# 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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# i 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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### 1 

# 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 Pete rs 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 Inventori es 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 PM2.5 2 

# component species (i.e., elemental carbon, organic carbon, nitrate, sulfate, and crustal material) and coarse particles 3



# . Each inventory includes anthropogenic emissions fo r the major sectors of the inventory: electric generating units (EGUs), industrial point s ources (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 bur ning, 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 defi nes 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 seve ral 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 andemissions 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+BART 4 

# 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 C ontrol Retrofits for Cost Effectiveness and Feasibility Analyses” Technical Support Document (EPA, 2005b).

# 3 

# 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 photochemi cal 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 pr ovided 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

### 5 

# The 8-hour ozone design value for a monitoring site is the 3-year average of the 4 th 

# highest 8- hour daily maximum ozone concentration in each of the three years at that site.



# 4 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 value 5 

# . 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

# 5 terms (i.e., distribution of concentration and fr equency 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-vect or wind components), temperature, water vapor mixing ratio, atmospheric air pressure, cloud cover, rainfall, and vertical diffusion coefficient. Values of wind, pressure, and vertical diffusiv ity 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 th ree 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 projecti on 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.

### 6 

# The inner nests were modeled with 34 layers while the outer 108 km domain was modeled with 28 layers.



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

12 km with 28-34 6 

# 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 Tropos pheric 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 di stort 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 albedofor 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 m 2 

# /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 domai n (i.e., Midwest, Northeast, Southeast, and Southwest), and each of 51 "local areas" w ithin 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.

# 8 

# 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 andChing, 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 su lfate, 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 m 2 

# /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

# 9 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 (Figur e IV-1 is provided at the end of Section IV). The domain extends from 126 degrees to 66 degr ees 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 paralle l 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 Pennsylvani a 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

# 11 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.

# 12 D. Initial and Boundary Condition Inputs to CMAQ

In this section we describe the approach used to provide the boundary conditions (BCs) andthe 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 mete orological 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 repres entations, 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-specifi c "zero-out" runs were made with CMAQ using the 2010 Base emissions scenario to quantify the c ontribution 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 prescrib ed 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 conduc ted 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:

C lean A ir S tatus and T rends Net work (CASTNet),

S peciation T rend N etwork (STN),

I nteragency M onitoring of PRO tected V isual E nvironments (IMPROVE),

A erometric I nformation R etrieval S ystem (AIRS),

N ational A cid D eposition P rogram (NADP), and

S outh E astern A erosol R esearch and CH aracterization (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

PM 2.5 

## Mass SO 4 

## NO 3 

## TNO 3 

## EC NH 4 

## OC O 3 

## SO 2 

## HNO 3 

## NO SO 4 

## NO 3 

## NH 4



## 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 TNO 3 

## = (NO 3 

## + HNO 3 

## )

### 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 ra nge 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 species 7 

# 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 in terstate 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 studies 8 

# . 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 u sed to project future concentrations for the final rule analysis. The draft ozone and PM2.5/Re gional Haze modeling guidance documents are being revised to reflect these changes.



# 16 

# V. Procedures for Projecting 8-Ho ur 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 th e 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 averageconcentrations 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 concentrat ions (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 concentrat ions (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 PM 2.5 

# 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/m 3 

# 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/m 3 

# 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 thelocation 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/m 3 

# (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/m 3 

# (as rounded up from 15.05 μg/m 3 

# ) 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/m 3 

# ) 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/m 3 

# ) 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 Stat es 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 dow nwind 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 th e 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 ar eas 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. Th e 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 modeli ng 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 emissi ons 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 contri butions 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 contributi ons 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, Indian a, Iowa, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, New Hampshire, New Jersey, New York, North Carolina, Ohio, Pennsylvania, R hode 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 emi ssions 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) fo r 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 zer o-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 zer o-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-hourlypredicted 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 averag e 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 C ounty, 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 TX 10 

## 87 N/A

Galveston TX 85 37 %

Harris TX 97 36 %

Jefferson TX 85 50 %

Tarrant TX 10 

## 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 particul ar 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 exam ple, 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 re latively 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 fre quent 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 apporti onment 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 Stat e, 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. Fo r some of the linkages, however, not all of the factors are consistent. For upwind-downwind linka ges in which some of the factors indicate high and/or frequent contributions while other fact ors 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. Cont ribution tables containing the values of the metrics for each linkage are provided in Appendix G.34Table VI-3a. Evaluation of the Contribution toDownwind Nonattainment in Middlesex Co., CT.Receptor Steps Evaluation of ContributionsMiddlesex Co.ConnecticutStep 1:Evaluation ofContributionsAgainstScreeningCriteria- 22 upwind States had contributions that did not exceed eitheror both of the screening criteria. These linkages were not evaluatedfurther. As an example, the contribution from MI did exceedthe screening criteria for the Source Apportionment modeling but did notexceed the criteria for the zero-out modeling, so this linkage was deemed not significant. Of the 22 linkages that failed thescreeningcriteria (i.e., were not significant), 15 werenot significant in both modeling techniques.- 8 upwind States (MA, VA, MD, OH, NJ, PA, WV, and NY) had contributions that exceeded both screening criteria and were carriedforward 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 contributions that were significantconsidering 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 ormore to 18% of the exeedances up to both NJ and NY whichcontributed 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 occurrence of a contribution greaterthan 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 tothat of the zero-out modeling in that 6 of the 8 States thatexceeded the screening criteria (VA, MD, OH, NJ, PA, and NY) madecontributions that were significant considering the metrics fromall 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 ormore to 58% of the exeedances up to both NJ and NY whichcontributed 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 exceedancesStep 4: FinalDeterminationof Significance- Of the 8 States that passed the screening criteria, 6 (VA,MA, OH, NJ, PA, and NY) had large and frequent contributions toMiddlesex Co for at least two of the three contribution factors based on each modeling technique. Thus, we determined that each ofthese 6 States makes a significant contribution to nonattainment inthis county. We determined that the contributions from theother 2States that passed the screening criteria (MD and WV) were notsignificant because the contributions based on the zero-out techniquewere not high and/or frequent.35Table VI-3b. Evaluation of the Contribution to Downwind Nonattainment in Bergen Co., NJ.Receptor Steps Evaluation of ContributionsBergen Co.New JerseyStep 1:Evaluation ofContributionsAgainst ScreeningCriteria- 24 upwind States had contributions that did not exceed eitheror both of the screening criteria. These linkages were not evaluatedfurther. As an example, the contribution from DE did exceedthe screening criteria for the Source Apportionment modeling but did notexceed the criteria for the Zero-Out modeling, so this linkagewas deemed not significant. Of the 24 linkages that passed thescreeningcriteria (i.e., were not significant), 17 werenot significant in both modeling techniques.- 6 upwind States (PA, VA, MD, OH, WV, and MI) had contributionsthat exceeded both screening criteria and were carried forwardforevaluation 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) madecontributions that were significant considering themetrics 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 moreto 46% of the exeedances up to PA which contributed 2 ppb ormore 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 the exceedances+ Relative Amount: total contribution from MI is 4%; the total contribution from WV is 13% of the total nonattainmentStep 3:Evaluation ofContributionsfrom SourceApportionmentModeling-In the source apportionment modeling 4 of the 6 States that exceeded the screening criteria (MD, OH, PA and VA) made contributionsthat 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 to PA which contributed 2 ppbor 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 theexceedances; WV contributed 2 ppb or more to 9%of the exceedancesStep 4: FinalDetermination ofSignificance- 4 of the States (MD, OH, PA and VA) had large and frequent contributions to Bergen Co. for at least two of the three contributionfactors based on each modeling technique. Therefore, we determined that each of these States makes a significant contributiontononattainment in this county. In addition, the contributions from MI and WV were large and frequent for two factors based on the SourceApportionment modeling and large based on one factor in the Zero-Outmodeling. Therefore, we determined that MI and WV make asignificant contribution to Bergen Co.

### 11 

# As 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 pr oximity 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 significan t 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 de termined 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 EP A found to be significant for 8-hour ozone are

listed in Tables VI-4 11 

# (by upwind State) and VI-5 (by downwind nonattainment county). Each upwind State contributed to nonattainment in coun ties 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 nonatta inment 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 sign ificant 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 Int erstate 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 i ndividual States to future PM2.5 nonattainment in other States. As part of the zero-out m odeling technique we removed the 2010 Base Case anthropogenic emissions of SO2 and NOx from 37 St ates in the East on a State-by-State basis in different CMAQ model runs. The States we an alyzed using zero-out modeling are: Alabama, Arkansas, Connecticut, Delaware, Florida, Georgi a, 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 Wisconsi n. 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/m 3 

# ) 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

### 12 

# As 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 pr oximity 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 significan t 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/m 3 

# for determining whether SO2 and NOx emissions in a State make a significant contribution to PM2.5 nonatta inment in another State. The rationale for choosing this criterion is described in the CAIR preamble. To determine whether the 0.2 μg/m 3 

# 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/m 3 

# then the contribution is significant. Examining the maxium contributions in Table VII-1 indicates that, of the 37 States analy zed, 28 States and the District of Columbia contribution 0.2 μg/m 3 

# or more to nonattainment in other States and therefore are found to make a significant contribution to PM2.5 nonattain ment. The upwind State-to-downwind nonattainment county significant linkages are listed in Table VII-2 12 

# . These downwind counties contain at least one nonattainment receptor site that receives a contribution of 0.2 μg/m 3 

# 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 nonatta inment 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 Ma rion WV Marshall WV

Montgomery OH Ohio WV St. Clair IL Scioto OH

Stark OH Summit OH Vanderburgh IN Walker GA

Washington PA West moreland PA Wood WV



## LA 2 Jefferson AL Russell AL



## MD/DC 13 Berkeley WV Berks PA Cambria PA Dauphin PA

Delaware PA District of Colu mbia 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 Ma rion WV Marshall WV

Montgomery OH New Castle DE Ohio WV Philadelphia PA

Scioto OH Stark OH Summit OH Union NJ

Washington PA Westmore land 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 De Kalb GA Delaware PA

District of Columbia Dubois IN Fayette KY Floyd GA

Fulton GA Hamilton TN Ha ncock 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 Co lumbia 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 Da vidson 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 Washi ngton 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

### 13 

# The 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 in clude 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 Massachu setts, 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 Ai r 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, vi sibility, and deposition of the SO2 and NOx emissions reductions expected to result from CAIR 13 

# . 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 emissions 15 

# 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/m 3 

# ) 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/m 3 

# ) 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/m 3 

# . 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/m 3 

# . 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/m 3 

# . 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 nona ttainment 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/m 3 

# . Also, after implementation of CAIR, we project that 18 of the 28 residual nonattainment counties in 2010 will be within 1.0 μg/m 3 

# of the NAAQS and 12 counties will be within 0.5 μg/m 3 

# of

attainment.

In 2015 we are projecting that PM2.5 in the 74 Base Case nonattainment counties will be reduced by 1.8 μg/m 3 

# , 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/m 3 

# . 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/m 3 

# . 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/m 3 

# in these 18 counties. The average reduction across these 18 residual nonattainment counties is 1.5 μg/m 3



# .

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/m 3 

# 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/m 3 

# . In 2010, the largest reductions of 1.5 μg/m 3 

# to more that 2.5 μg/m 3 

# 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/m 3 

# 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

### 16 

# We are using county-level maps to illustrate th e 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 concentr ations 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 analysis 16



# . 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 oz one 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 unmonitore d 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 data 17 

# . 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 a nd 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.

### 18 

# The baseline visibility is calculated as the aver age deciviews in the 1998-2002 period. This was the most recent period of available complete IMPR OVE 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.

19 

# There were also several model runs for 2015 which compared the CAIR controls to BART

controls. The results of these runs and a “better th an 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 impairment 18 

# . 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 I 19 

# . 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. Asimilar 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 s outhern 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 

# IX. References

Battelle, 2004: Draft Paper: Identification of Me teorological Regimes and Their Relationship to Ozone Concentrations, in conjunction with US EPA and Sonoma Technology Inc., January

2004.

Boylan, 2004: Calculating Statistics: Concentrati ons Related Performance Goals, Presentation at the EPA Model Performance Workshop, February 10, 2004.

Byun, D.W., and Ching, J.K.S., Eds, 1999. Science algorithms of EPA Models-3 Community Multiscale Air Quality (CMAQ modeling system , EPA/600/R-99/030, Office of Research and Development, U.S. Environmental Protection Agency.

Byun D.W., N.K.Moon, Daniel Jacob, and Rokjin Park, “Regional Transport Study of Air Pollutants with Linked Global Tropospheric Chemistry and Regional Air Quality Models”,2 nd 

# ICAP workshop, RTP, NC, October 2004, http://www.cep.unc.edu/empd/projects/ICAP/2004wkshp_pres.html

.

Byun, D.W., and Schere, K.L., 2004. Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 community Multiscale Air Quality (CMAQ) Modeling System. J. Applied Mechanics Reviews, XX, ps. XX

Dennis, R.L., Byun, D.W., Novak, J.H., Galluppi, K.J., Coats, C.J., and Vouk, M.A., 1996. The next generation of integrated air quality mode ling: EPA’s Models-3, Atmospheric Environment, 30, 1925-1938.

Eder, B.K., J.M. Davis, P. Bloomfield, (1994) , “A Characterization of the Spatiotemporal Variability of Non-urban Ozone Concentrations over the Eastern United States”, Atmospheric Environment, 30 (14), 2615-2625.

Environ, Enhanced Meteorological Modeling and Performance Evaluation for Two Texas Episodes . August 2001.

Environ, 2002: User’s Guide: Comprehensive Air Quality Model with Extensions (CAMx), Novato, CA.

EPA, 1999. Science Algorithms of the EPA MODELS-3 Community Multiscale Air Quality (CMAQ) Modeling System, EPA/600/R-99/030, March 1999.

EPA, 2001: Guidance for Demonstrating Attainment of Air Quality Goals for PM 2.5 

# and Regional Haze; Draft 1.1, Office of Air Quality Pl anning and Standards, Research Triangle Park,

NC.

EPA, 2004. Procedures for Estimating Future PM 2.5 

# Values for the CAIR Final Rule by Application of the (Revised) Speciat ed Modeled Attainment Test (SMAT)

# 72 Updated November 8, 2004, Office of Air Quality Planning and Standard, Research Triangle Park, NC.

EPA, 2005a. CAIR Emissions Inventory Tec hnical Support Document, Office of Air Quality Planning and Standards, Research Triangle Park, NC.

EPA, 2005b. Modeling of Control Costs, Emissions, and Control Retrofits for Cost Effectiveness and Feasibility AnalysesTechnical Support Document, Office of Atmospheric Programs, Washington, DC.

EPA, 2005c: Draft Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the 8-Hour Ozone NAAQS, O ffice of Air Quality Planning and Standards, Research Triangle Park, NC.

EPA, 2005d. Updated CMAQ Model Performance Evaluation for the 2001 Annual Simulation, Office of Air Quality Planning and St andard, Research Triangle Park, NC.

EPA, 2005e. Regulatory Impact Analysis of the Fi nal Clean Air Interstate Rule, Office of Air and Radiation, Washington, DC.

Fiore, A.M., D.J. Jacob, H. Liu, R.M. Yantosca, T.D. Fairlie, and Q. Li, “Variability in surface ozone background over the United States: Implications for air quality policy”, J. Geophys. Res., 108, 4787, 2003.

Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR ., 138 pp, National Center for Atmospheric Research, Boulder CO.

Griffin, R.J., D.R. Cocker III, R.C. Flagan, and J.H. Seinfeld, 1999: “Organic aerosol formation from the oxidation of biogenic hydrocarbons” J. Geophysical Research , Vol. 104, pp. 3555-

3567.

Hogrefe, C. et. al. “Evaluating the performance of regional-scale photochemical modeling systems: Part 1-meteorological predictions ”. Atmospherics Environment 35 (2001) 4159-4174.

Jacob, D.J., J. A. Logan, and P.P. Murti, "Eff ect of Rising Asian Emissions on Surface Ozone in the United States", Geophy. Res. Lett. , 26, 2175-2178, 1999.

Jaffe D., McKendry I., Anderson T., and Price H. “Six 'new' episodes of trans-Pacific transport of air pollutants”, Atmos. Envir. 37, 391-404, 2003.

Lagouvardos, K., G. Kallos, V. Kotroni, and S.T. Rao, 2000: “An Analysis of the Meteorological and Air Quality Conditions during an Extreme Ozone Episode over the Northeastern USA.” Int. J. Environment and Pollution, Vol. 14, Nos. 1-6, pp. 581-587.

# 73 Lehman, J.S., et. al., “Application of Automated Classification Scheme to Identify Meteorological Regimes and Their Relationship to Ozone Concentrations in the Eastern United States.” Technical Report to U.S. Environmen tal Protection Agency, Contract No. 68-D-02-061, September 2003.

McNally, D, Annual Application of MM5 for Calendar Year 2001, Topical report submitted to EPA, March 2003.

Moon N.K., and D.W. Byun, “A Simple User’s Guide for “geos2cmaq” Code: Linking CMAQ with GEOS-CHEM, Version 1.0”, Interim Report from Institute for Multidimensional Air Quality Studeis (IMAQS), University of Houston, TX, August 2004, http://www.math.uh.edu/~dwbyun/Meetings/icap/

.

Odum, J.R., T.P.W. Jungkamp, R.J. Griffin, R.C. Flagan, and J.H. Seinfeld, 1997: “The Atmospheric Aerosol-Forming Potential of Whole Gasoline Vapor.” Science, Vol. 276, pp. 96-
99.

Pielke, R.A., W.R. Cotton, R.L. Walko, C.J. Tremback, W.A. Lyons, L.D. Grasso, M.E. Nicholls, M.D. Moran, D.A. Wesley, T.J. Lee, and J.H. Copeland, 1992: A Comprehensive Meteorological Modeling System - RAMS, Meteor. Atmos. Phys ., Vol. 49, pp. 69-91.

Yantosca, B., 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry Modeling Group, Harvard University, Cambridge, MA, October 15, 2004.

# 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 below 1



# .

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 migrat ed 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 17 th 

# through the 19 th 

# , 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 19 th 

# and 20 th 

# , 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 19 th 

# and 20 th 

# 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 22 nd 

# . The episode came to an end on the 25 th 

# 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 8 th 

# , over Kansas on the 11 th 

# , over Illinois on the 13 th



# , and over Pennsylvania on the 15 th 

# . The ridge finally flattened out on the 16 th 

# 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 8 th 

# through the 10 th 

# , 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 11 th



# , 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 11 th 

# and 12 th



# .

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 14 th 

# and 15 th 

# , 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 16 th 

# (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 12 th 

# . By the 14 th 

# , high pressure aloft and at the surface dominated the eastern half of the U.S. Temperatures ranged from 90 to 100degrees 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 16 th 

# – 18 th



# , 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 18 th 

# featured high ozone across the South in cities such as: Atlanta, Charlotte, Birmingham, Augusta, as well as St. Louis. On the 19 th 

# and 20 th 

# , as the front slid further south, ozone air quality improved over this region as well. As the 21 st 

# 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 21 st 

# , 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 ValuesState County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbAlabama Clay 10270001 82.0 3 4 11Alabama Jefferson 10731003 81.7 3 7 15Alabama Jefferson 10731005 84.7 2 7 13Alabama Jefferson 10732006 86.7 3 9 15Alabama Jefferson 10735002 82.0 4 10 15Alabama Jefferson 10736002 81.3 5 10 14Alabama Lawrence 10790002 78.7 2 8 12Alabama Madison 10890014 82.7 0 4 9Alabama Mobile 10970003 78.5 0 2 3Alabama Shelby 11170004 91.7 2 3 8Alabama Sumter 11190002 74.0 1 3 5Arkansas Crittenden 50350005 92.7 0 2 4Arkansas Montgomery 50970001 68.0 3 4 12Arkansas Pulaski 51190007 80.0 0 5 15Arkansas Pulaski 51191002 84.7 2 6 7Connecticut Fairfield 90010017 97.0 1 2 3Connecticut Fairfield 90011123 97.0 0 0 0Connecticut Fairfield 90013007 98.7 1 1 2Connecticut Hartford 90031003 89.3 0 1 3Connecticut Middlesex 90070007 98.0 0 1 2Connecticut New Haven 90093002 99.0 0 0 2Connecticut New London 90110008 90.7 0 1 2Connecticut Tolland 90131001 93.0 0 0 0Delaware Kent 100010002 91.3 0 1 3Delaware New Castle 100031003 91.5 1 2 4Delaware New Castle 100031007 95.3 1 4 5Delaware New Castle 100031010 94.7 2 2 3Number of Days in 1995 Episodes with 8-hour maximum ozone within +/- "X" ppb of the 2001-2003 design value at that siteState County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbDelaware Sussex 100051002 93.3 1 1 2D.C. Washington 110010025 91.3 1 2 6D.C. Washington 110010041 90.3 1 4 6D.C. Washington 110010043 94.3 1 6 8Florida Brevard 120094001 73.3 0 0 2Florida Duval 120310077 70.3 1 3 7Florida Escambia 120330004 77.3 2 6 11Florida Escambia 120330018 83.7 1 3 5Florida Hillsborough 120570081 79.7 1 3 5Florida Hillsborough 120571035 75.7 0 0 4Florida Hillsborough 120571065 80.3 2 2 3Florida Orange 120950008 77.0 3 7 8Florida Orange 120952002 78.3 1 1 3Florida Osceola 120972002 73.7 0 1 4Florida Palm Beach 120992004 69.7 1 2 4Florida Pasco 121012001 77.7 0 1 3Florida Pinellas 121030004 77.3 0 1 3Florida Pinellas 121030018 74.3 0 0 1Florida Pinellas 121035002 76.0 0 0 1Florida Polk 121056005 75.7 0 1 2Florida Polk 121056006 78.0 1 1 3Florida St Lucie 121111002 69.3 0 1 2Florida Sarasota 121151005 81.7 0 0 1Florida Seminole 121171002 77.7 0 0 2Florida Volusia 121272001 70.3 0 2 3Florida Volusia 121275002 72.0 1 3 5Georgia Chatham 130510021 71.0 1 3 8Georgia De Kalb 130890002 94.3 3 6 11Georgia De Kalb 130893001 95.3 1 4 8State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbGeorgia Fulton 131210055 99.0 1 2 6Georgia Glynn 131270006 72.7 1 2 3Georgia Gwinnett 131350002 89.3 1 4 6Georgia Muscogee 132150008 76.5 0 4 12Georgia Muscogee 132151003 82.0 2 2 5Georgia Richmond 132450091 85.7 0 1 6Georgia Rockdale 132470001 96.3 2 4 7Illinois Adams 170010006 76.0 3 7 10Illinois Champaign 170190004 77.3 3 8 14Illinois Cook 170310001 78.3 3 6 10Illinois Cook 170310032 87.7 1 3 7Illinois Cook 170310050 71.7 1 6 9Illinois Cook 170310064 75.0 4 6 11Illinois Cook 170310072 78.0 1 3 5Illinois Cook 170311003 78.0 4 8 10Illinois Cook 170311601 73.0 3 6 9Illinois Cook 170314002 70.3 2 7 12Illinois Cook 170314006 76.0 4 8 12Illinois Cook 170317002 84.0 1 5 11Illinois Cook 170318003 72.5 3 5 8Illinois Du Page 170436001 70.7 2 3 11Illinois Effingham 170491001 77.7 2 4 11Illinois Jersey 170831001 89.0 3 5 8Illinois Kane 170890005 77.7 1 6 12Illinois Lake 170971002 80.7 1 1 8Illinois Lake 170971007 83.3 3 6 8Illinois Lake 170973001 76.5 4 4 8Illinois McHenry 171110001 83.3 0 3 9Illinois Macon 171150013 76.7 6 9 11State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbIllinois Macoupin 171170002 79.3 4 8 11Illinois Madison 171190008 84.7 1 6 12Illinois Madison 171191009 80.3 3 5 14Illinois Madison 171192007 81.3 4 6 14Illinois Madison 171193007 80.3 2 5 15Illinois Peoria 171430024 72.7 6 8 13Illinois Peoria 171431001 79.0 3 4 11Illinois Randolph 171570001 78.7 3 7 11Illinois St Clair 171630010 83.3 4 8 13Illinois Sangamon 171670010 76.0 4 10 12Illinois Will 171971008 76.0 2 4 11Illinois Will 171971011 79.3 3 5 10Illinois Winnebago 172010009 76.0 1 3 7Illinois Winnebago 172012001 74.0 2 5 6Indiana Allen 180030002 87.7 0 6 13Indiana Allen 180030004 83.3 2 2 5Indiana Clark 180190003 89.3 4 7 9Indiana Floyd 180431004 83.7 1 5 15Indiana Hamilton 180571001 93.3 5 7 8Indiana Hancock 180590003 91.7 0 2 4Indiana Lake 180890022 84.0 2 3 5Indiana Lake 180892008 90.7 5 6 10Indiana La Porte 180910005 90.0 1 4 8Indiana Madison 180950010 91.0 1 3 7Indiana Marion 180970042 82.7 2 6 9Indiana Marion 180970050 90.0 3 6 12Indiana Marion 180970057 85.3 1 7 11Indiana Marion 180970073 87.3 2 6 13Indiana Porter 181270020 84.7 0 2 7State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbIndiana Porter 181270024 89.0 1 3 10Indiana St Joseph 181410010 82.7 2 5 8Indiana St Joseph 181411007 89.0 2 5 8Indiana St Joseph 181411008 86.3 4 7 10Indiana Vanderburgh 181630012 83.3 0 3 14Indiana Vanderburgh 181630013 77.7 3 7 12Indiana Vigo 181670018 78.0 3 6 15Indiana Warrick 181730002 84.5 1 6 12Indiana Warrick 181730008 80.0 4 8 13Indiana Warrick 181730009 80.7 3 8 16Iowa Polk 191530058 58.7 2 2 4Iowa Scott 191632011 76.7 2 7 9Kansas Sedgwick 201730001 72.0 5 10 15Kansas Sedgwick 201730010 81.0 0 0 2Kentucky Bell 210130002 83.3 0 2 2Kentucky Boone 210150003 85.3 1 2 5Kentucky Boyd 210190015 86.0 2 5 11Kentucky Bullitt 210290006 83.7 2 3 9Kentucky Daviess 210590005 77.3 3 5 11Kentucky Fayette 210670001 71.0 2 8 19Kentucky Fayette 210670012 78.3 3 8 16Kentucky Graves 210830003 81.0 0 0 1Kentucky Greenup 210890007 84.0 1 3 7Kentucky Hancock 210910012 82.7 1 4 9Kentucky Henderson 211010013 77.0 3 4 13Kentucky Henderson 211010014 79.5 0 6 12Kentucky Jefferson 211110027 84.3 1 4 8Kentucky Jefferson 211110051 84.3 2 3 10Kentucky Jefferson 211111021 81.3 3 5 7State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbKentucky Jessamine 211130001 78.0 4 6 13Kentucky Kenton 211170007 86.3 2 7 10Kentucky Livingston 211390003 85.0 2 2 7Kentucky Livingston 211390004 83.0 2 6 8Kentucky McCracken 211451024 81.7 1 1 4Kentucky McLean 211490001 84.0 1 4 8Kentucky Oldham 211850004 88.0 4 7 11Kentucky Pike 211950002 76.3 2 4 17Kentucky Pulaski 211990003 81.3 3 6 9Kentucky Scott 212090001 70.3 1 7 16Kentucky Simpson 212130004 84.0 2 3 9Louisiana Ascension 220050004 81.7 1 4 8Louisiana Beauregard 220110002 75.0 2 6 11Louisiana Bossier 220150008 84.7 0 0 1Louisiana Caddo 220170001 79.7 1 4 8Louisiana Calcasieu 220190002 77.7 3 8 12Louisiana Calcasieu 220190008 76.0 0 0 0Louisiana Calcasieu 220190009 81.7 1 3 6Louisiana East Baton Rou 220330003 87.3 2 4 10Louisiana East Baton Rou 220330009 83.3 4 5 8Louisiana East Baton Rou 220330013 80.3 2 3 6Louisiana East Baton Rou 220331001 86.7 0 1 4Louisiana Grant 220430001 77.7 2 5 9Louisiana Iberville 220470007 82.3 2 4 7Louisiana Iberville 220470009 80.7 2 6 13Louisiana Jefferson 220511001 85.3 2 6 12Louisiana Lafayette 220550005 80.7 3 7 12Louisiana Livingston 220630002 83.3 1 1 3Louisiana Orleans 220710012 72.0 3 5 12State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbLouisiana Pointe Coupee 220770001 73.0 3 7 9Louisiana St Bernard 220870002 79.3 0 2 6Louisiana St Charles 220890003 81.7 1 4 7Louisiana St James 220930002 77.3 3 7 11Louisiana St John The Ba 220950002 81.7 3 8 13Louisiana St Mary 221010003 78.0 2 5 15Louisiana West Baton Rou 221210001 85.7 1 3 3Maine Cumberland 230052003 84.7 0 1 2Maine Hancock 230090102 92.0 0 0 0Maine Kennebec 230112005 77.7 0 3 3Maine Knox 230130004 83.3 0 1 5Maine Oxford 230173001 61.0 3 3 6Maine Penobscot 230194008 83.0 0 1 3Maine York 230312002 89.0 1 2 2Maine York 230313002 84.3 0 3 3Maryland Anne Arundel 240030014 101.0 0 2 4Maryland Anne Arundel 240030019 99.3 2 5 7Maryland Baltimore 240051007 91.3 2 4 6Maryland Baltimore 240053001 93.0 2 4 5Maryland Carroll 240130001 91.3 3 7 10Maryland Cecil 240150003 102.7 0 0 2Maryland Charles 240170010 94.7 0 1 5Maryland Harford 240251001 103.7 0 4 6Maryland Harford 240259001 98.7 3 5 7Maryland Kent 240290002 99.0 1 1 4Maryland Montgomery 240313001 88.7 2 5 6Maryland Prince Georges 240330002 95.0 3 5 8Maryland Prince Georges 240338001 93.0 0 4 8Massachusetts Barnstable 250010002 94.7 1 1 2State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbMassachusetts Berkshire 250034002 87.0 0 1 1Massachusetts Bristol 250051002 92.7 0 1 1Massachusetts Essex 250090005 66.5 1 1 5Massachusetts Essex 250092006 89.7 0 2 4Massachusetts Essex 250094004 86.0 0 0 1Massachusetts Hampden 250130003 80.0 1 2 6Massachusetts Hampden 250130008 90.3 0 1 1Massachusetts Hampshire 250150103 77.3 2 2 7Massachusetts Hampshire 250154002 87.3 0 0 1Massachusetts Worcester 250270015 85.3 2 3 4Michigan Allegan 260050003 92.0 0 2 7Michigan Benzie 260190003 87.7 1 2 5Michigan Berrien 260210014 88.3 1 2 6Michigan Cass 260270003 90.0 1 6 12Michigan Clinton 260370001 83.3 0 2 4Michigan Genesee 260490021 85.0 0 4 6Michigan Genesee 260492001 86.7 0 2 4Michigan Huron 260630007 84.0 2 4 5Michigan Ingham 260650012 83.3 2 7 10Michigan Kent 260810020 80.7 1 4 7Michigan Lenawee 260910007 85.0 2 4 7Michigan Macomb 260990009 91.0 4 5 5Michigan Macomb 260991003 89.7 2 4 8Michigan Muskegon 261210039 92.0 2 4 7Michigan Oakland 261250001 87.0 0 3 8Michigan Ottawa 261390005 86.0 0 4 7Michigan St Clair 261470005 87.7 0 1 3Michigan Wayne 261630001 80.0 2 4 6Michigan Wayne 261630016 84.7 0 1 2State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbMichigan Wayne 261630019 88.0 1 3 5Minnesota Anoka 270031001 70.5 2 4 9Minnesota Anoka 270031002 72.5 3 5 7Minnesota Dakota 270376018 68.0 0 3 6Mississippi Adams 280010004 79.7 0 1 3Mississippi De Soto 280330002 84.3 1 4 11Mississippi Hancock 280450001 83.7 4 6 11Mississippi Hinds 280490010 76.3 1 1 4Mississippi Jackson 280590006 83.0 1 1 3Mississippi Madison 280890002 76.3 1 1 7Mississippi Warren 281490004 76.7 3 5 6Missouri Clay 290470003 80.0 2 7 13Missouri Clay 290470005 84.3 4 9 12Missouri Clay 290470025 80.0 1 6 12Missouri Greene 290770026 74.7 1 3 5Missouri Greene 290770036 73.3 8 14 16Missouri Jefferson 290990012 87.3 4 5 11Missouri Monroe 291370001 79.3 2 6 11Missouri Platte 291650023 81.7 1 5 11Missouri St Charles 291831002 90.3 3 6 10Missouri St Charles 291831004 90.7 8 10 13Missouri St Louis 291890006 88.0 1 5 7Missouri St Louis 291893001 83.3 1 5 10Missouri St Louis 291895001 86.3 7 13 18Missouri St Louis City 295100007 82.0 2 3 7Missouri St Louis City 295100072 73.3 1 2 8Nebraska Douglas 310550028 62.7 2 6 14Nebraska Douglas 310550032 67.5 0 0 0Nebraska Douglas 310550035 54.0 2 8 12State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbNebraska Lancaster 311090016 54.0 4 14 21New Hampshire Cheshire 330050007 73.7 1 3 5New Hampshire Hillsborough 330111010 85.0 0 1 3New Hampshire Merrimack 330130007 73.0 1 1 4New Hampshire Rockingham 330150012 82.7 1 1 2New Jersey Atlantic 340010005 91.0 1 2 6New Jersey Camden 340070003 99.7 1 2 3New Jersey Camden 340071001 102.3 0 4 6New Jersey Cumberland 340110007 96.7 1 1 2New Jersey Gloucester 340150002 101.3 0 0 4New Jersey Hudson 340170006 89.0 1 2 4New Jersey Hunterdon 340190001 97.7 0 1 5New Jersey Mercer 340210005 103.0 0 3 6New Jersey Middlesex 340230011 100.7 1 1 5New Jersey Monmouth 340250005 96.0 1 1 1New Jersey Morris 340273001 97.7 2 5 6New Jersey Ocean 340290006 111.0 0 1 3New York Albany 360010012 83.0 1 2 5New York Bronx 360050083 82.7 0 3 5New York Chautauqua 360130011 87.0 1 3 5New York Chemung 360150003 81.0 1 1 3New York Dutchess 360270007 91.3 0 2 2New York Erie 360290002 96.0 1 2 2New York Essex 360310002 88.5 0 2 4New York Essex 360310003 82.7 1 2 5New York Hamilton 360410005 79.0 0 2 4New York Herkimer 360430005 74.0 1 2 2New York Jefferson 360450002 91.7 1 1 2New York Madison 360530006 80.0 1 3 4State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbNew York Monroe 360551004 86.5 0 2 5New York Niagara 360631006 91.0 0 2 3New York Oneida 360650004 79.0 2 3 4New York Onondaga 360671015 83.0 1 1 5New York Orange 360715001 86.0 0 2 5New York Putnam 360790005 91.3 0 0 3New York Richmond 360850067 96.0 2 5 6New York Saratoga 360910004 85.5 0 0 2New York Schenectady 360930003 77.3 1 5 9New York Suffolk 361030002 91.3 0 1 2New York Suffolk 361030004 86.7 1 2 3New York Ulster 361111005 81.7 1 1 3New York Wayne 361173001 84.0 0 2 4New York Westchester 361192004 92.0 1 2 5North Carolina Buncombe 370210030 82.0 0 1 4North Carolina Caldwell 370270003 85.7 0 1 7North Carolina Camden 370290099 80.0 0 1 2North Carolina Caswell 370330001 89.7 0 0 1North Carolina Chatham 370370004 82.0 5 8 14North Carolina Cumberland 370510008 87.0 1 2 4North Carolina Duplin 370610002 80.7 0 0 0North Carolina Durham 370630013 89.0 1 1 6North Carolina Forsyth 370670022 93.7 0 1 3North Carolina Forsyth 370670027 82.7 1 2 5North Carolina Forsyth 370671008 91.3 0 1 5North Carolina Franklin 370690001 89.0 0 0 1North Carolina Granville 370770001 92.0 0 2 10North Carolina Guilford 370810011 90.7 1 1 3North Carolina Haywood 370870035 85.0 1 2 5State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbNorth Carolina Haywood 370870036 86.3 0 0 4North Carolina Johnston 371010002 85.7 3 6 9North Carolina Lincoln 371090004 92.3 1 4 7North Carolina Martin 371170001 80.3 0 0 1North Carolina Mecklenburg 371191005 88.3 0 3 6North Carolina Mecklenburg 371191009 100.3 1 2 3North Carolina Northampton 371310002 83.3 0 2 4North Carolina Pitt 371470099 83.0 2 2 4North Carolina Rockingham 371570099 88.7 0 0 2North Carolina Rowan 371590021 98.7 1 1 2North Carolina Rowan 371590022 99.7 0 0 1North Carolina Swain 371730002 73.7 0 1 3North Carolina Wake 371830014 92.7 0 0 1North Carolina Wake 371830015 92.7 0 1 4North Carolina Wake 371830016 87.0 2 4 7North Carolina Wake 371830017 86.0 3 7 11North Carolina Yancey 371990003 86.3 2 2 10Ohio Allen 390030002 87.7 1 8 11Ohio Ashtabula 390071001 94.0 2 3 5Ohio Butler 390170004 89.0 1 5 8Ohio Butler 390171004 89.0 3 5 9Ohio Clark 390230001 88.3 3 6 8Ohio Clark 390230003 86.3 0 4 9Ohio Clinton 390271002 95.7 4 6 9Ohio Cuyahoga 390350034 78.3 3 7 10Ohio Cuyahoga 390350064 81.0 5 8 12Ohio Cuyahoga 390355002 86.3 2 2 9Ohio Franklin 390490081 84.3 2 4 11Ohio Hamilton 390610006 89.3 3 4 8State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbOhio Hamilton 390610010 84.3 1 4 9Ohio Knox 390830002 89.3 1 3 6Ohio Lake 390850003 92.7 4 11 12Ohio Lake 390853002 85.0 1 6 11Ohio Lawrence 390870006 85.0 3 5 10Ohio Lawrence 390870011 82.3 1 4 10Ohio Licking 390890005 89.0 2 5 10Ohio Lucas 390950034 88.7 3 6 9Ohio Lucas 390950081 88.3 0 3 7Ohio Madison 390970007 89.0 0 6 12Ohio Medina 391030003 87.7 3 11 12Ohio Miami 391090005 86.3 3 3 6Ohio Montgomery 391130019 86.7 2 5 6Ohio Portage 391331001 92.0 3 3 7Ohio Preble 391351001 80.3 2 4 12Ohio Stark 391510016 88.7 2 5 11Ohio Stark 391510019 88.0 2 7 10Ohio Stark 391511009 89.0 3 4 8Ohio Stark 391514005 88.7 3 6 9Ohio Summit 391530020 94.3 3 5 9Ohio Trumbull 391550008 88.0 1 2 8Ohio Trumbull 391550009 88.0 1 1 8Ohio Washington 391670004 87.0 1 4 7Oklahoma Cleveland 400270049 77.3 2 5 12Oklahoma Oklahoma 401090033 79.3 1 4 12Oklahoma Oklahoma 401091037 80.7 2 6 11Oklahoma Tulsa 401430137 85.0 2 5 7Oklahoma Tulsa 401430174 80.0 2 3 5Pennsylvania Allegheny 420030008 88.7 2 5 7State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbPennsylvania Allegheny 420030067 90.3 0 3 7Pennsylvania Allegheny 420030088 88.0 1 3 6Pennsylvania Allegheny 420031005 93.0 1 2 4Pennsylvania Beaver 420070002 91.0 2 3 6Pennsylvania Beaver 420070005 90.0 1 4 4Pennsylvania Beaver 420070014 86.3 1 4 10Pennsylvania Berks 420110001 85.7 1 5 9Pennsylvania Berks 420110009 92.7 1 1 5Pennsylvania Blair 420130801 84.3 1 2 8Pennsylvania Bucks 420170012 103.0 1 4 5Pennsylvania Cambria 420210011 87.7 0 3 5Pennsylvania Dauphin 420430401 86.3 2 3 8Pennsylvania Dauphin 420431100 91.0 2 3 7Pennsylvania Delaware 420450002 93.7 2 2 5Pennsylvania Erie 420490003 89.0 1 1 5Pennsylvania Lackawanna 420690101 85.3 1 5 7Pennsylvania Lackawanna 420692006 83.7 2 3 8Pennsylvania Lancaster 420710007 94.0 1 3 10Pennsylvania Lawrence 420730015 78.7 3 5 10Pennsylvania Lehigh 420770004 93.3 0 1 4Pennsylvania Luzerne 420791100 83.0 0 3 5Pennsylvania Luzerne 420791101 84.7 4 6 9Pennsylvania Lycoming 420810403 71.0 2 6 8Pennsylvania Mercer 420850100 91.3 3 4 6Pennsylvania Montgomery 420910013 96.3 2 4 6Pennsylvania Perry 420990301 84.7 1 3 6Pennsylvania Philadelphia 421010004 73.3 1 2 4Pennsylvania Philadelphia 421010014 91.3 0 2 2Pennsylvania Philadelphia 421010024 97.5 1 3 5State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbPennsylvania Philadelphia 421010136 86.3 1 1 4Pennsylvania Washington 421250005 87.3 1 6 12Pennsylvania Washington 421250200 86.7 2 3 8Pennsylvania Washington 421255001 87.7 3 5 6Pennsylvania Westmoreland 421290006 81.7 3 4 8Pennsylvania York 421330008 90.3 1 4 5Rhode Island Kent 440030002 95.3 0 1 3Rhode Island Providence 440071010 90.3 1 2 5South Carolina Abbeville 450010001 84.0 1 4 5South Carolina Aiken 450030003 84.7 0 1 2South Carolina Anderson 450070003 88.0 1 4 9South Carolina Barnwell 450110001 81.3 0 0 0South Carolina Berkeley 450150002 71.0 2 4 7South Carolina Charleston 450190042 74.0 1 3 5South Carolina Charleston 450190046 72.0 2 4 4South Carolina Cherokee 450210002 86.0 2 4 10South Carolina Chester 450230002 84.3 1 2 8South Carolina Darlington 450310003 84.7 1 1 1South Carolina Edgefield 450370001 80.7 0 3 6South Carolina Oconee 450730001 84.0 0 0 0South Carolina Pickens 450770002 85.3 1 3 8South Carolina Richland 450790007 85.7 0 2 5South Carolina Richland 450791002 93.0 1 1 1South Carolina Spartanburg 450830009 90.0 0 1 4South Carolina Union 450870001 80.7 0 1 3South Carolina Williamsburg 450890001 72.3 1 2 3South Carolina York 450910006 83.3 1 3 5Tennessee Anderson 470010101 89.7 0 3 9Tennessee Blount 470090101 94.0 0 4 6State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbTennessee Blount 470090102 77.5 2 2 5Tennessee Davidson 470370011 72.0 1 4 12Tennessee Davidson 470370026 81.3 2 10 16Tennessee Hamilton 470650028 90.7 1 3 4Tennessee Hamilton 470651011 90.7 0 2 4Tennessee Haywood 470750002 89.0 0 0 4Tennessee Knox 470930021 91.0 1 4 7Tennessee Knox 470931020 94.7 0 2 6Tennessee Rutherford 471490101 83.3 0 0 0Tennessee Sevier 471550101 94.7 0 4 6Tennessee Sevier 471550102 96.0 1 1 2Tennessee Shelby 471570021 88.0 0 2 6Tennessee Shelby 471571004 90.7 1 3 11Tennessee Sullivan 471632002 87.7 0 6 10Tennessee Sullivan 471632003 89.3 3 6 9Tennessee Sumner 471650007 89.0 2 6 16Tennessee Sumner 471650101 86.3 0 0 0Tennessee Wilson 471890103 84.7 1 2 6Texas Brazoria 480391003 88.0 1 3 7Texas Collin 480850005 93.3 0 2 5Texas Dallas 481130069 91.0 0 2 5Texas Dallas 481130087 83.0 1 5 7Texas Galveston 481671002 84.7 2 2 4Texas Gregg 481830001 88.3 3 6 10Texas Harris 482010024 105.0 0 4 6Texas Harris 482010029 102.0 0 1 3Texas Harris 482010046 91.0 2 6 11Texas Harris 482010047 84.0 2 6 11Texas Harris 482010051 101.7 0 1 3State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbTexas Harris 482010062 91.0 3 3 6Texas Harris 482010066 95.3 1 4 8Texas Harris 482011035 95.3 0 1 4Texas Jefferson 482450009 79.3 2 8 17Texas Jefferson 482450011 82.3 2 5 12Texas Orange 483611001 78.3 3 8 15Texas Tarrant 484391002 96.3 3 4 4Texas Tarrant 484392003 98.3 0 1 6Texas Travis 484530014 84.3 2 4 10Vermont Bennington 500030004 79.7 1 2 5Virginia Arlington 510130020 95.7 6 7 8Virginia Caroline 510330001 84.0 0 1 6Virginia Charles City 510360002 89.3 1 2 3Virginia Chesterfield 510410004 86.0 0 2 5Virginia Fairfax 510590005 88.3 0 3 8Virginia Fairfax 510590018 96.3 1 3 8Virginia Fairfax 510595001 88.0 5 7 9Virginia Fauquier 510610002 81.0 1 4 8Virginia Frederick 510690010 84.3 1 5 9Virginia Henrico 510870014 90.0 0 2 5Virginia Madison 511130003 86.3 4 5 10Virginia Prince William 511530009 85.7 1 1 10Virginia Roanoke 511611004 86.0 1 1 2Virginia Stafford 511790001 86.3 3 7 10Virginia Wythe 511970002 80.7 0 2 6Virginia Alexandria Cit 515100009 90.0 3 4 5Virginia Hampton City 516500004 88.7 1 1 3Virginia Suffolk City 518000004 87.3 1 3 3Virginia Suffolk City 518000005 82.7 0 2 5State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbWest Virginia Cabell 540110006 88.0 2 6 11West Virginia Hancock 540291004 84.3 1 5 10West Virginia Ohio 540690007 84.7 1 2 6West Virginia Wood 541071002 87.7 1 6 12Wisconsin Brown 550090026 81.7 0 1 4Wisconsin Columbia 550210015 77.7 4 6 8Wisconsin Dane 550250041 77.3 2 2 6Wisconsin Dodge 550270007 81.0 3 6 9Wisconsin Door 550290004 92.7 0 1 2Wisconsin Fond Du Lac 550390006 79.0 4 7 12Wisconsin Jefferson 550550002 84.5 1 4 5Wisconsin Kenosha 550590002 94.0 3 5 8Wisconsin Kenosha 550590019 98.7 0 4 10Wisconsin Kewaunee 550610002 90.0 0 1 4Wisconsin Manitowoc 550710004 82.0 1 1 4Wisconsin Manitowoc 550710007 90.0 0 1 3Wisconsin Marathon 550730012 73.7 1 4 10Wisconsin Milwaukee 550790041 85.5 0 0 6Wisconsin Milwaukee 550790044 71.3 0 6 8Wisconsin Milwaukee 550790085 90.7 1 1 3Wisconsin Milwaukee 550791025 90.3 1 5 6Wisconsin Outagamie 550870009 77.3 2 5 8Wisconsin Ozaukee 550890008 90.0 0 0 4Wisconsin Ozaukee 550890009 95.3 1 3 3Wisconsin Racine 551010017 91.7 3 5 8Wisconsin Rock 551050024 84.3 0 1 7Wisconsin St Croix 551091002 72.7 0 6 9Wisconsin Sauk 551110007 74.3 1 4 9Wisconsin Vernon 551230008 71.7 1 4 10State County AIRS Site Code2001-2003Design Value(ppb)# Days within +/-2 ppb# Days within +/-5 ppb# Days within +/-10 ppbWisconsin Walworth 551270005 83.3 0 2 4Wisconsin Washington 551310009 82.7 1 2 10Wisconsin Waukesha 551330017 82.7 1 3 6Wisconsin Winnebago 551390011 80.0 2 6 10State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Alabama Clay 10270001 69.5 71.7 70.1 75.7 51.1 53.1 60.2 70.3 83.8 75.5Alabama Jefferson 10731003 83.0 74.1 71.6 73.6 47.5 61.8 78.7 89.0 72.8 67.2Alabama Jefferson 10731005 79.6 65.0 64.5 69.7 48.5 56.6 75.8 85.7 89.0 70.7Alabama Jefferson 10732006 77.2 69.2 69.5 72.0 43.2 74.5 68.1 79.7 89.2 71.1Alabama Jefferson 10735002 81.3 64.8 72.1 65.8 47.7 60.3 67.2 72.1 65.0 79.1Alabama Jefferson 10736002 80.6 68.8 73.8 69.0 40.7 60.5 69.1 73.2 70.5 69.1Alabama Lawrence 10790002 74.7 67.7 66.3 76.5 69.0 52.7 62.8 81.5 66.6 58.8Alabama Madison 10890014 75.1 65.8 70.1 66.1 57.1 59.2 67.2 69.0 64.6 72.6Alabama Mobile 10970003 66.1 57.7 45.3 52.8 60.7 57.6 61.1 65.8 75.0 59.8Alabama Shelby 11170004 81.2 71.7 72.2 72.8 46.2 NA NA 82.7 93.2 71.2Alabama Sumter 11190002 67.6 56.8 53.3 59.7 60.0 43.3 65.8 62.1 69.3 54.2Arkansas Crittenden 50350005 63.6 76.5 82.2 85.7 89.7 74.6 66.6 82.5 77.0 71.6Arkansas Montgomery 50970001 62.2 60.8 61.2 56.7 68.2 67.3 64.5 58.5 49.8 50.8Arkansas Pulaski 51190007 75.5 76.0 73.3 72.0 85.2 64.8 70.3 69.6 87.5 60.0Arkansas Pulaski 51191002 82.6 83.1 83.2 72.3 80.7 64.5 71.1 73.6 94.7 63.5Connecticut Fairfield 90010017 47.8 68.1 80.0 95.6 102.2 94.8 41.7 42.0 43.1 33.8Connecticut Fairfield 90011123 34.7 56.0 76.5 78.3 57.5 NA 40.3 42.6 47.0 47.8Connecticut Fairfield 90013007 52.3 62.8 93.0 109.8 117.6 84.5 39.2 39.1 36.6 36.6Connecticut Hartford 90031003 43.6 59.7 65.8 92.6 84.1 74.3 54.8 44.8 54.8 45.6Connecticut Middlesex 90070007 40.3 58.5 74.7 90.6 78.1 65.3 42.2 46.3 41.6 34.3Connecticut New Haven 90093002 52.0 67.8 105.5 114.5 135.5 77.0 45.0 42.2 38.5 34.0Connecticut New London 90110008 40.1 56.6 88.5 101.3 119.3 75.0 41.3 38.7 34.5 30.6Connecticut Tolland 90131001 35.8 56.5 61.2 75.7 67.8 49.8 55.0 47.1 51.5 48.2Delaware Kent 100010002 57.3 69.0 72.8 88.3 83.2 62.5 66.0 52.7 36.8 36.0Delaware New Castle 100031003 53.1 63.3 81.6 98.6 122.0 105.0 36.2 32.8 22.2 28.2Delaware New Castle 100031007 52.1 67.8 82.5 99.2 109.6 95.6 NA 44.1 28.0 35.7Delaware New Castle 100031010 52.8 66.3 86.0 94.5 122.3 118.7 44.0 31.5 23.8 30.2Delaware Sussex 100051002 56.1 67.7 59.7 77.6 61.3 40.1 56.2 47.0 37.3 28.2D.C. Washington 110010025 NA 61.6 78.1 106.3 103.7 83.7 59.8 39.8 17.6 19.8D.C. Washington 110010041 NA 69.3 82.5 102.2 96.5 71.8 67.6 37.2 23.3 23.7Daily Maximum 8-Hour Ozone (ppb) Measured at Monitoring Sites in the East During the June 1995 EpisodeState CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24D.C. Washington 110010043 NA 72.0 89.2 111.6 89.6 80.7 71.6 50.0 28.1 27.7Florida Brevard 120094001 63.3 57.7 50.0 40.2 45.0 40.0 39.5 36.2 34.6 27.5Florida Duval 120310077 66.8 46.5 50.0 40.2 47.6 52.2 54.6 32.7 52.8 43.8Florida Escambia 120330004 77.8 67.3 56.3 67.2 60.1 64.6 88.1 80.0 79.3 56.5Florida Escambia 120330018 75.3 69.7 53.8 65.2 73.3 34.5 71.8 57.6 71.2 45.5Florida Hillsborough 120570081 66.8 52.7 55.8 55.0 57.8 58.6 27.8 48.5 36.6 12.3Florida Hillsborough 120571035 69.8 55.1 53.5 49.5 62.2 59.8 24.0 56.5 32.3 7.1Florida Hillsborough 120571065 70.0 47.2 55.1 57.3 57.8 66.2 22.8 50.6 34.3 14.0Florida Orange 120950008 64.7 56.8 50.1 46.5 62.7 53.6 36.3 40.1 31.6 13.1Florida Orange 120952002 56.2 50.0 41.8 39.3 47.7 56.8 34.8 37.8 27.7 8.5Florida Osceola 120972002 59.1 51.5 47.6 43.8 47.8 45.7 31.6 38.1 33.5 14.3Florida Palm Beach 120992004 68.0 38.7 39.1 29.5 50.3 32.0 27.5 15.1 16.5 22.0Florida Pasco 121012001 69.3 57.7 47.8 50.6 45.0 48.3 27.2 50.8 33.8 15.2Florida Pinellas 121030004 74.0 60.3 47.8 50.6 45.2 50.1 22.6 53.7 28.1 11.6Florida Pinellas 121030018 49.0 41.6 45.5 45.0 40.8 48.6 24.7 52.6 26.8 12.5Florida Pinellas 121035002 59.3 43.7 40.5 45.6 41.8 49.7 18.0 51.1 27.8 9.6Florida Polk 121056005 64.1 49.1 51.8 38.6 51.1 48.1 23.1 34.7 30.8 10.6Florida Polk 121056006 64.8 48.3 51.1 40.2 54.1 51.6 25.3 47.0 32.7 15.2Florida St Lucie 121111002 NA NA NA NA NA NA NA NA NA NAFlorida Sarasota 121151005 61.3 48.5 49.8 49.7 60.0 57.5 21.6 39.5 30.7 22.0Florida Seminole 121171002 63.6 54.8 47.8 43.0 41.8 61.5 42.0 41.4 28.0 13.2Florida Volusia 121272001 66.6 56.6 47.6 40.1 46.1 57.3 40.1 36.5 30.3 16.7Florida Volusia 121275002 70.6 56.5 47.8 41.3 47.5 55.5 41.7 38.5 31.0 16.5Georgia Chatham 130510021 69.3 NA 55.3 42.1 48.3 48.6 37.6 43.5 46.3 52.5Georgia De Kalb 130890002 72.7 70.2 69.5 73.7 31.5 67.5 57.3 71.2 106.1 87.5Georgia De Kalb 130893001 68.5 68.8 69.8 72.5 34.0 64.6 56.8 71.0 100.5 96.8Georgia Fulton 131210055 NA 65.2 68.3 73.8 43.0 68.6 51.0 73.5 88.5 86.1Georgia Glynn 131270006 NA NA NA NA NA NA NA NA NA NAGeorgia Gwinnett 131350002 63.1 66.0 63.2 67.5 38.5 59.2 51.0 58.7 100.8 90.0Georgia Muscogee 132150008 65.8 62.2 66.2 70.1 42.3 65.0 57.8 54.0 71.6 71.1Georgia Muscogee 132151003 63.5 62.0 65.0 70.1 42.7 60.2 54.1 52.6 63.8 69.2State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Georgia Richmond 132450091 73.2 72.5 74.0 57.2 54.7 58.1 59.6 59.1 76.2 64.6Georgia Rockdale 132470001 68.6 66.8 65.0 67.8 53.2 61.7 52.8 60.1 109.1 87.7Illinois Adams 170010006 59.3 60.7 74.7 68.3 68.0 71.2 74.3 76.2 71.7 78.8Illinois Champaign 170190004 78.5 73.1 89.5 87.0 78.8 79.7 77.7 79.7 79.5 72.1Illinois Cook 170310001 68.8 70.3 80.3 73.8 79.8 57.2 72.3 77.0 89.1 102.3Illinois Cook 170310032 65.2 73.2 80.3 90.0 86.0 54.8 51.3 73.8 84.2 101.0Illinois Cook 170310050 74.8 75.5 81.8 77.7 84.8 59.2 64.5 74.7 99.7 126.6Illinois Cook 170310064 69.0 76.8 87.0 80.5 83.1 56.0 55.6 75.0 89.3 103.3Illinois Cook 170310072 76.2 92.6 93.6 99.2 90.1 63.0 59.5 75.7 90.7 93.1Illinois Cook 170311003 80.0 75.2 81.0 72.3 82.1 63.5 63.1 79.7 100.7 107.8Illinois Cook 170311601 70.3 73.7 77.8 66.8 74.1 65.8 70.3 93.6 94.2 101.2Illinois Cook 170314002 74.5 79.2 87.8 77.8 81.8 60.3 67.1 72.7 90.3 100.5Illinois Cook 170314006 81.6 78.5 78.1 76.3 79.5 64.3 58.0 88.3 99.1 101.3Illinois Cook 170317002 75.6 79.3 90.8 91.0 83.7 59.2 57.5 72.8 79.8 75.3Illinois Cook 170318003 62.5 58.3 71.3 68.0 74.7 49.1 59.7 61.5 85.7 113.7Illinois Du Page 170436001 57.3 65.3 66.7 64.7 69.0 62.5 60.7 82.7 87.2 100.1Illinois Effingham 170491001 76.8 62.8 88.6 88.2 87.6 80.3 86.8 78.2 72.2 72.8Illinois Jersey 170831001 85.6 87.6 88.3 81.7 76.1 77.8 85.0 77.7 44.2 NAIllinois Kane 170890005 81.4 87.0 74.5 75.0 74.6 78.8 62.5 100.5 102.5 110.5Illinois Lake 170971002 73.0 88.1 94.1 66.5 67.1 55.8 52.1 69.8 75.6 86.2Illinois Lake 170971007 84.0 103.0 107.3 67.0 NA NA 54.0 76.6 83.6 80.2Illinois Lake 170973001 75.8 84.6 69.7 63.2 68.2 57.0 50.0 77.8 97.5 91.6Illinois McHenry 171110001 90.5 89.3 74.7 74.1 76.6 79.5 62.7 96.5 104.6 103.8Illinois Macon 171150013 79.5 75.6 96.6 89.0 79.5 76.7 76.3 74.7 65.7 79.5Illinois Macoupin 171170002 82.0 81.7 97.2 80.8 72.2 76.6 80.5 70.3 75.5 77.6Illinois Madison 171190008 72.6 72.3 99.7 96.2 82.3 79.2 90.2 81.8 77.8 83.1Illinois Madison 171191009 NA 66.6 80.5 87.1 89.0 72.7 78.3 75.5 67.3 74.0Illinois Madison 171192007 86.6 79.3 94.7 101.1 90.6 82.0 88.0 80.8 81.5 86.8Illinois Madison 171193007 73.1 68.2 90.3 88.0 76.2 75.2 87.0 74.8 69.7 79.3Illinois Peoria 171430024 74.5 78.7 79.1 62.1 72.1 73.3 76.8 75.6 59.8 74.6Illinois Peoria 171431001 84.3 89.3 86.7 67.3 69.6 80.1 86.5 80.8 69.5 80.1State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Illinois Randolph 171570001 81.5 81.3 80.3 80.1 108.1 83.6 72.2 78.2 70.3 74.1Illinois St Clair 171630010 73.6 71.0 84.7 81.0 84.1 78.1 80.2 80.1 58.6 84.3Illinois Sangamon 171670010 78.2 76.8 94.0 80.1 78.1 76.0 72.2 71.1 61.2 66.1Illinois Will 171971008 71.2 73.1 77.8 67.8 69.8 70.2 77.2 97.1 96.7 98.0Illinois Will 171971011 75.3 74.0 82.0 71.0 74.0 72.1 90.1 80.7 95.7 79.0Illinois Winnebago 172010009 69.8 87.8 67.2 67.7 65.2 78.6 62.5 84.8 98.3 91.6Illinois Winnebago 172012001 72.1 86.5 61.0 45.5 NA NA NA 87.8 96.3 88.5Indiana Allen 180030002 83.6 96.8 85.3 98.2 83.0 82.0 99.0 85.6 92.2 71.6Indiana Allen 180030004 91.7 99.6 90.1 100.1 75.3 NA NA 81.6 83.0 67.2Indiana Clark 180190003 87.7 86.5 88.1 90.3 75.5 69.7 73.0 68.8 47.7 68.7Indiana Floyd 180431004 99.7 90.2 94.1 89.2 72.3 80.0 79.6 75.2 50.5 67.2Indiana Hamilton 180571001 85.1 94.7 95.1 92.7 92.2 80.6 76.0 81.3 79.6 76.6Indiana Hancock 180590003 85.6 81.2 97.3 87.8 76.8 73.8 69.8 74.7 67.7 69.1Indiana Lake 180890022 NA NA NA NA NA NA NA NA NA NAIndiana Lake 180892008 89.2 80.7 92.2 89.6 90.7 61.5 63.7 74.8 113.5 129.3Indiana La Porte 180910005 96.3 90.6 102.3 117.7 109.2 80.2 79.1 93.6 114.8 93.0Indiana Madison 180950010 80.7 84.6 95.3 91.2 83.5 73.8 76.6 73.2 66.0 71.7Indiana Marion 180970042 78.7 67.0 83.7 93.0 85.8 77.3 69.5 83.0 NA NAIndiana Marion 180970050 91.3 90.1 101.2 99.7 94.1 83.1 72.5 85.0 76.0 80.8Indiana Marion 180970057 81.1 69.7 88.2 98.7 79.6 72.6 60.6 87.6 75.5 81.8Indiana Marion 180970073 81.5 80.7 90.7 96.6 87.8 78.8 59.0 83.1 72.1 78.6Indiana Porter 181270020 94.2 75.7 90.1 71.6 NA 69.1 56.1 74.0 104.2 90.6Indiana Porter 181270024 92.3 79.7 95.8 106.7 108.1 73.0 72.1 88.8 109.3 98.2Indiana St Joseph 181410010 79.8 83.7 82.0 91.5 88.7 87.6 101.8 102.4 106.3 88.3Indiana St Joseph 181411007 82.6 81.8 86.6 102.3 82.7 77.1 99.5 91.2 101.3 88.6Indiana St Joseph 181411008 74.3 79.2 83.7 102.8 81.6 73.6 84.7 84.6 95.1 84.5Indiana Vanderburgh 181630012 93.6 78.2 86.6 92.2 91.6 94.0 91.1 92.1 62.2 71.5Indiana Vanderburgh 181630013 90.2 77.2 89.5 89.1 97.0 96.6 91.5 86.8 58.6 70.2Indiana Vigo 181670018 79.6 69.8 83.3 83.7 75.2 75.5 64.6 72.5 76.0 72.0Indiana Warrick 181730002 88.7 76.8 74.6 88.5 77.8 69.0 75.3 78.7 64.5 70.8Indiana Warrick 181730008 85.0 73.3 88.6 90.7 86.0 77.8 79.7 80.8 67.5 73.0State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Indiana Warrick 181730009 78.3 66.8 85.7 86.7 78.1 80.6 75.0 82.2 65.7 64.2Iowa Polk 191530058 59.8 60.2 65.3 65.6 71.0 70.7 69.1 73.5 71.5 44.8Iowa Scott 191632011 73.7 77.0 74.7 79.6 81.0 87.7 88.3 81.2 79.5 87.7Kansas Sedgwick 201730001 70.0 70.0 71.2 70.0 65.0 70.0 74.2 77.5 52.5 55.0Kansas Sedgwick 201730010 58.1 58.1 59.3 58.7 51.8 56.8 54.2 71.8 50.0 54.3Kentucky Bell 210130002 52.7 59.7 67.3 55.1 61.3 42.6 55.1 49.7 45.6 52.3Kentucky Boone 210150003 73.5 76.5 73.2 83.1 47.7 52.1 70.6 37.1 43.1 44.5Kentucky Boyd 210190015 67.7 80.6 88.3 76.3 87.5 83.2 59.8 51.1 38.2 58.2Kentucky Bullitt 210290006 73.3 73.2 77.0 88.8 51.8 66.8 59.0 58.7 40.1 41.1Kentucky Daviess 210590005 80.0 69.3 72.8 88.7 65.1 69.6 68.7 71.2 64.8 61.2Kentucky Fayette 210670001 65.7 75.0 74.8 79.8 73.2 63.2 59.7 51.7 42.0 45.8Kentucky Fayette 210670012 73.0 82.3 85.1 91.2 81.6 70.6 64.2 54.8 46.8 50.8Kentucky Graves 210830003 63.6 55.3 65.6 75.3 48.1 62.3 50.6 57.3 65.2 54.1Kentucky Greenup 210890007 63.1 75.3 85.2 73.8 91.8 76.3 64.7 60.8 27.5 54.3Kentucky Hancock 210910012 75.7 63.1 77.1 90.3 66.2 72.2 64.2 63.8 71.2 59.0Kentucky Henderson 211010013 66.3 53.5 78.2 87.0 66.7 79.1 NA NA 63.1 69.5Kentucky Henderson 211010014 88.1 75.1 83.8 89.6 76.2 74.5 72.2 67.5 64.0 74.0Kentucky Jefferson 211110027 68.5 73.8 81.3 87.3 80.6 48.2 45.8 41.8 33.2 33.8Kentucky Jefferson 211110051 67.7 76.5 72.2 104.3 63.4 74.6 65.5 76.5 47.4 53.1Kentucky Jefferson 211111021 78.1 78.6 91.3 91.3 73.5 56.5 67.4 66.7 44.3 63.7Kentucky Jessamine 211130001 70.2 76.0 77.0 83.7 69.0 62.5 57.3 55.0 48.8 39.0Kentucky Kenton 211170007 57.0 72.7 78.0 102.7 100.2 52.6 69.7 63.7 48.8 51.2Kentucky Livingston 211390003 78.8 68.3 73.3 79.6 66.0 83.6 60.7 58.1 66.1 83.3Kentucky Livingston 211390004 80.1 70.0 76.1 78.8 62.3 85.5 59.6 67.2 71.8 89.2Kentucky McCracken 211451024 68.2 62.3 70.6 79.7 60.0 73.2 57.8 59.1 60.7 70.7Kentucky McLean 211490001 72.7 66.1 80.2 87.7 61.8 75.3 56.0 64.1 65.2 69.8Kentucky Oldham 211850004 75.8 80.7 81.7 92.1 79.6 74.5 68.3 65.8 46.8 57.0Kentucky Pike 211950002 69.2 68.2 80.7 75.6 25.5 78.6 62.6 35.1 48.6 59.3Kentucky Pulaski 211990003 65.5 66.6 79.5 79.7 68.6 66.1 65.3 57.5 51.7 46.2Kentucky Scott 212090001 67.6 74.8 78.3 84.5 79.3 80.8 69.2 46.5 42.2 44.7Kentucky Simpson 212130004 73.7 67.8 82.1 84.6 59.8 67.0 70.3 67.3 53.8 69.7State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Louisiana Ascension 220050004 77.7 70.7 53.7 58.3 68.5 84.7 71.2 72.3 91.1 78.3Louisiana Beauregard 220110002 74.0 68.3 54.5 50.7 68.7 77.1 75.7 69.8 68.5 69.7Louisiana Bossier 220150008 56.5 68.0 61.7 55.1 71.7 79.3 63.1 60.0 60.0 73.5Louisiana Caddo 220170001 84.3 76.7 66.6 55.8 79.8 88.0 69.3 62.5 60.8 77.6Louisiana Calcasieu 220190002 82.1 78.2 66.2 59.0 68.7 74.8 85.8 74.5 80.8 75.0Louisiana Calcasieu 220190008 65.5 62.5 51.8 49.2 54.0 57.5 61.2 54.6 51.1 50.8Louisiana Calcasieu 220190009 88.2 78.5 64.6 62.1 65.4 68.3 83.2 67.0 72.0 73.0Louisiana East Baton Rou 220330003 87.5 72.5 56.6 65.0 73.6 89.5 80.0 84.0 114.6 72.6Louisiana East Baton Rou 220330009 77.2 68.1 51.0 61.1 67.7 82.5 71.3 82.0 99.8 70.7Louisiana East Baton Rou 220330013 72.3 70.5 54.5 61.1 66.8 77.7 66.8 79.0 73.6 67.0Louisiana East Baton Rou 220331001 75.7 69.6 52.8 59.7 65.3 81.8 71.1 79.3 94.2 71.2Louisiana Grant 220430001 82.0 81.1 66.8 64.6 73.8 89.1 69.7 55.7 77.2 68.7Louisiana Iberville 220470007 92.6 68.3 53.5 61.6 71.7 78.5 77.6 71.2 74.2 67.5Louisiana Iberville 220470009 94.2 80.2 61.6 64.3 78.2 86.0 88.6 75.0 94.5 71.5Louisiana Jefferson 220511001 80.1 75.3 59.8 67.1 82.5 78.6 82.8 83.7 88.7 73.6Louisiana Lafayette 220550005 87.1 77.5 66.7 72.0 70.1 84.6 76.0 81.8 80.2 76.0Louisiana Livingston 220630002 72.2 70.2 51.2 57.6 68.3 81.5 61.1 61.6 70.6 68.0Louisiana Orleans 220710012 72.8 62.1 42.5 54.2 67.5 54.3 63.1 63.1 79.5 61.3Louisiana Pointe Coupee 220770001 72.1 68.6 51.0 57.7 62.6 80.7 71.8 71.8 70.8 66.6Louisiana St Bernard 220870002 75.7 65.5 48.3 60.0 77.1 71.1 67.6 67.2 70.0 63.8Louisiana St Charles 220890003 84.0 66.6 54.1 NA NA NA NA NA NA 66.7Louisiana St James 220930002 83.3 71.3 58.7 64.7 73.7 97.6 77.5 74.2 82.6 71.3Louisiana St John The Ba 220950002 NA NA 55.2 64.5 78.2 84.5 77.7 82.7 71.8 72.8Louisiana St Mary 221010003 NA NA 54.2 70.3 79.8 81.0 96.5 85.2 79.5 73.0Louisiana West Baton Rou 221210001 73.1 66.6 47.6 58.0 67.3 74.2 71.6 81.7 87.3 68.7Maine Cumberland 230052003 24.2 59.7 50.8 87.2 65.7 46.3 34.8 43.7 36.8 59.3Maine Hancock 230090102 NA NA NA NA NA NA NA NA NA NAMaine Kennebec 230112005 23.8 39.0 46.5 63.8 53.1 42.1 34.3 50.5 49.3 72.8Maine Knox 230130004 30.8 64.1 63.0 99.7 90.6 44.0 33.2 46.0 34.1 56.2Maine Oxford 230173001 19.2 39.1 49.5 59.6 54.2 40.1 37.7 49.7 60.7 54.2Maine Penobscot 230194008 29.6 37.8 50.6 68.2 60.1 38.6 30.8 51.7 54.2 76.5State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Maine York 230312002 28.4 62.2 55.2 87.1 74.0 50.1 38.1 47.3 37.2 60.6Maine York 230313002 25.2 50.8 50.2 80.2 65.7 48.2 34.2 46.0 35.7 68.5Maryland Anne Arundel 240030014 71.8 74.0 85.2 90.5 105.1 69.1 62.5 49.3 31.0 29.1Maryland Anne Arundel 240030019 62.5 78.1 88.0 105.7 134.7 107.8 70.3 50.3 21.5 33.5Maryland Baltimore 240051007 57.7 76.3 89.8 104.3 105.7 104.3 68.3 48.8 27.6 27.3Maryland Baltimore 240053001 57.0 59.0 73.8 98.0 108.7 81.5 67.1 50.3 26.8 22.6Maryland Carroll 240130001 52.3 89.4 94.8 89.3 97.0 104.5 77.7 60.5 31.2 22.3Maryland Cecil 240150003 54.1 76.0 91.2 111.6 124.0 133.6 82.3 42.6 34.1 52.8Maryland Charles 240170010 59.1 67.3 71.7 64.5 67.2 47.1 69.5 37.7 33.0 27.8Maryland Harford 240251001 56.3 71.6 76.5 101.0 99.0 87.1 80.2 51.5 26.5 36.6Maryland Harford 240259001 53.2 64.8 83.6 96.8 105.2 99.6 113.5 43.7 28.8 41.7Maryland Kent 240290002 55.2 71.8 83.6 106.0 99.1 76.6 71.5 56.0 32.2 36.6Maryland Montgomery 240313001 53.3 76.6 85.2 92.0 113.1 88.2 75.0 46.0 22.3 25.3Maryland Prince Georges 240330002 55.5 68.5 78.6 92.5 111.1 86.1 64.1 51.1 30.0 28.5Maryland Prince Georges 240338001 58.6 71.2 83.1 95.1 95.6 64.3 66.0 42.7 29.5 26.8Massachusetts Barnstable 250010002 37.0 74.5 96.5 105.8 125.7 62.2 36.8 35.6 32.0 35.3Massachusetts Berkshire 250034002 31.8 49.5 60.0 82.1 72.7 55.7 47.8 44.3 51.8 47.5Massachusetts Bristol 250051002 35.7 73.3 97.6 107.3 125.1 58.1 42.5 42.8 35.0 41.8Massachusetts Essex 250090005 20.3 31.2 39.0 53.5 48.4 35.7 22.6 31.1 29.3 61.1Massachusetts Essex 250092006 27.3 60.8 69.7 83.3 78.7 47.7 30.8 38.0 33.6 51.2Massachusetts Essex 250094004 28.0 52.5 56.8 77.1 69.3 50.1 34.0 41.6 34.5 62.8Massachusetts Hampden 250130003 30.3 53.2 60.2 80.3 70.6 68.0 59.0 45.2 62.1 55.6Massachusetts Hampden 250130008 41.8 57.0 68.5 87.1 78.7 57.6 66.8 54.8 74.2 64.8Massachusetts Hampshire 250150103 36.5 45.0 60.2 78.0 68.7 51.6 60.0 49.1 57.1 59.0Massachusetts Hampshire 250154002 38.1 50.8 64.6 78.2 70.0 50.3 63.2 50.2 66.3 65.5Massachusetts Worcester 250270015 38.3 53.8 66.3 83.7 76.3 53.5 66.2 61.6 64.1 68.2Michigan Allegan 260050003 83.1 103.3 118.1 95.6 80.1 68.5 73.5 82.2 86.8 76.2Michigan Benzie 260190003 67.1 99.2 95.1 86.7 74.3 66.8 53.0 68.5 80.5 76.2Michigan Berrien 260210014 68.0 85.6 90.1 77.0 74.1 67.0 67.2 81.5 97.6 71.3Michigan Cass 260270003 84.6 88.8 92.1 96.1 85.8 80.1 99.7 92.1 101.3 84.5Michigan Clinton 260370001 59.3 73.1 76.2 66.2 65.8 53.7 64.2 71.1 79.8 63.0State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Michigan Genesee 260490021 53.3 82.8 76.8 69.7 71.0 49.6 50.7 60.0 79.8 70.6Michigan Genesee 260492001 48.8 82.2 76.6 72.1 73.2 45.5 46.8 55.0 73.7 63.6Michigan Huron 260630007 37.1 68.2 83.8 76.5 83.3 56.7 44.5 61.6 81.2 68.7Michigan Ingham 260650012 65.3 85.7 88.0 79.7 82.7 68.5 77.8 83.1 98.8 75.2Michigan Kent 260810020 67.0 91.6 101.5 77.1 76.2 53.1 65.6 74.3 63.5 87.2Michigan Lenawee 260910007 64.3 82.7 85.7 91.2 81.7 90.3 75.7 60.5 97.0 49.2Michigan Macomb 260990009 51.1 72.6 106.7 92.7 78.6 47.2 64.0 71.7 89.1 53.3Michigan Macomb 260991003 54.2 69.6 78.8 89.7 85.8 57.2 59.5 78.5 94.7 46.0Michigan Muskegon 261210039 84.7 95.7 119.0 85.6 61.3 64.1 78.2 100.8 102.1 95.0Michigan Oakland 261250001 47.7 53.2 76.8 84.2 79.2 59.1 54.8 65.2 83.7 46.3Michigan Ottawa 261390005 62.0 75.3 81.5 63.6 61.0 55.5 74.5 79.0 83.2 72.6Michigan St Clair 261470005 35.6 47.2 100.0 94.7 76.3 49.1 49.1 48.5 77.0 44.8Michigan Wayne 261630001 NA NA 68.1 78.6 62.7 52.2 56.1 63.8 72.6 44.2Michigan Wayne 261630016 28.1 43.0 48.1 45.2 49.1 NA NA NA 70.6 35.0Michigan Wayne 261630019 34.1 58.0 64.6 71.5 46.2 62.7 67.2 66.1 98.0 52.6Minnesota Anoka 270031001 69.3 78.5 75.7 78.5 83.7 92.2 97.2 90.1 69.8 36.2Minnesota Anoka 270031002 70.7 80.2 74.2 84.0 82.7 89.6 98.4 84.8 72.2 56.7Minnesota Dakota 270376018 NA NA NA NA NA NA 79.7 76.0 72.5 64.2Mississippi Adams 280010004 65.8 66.3 53.5 58.2 66.2 76.6 64.1 59.7 69.8 71.8Mississippi De Soto 280330002 72.0 75.3 73.5 77.8 96.2 82.0 76.0 79.6 84.2 69.1Mississippi Hancock 280450001 79.2 74.6 56.1 63.6 81.7 66.2 85.7 78.1 82.6 69.7Mississippi Hinds 280490010 77.5 67.7 55.3 64.7 64.1 57.7 57.1 65.1 67.2 56.1Mississippi Jackson 280590006 75.2 69.1 51.1 63.5 72.4 61.0 82.0 73.0 72.1 63.2Mississippi Madison 280890002 76.0 67.2 54.1 65.3 67.7 67.8 55.5 67.1 66.8 61.1Mississippi Warren 281490004 75.0 75.5 60.7 62.5 76.2 62.6 57.7 62.0 72.2 72.6Missouri Clay 290470003 81.1 86.2 88.2 94.2 85.0 98.6 76.5 89.1 85.3 65.7Missouri Clay 290470005 81.6 85.7 91.2 97.5 87.8 97.5 79.4 83.7 91.1 67.0Missouri Clay 290470025 76.1 79.0 84.6 90.8 77.5 86.3 72.3 71.7 87.2 60.2Missouri Greene 290770026 59.0 58.6 61.2 64.3 73.2 72.5 61.2 61.8 65.6 56.1Missouri Greene 290770036 71.6 72.7 70.0 78.1 75.7 92.8 74.5 85.0 88.8 73.2Missouri Jefferson 290990012 89.2 83.0 89.1 95.5 103.8 92.6 94.6 94.0 75.2 93.8State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Missouri Monroe 291370001 70.1 73.0 82.2 74.5 79.6 80.0 75.6 87.3 86.5 83.1Missouri Platte 291650023 84.1 74.1 70.8 77.8 73.0 99.1 75.5 84.3 90.0 57.5Missouri St Charles 291831002 NA 95.2 104.7 106.3 94.3 89.0 112.0 91.8 97.1 114.5Missouri St Charles 291831004 94.2 94.7 92.5 98.5 90.2 91.5 91.0 89.8 91.8 125.3Missouri St Louis 291890006 75.2 73.5 75.7 74.5 83.6 83.5 93.2 83.2 75.7 91.2Missouri St Louis 291893001 68.1 63.5 73.6 80.5 86.7 77.7 91.3 89.1 66.0 87.8Missouri St Louis 291895001 88.1 86.8 90.8 87.1 85.6 81.7 92.3 85.1 81.6 101.3Missouri St Louis City 295100007 64.3 62.3 68.6 82.5 94.6 79.3 75.0 75.1 62.1 74.5Missouri St Louis City 295100072 54.5 53.6 64.2 65.8 67.5 59.6 57.7 57.8 45.8 67.1Nebraska Douglas 310550028 84.7 59.6 72.2 67.5 63.3 67.7 77.7 76.5 54.8 43.2Nebraska Douglas 310550032 51.3 36.0 38.8 32.8 32.8 31.6 29.2 25.8 13.6 8.3Nebraska Douglas 310550035 62.6 45.7 52.3 49.5 46.7 49.2 49.3 51.3 32.8 29.1Nebraska Lancaster 311090016 58.7 51.0 58.1 58.3 54.0 54.1 60.8 63.6 51.6 40.2New Hampshire Cheshire 330050007 26.6 42.2 56.7 74.7 64.5 45.6 55.7 48.0 58.6 69.7New Hampshire Hillsborough 330111010 29.7 48.2 58.2 79.6 67.7 51.6 47.3 63.7 62.8 88.2New Hampshire Merrimack 330130007 28.3 36.5 53.2 72.0 58.2 39.5 38.2 54.6 62.0 66.6New Hampshire Rockingham 330150012 14.8 25.2 21.8 36.8 45.2 31.6 22.2 49.3 38.6 65.0New Jersey Atlantic 340010005 61.5 70.8 68.2 100.1 93.7 79.0 40.1 48.1 32.0 28.6New Jersey Camden 340070003 49.7 65.3 87.8 98.5 110.3 104.7 34.7 31.2 20.6 21.7New Jersey Camden 340071001 62.0 71.1 89.7 107.0 107.1 96.0 47.2 50.0 28.7 30.6New Jersey Cumberland 340110007 53.8 67.5 74.3 95.5 87.1 69.2 43.4 43.3 27.6 25.6New Jersey Gloucester 340150002 54.3 69.6 91.7 108.3 118.1 115.1 NA 40.2 26.8 29.0New Jersey Hudson 340170006 48.1 76.5 85.7 90.7 104.1 80.0 23.3 22.6 9.5 25.1New Jersey Hunterdon 340190001 46.8 79.5 86.5 87.7 104.5 77.0 35.1 29.8 22.6 37.1New Jersey Mercer 340210005 50.3 78.8 96.7 90.3 122.6 93.8 34.3 31.2 24.0 34.7New Jersey Middlesex 340230011 42.7 81.5 91.5 88.7 113.3 90.3 33.8 29.0 19.1 32.2New Jersey Monmouth 340250005 61.1 61.6 75.6 94.7 130.3 111.2 45.8 34.5 30.2 25.0New Jersey Morris 340273001 43.8 83.8 93.1 94.3 110.0 71.3 41.2 28.6 38.2 51.1New Jersey Ocean 340290006 49.1 76.6 99.1 108.7 124.7 117.7 41.5 38.7 32.1 29.2New York Albany 360010012 37.1 48.6 67.7 92.2 79.7 70.6 61.8 49.1 76.2 74.8New York Bronx 360050083 39.5 54.8 80.0 87.6 110.8 93.7 37.6 34.3 29.2 35.6State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24New York Chautauqua 360130011 56.1 58.3 69.6 90.7 76.7 80.3 68.0 89.6 62.5 31.8New York Chemung 360150003 43.8 61.6 68.2 81.8 70.2 55.6 70.0 57.0 49.1 29.5New York Dutchess 360270007 42.6 62.7 68.5 78.3 71.2 52.8 50.0 48.3 53.3 52.2New York Erie 360290002 48.1 65.8 69.0 95.1 82.6 54.0 56.0 76.2 68.7 41.3New York Essex 360310002 47.6 50.6 98.1 93.2 75.5 58.6 48.8 67.7 65.3 60.7New York Essex 360310003 42.6 44.3 92.2 92.2 73.6 50.3 42.3 59.1 58.5 55.6New York Hamilton 360410005 40.1 43.8 70.2 98.2 76.2 52.7 54.0 59.3 62.1 52.8New York Herkimer 360430005 41.2 44.5 72.0 85.4 69.8 52.0 49.8 55.6 55.1 50.0New York Jefferson 360450002 39.8 46.0 90.6 105.6 108.2 46.0 47.2 58.5 55.8 60.7New York Madison 360530006 44.7 56.1 63.1 89.2 77.1 51.7 57.3 53.0 51.2 55.3New York Monroe 360551004 36.3 47.5 82.3 95.1 90.0 44.3 49.5 69.8 60.2 45.6New York Niagara 360631006 42.3 56.7 77.0 101.8 95.0 54.7 51.7 71.1 61.6 47.2New York Oneida 360650004 39.1 44.8 70.6 89.6 78.0 50.2 63.8 57.8 64.0 62.7New York Onondaga 360671015 38.3 56.0 76.6 95.2 90.2 54.1 60.2 52.2 49.8 56.5New York Orange 360715001 39.2 62.8 73.8 77.2 73.3 65.3 47.4 42.6 52.6 56.2New York Putnam 360790005 42.6 61.7 81.7 82.8 80.2 62.0 41.8 42.8 49.6 52.5New York Richmond 360850067 53.2 78.1 90.1 93.8 111.1 94.8 31.3 29.2 19.7 29.0New York Saratoga 360910004 33.5 45.1 61.6 77.2 72.0 56.2 57.5 52.3 64.0 74.8New York Schenectady 360930003 36.3 45.2 62.8 83.7 73.2 52.2 66.7 49.5 71.2 79.3New York Suffolk 361030002 48.6 54.7 72.8 95.0 NA 75.8 31.6 31.6 31.3 25.0New York Suffolk 361030004 47.0 67.3 85.6 102.8 108.5 82.0 37.8 36.1 33.3 23.3New York Ulster 361111005 42.2 NA NA NA NA NA NA NA NA 56.2New York Wayne 361173001 37.3 51.0 81.7 100.7 101.5 47.1 48.8 66.1 60.0 58.3New York Westchester 361192004 42.7 64.8 85.0 82.0 94.7 73.1 37.0 38.3 30.2 36.1North Carolina Buncombe 370210030 46.3 54.7 39.8 63.8 33.2 42.5 49.3 48.2 30.5 35.1North Carolina Caldwell 370270003 59.6 66.6 74.2 75.7 48.1 53.6 67.8 48.1 56.3 64.0North Carolina Camden 370290099 63.1 54.1 52.0 48.8 31.8 38.7 45.0 32.7 44.2 47.0North Carolina Caswell 370330001 61.1 66.6 65.0 55.7 30.2 39.3 54.0 27.8 47.6 51.1North Carolina Chatham 370370004 75.7 81.8 71.0 52.6 33.2 30.2 40.3 34.1 44.7 50.5North Carolina Cumberland 370510008 64.6 68.1 61.2 43.0 32.7 45.7 25.0 23.3 43.1 47.1North Carolina Duplin 370610002 63.8 65.3 50.2 33.5 25.7 45.5 27.1 27.2 35.2 39.0State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24North Carolina Durham 370630013 68.8 73.6 71.0 57.7 32.1 32.5 42.7 28.7 54.1 57.8North Carolina Forsyth 370670022 75.0 61.5 79.3 70.0 47.1 48.5 57.8 46.1 53.2 66.3North Carolina Forsyth 370670027 53.1 62.5 68.0 68.1 38.1 46.1 62.2 39.5 39.7 65.2North Carolina Forsyth 370671008 69.6 65.6 77.1 64.8 40.5 45.3 55.6 35.7 46.7 59.1North Carolina Franklin 370690001 67.6 68.6 60.3 52.0 26.2 35.7 36.2 24.8 46.0 56.3North Carolina Granville 370770001 70.6 74.0 74.3 59.5 31.8 40.2 47.1 31.1 63.1 64.5North Carolina Guilford 370810011 72.3 69.1 79.2 64.3 31.7 39.5 51.6 30.5 57.1 50.2North Carolina Haywood 370870035 61.6 65.7 64.8 69.8 64.6 52.8 55.3 57.2 49.8 61.6North Carolina Haywood 370870036 61.0 62.1 61.5 64.8 58.2 42.0 51.2 51.5 42.8 46.2North Carolina Johnston 371010002 70.0 78.0 64.1 46.7 25.8 39.5 33.7 31.2 45.0 63.1North Carolina Lincoln 371090004 63.1 72.6 78.0 68.2 42.2 52.0 57.6 46.1 58.8 66.6North Carolina Martin 371170001 68.7 61.6 53.8 43.8 27.8 38.2 38.3 28.3 41.1 47.8North Carolina Mecklenburg 371191005 60.2 63.6 72.8 57.1 31.7 46.7 46.2 36.0 52.2 67.1North Carolina Mecklenburg 371191009 68.1 65.8 74.6 61.2 44.7 49.1 44.5 38.6 67.1 83.1North Carolina Northampton 371310002 72.1 68.2 57.3 57.5 31.5 37.6 39.7 27.7 45.6 65.0North Carolina Pitt 371470099 65.1 63.8 54.6 45.0 29.7 44.3 33.0 29.5 40.8 55.1North Carolina Rockingham 371570099 59.2 55.1 64.7 53.5 30.0 39.8 47.6 29.5 33.2 42.3North Carolina Rowan 371590021 67.6 67.5 73.5 61.0 42.6 46.1 48.5 37.2 67.3 63.6North Carolina Rowan 371590022 NA 68.8 75.1 68.3 48.3 45.2 46.5 NA NA NANorth Carolina Swain 371730002 49.5 48.7 40.6 52.8 46.1 38.6 40.1 49.1 44.1 55.5North Carolina Wake 371830014 66.8 68.3 61.1 46.5 16.5 30.8 32.1 25.2 46.1 55.3North Carolina Wake 371830015 71.8 77.3 71.1 53.5 19.1 39.1 37.8 30.2 56.0 70.7North Carolina Wake 371830016 69.6 72.8 67.1 48.3 29.8 36.3 36.5 31.0 42.2 58.5North Carolina Wake 371830017 65.7 71.8 62.3 47.2 26.8 36.1 39.0 NA NA 61.3North Carolina Yancey 371990003 65.8 77.7 79.7 76.5 69.7 66.2 67.5 67.7 66.0 67.5Ohio Allen 390030002 78.0 84.8 93.0 90.2 83.2 88.0 72.3 85.0 74.6 55.8Ohio Ashtabula 390071001 57.6 73.6 78.3 96.0 91.0 88.8 71.0 92.1 69.1 45.2Ohio Butler 390170004 66.1 84.2 92.1 89.5 86.7 48.7 66.1 74.8 61.2 54.2Ohio Butler 390171004 57.2 81.1 100.5 91.8 90.2 62.0 74.3 83.3 65.5 62.2Ohio Clark 390230001 62.1 83.3 86.6 92.2 86.3 74.0 NA NA 58.0 38.8Ohio Clark 390230003 74.5 81.8 92.3 97.1 89.3 81.6 64.6 83.2 70.7 57.2State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Ohio Clinton 390271002 85.1 79.5 92.2 96.8 96.3 71.5 75.8 80.1 60.3 59.6Ohio Cuyahoga 390350034 43.8 29.6 43.3 47.6 59.8 67.1 58.6 62.2 76.5 42.2Ohio Cuyahoga 390350064 53.1 55.6 88.0 95.6 81.8 64.6 62.6 71.6 77.5 46.0Ohio Cuyahoga 390355002 47.6 59.5 86.3 84.7 74.0 78.8 69.1 78.3 72.3 47.1Ohio Franklin 390490081 67.0 83.3 81.3 88.7 90.1 67.6 74.3 72.1 68.3 52.0Ohio Hamilton 390610006 52.8 76.0 96.7 101.5 88.0 37.5 68.3 64.0 47.3 49.7Ohio Hamilton 390610010 67.7 71.5 77.8 84.2 78.6 33.3 74.6 65.5 33.5 54.2Ohio Knox 390830002 62.8 75.7 82.3 83.6 76.5 77.5 70.5 66.2 66.6 45.5Ohio Lake 390850003 59.5 70.8 96.6 95.0 87.7 90.1 68.8 85.3 79.7 52.6Ohio Lake 390853002 59.3 68.5 87.1 87.2 75.8 88.3 68.1 88.5 72.1 45.2Ohio Lawrence 390870006 74.0 83.7 89.3 78.8 91.0 83.6 65.7 62.2 35.1 61.1Ohio Lawrence 390870011 63.7 69.8 74.7 69.5 77.0 75.0 56.8 54.8 32.8 42.0Ohio Licking 390890005 60.7 79.3 77.8 74.6 82.6 61.1 86.0 83.6 69.2 51.6Ohio Lucas 390950034 49.8 61.6 82.6 91.0 88.3 79.1 59.5 79.8 101.1 56.5Ohio Lucas 390950081 58.2 68.6 85.0 94.0 77.0 79.0 67.0 83.5 104.1 58.6Ohio Madison 390970007 85.5 83.8 85.6 93.7 95.6 82.2 73.5 77.6 68.1 58.5Ohio Medina 391030003 51.6 66.8 88.7 83.0 71.8 77.8 85.6 91.7 85.7 56.8Ohio Miami 391090005 75.7 81.2 79.2 85.1 80.7 72.5 60.0 70.8 64.5 51.3Ohio Montgomery 391130019 62.6 69.6 83.2 86.0 82.5 70.3 56.7 73.7 51.7 52.0Ohio Portage 391331001 54.8 66.0 91.1 103.1 107.8 85.1 72.5 90.8 76.7 54.7Ohio Preble 391351001 70.8 66.5 87.1 88.6 84.3 70.5 66.5 75.6 60.0 61.0Ohio Stark 391510016 46.6 64.8 94.0 95.8 92.5 80.1 81.1 78.0 70.2 46.8Ohio Stark 391510019 56.7 76.0 96.0 92.5 92.1 74.7 81.2 77.5 76.1 53.2Ohio Stark 391511009 49.7 64.6 87.2 94.1 88.7 67.6 75.6 70.3 64.7 46.6Ohio Stark 391514005 59.0 68.3 90.6 99.6 105.8 76.3 80.0 83.0 67.8 50.7Ohio Summit 391530020 49.0 75.0 99.2 92.6 92.1 77.8 69.5 92.3 66.0 47.6Ohio Trumbull 391550008 65.0 70.6 71.0 101.5 97.0 80.7 70.2 78.0 59.7 47.5Ohio Trumbull 391550009 64.2 67.5 73.1 96.7 86.1 93.1 63.8 78.1 61.5 41.2Ohio Washington 391670004 65.2 76.7 84.7 90.7 98.3 99.3 NA 62.8 38.1 57.8Oklahoma Cleveland 400270049 70.0 77.7 68.3 64.0 57.8 70.0 78.6 82.1 58.6 56.2Oklahoma Oklahoma 401090033 66.3 74.6 69.5 64.1 61.3 72.1 85.1 83.7 69.8 59.7State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Oklahoma Oklahoma 401091037 68.7 78.2 73.7 68.3 67.8 62.0 73.5 88.1 68.3 56.8Oklahoma Tulsa 401430137 74.2 89.8 84.1 72.2 81.0 91.0 95.3 95.0 65.0 61.3Oklahoma Tulsa 401430174 57.6 62.7 61.5 50.0 59.2 59.2 69.6 59.2 48.6 44.7Pennsylvania Allegheny 420030008 61.2 72.3 87.3 109.5 125.6 44.0 64.2 69.8 32.7 44.8Pennsylvania Allegheny 420030067 57.8 63.7 84.1 105.5 102.0 60.3 66.0 64.6 35.5 49.8Pennsylvania Allegheny 420030088 60.1 72.3 80.3 102.0 118.8 49.1 63.7 57.8 28.7 40.1Pennsylvania Allegheny 420031005 66.1 80.3 91.2 101.1 113.1 74.2 62.8 71.5 40.2 43.1Pennsylvania Beaver 420070002 61.7 60.1 NA 95.8 91.6 62.8 84.8 NA 49.3 46.1Pennsylvania Beaver 420070005 64.6 70.6 74.1 93.3 89.8 51.7 93.0 78.2 43.5 49.8Pennsylvania Beaver 420070014 59.0 66.2 75.2 83.5 95.7 50.1 82.6 77.1 47.2 51.2Pennsylvania Berks 420110001 45.7 77.1 82.1 80.8 97.2 66.6 36.8 27.2 34.7 40.3Pennsylvania Berks 420110009 41.8 79.5 86.5 87.0 114.2 73.2 40.5 30.7 28.7 42.1Pennsylvania Blair 420130801 62.0 78.6 89.7 85.5 94.8 69.3 68.7 61.1 41.7 34.2Pennsylvania Bucks 420170012 50.6 73.0 102.3 100.7 122.7 108.6 41.1 37.5 25.5 32.3Pennsylvania Cambria 420210011 61.1 66.3 77.5 91.3 92.7 64.2 63.8 59.6 37.6 42.8Pennsylvania Dauphin 420430401 49.5 76.1 76.8 82.5 102.5 67.2 57.7 27.3 18.8 19.1Pennsylvania Dauphin 420431100 50.3 79.0 80.0 88.6 106.2 67.2 63.6 31.2 23.5 20.6Pennsylvania Delaware 420450002 51.6 67.7 86.1 99.0 111.3 100.7 34.6 34.3 18.2 26.8Pennsylvania Erie 420490003 52.7 58.5 57.7 87.5 71.8 76.0 55.1 80.2 58.6 35.5Pennsylvania Lackawanna 420690101 46.0 74.1 82.6 81.5 79.6 54.6 54.0 40.6 48.5 44.7Pennsylvania Lackawanna 420692006 45.1 76.2 91.1 91.0 88.8 63.2 57.1 43.7 49.7 45.5Pennsylvania Lancaster 420710007 54.2 76.8 85.7 93.1 109.2 101.8 63.6 37.7 31.7 35.8Pennsylvania Lawrence 420730015 55.3 61.1 79.2 94.7 100.0 65.3 72.7 77.8 52.1 42.3Pennsylvania Lehigh 420770004 46.2 78.5 84.3 83.8 96.7 64.3 28.3 24.0 32.5 29.6Pennsylvania Luzerne 420791100 40.3 66.3 79.5 75.8 78.6 52.6 55.7 44.1 44.5 30.8Pennsylvania Luzerne 420791101 45.7 72.8 88.7 85.5 90.1 65.7 56.3 47.2 43.7 36.3Pennsylvania Lycoming 420810403 43.7 55.8 72.7 69.6 73.1 50.5 NA NA 33.7 31.1Pennsylvania Mercer 420850100 58.5 63.1 72.0 102.5 97.1 75.6 70.2 78.2 54.2 42.3Pennsylvania Montgomery 420910013 52.8 68.8 85.5 87.8 114.5 96.1 36.7 28.8 28.3 26.7Pennsylvania Perry 420990301 53.2 69.3 73.1 71.7 93.6 50.1 66.3 35.1 24.7 20.2Pennsylvania Philadelphia 421010004 43.7 55.7 78.7 86.2 98.7 85.0 31.6 27.5 18.7 20.0State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Pennsylvania Philadelphia 421010014 52.5 72.5 88.7 88.3 NA NA 37.5 35.0 27.5 25.0Pennsylvania Philadelphia 421010024 57.5 73.7 101.2 102.5 127.5 111.2 41.2 40.0 26.2 33.7Pennsylvania Philadelphia 421010136 48.7 62.5 85.0 92.5 106.2 105.0 23.7 NA NA NAPennsylvania Washington 421250005 55.6 61.8 86.1 92.2 96.5 NA NA 57.2 37.3 49.3Pennsylvania Washington 421250200 59.1 56.2 77.5 90.8 92.2 59.0 67.3 58.8 35.1 NAPennsylvania Washington 421255001 53.6 62.6 67.3 88.6 NA NA 77.6 63.0 39.8 45.5Pennsylvania Westmoreland 421290006 53.6 71.6 83.8 99.7 107.3 54.6 58.1 62.5 32.8 45.6Pennsylvania York 421330008 49.0 78.1 79.3 86.3 91.7 82.5 68.8 35.1 27.2 20.7Rhode Island Kent 440030002 34.0 62.2 91.8 104.1 120.1 55.6 43.2 42.0 52.0 37.2Rhode Island Providence 440071010 35.3 61.2 76.2 99.5 89.3 63.6 44.8 43.1 46.6 39.8South Carolina Abbeville 450010001 56.0 55.7 62.5 50.3 NA NA 44.7 47.8 49.5 61.1South Carolina Aiken 450030003 57.3 53.3 60.2 43.5 44.1 41.5 45.6 43.4 47.7 52.5South Carolina Anderson 450070003 67.1 66.1 66.1 63.0 37.2 59.8 46.3 59.2 60.6 59.2South Carolina Barnwell 450110001 60.1 58.7 66.6 46.1 45.7 42.8 50.0 39.3 NA NASouth Carolina Berkeley 450150002 71.1 48.2 49.1 34.1 33.0 52.6 33.6 25.8 40.3 40.7South Carolina Charleston 450190042 68.3 51.7 50.5 35.5 32.2 48.1 35.1 28.6 44.6 43.3South Carolina Charleston 450190046 59.6 44.8 43.2 32.2 28.0 41.3 29.0 25.8 39.6 35.3South Carolina Cherokee 450210002 50.6 68.3 77.1 68.5 38.2 59.7 52.1 54.2 63.5 62.2South Carolina Chester 450230002 77.1 67.2 71.5 54.6 42.6 51.5 47.3 43.6 57.3 61.7South Carolina Darlington 450310003 69.2 65.5 67.0 47.1 36.1 55.0 36.0 33.7 60.0 50.3South Carolina Edgefield 450370001 61.2 63.8 67.3 46.5 48.6 55.1 57.0 43.6 69.5 67.8South Carolina Oconee 450730001 53.8 69.3 53.1 62.3 56.3 63.8 45.0 47.2 60.5 61.6South Carolina Pickens 450770002 64.6 73.7 65.8 67.8 43.7 66.3 NA NA 65.6 66.0South Carolina Richland 450790007 NA 63.6 68.3 45.6 37.8 58.1 40.7 35.6 58.6 74.0South Carolina Richland 450791002 55.5 62.7 65.8 45.7 41.0 60.8 39.1 38.0 60.2 74.7South Carolina Spartanburg 450830009 55.2 68.6 71.5 66.2 33.8 61.2 49.5 63.1 67.2 62.0South Carolina Union 450870001 NA 55.7 69.7 50.2 42.7 49.8 43.3 47.1 50.2 60.6South Carolina Williamsburg 450890001 69.0 58.3 53.6 41.3 38.1 53.2 39.6 33.0 36.1 40.1South Carolina York 450910006 65.7 65.0 69.0 55.6 40.0 45.7 41.8 40.3 53.1 57.5Tennessee Anderson 470010101 66.1 74.1 72.8 NA NA 62.0 52.8 52.8 57.1 66.2Tennessee Blount 470090101 NA NA NA NA NA NA NA NA NA NAState CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Tennessee Blount 470090102 60.1 55.6 67.1 66.1 54.5 37.0 28.5 48.0 34.0 64.8Tennessee Davidson 470370011 75.6 65.6 83.7 80.6 47.5 64.3 73.1 74.3 38.1 67.5Tennessee Davidson 470370026 69.3 66.2 82.5 77.5 46.8 61.8 75.0 73.7 50.6 91.8Tennessee Hamilton 470650028 67.6 66.3 66.8 74.1 50.1 68.5 55.7 68.0 60.2 76.2Tennessee Hamilton 470651011 58.8 63.1 60.8 67.3 46.3 67.5 58.5 59.8 49.1 65.3Tennessee Haywood 470750002 73.2 81.2 81.8 77.6 81.5 77.7 66.3 77.5 82.5 NATennessee Knox 470930021 55.8 68.0 64.0 64.0 51.5 52.7 48.5 44.3 64.5 69.5Tennessee Knox 470931020 55.8 65.0 64.5 62.6 45.0 50.5 42.6 46.8 57.8 69.0Tennessee Rutherford 471490101 56.8 63.1 NA NA NA NA NA NA NA 62.1Tennessee Sevier 471550101 70.8 70.7 NA 75.7 67.0 57.3 63.8 69.8 63.7 72.7Tennessee Sevier 471550102 59.0 67.2 69.5 71.0 68.1 54.6 62.1 71.7 58.8 61.7Tennessee Shelby 471570021 73.2 74.7 73.3 75.6 83.6 75.5 68.2 75.0 81.4 75.3Tennessee Shelby 471571004 78.5 79.0 79.7 83.3 89.1 82.5 68.3 82.0 81.5 85.1Tennessee Sullivan 471632002 53.3 61.7 64.1 62.2 37.5 39.0 57.1 49.1 64.5 63.8Tennessee Sullivan 471632003 55.5 64.1 68.8 70.7 38.0 39.3 60.7 51.5 63.7 58.3Tennessee Sumner 471650007 82.8 65.2 87.0 84.3 59.6 68.3 80.0 79.0 54.3 83.2Tennessee Sumner 471650101 68.3 56.3 67.7 69.6 51.3 49.8 65.1 54.0 40.2 59.7Tennessee Wilson 471890103 64.2 63.3 74.2 74.1 49.8 54.7 65.8 67.6 49.8 71.3Texas Brazoria 480391003 68.5 80.3 63.6 42.8 73.1 79.2 64.6 91.5 81.7 72.0Texas Collin 480850005 75.1 88.2 77.7 63.3 73.6 90.1 NA NA NA 74.0Texas Dallas 481130069 54.8 67.2 66.6 51.1 57.0 75.7 87.5 82.1 78.0 79.8Texas Dallas 481130087 62.0 73.2 68.3 50.2 64.7 85.1 87.3 85.8 82.0 96.7Texas Galveston 481671002 70.1 84.6 64.8 46.8 73.3 92.3 NA 153.0 116.5 120.6Texas Gregg 481830001 93.7 93.3 78.1 72.7 85.1 110.0 101.2 102.3 103.2 87.6Texas Harris 482010024 89.3 90.7 77.8 43.1 80.0 83.5 107.3 116.1 109.8 100.5Texas Harris 482010029 109.1 98.2 90.3 60.6 88.3 85.3 89.6 93.1 83.2 68.1Texas Harris 482010046 80.3 89.8 75.8 41.3 87.1 73.6 94.6 98.5 94.3 92.3Texas Harris 482010047 77.6 89.2 75.1 43.5 92.1 82.1 NA 87.0 79.7 78.8Texas Harris 482010051 72.7 90.1 74.1 44.1 80.8 112.8 103.7 84.0 79.3 80.8Texas Harris 482010062 74.8 82.7 64.5 43.6 71.1 97.1 101.0 91.8 74.6 75.5Texas Harris 482010066 71.0 102.2 92.0 47.3 90.8 86.7 84.1 83.3 72.1 70.1State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Texas Harris 482011035 66.3 85.3 66.8 41.8 NA NA 106.7 102.6 91.8 75.7Texas Jefferson 482450009 88.8 86.7 75.0 54.0 70.8 74.3 87.3 82.1 79.5 74.3Texas Jefferson 482450011 75.3 77.3 64.2 50.5 86.2 82.2 99.8 88.5 89.7 106.3Texas Orange 483611001 79.3 87.7 71.8 56.0 67.8 72.6 82.5 79.3 75.0 72.5Texas Tarrant 484391002 66.5 81.1 76.0 62.2 75.5 96.5 94.8 98.3 96.1 85.3Texas Tarrant 484392003 69.2 89.1 84.6 67.1 89.8 110.6 113.1 106.5 106.0 77.8Texas Travis 484530014 58.3 72.6 59.7 52.8 69.6 80.2 97.6 89.6 71.5 77.3Vermont Bennington 500030004 36.5 49.0 70.0 85.8 76.2 50.0 60.3 56.6 57.7 60.3Virginia Arlington 510130020 56.8 67.5 75.6 97.5 95.4 82.3 64.8 43.0 22.3 31.5Virginia Caroline 510330001 64.3 66.5 72.6 62.1 69.6 36.0 63.2 35.1 33.8 41.2Virginia Charles City 510360002 66.0 71.8 57.1 56.1 36.7 39.8 56.2 35.6 35.8 51.5Virginia Chesterfield 510410004 62.3 71.8 62.3 59.8 50.8 33.2 56.1 33.1 34.8 50.6Virginia Fairfax 510590005 55.2 78.2 84.5 99.1 93.3 68.5 65.3 48.8 18.5 40.8Virginia Fairfax 510590018 58.6 70.2 82.0 89.6 91.1 71.8 63.5 45.0 26.3 4.3Virginia Fairfax 510595001 62.3 67.6 86.5 122.1 107.5 63.5 59.1 49.5 24.8 39.2Virginia Fauquier 510610002 57.1 78.8 74.6 77.8 68.1 47.7 57.5 40.3 28.3 43.3Virginia Frederick 510690010 55.1 65.2 63.1 76.3 90.7 81.0 57.0 56.7 11.8 40.0Virginia Henrico 510870014 67.5 71.5 61.5 54.3 45.2 37.0 63.0 37.6 33.0 51.2Virginia Madison 511130003 61.8 72.8 68.0 77.5 89.2 79.1 64.0 52.8 39.6 48.2Virginia Prince William 511530009 56.2 76.2 75.8 100.3 97.0 71.2 64.0 52.0 28.7 46.7Virginia Roanoke 511611004 63.2 61.8 69.6 66.6 65.0 44.2 63.7 30.1 25.0 43.0Virginia Stafford 511790001 67.0 73.3 75.8 81.3 81.7 60.7 62.5 39.5 36.6 49.2Virginia Wythe 511970002 52.2 64.1 64.8 69.2 48.3 46.3 58.7 37.3 45.3 50.5Virginia Alexandria Cit 515100009 50.2 NA NA NA NA 63.4 62.6 43.6 25.0 25.6Virginia Hampton City 516500004 53.0 55.5 49.3 51.7 31.2 40.0 60.7 36.5 31.7 30.8Virginia Suffolk City 518000004 52.7 53.3 50.7 55.0 32.5 42.1 58.3 35.2 33.0 38.6Virginia Suffolk City 518000005 62.8 66.0 61.8 60.2 31.7 41.0 49.5 34.5 45.0 56.1West Virginia Cabell 540110006 76.1 76.1 81.7 69.0 84.2 83.0 54.7 49.1 37.7 57.3West Virginia Hancock 540291004 56.3 51.6 79.0 93.1 78.1 66.0 71.2 73.5 48.0 43.1West Virginia Ohio 540690007 56.3 57.3 64.6 77.0 89.1 61.0 64.3 66.3 45.6 44.2West Virginia Wood 541071002 62.8 69.2 73.8 79.6 87.3 83.0 NA 65.4 37.1 53.5State CountyAIRS SiteCode Jun 15 Jun 16 Jun 17 Jun 18 Jun 19 Jun 20 Jun 21 Jun 22 Jun 23 Jun 24Wisconsin Brown 550090026 71.2 99.8 70.8 62.0 61.7 66.6 47.2 69.8 76.6 76.1Wisconsin Columbia 550210015 81.8 88.0 79.1 68.7 79.2 76.7 66.8 96.6 99.2 88.2Wisconsin Dane 550250041 70.2 78.1 68.5 65.3 65.1 78.8 62.2 93.5 95.8 87.6Wisconsin Dodge 550270007 72.1 83.5 69.7 65.2 63.7 63.5 52.7 79.8 83.2 80.2Wisconsin Door 550290004 68.8 116.3 115.5 73.8 62.2 71.1 50.1 60.3 75.6 70.7Wisconsin Fond Du Lac 550390006 77.5 87.2 73.7 76.2 71.5 73.1 55.3 81.8 82.6 77.6Wisconsin Jefferson 550550002 75.7 87.5 71.1 64.2 70.8 70.1 65.0 88.2 94.8 88.6Wisconsin Kenosha 550590002 74.6 92.7 99.5 72.0 76.1 61.6 51.0 76.3 90.3 88.5Wisconsin Kenosha 550590019 89.2 110.8 118.6 78.1 93.7 71.0 56.7 85.6 102.2 91.5Wisconsin Kewaunee 550610002 70.2 102.7 109.3 71.5 67.0 69.2 52.1 72.5 96.2 81.7Wisconsin Manitowoc 550710004 67.0 100.1 65.7 62.3 66.1 59.8 48.0 75.7 74.7 67.3Wisconsin Manitowoc 550710007 74.7 106.8 115.1 71.6 56.1 62.6 49.6 76.8 80.7 78.2Wisconsin Marathon 550730012 75.2 78.2 61.3 66.0 64.8 70.8 70.6 68.3 81.3 80.7Wisconsin Milwaukee 550790041 74.5 101.5 99.5 70.5 72.3 65.2 49.7 72.1 94.3 94.5Wisconsin Milwaukee 550790044 61.0 88.1 68.7 68.6 68.6 69.1 49.5 75.6 92.1 93.1Wisconsin Milwaukee 550790085 81.8 109.0 107.3 75.2 77.1 69.1 52.8 75.3 103.0 100.0Wisconsin Milwaukee 550791025 77.8 102.0 93.1 73.2 76.1 68.0 51.3 76.1 93.8 93.2Wisconsin Outagamie 550870009 82.1 90.8 77.8 66.5 65.6 76.4 56.3 74.5 85.2 84.5Wisconsin Ozaukee 550890008 81.2 111.1 104.7 71.1 67.7 67.0 55.2 80.3 101.2 103.5Wisconsin Ozaukee 550890009 80.3 109.0 116.6 73.7 75.5 66.0 53.6 76.8 99.8 94.5Wisconsin Racine 551010017 72.2 93.5 102.7 66.5 69.8 54.1 42.2 66.1 80.8 84.8Wisconsin Rock 551050024 76.1 91.2 70.0 67.1 66.2 74.3 64.8 87.6 94.1 98.7Wisconsin St Croix 551091002 57.1 67.7 63.2 69.1 69.2 78.8 84.0 66.6 68.2 49.6Wisconsin Sauk 551110007 69.2 75.5 70.5 59.5 63.0 76.7 64.8 87.2 99.1 86.8Wisconsin Vernon 551230008 56.8 67.7 63.5 62.5 62.5 68.3 69.2 71.0 79.7 64.6Wisconsin Walworth 551270005 89.0 85.8 64.5 63.1 72.2 75.4 58.0 86.5 96.8 94.1Wisconsin Washington 551310009 80.0 94.1 75.5 73.2 76.5 73.3 56.1 89.2 89.8 90.7Wisconsin Waukesha 551330017 79.6 85.5 65.7 60.0 NA NA 55.0 84.6 88.8 92.7Wisconsin Winnebago 551390011 82.7 88.2 77.6 65.0 68.7 74.5 55.6 81.0 86.0 86.2State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Alabama Clay 10270001 72.7 64.1 60.1 80.7 72.3 84.3 66.3 90.0Alabama Jefferson 10731003 46.5 44.1 83.1 71.6 93.3 87.8 91.7 81.6Alabama Jefferson 10731005 45.6 40.1 66.1 88.8 102.2 85.6 80.1 69.6Alabama Jefferson 10732006 50.8 38.3 78.0 91.3 87.8 83.1 84.5 92.6Alabama Jefferson 10735002 55.2 51.3 70.3 67.5 79.2 83.0 77.6 80.5Alabama Jefferson 10736002 51.0 48.2 103.2 68.1 85.6 82.0 79.5 88.3Alabama Lawrence 10790002 51.7 43.0 49.5 77.2 82.6 85.0 76.2 72.5Alabama Madison 10890014 80.6 62.2 73.5 72.3 73.8 84.7 93.3 67.1Alabama Mobile 10970003 NA NA NA NA NA NA NA NAAlabama Shelby 11170004 56.1 35.1 78.3 98.8 89.5 81.5 82.5 97.7Alabama Sumter 11190002 39.5 38.3 42.5 60.8 74.7 50.2 50.6 46.1Arkansas Crittenden 50350005 59.5 55.2 56.2 118.7 89.8 123.8 67.0 63.3Arkansas Montgomery 50970001 49.3 50.0 45.6 42.7 44.6 54.5 58.0 47.8Arkansas Pulaski 51190007 57.3 55.8 60.3 75.8 77.5 72.0 70.7 57.0Arkansas Pulaski 51191002 56.8 57.3 53.3 99.5 100.6 86.7 79.8 69.1Connecticut Fairfield 90010017 68.1 31.8 59.1 61.5 64.8 81.3 122.1 NAConnecticut Fairfield 90011123 63.2 25.3 61.0 59.0 79.5 122.2 62.6 50.1Connecticut Fairfield 90013007 66.7 37.2 56.7 69.7 63.7 76.6 130.7 100.2Connecticut Hartford 90031003 56.5 26.8 63.8 64.3 71.3 113.7 95.1 64.2Connecticut Middlesex 90070007 54.1 26.1 74.1 69.1 67.2 110.8 121.5 71.5Connecticut New Haven 90093002 56.2 43.8 64.7 74.5 67.1 83.7 141.0 117.6Connecticut New London 90110008 35.5 41.3 60.1 63.2 55.2 64.8 113.5 97.0Connecticut Tolland 90131001 49.5 23.5 63.3 60.8 68.0 114.2 74.5 60.1Delaware Kent 100010002 75.3 47.7 60.6 68.2 76.6 78.1 117.5 111.2Delaware New Castle 100031003 67.5 31.8 65.6 68.6 89.0 90.6 115.5 122.7Delaware New Castle 100031007 63.8 40.6 51.1 73.5 91.0 93.0 118.2 135.2Delaware New Castle 100031010 63.8 30.8 67.0 64.1 78.8 95.5 NA NADelaware Sussex 100051002 82.6 58.1 62.3 76.0 78.8 79.3 101.3 104.78-Hour Daily Maximum Ozone (ppb) Measured at Monitoring Sites in the East During the July 1995 episodeState CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15D.C. Washington 110010025 58.8 52.5 61.3 68.1 91.8 93.6 84.7 99.0D.C. Washington 110010041 61.8 54.7 64.5 64.8 88.6 85.6 103.5 114.0D.C. Washington 110010043 70.1 61.8 63.5 81.1 98.3 93.2 108.3 123.7Florida Brevard 120094001 52.3 67.3 51.0 57.3 58.3 51.0 20.8 16.8Florida Duval 120310077 63.2 70.7 61.2 65.5 42.8 36.5 49.2 39.2Florida Escambia 120330004 59.5 42.3 59.6 73.8 71.3 59.1 48.1 33.7Florida Escambia 120330018 83.0 59.8 61.2 64.8 86.2 60.3 51.3 NAFlorida Hillsborough 120570081 62.7 64.5 60.1 58.1 NA 54.5 32.0 56.3Florida Hillsborough 120571035 57.7 67.2 61.8 64.0 44.7 52.3 30.0 51.6Florida Hillsborough 120571065 56.7 66.8 58.8 62.8 47.6 53.8 31.8 65.1Florida Orange 120950008 66.0 68.2 75.0 45.8 62.0 53.1 30.8 34.6Florida Orange 120952002 61.2 59.6 62.3 48.0 51.5 51.2 20.0 26.3Florida Osceola 120972002 49.2 62.3 66.1 42.0 54.1 52.3 27.7 42.2Florida Palm Beach 120992004 52.3 43.1 24.0 18.5 41.7 32.5 22.1 21.0Florida Pasco 121012001 53.3 66.7 59.0 55.6 52.8 75.3 57.7 44.2Florida Pinellas 121030004 48.5 65.2 59.1 55.5 43.7 54.8 26.5 49.8Florida Pinellas 121030018 36.3 51.2 47.3 49.2 46.0 22.0 13.7 23.1Florida Pinellas 121035002 49.0 63.3 57.0 52.7 35.5 65.1 36.7 42.0Florida Polk 121056005 48.6 63.1 53.2 63.2 44.0 47.6 26.0 31.8Florida Polk 121056006 54.0 72.5 61.6 49.8 48.8 50.5 29.0 30.0Florida St Lucie 121111002 45.6 58.0 31.1 40.2 50.8 39.2 17.5 16.5Florida Sarasota 121151005 53.7 61.2 60.3 54.2 57.6 49.3 30.7 43.3Florida Seminole 121171002 62.6 62.0 61.8 58.5 52.5 46.8 27.5 24.3Florida Volusia 121272001 51.1 67.2 61.1 55.3 56.7 44.5 21.3 18.7Florida Volusia 121275002 54.3 67.6 59.1 54.8 58.1 45.7 21.7 22.2Georgia Chatham 130510021 64.8 76.6 NA NA NA NA NA NAGeorgia De Kalb 130890002 84.5 95.7 107.0 115.1 75.2 73.6 104.0 120.3Georgia De Kalb 130893001 71.1 83.8 129.1 97.8 73.5 69.6 88.1 111.7Georgia Fulton 131210055 78.6 89.7 123.5 110.1 73.1 69.8 97.2 124.7Georgia Glynn 131270006 NA NA NA NA NA 37.5 34.5 42.3State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Georgia Gwinnett 131350002 66.5 73.8 109.0 81.3 68.3 67.5 78.7 92.6Georgia Muscogee 132150008 66.1 67.8 99.1 91.3 71.0 63.2 80.7 67.2Georgia Muscogee 132151003 74.5 65.2 74.0 69.3 71.1 63.8 82.0 68.0Georgia Richmond 132450091 70.0 78.8 99.4 88.2 73.8 68.7 49.7 76.5Georgia Rockdale 132470001 88.3 92.1 84.0 94.6 75.5 66.1 98.2 133.2Illinois Adams 170010006 69.3 47.2 48.5 50.2 65.5 79.2 49.0 59.3Illinois Champaign 170190004 62.0 75.1 65.0 89.5 71.6 84.6 67.3 64.1Illinois Cook 170310001 59.0 73.6 59.6 50.8 60.8 113.2 77.7 73.1Illinois Cook 170310032 54.7 72.2 59.7 82.1 69.6 112.8 82.0 69.6Illinois Cook 170310050 43.2 86.3 66.7 70.8 77.8 116.7 90.2 84.8Illinois Cook 170310064 53.7 79.2 60.7 74.8 77.0 117.3 85.7 78.0Illinois Cook 170310072 63.3 73.2 65.1 83.7 85.5 96.7 NA 95.3Illinois Cook 170311003 53.5 78.7 56.6 69.8 89.6 101.5 80.0 73.0Illinois Cook 170311601 54.6 66.8 54.1 73.6 83.8 NA 50.2 55.7Illinois Cook 170314002 44.7 71.0 61.5 49.1 77.3 85.0 73.1 67.1Illinois Cook 170314006 60.2 77.6 64.6 75.0 106.2 110.1 83.1 77.2Illinois Cook 170317002 61.1 75.3 57.3 78.7 86.2 104.3 80.2 104.2Illinois Cook 170318003 35.7 72.0 54.7 47.3 56.6 71.5 85.7 66.0Illinois Du Page 170436001 58.1 63.0 NA NA 79.8 71.1 63.6 55.8Illinois Effingham 170491001 64.7 67.5 68.8 93.7 66.1 71.2 71.0 62.1Illinois Jersey 170831001 59.8 60.1 61.2 90.2 79.6 80.0 56.6 55.0Illinois Kane 170890005 60.3 68.5 56.8 86.2 102.0 84.8 74.3 69.7Illinois Lake 170971002 62.0 62.2 58.2 79.3 87.3 86.1 75.3 66.3Illinois Lake 170971007 67.0 72.1 64.6 84.3 90.8 100.0 85.3 NAIllinois Lake 170973001 51.8 60.1 53.5 74.5 99.3 77.0 66.6 60.3Illinois McHenry 171110001 59.6 66.1 61.5 78.0 104.0 86.2 78.8 69.1Illinois Macon 171150013 62.7 64.3 62.3 78.3 68.1 76.2 59.1 62.0Illinois Macoupin 171170002 62.7 59.0 67.6 79.3 104.1 74.2 NA 54.8Illinois Madison 171190008 53.2 64.2 72.6 89.1 101.0 102.2 87.1 61.6Illinois Madison 171191009 56.8 58.5 79.7 52.7 67.6 95.1 84.3 74.0State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Illinois Madison 171192007 64.7 67.3 90.2 71.8 91.1 99.7 98.5 76.8Illinois Madison 171193007 54.1 59.1 68.3 83.0 88.1 95.1 76.3 53.6Illinois Peoria 171430024 57.3 56.5 66.3 58.7 78.5 57.6 74.6 73.0Illinois Peoria 171431001 62.6 59.1 70.2 67.6 87.7 82.1 62.6 65.6Illinois Randolph 171570001 63.1 71.5 68.7 66.1 55.0 58.0 64.5 65.8Illinois St Clair 171630010 48.7 60.6 97.2 60.2 64.5 92.1 76.6 70.8Illinois Sangamon 171670010 56.6 67.8 65.0 76.3 80.3 74.7 50.1 62.6Illinois Will 171971008 61.0 67.8 62.3 67.1 85.8 92.8 68.1 63.7Illinois Will 171971011 54.0 73.1 65.1 62.7 79.5 NA NA NAIllinois Winnebago 172010009 NA NA NA NA NA NA 75.8 72.1Illinois Winnebago 172012001 60.0 51.7 60.1 70.2 77.5 81.3 74.2 69.5Indiana Allen 180030002 61.1 59.5 63.7 78.8 96.1 97.5 91.1 79.6Indiana Allen 180030004 62.3 57.0 67.6 69.5 NA NA 72.0 NAIndiana Clark 180190003 64.5 87.0 74.5 91.6 108.0 83.7 90.7 NAIndiana Floyd 180431004 63.2 76.7 75.1 89.6 92.7 77.6 82.2 51.3Indiana Hamilton 180571001 58.1 80.6 64.3 76.5 111.0 94.6 97.5 65.8Indiana Hancock 180590003 57.2 74.7 NA 80.0 89.4 103.1 104.6 57.2Indiana Lake 180890022 59.0 89.1 78.8 94.2 84.1 105.6 104.6 85.8Indiana Lake 180892008 51.5 88.7 78.7 79.6 81.6 110.7 99.6 85.7Indiana La Porte 180910005 66.6 87.5 81.7 97.2 103.2 102.0 131.1 120.3Indiana Madison 180950010 62.5 76.6 69.3 83.8 95.7 103.0 106.8 70.1Indiana Marion 180970042 62.2 76.5 63.3 80.5 92.0 87.2 95.5 61.1Indiana Marion 180970050 71.7 91.7 69.1 83.0 95.2 94.0 106.3 69.6Indiana Marion 180970057 71.2 84.6 69.6 81.5 91.2 88.6 104.4 59.2Indiana Marion 180970073 79.7 89.7 72.5 85.3 91.0 94.0 104.1 57.3Indiana Porter 181270020 52.1 87.7 67.1 72.5 82.1 90.6Indiana Porter 181270024 69.1 103.5 70.0 NA 83.1 97.8 97.3 92.8Indiana St Joseph 181410010 58.7 57.3 69.6 65.0 NA NA 80.6 66.7Indiana St Joseph 181411007 49.1 59.7 67.5 77.8 88.1 NA NA NAIndiana St Joseph 181411008 49.5 63.1 70.0 67.7 90.1 94.7 NA NAState CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Indiana Vanderburgh 181630012 80.1 73.5 77.2 88.3 91.5 75.1 79.5 57.6Indiana Vanderburgh 181630013 80.0 81.0 77.3 92.7 99.2 90.8 78.0 61.5Indiana Vigo 181670018 69.8 74.7 60.5 78.1 87.5 64.5 85.0 56.8Indiana Warrick 181730002 77.1 71.2 85.3 104.7 NA 81.2 86.7 58.7Indiana Warrick 181730008 69.2 71.7 79.1 98.1 106.1 77.1 82.7 56.1Indiana Warrick 181730009 70.6 75.7 75.6 108.1 107.7 90.1 81.7 53.3Iowa Polk 191530058 28.2 26.7 25.3 36.3 42.5 39.8 33.5 30.5Iowa Scott 191632011 57.5 46.6 59.8 61.8 60.6 82.2 60.0 66.8Kansas Sedgwick 201730001 61.2 51.2 76.2 75.0 76.2 83.7 80.0 76.2Kansas Sedgwick 201730010 51.8 44.3 66.2 56.2 65.0 72.5 66.2 61.8Kentucky Bell 210130002 72.5 70.7 61.5 62.1 64.2 79.5 72.0 58.0Kentucky Boone 210150003 53.1 62.6 71.6 75.6 84.0 72.1 77.2 62.7Kentucky Boyd 210190015 54.3 69.8 85.3 79.8 91.0 103.6 68.7 102.1Kentucky Bullitt 210290006 61.3 68.2 90.7 77.2 86.1 72.7 105.8 44.6Kentucky Daviess 210590005 77.0 NA 65.6 NA NA 96.3 85.6 59.7Kentucky Fayette 210670001 47.8 63.5 63.3 77.6 104.2 79.7 77.0 70.2Kentucky Fayette 210670012 55.2 63.1 89.5 88.2 92.6 80.1 86.6 83.0Kentucky Graves 210830003 65.1 49.3 51.0 68.5 49.0 55.2 51.8 61.3Kentucky Greenup 210890007 50.1 64.7 62.7 71.5 80.3 81.0 65.3 77.5Kentucky Hancock 210910012 63.5 74.2 71.3 95.0 101.8 86.8 95.5 58.3Kentucky Henderson 211010013 NA 70.0 76.6 98.8 91.0 75.0 97.7 48.5Kentucky Henderson 211010014 81.7 70.5 73.2 93.0 97.5 65.7 92.8 53.1Kentucky Jefferson 211110027 47.2 48.5 62.5 74.5 79.2 60.7 102.2 40.2Kentucky Jefferson 211110051 67.1 64.0 77.2 82.6 103.1 69.1 89.7 50.1Kentucky Jefferson 211111021 57.1 81.8 68.8 68.8 103.6 80.0 87.1 65.3Kentucky Jessamine 211130001 54.1 57.3 70.8 61.4 80.2 62.8 73.1 77.3Kentucky Kenton 211170007 67.6 81.0 83.1 82.5 98.5 82.5 84.1 85.6Kentucky Livingston 211390003 71.5 61.1 53.3 75.8 60.3 69.6 92.2 75.8Kentucky Livingston 211390004 72.0 55.1 58.2 83.0 61.7 69.3 84.6 78.8Kentucky McCracken 211451024 72.3 51.5 50.8 69.2 55.2 67.8 69.7 72.0State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Kentucky McLean 211490001 76.8 62.6 78.0 103.6 93.8 84.0 80.1 56.8Kentucky Oldham 211850004 53.1 61.7 67.8 88.1 88.2 76.2 83.3 42.4Kentucky Pike 211950002 63.3 60.1 68.3 85.2 82.6 86.2 74.5 83.7Kentucky Pulaski 211990003 68.6 66.7 57.1 80.1 77.0 73.1 63.2 68.6Kentucky Scott 212090001 56.5 63.5 79.1 81.3 99.8 88.7 87.0 76.5Kentucky Simpson 212130004 74.8 66.3 77.6 76.8 NA 49.5 71.2 73.7Louisiana Ascension 220050004 57.1 67.7 83.2 88.1 70.5 59.1 26.1 31.7Louisiana Beauregard 220110002 51.6 52.6 71.6 58.1 56.8 70.0 51.8 34.6Louisiana Bossier 220150008 58.3 54.0 60.0 74.6 63.7 65.8 58.7 39.3Louisiana Caddo 220170001 60.8 54.2 66.5 66.8 64.3 64.0 74.5 44.7Louisiana Calcasieu 220190002 62.5 63.2 78.1 65.2 84.8 84.1 55.8 39.5Louisiana Calcasieu 220190008 37.2 37.1 46.8 32.3 39.6 51.5 39.6 22.8Louisiana Calcasieu 220190009 69.3 58.2 67.0 63.4 67.2 79.5 53.8 38.6Louisiana East Baton Rou 220330003 61.5 62.5 79.0 80.6 88.0 64.3 37.7 40.5Louisiana East Baton Rou 220330009 58.2 62.0 80.0 81.6 83.3 64.6 32.7 36.8Louisiana East Baton Rou 220330013 49.6 62.0 80.6 58.7 58.0 62.0 42.1 40.8Louisiana East Baton Rou 220331001 50.7 60.8 77.3 70.6 74.1 65.2 43.7 42.8Louisiana Grant 220430001 58.2 57.5 72.1 62.6 69.8 77.0 59.3 NALouisiana Iberville 220470007 58.6 54.5 75.6 65.2 89.3 61.1 39.8 31.8Louisiana Iberville 220470009 77.5 57.6 78.0 71.7 90.0 79.1 42.3 33.8Louisiana Jefferson 220511001 63.6 85.8 94.5 75.1 81.6 67.3 45.7 41.0Louisiana Lafayette 220550005 60.6 67.6 106.8 79.3 74.7 88.5 53.4 38.7Louisiana Livingston 220630002 49.0 78.0 89.0 69.6 63.5 59.7 37.3 35.3Louisiana Orleans 220710012 50.0 73.0 72.7 59.6 62.5 55.6 32.1 26.7Louisiana Pointe Coupee 220770001 47.7 56.8 70.6 60.3 57.8 60.6 40.8 38.5Louisiana St Bernard 220870002 44.2 58.1 55.6 49.1 45.0 38.3 27.1 21.0Louisiana St Charles 220890003 66.6 84.2 82.0 69.1 76.0 68.6 NA 35.7Louisiana St James 220930002 63.7 61.1 76.3 93.1 88.5 65.0 38.3 28.7Louisiana St John The Ba 220950002 58.6 79.5 79.5 75.2 68.1 63.7 38.6 28.1Louisiana St Mary 221010003 68.6 71.6 70.3 72.1 72.5 61.2 41.2 29.2State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Louisiana West Baton Rou 221210001 52.5 54.2 72.2 67.3 81.2 62.1 31.7 34.5Maine Cumberland 230052003 31.2 30.3 60.7 44.7 67.2 96.5 102.2 42.8Maine Hancock 230090102 NA NA NA NA NA NA NA NAMaine Kennebec 230112005 26.3 20.6 38.1 33.1 50.1 80.1 59.0 35.5Maine Knox 230130004 32.8 33.2 54.1 53.0 70.0 88.6 114.5 42.8Maine Oxford 230173001 40.3 24.6 35.1 39.1 45.5 53.0 62.7 35.6Maine Penobscot 230194008 36.0 33.0 43.6 52.1 60.3 88.8 62.3 56.0Maine York 230312002 37.2 30.3 64.7 39.5 75.6 103.2 103.1 50.7Maine York 230313002 40.6 23.5 53.5 39.5 67.5 88.6 79.8 50.8Maryland Anne Arundel 240030014 81.3 26.5 NA NA 112.6 107.5 123.3 109.0Maryland Anne Arundel 240030019 70.7 55.7 79.2 77.7 103.8 103.2 110.5 147.6Maryland Baltimore 240051007 57.3 36.2 66.7 59.1 95.3 110.2 91.0 100.1Maryland Baltimore 240053001 64.5 46.8 67.6 66.2 106.3 85.5 96.3 126.0Maryland Carroll 240130001 57.6 43.6 74.6 61.5 85.0 87.0 90.7 95.2Maryland Cecil 240150003 71.3 44.3 82.3 79.7 94.2 NA NA NAMaryland Charles 240170010 78.2 64.1 60.3 92.6 87.3 86.7 84.3 101.5Maryland Harford 240251001 65.3 40.0 74.2 79.2 113.0 93.8 123.6 132.0Maryland Harford 240259001 62.3 42.8 72.6 70.1 99.5 101.8 117.1 122.7Maryland Kent 240290002 72.5 44.6 67.2 75.0 91.2 87.5 117.1 107.8Maryland Montgomery 240313001 62.2 52.6 73.3 68.7 92.6 106.5Maryland Prince Georges 240330002 66.5 55.1 69.7 68.3 99.6 95.5 100.5 123.6Maryland Prince Georges 240338001 64.0 56.8 60.5 69.6 97.7 85.3 108.0 105.1Massachusetts Barnstable 250010002 40.1 44.8 65.7 54.3 60.2 67.0 122.3 74.7Massachusetts Berkshire 250034002 NA NA NA NA NA NA NA NAMassachusetts Bristol 250051002 43.3 34.0 71.3 53.7 70.0 65.5 107.2 73.0Massachusetts Essex 250090005 29.1 18.1 40.7 34.7 58.3 77.2 58.7 43.3Massachusetts Essex 250092006 37.1 28.1 62.3 50.8 63.1 100.7 91.7 63.5Massachusetts Essex 250094004 38.0 24.0 NA NA NA NA NA 52.0Massachusetts Hampden 250130003 56.0 25.1 55.8 62.5 71.7 109.3 76.2 60.8Massachusetts Hampden 250130008 56.2 25.2 51.5 63.1 77.2 101.6 78.8 63.1State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Massachusetts Hampshire 250150103 51.1 25.1 47.1 45.8 61.2 75.5 71.8 53.2Massachusetts Hampshire 250154002 52.0 25.7 57.2 60.8 68.5 101.0 76.7 62.3Massachusetts Worcester 250270015 47.7 25.1 60.5 56.1 60.6 106.3 83.5 59.8Michigan Allegan 260050003 46.3 78.1 80.6 63.2 101.3 162.7 133.3 82.0Michigan Benzie 260190003 53.0 57.0 80.6 55.0 102.1 83.3 101.6 65.5Michigan Berrien 260210014 42.2 69.7 72.7 70.7 74.8 101.0 107.3 65.7Michigan Cass 260270003 49.7 71.6 66.8 85.6 93.3 101.1 NA 63.5Michigan Clinton 260370001 46.5 48.1 44.8 46.2 76.0 71.8 80.7 62.1Michigan Genesee 260490021 46.6 60.3 47.1 46.1 89.0 80.3 81.5 67.0Michigan Genesee 260492001 NA NA NA 38.7 83.6 81.0 79.5 55.7Michigan Huron 260630007 35.0 51.2 60.1 38.3 NA NA NA NAMichigan Ingham 260650012 50.6 65.6 51.5 54.1 90.5 80.2 94.5 66.0Michigan Kent 260810020 42.5 60.1 61.8 46.1 94.6 133.1 107.7 76.7Michigan Lenawee 260910007 57.0 51.3 51.0 58.3 84.6 NA NA NAMichigan Macomb 260990009 51.7 48.5 54.8 51.0 57.8 92.7 95.3 78.8Michigan Macomb 260991003 51.7 45.6 47.5 53.0 69.3 85.7 88.6 84.1Michigan Muskegon 261210039 44.6 58.8 72.5 71.3 102.8 153.5 119.8 67.8Michigan Oakland 261250001 52.6 37.8 49.2 56.4 71.5 79.2 93.3 84.1Michigan Ottawa 261390005 41.6 61.1 64.8 62.0 83.2 NA NA NAMichigan St Clair 261470005 30.5 48.0 55.3 44.7 53.7 100.2 84.3 70.0Michigan Wayne 261630001 39.2 31.2 36.8 51.0 55.8 77.2 81.7 83.6Michigan Wayne 261630016 42.2 29.6 43.5 50.3 53.6 68.0 82.5 NAMichigan Wayne 261630019 57.3 41.6 55.2 61.3 67.2 91.2 99.2 75.6Minnesota Anoka 270031001 43.2 31.6 38.6 66.8 67.8 62.8 65.2 30.0Minnesota Anoka 270031002 NA NA NA NA 78.7 76.0 77.2 34.2Minnesota Dakota 270376018 50.1 46.0 52.7 52.7 58.6 55.1 63.7 44.6Mississippi Adams 280010004 51.2 57.1 54.6 65.2 56.1 56.6 42.7 27.0Mississippi De Soto 280330002 61.1 62.2 76.0 80.1 76.3 75.5 68.3 65.8Mississippi Hancock 280450001 63.3 65.3 82.7 77.1 78.0 76.7 49.2 38.0Mississippi Hinds 280490010 54.1 56.0 52.7 61.3 63.0 66.1 51.8 50.7State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Mississippi Jackson 280590006 57.3 56.5 54.1 68.2 70.1 64.6 40.1 27.0Mississippi Madison 280890002 58.0 61.5 58.3 60.5 56.5 63.1 51.7 50.5Mississippi Warren 281490004 53.8 64.3 58.7 67.8 59.3 59.1 52.5 39.3Missouri Clay 290470003 63.6 44.0 71.0 111.8 119.5 92.8 82.0 86.1Missouri Clay 290470005 62.2 44.7 68.6 105.0 125.2 99.1 85.0 87.6Missouri Clay 290470025 52.8 40.5 56.8 88.0 94.3 88.1 66.6 67.5Missouri Greene 290770026 46.6 53.8 71.6 46.1 63.5 47.0 54.0 52.3Missouri Greene 290770036 49.2 54.5 95.3 57.5 64.6 75.2 75.1 69.8Missouri Jefferson 290990012 63.0 74.0 102.2 61.8 62.1 73.2 62.2 67.6Missouri Monroe 291370001 60.5 52.7 57.3 NA NA NA 59.0 61.2Missouri Platte 291650023 51.3 38.8 55.2 83.3 78.5 76.2 66.8 69.5Missouri St Charles 291831002 65.7 73.5 89.7 112.8 105.4 121.6 97.2 72.0Missouri St Charles 291831004 67.3 66.1 69.1 74.1 89.8 96.5 78.2 71.1Missouri St Louis 291890006 52.6 65.5 74.7 47.5 54.2 86.6 65.6 62.1Missouri St Louis 291893001 57.7 65.6 76.2 57.7 55.6 85.3 70.5 62.8Missouri St Louis 291895001 61.7 65.8 88.5 92.5 88.2 113.5 89.5 77.3Missouri St Louis City 295100007 49.6 60.7 87.5 47.3 37.0 61.1 59.1 63.8Missouri St Louis City 295100072 NA 53.7 73.6 48.2 52.2 75.6 60.8 57.1Nebraska Douglas 310550028 60.1 39.3 68.0 68.6 75.0 71.8 68.1 61.0Nebraska Douglas 310550032 NA NA NA NA NA NA NA NANebraska Douglas 310550035 25.6 20.1 32.3 40.3 NA NA NA NANebraska Lancaster 311090016 52.7 40.5 51.1 63.2 49.2 53.3 56.8 45.3New Hampshire Cheshire 330050007 49.1 22.3 47.7 52.5 50.0 69.0 67.5 46.3New Hampshire Hillsborough 330111010 40.1 23.0 55.0 44.6 67.0 96.6 76.2 58.2New Hampshire Merrimack 330130007 39.1 18.1 41.1 25.3 44.8 65.1 64.8 44.3New Hampshire Rockingham 330150012 46.0 24.0 60.2 40.3 73.6 99.3 80.8 53.5New Jersey Atlantic 340010005 84.2 45.8 66.0 70.1 91.2 76.7 101.6 109.8New Jersey Camden 340070003 67.8 22.7 66.5 66.7 81.5 77.0 110.1 128.1New Jersey Camden 340071001 88.0 38.7 72.2 99.1 95.2 89.1 124.1 128.0New Jersey Cumberland 340110007 75.0 39.7 66.6 66.7 82.5 76.8 115.2 115.5State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15New Jersey Gloucester 340150002 77.1 29.6 73.2 77.6 107.0 91.2 126.2 125.8New Jersey Hudson 340170006 72.3 27.0 63.3 62.5 64.5 104.6 114.2 102.2New Jersey Hunterdon 340190001 65.7 30.2 74.8 72.6 86.2 106.2 101.8 85.6New Jersey Mercer 340210005 72.0 26.8 80.8 70.0 88.5 107.0 106.5 99.3New Jersey Middlesex 340230011 69.6 28.3 73.8 72.1 78.2 109.2 107.1 99.6New Jersey Monmouth 340250005 70.2 26.5 60.6 61.7 62.0 71.2 136.8 112.2New Jersey Morris 340273001 67.8 32.3 74.0 75.1 101.5 113.6 110.7 91.0New Jersey Ocean 340290006 79.0 27.3 77.6 75.8 86.3 87.1 147.7 124.8New York Albany 360010012 36.6 34.1 51.8 71.6 62.1 81.3 71.6 60.2New York Bronx 360050083 65.0 24.6 50.1 42.8 63.6 86.2 115.6 91.2New York Chautauqua 360130011 NA NA NA NA NA NA NA NANew York Chemung 360150003 25.3 38.1 54.0 56.8 68.6 75.6 74.1 69.6New York Dutchess 360270007 59.8 24.5 54.6 63.7 69.7 94.8 76.1 63.2New York Erie 360290002 21.8 56.6 NA 58.1 55.0 98.7 NA NANew York Essex 360310002 56.2 35.1 44.8 51.0 68.2 90.6 81.7 65.0New York Essex 360310003 45.3 33.3 42.0 39.5 60.6 86.3 82.6 58.2New York Hamilton 360410005 32.1 34.5 41.0 45.8 51.1 70.4 75.5 NANew York Herkimer 360430005 23.6 32.3 38.7 53.6 52.2 52.0 58.7 NANew York Jefferson 360450002 26.1 36.8 44.3 55.1 42.7 98.2 102.3 NANew York Madison 360530006 30.2 40.0 51.2 52.0 61.5 81.3 82.2 NANew York Monroe 360551004 22.2 43.6 53.8 NA 40.6 97.0 93.6 65.3New York Niagara 360631006 22.3 57.3 44.2 NA NA NA 95.6 66.1New York Oneida 360650004 24.0 33.3 44.1 54.0 52.7 82.2 77.0 52.6New York Onondaga 360671015 23.2 36.8 54.8 59.6 67.1 92.6 84.8 62.1New York Orange 360715001 57.8 24.6 59.3 64.2 77.2 102.7 78.8 67.8New York Putnam 360790005 63.7 27.7 62.3 59.8 75.3 107.3 74.8 63.6New York Richmond 360850067 76.7 27.1 68.7 60.1 58.5 97.6 110.7 99.2New York Saratoga 360910004 35.5 30.5 45.8 52.1 59.6 77.0 70.2 63.1New York Schenectady 360930003 34.6 33.2 49.1 61.2 55.3 74.1 75.6 56.1New York Suffolk 361030002 82.3 37.2 62.8 69.8 67.5 78.1 115.2 116.1State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15New York Suffolk 361030004 58.0 40.1 59.0 61.2 54.7 77.6 116.3 110.8New York Ulster 361111005 46.3 35.6 52.0 55.8 53.5 80.7 75.8 69.2New York Wayne 361173001 21.8 37.1 59.5 60.8 44.3 96.8 93.5 66.0New York Westchester 361192004 74.1 33.2 61.5 62.2 74.8 100.6 104.7 NANorth Carolina Buncombe 370210030 72.7 76.7 75.8 61.8 63.1 78.2 71.7 70.0North Carolina Caldwell 370270003 74.0 68.0 83.1 72.3 79.3 92.0 78.0 78.6North Carolina Camden 370290099 74.6 63.6 69.0 67.7 83.5 61.2 48.1 59.1North Carolina Caswell 370330001 69.1 57.6 69.6 69.7 69.8 76.6 100.7 96.1North Carolina Chatham 370370004 79.0 58.1 76.2 75.1 81.3 80.2 49.8 61.0North Carolina Cumberland 370510008 74.0 67.3 58.3 65.3 75.0 67.5 47.3 52.7North Carolina Duplin 370610002 57.7 62.3 45.6 46.1 48.6 37.1 28.6 31.0North Carolina Durham 370630013 80.3 65.2 78.6 88.3 82.3 81.1 73.3 72.6North Carolina Forsyth 370670022 76.3 78.1 90.5 74.7 87.0 100.5 112.1 106.8North Carolina Forsyth 370670027 68.8 61.6 72.5 63.1 76.2 82.2 80.5 74.5North Carolina Forsyth 370671008 75.8 71.1 77.7 64.1 81.5 84.1 99.6 87.8North Carolina Franklin 370690001 73.3 54.3 63.7 66.0 77.8 66.0 62.0 71.8North Carolina Granville 370770001 84.5 67.7 74.8 89.5 85.0 82.2 84.7 83.1North Carolina Guilford 370810011 73.3 62.0 69.1 73.8 73.7 75.2 103.5 92.1North Carolina Haywood 370870035 59.7 54.3 51.3 62.0 58.0 61.0 77.5 79.7North Carolina Haywood 370870036 64.8 55.5 52.2 63.8 63.8 60.5 75.8 76.4North Carolina Johnston 371010002 87.1 68.8 72.2 85.3 82.0 73.8 51.1 36.5North Carolina Lincoln 371090004 64.5 74.0 73.0 77.7 87.6 90.0 92.0 88.1North Carolina Martin 371170001 65.8 55.8 55.5 49.7 71.7 69.1 31.0 43.5North Carolina Mecklenburg 371191005 77.1 77.5 78.0 84.0 76.1 70.2 96.3 74.5North Carolina Mecklenburg 371191009 78.2 78.6 80.5 83.0 76.8 67.7 98.7 88.5North Carolina Northampton 371310002 74.3 64.1 60.3 64.5 70.5 63.5 64.7 64.2North Carolina Pitt 371470099 76.6 65.1 54.5 62.7 58.8 61.1 50.2 50.3North Carolina Rockingham 371570099 66.7 50.6 73.3 64.7 66.5 68.7 82.5 79.2North Carolina Rowan 371590021 80.1 59.7 72.0 97.0 78.5 64.6 83.6 79.2North Carolina Rowan 371590022 75.1 68.7 80.6 75.8 80.5 64.2 92.8 86.7State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15North Carolina Swain 371730002 61.5 53.6 56.0 51.6 51.7 63.2 64.7 54.3North Carolina Wake 371830014 84.6 60.7 67.5 65.6 73.2 70.1 73.1 65.7North Carolina Wake 371830015 87.8 72.7 69.7 78.5 82.7 79.2 65.8 63.2North Carolina Wake 371830016 84.0 70.6 70.0 77.7 86.6 76.3 50.1 53.3North Carolina Wake 371830017 85.2 65.2 71.3 79.0 81.6 70.1 51.1 51.0North Carolina Yancey 371990003 74.6 64.5 65.0 71.5 78.1 77.2 79.5 85.8Ohio Allen 390030002 56.0 61.1 73.3 79.8 83.0 92.3 102.5 68.7Ohio Ashtabula 390071001 35.8 44.0 82.5 51.1 69.8 119.6 NA 79.0Ohio Butler 390170004 56.1 84.5 77.1 79.2 121.6 78.1 79.8 71.6Ohio Butler 390171004 59.0 89.8 81.0 77.0 113.5 89.1 94.4 78.7Ohio Clark 390230001 51.0 42.3 83.5 82.7 77.7 112.7 87.1 NAOhio Clark 390230003 57.3 65.8 80.1 80.7 92.7 113.5 99.6 69.8Ohio Clinton 390271002 67.0 86.3 94.0 97.3 99.7 110.2 105.2 82.8Ohio Cuyahoga 390350034 46.3 49.7 75.3 58.8 66.2 90.8 76.1 82.3Ohio Cuyahoga 390350064 61.1 53.4 68.0 69.8 81.0 95.8 79.2 90.7Ohio Cuyahoga 390355002 47.8 54.8 76.3 62.1 74.6 93.0 94.6 101.2Ohio Franklin 390490081 50.1 58.0 73.0 76.3 92.0 75.5 NA 84.0Ohio Hamilton 390610006 58.8 90.7 75.0 87.5 106.0 101.3 102.3 86.3Ohio Hamilton 390610010 51.3 52.1 48.1 42.0 89.1 74.3 86.5 80.3Ohio Knox 390830002 50.2 58.6 70.8 77.7 79.6 94.2 89.2 68.8Ohio Lake 390850003 44.6 55.6 90.7 61.6 63.5 111.2 104.8 92.6Ohio Lake 390853002 34.6 52.1 79.3 51.6 57.3 103.3 101.5 87.8Ohio Lawrence 390870006 53.7 68.0 72.1 59.1 80.4 90.7 66.8 86.2Ohio Lawrence 390870011 48.0 70.7 86.5 78.6 74.7 90.0 74.2 83.7Ohio Licking 390890005 55.3 67.3 89.3 97.8 93.0 92.6 115.5 78.3Ohio Lucas 390950034 59.2 55.0 59.5 92.8 65.6 89.7 90.1 92.6Ohio Lucas 390950081 58.6 53.8 52.7 100.3 72.1 81.2 79.1 83.6Ohio Madison 390970007 57.8 72.7 84.5 93.2 82.7 108.6 110.2 75.3Ohio Medina 391030003 58.8 61.7 70.0 83.1 82.8 99.3 85.3 83.5Ohio Miami 391090005 54.0 57.2 70.2 73.3 100.5 84.7 87.6 59.0State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Ohio Montgomery 391130019 53.5 63.2 75.5 72.1 103.8 92.5 89.5 68.5Ohio Portage 391331001 57.1 58.3 69.2 73.7 84.0 85.0 72.7 86.6Ohio Preble 391351001 57.6 69.5 73.0 81.1 80.3 71.8 95.8 60.6Ohio Stark 391510016 52.1 64.3 70.8 80.1 84.1 87.5 87.0 84.3Ohio Stark 391510019 59.7 66.0 74.2 87.3 85.6 83.8 85.3 78.5Ohio Stark 391511009 55.1 63.8 71.3 80.7 80.5 83.2 86.8 87.5Ohio Stark 391514005 56.7 65.7 75.1 85.3 81.7 103.6 87.7 88.5Ohio Summit 391530020 59.5 26.3 63.0 80.0 88.7 86.1 82.2 84.3Ohio Trumbull 391550008 38.1 55.6 76.3 69.8 81.3 102.2 93.6 101.8Ohio Trumbull 391550009 30.0 48.0 71.6 57.2 62.0 100.5 93.1 93.1Ohio Washington 391670004 50.2 60.1 86.0 57.8 79.7 NA 94.1 NAOklahoma Cleveland 400270049 44.1 63.6 59.0 60.3 68.8 71.6 66.6 67.7Oklahoma Oklahoma 401090033 49.7 71.8 72.7 66.0 69.1 77.6 70.1 72.3Oklahoma Oklahoma 401091037 50.2 63.8 76.5 76.7 79.6 81.7 71.6 76.8Oklahoma Tulsa 401430137 47.1 62.3 80.0 66.6 62.5 NA NA NAOklahoma Tulsa 401430174 38.3 78.6 56.0 56.3 68.6 63.7 64.1 64.7Pennsylvania Allegheny 420030008 41.0 62.0 64.4 70.6 73.8 98.1 92.8 111.7Pennsylvania Allegheny 420030067 50.8 67.8 68.8 67.5 66.5 93.2 82.2 96.0Pennsylvania Allegheny 420030088 48.5 63.1 76.1 76.8 89.5 92.8 104.8 117.8Pennsylvania Allegheny 420031005 43.2 59.8 79.1 80.0 76.0 112.7 114.0 125.8Pennsylvania Beaver 420070002 53.0 59.1 58.6 76.1 75.7 89.0 81.0 85.5Pennsylvania Beaver 420070005 47.5 57.1 60.8 71.5 72.6 85.2 NA NAPennsylvania Beaver 420070014 42.0 58.6 68.3 71.7 71.2 80.5 77.1 77.2Pennsylvania Berks 420110001 47.5 33.3 71.8 66.5 72.1 103.2 84.1 80.1Pennsylvania Berks 420110009 47.2 31.5 74.8 59.3 73.0 107.0 84.5 86.3Pennsylvania Blair 420130801 34.5 51.0 58.7 68.1 77.0 77.1 87.1 95.6Pennsylvania Bucks 420170012 73.7 25.0 80.2 72.1 90.8 107.5 117.2 123.0Pennsylvania Cambria 420210011 34.7 46.2 58.5 72.1 73.0 NA 90.0 90.8Pennsylvania Dauphin 420430401 41.7 39.5 64.2 63.1 78.8 85.2 79.3 86.0Pennsylvania Dauphin 420431100 44.8 41.3 68.5 64.5 82.6 90.8 83.2 77.8State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Pennsylvania Delaware 420450002 70.3 32.6 72.2 76.7 92.2 91.7 108.1 108.0Pennsylvania Erie 420490003 30.7 49.7 76.2 49.0 55.8 113.8 97.6 75.1Pennsylvania Lackawanna 420690101 39.0 33.1 57.0 55.7 74.7 101.6 76.6 NAPennsylvania Lackawanna 420692006 39.3 32.6 60.2 57.6 75.8 104.0 82.2 72.0Pennsylvania Lancaster 420710007 58.0 43.5 76.7 70.0 96.3 100.5 99.6 103.5Pennsylvania Lawrence 420730015 28.1 52.1 65.2 62.7 70.0 70.8 75.7 79.2Pennsylvania Lehigh 420770004 51.2 31.1 71.0 62.8 78.6 103.7 84.3 68.1Pennsylvania Luzerne 420791100 38.5 28.7 64.3 48.1 64.1 90.0 72.0Pennsylvania Luzerne 420791101 40.3 35.0 69.1 61.3 73.4 96.8 85.3 78.8Pennsylvania Lycoming 420810403 31.5 38.0 51.2 54.0 55.7 78.0 74.6 68.5Pennsylvania Mercer 420850100 30.1 50.6 75.2 63.5 75.3 89.5 90.2 89.0Pennsylvania Montgomery 420910013 64.0 29.5 68.0 68.1 82.5 92.5 96.0 98.3Pennsylvania Perry 420990301 37.3 42.3 62.7 56.7 69.1 77.6 70.7 68.6Pennsylvania Philadelphia 421010004 58.7 22.5 58.7 58.5 72.5 81.2 91.4 97.5Pennsylvania Philadelphia 421010014 67.5 25.0 71.2 63.7 80.0 103.3 NA NAPennsylvania Philadelphia 421010024 73.7 30.0 77.5 77.5 98.3 107.5 117.5 117.5Pennsylvania Philadelphia 421010136 NA NA NA 68.5 80.0 81.2 98.7 NAPennsylvania Washington 421250005 53.7 68.1 74.0 74.6 89.8 84.1 103.0 91.5Pennsylvania Washington 421250200 49.3 59.0 65.1 62.3 73.8 86.5 78.2 NAPennsylvania Washington 421255001 47.1 59.0 65.2 65.6 49.1 85.1 85.7 76.8Pennsylvania Westmoreland 421290006 41.8 61.5 62.3 69.1 89.8 76.1 83.3 93.5Pennsylvania York 421330008 49.5 41.5 65.7 60.5 77.1 86.1 77.0 85.3Rhode Island Kent 440030002 37.8 29.5 68.1 55.0 63.3 74.5 139.2 68.1Rhode Island Providence 440071010 35.0 28.6 65.8 49.6 59.7 82.2 119.7 62.6South Carolina Abbeville 450010001 61.6 65.0 60.1 75.2 65.6 67.8 79.2 66.3South Carolina Aiken 450030003 56.5 61.3 82.3 63.6 64.5 67.2 33.6 63.2South Carolina Anderson 450070003 69.0 82.1 80.3 84.0 81.2 83.6 102.1 74.5South Carolina Barnwell 450110001 55.7 58.8 55.7 NA NA NA NA NASouth Carolina Berkeley 450150002 57.2 56.7 66.2 NA 44.1 30.5 30.3 42.3South Carolina Charleston 450190042 55.8 NA NA 63.1 41.1 32.5 31.1 41.8State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15South Carolina Charleston 450190046 46.3 48.7 52.0 44.1 39.8 30.6 31.1 41.5South Carolina Cherokee 450210002 63.1 70.2 69.6 70.1 78.8 76.2 84.1 85.2South Carolina Chester 450230002 77.5 64.3 62.7 91.6 78.8 73.6 69.0 60.8South Carolina Darlington 450310003 60.8 67.0 63.5 59.7 61.1 59.2 37.1 47.8South Carolina Edgefield 450370001 65.2 78.1 67.3 64.7 73.5 74.2 46.2 65.1South Carolina Oconee 450730001 55.0 69.0 59.5 NA NA NA NA NASouth Carolina Pickens 450770002 61.5 75.6 71.1 61.1 79.1 83.8 92.2 69.7South Carolina Richland 450790007 72.3 74.3 80.1 58.8 67.8 73.2 35.5 51.8South Carolina Richland 450791002 69.0 67.5 72.2 62.0 65.8 69.6 37.6 44.6South Carolina Spartanburg 450830009 70.0 78.2 67.3 66.5 78.2 78.2 98.7 95.0South Carolina Union 450870001 NA NA NA NA NA NA NA 55.2South Carolina Williamsburg 450890001 NA NA NA 49.1 50.8 41.8 30.0 36.7South Carolina York 450910006 68.0 62.6 60.8 86.0 76.5 72.3 68.2 60.1Tennessee Anderson 470010101 72.0 99.2 84.0 60.8 81.8 96.7 92.3 85.6Tennessee Blount 470090101 91.7 80.1 74.4 80.7 76.2 81.1 91.7 73.8Tennessee Blount 470090102 83.0 61.0 64.7 66.2 57.7 69.8 56.6 60.5Tennessee Davidson 470370011 63.7 42.5 63.7 92.5 94.3 82.5 41.4 78.1Tennessee Davidson 470370026 77.5 66.8 66.2 100.6 77.5 85.6 88.1 85.0Tennessee Hamilton 470650028 73.6 93.3 84.5 69.6 65.7 104.6 94.1 71.8Tennessee Hamilton 470651011 66.8 81.7 75.3 71.7 57.2 95.7 87.2 55.3Tennessee Haywood 470750002 52.6 56.5 63.0 76.7 59.8 64.0 60.3 63.6Tennessee Knox 470930021 71.6 78.7 89.8 60.7 71.3 94.8 83.3 87.2Tennessee Knox 470931020 77.8 92.5 92.3 63.1 70.0 103.5 88.8 101.8Tennessee Rutherford 471490101 72.6 43.0 57.0 55.7 47.7 58.7 52.2 38.6Tennessee Sevier 471550101 82.2 72.2 78.2 77.1 74.5 77.0 92.6 79.8Tennessee Sevier 471550102 84.3 60.8 65.2 65.7 NA NA NA NATennessee Shelby 471570021 50.7 55.6 53.7 108.1 101.5 81.7 90.8 78.6Tennessee Shelby 471571004 46.7 60.1 58.2 81.7 98.3 70.0 94.5 81.5Tennessee Sullivan 471632002 66.7 71.7 84.6 62.0 82.6 102.2 85.3 91.1Tennessee Sullivan 471632003 69.2 71.8 91.0 NA 85.7 92.2 84.5 98.3State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Tennessee Sumner 471650007 82.8 79.6 74.3 100.5 92.7 102.0 81.6 92.1Tennessee Sumner 471650101 64.1 58.1 56.7 61.8 52.1 74.7 68.2 64.1Tennessee Wilson 471890103 77.7 57.1 74.3 76.8 62.1 83.0 90.5 71.8Texas Brazoria 480391003 72.3 78.1 57.7 54.7 56.5 36.0 60.3 NATexas Collin 480850005 78.2 57.2 78.0 114.5 97.1 83.8 80.2 69.6Texas Dallas 481130069 58.2 60.2 86.8 68.5 99.7 67.2 60.6 54.0Texas Dallas 481130087 50.7 58.2 71.2 63.7 89.5 NA 59.1 58.2Texas Galveston 481671002 76.0 95.1 70.0 65.7 66.2 82.7 62.0 68.0Texas Gregg 481830001 68.8 68.3 79.6 99.3 88.1 75.3 73.8 51.6Texas Harris 482010024 78.1 135.5 90.3 95.5 96.1 87.5 79.1 59.2Texas Harris 482010029 72.6 88.1 NA NA 77.0 80.7 83.5 NATexas Harris 482010046 71.6 NA NA 83.7 81.2 85.8 61.6 43.3Texas Harris 482010047 70.1 121.8 72.3 79.3 79.2 67.5 66.6 53.2Texas Harris 482010051 113.8 91.6 64.4 75.1 69.8 58.0 71.2 63.0Texas Harris 482010062 89.5 78.5 65.1 78.8 62.7 68.7 59.7 61.2Texas Harris 482010066 102.7 86.2 52.8 63.5 57.0 58.6 74.6 64.7Texas Harris 482011035 85.3 111.2 NA NA 82.7 75.2 NA NATexas Jefferson 482450009 95.0 87.6 73.7 69.5 76.0 79.8 64.0 47.3Texas Jefferson 482450011 69.2 90.2 81.8 69.7 89.3 87.5 52.5 43.0Texas Orange 483611001 95.8 82.2 86.7 75.7 88.7 78.5 63.0 42.6Texas Tarrant 484391002 57.3 55.8 78.6 71.5 NA NA NA 77.7Texas Tarrant 484392003 60.1 51.6 80.1 78.5 123.8 101.3 87.5 88.8Texas Travis 484530014 73.3 80.8 83.7 83.6 75.7 59.6 44.1 73.0Vermont Bennington 500030004 47.7 33.6 58.4 55.5 59.7 79.5 72.1 50.5Virginia Arlington 510130020 64.3 58.2 65.3 78.1 94.2 93.0 102.5 117.0Virginia Caroline 510330001 68.6 59.8 57.8 73.6 73.6 79.7 73.7 89.3Virginia Charles City 510360002 75.6 61.7 56.4 76.7 78.1 67.7 86.6 82.8Virginia Chesterfield 510410004 65.5 52.1 53.5 59.8 63.1 62.1 82.7 80.5Virginia Fairfax 510590005 61.6 52.5 64.0 64.5 83.2 74.5 51.6 80.7Virginia Fairfax 510590018 62.0 56.3 61.3 65.0 90.6 87.7 99.2 110.3State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Virginia Fairfax 510595001 63.7 57.7 59.5 69.8 86.8 74.6 87.6 97.1Virginia Fauquier 510610002 62.8 55.3 56.5 67.3 NA NA NA NAVirginia Frederick 510690010 48.7 52.1 61.8 60.5 80.1 89.7 79.6 92.0Virginia Henrico 510870014 76.2 57.2 64.7 69.3 86.6 78.6 82.3 106.3Virginia Madison 511130003 74.0 58.7 62.7 73.0 71.5 76.4 85.8 93.7Virginia Prince William 511530009 62.0 56.0 58.5 64.6 87.2 79.6 76.0 94.2Virginia Roanoke 511611004 65.1 64.1 69.0 62.5 75.6 75.0 71.4 85.5Virginia Stafford 511790001 68.2 59.1 59.2 74.1 75.7 86.7 74.5 92.8Virginia Wythe 511970002 68.5 55.3 70.3 66.1 70.7 68.7 70.8 90.6Virginia Alexandria Cit 515100009 62.1 55.8 57.3 65.5 77.7 81.8 90.8 113.0Virginia Hampton City 516500004 78.7 69.3 71.2 75.1 86.7 81.7 71.5 70.1Virginia Suffolk City 518000004 82.7 70.0 71.1 77.2 90.3 89.1 70.1 71.2Virginia Suffolk City 518000005 72.7 65.0 63.5 72.8 86.5 62.1 57.8 66.3West Virginia Cabell 540110006 50.2 84.1 78.6 92.8 96.3 112.8 72.0 100.2West Virginia Hancock 540291004 53.5 63.7 61.8 79.2 79.7 91.1 NA NAWest Virginia Ohio 540690007 53.6 62.5 72.7 71.8 79.3 92.2 86.5 78.6West Virginia Wood 541071002 50.8 62.0 83.2 75.2 75.5 103.1 90.2 95.3Wisconsin Brown 550090026 35.1 39.0 48.2 53.6 89.5 67.0 85.6 46.3Wisconsin Columbia 550210015 54.0 47.5 55.3 71.7 92.3 81.8 78.6 61.3Wisconsin Dane 550250041 49.1 44.8 51.6 64.7 70.5 68.5 67.2 66.6Wisconsin Dodge 550270007 45.3 46.8 52.2 76.5 88.2 79.2 72.1 52.5Wisconsin Door 550290004 46.2 37.7 50.5 46.3 89.1 85.3 80.2 41.2Wisconsin Fond Du Lac 550390006 47.5 50.0 53.5 69.8 93.6 78.5 78.0 46.7Wisconsin Jefferson 550550002 53.5 50.6 52.0 74.2 84.0 NA 65.7 61.5Wisconsin Kenosha 550590002 62.2 73.6 60.7 90.0 85.1 92.5 83.1 68.6Wisconsin Kenosha 550590019 73.3 79.6 67.5 92.2 101.5 107.7 92.7 77.2Wisconsin Kewaunee 550610002 42.6 45.7 61.2 56.7 104.6 86.3 82.3 46.2Wisconsin Manitowoc 550710004 41.3 47.1 57.3 66.5 110.3 81.1 73.2 52.6Wisconsin Manitowoc 550710007 48.3 44.5 59.3 60.0 100.6 95.3 87.2 49.6Wisconsin Marathon 550730012 38.8 31.5 42.1 53.8 55.3 61.8 64.1 44.2State CountyAIRS SiteCode July 8 July 9 July 10 July 11 July 12 July 13 July 14 July 15Wisconsin Milwaukee 550790041 59.1 60.5 54.8 78.1 91.7 95.0 80.0 57.0Wisconsin Milwaukee 550790044 45.5 59.5 55.1 68.5 116.1 89.1 77.2 53.0Wisconsin Milwaukee 550790085 62.0 63.6 64.2 79.8 105.0 101.5 89.0 NAWisconsin Milwaukee 550791025 55.2 64.6 61.3 87.6 105.8 100.1 91.7 61.8Wisconsin Outagamie 550870009 40.1 42.0 47.1 62.1 90.0 72.5 69.6 52.8Wisconsin Ozaukee 550890008 57.3 56.7 54.6 82.0 104.6 80.8 66.8 59.7Wisconsin Ozaukee 550890009 61.8 57.3 62.5 71.8 105.5 90.6 81.7 58.0Wisconsin Racine 551010017 60.2 62.7 58.8 83.8 92.3 95.1 90.5 65.6Wisconsin Rock 551050024 58.3 49.8 54.7 70.5 77.7 77.3 70.3 62.1Wisconsin St Croix 551091002 45.0 31.6 37.7 47.0 46.0 68.0 76.5 NAWisconsin Sauk 551110007 40.6 42.8 42.3 66.5 69.7 65.8 67.0 61.7Wisconsin Vernon 551230008 45.2 32.5 44.7 50.5 61.1 62.7 61.3 51.5Wisconsin Walworth 551270005 55.2 57.6 58.5 72.2 NA NA NA NAWisconsin Washington 551310009 51.5 56.6 61.2 81.5 116.2 87.8 70.0 59.7Wisconsin Waukesha 551330017 43.2 50.8 37.6 63.6 102.7 88.6 73.2 NAWisconsin Winnebago 551390011 46.3 44.3 51.2 66.2 84.8 75.5 79.5 53.2State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Alabama Clay 10270001 65.0 68.1 100.1 83.6 71.5 89.8 92.2 97.3 89.5 97.4 53.4 43.5Alabama Jefferson 10731003 84.5 68.2 72.1 89.2 94.2 77.6 67.6 79.2 89.3 69.7 70.7 57.1Alabama Jefferson 10731005 105.6 82.5 90.8 89.5 69.5 92.2 93.1 101.2 123.3 77.3 74.2 64.5Alabama Jefferson 10732006 86.1 91.3 84.5 88.2 100.6 109.1 80.6 109.3 107.6 77.7 70.7 62.0Alabama Jefferson 10735002 59.2 56.8 65.6 74.7 87.8 77.0 68.3 79.5 79.3 72.6 82.6 56.7Alabama Jefferson 10736002 67.8 68.8 71.8 81.2 126.2 78.0 76.6 78.5 81.5 70.0 84.5 56.2Alabama Lawrence 10790002 64.6 58.6 69.8 67.7 53.6 61.8 63.8 68.1 74.7 80.6 46.1 60.5Alabama Madison 10890014 78.3 NA NA NA NA 77.7 68.0 76.5 72.0 72.7 62.5 59.6Alabama Mobile 10970003 43.2 55.8 48.5 62.6 47.1 60.3 53.1 85.3 81.2 58.6 68.1 52.8Alabama Shelby 11170004 80.0 90.1 80.7 79.5 81.1 107.5 100.1 106.3 110.2 77.1 68.1 53.7Alabama Sumter 11190002 54.7 50.7 48.2 45.1 42.0 43.8 53.8 59.0 71.2 60.7 42.0 46.5Arkansas Crittenden 50350005 52.8 64.5 72.3 56.1 57.7 43.4 57.6 79.3 84.8 68.0 57.6 58.6Arkansas Montgomery 50970001 46.7 55.5 56.6 52.5 47.6 39.0 44.6 51.0 58.7 68.1 74.0 59.6Arkansas Pulaski 51190007 60.7 70.8 70.3 63.6 48.8 52.3 56.2 84.7 56.5 72.5 65.8 58.8Arkansas Pulaski 51191002 64.6 74.3 72.0 68.1 53.0 49.3 54.2 77.0 61.3 71.3 54.0 60.5Connecticut Fairfield 90010017 60.1 38.8 72.0 30.5 62.1 49.1 24.1 NA NA NA NA NAConnecticut Fairfield 90011123 69.8 53.2 51.7 24.3 86.5 69.7 47.2 55.3 44.6 44.7 51.5 68.7Connecticut Fairfield 90013007 71.8 41.1 80.2 29.5 66.7 46.6 20.3 58.7 54.2 49.5 65.8 115.0Connecticut Hartford 90031003 72.6 61.1 57.7 25.2 73.0 50.1 40.3 72.8 47.8 51.3 60.1 NAConnecticut Middlesex 90070007 95.0 54.1 76.0 30.1 73.2 41.5 27.7 74.8 49.0 53.6 67.8 86.0Connecticut New Haven 90093002 90.1 43.3 71.5 40.8 68.0 32.1 19.1 56.5 55.7 48.2 53.3 126.6Connecticut New London 90110008 59.3 24.7 54.5 38.1 66.5 31.0 15.7 50.3 47.3 48.5 43.6 113.1Connecticut Tolland 90131001 75.2 65.5 50.6 26.3 65.0 49.6 34.7 66.1 44.6 47.1 44.7 73.0Delaware Kent 100010002 33.0 29.0 80.7 51.5 59.5 48.1 25.5 47.1 80.1 54.3 72.5 83.8Delaware New Castle 100031003 41.1 35.7 77.7 48.2 56.8 57.0 41.1 57.2 65.4 64.2 74.7 115.3Delaware New Castle 100031007 38.5 35.7 82.5 50.7 65.2 56.2 58.1 NA NA 80.2 89.5 120.0Delaware New Castle 100031010 34.1 37.0 66.3 41.2 58.6 62.0 52.3 63.8 57.2 65.7 66.1 111.1Delaware Sussex 100051002 32.0 30.2 91.6 60.6 64.6 47.6 24.7 42.3 77.1 50.5 72.1 74.8D.C. Washington 110010025 31.1 50.7 56.1 49.7 66.3 73.8 79.6 60.8 58.2 62.3 79.6 82.7D.C. Washington 110010041 33.1 44.5 68.6 59.8 73.6 67.7 71.7 62.0 63.0 65.0 94.3 94.78-Hour Daily Maximum 8-Hour Ozone (ppb) Measured at Monitoring Sites in the East During the August 1995 EpisodeState CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21D.C. Washington 110010043 38.7 56.1 71.7 64.3 87.0 77.2 90.6 69.7 68.6 75.3 98.8 99.1Florida Brevard 120094001 36.6 39.8 45.5 NA NA 58.6 34.5 57.0 61.5 56.5 59.6 42.8Florida Duval 120310077 40.8 35.0 54.7 52.1 55.0 57.5 62.3 81.0 61.1 39.3 46.8 38.5Florida Escambia 120330004 50.3 55.7 60.5 54.6 60.1 69.1 80.1 95.1 68.0 75.6 68.1 50.7Florida Escambia 120330018 49.0 55.5 57.1 45.3 55.7 79.6 96.8 91.7 59.5 61.1 65.2 52.3Florida Hillsborough 120570081 59.5 73.7 58.2 52.1 59.0 60.3 80.3 84.4 58.6 70.5 62.2 74.7Florida Hillsborough 120571035 48.6 56.5 51.6 52.5 56.6 67.7 65.0 82.7 60.7 56.2 50.0 59.8Florida Hillsborough 120571065 44.0 66.1 53.7 53.8 52.1 74.1 80.7 82.0 62.8 59.1 55.1 59.7Florida Orange 120950008 43.8 72.1 60.2 80.3 75.3 75.2 55.3 72.2 74.8 66.3 55.2 50.8Florida Orange 120952002 30.7 54.5 49.3 76.7 64.0 67.1 43.5 69.0 70.8 51.1 54.0 41.5Florida Osceola 120972002 36.6 50.5 50.7 54.6 54.5 69.0 61.5 66.6 56.1 68.3 46.2 43.2Florida Palm Beach 120992004 46.0 34.2 42.8 55.6 46.7 57.3 41.6 66.8 62.8 63.8 45.2 21.2Florida Pasco 121012001 37.0 49.6 47.6 38.3 45.7 54.8 71.6 62.0 49.7 41.1 55.8 63.0Florida Pinellas 121030004 41.7 48.5 47.5 41.2 43.6 NA 70.0 63.3 52.6 41.3 56.5 72.0Florida Pinellas 121030018 38.0 49.2 35.8 29.3 33.7 49.7 68.6 56.2 39.6 NA NA NAFlorida Pinellas 121035002 31.5 42.6 41.6 36.7 40.6 58.0 63.6 66.1 50.2 38.2 44.0 59.0Florida Polk 121056005 36.6 44.0 48.5 48.1 44.3 66.5 65.4 61.1 71.6 50.5 42.5 51.2Florida Polk 121056006 44.6 42.6 52.0 50.0 54.1 68.5 59.5 66.2 77.1 51.0 46.2 58.2Florida St Lucie 121111002 33.0 47.3 40.6 48.6 62.1 57.1 32.7 64.7 45.8 53.6 35.3 15.7Florida Sarasota 121151005 64.5 40.6 36.5 34.6 39.0 56.0 87.3 68.6 44.3 43.5 53.6 62.8Florida Seminole 121171002 31.6 47.8 46.3 54.2 59.5 70.1 47.6 70.8 59.1 51.2 52.1 45.3Florida Volusia 121272001 32.5 48.8 45.6 52.7 56.6 59.1 21.0 59.0 59.3 49.6 50.7 44.0Florida Volusia 121275002 35.0 52.0 48.8 56.6 58.2 62.5 40.8 67.5 62.8 50.6 53.6 40.1Georgia Chatham 130510021 35.6 54.5 67.8 64.3 56.3 51.8 65.6 76.1 73.3 56.5 42.3 44.7Georgia De Kalb 130890002 91.7 84.7 105.8 95.1 95.8 101.1 98.1 99.1 126.3 81.1 58.8 41.2Georgia De Kalb 130893001 82.0 72.6 75.1 98.0 87.7 97.6 86.6 NA 117.1 62.6 54.5 38.2Georgia Fulton 131210055 94.5 81.8 105.0 109.1 106.5 118.2 108.7 112.5 128.7 84.3 NA NAGeorgia Glynn 131270006 47.0 57.6 62.1 59.7 61.7 60.7 71.2 79.4 69.1 57.0 49.6 43.7Georgia Gwinnett 131350002 65.8 66.1 65.5 72.1 77.6 72.6 80.3 92.1 93.8 74.7 55.7 35.3Georgia Muscogee 132150008 49.7 63.2 78.6 84.8 66.3 83.5 80.6 87.1 89.7 82.0 59.5 40.6Georgia Muscogee 132151003 49.5 66.6 81.8 75.7 46.6 57.1 53.8 51.7 64.0 50.6 18.2 24.2State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Georgia Richmond 132450091 54.6 67.6 71.8 72.6 74.6 80.2 79.6 74.2 99.8 64.7 55.2 61.8Georgia Rockdale 132470001 67.8 81.6 87.8 82.2 100.1 70.3 74.7 111.6 120.8 72.2 59.1 45.5Illinois Adams 170010006 44.1 60.3 55.7 44.1 38.0 45.3 30.2 40.0 50.5 33.1 40.0 42.0Illinois Champaign 170190004 40.6 57.3 67.6 56.8 59.1 59.0 48.5 51.6 46.7 41.3 46.1 60.2Illinois Cook 170310001 34.7 49.2 64.1 59.2 46.1 31.8 28.0 48.7 54.1 42.5 38.6 43.2Illinois Cook 170310032 49.2 69.8 81.8 73.1 65.2 48.8 45.6 59.7 56.0 NA 44.1 59.3Illinois Cook 170310050 40.6 55.3 74.7 60.5 49.5 32.0 32.0 57.8 45.2 40.2 39.8 50.1Illinois Cook 170310064 37.1 58.0 82.8 65.6 49.8 40.3 32.2 59.1 48.6 41.7 41.3 55.0Illinois Cook 170310072 42.0 51.6 106.1 NA NA 49.7 40.5 56.8 55.2 41.3 43.0 62.6Illinois Cook 170311003 32.6 51.4 65.0 56.0 46.1 42.6 26.8 50.8 48.6 27.8 39.1 54.3Illinois Cook 170311601 39.2 51.8 59.3 54.2 46.3 26.6 33.6 36.2 62.7 35.8 46.8 52.0Illinois Cook 170314002 36.0 50.2 70.5 55.1 44.2 25.3 26.7 29.7 38.8 26.8 35.2 47.8Illinois Cook 170314006 49.2 69.8 81.8 73.1 65.2 48.8 45.6 59.7 56.0 44.1 59.3Illinois Cook 170317002 37.5 60.3 104.2 65.6 55.3 54.1 38.4 55.3 50.5 40.7 44.8 61.0Illinois Cook 170318003 38.3 51.7 79.7 59.8 51.6 36.1 39.1 59.6 46.7 46.7 38.8 40.5Illinois Du Page 170436001 38.0 46.5 59.2 44.0 49.1 39.5 36.1 40.6 62.1 39.8 48.7 52.7Illinois Effingham 170491001 55.1 63.0 57.7 53.2 60.1 49.6 55.5 58.2 72.5 43.5 48.2 57.5Illinois Jersey 170831001 39.8 48.0 64.5 54.3 58.1 72.6 47.2 51.2 50.8 60.0 42.7 51.1Illinois Kane 170890005 40.7 53.5 61.2 57.0 54.8 43.7 52.5 47.2 67.8 40.2 50.0 57.3Illinois Lake 170971002 44.5 61.6 102.8 52.2 51.7 44.3 43.8 46.1 41.0 31.5 38.0 44.3Illinois Lake 170971007 46.5 79.2 95.1 56.3 56.1 47.5 48.7 48.5 45.1 35.0 39.6 46.3Illinois Lake 170973001 39.3 47.7 59.6 48.6 46.5 40.3 41.6 34.0 32.8 23.5 38.0 41.0Illinois McHenry 171110001 42.8 57.7 65.8 58.2 60.3 52.6 60.3 NA NA NA NA NAIllinois Macon 171150013 38.1 51.7 68.8 60.2 50.5 65.6 55.2 53.3 51.1 44.6 43.2 58.0Illinois Macoupin 171170002 34.6 43.1 58.1 64.5 52.0 55.3 42.0 45.6 50.1 44.8 44.6 54.7Illinois Madison 171190008 46.0 69.5 88.7 70.6 75.6 75.7 67.0 65.1 78.3 63.6 42.8 55.8Illinois Madison 171191009 53.6 73.0 71.6 48.7 59.2 48.1 46.2 72.8 89.0 62.0 40.6 52.7Illinois Madison 171192007 57.8 94.6 96.5 68.6 77.1 65.1 63.8 76.0 94.0 64.6 45.3 55.5Illinois Madison 171193007 40.6 87.6 99.0 73.1 73.2 73.8 66.2 62.3 79.2 62.0 43.0 53.8Illinois Peoria 171430024 41.0 57.2 65.3 49.5 44.8 45.4 45.1 52.0 42.3 46.0 41.7 48.7Illinois Peoria 171431001 45.8 63.6 68.5 61.3 45.3 46.5 62.0 59.5 63.2 65.6 50.2 53.0State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Illinois Randolph 171570001 39.0 44.7 46.5 45.0 44.1 43.5 46.6 49.6 55.2 63.0 48.0 62.8Illinois St Clair 171630010 81.1 71.1 82.0 53.6 63.6 55.7 56.8 76.8 112.5 65.3 32.6 58.7Illinois Sangamon 171670010 38.2 60.8 63.2 60.1 48.5 45.7 45.5 46.6 50.1 50.2 39.7 45.3Illinois Will 171971008 41.8 51.3 58.5 56.5 47.5 51.0 33.7 43.7 63.3 39.1 51.7 56.6Illinois Will 171971011 34.6 41.8 56.5 49.2 46.1 49.5 39.0 37.5 56.1 35.8 51.8 64.6Illinois Winnebago 172010009 44.7 59.0 63.5 56.1 47.7 33.7 48.2 43.3 51.7 40.6 47.0 52.3Illinois Winnebago 172012001 43.2 58.0 62.5 55.0 47.2 37.7 49.7 40.2 49.1 40.3 44.5 50.5Indiana Allen 180030002 56.2 60.3 72.8 64.8 64.5 61.4 68.8 47.0 61.3 70.2 78.1 60.6Indiana Allen 180030004 51.5 53.0 64.8 NA NA 61.5 NA NA 59.5 67.6 70.3 57.1Indiana Clark 180190003 66.6 62.6 60.2 70.2 83.5 114.1 NA NA NA 71.6 75.0 73.3Indiana Floyd 180431004 63.8 68.3 56.0 66.8 86.2 79.5 74.6 67.0 53.7 59.3 75.7 66.7Indiana Hamilton 180571001 56.5 64.1 67.3 68.7 71.6 80.3 96.3 53.6 50.6 58.6 74.2 59.3Indiana Hancock 180590003 57.3 69.6 67.8 76.6 66.2 71.0 70.5 49.2 39.5 47.8 58.7 45.6Indiana Lake 180890022 49.5 65.0 95.1 64.3 58.6 51.1 47.3 79.5 60.8 59.3 45.2 70.8Indiana Lake 180892008 53.1 62.5 96.1 66.7 61.7 44.7 36.3 74.0 51.2 59.8 50.0 58.1Indiana La Porte 180910005 61.2 68.1 108.6 63.3 61.2 57.2 59.5 71.0 63.8 71.1 54.1 73.3Indiana Madison 180950010 63.2 73.5 74.7 82.2 73.5 76.2 80.0 56.7 51.7 58.6 67.2 58.1Indiana Marion 180970042 NA 61.5 58.3 59.7 57.2 37.6 61.1 NA 28.0 59.3 56.2 59.1Indiana Marion 180970050 69.0 72.5 69.5 76.5 68.2 72.0 81.3 53.1 48.6 59.3 70.6 59.2Indiana Marion 180970057 56.7 65.8 64.7 68.8 58.8 58.8 78.0 50.3 38.7 55.3 65.7 56.6Indiana Marion 180970073 64.1 66.7 70.5 73.5 64.3 62.1 NA NA 39.2 60.8 74.5 61.5Indiana Porter 181270020 45.5 51.8 62.8 NA 48.2 47.8 44.3 65.7 52.4 53.3 44.2 65.2Indiana Porter 181270024 55.0 65.0 95.0 63.0 61.1 54.7 NA NA NA 65.8 57.5 78.8Indiana St Joseph 181410010 48.2 58.2 70.2 54.8 58.4 53.8 58.4 50.2 50.8 60.3 53.7 60.3Indiana St Joseph 181411007 NA 40.8 86.3 53.1 44.5 43.6 47.6 48.2 55.0 44.2 50.8Indiana St Joseph 181411008 53.6 59.8 84.5 58.5 59.1 49.3 56.7 52.7 54.2 59.3 50.5 58.5Indiana Vanderburgh 181630012 56.8 68.5 64.1 58.1 70.0 68.6 63.1 52.3 75.7 63.7 53.1 72.3Indiana Vanderburgh 181630013 55.1 58.1 57.0 56.6 74.2 72.2 69.5 57.2 73.0 55.3 53.7 69.2Indiana Vigo 181670018 50.1 61.6 59.3 55.3 55.7 66.0 70.7 50.0 57.8 54.3 54.7 55.2Indiana Warrick 181730002 54.0 80.6 63.2 58.1 62.6 63.2 NA NA NA NA NA NAIndiana Warrick 181730008 51.8 69.4 66.8 62.8 64.3 69.1 61.1 66.0 78.7 56.6 48.1 62.1State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Indiana Warrick 181730009 67.6 76.1 66.6 63.6 75.1 72.1 75.1 55.5 78.0 57.3 53.7 64.3Iowa Polk 191530058 28.0 35.8 44.0 29.6 22.8 21.2 21.0 26.0 35.8 24.3 30.6 34.1Iowa Scott 191632011 42.5 63.6 60.7 47.5 41.5 44.0 49.1 53.2 51.6 28.8 40.5 42.6Kansas Sedgwick 201730001 51.2 62.5 65.0 40.0 45.0 40.0 33.3 30.0 32.5 32.5 51.2 33.7Kansas Sedgwick 201730010 41.8 47.5 54.3 28.1 36.8 33.1 20.0 24.3 28.7 32.5 48.7 38.7Kentucky Bell 210130002 47.7 55.6 55.1 56.2 51.8 53.7 86.8 72.2 64.6 58.5 42.5 55.7Kentucky Boone 210150003 49.2 49.3 58.2 60.2 74.1 68.5 73.7 70.2 43.8 44.5 66.3 60.1Kentucky Boyd 210190015 63.0 54.3 76.6 73.2 66.6 71.1 103.6 76.7 66.1 66.6 70.3 78.5Kentucky Bullitt 210290006 60.5 62.6 53.1 65.6 62.8 82.3 74.3 83.0 55.7 69.7 71.5 74.0Kentucky Daviess 210590005 58.2 77.8 63.7 66.0 58.6 53.2 72.1 65.5 76.3 53.6 39.3 59.5Kentucky Fayette 210670001 54.6 61.1 62.8 62.8 68.7 73.1 71.8 85.0 51.7 56.4 54.5 68.7Kentucky Fayette 210670012 60.7 69.0 75.2 71.1 77.7 86.1 83.0 99.5 61.0 63.6 63.8 77.5Kentucky Graves 210830003 49.0 53.6 47.1 45.5 49.6 52.8 55.6 48.5 65.1 59.3 42.5 53.6Kentucky Greenup 210890007 57.7 49.5 72.8 67.6 69.8 71.5 95.3 69.6 58.5 65.6 66.1 67.3Kentucky Hancock 210910012 59.2 84.2 65.1 64.8 79.5 66.8 78.8 65.3 77.3 66.6 49.0 64.2Kentucky Henderson 211010013 67.7 87.2 68.0 50.2 69.5 62.6 69.0 68.3 82.7 61.7 43.2 51.1Kentucky Henderson 211010014 54.6 71.7 58.1 51.6 55.5 60.8 61.0 60.2 74.6 65.2 46.0 55.8Kentucky Jefferson 211110027 52.2 63.2 64.8 68.7 83.5 77.2 72.0 92.1 65.5 64.0 65.3 73.8Kentucky Jefferson 211110051 60.2 65.0 58.7 66.8 75.3 89.1 73.7 83.0 62.0 58.1 73.5 78.6Kentucky Jefferson 211111021 50.1 60.6 54.2 68.3 69.3 81.5 67.7 66.0 53.3 59.1 66.6 69.8Kentucky Jessamine 211130001 54.2 63.1 70.6 65.0 68.6 79.8 87.5 104.2 52.2 58.2 66.0 65.0Kentucky Kenton 211170007 45.0 52.1 69.8 72.6 90.5 62.5 93.1 87.1 44.6 60.8 63.1 73.8Kentucky Livingston 211390003 61.5 69.2 50.7 53.8 56.5 58.3 62.6 55.3 57.3 58.5 43.6 60.1Kentucky Livingston 211390004 63.5 65.5 51.7 50.8 57.3 35.6 44.5 58.5 56.7 52.8 39.0 61.8Kentucky McCracken 211451024 59.0 62.5 51.3 51.3 54.6 55.2 60.1 52.2 64.3 57.8 41.0 49.5Kentucky McLean 211490001 61.0 62.6 63.8 60.5 57.7 52.1 56.0 64.5 66.2 69.5 44.6 63.6Kentucky Oldham 211850004 65.8 61.8 NA 79.0 83.0 88.8 NA 88.0 53.7 50.0 70.6 64.8Kentucky Pike 211950002 63.7 67.0 66.1 70.1 68.7 85.5 99.6 105.3 70.6 56.5 62.3 68.8Kentucky Pulaski 211990003 52.8 62.1 74.7 84.8 60.8 73.1 77.7 93.8 58.8 67.6 56.5 60.5Kentucky Scott 212090001 61.3 68.0 67.5 64.7 78.6 76.7 80.5 72.8 49.1 50.7 55.1 74.6Kentucky Simpson 212130004 61.0 56.8 76.1 79.3 65.8 70.3 75.0 97.3 66.1 76.6 57.8 54.3State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Louisiana Ascension 220050004 NA NA NA NA NA NA NA 53.5 54.6 69.5 88.2 67.3Louisiana Beauregard 220110002 54.0 38.8 30.5 16.1 24.1 45.5 40.1 78.6 49.8 58.0 47.1 50.8Louisiana Bossier 220150008 56.2 51.5 64.0 48.1 30.1 34.3 55.7 65.2 60.7 64.8 66.1 53.7Louisiana Caddo 220170001 59.0 58.0 70.7 46.6 33.0 36.3 64.5 84.7 68.1 59.5 60.6 46.1Louisiana Calcasieu 220190002 53.1 31.8 25.7 15.8 22.3 25.2 66.1 61.1 54.2 77.2 56.8 58.7Louisiana Calcasieu 220190008 35.3 19.0 13.8 4.2 7.1 12.6 23.8 39.1 22.6 30.1 23.0 24.5Louisiana Calcasieu 220190009 61.6 34.3 29.2 19.0 20.2 23.7 57.7 66.5 49.8 64.2 51.3 46.6Louisiana East Baton Rou 220330003 56.0 47.7 101.7 33.5 43.8 54.7 81.2 65.5 49.7 77.7 65.6 95.1Louisiana East Baton Rou 220330009 59.3 48.3 95.3 32.1 40.5 50.7 76.7 56.5 40.7 72.6 65.0 76.3Louisiana East Baton Rou 220330013 49.2 47.1 47.7 42.0 49.2 42.6 46.0 54.8 39.8 63.1 51.8 20.5Louisiana East Baton Rou 220331001 49.7 47.3 62.0 31.8 41.2 44.7 61.3 53.8 51.6 69.2 56.6 58.8Louisiana Grant 220430001 51.6 59.8 51.0 47.5 36.2 44.4 48.8 50.2 52.5 65.6 59.5 54.5Louisiana Iberville 220470007 60.2 58.3 83.3 37.8 17.7 45.8 83.5 52.3 33.6 99.1 59.6 68.3Louisiana Iberville 220470009 63.1 54.5 83.8 39.7 27.7 68.6 75.6 69.1 62.7 94.6 59.3 69.7Louisiana Jefferson 220511001 47.3 41.5 60.7 43.1 42.6 56.0 71.6 59.1 73.0 79.3 78.3 61.1Louisiana Lafayette 220550005 66.0 50.6 48.7 37.8 34.1 43.6 62.6 65.1 63.6 86.7 67.1 64.2Louisiana Livingston 220630002 46.2 44.6 61.3 35.1 33.1 40.3 60.8 49.7 56.5 61.1 65.3 51.8Louisiana Orleans 220710012 41.8 31.6 48.6 33.8 31.6 43.0 48.2 38.8 65.3 62.5 67.7 42.8Louisiana Pointe Coupee 220770001 45.3 51.3 59.3 33.3 28.2 34.5 44.7 45.6 39.1 68.5 48.5 42.2Louisiana St Bernard 220870002 44.3 30.3 47.2 33.7 37.1 46.8 70.3 52.0 72.8 59.8 68.6 56.1Louisiana St Charles 220890003 53.1 41.4 65.7 38.8 37.5 48.6 73.8 54.0 63.8 86.3 73.3 56.7Louisiana St James 220930002 49.1 36.1 61.0 32.2 29.8 48.2 78.8 46.2 56.7 82.2 80.6 53.7Louisiana St John The Ba 220950002 52.2 39.4 63.8 32.8 40.5 44.8 79.8 31.5 73.0 82.5 75.7 55.1Louisiana St Mary 221010003 45.2 37.5 40.1 40.2 25.1 57.6 65.6 73.1 69.2 83.2 84.1 88.2Louisiana West Baton Rou 221210001 53.0 39.5 72.2 25.3 32.3 40.6 63.5 43.1 37.0 62.6 58.3 53.0Maine Cumberland 230052003 98.6 91.5 58.0 26.5 36.2 29.5 29.5 57.5 34.8 33.5 35.6 74.1Maine Hancock 230090102 71.5 79.2 49.5 22.0 33.8 35.7 44.0 41.6 31.5 41.7 35.0 71.5Maine Kennebec 230112005 52.6 73.8 38.7 19.5 31.1 22.3 64.3 36.0 26.8 32.7 35.6 58.1Maine Knox 230130004 78.1 86.1 49.7 22.7 29.3 32.5 24.5 49.7 32.6 30.2 26.2 78.2Maine Oxford 230173001 41.1 50.6 31.8 21.8 31.6 29.2 43.0 31.5 29.2 33.0 36.6 44.5Maine Penobscot 230194008 57.6 78.5 45.8 20.8 30.6 28.1 67.6 41.0 35.1 34.6 37.0 58.8State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Maine York 230312002 106.1 93.0 53.3 30.0 38.0 37.2 33.3 57.8 32.0 32.3 33.3 72.8Maine York 230313002 99.6 NA NA NA 40.5 35.7 41.3 52.5 31.1 35.2 33.7 61.7Maryland Anne Arundel 240030014 38.3 42.3 82.7 73.2 98.2 66.8 87.8 68.1 74.5 80.3 84.8 112.7Maryland Anne Arundel 240030019 35.2 59.0 72.7 59.8 95.0 69.1 78.0 73.3 73.0 78.5 98.2 100.2Maryland Baltimore 240051007 45.3 34.8 68.1 61.2 79.5 64.5 84.2 77.5 66.7 61.7 81.0 95.2Maryland Baltimore 240053001 43.2 31.7 68.6 61.0 93.6 49.0 72.8 64.3 66.6 73.6 80.5 93.1Maryland Carroll 240130001 48.6 46.8 63.7 55.1 68.2 78.1 86.3 80.8 68.7 56.8 74.8 86.0Maryland Cecil 240150003 43.7 42.7 76.5 49.2 76.8 58.1 62.7 66.8 62.7 66.7 68.5 114.7Maryland Charles 240170010 31.0 33.7 79.3 81.0 86.0 53.8 70.6 79.3 79.1 65.5 76.3 84.6Maryland Harford 240251001 44.8 37.7 84.0 60.1 107.5 55.0 79.8 68.8 63.1 74.6 78.6 100.8Maryland Harford 240259001 45.7 38.6 70.1 54.5 102.0 51.8 75.5 71.7 59.3 70.3 70.0 106.1Maryland Kent 240290002 40.3 37.3 78.1 54.6 53.7 50.1 45.3 64.8 71.7 67.1 77.8 NAMaryland Montgomery 240313001 42.7 55.7 59.0 50.5 72.6 86.7 73.8 63.1 57.8 56.0 74.0 80.7Maryland Prince Georges 240330002 36.6 50.8 69.1 61.6 85.1 67.8 79.1 72.2 71.7 74.7 96.1 93.1Maryland Prince Georges 240338001 36.3 46.0 70.7 71.0 84.0 69.0 71.7 69.6 74.2 71.8 95.6 99.3Massachusetts Barnstable 250010002 100.5 55.5 46.5 30.6 51.6 31.1 31.7 45.7 45.5 32.5 32.2 122.2Massachusetts Berkshire 250034002 52.8 51.3 40.3 22.8 62.7 50.7 62.8 38.1 35.6 37.1 47.0 64.7Massachusetts Bristol 250051002 80.0 33.2 40.1 47.6 67.0 32.8 18.5 48.0 35.7 33.5 34.5 124.5Massachusetts Essex 250090005 68.1 58.0 43.3 26.7 32.7 44.2 39.3 NA 30.6 NA NA NAMassachusetts Essex 250092006 98.6 74.5 59.5 46.0 46.1 36.2 41.2 51.6 35.1 38.1 34.7 86.8Massachusetts Essex 250094004 101.2 68.2 48.6 28.7 42.5 33.1 NA 63.7 33.2 33.6 33.2 63.6Massachusetts Hampden 250130003 65.8 69.8 51.7 23.1 73.5 63.5 67.3 62.2 50.2 47.1 67.0 70.5Massachusetts Hampden 250130008 68.7 70.0 45.1 24.6 74.0 63.3 65.2 57.1 48.5 47.1 51.7 72.0Massachusetts Hampshire 250150103 69.5 66.3 38.5 23.6 58.2 62.6 72.0 40.7 48.7 45.8 48.1 67.8Massachusetts Hampshire 250154002 74.3 70.7 45.5 22.5 61.0 53.2 53.6 55.1 48.5 49.5 52.0 73.3Massachusetts Worcester 250270015 82.0 69.3 52.2 22.0 48.2 48.7 38.1 69.7 44.2 48.1 63.7 75.1Michigan Allegan 260050003 63.8 64.8 88.5 110.0 105.8 52.6 53.7 63.7 56.5 72.1 50.7 63.3Michigan Benzie 260190003 57.3 44.3 42.2 44.3 54.0 30.3 36.8 36.2 69.1 50.6 41.6 39.2Michigan Berrien 260210014 52.1 67.6 111.0 81.5 81.0 62.2 62.7 67.6 53.3 73.0 50.8 68.2Michigan Cass 260270003 52.0 68.5 98.3 69.1 65.6 55.3 54.7 56.3 51.8 62.0 49.2 58.8Michigan Clinton 260370001 48.7 51.3 43.1 61.1 50.8 54.7 41.2 38.0 56.6 64.3 32.7 40.2State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Michigan Genesee 260490021 66.7 56.8 43.1 59.6 52.2 54.8 62.5 35.5 56.7 57.8 39.3 37.8Michigan Genesee 260492001 NA NA NA 52.7 51.1 52.6 52.7 33.7 58.0 49.1 33.1 36.5Michigan Huron 260630007 80.5 50.7 43.8 38.0 63.6 52.1 44.6 38.6 45.8 56.1 43.3 45.1Michigan Ingham 260650012 52.2 54.7 51.2 71.8 51.3 56.1 46.3 34.1 67.8 80.1 41.5 44.5Michigan Kent 260810020 44.8 58.7 56.6 91.0 89.6 46.6 48.1 35.7 56.5 78.7 41.6 49.1Michigan Lenawee 260910007 50.8 55.1 60.6 NA NA NA NA NA 35.8 38.0 61.1 35.7Michigan Macomb 260990009 56.6 90.5 46.8 57.0 64.8 69.6 58.7 34.7 46.7 60.6 68.5 45.2Michigan Macomb 260991003 60.6 70.7 52.6 68.1 56.3 97.5 58.3 36.0 46.7 60.3 82.6 45.6Michigan Muskegon 261210039 51.2 63.5 55.1 93.5 94.0 49.3 38.7 51.6 50.6 62.0 44.8 55.5Michigan Oakland 261250001 47.1 68.7 52.1 60.0 51.3 81.2 50.6 25.0 5.8 73.2 96.2 46.5Michigan Ottawa 261390005 50.2 59.2 79.8 91.0 94.3 51.7 49.0 42.6 NA NA 43.2 52.8Michigan St Clair 261470005 52.5 81.8 42.8 48.0 74.8 58.8 55.2 23.1 54.1 53.3 48.2 47.2Michigan Wayne 261630001 42.5 55.3 58.7 61.6 52.2 61.0 60.8 32.7 53.8 58.1 85.2 39.5Michigan Wayne 261630016 26.5 48.7 57.6 56.8 54.8 63.7 50.1 33.6 48.7 60.3 77.8 46.8Michigan Wayne 261630019 60.5 80.6 58.7 63.6 61.2 85.2 59.5 41.6 53.0 60.1 88.0 52.1Minnesota Anoka 270031001 54.5 32.3 28.8 44.5 30.5 49.8 38.1 42.5 46.8 35.0 37.1 42.0Minnesota Anoka 270031002 57.7 37.3 41.5 51.6 31.2 44.8 36.5 47.2 50.0 39.7 42.5 60.5Minnesota Dakota 270376018 43.0 40.0 41.4 46.0 33.0 40.0 39.7 48.3 43.7 42.5 37.2 60.7Mississippi Adams 280010004 43.8 47.1 46.3 40.1 32.2 30.3 45.1 50.2 40.8 47.0 54.7 50.5Mississippi De Soto 280330002 NA 59.3 76.8 66.7 46.3 47.2 27.3 59.6 68.3 70.8 67.0 61.1Mississippi Hancock 280450001 59.6 50.5 64.0 59.0 52.8 NA NA NA NA NA NA 84.4Mississippi Hinds 280490010 50.4 53.6 61.1 54.2 56.0 41.2 51.2 51.7 69.8 61.2 55.5 48.3Mississippi Jackson 280590006 41.6 43.5 48.0 57.1 45.0 65.5 57.1 60.5 69.2 67.2 67.0 53.5Mississippi Madison 280890002 48.5 55.0 50.3 59.1 51.5 35.7 48.7 54.2 70.0 60.3 53.7 52.2Mississippi Warren 281490004 54.7 61.7 63.0 51.1 28.0 42.5 52.6 54.4 52.0 66.0 55.0 55.7Missouri Clay 290470003 67.2 82.2 84.8 47.7 51.6 53.0 44.5 54.0 76.5 38.5 41.7 46.5Missouri Clay 290470005 64.7 82.0 84.1 45.3 50.2 59.6 43.7 55.0 75.2 37.5 49.2 49.5Missouri Clay 290470025 52.1 76.5 77.0 41.5 40.1 47.1 36.1 46.0 68.3 34.2 50.6 41.8Missouri Greene 290770026 57.5 50.0 58.3 53.7 42.6 44.2 44.3 45.3 58.6 69.1 46.2 54.5Missouri Greene 290770036 64.8 68.4 68.5 62.5 52.6 54.7 53.5 50.7 73.1 73.1 49.3 56.2Missouri Jefferson 290990012 88.8 57.7 73.5 54.1 60.5 60.3 50.6 55.2 74.2 88.5 44.1 82.1State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Missouri Monroe 291370001 46.2 69.8 65.2 55.8 50.5 52.6 49.1 48.7 56.0 52.3 50.6 57.8Missouri Platte 291650023 46.5 57.5 72.3 41.0 43.5 43.0 41.3 44.1 64.8 33.3 42.3 46.1Missouri St Charles 291831002 54.5 80.1 97.8 72.6 87.0 98.7 74.0 71.7 78.7 72.8 48.5 59.0Missouri St Charles 291831004 49.8 72.2 81.7 63.7 75.5 88.8 55.5 63.2 71.3 76.5 46.3 55.5Missouri St Louis 291890006 56.2 69.0 65.3 47.1 60.8 58.8 49.5 57.8 80.8 70.7 42.3 59.3Missouri St Louis 291893001 54.8 64.7 70.5 49.0 62.3 58.6 47.7 57.7 82.0 71.1 39.7 57.6Missouri St Louis 291895001 71.0 78.5 86.7 59.2 75.3 77.0 58.0 62.1 89.1 72.5 36.7 55.2Missouri St Louis City 295100007 58.7 46.2 58.7 35.1 47.1 42.0 35.2 54.5 84.0 66.2 36.0 54.7Missouri St Louis City 295100072 57.7 42.2 65.2 45.7 48.6 40.3 36.1 47.7 64.8 47.5 24.0 41.0Nebraska Douglas 310550028 47.2 53.0 60.2 50.1 30.6 18.7 34.3 36.8 41.1 28.2 42.1 35.7Nebraska Douglas 310550032 39.8 36.3 38.6 32.0 NA NA NA 31.5 32.2 23.7 35.0 25.8Nebraska Douglas 310550035 49.7 55.3 62.2 50.1 29.0 12.8 24.3 35.5 41.1 33.2 38.7 29.8Nebraska Lancaster 311090016 44.0 49.8 56.5 46.6 28.6 11.8 30.5 32.5 35.6 31.0 44.0 37.0New Hampshire Cheshire 330050007 59.3 NA NA NA 26.4 49.5 62.8 37.1 40.6 43.6 48.5 56.1New Hampshire Hillsborough 330111010 73.1 NA NA NA 43.6 62.5 NA NA 37.0 41.0 56.1 68.1New Hampshire Merrimack 330130007 47.6 57.2 43.0 17.6 23.8 39.2 51.8 32.5 24.2 30.7 39.8 NANew Hampshire Rockingham 330150012 98.3 NA NA NA 33.8 36.8 35.6 61.0 27.3 29.7 31.6 64.8New Jersey Atlantic 340010005 33.3 24.3 81.6 51.5 56.0 35.5 18.7 34.2 64.3 52.3 56.1 96.7New Jersey Camden 340070003 50.8 42.8 76.8 48.6 69.7 55.3 34.3 55.8 67.1 65.7 73.6 102.0New Jersey Camden 340071001 54.6 42.8 92.0 67.2 75.0 49.8 31.8 50.6 91.3 66.5 79.8 99.7New Jersey Cumberland 340110007 39.7 26.3 77.0 62.6 58.7 42.6 22.6 42.5 72.7 53.2 65.1 86.1New Jersey Gloucester 340150002 47.7 38.8 80.6 53.8 65.7 56.6 39.7 60.5 70.2 64.8 73.5 108.5New Jersey Hudson 340170006 48.5 33.5 78.1 46.3 66.5 42.7 26.0 57.2 51.6 51.1 53.7 95.7New Jersey Hunterdon 340190001 65.1 56.7 68.5 43.1 83.8 60.0 45.0 72.2 58.5 66.3 66.3 90.3New Jersey Mercer 340210005 52.2 39.2 74.5 41.3 86.6 57.1 43.6 48.5 64.7 70.6 81.7 95.6New Jersey Middlesex 340230011 62.0 45.6 73.2 36.6 83.6 49.6 37.1 45.5 62.1 61.0 63.6 93.1New Jersey Monmouth 340250005 NA NA NA NA NA NA NA 37.6 66.0 53.8 59.2 116.8New Jersey Morris 340273001 66.6 58.7 71.5 37.8 95.7 65.6 77.1 73.8 58.2 63.3 80.7 98.6New Jersey Ocean 340290006 NA 33.2 91.3 50.0 68.8 53.0 29.1 47.2 75.7 64.1 70.1 117.6New York Albany 360010012 69.6 54.8 39.7 25.0 68.5 71.6 70.3 44.1 55.6 42.5 61.3 64.3New York Bronx 360050083 44.7 26.7 67.0 48.2 58.2 51.3 15.7 73.5 50.3 57.1 60.2 93.6State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21New York Chautauqua 360130011 65.2 70.5 57.2 42.0 76.3 71.5 72.5 59.1 81.7 67.8 85.7 74.2New York Chemung 360150003 48.1 52.0 45.3 31.1 57.8 52.2 58.1 49.6 47.8 51.5 66.5 49.0New York Dutchess 360270007 68.1 67.0 44.6 26.8 93.7 70.0 68.2 45.5 45.3 40.0 55.3 77.6New York Erie 360290002 52.5 54.6 43.2 42.7 69.2 61.0 42.2 47.1 38.6 64.1 83.1 50.3New York Essex 360310002 58.6 63.0 56.7 23.6 40.1 64.1 68.0 NA NA NA 78.1 65.7New York Essex 360310003 49.8 51.8 48.6 18.5 21.8 59.8 55.6 39.5 37.8 33.5 66.2 65.5New York Hamilton 360410005 53.8 53.2 42.6 21.3 29.1 53.7 53.0 34.7 42.1 43.1 60.2 53.2New York Herkimer 360430005 51.5 49.3 36.6 21.3 31.3 48.6 47.5 33.6 41.6 42.3 61.2 46.7New York Jefferson 360450002 59.2 63.1 46.8 25.1 47.7 78.0 46.7 44.5 46.1 60.7 79.6 60.7New York Madison 360530006 52.6 59.0 44.6 33.8 51.7 52.0 67.2 40.0 46.3 54.2 67.5 56.7New York Monroe 360551004 59.3 54.8 45.6 28.6 51.3 64.7 41.5 41.1 45.3 69.7 81.0 53.7New York Niagara 360631006 51.6 48.5 51.0 27.8 75.3 60.8 40.5 37.6 39.3 68.2 81.8 59.6New York Oneida 360650004 53.6 52.7 40.0 20.8 52.8 58.5 42.6 30.6 42.7 51.6 67.8 50.4New York Onondaga 360671015 52.7 55.0 47.8 29.2 60.8 67.5 49.3 36.6 46.5 55.7 76.5 60.7New York Orange 360715001 57.8 48.6 52.8 25.5 88.8 66.6 90.7 65.6 58.7 43.6 70.5 NANew York Putnam 360790005 64.5 58.3 52.5 25.3 85.8 60.0 49.2 48.3 43.0 39.7 47.8 77.0New York Richmond 360850067 42.8 29.7 73.0 45.7 61.3 37.2 NA NA NA 54.2 65.3 100.3New York Saratoga 360910004 75.3 29.1 5.5 8.7 17.0 25.8 18.0 8.5 11.2 7.8 13.5 41.1New York Schenectady 360930003 72.1 49.8 40.1 22.5 57.8 71.6 74.5 40.5 51.8 46.8 64.8 55.3New York Suffolk 361030002 48.2 30.5 77.6 47.2 65.8 44.5 35.5 67.6 63.8 53.0 58.1 112.1New York Suffolk 361030004 65.2 33.7 66.1 37.1 59.5 33.0 21.0 49.3 51.3 45.5 43.3 109.1New York Ulster 361111005 49.5 55.3 47.2 27.5 73.5 54.1 57.6 45.8 52.1 44.6 53.0 68.0New York Wayne 361173001 55.5 57.8 52.5 23.6 56.7 76.7 42.5 42.7 48.5 65.2 80.3 65.3New York Westchester 361192004 65.0 37.0 66.6 40.8 74.0 66.1 25.7 71.8 55.5 58.8 69.7 92.2North Carolina Buncombe 370210030 32.7 36.5 40.3 48.6 50.1 58.6 53.6 60.1 66.7 61.2 43.8 46.1North Carolina Caldwell 370270003 52.8 62.1 60.1 66.7 70.1 68.3 71.3 62.2 79.0 52.8 56.0 66.8North Carolina Camden 370290099 27.3 32.2 53.5 64.8 68.3 NA NA NA NA NA NA NANorth Carolina Caswell 370330001 25.3 32.2 56.6 66.2 58.3 49.5 66.0 62.3 68.7 57.7 64.7 79.3North Carolina Chatham 370370004 41.3 44.1 71.8 77.8 88.2 62.0 81.5 81.6 85.8 70.8 73.8 73.1North Carolina Cumberland 370510008 28.8 38.2 66.7 73.6 89.2 72.1 75.7 93.5 87.0 68.6 57.0 77.0North Carolina Duplin 370610002 26.3 26.8 58.6 56.7 53.0 53.0 46.6 45.6 62.5 48.7 42.6 57.2State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21North Carolina Durham 370630013 32.7 38.1 61.7 71.5 75.8 75.3 73.8 83.0 76.8 63.1 68.3 82.5North Carolina Forsyth 370670022 32.8 36.2 62.7 62.0 58.0 74.3 67.8 66.7 78.5 54.2 77.6 69.6North Carolina Forsyth 370670027 33.1 44.8 59.7 66.1 66.3 67.8 68.8 67.6 89.2 42.7 53.7 60.1North Carolina Forsyth 370671008 43.3 47.1 67.3 76.7 60.3 77.6 60.8 68.1 81.7 54.0 76.2 75.5North Carolina Franklin 370690001 26.6 38.6 71.5 60.2 55.2 56.3 69.6 72.8 67.0 60.8 61.8 79.2North Carolina Granville 370770001 38.0 44.3 82.3 74.3 74.5 79.8 83.2 86.5 76.7 72.0 77.8 88.6North Carolina Guilford 370810011 25.2 39.0 65.8 70.8 61.8 65.1 67.3 67.8 83.6 65.5 77.1 95.7North Carolina Haywood 370870035 NA 55.3 54.1 56.8 61.8 69.3 78.2 80.3 85.6 74.5 57.1 54.2North Carolina Haywood 370870036 44.1 58.2 54.2 56.0 66.3 74.5 92.1 93.3 92.8 62.8 46.0 58.5North Carolina Johnston 371010002 32.0 38.2 82.6 71.8 74.0 69.3 76.6 84.7 82.6 63.5 57.7 76.2North Carolina Lincoln 371090004 51.5 58.5 67.3 65.1 70.0 83.6 83.2 69.7 101.3 58.1 48.8 60.7North Carolina Martin 371170001 19.1 25.0 60.6 60.3 59.8 34.0 21.6 40.5 62.1 44.8 36.3 59.1North Carolina Mecklenburg 371191005 59.2 67.3 91.5 81.5 96.7 73.3 86.2 73.7 100.1 52.3 53.7 43.1North Carolina Mecklenburg 371191009 60.5 67.3 91.2 75.6 84.1 82.2 80.1 95.3 84.0 52.7 50.1 74.8North Carolina Northampton 371310002 27.2 30.5 60.5 75.3 58.0 68.1 40.6 62.3 86.7 50.8 66.3 81.2North Carolina Pitt 371470099 31.2 31.6 84.3 71.3 72.0 67.0 37.0 55.2 82.6 49.5 44.8 88.1North Carolina Rockingham 371570099 30.0 34.1 56.1 62.6 65.2 61.1 63.7 67.5 67.1 61.1 61.7 53.2North Carolina Rowan 371590021 55.0 51.0 70.7 87.7 110.7 82.5 90.4 72.6 80.6 49.6 66.3 82.8North Carolina Rowan 371590022 60.5 65.7 68.0 71.2 77.0 77.2 71.8 76.7 84.8 46.3 51.0 71.2North Carolina Swain 371730002 33.0 45.6 48.5 47.2 55.8 55.3 67.0 70.5 62.0 49.3 40.7 48.5North Carolina Wake 371830014 26.3 40.8 79.7 69.2 64.7 61.7 75.1 81.6 78.7 63.6 64.5 81.5North Carolina Wake 371830015 30.2 42.5 79.8 78.2 72.8 66.1 77.1 86.3 80.8 67.0 68.7 87.0North Carolina Wake 371830016 34.8 49.0 75.5 77.5 80.8 69.5 76.3 88.7 90.7 66.2 71.2 75.1North Carolina Wake 371830017 NA 43.3 81.5 79.6 88.0 77.2 82.5 86.0 82.7 66.0 63.7 77.3North Carolina Yancey 371990003 65.5 72.3 69.0 68.8 67.2 81.0 88.2 98.1 102.0 78.1 72.1 66.1Ohio Allen 390030002 52.5 48.8 65.2 65.3 58.1 54.1 73.1 42.5 53.2 83.2 74.1 53.6Ohio Ashtabula 390071001 67.1 71.0 62.7 58.8 82.7 74.2 72.3 72.3 69.7 77.7 81.3 85.1Ohio Butler 390170004 52.1 NA NA NA NA NA 83.2 34.5 43.2 59.8 71.0 59.1Ohio Butler 390171004 56.3 62.3 69.1 70.5 76.1 85.4 100.7 73.5 48.3 64.0 69.8 65.7Ohio Clark 390230001 63.3 55.8 62.3 62.7 72.3 66.5 79.8 69.7 49.3 58.1 70.5 63.6Ohio Clark 390230003 61.6 63.0 70.8 69.5 73.1 73.7 76.7 69.7 45.0 68.3 63.7 71.8State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Ohio Clinton 390271002 69.6 68.6 70.8 75.2 101.0 71.5 78.0 NA NA NA NA NAOhio Cuyahoga 390350034 71.5 76.7 79.2 66.7 58.0 70.2 67.8 59.6 50.6 87.3 75.2 66.3Ohio Cuyahoga 390350064 56.7 90.5 77.2 64.7 56.6 68.0 77.2 82.1 41.8 97.8 81.2 66.3Ohio Cuyahoga 390355002 60.7 73.3 78.8 72.0 60.6 73.1 62.3 57.8 50.8 78.5 76.0 64.6Ohio Franklin 390490081 49.0 49.2 55.7 NA NA NA NA 74.7 49.3 79.2 72.6 72.6Ohio Hamilton 390610006 58.3 67.1 69.3 69.0 76.1 81.0 94.6 79.3 39.5 54.0 58.6 63.6Ohio Hamilton 390610010 52.3 58.0 59.8 56.3 65.2 66.4 79.0 73.8 42.1 55.3 70.7 63.3Ohio Knox 390830002 64.3 54.7 58.1 62.7 64.3 62.7 56.0 65.1 54.1 84.5 69.3 68.3Ohio Lake 390850003 96.5 91.6 76.8 76.1 71.3 72.6 72.3 72.8 61.1 90.3 93.2 89.0Ohio Lake 390853002 85.2 79.0 79.6 70.8 71.1 71.1 58.2 56.2 47.0 62.1 74.5 78.0Ohio Lawrence 390870006 58.7 50.7 77.6 71.5 63.5 65.3 94.2 71.1 67.3 67.6 69.7 68.2Ohio Lawrence 390870011 64.1 39.0 69.7 58.2 71.6 61.8 77.8 58.5 48.5 48.7 55.0 59.3Ohio Licking 390890005 59.1 61.1 71.6 73.6 89.1 64.6 73.1 66.7 48.6 76.1 78.8 82.8Ohio Lucas 390950034 43.5 74.5 63.1 57.8 55.0 65.1 65.3 44.0 63.3 70.6 70.2 55.0Ohio Lucas 390950081 23.8 60.3 57.3 66.2 61.2 55.6 67.5 49.8 59.6 66.5 70.1 45.1Ohio Madison 390970007 66.8 72.6 73.6 72.8 91.1 65.8 70.8 81.6 46.2 77.6 67.1 82.0Ohio Medina 391030003 57.1 56.3 60.2 NA NA 63.1 77.6 65.0 40.7 92.0 87.1 69.6Ohio Miami 391090005 52.2 51.3 61.5 60.2 61.2 54.8 74.7 55.6 51.3 61.0 64.8 55.3Ohio Montgomery 391130019 48.5 58.6 68.6 63.3 64.7 61.1 87.6 59.2 45.0 61.3 63.3 65.0Ohio Portage 391331001 54.0 70.2 73.6 61.1 61.8 56.2 62.6 69.3 47.3 73.7 92.6 65.7Ohio Preble 391351001 52.5 58.3 61.6 54.3 NA NA 72.5 49.1 43.5 55.3 74.2 52.7Ohio Stark 391510016 57.0 62.2 61.7 68.3 68.8 64.8 71.8 69.0 62.7 80.5 72.2 63.2Ohio Stark 391510019 51.6 60.3 63.8 62.5 71.0 57.6 86.0 68.7 34.7 76.0 77.7 67.8Ohio Stark 391511009 54.0 62.8 62.3 69.5 67.7 65.6 75.3 68.5 56.1 77.2 67.5 67.6Ohio Stark 391514005 53.5 60.8 57.7 66.8 65.1 66.5 84.0 72.1 65.5 83.8 68.0 76.0Ohio Summit 391530020 47.3 63.5 67.6 67.0 63.2 60.6 69.8 82.3 49.2 86.6 93.6 70.0Ohio Trumbull 391550008 55.6 49.3 76.1 79.5 70.7 74.8 70.7 64.3 56.0 86.1 85.6 70.0Ohio Trumbull 391550009 57.2 51.6 67.3 66.8 67.1 68.5 69.0 56.1 47.6 81.1 78.6 61.5Ohio Washington 391670004 55.8 57.2 57.2 57.1 72.0 98.2 72.0 77.5 68.7 61.1 76.3 83.1Oklahoma Cleveland 400270049 58.5 58.3 43.7 27.7 41.0 34.5 31.1 39.5 49.1 85.5 73.8 74.7Oklahoma Oklahoma 401090033 57.8 60.2 46.7 29.0 42.6 36.7 33.2 40.7 51.6 82.7 66.3 64.0State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Oklahoma Oklahoma 401091037 62.8 64.5 49.1 30.2 45.1 38.8 32.1 44.3 56.6 74.8 58.1 58.6Oklahoma Tulsa 401430137 65.3 85.7 64.5 46.3 53.3 49.6 46.8 49.4 67.7 67.1 55.7 64.6Oklahoma Tulsa 401430174 69.6 84.1 67.7 48.8 52.2 48.0 53.3 56.6 71.0 97.0 71.8 81.2Pennsylvania Allegheny 420030008 56.3 31.6 55.1 85.7 66.7 90.2 66.0 71.5 71.6 77.2 93.0 82.1Pennsylvania Allegheny 420030067 57.6 49.3 57.8 76.3 67.1 81.2 73.6 87.3 78.7 71.6 86.1 76.6Pennsylvania Allegheny 420030088 57.5 35.2 55.3 98.0 69.1 100.1 54.5 60.5 55.6 69.2 82.5 84.4Pennsylvania Allegheny 420031005 67.8 36.7 58.5 80.7 71.6 115.7 72.2 70.2 62.6 78.1 89.6 85.0Pennsylvania Beaver 420070002 56.5 NA NA 60.3 63.7 NA NA 49.8 58.1 56.2 62.7 53.2Pennsylvania Beaver 420070005 42.3 39.5 56.7 70.8 68.1 65.3 NA 78.1 NA NA 79.5 76.6Pennsylvania Beaver 420070014 52.8 NA NA 67.0 58.1 62.1 65.7 69.0 66.7 86.3 84.1 78.0Pennsylvania Berks 420110001 43.0 47.0 56.8 35.7 82.6 37.5 76.6 63.0 52.6 57.1 77.5 82.8Pennsylvania Berks 420110009 37.6 46.0 61.7 37.8 78.7 NA 71.0 60.1 45.7 53.6 70.0 91.3Pennsylvania Blair 420130801 43.2 39.7 52.5 44.7 69.7 90.7 36.1 57.1 49.8 53.6 76.0 67.2Pennsylvania Bucks 420170012 61.2 54.2 84.7 45.8 86.3 60.1 35.2 52.5 69.5 71.7 83.0 107.1Pennsylvania Cambria 420210011 32.3 38.2 51.6 39.5 64.7 79.0 65.8 56.3 52.1 58.3 77.0 62.8Pennsylvania Dauphin 420430401 27.5 38.5 57.6 50.7 60.3 71.1 78.0 66.5 52.8 57.5 77.3 76.2Pennsylvania Dauphin 420431100 25.7 44.1 61.2 46.6 68.2 74.5 91.7 68.1 53.7 57.5 83.8 85.6Pennsylvania Delaware 420450002 46.2 41.8 74.3 48.2 65.3 66.2 34.6 54.0 62.5 63.6 74.3 110.5Pennsylvania Erie 420490003 NA NA 61.2 47.8 81.7 71.0 77.5 69.5 73.5 75.2 94.8 78.1Pennsylvania Lackawanna 420690101 44.1 56.6 52.2 34.8 82.1 86.6 57.3 50.8 47.2 44.1 82.6 68.0Pennsylvania Lackawanna 420692006 42.8 55.3 51.7 34.1 79.1 82.6 58.8 55.3 49.5 46.2 93.7 68.6Pennsylvania Lancaster 420710007 42.0 49.1 64.7 52.1 79.8 61.7 87.1 71.5 56.3 55.6 89.1 88.5Pennsylvania Lawrence 420730015 45.2 32.8 65.3 69.1 54.6 49.3 59.2 57.7 56.3 88.7 82.5 73.0Pennsylvania Lehigh 420770004 39.8 42.3 55.3 39.0 70.5 36.5 68.5 55.2 45.3 51.7 67.0 79.3Pennsylvania Luzerne 420791100 35.7 51.3 49.3 31.7 66.1 69.8 61.7 63.8 53.7 55.6 72.0 79.3Pennsylvania Luzerne 420791101 40.3 57.1 54.8 35.2 76.2 81.2 68.2 64.3 54.6 50.8 85.6 86.0Pennsylvania Lycoming 420810403 30.8 49.7 53.1 32.6 53.6 62.3 57.3 55.0 47.8 52.0 68.8 58.6Pennsylvania Mercer 420850100 49.1 42.0 70.7 74.6 67.5 65.0 63.7 59.6 47.5 91.0 84.6 66.0Pennsylvania Montgomery 420910013 44.1 45.1 69.1 43.7 71.1 49.7 54.4 59.0 55.0 70.6 62.4 101.3Pennsylvania Perry 420990301 38.3 48.6 66.3 44.0 54.7 88.1 70.2 80.2 54.8 70.8 85.8 79.5Pennsylvania Philadelphia 421010004 40.0 30.0 60.0 36.2 53.7 45.0 22.5 45.0 45.7 58.7 61.2 77.5State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Pennsylvania Philadelphia 421010014 56.6 50.0 72.5 46.2 76.2 51.2 50.0 66.2 67.1 78.7 71.2 108.7Pennsylvania Philadelphia 421010024 63.7 53.7 82.5 51.2 81.2 63.7 40.0 62.5 72.8 76.2 77.5 107.5Pennsylvania Philadelphia 421010136 28.7 27.5 62.5 37.5 48.5 NA NA NA NA NA NA 71.2Pennsylvania Washington 421250005 58.8 47.7 60.0 80.2 68.1 95.0 81.0 90.6 75.3 71.1 80.2 79.8Pennsylvania Washington 421250200 49.5 41.3 55.3 63.0 59.2 80.1 70.2 87.2 67.2 74.3 77.0 71.3Pennsylvania Washington 421255001 56.3 47.6 54.1 62.7 60.6 74.3 71.3 79.6 83.3 76.8 88.3 68.2Pennsylvania Westmoreland 421290006 49.0 31.3 38.7 73.0 49.2 83.2 62.0 66.2 61.3 66.6 79.7 74.5Pennsylvania York 421330008 39.8 39.7 58.8 51.5 63.0 64.2 79.8 58.0 57.2 56.2 78.8 74.1Rhode Island Kent 440030002 87.0 50.5 61.2 24.5 53.1 32.1 22.3 69.5 43.3 47.0 64.1 110.2Rhode Island Providence 440071010 95.2 55.5 65.0 31.8 59.8 29.6 24.8 55.6 37.5 38.1 43.5 96.6South Carolina Abbeville 450010001 62.0 64.5 64.3 61.2 71.5 82.8 87.5 70.0 88.6 62.3 46.7 50.0South Carolina Aiken 450030003 43.3 66.1 79.2 69.7 64.1 64.5 66.2 65.6 67.1 49.0 48.2 46.7South Carolina Anderson 450070003 71.8 64.3 76.0 72.5 75.5 93.7 84.2 88.0 97.5 75.3 53.0 46.0South Carolina Barnwell 450110001 NA NA NA NA NA NA NA NA NA NA NA NASouth Carolina Berkeley 450150002 24.8 46.8 66.8 62.0 NA 53.2 72.7 79.2 63.3 43.6 37.3 41.0South Carolina Charleston 450190042 29.6 49.3 63.1 71.0 77.7 55.3 72.1 81.1 NA NA NA NASouth Carolina Charleston 450190046 23.2 44.0 74.0 57.5 75.3 56.5 71.1 91.0 75.5 52.7 39.3 32.2South Carolina Cherokee 450210002 53.5 59.5 63.2 64.3 80.1 81.5 80.1 82.7 79.2 65.0 42.1 51.1South Carolina Chester 450230002 74.4 80.5 72.7 70.6 105.2 83.2 95.1 93.5 109.8 59.5 50.2 51.8South Carolina Darlington 450310003 31.2 31.5 69.2 61.7 64.3 63.0 69.8 84.2 72.6 46.8 50.6 56.6South Carolina Edgefield 450370001 51.3 58.0 58.5 51.3 67.2 75.6 69.6 77.1 76.8 60.3 59.7 53.0South Carolina Oconee 450730001 39.6 41.7 46.3 44.7 41.1 56.6 60.1 67.6 64.0 70.1 45.6 39.5South Carolina Pickens 450770002 55.8 65.5 NA NA NA 82.1 79.6 82.2 92.8 75.1 50.0 42.7South Carolina Richland 450790007 37.8 50.8 80.7 67.1 80.1 67.6 92.8 82.1 74.4 51.1 56.2 56.5South Carolina Richland 450791002 37.3 52.0 77.8 67.5 77.1 70.1 93.8 82.8 81.8 47.8 55.6 56.5South Carolina Spartanburg 450830009 56.5 56.0 61.5 72.8 76.3 71.2 74.8 83.0 83.7 74.7 46.2 44.8South Carolina Union 450870001 65.0 65.5 65.1 63.7 68.1 71.3 91.5 85.7 83.0 52.2 46.5 37.6South Carolina Williamsburg 450890001 24.7 32.7 46.6 52.1 56.0 53.8 62.7 71.8 48.4 43.5 38.8 47.3South Carolina York 450910006 64.6 68.5 73.6 61.2 95.1 81.8 87.6 96.1 108.2 52.3 48.1 47.2Tennessee Anderson 470010101 59.5 72.6 70.0 68.8 83.7 77.2 82.5 100.6 94.1 69.3 52.5 66.8Tennessee Blount 470090101 67.1 84.5 72.8 72.3 98.7 81.7 103.6 105.5 90.2 72.0 58.5 64.6State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Tennessee Blount 470090102 45.5 54.7 52.1 52.1 83.7 55.8 77.5 77.2 56.7 59.6 38.1 43.1Tennessee Davidson 470370011 66.2 55.0 81.8 86.2 58.7 60.0 43.7 55.0 31.2 43.1 44.3 45.0Tennessee Davidson 470370026 96.8 76.8 88.7 86.2 86.4 87.5 80.0 85.6 71.2 70.6 64.3 67.5Tennessee Hamilton 470650028 75.3 59.0 NA NA 73.6 57.1 75.7 89.6 77.6 57.5 45.1 56.8Tennessee Hamilton 470651011 57.6 NA 55.3 56.3 51.8 57.7 78.8 86.5 75.5 50.6 41.2 49.1Tennessee Haywood 470750002 60.0 NA 50.0 47.3 63.0 55.6 49.5 62.8 50.8 54.6 51.0 56.3Tennessee Knox 470930021 53.6 58.8 61.1 68.3 61.1 79.5 87.6 84.6 84.2 63.5 45.5 47.8Tennessee Knox 470931020 48.6 60.5 62.0 68.8 74.5 68.6 86.1 83.5 84.2 63.1 41.3 52.1Tennessee Rutherford 471490101 50.2 26.0 29.0 28.6 25.5 31.3 67.2 71.0 27.8 52.2 49.7 47.7Tennessee Sevier 471550101 67.0 71.7 68.8 75.2 84.8 86.0 97.6 98.8 91.5 70.7 62.8 64.7Tennessee Sevier 471550102 59.3 61.7 60.2 62.0 65.5 77.8 80.1 95.0 87.0 61.7 63.0 61.8Tennessee Shelby 471570021 51.7 57.2 81.8 55.6 45.0 30.3 71.6 108.6 NA NA 60.2 54.7Tennessee Shelby 471571004 80.3 59.0 72.0 87.5 75.5 62.2 78.0 63.7 76.3 62.5 61.5 54.1Tennessee Sullivan 471632002 58.3 57.7 81.1 78.0 83.8 72.5 85.6 94.0 91.0 69.0 61.1 62.2Tennessee Sullivan 471632003 51.7 58.2 78.7 75.7 83.6 68.3 83.6 89.2 91.1 72.0 56.3 67.1Tennessee Sumner 471650007 85.6 63.6 77.3 83.8 76.8 89.8 79.8 82.5 66.1 70.3 59.2 66.7Tennessee Sumner 471650101 56.8 51.8 59.7 71.5 56.8 48.3 65.0 66.6 53.8 68.2 54.0 46.7Tennessee Wilson 471890103 63.3 63.3 65.6 63.6 64.8 49.2 64.5 74.7 58.3 80.1 49.3 51.1Texas Brazoria 480391003 33.6 18.2 13.7 13.3 14.0 16.1 18.6 42.1 65.0 86.6 84.8 63.0Texas Collin 480850005 84.8 82.6 50.3 43.8 39.0 43.1 NA NA NA 68.7 68.5 NATexas Dallas 481130069 57.8 61.6 36.1 26.5 23.0 28.6 25.7 44.3 67.6 60.8 81.0 60.8Texas Dallas 481130087 54.0 56.1 31.6 19.3 18.6 26.1 21.2 39.7 62.7 66.5 80.3 70.0Texas Galveston 481671002 39.7 23.8 22.0 NA NA 16.8 22.6 60.5 61.0 128.1 112.4 140.5Texas Gregg 481830001 77.6 69.0 53.8 53.3 29.8 40.0 35.2 67.3 83.6 84.7 95.5 87.5Texas Harris 482010024 56.8 36.0 31.6 28.3 21.6 33.0 48.2 101.6 55.8 76.7 85.1 81.8Texas Harris 482010029 NA NA NA NA NA NA 50.7 83.2 61.1 77.0 82.8 65.8Texas Harris 482010046 51.8 35.1 24.1 25.0 21.7 27.6 47.0 84.6 44.5 74.3 95.6 78.5Texas Harris 482010047 53.8 31.7 22.6 18.1 13.3 23.5 38.7 64.8 47.1 83.2 105.5 69.7Texas Harris 482010051 58.6 25.5 21.6 17.5 17.5 29.5 35.2 52.3 55.0 118.0 107.1 93.1Texas Harris 482010062 48.0 25.7 21.2 18.2 24.3 23.1 27.2 48.3 48.8 107.2 111.7 89.0Texas Harris 482010066 63.7 38.0 25.2 17.0 16.2 26.5 28.6 56.2 55.8 96.0 100.1 71.8State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Texas Harris 482011035 47.7 22.2 17.7 17.1 18.8 18.6 26.0 58.0 42.6 NA NA NATexas Jefferson 482450009 69.6 31.0 29.7 21.0 19.2 18.5 57.7 75.1 52.6 107.6 74.1 96.6Texas Jefferson 482450011 50.8 25.1 16.1 13.0 19.2 11.7 66.2 86.5 51.5 112.0 76.8 105.5Texas Orange 483611001 61.5 32.2 22.2 22.7 20.0 22.0 73.8 84.6 59.0 96.6 52.2 71.8Texas Tarrant 484391002 67.7 76.7 44.8 31.5 33.2 38.1 37.2 55.8 80.0 79.1 82.3 76.0Texas Tarrant 484392003 75.3 NA NA NA NA NA NA 45.5 69.2 62.6 66.0 59.0Texas Travis 484530014 67.3 55.3 26.0 29.3 24.3 27.6 32.0 37.7 48.3 74.5 78.5 76.5Vermont Bennington 500030004 58.6 47.3 27.1 22.5 51.0 46.2 54.0 38.6 36.5 32.3 47.2 59.5Virginia Arlington 510130020 34.5 54.0 67.8 65.1 94.2 73.0 82.2 64.0 60.3 72.5 97.5 97.5Virginia Caroline 510330001 26.0 44.7 55.5 66.6 78.2 57.5 61.8 77.1 91.7 47.5 77.5 65.8Virginia Charles City 510360002 28.5 29.3 74.0 66.8 77.8 58.7 44.6 75.3 63.7 42.0 55.7 88.6Virginia Chesterfield 510410004 26.5 41.1 59.6 66.7 96.7 65.7 56.2 72.6 82.6 52.1 79.5 78.3Virginia Fairfax 510590005 39.5 56.0 55.7 56.5 63.1 93.1 84.8 72.8 60.3 67.0 81.3 79.0Virginia Fairfax 510590018 32.0 42.2 66.3 60.3 102.5 70.2 76.3 64.6 63.2 71.5 99.7 94.3Virginia Fairfax 510595001 34.7 43.5 67.1 58.0 80.5 83.0 84.3 77.6 65.6 70.2 89.3 86.0Virginia Fauquier 510610002 34.4 40.2 55.5 55.2 61.8 81.7 84.7 75.8 64.2 59.8 72.1 73.8Virginia Frederick 510690010 34.8 37.6 58.7 63.2 63.5 82.7 80.2 66.0 64.1 53.8 70.0 68.0Virginia Henrico 510870014 28.8 33.0 68.3 70.2 83.0 56.2 55.6 80.7 76.5 47.0 71.8 92.3Virginia Madison 511130003 33.0 47.1 62.8 69.8 69.6 78.4 85.1 85.5 64.4 54.5 59.5 87.0Virginia Prince William 511530009 37.0 50.0 67.5 63.7 68.7 100.8 91.7 75.7 67.2 60.5 76.8 77.7Virginia Roanoke 511611004 34.3 51.7 61.8 74.1 67.3 62.5 79.5 74.3 71.3 66.1 60.8 72.2Virginia Stafford 511790001 29.2 51.7 58.2 68.0 83.2 76.5 86.1 84.5 90.6 69.6 99.1 76.6Virginia Wythe 511970002 51.5 62.0 64.8 68.3 66.7 67.0 67.5 76.0 84.8 71.2 56.3 59.8Virginia Alexandria Cit 515100009 28.8 45.5 64.3 67.2 91.2 68.3 76.2 54.0 55.8 68.2 90.3 86.0Virginia Hampton City 516500004 28.6 29.4 74.1 74.1 69.0 37.2 22.1 54.8 57.6 42.7 43.1 76.2Virginia Suffolk City 518000004 23.1 28.7 75.6 63.8 62.6 34.0 27.8 62.1 57.2 43.0 42.6 75.0Virginia Suffolk City 518000005 27.0 36.0 62.5 67.5 86.6 45.5 28.5 52.5 68.0 48.5 51.0 74.2West Virginia Cabell 540110006 72.2 49.3 87.0 87.8 80.0 74.3 NA 63.5 72.5 59.0 65.8 79.6West Virginia Hancock 540291004 52.0 45.5 50.1 58.7 63.0 65.8 72.7 80.5 86.8 80.3 84.7 71.1West Virginia Ohio 540690007 42.2 37.3 55.2 58.0 63.7 73.5 56.7 66.8 52.3 66.3 71.6 57.8West Virginia Wood 541071002 56.1 61.2 61.0 56.7 79.7 95.7 90.1 92.0 62.8 64.7 78.8 81.3State CountyAIRS SiteCode Aug 10 Aug 11 Aug 12 Aug 13 Aug 14 Aug 15 Aug 16 Aug 17 Aug 18 Aug 19 Aug 20 Aug 21Wisconsin Brown 550090026 50.8 41.7 34.0 44.0 45.0 25.4 38.1 47.5 45.3 39.2 41.8 41.5Wisconsin Columbia 550210015 45.8 54.3 63.0 55.7 45.0 43.6 56.2 45.6 49.5 40.3 41.8 47.0Wisconsin Dane 550250041 40.7 51.8 58.8 46.1 44.1 42.8 45.2 40.8 45.8 32.8 44.6 44.2Wisconsin Dodge 550270007 NA 42.8 47.7 48.1 40.3 37.1 53.1 39.0 48.7 35.5 37.0 40.3Wisconsin Door 550290004 60.1 44.8 37.7 44.5 46.6 38.1 45.3 55.7 55.1 52.7 40.5 38.4Wisconsin Fond Du Lac 550390006 36.3 43.7 43.8 48.0 41.7 34.2 47.6 44.3 46.5 34.6 39.2 39.8Wisconsin Jefferson 550550002 41.1 57.8 56.7 58.8 49.5 42.1 57.6 50.8 53.8 38.2 43.7 48.7Wisconsin Kenosha 550590002 44.8 73.7 92.5 58.2 56.8 52.2 51.0 60.3 53.5 37.6 46.6 49.1Wisconsin Kenosha 550590019 55.1 94.8 105.0 64.1 63.3 57.1 61.0 65.5 58.5 43.0 49.1 53.6Wisconsin Kewaunee 550610002 52.6 45.8 43.2 46.7 46.8 37.1 42.1 50.3 57.7 44.8 40.1 39.3Wisconsin Manitowoc 550710004 39.3 44.1 39.1 45.3 45.6 27.7 47.3 46.2 48.2 39.3 45.8 47.3Wisconsin Manitowoc 550710007 45.0 43.6 40.2 46.0 43.7 37.1 45.1 46.7 49.6 40.1 41.7 40.3Wisconsin Marathon 550730012 34.5 37.6 28.8 42.2 40.8 33.0 32.1 34.7 44.2 37.4 34.3 36.4Wisconsin Milwaukee 550790041 35.5 61.6 75.0 61.3 54.6 47.7 59.0 61.5 67.2 45.1 44.7 45.1Wisconsin Milwaukee 550790044 37.3 56.1 63.5 58.7 49.6 39.2 52.8 52.7 58.6 37.0 44.2 41.0Wisconsin Milwaukee 550790085 45.5 74.0 70.1 63.6 56.7 49.0 61.8 62.6 61.1 37.7 44.3 46.7Wisconsin Milwaukee 550791025 45.8 58.3 77.6 59.5 55.2 51.5 60.5 63.1 NA NA NA NAWisconsin Outagamie 550870009 36.7 36.2 27.2 38.2 38.2 23.3 38.4 45.7 45.1 30.8 36.3 40.0Wisconsin Ozaukee 550890008 37.1 63.5 56.1 51.5 42.7 47.3 58.6 49.7 59.1 36.1 43.5 40.2Wisconsin Ozaukee 550890009 44.5 62.6 55.5 63.6 52.3 52.3 58.1 53.0 59.5 42.0 45.3 48.2Wisconsin Racine 551010017 41.7 85.2 87.2 56.6 51.0 46.7 52.5 59.3 53.6 32.8 41.0 46.0Wisconsin Rock 551050024 40.5 54.0 59.1 53.0 44.8 33.8 50.3 39.1 50.1 35.6 44.0 NAWisconsin St Croix 551091002 48.5 29.8 31.2 37.1 28.3 34.5 31.5 37.2 39.7 32.2 42.0 40.0Wisconsin Sauk 551110007 36.3 49.8 50.8 45.5 37.5 36.6 40.1 41.0 44.5 29.8 37.3 43.0Wisconsin Vernon 551230008 34.6 49.7 47.7 44.3 39.3 30.3 30.0 34.3 39.7 34.1 31.5 49.3Wisconsin Walworth 551270005 44.8 65.1 NA NA NA NA 52.4 43.6 61.0 34.1 44.8 51.5Wisconsin Washington 551310009 40.0 52.5 55.6 58.3 46.1 42.8 58.8 46.2 55.5 41.8 42.8 44.7Wisconsin Waukesha 551330017 39.1 55.0 58.3 56.5 48.1 36.1 48.2 46.1 60.5 47.0 42.5 44.8Wisconsin Winnebago 551390011 36.8 44.6 41.0 38.0 39.8 32.5 43.8 42.7 46.0 34.7 39.0 41.2

# 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 Norma lized 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 diffe r 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 CAIRTable E-1. 8-Hour Ozone Concentrations (ppb): Average 1999-2003, 2010 Base and CAIR, 2015 Base and CAIR; and the impact ofCAIR on 8-hour ozone in 2010 and 2015.CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Mobile, AL AL Baldwin Co 79.0 72.5 71.4 -1.1 71.3 67.4 -3.9AL Clay Co 82.0 64.3 63.1 -1.2 61.0 59.2 -1.8Montgomery, AL AL Elmore Co 78.3 64.3 63.5 -0.8 60.7 59.4 -1.3Birmingham, AL AL Jefferson Co 86.7 70.7 70.2 -0.5 66.4 65.5 -0.9Decatur, AL AL Lawrence Co 78.7 66.4 66.1 -0.3 63.7 62.3 -1.4Huntsville, AL AL Madison Co 82.7 68.8 67.9 -0.9 64.3 62.7 -1.6Mobile, AL AL Mobile Co 79.0 72.9 71.9 -1.0 71.9 68.0 -3.9Montgomery, AL AL Montgomery Co80.0 65.6 64.8 -0.8 62.0 60.8 -1.2Decatur, AL AL Morgan Co 83.0 70.6 70.0 -0.6 67.7 66.7 -1.0Birmingham, AL AL Shelby Co 91.7 73.8 73.2 -0.6 69.2 68.2 -1.0AL Sumter Co 74.0 61.5 60.9 -0.6 59.5 58.2 -1.3Tuscaloosa, AL AL Tuscaloosa Co78.0 62.5 62.1 -0.4 59.3 58.3 -1.0Memphis, TN AR Crittenden Co 92.7 81.5 80.8 -0.7 78.6 78.0 -0.6AR Montgomery Co 68.0 60.7 58.5 -2.2 58.6 55.9 -2.7Little Rock, AR AR Pulaski Co 84.7 76.3 71.8 -4.5 73.4 68.4 -5.0New Haven-Meriden, CT CT Fairfield Co 98.7 92.6 92.2 -0.4 91.4 90.6 -0.8Hartford, CT CT Hartford Co 89.3 80.4 80.1 -0.3 77.4 76.8 -0.6CT Litchfield Co 83.0 74.0 73.9 -0.1 71.3 70.8 -0.5Hartford, CT CT Middlesex Co 98.0 90.9 90.6 -0.3 89.1 88.4 -0.7New Haven-Meriden, CT CT New Haven Co 99.0 91.6 91.3 -0.3 89.8 89.1 -0.7New London-Norwich, CT CT New LondonCo 90.7 83.6 83.4 -0.2 81.8 81.1 -0.7Hartford, CT CT Tolland Co 93.0 83.0 82.7 -0.3 79.7 79.1 -0.6Dover, DE DE Kent Co 91.3 79.1 78.7 -0.4 76.6 75.5 -1.1Philadelphia, PA DE New CastleCo 95.3 85.0 84.7 -0.3 82.8 81.5 -1.3DE Sussex Co 93.3 80.9 80.3 -0.6 78.4 77.3 -1.1CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Washington-Baltimore, DC-MD DC Washington Co 94.3 85.2 85.0 -0.2 83.5 82.7 -0.8Gainesville, FL FL Alachua Co 75.3 65.3 59.8 -5.5 61.8 55.1 -6.7FL Baker Co 72.7 62.1 58.0 -4.1 58.7 53.8 -4.9Panama City, FL FL Bay Co 80.0 72.8 71.3 -1.5 70.8 68.7 -2.1Melbourne, FL FL Brevard Co 75.0 62.5 58.8 -3.7 58.1 53.9 -4.2FL Columbia Co 71.0 60.6 56.9 -3.7 57.1 52.8 -4.3Jacksonville, FL FL Duval Co 70.3 60.1 54.4 -5.7 57.2 50.6 -6.6Pensacola, FL FL Escambia Co 83.7 77.8 74.1 -3.7 76.1 70.2 -5.9FL Highlands Co 64.0 52.2 50.1 -2.1 48.9 46.3 -2.6Tampa-St Petersburg, FL FL Hillsborough Co 80.3 68.7 67.0 -1.7 65.6 63.4 -2.2FL Holmes Co 72.3 63.2 61.8 -1.4 60.5 58.3 -2.2Orlando, FL FL Lake Co 76.064.2 60.0 -4.2 60.2 54.8 -5.4Fort Myers, FL FL Lee Co 70.7 57.9 56.4 -1.5 53.8 51.8 -2.0Tallahassee, FL FL Leon Co 73.362.8 61.5 -1.3 59.4 57.5 -1.9Sarasota, FL FL Manatee Co 79.0 66.3 64.8 -1.5 63.0 60.9 -2.1Ocala, FL FL Marion Co 75.7 65.4 60.1 -5.3 61.8 55.1 -6.7Orlando, FL FL Orange Co 78.366.1 62.1 -4.0 61.7 56.8 -4.9Orlando, FL FL Osceola Co 73.762.1 58.2 -3.9 58.1 53.2 -4.9West Palm Beach-Boca Raton, FL FL PalmBeach Co 69.7 58.4 55.9 -2.5 54.2 51.3 -2.9Tampa-St Petersburg, FL FL PascoCo 77.7 66.7 64.3 -2.4 63.2 59.9 -3.3Tampa-St Petersburg, FL FL Pinellas Co 77.3 67.4 65.7 -1.7 64.5 62.3 -2.2Lakeland-Winter Haven, FL FL PolkCo 78.0 63.5 60.7 -2.8 59.4 55.7 -3.7Fort Pierce, FL FL St Lucie Co 69.3 58.8 55.4 -3.4 55.3 51.2 -4.1Pensacola, FL FL Santa Rosa Co82.0 75.8 72.5 -3.3 74.2 69.0 -5.2Sarasota, FL FL Sarasota Co 81.7 67.4 65.5 -1.9 63.5 61.1 -2.4Orlando, FL FL Seminole Co 77.765.2 61.3 -3.9 60.9 56.0 -4.9Daytona Beach, FL FL Volusia Co 72.0 60.4 56.3 -4.1 56.4 51.4 -5.0CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015FL Wakulla Co 76.0 66.7 65.6 -1.1 64.0 62.3 -1.7Macon, GA GA Bibb Co 92.0 82.5 80.0 -2.5 78.8 77.6 -1.2Savannah, GA GA Chatham Co 71.0 61.1 60.0 -1.1 58.2 56.6 -1.6Atlanta, GA GA Cherokee Co 77.0 63.1 62.2 -0.9 57.2 56.0 -1.2Atlanta, GA GA Cobb Co 94.7 80.6 79.4 -1.2 73.0 71.2 -1.8Atlanta, GA GA Coweta Co 92.0 78.4 76.6 -1.8 72.8 69.7 -3.1GA Dawson Co 82.0 67.1 66.2 -0.9 60.7 59.2 -1.5Atlanta, GA GA De Kalb Co 95.3 83.3 81.9 -1.4 76.6 74.5 -2.1Atlanta, GA GA Douglas Co 94.7 80.2 78.7 -1.5 73.3 71.2 -2.1Atlanta, GA GA Fayette Co 90.7 78.0 76.7 -1.3 71.8 70.1 -1.7Atlanta, GA GA Fulton Co 99.0 86.5 85.1 -1.4 79.7 77.6 -2.1GA Glynn Co 72.7 62.9 60.0 -2.9 60.0 56.6 -3.4Atlanta, GA GA Gwinnett Co 89.3 75.6 74.6 -1.0 68.0 66.6 -1.4Atlanta, GA GA Henry Co 98.0 82.4 80.3 -2.1 76.5 74.3 -2.2GA Murray Co 86.0 70.9 69.4 -1.5 65.4 63.1 -2.3Columbus, GA-AL GA Muscogee Co 82.0 68.7 67.6 -1.1 64.1 62.2 -1.9Atlanta, GA GA Paulding Co 90.3 72.2 70.7 -1.5 66.4 64.3 -2.1Augusta-Aiken, GA-SC GA Richmond Co 85.7 72.9 72.1 -0.8 69.7 68.4 -1.3Atlanta, GA GA Rockdale Co 96.3 81.6 80.4 -1.2 74.4 72.8 -1.6GA Sumter Co 80.3 68.4 66.7 -1.7 64.1 61.8 -2.3IL Adams Co 76.0 67.0 66.4 -0.6 64.7 63.4 -1.3Champaign-Urbana, IL IL ChampaignCo 77.3 66.3 65.9 -0.4 64.0 62.7 -1.3IL Clark Co 75.0 65.7 62.9 -2.8 61.7 60.0 -1.7Chicago, IL-IN IL Cook Co 87.782.1 81.8 -0.3 82.1 81.1 -1.0Chicago, IL-IN IL Du Page Co70.7 66.7 66.4 -0.3 67.1 66.1 -1.0IL Effingham Co 77.7 67.1 66.4 -0.7 64.1 63.1 -1.0IL Hamilton Co 78.7 67.1 66.5 -0.6 64.7 62.3 -2.4CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015St. Louis, MO-IL IL Jersey Co89.0 77.3 77.0 -0.3 73.8 72.8 -1.0Chicago, IL-IN IL Kane Co 77.7 72.0 71.7 -0.3 71.0 70.2 -0.8Chicago, IL-IN IL Lake Co 83.3 77.0 76.8 -0.2 75.4 75.0 -0.4Chicago, IL-IN IL McHenry Co 83.3 76.9 76.6 -0.3 75.4 74.6 -0.8Bloomington, IL IL McLean Co 77.0 65.2 64.7 -0.5 62.4 61.3 -1.1Chicago, IL-IN IL Macon Co 76.7 64.0 63.6 -0.4 61.7 60.6 -1.1IL Macoupin Co 79.3 66.8 66.4 -0.4 63.7 62.6 -1.1St. Louis, MO-IL IL Madison Co84.7 74.8 74.5 -0.3 71.7 70.7 -1.0Peoria, IL IL Peoria Co 79.067.2 65.5 -1.7 64.9 62.2 -2.7IL Randolph Co 78.7 66.2 66.0 -0.2 64.0 63.3 -0.7Davenport, IA-IL IL Rock Island Co71.0 64.1 63.2 -0.9 62.0 60.6 -1.4St. Louis, MO-IL IL St Clair Co 83.3 74.7 74.5 -0.2 72.0 71.1 -0.9Springfield, IL IL Sangamon Co 76.0 63.6 63.1 -0.5 60.8 59.4 -1.4Chicago, IL-IN IL Will Co 79.370.7 70.4 -0.3 69.9 68.9 -1.0Rockford, IL IL Winnebago Co 76.0 67.0 66.6 -0.4 63.6 63.0 -0.6Fort Wayne, IN IN Allen Co 87.7 76.8 76.4 -0.4 73.0 72.0 -1.0Indianapolis, IN IN Boone Co 89.0 78.6 78.1 -0.5 75.4 73.0 -2.4IN Carroll Co 84.0 73.7 73.1 -0.6 70.6 68.4 -2.2Louisville, KY-IN IN Clark Co 89.3 78.2 78.4 0.2 75.8 73.5 -2.3Muncie, IN IN Delaware Co 88.0 76.1 75.6 -0.5 72.1 70.4 -1.7Elkhart, IN IN Elkhart Co 80.069.8 69.5 -0.3 66.6 65.8 -0.8Louisville, KY-IN IN Floyd Co 83.7 74.9 75.2 0.3 72.8 70.3 -2.5IN Gibson Co 71.7 62.1 61.7 -0.4 60.2 57.9 -2.3IN Greene Co 88.5 76.6 75.3 -1.3 73.3 70.1 -3.2Indianapolis, IN IN Hamilton Co93.3 82.2 81.7 -0.5 78.3 76.2 -2.1Indianapolis, IN IN Hancock Co91.7 80.8 80.4 -0.4 77.1 75.0 -2.1Indianapolis, IN IN Hendricks Co86.5 76.4 75.9 -0.5 73.4 70.9 -2.5CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Fort Wayne, IN IN Huntington Co 85.0 74.1 73.7 -0.4 70.5 69.3 -1.2IN Jackson Co 85.0 71.7 71.4 -0.3 68.6 66.6 -2.0Indianapolis, IN IN Johnson Co 86.7 74.2 73.8 -0.4 71.0 68.8 -2.2Chicago, IL-IN IN Lake Co 90.7 83.2 82.8 -0.4 81.6 80.7 -0.9IN La Porte Co 90.0 82.1 81.8 -0.3 80.0 79.4 -0.6Indianapolis, IN IN Madison Co91.0 79.0 78.6 -0.4 74.8 72.9 -1.9Indianapolis, IN IN Marion Co 90.0 80.1 79.6 -0.5 76.9 74.6 -2.3Indianapolis, IN IN Morgan Co 86.7 76.0 75.7 -0.3 73.1 70.6 -2.5IN Perry Co 90.0 75.4 75.3 -0.1 73.0 70.5 -2.5Chicago, IL-IN IN Porter Co 89.0 81.4 81.1 -0.3 79.3 78.6 -0.7Evansville-Henderson, IN-KY IN PoseyCo 85.7 74.4 73.9 -0.5 72.1 70.5 -1.6South Bend, IN IN St Joseph Co89.0 78.1 77.8 -0.3 74.8 74.0 -0.8Indianapolis, IN IN Shelby Co 93.5 82.1 81.6 -0.5 78.7 76.2 -2.5Evansville-Henderson, IN-KY IN Vanderburgh Co 83.3 72.4 72.0 -0.4 69.9 68.3 -1.6Terre Haute, IN IN Vigo Co 87.0 76.7 75.5 -1.2 73.8 70.2 -3.6Evansville-Henderson, IN-KY IN WarrickCo 84.5 73.4 73.1 -0.3 71.4 69.5 -1.9IA Bremer Co 70.5 62.9 62.0 -0.9 60.5 58.8 -1.7IA Clinton Co 78.3 71.4 70.3 -1.1 69.4 67.5 -1.9IA Harrison Co 75.7 68.8 68.0 -0.8 66.4 64.8 -1.6Cedar Rapids, IA IA Linn Co 71.0 64.3 63.4 -0.9 62.5 60.8 -1.7IA Palo Alto Co 66.0 59.1 58.4 -0.7 56.5 55.1 -1.4Des Moines, IA IA Polk Co 58.7 51.4 50.6 -0.8 49.0 47.4 -1.6Davenport, IA-IL IA Scott Co 79.0 71.7 70.6 -1.1 69.3 67.8 -1.5IA Story Co 63.3 55.6 54.8 -0.8 53.0 51.5 -1.5IA Van Buren Co 74.0 65.8 65.0 -0.8 63.4 61.6 -1.8Des Moines, IA IA Warren Co 63.3 55.3 54.6 -0.7 52.8 51.4 -1.4KS Linn Co 76.7 71.6 71.4 -0.2 69.5 68.5 -1.0CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Wichita, KS KS Sedgwick Co 81.0 73.7 73.2 -0.5 71.2 70.5 -0.7KS Sumner Co 79.0 72.3 71.8 -0.5 69.8 69.4 -0.4Kansas City, MO-KS KS Wyandotte Co 80.3 73.9 73.7 -0.2 71.2 70.7 -0.5KY Bell Co 83.3 65.1 64.8 -0.3 61.5 60.1 -1.4Cincinnati, OH-KY KY Boone Co 85.3 73.1 73.1 0.0 71.0 68.0 -3.0Huntington-Ashland, WV-KY-OH KY Boyd Co 89.5 77.2 76.4 -0.8 74.8 74.0 -0.8Louisville, KY-IN KY Bullitt Co 83.7 73.0 73.1 0.1 70.8 69.3 -1.5Cincinnati, OH-KY KY Campbell Co92.5 81.6 81.5 -0.1 78.8 76.8 -2.0Huntington-Ashland, WV-KY-OH KY Carter Co 80.3 66.7 65.9 -0.8 64.2 63.2 -1.0Clarksville, TN-KY KY Christian Co85.0 64.4 63.6 -0.8 61.7 60.3 -1.4Owensboro, KY KY Daviess Co 77.3 65.7 65.5 -0.2 63.9 62.4 -1.5KY Edmonson Co 84.0 68.8 68.6 -0.2 65.8 64.0 -1.8Lexington, KY KY Fayette Co 78.3 67.2 67.0 -0.2 64.3 62.8 -1.5KY Graves Co 81.0 70.8 70.1 -0.7 68.5 65.5 -3.0Huntington-Ashland, WV-KY-OH KY Greenup Co 84.0 71.7 71.0 -0.7 69.4 68.5 -0.9KY Hancock Co 82.7 70.8 70.6 -0.2 68.8 66.9 -1.9KY Hardin Co 80.0 68.1 68.2 0.1 65.6 63.8 -1.8Evansville, IN-KY KY Henderson Co79.5 69.2 68.9 -0.3 67.3 65.7 -1.6Louisville, KY-IN KY Jefferson Co 84.3 74.7 74.9 0.2 72.8 71.1 -1.7Lexington, KY KY Jessamine Co 78.0 67.1 66.9 -0.2 64.3 62.3 -2.0Cincinnati, OH-KY KY Kenton Co 86.3 75.7 75.6 -0.1 73.3 71.3 -2.0KY Livingston Co 85.0 75.0 74.4 -0.6 72.7 68.2 -4.5KY McCracken Co 81.7 72.5 71.9 -0.6 70.4 65.9 -4.5KY McLean Co 84.0 70.0 69.6 -0.4 67.9 66.4 -1.5Louisville, KY-IN KY Oldham Co 88.0 75.5 75.6 0.1 73.2 71.0 -2.2KY Perry Co 75.5 61.2 60.3 -0.9 58.3 56.7 -1.6KY Pike Co 76.3 61.4 60.4 -1.0 58.1 56.8 -1.3CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015KY Pulaski Co 81.3 67.5 67.1 -0.4 64.7 62.4 -2.3Lexington, KY KY Scott Co 70.3 58.6 58.6 0.0 56.4 54.3 -2.1KY Simpson Co 84.0 67.7 67.2 -0.5 64.2 63.0 -1.2KY Trigg Co 76.7 64.4 63.8 -0.6 62.3 60.4 -1.9KY Warren Co 84.0 68.8 68.4 -0.4 65.6 63.9 -1.7Baton Rouge, LA LA Ascension Parish81.7 76.8 76.3 -0.5 76.0 75.2 -0.8LA Beauregard Parish 75.070.1 68.8 -1.3 69.2 67.5 -1.7Shreveport, LA LA Bossier Parish84.7 78.1 77.0 -1.1 76.6 74.1 -2.5Shreveport, LA LA Caddo Parish 79.7 73.3 72.3 -1.0 71.9 69.7 -2.2Lake Charles, LA LA Calcasieu Parish 81.7 76.0 74.9 -1.1 75.3 73.8 -1.5Baton Rouge, LA LAEast Baton RougeParish 87.3 81.4 80.6 -0.8 80.4 79.3 -1.1LA Grant Parish 77.7 70.8 69.3 -1.5 69.3 67.3 -2.0LA Iberville Parish 85.0 79.9 79.4 -0.5 79.1 78.2 -0.9New Orleans, LA LA Jefferson Parish 85.3 79.2 78.6 -0.6 78.3 77.1 -1.2Lafayette, LA LA Lafayette Parish80.7 74.2 73.4 -0.8 72.8 71.6 -1.2Houma, LA LA Lafourche Parish79.0 74.0 73.7 -0.3 73.3 72.7 -0.6Baton Rouge, LA LA Livingston Parish 83.3 78.3 77.8 -0.5 77.5 76.6 -0.9New Orleans, LA LA Orleans Parish 72.0 66.9 66.6 -0.3 66.3 65.4 -0.9Monroe, LA LA Ouachita Parish 78.7 72.3 71.6 -0.7 71.4 70.2 -1.2LAPointe CoupeeParish 73.0 67.5 66.6 -0.9 66.5 65.4 -1.1New Orleans, LA LA St Bernard Parish 79.3 73.3 72.9 -0.4 72.6 71.3 -1.3New Orleans, LA LA St Charles Parish 81.7 76.5 76.2 -0.3 75.8 75.0 -0.8New Orleans, LA LA St James Parish 77.3 72.7 72.3 -0.4 72.0 71.2 -0.8New Orleans, LA LASt John The BaptistParish 81.7 76.8 76.4 -0.4 76.1 75.3 -0.8LA St Mary Parish 78.0 73.2 72.7 -0.5 72.6 71.8 -0.8CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Baton Rouge, LA LAW. Baton RougeParish 85.7 79.6 78.8 -0.8 78.6 77.4 -1.2Portland, ME ME Cumberland Co 84.7 75.9 75.8 -0.1 73.4 73.0 -0.4ME Hancock Co 92.0 80.7 80.5 -0.2 77.2 76.8 -0.4ME Kennebec Co 77.7 68.1 68.0 -0.1 65.3 64.9 -0.4ME Knox Co 83.3 73.7 73.6 -0.1 70.7 70.4 -0.3ME Oxford Co 61.0 54.9 54.7 -0.2 53.2 52.7 -0.5Bangor, ME ME Penobscot Co 83.0 72.8 72.6 -0.2 70.0 69.5 -0.5ME York Co 89.0 80.3 80.2 -0.1 78.0 77.6 -0.4Washington-Baltimore, DC-MD MD Anne Arundel Co 101.0 88.8 88.6 -0.2 86.0 84.9 -1.1Washington-Baltimore, DC-MD MD Baltimore Co 93.0 83.9 83.7 -0.2 81.9 81.0 -0.9Washington-Baltimore, DC-MD MD CalvertCo 89.0 74.5 74.1 -0.4 72.1 71.2 -0.9Washington-Baltimore, DC-MD MD Carroll Co 91.3 80.3 80.0 -0.3 77.8 76.3 -1.5Philadelphia, PA-DE-NJ MD CecilCo 102.7 89.7 89.5 -0.2 86.9 85.4 -1.5Washington-Baltimore, DC-MD MD CharlesCo 94.7 79.0 78.7 -0.3 76.5 75.6 -0.9Washington-Baltimore, DC-MD MD Frederick Co 90.0 78.5 78.1 -0.4 75.9 74.2 -1.7Washington-Baltimore, DC-MD MD HarfordCo 103.7 93.0 92.8 -0.2 90.6 89.6 -1.0MD Kent Co 99.0 86.2 85.8 -0.4 83.4 82.3 -1.1Washington-Baltimore, DC-MD MD Montgomery Co 88.7 79.5 79.3 -0.2 77.4 76.4 -1.0Washington-Baltimore, DC-MD MD Prince Georges Co 95.0 84.4 84.2 -0.2 81.9 80.9 -1.0Washington-Baltimore, DC-MD MD Washington Co 86.0 74.2 73.3 -0.9 71.6 69.3 -2.3Washington-Baltimore, DC-MD MD Baltimore City 82.0 74.0 73.8 -0.2 72.3 71.4 -0.9Barnstable-Yarmouth, MA MA Barnstable Co 94.7 83.7 83.6 -0.1 80.8 80.2 -0.6MA Berkshire Co 87.0 76.3 76.1 -0.2 73.6 73.2 -0.4Boston, MA-NH MA Bristol Co 92.7 83.1 83.0 -0.1 80.3 80.0 -0.3Boston, MA-NH MA Essex Co 89.7 81.8 81.7 -0.1 80.6 80.2 -0.4Springfield, MA MA Hampden Co 90.3 80.4 80.2 -0.2 77.3 76.7 -0.6CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Springfield, MA MA Hampshire Co 87.3 78.2 78.0 -0.2 75.4 74.9 -0.5Boston, MA-NH MA Middlesex Co 88.7 79.3 79.1 -0.2 76.1 75.8 -0.3Boston, MA-NH MA Suffolk Co 88.0 78.3 78.1 -0.2 75.2 74.9 -0.3Boston, MA-NH MA Worcester Co 85.3 76.1 76.0 -0.1 73.3 72.9 -0.4Grand Rapids, MI MI Allegan Co 92.0 82.4 82.1 -0.3 79.2 79.5 0.3MI Benzie Co 87.7 78.5 77.9 -0.6 75.1 74.0 -1.1Benton Harbor, MI MI Berrien Co 88.3 78.4 78.1 -0.3 75.4 74.8 -0.6MI Cass Co 90.0 78.5 78.2 -0.3 75.1 74.4 -0.7Lansing, MI MI Clinton Co 83.3 73.5 73.3 -0.2 70.2 69.0 -1.2Detroit, MI MI Genesee Co 86.7 76.9 76.7 -0.2 73.4 72.5 -0.9MI Huron Co 84.0 75.0 74.8 -0.2 72.4 71.9 -0.5Lansing, MI MI Ingham Co 83.3 73.5 73.3 -0.2 70.1 69.0 -1.1Kalamazoo-Battle Creek, MI MI Kalamazoo Co 83.0 72.2 71.9 -0.3 68.6 67.9 -0.7Grand Rapids, MI MI Kent Co 84.7 75.0 74.6 -0.4 71.7 70.7 -1.0Detroit, MI MI Lenawee Co 85.0 75.0 74.8 -0.2 72.1 71.2 -0.9Detroit, MI MI Macomb Co 91.0 85.5 85.4 -0.1 85.1 84.2 -0.9MI Mason Co 89.0 79.3 78.9 -0.4 75.8 74.7 -1.1MI Missaukee Co 80.3 71.0 70.7 -0.3 68.0 67.4 -0.6Grand Rapids, MI MI Muskegon Co 92.0 82.3 82.0 -0.3 79.3 79.2 -0.1Detroit, MI MI Oakland Co 87.0 80.9 80.7 -0.2 80.0 79.2 -0.8Grand Rapids, MI MI Ottawa Co 86.0 76.9 76.6 -0.3 73.6 74.0 0.4Detroit, MI MI St Clair Co 87.7 80.8 80.6 -0.2 78.4 78.0 -0.4Detroit, MI MI Washtenaw Co 89.0 81.1 81.0 -0.1 78.6 78.0 -0.6Detroit, MI MI Wayne Co 88.0 84.8 84.7 -0.1 84.9 84.1 -0.8Minneapolis-St Paul, MN MN Anoka Co 72.5 66.4 64.7 -1.7 63.3 60.8 -2.5Minneapolis-St Paul, MN MN Dakota Co 68.0 62.7 62.0 -0.7 60.2 59.0 -1.2MN Mille Lacs Co 72.0 65.5 63.1 -2.4 63.1 59.9 -3.2CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Minneapolis-St Paul, MN MN Washington Co 74.5 68.1 65.7 -2.4 64.9 61.8 -3.1MS Adams Co 79.7 69.8 69.2 -0.6 68.2 67.2 -1.0MS Bolivar Co 78.0 66.6 66.0 -0.6 64.3 63.4 -0.9Memphis, TN MS De Soto Co 84.3 72.6 72.0 -0.6 70.4 69.7 -0.7Biloxi-Gulfport, MS MS HancockCo 83.7 75.5 74.4 -1.1 74.0 70.9 -3.1Biloxi-Gulfport, MS MS HarrisonCo 83.3 77.3 75.9 -1.4 75.8 69.3 -6.5Jackson, MS MS Hinds Co 76.3 63.8 63.4 -0.4 60.1 59.1 -1.0Biloxi-Gulfport, MS MS JacksonCo 83.0 77.0 76.0 -1.0 75.7 70.4 -5.3MS Lauderdale Co 76.0 63.0 62.3 -0.7 59.9 58.3 -1.6MS Lee Co 82.0 67.5 67.2 -0.3 63.9 62.1 -1.8Jackson, MS MS Madison Co 76.3 64.6 64.1 -0.5 62.4 61.2 -1.2MS Warren Co 76.7 58.8 58.4 -0.4 57.0 56.2 -0.8Kansas City, MO-KS MO Cass Co 79.0 73.2 73.0 -0.2 70.6 70.5 -0.1MO Cedar Co 82.0 74.2 73.8 -0.4 71.6 68.6 -3.0Kansas City, MO-KS MO Clay Co 84.3 76.7 76.5 -0.2 73.7 73.1 -0.6Springfield, MO MO Greene Co 74.7 63.5 63.6 0.1 60.2 59.0 -1.2St. Louis, MO-IL MO Jefferson Co87.3 76.9 76.7 -0.2 73.4 72.1 -1.3MO Monroe Co 79.3 68.7 68.4 -0.3 66.4 65.2 -1.2Kansas City, MO-KS MO Platte Co 81.7 75.2 75.0 -0.2 72.4 72.0 -0.4St. Louis, MO-IL MO St Charles Co 90.7 80.7 80.5 -0.2 77.5 76.5 -1.0MO Ste Genevieve Co 84.0 73.8 73.6 -0.2 70.8 69.8 -1.0St. Louis, MO-IL MO St Louis Co 89.3 80.7 80.5 -0.2 77.8 76.7 -1.1St. Louis, MO-IL MO St Louis City 88.5 79.6 79.4 -0.2 76.5 75.6 -0.9Omaha, NE NE Douglas Co 67.5 61.2 60.5 -0.7 59.0 58.0 -1.0Lincoln, NE NE Lancaster Co 54.0 49.3 49.2 -0.1 47.3 47.0 -0.3NH Belknap Co 78.0 68.5 68.2 -0.3 65.8 65.0 -0.8NH Carroll Co 66.5 60.3 60.0 -0.3 58.6 58.0 -0.6CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015NH Cheshire Co 73.7 65.2 64.9 -0.3 63.1 62.0 -1.1Boston, MA-NH NH Hillsborough Co 85.0 76.7 76.6 -0.1 74.1 73.9 -0.2NH Merrimack Co 73.0 64.8 64.6 -0.2 62.3 61.8 -0.5Boston, MA-NH NH Rockingham Co 82.7 75.2 75.1 -0.1 73.3 72.9 -0.4Boston, MA-NH NH Strafford Co 77.3 69.2 69.1 -0.1 66.8 66.5 -0.3Philadelphia, PA-DE-NJ NJ AtlanticCo 91.0 80.8 80.4 -0.4 78.5 77.7 -0.8New York City,NY-NJ-CT NJ BergenCo 92.5 86.9 86.0 -0.9 85.7 84.5 -1.2Philadelphia, PA-DE-NJ NJ CamdenCo 102.3 91.9 91.6 -0.3 89.5 88.3 -1.2Philadelphia, PA-DE-NJ NJ Cumberland Co 96.7 84.8 84.4 -0.4 82.2 80.9 -1.3New York City,NY-NJ-CT NJ EssexCo 68.0 64.6 64.4 -0.2 63.7 63.0 -0.7Philadelphia, PA-DE-NJ NJ Gloucester Co 101.3 91.8 91.3 -0.5 89.6 88.2 -1.4New York City,NY-NJ-CT NJ HudsonCo 89.0 84.6 84.3 -0.3 83.3 82.4 -0.9New York City,NY-NJ-CT NJ HunterdonCo 97.7 89.0 88.6 -0.4 86.5 85.4 -1.1New York City,NY-NJ-CT NJ MercerCo 103.0 95.6 95.2 -0.4 93.5 92.4 -1.1New York City,NY-NJ-CT NJ MiddlesexCo 100.7 92.4 92.1 -0.3 89.8 88.8 -1.0New York City,NY-NJ-CT NJ MonmouthCo 96.0 86.6 86.4 -0.2 83.9 83.2 -0.7New York City,NY-NJ-CT NJ MorrisCo 97.7 86.5 85.5 -1.0 83.4 81.8 -1.6New York City,NY-NJ-CT NJ OceanCo 111.0 100.5 100.3 -0.2 98.0 96.9 -1.1New York City,NY-NJ-CT NJ PassaicCo 88.3 80.4 79.7 -0.7 78.5 77.4 -1.1Albany-Schenectady, NY NY AlbanyCo 83.0 73.7 73.4 -0.3 71.2 70.7 -0.5New York City,NY-NJ-CT NY BronxCo 82.7 80.5 79.7 -0.8 80.8 79.5 -1.3Jamestown,NY NY Chautauqua Co 91.7 82.2 81.8 -0.4 79.9 78.6 -1.3Elmira, NY NY Chemung Co 81.0 70.8 70.1 -0.7 67.7 66.9 -0.8New York City,NY-NJ-CT NY Dutchess Co 91.3 81.4 81.0 -0.4 78.9 78.1 -0.8Buffalo, NY NY Erie Co 96.0 87.3 86.9 -0.4 85.2 84.2 -1.0NY Essex Co 88.5 77.9 77.6 -0.3 76.2 75.6 -0.6NY Hamilton Co 79.0 70.3 70.0 -0.3 68.7 68.2 -0.5CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Utica-Rome, NY NY Herkimer Co 74.0 65.7 65.4 -0.3 64.1 63.5 -0.6NY Jefferson Co 91.7 80.9 80.5 -0.4 78.7 78.0 -0.7Syracuse, NY NY Madison Co 80.0 71.8 71.6 -0.2 69.8 69.4 -0.4Rochester, NY NY Monroe Co 86.5 77.3 76.9 -0.4 75.0 74.3 -0.7Buffalo, NY NY Niagara Co 91.0 82.7 82.3 -0.4 80.8 80.3 -0.5Utica-Rome, NY NY Oneida Co 79.0 69.4 69.1 -0.3 67.0 66.3 -0.7Syracuse, NY NY Onondaga Co 83.0 72.7 72.3 -0.4 69.7 69.0 -0.7New York City,NY-NJ-CT NY OrangeCo 86.0 77.6 77.1 -0.5 75.4 74.6 -0.8New York City,NY-NJ-CT NY PutnamCo 91.3 82.8 82.3 -0.5 80.4 79.3 -1.1New York City,NY-NJ-CT NY QueensCo 86.0 78.5 78.3 -0.2 76.7 76.0 -0.7New York City,NY-NJ-CT NY RichmondCo 96.0 87.3 87.1 -0.2 84.6 83.9 -0.7Albany-Schenectady, NY NY SaratogaCo 85.5 75.3 75.0 -0.3 72.6 71.8 -0.8Albany-Schenectady, NY NY Schenectady Co 77.3 69.2 69.0 -0.2 66.9 66.4 -0.5New York City,NY-NJ-CT NY SuffolkCo 98.5 91.1 90.8 -0.3 89.9 89.0 -0.9NY Ulster Co 81.7 72.9 72.5 -0.4 70.6 70.0 -0.6Rochester, NY NY Wayne Co 84.0 74.4 74.1 -0.3 72.2 71.6 -0.6New York City,NY-NJ-CT NY Westchester Co 92.0 85.3 84.7 -0.6 84.2 83.1 -1.1Hickory, NC NC Alexander Co 88.7 72.9 71.7 -1.2 68.4 66.8 -1.6NC Avery Co 77.3 63.2 62.2 -1.0 58.7 57.4 -1.3Asheville, NC NC Buncombe Co 82.0 67.1 66.7 -0.4 62.7 61.1 -1.6Hickory, NC NC Caldwell Co 85.7 70.7 69.4 -1.3 66.0 64.4 -1.6NC Camden Co 80.0 72.9 72.3 -0.6 71.1 70.5 -0.6NC Caswell Co 89.7 72.2 71.7 -0.5 68.0 67.4 -0.6Raleigh-Durham, NC NC Chatham Co 82.0 67.1 67.0 -0.1 62.8 62.2 -0.6Fayetteville, NC NC Cumberland Co87.0 72.2 71.9 -0.3 67.2 66.3 -0.9Greensboro, NC NC Davie Co 94.7 76.2 75.5 -0.7 71.1 69.9 -1.2NC Duplin Co 80.7 67.2 66.9 -0.3 63.3 62.2 -1.1CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Raleigh-Durham, NC NC Durham Co 89.0 72.8 72.5 -0.3 67.8 67.2 -0.6Rocky Mount, NC NC Edgecombe Co 88.0 74.5 74.0 -0.5 71.0 70.1 -0.9Greensboro, NC NC Forsyth Co 93.7 75.3 74.9 -0.4 70.2 69.5 -0.7Raleigh-Durham, NC NC Franklin Co 89.0 73.6 73.3 -0.3 68.8 68.1 -0.7NC Granville Co 92.0 75.4 75.1 -0.3 70.2 69.6 -0.6Greensboro, NC NC Guilford Co 90.7 73.4 72.9 -0.5 68.4 67.9 -0.5NC Haywood Co 86.3 69.8 69.9 0.1 65.6 63.9 -1.7NC Jackson Co 85.0 68.5 68.6 0.1 64.5 62.6 -1.9Raleigh-Durham, NC NC Johnston Co 85.7 71.4 71.1 -0.3 66.2 65.4 -0.8NC Lenoir Co 81.3 67.6 67.3 -0.3 63.7 62.6 -1.1Charlotte, NC-SC NC Lincoln Co 92.3 76.1 74.5 -1.6 70.3 68.5 -1.8NC Martin Co 80.3 69.6 68.9 -0.7 66.7 65.6 -1.1Charlotte, NC-SC NC Mecklenburg Co 100.3 82.5 81.4 -1.1 76.6 75.0 -1.6Wilmington, NC NC New Hanover Co77.3 66.3 65.8 -0.5 64.4 63.2 -1.2NC Northampton Co 83.3 70.7 70.2 -0.5 67.6 66.9 -0.7Person Co, NC NC Person Co 90.071.9 71.5 -0.4 67.8 67.2 -0.6Greenville, NC NC Pitt Co 83.0 69.6 69.3 -0.3 65.8 65.1 -0.7Greensboro, NC NC Randolph Co 85.0 68.7 68.4 -0.3 63.7 63.1 -0.6NC Rockingham Co 88.7 70.6 70.2 -0.4 66.4 66.0 -0.4Charlotte, NC-SC NC Rowan Co 99.7 81.3 80.1 -1.2 75.7 74.1 -1.6NC Swain Co 73.7 58.6 58.7 0.1 55.6 53.3 -2.3Charlotte, NC-SC NC Union Co 87.7 71.9 71.1 -0.8 66.4 65.2 -1.2Raleigh-Durham, NC NC Wake Co 92.7 77.5 77.2 -0.3 71.5 70.8 -0.7NC Yancey Co 86.3 70.9 69.9 -1.0 66.3 64.6 -1.7Lima, OH OH Allen Co 87.7 77.2 76.8 -0.4 73.7 72.6 -1.1Cleveland, OH OH Ashtabula Co 94.0 83.8 83.5 -0.3 81.2 80.0 -1.2Cincinnati, OH-KY OH Butler Co 89.0 78.2 78.0 -0.2 75.2 73.6 -1.6CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Dayton, OH OH Clark Co 88.3 75.9 75.4 -0.5 71.2 69.9 -1.3Cincinnati, OH-KY OH Clermont Co 90.0 78.1 78.0 -0.1 75.1 72.5 -2.6OH Clinton Co 95.7 81.7 81.4 -0.3 77.4 75.7 -1.7Cleveland, OH OH Cuyahoga Co 86.3 77.7 77.3 -0.4 75.1 74.0 -1.1Columbus, OH OH Delaware Co 90.3 77.9 77.3 -0.6 73.4 72.1 -1.3Columbus, OH OH Franklin Co 95.0 82.8 81.9 -0.9 78.3 77.0 -1.3Cleveland, OH OH Geauga Co 98.3 87.1 86.6 -0.5 83.8 82.5 -1.3Dayton, OH OH Greene Co 87.0 74.7 74.4 -0.3 70.5 69.3 -1.2Cincinnati, OH-KY OH Hamilton Co89.3 78.8 78.6 -0.2 75.8 74.3 -1.5Steubenville-Weirton, OH-WV OH Jefferson Co 85.3 74.6 73.8 -0.8 72.3 71.2 -1.1OH Knox Co 89.3 77.3 76.5 -0.8 72.6 71.4 -1.2Cleveland, OH OH Lake Co 92.7 82.5 82.2 -0.3 80.1 78.9 -1.2Huntington-Ashland, WV-KY-OH OH Lawrence Co 85.0 72.5 71.8 -0.7 70.2 69.3 -0.9Columbus, OH OH Licking Co 89.0 76.5 75.2 -1.3 71.6 70.2 -1.4Cleveland, OH OH Lorain Co 87.5 78.8 78.5 -0.3 76.5 75.5 -1.0Toledo, OH OH Lucas Co 88.7 80.2 80.0 -0.2 77.9 76.8 -1.1Columbus, OH OH Madison Co 89.0 76.9 76.3 -0.6 72.5 71.2 -1.3Youngstown-Warren, OH OH Mahoning Co88.0 75.8 75.2 -0.6 72.0 70.5 -1.5Cleveland, OH OH Medina Co 87.7 76.9 76.5 -0.4 73.1 72.0 -1.1Dayton, OH OH Miami Co 86.3 74.1 73.7 -0.4 69.5 68.3 -1.2Dayton, OH OH Montgomery Co 86.7 75.1 74.7 -0.4 70.8 69.6 -1.2Cleveland, OH OH Portage Co 92.0 80.4 79.8 -0.6 76.1 74.9 -1.2OH Preble Co 80.3 68.6 68.2 -0.4 64.8 63.3 -1.5Canton, OH OH Stark Co 89.0 77.1 76.3 -0.8 72.9 71.7 -1.2Cleveland, OH OH Summit Co 94.3 82.9 82.4 -0.5 78.6 77.4 -1.2Youngstown-Warren, OH OH TrumbullCo 92.5 80.2 79.7 -0.5 76.1 74.7 -1.4Cincinnati, OH-KY OH Warren Co 92.0 80.2 80.0 -0.2 76.6 75.1 -1.5CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Parkersburg, WV-OH OH WashingtonCo 87.0 71.9 70.1 -1.8 69.4 65.1 -4.3Toledo, OH OH Wood Co 87.0 77.8 77.4 -0.4 75.0 73.8 -1.2OK Cherokee Co 76.0 71.9 71.2 -0.7 69.7 68.4 -1.3Oklahoma City, OK OK Cleveland Co 77.3 69.0 68.7 -0.3 65.8 65.2 -0.6OK Kay Co 75.0 69.2 68.9 -0.3 66.6 66.3 -0.3Oklahoma City, OK OK Mc Clain Co 78.5 70.3 70.0 -0.3 67.2 66.6 -0.6Oklahoma City, OK OK Oklahoma Co 80.7 72.0 71.7 -0.3 68.6 68.1 -0.5OK Ottawa Co 79.0 74.3 74.1 -0.2 72.3 71.7 -0.6Tulsa, OK OK Tulsa Co 85.0 79.4 79.2 -0.2 76.9 76.4 -0.5Pittsburgh, PA PA Allegheny Co 93.0 82.7 81.9 -0.8 80.4 78.9 -1.5PA Armstrong Co 92.0 80.6 79.7 -0.9 78.0 76.1 -1.9Pittsburgh, PA PA Beaver Co 91.0 80.5 79.6 -0.9 77.9 76.8 -1.1Reading, PA PA Berks Co 92.7 82.2 81.7 -0.5 79.4 76.9 -2.5Altoona, PA PA Blair Co 84.3 73.0 72.5 -0.5 70.9 67.7 -3.2Philadelphia, PA-DE-NJ PA BucksCo 103.0 94.7 94.3 -0.4 93.0 91.8 -1.2Johnstown, PA PA Cambria Co 87.7 77.6 76.9 -0.7 75.4 72.9 -2.5State College, PA PA Centre Co 85.5 75.4 74.7 -0.7 73.1 70.7 -2.4Philadelphia, PA-DE-NJ PA ChesterCo 96.5 85.7 85.4 -0.3 83.7 82.2 -1.5PA Clearfield Co 86.7 75.0 74.2 -0.8 72.3 70.5 -1.8Harrisburg, PA PA Dauphin Co 91.0 81.3 80.8 -0.5 78.6 76.0 -2.6Philadelphia, PA-DE-NJ PA DelawareCo 93.7 84.4 84.0 -0.4 82.4 81.0 -1.4Erie, PA PA Erie Co 89.0 79.4 79.1 -0.3 77.1 76.0 -1.1PA Franklin Co 93.0 80.7 80.2 -0.5 78.0 75.5 -2.5PA Greene Co 90.3 74.0 73.2 -0.8 72.0 68.9 -3.1Scranton--Wilkes-Barre, PA PA Lackawanna Co 85.3 74.3 73.6 -0.7 71.2 70.0 -1.2Lancaster, PA PA Lancaster Co94.0 83.9 83.6 -0.3 81.3 78.4 -2.9PA Lawrence Co 78.7 68.3 67.7 -0.6 65.3 63.9 -1.4CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Allentown, PA PA Lehigh Co 93.3 82.8 82.1 -0.7 80.1 78.3 -1.8Scranton--Wilkes-Barre, PA PA LuzerneCo 84.7 72.6 71.7 -0.9 69.4 67.9 -1.5Williamsport, PA PA Lycoming Co 83.0 71.8 71.1 -0.7 68.9 67.4 -1.5Sharon, PA PA Mercer Co 91.3 78.6 78.1 -0.5 74.6 73.2 -1.4Philadelphia, PA-DE-NJ PA Montgomery Co 96.3 88.0 87.6 -0.4 86.5 84.9 -1.6Allentown, PA PA Northampton Co93.0 82.6 81.8 -0.8 79.8 78.1 -1.7Harrisburg, PA PA Perry Co 84.7 74.5 74.0 -0.5 72.0 69.8 -2.2Philadelphia, PA-DE-NJ PA Philadelphia Co 97.5 90.3 89.9 -0.4 88.9 87.5 -1.4PA Tioga Co 83.7 72.4 71.8 -0.6 68.9 68.5 -0.4Pittsburgh, PA PA Washington Co 87.7 78.2 77.3 -0.9 75.9 74.8 -1.1Pittsburgh, PA PA Westmoreland Co87.7 77.4 76.7 -0.7 75.3 73.9 -1.4York, PA PA York Co 90.3 79.7 79.4 -0.3 77.1 74.8 -2.3Providence, RI RI Kent Co 95.3 86.4 86.2 -0.2 83.9 83.2 -0.7Providence, RI RI Providence Co 90.3 81.4 81.2 -0.2 78.6 78.1 -0.5Providence, RI RI Washington Co 93.3 84.3 84.2 -0.1 81.8 81.3 -0.5SC Abbeville Co 84.0 69.7 68.8 -0.9 65.3 63.6 -1.7Augusta-Aiken, GA-SC SC Aiken Co84.7 72.1 71.3 -0.8 69.0 67.7 -1.3Greenville-Spartanburg, SC SC Anderson Co 88.0 72.4 71.9 -0.5 67.6 65.8 -1.8SC Barnwell Co 81.3 67.9 67.2 -0.7 64.6 63.2 -1.4Charleston, SC SC Berkeley Co 71.0 58.6 58.0 -0.6 56.2 55.1 -1.1Charleston, SC SC Charleston Co74.0 61.1 60.4 -0.7 58.6 57.4 -1.2Greenville-Spartanburg, SC SC Cherokee Co 86.0 69.2 68.3 -0.9 64.2 62.8 -1.4SC Chester Co 84.3 70.9 69.5 -1.4 65.8 64.3 -1.5SC Colleton Co 78.7 66.3 65.6 -0.7 63.2 61.8 -1.4SC Darlington Co 84.7 70.8 70.2 -0.6 66.9 65.8 -1.1Augusta-Aiken, GA-SC SC EdgefieldCo 80.7 67.5 66.7 -0.8 63.2 61.9 -1.3SC Oconee Co 84.0 67.9 67.2 -0.7 62.4 61.1 -1.3CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Greenville-Spartanburg, SC SC Pickens Co 85.3 70.1 69.5 -0.6 65.2 63.7 -1.5Columbia, SC SC Richland Co 93.0 77.7 76.9 -0.8 72.1 70.7 -1.4Greenville-Spartanburg, SC SC Spartanburg Co 90.0 73.0 72.2 -0.8 67.6 66.3 -1.3Union Co, SC SC Union Co 80.7 66.4 65.3 -1.1 61.7 60.1 -1.6SC Williamsburg Co 72.3 59.9 59.0 -0.9 57.1 55.5 -1.6Charlotte, NC-SC SC York Co 83.3 70.0 68.5 -1.5 64.8 63.2 -1.6Knovville, TN TN Anderson Co 89.767.9 67.8 -0.1 63.9 62.8 -1.1Knoxville, TN TN Blount Co 94.0 74.3 74.1 -0.2 69.8 68.5 -1.3Nashville, TN TN Davidson Co 81.3 69.3 68.9 -0.4 65.6 64.9 -0.7Chattanooga, TN-GA TN Hamilton Co 90.7 74.3 72.7 -1.6 69.7 67.6 -2.1TN Haywood Co 89.0 75.6 75.0 -0.6 72.6 71.4 -1.2TN Jefferson Co 94.0 75.6 75.6 0.0 71.7 69.6 -2.1Knoxville, TN TN Knox Co 94.7 73.2 73.1 -0.1 69.4 68.1 -1.3TN Lawrence Co 79.3 65.0 64.2 -0.8 61.4 59.6 -1.8TN Meigs Co 90.5 72.4 71.1 -1.3 68.3 66.2 -2.1TN Putnam Co 85.0 69.9 69.2 -0.7 66.3 64.4 -1.9Nashville, TN TN Rutherford Co83.3 69.5 68.9 -0.6 64.9 63.7 -1.2Knoxville, TN TN Sevier Co 96.0 76.3 76.5 0.2 72.7 70.2 -2.5Memphis, TN TN Shelby Co 90.7 77.3 76.7 -0.6 74.3 73.6 -0.7Johnson City, TN-VA TN Sullivan Co 89.3 73.9 74.2 0.3 71.8 69.9 -1.9Nashville, TN TN Sumner Co 89.0 75.8 75.4 -0.4 72.0 71.3 -0.7Nashville, TN TN Williamson Co 86.3 71.2 70.2 -1.0 66.8 65.4 -1.4Nashville, TN TN Wilson Co 84.772.3 71.8 -0.5 68.6 67.6 -1.0San Antonio, TX TX Bexar Co 85.7 70.2 69.7 -0.5 67.6 66.7 -0.9Houston, TX TX Brazoria Co 91.0 84.5 84.1 -0.4 83.5 82.7 -0.8Dallas, TX TX Collin Co 93.383.1 82.5 -0.6 78.5 77.6 -0.9Dallas, TX TX Dallas Co 91.082.7 82.2 -0.5 78.8 77.9 -0.9CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Dallas, TX TX Denton Co 99.087.4 86.8 -0.6 82.3 81.3 -1.0Dallas, TX TX Ellis Co 85.375.2 74.6 -0.6 71.4 70.4 -1.0Houston, TX TX Galveston Co 92.0 85.1 84.6 -0.5 84.1 83.2 -0.9Longview, TX TX Gregg Co 88.3 79.8 79.1 -0.7 78.0 76.3 -1.7Houston, TX TX Harris Co 105.097.9 97.4 -0.5 97.3 96.4 -0.9Longview, TX TX Harrison Co 76.068.5 67.7 -0.8 67.2 65.2 -2.0Dallas, TX TX Hood Co 84.0 73.8 72.7 -1.1 70.1 68.7 -1.4Beaumont-Port Arthur, TX TX Jefferson Co 91.0 85.6 85.0 -0.6 85.0 84.1 -0.9Dallas, TX TX Johnson Co 89.579.0 78.2 -0.8 75.1 73.9 -1.2Dallas, TX TX Kaufman Co 71.563.9 63.4 -0.5 60.9 60.2 -0.7Houston, TX TX Montgomery Co 91.0 81.6 81.2 -0.4 78.7 77.9 -0.8Beaumont-Port Arthur, TX TX OrangeCo 78.3 72.7 71.8 -0.9 72.0 70.8 -1.2Dallas, TX TX Parker Co 87.577.1 76.0 -1.1 73.0 71.6 -1.4Dallas, TX TX Rockwall Co 82.073.9 73.4 -0.5 70.5 69.7 -0.8Tyler, TX TX Smith Co 82.573.8 73.1 -0.7 71.5 70.0 -1.5Dallas, TX TX Tarrant Co 98.387.8 87.2 -0.6 83.1 82.2 -0.9Austin-San Marcos, TX TX TravisCo 84.3 73.4 72.9 -0.5 70.3 69.4 -0.9VT Bennington Co 79.7 70.3 70.0 -0.3 67.9 67.2 -0.7Washington-Baltimore, DC-MD VA Arlington Co 95.7 86.2 86.0 -0.2 84.7 83.8 -0.9VA Caroline Co 84.0 72.1 71.7 -0.4 69.7 68.8 -0.9Richmond-Petersburg, VA VA Charles City Co 89.3 79.2 77.7 -1.5 75.7 74.9 -0.8Richmond-Petersburg, VA VA Chesterfield Co 86.0 75.8 74.7 -1.1 72.6 71.8 -0.8Washington-Baltimore, DC-MD VA Fairfax Co 96.3 85.7 85.4 -0.3 83.9 83.0 -0.9Washington-Baltimore, DC-MD VA FauquierCo 81.0 69.6 69.3 -0.3 67.6 66.8 -0.8VA Frederick Co 84.3 71.5 70.8 -0.7 68.9 67.1 -1.8Richmond-Petersburg, VA VA HanoverCo 94.0 82.0 80.9 -1.1 78.7 77.9 -0.8Richmond-Petersburg, VA VA HenricoCo 90.0 79.1 78.2 -0.9 76.3 75.5 -0.8CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Washington-Baltimore, DC-MD VA LoudounCo 89.3 78.9 78.6 -0.3 76.5 75.4 -1.1VA Madison Co 86.3 69.3 68.7 -0.6 66.7 65.0 -1.7VA Page Co 81.3 64.8 64.1 -0.7 62.5 60.5 -2.0Washington-Baltimore, DC-MD VA Prince William Co 85.7 75.1 74.9 -0.2 72.9 72.0 -0.9Roanoke, VA VA Roanoke Co 86.0 72.9 72.5 -0.4 69.6 68.8 -0.8VA Rockbridge Co 79.0 66.9 66.5 -0.4 64.3 63.2 -1.1Washington-Baltimore, DC-MD VA StaffordCo 86.3 73.6 73.3 -0.3 71.3 70.5 -0.8VA Wythe Co 80.7 64.3 63.8 -0.5 61.8 60.6 -1.2Washington-Baltimore, DC-MD VA AlexandriaCity 90.0 81.1 80.9 -0.2 79.6 78.8 -0.8Norfolk, VA-NC VA Hampton City 88.7 79.2 78.7 -0.5 77.3 76.6 -0.7Norfolk, VA-NC VA Suffolk City 87.3 78.0 77.5 -0.5 76.1 75.4 -0.7Washington-Baltimore, DC-MD WV Berkeley Co 86.0 74.3 73.3 -1.0 71.8 69.6 -2.2Huntington-Ashland, WV-KY-OH WV Cabell Co 88.0 76.3 75.5 -0.8 74.1 73.2 -0.9WV Greenbrier Co 81.7 65.2 64.8 -0.4 62.6 60.8 -1.8Steubenville-Weirton, OH-WV WV Hancock Co 84.3 73.7 72.9 -0.8 71.5 70.3 -1.2Charleston, WV WV Kanawha Co 87.0 71.1 70.6 -0.5 68.5 68.0 -0.5WV Monongalia Co 80.0 66.8 65.7 -1.1 65.0 63.6 -1.4Wheeling, WV-OH WV Ohio Co 84.7 71.4 70.4 -1.0 68.7 66.5 -2.2Parkersburg, WV-OH WV Wood Co 87.7 72.0 69.7 -2.3 68.7 65.0 -3.7Green Bay, WI WI Brown Co 81.7 73.9 72.6 -1.3 71.1 69.0 -2.1WI Columbia Co 77.7 68.4 67.9 -0.5 64.5 63.7 -0.8Madison, WI WI Dane Co 77.3 68.3 67.6 -0.7 64.5 63.6 -0.9WI Dodge Co 81.0 72.2 71.7 -0.5 68.4 67.7 -0.7WI Door Co 92.7 83.3 82.1 -1.2 79.9 77.9 -2.0WI Fond Du Lac Co 79.0 70.4 69.5 -0.9 67.2 65.9 -1.3WI Green Co 74.5 65.8 65.2 -0.6 62.7 61.6 -1.1WI Jefferson Co 84.5 74.5 73.9 -0.6 70.4 69.5 -0.9CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in2010 2015 Base 2015 CAIRImpact ofCAIR in2015Chicago, IL-IN WI Kenosha Co 98.7 91.3 91.0 -0.3 89.4 88.8 -0.6WI Kewaunee Co 90.0 81.0 79.9 -1.1 77.6 75.7 -1.9WI Manitowoc Co 90.0 80.9 80.0 -0.9 77.4 75.9 -1.5Wausau, WI WI Marathon Co 73.7 66.3 64.3 -2.0 63.3 60.8 -2.5Milwaukee WI Milwaukee Co 90.7 82.5 82.1 -0.4 79.6 79.0 -0.6Oshkosh, WI WI Outagamie Co 77.3 69.6 68.4 -1.2 66.8 64.9 -1.9Milwaukee, WI WI Ozaukee Co 95.3 86.2 85.8 -0.4 82.9 82.0 -0.9Milwaukee, WI WI Racine Co 91.7 84.2 83.9 -0.3 82.0 81.6 -0.4Janesville-Beloit, WI WI Rock Co 84.3 73.9 73.4 -0.5 69.9 69.1 -0.8Minneapolis-St Paul, MN WI St Croix Co 72.7 66.2 62.8 -3.4 63.1 58.9 -4.2WI Sauk Co 74.3 66.3 63.0 -3.3 63.2 59.9 -3.3Sheboyegan WI WI Sheboygan Co 98.0 88.3 87.7 -0.6 84.7 83.6 -1.1WI Vernon Co 71.7 64.2 63.1 -1.1 61.2 59.6 -1.6WI Walworth Co 83.3 74.2 73.7 -0.5 70.8 70.1 -0.7Milwaukee, WI WI Washington Co 82.7 75.4 75.0 -0.4 72.4 71.8 -0.6Milwaukee, WI WI Waukesha Co 82.7 75.1 74.7 -0.4 71.9 71.3 -0.6Oshkosh, WI WI Winnebago Co 80.071.7 69.9 -1.8 68.9 66.3 -2.6

# 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 CAIRTable F-1. Annual Average PM2.5 Concentrations (μg/m3): Average 1999-2003, 2010 Base and CAIR, 2015 Base and CAIR; and theimpact of CAIR on PM2.5 in 2010 and 2015.CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015Mobile, AL AL Baldwin Co 11.43 10.87 10.18 -0.69 10.88 9.83 -1.05AL Clay Co 14.27 13.72 12.46 -1.26 13.62 11.89 -1.73Florence, AL AL Colbert Co 13.94 13.60 12.38 -1.22 13.53 12.17 -1.36AL DeKalb Co 15.62 15.23 13.97 -1.26 15.24 13.46 -1.78AL Escambia Co 13.03 12.51 11.77 -0.74 12.50 11.37 -1.13Dothan, AL AL Houston Co 14.69 14.06 13.32 -0.74 14.07 12.98 -1.09Birmingham, AL AL Jefferson Co 19.04 18.57 17.46 -1.11 18.85 17.36 -1.49Huntsville, AL AL Madison Co 14.81 14.26 13.05 -1.21 14.20 12.62 -1.58Mobile, AL AL Mobile Co 13.69 13.20 12.58 -0.62 13.52 12.57 -0.95Montgomery, AL AL Montgomery Co 15.41 15.12 14.10 -1.02 15.24 13.87 -1.37Decatur, AL AL Morgan Co 15.81 15.29 14.11 -1.18 15.26 13.85 -1.41Columbus, GA-AL AL Russell Co 16.70 16.17 15.15 -1.02 16.10 14.66 -1.44Birmingham, AL AL Shelby Co 15.33 14.92 13.83 -1.09 14.96 13.48 -1.48AL Sumter Co 13.28 12.79 11.91 -0.88 12.74 11.53 -1.21AL Talladega Co 16.05 15.34 14.00 -1.34 15.22 13.35 -1.87AR Arkansas Co 11.90 11.37 10.71 -0.66 11.39 10.62 -0.77AR Ashley Co 11.80 11.30 10.66 -0.64 11.38 10.61 -0.77Jonesboro, AR AR Craighead Co 12.39 11.85 11.07 -0.78 11.79 10.89 -0.90Memphis, TN AR Crittenden Co 13.35 13.12 12.33 -0.79 13.59 12.64 -0.95Little Rock, AR AR Faulkner Co 12.57 12.14 11.55 -0.59 12.21 11.52 -0.69Pine Bluff, AR AR Jefferson Co 13.28 13.02 12.44 -0.58 13.33 12.65 -0.68AR Mississippi Co 12.04 11.74 10.93 -0.81 11.79 10.85 -0.94AR Phillips Co 12.49 11.86 11.09 -0.77 11.90 10.98 -0.92AR Polk Co 11.35 10.78 10.31 -0.47 10.85 10.26 -0.59AR Pope Co 12.48 12.24 11.76 -0.48 12.37 11.80 -0.57Little Rock, AR AR Pulaski Co 14.11 13.69 13.04 -0.65 13.85 13.09 -0.76CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015Fort Smith, AR-OK AR Sebastian Co 12.66 12.07 11.62 -0.45 12.12 11.55 -0.57AR Union Co 13.03 12.76 12.10 -0.66 13.09 12.46 -0.63AR White Co 11.91 11.46 10.83 -0.63 11.54 10.80 -0.74New Haven-Meriden, CT CT FairfieldCo 13.52 12.45 11.45 -1.00 12.14 10.97 -1.17Hartford, CT CT Hartford Co 12.88 11.89 11.10 -0.79 11.67 10.77 -0.90New London-Norwich, CT CT New London Co 11.92 11.00 10.16 -0.84 10.80 9.84 -0.96Dover, DE DE Kent Co 13.30 12.96 10.97 -1.99 12.76 10.43 -2.33Philadelphia, PA DE New Castle Co16.41 16.56 14.84 -1.72 16.47 14.41 -2.06DE Sussex Co 14.08 13.56 11.54 -2.02 13.30 10.97 -2.33Washington-Baltimore, DC-MD DCDistrict ofColumbia 16.25 15.84 13.68 -2.16 15.57 13.11 -2.46Gainesville, FL FL Alachua Co 10.36 9.69 9.21 -0.48 9.67 8.91 -0.76Melbourne, FL FL Brevard Co 7.88 6.73 6.32 -0.41 6.68 6.10 -0.58Miami-Fort Lauderdale, FL FL Broward Co 8.52 7.88 7.58 -0.30 7.93 7.55 -0.38FL Citrus Co 9.73 8.82 8.33 -0.49 8.79 7.90 -0.89Jacksonville, FL FL Duval Co 10.82 9.91 9.44 -0.47 9.93 9.23 -0.70Pensacola, FL FL Escambia Co 12.21 11.96 11.30 -0.66 12.13 11.05 -1.08Tampa-St. Petersburg, FL FL HillsboroughCo 11.64 9.92 9.50 -0.42 9.86 9.19 -0.67Fort Myers, FL FL Lee Co 8.94 7.33 6.95 -0.38 7.33 6.83 -0.50Tallahassee, FL FL Leon Co 12.93 12.31 11.78 -0.53 12.32 11.52 -0.80Sarasota, FL FL Manatee Co 9.96 8.14 7.71 -0.43 8.13 7.47 -0.66Ocala, FL FL Marion Co 10.37 9.38 8.96 -0.42 9.40 8.62 -0.78Miami-Fort Lauderdale, FL FL Miami-Dade Co 9.82 8.99 8.65 -0.34 8.93 8.51 -0.42Orlando, FL FL Orange Co 10.73 9.23 8.80 -0.43 9.16 8.43 -0.73West Palm Beach-Boca Raton, FL FL Palm Beach Co 7.69 6.60 6.25 -0.35 6.60 6.16 -0.44Tampa-St. Petersburg, FL FL PinellasCo 11.14 9.22 8.80 -0.42 9.22 8.58 -0.64Lakeland-Winter Haven, FL FL Polk Co 10.91 9.42 9.03 -0.39 9.42 8.82 -0.60Fort Pierce, FL FL St. Lucie Co 9.00 7.66 7.22 -0.44 7.70 7.12 -0.58Sarasota, FL FL Sarasota Co 9.86 8.13 7.70 -0.43 8.09 7.46 -0.63Orlando, FL FL Seminole Co 9.78 8.34 7.91 -0.43 8.27 7.56 -0.71CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015Daytona Beach, FL FL Volusia Co 9.81 8.58 8.07 -0.51 8.51 7.70 -0.81Macon, GA GA Bibb Co 16.42 16.27 15.17 -1.10 16.41 14.83 -1.58Savannah, GA GA Chatham Co 14.99 14.86 14.02 -0.84 15.06 13.86 -1.20Athens, GA GA Clarke Co 17.07 16.39 14.96 -1.43 16.15 14.10 -2.05Atlanta, GA GA Clayton Co 17.51 17.39 16.29 -1.10 17.46 15.85 -1.61Atlanta, GA GA Cobb Co 17.12 16.57 15.35 -1.22 16.51 14.67 -1.84Atlanta, GA GA DeKalb Co 16.86 16.75 15.70 -1.05 16.82 15.29 -1.53Albany, GA GA Dougherty Co 15.10 14.72 13.85 -0.87 14.71 13.43 -1.28GA Floyd Co 16.78 16.87 15.87 -1.00 17.33 15.79 -1.54Atlanta, GA GA Fulton Co 19.5118.02 16.98 16.98 18.00 16.47 16.47GA Glynn Co 12.01 11.61 10.99 -0.62 11.66 10.79 -0.87Atlanta, GA GA Gwinnett Co 16.34 14.99 14.02 -0.97 14.97 13.53 -1.44GA Hall Co 16.08 15.60 14.28 -1.32 15.36 13.48 -1.88Macon, GA GA Houston Co 12.85 12.57 11.59 -0.98 12.56 11.14 -1.42GA Lowndes Co 12.04 11.55 10.98 -0.57 11.57 10.74 -0.83Columbus, GA-AL GA Muscogee Co 16.19 15.65 14.57 -1.08 15.58 14.06 -1.52Atlanta, GA GA Paulding Co 15.34 14.86 13.47 -1.39 14.79 12.72 -2.07Augusta-Aiken, GA-SC GA Richmond Co 15.86 15.68 14.64 -1.04 15.76 14.23 -1.53Chattanooga, TN-GA GA Walker Co 15.73 15.43 14.22 -1.21 15.37 13.65 -1.72GA Washington Co 15.44 15.31 14.22 -1.09 15.34 13.67 -1.67GA Wilkinson Co 16.26 16.27 15.22 -1.05 16.54 15.01 -1.53IL Adams Co 13.04 12.41 11.90 -0.51 12.36 11.74 -0.62Champaign-Urbana, IL IL Champaign Co 12.93 12.60 11.64 -0.96 12.52 11.45 -1.07Chicago, IL-IN IL Cook Co 18.00 17.52 16.88 -0.64 17.71 16.95 -0.76Chicago, IL-IN IL DuPage Co 15.0114.42 13.81 -0.61 14.58 13.88 -0.70Chicago, IL-IN IL Kane Co 14.40 13.88 13.31 -0.57 13.84 13.16 -0.68Chicago, IL-IN IL Lake Co 12.9812.47 12.02 -0.45 12.43 11.90 -0.53Chicago, IL-IN IL McHenry Co 13.1412.66 12.09 -0.57 12.60 11.93 -0.67Bloomington, IL IL McLean Co 13.87 13.44 12.48 -0.96 13.42 12.32 -1.10Decatur, IL IL Macon Co 14.22 13.85 12.92 -0.93 13.85 12.81 -1.04CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015St. Louis, MO-IL IL Madison Co 17.40 16.66 15.96 -0.70 16.90 16.07 -0.83Peoria, IL IL Peoria Co 14.33 13.88 12.92 -0.96 13.99 12.88 -1.11IL Randolph Co 13.06 12.54 11.77 -0.77 12.48 11.58 -0.90Davenport, IA-IL IL Rock Island Co 12.44 11.73 11.20 -0.53 11.66 11.04 -0.62St. Louis, MO-IL IL St. Clair Co 16.87 16.24 15.54 -0.70 16.49 15.64 -0.85Springfield, IL IL Sangamon Co 13.60 13.14 12.30 -0.84 13.11 12.13 -0.98Chicago, IL-IN IL Will Co 15.35 15.03 14.30 -0.73 15.12 14.27 -0.85Fort Wayne, IN IN Allen Co 14.52 14.32 13.31 -1.01 14.21 13.07 -1.14Louisville, KY-IN IN Clark Co 16.90 16.51 15.15 -1.36 16.37 14.79 -1.58Muncie, IN IN Delaware Co 14.7114.45 13.21 -1.24 14.25 12.89 -1.36IN Dubois Co 16.02 15.73 14.37 -1.36 15.66 14.16 -1.50Elkhart, IN IN Elkhart Co 15.31 14.75 13.93 -0.82 14.73 13.80 -0.93Louisville, KY-IN IN Floyd Co 15.35 14.83 13.54 -1.29 14.75 13.27 -1.48IN Henry Co 13.55 13.28 12.06 -1.22 13.09 11.75 -1.34Kokomo, IN IN Howard Co 14.88 14.56 13.44 -1.12 14.39 13.14 -1.25IN Knox Co 13.83 13.55 12.10 -1.45 13.37 11.83 -1.54Chicago, IL-IN IN Lake Co 17.7617.26 16.48 -0.78 17.27 16.36 -0.91IN La Porte Co 13.52 13.17 12.47 -0.70 13.20 12.39 -0.81Indianapolis, IN IN Madison Co 14.82 14.51 13.28 -1.23 14.32 12.96 -1.36Indianapolis, IN IN Marion Co 16.88 16.83 15.54 -1.29 16.77 15.38 -1.39Chicago, IL-IN IN Porter Co 14.00 13.86 13.31 -0.55 14.38 13.73 -0.65South Bend, IN IN St. Joseph Co 14.35 13.91 13.17 -0.74 13.86 13.00 -0.86IN Spencer Co 14.43 13.96 12.63 -1.33 13.84 12.28 -1.56Evansville-Henderson, IN-KY IN Vanderburgh Co 15.60 15.54 14.26 -1.28 15.56 14.17 -1.39Terre Haute, IN IN Vigo Co 14.8814.61 13.26 -1.35 14.53 13.01 -1.52Cedar Falls, IA IA Black Hawk Co 11.48 10.75 10.39 -0.36 10.70 10.25 -0.45IA Cerro Gordo Co 10.55 9.74 9.45 -0.29 9.63 9.29 -0.34IA Clinton Co 12.26 11.63 11.08 -0.55 11.54 10.89 -0.65IA Emmet Co 8.82 8.14 7.90 -0.24 8.00 7.73 -0.27Iowa City, IA IA Johnson Co 11.52 10.86 10.45 -0.41 10.78 10.28 -0.50CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015Cedar Rapids, IA IA Linn Co 11.23 10.73 10.34 -0.39 10.70 10.22 -0.48IA Muscatine Co 13.03 12.40 11.88 -0.52 12.44 11.81 -0.63Des Moines, IA IA Polk Co 10.68 9.96 9.66 -0.30 9.88 9.52 -0.36Omaha, NE-IA IA Pottawattamie Co 10.48 9.83 9.61 -0.22 9.79 9.54 -0.25Davenport, IA-IL IA Scott Co 12.7612.00 11.49 -0.51 11.93 11.31 -0.62IA Van Buren Co 10.45 9.89 9.45 -0.44 9.81 9.27 -0.54Sioux City, IA-NE IA Woodbury Co 10.08 9.38 9.17 -0.21 9.29 9.07 -0.22Kansas City, MO-KS KS Johnson Co 11.95 11.17 10.85 -0.32 11.09 10.71 -0.38KS Linn Co 10.92 10.26 9.93 -0.33 10.20 9.78 -0.42Wichita, KS KS Sedgwick Co 11.39 10.53 10.27 -0.26 10.49 10.20 -0.29Topeka, KS KS Shawnee Co 11.03 10.47 10.22 -0.25 10.44 10.14 -0.30KS Sumner Co 10.31 9.55 9.29 -0.26 9.51 9.22 -0.29Kansas City, MO-KS KS Wyandotte Co 13.69 12.90 12.59 -0.31 12.98 12.60 -0.38KY Bell Co 15.26 14.67 13.20 13.20 14.39 12.54 12.54Huntington-Ashland, WV-KY-OH KY Boyd Co 15.37 15.23 13.38 -1.85 15.06 12.95 -2.11Louisville, KY-IN KY Bullitt Co 15.61 15.10 13.67 -1.43 14.83 13.13 -1.70Cincinnati, OH-KY KY Campbell Co 15.09 14.89 13.35 -1.54 14.68 12.89 -1.79Huntington-Ashland, WV-KY-OH KY Carter Co 12.71 12.54 10.73 -1.81 12.21 10.17 -2.04Clarksville, TN-KY KY Christian Co 14.10 13.63 12.39 -1.24 13.50 12.11 -1.39Owensboro, KY KY Daviess Co 14.94 14.15 12.88 -1.27 14.05 12.65 -1.40Lexington, KY KY Fayette Co 16.36 15.95 14.17 -1.78 15.62 13.54 -2.08KY Franklin Co 14.19 13.86 12.27 -1.59 13.56 11.72 -1.84KY Hardin Co 14.59 14.10 12.68 -1.42 13.85 12.14 -1.71Louisville, KY-IN KY Jefferson Co 17.07 16.71 15.44 -1.27 16.61 15.13 -1.48Cincinnati, OH-KY KY Kenton Co 15.50 15.30 13.72 -1.58 15.09 13.26 -1.83KY McCracken Co 14.22 13.91 12.81 -1.10 13.88 12.66 -1.22Lexington, KY KY Madison Co 14.26 13.87 12.20 -1.67 13.54 11.55 -1.99KY Perry Co 13.54 13.12 11.51 -1.61 12.80 10.93 -1.87KY Pike Co 14.86 14.41 12.59 -1.82 14.05 11.95 -2.10KY Warren Co 14.55 14.07 12.72 -1.35 13.88 12.30 -1.58CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015Shreveport, LA LA Caddo Parish 13.1312.58 12.06 -0.52 12.74 12.04 -0.70Lake Charles, LA LA Calcasieu Parish 12.02 11.68 11.04 -0.64 11.82 11.01 -0.81Baton Rouge, LA LAEast Baton RougeParish 13.71 13.78 13.30 -0.48 14.27 13.66 -0.61LA Iberville Parish 13.08 12.93 12.47 -0.46 13.34 12.76 -0.58New Orleans, LA LA Jefferson Parish12.82 12.50 11.99 -0.51 12.79 12.07 -0.72Lafayette, LA LA Lafayette Parish 11.60 11.38 10.78 -0.60 11.55 10.81 -0.74New Orleans, LA LA Orleans Parish 13.05 12.74 12.20 -0.54 13.03 12.29 -0.74Monroe, LA LA Ouachita Parish 12.15 11.95 11.41 -0.54 12.28 11.59 -0.69New Orleans, LA LA St. Bernard Parish 10.88 10.41 9.79 -0.62 10.50 9.63 -0.87LA Tangipahoa Parish 12.15 11.68 11.04 -0.64 11.84 11.00 -0.84Houma, LA LA Terrebonne Parish 10.6110.40 9.83 -0.57 10.58 9.82 -0.76Baton Rouge, LA LAWest Baton RougeParish 13.29 13.37 12.89 -0.48 13.84 13.23 -0.61Lewiston, ME ME Androscoggin Co 10.60 10.16 9.75 -0.41 10.04 9.58 -0.46ME Aroostook Co 11.16 11.41 11.11 -0.30 11.38 11.04 -0.34Portland, ME ME Cumberland Co 11.44 10.66 10.23 -0.43 10.50 10.02 -0.48ME Hancock Co 6.20 5.98 5.65 -0.33 5.95 5.58 -0.37ME Kennebec Co 10.55 10.21 9.81 -0.40 10.12 9.67 -0.45ME Oxford Co 10.29 10.23 9.74 -0.49 10.15 9.60 -0.55Bangor, ME ME Penobscot Co 9.87 9.81 9.46 -0.35 9.73 9.34 -0.39ME York Co 9.63 9.07 8.64 -0.43 8.97 8.50 -0.47Washington-Baltimore, DC-MD MD Anne Arundel Co 15.59 15.26 12.98 -2.28 15.02 12.38 -2.64Washington-Baltimore, DC-MD MD BaltimoreCo 15.20 15.02 13.09 -1.93 15.08 12.75 -2.33Washington-Baltimore, DC-MD MD HarfordCo 13.54 13.40 11.62 -1.78 13.46 11.30 -2.16Washington-Baltimore, DC-MD MD MontgomeryCo 13.01 12.82 10.84 -1.98 12.59 10.34 -2.25Washington-Baltimore, DC-MD MD WashingtonCo 14.40 13.99 11.69 -2.30 13.63 11.03 -2.60Washington-Baltimore, DC-MD MD Baltimorecity 17.18 16.96 14.88 -2.08 17.04 14.50 -2.54Pittsfield, MA MA Berkshire Co 12.40 11.68 10.93 -0.75 11.49 10.66 -0.83Springfield, MA MA Hampden Co 13.80 12.81 12.10 -0.71 12.59 11.79 -0.80CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015Boston, MA-NH MA Plymouth Co 11.34 10.65 10.02 -0.63 10.50 9.78 -0.72Boston, MA-NH MA Suffolk Co 12.81 12.05 11.48 -0.57 11.80 11.15 -0.65Grand Rapids, MI MI Allegan Co 12.36 12.02 11.41 -0.61 11.93 11.21 -0.72Saginaw, MI MI Bay Co 11.22 11.04 10.42 -0.62 11.01 10.30 -0.71Benton Harbor, MI MI Berrien Co 12.60 12.26 11.62 -0.64 12.18 11.42 -0.76MI Chippewa Co 8.28 8.06 7.83 -0.23 7.99 7.73 -0.26Detroit-Ann Arbor-Flint, MI MI GeneseeCo 12.70 12.34 11.55 -0.79 12.19 11.30 -0.89Lansing, MI MI Ingham Co 13.35 12.97 12.19 -0.78 12.80 11.91 -0.89Kalamazoo-Battle Creek, MI MI Kalamazoo Co 14.92 14.45 13.66 -0.79 14.31 13.39 -0.92Grand Rapids, MI MI Kent Co 13.91 13.46 12.85 -0.61 13.33 12.60 -0.73Detroit, MI MI Macomb Co 13.31 13.17 12.23 -0.94 13.05 11.99 -1.06Detroit, MI MI Monroe Co 15.33 15.00 14.03 -0.97 14.87 13.76 -1.11Grand Rapids, MI MI Muskegon Co 12.23 11.88 11.39 -0.49 11.81 11.19 -0.62Detroit, MI MI Oakland Co 14.85 14.73 13.70 -1.03 14.63 13.48 -1.15Grand Rapids, MI MI Ottawa Co 13.41 12.97 12.41 -0.56 12.86 12.17 -0.69Saginaw, MI MI Saginaw Co 10.80 10.68 10.07 -0.61 10.66 9.97 -0.69Detroit, MI MI St. Clair Co 13.92 13.82 12.91 -0.91 13.72 12.67 -1.05Detroit, MI MI Washtenaw Co 14.57 14.30 13.28 -1.02 14.13 12.99 -1.14Detroit, MI MI Wayne Co 19.62 19.41 18.23 -1.18 19.28 17.95 -1.33Minneapolis-St Paul, MN MN DakotaCo 10.32 9.44 9.17 9.17 9.31 8.99 8.99Minneapolis-St Paul, MN MN Hennepin Co 10.78 9.70 9.46 -0.24 9.60 9.34 -0.26MN Mille Lacs Co 7.40 6.89 6.71 -0.18 6.81 6.62 -0.19Rochester, MN MN Olmsted Co 11.17 10.25 9.92 -0.33 10.05 9.66 -0.39Minneapolis-St Paul, MN MN Ramsey Co 12.24 10.98 10.73 -0.25 10.86 10.58 -0.28Duluth, MN-WI MN St. Louis Co 8.41 7.99 7.85 -0.14 8.06 7.90 -0.16Minneapolis-St Paul, MN MN Scott Co 10.42 9.53 9.29 -0.24 9.38 9.11 -0.27St. Cloud, MN MN Stearns Co 9.65 8.88 8.67 -0.21 8.77 8.53 -0.24MS Adams Co 11.35 11.08 10.48 -0.60 11.31 10.57 -0.74CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015MS Bolivar Co 12.81 11.84 11.15 -0.69 11.87 11.04 -0.83Memphis, TN MS DeSoto Co 13.18 12.65 11.84 -0.81 12.76 11.78 -0.98Hattiesburg, MS MS Forrest Co 13.54 13.02 12.34 -0.68 13.07 12.09 -0.98Biloxi-Gulfport, MS MS Hancock Co 10.97 10.55 9.92 -0.63 10.63 9.73 -0.90Biloxi-Gulfport, MS MS Harrison Co 11.56 11.11 10.55 -0.56 11.16 10.19 -0.97Jackson, MS MS Hinds Co 14.06 13.42 12.73 -0.69 13.55 12.67 -0.88Biloxi-Gulfport, MS MS Jackson Co 12.56 12.19 11.58 -0.61 12.32 11.36 -0.96MS Jones Co 15.29 15.01 14.21 -0.80 15.18 14.06 -1.12MS Lauderdale Co 13.34 12.82 12.00 -0.82 12.77 11.66 -1.11MS Lee Co 13.20 12.76 11.80 -0.96 12.72 11.54 -1.18MS Lowndes Co 13.68 13.20 12.31 -0.89 13.23 12.10 -1.13MS Pearl River Co 11.68 11.26 10.68 -0.58 11.33 10.50 -0.83Jackson, MS MS Rankin Co 13.35 12.75 12.12 -0.63 12.88 12.08 -0.80MS Scott Co 11.88 11.32 10.61 -0.71 11.33 10.40 -0.93MS Warren Co 12.50 11.02 10.42 -0.60 11.18 10.43 -0.75St. Joseph, MO MO Buchanan Co 12.54 11.68 11.37 -0.31 11.60 11.23 -0.37Kansas City, MO-KS MO Cass Co 11.39 10.66 10.35 -0.31 10.59 10.21 -0.38MO Cedar Co 11.61 10.88 10.49 -0.39 10.82 10.32 -0.50Kansas City, MO-KS MO Clay Co 12.88 12.15 11.85 -0.30 12.22 11.85 -0.37Springfield, MO MO Greene Co 12.27 11.56 11.14 -0.42 11.53 11.02 -0.51Kansas City, MO-KS MO Jackson Co 12.27 11.44 11.10 -0.34 11.38 10.96 -0.42Joplin, MO MO Jasper Co 13.85 12.65 12.26 -0.39 12.66 12.18 -0.48St. Louis, MO-IL MO Jefferson Co 14.79 13.97 13.32 -0.65 13.90 13.11 -0.79MO Monroe Co 11.16 10.60 10.09 -0.51 10.53 9.93 -0.60St. Louis, MO-IL MO St. Charles Co 14.52 14.23 13.54 -0.69 14.27 13.45 -0.82MO Ste. Genevieve Co 13.98 13.42 12.68 -0.74 13.45 12.59 -0.86St. Louis, MO-IL MO St. Louis Co 14.46 13.61 12.92 -0.69 13.56 12.74 -0.82St. Louis, MO-IL MO St. Louis City 15.62 15.10 14.40 -0.70 15.34 14.50 -0.84CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015Omaha, NE NE Cass Co 10.38 9.67 9.46 -0.21 9.59 9.34 -0.25Omaha, NE NE Douglas Co 10.77 10.10 9.88 -0.22 10.06 9.80 -0.26NE Hall Co 8.55 7.97 7.81 -0.16 7.90 7.73 -0.17Lincoln, NE NE Lancaster Co 10.02 9.40 9.19 -0.21 9.34 9.11 -0.23Omaha, NE NE Sarpy Co 10.32 9.68 9.47 -0.21 9.64 9.40 -0.24Omaha, NE NE Washington Co 9.90 9.29 9.09 -0.20 9.21 8.99 -0.22NH Cheshire Co 11.81 11.04 10.39 -0.65 10.85 10.11 -0.74NH Coos Co 10.11 9.98 9.49 -0.49 9.93 9.37 -0.56NH Merrimack Co 9.96 9.22 8.67 -0.55 9.11 8.49 -0.62NH Sullivan Co 9.95 9.40 8.75 -0.65 9.26 8.53 -0.73New York-New Jersey,NY-NJ-CT-PA NJ Bergen Co 14.09 13.31 12.05 -1.26 13.06 11.57 -1.49Philadelphia, PA-DE-NJ NJ Camden Co 14.54 14.11 12.48 -1.63 13.92 12.02 -1.90Philadelphia, PA-DE-NJ NJ GloucesterCo 13.99 14.15 12.52 -1.63 14.07 12.13 -1.94New York City, NY-NJ-CT NJ Hudson Co 15.39 14.52 13.14 -1.38 14.22 12.64 -1.58New York City, NY-NJ-CT NJ Mercer Co 14.27 13.74 12.12 -1.62 13.47 11.66 -1.81New York City, NY-NJ-CT NJ Middlesex Co 12.67 12.14 10.76 -1.38 11.91 10.32 -1.59New York City, NY-NJ-CT NJ Morris Co 12.68 12.27 10.79 -1.48 12.06 10.38 -1.68New York City, NY-NJ-CT NJ Union Co 15.94 15.05 13.60 -1.45 14.73 13.08 -1.65New York City, NY-NJ-CT NJ Warren Co 13.55 13.26 11.71 -1.55 13.10 11.31 -1.79New York City, NY-NJ-CT NY Bronx Co 15.99 14.79 13.62 -1.17 14.41 13.06 -1.35Jamestown, NY NY Chautauqua Co 10.97 10.74 9.40 -1.34 10.54 9.05 -1.49Buffalo, NY NY Erie Co 14.35 13.94 12.39 -1.55 13.69 11.98 -1.71NY Essex Co 6.49 6.33 5.79 -0.54 6.25 5.64 -0.61New York City, NY-NJ-CT NY Kings Co 14.90 14.02 12.69 -1.33 13.79 12.25 -1.54Rochester, NY NY Monroe Co 11.52 11.00 9.94 -1.06 10.84 9.66 -1.18New York City, NY-NJ-CT NY Nassau Co 12.36 11.70 10.52 -1.18 11.52 10.16 -1.36New York City, NY-NJ-CT NY New York Co 17.56 16.19 14.95 -1.24 15.76 14.33 -1.43CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015Buffalo, NY NY Niagara Co 12.25 11.99 10.65 10.65 11.82 10.36 10.36Syracuse, NY NY Onondaga Co 10.68 10.28 9.22 -1.06 10.06 8.86 -1.20New York City, NY-NJ-CT NY Orange Co 11.64 11.02 10.02 -1.00 10.79 9.56 -1.23New York City, NY-NJ-CT NY Queens Co 13.56 12.69 11.56 -1.13 12.41 11.11 -1.30New York City, NY-NJ-CT NY Richmond Co 12.35 11.67 10.53 -1.14 11.43 10.12 -1.31NY St. Lawrence Co 8.62 8.44 7.82 -0.62 8.36 7.66 -0.70NY Steuben Co 9.95 9.72 8.40 -1.32 9.55 8.09 -1.46New York City, NY-NJ-CT NY Suffolk Co 12.41 11.51 10.32 -1.19 11.23 9.85 -1.38New York City, NY-NJ-CT NY WestchesterCo 12.55 11.77 10.68 -1.09 11.51 10.26 -1.25Greensboro, NC NC Alamance Co 14.47 13.98 12.62 -1.36 13.65 12.02 -1.63Asheville, NC NC Buncombe Co 14.09 13.45 12.25 -1.20 13.14 11.58 -1.56Charlotte, NC-SC NC Cabarrus Co 15.09 14.38 13.11 -1.27 13.97 12.34 -1.63NC Caswell Co 13.90 13.43 12.09 -1.34 13.11 11.50 -1.61Hickory, NC NC Catawba Co 16.32 15.48 14.07 -1.41 15.19 13.45 -1.74Raleigh-Durham, NC NC Chatham Co 12.81 12.26 11.01 -1.25 11.92 10.40 -1.52Fayetteville, NC NC Cumberland Co 14.69 14.09 12.89 -1.20 13.82 12.28 -1.54Greensboro--Winston-Salem, NC NC Davidson Co 16.60 15.76 14.36 -1.40 15.34 13.61 -1.73NC Duplin Co 12.37 11.97 10.82 -1.15 11.75 10.28 -1.47Raleigh-Durham, NC NC Durham Co 14.65 13.98 12.60 -1.38 13.55 11.91 -1.64Greensboro, NC NC Forsyth Co 15.46 14.72 13.16 -1.56 14.28 12.40 -1.88Charlotte, NC-SC NC Gaston Co 14.65 14.00 12.71 -1.29 13.59 11.92 -1.67Greensboro, NC NC Guilford Co 14.04 13.54 12.21 -1.33 13.16 11.57 -1.59NC Haywood Co 14.53 13.93 12.70 -1.23 13.68 12.03 -1.65NC Jackson Co 12.95 12.41 11.24 -1.17 12.18 10.60 -1.58NC Lenoir Co 11.91 11.46 10.25 -1.21 11.21 9.67 -1.54NC McDowell Co 15.32 14.52 13.22 -1.30 14.15 12.49 -1.66Charlotte, NC-SC NC Mecklenburg Co 15.83 15.22 13.92 -1.30 14.83 13.14 -1.69NC Mitchell Co 14.54 13.69 12.42 -1.27 13.36 11.78 -1.58CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015NC Montgomery Co 12.57 12.01 10.80 -1.21 11.66 10.15 -1.51Jacksonville, NC NC Onslow Co 11.60 11.19 10.07 -1.12 10.93 9.53 -1.40Raleigh-Durham, NC NC Orange Co 13.67 13.08 11.75 -1.33 12.72 11.10 -1.62Greenville, NC NC Pitt Co 12.56 12.11 10.86 -1.25 11.87 10.30 -1.57NC Robeson Co 12.75 12.26 11.11 -1.15 11.99 10.50 -1.49NC Swain Co 13.47 13.03 11.68 -1.35 12.78 10.97 -1.81Raleigh-Durham, NC NC Wake Co 14.57 14.01 12.66 -1.35 13.69 12.03 -1.66Goldsboro, NC NC Wayne Co 14.51 13.66 12.34 -1.32 13.40 11.68 -1.72Fargo, ND-MN ND Cass Co 8.11 7.51 7.41 -0.10 7.38 7.29 -0.09OH Athens Co 12.47 12.33 10.31 -2.02 12.04 9.81 -2.23Cincinnati, OH-KY-IN OH Butler Co 16.78 16.45 15.03 -1.42 16.32 14.67 -1.65Dayton-Springfield, OH OH Clark Co 14.67 14.55 13.10 -1.45 14.30 12.64 -1.66Cleveland, OH OH Cuyahoga Co 19.25 18.84 17.11 -1.73 18.60 16.67 -1.93Columbus, OH OH Franklin Co 17.23 16.98 15.13 -1.85 16.64 14.57 -2.07Cincinnati, OH-KY OH Hamilton Co 18.55 18.23 16.61 -1.62 18.03 16.10 -1.93Steubenville-Weirton, OH-WV OH JeffersonCo 18.30 17.94 15.64 -2.30 17.83 15.26 -2.57Cleveland, OH OH Lake Co 13.72 13.62 12.15 -1.47 13.45 11.80 -1.65Huntington-Ashland, WV-KY-OH OH Lawrence Co 16.26 16.10 14.11 -1.99 15.92 13.71 -2.21Cleveland, OH OH Lorain Co 13.88 13.60 12.33 -1.27 13.46 12.04 -1.42Toledo, OH OH Lucas Co 15.07 14.77 13.76 -1.01 14.64 13.48 -1.16Youngstown-Warren, OH OH Mahoning Co 15.77 15.39 13.40 -1.99 15.13 12.94 -2.19Dayton, OH OH Montgomery Co 15.57 15.41 13.83 -1.58 15.16 13.33 -1.83Cleveland, OH OH Portage Co 14.86 14.40 12.64 -1.76 14.11 12.17 -1.94OH Preble Co 13.51 13.34 11.89 -1.45 13.11 11.45 -1.66OH Scioto Co 18.26 18.13 15.98 -2.15 17.92 15.55 -2.37Canton, OH OH Stark Co 17.85 17.14 15.08 -2.06 16.86 14.58 -2.28Cleveland, OH OH Summit Co 16.95 16.47 14.69 -1.78 16.14 14.18 -1.96Youngstown-Warren, OH OH Trumbull Co15.60 15.28 13.50 -1.78 15.05 13.08 -1.97CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015OK Caddo Co 8.66 8.32 8.06 -0.26 8.32 7.96 -0.36Oklahoma City, OK OK Canadian Co 8.99 8.34 8.07 -0.27 8.28 7.93 -0.35OK Carter Co 10.21 9.60 9.29 -0.31 9.60 9.18 -0.42OK Cherokee Co 11.72 11.04 10.62 -0.42 11.04 10.50 -0.54Enid, OK OK Garfield Co 10.03 9.60 9.37 -0.23 9.64 9.34 -0.30OK Kay Co 10.71 10.01 9.74 -0.27 9.94 9.60 -0.34OK Lincoln Co 10.08 9.44 9.11 -0.33 9.41 8.97 -0.44OK Mayes Co 12.01 11.99 11.63 -0.36 12.07 11.60 -0.47OK Muskogee Co 12.17 11.61 11.22 11.22 11.63 11.13 11.13Oklahoma City, OK OK Oklahoma Co 10.61 10.04 9.76 -0.28 10.02 9.65 -0.37OK Ottawa Co 11.78 11.12 10.75 -0.37 11.08 10.62 -0.46OK Pittsburg Co 11.52 10.86 10.50 -0.36 10.87 10.38 -0.49OK Seminole Co 9.47 8.87 8.55 -0.32 8.86 8.43 -0.43Tulsa, OK OK Tulsa Co 12.03 11.53 11.21 -0.32 11.58 11.17 -0.41PA Adams Co 13.39 13.05 10.77 -2.28 12.77 10.21 -2.56Pittsburgh, PA PA Allegheny Co 21.2020.55 18.01 -2.54 20.33 17.47 -2.86Pittsburgh, PA PA Beaver Co 15.97 15.78 13.61 -2.17 15.54 13.09 -2.45Reading, PA PA Berks Co 16.24 15.89 13.56 -2.33 15.66 12.99 -2.67Philadelphia, PA-DE-NJ PA Bucks Co 14.07 13.65 12.07 -1.58 13.46 11.62 -1.84Johnstown, PA PA Cambria Co 15.62 15.14 12.72 -2.42 14.80 12.08 -2.72State College, PA PA Centre Co 13.01 12.67 10.77 -1.90 12.43 10.26 -2.17Harrisburg, PA PA Dauphin Co 15.6515.17 12.88 -2.29 14.87 12.27 -2.60Philadelphia, PA-DE-NJ PA DelawareCo 15.45 15.61 13.94 -1.67 15.52 13.52 -2.00Erie, PA PA Erie Co 13.43 13.08 11.73 -1.35 12.92 11.42 -1.50Scranton--Wilkes-Barre, PA PA LackawannaCo 12.29 11.91 10.31 -1.60 11.71 9.87 -1.84Lancaster, PA PA Lancaster Co 16.99 16.55 14.09 -2.46 16.28 13.33 -2.95Allentown, PA PA Lehigh Co 14.2113.77 12.01 -1.76 13.53 11.52 -2.01Scranton--Wilkes-Barre, PA PA LuzerneCo 12.95 12.62 10.86 -1.76 12.42 10.41 -2.01CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015Sharon, PA PA Mercer Co 14.28 14.01 12.27 -1.74 13.77 11.87 -1.90Philadelphia, PA-DE-NJ PA MontgomeryCo 14.10 13.80 12.02 -1.78 13.62 11.55 -2.07Allentown, PA PA Northampton Co 14.42 13.93 12.35 -1.58 13.70 11.89 -1.81Harrisburg, PA PA Perry Co 12.8312.46 10.54 -1.92 12.24 10.07 -2.17Philadelphia, PA-DE-NJ PA PhiladelphiaCo 16.54 16.65 14.98 -1.67 16.53 14.53 -2.00Pittsburgh, PA PA Washington Co 15.58 15.23 12.99 -2.24 15.01 12.54 -2.47Pittsburgh, PA PA Westmoreland Co 15.56 15.16 12.60 -2.56 14.75 11.89 -2.86York, PA PA York Co 16.86 16.49 14.20 -2.29 16.22 13.46 -2.76Providence, RI RI Kent Co 8.96 8.45 7.69 -0.76 8.33 7.45 -0.88Providence, RI RI Providence Co 11.45 10.78 9.99 -0.79 10.60 9.71 -0.89SC Beaufort Co 11.04 10.68 9.86 -0.82 10.70 9.52 -1.18Charleston, SC SC Charleston Co 11.94 11.63 10.79 -0.84 11.54 10.32 -1.22SC Chesterfield Co 12.42 11.93 10.83 -1.10 11.65 10.21 -1.44Augusta-Aiken, GA-SC SC Edgefield Co 12.83 12.54 11.54 -1.00 12.45 10.99 -1.46Florence, SC SC Florence Co 13.23 12.80 11.71 -1.09 12.58 11.10 -1.48SC Georgetown Co 13.26 12.69 11.74 -0.95 12.54 11.25 -1.29Greenville-Spartanburg, SC SC GreenvilleCo 15.43 14.80 13.36 -1.44 14.45 12.54 -1.91SC Greenwood Co 14.01 13.47 12.18 -1.29 13.18 11.40 -1.78Myrtle Beach, SC SC Horry Co 11.16 10.68 9.70 -0.98 10.50 9.20 -1.30Columbia, SC SC Lexington Co 14.57 14.08 12.95 -1.13 13.83 12.27 -1.56SC Oconee Co 11.47 11.13 9.86 -1.27 10.88 9.15 -1.73Columbia, SC SC Richland Co 14.50 14.01 12.88 -1.13 13.76 12.20 -1.56Greenville-Spartanburg, SC SC SpartanburgCo 14.51 13.99 12.58 -1.41 13.63 11.79 -1.84SD Brookings Co 9.37 8.64 8.45 -0.19 8.50 8.31 -0.19SD Brown Co 8.31 7.76 7.64 -0.12 7.63 7.53 -0.10Sioux Falls, SD SD Minnehaha Co 9.82 9.09 8.88 -0.21 8.96 8.73 -0.23Knoxville, TN TN Blount Co 14.42 13.63 12.48 -1.15 13.52 11.98 -1.54Nashville, TN TN Davidson Co 15.5615.36 14.26 -1.10 15.36 14.02 -1.34CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015TN Dyer Co 12.35 12.03 11.16 -0.87 12.03 11.03 -1.00Chattanooga, TN-GA TN Hamilton Co 17.23 16.89 15.57 -1.32 16.82 14.94 -1.88Knoxville, TN TN Knox Co 18.49 17.44 16.16 -1.28 17.34 15.61 -1.73TN Lawrence Co 12.65 12.41 11.19 -1.22 12.32 10.95 -1.37TN McMinn Co 15.34 14.92 13.64 -1.28 14.88 13.14 -1.74TN Maury Co 13.65 14.28 13.21 -1.07 14.88 13.62 -1.26Clarksville, TN-KY TN Montgomery Co 13.75 13.41 12.23 -1.18 13.34 12.02 -1.32TN Putnam Co 13.94 13.56 12.27 -1.29 13.37 11.62 -1.75TN Roane Co 15.56 14.97 13.58 -1.39 14.80 12.94 -1.86Memphis, TN TN Shelby Co 14.81 14.59 13.77 -0.82 15.17 14.19 -0.98Johnson City, TN-VA TN Sullivan Co 15.71 15.32 14.01 -1.31 15.37 13.77 -1.60Nashville, TN TN Sumner Co 14.47 14.18 13.05 -1.13 14.12 12.73 -1.39Texarkana, TX-AR TX Bowie Co 14.10 13.36 12.88 -0.48 13.48 12.86 -0.62Brownsville, TX TX Cameron Co 9.90 9.61 9.25 -0.36 9.64 9.15 -0.49Dallas, TX TX Dallas Co 13.82 13.03 12.68 -0.35 12.99 12.45 -0.54Houston-Galveston, TX TX GalvestonCo 9.64 9.23 8.67 -0.56 9.33 8.60 -0.73Longview, TX TX Gregg Co 12.49 11.81 11.36 11.36 11.91 11.26 11.26Houston, TX TX Harris Co 14.13 14.13 13.78 -0.35 14.46 13.90 -0.56McAllen, TX TX Hidalgo Co 10.8410.70 10.42 -0.28 10.89 10.50 -0.39Beaumont-Port Arthur, TX TX JeffersonCo 11.25 11.12 10.63 -0.49 11.40 10.77 -0.63Corpus Christi, TX TX Nueces Co 10.30 10.28 9.94 -0.34 10.49 9.98 -0.51Beaumont-Port Arthur, TX TX Orange Co 11.41 11.27 10.80 -0.47 11.56 10.95 -0.61Dallas, TX TX Tarrant Co 12.36 11.42 11.08 -0.34 11.42 10.93 -0.49Burlington, VT VT Chittenden Co 9.49 9.19 8.55 -0.64 9.08 8.35 -0.73Washington-Baltimore, DC-MD VA ArlingtonCo 14.64 14.30 12.31 -1.99 14.06 11.80 -2.26Richmond-Petersburg, VA VA Charles CityCo 13.32 12.77 11.24 -1.53 12.47 10.71 -1.76Richmond-Petersburg, VA VA ChesterfieldCo 13.91 13.35 11.81 -1.54 13.05 11.28 -1.77Washington-Baltimore, DC-MD VA FairfaxCo 14.33 13.88 11.75 -2.13 13.54 11.13 -2.41CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015Richmond-Petersburg, VA VA Henrico Co13.92 13.35 11.82 -1.53 13.05 11.29 -1.76Washington-Baltimore, DC-MD VA Loudoun Co 13.65 13.23 11.14 -2.09 12.90 10.55 -2.35VA Page Co 13.17 12.80 10.72 -2.08 12.44 10.10 -2.34Johnson City, TN-VA VA Bristol city15.21 14.53 13.03 -1.50 14.23 12.42 -1.81Norfolk, VA-NC VA Chesapeake city 12.99 12.58 11.09 -1.49 12.45 10.63 -1.82Norfolk, VA-NC VA Hampton city 12.95 12.67 11.16 -1.51 12.56 10.77 -1.79Norfolk, VA-NC VA Newport News city 12.30 11.87 10.39 -1.48 11.66 9.93 -1.73Norfolk, VA-NC VA Norfolk city 13.33 13.03 11.51 -1.52 12.93 11.12 -1.81Richmond, VA VA Richmond city 14.48 13.88 12.29 -1.59 13.57 11.75 -1.82Roanoke, VA VA Roanoke city 14.84 14.38 12.55 -1.83 13.94 11.86 -2.08Roanoke, VA VA Salem city 14.95 14.49 12.70 -1.79 14.07 12.02 -2.05Norfolk, VA-NC VA Virginia Beach city 12.88 12.59 11.14 -1.45 12.50 10.76 -1.74Washington-Baltimore, DC-MD WV BerkeleyCo 16.18 15.69 13.43 -2.26 15.32 12.73 -2.59Steubenville-Weirton, OH-WV WV Brooke Co16.96 16.63 14.42 -2.21 16.51 14.05 -2.46Huntington-Ashland, WV-KY-OH WV CabellCo 17.22 17.03 15.08 -1.95 16.86 14.64 -2.22Steubenville-Weirton, OH-WV WV HancockCo 17.40 17.06 14.89 -2.17 16.97 14.54 -2.43WV Harrison Co 14.40 14.15 11.90 -2.25 13.82 11.31 -2.51Charleston, WV WV Kanawha Co 17.75 17.56 15.27 -2.29 17.17 14.66 -2.51WV Marion Co 15.58 15.32 12.90 -2.42 14.98 12.23 -2.75Wheeling, WV-OH WV Marshall Co 16.07 15.81 13.46 -2.35 15.52 12.87 -2.65WV Mercer Co 12.97 12.52 10.82 -1.70 12.14 10.16 -1.98WV Monongalia Co 14.96 14.77 12.31 -2.46 14.37 11.40 -2.97Wheeling, WV-OH WV Ohio Co 15.37 15.14 12.81 -2.33 14.84 12.22 -2.62WV Raleigh Co 13.54 13.19 11.33 -1.86 12.80 10.66 -2.14WV Summers Co 10.46 10.21 8.63 -1.58 9.89 8.07 -1.82Parkersburg, WV-OH WV Wood Co 16.88 16.66 14.14 -2.52 16.69 13.88 -2.81Green Bay, WI WI Brown Co 11.52 10.99 10.67 -0.32 10.93 10.54 -0.39Madison, WI WI Dane Co 12.81 12.07 11.62 -0.45 11.86 11.33 -0.53CMSA/MSA State CountyAverage1999-2003 2010 Base 2010 CAIRImpact ofCAIR in 2010 2015 Base 2015 CAIRImpact ofCAIR in2015WI Dodge Co 11.41 10.79 10.39 -0.40 10.63 10.14 -0.49WI Grant Co 11.78 11.17 10.75 -0.42 11.11 10.60 -0.51Chicago, IL-IN-WI WI Kenosha Co 11.90 11.44 10.97 -0.47 11.40 10.85 -0.55WI Manitowoc Co 10.09 9.55 9.21 -0.34 9.41 9.00 -0.41Milwaukee, WI WI Milwaukee Co 13.74 13.35 12.88 -0.47 13.33 12.76 -0.57Appleton, WI WI Outagamie Co 11.04 10.43 10.10 -0.33 10.28 9.88 -0.40WI Vilas Co 6.26 5.94 5.75 -0.19 5.88 5.65 -0.23Milwaukee, WI WI Waukesha Co 13.55 12.95 12.51 -0.44 12.82 12.28 -0.54

# Technical Support Document

for the Final

Clean Air Interstate Rule

Air Quality Modeling Analyses

Appendix G

8-Hour Contributions to Each Nonattainment County in 2010Fairfield CTUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbMax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbMax 8-hr ppbcontributionPA24 20 20 65 100 25 64 66 10 100 19.3NJ21 18 18 65 100 23 42 37 10 100 14.1NY19 17 19 65 100 23 -11 -19 4 40 8.4VA5 6 7 62 95 6 8 9 4 40 6.4OH9 8 8 65 100 10 16 15 9 90 6.3MD4 7 8 56 86 7 8 8 3 30 4.1WV3 3 3 62 95 3 9 9 5 50 2.5IN2 2 2 30 46 3 3 3 0 0 1.1NC2 2 2 26 40 2 3 3 0 0 1.1DE2 2 2 11 17 3 2 2 0 0 0.8IL2 2 2 13 20 2 2 2 0 0 0.8MI1 1 2 0 0 2 1 1 0 0 0.8KY2 2 2 18 28 3 2 2 0 0 0.7WI1 1 1 0 0 1 1 1 0 0 0.4MO1 1 1 0 0 1 1 1 0 0 0.3IA1 1 1 0 0 1 1 1 0 0 0.2MN0 0 0 0 0 0 0 0 0 0 0.1SC0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0AR0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0TN0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions exceedscreening criteriaContributions do notexceed screening criteriaCAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grids-days) = 10CAMX Source Apportionment ModelingBase Case: Total Number of Exceedances (grid-hours) = 65Middlesex CTUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionNY26 21 24 153 100 31 48 48 22 100 18.6PA20 20 22 153 100 25 47 47 21 96 14.2NJ18 16 17 153 100 22 37 38 22 100 10.0OH6 8 7 91 60 10 11 15 10 46 6.7MA1 8 9 5 3 8 1 1 1 5 6.0VA4 4 4 89 58 7 5 5 4 18 3.4WV2 3 3 83 54 3 5 6 1 5 2.2MD4 5 5 116 76 7 6 6 1 5 2.1MI1 2 2 22 14 3 2 2 0 0 1.6IN2 2 2 44 29 3 3 4 0 0 1.3NC2 2 3 45 29 3 2 2 0 0 1.1DE2 3 3 37 24 4 2 2 0 0 1.0IL2 2 2 28 18 3 2 3 0 0 0.9KY1 2 2 14 9 2 1 2 0 0 0.7NH0 1 1 0 0 1 0 0 0 0 0.5RI0 2 3 2 1 2 0 0 0 0 0.5MO1 1 1 0 0 1 1 1 0 0 0.3VT0 0 0 0 0 0 0 0 0 0 0.3IA1 1 1 0 0 1 0 1 0 0 0.2TN0 0 0 0 0 0 0 0 0 0 0.2WI1 1 1 0 0 1 1 1 0 0 0.2AR0 0 0 0 0 0 0 0 0 0 0.1ME0 0 1 0 0 1 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MN0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions do notexceed screening criteriaContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 153 Base Case: Total Number of Exceedances (grids-days) = 22New Haven CTUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionPA21 21 23 117 100 24 51 50 23 100 16.5NY25 21 24 117 100 29 32 27 20 87 12.8NJ19 15 17 117 100 23 39 38 23 100 11.5OH7 8 7 96 82 10 15 14 9 39 6.6MD4 8 9 95 81 9 12 11 6 26 5.6VA4 5 6 87 74 6 11 10 10 44 5.6WV2 3 3 71 61 3 7 7 4 17 2.4MI1 2 3 16 14 3 4 4 0 0 1.6IN2 2 2 35 30 3 4 4 0 0 1.3NC2 2 2 38 33 3 3 3 0 0 1.0IL2 2 2 15 13 3 3 3 0 0 0.9DE2 3 3 22 19 3 3 2 0 0 0.8KY1 2 2 13 11 2 2 2 0 0 0.8WI1 1 1 0 0 1 1 1 0 0 0.5MA0 1 1 0 0 1 0 1 0 0 0.4MO1 1 1 0 0 1 1 1 0 0 0.4IA1 1 1 0 0 1 1 1 0 0 0.2AR0 0 0 0 0 0 0 0 0 0 0.1FL0 0 0 0 0 0 0 0 0 0 0.1GA0 0 0 0 0 0 0 0 0 0 0.1ME0 0 0 0 0 0 0 0 0 0 0.1MN0 0 0 0 0 0 0 0 0 0 0.1NH0 0 0 0 0 0 0 0 0 0 0.1RI0 0 0 0 0 0 0 0 0 0 0.1SC0 0 0 0 0 0 0 0 0 0 0.1TN0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions do notexceed screening criteriaContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 117 Base Case: Total Number of Exceedances (grids-days) = 23Washington DCUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionVA8 7 8 10 100 7 79 79 3 100 17.4MD54 42 49 10 100 49 52 52 3 100 10.1OH8 9 11 9 90 10 25 25 2 67 6.1PA6 6 7 10 100 6 17 17 2 67 3.9WV2 2 2 1 10 2 19 19 2 67 2.5IL4 4 4 9 90 4 9 9 0 0 1.5IN3 3 3 3 30 3 8 8 0 0 1.3MI2 2 2 0 0 2 5 5 0 0 0.8KY1 3 3 3 30 3 3 3 0 0 0.7MO2 3 3 3 30 3 5 5 0 0 0.7IA1 1 2 0 0 1 3 3 0 0 0.6NY0 1 1 0 0 1 2 2 0 0 0.6DE0 1 1 0 0 1 2 2 0 0 0.5NJ0 1 1 0 0 1 2 2 0 0 0.5LA1 1 1 0 0 1 1 1 0 0 0.4AR1 1 1 0 0 1 1 1 0 0 0.2MS0 0 0 0 0 0 0 0 0 0 0.1TN0 1 1 0 0 1 0 0 0 0 0.1WI0 0 0 0 0 0 1 1 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MN0 0 0 0 0 0 0 0 0 0 0.0NC0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions exceedscreening criteriaContributions do notexceed screening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 10 Base Case: Total Number of Exceedances (grids-days) = 3New Castle DEUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionMD45 36 42 108 100 44 100 100 23 100 36.8VA6 7 7 75 69 9 41 32 17 74 14.9PA10 14 17 84 78 17 40 45 17 74 12.8OH7 9 10 86 80 9 24 20 15 65 5.1WV4 5 5 58 54 6 28 21 9 39 5.1NC3 4 4 52 48 6 8 4 2 9 2.7MI2 4 4 32 30 5 8 8 2 9 2.6NJ0 3 3 2 2 3 1 1 0 0 1.5IN2 3 3 25 23 3 6 5 0 0 1.2IL3 3 4 33 31 4 5 4 0 0 1.0IA1 2 2 8 7 2 4 3 0 0 0.8KY2 2 3 36 33 4 4 3 0 0 0.8NY0 2 2 0 0 2 2 1 0 0 0.6MO1 2 2 20 19 2 2 2 0 0 0.4WI1 1 1 0 0 1 2 1 0 0 0.3CT0 0 0 0 0 0 0 0 0 0 0.2MA0 1 1 0 0 1 0 0 0 0 0.2AR0 1 1 0 0 1 0 0 0 0 0.1LA0 1 1 0 0 1 0 0 0 0 0.1ME0 0 0 0 0 0 0 0 0 0 0.1MN0 0 0 0 0 0 1 0 0 0 0.1NH0 0 0 0 0 0 0 0 0 0 0.1SC0 0 0 0 0 1 0 0 0 0 0.1TN0 1 1 0 0 1 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 1 0 0 0 0 0.0GA0 0 0 0 0 1 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions exceedscreening criteriaDownwindNonattainment ReceptorContributions do notexceed screening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 108 Base Case: Total Number of Exceedances (grids-days) = 23Fulton GAUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionAL4 14 15 585 47 23 9 7 39 20 17.8TN4 7 7 796 64 8 11 9 67 34 7.1SC2 7 7 316 26 8 6 4 29 15 7.0NC2 5 5 238 19 7 5 4 35 18 4.7KY3 6 6 608 49 7 5 4 26 13 3.6FL1 5 5 162 13 6 2 3 20 10 3.2WV1 3 3 120 10 3 2 2 9 5 2.8VA1 2 2 46 4 3 2 2 1 1 2.4MS1 2 2 37 3 3 1 1 2 1 2.3AR1 2 2 73 6 3 1 1 0 0 1.8OH1 3 3 101 8 4 1 1 0 0 1.1PA0 3 3 58 5 3 1 1 0 0 1.1IN1 2 2 6 1 2 2 1 0 0 1.0IL1 2 2 80 7 2 1 1 0 0 0.8MO1 2 2 0 0 2 1 1 0 0 0.7NY0 1 1 0 0 1 0 0 0 0 0.3LA0 1 1 0 0 1 0 0 0 0 0.2MD0 1 1 0 0 1 0 0 0 0 0.2CT0 0 0 0 0 0 0 0 0 0 0.1IA0 0 0 0 0 0 0 0 0 0 0.1MA0 0 0 0 0 0 0 0 0 0 0.1NJ0 1 1 0 0 1 0 0 0 0 0.1WI0 0 0 0 0 0 0 0 0 0 0.1DE0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MI0 0 0 0 0 0 0 0 0 0 0.0MN0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions exceedscreening criteriaDownwindNonattainment ReceptorContributions do notexceed screening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 1240 Base Case: Total Number of Exceedances (grids-days) = 199Anne Arundel MDUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionVA9 15 18 304 88 18 60 52 68 90 29.5OH8 10 10 285 82 11 26 22 45 59 7.3PA6 10 12 285 82 11 23 25 47 62 6.9WV3 5 5 113 33 6 16 13 16 21 5.9NC1 5 5 56 16 9 4 2 7 9 3.5MI2 4 4 70 20 5 6 5 4 5 2.9WI1 3 3 29 8 3 3 3 0 0 1.8IL3 4 4 135 39 4 6 6 0 0 1.5KY2 3 3 135 39 4 3 2 0 0 1.4IN2 3 3 145 42 3 5 4 0 0 1.3IA1 1 2 0 0 2 2 2 0 0 0.7MO2 3 3 106 31 3 2 2 0 0 0.7NY0 1 2 0 0 1 1 1 0 0 0.6DE0 1 1 0 0 1 1 1 0 0 0.5MN0 1 1 0 0 1 1 1 0 0 0.5NJ0 1 1 0 0 1 1 1 0 0 0.5LA0 1 1 0 0 1 0 0 0 0 0.3AR1 1 1 0 0 1 0 0 0 0 0.2CT0 0 0 0 0 0 0 0 0 0 0.2MA0 1 1 0 0 1 0 0 0 0 0.2FL0 1 1 0 0 1 0 0 0 0 0.1GA0 1 1 0 0 1 0 0 0 0 0.1ME0 0 0 0 0 0 0 0 0 0 0.1MS0 0 0 0 0 0 0 0 0 0 0.1NH0 0 0 0 0 0 0 0 0 0 0.1SC0 1 1 0 0 1 0 0 0 0 0.1TN1 1 1 0 0 1 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaContributions exceedscreening criteriaDownwindNonattainment ReceptorCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 347 Base Case: Total Number of Exceedances (grids-days) = 76Cecil MDUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionPA12 14 15 150 89 22 44 43 25 81 15.6VA6 13 15 103 61 14 34 32 21 68 12.9WV3 5 5 64 38 6 19 18 9 29 5.8OH7 9 10 126 75 9 19 17 16 52 5.1MI2 3 3 58 35 4 6 6 2 7 2.8NC2 4 4 48 29 6 4 3 0 0 2.0IN2 3 3 67 40 3 5 4 0 0 1.2KY2 3 3 38 23 4 3 3 0 0 1.1IL3 4 4 59 35 4 4 4 0 0 0.9IA1 2 2 2 1 2 3 2 0 0 0.8DE1 2 2 26 16 4 1 1 0 0 0.6NY0 1 2 0 0 2 2 2 0 0 0.6NJ0 1 1 0 0 1 1 1 0 0 0.5MO1 2 2 57 34 2 2 2 0 0 0.4MA0 1 1 0 0 1 0 0 0 0 0.3WI1 1 1 0 0 1 1 1 0 0 0.3CT0 0 0 0 0 0 0 0 0 0 0.2FL0 1 1 0 0 1 0 0 0 0 0.2LA0 1 1 0 0 1 0 0 0 0 0.2AR0 1 1 0 0 1 0 0 0 0 0.1GA0 1 1 0 0 1 0 0 0 0 0.1ME0 0 0 0 0 0 0 0 0 0 0.1MN0 0 0 0 0 0 0 0 0 0 0.1MS0 0 0 0 0 0 0 0 0 0 0.1NH0 0 0 0 0 0 0 0 0 0 0.1SC0 0 1 0 0 1 0 0 0 0 0.1TN0 1 1 0 0 1 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions do notexceed screening criteriaContributions exceedscreening criteriaCAMX Source Apportionment ModelingBase Case: Total Number of Exceedances (grid-hours) = 168CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grids-days) = 31Harford MDUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionVA7 13 15 136 52 15 34 32 29 55 15.9PA11 15 17 229 88 25 34 29 39 74 15.8WV2 5 6 45 17 6 12 13 9 17 7.1OH6 9 10 192 74 10 17 17 22 42 5.3NC1 3 4 48 19 5 3 3 4 8 2.6MI2 3 3 84 32 4 6 5 1 2 2.2WI1 3 3 15 6 3 2 2 0 0 1.7IN2 3 3 74 29 3 4 4 0 0 1.5KY1 3 3 41 16 4 2 2 0 0 1.5IL2 4 4 74 29 4 4 4 0 0 1.2IA1 2 2 0 0 2 2 2 0 0 0.8NY0 1 2 0 0 1 1 1 0 0 0.6MO1 2 2 53 20 3 2 2 0 0 0.5NJ0 1 1 0 0 1 1 1 0 0 0.5LA0 1 1 0 0 1 0 0 0 0 0.4MN0 1 1 0 0 1 1 1 0 0 0.4FL0 1 1 0 0 1 0 0 0 0 0.3AR0 1 1 0 0 1 0 0 0 0 0.2CT0 0 0 0 0 0 0 0 0 0 0.2DE0 1 1 0 0 2 0 1 0 0 0.2GA0 1 1 0 0 1 0 0 0 0 0.2MA0 1 1 0 0 1 0 0 0 0 0.2SC0 0 1 0 0 1 0 0 0 0 0.2ME0 0 0 0 0 0 0 0 0 0 0.1MS0 0 0 0 0 0 0 0 0 0 0.1NH0 0 0 0 0 0 0 0 0 0 0.1TN0 1 1 0 0 1 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 260 Base Case: Total Number of Exceedances (grids-days) = 53Contributions do notexceed screening criteriaKent MDUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionVA7 13 15 118 69 13 45 49 20 69 22.8PA6 10 11 116 67 13 18 17 14 48 8.7OH9 9 10 157 91 11 27 29 22 76 6.8WV4 4 4 87 51 5 20 21 14 48 4.7NC3 6 6 56 33 9 5 7 6 21 3.6MI2 4 5 34 20 5 5 5 3 10 2.6IN3 3 3 98 57 3 6 7 0 0 1.5IL3 4 4 82 48 4 6 6 0 0 1.2IA1 1 1 0 0 2 3 3 0 0 0.8KY2 2 2 73 42 4 3 3 0 0 0.8NY0 1 2 0 0 1 1 1 0 0 0.6MO2 2 2 56 33 3 2 2 0 0 0.5NJ0 1 1 0 0 1 1 1 0 0 0.5DE0 0 0 0 0 1 0 0 0 0 0.4MA0 1 1 0 0 1 0 0 0 0 0.3WI1 1 1 0 0 1 1 1 0 0 0.3CT0 0 0 0 0 0 0 0 0 0 0.2MN0 0 0 0 0 0 0 0 0 0 0.2AR0 1 1 0 0 1 0 0 0 0 0.1ME0 0 0 0 0 0 0 0 0 0 0.1NH0 0 0 0 0 0 0 0 0 0 0.1SC0 1 1 0 0 1 0 0 0 0 0.1TN1 1 1 0 0 1 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 1 0 0 0 0 0.0GA0 0 0 0 0 1 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 172 Base Case: Total Number of Exceedances (grids-days) = 29Contributions do notexceed screening criteriaMacomb MIUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionIL14 13 14 102 100 16 60 68 27 96 10.9IN4 8 9 75 74 9 24 27 11 39 7.8OH3 8 9 37 36 9 18 19 7 25 6.9WI3 4 5 43 42 5 11 8 6 21 3.1IA3 4 5 55 54 5 15 20 3 11 2.7KY1 6 7 9 9 6 4 2 3 11 2.4MO3 3 3 40 39 4 12 14 0 0 1.7AR2 2 2 1 1 2 3 3 0 0 0.8LA1 2 2 1 1 2 1 1 0 0 0.4TN0 1 1 0 0 1 1 1 0 0 0.2AL0 0 0 0 0 0 0 0 0 0 0.1MN0 0 0 0 0 0 0 0 0 0 0.1MS1 1 1 0 0 1 0 0 0 0 0.1CT0 0 0 0 0 0 0 0 0 0 0.0DE0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0MD0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0NC0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0NJ0 0 0 0 0 0 0 0 0 0 0.0NY0 0 0 0 0 0 0 0 0 0 0.0PA0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0VA0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0WV0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 102 Base Case: Total Number of Exceedances (grids-days) = 28Contributions do notexceed screening criteriaContributions exceedscreening criteriaBergen NJUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionPA31 27 29 54 100 31 97 99 13 100 23.8VA5 8 9 42 78 8 19 15 6 46 7.3OH7 7 7 44 82 9 19 15 6 46 4.9MD5 6 7 41 76 7 16 18 5 39 4.6WV3 3 3 37 69 3 13 12 4 31 2.2MI1 4 5 7 13 5 4 5 2 15 2.1DE3 3 3 25 46 4 6 6 0 0 1.8NC2 2 2 20 37 3 6 6 0 0 1.2IN2 2 2 13 24 3 4 4 0 0 0.9KY2 2 2 13 24 3 2 2 0 0 0.8WI1 1 1 0 0 1 2 3 0 0 0.7IA1 2 2 0 0 2 2 2 0 0 0.6IL2 2 2 6 11 2 3 3 0 0 0.6NY3 14 16 18 33 15 -34 -56 0 0 0.3MN0 0 0 0 0 0 1 1 0 0 0.2MO1 1 1 0 0 1 1 1 0 0 0.2CT0 0 0 0 0 0 0 0 0 0 0.1FL0 0 0 0 0 0 0 0 0 0 0.1GA0 0 0 0 0 0 0 0 0 0 0.1SC0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0AR0 0 0 0 0 0 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0TN0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaDownwindNonattainment ReceptorCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 54 Base Case: Total Number of Exceedances (grids-days) = 13Contributions exceedscreening criteriaCamden NJUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionPA35 32 36 122 100 39 71 61 24 86 25.1MD18 24 25 105 86 29 57 66 22 79 21.1VA4 6 6 62 51 8 16 16 13 46 6.8DE11 10 11 122 100 12 40 45 24 86 6.6OH5 7 8 87 71 8 16 14 15 54 4.5WV2 4 4 37 30 5 10 11 5 18 4.4MI3 5 5 45 37 6 12 12 7 25 3.8NC2 4 4 34 28 5 3 4 0 0 1.8NY1 3 3 8 7 3 2 3 0 0 1.4IA1 2 2 28 23 2 3 3 0 0 1.1IL2 3 3 43 35 3 5 4 0 0 1.0IN2 2 2 6 5 2 4 4 0 0 1.0WI1 2 2 0 0 2 3 3 0 0 0.9KY1 2 2 11 9 3 2 1 0 0 0.5MN0 0 0 0 0 0 1 1 0 0 0.3MO1 2 2 0 0 2 1 1 0 0 0.3SC0 1 1 0 0 1 0 0 0 0 0.2GA0 1 1 0 0 1 0 0 0 0 0.1LA0 0 0 0 0 0 0 0 0 0 0.1TN0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0AR0 1 1 0 0 1 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 1 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaDownwindNonattainment ReceptorCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 122 Base Case: Total Number of Exceedances (grids-days) = 28Contributions exceedscreening criteriaGloucester NJUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionMD28 30 32 93 99 35 87 90 20 100 27.9PA21 23 27 94 100 31 55 50 14 70 14.3DE14 18 21 94 100 18 64 58 19 95 13.2VA5 6 7 52 55 8 26 24 11 55 7.4WV3 4 5 36 38 5 17 17 6 30 4.8MI3 4 4 40 43 5 15 12 8 40 3.2OH7 6 6 76 81 6 23 19 14 70 3.1NC2 4 4 36 38 5 5 5 0 0 1.6IA2 2 2 22 23 2 5 4 0 0 1.0IL3 3 4 40 43 3 7 5 0 0 1.0IN2 2 2 7 7 2 5 5 0 0 0.7KY2 2 2 19 20 3 2 2 0 0 0.7NY0 1 2 0 0 1 1 2 0 0 0.5WI1 1 1 0 0 1 3 2 0 0 0.4MO1 2 2 0 0 2 2 1 0 0 0.3LA0 0 1 0 0 0 0 0 0 0 0.1MN0 0 0 0 0 0 1 1 0 0 0.1SC0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0AR0 1 1 0 0 1 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 1 0 0 0 0 0.0GA0 0 0 0 0 1 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0TN0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaDownwindNonattainment ReceptorContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 94 Base Case: Total Number of Exceedances (grids-days) = 20Hunterton NJUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionPA46 34 39 71 100 46 100 100 20 100 32.9VA2 10 11 15 21 10 9 11 4 20 9.4MD4 8 10 24 34 10 16 18 6 30 7.3DE4 5 6 60 85 6 18 19 4 20 3.8OH2 7 8 19 27 8 13 14 2 10 3.3WV1 4 4 8 11 4 9 10 2 10 2.8NY3 3 3 45 63 4 14 15 0 0 1.9MI2 3 4 13 18 4 9 9 0 0 1.6NC1 2 2 4 6 3 4 4 0 0 0.9WI1 2 2 0 0 2 4 4 0 0 0.8IN1 2 2 5 7 3 3 3 0 0 0.7KY1 2 3 5 7 3 2 2 0 0 0.7IL1 2 2 0 0 2 4 4 0 0 0.4MN0 0 0 0 0 0 1 1 0 0 0.3FL0 0 1 0 0 1 0 0 0 0 0.1GA0 0 1 0 0 1 0 0 0 0 0.1IA1 1 1 0 0 1 1 1 0 0 0.1LA0 0 0 0 0 0 1 1 0 0 0.1MO0 1 1 0 0 1 1 1 0 0 0.1SC0 0 0 0 0 0 0 0 0 0 0.1TN0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0AR0 0 0 0 0 0 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions exceedscreening criteriaDownwindNonattainment ReceptorCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 71 Base Case: Total Number of Exceedances (grids-days) = 20Contributions do notexceed screening criteriaMercer NJUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionPA41 40 41 86 100 47 90 90 16 100 36.0MD8 12 12 56 65 17 25 27 8 50 13.4MI3 5 6 40 47 6 8 8 4 25 3.9DE5 5 6 61 71 7 10 11 3 19 3.6VA2 5 5 29 34 6 7 7 3 19 3.4NY2 5 5 13 15 6 6 5 3 19 3.3WV1 3 4 23 27 4 6 6 4 25 3.2OH4 5 6 62 72 7 8 8 2 13 2.6NC1 3 3 22 26 4 2 2 0 0 1.4IA1 2 2 17 20 2 2 2 0 0 0.8IL2 3 3 19 22 3 3 3 0 0 0.7IN1 2 2 2 2 2 3 3 0 0 0.6KY1 2 2 9 11 3 1 1 0 0 0.5WI1 1 1 0 0 1 2 2 0 0 0.4MO1 1 2 0 0 2 1 1 0 0 0.2MN0 0 0 0 0 0 0 0 0 0 0.1TN0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0AR0 1 1 0 0 1 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions exceedscreening criteriaContributions do notexceed screening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 86 Base Case: Total Number of Exceedances (grids-days) = 16Middlesex NJUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionPA30 33 38 172 100 36 80 78 31 97 28.7MD5 9 8 86 50 13 12 11 9 28 7.7VA2 7 8 59 34 8 7 8 9 28 7.2NY3 8 8 66 38 12 8 7 8 25 3.2MI3 5 5 73 42 6 6 6 6 19 3.0DE3 4 5 68 40 6 5 5 2 6 2.4OH3 5 5 103 60 7 6 6 2 6 2.4WV1 3 3 47 27 4 4 4 7 22 2.4NC1 3 3 37 22 3 2 2 0 0 1.3IA1 2 2 27 16 2 1 1 0 0 0.7IL2 2 3 39 23 3 2 2 0 0 0.5IN1 2 2 8 5 2 2 2 0 0 0.5WI1 1 1 0 0 1 1 1 0 0 0.5KY1 2 2 17 10 3 1 1 0 0 0.4CT0 0 0 0 0 0 0 0 0 0 0.1FL0 0 1 0 0 1 0 0 0 0 0.1GA0 1 1 0 0 1 0 0 0 0 0.1MN0 0 0 0 0 0 0 0 0 0 0.1MO1 1 1 0 0 1 0 0 0 0 0.1SC0 0 0 0 0 0 0 0 0 0 0.1TN0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0AR0 0 1 0 0 1 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions exceedscreening criteriaContributions do notexceed screening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 172 Base Case: Total Number of Exceedances (grids-days) = 32Monmouth NJUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionPA31 33 37 318 100 45 73 72 54 98 33.6MD8 10 11 217 68 21 17 17 28 51 11.4VA3 6 6 113 36 8 7 7 10 18 5.0NY3 11 12 66 21 17 6 6 8 15 4.8DE4 5 6 218 69 8 9 10 12 22 4.0OH3 5 6 190 60 6 6 6 7 13 3.1WV1 3 3 71 22 4 5 5 9 16 3.0MI2 4 4 99 31 6 5 5 8 15 2.8NC1 3 3 80 25 4 2 2 0 0 1.5IA1 2 2 19 6 2 1 1 0 0 0.8IN1 2 2 1 0 2 2 2 0 0 0.7IL2 2 2 69 22 3 2 2 0 0 0.6WI1 1 1 0 0 1 1 1 0 0 0.5MA0 1 1 0 0 1 0 0 0 0 0.4CT0 0 1 0 0 0 0 0 0 0 0.3KY1 1 1 17 5 2 1 1 0 0 0.3MN0 0 0 0 0 0 0 0 0 0 0.2FL0 1 1 0 0 1 0 0 0 0 0.1GA0 1 1 0 0 1 0 0 0 0 0.1ME0 0 0 0 0 0 0 0 0 0 0.1MO1 1 1 0 0 2 0 0 0 0 0.1NH0 0 0 0 0 0 0 0 0 0 0.1RI0 0 0 0 0 0 0 0 0 0 0.1SC0 0 1 0 0 0 0 0 0 0 0.1TN0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0AR0 0 0 0 0 1 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaDownwindNonattainment ReceptorContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 318 Base Case: Total Number of Exceedances (grids-days) = 55Morris NJUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionPA32 31 34 151 100 37 88 88 34 100 27.6VA5 9 10 78 52 10 30 32 12 35 8.6MD4 7 8 75 50 11 16 16 12 35 5.7OH3 8 8 79 52 9 11 12 6 18 5.0NY4 11 13 71 47 12 10 10 6 18 3.8MI1 6 7 5 3 6 4 4 1 3 3.1DE3 4 4 72 48 5 12 11 6 18 3.0WV1 3 4 24 16 3 8 10 5 15 2.4IN1 2 2 17 11 3 3 3 0 0 1.0KY1 2 2 17 11 3 2 2 0 0 0.9NC1 2 2 8 5 3 3 4 0 0 0.9IA0 2 3 1 1 2 1 1 0 0 0.7WI1 1 2 0 0 1 2 2 0 0 0.7IL1 2 2 4 3 2 3 3 0 0 0.6MO0 1 1 0 0 1 1 1 0 0 0.3MN0 0 0 0 0 0 1 1 0 0 0.2AR0 0 1 0 0 0 0 0 0 0 0.1FL0 0 0 0 0 1 0 0 0 0 0.1GA0 0 0 0 0 1 0 0 0 0 0.1LA0 0 0 0 0 0 0 0 0 0 0.1SC0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0TN0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do not exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 151 Base Case: Total Number of Exceedances (grids-days) = 34Contributions exceedscreening criteriaDownwindNonattainment ReceptorOcean NJUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionPA34 30 35 359 100 46 86 90 58 98 30.0MD12 17 17 298 83 25 31 35 39 66 14.0VA3 7 7 164 46 9 12 15 16 27 6.8DE8 7 8 336 94 9 24 25 47 80 5.9WV2 4 4 77 21 4 8 9 8 14 4.0OH5 6 7 264 74 7 13 12 19 32 3.9NY1 7 8 12 3 7 2 3 1 2 3.7MI3 4 4 144 40 6 8 7 10 17 3.1NC1 4 4 66 18 5 2 3 1 2 2.0IA1 2 2 52 15 2 3 2 0 0 0.9IN2 2 2 18 5 2 3 3 0 0 0.9IL3 3 3 174 49 4 3 3 0 0 0.8WI1 1 1 0 0 1 2 2 0 0 0.5KY1 1 2 15 4 3 1 1 0 0 0.4MA0 1 1 0 0 1 0 1 0 0 0.4CT0 1 1 0 0 1 0 0 0 0 0.3MN0 0 0 0 0 0 1 1 0 0 0.2MO1 1 2 0 0 2 1 1 0 0 0.2SC0 1 1 0 0 1 0 0 0 0 0.2GA0 1 1 0 0 1 0 0 0 0 0.1ME0 0 0 0 0 0 0 0 0 0 0.1NH0 0 0 0 0 0 0 0 0 0 0.1RI0 0 0 0 0 0 0 0 0 0 0.1TN0 0 0 0 0 1 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0AR0 1 1 0 0 1 0 0 0 0 0.0FL0 0 0 0 0 1 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaDownwindNonattainment ReceptorContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 359 Base Case: Total Number of Exceedances (grids-days) = 59Erie NYUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionPA2 12 14 2 13 12 5 1 1 25 6.8MD2 8 9 2 13 8 4 1 1 25 3.9WI6 5 6 14 88 5 62 72 3 75 3.8NJ1 5 6 2 13 5 4 1 1 25 3.4VA1 6 7 2 13 6 4 1 1 25 3.1MI4 3 4 14 88 4 38 42 2 50 2.1DE0 2 3 2 13 2 4 1 0 0 1.3IL4 3 3 14 88 3 18 16 0 0 1.2NC1 3 4 2 13 3 4 1 0 0 1.1IA3 2 2 14 88 2 12 14 0 0 0.7MO3 2 2 6 38 2 13 14 0 0 0.7AR1 1 1 0 0 1 5 6 0 0 0.3CT0 0 0 0 0 0 1 0 0 0 0.1MA0 0 1 0 0 0 1 0 0 0 0.1MN0 0 0 0 0 0 2 2 0 0 0.1OH0 0 0 0 0 0 1 1 0 0 0.1SC0 1 1 0 0 1 1 0 0 0 0.1WV0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0IN0 0 0 0 0 0 1 1 0 0 0.0KY0 0 0 0 0 0 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0TN0 0 0 0 0 0 0 1 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaDownwindNonattainment ReceptorContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 16 Base Case: Total Number of Exceedances (grids-days) = 4Richmond NYUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionNJ41 40 45 47 100 52 74 75 8 100 41.2PA25 30 29 47 100 34 63 59 6 75 21.2MD5 8 8 28 60 10 9 9 2 25 5.4VA3 6 7 20 43 7 7 7 4 50 5.3MI3 5 5 25 53 5 5 5 2 25 2.3WV1 3 3 15 32 3 4 3 2 25 2.2DE2 5 4 19 40 5 4 4 0 0 2.0OH4 5 5 29 62 6 5 5 0 0 1.8NC1 3 3 14 30 3 2 2 0 0 1.3IA1 2 2 7 15 2 1 1 0 0 0.6IL2 2 2 12 26 3 2 1 0 0 0.5WI1 1 1 0 0 1 1 1 0 0 0.5IN1 2 1 0 0 2 1 1 0 0 0.4KY1 1 1 2 4 2 0 0 0 0 0.2MN0 0 0 0 0 0 0 0 0 0 0.2FL0 1 1 0 0 1 0 0 0 0 0.1GA0 1 1 0 0 1 0 0 0 0 0.1MO1 1 1 0 0 1 0 0 0 0 0.1SC0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0AR0 0 0 0 0 0 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0TN0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaDownwindNonattainment ReceptorContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 47 Base Case: Total Number of Exceedances (grids-days) = 8Suffolk NYUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionNJ22 19 21 1088 100 39 64 64 151 99 36.7PA18 25 27 1050 97 34 42 42 136 89 22.2CT4 9 8 395 36 26 13 7 42 28 16.1VA3 8 8 355 33 11 9 8 35 23 8.4MD6 10 12 790 73 15 13 12 80 52 6.5DE3 5 5 528 49 6 7 7 26 17 4.6OH3 5 5 310 29 7 5 5 12 8 3.3NC1 4 4 282 26 6 3 2 11 7 3.2WV1 2 3 235 22 3 4 4 16 11 2.8MA1 3 3 93 9 7 1 1 1 1 2.2MI2 3 3 220 20 5 4 5 2 1 2.1WI1 1 1 0 0 2 2 2 0 0 1.0IA1 1 2 4 0 2 1 1 0 0 0.7IN1 1 1 0 0 2 2 2 0 0 0.7IL1 2 2 125 12 3 2 2 0 0 0.5MN0 0 0 0 0 1 1 1 0 0 0.5KY1 1 1 1 0 2 1 1 0 0 0.4RI0 1 1 3 0 2 0 0 0 0 0.3MO1 1 1 0 0 1 0 0 0 0 0.2AR0 0 0 0 0 0 0 0 0 0 0.1FL0 1 1 0 0 1 0 0 0 0 0.1ME0 0 0 0 0 1 0 0 0 0 0.1NH0 0 0 0 0 1 0 0 0 0 0.1SC0 0 0 0 0 1 0 0 0 0 0.1TN0 0 0 0 0 0 0 0 0 0 0.1VT0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 1 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0Contributions exceedscreening criteriaDownwindNonattainment ReceptorContributions do notexceed screening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 1088 Base Case: Total Number of Exceedances (grids-days) = 153Westchester NYUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionNJ29 29 32 81 100 35 79 67 15 100 25.6PA24 22 23 81 100 28 75 83 15 100 20.1VA6 7 8 66 82 8 21 20 8 53 7.5OH7 8 8 70 86 10 15 18 7 47 5.6MD5 7 7 65 80 8 11 13 6 40 3.0WV2 3 3 40 49 3 9 13 4 27 2.3DE2 2 3 22 27 3 5 5 0 0 1.4NC2 2 2 13 16 2 3 5 0 0 1.2IN2 2 2 25 31 3 3 4 0 0 1.0IL2 2 2 10 12 2 2 2 0 0 0.7KY1 2 2 17 21 3 2 2 0 0 0.7MI1 1 2 0 0 2 2 2 0 0 0.7WI1 1 1 0 0 1 1 1 0 0 0.4IA1 1 1 0 0 1 1 1 0 0 0.2MO0 1 1 0 0 1 1 1 0 0 0.2CT0 0 0 0 0 0 0 0 0 0 0.1LA0 0 0 0 0 0 0 0 0 0 0.1MN0 0 0 0 0 0 0 0 0 0 0.1SC0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0AR0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0TN0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions exceedscreening criteriaContributions do notexceed screening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 81 Base Case: Total Number of Exceedances (grids-days) = 15Geauga OHUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionIL10 15 17 60 100 15 46 42 15 88 10.9IN5 8 10 56 93 9 28 23 6 35 5.5MI9 7 8 47 78 9 50 54 11 65 5.1KY1 8 9 5 8 9 6 2 2 12 4.6MO4 6 7 49 82 6 16 14 6 35 2.9IA2 2 2 0 0 2 8 6 0 0 0.7AR1 2 2 1 2 2 5 4 0 0 0.6LA2 2 2 15 25 2 6 6 0 0 0.6WI1 1 1 0 0 1 4 3 0 0 0.4WV0 1 1 0 0 1 1 0 0 0 0.4MN0 0 0 0 0 0 1 1 0 0 0.1MS1 0 1 0 0 1 1 1 0 0 0.1PA0 0 0 0 0 0 0 0 0 0 0.1TN0 1 1 0 0 1 1 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0DE0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0MD0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0NC0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0NJ0 0 0 0 0 0 0 0 0 0 0.0NY0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0VA0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions exceedscreening criteriaContributions do notexceed screening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 60 Base Case: Total Number of Exceedances (grids-days) = 17Bucks PAUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionNJ12 18 20 83 76 27 38 25 18 58 14.4MD7 10 11 77 70 15 38 45 11 36 12.7OH4 7 7 62 56 8 16 16 6 19 4.1MI2 6 7 38 35 6 14 12 5 16 3.9DE7 6 6 106 96 8 25 24 9 29 3.7WV1 4 4 23 21 4 8 10 6 19 3.4VA2 4 4 23 21 6 7 9 3 10 2.9NY2 2 2 18 16 3 8 7 0 0 1.7WI1 2 2 0 0 2 5 5 0 0 1.3NC1 2 3 16 15 3 2 3 0 0 1.2IA1 2 3 3 3 2 2 2 0 0 0.9IN1 2 2 13 12 3 4 5 0 0 0.9KY1 2 3 15 14 3 2 2 0 0 0.9IL2 3 3 3 3 3 5 5 0 0 0.7MN0 0 0 0 0 0 1 1 0 0 0.4MO1 2 2 0 0 2 2 2 0 0 0.3LA0 0 0 0 0 1 1 1 0 0 0.2AR0 1 1 0 0 1 0 0 0 0 0.1MS0 0 0 0 0 0 0 0 0 0 0.1TN0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions do notexceed screening criteriaContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 110 Base Case: Total Number of Exceedances (grids-days) = 31Chester PAUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionMD30 32 37 126 100 39 95 94 33 85 28.4DE8 15 17 92 73 22 29 40 10 26 14.3VA5 12 14 85 68 13 41 28 17 44 12.1NJ2 15 17 15 12 15 11 11 5 13 10.4WV3 5 5 40 32 5 31 26 11 28 5.1OH6 7 8 87 69 9 35 28 13 33 4.8MI2 5 6 15 12 6 16 13 3 8 3.7NY1 3 4 8 6 3 8 5 1 3 2.4NC1 3 4 30 24 4 7 5 0 0 1.6KY2 3 3 35 28 5 9 7 0 0 1.5IN2 2 3 30 24 4 14 10 0 0 1.3IA1 2 2 5 4 2 4 4 0 0 1.0IL2 3 4 15 12 3 11 8 0 0 0.8CT0 1 1 0 0 1 2 0 0 0 0.7MA0 1 1 0 0 1 1 0 0 0 0.4MO1 2 2 7 6 2 4 3 0 0 0.4LA0 1 1 0 0 1 1 1 0 0 0.3WI1 1 1 0 0 1 4 3 0 0 0.3FL0 1 1 0 0 1 0 0 0 0 0.2AL0 0 0 0 0 0 0 0 0 0 0.1AR0 1 1 0 0 1 1 1 0 0 0.1GA0 1 1 0 0 1 0 0 0 0 0.1MN0 0 0 0 0 0 1 1 0 0 0.1MS0 0 0 0 0 0 0 0 0 0 0.1RI0 0 0 0 0 0 0 0 0 0 0.1SC0 0 0 0 0 0 0 0 0 0 0.1TN0 0 0 0 0 0 1 1 0 0 0.1ME0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions exceedscreening criteriaDownwindNonattainment ReceptorCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 126 Base Case: Total Number of Exceedances (grids-days) = 39Contributions do notexceed screening criteriaMontgomery PAUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionMD13 13 14 46 100 21 69 69 11 85 11.7DE11 11 12 46 100 15 53 50 7 54 9.9NJ4 10 12 19 41 11 24 21 6 46 6.8OH5 7 8 26 57 8 21 20 3 23 3.9WV2 4 5 13 28 4 16 16 3 23 3.8NY1 3 4 4 9 3 8 6 1 8 2.2MI2 4 4 6 13 4 9 9 0 0 2.0VA2 3 4 16 35 5 12 12 0 0 1.9WI1 2 3 1 2 2 3 2 0 0 1.3IN2 2 3 9 20 3 7 6 0 0 0.9KY1 3 3 11 24 3 4 4 0 0 0.9NC1 2 3 8 17 3 3 3 0 0 0.9CT0 1 1 0 0 1 2 0 0 0 0.6IL2 2 2 0 0 2 6 6 0 0 0.5MN0 1 1 0 0 1 1 1 0 0 0.4MA0 1 1 0 0 1 1 0 0 0 0.3IA1 1 1 0 0 1 2 2 0 0 0.2LA0 1 1 0 0 1 1 1 0 0 0.2MO1 1 1 0 0 1 3 2 0 0 0.2AR0 0 0 0 0 0 1 1 0 0 0.1MS0 0 0 0 0 0 0 0 0 0 0.1RI0 0 0 0 0 0 0 0 0 0 0.1TN0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions exceedscreening criteriaContributions do notexceed screening criteriaDownwindNonattainment ReceptorCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 46 Base Case: Total Number of Exceedances (grids-days) = 13Philadelphia PAUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionMD18 19 20 31 86 24 81 79 7 78 21.5NJ3 10 11 6 17 14 10 7 2 22 10.1DE11 9 9 36 100 11 42 42 8 89 6.3MI2 6 7 3 8 6 9 10 1 11 4.2VA4 5 6 21 58 7 20 21 4 44 4.2WV3 4 4 15 42 4 17 17 3 33 3.9OH5 6 6 26 72 7 15 15 2 22 2.5NC2 3 4 15 42 4 6 6 0 0 1.7NY1 2 2 3 8 2 4 4 0 0 1.1IA1 2 3 3 8 2 2 3 0 0 1.0IL2 3 3 3 8 3 4 4 0 0 0.8IN2 2 2 1 3 2 4 4 0 0 0.6KY1 2 2 6 17 3 2 2 0 0 0.5WI1 1 1 0 0 1 2 2 0 0 0.4MO1 2 2 0 0 2 1 1 0 0 0.2LA0 0 0 0 0 0 0 0 0 0 0.1MN0 0 0 0 0 0 0 0 0 0 0.1TN0 0 0 0 0 0 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0AR0 1 1 0 0 1 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 1 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions exceedscreening criteriaContributions do notexceed screening criteriaDownwindNonattainment ReceptorCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 36 Base Case: Total Number of Exceedances (grids-days) = 9Kent RIUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionMA1 26 30 3 2 27 1 1 1 6 26.3NY26 22 23 134 100 29 77 77 17 94 20.3PA17 22 25 131 98 22 39 37 17 94 12.2NJ16 14 17 131 98 18 45 41 17 94 9.8CT10 8 9 125 93 15 29 35 17 94 9.7OH6 7 7 75 56 9 9 12 7 39 3.6VA4 5 5 75 56 6 6 5 3 17 2.9MD4 5 5 113 84 5 7 5 0 0 1.6WV2 2 2 50 37 3 4 4 0 0 1.4NH0 3 3 3 2 3 1 0 0 0 1.3MI1 2 2 28 21 3 3 4 0 0 1.2IN2 2 2 22 16 2 3 4 0 0 0.8DE2 3 3 27 20 3 3 2 0 0 0.7NC2 3 3 42 31 3 1 1 0 0 0.7IL2 2 2 8 6 2 2 3 0 0 0.5KY1 2 2 0 0 2 1 1 0 0 0.3ME0 1 1 0 0 1 0 0 0 0 0.3MO1 1 1 0 0 1 1 1 0 0 0.2VT0 0 0 0 0 0 0 0 0 0 0.2AR0 0 0 0 0 0 0 0 0 0 0.1IA1 1 1 0 0 1 0 1 0 0 0.1TN0 0 0 0 0 0 0 0 0 0 0.1WI1 0 1 0 0 1 1 1 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MN0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0Contributions exceedscreening criteriaContributions do notexceed screening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 134 Base Case: Total Number of Exceedances (grids-days) = 18DownwindNonattainment ReceptorGalveston TXUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionLA13 10 12 188 100 13 64 58 48 100 6.7AR3 3 4 85 45 3 16 13 9 19 2.7AL3 4 4 101 54 4 9 9 0 0 1.7MS3 3 4 108 57 3 15 14 0 0 1.6GA2 3 3 2 1 3 7 7 0 0 1.5NC1 2 2 0 0 2 5 6 0 0 1.1TN3 2 3 49 26 2 10 9 0 0 0.9KY2 2 2 0 0 2 7 6 0 0 0.7MO1 1 2 0 0 1 3 3 0 0 0.7IL1 2 2 0 0 2 3 2 0 0 0.6OH1 2 2 0 0 2 5 5 0 0 0.6VA1 1 1 0 0 1 4 4 0 0 0.6IN1 1 1 0 0 1 3 3 0 0 0.5SC1 1 1 0 0 1 2 3 0 0 0.5WV1 1 1 0 0 1 4 4 0 0 0.4FL0 1 1 0 0 1 1 1 0 0 0.3PA1 1 1 0 0 1 2 2 0 0 0.3MI0 1 1 0 0 1 2 2 0 0 0.2MD0 0 1 0 0 0 1 1 0 0 0.1NY0 0 0 0 0 0 0 0 0 0 0.1WI0 0 0 0 0 0 1 1 0 0 0.1CT0 0 0 0 0 0 0 0 0 0 0.0DE0 0 0 0 0 0 0 0 0 0 0.0IA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MN0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0NJ0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaContributions exceedscreening criteriaDownwindNonattainment ReceptorCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 188 Base Case: Total Number of Exceedances (grids-days) = 48Harris TXUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionLA13 13 15 740 100 15 68 66 190 100 9.1AR3 3 3 424 57 4 18 15 39 20 2.6MS3 4 5 315 43 5 15 16 15 8 2.6AL3 4 4 301 41 4 12 13 15 8 2.4GA2 3 3 179 24 3 9 9 0 0 1.5NC1 2 2 0 0 2 4 4 0 0 1.0TN2 2 3 126 17 3 9 9 0 0 0.9IL1 2 2 0 0 2 3 3 0 0 0.8KY2 2 2 0 0 2 6 5 0 0 0.8MO1 1 2 0 0 2 4 3 0 0 0.8IN1 1 1 0 0 1 4 3 0 0 0.7OH1 2 2 0 0 2 5 4 0 0 0.6VA1 1 1 0 0 1 3 3 0 0 0.6SC1 1 1 0 0 1 2 2 0 0 0.4WV0 1 1 0 0 1 3 3 0 0 0.4FL0 1 1 0 0 1 1 1 0 0 0.3MI0 1 1 0 0 1 1 1 0 0 0.2PA1 1 1 0 0 1 2 2 0 0 0.2IA0 0 0 0 0 0 0 0 0 0 0.1MD0 0 1 0 0 1 1 1 0 0 0.1NY0 0 0 0 0 0 0 0 0 0 0.1WI0 0 0 0 0 0 1 1 0 0 0.1CT0 0 0 0 0 0 0 0 0 0 0.0DE0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MN0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0NJ0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0DownwindNonattainment ReceptorContributions exceedscreening criteriaContributions do notexceed screening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 740 Base Case: Total Number of Exceedances (grids-days) = 191Jefferson TXUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionLA26 33 38 356 100 39 97 98 98 99 23.3AR4 4 4 271 76 5 33 29 29 29 3.3MS3 5 6 141 40 5 20 21 13 13 3.0AL2 4 5 135 38 4 15 17 11 11 2.3GA1 2 3 69 19 2 9 9 0 0 1.1MO1 2 2 0 0 2 10 10 0 0 1.0KY2 2 2 0 0 2 9 7 0 0 0.8TN2 2 3 96 27 3 12 11 0 0 0.8IL1 2 2 0 0 2 7 7 0 0 0.6OH1 1 2 0 0 2 7 6 0 0 0.6IN1 1 1 0 0 1 6 5 0 0 0.4WV0 1 1 0 0 1 3 3 0 0 0.4NC1 1 1 0 0 1 3 3 0 0 0.3VA0 1 1 0 0 1 2 2 0 0 0.3FL0 1 1 0 0 1 2 2 0 0 0.2MI0 1 1 0 0 1 2 1 0 0 0.2IA0 0 0 0 0 0 1 1 0 0 0.1PA0 0 1 0 0 1 2 2 0 0 0.1SC0 0 1 0 0 1 2 1 0 0 0.1WI0 0 0 0 0 0 1 1 0 0 0.1CT0 0 0 0 0 0 0 0 0 0 0.0DE0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0MD0 0 0 0 0 0 1 1 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MN0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0NJ0 0 0 0 0 0 0 0 0 0 0.0NY0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaDownwindNonattainment ReceptorContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 356 Base Case: Total Number of Exceedances (grids-days) = 99Arlington VAUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionMD51 45 44 11 100 51 24 24 1 50 6.8OH8 10 11 11 100 10 26 26 2 100 6.5PA6 6 6 7 64 6 12 12 1 50 3.3WV2 2 2 1 9 2 11 11 1 50 2.0IL4 4 4 11 100 4 7 7 0 0 1.5IN3 3 3 4 36 3 6 6 0 0 1.3KY1 3 3 4 36 3 2 2 0 0 0.8MI2 2 2 2 18 2 4 4 0 0 0.8MO2 3 3 4 36 3 4 4 0 0 0.7IA1 1 2 0 0 1 2 2 0 0 0.6LA1 1 1 0 0 1 1 1 0 0 0.3AR1 1 1 0 0 1 1 1 0 0 0.2WI1 0 0 0 0 0 1 1 0 0 0.2MS0 0 0 0 0 0 0 0 0 0 0.1TN1 1 1 0 0 1 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0DE0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MN0 0 0 0 0 0 0 0 0 0 0.0NC0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0NJ0 0 0 0 0 0 0 0 0 0 0.0NY0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaDownwindNonattainment ReceptorContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 11 Base Case: Total Number of Exceedances (grids-days) = 2Fairfax VAUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionMD53 41 47 73 100 52 64 65 15 100 20.3PA8 6 7 73 100 7 29 31 15 100 4.5OH4 5 6 63 86 6 11 13 3 20 3.5WV2 2 2 3 4 2 11 12 4 27 2.7IN2 2 2 17 23 3 5 5 0 0 1.1DE1 1 1 1 1 2 4 5 0 0 1.0IL2 2 3 14 19 3 4 5 0 0 1.0NJ1 1 1 0 0 2 4 5 0 0 1.0MI1 2 2 1 1 2 3 3 0 0 0.9NY2 2 2 0 0 2 4 5 0 0 0.9MO2 2 2 0 0 2 3 3 0 0 0.7LA1 1 1 0 0 2 2 2 0 0 0.5AR1 1 1 0 0 1 1 1 0 0 0.3WI0 0 0 0 0 0 1 1 0 0 0.2CT0 0 0 0 0 0 0 0 0 0 0.1IA0 0 1 0 0 1 1 1 0 0 0.1MS0 0 0 0 0 1 1 1 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0KY0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MN0 0 0 0 0 0 0 0 0 0 0.0NC0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0TN0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaContributions exceedscreening criteriaDownwindNonattainment ReceptorCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 73 Base Case: Total Number of Exceedances (grids-days) = 15Kenosha WIUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionIL55 47 48 136 100 57 100 100 29 100 48.6IN11 14 15 113 83 19 32 31 15 52 12.7MO9 10 10 136 100 12 35 35 25 86 7.2IA1 4 5 14 10 5 5 6 2 7 2.4AR3 3 4 59 43 4 5 6 0 0 1.9GA1 2 2 3 2 2 1 2 0 0 1.2AL1 2 3 21 15 3 2 2 0 0 1.1MI1 1 1 6 4 3 2 2 0 0 1.1LA2 3 4 38 28 4 2 2 0 0 0.9MS1 2 2 1 1 2 1 2 0 0 0.9TN1 2 2 0 0 2 1 1 0 0 0.4KY0 1 1 0 0 1 0 0 0 0 0.1MN0 0 0 0 0 0 0 0 0 0 0.1CT0 0 0 0 0 0 0 0 0 0 0.0DE0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0MD0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0NC0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0NJ0 0 0 0 0 0 0 0 0 0 0.0NY0 0 0 0 0 0 0 0 0 0 0.0OH0 0 0 0 0 0 0 0 0 0 0.0PA0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0VA0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0WV0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaDownwindNonattainment ReceptorContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 136 Base Case: Total Number of Exceedances (grids-days) = 29Ozaukee WIUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionIL49 37 40 62 100 43 100 100 11 100 34.4IN10 10 10 47 76 14 33 33 7 64 9.6MO11 9 10 62 100 10 38 40 8 73 6.1IA2 3 4 7 11 4 5 4 0 0 1.9LA1 3 4 14 23 3 1 2 0 0 0.8AR1 1 1 0 0 1 0 1 0 0 0.2MI0 0 0 0 0 1 1 1 0 0 0.2MN0 0 0 0 0 0 1 1 0 0 0.1MS0 1 1 0 0 1 0 0 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0DE0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0KY0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0MD0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0NC0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0NJ0 0 0 0 0 0 0 0 0 0 0.0NY0 0 0 0 0 0 0 0 0 0 0.0OH0 0 0 0 0 0 0 0 0 0 0.0PA0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0TN0 0 0 0 0 0 0 0 0 0 0.0VA0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0WV0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaDownwindNonattainment ReceptorContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 62 Base Case: Total Number of Exceedances (grids-days) = 11Sheboygan WIUpwindStateAverage 3-episode %contributionHighest dailyaverage (ppb)Highest dailyaverage (%)# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontribution% total ppbreduced% pop-wgt totalppb reduced# reduced>= 2 ppb% reduced>= 2 ppbmax 8-hr ppbcontributionIL41 32 36 39 100 36 100 100 10 100 24.6IN9 9 10 26 67 12 47 29 7 70 8.8MO11 9 10 39 100 10 59 49 7 70 5.5IA3 4 4 13 33 4 15 31 3 30 2.4MI1 1 1 0 0 1 1 1 0 0 0.2AR1 1 1 0 0 1 -1 -3 0 0 0.1MN0 0 0 0 0 0 1 1 0 0 0.1AL0 0 0 0 0 0 0 0 0 0 0.0CT0 0 0 0 0 0 0 0 0 0 0.0DE0 0 0 0 0 0 0 0 0 0 0.0FL0 0 0 0 0 0 0 0 0 0 0.0GA0 0 0 0 0 0 0 0 0 0 0.0KY0 0 0 0 0 0 0 0 0 0 0.0LA0 0 0 0 0 0 0 0 0 0 0.0MA0 0 0 0 0 0 0 0 0 0 0.0MD0 0 0 0 0 0 0 0 0 0 0.0ME0 0 0 0 0 0 0 0 0 0 0.0MS0 0 0 0 0 0 0 0 0 0 0.0NC0 0 0 0 0 0 0 0 0 0 0.0NH0 0 0 0 0 0 0 0 0 0 0.0NJ0 0 0 0 0 0 0 0 0 0 0.0NY0 0 0 0 0 0 0 0 0 0 0.0OH0 0 0 0 0 0 0 0 0 0 0.0PA0 0 0 0 0 0 0 0 0 0 0.0RI0 0 0 0 0 0 0 0 0 0 0.0SC0 0 0 0 0 0 0 0 0 0 0.0TN0 0 0 0 0 0 0 0 0 0 0.0VA0 0 0 0 0 0 0 0 0 0 0.0VT0 0 0 0 0 0 0 0 0 0 0.0WV0 0 0 0 0 0 0 0 0 0 0.0Contributions do notexceed screening criteriaDownwindNonattainment ReceptorContributions exceedscreening criteriaCAMX Source Apportionment Modeling CAMX State Zero-Out ModelingBase Case: Total Number of Exceedances (grid-hours) = 39 Base Case: Total Number of Exceedances (grids-days) = 10

# Technical Support Document

for the Final

Clean Air Interstate Rule

Air Quality Modeling Analyses

Appendix H

PM2.5 Contributions to Each Nonattainment County in 2010Downwind Nonattainment Counties Upwind States - Part 1State County AL FL GA IL IN IA KY LAAlabama Jefferson 2.29 0.24 0.72 0.21 0.31 0.07 0.23 0.25Alabama Russell 1.05 0.45 1.24 0.14 0.22 0.05 0.18 0.24Delaware New Castle 0.05 < 0.05 0.10 0.12 0.15 < 0.05 0.11 < 0.05District of Columbia 0.08 < 0.05 0.15 0.13 0.19 0.05 0.16 < 0.05Georgia Bibb 0.62 0.35 1.85 0.13 0.20 < 0.05 0.17 0.19Georgia Clarke 0.63 0.25 2.42 0.17 0.27 0.05 0.23 0.18Georgia Clayton 0.66 0.22 1.81 0.13 0.21 < 0.05 0.18 0.16Georgia Cobb 0.73 0.20 2.07 0.14 0.23 < 0.05 0.20 0.16Georgia DeKalb 0.64 0.22 1.84 0.13 0.21 < 0.05 0.18 0.16Georgia Floyd 0.95 0.13 1.42 0.14 0.22 < 0.05 0.20 0.14Georgia Fulton 0.54 0.18 1.87 0.13 0.20 < 0.05 0.18 0.14Georgia Walker 0.91 0.13 1.16 0.18 0.32 0.05 0.30 0.15Illinois Cook 0.06 < 0.05 < 0.05 1.04 0.66 0.28 0.13 0.06Illinois Madison 0.13 < 0.05 0.09 0.80 0.47 0.27 0.21 0.18Illinois St. Clair 0.12 < 0.05 0.08 0.83 0.48 0.28 0.20 0.18Indiana Clark 0.29 0.05 0.32 0.39 0.90 0.11 0.90 0.11Indiana Dubois 0.26 0.05 0.19 0.58 1.22 0.14 0.64 0.13Indiana Lake 0.07 < 0.05 0.05 1.02 0.89 0.26 0.16 0.06Indiana Marion 0.16 < 0.05 0.12 0.76 1.44 0.21 0.42 0.08Indiana Vanderburgh 0.33 0.06 0.21 0.76 1.11 0.18 0.52 0.17Kentucky Fayette 0.30 0.06 0.39 0.32 0.68 0.09 1.10 0.10Kentucky Jefferson 0.27 0.05 0.30 0.38 0.87 0.11 0.86 0.11Maryland Anne Arundel 0.07 < 0.05 0.13 0.13 0.18 0.05 0.14 < 0.05Maryland Baltimore City 0.07 < 0.05 0.12 0.13 0.17 0.05 0.13 < 0.05Michigan Wayne 0.09 < 0.05 0.10 0.42 0.57 0.14 0.19 < 0.05Downwind Nonattainment Counties Upwind States - Part 1State County AL FL GA IL IN IA KY LANew Jersey Union 0.05 < 0.05 0.08 0.12 0.15 0.05 0.10 < 0.05New York New York < 0.05 < 0.05 0.08 0.11 0.14 < 0.05 0.09 < 0.05North Carolina Catawba 0.28 0.09 0.56 0.15 0.26 0.05 0.29 0.08North Carolina Davidson 0.23 0.10 0.56 0.12 0.20 < 0.05 0.21 0.07Ohio Butler 0.18 < 0.05 0.24 0.38 0.87 0.11 0.65 0.08Ohio Cuyahoga 0.08 < 0.05 0.11 0.32 0.43 0.11 0.23 < 0.05Ohio Franklin 0.20 < 0.05 0.26 0.40 0.67 0.11 0.43 0.05Ohio Hamilton 0.18 < 0.05 0.24 0.38 0.91 0.12 0.68 0.09Ohio Jefferson 0.08 < 0.05 0.11 0.18 0.25 0.07 0.20 < 0.05Ohio Lawrence 0.21 < 0.05 0.25 0.21 0.40 0.06 0.62 0.07Ohio Mahoning 0.08 < 0.05 0.10 0.25 0.32 0.09 0.21 < 0.05Ohio Montgomery 0.15 < 0.05 0.20 0.44 0.85 0.12 0.46 0.06Ohio Scioto 0.25 0.05 0.29 0.25 0.48 0.08 0.67 0.09Ohio Stark 0.09 < 0.05 0.12 0.26 0.36 0.09 0.25 < 0.05Ohio Summit 0.09 < 0.05 0.12 0.30 0.41 0.10 0.25 < 0.05Pennsylvania Allegheny 0.10 < 0.05 0.14 0.21 0.31 0.08 0.24 < 0.05Pennsylvania Beaver 0.08 < 0.05 0.10 0.19 0.26 0.07 0.18 < 0.05Pennsylvania Berks 0.07 < 0.05 0.10 0.14 0.19 0.06 0.15 < 0.05Pennsylvania Cambria 0.07 < 0.05 0.09 0.14 0.20 0.05 0.16 < 0.05Pennsylvania Dauphin 0.07 < 0.05 0.12 0.14 0.19 0.06 0.15 < 0.05Pennsylvania Delaware 0.05 < 0.05 0.10 0.12 0.15 0.05 0.11 < 0.05Pennsylvania Lancaster 0.07 < 0.05 0.12 0.15 0.20 0.06 0.16 < 0.05Pennsylvania Philadelphia 0.05 <0.05 0.10 0.12 0.15 0.05 0.11 < 0.05Pennsylvania Washington 0.09 < 0.05 0.12 0.18 0.27 0.07 0.21 < 0.05Pennsylvania Westmoreland 0.09 < 0.05 0.12 0.18 0.26 0.06 0.22 < 0.05Pennsylvania York 0.06 < 0.05 0.10 0.14 0.18 0.05 0.14 < 0.05Tennessee Hamilton 0.98 0.14 1.27 0.20 0.34 0.06 0.32 0.15Downwind Nonattainment Counties Upwind States - Part 1State County AL FL GA IL IN IA KY LATennessee Knox 0.48 0.10 0.86 0.17 0.32 0.05 0.44 0.13West Virginia Berkeley 0.08 < 0.05 0.13 0.14 0.21 0.05 0.17 < 0.05West Virginia Brooke 0.07 < 0.05 0.10 0.17 0.24 0.06 0.19 < 0.05West Virginia Cabell 0.24 0.06 0.29 0.21 0.42 0.07 0.71 0.08West Virginia Hancock 0.08 < 0.05 0.11 0.18 0.26 0.07 0.19 < 0.05West Virginia Kanawha 0.18 0.05 0.24 0.20 0.35 0.06 0.46 0.06West Virginia Marion 0.10 < 0.05 0.15 0.17 0.26 0.05 0.24 < 0.05West Virginia Marshall 0.09 < 0.05 0.13 0.18 0.27 0.07 0.23 < 0.05West Virginia Ohio 0.09 < 0.05 0.13 0.18 0.27 0.06 0.23 < 0.05West Virginia Wood 0.12 < 0.05 0.16 0.19 0.31 0.06 0.31 < 0.05Downwind Nonattainment Counties Upwind States - Part 2State County MD/DC MI MN MS MO NY NC OHAlabama Jefferson 0.07 0.08 < 0.05 0.23 0.15 < 0.05 0.13 0.32Alabama Russell 0.09 0.07 < 0.05 0.17 0.11 < 0.05 0.17 0.29Connecticut New Haven 0.15 0.12 < 0.05 < 0.05 < 0.05 0.31 0.07 0.31Delaware New Castle 0.45 0.21 < 0.05 < 0.05 0.05 0.20 0.16 0.58District of Columbia 0.51 0.17 < 0.05 < 0.05 0.06 0.11 0.26 0.77Georgia Bibb 0.11 0.07 < 0.05 0.12 0.10 < 0.05 0.22 0.32Georgia Clarke 0.10 0.10 < 0.05 0.10 0.12 < 0.05 0.31 0.43Georgia Clayton 0.06 0.07 < 0.05 0.09 0.09 < 0.05 0.19 0.30Georgia Cobb 0.06 0.08 < 0.05 0.09 0.10 < 0.05 0.18 0.34Georgia DeKalb 0.06 0.07 < 0.05 0.09 0.09 < 0.05 0.19 0.31Georgia Floyd 0.05 0.08 < 0.05 0.08 0.10 < 0.05 0.12 0.29Georgia Fulton 0.06 0.07 < 0.05 0.08 0.09 < 0.05 0.19 0.30Georgia Walker 0.06 0.11 < 0.05 0.09 0.13 < 0.05 0.14 0.41Illinois Cook < 0.05 0.40 0.21 < 0.05 0.31 < 0.05 < 0.05 0.24Illinois Madison < 0.05 0.13 0.13 0.09 1.05 < 0.05 < 0.05 0.21Illinois St. Clair < 0.05 0.13 0.13 0.08 1.07 < 0.05 < 0.05 0.21Indiana Clark < 0.05 0.20 < 0.05 0.06 0.22 < 0.05 0.06 0.66Indiana Dubois < 0.05 0.19 0.05 0.06 0.30 < 0.05 < 0.05 0.44Indiana Lake < 0.05 0.35 0.20 < 0.05 0.29 < 0.05 < 0.05 0.30Indiana Marion < 0.05 0.30 0.10 0.05 0.27 < 0.05 < 0.05 0.66Indiana Vanderburgh < 0.05 0.17 0.07 0.09 0.40 < 0.05 < 0.05 0.36Kentucky Fayette < 0.05 0.25 < 0.05 0.06 0.17 < 0.05 0.08 1.08Kentucky Jefferson < 0.05 0.19 < 0.05 0.06 0.20 < 0.05 0.05 0.62Maryland Anne Arundel 0.86 0.18 < 0.05 < 0.05 0.06 0.14 0.24 0.72Maryland Baltimore City 0.75 0.19 < 0.05 < 0.05 0.06 0.15 0.20 0.71Downwind Nonattainment Counties Upwind States - Part 2State County MD/DC MI MN MS MO NY NC OHMichigan Wayne 0.05 1.04 0.11 < 0.05 0.14 0.08 < 0.05 1.04New Jersey Union 0.25 0.20 < 0.05 < 0.05 0.05 0.34 0.11 0.51New York New York 0.25 0.19 < 0.05 < 0.05 < 0.05 0.51 0.11 0.51North Carolina Catawba 0.10 0.13 < 0.05 < 0.05 0.10 < 0.05 0.92 0.63North Carolina Davidson 0.14 0.12 < 0.05 < 0.05 0.07 0.06 1.21 0.59Ohio Butler < 0.05 0.29 < 0.05 < 0.05 0.17 < 0.05 0.05 1.25Ohio Cuyahoga 0.08 0.62 0.06 < 0.05 0.12 0.09 0.07 1.75Ohio Franklin < 0.05 0.41 0.05 < 0.05 0.15 < 0.05 0.06 1.67Ohio Hamilton < 0.05 0.30 < 0.05 0.05 0.18 < 0.05 0.06 1.29Ohio Jefferson 0.13 0.29 < 0.05 < 0.05 0.09 0.08 0.10 1.54Ohio Lawrence 0.06 0.20 < 0.05 < 0.05 0.11 < 0.05 0.14 1.28Ohio Mahoning 0.11 0.44 0.05 < 0.05 0.10 0.11 0.08 1.67Ohio Montgomery < 0.05 0.36 0.05 < 0.05 0.18 < 0.05 < 0.05 1.38Ohio Scioto 0.06 0.24 < 0.05 0.05 0.13 < 0.05 0.13 1.51Ohio Stark 0.10 0.42 < 0.05 < 0.05 0.11 0.09 0.08 1.75Ohio Summit 0.08 0.54 0.05 < 0.05 0.12 0.09 0.07 1.75Pennsylvania Allegheny 0.19 0.33 0.05 < 0.05 0.10 0.11 0.13 1.67Pennsylvania Beaver 0.13 0.34 < 0.05 < 0.05 0.09 0.10 0.09 1.67Pennsylvania Berks 0.48 0.25 < 0.05 < 0.05 0.07 0.24 0.13 0.76Pennsylvania Cambria 0.23 0.21 < 0.05 < 0.05 0.08 0.11 0.09 1.08Pennsylvania Dauphin 0.43 0.23 < 0.05 < 0.05 0.07 0.17 0.15 0.87Pennsylvania Delaware 0.44 0.20 < 0.05 < 0.05 0.05 0.19 0.15 0.57Pennsylvania Lancaster 0.69 0.27 < 0.05 < 0.05 0.07 0.21 0.17 0.84Pennsylvania Philadelphia 0.44 0.20 < 0.05 < 0.05 0.05 0.19 0.16 0.57Pennsylvania Washington 0.16 0.29 < 0.05 < 0.05 0.09 0.09 0.12 1.44Pennsylvania Westmoreland 0.20 0.27 < 0.05 < 0.05 0.09 0.09 0.12 1.43Pennsylvania York 0.47 0.24 < 0.05 < 0.05 0.06 0.18 0.14 0.83Downwind Nonattainment Counties Upwind States - Part 2State County MD/DC MI MN MS MO NY NC OHTennessee Hamilton 0.06 0.12 < 0.05 0.10 0.14 < 0.05 0.15 0.44Tennessee Knox 0.06 0.12 < 0.05 0.06 0.14 < 0.05 0.20 0.54West Virginia Berkeley 0.34 0.18 < 0.05 < 0.05 0.08 0.10 0.15 0.91West Virginia Brooke 0.12 0.27 < 0.05 < 0.05 0.08 0.07 0.09 1.47West Virginia Cabell 0.06 0.20 < 0.05 < 0.05 0.12 < 0.05 0.17 1.12West Virginia Hancock 0.12 0.29 < 0.05 < 0.05 0.09 0.08 0.10 1.50West Virginia Kanawha 0.09 0.19 < 0.05 < 0.05 0.11 < 0.05 0.22 1.15West Virginia Marion 0.15 0.21 < 0.05 < 0.05 0.08 0.06 0.15 1.24West Virginia Marshall 0.13 0.27 < 0.05 < 0.05 0.08 0.07 0.12 1.48West Virginia Ohio 0.13 0.27 < 0.05 < 0.05 0.08 0.07 0.12 1.49West Virginia Wood 0.09 0.24 < 0.05 < 0.05 0.08 < 0.05 0.13 1.59Downwind Nonattainment Counties Upwind States - Part 3State County PA SC TN TX VA WV WIAlabama Jefferson 0.10 0.12 0.32 0.19 0.06 0.13 0.06Alabama Russell 0.14 0.23 0.22 0.15 0.08 0.16 < 0.05Connecticut New Haven 0.39 < 0.05 < 0.05 < 0.05 0.08 0.13 < 0.05Delaware New Castle 0.88 0.06 0.08 < 0.05 0.25 0.33 0.07District of Columbia 0.69 0.09 0.13 < 0.05 0.44 0.46 0.07Georgia Bibb 0.19 0.32 0.23 0.14 0.10 0.16 < 0.05Georgia Clarke 0.21 0.40 0.40 0.13 0.11 0.21 0.06Georgia Clayton 0.13 0.23 0.27 0.11 0.07 0.14 < 0.05Georgia Cobb 0.12 0.20 0.34 0.11 0.07 0.15 < 0.05Georgia DeKalb 0.13 0.22 0.31 0.11 0.07 0.14 < 0.05Georgia Floyd 0.08 0.13 0.34 0.10 0.05 0.13 < 0.05Georgia Fulton 0.12 0.21 0.30 0.10 0.07 0.14 < 0.05Georgia Walker 0.10 0.16 0.65 0.12 0.06 0.16 0.06Illinois Cook < 0.05 < 0.05 0.08 0.12 < 0.05 < 0.05 0.56Illinois Madison < 0.05 < 0.05 0.18 0.29 < 0.05 0.05 0.16Illinois St. Clair < 0.05 < 0.05 0.17 0.28 < 0.05 < 0.05 0.16Indiana Clark 0.08 0.05 0.36 0.13 < 0.05 0.16 0.12Indiana Dubois 0.06 < 0.05 0.30 0.16 < 0.05 0.10 0.14Indiana Lake < 0.05 < 0.05 0.09 0.11 < 0.05 0.05 0.43Indiana Marion 0.08 < 0.05 0.18 0.12 < 0.05 0.11 0.23Indiana Vanderburgh 0.06 < 0.05 0.36 0.19 < 0.05 0.09 0.15Kentucky Fayette 0.12 0.08 0.44 0.11 0.05 0.29 0.12Kentucky Jefferson 0.08 0.05 0.33 0.12 < 0.05 0.15 0.11Maryland Anne Arundel 0.78 0.08 0.12 < 0.05 0.39 0.41 0.07Maryland Baltimore City 0.89 0.07 0.11 < 0.05 0.31 0.41 0.07Downwind Nonattainment Counties Upwind States - Part 3State County PA SC TN TX VA WV WIMichigan Wayne 0.28 < 0.05 0.09 0.08 < 0.05 0.17 0.27New Jersey Union 0.81 0.05 0.07 < 0.05 0.14 0.25 0.07New York New York 0.81 < 0.05 0.06 < 0.05 0.13 0.24 0.06North Carolina Catawba 0.21 0.28 0.62 0.09 0.22 0.33 0.06North Carolina Davidson 0.30 0.39 0.30 0.07 0.25 0.30 0.05Ohio Butler 0.10 < 0.05 0.23 0.10 < 0.05 0.22 0.13Ohio Cuyahoga 0.34 < 0.05 0.12 0.06 0.05 0.29 0.17Ohio Franklin 0.19 < 0.05 0.19 0.09 < 0.05 0.34 0.15Ohio Hamilton 0.11 0.05 0.25 0.11 < 0.05 0.23 0.15Ohio Jefferson 0.54 < 0.05 0.13 0.06 0.08 0.61 0.10Ohio Lawrence 0.20 0.07 0.33 0.07 0.09 0.60 0.09Ohio Mahoning 0.54 < 0.05 0.11 0.06 0.07 0.43 0.13Ohio Montgomery 0.11 < 0.05 0.18 0.10 < 0.05 0.24 0.16Ohio Scioto 0.18 0.09 0.38 0.10 0.08 0.52 0.11Ohio Stark 0.44 < 0.05 0.12 0.07 0.07 0.43 0.13Ohio Summit 0.36 < 0.05 0.12 0.06 0.06 0.34 0.15Pennsylvania Allegheny 0.92 < 0.05 0.16 0.07 0.13 0.84 0.11Pennsylvania Beaver 0.59 < 0.05 0.12 0.05 0.08 0.48 0.11Pennsylvania Berks 1.38 0.05 0.10 < 0.05 0.21 0.40 0.08Pennsylvania Cambria 1.12 < 0.05 0.10 0.05 0.15 0.53 0.07Pennsylvania Dauphin 1.17 0.05 0.11 < 0.05 0.23 0.45 0.08Pennsylvania Delaware 0.87 0.06 0.08 < 0.05 0.24 0.32 0.07Pennsylvania Lancaster 1.54 0.06 0.11 < 0.05 0.28 0.47 0.09Pennsylvania Philadelphia 0.85 0.06 0.09 < 0.05 0.24 0.32 0.07Pennsylvania Washington 0.67 < 0.05 0.14 0.06 0.11 0.72 0.10Pennsylvania Westmoreland 0.94 < 0.05 0.14 0.05 0.14 0.81 0.10Pennsylvania York 1.43 < 0.05 0.10 < 0.05 0.23 0.44 0.07Downwind Nonattainment Counties Upwind States - Part 3State County PA SC TN TX VA WV WITennessee Hamilton 0.10 0.18 0.69 0.12 0.06 0.17 0.06Tennessee Knox 0.11 0.19 1.20 0.11 0.06 0.24 0.06West Virginia Berkeley 0.84 0.05 0.12 0.05 0.24 0.59 0.07West Virginia Brooke 0.52 < 0.05 0.12 0.05 0.08 0.57 0.09West Virginia Cabell 0.20 0.09 0.39 0.08 0.11 0.56 0.09West Virginia Hancock 0.52 < 0.05 0.12 0.06 0.08 0.58 0.10West Virginia Kanawha 0.29 0.08 0.31 0.07 0.17 1.10 0.09West Virginia Marion 0.53 < 0.05 0.17 0.05 0.14 1.06 0.08West Virginia Marshall 0.53 < 0.05 0.15 0.06 0.10 0.74 0.09West Virginia Ohio 0.54 < 0.05 0.15 0.05 0.10 0.75 0.09West Virginia Wood 0.32 0.05 0.18 0.06 0.09 0.72 0.09

# Technic al Suppor t Documen t

for t he Fi nal

Clean Air Interstate Rule

Appen dix I

Proje cted Visibil ity for the

20% Best and 20 % W orst Days in

Class I Ar eas

## Example Calculation of the Predicted Change in Visibility on the 20% Worst Days at an IMPROVE Sit e

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 Ap pend ix I-2

. Current vi sibility (199 8-2002) on the 20% best days and 20% worst days at 11 6 IMPROVE site s.

Class I Area IMP ROVE
Rep resentativ e Site IMP ROVE

Site

Ident ifi er State Numberof Year s of

Com plete Da ta 19 98 -20 02 Ba seline

Visib il it y (i n dv)
20% Best Days 19 98 -20 02 Ba seline

Visib il it y (i n dv)

20% W orst

Days

Acadia Acadia ACAD ME 5 8.4 22 .7

Agua Tib ia Agua Tib ia AG TI CA 2 10 .0 23 .2

Alpine La

kes Snoq ualmie Pass SNPA WA 3 5.6 18 .0

Anaconda - Pintle r Sula SULA MT 2 3.1 12 .3

Arches Cany onlands CANY UT 5 5.3 12 .0

Badla nds Badla nds BADL SD 5 7.1 17 .3

Bandelier Bandelier BAND NM 5 6.3 13 .2

Big Bend Big Bend BI BE TX 4 7.7 18 .4

Black Cany on of

the

Gunnison Weminuche WEMI CO 4 4.4 11 .6

Bob Marshall M ontu re MONT MT 2 4.5 14 .2

Boundary

Water s Canoe

Area Bo und ary Waters BOWA MN 3 6.4 20 .0

Bridger Bridger B R ID WY 5 3.8 11 .5

Brig anti ne Brig anti ne B R IG NJ 4 13 .6 27 .6

Bry ce Cany on Bry ce Cany on BRCA UT 5 4.1 12 .0

Cabinet Mountains Cabinet Mountains CABI MT 2 4.1 13 .8

Caney Cre ek Caney Cre ek CACR AR 2 12 .1 25 .9

Cany onlands Cany onlands CANY UT 5 5.3 12 .0

Cape R omain Cape R omain RO MA SC 4 13 .8 25 .9

Cari bou Lassen Vo lcanic LAVO CA 5 3.3 14 .8

Carl sbad Caver

ns Guada lupe Mounta ins GU MO NM 5 7.2 17 .6

Chassaho witzka Chassaho witzka CHAS FL 4 16 .4 25 .7

Chiri cahua

NM Chiri cahua C HIR AZ 5 5.9 13 .9

## Class I Area IMP ROVE
Rep resentativ e Site IMP ROVE

Site

Ident ifi er State Numberof Year s of

Com plete Da ta 19 98 -20 02 Ba seline

Visib il it y (i n dv)
20% Best Days 19 98 -20 02 Ba seline

Visib il it y (i n dv)

20% W orst

Days

Ch iricahu a W Chiri cahua C HIR AZ 5 5.9 13 .9

Crat ers of

the Moon Crat ers of the Moon CRMO ID 2 5.0 14 .7

Desol ati on Bliss B LIS CA 3 3.5 12 .9

Dolly Sods Dolly Sods DOSO WV 5 13 .0 27 .6

Dome Land Dome Land DO ME CA 2 5.4 20 .3

Eagle C ap Star key STAR OR 2 5.6 19 .6

Eagles Nest White River WHRI CO 2 2.8 11 .3

Emigr ant Yosem ite YOSE CA 5 4.0 17 .6

Evergl ades Evergl ades EVER FL 2 10 .8 20 .3

Fitzpa tri ck Bridger B R ID WY 5 3.8 11 .5

Fla t Tops White River WHRI CO 2 2.8 11 .3

Galiuro Chiri cahua C HIR AZ 5 5.9 13 .9

Gat es of the Mounta ins Gat es of the Mounta ins GA MO MT 2 3.1 11 .2

Gila Gi la C liffs GI CL NM 4 5.1 13 .5

Glaci er Glaci er GLAC MT 4 7.3 19 .5

Glacie r Peak North Casca des NOCA WA 2 2.8 14 .0

Gra nd Teton Yell owstone 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

Grea t Smoky Mountains Grea

t Smoky Mountains GRSM TN 5 14 .2 29 .5

Guada lupe Mounta ins Guada lupe Mounta ins GU MO TX 5 7.2 17 .6

Hell s Cany on Hell s Cany on HECA OR 2 5.4 18 .1

Isle Royale Isle Royale I SLE MI 3 6.2 21 .1

James Riv er Fa ce James Riv er Fa ce JA RI VA 3 15 .5 28 .5

Jarbidge Jarbidge JA RB NV 3 3.0 12 .6

## Class I Area IMP ROVE
Rep resentativ e Site IMP ROVE

Site

Ident ifi er State Numberof Year s of

Com plete Da ta 19 98 -20 02 Ba seline

Visib il it y (i n dv)
20% Best Days 19 98 -20 02 Ba seline

Visib il it y (i n dv)

20% W orst

Days

Joshua Tree Joshua Tree JOSH CA 2 6.6 19 .5

Joyce Kilmer - Slick

rock Grea

t Smoky Mountains GRSM NC 5 14 .2 29 .5

Kalmiop sis Kalmiop sis KALM OR 2 4.7 14 .8

Kings Cany on Sequ oia SEQU CA 3 8.8 23 .5

La Garita Weminuche WEMI CO 4 4.4 11 .6

Lassen Vo lcanic Lassen Vo lcanic LAVO CA 5 3.3 14 .8

Lava Beds Lava Beds LABE CA 2 3.7 16 .6

Linville Go rge Linville Go rge 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

Mam moth Cave Mam moth Cave MACA KY 4 16 .5 30 .2

Marble M oun tain Trinity T R IN CA 1 3.5 17 .1

Maroo n Be lls -

Snow mass White River WHRI CO 2 2.8 11 .3

M azatzal Ik e' s Back bone I KBA AZ 2 6.1 13 .1

Me dicine L ake Me dicine L ake MELA MT 3 7.5 17 .7

Mesa Ver

de Mesa Ver de MEVE CO 5 5.5 12 .8

Min go Min go MING MO 2 14 .1 27 .5

Missi on Mountains M ontu re MONT MT 2 4.5 14 .2

Mokelumne Bliss B LIS CA 3 3.5 12 .9

M oos eho rn M oos eho rn MOOS ME 5 8.6 21 .4

Mount Hood Mount Hood MOHO OR 2 2.5 14 .0

Mount Jef

fe rson Th ree Siste rs THSI OR 5 2.8 15 .7

Moun t Rainie r Moun t Rainie r MORA WA 5 4.9 18 .9

Mount Washing ton Th ree Siste rs THSI OR 5 2.8 15 .7

Moun t Zir

kel Moun t Zir kel MOZI CO 4 4.4 11 .7

## Class I Area IMP ROVE
Rep resentativ e Site IMP ROVE

Site

Ident ifi er State Numberof Year s of

Com plete Da ta 19 98 -20 02 Ba seline

Visib il it y (i n dv)
20% Best Days 19 98 -20 02 Ba seline

Visib il it y (i n dv)

20% W orst

Days

North Casca des North Casca des NOCA WA 2 2.8 14 .0

Okef enokee Okef enokee OKEF GA 5 15 .5 26 .4

Otter Creek Dolly Sods DOSO WV 5 13 .0 27 .6

Pasa yt en Pasa yt en PASA WA 2 2.9 14 .7

Petrified Forest Petrified Forest PEFO AZ 5 6.3 13 .5

Pine M oun tain Ik e' s Back bone I KBA AZ 2 6.1 13 .1

Presid ential R ange - D ry Great Gulf GRGU NH 2 7.8 23 .2

Raw ah Moun t Zir kel MOZI CO 4 4.4 11 .7

Red Rock Lakes Yell owstone YELL WY 4 3.7 12 .1

Redwood Redwood REDW CA 5 5.0 16 .5

Roc ky Mou ntain Roc ky Mou ntain RO MO CO 5 3.7 14 .1

Roo sevelt Camp obello M oos eho rn MOOS ME 5 8.6 21 .4

Salt Creek Salt Creek SA CR NM 2 8.3 17 .7

San Gorgo nio San Gorgo nio SAGO CA 4 6.8 21 .5

San Jacinto San Gorgo nio SAGO CA 4 6.8 21 .5

San Ped ro Parks San Ped ro Parks SAPE NM 2 3.5 11 .4

Sawtooth Sawtooth SA WT ID 2 4.3 13 .6

Scap egoat M ontu re MONT MT 2 4.5 14 .2

Selwa y - Bitt err oot Sula SULA MT 2 3.1 12 .3

Seney Seney SENE MI 3 6.4 23 .8

Sequ oia Sequ oia SEQU CA 3 8.8 23 .5

She nand oah She nand oah SHEN VA 4 12 .2 27 .6

Sier ra Ancha Sier ra 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 IMP ROVE
Rep resentativ e Site IMP ROVE

Site

Ident ifi er State Numberof Year s of

Com plete Da ta 19 98 -20 02 Ba seline

Visib il it y (i n dv)
20% Best Days 19 98 -20 02 Ba seline

Visib il it y (i n dv)

20% W orst

Days

Strawberry Mou ntain Star key STAR OR 2 5.6 19 .6

Supers tit ion To nto TONT AZ 3 7.4 14 .7

Swanquart er Swanquart er SWAN NC 2 12 .4 24 .6

Sy camor e Cany on Sy camor e Cany on SYCA AZ 2 6.7 16 .1

Teton Yell owstone YELL WY 4 3.7 12 .1

Th eodo re Roo sevelt Th eodo re Roo sevelt THRO ND 2 7.8 17 .6

Thousand Lakes Lassen Vo lcanic LAVO CA 5 3.3 14 .8

Th ree Siste rs Th ree Siste rs THSI OR 5 2.8 15 .7

UL Bend UL Bend ULBE MT 2 4.6 14 .7

Upp er Buffalo Upp er Buffalo UPBU AR 5 12 .2 25 .5

Voyageu rs Voyageu rs VOYA MN 3 6.4 18 .4

Weminuche Weminuche WEMI CO 4 4.4 11 .6

West E lk White River WHRI CO 2 2.8 11 .3

Win d Cave Win d Cave WI CA SD 3 5.7 16 .0

Wolf Is land Okef enokee OKEF GA 5 15 .5 26 .4

Yell owstone Yell owstone YELL WY 4 3.7 12 .1

Yolla Boll y - Middle Eel Trinity T R IN CA 1 3.5 17 .1

Yosem ite Yosem ite YOSE CA 5 4.0 17 .6

Zion Zion ZION UT 2 5.4 13 .5

# Table Ap pend ix I-3

. Pr oje ct ed vi sibi li ty for the 201 0 an d 201 5 bas el ine and v isi bili ty improv emen t fr om the CAIR str ate gy on the 20% best days, at 116 Class I areas.

Clas s I Ar eas (IMPROV E Site) State 19 98 -20 02 Ba seline

Visib il it y (i n dv)

20% Best Da ys 20 10 Ba seline

Visib il it y (dv) 201 0 V isibility

Improvemen t f

rom CAIR ( dv) 20 15 Ba seline

Visib il it y (dv) 201 5 V isibility

Improvemen t f

rom CAIR ( dv)

Acadia ME 8.4 8.0 -0.2 8.2 -0.2

Agua Tib ia CA 10 .0 10 .0 0.0 10 .1 0.0

Alpine La

kes WA 5.6 4.9 0.0 5.6 0.0

Anaconda - Pintle r MT 3.1 3.1 0.0 3.1 0.0

Arches UT 5.3 5.3 0.0 5.4 0.0

Badla nds 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 Cany on of

the Gunni son 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 Wat ers Canoe A rea MN 6.4 6.1 0.0 6.2 0.0

Bridger WY 3.8 3.5 0.0 3.6 0.0

Brig anti ne NJ 13 .6 13 .4 -0.4 15 .0 -0.4

Bry ce Cany on 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 Cre ek AR 12 .1 11 .5 -0.1 11 .6 -0.1

Cany onlands UT 5.3 5.3 0.0 5.4 0.0

Cape R omain SC 13 .8 13 .2 -0.3 13 .4 -0.4

Cari bou CA 3.3 3.2 0.0 3.3 0.0

Carl sbad Caver

ns NM 7.2 7.3 0.0 7.4 0.0

Chassaho witzka FL 16 .4 15 .1 -0.2 15 .2 -0.6

Chiri cahua

NM AZ 5.9 5.8 0.0 5.9 0.0

Ch iricahu a W AZ 5.9 5.8 0.0 5.9 0.0

## Clas s I Ar eas

(IMPROV E Site) State 19 98 -20 02 Ba seline

Visib il it y (i n dv)

20% Best Da ys 20 10 Ba seline

Visib il it y (dv) 201 0 V isibility

Improvemen t f

rom CAIR ( dv) 20 15 Ba seline

Visib il it y (dv) 201 5 V isibility

Improvemen t f

rom CAIR ( dv)

Crat ers of

the Moon ID 5.0 4.9 0.0 4.9 0.0

Desol ati on 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 C ap OR 5.6 5.3 0.0 5.3 0.0

Eagles Nest CO 2.8 2.7 0.0 2.8 0.0

Emigr ant CA 4.0 4.0 0.0 4.1 0.0

Evergl ades FL 10 .8 10 .5 -0.1 11 .3 -0.1

Fitzpa tri ck WY 3.8 3.5 0.0 3.6 0.0

Fla t Tops CO 2.8 2.7 0.0 2.7 0.0

Galiuro AZ 5.9 6.0 0.0 6.1 0.0

Gat es of the Mounta ins MT 3.1 2.9 0.0 2.9 0.0

Gila NM 5.1 5.2 0.0 5.3 0.0

Glaci er MT 7.3 6.9 0.0 6.9 0.0

Glacie r Peak WA 2.8 2.4 0.0 2.5 0.0

Gra nd 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

Grea t Smoky Mountains TN 14 .2 14 .0 -0.5 14 .0 -0.7

Guada lupe Mounta ins TX 7.2 7.4 0.0 7.4 0.0

Hell s Cany on 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 Riv er Fa ce 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 - Slick

rock NC 14 .2 14 .0 -0.5 14 .0 -0.7

## Clas s I Ar eas

(IMPROV E Site) State 19 98 -20 02 Ba seline

Visib il it y (i n dv)

20% Best Da ys 20 10 Ba seline

Visib il it y (dv) 201 0 V isibility

Improvemen t f

rom CAIR ( dv) 20 15 Ba seline

Visib il it y (dv) 201 5 V isibility

Improvemen t f

rom CAIR ( dv)

Kalmiop sis OR 4.7 4.6 0.0 4.9 0.0

Kings Cany on 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 Vo lcanic 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 Go rge 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

Mam moth Cave KY 16 .5 16 .1 -0.5 16 .0 -0.6

Marble M oun tain CA 3.5 3.5 0.0 3.6 0.0

Maroo n Be lls - Snowm ass CO 2.8 2.6 0.0 2.7 0.0

M azatzal AZ 6.1 6.3 0.0 6.4 0.0

Me dicine L ake MT 7.5 7.1 0.0 7.1 0.0

Mesa Ver

de CO 5.5 5.4 0.0 5.5 0.0

Min go MO 14 .1 13 .6 -0.2 13 .7 -0.2

Missi on Mountains MT 4.5 4.1 0.0 4.0 0.0

Mokelumne CA 3.5 3.5 0.0 3.6 0.0

M oos eho rn 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 Jef

fe rson OR 2.8 2.6 0.0 2.7 0.0

Moun t Rainie r WA 4.9 4.3 0.0 6.8 0.0

Mount Washing ton OR 2.8 2.7 0.0 3.3 0.0

Moun t Zir

kel CO 4.4 4.3 0.0 4.4 0.0

North Casca des WA 2.8 2.6 0.0 2.8 0.0

Okef enokee GA 15 .5 15 .1 -0.3 15 .4 -0.4

Otter Creek WV 13 .0 12 .9 -1.1 12 .7 -1.3

## Clas s I Ar eas

(IMPROV E Site) State 19 98 -20 02 Ba seline

Visib il it y (i n dv)

20% Best Da ys 20 10 Ba seline

Visib il it y (dv) 201 0 V isibility

Improvemen t f

rom CAIR ( dv) 20 15 Ba seline

Visib il it y (dv) 201 5 V isibility

Improvemen t f

rom CAIR ( dv)

Pasa yt en 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 M oun tain AZ 6.1 6.2 0.0 6.4 0.0

Presid ential R ange - D ry NH 7.8 7.6 -0.1 7.7 -0.1

Raw ah 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

Roc ky Mou ntain CO 3.7 3.7 0.0 3.8 0.0

Roo sevelt Camp obello 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 Gorgo nio 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 Ped ro Parks NM 3.5 3.5 0.0 3.5 0.0

Sawtooth ID 4.3 4.2 0.0 4.2 0.0

Scap egoat MT 4.5 4.3 0.0 4.3 0.0

Selwa y - Bitt err oot MT 3.1 3.1 0.0 3.1 0.0

Seney MI 6.4 6.2 0.0 6.3 0.0

Sequ oia CA 8.8 8.7 0.0 9.0 -0.0

She nand oah VA 12 .2 12 .0 -1.0 12 .1 -1.3

Sier ra 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 Mou ntain OR 5.6 5.4 0.0 5.4 0.0

Supers tit ion AZ 7.4 7.4 0.0 7.5 0.0

Swanquart er NC 12 .4 12 .1 -0.1 12 .2 -0.2

Sy camor e Cany on AZ 6.7 6.7 0.0 6.9 0.0

## Clas s I Ar eas

(IMPROV E Site) State 19 98 -20 02 Ba seline

Visib il it y (i n dv)

20% Best Da ys 20 10 Ba seline

Visib il it y (dv) 201 0 V isibility

Improvemen t f

rom CAIR ( dv) 20 15 Ba seline

Visib il it y (dv) 201 5 V isibility

Improvemen t f

rom CAIR ( dv)

Teton WY 3.7 3.6 0.0 3.6 0.0

Th eodo re Roo sevelt ND 7.8 7.5 0.0 7.5 0.0

Thousand Lakes CA 3.3 3.2 0.0 3.3 0.0

Th ree Siste rs OR 2.8 2.7 0.0 3.3 0.0

UL Bend MT 4.6 4.3 0.0 4.3 0.0

Upp er Buffalo AR 12 .2 11 .8 -0.1 11 .8 -0.1

Voyageu rs MN 6.4 6.3 0.0 6.5 0.0

Weminuche CO 4.4 4.3 0.0 4.3 0.0

West E lk CO 2.8 2.6 0.0 2.7 0.0

Win d Cave SD 5.7 5.4 0.0 5.5 0.0

Wolf Is land GA 15 .5 15 .2 -0.2 15 .5 -0.3

Yell owstone WY 3.7 3.7 0.0 3.7 0.0

Yolla Boll y - Middle Eel CA 3.5 3.5 0.0 3.5 0.0

Yosem ite CA 4.0 3.9 0.0 4.0 0.0

Zion UT 5.4 5.3 0.0 5.4 0.0

# Tabl e Appe ndix I- 4. P roj ec te d vis ibil ity for the 201 0 an d 201 5 bas el ine and v isi bili ty improv emen t fr om the CAIR str ate gy on

the 20% worst days at 116 IM PROVE sites.

Clas s I Ar eas (IMPROV E Site) State 1998-2002

Ba seline

Visib il it y (i n dv)

20% Best Da ys 20 10 Ba seline

Visib il it y (dv) 201 0 V isibility

Improvemen t f

rom CAIR ( dv) 20 15 Ba seline

Visib il it y (dv) 201 5 V isibility

Improvemen t f

rom CAIR ( dv)

Acadia ME 22 .7 22 .1 -0.9 22 .0 -1.0

Agua Tib ia CA 23 .2 23 .3 0.0 23 .3 0.0

Alpine La

kes WA 18 .0 17 .3 0.0 17 .4 0.0

Anaconda - Pintle r MT 12 .3 12 .1 0.0 12 .2 0.0

Arches UT 12 .0 11 .9 0.0 12 .1 0.0

Badla nds 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 Cany on of

the Gunni son 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 Wat ers Canoe A rea MN 20 .0 19 .4 -0.3 19 .3 -0.3

Bridger WY 11 .5 11 .2 0.0 11 .3 0.0

Brig anti ne NJ 27 .6 27 .3 -1.9 27 .4 -2.1

Bry ce Cany on 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 Cre ek AR 25 .9 25 .4 -1.1 25 .4 -1.3

Cany onlands UT 12 .0 11 .9 0.0 12 .0 0.0

Cape R omain SC 25 .9 25 .4 -1.0 25 .3 -1.4

Cari bou CA 14 .8 14 .6 0.0 14 .6 0.0

Carl sbad Caver

ns NM 17 .6 17 .6 0.0 17 .8 -0.1

Chassaho witzka FL 25 .7 24 .6 -0.9 24 .6 -1.7

Chiri cahua

NM AZ 13 .9 13 .8 -0.1 14 .0 -0.1

## Clas s I Ar eas

(IMPROV E Site) State 1998-2002

Ba seline

Visib il it y (i n dv)

20% Best Da ys 20 10 Ba seline

Visib il it y (dv) 201 0 V isibility

Improvemen t f

rom CAIR ( dv) 20 15 Ba seline

Visib il it y (dv) 201 5 V isibility

Improvemen t f

rom CAIR ( dv)

Ch iricahu a W AZ 13 .9 13 .8 -0.1 14 .0 -0.1

Crat ers of

the Moon ID 14 .7 14 .7 0.0 14 .7 0.0

Desol ati on 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 C ap 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

Evergl ades FL 20 .3 19 .5 -0.4 19 .7 -0.5

Fitzpa tri ck WY 11 .5 11 .2 0.0 11 .3 0.0

Fla t Tops CO 11 .3 11 .3 0.0 11 .4 0.0

Galiuro AZ 13 .9 13 .9 -0.1 14 .2 -0.1

Gat es of the Mounta ins MT 11 .2 10 .8 0.0 10 .8 0.0

Gila NM 13 .5 13 .5 -0.1 13 .6 -0.1

Glaci er MT 19 .5 19 .1 0.0 19 .1 0.0

Glacie r Peak WA 14 .0 13 .5 0.0 13 .8 0.0

Gra nd 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

Grea t Smoky Mountains TN 29 .5 29 .1 -1.9 28 .7 -2.6

Guada lupe Mounta ins TX 17 .6 17 .5 -0.1 17 .6 -0.1

Hell s Cany on 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 Riv er Fa ce VA 28 .5 28 .0 -2.1 27 .6 -2.4

Jarbidge NV 12 .6 12 .6 0.0 12 .8 0.0

## Clas s I Ar eas

(IMPROV E Site) State 1998-2002

Ba seline

Visib il it y (i n dv)

20% Best Da ys 20 10 Ba seline

Visib il it y (dv) 201 0 V isibility

Improvemen t f

rom CAIR ( dv) 20 15 Ba seline

Visib il it y (dv) 201 5 V isibility

Improvemen t f

rom CAIR ( dv)

Joshua Tree CA 19 .5 19 .9 0.0 20 .4 0.0

Joyce Kilmer - Slick

rock NC 29 .5 29 .1 -1.9 28 .7 -2.6

Kalmiop sis OR 14 .8 14 .0 0.0 14 .4 0.0

Kings Cany on 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 Vo lcanic 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 Go rge 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

Mam moth Cave KY 30 .2 29 .8 -1.7 29 .5 -2.5

Marble M oun tain CA 17 .1 16 .7 0.0 16 .8 0.0

Maroo n Be lls - Snowm ass CO 11 .3 11 .2 0.0 11 .3 0.0

M azatzal AZ 13 .1 13 .3 0.0 13 .5 0.0

Me dicine L ake MT 17 .7 17 .2 0.0 17 .1 0.0

Mesa Ver

de CO 12 .8 12 .7 0.0 12 .8 0.0

Min go MO 27 .5 27 .0 -0.8 26 .9 -0.9

Missi on Mountains MT 14 .2 13 .9 0.0 14 .0 0.0

Mokelumne CA 12 .9 12 .7 0.0 12 .8 0.0

M oos eho rn 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 Jef

fe rson OR 15 .7 14 .9 0.0 15 .2 0.0

Moun t Rainie r WA 18 .9 17 .3 0.0 19 .4 0.0

Mount Washing ton OR 15 .7 14 .9 0.0 15 .2 0.0

Moun t Zir

kel CO 11 .7 11 .7 0.0 11 .8 0.0

## Clas s I Ar eas

(IMPROV E Site) State 1998-2002

Ba seline

Visib il it y (i n dv)

20% Best Da ys 20 10 Ba seline

Visib il it y (dv) 201 0 V isibility

Improvemen t f

rom CAIR ( dv) 20 15 Ba seline

Visib il it y (dv) 201 5 V isibility

Improvemen t f

rom CAIR ( dv)

North Casca des WA 14 .0 13 .7 0.0 14 .0 0.0

Okef enokee GA 26 .4 26 .1 -1.0 26 .1 -1.4

Otter Creek WV 27 .6 27 .3 -2.5 27 .0 -2.9

Pasa yt en 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 M oun tain AZ 13 .1 13 .2 0.0 13 .5 0.0

Presid ential R ange - D ry NH 23 .2 22 .7 -1.5 22 .6 -1.8

Raw ah 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

Roc ky Mou ntain CO 14 .1 14 .1 0.0 14 .1 0.0

Roo sevelt Camp obello 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 Gorgo nio 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 Ped ro Parks NM 11 .4 11 .4 0.0 11 .5 0.0

Sawtooth ID 13 .6 13 .4 0.0 13 .5 0.0

Scap egoat MT 14 .2 14 .0 0.0 14 .1 0.0

Selwa y - Bitt err oot MT 12 .3 12 .1 0.0 12 .1 0.0

Seney MI 23 .8 23 .5 -0.7 23 .4 -0.8

Sequ oia CA 23 .5 24 .0 0.0 24 .1 0.0

She nand oah VA 27 .6 27 .2 -2.8 26 .7 -3.3

Sier ra 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

## Clas s I Ar eas

(IMPROV E Site) State 1998-2002

Ba seline

Visib il it y (i n dv)

20% Best Da ys 20 10 Ba seline

Visib il it y (dv) 201 0 V isibility

Improvemen t f

rom CAIR ( dv) 20 15 Ba seline

Visib il it y (dv) 201 5 V isibility

Improvemen t f

rom CAIR ( dv)

Strawberry Mou ntain OR 19 .6 19 .2 0.0 19 .2 0.0

Supers tit ion AZ 14 .7 14 .8 0.0 15 .0 0.0

Swanquart er NC 24 .6 24 .1 -1.4 23 .8 -1.9

Sy camor e Cany on AZ 16 .1 16 .2 0.0 16 .6 0.0

Teton WY 12 .1 12 .1 0.0 12 .1 0.0

Th eodo re Roo sevelt ND 17 .6 17 .0 0.0 16 .9 0.0

Thousand Lakes CA 14 .8 14 .6 0.0 14 .6 0.0

Th ree Siste rs OR 15 .7 14 .9 0.0 15 .2 0.0

UL Bend MT 14 .7 14 .1 0.0 14 .1 0.0

Upp er Buffalo AR 25 .5 25 .0 -0.7 25 .1 -0.8

Voyageu rs MN 18 .4 17 .8 -0.1 17 .7 -0.1

Weminuche CO 11 .6 11 .4 0.0 11 .5 0.0

West E lk CO 11 .3 11 .2 0.0 11 .3 0.0

Win d Cave SD 16 .0 15 .5 -0.1 15 .4 0.0

Wolf Is land GA 26 .4 26 .0 -0.8 26 .1 -1.1

Yell owstone WY 12 .1 12 .1 0.0 12 .1 0.0

Yolla Boll y - Middle Eel CA 17 .1 16 .8 0.0 16 .9 0.0

Yosem ite CA 17 .6 17 .4 0.0 17 .4 0.0

Zion UT 13 .5 13 .2 0.0 13 .3 0.0