Technical Support Document

for the Final

Clean Air Interstate Rule

Air Quality Modeling

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

March 2005
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Table of Contents

I. Introduction................................................................................................................. 1

II. Overview of Emission Inventories Used for Air Quality Modeling........................... 1

III. Episodic Ozone Modeling: Model Configuration, Inputs, and Performance

Evaluation................................................................................................................... 3

IV. Annual PM2.5, Visibility, and Deposition Modeling: Model Configuration, 

Inputs, and Performance Evaluation........................................................................... 8

V. Procedures for Projecting 8-Hour Ozone and PM2.5 for Future Year Scenarios..... 16

VI. Modeling to Assess Interstate Ozone Contributions................................................. 26

VII. Modeling to Assess Interstate PM2.5 Contributions................................................. 41

VIII. Expected Impacts of CAIR SO2 and NOx Emissions Reductions........................... 49

IX. References................................................................................................................. 71

Appendix A. Description of 1995 Ozone Episodes

Appendix B. Transport Patterns During 1995 Episodes

Appendix C. Ozone During 1995 Episodes Compared to Current Design Values

Appendix D. CAMx Ozone Model Performance Evaluation for the 1995 Episodes

Appendix E. Average Ambient and Projected 2010 and 2015 Base and CAIR Control 8-hour

Ozone Concentrations

Appendix F. Average Ambient and Projected 2010 and 2015 Base and CAIR Control Annual

PM2.5 Concentrations

Appendix G. 8-Hour Ozone Contributions to Downwind Nonattainment Counties in 2010

Appendix H. PM2.5 Contributions to Downwind Nonattainment Counties in 2010

Appendix I. Projected Visibility for 20% Best and 20% Worst Days in Class I Areas
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All model input files used for the CAIR air quality modeling can be obtained in electronic form

from our public ftp site. Please contact Warren Peters at peters.warren@epa.gov to obtain instructions

for accessing this site.

2

Particles with a diameter of 2.5 microns or less.

3

Particles with a diameter between 2.5 and 10 microns.

1

I. Introduction

This document was prepared to describe the air quality modeling performed by EPA in

support of the Clean Air Interstate Rule (CAIR)1. Included is information on (1) the air quality

models and the development of model inputs, (2) the performance of the models as compared to

measured data, (3) the procedures for projecting current air quality to future year base and

control scenarios, (4) the evaluation of interstate contribution to ozone and PM2.5 in downwind

nonattainment areas, and (5) an assessment of the expected air quality improvements from the

regional SO2 and NOx emissions reductions in CAIR. 

II. Overview of Emission Inventories Used for Air Quality Modeling

Emission inventories were developed for the 48 contiguous States, the District of

Columbia, and portions of Canada and Mexico in part to support the air quality modeling

analyses for CAIR. Inventories were prepared for a 2001 Base Year, for 2010 and 2015 future

baseline scenarios, and for 2010 and 2015 regional control scenarios. The inventories include

emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), volatile organic compounds (VOC),

carbon monoxide (CO), ammonia (NH3), directly emitted PM2.52

 component species (i.e.,

elemental carbon, organic carbon, nitrate, sulfate, and crustal material) and coarse particles3. 

Each inventory includes anthropogenic emissions for the major sectors of the inventory: electric

generating units (EGUs), industrial point sources (non-EGUs), nonroad engines, highway

vehicles, stationary area sources, fugitive dust, agricultural sources (i.e., fertilizer and animal

husbandry), fires (i.e., wild fires, open burning, and prescribed burning), and biogenic

processes. Emissions for EGU and non-EGU point sources are specified for each plant by boiler

and by source classification code (SCC) which defines the emissions process. The inventory for

these sources include stack parameters used for calculating plume rise. Emissions from other

anthropogenic sectors are calculated on a county and SCC basis. Emissions from biogenic

sources are calculated for each grid cell in the air quality modeling domain using grid-specific

land use and meteorological data. 

The 2001 inventory is a combination of several different data sources. EGU emissions

are based on 2001 Continuous Emissions Monitoring (CEM). On-road and nonroad mobile

sources were created for 2001 from the MOBILE6.2 and Draft NONROAD 2004 models. NonEGU and stationary area source emissions were either (1) projected from 1999 to 2001, (2)

computed for 2001, or (3) based on 2002 emission values using the latest approaches that were
4

BART is Best Available Retrofit Technology. The CAIR+BART and BART-only runs were

performed to support the "Better-than-BART" analysis performed as part of the analysis for CAIR. The

results of this analysis can be found in the “Supplemental Air Quality Modeling Technical Support

Document (TSD) for the Clean Air Interstate Rule (CAIR) Better-than-BART Demonstration."

2

not available for 2001 inventories. Finally, biogenic emission estimates for 2001 were

calculated using the BEIS3.12 model.

The 2010 and 2015 Base Case emissions reflect the net effects of economic growth and

emissions reductions expected to result from existing and promulgated control programs. Yearspecific data for 2010 and 2015 from the MOBILE6.2 and NONROAD models were used to

provide estimates of future year emissions for the highway vehicles and nonroad engines,

respectively. Outputs for 2010 and 2015 from the Integrated Planning Model (IPM) we used for

future year base and control case emissions from the EGU sector.

The future control case scenarios that were simulated with air quality modeling are:

- CAIR controls in 2010

- CAIR controls in 2015

- CAIR+BART4

 in 2015

- BART-only in 2015

In each of the above control cases, only emissions from EGUs were controlled. Emissions from

sources in all other sectors remained at the level of the corresponding 2010 or 2015 Base Case.

Emissions data for 2001 and the future base and control scenarios were processed using

the Sparse Matrix Operator Kernel (SMOKE) emissions model to prepare hourly, chemically

speciated emissions in each model grid cell and layer as inputs for the air quality modeling. 

Emissions of VOC were speciated into the Carbon Bond IV classes for both the episodic ozone

and annual PM2.5 air quality modeling.

Details on the development of the 2001 and 2010 and 2015 Base and control scenario

emissions, emissions processing to create model-ready inputs, and summaries of the emissions

data for each scenario can be found in the CAIR Emissions Inventory Technical Support

Document (EI TSD) (EPA. 2005a). Details on the EGU control scenarios can be found in the

“Modeling of Control Costs, Emissions, and Control Retrofits for Cost Effectiveness and

Feasibility Analyses” Technical Support Document (EPA, 2005b).
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III. Episodic Ozone Modeling: Model Configuration, Inputs, and

Performance Evaluation

Air quality modeling for ozone was conducted using the Comprehensive Air Quality

Model with Extensions (CAMx), version 3.10. CAMx is a non-proprietary computer model that

simulates the formation and fate of photochemical oxidants including ozone for an input set of

meteorological conditions and emissions. CAMx also contains a source apportionment tool

which is designed to attribute ozone concentrations predicted at a given set of receptors to

emissions from individual source areas, as specified by the user. More information on the

CAMx model can be found in the model's user guide (Environ, 2002). 

A. CAMx Modeling Domain and Grid Configuration

The CAMx model applications were performed for a domain covering most of the

Eastern U.S., as shown in Figure III-1 (figure is provided at the end of this section). The domain

has nested horizontal grids of 36 km (Outer Grid) and 12 km (Inner Grid). The configuration of

the Outer Grid and Inner grid are provided in Table III-1.

Table III-1. Configuration of ozone modeling domain.

Eastern US Domain

Coarse Grid Fine Grid

Map Projection latitude/longitude latitude/longitude

Grid Resolution 1/2/ longitude, 1/3/ latitude 

(~ 36 km)

1/6/ longitude, 1/9/ latitude 

(~ 12 km)

East/West extent -99 W to -67 W -92 W to -69.5 W

North/South extent 26 N to 47 N 32 N to 44 N

Dimensions 64 x 63 x 9 137 x 110 x 9

Vertical extent 9 Layers: surface to 4 km

Layer structure (m) 0-50, 50-100, 100-300, 300-600, 600-1000, 1000-1500, 1500-2000, 2000-

2500, 2500-4000
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The 8-hour ozone design value for a monitoring site is the 3-year average of the 4th highest 8-

hour daily maximum ozone concentration in each of the three years at that site.

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B. Ozone Episodes Modeled

 There are several considerations involved in selecting episodes for an ozone modeling

analysis (EPA, 1999a). In general, the goal is to model several differing sets of meteorological

conditions leading to ambient ozone levels similar to an area’s design value5

. Warm

temperatures, light winds, cloud-free skies, and stable boundary layers are some of the typical

characteristics of ozone episodes in the Eastern U.S. On a synoptic scale, these conditions

usually result from a combination of high pressure aloft (e.g., at the 500 millibar pressure level)

and at the surface. On the local scale, the conditions that lead to exceedances of the ozone

National Ambient Air Quality Standards (NAAQS) can vary from location to location based on

factors such as wind direction, sea/lake breezes, etc.

The ozone episode dates modeled for the CAIR are listed in Table III-2. The first three

days of each period are considered ramp-up days and the results from these days were not used

in the analyses. In all, 30 episode days were modeled for each emissions scenario. The synoptic

meteorological patterns during these episodes and the ambient 8-hour ozone concentrations are

described in Appendix A. A series of inert tracer model runs were performed to reveal the

overall transport patterns during each episode. The results of this analysis are presented in

Appendix B.

Table III-2. Dates of ozone episodes modeled including initialization (ramp-up) days.

Ozone Episodes

Episode 1 June 12-24, 1995

Episode 2 July 5-15, 1995

Episode 3 August 7-21, 1995

In order to determine whether the 1995 modeling days correspond to commonly

occurring ozone-conducive meteorology, EPA applied a multi-variate statistical approach to

characterize daily meteorological patterns and investigate their relationship to 8-hour ozone

concentrations in the Eastern U.S. (Lehman, et al., 2003). The approach relies on procedures

presented in the report “A Characterization of the Spatiotemporal Variability of Non-urban

Ozone Concentrations over the Eastern United States” (Eder, et al.,1994). The analysis by

Lehman was conducted for 16 locations in the East that have both surface and upper air

meteorological observations. Collectively, the locations included in this analysis provide

coverage for the Northeast, Southeast, Gulf Coast, Midwest, and Great Plains. For most of these

locations, there were five to six distinct sets of meteorological conditions, called regimes, which

were defined based on seven to ten years of data. An analysis of the 8-hour daily maximum

ozone concentrations for each of the meteorological regimes was performed to determine the

distribution of ozone concentrations and the frequency of occurrence of each regime. These two
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terms (i.e., distribution of concentration and frequency of regime) were analyzed to identify

which regimes are most closely associated with high ozone concentrations in each location. For

the three 1995 episodes, between 60 and 70 percent of the episode days modeled were found to

be associated with the most frequently occurring, high ozone potential, meteorological regimes. 

In general, these results provide support that the episodes modeled are representative of

meteorological conditions present when elevated ozone is observed throughout the modeling

domain. 

To respond to comments on the proposed rule, we performed several analyses to examine

whether ambient ozone concentrations during the 1995 episodes selected for modeling were in

the range of the most recent (i.e., 2001-2003) 8-hour ozone design values available at the time of

the analysis. These analyses indicate that, in general, ozone levels during the 1995 episodes

approximate recent ambient concentrations over the eastern U.S. Ambient concentrations of 8-

hour ozone during the episodes do not appear to be biased high or low, compared to current

design values. For example, of the 524 monitoring sites in the East that collected data both

during the 1995 episodes and during the 2001-2003 design value period, 267 sites (51%) had

fourth-highest values during the 1995 episodes that were higher than the current baseline design

values while 257 sites (49%) had fourth-high values that were less than the current ozone levels. 

Also, during the 1995 episodes there was at least one day with measured ozone within + 5 ppb of

the corresponding 2001-2003 design value at over 90 percent of monitoring sites with design

values at or above 75 ppb. The data used for these analyses are provided in Appendix C.

C. Meteorological and Other Model Inputs

In order to solve for the change in pollutant concentrations over time and space, the

CAMx model requires certain meteorological inputs that, in part, govern the formation,

transport, and destruction of pollutant material. In particular, the model requires seven

meteorological input files: wind (u- and v-vector wind components), temperature, water vapor

mixing ratio, atmospheric air pressure, cloud cover, rainfall, and vertical diffusion coefficient. 

Values of wind, pressure, and vertical diffusivity are calculated for the outer and inner grids

whereas the other meteorological inputs are calculated at 36 km resolution for the entire domain

and then interpolated from 36 km to 12 km for the inner grid..

The gridded meteorological data for the three historical 1995 episodes were developed

using the Regional Atmospheric Modeling System (RAMS), version 3b. RAMS (Pielke et al.,

1992) is a numerical meteorological model that solves the full set of physical and

thermodynamic equations which govern atmospheric motions. The output data from RAMS,

which is run in a polar stereographic projection and a sigma-p coordinate system, were mapped

to the CAMx grid. Two separate meteorological CAMx inputs, cloud fractions and rainfall rates,

were developed based on observed data.
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 The inner nests were modeled with 34 layers while the outer 108 km domain was modeled with

28 layers.

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RAMS was run in a nested-grid mode with three levels of resolution: 108 km, 36 km, and

12 km with 28-346 vertical layers. The top of the surface layer was 16.7 m in the 36 and 12km

grids. These two finer grids were at least as large as their CAMx counterparts. In order to keep

the model results in line with reality, the simulated fields were nudged to an European Center for

Medium-Range Weather Forecasting (ECMWF) analysis field every six hours. This assimilation

data set was bolstered by every four-hourly special soundings regularly collected as part of the

North American Research Strategy on Tropospheric Ozone (NARSTO) field study in the

northeast U.S.

A model performance evaluation (Lagouvardos et al., 2000) was completed for a portion

of the 1995 meteorological modeling (July 12-15). Observed data not used in the assimilation

procedure were compared against modeled data at the surface and aloft. In general, the model

accurately reproduced the synoptic meteorological conditions of the episode days. Furthermore,

the meteorological fields were compared before and after being processed into CAMx inputs. It

was concluded that this preprocessing did not distort the meteorological fields. In addition, a

peer-reviewed, quantitative evaluation was performed for the RAMS model predictions over the

period June through August 1995 (Hogrefe, 2001). The results show that the RAMS biases and

errors are generally in line with the better performing meteorological model simulations found

by other groups outside EPA, as compiled by Environ (Environ, 2001). Specifically, the RAMS

predictions of surface water vapor mixing ratio are within the performance goals for this

parameter and the root mean square error for the RAMS-predicted hourly wind speed is 1.8 m/s

which is within the goal of 2.0 m/s. The model temperatures had an overall bias of 1.4 degrees C

and error of 2.3 C which are somewhat outside the target goals of + 0.5 and 2.0 degrees C, but

still within the range of performance in other non-EPA meteorological model applications. 

In addition to the meteorological data, the photochemical grid model requires several

other types of data. In general, most of these miscellaneous model files have been taken from

existing regional modeling applications. Clean conditions were used to initialize the model and

were also used as lateral and top boundary conditions as in previous regional modeling

applications. The model also requires information regarding land use type and surface albedo

for all layer 1 grid cells in the domain. Existing regional data were used for these non-dayspecific files. Photolysis rates were developed using the JCALC preprocessor. Turbidity values

were set equal to a constant thought to be representative of regional conditions. A minimum of

1.0 m2

/sec was applied for vertical diffusivity (Kv).

D. CAMx Model Applications for CAIR

For CAIR, CAMx was run for five emissions scenarios: 2001 Base Year, 2010 Base

Case, 2015 Base Case, 2010 CAIR control, and 2015 CAIR control. In addition, State-specific

"zero-out" and source apportionment runs were made with CAMx to quantify the contribution of

emissions in "upwind" States to 8-hour ozone nonattainment in other "downwind" States, as
7

described below. The 2010 Base Case emissions inventory was used as the baseline for these

State-by-State runs.

E. CAMx Model Performance Evaluation

EPA conducted a performance evaluation of CAMx for the 1995 episodes as part of the

modeling analysis for the Nonroad Rule. The details of that ozone performance evaluation are

provided in Appendix D. In summary, model performance statistics were calculated for

observed/predicted pairs of hourly and 8-hour concentrations. Statistics were generated for each

episode and for all three episodes combined for the following geographic groupings:

domainwide, each of four quadrants of the domain (i.e., Midwest, Northeast, Southeast, and

Southwest), and each of 51 "local areas" within the domain. The “acceptability” of model

performance was judged by comparing our results to those from past regional ozone model

applications. Overall, the performance for this application is similar to or improved over that of

applications for earlier studies. In addition, model performance for this analysis compares

favorably against performance goals for urban scale modeling that are recommended in EPA's

draft 8-hour ozone attainment modeling guidance (EPA, 2005c). 

Figure III-1. Map of the CAMx modeling domain used for CAIR.
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IV. PM2.5, Visibility, and Deposition Modeling: Model Configuration,

Inputs, and Evaluation

Air quality modeling for PM2.5, visibility, and deposition was conducted using the

Community Multiscale Air Quality Model (CMAQ). The CMAQ modeling system is a

comprehensive three-dimensional grid-based Eulerian air quality model designed to estimate

particulate concentrations and deposition over large spatial scales (Dennis et al., 1996; Byun and

Ching, 1999; Byun and Schere, 2004). The CMAQ model is a publically available, peerreviewed, state-of-the-science model consisting of a number of science attributes that are critical

for simulating the oxidant precursors and non-linear organic and inorganic chemical

relationships associated with the formation of sulfate, nitrate, and organic aerosols. CMAQ also

simulates the transport and removal of directly emitted particles which are speciated as elemental

carbon, crustal material, nitrate, sulfate, and organic aerosols.

Version 4.3 of CMAQ (Byun and Schere, 2004) was used for CAIR. This version

reflects updates to earlier versions in a number of areas to improve the underlying science and

address comments from the peer review. The improvements in version 4.3 compared to earlier

versions include (1) use of a state-of-the-science inorganic nitrate partitioning module

(ISORROPIA) and updated gaseous, heterogeneous chemistry in the calculation of nitrate

formation, (2) a state-of-the-science secondary organic aerosol (SOA) module that includes a

more comprehensive gas-particle partioning algorithm from both anthropogenic and biogenic

SOA, (3) an in-cloud sulfate chemistry module that accounts for the nonlinear sensitivity of

sulfate formation to varying pH, and (4) an updated CB-IV gas-phase chemistry mechanism and

aqueous chemistry mechanism that provide a comprehensive simulation of aerosol precursor

oxidants. Key science aspects of CMAQ as applied for CAIR include:

• Gas-Phase Chemical Solver: Euler Backward Iterative (EBI) scheme

• PM Module: 

Three-mode approach: One coarse mode, two fine modes with variable standard

deviations

Inorganic PM module: ISORROPIA 

Organic PM module: SOA module based on Odum (1997) and Griffin (1999)

• Advection Scheme (vertical and horizontal): Piecewise Parabolic Method (PPM) scheme 

• Vertical Diffusion: K-theory eddy diffusivity scheme; minimum diffusivity is 1 m2

/sec

• Dry Deposition: M3DRY module, modified RADM scheme with Pleim-Xiu land surface

model 

• Aqueous Chemistry: RADM Bulk scheme

• Cloud Scheme: RADM Cloud scheme

• Vertical Coordinate: Terrain-following Sigma coordinate 
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A. CMAQ Modeling Domain and Configuration

As shown in Figure IV-1, the CMAQ modeling domain encompasses all of the lower 48

States and portions of Canada and Mexico (Figure IV-1 is provided at the end of Section IV). 

The domain extends from 126 degrees to 66 degrees west longitude and from 24 degrees north

latitude to 52 degrees north latitude. The horizontal grid cells are approximately 36 km by 36

km. The modeling domain contains 14 vertical layers with the top of the modeling domain at

about 16,200 meters, or 100 mb. The vertical layer structure for CMAQ used for the CAIR

applications is shown in Table IV-1 (this table can be found below, in the section IV.C.).

B. Time Period Modeled For PM2.5/Visibility/Depostion

The CMAQ model was applied for the year 2001 in order to provide annual PM2.5

concentration s, visibility, and deposition estimates for each of the CAIR emissions scenarios. 

The overall model run time for completing an annual simulation was reduced by dividing the

year into four quarters which were run in parallel on different computer processors. That is, the

annual simulation was performed as four separate quarterly model runs. As an example, the

model run for the first quarter included the months of January, February, and March. Each

quarterly run included a 10-day ramp-up (i.e., "spin-up") period designed to minimize the

influence of the initial concentration fields (i.e., initial conditions) used at the start of the model

run. The development of initial condition concentrations is described in Section IV.D, below. 

The ramp-up periods used for the CAIR CMAQ applications are as follows:

- first quarter ramp-up period is December 22 - 31, 2000

- second quarter ramp-up period is March 22 - 31, 2001

- third quarter ramp-up period is June 21 - 30, 2001

- fourth quarter ramp-up period is September 21 - 30, 2001

Model predictions from these periods were discarded and not used in analyses of the modeling

results.

C. Meteorological Inputs to CMAQ

Meteorological data, such as temperature, wind, stability parameters, and atmospheric

moisture contents influence the formation, transport, and removal of air pollution. The CMAQ

model requires a specific suite of meteorological input files in order to simulate these physical

and chemical processes. For the CAIR CMAQ modeling, meteorological input files were

derived from a simulation of the Pennsylvania State University / National Center for

Atmospheric Research Mesoscale Model (Grell et al., 1994) for the entire year of 2001. This

model, commonly referred to as MM5, is a limited-area, nonhydrostatic, terrain-following

system that solves for the full set of physical and thermodynamic equations which govern

atmospheric motions. For this analysis, version 3.6.1 of MM5 was used. The horizontal domain

consisted of a single 36 x 36 km grid with 165 by 129 cells, selected to maximize the coverage

of the Eta model analysis region and completely cover the CMAQ modeling domain with some
10

buffer to avoid boundary effects. The MM5 was run on the same map projection as CMAQ. 

The 2001 meteorological modeling utilized 34 vertical layers with a surface layer of 

approximately 38 meters. The MM5 and CMAQ vertical structures are shown in Table IV-1.

Table IV-1. Vertical layer structure for MM5 and CMAQ (heights are the top of layer).

CMAQ Layers

(14)

MM5 Layers

(34)

Sigma Approximate

Height (m)

Approximate

Pressure (mb) 

0 0 1.000 0 1000

1 1 0.995 38 995

2 2 0.990 77 991

3 0.985 115 987

3 4 0.980 154 982

5 0.970 232 973

4 6 0.960 310 964

7 0.950 389 955

5 8 0.940 469 946

9 0.930 550 937

10 0.920 631 928

6 11 0.910 712 919

12 0.900 794 910

13 0.880 961 892

7 14 0.860 1130 874

15 0.840 1303 856

16 0.820 1478 838

8 17 0.800 1657 820

18 0.770 1930 793

9 19 0.740 2212 766

20 0.700 2600 730

10 21 0.650 3108 685

22 0.600 3644 640

11 23 0.550 4212 595

24 0.500 4816 550

25 0.450 5461 505

12 26 0.400 6153 460

27 0.350 6903 415

28 0.300 7720 370

29 0.250 8621 325

13 30 0.200 9625 280

31 0.150 10764 235

32 0.100 12085 190

33 0.050 13670 145

14 34 0.000 15674 100
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A complete description of the configuration and evaluation of the 2001 meteorological modeling

is contained in McNally (2003), however some of the key model physics options are as follows:

• Cumulus Parameterization: Kain-Fritsch 

• Planetary Boundary Layer Scheme: Pleim-Chang

• Explicit Moisture Scheme: Reisner 2

• Radiation Scheme: RRTM longwave scheme

• Land Surface Model: Pleim-Xiu

• Four-Dimensional Data Assimilation (FDDA): analysis nudging only

The annual MM5 simulation was divided into four separate periods: 12/16/00 to 4/05/01,

3/16/01 to 7/05/01, 6/14/01 to 10/02/01, and 9/17/01 to 2/04/02. Within each of these periods

the model was run for 5 1⁄2 days blocks with a restart occurring at 1200 UTC every fifth day. To

assure continuity in the surface moisture, the model initial conditions were updated with the soil

conditions from the end of the previous 5 1⁄2 day period using the EPA “INTERPX” processor.

In terms of the 2001 MM5 model performance evaluation, we used an approach which

included a combination of qualitative and quantitative analyses to assess the adequacy of the

MM5 simulated fields. The qualitative aspects involved comparisons of the model estimated sea

level pressure and radar reflectivity fields against observed values of the same parameters from

historical weather chart archives. The statistical portion of the evaluation examined the model

bias and error for temperature, water vapor mixing ratio, and the index of agreement for the wind

fields. These statistical values were calculated on a regional basis. The results of the evaluation

indicate that the 2001 model data had a bias in surface temperature of -0.6 degrees Celsius and

the error averaged 2.1 degrees C. The humidity fields had a bias of -0.2 g/kg and an error of 1.0

g/kg. The wind speed index of agreement averaged 0.86. The model was found to overestimate

precipitation, on average by about 1.6 cm. The precipitation bias was strongest in the summer. 

Qualitatively, the model fields closely matched the observed synoptic patterns, which is expected

given the use of FDDA. In general, the bias and error values associated with the 2001 data are in

the range of model performance found from other non-EPA regional meteorological model

applications (Environ, 2001). 

The MM5 outputs were processed to create model-ready inputs for CMAQ using the

Meteorology-Chemistry Interface Processor (MCIP) as described in EPA (1999). MCIP version

2.2gv was used to convert the MM5 output to CMAQ meteorological input. This version

contained two differences from the main MCIP version 2.2 in that: 1) it allowed for treatment of

the graupel associated with the Reisner 2 microphysics scheme and 2) it included a patch to

compensate for a minor error in MM5 associated with vegetation fractions.
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D. Initial and Boundary Condition Inputs to CMAQ

In this section we describe the approach used to provide the boundary conditions (BCs) and

the concentrations used to initialize the model runs for the CAIR CMAQ modeling. Nonepisodic national modeling, such as the CAIR annual PM2.5 CMAQ modeling, requires the

prescription of BCs to account for the influx of pollutants and precursors from the upwind source

areas outside the modeling domain. The pollutant influxes from the upwind boundaries, which

are often dynamic in nature, can affect pollutant concentrations within the modeling domain. 

For example, a number of recent studies show that long-range, intercontinental transport of

pollutants is important for simulating seasonal/annual ozone and PM (Jacob, et al., 1999; Jaffe

et al., 2003; Fiore, et al., 2003). A scientifically sound approach to estimate incoming pollutant

concentrations associated with intercontinental transport is to use a global chemistry model to

provide dynamic BCs for the regional model simulations.

For the CAIR annual PM2.5 modeling, we used the predictions from a global threedimensional chemistry model, the GEOS-CHEM model (Yantosca, 2004), to provide the BCs

and initial concentrations. The global GEOS-CHEM model simulates atmospheric chemical and

physical processes driven by assimilated meteorological observations from the NASA’s Goddard

Earth Observing System (GEOS). This model was run for 2001 with a grid resolution of 2

degree x 2.5 degree (latitude-longitude) and 20 vertical layers. The predictions were used to

provide one-way dynamic BCs at 3-hour intervals and initial concentration field for the CMAQ

simulations. We used an interface utility tool developed at the University of Houston (Byun and

Moon, 2004; Moon and Byun, 2004) to link the GOES-CHEM with CMAQ. The scale,

chemical, and dynamic linking between the two models are needed since the horizontal and

vertical coordinates, chemical species representations, and model output time are different. A

detailed description of how the GEOS-CHEM model outputs were used to develop inputs to

CMAQ including the data preparation, spatial and temporal conversion procedures, and species

mapping tables are given in Moon and Byun (2004).

E. CMAQ Model Applications 

For CAIR, CMAQ was run for seven emissions scenarios: 2001 Base Year, 2010 Base Case,

2015 Base Case, 2010 CAIR control, 2015 CAIR control, 2015 BART control, and 2015

CAIR+BART control. In addition, State-specific "zero-out" runs were made with CMAQ using

the 2010 Base emissions scenario to quantify the contribution of emissions in "upwind" States to

annual PM2.5 nonattainment in other "downwind" States, as described in Section V, below.

As described in the EI TSD, two versions of the 2001 inventory were created each with

different emissions for wild fires and prescribed burning. One version of the 2001 inventory

incorporated year-specific 2001 emissions for wild fires and prescribed burning. This version

was used in an annual CMAQ run to evaluate model predictions against 2001 ambient

measurements. A second version of the 2001 inventory was created using seven year average

emissions (i.e., 1996-2002) for wild fires and prescribed burning. The CMAQ model run with

this second 2001 inventory (i.e., the inventory with average fires) was used as the baseline for
13

projecting PM2.5 concentrations and visibility for the future case scenarios. Note that the 1996-

2001 average fire emissions were also used in all of the future base and control case CMAQ

runs. Please see the EI TSD for more details on the development of the 2001 and future year

emissions used for air quality modeling.

F. CMAQ Model Performance Evaluation

A performance evaluation of CMAQ was conducted by comparing model predictions from

the 2001 annual simulation to the corresponding ambient measurements. This evaluation was

performed for PM2.5 component species and precursor gases (for which measurements were

available), visibility, and deposition of ammonium, nitrate, and sulfate. The PM2.5 components

covered in this evaluation include sulfate, nitrate, elemental carbon, organic carbon, and crustal

material. The precursor gases included in this analysis are sulfur dioxide, ozone, nitric acid, and

nitrogen oxide. Ambient measurements for 2001 were obtained from the following networks:

Clean Air Status and Trends Network (CASTNet), 

Speciation Trend Network (STN),

Interagency Monitoring of PROtected Visual Environments (IMPROVE),

Aerometric Information Retrieval System (AIRS),

National Acid Deposition Program (NADP), and

SouthEastern Aerosol Research and CHaracterization (SEARCH). 

The pollutant species included in the evaluation for each network are listed in Table VI-2.

Table VI-2. Monitoring networks and pollutants species included in the CMAQ

performance evaluation.

Ambient

Monitoring

Networks

Particulate 

Species

 Gaseous Precursor

Species

Wet Deposition

Species

PM2.5

Mass

SO4 NO3 TNO3 EC NH4 OC O3 SO2 HNO3 NO SO4 NO3 NH4

IMPROVE X X X X X X

CASTNet X X X X

STN X X X X X X

NADP X X X X

AIRS X

SEARCH X X X X X X X X X X

Note that TNO3 = (NO3 + HNO3)
7

Both SO2 and ozone are precursors for sulfate particles and ozone has a major role in the

formation of nitrate and secondary organic aerosols.

8

 These other modeling studies represent a wide range of modeling analyses which cover various

models, model configurations, domains, years and/or episodes, chemical mechanisms, and aerosol

modules.

14

The evaluation includes several types of analyses to compare predictions with observations. 

- model performance statistics

- scatter plots

- time series plots

- spatial maps

For PM2.5 component species, visiblity, and deposition, model performance statistics were

calculated using observed/predicted data pairs which correspond to the sampling frequency of

each network (i.e., daily average or weekly average). Performance statistics were calculated for

each season individually and for the entire year, as a whole. For these time periods, separate

statistics were calculated for the eastern and western portions of the modeling domain. For

PM2.5 species that are measured by more than one network, we calculated separate sets of

statistics for each network. Model performance statistics were also calculated for SO2 (using

weekly measurements from CASTNET) and ozone (using 1-hour and 8-hour daily maximum

concentrations from AIRS) since these two pollutants are important as precursor gases in the

formation of several PM2.5 component species7

 and there exists abundant ambient

measurements for SO2 and ozone for use in calculating performance statistics.

Given that a purpose of CAIR is to reduce interstate transport of PM2.5, the focus of the

evaluation was on model predictions of PM2.5 component species. The “acceptability” of model

performance was judged by comparing our results to the results found in recent regional PM2.5

model applications for other, non-EPA studies8

. In addition, we compared our performance

results against benchmark performance goals (Boylan, 2004) suggested as part of comments on

the proposed rule PM2.5 modeling. The results indicate that the performance for CMAQ is

within the range or better than these other applications and also meets the suggested benchmark

performance goals. Details on the CMAQ model evaluation can be found in the report: Updated

CMAQ Model Performance Evaluation for 2001 Annual Simulation (EPA, 2005d).
15

Figure IV-1. Map of the CMAQ modeling domain used for CAIR.
9

Note that, in response to comments on modeling procedures in the proposed CAIR, EPA has

made modifications to several aspects of the projection methodology for ozone and PM2.5. These

modifications were incorporated into the procedures used to project future concentrations for the final

rule analysis. The draft ozone and PM2.5/Regional Haze modeling guidance documents are being

revised to reflect these changes.

16

V. Procedures for Projecting 8-Hour Ozone and PM2.5 for Future Year

Scenarios

A. Introduction

In this section we describe the procedures for estimating 8-hour ozone and PM2.5

concentrations for the 2010 and 2015 Base Cases and CAIR regional strategy. The projected

future case concentrations for these scenarios are used to quantify the expected air quality

impacts (e.g., reduction in nonattainment) and monetized benefits of this rule. In addition, those

counties that are currently measuring nonattainment and are also projected to be nonattainment

for the 2010 Base are used as nonattainment receptors in the analysis to determine which upwind

States make a significant contribution to nonattainment in other downwind States.

In general, the procedures for projecting both PM2.5 and 8-hour ozone are based on using

model predictions in a relative sense. In this manner, the 2001 Base Year and the future case

model predictions are coupled with ambient data to forecast future concentrations. This

approach is consistent with the EPA draft guidance documents for modeling 8-hour ozone (EPA,

2005c) and PM2.5/Regional (EPA, 2001).9

B. Ambient Data Used for Projections

For the 2010 and 2015 Base and CAIR control scenarios we have projected 8-hour ozone and

PM2.5 concentrations for the location of monitoring sites in the Eastern U.S. The starting point

for these projections is the average of the 1999-2001, 2000-2002, and 2001-2003 design values

at each monitoring site. The average of the three design values is not a straight five-year

average. Rather, it is a weighted average over the period 1999-2003. That is, by averaging

1999-2001, 2000-2002, and 2001-2003, the value from 2001 is weighted three times, whereas,

values for 2000 and 2002 are each weighted twice, and 1999 and 2003 are each weighted once. 

This approach has the desired benefits of (1) weighting the ozone and PM2.5 values towards the

middle year of the five-year period, which is the 2001 Base Year for our emissions projections,

and (2) smoothing out the effects of year-to-year variability in emissions and meteorology that

occurs over the full five-year period. This approach provides a robust estimate of current air

quality for use as a basis for future year projections. In the remainder of this document we refer

to the weighted average values as the average 1999-2003 concentrations.

In the analysis to project future 8-hour ozone concentrations, we included 1999-2003 8-hour

ozone measurements from monitoring sites in 525 counties in the Eastern U.S. For projecting

PM2.5, we included measurements from Federal Reference Method (FRM) monitoring sites in
17

433 counties in the East. The 1999-2003 weighted-average concentrations in these counties are

provided in Appendix E for ozone and Appendix F for PM2.5. For those counties with multiple

monitoring sites, future case concentrations were calculated for each site in the county. The

highest concentration from among the sites in a given county was selected as the representative

value for that county. Note that only the high sites in each county are provided in the

appendices.

C. Projection of Future 8-Hour Ozone Concentrations

1. Ozone Projection Procedures

The CAMx 8-hour ozone predictions for the 2001 Base Year run were coupled with

predictions for each of the four future year scenarios (i.e., 2010 and 2015 baselines and the

CAIR control cases projected for these two future years) to project 8-hour ozone for the future

base and control case scenarios. As mentioned above, the approach for projecting 8-hour ozone

concentrations involves using the model in a relative sense to estimate the change in ozone

between 2001 and each future scenario. For example, to calculate the projected ozone for the

2010 Base scenario, the average 1999-2003 ambient 8-hour ozone concentrations were

multiplied by the relative change in model predicted ozone between the 2001 and 2010 Base. 

The steps we followed for projecting future 8-hour ozone concentrations for each future case

scenario are described below. This procedure was performed to project future case

concentrations at each monitoring site. Note that prior to processing the model predictions, the

location of each monitoring site was mapped onto the CAMx modeling grid network (i.e., match

the location of each monitor to a grid cell).

Step 1: Using hourly model predictions, calculate the daily maximum 8-hour average

concentrations in the vicinity of each ozone monitor for each episode day modeled. The multiday mean (excluding ramp-up days) of the 8-hour daily maximum predictions is calculated using

only those predictions greater than or equal to 70 ppb, as recommended in EPA's draft 8-hour

ozone modeling guidance. This calculation is performed using model predictions for the 2001

Base Year and each future-year scenario. A relative reduction factor (RRF) is then determined

for each monitoring site. The RRF for a site is the ratio of the mean prediction in the future-year

scenario (e.g., 2010) to the mean prediction in the 2001 Base Year scenario.

Step 2: Multiple the RRF for each site by the average 1999-2003 concentration for that site,

to yield an estimate of the future concentration at that particular monitoring location.

Step 3: For counties with only one monitoring site, the value at that site is selected as the

value for that county. As indicated above, for counties with more than one monitor, the highest

value in the county is selected as the value for that county.

2. Projected 8-Hour Ozone Concentrations and Future Nonattainment

The procedures described above were used to project 8-hour ozone concentrations for the

2010 Base and 2015 Base Case and CAIR control scenarios. The projected concentrations for
18

each county analyzed are provided in Appendix E. Counties with 8-hour ozone concentrations

of 85.0 ppb or more are projected to be nonattainment in the future case scenarios. An analysis

of the impacts of CAIR on 8-hour ozone nonattainment in 2010 and 2015 is provided below in

Section VIII.

The 2010 and 2015 Base Case 8-hour ozone nonattainment counties are listed in Table V-1

and Table V-2, respectively. As noted above, those counties that are currently nonattainment

based on the most recent measured data (i.e., 2001-2003 design values) and are also projected to

be nonattainment in the 2010 Base Case comprise the set of future nonattainment receptors

which were used in the analysis of significant contribution, as described below in Section VI. 

We refer to these counties as "monitored plus modeled" nonattainment. For 8-hour ozone, the 40

counties projected to be nonattainment for the 2010 Base are also measured nonattainment,

based on 2001-2003 design values. Thus, these 40 counties are used as the "downwind"

receptors in the analysis of significant contribution.

Table V-1. Projected 8-hour ozone concentrations (ppb) for nonattainment counties in the

2010 Base Case.

State County 2010 Base

Connecticut Fairfield Co 92.6

Connecticut Middlesex Co 90.9

Connecticut New Haven Co 91.6

Delaware New Castle Co 85.0

District of Columbia 85.2

Georgia Fulton Co 86.5

Maryland Anne Arundel Co 88.8

Maryland Cecil Co 89.7

Maryland Harford Co 93.0

Maryland Kent Co 86.2

Michigan Macomb Co 85.5

New Jersey Bergen Co 86.9

New Jersey Camden Co 91.9

New Jersey Gloucester Co 91.8

New Jersey Hunterdon Co 89.0

New Jersey Mercer Co 95.6

New Jersey Middlesex Co 92.4

New Jersey Monmouth Co 86.6

New Jersey Morris Co 86.5

New Jersey Ocean Co 100.5

New York Erie Co 87.3

New York Richmond Co 87.3

New York Suffolk Co 91.1

New York Westchester Co 85.3

Ohio Geauga Co 87.1

Pennsylvania Bucks Co 94.7
State County 2010 Base

19

Pennsylvania Chester Co 85.7

Pennsylvania Montgomery Co 88.0

Pennsylvania Philadelphia Co 90.3

Rhode Island Kent Co 86.4

Texas Denton Co 87.4

Texas Galveston Co 85.1

Texas Harris Co 97.9

Texas Jefferson Co 85.6

Texas Tarrant Co 87.8

Virginia Arlington Co 86.2

Virginia Fairfax Co 85.7

Wisconsin Kenosha Co 91.3

Wisconsin Ozaukee Co 86.2

Wisconsin Sheboygan Co 88.3

Table V-2. Projected 8-hour ozone concentrations (ppb) for nonattainment counties in the

2015 Base Case.

State County 2015 Base 

Connecticut Fairfield Co 91.4

Connecticut Middlesex Co 89.1

Connecticut New Haven Co 89.8

Maryland Anne Arundel Co 86.0

Maryland Cecil Co 86.9

Maryland Harford Co 90.6

Michigan Macomb Co 85.1

New Jersey Bergen Co 85.7

New Jersey Camden Co 89.5

New Jersey Gloucester Co 89.6

New Jersey Hunterdon Co 86.5

New Jersey Mercer Co 93.5

New Jersey Middlesex Co 89.8

New Jersey Ocean Co 98.0

New York Erie Co 85.2

New York Suffolk Co 89.9

Pennsylvania Bucks Co 93.0

Pennsylvania Montgomery Co 86.5

Pennsylvania Philadelphia Co 88.9

Texas Harris Co 97.3

Texas Jefferson Co 85.0

Wisconsin Kenosha Co 89.4
20

D. Projection of Future PM2.5 Concentrations

1. PM2.5 Projection Procedures

As with ozone, the approach for estimating PM2.5 concentrations for the future case

scenarios involves using model predictions in a relative manner. However, for PM2.5 we project

concentrations for each of the PM2.5 components species which are then combined to estimate

total PM2.5. This approach, which is consistent with the procedures in the draft PM2.5/Regional

Haze air quality modeling guidance, is named the Speciated Modeled Attainment Test (SMAT). 

Details on SMAT can be found in the report "Procedures for Estimating Future PM2.5 Values for

the CAIR Final Rule by Application of the (Revised) Speciated Modeled Attainment Test

(SMAT)" (EPA, 2004).

Below are the steps we followed for projecting future PM2.5 concentrations for each future

case scenario. These steps were performed to estimate future case concentrations at each FRM

monitoring site. Note that before processing the model predictions, the location of each

monitoring site was mapped onto the CMAQ modeling grid network.

Step 1: Calculate quarterly mean ambient concentrations for each of the six major

components of PM2.5 (i.e., sulfate, nitrate, ammonium, elemental carbon, organic carbon, and

crustal material) using the component species concentrations estimated for each FRM site.

Step 1a: Since roughly 80 percent of the FRM sites do not have co-located speciation

monitors, species data from the IMPROVE and EPA's speciation network were spatially

interpolated to each FRM site. In this analysis we used species data for the calendar year

2002.

Step 1b: A number of adjustments and additions were made to the measured species data

to provide for consistency with the chemical components retained on the FRM Teflon

filter. These include (a) a reduction in nitrates, (b) estimated ammonium associated with

nitrates and sulfates based on the degree of neutralization of sulfate, (c) estimated

particle-bound water using an empirical relationship derived from the AIM model (see

the SMAT report for details on this model), and (d) a 0.5 μg/m3

 PM2.5 blank mass

correction.

Step 1c: Once the adjustments/additions were made to the measured species, organic

carbon was estimated by a mass balance approach in which organic carbon is calculated

as the difference between total PM2.5 and the sum of the non-organic carbon constituents

(i.e., sulfate, nitrate, ammonium, water, elemental carbon, crustal material, and blank

mass). This mass balance approach for carbon was adopted in view of the uncertainties

associated with measurements of organic carbon. 

Step 1d: The species concentrations calculated above were used to estimate the species

fractions at each FRM site.

Step 1e: The average 1999-2003 FRM quarterly mean concentration at each site was

multiplied by the estimated fractional composition of PM2.5 species, by quarter (e.g., 20

percent sulfate multiplied by 15.0 μg/m3 of PM2.5 equals 3 μg/m3

 sulfate). 
21

Step 2: Calculate quarterly average Relative Reduction Factors (RRFs) for sulfate, nitrate,

elemental carbon, organic carbon, and crustal material. The species-specific RRFs for the

location of each FRM are the ratio of future case to 2001 Base Year quarterly average modelpredicted species concentrations. The species-specific quarterly RRF are then multiplied by the

corresponding 1999-2003 quarterly species concentration from Step 1. The result is the future

case quarterly average concentration for each of these species.

Step 3: Calculate quarterly average concentrations for ammonium and particle-bound water. 

The future case concentrations for ammonium are calculated using the future case sulfate and

nitrate concentrations determined from Step 2 along with the degree of neutralization of sulfate

(held constant from the base year). Concentrations of particle-bound water are calculated using

the empirical relationship derived from the AIM model using the future case concentrations of

sulfate, nitrate, and ammonium as inputs.

Step 4: Calculate the mean of the four quarterly average future case concentrations to

estimate future annual average concentration for each component specie. The annual average

concentrations of the components are added together to obtain the future annual average

concentration for PM2.5. In calculating the projected PM2.5 concentrations, any amount of the

concentration less than 0.01 μg/m3

 (i.e., more than two places to the right of the decimal) is

discarded (i.e., truncated).

Step 5: For counties with only one monitoring site, the projected value at that site is the

future case value for that county. As indicated above, for counties with more than one monitor,

the highest value in the county is selected as the concentration for that county. However, the

site-specific 2010 Base concentrations were retained for use in the PM2.5 significant

contribution analysis, as described in Section VII.

2. Projected PM2.5 Concentrations and Future Nonattainment

The procedures described above were used to estimate annual average PM2.5 concentrations

for the 2010 Base and 2015 Base Case and CAIR control scenarios. The projected

concentrations for each county were analyzed and are provided in Appendix F. Counties with

PM2.5 concentrations of 15.1 μg/m3

 (as rounded up from 15.05 μg/m3) or more are projected to

be nonattainment for the future case scenarios. An analysis of the impacts of CAIR on PM2.5

nonattainment in 2010 and 2015 is provided below in Section VIII. The 2010 and 2015 Base

Case PM2.5 nonattainment counties are listed in Table V-3 and Table V-4, respectively. As

noted above, those counties that are currently nonattainment based on the most recent measured

data (i.e., 2001-2003 design values) and are also projected to be nonattainment in the 2010 Base

Case are used in the analysis of significant contribution. Of the 79 counties projected to be

nonattainment for the 2010 Base, 62 also measured nonattainment in 2001-2003. The modeled

plus monitored nonattainment counties are identified in Table V-3. Note that some counties

contain multiple FRM sites that are projected to be modeled plus monitored nonattainment. 

Within the 62 counties there are a total of 113 FRM sites that are modeled plus monitored

nonattainment. These 113 sites are the "downwind" nonattainment receptors used the significant

contribution analysis in Section VI. 
22

Table V-3. Projected PM2.5 concentrations (μg/m3) for nonattainment counties in the 2010

Base Case.

State County 2010 Base

"Modeled + Monitored"

Nonattainment in 2010 Base

Alabama DeKalb Co 15.23 No

Alabama Jefferson Co 18.57 Yes

Alabama Montgomery Co 15.12 No

Alabama Morgan Co 15.29 No

Alabama Russell Co 16.17 Yes

Alabama Talladega Co 15.34 No

Delaware New Castle Co 16.56 Yes

District of Columbia 15.84 Yes

Georgia Bibb Co 16.27 Yes

Georgia Clarke Co 16.39 Yes

Georgia Clayton Co 17.39 Yes

Georgia Cobb Co 16.57 Yes

Georgia DeKalb Co 16.75 Yes

Georgia Floyd Co 16.87 Yes

Georgia Fulton Co 18.02 Yes

Georgia Hall Co 15.60 No

Georgia Muscogee Co 15.65 No

Georgia Richmond Co 15.68 No

Georgia Walker Co 15.43 Yes

Georgia Washington Co 15.31 No

Georgia Wilkinson Co 16.27 No

Illinois Cook Co 17.52 Yes

Illinois Madison Co 16.66 Yes

Illinois St. Clair Co 16.24 Yes

Indiana Clark Co 16.51 Yes

Indiana Dubois Co 15.73 Yes

Indiana Lake Co 17.26 Yes

Indiana Marion Co 16.83 Yes

Indiana Vanderburgh Co 15.54 Yes

Kentucky Boyd Co 15.23 No

Kentucky Bullitt Co 15.10 No

Kentucky Fayette Co 15.95 Yes

Kentucky Jefferson Co 16.71 Yes

Kentucky Kenton Co 15.30 No

Maryland Anne Arundel Co 15.26 Yes

Maryland Baltimore City 16.96 Yes

Michigan Wayne Co 19.41 Yes

Missouri St. Louis City 15.10 No

New Jersey Union Co 15.05 Yes

New York New York Co 16.19 Yes
State County 2010 Base

"Modeled + Monitored"

Nonattainment in 2010 Base

23

North Carolina Catawba Co 15.48 Yes

North Carolina Davidson Co 15.76 Yes

North Carolina Mecklenburg Co 15.22 No

Ohio Butler Co 16.45 Yes

Ohio Cuyahoga Co 18.84 Yes

Ohio Franklin Co 16.98 Yes

Ohio Hamilton Co 18.23 Yes

Ohio Jefferson Co 17.94 Yes

Ohio Lawrence Co 16.10 Yes

Ohio Mahoning Co 15.39 Yes

Ohio Montgomery Co 15.41 Yes

Ohio Scioto Co 18.13 Yes

Ohio Stark Co 17.14 Yes

Ohio Summit Co 16.47 Yes

Ohio Trumbull Co 15.28 No

Pennsylvania Allegheny Co 20.55 Yes

Pennsylvania Beaver Co 15.78 Yes

Pennsylvania Berks Co 15.89 Yes

Pennsylvania Cambria Co 15.14 Yes

Pennsylvania Dauphin Co 15.17 Yes

Pennsylvania Delaware Co 15.61 Yes

Pennsylvania Lancaster Co 16.55 Yes

Pennsylvania Philadelphia Co 16.65 Yes

Pennsylvania Washington Co 15.23 Yes

Pennsylvania Westmoreland Co 15.16 Yes

Pennsylvania York Co 16.49 Yes

Tennessee Davidson Co 15.36 No

Tennessee Hamilton Co 16.89 Yes

Tennessee Knox Co 17.44 Yes

Tennessee Sullivan Co 15.32 No

West Virginia Berkeley Co 15.69 Yes

West Virginia Brooke Co 16.63 Yes

West Virginia Cabell Co 17.03 Yes

West Virginia Hancock Co 17.06 Yes

West Virginia Kanawha Co 17.56 Yes

West Virginia Marion Co 15.32 Yes

West Virginia Marshall Co 15.81 Yes

West Virginia Ohio Co 15.14 Yes

West Virginia Wood Co 16.66 Yes
24

Table V-4. Projected PM2.5 concentrations (μg/m3) for nonattainment counties in the 2015

Base Case.

State County 2015 Base

Alabama DeKalb Co 15.24

Alabama Jefferson Co 18.85

Alabama Montgomery Co 15.24

Alabama Morgan Co 15.26

Alabama Russell Co 16.10

Alabama Talladega Co 15.22

Delaware New Castle Co 16.47

District of Columbia 15.57

Georgia Bibb Co 16.41

Georgia Chatham Co 15.06

Georgia Clarke Co 16.15

Georgia Clayton Co 17.46

Georgia Cobb Co 16.51

Georgia DeKalb Co 16.82

Georgia Floyd Co 17.33

Georgia Fulton Co 18.00

Georgia Hall Co 15.36

Georgia Muscogee Co 15.58

Georgia Richmond Co 15.76

Georgia Walker Co 15.37

Georgia Washington Co 15.34

Georgia Wilkinson Co 16.54

Illinois Cook Co 17.71

Illinois Madison Co 16.90

Illinois St. Clair Co 16.49

Illinois Will Co 15.12

Indiana Clark Co 16.37

Indiana Dubois Co 15.66

Indiana Lake Co 17.27

Indiana Marion Co 16.77

Indiana Vanderburgh Co 15.56

Kentucky Boyd Co 15.06

Kentucky Fayette Co 15.62

Kentucky Jefferson Co 16.61

Kentucky Kenton Co 15.09

Maryland Baltimore City 17.04

Maryland Baltimore Co 15.08

Michigan Wayne Co 19.28

Mississippi Jones Co 15.18
State County 2015 Base

25

Missouri St. Louis City 15.34

New York New York Co 15.76

North Carolina Catawba Co 15.19

North Carolina Davidson Co 15.34

Ohio Butler Co 16.32

Ohio Cuyahoga Co 18.60

Ohio Franklin Co 16.64

Ohio Hamilton Co 18.03

Ohio Jefferson Co 17.83

Ohio Lawrence Co 15.92

Ohio Mahoning Co 15.13

Ohio Montgomery Co 15.16

Ohio Scioto Co 17.92

Ohio Stark Co 16.86

Ohio Summit Co 16.14

Ohio Trumbull Co 15.05

Pennsylvania Allegheny Co 20.33

Pennsylvania Beaver Co 15.54

Pennsylvania Berks Co 15.66

Pennsylvania Delaware Co 15.52

Pennsylvania Lancaster Co 16.28

Pennsylvania Philadelphia Co 16.53

Pennsylvania York Co 16.22

Tennessee Davidson Co 15.36

Tennessee Hamilton Co 16.82

Tennessee Knox Co 17.34

Tennessee Shelby Co 15.17

Tennessee Sullivan Co 15.37

West Virginia Berkeley Co 15.32

West Virginia Brooke Co 16.51

West Virginia Cabell Co 16.86

West Virginia Hancock Co 16.97

West Virginia Kanawha Co 17.17

West Virginia Marshall Co 15.52

West Virginia Wood Co 16.69
26

VI. Modeling to Assess Interstate Ozone Contributions

This section documents the procedures used by EPA to quantify the impact of ozone

precursor emissions in specific upwind States on 8-hour ozone in projected downwind

nonattainment counties. These procedures are the first of the two-step process for determining

significant contribution, in which the second step involves a control cost assessment to determine

the amount of upwind emissions that should be reduced. In this section we use the phrase

“significant contribution” to refer to the ozone air quality step of the significance determination.

Included in this section are descriptions of: 1) the analytic approach for modeling the

contribution of upwind States to ozone in downwind nonattainment counties, 2) the methodology

for analyzing the modeling results, 3) the decision rules used to determine whether individual

States make a significant contribution (before considering cost), and d) the results of the

interstate ozone significant contribution analysis. As discussed in section III, the air quality

modeling analyses for ozone were conducted for an Eastern U.S. domain with CAMx, version

3.10.

The interstate ozone contribution analysis focuses on the 40 counties that contain monitors

that are both predicted to be nonattainment in the 2010 Base Case and are also measuring

nonattainment based on 2001-2003 design values. As noted above in Section V, we refer to

these counties as being modeled plus monitored nonattainment. These counties are identified in

Table V-1. It should be noted that the approach used to identify the nonattainment receptors for

this analysis differed from that used in the NOx SIP Call where we aggregated on a State-byState basis all grid cells which were both (a) associated with counties that violated the 8-hour

NAAQS (based on 1994-1996 data) and (b) had future base case predictions of 85 ppb or more. 

For the CAIR analysis of interstate ozone contributions, we have treated each individual

modeled plus monitored nonattainment county as a downwind nonattainment receptor.

A. Zero Out and Source Apportionment Techniques

The modeling approach used by EPA to quantify the impact of emissions in specific upwind

States on projected downwind nonattainment areas for 8-hour ozone includes two different

techniques, zero-out and source apportionment. The outputs of the two types of modeling were

used to calculate certain measures of contribution, called “metrics”. The metrics were evaluated

in terms of three key contribution factors to determine which States make a significant

contribution to downwind ozone nonattainment. The significant contribution analysis completed

for CAIR uses the same modeling techniques, the same metrics, and the same three contribution

factors as those used by EPA for the State-by-State determination in the NOx SIP Call.

The zero-out and source apportionment modeling techniques provide different technical

approaches to quantifying the downwind impact of emissions in upwind States. The zero-out

modeling provides an estimate of downwind impacts by calculating the difference between the

model estimates from a base case run to the estimates from a simulation in which the base case

man-made emissions of NOx and VOC are removed from a specific State.
27

EPA also used the source apportionment technique as part of the modeling analysis to

evaluate the downwind contributions of emissions in upwind States. The source apportionment

technique in CAMx was developed to provide a means of estimating the contributions of many

individual source areas/categories to ozone formation in one single model run. This is achieved

by using multiple tracer species to track the fate of ozone precursor emission (VOC and NOx)

and the ozone formation caused by these emissions within a CAMx simulation. The

methodology is designed so that all ozone and precursor concentrations are attributed to the

selected source areas/categories at all times. Thus, for all receptor locations and times, the ozone

concentrations predicted by the CAMx are attributed to the source areas/categories selected for

analysis. EPA used the Anthropogenic Precursor Culpability Assessment (APCA) option for the

CAIR source apportionment modeling. The key feature of APCA is that it allocates ozone

production to manmade precursor emissions, either through reactions among various manmade

sources and/or through reactions between manmade emissions and biogenic emissions. 

Additional information on the source apportionment technique can be found in the CAMx User’s

Guide (Environ, 2002). In general, EPA found that the source apportionment modeling tends to

show greater magnitude and frequency of contributions than the zero-out modeling for individual

linkages. However, because there is no technical evidence showing that one technique is clearly

superior to the other for evaluating contributions to ozone from various emission sources; both

approaches were given equal consideration in the significance analysis.

The EPA performed State-by-State zero-out modeling and source apportionment modeling

for 31 States in the Eastern U.S. These States are as follows: Alabama, Arkansas, Connecticut,

Delaware, Florida, Georgia, Illinois, Indiana, Iowa, Kentucky, Louisiana, Maine, Maryland,

Massachusetts, Michigan, Minnesota, Mississippi, Missouri, New Hampshire, New Jersey, New

York, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina, Tennessee, Vermont,

Virginia, West Virginia, and Wisconsin. In the application of both modeling techniques,

emissions from the District of Columbia were combined with those from Maryland. 

B. Ozone Contribution Factors and Metrics

EPA selected several metrics to quantify the projected downwind contributions from

emissions in upwind States. The metrics were designed to provide information on three

fundamental factors for evaluating whether emissions in an upwind State make large and/or

frequent contributions to downwind nonattainment. These factors are: a) the magnitude of the

contribution, b) the frequency of the contribution, and c) the relative amount of the contribution. 

The magnitude of contribution factor refers to the actual amount of ozone contributed by

emissions in the upwind State to nonattainment in the downwind area. The frequency of the

contribution refers to how often contributions above certain thresholds occur. The relative

amount of the contribution is used to compare the total ozone contributed by the upwind State to

the total amount of nonattainment ozone in the downwind area. These factors are the basis for

eight separate metrics that can be used to assess a particular impact. These metrics are described

below for the zero-out modeling and for the source apportionment modeling. Table VI-1 lists the

four metrics for each factor.
28

Table VI-1. Ozone Contribution Factors and Metrics.

Factor: Zero-out Metrics Source Apportionment Metrics

Magnitude of

Contribution

1) Maximum contribution 5) Maximum contribution; and

6) Highest daily average contribution (ppb

and percent)

Frequency of

Contribution

2) Number and percent of exceedances

with contributions in various

concentration ranges

7) Number and percent of exceedances with

contributions in various concentration

ranges

Relative Amount of

Contribution

3) Total contribution relative to the total

exceedance ozone in the downwind area

4) Population-weighted total contribution

relative to the total population-weighted

exceedance ozone in the downwind area

8) Total average contribution to exceedance

ozone in the downwind area

The values for each metric were calculated using only those periods during which modelpredicted 8-hour average ozone concentration were 85.0 ppb or more in at least one of the model

grid cells that are associated with the receptor county. That is, we only analyzed interstate ozone

contributions for the nonattainment receptor counties when the model predicted an exceedance in

the 2010 Base Case. Grid cells were linked to a specific nonattainment county if any part of the

grid cell covered any portion of the projected 2010 nonattainment county. In cases where a grid

cell covered two or more nonattainment counties, the grid was tied to the nonattainment county

that contained the largest portion of the area of the grid cell. The exception to that rule involves

cells that encompass a border of two adjacent States and more than two counties. In that case,

grids are assigned to the county in the State with the largest area of the grid cell.

As in the NOx SIP Call, the ozone contribution metrics are calculated and evaluated for each

upwind State to each downwind nonattainment receptor. These source-receptor pairs are referred

to as “linkages”.

1. Zero-out Metrics

A central component of several of the metrics is the number of predicted exceedances in the

2010 Base Case for each nonattainment receptor. The number of exceedances in a particular

nonattainment receptor was determined by the total number of daily predicted peak 8-hour

concentrations of 85 ppb or more across all the episode days in the grid cell(s) assigned to the

receptor. For example, the Fairfield County, CT receptor area consists of 11 grid cells. There are

30 days in the modeling simulations. Thus, the maximum possible number of exceedances for

this area is 330. The actual number of exceedances for this area was 10 grid-days.

The Maximum Contribution Metric (metric 1) for a particular upwind State to an individual

downwind nonattainment receptor was determined by first calculating the concentration

differences between the 2010 Base Case and the zero-out simulation for that upwind State. This

calculation was performed for all 2010 Base Case exceedances predicted within the grid cells
29

associated with the nonattainment county. The largest difference (i.e., contribution) for the

linkage across all of the exceedances at the downwind receptor is the maximum contribution. 

The Frequency of Contribution Metric (metric 2) for a particular linkage was determined by

first sorting the contributions by concentration range (e.g., >= 2 ppb, >= 5 ppb, etc.). The number

of impacts in each range was used to assess the frequency of contribution. Frequency of

Contribution was also expressed in terms of the percent of the 2010 Base exceedances that

receive contributions in each range. For example, Ohio contributes 2 ppb or more to 9 of the 10

exceedances in Fairfield County, CT. Thus, Ohio contributes >= 2 ppb to 90% of the

exceedances predicted in this county.

Determining the Total Ozone Contribution Relative to the Base Case Exceedance Metric

(metric 3) for a particular linkage involves first calculating the total ozone of 85 ppb or more in

the 2010 Base Case and in the upwind State’s zero-out run. This calculation was performed by

summing the amount of ozone above the NAAQS for each predicted exceedance at the downwind

receptor area. Second, the amount of ozone above the NAAQS from the zero-out run was

subtracted from the amount of ozone above the NAAQS in the 2010 Base run. The difference in

contribution (between the base and zero-out run) was then divided by the total ozone above the

NAAQS in the base run to form this metric. For example in Fairfield County CT, the sum of the

ozone above 85 ppb for the 2010 Base run in the 10 exceedances equals 217.3 ppb. When the

emissions from Ohio are zeroed, the total ozone above the NAAQS equals 181.7 ppb. The

difference between the base and zero-out amounts is 35.6 ppb. Thus, the total relative

contribution from emissions in Ohio is 16 percent (35.6 divided by 217.3).

The Population-Weighted Relative Contribution Metric (metric 4) is similar to the total ozone

contribution metric described in the preceding paragraph, except that during the calculation the

amount of ozone above the NAAQS in both the base case and the zero-out simulation was

weighted by (i.e., multiplied by) the 2000 population in the receptor grid cell. Note that this

metric is used solely to provide an additional perspective. It is not considered as an independent

metric and it did not provide the basis for any decisions.

2. Source Apportionment Metrics

Despite the fundamental differences between the zero out and source apportionment

techniques, the definitions of the source apportionment metrics are generally similar to the zero

out metrics. One exception is that all 8-hour periods with averages above or equal to 85 ppb are

considered in the source apportionment metrics, as opposed to just the peak 8-hour average per

day. Similar analyses completed as part of the NOx SIP call concluded that the differences

resulting from considering only daily maximum 8-hour averages (zero out) versus considering all

8-hour periods (source apportionment) was very small and did not influence the significance

determinations. Therefore, the number of “exceedance periods” are the total number of 8-hourly

predicted concentrations greater than or equal to 85 ppb within the downwind area on a cell-bycell basis. Again using the Fairfield County, CT receptor area as an example, the maximum

possible number of exceedances for this area is 5,610 (11 cells * 30 days * 17 eight-hour averages

per day). The actual number of exceedance periods for this area was 65.
30

For a given upwind State to downwind nonattainment receptor linkage, the Maximum

Contribution Metric (metric 5) is the highest contribution from among the contributions to all

exceedances at the downwind receptor. 

The Highest Daily Average Contribution Metric (metric 6) was determined for each day with

predicted exceedances at the downwind receptor. The metric is calculated by first summing the

contributions for that linkage over all exceedances on a particular day, then dividing by the

number of exceedances on that day to produce a daily average contribution to nonattainment. The

daily average contribution values across all days with exceedances were examined to identify the

highest value which is then selected for use in the determination of significance. We also express

this metric as a percent by dividing the highest daily average contribution by the corresponding

ozone exceedance concentration on the same day. As an example of how this metric was

calculated, consider the following two modeling days in Fairfield County, CT.

7/13/95: There were 4 exceedance periods. The total contribution from Ohio was 11 ppb.

Therefore, the daily average contribution from Ohio to Fairfield County, CT was 2.8 ppb on that

day. The average exceedance ozone on that day was 87 ppb, so the percentage contribution from

Ohio on that day was 3.1 percent.

7/14/95: There were 62 exceedance periods. The total contribution from Ohio was 471 ppb

for those cell-hours. Therefore the daily average contribution from Ohio to Fairfield County, CT

was 7.6 ppb on that day. The average exceedance ozone on that day was 103 ppb, so the

percentage contribution from Ohio on that day was 7.5 percent. This day had the highest daily

average contribution from Ohio to Fairfield County, CT (7.6 ppb) of any of the 30 modeling days,

so the ppb and percent contributions on this day were used as the values for this metric.

The Frequency of Contribution Metric (metric 7) for the source apportionment technique is

determined similar to the way this metric is calculated for the zero-out modeling. Looking at the

impact of Ohio man-made NOx and VOC emissions on Fairfield County, CT as an example, all of

the 65 exceedance hours (1.e., 100 percent) were received a contribution of at least 2 ppb.

The Total Average Contribution Metric is determined for each of the three episodes

individually as well as for all 30 days (i.e., all three episodes) combined. There are three parts to

the calculation of this metric. In step 1, the ozone values for each of the exceedance periods in a

particular downwind area are summed over the episode(s). In step 2, the total ozone from the

previous step that is due to anthropogenic sources is calculated based on the source

apportionment results. In step 3, the contributions from a given source region to this downwind

area are summed over the exceedance periods. The total contribution calculated in step 3 is then

divided by the total ozone resulting from manmade sources in step 2 to determine the fraction of

ozone that is due to emissions from the upwind source area. This fraction was multiplied by 100

to express the result as a percentage. For example, for the 65 exceedances in Fairfield County,

CT there is a total of 6,527 ppb of ozone. Of the total base ozone, the source apportionment

results indicate that 5,362 ppb is due to anthropogenic sources. The sources in Ohio contribute a

total of 479 ppb which is 8.9 percent of the base case total (i.e., 479 divided by 5,362).
31

The source apportionment total contribution metric is also used to estimate the percent of 8-

hour nonattainment that is due to transport from other States versus in-State emissions. The

results are provided in Table VI-2 for each 2010 Base nonattainment county along with the

projected 8-hour ozone concentration in the county. The results in this table indicate that ozone

transport constitutes a sizable portion of projected nonattainment in most eastern areas in 2010

(even after implementation of the NOx SIP call and other controls in the 2010 Base). In many

cases, over 50 percent of the ozone nonattainment problem is due to emissions in other States. 

All of the future nonattainment areas are projected to have at least a 20 percent overall

contribution from transported ozone or ozone precursors.

Table VI-2. Percent contribution to 8-hour ozone nonattainment due to transport from

upwind States.

2010 Base

Nonattainment

Counties

2010 Base

8-Hour Ozone (ppb)

Percent of 8-Hour

Ozone due to

Transport

Fairfield CT 92 80 %

Middlesex CT 90 93 %

New Haven CT 91 95 %

Washington DC 85 38 %

Newcastle DE 85 37 %

Fulton GA 86 24 %

Anne Arundel MD 88 45 %

Cecil MD 89 35 %

Harford MD 93 31 %

Kent MD 86 47 %

Macomb MI 85 43 %

Bergen NJ 86 38 %

Camden NJ 91 57 %

Gloucester NJ 91 62 %

Hunterdon NJ 89 26 %

Mercer NJ 95 36 %

Middlesex NJ 92 62 %

Monmouth NJ 86 65 %

Morris NJ 86 63 %

Ocean NJ 100 82 %

Erie NY 87 37 %

Richmond NY 87 55 %

Suffolk NY 91 52 %

Westchester NY 85 56 %

Geauga OH 87 47 %
2010 Base

Nonattainment

Counties

2010 Base

8-Hour Ozone (ppb)

Percent of 8-Hour

Ozone due to

Transport

10 We could not calculate contribution metrics for Denton and Tarrant counties since there were

no predicted 8-hour daily maximum concentrations > 85 ppb in grid cells associated with either of these

counties in the 2010 Base Case CAMx modeling.

32

Bucks PA 94 35 %

Chester PA 85 39 %

Montgomery PA 88 47 %

Philadelphia PA 90 55 %

Kent RI 86 88 %

Denton TX10 87 N/A

Galveston TX 85 37 %

Harris TX 97 36 %

Jefferson TX 85 50 %

Tarrant TX10 87 N/A

Arlington VA 86 39 %

Fairfax VA 85 33 %

Kenosha WI 91 37 %

Ozaukee WI 86 81 %

Sheboygan WI 88 74 %

C. Basis for Identifying which Ozone Linkages are Significant

EPA compiled the 8-hour metrics by downwind nonattainment receptor county (referred to

below as “downwind area”) in order to evaluate the contributions to downwind nonattainment in

2010. The contribution metrics were reviewed to determine how large of a contribution a

particular upwind State makes to nonattainment in each downwind area in terms of the magnitude

of the contribution, the frequency of the contributions, and the relative amount of the total

contribution. Determining whether a particular linkage indicated a significant amount of

transport from an upwind source State to a downwind county is a four step process.

The first step in evaluating the contribution factors was to screen out linkages for which the

contributions were clearly small. This initial screening was based on: 1) a maximum contribution

of less than 2 ppb from either of the two modeling techniques and/or, 2) a percent of total

nonattainment of less than 1 percent. Any upwind State that contributed to a particular downwind

area in amounts that were less than the screening criteria was considered not to make a significant

contribution to that downwind area. As an example, Virginia had a maximum contribution of 3

ppb on Fulton County, GA in the source apportionment modeling and a 2 ppb contribution in the

zero out modeling, and the percent of total nonattainment metric for Virginia/Fulton was 1

percent. This linkage was carried on for further analysis.
33

Those linkages that had contributions which exceeded the screening criteria were evaluated

further in steps 2 through 4. In step 2 we evaluated the contributions in each linkage based on the

zero-out modeling and in step 3 we evaluated the contributions in each linkage based on the

source apportionment modeling. In step 4 we considered the results of both step 2 and step 3 to

determine which of the linkages are significant. For both techniques, EPA determined whether

the linkage is significant by evaluating the magnitude, frequency, and relative amount of the

contributions. Each upwind State that made relatively large and/or frequent contributions to

nonattainment in the downwind area, based on these factors, is considered as contributing

significantly to nonattainment in the downwind area. The EPA believes that each of the factors

provides an independent legitimate measure of contribution. However, there had to be at least

two different factors that indicate large and/or frequent contributions in order for the linkage to be

found significant. In this regard, the finding of a significant contribution for an individual linkage

was not based on any single factor.

As indicated above, in step 4 we considered the results of evaluating the contributions zeroout contributions from step 1 and source apportionment contributions from step 2. For many of

the individual linkages the analyses of zero-out and source apportionment contributions yield a

consistent result (i.e., either large and/or frequent contributions or small and infrequent

contributions). Indeed, for each affected State, EPA’s proposed determination that the State

contributes significantly downwind is based on at least one linkage for which each of the factors

indicates large and/or frequent contributions. For some of the linkages, however, not all of the

factors are consistent. For upwind-downwind linkages in which some of the factors indicate high

and/or frequent contributions while other factors do not, EPA considered the overall number and

magnitude of those factors that indicate large and/or infrequent contributions compared to those

factors that do not. As part of the process of evaluating these types of linkages, we required that

two of the three factors had to indicate large and frequent contributions for one of the modeling

techniques and large and/or frequent contributions for at least one factor in the other modeling

technique in order to find that the linkage was significant. Thus, based on an assessment of all the

factors in such cases, EPA determined that the upwind State contributes significantly to

nonattainment in the downwind area if, on balance, the factors indicate large and frequent

contributions from the upwind State to the downwind area. Table VI-3, below, provides

examples of the four step process to illustrate how the metrics were evaluated to determine

whether individual linkages are significant. Contribution tables containing the values of the

metrics for each linkage are provided in Appendix G.
34

Table VI-3a. Evaluation of the Contribution to Downwind Nonattainment in Middlesex Co., CT.

Receptor Steps Evaluation of ContributionsMiddlesex Co. Connecticut

Step 1: Evaluation ofContributionsAgainstScreeningCriteria

- 22 upwind States had contributions that did not exceed either or both of the screening criteria. These 

further. As an example, the contribution from MI did exceed the screening criteria for the Source Appo

exceed the criteria for the zero-out modeling, so this linkage was deemed not significant. Of the 22 link

criteria (i.e., were not significant), 15 were not significant in both modeling techniques.

- 8 upwind States (MA, VA, MD, OH, NJ, PA, WV, and NY) had contributions that exceeded both screforward for evaluation in Steps 2 - 4.

Step 2: Evaluation ofContributionsfrom Zero-OutModeling

- Of the 8 States that exceeded the screening criteria, 6 (VA, MA, OH, NJ, PA, and NY) made contribut

considering the metrics for all three factors.- Contributions from VA, MA, OH, NJ, PA, and NY:

+ Magnitude: values ranged from 3.4 ppb (VA) up to 18.6 ppb (NY)+ Frequency: values ranged from VA which contributed 2 ppb or more to 18% of the exeedances ucontributed 2 ppb or more to all of the exceedances

- Contributions from MD and WV were determined to be not significant since there was only one occur

than 2 ppb and the amount of the contribution was less than 3 ppb.

Step 3: Evaluation ofContributionsfrom SourceApportionmentModeling

- The findings from the source apportionment modeling were similar to that of the zero-out modeling in exceeded the screening criteria (VA, MD, OH, NJ, PA, and NY) made contributions that were significa

all three factors:- Contributions from VA, MD, OH, NJ, PA, and NY:

+ Magnitude: values ranged from 7 ppb (MD and VA) to 31 ppb (NY)+ Frequency: values ranged from VA which contributed 2 ppb or more to 58% of the exeedances ucontributed 2 ppb or more to all of the exceedances

- Contributions from MA and WV were large in terms of two of the three factors:

+ Magnitude: the maximum contribution was 3 ppb from WV and 8 ppb from MA+ Frequency: MA contributed 2.0 ppb or more to 3% of the exceedances

Step 4: FinalDeterminationof Significance

- Of the 8 States that passed the screening criteria, 6 (VA, MA, OH, NJ, PA, and NY) had large and fre

Middlesex Co for at least two of the three contribution factors based on each modeling technique. Thusthese 6 States makes a significant contribution to nonattainment in this county. We determined that the 

States that passed the screening criteria (MD and WV) were not significant because the contributions ba

were not high and/or frequent.
35

Table VI-3b. Evaluation of the Contribution to Downwind Nonattainment in Bergen Co., NJ.

Receptor Steps Evaluation of Contributions

Bergen Co.New Jersey

Step 1: Evaluation ofContributionsAgainst ScreeningCriteria

- 24 upwind States had contributions that did not exceed either or both of the screening criteria. These lin

further. As an example, the contribution from DE did exceed the screening criteria for the Source Apporti

exceed the criteria for the Zero-Out modeling, so this linkage was deemed not significant. Of the 24 linkacriteria (i.e., were not significant), 17 were not significant in both modeling techniques.

- 6 upwind States (PA, VA, MD, OH, WV, and MI) had contributions that exceeded both screening criteri

evaluation in Steps 2 - 4.

Step 2: Evaluation ofContributionsfrom Zero-OutModeling

- Of the 6 States that exceeded the screening criteria, 4 (PA, VA, MD, and OH) made contributions that wmetrics for all three factors.- Contributions from PA, VA, MD, and OH:

+ Magnitude: values ranged from 4.7 ppb (OH) up to 23.8 ppb (PA)+ Frequency: values ranged from OH which contributed 2 ppb or more to 46% of the exeedances up tmore to all of the exceedances+ Relative Amount: values ranges from 19% (VA) up to 97% (PA)

- Contributions from WV and MI were large in terms of two of the three factors: + Frequency: MI contributed 2.0 ppb or more to 15% and WV contributed 2.0 ppb or more to 31% of 

+ Relative Amount: total contribution from MI is 4%; the total contribution from WV is 13% of the to

Step 3: Evaluation ofContributionsfrom SourceApportionmentModeling

- In the source apportionment modeling 4 of the 6 States that exceeded the screening criteria (MD, OH, PA 

that were significant considering the metrics from all three factors:- Contributions from (MD, OH, PA and VA):

+ Magnitude: maximum contributions ranged from 7 ppb (MD) to 31 ppb (PA)+ Frequency: values ranged from MD which contributed 2 ppb or more to 76% of the exeedances up 

or more to all of the exceedances+ Relative Amount: values ranged from 5% (MD and VA) up to 31% (PA)

- Contributions from MI and WV were large in terms of two of the three factors:

+ Magnitude: maximum contribution was 6 ppb from MI and 3 ppb from WV+ Frequency: MI contributed 2.0 ppb or more to 15% of the exceedances; WV contributed 2 ppb or m

Step 4: FinalDetermination ofSignificance

- 4 of the States (MD, OH, PA and VA) had large and frequent contributions to Bergen Co. for at least tw

factors based on each modeling technique. Therefore, we determined that each of these States makes a signonattainment in this county. In addition, the contributions from MI and WV were large and frequent for 

Apportionment modeling and large based on one factor in the Zero-Out modeling. Therefore, we determi

significant contribution to Bergen Co.
11As noted above, we combined Maryland and the District of Columbia as a single entity in our

ozone contribution modeling. This is a logical approach because of the small size of the District of

Columbia and, hence, its emissions and its close proximity to Maryland. Under our analysis, Maryland

and the District of Columbia are linked as significant contributors to the same downwind nonattainment

counties. We also considered these entities separately, and in view of the close proximity of these two

areas we believe that Maryland is linked as a significant contributor to nonattainment in the District of

Columbia and that the District of Columbia is linked as a significant contributor to nonattainment in

Maryland.

36

D. Results of Interstate Ozone Contribution Analysis 

Using the procedures described above, EPA determined which States contribute significantly

to nonattainment in the 40 2010 modeled plus monitored downwind receptor counties. Of the 31

States included in the assessment of interstate ozone contributions, 25 States and the District of

Columbia were found to have emissions which make a significant contribution to downwind 8-

hour ozone nonattainment. The linkages which EPA found to be significant for 8-hour ozone are

listed in Tables VI-411 (by upwind State) and VI-5 (by downwind nonattainment county). Each

upwind State contributed to nonattainment in counties in at least two downwind States (except

for Louisiana and Arkansas which contributed to nonattainment only in Texas counties). Of the

31 States included in the assessment of interstate ozone transport, the following six States are

found to not make a significant contribution to downwind nonattainment: Georgia, Maine,

Minnesota, New Hampshire, Rhode Island, and Vermont.
37

Table VI-4. Upwind State-to-downwind nonattainment county significant “linkages” for 8-

hour ozone.

Upwind

States Total Linkages Downwind Counties

AL 3 Fulton GA Harris TX Jefferson TX

AR 3 Galveston TX Harris TX Jefferson TX

CT 2 Kent RI Suffolk NY

DE 13

Bucks PA Camden NJ Chester PA Gloucester NJ

Hunterdon NJ Mercer NJ Middlesex NJ Monmouth NJ

Montgomery PA Morris NJ Ocean NJ Philadelphia PA

Suffolk NY

FL 1 Fulton GA

IA 3 Kenosha WI Macomb MI Sheboygan WI

IL 5 Geauga OH Kenosha WI Macomb MI Ozaukee WI

Sheboygan WI

IN 5 Geauga OH Kenosha WI Macomb MI Ozaukee WI

Sheboygan WI

KY 3 Fulton GA Geauga OH Macomb MI

LA 3 Galveston TX Harris TX Jefferson TX

MA 2 Kent RI Middlesex NJ

MD/DC 23

Arlington VA Bergen NJ Bucks PA Camden NJ

Chester PA District of Columbia Erie NY Fairfax VA

Fairfield CT Gloucester NJ Hunterton NJ Mercer NJ

Middlesex NJ Monmouth NJ Montgomery PA Morris NJ

New Castle DE New Haven CT Ocean NJ Philadelphia PA

Richmond NY Suffolk NY Westchester NY

MI

19

Anne Arundel MD Bergen NJ Bucks PA Camden NJ

Cecil MD Chester PA Erie NY Geauga OH

Gloucester NJ Kent MD Mercer NJ Middlesex NJ

Monmouth NJ Morris NJ New Castle DE Ocean NJ

Philadelphia PA Richmond NY Suffolk NY

MO 4 Geauga OH Kenosha WI Ozaukee WI Sheboygan WI

MS 2 Harris TX Jefferson TX

NC 8 Anne Arundel MD Fulton GA Harford MD Kent MD

Newcastle DE Suffolk NY Bucks PA Chester PA

NJ 10

Erie NY Fairfield CT Kent RI Middlesex CT

Montgomery PA New Haven CT Philadelphia PA Richmond NY

Suffolk NY Westchester NY

NY 9

Fairfield CT Kent RI Mercer NJ Middlesex CT

Middlesex NJ Monmouth NJ Morris NJ New Haven CT

Ocean NJ
38

OH 28

Anne Arundel MD Arlington VA Bergen NJ Bucks PA

Camden NJ Cecil MD Chester PA District of Columbia

Fairfax VA Fairfield CT Gloucester NJ Harford MD

Hunterton NJ Kent MD Kent RI Macomb MI

Mercer NJ Middlesex CT Middlesex NJ Monmouth NJ

Montgomery PA Morris NJ New Castle DE New Haven CT

Ocean NJ Philadelphia PA Suffolk NY Westchester NY

PA 25

Anne Arundel MD Arlington VA Bergen NJ Camden NJ

Cecil MD District of Columbia Erie NY Fairfax VA

Fairfield CT Gloucester NJ Harford MD Hunterton NJ

Kent MD Kent RI Mercer NJ Middlesex CT

Middlesex NJ Monmouth NJ Morris NJ New Castle DE

New Haven CT Ocean NJ Richmond NY Suffolk NY

Westchester NY

SC 1 Fulton GA

TN 1 Fulton GA

VA 26

Anne Arundel MD Bergen NJ Bucks PA Camden NJ

Cecil MD Chester PA District of Columbia Erie NY

Fairfield CT Gloucester NJ Harford MD Hunterton NJ

Kent MD Kent RI Mercer NJ Middlesex CT

Middlesex NJ Monmouth NJ Morris NJ New Castle DE

New Haven CT Ocean NJ Philadelphia PA Richmond NY

Suffolk NY Westchester NY

WI 2 Erie NY Macomb MI

WV 25

Anne Arundel MD Bergen NJ Bucks PA Camden NJ

Cecil MD Chester PA Fairfax VA Fairfield CT

Fulton GA Gloucester NJ Harford MD Hunterton NJ

Kent MD Mercer NJ Middlesex NJ Monmouth NJ

Montgomery PA Morris NJ New Castle DE New Haven CT

Ocean NJ Philadelphia PA Richmond NY Suffolk NY

Westchester NY
39

Table VI-5. Upwind States that make a significant contribution to 8-hour ozone in each

downwind nonattainment county.

Downwind State/

County Upwind States

CT Middlesex MA NJ NY OH PA VA

CT New Haven MD/DC NJ NY OH PA VA WV

CT Fairfield MD/DC NJ NY OH PA VA WV

District of Columbia MD/DC OH PA VA

DE New Castle MD/DC MI NC OH PA VA WV

GA Fulton AL FL KY NC SC TN WV

MD Harford NC OH PA VA WV

MD Kent MI NC OH PA VA WV

MD Cecil MI OH PA VA WV

MD

Anne

Arundel MI NC OH PA VA WV

MI Macomb IA IL IN KY OH WI

NJ Ocean DE MD/DC MI NY OH PA VA WV

NJ Bergen MD/DC MI OH PA VA WV

NJ Gloucester DE MD/DC MI OH PA VA WV

NJ Morris DE MD/DC MI NY OH PA VA WV

NJ Middlesex DE MD/DC MI NY OH PA VA WV

NJ Hunterton DE MD/DC OH PA VA WV

NJ Camden DE MD/DC MI OH PA VA WV

NJ Mercer DE MD/DC MI NY OH PA VA WV

NJ Monmouth DE MD/DC MI NY OH PA VA WV

NY Erie MD/DC MI NJ PA VA WI

NY Westchester MD/DC NJ OH PA VA WV

NY Richmond MD/DC MI NJ PA VA WV

NY Suffolk CT DE MD/DC MI NC NJ OH PA VA WV

OH Geauga IL IN KY MI MO

PA Montgomery DE MD/DC NJ OH WV

PA Philadelphia DE MD/DC MI NJ OH VA WV

PA Chester DE MD/DC MI NJ OH VA WV

PA Bucks DE MD/DC MI NJ OH VA WV

RI Kent CT MA NJ NY OH PA VA

TX Harris AL AR LA MS

TX Jefferson AL AR LA MS

TX Galveston AR LA

VA Arlington MD/DC OH PA

VA Fairfax MD/DC OH PA WV

WI Ozaukee IL IN MO

WI Kenosha IA IL IN MO

WI Sheboygan IA IL IN MO
40

As a refinement to the preceding procedures for evaluating the contributions for each

linkage, EPA prepared the following criteria for the three contribution factors to distinguish

between the values which comprise a significant contribution versus those that do not:

Magnitude Metrics: considered large enough to be significant if the contribution is >= 3 ppb.

Frequency Metrics: considered frequent enough to be significant if there is a 3 ppb or more

contribution to at least 3 percent of the exceedances and, for linkages in which the maximum

contribution was in the range of >= 2 to < 3 ppb, there has to be contributions in this range to at

least two exceedances in the downwind area.

Relative Amount Metrics: considered large enough to be significant if the total contribution

relative to the total amount of nonattainment is >= 3 percent.

Applying these criteria to the contribution metrics for each linkage in the evaluation steps 2

through 4 yields the same result in terms of which linkages are significant, as provided in Tables

VI-4 and VI-5.
41

VII. Modeling to Assess Interstate PM2.5 Contributions

This section documents the procedures used by EPA to quantify the impact of SO2 ad Nox

emissions in specific States on projected PM2.5 nonattainment in other States. The analytic

approach for modeling the contribution of upwind States to PM2.5 in downwind nonattainment

areas and the methodology for analyzing the modeling results are described in Section VII.A.

These procedures are the first of the two-step process for determining significant contribution, in

which the second step involves a control cost assessment to determine the amount of upwind

emissions that should be reduced. The findings as to whether individual States meet the air

quality component of the significant contribution test is provided in Section VII.B. In this

section we use the phase “significant contribution” to refer to the PM2.5 air quality step of the

significance determination.

The interstate PM2.5 contribution analysis focuses on the 113 receptors in 62 counties that

are both predicted to be nonattainment in the 2010 Base Case and are also measuring

nonattainment based on 2001-2003 design values. As noted above in Section V, we refer to

these counties as being modeled plus monitored nonattainment. 

A. Analytical Techniques for Modeling Interstate Contributions to Annual Average PM2.5

Nonattainment

1. State-by-State Zero-Out Modeling

State-by-State zero-out modeling was used as the modeling technique to quantify the

contribution from SO2 and NOx emissions in individual States to future PM2.5 nonattainment in

other States. As part of the zero-out modeling technique we removed the 2010 Base Case

anthropogenic emissions of SO2 and NOx from 37 States in the East on a State-by-State basis in

different CMAQ model runs. The States we analyzed using zero-out modeling are: Alabama,

Arkansas, Connecticut, Delaware, Florida, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky,

Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri,

Nebraska, New Hampshire, New Jersey, New York, North Carolina, North Dakota, Ohio,

Oklahoma, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas,

Vermont, Virginia, West Virginia, and Wisconsin. Emissions from the District of Columbia

were combined with those from Maryland. 

The model predictions from the zero-out runs were used to calculate the contribution from

each State to PM2.5 at nonattainment receptors in other States through the following procedures:

Step 1: The SMAT technique is applied for each zero-out run to calculate PM2.5

concentrations at each FRM site (see Section IV.B. for a general description of this technique). 

Step 2: For each of the 113 receptors, the difference between the 2010 Base PM2.5

concentration and the PM2.5 concentration for the zero-out run is calculated at that same

receptor. This difference is defined as the contribution from the zero-out State to the

nonattainment receptor. 
42

2. Interstate PM2.5 Contribution Metric

As described in the CAIR preamble, EPA has selected the maximum contribution as the

metric to use in determining significant contribution to annual average PM2.5 nonattainment. 

The maximum contribution for a given State is the highest contribution made by that State to any

downwind nonattianment receptor. The procedures for calculating the maximum contribution

metric are as follows:

Step 1: Examine the contribution from each upwind State to PM2.5 at each of the 113 2010

Base "modeled plus monitored" nonattainment site in other States. 

Step 2: Select the largest contribution from among the set of downwind contributions

determined in Step 1. This is the maximum downwind contribution.

The maximum contribution from each State to annual average PM2.5 nonattainment in a

downwind State is provided in Table VII-1. The contribution from each State to each

nonattainment receptor county is provided in Appendix H. For those counties with more than

one 2010 Base nonattainment site, the largest contribution across all nonattainment sites in the

county is the value shown for that county in Appendix H.

Table VII-1. Maximum downwind PM2.5 contribution (μg/m3) for each of the 37 upwind

States.

Upwind State

 Maximum

Downwind

 Contribution 

Upwind State

Maximum

Downwind

Contribution

Alabama 0.98 Nebraska 0.07

Arkansas 0.19 New Hampshire <0.05

Connecticut <0.05 New Jersey 0.13

Delaware 0.14 New York 0.34

Florida 0.45 North Carolina 0.31

Georgia 1.27 North Dakota 0.11

Illinois 1.02 Ohio 1.67

Indiana 0.91 Oklahoma 0.12

Iowa 0.28 Pennsylvania 0.89

Kansas 0.11 Rhode Island <0.05

Kentucky 0.90 South Carolina 0.40

Louisiana 0.25 South Dakota <0.05

Maine <0.05 Tennessee 0.65

Maryland/DC 0.69 Texas 0.29

Massachusetts 0.07 Vermont <0.05

Michigan 0.62 Virginia 0.44

Minnesota 0.21 West Virginia 0.84

Mississippi 0.23 Wisconsin 0.56

Missouri 1.07
12As noted above, we combined Maryland and the District of Columbia as a single entity in our

PM2.5 contribution modeling. This is a logical approach because of the small size of the District of

Columbia and, hence, its emissions and its close proximity to Maryland. Under our analysis, Maryland

and the District of Columbia are linked as significant contributors to the same downwind nonattainment

counties. We also considered these entities separately, and in view of the close proximity of these two

areas we believe that Maryland is linked as a significant contributor to nonattainment in the District of

Columbia and that the District of Columbia is linked as a significant contributor to nonattainment in

Maryland.

43

B. Evaluation of Upwind State Contributions to Downwind PM2.5 Nonattainment

The EPA used a criterion of 0.2 μg/m3

 for determining whether SO2 and NOx emissions in a

State make a significant contribution to PM2.5 nonattainment in another State. The rationale for

choosing this criterion is described in the CAIR preamble. To determine whether the 0.2 μg/m3

significance criteria is met, the amount of the contributions specified in Table VIII-1 are each

truncated to a tenth of microgram. If the resulting (truncated) value is greater than or equal to

0.2 μg/m3 then the contribution is significant. Examining the maxium contributions in Table

VII-1 indicates that, of the 37 States analyzed, 28 States and the District of Columbia

contribution 0.2 μg/m3 or more to nonattainment in other States and therefore are found to make

a significant contribution to PM2.5 nonattainment. The upwind State-to-downwind

nonattainment county significant linkages are listed in Table VII-212. These downwind counties

contain at least one nonattainment receptor site that receives a contribution of 0.2 μg/m3 or more

from the upwind State. In Table VII-3 we present these linkages by downwind nonattainment

county. 
44

Table VII-2. Upwind States-to-downwind nonattainment county significant “linkages” for

PM2.5.

Upwind

States

Total

Linkages Downwind Counties

AL 21

Bibb GA Cabell WV Catawba NC Clark IN

Clarke GA Clayton GA Cobb GA Davidson NC

DeKalb GA Dubois IN Fayette KY Floyd GA

Fulton GA Hamilton OH Hamilton TN Jefferson KY

Knox TN Lawrence OH Scioto OH Vanderburgh IN

Walker GA

FL 7 Bibb GA Clarke GA Clayton GA Cobb GA

DeKalb GA Jefferson AL Russell AL

GA 17

Butler OH Cabell WV Catawba NC Clark IN

Davidson NC Fayette KY Hamilton OH Hamilton TN

Jefferson AL Jefferson KY Kanawha WV Knox TN

Lawrence OH Montgomery OH Russell AL Scioto OH

Vanderburgh IN

IL 23

Allegheny PA Butler OH Cabell WV Clark IN

Cuyahoga OH Dubois IN Fayette KY Franklin OH

Hamilton OH Hamilton TN Jefferson AL Jefferson KY

Kanawha WV Lake IN Lawrence OH Mahoning OH

Marion IN Montgomery OH Scioto OH Stark OH

Summit OH Vanderburgh IN Wayne MI

IN 46

Allegheny PA Beaver PA Berkeley WV Bibb GA

Brooke WV Butler OH Cabell WV Cambria PA

Catawba NC Clarke GA Clayton GA Cobb GA

Cook IL Cuyahoga OH Davidson NC DeKalb GA

Fayette KY Floyd GA Franklin OH Fulton GA

Hamilton OH Hamilton TN Hancock WV Jefferson AL

Jefferson KY Jefferson OH Kanawha WV Knox TN

Lancaster PA Lawrence OH Madison IL Mahoning OH

Marion WV Marshall WV Montgomery OH Ohio WV

Russell AL St. Clair IL Scioto OH Stark OH

Summit OH Walker GA Wayne MI Washington PA

Westmoreland PA Wood WV

IA 5 Cook IL Lake IN Madison IL Marion IN

St. Clair IL
45

KY 35

Allegheny PA Butler OH Cabell WV Catawba NC

Clark IN Clarke GA Cobb GA Cuyahoga OH

Davidson NC Dubois IN Floyd GA Franklin OH

Hamilton OH Hamilton TN Jefferson AL Jefferson OH

Kanawha WV Knox TN Lawrence OH Madison IL

Mahoning OH Marion IN Marion WV Marshall WV

Montgomery OH Ohio WV St. Clair IL Scioto OH

Stark OH Summit OH Vanderburgh IN Walker GA

Washington PA Westmoreland PA Wood WV

LA 2 Jefferson AL Russell AL

MD/DC 13

Berkeley WV Berks PA Cambria PA Dauphin PA

Delaware PA District of Columbia Lancaster PA New Castle DE

New York NY Philadelphia PA Union NJ Westmoreland PA

York PA

MI 36

Allegheny PA Beaver PA Berks PA Brooke WV

Butler OH Cabell WV Cambria PA Clark IN

Cook IL Cuyahoga OH Dauphin PA Delaware PA

Fayette KY Franklin OH Hamilton OH Hancock WV

Jefferson OH Lake IN Lancaster PA Lawrence OH

Mahoning OH Marion IN Marion WV Marshall WV

Montgomery OH New Castle DE Ohio WV Philadelphia PA

Scioto OH Stark OH Summit OH Union NJ

Washington PA Westmoreland PA Wood WV York PA

MN 2 Cook IL Lake IN

MO 9

Clark IN Cook IL Dubois IN Jefferson KY

Lake IN Madison IL Marion IN St. Clair IL

Vanderburgh IN

MS 1 Jefferson AL

NY 5 Berks PA Lancaster PA New Castle DE New Haven CT

Union NJ

NC 7

Anne Arundel MD Baltimore City Bibb GA Clarke GA

District of Columbia Kanawha WV Knox TN

OH 51

Anne Arundel MD Allegheny PA Baltimore City Beaver PA

Berkeley WV Berks PA Bibb GA Brooke WV

Cabell WV Cambria PA Catawba NC Clark IN

Clarke GA Clayton GA Cobb GA Cook IL

Dauphin PA Davidson NC DeKalb GA Delaware PA

District of Columbia Dubois IN Fayette KY Floyd GA

Fulton GA Hamilton TN Hancock WV Jefferson AL

Jefferson KY Kanawha WV Knox TN Lake IN

Lancaster PA Madison IL Marion IN Marion WV

Marshall WV New Castle DE New York NY Ohio WV

Philadelphia PA Russell AL St. Clair IL Union NJ
46

Vanderburgh IN Walker GA Washington PA Wayne MI

Westmoreland PA Wood WV York PA

PA 25

Anne Arundel MD Baltimore City Berkeley WV Brooke WV

Cabell WV Catawba NC Clarke GA Cuyahoga OH

Davidson NC District of Columbia Hancock WV Jefferson OH

Kanawha WV Lawrence OH Mahoning OH Marion WV

Marshall WV New Castle DE New York NY Ohio WV

Stark OH Summit OH Union NJ Wayne MI

Wood WV

SC 9

Bibb GA Catawba NC Clarke GA Clayton GA

Cobb GA Davidson NC DeKalb GA Fulton GA

Russell AL

TN 23

Bibb GA Butler OH Cabell WV Catawba NC

Clark IN Clarke GA Clayton GA Cobb GA

Davidson NC DeKalb GA Dubois IN Fayette KY

Floyd GA Fulton GA Hamilton OH Jefferson AL

Jefferson KY Kanawha WV Lawrence OH Russell AL

Scioto OH Vanderburgh TN Walker GA

TX 2 Madison IL St Clair IL

VA 13

Anne Arundel MD Baltimore City Berkeley WV Berks PA

Catawba NC Dauphin PA Davidson NC Delaware PA

District of Columbia Lancaster PA New Castle DE Philadelphia PA

York PA

WV 33

Anne Arundel MD Allegheny PA Baltimore City Beaver PA

Berks PA Butler OH Cambria PA Catawba NC

Clarke GA Cuyahoga OH Dauphin PA Davidson NC

Delaware PA District of Columbia Fayette KY Franklin OH

Hamilton OH Jefferson OH Knox TN Lancaster PA

Lawrence OH Mahoning OH Montgomery OH New Castle DE

New York NY Philadelphia PA Scioto OH Stark OH

Summit OH Union NJ Washington PA Westmoreland PA

York PA

WI 4 Cook IL Lake IN Marion IN Wayne MI
47

Table VII-3. Upwind States that make a significant contribution to PM2.5 in each

downwind nonattainment county.

Downwind State

/County Upwind States

AL Jefferson FL GA IL IN KY LA MS OH TN

AL Russell FL GA IN LA OH SC TN

DE New Castle MD/DC MI NY OH PA VA WV

District of Columbia NC OH PA VA WV

GA Bibb AL FL IN NC OH SC TN

GA Clarke AL FL IN KY NC OH PA SC TN WV

GA Clayton AL FL IN OH SC TN

GA Cobb AL FL IN KY OH SC TN

GA DeKalb AL FL IN OH SC TN

GA Floyd AL IN KY OH TN

GA Fulton AL IN OH SC TN

GA Walker AL IN KY OH TN

IL Cook IN IA MI MN MO OH WI

IL Madison IN IA KY MO OH TX

IL St. Clair IN IA KY MO OH TX

IN Clark AL GA IL KY MI MO OH TN

IN Dubois AL IL KY MO OH TN

IN Lake IL IA MI MN MO OH WI

IN Marion IL IA KY MI MO OH WI

IN Vanderburgh AL GA IL KY MO OH TN

KY Fayette AL GA IL IN MI OH TN WV

KY Jefferson AL GA IL IN MO OH TN

MD Anne Arundel NC OH PA VA WV

MD Baltimore City NC OH PA VA WV

MI Wayne IL IN OH PA WI

NJ Union MD/DC MI NY OH PA WV

NY New York MD/DC OH PA WV

NC Catawba AL GA IN KY OH PA SC TN VA WV

NC Davidson AL GA IN KY OH PA SC TN VA WV

OH Butler GA IL IN KY MI TN WV

OH Cuyahoga IL IN KY MI PA WV

OH Franklin IL IN KY MI WV

OH Hamilton AL GA IL IN KY MI TN WV

OH Jefferson IN KY MI PA WV

OH Lawrence AL GA IL IN KY MI PA TN WV

OH Mahoning IL IN KY MI PA WV

OH Montgomery GA IL IN KY MI WV

OH Scioto AL GA IL IN KY MI TN WV

OH Stark IL IN KY MI PA WV

OH Summit IL IN KY MI PA WV
Downwind State

/County Upwind States

48

PA Allegheny IL IN KY MI OH WV

PA Beaver IN MI OH WV

PA Berks MD/DC MI NY OH VA WV

PA Cambria IN MD/DC MI OH WV

PA Dauphin MD/DC MI OH VA WV

PA Delaware MD/DC MI OH VA WV

PA Lancaster IN MD/DC MI NY OH VA WV

PA Philadelphia MD/DC MI OH VA WV

PA Washington IN KY MI OH WV

PA Westmoreland IN KY MD/DC MI OH WV

PA York MD/DC MI OH VA WV

TN Hamilton AL GA IL IN KY OH

TN Knox AL GA IN KY NC OH WV

WV Berkeley IN MD/DC OH PA VA

WV Brooke IN MI OH PA

WV Cabell AL GA IL IN KY MI OH PA TN

WV Hancock IN MI OH PA

WV Kanawha GA IL IN KY NC OH PA TN

WV Marion IN KY MI OH PA

WV Marshall IN KY MI OH PA

WV Ohio IN KY MI OH PA

WV Wood IN KY MI OH PA
13The modeling used to estimate the impacts of CAIR on PM2.5 assumed annual SO2 and NOx

controls for Arkansas, Delaware, and New Jersey in addition to the 23-States plus the District of

Columbia. However, the EPA plans to propose to include these three States in the CAIR region through a

separate regulatory process. Thus, the results of our modeling represents the total impacts expected for

CAIR assuming Arkansas, Delaware, and New Jersey will become part of the annual SO2 and NOx

trading program.

14 In addition to the SO2 and NOx reductions in these States, we also modeled summer-season

only EGU NOx controls for Connecticut and Massachusetts, which significantly contribute to ozone, but

not to PM2.5 nonattainment in downwind areas.

15 For the purposes of this discussion, we have calculated the percent reduction in total EGU

emissions which includes units greater than and less than 25 MW.

49

VIII. Expected Impacts of CAIR on Air Quality, Visibility, and Deposition

A. Introduction

In this section we describe the results of air quality modeling performed to determine the

projected impacts on PM2.5, 8-hour ozone, visibility, and deposition of the SO2 and NOx

emissions reductions expected to result from CAIR13. In this assessment, we focus on the air

quality related improvements in two analysis years, 2010 and 2015. These improvements are

quantified by comparing modeling results for the CAIR scenario to the modeling results for the

corresponding 2010 and 2015 Base Case scenarios. The impacts of CAIR on PM2.5 and 8-hour

ozone are described below in terms of the reductions from CAIR in the concentrations of these

two pollutants and the extent of nonattainment in 2010 and 2015. For visibility, we present the

expected change in visibility in Class I areas on the 20 percent best and 20 percent worst days. 

For deposition, we examine the percent reduction in nitrogen and sulfur deposition across the

Eastern U.S. expected to occur with CAIR.

B. Overview of Emissions Reductions from CAIR

The CAIR 2010 and 2015 emissions reductions from the power generation sector include a

two Phase cap and trade program covering the control region modeled (i.e., the 23 States plus the

District of Columbia included in CAIR and Arkansas, Delaware, and New Jersey).14 Phase 1 of

the regional strategy (the 2010 reductions) is forecast to reduce total EGU SO2 emissions15 in the

control region modeled by 40 percent in 2010. Phase 2 (the 2015 reductions) is forecast to

provide a 48 percent reduction in EGU SO2 emissions compared to the Base Case in 2015. The

net effect of CAIR on total SO2 emissions in the control region modeled considering all sources

of emissions, is a 28 percent reduction in 2010 and a 32 percent reduction in 2015. 

For NOx, Phase 1 of the strategy is forecast to reduce total EGU emissions by 44 percent in

2009. Total NOx emissions across the control region (i.e., includes all sources) are 11 percent

lower in the 2010 CAIR scenario compared to the emissions in the 2010 Base Case. In Phase 2,

EGU NOx emissions are projected to decline by 54 percent in 2015 in this region. Total NOx

emissions from all anthropogenic sources are projected to be reduced by 14 percent in 2015. 
50

The percent change in emissions by State for SO2 and NOx in 2010 and 2015 for the regional

control strategy modeled are provided in the NFR EITSD.

C. Impacts of CAIR on Air Quality Concentrations and Nonattainment

We quantified the expected impacts of CAIR on PM2.5 and 8-hour ozone by comparing the

projected concentrations for the 2010 and 2015 Base Cases to the concentrations projected for

the CAIR scenario in both of these future years. Concentrations of PM2.5 and 8-hour ozone for

CAIR in 2010 and 2015 were calculated using the methods described in Section VI. The 2010

and 2015 CAIR PM2.5 concentrations for the 432 counties included in this analysis are provided

in Appendix F. The 8-hour ozone concentrations in 2010 and 2015 with CAIR are provided in

Appendix E. The focus of our analysis of the impacts of CAIR on air quality is on

nonattainment counties. The number of PM2.5 and 8-hour ozone nonattainment counties in the

East for the base year and 2010 and 2015 Base and CAIR scenarios is provided in Table VIII-1. 

Maps showing the location of these counties are provided in Figures VIII-1 through VIII-3 at the

end of this section. 

Table VIII-1. Summary of the number of PM2.5 and 8-hour ozone nonattainment counties

in the East.

PM2.5 Ozone PM2.5-only Ozone-only PM2.5 + Ozone

1999-2003

Average 101 270 46 215 55

2010 Base Case 79 40 74 35 5

2010 CAIR 28 37 27 36 1

2015 Base Case 74 22 73 21 1

2015 CAIR 18 16 18 16 0

1. Impacts of CAIR on PM2.5 Concentrations and Nonattainment

In our analysis of PM2.5 we have quantified the impacts of CAIR on those counties

projected to be nonattainment in the 2010 and 2015 Base Case scenarios. The results for these

counties are provided for 2010 and 2015 in Table VIII-2 and Table VIII-3, respectively. Note

that in both tables, counties shown in bold/italics are projected to come into attainment with

CAIR. Maps showing the location of the 2010 and 2015 Base nonattainment counties and those

counties projected to come into attainment with CAIR are provided in Figure VIII-4, at the end

of this section.
51

Table VIII-2. Projected PM2.5 concentrations (μg/m3) for the 2010 base case and CAIR,

and the impact of CAIR in 2010. 

State County 2010 Base Case 2010 CAIR Impact of CAIR

Alabama DeKalb Co 15.23 13.97 -1.26

Alabama Jefferson Co 18.57 17.46 -1.11

Alabama Montgomery Co 15.12 14.10 -1.02

Alabama Morgan Co 15.29 14.11 -1.18

Alabama Russell Co 16.17 15.15 -1.02

Alabama Talladega Co 15.34 14.00 -1.34

Delaware New Castle Co 16.56 14.84 -1.72

District of Columbia 15.84 13.68 -2.16

Georgia Bibb Co 16.27 15.17 -1.10

Georgia Clarke Co 16.39 14.96 -1.43

Georgia Clayton Co 17.39 16.29 -1.10

Georgia Cobb Co 16.57 15.35 -1.22

Georgia DeKalb Co 16.75 15.70 -1.05

Georgia Floyd Co 16.87 15.87 -1.00

Georgia Fulton Co 18.02 16.98 -1.04

Georgia Hall Co 15.60 14.28 -1.32

Georgia Muscogee Co 15.65 14.57 -1.08

Georgia Richmond Co 15.68 14.64 -1.04

Georgia Walker Co 15.43 14.22 -1.21

Georgia Washington Co 15.31 14.22 -1.09

Georgia Wilkinson Co 16.27 15.22 -1.05

Illinois Cook Co 17.52 16.88 -0.64

Illinois Madison Co 16.66 15.96 -0.70

Illinois St. Clair Co 16.24 15.54 -0.70

Indiana Clark Co 16.51 15.15 -1.36

Indiana Dubois Co 15.73 14.37 -1.36

Indiana Lake Co 17.26 16.48 -0.78

Indiana Marion Co 16.83 15.54 -1.29

Indiana Vanderburgh Co 15.54 14.26 -1.28

Kentucky Boyd Co 15.23 13.38 -1.85

Kentucky Bullitt Co 15.10 13.67 -1.43

Kentucky Fayette Co 15.95 14.17 -1.78

Kentucky Jefferson Co 16.71 15.44 -1.27

Kentucky Kenton Co 15.30 13.72 -1.58

Maryland Anne Arundel Co 15.26 12.98 -2.28

Maryland Baltimore city 16.96 14.88 -2.08

Michigan Wayne Co 19.41 18.23 -1.18

Missouri St. Louis city 15.10 14.40 -0.70

New Jersey Union Co 15.05 13.60 -1.45

New York New York Co 16.19 14.95 -1.24

North Carolina Catawba Co 15.48 14.07 -1.41
State County 2010 Base Case 2010 CAIR Impact of CAIR

52

North Carolina Davidson Co 15.76 14.36 -1.40

North Carolina Mecklenburg Co 15.22 13.92 -1.30

Ohio Butler Co 16.45 15.03 -1.42

Ohio Cuyahoga Co 18.84 17.11 -1.73

Ohio Franklin Co 16.98 15.13 -1.85

Ohio Hamilton Co 18.23 16.61 -1.62

Ohio Jefferson Co 17.94 15.64 -2.30

Ohio Lawrence Co 16.10 14.11 -1.99

Ohio Mahoning Co 15.39 13.40 -1.99

Ohio Montgomery Co 15.41 13.83 -1.58

Ohio Scioto Co 18.13 15.98 -2.15

Ohio Stark Co 17.14 15.08 -2.06

Ohio Summit Co 16.47 14.69 -1.78

Ohio Trumbull Co 15.28 13.50 -1.78

Pennsylvania Allegheny Co 20.55 18.01 -2.54

Pennsylvania Beaver Co 15.78 13.61 -2.17

Pennsylvania Berks Co 15.89 13.56 -2.33

Pennsylvania Cambria Co 15.14 12.72 -2.42

Pennsylvania Dauphin Co 15.17 12.88 -2.29

Pennsylvania Delaware Co 15.61 13.94 -1.67

Pennsylvania Lancaster Co 16.55 14.09 -2.46

Pennsylvania Philadelphia Co 16.65 14.98 -1.67

Pennsylvania Washington Co 15.23 12.99 -2.24

Pennsylvania Westmoreland Co 15.16 12.60 -2.56

Pennsylvania York Co 16.49 14.20 -2.29

Tennessee Davidson Co 15.36 14.26 -1.10

Tennessee Hamilton Co 16.89 15.57 -1.32

Tennessee Knox Co 17.44 16.16 -1.28

Tennessee Sullivan Co 15.32 14.01 -1.31

West Virginia Berkeley Co 15.69 13.43 -2.26

West Virginia Brooke Co 16.63 14.42 -2.21

West Virginia Cabell Co 17.03 15.08 -1.95

West Virginia Hancock Co 17.06 14.89 -2.17

West Virginia Kanawha Co 17.56 15.27 -2.29

West Virginia Marion Co 15.32 12.90 -2.42

West Virginia Marshall Co 15.81 13.46 -2.35

West Virginia Ohio Co 15.14 12.81 -2.33

West Virginia Wood Co 16.66 14.14 -2.52
53

Table VIII-3. Projected PM2.5 concentrations (μg/m3) for the 2015 base case and CAIR,

and the impact of CAIR controls in 2015.

State County 2015 Base Case 2015 CAIR Impact of CAIR

Alabama DeKalb Co 15.24 13.46 -1.78

Alabama Jefferson Co 18.85 17.36 -1.49

Alabama Montgomery Co 15.24 13.87 -1.37

Alabama Morgan Co 15.26 13.85 -1.41

Alabama Russell Co 16.10 14.66 -1.44

Alabama Talladega Co 15.22 13.35 -1.87

Delaware New Castle Co 16.47 14.41 -2.06

District of Columbia 15.57 13.11 -2.46

Georgia Bibb Co 16.41 14.83 -1.58

Georgia Chatham Co 15.06 13.86 -1.20

Georgia Clarke Co 16.15 14.10 -2.05

Georgia Clayton Co 17.46 15.85 -1.61

Georgia Cobb Co 16.51 14.67 -1.84

Georgia DeKalb Co 16.82 15.29 -1.53

Georgia Floyd Co 17.33 15.79 -1.54

Georgia Fulton Co 18.00 16.47 -1.53

Georgia Hall Co 15.36 13.48 -1.88

Georgia Muscogee Co 15.58 14.06 -1.52

Georgia Richmond Co 15.76 14.23 -1.53

Georgia Walker Co 15.37 13.65 -1.72

Georgia Washington Co 15.34 13.67 -1.67

Georgia Wilkinson Co 16.54 15.01 -1.53

Illinois Cook Co 17.71 16.95 -0.76

Illinois Madison Co 16.90 16.07 -0.83

Illinois St. Clair Co 16.49 15.64 -0.85

Illinois Will Co 15.12 14.27 -0.85

Indiana Clark Co 16.37 14.79 -1.58

Indiana Dubois Co 15.66 14.16 -1.50

Indiana Lake Co 17.27 16.36 -0.91

Indiana Marion Co 16.77 15.38 -1.39

Indiana Vanderburgh Co 15.56 14.17 -1.39

Kentucky Boyd Co 15.06 12.95 -2.11

Kentucky Fayette Co 15.62 13.54 -2.08

Kentucky Jefferson Co 16.61 15.13 -1.48

Kentucky Kenton Co 15.09 13.26 -1.83

Maryland Baltimore city 17.04 14.50 -2.54

Maryland Baltimore Co 15.08 12.75 -2.33

Michigan Wayne Co 19.28 17.95 -1.33

Mississippi Jones Co 15.18 14.06 -1.12

Missouri St. Louis city 15.34 14.50 -0.84

New York New York Co 15.76 14.33 -1.43

North Carolina Catawba Co 15.19 13.45 -1.74
State County 2015 Base Case 2015 CAIR Impact of CAIR

54

North Carolina Davidson Co 15.34 13.61 -1.73

Ohio Butler Co 16.32 14.67 -1.65

Ohio Cuyahoga Co 18.60 16.67 -1.93

Ohio Franklin Co 16.64 14.57 -2.07

Ohio Hamilton Co 18.03 16.10 -1.93

Ohio Jefferson Co 17.83 15.26 -2.57

Ohio Lawrence Co 15.92 13.71 -2.21

Ohio Mahoning Co 15.13 12.94 -2.19

Ohio Montgomery Co 15.16 13.33 -1.83

Ohio Scioto Co 17.92 15.55 -2.37

Ohio Stark Co 16.86 14.58 -2.28

Ohio Summit Co 16.14 14.18 -1.96

Ohio Trumbull Co 15.05 13.08 -1.97

Pennsylvania Allegheny Co 20.33 17.47 -2.86

Pennsylvania Beaver Co 15.54 13.09 -2.45

Pennsylvania Berks Co 15.66 12.99 -2.67

Pennsylvania Delaware Co 15.52 13.52 -2.00

Pennsylvania Lancaster Co 16.28 13.33 -2.95

Pennsylvania Philadelphia Co 16.53 14.53 -2.00

Pennsylvania York Co 16.22 13.46 -2.76

Tennessee Davidson Co 15.36 14.02 -1.34

Tennessee Hamilton Co 16.82 14.94 -1.88

Tennessee Knox Co 17.34 15.61 -1.73

Tennessee Shelby Co 15.17 14.19 -0.98

Tennessee Sullivan Co 15.37 13.77 -1.60

West Virginia Berkeley Co 15.32 12.73 -2.59

West Virginia Brooke Co 16.51 14.05 -2.46

West Virginia Cabell Co 16.86 14.64 -2.22

West Virginia Hancock Co 16.97 14.54 -2.43

West Virginia Kanawha Co 17.17 14.66 -2.51

West Virginia Marshall Co 15.52 12.87 -2.65

West Virginia Wood Co 16.69 13.88 -2.81

As described in Section VI.B.1, we project that 79 counties in the East will be nonattainment

for PM2.5 in the 2010 Base Case. We estimate that, on average, CAIR will reduce PM2.5 in

these 79 counties by 1.6 μg/m3

. In over 90 percent of the nonattainment counties (i.e., 74 out of

79 counties), we project that PM2.5 will be reduced by at least 1.0 μg/m3

. In over 25 percent of

the 79 nonattainment counties (i.e., 23 of the 79 counties) we project a decline of more than 2.0

μg/m3

. Of the 79 counties that are nonattainment in the 2010 Base, we project that 51 counties

will come into attainment as a result of the SO2 and NOx emissions reductions expected from

CAIR. Even those 28 counties that remain nonattainment in 2010 after implementation of CAIR

will be closer to attainment as a result of these emissions reductions. Specifically, the average
55

reduction of PM2.5 in the 28 residual nonattainment counties is projected to be 1.3 μg/m3. Also,

after implementation of CAIR, we project that 18 of the 28 residual nonattainment counties in

2010 will be within 1.0 μg/m3 of the NAAQS and 12 counties will be within 0.5 μg/m3 of

attainment.

In 2015 we are projecting that PM2.5 in the 74 Base Case nonattainment counties will be

reduced by 1.8 μg/m3

, on average, as a result of CAIR. In over 90 percent of the nonattainment

counties (i.e., 67 of the 74 counties) concentrations of PM2.5 are predicted to be reduced by at

least 1.0 μg/m3

. In over 35 percent of the counties (i.e., 27 of the 74 counties), we project the

regional strategy will reduce PM2.5 by more than 2.0 μg/m3. As a result of the reductions in

PM2.5, 56 nonattainment counties are projected to come into attainment in 2015. The remaining

18 nonattainment counties are projected to be closer to attainment with CAIR. Our modeling

results indicate that PM2.5 will be reduced in the range of 0.7 μg/m3 to 2.9 μg/m3 in these 18

counties. The average reduction across these 18 residual nonattainment counties is 1.5 μg/m3. 

To illustrate the broad benefits expected from CAIR on PM2.5 in the East, we prepared maps

showing the regionwide reductions in PM2.5 with CAIR in both 2010 and 2015. The data for

these maps were created using the basic SMAT procedures described in Section V.C., above. 

However, PM2.5 concentrations were projected for each CMAQ grid cell rather than just for

individual counties. Details on the approach for projecting the gridded PM2.5 concentrations is

provided in the CAIR Regulatory Impact Assessment, Chapter 4 (EPA, 2005e). The regionwide

reductions from CAIR in 2010 and 2015 are shown in Figure VIII-5, below. In 2010, reductions

in PM2.5 of more that 0.5 μg/m3

 are predicted over much of the East in a broad area extending

from Texas to Illinois eastward. From northern Georgia and Alabama across the Tennessee

Valley and Ohio Valley, the mid-Atlantic States and portions of the Northeast, we expect

reductions in PM2.5 of more than 1.0 μg/m3. In 2010, the largest reductions of 1.5 μg/m3 to

more that 2.5 μg/m3

 are predicted for the Ohio Valley across Virginia and Pennsylvania to

Delaware and parts of New Jersey. Greater impactss are expected by 2015 with reductions in

PM2.5 of 1.5 μg/m3

 or more from Georgia and Alabama northeastward to New York State and

Kansas. The largest reductions in 2015 are projected to occur in portions of Ohio, West

Virginia, Pennsylvania, and western Maryland.

2. Impacts on 8-Hour Ozone Concentrations and Nonattainment

In our analysis of the impacts of CAIR on 8-hour ozone, we have quantified the impacts of

CAIR on those counties projected to be nonattainment in the 2010 and 2015 Base Case

scenarios. The results for these counties are provided for 2010 and 2015 in Table VIII-4 and

Table VIII-5, respectively. Note that in both tables, counties shown in bold/italics are projected

to come into attainment with CAIR. Maps showing the location of the 2010 and 2015 Base 8-

hour ozone nonattainment counties and those counties projected to come into attainment with

CAIR are provided in Figure VIII-6, at the end of this section.
56

Table VIII-4. Projected 8-hour concentrations (ppb) for the 2010 base case and CAIR, and

the impact of CAIR in 2010.

State County 2010 Base Case 2010 CAIR

Impact of

CAIR

Connecticut Fairfield Co 92.6 92.2 -0.4

Connecticut Middlesex Co 90.9 90.6 -0.3

Connecticut New Haven Co 91.6 91.3 -0.3

District of Columbia District of Columbia 85.2 85.0 -0.2

Delaware New Castle Co 85.0 84.7 -0.3

Georgia Fulton Co 86.5 85.1 -1.4

Maryland Anne Arundel Co 88.8 88.6 -0.2

Maryland Cecil Co 89.7 89.5 -0.2

Maryland Harford Co 93.0 92.8 -0.2

Maryland Kent Co 86.2 85.8 -0.4

Michigan Macomb Co 85.5 85.4 -0.1

New Jersey Bergen Co 86.9 86.0 -0.9

New Jersey Camden Co 91.9 91.6 -0.3

New Jersey Gloucester Co 91.8 91.3 -0.5

New Jersey Hunterdon Co 89.0 88.6 -0.4

New Jersey Mercer Co 95.6 95.2 -0.4

New Jersey Middlesex Co 92.4 92.1 -0.3

New Jersey Monmouth Co 86.6 86.4 -0.2

New Jersey Morris Co 86.5 85.5 -1.0

New Jersey Ocean Co 100.5 100.3 -0.2

New York Erie Co 87.3 86.9 -0.4

New York Richmond Co 87.3 87.1 -0.2

New York Suffolk Co 91.1 90.8 -0.3

New York Westchester Co 85.3 84.7 -0.6

Ohio Geauga Co 87.1 86.6 -0.5

Pennsylvania Bucks Co 94.7 94.3 -0.4

Pennsylvania Chester Co 85.7 85.4 -0.3

Pennsylvania Montgomery Co 88.0 87.6 -0.4

Pennsylvania Philadelphia Co 90.3 89.9 -0.4

Rhode Island Kent Co 86.4 86.2 -0.2

Texas Denton Co 87.4 86.8 -0.6

Texas Galveston Co 85.1 84.6 -0.5

Texas Harris Co 97.9 97.4 -0.5

Texas Jefferson Co 85.6 85.0 -0.6

Texas Tarrant Co 87.8 87.2 -0.6
State County 2010 Base Case 2010 CAIR

Impact of

CAIR

57

Virginia Arlington Co 86.2 86.0 -0.2

Virginia Fairfax Co 85.7 85.4 -0.3

Wisconsin Kenosha Co 91.3 91.0 -0.3

Wisconsin Ozaukee Co 86.2 85.8 -0.4

Wisconsin Sheboygan Co 88.3 87.7 -0.6

Table VIII-5. Projected 8-hour concentrations (ppb) for the 2015 base case and CAIR, and

the impact of CAIR in 2015.

State County 2015 Base Case 2015 CAIR

Impact of

CAIR

Connecticut Fairfield Co 91.4 90.6 -0.8

Connecticut Middlesex Co 89.1 88.4 -0.7

Connecticut New Haven Co 89.8 89.1 -0.7

Maryland Anne Arundel Co 86.0 84.9 -1.1

Maryland Cecil Co 86.9 85.4 -1.5

Maryland Harford Co 90.6 89.6 -1.0

Michigan Macomb Co 85.1 84.2 -0.9

New Jersey Bergen Co 85.7 84.5 -1.2

New Jersey Camden Co 89.5 88.3 -1.2

New Jersey Gloucester Co 89.6 88.2 -1.4

New Jersey Hunterdon Co 86.5 85.4 -1.1

New Jersey Mercer Co 93.5 92.4 -1.1

New Jersey Middlesex Co 89.8 88.8 -1.0

New Jersey Ocean Co 98.0 96.9 -1.1

New York Erie Co 85.2 84.2 -1.0

New York Suffolk Co 89.9 89.0 -0.9

Pennsylvania Bucks Co 93.0 91.8 -1.2

Pennsylvania Montgomery Co 86.5 84.9 -1.6

Pennsylvania Philadelphia Co 88.9 87.5 -1.4

Texas Harris Co 97.3 96.4 -0.9

Texas Jefferson Co 85.0 84.1 -0.9

Wisconsin Kenosha Co 89.4 88.8 -0.6
16We are using county-level maps to illustrate the spatial extend of the impact of CAIR on 8-hour

ozone as opposed to maps of gridded values as we used for PM2.5 because the methodologies for spatial

interpolation for 8-hour concentrations is still under development.

58

As described in Section VI.B.1, we project that 40 counties in the East would be

nonattainment for 8-hour ozone under the assumptions in the 2010 Base Case. Our modeling of

CAIR in 2010 indicates that 3 of these counties will come into attainment of the 8-hour ozone

NAAQS and that ozone in 16 of the 40 nonattainment counties will be reduced by 1 ppb or more. 

In addition, our modeling predicts that 8-hour ozone exceedances (i.e., 8-hour ozone of 85 ppb

or higher) within nonattainment areas are expected to decline by 5 percent in 2010 with CAIR. 

Of the 37 counties that are projected to remain nonattainment in 2010 after CAIR, nearly half

(i.e., 16 of the 37 counties) are within 2 ppb of attainment.

In 2015, we project that 6 of the 22 counties which are nonattainment for 8-hour ozone in the

Base Case will come into attainment with CAIR. Ozone concentrations in over 70 percent (i.e.,

16 of 22 counties) of the 2015 Base Case nonattainment counties are projected to be reduced by

1 ppb or more as a result of CAIR. Exceedances of the 8-hour ozone NAAQS are predicted to

decline in nonattainment areas by 14 percent with CAIR controls in place in 2015. Thus, the

NOx emissions reductions which will result from CAIR will help to bring 8-hour ozone

nonattainment areas in the East closer to attainment by 2010 and beyond.

To illustrate the spatial extent of the impact of CAIR on 8-hour ozone across the East we

prepared maps showing the ozone reductions or the 525 counties included in this analysis16. 

Maps showing the projected impacts in 2010 and 2015 on 8-hour ozone in each county are

provided in Figure VIII-7 at the end of this section. Note that the data used to create these maps

are provided in Appendix E. The maps show that 8-hour ozone reductions from CAIR are

extend across nearly all areas of the East. In 2010, 78 percent of the counties are expected to

experience ozone reductions of up to 1 ppb while 14 percent will see reductions of 1 to 2 ppb,

with more than 2 ppb reduction in 6 percent of the counties. In 2015, 8-hour ozone reductions of

more than 2 ppb are expected in 47 percent of the counties with reductions more that 2 ppb in 18

percent of the counties. 
59

PM2.5 and 8-Hour Ozone Nonattainment

Based on 1999-2003 Average Concentrations

Both Nonattainment

Nonattainment for ozone only

Nonattainment for PM2.5 only

Number of Counties:

55

215

46

PM2.5 and 8-Hour Ozone Nonattainment

Based on 1999-2003 Average Concentrations

Both Nonattainment

Nonattainment for ozone only

Nonattainment for PM2.5 only

Number of Counties:

55

215

46

3. Figures for Section VIII.C. - Impacts of CAIR on Air Quality Concentrations and

Nonattainment

Figure VIII-1. PM2.5 and/or 8-hour ozone nonattainment counties in the East based on average

1999-2003 ambient concentrations.
60

2010 Base Case

Projected 8-Hour Ozone and Annual PM2.5 Nonattainment

Both Nonattainment

Nonattainment for ozone only

Nonattainment for PM2.5 only

Number of Counties:

5

35

74

2010 Base Case

Projected 8-Hour Ozone and Annual PM2.5 Nonattainment

Both Nonattainment

Nonattainment for ozone only

Nonattainment for PM2.5 only

Number of Counties:

5

35

74

Both Nonattainment

Nonattainment for ozone only

Nonattainment for PM2.5 only

Number of Counties:

1

36

27

2010 CAIR Control Case

Projected 8-Hour Ozone and Annual PM2.5 Nonattainment

Both Nonattainment

Nonattainment for ozone only

Nonattainment for PM2.5 only

Number of Counties:

1

36

27

2010 CAIR Control Case

Projected 8-Hour Ozone and Annual PM2.5 Nonattainment

Figure VIII-2. PM2.5 and 8-hour ozone nonattainment counties in the East for the 2010 Base

Case (top) and 2010 CAIR control case (bottom).
61

Both Nonattainment

Nonattainment for ozone only

Nonattainment for PM2.5 only

Number of Counties:

1

21

73

2015 Base Case

Projected 8-Hour Ozone and Annual PM2.5 Nonattainment

Both Nonattainment

Nonattainment for ozone only

Nonattainment for PM2.5 only

Number of Counties:

1

21

73

2015 Base Case

Projected 8-Hour Ozone and Annual PM2.5 Nonattainment

Both Nonattainment

Nonattainment for ozone only

Nonattainment for PM2.5 only

Number of Counties:

0

16

18

2015 CAIR Control Case

Projected 8-Hour Ozone and Annual PM2.5 Nonattainment

Both Nonattainment

Nonattainment for ozone only

Nonattainment for PM2.5 only

Number of Counties:

0

16

18

2015 CAIR Control Case

Projected 8-Hour Ozone and Annual PM2.5 Nonattainment

Figure VIII-3. PM2.5 and 8-hour ozone nonattainment counties in the East for the 2015 Base

Case (top) and 2015 CAIR control case (bottom).
62

2010 CAIR Control Case: PM2.5 Nonattainment Counties

2010 Control Residual Nonattinment: 28

Come into attainment by CAIR in 2010: 51

Total 2010 Base Nonattainment: 79 

Manhattan, NY comes into

attainment with CAIR in 2010

2010 CAIR Control Case: PM2.5 Nonattainment Counties

2010 Control Residual Nonattinment: 28

Come into attainment by CAIR in 2010: 51

Total 2010 Base Nonattainment: 79 

Manhattan, NY comes into

attainment with CAIR in 2010

2015 Control Residual Nonattinment: 18

Come into attainment by CAIR in 2015: 56

Total 2015 Base Nonattainment: 74 

2015 CAIR Control Case: PM2.5 Nonattainment Counties

Manhattan, NY comes into

attainment with CAIR in 2015

2015 Control Residual Nonattinment: 18

Come into attainment by CAIR in 2015: 56

Total 2015 Base Nonattainment: 74 

2015 CAIR Control Case: PM2.5 Nonattainment Counties

Manhattan, NY comes into

attainment with CAIR in 2015

Figure VIII-4. 2010 and 2015 Base Case 8-hour nonattainment counties that are expected to

come into attainment with CAIR in 2010 (top) and 2015 (bottom).
63

Figure VIII-5. Regionwide reduction in annual average PM2.5 expected from CAIR in 2010

(top) and 2015 (bottom).
64

2010 CAIR Control Case: 8-Hour Ozone Nonattainment Counties

2010 Control Residual Nonattainment: 37

Come into attainment by CAIR in 2010: 3

Total 2010 Base Nonattainment: 40

2015 CAIR Control Case: 8-Hour Ozone Nonattainment Counties

2015 Control Residual Nonattainment: 16

Come into attainment by CAIR in 2015: 6

Total 2015 Base Nonattainment: 22

Figure VIII-6. 2010 and 2015 Base Case 8-hour nonattainment counties that are expected to

come into attainment with CAIR in 2010 (top) and 2015 (bottom).
65

Figure VIII-7. Regionwide reduction in 8-hour ozone concentrations expected from CAIR in

2010 (top) and 2015 (bottom).
17 There are 81 IMPROVE sites with complete data for 2001. Many of these sites collect data

that is “representative” of other nearby unmonitored Class I areas. There are a total of 116 Class I areas

that are represented by the 81 sites. The matching of sites to monitors is taken from “Guidance for

Tracking Progress Under the Regional Haze Rule”.

66

D. Estimated Impacts on Visibility in Class 1 Areas

The impacts of the CAIR regional SO2 and NOx emissions reductions were examined in

terms of the projected improvements in visibility on the 20 percent best and worst days from

2001 at Class I areas. We quantified visibility impacts at the 116 Class I areas which have

complete IMPROVE ambient data for 2001 or are represented by IMPROVE monitors with

complete data17. There are currently 110 IMPROVE monitoring sites (representing all 156 Class

I areas) collecting ambient PM2.5 data at Class I areas, but only 81 of these sites have complete

data for 2001.

Visibility for the future year base and CAIR control scenario were calculated using a

methodology which applies modeling results in a relative sense similar to the Speciated Modeled

Attainment Test (SMAT). The draft PM2.5 and Regional Haze modeling guidance recommends

the calculation of future year changes in visibility in a similar manner to the calculation of

changes in PM2.5. We generally followed the procedures in the guidance. 

In calculating visibility impairment, the extinction coefficient and deciview values are

made up of individual component species (sulfate, nitrate, organics, etc). The predicted change

in visibility (on the 20 percent best and worst days) is calculated as the percent change in the

extinction coefficient for each of the PM2.5 species (on a daily average basis). The individual

daily species extinction coefficients are summed to get a daily total extinction value. The daily

extinction coefficients are converted to deciviews and then averaged across all 20 percent best

and all 20 percent worst days separately. In this way, we calculate an average change in

deciviews from the base case to a future case at each IMPROVE site. Additionally, subtracting

the CAIR scenario deciview values from the corresponding future base case deciview values

gives an estimate of the visibility benefits in Class I areas expected to occur with CAIR. 

Additional details on the visibility calculation methodology can be found in the report

“Supplemental Air Quality Modeling Technical Support Document (TSD) for the Clean Air Interstate

Rule (CAIR) Better-than-BART Demonstration”(EPA, 2005e).

As described in Section V.C., we have updated the SMAT procedures to use PM2.5

component species that emulate the FRM measurements as part of the methodology for

projecting future PM2.5 concentrations. For visibility calculations, we are continuing to use the

IMPROVE program species definitions and visibility formulas which are recommended in the

draft modeling guidance. Each IMPROVE site has measurements of PM2.5 species and

therefore we do not need to estimate the species fractions in the same way that we did for FRM

sites (i.e., using interpolation techniques and other assumptions concerning volatilization of

species). Therefore, the methodology for calculating PM2.5 species fractions for the visibility

calculations (at IMPROVE sites) differs from the calculations in the revised SMAT

methodology. 
18The baseline visibility is calculated as the average deciviews in the 1998-2002 period. This was

the most recent period of available complete IMPROVE data. The future year deciview values were

normalized to the 1998-2002 5 year average values by adjusting the future deciview values by the

difference between the 2001 visibility and the 1998-2002 visibility at each site. 

19There were also several model runs for 2015 which compared the CAIR controls to BART

controls. The results of these runs and a “better than BART” analysis is contained in the better than

BART TSD.

67

Appendix I contains an example calculation of the predicted improvement in visibility on

the 20 percent worst days at an IMPROVE site. We used data for the period 1998-2002 as the

baseline for calculating visibility impairment18. The current visibility degradation as well as the

future visibility degradation in Class I areas on the 20 percent best visibility days and the 20

percent worst visibility days for the 2010 and 2015 base and CAIR are also provided in

Appendix I19. The calculated visibility in deciviews (dv) is based on the model predicted

changes in PM species between the 2001 Base Year and the 2010 and 2015 Base and CAIR

CMAQ model runs. 

As an example of the information in Appendix I, the following is a discussion of the

expected improvement in visibility on the 20 percent worst visibility days at the Great Smoky

Mountains National Park (GRSM). Between the current base year and the 2010 Base Case,

visibility is expected to improve by 0.4 dv. The improvement between the current base year and

2010 with CAIR is 2.3 dv. In 2010, visibility is projected to improve by 1.9 dv solely due to

CAIR, compared to the base case. The improvement in visibility by 2015 is larger than in 2010. 

The visibility improvement from the current base year to the 2015 Base 0.8 dv. The

improvement from the base year to 2015 with CAIR is 3.4 dv. The improvement solely due to

CAIR in 2015 is projected to be 2.6 dv. The modeling predicts somewhat smaller improvements

in visibility on the 20 percent best days forecast for both 2010 and 2015. Note that there are no

cases in which visibility deteriorated due to the regional strategy.
68

E. Estimated Impacts on Nitrogen and Sulfur Deposition

The impacts of CAIR on nitrogen and sulfur deposition were calculated for 2010 and

2015 using the CMAQ deposition predictions. As described in the CAIR RIA, Chapter 5 (EPA,

2005e), nitrogen and sulfur deposition can contribute to a number of harmful environmental

effects in sensitive forests and to fish and other aquatic life in sensitive lakes, streams, and

coastal ecosystems. Total sulfur deposition is comprised of the sulfur mass in SO2 and sulfate

deposition. For this analysis, nitrogen deposition was determined by calculating the sum of wet

and dry deposition for nitrogen mass contained in the nitrogen-containing species predicted by

CMAQ. These species include nitrate, nitric acid, nitric oxide, nitrogen dioxide, and

ammonium. The calculation of total deposition was performed for each model grid cell over the

annual model simulations to yield an estimate of annual total nitrogen deposition in each grid. A

similar procedure was followed for calculating annual total sulfur deposition using CMAQpredicted deposition of sulfate and SO2.

We used the annual deposition estimates for the 2010 and 2015 Base Cases along with

deposition from the corresponding CAIR scenario to determine the percent change in nitrogen

and sulfur deposition expected from CAIR, compared to what would be expected in these future

years without CAIR. Maps showing the percent change in nitrogen deposition in 2010 and 2015

(future base case versus CAIR) are provided in Figure VIII-8. The results indicate that CAIR is

expected to result in lower nitrogen deposition by 5 percent or more over much of the area east

of the Mississippi River including estuaries along the eastern Gulf Coast and in the mid-Atlantic

region. The largest expected decline in nitrogen deposition in 2010, compared to the 2010 Base,

are in the range of 10 to 15 percent. This range of improvement is most notable along portions

of the Appalachian Mountains from southwest Virginia to Pennsylvania. In 2015, the reductions

in nitrogen deposition compared to base case estimates are larger than in 2010, especially in and

near the Appalachian Mountains and the Adirondacks.

The percent reductions in sulfur deposition across the East with CAIR versus the 2010

and 2015 Base Cases are shown in Figure VIII-9. As indicted by this figure, CAIR is expected

to result in lower sulfur deposition by 15 percent or more across most portions of the East to the

east of the Mississippi River. The expected improvement in sulfur deposition is in the range of

20 to 25 percent in the southern portion of the Appalachians in 2010. In 2015, the sulfure

deposition is reduced further, by up to 30 to 40 percent. Along the Appalachians from West

Virginia and western Virginia northeastward to southern New York State, CAIR is expected to

reduce sulfur deposition by 30 to 40 percent in 2010, and by over 50 percent in portions of this

area in 2015. Across the forests of upstate New York and northern New England, sulfur

deposition is projected to decline by 30 to 40 percent in 2015 with CAIR, compared to the

forecast base case conditions.
69

2010

2015

Figure VIII-8. Percent reduction in total nitrogen deposition expected from CAIR in 2010 (top)

and 2015 (bottom).
70

2010

2015

Figure VIII-9. Percent reduction in total sulfur deposition expected from CAIR in 2010 (top)

and 2015 (bottom).
71

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Pollutants with Linked Global Tropospheric Chemistry and Regional Air Quality Models”,2nd

ICAP workshop, RTP, NC, October 2004,

http://www.cep.unc.edu/empd/projects/ICAP/2004wkshp_pres.html.

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Technical Support Document

for the Final

Clean Air Interstate Rule

Air Quality Modeling Analyses

Appendix A

Analysis of 1995 Ozone Episodes
1

The 8-hour ozone maps were created and provided by the New York State Department

of Environmental Conservation.

Meteorological Conditions and 8-Hour Ozone Concentrations

During the 1995 Episodes Modeled for CAIR

Warm temperatures, light winds, cloud-free skies, and stable boundary layers are some of

the typical characteristics of ozone episodes. On a synoptic scale, these conditions usually result

from a combination of high pressure aloft (500 millibars) and at the surface. At a smaller scale,

the conditions that lead to local ozone exceedances can vary from location to location (based on

factors such as wind direction, sea/lake breezes, etc.) The meteorological and resultant ozone

patterns for the three 1995 modeling episodes are discussed in more detail below. Maps showing

the observed daily maximum 8-hour ozone concentrations for each day in the three episodes are

provided below1

.

June 12-24, 1995

The initial stages of this episode were fairly typical from the standpoint of regional

meteorology. A 500-millibar ridge propagated into the eastern U.S. from the west. The ridge

was associated with a surface high that migrated south from Canada. A cold front passed

completely through the region by Wednesday, June 13 allowing the modeling to start with a

clean set of initial conditions. Maximum temperatures during the June 15 - 16 period were

generally in the 80s and little precipitation was measured. By June 17, a strong, 1028 millibar,

surface high pressure system was anchored over the region. High 8-hour ozone values (e.g., >=

85 ppb) were most widespread from the 17th through the 19th, especially over the Lake Michigan

region, the Ohio Valley, the mid-Atlantic States, and the southern portions of the Northeast

Corridor.

As the aloft pattern amplified, a cut off low pressure system developed over the

southeastern U.S. On the 19th and 20th, cooler temperatures and occasional rain prevailed in the

Southeast. This resulted in an atypical temperature pattern which featured maximums of 90-100

degrees F over the northern tier of States and 75-85 degrees F in the south. Additionally, the

strong cyclonic circulation around this low resulted in aloft flow from east to west over the midAtlantic and Ohio Valley States. Ozone continued to build throughout this period in the

Northeast, peaking on the 19th and 20th when 8-hour ozone daily maximum values of 100 to 125

ppb were common from Washington, D.C. to Boston.

The last four days of the episode were relatively clean in the Northeast due to the

combination of a “backdoor” cold front and the northward migration of the cut off low. 

Meanwhile ozone conducive conditions returned to the Texas Gulf Coast and Lake Michigan

areas. The highest value over the June 1995 episode (153 ppb) was recorded near Galveston TX

on the 22nd. The episode came to an end on the 25th as a long-wave trough replaced the 500-

millibar ridge over the eastern U.S. resulting in more clouds, precipitation, and diffusive mixing.

Table 1 shows a State-by-State listing of daily “exceedance” counts during the June 1995

CAIR episode, where exceedances are defined as daily maximum 8-hour ozone concentrations

greater than or equal to 85 ppb. There were 900 exceedances of the ozone NAAQS during this

period. The peak day of the episode in terms of exceedance count was June 18. Indiana had the

most exceedance monitor-days (104).
Table 1. Summary of exceedance-monitor days, by State/day, for the June 1995 episode.

June AL AR CT DC DE FL GA IL IN IA KS KY LA ME MD MA MI MN MS MO NE NH NJ NY NC OH OK PA RI SC TN TX VT VA WV WI TOT

6/15/95 0 0 0 0 0 0 0 3 14 0 0 1 5 0 0 0 0 0 0 3 0 0 0 0 0 2 0 0 0 0 0 4 0 0 0 2 34

6/16/95 0 0 0 0 0 0 0 9 7 0 0 0 0 0 1 0 7 0 0 5 0 0 0 0 0 0 1 0 0 0 0 12 0 0 0 23 65

6/17/95 0 0 3 1 1 0 0 16 19 0 0 4 0 0 6 2 10 0 0 6 0 0 9 6 0 22 0 14 1 0 1 2 0 1 0 11 135

6/18/95 0 1 6 3 4 0 0 10 22 0 0 12 0 3 12 3 8 0 0 7 0 0 12 18 0 27 0 28 2 0 0 0 1 5 1 0 185

6/19/95 0 2 4 3 3 0 0 6 11 0 0 3 0 1 12 2 2 0 1 8 0 0 12 9 0 21 0 30 2 0 1 7 0 7 2 1 150

6/20/95 0 0 1 0 3 0 0 0 3 1 0 1 5 0 8 0 1 2 0 8 0 0 7 2 0 7 1 7 0 0 0 10 0 0 0 0 67

6/21/95 0 0 0 0 0 1 0 7 5 1 0 0 3 0 1 0 1 2 1 6 0 0 0 0 0 2 2 1 0 0 0 14 0 0 0 0 47

6/22/95 2 0 0 0 0 0 0 6 9 0 0 0 1 0 0 0 2 1 0 8 0 0 0 1 0 7 2 0 0 0 0 14 0 0 0 8 61

6/23/95 3 2 0 0 0 0 5 17 8 0 0 0 7 0 0 0 9 0 0 8 0 0 0 0 0 3 0 0 0 0 0 8 0 0 0 19 89

6/24/95 0 0 0 0 0 0 5 19 6 1 0 1 0 0 0 0 2 0 0 6 0 1 0 0 0 0 0 0 0 0 2 7 0 0 0 17 67

July 5-15, 1995

The mid-July episode, which covered most of the Ozone Transport Assessment Group

(OTAG) July 1995 episode, is much easier to characterize from a meteorological perspective. A

strong 500-mb ridge progressed from west to east across the eastern U.S. over the period. This

feature was centered over Colorado on the 8th, over Kansas on the 11th, over Illinois on the 13th,

and over Pennsylvania on the 15th. The ridge finally flattened out on the 16th allowing a surface

cold front to clean out the northern portions of the domain and less stable conditions to prevail

over the southern portions.

Excessively hot temperatures accompanied the core of this strong ridge. Temperatures in

the 90s and 100s were common throughout the episode. Rainfall was confined primarily to the

coastal regions in the south and southeast. Wind speeds were moderate and the mean transport

direction was southwest to northeast, especially over the northern half of the domain.

From the 8th through the 10th, the airmass over the eastern U.S. was gradually becoming

hazy. Ozone hot spots occurred in urban areas like Houston, Dallas, and Atlanta. By the 11th,

the area of regional haze (roughly defined as the area where peak 8-hour ozone was greater than

80-85 ppb) had expanded to encompass most of the domain. On top of that “background,” local

contributions from urban emissions yielded ozone values > 100 ppb in places like Memphis,

Atlanta, St. Louis, Evansville, and Nashville on the 11th and 12th. 

July 13 and 14 marked the highest regional ozone levels of the summer as almost all

major metropolitan areas in the northern two-thirds of the domain measured 8-hour ozone values

greater than 85 ppb on this day. For the 14th and 15th, most of the ozone problem shifted east and

south due to both transport and the location of the aloft core of warm air. The Northeast

Corridor, Charlotte, Greensboro, Birmingham, and Atlanta all had exceedances of the standard

on this day. The episode ended abruptly on the 16th (Sunday) for most of the domain, although

elevated ozone lingered over the southern regions into the early part of the next week.

Table 2 shows a State-by-State listing of daily exceedance counts during the July 1995

CAIR episode. There were 809 exceedances of the ozone NAAQS during this eight-day period. 

The peak day of the episode, in terms of exceedance monitors was July 13. Ohio had the most

exceedances (91).
Table 2. Summary of exceedance-monitor days, by State/day, for the July 1995 episode.

July AL AR CT DC DE FL GA IL IN IA KS KY LA ME MD MA MI MN MS MO NE NH NJ NY NC OH OK PA RI SC TN TX VT VA WV WI TOT

7/8/95 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 3 0 0 0 0 0 1 6 0 0 0 0 12

7/9/95 0 0 0 0 0 0 3 1 8 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 3 9 0 0 0 0 28

7/10/95 1 0 0 0 0 0 6 2 1 0 0 3 3 0 0 0 0 0 0 5 0 0 0 0 1 5 0 0 0 0 3 3 0 0 0 0 33

7/11/95 3 2 0 0 0 0 6 5 10 0 0 7 2 0 1 0 1 0 0 5 0 0 1 0 4 8 0 0 0 2 4 3 0 0 1 3 68

7/12/95 5 2 0 3 2 1 0 15 18 0 0 13 4 0 13 0 7 0 0 6 0 0 7 0 4 12 0 7 0 0 5 8 0 8 1 20 161

7/13/95 3 2 4 3 3 0 0 18 15 0 0 5 1 5 12 5 9 0 0 8 0 2 8 13 3 26 0 26 0 0 9 4 0 5 4 13 206

7/14/95 2 0 6 2 4 0 4 6 15 0 0 11 0 3 10 3 10 0 0 3 0 0 12 9 8 25 0 17 2 3 11 1 0 6 2 6 181

7/15/95 4 0 3 3 4 0 5 2 5 0 0 2 0 0 11 0 0 0 0 2 0 0 12 4 8 12 0 19 0 2 7 1 0 12 2 0 120

August 7-21, 1995

A one-day ozone event occurred over New England on August 10, and a separate oneday event occurred in the Lake Michigan region on the 12th. By the 14th, high pressure aloft and

at the surface dominated the eastern half of the U.S. Temperatures ranged from 90 to 100

degrees F over most of the domain throughout this period. Ozone was highest over the midAtlantic States during this period. Hurricane Felix brushed the East Coast from the 16th – 18th,

but appeared to have little effect on ozone or ozone transport away from the immediate eastern

seaboard.

A weak cold front, draped across the Great Lakes over most of the episode, moved

slowly southward over the eastern half of the Appalachians during the August 18-21 period. 

This front initiated precipitation that helped keep ozone concentrations low in the upper

Midwest. The 18th featured high ozone across the South in cities such as: Atlanta, Charlotte,

Birmingham, Augusta, as well as St. Louis. On the 19th and 20th, as the front slid further south,

ozone air quality improved over this region as well. As the 21st marked the fourth day on which

the same airmass has resided over the Northeast, it had become fairly polluted by that point.

Table 3 shows a State-by-State listing of daily exceedance counts (i.e., daily maximum

values greater than 85 ppb) during the August 1995 episode. There were 437 exceedances of the

ozone NAAQS during this period. The peak day of the episode, in terms of exceedance monitors

was August 21st, although high ozone days were interspersed fairly regularly over the 12 day

episode. Pennsylvania had the most exceedances (37), but several 10 other States had at least 20

monitor-days with 8-hour peaks greater than 85 ppb.

Table 3. Summary of exceedance-monitor days, by State/day, for the August 1995 episode.

August AL AR CT DC DE FL GA IL IN IA KS KY LA ME MD MA MI MN MS MO NE NH NJ NY NC OH OK PA RI SC TN TX VT VA WV WI TOT

8/10/95 2 0 2 0 0 0 2 0 0 0 0 0 0 3 0 3 0 0 0 1 0 1 0 0 0 2 0 0 2 0 2 0 0 0 0 0 20

8/11/95 2 0 0 0 0 0 0 2 0 0 0 1 0 3 0 0 1 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 2 14

8/12/95 2 0 0 0 1 0 3 7 5 0 0 0 2 0 0 0 3 0 0 2 0 0 2 0 2 0 0 0 0 0 1 0 0 0 1 3 34

8/13/95 3 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 0 1 0 0 2 0 0 3 0 0 0 1 0 17

8/14/95 4 0 1 1 0 0 4 0 1 0 0 1 0 0 7 0 4 0 0 1 0 0 2 3 5 3 0 1 0 2 2 0 0 5 0 0 47

8/15/95 4 0 0 0 0 0 3 0 1 0 0 4 0 0 1 0 2 0 0 2 0 0 0 0 0 2 0 7 0 1 3 0 0 2 1 0 33

8/16/95 3 0 0 1 0 2 3 0 1 0 0 6 0 0 2 0 0 0 0 0 0 0 0 1 4 5 0 2 0 6 5 0 0 3 1 0 45

8/17/95 5 0 0 0 0 2 5 0 0 0 0 9 0 0 0 0 0 0 0 0 0 0 0 0 8 0 0 3 0 5 10 2 0 1 1 0 51

8/18/95 5 0 0 0 0 0 7 3 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 10 0 0 0 0 5 6 0 0 2 1 0 41

8/19/95 1 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 0 0 0 1 0 0 0 0 0 6 2 3 0 0 0 8 0 0 0 0 25

8/20/95 0 0 0 2 1 0 0 0 0 0 0 0 1 0 3 0 3 0 0 0 0 0 0 1 0 5 0 9 0 0 0 8 0 5 0 0 38

8/21/95 0 0 4 2 3 0 0 0 0 0 0 0 2 0 10 3 0 0 0 0 0 0 12 5 4 2 0 10 2 0 0 6 0 7 0 0 72
8-Hour Daily Maximum Ozone Maps

June 1995 Episode: June 12 - 24, 1995



8-Hour Daily Maximum Ozone Maps

July 1995 Episode: July 5 - 15, 1995



8-Hour Daily Maximum Ozone Maps

August 1995 Episode: August 7 - 15, 1995



Technical Support Document

for the Final

Clean Air Interstate Rule

Air Quality Modeling Analyses

Appendix B

1995 Episode Transport Patterns
June 1995 Episode

Depiction of Transport Patterns

Based on Selected Inert Tracer Model Simulations

Maximum impact of tracer material is used to denote the general

transport pattern over the time period of the episode.
July 1995 Episode

Depiction of Transport Patterns

Based on Selected Inert Tracer Model Simulations

Maximum impact of tracer material is used to denote the general

transport pattern over the time period of the episode.
August 1995 Episode

Depiction of Transport Patterns

Based on Selected Inert Tracer Model Simulations

Maximum impact of tracer material is used to denote the general

transport pattern over the time period of the episode.
Technical Support Document

for the Final

Clean Air Interstate Rule

Appendix C

Ozone During 1995 Episode 

Compared to Current Design Values
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Alabama Clay 10270001 82.0 3 4 Alabama Jefferson 10731003 81.7 3 7 Alabama Jefferson 10731005 84.7 2 7 Alabama Jefferson 10732006 86.7 3 9 Alabama Jefferson 10735002 82.0 4 10 Alabama Jefferson 10736002 81.3 5 10 Alabama Lawrence 10790002 78.7 2 8 Alabama Madison 10890014 82.7 0 4 Alabama Mobile 10970003 78.5 0 2 Alabama Shelby 11170004 91.7 2 3 Alabama Sumter 11190002 74.0 1 3 Arkansas Crittenden 50350005 92.7 0 2 Arkansas Montgomery 50970001 68.0 3 4 Arkansas Pulaski 51190007 80.0 0 5 Arkansas Pulaski 51191002 84.7 2 6 Connecticut Fairfield 90010017 97.0 1 2 Connecticut Fairfield 90011123 97.0 0 0 Connecticut Fairfield 90013007 98.7 1 1 Connecticut Hartford 90031003 89.3 0 1 Connecticut Middlesex 90070007 98.0 0 1 Connecticut New Haven 90093002 99.0 0 0 Connecticut New London 90110008 90.7 0 1 Connecticut Tolland 90131001 93.0 0 0 Delaware Kent 100010002 91.3 0 1 Delaware New Castle 100031003 91.5 1 2 Delaware New Castle 100031007 95.3 1 4 Delaware New Castle 100031010 94.7 2 2 

Number of Days in 1995 Episodes with 8-hour maximum ozone within +/- "X" ppb of the 2001-2003 design v
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Delaware Sussex 100051002 93.3 1 1 D.C. Washington 110010025 91.3 1 2 D.C. Washington 110010041 90.3 1 4 D.C. Washington 110010043 94.3 1 6 Florida Brevard 120094001 73.3 0 0 Florida Duval 120310077 70.3 1 3 Florida Escambia 120330004 77.3 2 6 Florida Escambia 120330018 83.7 1 3 Florida Hillsborough 120570081 79.7 1 3 Florida Hillsborough 120571035 75.7 0 0 Florida Hillsborough 120571065 80.3 2 2 Florida Orange 120950008 77.0 3 7 Florida Orange 120952002 78.3 1 1 Florida Osceola 120972002 73.7 0 1 Florida Palm Beach 120992004 69.7 1 2 Florida Pasco 121012001 77.7 0 1 Florida Pinellas 121030004 77.3 0 1 Florida Pinellas 121030018 74.3 0 0 Florida Pinellas 121035002 76.0 0 0 Florida Polk 121056005 75.7 0 1 Florida Polk 121056006 78.0 1 1 Florida St Lucie 121111002 69.3 0 1 Florida Sarasota 121151005 81.7 0 0 Florida Seminole 121171002 77.7 0 0 Florida Volusia 121272001 70.3 0 2 Florida Volusia 121275002 72.0 1 3 Georgia Chatham 130510021 71.0 1 3 Georgia De Kalb 130890002 94.3 3 6 Georgia De Kalb 130893001 95.3 1 4 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Georgia Fulton 131210055 99.0 1 2 Georgia Glynn 131270006 72.7 1 2 Georgia Gwinnett 131350002 89.3 1 4 Georgia Muscogee 132150008 76.5 0 4 Georgia Muscogee 132151003 82.0 2 2 Georgia Richmond 132450091 85.7 0 1 Georgia Rockdale 132470001 96.3 2 4 Illinois Adams 170010006 76.0 3 7 Illinois Champaign 170190004 77.3 3 8 Illinois Cook 170310001 78.3 3 6 Illinois Cook 170310032 87.7 1 3 Illinois Cook 170310050 71.7 1 6 Illinois Cook 170310064 75.0 4 6 Illinois Cook 170310072 78.0 1 3 Illinois Cook 170311003 78.0 4 8 Illinois Cook 170311601 73.0 3 6 Illinois Cook 170314002 70.3 2 7 Illinois Cook 170314006 76.0 4 8 Illinois Cook 170317002 84.0 1 5 Illinois Cook 170318003 72.5 3 5 Illinois Du Page 170436001 70.7 2 3 Illinois Effingham 170491001 77.7 2 4 Illinois Jersey 170831001 89.0 3 5 Illinois Kane 170890005 77.7 1 6 Illinois Lake 170971002 80.7 1 1 Illinois Lake 170971007 83.3 3 6 Illinois Lake 170973001 76.5 4 4 Illinois McHenry 171110001 83.3 0 3 Illinois Macon 171150013 76.7 6 9 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Illinois Macoupin 171170002 79.3 4 8 Illinois Madison 171190008 84.7 1 6 Illinois Madison 171191009 80.3 3 5 Illinois Madison 171192007 81.3 4 6 Illinois Madison 171193007 80.3 2 5 Illinois Peoria 171430024 72.7 6 8 Illinois Peoria 171431001 79.0 3 4 Illinois Randolph 171570001 78.7 3 7 Illinois St Clair 171630010 83.3 4 8 Illinois Sangamon 171670010 76.0 4 10 Illinois Will 171971008 76.0 2 4 Illinois Will 171971011 79.3 3 5 Illinois Winnebago 172010009 76.0 1 3 Illinois Winnebago 172012001 74.0 2 5 Indiana Allen 180030002 87.7 0 6 Indiana Allen 180030004 83.3 2 2 Indiana Clark 180190003 89.3 4 7 Indiana Floyd 180431004 83.7 1 5 Indiana Hamilton 180571001 93.3 5 7 Indiana Hancock 180590003 91.7 0 2 Indiana Lake 180890022 84.0 2 3 Indiana Lake 180892008 90.7 5 6 Indiana La Porte 180910005 90.0 1 4 Indiana Madison 180950010 91.0 1 3 Indiana Marion 180970042 82.7 2 6 Indiana Marion 180970050 90.0 3 6 Indiana Marion 180970057 85.3 1 7 Indiana Marion 180970073 87.3 2 6 Indiana Porter 181270020 84.7 0 2 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Indiana Porter 181270024 89.0 1 3 Indiana St Joseph 181410010 82.7 2 5 Indiana St Joseph 181411007 89.0 2 5 Indiana St Joseph 181411008 86.3 4 7 Indiana Vanderburgh 181630012 83.3 0 3 Indiana Vanderburgh 181630013 77.7 3 7 Indiana Vigo 181670018 78.0 3 6 Indiana Warrick 181730002 84.5 1 6 Indiana Warrick 181730008 80.0 4 8 Indiana Warrick 181730009 80.7 3 8 Iowa Polk 191530058 58.7 2 2 Iowa Scott 191632011 76.7 2 7 Kansas Sedgwick 201730001 72.0 5 10 Kansas Sedgwick 201730010 81.0 0 0 Kentucky Bell 210130002 83.3 0 2 Kentucky Boone 210150003 85.3 1 2 Kentucky Boyd 210190015 86.0 2 5 Kentucky Bullitt 210290006 83.7 2 3 Kentucky Daviess 210590005 77.3 3 5 Kentucky Fayette 210670001 71.0 2 8 Kentucky Fayette 210670012 78.3 3 8 Kentucky Graves 210830003 81.0 0 0 Kentucky Greenup 210890007 84.0 1 3 Kentucky Hancock 210910012 82.7 1 4 Kentucky Henderson 211010013 77.0 3 4 Kentucky Henderson 211010014 79.5 0 6 Kentucky Jefferson 211110027 84.3 1 4 Kentucky Jefferson 211110051 84.3 2 3 Kentucky Jefferson 211111021 81.3 3 5 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Kentucky Jessamine 211130001 78.0 4 6 Kentucky Kenton 211170007 86.3 2 7 Kentucky Livingston 211390003 85.0 2 2 Kentucky Livingston 211390004 83.0 2 6 Kentucky McCracken 211451024 81.7 1 1 Kentucky McLean 211490001 84.0 1 4 Kentucky Oldham 211850004 88.0 4 7 Kentucky Pike 211950002 76.3 2 4 Kentucky Pulaski 211990003 81.3 3 6 Kentucky Scott 212090001 70.3 1 7 Kentucky Simpson 212130004 84.0 2 3 Louisiana Ascension 220050004 81.7 1 4 Louisiana Beauregard 220110002 75.0 2 6 Louisiana Bossier 220150008 84.7 0 0 Louisiana Caddo 220170001 79.7 1 4 Louisiana Calcasieu 220190002 77.7 3 8 Louisiana Calcasieu 220190008 76.0 0 0 Louisiana Calcasieu 220190009 81.7 1 3 Louisiana East Baton Rou 220330003 87.3 2 4 Louisiana East Baton Rou 220330009 83.3 4 5 Louisiana East Baton Rou 220330013 80.3 2 3 Louisiana East Baton Rou 220331001 86.7 0 1 Louisiana Grant 220430001 77.7 2 5 Louisiana Iberville 220470007 82.3 2 4 Louisiana Iberville 220470009 80.7 2 6 Louisiana Jefferson 220511001 85.3 2 6 Louisiana Lafayette 220550005 80.7 3 7 Louisiana Livingston 220630002 83.3 1 1 Louisiana Orleans 220710012 72.0 3 5 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Louisiana Pointe Coupee 220770001 73.0 3 7 Louisiana St Bernard 220870002 79.3 0 2 Louisiana St Charles 220890003 81.7 1 4 Louisiana St James 220930002 77.3 3 7 Louisiana St John The Ba 220950002 81.7 3 8 Louisiana St Mary 221010003 78.0 2 5 Louisiana West Baton Rou 221210001 85.7 1 3 Maine Cumberland 230052003 84.7 0 1 Maine Hancock 230090102 92.0 0 0 Maine Kennebec 230112005 77.7 0 3 Maine Knox 230130004 83.3 0 1 Maine Oxford 230173001 61.0 3 3 Maine Penobscot 230194008 83.0 0 1 Maine York 230312002 89.0 1 2 Maine York 230313002 84.3 0 3 Maryland Anne Arundel 240030014 101.0 0 2 Maryland Anne Arundel 240030019 99.3 2 5 Maryland Baltimore 240051007 91.3 2 4 Maryland Baltimore 240053001 93.0 2 4 Maryland Carroll 240130001 91.3 3 7 Maryland Cecil 240150003 102.7 0 0 Maryland Charles 240170010 94.7 0 1 Maryland Harford 240251001 103.7 0 4 Maryland Harford 240259001 98.7 3 5 Maryland Kent 240290002 99.0 1 1 Maryland Montgomery 240313001 88.7 2 5 Maryland Prince Georges 240330002 95.0 3 5 Maryland Prince Georges 240338001 93.0 0 4 Massachusetts Barnstable 250010002 94.7 1 1 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Massachusetts Berkshire 250034002 87.0 0 1 Massachusetts Bristol 250051002 92.7 0 1 Massachusetts Essex 250090005 66.5 1 1 Massachusetts Essex 250092006 89.7 0 2 Massachusetts Essex 250094004 86.0 0 0 Massachusetts Hampden 250130003 80.0 1 2 Massachusetts Hampden 250130008 90.3 0 1 Massachusetts Hampshire 250150103 77.3 2 2 Massachusetts Hampshire 250154002 87.3 0 0 Massachusetts Worcester 250270015 85.3 2 3 Michigan Allegan 260050003 92.0 0 2 Michigan Benzie 260190003 87.7 1 2 Michigan Berrien 260210014 88.3 1 2 Michigan Cass 260270003 90.0 1 6 Michigan Clinton 260370001 83.3 0 2 Michigan Genesee 260490021 85.0 0 4 Michigan Genesee 260492001 86.7 0 2 Michigan Huron 260630007 84.0 2 4 Michigan Ingham 260650012 83.3 2 7 Michigan Kent 260810020 80.7 1 4 Michigan Lenawee 260910007 85.0 2 4 Michigan Macomb 260990009 91.0 4 5 Michigan Macomb 260991003 89.7 2 4 Michigan Muskegon 261210039 92.0 2 4 Michigan Oakland 261250001 87.0 0 3 Michigan Ottawa 261390005 86.0 0 4 Michigan St Clair 261470005 87.7 0 1 Michigan Wayne 261630001 80.0 2 4 Michigan Wayne 261630016 84.7 0 1 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Michigan Wayne 261630019 88.0 1 3 Minnesota Anoka 270031001 70.5 2 4 Minnesota Anoka 270031002 72.5 3 5 Minnesota Dakota 270376018 68.0 0 3 Mississippi Adams 280010004 79.7 0 1 Mississippi De Soto 280330002 84.3 1 4 Mississippi Hancock 280450001 83.7 4 6 Mississippi Hinds 280490010 76.3 1 1 Mississippi Jackson 280590006 83.0 1 1 Mississippi Madison 280890002 76.3 1 1 Mississippi Warren 281490004 76.7 3 5 Missouri Clay 290470003 80.0 2 7 Missouri Clay 290470005 84.3 4 9 Missouri Clay 290470025 80.0 1 6 Missouri Greene 290770026 74.7 1 3 Missouri Greene 290770036 73.3 8 14 Missouri Jefferson 290990012 87.3 4 5 Missouri Monroe 291370001 79.3 2 6 Missouri Platte 291650023 81.7 1 5 Missouri St Charles 291831002 90.3 3 6 Missouri St Charles 291831004 90.7 8 10 Missouri St Louis 291890006 88.0 1 5 Missouri St Louis 291893001 83.3 1 5 Missouri St Louis 291895001 86.3 7 13 Missouri St Louis City 295100007 82.0 2 3 Missouri St Louis City 295100072 73.3 1 2 Nebraska Douglas 310550028 62.7 2 6 Nebraska Douglas 310550032 67.5 0 0 Nebraska Douglas 310550035 54.0 2 8 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Nebraska Lancaster 311090016 54.0 4 14 New Hampshire Cheshire 330050007 73.7 1 3 New Hampshire Hillsborough 330111010 85.0 0 1 New Hampshire Merrimack 330130007 73.0 1 1 New Hampshire Rockingham 330150012 82.7 1 1 New Jersey Atlantic 340010005 91.0 1 2 New Jersey Camden 340070003 99.7 1 2 New Jersey Camden 340071001 102.3 0 4 New Jersey Cumberland 340110007 96.7 1 1 New Jersey Gloucester 340150002 101.3 0 0 New Jersey Hudson 340170006 89.0 1 2 New Jersey Hunterdon 340190001 97.7 0 1 New Jersey Mercer 340210005 103.0 0 3 New Jersey Middlesex 340230011 100.7 1 1 New Jersey Monmouth 340250005 96.0 1 1 New Jersey Morris 340273001 97.7 2 5 New Jersey Ocean 340290006 111.0 0 1 New York Albany 360010012 83.0 1 2 New York Bronx 360050083 82.7 0 3 New York Chautauqua 360130011 87.0 1 3 New York Chemung 360150003 81.0 1 1 New York Dutchess 360270007 91.3 0 2 New York Erie 360290002 96.0 1 2 New York Essex 360310002 88.5 0 2 New York Essex 360310003 82.7 1 2 New York Hamilton 360410005 79.0 0 2 New York Herkimer 360430005 74.0 1 2 New York Jefferson 360450002 91.7 1 1 New York Madison 360530006 80.0 1 3 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

New York Monroe 360551004 86.5 0 2 New York Niagara 360631006 91.0 0 2 New York Oneida 360650004 79.0 2 3 New York Onondaga 360671015 83.0 1 1 New York Orange 360715001 86.0 0 2 New York Putnam 360790005 91.3 0 0 New York Richmond 360850067 96.0 2 5 New York Saratoga 360910004 85.5 0 0 New York Schenectady 360930003 77.3 1 5 New York Suffolk 361030002 91.3 0 1 New York Suffolk 361030004 86.7 1 2 New York Ulster 361111005 81.7 1 1 New York Wayne 361173001 84.0 0 2 New York Westchester 361192004 92.0 1 2 North Carolina Buncombe 370210030 82.0 0 1 North Carolina Caldwell 370270003 85.7 0 1 North Carolina Camden 370290099 80.0 0 1 North Carolina Caswell 370330001 89.7 0 0 North Carolina Chatham 370370004 82.0 5 8 North Carolina Cumberland 370510008 87.0 1 2 North Carolina Duplin 370610002 80.7 0 0 North Carolina Durham 370630013 89.0 1 1 North Carolina Forsyth 370670022 93.7 0 1 North Carolina Forsyth 370670027 82.7 1 2 North Carolina Forsyth 370671008 91.3 0 1 North Carolina Franklin 370690001 89.0 0 0 North Carolina Granville 370770001 92.0 0 2 North Carolina Guilford 370810011 90.7 1 1 North Carolina Haywood 370870035 85.0 1 2 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

North Carolina Haywood 370870036 86.3 0 0 North Carolina Johnston 371010002 85.7 3 6 North Carolina Lincoln 371090004 92.3 1 4 North Carolina Martin 371170001 80.3 0 0 North Carolina Mecklenburg 371191005 88.3 0 3 North Carolina Mecklenburg 371191009 100.3 1 2 North Carolina Northampton 371310002 83.3 0 2 North Carolina Pitt 371470099 83.0 2 2 North Carolina Rockingham 371570099 88.7 0 0 North Carolina Rowan 371590021 98.7 1 1 North Carolina Rowan 371590022 99.7 0 0 North Carolina Swain 371730002 73.7 0 1 North Carolina Wake 371830014 92.7 0 0 North Carolina Wake 371830015 92.7 0 1 North Carolina Wake 371830016 87.0 2 4 North Carolina Wake 371830017 86.0 3 7 North Carolina Yancey 371990003 86.3 2 2 Ohio Allen 390030002 87.7 1 8 Ohio Ashtabula 390071001 94.0 2 3 Ohio Butler 390170004 89.0 1 5 Ohio Butler 390171004 89.0 3 5 Ohio Clark 390230001 88.3 3 6 Ohio Clark 390230003 86.3 0 4 Ohio Clinton 390271002 95.7 4 6 Ohio Cuyahoga 390350034 78.3 3 7 Ohio Cuyahoga 390350064 81.0 5 8 Ohio Cuyahoga 390355002 86.3 2 2 Ohio Franklin 390490081 84.3 2 4 Ohio Hamilton 390610006 89.3 3 4 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Ohio Hamilton 390610010 84.3 1 4 Ohio Knox 390830002 89.3 1 3 Ohio Lake 390850003 92.7 4 11 Ohio Lake 390853002 85.0 1 6 Ohio Lawrence 390870006 85.0 3 5 Ohio Lawrence 390870011 82.3 1 4 Ohio Licking 390890005 89.0 2 5 Ohio Lucas 390950034 88.7 3 6 Ohio Lucas 390950081 88.3 0 3 Ohio Madison 390970007 89.0 0 6 Ohio Medina 391030003 87.7 3 11 Ohio Miami 391090005 86.3 3 3 Ohio Montgomery 391130019 86.7 2 5 Ohio Portage 391331001 92.0 3 3 Ohio Preble 391351001 80.3 2 4 Ohio Stark 391510016 88.7 2 5 Ohio Stark 391510019 88.0 2 7 Ohio Stark 391511009 89.0 3 4 Ohio Stark 391514005 88.7 3 6 Ohio Summit 391530020 94.3 3 5 Ohio Trumbull 391550008 88.0 1 2 Ohio Trumbull 391550009 88.0 1 1 Ohio Washington 391670004 87.0 1 4 Oklahoma Cleveland 400270049 77.3 2 5 Oklahoma Oklahoma 401090033 79.3 1 4 Oklahoma Oklahoma 401091037 80.7 2 6 Oklahoma Tulsa 401430137 85.0 2 5 Oklahoma Tulsa 401430174 80.0 2 3 Pennsylvania Allegheny 420030008 88.7 2 5 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Pennsylvania Allegheny 420030067 90.3 0 3 Pennsylvania Allegheny 420030088 88.0 1 3 Pennsylvania Allegheny 420031005 93.0 1 2 Pennsylvania Beaver 420070002 91.0 2 3 Pennsylvania Beaver 420070005 90.0 1 4 Pennsylvania Beaver 420070014 86.3 1 4 Pennsylvania Berks 420110001 85.7 1 5 Pennsylvania Berks 420110009 92.7 1 1 Pennsylvania Blair 420130801 84.3 1 2 Pennsylvania Bucks 420170012 103.0 1 4 Pennsylvania Cambria 420210011 87.7 0 3 Pennsylvania Dauphin 420430401 86.3 2 3 Pennsylvania Dauphin 420431100 91.0 2 3 Pennsylvania Delaware 420450002 93.7 2 2 Pennsylvania Erie 420490003 89.0 1 1 Pennsylvania Lackawanna 420690101 85.3 1 5 Pennsylvania Lackawanna 420692006 83.7 2 3 Pennsylvania Lancaster 420710007 94.0 1 3 Pennsylvania Lawrence 420730015 78.7 3 5 Pennsylvania Lehigh 420770004 93.3 0 1 Pennsylvania Luzerne 420791100 83.0 0 3 Pennsylvania Luzerne 420791101 84.7 4 6 Pennsylvania Lycoming 420810403 71.0 2 6 Pennsylvania Mercer 420850100 91.3 3 4 Pennsylvania Montgomery 420910013 96.3 2 4 Pennsylvania Perry 420990301 84.7 1 3 Pennsylvania Philadelphia 421010004 73.3 1 2 Pennsylvania Philadelphia 421010014 91.3 0 2 Pennsylvania Philadelphia 421010024 97.5 1 3 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Pennsylvania Philadelphia 421010136 86.3 1 1 Pennsylvania Washington 421250005 87.3 1 6 Pennsylvania Washington 421250200 86.7 2 3 Pennsylvania Washington 421255001 87.7 3 5 Pennsylvania Westmoreland 421290006 81.7 3 4 Pennsylvania York 421330008 90.3 1 4 Rhode Island Kent 440030002 95.3 0 1 Rhode Island Providence 440071010 90.3 1 2 South Carolina Abbeville 450010001 84.0 1 4 South Carolina Aiken 450030003 84.7 0 1 South Carolina Anderson 450070003 88.0 1 4 South Carolina Barnwell 450110001 81.3 0 0 South Carolina Berkeley 450150002 71.0 2 4 South Carolina Charleston 450190042 74.0 1 3 South Carolina Charleston 450190046 72.0 2 4 South Carolina Cherokee 450210002 86.0 2 4 South Carolina Chester 450230002 84.3 1 2 South Carolina Darlington 450310003 84.7 1 1 South Carolina Edgefield 450370001 80.7 0 3 South Carolina Oconee 450730001 84.0 0 0 South Carolina Pickens 450770002 85.3 1 3 South Carolina Richland 450790007 85.7 0 2 South Carolina Richland 450791002 93.0 1 1 South Carolina Spartanburg 450830009 90.0 0 1 South Carolina Union 450870001 80.7 0 1 South Carolina Williamsburg 450890001 72.3 1 2 South Carolina York 450910006 83.3 1 3 Tennessee Anderson 470010101 89.7 0 3 Tennessee Blount 470090101 94.0 0 4 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Tennessee Blount 470090102 77.5 2 2 Tennessee Davidson 470370011 72.0 1 4 Tennessee Davidson 470370026 81.3 2 10 Tennessee Hamilton 470650028 90.7 1 3 Tennessee Hamilton 470651011 90.7 0 2 Tennessee Haywood 470750002 89.0 0 0 Tennessee Knox 470930021 91.0 1 4 Tennessee Knox 470931020 94.7 0 2 Tennessee Rutherford 471490101 83.3 0 0 Tennessee Sevier 471550101 94.7 0 4 Tennessee Sevier 471550102 96.0 1 1 Tennessee Shelby 471570021 88.0 0 2 Tennessee Shelby 471571004 90.7 1 3 Tennessee Sullivan 471632002 87.7 0 6 Tennessee Sullivan 471632003 89.3 3 6 Tennessee Sumner 471650007 89.0 2 6 Tennessee Sumner 471650101 86.3 0 0 Tennessee Wilson 471890103 84.7 1 2 Texas Brazoria 480391003 88.0 1 3 Texas Collin 480850005 93.3 0 2 Texas Dallas 481130069 91.0 0 2 Texas Dallas 481130087 83.0 1 5 Texas Galveston 481671002 84.7 2 2 Texas Gregg 481830001 88.3 3 6 Texas Harris 482010024 105.0 0 4 Texas Harris 482010029 102.0 0 1 Texas Harris 482010046 91.0 2 6 Texas Harris 482010047 84.0 2 6 Texas Harris 482010051 101.7 0 1 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Texas Harris 482010062 91.0 3 3 Texas Harris 482010066 95.3 1 4 Texas Harris 482011035 95.3 0 1 Texas Jefferson 482450009 79.3 2 8 Texas Jefferson 482450011 82.3 2 5 Texas Orange 483611001 78.3 3 8 Texas Tarrant 484391002 96.3 3 4 Texas Tarrant 484392003 98.3 0 1 Texas Travis 484530014 84.3 2 4 Vermont Bennington 500030004 79.7 1 2 Virginia Arlington 510130020 95.7 6 7 Virginia Caroline 510330001 84.0 0 1 Virginia Charles City 510360002 89.3 1 2 Virginia Chesterfield 510410004 86.0 0 2 Virginia Fairfax 510590005 88.3 0 3 Virginia Fairfax 510590018 96.3 1 3 Virginia Fairfax 510595001 88.0 5 7 Virginia Fauquier 510610002 81.0 1 4 Virginia Frederick 510690010 84.3 1 5 Virginia Henrico 510870014 90.0 0 2 Virginia Madison 511130003 86.3 4 5 Virginia Prince William 511530009 85.7 1 1 Virginia Roanoke 511611004 86.0 1 1 Virginia Stafford 511790001 86.3 3 7 Virginia Wythe 511970002 80.7 0 2 Virginia Alexandria Cit 515100009 90.0 3 4 Virginia Hampton City 516500004 88.7 1 1 Virginia Suffolk City 518000004 87.3 1 3 Virginia Suffolk City 518000005 82.7 0 2 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

West Virginia Cabell 540110006 88.0 2 6 West Virginia Hancock 540291004 84.3 1 5 West Virginia Ohio 540690007 84.7 1 2 West Virginia Wood 541071002 87.7 1 6 Wisconsin Brown 550090026 81.7 0 1 Wisconsin Columbia 550210015 77.7 4 6 Wisconsin Dane 550250041 77.3 2 2 Wisconsin Dodge 550270007 81.0 3 6 Wisconsin Door 550290004 92.7 0 1 Wisconsin Fond Du Lac 550390006 79.0 4 7 Wisconsin Jefferson 550550002 84.5 1 4 Wisconsin Kenosha 550590002 94.0 3 5 Wisconsin Kenosha 550590019 98.7 0 4 Wisconsin Kewaunee 550610002 90.0 0 1 Wisconsin Manitowoc 550710004 82.0 1 1 Wisconsin Manitowoc 550710007 90.0 0 1 Wisconsin Marathon 550730012 73.7 1 4 Wisconsin Milwaukee 550790041 85.5 0 0 Wisconsin Milwaukee 550790044 71.3 0 6 Wisconsin Milwaukee 550790085 90.7 1 1 Wisconsin Milwaukee 550791025 90.3 1 5 Wisconsin Outagamie 550870009 77.3 2 5 Wisconsin Ozaukee 550890008 90.0 0 0 Wisconsin Ozaukee 550890009 95.3 1 3 Wisconsin Racine 551010017 91.7 3 5 Wisconsin Rock 551050024 84.3 0 1 Wisconsin St Croix 551091002 72.7 0 6 Wisconsin Sauk 551110007 74.3 1 4 Wisconsin Vernon 551230008 71.7 1 4 
State County AIRS Site Code

2001-2003 

Design Value 

(ppb)

# Days within +/- 

2 ppb 

# Days within 

5 ppb 

Wisconsin Walworth 551270005 83.3 0 2 Wisconsin Washington 551310009 82.7 1 2 Wisconsin Waukesha 551330017 82.7 1 3 Wisconsin Winnebago 551390011 80.0 2 6 
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Alabama Clay 10270001 69.5 71.7 70.1 75.7 51.1 53.1 60.2 70Alabama Jefferson 10731003 83.0 74.1 71.6 73.6 47.5 61.8 78.7 89Alabama Jefferson 10731005 79.6 65.0 64.5 69.7 48.5 56.6 75.8 85Alabama Jefferson 10732006 77.2 69.2 69.5 72.0 43.2 74.5 68.1 79Alabama Jefferson 10735002 81.3 64.8 72.1 65.8 47.7 60.3 67.2 72Alabama Jefferson 10736002 80.6 68.8 73.8 69.0 40.7 60.5 69.1 73Alabama Lawrence 10790002 74.7 67.7 66.3 76.5 69.0 52.7 62.8 81Alabama Madison 10890014 75.1 65.8 70.1 66.1 57.1 59.2 67.2 69Alabama Mobile 10970003 66.1 57.7 45.3 52.8 60.7 57.6 61.1 65Alabama Shelby 11170004 81.2 71.7 72.2 72.8 46.2 NA NA 82Alabama Sumter 11190002 67.6 56.8 53.3 59.7 60.0 43.3 65.8 62Arkansas Crittenden 50350005 63.6 76.5 82.2 85.7 89.7 74.6 66.6 82Arkansas Montgomery 50970001 62.2 60.8 61.2 56.7 68.2 67.3 64.5 58Arkansas Pulaski 51190007 75.5 76.0 73.3 72.0 85.2 64.8 70.3 69Arkansas Pulaski 51191002 82.6 83.1 83.2 72.3 80.7 64.5 71.1 73Connecticut Fairfield 90010017 47.8 68.1 80.0 95.6 102.2 94.8 41.7 42Connecticut Fairfield 90011123 34.7 56.0 76.5 78.3 57.5 NA 40.3 42Connecticut Fairfield 90013007 52.3 62.8 93.0 109.8 117.6 84.5 39.2 39Connecticut Hartford 90031003 43.6 59.7 65.8 92.6 84.1 74.3 54.8 44Connecticut Middlesex 90070007 40.3 58.5 74.7 90.6 78.1 65.3 42.2 46Connecticut New Haven 90093002 52.0 67.8 105.5 114.5 135.5 77.0 45.0 42Connecticut New London 90110008 40.1 56.6 88.5 101.3 119.3 75.0 41.3 38Connecticut Tolland 90131001 35.8 56.5 61.2 75.7 67.8 49.8 55.0 47Delaware Kent 100010002 57.3 69.0 72.8 88.3 83.2 62.5 66.0 52Delaware New Castle 100031003 53.1 63.3 81.6 98.6 122.0 105.0 36.2 32Delaware New Castle 100031007 52.1 67.8 82.5 99.2 109.6 95.6 NA 44Delaware New Castle 100031010 52.8 66.3 86.0 94.5 122.3 118.7 44.0 31Delaware Sussex 100051002 56.1 67.7 59.7 77.6 61.3 40.1 56.2 47D.C. Washington 110010025 NA 61.6 78.1 106.3 103.7 83.7 59.8 39D.C. Washington 110010041 NA 69.3 82.5 102.2 96.5 71.8 67.6 37

Daily Maximum 8-Hour Ozone (ppb) Measured at Monitoring Sites in the East During the June 1995 Epi
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

D.C. Washington 110010043 NA 72.0 89.2 111.6 89.6 80.7 71.6 50Florida Brevard 120094001 63.3 57.7 50.0 40.2 45.0 40.0 39.5 36Florida Duval 120310077 66.8 46.5 50.0 40.2 47.6 52.2 54.6 32Florida Escambia 120330004 77.8 67.3 56.3 67.2 60.1 64.6 88.1 80Florida Escambia 120330018 75.3 69.7 53.8 65.2 73.3 34.5 71.8 57Florida Hillsborough 120570081 66.8 52.7 55.8 55.0 57.8 58.6 27.8 48Florida Hillsborough 120571035 69.8 55.1 53.5 49.5 62.2 59.8 24.0 56Florida Hillsborough 120571065 70.0 47.2 55.1 57.3 57.8 66.2 22.8 50Florida Orange 120950008 64.7 56.8 50.1 46.5 62.7 53.6 36.3 40Florida Orange 120952002 56.2 50.0 41.8 39.3 47.7 56.8 34.8 37Florida Osceola 120972002 59.1 51.5 47.6 43.8 47.8 45.7 31.6 38Florida Palm Beach 120992004 68.0 38.7 39.1 29.5 50.3 32.0 27.5 15Florida Pasco 121012001 69.3 57.7 47.8 50.6 45.0 48.3 27.2 50Florida Pinellas 121030004 74.0 60.3 47.8 50.6 45.2 50.1 22.6 53Florida Pinellas 121030018 49.0 41.6 45.5 45.0 40.8 48.6 24.7 52Florida Pinellas 121035002 59.3 43.7 40.5 45.6 41.8 49.7 18.0 51Florida Polk 121056005 64.1 49.1 51.8 38.6 51.1 48.1 23.1 34Florida Polk 121056006 64.8 48.3 51.1 40.2 54.1 51.6 25.3 47Florida St Lucie 121111002 NA NA NA NA NA NA NA NFlorida Sarasota 121151005 61.3 48.5 49.8 49.7 60.0 57.5 21.6 39Florida Seminole 121171002 63.6 54.8 47.8 43.0 41.8 61.5 42.0 41Florida Volusia 121272001 66.6 56.6 47.6 40.1 46.1 57.3 40.1 36Florida Volusia 121275002 70.6 56.5 47.8 41.3 47.5 55.5 41.7 38Georgia Chatham 130510021 69.3 NA 55.3 42.1 48.3 48.6 37.6 43Georgia De Kalb 130890002 72.7 70.2 69.5 73.7 31.5 67.5 57.3 71Georgia De Kalb 130893001 68.5 68.8 69.8 72.5 34.0 64.6 56.8 71Georgia Fulton 131210055 NA 65.2 68.3 73.8 43.0 68.6 51.0 73Georgia Glynn 131270006 NA NA NA NA NA NA NA NGeorgia Gwinnett 131350002 63.1 66.0 63.2 67.5 38.5 59.2 51.0 58Georgia Muscogee 132150008 65.8 62.2 66.2 70.1 42.3 65.0 57.8 54Georgia Muscogee 132151003 63.5 62.0 65.0 70.1 42.7 60.2 54.1 52
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Georgia Richmond 132450091 73.2 72.5 74.0 57.2 54.7 58.1 59.6 59Georgia Rockdale 132470001 68.6 66.8 65.0 67.8 53.2 61.7 52.8 60Illinois Adams 170010006 59.3 60.7 74.7 68.3 68.0 71.2 74.3 76Illinois Champaign 170190004 78.5 73.1 89.5 87.0 78.8 79.7 77.7 79Illinois Cook 170310001 68.8 70.3 80.3 73.8 79.8 57.2 72.3 77Illinois Cook 170310032 65.2 73.2 80.3 90.0 86.0 54.8 51.3 73Illinois Cook 170310050 74.8 75.5 81.8 77.7 84.8 59.2 64.5 74Illinois Cook 170310064 69.0 76.8 87.0 80.5 83.1 56.0 55.6 75Illinois Cook 170310072 76.2 92.6 93.6 99.2 90.1 63.0 59.5 75Illinois Cook 170311003 80.0 75.2 81.0 72.3 82.1 63.5 63.1 79Illinois Cook 170311601 70.3 73.7 77.8 66.8 74.1 65.8 70.3 93Illinois Cook 170314002 74.5 79.2 87.8 77.8 81.8 60.3 67.1 72Illinois Cook 170314006 81.6 78.5 78.1 76.3 79.5 64.3 58.0 88Illinois Cook 170317002 75.6 79.3 90.8 91.0 83.7 59.2 57.5 72Illinois Cook 170318003 62.5 58.3 71.3 68.0 74.7 49.1 59.7 61Illinois Du Page 170436001 57.3 65.3 66.7 64.7 69.0 62.5 60.7 82Illinois Effingham 170491001 76.8 62.8 88.6 88.2 87.6 80.3 86.8 78Illinois Jersey 170831001 85.6 87.6 88.3 81.7 76.1 77.8 85.0 77Illinois Kane 170890005 81.4 87.0 74.5 75.0 74.6 78.8 62.5 10Illinois Lake 170971002 73.0 88.1 94.1 66.5 67.1 55.8 52.1 69Illinois Lake 170971007 84.0 103.0 107.3 67.0 NA NA 54.0 76Illinois Lake 170973001 75.8 84.6 69.7 63.2 68.2 57.0 50.0 77Illinois McHenry 171110001 90.5 89.3 74.7 74.1 76.6 79.5 62.7 96Illinois Macon 171150013 79.5 75.6 96.6 89.0 79.5 76.7 76.3 74Illinois Macoupin 171170002 82.0 81.7 97.2 80.8 72.2 76.6 80.5 70Illinois Madison 171190008 72.6 72.3 99.7 96.2 82.3 79.2 90.2 81Illinois Madison 171191009 NA 66.6 80.5 87.1 89.0 72.7 78.3 75Illinois Madison 171192007 86.6 79.3 94.7 101.1 90.6 82.0 88.0 80Illinois Madison 171193007 73.1 68.2 90.3 88.0 76.2 75.2 87.0 74Illinois Peoria 171430024 74.5 78.7 79.1 62.1 72.1 73.3 76.8 75Illinois Peoria 171431001 84.3 89.3 86.7 67.3 69.6 80.1 86.5 80
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Illinois Randolph 171570001 81.5 81.3 80.3 80.1 108.1 83.6 72.2 78Illinois St Clair 171630010 73.6 71.0 84.7 81.0 84.1 78.1 80.2 80Illinois Sangamon 171670010 78.2 76.8 94.0 80.1 78.1 76.0 72.2 71Illinois Will 171971008 71.2 73.1 77.8 67.8 69.8 70.2 77.2 97Illinois Will 171971011 75.3 74.0 82.0 71.0 74.0 72.1 90.1 80Illinois Winnebago 172010009 69.8 87.8 67.2 67.7 65.2 78.6 62.5 84Illinois Winnebago 172012001 72.1 86.5 61.0 45.5 NA NA NA 87Indiana Allen 180030002 83.6 96.8 85.3 98.2 83.0 82.0 99.0 85Indiana Allen 180030004 91.7 99.6 90.1 100.1 75.3 NA NA 81Indiana Clark 180190003 87.7 86.5 88.1 90.3 75.5 69.7 73.0 68Indiana Floyd 180431004 99.7 90.2 94.1 89.2 72.3 80.0 79.6 75Indiana Hamilton 180571001 85.1 94.7 95.1 92.7 92.2 80.6 76.0 81Indiana Hancock 180590003 85.6 81.2 97.3 87.8 76.8 73.8 69.8 74Indiana Lake 180890022 NA NA NA NA NA NA NA NIndiana Lake 180892008 89.2 80.7 92.2 89.6 90.7 61.5 63.7 74Indiana La Porte 180910005 96.3 90.6 102.3 117.7 109.2 80.2 79.1 93Indiana Madison 180950010 80.7 84.6 95.3 91.2 83.5 73.8 76.6 73Indiana Marion 180970042 78.7 67.0 83.7 93.0 85.8 77.3 69.5 83Indiana Marion 180970050 91.3 90.1 101.2 99.7 94.1 83.1 72.5 85Indiana Marion 180970057 81.1 69.7 88.2 98.7 79.6 72.6 60.6 87Indiana Marion 180970073 81.5 80.7 90.7 96.6 87.8 78.8 59.0 83Indiana Porter 181270020 94.2 75.7 90.1 71.6 NA 69.1 56.1 74Indiana Porter 181270024 92.3 79.7 95.8 106.7 108.1 73.0 72.1 88Indiana St Joseph 181410010 79.8 83.7 82.0 91.5 88.7 87.6 101.8 10Indiana St Joseph 181411007 82.6 81.8 86.6 102.3 82.7 77.1 99.5 91Indiana St Joseph 181411008 74.3 79.2 83.7 102.8 81.6 73.6 84.7 84Indiana Vanderburgh 181630012 93.6 78.2 86.6 92.2 91.6 94.0 91.1 92Indiana Vanderburgh 181630013 90.2 77.2 89.5 89.1 97.0 96.6 91.5 86Indiana Vigo 181670018 79.6 69.8 83.3 83.7 75.2 75.5 64.6 72Indiana Warrick 181730002 88.7 76.8 74.6 88.5 77.8 69.0 75.3 78Indiana Warrick 181730008 85.0 73.3 88.6 90.7 86.0 77.8 79.7 80
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Indiana Warrick 181730009 78.3 66.8 85.7 86.7 78.1 80.6 75.0 82Iowa Polk 191530058 59.8 60.2 65.3 65.6 71.0 70.7 69.1 73Iowa Scott 191632011 73.7 77.0 74.7 79.6 81.0 87.7 88.3 81Kansas Sedgwick 201730001 70.0 70.0 71.2 70.0 65.0 70.0 74.2 77Kansas Sedgwick 201730010 58.1 58.1 59.3 58.7 51.8 56.8 54.2 71Kentucky Bell 210130002 52.7 59.7 67.3 55.1 61.3 42.6 55.1 49Kentucky Boone 210150003 73.5 76.5 73.2 83.1 47.7 52.1 70.6 37Kentucky Boyd 210190015 67.7 80.6 88.3 76.3 87.5 83.2 59.8 51Kentucky Bullitt 210290006 73.3 73.2 77.0 88.8 51.8 66.8 59.0 58Kentucky Daviess 210590005 80.0 69.3 72.8 88.7 65.1 69.6 68.7 71Kentucky Fayette 210670001 65.7 75.0 74.8 79.8 73.2 63.2 59.7 51Kentucky Fayette 210670012 73.0 82.3 85.1 91.2 81.6 70.6 64.2 54Kentucky Graves 210830003 63.6 55.3 65.6 75.3 48.1 62.3 50.6 57Kentucky Greenup 210890007 63.1 75.3 85.2 73.8 91.8 76.3 64.7 60Kentucky Hancock 210910012 75.7 63.1 77.1 90.3 66.2 72.2 64.2 63Kentucky Henderson 211010013 66.3 53.5 78.2 87.0 66.7 79.1 NA NKentucky Henderson 211010014 88.1 75.1 83.8 89.6 76.2 74.5 72.2 67Kentucky Jefferson 211110027 68.5 73.8 81.3 87.3 80.6 48.2 45.8 41Kentucky Jefferson 211110051 67.7 76.5 72.2 104.3 63.4 74.6 65.5 76Kentucky Jefferson 211111021 78.1 78.6 91.3 91.3 73.5 56.5 67.4 66Kentucky Jessamine 211130001 70.2 76.0 77.0 83.7 69.0 62.5 57.3 55Kentucky Kenton 211170007 57.0 72.7 78.0 102.7 100.2 52.6 69.7 63Kentucky Livingston 211390003 78.8 68.3 73.3 79.6 66.0 83.6 60.7 58Kentucky Livingston 211390004 80.1 70.0 76.1 78.8 62.3 85.5 59.6 67Kentucky McCracken 211451024 68.2 62.3 70.6 79.7 60.0 73.2 57.8 59Kentucky McLean 211490001 72.7 66.1 80.2 87.7 61.8 75.3 56.0 64Kentucky Oldham 211850004 75.8 80.7 81.7 92.1 79.6 74.5 68.3 65Kentucky Pike 211950002 69.2 68.2 80.7 75.6 25.5 78.6 62.6 35Kentucky Pulaski 211990003 65.5 66.6 79.5 79.7 68.6 66.1 65.3 57Kentucky Scott 212090001 67.6 74.8 78.3 84.5 79.3 80.8 69.2 46Kentucky Simpson 212130004 73.7 67.8 82.1 84.6 59.8 67.0 70.3 67
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Louisiana Ascension 220050004 77.7 70.7 53.7 58.3 68.5 84.7 71.2 72Louisiana Beauregard 220110002 74.0 68.3 54.5 50.7 68.7 77.1 75.7 69Louisiana Bossier 220150008 56.5 68.0 61.7 55.1 71.7 79.3 63.1 60Louisiana Caddo 220170001 84.3 76.7 66.6 55.8 79.8 88.0 69.3 62Louisiana Calcasieu 220190002 82.1 78.2 66.2 59.0 68.7 74.8 85.8 74Louisiana Calcasieu 220190008 65.5 62.5 51.8 49.2 54.0 57.5 61.2 54Louisiana Calcasieu 220190009 88.2 78.5 64.6 62.1 65.4 68.3 83.2 67Louisiana East Baton Rou 220330003 87.5 72.5 56.6 65.0 73.6 89.5 80.0 84Louisiana East Baton Rou 220330009 77.2 68.1 51.0 61.1 67.7 82.5 71.3 82Louisiana East Baton Rou 220330013 72.3 70.5 54.5 61.1 66.8 77.7 66.8 79Louisiana East Baton Rou 220331001 75.7 69.6 52.8 59.7 65.3 81.8 71.1 79Louisiana Grant 220430001 82.0 81.1 66.8 64.6 73.8 89.1 69.7 55Louisiana Iberville 220470007 92.6 68.3 53.5 61.6 71.7 78.5 77.6 71Louisiana Iberville 220470009 94.2 80.2 61.6 64.3 78.2 86.0 88.6 75Louisiana Jefferson 220511001 80.1 75.3 59.8 67.1 82.5 78.6 82.8 83Louisiana Lafayette 220550005 87.1 77.5 66.7 72.0 70.1 84.6 76.0 81Louisiana Livingston 220630002 72.2 70.2 51.2 57.6 68.3 81.5 61.1 61Louisiana Orleans 220710012 72.8 62.1 42.5 54.2 67.5 54.3 63.1 63Louisiana Pointe Coupee 220770001 72.1 68.6 51.0 57.7 62.6 80.7 71.8 71Louisiana St Bernard 220870002 75.7 65.5 48.3 60.0 77.1 71.1 67.6 67Louisiana St Charles 220890003 84.0 66.6 54.1 NA NA NA NA NLouisiana St James 220930002 83.3 71.3 58.7 64.7 73.7 97.6 77.5 74Louisiana St John The Ba 220950002 NA NA 55.2 64.5 78.2 84.5 77.7 82Louisiana St Mary 221010003 NA NA 54.2 70.3 79.8 81.0 96.5 85Louisiana West Baton Rou 221210001 73.1 66.6 47.6 58.0 67.3 74.2 71.6 81Maine Cumberland 230052003 24.2 59.7 50.8 87.2 65.7 46.3 34.8 43Maine Hancock 230090102 NA NA NA NA NA NA NA NMaine Kennebec 230112005 23.8 39.0 46.5 63.8 53.1 42.1 34.3 50Maine Knox 230130004 30.8 64.1 63.0 99.7 90.6 44.0 33.2 46Maine Oxford 230173001 19.2 39.1 49.5 59.6 54.2 40.1 37.7 49Maine Penobscot 230194008 29.6 37.8 50.6 68.2 60.1 38.6 30.8 51
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Maine York 230312002 28.4 62.2 55.2 87.1 74.0 50.1 38.1 47Maine York 230313002 25.2 50.8 50.2 80.2 65.7 48.2 34.2 46Maryland Anne Arundel 240030014 71.8 74.0 85.2 90.5 105.1 69.1 62.5 49Maryland Anne Arundel 240030019 62.5 78.1 88.0 105.7 134.7 107.8 70.3 50Maryland Baltimore 240051007 57.7 76.3 89.8 104.3 105.7 104.3 68.3 48Maryland Baltimore 240053001 57.0 59.0 73.8 98.0 108.7 81.5 67.1 50Maryland Carroll 240130001 52.3 89.4 94.8 89.3 97.0 104.5 77.7 60Maryland Cecil 240150003 54.1 76.0 91.2 111.6 124.0 133.6 82.3 42Maryland Charles 240170010 59.1 67.3 71.7 64.5 67.2 47.1 69.5 37Maryland Harford 240251001 56.3 71.6 76.5 101.0 99.0 87.1 80.2 51Maryland Harford 240259001 53.2 64.8 83.6 96.8 105.2 99.6 113.5 43Maryland Kent 240290002 55.2 71.8 83.6 106.0 99.1 76.6 71.5 56Maryland Montgomery 240313001 53.3 76.6 85.2 92.0 113.1 88.2 75.0 46Maryland Prince Georges 240330002 55.5 68.5 78.6 92.5 111.1 86.1 64.1 51Maryland Prince Georges 240338001 58.6 71.2 83.1 95.1 95.6 64.3 66.0 42Massachusetts Barnstable 250010002 37.0 74.5 96.5 105.8 125.7 62.2 36.8 35Massachusetts Berkshire 250034002 31.8 49.5 60.0 82.1 72.7 55.7 47.8 44Massachusetts Bristol 250051002 35.7 73.3 97.6 107.3 125.1 58.1 42.5 42Massachusetts Essex 250090005 20.3 31.2 39.0 53.5 48.4 35.7 22.6 31Massachusetts Essex 250092006 27.3 60.8 69.7 83.3 78.7 47.7 30.8 38Massachusetts Essex 250094004 28.0 52.5 56.8 77.1 69.3 50.1 34.0 41Massachusetts Hampden 250130003 30.3 53.2 60.2 80.3 70.6 68.0 59.0 45Massachusetts Hampden 250130008 41.8 57.0 68.5 87.1 78.7 57.6 66.8 54Massachusetts Hampshire 250150103 36.5 45.0 60.2 78.0 68.7 51.6 60.0 49Massachusetts Hampshire 250154002 38.1 50.8 64.6 78.2 70.0 50.3 63.2 50Massachusetts Worcester 250270015 38.3 53.8 66.3 83.7 76.3 53.5 66.2 61Michigan Allegan 260050003 83.1 103.3 118.1 95.6 80.1 68.5 73.5 82Michigan Benzie 260190003 67.1 99.2 95.1 86.7 74.3 66.8 53.0 68Michigan Berrien 260210014 68.0 85.6 90.1 77.0 74.1 67.0 67.2 81Michigan Cass 260270003 84.6 88.8 92.1 96.1 85.8 80.1 99.7 92Michigan Clinton 260370001 59.3 73.1 76.2 66.2 65.8 53.7 64.2 71
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Michigan Genesee 260490021 53.3 82.8 76.8 69.7 71.0 49.6 50.7 60Michigan Genesee 260492001 48.8 82.2 76.6 72.1 73.2 45.5 46.8 55Michigan Huron 260630007 37.1 68.2 83.8 76.5 83.3 56.7 44.5 61Michigan Ingham 260650012 65.3 85.7 88.0 79.7 82.7 68.5 77.8 83Michigan Kent 260810020 67.0 91.6 101.5 77.1 76.2 53.1 65.6 74Michigan Lenawee 260910007 64.3 82.7 85.7 91.2 81.7 90.3 75.7 60Michigan Macomb 260990009 51.1 72.6 106.7 92.7 78.6 47.2 64.0 71Michigan Macomb 260991003 54.2 69.6 78.8 89.7 85.8 57.2 59.5 78Michigan Muskegon 261210039 84.7 95.7 119.0 85.6 61.3 64.1 78.2 10Michigan Oakland 261250001 47.7 53.2 76.8 84.2 79.2 59.1 54.8 65Michigan Ottawa 261390005 62.0 75.3 81.5 63.6 61.0 55.5 74.5 79Michigan St Clair 261470005 35.6 47.2 100.0 94.7 76.3 49.1 49.1 48Michigan Wayne 261630001 NA NA 68.1 78.6 62.7 52.2 56.1 63Michigan Wayne 261630016 28.1 43.0 48.1 45.2 49.1 NA NA NMichigan Wayne 261630019 34.1 58.0 64.6 71.5 46.2 62.7 67.2 66Minnesota Anoka 270031001 69.3 78.5 75.7 78.5 83.7 92.2 97.2 90Minnesota Anoka 270031002 70.7 80.2 74.2 84.0 82.7 89.6 98.4 84Minnesota Dakota 270376018 NA NA NA NA NA NA 79.7 76Mississippi Adams 280010004 65.8 66.3 53.5 58.2 66.2 76.6 64.1 59Mississippi De Soto 280330002 72.0 75.3 73.5 77.8 96.2 82.0 76.0 79Mississippi Hancock 280450001 79.2 74.6 56.1 63.6 81.7 66.2 85.7 78Mississippi Hinds 280490010 77.5 67.7 55.3 64.7 64.1 57.7 57.1 65Mississippi Jackson 280590006 75.2 69.1 51.1 63.5 72.4 61.0 82.0 73Mississippi Madison 280890002 76.0 67.2 54.1 65.3 67.7 67.8 55.5 67Mississippi Warren 281490004 75.0 75.5 60.7 62.5 76.2 62.6 57.7 62Missouri Clay 290470003 81.1 86.2 88.2 94.2 85.0 98.6 76.5 89Missouri Clay 290470005 81.6 85.7 91.2 97.5 87.8 97.5 79.4 83Missouri Clay 290470025 76.1 79.0 84.6 90.8 77.5 86.3 72.3 71Missouri Greene 290770026 59.0 58.6 61.2 64.3 73.2 72.5 61.2 61Missouri Greene 290770036 71.6 72.7 70.0 78.1 75.7 92.8 74.5 85Missouri Jefferson 290990012 89.2 83.0 89.1 95.5 103.8 92.6 94.6 94
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Missouri Monroe 291370001 70.1 73.0 82.2 74.5 79.6 80.0 75.6 87Missouri Platte 291650023 84.1 74.1 70.8 77.8 73.0 99.1 75.5 84Missouri St Charles 291831002 NA 95.2 104.7 106.3 94.3 89.0 112.0 91Missouri St Charles 291831004 94.2 94.7 92.5 98.5 90.2 91.5 91.0 89Missouri St Louis 291890006 75.2 73.5 75.7 74.5 83.6 83.5 93.2 83Missouri St Louis 291893001 68.1 63.5 73.6 80.5 86.7 77.7 91.3 89Missouri St Louis 291895001 88.1 86.8 90.8 87.1 85.6 81.7 92.3 85Missouri St Louis City 295100007 64.3 62.3 68.6 82.5 94.6 79.3 75.0 75Missouri St Louis City 295100072 54.5 53.6 64.2 65.8 67.5 59.6 57.7 57Nebraska Douglas 310550028 84.7 59.6 72.2 67.5 63.3 67.7 77.7 76Nebraska Douglas 310550032 51.3 36.0 38.8 32.8 32.8 31.6 29.2 25Nebraska Douglas 310550035 62.6 45.7 52.3 49.5 46.7 49.2 49.3 51Nebraska Lancaster 311090016 58.7 51.0 58.1 58.3 54.0 54.1 60.8 63New Hampshire Cheshire 330050007 26.6 42.2 56.7 74.7 64.5 45.6 55.7 48New Hampshire Hillsborough 330111010 29.7 48.2 58.2 79.6 67.7 51.6 47.3 63New Hampshire Merrimack 330130007 28.3 36.5 53.2 72.0 58.2 39.5 38.2 54New Hampshire Rockingham 330150012 14.8 25.2 21.8 36.8 45.2 31.6 22.2 49New Jersey Atlantic 340010005 61.5 70.8 68.2 100.1 93.7 79.0 40.1 48New Jersey Camden 340070003 49.7 65.3 87.8 98.5 110.3 104.7 34.7 31New Jersey Camden 340071001 62.0 71.1 89.7 107.0 107.1 96.0 47.2 50New Jersey Cumberland 340110007 53.8 67.5 74.3 95.5 87.1 69.2 43.4 43New Jersey Gloucester 340150002 54.3 69.6 91.7 108.3 118.1 115.1 NA 40New Jersey Hudson 340170006 48.1 76.5 85.7 90.7 104.1 80.0 23.3 22New Jersey Hunterdon 340190001 46.8 79.5 86.5 87.7 104.5 77.0 35.1 29New Jersey Mercer 340210005 50.3 78.8 96.7 90.3 122.6 93.8 34.3 31New Jersey Middlesex 340230011 42.7 81.5 91.5 88.7 113.3 90.3 33.8 29New Jersey Monmouth 340250005 61.1 61.6 75.6 94.7 130.3 111.2 45.8 34New Jersey Morris 340273001 43.8 83.8 93.1 94.3 110.0 71.3 41.2 28New Jersey Ocean 340290006 49.1 76.6 99.1 108.7 124.7 117.7 41.5 38New York Albany 360010012 37.1 48.6 67.7 92.2 79.7 70.6 61.8 49New York Bronx 360050083 39.5 54.8 80.0 87.6 110.8 93.7 37.6 34
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

New York Chautauqua 360130011 56.1 58.3 69.6 90.7 76.7 80.3 68.0 89New York Chemung 360150003 43.8 61.6 68.2 81.8 70.2 55.6 70.0 57New York Dutchess 360270007 42.6 62.7 68.5 78.3 71.2 52.8 50.0 48New York Erie 360290002 48.1 65.8 69.0 95.1 82.6 54.0 56.0 76New York Essex 360310002 47.6 50.6 98.1 93.2 75.5 58.6 48.8 67New York Essex 360310003 42.6 44.3 92.2 92.2 73.6 50.3 42.3 59New York Hamilton 360410005 40.1 43.8 70.2 98.2 76.2 52.7 54.0 59New York Herkimer 360430005 41.2 44.5 72.0 85.4 69.8 52.0 49.8 55New York Jefferson 360450002 39.8 46.0 90.6 105.6 108.2 46.0 47.2 58New York Madison 360530006 44.7 56.1 63.1 89.2 77.1 51.7 57.3 53New York Monroe 360551004 36.3 47.5 82.3 95.1 90.0 44.3 49.5 69New York Niagara 360631006 42.3 56.7 77.0 101.8 95.0 54.7 51.7 71New York Oneida 360650004 39.1 44.8 70.6 89.6 78.0 50.2 63.8 57New York Onondaga 360671015 38.3 56.0 76.6 95.2 90.2 54.1 60.2 52New York Orange 360715001 39.2 62.8 73.8 77.2 73.3 65.3 47.4 42New York Putnam 360790005 42.6 61.7 81.7 82.8 80.2 62.0 41.8 42New York Richmond 360850067 53.2 78.1 90.1 93.8 111.1 94.8 31.3 29New York Saratoga 360910004 33.5 45.1 61.6 77.2 72.0 56.2 57.5 52New York Schenectady 360930003 36.3 45.2 62.8 83.7 73.2 52.2 66.7 49New York Suffolk 361030002 48.6 54.7 72.8 95.0 NA 75.8 31.6 31New York Suffolk 361030004 47.0 67.3 85.6 102.8 108.5 82.0 37.8 36New York Ulster 361111005 42.2 NA NA NA NA NA NA NNew York Wayne 361173001 37.3 51.0 81.7 100.7 101.5 47.1 48.8 66New York Westchester 361192004 42.7 64.8 85.0 82.0 94.7 73.1 37.0 38North Carolina Buncombe 370210030 46.3 54.7 39.8 63.8 33.2 42.5 49.3 48North Carolina Caldwell 370270003 59.6 66.6 74.2 75.7 48.1 53.6 67.8 48North Carolina Camden 370290099 63.1 54.1 52.0 48.8 31.8 38.7 45.0 32North Carolina Caswell 370330001 61.1 66.6 65.0 55.7 30.2 39.3 54.0 27North Carolina Chatham 370370004 75.7 81.8 71.0 52.6 33.2 30.2 40.3 34North Carolina Cumberland 370510008 64.6 68.1 61.2 43.0 32.7 45.7 25.0 23North Carolina Duplin 370610002 63.8 65.3 50.2 33.5 25.7 45.5 27.1 27
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

North Carolina Durham 370630013 68.8 73.6 71.0 57.7 32.1 32.5 42.7 28North Carolina Forsyth 370670022 75.0 61.5 79.3 70.0 47.1 48.5 57.8 46North Carolina Forsyth 370670027 53.1 62.5 68.0 68.1 38.1 46.1 62.2 39North Carolina Forsyth 370671008 69.6 65.6 77.1 64.8 40.5 45.3 55.6 35North Carolina Franklin 370690001 67.6 68.6 60.3 52.0 26.2 35.7 36.2 24North Carolina Granville 370770001 70.6 74.0 74.3 59.5 31.8 40.2 47.1 31North Carolina Guilford 370810011 72.3 69.1 79.2 64.3 31.7 39.5 51.6 30North Carolina Haywood 370870035 61.6 65.7 64.8 69.8 64.6 52.8 55.3 57North Carolina Haywood 370870036 61.0 62.1 61.5 64.8 58.2 42.0 51.2 51North Carolina Johnston 371010002 70.0 78.0 64.1 46.7 25.8 39.5 33.7 31North Carolina Lincoln 371090004 63.1 72.6 78.0 68.2 42.2 52.0 57.6 46North Carolina Martin 371170001 68.7 61.6 53.8 43.8 27.8 38.2 38.3 28North Carolina Mecklenburg 371191005 60.2 63.6 72.8 57.1 31.7 46.7 46.2 36North Carolina Mecklenburg 371191009 68.1 65.8 74.6 61.2 44.7 49.1 44.5 38North Carolina Northampton 371310002 72.1 68.2 57.3 57.5 31.5 37.6 39.7 27North Carolina Pitt 371470099 65.1 63.8 54.6 45.0 29.7 44.3 33.0 29North Carolina Rockingham 371570099 59.2 55.1 64.7 53.5 30.0 39.8 47.6 29North Carolina Rowan 371590021 67.6 67.5 73.5 61.0 42.6 46.1 48.5 37North Carolina Rowan 371590022 NA 68.8 75.1 68.3 48.3 45.2 46.5 NNorth Carolina Swain 371730002 49.5 48.7 40.6 52.8 46.1 38.6 40.1 49North Carolina Wake 371830014 66.8 68.3 61.1 46.5 16.5 30.8 32.1 25North Carolina Wake 371830015 71.8 77.3 71.1 53.5 19.1 39.1 37.8 30North Carolina Wake 371830016 69.6 72.8 67.1 48.3 29.8 36.3 36.5 31North Carolina Wake 371830017 65.7 71.8 62.3 47.2 26.8 36.1 39.0 NNorth Carolina Yancey 371990003 65.8 77.7 79.7 76.5 69.7 66.2 67.5 67Ohio Allen 390030002 78.0 84.8 93.0 90.2 83.2 88.0 72.3 85Ohio Ashtabula 390071001 57.6 73.6 78.3 96.0 91.0 88.8 71.0 92Ohio Butler 390170004 66.1 84.2 92.1 89.5 86.7 48.7 66.1 74Ohio Butler 390171004 57.2 81.1 100.5 91.8 90.2 62.0 74.3 83Ohio Clark 390230001 62.1 83.3 86.6 92.2 86.3 74.0 NA NOhio Clark 390230003 74.5 81.8 92.3 97.1 89.3 81.6 64.6 83
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Ohio Clinton 390271002 85.1 79.5 92.2 96.8 96.3 71.5 75.8 80Ohio Cuyahoga 390350034 43.8 29.6 43.3 47.6 59.8 67.1 58.6 62Ohio Cuyahoga 390350064 53.1 55.6 88.0 95.6 81.8 64.6 62.6 71Ohio Cuyahoga 390355002 47.6 59.5 86.3 84.7 74.0 78.8 69.1 78Ohio Franklin 390490081 67.0 83.3 81.3 88.7 90.1 67.6 74.3 72Ohio Hamilton 390610006 52.8 76.0 96.7 101.5 88.0 37.5 68.3 64Ohio Hamilton 390610010 67.7 71.5 77.8 84.2 78.6 33.3 74.6 65Ohio Knox 390830002 62.8 75.7 82.3 83.6 76.5 77.5 70.5 66Ohio Lake 390850003 59.5 70.8 96.6 95.0 87.7 90.1 68.8 85Ohio Lake 390853002 59.3 68.5 87.1 87.2 75.8 88.3 68.1 88Ohio Lawrence 390870006 74.0 83.7 89.3 78.8 91.0 83.6 65.7 62Ohio Lawrence 390870011 63.7 69.8 74.7 69.5 77.0 75.0 56.8 54Ohio Licking 390890005 60.7 79.3 77.8 74.6 82.6 61.1 86.0 83Ohio Lucas 390950034 49.8 61.6 82.6 91.0 88.3 79.1 59.5 79Ohio Lucas 390950081 58.2 68.6 85.0 94.0 77.0 79.0 67.0 83Ohio Madison 390970007 85.5 83.8 85.6 93.7 95.6 82.2 73.5 77Ohio Medina 391030003 51.6 66.8 88.7 83.0 71.8 77.8 85.6 91Ohio Miami 391090005 75.7 81.2 79.2 85.1 80.7 72.5 60.0 70Ohio Montgomery 391130019 62.6 69.6 83.2 86.0 82.5 70.3 56.7 73Ohio Portage 391331001 54.8 66.0 91.1 103.1 107.8 85.1 72.5 90Ohio Preble 391351001 70.8 66.5 87.1 88.6 84.3 70.5 66.5 75Ohio Stark 391510016 46.6 64.8 94.0 95.8 92.5 80.1 81.1 78Ohio Stark 391510019 56.7 76.0 96.0 92.5 92.1 74.7 81.2 77Ohio Stark 391511009 49.7 64.6 87.2 94.1 88.7 67.6 75.6 70Ohio Stark 391514005 59.0 68.3 90.6 99.6 105.8 76.3 80.0 83Ohio Summit 391530020 49.0 75.0 99.2 92.6 92.1 77.8 69.5 92Ohio Trumbull 391550008 65.0 70.6 71.0 101.5 97.0 80.7 70.2 78Ohio Trumbull 391550009 64.2 67.5 73.1 96.7 86.1 93.1 63.8 78Ohio Washington 391670004 65.2 76.7 84.7 90.7 98.3 99.3 NA 62Oklahoma Cleveland 400270049 70.0 77.7 68.3 64.0 57.8 70.0 78.6 82Oklahoma Oklahoma 401090033 66.3 74.6 69.5 64.1 61.3 72.1 85.1 83
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Oklahoma Oklahoma 401091037 68.7 78.2 73.7 68.3 67.8 62.0 73.5 88Oklahoma Tulsa 401430137 74.2 89.8 84.1 72.2 81.0 91.0 95.3 95Oklahoma Tulsa 401430174 57.6 62.7 61.5 50.0 59.2 59.2 69.6 59Pennsylvania Allegheny 420030008 61.2 72.3 87.3 109.5 125.6 44.0 64.2 69Pennsylvania Allegheny 420030067 57.8 63.7 84.1 105.5 102.0 60.3 66.0 64Pennsylvania Allegheny 420030088 60.1 72.3 80.3 102.0 118.8 49.1 63.7 57Pennsylvania Allegheny 420031005 66.1 80.3 91.2 101.1 113.1 74.2 62.8 71Pennsylvania Beaver 420070002 61.7 60.1 NA 95.8 91.6 62.8 84.8 NPennsylvania Beaver 420070005 64.6 70.6 74.1 93.3 89.8 51.7 93.0 78Pennsylvania Beaver 420070014 59.0 66.2 75.2 83.5 95.7 50.1 82.6 77Pennsylvania Berks 420110001 45.7 77.1 82.1 80.8 97.2 66.6 36.8 27Pennsylvania Berks 420110009 41.8 79.5 86.5 87.0 114.2 73.2 40.5 30Pennsylvania Blair 420130801 62.0 78.6 89.7 85.5 94.8 69.3 68.7 61Pennsylvania Bucks 420170012 50.6 73.0 102.3 100.7 122.7 108.6 41.1 37Pennsylvania Cambria 420210011 61.1 66.3 77.5 91.3 92.7 64.2 63.8 59Pennsylvania Dauphin 420430401 49.5 76.1 76.8 82.5 102.5 67.2 57.7 27Pennsylvania Dauphin 420431100 50.3 79.0 80.0 88.6 106.2 67.2 63.6 31Pennsylvania Delaware 420450002 51.6 67.7 86.1 99.0 111.3 100.7 34.6 34Pennsylvania Erie 420490003 52.7 58.5 57.7 87.5 71.8 76.0 55.1 80Pennsylvania Lackawanna 420690101 46.0 74.1 82.6 81.5 79.6 54.6 54.0 40Pennsylvania Lackawanna 420692006 45.1 76.2 91.1 91.0 88.8 63.2 57.1 43Pennsylvania Lancaster 420710007 54.2 76.8 85.7 93.1 109.2 101.8 63.6 37Pennsylvania Lawrence 420730015 55.3 61.1 79.2 94.7 100.0 65.3 72.7 77Pennsylvania Lehigh 420770004 46.2 78.5 84.3 83.8 96.7 64.3 28.3 24Pennsylvania Luzerne 420791100 40.3 66.3 79.5 75.8 78.6 52.6 55.7 44Pennsylvania Luzerne 420791101 45.7 72.8 88.7 85.5 90.1 65.7 56.3 47Pennsylvania Lycoming 420810403 43.7 55.8 72.7 69.6 73.1 50.5 NA NPennsylvania Mercer 420850100 58.5 63.1 72.0 102.5 97.1 75.6 70.2 78Pennsylvania Montgomery 420910013 52.8 68.8 85.5 87.8 114.5 96.1 36.7 28Pennsylvania Perry 420990301 53.2 69.3 73.1 71.7 93.6 50.1 66.3 35Pennsylvania Philadelphia 421010004 43.7 55.7 78.7 86.2 98.7 85.0 31.6 27
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Pennsylvania Philadelphia 421010014 52.5 72.5 88.7 88.3 NA NA 37.5 35Pennsylvania Philadelphia 421010024 57.5 73.7 101.2 102.5 127.5 111.2 41.2 40Pennsylvania Philadelphia 421010136 48.7 62.5 85.0 92.5 106.2 105.0 23.7 NPennsylvania Washington 421250005 55.6 61.8 86.1 92.2 96.5 NA NA 57Pennsylvania Washington 421250200 59.1 56.2 77.5 90.8 92.2 59.0 67.3 58Pennsylvania Washington 421255001 53.6 62.6 67.3 88.6 NA NA 77.6 63Pennsylvania Westmoreland 421290006 53.6 71.6 83.8 99.7 107.3 54.6 58.1 62Pennsylvania York 421330008 49.0 78.1 79.3 86.3 91.7 82.5 68.8 35Rhode Island Kent 440030002 34.0 62.2 91.8 104.1 120.1 55.6 43.2 42Rhode Island Providence 440071010 35.3 61.2 76.2 99.5 89.3 63.6 44.8 43South Carolina Abbeville 450010001 56.0 55.7 62.5 50.3 NA NA 44.7 47South Carolina Aiken 450030003 57.3 53.3 60.2 43.5 44.1 41.5 45.6 43South Carolina Anderson 450070003 67.1 66.1 66.1 63.0 37.2 59.8 46.3 59South Carolina Barnwell 450110001 60.1 58.7 66.6 46.1 45.7 42.8 50.0 39South Carolina Berkeley 450150002 71.1 48.2 49.1 34.1 33.0 52.6 33.6 25South Carolina Charleston 450190042 68.3 51.7 50.5 35.5 32.2 48.1 35.1 28South Carolina Charleston 450190046 59.6 44.8 43.2 32.2 28.0 41.3 29.0 25South Carolina Cherokee 450210002 50.6 68.3 77.1 68.5 38.2 59.7 52.1 54South Carolina Chester 450230002 77.1 67.2 71.5 54.6 42.6 51.5 47.3 43South Carolina Darlington 450310003 69.2 65.5 67.0 47.1 36.1 55.0 36.0 33South Carolina Edgefield 450370001 61.2 63.8 67.3 46.5 48.6 55.1 57.0 43South Carolina Oconee 450730001 53.8 69.3 53.1 62.3 56.3 63.8 45.0 47South Carolina Pickens 450770002 64.6 73.7 65.8 67.8 43.7 66.3 NA NSouth Carolina Richland 450790007 NA 63.6 68.3 45.6 37.8 58.1 40.7 35South Carolina Richland 450791002 55.5 62.7 65.8 45.7 41.0 60.8 39.1 38South Carolina Spartanburg 450830009 55.2 68.6 71.5 66.2 33.8 61.2 49.5 63South Carolina Union 450870001 NA 55.7 69.7 50.2 42.7 49.8 43.3 47South Carolina Williamsburg 450890001 69.0 58.3 53.6 41.3 38.1 53.2 39.6 33South Carolina York 450910006 65.7 65.0 69.0 55.6 40.0 45.7 41.8 40Tennessee Anderson 470010101 66.1 74.1 72.8 NA NA 62.0 52.8 52Tennessee Blount 470090101 NA NA NA NA NA NA NA N
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Tennessee Blount 470090102 60.1 55.6 67.1 66.1 54.5 37.0 28.5 48Tennessee Davidson 470370011 75.6 65.6 83.7 80.6 47.5 64.3 73.1 74Tennessee Davidson 470370026 69.3 66.2 82.5 77.5 46.8 61.8 75.0 73Tennessee Hamilton 470650028 67.6 66.3 66.8 74.1 50.1 68.5 55.7 68Tennessee Hamilton 470651011 58.8 63.1 60.8 67.3 46.3 67.5 58.5 59Tennessee Haywood 470750002 73.2 81.2 81.8 77.6 81.5 77.7 66.3 77Tennessee Knox 470930021 55.8 68.0 64.0 64.0 51.5 52.7 48.5 44Tennessee Knox 470931020 55.8 65.0 64.5 62.6 45.0 50.5 42.6 46Tennessee Rutherford 471490101 56.8 63.1 NA NA NA NA NA NTennessee Sevier 471550101 70.8 70.7 NA 75.7 67.0 57.3 63.8 69Tennessee Sevier 471550102 59.0 67.2 69.5 71.0 68.1 54.6 62.1 71Tennessee Shelby 471570021 73.2 74.7 73.3 75.6 83.6 75.5 68.2 75Tennessee Shelby 471571004 78.5 79.0 79.7 83.3 89.1 82.5 68.3 82Tennessee Sullivan 471632002 53.3 61.7 64.1 62.2 37.5 39.0 57.1 49Tennessee Sullivan 471632003 55.5 64.1 68.8 70.7 38.0 39.3 60.7 51Tennessee Sumner 471650007 82.8 65.2 87.0 84.3 59.6 68.3 80.0 79Tennessee Sumner 471650101 68.3 56.3 67.7 69.6 51.3 49.8 65.1 54Tennessee Wilson 471890103 64.2 63.3 74.2 74.1 49.8 54.7 65.8 67Texas Brazoria 480391003 68.5 80.3 63.6 42.8 73.1 79.2 64.6 91Texas Collin 480850005 75.1 88.2 77.7 63.3 73.6 90.1 NA NTexas Dallas 481130069 54.8 67.2 66.6 51.1 57.0 75.7 87.5 82Texas Dallas 481130087 62.0 73.2 68.3 50.2 64.7 85.1 87.3 85Texas Galveston 481671002 70.1 84.6 64.8 46.8 73.3 92.3 NA 15Texas Gregg 481830001 93.7 93.3 78.1 72.7 85.1 110.0 101.2 10Texas Harris 482010024 89.3 90.7 77.8 43.1 80.0 83.5 107.3 11Texas Harris 482010029 109.1 98.2 90.3 60.6 88.3 85.3 89.6 93Texas Harris 482010046 80.3 89.8 75.8 41.3 87.1 73.6 94.6 98Texas Harris 482010047 77.6 89.2 75.1 43.5 92.1 82.1 NA 87Texas Harris 482010051 72.7 90.1 74.1 44.1 80.8 112.8 103.7 84Texas Harris 482010062 74.8 82.7 64.5 43.6 71.1 97.1 101.0 91Texas Harris 482010066 71.0 102.2 92.0 47.3 90.8 86.7 84.1 83
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Texas Harris 482011035 66.3 85.3 66.8 41.8 NA NA 106.7 10Texas Jefferson 482450009 88.8 86.7 75.0 54.0 70.8 74.3 87.3 82Texas Jefferson 482450011 75.3 77.3 64.2 50.5 86.2 82.2 99.8 88Texas Orange 483611001 79.3 87.7 71.8 56.0 67.8 72.6 82.5 79Texas Tarrant 484391002 66.5 81.1 76.0 62.2 75.5 96.5 94.8 98Texas Tarrant 484392003 69.2 89.1 84.6 67.1 89.8 110.6 113.1 10Texas Travis 484530014 58.3 72.6 59.7 52.8 69.6 80.2 97.6 89Vermont Bennington 500030004 36.5 49.0 70.0 85.8 76.2 50.0 60.3 56Virginia Arlington 510130020 56.8 67.5 75.6 97.5 95.4 82.3 64.8 43Virginia Caroline 510330001 64.3 66.5 72.6 62.1 69.6 36.0 63.2 35Virginia Charles City 510360002 66.0 71.8 57.1 56.1 36.7 39.8 56.2 35Virginia Chesterfield 510410004 62.3 71.8 62.3 59.8 50.8 33.2 56.1 33Virginia Fairfax 510590005 55.2 78.2 84.5 99.1 93.3 68.5 65.3 48Virginia Fairfax 510590018 58.6 70.2 82.0 89.6 91.1 71.8 63.5 45Virginia Fairfax 510595001 62.3 67.6 86.5 122.1 107.5 63.5 59.1 49Virginia Fauquier 510610002 57.1 78.8 74.6 77.8 68.1 47.7 57.5 40Virginia Frederick 510690010 55.1 65.2 63.1 76.3 90.7 81.0 57.0 56Virginia Henrico 510870014 67.5 71.5 61.5 54.3 45.2 37.0 63.0 37Virginia Madison 511130003 61.8 72.8 68.0 77.5 89.2 79.1 64.0 52Virginia Prince William 511530009 56.2 76.2 75.8 100.3 97.0 71.2 64.0 52Virginia Roanoke 511611004 63.2 61.8 69.6 66.6 65.0 44.2 63.7 30Virginia Stafford 511790001 67.0 73.3 75.8 81.3 81.7 60.7 62.5 39Virginia Wythe 511970002 52.2 64.1 64.8 69.2 48.3 46.3 58.7 37Virginia Alexandria Cit 515100009 50.2 NA NA NA NA 63.4 62.6 43Virginia Hampton City 516500004 53.0 55.5 49.3 51.7 31.2 40.0 60.7 36Virginia Suffolk City 518000004 52.7 53.3 50.7 55.0 32.5 42.1 58.3 35Virginia Suffolk City 518000005 62.8 66.0 61.8 60.2 31.7 41.0 49.5 34West Virginia Cabell 540110006 76.1 76.1 81.7 69.0 84.2 83.0 54.7 49West Virginia Hancock 540291004 56.3 51.6 79.0 93.1 78.1 66.0 71.2 73West Virginia Ohio 540690007 56.3 57.3 64.6 77.0 89.1 61.0 64.3 66West Virginia Wood 541071002 62.8 69.2 73.8 79.6 87.3 83.0 NA 65
State County

AIRS Site 

Code Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 

Wisconsin Brown 550090026 71.2 99.8 70.8 62.0 61.7 66.6 47.2 69Wisconsin Columbia 550210015 81.8 88.0 79.1 68.7 79.2 76.7 66.8 96Wisconsin Dane 550250041 70.2 78.1 68.5 65.3 65.1 78.8 62.2 93Wisconsin Dodge 550270007 72.1 83.5 69.7 65.2 63.7 63.5 52.7 79Wisconsin Door 550290004 68.8 116.3 115.5 73.8 62.2 71.1 50.1 60Wisconsin Fond Du Lac 550390006 77.5 87.2 73.7 76.2 71.5 73.1 55.3 81Wisconsin Jefferson 550550002 75.7 87.5 71.1 64.2 70.8 70.1 65.0 88Wisconsin Kenosha 550590002 74.6 92.7 99.5 72.0 76.1 61.6 51.0 76Wisconsin Kenosha 550590019 89.2 110.8 118.6 78.1 93.7 71.0 56.7 85Wisconsin Kewaunee 550610002 70.2 102.7 109.3 71.5 67.0 69.2 52.1 72Wisconsin Manitowoc 550710004 67.0 100.1 65.7 62.3 66.1 59.8 48.0 75Wisconsin Manitowoc 550710007 74.7 106.8 115.1 71.6 56.1 62.6 49.6 76Wisconsin Marathon 550730012 75.2 78.2 61.3 66.0 64.8 70.8 70.6 68Wisconsin Milwaukee 550790041 74.5 101.5 99.5 70.5 72.3 65.2 49.7 72Wisconsin Milwaukee 550790044 61.0 88.1 68.7 68.6 68.6 69.1 49.5 75Wisconsin Milwaukee 550790085 81.8 109.0 107.3 75.2 77.1 69.1 52.8 75Wisconsin Milwaukee 550791025 77.8 102.0 93.1 73.2 76.1 68.0 51.3 76Wisconsin Outagamie 550870009 82.1 90.8 77.8 66.5 65.6 76.4 56.3 74Wisconsin Ozaukee 550890008 81.2 111.1 104.7 71.1 67.7 67.0 55.2 80Wisconsin Ozaukee 550890009 80.3 109.0 116.6 73.7 75.5 66.0 53.6 76Wisconsin Racine 551010017 72.2 93.5 102.7 66.5 69.8 54.1 42.2 66Wisconsin Rock 551050024 76.1 91.2 70.0 67.1 66.2 74.3 64.8 87Wisconsin St Croix 551091002 57.1 67.7 63.2 69.1 69.2 78.8 84.0 66Wisconsin Sauk 551110007 69.2 75.5 70.5 59.5 63.0 76.7 64.8 87Wisconsin Vernon 551230008 56.8 67.7 63.5 62.5 62.5 68.3 69.2 71Wisconsin Walworth 551270005 89.0 85.8 64.5 63.1 72.2 75.4 58.0 86Wisconsin Washington 551310009 80.0 94.1 75.5 73.2 76.5 73.3 56.1 89Wisconsin Waukesha 551330017 79.6 85.5 65.7 60.0 NA NA 55.0 84Wisconsin Winnebago 551390011 82.7 88.2 77.6 65.0 68.7 74.5 55.6 81
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Alabama Clay 10270001 72.7 64.1 60.1 80.7 72.3 84.3 Alabama Jefferson 10731003 46.5 44.1 83.1 71.6 93.3 87.8 Alabama Jefferson 10731005 45.6 40.1 66.1 88.8 102.2 85.6 Alabama Jefferson 10732006 50.8 38.3 78.0 91.3 87.8 83.1 Alabama Jefferson 10735002 55.2 51.3 70.3 67.5 79.2 83.0 Alabama Jefferson 10736002 51.0 48.2 103.2 68.1 85.6 82.0 Alabama Lawrence 10790002 51.7 43.0 49.5 77.2 82.6 85.0 Alabama Madison 10890014 80.6 62.2 73.5 72.3 73.8 84.7 Alabama Mobile 10970003 NA NA NA NA NA NA Alabama Shelby 11170004 56.1 35.1 78.3 98.8 89.5 81.5 Alabama Sumter 11190002 39.5 38.3 42.5 60.8 74.7 50.2 Arkansas Crittenden 50350005 59.5 55.2 56.2 118.7 89.8 123.8 Arkansas Montgomery 50970001 49.3 50.0 45.6 42.7 44.6 54.5 Arkansas Pulaski 51190007 57.3 55.8 60.3 75.8 77.5 72.0 Arkansas Pulaski 51191002 56.8 57.3 53.3 99.5 100.6 86.7 Connecticut Fairfield 90010017 68.1 31.8 59.1 61.5 64.8 81.3 Connecticut Fairfield 90011123 63.2 25.3 61.0 59.0 79.5 122.2 Connecticut Fairfield 90013007 66.7 37.2 56.7 69.7 63.7 76.6 Connecticut Hartford 90031003 56.5 26.8 63.8 64.3 71.3 113.7 Connecticut Middlesex 90070007 54.1 26.1 74.1 69.1 67.2 110.8 Connecticut New Haven 90093002 56.2 43.8 64.7 74.5 67.1 83.7 Connecticut New London 90110008 35.5 41.3 60.1 63.2 55.2 64.8 Connecticut Tolland 90131001 49.5 23.5 63.3 60.8 68.0 114.2 Delaware Kent 100010002 75.3 47.7 60.6 68.2 76.6 78.1 Delaware New Castle 100031003 67.5 31.8 65.6 68.6 89.0 90.6 Delaware New Castle 100031007 63.8 40.6 51.1 73.5 91.0 93.0 Delaware New Castle 100031010 63.8 30.8 67.0 64.1 78.8 95.5 Delaware Sussex 100051002 82.6 58.1 62.3 76.0 78.8 79.3 

8-Hour Daily Maximum Ozone (ppb) Measured at Monitoring Sites in the East During the July 1995 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

D.C. Washington 110010025 58.8 52.5 61.3 68.1 91.8 93.6 D.C. Washington 110010041 61.8 54.7 64.5 64.8 88.6 85.6 D.C. Washington 110010043 70.1 61.8 63.5 81.1 98.3 93.2 Florida Brevard 120094001 52.3 67.3 51.0 57.3 58.3 51.0 Florida Duval 120310077 63.2 70.7 61.2 65.5 42.8 36.5 Florida Escambia 120330004 59.5 42.3 59.6 73.8 71.3 59.1 Florida Escambia 120330018 83.0 59.8 61.2 64.8 86.2 60.3 Florida Hillsborough 120570081 62.7 64.5 60.1 58.1 NA 54.5 Florida Hillsborough 120571035 57.7 67.2 61.8 64.0 44.7 52.3 Florida Hillsborough 120571065 56.7 66.8 58.8 62.8 47.6 53.8 Florida Orange 120950008 66.0 68.2 75.0 45.8 62.0 53.1 Florida Orange 120952002 61.2 59.6 62.3 48.0 51.5 51.2 Florida Osceola 120972002 49.2 62.3 66.1 42.0 54.1 52.3 Florida Palm Beach 120992004 52.3 43.1 24.0 18.5 41.7 32.5 Florida Pasco 121012001 53.3 66.7 59.0 55.6 52.8 75.3 Florida Pinellas 121030004 48.5 65.2 59.1 55.5 43.7 54.8 Florida Pinellas 121030018 36.3 51.2 47.3 49.2 46.0 22.0 Florida Pinellas 121035002 49.0 63.3 57.0 52.7 35.5 65.1 Florida Polk 121056005 48.6 63.1 53.2 63.2 44.0 47.6 Florida Polk 121056006 54.0 72.5 61.6 49.8 48.8 50.5 Florida St Lucie 121111002 45.6 58.0 31.1 40.2 50.8 39.2 Florida Sarasota 121151005 53.7 61.2 60.3 54.2 57.6 49.3 Florida Seminole 121171002 62.6 62.0 61.8 58.5 52.5 46.8 Florida Volusia 121272001 51.1 67.2 61.1 55.3 56.7 44.5 Florida Volusia 121275002 54.3 67.6 59.1 54.8 58.1 45.7 Georgia Chatham 130510021 64.8 76.6 NA NA NA NA Georgia De Kalb 130890002 84.5 95.7 107.0 115.1 75.2 73.6 Georgia De Kalb 130893001 71.1 83.8 129.1 97.8 73.5 69.6 Georgia Fulton 131210055 78.6 89.7 123.5 110.1 73.1 69.8 Georgia Glynn 131270006 NA NA NA NA NA 37.5 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Georgia Gwinnett 131350002 66.5 73.8 109.0 81.3 68.3 67.5 Georgia Muscogee 132150008 66.1 67.8 99.1 91.3 71.0 63.2 Georgia Muscogee 132151003 74.5 65.2 74.0 69.3 71.1 63.8 Georgia Richmond 132450091 70.0 78.8 99.4 88.2 73.8 68.7 Georgia Rockdale 132470001 88.3 92.1 84.0 94.6 75.5 66.1 Illinois Adams 170010006 69.3 47.2 48.5 50.2 65.5 79.2 Illinois Champaign 170190004 62.0 75.1 65.0 89.5 71.6 84.6 Illinois Cook 170310001 59.0 73.6 59.6 50.8 60.8 113.2 Illinois Cook 170310032 54.7 72.2 59.7 82.1 69.6 112.8 Illinois Cook 170310050 43.2 86.3 66.7 70.8 77.8 116.7 Illinois Cook 170310064 53.7 79.2 60.7 74.8 77.0 117.3 Illinois Cook 170310072 63.3 73.2 65.1 83.7 85.5 96.7 Illinois Cook 170311003 53.5 78.7 56.6 69.8 89.6 101.5 Illinois Cook 170311601 54.6 66.8 54.1 73.6 83.8 NA Illinois Cook 170314002 44.7 71.0 61.5 49.1 77.3 85.0 Illinois Cook 170314006 60.2 77.6 64.6 75.0 106.2 110.1 Illinois Cook 170317002 61.1 75.3 57.3 78.7 86.2 104.3 Illinois Cook 170318003 35.7 72.0 54.7 47.3 56.6 71.5 Illinois Du Page 170436001 58.1 63.0 NA NA 79.8 71.1 Illinois Effingham 170491001 64.7 67.5 68.8 93.7 66.1 71.2 Illinois Jersey 170831001 59.8 60.1 61.2 90.2 79.6 80.0 Illinois Kane 170890005 60.3 68.5 56.8 86.2 102.0 84.8 Illinois Lake 170971002 62.0 62.2 58.2 79.3 87.3 86.1 Illinois Lake 170971007 67.0 72.1 64.6 84.3 90.8 100.0 Illinois Lake 170973001 51.8 60.1 53.5 74.5 99.3 77.0 Illinois McHenry 171110001 59.6 66.1 61.5 78.0 104.0 86.2 Illinois Macon 171150013 62.7 64.3 62.3 78.3 68.1 76.2 Illinois Macoupin 171170002 62.7 59.0 67.6 79.3 104.1 74.2 Illinois Madison 171190008 53.2 64.2 72.6 89.1 101.0 102.2 Illinois Madison 171191009 56.8 58.5 79.7 52.7 67.6 95.1 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Illinois Madison 171192007 64.7 67.3 90.2 71.8 91.1 99.7 Illinois Madison 171193007 54.1 59.1 68.3 83.0 88.1 95.1 Illinois Peoria 171430024 57.3 56.5 66.3 58.7 78.5 57.6 Illinois Peoria 171431001 62.6 59.1 70.2 67.6 87.7 82.1 Illinois Randolph 171570001 63.1 71.5 68.7 66.1 55.0 58.0 Illinois St Clair 171630010 48.7 60.6 97.2 60.2 64.5 92.1 Illinois Sangamon 171670010 56.6 67.8 65.0 76.3 80.3 74.7 Illinois Will 171971008 61.0 67.8 62.3 67.1 85.8 92.8 Illinois Will 171971011 54.0 73.1 65.1 62.7 79.5 NA Illinois Winnebago 172010009 NA NA NA NA NA NA Illinois Winnebago 172012001 60.0 51.7 60.1 70.2 77.5 81.3 Indiana Allen 180030002 61.1 59.5 63.7 78.8 96.1 97.5 Indiana Allen 180030004 62.3 57.0 67.6 69.5 NA NA Indiana Clark 180190003 64.5 87.0 74.5 91.6 108.0 83.7 Indiana Floyd 180431004 63.2 76.7 75.1 89.6 92.7 77.6 Indiana Hamilton 180571001 58.1 80.6 64.3 76.5 111.0 94.6 Indiana Hancock 180590003 57.2 74.7 NA 80.0 89.4 103.1 Indiana Lake 180890022 59.0 89.1 78.8 94.2 84.1 105.6 Indiana Lake 180892008 51.5 88.7 78.7 79.6 81.6 110.7 Indiana La Porte 180910005 66.6 87.5 81.7 97.2 103.2 102.0 Indiana Madison 180950010 62.5 76.6 69.3 83.8 95.7 103.0 Indiana Marion 180970042 62.2 76.5 63.3 80.5 92.0 87.2 Indiana Marion 180970050 71.7 91.7 69.1 83.0 95.2 94.0 Indiana Marion 180970057 71.2 84.6 69.6 81.5 91.2 88.6 Indiana Marion 180970073 79.7 89.7 72.5 85.3 91.0 94.0 Indiana Porter 181270020 52.1 87.7 67.1 72.5 82.1 Indiana Porter 181270024 69.1 103.5 70.0 NA 83.1 97.8 Indiana St Joseph 181410010 58.7 57.3 69.6 65.0 NA NA Indiana St Joseph 181411007 49.1 59.7 67.5 77.8 88.1 NA Indiana St Joseph 181411008 49.5 63.1 70.0 67.7 90.1 94.7 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Indiana Vanderburgh 181630012 80.1 73.5 77.2 88.3 91.5 75.1 Indiana Vanderburgh 181630013 80.0 81.0 77.3 92.7 99.2 90.8 Indiana Vigo 181670018 69.8 74.7 60.5 78.1 87.5 64.5 Indiana Warrick 181730002 77.1 71.2 85.3 104.7 NA 81.2 Indiana Warrick 181730008 69.2 71.7 79.1 98.1 106.1 77.1 Indiana Warrick 181730009 70.6 75.7 75.6 108.1 107.7 90.1 Iowa Polk 191530058 28.2 26.7 25.3 36.3 42.5 39.8 Iowa Scott 191632011 57.5 46.6 59.8 61.8 60.6 82.2 Kansas Sedgwick 201730001 61.2 51.2 76.2 75.0 76.2 83.7 Kansas Sedgwick 201730010 51.8 44.3 66.2 56.2 65.0 72.5 Kentucky Bell 210130002 72.5 70.7 61.5 62.1 64.2 79.5 Kentucky Boone 210150003 53.1 62.6 71.6 75.6 84.0 72.1 Kentucky Boyd 210190015 54.3 69.8 85.3 79.8 91.0 103.6 Kentucky Bullitt 210290006 61.3 68.2 90.7 77.2 86.1 72.7 Kentucky Daviess 210590005 77.0 NA 65.6 NA NA 96.3 Kentucky Fayette 210670001 47.8 63.5 63.3 77.6 104.2 79.7 Kentucky Fayette 210670012 55.2 63.1 89.5 88.2 92.6 80.1 Kentucky Graves 210830003 65.1 49.3 51.0 68.5 49.0 55.2 Kentucky Greenup 210890007 50.1 64.7 62.7 71.5 80.3 81.0 Kentucky Hancock 210910012 63.5 74.2 71.3 95.0 101.8 86.8 Kentucky Henderson 211010013 NA 70.0 76.6 98.8 91.0 75.0 Kentucky Henderson 211010014 81.7 70.5 73.2 93.0 97.5 65.7 Kentucky Jefferson 211110027 47.2 48.5 62.5 74.5 79.2 60.7 Kentucky Jefferson 211110051 67.1 64.0 77.2 82.6 103.1 69.1 Kentucky Jefferson 211111021 57.1 81.8 68.8 68.8 103.6 80.0 Kentucky Jessamine 211130001 54.1 57.3 70.8 61.4 80.2 62.8 Kentucky Kenton 211170007 67.6 81.0 83.1 82.5 98.5 82.5 Kentucky Livingston 211390003 71.5 61.1 53.3 75.8 60.3 69.6 Kentucky Livingston 211390004 72.0 55.1 58.2 83.0 61.7 69.3 Kentucky McCracken 211451024 72.3 51.5 50.8 69.2 55.2 67.8 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Kentucky McLean 211490001 76.8 62.6 78.0 103.6 93.8 84.0 Kentucky Oldham 211850004 53.1 61.7 67.8 88.1 88.2 76.2 Kentucky Pike 211950002 63.3 60.1 68.3 85.2 82.6 86.2 Kentucky Pulaski 211990003 68.6 66.7 57.1 80.1 77.0 73.1 Kentucky Scott 212090001 56.5 63.5 79.1 81.3 99.8 88.7 Kentucky Simpson 212130004 74.8 66.3 77.6 76.8 NA 49.5 Louisiana Ascension 220050004 57.1 67.7 83.2 88.1 70.5 59.1 Louisiana Beauregard 220110002 51.6 52.6 71.6 58.1 56.8 70.0 Louisiana Bossier 220150008 58.3 54.0 60.0 74.6 63.7 65.8 Louisiana Caddo 220170001 60.8 54.2 66.5 66.8 64.3 64.0 Louisiana Calcasieu 220190002 62.5 63.2 78.1 65.2 84.8 84.1 Louisiana Calcasieu 220190008 37.2 37.1 46.8 32.3 39.6 51.5 Louisiana Calcasieu 220190009 69.3 58.2 67.0 63.4 67.2 79.5 Louisiana East Baton Rou 220330003 61.5 62.5 79.0 80.6 88.0 64.3 Louisiana East Baton Rou 220330009 58.2 62.0 80.0 81.6 83.3 64.6 Louisiana East Baton Rou 220330013 49.6 62.0 80.6 58.7 58.0 62.0 Louisiana East Baton Rou 220331001 50.7 60.8 77.3 70.6 74.1 65.2 Louisiana Grant 220430001 58.2 57.5 72.1 62.6 69.8 77.0 Louisiana Iberville 220470007 58.6 54.5 75.6 65.2 89.3 61.1 Louisiana Iberville 220470009 77.5 57.6 78.0 71.7 90.0 79.1 Louisiana Jefferson 220511001 63.6 85.8 94.5 75.1 81.6 67.3 Louisiana Lafayette 220550005 60.6 67.6 106.8 79.3 74.7 88.5 Louisiana Livingston 220630002 49.0 78.0 89.0 69.6 63.5 59.7 Louisiana Orleans 220710012 50.0 73.0 72.7 59.6 62.5 55.6 Louisiana Pointe Coupee 220770001 47.7 56.8 70.6 60.3 57.8 60.6 Louisiana St Bernard 220870002 44.2 58.1 55.6 49.1 45.0 38.3 Louisiana St Charles 220890003 66.6 84.2 82.0 69.1 76.0 68.6 Louisiana St James 220930002 63.7 61.1 76.3 93.1 88.5 65.0 Louisiana St John The Ba 220950002 58.6 79.5 79.5 75.2 68.1 63.7 Louisiana St Mary 221010003 68.6 71.6 70.3 72.1 72.5 61.2 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Louisiana West Baton Rou 221210001 52.5 54.2 72.2 67.3 81.2 62.1 Maine Cumberland 230052003 31.2 30.3 60.7 44.7 67.2 96.5 Maine Hancock 230090102 NA NA NA NA NA NA Maine Kennebec 230112005 26.3 20.6 38.1 33.1 50.1 80.1 Maine Knox 230130004 32.8 33.2 54.1 53.0 70.0 88.6 Maine Oxford 230173001 40.3 24.6 35.1 39.1 45.5 53.0 Maine Penobscot 230194008 36.0 33.0 43.6 52.1 60.3 88.8 Maine York 230312002 37.2 30.3 64.7 39.5 75.6 103.2 Maine York 230313002 40.6 23.5 53.5 39.5 67.5 88.6 Maryland Anne Arundel 240030014 81.3 26.5 NA NA 112.6 107.5 Maryland Anne Arundel 240030019 70.7 55.7 79.2 77.7 103.8 103.2 Maryland Baltimore 240051007 57.3 36.2 66.7 59.1 95.3 110.2 Maryland Baltimore 240053001 64.5 46.8 67.6 66.2 106.3 85.5 Maryland Carroll 240130001 57.6 43.6 74.6 61.5 85.0 87.0 Maryland Cecil 240150003 71.3 44.3 82.3 79.7 94.2 NA Maryland Charles 240170010 78.2 64.1 60.3 92.6 87.3 86.7 Maryland Harford 240251001 65.3 40.0 74.2 79.2 113.0 93.8 Maryland Harford 240259001 62.3 42.8 72.6 70.1 99.5 101.8 Maryland Kent 240290002 72.5 44.6 67.2 75.0 91.2 87.5 Maryland Montgomery 240313001 62.2 52.6 73.3 68.7 92.6 106.5 Maryland Prince Georges 240330002 66.5 55.1 69.7 68.3 99.6 95.5 Maryland Prince Georges 240338001 64.0 56.8 60.5 69.6 97.7 85.3 Massachusetts Barnstable 250010002 40.1 44.8 65.7 54.3 60.2 67.0 Massachusetts Berkshire 250034002 NA NA NA NA NA NA Massachusetts Bristol 250051002 43.3 34.0 71.3 53.7 70.0 65.5 Massachusetts Essex 250090005 29.1 18.1 40.7 34.7 58.3 77.2 Massachusetts Essex 250092006 37.1 28.1 62.3 50.8 63.1 100.7 Massachusetts Essex 250094004 38.0 24.0 NA NA NA NA Massachusetts Hampden 250130003 56.0 25.1 55.8 62.5 71.7 109.3 Massachusetts Hampden 250130008 56.2 25.2 51.5 63.1 77.2 101.6 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Massachusetts Hampshire 250150103 51.1 25.1 47.1 45.8 61.2 75.5 Massachusetts Hampshire 250154002 52.0 25.7 57.2 60.8 68.5 101.0 Massachusetts Worcester 250270015 47.7 25.1 60.5 56.1 60.6 106.3 Michigan Allegan 260050003 46.3 78.1 80.6 63.2 101.3 162.7 Michigan Benzie 260190003 53.0 57.0 80.6 55.0 102.1 83.3 Michigan Berrien 260210014 42.2 69.7 72.7 70.7 74.8 101.0 Michigan Cass 260270003 49.7 71.6 66.8 85.6 93.3 101.1 Michigan Clinton 260370001 46.5 48.1 44.8 46.2 76.0 71.8 Michigan Genesee 260490021 46.6 60.3 47.1 46.1 89.0 80.3 Michigan Genesee 260492001 NA NA NA 38.7 83.6 81.0 Michigan Huron 260630007 35.0 51.2 60.1 38.3 NA NA Michigan Ingham 260650012 50.6 65.6 51.5 54.1 90.5 80.2 Michigan Kent 260810020 42.5 60.1 61.8 46.1 94.6 133.1 Michigan Lenawee 260910007 57.0 51.3 51.0 58.3 84.6 NA Michigan Macomb 260990009 51.7 48.5 54.8 51.0 57.8 92.7 Michigan Macomb 260991003 51.7 45.6 47.5 53.0 69.3 85.7 Michigan Muskegon 261210039 44.6 58.8 72.5 71.3 102.8 153.5 Michigan Oakland 261250001 52.6 37.8 49.2 56.4 71.5 79.2 Michigan Ottawa 261390005 41.6 61.1 64.8 62.0 83.2 NA Michigan St Clair 261470005 30.5 48.0 55.3 44.7 53.7 100.2 Michigan Wayne 261630001 39.2 31.2 36.8 51.0 55.8 77.2 Michigan Wayne 261630016 42.2 29.6 43.5 50.3 53.6 68.0 Michigan Wayne 261630019 57.3 41.6 55.2 61.3 67.2 91.2 Minnesota Anoka 270031001 43.2 31.6 38.6 66.8 67.8 62.8 Minnesota Anoka 270031002 NA NA NA NA 78.7 76.0 Minnesota Dakota 270376018 50.1 46.0 52.7 52.7 58.6 55.1 Mississippi Adams 280010004 51.2 57.1 54.6 65.2 56.1 56.6 Mississippi De Soto 280330002 61.1 62.2 76.0 80.1 76.3 75.5 Mississippi Hancock 280450001 63.3 65.3 82.7 77.1 78.0 76.7 Mississippi Hinds 280490010 54.1 56.0 52.7 61.3 63.0 66.1 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Mississippi Jackson 280590006 57.3 56.5 54.1 68.2 70.1 64.6 Mississippi Madison 280890002 58.0 61.5 58.3 60.5 56.5 63.1 Mississippi Warren 281490004 53.8 64.3 58.7 67.8 59.3 59.1 Missouri Clay 290470003 63.6 44.0 71.0 111.8 119.5 92.8 Missouri Clay 290470005 62.2 44.7 68.6 105.0 125.2 99.1 Missouri Clay 290470025 52.8 40.5 56.8 88.0 94.3 88.1 Missouri Greene 290770026 46.6 53.8 71.6 46.1 63.5 47.0 Missouri Greene 290770036 49.2 54.5 95.3 57.5 64.6 75.2 Missouri Jefferson 290990012 63.0 74.0 102.2 61.8 62.1 73.2 Missouri Monroe 291370001 60.5 52.7 57.3 NA NA NA Missouri Platte 291650023 51.3 38.8 55.2 83.3 78.5 76.2 Missouri St Charles 291831002 65.7 73.5 89.7 112.8 105.4 121.6 Missouri St Charles 291831004 67.3 66.1 69.1 74.1 89.8 96.5 Missouri St Louis 291890006 52.6 65.5 74.7 47.5 54.2 86.6 Missouri St Louis 291893001 57.7 65.6 76.2 57.7 55.6 85.3 Missouri St Louis 291895001 61.7 65.8 88.5 92.5 88.2 113.5 Missouri St Louis City 295100007 49.6 60.7 87.5 47.3 37.0 61.1 Missouri St Louis City 295100072 NA 53.7 73.6 48.2 52.2 75.6 Nebraska Douglas 310550028 60.1 39.3 68.0 68.6 75.0 71.8 Nebraska Douglas 310550032 NA NA NA NA NA NA Nebraska Douglas 310550035 25.6 20.1 32.3 40.3 NA NA Nebraska Lancaster 311090016 52.7 40.5 51.1 63.2 49.2 53.3 New Hampshire Cheshire 330050007 49.1 22.3 47.7 52.5 50.0 69.0 New Hampshire Hillsborough 330111010 40.1 23.0 55.0 44.6 67.0 96.6 New Hampshire Merrimack 330130007 39.1 18.1 41.1 25.3 44.8 65.1 New Hampshire Rockingham 330150012 46.0 24.0 60.2 40.3 73.6 99.3 New Jersey Atlantic 340010005 84.2 45.8 66.0 70.1 91.2 76.7 New Jersey Camden 340070003 67.8 22.7 66.5 66.7 81.5 77.0 New Jersey Camden 340071001 88.0 38.7 72.2 99.1 95.2 89.1 New Jersey Cumberland 340110007 75.0 39.7 66.6 66.7 82.5 76.8 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

New Jersey Gloucester 340150002 77.1 29.6 73.2 77.6 107.0 91.2 New Jersey Hudson 340170006 72.3 27.0 63.3 62.5 64.5 104.6 New Jersey Hunterdon 340190001 65.7 30.2 74.8 72.6 86.2 106.2 New Jersey Mercer 340210005 72.0 26.8 80.8 70.0 88.5 107.0 New Jersey Middlesex 340230011 69.6 28.3 73.8 72.1 78.2 109.2 New Jersey Monmouth 340250005 70.2 26.5 60.6 61.7 62.0 71.2 New Jersey Morris 340273001 67.8 32.3 74.0 75.1 101.5 113.6 New Jersey Ocean 340290006 79.0 27.3 77.6 75.8 86.3 87.1 New York Albany 360010012 36.6 34.1 51.8 71.6 62.1 81.3 New York Bronx 360050083 65.0 24.6 50.1 42.8 63.6 86.2 New York Chautauqua 360130011 NA NA NA NA NA NA New York Chemung 360150003 25.3 38.1 54.0 56.8 68.6 75.6 New York Dutchess 360270007 59.8 24.5 54.6 63.7 69.7 94.8 New York Erie 360290002 21.8 56.6 NA 58.1 55.0 98.7 New York Essex 360310002 56.2 35.1 44.8 51.0 68.2 90.6 New York Essex 360310003 45.3 33.3 42.0 39.5 60.6 86.3 New York Hamilton 360410005 32.1 34.5 41.0 45.8 51.1 70.4 New York Herkimer 360430005 23.6 32.3 38.7 53.6 52.2 52.0 New York Jefferson 360450002 26.1 36.8 44.3 55.1 42.7 98.2 New York Madison 360530006 30.2 40.0 51.2 52.0 61.5 81.3 New York Monroe 360551004 22.2 43.6 53.8 NA 40.6 97.0 New York Niagara 360631006 22.3 57.3 44.2 NA NA NA New York Oneida 360650004 24.0 33.3 44.1 54.0 52.7 82.2 New York Onondaga 360671015 23.2 36.8 54.8 59.6 67.1 92.6 New York Orange 360715001 57.8 24.6 59.3 64.2 77.2 102.7 New York Putnam 360790005 63.7 27.7 62.3 59.8 75.3 107.3 New York Richmond 360850067 76.7 27.1 68.7 60.1 58.5 97.6 New York Saratoga 360910004 35.5 30.5 45.8 52.1 59.6 77.0 New York Schenectady 360930003 34.6 33.2 49.1 61.2 55.3 74.1 New York Suffolk 361030002 82.3 37.2 62.8 69.8 67.5 78.1 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

New York Suffolk 361030004 58.0 40.1 59.0 61.2 54.7 77.6 New York Ulster 361111005 46.3 35.6 52.0 55.8 53.5 80.7 New York Wayne 361173001 21.8 37.1 59.5 60.8 44.3 96.8 New York Westchester 361192004 74.1 33.2 61.5 62.2 74.8 100.6 North Carolina Buncombe 370210030 72.7 76.7 75.8 61.8 63.1 78.2 North Carolina Caldwell 370270003 74.0 68.0 83.1 72.3 79.3 92.0 North Carolina Camden 370290099 74.6 63.6 69.0 67.7 83.5 61.2 North Carolina Caswell 370330001 69.1 57.6 69.6 69.7 69.8 76.6 North Carolina Chatham 370370004 79.0 58.1 76.2 75.1 81.3 80.2 North Carolina Cumberland 370510008 74.0 67.3 58.3 65.3 75.0 67.5 North Carolina Duplin 370610002 57.7 62.3 45.6 46.1 48.6 37.1 North Carolina Durham 370630013 80.3 65.2 78.6 88.3 82.3 81.1 North Carolina Forsyth 370670022 76.3 78.1 90.5 74.7 87.0 100.5 North Carolina Forsyth 370670027 68.8 61.6 72.5 63.1 76.2 82.2 North Carolina Forsyth 370671008 75.8 71.1 77.7 64.1 81.5 84.1 North Carolina Franklin 370690001 73.3 54.3 63.7 66.0 77.8 66.0 North Carolina Granville 370770001 84.5 67.7 74.8 89.5 85.0 82.2 North Carolina Guilford 370810011 73.3 62.0 69.1 73.8 73.7 75.2 North Carolina Haywood 370870035 59.7 54.3 51.3 62.0 58.0 61.0 North Carolina Haywood 370870036 64.8 55.5 52.2 63.8 63.8 60.5 North Carolina Johnston 371010002 87.1 68.8 72.2 85.3 82.0 73.8 North Carolina Lincoln 371090004 64.5 74.0 73.0 77.7 87.6 90.0 North Carolina Martin 371170001 65.8 55.8 55.5 49.7 71.7 69.1 North Carolina Mecklenburg 371191005 77.1 77.5 78.0 84.0 76.1 70.2 North Carolina Mecklenburg 371191009 78.2 78.6 80.5 83.0 76.8 67.7 North Carolina Northampton 371310002 74.3 64.1 60.3 64.5 70.5 63.5 North Carolina Pitt 371470099 76.6 65.1 54.5 62.7 58.8 61.1 North Carolina Rockingham 371570099 66.7 50.6 73.3 64.7 66.5 68.7 North Carolina Rowan 371590021 80.1 59.7 72.0 97.0 78.5 64.6 North Carolina Rowan 371590022 75.1 68.7 80.6 75.8 80.5 64.2 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

North Carolina Swain 371730002 61.5 53.6 56.0 51.6 51.7 63.2 North Carolina Wake 371830014 84.6 60.7 67.5 65.6 73.2 70.1 North Carolina Wake 371830015 87.8 72.7 69.7 78.5 82.7 79.2 North Carolina Wake 371830016 84.0 70.6 70.0 77.7 86.6 76.3 North Carolina Wake 371830017 85.2 65.2 71.3 79.0 81.6 70.1 North Carolina Yancey 371990003 74.6 64.5 65.0 71.5 78.1 77.2 Ohio Allen 390030002 56.0 61.1 73.3 79.8 83.0 92.3 Ohio Ashtabula 390071001 35.8 44.0 82.5 51.1 69.8 119.6 Ohio Butler 390170004 56.1 84.5 77.1 79.2 121.6 78.1 Ohio Butler 390171004 59.0 89.8 81.0 77.0 113.5 89.1 Ohio Clark 390230001 51.0 42.3 83.5 82.7 77.7 112.7 Ohio Clark 390230003 57.3 65.8 80.1 80.7 92.7 113.5 Ohio Clinton 390271002 67.0 86.3 94.0 97.3 99.7 110.2 Ohio Cuyahoga 390350034 46.3 49.7 75.3 58.8 66.2 90.8 Ohio Cuyahoga 390350064 61.1 53.4 68.0 69.8 81.0 95.8 Ohio Cuyahoga 390355002 47.8 54.8 76.3 62.1 74.6 93.0 Ohio Franklin 390490081 50.1 58.0 73.0 76.3 92.0 75.5 Ohio Hamilton 390610006 58.8 90.7 75.0 87.5 106.0 101.3 Ohio Hamilton 390610010 51.3 52.1 48.1 42.0 89.1 74.3 Ohio Knox 390830002 50.2 58.6 70.8 77.7 79.6 94.2 Ohio Lake 390850003 44.6 55.6 90.7 61.6 63.5 111.2 Ohio Lake 390853002 34.6 52.1 79.3 51.6 57.3 103.3 Ohio Lawrence 390870006 53.7 68.0 72.1 59.1 80.4 90.7 Ohio Lawrence 390870011 48.0 70.7 86.5 78.6 74.7 90.0 Ohio Licking 390890005 55.3 67.3 89.3 97.8 93.0 92.6 Ohio Lucas 390950034 59.2 55.0 59.5 92.8 65.6 89.7 Ohio Lucas 390950081 58.6 53.8 52.7 100.3 72.1 81.2 Ohio Madison 390970007 57.8 72.7 84.5 93.2 82.7 108.6 Ohio Medina 391030003 58.8 61.7 70.0 83.1 82.8 99.3 Ohio Miami 391090005 54.0 57.2 70.2 73.3 100.5 84.7 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Ohio Montgomery 391130019 53.5 63.2 75.5 72.1 103.8 92.5 Ohio Portage 391331001 57.1 58.3 69.2 73.7 84.0 85.0 Ohio Preble 391351001 57.6 69.5 73.0 81.1 80.3 71.8 Ohio Stark 391510016 52.1 64.3 70.8 80.1 84.1 87.5 Ohio Stark 391510019 59.7 66.0 74.2 87.3 85.6 83.8 Ohio Stark 391511009 55.1 63.8 71.3 80.7 80.5 83.2 Ohio Stark 391514005 56.7 65.7 75.1 85.3 81.7 103.6 Ohio Summit 391530020 59.5 26.3 63.0 80.0 88.7 86.1 Ohio Trumbull 391550008 38.1 55.6 76.3 69.8 81.3 102.2 Ohio Trumbull 391550009 30.0 48.0 71.6 57.2 62.0 100.5 Ohio Washington 391670004 50.2 60.1 86.0 57.8 79.7 NA Oklahoma Cleveland 400270049 44.1 63.6 59.0 60.3 68.8 71.6 Oklahoma Oklahoma 401090033 49.7 71.8 72.7 66.0 69.1 77.6 Oklahoma Oklahoma 401091037 50.2 63.8 76.5 76.7 79.6 81.7 Oklahoma Tulsa 401430137 47.1 62.3 80.0 66.6 62.5 NA Oklahoma Tulsa 401430174 38.3 78.6 56.0 56.3 68.6 63.7 Pennsylvania Allegheny 420030008 41.0 62.0 64.4 70.6 73.8 98.1 Pennsylvania Allegheny 420030067 50.8 67.8 68.8 67.5 66.5 93.2 Pennsylvania Allegheny 420030088 48.5 63.1 76.1 76.8 89.5 92.8 Pennsylvania Allegheny 420031005 43.2 59.8 79.1 80.0 76.0 112.7 Pennsylvania Beaver 420070002 53.0 59.1 58.6 76.1 75.7 89.0 Pennsylvania Beaver 420070005 47.5 57.1 60.8 71.5 72.6 85.2 Pennsylvania Beaver 420070014 42.0 58.6 68.3 71.7 71.2 80.5 Pennsylvania Berks 420110001 47.5 33.3 71.8 66.5 72.1 103.2 Pennsylvania Berks 420110009 47.2 31.5 74.8 59.3 73.0 107.0 Pennsylvania Blair 420130801 34.5 51.0 58.7 68.1 77.0 77.1 Pennsylvania Bucks 420170012 73.7 25.0 80.2 72.1 90.8 107.5 Pennsylvania Cambria 420210011 34.7 46.2 58.5 72.1 73.0 NA Pennsylvania Dauphin 420430401 41.7 39.5 64.2 63.1 78.8 85.2 Pennsylvania Dauphin 420431100 44.8 41.3 68.5 64.5 82.6 90.8 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Pennsylvania Delaware 420450002 70.3 32.6 72.2 76.7 92.2 91.7 Pennsylvania Erie 420490003 30.7 49.7 76.2 49.0 55.8 113.8 Pennsylvania Lackawanna 420690101 39.0 33.1 57.0 55.7 74.7 101.6 Pennsylvania Lackawanna 420692006 39.3 32.6 60.2 57.6 75.8 104.0 Pennsylvania Lancaster 420710007 58.0 43.5 76.7 70.0 96.3 100.5 Pennsylvania Lawrence 420730015 28.1 52.1 65.2 62.7 70.0 70.8 Pennsylvania Lehigh 420770004 51.2 31.1 71.0 62.8 78.6 103.7 Pennsylvania Luzerne 420791100 38.5 28.7 64.3 48.1 64.1 90.0 Pennsylvania Luzerne 420791101 40.3 35.0 69.1 61.3 73.4 96.8 Pennsylvania Lycoming 420810403 31.5 38.0 51.2 54.0 55.7 78.0 Pennsylvania Mercer 420850100 30.1 50.6 75.2 63.5 75.3 89.5 Pennsylvania Montgomery 420910013 64.0 29.5 68.0 68.1 82.5 92.5 Pennsylvania Perry 420990301 37.3 42.3 62.7 56.7 69.1 77.6 Pennsylvania Philadelphia 421010004 58.7 22.5 58.7 58.5 72.5 81.2 Pennsylvania Philadelphia 421010014 67.5 25.0 71.2 63.7 80.0 103.3 Pennsylvania Philadelphia 421010024 73.7 30.0 77.5 77.5 98.3 107.5 Pennsylvania Philadelphia 421010136 NA NA NA 68.5 80.0 81.2 Pennsylvania Washington 421250005 53.7 68.1 74.0 74.6 89.8 84.1 Pennsylvania Washington 421250200 49.3 59.0 65.1 62.3 73.8 86.5 Pennsylvania Washington 421255001 47.1 59.0 65.2 65.6 49.1 85.1 Pennsylvania Westmoreland 421290006 41.8 61.5 62.3 69.1 89.8 76.1 Pennsylvania York 421330008 49.5 41.5 65.7 60.5 77.1 86.1 Rhode Island Kent 440030002 37.8 29.5 68.1 55.0 63.3 74.5 Rhode Island Providence 440071010 35.0 28.6 65.8 49.6 59.7 82.2 South Carolina Abbeville 450010001 61.6 65.0 60.1 75.2 65.6 67.8 South Carolina Aiken 450030003 56.5 61.3 82.3 63.6 64.5 67.2 South Carolina Anderson 450070003 69.0 82.1 80.3 84.0 81.2 83.6 South Carolina Barnwell 450110001 55.7 58.8 55.7 NA NA NA South Carolina Berkeley 450150002 57.2 56.7 66.2 NA 44.1 30.5 South Carolina Charleston 450190042 55.8 NA NA 63.1 41.1 32.5 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

South Carolina Charleston 450190046 46.3 48.7 52.0 44.1 39.8 30.6 South Carolina Cherokee 450210002 63.1 70.2 69.6 70.1 78.8 76.2 South Carolina Chester 450230002 77.5 64.3 62.7 91.6 78.8 73.6 South Carolina Darlington 450310003 60.8 67.0 63.5 59.7 61.1 59.2 South Carolina Edgefield 450370001 65.2 78.1 67.3 64.7 73.5 74.2 South Carolina Oconee 450730001 55.0 69.0 59.5 NA NA NA South Carolina Pickens 450770002 61.5 75.6 71.1 61.1 79.1 83.8 South Carolina Richland 450790007 72.3 74.3 80.1 58.8 67.8 73.2 South Carolina Richland 450791002 69.0 67.5 72.2 62.0 65.8 69.6 South Carolina Spartanburg 450830009 70.0 78.2 67.3 66.5 78.2 78.2 South Carolina Union 450870001 NA NA NA NA NA NA South Carolina Williamsburg 450890001 NA NA NA 49.1 50.8 41.8 South Carolina York 450910006 68.0 62.6 60.8 86.0 76.5 72.3 Tennessee Anderson 470010101 72.0 99.2 84.0 60.8 81.8 96.7 Tennessee Blount 470090101 91.7 80.1 74.4 80.7 76.2 81.1 Tennessee Blount 470090102 83.0 61.0 64.7 66.2 57.7 69.8 Tennessee Davidson 470370011 63.7 42.5 63.7 92.5 94.3 82.5 Tennessee Davidson 470370026 77.5 66.8 66.2 100.6 77.5 85.6 Tennessee Hamilton 470650028 73.6 93.3 84.5 69.6 65.7 104.6 Tennessee Hamilton 470651011 66.8 81.7 75.3 71.7 57.2 95.7 Tennessee Haywood 470750002 52.6 56.5 63.0 76.7 59.8 64.0 Tennessee Knox 470930021 71.6 78.7 89.8 60.7 71.3 94.8 Tennessee Knox 470931020 77.8 92.5 92.3 63.1 70.0 103.5 Tennessee Rutherford 471490101 72.6 43.0 57.0 55.7 47.7 58.7 Tennessee Sevier 471550101 82.2 72.2 78.2 77.1 74.5 77.0 Tennessee Sevier 471550102 84.3 60.8 65.2 65.7 NA NA Tennessee Shelby 471570021 50.7 55.6 53.7 108.1 101.5 81.7 Tennessee Shelby 471571004 46.7 60.1 58.2 81.7 98.3 70.0 Tennessee Sullivan 471632002 66.7 71.7 84.6 62.0 82.6 102.2 Tennessee Sullivan 471632003 69.2 71.8 91.0 NA 85.7 92.2 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Tennessee Sumner 471650007 82.8 79.6 74.3 100.5 92.7 102.0 Tennessee Sumner 471650101 64.1 58.1 56.7 61.8 52.1 74.7 Tennessee Wilson 471890103 77.7 57.1 74.3 76.8 62.1 83.0 Texas Brazoria 480391003 72.3 78.1 57.7 54.7 56.5 36.0 Texas Collin 480850005 78.2 57.2 78.0 114.5 97.1 83.8 Texas Dallas 481130069 58.2 60.2 86.8 68.5 99.7 67.2 Texas Dallas 481130087 50.7 58.2 71.2 63.7 89.5 NA Texas Galveston 481671002 76.0 95.1 70.0 65.7 66.2 82.7 Texas Gregg 481830001 68.8 68.3 79.6 99.3 88.1 75.3 Texas Harris 482010024 78.1 135.5 90.3 95.5 96.1 87.5 Texas Harris 482010029 72.6 88.1 NA NA 77.0 80.7 Texas Harris 482010046 71.6 NA NA 83.7 81.2 85.8 Texas Harris 482010047 70.1 121.8 72.3 79.3 79.2 67.5 Texas Harris 482010051 113.8 91.6 64.4 75.1 69.8 58.0 Texas Harris 482010062 89.5 78.5 65.1 78.8 62.7 68.7 Texas Harris 482010066 102.7 86.2 52.8 63.5 57.0 58.6 Texas Harris 482011035 85.3 111.2 NA NA 82.7 75.2 Texas Jefferson 482450009 95.0 87.6 73.7 69.5 76.0 79.8 Texas Jefferson 482450011 69.2 90.2 81.8 69.7 89.3 87.5 Texas Orange 483611001 95.8 82.2 86.7 75.7 88.7 78.5 Texas Tarrant 484391002 57.3 55.8 78.6 71.5 NA NA Texas Tarrant 484392003 60.1 51.6 80.1 78.5 123.8 101.3 Texas Travis 484530014 73.3 80.8 83.7 83.6 75.7 59.6 Vermont Bennington 500030004 47.7 33.6 58.4 55.5 59.7 79.5 Virginia Arlington 510130020 64.3 58.2 65.3 78.1 94.2 93.0 Virginia Caroline 510330001 68.6 59.8 57.8 73.6 73.6 79.7 Virginia Charles City 510360002 75.6 61.7 56.4 76.7 78.1 67.7 Virginia Chesterfield 510410004 65.5 52.1 53.5 59.8 63.1 62.1 Virginia Fairfax 510590005 61.6 52.5 64.0 64.5 83.2 74.5 Virginia Fairfax 510590018 62.0 56.3 61.3 65.0 90.6 87.7 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Virginia Fairfax 510595001 63.7 57.7 59.5 69.8 86.8 74.6 Virginia Fauquier 510610002 62.8 55.3 56.5 67.3 NA NA Virginia Frederick 510690010 48.7 52.1 61.8 60.5 80.1 89.7 Virginia Henrico 510870014 76.2 57.2 64.7 69.3 86.6 78.6 Virginia Madison 511130003 74.0 58.7 62.7 73.0 71.5 76.4 Virginia Prince William 511530009 62.0 56.0 58.5 64.6 87.2 79.6 Virginia Roanoke 511611004 65.1 64.1 69.0 62.5 75.6 75.0 Virginia Stafford 511790001 68.2 59.1 59.2 74.1 75.7 86.7 Virginia Wythe 511970002 68.5 55.3 70.3 66.1 70.7 68.7 Virginia Alexandria Cit 515100009 62.1 55.8 57.3 65.5 77.7 81.8 Virginia Hampton City 516500004 78.7 69.3 71.2 75.1 86.7 81.7 Virginia Suffolk City 518000004 82.7 70.0 71.1 77.2 90.3 89.1 Virginia Suffolk City 518000005 72.7 65.0 63.5 72.8 86.5 62.1 West Virginia Cabell 540110006 50.2 84.1 78.6 92.8 96.3 112.8 West Virginia Hancock 540291004 53.5 63.7 61.8 79.2 79.7 91.1 West Virginia Ohio 540690007 53.6 62.5 72.7 71.8 79.3 92.2 West Virginia Wood 541071002 50.8 62.0 83.2 75.2 75.5 103.1 Wisconsin Brown 550090026 35.1 39.0 48.2 53.6 89.5 67.0 Wisconsin Columbia 550210015 54.0 47.5 55.3 71.7 92.3 81.8 Wisconsin Dane 550250041 49.1 44.8 51.6 64.7 70.5 68.5 Wisconsin Dodge 550270007 45.3 46.8 52.2 76.5 88.2 79.2 Wisconsin Door 550290004 46.2 37.7 50.5 46.3 89.1 85.3 Wisconsin Fond Du Lac 550390006 47.5 50.0 53.5 69.8 93.6 78.5 Wisconsin Jefferson 550550002 53.5 50.6 52.0 74.2 84.0 NA Wisconsin Kenosha 550590002 62.2 73.6 60.7 90.0 85.1 92.5 Wisconsin Kenosha 550590019 73.3 79.6 67.5 92.2 101.5 107.7 Wisconsin Kewaunee 550610002 42.6 45.7 61.2 56.7 104.6 86.3 Wisconsin Manitowoc 550710004 41.3 47.1 57.3 66.5 110.3 81.1 Wisconsin Manitowoc 550710007 48.3 44.5 59.3 60.0 100.6 95.3 Wisconsin Marathon 550730012 38.8 31.5 42.1 53.8 55.3 61.8 
State County 

AIRS Site 

Code July 8 July 9 July 10 July 11 July 12 July 13 

Wisconsin Milwaukee 550790041 59.1 60.5 54.8 78.1 91.7 95.0 Wisconsin Milwaukee 550790044 45.5 59.5 55.1 68.5 116.1 89.1 Wisconsin Milwaukee 550790085 62.0 63.6 64.2 79.8 105.0 101.5 Wisconsin Milwaukee 550791025 55.2 64.6 61.3 87.6 105.8 100.1 Wisconsin Outagamie 550870009 40.1 42.0 47.1 62.1 90.0 72.5 Wisconsin Ozaukee 550890008 57.3 56.7 54.6 82.0 104.6 80.8 Wisconsin Ozaukee 550890009 61.8 57.3 62.5 71.8 105.5 90.6 Wisconsin Racine 551010017 60.2 62.7 58.8 83.8 92.3 95.1 Wisconsin Rock 551050024 58.3 49.8 54.7 70.5 77.7 77.3 Wisconsin St Croix 551091002 45.0 31.6 37.7 47.0 46.0 68.0 Wisconsin Sauk 551110007 40.6 42.8 42.3 66.5 69.7 65.8 Wisconsin Vernon 551230008 45.2 32.5 44.7 50.5 61.1 62.7 Wisconsin Walworth 551270005 55.2 57.6 58.5 72.2 NA NA Wisconsin Washington 551310009 51.5 56.6 61.2 81.5 116.2 87.8 Wisconsin Waukesha 551330017 43.2 50.8 37.6 63.6 102.7 88.6 Wisconsin Winnebago 551390011 46.3 44.3 51.2 66.2 84.8 75.5 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Alabama Clay 10270001 65.0 68.1 100.1 83.6 71.5 89.8 92.2 97.3 89.5 Alabama Jefferson 10731003 84.5 68.2 72.1 89.2 94.2 77.6 67.6 79.2 89.3 Alabama Jefferson 10731005 105.6 82.5 90.8 89.5 69.5 92.2 93.1 101.2 123.3 Alabama Jefferson 10732006 86.1 91.3 84.5 88.2 100.6 109.1 80.6 109.3 107.6 Alabama Jefferson 10735002 59.2 56.8 65.6 74.7 87.8 77.0 68.3 79.5 79.3 Alabama Jefferson 10736002 67.8 68.8 71.8 81.2 126.2 78.0 76.6 78.5 81.5 Alabama Lawrence 10790002 64.6 58.6 69.8 67.7 53.6 61.8 63.8 68.1 74.7 Alabama Madison 10890014 78.3 NA NA NA NA 77.7 68.0 76.5 72.0 Alabama Mobile 10970003 43.2 55.8 48.5 62.6 47.1 60.3 53.1 85.3 81.2 Alabama Shelby 11170004 80.0 90.1 80.7 79.5 81.1 107.5 100.1 106.3 110.2 Alabama Sumter 11190002 54.7 50.7 48.2 45.1 42.0 43.8 53.8 59.0 71.2 Arkansas Crittenden 50350005 52.8 64.5 72.3 56.1 57.7 43.4 57.6 79.3 84.8 Arkansas Montgomery 50970001 46.7 55.5 56.6 52.5 47.6 39.0 44.6 51.0 58.7 Arkansas Pulaski 51190007 60.7 70.8 70.3 63.6 48.8 52.3 56.2 84.7 56.5 Arkansas Pulaski 51191002 64.6 74.3 72.0 68.1 53.0 49.3 54.2 77.0 61.3 Connecticut Fairfield 90010017 60.1 38.8 72.0 30.5 62.1 49.1 24.1 NA NA Connecticut Fairfield 90011123 69.8 53.2 51.7 24.3 86.5 69.7 47.2 55.3 44.6 Connecticut Fairfield 90013007 71.8 41.1 80.2 29.5 66.7 46.6 20.3 58.7 54.2 Connecticut Hartford 90031003 72.6 61.1 57.7 25.2 73.0 50.1 40.3 72.8 47.8 Connecticut Middlesex 90070007 95.0 54.1 76.0 30.1 73.2 41.5 27.7 74.8 49.0 Connecticut New Haven 90093002 90.1 43.3 71.5 40.8 68.0 32.1 19.1 56.5 55.7 Connecticut New London 90110008 59.3 24.7 54.5 38.1 66.5 31.0 15.7 50.3 47.3 Connecticut Tolland 90131001 75.2 65.5 50.6 26.3 65.0 49.6 34.7 66.1 44.6 Delaware Kent 100010002 33.0 29.0 80.7 51.5 59.5 48.1 25.5 47.1 80.1 Delaware New Castle 100031003 41.1 35.7 77.7 48.2 56.8 57.0 41.1 57.2 65.4 Delaware New Castle 100031007 38.5 35.7 82.5 50.7 65.2 56.2 58.1 NA NA Delaware New Castle 100031010 34.1 37.0 66.3 41.2 58.6 62.0 52.3 63.8 57.2 Delaware Sussex 100051002 32.0 30.2 91.6 60.6 64.6 47.6 24.7 42.3 77.1 D.C. Washington 110010025 31.1 50.7 56.1 49.7 66.3 73.8 79.6 60.8 58.2 D.C. Washington 110010041 33.1 44.5 68.6 59.8 73.6 67.7 71.7 62.0 63.0 

8-Hour Daily Maximum 8-Hour Ozone (ppb) Measured at Monitoring Sites in the East During the August 19
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

D.C. Washington 110010043 38.7 56.1 71.7 64.3 87.0 77.2 90.6 69.7 68.6 Florida Brevard 120094001 36.6 39.8 45.5 NA NA 58.6 34.5 57.0 61.5 Florida Duval 120310077 40.8 35.0 54.7 52.1 55.0 57.5 62.3 81.0 61.1 Florida Escambia 120330004 50.3 55.7 60.5 54.6 60.1 69.1 80.1 95.1 68.0 Florida Escambia 120330018 49.0 55.5 57.1 45.3 55.7 79.6 96.8 91.7 59.5 Florida Hillsborough 120570081 59.5 73.7 58.2 52.1 59.0 60.3 80.3 84.4 58.6 Florida Hillsborough 120571035 48.6 56.5 51.6 52.5 56.6 67.7 65.0 82.7 60.7 Florida Hillsborough 120571065 44.0 66.1 53.7 53.8 52.1 74.1 80.7 82.0 62.8 Florida Orange 120950008 43.8 72.1 60.2 80.3 75.3 75.2 55.3 72.2 74.8 Florida Orange 120952002 30.7 54.5 49.3 76.7 64.0 67.1 43.5 69.0 70.8 Florida Osceola 120972002 36.6 50.5 50.7 54.6 54.5 69.0 61.5 66.6 56.1 Florida Palm Beach 120992004 46.0 34.2 42.8 55.6 46.7 57.3 41.6 66.8 62.8 Florida Pasco 121012001 37.0 49.6 47.6 38.3 45.7 54.8 71.6 62.0 49.7 Florida Pinellas 121030004 41.7 48.5 47.5 41.2 43.6 NA 70.0 63.3 52.6 Florida Pinellas 121030018 38.0 49.2 35.8 29.3 33.7 49.7 68.6 56.2 39.6 Florida Pinellas 121035002 31.5 42.6 41.6 36.7 40.6 58.0 63.6 66.1 50.2 Florida Polk 121056005 36.6 44.0 48.5 48.1 44.3 66.5 65.4 61.1 71.6 Florida Polk 121056006 44.6 42.6 52.0 50.0 54.1 68.5 59.5 66.2 77.1 Florida St Lucie 121111002 33.0 47.3 40.6 48.6 62.1 57.1 32.7 64.7 45.8 Florida Sarasota 121151005 64.5 40.6 36.5 34.6 39.0 56.0 87.3 68.6 44.3 Florida Seminole 121171002 31.6 47.8 46.3 54.2 59.5 70.1 47.6 70.8 59.1 Florida Volusia 121272001 32.5 48.8 45.6 52.7 56.6 59.1 21.0 59.0 59.3 Florida Volusia 121275002 35.0 52.0 48.8 56.6 58.2 62.5 40.8 67.5 62.8 Georgia Chatham 130510021 35.6 54.5 67.8 64.3 56.3 51.8 65.6 76.1 73.3 Georgia De Kalb 130890002 91.7 84.7 105.8 95.1 95.8 101.1 98.1 99.1 126.3 Georgia De Kalb 130893001 82.0 72.6 75.1 98.0 87.7 97.6 86.6 NA 117.1 Georgia Fulton 131210055 94.5 81.8 105.0 109.1 106.5 118.2 108.7 112.5 128.7 Georgia Glynn 131270006 47.0 57.6 62.1 59.7 61.7 60.7 71.2 79.4 69.1 Georgia Gwinnett 131350002 65.8 66.1 65.5 72.1 77.6 72.6 80.3 92.1 93.8 Georgia Muscogee 132150008 49.7 63.2 78.6 84.8 66.3 83.5 80.6 87.1 89.7 Georgia Muscogee 132151003 49.5 66.6 81.8 75.7 46.6 57.1 53.8 51.7 64.0 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Georgia Richmond 132450091 54.6 67.6 71.8 72.6 74.6 80.2 79.6 74.2 99.8 Georgia Rockdale 132470001 67.8 81.6 87.8 82.2 100.1 70.3 74.7 111.6 120.8 Illinois Adams 170010006 44.1 60.3 55.7 44.1 38.0 45.3 30.2 40.0 50.5 Illinois Champaign 170190004 40.6 57.3 67.6 56.8 59.1 59.0 48.5 51.6 46.7 Illinois Cook 170310001 34.7 49.2 64.1 59.2 46.1 31.8 28.0 48.7 54.1 Illinois Cook 170310032 49.2 69.8 81.8 73.1 65.2 48.8 45.6 59.7 56.0 Illinois Cook 170310050 40.6 55.3 74.7 60.5 49.5 32.0 32.0 57.8 45.2 Illinois Cook 170310064 37.1 58.0 82.8 65.6 49.8 40.3 32.2 59.1 48.6 Illinois Cook 170310072 42.0 51.6 106.1 NA NA 49.7 40.5 56.8 55.2 Illinois Cook 170311003 32.6 51.4 65.0 56.0 46.1 42.6 26.8 50.8 48.6 Illinois Cook 170311601 39.2 51.8 59.3 54.2 46.3 26.6 33.6 36.2 62.7 Illinois Cook 170314002 36.0 50.2 70.5 55.1 44.2 25.3 26.7 29.7 38.8 Illinois Cook 170314006 49.2 69.8 81.8 73.1 65.2 48.8 45.6 59.7 56.0 Illinois Cook 170317002 37.5 60.3 104.2 65.6 55.3 54.1 38.4 55.3 50.5 Illinois Cook 170318003 38.3 51.7 79.7 59.8 51.6 36.1 39.1 59.6 46.7 Illinois Du Page 170436001 38.0 46.5 59.2 44.0 49.1 39.5 36.1 40.6 62.1 Illinois Effingham 170491001 55.1 63.0 57.7 53.2 60.1 49.6 55.5 58.2 72.5 Illinois Jersey 170831001 39.8 48.0 64.5 54.3 58.1 72.6 47.2 51.2 50.8 Illinois Kane 170890005 40.7 53.5 61.2 57.0 54.8 43.7 52.5 47.2 67.8 Illinois Lake 170971002 44.5 61.6 102.8 52.2 51.7 44.3 43.8 46.1 41.0 Illinois Lake 170971007 46.5 79.2 95.1 56.3 56.1 47.5 48.7 48.5 45.1 Illinois Lake 170973001 39.3 47.7 59.6 48.6 46.5 40.3 41.6 34.0 32.8 Illinois McHenry 171110001 42.8 57.7 65.8 58.2 60.3 52.6 60.3 NA NA Illinois Macon 171150013 38.1 51.7 68.8 60.2 50.5 65.6 55.2 53.3 51.1 Illinois Macoupin 171170002 34.6 43.1 58.1 64.5 52.0 55.3 42.0 45.6 50.1 Illinois Madison 171190008 46.0 69.5 88.7 70.6 75.6 75.7 67.0 65.1 78.3 Illinois Madison 171191009 53.6 73.0 71.6 48.7 59.2 48.1 46.2 72.8 89.0 Illinois Madison 171192007 57.8 94.6 96.5 68.6 77.1 65.1 63.8 76.0 94.0 Illinois Madison 171193007 40.6 87.6 99.0 73.1 73.2 73.8 66.2 62.3 79.2 Illinois Peoria 171430024 41.0 57.2 65.3 49.5 44.8 45.4 45.1 52.0 42.3 Illinois Peoria 171431001 45.8 63.6 68.5 61.3 45.3 46.5 62.0 59.5 63.2 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Illinois Randolph 171570001 39.0 44.7 46.5 45.0 44.1 43.5 46.6 49.6 55.2 Illinois St Clair 171630010 81.1 71.1 82.0 53.6 63.6 55.7 56.8 76.8 112.5 Illinois Sangamon 171670010 38.2 60.8 63.2 60.1 48.5 45.7 45.5 46.6 50.1 Illinois Will 171971008 41.8 51.3 58.5 56.5 47.5 51.0 33.7 43.7 63.3 Illinois Will 171971011 34.6 41.8 56.5 49.2 46.1 49.5 39.0 37.5 56.1 Illinois Winnebago 172010009 44.7 59.0 63.5 56.1 47.7 33.7 48.2 43.3 51.7 Illinois Winnebago 172012001 43.2 58.0 62.5 55.0 47.2 37.7 49.7 40.2 49.1 Indiana Allen 180030002 56.2 60.3 72.8 64.8 64.5 61.4 68.8 47.0 61.3 Indiana Allen 180030004 51.5 53.0 64.8 NA NA 61.5 NA NA 59.5 Indiana Clark 180190003 66.6 62.6 60.2 70.2 83.5 114.1 NA NA NA Indiana Floyd 180431004 63.8 68.3 56.0 66.8 86.2 79.5 74.6 67.0 53.7 Indiana Hamilton 180571001 56.5 64.1 67.3 68.7 71.6 80.3 96.3 53.6 50.6 Indiana Hancock 180590003 57.3 69.6 67.8 76.6 66.2 71.0 70.5 49.2 39.5 Indiana Lake 180890022 49.5 65.0 95.1 64.3 58.6 51.1 47.3 79.5 60.8 Indiana Lake 180892008 53.1 62.5 96.1 66.7 61.7 44.7 36.3 74.0 51.2 Indiana La Porte 180910005 61.2 68.1 108.6 63.3 61.2 57.2 59.5 71.0 63.8 Indiana Madison 180950010 63.2 73.5 74.7 82.2 73.5 76.2 80.0 56.7 51.7 Indiana Marion 180970042 NA 61.5 58.3 59.7 57.2 37.6 61.1 NA 28.0 Indiana Marion 180970050 69.0 72.5 69.5 76.5 68.2 72.0 81.3 53.1 48.6 Indiana Marion 180970057 56.7 65.8 64.7 68.8 58.8 58.8 78.0 50.3 38.7 Indiana Marion 180970073 64.1 66.7 70.5 73.5 64.3 62.1 NA NA 39.2 Indiana Porter 181270020 45.5 51.8 62.8 NA 48.2 47.8 44.3 65.7 52.4 Indiana Porter 181270024 55.0 65.0 95.0 63.0 61.1 54.7 NA NA NA Indiana St Joseph 181410010 48.2 58.2 70.2 54.8 58.4 53.8 58.4 50.2 50.8 Indiana St Joseph 181411007 NA 40.8 86.3 53.1 44.5 43.6 47.6 48.2 Indiana St Joseph 181411008 53.6 59.8 84.5 58.5 59.1 49.3 56.7 52.7 54.2 Indiana Vanderburgh 181630012 56.8 68.5 64.1 58.1 70.0 68.6 63.1 52.3 75.7 Indiana Vanderburgh 181630013 55.1 58.1 57.0 56.6 74.2 72.2 69.5 57.2 73.0 Indiana Vigo 181670018 50.1 61.6 59.3 55.3 55.7 66.0 70.7 50.0 57.8 Indiana Warrick 181730002 54.0 80.6 63.2 58.1 62.6 63.2 NA NA NA Indiana Warrick 181730008 51.8 69.4 66.8 62.8 64.3 69.1 61.1 66.0 78.7 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Indiana Warrick 181730009 67.6 76.1 66.6 63.6 75.1 72.1 75.1 55.5 78.0 Iowa Polk 191530058 28.0 35.8 44.0 29.6 22.8 21.2 21.0 26.0 35.8 Iowa Scott 191632011 42.5 63.6 60.7 47.5 41.5 44.0 49.1 53.2 51.6 Kansas Sedgwick 201730001 51.2 62.5 65.0 40.0 45.0 40.0 33.3 30.0 32.5 Kansas Sedgwick 201730010 41.8 47.5 54.3 28.1 36.8 33.1 20.0 24.3 28.7 Kentucky Bell 210130002 47.7 55.6 55.1 56.2 51.8 53.7 86.8 72.2 64.6 Kentucky Boone 210150003 49.2 49.3 58.2 60.2 74.1 68.5 73.7 70.2 43.8 Kentucky Boyd 210190015 63.0 54.3 76.6 73.2 66.6 71.1 103.6 76.7 66.1 Kentucky Bullitt 210290006 60.5 62.6 53.1 65.6 62.8 82.3 74.3 83.0 55.7 Kentucky Daviess 210590005 58.2 77.8 63.7 66.0 58.6 53.2 72.1 65.5 76.3 Kentucky Fayette 210670001 54.6 61.1 62.8 62.8 68.7 73.1 71.8 85.0 51.7 Kentucky Fayette 210670012 60.7 69.0 75.2 71.1 77.7 86.1 83.0 99.5 61.0 Kentucky Graves 210830003 49.0 53.6 47.1 45.5 49.6 52.8 55.6 48.5 65.1 Kentucky Greenup 210890007 57.7 49.5 72.8 67.6 69.8 71.5 95.3 69.6 58.5 Kentucky Hancock 210910012 59.2 84.2 65.1 64.8 79.5 66.8 78.8 65.3 77.3 Kentucky Henderson 211010013 67.7 87.2 68.0 50.2 69.5 62.6 69.0 68.3 82.7 Kentucky Henderson 211010014 54.6 71.7 58.1 51.6 55.5 60.8 61.0 60.2 74.6 Kentucky Jefferson 211110027 52.2 63.2 64.8 68.7 83.5 77.2 72.0 92.1 65.5 Kentucky Jefferson 211110051 60.2 65.0 58.7 66.8 75.3 89.1 73.7 83.0 62.0 Kentucky Jefferson 211111021 50.1 60.6 54.2 68.3 69.3 81.5 67.7 66.0 53.3 Kentucky Jessamine 211130001 54.2 63.1 70.6 65.0 68.6 79.8 87.5 104.2 52.2 Kentucky Kenton 211170007 45.0 52.1 69.8 72.6 90.5 62.5 93.1 87.1 44.6 Kentucky Livingston 211390003 61.5 69.2 50.7 53.8 56.5 58.3 62.6 55.3 57.3 Kentucky Livingston 211390004 63.5 65.5 51.7 50.8 57.3 35.6 44.5 58.5 56.7 Kentucky McCracken 211451024 59.0 62.5 51.3 51.3 54.6 55.2 60.1 52.2 64.3 Kentucky McLean 211490001 61.0 62.6 63.8 60.5 57.7 52.1 56.0 64.5 66.2 Kentucky Oldham 211850004 65.8 61.8 NA 79.0 83.0 88.8 NA 88.0 53.7 Kentucky Pike 211950002 63.7 67.0 66.1 70.1 68.7 85.5 99.6 105.3 70.6 Kentucky Pulaski 211990003 52.8 62.1 74.7 84.8 60.8 73.1 77.7 93.8 58.8 Kentucky Scott 212090001 61.3 68.0 67.5 64.7 78.6 76.7 80.5 72.8 49.1 Kentucky Simpson 212130004 61.0 56.8 76.1 79.3 65.8 70.3 75.0 97.3 66.1 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Louisiana Ascension 220050004 NA NA NA NA NA NA NA 53.5 54.6 Louisiana Beauregard 220110002 54.0 38.8 30.5 16.1 24.1 45.5 40.1 78.6 49.8 Louisiana Bossier 220150008 56.2 51.5 64.0 48.1 30.1 34.3 55.7 65.2 60.7 Louisiana Caddo 220170001 59.0 58.0 70.7 46.6 33.0 36.3 64.5 84.7 68.1 Louisiana Calcasieu 220190002 53.1 31.8 25.7 15.8 22.3 25.2 66.1 61.1 54.2 Louisiana Calcasieu 220190008 35.3 19.0 13.8 4.2 7.1 12.6 23.8 39.1 22.6 Louisiana Calcasieu 220190009 61.6 34.3 29.2 19.0 20.2 23.7 57.7 66.5 49.8 Louisiana East Baton Rou 220330003 56.0 47.7 101.7 33.5 43.8 54.7 81.2 65.5 49.7 Louisiana East Baton Rou 220330009 59.3 48.3 95.3 32.1 40.5 50.7 76.7 56.5 40.7 Louisiana East Baton Rou 220330013 49.2 47.1 47.7 42.0 49.2 42.6 46.0 54.8 39.8 Louisiana East Baton Rou 220331001 49.7 47.3 62.0 31.8 41.2 44.7 61.3 53.8 51.6 Louisiana Grant 220430001 51.6 59.8 51.0 47.5 36.2 44.4 48.8 50.2 52.5 Louisiana Iberville 220470007 60.2 58.3 83.3 37.8 17.7 45.8 83.5 52.3 33.6 Louisiana Iberville 220470009 63.1 54.5 83.8 39.7 27.7 68.6 75.6 69.1 62.7 Louisiana Jefferson 220511001 47.3 41.5 60.7 43.1 42.6 56.0 71.6 59.1 73.0 Louisiana Lafayette 220550005 66.0 50.6 48.7 37.8 34.1 43.6 62.6 65.1 63.6 Louisiana Livingston 220630002 46.2 44.6 61.3 35.1 33.1 40.3 60.8 49.7 56.5 Louisiana Orleans 220710012 41.8 31.6 48.6 33.8 31.6 43.0 48.2 38.8 65.3 Louisiana Pointe Coupee 220770001 45.3 51.3 59.3 33.3 28.2 34.5 44.7 45.6 39.1 Louisiana St Bernard 220870002 44.3 30.3 47.2 33.7 37.1 46.8 70.3 52.0 72.8 Louisiana St Charles 220890003 53.1 41.4 65.7 38.8 37.5 48.6 73.8 54.0 63.8 Louisiana St James 220930002 49.1 36.1 61.0 32.2 29.8 48.2 78.8 46.2 56.7 Louisiana St John The Ba 220950002 52.2 39.4 63.8 32.8 40.5 44.8 79.8 31.5 73.0 Louisiana St Mary 221010003 45.2 37.5 40.1 40.2 25.1 57.6 65.6 73.1 69.2 Louisiana West Baton Rou 221210001 53.0 39.5 72.2 25.3 32.3 40.6 63.5 43.1 37.0 Maine Cumberland 230052003 98.6 91.5 58.0 26.5 36.2 29.5 29.5 57.5 34.8 Maine Hancock 230090102 71.5 79.2 49.5 22.0 33.8 35.7 44.0 41.6 31.5 Maine Kennebec 230112005 52.6 73.8 38.7 19.5 31.1 22.3 64.3 36.0 26.8 Maine Knox 230130004 78.1 86.1 49.7 22.7 29.3 32.5 24.5 49.7 32.6 Maine Oxford 230173001 41.1 50.6 31.8 21.8 31.6 29.2 43.0 31.5 29.2 Maine Penobscot 230194008 57.6 78.5 45.8 20.8 30.6 28.1 67.6 41.0 35.1 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Maine York 230312002 106.1 93.0 53.3 30.0 38.0 37.2 33.3 57.8 32.0 Maine York 230313002 99.6 NA NA NA 40.5 35.7 41.3 52.5 31.1 Maryland Anne Arundel 240030014 38.3 42.3 82.7 73.2 98.2 66.8 87.8 68.1 74.5 Maryland Anne Arundel 240030019 35.2 59.0 72.7 59.8 95.0 69.1 78.0 73.3 73.0 Maryland Baltimore 240051007 45.3 34.8 68.1 61.2 79.5 64.5 84.2 77.5 66.7 Maryland Baltimore 240053001 43.2 31.7 68.6 61.0 93.6 49.0 72.8 64.3 66.6 Maryland Carroll 240130001 48.6 46.8 63.7 55.1 68.2 78.1 86.3 80.8 68.7 Maryland Cecil 240150003 43.7 42.7 76.5 49.2 76.8 58.1 62.7 66.8 62.7 Maryland Charles 240170010 31.0 33.7 79.3 81.0 86.0 53.8 70.6 79.3 79.1 Maryland Harford 240251001 44.8 37.7 84.0 60.1 107.5 55.0 79.8 68.8 63.1 Maryland Harford 240259001 45.7 38.6 70.1 54.5 102.0 51.8 75.5 71.7 59.3 Maryland Kent 240290002 40.3 37.3 78.1 54.6 53.7 50.1 45.3 64.8 71.7 Maryland Montgomery 240313001 42.7 55.7 59.0 50.5 72.6 86.7 73.8 63.1 57.8 Maryland Prince Georges 240330002 36.6 50.8 69.1 61.6 85.1 67.8 79.1 72.2 71.7 Maryland Prince Georges 240338001 36.3 46.0 70.7 71.0 84.0 69.0 71.7 69.6 74.2 Massachusetts Barnstable 250010002 100.5 55.5 46.5 30.6 51.6 31.1 31.7 45.7 45.5 Massachusetts Berkshire 250034002 52.8 51.3 40.3 22.8 62.7 50.7 62.8 38.1 35.6 Massachusetts Bristol 250051002 80.0 33.2 40.1 47.6 67.0 32.8 18.5 48.0 35.7 Massachusetts Essex 250090005 68.1 58.0 43.3 26.7 32.7 44.2 39.3 NA 30.6 Massachusetts Essex 250092006 98.6 74.5 59.5 46.0 46.1 36.2 41.2 51.6 35.1 Massachusetts Essex 250094004 101.2 68.2 48.6 28.7 42.5 33.1 NA 63.7 33.2 Massachusetts Hampden 250130003 65.8 69.8 51.7 23.1 73.5 63.5 67.3 62.2 50.2 Massachusetts Hampden 250130008 68.7 70.0 45.1 24.6 74.0 63.3 65.2 57.1 48.5 Massachusetts Hampshire 250150103 69.5 66.3 38.5 23.6 58.2 62.6 72.0 40.7 48.7 Massachusetts Hampshire 250154002 74.3 70.7 45.5 22.5 61.0 53.2 53.6 55.1 48.5 Massachusetts Worcester 250270015 82.0 69.3 52.2 22.0 48.2 48.7 38.1 69.7 44.2 Michigan Allegan 260050003 63.8 64.8 88.5 110.0 105.8 52.6 53.7 63.7 56.5 Michigan Benzie 260190003 57.3 44.3 42.2 44.3 54.0 30.3 36.8 36.2 69.1 Michigan Berrien 260210014 52.1 67.6 111.0 81.5 81.0 62.2 62.7 67.6 53.3 Michigan Cass 260270003 52.0 68.5 98.3 69.1 65.6 55.3 54.7 56.3 51.8 Michigan Clinton 260370001 48.7 51.3 43.1 61.1 50.8 54.7 41.2 38.0 56.6 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Michigan Genesee 260490021 66.7 56.8 43.1 59.6 52.2 54.8 62.5 35.5 56.7 Michigan Genesee 260492001 NA NA NA 52.7 51.1 52.6 52.7 33.7 58.0 Michigan Huron 260630007 80.5 50.7 43.8 38.0 63.6 52.1 44.6 38.6 45.8 Michigan Ingham 260650012 52.2 54.7 51.2 71.8 51.3 56.1 46.3 34.1 67.8 Michigan Kent 260810020 44.8 58.7 56.6 91.0 89.6 46.6 48.1 35.7 56.5 Michigan Lenawee 260910007 50.8 55.1 60.6 NA NA NA NA NA 35.8 Michigan Macomb 260990009 56.6 90.5 46.8 57.0 64.8 69.6 58.7 34.7 46.7 Michigan Macomb 260991003 60.6 70.7 52.6 68.1 56.3 97.5 58.3 36.0 46.7 Michigan Muskegon 261210039 51.2 63.5 55.1 93.5 94.0 49.3 38.7 51.6 50.6 Michigan Oakland 261250001 47.1 68.7 52.1 60.0 51.3 81.2 50.6 25.0 5.8 Michigan Ottawa 261390005 50.2 59.2 79.8 91.0 94.3 51.7 49.0 42.6 NA Michigan St Clair 261470005 52.5 81.8 42.8 48.0 74.8 58.8 55.2 23.1 54.1 Michigan Wayne 261630001 42.5 55.3 58.7 61.6 52.2 61.0 60.8 32.7 53.8 Michigan Wayne 261630016 26.5 48.7 57.6 56.8 54.8 63.7 50.1 33.6 48.7 Michigan Wayne 261630019 60.5 80.6 58.7 63.6 61.2 85.2 59.5 41.6 53.0 Minnesota Anoka 270031001 54.5 32.3 28.8 44.5 30.5 49.8 38.1 42.5 46.8 Minnesota Anoka 270031002 57.7 37.3 41.5 51.6 31.2 44.8 36.5 47.2 50.0 Minnesota Dakota 270376018 43.0 40.0 41.4 46.0 33.0 40.0 39.7 48.3 43.7 Mississippi Adams 280010004 43.8 47.1 46.3 40.1 32.2 30.3 45.1 50.2 40.8 Mississippi De Soto 280330002 NA 59.3 76.8 66.7 46.3 47.2 27.3 59.6 68.3 Mississippi Hancock 280450001 59.6 50.5 64.0 59.0 52.8 NA NA NA NA Mississippi Hinds 280490010 50.4 53.6 61.1 54.2 56.0 41.2 51.2 51.7 69.8 Mississippi Jackson 280590006 41.6 43.5 48.0 57.1 45.0 65.5 57.1 60.5 69.2 Mississippi Madison 280890002 48.5 55.0 50.3 59.1 51.5 35.7 48.7 54.2 70.0 Mississippi Warren 281490004 54.7 61.7 63.0 51.1 28.0 42.5 52.6 54.4 52.0 Missouri Clay 290470003 67.2 82.2 84.8 47.7 51.6 53.0 44.5 54.0 76.5 Missouri Clay 290470005 64.7 82.0 84.1 45.3 50.2 59.6 43.7 55.0 75.2 Missouri Clay 290470025 52.1 76.5 77.0 41.5 40.1 47.1 36.1 46.0 68.3 Missouri Greene 290770026 57.5 50.0 58.3 53.7 42.6 44.2 44.3 45.3 58.6 Missouri Greene 290770036 64.8 68.4 68.5 62.5 52.6 54.7 53.5 50.7 73.1 Missouri Jefferson 290990012 88.8 57.7 73.5 54.1 60.5 60.3 50.6 55.2 74.2 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Missouri Monroe 291370001 46.2 69.8 65.2 55.8 50.5 52.6 49.1 48.7 56.0 Missouri Platte 291650023 46.5 57.5 72.3 41.0 43.5 43.0 41.3 44.1 64.8 Missouri St Charles 291831002 54.5 80.1 97.8 72.6 87.0 98.7 74.0 71.7 78.7 Missouri St Charles 291831004 49.8 72.2 81.7 63.7 75.5 88.8 55.5 63.2 71.3 Missouri St Louis 291890006 56.2 69.0 65.3 47.1 60.8 58.8 49.5 57.8 80.8 Missouri St Louis 291893001 54.8 64.7 70.5 49.0 62.3 58.6 47.7 57.7 82.0 Missouri St Louis 291895001 71.0 78.5 86.7 59.2 75.3 77.0 58.0 62.1 89.1 Missouri St Louis City 295100007 58.7 46.2 58.7 35.1 47.1 42.0 35.2 54.5 84.0 Missouri St Louis City 295100072 57.7 42.2 65.2 45.7 48.6 40.3 36.1 47.7 64.8 Nebraska Douglas 310550028 47.2 53.0 60.2 50.1 30.6 18.7 34.3 36.8 41.1 Nebraska Douglas 310550032 39.8 36.3 38.6 32.0 NA NA NA 31.5 32.2 Nebraska Douglas 310550035 49.7 55.3 62.2 50.1 29.0 12.8 24.3 35.5 41.1 Nebraska Lancaster 311090016 44.0 49.8 56.5 46.6 28.6 11.8 30.5 32.5 35.6 New Hampshire Cheshire 330050007 59.3 NA NA NA 26.4 49.5 62.8 37.1 40.6 New Hampshire Hillsborough 330111010 73.1 NA NA NA 43.6 62.5 NA NA 37.0 New Hampshire Merrimack 330130007 47.6 57.2 43.0 17.6 23.8 39.2 51.8 32.5 24.2 New Hampshire Rockingham 330150012 98.3 NA NA NA 33.8 36.8 35.6 61.0 27.3 New Jersey Atlantic 340010005 33.3 24.3 81.6 51.5 56.0 35.5 18.7 34.2 64.3 New Jersey Camden 340070003 50.8 42.8 76.8 48.6 69.7 55.3 34.3 55.8 67.1 New Jersey Camden 340071001 54.6 42.8 92.0 67.2 75.0 49.8 31.8 50.6 91.3 New Jersey Cumberland 340110007 39.7 26.3 77.0 62.6 58.7 42.6 22.6 42.5 72.7 New Jersey Gloucester 340150002 47.7 38.8 80.6 53.8 65.7 56.6 39.7 60.5 70.2 New Jersey Hudson 340170006 48.5 33.5 78.1 46.3 66.5 42.7 26.0 57.2 51.6 New Jersey Hunterdon 340190001 65.1 56.7 68.5 43.1 83.8 60.0 45.0 72.2 58.5 New Jersey Mercer 340210005 52.2 39.2 74.5 41.3 86.6 57.1 43.6 48.5 64.7 New Jersey Middlesex 340230011 62.0 45.6 73.2 36.6 83.6 49.6 37.1 45.5 62.1 New Jersey Monmouth 340250005 NA NA NA NA NA NA NA 37.6 66.0 New Jersey Morris 340273001 66.6 58.7 71.5 37.8 95.7 65.6 77.1 73.8 58.2 New Jersey Ocean 340290006 NA 33.2 91.3 50.0 68.8 53.0 29.1 47.2 75.7 New York Albany 360010012 69.6 54.8 39.7 25.0 68.5 71.6 70.3 44.1 55.6 New York Bronx 360050083 44.7 26.7 67.0 48.2 58.2 51.3 15.7 73.5 50.3 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

New York Chautauqua 360130011 65.2 70.5 57.2 42.0 76.3 71.5 72.5 59.1 81.7 New York Chemung 360150003 48.1 52.0 45.3 31.1 57.8 52.2 58.1 49.6 47.8 New York Dutchess 360270007 68.1 67.0 44.6 26.8 93.7 70.0 68.2 45.5 45.3 New York Erie 360290002 52.5 54.6 43.2 42.7 69.2 61.0 42.2 47.1 38.6 New York Essex 360310002 58.6 63.0 56.7 23.6 40.1 64.1 68.0 NA NA New York Essex 360310003 49.8 51.8 48.6 18.5 21.8 59.8 55.6 39.5 37.8 New York Hamilton 360410005 53.8 53.2 42.6 21.3 29.1 53.7 53.0 34.7 42.1 New York Herkimer 360430005 51.5 49.3 36.6 21.3 31.3 48.6 47.5 33.6 41.6 New York Jefferson 360450002 59.2 63.1 46.8 25.1 47.7 78.0 46.7 44.5 46.1 New York Madison 360530006 52.6 59.0 44.6 33.8 51.7 52.0 67.2 40.0 46.3 New York Monroe 360551004 59.3 54.8 45.6 28.6 51.3 64.7 41.5 41.1 45.3 New York Niagara 360631006 51.6 48.5 51.0 27.8 75.3 60.8 40.5 37.6 39.3 New York Oneida 360650004 53.6 52.7 40.0 20.8 52.8 58.5 42.6 30.6 42.7 New York Onondaga 360671015 52.7 55.0 47.8 29.2 60.8 67.5 49.3 36.6 46.5 New York Orange 360715001 57.8 48.6 52.8 25.5 88.8 66.6 90.7 65.6 58.7 New York Putnam 360790005 64.5 58.3 52.5 25.3 85.8 60.0 49.2 48.3 43.0 New York Richmond 360850067 42.8 29.7 73.0 45.7 61.3 37.2 NA NA NA New York Saratoga 360910004 75.3 29.1 5.5 8.7 17.0 25.8 18.0 8.5 11.2 New York Schenectady 360930003 72.1 49.8 40.1 22.5 57.8 71.6 74.5 40.5 51.8 New York Suffolk 361030002 48.2 30.5 77.6 47.2 65.8 44.5 35.5 67.6 63.8 New York Suffolk 361030004 65.2 33.7 66.1 37.1 59.5 33.0 21.0 49.3 51.3 New York Ulster 361111005 49.5 55.3 47.2 27.5 73.5 54.1 57.6 45.8 52.1 New York Wayne 361173001 55.5 57.8 52.5 23.6 56.7 76.7 42.5 42.7 48.5 New York Westchester 361192004 65.0 37.0 66.6 40.8 74.0 66.1 25.7 71.8 55.5 North Carolina Buncombe 370210030 32.7 36.5 40.3 48.6 50.1 58.6 53.6 60.1 66.7 North Carolina Caldwell 370270003 52.8 62.1 60.1 66.7 70.1 68.3 71.3 62.2 79.0 North Carolina Camden 370290099 27.3 32.2 53.5 64.8 68.3 NA NA NA NA North Carolina Caswell 370330001 25.3 32.2 56.6 66.2 58.3 49.5 66.0 62.3 68.7 North Carolina Chatham 370370004 41.3 44.1 71.8 77.8 88.2 62.0 81.5 81.6 85.8 North Carolina Cumberland 370510008 28.8 38.2 66.7 73.6 89.2 72.1 75.7 93.5 87.0 North Carolina Duplin 370610002 26.3 26.8 58.6 56.7 53.0 53.0 46.6 45.6 62.5 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

North Carolina Durham 370630013 32.7 38.1 61.7 71.5 75.8 75.3 73.8 83.0 76.8 North Carolina Forsyth 370670022 32.8 36.2 62.7 62.0 58.0 74.3 67.8 66.7 78.5 North Carolina Forsyth 370670027 33.1 44.8 59.7 66.1 66.3 67.8 68.8 67.6 89.2 North Carolina Forsyth 370671008 43.3 47.1 67.3 76.7 60.3 77.6 60.8 68.1 81.7 North Carolina Franklin 370690001 26.6 38.6 71.5 60.2 55.2 56.3 69.6 72.8 67.0 North Carolina Granville 370770001 38.0 44.3 82.3 74.3 74.5 79.8 83.2 86.5 76.7 North Carolina Guilford 370810011 25.2 39.0 65.8 70.8 61.8 65.1 67.3 67.8 83.6 North Carolina Haywood 370870035 NA 55.3 54.1 56.8 61.8 69.3 78.2 80.3 85.6 North Carolina Haywood 370870036 44.1 58.2 54.2 56.0 66.3 74.5 92.1 93.3 92.8 North Carolina Johnston 371010002 32.0 38.2 82.6 71.8 74.0 69.3 76.6 84.7 82.6 North Carolina Lincoln 371090004 51.5 58.5 67.3 65.1 70.0 83.6 83.2 69.7 101.3 North Carolina Martin 371170001 19.1 25.0 60.6 60.3 59.8 34.0 21.6 40.5 62.1 North Carolina Mecklenburg 371191005 59.2 67.3 91.5 81.5 96.7 73.3 86.2 73.7 100.1 North Carolina Mecklenburg 371191009 60.5 67.3 91.2 75.6 84.1 82.2 80.1 95.3 84.0 North Carolina Northampton 371310002 27.2 30.5 60.5 75.3 58.0 68.1 40.6 62.3 86.7 North Carolina Pitt 371470099 31.2 31.6 84.3 71.3 72.0 67.0 37.0 55.2 82.6 North Carolina Rockingham 371570099 30.0 34.1 56.1 62.6 65.2 61.1 63.7 67.5 67.1 North Carolina Rowan 371590021 55.0 51.0 70.7 87.7 110.7 82.5 90.4 72.6 80.6 North Carolina Rowan 371590022 60.5 65.7 68.0 71.2 77.0 77.2 71.8 76.7 84.8 North Carolina Swain 371730002 33.0 45.6 48.5 47.2 55.8 55.3 67.0 70.5 62.0 North Carolina Wake 371830014 26.3 40.8 79.7 69.2 64.7 61.7 75.1 81.6 78.7 North Carolina Wake 371830015 30.2 42.5 79.8 78.2 72.8 66.1 77.1 86.3 80.8 North Carolina Wake 371830016 34.8 49.0 75.5 77.5 80.8 69.5 76.3 88.7 90.7 North Carolina Wake 371830017 NA 43.3 81.5 79.6 88.0 77.2 82.5 86.0 82.7 North Carolina Yancey 371990003 65.5 72.3 69.0 68.8 67.2 81.0 88.2 98.1 102.0 Ohio Allen 390030002 52.5 48.8 65.2 65.3 58.1 54.1 73.1 42.5 53.2 Ohio Ashtabula 390071001 67.1 71.0 62.7 58.8 82.7 74.2 72.3 72.3 69.7 Ohio Butler 390170004 52.1 NA NA NA NA NA 83.2 34.5 43.2 Ohio Butler 390171004 56.3 62.3 69.1 70.5 76.1 85.4 100.7 73.5 48.3 Ohio Clark 390230001 63.3 55.8 62.3 62.7 72.3 66.5 79.8 69.7 49.3 Ohio Clark 390230003 61.6 63.0 70.8 69.5 73.1 73.7 76.7 69.7 45.0 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Ohio Clinton 390271002 69.6 68.6 70.8 75.2 101.0 71.5 78.0 NA NA Ohio Cuyahoga 390350034 71.5 76.7 79.2 66.7 58.0 70.2 67.8 59.6 50.6 Ohio Cuyahoga 390350064 56.7 90.5 77.2 64.7 56.6 68.0 77.2 82.1 41.8 Ohio Cuyahoga 390355002 60.7 73.3 78.8 72.0 60.6 73.1 62.3 57.8 50.8 Ohio Franklin 390490081 49.0 49.2 55.7 NA NA NA NA 74.7 49.3 Ohio Hamilton 390610006 58.3 67.1 69.3 69.0 76.1 81.0 94.6 79.3 39.5 Ohio Hamilton 390610010 52.3 58.0 59.8 56.3 65.2 66.4 79.0 73.8 42.1 Ohio Knox 390830002 64.3 54.7 58.1 62.7 64.3 62.7 56.0 65.1 54.1 Ohio Lake 390850003 96.5 91.6 76.8 76.1 71.3 72.6 72.3 72.8 61.1 Ohio Lake 390853002 85.2 79.0 79.6 70.8 71.1 71.1 58.2 56.2 47.0 Ohio Lawrence 390870006 58.7 50.7 77.6 71.5 63.5 65.3 94.2 71.1 67.3 Ohio Lawrence 390870011 64.1 39.0 69.7 58.2 71.6 61.8 77.8 58.5 48.5 Ohio Licking 390890005 59.1 61.1 71.6 73.6 89.1 64.6 73.1 66.7 48.6 Ohio Lucas 390950034 43.5 74.5 63.1 57.8 55.0 65.1 65.3 44.0 63.3 Ohio Lucas 390950081 23.8 60.3 57.3 66.2 61.2 55.6 67.5 49.8 59.6 Ohio Madison 390970007 66.8 72.6 73.6 72.8 91.1 65.8 70.8 81.6 46.2 Ohio Medina 391030003 57.1 56.3 60.2 NA NA 63.1 77.6 65.0 40.7 Ohio Miami 391090005 52.2 51.3 61.5 60.2 61.2 54.8 74.7 55.6 51.3 Ohio Montgomery 391130019 48.5 58.6 68.6 63.3 64.7 61.1 87.6 59.2 45.0 Ohio Portage 391331001 54.0 70.2 73.6 61.1 61.8 56.2 62.6 69.3 47.3 Ohio Preble 391351001 52.5 58.3 61.6 54.3 NA NA 72.5 49.1 43.5 Ohio Stark 391510016 57.0 62.2 61.7 68.3 68.8 64.8 71.8 69.0 62.7 Ohio Stark 391510019 51.6 60.3 63.8 62.5 71.0 57.6 86.0 68.7 34.7 Ohio Stark 391511009 54.0 62.8 62.3 69.5 67.7 65.6 75.3 68.5 56.1 Ohio Stark 391514005 53.5 60.8 57.7 66.8 65.1 66.5 84.0 72.1 65.5 Ohio Summit 391530020 47.3 63.5 67.6 67.0 63.2 60.6 69.8 82.3 49.2 Ohio Trumbull 391550008 55.6 49.3 76.1 79.5 70.7 74.8 70.7 64.3 56.0 Ohio Trumbull 391550009 57.2 51.6 67.3 66.8 67.1 68.5 69.0 56.1 47.6 Ohio Washington 391670004 55.8 57.2 57.2 57.1 72.0 98.2 72.0 77.5 68.7 Oklahoma Cleveland 400270049 58.5 58.3 43.7 27.7 41.0 34.5 31.1 39.5 49.1 Oklahoma Oklahoma 401090033 57.8 60.2 46.7 29.0 42.6 36.7 33.2 40.7 51.6 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Oklahoma Oklahoma 401091037 62.8 64.5 49.1 30.2 45.1 38.8 32.1 44.3 56.6 Oklahoma Tulsa 401430137 65.3 85.7 64.5 46.3 53.3 49.6 46.8 49.4 67.7 Oklahoma Tulsa 401430174 69.6 84.1 67.7 48.8 52.2 48.0 53.3 56.6 71.0 Pennsylvania Allegheny 420030008 56.3 31.6 55.1 85.7 66.7 90.2 66.0 71.5 71.6 Pennsylvania Allegheny 420030067 57.6 49.3 57.8 76.3 67.1 81.2 73.6 87.3 78.7 Pennsylvania Allegheny 420030088 57.5 35.2 55.3 98.0 69.1 100.1 54.5 60.5 55.6 Pennsylvania Allegheny 420031005 67.8 36.7 58.5 80.7 71.6 115.7 72.2 70.2 62.6 Pennsylvania Beaver 420070002 56.5 NA NA 60.3 63.7 NA NA 49.8 58.1 Pennsylvania Beaver 420070005 42.3 39.5 56.7 70.8 68.1 65.3 NA 78.1 NA Pennsylvania Beaver 420070014 52.8 NA NA 67.0 58.1 62.1 65.7 69.0 66.7 Pennsylvania Berks 420110001 43.0 47.0 56.8 35.7 82.6 37.5 76.6 63.0 52.6 Pennsylvania Berks 420110009 37.6 46.0 61.7 37.8 78.7 NA 71.0 60.1 45.7 Pennsylvania Blair 420130801 43.2 39.7 52.5 44.7 69.7 90.7 36.1 57.1 49.8 Pennsylvania Bucks 420170012 61.2 54.2 84.7 45.8 86.3 60.1 35.2 52.5 69.5 Pennsylvania Cambria 420210011 32.3 38.2 51.6 39.5 64.7 79.0 65.8 56.3 52.1 Pennsylvania Dauphin 420430401 27.5 38.5 57.6 50.7 60.3 71.1 78.0 66.5 52.8 Pennsylvania Dauphin 420431100 25.7 44.1 61.2 46.6 68.2 74.5 91.7 68.1 53.7 Pennsylvania Delaware 420450002 46.2 41.8 74.3 48.2 65.3 66.2 34.6 54.0 62.5 Pennsylvania Erie 420490003 NA NA 61.2 47.8 81.7 71.0 77.5 69.5 73.5 Pennsylvania Lackawanna 420690101 44.1 56.6 52.2 34.8 82.1 86.6 57.3 50.8 47.2 Pennsylvania Lackawanna 420692006 42.8 55.3 51.7 34.1 79.1 82.6 58.8 55.3 49.5 Pennsylvania Lancaster 420710007 42.0 49.1 64.7 52.1 79.8 61.7 87.1 71.5 56.3 Pennsylvania Lawrence 420730015 45.2 32.8 65.3 69.1 54.6 49.3 59.2 57.7 56.3 Pennsylvania Lehigh 420770004 39.8 42.3 55.3 39.0 70.5 36.5 68.5 55.2 45.3 Pennsylvania Luzerne 420791100 35.7 51.3 49.3 31.7 66.1 69.8 61.7 63.8 53.7 Pennsylvania Luzerne 420791101 40.3 57.1 54.8 35.2 76.2 81.2 68.2 64.3 54.6 Pennsylvania Lycoming 420810403 30.8 49.7 53.1 32.6 53.6 62.3 57.3 55.0 47.8 Pennsylvania Mercer 420850100 49.1 42.0 70.7 74.6 67.5 65.0 63.7 59.6 47.5 Pennsylvania Montgomery 420910013 44.1 45.1 69.1 43.7 71.1 49.7 54.4 59.0 55.0 Pennsylvania Perry 420990301 38.3 48.6 66.3 44.0 54.7 88.1 70.2 80.2 54.8 Pennsylvania Philadelphia 421010004 40.0 30.0 60.0 36.2 53.7 45.0 22.5 45.0 45.7 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Pennsylvania Philadelphia 421010014 56.6 50.0 72.5 46.2 76.2 51.2 50.0 66.2 67.1 Pennsylvania Philadelphia 421010024 63.7 53.7 82.5 51.2 81.2 63.7 40.0 62.5 72.8 Pennsylvania Philadelphia 421010136 28.7 27.5 62.5 37.5 48.5 NA NA NA NA Pennsylvania Washington 421250005 58.8 47.7 60.0 80.2 68.1 95.0 81.0 90.6 75.3 Pennsylvania Washington 421250200 49.5 41.3 55.3 63.0 59.2 80.1 70.2 87.2 67.2 Pennsylvania Washington 421255001 56.3 47.6 54.1 62.7 60.6 74.3 71.3 79.6 83.3 Pennsylvania Westmoreland 421290006 49.0 31.3 38.7 73.0 49.2 83.2 62.0 66.2 61.3 Pennsylvania York 421330008 39.8 39.7 58.8 51.5 63.0 64.2 79.8 58.0 57.2 Rhode Island Kent 440030002 87.0 50.5 61.2 24.5 53.1 32.1 22.3 69.5 43.3 Rhode Island Providence 440071010 95.2 55.5 65.0 31.8 59.8 29.6 24.8 55.6 37.5 South Carolina Abbeville 450010001 62.0 64.5 64.3 61.2 71.5 82.8 87.5 70.0 88.6 South Carolina Aiken 450030003 43.3 66.1 79.2 69.7 64.1 64.5 66.2 65.6 67.1 South Carolina Anderson 450070003 71.8 64.3 76.0 72.5 75.5 93.7 84.2 88.0 97.5 South Carolina Barnwell 450110001 NA NA NA NA NA NA NA NA NA South Carolina Berkeley 450150002 24.8 46.8 66.8 62.0 NA 53.2 72.7 79.2 63.3 South Carolina Charleston 450190042 29.6 49.3 63.1 71.0 77.7 55.3 72.1 81.1 NA South Carolina Charleston 450190046 23.2 44.0 74.0 57.5 75.3 56.5 71.1 91.0 75.5 South Carolina Cherokee 450210002 53.5 59.5 63.2 64.3 80.1 81.5 80.1 82.7 79.2 South Carolina Chester 450230002 74.4 80.5 72.7 70.6 105.2 83.2 95.1 93.5 109.8 South Carolina Darlington 450310003 31.2 31.5 69.2 61.7 64.3 63.0 69.8 84.2 72.6 South Carolina Edgefield 450370001 51.3 58.0 58.5 51.3 67.2 75.6 69.6 77.1 76.8 South Carolina Oconee 450730001 39.6 41.7 46.3 44.7 41.1 56.6 60.1 67.6 64.0 South Carolina Pickens 450770002 55.8 65.5 NA NA NA 82.1 79.6 82.2 92.8 South Carolina Richland 450790007 37.8 50.8 80.7 67.1 80.1 67.6 92.8 82.1 74.4 South Carolina Richland 450791002 37.3 52.0 77.8 67.5 77.1 70.1 93.8 82.8 81.8 South Carolina Spartanburg 450830009 56.5 56.0 61.5 72.8 76.3 71.2 74.8 83.0 83.7 South Carolina Union 450870001 65.0 65.5 65.1 63.7 68.1 71.3 91.5 85.7 83.0 South Carolina Williamsburg 450890001 24.7 32.7 46.6 52.1 56.0 53.8 62.7 71.8 48.4 South Carolina York 450910006 64.6 68.5 73.6 61.2 95.1 81.8 87.6 96.1 108.2 Tennessee Anderson 470010101 59.5 72.6 70.0 68.8 83.7 77.2 82.5 100.6 94.1 Tennessee Blount 470090101 67.1 84.5 72.8 72.3 98.7 81.7 103.6 105.5 90.2 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Tennessee Blount 470090102 45.5 54.7 52.1 52.1 83.7 55.8 77.5 77.2 56.7 Tennessee Davidson 470370011 66.2 55.0 81.8 86.2 58.7 60.0 43.7 55.0 31.2 Tennessee Davidson 470370026 96.8 76.8 88.7 86.2 86.4 87.5 80.0 85.6 71.2 Tennessee Hamilton 470650028 75.3 59.0 NA NA 73.6 57.1 75.7 89.6 77.6 Tennessee Hamilton 470651011 57.6 NA 55.3 56.3 51.8 57.7 78.8 86.5 75.5 Tennessee Haywood 470750002 60.0 NA 50.0 47.3 63.0 55.6 49.5 62.8 50.8 Tennessee Knox 470930021 53.6 58.8 61.1 68.3 61.1 79.5 87.6 84.6 84.2 Tennessee Knox 470931020 48.6 60.5 62.0 68.8 74.5 68.6 86.1 83.5 84.2 Tennessee Rutherford 471490101 50.2 26.0 29.0 28.6 25.5 31.3 67.2 71.0 27.8 Tennessee Sevier 471550101 67.0 71.7 68.8 75.2 84.8 86.0 97.6 98.8 91.5 Tennessee Sevier 471550102 59.3 61.7 60.2 62.0 65.5 77.8 80.1 95.0 87.0 Tennessee Shelby 471570021 51.7 57.2 81.8 55.6 45.0 30.3 71.6 108.6 NA Tennessee Shelby 471571004 80.3 59.0 72.0 87.5 75.5 62.2 78.0 63.7 76.3 Tennessee Sullivan 471632002 58.3 57.7 81.1 78.0 83.8 72.5 85.6 94.0 91.0 Tennessee Sullivan 471632003 51.7 58.2 78.7 75.7 83.6 68.3 83.6 89.2 91.1 Tennessee Sumner 471650007 85.6 63.6 77.3 83.8 76.8 89.8 79.8 82.5 66.1 Tennessee Sumner 471650101 56.8 51.8 59.7 71.5 56.8 48.3 65.0 66.6 53.8 Tennessee Wilson 471890103 63.3 63.3 65.6 63.6 64.8 49.2 64.5 74.7 58.3 Texas Brazoria 480391003 33.6 18.2 13.7 13.3 14.0 16.1 18.6 42.1 65.0 Texas Collin 480850005 84.8 82.6 50.3 43.8 39.0 43.1 NA NA NA Texas Dallas 481130069 57.8 61.6 36.1 26.5 23.0 28.6 25.7 44.3 67.6 Texas Dallas 481130087 54.0 56.1 31.6 19.3 18.6 26.1 21.2 39.7 62.7 Texas Galveston 481671002 39.7 23.8 22.0 NA NA 16.8 22.6 60.5 61.0 Texas Gregg 481830001 77.6 69.0 53.8 53.3 29.8 40.0 35.2 67.3 83.6 Texas Harris 482010024 56.8 36.0 31.6 28.3 21.6 33.0 48.2 101.6 55.8 Texas Harris 482010029 NA NA NA NA NA NA 50.7 83.2 61.1 Texas Harris 482010046 51.8 35.1 24.1 25.0 21.7 27.6 47.0 84.6 44.5 Texas Harris 482010047 53.8 31.7 22.6 18.1 13.3 23.5 38.7 64.8 47.1 Texas Harris 482010051 58.6 25.5 21.6 17.5 17.5 29.5 35.2 52.3 55.0 Texas Harris 482010062 48.0 25.7 21.2 18.2 24.3 23.1 27.2 48.3 48.8 Texas Harris 482010066 63.7 38.0 25.2 17.0 16.2 26.5 28.6 56.2 55.8 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Texas Harris 482011035 47.7 22.2 17.7 17.1 18.8 18.6 26.0 58.0 42.6 Texas Jefferson 482450009 69.6 31.0 29.7 21.0 19.2 18.5 57.7 75.1 52.6 Texas Jefferson 482450011 50.8 25.1 16.1 13.0 19.2 11.7 66.2 86.5 51.5 Texas Orange 483611001 61.5 32.2 22.2 22.7 20.0 22.0 73.8 84.6 59.0 Texas Tarrant 484391002 67.7 76.7 44.8 31.5 33.2 38.1 37.2 55.8 80.0 Texas Tarrant 484392003 75.3 NA NA NA NA NA NA 45.5 69.2 Texas Travis 484530014 67.3 55.3 26.0 29.3 24.3 27.6 32.0 37.7 48.3 Vermont Bennington 500030004 58.6 47.3 27.1 22.5 51.0 46.2 54.0 38.6 36.5 Virginia Arlington 510130020 34.5 54.0 67.8 65.1 94.2 73.0 82.2 64.0 60.3 Virginia Caroline 510330001 26.0 44.7 55.5 66.6 78.2 57.5 61.8 77.1 91.7 Virginia Charles City 510360002 28.5 29.3 74.0 66.8 77.8 58.7 44.6 75.3 63.7 Virginia Chesterfield 510410004 26.5 41.1 59.6 66.7 96.7 65.7 56.2 72.6 82.6 Virginia Fairfax 510590005 39.5 56.0 55.7 56.5 63.1 93.1 84.8 72.8 60.3 Virginia Fairfax 510590018 32.0 42.2 66.3 60.3 102.5 70.2 76.3 64.6 63.2 Virginia Fairfax 510595001 34.7 43.5 67.1 58.0 80.5 83.0 84.3 77.6 65.6 Virginia Fauquier 510610002 34.4 40.2 55.5 55.2 61.8 81.7 84.7 75.8 64.2 Virginia Frederick 510690010 34.8 37.6 58.7 63.2 63.5 82.7 80.2 66.0 64.1 Virginia Henrico 510870014 28.8 33.0 68.3 70.2 83.0 56.2 55.6 80.7 76.5 Virginia Madison 511130003 33.0 47.1 62.8 69.8 69.6 78.4 85.1 85.5 64.4 Virginia Prince William 511530009 37.0 50.0 67.5 63.7 68.7 100.8 91.7 75.7 67.2 Virginia Roanoke 511611004 34.3 51.7 61.8 74.1 67.3 62.5 79.5 74.3 71.3 Virginia Stafford 511790001 29.2 51.7 58.2 68.0 83.2 76.5 86.1 84.5 90.6 Virginia Wythe 511970002 51.5 62.0 64.8 68.3 66.7 67.0 67.5 76.0 84.8 Virginia Alexandria Cit 515100009 28.8 45.5 64.3 67.2 91.2 68.3 76.2 54.0 55.8 Virginia Hampton City 516500004 28.6 29.4 74.1 74.1 69.0 37.2 22.1 54.8 57.6 Virginia Suffolk City 518000004 23.1 28.7 75.6 63.8 62.6 34.0 27.8 62.1 57.2 Virginia Suffolk City 518000005 27.0 36.0 62.5 67.5 86.6 45.5 28.5 52.5 68.0 West Virginia Cabell 540110006 72.2 49.3 87.0 87.8 80.0 74.3 NA 63.5 72.5 West Virginia Hancock 540291004 52.0 45.5 50.1 58.7 63.0 65.8 72.7 80.5 86.8 West Virginia Ohio 540690007 42.2 37.3 55.2 58.0 63.7 73.5 56.7 66.8 52.3 West Virginia Wood 541071002 56.1 61.2 61.0 56.7 79.7 95.7 90.1 92.0 62.8 
State County

AIRS Site 

Code Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 1

Wisconsin Brown 550090026 50.8 41.7 34.0 44.0 45.0 25.4 38.1 47.5 45.3 Wisconsin Columbia 550210015 45.8 54.3 63.0 55.7 45.0 43.6 56.2 45.6 49.5 Wisconsin Dane 550250041 40.7 51.8 58.8 46.1 44.1 42.8 45.2 40.8 45.8 Wisconsin Dodge 550270007 NA 42.8 47.7 48.1 40.3 37.1 53.1 39.0 48.7 Wisconsin Door 550290004 60.1 44.8 37.7 44.5 46.6 38.1 45.3 55.7 55.1 Wisconsin Fond Du Lac 550390006 36.3 43.7 43.8 48.0 41.7 34.2 47.6 44.3 46.5 Wisconsin Jefferson 550550002 41.1 57.8 56.7 58.8 49.5 42.1 57.6 50.8 53.8 Wisconsin Kenosha 550590002 44.8 73.7 92.5 58.2 56.8 52.2 51.0 60.3 53.5 Wisconsin Kenosha 550590019 55.1 94.8 105.0 64.1 63.3 57.1 61.0 65.5 58.5 Wisconsin Kewaunee 550610002 52.6 45.8 43.2 46.7 46.8 37.1 42.1 50.3 57.7 Wisconsin Manitowoc 550710004 39.3 44.1 39.1 45.3 45.6 27.7 47.3 46.2 48.2 Wisconsin Manitowoc 550710007 45.0 43.6 40.2 46.0 43.7 37.1 45.1 46.7 49.6 Wisconsin Marathon 550730012 34.5 37.6 28.8 42.2 40.8 33.0 32.1 34.7 44.2 Wisconsin Milwaukee 550790041 35.5 61.6 75.0 61.3 54.6 47.7 59.0 61.5 67.2 Wisconsin Milwaukee 550790044 37.3 56.1 63.5 58.7 49.6 39.2 52.8 52.7 58.6 Wisconsin Milwaukee 550790085 45.5 74.0 70.1 63.6 56.7 49.0 61.8 62.6 61.1 Wisconsin Milwaukee 550791025 45.8 58.3 77.6 59.5 55.2 51.5 60.5 63.1 NA Wisconsin Outagamie 550870009 36.7 36.2 27.2 38.2 38.2 23.3 38.4 45.7 45.1 Wisconsin Ozaukee 550890008 37.1 63.5 56.1 51.5 42.7 47.3 58.6 49.7 59.1 Wisconsin Ozaukee 550890009 44.5 62.6 55.5 63.6 52.3 52.3 58.1 53.0 59.5 Wisconsin Racine 551010017 41.7 85.2 87.2 56.6 51.0 46.7 52.5 59.3 53.6 Wisconsin Rock 551050024 40.5 54.0 59.1 53.0 44.8 33.8 50.3 39.1 50.1 Wisconsin St Croix 551091002 48.5 29.8 31.2 37.1 28.3 34.5 31.5 37.2 39.7 Wisconsin Sauk 551110007 36.3 49.8 50.8 45.5 37.5 36.6 40.1 41.0 44.5 Wisconsin Vernon 551230008 34.6 49.7 47.7 44.3 39.3 30.3 30.0 34.3 39.7 Wisconsin Walworth 551270005 44.8 65.1 NA NA NA NA 52.4 43.6 61.0 Wisconsin Washington 551310009 40.0 52.5 55.6 58.3 46.1 42.8 58.8 46.2 55.5 Wisconsin Waukesha 551330017 39.1 55.0 58.3 56.5 48.1 36.1 48.2 46.1 60.5 Wisconsin Winnebago 551390011 36.8 44.6 41.0 38.0 39.8 32.5 43.8 42.7 46.0 
Technical Support Document 

for the Final

Clean Air Interstate Rule

Air Quality Modeling Analyses

Appendix D

CAMx Model Performance Evaluation
1

Introduction

An operational model performance evaluation for surface ozone for the five episodes was

performed in order to estimate the ability of the modeling system to replicate base year ozone

concentrations. This evaluation is comprised principally of statistical assessments of model

versus observed pairs. The robustness of an operational evaluation is directly proportional to the

amount and quality of the ambient data available for comparison.

a. Statistical Definitions

Below are the definitions of those statistics used for the evaluation. The format of all the

statistics is such that negative values indicate model ozone predictions that were less than their

observed counterparts. Positively-valued statistics indicate model overestimation of surface

ozone. Statistics were not generated for the first three days of an episode to avoid the

initialization period. The statistics were calculated for (a) the entire HDE domain, (b) four

quadrants (Midwest, Northeast, Southeast, Southwest), and (c) 51 local areas. The statistics that

were calculated for each of these sets of areas are described below.

Domainwide unpaired peak prediction accuracy: This metric simply compares the peak

concentration modeled anywhere in the selected area against the peak ambient concentration

anywhere in the same area. The difference of the peaks (model - observed) is then normalized

by the peak observed concentration.

Peak prediction accuracy: This metric averages the paired peak prediction accuracy calculated

for each monitor in the subregion. It characterizes the capacity of the model to replicate peak

(afternoon) ozone over a subregion. The daily peak model versus daily peak observed residuals

are paired in space but not in time.

Mean normalized bias: This performance statistic averages the normalized (by observation)

difference (model - observed) over all pairs in which the observed values were greater than 60

ppb. A value of zero would indicate that the model over predictions and model under

predictions exactly cancel each other out.

Mean normalized gross error: The last metric used to assess the performance of the HDE base

cases is similar to the above statistic, except in this case it is the absolute value of the residual

which is normalized by the observation, and then averaged over all sites. A zero gross error

value would indicate that all model concentrations (in which their observed counterpart was

greater than 60 ppb) exactly matched the ambient values.

b. Domainwide Model Performance

As with previous regional photochemical modeling studies, the degree that model

predictions replicate observed concentrations varies by day and location over the large eastern

U.S. modeling domain. From a qualitative standpoint, there appears to be considerable
2

similarity on most days between the observed and simulated ozone patterns. Additionally, where

possible to discern, the model appears to follow the day-to-day variations in synoptic-scale

ozone fairly closely. More quantitative comparisons of the model predictions and ambient data

are provided below.

When all hourly observed ozone values (greater than 60 ppb) are compared to their

model counterparts for the 30 episode modeling days in the eastern U.S. simulations, the mean

normalized bias is -1.1 percent and the mean normalized gross error is 20.5 percent As shown in

Table III-3, the model generally underestimates observed ozone values for the June and July

episodes, but predicts higher than observed amounts for the August episode.

Table III-3. Performance statistics for hourly ozone in the Eastern U.S. CAMx simulations.

Average Accuracy of the Peak Mean Normalized Bias Mean Normalized Gross Error

June 1995 -7.3 -8.8 19.6

July 1995 -3.3 -5.0 19.1

August 1995 9.6 8.6 23.3

Depending on the episode and region, the normalized biases can range from an

underestimation of 18 percent to an overestimation of 16 percent. Gross errors tend to average

between 17 and 25 percent. As shown in Table III-4, when the model domain is subdivided into

four quadrants, it is found that most of the underestimations in the June and July episodes are

driven by the Northeast and Midwest quadrants (i.e., the two northern ones). Conversely, most

of the overestimated ozone in the August episode is due to the Midwest, Southeast and

Southwest quadrants. Hourly ozone is consistently underestimated in the Northeast quadrant. 

The model does slightly better in replicating the peak values for each monitoring site than it does

at replicating the mean values, especially in the Northeast where the underpredictions are not as

large for the highest ozone observations.

Table III-4. Regional/Episodic performance statistics for hourly ozone predictions.

Average Accuracy of the

Peak

Mean Normalized Bias Mean Normalized Gross

Error

June July August June July August June July August

Whole Grid -7.3 -3.3 9.6 -8.8 -5.0 8.6 19.6 19.1 23.3

Northeast -14.7 -5.0 -4.3 -18.4 -7.2 -6.0 24.7 19.1 22.6

Midwest -7.3 -6.2 15.5 -8.7 -7.2 15.5 18.0 19.4 23.7

Southeast -2.9 1.9 15.1 -3.0 1.3 14.7 17.4 19.1 24.1

Southwest -0.9 1.3 7.0 0.7 3.1 10.3 19.0 20.0 22.6

At present, there are no accepted criteria by which one can determine if a regional ozone
3

modeling exercise is exhibiting adequate model performance. As a result, EPA compares the

evaluation results of regional models against applicable previous analyses. For instance, the

Heavy Duty Engine (HDE) base case simulations were determined to be appropriate for use

based on comparisons to previously accepted modeling analyses (e.g., OTAG and Tier-2). 

Model performance in the base year simulations is generally similar or better than its predecessor

regional ozone modeling efforts. In particular, the gross error metric is almost universally

improved in the more recent modeling. In general, the CAMx modeling results are

approximately 3-6 ppb higher on average than what was generated in the HDE/UAM-V

modeling. In some previous regional modeling applications, there had been a tendency for the

model to underestimate ozone in the early parts of an episode and then overestimate ozone at the

end of an episode. The trend toward positive bias would increase throughout the episode, which

may be a sign of an imbalance in the model chemistry which in turn could affect control strategy

signal. In general, there does not appear to be an issue with bias creep in the base year modeling. 

Finally, as noted above, the base year CAMx modeling has been used before to support proposed

emission control regulations (e.g., Non-Road Rulemaking). 

Table III-5 presents the results from the eight-hourly ozone evaluation. In general, the

gross error is noticeably less for the eight-hour ambient versus observed ozone comparisons. 

However, the eight-hour ozone model predictions are large overestimates of the actual observed

values for the August episode, especially outside of the Northeast quadrant.

Table III-5. Regional/Episodic performance statistics for 8-hourly ozone predictions. 

Average Accuracy of the

Peak

Mean Normalized Bias Mean Normalized Gross

Error

June July August June July August June July August

Whole Grid -3.9 0.9 13.9 -5.7 -2.1 11.0 17.5 16.4 22.6

Northeast -13.5 -2.4 -1.6 -15.4 -4.9 -3.8 21.3 14.6 20.8

Midwest -4.0 -0.9 20.6 -5.8 -4.4 17.6 16.0 16.7 23.7

Southeast 1.3 5.3 20.5 0.9 4.0 18.4 16.4 17.5 24.1

Southwest 5.0 8.2 16.2 3.9 3.6 12.4 17.8 18.1 21.1

c. Local-scale Model Performance

The CAMx modeling results were also evaluated at a “local” level. For this analysis, the

modeling domain was broken up into 51 local subregions as shown in Figure III-2. The primary

statistics for each of the 51 subregions is shown in Table III-6.

As noted above, there is no set of established statistical benchmarks to determine the

adequacy of a regional modeling operation evaluation. If one were to evaluate the performance

of the 1995 eastern base cases against existing EPA requirements for acceptable levels of

accuracy, bias, and error in local attainment demonstration modeling, 69% of the regions would
4

pass for the June episode, 80% of the regions would pass for the July episodes, and 61% of the

regions would pass for the August episode. This is an improvement from the HDE base case

analyses where the numbers were: 57%, 45%, and 55%, respectively. The local eight-hour

metrics (not shown) generally do not greatly differ from their hourly counterparts. There is a

slight tendency toward greater overprediction of the eight-hourly values.

Table III-6. Local performance statistics for hourly ozone predictions.

Average Accuracy of

the Peak

Mean Normalized

Bias

Mean Normalized

Gross Error

June July August June July August June July August

Dallas -9.6 -12.3 2.2 -10.6 -11.5 3.2 16.6 18.7 15.7

Houston/Galveston -3.0 -5.1 0.3 -3.5 -3.9 2.2 20.8 19.0 25.7

Beaumont/Port Arthur 14.0 16.7 8.8 16.0 19.3 12.9 20.4 24.5 24.6

Baton Rouge 15.6 24.7 31.4 22.6 26.6 37.4 26.1 31.0 40.5

New Orleans 15.6 29.1 42.1 15.9 28.9 48.9 21.9 32.0 50.2

St. Louis -0.5 -4.0 8.4 -0.6 0.6 10.5 17.0 18.4 18.2

Memphis -7.7 -4.9 13.7 -5.9 -0.3 13.6 15.5 19.3 22.0

Alabama 5.2 -1.7 16.0 6.5 6.7 23.1 14.4 16.6 25.2

Atlanta -3.1 5.4 19.0 -3.4 6.8 26.1 16.7 20.1 31.0

Nashville -2.9 7.8 31.5 -2.4 9.1 36.1 18.1 24.7 37.4

Eastern TN -14.2 -16.0 -2.7 -21.0 -17.1 -5.9 22.7 20.7 18.3

Charlotte 8.3 -2.1 6.0 5.8 4.1 14.5 13.0 16.3 18.2

Greensboro -1.7 -1.1 17.2 -4.2 1.2 18.2 14.1 15.3 21.7

Raleigh-Durham -11.8 1.3 -2.3 -10.7 4.2 -1.9 14.6 13.9 16.9

Evansville/Owensboro 1.2 -0.9 28.3 4.5 5.4 32.8 15.1 21.2 33.9

Indianapolis -8.3 -13.5 15.9 -3.6 -14.4 18.0 13.1 19.3 19.7

Louisville 2.8 4.2 36.6 4.8 6.1 42.1 14.7 17.9 42.5

Cincinnati/Dayton -4.7 -8.5 29.0 0.1 -5.6 32.7 12.8 19.1 33.5

Columbus -8.5 -14.5 9.2 -6.2 -11.0 14.2 14.6 17.3 18.7

West Virginia -8.8 -5.7 12.7 -7.5 -3.2 13.7 15.7 16.6 24.5

Chicago -9.9 -4.3 10.4 -17.1 -11.1 3.5 24.5 23.5 22.3

Milwaukee -14.8 -12.9 21.5 -16.5 -16.9 12.3 19.1 23.3 18.2

Muskegon/Grand

Rapids

-10.8 -12.3 3.1 -11.6 -12.9 1.7 17.7 20.4 16.4

Gary/South Bend -13.0 -10.0 11.8 -15.0 -14.5 9.3 19.2 24.4 20.7

Detroit -17.2 -5.8 3.9 -20.1 -13.2 -3.2 25.1 22.5 23.4
5

Pittsburgh -10.0 -3.2 9.2 -9.2 -2.1 7.9 23.1 16.1 20.4

Central PA -6.0 -7.6 1.0 -8.5 -6.0 1.1 21.9 15.5 18.6

Norfolk -9.0 0.0 8.3 -13.4 -5.6 5.7 19.1 18.6 24.7

Richmond -1.2 4.8 2.6 -1.3 10.7 4.5 8.4 18.3 20.3

Baltimore/Washington -4.7 -3.1 1.7 -6.8 -5.2 0.7 18.6 15.6 23.4

Delaware -6.1 -5.2 2.3 -6.3 -0.2 7.5 12.9 11.6 16.2

Philadelphia -14.1 -1.8 -8.7 -22.0 -10.5 -13.9 26.4 19.5 28.9

New York City -16.2 -3.9 -12.2 -24.6 -14.1 -17.9 31.3 22.5 29.8

Hartford -16.9 -5.0 -9.9 -18.5 -4.0 -7.7 23.6 18.2 20.1

Boston -13.7 -4.7 -15.6 -19.6 -9.2 -19.6 25.9 20.9 26.5

Maine -20.4 -4.7 -6.9 -25.0 -9.4 -6.9 25.3 19.0 15.5

Longview/Shreveport -2.1 11.3 7.7 0.8 11.1 11.4 16.2 16.5 17.9

Kansas City -8.5 -7.8 -4.3 -7.9 -1.5 -8.3 15.7 13.0 12.4

Western NY -23.1 -20.6 -9.0 -25.6 -20.5 -12.1 28.1 23.8 19.0

Northeast OH -4.0 -6.5 6.9 -6.6 -6.8 7.7 20.4 15.5 16.5

South Carolina -2.5 1.3 11.4 -3.4 1.5 15.7 12.5 17.7 19.4

Gulf Coast 0.5 23.1 29.3 4.5 30.0 33.7 15.4 31.6 34.9

FL West Coast -6.4 22.8 41.2 -7.3 11.9 42.8 11.3 22.7 43.7

FL East Coast -15.9 16.2 23.3 -16.8 16.6 26.3 18.0 18.4 29.4

Jackson 0.6 10.9 21.0 1.8 10.0 24.0 16.0 16.0 24.9

Central MI -6.9 -10.4 12.0 -9.6 -14.8 6.6 18.1 18.7 17.5

Macon/Columbus -9.5 -11.1 21.6 -8.8 -5.7 26.4 10.9 13.0 26.9

Austin/San Antonio -14.1 -19.6 -1.9 -11.0 -15.5 4.1 14.1 17.2 12.4

Oklahoma City/Tulsa -12.3 -5.6 -5.2 -12.9 -3.2 -2.8 17.2 14.6 12.6

Ft. Wayne/Lima -9.1 -13.1 3.9 -8.3 -14.1 5.1 16.0 18.2 10.6

Bangor/Hancock Co. -17.8 -6.9 -17.7 -24.4 -8.5 -19.9 25.2 15.3 21.0
6

Figure III-2. Map of the 51 local-scale evaluation zones.

Technical Support Document

for the Final

Clean Air Interstate Rule

Air Quality Modeling Analyses

Appendix E

8-Hour Ozone: Average Ambient and

Projected 2010/2015 Base and CAIR
Table E-1. 8-Hour Ozone Concentrations (ppb): Average 1999-2003, 2010 Base and CAIR, 2015 Base and CAIR

CAIR on 8-hour ozone in 2010 and 2015. 

CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Mobile, AL AL Baldwin Co 79.0 72.5 71.4 -1.1 71.3 

AL Clay Co 82.0 64.3 63.1 -1.2 61.0 

Montgomery, AL AL Elmore Co 78.3 64.3 63.5 -0.8 60.7 Birmingham, AL AL Jefferson Co 86.7 70.7 70.2 -0.5 66.4 Decatur, AL AL Lawrence Co 78.7 66.4 66.1 -0.3 63.7 Huntsville, AL AL Madison Co 82.7 68.8 67.9 -0.9 64.3 Mobile, AL AL Mobile Co 79.0 72.9 71.9 -1.0 71.9 Montgomery, AL AL Montgomery Co 80.0 65.6 64.8 -0.8 62.0 Decatur, AL AL Morgan Co 83.0 70.6 70.0 -0.6 67.7 Birmingham, AL AL Shelby Co 91.7 73.8 73.2 -0.6 69.2 

AL Sumter Co 74.0 61.5 60.9 -0.6 59.5 

Tuscaloosa, AL AL Tuscaloosa Co 78.0 62.5 62.1 -0.4 59.3 Memphis, TN AR Crittenden Co 92.7 81.5 80.8 -0.7 78.6 

AR Montgomery Co 68.0 60.7 58.5 -2.2 58.6 

Little Rock, AR AR Pulaski Co 84.7 76.3 71.8 -4.5 73.4 New Haven-Meriden, CT CT Fairfield Co 98.7 92.6 92.2 -0.4 91.4 

Hartford, CT CT Hartford Co 89.3 80.4 80.1 -0.3 77.4 

CT Litchfield Co 83.0 74.0 73.9 -0.1 71.3 

Hartford, CT CT Middlesex Co 98.0 90.9 90.6 -0.3 89.1 New Haven-Meriden, CT CT New Haven Co 99.0 91.6 91.3 -0.3 89.8 New London-Norwich, CT CT New London Co 90.7 83.6 83.4 -0.2 81.8 Hartford, CT CT Tolland Co 93.0 83.0 82.7 -0.3 79.7 Dover, DE DE Kent Co 91.3 79.1 78.7 -0.4 76.6 Philadelphia, PA DE New Castle Co 95.3 85.0 84.7 -0.3 82.8 

DE Sussex Co 93.3 80.9 80.3 -0.6 78.4 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Washington-Baltimore, DC-MD DC Washington Co 94.3 85.2 85.0 -0.2 83.5 Gainesville, FL FL Alachua Co 75.3 65.3 59.8 -5.5 61.8 

FL Baker Co 72.7 62.1 58.0 -4.1 58.7 

Panama City, FL FL Bay Co 80.0 72.8 71.3 -1.5 70.8 Melbourne, FL FL Brevard Co 75.0 62.5 58.8 -3.7 58.1 

FL Columbia Co 71.0 60.6 56.9 -3.7 57.1 

Jacksonville, FL FL Duval Co 70.3 60.1 54.4 -5.7 57.2 

Pensacola, FL FL Escambia Co 83.7 77.8 74.1 -3.7 76.1 

FL Highlands Co 64.0 52.2 50.1 -2.1 48.9 

Tampa-St Petersburg, FL FL Hillsborough Co 80.3 68.7 67.0 -1.7 65.6 

FL Holmes Co 72.3 63.2 61.8 -1.4 60.5 

Orlando, FL FL Lake Co 76.0 64.2 60.0 -4.2 60.2 

Fort Myers, FL FL Lee Co 70.7 57.9 56.4 -1.5 53.8 Tallahassee, FL FL Leon Co 73.3 62.8 61.5 -1.3 59.4 Sarasota, FL FL Manatee Co 79.0 66.3 64.8 -1.5 63.0 Ocala, FL FL Marion Co 75.7 65.4 60.1 -5.3 61.8 Orlando, FL FL Orange Co 78.3 66.1 62.1 -4.0 61.7 Orlando, FL FL Osceola Co 73.7 62.1 58.2 -3.9 58.1 West Palm Beach-Boca Raton, FL FL Palm Beach Co 69.7 58.4 55.9 -2.5 54.2 Tampa-St Petersburg, FL FL Pasco Co 77.7 66.7 64.3 -2.4 63.2 Tampa-St Petersburg, FL FL Pinellas Co 77.3 67.4 65.7 -1.7 64.5 Lakeland-Winter Haven, FL FL Polk Co 78.0 63.5 60.7 -2.8 59.4 Fort Pierce, FL FL St Lucie Co 69.3 58.8 55.4 -3.4 55.3 Pensacola, FL FL Santa Rosa Co 82.0 75.8 72.5 -3.3 74.2 Sarasota, FL FL Sarasota Co 81.7 67.4 65.5 -1.9 63.5 Orlando, FL FL Seminole Co 77.7 65.2 61.3 -3.9 60.9 Daytona Beach, FL FL Volusia Co 72.0 60.4 56.3 -4.1 56.4 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

FL Wakulla Co 76.0 66.7 65.6 -1.1 64.0 

Macon, GA GA Bibb Co 92.0 82.5 80.0 -2.5 78.8 Savannah, GA GA Chatham Co 71.0 61.1 60.0 -1.1 58.2 Atlanta, GA GA Cherokee Co 77.0 63.1 62.2 -0.9 57.2 Atlanta, GA GA Cobb Co 94.7 80.6 79.4 -1.2 73.0 Atlanta, GA GA Coweta Co 92.0 78.4 76.6 -1.8 72.8 

GA Dawson Co 82.0 67.1 66.2 -0.9 60.7 

Atlanta, GA GA De Kalb Co 95.3 83.3 81.9 -1.4 76.6 Atlanta, GA GA Douglas Co 94.7 80.2 78.7 -1.5 73.3 Atlanta, GA GA Fayette Co 90.7 78.0 76.7 -1.3 71.8 Atlanta, GA GA Fulton Co 99.0 86.5 85.1 -1.4 79.7 

GA Glynn Co 72.7 62.9 60.0 -2.9 60.0 

Atlanta, GA GA Gwinnett Co 89.3 75.6 74.6 -1.0 68.0 Atlanta, GA GA Henry Co 98.0 82.4 80.3 -2.1 76.5 

GA Murray Co 86.0 70.9 69.4 -1.5 65.4 

Columbus, GA-AL GA Muscogee Co 82.0 68.7 67.6 -1.1 64.1 Atlanta, GA GA Paulding Co 90.3 72.2 70.7 -1.5 66.4 Augusta-Aiken, GA-SC GA Richmond Co 85.7 72.9 72.1 -0.8 69.7 Atlanta, GA GA Rockdale Co 96.3 81.6 80.4 -1.2 74.4 

GA Sumter Co 80.3 68.4 66.7 -1.7 64.1 

IL Adams Co 76.0 67.0 66.4 -0.6 64.7 

Champaign-Urbana, IL IL Champaign Co 77.3 66.3 65.9 -0.4 64.0 

IL Clark Co 75.0 65.7 62.9 -2.8 61.7 

Chicago, IL-IN IL Cook Co 87.7 82.1 81.8 -0.3 82.1 Chicago, IL-IN IL Du Page Co 70.7 66.7 66.4 -0.3 67.1 

IL Effingham Co 77.7 67.1 66.4 -0.7 64.1 IL Hamilton Co 78.7 67.1 66.5 -0.6 64.7 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

St. Louis, MO-IL IL Jersey Co 89.0 77.3 77.0 -0.3 73.8 Chicago, IL-IN IL Kane Co 77.7 72.0 71.7 -0.3 71.0 Chicago, IL-IN IL Lake Co 83.3 77.0 76.8 -0.2 75.4 Chicago, IL-IN IL McHenry Co 83.3 76.9 76.6 -0.3 75.4 Bloomington, IL IL McLean Co 77.0 65.2 64.7 -0.5 62.4 Chicago, IL-IN IL Macon Co 76.7 64.0 63.6 -0.4 61.7 

IL Macoupin Co 79.3 66.8 66.4 -0.4 63.7 

St. Louis, MO-IL IL Madison Co 84.7 74.8 74.5 -0.3 71.7 

Peoria, IL IL Peoria Co 79.0 67.2 65.5 -1.7 64.9 

IL Randolph Co 78.7 66.2 66.0 -0.2 64.0 

Davenport, IA-IL IL Rock Island Co 71.0 64.1 63.2 -0.9 62.0 

St. Louis, MO-IL IL St Clair Co 83.3 74.7 74.5 -0.2 72.0 Springfield, IL IL Sangamon Co 76.0 63.6 63.1 -0.5 60.8 Chicago, IL-IN IL Will Co 79.3 70.7 70.4 -0.3 69.9 

Rockford, IL IL Winnebago Co 76.0 67.0 66.6 -0.4 63.6 Fort Wayne, IN IN Allen Co 87.7 76.8 76.4 -0.4 73.0 Indianapolis, IN IN Boone Co 89.0 78.6 78.1 -0.5 75.4 

IN Carroll Co 84.0 73.7 73.1 -0.6 70.6 

Louisville, KY-IN IN Clark Co 89.3 78.2 78.4 0.2 75.8 Muncie, IN IN Delaware Co 88.0 76.1 75.6 -0.5 72.1 

Elkhart, IN IN Elkhart Co 80.0 69.8 69.5 -0.3 66.6 

Louisville, KY-IN IN Floyd Co 83.7 74.9 75.2 0.3 72.8 

IN Gibson Co 71.7 62.1 61.7 -0.4 60.2 IN Greene Co 88.5 76.6 75.3 -1.3 73.3 

Indianapolis, IN IN Hamilton Co 93.3 82.2 81.7 -0.5 78.3 Indianapolis, IN IN Hancock Co 91.7 80.8 80.4 -0.4 77.1 Indianapolis, IN IN Hendricks Co 86.5 76.4 75.9 -0.5 73.4 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Fort Wayne, IN IN Huntington Co 85.0 74.1 73.7 -0.4 70.5 

IN Jackson Co 85.0 71.7 71.4 -0.3 68.6 

Indianapolis, IN IN Johnson Co 86.7 74.2 73.8 -0.4 71.0 Chicago, IL-IN IN Lake Co 90.7 83.2 82.8 -0.4 81.6 

IN La Porte Co 90.0 82.1 81.8 -0.3 80.0 

Indianapolis, IN IN Madison Co 91.0 79.0 78.6 -0.4 74.8 

Indianapolis, IN IN Marion Co 90.0 80.1 79.6 -0.5 76.9 Indianapolis, IN IN Morgan Co 86.7 76.0 75.7 -0.3 73.1 

IN Perry Co 90.0 75.4 75.3 -0.1 73.0 

Chicago, IL-IN IN Porter Co 89.0 81.4 81.1 -0.3 79.3 Evansville-Henderson, IN-KY IN Posey Co 85.7 74.4 73.9 -0.5 72.1 South Bend, IN IN St Joseph Co 89.0 78.1 77.8 -0.3 74.8 Indianapolis, IN IN Shelby Co 93.5 82.1 81.6 -0.5 78.7 Evansville-Henderson, IN-KY IN Vanderburgh Co 83.3 72.4 72.0 -0.4 69.9 Terre Haute, IN IN Vigo Co 87.0 76.7 75.5 -1.2 73.8 Evansville-Henderson, IN-KY IN Warrick Co 84.5 73.4 73.1 -0.3 71.4 

IA Bremer Co 70.5 62.9 62.0 -0.9 60.5 IA Clinton Co 78.3 71.4 70.3 -1.1 69.4 IA Harrison Co 75.7 68.8 68.0 -0.8 66.4 

Cedar Rapids, IA IA Linn Co 71.0 64.3 63.4 -0.9 62.5 

IA Palo Alto Co 66.0 59.1 58.4 -0.7 56.5 

Des Moines, IA IA Polk Co 58.7 51.4 50.6 -0.8 49.0 Davenport, IA-IL IA Scott Co 79.0 71.7 70.6 -1.1 69.3 

IA Story Co 63.3 55.6 54.8 -0.8 53.0 IA Van Buren Co 74.0 65.8 65.0 -0.8 63.4 

Des Moines, IA IA Warren Co 63.3 55.3 54.6 -0.7 52.8 

KS Linn Co 76.7 71.6 71.4 -0.2 69.5 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Wichita, KS KS Sedgwick Co 81.0 73.7 73.2 -0.5 71.2 

KS Sumner Co 79.0 72.3 71.8 -0.5 69.8 

Kansas City, MO-KS KS Wyandotte Co 80.3 73.9 73.7 -0.2 71.2 

KY Bell Co 83.3 65.1 64.8 -0.3 61.5 

Cincinnati, OH-KY KY Boone Co 85.3 73.1 73.1 0.0 71.0 Huntington-Ashland, WV-KY-OH KY Boyd Co 89.5 77.2 76.4 -0.8 74.8 Louisville, KY-IN KY Bullitt Co 83.7 73.0 73.1 0.1 70.8 Cincinnati, OH-KY KY Campbell Co 92.5 81.6 81.5 -0.1 78.8 Huntington-Ashland, WV-KY-OH KY Carter Co 80.3 66.7 65.9 -0.8 64.2 Clarksville, TN-KY KY Christian Co 85.0 64.4 63.6 -0.8 61.7 Owensboro, KY KY Daviess Co 77.3 65.7 65.5 -0.2 63.9 

KY Edmonson Co 84.0 68.8 68.6 -0.2 65.8 

Lexington, KY KY Fayette Co 78.3 67.2 67.0 -0.2 64.3 

KY Graves Co 81.0 70.8 70.1 -0.7 68.5 

Huntington-Ashland, WV-KY-OH KY Greenup Co 84.0 71.7 71.0 -0.7 69.4 

KY Hancock Co 82.7 70.8 70.6 -0.2 68.8 KY Hardin Co 80.0 68.1 68.2 0.1 65.6 

Evansville, IN-KY KY Henderson Co 79.5 69.2 68.9 -0.3 67.3 Louisville, KY-IN KY Jefferson Co 84.3 74.7 74.9 0.2 72.8 Lexington, KY KY Jessamine Co 78.0 67.1 66.9 -0.2 64.3 Cincinnati, OH-KY KY Kenton Co 86.3 75.7 75.6 -0.1 73.3 

KY Livingston Co 85.0 75.0 74.4 -0.6 72.7 KY McCracken Co 81.7 72.5 71.9 -0.6 70.4 KY McLean Co 84.0 70.0 69.6 -0.4 67.9 

Louisville, KY-IN KY Oldham Co 88.0 75.5 75.6 0.1 73.2 

KY Perry Co 75.5 61.2 60.3 -0.9 58.3 KY Pike Co 76.3 61.4 60.4 -1.0 58.1 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

KY Pulaski Co 81.3 67.5 67.1 -0.4 64.7 

Lexington, KY KY Scott Co 70.3 58.6 58.6 0.0 56.4 

KY Simpson Co 84.0 67.7 67.2 -0.5 64.2 KY Trigg Co 76.7 64.4 63.8 -0.6 62.3 KY Warren Co 84.0 68.8 68.4 -0.4 65.6 

Baton Rouge, LA LA Ascension Parish 81.7 76.8 76.3 -0.5 76.0 

LA Beauregard Parish 75.0 70.1 68.8 -1.3 69.2 

Shreveport, LA LA Bossier Parish 84.7 78.1 77.0 -1.1 76.6 Shreveport, LA LA Caddo Parish 79.7 73.3 72.3 -1.0 71.9 Lake Charles, LA LA Calcasieu Parish 81.7 76.0 74.9 -1.1 75.3 Baton Rouge, LA LA

East Baton RougeParish 87.3 81.4 80.6 -0.8 80.4 

LA Grant Parish 77.7 70.8 69.3 -1.5 69.3 LA Iberville Parish 85.0 79.9 79.4 -0.5 79.1 

New Orleans, LA LA Jefferson Parish 85.3 79.2 78.6 -0.6 78.3 Lafayette, LA LA Lafayette Parish 80.7 74.2 73.4 -0.8 72.8 Houma, LA LA Lafourche Parish 79.0 74.0 73.7 -0.3 73.3 Baton Rouge, LA LA Livingston Parish 83.3 78.3 77.8 -0.5 77.5 New Orleans, LA LA Orleans Parish 72.0 66.9 66.6 -0.3 66.3 Monroe, LA LA Ouachita Parish 78.7 72.3 71.6 -0.7 71.4 

LA

Pointe CoupeeParish 73.0 67.5 66.6 -0.9 66.5 

New Orleans, LA LA St Bernard Parish 79.3 73.3 72.9 -0.4 72.6 New Orleans, LA LA St Charles Parish 81.7 76.5 76.2 -0.3 75.8 New Orleans, LA LA St James Parish 77.3 72.7 72.3 -0.4 72.0 New Orleans, LA LA

St John The BaptistParish 81.7 76.8 76.4 -0.4 76.1 

LA St Mary Parish 78.0 73.2 72.7 -0.5 72.6 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Baton Rouge, LA LA

W. Baton RougeParish 85.7 79.6 78.8 -0.8 78.6 

Portland, ME ME Cumberland Co 84.7 75.9 75.8 -0.1 73.4 

ME Hancock Co 92.0 80.7 80.5 -0.2 77.2 ME Kennebec Co 77.7 68.1 68.0 -0.1 65.3 ME Knox Co 83.3 73.7 73.6 -0.1 70.7 ME Oxford Co 61.0 54.9 54.7 -0.2 53.2 

Bangor, ME ME Penobscot Co 83.0 72.8 72.6 -0.2 70.0 

ME York Co 89.0 80.3 80.2 -0.1 78.0 

Washington-Baltimore, DC-MD MD Anne Arundel Co 101.0 88.8 88.6 -0.2 86.0 Washington-Baltimore, DC-MD MD Baltimore Co 93.0 83.9 83.7 -0.2 81.9 Washington-Baltimore, DC-MD MD Calvert Co 89.0 74.5 74.1 -0.4 72.1 Washington-Baltimore, DC-MD MD Carroll Co 91.3 80.3 80.0 -0.3 77.8 Philadelphia, PA-DE-NJ MD Cecil Co 102.7 89.7 89.5 -0.2 86.9 Washington-Baltimore, DC-MD MD Charles Co 94.7 79.0 78.7 -0.3 76.5 Washington-Baltimore, DC-MD MD Frederick Co 90.0 78.5 78.1 -0.4 75.9 Washington-Baltimore, DC-MD MD Harford Co 103.7 93.0 92.8 -0.2 90.6 

MD Kent Co 99.0 86.2 85.8 -0.4 83.4 

Washington-Baltimore, DC-MD MD Montgomery Co 88.7 79.5 79.3 -0.2 77.4 

Washington-Baltimore, DC-MD MD Prince Georges Co 95.0 84.4 84.2 -0.2 81.9 Washington-Baltimore, DC-MD MD Washington Co 86.0 74.2 73.3 -0.9 71.6 Washington-Baltimore, DC-MD MD Baltimore City 82.0 74.0 73.8 -0.2 72.3 Barnstable-Yarmouth, MA MA Barnstable Co 94.7 83.7 83.6 -0.1 80.8 

MA Berkshire Co 87.0 76.3 76.1 -0.2 73.6 

Boston, MA-NH MA Bristol Co 92.7 83.1 83.0 -0.1 80.3 Boston, MA-NH MA Essex Co 89.7 81.8 81.7 -0.1 80.6 Springfield, MA MA Hampden Co 90.3 80.4 80.2 -0.2 77.3 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Springfield, MA MA Hampshire Co 87.3 78.2 78.0 -0.2 75.4 Boston, MA-NH MA Middlesex Co 88.7 79.3 79.1 -0.2 76.1 Boston, MA-NH MA Suffolk Co 88.0 78.3 78.1 -0.2 75.2 Boston, MA-NH MA Worcester Co 85.3 76.1 76.0 -0.1 73.3 Grand Rapids, MI MI Allegan Co 92.0 82.4 82.1 -0.3 79.2 

MI Benzie Co 87.7 78.5 77.9 -0.6 75.1 

Benton Harbor, MI MI Berrien Co 88.3 78.4 78.1 -0.3 75.4 

MI Cass Co 90.0 78.5 78.2 -0.3 75.1 

Lansing, MI MI Clinton Co 83.3 73.5 73.3 -0.2 70.2 Detroit, MI MI Genesee Co 86.7 76.9 76.7 -0.2 73.4 

MI Huron Co 84.0 75.0 74.8 -0.2 72.4 

Lansing, MI MI Ingham Co 83.3 73.5 73.3 -0.2 70.1 Kalamazoo-Battle Creek, MI MI Kalamazoo Co 83.0 72.2 71.9 -0.3 68.6 Grand Rapids, MI MI Kent Co 84.7 75.0 74.6 -0.4 71.7 Detroit, MI MI Lenawee Co 85.0 75.0 74.8 -0.2 72.1 Detroit, MI MI Macomb Co 91.0 85.5 85.4 -0.1 85.1 

MI Mason Co 89.0 79.3 78.9 -0.4 75.8 MI Missaukee Co 80.3 71.0 70.7 -0.3 68.0 

Grand Rapids, MI MI Muskegon Co 92.0 82.3 82.0 -0.3 79.3 Detroit, MI MI Oakland Co 87.0 80.9 80.7 -0.2 80.0 Grand Rapids, MI MI Ottawa Co 86.0 76.9 76.6 -0.3 73.6 Detroit, MI MI St Clair Co 87.7 80.8 80.6 -0.2 78.4 Detroit, MI MI Washtenaw Co 89.0 81.1 81.0 -0.1 78.6 Detroit, MI MI Wayne Co 88.0 84.8 84.7 -0.1 84.9 Minneapolis-St Paul, MN MN Anoka Co 72.5 66.4 64.7 -1.7 63.3 Minneapolis-St Paul, MN MN Dakota Co 68.0 62.7 62.0 -0.7 60.2 

MN Mille Lacs Co 72.0 65.5 63.1 -2.4 63.1 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Minneapolis-St Paul, MN MN Washington Co 74.5 68.1 65.7 -2.4 64.9 

MS Adams Co 79.7 69.8 69.2 -0.6 68.2 MS Bolivar Co 78.0 66.6 66.0 -0.6 64.3 

Memphis, TN MS De Soto Co 84.3 72.6 72.0 -0.6 70.4 Biloxi-Gulfport, MS MS Hancock Co 83.7 75.5 74.4 -1.1 74.0 Biloxi-Gulfport, MS MS Harrison Co 83.3 77.3 75.9 -1.4 75.8 Jackson, MS MS Hinds Co 76.3 63.8 63.4 -0.4 60.1 Biloxi-Gulfport, MS MS Jackson Co 83.0 77.0 76.0 -1.0 75.7 

MS Lauderdale Co 76.0 63.0 62.3 -0.7 59.9 MS Lee Co 82.0 67.5 67.2 -0.3 63.9 

Jackson, MS MS Madison Co 76.3 64.6 64.1 -0.5 62.4 

MS Warren Co 76.7 58.8 58.4 -0.4 57.0 

Kansas City, MO-KS MO Cass Co 79.0 73.2 73.0 -0.2 70.6 

MO Cedar Co 82.0 74.2 73.8 -0.4 71.6 

Kansas City, MO-KS MO Clay Co 84.3 76.7 76.5 -0.2 73.7 Springfield, MO MO Greene Co 74.7 63.5 63.6 0.1 60.2 St. Louis, MO-IL MO Jefferson Co 87.3 76.9 76.7 -0.2 73.4 

MO Monroe Co 79.3 68.7 68.4 -0.3 66.4 

Kansas City, MO-KS MO Platte Co 81.7 75.2 75.0 -0.2 72.4 St. Louis, MO-IL MO St Charles Co 90.7 80.7 80.5 -0.2 77.5 

MO Ste Genevieve Co 84.0 73.8 73.6 -0.2 70.8 

St. Louis, MO-IL MO St Louis Co 89.3 80.7 80.5 -0.2 77.8 St. Louis, MO-IL MO St Louis City 88.5 79.6 79.4 -0.2 76.5 Omaha, NE NE Douglas Co 67.5 61.2 60.5 -0.7 59.0 Lincoln, NE NE Lancaster Co 54.0 49.3 49.2 -0.1 47.3 

NH Belknap Co 78.0 68.5 68.2 -0.3 65.8 NH Carroll Co 66.5 60.3 60.0 -0.3 58.6 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

NH Cheshire Co 73.7 65.2 64.9 -0.3 63.1 

Boston, MA-NH NH Hillsborough Co 85.0 76.7 76.6 -0.1 74.1 

NH Merrimack Co 73.0 64.8 64.6 -0.2 62.3 

Boston, MA-NH NH Rockingham Co 82.7 75.2 75.1 -0.1 73.3 Boston, MA-NH NH Strafford Co 77.3 69.2 69.1 -0.1 66.8 Philadelphia, PA-DE-NJ NJ Atlantic Co 91.0 80.8 80.4 -0.4 78.5 New York City,NY-NJ-CT NJ Bergen Co 92.5 86.9 86.0 -0.9 85.7 Philadelphia, PA-DE-NJ NJ Camden Co 102.3 91.9 91.6 -0.3 89.5 Philadelphia, PA-DE-NJ NJ Cumberland Co 96.7 84.8 84.4 -0.4 82.2 New York City,NY-NJ-CT NJ Essex Co 68.0 64.6 64.4 -0.2 63.7 Philadelphia, PA-DE-NJ NJ Gloucester Co 101.3 91.8 91.3 -0.5 89.6 New York City,NY-NJ-CT NJ Hudson Co 89.0 84.6 84.3 -0.3 83.3 New York City,NY-NJ-CT NJ Hunterdon Co 97.7 89.0 88.6 -0.4 86.5 New York City,NY-NJ-CT NJ Mercer Co 103.0 95.6 95.2 -0.4 93.5 New York City,NY-NJ-CT NJ Middlesex Co 100.7 92.4 92.1 -0.3 89.8 New York City,NY-NJ-CT NJ Monmouth Co 96.0 86.6 86.4 -0.2 83.9 New York City,NY-NJ-CT NJ Morris Co 97.7 86.5 85.5 -1.0 83.4 New York City,NY-NJ-CT NJ Ocean Co 111.0 100.5 100.3 -0.2 98.0 New York City,NY-NJ-CT NJ Passaic Co 88.3 80.4 79.7 -0.7 78.5 Albany-Schenectady, NY NY Albany Co 83.0 73.7 73.4 -0.3 71.2 New York City,NY-NJ-CT NY Bronx Co 82.7 80.5 79.7 -0.8 80.8 Jamestown,NY NY Chautauqua Co 91.7 82.2 81.8 -0.4 79.9 Elmira, NY NY Chemung Co 81.0 70.8 70.1 -0.7 67.7 New York City,NY-NJ-CT NY Dutchess Co 91.3 81.4 81.0 -0.4 78.9 Buffalo, NY NY Erie Co 96.0 87.3 86.9 -0.4 85.2 

NY Essex Co 88.5 77.9 77.6 -0.3 76.2 NY Hamilton Co 79.0 70.3 70.0 -0.3 68.7 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Utica-Rome, NY NY Herkimer Co 74.0 65.7 65.4 -0.3 64.1 

NY Jefferson Co 91.7 80.9 80.5 -0.4 78.7 

Syracuse, NY NY Madison Co 80.0 71.8 71.6 -0.2 69.8 Rochester, NY NY Monroe Co 86.5 77.3 76.9 -0.4 75.0 Buffalo, NY NY Niagara Co 91.0 82.7 82.3 -0.4 80.8 Utica-Rome, NY NY Oneida Co 79.0 69.4 69.1 -0.3 67.0 Syracuse, NY NY Onondaga Co 83.0 72.7 72.3 -0.4 69.7 New York City,NY-NJ-CT NY Orange Co 86.0 77.6 77.1 -0.5 75.4 New York City,NY-NJ-CT NY Putnam Co 91.3 82.8 82.3 -0.5 80.4 New York City,NY-NJ-CT NY Queens Co 86.0 78.5 78.3 -0.2 76.7 New York City,NY-NJ-CT NY Richmond Co 96.0 87.3 87.1 -0.2 84.6 Albany-Schenectady, NY NY Saratoga Co 85.5 75.3 75.0 -0.3 72.6 Albany-Schenectady, NY NY Schenectady Co 77.3 69.2 69.0 -0.2 66.9 New York City,NY-NJ-CT NY Suffolk Co 98.5 91.1 90.8 -0.3 89.9 

NY Ulster Co 81.7 72.9 72.5 -0.4 70.6 

Rochester, NY NY Wayne Co 84.0 74.4 74.1 -0.3 72.2 New York City,NY-NJ-CT NY Westchester Co 92.0 85.3 84.7 -0.6 84.2 Hickory, NC NC Alexander Co 88.7 72.9 71.7 -1.2 68.4 

NC Avery Co 77.3 63.2 62.2 -1.0 58.7 

Asheville, NC NC Buncombe Co 82.0 67.1 66.7 -0.4 62.7 Hickory, NC NC Caldwell Co 85.7 70.7 69.4 -1.3 66.0 

NC Camden Co 80.0 72.9 72.3 -0.6 71.1 NC Caswell Co 89.7 72.2 71.7 -0.5 68.0 

Raleigh-Durham, NC NC Chatham Co 82.0 67.1 67.0 -0.1 62.8 Fayetteville, NC NC Cumberland Co 87.0 72.2 71.9 -0.3 67.2 

Greensboro, NC NC Davie Co 94.7 76.2 75.5 -0.7 71.1 

NC Duplin Co 80.7 67.2 66.9 -0.3 63.3 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Raleigh-Durham, NC NC Durham Co 89.0 72.8 72.5 -0.3 67.8 Rocky Mount, NC NC Edgecombe Co 88.0 74.5 74.0 -0.5 71.0 Greensboro, NC NC Forsyth Co 93.7 75.3 74.9 -0.4 70.2 Raleigh-Durham, NC NC Franklin Co 89.0 73.6 73.3 -0.3 68.8 

NC Granville Co 92.0 75.4 75.1 -0.3 70.2 

Greensboro, NC NC Guilford Co 90.7 73.4 72.9 -0.5 68.4 

NC Haywood Co 86.3 69.8 69.9 0.1 65.6 NC Jackson Co 85.0 68.5 68.6 0.1 64.5 

Raleigh-Durham, NC NC Johnston Co 85.7 71.4 71.1 -0.3 66.2 

NC Lenoir Co 81.3 67.6 67.3 -0.3 63.7 

Charlotte, NC-SC NC Lincoln Co 92.3 76.1 74.5 -1.6 70.3 

NC Martin Co 80.3 69.6 68.9 -0.7 66.7 

Charlotte, NC-SC NC Mecklenburg Co 100.3 82.5 81.4 -1.1 76.6 Wilmington, NC NC New Hanover Co 77.3 66.3 65.8 -0.5 64.4 

NC Northampton Co 83.3 70.7 70.2 -0.5 67.6 

Person Co, NC NC Person Co 90.0 71.9 71.5 -0.4 67.8 

Greenville, NC NC Pitt Co 83.0 69.6 69.3 -0.3 65.8 Greensboro, NC NC Randolph Co 85.0 68.7 68.4 -0.3 63.7 

NC Rockingham Co 88.7 70.6 70.2 -0.4 66.4 

Charlotte, NC-SC NC Rowan Co 99.7 81.3 80.1 -1.2 75.7 

NC Swain Co 73.7 58.6 58.7 0.1 55.6 

Charlotte, NC-SC NC Union Co 87.7 71.9 71.1 -0.8 66.4 Raleigh-Durham, NC NC Wake Co 92.7 77.5 77.2 -0.3 71.5 

NC Yancey Co 86.3 70.9 69.9 -1.0 66.3 

Lima, OH OH Allen Co 87.7 77.2 76.8 -0.4 73.7 Cleveland, OH OH Ashtabula Co 94.0 83.8 83.5 -0.3 81.2 Cincinnati, OH-KY OH Butler Co 89.0 78.2 78.0 -0.2 75.2 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Dayton, OH OH Clark Co 88.3 75.9 75.4 -0.5 71.2 Cincinnati, OH-KY OH Clermont Co 90.0 78.1 78.0 -0.1 75.1 

OH Clinton Co 95.7 81.7 81.4 -0.3 77.4 

Cleveland, OH OH Cuyahoga Co 86.3 77.7 77.3 -0.4 75.1 Columbus, OH OH Delaware Co 90.3 77.9 77.3 -0.6 73.4 Columbus, OH OH Franklin Co 95.0 82.8 81.9 -0.9 78.3 Cleveland, OH OH Geauga Co 98.3 87.1 86.6 -0.5 83.8 Dayton, OH OH Greene Co 87.0 74.7 74.4 -0.3 70.5 Cincinnati, OH-KY OH Hamilton Co 89.3 78.8 78.6 -0.2 75.8 Steubenville-Weirton, OH-WV OH Jefferson Co 85.3 74.6 73.8 -0.8 72.3 

OH Knox Co 89.3 77.3 76.5 -0.8 72.6 

Cleveland, OH OH Lake Co 92.7 82.5 82.2 -0.3 80.1 Huntington-Ashland, WV-KY-OH OH Lawrence Co 85.0 72.5 71.8 -0.7 70.2 Columbus, OH OH Licking Co 89.0 76.5 75.2 -1.3 71.6 Cleveland, OH OH Lorain Co 87.5 78.8 78.5 -0.3 76.5 Toledo, OH OH Lucas Co 88.7 80.2 80.0 -0.2 77.9 Columbus, OH OH Madison Co 89.0 76.9 76.3 -0.6 72.5 Youngstown-Warren, OH OH Mahoning Co 88.0 75.8 75.2 -0.6 72.0 Cleveland, OH OH Medina Co 87.7 76.9 76.5 -0.4 73.1 Dayton, OH OH Miami Co 86.3 74.1 73.7 -0.4 69.5 Dayton, OH OH Montgomery Co 86.7 75.1 74.7 -0.4 70.8 Cleveland, OH OH Portage Co 92.0 80.4 79.8 -0.6 76.1 

OH Preble Co 80.3 68.6 68.2 -0.4 64.8 

Canton, OH OH Stark Co 89.0 77.1 76.3 -0.8 72.9 Cleveland, OH OH Summit Co 94.3 82.9 82.4 -0.5 78.6 Youngstown-Warren, OH OH Trumbull Co 92.5 80.2 79.7 -0.5 76.1 Cincinnati, OH-KY OH Warren Co 92.0 80.2 80.0 -0.2 76.6 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Parkersburg, WV-OH OH Washington Co 87.0 71.9 70.1 -1.8 69.4 Toledo, OH OH Wood Co 87.0 77.8 77.4 -0.4 75.0 

OK Cherokee Co 76.0 71.9 71.2 -0.7 69.7 

Oklahoma City, OK OK Cleveland Co 77.3 69.0 68.7 -0.3 65.8 

OK Kay Co 75.0 69.2 68.9 -0.3 66.6 

Oklahoma City, OK OK Mc Clain Co 78.5 70.3 70.0 -0.3 67.2 Oklahoma City, OK OK Oklahoma Co 80.7 72.0 71.7 -0.3 68.6 

OK Ottawa Co 79.0 74.3 74.1 -0.2 72.3 

Tulsa, OK OK Tulsa Co 85.0 79.4 79.2 -0.2 76.9 Pittsburgh, PA PA Allegheny Co 93.0 82.7 81.9 -0.8 80.4 

PA Armstrong Co 92.0 80.6 79.7 -0.9 78.0 

Pittsburgh, PA PA Beaver Co 91.0 80.5 79.6 -0.9 77.9 

Reading, PA PA Berks Co 92.7 82.2 81.7 -0.5 79.4 Altoona, PA PA Blair Co 84.3 73.0 72.5 -0.5 70.9 Philadelphia, PA-DE-NJ PA Bucks Co 103.0 94.7 94.3 -0.4 93.0 Johnstown, PA PA Cambria Co 87.7 77.6 76.9 -0.7 75.4 State College, PA PA Centre Co 85.5 75.4 74.7 -0.7 73.1 Philadelphia, PA-DE-NJ PA Chester Co 96.5 85.7 85.4 -0.3 83.7 

PA Clearfield Co 86.7 75.0 74.2 -0.8 72.3 

Harrisburg, PA PA Dauphin Co 91.0 81.3 80.8 -0.5 78.6 

Philadelphia, PA-DE-NJ PA Delaware Co 93.7 84.4 84.0 -0.4 82.4 Erie, PA PA Erie Co 89.0 79.4 79.1 -0.3 77.1 

PA Franklin Co 93.0 80.7 80.2 -0.5 78.0 PA Greene Co 90.3 74.0 73.2 -0.8 72.0 

Scranton--Wilkes-Barre, PA PA Lackawanna Co 85.3 74.3 73.6 -0.7 71.2 Lancaster, PA PA Lancaster Co 94.0 83.9 83.6 -0.3 81.3 

PA Lawrence Co 78.7 68.3 67.7 -0.6 65.3 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Allentown, PA PA Lehigh Co 93.3 82.8 82.1 -0.7 80.1 Scranton--Wilkes-Barre, PA PA Luzerne Co 84.7 72.6 71.7 -0.9 69.4 Williamsport, PA PA Lycoming Co 83.0 71.8 71.1 -0.7 68.9 Sharon, PA PA Mercer Co 91.3 78.6 78.1 -0.5 74.6 Philadelphia, PA-DE-NJ PA Montgomery Co 96.3 88.0 87.6 -0.4 86.5 Allentown, PA PA Northampton Co 93.0 82.6 81.8 -0.8 79.8 Harrisburg, PA PA Perry Co 84.7 74.5 74.0 -0.5 72.0 Philadelphia, PA-DE-NJ PA Philadelphia Co 97.5 90.3 89.9 -0.4 88.9 

PA Tioga Co 83.7 72.4 71.8 -0.6 68.9 

Pittsburgh, PA PA Washington Co 87.7 78.2 77.3 -0.9 75.9 Pittsburgh, PA PA Westmoreland Co 87.7 77.4 76.7 -0.7 75.3 York, PA PA York Co 90.3 79.7 79.4 -0.3 77.1 Providence, RI RI Kent Co 95.3 86.4 86.2 -0.2 83.9 Providence, RI RI Providence Co 90.3 81.4 81.2 -0.2 78.6 Providence, RI RI Washington Co 93.3 84.3 84.2 -0.1 81.8 

SC Abbeville Co 84.0 69.7 68.8 -0.9 65.3 

Augusta-Aiken, GA-SC SC Aiken Co 84.7 72.1 71.3 -0.8 69.0 Greenville-Spartanburg, SC SC Anderson Co 88.0 72.4 71.9 -0.5 67.6 

SC Barnwell Co 81.3 67.9 67.2 -0.7 64.6 

Charleston, SC SC Berkeley Co 71.0 58.6 58.0 -0.6 56.2 Charleston, SC SC Charleston Co 74.0 61.1 60.4 -0.7 58.6 Greenville-Spartanburg, SC SC Cherokee Co 86.0 69.2 68.3 -0.9 64.2 

SC Chester Co 84.3 70.9 69.5 -1.4 65.8 SC Colleton Co 78.7 66.3 65.6 -0.7 63.2 SC Darlington Co 84.7 70.8 70.2 -0.6 66.9 

Augusta-Aiken, GA-SC SC Edgefield Co 80.7 67.5 66.7 -0.8 63.2 

SC Oconee Co 84.0 67.9 67.2 -0.7 62.4 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Greenville-Spartanburg, SC SC Pickens Co 85.3 70.1 69.5 -0.6 65.2 Columbia, SC SC Richland Co 93.0 77.7 76.9 -0.8 72.1 Greenville-Spartanburg, SC SC Spartanburg Co 90.0 73.0 72.2 -0.8 67.6 Union Co, SC SC Union Co 80.7 66.4 65.3 -1.1 61.7 

SC Williamsburg Co 72.3 59.9 59.0 -0.9 57.1 

Charlotte, NC-SC SC York Co 83.3 70.0 68.5 -1.5 64.8 Knovville, TN TN Anderson Co 89.7 67.9 67.8 -0.1 63.9 Knoxville, TN TN Blount Co 94.0 74.3 74.1 -0.2 69.8 Nashville, TN TN Davidson Co 81.3 69.3 68.9 -0.4 65.6 Chattanooga, TN-GA TN Hamilton Co 90.7 74.3 72.7 -1.6 69.7 

TN Haywood Co 89.0 75.6 75.0 -0.6 72.6 TN Jefferson Co 94.0 75.6 75.6 0.0 71.7 

Knoxville, TN TN Knox Co 94.7 73.2 73.1 -0.1 69.4 

TN Lawrence Co 79.3 65.0 64.2 -0.8 61.4 TN Meigs Co 90.5 72.4 71.1 -1.3 68.3 TN Putnam Co 85.0 69.9 69.2 -0.7 66.3 

Nashville, TN TN Rutherford Co 83.3 69.5 68.9 -0.6 64.9 Knoxville, TN TN Sevier Co 96.0 76.3 76.5 0.2 72.7 Memphis, TN TN Shelby Co 90.7 77.3 76.7 -0.6 74.3 Johnson City, TN-VA TN Sullivan Co 89.3 73.9 74.2 0.3 71.8 Nashville, TN TN Sumner Co 89.0 75.8 75.4 -0.4 72.0 Nashville, TN TN Williamson Co 86.3 71.2 70.2 -1.0 66.8 Nashville, TN TN Wilson Co 84.7 72.3 71.8 -0.5 68.6 San Antonio, TX TX Bexar Co 85.7 70.2 69.7 -0.5 67.6 Houston, TX TX Brazoria Co 91.0 84.5 84.1 -0.4 83.5 Dallas, TX TX Collin Co 93.3 83.1 82.5 -0.6 78.5 Dallas, TX TX Dallas Co 91.0 82.7 82.2 -0.5 78.8 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Dallas, TX TX Denton Co 99.0 87.4 86.8 -0.6 82.3 Dallas, TX TX Ellis Co 85.3 75.2 74.6 -0.6 71.4 Houston, TX TX Galveston Co 92.0 85.1 84.6 -0.5 84.1 Longview, TX TX Gregg Co 88.3 79.8 79.1 -0.7 78.0 Houston, TX TX Harris Co 105.0 97.9 97.4 -0.5 97.3 Longview, TX TX Harrison Co 76.0 68.5 67.7 -0.8 67.2 Dallas, TX TX Hood Co 84.0 73.8 72.7 -1.1 70.1 Beaumont-Port Arthur, TX TX Jefferson Co 91.0 85.6 85.0 -0.6 85.0 Dallas, TX TX Johnson Co 89.5 79.0 78.2 -0.8 75.1 Dallas, TX TX Kaufman Co 71.5 63.9 63.4 -0.5 60.9 Houston, TX TX Montgomery Co 91.0 81.6 81.2 -0.4 78.7 Beaumont-Port Arthur, TX TX Orange Co 78.3 72.7 71.8 -0.9 72.0 Dallas, TX TX Parker Co 87.5 77.1 76.0 -1.1 73.0 Dallas, TX TX Rockwall Co 82.0 73.9 73.4 -0.5 70.5 Tyler, TX TX Smith Co 82.5 73.8 73.1 -0.7 71.5 Dallas, TX TX Tarrant Co 98.3 87.8 87.2 -0.6 83.1 Austin-San Marcos, TX TX Travis Co 84.3 73.4 72.9 -0.5 70.3 

VT Bennington Co 79.7 70.3 70.0 -0.3 67.9 

Washington-Baltimore, DC-MD VA Arlington Co 95.7 86.2 86.0 -0.2 84.7 

VA Caroline Co 84.0 72.1 71.7 -0.4 69.7 

Richmond-Petersburg, VA VA Charles City Co 89.3 79.2 77.7 -1.5 75.7 Richmond-Petersburg, VA VA Chesterfield Co 86.0 75.8 74.7 -1.1 72.6 Washington-Baltimore, DC-MD VA Fairfax Co 96.3 85.7 85.4 -0.3 83.9 Washington-Baltimore, DC-MD VA Fauquier Co 81.0 69.6 69.3 -0.3 67.6 

VA Frederick Co 84.3 71.5 70.8 -0.7 68.9 

Richmond-Petersburg, VA VA Hanover Co 94.0 82.0 80.9 -1.1 78.7 

Richmond-Petersburg, VA VA Henrico Co 90.0 79.1 78.2 -0.9 76.3 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Washington-Baltimore, DC-MD VA Loudoun Co 89.3 78.9 78.6 -0.3 76.5 

VA Madison Co 86.3 69.3 68.7 -0.6 66.7 VA Page Co 81.3 64.8 64.1 -0.7 62.5 

Washington-Baltimore, DC-MD VA Prince William Co 85.7 75.1 74.9 -0.2 72.9 Roanoke, VA VA Roanoke Co 86.0 72.9 72.5 -0.4 69.6 

VA Rockbridge Co 79.0 66.9 66.5 -0.4 64.3 

Washington-Baltimore, DC-MD VA Stafford Co 86.3 73.6 73.3 -0.3 71.3 

VA Wythe Co 80.7 64.3 63.8 -0.5 61.8 

Washington-Baltimore, DC-MD VA Alexandria City 90.0 81.1 80.9 -0.2 79.6 Norfolk, VA-NC VA Hampton City 88.7 79.2 78.7 -0.5 77.3 Norfolk, VA-NC VA Suffolk City 87.3 78.0 77.5 -0.5 76.1 Washington-Baltimore, DC-MD WV Berkeley Co 86.0 74.3 73.3 -1.0 71.8 Huntington-Ashland, WV-KY-OH WV Cabell Co 88.0 76.3 75.5 -0.8 74.1 

WV Greenbrier Co 81.7 65.2 64.8 -0.4 62.6 

Steubenville-Weirton, OH-WV WV Hancock Co 84.3 73.7 72.9 -0.8 71.5 

Charleston, WV WV Kanawha Co 87.0 71.1 70.6 -0.5 68.5 

WV Monongalia Co 80.0 66.8 65.7 -1.1 65.0 

Wheeling, WV-OH WV Ohio Co 84.7 71.4 70.4 -1.0 68.7 Parkersburg, WV-OH WV Wood Co 87.7 72.0 69.7 -2.3 68.7 Green Bay, WI WI Brown Co 81.7 73.9 72.6 -1.3 71.1 

WI Columbia Co 77.7 68.4 67.9 -0.5 64.5 

Madison, WI WI Dane Co 77.3 68.3 67.6 -0.7 64.5 

WI Dodge Co 81.0 72.2 71.7 -0.5 68.4 WI Door Co 92.7 83.3 82.1 -1.2 79.9 WI Fond Du Lac Co 79.0 70.4 69.5 -0.9 67.2 WI Green Co 74.5 65.8 65.2 -0.6 62.7 WI Jefferson Co 84.5 74.5 73.9 -0.6 70.4 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in

2010 2015 Base 

Chicago, IL-IN WI Kenosha Co 98.7 91.3 91.0 -0.3 89.4 

WI Kewaunee Co 90.0 81.0 79.9 -1.1 77.6 WI Manitowoc Co 90.0 80.9 80.0 -0.9 77.4 

Wausau, WI WI Marathon Co 73.7 66.3 64.3 -2.0 63.3 Milwaukee WI Milwaukee Co 90.7 82.5 82.1 -0.4 79.6 Oshkosh, WI WI Outagamie Co 77.3 69.6 68.4 -1.2 66.8 Milwaukee, WI WI Ozaukee Co 95.3 86.2 85.8 -0.4 82.9 Milwaukee, WI WI Racine Co 91.7 84.2 83.9 -0.3 82.0 Janesville-Beloit, WI WI Rock Co 84.3 73.9 73.4 -0.5 69.9 Minneapolis-St Paul, MN WI St Croix Co 72.7 66.2 62.8 -3.4 63.1 

WI Sauk Co 74.3 66.3 63.0 -3.3 63.2 

Sheboyegan WI WI Sheboygan Co 98.0 88.3 87.7 -0.6 84.7 

WI Vernon Co 71.7 64.2 63.1 -1.1 61.2 WI Walworth Co 83.3 74.2 73.7 -0.5 70.8 

Milwaukee, WI WI Washington Co 82.7 75.4 75.0 -0.4 72.4 Milwaukee, WI WI Waukesha Co 82.7 75.1 74.7 -0.4 71.9 Oshkosh, WI WI Winnebago Co 80.0 71.7 69.9 -1.8 68.9 

Technical Support Document

for the Final

Clean Air Interstate Rule

Air Quality Modeling Analyses

Appendix F

Annual PM2.5: Average Ambient and

Projected 2010/2015 Base and CAIR
Table F-1. Annual Average PM2.5 Concentrations (μg/m3): Average 1999-2003, 2010 Base and CAIR, 2015 Ba

impact of CAIR on PM2.5 in 2010 and 2015. 

CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

Mobile, AL AL Baldwin Co 11.43 10.87 10.18 -0.69 10.88 

AL Clay Co 14.27 13.72 12.46 -1.26 13.62 

Florence, AL AL Colbert Co 13.94 13.60 12.38 -1.22 13.53 

AL DeKalb Co 15.62 15.23 13.97 -1.26 15.24 AL Escambia Co 13.03 12.51 11.77 -0.74 12.50 

Dothan, AL AL Houston Co 14.69 14.06 13.32 -0.74 14.07 Birmingham, AL AL Jefferson Co 19.04 18.57 17.46 -1.11 18.85 Huntsville, AL AL Madison Co 14.81 14.26 13.05 -1.21 14.20 Mobile, AL AL Mobile Co 13.69 13.20 12.58 -0.62 13.52 Montgomery, AL AL Montgomery Co 15.41 15.12 14.10 -1.02 15.24 Decatur, AL AL Morgan Co 15.81 15.29 14.11 -1.18 15.26 Columbus, GA-AL AL Russell Co 16.70 16.17 15.15 -1.02 16.10 Birmingham, AL AL Shelby Co 15.33 14.92 13.83 -1.09 14.96 

AL Sumter Co 13.28 12.79 11.91 -0.88 12.74 AL Talladega Co 16.05 15.34 14.00 -1.34 15.22 

AR Arkansas Co 11.90 11.37 10.71 -0.66 11.39 AR Ashley Co 11.80 11.30 10.66 -0.64 11.38 

Jonesboro, AR AR Craighead Co 12.39 11.85 11.07 -0.78 11.79 Memphis, TN AR Crittenden Co 13.35 13.12 12.33 -0.79 13.59 Little Rock, AR AR Faulkner Co 12.57 12.14 11.55 -0.59 12.21 Pine Bluff, AR AR Jefferson Co 13.28 13.02 12.44 -0.58 13.33 

AR Mississippi Co 12.04 11.74 10.93 -0.81 11.79 AR Phillips Co 12.49 11.86 11.09 -0.77 11.90 AR Polk Co 11.35 10.78 10.31 -0.47 10.85 AR Pope Co 12.48 12.24 11.76 -0.48 12.37 

Little Rock, AR AR Pulaski Co 14.11 13.69 13.04 -0.65 13.85 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

Fort Smith, AR-OK AR Sebastian Co 12.66 12.07 11.62 -0.45 12.12 

AR Union Co 13.03 12.76 12.10 -0.66 13.09 AR White Co 11.91 11.46 10.83 -0.63 11.54 

New Haven-Meriden, CT CT Fairfield Co 13.52 12.45 11.45 -1.00 12.14 Hartford, CT CT Hartford Co 12.88 11.89 11.10 -0.79 11.67 New London-Norwich, CT CT New London Co 11.92 11.00 10.16 -0.84 10.80 Dover, DE DE Kent Co 13.30 12.96 10.97 -1.99 12.76 Philadelphia, PA DE New Castle Co 16.41 16.56 14.84 -1.72 16.47 

DE Sussex Co 14.08 13.56 11.54 -2.02 13.30 

Washington-Baltimore, DC-MD DC

District ofColumbia 16.25 15.84 13.68 -2.16 15.57 

Gainesville, FL FL Alachua Co 10.36 9.69 9.21 -0.48 9.67 Melbourne, FL FL Brevard Co 7.88 6.73 6.32 -0.41 6.68 Miami-Fort Lauderdale, FL FL Broward Co 8.52 7.88 7.58 -0.30 7.93 

FL Citrus Co 9.73 8.82 8.33 -0.49 8.79 

Jacksonville, FL FL Duval Co 10.82 9.91 9.44 -0.47 9.93 Pensacola, FL FL Escambia Co 12.21 11.96 11.30 -0.66 12.13 Tampa-St. Petersburg, FL FL Hillsborough Co 11.64 9.92 9.50 -0.42 9.86 Fort Myers, FL FL Lee Co 8.94 7.33 6.95 -0.38 7.33 Tallahassee, FL FL Leon Co 12.93 12.31 11.78 -0.53 12.32 

Sarasota, FL FL Manatee Co 9.96 8.14 7.71 -0.43 8.13 Ocala, FL FL Marion Co 10.37 9.38 8.96 -0.42 9.40 Miami-Fort Lauderdale, FL FL Miami-Dade Co 9.82 8.99 8.65 -0.34 8.93 Orlando, FL FL Orange Co 10.73 9.23 8.80 -0.43 9.16 West Palm Beach-Boca Raton, FL FL Palm Beach Co 7.69 6.60 6.25 -0.35 6.60 Tampa-St. Petersburg, FL FL Pinellas Co 11.14 9.22 8.80 -0.42 9.22 Lakeland-Winter Haven, FL FL Polk Co 10.91 9.42 9.03 -0.39 9.42 Fort Pierce, FL FL St. Lucie Co 9.00 7.66 7.22 -0.44 7.70 Sarasota, FL FL Sarasota Co 9.86 8.13 7.70 -0.43 8.09 Orlando, FL FL Seminole Co 9.78 8.34 7.91 -0.43 8.27 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

Daytona Beach, FL FL Volusia Co 9.81 8.58 8.07 -0.51 8.51 Macon, GA GA Bibb Co 16.42 16.27 15.17 -1.10 16.41 Savannah, GA GA Chatham Co 14.99 14.86 14.02 -0.84 15.06 Athens, GA GA Clarke Co 17.07 16.39 14.96 -1.43 16.15 Atlanta, GA GA Clayton Co 17.51 17.39 16.29 -1.10 17.46 Atlanta, GA GA Cobb Co 17.12 16.57 15.35 -1.22 16.51 Atlanta, GA GA DeKalb Co 16.86 16.75 15.70 -1.05 16.82 Albany, GA GA Dougherty Co 15.10 14.72 13.85 -0.87 14.71 

GA Floyd Co 16.78 16.87 15.87 -1.00 17.33 

Atlanta, GA GA Fulton Co 19.51 18.02 16.98 16.98 18.00 

GA Glynn Co 12.01 11.61 10.99 -0.62 11.66 

Atlanta, GA GA Gwinnett Co 16.34 14.99 14.02 -0.97 14.97 

GA Hall Co 16.08 15.60 14.28 -1.32 15.36 

Macon, GA GA Houston Co 12.85 12.57 11.59 -0.98 12.56 

GA Lowndes Co 12.04 11.55 10.98 -0.57 11.57 

Columbus, GA-AL GA Muscogee Co 16.19 15.65 14.57 -1.08 15.58 Atlanta, GA GA Paulding Co 15.34 14.86 13.47 -1.39 14.79 Augusta-Aiken, GA-SC GA Richmond Co 15.86 15.68 14.64 -1.04 15.76 Chattanooga, TN-GA GA Walker Co 15.73 15.43 14.22 -1.21 15.37 

GA Washington Co 15.44 15.31 14.22 -1.09 15.34 GA Wilkinson Co 16.26 16.27 15.22 -1.05 16.54 

IL Adams Co 13.04 12.41 11.90 -0.51 12.36 

Champaign-Urbana, IL IL Champaign Co 12.93 12.60 11.64 -0.96 12.52 Chicago, IL-IN IL Cook Co 18.00 17.52 16.88 -0.64 17.71 Chicago, IL-IN IL DuPage Co 15.01 14.42 13.81 -0.61 14.58 

Chicago, IL-IN IL Kane Co 14.40 13.88 13.31 -0.57 13.84 Chicago, IL-IN IL Lake Co 12.98 12.47 12.02 -0.45 12.43 Chicago, IL-IN IL McHenry Co 13.14 12.66 12.09 -0.57 12.60 Bloomington, IL IL McLean Co 13.87 13.44 12.48 -0.96 13.42 Decatur, IL IL Macon Co 14.22 13.85 12.92 -0.93 13.85 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

St. Louis, MO-IL IL Madison Co 17.40 16.66 15.96 -0.70 16.90 Peoria, IL IL Peoria Co 14.33 13.88 12.92 -0.96 13.99 

IL Randolph Co 13.06 12.54 11.77 -0.77 12.48 

Davenport, IA-IL IL Rock Island Co 12.44 11.73 11.20 -0.53 11.66 St. Louis, MO-IL IL St. Clair Co 16.87 16.24 15.54 -0.70 16.49 Springfield, IL IL Sangamon Co 13.60 13.14 12.30 -0.84 13.11 Chicago, IL-IN IL Will Co 15.35 15.03 14.30 -0.73 15.12 Fort Wayne, IN IN Allen Co 14.52 14.32 13.31 -1.01 14.21 Louisville, KY-IN IN Clark Co 16.90 16.51 15.15 -1.36 16.37 Muncie, IN IN Delaware Co 14.71 14.45 13.21 -1.24 14.25 

IN Dubois Co 16.02 15.73 14.37 -1.36 15.66 

Elkhart, IN IN Elkhart Co 15.31 14.75 13.93 -0.82 14.73 Louisville, KY-IN IN Floyd Co 15.35 14.83 13.54 -1.29 14.75 

IN Henry Co 13.55 13.28 12.06 -1.22 13.09 

Kokomo, IN IN Howard Co 14.88 14.56 13.44 -1.12 14.39 

IN Knox Co 13.83 13.55 12.10 -1.45 13.37 

Chicago, IL-IN IN Lake Co 17.76 17.26 16.48 -0.78 17.27 

IN La Porte Co 13.52 13.17 12.47 -0.70 13.20 

Indianapolis, IN IN Madison Co 14.82 14.51 13.28 -1.23 14.32 Indianapolis, IN IN Marion Co 16.88 16.83 15.54 -1.29 16.77 Chicago, IL-IN IN Porter Co 14.00 13.86 13.31 -0.55 14.38 South Bend, IN IN St. Joseph Co 14.35 13.91 13.17 -0.74 13.86 

IN Spencer Co 14.43 13.96 12.63 -1.33 13.84 

Evansville-Henderson, IN-KY IN Vanderburgh Co 15.60 15.54 14.26 -1.28 15.56 

Terre Haute, IN IN Vigo Co 14.88 14.61 13.26 -1.35 14.53 

Cedar Falls, IA IA Black Hawk Co 11.48 10.75 10.39 -0.36 10.70 

IA Cerro Gordo Co 10.55 9.74 9.45 -0.29 9.63 IA Clinton Co 12.26 11.63 11.08 -0.55 11.54 IA Emmet Co 8.82 8.14 7.90 -0.24 8.00 

Iowa City, IA IA Johnson Co 11.52 10.86 10.45 -0.41 10.78 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

Cedar Rapids, IA IA Linn Co 11.23 10.73 10.34 -0.39 10.70 

IA Muscatine Co 13.03 12.40 11.88 -0.52 12.44 

Des Moines, IA IA Polk Co 10.68 9.96 9.66 -0.30 9.88 Omaha, NE-IA IA Pottawattamie Co 10.48 9.83 9.61 -0.22 9.79 Davenport, IA-IL IA Scott Co 12.76 12.00 11.49 -0.51 11.93 

IA Van Buren Co 10.45 9.89 9.45 -0.44 9.81 

Sioux City, IA-NE IA Woodbury Co 10.08 9.38 9.17 -0.21 9.29 Kansas City, MO-KS KS Johnson Co 11.95 11.17 10.85 -0.32 11.09 

KS Linn Co 10.92 10.26 9.93 -0.33 10.20 

Wichita, KS KS Sedgwick Co 11.39 10.53 10.27 -0.26 10.49 Topeka, KS KS Shawnee Co 11.03 10.47 10.22 -0.25 10.44 

KS Sumner Co 10.31 9.55 9.29 -0.26 9.51 

Kansas City, MO-KS KS Wyandotte Co 13.69 12.90 12.59 -0.31 12.98 

KY Bell Co 15.26 14.67 13.20 13.20 14.39 

Huntington-Ashland, WV-KY-OH KY Boyd Co 15.37 15.23 13.38 -1.85 15.06 Louisville, KY-IN KY Bullitt Co 15.61 15.10 13.67 -1.43 14.83 Cincinnati, OH-KY KY Campbell Co 15.09 14.89 13.35 -1.54 14.68 Huntington-Ashland, WV-KY-OH KY Carter Co 12.71 12.54 10.73 -1.81 12.21 Clarksville, TN-KY KY Christian Co 14.10 13.63 12.39 -1.24 13.50 Owensboro, KY KY Daviess Co 14.94 14.15 12.88 -1.27 14.05 Lexington, KY KY Fayette Co 16.36 15.95 14.17 -1.78 15.62 

KY Franklin Co 14.19 13.86 12.27 -1.59 13.56 KY Hardin Co 14.59 14.10 12.68 -1.42 13.85 

Louisville, KY-IN KY Jefferson Co 17.07 16.71 15.44 -1.27 16.61 Cincinnati, OH-KY KY Kenton Co 15.50 15.30 13.72 -1.58 15.09 

KY McCracken Co 14.22 13.91 12.81 -1.10 13.88 

Lexington, KY KY Madison Co 14.26 13.87 12.20 -1.67 13.54 

KY Perry Co 13.54 13.12 11.51 -1.61 12.80 KY Pike Co 14.86 14.41 12.59 -1.82 14.05 KY Warren Co 14.55 14.07 12.72 -1.35 13.88 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

Shreveport, LA LA Caddo Parish 13.13 12.58 12.06 -0.52 12.74 Lake Charles, LA LA Calcasieu Parish 12.02 11.68 11.04 -0.64 11.82 Baton Rouge, LA LA

East Baton RougeParish 13.71 13.78 13.30 -0.48 14.27 

LA Iberville Parish 13.08 12.93 12.47 -0.46 13.34 

New Orleans, LA LA Jefferson Parish 12.82 12.50 11.99 -0.51 12.79 

Lafayette, LA LA Lafayette Parish 11.60 11.38 10.78 -0.60 11.55 New Orleans, LA LA Orleans Parish 13.05 12.74 12.20 -0.54 13.03 Monroe, LA LA Ouachita Parish 12.15 11.95 11.41 -0.54 12.28 New Orleans, LA LA St. Bernard Parish 10.88 10.41 9.79 -0.62 10.50 

LA Tangipahoa Parish 12.15 11.68 11.04 -0.64 11.84 

Houma, LA LA Terrebonne Parish 10.61 10.40 9.83 -0.57 10.58 

Baton Rouge, LA LA

West Baton RougeParish 13.29 13.37 12.89 -0.48 13.84 

Lewiston, ME ME Androscoggin Co 10.60 10.16 9.75 -0.41 10.04 

ME Aroostook Co 11.16 11.41 11.11 -0.30 11.38 

Portland, ME ME Cumberland Co 11.44 10.66 10.23 -0.43 10.50 

ME Hancock Co 6.20 5.98 5.65 -0.33 5.95 ME Kennebec Co 10.55 10.21 9.81 -0.40 10.12 ME Oxford Co 10.29 10.23 9.74 -0.49 10.15 

Bangor, ME ME Penobscot Co 9.87 9.81 9.46 -0.35 9.73 

ME York Co 9.63 9.07 8.64 -0.43 8.97 

Washington-Baltimore, DC-MD MD Anne Arundel Co 15.59 15.26 12.98 -2.28 15.02 Washington-Baltimore, DC-MD MD Baltimore Co 15.20 15.02 13.09 -1.93 15.08 Washington-Baltimore, DC-MD MD Harford Co 13.54 13.40 11.62 -1.78 13.46 Washington-Baltimore, DC-MD MD Montgomery Co 13.01 12.82 10.84 -1.98 12.59 Washington-Baltimore, DC-MD MD Washington Co 14.40 13.99 11.69 -2.30 13.63 Washington-Baltimore, DC-MD MD Baltimore city 17.18 16.96 14.88 -2.08 17.04 Pittsfield, MA MA Berkshire Co 12.40 11.68 10.93 -0.75 11.49 Springfield, MA MA Hampden Co 13.80 12.81 12.10 -0.71 12.59 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

Boston, MA-NH MA Plymouth Co 11.34 10.65 10.02 -0.63 10.50 Boston, MA-NH MA Suffolk Co 12.81 12.05 11.48 -0.57 11.80 Grand Rapids, MI MI Allegan Co 12.36 12.02 11.41 -0.61 11.93 Saginaw, MI MI Bay Co 11.22 11.04 10.42 -0.62 11.01 Benton Harbor, MI MI Berrien Co 12.60 12.26 11.62 -0.64 12.18 

MI Chippewa Co 8.28 8.06 7.83 -0.23 7.99 

Detroit-Ann Arbor-Flint, MI MI Genesee Co 12.70 12.34 11.55 -0.79 12.19 Lansing, MI MI Ingham Co 13.35 12.97 12.19 -0.78 12.80 Kalamazoo-Battle Creek, MI MI Kalamazoo Co 14.92 14.45 13.66 -0.79 14.31 Grand Rapids, MI MI Kent Co 13.91 13.46 12.85 -0.61 13.33 Detroit, MI MI Macomb Co 13.31 13.17 12.23 -0.94 13.05 Detroit, MI MI Monroe Co 15.33 15.00 14.03 -0.97 14.87 Grand Rapids, MI MI Muskegon Co 12.23 11.88 11.39 -0.49 11.81 Detroit, MI MI Oakland Co 14.85 14.73 13.70 -1.03 14.63 Grand Rapids, MI MI Ottawa Co 13.41 12.97 12.41 -0.56 12.86 Saginaw, MI MI Saginaw Co 10.80 10.68 10.07 -0.61 10.66 Detroit, MI MI St. Clair Co 13.92 13.82 12.91 -0.91 13.72 Detroit, MI MI Washtenaw Co 14.57 14.30 13.28 -1.02 14.13 Detroit, MI MI Wayne Co 19.62 19.41 18.23 -1.18 19.28 Minneapolis-St Paul, MN MN Dakota Co 10.32 9.44 9.17 9.17 9.31 Minneapolis-St Paul, MN MN Hennepin Co 10.78 9.70 9.46 -0.24 9.60 

MN Mille Lacs Co 7.40 6.89 6.71 -0.18 6.81 

Rochester, MN MN Olmsted Co 11.17 10.25 9.92 -0.33 10.05 Minneapolis-St Paul, MN MN Ramsey Co 12.24 10.98 10.73 -0.25 10.86 Duluth, MN-WI MN St. Louis Co 8.41 7.99 7.85 -0.14 8.06 Minneapolis-St Paul, MN MN Scott Co 10.42 9.53 9.29 -0.24 9.38 St. Cloud, MN MN Stearns Co 9.65 8.88 8.67 -0.21 8.77 

MS Adams Co 11.35 11.08 10.48 -0.60 11.31 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

MS Bolivar Co 12.81 11.84 11.15 -0.69 11.87 

Memphis, TN MS DeSoto Co 13.18 12.65 11.84 -0.81 12.76 Hattiesburg, MS MS Forrest Co 13.54 13.02 12.34 -0.68 13.07 Biloxi-Gulfport, MS MS Hancock Co 10.97 10.55 9.92 -0.63 10.63 Biloxi-Gulfport, MS MS Harrison Co 11.56 11.11 10.55 -0.56 11.16 Jackson, MS MS Hinds Co 14.06 13.42 12.73 -0.69 13.55 Biloxi-Gulfport, MS MS Jackson Co 12.56 12.19 11.58 -0.61 12.32 

MS Jones Co 15.29 15.01 14.21 -0.80 15.18 MS Lauderdale Co 13.34 12.82 12.00 -0.82 12.77 MS Lee Co 13.20 12.76 11.80 -0.96 12.72 MS Lowndes Co 13.68 13.20 12.31 -0.89 13.23 MS Pearl River Co 11.68 11.26 10.68 -0.58 11.33 

Jackson, MS MS Rankin Co 13.35 12.75 12.12 -0.63 12.88 

MS Scott Co 11.88 11.32 10.61 -0.71 11.33 MS Warren Co 12.50 11.02 10.42 -0.60 11.18 

St. Joseph, MO MO Buchanan Co 12.54 11.68 11.37 -0.31 11.60 Kansas City, MO-KS MO Cass Co 11.39 10.66 10.35 -0.31 10.59 

MO Cedar Co 11.61 10.88 10.49 -0.39 10.82 

Kansas City, MO-KS MO Clay Co 12.88 12.15 11.85 -0.30 12.22 Springfield, MO MO Greene Co 12.27 11.56 11.14 -0.42 11.53 Kansas City, MO-KS MO Jackson Co 12.27 11.44 11.10 -0.34 11.38 Joplin, MO MO Jasper Co 13.85 12.65 12.26 -0.39 12.66 St. Louis, MO-IL MO Jefferson Co 14.79 13.97 13.32 -0.65 13.90 

MO Monroe Co 11.16 10.60 10.09 -0.51 10.53 

St. Louis, MO-IL MO St. Charles Co 14.52 14.23 13.54 -0.69 14.27 

MO Ste. Genevieve Co 13.98 13.42 12.68 -0.74 13.45 

St. Louis, MO-IL MO St. Louis Co 14.46 13.61 12.92 -0.69 13.56 St. Louis, MO-IL MO St. Louis City 15.62 15.10 14.40 -0.70 15.34 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

Omaha, NE NE Cass Co 10.38 9.67 9.46 -0.21 9.59 Omaha, NE NE Douglas Co 10.77 10.10 9.88 -0.22 10.06 

NE Hall Co 8.55 7.97 7.81 -0.16 7.90 

Lincoln, NE NE Lancaster Co 10.02 9.40 9.19 -0.21 9.34 Omaha, NE NE Sarpy Co 10.32 9.68 9.47 -0.21 9.64 Omaha, NE NE Washington Co 9.90 9.29 9.09 -0.20 9.21 

NH Cheshire Co 11.81 11.04 10.39 -0.65 10.85 NH Coos Co 10.11 9.98 9.49 -0.49 9.93 NH Merrimack Co 9.96 9.22 8.67 -0.55 9.11 NH Sullivan Co 9.95 9.40 8.75 -0.65 9.26 

New York-New Jersey,NY-NJ-CT-PA NJ Bergen Co 14.09 13.31 12.05 -1.26 13.06 Philadelphia, PA-DE-NJ NJ Camden Co 14.54 14.11 12.48 -1.63 13.92 Philadelphia, PA-DE-NJ NJ Gloucester Co 13.99 14.15 12.52 -1.63 14.07 New York City, NY-NJ-CT NJ Hudson Co 15.39 14.52 13.14 -1.38 14.22 New York City, NY-NJ-CT NJ Mercer Co 14.27 13.74 12.12 -1.62 13.47 New York City, NY-NJ-CT NJ Middlesex Co 12.67 12.14 10.76 -1.38 11.91 New York City, NY-NJ-CT NJ Morris Co 12.68 12.27 10.79 -1.48 12.06 New York City, NY-NJ-CT NJ Union Co 15.94 15.05 13.60 -1.45 14.73 New York City, NY-NJ-CT NJ Warren Co 13.55 13.26 11.71 -1.55 13.10 New York City, NY-NJ-CT NY Bronx Co 15.99 14.79 13.62 -1.17 14.41 Jamestown, NY NY Chautauqua Co 10.97 10.74 9.40 -1.34 10.54 Buffalo, NY NY Erie Co 14.35 13.94 12.39 -1.55 13.69 

NY Essex Co 6.49 6.33 5.79 -0.54 6.25 

New York City, NY-NJ-CT NY Kings Co 14.90 14.02 12.69 -1.33 13.79 Rochester, NY NY Monroe Co 11.52 11.00 9.94 -1.06 10.84 New York City, NY-NJ-CT NY Nassau Co 12.36 11.70 10.52 -1.18 11.52 New York City, NY-NJ-CT NY New York Co 17.56 16.19 14.95 -1.24 15.76 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

Buffalo, NY NY Niagara Co 12.25 11.99 10.65 10.65 11.82 Syracuse, NY NY Onondaga Co 10.68 10.28 9.22 -1.06 10.06 New York City, NY-NJ-CT NY Orange Co 11.64 11.02 10.02 -1.00 10.79 New York City, NY-NJ-CT NY Queens Co 13.56 12.69 11.56 -1.13 12.41 New York City, NY-NJ-CT NY Richmond Co 12.35 11.67 10.53 -1.14 11.43 

NY St. Lawrence Co 8.62 8.44 7.82 -0.62 8.36 NY Steuben Co 9.95 9.72 8.40 -1.32 9.55 

New York City, NY-NJ-CT NY Suffolk Co 12.41 11.51 10.32 -1.19 11.23 New York City, NY-NJ-CT NY Westchester Co 12.55 11.77 10.68 -1.09 11.51 Greensboro, NC NC Alamance Co 14.47 13.98 12.62 -1.36 13.65 Asheville, NC NC Buncombe Co 14.09 13.45 12.25 -1.20 13.14 Charlotte, NC-SC NC Cabarrus Co 15.09 14.38 13.11 -1.27 13.97 

NC Caswell Co 13.90 13.43 12.09 -1.34 13.11 

Hickory, NC NC Catawba Co 16.32 15.48 14.07 -1.41 15.19 Raleigh-Durham, NC NC Chatham Co 12.81 12.26 11.01 -1.25 11.92 Fayetteville, NC NC Cumberland Co 14.69 14.09 12.89 -1.20 13.82 Greensboro--Winston-Salem, NC NC Davidson Co 16.60 15.76 14.36 -1.40 15.34 

NC Duplin Co 12.37 11.97 10.82 -1.15 11.75 

Raleigh-Durham, NC NC Durham Co 14.65 13.98 12.60 -1.38 13.55 Greensboro, NC NC Forsyth Co 15.46 14.72 13.16 -1.56 14.28 Charlotte, NC-SC NC Gaston Co 14.65 14.00 12.71 -1.29 13.59 Greensboro, NC NC Guilford Co 14.04 13.54 12.21 -1.33 13.16 

NC Haywood Co 14.53 13.93 12.70 -1.23 13.68 NC Jackson Co 12.95 12.41 11.24 -1.17 12.18 NC Lenoir Co 11.91 11.46 10.25 -1.21 11.21 NC McDowell Co 15.32 14.52 13.22 -1.30 14.15 

Charlotte, NC-SC NC Mecklenburg Co 15.83 15.22 13.92 -1.30 14.83 

NC Mitchell Co 14.54 13.69 12.42 -1.27 13.36 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

NC Montgomery Co 12.57 12.01 10.80 -1.21 11.66 

Jacksonville, NC NC Onslow Co 11.60 11.19 10.07 -1.12 10.93 Raleigh-Durham, NC NC Orange Co 13.67 13.08 11.75 -1.33 12.72 Greenville, NC NC Pitt Co 12.56 12.11 10.86 -1.25 11.87 

NC Robeson Co 12.75 12.26 11.11 -1.15 11.99 NC Swain Co 13.47 13.03 11.68 -1.35 12.78 

Raleigh-Durham, NC NC Wake Co 14.57 14.01 12.66 -1.35 13.69 Goldsboro, NC NC Wayne Co 14.51 13.66 12.34 -1.32 13.40 Fargo, ND-MN ND Cass Co 8.11 7.51 7.41 -0.10 7.38 

OH Athens Co 12.47 12.33 10.31 -2.02 12.04 

Cincinnati, OH-KY-IN OH Butler Co 16.78 16.45 15.03 -1.42 16.32 Dayton-Springfield, OH OH Clark Co 14.67 14.55 13.10 -1.45 14.30 Cleveland, OH OH Cuyahoga Co 19.25 18.84 17.11 -1.73 18.60 Columbus, OH OH Franklin Co 17.23 16.98 15.13 -1.85 16.64 Cincinnati, OH-KY OH Hamilton Co 18.55 18.23 16.61 -1.62 18.03 Steubenville-Weirton, OH-WV OH Jefferson Co 18.30 17.94 15.64 -2.30 17.83 Cleveland, OH OH Lake Co 13.72 13.62 12.15 -1.47 13.45 Huntington-Ashland, WV-KY-OH OH Lawrence Co 16.26 16.10 14.11 -1.99 15.92 Cleveland, OH OH Lorain Co 13.88 13.60 12.33 -1.27 13.46 Toledo, OH OH Lucas Co 15.07 14.77 13.76 -1.01 14.64 Youngstown-Warren, OH OH Mahoning Co 15.77 15.39 13.40 -1.99 15.13 Dayton, OH OH Montgomery Co 15.57 15.41 13.83 -1.58 15.16 Cleveland, OH OH Portage Co 14.86 14.40 12.64 -1.76 14.11 

OH Preble Co 13.51 13.34 11.89 -1.45 13.11 OH Scioto Co 18.26 18.13 15.98 -2.15 17.92 

Canton, OH OH Stark Co 17.85 17.14 15.08 -2.06 16.86 Cleveland, OH OH Summit Co 16.95 16.47 14.69 -1.78 16.14 Youngstown-Warren, OH OH Trumbull Co 15.60 15.28 13.50 -1.78 15.05 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

OK Caddo Co 8.66 8.32 8.06 -0.26 8.32 

Oklahoma City, OK OK Canadian Co 8.99 8.34 8.07 -0.27 8.28 

OK Carter Co 10.21 9.60 9.29 -0.31 9.60 OK Cherokee Co 11.72 11.04 10.62 -0.42 11.04 

Enid, OK OK Garfield Co 10.03 9.60 9.37 -0.23 9.64 

OK Kay Co 10.71 10.01 9.74 -0.27 9.94 OK Lincoln Co 10.08 9.44 9.11 -0.33 9.41 OK Mayes Co 12.01 11.99 11.63 -0.36 12.07 OK Muskogee Co 12.17 11.61 11.22 11.22 11.63 

Oklahoma City, OK OK Oklahoma Co 10.61 10.04 9.76 -0.28 10.02 

OK Ottawa Co 11.78 11.12 10.75 -0.37 11.08 OK Pittsburg Co 11.52 10.86 10.50 -0.36 10.87 OK Seminole Co 9.47 8.87 8.55 -0.32 8.86 

Tulsa, OK OK Tulsa Co 12.03 11.53 11.21 -0.32 11.58 

PA Adams Co 13.39 13.05 10.77 -2.28 12.77 

Pittsburgh, PA PA Allegheny Co 21.20 20.55 18.01 -2.54 20.33 Pittsburgh, PA PA Beaver Co 15.97 15.78 13.61 -2.17 15.54 Reading, PA PA Berks Co 16.24 15.89 13.56 -2.33 15.66 Philadelphia, PA-DE-NJ PA Bucks Co 14.07 13.65 12.07 -1.58 13.46 Johnstown, PA PA Cambria Co 15.62 15.14 12.72 -2.42 14.80 State College, PA PA Centre Co 13.01 12.67 10.77 -1.90 12.43 Harrisburg, PA PA Dauphin Co 15.65 15.17 12.88 -2.29 14.87 Philadelphia, PA-DE-NJ PA Delaware Co 15.45 15.61 13.94 -1.67 15.52 Erie, PA PA Erie Co 13.43 13.08 11.73 -1.35 12.92 Scranton--Wilkes-Barre, PA PA Lackawanna Co 12.29 11.91 10.31 -1.60 11.71 Lancaster, PA PA Lancaster Co 16.99 16.55 14.09 -2.46 16.28 Allentown, PA PA Lehigh Co 14.21 13.77 12.01 -1.76 13.53 Scranton--Wilkes-Barre, PA PA Luzerne Co 12.95 12.62 10.86 -1.76 12.42 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

Sharon, PA PA Mercer Co 14.28 14.01 12.27 -1.74 13.77 Philadelphia, PA-DE-NJ PA Montgomery Co 14.10 13.80 12.02 -1.78 13.62 Allentown, PA PA Northampton Co 14.42 13.93 12.35 -1.58 13.70 Harrisburg, PA PA Perry Co 12.83 12.46 10.54 -1.92 12.24 Philadelphia, PA-DE-NJ PA Philadelphia Co 16.54 16.65 14.98 -1.67 16.53 Pittsburgh, PA PA Washington Co 15.58 15.23 12.99 -2.24 15.01 Pittsburgh, PA PA Westmoreland Co 15.56 15.16 12.60 -2.56 14.75 York, PA PA York Co 16.86 16.49 14.20 -2.29 16.22 Providence, RI RI Kent Co 8.96 8.45 7.69 -0.76 8.33 Providence, RI RI Providence Co 11.45 10.78 9.99 -0.79 10.60 

SC Beaufort Co 11.04 10.68 9.86 -0.82 10.70 

Charleston, SC SC Charleston Co 11.94 11.63 10.79 -0.84 11.54 

SC Chesterfield Co 12.42 11.93 10.83 -1.10 11.65 

Augusta-Aiken, GA-SC SC Edgefield Co 12.83 12.54 11.54 -1.00 12.45 Florence, SC SC Florence Co 13.23 12.80 11.71 -1.09 12.58 

SC Georgetown Co 13.26 12.69 11.74 -0.95 12.54 

Greenville-Spartanburg, SC SC Greenville Co 15.43 14.80 13.36 -1.44 14.45 

SC Greenwood Co 14.01 13.47 12.18 -1.29 13.18 

Myrtle Beach, SC SC Horry Co 11.16 10.68 9.70 -0.98 10.50 Columbia, SC SC Lexington Co 14.57 14.08 12.95 -1.13 13.83 

SC Oconee Co 11.47 11.13 9.86 -1.27 10.88 

Columbia, SC SC Richland Co 14.50 14.01 12.88 -1.13 13.76 Greenville-Spartanburg, SC SC Spartanburg Co 14.51 13.99 12.58 -1.41 13.63 

SD Brookings Co 9.37 8.64 8.45 -0.19 8.50 SD Brown Co 8.31 7.76 7.64 -0.12 7.63 

Sioux Falls, SD SD Minnehaha Co 9.82 9.09 8.88 -0.21 8.96 Knoxville, TN TN Blount Co 14.42 13.63 12.48 -1.15 13.52 Nashville, TN TN Davidson Co 15.56 15.36 14.26 -1.10 15.36 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

TN Dyer Co 12.35 12.03 11.16 -0.87 12.03 

Chattanooga, TN-GA TN Hamilton Co 17.23 16.89 15.57 -1.32 16.82 Knoxville, TN TN Knox Co 18.49 17.44 16.16 -1.28 17.34 

TN Lawrence Co 12.65 12.41 11.19 -1.22 12.32 TN McMinn Co 15.34 14.92 13.64 -1.28 14.88 TN Maury Co 13.65 14.28 13.21 -1.07 14.88 

Clarksville, TN-KY TN Montgomery Co 13.75 13.41 12.23 -1.18 13.34 

TN Putnam Co 13.94 13.56 12.27 -1.29 13.37 TN Roane Co 15.56 14.97 13.58 -1.39 14.80 

Memphis, TN TN Shelby Co 14.81 14.59 13.77 -0.82 15.17 Johnson City, TN-VA TN Sullivan Co 15.71 15.32 14.01 -1.31 15.37 Nashville, TN TN Sumner Co 14.47 14.18 13.05 -1.13 14.12 Texarkana, TX-AR TX Bowie Co 14.10 13.36 12.88 -0.48 13.48 Brownsville, TX TX Cameron Co 9.90 9.61 9.25 -0.36 9.64 Dallas, TX TX Dallas Co 13.82 13.03 12.68 -0.35 12.99 Houston-Galveston, TX TX Galveston Co 9.64 9.23 8.67 -0.56 9.33 Longview, TX TX Gregg Co 12.49 11.81 11.36 11.36 11.91 Houston, TX TX Harris Co 14.13 14.13 13.78 -0.35 14.46 McAllen, TX TX Hidalgo Co 10.84 10.70 10.42 -0.28 10.89 Beaumont-Port Arthur, TX TX Jefferson Co 11.25 11.12 10.63 -0.49 11.40 Corpus Christi, TX TX Nueces Co 10.30 10.28 9.94 -0.34 10.49 Beaumont-Port Arthur, TX TX Orange Co 11.41 11.27 10.80 -0.47 11.56 Dallas, TX TX Tarrant Co 12.36 11.42 11.08 -0.34 11.42 Burlington, VT VT Chittenden Co 9.49 9.19 8.55 -0.64 9.08 Washington-Baltimore, DC-MD VA Arlington Co 14.64 14.30 12.31 -1.99 14.06 Richmond-Petersburg, VA VA Charles City Co 13.32 12.77 11.24 -1.53 12.47 Richmond-Petersburg, VA VA Chesterfield Co 13.91 13.35 11.81 -1.54 13.05 Washington-Baltimore, DC-MD VA Fairfax Co 14.33 13.88 11.75 -2.13 13.54 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

Richmond-Petersburg, VA VA Henrico Co 13.92 13.35 11.82 -1.53 13.05 Washington-Baltimore, DC-MD VA Loudoun Co 13.65 13.23 11.14 -2.09 12.90 

VA Page Co 13.17 12.80 10.72 -2.08 12.44 

Johnson City, TN-VA VA Bristol city 15.21 14.53 13.03 -1.50 14.23 Norfolk, VA-NC VA Chesapeake city 12.99 12.58 11.09 -1.49 12.45 Norfolk, VA-NC VA Hampton city 12.95 12.67 11.16 -1.51 12.56 Norfolk, VA-NC VA Newport News city 12.30 11.87 10.39 -1.48 11.66 Norfolk, VA-NC VA Norfolk city 13.33 13.03 11.51 -1.52 12.93 Richmond, VA VA Richmond city 14.48 13.88 12.29 -1.59 13.57 Roanoke, VA VA Roanoke city 14.84 14.38 12.55 -1.83 13.94 Roanoke, VA VA Salem city 14.95 14.49 12.70 -1.79 14.07 Norfolk, VA-NC VA Virginia Beach city 12.88 12.59 11.14 -1.45 12.50 Washington-Baltimore, DC-MD WV Berkeley Co 16.18 15.69 13.43 -2.26 15.32 Steubenville-Weirton, OH-WV WV Brooke Co 16.96 16.63 14.42 -2.21 16.51 Huntington-Ashland, WV-KY-OH WV Cabell Co 17.22 17.03 15.08 -1.95 16.86 Steubenville-Weirton, OH-WV WV Hancock Co 17.40 17.06 14.89 -2.17 16.97 

WV Harrison Co 14.40 14.15 11.90 -2.25 13.82 

Charleston, WV WV Kanawha Co 17.75 17.56 15.27 -2.29 17.17 

WV Marion Co 15.58 15.32 12.90 -2.42 14.98 

Wheeling, WV-OH WV Marshall Co 16.07 15.81 13.46 -2.35 15.52 

WV Mercer Co 12.97 12.52 10.82 -1.70 12.14 WV Monongalia Co 14.96 14.77 12.31 -2.46 14.37 

Wheeling, WV-OH WV Ohio Co 15.37 15.14 12.81 -2.33 14.84 

WV Raleigh Co 13.54 13.19 11.33 -1.86 12.80 WV Summers Co 10.46 10.21 8.63 -1.58 9.89 

Parkersburg, WV-OH WV Wood Co 16.88 16.66 14.14 -2.52 16.69 Green Bay, WI WI Brown Co 11.52 10.99 10.67 -0.32 10.93 Madison, WI WI Dane Co 12.81 12.07 11.62 -0.45 11.86 
CMSA/MSA State County

Average

1999-2003 2010 Base 2010 CAIR

Impact of

CAIR in 2010 2015 Base 

WI Dodge Co 11.41 10.79 10.39 -0.40 10.63 WI Grant Co 11.78 11.17 10.75 -0.42 11.11 

Chicago, IL-IN-WI WI Kenosha Co 11.90 11.44 10.97 -0.47 11.40 

WI Manitowoc Co 10.09 9.55 9.21 -0.34 9.41 

Milwaukee, WI WI Milwaukee Co 13.74 13.35 12.88 -0.47 13.33 Appleton, WI WI Outagamie Co 11.04 10.43 10.10 -0.33 10.28 

WI Vilas Co 6.26 5.94 5.75 -0.19 5.88 

Milwaukee, WI WI Waukesha Co 13.55 12.95 12.51 -0.44 12.82 

Technical Support Document

for the Final

Clean Air Interstate Rule

Air Quality Modeling Analyses

Appendix G

8-Hour Contributions to 

Each Nonattainment County in 2010
Fairfield CT

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

Max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

PA 24 20 20 65 100 25 64 66 NJ 21 18 18 65 100 23 42 37 NY 19 17 19 65 100 23 -11 -19 VA 5 6 7 62 95 6 8 9 OH 9 8 8 65 100 10 16 15 MD 4 7 8 56 86 7 8 8 WV 3 3 3 62 95 3 9 9 IN 2 2 2 30 46 3 3 3 NC 2 2 2 26 40 2 3 3 DE 2 2 2 11 17 3 2 2 IL 2 2 2 13 20 2 2 2 MI 1 1 2 0 0 2 1 1 KY 2 2 2 18 28 3 2 2 WI 1 1 1 0 0 1 1 1 MO 1 1 1 0 0 1 1 1 IA 1 1 1 0 0 1 1 1 MN 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 AR 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 LA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions exceed screening criteriaContributions do not exceed screening criteria

CAMX Sta

Base Case: Total Num

CAMX Source Apportionment Modeling

Base Case: Total Number of Exceedances (grid-hours) = 65
Middlesex CT

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

NY 26 21 24 153 100 31 48 48 PA 20 20 22 153 100 25 47 47 NJ 18 16 17 153 100 22 37 38 OH 6 8 7 91 60 10 11 15 MA 1 8 9 5 3 8 1 1 VA 4 4 4 89 58 7 5 5 WV 2 3 3 83 54 3 5 6 MD 4 5 5 116 76 7 6 6 MI 1 2 2 22 14 3 2 2 IN 2 2 2 44 29 3 3 4 NC 2 2 3 45 29 3 2 2 DE 2 3 3 37 24 4 2 2 IL 2 2 2 28 18 3 2 3 KY 1 2 2 14 9 2 1 2 NH 0 1 1 0 0 1 0 0 RI 0 2 3 2 1 2 0 0 MO 1 1 1 0 0 1 1 1 VT 0 0 0 0 0 0 0 0 IA 1 1 1 0 0 1 0 1 TN 0 0 0 0 0 0 0 0 WI 1 1 1 0 0 1 1 1 AR 0 0 0 0 0 0 0 0 ME 0 0 1 0 0 1 0 0 AL 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 LA 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions do not exceed screening criteriaContributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 153 Base Case: Total Num
New Haven CT

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

PA 21 21 23 117 100 24 51 50 NY 25 21 24 117 100 29 32 27 NJ 19 15 17 117 100 23 39 38 OH 7 8 7 96 82 10 15 14 MD 4 8 9 95 81 9 12 11 VA 4 5 6 87 74 6 11 10 WV 2 3 3 71 61 3 7 7 MI 1 2 3 16 14 3 4 4 IN 2 2 2 35 30 3 4 4 NC 2 2 2 38 33 3 3 3 IL 2 2 2 15 13 3 3 3 DE 2 3 3 22 19 3 3 2 KY 1 2 2 13 11 2 2 2 WI 1 1 1 0 0 1 1 1 MA 0 1 1 0 0 1 0 1 MO 1 1 1 0 0 1 1 1 IA 1 1 1 0 0 1 1 1 AR 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 LA 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions do not exceed screening criteriaContributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 117 Base Case: Total Num
Washington DC

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

VA 8 7 8 10 100 7 79 79 MD 54 42 49 10 100 49 52 52 OH 8 9 11 9 90 10 25 25 PA 6 6 7 10 100 6 17 17 WV 2 2 2 1 10 2 19 19 IL 4 4 4 9 90 4 9 9 IN 3 3 3 3 30 3 8 8 MI 2 2 2 0 0 2 5 5 KY 1 3 3 3 30 3 3 3 MO 2 3 3 3 30 3 5 5 IA 1 1 2 0 0 1 3 3 NY 0 1 1 0 0 1 2 2 DE 0 1 1 0 0 1 2 2 NJ 0 1 1 0 0 1 2 2 LA 1 1 1 0 0 1 1 1 AR 1 1 1 0 0 1 1 1 MS 0 0 0 0 0 0 0 0 TN 0 1 1 0 0 1 0 0 WI 0 0 0 0 0 0 1 1 AL 0 0 0 0 0 0 0 0 CT 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 NC 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions exceed screening criteriaContributions do not exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 10 Base Case: Total Num
New Castle DE

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

MD 45 36 42 108 100 44 100 100 VA 6 7 7 75 69 9 41 32 PA 10 14 17 84 78 17 40 45 OH 7 9 10 86 80 9 24 20 WV 4 5 5 58 54 6 28 21 NC 3 4 4 52 48 6 8 4 MI 2 4 4 32 30 5 8 8 NJ 0 3 3 2 2 3 1 1 IN 2 3 3 25 23 3 6 5 IL 3 3 4 33 31 4 5 4 IA 1 2 2 8 7 2 4 3 KY 2 2 3 36 33 4 4 3 NY 0 2 2 0 0 2 2 1 MO 1 2 2 20 19 2 2 2 WI 1 1 1 0 0 1 2 1 CT 0 0 0 0 0 0 0 0 MA 0 1 1 0 0 1 0 0 AR 0 1 1 0 0 1 0 0 LA 0 1 1 0 0 1 0 0 ME 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 1 0 NH 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 1 0 0 TN 0 1 1 0 0 1 0 0 AL 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 1 0 0 GA 0 0 0 0 0 1 0 0 MS 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions exceed screening criteria

Downwind Nonattainment Receptor

Contributions do not exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 108 Base Case: Total Num
Fulton GA

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

AL 4 14 15 585 47 23 9 7 TN 4 7 7 796 64 8 11 9 SC 2 7 7 316 26 8 6 4 NC 2 5 5 238 19 7 5 4 KY 3 6 6 608 49 7 5 4 FL 1 5 5 162 13 6 2 3 WV 1 3 3 120 10 3 2 2 VA 1 2 2 46 4 3 2 2 MS 1 2 2 37 3 3 1 1 AR 1 2 2 73 6 3 1 1 OH 1 3 3 101 8 4 1 1 PA 0 3 3 58 5 3 1 1 IN 1 2 2 6 1 2 2 1 IL 1 2 2 80 7 2 1 1 MO 1 2 2 0 0 2 1 1 NY 0 1 1 0 0 1 0 0 LA 0 1 1 0 0 1 0 0 MD 0 1 1 0 0 1 0 0 CT 0 0 0 0 0 0 0 0 IA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 NJ 0 1 1 0 0 1 0 0 WI 0 0 0 0 0 0 0 0 DE 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MI 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions exceed screening criteria

Downwind Nonattainment Receptor

Contributions do not exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 1240 Base Case: Total Numb
Anne Arundel MD

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

VA 9 15 18 304 88 18 60 52 OH 8 10 10 285 82 11 26 22 PA 6 10 12 285 82 11 23 25 WV 3 5 5 113 33 6 16 13 NC 1 5 5 56 16 9 4 2 MI 2 4 4 70 20 5 6 5 WI 1 3 3 29 8 3 3 3 IL 3 4 4 135 39 4 6 6 KY 2 3 3 135 39 4 3 2 IN 2 3 3 145 42 3 5 4 IA 1 1 2 0 0 2 2 2 MO 2 3 3 106 31 3 2 2 NY 0 1 2 0 0 1 1 1 DE 0 1 1 0 0 1 1 1 MN 0 1 1 0 0 1 1 1 NJ 0 1 1 0 0 1 1 1 LA 0 1 1 0 0 1 0 0 AR 1 1 1 0 0 1 0 0 CT 0 0 0 0 0 0 0 0 MA 0 1 1 0 0 1 0 0 FL 0 1 1 0 0 1 0 0 GA 0 1 1 0 0 1 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 SC 0 1 1 0 0 1 0 0 TN 1 1 1 0 0 1 0 0 AL 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteriaContributions exceed screening criteria

Downwind Nonattainment Receptor

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 347 Base Case: Total Num
Cecil MD

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

PA 12 14 15 150 89 22 44 43 VA 6 13 15 103 61 14 34 32 WV 3 5 5 64 38 6 19 18 OH 7 9 10 126 75 9 19 17 MI 2 3 3 58 35 4 6 6 NC 2 4 4 48 29 6 4 3 IN 2 3 3 67 40 3 5 4 KY 2 3 3 38 23 4 3 3 IL 3 4 4 59 35 4 4 4 IA 1 2 2 2 1 2 3 2 DE 1 2 2 26 16 4 1 1 NY 0 1 2 0 0 2 2 2 NJ 0 1 1 0 0 1 1 1 MO 1 2 2 57 34 2 2 2 MA 0 1 1 0 0 1 0 0 WI 1 1 1 0 0 1 1 1 CT 0 0 0 0 0 0 0 0 FL 0 1 1 0 0 1 0 0 LA 0 1 1 0 0 1 0 0 AR 0 1 1 0 0 1 0 0 GA 0 1 1 0 0 1 0 0 ME 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 SC 0 0 1 0 0 1 0 0 TN 0 1 1 0 0 1 0 0 AL 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions do not exceed screening criteriaContributions exceed screening criteria

CAMX Source Apportionment Modeling

Base Case: Total Number of Exceedances (grid-hours) = 168

CAMX Sta

Base Case: Total Num
Harford MD

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

VA 7 13 15 136 52 15 34 32 PA 11 15 17 229 88 25 34 29 WV 2 5 6 45 17 6 12 13 OH 6 9 10 192 74 10 17 17 NC 1 3 4 48 19 5 3 3 MI 2 3 3 84 32 4 6 5 WI 1 3 3 15 6 3 2 2 IN 2 3 3 74 29 3 4 4 KY 1 3 3 41 16 4 2 2 IL 2 4 4 74 29 4 4 4 IA 1 2 2 0 0 2 2 2 NY 0 1 2 0 0 1 1 1 MO 1 2 2 53 20 3 2 2 NJ 0 1 1 0 0 1 1 1 LA 0 1 1 0 0 1 0 0 MN 0 1 1 0 0 1 1 1 FL 0 1 1 0 0 1 0 0 AR 0 1 1 0 0 1 0 0 CT 0 0 0 0 0 0 0 0 DE 0 1 1 0 0 2 0 1 GA 0 1 1 0 0 1 0 0 MA 0 1 1 0 0 1 0 0 SC 0 0 1 0 0 1 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 TN 0 1 1 0 0 1 0 0 AL 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 260 Base Case: Total Num

Contributions do not exceed screening criteria
Kent MD

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

VA 7 13 15 118 69 13 45 49 PA 6 10 11 116 67 13 18 17 OH 9 9 10 157 91 11 27 29 WV 4 4 4 87 51 5 20 21 NC 3 6 6 56 33 9 5 7 MI 2 4 5 34 20 5 5 5 IN 3 3 3 98 57 3 6 7 IL 3 4 4 82 48 4 6 6 IA 1 1 1 0 0 2 3 3 KY 2 2 2 73 42 4 3 3 NY 0 1 2 0 0 1 1 1 MO 2 2 2 56 33 3 2 2 NJ 0 1 1 0 0 1 1 1 DE 0 0 0 0 0 1 0 0 MA 0 1 1 0 0 1 0 0 WI 1 1 1 0 0 1 1 1 CT 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 AR 0 1 1 0 0 1 0 0 ME 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 SC 0 1 1 0 0 1 0 0 TN 1 1 1 0 0 1 0 0 AL 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 1 0 0 GA 0 0 0 0 0 1 0 0 LA 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 172 Base Case: Total Num

Contributions do not exceed screening criteria
Macomb MI

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

IL 14 13 14 102 100 16 60 68 IN 4 8 9 75 74 9 24 27 OH 3 8 9 37 36 9 18 19 WI 3 4 5 43 42 5 11 8 IA 3 4 5 55 54 5 15 20 KY 1 6 7 9 9 6 4 2 MO 3 3 3 40 39 4 12 14 AR 2 2 2 1 1 2 3 3 LA 1 2 2 1 1 2 1 1 TN 0 1 1 0 0 1 1 1 AL 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 MS 1 1 1 0 0 1 0 0 CT 0 0 0 0 0 0 0 0 DE 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 MD 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 NC 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 NJ 0 0 0 0 0 0 0 0 NY 0 0 0 0 0 0 0 0 PA 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 VA 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 WV 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 102 Base Case: Total Num

Contributions do not exceed screening criteriaContributions exceed screening criteria
Bergen NJ

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

PA 31 27 29 54 100 31 97 99 VA 5 8 9 42 78 8 19 15 OH 7 7 7 44 82 9 19 15 MD 5 6 7 41 76 7 16 18 WV 3 3 3 37 69 3 13 12 MI 1 4 5 7 13 5 4 5 DE 3 3 3 25 46 4 6 6 NC 2 2 2 20 37 3 6 6 IN 2 2 2 13 24 3 4 4 KY 2 2 2 13 24 3 2 2 WI 1 1 1 0 0 1 2 3 IA 1 2 2 0 0 2 2 2 IL 2 2 2 6 11 2 3 3 NY 3 14 16 18 33 15 -34 -56 MN 0 0 0 0 0 0 1 1 MO 1 1 1 0 0 1 1 1 CT 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 AR 0 0 0 0 0 0 0 0 LA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

Downwind Nonattainment Receptor

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 54 Base Case: Total Num

Contributions exceed screening criteria
Camden NJ

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

PA 35 32 36 122 100 39 71 61 MD 18 24 25 105 86 29 57 66 VA 4 6 6 62 51 8 16 16 DE 11 10 11 122 100 12 40 45 OH 5 7 8 87 71 8 16 14 WV 2 4 4 37 30 5 10 11 MI 3 5 5 45 37 6 12 12 NC 2 4 4 34 28 5 3 4 NY 1 3 3 8 7 3 2 3 IA 1 2 2 28 23 2 3 3 IL 2 3 3 43 35 3 5 4 IN 2 2 2 6 5 2 4 4 WI 1 2 2 0 0 2 3 3 KY 1 2 2 11 9 3 2 1 MN 0 0 0 0 0 0 1 1 MO 1 2 2 0 0 2 1 1 SC 0 1 1 0 0 1 0 0 GA 0 1 1 0 0 1 0 0 LA 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 AR 0 1 1 0 0 1 0 0 CT 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 1 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

Downwind Nonattainment Receptor

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 122 Base Case: Total Num

Contributions exceed screening criteria
Gloucester NJ

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

MD 28 30 32 93 99 35 87 90 PA 21 23 27 94 100 31 55 50 DE 14 18 21 94 100 18 64 58 VA 5 6 7 52 55 8 26 24 WV 3 4 5 36 38 5 17 17 MI 3 4 4 40 43 5 15 12 OH 7 6 6 76 81 6 23 19 NC 2 4 4 36 38 5 5 5 IA 2 2 2 22 23 2 5 4 IL 3 3 4 40 43 3 7 5 IN 2 2 2 7 7 2 5 5 KY 2 2 2 19 20 3 2 2 NY 0 1 2 0 0 1 1 2 WI 1 1 1 0 0 1 3 2 MO 1 2 2 0 0 2 2 1 LA 0 0 1 0 0 0 0 0 MN 0 0 0 0 0 0 1 1 SC 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 AR 0 1 1 0 0 1 0 0 CT 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 1 0 0 GA 0 0 0 0 0 1 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

Downwind Nonattainment Receptor

Contributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 94 Base Case: Total Num
Hunterton NJ

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

PA 46 34 39 71 100 46 100 100 VA 2 10 11 15 21 10 9 11 MD 4 8 10 24 34 10 16 18 DE 4 5 6 60 85 6 18 19 OH 2 7 8 19 27 8 13 14 WV 1 4 4 8 11 4 9 10 NY 3 3 3 45 63 4 14 15 MI 2 3 4 13 18 4 9 9 NC 1 2 2 4 6 3 4 4 WI 1 2 2 0 0 2 4 4 IN 1 2 2 5 7 3 3 3 KY 1 2 3 5 7 3 2 2 IL 1 2 2 0 0 2 4 4 MN 0 0 0 0 0 0 1 1 FL 0 0 1 0 0 1 0 0 GA 0 0 1 0 0 1 0 0 IA 1 1 1 0 0 1 1 1 LA 0 0 0 0 0 0 1 1 MO 0 1 1 0 0 1 1 1 SC 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 AR 0 0 0 0 0 0 0 0 CT 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions exceed screening criteria

Downwind Nonattainment Receptor

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 71 Base Case: Total Num

Contributions do not exceed screening criteria
Mercer NJ

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

PA 41 40 41 86 100 47 90 90 MD 8 12 12 56 65 17 25 27 MI 3 5 6 40 47 6 8 8 DE 5 5 6 61 71 7 10 11 VA 2 5 5 29 34 6 7 7 NY 2 5 5 13 15 6 6 5 WV 1 3 4 23 27 4 6 6 OH 4 5 6 62 72 7 8 8 NC 1 3 3 22 26 4 2 2 IA 1 2 2 17 20 2 2 2 IL 2 3 3 19 22 3 3 3 IN 1 2 2 2 2 2 3 3 KY 1 2 2 9 11 3 1 1 WI 1 1 1 0 0 1 2 2 MO 1 1 2 0 0 2 1 1 MN 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 AR 0 1 1 0 0 1 0 0 CT 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 LA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions exceed screening criteriaContributions do not exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 86 Base Case: Total Num
Middlesex NJ

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

PA 30 33 38 172 100 36 80 78 MD 5 9 8 86 50 13 12 11 VA 2 7 8 59 34 8 7 8 NY 3 8 8 66 38 12 8 7 MI 3 5 5 73 42 6 6 6 DE 3 4 5 68 40 6 5 5 OH 3 5 5 103 60 7 6 6 WV 1 3 3 47 27 4 4 4 NC 1 3 3 37 22 3 2 2 IA 1 2 2 27 16 2 1 1 IL 2 2 3 39 23 3 2 2 IN 1 2 2 8 5 2 2 2 WI 1 1 1 0 0 1 1 1 KY 1 2 2 17 10 3 1 1 CT 0 0 0 0 0 0 0 0 FL 0 0 1 0 0 1 0 0 GA 0 1 1 0 0 1 0 0 MN 0 0 0 0 0 0 0 0 MO 1 1 1 0 0 1 0 0 SC 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 AR 0 0 1 0 0 1 0 0 LA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions exceed screening criteriaContributions do not exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 172 Base Case: Total Num
Monmouth NJ

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

PA 31 33 37 318 100 45 73 72 MD 8 10 11 217 68 21 17 17 VA 3 6 6 113 36 8 7 7 NY 3 11 12 66 21 17 6 6 DE 4 5 6 218 69 8 9 10 OH 3 5 6 190 60 6 6 6 WV 1 3 3 71 22 4 5 5 MI 2 4 4 99 31 6 5 5 NC 1 3 3 80 25 4 2 2 IA 1 2 2 19 6 2 1 1 IN 1 2 2 1 0 2 2 2 IL 2 2 2 69 22 3 2 2 WI 1 1 1 0 0 1 1 1 MA 0 1 1 0 0 1 0 0 CT 0 0 1 0 0 0 0 0 KY 1 1 1 17 5 2 1 1 MN 0 0 0 0 0 0 0 0 FL 0 1 1 0 0 1 0 0 GA 0 1 1 0 0 1 0 0 ME 0 0 0 0 0 0 0 0 MO 1 1 1 0 0 2 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 1 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 AR 0 0 0 0 0 1 0 0 LA 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

Downwind Nonattainment Receptor

Contributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 318 Base Case: Total Num
Morris NJ

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

PA 32 31 34 151 100 37 88 88 VA 5 9 10 78 52 10 30 32 MD 4 7 8 75 50 11 16 16 OH 3 8 8 79 52 9 11 12 NY 4 11 13 71 47 12 10 10 MI 1 6 7 5 3 6 4 4 DE 3 4 4 72 48 5 12 11 WV 1 3 4 24 16 3 8 10 IN 1 2 2 17 11 3 3 3 KY 1 2 2 17 11 3 2 2 NC 1 2 2 8 5 3 3 4 IA 0 2 3 1 1 2 1 1 WI 1 1 2 0 0 1 2 2 IL 1 2 2 4 3 2 3 3 MO 0 1 1 0 0 1 1 1 MN 0 0 0 0 0 0 1 1 AR 0 0 1 0 0 0 0 0 FL 0 0 0 0 0 1 0 0 GA 0 0 0 0 0 1 0 0 LA 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 CT 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 151 Base Case: Total Num

Contributions exceed screening criteria

Downwind Nonattainment Receptor
Ocean NJ

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

PA 34 30 35 359 100 46 86 90 MD 12 17 17 298 83 25 31 35 VA 3 7 7 164 46 9 12 15 DE 8 7 8 336 94 9 24 25 WV 2 4 4 77 21 4 8 9 OH 5 6 7 264 74 7 13 12 NY 1 7 8 12 3 7 2 3 MI 3 4 4 144 40 6 8 7 NC 1 4 4 66 18 5 2 3 IA 1 2 2 52 15 2 3 2 IN 2 2 2 18 5 2 3 3 IL 3 3 3 174 49 4 3 3 WI 1 1 1 0 0 1 2 2 KY 1 1 2 15 4 3 1 1 MA 0 1 1 0 0 1 0 1 CT 0 1 1 0 0 1 0 0 MN 0 0 0 0 0 0 1 1 MO 1 1 2 0 0 2 1 1 SC 0 1 1 0 0 1 0 0 GA 0 1 1 0 0 1 0 0 ME 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 1 0 0 AL 0 0 0 0 0 0 0 0 AR 0 1 1 0 0 1 0 0 FL 0 0 0 0 0 1 0 0 LA 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

Downwind Nonattainment Receptor

Contributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 359 Base Case: Total Num
Erie NY

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

PA 2 12 14 2 13 12 5 1 MD 2 8 9 2 13 8 4 1 WI 6 5 6 14 88 5 62 72 NJ 1 5 6 2 13 5 4 1 VA 1 6 7 2 13 6 4 1 MI 4 3 4 14 88 4 38 42 DE 0 2 3 2 13 2 4 1 IL 4 3 3 14 88 3 18 16 NC 1 3 4 2 13 3 4 1 IA 3 2 2 14 88 2 12 14 MO 3 2 2 6 38 2 13 14 AR 1 1 1 0 0 1 5 6 CT 0 0 0 0 0 0 1 0 MA 0 0 1 0 0 0 1 0 MN 0 0 0 0 0 0 2 2 OH 0 0 0 0 0 0 1 1 SC 0 1 1 0 0 1 1 0 WV 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 IN 0 0 0 0 0 0 1 1 KY 0 0 0 0 0 0 0 0 LA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 1 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

Downwind Nonattainment Receptor

Contributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 16 Base Case: Total Num
Richmond NY

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

NJ 41 40 45 47 100 52 74 75 PA 25 30 29 47 100 34 63 59 MD 5 8 8 28 60 10 9 9 VA 3 6 7 20 43 7 7 7 MI 3 5 5 25 53 5 5 5 WV 1 3 3 15 32 3 4 3 DE 2 5 4 19 40 5 4 4 OH 4 5 5 29 62 6 5 5 NC 1 3 3 14 30 3 2 2 IA 1 2 2 7 15 2 1 1 IL 2 2 2 12 26 3 2 1 WI 1 1 1 0 0 1 1 1 IN 1 2 1 0 0 2 1 1 KY 1 1 1 2 4 2 0 0 MN 0 0 0 0 0 0 0 0 FL 0 1 1 0 0 1 0 0 GA 0 1 1 0 0 1 0 0 MO 1 1 1 0 0 1 0 0 SC 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 AR 0 0 0 0 0 0 0 0 CT 0 0 0 0 0 0 0 0 LA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

Downwind Nonattainment Receptor

Contributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 47 Base Case: Total Num
Suffolk NY

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

NJ 22 19 21 1088 100 39 64 64 PA 18 25 27 1050 97 34 42 42 CT 4 9 8 395 36 26 13 7 VA 3 8 8 355 33 11 9 8 MD 6 10 12 790 73 15 13 12 DE 3 5 5 528 49 6 7 7 OH 3 5 5 310 29 7 5 5 NC 1 4 4 282 26 6 3 2 WV 1 2 3 235 22 3 4 4 MA 1 3 3 93 9 7 1 1 MI 2 3 3 220 20 5 4 5 WI 1 1 1 0 0 2 2 2 IA 1 1 2 4 0 2 1 1 IN 1 1 1 0 0 2 2 2 IL 1 2 2 125 12 3 2 2 MN 0 0 0 0 0 1 1 1 KY 1 1 1 1 0 2 1 1 RI 0 1 1 3 0 2 0 0 MO 1 1 1 0 0 1 0 0 AR 0 0 0 0 0 0 0 0 FL 0 1 1 0 0 1 0 0 ME 0 0 0 0 0 1 0 0 NH 0 0 0 0 0 1 0 0 SC 0 0 0 0 0 1 0 0 TN 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 1 0 0 LA 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 

Contributions exceed screening criteria

Downwind Nonattainment Receptor

Contributions do not exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 1088 Base Case: Total Numb
Westchester NY

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

NJ 29 29 32 81 100 35 79 67 PA 24 22 23 81 100 28 75 83 VA 6 7 8 66 82 8 21 20 OH 7 8 8 70 86 10 15 18 MD 5 7 7 65 80 8 11 13 WV 2 3 3 40 49 3 9 13 DE 2 2 3 22 27 3 5 5 NC 2 2 2 13 16 2 3 5 IN 2 2 2 25 31 3 3 4 IL 2 2 2 10 12 2 2 2 KY 1 2 2 17 21 3 2 2 MI 1 1 2 0 0 2 2 2 WI 1 1 1 0 0 1 1 1 IA 1 1 1 0 0 1 1 1 MO 0 1 1 0 0 1 1 1 CT 0 0 0 0 0 0 0 0 LA 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 AR 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions exceed screening criteriaContributions do not exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 81 Base Case: Total Num
Geauga OH

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

IL 10 15 17 60 100 15 46 42 IN 5 8 10 56 93 9 28 23 MI 9 7 8 47 78 9 50 54 KY 1 8 9 5 8 9 6 2 MO 4 6 7 49 82 6 16 14 IA 2 2 2 0 0 2 8 6 AR 1 2 2 1 2 2 5 4 LA 2 2 2 15 25 2 6 6 WI 1 1 1 0 0 1 4 3 WV 0 1 1 0 0 1 1 0 MN 0 0 0 0 0 0 1 1 MS 1 0 1 0 0 1 1 1 PA 0 0 0 0 0 0 0 0 TN 0 1 1 0 0 1 1 0 AL 0 0 0 0 0 0 0 0 CT 0 0 0 0 0 0 0 0 DE 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 MD 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 NC 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 NJ 0 0 0 0 0 0 0 0 NY 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 VA 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions exceed screening criteriaContributions do not exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 60 Base Case: Total Num
Bucks PA

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

NJ 12 18 20 83 76 27 38 25 MD 7 10 11 77 70 15 38 45 OH 4 7 7 62 56 8 16 16 MI 2 6 7 38 35 6 14 12 DE 7 6 6 106 96 8 25 24 WV 1 4 4 23 21 4 8 10 VA 2 4 4 23 21 6 7 9 NY 2 2 2 18 16 3 8 7 WI 1 2 2 0 0 2 5 5 NC 1 2 3 16 15 3 2 3 IA 1 2 3 3 3 2 2 2 IN 1 2 2 13 12 3 4 5 KY 1 2 3 15 14 3 2 2 IL 2 3 3 3 3 3 5 5 MN 0 0 0 0 0 0 1 1 MO 1 2 2 0 0 2 2 2 LA 0 0 0 0 0 1 1 1 AR 0 1 1 0 0 1 0 0 MS 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 CT 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions do not exceed screening criteriaContributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 110 Base Case: Total Num
Chester PA

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

MD 30 32 37 126 100 39 95 94 DE 8 15 17 92 73 22 29 40 VA 5 12 14 85 68 13 41 28 NJ 2 15 17 15 12 15 11 11 WV 3 5 5 40 32 5 31 26 OH 6 7 8 87 69 9 35 28 MI 2 5 6 15 12 6 16 13 NY 1 3 4 8 6 3 8 5 NC 1 3 4 30 24 4 7 5 KY 2 3 3 35 28 5 9 7 IN 2 2 3 30 24 4 14 10 IA 1 2 2 5 4 2 4 4 IL 2 3 4 15 12 3 11 8 CT 0 1 1 0 0 1 2 0 MA 0 1 1 0 0 1 1 0 MO 1 2 2 7 6 2 4 3 LA 0 1 1 0 0 1 1 1 WI 1 1 1 0 0 1 4 3 FL 0 1 1 0 0 1 0 0 AL 0 0 0 0 0 0 0 0 AR 0 1 1 0 0 1 1 1 GA 0 1 1 0 0 1 0 0 MN 0 0 0 0 0 0 1 1 MS 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 1 1 ME 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions exceed screening criteria

Downwind Nonattainment Receptor

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 126 Base Case: Total Num

Contributions do not exceed screening criteria
Montgomery PA

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

MD 13 13 14 46 100 21 69 69 DE 11 11 12 46 100 15 53 50 NJ 4 10 12 19 41 11 24 21 OH 5 7 8 26 57 8 21 20 WV 2 4 5 13 28 4 16 16 NY 1 3 4 4 9 3 8 6 MI 2 4 4 6 13 4 9 9 VA 2 3 4 16 35 5 12 12 WI 1 2 3 1 2 2 3 2 IN 2 2 3 9 20 3 7 6 KY 1 3 3 11 24 3 4 4 NC 1 2 3 8 17 3 3 3 CT 0 1 1 0 0 1 2 0 IL 2 2 2 0 0 2 6 6 MN 0 1 1 0 0 1 1 1 MA 0 1 1 0 0 1 1 0 IA 1 1 1 0 0 1 2 2 LA 0 1 1 0 0 1 1 1 MO 1 1 1 0 0 1 3 2 AR 0 0 0 0 0 0 1 1 MS 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions exceed screening criteriaContributions do not exceed screening criteria

Downwind Nonattainment Receptor

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 46 Base Case: Total Num
Philadelphia PA

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

MD 18 19 20 31 86 24 81 79 NJ 3 10 11 6 17 14 10 7 DE 11 9 9 36 100 11 42 42 MI 2 6 7 3 8 6 9 10 VA 4 5 6 21 58 7 20 21 WV 3 4 4 15 42 4 17 17 OH 5 6 6 26 72 7 15 15 NC 2 3 4 15 42 4 6 6 NY 1 2 2 3 8 2 4 4 IA 1 2 3 3 8 2 2 3 IL 2 3 3 3 8 3 4 4 IN 2 2 2 1 3 2 4 4 KY 1 2 2 6 17 3 2 2 WI 1 1 1 0 0 1 2 2 MO 1 2 2 0 0 2 1 1 LA 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 AL 0 0 0 0 0 0 0 0 AR 0 1 1 0 0 1 0 0 CT 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 1 0 0 GA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions exceed screening criteriaContributions do not exceed screening criteria

Downwind Nonattainment Receptor

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 36 Base Case: Total Num
Kent RI

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

MA 1 26 30 3 2 27 1 1 NY 26 22 23 134 100 29 77 77 PA 17 22 25 131 98 22 39 37 NJ 16 14 17 131 98 18 45 41 CT 10 8 9 125 93 15 29 35 OH 6 7 7 75 56 9 9 12 VA 4 5 5 75 56 6 6 5 MD 4 5 5 113 84 5 7 5 WV 2 2 2 50 37 3 4 4 NH 0 3 3 3 2 3 1 0 MI 1 2 2 28 21 3 3 4 IN 2 2 2 22 16 2 3 4 DE 2 3 3 27 20 3 3 2 NC 2 3 3 42 31 3 1 1 IL 2 2 2 8 6 2 2 3 KY 1 2 2 0 0 2 1 1 ME 0 1 1 0 0 1 0 0 MO 1 1 1 0 0 1 1 1 VT 0 0 0 0 0 0 0 0 AR 0 0 0 0 0 0 0 0 IA 1 1 1 0 0 1 0 1 TN 0 0 0 0 0 0 0 0 WI 1 0 1 0 0 1 1 1 AL 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 LA 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 

Contributions exceed screening criteriaContributions do not exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 134 Base Case: Total Num

Downwind Nonattainment Receptor
Galveston TX

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

LA 13 10 12 188 100 13 64 58 AR 3 3 4 85 45 3 16 13 AL 3 4 4 101 54 4 9 9 MS 3 3 4 108 57 3 15 14 GA 2 3 3 2 1 3 7 7 NC 1 2 2 0 0 2 5 6 TN 3 2 3 49 26 2 10 9 KY 2 2 2 0 0 2 7 6 MO 1 1 2 0 0 1 3 3 IL 1 2 2 0 0 2 3 2 OH 1 2 2 0 0 2 5 5 VA 1 1 1 0 0 1 4 4 IN 1 1 1 0 0 1 3 3 SC 1 1 1 0 0 1 2 3 WV 1 1 1 0 0 1 4 4 FL 0 1 1 0 0 1 1 1 PA 1 1 1 0 0 1 2 2 MI 0 1 1 0 0 1 2 2 MD 0 0 1 0 0 0 1 1 NY 0 0 0 0 0 0 0 0 WI 0 0 0 0 0 0 1 1 CT 0 0 0 0 0 0 0 0 DE 0 0 0 0 0 0 0 0 IA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 NJ 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteriaContributions exceed screening criteria

Downwind Nonattainment Receptor

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 188 Base Case: Total Num
Harris TX

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

LA 13 13 15 740 100 15 68 66 AR 3 3 3 424 57 4 18 15 MS 3 4 5 315 43 5 15 16 AL 3 4 4 301 41 4 12 13 GA 2 3 3 179 24 3 9 9 NC 1 2 2 0 0 2 4 4 TN 2 2 3 126 17 3 9 9 IL 1 2 2 0 0 2 3 3 KY 2 2 2 0 0 2 6 5 MO 1 1 2 0 0 2 4 3 IN 1 1 1 0 0 1 4 3 OH 1 2 2 0 0 2 5 4 VA 1 1 1 0 0 1 3 3 SC 1 1 1 0 0 1 2 2 WV 0 1 1 0 0 1 3 3 FL 0 1 1 0 0 1 1 1 MI 0 1 1 0 0 1 1 1 PA 1 1 1 0 0 1 2 2 IA 0 0 0 0 0 0 0 0 MD 0 0 1 0 0 1 1 1 NY 0 0 0 0 0 0 0 0 WI 0 0 0 0 0 0 1 1 CT 0 0 0 0 0 0 0 0 DE 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 NJ 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Downwind Nonattainment Receptor

Contributions exceed screening criteriaContributions do not exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 740 Base Case: Total Numb
Jefferson TX

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

LA 26 33 38 356 100 39 97 98 AR 4 4 4 271 76 5 33 29 MS 3 5 6 141 40 5 20 21 AL 2 4 5 135 38 4 15 17 GA 1 2 3 69 19 2 9 9 MO 1 2 2 0 0 2 10 10 KY 2 2 2 0 0 2 9 7 TN 2 2 3 96 27 3 12 11 IL 1 2 2 0 0 2 7 7 OH 1 1 2 0 0 2 7 6 IN 1 1 1 0 0 1 6 5 WV 0 1 1 0 0 1 3 3 NC 1 1 1 0 0 1 3 3 VA 0 1 1 0 0 1 2 2 FL 0 1 1 0 0 1 2 2 MI 0 1 1 0 0 1 2 1 IA 0 0 0 0 0 0 1 1 PA 0 0 1 0 0 1 2 2 SC 0 0 1 0 0 1 2 1 WI 0 0 0 0 0 0 1 1 CT 0 0 0 0 0 0 0 0 DE 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 MD 0 0 0 0 0 0 1 1 ME 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 NJ 0 0 0 0 0 0 0 0 NY 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

Downwind Nonattainment Receptor

Contributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 356 Base Case: Total Num
Arlington VA

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

MD 51 45 44 11 100 51 24 24 OH 8 10 11 11 100 10 26 26 PA 6 6 6 7 64 6 12 12 WV 2 2 2 1 9 2 11 11 IL 4 4 4 11 100 4 7 7 IN 3 3 3 4 36 3 6 6 KY 1 3 3 4 36 3 2 2 MI 2 2 2 2 18 2 4 4 MO 2 3 3 4 36 3 4 4 IA 1 1 2 0 0 1 2 2 LA 1 1 1 0 0 1 1 1 AR 1 1 1 0 0 1 1 1 WI 1 0 0 0 0 0 1 1 MS 0 0 0 0 0 0 0 0 TN 1 1 1 0 0 1 0 0 AL 0 0 0 0 0 0 0 0 CT 0 0 0 0 0 0 0 0 DE 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 NC 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 NJ 0 0 0 0 0 0 0 0 NY 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

Downwind Nonattainment Receptor

Contributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 11 Base Case: Total Num
Fairfax VA

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

MD 53 41 47 73 100 52 64 65 PA 8 6 7 73 100 7 29 31 OH 4 5 6 63 86 6 11 13 WV 2 2 2 3 4 2 11 12 IN 2 2 2 17 23 3 5 5 DE 1 1 1 1 1 2 4 5 IL 2 2 3 14 19 3 4 5 NJ 1 1 1 0 0 2 4 5 MI 1 2 2 1 1 2 3 3 NY 2 2 2 0 0 2 4 5 MO 2 2 2 0 0 2 3 3 LA 1 1 1 0 0 2 2 2 AR 1 1 1 0 0 1 1 1 WI 0 0 0 0 0 0 1 1 CT 0 0 0 0 0 0 0 0 IA 0 0 1 0 0 1 1 1 MS 0 0 0 0 0 1 1 1 AL 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 KY 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MN 0 0 0 0 0 0 0 0 NC 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteriaContributions exceed screening criteria

Downwind Nonattainment Receptor

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 73 Base Case: Total Num
Kenosha WI

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

IL 55 47 48 136 100 57 100 100 IN 11 14 15 113 83 19 32 31 MO 9 10 10 136 100 12 35 35 IA 1 4 5 14 10 5 5 6 AR 3 3 4 59 43 4 5 6 GA 1 2 2 3 2 2 1 2 AL 1 2 3 21 15 3 2 2 MI 1 1 1 6 4 3 2 2 LA 2 3 4 38 28 4 2 2 MS 1 2 2 1 1 2 1 2 TN 1 2 2 0 0 2 1 1 KY 0 1 1 0 0 1 0 0 MN 0 0 0 0 0 0 0 0 CT 0 0 0 0 0 0 0 0 DE 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 MD 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 NC 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 NJ 0 0 0 0 0 0 0 0 NY 0 0 0 0 0 0 0 0 OH 0 0 0 0 0 0 0 0 PA 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 VA 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 WV 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

Downwind Nonattainment Receptor

Contributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 136 Base Case: Total Num
Ozaukee WI

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

IL 49 37 40 62 100 43 100 100 IN 10 10 10 47 76 14 33 33 MO 11 9 10 62 100 10 38 40 IA 2 3 4 7 11 4 5 4 LA 1 3 4 14 23 3 1 2 AR 1 1 1 0 0 1 0 1 MI 0 0 0 0 0 1 1 1 MN 0 0 0 0 0 0 1 1 MS 0 1 1 0 0 1 0 0 AL 0 0 0 0 0 0 0 0 CT 0 0 0 0 0 0 0 0 DE 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 KY 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 MD 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 NC 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 NJ 0 0 0 0 0 0 0 0 NY 0 0 0 0 0 0 0 0 OH 0 0 0 0 0 0 0 0 PA 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 VA 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 WV 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

Downwind Nonattainment Receptor

Contributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 62 Base Case: Total Num
Sheboygan WI

Upwind State

Average 3-episode % contribution

Highest daily average (ppb)

Highest daily average (%)

# reduced >= 2 ppb

% reduced >= 2 ppb

max 8-hr ppb contribution

% total ppb reduced

% pop-wgt total ppb reduced

IL 41 32 36 39 100 36 100 100 IN 9 9 10 26 67 12 47 29 MO 11 9 10 39 100 10 59 49 IA 3 4 4 13 33 4 15 31 MI 1 1 1 0 0 1 1 1 AR 1 1 1 0 0 1 -1 -3 MN 0 0 0 0 0 0 1 1 AL 0 0 0 0 0 0 0 0 CT 0 0 0 0 0 0 0 0 DE 0 0 0 0 0 0 0 0 FL 0 0 0 0 0 0 0 0 GA 0 0 0 0 0 0 0 0 KY 0 0 0 0 0 0 0 0 LA 0 0 0 0 0 0 0 0 MA 0 0 0 0 0 0 0 0 MD 0 0 0 0 0 0 0 0 ME 0 0 0 0 0 0 0 0 MS 0 0 0 0 0 0 0 0 NC 0 0 0 0 0 0 0 0 NH 0 0 0 0 0 0 0 0 NJ 0 0 0 0 0 0 0 0 NY 0 0 0 0 0 0 0 0 OH 0 0 0 0 0 0 0 0 PA 0 0 0 0 0 0 0 0 RI 0 0 0 0 0 0 0 0 SC 0 0 0 0 0 0 0 0 TN 0 0 0 0 0 0 0 0 VA 0 0 0 0 0 0 0 0 VT 0 0 0 0 0 0 0 0 WV 0 0 0 0 0 0 0 0 

Contributions do not exceed screening criteria

Downwind Nonattainment Receptor

Contributions exceed screening criteria

CAMX Source Apportionment Modeling CAMX Sta

Base Case: Total Number of Exceedances (grid-hours) = 39 Base Case: Total Num

Technical Support Document

for the Final

Clean Air Interstate Rule

Air Quality Modeling Analyses

Appendix H

PM2.5 Contributions to 

Each Nonattainment County in 2010
Downwind Nonattainment Counties Upwind States - Part 1

State County AL FL GA IL IN IA 

Alabama Jefferson 2.29 0.24 0.72 0.21 0.31 0.07 Alabama Russell 1.05 0.45 1.24 0.14 0.22 0.05 Delaware New Castle 0.05 < 0.05 0.10 0.12 0.15 < 0.05 District of Columbia 0.08 < 0.05 0.15 0.13 0.19 0.05 Georgia Bibb 0.62 0.35 1.85 0.13 0.20 < 0.05 Georgia Clarke 0.63 0.25 2.42 0.17 0.27 0.05 Georgia Clayton 0.66 0.22 1.81 0.13 0.21 < 0.05 Georgia Cobb 0.73 0.20 2.07 0.14 0.23 < 0.05 Georgia DeKalb 0.64 0.22 1.84 0.13 0.21 < 0.05 Georgia Floyd 0.95 0.13 1.42 0.14 0.22 < 0.05 Georgia Fulton 0.54 0.18 1.87 0.13 0.20 < 0.05 Georgia Walker 0.91 0.13 1.16 0.18 0.32 0.05 Illinois Cook 0.06 < 0.05 < 0.05 1.04 0.66 0.28 Illinois Madison 0.13 < 0.05 0.09 0.80 0.47 0.27 Illinois St. Clair 0.12 < 0.05 0.08 0.83 0.48 0.28 Indiana Clark 0.29 0.05 0.32 0.39 0.90 0.11 Indiana Dubois 0.26 0.05 0.19 0.58 1.22 0.14 Indiana Lake 0.07 < 0.05 0.05 1.02 0.89 0.26 Indiana Marion 0.16 < 0.05 0.12 0.76 1.44 0.21 Indiana Vanderburgh 0.33 0.06 0.21 0.76 1.11 0.18 Kentucky Fayette 0.30 0.06 0.39 0.32 0.68 0.09 Kentucky Jefferson 0.27 0.05 0.30 0.38 0.87 0.11 Maryland Anne Arundel 0.07 < 0.05 0.13 0.13 0.18 0.05 Maryland Baltimore City 0.07 < 0.05 0.12 0.13 0.17 0.05 Michigan Wayne 0.09 < 0.05 0.10 0.42 0.57 0.14 
Downwind Nonattainment Counties Upwind States - Part 1

State County AL FL GA IL IN IA 

New Jersey Union 0.05 < 0.05 0.08 0.12 0.15 0.05 New York New York < 0.05 < 0.05 0.08 0.11 0.14 < 0.05 North Carolina Catawba 0.28 0.09 0.56 0.15 0.26 0.05 North Carolina Davidson 0.23 0.10 0.56 0.12 0.20 < 0.05 Ohio Butler 0.18 < 0.05 0.24 0.38 0.87 0.11 Ohio Cuyahoga 0.08 < 0.05 0.11 0.32 0.43 0.11 Ohio Franklin 0.20 < 0.05 0.26 0.40 0.67 0.11 Ohio Hamilton 0.18 < 0.05 0.24 0.38 0.91 0.12 Ohio Jefferson 0.08 < 0.05 0.11 0.18 0.25 0.07 Ohio Lawrence 0.21 < 0.05 0.25 0.21 0.40 0.06 Ohio Mahoning 0.08 < 0.05 0.10 0.25 0.32 0.09 Ohio Montgomery 0.15 < 0.05 0.20 0.44 0.85 0.12 Ohio Scioto 0.25 0.05 0.29 0.25 0.48 0.08 Ohio Stark 0.09 < 0.05 0.12 0.26 0.36 0.09 Ohio Summit 0.09 < 0.05 0.12 0.30 0.41 0.10 Pennsylvania Allegheny 0.10 < 0.05 0.14 0.21 0.31 0.08 Pennsylvania Beaver 0.08 < 0.05 0.10 0.19 0.26 0.07 Pennsylvania Berks 0.07 < 0.05 0.10 0.14 0.19 0.06 Pennsylvania Cambria 0.07 < 0.05 0.09 0.14 0.20 0.05 Pennsylvania Dauphin 0.07 < 0.05 0.12 0.14 0.19 0.06 Pennsylvania Delaware 0.05 < 0.05 0.10 0.12 0.15 0.05 Pennsylvania Lancaster 0.07 < 0.05 0.12 0.15 0.20 0.06 Pennsylvania Philadelphia 0.05 < 0.05 0.10 0.12 0.15 0.05 Pennsylvania Washington 0.09 < 0.05 0.12 0.18 0.27 0.07 Pennsylvania Westmoreland 0.09 < 0.05 0.12 0.18 0.26 0.06 Pennsylvania York 0.06 < 0.05 0.10 0.14 0.18 0.05 Tennessee Hamilton 0.98 0.14 1.27 0.20 0.34 0.06 
Downwind Nonattainment Counties Upwind States - Part 1

State County AL FL GA IL IN IA 

Tennessee Knox 0.48 0.10 0.86 0.17 0.32 0.05 West Virginia Berkeley 0.08 < 0.05 0.13 0.14 0.21 0.05 West Virginia Brooke 0.07 < 0.05 0.10 0.17 0.24 0.06 West Virginia Cabell 0.24 0.06 0.29 0.21 0.42 0.07 West Virginia Hancock 0.08 < 0.05 0.11 0.18 0.26 0.07 West Virginia Kanawha 0.18 0.05 0.24 0.20 0.35 0.06 West Virginia Marion 0.10 < 0.05 0.15 0.17 0.26 0.05 West Virginia Marshall 0.09 < 0.05 0.13 0.18 0.27 0.07 West Virginia Ohio 0.09 < 0.05 0.13 0.18 0.27 0.06 West Virginia Wood 0.12 < 0.05 0.16 0.19 0.31 0.06 
Downwind Nonattainment Counties Upwind States - Part 2

State County MD/DC MI MN MS MO NY 

Alabama Jefferson 0.07 0.08 < 0.05 0.23 0.15 < 0.05 Alabama Russell 0.09 0.07 < 0.05 0.17 0.11 < 0.05 Connecticut New Haven 0.15 0.12 < 0.05 < 0.05 < 0.05 0.31 Delaware New Castle 0.45 0.21 < 0.05 < 0.05 0.05 0.20 District of Columbia 0.51 0.17 < 0.05 < 0.05 0.06 0.11 Georgia Bibb 0.11 0.07 < 0.05 0.12 0.10 < 0.05 Georgia Clarke 0.10 0.10 < 0.05 0.10 0.12 < 0.05 Georgia Clayton 0.06 0.07 < 0.05 0.09 0.09 < 0.05 Georgia Cobb 0.06 0.08 < 0.05 0.09 0.10 < 0.05 Georgia DeKalb 0.06 0.07 < 0.05 0.09 0.09 < 0.05 Georgia Floyd 0.05 0.08 < 0.05 0.08 0.10 < 0.05 Georgia Fulton 0.06 0.07 < 0.05 0.08 0.09 < 0.05 Georgia Walker 0.06 0.11 < 0.05 0.09 0.13 < 0.05 Illinois Cook < 0.05 0.40 0.21 < 0.05 0.31 < 0.05 Illinois Madison < 0.05 0.13 0.13 0.09 1.05 < 0.05 Illinois St. Clair < 0.05 0.13 0.13 0.08 1.07 < 0.05 Indiana Clark < 0.05 0.20 < 0.05 0.06 0.22 < 0.05 Indiana Dubois < 0.05 0.19 0.05 0.06 0.30 < 0.05 Indiana Lake < 0.05 0.35 0.20 < 0.05 0.29 < 0.05 Indiana Marion < 0.05 0.30 0.10 0.05 0.27 < 0.05 Indiana Vanderburgh < 0.05 0.17 0.07 0.09 0.40 < 0.05 Kentucky Fayette < 0.05 0.25 < 0.05 0.06 0.17 < 0.05 Kentucky Jefferson < 0.05 0.19 < 0.05 0.06 0.20 < 0.05 Maryland Anne Arundel 0.86 0.18 < 0.05 < 0.05 0.06 0.14 Maryland Baltimore City 0.75 0.19 < 0.05 < 0.05 0.06 0.15 
Downwind Nonattainment Counties Upwind States - Part 2

State County MD/DC MI MN MS MO NY 

Michigan Wayne 0.05 1.04 0.11 < 0.05 0.14 0.08 New Jersey Union 0.25 0.20 < 0.05 < 0.05 0.05 0.34 New York New York 0.25 0.19 < 0.05 < 0.05 < 0.05 0.51 North Carolina Catawba 0.10 0.13 < 0.05 < 0.05 0.10 < 0.05 North Carolina Davidson 0.14 0.12 < 0.05 < 0.05 0.07 0.06 Ohio Butler < 0.05 0.29 < 0.05 < 0.05 0.17 < 0.05 Ohio Cuyahoga 0.08 0.62 0.06 < 0.05 0.12 0.09 Ohio Franklin < 0.05 0.41 0.05 < 0.05 0.15 < 0.05 Ohio Hamilton < 0.05 0.30 < 0.05 0.05 0.18 < 0.05 Ohio Jefferson 0.13 0.29 < 0.05 < 0.05 0.09 0.08 Ohio Lawrence 0.06 0.20 < 0.05 < 0.05 0.11 < 0.05 Ohio Mahoning 0.11 0.44 0.05 < 0.05 0.10 0.11 Ohio Montgomery < 0.05 0.36 0.05 < 0.05 0.18 < 0.05 Ohio Scioto 0.06 0.24 < 0.05 0.05 0.13 < 0.05 Ohio Stark 0.10 0.42 < 0.05 < 0.05 0.11 0.09 Ohio Summit 0.08 0.54 0.05 < 0.05 0.12 0.09 Pennsylvania Allegheny 0.19 0.33 0.05 < 0.05 0.10 0.11 Pennsylvania Beaver 0.13 0.34 < 0.05 < 0.05 0.09 0.10 Pennsylvania Berks 0.48 0.25 < 0.05 < 0.05 0.07 0.24 Pennsylvania Cambria 0.23 0.21 < 0.05 < 0.05 0.08 0.11 Pennsylvania Dauphin 0.43 0.23 < 0.05 < 0.05 0.07 0.17 Pennsylvania Delaware 0.44 0.20 < 0.05 < 0.05 0.05 0.19 Pennsylvania Lancaster 0.69 0.27 < 0.05 < 0.05 0.07 0.21 Pennsylvania Philadelphia 0.44 0.20 < 0.05 < 0.05 0.05 0.19 Pennsylvania Washington 0.16 0.29 < 0.05 < 0.05 0.09 0.09 Pennsylvania Westmoreland 0.20 0.27 < 0.05 < 0.05 0.09 0.09 Pennsylvania York 0.47 0.24 < 0.05 < 0.05 0.06 0.18 
Downwind Nonattainment Counties Upwind States - Part 2

State County MD/DC MI MN MS MO NY 

Tennessee Hamilton 0.06 0.12 < 0.05 0.10 0.14 < 0.05 Tennessee Knox 0.06 0.12 < 0.05 0.06 0.14 < 0.05 West Virginia Berkeley 0.34 0.18 < 0.05 < 0.05 0.08 0.10 West Virginia Brooke 0.12 0.27 < 0.05 < 0.05 0.08 0.07 West Virginia Cabell 0.06 0.20 < 0.05 < 0.05 0.12 < 0.05 West Virginia Hancock 0.12 0.29 < 0.05 < 0.05 0.09 0.08 West Virginia Kanawha 0.09 0.19 < 0.05 < 0.05 0.11 < 0.05 West Virginia Marion 0.15 0.21 < 0.05 < 0.05 0.08 0.06 West Virginia Marshall 0.13 0.27 < 0.05 < 0.05 0.08 0.07 West Virginia Ohio 0.13 0.27 < 0.05 < 0.05 0.08 0.07 West Virginia Wood 0.09 0.24 < 0.05 < 0.05 0.08 < 0.05 
Downwind Nonattainment Counties Upwind States - Part 3

State County PA SC TN TX VA W

Alabama Jefferson 0.10 0.12 0.32 0.19 0.06 0.1Alabama Russell 0.14 0.23 0.22 0.15 0.08 0.1Connecticut New Haven 0.39 < 0.05 < 0.05 < 0.05 0.08 0.1Delaware New Castle 0.88 0.06 0.08 < 0.05 0.25 0.3District of Columbia 0.69 0.09 0.13 < 0.05 0.44 0.4Georgia Bibb 0.19 0.32 0.23 0.14 0.10 0.1Georgia Clarke 0.21 0.40 0.40 0.13 0.11 0.2Georgia Clayton 0.13 0.23 0.27 0.11 0.07 0.1Georgia Cobb 0.12 0.20 0.34 0.11 0.07 0.1Georgia DeKalb 0.13 0.22 0.31 0.11 0.07 0.1Georgia Floyd 0.08 0.13 0.34 0.10 0.05 0.1Georgia Fulton 0.12 0.21 0.30 0.10 0.07 0.1Georgia Walker 0.10 0.16 0.65 0.12 0.06 0.1Illinois Cook < 0.05 < 0.05 0.08 0.12 < 0.05 < 0Illinois Madison < 0.05 < 0.05 0.18 0.29 < 0.05 0.0Illinois St. Clair < 0.05 < 0.05 0.17 0.28 < 0.05 < 0Indiana Clark 0.08 0.05 0.36 0.13 < 0.05 0.1Indiana Dubois 0.06 < 0.05 0.30 0.16 < 0.05 0.1Indiana Lake < 0.05 < 0.05 0.09 0.11 < 0.05 0.0Indiana Marion 0.08 < 0.05 0.18 0.12 < 0.05 0.1Indiana Vanderburgh 0.06 < 0.05 0.36 0.19 < 0.05 0.0Kentucky Fayette 0.12 0.08 0.44 0.11 0.05 0.2Kentucky Jefferson 0.08 0.05 0.33 0.12 < 0.05 0.1Maryland Anne Arundel 0.78 0.08 0.12 < 0.05 0.39 0.4Maryland Baltimore City 0.89 0.07 0.11 < 0.05 0.31 0.4
Downwind Nonattainment Counties Upwind States - Part 3

State County PA SC TN TX VA W

Michigan Wayne 0.28 < 0.05 0.09 0.08 < 0.05 0.1New Jersey Union 0.81 0.05 0.07 < 0.05 0.14 0.2New York New York 0.81 < 0.05 0.06 < 0.05 0.13 0.2North Carolina Catawba 0.21 0.28 0.62 0.09 0.22 0.3North Carolina Davidson 0.30 0.39 0.30 0.07 0.25 0.3Ohio Butler 0.10 < 0.05 0.23 0.10 < 0.05 0.2Ohio Cuyahoga 0.34 < 0.05 0.12 0.06 0.05 0.2Ohio Franklin 0.19 < 0.05 0.19 0.09 < 0.05 0.3Ohio Hamilton 0.11 0.05 0.25 0.11 < 0.05 0.2Ohio Jefferson 0.54 < 0.05 0.13 0.06 0.08 0.6Ohio Lawrence 0.20 0.07 0.33 0.07 0.09 0.6Ohio Mahoning 0.54 < 0.05 0.11 0.06 0.07 0.4Ohio Montgomery 0.11 < 0.05 0.18 0.10 < 0.05 0.2Ohio Scioto 0.18 0.09 0.38 0.10 0.08 0.5Ohio Stark 0.44 < 0.05 0.12 0.07 0.07 0.4Ohio Summit 0.36 < 0.05 0.12 0.06 0.06 0.3Pennsylvania Allegheny 0.92 < 0.05 0.16 0.07 0.13 0.8Pennsylvania Beaver 0.59 < 0.05 0.12 0.05 0.08 0.4Pennsylvania Berks 1.38 0.05 0.10 < 0.05 0.21 0.4Pennsylvania Cambria 1.12 < 0.05 0.10 0.05 0.15 0.5Pennsylvania Dauphin 1.17 0.05 0.11 < 0.05 0.23 0.4Pennsylvania Delaware 0.87 0.06 0.08 < 0.05 0.24 0.3Pennsylvania Lancaster 1.54 0.06 0.11 < 0.05 0.28 0.4Pennsylvania Philadelphia 0.85 0.06 0.09 < 0.05 0.24 0.3Pennsylvania Washington 0.67 < 0.05 0.14 0.06 0.11 0.7Pennsylvania Westmoreland 0.94 < 0.05 0.14 0.05 0.14 0.8Pennsylvania York 1.43 < 0.05 0.10 < 0.05 0.23 0.4
Downwind Nonattainment Counties Upwind States - Part 3

State County PA SC TN TX VA W

Tennessee Hamilton 0.10 0.18 0.69 0.12 0.06 0.1Tennessee Knox 0.11 0.19 1.20 0.11 0.06 0.2West Virginia Berkeley 0.84 0.05 0.12 0.05 0.24 0.5West Virginia Brooke 0.52 < 0.05 0.12 0.05 0.08 0.5West Virginia Cabell 0.20 0.09 0.39 0.08 0.11 0.5West Virginia Hancock 0.52 < 0.05 0.12 0.06 0.08 0.5West Virginia Kanawha 0.29 0.08 0.31 0.07 0.17 1.1West Virginia Marion 0.53 < 0.05 0.17 0.05 0.14 1.0West Virginia Marshall 0.53 < 0.05 0.15 0.06 0.10 0.7West Virginia Ohio 0.54 < 0.05 0.15 0.05 0.10 0.7West Virginia Wood 0.32 0.05 0.18 0.06 0.09 0.7
Technical Support Document

for the Final

Clean Air Interstate Rule

Appendix I

Projected Visibility for the

20% Best and 20% Worst Days in

Class I Areas
Example Calculation of the Predicted Change in Visibility on the 20% Worst Days at an IMPROVE Site

Day

2001 

IMPROVE 

Deciviews

 IMPROVE 

bext

 IMPROVE 

SO4 bext

IMPROVE 

NO3 bext

IMPROVE 

OMC bext

IMPROVE 

EC bext

 IMPROVE 

soil bext

 IMPROVE 

coarse bext

2015c RRF 

SO4 

2015c RRF 

NO3 

2015c RRF 

OMC 2015c RRF EC 

2015c RRF 

soil 

2015c RRF 

coarse 

Day 1 21.66 87.27 46.45 14.53 9.10 3.87 0.46 2.86 1.09 1.01 0.85 0.72 0.98 1.08

Day 2 21.50 85.83 53.58 4.50 7.79 3.92 0.65 5.38 0.94 1.04 0.95 0.76 1.02 1.10

Day 3 23.54 105.31 67.83 11.24 8.32 4.26 0.30 3.35 0.54 0.74 0.80 0.62 1.08 1.10

Day 4 20.11 74.72 53.60 1.93 4.84 3.04 0.13 1.18 0.57 1.70 0.70 0.50 1.06 1.08

Day 5 21.85 88.93 51.43 7.38 10.18 1.43 0.25 8.26 0.58 1.84 0.77 0.48 1.13 1.13

Day 6 23.83 108.41 74.33 4.42 10.62 4.13 0.28 4.63 0.58 1.14 0.83 0.52 1.13 1.11

Day 7 24.69 118.16 80.05 5.64 12.08 7.95 0.47 1.98 0.59 0.98 0.73 0.43 1.13 1.11

Day 8 22.34 93.35 41.44 1.65 30.37 6.73 0.28 2.88 0.74 0.70 0.91 0.81 1.07 1.11

Day 9 22.47 94.56 59.46 4.35 12.62 3.66 0.29 4.19 0.87 0.43 0.80 0.55 1.13 1.13

Day 10 24.11 111.40 88.74 1.26 6.50 3.49 0.02 1.39 0.61 0.42 0.78 0.45 1.14 1.12

Day 11 32.94 269.47 235.95 1.29 15.09 6.52 0.09 0.54 0.55 0.99 0.77 0.50 1.11 1.11

Day 12 25.23 124.60 90.91 4.25 12.87 4.67 0.33 1.58 0.70 0.38 0.88 0.62 1.12 1.13

Day 13 30.50 211.16 179.59 3.26 11.20 5.42 0.07 1.62 0.53 1.27 0.73 0.48 1.11 1.10

Day 14 22.30 93.00 57.95 8.26 10.37 3.50 0.28 2.65 0.60 1.28 0.76 0.44 1.11 1.11

Day 15 24.07 111.05 77.79 3.03 13.68 3.47 0.23 2.85 0.73 1.26 0.87 0.57 1.08 1.09

Day 16 23.37 103.49 69.05 2.34 10.74 4.87 0.33 6.15 0.82 0.88 0.89 0.77 1.08 1.10

Day 17 20.27 75.91 54.19 1.77 4.66 2.45 0.16 2.69 1.00 0.94 0.94 0.97 1.00 1.06

Day 18 19.98 73.76 46.74 2.46 6.05 5.49 0.15 2.85 0.74 0.66 0.80 0.68 0.96 1.07

Day 19 22.15 91.58 65.68 2.83 6.14 4.43 0.11 2.38 0.76 1.18 0.82 0.65 0.99 1.06

Average dv 23.52 Step 2

2001 Observed values at an IMPROVE site Relative Reduction Factors are calculated from CMAQ for each species

Step 1 (10 Mm-1 is added to each total bext value to account for Rayleigh scattering) based on the model predicted % reduction for each day.

RRFs represent the predicted reduction from the 2001 base case to the

2015 CAIR control case. An RRF of 0.85 indicates a 15% reduction.

2015c SO4 

bext

2015c 

NO3 bext

2015c 

OMC bext

2015c EC 

bext

2015c soil 

bext

2015c 

coarse 

bext

2015c Total 

bext

2015c 

Deciviews 2001 dv

1998-2002 

dv

2015c final 

"normalized" 

dv

50.72 14.73 7.74 2.80 0.45 3.08 89.52 21.92 23.5 24.7 22.0

50.56 4.67 7.43 2.99 0.66 5.93 82.24 21.07 Step 4

36.96 8.35 6.68 2.62 0.32 3.67 68.60 19.26 The future year dv value is normalized 

30.32 3.27 3.39 1.52 0.13 1.27 49.90 16.07 by the difference between the observed 

29.60 13.55 7.84 0.69 0.28 9.29 71.25 19.64 value in 2001 and the 5 year average for 

43.09 5.01 8.77 2.14 0.32 5.13 74.46 20.08 the 1998-2002 time period. In this case

47.55 5.52 8.80 3.39 0.53 2.20 77.98 20.54 the future year dv value is adjusted 

30.47 1.16 27.58 5.48 0.30 3.20 78.18 20.56 upwards by the difference between 

51.88 1.88 10.11 2.01 0.33 4.74 80.94 20.91 23.52 dv and 24.67 dv. In this way,

54.45 0.52 5.04 1.58 0.02 1.56 73.18 19.90 the future year dv values are 

129.76 1.28 11.58 3.28 0.10 0.60 156.59 27.51 numerically consistant with the base

63.97 1.63 11.29 2.90 0.36 1.78 91.93 22.18 year 5 year average visibility impairment.

95.33 4.13 8.23 2.59 0.08 1.79 122.15 25.03 The final values are rounded to the 

34.82 10.55 7.86 1.55 0.31 2.93 68.03 19.17 tenths digit.

57.15 3.82 11.93 1.99 0.25 3.11 88.25 21.78

56.81 2.06 9.60 3.76 0.35 6.77 89.36 21.90

54.39 1.66 4.36 2.38 0.16 2.85 75.80 20.26

34.51 1.62 4.82 3.73 0.15 3.05 57.87 17.56

49.62 3.34 5.05 2.87 0.10 2.52 73.50 19.95

Average dv 2015c 20.80

Step 3

The RRFs are multiplied by the base year bext values to get the 2015 control bext predictions.

The daily total bext values are converted to deciviews and then the deciview values are 

averaged across all days. 

Page 1
Table Appendix I-2. Current visibility (1998-2002) on the 20% best days and 20% worst days at 116 IMPROVE sites.

Class I Area

IMPROVE

Representative Site

IMPROVE

Site

Identifier State

Number of Years of

Complete Data

1998-2002 Baseline

Visibility (in dv)

20% Best Days

1998-2002 Baseline

Visibility (in dv)

20% Worst Days

Acadia Acadia ACAD ME 5 8.4 22.7

Agua Tibia Agua Tibia AGTI CA 2 10.0 23.2

Alpine Lakes Snoqualmie Pass SNPA WA 3 5.6 18.0

Anaconda - Pintler Sula SULA MT 2 3.1 12.3

Arches Canyonlands CANY UT 5 5.3 12.0

Badlands Badlands BADL SD 5 7.1 17.3

Bandelier Bandelier BAND NM 5 6.3 13.2

Big Bend Big Bend BIBE TX 4 7.7 18.4

Black Canyon of the

Gunnison

Weminuche WEMI CO 4 4.4 11.6

Bob Marshall Monture MONT MT 2 4.5 14.2

Boundary Waters Canoe

Area

Boundary Waters BOWA MN 3 6.4 20.0

Bridger Bridger BRID WY 5 3.8 11.5

Brigantine Brigantine BRIG NJ 4 13.6 27.6

Bryce Canyon Bryce Canyon BRCA UT 5 4.1 12.0

Cabinet Mountains Cabinet Mountains CABI MT 2 4.1 13.8

Caney Creek Caney Creek CACR AR 2 12.1 25.9

Canyonlands Canyonlands CANY UT 5 5.3 12.0

Cape Romain Cape Romain ROMA SC 4 13.8 25.9

Caribou Lassen Volcanic LAVO CA 5 3.3 14.8

Carlsbad Caverns Guadalupe Mountains GUMO NM 5 7.2 17.6

Chassahowitzka Chassahowitzka CHAS FL 4 16.4 25.7

Chiricahua NM Chiricahua CHIR AZ 5 5.9 13.9
Class I Area

IMPROVE

Representative Site

IMPROVE

Site

Identifier State

Number of Years of

Complete Data

1998-2002 Baseline

Visibility (in dv)

20% Best Days

1998-2002 Baseline

Visibility (in dv)

20% Worst Days

Chiricahua W Chiricahua CHIR AZ 5 5.9 13.9

Craters of the Moon Craters of the Moon CRMO ID 2 5.0 14.7

Desolation Bliss BLIS CA 3 3.5 12.9

Dolly Sods Dolly Sods DOSO WV 5 13.0 27.6

Dome Land Dome Land DOME CA 2 5.4 20.3

Eagle Cap Starkey STAR OR 2 5.6 19.6

Eagles Nest White River WHRI CO 2 2.8 11.3

Emigrant Yosemite YOSE CA 5 4.0 17.6

Everglades Everglades EVER FL 2 10.8 20.3

Fitzpatrick Bridger BRID WY 5 3.8 11.5

Flat Tops White River WHRI CO 2 2.8 11.3

Galiuro Chiricahua CHIR AZ 5 5.9 13.9

Gates of the Mountains Gates of the Mountains GAMO MT 2 3.1 11.2

Gila Gila Cliffs GICL NM 4 5.1 13.5

Glacier Glacier GLAC MT 4 7.3 19.5

Glacier Peak North Cascades NOCA WA 2 2.8 14.0

Grand Teton Yellowstone YELL WY 4 3.7 12.1

Great Gulf Great Gulf GRGU NH 2 7.8 23.2

Great Sand Dunes Great Sand Dunes GRSA CO 5 5.7 13.1

Great Smoky Mountains Great Smoky Mountains GRSM TN 5 14.2 29.5

Guadalupe Mountains Guadalupe Mountains GUMO TX 5 7.2 17.6

Hells Canyon Hells Canyon HECA OR 2 5.4 18.1

Isle Royale Isle Royale ISLE MI 3 6.2 21.1

James River Face James River Face JARI VA 3 15.5 28.5

Jarbidge Jarbidge JARB NV 3 3.0 12.6
Class I Area

IMPROVE

Representative Site

IMPROVE

Site

Identifier State

Number of Years of

Complete Data

1998-2002 Baseline

Visibility (in dv)

20% Best Days

1998-2002 Baseline

Visibility (in dv)

20% Worst Days

Joshua Tree Joshua Tree JOSH CA 2 6.6 19.5

Joyce Kilmer - Slickrock Great Smoky Mountains GRSM NC 5 14.2 29.5

Kalmiopsis Kalmiopsis KALM OR 2 4.7 14.8

Kings Canyon Sequoia SEQU CA 3 8.8 23.5

La Garita Weminuche WEMI CO 4 4.4 11.6

Lassen Volcanic Lassen Volcanic LAVO CA 5 3.3 14.8

Lava Beds Lava Beds LABE CA 2 3.7 16.6

Linville Gorge Linville Gorge LIGO NC 2 12.2 27.9

Lostwood Lostwood LOST ND 3 8.3 19.6

Lye Brook Lye Brook LYBR VT 4 6.6 23.9

Mammoth Cave Mammoth Cave MACA KY 4 16.5 30.2

Marble Mountain Trinity TRIN CA 1 3.5 17.1

Maroon Bells -

Snowmass

White River WHRI CO 2 2.8 11.3

Mazatzal Ike's Backbone IKBA AZ 2 6.1 13.1

Medicine Lake Medicine Lake MELA MT 3 7.5 17.7

Mesa Verde Mesa Verde MEVE CO 5 5.5 12.8

Mingo Mingo MING MO 2 14.1 27.5

Mission Mountains Monture MONT MT 2 4.5 14.2

Mokelumne Bliss BLIS CA 3 3.5 12.9

Moosehorn Moosehorn MOOS ME 5 8.6 21.4

Mount Hood Mount Hood MOHO OR 2 2.5 14.0

Mount Jefferson Three Sisters THSI OR 5 2.8 15.7

Mount Rainier Mount Rainier MORA WA 5 4.9 18.9

Mount Washington Three Sisters THSI OR 5 2.8 15.7

Mount Zirkel Mount Zirkel MOZI CO 4 4.4 11.7
Class I Area

IMPROVE

Representative Site

IMPROVE

Site

Identifier State

Number of Years of

Complete Data

1998-2002 Baseline

Visibility (in dv)

20% Best Days

1998-2002 Baseline

Visibility (in dv)

20% Worst Days

North Cascades North Cascades NOCA WA 2 2.8 14.0

Okefenokee Okefenokee OKEF GA 5 15.5 26.4

Otter Creek Dolly Sods DOSO WV 5 13.0 27.6

Pasayten Pasayten PASA WA 2 2.9 14.7

Petrified Forest Petrified Forest PEFO AZ 5 6.3 13.5

Pine Mountain Ike's Backbone IKBA AZ 2 6.1 13.1

Presidential Range - Dry Great Gulf GRGU NH 2 7.8 23.2

Rawah Mount Zirkel MOZI CO 4 4.4 11.7

Red Rock Lakes Yellowstone YELL WY 4 3.7 12.1

Redwood Redwood REDW CA 5 5.0 16.5

Rocky Mountain Rocky Mountain ROMO CO 5 3.7 14.1

Roosevelt Campobello Moosehorn MOOS ME 5 8.6 21.4

Salt Creek Salt Creek SACR NM 2 8.3 17.7

San Gorgonio San Gorgonio SAGO CA 4 6.8 21.5

San Jacinto San Gorgonio SAGO CA 4 6.8 21.5

San Pedro Parks San Pedro Parks SAPE NM 2 3.5 11.4

Sawtooth Sawtooth SAWT ID 2 4.3 13.6

Scapegoat Monture MONT MT 2 4.5 14.2

Selway - Bitterroot Sula SULA MT 2 3.1 12.3

Seney Seney SENE MI 3 6.4 23.8

Sequoia Sequoia SEQU CA 3 8.8 23.5

Shenandoah Shenandoah SHEN VA 4 12.2 27.6

Sierra Ancha Sierra Ancha SIAN AZ 2 6.8 13.4

Sipsey Sipsey SIPS AL 4 16.3 28.7

South Warner Lava Beds LABE CA 2 3.7 16.6
Class I Area

IMPROVE

Representative Site

IMPROVE

Site

Identifier State

Number of Years of

Complete Data

1998-2002 Baseline

Visibility (in dv)

20% Best Days

1998-2002 Baseline

Visibility (in dv)

20% Worst Days

Strawberry Mountain Starkey STAR OR 2 5.6 19.6

Superstition Tonto TONT AZ 3 7.4 14.7

Swanquarter Swanquarter SWAN NC 2 12.4 24.6

Sycamore Canyon Sycamore Canyon SYCA AZ 2 6.7 16.1

Teton Yellowstone YELL WY 4 3.7 12.1

Theodore Roosevelt Theodore Roosevelt THRO ND 2 7.8 17.6

Thousand Lakes Lassen Volcanic LAVO CA 5 3.3 14.8

Three Sisters Three Sisters THSI OR 5 2.8 15.7

UL Bend UL Bend ULBE MT 2 4.6 14.7

Upper Buffalo Upper Buffalo UPBU AR 5 12.2 25.5

Voyageurs Voyageurs VOYA MN 3 6.4 18.4

Weminuche Weminuche WEMI CO 4 4.4 11.6

West Elk White River WHRI CO 2 2.8 11.3

Wind Cave Wind Cave WICA SD 3 5.7 16.0

Wolf Island Okefenokee OKEF GA 5 15.5 26.4

Yellowstone Yellowstone YELL WY 4 3.7 12.1

Yolla Bolly - Middle Eel Trinity TRIN CA 1 3.5 17.1

Yosemite Yosemite YOSE CA 5 4.0 17.6

Zion Zion ZION UT 2 5.4 13.5
Table Appendix I-3. Projected visibility for the 2010 and 2015 baseline and visibility improvement from the CAIR strategy on

the 20% best days, at 116 Class I areas.

Class I Areas

(IMPROVE Site) State

1998-2002 Baseline

Visibility (in dv)

20% Best Days 

2010 Baseline

Visibility (dv)

2010 Visibility

Improvement from

CAIR (dv)

2015 Baseline

Visibility (dv)

2015 Visibility

Improvement from

CAIR (dv)

Acadia ME 8.4 8.0 -0.2 8.2 -0.2

Agua Tibia CA 10.0 10.0 0.0 10.1 0.0

Alpine Lakes WA 5.6 4.9 0.0 5.6 0.0

Anaconda - Pintler MT 3.1 3.1 0.0 3.1 0.0

Arches UT 5.3 5.3 0.0 5.4 0.0

Badlands SD 7.1 6.7 0.0 6.8 0.0

Bandelier NM 6.3 6.2 0.0 6.3 0.0

Big Bend TX 7.7 7.6 0.0 7.7 0.0

Black Canyon of the Gunnison CO 4.4 4.2 0.0 4.2 0.0

Bob Marshall MT 4.5 4.4 0.0 4.4 0.0

Boundary Waters Canoe Area MN 6.4 6.1 0.0 6.2 0.0

Bridger WY 3.8 3.5 0.0 3.6 0.0

Brigantine NJ 13.6 13.4 -0.4 15.0 -0.4

Bryce Canyon UT 4.1 3.9 0.0 4.0 0.0

Cabinet Mountains MT 4.1 4.0 0.0 4.0 0.0

Caney Creek AR 12.1 11.5 -0.1 11.6 -0.1

Canyonlands UT 5.3 5.3 0.0 5.4 0.0

Cape Romain SC 13.8 13.2 -0.3 13.4 -0.4

Caribou CA 3.3 3.2 0.0 3.3 0.0

Carlsbad Caverns NM 7.2 7.3 0.0 7.4 0.0

Chassahowitzka FL 16.4 15.1 -0.2 15.2 -0.6

Chiricahua NM AZ 5.9 5.8 0.0 5.9 0.0

Chiricahua W AZ 5.9 5.8 0.0 5.9 0.0
Class I Areas

(IMPROVE Site) State

1998-2002 Baseline

Visibility (in dv)

20% Best Days 

2010 Baseline

Visibility (dv)

2010 Visibility

Improvement from

CAIR (dv)

2015 Baseline

Visibility (dv)

2015 Visibility

Improvement from

CAIR (dv)

Craters of the Moon ID 5.0 4.9 0.0 4.9 0.0

Desolation CA 3.5 3.6 0.0 3.7 0.0

Dolly Sods WV 13.0 12.7 -1.0 12.6 -1.1

Dome Land CA 5.4 5.3 0.0 6.0 0.0

Eagle Cap OR 5.6 5.3 0.0 5.3 0.0

Eagles Nest CO 2.8 2.7 0.0 2.8 0.0

Emigrant CA 4.0 4.0 0.0 4.1 0.0

Everglades FL 10.8 10.5 -0.1 11.3 -0.1

Fitzpatrick WY 3.8 3.5 0.0 3.6 0.0

Flat Tops CO 2.8 2.7 0.0 2.7 0.0

Galiuro AZ 5.9 6.0 0.0 6.1 0.0

Gates of the Mountains MT 3.1 2.9 0.0 2.9 0.0

Gila NM 5.1 5.2 0.0 5.3 0.0

Glacier MT 7.3 6.9 0.0 6.9 0.0

Glacier Peak WA 2.8 2.4 0.0 2.5 0.0

Grand Teton WY 3.7 3.6 0.0 3.6 0.0

Great Gulf NH 7.8 7.8 0.0 7.9 0.0

Great Sand Dunes CO 5.7 5.6 0.0 5.6 0.0

Great Smoky Mountains TN 14.2 14.0 -0.5 14.0 -0.7

Guadalupe Mountains TX 7.2 7.4 0.0 7.4 0.0

Hells Canyon OR 5.4 5.2 0.0 5.2 0.0

Isle Royale MI 6.2 6.1 0.0 6.1 0.0

James River Face VA 15.5 15.4 -0.8 15.4 -0.9

Jarbidge NV 3.0 3.0 0.0 3.0 0.0

Joshua Tree CA 6.6 6.7 -0.1 7.3 0.0

Joyce Kilmer - Slickrock NC 14.2 14.0 -0.5 14.0 -0.7
Class I Areas

(IMPROVE Site) State

1998-2002 Baseline

Visibility (in dv)

20% Best Days 

2010 Baseline

Visibility (dv)

2010 Visibility

Improvement from

CAIR (dv)

2015 Baseline

Visibility (dv)

2015 Visibility

Improvement from

CAIR (dv)

Kalmiopsis OR 4.7 4.6 0.0 4.9 0.0

Kings Canyon CA 8.8 9.2 0.0 9.4 0.0

La Garita CO 4.4 4.3 0.0 4.3 0.0

Lassen Volcanic CA 3.3 3.2 0.0 3.3 0.0

Lava Beds CA 3.7 3.6 0.0 3.7 0.0

Linville Gorge NC 12.2 11.9 -0.4 11.9 -0.5

Lostwood ND 8.3 8.0 0.0 8.0 0.0

Lye Brook VT 6.6 6.3 0.0 6.3 -0.0

Mammoth Cave KY 16.5 16.1 -0.5 16.0 -0.6

Marble Mountain CA 3.5 3.5 0.0 3.6 0.0

Maroon Bells - Snowmass CO 2.8 2.6 0.0 2.7 0.0

Mazatzal AZ 6.1 6.3 0.0 6.4 0.0

Medicine Lake MT 7.5 7.1 0.0 7.1 0.0

Mesa Verde CO 5.5 5.4 0.0 5.5 0.0

Mingo MO 14.1 13.6 -0.2 13.7 -0.2

Mission Mountains MT 4.5 4.1 0.0 4.0 0.0

Mokelumne CA 3.5 3.5 0.0 3.6 0.0

Moosehorn ME 8.6 8.6 -0.1 8.7 -0.1

Mount Hood OR 2.5 2.3 0.0 2.6 0.0

Mount Jefferson OR 2.8 2.6 0.0 2.7 0.0

Mount Rainier WA 4.9 4.3 0.0 6.8 0.0

Mount Washington OR 2.8 2.7 0.0 3.3 0.0

Mount Zirkel CO 4.4 4.3 0.0 4.4 0.0

North Cascades WA 2.8 2.6 0.0 2.8 0.0

Okefenokee GA 15.5 15.1 -0.3 15.4 -0.4

Otter Creek WV 13.0 12.9 -1.1 12.7 -1.3
Class I Areas

(IMPROVE Site) State

1998-2002 Baseline

Visibility (in dv)

20% Best Days 

2010 Baseline

Visibility (dv)

2010 Visibility

Improvement from

CAIR (dv)

2015 Baseline

Visibility (dv)

2015 Visibility

Improvement from

CAIR (dv)

Pasayten WA 2.9 2.5 0.0 2.5 0.0

Petrified Forest AZ 6.3 6.3 0.0 6.4 0.0

Pine Mountain AZ 6.1 6.2 0.0 6.4 0.0

Presidential Range - Dry NH 7.8 7.6 -0.1 7.7 -0.1

Rawah CO 4.4 4.3 0.0 4.4 0.0

Red Rock Lakes WY 3.7 3.6 0.0 3.7 0.0

Redwood CA 5.0 4.9 0.0 5.5 0.0

Rocky Mountain CO 3.7 3.7 0.0 3.8 0.0

Roosevelt Campobello ME 8.6 8.6 -0.1 8.7 -0.1

Salt Creek NM 8.3 8.0 0.0 8.1 0.0

San Gorgonio CA 6.8 6.8 0.0 6.9 0.0

San Jacinto CA 6.8 6.8 0.0 6.8 0.0

San Pedro Parks NM 3.5 3.5 0.0 3.5 0.0

Sawtooth ID 4.3 4.2 0.0 4.2 0.0

Scapegoat MT 4.5 4.3 0.0 4.3 0.0

Selway - Bitterroot MT 3.1 3.1 0.0 3.1 0.0

Seney MI 6.4 6.2 0.0 6.3 0.0

Sequoia CA 8.8 8.7 0.0 9.0 -0.0

Shenandoah VA 12.2 12.0 -1.0 12.1 -1.3

Sierra Ancha AZ 6.8 7.0 0.0 7.2 0.0

Sipsey AL 16.3 16.0 -0.5 16.0 -0.6

South Warner CA 3.7 3.6 0.0 3.7 0.0

Strawberry Mountain OR 5.6 5.4 0.0 5.4 0.0

Superstition AZ 7.4 7.4 0.0 7.5 0.0

Swanquarter NC 12.4 12.1 -0.1 12.2 -0.2

Sycamore Canyon AZ 6.7 6.7 0.0 6.9 0.0
Class I Areas

(IMPROVE Site) State

1998-2002 Baseline

Visibility (in dv)

20% Best Days 

2010 Baseline

Visibility (dv)

2010 Visibility

Improvement from

CAIR (dv)

2015 Baseline

Visibility (dv)

2015 Visibility

Improvement from

CAIR (dv)

Teton WY 3.7 3.6 0.0 3.6 0.0

Theodore Roosevelt ND 7.8 7.5 0.0 7.5 0.0

Thousand Lakes CA 3.3 3.2 0.0 3.3 0.0

Three Sisters OR 2.8 2.7 0.0 3.3 0.0

UL Bend MT 4.6 4.3 0.0 4.3 0.0

Upper Buffalo AR 12.2 11.8 -0.1 11.8 -0.1

Voyageurs MN 6.4 6.3 0.0 6.5 0.0

Weminuche CO 4.4 4.3 0.0 4.3 0.0

West Elk CO 2.8 2.6 0.0 2.7 0.0

Wind Cave SD 5.7 5.4 0.0 5.5 0.0

Wolf Island GA 15.5 15.2 -0.2 15.5 -0.3

Yellowstone WY 3.7 3.7 0.0 3.7 0.0

Yolla Bolly - Middle Eel CA 3.5 3.5 0.0 3.5 0.0

Yosemite CA 4.0 3.9 0.0 4.0 0.0

Zion UT 5.4 5.3 0.0 5.4 0.0
Table Appendix I-4. Projected visibility for the 2010 and 2015 baseline and visibility improvement from the CAIR strategy on

the 20% worst days at 116 IMPROVE sites.

Class I Areas

(IMPROVE Site) State

1998-2002

Baseline

Visibility (in dv)

20% Best Days 

2010 Baseline

Visibility (dv)

2010 Visibility

Improvement from

CAIR (dv)

2015 Baseline

Visibility (dv)

2015 Visibility

Improvement from

CAIR (dv)

Acadia ME 22.7 22.1 -0.9 22.0 -1.0

Agua Tibia CA 23.2 23.3 0.0 23.3 0.0

Alpine Lakes WA 18.0 17.3 0.0 17.4 0.0

Anaconda - Pintler MT 12.3 12.1 0.0 12.2 0.0

Arches UT 12.0 11.9 0.0 12.1 0.0

Badlands SD 17.3 16.9 -0.1 16.9 -0.1

Bandelier NM 13.2 13.2 0.0 13.2 0.0

Big Bend TX 18.4 18.4 -0.1 18.4 -0.2

Black Canyon of the Gunnison CO 11.6 11.4 0.0 11.4 0.0

Bob Marshall MT 14.2 14.0 0.0 14.0 0.0

Boundary Waters Canoe Area MN 20.0 19.4 -0.3 19.3 -0.3

Bridger WY 11.5 11.2 0.0 11.3 0.0

Brigantine NJ 27.6 27.3 -1.9 27.4 -2.1

Bryce Canyon UT 12.0 11.9 0.0 11.9 0.0

Cabinet Mountains MT 13.8 13.5 0.0 13.5 0.0

Caney Creek AR 25.9 25.4 -1.1 25.4 -1.3

Canyonlands UT 12.0 11.9 0.0 12.0 0.0

Cape Romain SC 25.9 25.4 -1.0 25.3 -1.4

Caribou CA 14.8 14.6 0.0 14.6 0.0

Carlsbad Caverns NM 17.6 17.6 0.0 17.8 -0.1

Chassahowitzka FL 25.7 24.6 -0.9 24.6 -1.7

Chiricahua NM AZ 13.9 13.8 -0.1 14.0 -0.1
Class I Areas

(IMPROVE Site) State

1998-2002

Baseline

Visibility (in dv)

20% Best Days 

2010 Baseline

Visibility (dv)

2010 Visibility

Improvement from

CAIR (dv)

2015 Baseline

Visibility (dv)

2015 Visibility

Improvement from

CAIR (dv)

Chiricahua W AZ 13.9 13.8 -0.1 14.0 -0.1

Craters of the Moon ID 14.7 14.7 0.0 14.7 0.0

Desolation CA 12.9 12.8 0.0 12.8 0.0

Dolly Sods WV 27.6 27.0 -2.4 26.7 -2.7

Dome Land CA 20.3 20.0 0.0 19.9 0.0

Eagle Cap OR 19.6 19.1 0.0 19.0 0.0

Eagles Nest CO 11.3 11.3 0.0 11.4 0.0

Emigrant CA 17.6 17.4 0.0 17.4 0.0

Everglades FL 20.3 19.5 -0.4 19.7 -0.5

Fitzpatrick WY 11.5 11.2 0.0 11.3 0.0

Flat Tops CO 11.3 11.3 0.0 11.4 0.0

Galiuro AZ 13.9 13.9 -0.1 14.2 -0.1

Gates of the Mountains MT 11.2 10.8 0.0 10.8 0.0

Gila NM 13.5 13.5 -0.1 13.6 -0.1

Glacier MT 19.5 19.1 0.0 19.1 0.0

Glacier Peak WA 14.0 13.5 0.0 13.8 0.0

Grand Teton WY 12.1 12.0 0.0 12.0 0.0

Great Gulf NH 23.2 22.9 -1.3 22.8 -1.6

Great Sand Dunes CO 13.1 13.0 0.0 13.0 0.0

Great Smoky Mountains TN 29.5 29.1 -1.9 28.7 -2.6

Guadalupe Mountains TX 17.6 17.5 -0.1 17.6 -0.1

Hells Canyon OR 18.1 18.0 0.0 18.0 0.0

Isle Royale MI 21.1 20.6 -0.3 20.5 -0.4

James River Face VA 28.5 28.0 -2.1 27.6 -2.4

Jarbidge NV 12.6 12.6 0.0 12.8 0.0
Class I Areas

(IMPROVE Site) State

1998-2002

Baseline

Visibility (in dv)

20% Best Days 

2010 Baseline

Visibility (dv)

2010 Visibility

Improvement from

CAIR (dv)

2015 Baseline

Visibility (dv)

2015 Visibility

Improvement from

CAIR (dv)

Joshua Tree CA 19.5 19.9 0.0 20.4 0.0

Joyce Kilmer - Slickrock NC 29.5 29.1 -1.9 28.7 -2.6

Kalmiopsis OR 14.8 14.0 0.0 14.4 0.0

Kings Canyon CA 23.5 24.2 0.0 24.2 0.0

La Garita CO 11.6 11.5 0.0 11.5 0.0

Lassen Volcanic CA 14.8 14.6 0.0 14.6 0.0

Lava Beds CA 16.6 16.4 0.0 16.5 0.0

Linville Gorge NC 27.9 27.2 -1.7 26.8 -2.1

Lostwood ND 19.6 18.8 -0.1 18.7 0.0

Lye Brook VT 23.9 23.4 -1.8 23.2 -2.1

Mammoth Cave KY 30.2 29.8 -1.7 29.5 -2.5

Marble Mountain CA 17.1 16.7 0.0 16.8 0.0

Maroon Bells - Snowmass CO 11.3 11.2 0.0 11.3 0.0

Mazatzal AZ 13.1 13.3 0.0 13.5 0.0

Medicine Lake MT 17.7 17.2 0.0 17.1 0.0

Mesa Verde CO 12.8 12.7 0.0 12.8 0.0

Mingo MO 27.5 27.0 -0.8 26.9 -0.9

Mission Mountains MT 14.2 13.9 0.0 14.0 0.0

Mokelumne CA 12.9 12.7 0.0 12.8 0.0

Moosehorn ME 21.4 21.2 -0.8 21.2 -0.9

Mount Hood OR 14.0 13.3 0.0 13.7 0.0

Mount Jefferson OR 15.7 14.9 0.0 15.2 0.0

Mount Rainier WA 18.9 17.3 0.0 19.4 0.0

Mount Washington OR 15.7 14.9 0.0 15.2 0.0

Mount Zirkel CO 11.7 11.7 0.0 11.8 0.0
Class I Areas

(IMPROVE Site) State

1998-2002

Baseline

Visibility (in dv)

20% Best Days 

2010 Baseline

Visibility (dv)

2010 Visibility

Improvement from

CAIR (dv)

2015 Baseline

Visibility (dv)

2015 Visibility

Improvement from

CAIR (dv)

North Cascades WA 14.0 13.7 0.0 14.0 0.0

Okefenokee GA 26.4 26.1 -1.0 26.1 -1.4

Otter Creek WV 27.6 27.3 -2.5 27.0 -2.9

Pasayten WA 14.7 14.4 0.0 14.5 0.0

Petrified Forest AZ 13.5 13.6 0.0 13.8 0.0

Pine Mountain AZ 13.1 13.2 0.0 13.5 0.0

Presidential Range - Dry NH 23.2 22.7 -1.5 22.6 -1.8

Rawah CO 11.7 11.6 0.0 11.7 0.0

Red Rock Lakes WY 12.1 12.0 0.0 12.1 0.0

Redwood CA 16.5 15.6 0.0 16.5 0.0

Rocky Mountain CO 14.1 14.1 0.0 14.1 0.0

Roosevelt Campobello ME 21.4 21.0 -0.8 21.0 -0.9

Salt Creek NM 17.7 17.4 -0.1 17.5 -0.2

San Gorgonio CA 21.5 21.7 0.0 22.1 0.0

San Jacinto CA 21.5 21.6 0.0 21.4 0.0

San Pedro Parks NM 11.4 11.4 0.0 11.5 0.0

Sawtooth ID 13.6 13.4 0.0 13.5 0.0

Scapegoat MT 14.2 14.0 0.0 14.1 0.0

Selway - Bitterroot MT 12.3 12.1 0.0 12.1 0.0

Seney MI 23.8 23.5 -0.7 23.4 -0.8

Sequoia CA 23.5 24.0 0.0 24.1 0.0

Shenandoah VA 27.6 27.2 -2.8 26.7 -3.3

Sierra Ancha AZ 13.4 13.5 0.0 13.7 0.0

Sipsey AL 28.7 28.3 -1.5 28.1 -2.1

South Warner CA 16.6 16.5 0.0 16.5 0.0
Class I Areas

(IMPROVE Site) State

1998-2002

Baseline

Visibility (in dv)

20% Best Days 

2010 Baseline

Visibility (dv)

2010 Visibility

Improvement from

CAIR (dv)

2015 Baseline

Visibility (dv)

2015 Visibility

Improvement from

CAIR (dv)

Strawberry Mountain OR 19.6 19.2 0.0 19.2 0.0

Superstition AZ 14.7 14.8 0.0 15.0 0.0

Swanquarter NC 24.6 24.1 -1.4 23.8 -1.9

Sycamore Canyon AZ 16.1 16.2 0.0 16.6 0.0

Teton WY 12.1 12.1 0.0 12.1 0.0

Theodore Roosevelt ND 17.6 17.0 0.0 16.9 0.0

Thousand Lakes CA 14.8 14.6 0.0 14.6 0.0

Three Sisters OR 15.7 14.9 0.0 15.2 0.0

UL Bend MT 14.7 14.1 0.0 14.1 0.0

Upper Buffalo AR 25.5 25.0 -0.7 25.1 -0.8

Voyageurs MN 18.4 17.8 -0.1 17.7 -0.1

Weminuche CO 11.6 11.4 0.0 11.5 0.0

West Elk CO 11.3 11.2 0.0 11.3 0.0

Wind Cave SD 16.0 15.5 -0.1 15.4 0.0

Wolf Island GA 26.4 26.0 -0.8 26.1 -1.1

Yellowstone WY 12.1 12.1 0.0 12.1 0.0

Yolla Bolly - Middle Eel CA 17.1 16.8 0.0 16.9 0.0

Yosemite CA 17.6 17.4 0.0 17.4 0.0

Zion UT 13.5 13.2 0.0 13.3 0.0