Air Quality Modeling 

Final Rule Technical Support Document 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711 

June 2011 
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i

Table of Contents 

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

II. Air Quality Modeling Platform...................................................................5 

 A. Air Quality Model................................................................................5 

 B. Modeling Domains...............................................................................5 

 C. Model Simulation Periods.......................................................................7 

 D. Meteorological Input Data......................................................................7 

 E. Initial and Boundary Concentrations .........................................................10 

F. Model Performance Evaluation for Ozone and PM2.5......................................11 

 III. Projection of Future Nonattainment and Maintenance for Annual PM2.5, 

24-Hour PM2.5, 8-Hour and Ozone.............................................................12 

 A. Procedures for Processing Ambient Ozone and PM2.5 Data................................13 

 B. Projection of Future Design Values and Determination of 

 Nonattainment and Maintenance for Annual and 24-Hour PM2.5 .....................14 

1. Methodology for Projecting Future Annual PM2.5 

Nonattainment and Maintenance ................................................15 

2. Methodology for Projecting Future 24-Hour PM2.5 

Nonattainment and Maintenance ................................................18 

 C. Projection of Future Design Values and Determination of 

Nonattainment and Maintenance for 8-Hour Ozone...................................25 

 

IV. State-by-State Source Contribution Modeling and Results...............................27 

 A. Nonattainment and Maintenance Receptors for the Assessment of PM2.5 

and Ozone Contributions.......................................................................28 

B. Overview of Approach for Source Apportionment Modeling.......................... 34 

C. Approach to Calculating Contributions for Annual and 24-Hour PM2.5...............35 

D. Approach to Calculating Contributions for 8-Hour Ozone .............................38 

 E. Evaluation of Interstate Contributions .....................................................40 

1. Contributions to Annual PM2.5 Nonattainment and Maintenance............42 

2. Contributions to 24-Hour PM2.5 Nonattainment and Maintenance.........43 

3. Contributions to 8-Hour Ozone Nonattainment and Maintenance.........45 

 F. Analysis of Contributions Captured by Various Thresholds.............................47 

V. Air Quality Impacts of the Transport Rule SO2 and NOx Emissions Reductions ......50 

 A. Characterization of Emissions in 2014 for the Remedy Scenario.......................50 

 B. Impacts on Projected PM2.5 Design Value Concentrations...............................54 

 C. Impacts on Annual and 24-Hour PM2.5 Nonattainment and Maintenance............ 57 

 D. Impacts on Projected 8-Hour Ozone Design Value Concentrations...................60 

E. Impacts on Projected 8-Hour Ozone Nonattainment and Maintenance...............61 

 F. Impacts on Visibility in Class 1 Areas.....................................................64 

Appendices 
ii

List of Appendices 

Appendix A. 

Model Performance Evaluation for the 2005-Based Final Transport Rule Air Quality 

Modeling Platform 

Appendix B. 

2003-2007 Base Period and Projected 2012 and 2014 Design Values for Monitoring Sites in 

the Eastern Modeling Domain 

Appendix C. 

Ozone and PM2.5 Contribution Calculation Examples 

Appendix D. 

2012 Base Case State-by-State Contributions to Nonattainment and Maintenance for 8-Hour 

Ozone, Annual PM2.5, and 24-Hour PM2.5

Appendix E. 

Upwind States to Downwind Nonattainment and Maintenance Linkages 

Appendix F. 

Analysis of Contribution Thresholds 

Appendix G. 

Summary of Aggregated Emissions of SO2 and NOx 

Appendix H. 

Concentration and Difference Maps for Annual PM2.5, 24-Hour PM2.5, and 8-Hour Ozone 
1

I. Introduction

In this technical support document (TSD) we describe the air quality modeling 

performed to support the Final Transport Rule (TR)1

. For this rule the Environmental 

Protection Agency (EPA) used air quality modeling to: 

(1) identify locations expected to be nonattainment or have maintenance problems 

for annual (average) PM2.5, 24-hour PM2.5, and/or 8-hour ozone for the analytic 

years chosen for the TR, 

(2) quantify the impacts (i.e., air quality contributions) of SO2 and NOX emissions 

from upwind states on downwind annual and 24-hour PM2.5 concentrations at 

monitoring sites projected to be nonattainment or have maintenance problems in 

2012 for the 1997 annual and 2006 24-hour PM2.5 NAAQS2, respectively, 

(3) quantify the contributions of NOX emissions on downwind 8-hour ozone 

concentrations at monitoring sites projected to be nonattainment or have 

maintenance problems in 2012 for the 1997 ozone NAAQS from upwind states, 

(4) “calibrate” the Air Quality Assessment Tool (AQAT) for use in the process of 

determining the amount of significant contribution, 

(5) examine issues related to the need for NOx emissions reductions from upwind 

states, and 

(6) assess the health and welfare benefits of the emissions reductions expected to 

result from this rule. 

This TSD includes information on the following analytical aspects of the TR air quality 

modeling: 

• a description of the modeling platform, 

• an evaluation of model predictions compared to measured concentrations, 

• the procedures and results of projecting ozone and PM2.5 concentrations for future 

year scenarios, 

• the procedures used to quantify interstate contributions for annual and 24-hour 

PM2.5, and 8-hour ozone, 

• the results of evaluating the interstate contributions using concentration 

thresholds, and 

 

1

 See docket item (placeholder) for the input files and output files from the Final Transport Rule 

air quality modeling. 

2

 National Ambient Air Quality Standards (NAAQS). 
2

• the air quality impacts from the SO2 and NOX emissions reductions expected from 

the “Air Quality-Assured Trading” scenario (i.e., remedy scenario), as described 

in the rule preamble. 

Air quality modeling was performed for several emissions cases: a 2005 base 

year, a 2012 “no CAIR” base case, a 2014 “no CAIR” base case, the 2014 remedy 

scenario, a 2014 “AQAT” calibration scenario, and two emissions sensitivity scenarios. 

There were two separate annual model simulations for 2005. One of the 2005 

simulations was used as the base year for projecting air quality and the second 2005 

simulation, which included additional year-specific emissions for 2005, was used in the 

analysis of model performance. The year 2005 was selected for the Transport Rule base 

year because this is the most recent year for which EPA has a complete national 

emissions inventory. The 2012 base case modeling was used to identify future 

nonattainment and maintenance locations and to quantify the contributions of emissions 

in upwind states to annual and 24-hour PM2.5, and 8-hour ozone at downwind receptors. 

The 2014 base case and 2014 remedy case modeling were used to quantify the benefits of 

the emissions reductions from the rule. The rationale for selecting 2012 and 2014 as the 

analytic years for the TR is provided in the preamble. 

As indicated above, we used the air quality modeling platform described in this 

TSD to simulate a 2014 emissions scenario developed to calibrate AQAT. This scenario 

is referred to as the AQAT calibration scenario and the modeling results from this 

scenario were used both as direct inputs to EPA’s evaluation of downwind air quality 

under multiple EGU emission reduction scenarios and as data points valuable to 

calibrating AQAT3. For more information on AQAT and how the AQAT calibration was 

 3 The 2014 AQAT calibration scenario was designed to be similar to the 2014 preferred 

remedy case modeled for the proposed rule. In the calibration scenario EGU SO2 emissions for 

states included in Group 1 at proposal were modeled using IPM under a cost threshold of 

$2000/ton. Emissions of SO2 from EGUs in the proposed Group 2 states were modeled using 

IPM under a cost threshold of $500/ton. Annual and ozone season EGU NOx emissions for states 

included in the proposed Transport Rule for contributions to PM2.5 and ozone respectively were 

modeled for a cost threshold of $500/ton. Note that the geography of states participating in each 

program and the geography of states defined as Group 1 and Group 2 for SO2 in the final 

Transport Rule differs from the proposed Transport Rule. The emissions from EGUs for the 

AQAT calibration scenario were merged with the 2014 base case emissions from other source 

sectors for input to CAMx. The annual CAMx simulation for this scenario was performed for the 

modeling domain covering the Eastern U.S with 12 km horizontal resolution. Initial and 

boundary concentrations for modeling this scenario were obtained from outputs of the 2014 base 

case simulation performed at 36 km resolution for the Continental U.S. domain. The CAMx 
3

used as part of the approach for identifying emissions that constitute a state’s significant 

contribution to nonattainment and interference with maintenance, please refer to 

preamble section VI.C and the Significant Contribution and State Emissions Budgets 

Final Rule TSD. We also performed two sensitivity model simulations to further 

examine the impacts of NOx and SO2 emissions on concentrations of PM2.5. The 

sensitivity scenarios included (1) a simulation for 2014 which was identical to the 2014 

base case, except that we included the SO2 emissions reductions from the AQAT 

calibration scenario (without any NOx reductions) and (2) a simulation for 2012 in which 

“removed” (i.e., zero-out) NOx emissions from all anthropogenic emissions in Georgia. 

The scenario with SO2-only emissions reductions was performed to quantify the nitrate 

increase that occurs when SO2 emissions are reduced (“nitrate replacement”). The NOx 

zero-out simulation was performed to quantify the role of NOx emissions in the 

formation of sulfate. The results of these two sensitivity scenarios are provided in 

separate technical report in the docket4

. 

The remaining sections of the Air Quality Modeling Final Rule TSD are as 

follows. Section II describes the air quality modeling platform and the evaluation of 

model predictions of PM2.5 and ozone using corresponding ambient measurements. 

Section III defines the procedures for projecting future case PM2.5 and ozone 

concentrations and the approach for determining locations with projected nonattainment 

and/or maintenance problems. Section IV describes (1) the source apportionment (i.e., 

contribution) modeling, (2) the procedures for quantifying contributions to nonattainment 

and/or maintenance, (3) the evaluation of contributions to determine which states are 

covered by this rule, and (4) an analysis of contributions relative to various air quality 

thresholds. In section V we present the results of modeling performed for 2014 to assess 

 

model predictions from the AQAT calibration scenario were used to characterize the seasonspecific response of sulfate to the SO2 and NOx emissions reductions modeled. The seasonal 

response in sulfate, as predicted for individual sites by CAMx, was used along with the CAMx 

predictions from the 2012 base case to develop calibration factors that were applied when running 

AQAT in order to align the sulfate estimates from AQAT with what would be expected from 

simulations with CAMx. The CAMx model predictions of the other PM2.5 component species 

(e.g., nitrate) from the AQAT calibration scenario were used directly along with the calibrated 

sulfate estimates from AQAT to estimate total PM2.5 concentrations at the cost threshold levels 

analyzed for the final rule. Details on AQAT, the calibration scenario, the approach for using air 

quality modeling results to calibrate AQAT, and the AQAT PM2.5 concentration estimates are 

provided in the Significant Contribution and State Emissions Budgets Final Rule TSD. 

4

 “Technical Analyses in Support of the Need for Annual NOx Controls in the Final Transport 

Rule”. 
4

the impacts on air quality of the emissions reductions expected from this rule. 

Information on the development of emissions inventories for the TR and the steps and 

data used in creating emissions inputs for air quality modeling can be found in the 

Emissions Inventory Final Rule TSD (EITSD). The EITSD also contains 

state/sector/pollutant emissions summaries for each of the emissions scenarios modeled. 
5

II. Air Quality Modeling Platform 

A. Air Quality Model 

 We used the Comprehensive Air Quality Model with Extension5 (CAMx) version 

5.3 to simulate ozone and PM2.5 concentrations for the 2005 base year and the 2012 and 

2014 future year scenarios modeled for the TR. CAMx was also used for the 2012 source 

apportionment modeling to quantify interstate transport of ozone and PM2.5. CAMx is a 

three-dimensional Eulerian grid-based photochemical model designed to simulate the 

formation, chemical transformation, transport, and removal of ozone, secondary and 

directly emitted PM2.5, and their precursor species on national, regional, and local scales 

for short-term episodes up to annual time periods. Using this model, EPA simulated the 

emissions from many different sources of pollution (e.g., EGUs, mobile sources). The 

emission inputs represent the outputs of other sector-specific models, reported 

measurements, or other sources. Meteorological model outputs of parameters such as 

wind speed, wind direction, temperature, pressure, and precipitation are important inputs 

to CAMx. The emission, transport, diffusion, chemical transformation, and 

concentrations of pollution were simulated at 1-hour intervals across the modeling 

domains. 

B. Modeling Domains 

The CAMx model applications for the TR focus on states in the Eastern U.S. 

using a horizontal grid resolution of 12 x 12 km (Table II-1). The Eastern modeling 

region (i.e., Eastern modeling domain) extends from Texas northward to North Dakota 

and eastward to the East Coast. Thirty seven states and the District of Columbia are 

wholly contained with this modeling domain. The Eastern modeling domain lies within a 

coarse grid, 36 x 36 km modeling domain which covers the lower 48 states and adjacent 

portions of Canada and Mexico. The 36 km and 12 km modeling domains extend 

vertically from the surface to 100 millibars (approximately 15 km) using a sigmapressure coordinate system. Predictions from the 36 km Continental U.S. (CONUS) 

domain were used to provide initial and boundary concentrations for simulations in the 

12 km domain. The 2005 base year scenarios, and the 2012 base case, 2014 base case, 

and 2014 remedy emissions scenarios were modeled for the 36 km and 12 km domains. 

 

5

 Comprehensive Air Quality Model with Extensions Version 5.3 User’s Guide. Environ 

International Corporation. Novato, CA. December 2010 (www.camx.com). 
6

However, as described below, the source apportionment modeling was performed for the 

37 states wholly within the 12 km Eastern domain only using boundary concentrations 

from the 2012 base case 36 km CAMx simulation. The 36 km and 12 km modeling 

domains are shown in Figure II-1. Table II-1 provides geographic specifications for these 

domains. 

In addition to the CAMx model, the TR modeling platform includes (1) emissions 

for the 2005 base year, 2012 base case, 2014 base case, and 2014 remedy scenario, (2) 

meteorology for the year 2005, and (3) estimates of intercontinental transport (i.e., 

boundary concentrations) from a global photochemical model. Using these input data, 

CAMx was run to generate hourly predictions of ozone and PM2.5 component species 

concentrations for each grid cell in the modeling domains. The development of 2005 

meteorological inputs and initial and boundary concentrations for the CONUS domain 

are described below. The emissions inventories used in the TR air quality modeling are 

described in the EITSD. 

Figure II-1. Transport Rule air quality modeling domains. 

CONUS 36 km Domain 

Eastern 12 km Domain 
7

Table II-1. Specifications of the air quality modeling domains. 

36 x 36 km Domain 12 x 12 km Domain 

Map Projection Lambert Conformal Projection 

Grid Resolution 36 km 12 km 

Coordinate Center 97 deg W, 40 deg N 

True Latitudes 33 deg N and 45 deg N 

Dimensions 148 x 112 x 14 279 x 240 x 14 

Vertical extent 14 Layers: Surface to 100 millibar level 

 (see Table II-2) 

C. Model Simulation Periods 

Annual simulations of CAMx were performed in quarterly segments (i.e., January 

through March, April through June, July through September, and October through 

December) for each emissions scenario. With this approach to segmenting an annual 

simulation we were able to model several quarters at the same time and, thus, reduce the 

overall throughput time for an annual simulation. The CONUS domain simulations 

included a “ramp-up” period, comprised of 10 days before the beginning of each quarter, 

to mitigate the effects of initial concentrations. For the 12 km Eastern domain 

simulations we used a 3-day ramp-up period for each quarter. Fewer ramp-up days were 

used for the 12 km simulations because the initial concentrations were derived from the 

parent 36 km simulations. 

D. Meteorological Input Data

All of the TR CAMx simulations used meteorology for 2005. The gridded 

meteorological input data for 2005 were derived from simulations of the Pennsylvania 

State University / National Center for Atmospheric Research Mesoscale Model. This 

model, commonly referred to as MM5, is a limited-area, non-hydrostatic, terrainfollowing system that solves for the full set of physical and thermodynamic equations 
8

which govern atmospheric motions.6

 Meteorological model input fields for CAMx were 

prepared separately for each of the domains shown in Figure II-1 using MM5 version 

3.7.4. The MM5 simulations were run on the same map projection and with the same 

horizontal resolution as CAMx. 

The CONUS and Eastern meteorological model runs were configured similarly. 

The selections for key MM5 physics options are as follows: 

• Pleim-Xiu PBL and land surface schemes 

• Kain-Fritsh 2 cumulus parameterization 

• Reisner 2 mixed phase moisture scheme 

• RRTM longwave radiation scheme 

• Dudhia shortwave radiation scheme 

Three dimensional analysis nudging7 for temperature and moisture was applied 

above the boundary layer only. Analysis nudging for the wind field was applied above 

and below the boundary layer. The 36 km domain nudging weighting factors were 3.0 x 

104 for wind fields and temperatures and 1.0 x 105 for moisture fields. The 12 km domain 

nudging weighting factors were 1.0 x 104 for wind fields and temperatures and 1.0 x 105

for moisture fields. 

The meteorological model runs were conducted in 5.5 day segments with 12 

hours of overlap for spin-up purposes. The meteorological modeling domains contain 34 

vertical layers with an approximately 38 m deep surface layer and a 100 millibar top. 

The MM5 and CAMx vertical structures are shown in Table II-2 and do not vary by 

horizontal grid resolution. The meteorological outputs from the MM5 simulations were 

processed to create model-ready inputs for CAMx using the mm5camx meteorological 

processor. 

 

6

 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. 

7

 “nudging” is a common practice in meteorological modeling in which measured values for 

certain meteorological parameters are input to the model simulation with weighting factors to 

more closely align or “nudge” the predictions to the observations. 
9

Table II-2. Vertical layer structure for MM5 and CAMx (heights are layer top). 

CAMx Layers MM5 Layers Sigma P 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 

6 10 0.920 631 928 

11 0.910 712 919 

12 0.900 794 910 

7 13 0.880 961 892 

14 0.860 1,130 874 

15 0.840 1,303 856 

8 16 0.820 1,478 838 

17 0.800 1,657 820 

18 0.770 1,930 793 9 19 0.740 2,212 766 

20 0.700 2,600 730 10 21 0.650 3,108 685 

22 0.600 3,644 640 11 23 0.550 4,212 595 

24 0.500 4,816 550 

12 25 0.450 5,461 505 

26 0.400 6,153 460 

27 0.350 6,903 415 

28 0.300 7,720 370 

29 0.250 8,621 325 13 

30 0.200 9,625 280 

31 0.150 10,764 235 

32 0.100 12,085 190 

33 0.050 13,670 145 14 

34 0.000 15,674 100 

The 2005 MM5 meteorological predictions were compared to the corresponding 

observations as part of a model performance evaluation to assess the adequacy of the 

MM5 simulated fields. The qualitative aspects of this evaluation included the 

comparison of the model-estimated synoptic patterns against observed patterns from 

historical weather chart archives for 2005. Additionally, the evaluation compared spatial 

patterns of monthly average rainfall and monthly maximum planetary boundary layer 

(PBL) heights. Qualitatively, the model fields closely matched the observed synoptic 

patterns, which is not unexpected given the use of nudging. An operational evaluation 

for 2005 was performed using statistical comparisons of model/observed pairs (e.g., mean 
10

normalized bias, mean normalized error, index of agreement, root mean square errors, 

etc.) for multiple meteorological parameters. For this portion of the evaluation, five 

meteorological parameters were investigated: temperature, humidity, shortwave 

downward radiation, wind speed, and wind direction. The three individual MM5 

evaluations are described elsewhere.8,9,10 The results of these analyses indicate that the 

bias and error values associated with the 2005 meteorological data were generally within 

the range of past meteorological modeling results that have been used for air quality 

applications11. 

E. Initial and Boundary Concentrations 

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM model12 (standard 

version 7-04-1113). The global GEOS-CHEM model simulates atmospheric chemical and 

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

Goddard Earth Observing System (GEOS). The GEOS-CHEM model was run for 2005 

with a grid resolution of 2.0 degree x 2.5 degree (latitude-longitude) and 30 vertical 

layers up to 100 mb. The predictions were used to provide one-way dynamic boundary 

conditions at three-hour intervals and an initial concentration field for the 36 km CAMx 

simulations. The concentrations from the 36 km CONUS domain model simulations 

were used to develop the initial/boundary concentrations for the corresponding 12 km 

Eastern domain model simulations. 

 

8

Baker, K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 

2005 Eastern U.S. 12-km Domain Simulation, USEPA/OAQPS, February 2, 2009. 

http://www.epa.gov/scram001/meteorology/metgridmodeling/met.2005.12EUS1.pdf 

9

 Baker, K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 

2005 Western U.S. 12-km Domain Simulation, USEPA/OAQPS, February 2, 2009. 

http://www.epa.gov/scram001/meteorology/metgridmodeling/met.2005.12WUS1.pdf

10 Baker, K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 

2005 Continental U.S. 36-km Domain Simulation, USEPA/OAQPS, February 2, 2009. 

http://www.epa.gov/scram001/meteorology/metgridmodeling/met.2005.36US1.pdf

11 Dolwick, P, R. Gilliam, L. Reynolds, and A. Huffman. Regional and Local-Scale Evaluation of 

2002 MM5 Meteorological Fields for Various Air Quality Modeling Applications, 6th Annual 

CMAS Conference, Chapel Hill, NC, October 1-3, 2007. 

http://www.cmascenter.org/conference/2007/abstracts/dolwick_session3_2007.pdf 

12 Yantosca, B., 2006. GEOS-CHEMv7-04112 User’s Guide, Atmospheric Chemistry Modeling 

Group, Harvard University, Cambridge, MA, March 05, 2006.

13 Henze, D.K., J.H. Seinfeld, N.L. Ng, J.H. Kroll, T-M. Fu, D.J. Jacob, C.L. Heald, 2008. Global 

modeling of secondary organic aerosol formation from aromatic hydrocarbons: high-vs.low-yield 

pathways. Atmos. Chem. Phys., 8, 2405-2420. 
11

F. Model Performance Evaluation for Ozone and PM2.5 

The predictions from CAMx for ozone and fine particulate sulfate, nitrate, 

ammonium, organic carbon, elemental carbon, and crustal material from the 2005 base 

year evaluation case were compared to measured concentrations in order to evaluate the 

performance of the modeling platform for replicating observed concentrations. This 

evaluation was comprised of statistical and graphical comparisons of paired modeled and 

observed data. Details on the model performance evaluation including a description of 

the methodology, the model performance statistics, and results are provided in the 

Appendix A. 
12

III. Projection of Future Nonattainment and Maintenance for Annual 

PM2.5, 24-Hour PM2.5, and 8-Hour Ozone 

In this section we describe the approach for projecting future concentrations of 

ozone and PM2.5 to identify locations that are expected to be nonattainment or have a 

maintenance problem in the 2012 and 2014 future year scenarios modeled for the TR. 

The nonattainment and maintenance locations are based on projections of future air 

quality at existing ozone and PM2.5 monitoring sites. The nonattainment and 

maintenance sites for the 2012 base case are used as the “receptors” for quantifying the 

contributions of emissions in upwind states to nonattainment and maintenance in 

downwind locations. Future year concentrations for the 2014 base case and 2014 remedy 

scenario were used to assess the impacts of the emissions reductions from the TR on 

ozone and PM2.5 air quality. 

For this analysis we are using air quality modeling results in a “relative” sense to 

project future concentrations of ozone and PM2.5. Rather than use the absolute modelpredicted future year ozone and PM2.5 concentrations, the base year and future year 

predictions are used to calculate a (relative) percent change in ozone and PM2.5

concentrations. In this approach, the ratio of future year model predictions (i.e., 2012 or 

2014) to 2005 base year model predictions are used to adjust (2003-2007) ambient 

measured data up or down depending on the relative (percent) change in model 

predictions for each location. The use of ambient data as part of the calculation helps to 

constrain the future year design value predictions, even if the absolute model 

concentrations are over-predicted or under-predicted. For example, assume that a 

monitoring site has a measured sulfate concentration of 6.0 μg/m3

 in the 2003-2007 base 

period and a modeled concentration (in 2005) of 4.0 μg/m3

. In the 2012 base case, the 

modeled concentration is 3.2 μg/m3

. Therefore the relative response factor is 0.80 or a 

20% reduction [100*(1-(3.2/4.0))]. Consequently, the estimated 2012 base case sulfate 

value would be 6 μg/m3 multiplied by the relative response factor of 0.80, or 4.8 μg/m3. 

In this way, the predicted future year pollutant concentration is always anchored by the 

observed data. As described below, the procedures for projecting annual and 24-hour 

PM2.5 and 8-hour ozone conform to the methodologies contained in the final guidance for 

attainment demonstration modeling14 (referred to below as the “modeling guidance”). 

 

14 U.S. EPA, 2007: Guidance on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze; Office of Air Quality 
13

A. Procedures for Processing Ambient Ozone and PM2.5 Data 

In this analysis we use measurements of ambient ozone and PM2.5 data from 

several state and federal monitoring networks. This includes data from over 500 ozone 

monitoring sites as well as over 500 Federal Reference Method (FRM) PM2.5 sites in the 

Eastern U.S.. In addition, speciated PM2.5 data from the Chemical Speciation Network 

(CSN) and IMPROVE network are used to estimate PM2.5 species concentrations at each 

FRM site. The ambient data used in this analysis were obtained from EPA’s Air Quality 

System (AQS)15 for the time-period between 2003 and 2007. 

In order to utilize the ambient data, the raw measurements must be processed into 

a form pertinent for useful interpretations. The ozone data were processed consistent 

with the formats associated with the 1997 8-hour NAAQS for ozone. The level of the 

1997 8-hour O3 NAAQS is 0.08 ppm. The 8-hour ozone standard is not met if the 3-year 

average of the annual 4th highest daily maximum 8-hour O3 concentration is greater than 

0.08 ppm (0.085 ppm or greater when rounded up). This 3-year average is referred to as 

the design value. 

The PM2.5 ambient data were processed consistent with the formats associated 

with the NAAQS for PM2.5. For PM2.5, we evaluated concentrations of both the annual 

PM2.5 NAAQS and the 24-hour PM2.5 NAAQS. The annual PM2.5 standard is not met if 

the 3-year average of the annual mean concentration is greater than 15.0 μg/m3 (15.05 

μg/m3 or greater when rounded up). The 3-year average annual mean concentration is 

computed at each site by averaging the daily Federal Reference Method (FRM) samples 

by quarter, averaging these quarterly averages to obtain an annual average, and then 

averaging the three annual averages. The 3-year average annual mean concentration is 

referred to as the annual design value. 

The 24-hour standard is not met when the 3-year average of the annual 98th

percentile PM2.5 concentration is greater than 35 μg/m3 (35.5 μg/m3 or greater when 

rounded up). The 3-year average mean 98th percentile concentration is computed at each 

site by averaging the 3 individual annual 98th percentile values at each site. The 3-year 

average 98th percentile concentration is referred to as the 24-hour design value. 

 

Planning and Standards, Research Triangle Park, NC. 

http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf

15 See http://www.epa.gov/ttn/airs/airsaqs/ for access to raw data. 
14

When projecting ambient monitoring data to future time periods using relative 

response factors, the modeling guidance recommends using the average of the three 

design value periods centered on the year of the base year emissions. Since 2005 was the 

base emissions year for the TR modeling, we used the design value for 2003-2005, 2004-

2006, and 2005-2007 to represent the base period ozone and PM2.5 concentrations. 

Specifically, we used ambient ozone and annual and 24-hour PM2.5 design values for the 

periods 2003-2005, 2004-2006, and 2005-2007 as the starting point for our projections of 

design values for each of the future year scenarios modeled. The 2003–2005, 2004-2006, 

and 2005-2007 design values are accessible at www.epa.gov/airtrends/values.html. 

Ambient design values from monitoring sites were included in our analysis if the 

site had at least one complete16 design value in the 2003-2007 five-year period.17 There 

were 723 monitoring sites in the 12 km Eastern modeling domain which had at least one 

complete design value period for the annual PM2.5 NAAQS, and 722 sites which met this 

criteria for the 24-hour NAAQS18 and 744 sites which met the criteria for the 8-hour 

ozone NAAQS. 

B. Projection of Future Design Values and Determination of Nonattainment and 

Maintenance for Annual and 24-Hour PM2.5

As noted above, the projection methodology for PM2.5 involves using the model 

predictions in a relative sense to estimate the change in PM2.5 between 2005 and each 

future year scenario. For a particular location, the percent change in modeled 

concentration (relative response factor) is multiplied by the corresponding observed base 

period ambient concentration to estimate the future year design value for that location. 

The procedure for calculating future year annual and 24-hour PM2.5 design values is 

called the Speciated Modeled Attainment Test (SMAT). The SMAT approach is codified 

in a software tool available from EPA called Modeled Attainment Test Software, or 

 

16 Design value completeness was determined according to the monitoring rules in CFR 40 Part 

50 Appendix I (8-hour ozone) and Appendix N (annual and 24-hr PM2.5). 

17 If there is only one complete design value, then the nonattainment and maintenance design 

values are the same. 

18 Design values were only used if they were deemed to be officially complete based on CFR 40 

Part 50 Appendix N. The completeness criteria for the annual and 24-hour PM2.5 NAAQS are 

different. Therefore, there are fewer complete sites for the annual NAAQS. 
15

MATS19. The software (including documentation) is available at: 

http://www.epa.gov/scram001/modelingapps_mats.htm. 

 As described below, design values of PM2.5 in 2012 and 2014 were estimated by 

applying the 2005 to 2012 or 2005 to 2014 relative change in model-predicted PM2.5

species concentrations to the measured (2003-2007) PM2.5 species concentrations. The 

PM2.5 species include sulfate, nitrate, ammonium, particle bound water, elemental carbon, 

salt, other primary PM2.5, and organic aerosol mass (by difference). Organic aerosol 

mass by difference is defined as the difference between FRM PM2.5 and the sum of the 

other components. 

For each FRM PM2.5 monitoring site, all valid design values (up to 3) from this 

period were averaged together, resulting in the “average” design value. Averaging the 

three values together has the effect of creating a 5-year weighted average. The middle 

year (2005) is weighted 3 times, the 2nd and 4th years (2004 and 2006) are weighted 

twice, and the 1st and 5th years (2003 and 2007) are weighted once. We refer to this as 

the 5-year weighted average design value concentration. For sites that did not have three 

valid design values, the “average” of all valid design values from the time-period were 

used. 

The average design values were used to project concentrations for the 2012 and 

2014 scenarios in order to determine which monitoring sites are expected to be 

nonattainment for these future year scenarios. We also projected design values for each 

of the individual valid 3-year design value periods (i.e., 2003-2005, 2004-2006, and 

2003-2007). The projection of design values for these individual periods was used in the 

interstate contribution analysis to determine sites expected to have maintenance 

problems, as described below. 

1. Methodology for Projecting Future Annual PM2.5 Nonattainment and 

Maintenance 

The following is a summary of the method used to project future year annual 

PM2.5 design values. Additional details are provided in the modeling guidance and 

MATS documentation. 

 

19 U.S. EPA, 2009: Modeled Attainment Test Software; User’s Manual 

http://www.epa.gov/ttn/scram/guidance/guide/MATS%20manual-1-5-1.pdf
16

As described above, base period (i.e., 2003 – 2007) FRM data are the starting 

point for projecting future design values since these quality assured measurements are 

used to determine attainment status. In order to apply SMAT to the FRM data, 

information on PM2.5 speciation is needed for the location of each FRM monitoring site. 

Since co-located PM2.5 speciation data are only available at ~15 percent of FRM 

monitoring sites, spatial interpolation techniques are used to calculate species 

concentrations for each FRM monitoring site. Speciation data from the IMPROVE and 

CSN were interpolated on a quarterly basis to each FRM monitor location by applying 

the Voronoi Neighbor Averaging (VNA) technique (using MATS). Additional 

information on the VNA interpolation techniques and data handling procedures can be 

found in the MATS User’s Guide20. After the species fractions are calculated for each 

FRM site, the following procedures were used to estimate future year design values: 

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

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

mass, particle bound water, salt, and blank mass). This is done by multiplying the 

monitored quarterly mean concentration of FRM-derived total PM2.5 by the monitored 

fractional composition of PM2.5 species for each quarter averaged over 3 years21 (e.g., 20 

percent sulfate fraction multiplied by 15 μg/m3 PM2.5 equals 3 μg/m3 sulfate). 

Step 2: For each quarter, calculate the ratio of future year to base year model 

predictions for sulfate, nitrate, elemental carbon, organic carbon, and other primary 

PM2.5. The result is a set of species-specific quarterly average relative response factors 

(RRF) (e.g., assume that the model-predicted 2005 base year sulfate for a particular 

location is 10.0 μg/m3

 and the 2012 future concentration is 8.0 μg/m3, then the RRF for 

sulfate is 0.8). The RRFs are calculated based on the modeled concentrations averaged 

over the nine grid cells22 centered at the location of the monitor. 

Step 3: For each quarter and each of the species, multiply the base year quarterly 

mean component concentration (Step 1) by the species-specific quarterly RRF obtained 

in Step 2. This results in an estimated future year quarterly mean concentration for each 

 

20 Available at http://www.epa.gov/ttn/scram/guidance/guide/MATS-2-3-1_manual.pdf

21 For this analysis, species fractions were calculated using FRM and speciation data for the 

2004-2006 time period. This was deemed to be representative of the 2005 base year period. 

22 The modeling guidance recommends calculating annual PM2.5 RRFs using a 3 X 3 grid cell 

array (9 grid cells) when using a model resolution of 12km. 
17

species (e.g., 3 μg/m3

 sulfate multiplied by the 0.8 RRF equals a future sulfate 

concentration of 2.4 μg/m3). 

Step 4: The future year concentrations for the remaining species are then 

calculated23. The future year ammonium is calculated based on the calculated future year 

sulfate and nitrate concentrations, using a constant value for the degree of neutralization 

of sulfate24 (from the ambient data). The future year particle bound water concentration 

is calculated from an empirical formula derived from the Aerosol Inorganic Model 

(AIM)25. The inputs to the formula are the future year concentrations of sulfate, nitrate, 

and ammonium (from step 3). 

Step 5: Average the four quarterly mean future concentrations to obtain the future 

year annual design value concentration for each of the component species. Sum the 

species concentrations to obtain the future year annual design value for PM2.5. 

Step 6: Calculate the maximum future design value by processing each of the 

three base design value periods (2003-2005, 2004-2006, and 2005-2007) separately. The 

highest of the three future values is the maximum design value. The maximum design 

values are used to determine future year maintenance sites. 

The preceding procedures for determining future year PM2.5 concentrations were 

applied for each FRM site. The calculated annual PM2.5 design values are truncated after 

the second decimal place26. This is consistent with the truncation and rounding 

procedures for the annual PM2.5 NAAQS. Any value that is greater than or equal to 15.05 

μg/m3 is rounded to 15.1 μg/m3 and is considered to be violating the NAAQS. Thus, 

sites with future year annual PM2.5 design values of 15.05 μg/m3 or greater, based on the 

projection of 5-year weighted average concentrations, are predicted to be nonattainment 

sites. Sites with future year maximum design values of 15.05 μg/m3 or greater are 

predicted to be maintenance sites. Note that nonattainment sites are also maintenance 

sites because the maximum design value at a site is always greater than or equal to the 5-

year weighted average at that site. For ease of reference we use the term “nonattainment 

 

23 All of the calculations and assumptions are consistent with the default MATS settings (as 

described in the MATS user’s guide and the photochemical modeling guidance). Additionally, 

we did not explicitly model salt and therefore the salt concentration was held constant from the 

base to future. Blank mass was assumed to be a constant mass of 0.5 μg/m3 in both the base and 

future year. 

24 See section 5.1.2.2 of the MATS User’s guide for details on the ammonium calculation. 

25 See section 5.1.2.2 of the MATS User’s guide for details on the particle bound water formula. 

26 For example, a calculated annual average concentration of 14.94753 becomes 14.94 when 

digits beyond two places to the right are truncated. 
18

sites” to refer to those sites that are projected to exceed the NAAQS based on both the 

average and maximum design values. Those sites that are projected to be in attainment 

based on the average design value but are projected to exceed the NAAQS based on the 

maximum design value are referred to as maintenance sites. The monitoring sites that we 

project to be nonattainment and/or maintenance for the annual PM2.5 NAAQS in the 2012 

base case are used as the nonattainment/maintenance receptors for assessing the 

contribution of emissions in upwind states to downwind nonattainment and maintenance 

of the annual PM2.5 NAAQS (see section IV, below). The 2003-2007 base period and 

projected annual PM2.5 design values for each site for the 2012 and 2014 scenarios are 

provided in Appendix B. 

2. Methodology for Projecting Future 24-Hour PM2.5 Nonattainment and 

Maintenance 

The following is a summary of the procedures used for calculating future year 24-

hour PM2.5 design values. Additional details are provided in the modeling guidance and 

MATS documentation. The procedures for calculating the future year 24-hour PM2.5 

design values have been updated for the final rule27. The updates are intended to make 

the projection methodology more consistent with the procedures for calculating ambient 

design values. In the 24-hr attainment test used for the proposed TR, for each PM2.5 

monitor, we projected the measured 98th percentile concentrations from the 2003-2007 

period to the future. As an additional check, we also projected the next highest 

concentrations from the other calendar quarters for each year (the three quarters when the 

98th percentile did not occur) to ensure that the future year 98th percentile did not switch 

seasons in the future year compared to the base year. A basic assumption in this 

methodology is that the overall distribution of high measured days in the base period will 

be the same as in the future. Examination of several future year modeling scenarios 

showed that many of the highest PM2.5 days switched from the summer in the base 

period to the winter in the future period. As a result, the distribution of days shifted 

between the base and future periods. This is especially true in areas such as the upper 

Midwest which experience both high summer and winter PM2.5 episodes. 

 

27 See “Update to the 24-hour PM2.5 NAAQS Modeled Attainment Test”, memorandum from 

Tyler Fox. Docket item EPA-HQ-OAR-2009-0491-4360. There were no methodological updates 

to either the ozone or annual PM2.5 attainment test methodology. 
19

In the revised methodology, used for the final TR, we do not assume that the 

temporal distribution of high days in the base and future periods will remain the same. 

We project a larger set of ambient days from the base period to the future and then rerank the entire set of days to find the new future 98th percentile value (for each year). 

Similar to the annual PM2.5 calculations, we are using the 2003-2007 base period 

FRM data for projecting future year design values. The 24-hour PM2.5 calculations are 

computationally similar to the annual average calculations. The main difference is that 

the base period “high day” 24-hour PM2.5 concentrations are projected to the future year, 

instead of the annual average concentrations. Also, the PM2.5 species fractions and 

relative response factors are calculated from observed and modeled high concentration 

days, instead of quarterly average data. 

Both the annual PM2.5 and 24-hour PM2.5 calculations are performed on a calendar 

quarter basis. Since all years and quarters are averaged together in the annual PM2.5 

calculations, the individual years can be averaged together early in the calculations. 

However, in the 24-hour PM2.5 calculations, the 98th percentile value from each year is 

used in the final calculations. Since the 98th percentile value can come from any day in 

the year, all potential 98th percentile days in the four quarters must be carried through to 

near the end of the calculations. To calculate final future year design values, the 98th

percentile for each year is identified and then, three yearly values from consecutive years 

were averaged resulting in a design value. Three design values were calculated for the 

2003-2007 time period, as was done for the annual PM2.5 standard. The average of the 

valid design values was used to calculate the “average” design value. When all five years 

of data were valid, resulting in three design values, the average of the three design values 

is a five year weighted average of the 98th percentile values for each site. 

 In the revised 24-hour attainment test methodology, the 8 highest ambient PM2.5 

days in each quarter at each site are projected to the future (32 days per year) using 

species specific quarterly relative response factors. After all 32 days are projected to the 

future, the days are re-ranked to determine the future year 98th percentile day. The 98th

percentile is selected based on the rank of the 98th percentile day in the base year ambient 

data. For example, at site A, if the observed 98th percentile value in year 1 was the 3rd

high day for the year, then the future year 98th percentile is selected as the 3rd high future 

day for the year. 
20

This update to the methodology ensures that the 98th percentile day is not overestimated or under-estimated due to changes in the temporal distribution of high days 

between the base and future days. In practice, we have found that in almost all cases, the 

revised methodology leads to lower future 98th percentile values and therefore lower 

projected future 24-hour PM2.5 design values. 

Table III-1 and Table III-2 show an example design value calculation for a single 

site in Baldwin County Alabama. Table III-1 lists the projected high future year days at 

the site for 2003 (32 days) as well as base year values. The values in Table III-1 are 

ranked in decreasing order based on the Future Year PM2.5 concentration. At this site for 

2003, the 98th percentile day is the 3rd high for the year (based on the number of samples 

collected in 2003). Therefore the 3rd high day is flagged (future year concentration of 

20.5718 μg/m3

) and carried through to the final design value calculation. Note that the 

3rd high day in the base year is on a different day (20030524), which had a concentration 

of 29.1 μg/m3

Table III-1. Example high base and future year days for 2003 at a single monitoring site. 

AIRS ID State County Date Year Quarter 

Base Year 

PM2.5 

(ug/m3) 

Future 

Year 

PM2.5 

(ug/m3) 

10030010 Alabama Baldwin 20030415 2003 2 32.2 22.8255 

10030010 Alabama Baldwin 20030524 2003 2 29.1 20.6422 

10030010 Alabama Baldwin 20030530 2003 2 29 20.5718 

10030010 Alabama Baldwin 20031006 2003 4 28.4 19.6670 

10030010 Alabama Baldwin 20030810 2003 3 30.5 18.8999 

10030010 Alabama Baldwin 20030310 2003 1 19.7 14.8373 

10030010 Alabama Baldwin 20030506 2003 2 20.5 14.5855 

10030010 Alabama Baldwin 20030605 2003 2 20.5 14.5855 

10030010 Alabama Baldwin 20030626 2003 2 20 14.2333 

10030010 Alabama Baldwin 20030909 2003 3 22.7 14.1159 

10030010 Alabama Baldwin 20031003 2003 4 20 13.8963 

10030010 Alabama Baldwin 20030115 2003 1 18.1 13.6426 

10030010 Alabama Baldwin 20030918 2003 3 21.9 13.6252 

10030010 Alabama Baldwin 20030427 2003 2 19 13.5291 

10030010 Alabama Baldwin 20030430 2003 2 18.9 13.4586 

10030010 Alabama Baldwin 20031009 2003 4 18.2 12.6597 

10030010 Alabama Baldwin 20031018 2003 4 17.5 12.1788 

10030010 Alabama Baldwin 20031021 2003 4 17.4 12.1101 

10030010 Alabama Baldwin 20030912 2003 3 19.3 12.0306 

10030010 Alabama Baldwin 20030127 2003 1 15.5 11.7010 
21

AIRS ID State County Date Year Quarter 

Base Year 

PM2.5 

(ug/m3) 

Future 

Year 

PM2.5 

(ug/m3) 

10030010 Alabama Baldwin 20030325 2003 1 15.3 11.5517 

10030010 Alabama Baldwin 20031024 2003 4 16.5 11.4918 

10030010 Alabama Baldwin 20030214 2003 1 15 11.3277 

10030010 Alabama Baldwin 20030316 2003 1 14.8 11.1783 

10030010 Alabama Baldwin 20031208 2003 4 15.6 10.8735 

10030010 Alabama Baldwin 20030301 2003 1 14.3 10.8050 

10030010 Alabama Baldwin 20030322 2003 1 13.9 10.5063 

10030010 Alabama Baldwin 20030924 2003 3 16.4 10.2519 

10030010 Alabama Baldwin 20030720 2003 3 14.6 9.1479 

10030010 Alabama Baldwin 20030729 2003 3 14.6 9.1479 

10030010 Alabama Baldwin 20030819 2003 3 14.3 8.9639 

10030010 Alabama Baldwin 20031108 2003 4 12.5 8.7439 

 Table III-2 shows the 98th percentile values for each of the five years (2003-

2007). The future year 98th percentile values from all five years of the 2003-2007 period 

(the 3rd high future year day in each year) are used to calculate the final five year 

weighted design value. 

Table III-2. Example design value calculation for site 010030010 

AIRS ID State County 

98th 

Percentile 

Day Rank Year 

Date Base 

Year 

Base 

High 

Quarter 

Base 

Year 

PM2.5 

(ug/m3) 

Date 

Future 

Year 

Future 

High 

Quarter 

Future 

Year 

PM2.5 

(ug/m3) 

10030010 AL Baldwin 3 2003 20030524 2 29.1 20030530 2 20.5718 

10030010 AL Baldwin 3 2004 20040720 3 27.5 20040313 1 19.1684 

10030010 AL Baldwin 3 2005 20050624 2 26 20050128 1 17.6003 

10030010 AL Baldwin 3 2006 20060701 3 25.7 20060601 2 18.1773 

10030010 AL Baldwin 3 2007 20070503 2 22.4 20070503 2 15.9236 

 5 year weighted average = 26.2 18.2 

The following are the individual steps for calculating the future year 24-hour 

PM2.5 design values: 

Step 1: At each FRM monitoring site, identify the 8 highest observed 24-hour 

PM2.5 concentration days in each quarter for each year (2003-2007) and identify the day 

rank of the official 98th percentile value based on the number of collected ambient 

samples (e.g., the 3rd highest day of the year). This results in a data set for each year (for 

up to 5 years) for each site containing 32 days of data. 
22

Step 2: In this step we calculate quarterly ambient species fractions on “high”28

days for each of the major component species of PM2.5 (sulfate, nitrate, ammonium, 

elemental carbon, organic carbon mass, particle bound water, salt and blank mass). This 

calculation is performed by multiplying the monitored concentrations of FRM-derived 

total PM2.5 mass on the 10 percent of highest PM2.5 days at each site for each quarter, by 

the average monitored species composition29 on the 10 percent highest PM2.5 days for 

each quarter, averaged over 3 years30. The end result is a set of quarterly species 

fractions for each FRM site. 

Step 3: Multiply the quarterly “high day” species fractions from step 2 by the 

PM2.5 mass concentration for the 8 high days per quarter identified in step 1. This 

results in a set of species concentrations for each of the 32 days per year identified in step 

1. 

Step 4: For each quarter, calculate the ratio of future year (e.g., 2012) to base 

year (e.g., 2005) modeled predictions for sulfate, nitrate, elemental carbon, organic 

carbon, and other primary PM2.5 for the average of the top 10 percent of modeled days31

based on predicted concentrations of 24-hour PM2.5. The result is a single set of speciesspecific “high day” relative response factors (RRF), one for each quarter (e.g., assume 

that 2005 predicted sulfate on the 10 percent highest PM2.5 days for a quarter for a 

particular location is 20 μg/m3 and the 2012 base case concentration is 16 μg/m3, then the 

RRF for sulfate is 0.8). The RRFs are calculated based on the modeled concentrations at 

the single32 grid cell where the monitor is located. 

 

28 High ambient data and model days were defined as the top 10 percent days in each quarter 

based on 24-hour average concentrations of PM2.5 mass. 

29 Similar to the annual average calculations, the quarterly species fractions are calculated at each 

FRM site using interpolated species data (using VNA). For the 24-hour species interpolations, 

the highest 10% of monitor days in each quarter are interpolated. 

30 For this analysis, species fractions were calculated using an average of FRM and speciation 

data for the 2004-2006 time period. This was deemed to be most representative of the 2005 

modeling year. 

31 The calculation of the top 10% of modeled PM2.5 days in each quarter did not include “other 

primary PM2.5” (crustal) concentrations. As indicated in Appendix A, the crustal component of 

PM2.5 was severely over predicted. Therefore, we did not allow the selection of the “high” 

modeled days (which are used for the RRF calculations) to be unduly influenced by the overpredicted crustal concentrations. However, the modeled crustal concentrations were used to 

calculate the “crustal” component RRF. 

32 The modeling guidance recommends using a single grid cell to calculate RRFs for the 24-hr 

PM2.5 NAAQS. 
23

Step 5: For each of the 8 days in each quarter, multiply the species concentrations 

(step 3) by the quarterly33 species-specific RRF obtained in step 4. This leads to an 

estimated future concentration for each component for each day. (e.g., 21.0 μg/m3 of 

nitrate for day 2 in quarter 1 of 2003 x 0.75 (the quarter 1 nitrate RRF) = future nitrate for 

that day of 15.75 μg/m3). 

Step 6: The future year concentrations for the remaining species are then 

calculated34 for each of the days. The future year ammonium is determined based on the 

calculated future year sulfate and nitrate concentrations, using a constant value for the 

degree of neutralization of sulfate (from the ambient data). The future year particlebound water concentration is calculated from an empirical formula derived from the AIM 

model35. The inputs to the formula are the calculated future year concentrations of 

sulfate, nitrate, and ammonium (from step 5). 

Step 7: Sum the species concentrations to obtain total PM2.5 concentration values 

for each of the 32 days per year. 

Step 8: For each monitoring site, rank the 32 days per year for each site for each 

year by total PM2.5 concentration. For each year, the previously identified 98th percentile 

rank (for each year) is used to identify the 98th percentile concentration for each year. 

For example, if the base year 98th percentile value for year 1 was the 3rd high 

concentration, then the future year 98th percentile concentration is the future year 3rd high 

PM2.5 concentration (out of 32 days). For each monitoring site, a single 98th percentile 

concentration is identified for each year (2003-2007) that has been projected to the future 

year. 

Step 9: The estimated 98th percentile values for each of the 5 years (identified in 

step 8) are averaged over 3 year consecutive intervals to create the 3 year average design 

values (e.g., the 98th percentile values from 2003, 2004, and 2005 are averaged. Then the 

98th percentile values from 2004, 2005, and 2006 are averaged, etc.). The (up to 3) valid 

 

33 Since there is only one modeled base year (2005), there are four quarterly RRFs at each 

monitor. The modeled quarterly RRF for quarter 1 is multiplied by the ambient data for quarter 1 

(8 days in each year) for each of the 5 years of ambient data. The same procedure is applied for 

the 8 high days per quarter in the other 3 quarters. 

34 We did not explicitly model salt and therefore the salt concentration was held constant from the 

base to future. Blank mass was assumed to be a constant mass of 0.5 μg/m3 in both the base and 

future year. 

35 Similar to the annual PM2.5 calculations, the ammonium and particle bound water formulas 

can be found in section 5.1.2.2 of the MATS user’s guide: 

http://www.epa.gov/ttn/scram/guidance/guide/MATS-2-3-1_manual.pdf
24

design values are then averaged to create the “average” future design value that is used 

for assessing nonattainment. For sites that have valid measurements across the entire 

2003-2007 time period, the result is 3 valid design values. When these design values are 

averaged, the result is a 5 year weighted average for that monitoring site. 

Step 10: Calculate the maximum future design value by processing each of the 

three projected base design value periods (2003-2005, 2004-2006, and 2005-2007) 

separately. The highest of the three future values is the maximum design value. The 

maximum design values are used to determine future year 24-hour PM2.5 maintenance 

receptors. 

The preceding procedures for determining future year 24-hour PM2.5

concentrations were applied for each FRM site. The 24-hour PM2.5 design values are 

truncated after the first decimal place. This approach is consistent with the truncation 

and rounding procedures for the 24-hour PM2.5 NAAQS. Any value that is greater than 

or equal to 35.5 μg/m3 is rounded to 36 μg/m3 and is violating the NAAQS. Sites with 

future year 5 year weighted average design values of 35.5 μg/m3

 or greater, based on the 

projection of 5-year weighted average concentrations, are predicted to be nonattainment. 

Sites with future year maximum design values of 35.5 μg/m3 or greater, based on a 

single design value period, are predicted to be “maintenance” sites. Note that 

nonattainment sites for the 24-hour NAAQS are also maintenance sites because the 

maximum design value is at a site is always greater than or equal to the 5-year weighted 

average for that site. For ease of reference we use the term “nonattainment sites” to refer 

to those sites that are projected to exceed the NAAQS based on both the average and 

maximum design values. Those sites that are projected to be attainment based on the 

average design value but exceed the NAAQS based on the maximum design value are 

referred to as maintenance sites. The monitoring sites that we project to be 

nonattainment and/or maintenance for the 24-hour PM2.5 NAAQS in the 2012 base case 

are used as the nonattainment/maintenance receptors for assessing the contribution of 

emissions in upwind states to downwind nonattainment and maintenance of 24-hour 

PM2.5 NAAQS (see section IV, below). The 2003-2007 base period and projected 24-

hour PM2.5 design values for each site for the 2012 and 2014 scenarios are provided in 

Appendix B. 
25

C. Projection of Future Design Values and Determination of Nonattainment and 

Maintenance for 8-Hour Ozone 

The following is a summary of the future year 8-hour ozone calculations. 

Additional details are provided in the modeling guidance and MATS documentation. 

Base period 2003-2007 ambient ozone design value data taken from AQS are the starting 

point for projecting future year design values. Since ozone is a single species, the ozone 

projection procedure is relatively simple (compared to the PM2.5 projection 

methodologies). It is not necessary to interpolate ambient ozone data, since ambient 

ozone design values and gridded, modeled ozone data are all that are needed for the 

projections. 

To project 8-hour ozone design values we used the 2005 base year, the 2012 

future base case, and 2014 future base and control case model-predicted ozone 

concentrations to calculate relative response factors. The methodology we followed is 

consistent with the attainment demonstration modeling guidance. The RRFs were 

applied to the 2003-2007 ozone design values through the following steps: 

Step 1: The first step in the procedure is to calculate a RRF based on the change 

in modeled ozone concentration from the 2005 base case simulation to the future year 

simulation. For each monitoring site we calculate the mean of the modeled 8-hour 

average daily maximum predictions across all days with 8-hour average daily maximum 

predictions greater than or equal to 85 ppb36. The mean concentrations are calculated 

using the predictions in the nine grid cells (3 by 3) that include or surround the location 

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

year to the mean prediction in the 2005 base year. The RRFs were calculated on a siteby-site basis. 

Step 2: The RRF for each site is then multiplied by the 2003-2007 5-year 

weighted average ambient design value for that site, yielding an estimate of the future 

year (2012) design value at that particular monitoring location. 

Step 3: We calculate the maximum future design value by projecting design 

values for each of the three base periods (2003-2005, 2004-2006, and 2005-2007) 

 

36 As specified in the attainment demonstration modeling guidance, if there are less than 10 

modeled days > 85 ppb, then the threshold is lowered in 1 ppb increments (to as low as 70 ppb) 

until there 10 days. If there are less than 5 days > 70 ppb, then an RRF calculation is not 

completed for that site. 
26

separately. The highest of the three future values is the maximum design value. This 

maximum value is used to identify the 8-hour ozone maintenance receptors. 

The preceding procedures for determining future year 8-hour ozone design values 

were applied for each ozone monitoring site for each future year scenario that was 

modeled (i.e., the 2012 base case, 2014 base case, and 2014 control). Each future year 

design value was truncated to integers in units of ppb. This approach is consistent with 

the rounding and truncation procedures for the 8-hour ozone NAAQS. Future year 

design values that are greater than or equal to 85 ppb are considered to be violating the 

NAAQS. Sites with future year 5 year weighted average design values (the average of all 

valid design values from the projected 2003-2007 time-period) of 85 ppb or greater are 

predicted to be nonattainment. Sites with future year maximum design values of 85 ppb 

or greater are predicted to be future year maintenance sites. Note that, as described 

previously for the annual and 24-hour PM2.5 NAAQS, nonattainment sites for the ozone 

NAAQS are also maintenance sites because the maximum design value is always greater 

than or equal to the 5-year weighted average. For ease of reference we use the term 

“nonattainment sites” to refer to those sites that are projected to exceed the NAAQS 

based on both the average and maximum design values. Those sites that are projected to 

be attainment based on the average design value but exceed the NAAQS based on the 

maximum design value are referred to as maintenance sites. The monitoring sites that we 

project to be nonattainment and/or maintenance for the ozone NAAQS in the 2012 base 

case are the nonattainment/maintenance receptors used for assessing the contribution of 

emissions in upwind states to downwind nonattainment and maintenance of the 8-hour 

ozone NAAQS. The 2003-2007 base period and projected 8-hour ozone design values 

for all sites for the 2012 and 2014 scenarios are provided in Appendix B37. 

 

37 Note that there are a number of ozone monitoring sites for which we did not calculate projected 

design values because there were less than 5 days with predicted ozone concentrations > 70 ppb. 
27

IV. State-by-State Source Contribution Modeling and Results 

This section documents the procedures used to quantify the air quality impacts 

(i.e., the contributions) of emissions in individual states on annual PM2.5, 24-hour PM2.5, 

and 8-hour ozone concentrations at projected 2012 nonattainment and maintenance 

receptors in the East. The purpose of this assessment is to determine which states 

contribute to ozone, and/or annual PM2.5, and/or 24-hour PM2.5 nonattainment and/or 

maintenance problems in other states38 in amounts at or above thresholds based on 1 

percent of the corresponding NAAQS (see section V of the preamble for more details). 

The air quality contributions were quantified using source apportionment techniques in 

CAMx. In general, these techniques track the formation and transport of ozone and 

particulate matter from specific emissions sources and calculate the contribution of 

sources and precursors to ozone and PM2.5 at individual receptor locations. The strength 

of the photochemical model source apportionment technique is that all modeled ozone 

and/or PM2.5 mass at a given receptor location in the modeling domain can be tracked 

back to specific sources of emissions and boundary conditions in order to fully 

characterize culpable sources. Furthermore, in contrast to other techniques, such as 

“zero-out” modeling (such as was used in development of CAIR), the tracking of 

emissions and resulting pollution in source apportionment modeling does not affect the 

transport, chemical transformation, or atmospheric chemical relationships within the 

modeling. This type of source apportionment is useful for understanding the downwind 

air quality impacts of both the types of sources or regions, and the magnitude of the 

contributions that are associated with ozone and PM2.5, as estimated by the model. More 

details on the implementation of source apportionment in CAMx can be found in the 

CAMx user’s guide39. 

 

38 In this assessment we refer to source states as “upwind” States and nonattainment and/or 

maintenance sites in other states as “downwind” nonattainment and/or maintenance receptors. 

39 Comprehensive Air Quality Model with Extensions Version 5 User’s Guide. Environ 

International Corporation. Novato, CA. March 2009. 
28

A. Nonattainment and Maintenance Receptors for the Assessment of PM2.5 and Ozone 

Contributions 

As described above in section III, we identified monitoring sites in the East that 

are expected to be nonattainment and/or have maintenance problems for the 2012 base 

case for the 1997 annual and 2006 24-hour PM2.5 standards and the 1997 8-hour 

standard40. These 2012 nonattainment and/or maintenance sites are used as the receptors 

in the analysis of interstate contributions. For each of the NAAQS, the projected 

nonattainment and maintenance sites are listed, respectively, in the preamble in Tables 

V.C-1 and V.C-2 for Annual PM2.5, Tables V.C-3 and V.C-4 for 24-hour PM2.5, and 

Tables V.C-5 and V.C-6 for 8-hour ozone. These data are repeated, below, for the 

convenience of the reader. Note that counties with multiple monitoring sites can have 

both nonattainment sites and maintenance only sites. 

Table IV-1 contains the 2003-2007 base period average and maximum annual 

PM2.5 design values and the corresponding 2012 base case average and maximum design 

values for sites projected to be nonattainment of the annual PM2.5 NAAQS in 2012. Table 

IV-2 contains this same information for projected 2012 base case maintenance-only sites. 

There are 12 sites in 7 counties projected to be nonattainment for the annual PM2.5

NAAQS in 2012. There are 4 sites in 3 counties projected to have maintenance only 

problems for the annual PM2.5 NAAQS in 2012. The 2012 base case annual PM2.5

nonattainment and/or maintenance sites in the East are shown in Figure IV-1. 

Table IV-1. Average and maximum 2003-2007 and 2012 base case annual PM2.5 design 

values (μg/m3

) at projected nonattainment sites. 

Monitor ID State County 

Average 

Design 

Value 2003-

2007 

Maximum 

Design 

Value 2003-

2007 

Final Rule 

Average 

Design 

Value 2012 

Final Rule 

Maximum 

Design 

Value 2012 

010730023 Alabama Jefferson 18.57 18.94 16.15 16.46 

010732003 Alabama Jefferson 17.15 17.69 15.16 15.64 

131210039 Georgia Fulton 17.43 17.47 15.07 15.10 

171191007 Illinois Madison 16.72 17.01 15.46 15.73 

261630033 Michigan Wayne 17.50 18.16 15.73 16.32 

390350038 Ohio Cuyahoga 17.37 18.10 15.99 16.66 

390350045 Ohio Cuyahoga 16.47 16.98 15.14 15.61 

 

40 As indicated previously, average design values are used to indentify sites that are 

nonattainment and maximum design values are used to identify sites that have a maintenance 

problem. 
29

Monitor ID State County 

Average 

Design 

Value 2003-

2007 

Maximum 

Design 

Value 2003-

2007 

Final Rule 

Average 

Design 

Value 2012 

Final Rule 

Maximum 

Design 

Value 2012 

390350060 Ohio Cuyahoga 17.11 17.66 15.67 16.18 

390610014 Ohio Hamilton 17.29 17.53 15.76 15.98 

390610042 Ohio Hamilton 16.85 17.25 15.40 15.77 

390618001 Ohio Hamilton 17.54 17.90 16.01 16.33 

420030064 Pennsylvania Allegheny 20.31 20.75 17.94 18.33 

Table IV-2. Average and maximum 2003-2007 and 2012 base case annual PM2.5 design 

values (μg/m3) at projected maintenance-only sites. 

Monitor ID State County 

Average 

Design 

Value 2003-

2007 

Maximum 

Design 

Value 2003-

2007 

Final Rule 

Average 

Design 

Value 2012 

Final Rule 

Maximum 

Design 

Value 2012 

180970081 Indiana Marion 16.05 16.36 14.86 15.16 

180970083 Indiana Marion 15.90 16.27 14.71 15.06 

390350065 Ohio Cuyahoga 15.97 16.44 14.67 15.10 

390617001 Ohio Hamilton 16.17 16.56 14.74 15.10 

Figure IV-1. Projected 2012 base case annual PM2.5 nonattainment and/or maintenance 

sites in the East. 
30

Table IV-3 contains the 2003-2007 base period average and maximum 24-hour 

PM2.5 design values and the 2012 base case average and maximum design values for sites 

projected to be 2012 nonattainment of the 24-hour PM2.5 NAAQS in 2012. Table IV-4 

contains this same information for projected 2012 24-hour maintenance-only sites. There 

are 20 sites in 12 counties projected to be nonattainment for the 24-hour PM2.5 NAAQS 

in 2012. There are 21 sites in 13 counties projected to have maintenance only problems 

for the 24-hour PM2.5 NAAQS in 2012. The 2012 base case 24-hour PM2.5

nonattainment and/or maintenance site in the East are shown in Figure IV-2. 

Table IV-3. Average and maximum 2003-2007 and 2012 base case 24-hour PM2.5 design 

values (μg/m3

) at projected nonattainment sites. 

Monitor ID State County 

Average 

Design 

Value 

2003-2007 

Maximum 

Design 

Value 

2003-2007 

Final Rule 

Average 

Design Value 

2012 

Final Rule 

Maximum 

Design 

Value 2012 

010730023 Alabama Jefferson 44.0 44.2 36.9 37.3 

170311016 Illinois Cook 43.0 46.3 37.5 40.4 

171191007 Illinois Madison 39.1 40.1 36.5 36.8 

180970043 Indiana Marion 38.4 39.9 35.7 37.1 

180970066 Indiana Marion 38.3 39.6 35.7 36.9 

180970081 Indiana Marion 38.2 39.2 35.8 36.9 

261470005 Michigan St Clair 39.6 40.6 36.2 37.1 

261630015 Michigan Wayne 40.1 40.6 35.5 36.0 

261630016 Michigan Wayne 42.9 45.4 38.9 41.2 

261630019 Michigan Wayne 40.9 41.4 37.3 37.8 

261630033 Michigan Wayne 43.8 44.2 39.4 39.8 

390350038 Ohio Cuyahoga 44.2 47.0 39.4 41.8 

390350060 Ohio Cuyahoga 42.1 45.7 37.7 40.8 

420030064 Pennsylvania Allegheny 64.2 68.2 56.7 59.9 

420030093 Pennsylvania Allegheny 45.6 51.5 39.1 44.3 

420030116 Pennsylvania Allegheny 42.5 42.5 35.5 35.5 

420070014 Pennsylvania Beaver 43.4 44.6 36.2 37.4 

420710007 Pennsylvania Lancaster 40.8 44.0 35.9 38.3 

540090011 West Virginia Brooke 43.9 44.9 37.5 38.3 

550790043 Wisconsin Milwaukee 39.9 40.8 36.2 37.1 
31

Table IV-4. Average and maximum 2003-2007 and 2012 base case 24-hour PM2.5 design 

values (μg/m3

) at projected maintenance-only sites. 

Monitor ID State County 

Average 

Design 

Value 

2003-2007 

Maximum 

Design 

Value 

2003-2007 

Final Rule 

Average 

Design 

Value 2012 

Final Rule 

Maximum 

Design 

Value 2012 

010732003 Alabama Jefferson 40.3 40.8 35.3 35.9 

170310052 Illinois Cook 40.2 41.4 34.9 36.0 

170312001 Illinois Cook 37.7 40.6 33.6 36.1 

170313301 Illinois Cook 40.2 43.3 34.9 37.6 

170316005 Illinois Cook 39.1 41.8 34.1 36.4 

171190023 Illinois Madison 37.3 38.1 35.1 35.8 

180890022 Indiana Lake 38.9 44.0 34.9 39.5 

180890026 Indiana Lake 38.4 41.3 34.0 37.0 

261610008 Michigan Washtenaw 39.4 40.8 35.0 36.3 

390170003 Ohio Butler 39.2 41.1 34.4 36.5 

390350045 Ohio Cuyahoga 38.5 41.5 34.7 38.1 

390350065 Ohio Cuyahoga 38.6 41.0 34.9 37.6 

390618001 Ohio Hamilton 40.6 40.9 35.2 35.8 

390811001 Ohio Jefferson 41.9 45.5 34.5 37.8 

391130032 Ohio Montgomery 37.8 40.0 33.6 35.6 

420031008 Pennsylvania Allegheny 41.3 42.8 35.0 36.3 

420031301 Pennsylvania Allegheny 40.3 42.4 33.9 35.6 

420033007 Pennsylvania Allegheny 37.5 43.1 32.3 37.3 

421330008 Pennsylvania York 38.2 40.7 33.3 36.0 

550790010 Wisconsin Milwaukee 38.6 40.0 35.4 36.7 

550790026 Wisconsin Milwaukee 37.3 41.3 33.6 37.2 
32

Figure IV-2. Projected 2012 base case 24-hour PM2.5 nonattainment and/or maintenance 

sites in the East. 

Table IV-5 contains the 2003-2007 base period average and maximum 8-hour 

ozone design values and the 2012 base case average and maximum design values for sites 

projected to be 2012 nonattainment of the 8-hour ozone NAAQS in 2012. Table IV-6 

contains this same information for projected 2012 8-hour ozone maintenance-only sites. 

There are 7 sites in 3 counties projected to be nonattainment for the 1997 8-hour ozone 

NAAQS in 2012. There are 9 sites in 5 counties projected to have maintenance only 

problems for the 1997 8-hour ozone NAAQS in 2012. The 2012 base case 8-hour ozone 

nonattainment and/or maintenance sites in the East are shown in Figure IV-3. 
33

Table IV-5. Average and maximum 2003-2007 and 2012 base case 8-hour ozone design 

values (ppb) at projected nonattainment sites. 

Monitor ID State County 

Average 

Design 

Value 

2003-2007 

Maximum 

Design 

Value 

2003-2007 

Final Rule 

Average 

Design 

Value 2012 

Final Rule 

Maximum 

Design Value 

2012 

220330003 Louisiana East Baton Rouge 92.0 96 85.6 89.3 

480391004 Texas Brazoria 94.7 97 86.7 88.8 

482010051 Texas Harris 93.0 98 86.1 90.8 

482010055 Texas Harris 100.7 103 93.3 95.4 

482010062 Texas Harris 95.7 99 88.8 91.8 

482010066 Texas Harris 92.3 96 87.1 90.6 

482011039 Texas Harris 96.3 100 88.8 92.2 

Table IV-6. Average and maximum 2003-2007 and 2012 base case 8-hour ozone design 

values (ppb) at projected maintenance-only sites. 

Monitor ID State County 

Average 

Design 

Value 2003-

2007 

Maximum 

Design 

Value 

2003-2007 

Final Rule 

Average 

Design Value 

2012 

Final Rule 

Maximum 

Design 

Value 2012 

090011123 Connecticut Fairfield 92.3 94 83.9 85.5 

090093002 Connecticut New Haven 90.3 93 82.7 85.1 

240251001 Maryland Harford 92.7 94 84.4 85.6 

260050003 Michigan Allegan 90.0 93 82.4 85.1 

482010024 Texas Harris 88.0 92 83.4 87.2 

482010029 Texas Harris 91.7 93 84.2 85.4 

482011015 Texas Harris 89.0 96 82.4 88.9 

482011035 Texas Harris 86.3 95 79.9 88.0 

482011050 Texas Harris 89.3 92 82.8 85.4 
34

Figure IV-3. Projected 2012 base case 8-hour ozone nonattainment and/or maintenance 

sites in the East. 

B. Overview of Approach for Source Apportionment Modeling 

Source apportionment modeling for both ozone and PM2.5 was performed using 

the 2012 base case emissions. In this modeling we tracked ozone and PM2.5 formed from 

anthropogenic emissions in each state that is wholly within the 12 km modeling domain. 

The modeling results were used to calculate the contributions of the emissions in these 

states to pollution concentrations at each of the projected nonattainment and maintenance 

receptors. The states included in the source apportionment modeling for ozone and PM2.5

are: Alabama, Arkansas, Connecticut, Delaware, the District of Columbia41, Florida, 

Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, 

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

New Jersey, New York, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, 

Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Vermont, Virginia, West 

Virginia, and Wisconsin. 

 

41 As in the proposal, EPA has combined the contributions from Maryland and the District of 

Columbia as a single entity in our contribution analysis for the final rule. EPA believes that this 

is a fair representation of emissions for transport analysis because of the small size of the District 

of Columbia and its close proximity to Maryland. However, the District of Columbia is not 

included in the Transport Rule due to the significant contribution analysis findings in section 

VI.D of the preamble. 
35

There are several other states that are only partially contained within the 12 km 

modeling domain (i.e., Colorado, Montana, New Mexico, and Wyoming). We did not 

track the emissions or assess the contribution from emissions in these states individually. 

Rather, the emissions from these states were tracked as a combined set of emissions 

together with emissions from sources in Canada and Mexico in a category named “other”. 

Emissions from wildfires in all states were also included in the “other” category. In the 

CAMx source apportionment techniques we also tracked the contributions from biogenic 

emissions (regionally) and initial and boundary concentrations (IC/BCs). The 

contributions from these three categories (i.e., other, biogenic, and IC/BCs) were not used 

in the analysis to determine upwind state contributions to downwind areas. 

C. Approach to Calculating Contributions for Annual and 24-Hour PM2.5 

 We used the CAMx Particulate Source Apportionment Technique (PSAT) to 

calculate contributions to annual and 24-hour PM2.5 nonattainment and maintenance. The 

CAMx PSAT is capable of “tagging” (i.e., tracking) source category emissions for certain 

PM species and precursor emissions. For the Transport Rule, we ran PSAT to tag 

emissions of NOX, SO2, and primary PM2.5 from the relevant sources within the 

individual states listed above. Each state was a separate tag, and the tagged emissions 

followed state boundaries (not grid cells). Information on the creation of tagged 

emissions can be found in the Transport Rule EITSD. Due to the relatively small 

modeled concentrations of secondary organic aerosols (SOA), and the relatively large 

runtime penalty of the SOA PSAT mechanism, we chose not to track SOA. 

 In the PSAT simulation, NOX emissions are tracked to contributions to particulate 

nitrate concentrations, SO2 emissions are tracked to contributions to particulate sulfate 

concentrations, and primary particulates (organic carbon, elemental carbon, and other, 

unspeciated PM2.5) are tracked as contributions to primary particulates. We combined the 

contributions of nitrate and sulfate in calculating the net contributions to PM2.5 for the 

purpose of evaluating the interstate contributions against specific threshold criteria, as 

described in section IV.E, below. The rationale for including mass associated with nitrate 

plus sulfate and excluding primary PM2.5 and SOA in the contributions for the TR is 

articulated in the section V of the TR preamble. 

 We developed and applied several post-processing steps to transform the PSAT 

modeling outputs to PM2.5 contributions. The approach involved processing the PSAT 
36

model outputs using MATS along with other post-processing software to calculate the 

contribution of each upwind state to each nonattainment and/or maintenance site (i.e., 

receptor). 

The following is a description of the procedures for calculating contributions for 

annual PM2.5. These procedures were applied separately for each source state. 

1. The analysis identified the air quality contributions from each state modeled using 

source apportionment to calculate contributions to each monitoring site. Particularly 

important monitoring sites included the receptor sites identified in the 2012 base case 

modeling as being nonattainment and/or maintenance receptors. 

2. Contributions for each of the PM2.5 species from each state, as predicted by PSAT, are 

subtracted from the standard 2012 base case CAMx model output to generate a new set of 

model output files for each source region (state). 

3. Daily, 24-hr average PM2.5 species are calculated for the “standard” model output files 

and newly generated source contribution output files. 

4. The relative response factors (RRFs) for each of the PM2.5 species is calculated for 

each source region at all receptors using the MATS model attainment software. In this 

approach, the MATS “baseline” model file is defined as the standard 2012 base case 

CAMx model output file and the “future case” model file is defined as the 2012 state 

contribution model output file (from step 3). 

5. The species-specific annual average RRFs (generated by MATS in step 4) for each 

source state are multiplied by the annual average future year (2012) species 

concentrations generated for the 2012 base case to estimate PM2.5 species contributions in 

μg/m3

 from each species for each source-tagged state42. [The actual calculation is 

(species concentration – (RRF*species concentration))] 

6. The annual average contributions of sulfate ion, nitrate ion, ammonium, and water for 

each source state are combined to calculate the nitrate plus sulfate PM2.5 contribution 

used in the TR analysis43. 

 

42 The calculations in step 5 use MATS outputs and additional post-processing software 

developed for this rule. More details on the post-processing steps and software can be found in 

Appendix C. 

43 Particulate ammonium and particle bound water are attached to the nitrate and sulfate. 

Therefore, they are counted as part of the downwind contribution of total sulfate and total nitrate. 

As in the design value calculations in section III, the ammonium contribution is calculated based 

on the calculated sulfate and nitrate concentrations, using a constant value for the degree of 

neutralization of sulfate (from the ambient data). The particle bound water contribution 

concentration is calculated from the empirical formula contained in MATS. . 
37

7. Annual PM2.5 (i.e., nitrate plus sulfate) contributions are expressed in units of μg/m3

. 

Values of annual PM2.5 contribution are truncated after two places to the right of the 

decimal (e.g., a contribution of 0.149 μg/m3 is truncated to 0.14 μg/m3). 

The 24-hour PM2.5 contributions were calculated in a manner similar to the 

procedures for annual PM2.5. However, there are several more steps in the 24-hour 

calculations which are designed to retain the contributions in each quarter through most 

of the post-processing. For 24-hour PM2.5, the contributions are calculated as the five 

year average contributions to the 98th percentile concentrations at each site. 

The following is a description of the procedures for calculating contributions for 

24-hour PM2.5. These procedures were applied separately for each source-tagged state. 

1. The analysis identified the air quality contributions from each state modeled using 

source apportionment to calculate contributions to each monitoring site. Particularly 

important monitoring sites included the receptor sites identified in the 2012 base case 

modeling as being nonattainment and/or maintenance for 24-hour PM2.5. 

2. Contributions for each of the PM2.5 species from each state, as predicted by PSAT, are 

subtracted from the standard 2012 CAMx model output to generate a new set of model 

output files for each source region. 

3. Daily, 24-hr average PM2.5 species model output files are generated for the “standard” 

2012 base case model output files and newly generated source contribution output files. 

4. Relative response factors (RRFs) are calculated for each of the PM2.5 species44 for each 

source state at all receptors using the MATS model attainment software. Quarterly RRFs 

are calculated using the average of the “high” concentration model days in each quarter. 

Here, the “high” concentration days are based on the highest 10% of modeled PM2.5

days45 (in each grid cell) in the 2012 base case. The MATS “baseline” model file is 

defined as the standard 2012 base case CAMx model output file and the “future case” 

model file is defined as the 2012 state contribution region model output file (from step 3). 

 

44 As in the design value calculations in section III, the ammonium contribution is calculated 

based on the calculated sulfate and nitrate contribution concentrations, using a constant value for 

the degree of neutralization of sulfate (from the ambient data). The particle bound water 

contribution concentration is calculated from the empirical formula contained in MATS. 

45 Due to large over-predictions of crustal PM (other primary PM2.5), the modeled PM2.5 

concentrations used to determine the high modeled days in each quarter do not include crustal 

PM2.5. We did not want the crustal PM2.5 to unduly influence the selection of the high modeled 

days. The model performance for crustal PM2.5 and all other PM2.5 components is documented 

in Appendix A. 
38

5. The species “high days” quarterly average RRFs (generated by MATS in step 4) for 

each source region are multiplied46 by the previously identified 2012 98th percentile day 

species concentrations for each site for each year (2003-2007) projected to the 2012 base 

case. [The actual calculation is (species concentration – (RRF*species concentration))]. 

This calculation is done for all valid years of data (up to 5 years). The quarterly species 

RRFs are applied based on the quarter in which the 98th percentile value (in 2012) 

occurred. For example, if the projected 98th percentile value for 2003 occurs in January, 

then the modeled RRFs for quarter 1 will be applied to the species concentrations for that 

particular day. The result of these calculations are the contribution of each of the species 

from the upwind source to each of the 98th percentile days, for up to 5 years. 

6. For each receptor, the contributions of each species for the 98th percentile days (2003-

2007 projected to the 2012 base case) are averaged47 together. This represents the 

species contributions to 24-hour PM2.5 concentrations. 

7. The 24-hour contributions of sulfate ion, nitrate ion, ammonium, and water48 for each 

source state are combined to calculate the total PM2.5 contribution used in the TR 

analysis, as in the calculations for annual PM2.5 , described above.. 

8. 24-hour PM2.5 contributions are expressed in units of μg/m3

. For the contribution 

assessment, values of 24-hour PM2.5 contribution are truncated after two places to the 

right of the decimal (e.g., a contribution of 0.349 μg/m3 is truncated to 0.34 μg/m3). 

D. Approach to Calculating Contributions for 8-Hour Ozone 

We used the CAMx Anthropogenic Precursor Culpability Assessment (APCA) 

source apportionment technique to calculate the contributions to 8-hour ozone 

nonattainment and maintenance in the 2012 base case. APCA tracks the formation of 

ozone from NOX and VOC emissions. Through emissions pre-processing procedures, we 

tagged all of the anthropogenic NOX and VOC emissions in each state wholly within the 

 

46 The calculations in steps 5 and 6 use MATS outputs and post-processing software written for 

this rule. More details on the post-processing steps and software can be found in Appendix C. 

47 For the 24-hr PM2.5 NAAQs contribution calculations, the five year average contributions are 

not weighted towards the middle year of the period. Since the 98th percentile day for each year 

can be in a different season, EPA did not want to weight the upwind contribution to a particular 

season. Upwind states should not be penalized for having a large contribution in the high season 

for 2005, compared to other years. Likewise, upwind states should not be rewarded for having a 

small contribution to 2005 compared to other years 

48 Particulate ammonium and particle bound water are attached to the nitrate and sulfate. 

Therefore, they are counted as part of the downwind contribution of total sulfate and total nitrate. 
39

Eastern modeling domain. A separate tag was created for each state, and the tagged 

emissions followed state boundaries. The list of tagged states can be found in section B. 

The emissions from all anthropogenic sources of NOX and VOC were tracked in 

the APCA simulation. Several post-processing steps were developed to transform the 

raw model outputs to ozone contributions. The approach for calculating ozone 

contributions was similar to the approach used for PM2.5, except that the APCA model 

outputs were processed using MATS instead of the PSAT outputs used for PM2.5.

The following is a description of the procedures for calculating contributions for 

8-hour ozone. These procedures were applied separately for each source state. 

1. The analysis identified the air quality contributions from each state modeled using 

source apportionment to calculate contributions to each monitoring site. Particularly 

important monitoring sites included the receptor sites identified in the 2012 base case 

modeling as being nonattainment and/or maintenance for 8-hour ozone. 

2. The “raw” contributions from each source state are subtracted from the “raw” standard 

2012 CAMx model output to generate a new set of model output files for each state. 

3. Daily 8-hr maximum ozone model output files are generated from hourly ozone 

predictions in the “standard” model output files and newly generated source contribution 

output files. 

4. Relative response factors (RRFs) are calculated for each source state for all receptors 

using MATS. The “baseline” ozone values in this calculation are the 2012 base case 8-

hour daily maximum concentrations and the “future case” ozone values are the 8-hour 

daily maximum concentrations in the 2012 source state file created in step 3. 

5. A “modified”49 model attainment test is applied with MATS using an initial ozone 

criterion of 85 ppb and a minimum allowable criterion of 70 ppb. A minimum number of 

5 days at or above the 85 ppb criterion or 5 days of modeled ozone greater than the 

minimum allowable criterion (70 ppb) are required50 for a relative response factor to be 

estimated at a receptor site. For sites at which there are not 5 modeled days with 

 

49 We refer to this as a “modified” attainment test because the contribution analysis uses a 

different set of concentration thresholds compared to the attainment test used to calculate future 

year design values. 

50 The contribution analysis uses a minimum number of days above the threshold of 5 days. This 

differs from the default attainment test minimum number of 10 days > 85 ppb. It was reasoned 

that the contribution should be averaged over multiple days, but requiring a minimum of 10 days 

was not necessary. Since the attainment test uses an absolute minimum of 5 days in order to 

perform an RRF calculation, we chose to use 5 days as the default minimum number of days 

needed for each contribution calculation. 
40

predicted 8-hour daily maximum concentrations above the initial ozone criterion, the 

criterion is decreased by 1 ppb until 5 days are included in the RRF calculation until the 

minimum threshold (70 ppb) is reached. If there are less than 5 modeled days above 70 

ppb, then a contribution to the receptor is not calculated and the receptor is not used in 

the contribution analysis. 

 6. The relative response factor (from step 4) is multiplied by the 2012 base case design 

value to estimate ozone contribution in ppb from the source state51 [The actual 

calculation is (ozone concentration – (RRF*ozone concentration))] 

7. Ozone contributions are expressed in units of ppb. Ozone contribution values are 

truncated after 1 place to the right of the decimal (e.g. a contribution of 0.79 ppb is 

truncated to 0.7 ppb). 

E. Evaluation of Interstate Contributions 

The previous section describes the procedures for calculating 8-hour ozone and 

annual and 24-hour PM2.5 contributions to the nonattainment and maintenance receptor 

sites52. The contributions to each 2012 nonattainment receptor and each maintenance 

receptor are provided in Appendix D53. For annual PM2.5 we calculated each state’s 

contribution to each of the 12 monitoring sites that are projected to be nonattainment and 

each of the 4 sites that are projected to have maintenance problems for the annual PM2.5

NAAQS in the 2012 base case. For 24-hour PM2.5, we calculated each state’s 

contribution to each of the 20 monitoring sites that are projected to be nonattainment and 

each of the 21 sites that are projected to have maintenance problems for the 24-hour 

PM2.5 NAAQS in the 2012 base case. Similarly, for ozone we calculated each state’s 

contribution to each of the 4 monitoring sites that are projected to be nonattainment and 

 

51 The calculation in step 6 uses MATS outputs and post-processing software written for this rule. 

More details on the post-processing steps and software can be found in Appendix C. 

52 As noted previously, the nonattainment receptors are those monitoring sites which are projected 

to exceed the NAAQS in the 2012 base case, based on the 5-year weighted average design values. 

The maintenance receptors are those monitoring sites which are projected to exceed the NAAQS 

in the 2012 base case based on projections of the maximum design value in the 5-year period. 

Monitoring sites with average design values at or below the level of the NAAQS, but with 

maximum design values exceeding the NAAQS are referred to as maintenance only receptors. 

53 The contributions to all monitoring sites in the 12 km Eastern modeling domain are provided in 

the TR docket. 
41

each of 654 sites that are projected to have maintenance problems for the 8-hour ozone 

NAAQS in the 2012 Base Case. 

The upwind state-to-downwind receptor combinations (i.e., linkages) were 

evaluated to determine if the contributions to downwind nonattainment and/or 

maintenance are at or above specific contribution thresholds. As described in the TR 

preamble, EPA is using 1 percent of the NAAQS as the threshold for determining which 

states to include in this rule. The thresholds for the 1997 annual PM2.5 NAAQS, 2006 24-

hour PM2.5 NAAQS, and 1997 8-hour ozone NAAQS are shown in Table IV-7. 

Table IV-7. Threshold concentrations used for evaluating interstate contributions to 

annual and 24-hour PM2.5 and 8-hour ozone. 

Pollutant Level of the NAAQS 1 Percent Threshold

Annual PM2.5 15.0 μg/m3 0.15 μg/m3

24-Hour PM2.5 35 μg/m3 0.35 μg/m3

8-Hour Ozone 0.08 ppm 0.0008 ppm (0.8 ppb)

To determine which states contribute amounts at or above these thresholds to 

nonattainment and/or maintenance, we examined the amount of contribution from each 

state to each downwind nonattainment receptor and each maintenance only receptor for 

the three NAAQS and noted the magnitude of the contribution relative to the threshold. 

States that contribute amounts at or above the threshold to at least one downwind 

nonattainment receptor or one maintenance receptor for the annual PM2.5 NAAQS or the 

24-hour PM2.5 NAAQS are included in the TR for annual SO2 and NOX emissions 

budgets. States that contribute amounts that are at or above the threshold to at least one 

8-hour ozone downwind nonattainment or maintenance receptor are included in the TR 

for ozone season (i.e., May through September) NOX emissions budgets. 

 

54 There are 6 additional sites with projected 2012 nonattainment or maintenance (Harris Co., 

Texas sites 482010024, 482010062, 482010066, 482011015, 482011035, and 482011039) for 

which there are less than 5 days with 8-hour ozone predictions of at least 70 ppb. Thus, we did 

not calculate contributions for these 6 sites. 
42

1. Contributions to Annual PM2.5 Nonattainment and Maintenance 

 The largest contribution from each state to annual PM2.5 across all downwind 

nonattainment receptors is provided in Table IV-8. The largest contribution from each 

State to annual PM2.5 across all downwind maintenance-only receptors is also provided in 

Table IV-8. 

Table IV-8. Largest contribution to downwind annual PM2.5 (μg/m3) nonattainment and 

maintenance for each of 37 states. 

Upwind State

Largest Downwind 

Contribution to Nonattainment 

for Annual PM2.5 (μg/m3

)

Largest Downwind Contribution 

to Maintenance 

for Annual PM2.5 (μg/m3

)

Alabama 0.51 0.19

Arkansas 0.10 0.04

Connecticut 0.00 0.00

Delaware 0.00 0.00

Florida 0.08 0.01

Georgia 0.46 0.13

Illinois 0.50 0.65

Indiana 1.34 1.27

Iowa 0.26 0.14

Kansas 0.09 0.04

Kentucky 0.94 0.81

Louisiana 0.09 0.03

Maine 0.00 0.00

Maryland 0.15 0.06

Massachusetts 0.00 0.00

Michigan 0.64 0.64

Minnesota 0.14 0.09

Mississippi 0.05 0.01

Missouri 1.22 0.27

Nebraska 0.06 0.03

New Hampshire 0.00 0.00

New Jersey 0.02 0.01

New York 0.21 0.21

North Carolina 0.20 0.06

North Dakota 0.06 0.04

Ohio 1.34 0.94

Oklahoma 0.08 0.03
43

Upwind State

Largest Downwind 

Contribution to Nonattainment 

for Annual PM2.5 (μg/m3

)

Largest Downwind Contribution 

to Maintenance 

for Annual PM2.5 (μg/m3

)

Pennsylvania 0.54 0.54

Rhode Island 0.00 0.00

South Carolina 0.24 0.04

South Dakota 0.03 0.01

Tennessee 0.32 0.32

Texas 0.18 0.07

Vermont 0.00 0.00

Virginia 0.12 0.06

West Virginia 0.95 0.40

Wisconsin 0.22 0.19

 

 Based on the state-by-state contribution analysis, there are 18 states which 

contribute 0.15 μg/m3

 or more to downwind annual PM2.5 nonattainment. These states 

are: Alabama, Georgia, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Missouri, 

New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Texas, West 

Virginia, and Wisconsin. A list of the downwind nonattainment sites to which each 

upwind state contributes 0.15 μg/m3 or more (i.e., the upwind state-to-downwind 

nonattainment “linkages”) are provided in Appendix E. 

 There are 12 states which contribute 0.15 μg/m3 or more to downwind annual 

PM2.5 maintenance. These states are: Alabama, Illinois, Indiana, Kentucky, Michigan, 

Missouri, New York, Ohio, Pennsylvania, Tennessee, West Virginia, and Wisconsin. A 

list of the downwind maintenance sites to which each upwind state contributes 0.15 

μg/m3 or more (i.e., the upwind state-to-downwind maintenance “linkages”) are provided 

in Appendix E. 

2. Contributions to 24-Hour PM2.5 Nonattainment and Maintenance 

The largest contribution from each state to 24-hour PM2.5 across all downwind 

nonattainment receptors is provided in Table IV-9. The largest contribution from each 

state to 24-hour PM2.5 across all downwind maintenance-only receptors is also provided 

in Table IV-9. 
44

Table IV-9. Largest contribution to downwind 24-hour PM2.5 (μg/m3

) nonattainment and 

maintenance for each of 37 states. 

Upwind State

Largest Downwind 

Contribution to 

Nonattainment 

for 24-hour PM2.5 (μg/m3

)

Largest Downwind 

Contribution to Maintenance 

for 24-hour PM2.5 (μg/m3

)

Alabama 0.51 0.42

Arkansas 0.24 0.23

Connecticut 0.10 0.18

Delaware 0.22 0.20

Florida 0.07 0.03

Georgia 1.10 0.92

Illinois 3.72 5.70

Indiana 3.56 5.15

Iowa 0.82 1.55

Kansas 0.37 0.81

Kentucky 4.38 3.58

Louisiana 0.11 0.13

Maine 0.06 0.10

Maryland 2.83 2.11

Massachusetts 0.19 0.30

Michigan 1.86 2.03

Minnesota 0.61 1.01

Mississippi 0.06 0.07

Missouri 3.73 3.71

Nebraska 0.24 0.52

New Hampshire 0.05 0.10

New Jersey 0.68 0.75

New York 0.83 1.34

North Carolina 0.40 0.38

North Dakota 0.21 0.33

Ohio 5.85 4.74

Oklahoma 0.17 0.20

Pennsylvania 2.85 2.29

Rhode Island 0.02 0.03

South Carolina 0.29 0.25

South Dakota 0.10 0.17

Tennessee 1.38 1.30

Texas 0.37 0.33
45

Upwind State

Largest Downwind 

Contribution to 

Nonattainment 

for 24-hour PM2.5 (μg/m3

)

Largest Downwind 

Contribution to Maintenance 

for 24-hour PM2.5 (μg/m3

)

Vermont 0.03 0.05

Virginia 1.21 1.01

West Virginia 4.02 3.33

Wisconsin 0.69 0.97

 Based on the state-by-state contribution analysis, there are 21 states which 

contribute 0.35 μg/m3 or more to downwind 24-hour PM2.5 nonattainment. These states 

are: Alabama, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Maryland, Michigan, 

Minnesota, Missouri, New Jersey, New York, North Carolina, Ohio, Pennsylvania, 

Tennessee, Texas, Virginia, West Virginia, and Wisconsin. A list of the downwind 

nonattainment counties to which each upwind state contributes 0.35 μg/m3 or more (i.e., 

the upwind state-to-downwind nonattainment “linkages”) are provided in Appendix E. 

 There are 21 states which contribute (individually) 0.35 μg/m3 or more to 

downwind 24-hour PM2.5 maintenance. These states are: Alabama, Georgia, Illinois, 

Indiana, Iowa, Kansas, Kentucky, Maryland, Michigan, Minnesota, Missouri, Nebraska, 

New Jersey, New York, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West 

Virginia, and Wisconsin. A list of the downwind maintenance sites to which each 

upwind state contributes 0.35 μg/m3 or more (i.e., the upwind state-to-downwind 

maintenance “linkages”) are provided in Appendix E. 

3. Contributions to 8-Hour Ozone Nonattainment and Maintenance 

The largest contribution from each state to 8-hour ozone across all downwind 

nonattainment receptors is provided in Table IV-10. The largest contribution from each 

state to 8-hour ozone across all downwind maintenance-only receptors is also provided in 

Table IV-10. 
46

Table IV-10. Largest contribution to downwind 8-hour ozone nonattainment and 

maintenance for each of 37 states. 

Upwind State

Largest Downwind 

Contribution to 

Nonattainment 

for Ozone (ppb)

Largest Downwind 

Contribution to Maintenance

for Ozone (ppb)

Alabama 4.0 2.8

Arkansas 2.1 2.0

Connecticut 0.0 0.2

Delaware 0.0 0.6

Florida 0.5 3.6

Georgia 1.6 2.8

Illinois 1.9 26.8

Indiana 1.3 9.4

Iowa 0.6 0.9

Kansas 0.5 1.0

Kentucky 1.6 1.6

Louisiana 8.0 11.1

Maine 0.0 0.0

Maryland 0.0 2.7 

Massachusetts 0.0 0.6

Michigan 0.0 0.9

Minnesota 0.3 0.2

Mississippi 4.0 3.3

Missouri 1.1 4.8

Nebraska 0.2 0.2

New Hampshire 0.0 0.1

New Jersey 0.0 11.5

New York 0.0 18.8

North Carolina 0.5 1.3

North Dakota 0.2 0.1

Ohio 0.1 3.2

Oklahoma 0.3 2.8

Pennsylvania 0.1 8.2

Rhode Island 0.0 0.0

South Carolina 0.4 0.9

South Dakota 0.1 0.1

Tennessee 2.2 1.1

Texas 3.9 1.9
47

Upwind State

Largest Downwind 

Contribution to 

Nonattainment 

for Ozone (ppb)

Largest Downwind 

Contribution to Maintenance

for Ozone (ppb)

Vermont 0.0 0.0

Virginia 0.2 8.2

West Virginia 0.0 2.8

Wisconsin 0.2 2.2

Based on the state-by-state contribution analysis, there are 11 states (which 

contribute 0.8 ppb or more to downwind 8-hour ozone nonattainment. These states are: 

Alabama, Arkansas, Georgia, Illinois, Indiana, Kentucky, Louisiana, Mississippi, 

Missouri, Tennessee, and Texas. A list of the downwind nonattainment counties to 

which each upwind state contributes 0.8 ppb or more (i.e., the upwind state-to-downwind 

nonattainment “linkages”) is provided in Appendix E. 

There are 26 states which contribute 0.8 ppb or more to downwind 8-hour ozone 

maintenance. These states are: Alabama, Arkansas, Florida, Georgia, Illinois, Indiana, 

Iowa, Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Missouri, New 

Jersey, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, South Carolina, 

Tennessee, Texas, Virginia, West Virginia, and Wisconsin. A list of the downwind 

nonattainment counties to which each upwind state contributes 0.8 ppb or more (i.e., the 

upwind state-to-downwind nonattainment “linkages”) is provided in Appendix E. 

F. Analysis of Contributions Captured by Various Thresholds 

 In this section we present a summary of the amount of upwind contribution to 

each receptor based on the 1 percent threshold in comparison to the amount of 

contribution based on two other thresholds: 0.5 percent of the NAAQS and 5 percent of 

the NAAQS. This analysis was performed to support the development of responses to 

comments on the choice of thresholds in the proposed rule. The concentration associated 

with each of these thresholds, as used in this analysis, is given in Table IV-11. 
48

Table IV-11. Concentrations associated with thresholds of 0.5 percent, 1 percent, and 5 

percent. 

0.5 Percent 1 Percent 5 Percent

8-hour Ozone (ppb) 0.4 0.8 4

Annual PM2.5 (μg/m3) 0.075 0.15 0.75

24-Hour PM2.5 (μg/m3) 0.175 0.35 1.75

For the analysis of thresholds we calculate several “metrics” (i.e., measures of 

contribution) for each downwind receptor for the 8-hour ozone, annual PM2.5, and 24-

hour PM2.5 NAAQS, individually. These metrics are listed in Table IV-12. In this table 

“x” refers to one the thresholds included in this analysis, namely, 0.5 percent, 1 percent, 

and 5 percent. 

Table IV-12. Contribution metrics used for the analysis of thresholds. 

Contribution Metrics 

In-State Contribution

Total Contribution from All Upwind States

Upwind Contribution as a Percent of Receptor Concentration

Number of Upwind States that Contribute At or Above “x” Percent Threshold

Total Contribution from Upwind States using a “x” Percent Threshold

Percent of Upwind Contribution Captured with “x” Percent Threshold

The method for calculating each metric of the metrics in Table 1 is as follows: 

1. In-State Contribution 

- Amount of contribution from emissions from the state in which the receptor is located. 

These data are available from the contribution calculations described previously in this 

document. 

2. Total Contribution from All Upwind States 

- Sum of contributions from all upwind states, without consideration of any contribution 

threshold. 
49

3. Upwind Contribution as a Percent of Receptor Concentration 

- Ratio of total contribution from all upwind states (metric 2) divided by the sum of the 

in-state contribution (metric 1) and the total upwind contribution (metric 2), expressed as 

a percent. 

4. Number of Upwind States that Contribute At or Above “x” Percent Threshold 

- Count of the number of upwind states that contribute amounts at or above the given 

threshold. 

5. Total Contribution from Upwind States using a “x” Percent Threshold 

- Sum of contributions from all upwind states the individually contribute at or above the 

given threshold. 

6. Percent of Upwind Contribution Captured with “x” Percent Threshold 

- Total contribution using an “x” percent threshold (metric 5) divided by the total 

contribution from all upwind states (metric 2), expressed as a percent. 

The data for each of the above contribution metrics for each 2012 nonattainment and 

maintenance receptor are provided in Appendix F Tables F-1, F-2, and F-3 for 8-hour 

ozone, annual PM2.5 and 24-hour PM2.5, respectively. 

The contribution data in Tables F-1 through F-3 indicate that the amount of 

transport from upwind states comprises a very large portion of the concentration at the 8-

hour ozone, annual PM2.5 and 24-hour PM2.5 nonattainment and maintenance sites. For 

ozone, more than 90 percent of the concentration at the Allegan, MI, Fairfield County, 

CT and New Haven County, CT receptors is due to transport from upwind states. For 

Harford County, MD, Baton Rouge Parish, LA, and Harris County, TX, transport is 35 to 

50 percent of the concentration. For annual PM2.5, 60 to 70 percent of the concentration 

is due to upwind transport at all receptors, except for Jefferson County, AL where 

transport is somewhat less, but still substantial at 45 to 50 percent. The amount of PM2.5

due to transport is 60 to 80 percent of the concentration at a majority of the 24-hour PM2.5

receptors. The interpretation of the contribution summaries presented in Tables F-1 

through F-3 with respect to decisions on the selection of thresholds for the final rule can 

be found in section V.D.1 of the final rule preamble. 
50

V. Air Quality Impacts of the Transport Rule SO2 and NOX Emissions 

Reductions

In this section we present the air quality impacts of the SO2 and NOX emissions 

reductions in the 2014 remedy scenario in comparison to air quality predicted for the 

2012 base case and 2014 base case scenarios55. We first describe the magnitude and 

spatial patterns in emissions reductions (and increases) in the East. Then we quantify the 

impacts of these emissions changes on projected design value concentrations for annual 

PM2.5, 24-hour PM2.5, and 8-hour ozone at monitoring sites in the Eastern modeling 

domain56. We also provide the impacts on visibility for the 20 percent worst days in 

Class I Areas in the East. 

A. Characterization of Emissions in 2014 for the Transport Rule Remedy Scenario 

The approach for developing the 2014 remedy scenario is described in the TR 

preamble. The methods for projecting emissions for the 2012 and 2014 base case 

scenarios, including the baseline control programs, are presented in the EITSD57. The 23 

states that are included in the TR for annual SO2 and NOX emissions controls to reduce 

interstate contributions of PM2.5 are shown on the map in Figure V-1. These states are 

divided into two groups with different levels of SO2 emissions reductions. The states in 

Group 1 have deeper reductions than those in Group 258. The 20 states subject to ozone 

season NOX controls to reduce interstate transport of ozone are shown on the map in 

Figure V-2. Also shown on the map are the 6 additional states (Iowa, Kansas, Michigan, 

Missouri, Oklahoma, and Wisconsin)59 that EPA is proposing to include in the Transport 

Rule ozone season NOX trading program. The aggregate emissions of annual SO2, annual 

 

55 Note that the comparisons between the 2012 base case and the 2014 remedy scenario include 

the impacts of emissions reductions from other rules and control programs that are projected to 

come into effect between 2012 and 2014, in addition to the impacts of the emissions reductions in 

the TR. The comparison between the 2014 base case and the 2014 remedy reflects the SO2 and 

NOx emissions reductions in the TR, only. 

56 This analysis does not include projections for monitoring sites that do not meet certain 

completeness criteria or other projection criteria, as described in section III of this TSD. 

57 State/sector/pollutant emissions summaries for all of the scenarios simulated with CAMx can 

be found in the EITSD. 

58 See section IV.D of the TR preamble for details on the SO2 emissions reduction requirements 

for Group 1 and Group 2 States. 

59 These 6 states were included in the remedy scenario modeled for assessing the air quality 

impacts of the Transport Rule. 
51

NOX, and ozone season NOX for these states are provided in Appendix G for the 2014 

base case and remedy scenarios. 

Figure V-1. States modeled for annual SO2 and NOX emission reductions. 

Figure V-2. States modeled for ozone season NOX emission reductions. 

For the Group 1 states as a whole, annual SO2 emissions from electric generating 

units (EGUs) are projected to be 67 percent lower in 2014 with the TR controls compared 

to those in the 2014 base case. The reduction in EGU emissions yields a net 52 percent 
52

reduction in total SO2 for the Group 1 states. Overall, for both Group 1 and Group 2 

states, total SO2 emissions across all source categories are 47 percent lower in the 2014 

remedy scenario, compared to the 2014 base case. 

The difference in annual total county-level SO2 emissions between the 2014 base 

and remedy scenarios is shown in Figure V-3. This figure reveals that substantial SO2

emissions reductions will occur in the states modeled as part of the TR with the largest 

reductions in Alabama northward across Tennessee into the Ohio Valley extending 

eastward across Pennsylvania. Some increases in SO2 emissions are noted in a few states 

not included in the TR for annual SO2 reductions, most notably in several counties in 

Arkansas, Colorado, Florida, Louisiana, Montana, and Wyoming. 

Figure V-3. Change in county total annual EGU SO2 emissions between the 2014 base 

and control scenarios. 

Annual emissions of NOX from EGUs are lower by 11 percent in the remedy 

scenario compared to the 2014 base case for the states that were modeled as part of the 

TR for annual NOX emissions reductions. The reduction in EGU NOX produces a 2 

percent net reduction in total NOX for these states. For the states included in the air 
53

quality modeling of ozone season NOX controls, ozone season EGU NOX emission are 11 

percent lower in the 2014 remedy scenario, compared to the 2014 base case. 

The changes in winter and ozone season NOX emissions between the 2014 

remedy and 2014 base case at individual EGU facilities are presented in Figures V-4 and 

V-5, respectively. As is evident from Figure V-4, the largest reductions in winter NOX

are at facilities located in the area extending from Texas, Oklahoma, Kansas, and 

Nebraska eastward across the Midwest and Ohio Valley into Pennsylvania. Increases in 

EGU NOX emissions are seen in locations scattered across the East. Comparing Figures 

V-4 and V-5 indicates that the spatial pattern in NOX emissions reductions in the ozone 

season is similar to that in the winter. 

Figure V-4. Change in winter season county total EGU NOX emissions between the 2014 

base and control scenarios at individual facilities. 
54

Figure V-5. Change in ozone season county total EGU NOX emissions between the 2014 

base and control scenarios at individual facilities. 

B. Impacts on Projected PM2.5 Design Value Concentrations 

The projected annual and 24-hour PM2.5 design values for the 2014 base case and 

2014 remedy scenario at individual monitoring sites are provided in Appendix B. The 

impacts of the annual SO2 and NOX emissions reductions on annual and 24-hour PM2.5

design values are also provided in Appendix B. The impacts in 2014 on annual and 24-

hour PM2.5 design values for each monitored county are shown in Figures V-6 and V-7, 

respectively60,61. The results, as shown in these figures indicate that the emissions 

reductions from the TR will substantially lower annual and 24-hour PM2.5 concentrations 

at most locations in the Eastern U.S, compared to the 2014 base case. Annual PM2.5 is 

 

60 These maps, which show the difference between the 2014 remedy scenario and the 2014 base 

case, are based upon 5-year weighted average design values. Additional maps showing average 

design value concentrations for 2003-2007, the 2012 and 2014 base cases, and the 2014 remedy 

scenario for annual and 24-hour PM2.5 are provided in Appendix H. Maps showing the predicted 

change in annual and 24-hour PM2.5 between the 2014 remedy and the 2012 base case are also 

provided in Appendix H. 

61 For counties with multiple monitoring sites, the maps show the largest difference from among 

the sites in a county. 
55

lower by more than 2 μg/m3

 in areas of the East extending from Tennessee northward to 

Indiana and Ohio, and eastward across Pennsylvania. Along the highly populated 

Northeast Corridor, across the Southeast and in the upper Midwest, including the cities of 

Atlanta, Birmingham, St. Louis, Chicago, and Detroit annual PM2.5 concentrations are 

lower by 1 to 2 μg/m3

. 

As shown in Figure V-7, the predicted reductions in 24-hour PM2.5 are greater 

than those for annual PM2.5 concentrations. Daily PM2.5 concentrations in the remedy 

scenario are lower than in the 2014 base case by 2 to 6 μg/m3

 across much of the 

Southeast and along portions of the Northeast Corridor. In the multi-state area from 

Tennessee to western Pennsylvania, our modeling predicts 24-hour PM2.5 concentrations 

that are 6 to 10 μg/m3 lower than those in the 2014 base case. 

Figure V-6. Impacts on annual average PM2.5 concentrations (μg/m3

) resulting from the 

emissions reductions in the 2014 remedy scenario compared to the 2014 base case 

scenario. 
56

Figure V-7. Impacts on 24-hour PM2.5 concentrations (μg/m3) resulting from the 

emissions reductions in the 2014 remedy scenario compared to the 2014 base case 

scenario. 

The spatial patterns of the magnitude of reduction in annual and 24-hour PM2.5

across the East are generally consistent with the patterns in annual SO2 and winter NOX

emissions reductions from EGUs, as evident by comparing Figures V-6 and V-7 to 

Figures V-3 and V-4, respectively. These figures also indicate that PM2.5 concentrations 

are lower in the remedy scenario in those states in the East not included in the TR for 

annual SO2 and NOX reductions, like Arkansas, Louisiana, Mississippi, Oklahoma, and 

several northern New England. In Arkansas, Florida, and Louisiana, the reductions in 

transported PM2.5 are large enough to more than offset the forecasted increases in local 

emissions of SO2 in these states. 

There are a few locations mainly in the West where SO2 emissions and, therefore, 

PM2.5 is predicted to be higher in the 2014 remedy scenario compared to the 2014 base 

case. Locations where annual PM2.5 levels are higher in the remedy scenario are evident 

in Colorado and Wyoming. 
57

C. Impacts on Annual and 24-Hour PM2.5 Nonattainment and Maintenance 

The number of projected nonattainment and/or maintenance sites in the East for 

the 2003-2007 base period, 2012 base case, 2014 base case, and 2014 remedy scenario 

for annual PM2.5 and 24-hour PM2.5 are provided in Table V-1. The percent difference in 

the number of nonattainment and maintenance sites between the 2014 remedy scenario 

and both the 2012 and 2014 base case scenarios are also provided in this table. The 

average and peak reductions in annual PM2.5 and 24-hour PM2.5 concentrations for those 

sites with 2012 nonattainment and/or maintenance problems are provided in Table V-262. 

The annual PM2.5 nonattainment and/or maintenance sites in the East for the 2014 base 

case are shown in Figure V-8. There are no annual PM2.5 nonattainment and/or 

maintenance sites projected for the 2014 remedy scenario. Maps showing the location of 

24-hour PM2.5 nonattainment and/or maintenance sites for the 2014 base case and 2014 

remedy scenario are provided in Figures V-9 and V-10, respectively. 

 

Table V-1. Percent reduction in annual and 24-hour PM2.5 nonattainment and/or 

maintenance in the Eastern U.S. 

Number of Nonattainment and Maintenance 

Sites

Percent Reduction in

Nonattainment/Maintenance 

Sites

Ambient 

(2003-2007)

 2012 

Base Case

2014 

Base Case

2014 

Remedy

2012 Base vs 

2014 Remedy

2014 Base vs 

2014 Remedy

Annual PM2.5

Nonattainment Sitesa 103 12 7 0 100 percent 100 percent

Annual PM2.5

Maintenance-Only 

Sites 22 4 3 0 100 percent 100 percent

Daily PM2.5

Nonattainment Sites 151 20 10 1 95 percent 90 percent

Daily PM2.5

Maintenance-Only 

Sites 48 21 12 4 81 percent 67 percent a

 As indicated in section III, the term “nonattainment” is used to denote sites that are projected to 

have both nonattainment and maintenance problems. 

 

62 The average and peak reductions in concentrations are calculated using the projected average 

design values. 
58

Table V-2. Average and peak reductions in annual PM2.5 and 24-hour PM2.5 for the 2012 

base case nonattainment and/or maintenance sites for the 2014 remedy compared to the 

2012 base case and the 2014 base case. 

2014 Remedy vs 

2012 Base Case 

2014 Remedy vs 

2014 Base Case 

 Average 

Reduction 

Peak 

Reduction 

Average 

Reduction 

Peak 

Reduction 

Annual PM2.5

Nonattainment Sites 2.73 μg/m3

 3.32 μg/m3 2.18 μg/m3 2.69 μg/m3

Annual PM2.5

Maintenance-Only Sites 2.99 μg/m3

 3.26 μg/m3 2.49 μg/m3 2.69 μg/m3 

 

24-Hour PM2.5

Nonattainment Sites 6.8 μg/m3 11.7 μg/m3 5.3 μg/m3 9.1 μg/m3

24-Hour PM2.5

Maintenance-Only Sites 6.5 μg/m3

 11.0 μg/m3 5.1 μg/m3 9.3 μg/m3

The information in Table V-1 shows that the extent of nonattainment and 

maintenance problems for annual PM2.5 and 24-hour PM2.5 is significantly lower in the 

2014 remedy scenario compared to both the 2012 base case and the 2014 base case. All 

annual PM2.5 nonattainment and maintenance problems are projected to be resolved by 

2014 with the remedy coupled with other existing control programs. Annual PM2.5

concentrations are, on average, more than 2 μg/m3

 lower across the 12 sites that were 

nonattainment in the 2012 base case. The impacts on annual PM2.5 at maintenance-only 

sites are comparable to those at the nonattainment sites. 

For 24-hour PM2.5, there are significantly fewer nonattainment and/or 

maintenance sites in the 2014 remedy scenario compared to both the 2012 and 2014 base 

cases. There is only one remaining nonattainment site63 and 4 remaining maintenance 

sites64 for 24-hour PM2.5 with the 2014 remedy scenario.. The average reduction in 24-

hour PM2.5 across the 20 2012 nonattainment sites is 6.8 μg/m3 and the peak reduction at 

an individual nonattainment site is 11.7 μg/m3

. Comparable reductions are projected at 

24-hour PM2.5 maintenance-only sites. 

 

63 Allegheny Co, PA site 420030064 

64 Cook Co, IL site 170311016, Lake Co, IN site 180890022, Wayne Co, MI site 261630019, and 

Lancaster Co, PA site 420710007 
59

Figure V-8. Projected annual PM2.5 nonattainment and/or maintenance sites in the East 

for the 2014 base case. 

Figure V-9. Projected 24-hour PM2.5 nonattainment and/or maintenance sites in the East 

for the 2014 base case. 
60

Figure V-10. Projected 24-hour PM2.5 nonattainment and/or maintenance sites in the East 

for the 2014 remedy scenario. 

D. Impacts on Projected 8-Hour Ozone Design Value Concentrations 

The projected 8-hour ozone design values for the 2014 base case and 2014 

remedy scenario at individual monitoring sites are provided in Appendix B. The impacts 

of the ozone season NOX emissions reductions on 8-hour ozone design values for 

monitored counties in the Eastern 12 km modeling domain are also provided in Appendix 

B. The impacts are shown in Figure V-1165. This figure indicates that the emissions 

reductions modeled will result in lower ozone concentrations in most counties in the 

Eastern U.S., compared to the 2014 base case. Concentrations of 8-hour ozone are 

projected to be lower by 0.2 ppb or more for the 2014 remedy scenario in portions of the 

Southeast, Midwest, and Northeast. Reductions in ozone of 1 ppb or more are predicted 

at sites in 23 counties in portions Florida, Georgia, Iowa, Kansas, Oklahoma, Ohio, West 

Virginia, and Pennsylvania. 

 

 

65 These maps, which show the difference between the 2014 remedy scenario and the 2014 base 

case, are based upon 5-year weighted average design values. Additional maps showing average 

design value concentrations for 2003-2007, the 2012 and 2014 base cases, and the 2014 remedy 

scenario for 8-hour are provided in Appendix H. Maps showing the predicted change in 8-hour 

between the 2014 remedy and the 2012 base case are also provided in Appendix H. 
61

Figure V-11. Impacts on 8-hour ozone concentrations in 2014 resulting from the 

emissions reductions in the 2014 remedy scenario. 

E. Impacts on 8-Hour Ozone Nonattainment and Maintenance 

The number of projected 8-hour ozone nonattainment and/or maintenance sites in 

the East for the 2012 base case, 2014 base case, and 2014 remedy for 8-hour ozone are 

provided in Table V-3. The percent difference in the number of nonattainment and 

maintenance sites between the 2014 remedy scenario and both the 2012 and 2014 base 

case scenarios are also provided in this table. The average and peak reductions in 8-hour 

ozone concentrations for those sites with 2012 nonattainment and/or maintenance 

problems are provided in Table V-4. The location of 8-hour ozone nonattainment and/or 

maintenance sites in the East for the 2014 base case is shown in Figure V-12 and V-13, 

respectively. 
62

Table V-3. Percent reduction in 8-hour ozone nonattainment and/or maintenance in the 

Eastern U.S. 

Number of Nonattainment and Maintenance Sites

Percent Reduction in

Nonattainment/

Maintenance Sites

8-Hour Ozone Ambient 

(2003-2007)

 2012 

Base Case

2014 

Base Case

2014 

Remedy

2012 Base vs 

2014 Remedy

2014 Base vs 

2014 Remedy

Nonattainment 

Sites 104 7 4 4 43 percent No Change

Maintenance-Only 

Sites 65 9 6 6 33 percent No Change

Table V-4. Average and peak reductions in 8-hour ozone for the 2012 base case 

nonattainment and/or maintenance sites for the 2014 remedy compared to the 2012 base 

case and the 2014 base case. 

2014 Remedy vs 2012 Base Case 2014 Remedy vs 2014 Base Case 8- Hour Ozone

Average 

Reduction

Greatest 

Reduction

Average 

Reduction

Greatest 

Reduction

Nonattainment Sites 1.9 ppb 2.3 ppb 0.1 ppb 0.3 ppb

Maintenance-Only Sites 1.8 ppb 2.1 ppb 0.1 ppb 0.5 ppb

The information in Table V-4 shows that the extent of nonattainment for 8-hour 

ozone is predicted to be lower by 43 percent and the number maintenance-only sites by 

33 percent in the 2014 remedy scenario compared to the 2012 base case. For the 10 sites 

with residual nonattainment and/or maintenance problems in the 2014 remedy scenario, 

the predicted ozone reductions provide 5 percent of the amount needed for these sites to 

attain and/or maintain the ozone standard. Of these 12 sites, 4 are predicted to be 

nonattainment and 6 are predicted to have maintenance problems. The 4 nonattainment 

sites and 5 of the maintenance sites are located in Houston. One maintenance site is in 

Baton Rouge. 
63

Figure V-12. Projected 8-hour ozone nonattainment and/or maintenance sites in the East 

for the 2014 base case. 

Figure V-13. Projected 8-hour ozone nonattainment and/or maintenance sites in the East 

for the 2014 remedy scenario. 
64

F. Impacts on Visibility in Class 1 Areas 

The impacts of the TR regional SO2 and NOX emissions reductions were 

examined in terms of the projected improvements in visibility on the 20 percent best and 

worst visibility days at Class I areas66. We quantified visibility impacts at the 60 Class I 

areas in the Eastern 12km modeling domain which have complete IMPROVE ambient 

data for 2005 or are represented by IMPROVE monitors with complete data. Sites were 

used in this analysis if they had at least 3 years of complete data for the 2003-2007 

period67. 

Visibility for the 2012 base case, 2014 base case and 2014 remedy scenario were 

calculated using the regional haze methodology outlined in section 6 of the 

photochemical modeling guidance, which applies modeling results in a relative sense, 

using base year ambient data. The 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 design values. The regional haze methodology for 

calculating future year visibility impairment is included in MATS 

(http://www.epa.gov/scram001/modelingapps_mats.htm) 

 In calculating visibility impairment, the extinction coefficient values68 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 modeled percent 

change in the mass for each of the PM2.5 species (on the 20% best and worst observed 

days) multiplied by the observed concentrations. The future mass is converted to 

extinction and then daily species extinction coefficients are summed to get a daily total 

extinction value (including Rayleigh scattering). The daily extinction coefficients are 

converted to deciviews and averaged across all 20 percent best or worst days. In this way, 

we calculate an average change in deciviews from the base case to a future case at each 

IMPROVE site. Subtracting the 2014 Transport rule remedy scenario deciview values 

 

66 The focus of this analysis is on visibility impacts at Class I areas within our 12 km Eastern 

modeling domain. We have also calculated visibility impacts for Class I areas farther to the west 

using results of the modeling for the 36 km modeling domain. These results can be found in 

Appendix I. 

67 Since the base case modeling used meteorology for 2005, one of the complete years must be 

2005. 

68 Extinction coefficient is in units of inverse megameters (Mm-1). It is a measure of how much 

light is absorbed or scattered as it passes through a medium. Light extinction is commonly used 

as a measure of visibility impairment in the regional haze program. 
65

from the corresponding 2014 base case deciview values gives an estimate of the visibility 

benefits in Class I areas that are expected to occur from the TR. 

The following options were chosen in MATS for calculating the future year 

visibility values for the rule: 

New IMPROVE algorithm 

Use model grid cells at (IMPROVE) monitor 

Temporal adjustment at monitor- 3x3 for 12km grid, (1x1 for 36km grid) 

Start monitor year- 2003 

End monitor year- 2007 

Base model year 2005 

Minimum years required for a valid monitor- 3 

 The “base model year” was chosen as 2005 because it is the base case 

meteorological year for the final Transport Rule modeling. The start and end years were 

chosen as 2003 and 2007 because that is the 5 year period which is centered on the base 

model year of 2005. These choices are consistent with using a 5 year base period for 

regional haze calculations. The predicted visibility and change in visibility on the 20 

percent worst days is provided in Table V-5 for each Class I area in the Eastern modeling 

domain. The predicted visibility and change in visibility on the 20 percent best days is 

provided in Table V-6 for each Class I area in the Eastern modeling domain. 

 Visibility on the 20 percent worst days is predicted to improve in the 2014 remedy 

scenario compared to the 2014 base case at 55 of the 60 locations included in this 

analysis. Similarly, visibility on the 20 percent best days is predicted to improve or not 

change in 2014 at 48 of these 60 locations. A very small degradation in visibility is 

predicted for a few locations in five states not covered by the Transport Rule including 

Colorado, Maine, New Mexico, North Dakota, South Dakota. Note that the modeling did 

not include Best Available Retrofit Technology (BART) controls69. 

 

69 All known enforceable controls are included in the modeling. EPA does not know of any 

specific state BART rules that are currently in place. We did not attempt to apply generic BART 

emissions reductions for any sources. 
66

Table V-5. Visibility (in deciviews) on the 20 percent worst days at Class I areas in the 

Eastern 12km modeling domain. 

Class I Area 

(IMPROVE Site) Site Code State 

2003-2007 

Baseline 

Visibility 

20% 

Worst 

Days (dv) 

2012 Base 

Case 

Visibility 

20% 

Worst 

Days (dv) 

2014 Base 

Case 

Visibility 

20% 

Worst 

Days(dv) 

2014 

Remedy 

Case 

Visibility 

20% 

Worst 

Days(dv) 

2014 

Visibility 

Change 

from 

2014 Base 

Case (dv) 

Acadia NP ACAD ME 22.75 20.26 20.08 19.12 -0.96 

Badlands NP BADL SD 16.82 16.11 15.98 15.66 -0.32 

Bandelier NM BAND NM 11.97 11.28 11.09 10.87 -0.22 

Big Bend NP BIBE TX 17.21 16.49 16.26 15.35 -0.91 

Black Canyon of the Gunnison 

NM BLCA CO 10.00 9.54 9.49 9.46 -0.03 

Bosque del Apache BOAP NM 13.81 13.20 13.01 12.74 -0.27 

Boundary Waters Canoe Area BOWA MN 20.20 19.15 18.84 17.78 -1.06 

Brigantine BRIG NJ 28.68 25.75 25.35 22.88 -2.47 

Caney Creek Wilderness CACR AR 26.69 25.20 24.38 21.55 -2.83 

Carlsbad Caverns NP CAVE TX 16.51 15.76 15.54 14.80 -0.74 

Cohutta Wilderness COHU GA 30.45 27.68 26.62 22.85 -3.77 

Dolly Sods Wilderness DOSO WV 29.94 27.84 27.11 21.39 -5.72 

Eagles Nest Wilderness EANE CO 8.82 8.32 8.28 8.30 0.02 

Everglades NP EVER FL 22.48 20.6 20.36 19.47 -0.89 

Flat Tops Wilderness FLTO CO 8.82 8.32 8.28 8.3 0.02 

Great Gulf Wilderness GRGU NH 21.43 19.56 19.22 17.48 -1.74 

Great Sand Dunes NM GRSA CO 11.82 11.30 11.28 11.25 -0.03 

Great Smoky Mountains NP GRSM TN 30.56 27.93 27.02 23.34 -3.68 

Guadalupe Mountains NP GUMO TX 16.51 15.76 15.54 14.8 -0.74 

Hercules-Glades Wilderness HEGL MO 26.95 25.82 25.19 22.87 -2.32 

Isle Royale NP ISLE MI 21.31 20.22 19.92 18.95 -0.97 

James River Face Wilderness JARI VA 28.93 26.56 25.80 21.61 -4.19 

Joyce-Kilmer-Slickrock 

Wilderness JOYC TN 30.56 27.93 27.02 23.34 -3.68 

La Garita Wilderness LAGA CO 10.00 9.54 9.49 9.46 -0.03 

Linville Gorge Wilderness LIGO NC 29.66 26.83 26.01 21.73 -4.28 

Lostwood LOST ND 19.61 18.89 18.79 18.63 -0.16 

Lye Brook Wilderness LYBR VT 24.11 21.17 20.74 18.22 -2.52 

Maroon Bells-Snowmass 

Wilderness MABE CO 8.82 8.32 8.28 8.3 0.02 

Mammoth Cave NP MACA KY 32.00 30.42 29.54 24.37 -5.17 

Medicine Lake MELA MT 18.21 17.75 17.68 17.58 -0.10 

Mesa Verde NP MEVE CO 12.14 11.39 11.40 11.34 -0.06 

Moosehorn MOOS ME 21.19 19.17 19.01 18.11 -0.90 

Mount Zirkel Wilderness MOZI CO 9.72 9.29 9.23 9.22 -0.01 

North Absaroka Wilderness NOAB WY 11.30 11.08 11.05 11.04 -0.01 

Okefenokee OKEF GA 27.21 24.58 24.05 21.82 -2.23 
67

Class I Area 

(IMPROVE Site) Site Code State 

2003-2007 

Baseline 

Visibility 

20% 

Worst 

Days (dv) 

2012 Base 

Case 

Visibility 

20% 

Worst 

Days (dv) 

2014 Base 

Case 

Visibility 

20% 

Worst 

Days(dv) 

2014 

Remedy 

Case 

Visibility 

20% 

Worst 

Days(dv) 

2014 

Visibility 

Change 

from 

2014 Base 

Case (dv) 

Otter Creek Wilderness OTCR WV 29.94 27.84 27.11 21.39 -5.72 

Pecos Wilderness PECO NM 9.60 9.14 9.04 8.94 -0.10 

Presidential Range-Dry River 

Wilderness PRRA NH 21.43 19.56 19.22 17.48 -1.74 

Rawah Wilderness RAWA CO 9.72 9.29 9.23 9.22 -0.01 

Roosevelt Campobello 

International Park ROCA ME 21.19 19.17 19.01 18.11 -0.90 

Cape Romain ROMA SC 27.43 24.59 24.02 21.27 -2.75 

Rocky Mountain NP ROMO CO 12.85 12.33 12.22 12.24 0.02 

Salt Creek SACR NM 18.27 17.40 17.14 16.52 -0.62 

San Pedro Parks Wilderness SAPE NM 10.42 9.99 9.90 9.76 -0.14 

Seney SENE MI 25.05 23.97 23.34 21.78 -1.56 

Shenandoah NP SHEN VA 29.42 26.97 26.21 21.15 -5.06 

Shining Rock Wilderness SHRO NC 28.54 25.71 24.84 21.08 -3.76 

Sipsey Wilderness SIPS AL 29.88 27.49 26.51 22.88 -3.63 

Theodore Roosevelt NP THRO ND 17.88 17.16 17.04 16.92 -0.12 

UL Bend ULBE MT 15.49 15.25 15.21 15.20 -0.01 

Upper Buffalo Wilderness UPBU AR 26.97 25.50 24.73 22.05 -2.68 

Voyageurs NP VOYA MN 19.62 18.70 18.41 17.48 -0.93 

Washakie Wilderness WASH WY 11.30 11.08 11.05 11.04 -0.01 

West Elk Wilderness WEEL CO 8.82 8.32 8.28 8.30 0.02 

Weminuche Wilderness WEMI CO 10.00 9.54 9.49 9.46 -0.03 

White Mountain Wilderness WHIT NM 13.01 12.46 12.29 11.71 -0.58 

Wheeler Peak Wilderness WHPE NM 9.60 9.14 9.04 8.94 -0.10 

Wind Cave NP WICA SD 15.95 15.26 15.14 15.03 -0.11 

Wichita Mountains WIMO OK 23.63 22.20 21.72 20.34 -1.38 

Wolf Island WOLF GA 27.21 24.58 24.05 21.82 -2.23 
68

Table V-6. Visibility (in deciviews) on the 20 percent best days at Class I areas in the 

Eastern 12km modeling domain. 

Class I Area 

(IMPROVE Site) Site Code State 

2003-2007 

Baseline 

Visibility 

20% Best 

Days (dv) 

2012 Base 

Case 

Visibility 

20% Best 

Days(dv) 

2014 Base 

Case 

Visibility 

20% Best 

Days(dv) 

2014 

Remedy 

Case 

Visibility 

20% Best 

Days(dv) 

2014 

Visibility 

Change 

from 

2014 Base 

Case (dv) 

Acadia NP ACAD ME 8.26 8.00 7.95 7.91 -0.04 

Badlands NP BADL SD 6.65 6.38 6.34 6.34 0.00 

Bandelier NM BAND NM 4.62 4.22 4.15 4.15 0.00 

Big Bend NP BIBE TX 5.73 5.42 5.39 5.31 -0.08 

Black Canyon of the Gunnison 

NM BLCA CO 2.76 2.36 2.32 2.33 0.01 

Bosque del Apache BOAP NM 6.07 5.61 5.56 5.54 -0.02 

Boundary Waters Canoe Area BOWA MN 5.90 5.79 5.77 5.69 -0.08 

Brigantine BRIG NJ 14.29 13.38 13.24 12.87 -0.37 

Caney Creek Wilderness CACR AR 11.82 11.40 11.33 11.09 -0.24 

Carlsbad Caverns NP CAVE TX 5.60 5.28 5.24 5.18 -0.06 

Cohutta Wilderness COHU GA 13.84 13.13 12.88 12.21 -0.67 

Dolly Sods Wilderness DOSO WV 11.23 10.58 10.33 9.25 -1.08 

Eagles Nest Wilderness EANE CO 0.92 0.46 0.42 0.41 -0.01 

Everglades NP EVER FL 12.29 11.52 11.45 11.21 -0.24 

Flat Tops Wilderness FLTO CO 0.92 0.46 0.42 0.41 -0.01 

Great Gulf Wilderness GRGU NH 6.96 6.75 6.71 6.62 -0.09 

Great Sand Dunes NM GRSA CO 3.86 3.59 3.53 3.51 -0.02 

Great Smoky Mountains NP GRSM TN 13.17 12.58 12.24 11.53 -0.71 

Guadalupe Mountains NP GUMO TX 5.60 5.28 5.24 5.18 -0.06 

Hercules-Glades Wilderness HEGL MO 12.97 12.40 12.23 11.76 -0.47 

Isle Royale NP ISLE MI 6.59 6.40 6.37 6.27 -0.10 

James River Face Wilderness JARI VA 14.02 13.23 12.92 12.10 -0.82 

Joyce-Kilmer-Slickrock 

Wilderness JOYC TN 13.17 12.58 12.24 11.53 -0.71 

La Garita Wilderness LAGA CO 2.76 2.36 2.32 2.33 0.01 

Linville Gorge Wilderness LIGO NC 11.33 10.52 10.32 9.63 -0.69 

Lostwood LOST ND 8.01 7.88 7.86 7.84 -0.02 

Lye Brook Wilderness LYBR VT 5.82 5.57 5.51 5.39 -0.12 

Maroon Bells-Snowmass 

Wilderness MABE CO 0.92 0.46 0.42 0.41 -0.01 

Mammoth Cave NP MACA KY 16.50 15.57 15.27 14.15 -1.12 

Medicine Lake MELA MT 6.64 6.54 6.52 6.52 0.00 

Mesa Verde NP MEVE CO 3.70 3.23 3.20 3.21 0.01 

Moosehorn MOOS ME 8.56 8.42 8.39 8.41 0.02 

Mount Zirkel Wilderness MOZI CO 1.19 1.02 0.97 0.98 0.01 

North Absaroka Wilderness NOAB WY 1.67 1.56 1.54 1.54 0.00 

Okefenokee OKEF GA 14.90 14.09 13.91 13.12 -0.79 
69

Class I Area 

(IMPROVE Site) Site Code State 

2003-2007 

Baseline 

Visibility 

20% Best 

Days (dv) 

2012 Base 

Case 

Visibility 

20% Best 

Days(dv) 

2014 Base 

Case 

Visibility 

20% Best 

Days(dv) 

2014 

Remedy 

Case 

Visibility 

20% Best 

Days(dv) 

2014 

Visibility 

Change 

from 

2014 Base 

Case (dv) 

Otter Creek Wilderness OTCR WV 11.23 10.58 10.33 9.25 -1.08 

Pecos Wilderness PECO NM 1.37 1.04 1.00 1.02 0.02 

Presidential Range-Dry River 

Wilderness PRRA NH 6.96 6.75 6.71 6.62 -0.09 

Rawah Wilderness RAWA CO 1.19 1.02 0.97 0.98 0.01 

Roosevelt Campobello 

International Park ROCA ME 8.56 8.42 8.39 8.41 0.02 

Cape Romain ROMA SC 15.05 13.77 13.58 12.97 -0.61 

Rocky Mountain NP ROMO CO 2.27 1.99 1.96 1.96 0.00 

Salt Creek SACR NM 7.88 7.42 7.32 7.24 -0.08 

San Pedro Parks Wilderness SAPE NM 1.46 1.24 1.18 1.13 -0.05 

Seney SENE MI 7.13 6.93 6.89 6.83 -0.06 

Shenandoah NP SHEN VA 10.21 9.17 8.98 8.21 -0.77 

Shining Rock Wilderness SHRO NC 7.28 6.53 6.33 5.71 -0.62 

Sipsey Wilderness SIPS AL 15.33 14.71 14.52 13.85 -0.67 

Theodore Roosevelt NP THRO ND 7.07 6.86 6.82 6.83 0.01 

UL Bend ULBE MT 4.40 4.24 4.21 4.21 0.00 

Upper Buffalo Wilderness UPBU AR 12.03 11.47 11.33 11.05 -0.28 

Voyageurs NP VOYA MN 6.79 6.64 6.62 6.56 -0.06 

Washakie Wilderness WASH WY 1.67 1.56 1.54 1.54 0.00 

West Elk Wilderness WEEL CO 0.92 0.46 0.42 0.41 -0.01 

Weminuche Wilderness WEMI CO 2.76 2.36 2.32 2.33 0.01 

White Mountain Wilderness WHIT NM 3.35 3.11 3.07 3.00 -0.07 

Wheeler Peak Wilderness WHPE NM 1.37 1.04 1.00 1.02 0.02 

Wind Cave NP WICA SD 4.91 4.65 4.62 4.64 0.02 

Wichita Mountains WIMO OK 9.85 9.21 9.11 9.00 -0.11 

Wolf Island WOLF GA 14.9 14.09 13.91 13.12 -0.79 
A-1 

Air Quality Modeling Final Rule 

Technical Support Document 

Appendix A 

Model Performance Evaluation for the 2005-Based 

 Final Transport Rule Air Quality Modeling Platform
A-2 

A.1. Introduction 

An operational model evaluation was conducted for the 2005 base year CAMx annual 

model simulation performed for the 12 km Eastern U.S. modeling domain.1,2

 The purpose of this 

evaluation was to examine the ability of the Transport Rule air quality modeling platform to 

replicate the magnitude and spatial and temporal variability of measured (i.e., observed) ozone 

and PM2.5 component species concentrations within the modeling domain. Included in the 

evaluation are statistical measures of model performance based upon model-predicted versus 

observed concentrations that were paired in space and time on a daily or weekly basis, depending 

on the sampling frequency of the particular monitoring network, as described below. 

Model performance statistics were calculated for several spatial scales and temporal 

periods. Statistics were calculated for individual monitoring sites and for each of four 

subregions of the Eastern modeling domain. The subregions include the Northeast, Midwest, 

Southeast, and Central states which are defined based upon the states contained within the 

Regional Planning Organizations (RPOs)3

 in the East. The statistics for each site and subregion 

were calculated by season (e.g., “winter” is defined as January through March). For 8-hour daily 

maximum ozone, we also calculated performance statistics by subregion for the May through 

September ozone season4. In addition to the performance statistics, we prepared several 

graphical presentations of model performance for 8-hour daily maximum ozone, sulfate, and 

nitrate which are the three key pollutants for the Transport Rule. These graphical presentations 

include: 

(1) regional maps which show the normalized mean bias and error calculated for each season at 

individual monitoring sites, 

(2) bar and whisker plots which show the distribution of the predicted and observed data by 

month by subregion, and 

(3) time series plots of observed and predicted concentrations for those monitoring sites 

predicted to be nonattainment or have maintenance problems for ozone or PM2.5 in the 

Transport Rule 2012 base case. 

 

1

 See section II.B of the main document for a description of the Eastern modeling domain. 

2

 As noted in section II of the main document, II.F. of the main document, the 2005 base year simulation used 

Continuous Emissions Monitoring (CEM) data for specifying emissions from electric generating units as well as 

year-specific wild and prescribed fires. These data were used for the 2005 evaluation case in order to provide more 

resolved year-specific emissions data for these two sectors than was included in the 2005 base year emissions used 

as the baseline for projecting future air quality. See the Emissions Inventory Technical Support Document for the 

Final Transport Rule for additional details on these emissions. 

3

 The subregions are defined by States where: Midwest is IL, IN, MI, OH, and WI; Northeast is CT, DE, 

MA, MD, ME, NH, NJ, NY, PA, RI, and VT; Southeast is AL, FL, GA, KY, MS, NC, SC, TN, VA, and 

WV; Central is AR, IA, KS, LA, MN, MO, NE, OK, and TX; West is AK, CA, OR, WA, AZ, NM, CO, UT, WY, 

SD, ND, MT, ID, and NV. 

4

 In calculating the ozone season statistics we limited the data to those observed and predicted pairs with 

observations that exceeded 60 ppb in order to focus on concentrations at the upper portion of the distribution of 

values. 
A-3 

A.1.1 Monitoring Networks 

The model evaluation for ozone was based upon comparisons of model predicted 8-hour 

daily maximum concentrations to the corresponding observed data at monitoring sites in the EPA 

Air Quality System (AQS). The PM2.5 evaluation focused on PM2.5 component species 

including sulfate (SO4), nitrate (NO3), total nitrate (TNO3=NO3+HNO3), ammonium (NH4), 

elemental carbon (EC), organic carbon (OC), and crustal material (CR). PM2.5 observed data for 

2005 were obtained from the following networks: Chemical Speciation Network (CSN), 

Interagency Monitoring of PROtected Visual Environments (IMPROVE) network, and the Clean 

Air Status and Trends Network (CASTNet). The pollutant species included in the evaluation for 

each network are listed in Table A-1. For PM2.5 species that are measured by more than one 

network, we calculated separate sets of statistics for each network. The CSN and IMPROVE 

networks provide 24-hour average concentrations on a 1 in every 3 day, or 1 in every 6 day 

sampling cycle. The PM2.5 species data at CASTNet sites are weekly integrated samples. In this 

analysis we use the term “urban sites” to refer to CSN sites; “suburban/rural sites” to refer to 

CASTNet sites; and “rural sites” to refer to IMPROVE sites.

Table A-1. PM2.5 monitoring networks and pollutants species included in the evaluation of the 

2005-based modeling platform. 

Ambient Particulate Species 

Monitoring 

Networks 

SO4 NO3 TNO3

a

 EC NH4 OC 

IMPROVE X X X X 

CASTNet X X X 

CSN X X X X X 

a

 TNO3 = (NO3 + HNO3) 

A.1.2 Model Performance Statistics 

The Atmospheric Model Evaluation Tool (AMET) was used to calculate the model 

performance statistics used in this document5

. Some of the statistics calculated for the 2005 

CAMx model simulation are identified in Table A-2. For this analysis we have selected the 

normalized mean bias, normalized error, fractional bias, and fractional error to characterize 

model performance. All of the statistics listed in Table A-2 are provided in csv or xls files in the 

Transport Rule docket. As noted above, we calculated the performance statistics by season. In 

 

5

 Gilliam, R. C., W. Appel, and S. Phillips. The Atmospheric Model Evaluation Tool (AMET): Meteorology 

Module. Presented at 4th Annual CMAS Models-3 Users Conference, Chapel Hill, NC, September 26 - 28, 2005. 

(http://www.cmascenter.org/) 
A-4 

this analysis “winter” includes the months of January through March; “spring” includes the 

months of April through June; “summer” includes the months of July through September; and 

“fall” includes the months of October through December. 

Table A-2. AMET model performance statistics calculated for this analysis. 

Mean Observed Percent Normalized Bias 

Mean Predicted Percent Normalized Error 

Median Observed Percent Normalized Mean Bias 

Median Predicted Percent Normalized Mean Error 

Standard Deviation of Observed Normalized Median Bias 

Standard Deviation of Predictions Normalized Median Error 

Correlation Coefficient Fractional Bias 

R Squared Fractional Error 

Mean Bias Index of Agreement 

Mean Error Root Mean Square Error 

Median Bias 

Median Error 

 Normalized mean bias (NMB) is used as a normalization to facilitate a range of 

concentration magnitudes. This statistic averages the difference (model - observed) over the sum 

of observed values. NMB is a useful model performance indicator because it avoids over 

inflating the observed range of values, especially at low concentrations. 

Normalized mean bias is defined as:

NMB = 

( )

( )

P O

O

n

n

∑ −

∑

1

1

*100 

Normalized mean error (NME) is also similar to NMB, where the performance statistic is used as 

a normalization of the mean error. NME calculates the absolute value of the difference (model - 

observed) over the sum of observed values. 

Normalized mean error is defined as: 

NME = 

( )

P O

O

n

n

∑ −

∑

1

1

*100 
A-5 

Fractional bias is defined as: 

FB = 

( )

( )

1

2

1

1

n

P O

P O

n

n

−

⎛ +

⎝

⎜ ⎞

⎠

⎟

⎛

⎝

⎜

⎜

⎜

⎜

⎞

⎠

⎟

⎟

⎟

⎟

∑

∑

*100, where P = predicted and O = observed concentrations. 

FB is a useful model performance indicator because it has the advantage of equally weighting 

positive and negative bias estimates. The single largest disadvantage in this estimate of model 

performance is that the estimated concentration (i.e., prediction, P) is found in both the 

numerator and denominator. Fractional error (FE) is similar to fractional bias except the 

absolute value of the difference is used so that the error is always positive. 

Fractional error is defined as: 

FE = ( )

1

2

1

1

n

P O

P O

n

n

−

⎛ +

⎝

⎜ ⎞

⎠

⎟

⎛

⎝

⎜

⎜

⎜

⎜

⎞

⎠

⎟

⎟

⎟

⎟

∑

∑

*100 

A.1.3 Highlights of the Model Evaluation Results 

 In this section we present a summary of the model performance results for 8-hour daily 

maximum ozone and each of the PM2.5 component species included in this evaluation. Details on 

the performance of the 2005 modeling platform for each of these pollutants are provided in 

subsequent sections of this Appendix. 

Ozone 

● In general, the model performance statistics indicate that the 8-hour daily maximum ozone 

concentrations predicted by the 2005 CAMx modeling platform closely reflect the corresponding 

8-hour observed ozone concentrations in space and time in each subregion of the Eastern 

modeling domain. 

● The day to day fluctuations in observed 8-hour daily maximum concentrations appear to be 

captured well, based on a time series analysis for monitoring sites projected to be nonattainment 

or have maintenance problems for the 8-hour ozone NAAQS in the Transport Rule 2012 base 

case. 

Sulfate and Nitrate

● Model predictions of sulfate correspond closest to observations during the late spring and 

summer when observed sulfate concentrations are highest. Average positive bias across the 

subregions during the spring and summer is less than 10 percent for both urban and rural 
A-6 

locations. During the fall and winter, when sulfate is generally low, the modeling platform 

shows more moderate over prediction of nearly 60 percent, on average across the subregions at 

both urban and rural locations. 

● Nitrate predictions best match the observations during the fall and winter when observations 

are at their highest levels. The average low bias across the subregions during the fall and winter 

is smaller than 17 percent for urban locations and 5 percent for rural locations. There is an 

tendency for more moderate under prediction in the spring and summer, when nitrate 

concentrations are very low, as indicated by the 30 percent average low bias at both urban and 

rural locations for the spring and summer seasons. 

● The modeling platform closely replicates the short-term variability, the general magnitude, 

and site-to-site differences in both sulfate and nitrate, as measured in counties with sites 

projected to be nonattainment or have maintenance problems for PM2.5 in the Transport Rule 

2012 base case. 

● The modeling platform shows considerable skill at distinguishing between days with high and 

low concentrations for both sulfate and nitrate at different times of the year. The time series 

analysis shows that the model consistently captures the correct timing of the observed peak 

episode days for sulfate and for nitrate. 

Ammonium, Elemental Carbon, Organic Carbon, and Crustal Material 

● The model performance statistics indicate that predictions of ammonium are close to the 

observations. During the summer, there is slight under prediction with a low bias of smaller than 

10 percent, on average across the subregions for urban locations. In other times of the year 

ammonium tends to be somewhat over predicted with a positive bias of 19 percent, on average 

across the subregions for urban locations. 

● For elemental carbon, the model performance statistics show clear over prediction throughout 

the year at urban locations where the average positive bias across the subregions is 100 percent. 

The degree of over prediction is less at rural locations, particularly in the summer. The 

performance issues with elemental carbon may be related to problems with the total mass or 

speciation of directly emitted PM2.5 emissions from sources located mainly in urban areas. 

However, model performance for elemental carbon, as well as for organic carbon, must be 

viewed in light of measurement procedures which may affect the comparability of observations 

to model predictions6. 

 

6

 Ambient measurements of EC and OC are based on speciation of observed total carbon by one of several 

techniques and protocols. Since the relative contributions of EC and OC to total carbon depend on the measurement 

technique and protocol used, measured EC and OC concentrations are considered to be "operationally defined" and 

may not correspond exactly to modeled EC and OC. Thus, a high bias in modeled EC in combination with low bias 

in modeled OC could be caused in part by the different definitions of EC and OC inherent in the model and 

observations. 
A-7 

● Observations of organic carbon are somewhat under predicted during the spring and summer 

when the low bias is 15 percent, on average at urban locations across the four subregions. In 

contrast, during the winter organic carbon is somewhat over predicted with an average positive 

bias of 27 percent at urban locations. 

● Crustal material is over predicted by a considerable amount at both urban and rural locations 

in all seasons and subregions with an average positive bias well in excess of 100 percent. 

Performance issues for crustal material found in the 2005 CAMx simulations have also been 

reported in other applications13 and may be associated with problems in properly characterizing 

the magnitude of emissions inventories for directly emitted PM2.5 from fugitive sources. 

The “acceptability” of model performance was judged by considering the 2005 CAMx 

performance results in light of the range of performance found in recent regional ozone and 

PM2.5 model applications.7,8,9,10,11,12,13,14,15,16,17 These other modeling studies represent a wide range 

of modeling analyses which cover various models, model configurations, domains, years and/or 

episodes, chemical mechanisms, and aerosol modules. Overall, the ozone and PM2.5 model 

performance results for the 2005 CAMx simulations performed for the Transport Rule are within 

the range, or close to that found in other recent applications. The model performance results, as 

described in this report, demonstrate that the predictions from the Transport Rule modeling 

platform closely replicate the corresponding observed concentrations in terms of the magnitude, 

 

7 Appel, K.W., Bhave, P.V., Gilliland, A.B., Sarwar, G., and Roselle, S.J.: evaluation of the community multiscale 

air quality (CMAQ) model version 4.5: sensitivities impacting model performance: Part II – particulate matter. 

Atmospheric Environment 42, 6057-6066, 2008.

8 Appel, K.W., Gilliland, A.B., Sarwar, G., Gilliam, R.C., 2007. Evaluation of the community multiscale air quality 

(CMAQ) model version 4.5: sensitivities impacting model performance: Part I – ozone. Atmospheric Environment 

41, 9603-9615.

9 Appel, K.W., Roselle, S.J., Gilliam, R.C., and Pleim, J.E.,: Sensitivity of the Community Multiscale Air Quality 

(CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers. Geoscientific 

Model Development, 3, 169-188, 2010.

10 Foley, K.M., Roselle, S.J., Appel, K.W., Bhave, P.V., Pleim, J.E., Otte, T.L., Mathur, R., Sarwar, G., Young, J.O., 

Gilliam, R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., and Bash, J.O.,: Incremental testing of the Community 

multiscale air quality (CMAQ) modeling system version 4.7. Geoscientific Model Development, 3, 205-226, 2010. 11 Hogrefe, G., Civeroio, K.L., Hao, W., Ku, J-Y., Zalewsky, E.E., and Sistla, G., Rethinking the Assessment of 

Photochemical Modeling Systems in Air Quality Planning Applications. Air & Waste Management Assoc., 

58:1086-1099, 2008.

12 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform: 

Air Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008. 13 Strum, M., Wesson, K., Phillips, S.,Pollack, A., Shepard, S., Jimenez, M., et.al.. Link Based vs NEI Onroad 

Emissions Impact on Air Quality Model Predictions. 17th Annual International Emission Inventory Conference, 

Portland, Oregon , June 2-5, 2008. 

14 Tesche, T.W., Morris, R., Tonnesen, G., McNally, D., Boylan, J., Brewer, P., 2006. CMAQ/CAMx annual 2002 

performance evaluation over the eastern United States. Atmospheric Environment 40, 4906-4919. 15 U.S. Environmental Protection Agency; Technical Support Document for the Final Clean Air Interstate Rule: Air 

Quality Modeling; Office of Air Quality Planning and Standards; RTP, NC; March 2005 (CAIR Docket OAR-2005-

0053-2149). 

16 U.S. Environmental Protection Agency; Technical Support Document for the Proposal to Designate an Emissions 

Control Area for Nitrogen Oxides, Sulfur Oxides, and Particulate Matter: EPA-420-R-007, 329pp., 2009. 

(http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09007.pdf) 17 U.S. Environmental Protection Agency; Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. 

EPA-420-R-10-006. February 2010. Sections 3.4.2.1.2 and 3.4.3.3. Docket EPA-HQ-OAR-2009-0472-11332. 
A-8 

temporal fluctuations, and spatial differences for 8-hour daily maximum ozone, sulfate, and 

nitrate, which are the key pollutants for the Transport Rule. In addition, the modeling platform 

captures the general magnitude and seasonal variations in ammonium and organic carbon, two 

other components of PM2.5. As noted above, model predictions of elemental carbon and crustal 

material are over predicted, most likely due to problems in the emissions for these pollutants. 

Consistent with EPA’s guidance for attainment demonstration modeling, we have applied 

the model predictions performed as part of the Transport Rule in a relative manner for projecting 

future concentrations and for calculating interstate contributions of ozone and PM2.5. The 

National Research Council18 states that using air quality modeling in a relative manner “may help 

reduce the bias introduced by modeling errors and, therefore, may be more accurate than using 

model results directly (absolute values) to estimate future pollutant levels”. Thus, the results of 

this evaluation together with the manner in which we are applying model predictions gives us 

confidence that our air quality model applications using the CAMx 2005 modeling platform 

provides a scientifically credible approach for assessing ozone and PM2.5 concentrations for the 

Final Transport Rule. 

 

18 National Research Council, 2002. Estimating the Public Health Benefits of Proposed Air Pollution Regulations, 

Washington, DC: National Academies Press. 
A-9 

A.2. Evaluation for 8-Hour Daily Maximum Ozone 

The 8-hour ozone model performance bias and error statistics for each subregion and 

each season are provided in Table A-3. The distributions of observed and predicted 8-hour 

ozone by month in the 5-month ozone season for each subregion are shown in Figures A-1 

through A-4. Spatial plots of the normalized mean bias and error for individual monitors are 

shown in Figures A-5 and A-6, respectively. The statistics shown in these two figures were 

calculated over the ozone season using data pairs on days with observed 8-hour ozone of > 60 

ppb. Time series plots of observed and predicted 8-hour ozone during the ozone season at 

monitoring sites identified as nonattainment or maintenance in the Transport Rule 2012 base 

case19 are provided in Figure A-7a-h20. These sites are listed in Table A-4. 

Table A-4. Monitoring sites used for the ozone time series analysis. 

County State 

Monitoring 

Site ID 

Fairfield Connecticut 90011123

New Haven Connecticut 90093002

East Baton 

Rouge Louisiana 220330003

Harford Maryland 240251001

Allegan Michigan 260050003

Brazoria Texas 480391004

Harris Texas 482010029*

Harris Texas 482010051*

Harris Texas 482010055

Harris Texas 482011050

*Data for 2005 were not available in AMET for this site, thus no time series plot was created for this site. 

In general, the model performance statistics indicate that the 8-hour daily maximum 

ozone concentrations predicted by the 2005 CAMx modeling platform closely reflect the 

corresponding 8-hour observed ozone concentrations in space and time in each subregion of the 

Eastern modeling domain. As indicated by the statistics in Table A-3, bias and error for 8-hour 

daily maximum ozone are relatively low in each subregion, not only in the summer when 

concentrations are highest, but also during other times of the year. Specifically, 8-hour ozone in 

the summer is slightly over predicted with the greatest over prediction in the Southeast (NMB is 

14.6 percent). In the spring ozone is slightly over predicted in the Northeast and Southeast and 

slightly under predicted in the Midwest and Central, with NMBs less than + 10 percent in each 

 

19 The procedures for identifying projected nonattainment and maintenance sites are described in section IV of the 

main body of this document. 

20 Ozone data are not available for 2005 for two of the projected 2012 base case nonattainment sites in Harris 

county, TX (sites: 482010029 and 482010051) 
A-10 

region during this season. In the fall and winter, when concentrations are generally low, the bias 

is less than + 25 percent in each subregion. 

The monthly distribution of 8-hour daily maximum ozone during the ozone season 

corresponds well with that of the observed concentrations, as indicated by the graphics in Figures 

A-1 through A-4. Specifically, the predicted concentrations tend to be close to, or somewhat 

greater than, the observed median and 75th percentile values for each subregion. Model 

estimates in the Midwest and Central states capture the entire 25th to 75th percentile range of the 

observed concentrations. 

Model bias at individual sites during the ozone season is similar to that seen on a 

subregional basis for the summer. The information in Figure A-5 indicates that the bias for days 

with observed 8-hour daily maximum ozone greater than 60 ppb is within + 20 percent at the 

vast majority of monitoring sites across the East. The exceptions are sites in and/or near Miami, 

Minneapolis, Baton Rouge, and Houston, as well as at several sites in the extreme western most 

part of the modeling domain. At these sites observed concentrations greater than 60 ppb are 

under predicted generally in the range of 20 to 40 percent. Looking at the map of bias in Figure 

A-5 indicates that the low bias at these sites is not evident at other sites in these same areas. This 

suggests that the under prediction at these sites is likely due to very local features (e.g., 

meteorology and/or emissions) and not indicative of a systematic problem in the modeling 

platform. 

Model error, as seen from Figure A-6, is 14 percent or less at most of the sites across the 

Eastern modeling domain. Somewhat greater error is evident at sites in several areas most 

notably along portions of the Northeast Corridor and in portions of Florida, Louisiana, Texas, 

and the western most part of the modeling domain. 

In addition to the above analysis of overall model performance, we also examine how 

well the modeling platform replicates day to day fluctuations in observed 8-hour daily maximum 

concentrations at those monitoring sites predicted to be nonattainment or have maintenance 

problems for the 8-hour ozone NAAQS in the Transport Rule 2012 base case. For this sitespecific analysis we present the time series of observed and predicted 8-hour daily maximum 

concentrations by site over the ozone season, May through September. The results, as shown in 

Figures A-7a through h, indicate that the modeling platform replicates the day-to-day variability 

in ozone during this time period. This is particularly true for the three sites in the Northeast (i.e., 

Harford County, MD, New Haven County, CT, and Fairfield County, CT) and for Allegan 

County, MI. Looking across all the sites indicates that the modeling platform is able to capture 

the site to site differences in the short-term variability of ozone concentrations. Still, some 

observed episode days are over predicted and some under predicted. Under prediction of peak 

episodes is evident at the site in Baton Rouge and the sites near Houston. 
A-11 

Table A-3. Daily maximum 8-hour ozone performance statistics by subregion, by season. 

Subregion

No. of 

Obs NMB (%) NME (%) FB (%) FE (%)

Winter 9455 -2.1 19.1 -1.1 22.8

Spring 14103 -5.8 14.6 -4.4 15.7

Summer 12776 1.5 19.4 3.8 21.3

Central

States

Fall 8361 3.3 18.1 4.1 19.7 

Winter 2738 -11.7 19.6 -15.6 25.2

Spring 15153 -3.0 14.8 -1.6 15.5

Summer 15727 3.6 15.5 4.7 16.2

Midwest

Fall 4056 17.6 24.5 18.1 25.5 

Winter 9107 4.6 15.1 5.8 16.1

Spring 19753 0.9 13.9 3.2 15.4

Summer 19423 14.6 22.6 16.1 23.8

Southeast

Fall 6193 22.3 26.7 23.9 28.6 

Winter 4667 -5.4 19.1 -5.3 22.9

Spring 15063 3.4 16.6 4.7 17.4

Summer 15527 8.7 18.9 9.8 19.5

Northeast

Fall 5284 11.3 24.3 13.0 28.3

Figure A-1. Distribution of observed and predicted 8-hour daily maximum ozone by month for 

the period May through September for the Northeast subregion. [symbol = median; top/bottom 

of box = 75th/25th percentiles; top/bottom line = max/min values] 
A-12 

Figure A-2. Distribution of observed and predicted 8-hour daily maximum ozone by month for 

the period May through September 2005 for the Southeast subregion. 

Figure A-3. Distribution of observed and predicted 8-hour daily maximum ozone by month for 

the period May through September for the Midwest subregion. 
A-13 

Figure A-4. Distribution of observed and predicted 8-hour daily maximum ozone by month for 

the period May through September for the Central states subregion. 
A-14 

.

Figure A-5. Normalized Mean Bias (%) of 8 hour daily maximum ozone greater than 60 ppb 

over the period May-September 2005 at monitoring sites in Eastern modeling domain. 

Figure A-6. Normalized Mean Error (%) of 8 hour daily maximum ozone greater than 60 ppb 

over the period May-September 2005 at monitoring sites in Eastern modeling domain. 
A-15 

Figure A-7a. Time series of observed (black) and predicted (blue) 8-hour daily maximum ozone 

for the period May through September 2005 at site 090011123 in Fairfield County, CT. 

Figure A-7b. Time series of observed (black) and predicted (blue) 8-hour daily maximum ozone 

for the period May through September 2005 at site 090093002 in New Haven County, CT. 

Figure A-7c. Time series of observed (black) and predicted (blue) 8-hour daily maximum ozone 

for the period May through September 2005 at site 220330003 in East Baton Rouge Parish, LA. 
A-16 

Figure A-7d. Time series of observed (black) and predicted (blue) 8-hour daily maximum ozone 

for the period May through September 2005 at site 240251001 in Harford County, MD. 

Figure A-7e. Time series of observed (black) and predicted (blue) 8-hour daily maximum ozone 

for the period May through September 2005 at site 2600050003 in Allegan County, MI. 

Figure A-7f. Time series of observed (black) and predicted (blue) 8-hour daily maximum ozone 

for the period May through September 2005 at site 480391004 in Brazoria County, TX. 
A-17 

Figure A-7g. Time series of observed (black) and predicted (blue) 8-hour daily maximum ozone 

for the period May through September 2005 at site 482010055 in Harris County, TX. 

Figure A-7h. Time series of observed (black) and predicted (blue) 8-hour daily maximum ozone 

for the period May through September 2005 at site 482011050 in Harris County, TX. 
A-18 

A.3. Evaluation of PM2.5 Component Species 

 The evaluation of 2005 model predictions for PM2.5 covers the performance for the 

individual PM2.5 component species (i.e., sulfate, nitrate, organic carbon, elemental carbon, and 

crustal material). Performance results are provided for each species with a focus on sulfate and 

nitrate, which are the key PM2.5 species for the Transport Rule. As indicated above, for each 

species we present tabular summaries of bias and error statistics by subregion for each season. 

These statistics are based on the set of observed-predicted pairs of data for the particular quarter 

at monitoring sites within the subregion. Separate statistics are provided for each monitoring 

network, as applicable for the particular species measured. For sulfate and nitrate we also 

provide a more refined temporal and spatial analysis of model performance that includes (1) 

graphics of the distribution of 24-hour average concentrations and predictions by month for each 

subregion, (2) spatial maps which show the normalized mean bias and error by site, aggregated 

by quarter, and (3) time series plots of observed and predicted concentrations for CSN sites in 

counties with predicted Transport Rule 2012 base case nonattainment and/or maintenance for the 

annual and/or 24-hour PM2.5 NAAQS21,22. The 2012 base case nonattainment or maintenance 

counties and the CSN sites in these counties used for the time series analysis are listed in Table 

A-4. 

Table A-4. CSN sites used for the sulfate and nitrate time series analysis. 

County State CSN Sites Used for Time Series Analysis 

Jefferson Alabama 

10730023 

10732003 

Fulton Georgia 

No CSN data for in AMET for this county; used CSN site 

130890002 in DeKalb County which is adjacent to Fulton County 

Cook Illinois 

170310057 

170310076 

170314201 

Madison Illinois 171192009 

Lake Indiana 

180890022 

180892004 

Marion Indiana 180970078 

St Clair Michigan 

No CSN data in AMET for this county nor in adjacent counties in 

Michigan 

Washtenaw Michigan 261610008 

Wayne Michigan 

261630001 

261630033 

Butler Ohio 390171004 

 

21 The procedures for identifying projected nonattainment and maintenance sites are described in section IV of the 

main body of this document. 

22 We have included time series for all CSN sites (with data available in AMET for 2005) in the projected 

nonattainment/maintenance counties because some of the nonattainment/maintenance sites do not have co-located 

PM2.5 speciation monitors. 
A-19 

County State CSN Sites Used for Time Series Analysis 

Cuyahoga Ohio 

390350038 

390350060 

Hamilton Ohio 390610040 

Jefferson Ohio 390810017 

Montgomery Ohio 391130031 

Allegheny Pennsylvania 

420030008 

420030064 

Beaver Pennsylvania 

No CSN data in AMET for this county; see results for CSN sites 

in Allegheny County which is adjacent to Beaver County 

Lancaster Pennsylvania 420710007 

York Pennsylvania 421330008 

Brooke West Virginia 

No CSN data in AMET for this county nor in adjacent counties in 

West Virginia 

Milwaukee Wisconsin 550790026 

A.3.1. Evaluation for Sulfate 

 The model performance bias and error statistics for sulfate for each subregion and each 

season are provided in Table A-5. The distributions of observed and predicted suflate by month 

for each subregion are shown in Figures A-8 through A-11. Spatial plots of the normalized mean 

bias and error by season for individual monitors are shown in Figures A-12 through A-15. Time 

series plots of observed and predicted 24-hour average sulfate during the ozone season at CSN 

monitoring sites in counties identified as nonattainment or maintenance for the annual and/or 24-

hour PM2.5 NAAQS are provided in Figure A-16a-x23. 

The results of this analysis indicate that sulfate predictions from our modeling platform 

correspond closest to observations during the late spring and summer when observed sulfate 

concentrations are highest. During the fall and winter, when sulfate is generally low, the 

modeling platform tends to over predict observed concentrations. The bias and error statistics in 

Table A-5 indicate that model performance for sulfate is best during the spring and summer 

seasons at both urban and rural sites in the Midwest, Southeast, and Central subregions. In these 

subregions NMB is generally + 15 percent or less. In the Northeast subregion the bias is in this 

same range during the summer, but there is a somewhat stronger signal of over prediction in the 

spring when NMB and FB are in the range of 30 to 50 percent. Average bias across the 

subregions during the spring and summer is less than 10 percent for both urban and rural 

locations. 

In the fall and winter observed sulfate is over predicted, particularly in the Midwest and 

Southeast. During the cool seasons NMBs are generally in the range of 60 to 70% and FBs are 

 

23 There is one county, Fulton County, GA, that is projected to be nonattainment for the annual PM2.5 NAAQS in 

2012 for which there is no site with 2005 PM2.5 speciation data available in AMET. Instead, we provide time series 

plots based on speciation measurements at the CSN site in neighboring DeKalb County, GA. 
A-20 

generally 30 to 45 percent at urban and rural sites in these two subregions. The tendency for 

over prediction during the cool seasons is less evident in the Northeast and Central subregions 

where NMBs are generally less than 40 percent. On average across the subregions for both urban 

and rural locations, the over prediction bias nearly 60 percent. 

The close relationship between observations and predictions during portions of the spring 

and summer is further revealed in Figures A8 through A-11 which show the distribution of 

observed 24-hour average (CSN and IMPROVE) and weekly average (CASTNet) sulfate 

concentrations and the corresponding model predictions by month for January through December 

2005. These figures indicate that the distribution of predictions is most consistent with that of 

the observations for the months of June through September. Over prediction is evident during 

the months of Jan through April and October through December. 

Model bias and error at individual sites for each season, as displayed in Figures A-12 

through A-15, suggest spatial patterns in model performance that vary by season. In the winter 

(Figure A-12a) and fall (Figure A-15a) over prediction is fairly consistent at sites across the 

Eastern modeling domain. However, Figures 12 b and 15b indicate that there is no clear regional 

trend in error for these two seasons. During the spring (Figure A-13a) and summer (Figure A14a) bias appears to be much less than in fall and winter at most sites. The few sites in extreme 

northern New England that have biases much greater than 20 percent have relatively low 

measured spring and summer average sulfate concentrations (e.g., 2 μg/m3

 or less). Normalized 

mean bias is only + 20 percent across much of the southern two-thirds of the modeling domain 

during these seasons. In portions of the northern third of the modeling domain, NMBs at most 

sites are somewhat greater, but are still in the range of only 20 to 40 percent. Model error is also 

much less in spring and summer compared to fall and winter at most sites in the domain. 

In addition to the above analysis of overall model performance, we also examine how 

well the modeling platform replicates the magnitude and short-term fluctuations in observed 24-

hour average sulfate concentrations at CSN monitoring sites in counties with predicted 

nonattainment and/or maintenance problems for the annual and/or 24-hour PM2.5 NAAQS in the 

Transport Rule 2012 base case. The annual time series plots of observed and predicted 24-hour 

average concentrations for the 24 CSN sites in these counties are provided in Figures A-16a 

through x. These figures indicate that the modeling platform closely replicates the short-term 

variability, the general magnitude, and site-to-site differences in sulfate concentrations 

throughout much of the year in counties predicted by our modeling to have future nonattainment 

and/or maintenance problems for PM2.5. The modeling platform shows considerable skill at 

distinguishing between high and low days at each site. The over prediction during winter and 

fall, as seen in the performance statistics, is evident on some days at these sites. For example, 

the time series for site 26263001 in Wayne County, MI (Figure A-16a) shows that the 

observations at this site exhibit considerable short-term variability, particularly during the 

summer. As evident from this figure, the model predictions closely track these temporal 

fluctuations and the magnitude of the observations in each season. Notable over predictions are 

limited to a few days in January, April, and October. Time series for all of the sites examined 
A-21 

indicates that the model consistently captures the correct timing of the observed peak episode 

days. 

Table A-5. Sulfate performance statistics by subregion, by season for the 2005 CAMx model 

simulation. 

Subregion Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Winter 862 57.7 67.3 40.6 49.7

Spring 884 3.0 38.3 3.4 41.0

Summer 758 -12.4 43.4 -20.7 55.5

CSN 

Fall 580 46.7 62.6 36.9 48.4

Winter 657 64.6 73.7 43.0 52.3

Spring 722 5.7 38.3 13.6 41.1

Summer 654 1.6 41.5 6.9 48.4

IMPROVE 

Fall 607 43.5 56.6 39.8 49.7

Winter 72 47.7 49.1 35.4 37.9

Spring 76 -1.5 27.0 2.5 31.0

Summer 72 -8.3 27.6 -8.1 38.5

CENRAP 

CASTNet 

Fall 76 28.3 34.6 25.6 31.9

 

Winter 618 73.9 86.4 37.7 54.0

Spring 633 15.5 39.1 28.6 40.0

Summer 616 11.3 34.3 22.9 39.8

CSN 

Fall 628 51.8 59.6 39.1 45.9

Winter 164 63.7 71.0 31.7 45.2

Spring 177 14.2 40.2 31.3 45.0

Summer 153 1.1 33.2 18.4 42.5

IMPROVE 

Fall 128 44.7 51.4 42.7 48.3

Winter 140 38.3 41.9 28.3 33.9

Spring 156 13.3 28.1 22.1 30.6

Summer 162 1.6 23.2 6.9 23.9

MWRPO 

CASTNet 

Fall 157 35.3 36.5 29.9 31.1

 

 

Winter 963 60.3 66.4 42.0 47.7

Spring 969 10.1 30.6 9.1 30.6

Summer 914 -4.0 31.5 -9.2 36.9

VISTAS 

CSN 

Fall 977 57.2 61.5 40.1 45.4
A-22 

Subregion Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Winter 493 70.5 74.5 47.8 52.3

Spring 527 5.4 35.3 6.5 39.0

Summer 502 -6.1 35.5 -16.7 45.0

IMPROVE 

Fall 468 60.9 68.5 44.7 54.3

Winter 270 42.8 45.6 33.7 37.1

Spring 288 4.4 20.2 4.2 22.1

Summer 263 -7.9 20.9 -14.4 27.5

CASTNet 

Fall 276 40.4 43.8 32.4 36.7

 

Winter 872 33.0 56.0 14.8 43.1

Spring 881 30.9 46.4 34.9 44.5

Summer 884 4.0 30.7 18.2 37.0

CSN 

Fall 861 44.0 52.2 35.6 43.7

Winter 596 46.7 61.4 21.4 44.6

Spring 687 38.5 55.7 45.9 55.1

Summer 626 11.7 39.5 31.0 46.9

IMPROVE 

Fall 581 63.3 67.2 50.3 56.2

Winter 195 24.3 33.3 16.7 30.7

Spring 199 27.2 37.1 35.7 40.4

Summer 197 5.9 23.4 15.7 26.6

MANEVU 

CASTNet 

Fall 195 49.7 50.8 42.8 43.3
A-23 

Figure A-8a. Distribution of observed and predicted 24-hour average sulfate by month for 2005 

at IMPROVE sites in the Northeast subregion. [symbol = median; top/bottom of box = 75th/25th

percentiles; top/bottom line = max/min values] 

Figure A-8b. Distribution of observed and predicted 24-hour average sulfate by month for 2005 

at CSN sites in the Northeast subregion. 
A-24 

Figure A-8c. Distribution of observed and predicted weekly average sulfate by month for 2005 

at CASTNet sites in the Northeast subregion. 

Figure A-9a. Distribution of observed and predicted 24-hour average sulfate by month for 2005 

at IMPROVE sites in the Southeast subregion. 
A-25 

Figure A-9b. Distribution of observed and predicted 24-hour average sulfate by month for 2005 

at CSN sites in the Southeast subregion. 

Figure A-9c. Distribution of observed and predicted weekly average sulfate by month for 2005 

at CASTNet sites in the Southeast subregion. 
A-26 

Figure A-10a. Distribution of observed and predicted 24-hour average sulfate by month for 2005 

at IMPROVE sites in the Midwest subregion. 

Figure A-10b. Distribution of observed and predicted 24-hour average sulfate by month for 

2005 at CSN sites in the Midwest subregion. 
A-27 

Figure A-10c. Distribution of observed and predicted weekly average sulfate by month for 2005 

at CASTNet sites in the Midwest subregion. 

Figure A-11a. Distribution of observed and predicted 24-hour average sulfate by month for 2005 

at IMPROVE sites in the Central states subregion. 
A-28 

Figure A-11b. Distribution of observed and predicted 24-hour average sulfate by month for 

2005 at CSN sites in the Central states subregion. 

Figure A-11c. Distribution of observed and predicted weekly average sulfate by month for 2005 

at CASTNet sites in the Central states subregion. 
A-29 

Figure A-12a. Normalized Mean Bias (%) of sulfate during January through March 2005 at 

monitoring sites in Eastern modeling domain. 

Figure A-12b. Normalized Mean Error (%) of sulfate during January through March 2005 at 

monitoring sites in Eastern modeling domain. 
A-30 

Figure A-13a. Normalized Mean Bias (%) of sulfate during April through June 2005 at 

monitoring sites in Eastern modeling domain. 

Figure A-13b. Normalized Mean Error (%) of sulfate during April through June 2005 at 

monitoring sites in Eastern modeling domain. 
A-31 

Figure A-14a. Normalized Mean Bias (%) of sulfate during July through September 2005 at 

monitoring sites in Eastern modeling domain. 

Figure A-14b. Normalized Mean Error (%) of sulfate during July through September 2005 at 

monitoring sites in Eastern modeling domain. 
A-32 

Figure A-15a. Normalized Mean Bias (%) of sulfate during October through December 2005 at 

monitoring sites in Eastern modeling domain. 

Figure A-15b. Normalized Mean Error (%) of sulfate during October through December 2005 at 

monitoring sites in Eastern modeling domain. 
A-33 

Figure A-16a. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 010730023 in Jefferson County, AL. 

Figure A-16b. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 010732003 in Jefferson County, AL. 

Figure A-16c. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 130890002 in DeKalb County, GA (this site in DeKalb County is used since there 

were no CSN data available for Fulton County). 
A-34 

Figure A-16d. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 170310057 in Cook County, IL. 

Figure A-16e. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 170310076 in Cook County, IL. 

Figure A-16f. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 170314201 in Cook County, IL. 
A-35 

Figure A-16g. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 1711192009 in Madison County, IL. 

Figure A-16h. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 180890022 in Lake County, IN. 

Figure A-16i. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 180892004 in Lake County, IN. 
A-36 

Figure A-16j. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 180970078 in Marion County, IN. 

Figure A-16k. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 261610008 in Washtenaw County, MI. 

Figure A-16l. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 261630001 in Wayne County, MI. 
A-37 

Figure A-16m. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 261630033 in Wayne County, MI. 

Figure A-16n. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 390171004 in Butler County, OH. 

Figure A-16o. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 390350038 in Cuyahoga County, OH. 
A-38 

Figure A-16p. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 390350060 in Cuyahoga County, OH. 

Figure A-16q. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 390610040 in Hamilton County, OH. 

Figure A-16r. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 390810017 in Jefferson County, OH (blank sections indicate extended periods with 

missing data). 
A-39 

Figure A-16s. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 391130031 in Montgomery County, OH. 

Figure A-16t. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 420030008 in Allegheny County, PA. 

Figure A-16u. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 420030064 in Allegheny County, PA. 
A-40 

Figure A-16v. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 420710007 in Lancaster County, PA. 

Figure A-16w. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 421330008 in York County, PA. 

Figure A-16x. Time series of observed (black) and predicted (blue) 24-hour average sulfate for 

2005 at site 550790026 in Milwaukee County, WI. 
A-41 

3.2 Evaluation for Nitrate 

 The model performance bias and error statistics for nitrate for each subregion and each 

season are provided in Table A-6. This table includes statistics for particulate nitrate, as 

measured at CSN and IMPROVE sites, and statistics for total nitrate, as measured at CASTNet 

sites. The distributions of observed and predicted nitrate by month for each subregion are shown 

in Figures A-17 through A-20. Spatial plots of the normalized mean bias and error by season for 

individual monitors are shown in Figures A-21 through A-24. Time series plots of observed and 

predicted 24-hour average nitrate at CSN monitoring sites in counties identified as nonattainment 

or maintenance for the annual and/or 24-hour PM2.5 NAAQS in the 2012 Transport Rule base 

case are provided in Figure A-25a-x24. 

The nitrate predictions from our modeling platform correspond well with the 

observations, particularly during the fall and winter when observations are at their highest levels. 

The bias and error statistics in Table A-6 indicate that model performance for nitrate is generally 

best during the winter and fall seasons at both urban and rural sites in each of the four 

subregions. The bias statistics indicate that the predictions are generally within + 20 percent of 

the observations at urban, suburban, and rural locations in most subregions during this season. 

Average bias across the subregions during the fall and winter is smaller than 17 percent for urban 

locations and 5 percent for rural locations. Somewhat greater under prediction is indicated by 

the FBs, especially in the Southeast where winter nitrate concentrations are lower than in the 

other subregions. In the spring and summer, when nitrate concentrations are low in all 

subregions, the under prediction bias is larger than in fall and winter, as indicated by the 30 

percent average bias at both urban and rural locations for the spring and summer seasons. Model 

error for nitrate is somewhat greater in all seasons for each subregions compared to sulfate, 

particularly with the FE statistic. 

Examining the distribution of observations and predictions for each subregion and 

network, as shown in Figures A-17 through A-20 provides perhaps a clearer understanding of 

model performance for nitrate than the performance statistics in Table A-6. These figures are 

based on observed 24-hour average (CSN and IMPROVE) particle nitrate and weekly average 

(CASTNet) total nitrate concentrations and the corresponding model predictions by month for 

January through December 2005. The figures indicate that the modeling platform closely 

replicates the observations in terms of the seasonal variability in nitrate concentrations and the 

large differences between the subregions and between urban, suburban, and rural sites in terms of 

the magnitude of nitrate concentrations. Median predicted concentrations are very similar in 

magnitude to the median of the measurements in nearly all months for each subregion for urban, 

suburban, and rural sites. However, a tendency for under prediction is evident in the 75th

percentile concentrations during the winter months. 

 

24 There is one county, Fulton County, GA, that is projected to be nonattainment for the annual PM2.5 NAAQS in 

2012 for which there is no site with 2005 PM2.5 speciation data available in AMET. Instead, we provide time series 

plots based on speciation measurements at the CSN site in neighboring DeKalb County, GA. 
A-42 

Model bias and error at individual sites indicates mainly under prediction biases of less 

than 40 percent at most sites during the winter and fall, as indicated in Figures A-21a and A-24a. 

The exception to this is the Southeast and extreme western portion of the modeling domain 

where there appears to be a greater number of sites with under prediction in the range of 40 to 60 

percent. During the spring and summer, when particle nitrate concentrations are very low, most 

sites across the modeling domain show under prediction of nitrate of 40 to 80 percent. Model 

error during the fall and winter, as shown in Figures A-21b and A-24b, is least at sites located in 

portions of the northern Plains states eastward to the mid-Altlantic states. Nitrate concentrations 

are typically higher in these areas than in other portions of the modeling domain. 

Like sulfate, the modeling platform closely matches the magnitude and short-term 

fluctuations in observed 24-hour average nitrate concentrations at CSN monitoring sites in 

counties with predicted nonattainment and/or maintenance problems for the annual or 24-hour 

PM2.5 NAAQS in the Transport Rule 2012 base case. The annual time series plots of observed 

and predicted 24-hour average nitrate concentrations for the 24 CSN sites in these counties are 

provided in Figures A-25a through x. These figures indicate that the short-term variability, 

general magnitude, and site-to-site differences in nitrate observations are captured by the model 

predictions in counties predicted to have future nonattainment and/or maintenance problems. In 

addition, the modeling platform shows excellent skill at replicating the timing of the transition 

between high nitrate episodes in the fall and winter and the very low concentrations during the 

spring and summer. For example, the time series for site 262610008 in Washtenaw County, MI 

(Figure A-25k) shows that the model predictions not only closely track the periods with high 

observed nitrate during cool season months (i.e., January, February, March and November and 

December), but also the more moderate concentrations in the transition months of April, May, 

and October, as well as the very low concentrations during July and August. At most sites there 

are a few peak days that are under predicted and others that are over predicted by the model. For 

example, at site 170310057 in Cook County, IL (Figure A-25d), the model under predicted the 

observed peak on February 3, closely replicated the peak on March 5, and over predicted the 

peak on March 29. Even at this site, however, the timing of observed high nitrate episodes is 

accurately captured by the model. Overall, the model predictions of nitrate track the temporal 

variability in the observations throughout the year at the monitoring sites examined in this 

analysis. 

Table A-6. Nitrate performance statistics by subregion, by season for the 2005 CAMx model 

simulation. 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Winter 514 -21.8 46.2 -32.6 64.3

Spring 493 -36.9 62.4 -92.3 111.0

Summer 478 -39.1 86.3 -112.0 131.0

CENRAP 

CSN 

Fall 442 -3.9 58.3 -40.6 84.7
A-43 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Winter 657 -10.4 49.1 -39.1 78.0

Spring 722 -39.6 63.5 -112.0 128.0

Summer 654 -31.8 93.1 -126.0 146.0

IMPROVE 

Fall 607 2.2 73.2 -40.3 104.0

Winter 72 9.6 29.4 10.6 31.0

Spring 76 -28.7 31.8 -37.9 40.2

Summer 72 -12.4 27.7 -20.8 32.4

CASTNet 

Fall 76 13.8 38.7 18.3 37.8

 

Winter 618 -13.9 45.6 -33.2 56.3

Spring 633 1.0 68.6 -36.9 81.6

Summer 616 -35.0 75.4 -85.1 112.0

CSN 

Fall 628 -17.7 49.1 -46.1 76.2

Winter 164 -19.2 46.7 -46.7 77.3

Spring 177 -8.6 71.1 -86.5 113.0

Summer 153 -51.4 79.4 -124.0 143.0

IMPROVE 

Fall 128 -14.6 59.4 -69.0 100.0

Winter 140 -12.3 25.2 -10.6 25.9

Spring 156 -9.1 25.4 -11.9 28.6

Summer 162 22.0 35.5 11.2 30.3

MWRPO 

CASTNet 

Fall 157 8.9 37.7 6.6 32.0

 

Winter 963 -29.3 67.5 -72.1 101.0

Spring 969 -43.4 73.3 -104.0 121.0

Summer 914 -56.5 82.7 -125.0 140.0

CSN 

Fall 977 -40.6 76.7 -96.2 120.0

Winter 493 -20.9 79.5 -70.3 112.0

Spring 527 -43.0 85.5 -122.0 142.0

Summer 502 -43.6 108.0 -134.0 157.0

IMPROVE 

Fall 468 -31.9 95.0 -105.0 142.0

VISTAS 

CASTNet Winter 270 3.9 24.8 4.2 25.2
A-44 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Spring 288 -14.6 25.4 -14.2 27.6

Summer 263 9.6 30.1 8.2 30.1

Fall 276 30.5 44.4 27.6 40.1

 

Winter 872 -14.8 43.7 -18.9 53.5

Spring 881 -2.1 67.0 -47.8 90.5

Summer 884 -39.1 81.0 -111.0 132.0

CSN 

Fall 862 6.9 65.4 -29.8 78.7

Winter 596 18.2 68.7 13.6 75.5

Spring 687 3.0 79.2 -52.9 105.0

Summer 626 -21.3 98.1 -112.0 136.0

IMPROVE 

Fall 576 40.3 86.9 -22.4 98.5

Winter 195 11.6 26.1 16.7 29.6

Spring 199 16.7 30.3 13.2 30.2

Summer 197 32.3 40.7 20.4 36.9

MANEVU 

CASTNet 

Fall 195 40.4 52.1 41.1 49.1
A-45 

Figure A-17a. Distribution of observed and predicted 24-hour average nitrate by month for 2005 

at IMPROVE sites in the Northeast subregion. [symbol = median; top/bottom of box = 75th/25th

percentiles; top/bottom line = max/min values] 

Figure A-17b. Distribution of observed and predicted 24-hour average nitrate by month for 2005 

at CSN sites in the Northeast subregion. 
A-46 

Figure A-17c. Distribution of observed and predicted weekly average total nitrate by month for 

2005 at CASTNet sites in the Northeast subregion. 

Figure A-18a. Distribution of observed and predicted 24-hour average nitrate by month for 2005 

at IMPROVE sites in the Southeast subregion. 
A-47 

Figure A-18b. Distribution of observed and predicted 24-hour average nitrate by month for 2005 

at CSN sites in the Southeast subregion. 

Figure A-18c. Distribution of observed and predicted weekly average total nitrate by month for 

2005 at CASTNet sites in the Southeast subregion. 
A-48 

Figure A-19a. Distribution of observed and predicted 24-hour average nitrate by month for 2005 

at IMPROVE sites in the Midwest subregion. 

Figure A-19b. Distribution of observed and predicted 24-hour average nitrate by month for 2005 

at CSN sites in the Midwest subregion. 
A-49 

Figure A-19c. Distribution of observed and predicted weekly average total nitrate by month for 

2005 at CASTNet sites in the Midwest subregion. 

Figure A-20a. Distribution of observed and predicted 24-hour average nitrate by month for 2005 

at IMPROVE sites in the Central states subregion. 
A-50 

Figure A-20b. Distribution of observed and predicted 24-hour average nitrate by month for 2005 

at CSN sites in the Central states subregion. 

Figure A-20c. Distribution of observed and predicted weekly average total nitrate by month for 

2005 at CASTNet sites in the Central states subregion. 
A-51 

Figure A-21a. Normalized Mean Bias (%) for nitrate during January through March 2005 at 

monitoring sites in Eastern modeling domain. 

Figure A-21b. Normalized Mean Error (%) for nitrate during January through March 2005 at 

monitoring sites in Eastern modeling domain. 
A-52 

Figure A-22a. Normalized Mean Bias (%) for nitrate during April through June 2005 at 

monitoring sites in Eastern modeling domain. 

Figure A-22b. Normalized Mean Error (%) for nitrate during April through June 2005 at 

monitoring sites in Eastern modeling domain.
A-53 

Figure A-23a. Normalized Mean Bias (%) for nitrate during July through September 2005 at 

monitoring sites in Eastern modeling domain. 

Figure A-23b. Normalized Mean Error (%) for nitrate during July through September 2005 at 

monitoring sites in Eastern modeling domain. 
A-54 

Figure A-24a. Normalized Mean Bias (%) for nitrate during October through December 2005 at 

monitoring sites in Eastern modeling domain. 

Figure A-24b. Normalized Mean Error (%) for nitrate during October through December 2005 

at monitoring sites in Eastern modeling domain. 
A-55 

Figure A-25a. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 010730023 in Jefferson County, AL. 

Figure A-25b. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 010732003 in Jefferson County, AL. 

Figure A-25c. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 130890002 in DeKalb County, GA (this site in DeKalb County is used since there 

were no CSN data available for Fulton County). 
A-56 

Figure A-25d. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 170310057 in Cook County, IL. 

Figure A-25e. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 170310076 in Cook County, IL. 

Figure A-25f. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 170314201 in Cook County, IL. 
A-57 

Figure A-25g. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 1711192009 in Madison County, IL. 

Figure A-25h. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 180890022 in Lake County, IN. 

Figure A-25i. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 180892004 in Lake County, IN. 
A-58 

Figure A-25j. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 180970078 in Marion County, IN. 

Figure A-25k. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 261610008 in Washtenaw County, MI. 

Figure A-25l. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 261630001 in Wayne County, MI. 
A-59 

Figure A-25m. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 261630033 in Wayne County, MI. 

Figure A-25n. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 390171004 in Butler County, OH. 

Figure A-25o. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 390350038 in Cuyahoga County, OH. 
A-60 

Figure A-25p. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 390350060 in Cuyahoga County, OH. 

Figure A-25q. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 390610040 in Hamilton County, OH. 

Figure A-25r. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 390810017 in Jefferson County, OH. (blank sections indicate extensive periods with 

missing data). 
A-61 

Figure A-25s. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 391130031 in Montgomery County, OH. 

Figure A-25t. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 420030008 in Allegheny County, PA. 

Figure A-25u. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 420030064 in Allegheny County, PA. 
A-62 

Figure A-25v. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 420710007 in Lancaster County, PA. 

Figure A-25w. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 421330008 in York County, PA. 

Figure A-25x. Time series of observed (black) and predicted (blue) 24-hour average nitrate for 

2005 at site 550790026 in Milwaukee County, WI. 
A-63 

A.3.3. Evaluation for Ammonium 

 The model performance bias and error statistics for ammonium for each subregion and 

each season are provided in Table A-7. These statistics indicate model bias for ammonium is 

generally + 40 percent or less for all seasons in each subregion. During the summer, there is 

slight under prediction with a low bias smaller than 10 percent, on average across the subregions 

for urban locations. In other times of the year ammonium tends to be somewhat over predicted 

with a bias of 19 percent, on average across the subregions for urban locations. There is not a 

large variation from subregion to subregion or at urban versus rural sites in the error statistics for 

ammonium. 

Table A-7. Ammonium performance statistics by subregion, by season for the 2005 CAMx 

model simulation. 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Winter 862 26.7 51.0 25.9 47.1

Spring 884 -2.1 41.7 1.4 48.0

Summer 758 -11.1 44.0 -15.0 58.4

CSN 

Fall 580 33.8 59.5 31.8 52.9

Winter 72 37.7 44.1 32.4 37.6

Spring 76 14.4 35.6 14.0 35.2

Summer 72 -1.8 32.0 0.6 39.5

CENRAP 

CASTNet 

Fall 76 39.0 56.6 37.9 50.9

 

Winter 618 15.5 40.8 9.7 35.1

Spring 633 17.3 42.6 26.4 42.8

Summer 616 0.3 37.0 19.2 44.8

CSN 

Fall 628 17.7 38.7 25.1 40.0

Winter 140 7.8 22.6 10.0 22.5

Spring 156 30.2 38.7 30.5 36.1

Summer 162 -3.7 26.8 2.1 26.8

MWRPO 

CASTNet 

Fall 157 31.1 39.9 30.2 36.2

 

Winter 963 21.4 46.1 17.9 42.0

Spring 969 5.0 33.8 9.4 36.0

Summer 914 -10.8 35.9 -5.8 42.0

CSN 

Fall 977 22.9 40.1 24.1 39.0

VISTAS 

CASTNet Winter 380 21.1 29.6 16.7 26.3
A-64 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Spring 375 -6.0 26.8 -6.2 29.8

Summer 364 -29.0 33.7 -42.4 47.0

Fall 351 18.3 31.1 18.0 31.6

 

Winter 872 6.7 35.7 10.9 35.4

Spring 881 26.0 45.8 35.8 49.3

Summer 884 -7.6 36.9 17.0 47.2

CSN 

Fall 861 32.0 51.5 39.0 52.6

Winter 195 34.8 42.6 35.7 40.4

Spring 199 23.9 38.4 29.7 38.6

Summer 197 -15.8 27.0 -9.1 27.9

MANEVU 

CASTNet 

Fall 195 50.2 53.7 46.3 48.1

A.3.4. Evaluation for Elemental Carbon 

 The model performance bias and error statistics for elemental carbon for each subregion 

and each season are provided in Table A-8. The statistics show clear over prediction at urban 

sites in all subregions. For example, NMBs are greater than 100 percent at urban sites in the 

Midwest, Northeast, and Central subregions with only slightly less over prediction at urban sites 

in the Southeast. Rural sites show much less over prediction than at urban sites with under 

prediction in the spring and summer at rural sites in the Southeast and Central subregions. In 

addition, the predictions for urban sites have greater error than the predictions for rural locations. 

Table A-8. Elemental Carbon performance statistics by subregion, by season for the 2005 

CAMx model simulation. 

Subregion Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Winter 939 163.0 179.0 76.4 87.3

Spring 913 105.0 127.0 50.5 76.2

Summer 780 162.0 173.0 63.9 86.5

CSN 

Fall 615 112.0 139.0 69.0 79.9

Winter 624 54.0 80.9 38.4 55.7

Spring 735 -20.3 45.8 -16.3 47.6

Summer 653 -9.6 39.2 -11.0 41.9

CENRAP 

IMPROVE 

Fall 614 2.9 34.8 10.3 38.0

 

MWRPO CSN Winter 621 152.0 167.0 81.3 85.4
A-65 

Subregion Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Spring 634 76.6 92.2 58.0 66.8

Summer 618 73.7 83.1 52.2 60.6

Fall 629 111.0 125.0 66.4 73.8

Winter 202 108.0 114.0 47.3 59.5

Spring 188 15.4 44.2 -1.2 37.5

Summer 163 1.1 37.1 -12.7 34.9

IMPROVE 

Fall 143 26.5 38.5 28.2 42.3

 

Winter 959 84.5 95.3 57.1 64.1

Spring 968 49.2 74.3 43.7 60.2

Summer 910 66.5 84.4 54.0 67.0

CSN 

Fall 981 33.6 61.2 33.4 52.9

Winter 515 17.7 55.0 19.3 50.2

Spring 526 -25.5 45.0 -25.3 46.4

Summer 495 -19.8 39.6 -29.7 51.3

VISTAS 

IMPROVE 

Fall 459 -4.0 38.5 8.2 41.3

 

Winter 870 132.0 139.0 71.8 75.5

Spring 868 124.0 139.0 73.3 80.1

Summer 879 95.6 114.0 62.2 70.0

CSN 

Fall 862 98.4 117.0 60.2 68.7

Winter 622 77.6 95.9 46.0 63.4

Spring 650 32.0 61.5 19.7 50.2

Summer 565 14.5 46.9 0.6 39.7

MANEVU 

IMPROVE 

Fall 611 75.4 88.4 43.9 58.7

A.3.5. Evaluation for Organic Carbon 

The model performance bias and error statistics for organic carbon for each subregion 

and each season are provided in Table A-9. The statistics in this table indicate a tendency for the 

modeling platform to somewhat under predict observed organic carbon concentrations during the 

spring and summer at urban locations with an NMB of -15 percent, on average at urban locations 

across the four subregions. In contrast, during the winter organic carbon is somewhat over 

predicted with an average NMB of 27 percent at urban locations. Organic carbon at rural 

locations in the spring and summer tends to be slightly under predicted in some subregions and 

over predicted in others with NMBs generally within + 30 percent. At rural locations in the 

winter organic carbon is generally under predicted with NMBs generally smaller than -20 

percent. 
A-66 

Table A-9. Organic Carbon performance statistics by subregion, by season for the 2005 CAMx 

model simulation. 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Winter 607 23.9 68.8 32.9 64.4

Spring 620 -27.8 39.3 -27.6 47.9

Summer 557 -4.1 34.7 -4.3 41.3

CSN 

Fall 476 -7.8 47.4 2.3 51.9

Winter 624 18.8 65.0 18.0 50.0

Spring 734 -21.8 35.8 -17.0 39.2

Summer 651 16.3 34.4 14.1 34.5

CENRAP 

IMPROVE 

Fall 613 -20.8 39.3 -13.1 40.8

Winter 574 33.3 61.1 45.0 61.0

Spring 618 -27.9 39.0 -17.7 44.1

Summer 616 -13.8 33.3 -11.8 38.6

CSN 

Fall 577 8.4 53.7 24.2 58.4

Winter 202 37.1 53.4 26.2 45.1

Spring 188 -8.7 27.4 -7.6 27.8

Summer 163 8.1 40.7 10.8 34.2

MWRPO 

IMPROVE 

Fall 142 1.9 37.4 14.6 41.9

 

Winter 931 0.1 48.7 14.3 51.2

Spring 963 -33.2 40.1 -32.0 47.4

Summer 902 -8.2 31.0 -7.2 35.8

CSN 

Fall 951 -26.2 45.2 -16.0 52.4

Winter 514 -5.8 43.2 -1.1 44.3

Spring 525 -27.3 39.4 -24.4 38.9

Summer 495 15.4 39.6 3.4 38.9

VISTAS 

IMPROVE 

Fall 459 -17.4 35.8 -12.6 40.6

 

Winter 831 52.0 76.5 46.2 62.9

Spring 833 -0.6 49.3 8.5 51.4

Summer 877 -8.0 34.2 -5.8 37.8

CSN 

Fall 786 36.1 66.6 43.0 63.9

Winter 621 69.9 85.3 45.0 58.0

MANEVU 

IMPROVE 

Spring 649 0.2 40.9 8.2 39.1
A-67 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Summer 565 15.3 37.0 13.9 33.4

Fall 608 73.6 83.9 48.2 57.3

A.3.6. Evaluation for Crustal Material 

The model performance bias and error statistics for crustal material for each subregion 

and each season are provided in Table A-10. As indicated by the performance statistics in this 

table, the modeling platform over predicts observed concentrations at urban and rural sites in 

each season for all four subregions. The magnitude of the over prediction is quite large with 

NMBs that exceed 100 percent. 

Table A-10. Crustal Material performance statistics by subregion, by season for the 2005 CAMx 

model simulation 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Winter 861 735.0 761.0 153.0 157.0

Spring 874 212.0 261.0 98.4 119.0

Summer 758 196.0 256.0 99.9 127.0

CSN 

Fall 578 394.0 426.0 136.0 142.0

Winter 683 554.0 582.0 129.0 139.0

Spring 742 66.0 109.0 43.1 76.1

Summer 673 53.0 116.0 51.8 85.9

CENRAP 

IMPROVE 

Fall 672 204.0 229.0 100.0 110.0

Winter 619 935.0 937.0 161.0 162.0

Spring 634 439.0 442.0 134.0 135.0

Summer 612 466.0 477.0 138.0 140.0

CSN 

Fall 625 632.0 643.0 150.0 151.0

Winter 174 957.0 958.0 151.0 153.0

Spring 183 280.0 280.0 105.0 108.0

Summer 159 343.0 351.0 110.0 117.0

MWRPO 

IMPROVE 

Fall 152 662.0 662.0 141.0 143.0

VISTAS CSN Winter 963 897.0 906.0 159.0 160.0

Spring 964 378.0 398.0 120.0 126.0
A-68 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Summer 923 339.0 372.0 116.0 127.0

Fall 979 641.0 642.0 145.0 145.0

Winter 471 772.0 779.0 148.0 152.0

Spring 519 176.0 216.0 76.8 101.0

Summer 503 129.0 208.0 70.0 112.0

IMPROVE 

Fall 471 539.0 542.0 138.0 141.0

 

Winter 863 1030.0 1030.0 165.0 165.0

Spring 877 496.0 499.0 139.0 140.0

Summer 880 517.0 519.0 142.0 143.0

CSN 

Fall 859 841.0 841.0 157.0 157.0

Winter 661 886.0 890.0 155.0 156.0

Spring 686 349.0 355.0 116.0 121.0

Summer 613 365.0 369.0 122.0 123.0

MANEVU 

IMPROVE 

Fall 623 843.0 844.0 155.0 156.0
B-1 

Air Quality Modeling Final Rule 

Technical Support Document 

Appendix B 

 

2003-2007 Base Period and Projected 2012 and 

2014 Design Values 

for Monitoring Sites in the Eastern Modeling Domain 
B-2 

The tables in this appendix provide the 5-year weighted average and maximum 

design values for 8-hour ozone, annual PM2.5, and 24-hour PM2.5 for 

monitoring sites in the 12 km Eastern U.S. modeling domain1

. Design values 

are provided for the 2003-2007 base period, 2012 base case, 2014 base case, 

and 2014 remedy scenario. 

Also included in this appendix are tables of the differences (i.e., increases and 

decreases) by site in average design values between the 2014 remedy scenario 

and both the 2012 base case and 2014 base case. Note that increases and 

decreases reported as + 0.1 ppb for ozone, + 0.01 μg/m3

 for annual PM2.5, and 

+ 0.1 μg/m3

 for 24-hour PM2.5 may overstate the actual difference in 

concentrations because the design value concentrations are truncated before 

the differences are calculated. 

 1

 Design values are provided for those sites which meet the data completeness and 

projection criteria described in Section III of the TSD. 
B-3 

This page intentionally left blank
B-4 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

010030010 Alabama Baldwin 77.3 78.0 69.6 70.2 67.6 68.010270001 Alabama Clay 74.0 76.0 63.8 65.5 61.2 62.010331002 Alabama Colbert 72.0 74.0 64.0 65.8 62.3 64.010510001 Alabama Elmore 70.7 71.0 62.2 62.5 60.1 60.010550011 Alabama Etowah 71.7 73.0 61.8 63.0 59.8 60.010730023 Alabama Jefferson 78.7 86.0 68.7 75.0 66.4 72.010731003 Alabama Jefferson 79.0 84.0 68.2 72.5 65.9 70.010731005 Alabama Jefferson 81.0 86.0 70.0 74.3 67.3 71.010731009 Alabama Jefferson 77.0 82.0 66.2 70.5 64.1 68.010731010 Alabama Jefferson 73.0 75.0 62.5 64.2 60.2 61.010732006 Alabama Jefferson 83.7 89.0 72.9 77.5 70.3 74.010735002 Alabama Jefferson 74.0 77.0 64.9 67.5 63.1 65.010735003 Alabama Jefferson 77.0 82.0 66.9 71.2 64.9 69.010736002 Alabama Jefferson 81.3 89.0 71.7 78.4 69.6 76.010790002 Alabama Lawrence 72.0 74.0 63.7 65.4 61.6 63.010890014 Alabama Madison 77.3 78.0 68.0 68.7 65.5 66.010970003 Alabama Mobile 76.7 78.0 69.2 70.4 67.5 68.010972005 Alabama Mobile 76.7 77.0 69.1 69.4 67.2 67.011011002 Alabama Montgomery 69.3 74.0 60.4 64.5 58.3 62.011030011 Alabama Morgan 77.3 79.0 69.3 70.8 67.4 68.011130002 Alabama Russell 71.3 75.0 64.6 68.0 62.2 65.011170004 Alabama Shelby 85.7 88.0 74.1 76.1 71.3 73.011190002 Alabama Sumter 64.0 66.0 57.5 59.3 58.0 59.011210003 Alabama Talladega 72.0 72.0 62.4 62.4 60.5 60.011250010 Alabama Tuscaloosa 73.3 77.0 63.6 66.8 61.3 64.
B-5 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

050350005 Arkansas Crittenden 87.3 89.0 77.1 78.6 74.4 75.051190007 Arkansas Pulaski 78.3 80.0 67.9 69.4 65.5 66.051191002 Arkansas Pulaski 79.7 83.0 69.5 72.4 67.2 69.051191005 Arkansas Pulaski 74.0 75.0 64.4 65.2 62.1 62.080013001 Colorado Adams 69.0 70.0 65.1 66.1 64.1 65.080050002 Colorado Arapahoe 78.7 81.0 73.8 75.9 72.5 74.080130011 Colorado Boulder 77.0 81.0 71.8 75.5 70.4 74.080310014 Colorado Denver 73.0 75.0 68.9 70.8 67.8 69.080350004 Colorado Douglas 83.7 85.0 78.8 80.0 77.5 78.080590002 Colorado Jefferson 76.3 79.0 72.5 75.1 71.5 74.080590005 Colorado Jefferson 70.3 75.0 66.4 70.8 65.4 69.080590006 Colorado Jefferson 81.7 85.0 76.7 79.8 75.4 78.080590011 Colorado Jefferson 80.7 82.0 76.7 77.9 75.6 76.080690007 Colorado Larimer 76.0 78.0 70.5 72.3 68.7 70.080691004 Colorado Larimer 72.3 74.0 66.7 68.3 65.2 66.081230009 Colorado Weld 76.7 78.0 71.5 72.7 70.1 71.090010017 Connecticut Fairfield 88.0 90.0 81.1 82.9 79.5 81.090011123 Connecticut Fairfield 92.3 94.0 83.9 85.5 82.0 83.090013007 Connecticut Fairfield 90.0 92.0 82.9 84.7 81.4 83.090019003 Connecticut Fairfield 87.7 89.0 80.9 82.1 79.4 80.090031003 Connecticut Hartford 84.3 90.0 74.3 79.3 72.3 77.090050005 Connecticut Litchfield 87.7 89.0 78.2 79.4 76.1 77.090070007 Connecticut Middlesex 90.3 92.0 80.9 82.4 78.9 80.090090027 Connecticut New Haven 79.3 81.0 72.8 74.4 71.5 73.090093002 Connecticut New Haven 90.3 93.0 82.7 85.1 81.0 83.090110008 Connecticut New London 85.3 88.0 76.7 79.2 74.9 77.
B-6 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

090131001 Connecticut Tolland 88.7 91.0 78.4 80.5 76.3 78.100010002 Delaware Kent 80.3 81.0 71.2 71.8 69.0 69.100031007 Delaware New Castle 80.0 82.0 70.3 72.0 68.1 69.100031010 Delaware New Castle 82.3 83.0 73.6 74.2 71.6 72.100031013 Delaware New Castle 81.3 82.0 72.1 72.7 69.9 70.100051002 Delaware Sussex 81.3 82.0 71.2 71.8 69.0 69.100051003 Delaware Sussex 82.7 84.0 74.1 75.2 72.2 73.110010025 D.C. Washington 79.7 81.0 72.7 73.9 70.9 72.110010041 D.C. Washington 80.3 85.0 72.9 77.2 71.1 75.110010043 D.C. Washington 84.7 87.0 76.9 79.0 75.0 77.120010025 Florida Alachua 72.0 72.0 62.3 62.3 60.5 60.120030002 Florida Baker 68.7 70.0 59.7 60.8 57.7 58.120050006 Florida Bay 78.7 81.0 69.6 71.6 67.9 69.120090007 Florida Brevard 69.0 69.0 61.9 61.9 60.5 60.120094001 Florida Brevard 71.3 72.0 63.7 64.4 62.3 63.120112003 Florida Broward 61.3 63.0 56.9 58.5 55.9 57.120118002 Florida Broward 65.0 67.0 60.8 62.7 59.8 61.120210004 Florida Collier 68.3 69.0 59.2 59.8 57.2 57.120310077 Florida Duval 77.0 77.0 69.1 69.1 67.9 67.120310100 Florida Duval 77.7 79.0 69.7 70.8 68.2 69.120330004 Florida Escambia 79.7 81.0 72.4 73.6 71.0 72.120330018 Florida Escambia 82.7 83.0 73.8 74.0 71.9 72.120330024 Florida Escambia 80.7 81.0 72.0 72.3 70.2 70.120570081 Florida Hillsborough 78.7 80.0 73.6 74.9 72.1 73.120570110 Florida Hillsborough 76.0 76.0 67.4 67.4 65.8 65.120571035 Florida Hillsborough 74.0 76.0 69.3 71.2 67.9 69.
B-7 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

120571065 Florida Hillsborough 80.7 81.0 76.6 76.9 75.1 75.120573002 Florida Hillsborough 77.5 78.0 70.0 70.4 68.4 68.120574004 Florida Hillsborough 74.0 74.0 66.4 66.4 64.7 64.120690002 Florida Lake 76.7 77.0 68.8 69.1 66.9 67.120712002 Florida Lee 70.3 72.0 62.0 63.5 60.1 61.120713002 Florida Lee 70.3 71.0 61.5 62.1 59.5 60.120730012 Florida Leon 70.0 70.0 60.8 60.8 58.3 58.120730013 Florida Leon 71.0 72.0 61.6 62.5 58.9 59.120813002 Florida Manatee 77.3 79.0 69.9 71.5 68.3 69.120814012 Florida Manatee 76.3 77.0 67.4 68.0 65.6 66.120814013 Florida Manatee 72.7 73.0 64.4 64.6 62.7 63.120830003 Florida Marion 71.7 72.0 62.6 62.8 60.7 60.120830004 Florida Marion 73.0 73.0 63.2 63.2 61.3 61.120950008 Florida Orange 79.3 81.0 72.0 73.6 70.5 72.120952002 Florida Orange 79.3 80.0 72.3 72.9 70.5 71.120972002 Florida Osceola 72.0 73.0 64.4 65.3 62.7 63.120990020 Florida Palm Beach 65.0 65.0 59.7 59.7 58.6 58.121010005 Florida Pasco 76.3 77.0 67.0 67.6 66.0 66.121012001 Florida Pasco 73.3 75.0 64.7 66.2 63.4 64.121030004 Florida Pinellas 72.3 73.0 64.7 65.3 63.2 63.121030018 Florida Pinellas 69.7 71.0 61.7 62.9 60.2 61.121035002 Florida Pinellas 72.7 74.0 65.1 66.3 63.8 64.121056005 Florida Polk 73.3 75.0 65.6 67.1 64.1 65.121056006 Florida Polk 74.7 75.0 66.4 66.7 65.0 65.121111002 Florida St Lucie 66.5 68.0 59.7 61.1 58.3 59.121130015 Florida Santa Rosa 80.0 80.0 71.9 71.9 70.1 70.
B-8 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

121151005 Florida Sarasota 77.3 78.0 67.3 68.0 65.4 66.121151006 Florida Sarasota 73.3 74.0 64.4 65.0 62.5 63.121152002 Florida Sarasota 73.5 74.0 64.0 64.5 62.2 62.121171002 Florida Seminole 76.0 77.0 68.8 69.7 66.9 67.121272001 Florida Volusia 68.3 69.0 59.6 60.2 57.6 58.121275002 Florida Volusia 68.3 69.0 59.3 59.9 57.3 57.121290001 Florida Wakulla 71.3 74.0 63.1 65.4 61.1 63.130210012 Georgia Bibb 81.0 83.0 71.2 73.0 66.5 68.130210013 Georgia Bibb 81.0 81.0 71.5 71.5 67.0 67.130510021 Georgia Chatham 68.3 69.0 61.7 62.4 59.8 60.130550001 Georgia Chattooga 75.0 76.0 64.9 65.7 62.5 63.130590002 Georgia Clarke 80.7 83.0 68.9 70.8 65.8 67.130670003 Georgia Cobb 82.7 87.0 72.5 76.2 69.7 73.130730001 Georgia Columbia 73.0 73.0 64.0 64.0 61.3 61.130770002 Georgia Coweta 82.0 85.0 71.9 74.6 68.4 70.130850001 Georgia Dawson 76.3 79.0 66.3 68.6 63.5 65.130890002 Georgia De Kalb 88.7 93.0 79.9 83.7 77.5 81.130893001 Georgia De Kalb 87.5 88.0 79.5 80.0 77.7 78.130970004 Georgia Douglas 87.3 90.0 76.8 79.2 73.8 76.131130001 Georgia Fayette 85.7 89.0 76.2 79.1 73.5 76.131210055 Georgia Fulton 91.7 94.0 82.6 84.6 80.1 82.131270006 Georgia Glynn 67.0 68.0 59.4 60.3 58.0 58.131350002 Georgia Gwinnett 88.7 90.0 78.0 79.2 74.8 75.131510002 Georgia Henry 89.7 95.0 78.9 83.6 75.8 80.132130003 Georgia Murray 78.0 79.0 68.2 69.1 65.6 66.132150008 Georgia Muscogee 75.7 80.0 66.6 70.4 63.8 67.
B-9 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

132151003 Georgia Muscogee 70.7 73.0 62.1 64.1 59.5 61.132230003 Georgia Paulding 80.3 83.0 68.1 70.4 65.4 67.132450091 Georgia Richmond 80.3 81.0 70.2 70.8 67.4 68.132470001 Georgia Rockdale 90.0 95.0 78.3 82.7 74.7 78.132611001 Georgia Sumter 72.3 74.0 64.1 65.6 61.4 62.170010006 Illinois Adams 70.0 70.0 63.8 63.8 62.7 62.170190004 Illinois Champaign 68.3 70.0 61.7 63.3 60.5 62.170230001 Illinois Clark 66.0 66.0 59.2 59.2 57.8 57.170310001 Illinois Cook 77.3 82.0 72.2 76.6 71.1 75.170310032 Illinois Cook 74.3 77.0 68.9 71.4 67.8 70.170310064 Illinois Cook 71.3 77.0 66.1 71.4 65.1 70.170310072 Illinois Cook 71.0 73.0 66.3 68.2 65.5 67.170310076 Illinois Cook 77.0 79.0 72.3 74.2 71.3 73.170311003 Illinois Cook 76.3 79.0 72.2 74.7 71.3 73.170311601 Illinois Cook 76.7 80.0 71.7 74.8 70.6 73.170314002 Illinois Cook 66.3 68.0 63.4 65.0 62.9 64.170314007 Illinois Cook 71.7 74.0 66.9 69.0 65.8 67.170314201 Illinois Cook 74.3 76.0 69.3 70.9 68.2 69.170317002 Illinois Cook 77.7 79.0 72.9 74.1 71.9 73.170436001 Illinois Du Page 69.0 70.0 65.1 66.1 64.3 65.170491001 Illinois Effingham 70.0 72.0 63.4 65.2 61.7 63.170650002 Illinois Hamilton 73.0 73.0 66.3 66.3 64.7 64.170831001 Illinois Jersey 78.7 80.0 70.5 71.7 68.3 69.170890005 Illinois Kane 74.3 77.0 68.0 70.5 66.5 68.170971002 Illinois Lake 76.7 79.0 71.9 74.1 70.9 73.170971007 Illinois Lake 78.0 79.0 73.1 74.1 72.1 73.
B-10 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

171110001 Illinois McHenry 73.3 78.0 66.8 71.0 65.2 69.171132003 Illinois McLean 73.0 74.0 65.3 66.2 63.9 64.171150013 Illinois Macon 71.3 74.0 64.7 67.2 63.5 65.171170002 Illinois Macoupin 73.0 74.0 64.4 65.3 62.4 63.171190008 Illinois Madison 82.7 84.0 74.7 75.9 72.6 73.171191009 Illinois Madison 83.0 84.0 75.7 76.6 73.8 74.171193007 Illinois Madison 81.0 83.0 73.2 75.0 71.1 72.171430024 Illinois Peoria 68.3 71.0 61.4 63.9 60.3 62.171431001 Illinois Peoria 72.7 76.0 65.4 68.4 64.2 67.171570001 Illinois Randolph 72.0 75.0 65.8 68.5 64.6 67.171613002 Illinois Rock Island 65.3 68.0 59.5 62.0 58.2 60.171630010 Illinois St Clair 81.7 82.0 75.5 75.8 73.7 74.171670010 Illinois Sangamon 70.0 71.0 61.6 62.5 60.5 61.171971011 Illinois Will 71.7 72.0 66.0 66.2 64.7 65.172010009 Illinois Winnebago 69.0 71.0 61.9 63.7 60.4 62.172012001 Illinois Winnebago 68.3 70.0 61.3 62.8 59.8 61.180030002 Indiana Allen 79.3 83.0 71.4 74.7 69.8 73.180030004 Indiana Allen 74.3 76.0 66.8 68.4 65.3 66.180110001 Indiana Boone 79.7 81.0 72.2 73.4 70.5 71.180150002 Indiana Carroll 74.0 75.0 66.4 67.3 64.9 65.180190008 Indiana Clark 80.3 83.0 72.1 74.5 70.0 72.180350010 Indiana Delaware 76.3 78.0 67.9 69.5 66.2 67.180390007 Indiana Elkhart 79.0 83.0 70.8 74.4 69.2 72.180431004 Indiana Floyd 77.7 79.0 71.0 72.2 69.3 70.180550001 Indiana Greene 78.3 80.0 70.1 71.6 68.4 69.180570005 Indiana Hamilton 82.7 87.0 75.0 78.9 73.3 77.
B-11 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

180590003 Indiana Hancock 78.0 81.0 70.6 73.4 69.0 71.180630004 Indiana Hendricks 75.3 76.0 68.2 68.9 66.8 67.180690002 Indiana Huntington 75.0 76.0 67.6 68.5 66.0 66.180710001 Indiana Jackson 74.7 76.0 66.6 67.7 65.0 66.180810002 Indiana Johnson 76.7 78.0 69.6 70.7 68.2 69.180890022 Indiana Lake 77.7 82.0 72.1 76.1 71.2 75.180890030 Indiana Lake 81.0 85.0 75.8 79.5 74.9 78.180892008 Indiana Lake 77.7 79.0 72.7 73.9 71.8 73.180910005 Indiana La Porte 77.0 78.0 71.2 72.1 70.1 71.180910010 Indiana La Porte 78.5 79.0 71.5 71.9 70.2 70.180950010 Indiana Madison 76.7 80.0 68.8 71.8 67.1 70.180970042 Indiana Marion 72.7 76.0 66.7 69.7 65.4 68.180970050 Indiana Marion 78.7 81.0 72.2 74.3 70.7 72.180970057 Indiana Marion 75.0 77.0 69.1 70.9 67.8 69.180970073 Indiana Marion 75.7 77.0 69.3 70.5 67.9 69.181090005 Indiana Morgan 77.0 79.0 69.9 71.7 68.4 70.181230009 Indiana Perry 81.0 81.0 75.1 75.1 73.1 73.181270024 Indiana Porter 78.3 81.0 71.9 74.4 70.9 73.181270026 Indiana Porter 75.3 77.0 68.4 69.9 67.1 68.181290003 Indiana Posey 71.7 75.0 64.9 67.9 63.2 66.181410010 Indiana St Joseph 74.7 77.0 67.8 69.9 66.5 68.181410015 Indiana St Joseph 75.0 79.0 67.1 70.7 65.7 69.181411007 Indiana St Joseph 79.3 82.0 71.0 73.4 69.5 71.181450001 Indiana Shelby 77.3 80.0 70.9 73.4 69.6 72.181630013 Indiana Vanderburgh 67.7 75.0 61.1 67.7 59.5 65.181630021 Indiana Vanderburgh 77.3 80.0 70.4 72.8 68.5 70.
B-12 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

181670018 Indiana Vigo 63.0 67.0 56.0 59.5 55.1 58.181670024 Indiana Vigo 74.0 76.0 65.5 67.2 64.6 66.181730008 Indiana Warrick 77.7 80.0 71.3 73.4 69.6 71.181730009 Indiana Warrick 72.7 75.0 65.3 67.4 63.6 65.181730011 Indiana Warrick 76.7 77.0 70.6 70.8 68.7 69.190450021 Iowa Clinton 71.3 73.0 64.1 65.6 62.6 64.190851101 Iowa Harrison 74.7 75.0 67.6 67.8 65.9 66.191130028 Iowa Linn 67.7 71.0 60.9 63.9 59.5 62.191530058 Iowa Polk 63.0 66.0 56.9 59.6 55.6 58.191630014 Iowa Scott 70.3 74.0 63.4 66.8 61.8 65.191632011 Iowa Scott 72.0 72.0 65.0 65.0 63.4 63.191690011 Iowa Story 61.0 66.0 54.9 59.4 53.7 58.200450004 Kansas Douglas 73.0 73.0 65.3 65.3 63.8 63.200910010 Kansas Johnson 75.3 76.0 67.9 68.5 66.2 66.201030003 Kansas Leavenworth 75.0 77.0 67.4 69.1 65.8 67.201070002 Kansas Linn 73.3 74.0 64.8 65.4 63.3 63.201730001 Kansas Sedgwick 64.3 66.0 57.2 58.7 55.5 57.202090021 Kansas Wyandotte 75.3 77.0 68.7 70.3 67.2 68.210130002 Kentucky Bell 71.7 74.0 62.0 64.0 60.0 61.210150003 Kentucky Boone 75.7 77.0 68.1 69.3 66.1 67.210190017 Kentucky Boyd 77.3 79.0 69.7 71.3 68.2 69.210290006 Kentucky Bullitt 74.0 76.0 67.7 69.5 66.0 67.210370003 Kentucky Campbell 83.0 83.0 76.5 76.5 74.8 74.210430500 Kentucky Carter 71.0 72.0 62.9 63.8 61.3 62.210470006 Kentucky Christian 78.0 81.0 68.6 71.2 66.5 69.210590005 Kentucky Daviess 75.7 81.0 69.9 74.8 68.0 72.
B-13 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

210610501 Kentucky Edmonson 73.7 76.0 65.2 67.3 63.3 65.210670001 Kentucky Fayette 62.7 65.0 56.6 58.7 55.1 57.210670012 Kentucky Fayette 70.3 73.0 63.5 65.9 61.8 64.210890007 Kentucky Greenup 76.7 78.0 69.0 70.2 67.5 68.210910012 Kentucky Hancock 74.0 76.0 68.7 70.5 66.7 68.210930006 Kentucky Hardin 74.7 78.0 68.0 71.0 66.2 69.211010014 Kentucky Henderson 75.3 78.0 69.1 71.6 67.4 69.211110027 Kentucky Jefferson 75.3 79.0 68.6 72.0 66.9 70.211110051 Kentucky Jefferson 78.3 82.0 72.1 75.5 70.4 73.211111021 Kentucky Jefferson 71.0 73.0 64.6 66.4 62.8 64.211130001 Kentucky Jessamine 73.3 77.0 67.0 70.4 64.9 68.211170007 Kentucky Kenton 78.7 81.0 72.2 74.3 70.5 72.211390003 Kentucky Livingston 73.7 75.0 66.9 68.1 65.1 66.211451024 Kentucky McCracken 73.3 76.0 67.4 69.9 66.0 68.211490001 Kentucky McLean 73.0 73.0 65.9 65.9 64.2 64.211850004 Kentucky Oldham 83.0 85.0 74.1 75.8 71.9 73.211990003 Kentucky Pulaski 70.3 72.0 65.1 66.7 61.9 63.212130004 Kentucky Simpson 75.7 78.0 66.7 68.8 64.6 66.212210013 Kentucky Trigg 70.0 70.0 62.6 62.6 60.6 60.212270008 Kentucky Warren 72.0 73.0 63.6 64.4 61.6 62.220050004 Louisiana Ascension 82.0 84.0 75.9 77.8 74.5 76.220110002 Louisiana Beauregard 75.0 75.0 70.4 70.4 69.4 69.220150008 Louisiana Bossier 78.0 79.0 69.8 70.7 67.3 68.220170001 Louisiana Caddo 79.0 80.0 70.4 71.3 68.0 68.220190002 Louisiana Calcasieu 82.0 83.0 76.6 77.5 75.4 76.220190008 Louisiana Calcasieu 70.0 71.0 65.2 66.1 64.1 65.
B-14 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

220190009 Louisiana Calcasieu 78.7 80.0 72.4 73.6 71.0 72.220330003 Louisiana

East Baton Rouge 92.0 96.0 85.6 89.3 84.1 87.

220330009 Louisiana

East Baton Rouge 80.3 81.0 74.8 75.4 73.4 74.

220330013 Louisiana

East Baton Rouge 81.3 82.0 75.4 76.0 73.8 74.

220331001 Louisiana

East Baton Rouge 86.0 87.0 80.5 81.4 79.0 79.

220470007 Louisiana Iberville 84.0 86.0 77.2 79.1 75.6 77.220470009 Louisiana Iberville 80.0 81.0 73.5 74.4 72.0 72.220470012 Louisiana Iberville 85.0 86.0 78.9 79.9 77.5 78.220511001 Louisiana Jefferson 83.0 84.0 76.6 77.5 74.9 75.220550005 Louisiana Lafayette 82.0 82.0 74.6 74.6 72.9 72.220570004 Louisiana Lafourche 79.3 80.0 72.9 73.6 71.6 72.220630002 Louisiana Livingston 78.3 80.0 72.3 73.8 70.8 72.220730004 Louisiana Ouachita 75.3 77.0 67.2 68.7 65.0 66.220770001 Louisiana Pointe Coupee 83.7 86.0 77.8 80.0 76.4 78.220870002 Louisiana St Bernard 78.0 78.0 71.3 71.3 69.8 69.220890003 Louisiana St Charles 77.3 78.0 71.4 72.0 69.8 70.220930002 Louisiana St James 76.3 77.0 70.7 71.3 69.3 70.220950002 Louisiana

St John The Baptis 79.0 80.0 73.1 74.0 71.8 72.

221010003 Louisiana St Mary 76.0 76.0 69.5 69.5 68.2 68.221210001 Louisiana

West Baton Rouge 84.3 85.0 78.5 79.1 77.0 77.

230050027 Maine Cumberland 59.0 59.0 52.2 52.2 50.8 50.230052003 Maine Cumberland 72.0 75.0 63.7 66.3 62.1 64.
B-15 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

230090102 Maine Hancock 82.0 83.0 72.5 73.4 70.8 71.230090103 Maine Hancock 73.0 74.0 64.6 65.5 63.0 63.230090301 Maine Hancock 68.7 70.0 60.6 61.8 59.1 60.230112005 Maine Kennebec 69.7 71.0 61.0 62.2 59.5 60.230130004 Maine Knox 75.3 77.0 66.3 67.8 64.7 66.230194008 Maine Penobscot 67.0 68.0 59.6 60.5 58.3 59.230230004 Maine Sagadahoc 68.5 70.0 60.3 61.6 58.7 60.230310038 Maine York 73.7 75.0 65.1 66.2 63.4 64.230312002 Maine York 74.0 74.0 66.0 66.0 64.3 64.230313002 Maine York 74.0 77.0 66.3 68.9 64.6 67.240030014 Maryland Anne Arundel 89.7 90.0 80.0 80.2 77.6 77.240051007 Maryland Baltimore 77.3 78.0 70.0 70.6 68.3 68.240053001 Maryland Baltimore 85.3 87.0 78.7 80.3 77.3 78.240090011 Maryland Calvert 81.0 81.0 71.3 71.3 69.4 69.240130001 Maryland Carroll 83.3 86.0 74.2 76.6 72.2 74.240150003 Maryland Cecil 90.7 93.0 79.4 81.4 77.0 79.240170010 Maryland Charles 86.0 88.0 75.1 76.8 73.0 74.240210037 Maryland Frederick 80.3 83.0 71.3 73.7 69.3 71.240230002 Maryland Garrett 75.5 76.0 69.4 69.9 68.1 68.240251001 Maryland Harford 92.7 94.0 84.4 85.6 82.5 83.240259001 Maryland Harford 88.3 91.0 78.6 81.0 76.4 78.240290002 Maryland Kent 82.0 83.0 72.0 72.9 69.9 70.240313001 Maryland Montgomery 83.0 86.0 75.3 78.0 73.4 76.240330030 Maryland Prince Georges 85.0 85.0 77.0 77.0 75.0 75.240338003 Maryland Prince Georges 91.0 91.0 80.9 80.9 78.6 78.240430009 Maryland Washington 78.3 79.0 69.9 70.5 68.2 68.
B-16 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

250010002 Massachusetts Barnstable 84.7 86.0 75.6 76.7 73.9 75.250034002 Massachusetts Berkshire 79.7 81.0 71.2 72.3 69.3 70.250051002 Massachusetts Bristol 82.7 86.0 73.5 76.4 71.9 74.250070001 Massachusetts Dukes 83.0 84.0 75.0 75.9 73.4 74.250092006 Massachusetts Essex 83.3 84.0 75.6 76.3 73.9 74.250094004 Massachusetts Essex 77.7 79.0 69.9 71.1 68.3 69.250095005 Massachusetts Essex 80.0 80.0 71.0 71.0 69.0 69.250130008 Massachusetts Hampden 87.3 92.0 76.8 80.9 74.5 78.250150103 Massachusetts Hampshire 71.7 77.0 63.3 67.9 61.4 66.250154002 Massachusetts Hampshire 85.0 87.0 75.1 76.9 73.1 74.250170009 Massachusetts Middlesex 79.0 79.0 70.5 70.5 68.6 68.250171102 Massachusetts Middlesex 77.3 81.0 69.5 72.8 67.8 71.250213003 Massachusetts Norfolk 84.7 86.0 75.3 76.5 73.5 74.250250041 Massachusetts Suffolk 80.3 81.0 72.6 73.2 71.2 71.250250042 Massachusetts Suffolk 67.3 68.0 61.3 61.9 60.1 60.250270015 Massachusetts Worcester 80.0 83.0 71.0 73.7 69.2 71.260050003 Michigan Allegan 90.0 93.0 82.4 85.1 80.9 83.260190003 Michigan Benzie 81.7 83.0 74.1 75.3 72.6 73.260210014 Michigan Berrien 82.3 84.0 75.4 76.9 73.9 75.260270003 Michigan Cass 80.7 84.0 72.0 74.9 70.2 73.260370001 Michigan Clinton 75.7 78.0 67.0 69.1 65.2 67.260490021 Michigan Genesee 77.3 80.0 69.1 71.5 67.3 69.260492001 Michigan Genesee 79.3 82.0 71.1 73.5 69.4 71.260630007 Michigan Huron 75.7 78.0 68.6 70.7 67.3 69.260650012 Michigan Ingham 76.0 78.0 67.6 69.3 65.8 67.260770008 Michigan Kalamazoo 75.3 78.0 67.8 70.2 66.2 68.
B-17 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

260810020 Michigan Kent 79.3 83.0 70.7 74.0 69.0 72.260810022 Michigan Kent 81.0 83.0 71.9 73.7 70.0 71.260890001 Michigan Leelanau 75.7 77.0 69.2 70.4 67.8 68.260910007 Michigan Lenawee 78.7 81.0 70.6 72.7 69.2 71.260990009 Michigan Macomb 86.0 90.0 78.0 81.6 76.7 80.260991003 Michigan Macomb 84.0 87.0 76.8 79.6 75.6 78.261050007 Michigan Mason 79.7 81.0 71.7 72.9 70.2 71.261130001 Michigan Missaukee 73.7 75.0 66.1 67.3 64.8 65.261210039 Michigan Muskegon 85.0 88.0 77.0 79.7 75.5 78.261250001 Michigan Oakland 78.0 81.0 71.7 74.5 70.6 73.261390005 Michigan Ottawa 81.7 85.0 73.2 76.1 71.5 74.261470005 Michigan St Clair 82.3 85.0 73.9 76.3 72.3 74.261530001 Michigan Schoolcraft 79.3 82.0 71.4 73.8 69.8 72.261610008 Michigan Washtenaw 78.3 81.0 70.8 73.3 69.4 71.261630001 Michigan Wayne 73.0 75.0 66.2 68.0 65.0 66.261630016 Michigan Wayne 73.5 76.0 66.2 68.5 64.9 67.261630019 Michigan Wayne 82.0 83.0 75.5 76.4 74.2 75.270031002 Minnesota Anoka 67.7 69.0 65.4 66.7 64.8 66.280330002 Mississippi De Soto 82.7 85.0 73.3 75.3 70.8 72.280450001 Mississippi Hancock 79.0 79.0 71.3 71.3 69.7 69.280470008 Mississippi Harrison 83.0 83.0 74.0 74.0 72.0 72.280470009 Mississippi Harrison 77.0 77.0 68.5 68.5 67.0 67.280490010 Mississippi Hinds 71.3 73.0 62.6 64.1 59.9 61.280590006 Mississippi Jackson 80.3 81.0 72.4 73.1 70.8 71.280590007 Mississippi Jackson 74.0 74.0 66.4 66.4 65.2 65.280750003 Mississippi Lauderdale 74.3 76.0 66.5 68.0 65.6 67.
B-18 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

280810005 Mississippi Lee 73.7 75.0 64.6 65.8 62.2 63.290370003 Missouri Cass 74.7 77.0 66.4 68.4 64.8 66.290470003 Missouri Clay 79.0 81.0 70.4 72.2 68.6 70.290470005 Missouri Clay 84.7 87.0 76.2 78.2 74.3 76.290470006 Missouri Clay 83.0 87.0 74.2 77.8 72.3 75.290490001 Missouri Clinton 83.0 85.0 74.3 76.1 72.4 74.290770036 Missouri Greene 73.0 77.0 64.9 68.4 63.4 66.290990012 Missouri Jefferson 82.3 86.0 76.0 79.4 74.9 78.291130003 Missouri Lincoln 87.0 87.0 78.9 78.9 76.8 76.291370001 Missouri Monroe 71.7 75.0 64.4 67.4 63.0 66.291570001 Missouri Perry 77.5 80.0 69.8 72.0 68.4 70.291650023 Missouri Platte 77.0 77.0 69.3 69.3 67.5 67.291831002 Missouri St Charles 86.3 89.0 78.3 80.8 76.3 78.291831004 Missouri St Charles 87.0 89.0 79.2 81.0 77.1 78.291860005 Missouri Ste Genevieve 79.7 83.0 72.3 75.3 71.8 74.291890004 Missouri St Louis 82.3 86.0 76.6 80.1 75.3 78.291890005 Missouri St Louis 83.0 83.0 76.1 76.1 74.4 74.291890006 Missouri St Louis 78.0 78.0 72.1 72.1 70.5 70.291890014 Missouri St Louis 88.0 88.0 81.4 81.4 79.6 79.295100085 Missouri St Louis City 84.0 84.0 77.8 77.8 76.1 76.295100086 Missouri St Louis City 83.0 86.0 76.9 79.7 75.2 77.310550028 Nebraska Douglas 68.7 70.0 63.3 64.5 62.1 63.310550032 Nebraska Douglas 64.7 66.0 59.3 60.5 58.2 59.310550035 Nebraska Douglas 66.3 68.0 61.1 62.6 59.8 61.330012004

New Hampshire Belknap 71.3 73.0 63.3 64.9 61.5 62.

330050007 New Cheshire 70.7 71.0 63.1 63.4 61.5 61.
B-19 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

Hampshire

330074001

New Hampshire Coos 77.0 77.0 69.0 69.0 67.3 67.

330074002

New Hampshire Coos 65.0 67.0 58.3 60.0 56.8 58.

330090010

New Hampshire Grafton 67.0 67.0 59.1 59.1 57.5 57.

330110020

New Hampshire Hillsborough 70.3 71.0 61.9 62.5 60.1 60.

330111011

New Hampshire Hillsborough 78.7 80.0 69.8 71.0 67.8 68.

330115001

New Hampshire Hillsborough 78.3 80.0 69.8 71.3 68.0 69.

330131007

New Hampshire Merrimack 71.7 72.0 63.1 63.3 61.3 61.

330150014

New Hampshire Rockingham 75.3 77.0 67.4 68.9 65.8 67.

330150016

New Hampshire Rockingham 77.0 79.0 68.9 70.7 67.2 69.

330190003

New Hampshire Sullivan 70.0 72.0 61.7 63.5 60.0 61.

340010005 New Jersey Atlantic 79.3 82.0 71.1 73.5 69.3 71.340030005 New Jersey Bergen 86.0 86.0 79.4 79.4 77.8 77.340070003 New Jersey Camden 85.7 88.0 76.7 78.8 74.8 76.340071001 New Jersey Camden 89.3 91.0 79.9 81.4 77.8 79.340110007 New Jersey Cumberland 83.3 84.0 73.4 74.0 71.2 71.340150002 New Jersey Gloucester 87.0 88.0 77.5 78.4 75.4 76.340170006 New Jersey Hudson 85.7 89.0 79.8 82.9 78.9 81.340190001 New Jersey Hunterdon 89.0 90.0 79.7 80.6 77.4 78.340210005 New Jersey Mercer 88.0 91.0 79.7 82.4 77.7 80.
B-20 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

340230011 New Jersey Middlesex 88.3 91.0 79.2 81.6 77.1 79.340250005 New Jersey Monmouth 87.3 89.0 79.7 81.2 78.1 79.340273001 New Jersey Morris 83.3 86.0 74.7 77.1 72.7 75.340290006 New Jersey Ocean 93.0 94.0 83.1 84.0 80.9 81.340315001 New Jersey Passaic 81.0 83.0 73.4 75.2 71.6 73.360010012 New York Albany 73.7 76.0 66.0 68.1 64.4 66.360050110 New York Bronx 74.0 76.0 68.1 69.9 67.1 68.360050133 New York Bronx 74.7 75.0 70.0 70.3 69.1 69.360130006 New York Chautauqua 86.7 89.0 79.2 81.3 77.7 79.360130011 New York Chautauqua 77.3 79.0 71.0 72.5 69.6 71.360270007 New York Dutchess 75.7 79.0 67.6 70.5 65.9 68.360290002 New York Erie 85.0 86.0 77.4 78.3 76.1 77.360410005 New York Hamilton 71.7 73.0 64.9 66.1 63.7 64.360430005 New York Herkimer 68.3 70.0 62.4 64.0 61.2 62.360450002 New York Jefferson 78.0 80.0 71.2 73.0 69.8 71.360551007 New York Monroe 76.3 80.0 69.6 73.0 68.2 71.360631006 New York Niagara 82.7 86.0 77.3 80.4 76.2 79.360650004 New York Oneida 68.3 72.0 62.0 65.4 60.7 64.360671015 New York Onondaga 73.7 76.0 66.0 68.1 64.5 66.360715001 New York Orange 82.0 84.0 74.4 76.2 72.7 74.360750003 New York Oswego 78.0 82.0 71.1 74.7 69.7 73.360790005 New York Putnam 84.3 86.0 76.2 77.8 74.4 75.360810098 New York Queens 69.0 69.0 63.5 63.5 62.6 62.360810124 New York Queens 80.0 82.0 73.7 75.5 72.4 74.360830004 New York Rensselaer 77.3 80.0 69.2 71.6 67.6 69.360850067 New York Richmond 88.3 89.0 82.5 83.2 81.4 82.
B-21 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

360910004 New York Saratoga 79.7 82.0 71.2 73.2 69.4 71.360930003 New York Schenectady 70.0 74.0 62.9 66.5 61.5 65.361030002 New York Suffolk 90.0 91.0 83.0 83.9 81.8 82.361030004 New York Suffolk 84.7 90.0 77.9 82.7 76.3 81.361030009 New York Suffolk 90.3 91.0 83.2 83.8 81.7 82.361111005 New York Ulster 77.3 79.0 69.0 70.5 67.2 68.361173001 New York Wayne 68.0 71.0 61.7 64.5 60.5 63.361192004 New York Westchester 87.7 90.0 81.1 83.2 79.7 81.370030004

North Carolina Alexander 77.0 79.0 66.9 68.6 64.9 66.

370110002

North Carolina Avery 70.0 72.0 61.1 62.9 58.6 60.

370210030

North Carolina Buncombe 74.0 74.0 62.7 62.7 60.8 60.

370270003

North Carolina Caldwell 74.3 76.0 64.4 65.9 62.3 63.

370330001

North Carolina Caswell 76.3 77.0 66.5 67.2 64.2 64.

370370004

North Carolina Chatham 73.3 74.0 63.7 64.3 61.8 62.

370510008

North Carolina Cumberland 78.0 80.0 67.7 69.4 65.4 67.

370511003

North Carolina Cumberland 81.7 83.0 70.7 71.8 68.4 69.

370590002

North Carolina Davie 81.3 83.0 70.9 72.4 68.8 70.

370630013

North Carolina Durham 77.0 78.0 67.4 68.3 65.3 66.

370650099

North Carolina Edgecombe 77.0 79.0 67.6 69.4 65.5 67.
B-22 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

370670022

North Carolina Forsyth 78.0 79.0 68.4 69.2 66.3 67.

370670028

North Carolina Forsyth 73.0 74.0 62.6 63.5 60.9 61.

370670030

North Carolina Forsyth 76.0 76.0 66.6 66.6 64.4 64.

370671008

North Carolina Forsyth 80.0 81.0 69.6 70.5 67.5 68.

370690001

North Carolina Franklin 78.7 81.0 69.0 71.0 66.7 68.

370750001

North Carolina Graham 78.3 79.0 67.2 67.8 64.8 65.

370770001

North Carolina Granville 82.0 85.0 70.9 73.5 68.8 71.

370810011

North Carolina Guilford 77.0 77.0 67.4 67.4 64.9 64.

370810013

North Carolina Guilford 82.0 82.0 71.4 71.4 68.9 68.

370870036

North Carolina Haywood 77.3 78.0 67.7 68.4 65.4 66.

370990005

North Carolina Jackson 76.0 76.0 66.1 66.1 63.7 63.

371010002

North Carolina Johnston 77.3 79.0 67.5 69.0 65.2 66.

371070004

North Carolina Lenoir 75.3 77.0 66.0 67.5 63.3 64.

371090004

North Carolina Lincoln 81.0 83.0 70.7 72.4 68.6 70.

371170001

North Carolina Martin 75.0 77.0 67.2 69.0 65.5 67.

371190041

North Carolina Mecklenburg 88.0 90.0 77.7 79.4 76.0 77.
B-23 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

371191005

North Carolina Mecklenburg 80.3 83.0 70.8 73.2 69.3 71.

371191009

North Carolina Mecklenburg 89.3 93.0 78.0 81.3 75.8 78.

371290002

North Carolina New Hanover 72.3 73.0 65.4 66.0 63.0 63.

371450003

North Carolina Person 77.3 79.0 64.8 66.2 64.2 65.

371470099

North Carolina Pitt 76.3 77.0 66.5 67.1 63.8 64.

371570099

North Carolina Rockingham 77.0 78.0 65.6 66.4 63.5 64.

371590021

North Carolina Rowan 86.7 89.0 75.3 77.3 72.9 74.

371590022

North Carolina Rowan 86.7 90.0 75.6 78.4 73.2 76.

371730002

North Carolina Swain 66.3 68.0 57.4 58.9 55.3 56.

371790003

North Carolina Union 79.3 81.0 68.2 69.7 65.8 67.

371830014

North Carolina Wake 80.3 82.0 71.0 72.5 68.8 70.

371830016

North Carolina Wake 80.0 83.0 70.1 72.7 67.7 70.

390030009 Ohio Allen 78.7 81.0 70.2 72.3 68.4 70.390071001 Ohio Ashtabula 89.0 91.0 81.4 83.2 79.7 81.390170004 Ohio Butler 83.3 85.0 75.3 76.9 73.2 74.390171004 Ohio Butler 82.3 85.0 74.2 76.7 72.1 74.390230001 Ohio Clark 81.0 83.0 71.9 73.7 69.7 71.390230003 Ohio Clark 77.0 78.0 69.1 70.0 67.2 68.
B-24 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

390250022 Ohio Clermont 81.0 83.0 74.4 76.3 72.7 74.390271002 Ohio Clinton 82.3 85.0 73.2 75.7 70.8 73.390350034 Ohio Cuyahoga 71.0 76.0 64.7 69.3 63.4 67.390350064 Ohio Cuyahoga 73.0 76.0 67.8 70.6 66.8 69.390355002 Ohio Cuyahoga 79.7 81.0 72.9 74.1 71.5 72.390410002 Ohio Delaware 78.3 81.0 69.7 72.1 67.8 70.390490028 Ohio Franklin 80.3 82.0 71.9 73.4 70.0 71.390490029 Ohio Franklin 86.3 88.0 77.2 78.7 75.1 76.390490037 Ohio Franklin 80.3 81.0 72.6 73.3 70.9 71.390490081 Ohio Franklin 79.7 80.0 71.4 71.7 69.6 69.390550004 Ohio Geauga 79.3 86.0 71.0 77.0 69.4 75.390570006 Ohio Greene 80.3 83.0 71.5 73.9 69.3 71.390610006 Ohio Hamilton 84.7 86.0 77.4 78.6 75.5 76.390610010 Ohio Hamilton 82.0 84.0 75.1 76.9 73.2 75.390610040 Ohio Hamilton 81.7 83.0 75.9 77.1 74.3 75.390810017 Ohio Jefferson 78.0 80.0 69.1 70.9 67.6 69.390830002 Ohio Knox 77.7 79.0 68.9 70.1 67.0 68.390850003 Ohio Lake 86.3 89.0 78.7 81.1 77.1 79.390850007 Ohio Lake 80.7 81.0 73.5 73.8 71.9 72.390870006 Ohio Lawrence 70.7 73.0 63.6 65.7 62.2 64.390870011 Ohio Lawrence 63.3 69.0 55.2 60.2 53.8 58.390890005 Ohio Licking 78.0 81.0 69.2 71.8 67.2 69.390930018 Ohio Lorain 76.7 80.0 69.4 72.4 67.9 70.390950024 Ohio Lucas 76.3 78.0 69.9 71.5 68.5 70.390950027 Ohio Lucas 77.7 82.0 71.0 75.0 69.5 73.390950034 Ohio Lucas 81.3 86.0 73.8 78.1 72.2 76.
B-25 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

390950081 Ohio Lucas 80.7 84.0 73.9 77.0 72.4 75.390970007 Ohio Madison 79.7 82.0 70.5 72.5 68.4 70.390990013 Ohio Mahoning 78.7 80.0 70.2 71.4 68.6 69.391030003 Ohio Medina 80.3 84.0 71.9 75.2 70.2 73.391090005 Ohio Miami 76.7 80.0 68.0 70.9 65.8 68.391130037 Ohio Montgomery 74.0 75.0 65.6 66.5 63.6 64.391331001 Ohio Portage 83.7 88.0 74.7 78.5 72.9 76.391351001 Ohio Preble 73.0 75.0 64.9 66.7 63.1 64.391510016 Ohio Stark 78.0 79.0 70.0 70.9 68.3 69.391510022 Ohio Stark 77.3 80.0 69.3 71.7 67.7 70.391514005 Ohio Stark 81.0 82.0 72.0 72.9 70.3 71.391530020 Ohio Summit 83.7 85.0 75.2 76.4 73.5 74.391550009 Ohio Trumbull 80.0 83.0 71.3 73.9 69.5 72.391550011 Ohio Trumbull 84.3 86.0 75.1 76.6 73.3 74.391650007 Ohio Warren 88.3 91.0 79.3 81.7 76.9 79.391670004 Ohio Washington 82.7 85.0 75.3 77.4 73.2 75.391730003 Ohio Wood 80.0 83.0 72.2 74.9 70.5 73.400170101 Oklahoma Canadian 76.0 76.0 68.7 68.7 66.8 66.400219002 Oklahoma Cherokee 75.7 76.0 69.0 69.2 67.9 68.400270049 Oklahoma Cleveland 74.7 75.0 67.0 67.2 65.1 65.400370144 Oklahoma Creek 76.7 77.0 68.1 68.4 66.5 66.400719010 Oklahoma Kay 78.0 79.0 70.0 70.9 68.1 68.400871073 Oklahoma Mc Clain 72.0 72.0 64.5 64.5 62.6 62.400979014 Oklahoma Mayes 78.5 79.0 71.6 72.1 70.6 71.401090033 Oklahoma Oklahoma 77.0 77.0 69.8 69.8 68.0 68.401090096 Oklahoma Oklahoma 76.3 78.0 69.1 70.6 67.4 68.
B-26 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

401091037 Oklahoma Oklahoma 80.0 81.0 71.5 72.4 69.4 70.401159004 Oklahoma Ottawa 78.0 79.0 69.4 70.3 67.9 68.401430137 Oklahoma Tulsa 79.3 80.0 70.9 71.6 69.1 69.401430174 Oklahoma Tulsa 74.0 76.0 66.6 68.4 65.1 66.401430178 Oklahoma Tulsa 79.3 80.0 71.9 72.5 70.3 70.401431127 Oklahoma Tulsa 77.0 78.0 69.0 69.9 67.2 68.420010002 Pennsylvania Adams 76.3 78.0 67.8 69.4 65.9 67.420030008 Pennsylvania Allegheny 79.3 81.0 72.5 74.1 71.2 72.420030010 Pennsylvania Allegheny 82.3 84.0 75.3 76.8 73.9 75.420030067 Pennsylvania Allegheny 80.3 82.0 72.7 74.2 71.2 72.420031005 Pennsylvania Allegheny 83.7 87.0 76.2 79.2 74.5 77.420050001 Pennsylvania Armstrong 83.0 84.0 74.5 75.4 72.7 73.420070002 Pennsylvania Beaver 83.0 84.0 74.3 75.2 72.7 73.420070005 Pennsylvania Beaver 79.3 81.0 71.7 73.3 70.2 71.420070014 Pennsylvania Beaver 74.0 75.0 67.0 67.9 65.5 66.420110009 Pennsylvania Berks 80.0 80.0 71.9 71.9 69.8 69.420130801 Pennsylvania Blair 74.3 77.0 68.0 70.4 66.6 69.420170012 Pennsylvania Bucks 88.0 92.0 80.2 83.9 78.2 81.420210011 Pennsylvania Cambria 74.7 77.0 68.1 70.2 66.8 68.420270100 Pennsylvania Centre 78.3 79.0 70.9 71.6 69.3 69.420290100 Pennsylvania Chester 86.0 87.0 75.6 76.5 73.4 74.420334000 Pennsylvania Clearfield 78.3 82.0 69.9 73.2 68.3 71.420430401 Pennsylvania Dauphin 79.3 81.0 71.9 73.4 70.2 71.420431100 Pennsylvania Dauphin 79.3 81.0 72.3 73.8 70.6 72.420450002 Pennsylvania Delaware 83.3 85.0 74.5 76.0 72.5 73.420490003 Pennsylvania Erie 81.3 83.0 74.3 75.9 72.8 74.
B-27 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

420550001 Pennsylvania Franklin 72.3 75.0 64.4 66.8 62.6 64.420590002 Pennsylvania Greene 80.0 81.0 73.9 74.8 72.5 73.420630004 Pennsylvania Indiana 80.0 80.0 72.8 72.8 71.3 71.420690101 Pennsylvania Lackawanna 74.3 75.0 66.2 66.9 64.5 65.420692006 Pennsylvania Lackawanna 75.3 76.0 67.1 67.8 65.5 66.420710007 Pennsylvania Lancaster 83.3 84.0 74.8 75.5 72.9 73.420730015 Pennsylvania Lawrence 72.3 73.0 64.7 65.3 63.2 63.420770004 Pennsylvania Lehigh 83.3 85.0 75.0 76.5 73.0 74.420791100 Pennsylvania Luzerne 69.3 73.0 61.8 65.1 60.3 63.420791101 Pennsylvania Luzerne 76.3 77.0 67.9 68.5 66.1 66.420810100 Pennsylvania Lycoming 77.3 79.0 69.7 71.3 68.3 69.420850100 Pennsylvania Mercer 82.0 83.0 73.1 74.0 71.4 72.420910013 Pennsylvania Montgomery 85.7 86.0 77.4 77.6 75.4 75.420950025 Pennsylvania Northampton 84.3 87.0 75.9 78.4 73.9 76.420958000 Pennsylvania Northampton 80.0 82.0 71.7 73.5 69.8 71.420990301 Pennsylvania Perry 77.0 78.0 69.2 70.1 67.4 68.421010004 Pennsylvania Philadelphia 64.3 68.0 58.8 62.1 57.4 60.421010014 Pennsylvania Philadelphia 79.7 81.0 72.6 73.7 70.9 72.421010024 Pennsylvania Philadelphia 90.3 91.0 81.8 82.4 79.8 80.421010136 Pennsylvania Philadelphia 77.0 77.0 69.4 69.4 67.6 67.421174000 Pennsylvania Tioga 77.7 81.0 69.9 72.9 68.4 71.421250005 Pennsylvania Washington 78.3 80.0 71.9 73.5 70.6 72.421250200 Pennsylvania Washington 77.3 81.0 70.5 73.9 69.1 72.421255001 Pennsylvania Washington 78.0 78.0 69.7 69.7 68.2 68.421290006 Pennsylvania Westmoreland 78.3 80.0 71.3 72.9 69.9 71.421290008 Pennsylvania Westmoreland 79.0 82.0 71.9 74.6 70.4 73.
B-28 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

421330008 Pennsylvania York 82.0 83.0 73.8 74.7 72.1 73.440030002 Rhode Island Kent 84.3 86.0 74.9 76.4 73.1 74.440071010 Rhode Island Providence 82.3 84.0 73.5 75.0 71.7 73.440090007 Rhode Island Washington 86.0 89.0 77.2 79.9 75.4 78.450010001

South Carolina Abbeville 79.0 81.0 68.5 70.2 66.2 67.

450030003

South Carolina Aiken 76.0 77.0 66.1 67.0 63.4 64.

450070003

South Carolina Anderson 76.5 78.0 65.7 67.0 63.2 64.

450110001

South Carolina Barnwell 73.0 73.0 64.0 64.0 61.7 61.

450150002

South Carolina Berkeley 67.3 70.0 60.0 62.4 58.5 60.

450190046

South Carolina Charleston 74.0 75.0 67.2 68.1 65.4 66.

450210002

South Carolina Cherokee 74.0 75.0 64.1 64.9 61.8 62.

450230002

South Carolina Chester 75.7 76.0 66.3 66.5 64.4 64.

450250001

South Carolina Chesterfield 75.0 75.0 65.7 65.7 63.8 63.

450290002

South Carolina Colleton 72.3 74.0 63.5 65.0 61.1 62.

450310003

South Carolina Darlington 76.3 77.0 66.4 67.0 64.3 64.

450370001

South Carolina Edgefield 70.0 70.0 61.0 61.0 58.4 58.

450730001

South Carolina Oconee 73.0 76.0 62.9 65.5 60.6 63.

450770002 South Pickens 78.7 81.0 66.9 68.9 64.2 66.
B-29 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

Carolina

450790007

South Carolina Richland 80.3 82.0 68.9 70.4 65.8 67.

450790021

South Carolina Richland 72.7 73.0 62.2 62.4 59.4 59.

450791001

South Carolina Richland 82.3 83.0 70.6 71.2 67.4 68.

450830009

South Carolina Spartanburg 82.3 83.0 71.2 71.8 68.3 68.

450870001

South Carolina Union 76.0 77.0 66.7 67.6 64.6 65.

450890001

South Carolina Williamsburg 69.3 70.0 61.3 61.9 59.6 60.

450910006

South Carolina York 76.7 79.0 66.9 68.9 64.9 66.

470010101 Tennessee Anderson 77.3 80.0 65.8 68.1 63.6 65.470090101 Tennessee Blount 85.3 86.0 74.2 74.8 71.5 72.470090102 Tennessee Blount 68.5 70.0 59.8 61.1 57.6 58.470370011 Tennessee Davidson 68.7 72.0 60.4 63.3 58.0 60.470370026 Tennessee Davidson 77.7 79.0 67.6 68.8 64.8 65.470651011 Tennessee Hamilton 80.0 83.0 71.1 73.7 68.3 70.470654003 Tennessee Hamilton 81.0 84.0 71.7 74.4 68.8 71.470890002 Tennessee Jefferson 82.3 84.0 72.3 73.8 68.6 70.470930021 Tennessee Knox 78.7 81.0 68.9 70.9 66.2 68.470931020 Tennessee Knox 85.0 88.0 74.4 77.0 71.6 74.471050109 Tennessee Loudon 85.0 87.0 71.3 73.0 69.0 70.471210104 Tennessee Meigs 80.0 81.0 70.3 71.2 67.5 68.471490101 Tennessee Rutherford 76.3 80.0 66.4 69.6 63.6 66.471550101 Tennessee Sevier 79.0 82.0 68.6 71.2 65.9 68.
B-30 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

471550102 Tennessee Sevier 80.7 83.0 70.5 72.5 67.9 69.471570021 Tennessee Shelby 80.0 82.0 70.9 72.6 68.4 70.471571004 Tennessee Shelby 80.7 81.0 71.2 71.4 68.6 68.471632002 Tennessee Sullivan 80.3 83.0 73.5 76.0 71.5 73.471632003 Tennessee Sullivan 80.0 83.0 73.4 76.2 71.5 74.471650007 Tennessee Sumner 83.0 84.0 72.7 73.5 69.8 70.471650101 Tennessee Sumner 79.0 82.0 69.4 72.0 66.7 69.471870106 Tennessee Williamson 75.3 77.0 65.2 66.7 62.6 64.471890103 Tennessee Wilson 78.7 82.0 68.7 71.6 65.9 68.480290032 Texas Bexar 82.0 85.0 74.8 77.5 72.8 75.480391004 Texas Brazoria 94.7 97.0 86.7 88.8 84.7 86.480391016 Texas Brazoria 78.0 79.0 71.9 72.8 70.3 71.480850005 Texas Collin 90.3 92.0 79.9 81.4 76.6 78.481130069 Texas Dallas 87.0 90.0 80.7 83.4 78.0 80.481130075 Texas Dallas 88.3 90.0 80.4 81.9 77.4 78.481130087 Texas Dallas 87.0 88.0 81.3 82.3 78.8 79.481133003 Texas Dallas 84.0 84.0 76.7 76.7 74.1 74.481210034 Texas Denton 94.0 95.0 81.7 82.5 77.9 78.481390015 Texas Ellis 81.7 84.0 71.7 73.7 68.2 70.481670014 Texas Galveston 85.0 87.0 79.0 80.8 77.5 79.481830001 Texas Gregg 84.3 85.0 78.0 78.6 75.9 76.482010024 Texas Harris 88.0 92.0 83.4 87.2 82.2 86.482010026 Texas Harris 85.7 89.0 79.4 82.4 77.8 80.482010029 Texas Harris 91.7 93.0 84.2 85.4 82.4 83.482010046 Texas Harris 78.7 82.0 74.5 77.7 73.5 76.482010047 Texas Harris 78.7 80.0 74.1 75.4 72.9 74.
B-31 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

482010051 Texas Harris 93.0 98.0 86.1 90.8 84.3 88.482010055 Texas Harris 100.7 103.0 93.3 95.4 91.3 93.482010062 Texas Harris 95.7 99.0 88.8 91.8 87.0 90.482010066 Texas Harris 92.3 96.0 87.1 90.6 85.5 88.482010070 Texas Harris 84.3 88.0 78.8 82.2 77.3 80.482010075 Texas Harris 83.3 88.0 77.8 82.2 76.4 80.482011015 Texas Harris 89.0 96.0 82.4 88.9 80.8 87.482011034 Texas Harris 82.7 87.0 76.6 80.6 75.1 79.482011035 Texas Harris 86.3 95.0 79.9 88.0 78.4 86.482011039 Texas Harris 96.3 100.0 88.8 92.2 86.8 90.482011050 Texas Harris 89.3 92.0 82.8 85.4 81.2 83.482030002 Texas Harrison 79.0 80.0 71.6 72.5 69.3 70.482210001 Texas Hood 83.0 84.0 71.0 71.8 67.6 68.482450009 Texas Jefferson 81.7 82.0 74.7 75.0 73.2 73.482450011 Texas Jefferson 80.0 81.0 73.8 74.7 72.3 73.482450018 Texas Jefferson 83.3 84.0 76.8 77.5 75.4 76.482450022 Texas Jefferson 79.7 81.0 72.7 73.8 71.0 72.482450101 Texas Jefferson 84.7 88.0 78.4 81.4 76.8 79.482450628 Texas Jefferson 78.0 78.0 71.9 71.9 70.5 70.482510003 Texas Johnson 87.0 89.0 75.2 76.9 71.4 73.483390078 Texas Montgomery 85.0 86.0 78.0 78.9 76.0 76.483611001 Texas Orange 78.0 80.0 71.4 73.2 69.8 71.483611100 Texas Orange 73.0 74.0 67.0 67.9 65.7 66.483670081 Texas Parker 88.7 91.0 76.1 78.1 72.5 74.484230007 Texas Smith 81.0 82.0 74.0 74.9 71.9 72.484390075 Texas Tarrant 95.3 96.0 82.4 83.0 78.5 79.
B-32 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

484391002 Texas Tarrant 93.3 95.0 82.2 83.7 78.5 79.484392003 Texas Tarrant 93.7 95.0 81.8 83.0 78.1 79.484393009 Texas Tarrant 92.7 93.0 81.3 81.6 77.8 78.484393011 Texas Tarrant 86.0 87.0 77.1 78.0 73.9 74.484530014 Texas Travis 81.3 82.0 72.6 73.2 70.5 71.484530020 Texas Travis 79.3 81.0 71.2 72.8 69.4 70.484530613 Texas Travis 76.0 76.0 68.7 68.7 66.8 66.500030004 Vermont Bennington 72.0 73.0 64.3 65.2 62.7 63.510130020 Virginia Arlington 86.7 87.0 79.1 79.3 77.1 77.510330001 Virginia Caroline 80.0 81.0 71.0 71.9 68.9 69.510360002 Virginia Charles City 80.3 82.0 72.3 73.9 70.5 71.510410004 Virginia Chesterfield 76.7 77.0 68.6 68.9 66.9 67.510590005 Virginia Fairfax 78.3 79.0 70.5 71.2 68.5 69.510590018 Virginia Fairfax 90.0 91.0 82.4 83.3 80.4 81.510590030 Virginia Fairfax 88.0 89.0 80.2 81.1 78.2 79.510591005 Virginia Fairfax 85.7 87.0 78.1 79.3 76.1 77.510595001 Virginia Fairfax 82.0 84.0 74.3 76.1 72.2 74.510610002 Virginia Fauquier 72.7 73.0 64.8 65.1 63.2 63.510690010 Virginia Frederick 72.3 73.0 63.8 64.4 62.4 63.510850003 Virginia Hanover 81.3 82.0 72.6 73.3 70.5 71.510870014 Virginia Henrico 82.0 85.0 73.4 76.1 71.3 73.511071005 Virginia Loudoun 80.7 82.0 72.1 73.3 69.9 71.511130003 Virginia Madison 77.7 80.0 69.7 71.7 68.0 70.511390004 Virginia Page 74.0 76.0 66.3 68.1 64.7 66.511530009 Virginia Prince William 78.7 79.0 70.2 70.4 68.2 68.511611004 Virginia Roanoke 74.7 76.0 67.8 68.9 65.9 67.
B-33 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

511630003 Virginia Rockbridge 69.7 71.0 62.2 63.4 60.5 61.511790001 Virginia Stafford 81.7 85.0 74.0 77.0 72.0 74.515100009 Virginia Alexandria City 81.7 83.0 74.8 76.0 73.0 74.516500004 Virginia Hampton City 76.7 78.0 70.7 71.9 69.6 70.518000004 Virginia Suffolk City 76.7 78.0 71.4 72.6 70.6 71.518000005 Virginia Suffolk City 75.3 77.0 66.9 68.4 65.1 66.540030003 West Virginia Berkeley 75.0 76.0 66.9 67.8 65.3 66.540110006 West Virginia Cabell 78.7 84.0 70.9 75.7 69.3 74.540291004 West Virginia Hancock 75.7 77.0 67.5 68.6 66.1 67.540390010 West Virginia Kanawha 77.3 79.0 68.1 69.6 66.4 67.540610003 West Virginia Monongalia 75.3 78.0 72.0 74.6 70.8 73.540690010 West Virginia Ohio 78.3 82.0 69.1 72.4 67.6 70.541071002 West Virginia Wood 79.0 82.0 71.1 73.8 68.4 71.550090026 Wisconsin Brown 73.7 75.0 66.9 68.0 65.5 66.550210015 Wisconsin Columbia 72.7 74.0 65.1 66.3 63.5 64.550250041 Wisconsin Dane 72.0 74.0 64.8 66.6 63.4 65.550270007 Wisconsin Dodge 74.7 77.0 67.6 69.7 66.0 68.550290004 Wisconsin Door 88.7 90.0 80.3 81.4 78.5 79.550390006 Wisconsin Fond Du Lac 73.7 75.0 68.1 69.3 66.8 68.550550002 Wisconsin Jefferson 74.3 76.0 66.7 68.2 65.0 66.550590019 Wisconsin Kenosha 84.7 86.0 79.8 81.1 78.8 80.550610002 Wisconsin Kewaunee 82.7 86.0 75.3 78.3 73.7 76.550710007 Wisconsin Manitowoc 85.0 87.0 78.0 79.8 76.4 78.550790010 Wisconsin Milwaukee 69.0 70.0 65.0 65.9 64.1 65.550790026 Wisconsin Milwaukee 76.0 77.0 71.6 72.5 70.6 71.550790041 Wisconsin Milwaukee 81.3 83.0 76.6 78.2 75.6 77.
B-34 

8-Hour Ozone (ppb) 

Monitor ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

550790044 Wisconsin Milwaukee 71.0 71.0 66.6 66.6 65.8 65.550790085 Wisconsin Milwaukee 82.7 86.0 77.7 80.8 76.6 79.550870009 Wisconsin Outagamie 74.0 75.0 67.1 68.0 65.8 66.550890008 Wisconsin Ozaukee 81.3 85.0 76.1 79.6 75.1 78.550890009 Wisconsin Ozaukee 83.3 88.0 77.9 82.3 76.7 81.551010017 Wisconsin Racine 80.3 82.0 75.6 77.2 74.7 76.551050024 Wisconsin Rock 74.0 76.0 66.4 68.2 64.8 66.551091002 Wisconsin St Croix 69.0 71.0 61.4 63.2 60.2 62.551110007 Wisconsin Sauk 69.7 71.0 62.5 63.6 61.0 62.551170006 Wisconsin Sheboygan 88.0 89.0 81.6 82.5 80.1 81.551270005 Wisconsin Walworth 75.7 77.0 68.8 70.0 67.1 68.551310009 Wisconsin Washington 72.3 75.0 65.7 68.1 64.3 66.551330017 Wisconsin Waukesha 75.0 75.0 68.7 68.7 67.3 67.551330027 Wisconsin Waukesha 71.5 72.0 65.5 65.9 64.2 64.
B-35 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

010030010 Alabama Baldwin 11.44 11.75 9.87 10.15 9.58 9.85 010270001 Alabama Clay 13.27 13.32 11.77 11.82 11.25 11.3010331002 Alabama Colbert 12.75 13.11 11.65 11.97 11.19 11.50 010491003 Alabama DeKalb 14.13 14.42 12.44 12.70 11.93 12.19 010530002 Alabama Escambia 13.19 13.37 11.71 11.88 11.42 11.59 010550010 Alabama Etowah 14.87 15.22 13.10 13.40 12.59 12.88 010690003 Alabama Houston 13.22 13.22 11.69 11.69 11.44 11.44 010730023 Alabama Jefferson 18.57 18.94 16.15 16.46 15.58 15.89 010731005 Alabama Jefferson 15.46 15.89 13.81 14.20 13.33 13.7010731009 Alabama Jefferson 13.52 14.05 12.21 12.70 11.78 12.25 010731010 Alabama Jefferson 15.89 16.19 13.83 14.10 13.29 13.55 010732003 Alabama Jefferson 17.15 17.69 15.16 15.64 14.69 15.16 010732006 Alabama Jefferson 15.10 15.42 13.21 13.49 12.75 13.0010735002 Alabama Jefferson 14.42 14.85 12.48 12.85 11.98 12.34 010735003 Alabama Jefferson 14.53 14.88 12.86 13.17 12.37 12.68 010890014 Alabama Madison 13.83 13.97 12.34 12.46 11.80 11.9010970002 Alabama Mobile 12.90 12.90 11.41 11.41 11.03 11.03 010970003 Alabama Mobile 12.36 12.49 10.88 10.99 10.51 10.6010972005 Alabama Mobile 11.51 11.92 9.96 10.31 9.63 9.97 011010007 Alabama Montgomery 14.24 14.41 12.79 12.95 12.35 12.5011030011 Alabama Morgan 13.32 13.74 11.95 12.31 11.46 11.8011130001 Alabama Russell 15.73 15.83 13.91 13.99 13.36 13.44 011170006 Alabama Shelby 14.43 14.61 12.75 12.91 12.26 12.42 011190002 Alabama Sumter 11.92 11.92 10.71 10.71 10.31 10.3
B-36 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

011210002 Alabama Talladega 14.51 14.51 12.97 12.97 12.45 12.45 011250004 Alabama Tuscaloosa 13.56 13.79 12.19 12.39 11.73 11.93 011270002 Alabama Walker 13.86 14.30 12.46 12.86 11.97 12.36 050010011 Arkansas Arkansas 12.45 12.54 11.54 11.62 11.11 11.19 050030005 Arkansas Ashley 12.83 13.30 11.92 12.35 11.56 11.98 050350005 Arkansas Crittenden 13.36 13.36 12.15 12.15 11.58 11.58 050450002 Arkansas Faulkner 12.79 13.10 11.87 12.16 11.44 11.72 050510003 Arkansas Garland 12.40 12.67 11.50 11.74 11.07 11.3050930007 Arkansas Mississippi 12.61 12.61 11.60 11.60 11.08 11.08 051070001 Arkansas Phillips 12.10 12.40 11.13 11.40 10.67 10.92 051130002 Arkansas Polk 11.65 12.04 10.79 11.16 10.45 10.8051150003 Arkansas Pope 12.79 13.14 11.94 12.26 11.55 11.86 051190007 Arkansas Pulaski 13.17 13.27 12.07 12.15 11.59 11.67 051191004 Arkansas Pulaski 14.05 14.23 12.94 13.11 12.46 12.62 051191005 Arkansas Pulaski 13.59 13.62 12.49 12.52 12.02 12.05 051390006 Arkansas Union 12.86 13.09 11.88 12.09 11.51 11.7051450001 Arkansas White 12.57 12.74 11.72 11.88 11.33 11.48 080010006 Colorado Adams 10.06 10.16 9.10 9.19 8.89 8.97 080050005 Colorado Arapahoe 7.96 8.10 7.19 7.32 7.03 7.16 080130003 Colorado Boulder 8.32 8.54 7.74 7.95 7.62 7.83 080130012 Colorado Boulder 6.96 7.06 6.42 6.51 6.32 6.41 080290004 Colorado Delta 7.44 7.62 6.82 6.99 6.71 6.88 080310002 Colorado Denver 9.37 9.74 8.45 8.78 8.25 8.57 080310023 Colorado Denver 9.76 9.76 8.81 8.81 8.60 8.60 080390001 Colorado Elbert 4.40 4.54 4.13 4.26 4.08 4.22 080410008 Colorado El Paso 6.73 6.73 6.13 6.13 6.01 6.02 
B-37 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

080410011 Colorado El Paso 7.94 8.11 7.27 7.42 7.15 7.30 080690009 Colorado Larimer 7.33 7.40 6.88 6.94 6.81 6.88 080770017 Colorado Mesa 9.28 9.48 8.47 8.65 8.31 8.48 081010012 Colorado Pueblo 7.45 7.65 6.90 7.09 6.80 6.99 081130004 Colorado San Miguel 4.65 4.81 4.41 4.56 4.39 4.54 081230006 Colorado Weld 8.19 8.35 7.54 7.70 7.45 7.61 081230008 Colorado Weld 8.78 9.28 8.14 8.59 8.03 8.48 090010010 Connecticut Fairfield 13.21 13.27 11.30 11.34 10.96 11.00 090011123 Connecticut Fairfield 12.49 12.59 10.71 10.78 10.39 10.46 090013005 Connecticut Fairfield 12.43 12.72 10.50 10.74 10.16 10.38 090019003 Connecticut Fairfield 11.48 11.73 9.66 9.85 9.34 9.52 090031003 Connecticut Hartford 11.03 11.36 9.54 9.82 9.30 9.57 090050005 Connecticut Litchfield 8.01 8.01 6.72 6.72 6.52 6.52 090090026 Connecticut New Haven 12.12 12.13 10.30 10.31 9.99 10.0090090027 Connecticut New Haven 12.45 12.57 10.61 10.71 10.30 10.40 090091123 Connecticut New Haven 13.12 13.54 11.22 11.57 10.90 11.24 090092008 Connecticut New Haven 11.17 11.22 9.45 9.48 9.16 9.19 090092123 Connecticut New Haven 12.74 12.91 10.98 11.12 10.69 10.82 090113002 Connecticut New London 10.96 11.33 9.46 9.77 9.25 9.55 100010002 Delaware Kent 12.61 12.84 10.50 10.70 10.21 10.4100010003 Delaware Kent 12.52 12.66 10.45 10.59 10.17 10.30 100031003 Delaware New Castle 13.73 14.33 11.48 11.97 11.16 11.64 100031007 Delaware New Castle 12.92 13.41 10.74 11.16 10.45 10.86 100031012 Delaware New Castle 13.69 13.88 11.46 11.62 11.15 11.30 100032004 Delaware New Castle 14.87 15.11 12.51 12.72 12.17 12.37 100051002 Delaware Sussex 13.39 13.45 11.14 11.18 10.82 10.86 
B-38 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

110010041 District Of Columbia 14.16 14.38 11.96 12.15 11.57 11.75 110010042 District Of Columbia 14.41 14.52 12.01 12.10 11.59 11.68 110010043 District Of Columbia 13.99 14.44 11.69 12.08 11.29 11.66 120010023 Florida Alachua 9.32 9.51 7.83 7.98 7.67 7.82 120010024 Florida Alachua 9.59 9.64 8.08 8.13 7.92 7.97 120051004 Florida Bay 11.46 11.69 9.97 10.15 9.92 10.10 120090007 Florida Brevard 8.32 8.64 6.95 7.22 6.79 7.05 120111002 Florida Broward 8.22 8.30 6.99 7.06 6.87 6.94 120112004 Florida Broward 8.18 8.28 6.90 6.98 6.77 6.85 120113002 Florida Broward 8.21 8.21 6.91 6.91 6.79 6.79 120170005 Florida Citrus 9.00 9.14 7.31 7.42 7.17 7.27 120310098 Florida Duval 9.90 9.99 8.47 8.57 8.28 8.38 120310099 Florida Duval 10.44 10.53 9.05 9.12 8.85 8.92 120330004 Florida Escambia 11.72 11.91 10.29 10.44 10.06 10.2120570030 Florida Hillsborough 10.74 10.95 8.82 8.99 8.63 8.80 120573002 Florida Hillsborough 10.52 10.75 8.70 8.90 8.51 8.71 120710005 Florida Lee 8.36 8.41 6.96 7.00 6.82 6.86 120730012 Florida Leon 12.56 12.71 10.93 11.05 10.74 10.86 120814012 Florida Manatee 8.81 8.91 6.98 7.06 6.82 6.90 120830003 Florida Marion 10.11 10.20 8.53 8.60 8.36 8.43 120861016 Florida Miami-Dade 9.45 9.54 7.95 8.02 7.76 7.83 120866001 Florida Miami-Dade 8.14 8.28 7.13 7.25 7.04 7.16 120951004 Florida Orange 9.61 9.76 7.99 8.12 7.80 7.92 120952002 Florida Orange 9.50 9.60 7.87 7.95 7.67 7.75 120990009 Florida Palm Beach 7.84 7.84 6.75 6.75 6.63 6.63 120992005 Florida Palm Beach 7.70 7.81 6.59 6.68 6.47 6.56 
B-39 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

121030018 Florida Pinellas 9.82 9.85 7.95 7.96 7.78 7.80 121031009 Florida Pinellas 9.52 9.69 7.70 7.84 7.54 7.68 121056006 Florida Polk 9.53 9.62 7.86 7.94 7.71 7.78 121111002 Florida St. Lucie 8.34 8.47 7.03 7.13 6.87 6.97 121150013 Florida Sarasota 8.77 8.86 7.07 7.16 6.92 7.00 121171002 Florida Seminole 9.51 9.64 7.90 8.00 7.71 7.80 121275002 Florida Volusia 9.27 9.34 7.73 7.78 7.54 7.59 130210007 Georgia Bibb 16.54 16.78 14.58 14.79 13.95 14.15 130210012 Georgia Bibb 13.94 14.17 12.20 12.40 11.57 11.75 130510017 Georgia Chatham 13.74 13.87 12.00 12.10 11.65 11.75 130510091 Georgia Chatham 13.93 14.11 12.23 12.39 11.87 12.03 130590002 Georgia Clarke 14.90 14.90 13.15 13.15 12.48 12.48 130630091 Georgia Clayton 16.50 16.71 14.30 14.48 13.66 13.83 130670003 Georgia Cobb 16.15 16.21 14.05 14.10 13.50 13.54 130670004 Georgia Cobb 15.42 15.50 13.30 13.37 12.75 12.8130890002 Georgia DeKalb 15.48 15.66 13.25 13.40 12.59 12.74 130892001 Georgia DeKalb 15.37 15.56 13.20 13.36 12.56 12.73 130950007 Georgia Dougherty 14.46 14.83 12.73 13.07 12.41 12.75 131150005 Georgia Floyd 16.13 16.18 14.21 14.25 13.70 13.75 131210032 Georgia Fulton 15.84 16.02 13.56 13.72 12.93 13.08 131210039 Georgia Fulton 17.43 17.47 15.07 15.10 14.42 14.45 131270006 Georgia Glynn 12.25 12.30 10.73 10.78 10.49 10.54 131350002 Georgia Gwinnett 16.07 16.36 14.01 14.26 13.38 13.63 131390003 Georgia Hall 14.16 14.37 12.43 12.62 11.85 12.03 131530001 Georgia Houston 14.19 14.58 12.35 12.69 11.82 12.14 131850003 Georgia Lowndes 12.58 12.83 11.14 11.36 10.91 11.13 
B-40 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

132150001 Georgia Muscogee 14.94 15.14 13.16 13.33 12.63 12.80 132150008 Georgia Muscogee 15.39 15.39 13.65 13.65 13.11 13.1132150011 Georgia Muscogee 14.16 14.48 12.52 12.81 12.04 12.32 132230003 Georgia Paulding 14.12 14.43 12.13 12.39 11.64 11.89 132450005 Georgia Richmond 15.61 15.82 13.96 14.15 13.43 13.6132450091 Georgia Richmond 15.68 15.88 14.04 14.21 13.50 13.67 132950002 Georgia Walker 15.49 15.75 13.77 14.00 13.19 13.40 133030001 Georgia Washington 15.14 15.54 13.64 13.99 12.93 13.26 133190001 Georgia Wilkinson 15.27 15.50 13.59 13.79 12.86 13.05 170010006 Illinois Adams 12.50 12.87 11.48 11.81 11.08 11.40 170190004 Illinois Champaign 12.50 12.89 11.41 11.77 10.94 11.29 170191001 Illinois Champaign 12.53 12.92 11.46 11.83 10.98 11.34 170310022 Illinois Cook 15.21 15.55 13.73 14.05 13.25 13.55 170310050 Illinois Cook 14.81 15.22 13.29 13.66 12.77 13.13 170310052 Illinois Cook 15.75 16.02 14.09 14.33 13.50 13.74 170310057 Illinois Cook 15.03 15.34 13.38 13.66 12.80 13.07 170310076 Illinois Cook 14.89 15.18 13.30 13.56 12.74 12.98 170312001 Illinois Cook 14.77 15.14 13.30 13.63 12.82 13.14 170313301 Illinois Cook 15.24 15.59 13.62 13.93 13.05 13.35 170314007 Illinois Cook 12.78 13.15 11.47 11.80 10.95 11.26 170314201 Illinois Cook 12.76 13.18 11.46 11.84 10.93 11.29 170316005 Illinois Cook 15.48 16.07 13.80 14.32 13.21 13.7170434002 Illinois DuPage 13.82 14.01 12.52 12.69 11.96 12.13 170831001 Illinois Jersey 12.89 13.20 12.02 12.32 11.49 11.77 170890003 Illinois Kane 13.32 13.54 12.06 12.26 11.55 11.74 170890007 Illinois Kane 14.34 14.34 13.03 13.03 12.50 12.50 
B-41 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

170971007 Illinois Lake 11.81 12.10 10.75 11.01 10.29 10.54 171110001 Illinois McHenry 12.40 12.47 11.26 11.33 10.80 10.86 171132003 Illinois McLean 12.39 12.39 11.37 11.37 10.87 10.87 171150013 Illinois Macon 13.24 13.57 12.21 12.51 11.69 11.98 171191007 Illinois Madison 16.72 17.01 15.46 15.73 14.76 15.02 171192009 Illinois Madison 14.01 14.66 13.16 13.77 12.57 13.15 171193007 Illinois Madison 14.32 14.45 13.45 13.58 12.86 12.98 171430037 Illinois Peoria 13.34 13.66 12.24 12.54 11.75 12.03 171570001 Illinois Randolph 13.11 13.64 12.16 12.64 11.57 12.03 171613002 Illinois Rock Island 12.01 12.31 10.97 11.25 10.59 10.85 171630010 Illinois Saint Clair 15.58 15.74 14.40 14.54 13.71 13.85 171634001 Illinois Saint Clair 14.29 14.49 13.23 13.41 12.58 12.75 171670012 Illinois Sangamon 13.13 13.29 12.23 12.38 11.71 11.85 171971002 Illinois Will 13.63 14.05 12.34 12.72 11.77 12.12 171971011 Illinois Will 11.52 11.78 10.47 10.70 10.00 10.2172010013 Illinois Winnebago 13.57 13.57 12.38 12.38 11.96 11.96 180030004 Indiana Allen 13.67 14.08 12.51 12.89 12.16 12.53 180030014 Indiana Allen 13.55 13.91 12.41 12.74 12.06 12.38 180190006 Indiana Clark 16.44 16.67 14.83 15.03 14.20 14.39 180350006 Indiana Delaware 13.69 13.86 12.62 12.78 12.16 12.3180372001 Indiana Dubois 15.19 15.68 13.90 14.35 13.29 13.72 180431004 Indiana Floyd 14.85 14.99 13.43 13.56 12.84 12.96 180650003 Indiana Henry 13.64 13.64 12.59 12.59 12.17 12.17 180670003 Indiana Howard 13.93 14.29 12.82 13.15 12.40 12.73 180830004 Indiana Knox 14.03 14.21 12.77 12.92 12.18 12.32 180890006 Indiana Lake 14.33 14.51 12.95 13.11 12.47 12.63 
B-42 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

180890027 Indiana Lake 13.83 14.13 12.53 12.80 12.07 12.33 180891003 Indiana Lake 14.02 14.25 12.73 12.94 12.29 12.49 180892004 Indiana Lake 14.05 14.41 12.71 13.04 12.25 12.57 180892010 Indiana Lake 13.89 14.10 12.54 12.73 12.07 12.26 180910011 Indiana LaPorte 12.49 12.84 11.29 11.60 10.91 11.2180910012 Indiana LaPorte 12.69 13.07 11.50 11.84 11.12 11.45 180950009 Indiana Madison 13.97 14.37 12.90 13.27 12.47 12.83 180970042 Indiana Marion 14.24 14.51 13.20 13.45 12.73 12.97 180970078 Indiana Marion 15.26 15.43 14.12 14.28 13.64 13.79 180970079 Indiana Marion 14.71 14.99 13.56 13.81 13.08 13.33 180970081 Indiana Marion 16.05 16.36 14.86 15.16 14.36 14.65 180970083 Indiana Marion 15.90 16.27 14.71 15.06 14.21 14.55 181270020 Indiana Porter 12.66 13.01 11.42 11.74 11.01 11.32 181270024 Indiana Porter 13.21 13.39 11.92 12.09 11.49 11.65 181410014 Indiana St. Joseph 13.29 13.65 12.21 12.54 11.89 12.2181411008 Indiana St. Joseph 13.69 13.69 12.57 12.57 12.23 12.23 181412004 Indiana St. Joseph 12.82 13.22 11.77 12.14 11.45 11.8181470009 Indiana Spencer 14.32 14.55 13.06 13.27 12.47 12.66 181570008 Indiana Tippecanoe 13.70 14.06 12.54 12.87 12.10 12.42 181630006 Indiana Vanderburgh 14.69 14.89 13.53 13.71 13.01 13.19 181630012 Indiana Vanderburgh 14.82 15.00 13.61 13.78 13.08 13.25 181630016 Indiana Vanderburgh 14.99 15.14 13.81 13.95 13.29 13.43 181670018 Indiana Vigo 13.99 14.17 12.65 12.82 12.12 12.27 181670023 Indiana Vigo 13.46 13.69 12.16 12.37 11.63 11.83 190130008 Iowa Black Hawk 11.16 11.52 10.20 10.54 9.91 10.23 190450021 Iowa Clinton 12.52 12.63 11.44 11.54 11.07 11.16 
B-43 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

191032001 Iowa Johnson 12.08 12.26 11.04 11.20 10.73 10.88 191130037 Iowa Linn 10.79 10.95 9.78 9.92 9.48 9.63 191370002 Iowa Montgomery 10.02 10.33 9.09 9.37 8.84 9.11 191390015 Iowa Muscatine 12.92 13.29 11.85 12.20 11.50 11.83 191471002 Iowa Palo Alto 9.53 9.53 8.72 8.72 8.51 8.51 191530030 Iowa Polk 10.41 10.54 9.44 9.55 9.17 9.29 191532510 Iowa Polk 9.95 10.14 9.01 9.19 8.76 8.93 191532520 Iowa Polk 10.64 10.64 9.65 9.65 9.38 9.38 191550009 Iowa Pottawattamie 11.13 11.52 10.08 10.44 9.82 10.17 191630015 Iowa Scott 11.86 12.06 10.84 11.03 10.47 10.64 191630018 Iowa Scott 11.64 11.89 10.64 10.87 10.27 10.48 191630019 Iowa Scott 14.42 14.42 13.27 13.27 12.86 12.86 191770006 Iowa Van Buren 10.84 10.84 9.94 9.94 9.65 9.65 191930017 Iowa Woodbury 10.32 10.53 9.47 9.66 9.27 9.46 191970004 Iowa Wright 10.37 10.51 9.47 9.59 9.22 9.34 200910007 Kansas Johnson 10.59 10.86 9.60 9.84 9.33 9.56 200910009 Kansas Johnson 11.10 11.10 10.08 10.08 9.79 9.79 200910010 Kansas Johnson 9.68 9.74 8.81 8.86 8.56 8.61 201070002 Kansas Linn 10.47 10.62 9.66 9.79 9.41 9.53 201730008 Kansas Sedgwick 10.26 10.85 9.28 9.81 9.05 9.57 201730009 Kansas Sedgwick 10.29 10.95 9.31 9.89 9.07 9.64 201730010 Kansas Sedgwick 10.36 10.96 9.37 9.92 9.14 9.67 201770010 Kansas Shawnee 10.79 10.79 9.86 9.86 9.61 9.61 201770011 Kansas Shawnee 10.93 10.93 10.00 10.00 9.76 9.76 201910002 Kansas Sumner 9.89 10.31 9.01 9.39 8.80 9.17 202090021 Kansas Wyandotte 12.73 13.37 11.54 12.12 11.23 11.80 
B-44 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

202090022 Kansas Wyandotte 10.93 11.25 9.90 10.18 9.61 9.90 210130002 Kentucky Bell 14.10 14.18 12.52 12.58 11.93 11.99 210190017 Kentucky Boyd 14.49 14.70 12.99 13.17 12.36 12.54 210290006 Kentucky Bullitt 14.92 15.27 13.57 13.89 12.96 13.26 210370003 Kentucky Campbell 13.67 13.67 12.46 12.46 11.94 11.94 210430500 Kentucky Carter 12.22 12.61 10.85 11.19 10.29 10.62 210470006 Kentucky Christian 13.20 13.55 12.16 12.47 11.59 11.88 210590005 Kentucky Daviess 14.10 14.10 13.03 13.03 12.42 12.42 210670012 Kentucky Fayette 14.36 14.60 12.87 13.08 12.30 12.5210670014 Kentucky Fayette 14.87 15.13 13.36 13.59 12.79 13.0210730006 Kentucky Franklin 13.37 13.52 12.04 12.17 11.49 11.62 210930006 Kentucky Hardin 13.58 14.04 12.37 12.78 11.78 12.17 211010014 Kentucky Henderson 13.93 14.28 12.69 13.00 12.18 12.47 211110043 Kentucky Jefferson 15.55 15.77 14.04 14.24 13.42 13.62 211110044 Kentucky Jefferson 15.35 15.52 13.79 13.94 13.18 13.32 211110048 Kentucky Jefferson 15.31 15.33 13.74 13.76 13.12 13.14 211110051 Kentucky Jefferson 14.74 15.28 13.30 13.79 12.69 13.15 211170007 Kentucky Kenton 14.39 14.52 13.12 13.24 12.57 12.70 211250004 Kentucky Laurel 12.55 12.55 11.19 11.19 10.67 10.67 211451004 Kentucky McCracken 13.41 13.91 12.33 12.78 11.73 12.16 211510003 Kentucky Madison 13.61 13.70 12.17 12.26 11.62 11.7211930003 Kentucky Perry 13.21 13.88 11.76 12.36 11.21 11.78 211950002 Kentucky Pike 13.49 13.98 11.94 12.37 11.37 11.77 212270007 Kentucky Warren 13.83 13.92 12.71 12.78 12.11 12.19 220171002 Louisiana Caddo 12.53 12.53 11.49 11.49 11.03 11.03 220190009 Louisiana Calcasieu 10.58 10.78 9.55 9.74 9.31 9.49 
B-45 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

220190010 Louisiana Calcasieu 11.07 11.24 10.00 10.15 9.75 9.90 220290003 Louisiana Concordia 11.42 11.42 10.32 10.32 9.98 9.98 220330009 Louisiana

East Baton Rouge 13.38 13.57 12.07 12.24 11.75 11.9

220331001 Louisiana

East Baton Rouge 12.08 12.18 10.87 10.95 10.59 10.66 

220470005 Louisiana Iberville 12.90 12.98 11.47 11.54 11.15 11.22 220470009 Louisiana Iberville 11.02 11.16 9.77 9.89 9.47 9.59 220511001 Louisiana Jefferson 11.52 11.68 9.92 10.05 9.59 9.71 220550006 Louisiana Lafayette 11.08 11.18 9.85 9.95 9.56 9.65 220730004 Louisiana Ouachita 11.97 12.21 11.02 11.23 10.68 10.89 220790002 Louisiana Rapides 11.03 11.04 9.92 9.94 9.62 9.64 221050001 Louisiana Tangipahoa 12.03 12.39 10.61 10.92 10.27 10.58 221090001 Louisiana Terrebonne 10.74 10.83 9.40 9.47 9.11 9.18 221210001 Louisiana

West Baton Rouge 13.51 13.68 12.19 12.35 11.87 12.03 

230010011 Maine Androscoggin 9.90 10.62 8.92 9.58 8.75 9.39 230030013 Maine Aroostook 9.74 10.27 9.35 9.87 9.28 9.78 230031011 Maine Aroostook 8.27 8.92 7.80 8.41 7.71 8.32 230050015 Maine Cumberland 11.06 11.68 9.88 10.44 9.70 10.24 230050027 Maine Cumberland 11.13 11.53 9.95 10.32 9.77 10.12 230090103 Maine Hancock 5.76 6.17 5.02 5.38 4.95 5.31 230110016 Maine Kennebec 9.99 10.70 9.02 9.67 8.86 9.49 230172011 Maine Oxford 10.13 10.38 9.34 9.57 9.21 9.43 230190002 Maine Penobscot 9.12 9.65 8.20 8.67 8.05 8.52 240030014 Maryland Anne Arundel 11.91 11.91 9.91 9.91 9.59 9.59 
B-46 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

240031003 Maryland Anne Arundel 14.82 15.26 12.59 12.96 12.23 12.59 240032002 Maryland Anne Arundel 14.57 14.57 12.32 12.32 11.98 11.98 240051007 Maryland Baltimore 13.77 14.04 11.58 11.80 11.24 11.45 240053001 Maryland Baltimore 14.76 15.05 12.42 12.67 12.08 12.33 240150003 Maryland Cecil 12.68 12.82 10.58 10.69 10.29 10.40 240251001 Maryland Harford 12.51 12.62 10.36 10.45 10.07 10.16 240313001 Maryland Montgomery 12.47 12.70 10.57 10.76 10.23 10.4240330030 Maryland

Prince George's 12.24 12.24 10.34 10.34 10.02 10.02 

240338003 Maryland

Prince George's 13.03 13.23 10.88 11.06 10.54 10.72 

240430009 Maryland Washington 13.70 14.14 12.00 12.38 11.64 12.0245100006 Maryland

Baltimore City 14.12 14.37 11.86 12.07 11.52 11.72 

245100007 Maryland

Baltimore City 14.38 15.01 12.15 12.67 11.80 12.29 

245100008 Maryland

Baltimore City 15.76 15.87 13.34 13.43 12.96 13.05 

245100049 Maryland

Baltimore City 15.63 15.63 13.27 13.27 12.90 12.90 

250035001 Massachusetts Berkshire 10.65 11.15 9.44 9.90 9.24 9.68 250051004 Massachusetts Bristol 9.58 10.16 8.06 8.56 7.99 8.48 250092006 Massachusetts Essex 9.03 9.09 7.71 7.76 7.58 7.63 250095005 Massachusetts Essex 9.10 9.32 7.89 8.07 7.76 7.94 250096001 Massachusetts Essex 9.58 9.74 8.32 8.46 8.18 8.31 250130008 Massachusetts Hampden 9.85 10.14 8.56 8.82 8.37 8.62 
B-47 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

250130016 Massachusetts Hampden 12.17 12.52 10.70 11.00 10.47 10.76 250132009 Massachusetts Hampden 11.85 11.90 10.41 10.45 10.19 10.23 250230004 Massachusetts Plymouth 9.87 10.14 8.39 8.63 8.30 8.53 250250002 Massachusetts Suffolk 12.34 13.00 10.68 11.24 10.50 11.05 250250027 Massachusetts Suffolk 11.86 12.27 10.21 10.57 10.03 10.39 250250042 Massachusetts Suffolk 10.88 11.36 9.32 9.74 9.17 9.57 250250043 Massachusetts Suffolk 13.07 13.88 11.34 12.05 11.15 11.84 250270016 Massachusetts Worcester 10.55 10.70 9.17 9.30 8.98 9.10 250270023 Massachusetts Worcester 11.29 11.32 9.86 9.87 9.66 9.67 260050003 Michigan Allegan 11.84 11.99 10.76 10.89 10.41 10.53 260170014 Michigan Bay 10.93 11.05 9.89 10.01 9.61 9.72 260210014 Michigan Berrien 11.72 11.91 10.63 10.81 10.27 10.44 260490021 Michigan Genesee 11.61 11.79 10.47 10.63 10.14 10.29 260650012 Michigan Ingham 12.23 12.51 11.07 11.32 10.73 10.98 260770008 Michigan Kalamazoo 12.84 13.06 11.68 11.88 11.32 11.5260810020 Michigan Kent 12.89 13.09 11.56 11.74 11.18 11.35 260990009 Michigan Macomb 12.70 13.04 11.53 11.83 11.19 11.48 261130001 Michigan Missaukee 8.26 8.36 7.68 7.77 7.46 7.55 261150005 Michigan Monroe 13.92 14.13 12.55 12.74 12.17 12.36 261210040 Michigan Muskegon 11.61 11.70 10.48 10.56 10.13 10.20 261250001 Michigan Oakland 13.78 14.26 12.29 12.73 11.89 12.3261390005 Michigan Ottawa 12.55 12.67 11.28 11.38 10.91 11.0261450018 Michigan Saginaw 10.61 10.61 9.61 9.61 9.33 9.33 261470005 Michigan St. Clair 13.34 13.75 12.27 12.64 11.95 12.32 261610005 Michigan Washtenaw 12.30 12.30 11.04 11.04 10.69 10.69 261610008 Michigan Washtenaw 13.88 14.29 12.41 12.78 12.02 12.37 
B-48 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

261630001 Michigan Wayne 14.52 15.11 12.94 13.47 12.52 13.03 261630015 Michigan Wayne 15.88 16.40 14.26 14.72 13.81 14.27 261630016 Michigan Wayne 14.57 15.17 13.09 13.63 12.69 13.2261630019 Michigan Wayne 14.32 14.77 12.96 13.37 12.59 12.98 261630025 Michigan Wayne 13.39 13.88 11.93 12.36 11.52 11.94 261630033 Michigan Wayne 17.50 18.16 15.73 16.32 15.25 15.82 261630036 Michigan Wayne 14.67 15.44 13.17 13.86 12.78 13.44 270210001 Minnesota Cass 5.70 5.80 5.37 5.47 5.29 5.38 270370470 Minnesota Dakota 9.30 9.55 8.49 8.72 8.27 8.49 270530050 Minnesota Hennepin 9.76 9.82 8.87 8.92 8.62 8.67 270530961 Minnesota Hennepin 9.14 9.27 8.30 8.42 8.07 8.18 270530963 Minnesota Hennepin 9.59 9.81 8.69 8.89 8.44 8.63 270530965 Minnesota Hennepin 9.54 9.58 8.67 8.71 8.42 8.46 270531007 Minnesota Hennepin 9.56 9.75 8.68 8.86 8.44 8.61 270532006 Minnesota Hennepin 9.33 9.39 8.48 8.52 8.24 8.28 270953051 Minnesota Mille Lacs 6.54 6.67 6.09 6.22 5.98 6.11 271095008 Minnesota Olmsted 10.13 10.13 9.26 9.26 9.04 9.04 271230866 Minnesota Ramsey 11.32 11.61 10.41 10.67 10.16 10.42 271230868 Minnesota Ramsey 11.02 11.31 10.06 10.32 9.79 10.04 271230871 Minnesota Ramsey 9.63 9.80 8.77 8.93 8.54 8.69 271377001 Minnesota Saint Louis 6.10 6.15 5.73 5.78 5.65 5.70 271377550 Minnesota Saint Louis 6.19 6.25 5.78 5.84 5.67 5.73 271377551 Minnesota Saint Louis 7.51 7.62 7.02 7.12 6.90 7.00 271390505 Minnesota Scott 9.00 9.16 8.24 8.39 8.04 8.17 271453052 Minnesota Stearns 8.58 8.80 7.96 8.17 7.81 8.01 280010004 Mississippi Adams 11.29 11.46 10.17 10.33 9.84 9.99 
B-49 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

280110001 Mississippi Bolivar 12.36 12.58 11.39 11.60 10.97 11.16 280330002 Mississippi DeSoto 12.43 12.51 11.24 11.31 10.70 10.76 280350004 Mississippi Forrest 13.62 13.99 12.08 12.41 11.69 12.00 280470008 Mississippi Harrison 12.20 12.34 10.69 10.81 10.38 10.50 280490010 Mississippi Hinds 12.56 12.66 11.29 11.38 10.87 10.95 280590006 Mississippi Jackson 12.04 12.16 10.42 10.54 10.10 10.2280670002 Mississippi Jones 14.39 14.59 12.79 12.96 12.35 12.52 280750003 Mississippi Lauderdale 13.07 13.09 11.76 11.78 11.32 11.34 280810005 Mississippi Lee 12.57 12.83 11.42 11.65 10.89 11.1280870001 Mississippi Lowndes 12.79 13.11 11.66 11.94 11.17 11.44 281090001 Mississippi Pearl River 12.14 12.14 10.72 10.72 10.40 10.40 281490004 Mississippi Warren 12.32 12.32 11.22 11.22 10.83 10.83 290190004 Missouri Boone 11.84 11.84 10.91 10.91 10.55 10.55 290210005 Missouri Buchanan 12.80 12.80 11.78 11.78 11.52 11.52 290370003 Missouri Cass 10.67 10.83 9.74 9.89 9.46 9.60 290390001 Missouri Cedar 11.12 11.12 10.24 10.24 9.94 9.94 290470005 Missouri Clay 11.03 11.25 10.03 10.24 9.76 9.96 290770032 Missouri Greene 11.75 11.87 10.92 11.03 10.57 10.68 290950034 Missouri Jackson 12.78 13.19 11.56 11.93 11.24 11.60 290990012 Missouri Jefferson 13.79 13.97 12.81 12.98 12.27 12.43 291370001 Missouri Monroe 10.87 10.87 9.99 9.99 9.62 9.62 291831002 Missouri Saint Charles 13.29 13.70 12.44 12.83 11.88 12.25 291860006 Missouri

Sainte Genevieve 13.34 13.66 12.38 12.67 11.86 12.13 

291890004 Missouri Saint Louis 13.04 13.04 12.06 12.06 11.51 11.5291892003 Missouri Saint Louis 13.46 13.79 12.39 12.69 11.79 12.08 
B-50 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

295100007 Missouri St. Louis City 14.27 14.45 13.17 13.34 12.56 12.72 295100085 Missouri St. Louis City 14.36 14.45 13.21 13.29 12.56 12.64 295100086 Missouri St. Louis City 13.44 13.60 12.37 12.52 11.74 11.89 295100087 Missouri St. Louis City 14.56 14.70 13.44 13.57 12.77 12.90 300870307 Montana Rosebud 6.58 6.58 6.34 6.34 6.32 6.32 301111065 Montana Yellowstone 8.14 8.18 7.76 7.79 7.69 7.72 310250002 Nebraska Cass 9.99 9.99 9.03 9.03 8.80 8.80 310550019 Nebraska Douglas 9.88 10.06 8.91 9.08 8.67 8.84 310550052 Nebraska Douglas 9.85 10.18 8.88 9.17 8.64 8.92 310790004 Nebraska Hall 7.95 7.95 7.22 7.22 7.06 7.06 311090022 Nebraska Lancaster 8.90 9.28 7.99 8.33 7.77 8.10 311111002 Nebraska Lincoln 7.57 7.57 7.04 7.04 6.95 6.95 311530007 Nebraska Sarpy 9.79 9.86 8.83 8.89 8.59 8.65 311570003 Nebraska Scotts Bluff 6.04 6.04 5.64 5.64 5.60 5.60 311770002 Nebraska Washington 9.29 9.35 8.42 8.47 8.21 8.26 330012004

New Hampshire Belknap 7.28 7.33 6.39 6.43 6.27 6.31 

330050007

New Hampshire Cheshire 11.53 11.61 10.29 10.36 10.11 10.17 

330070014

New Hampshire Coos 10.24 10.39 9.49 9.63 9.36 9.50 

330090010

New Hampshire Grafton 8.43 8.43 7.59 7.59 7.45 7.45 

330110020

New Hampshire Hillsborough 10.18 10.18 8.86 8.86 8.71 8.71 

330111015

New Hampshire Hillsborough 10.01 10.01 8.75 8.75 8.59 8.59 

330115001

New Hampshire Hillsborough 6.27 6.27 5.44 5.44 5.34 5.34 
B-51 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

330131006

New Hampshire Merrimack 9.72 9.73 8.46 8.47 8.31 8.32 

330150014

New Hampshire Rockingham 9.00 9.20 7.84 8.02 7.74 7.91 

330190003

New Hampshire Sullivan 9.86 10.17 8.86 9.12 8.70 8.96 

340011006 New Jersey Atlantic 11.47 11.47 9.61 9.61 9.39 9.39 340030003 New Jersey Bergen 13.09 13.29 11.05 11.21 10.58 10.73 340070003 New Jersey Camden 13.31 13.40 11.20 11.28 10.89 10.96 340071007 New Jersey Camden 13.51 13.78 11.31 11.52 10.96 11.17 340130015 New Jersey Essex 13.27 13.29 11.09 11.11 10.66 10.68 340155001 New Jersey Gloucester 13.46 13.46 11.28 11.28 10.97 10.97 340171003 New Jersey Hudson 14.24 14.66 12.04 12.39 11.55 11.88 340210008 New Jersey Mercer 12.71 12.97 10.80 10.99 10.48 10.67 340218001 New Jersey Mercer 11.14 11.66 9.37 9.80 9.09 9.50 340230006 New Jersey Middlesex 12.15 12.52 10.32 10.63 10.01 10.30 340270004 New Jersey Morris 11.50 11.92 9.73 10.07 9.41 9.74 340273001 New Jersey Morris 10.21 10.59 8.73 9.04 8.47 8.78 340292002 New Jersey Ocean 10.92 11.27 9.17 9.45 8.90 9.17 340310005 New Jersey Passaic 12.88 13.09 10.78 10.94 10.37 10.5340390004 New Jersey Union 14.94 15.53 12.57 13.08 12.10 12.59 340390006 New Jersey Union 13.32 13.64 11.10 11.35 10.66 10.9340392003 New Jersey Union 13.06 13.32 10.90 11.10 10.48 10.68 340410006 New Jersey Warren 12.72 13.11 10.87 11.19 10.56 10.86 350010023 New Mexico Bernalillo 7.03 7.13 6.41 6.49 6.28 6.37 350010024 New Mexico Bernalillo 6.64 6.81 6.03 6.20 5.92 6.08 350050005 New Mexico Chaves 6.54 6.58 6.13 6.17 6.06 6.11 
B-52 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

350130017 New Mexico Dona Ana 9.95 10.40 9.42 9.84 9.34 9.76 350130025 New Mexico Dona Ana 6.31 6.35 5.89 5.93 5.81 5.85 350431003 New Mexico Sandoval 5.00 5.02 4.55 4.58 4.47 4.49 350439011 New Mexico Sandoval 7.99 8.34 7.61 7.94 7.55 7.88 350450006 New Mexico San Juan 5.92 6.08 5.61 5.76 5.59 5.74 350490020 New Mexico Santa Fe 4.76 4.81 4.49 4.54 4.45 4.50 360010005 New York Albany 11.83 11.83 10.71 10.71 10.48 10.48 360050080 New York Bronx 15.43 15.72 13.30 13.54 12.79 13.02 360050083 New York Bronx 13.09 13.35 10.98 11.18 10.47 10.67 360050110 New York Bronx 13.45 14.06 11.51 12.04 11.04 11.54 360130011 New York Chautauqua 9.80 10.09 8.73 8.98 8.47 8.71 360290005 New York Erie 12.62 12.79 11.30 11.45 10.98 11.13 360291007 New York Erie 12.64 13.00 11.29 11.62 10.97 11.29 360310003 New York Essex 5.94 5.98 5.44 5.48 5.33 5.36 360470122 New York Kings 14.20 14.64 12.04 12.42 11.54 11.90 360551007 New York Monroe 10.64 10.64 9.52 9.52 9.28 9.28 360590008 New York Nassau 11.66 12.07 9.79 10.13 9.43 9.75 360610056 New York New York 16.18 17.02 13.80 14.49 13.23 13.89 360610062 New York New York 14.80 15.31 12.53 12.96 12.02 12.43 360610079 New York New York 13.61 14.00 11.62 11.95 11.14 11.45 360610128 New York New York 15.41 15.80 13.14 13.46 12.61 12.9360632008 New York Niagara 11.96 12.29 10.84 11.13 10.56 10.85 360671015 New York Onondaga 10.08 10.46 8.98 9.32 8.71 9.03 360710002 New York Orange 10.99 11.43 9.38 9.74 9.07 9.42 360810124 New York Queens 12.18 12.69 10.30 10.74 9.85 10.27 360850055 New York Richmond 13.31 13.37 11.13 11.18 10.68 10.73 
B-53 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

360850067 New York Richmond 11.59 11.82 9.70 9.90 9.32 9.51 360893001 New York St. Lawrence 7.29 7.71 6.76 7.14 6.64 7.01 361010003 New York Steuben 9.00 9.38 7.96 8.29 7.76 8.08 361030001 New York Suffolk 11.52 11.52 9.63 9.63 9.26 9.26 361191002 New York Westchester 11.73 11.94 9.83 9.99 9.42 9.57 370010002 North Carolina Alamance 13.94 14.07 11.90 12.01 11.42 11.54 370210034 North Carolina Buncombe 12.60 12.65 10.91 10.95 10.44 10.48 370330001 North Carolina Caswell 13.19 13.33 11.18 11.30 10.70 10.8370350004 North Carolina Catawba 15.31 15.37 12.92 12.97 12.38 12.43 370370004 North Carolina Chatham 11.99 12.15 10.23 10.37 9.83 9.97 370510009 North Carolina Cumberland 13.73 13.85 12.01 12.12 11.53 11.63 370570002 North Carolina Davidson 15.17 15.24 12.86 12.92 12.39 12.45 370610002 North Carolina Duplin 11.30 11.54 9.73 9.94 9.32 9.53 370630001 North Carolina Durham 13.57 13.67 11.67 11.75 11.23 11.32 370650004 North Carolina Edgecombe 12.37 12.38 10.69 10.69 10.24 10.24 370670022 North Carolina Forsyth 14.28 14.45 12.02 12.16 11.50 11.63 370710016 North Carolina Gaston 14.26 14.40 12.01 12.12 11.53 11.64 370810013 North Carolina Guilford 13.79 13.79 11.73 11.73 11.22 11.22 370870010 North Carolina Haywood 12.98 13.00 11.44 11.46 11.00 11.02 370990006 North Carolina Jackson 12.09 12.43 10.54 10.85 10.06 10.35 371070004 North Carolina Lenoir 11.12 11.29 9.57 9.73 9.17 9.32 371110004 North Carolina McDowell 14.24 14.35 12.34 12.43 11.86 11.95 371170001 North Carolina Martin 10.86 11.12 9.27 9.50 8.91 9.14 371190010 North Carolina Mecklenburg 15.31 15.31 12.96 12.96 12.50 12.50 371190041 North Carolina Mecklenburg 14.74 14.94 12.43 12.60 11.97 12.14 371190042 North Carolina Mecklenburg 14.80 14.93 12.49 12.59 12.03 12.13 
B-54 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

371210001 North Carolina Mitchell 12.75 13.00 10.95 11.17 10.48 10.68 371230001 North Carolina Montgomery 12.35 12.52 10.58 10.72 10.16 10.30 371290002 North Carolina New Hanover 9.96 10.18 8.54 8.72 8.17 8.34 371330005 North Carolina Onslow 10.98 11.26 9.40 9.66 9.03 9.28 371350007 North Carolina Orange 13.12 13.25 11.21 11.32 10.78 10.88 371470005 North Carolina Pitt 11.59 11.90 9.99 10.26 9.57 9.84 371550005 North Carolina Robeson 12.78 12.79 11.18 11.21 10.65 10.68 371590021 North Carolina Rowan 14.02 14.02 11.93 11.93 11.52 11.52 371730002 North Carolina Swain 12.65 12.82 11.06 11.21 10.54 10.68 371830014 North Carolina Wake 13.54 13.57 11.64 11.67 11.19 11.22 371890003 North Carolina Watauga 12.05 12.06 10.24 10.25 9.75 9.76 371910005 North Carolina Wayne 12.96 13.14 11.33 11.49 10.78 10.93 380070002 North Dakota Billings 4.61 4.68 4.42 4.49 4.40 4.46 380130002 North Dakota Burke 5.90 5.90 5.74 5.74 5.72 5.72 380130003 North Dakota Burke 5.78 5.78 5.61 5.61 5.58 5.58 380150003 North Dakota Burleigh 6.61 6.71 6.24 6.33 6.18 6.27 380171004 North Dakota Cass 7.72 7.75 7.22 7.25 7.10 7.13 380530002 North Dakota McKenzie 5.01 5.09 4.85 4.93 4.82 4.91 380570004 North Dakota Mercer 6.04 6.15 5.80 5.90 5.76 5.87 390090003 Ohio Athens 12.39 12.67 10.88 11.13 10.39 10.63 390170016 Ohio Butler 15.74 16.11 14.34 14.68 13.78 14.1390170017 Ohio Butler 15.36 15.36 14.11 14.11 13.63 13.63 390171004 Ohio Butler 14.90 15.14 13.67 13.89 13.19 13.40 390230005 Ohio Clark 14.64 14.77 13.35 13.47 12.90 13.02 390250022 Ohio Clermont 14.15 14.15 12.94 12.94 12.43 12.43 390350027 Ohio Cuyahoga 15.46 16.13 14.22 14.83 13.80 14.40 
B-55 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

390350034 Ohio Cuyahoga 13.76 14.07 12.75 13.04 12.37 12.65 390350038 Ohio Cuyahoga 17.37 18.10 15.99 16.66 15.54 16.20 390350045 Ohio Cuyahoga 16.47 16.98 15.14 15.61 14.70 15.16 390350060 Ohio Cuyahoga 17.11 17.66 15.67 16.18 15.21 15.7390350065 Ohio Cuyahoga 15.97 16.44 14.67 15.10 14.25 14.67 390351002 Ohio Cuyahoga 14.14 14.64 12.95 13.41 12.58 13.03 390490024 Ohio Franklin 15.27 15.95 13.62 14.23 13.12 13.7390490025 Ohio Franklin 15.08 15.45 13.45 13.78 12.95 13.27 390490081 Ohio Franklin 14.33 14.33 12.80 12.80 12.33 12.33 390570005 Ohio Greene 13.36 13.55 12.07 12.24 11.61 11.78 390610006 Ohio Hamilton 14.84 14.84 13.50 13.50 12.97 12.97 390610014 Ohio Hamilton 17.29 17.53 15.76 15.98 15.16 15.37 390610040 Ohio Hamilton 15.50 15.88 14.12 14.46 13.55 13.89 390610042 Ohio Hamilton 16.85 17.25 15.40 15.77 14.81 15.17 390610043 Ohio Hamilton 15.55 15.82 14.16 14.41 13.61 13.86 390617001 Ohio Hamilton 16.17 16.56 14.74 15.10 14.17 14.52 390618001 Ohio Hamilton 17.54 17.90 16.01 16.33 15.41 15.72 390810017 Ohio Jefferson 15.41 15.46 13.37 13.41 12.91 12.95 390811001 Ohio Jefferson 16.51 17.17 14.31 14.88 13.82 14.37 390851001 Ohio Lake 13.02 13.02 12.15 12.15 11.81 11.8390870010 Ohio Lawrence 15.14 15.44 13.66 13.92 13.07 13.32 390930016 Ohio Lorain 13.87 14.13 12.80 13.04 12.43 12.67 390933002 Ohio Lorain 12.78 12.98 11.88 12.06 11.57 11.75 390950024 Ohio Lucas 14.38 14.68 13.01 13.28 12.62 12.88 390950025 Ohio Lucas 13.95 14.37 12.62 13.00 12.24 12.6390950026 Ohio Lucas 14.08 14.30 12.78 12.97 12.38 12.58 
B-56 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

390990005 Ohio Mahoning 14.68 14.99 13.13 13.41 12.74 13.02 390990014 Ohio Mahoning 15.12 15.53 13.61 13.99 13.22 13.58 391130031 Ohio Montgomery 14.58 14.58 13.22 13.22 12.76 12.76 391130032 Ohio Montgomery 15.54 15.92 14.10 14.44 13.62 13.95 391330002 Ohio Portage 13.37 13.56 12.06 12.22 11.69 11.84 391351001 Ohio Preble 13.70 13.88 12.68 12.84 12.25 12.4391450013 Ohio Scioto 14.65 14.84 13.10 13.26 12.55 12.7391510017 Ohio Stark 16.26 16.69 14.47 14.87 14.02 14.4391510020 Ohio Stark 15.23 15.23 13.61 13.61 13.21 13.2391530017 Ohio Summit 15.17 15.61 13.70 14.10 13.31 13.70 391530023 Ohio Summit 14.26 14.58 12.88 13.18 12.52 12.8391550007 Ohio Trumbull 14.53 14.73 13.16 13.35 12.78 12.96 400159008 Oklahoma Caddo 9.22 9.77 8.37 8.87 8.19 8.67 400219002 Oklahoma Cherokee 11.79 12.28 10.92 11.37 10.63 11.06 400719010 Oklahoma Kay 10.26 10.62 9.47 9.81 9.26 9.59 400819005 Oklahoma Lincoln 10.28 10.28 9.40 9.40 9.17 9.17 400970186 Oklahoma Mayes 11.70 11.83 10.87 11.00 10.61 10.73 400979014 Oklahoma Mayes 11.44 11.62 10.62 10.79 10.35 10.5401010169 Oklahoma Muskogee 11.89 12.10 11.08 11.28 10.80 11.00 401090035 Oklahoma Oklahoma 10.07 10.23 9.04 9.19 8.79 8.94 401091037 Oklahoma Oklahoma 9.86 10.07 8.86 9.04 8.61 8.79 401159004 Oklahoma Ottawa 11.69 11.95 10.84 11.07 10.57 10.79 401210415 Oklahoma Pittsburg 11.09 11.36 10.23 10.48 9.97 10.2401359015 Oklahoma Sequoyah 12.99 12.99 12.09 12.09 11.78 11.78 401430110 Oklahoma Tulsa 11.52 11.52 10.61 10.61 10.30 10.30 401431127 Oklahoma Tulsa 11.37 11.56 10.47 10.64 10.18 10.34 
B-57 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

420010001 Pennsylvania Adams 13.05 13.59 11.25 11.72 10.92 11.38 420030008 Pennsylvania Allegheny 15.24 15.48 13.51 13.73 13.05 13.27 420030021 Pennsylvania Allegheny 14.66 14.66 12.99 12.99 12.54 12.54 420030064 Pennsylvania Allegheny 20.31 20.75 17.94 18.33 17.30 17.68 420030067 Pennsylvania Allegheny 13.07 13.48 11.38 11.74 10.97 11.3420030095 Pennsylvania Allegheny 13.84 14.54 12.12 12.73 11.70 12.29 420030116 Pennsylvania Allegheny 15.36 15.36 13.54 13.54 13.09 13.09 420031008 Pennsylvania Allegheny 15.25 15.49 13.48 13.70 13.03 13.24 420031301 Pennsylvania Allegheny 16.26 16.57 14.32 14.59 13.79 14.05 420033007 Pennsylvania Allegheny 15.30 15.72 13.44 13.81 12.94 13.30 420039002 Pennsylvania Allegheny 14.44 14.44 12.72 12.72 12.26 12.26 420070014 Pennsylvania Beaver 16.38 16.45 14.44 14.51 14.03 14.09 420110011 Pennsylvania Berks 15.82 16.19 13.74 14.06 13.42 13.74 420170012 Pennsylvania Bucks 13.42 13.93 11.32 11.74 10.98 11.38 420210011 Pennsylvania Cambria 15.40 15.55 13.61 13.74 13.22 13.34 420270100 Pennsylvania Centre 12.78 13.42 11.17 11.73 10.85 11.4420290100 Pennsylvania Chester 15.22 15.22 12.88 12.88 12.54 12.54 420410101 Pennsylvania Cumberland 14.45 15.12 12.52 13.11 12.20 12.77 420430401 Pennsylvania Dauphin 15.13 15.78 12.79 13.35 12.44 12.98 420450002 Pennsylvania Delaware 15.23 15.69 12.85 13.23 12.51 12.88 420490003 Pennsylvania Erie 12.54 12.57 11.33 11.36 11.01 11.04 420692006 Pennsylvania Lackawanna 11.73 12.17 10.05 10.43 9.76 10.12 420710007 Pennsylvania Lancaster 16.55 17.46 14.06 14.83 13.70 14.45 420770004 Pennsylvania Lehigh 14.50 14.50 12.48 12.48 12.13 12.13 420791101 Pennsylvania Luzerne 12.76 12.76 10.99 10.99 10.69 10.69 420850100 Pennsylvania Mercer 13.28 13.74 11.91 12.32 11.55 11.95 
B-58 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

420950025 Pennsylvania Northampton 13.68 14.07 11.72 12.06 11.38 11.72 420990301 Pennsylvania Perry 12.81 12.81 11.13 11.13 10.86 10.86 421010047 Pennsylvania Philadelphia 15.19 15.19 12.90 12.90 12.52 12.52 421250005 Pennsylvania Washington 15.17 15.48 13.26 13.53 12.76 13.02 421250200 Pennsylvania Washington 14.92 14.92 13.00 13.00 12.52 12.52 421255001 Pennsylvania Washington 13.37 13.61 11.67 11.89 11.30 11.5421290008 Pennsylvania Westmoreland 15.49 15.68 13.65 13.82 13.20 13.36 421330008 Pennsylvania York 16.52 17.25 14.01 14.63 13.64 14.24 440070022 Rhode Island Providence 10.07 10.07 8.68 8.68 8.54 8.54 440070026 Rhode Island Providence 12.14 12.41 10.55 10.79 10.37 10.6440070028 Rhode Island Providence 10.82 11.10 9.37 9.61 9.21 9.45 440071010 Rhode Island Providence 9.93 10.23 8.53 8.78 8.38 8.63 450130007 South Carolina Beaufort 11.52 11.71 9.89 10.05 9.57 9.73 450190048 South Carolina Charleston 12.21 12.48 10.63 10.87 10.31 10.55 450190049 South Carolina Charleston 11.60 11.83 9.89 10.08 9.56 9.74 450250001 South Carolina Chesterfield 12.56 12.75 11.03 11.19 10.63 10.79 450370001 South Carolina Edgefield 13.17 13.38 11.64 11.83 11.14 11.32 450410002 South Carolina Florence 12.65 12.73 11.08 11.15 10.70 10.76 450430009 South Carolina Georgetown 12.85 12.90 11.10 11.13 10.78 10.8450450008 South Carolina Greenville 15.65 15.92 13.54 13.78 13.01 13.24 450450009 South Carolina Greenville 14.66 14.82 12.61 12.75 12.09 12.22 450470003 South Carolina Greenwood 13.53 13.70 11.81 11.96 11.25 11.38 450510002 South Carolina Horry 12.04 12.16 10.44 10.53 10.12 10.2450630008 South Carolina Lexington 14.64 15.07 12.91 13.28 12.38 12.75 450730001 South Carolina Oconee 10.95 11.24 9.37 9.62 8.93 9.17 450790007 South Carolina Richland 13.59 13.79 11.90 12.08 11.39 11.57 
B-59 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

450790019 South Carolina Richland 14.24 14.45 12.51 12.70 11.99 12.17 450830010 South Carolina Spartanburg 14.17 14.33 12.12 12.25 11.63 11.76 460110002 South Dakota Brookings 9.37 9.77 8.68 9.04 8.52 8.87 460130003 South Dakota Brown 8.42 8.48 7.88 7.94 7.77 7.83 460290002 South Dakota Codington 10.14 10.34 9.48 9.66 9.33 9.52 460330132 South Dakota Custer 5.64 5.64 5.33 5.33 5.31 5.31 460710001 South Dakota Jackson 5.39 5.45 5.08 5.14 5.06 5.11 460990006 South Dakota Minnehaha 10.18 10.39 9.34 9.53 9.14 9.32 460990007 South Dakota Minnehaha 9.58 9.82 8.79 9.00 8.60 8.80 461030016 South Dakota Pennington 7.48 7.59 7.06 7.16 7.01 7.10 461030020 South Dakota Pennington 8.77 8.83 8.31 8.36 8.24 8.29 461031001 South Dakota Pennington 7.32 7.35 6.92 6.95 6.87 6.90 470090011 Tennessee Blount 14.30 14.71 12.61 12.96 12.03 12.37 470370023 Tennessee Davidson 14.21 14.32 13.07 13.18 12.49 12.59 470370025 Tennessee Davidson 13.99 14.19 12.82 13.00 12.24 12.40 470370036 Tennessee Davidson 12.97 12.98 11.84 11.85 11.27 11.27 470450004 Tennessee Dyer 12.28 12.57 11.23 11.49 10.69 10.93 470650031 Tennessee Hamilton 15.67 16.05 13.91 14.25 13.31 13.64 470651011 Tennessee Hamilton 13.73 13.89 12.09 12.23 11.50 11.64 470654002 Tennessee Hamilton 15.16 15.43 13.42 13.65 12.81 13.03 470930028 Tennessee Knox 15.47 15.74 13.53 13.76 12.90 13.12 470931017 Tennessee Knox 15.64 15.71 13.71 13.76 13.04 13.09 470931020 Tennessee Knox 15.18 15.26 13.25 13.32 12.58 12.64 470990002 Tennessee Lawrence 11.69 12.13 10.69 11.09 10.26 10.64 471050108 Tennessee Loudon 15.49 15.72 13.67 13.87 13.11 13.30 471071002 Tennessee McMinn 14.29 14.70 12.63 13.00 12.08 12.43 
B-60 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

471192007 Tennessee Maury 13.21 13.51 12.14 12.40 11.61 11.87 471251009 Tennessee Montgomery 13.80 13.91 12.72 12.82 12.14 12.23 471410001 Tennessee Putnam 13.37 13.37 12.06 12.06 11.51 11.5471450004 Tennessee Roane 14.49 14.77 12.73 12.99 12.18 12.42 471570014 Tennessee Shelby 13.71 13.79 12.36 12.43 11.76 11.83 471570038 Tennessee Shelby 13.43 13.57 12.09 12.21 11.49 11.60 471570047 Tennessee Shelby 13.68 13.79 12.36 12.46 11.74 11.85 471571004 Tennessee Shelby 12.04 12.08 10.88 10.92 10.32 10.36 471631007 Tennessee Sullivan 14.16 14.46 12.51 12.78 11.96 12.22 471650007 Tennessee Sumner 13.68 13.83 12.64 12.77 12.02 12.14 480370004 Texas Bowie 12.85 12.95 11.80 11.88 11.39 11.46 481130050 Texas Dallas 12.77 12.89 11.22 11.33 10.92 11.02 481130069 Texas Dallas 11.80 12.26 10.32 10.72 10.03 10.42 481130087 Texas Dallas 11.15 11.61 9.73 10.13 9.43 9.82 481350003 Texas Ector 7.78 7.99 7.26 7.45 7.15 7.34 481410037 Texas El Paso 9.09 9.09 8.60 8.60 8.53 8.53 482010058 Texas Harris 11.77 12.39 10.54 11.08 10.26 10.79 482011035 Texas Harris 15.42 15.84 13.93 14.31 13.57 13.94 482030002 Texas Harrison 11.69 11.79 10.67 10.75 10.18 10.26 482150043 Texas Hidalgo 10.98 11.16 10.23 10.40 10.05 10.22 482450021 Texas Jefferson 11.56 11.66 10.38 10.48 10.09 10.18 483550032 Texas Nueces 10.42 10.87 9.31 9.71 9.04 9.43 483550034 Texas Nueces 9.63 10.24 8.58 9.12 8.33 8.85 483611001 Texas Orange 11.51 11.72 10.38 10.58 10.12 10.3484391002 Texas Tarrant 11.41 11.79 9.92 10.25 9.63 9.95 484391006 Texas Tarrant 12.23 12.81 10.66 11.16 10.36 10.85 
B-61 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

500010002 Vermont Addison 8.94 8.94 8.22 8.22 8.08 8.08 500010003 Vermont Addison 8.91 8.91 8.20 8.20 8.05 8.05 500030004 Vermont Bennington 8.52 9.00 7.66 8.08 7.50 7.92 500070012 Vermont Chittenden 9.27 9.56 8.53 8.79 8.38 8.64 500070014 Vermont Chittenden 10.02 10.02 9.23 9.23 9.08 9.08 500210002 Vermont Rutland 11.08 11.13 10.18 10.22 10.02 10.06 510130020 Virginia Arlington 14.27 14.62 12.07 12.37 11.65 11.94 510360002 Virginia Charles 12.37 12.48 10.32 10.42 9.96 10.06 510410003 Virginia Chesterfield 13.44 13.59 11.25 11.39 10.85 10.98 510590030 Virginia Fairfax 13.33 13.59 11.30 11.54 10.92 11.14 510591005 Virginia Fairfax 13.62 13.77 11.56 11.70 11.16 11.30 510595001 Virginia Fairfax 13.88 14.12 11.95 12.16 11.55 11.75 510870014 Virginia Henrico 13.51 13.83 11.31 11.58 10.90 11.16 510870015 Virginia Henrico 12.93 12.99 10.96 11.02 10.51 10.57 511071005 Virginia Loudoun 13.57 13.90 11.82 12.10 11.43 11.7511390004 Virginia Page 12.79 12.85 11.07 11.11 10.65 10.69 515200006 Virginia Bristol City 13.93 14.00 12.02 12.08 11.43 11.48 516500004 Virginia Hampton City 12.17 12.35 10.11 10.28 9.77 9.92 516800015 Virginia

Lynchburg City 12.84 13.00 10.96 11.10 10.48 10.6

517100024 Virginia Norfolk City 12.78 13.01 10.69 10.88 10.33 10.5517700014 Virginia Roanoke City 14.27 14.48 12.48 12.67 11.88 12.07 517750010 Virginia Salem City 14.69 14.69 12.94 12.94 12.35 12.35 518100008 Virginia

Virginia Beach City 12.40 12.57 10.30 10.45 9.95 10.09 

540030003 West Virginia Berkeley 15.93 16.19 14.09 14.32 13.70 13.92 
B-62 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

540090005 West Virginia Brooke 16.52 16.80 14.33 14.58 13.84 14.09 540090011 West Virginia Brooke 16.04 16.37 13.87 14.15 13.39 13.66 540110006 West Virginia Cabell 16.30 16.57 14.71 14.94 14.05 14.28 540291004 West Virginia Hancock 15.76 16.64 13.73 14.51 13.28 14.03 540330003 West Virginia Harrison 13.99 14.19 13.17 13.36 12.71 12.90 540390010 West Virginia Kanawha 15.15 15.38 13.40 13.60 12.78 12.97 540390011 West Virginia Kanawha 13.17 13.17 11.67 11.67 11.13 11.13 540391005 West Virginia Kanawha 16.52 16.59 14.65 14.71 14.01 14.06 540490006 West Virginia Marion 15.03 15.25 14.22 14.43 13.76 13.95 540511002 West Virginia Marshall 15.19 15.33 13.25 13.38 12.75 12.87 540610003 West Virginia Monongalia 14.35 14.47 12.89 13.01 12.41 12.53 540690010 West Virginia Ohio 14.58 14.58 12.53 12.53 12.05 12.05 540810002 West Virginia Raleigh 12.90 13.03 11.22 11.33 10.70 10.80 541071002 West Virginia Wood 15.40 15.44 13.74 13.77 13.16 13.20 550030010 Wisconsin Ashland 6.07 6.21 5.66 5.79 5.57 5.69 550090005 Wisconsin Brown 11.39 11.86 10.60 11.03 10.36 10.78 550250047 Wisconsin Dane 12.20 12.65 11.13 11.54 10.80 11.20 550270007 Wisconsin Dodge 11.04 11.26 10.13 10.34 9.82 10.02 550410007 Wisconsin Forest 7.41 7.41 6.90 6.90 6.75 6.75 550430009 Wisconsin Grant 11.79 12.24 10.75 11.16 10.45 10.85 550590019 Wisconsin Kenosha 11.98 12.93 10.94 11.80 10.49 11.32 550710007 Wisconsin Manitowoc 10.20 10.89 9.50 10.13 9.24 9.85 550790010 Wisconsin Milwaukee 13.32 13.94 12.16 12.72 11.72 12.26 550790026 Wisconsin Milwaukee 12.88 13.83 11.73 12.59 11.30 12.12 550790043 Wisconsin Milwaukee 14.08 14.62 12.87 13.36 12.41 12.89 550790059 Wisconsin Milwaukee 13.68 14.87 12.51 13.58 12.06 13.10 
B-63 

Annual PM2.5 Design Values (μg/m3

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Case 

Maxim

Value

550790099 Wisconsin Milwaukee 13.54 14.40 12.36 13.14 11.92 12.68 550870009 Wisconsin Outagamie 10.96 11.45 10.19 10.63 9.95 10.39 550890009 Wisconsin Ozaukee 11.60 12.22 10.66 11.23 10.30 10.85 551091002 Wisconsin St. Croix 10.09 10.09 9.31 9.31 9.11 9.11 551110007 Wisconsin Sauk 10.22 10.57 9.35 9.67 9.06 9.37 551198001 Wisconsin Taylor 8.24 8.43 7.67 7.85 7.51 7.69 551250001 Wisconsin Vilas 6.78 6.87 6.33 6.42 6.21 6.30 551330027 Wisconsin Waukesha 13.91 14.34 12.77 13.17 12.36 12.75 560050877 Wyoming Campbell 6.29 6.29 6.12 6.12 6.10 6.10 560050892 Wyoming Campbell 5.11 5.33 4.96 5.17 4.95 5.16 560050899 Wyoming Campbell 5.26 5.49 5.07 5.30 5.06 5.28 560090819 Wyoming Converse 3.58 3.68 3.43 3.53 3.42 3.52 560131003 Wyoming Fremont 8.17 8.50 7.81 8.12 7.74 8.05 560210001 Wyoming Laramie 4.48 4.69 4.15 4.34 4.11 4.31 560330002 Wyoming Sheridan 9.70 9.84 9.32 9.45 9.25 9.38 
B-64 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

010030010 Alabama Baldwin 26.2 27.5 22.7 23.8 21.7 22.010270001 Alabama Clay 31.8 32.4 26.4 27.1 24.9 25.010331002 Alabama Colbert 30.4 30.4 26.2 26.3 24.9 25.010491003 Alabama De Kalb 32.0 32.6 26.0 26.9 24.7 25.010530002 Alabama Escambia 29.0 29.7 24.8 25.4 24.0 24.010550010 Alabama Etowah 35.1 35.7 29.3 29.5 28.1 28.010690003 Alabama Houston 28.6 28.6 24.6 24.6 23.7 23.010730023 Alabama Jefferson 44.0 44.2 36.9 37.3 35.6 36.010731005 Alabama Jefferson 34.8 35.5 30.1 30.6 28.8 29.010731009 Alabama Jefferson 34.5 34.9 28.8 29.0 27.6 27.010731010 Alabama Jefferson 34.1 34.3 28.1 28.3 26.6 26.010732003 Alabama Jefferson 40.3 40.8 35.3 35.9 34.2 34.010732006 Alabama Jefferson 33.1 34.1 27.9 28.6 26.9 27.010735002 Alabama Jefferson 33.0 34.8 27.9 29.3 26.5 27.010735003 Alabama Jefferson 35.8 36.5 29.2 29.7 27.9 28.010890014 Alabama Madison 33.5 34.1 27.9 28.0 26.5 26.010970002 Alabama Mobile 30.0 30.0 26.2 26.2 24.9 24.010970003 Alabama Mobile 28.5 29.4 24.8 25.5 23.6 24.011010007 Alabama Montgomery 32.0 33.5 27.1 28.5 26.0 27.011030011 Alabama Morgan 31.5 31.9 25.1 25.4 23.8 24.011130001 Alabama Russell 35.5 36.9 31.0 32.1 29.6 30.011170006 Alabama Shelby 32.0 32.8 27.4 27.6 26.3 26.011190002 Alabama Sumter 28.9 28.9 24.8 24.8 23.8 23.011210002 Alabama Talladega 33.4 33.4 28.9 28.9 27.8 27.011250004 Alabama Tuscaloosa 29.8 30.1 25.5 25.5 24.4 24.
B-65 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

011270002 Alabama Walker 32.8 33.3 26.5 27.6 25.2 26.050010011 Arkansas Arkansas 29.1 29.6 27.0 27.4 25.8 26.050030005 Arkansas Ashley 28.9 29.9 26.6 27.1 25.6 26.050350005 Arkansas Crittenden 35.0 35.0 30.9 30.9 29.0 29.050450002 Arkansas Faulkner 29.8 30.6 27.2 27.8 26.0 26.050510003 Arkansas Garland 29.2 29.3 26.3 26.4 25.0 25.051070001 Arkansas Phillips 29.1 30.2 26.6 27.6 25.2 26.051130002 Arkansas Polk 26.1 27.7 22.9 24.0 21.6 22.051150003 Arkansas Pope 28.3 29.1 25.9 26.2 25.0 25.051190007 Arkansas Pulaski 31.1 31.4 28.4 28.7 27.0 27.051191004 Arkansas Pulaski 31.9 32.4 29.4 29.9 28.5 28.051191005 Arkansas Pulaski 31.9 32.3 29.5 29.9 28.2 28.051390006 Arkansas Union 28.7 29.6 26.1 26.7 25.2 25.051450001 Arkansas White 29.9 31.1 27.4 28.5 26.2 27.080010006 Colorado Adams 25.3 29.2 21.9 25.4 21.0 24.080050005 Colorado Arapahoe 21.2 22.2 18.3 19.1 17.6 18.080130003 Colorado Boulder 21.1 22.3 19.3 20.4 19.0 20.080130012 Colorado Boulder 18.7 19.7 16.6 17.5 16.2 17.080290004 Colorado Delta 20.7 22.3 18.4 19.8 17.9 19.080310002 Colorado Denver 26.4 27.6 23.3 24.3 22.5 23.080310023 Colorado Denver 26.3 26.3 23.5 23.5 22.9 22.080390001 Colorado Elbert 13.1 15.1 11.7 13.4 11.3 13.080410008 Colorado El Paso 16.4 16.7 14.8 15.0 14.4 14.080410011 Colorado El Paso 16.5 17.4 14.8 15.6 14.4 15.080690009 Colorado Larimer 18.3 18.8 16.8 17.3 16.5 17.080770017 Colorado Mesa 23.5 24.6 21.6 22.5 20.8 21.
B-66 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

081010012 Colorado Pueblo 15.4 15.8 14.0 14.3 13.7 14.081130004 Colorado San Miguel 10.1 11.3 9.7 10.9 9.6 10.081230006 Colorado Weld 22.9 23.9 20.8 21.9 20.6 21.081230008 Colorado Weld 18.3 18.9 16.7 17.1 16.4 16.090010010 Connecticut Fairfield 36.2 37.3 29.9 30.6 28.6 29.090011123 Connecticut Fairfield 32.2 32.7 28.2 28.6 27.2 27.090013005 Connecticut Fairfield 34.9 35.6 28.8 29.2 27.5 28.090019003 Connecticut Fairfield 33.6 36.7 27.0 28.3 25.7 26.090031003 Connecticut Hartford 31.8 32.6 26.0 26.4 24.9 25.090050005 Connecticut Litchfield 27.1 27.1 20.2 20.2 19.2 19.090090026 Connecticut New Haven 35.6 37.9 28.3 29.4 27.1 28.090090027 Connecticut New Haven 35.5 36.0 28.9 29.3 27.8 28.090091123 Connecticut New Haven 38.3 40.3 30.8 31.5 29.4 29.090092008 Connecticut New Haven 33.6 36.4 27.1 28.2 26.0 26.090092123 Connecticut New Haven 34.4 34.7 28.3 28.6 27.3 27.090113002 Connecticut New London 32.0 34.4 25.4 26.4 24.5 25.100010002 Delaware Kent 32.1 32.2 24.8 26.0 24.0 25.100010003 Delaware Kent 31.5 32.4 24.7 26.4 24.0 25.100031003 Delaware New Castle 34.3 34.3 28.0 28.0 27.1 27.100031007 Delaware New Castle 32.6 34.5 24.6 25.6 23.5 24.100031012 Delaware New Castle 33.5 33.5 27.7 27.7 26.8 26.100032004 Delaware New Castle 36.6 36.7 29.5 29.7 28.4 28.100051002 Delaware Sussex 33.7 34.3 25.6 26.5 24.9 25.110010041 District Of Columbia 36.3 37.8 30.3 31.5 29.1 30.110010042 District Of Columbia 34.9 37.0 29.2 31.4 27.9 30.110010043 District Of Columbia 34.1 34.8 28.7 29.4 27.5 28.
B-67 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

120010023 Florida Alachua 21.3 21.5 17.1 17.4 16.4 16.120010024 Florida Alachua 20.9 22.2 17.4 18.8 17.1 18.120051004 Florida Bay 28.0 29.2 24.1 24.9 23.5 24.120090007 Florida Brevard 20.7 22.0 17.2 18.3 16.5 17.120111002 Florida Broward 18.3 18.9 15.7 16.2 15.3 15.120112004 Florida Broward 18.6 19.0 16.2 16.5 15.7 16.120113002 Florida Broward 15.9 15.9 13.6 13.6 13.1 13.120170005 Florida Citrus 21.2 21.9 17.1 18.0 16.6 17.120310098 Florida Duval 23.7 24.1 19.6 20.3 19.2 20.120310099 Florida Duval 24.3 24.5 21.1 21.5 20.7 21.120330004 Florida Escambia 28.8 30.6 25.2 26.4 24.5 25.120570030 Florida Hillsborough 23.4 24.0 19.2 19.8 18.7 19.120573002 Florida Hillsborough 22.2 22.5 17.6 18.0 17.0 17.120710005 Florida Lee 17.7 17.9 14.5 14.8 14.0 14.120730012 Florida Leon 27.0 28.2 23.5 24.7 22.7 23.120814012 Florida Manatee 19.5 20.2 15.5 15.9 14.9 15.120830003 Florida Marion 22.5 22.5 18.3 18.3 17.7 17.120861016 Florida Miami-Dade 19.1 19.1 15.2 15.2 14.8 14.120866001 Florida Miami-Dade 18.6 19.1 15.8 16.3 15.3 15.120952002 Florida Orange 21.8 21.8 17.1 17.1 16.5 16.120990009 Florida Palm Beach 17.7 17.7 15.8 15.8 15.5 15.120992005 Florida Palm Beach 18.2 18.4 15.2 15.4 14.7 15.121030018 Florida Pinellas 21.7 22.4 18.1 18.2 17.6 17.121031009 Florida Pinellas 20.8 21.1 16.7 17.0 16.2 16.121056006 Florida Polk 19.3 19.9 15.9 16.4 15.6 16.121111002 Florida St Lucie 18.1 18.3 15.1 15.3 14.6 14.
B-68 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

121150013 Florida Sarasota 19.2 19.9 16.0 16.6 15.4 16.121171002 Florida Seminole 22.0 23.0 17.3 17.9 16.5 17.121275002 Florida Volusia 22.0 23.9 17.2 18.9 16.5 18.130210007 Georgia Bibb 33.5 33.6 28.6 29.0 27.1 27.130210012 Georgia Bibb 30.7 31.4 25.9 26.4 24.3 24.130510017 Georgia Chatham 28.4 28.9 24.4 24.7 23.5 23.130510091 Georgia Chatham 27.9 28.6 24.5 25.3 23.7 24.130630091 Georgia Clayton 35.8 37.7 30.0 31.4 28.7 29.130670003 Georgia Cobb 35.0 36.3 29.0 29.6 27.7 28.130670004 Georgia Cobb 34.1 35.7 28.0 29.1 26.3 27.130890002 Georgia De Kalb 33.4 33.7 27.5 27.8 25.9 26.130892001 Georgia De Kalb 33.9 35.4 28.0 29.2 26.5 27.130950007 Georgia Dougherty 34.1 35.2 29.8 30.8 28.9 29.131150005 Georgia Floyd 35.1 36.4 29.8 31.0 28.6 29.131210032 Georgia Fulton 34.1 35.0 28.2 29.6 27.0 28.131210039 Georgia Fulton 37.6 37.6 31.8 31.8 30.2 30.131270006 Georgia Glynn 26.1 26.7 23.3 23.5 22.6 22.131350002 Georgia Gwinnett 32.8 35.0 27.1 27.7 25.4 26.131390003 Georgia Hall 30.1 30.3 25.5 25.9 24.1 24.131530001 Georgia Houston 29.6 30.6 25.4 27.1 23.9 25.131850003 Georgia Lowndes 25.6 26.2 21.6 22.2 20.7 21.132150001 Georgia Muscogee 31.3 32.9 27.6 28.6 26.4 27.132150008 Georgia Muscogee 34.5 37.6 29.2 31.4 27.9 29.132150011 Georgia Muscogee 30.2 30.9 26.2 26.2 25.0 25.132230003 Georgia Paulding 33.0 33.3 26.6 26.9 25.4 25.132450005 Georgia Richmond 32.7 33.4 30.0 30.6 28.9 29.
B-69 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

132450091 Georgia Richmond 31.9 32.6 28.4 28.8 27.5 28.132950002 Georgia Walker 30.9 32.5 26.0 27.4 24.8 26.133030001 Georgia Washington 30.8 31.4 26.5 26.9 24.8 25.133190001 Georgia Wilkinson 33.1 33.7 29.4 29.9 27.7 28.170010006 Illinois Adams 31.4 32.3 28.0 28.8 26.3 26.170190004 Illinois Champaign 31.3 32.5 28.2 29.9 26.6 28.170191001 Illinois Champaign 30.0 31.7 27.2 28.5 26.1 27.170310022 Illinois Cook 36.6 38.6 33.2 34.9 32.4 34.170310050 Illinois Cook 36.1 38.0 31.5 33.1 30.0 31.170310052 Illinois Cook 40.2 41.4 34.9 36.0 33.2 34.170310057 Illinois Cook 37.3 38.6 32.6 33.7 30.9 32.170310076 Illinois Cook 38.0 39.1 33.0 34.0 31.5 32.170311016 Illinois Cook 43.0 46.3 37.5 40.4 35.8 38.170312001 Illinois Cook 37.7 40.6 33.6 36.1 32.4 34.170313103 Illinois Cook 39.6 40.3 35.0 35.4 33.5 33.170313301 Illinois Cook 40.2 43.3 34.9 37.6 33.4 36.170314007 Illinois Cook 34.3 36.4 30.5 32.3 28.8 30.170314201 Illinois Cook 32.0 33.8 27.7 29.6 26.0 27.170316005 Illinois Cook 39.1 41.8 34.1 36.4 32.7 34.170434002 Illinois Du Page 34.6 35.9 31.0 32.3 29.7 31.170650002 Illinois Hamilton 31.6 31.6 29.6 29.6 27.8 27.170831001 Illinois Jersey 32.1 33.2 29.6 30.5 27.8 28.170890003 Illinois Kane 33.8 35.4 29.9 31.2 28.2 29.170890007 Illinois Kane 34.8 34.8 31.2 31.2 29.5 29.170971007 Illinois Lake 33.0 35.0 29.9 31.5 27.8 29.170990007 Illinois La Salle 28.9 29.9 25.8 26.6 24.6 25.
B-70 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

171110001 Illinois McHenry 31.5 32.6 28.3 29.3 26.9 27.171132003 Illinois McLean 33.4 33.4 30.2 30.2 28.8 28.171150013 Illinois Macon 33.2 34.4 30.3 31.5 28.3 29.171190023 Illinois Madison 37.3 38.1 35.1 35.8 32.8 33.171191007 Illinois Madison 39.1 40.1 36.5 36.8 34.6 35.171192009 Illinois Madison 34.9 35.9 33.3 34.3 31.5 32.171193007 Illinois Madison 34.0 34.6 32.0 32.7 30.6 31.171430037 Illinois Peoria 32.7 34.1 29.3 30.6 27.5 28.171570001 Illinois Randolph 28.9 29.9 27.3 28.3 25.6 26.171613002 Illinois Rock Island 30.9 31.4 28.2 28.9 27.4 28.171630010 Illinois St Clair 33.7 34.1 31.7 32.3 29.7 30.171634001 Illinois St Clair 31.9 32.9 30.1 31.2 28.2 29.171670012 Illinois Sangamon 33.4 34.1 30.4 30.9 29.2 29.171971002 Illinois Will 36.4 37.1 32.3 32.9 30.6 31.171971011 Illinois Will 30.7 31.7 27.1 27.9 25.4 26.172010013 Illinois Winnebago 34.7 34.7 31.3 31.3 29.9 29.180030004 Indiana Allen 33.1 34.6 30.7 32.0 30.0 31.180030014 Indiana Allen 30.5 31.1 28.2 28.8 27.6 28.180190006 Indiana Clark 37.5 39.4 33.1 34.5 31.4 32.180350006 Indiana Delaware 32.0 33.6 29.0 30.3 27.6 28.180372001 Indiana Dubois 35.3 36.9 33.2 34.8 31.8 33.180390003 Indiana Elkhart 34.4 36.3 31.1 32.8 29.9 31.180431004 Indiana Floyd 33.2 34.5 30.2 31.2 28.5 29.180650003 Indiana Henry 31.8 31.8 28.5 28.5 27.2 27.180670003 Indiana Howard 32.2 32.9 28.9 29.7 27.4 28.180830004 Indiana Knox 35.9 36.3 33.0 33.2 31.6 31.
B-71 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

180890006 Indiana Lake 34.9 35.5 31.3 31.8 30.3 30.180890022 Indiana Lake 38.9 44.0 34.9 39.5 33.7 38.180890026 Indiana Lake 38.4 41.3 34.0 37.0 32.8 35.180890027 Indiana Lake 32.6 34.2 29.1 30.8 28.1 29.180890031 Indiana Lake 34.0 34.0 30.6 30.6 29.2 29.180891003 Indiana Lake 32.7 33.5 29.5 30.3 28.6 29.180892004 Indiana Lake 32.9 33.9 29.6 30.6 28.6 29.180892010 Indiana Lake 34.2 35.6 30.7 31.9 29.6 31.180910011 Indiana La Porte 33.0 33.6 28.5 29.1 27.2 27.180910012 Indiana La Porte 30.6 31.8 27.2 28.1 26.3 27.180950009 Indiana Madison 32.8 34.0 29.8 30.8 28.5 29.180970042 Indiana Marion 34.2 35.3 32.2 33.5 30.8 32.180970043 Indiana Marion 38.4 39.9 35.7 37.1 34.3 35.180970066 Indiana Marion 38.3 39.6 35.7 36.9 34.2 35.180970078 Indiana Marion 36.6 37.6 34.1 34.9 32.4 33.180970079 Indiana Marion 35.6 36.7 32.5 33.4 30.7 31.180970081 Indiana Marion 38.2 39.2 35.8 36.9 34.1 35.180970083 Indiana Marion 36.6 37.0 34.0 34.5 32.4 32.181270020 Indiana Porter 32.9 34.6 28.7 30.6 27.4 29.181270024 Indiana Porter 31.8 32.4 28.1 28.8 26.9 27.181410014 Indiana St Joseph 32.4 33.9 29.8 31.4 29.0 30.181411008 Indiana St Joseph 33.1 33.1 29.5 29.5 28.8 28.181412004 Indiana St Joseph 30.0 31.4 27.2 28.4 26.5 27.181470009 Indiana Spencer 32.3 33.1 30.4 31.3 28.9 29.181570008 Indiana Tippecanoe 35.6 36.7 32.8 33.9 31.3 32.181630006 Indiana Vanderburgh 34.8 35.5 31.9 32.5 30.8 31.
B-72 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

181630012 Indiana Vanderburgh 33.2 34.4 31.0 32.2 30.0 31.181630016 Indiana Vanderburgh 32.6 33.7 30.3 31.3 29.4 30.181670018 Indiana Vigo 34.6 35.1 32.1 32.6 30.8 31.181670023 Indiana Vigo 34.8 36.1 32.2 33.5 30.4 31.190130008 Iowa Black Hawk 30.7 32.0 27.9 29.3 27.0 28.190450021 Iowa Clinton 33.9 35.9 30.3 31.9 29.3 30.191032001 Iowa Johnson 34.6 35.2 30.9 31.6 29.4 30.191130037 Iowa Linn 30.6 33.3 27.5 30.0 26.6 29.191370002 Iowa Montgomery 27.5 27.7 24.4 24.8 23.3 23.191390015 Iowa Muscatine 36.0 37.7 33.2 34.5 32.2 33.191471002 Iowa Palo Alto 25.7 25.7 23.5 23.5 22.6 22.191530030 Iowa Polk 28.4 29.0 25.7 26.2 24.7 25.191532510 Iowa Polk 27.2 29.5 24.3 26.0 23.3 25.191532520 Iowa Polk 31.4 31.4 27.9 27.9 27.1 27.191550009 Iowa Pottawattamie 28.6 28.9 25.6 25.8 24.8 25.191630015 Iowa Scott 31.0 32.1 27.7 28.6 26.7 27.191630018 Iowa Scott 32.3 33.3 29.7 31.0 28.8 30.191630019 Iowa Scott 37.1 37.1 33.2 33.2 31.5 31.191770006 Iowa Van Buren 28.3 28.3 25.5 25.5 24.7 24.191930017 Iowa Woodbury 26.4 28.4 23.6 25.4 23.0 24.191970004 Iowa Wright 28.6 28.8 25.6 25.8 24.9 25.200910007 Kansas Johnson 25.3 26.5 23.0 24.1 22.1 23.200910009 Kansas Johnson 29.3 29.3 26.3 26.3 25.5 25.200910010 Kansas Johnson 23.5 23.6 20.9 21.0 20.0 20.201070002 Kansas Linn 25.3 26.2 23.0 23.7 21.9 22.201730008 Kansas Sedgwick 23.7 25.3 20.8 22.3 20.0 21.
B-73 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

201730009 Kansas Sedgwick 25.0 26.4 22.2 23.5 21.5 22.201730010 Kansas Sedgwick 25.3 27.0 22.5 23.9 21.9 23.201770010 Kansas Shawnee 29.1 29.1 26.1 26.1 25.2 25.201910002 Kansas Sumner 22.8 23.7 20.1 20.9 19.3 20.202090021 Kansas Wyandotte 29.5 32.1 26.3 28.9 25.1 27.202090022 Kansas Wyandotte 26.6 28.6 24.2 26.0 23.5 25.210130002 Kentucky Bell 29.9 31.0 25.4 26.1 24.2 24.210190017 Kentucky Boyd 33.1 34.4 28.7 29.8 26.9 28.210290006 Kentucky Bullitt 34.6 35.8 30.9 32.0 29.1 30.210370003 Kentucky Campbell 31.2 31.2 27.3 27.3 26.1 26.210430500 Kentucky Carter 29.9 31.2 25.4 26.6 23.7 24.210470006 Kentucky Christian 33.6 33.6 29.6 29.6 27.8 27.210590005 Kentucky Daviess 33.8 33.8 32.0 32.0 30.1 30.210670012 Kentucky Fayette 31.9 33.2 27.9 28.9 25.8 26.210670014 Kentucky Fayette 32.2 33.2 28.4 29.2 26.5 27.210730006 Kentucky Franklin 32.1 33.8 27.7 29.0 25.8 27.210930006 Kentucky Hardin 32.8 35.1 29.0 31.1 27.1 29.211010014 Kentucky Henderson 31.8 32.7 28.5 29.4 27.4 28.211110043 Kentucky Jefferson 35.4 36.1 31.4 32.2 29.8 30.211110044 Kentucky Jefferson 36.1 36.6 31.8 32.2 30.0 30.211110048 Kentucky Jefferson 36.4 37.2 31.5 32.3 29.8 30.211110051 Kentucky Jefferson 32.4 33.8 28.4 29.7 26.6 27.211170007 Kentucky Kenton 34.7 35.4 30.2 31.3 28.8 29.211250004 Kentucky Laurel 25.1 25.1 21.5 21.5 20.4 20.211451004 Kentucky McCracken 33.6 35.9 30.5 32.5 28.5 30.211510003 Kentucky Madison 30.1 30.9 24.5 25.4 23.1 24.
B-74 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

211930003 Kentucky Perry 28.5 29.8 24.4 25.5 22.8 23.211950002 Kentucky Pike 30.5 31.4 26.0 26.8 24.6 25.212270007 Kentucky Warren 33.1 35.1 29.2 31.0 27.3 29.220171002 Louisiana Caddo 27.5 27.5 24.5 24.5 23.3 23.220190009 Louisiana Calcasieu 24.2 25.8 22.0 23.6 21.4 22.220190010 Louisiana Calcasieu 26.3 27.2 23.7 24.8 22.9 23.220290003 Louisiana Concordia 26.1 26.1 23.8 23.8 22.6 22.220330009 Louisiana

East Baton Rouge 29.3 30.5 25.9 27.0 25.0 26.

220331001 Louisiana

East Baton Rouge 25.4 26.1 21.9 22.6 21.2 21.

220470005 Louisiana Iberville 28.6 29.3 25.2 25.9 24.4 25.220470009 Louisiana Iberville 26.1 27.8 22.8 23.9 21.9 23.220511001 Louisiana Jefferson 27.0 27.9 22.3 22.9 21.6 22.220550006 Louisiana Lafayette 24.2 24.4 21.7 22.0 20.9 21.220730004 Louisiana Ouachita 28.9 30.6 26.1 27.5 25.0 26.220790002 Louisiana Rapides 30.2 34.3 26.3 30.3 25.1 28.221050001 Louisiana Tangipahoa 29.6 30.3 25.1 25.7 24.0 24.221090001 Louisiana Terrebonne 26.2 26.4 22.6 22.7 21.7 21.221210001 Louisiana

West Baton Rouge 29.0 29.8 25.6 26.5 24.8 25.

230010011 Maine Androscoggin 26.5 29.0 23.6 25.2 22.9 24.230030013 Maine Aroostook 24.2 25.4 22.5 23.6 22.2 23.230031011 Maine Aroostook 22.9 24.3 20.8 22.2 20.4 21.230050015 Maine Cumberland 27.7 30.1 23.8 26.5 23.2 25.230050027 Maine Cumberland 29.2 31.1 24.5 26.0 23.8 25.230090103 Maine Hancock 19.4 19.7 13.5 13.7 13.4 13.
B-75 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

230110016 Maine Kennebec 26.2 28.1 23.1 25.1 22.4 24.230172011 Maine Oxford 28.3 30.4 25.6 26.6 25.0 26.230190002 Maine Penobscot 22.0 24.3 18.7 20.6 18.1 20.240030014 Maryland Anne Arundel 33.2 33.2 27.5 27.5 26.6 26.240031003 Maryland Anne Arundel 35.5 37.4 29.5 30.9 28.3 29.240032002 Maryland Anne Arundel 36.1 36.1 31.4 31.4 30.4 30.240051007 Maryland Baltimore 33.3 34.1 27.6 28.2 26.2 26.240053001 Maryland Baltimore 35.8 37.0 29.8 30.7 28.9 29.240150003 Maryland Cecil 30.8 32.5 25.3 26.6 24.4 25.240251001 Maryland Harford 31.2 31.4 24.0 24.3 23.1 23.240313001 Maryland Montgomery 30.9 31.8 25.8 26.3 24.7 25.240330030 Maryland

Prince Georges 31.7 31.7 26.2 26.2 25.1 25.

240338003 Maryland

Prince Georges 33.4 34.6 27.7 28.6 26.7 27.

240430009 Maryland Washington 33.4 35.5 29.1 30.6 28.2 29.245100006 Maryland

Baltimore City 33.3 33.8 27.9 28.1 26.8 26.

245100007 Maryland

Baltimore City 34.7 36.1 28.7 29.5 27.5 28.

245100008 Maryland

Baltimore City 37.2 37.3 31.3 32.0 30.0 30.

245100035 Maryland

Baltimore City 37.7 39.2 32.2 33.0 31.1 31.

245100040 Maryland

Baltimore City 39.0 40.9 33.0 34.4 31.8 33.
B-76 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

245100049 Maryland

Baltimore City 38.1 38.1 33.2 33.2 32.1 32.

250035001 Massachusetts Berkshire 31.0 33.5 27.1 28.8 26.2 27.250051004 Massachusetts Bristol 25.0 26.8 19.3 20.2 19.3 20.250092006 Massachusetts Essex 28.7 33.4 23.2 27.0 22.7 26.250095005 Massachusetts Essex 26.8 27.1 20.7 20.8 20.3 20.250096001 Massachusetts Essex 27.8 27.9 23.3 23.7 22.8 23.250130008 Massachusetts Hampden 27.2 27.9 22.8 23.3 22.1 22.250130016 Massachusetts Hampden 32.3 33.5 28.1 29.3 27.4 28.250132009 Massachusetts Hampden 33.1 33.3 28.6 28.7 27.9 27.250230004 Massachusetts Plymouth 28.4 28.8 21.7 22.2 21.4 21.250250002 Massachusetts Suffolk 29.4 29.7 24.3 24.4 23.7 23.250250027 Massachusetts Suffolk 29.2 29.5 23.6 23.6 23.0 23.250250042 Massachusetts Suffolk 28.6 29.1 23.7 23.7 23.1 23.250250043 Massachusetts Suffolk 32.1 33.9 26.1 27.8 25.6 27.250270016 Massachusetts Worcester 30.0 30.0 23.9 24.0 23.2 23.250270023 Massachusetts Worcester 30.6 30.6 25.2 25.2 24.6 24.260050003 Michigan Allegan 33.8 34.0 30.1 30.3 29.1 29.260170014 Michigan Bay 31.6 32.1 28.3 28.8 27.4 28.260210014 Michigan Berrien 31.3 32.3 27.8 28.5 26.5 27.260490021 Michigan Genesee 30.4 32.0 27.4 28.8 26.5 27.260650012 Michigan Ingham 31.9 32.1 29.3 29.6 28.4 28.260770008 Michigan Kalamazoo 31.1 32.8 28.3 29.9 27.4 28.260810020 Michigan Kent 36.5 37.1 32.2 32.7 30.9 31.260990009 Michigan Macomb 35.3 35.9 32.0 32.7 31.0 31.261130001 Michigan Missaukee 24.8 25.2 22.0 22.2 20.9 21.
B-77 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

261150005 Michigan Monroe 38.8 39.6 34.4 34.8 32.6 33.261210040 Michigan Muskegon 34.7 36.6 30.5 32.1 29.0 30.261250001 Michigan Oakland 39.9 40.4 35.0 35.4 33.4 33.261390005 Michigan Ottawa 34.2 34.7 31.0 31.4 30.2 30.261450018 Michigan Saginaw 30.6 30.6 27.2 27.2 26.1 26.261470005 Michigan St Clair 39.6 40.6 36.2 37.1 35.0 36.261610005 Michigan Washtenaw 33.6 33.6 29.8 29.8 28.7 28.261610008 Michigan Washtenaw 39.4 40.8 35.0 36.3 33.9 35.261630001 Michigan Wayne 37.8 40.1 32.9 35.1 31.3 33.261630015 Michigan Wayne 40.1 40.6 35.5 36.0 34.1 34.261630016 Michigan Wayne 42.9 45.4 38.9 41.2 37.9 40.261630019 Michigan Wayne 40.9 41.4 37.3 37.8 36.3 36.261630025 Michigan Wayne 35.1 36.8 30.8 31.9 29.3 30.261630033 Michigan Wayne 43.8 44.2 39.4 39.8 38.2 38.261630036 Michigan Wayne 37.1 37.9 34.0 34.9 33.0 33.261630039 Michigan Wayne 37.0 37.0 32.9 32.9 31.8 31.270210001 Minnesota Cass 18.0 19.1 16.4 17.4 15.9 16.270370470 Minnesota Dakota 25.4 25.6 23.0 23.2 22.0 22.270530050 Minnesota Hennepin 27.2 28.0 24.0 24.7 23.0 23.270530961 Minnesota Hennepin 25.5 26.2 22.9 23.5 22.2 22.270530963 Minnesota Hennepin 26.0 27.8 23.0 24.6 22.1 23.270530965 Minnesota Hennepin 24.7 25.5 21.8 22.5 21.1 21.270531007 Minnesota Hennepin 25.4 25.7 22.4 22.7 21.5 21.270532006 Minnesota Hennepin 26.7 27.5 24.0 24.7 23.0 23.270953051 Minnesota Mille Lacs 22.0 22.2 20.5 20.8 19.9 20.271230866 Minnesota Ramsey 28.0 29.4 25.5 26.8 24.7 25.
B-78 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

271230868 Minnesota Ramsey 28.3 30.5 25.4 27.1 24.4 26.271230871 Minnesota Ramsey 26.3 26.8 23.4 24.0 22.3 22.271377001 Minnesota St Louis 20.3 21.2 18.5 19.4 18.1 18.271377550 Minnesota St Louis 19.5 20.1 17.9 18.2 17.2 17.271377551 Minnesota St Louis 23.5 23.8 21.2 21.4 20.7 20.271390505 Minnesota Scott 24.9 25.8 22.7 23.4 22.1 22.280010004 Mississippi Adams 27.4 27.8 24.3 24.8 23.2 23.280110001 Mississippi Bolivar 28.9 29.9 26.4 27.2 25.1 26.280330002 Mississippi De Soto 30.8 31.3 27.1 27.3 25.4 25.280350004 Mississippi Forrest 30.4 31.1 27.0 27.6 26.0 26.280470008 Mississippi Harrison 29.0 29.0 25.6 25.6 24.6 24.280490010 Mississippi Hinds 28.8 29.1 25.4 25.5 24.2 24.280590006 Mississippi Jackson 26.9 26.9 23.4 23.4 22.4 22.280670002 Mississippi Jones 31.2 31.6 27.8 28.3 26.7 27.280810005 Mississippi Lee 32.1 33.4 27.7 28.5 26.2 26.280870001 Mississippi Lowndes 32.4 33.0 27.5 28.0 26.0 26.281490004 Mississippi Warren 30.2 30.2 26.7 26.7 25.6 25.290190004 Missouri Boone 30.2 30.2 27.0 27.0 25.8 25.290210005 Missouri Buchanan 30.1 30.1 26.9 26.9 26.2 26.290370003 Missouri Cass 25.6 26.1 23.0 23.3 21.7 21.290390001 Missouri Cedar 28.7 28.7 25.7 25.7 24.1 24.290470005 Missouri Clay 28.0 28.6 24.9 25.6 24.1 24.290770032 Missouri Greene 28.2 29.4 25.9 26.9 24.3 25.290950034 Missouri Jackson 27.8 27.9 25.2 25.2 24.0 24.290990012 Missouri Jefferson 33.4 34.2 31.2 31.9 29.4 29.291370001 Missouri Monroe 27.8 27.8 24.9 24.9 23.6 23.
B-79 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

291831002 Missouri St Charles 33.1 34.7 31.6 33.0 30.0 31.291860006 Missouri Ste Genevieve 31.4 32.0 28.6 29.3 27.1 27.291890004 Missouri St Louis 32.0 32.0 29.5 29.5 27.5 27.291892003 Missouri St Louis 33.2 33.8 30.1 30.9 29.0 29.295100007 Missouri St Louis City 33.1 33.5 31.2 31.7 28.8 29.295100085 Missouri St Louis City 33.2 33.8 30.7 31.4 28.9 29.295100086 Missouri St Louis City 32.5 32.6 30.1 30.3 28.2 28.295100087 Missouri St Louis City 34.3 34.7 32.2 32.7 30.0 30.300870307 Montana Rosebud 19.7 19.7 18.9 18.9 18.7 18.301111065 Montana Yellowstone 19.3 19.4 18.2 18.5 18.1 18.310250002 Nebraska Cass 28.3 28.3 25.0 25.0 24.3 24.310550019 Nebraska Douglas 25.7 25.7 22.7 22.7 22.1 22.310550052 Nebraska Douglas 25.7 26.8 23.1 24.0 22.3 23.310790004 Nebraska Hall 19.1 19.1 16.7 16.7 16.3 16.311090022 Nebraska Lancaster 24.7 26.6 22.2 23.8 21.5 23.311570003 Nebraska Scotts Bluff 16.6 16.6 15.4 15.4 15.2 15.311770002 Nebraska Washington 24.0 24.8 21.8 22.7 21.3 22.330012004 New Hampshire Belknap 20.5 21.4 15.6 16.2 15.1 15.330050007 New Hampshire Cheshire 30.2 31.0 26.2 26.4 25.5 25.330070014 New Hampshire Coos 26.5 26.9 22.7 22.8 22.0 22.330090010 New Hampshire Grafton 23.0 23.0 18.8 18.8 18.2 18.330110020 New Hampshire Hillsborough 28.6 28.6 24.9 24.9 24.2 24.330111015 New Hampshire Hillsborough 27.3 27.3 24.6 24.6 23.9 23.330115001 New Hampshire Hillsborough 25.9 25.9 20.0 20.0 19.4 19.330131006 New Hampshire Merrimack 25.6 26.1 20.7 21.0 20.1 20.330150014 New Hampshire Rockingham 26.3 27.8 21.5 22.6 21.1 22.
B-80 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

330190003 New Hampshire Sullivan 28.9 30.8 22.7 24.1 21.8 23.340030003 New Jersey Bergen 37.0 37.7 30.5 31.0 28.9 29.340070003 New Jersey Camden 36.5 38.6 29.3 31.2 28.1 30.340071007 New Jersey Camden 37.3 38.8 29.7 30.8 28.3 29.340130015 New Jersey Essex 38.3 38.4 30.2 30.7 28.7 29.340171003 New Jersey Hudson 39.0 40.5 32.2 32.7 30.7 31.340172002 New Jersey Hudson 41.4 41.4 35.4 35.4 33.7 33.340210008 New Jersey Mercer 34.7 35.8 28.2 28.4 26.9 27.340230006 New Jersey Middlesex 34.8 38.1 27.6 30.0 26.5 28.340270004 New Jersey Morris 32.3 33.6 26.5 27.4 25.3 26.340273001 New Jersey Morris 31.5 32.9 25.6 26.8 24.4 25.340292002 New Jersey Ocean 31.5 33.9 24.5 26.1 23.4 24.340310005 New Jersey Passaic 36.3 37.1 29.6 30.5 28.1 29.340390004 New Jersey Union 40.4 41.4 32.3 33.4 30.8 31.340390006 New Jersey Union 37.3 37.7 30.7 31.5 29.1 29.340392003 New Jersey Union 36.8 37.4 29.4 29.9 28.0 28.340410006 New Jersey Warren 34.0 34.7 29.1 29.9 28.2 29.350010023 New Mexico Bernalillo 18.6 18.8 16.9 17.2 16.3 16.350010024 New Mexico Bernalillo 16.4 17.1 14.8 15.4 14.4 14.350050005 New Mexico Chaves 15.6 16.5 14.3 15.0 14.0 14.350130017 New Mexico Dona Ana 32.9 35.5 31.1 33.5 30.8 33.350130025 New Mexico Dona Ana 13.8 14.1 12.7 13.1 12.5 12.350431003 New Mexico Sandoval 10.3 10.9 9.3 9.9 9.1 9.6 350439011 New Mexico Sandoval 15.6 16.2 14.9 15.6 14.7 15.350450006 New Mexico San Juan 12.4 12.6 11.7 12.0 11.7 11.350490020 New Mexico Santa Fe 9.7 10.2 9.1 9.6 9.0 9.4 
B-81 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

360010005 New York Albany 34.2 34.2 30.3 30.3 29.5 29.360050080 New York Bronx 38.8 40.2 33.0 33.4 31.2 31.360050083 New York Bronx 34.7 35.3 29.4 30.2 27.4 28.360050110 New York Bronx 36.1 36.6 31.3 32.4 29.5 30.360130011 New York Chautauqua 29.1 30.1 24.4 25.1 23.4 24.360290005 New York Erie 35.3 37.0 31.6 33.2 30.8 32.360291007 New York Erie 33.6 35.2 30.1 32.2 29.3 31.360310003 New York Essex 22.4 22.8 19.7 19.9 18.8 19.360470122 New York Kings 36.9 38.0 30.4 31.0 28.7 29.360551007 New York Monroe 32.2 33.0 27.2 27.8 26.2 26.360590008 New York Nassau 34.0 35.0 26.9 27.6 25.5 26.360610056 New York New York 39.7 40.6 33.3 34.0 31.3 31.360610062 New York New York 38.8 41.6 31.2 32.1 29.8 30.360610079 New York New York 37.9 40.2 31.6 33.5 29.8 31.360610128 New York New York 39.4 41.8 32.4 33.8 30.8 32.360632008 New York Niagara 33.8 34.3 29.8 30.7 29.0 30.360671015 New York Onondaga 27.3 28.5 23.6 24.1 22.7 23.360710002 New York Orange 28.9 29.4 23.9 24.4 23.0 23.360810124 New York Queens 35.5 35.5 29.6 29.6 28.0 28.360850055 New York Richmond 34.9 37.0 29.1 30.8 27.7 29.360850067 New York Richmond 32.4 33.0 25.4 26.0 24.0 24.360893001 New York St Lawrence 22.0 22.5 19.5 20.1 19.0 19.361010003 New York Steuben 27.8 28.2 23.3 23.6 22.4 22.361030001 New York Suffolk 34.6 34.6 26.7 26.7 25.3 25.361191002 New York Westchester 33.5 34.3 25.8 26.5 24.3 25.370010002 North Carolina Alamance 31.7 32.1 26.6 26.8 25.2 25.
B-82 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

370210034 North Carolina Buncombe 30.0 30.2 24.5 24.7 23.5 23.370330001 North Carolina Caswell 29.4 29.5 24.0 24.2 22.9 23.370350004 North Carolina Catawba 34.5 35.5 27.1 27.9 26.1 26.370370004 North Carolina Chatham 26.9 27.4 22.2 23.0 21.3 22.370510009 North Carolina Cumberland 30.7 31.2 25.8 26.6 24.7 25.370570002 North Carolina Davidson 31.3 32.1 25.7 26.4 24.8 25.370610002 North Carolina Duplin 28.3 29.6 22.8 23.5 21.7 22.370630001 North Carolina Durham 31.0 32.0 24.8 25.4 23.7 24.370650004 North Carolina Edgecombe 26.7 26.9 23.8 24.0 22.6 22.370670022 North Carolina Forsyth 31.9 32.2 26.1 26.3 25.1 25.370710016 North Carolina Gaston 30.8 30.9 24.1 24.3 23.1 23.370810013 North Carolina Guilford 30.6 30.6 26.1 26.1 24.9 24.370870010 North Carolina Haywood 27.7 28.5 23.6 24.0 22.6 22.370990006 North Carolina Jackson 24.9 25.4 20.6 20.7 19.4 19.371070004 North Carolina Lenoir 25.2 25.5 21.2 21.4 20.1 20.371110004 North Carolina McDowell 31.5 32.0 25.3 25.8 24.4 24.371170001 North Carolina Martin 24.8 26.2 21.3 22.3 20.2 21.371190010 North Carolina Mecklenburg 32.3 32.3 26.2 26.2 25.2 25.371190041 North Carolina Mecklenburg 31.7 32.1 24.6 24.9 23.6 23.371190042 North Carolina Mecklenburg 30.7 31.4 23.5 23.8 22.5 22.371210001 North Carolina Mitchell 30.2 31.6 24.1 26.0 22.9 24.371230001 North Carolina Montgomery 28.2 29.0 23.1 23.9 22.3 23.371290002 North Carolina New Hanover 25.4 25.4 20.6 20.6 19.3 19.371330005 North Carolina Onslow 24.6 25.1 21.4 21.9 20.4 20.371350007 North Carolina Orange 29.3 29.8 23.5 24.0 22.3 22.371470005 North Carolina Pitt 26.2 26.9 22.5 22.7 21.3 21.
B-83 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

371550005 North Carolina Robeson 29.9 30.4 24.7 24.9 23.1 23.371590021 North Carolina Rowan 30.2 30.2 24.4 24.4 23.6 23.371730002 North Carolina Swain 27.3 28.3 22.0 22.7 20.9 21.371830014 North Carolina Wake 31.6 32.2 25.9 26.5 24.8 25.371890003 North Carolina Watauga 30.4 30.5 24.6 24.8 23.4 23.371910005 North Carolina Wayne 29.7 30.4 24.5 25.4 22.6 23.380070002 North Dakota Billings 13.0 13.6 12.4 12.9 12.3 12.380130003 North Dakota Burke 16.7 16.7 16.1 16.1 16.0 16.380150003 North Dakota Burleigh 17.6 18.7 16.7 17.6 16.4 17.380171004 North Dakota Cass 21.2 22.5 19.5 20.6 18.9 20.380530002 North Dakota McKenzie 11.9 13.0 11.4 12.4 11.3 12.380570004 North Dakota Mercer 16.9 17.9 16.1 17.0 16.0 16.390090003 Ohio Athens 32.3 33.2 26.8 27.3 25.1 25.390170003 Ohio Butler 39.2 41.1 34.4 36.5 33.0 35.390170016 Ohio Butler 37.1 37.7 32.3 33.2 30.4 31.390170017 Ohio Butler 37.9 37.9 33.4 33.4 32.0 32.390171004 Ohio Butler 37.1 38.1 32.6 33.8 30.9 32.390230005 Ohio Clark 35.3 36.4 31.7 32.6 30.2 31.390250022 Ohio Clermont 34.4 34.4 29.9 29.9 28.5 28.390350027 Ohio Cuyahoga 36.6 38.8 33.4 35.3 32.4 34.390350034 Ohio Cuyahoga 36.5 37.9 32.3 34.0 31.4 33.390350038 Ohio Cuyahoga 44.2 47.0 39.4 41.8 38.2 40.390350045 Ohio Cuyahoga 38.5 41.5 34.7 38.1 33.7 37.390350060 Ohio Cuyahoga 42.1 45.7 37.7 40.8 36.7 39.390350065 Ohio Cuyahoga 38.6 41.0 34.9 37.6 33.7 36.390351002 Ohio Cuyahoga 34.2 34.8 30.6 31.5 29.8 30.
B-84 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

390490024 Ohio Franklin 38.5 39.7 33.1 34.3 31.2 32.390490025 Ohio Franklin 38.4 39.1 33.1 33.7 31.2 31.390490081 Ohio Franklin 34.1 34.1 28.4 28.4 26.6 26.390570005 Ohio Greene 32.2 33.0 29.0 29.8 27.0 27.390610006 Ohio Hamilton 37.6 37.6 33.1 33.1 31.6 31.390610014 Ohio Hamilton 38.2 39.4 33.2 34.1 31.3 32.390610040 Ohio Hamilton 36.7 37.7 31.5 32.6 29.7 30.390610042 Ohio Hamilton 37.3 38.2 32.6 33.3 31.2 31.390610043 Ohio Hamilton 35.9 36.2 32.2 32.8 31.1 31.390617001 Ohio Hamilton 38.8 39.6 33.6 34.2 32.2 32.390618001 Ohio Hamilton 40.6 40.9 35.2 35.8 33.5 33.390810017 Ohio Jefferson 40.7 42.4 33.4 34.9 31.9 33.390811001 Ohio Jefferson 41.9 45.5 34.5 37.8 33.1 36.390851001 Ohio Lake 37.1 37.1 34.3 34.3 32.9 32.390870010 Ohio Lawrence 33.7 34.8 28.4 29.4 26.7 27.390933002 Ohio Lorain 31.5 32.1 29.0 29.5 28.1 28.390950024 Ohio Lucas 36.3 38.6 32.8 35.1 31.9 33.390950025 Ohio Lucas 35.1 36.8 31.7 33.2 30.8 32.390950026 Ohio Lucas 34.9 36.7 31.4 33.1 30.3 31.390990005 Ohio Mahoning 35.1 35.7 29.8 30.4 28.8 29.390990014 Ohio Mahoning 36.8 38.2 31.8 32.9 30.7 31.391130031 Ohio Montgomery 35.7 37.1 32.2 33.2 31.0 32.391130032 Ohio Montgomery 37.8 40.0 33.6 35.6 32.1 33.391330002 Ohio Portage 34.3 34.5 29.8 30.0 28.9 29.391351001 Ohio Preble 32.8 33.9 30.4 31.6 28.9 30.391450013 Ohio Scioto 34.5 36.1 29.4 30.9 27.8 29.
B-85 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

391510017 Ohio Stark 36.9 37.6 32.3 33.0 31.2 32.391530017 Ohio Summit 38.0 39.6 33.1 34.7 31.9 33.391530023 Ohio Summit 35.8 37.5 32.1 33.8 30.8 32.391550007 Ohio Trumbull 36.2 37.8 32.1 33.4 31.1 32.400159008 Oklahoma Caddo 23.9 26.0 21.3 22.9 20.6 22.400219002 Oklahoma Cherokee 27.5 28.0 24.6 25.2 23.6 24.400710602 Oklahoma Kay 31.8 31.8 28.9 28.9 28.2 28.400719010 Oklahoma Kay 27.9 32.2 25.3 29.3 24.6 28.400819005 Oklahoma Lincoln 27.8 27.8 25.0 25.0 24.1 24.400970186 Oklahoma Mayes 28.7 29.1 26.2 26.8 25.4 26.400979014 Oklahoma Mayes 26.1 26.3 23.3 23.3 22.3 22.401010169 Oklahoma Muskogee 29.5 30.5 27.4 28.0 26.7 27.401090035 Oklahoma Oklahoma 23.4 24.0 20.6 21.1 19.7 20.401091037 Oklahoma Oklahoma 27.1 28.2 23.7 24.9 22.8 23.401159004 Oklahoma Ottawa 29.1 29.6 26.0 27.1 25.2 26.401210415 Oklahoma Pittsburg 26.3 27.2 23.9 25.0 22.9 24.401359015 Oklahoma Sequoyah 31.4 31.4 29.1 29.1 28.2 28.401430110 Oklahoma Tulsa 28.4 28.4 25.8 25.8 24.7 24.401431127 Oklahoma Tulsa 30.3 31.0 27.7 28.6 26.6 27.420010001 Pennsylvania Adams 34.9 36.2 28.8 30.1 27.9 29.420030008 Pennsylvania Allegheny 39.4 39.9 33.8 34.2 32.6 32.420030021 Pennsylvania Allegheny 35.1 35.1 30.1 30.1 28.9 28.420030064 Pennsylvania Allegheny 64.2 68.2 56.7 59.9 54.1 57.420030067 Pennsylvania Allegheny 36.4 39.4 29.7 32.2 28.2 30.420030093 Pennsylvania Allegheny 45.6 51.5 39.1 44.3 37.5 42.420030095 Pennsylvania Allegheny 38.7 40.7 32.8 35.1 31.3 33.
B-86 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

420030116 Pennsylvania Allegheny 42.5 42.5 35.5 35.5 33.8 33.420030133 Pennsylvania Allegheny 39.2 39.2 34.3 34.3 32.8 32.420031008 Pennsylvania Allegheny 41.3 42.8 35.0 36.3 33.2 34.420031301 Pennsylvania Allegheny 40.3 42.4 33.9 35.6 32.3 33.420033007 Pennsylvania Allegheny 37.5 43.1 32.3 37.3 30.9 35.420039002 Pennsylvania Allegheny 37.8 37.8 32.2 32.2 30.6 30.420070014 Pennsylvania Beaver 43.4 44.6 36.2 37.4 34.5 35.420110011 Pennsylvania Berks 37.7 39.1 33.7 35.3 32.9 34.420170012 Pennsylvania Bucks 34.0 34.8 28.5 29.0 27.0 27.420210011 Pennsylvania Cambria 39.0 39.4 34.3 34.7 32.8 33.420270100 Pennsylvania Centre 36.2 37.6 30.1 31.2 29.3 30.420290100 Pennsylvania Chester 36.7 36.7 31.0 31.0 30.0 30.420410101 Pennsylvania Cumberland 38.0 40.2 31.4 32.7 30.9 32.420430401 Pennsylvania Dauphin 38.0 39.0 32.9 34.1 32.2 33.420450002 Pennsylvania Delaware 35.2 35.9 29.3 30.0 28.3 29.420490003 Pennsylvania Erie 34.4 34.4 30.1 30.1 28.8 28.420692006 Pennsylvania Lackawanna 31.5 32.6 26.5 27.2 25.4 26.420710007 Pennsylvania Lancaster 40.8 44.0 35.9 38.3 35.3 37.420770004 Pennsylvania Lehigh 36.4 36.4 31.4 31.4 30.4 30.420791101 Pennsylvania Luzerne 32.4 32.4 27.1 27.1 25.8 25.420850100 Pennsylvania Mercer 36.3 36.3 30.5 30.5 29.6 29.420950025 Pennsylvania Northampton 36.7 37.4 30.6 31.1 29.6 30.420990301 Pennsylvania Perry 30.4 30.4 27.1 27.1 26.4 26.421010004 Pennsylvania Philadelphia 36.5 36.7 29.7 30.0 28.5 28.421010024 Pennsylvania Philadelphia 35.9 35.9 28.9 28.9 27.3 27.421010047 Pennsylvania Philadelphia 37.3 37.7 30.2 30.6 29.2 29.
B-87 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

421250005 Pennsylvania Washington 35.5 36.3 29.5 30.3 28.0 28.421250200 Pennsylvania Washington 33.5 33.5 27.1 27.1 25.9 25.421255001 Pennsylvania Washington 38.1 39.9 31.0 32.4 29.6 31.421290008 Pennsylvania Westmoreland 37.1 37.5 32.1 32.2 30.7 30.421330008 Pennsylvania York 38.2 40.7 33.3 36.0 32.6 35.440070022 Rhode Island Providence 29.4 29.4 23.1 23.1 22.6 22.440070026 Rhode Island Providence 30.6 32.2 25.5 26.1 24.8 25.440070028 Rhode Island Providence 28.1 28.1 23.3 23.3 22.6 22.440071010 Rhode Island Providence 28.8 29.7 22.9 23.1 22.3 22.450190049 South Carolina Charleston 27.9 27.9 22.6 22.6 21.8 21.450250001 South Carolina Chesterfield 28.7 29.9 23.9 24.7 22.9 23.450370001 South Carolina Edgefield 32.2 33.7 26.8 27.9 25.2 26.450410002 South Carolina Florence 28.8 28.9 24.3 24.6 23.2 23.450450008 South Carolina Greenville 31.8 31.8 27.0 27.0 25.8 25.450450009 South Carolina Greenville 32.5 33.5 26.7 27.3 25.4 26.450470003 South Carolina Greenwood 30.0 31.0 24.9 25.5 23.4 24.450510002 South Carolina Horry 28.3 29.0 24.0 24.7 22.9 23.450630008 South Carolina Lexington 32.8 33.4 28.0 28.8 26.6 27.450730001 South Carolina Oconee 27.9 28.2 23.4 23.4 22.1 22.450790007 South Carolina Richland 31.3 32.0 26.0 26.4 24.6 24.450790019 South Carolina Richland 33.2 33.8 27.8 28.2 26.3 26.450830010 South Carolina Spartanburg 32.4 32.4 26.3 26.3 25.1 25.460110002 South Dakota Brookings 23.5 24.9 20.9 22.0 20.3 21.460130003 South Dakota Brown 18.7 19.2 17.0 17.3 16.6 16.460290002 South Dakota Codington 23.6 24.0 21.9 22.4 21.3 21.460330132 South Dakota Custer 14.3 14.3 13.5 13.5 13.3 13.
B-88 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

460710001 South Dakota Jackson 12.7 13.4 11.8 12.4 11.6 12.460990006 South Dakota Minnehaha 24.1 25.5 21.7 23.0 21.0 22.460990007 South Dakota Minnehaha 23.9 25.1 21.3 22.1 20.6 21.461030016 South Dakota Pennington 17.2 17.2 15.9 16.0 15.7 15.461030020 South Dakota Pennington 18.5 19.2 17.5 18.1 17.3 17.461031001 South Dakota Pennington 15.9 16.6 14.8 15.6 14.5 15.470090011 Tennessee Blount 32.5 34.2 28.1 29.5 26.7 28.470370023 Tennessee Davidson 33.5 34.3 30.7 31.4 29.3 30.470370025 Tennessee Davidson 30.9 31.5 28.1 28.6 26.8 27.470370036 Tennessee Davidson 32.7 33.5 29.6 30.4 28.0 28.470450004 Tennessee Dyer 31.9 33.3 28.8 29.8 27.0 28.470650031 Tennessee Hamilton 33.2 35.2 28.1 29.0 26.8 27.470651011 Tennessee Hamilton 29.7 31.3 24.2 25.1 22.9 23.470654002 Tennessee Hamilton 33.5 34.3 27.5 28.0 26.1 26.470930028 Tennessee Knox 36.6 36.6 30.8 30.8 29.3 29.470931017 Tennessee Knox 33.4 33.9 28.9 29.2 27.6 27.470990002 Tennessee Lawrence 28.4 31.7 25.2 28.2 23.9 26.471050108 Tennessee Loudon 32.2 33.3 27.8 28.6 26.5 27.471071002 Tennessee Mc Minn 32.7 33.9 27.2 28.0 25.7 26.471192007 Tennessee Maury 30.9 31.7 28.4 29.1 27.1 27.471251009 Tennessee Montgomery 36.3 37.5 33.0 34.2 31.2 32.471410001 Tennessee Putnam 32.6 32.6 28.5 28.5 26.8 26.471450004 Tennessee Roane 30.2 31.4 25.0 26.1 23.6 24.471570014 Tennessee Shelby 32.2 33.5 28.0 28.9 26.2 27.471570038 Tennessee Shelby 32.5 33.5 28.2 28.8 26.2 26.471570047 Tennessee Shelby 33.5 33.8 28.8 29.4 26.8 27.
B-89 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

471571004 Tennessee Shelby 29.8 30.4 26.9 27.1 25.3 25.471631007 Tennessee Sullivan 31.1 32.0 25.9 26.7 24.8 25.471650007 Tennessee Sumner 33.6 34.5 31.0 31.9 29.3 30.480370004 Texas Bowie 29.4 30.0 26.2 26.9 25.3 26.481130050 Texas Dallas 27.4 29.9 23.3 25.9 22.1 24.481130069 Texas Dallas 25.7 27.7 22.2 23.8 21.2 22.481130087 Texas Dallas 24.2 27.0 20.4 22.7 19.5 21.481350003 Texas Ector 17.8 18.2 16.5 16.9 16.1 16.481410037 Texas El Paso 22.9 22.9 21.6 21.6 21.5 21.482011035 Texas Harris 30.8 31.4 27.5 27.9 26.4 26.482030002 Texas Harrison 25.9 26.2 23.6 23.9 22.6 23.482150043 Texas Hidalgo 26.4 27.9 24.8 26.2 24.1 25.483550032 Texas Nueces 27.5 30.2 24.1 26.8 23.1 25.483550034 Texas Nueces 20.7 22.2 17.7 18.7 16.7 17.483611001 Texas Orange 27.7 28.7 25.1 25.8 24.1 24.484391002 Texas Tarrant 25.3 26.8 21.6 23.3 20.8 22.484391006 Texas Tarrant 25.7 27.9 22.3 24.3 21.6 23.500010002 Vermont Addison 28.2 28.2 23.8 23.8 22.8 22.500010003 Vermont Addison 31.7 31.7 26.9 26.9 25.8 25.500030004 Vermont Bennington 26.4 28.5 22.3 24.4 21.7 23.500070012 Vermont Chittenden 29.8 30.3 25.3 25.8 24.4 25.500070014 Vermont Chittenden 30.1 30.1 26.4 26.4 25.8 25.500210002 Vermont Rutland 30.6 31.3 28.0 28.1 27.5 27.510130020 Virginia Arlington 34.1 36.3 28.6 30.6 27.2 29.510360002 Virginia Charles City 31.7 32.1 25.6 26.0 24.7 25.510410003 Virginia Chesterfield 31.2 32.6 23.7 24.6 22.7 23.
B-90 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

510590030 Virginia Fairfax 34.4 35.0 28.7 29.1 27.6 27.510591005 Virginia Fairfax 33.7 35.2 28.2 29.9 26.9 28.510595001 Virginia Fairfax 33.3 33.7 28.7 29.0 27.4 27.510870014 Virginia Henrico 31.9 32.9 25.5 26.5 24.6 25.510870015 Virginia Henrico 29.1 29.6 23.5 23.8 22.4 22.511071005 Virginia Loudoun 34.4 35.7 29.6 30.8 28.5 29.511390004 Virginia Page 30.0 30.9 26.2 27.2 25.1 26.515200006 Virginia Bristol City 30.2 30.5 24.7 24.9 23.3 23.516500004 Virginia Hampton City 29.0 29.2 23.8 24.6 22.8 23.516800015 Virginia

Lynchburg City 30.7 31.1 25.9 26.2 24.6 24.

517100024 Virginia Norfolk City 29.6 29.9 24.0 24.3 22.9 23.517700014 Virginia Roanoke City 32.7 33.2 27.2 27.5 25.8 26.517750010 Virginia Salem City 34.0 34.0 28.7 28.7 27.3 27.540030003 West Virginia Berkeley 34.5 35.8 30.6 31.1 29.8 30.540090005 West Virginia Brooke 39.4 41.5 31.9 33.4 30.4 31.540090011 West Virginia Brooke 43.9 44.9 37.5 38.3 36.1 37.540110006 West Virginia Cabell 35.1 36.6 30.9 32.2 29.3 30.540291004 West Virginia Hancock 40.6 41.2 32.5 32.7 30.7 31.540330003 West Virginia Harrison 33.5 34.6 31.3 32.2 30.1 31.540390010 West Virginia Kanawha 34.7 35.5 29.8 30.4 28.4 29.540390011 West Virginia Kanawha 33.1 33.1 28.4 28.4 27.1 27.540391005 West Virginia Kanawha 36.9 37.7 32.0 32.6 30.5 31.540490006 West Virginia Marion 33.6 33.7 30.9 31.0 29.7 29.540511002 West Virginia Marshall 33.9 34.8 28.1 28.3 27.0 27.540610003 West Virginia Monongalia 35.6 36.2 30.3 31.0 28.3 28.
B-91 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

540690010 West Virginia Ohio 32.0 32.0 26.3 26.3 25.1 25.540810002 West Virginia Raleigh 30.6 31.3 25.7 26.3 24.5 25.540890001 West Virginia Summers 31.2 31.2 26.4 26.4 25.1 25.541071002 West Virginia Wood 35.4 36.7 30.1 31.2 27.5 28.550030010 Wisconsin Ashland 18.6 20.5 16.8 18.5 16.2 17.550090005 Wisconsin Brown 36.5 37.0 33.7 34.1 32.8 33.550090009 Wisconsin Brown 35.8 35.8 33.1 33.1 32.6 32.550250047 Wisconsin Dane 35.5 36.9 32.5 33.8 31.3 32.550270007 Wisconsin Dodge 31.8 33.1 29.2 30.4 28.2 29.550410007 Wisconsin Forest 25.2 25.2 22.8 22.8 22.2 22.550430009 Wisconsin Grant 34.3 35.0 31.2 31.7 30.4 30.550590019 Wisconsin Kenosha 32.7 34.0 29.3 30.3 27.8 28.550710007 Wisconsin Manitowoc 29.7 31.6 27.5 29.3 26.7 28.550790010 Wisconsin Milwaukee 38.6 40.0 35.4 36.7 34.0 35.550790026 Wisconsin Milwaukee 37.3 41.3 33.6 37.2 32.3 35.550790043 Wisconsin Milwaukee 39.9 40.8 36.2 37.1 34.5 35.550790059 Wisconsin Milwaukee 35.5 37.0 32.3 33.6 30.4 31.550790099 Wisconsin Milwaukee 37.7 38.7 34.0 35.0 32.5 33.550870009 Wisconsin Outagamie 32.8 34.4 30.2 31.7 29.7 31.550890009 Wisconsin Ozaukee 32.5 34.0 30.0 31.5 29.0 30.551091002 Wisconsin St Croix 26.6 26.6 24.2 24.2 23.4 23.551110007 Wisconsin Sauk 28.6 28.8 26.2 26.4 25.5 25.551198001 Wisconsin Taylor 25.3 26.0 23.0 23.7 22.4 22.551250001 Wisconsin Vilas 22.6 23.8 20.4 21.4 19.8 20.551330027 Wisconsin Waukesha 35.4 36.2 32.4 33.1 31.0 31.560050877 Wyoming Campbell 18.6 18.6 17.8 17.8 17.7 17.
B-92 

24-Hour PM2.5 Design Values (μg/m

Site ID State County

2003-2007 

Average 

Ambient 

Values

2003-2007 

Maximum 

Ambient 

Values

2012 Base 

Case 

Average 

Values

2012 Base 

Case 

Maximum 

Values

2014 Base 

Case 

Average 

Values

2014 B

Cas

Maxim

Valu

560050892 Wyoming Campbell 12.5 13.3 12.2 13.0 12.2 13.560050899 Wyoming Campbell 12.6 13.5 12.4 13.2 12.3 13.560090819 Wyoming Converse 9.9 10.5 9.6 10.1 9.6 10.560131003 Wyoming Fremont 29.8 31.9 28.0 30.0 27.7 29.560210001 Wyoming Laramie 11.9 12.7 11.1 11.8 11.0 11.560330002 Wyoming Sheridan 30.8 32.5 29.6 31.1 29.4 30.
B-93 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

10030010 Alabama Baldwin -2.2 -0.2 

10270001 Alabama Clay -2.9 -0.3 

10331002 Alabama Colbert -2.5 -0.8 

10510001 Alabama Elmore -2.3 -0.2 

10550011 Alabama Etowah -2.3 -0.3 

10730023 Alabama Jefferson -2.6 -0.3 

10731003 Alabama Jefferson -2.5 -0.2 

10731005 Alabama Jefferson -2.9 -0.2 

10731009 Alabama Jefferson -2.5 -0.4 

10731010 Alabama Jefferson -2.5 -0.2 

10732006 Alabama Jefferson -2.8 -0.2 

10735002 Alabama Jefferson -2.1 -0.3 

10735003 Alabama Jefferson -2.3 -0.3 

10736002 Alabama Jefferson -2.4 -0.3 

10790002 Alabama Lawrence -2.2 -0.1 

10890014 Alabama Madison -2.6 -0.1 

10970003 Alabama Mobile -2.1 -0.4 

10972005 Alabama Mobile -2.1 -0.2 

11011002 Alabama Montgomery -2.4 -0.3 

11030011 Alabama Morgan -2.1 -0.2 

11130002 Alabama Russell -2.5 -0.1 

11170004 Alabama Shelby -3.0 -0.2 

11190002 Alabama Sumter 0.4 -0.1 

11210003 Alabama Talladega -2.4 -0.5 

11250010 Alabama Tuscaloosa -2.6 -0.3 

50350005 Arkansas Crittenden -2.7 0.0 

51190007 Arkansas Pulaski -2.4 0.0 

51191002 Arkansas Pulaski -2.4 -0.1 

51191005 Arkansas Pulaski -2.4 -0.1 

80013001 Colorado Adams -1.0 0.0 

80050002 Colorado Arapahoe -1.3 0.0 

80130011 Colorado Boulder -1.4 0.0 

80310014 Colorado Denver -1.0 0.1 

80350004 Colorado Douglas -1.3 0.0 

80590002 Colorado Jefferson -1.0 0.0 
B-94 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

80590005 Colorado Jefferson -1.0 0.0 

80590006 Colorado Jefferson -1.3 0.0 

80590011 Colorado Jefferson -1.1 0.0 

80690007 Colorado Larimer -1.8 0.0 

80691004 Colorado Larimer -1.5 0.0 

81230009 Colorado Weld -1.4 0.0 

90010017 Connecticut Fairfield -1.7 -0.1 

90011123 Connecticut Fairfield -2.1 -0.2 

90013007 Connecticut Fairfield -1.6 -0.1 

90019003 Connecticut Fairfield -1.6 -0.1 

90031003 Connecticut Hartford -2.2 -0.2 

90050005 Connecticut Litchfield -2.2 -0.1 

90070007 Connecticut Middlesex -2.1 -0.1 

90090027 Connecticut New Haven -1.4 -0.1 

90093002 Connecticut New Haven -1.8 -0.1 

90110008 Connecticut New London -1.9 -0.1 

90131001 Connecticut Tolland -2.2 -0.1 

100010002 Delaware Kent -2.3 -0.1 

100031007 Delaware New Castle -2.3 -0.1 

100031010 Delaware New Castle -2.1 -0.1 

100031013 Delaware New Castle -2.4 -0.2 

100051002 Delaware Sussex -2.3 -0.1 

100051003 Delaware Sussex -2.0 -0.1 

110010025 District of Columbia -1.9 -0.1 

110010041 District of Columbia -2.0 -0.2 

110010043 District of Columbia -2.1 -0.2 

120010025 Florida Alachua -3.0 -1.2 

120030002 Florida Baker -2.6 -0.6 

120050006 Florida Bay -2.9 -1.2 

120090007 Florida Brevard -1.6 -0.2 

120094001 Florida Brevard -1.6 -0.2 

120112003 Florida Broward -1.2 -0.2 

120118002 Florida Broward -1.2 -0.2 

120210004 Florida Collier -2.1 -0.1 

120310077 Florida Duval -4.8 -3.6 
B-95 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

120310100 Florida Duval -4.4 -2.9 

120330004 Florida Escambia -2.8 -1.4 

120330018 Florida Escambia -3.5 -1.6 

120330024 Florida Escambia -3.4 -1.6 

120570081 Florida Hillsborough -1.6 -0.1 

120570110 Florida Hillsborough -1.7 -0.1 

120571035 Florida Hillsborough -1.6 -0.2 

120571065 Florida Hillsborough -1.7 -0.2 

120573002 Florida Hillsborough -1.8 -0.2 

120574004 Florida Hillsborough -2.0 -0.3 

120690002 Florida Lake -2.1 -0.2 

120712002 Florida Lee -2.0 -0.1 

120713002 Florida Lee -2.2 -0.2 

120730012 Florida Leon -2.6 -0.1 

120730013 Florida Leon -2.8 -0.1 

120813002 Florida Manatee -1.8 -0.2 

120814012 Florida Manatee -1.9 -0.1 

120814013 Florida Manatee -1.8 -0.1 

120830003 Florida Marion -2.9 -1.0 

120830004 Florida Marion -2.9 -1.0 

120950008 Florida Orange -1.6 -0.1 

120952002 Florida Orange -1.9 -0.1 

120972002 Florida Osceola -2.0 -0.3 

120990020 Florida Palm Beach -1.3 -0.2 

121010005 Florida Pasco -2.1 -1.1 

121012001 Florida Pasco -2.6 -1.3 

121030004 Florida Pinellas -1.9 -0.4 

121030018 Florida Pinellas -1.7 -0.2 

121035002 Florida Pinellas -1.9 -0.6 

121056005 Florida Polk -2.7 -1.2 

121056006 Florida Polk -2.8 -1.4 

121111002 Florida St Lucie -1.7 -0.3 

121130015 Florida Santa Rosa -3.0 -1.2 

121151005 Florida Sarasota -2.0 -0.1 

121151006 Florida Sarasota -1.9 0.0 
B-96 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

121152002 Florida Sarasota -2.0 -0.2 

121171002 Florida Seminole -2.0 -0.1 

121272001 Florida Volusia -2.3 -0.3 

121275002 Florida Volusia -2.2 -0.2 

121290001 Florida Wakulla -2.2 -0.2 

130210012 Georgia Bibb -4.8 -0.1 

130210013 Georgia Bibb -4.7 -0.2 

130510021 Georgia Chatham -2.7 -0.8 

130550001 Georgia Chattooga -2.6 -0.2 

130590002 Georgia Clarke -3.3 -0.2 

130670003 Georgia Cobb -2.9 -0.1 

130730001 Georgia Columbia -2.8 -0.1 

130770002 Georgia Coweta -3.7 -0.2 

130850001 Georgia Dawson -2.8 0.0 

130890002 Georgia De Kalb -2.6 -0.2 

130893001 Georgia De Kalb -1.9 -0.1 

130970004 Georgia Douglas -3.1 -0.1 

131130001 Georgia Fayette -2.8 -0.1 

131210055 Georgia Fulton -2.6 -0.1 

131270006 Georgia Glynn -3.0 -1.6 

131350002 Georgia Gwinnett -3.4 -0.2 

131510002 Georgia Henry -3.2 -0.1 

132130003 Georgia Murray -2.8 -0.2 

132150008 Georgia Muscogee -2.9 -0.1 

132151003 Georgia Muscogee -2.7 -0.1 

132230003 Georgia Paulding -2.8 -0.1 

132450091 Georgia Richmond -2.9 -0.1 

132470001 Georgia Rockdale -3.7 -0.1 

132611001 Georgia Sumter -2.8 -0.1 

170010006 Illinois Adams -1.5 -0.4 

170190004 Illinois Champaign -1.4 -0.2 

170230001 Illinois Clark -1.5 -0.1 

170310001 Illinois Cook -1.2 -0.1 

170310032 Illinois Cook -1.3 -0.2 

170310064 Illinois Cook -1.2 -0.2 
B-97 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

170310072 Illinois Cook -0.9 -0.1 

170310076 Illinois Cook -1.1 -0.1 

170311003 Illinois Cook -1.1 -0.2 

170311601 Illinois Cook -1.1 0.0 

170314002 Illinois Cook -0.6 -0.1 

170314007 Illinois Cook -1.2 -0.1 

170314201 Illinois Cook -1.2 -0.1 

170317002 Illinois Cook -1.1 -0.1 

170436001 Illinois Du Page -0.8 0.0 

170491001 Illinois Effingham -1.8 -0.1 

170650002 Illinois Hamilton -1.6 0.0 

170831001 Illinois Jersey -2.4 -0.2 

170890005 Illinois Kane -1.6 -0.1 

170971002 Illinois Lake -1.2 -0.2 

170971007 Illinois Lake -1.2 -0.2 

171110001 Illinois McHenry -1.7 -0.1 

171132003 Illinois McLean -1.5 -0.1 

171150013 Illinois Macon -1.3 -0.1 

171170002 Illinois Macoupin -2.2 -0.2 

171190008 Illinois Madison -2.4 -0.3 

171191009 Illinois Madison -2.2 -0.3 

171193007 Illinois Madison -2.4 -0.3 

171430024 Illinois Peoria -1.4 -0.3 

171431001 Illinois Peoria -1.6 -0.4 

171570001 Illinois Randolph -1.2 0.0 

171613002 Illinois Rock Island -1.6 -0.3 

171630010 Illinois St Clair -2.1 -0.3 

171670010 Illinois Sangamon -1.2 -0.1 

171971011 Illinois Will -1.4 -0.1 

172010009 Illinois Winnebago -1.7 -0.2 

172012001 Illinois Winnebago -1.7 -0.2 

180030002 Indiana Allen -1.8 -0.2 

180030004 Indiana Allen -1.6 -0.1 

180110001 Indiana Boone -2.1 -0.4 

180150002 Indiana Carroll -1.7 -0.2 
B-98 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

180190008 Indiana Clark -2.5 -0.4 

180350010 Indiana Delaware -2.0 -0.3 

180390007 Indiana Elkhart -1.8 -0.2 

180431004 Indiana Floyd -2.1 -0.4 

180550001 Indiana Greene -2.0 -0.3 

180570005 Indiana Hamilton -2.2 -0.5 

180590003 Indiana Hancock -2.1 -0.5 

180630004 Indiana Hendricks -1.9 -0.5 

180690002 Indiana Huntington -1.7 -0.1 

180710001 Indiana Jackson -1.9 -0.3 

180810002 Indiana Johnson -1.8 -0.4 

180890022 Indiana Lake -1.2 -0.3 

180890030 Indiana Lake -1.1 -0.2 

180892008 Indiana Lake -1.0 -0.1 

180910005 Indiana La Porte -1.3 -0.2 

180910010 Indiana La Porte -1.6 -0.3 

180950010 Indiana Madison -2.0 -0.3 

180970042 Indiana Marion -1.8 -0.5 

180970050 Indiana Marion -1.9 -0.4 

180970057 Indiana Marion -1.7 -0.4 

180970073 Indiana Marion -1.8 -0.4 

181090005 Indiana Morgan -2.1 -0.6 

181230009 Indiana Perry -2.5 -0.5 

181270024 Indiana Porter -1.3 -0.3 

181270026 Indiana Porter -1.5 -0.2 

181290003 Indiana Posey -2.0 -0.3 

181410010 Indiana St Joseph -1.5 -0.2 

181410015 Indiana St Joseph -1.6 -0.2 

181411007 Indiana St Joseph -1.7 -0.2 

181450001 Indiana Shelby -1.8 -0.5 

181630013 Indiana Vanderburgh -1.8 -0.2 

181630021 Indiana Vanderburgh -2.1 -0.2 

181670018 Indiana Vigo -1.4 -0.5 

181670024 Indiana Vigo -1.6 -0.7 

181730008 Indiana Warrick -2.1 -0.4 
B-99 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

181730009 Indiana Warrick -2.0 -0.3 

181730011 Indiana Warrick -2.1 -0.2 

190450021 Iowa Clinton -1.9 -0.4 

190851101 Iowa Harrison -3.1 -1.4 

191130028 Iowa Linn -1.7 -0.3 

191530058 Iowa Polk -1.6 -0.3 

191630014 Iowa Scott -1.9 -0.3 

191632011 Iowa Scott -1.9 -0.3 

191690011 Iowa Story -1.4 -0.2 

200450004 Kansas Douglas -2.4 -0.9 

200910010 Kansas Johnson -2.3 -0.6 

201030003 Kansas Leavenworth -2.3 -0.7 

201070002 Kansas Linn -2.6 -1.1 

201730001 Kansas Sedgwick -2.0 -0.3 

202090021 Kansas Wyandotte -2.0 -0.5 

210130002 Kentucky Bell -2.1 -0.1 

210150003 Kentucky Boone -2.3 -0.3 

210190017 Kentucky Boyd -1.7 -0.2 

210290006 Kentucky Bullitt -2.3 -0.6 

210370003 Kentucky Campbell -2.7 -1.0 

210430500 Kentucky Carter -1.8 -0.2 

210470006 Kentucky Christian -2.5 -0.4 

210590005 Kentucky Daviess -2.1 -0.2 

210610501 Kentucky Edmonson -2.1 -0.2 

210670001 Kentucky Fayette -2.0 -0.5 

210670012 Kentucky Fayette -2.2 -0.5 

210890007 Kentucky Greenup -1.8 -0.3 

210910012 Kentucky Hancock -2.2 -0.2 

210930006 Kentucky Hardin -2.3 -0.5 

211010014 Kentucky Henderson -2.1 -0.4 

211110027 Kentucky Jefferson -2.2 -0.5 

211110051 Kentucky Jefferson -2.2 -0.5 

211111021 Kentucky Jefferson -2.1 -0.3 

211130001 Kentucky Jessamine -2.6 -0.5 

211170007 Kentucky Kenton -2.4 -0.7 
B-100 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

211390003 Kentucky Livingston -2.6 -0.8 

211451024 Kentucky McCracken -2.1 -0.7 

211490001 Kentucky McLean -2.0 -0.3 

211850004 Kentucky Oldham -2.7 -0.5 

211990003 Kentucky Pulaski -3.6 -0.4 

212130004 Kentucky Simpson -2.3 -0.2 

212210013 Kentucky Trigg -2.2 -0.2 

212270008 Kentucky Warren -2.2 -0.2 

220050004 Louisiana Ascension -1.5 -0.1 

220110002 Louisiana Beauregard -1.0 0.0 

220150008 Louisiana Bossier -2.5 0.0 

220170001 Louisiana Caddo -2.5 -0.1 

220190002 Louisiana Calcasieu -1.3 -0.1 

220190008 Louisiana Calcasieu -1.1 0.0 

220190009 Louisiana Calcasieu -1.5 -0.1 

220330003 Louisiana East Baton Rouge -1.6 -0.1 

220330009 Louisiana East Baton Rouge -1.5 -0.1 

220330013 Louisiana East Baton Rouge -1.6 0.0 

220331001 Louisiana East Baton Rouge -1.5 0.0 

220470007 Louisiana Iberville -1.7 -0.1 

220470009 Louisiana Iberville -1.6 -0.1 

220470012 Louisiana Iberville -1.5 -0.1 

220511001 Louisiana Jefferson -1.7 0.0 

220550005 Louisiana Lafayette -1.9 -0.2 

220570004 Louisiana Lafourche -1.5 -0.2 

220630002 Louisiana Livingston -1.5 0.0 

220730004 Louisiana Ouachita -2.2 0.0 

220770001 Louisiana Pointe Coupee -1.4 0.0 

220870002 Louisiana St Bernard -1.6 -0.1 

220890003 Louisiana St Charles -1.6 0.0 

220930002 Louisiana St James -1.4 0.0 

220950002 Louisiana 

St John The 

Baptis -1.4 -0.1 

221010003 Louisiana St Mary -1.5 -0.2 

221210001 Louisiana 

West Baton 

Rouge -1.5 0.0 
B-101 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

230050027 Maine Cumberland -1.4 0.0 

230052003 Maine Cumberland -1.7 -0.1 

230090102 Maine Hancock -1.7 0.0 

230090103 Maine Hancock -1.6 0.0 

230090301 Maine Hancock -1.5 0.0 

230112005 Maine Kennebec -1.6 -0.1 

230130004 Maine Knox -1.7 -0.1 

230194008 Maine Penobscot -1.3 0.0 

230230004 Maine Sagadahoc -1.6 0.0 

230310038 Maine York -1.7 0.0 

230312002 Maine York -1.7 0.0 

230313002 Maine York -1.7 0.0 

240030014 Maryland Anne Arundel -2.7 -0.3 

240051007 Maryland Baltimore -1.8 -0.1 

240053001 Maryland Baltimore -1.6 -0.2 

240090011 Maryland Calvert -2.2 -0.3 

240130001 Maryland Carroll -2.2 -0.2 

240150003 Maryland Cecil -2.5 -0.1 

240170010 Maryland Charles -2.5 -0.4 

240210037 Maryland Frederick -2.1 -0.1 

240230002 Maryland Garrett -1.8 -0.5 

240251001 Maryland Harford -2.1 -0.2 

240259001 Maryland Harford -2.4 -0.2 

240290002 Maryland Kent -2.3 -0.2 

240313001 Maryland Montgomery -2.1 -0.2 

240330030 Maryland Prince Georges -2.2 -0.2 

240338003 Maryland Prince Georges -2.6 -0.3 

240430009 Maryland Washington -2.0 -0.3 

250010002 Massachusetts Barnstable -1.8 -0.1 

250034002 Massachusetts Berkshire -2.0 -0.1 

250051002 Massachusetts Bristol -1.6 0.0 

250070001 Massachusetts Dukes -1.6 0.0 

250092006 Massachusetts Essex -1.7 0.0 

250094004 Massachusetts Essex -1.7 -0.1 

250095005 Massachusetts Essex -2.0 0.0 
B-102 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

250130008 Massachusetts Hampden -2.3 0.0 

250150103 Massachusetts Hampshire -2.0 -0.1 

250154002 Massachusetts Hampshire -2.1 -0.1 

250170009 Massachusetts Middlesex -2.0 -0.1 

250171102 Massachusetts Middlesex -1.9 -0.2 

250213003 Massachusetts Norfolk -1.9 -0.1 

250250041 Massachusetts Suffolk -1.5 -0.1 

250250042 Massachusetts Suffolk -1.3 -0.1 

250270015 Massachusetts Worcester -1.9 -0.1 

260050003 Michigan Allegan -2.0 -0.5 

260190003 Michigan Benzie -1.7 -0.2 

260210014 Michigan Berrien -1.8 -0.3 

260270003 Michigan Cass -2.1 -0.3 

260370001 Michigan Clinton -2.1 -0.3 

260490021 Michigan Genesee -2.1 -0.3 

260492001 Michigan Genesee -2.0 -0.3 

260630007 Michigan Huron -1.5 -0.2 

260650012 Michigan Ingham -2.1 -0.3 

260770008 Michigan Kalamazoo -1.9 -0.3 

260810020 Michigan Kent -2.2 -0.5 

260810022 Michigan Kent -2.3 -0.4 

260890001 Michigan Leelanau -1.7 -0.3 

260910007 Michigan Lenawee -2.0 -0.6 

260990009 Michigan Macomb -1.6 -0.3 

260991003 Michigan Macomb -1.5 -0.3 

261050007 Michigan Mason -1.9 -0.4 

261130001 Michigan Missaukee -1.7 -0.4 

261210039 Michigan Muskegon -2.0 -0.5 

261250001 Michigan Oakland -1.3 -0.2 

261390005 Michigan Ottawa -2.3 -0.6 

261470005 Michigan St Clair -1.9 -0.3 

261530001 Michigan Schoolcraft -1.8 -0.2 

261610008 Michigan Washtenaw -1.7 -0.3 

261630001 Michigan Wayne -1.5 -0.3 

261630016 Michigan Wayne -1.6 -0.3 
B-103 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

261630019 Michigan Wayne -1.6 -0.3 

270031002 Minnesota Anoka -1.1 -0.5 

280330002 Mississippi De Soto -2.6 -0.1 

280450001 Mississippi Hancock -1.8 -0.2 

280470008 Mississippi Harrison -2.2 -0.2 

280470009 Mississippi Harrison -1.7 -0.2 

280490010 Mississippi Hinds -2.7 0.0 

280590006 Mississippi Jackson -1.8 -0.2 

280590007 Mississippi Jackson -1.4 -0.2 

280750003 Mississippi Lauderdale -1.0 -0.1 

280810005 Mississippi Lee -2.6 -0.2 

290370003 Missouri Cass -2.4 -0.8 

290470003 Missouri Clay -2.5 -0.7 

290470005 Missouri Clay -2.5 -0.6 

290470006 Missouri Clay -2.5 -0.6 

290490001 Missouri Clinton -2.5 -0.6 

290770036 Missouri Greene -1.9 -0.4 

290990012 Missouri Jefferson -1.5 -0.4 

291130003 Missouri Lincoln -2.4 -0.3 

291370001 Missouri Monroe -1.8 -0.4 

291570001 Missouri Perry -1.6 -0.2 

291650023 Missouri Platte -2.3 -0.5 

291831002 Missouri St Charles -2.3 -0.3 

291831004 Missouri St Charles -2.3 -0.2 

291860005 Missouri Ste Genevieve -0.7 -0.2 

291890004 Missouri St Louis -1.6 -0.3 

291890005 Missouri St Louis -2.0 -0.3 

291890006 Missouri St Louis -1.9 -0.3 

291890014 Missouri St Louis -2.1 -0.3 

295100085 Missouri St Louis City -1.9 -0.2 

295100086 Missouri St Louis City -1.9 -0.2 

310550028 Nebraska Douglas -2.0 -0.8 

310550032 Nebraska Douglas -1.9 -0.8 

310550035 Nebraska Douglas -2.1 -0.8 

330012004 New Hampshire Belknap -1.9 -0.1 
B-104 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

330050007 New Hampshire Cheshire -1.7 -0.1 

330074001 New Hampshire Coos -1.7 0.0 

330074002 New Hampshire Coos -1.5 0.0 

330090010 New Hampshire Grafton -1.7 -0.1 

330110020 New Hampshire Hillsborough -1.9 -0.1 

330111011 New Hampshire Hillsborough -2.1 -0.1 

330115001 New Hampshire Hillsborough -1.9 -0.1 

330131007 New Hampshire Merrimack -1.9 -0.1 

330150014 New Hampshire Rockingham -1.7 -0.1 

330150016 New Hampshire Rockingham -1.7 0.0 

330190003 New Hampshire Sullivan -1.8 -0.1 

340010005 New Jersey Atlantic -1.9 -0.1 

340030005 New Jersey Bergen -1.9 -0.3 

340070003 New Jersey Camden -2.1 -0.2 

340071001 New Jersey Camden -2.3 -0.2 

340110007 New Jersey Cumberland -2.4 -0.2 

340150002 New Jersey Gloucester -2.3 -0.2 

340170006 New Jersey Hudson -1.0 -0.1 

340190001 New Jersey Hunterdon -2.5 -0.2 

340210005 New Jersey Mercer -2.2 -0.2 

340230011 New Jersey Middlesex -2.4 -0.3 

340250005 New Jersey Monmouth -1.7 -0.1 

340273001 New Jersey Morris -2.2 -0.2 

340290006 New Jersey Ocean -2.4 -0.2 

340315001 New Jersey Passaic -2.0 -0.2 

360010012 New York Albany -1.7 -0.1 

360050110 New York Bronx -1.2 -0.2 

360050133 New York Bronx -1.0 -0.1 

360130006 New York Chautauqua -2.1 -0.6 

360130011 New York Chautauqua -1.9 -0.5 

360270007 New York Dutchess -1.8 -0.1 

360290002 New York Erie -1.7 -0.4 

360410005 New York Hamilton -1.3 -0.1 

360430005 New York Herkimer -1.2 0.0 

360450002 New York Jefferson -1.5 -0.1 
B-105 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

360551007 New York Monroe -1.6 -0.2 

360631006 New York Niagara -1.2 -0.1 

360650004 New York Oneida -1.5 -0.2 

360671015 New York Onondaga -1.7 -0.2 

360715001 New York Orange -1.7 0.0 

360750003 New York Oswego -1.6 -0.2 

360790005 New York Putnam -2.0 -0.2 

360810098 New York Queens -1.1 -0.2 

360810124 New York Queens -1.5 -0.2 

360830004 New York Rensselaer -1.7 -0.1 

360850067 New York Richmond -1.3 -0.2 

360910004 New York Saratoga -1.9 -0.1 

360930003 New York Schenectady -1.5 -0.1 

361030002 New York Suffolk -1.4 -0.2 

361030004 New York Suffolk -1.7 -0.1 

361030009 New York Suffolk -1.6 -0.1 

361111005 New York Ulster -2.0 -0.2 

361173001 New York Wayne -1.4 -0.2 

361192004 New York Westchester -1.6 -0.2 

370030004 North Carolina Alexander -2.3 -0.3 

370110002 North Carolina Avery -2.7 -0.2 

370210030 North Carolina Buncombe -2.0 -0.1 

370270003 North Carolina Caldwell -2.3 -0.2 

370330001 North Carolina Caswell -2.4 -0.1 

370370004 North Carolina Chatham -2.4 -0.5 

370510008 North Carolina Cumberland -2.7 -0.4 

370511003 North Carolina Cumberland -2.6 -0.3 

370590002 North Carolina Davie -2.9 -0.8 

370630013 North Carolina Durham -2.2 -0.1 

370650099 North Carolina Edgecombe -2.4 -0.3 

370670022 North Carolina Forsyth -2.3 -0.2 

370670028 North Carolina Forsyth -1.9 -0.2 

370670030 North Carolina Forsyth -2.4 -0.2 

370671008 North Carolina Forsyth -2.3 -0.2 

370690001 North Carolina Franklin -2.5 -0.2 
B-106 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

370750001 North Carolina Graham -2.6 -0.2 

370770001 North Carolina Granville -2.3 -0.2 

370810011 North Carolina Guilford -2.6 -0.1 

370810013 North Carolina Guilford -2.7 -0.2 

370870036 North Carolina Haywood -2.5 -0.2 

370990005 North Carolina Jackson -2.7 -0.3 

371010002 North Carolina Johnston -2.6 -0.3 

371070004 North Carolina Lenoir -2.9 -0.2 

371090004 North Carolina Lincoln -2.5 -0.4 

371170001 North Carolina Martin -1.8 -0.1 

371190041 North Carolina Mecklenburg -2.4 -0.7 

371191005 North Carolina Mecklenburg -2.2 -0.7 

371191009 North Carolina Mecklenburg -2.8 -0.6 

371290002 North Carolina New Hanover -2.6 -0.2 

371450003 North Carolina Person -0.7 -0.1 

371470099 North Carolina Pitt -3.0 -0.3 

371570099 North Carolina Rockingham -2.2 -0.1 

371590021 North Carolina Rowan -3.0 -0.6 

371590022 North Carolina Rowan -2.9 -0.5 

371730002 North Carolina Swain -2.3 -0.2 

371790003 North Carolina Union -2.8 -0.4 

371830014 North Carolina Wake -2.5 -0.3 

371830016 North Carolina Wake -2.7 -0.3 

390030009 Ohio Allen -2.0 -0.2 

390071001 Ohio Ashtabula -2.4 -0.7 

390170004 Ohio Butler -2.5 -0.4 

390171004 Ohio Butler -2.5 -0.4 

390230001 Ohio Clark -2.5 -0.3 

390230003 Ohio Clark -2.2 -0.3 

390250022 Ohio Clermont -2.6 -0.9 

390271002 Ohio Clinton -2.9 -0.5 

390350034 Ohio Cuyahoga -1.8 -0.5 

390350064 Ohio Cuyahoga -1.5 -0.5 

390355002 Ohio Cuyahoga -2.0 -0.6 

390410002 Ohio Delaware -2.2 -0.3 
B-107 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

390490028 Ohio Franklin -2.3 -0.4 

390490029 Ohio Franklin -2.4 -0.3 

390490037 Ohio Franklin -2.0 -0.3 

390490081 Ohio Franklin -2.2 -0.4 

390550004 Ohio Geauga -2.2 -0.6 

390570006 Ohio Greene -2.4 -0.2 

390610006 Ohio Hamilton -2.5 -0.6 

390610010 Ohio Hamilton -2.6 -0.7 

390610040 Ohio Hamilton -2.5 -0.9 

390810017 Ohio Jefferson -2.2 -0.7 

390830002 Ohio Knox -2.2 -0.3 

390850003 Ohio Lake -2.2 -0.6 

390850007 Ohio Lake -2.3 -0.7 

390870006 Ohio Lawrence -1.6 -0.2 

390870011 Ohio Lawrence -1.7 -0.3 

390890005 Ohio Licking -2.3 -0.3 

390930018 Ohio Lorain -1.9 -0.4 

390950024 Ohio Lucas -2.0 -0.6 

390950027 Ohio Lucas -2.0 -0.5 

390950034 Ohio Lucas -2.1 -0.5 

390950081 Ohio Lucas -2.1 -0.6 

390970007 Ohio Madison -2.4 -0.3 

390990013 Ohio Mahoning -2.3 -0.7 

391030003 Ohio Medina -2.3 -0.6 

391090005 Ohio Miami -2.5 -0.3 

391130037 Ohio Montgomery -2.4 -0.4 

391331001 Ohio Portage -2.4 -0.6 

391351001 Ohio Preble -2.1 -0.3 

391510016 Ohio Stark -2.0 -0.3 

391510022 Ohio Stark -2.0 -0.4 

391514005 Ohio Stark -2.1 -0.4 

391530020 Ohio Summit -2.1 -0.4 

391550009 Ohio Trumbull -2.5 -0.7 

391550011 Ohio Trumbull -2.6 -0.8 

391650007 Ohio Warren -2.8 -0.4 
B-108 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

391670004 Ohio Washington -6.0 -3.9 

391730003 Ohio Wood -2.1 -0.4 

400170101 Oklahoma Canadian -2.2 -0.3 

400219002 Oklahoma Cherokee -4.5 -3.4 

400270049 Oklahoma Cleveland -2.5 -0.6 

400370144 Oklahoma Creek -2.4 -0.8 

400719010 Oklahoma Kay -3.4 -1.5 

400871073 Oklahoma Mc Clain -2.3 -0.4 

400979014 Oklahoma Mayes -3.8 -2.8 

401090033 Oklahoma Oklahoma -2.2 -0.4 

401090096 Oklahoma Oklahoma -2.2 -0.5 

401091037 Oklahoma Oklahoma -2.3 -0.2 

401159004 Oklahoma Ottawa -3.2 -1.7 

401430137 Oklahoma Tulsa -2.7 -0.9 

401430174 Oklahoma Tulsa -2.5 -1.0 

401430178 Oklahoma Tulsa -2.9 -1.3 

401431127 Oklahoma Tulsa -2.3 -0.5 

420010002 Pennsylvania Adams -2.0 -0.1 

420030008 Pennsylvania Allegheny -1.9 -0.6 

420030010 Pennsylvania Allegheny -2.1 -0.7 

420030067 Pennsylvania Allegheny -2.0 -0.5 

420031005 Pennsylvania Allegheny -2.3 -0.6 

420050001 Pennsylvania Armstrong -2.3 -0.5 

420070002 Pennsylvania Beaver -2.1 -0.5 

420070005 Pennsylvania Beaver -2.1 -0.6 

420070014 Pennsylvania Beaver -2.0 -0.5 

420110009 Pennsylvania Berks -2.3 -0.2 

420130801 Pennsylvania Blair -1.9 -0.5 

420170012 Pennsylvania Bucks -2.2 -0.2 

420210011 Pennsylvania Cambria -1.9 -0.6 

420270100 Pennsylvania Centre -1.9 -0.3 

420290100 Pennsylvania Chester -2.4 -0.2 

420334000 Pennsylvania Clearfield -2.2 -0.6 

420430401 Pennsylvania Dauphin -1.9 -0.2 

420431100 Pennsylvania Dauphin -1.9 -0.2 
B-109 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

420450002 Pennsylvania Delaware -2.2 -0.2 

420490003 Pennsylvania Erie -2.1 -0.6 

420550001 Pennsylvania Franklin -2.0 -0.2 

420590002 Pennsylvania Greene -2.9 -1.5 

420630004 Pennsylvania Indiana -2.1 -0.6 

420690101 Pennsylvania Lackawanna -1.9 -0.2 

420692006 Pennsylvania Lackawanna -1.9 -0.3 

420710007 Pennsylvania Lancaster -2.1 -0.2 

420730015 Pennsylvania Lawrence -2.0 -0.5 

420770004 Pennsylvania Lehigh -2.2 -0.2 

420791100 Pennsylvania Luzerne -1.9 -0.4 

420791101 Pennsylvania Luzerne -2.0 -0.2 

420810100 Pennsylvania Lycoming -2.5 -1.1 

420850100 Pennsylvania Mercer -2.6 -0.9 

420910013 Pennsylvania Montgomery -2.2 -0.2 

420950025 Pennsylvania Northampton -2.1 -0.1 

420958000 Pennsylvania Northampton -2.1 -0.2 

420990301 Pennsylvania Perry -2.0 -0.2 

421010004 Pennsylvania Philadelphia -1.5 -0.1 

421010014 Pennsylvania Philadelphia -1.8 -0.1 

421010024 Pennsylvania Philadelphia -2.2 -0.2 

421010136 Pennsylvania Philadelphia -1.9 -0.1 

421174000 Pennsylvania Tioga -1.9 -0.4 

421250005 Pennsylvania Washington -2.0 -0.7 

421250200 Pennsylvania Washington -2.3 -0.9 

421255001 Pennsylvania Washington -2.2 -0.7 

421290006 Pennsylvania Westmoreland -1.9 -0.5 

421290008 Pennsylvania Westmoreland -2.1 -0.6 

421330008 Pennsylvania York -1.9 -0.2 

440030002 Rhode Island Kent -1.9 -0.1 

440071010 Rhode Island Providence -1.9 -0.1 

440090007 Rhode Island Washington -1.9 -0.1 

450010001 South Carolina Abbeville -2.5 -0.2 

450030003 South Carolina Aiken -2.9 -0.2 

450070003 South Carolina Anderson -2.7 -0.2 
B-110 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

450110001 South Carolina Barnwell -2.5 -0.2 

450150002 South Carolina Berkeley -1.9 -0.4 

450190046 South Carolina Charleston -2.3 -0.5 

450210002 South Carolina Cherokee -2.5 -0.2 

450230002 South Carolina Chester -2.3 -0.4 

450250001 South Carolina Chesterfield -2.3 -0.4 

450290002 South Carolina Colleton -2.7 -0.3 

450310003 South Carolina Darlington -2.3 -0.2 

450370001 South Carolina Edgefield -2.7 -0.1 

450730001 South Carolina Oconee -2.4 -0.1 

450770002 South Carolina Pickens -2.7 0.0 

450790007 South Carolina Richland -3.4 -0.3 

450790021 South Carolina Richland -3.0 -0.2 

450791001 South Carolina Richland -3.5 -0.3 

450830009 South Carolina Spartanburg -3.0 -0.1 

450870001 South Carolina Union -2.4 -0.3 

450890001 South Carolina Williamsburg -2.1 -0.4 

450910006 South Carolina York -2.6 -0.6 

470010101 Tennessee Anderson -2.4 -0.2 

470090101 Tennessee Blount -2.9 -0.2 

470090102 Tennessee Blount -2.3 -0.1 

470370011 Tennessee Davidson -2.8 -0.4 

470370026 Tennessee Davidson -3.2 -0.4 

470651011 Tennessee Hamilton -3.0 -0.2 

470654003 Tennessee Hamilton -3.0 -0.1 

470890002 Tennessee Jefferson -4.0 -0.3 

470930021 Tennessee Knox -2.8 -0.1 

470931020 Tennessee Knox -2.9 -0.1 

471050109 Tennessee Loudon -2.4 -0.1 

471210104 Tennessee Meigs -3.0 -0.2 

471490101 Tennessee Rutherford -3.2 -0.4 

471550101 Tennessee Sevier -3.0 -0.3 

471550102 Tennessee Sevier -2.9 -0.3 

471570021 Tennessee Shelby -2.6 -0.1 

471571004 Tennessee Shelby -2.6 0.0 
B-111 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

471632002 Tennessee Sullivan -2.1 -0.1 

471632003 Tennessee Sullivan -2.0 -0.1 

471650007 Tennessee Sumner -3.4 -0.5 

471650101 Tennessee Sumner -3.2 -0.5 

471870106 Tennessee Williamson -2.8 -0.2 

471890103 Tennessee Wilson -3.3 -0.5 

480290032 Texas Bexar -2.1 -0.1 

480391004 Texas Brazoria -2.3 -0.3 

480391016 Texas Brazoria -1.7 -0.1 

480850005 Texas Collin -3.4 -0.1 

481130069 Texas Dallas -2.8 -0.1 

481130075 Texas Dallas -3.1 -0.1 

481130087 Texas Dallas -2.6 -0.1 

481133003 Texas Dallas -2.8 -0.2 

481210034 Texas Denton -3.8 0.0 

481390015 Texas Ellis -3.6 -0.1 

481670014 Texas Galveston -1.6 -0.1 

481830001 Texas Gregg -2.2 -0.1 

482010024 Texas Harris -1.3 -0.1 

482010026 Texas Harris -1.7 -0.1 

482010029 Texas Harris -1.9 -0.1 

482010046 Texas Harris -1.1 -0.1 

482010047 Texas Harris -1.2 0.0 

482010051 Texas Harris -2.0 -0.2 

482010055 Texas Harris -2.2 -0.2 

482010062 Texas Harris -1.9 -0.1 

482010066 Texas Harris -1.7 -0.1 

482010070 Texas Harris -1.6 -0.1 

482010075 Texas Harris -1.6 -0.2 

482011015 Texas Harris -1.7 -0.1 

482011034 Texas Harris -1.7 -0.2 

482011035 Texas Harris -1.7 -0.2 

482011039 Texas Harris -2.0 0.0 

482011050 Texas Harris -1.7 -0.1 

482030002 Texas Harrison -2.4 -0.1 
B-112 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

482210001 Texas Hood -3.5 -0.1 

482450009 Texas Jefferson -1.6 -0.1 

482450011 Texas Jefferson -1.6 -0.1 

482450018 Texas Jefferson -1.5 -0.1 

482450022 Texas Jefferson -1.8 -0.1 

482450101 Texas Jefferson -1.7 -0.1 

482450628 Texas Jefferson -1.5 -0.1 

482510003 Texas Johnson -3.9 -0.1 

483390078 Texas Montgomery -2.1 -0.1 

483611001 Texas Orange -1.7 -0.1 

483611100 Texas Orange -1.4 -0.1 

483670081 Texas Parker -3.7 -0.1 

484230007 Texas Smith -2.3 -0.2 

484390075 Texas Tarrant -4.0 -0.1 

484391002 Texas Tarrant -3.8 -0.1 

484392003 Texas Tarrant -3.8 -0.1 

484393009 Texas Tarrant -3.6 -0.1 

484393011 Texas Tarrant -3.3 -0.1 

484530014 Texas Travis -2.3 -0.2 

484530020 Texas Travis -1.9 -0.1 

484530613 Texas Travis -2.1 -0.2 

500030004 Vermont Bennington -1.7 -0.1 

510130020 Virginia Arlington -2.2 -0.2 

510330001 Virginia Caroline -2.3 -0.2 

510360002 Virginia Charles City -2.1 -0.3 

510410004 Virginia Chesterfield -2.0 -0.3 

510590005 Virginia Fairfax -2.2 -0.2 

510590018 Virginia Fairfax -2.3 -0.3 

510590030 Virginia Fairfax -2.2 -0.2 

510591005 Virginia Fairfax -2.2 -0.2 

510595001 Virginia Fairfax -2.2 -0.1 

510610002 Virginia Fauquier -1.8 -0.2 

510690010 Virginia Frederick -1.5 -0.1 

510850003 Virginia Hanover -2.3 -0.2 

510870014 Virginia Henrico -2.3 -0.2 
B-113 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

511071005 Virginia Loudoun -2.3 -0.1 

511130003 Virginia Madison -1.8 -0.1 

511390004 Virginia Page -1.7 -0.1 

511530009 Virginia Prince William -2.1 -0.1 

511611004 Virginia Roanoke -2.1 -0.2 

511630003 Virginia Rockbridge -1.9 -0.2 

511790001 Virginia Stafford -2.2 -0.2 

515100009 Virginia Alexandria City -2.1 -0.3 

516500004 Virginia Hampton City -1.2 -0.1 

518000004 Virginia Suffolk City -0.9 -0.1 

518000005 Virginia Suffolk City -1.9 -0.1 

540030003 West Virginia Berkeley -1.9 -0.3 

540110006 West Virginia Cabell -1.8 -0.2 

540291004 West Virginia Hancock -2.1 -0.7 

540390010 West Virginia Kanawha -1.8 -0.1 

540610003 West Virginia Monongalia -2.7 -1.5 

540690010 West Virginia Ohio -2.6 -1.1 

541071002 West Virginia Wood -3.9 -1.2 

550090026 Wisconsin Brown -1.7 -0.3 

550210015 Wisconsin Columbia -1.8 -0.2 

550250041 Wisconsin Dane -1.6 -0.2 

550270007 Wisconsin Dodge -1.8 -0.2 

550290004 Wisconsin Door -2.1 -0.3 

550390006 Wisconsin Fond Du Lac -1.5 -0.2 

550550002 Wisconsin Jefferson -1.9 -0.2 

550590019 Wisconsin Kenosha -1.2 -0.2 

550610002 Wisconsin Kewaunee -1.9 -0.3 

550710007 Wisconsin Manitowoc -1.8 -0.2 

550790010 Wisconsin Milwaukee -1.0 -0.1 

550790026 Wisconsin Milwaukee -1.2 -0.2 

550790041 Wisconsin Milwaukee -1.2 -0.2 

550790044 Wisconsin Milwaukee -1.1 -0.3 

550790085 Wisconsin Milwaukee -1.3 -0.2 

550870009 Wisconsin Outagamie -1.6 -0.3 

550890008 Wisconsin Ozaukee -1.2 -0.2 
B-114 

8-Hour Ozone (ppb) 

Monitor 

ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case

550890009 Wisconsin Ozaukee -1.5 -0.3 

551010017 Wisconsin Racine -1.1 -0.2 

551050024 Wisconsin Rock -1.8 -0.2 

551091002 Wisconsin St Croix -1.8 -0.6 

551110007 Wisconsin Sauk -1.6 -0.1 

551170006 Wisconsin Sheboygan -1.8 -0.3 

551270005 Wisconsin Walworth -2.0 -0.3 

551310009 Wisconsin Washington -1.7 -0.3 

551330017 Wisconsin Waukesha -1.5 -0.1 

551330027 Wisconsin Waukesha -1.5 -0.2 
B-115 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

010030010 Alabama Baldwin -1.47 -1.18 

010270001 Alabama Clay -2.09 -1.57 

010331002 Alabama Colbert -2.28 -1.82 

010491003 Alabama DeKalb -2.38 -1.87 

010530002 Alabama Escambia -1.47 -1.18 

010550010 Alabama Etowah -2.28 -1.77 

010690003 Alabama Houston -1.42 -1.17 

010730023 Alabama Jefferson -2.21 -1.64 

010731005 Alabama Jefferson -2.14 -1.66 

010731009 Alabama Jefferson -2.18 -1.75 

010731010 Alabama Jefferson -2.27 -1.73 

010732003 Alabama Jefferson -2.05 -1.58 

010732006 Alabama Jefferson -1.98 -1.52 

010735002 Alabama Jefferson -2.12 -1.62 

010735003 Alabama Jefferson -2.22 -1.73 

010890014 Alabama Madison -2.64 -2.10 

010970002 Alabama Mobile -1.56 -1.18 

010970003 Alabama Mobile -1.54 -1.17 

010972005 Alabama Mobile -1.49 -1.16 

011010007 Alabama Montgomery -1.87 -1.43 

011030011 Alabama Morgan -2.39 -1.90 

011130001 Alabama Russell -2.06 -1.51 

011170006 Alabama Shelby -2.18 -1.69 

011190002 Alabama Sumter -1.94 -1.54 

011210002 Alabama Talladega -2.43 -1.91 

011250004 Alabama Tuscaloosa -2.20 -1.74 

011270002 Alabama Walker -2.30 -1.81 

050010011 Arkansas Arkansas -1.79 -1.36 

050030005 Arkansas Ashley -1.53 -1.17 

050350005 Arkansas Crittenden -2.29 -1.72 

050450002 Arkansas Faulkner -1.69 -1.26 

050510003 Arkansas Garland -1.66 -1.23 

050930007 Arkansas Mississippi -2.20 -1.68 

051070001 Arkansas Phillips -1.96 -1.50 

051130002 Arkansas Polk -1.42 -1.08 

051150003 Arkansas Pope -1.54 -1.15 

051190007 Arkansas Pulaski -1.82 -1.34 
B-116 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

051191004 Arkansas Pulaski -1.82 -1.34 

051191005 Arkansas Pulaski -1.79 -1.32 

051390006 Arkansas Union -1.56 -1.19 

051450001 Arkansas White -1.57 -1.18 

080010006 Colorado Adams -0.16 0.05 

080050005 Colorado Arapahoe -0.12 0.04 

080130003 Colorado Boulder -0.09 0.03 

080130012 Colorado Boulder -0.06 0.04 

080290004 Colorado Delta -0.12 -0.01 

080310002 Colorado Denver -0.15 0.05 

080310023 Colorado Denver -0.15 0.06 

080390001 Colorado Elbert -0.04 0.01 

080410008 Colorado El Paso -0.10 0.02 

080410011 Colorado El Paso -0.09 0.03 

080690009 Colorado Larimer -0.05 0.02 

080770017 Colorado Mesa -0.17 -0.01 

081010012 Colorado Pueblo -0.11 -0.01 

081130004 Colorado San Miguel -0.03 -0.01 

081230006 Colorado Weld -0.08 0.01 

081230008 Colorado Weld -0.08 0.03 

090010010 Connecticut Fairfield -1.17 -0.83 

090011123 Connecticut Fairfield -1.16 -0.84 

090013005 Connecticut Fairfield -1.22 -0.88 

090019003 Connecticut Fairfield -1.17 -0.85 

090031003 Connecticut Hartford -0.90 -0.66 

090050005 Connecticut Litchfield -1.00 -0.80 

090090026 Connecticut New Haven -1.13 -0.82 

090090027 Connecticut New Haven -1.14 -0.83 

090091123 Connecticut New Haven -1.12 -0.80 

090092008 Connecticut New Haven -1.07 -0.78 

090092123 Connecticut New Haven -1.06 -0.77 

090113002 Connecticut New London -0.88 -0.67 

100010002 Delaware Kent -1.65 -1.36 

100010003 Delaware Kent -1.57 -1.29 

100031003 Delaware New Castle -1.67 -1.35 

100031007 Delaware New Castle -1.63 -1.34 

100031012 Delaware New Castle -1.63 -1.32 
B-117 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

100032004 Delaware New Castle -1.72 -1.38 

100051002 Delaware Sussex -1.70 -1.38 

110010041 District Of Columbia -1.86 -1.47 

110010042 District Of Columbia -2.08 -1.66 

110010043 District Of Columbia -2.05 -1.65 

120010023 Florida Alachua -0.99 -0.83 

120010024 Florida Alachua -0.97 -0.81 

120051004 Florida Bay -0.99 -0.94 

120090007 Florida Brevard -0.82 -0.66 

120111002 Florida Broward -0.50 -0.38 

120112004 Florida Broward -0.52 -0.39 

120113002 Florida Broward -0.51 -0.39 

120170005 Florida Citrus -0.82 -0.68 

120310098 Florida Duval -1.02 -0.83 

120310099 Florida Duval -0.99 -0.79 

120330004 Florida Escambia -1.17 -0.94 

120570030 Florida Hillsborough -0.75 -0.56 

120573002 Florida Hillsborough -0.77 -0.58 

120710005 Florida Lee -0.70 -0.56 

120730012 Florida Leon -1.13 -0.94 

120814012 Florida Manatee -0.76 -0.60 

120830003 Florida Marion -0.91 -0.74 

120861016 Florida Miami-Dade -0.58 -0.39 

120866001 Florida Miami-Dade -0.48 -0.39 

120951004 Florida Orange -0.93 -0.74 

120952002 Florida Orange -0.93 -0.73 

120990009 Florida Palm Beach -0.55 -0.43 

120992005 Florida Palm Beach -0.54 -0.42 

121030018 Florida Pinellas -0.72 -0.55 

121031009 Florida Pinellas -0.71 -0.55 

121056006 Florida Polk -0.76 -0.61 

121111002 Florida St. Lucie -0.76 -0.60 

121150013 Florida Sarasota -0.75 -0.60 

121171002 Florida Seminole -0.94 -0.75 

121275002 Florida Volusia -1.02 -0.83 

130210007 Georgia Bibb -2.06 -1.43 

130210012 Georgia Bibb -2.01 -1.38 
B-118 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

130510017 Georgia Chatham -1.60 -1.25 

130510091 Georgia Chatham -1.61 -1.25 

130590002 Georgia Clarke -2.19 -1.52 

130630091 Georgia Clayton -2.09 -1.45 

130670003 Georgia Cobb -2.02 -1.47 

130670004 Georgia Cobb -1.99 -1.44 

130890002 Georgia DeKalb -2.10 -1.44 

130892001 Georgia DeKalb -2.09 -1.45 

130950007 Georgia Dougherty -1.49 -1.17 

131150005 Georgia Floyd -2.20 -1.69 

131210032 Georgia Fulton -2.06 -1.43 

131210039 Georgia Fulton -2.08 -1.43 

131270006 Georgia Glynn -1.22 -0.98 

131350002 Georgia Gwinnett -2.08 -1.45 

131390003 Georgia Hall -2.13 -1.55 

131530001 Georgia Houston -1.93 -1.40 

131850003 Georgia Lowndes -1.17 -0.94 

132150001 Georgia Muscogee -2.04 -1.51 

132150008 Georgia Muscogee -2.03 -1.49 

132150011 Georgia Muscogee -1.86 -1.38 

132230003 Georgia Paulding -2.06 -1.57 

132450005 Georgia Richmond -1.97 -1.44 

132450091 Georgia Richmond -1.99 -1.45 

132950002 Georgia Walker -2.55 -1.97 

133030001 Georgia Washington -2.09 -1.38 

133190001 Georgia Wilkinson -2.12 -1.39 

170010006 Illinois Adams -1.40 -1.00 

170190004 Illinois Champaign -1.93 -1.46 

170191001 Illinois Champaign -1.96 -1.48 

170310022 Illinois Cook -1.49 -1.01 

170310050 Illinois Cook -1.44 -0.92 

170310052 Illinois Cook -1.50 -0.91 

170310057 Illinois Cook -1.48 -0.90 

170310076 Illinois Cook -1.47 -0.91 

170312001 Illinois Cook -1.35 -0.87 

170313301 Illinois Cook -1.45 -0.88 

170314007 Illinois Cook -1.47 -0.95 
B-119 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

170314201 Illinois Cook -1.48 -0.95 

170316005 Illinois Cook -1.49 -0.90 

170434002 Illinois DuPage -1.51 -0.95 

170831001 Illinois Jersey -2.01 -1.48 

170890003 Illinois Kane -1.50 -0.99 

170890007 Illinois Kane -1.49 -0.96 

170971007 Illinois Lake -1.39 -0.93 

171110001 Illinois McHenry -1.40 -0.94 

171132003 Illinois McLean -1.72 -1.22 

171150013 Illinois Macon -1.88 -1.36 

171191007 Illinois Madison -2.18 -1.48 

171192009 Illinois Madison -2.09 -1.50 

171193007 Illinois Madison -2.06 -1.47 

171430037 Illinois Peoria -1.55 -1.06 

171570001 Illinois Randolph -2.12 -1.53 

171613002 Illinois Rock Island -1.27 -0.89 

171630010 Illinois Saint Clair -2.19 -1.50 

171634001 Illinois Saint Clair -2.09 -1.44 

171670012 Illinois Sangamon -1.80 -1.28 

171971002 Illinois Will -1.61 -1.04 

171971011 Illinois Will -1.61 -1.14 

172010013 Illinois Winnebago -1.26 -0.84 

180030004 Indiana Allen -1.61 -1.26 

180030014 Indiana Allen -1.60 -1.25 

180190006 Indiana Clark -3.14 -2.51 

180350006 Indiana Delaware -2.41 -1.95 

180372001 Indiana Dubois -3.24 -2.63 

180431004 Indiana Floyd -3.13 -2.54 

180650003 Indiana Henry -2.49 -2.07 

180670003 Indiana Howard -2.17 -1.75 

180830004 Indiana Knox -2.75 -2.16 

180890006 Indiana Lake -1.53 -1.05 

180890027 Indiana Lake -1.56 -1.10 

180891003 Indiana Lake -1.50 -1.06 

180892004 Indiana Lake -1.50 -1.04 

180892010 Indiana Lake -1.48 -1.01 

180910011 Indiana LaPorte -1.48 -1.10 
B-120 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

180910012 Indiana LaPorte -1.53 -1.15 

180950009 Indiana Madison -2.46 -2.03 

180970042 Indiana Marion -2.86 -2.39 

180970078 Indiana Marion -2.83 -2.35 

180970079 Indiana Marion -2.75 -2.27 

180970081 Indiana Marion -2.85 -2.35 

180970083 Indiana Marion -2.85 -2.35 

181270020 Indiana Porter -1.52 -1.11 

181270024 Indiana Porter -1.56 -1.13 

181410014 Indiana St. Joseph -1.22 -0.90 

181411008 Indiana St. Joseph -1.20 -0.86 

181412004 Indiana St. Joseph -1.20 -0.88 

181470009 Indiana Spencer -3.24 -2.65 

181570008 Indiana Tippecanoe -2.13 -1.69 

181630006 Indiana Vanderburgh -2.57 -2.05 

181630012 Indiana Vanderburgh -2.53 -2.00 

181630016 Indiana Vanderburgh -2.58 -2.06 

181670018 Indiana Vigo -2.48 -1.95 

181670023 Indiana Vigo -2.50 -1.97 

190130008 Iowa Black Hawk -0.98 -0.69 

190450021 Iowa Clinton -1.24 -0.87 

191032001 Iowa Johnson -1.07 -0.76 

191130037 Iowa Linn -1.03 -0.73 

191370002 Iowa Montgomery -0.96 -0.71 

191390015 Iowa Muscatine -1.19 -0.84 

191471002 Iowa Palo Alto -0.73 -0.52 

191530030 Iowa Polk -0.92 -0.65 

191532510 Iowa Polk -0.88 -0.63 

191532520 Iowa Polk -0.90 -0.63 

191550009 Iowa Pottawattamie -0.83 -0.57 

191630015 Iowa Scott -1.25 -0.88 

191630018 Iowa Scott -1.26 -0.89 

191630019 Iowa Scott -1.26 -0.85 

191770006 Iowa Van Buren -1.02 -0.73 

191930017 Iowa Woodbury -0.67 -0.47 

191970004 Iowa Wright -0.88 -0.63 

200910007 Kansas Johnson -1.01 -0.74 
B-121 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

200910009 Kansas Johnson -1.11 -0.82 

200910010 Kansas Johnson -0.99 -0.74 

201070002 Kansas Linn -1.11 -0.86 

201730008 Kansas Sedgwick -0.89 -0.66 

201730009 Kansas Sedgwick -0.89 -0.65 

201730010 Kansas Sedgwick -0.88 -0.65 

201770010 Kansas Shawnee -0.99 -0.74 

201770011 Kansas Shawnee -0.94 -0.70 

201910002 Kansas Sumner -0.88 -0.67 

202090021 Kansas Wyandotte -1.10 -0.79 

202090022 Kansas Wyandotte -1.04 -0.75 

210130002 Kentucky Bell -2.68 -2.09 

210190017 Kentucky Boyd -3.14 -2.51 

210290006 Kentucky Bullitt -3.16 -2.55 

210370003 Kentucky Campbell -3.13 -2.61 

210430500 Kentucky Carter -2.76 -2.20 

210470006 Kentucky Christian -3.03 -2.46 

210590005 Kentucky Daviess -3.63 -3.02 

210670012 Kentucky Fayette -3.05 -2.48 

210670014 Kentucky Fayette -3.06 -2.49 

210730006 Kentucky Franklin -3.00 -2.45 

210930006 Kentucky Hardin -3.19 -2.60 

211010014 Kentucky Henderson -2.70 -2.19 

211110043 Kentucky Jefferson -3.16 -2.54 

211110044 Kentucky Jefferson -3.09 -2.48 

211110048 Kentucky Jefferson -3.11 -2.49 

211110051 Kentucky Jefferson -3.17 -2.56 

211170007 Kentucky Kenton -3.18 -2.63 

211250004 Kentucky Laurel -2.61 -2.09 

211451004 Kentucky McCracken -2.70 -2.10 

211510003 Kentucky Madison -3.01 -2.46 

211930003 Kentucky Perry -2.64 -2.09 

211950002 Kentucky Pike -2.89 -2.32 

212270007 Kentucky Warren -3.27 -2.67 

220171002 Louisiana Caddo -1.59 -1.13 

220190009 Louisiana Calcasieu -1.08 -0.84 

220190010 Louisiana Calcasieu -1.11 -0.86 
B-122 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

220290003 Louisiana Concordia -1.53 -1.19 

220330009 Louisiana East Baton Rouge -1.20 -0.88 

220331001 Louisiana East Baton Rouge -1.15 -0.87 

220470005 Louisiana Iberville -1.19 -0.87 

220470009 Louisiana Iberville -1.32 -1.02 

220511001 Louisiana Jefferson -1.37 -1.04 

220550006 Louisiana Lafayette -1.40 -1.11 

220730004 Louisiana Ouachita -1.45 -1.11 

220790002 Louisiana Rapides -1.37 -1.07 

221050001 Louisiana Tangipahoa -1.49 -1.15 

221090001 Louisiana Terrebonne -1.34 -1.05 

221210001 Louisiana 

West Baton 

Rouge -1.20 -0.88 

230010011 Maine Androscoggin -0.49 -0.32 

230030013 Maine Aroostook -0.32 -0.25 

230031011 Maine Aroostook -0.31 -0.22 

230050015 Maine Cumberland -0.51 -0.33 

230050027 Maine Cumberland -0.51 -0.33 

230090103 Maine Hancock -0.37 -0.30 

230110016 Maine Kennebec -0.47 -0.31 

230172011 Maine Oxford -0.46 -0.33 

230190002 Maine Penobscot -0.48 -0.33 

240030014 Maryland Anne Arundel -1.63 -1.31 

240031003 Maryland Anne Arundel -1.64 -1.28 

240032002 Maryland Anne Arundel -1.60 -1.26 

240051007 Maryland Baltimore -1.85 -1.51 

240053001 Maryland Baltimore -1.73 -1.39 

240150003 Maryland Cecil -1.60 -1.31 

240251001 Maryland Harford -1.63 -1.34 

240313001 Maryland Montgomery -1.82 -1.48 

240330030 Maryland Prince George's -1.73 -1.41 

240338003 Maryland Prince George's -1.71 -1.37 

240430009 Maryland Washington -2.19 -1.83 

245100006 Maryland Baltimore City -1.73 -1.39 

245100007 Maryland Baltimore City -1.82 -1.47 

245100008 Maryland Baltimore City -1.76 -1.38 

245100049 Maryland Baltimore City -1.70 -1.33 
B-123 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

250035001 Massachusetts Berkshire -0.89 -0.69 

250051004 Massachusetts Bristol -0.59 -0.52 

250092006 Massachusetts Essex -0.62 -0.49 

250095005 Massachusetts Essex -0.60 -0.47 

250096001 Massachusetts Essex -0.63 -0.49 

250130008 Massachusetts Hampden -0.82 -0.63 

250130016 Massachusetts Hampden -0.86 -0.63 

250132009 Massachusetts Hampden -0.84 -0.62 

250230004 Massachusetts Plymouth -0.61 -0.52 

250250002 Massachusetts Suffolk -0.70 -0.52 

250250027 Massachusetts Suffolk -0.70 -0.52 

250250042 Massachusetts Suffolk -0.67 -0.52 

250250043 Massachusetts Suffolk -0.71 -0.52 

250270016 Massachusetts Worcester -0.80 -0.61 

250270023 Massachusetts Worcester -0.80 -0.60 

260050003 Michigan Allegan -1.57 -1.22 

260170014 Michigan Bay -1.29 -1.01 

260210014 Michigan Berrien -1.45 -1.09 

260490021 Michigan Genesee -1.54 -1.21 

260650012 Michigan Ingham -1.58 -1.24 

260770008 Michigan Kalamazoo -1.55 -1.19 

260810020 Michigan Kent -1.42 -1.04 

260990009 Michigan Macomb -1.68 -1.34 

261130001 Michigan Missaukee -0.82 -0.60 

261150005 Michigan Monroe -2.02 -1.64 

261210040 Michigan Muskegon -1.32 -0.97 

261250001 Michigan Oakland -1.73 -1.33 

261390005 Michigan Ottawa -1.46 -1.09 

261450018 Michigan Saginaw -1.27 -0.99 

261470005 Michigan St. Clair -1.69 -1.37 

261610005 Michigan Washtenaw -1.69 -1.34 

261610008 Michigan Washtenaw -1.71 -1.32 

261630001 Michigan Wayne -1.63 -1.21 

261630015 Michigan Wayne -2.02 -1.57 

261630016 Michigan Wayne -1.80 -1.40 

261630019 Michigan Wayne -1.76 -1.39 

261630025 Michigan Wayne -1.79 -1.38 
B-124 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

261630033 Michigan Wayne -2.14 -1.66 

261630036 Michigan Wayne -1.73 -1.34 

270210001 Minnesota Cass -0.31 -0.23 

270370470 Minnesota Dakota -0.63 -0.41 

270530050 Minnesota Hennepin -0.63 -0.38 

270530961 Minnesota Hennepin -0.62 -0.39 

270530963 Minnesota Hennepin -0.64 -0.39 

270530965 Minnesota Hennepin -0.62 -0.37 

270531007 Minnesota Hennepin -0.63 -0.39 

270532006 Minnesota Hennepin -0.63 -0.39 

270953051 Minnesota Mille Lacs -0.37 -0.26 

271095008 Minnesota Olmsted -0.78 -0.56 

271230866 Minnesota Ramsey -0.63 -0.38 

271230868 Minnesota Ramsey -0.63 -0.36 

271230871 Minnesota Ramsey -0.59 -0.36 

271377001 Minnesota Saint Louis -0.28 -0.20 

271377550 Minnesota Saint Louis -0.34 -0.23 

271377551 Minnesota Saint Louis -0.36 -0.24 

271390505 Minnesota Scott -0.62 -0.42 

271453052 Minnesota Stearns -0.50 -0.35 

280010004 Mississippi Adams -1.54 -1.21 

280110001 Mississippi Bolivar -1.84 -1.42 

280330002 Mississippi DeSoto -2.16 -1.62 

280350004 Mississippi Forrest -1.80 -1.41 

280470008 Mississippi Harrison -1.43 -1.12 

280490010 Mississippi Hinds -1.71 -1.29 

280590006 Mississippi Jackson -1.43 -1.11 

280670002 Mississippi Jones -1.94 -1.50 

280750003 Mississippi Lauderdale -2.00 -1.56 

280810005 Mississippi Lee -2.23 -1.70 

280870001 Mississippi Lowndes -2.14 -1.65 

281090001 Mississippi Pearl River -1.48 -1.16 

281490004 Mississippi Warren -1.66 -1.27 

290190004 Missouri Boone -1.39 -1.03 

290210005 Missouri Buchanan -0.99 -0.73 

290370003 Missouri Cass -1.12 -0.84 

290390001 Missouri Cedar -1.39 -1.09 
B-125 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

290470005 Missouri Clay -1.09 -0.82 

290770032 Missouri Greene -1.43 -1.08 

290950034 Missouri Jackson -1.12 -0.80 

290990012 Missouri Jefferson -1.85 -1.31 

291370001 Missouri Monroe -1.42 -1.05 

291831002 Missouri Saint Charles -2.06 -1.50 

291860006 Missouri Sainte Genevieve -1.94 -1.42 

291890004 Missouri Saint Louis -1.90 -1.35 

291892003 Missouri Saint Louis -2.07 -1.47 

295100007 Missouri St. Louis City -2.02 -1.41 

295100085 Missouri St. Louis City -2.14 -1.49 

295100086 Missouri St. Louis City -2.10 -1.47 

295100087 Missouri St. Louis City -2.18 -1.51 

300870307 Montana Rosebud 0.05 0.07 

301111065 Montana Yellowstone -0.06 0.01 

310250002 Nebraska Cass -0.80 -0.57 

310550019 Nebraska Douglas -0.81 -0.57 

310550052 Nebraska Douglas -0.80 -0.56 

310790004 Nebraska Hall -0.56 -0.40 

311090022 Nebraska Lancaster -0.74 -0.52 

311111002 Nebraska Lincoln -0.27 -0.18 

311530007 Nebraska Sarpy -0.82 -0.58 

311570003 Nebraska Scotts Bluff -0.07 -0.03 

311770002 Nebraska Washington -0.75 -0.54 

330012004 

New 

Hampshire Belknap -0.57 -0.45 

330050007 

New 

Hampshire Cheshire -0.74 -0.56 

330070014 

New 

Hampshire Coos -0.52 -0.39 

330090010 

New 

Hampshire Grafton -0.60 -0.46 

330110020 

New 

Hampshire Hillsborough -0.67 -0.52 

330111015 

New 

Hampshire Hillsborough -0.68 -0.52 

330115001 

New 

Hampshire Hillsborough -0.50 -0.40 
B-126 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

330131006 

New 

Hampshire Merrimack -0.66 -0.51 

330150014 

New 

Hampshire Rockingham -0.52 -0.42 

330190003 

New 

Hampshire Sullivan -0.68 -0.52 

340011006 New Jersey Atlantic -1.33 -1.11 

340030003 New Jersey Bergen -1.33 -0.86 

340070003 New Jersey Camden -1.48 -1.17 

340071007 New Jersey Camden -1.56 -1.21 

340130015 New Jersey Essex -1.38 -0.95 

340155001 New Jersey Gloucester -1.57 -1.26 

340171003 New Jersey Hudson -1.41 -0.92 

340210008 New Jersey Mercer -1.48 -1.16 

340218001 New Jersey Mercer -1.38 -1.10 

340230006 New Jersey Middlesex -1.39 -1.08 

340270004 New Jersey Morris -1.37 -1.05 

340273001 New Jersey Morris -1.28 -1.02 

340292002 New Jersey Ocean -1.30 -1.03 

340310005 New Jersey Passaic -1.37 -0.96 

340390004 New Jersey Union -1.46 -0.99 

340390006 New Jersey Union -1.38 -0.94 

340392003 New Jersey Union -1.39 -0.97 

340410006 New Jersey Warren -1.53 -1.22 

350010023 New Mexico Bernalillo -0.20 -0.07 

350010024 New Mexico Bernalillo -0.19 -0.08 

350050005 New Mexico Chaves -0.29 -0.22 

350130017 New Mexico Dona Ana -0.24 -0.16 

350130025 New Mexico Dona Ana -0.23 -0.15 

350431003 New Mexico Sandoval -0.14 -0.06 

350439011 New Mexico Sandoval -0.12 -0.06 

350450006 New Mexico San Juan -0.03 -0.01 

350490020 New Mexico Santa Fe -0.09 -0.05 

360010005 New York Albany -0.93 -0.70 

360050080 New York Bronx -1.29 -0.78 

360050083 New York Bronx -1.44 -0.93 

360050110 New York Bronx -1.19 -0.72 
B-127 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

360130011 New York Chautauqua -1.76 -1.50 

360290005 New York Erie -1.77 -1.45 

360291007 New York Erie -1.82 -1.50 

360310003 New York Essex -0.67 -0.56 

360470122 New York Kings -1.40 -0.90 

360551007 New York Monroe -1.19 -0.95 

360590008 New York Nassau -1.23 -0.87 

360610056 New York New York -1.50 -0.93 

360610062 New York New York -1.46 -0.95 

360610079 New York New York -1.23 -0.75 

360610128 New York New York -1.45 -0.92 

360632008 New York Niagara -1.67 -1.39 

360671015 New York Onondaga -1.34 -1.07 

360710002 New York Orange -1.23 -0.92 

360810124 New York Queens -1.34 -0.89 

360850055 New York Richmond -1.37 -0.92 

360850067 New York Richmond -1.19 -0.81 

360893001 New York St. Lawrence -0.74 -0.62 

361010003 New York Steuben -1.41 -1.21 

361030001 New York Suffolk -1.25 -0.88 

361191002 New York Westchester -1.28 -0.87 

370010002 North Carolina Alamance -2.35 -1.87 

370210034 North Carolina Buncombe -2.15 -1.68 

370330001 North Carolina Caswell -2.34 -1.86 

370350004 North Carolina Catawba -2.46 -1.92 

370370004 North Carolina Chatham -2.17 -1.77 

370510009 North Carolina Cumberland -2.07 -1.59 

370570002 North Carolina Davidson -2.54 -2.07 

370610002 North Carolina Duplin -1.84 -1.43 

370630001 North Carolina Durham -2.21 -1.77 

370650004 North Carolina Edgecombe -2.03 -1.58 

370670022 North Carolina Forsyth -2.51 -1.99 

370710016 North Carolina Gaston -2.30 -1.82 

370810013 North Carolina Guilford -2.33 -1.82 

370870010 North Carolina Haywood -1.88 -1.44 

370990006 North Carolina Jackson -2.09 -1.61 

371070004 North Carolina Lenoir -1.81 -1.41 
B-128 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

371110004 North Carolina McDowell -2.23 -1.75 

371170001 North Carolina Martin -1.75 -1.39 

371190010 North Carolina Mecklenburg -2.26 -1.80 

371190041 North Carolina Mecklenburg -2.29 -1.83 

371190042 North Carolina Mecklenburg -2.29 -1.83 

371210001 North Carolina Mitchell -2.28 -1.81 

371230001 North Carolina Montgomery -2.18 -1.76 

371290002 North Carolina New Hanover -1.60 -1.23 

371330005 North Carolina Onslow -1.74 -1.37 

371350007 North Carolina Orange -2.25 -1.82 

371470005 North Carolina Pitt -1.86 -1.44 

371550005 North Carolina Robeson -2.09 -1.56 

371590021 North Carolina Rowan -2.32 -1.91 

371730002 North Carolina Swain -2.22 -1.70 

371830014 North Carolina Wake -2.18 -1.73 

371890003 North Carolina Watauga -2.42 -1.93 

371910005 North Carolina Wayne -2.02 -1.47 

380070002 North Dakota Billings -0.05 -0.03 

380130002 North Dakota Burke -0.05 -0.03 

380130003 North Dakota Burke -0.06 -0.03 

380150003 North Dakota Burleigh -0.13 -0.07 

380171004 North Dakota Cass -0.31 -0.19 

380530002 North Dakota McKenzie -0.05 -0.02 

380570004 North Dakota Mercer -0.09 -0.05 

390090003 Ohio Athens -2.74 -2.25 

390170016 Ohio Butler -3.06 -2.50 

390170017 Ohio Butler -2.73 -2.25 

390171004 Ohio Butler -2.82 -2.34 

390230005 Ohio Clark -2.61 -2.16 

390250022 Ohio Clermont -3.12 -2.61 

390350027 Ohio Cuyahoga -2.94 -2.52 

390350034 Ohio Cuyahoga -2.77 -2.39 

390350038 Ohio Cuyahoga -3.00 -2.55 

390350045 Ohio Cuyahoga -2.99 -2.55 

390350060 Ohio Cuyahoga -2.97 -2.51 

390350065 Ohio Cuyahoga -2.98 -2.56 

390351002 Ohio Cuyahoga -2.57 -2.20 
B-129 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

390490024 Ohio Franklin -2.50 -2.00 

390490025 Ohio Franklin -2.50 -2.00 

390490081 Ohio Franklin -2.45 -1.98 

390570005 Ohio Greene -2.64 -2.18 

390610006 Ohio Hamilton -3.03 -2.50 

390610014 Ohio Hamilton -3.29 -2.69 

390610040 Ohio Hamilton -3.26 -2.69 

390610042 Ohio Hamilton -3.24 -2.65 

390610043 Ohio Hamilton -3.03 -2.48 

390617001 Ohio Hamilton -3.26 -2.69 

390618001 Ohio Hamilton -3.28 -2.68 

390810017 Ohio Jefferson -2.80 -2.34 

390811001 Ohio Jefferson -3.03 -2.54 

390851001 Ohio Lake -2.62 -2.28 

390870010 Ohio Lawrence -2.95 -2.36 

390930016 Ohio Lorain -2.65 -2.28 

390933002 Ohio Lorain -2.16 -1.85 

390950024 Ohio Lucas -2.03 -1.64 

390950025 Ohio Lucas -2.05 -1.67 

390950026 Ohio Lucas -2.03 -1.63 

390990005 Ohio Mahoning -2.53 -2.14 

390990014 Ohio Mahoning -2.54 -2.15 

391130031 Ohio Montgomery -2.64 -2.18 

391130032 Ohio Montgomery -2.72 -2.24 

391330002 Ohio Portage -2.40 -2.03 

391351001 Ohio Preble -2.76 -2.33 

391450013 Ohio Scioto -2.98 -2.43 

391510017 Ohio Stark -2.77 -2.32 

391510020 Ohio Stark -2.30 -1.90 

391530017 Ohio Summit -2.37 -1.98 

391530023 Ohio Summit -2.29 -1.93 

391550007 Ohio Trumbull -2.50 -2.12 

400159008 Oklahoma Caddo -0.81 -0.63 

400219002 Oklahoma Cherokee -1.30 -1.01 

400719010 Oklahoma Kay -0.94 -0.73 

400819005 Oklahoma Lincoln -1.05 -0.82 

400970186 Oklahoma Mayes -1.18 -0.92 
B-130 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

400979014 Oklahoma Mayes -1.21 -0.94 

401010169 Oklahoma Muskogee -1.23 -0.95 

401090035 Oklahoma Oklahoma -0.99 -0.74 

401091037 Oklahoma Oklahoma -0.99 -0.74 

401159004 Oklahoma Ottawa -1.21 -0.94 

401210415 Oklahoma Pittsburg -1.22 -0.96 

401359015 Oklahoma Sequoyah -1.31 -1.00 

401430110 Oklahoma Tulsa -1.18 -0.87 

401431127 Oklahoma Tulsa -1.15 -0.86 

420010001 Pennsylvania Adams -2.03 -1.70 

420030008 Pennsylvania Allegheny -2.54 -2.08 

420030021 Pennsylvania Allegheny -2.61 -2.16 

420030064 Pennsylvania Allegheny -3.32 -2.68 

420030067 Pennsylvania Allegheny -2.58 -2.17 

420030095 Pennsylvania Allegheny -2.60 -2.18 

420030116 Pennsylvania Allegheny -2.57 -2.12 

420031008 Pennsylvania Allegheny -2.95 -2.50 

420031301 Pennsylvania Allegheny -2.99 -2.46 

420033007 Pennsylvania Allegheny -2.79 -2.29 

420039002 Pennsylvania Allegheny -2.78 -2.32 

420070014 Pennsylvania Beaver -2.59 -2.18 

420110011 Pennsylvania Berks -1.73 -1.41 

420170012 Pennsylvania Bucks -1.55 -1.21 

420210011 Pennsylvania Cambria -2.86 -2.47 

420270100 Pennsylvania Centre -2.16 -1.84 

420290100 Pennsylvania Chester -1.76 -1.42 

420410101 Pennsylvania Cumberland -1.90 -1.58 

420430401 Pennsylvania Dauphin -1.96 -1.61 

420450002 Pennsylvania Delaware -1.68 -1.34 

420490003 Pennsylvania Erie -2.06 -1.74 

420692006 Pennsylvania Lackawanna -1.52 -1.23 

420710007 Pennsylvania Lancaster -1.85 -1.49 

420770004 Pennsylvania Lehigh -1.59 -1.24 

420791101 Pennsylvania Luzerne -1.51 -1.21 

420850100 Pennsylvania Mercer -2.44 -2.08 

420950025 Pennsylvania Northampton -1.56 -1.22 

420990301 Pennsylvania Perry -1.72 -1.45 
B-131 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

421010047 Pennsylvania Philadelphia -1.54 -1.16 

421250005 Pennsylvania Washington -3.10 -2.60 

421250200 Pennsylvania Washington -3.06 -2.58 

421255001 Pennsylvania Washington -2.43 -2.06 

421290008 Pennsylvania Westmoreland -3.11 -2.66 

421330008 Pennsylvania York -1.96 -1.59 

440070022 Rhode Island Providence -0.68 -0.54 

440070026 Rhode Island Providence -0.75 -0.57 

440070028 Rhode Island Providence -0.72 -0.56 

440071010 Rhode Island Providence -0.72 -0.57 

450130007 South Carolina Beaufort -1.66 -1.34 

450190048 South Carolina Charleston -1.68 -1.36 

450190049 South Carolina Charleston -1.67 -1.34 

450250001 South Carolina Chesterfield -1.99 -1.59 

450370001 South Carolina Edgefield -1.95 -1.45 

450410002 South Carolina Florence -1.94 -1.56 

450430009 South Carolina Georgetown -1.65 -1.33 

450450008 South Carolina Greenville -2.21 -1.68 

450450009 South Carolina Greenville -2.22 -1.70 

450470003 South Carolina Greenwood -2.16 -1.60 

450510002 South Carolina Horry -1.70 -1.38 

450630008 South Carolina Lexington -2.13 -1.60 

450730001 South Carolina Oconee -1.96 -1.52 

450790007 South Carolina Richland -2.17 -1.66 

450790019 South Carolina Richland -2.15 -1.63 

450830010 South Carolina Spartanburg -2.19 -1.70 

460110002 South Dakota Brookings -0.51 -0.35 

460130003 South Dakota Brown -0.31 -0.20 

460290002 South Dakota Codington -0.41 -0.26 

460330132 South Dakota Custer -0.05 -0.03 

460710001 South Dakota Jackson -0.13 -0.11 

460990006 South Dakota Minnehaha -0.59 -0.39 

460990007 South Dakota Minnehaha -0.59 -0.40 

461030016 South Dakota Pennington -0.07 -0.02 

461030020 South Dakota Pennington -0.08 -0.01 

461031001 South Dakota Pennington -0.07 -0.02 

470090011 Tennessee Blount -2.34 -1.76 
B-132 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

470370023 Tennessee Davidson -2.90 -2.32 

470370025 Tennessee Davidson -2.92 -2.34 

470370036 Tennessee Davidson -2.86 -2.29 

470450004 Tennessee Dyer -2.42 -1.88 

470650031 Tennessee Hamilton -2.62 -2.02 

470651011 Tennessee Hamilton -2.69 -2.10 

470654002 Tennessee Hamilton -2.67 -2.06 

470930028 Tennessee Knox -2.53 -1.90 

470931017 Tennessee Knox -2.56 -1.89 

470931020 Tennessee Knox -2.61 -1.94 

470990002 Tennessee Lawrence -2.25 -1.82 

471050108 Tennessee Loudon -2.39 -1.83 

471071002 Tennessee McMinn -2.43 -1.88 

471192007 Tennessee Maury -2.64 -2.11 

471251009 Tennessee Montgomery -2.93 -2.35 

471410001 Tennessee Putnam -2.80 -2.25 

471450004 Tennessee Roane -2.49 -1.94 

471570014 Tennessee Shelby -2.33 -1.73 

471570038 Tennessee Shelby -2.34 -1.74 

471570047 Tennessee Shelby -2.45 -1.83 

471571004 Tennessee Shelby -2.28 -1.72 

471631007 Tennessee Sullivan -2.50 -1.95 

471650007 Tennessee Sumner -3.42 -2.80 

480370004 Texas Bowie -1.55 -1.14 

481130050 Texas Dallas -1.25 -0.95 

481130069 Texas Dallas -1.26 -0.97 

481130087 Texas Dallas -1.29 -0.99 

481350003 Texas Ector -0.44 -0.33 

481410037 Texas El Paso -0.22 -0.15 

482010058 Texas Harris -0.99 -0.71 

482011035 Texas Harris -1.04 -0.68 

482030002 Texas Harrison -1.65 -1.16 

482150043 Texas Hidalgo -0.75 -0.57 

482450021 Texas Jefferson -1.15 -0.86 

483550032 Texas Nueces -1.00 -0.73 

483550034 Texas Nueces -0.97 -0.72 

483611001 Texas Orange -1.14 -0.88 
B-133 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

484391002 Texas Tarrant -1.22 -0.93 

484391006 Texas Tarrant -1.23 -0.93 

500010002 Vermont Addison -0.67 -0.53 

500010003 Vermont Addison -0.68 -0.53 

500030004 Vermont Bennington -0.75 -0.59 

500070012 Vermont Chittenden -0.68 -0.53 

500070014 Vermont Chittenden -0.69 -0.54 

500210002 Vermont Rutland -0.70 -0.54 

510130020 Virginia Arlington -2.00 -1.58 

510360002 Virginia Charles -1.89 -1.53 

510410003 Virginia Chesterfield -2.05 -1.65 

510590030 Virginia Fairfax -1.84 -1.46 

510591005 Virginia Fairfax -1.88 -1.48 

510595001 Virginia Fairfax -1.93 -1.53 

510870014 Virginia Henrico -2.07 -1.66 

510870015 Virginia Henrico -2.16 -1.71 

511071005 Virginia Loudoun -1.99 -1.60 

511390004 Virginia Page -2.40 -1.98 

515200006 Virginia Bristol City -2.70 -2.11 

516500004 Virginia Hampton City -1.69 -1.35 

516800015 Virginia Lynchburg City -2.36 -1.88 

517100024 Virginia Norfolk City -1.67 -1.31 

517700014 Virginia Roanoke City -2.67 -2.07 

517750010 Virginia Salem City -2.59 -2.00 

518100008 Virginia 

Virginia Beach 

City -1.69 -1.34 

540030003 West Virginia Berkeley -2.28 -1.89 

540090005 West Virginia Brooke -2.99 -2.50 

540090011 West Virginia Brooke -2.98 -2.50 

540110006 West Virginia Cabell -3.14 -2.48 

540291004 West Virginia Hancock -2.92 -2.47 

540330003 West Virginia Harrison -3.81 -3.35 

540390010 West Virginia Kanawha -3.27 -2.65 

540390011 West Virginia Kanawha -3.00 -2.46 

540391005 West Virginia Kanawha -3.35 -2.71 

540490006 West Virginia Marion -4.06 -3.60 

540511002 West Virginia Marshall -3.18 -2.68 
B-134 

Annual PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

540610003 West Virginia Monongalia -3.61 -3.13 

540690010 West Virginia Ohio -3.02 -2.54 

540810002 West Virginia Raleigh -2.83 -2.31 

541071002 West Virginia Wood -2.90 -2.32 

550030010 Wisconsin Ashland -0.37 -0.28 

550090005 Wisconsin Brown -0.75 -0.51 

550250047 Wisconsin Dane -1.08 -0.75 

550270007 Wisconsin Dodge -1.02 -0.71 

550410007 Wisconsin Forest -0.54 -0.39 

550430009 Wisconsin Grant -1.05 -0.75 

550590019 Wisconsin Kenosha -1.36 -0.91 

550710007 Wisconsin Manitowoc -0.79 -0.53 

550790010 Wisconsin Milwaukee -1.27 -0.83 

550790026 Wisconsin Milwaukee -1.26 -0.83 

550790043 Wisconsin Milwaukee -1.30 -0.84 

550790059 Wisconsin Milwaukee -1.27 -0.82 

550790099 Wisconsin Milwaukee -1.26 -0.82 

550870009 Wisconsin Outagamie -0.78 -0.54 

550890009 Wisconsin Ozaukee -1.13 -0.77 

551091002 Wisconsin St. Croix -0.61 -0.41 

551110007 Wisconsin Sauk -1.03 -0.74 

551198001 Wisconsin Taylor -0.60 -0.44 

551250001 Wisconsin Vilas -0.48 -0.36 

551330027 Wisconsin Waukesha -1.16 -0.75 

560050877 Wyoming Campbell -0.01 0.01 

560050892 Wyoming Campbell 0.00 0.01 

560050899 Wyoming Campbell 0.00 0.01 

560090819 Wyoming Converse 0.00 0.01 

560131003 Wyoming Fremont -0.06 0.01 

560210001 Wyoming Laramie -0.03 0.01 

560330002 Wyoming Sheridan -0.06 0.01 
B-135 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

010030010 Alabama Baldwin -4.5 -3.5 

010270001 Alabama Clay -6.7 -5.2 

010331002 Alabama Colbert -8.1 -6.8 

010491003 Alabama De Kalb -6.4 -5.1 

010530002 Alabama Escambia -3.8 -3.0 

010550010 Alabama Etowah -6.9 -5.7 

010690003 Alabama Houston -4.3 -3.4 

010730023 Alabama Jefferson -5.8 -4.5 

010731005 Alabama Jefferson -5.9 -4.6 

010731009 Alabama Jefferson -8.2 -7.0 

010731010 Alabama Jefferson -7.3 -5.8 

010732003 Alabama Jefferson -4.8 -3.7 

010732006 Alabama Jefferson -6.5 -5.5 

010735002 Alabama Jefferson -7.2 -5.8 

010735003 Alabama Jefferson -6.9 -5.6 

010890014 Alabama Madison -8.9 -7.5 

010970002 Alabama Mobile -4.6 -3.3 

010970003 Alabama Mobile -4.6 -3.4 

011010007 Alabama Montgomery -5.9 -4.8 

011030011 Alabama Morgan -7.8 -6.5 

011130001 Alabama Russell -5.8 -4.4 

011170006 Alabama Shelby -6.7 -5.6 

011190002 Alabama Sumter -5.9 -4.9 

011210002 Alabama Talladega -6.8 -5.7 

011250004 Alabama Tuscaloosa -6.0 -4.9 

011270002 Alabama Walker -6.8 -5.5 

050010011 Arkansas Arkansas -6.6 -5.4 

050030005 Arkansas Ashley -4.1 -3.1 

050350005 Arkansas Crittenden -10.0 -8.1 

050450002 Arkansas Faulkner -6.1 -4.9 

050510003 Arkansas Garland -5.9 -4.6 

051070001 Arkansas Phillips -6.5 -5.1 

051130002 Arkansas Polk -5.1 -3.8 

051150003 Arkansas Pope -6.0 -5.1 

051190007 Arkansas Pulaski -6.0 -4.6 

051191004 Arkansas Pulaski -4.9 -4.0 
B-136 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

051191005 Arkansas Pulaski -5.5 -4.2 

051390006 Arkansas Union -4.3 -3.4 

051450001 Arkansas White -5.7 -4.5 

080010006 Colorado Adams -0.8 0.1 

080050005 Colorado Arapahoe -0.8 -0.1 

080130003 Colorado Boulder -0.2 0.1 

080130012 Colorado Boulder -0.4 0.0 

080290004 Colorado Delta -0.6 -0.1 

080310002 Colorado Denver -0.7 0.1 

080310023 Colorado Denver -0.4 0.2 

080390001 Colorado Elbert -0.5 -0.1 

080410008 Colorado El Paso -0.4 0.0 

080410011 Colorado El Paso -0.4 0.0 

080690009 Colorado Larimer -0.2 0.1 

080770017 Colorado Mesa -0.9 -0.1 

081010012 Colorado Pueblo -0.3 0.0 

081130004 Colorado San Miguel -0.1 0.0 

081230006 Colorado Weld 0.0 0.2 

081230008 Colorado Weld -0.3 0.0 

090010010 Connecticut Fairfield -4.2 -2.9 

090011123 Connecticut Fairfield -3.9 -2.9 

090013005 Connecticut Fairfield -6.5 -5.2 

090019003 Connecticut Fairfield -6.4 -5.1 

090031003 Connecticut Hartford -4.1 -3.0 

090050005 Connecticut Litchfield -5.2 -4.2 

090090026 Connecticut New Haven -4.9 -3.7 

090090027 Connecticut New Haven -5.2 -4.1 

090091123 Connecticut New Haven -5.0 -3.6 

090092008 Connecticut New Haven -5.5 -4.4 

090092123 Connecticut New Haven -4.4 -3.4 

090113002 Connecticut New London -5.3 -4.4 

100010002 Delaware Kent -3.8 -3.0 

100010003 Delaware Kent -4.5 -3.8 

100031003 Delaware New Castle -3.5 -2.6 

100031007 Delaware New Castle -5.3 -4.2 

100031012 Delaware New Castle -2.2 -1.3 

100032004 Delaware New Castle -3.8 -2.7 
B-137 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

100051002 Delaware Sussex -3.1 -2.4 

110010041 District Of Columbia -6.9 -5.7 

110010042 District Of Columbia -6.6 -5.3 

110010043 District Of Columbia -6.6 -5.4 

120010023 Florida Alachua -3.6 -2.9 

120010024 Florida Alachua -2.7 -2.4 

120051004 Florida Bay -3.3 -2.7 

120090007 Florida Brevard -2.9 -2.2 

120111002 Florida Broward -1.6 -1.2 

120112004 Florida Broward -1.8 -1.3 

120113002 Florida Broward -1.6 -1.1 

120170005 Florida Citrus -3.7 -3.2 

120310098 Florida Duval -2.0 -1.6 

120310099 Florida Duval -2.0 -1.6 

120330004 Florida Escambia -3.4 -2.7 

120570030 Florida Hillsborough -2.3 -1.8 

120573002 Florida Hillsborough -2.7 -2.1 

120710005 Florida Lee -1.8 -1.3 

120730012 Florida Leon -3.4 -2.6 

120814012 Florida Manatee -2.6 -2.0 

120830003 Florida Marion -3.3 -2.7 

120861016 Florida Miami-Dade -1.7 -1.3 

120866001 Florida Miami-Dade -1.9 -1.4 

120952002 Florida Orange -2.6 -2.0 

120990009 Florida Palm Beach -1.2 -0.9 

120992005 Florida Palm Beach -1.6 -1.1 

121030018 Florida Pinellas -1.8 -1.3 

121031009 Florida Pinellas -1.6 -1.1 

121056006 Florida Polk -1.7 -1.4 

121111002 Florida St Lucie -2.4 -1.9 

121150013 Florida Sarasota -2.6 -2.0 

121171002 Florida Seminole -3.4 -2.6 

121275002 Florida Volusia -3.2 -2.5 

130210007 Georgia Bibb -4.3 -2.8 

130210012 Georgia Bibb -5.9 -4.3 

130510017 Georgia Chatham -3.3 -2.4 

130510091 Georgia Chatham -3.6 -2.8 
B-138 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

130630091 Georgia Clayton -5.3 -4.0 

130670003 Georgia Cobb -5.8 -4.5 

130670004 Georgia Cobb -5.3 -3.6 

130890002 Georgia De Kalb -5.2 -3.6 

130892001 Georgia De Kalb -5.2 -3.7 

130950007 Georgia Dougherty -4.1 -3.2 

131150005 Georgia Floyd -5.5 -4.3 

131210032 Georgia Fulton -4.8 -3.6 

131210039 Georgia Fulton -4.6 -3.0 

131270006 Georgia Glynn -2.8 -2.1 

131350002 Georgia Gwinnett -5.9 -4.2 

131390003 Georgia Hall -5.6 -4.2 

131530001 Georgia Houston -6.0 -4.5 

131850003 Georgia Lowndes -3.4 -2.5 

132150001 Georgia Muscogee -4.1 -2.9 

132150008 Georgia Muscogee -4.6 -3.3 

132150011 Georgia Muscogee -4.1 -2.9 

132230003 Georgia Paulding -5.4 -4.2 

132450005 Georgia Richmond -3.8 -2.7 

132450091 Georgia Richmond -4.2 -3.3 

132950002 Georgia Walker -4.7 -3.5 

133030001 Georgia Washington -5.5 -3.8 

133190001 Georgia Wilkinson -6.2 -4.5 

170010006 Illinois Adams -6.7 -5.0 

170190004 Illinois Champaign -5.3 -3.7 

170191001 Illinois Champaign -4.8 -3.7 

170310022 Illinois Cook -3.0 -2.2 

170310050 Illinois Cook -3.6 -2.1 

170310052 Illinois Cook -5.4 -3.7 

170310057 Illinois Cook -5.2 -3.5 

170310076 Illinois Cook -4.1 -2.6 

170311016 Illinois Cook -4.9 -3.2 

170312001 Illinois Cook -4.2 -3.0 

170313103 Illinois Cook -4.7 -3.2 

170313301 Illinois Cook -4.7 -3.2 

170314007 Illinois Cook -5.5 -3.8 

170314201 Illinois Cook -3.5 -1.8 
B-139 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

170316005 Illinois Cook -2.2 -0.8 

170434002 Illinois Du Page -3.3 -2.0 

170650002 Illinois Hamilton -8.4 -6.6 

170831001 Illinois Jersey -6.3 -4.5 

170890003 Illinois Kane -3.2 -1.5 

170890007 Illinois Kane -2.7 -1.0 

170971007 Illinois Lake -6.4 -4.3 

170990007 Illinois La Salle -3.5 -2.3 

171110001 Illinois McHenry -4.5 -3.1 

171132003 Illinois McLean -5.9 -4.5 

171150013 Illinois Macon -8.1 -6.1 

171190023 Illinois Madison -6.8 -4.5 

171191007 Illinois Madison -7.3 -5.4 

171192009 Illinois Madison -7.3 -5.5 

171193007 Illinois Madison -8.7 -7.3 

171430037 Illinois Peoria -4.8 -3.0 

171570001 Illinois Randolph -3.6 -1.9 

171613002 Illinois Rock Island -2.2 -1.4 

171630010 Illinois St Clair -6.2 -4.2 

171634001 Illinois St Clair -3.4 -1.5 

171670012 Illinois Sangamon -5.6 -4.4 

171971002 Illinois Will -5.4 -3.7 

171971011 Illinois Will -7.2 -5.5 

172010013 Illinois Winnebago -3.4 -2.0 

180030004 Indiana Allen -4.0 -3.3 

180030014 Indiana Allen -4.5 -3.9 

180190006 Indiana Clark -8.8 -7.1 

180350006 Indiana Delaware -6.2 -4.8 

180372001 Indiana Dubois -6.8 -5.4 

180390003 Indiana Elkhart -3.6 -2.4 

180431004 Indiana Floyd -9.5 -7.8 

180650003 Indiana Henry -6.8 -5.5 

180670003 Indiana Howard -5.7 -4.2 

180830004 Indiana Knox -7.1 -5.7 

180890006 Indiana Lake -4.1 -3.1 

180890022 Indiana Lake -2.8 -1.6 

180890026 Indiana Lake -3.7 -2.5 
B-140 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

180890027 Indiana Lake -3.5 -2.5 

180890031 Indiana Lake -6.2 -4.8 

180891003 Indiana Lake -2.6 -1.7 

180892004 Indiana Lake -2.2 -1.2 

180892010 Indiana Lake -3.9 -2.8 

180910011 Indiana La Porte -4.8 -3.5 

180910012 Indiana La Porte -3.2 -2.3 

180950009 Indiana Madison -7.2 -5.9 

180970042 Indiana Marion -8.6 -7.2 

180970043 Indiana Marion -9.1 -7.7 

180970066 Indiana Marion -8.1 -6.6 

180970078 Indiana Marion -8.9 -7.2 

180970079 Indiana Marion -8.4 -6.6 

180970081 Indiana Marion -8.9 -7.2 

180970083 Indiana Marion -8.2 -6.6 

181270020 Indiana Porter -4.5 -3.2 

181270024 Indiana Porter -3.4 -2.2 

181410014 Indiana St Joseph -3.6 -2.8 

181411008 Indiana St Joseph -1.2 -0.5 

181412004 Indiana St Joseph -1.3 -0.6 

181470009 Indiana Spencer -10.5 -9.0 

181570008 Indiana Tippecanoe -9.0 -7.5 

181630006 Indiana Vanderburgh -5.6 -4.5 

181630012 Indiana Vanderburgh -5.2 -4.2 

181630016 Indiana Vanderburgh -3.9 -3.0 

181670018 Indiana Vigo -8.4 -7.1 

181670023 Indiana Vigo -9.7 -7.9 

190130008 Iowa Black Hawk -1.9 -1.0 

190450021 Iowa Clinton -2.9 -1.9 

191032001 Iowa Johnson -2.6 -1.1 

191130037 Iowa Linn -4.3 -3.4 

191370002 Iowa Montgomery -4.7 -3.6 

191390015 Iowa Muscatine -2.2 -1.2 

191471002 Iowa Palo Alto -3.3 -2.4 

191530030 Iowa Polk -2.9 -1.9 

191532510 Iowa Polk -3.2 -2.2 

191532520 Iowa Polk -2.5 -1.7 
B-141 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

191550009 Iowa Pottawattamie -2.3 -1.5 

191630015 Iowa Scott -3.0 -2.0 

191630018 Iowa Scott -2.9 -2.0 

191630019 Iowa Scott -3.8 -2.1 

191770006 Iowa Van Buren -1.3 -0.5 

191930017 Iowa Woodbury -2.0 -1.4 

191970004 Iowa Wright -3.5 -2.8 

200910007 Kansas Johnson -3.6 -2.7 

200910009 Kansas Johnson -2.5 -1.7 

200910010 Kansas Johnson -4.5 -3.6 

201070002 Kansas Linn -3.9 -2.8 

201730008 Kansas Sedgwick -3.2 -2.4 

201730009 Kansas Sedgwick -3.3 -2.6 

201730010 Kansas Sedgwick -2.5 -1.9 

201770010 Kansas Shawnee -2.4 -1.5 

201910002 Kansas Sumner -2.3 -1.5 

202090021 Kansas Wyandotte -3.0 -1.8 

202090022 Kansas Wyandotte -3.6 -2.9 

210130002 Kentucky Bell -5.8 -4.6 

210190017 Kentucky Boyd -10.0 -8.2 

210290006 Kentucky Bullitt -10.5 -8.7 

210370003 Kentucky Campbell -8.7 -7.5 

210430500 Kentucky Carter -9.6 -7.9 

210470006 Kentucky Christian -10.9 -9.1 

210590005 Kentucky Daviess -12.3 -10.4 

210670012 Kentucky Fayette -8.9 -6.8 

210670014 Kentucky Fayette -7.9 -6.0 

210730006 Kentucky Franklin -8.3 -6.4 

210930006 Kentucky Hardin -10.7 -8.8 

211010014 Kentucky Henderson -7.7 -6.6 

211110043 Kentucky Jefferson -9.3 -7.7 

211110044 Kentucky Jefferson -9.6 -7.8 

211110048 Kentucky Jefferson -7.3 -5.6 

211110051 Kentucky Jefferson -10.4 -8.6 

211170007 Kentucky Kenton -7.7 -6.3 

211250004 Kentucky Laurel -5.6 -4.5 

211451004 Kentucky McCracken -9.9 -7.9 
B-142 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

211510003 Kentucky Madison -7.3 -5.9 

211930003 Kentucky Perry -8.3 -6.7 

211950002 Kentucky Pike -7.9 -6.5 

212270007 Kentucky Warren -10.7 -8.8 

220171002 Louisiana Caddo -3.7 -2.5 

220190009 Louisiana Calcasieu -3.5 -2.9 

220190010 Louisiana Calcasieu -4.6 -3.8 

220290003 Louisiana Concordia -5.9 -4.7 

220330009 Louisiana East Baton Rouge -3.0 -2.1 

220331001 Louisiana East Baton Rouge -2.5 -1.8 

220470005 Louisiana Iberville -2.1 -1.3 

220470009 Louisiana Iberville -4.5 -3.6 

220511001 Louisiana Jefferson -3.5 -2.8 

220550006 Louisiana Lafayette -4.4 -3.6 

220730004 Louisiana Ouachita -4.4 -3.3 

220790002 Louisiana Rapides -5.7 -4.5 

221050001 Louisiana Tangipahoa -4.2 -3.1 

221090001 Louisiana Terrebonne -4.4 -3.5 

221210001 Louisiana West Baton Rouge -2.7 -1.9 

230010011 Maine Androscoggin -2.4 -1.7 

230030013 Maine Aroostook -1.2 -0.9 

230031011 Maine Aroostook -1.3 -0.9 

230050015 Maine Cumberland -1.5 -0.9 

230050027 Maine Cumberland -2.2 -1.5 

230090103 Maine Hancock -1.6 -1.5 

230110016 Maine Kennebec -2.3 -1.6 

230172011 Maine Oxford -1.8 -1.2 

230190002 Maine Penobscot -1.7 -1.1 

240030014 Maryland Anne Arundel -7.1 -6.2 

240031003 Maryland Anne Arundel -5.5 -4.3 

240032002 Maryland Anne Arundel -4.6 -3.6 

240051007 Maryland Baltimore -5.6 -4.2 

240053001 Maryland Baltimore -5.0 -4.1 

240150003 Maryland Cecil -3.4 -2.5 

240251001 Maryland Harford -4.4 -3.5 

240313001 Maryland Montgomery -7.2 -6.1 

240330030 Maryland Prince Georges -6.8 -5.7 
B-143 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

240338003 Maryland Prince Georges -7.5 -6.5 

240430009 Maryland Washington -6.3 -5.4 

245100006 Maryland Baltimore City -3.6 -2.5 

245100007 Maryland Baltimore City -4.0 -2.8 

245100008 Maryland Baltimore City -3.7 -2.4 

245100035 Maryland Baltimore City -4.6 -3.5 

245100040 Maryland Baltimore City -4.4 -3.2 

245100049 Maryland Baltimore City -3.4 -2.3 

250035001 Massachusetts Berkshire -4.0 -3.1 

250051004 Massachusetts Bristol -1.5 -1.5 

250092006 Massachusetts Essex -2.5 -2.0 

250095005 Massachusetts Essex -3.0 -2.6 

250096001 Massachusetts Essex -2.2 -1.7 

250130008 Massachusetts Hampden -2.6 -1.9 

250130016 Massachusetts Hampden -2.9 -2.2 

250132009 Massachusetts Hampden -2.5 -1.8 

250230004 Massachusetts Plymouth -2.6 -2.3 

250250002 Massachusetts Suffolk -1.5 -0.9 

250250027 Massachusetts Suffolk -1.5 -0.9 

250250042 Massachusetts Suffolk -2.1 -1.5 

250250043 Massachusetts Suffolk -3.0 -2.5 

250270016 Massachusetts Worcester -2.7 -2.0 

250270023 Massachusetts Worcester -2.6 -2.0 

260050003 Michigan Allegan -3.0 -2.0 

260170014 Michigan Bay -3.1 -2.2 

260210014 Michigan Berrien -3.7 -2.4 

260490021 Michigan Genesee -2.6 -1.7 

260650012 Michigan Ingham -2.8 -1.9 

260770008 Michigan Kalamazoo -4.0 -3.1 

260810020 Michigan Kent -3.9 -2.6 

260990009 Michigan Macomb -2.4 -1.4 

261130001 Michigan Missaukee -4.6 -3.5 

261150005 Michigan Monroe -7.5 -5.7 

261210040 Michigan Muskegon -3.8 -2.3 

261250001 Michigan Oakland -7.2 -5.6 

261390005 Michigan Ottawa -2.4 -1.6 

261450018 Michigan Saginaw -3.2 -2.1 
B-144 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

261470005 Michigan St Clair -4.0 -2.8 

261610005 Michigan Washtenaw -3.2 -2.1 

261610008 Michigan Washtenaw -6.6 -5.5 

261630001 Michigan Wayne -4.0 -2.4 

261630015 Michigan Wayne -4.7 -3.3 

261630016 Michigan Wayne -5.2 -4.2 

261630019 Michigan Wayne -2.6 -1.6 

261630025 Michigan Wayne -4.4 -2.9 

261630033 Michigan Wayne -5.1 -3.9 

261630036 Michigan Wayne -4.9 -3.9 

261630039 Michigan Wayne -3.5 -2.4 

270210001 Minnesota Cass -1.3 -0.8 

270370470 Minnesota Dakota -2.3 -1.3 

270530050 Minnesota Hennepin -2.5 -1.5 

270530961 Minnesota Hennepin -2.4 -1.7 

270530963 Minnesota Hennepin -2.2 -1.3 

270530965 Minnesota Hennepin -1.7 -1.0 

270531007 Minnesota Hennepin -2.0 -1.1 

270532006 Minnesota Hennepin -3.1 -2.1 

270953051 Minnesota Mille Lacs -2.0 -1.4 

271230866 Minnesota Ramsey -2.7 -1.9 

271230868 Minnesota Ramsey -2.5 -1.5 

271230871 Minnesota Ramsey -2.2 -1.1 

271377001 Minnesota St Louis -1.3 -0.9 

271377550 Minnesota St Louis -2.2 -1.5 

271377551 Minnesota St Louis -2.7 -2.2 

271390505 Minnesota Scott -2.6 -2.0 

280010004 Mississippi Adams -5.6 -4.5 

280110001 Mississippi Bolivar -5.5 -4.2 

280330002 Mississippi De Soto -8.7 -7.0 

280350004 Mississippi Forrest -3.5 -2.5 

280470008 Mississippi Harrison -4.4 -3.4 

280490010 Mississippi Hinds -5.2 -4.0 

280590006 Mississippi Jackson -4.2 -3.2 

280670002 Mississippi Jones -4.3 -3.2 

280810005 Mississippi Lee -8.3 -6.8 

280870001 Mississippi Lowndes -7.6 -6.1 
B-145 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

281490004 Mississippi Warren -4.9 -3.8 

290190004 Missouri Boone -5.4 -4.2 

290210005 Missouri Buchanan -3.3 -2.6 

290370003 Missouri Cass -4.9 -3.6 

290390001 Missouri Cedar -5.0 -3.4 

290470005 Missouri Clay -2.6 -1.8 

290770032 Missouri Greene -4.9 -3.3 

290950034 Missouri Jackson -2.8 -1.6 

290990012 Missouri Jefferson -7.0 -5.2 

291370001 Missouri Monroe -3.9 -2.6 

291831002 Missouri St Charles -8.1 -6.5 

291860006 Missouri Ste Genevieve -6.4 -4.9 

291890004 Missouri St Louis -5.4 -3.4 

291892003 Missouri St Louis -2.6 -1.5 

295100007 Missouri St Louis City -7.6 -5.2 

295100085 Missouri St Louis City -6.2 -4.4 

295100086 Missouri St Louis City -4.2 -2.3 

295100087 Missouri St Louis City -6.7 -4.5 

300870307 Montana Rosebud 0.0 0.2 

301111065 Montana Yellowstone -0.1 0.0 

310250002 Nebraska Cass -2.1 -1.4 

310550019 Nebraska Douglas -1.8 -1.2 

310550052 Nebraska Douglas -2.6 -1.8 

310790004 Nebraska Hall -1.1 -0.7 

311090022 Nebraska Lancaster -2.1 -1.4 

311570003 Nebraska Scotts Bluff -0.4 -0.2 

311770002 Nebraska Washington -2.7 -2.2 

330012004 New Hampshire Belknap -2.7 -2.2 

330050007 New Hampshire Cheshire -2.9 -2.2 

330070014 New Hampshire Coos -3.2 -2.5 

330090010 New Hampshire Grafton -2.7 -2.1 

330110020 New Hampshire Hillsborough -2.5 -1.8 

330111015 New Hampshire Hillsborough -2.0 -1.3 

330115001 New Hampshire Hillsborough -4.6 -4.0 

330131006 New Hampshire Merrimack -2.5 -1.9 

330150014 New Hampshire Rockingham -2.7 -2.3 

330190003 New Hampshire Sullivan -3.1 -2.2 
B-146 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

340030003 New Jersey Bergen -6.1 -4.5 

340070003 New Jersey Camden -5.8 -4.6 

340071007 New Jersey Camden -6.0 -4.6 

340130015 New Jersey Essex -4.8 -3.3 

340171003 New Jersey Hudson -4.1 -2.6 

340172002 New Jersey Hudson -2.7 -1.0 

340210008 New Jersey Mercer -6.8 -5.5 

340230006 New Jersey Middlesex -5.4 -4.3 

340270004 New Jersey Morris -6.1 -4.9 

340273001 New Jersey Morris -7.2 -6.0 

340292002 New Jersey Ocean -6.2 -5.1 

340310005 New Jersey Passaic -6.5 -5.0 

340390004 New Jersey Union -4.8 -3.3 

340390006 New Jersey Union -6.9 -5.3 

340392003 New Jersey Union -5.1 -3.7 

340410006 New Jersey Warren -5.8 -4.9 

350010023 New Mexico Bernalillo -0.8 -0.2 

350010024 New Mexico Bernalillo -0.7 -0.3 

350050005 New Mexico Chaves -1.4 -1.1 

350130017 New Mexico Dona Ana -0.7 -0.4 

350130025 New Mexico Dona Ana -0.9 -0.7 

350431003 New Mexico Sandoval -0.6 -0.4 

350439011 New Mexico Sandoval -0.4 -0.2 

350450006 New Mexico San Juan -0.1 -0.1 

350490020 New Mexico Santa Fe -0.3 -0.2 

360010005 New York Albany -2.7 -1.9 

360050080 New York Bronx -4.6 -2.8 

360050083 New York Bronx -6.5 -4.5 

360050110 New York Bronx -4.4 -2.6 

360130011 New York Chautauqua -7.2 -6.2 

360290005 New York Erie -3.9 -3.1 

360291007 New York Erie -4.4 -3.6 

360310003 New York Essex -4.6 -3.7 

360470122 New York Kings -5.4 -3.7 

360551007 New York Monroe -5.0 -4.0 

360590008 New York Nassau -5.7 -4.3 

360610056 New York New York -5.5 -3.5 
B-147 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

360610062 New York New York -5.5 -4.1 

360610079 New York New York -4.7 -2.9 

360610128 New York New York -4.6 -3.0 

360632008 New York Niagara -5.3 -4.5 

360671015 New York Onondaga -5.3 -4.4 

360710002 New York Orange -3.0 -2.1 

360810124 New York Queens -5.6 -4.0 

360850055 New York Richmond -5.9 -4.5 

360850067 New York Richmond -5.7 -4.3 

360893001 New York St Lawrence -2.4 -1.9 

361010003 New York Steuben -5.9 -5.0 

361030001 New York Suffolk -6.8 -5.4 

361191002 New York Westchester -4.7 -3.2 

370010002 North Carolina Alamance -6.0 -4.6 

370210034 North Carolina Buncombe -6.3 -5.3 

370330001 North Carolina Caswell -5.3 -4.2 

370350004 North Carolina Catawba -5.4 -4.4 

370370004 North Carolina Chatham -6.0 -5.1 

370510009 North Carolina Cumberland -5.2 -4.1 

370570002 North Carolina Davidson -4.2 -3.3 

370610002 North Carolina Duplin -4.9 -3.8 

370630001 North Carolina Durham -6.1 -5.0 

370650004 North Carolina Edgecombe -5.1 -3.9 

370670022 North Carolina Forsyth -5.1 -4.1 

370710016 North Carolina Gaston -5.6 -4.6 

370810013 North Carolina Guilford -5.7 -4.5 

370870010 North Carolina Haywood -5.1 -4.1 

370990006 North Carolina Jackson -5.0 -3.8 

371070004 North Carolina Lenoir -3.7 -2.6 

371110004 North Carolina McDowell -5.5 -4.6 

371170001 North Carolina Martin -4.3 -3.2 

371190010 North Carolina Mecklenburg -4.8 -3.8 

371190041 North Carolina Mecklenburg -5.0 -4.0 

371190042 North Carolina Mecklenburg -4.8 -3.8 

371210001 North Carolina Mitchell -6.7 -5.5 

371230001 North Carolina Montgomery -5.9 -5.1 

371290002 North Carolina New Hanover -4.9 -3.6 
B-148 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

371330005 North Carolina Onslow -4.5 -3.5 

371350007 North Carolina Orange -5.7 -4.5 

371470005 North Carolina Pitt -4.4 -3.2 

371550005 North Carolina Robeson -5.9 -4.3 

371590021 North Carolina Rowan -4.6 -3.8 

371730002 North Carolina Swain -5.3 -4.2 

371830014 North Carolina Wake -6.8 -5.7 

371890003 North Carolina Watauga -6.7 -5.5 

371910005 North Carolina Wayne -4.9 -3.0 

380070002 North Dakota Billings -0.2 -0.1 

380130003 North Dakota Burke -0.3 -0.2 

380150003 North Dakota Burleigh -0.8 -0.5 

380171004 North Dakota Cass -1.8 -1.2 

380530002 North Dakota McKenzie -0.3 -0.2 

380570004 North Dakota Mercer -0.5 -0.4 

390090003 Ohio Athens -9.1 -7.4 

390170003 Ohio Butler -8.3 -6.9 

390170016 Ohio Butler -9.5 -7.6 

390170017 Ohio Butler -10.6 -9.2 

390171004 Ohio Butler -10.7 -9.0 

390230005 Ohio Clark -9.2 -7.7 

390250022 Ohio Clermont -10.4 -9.0 

390350027 Ohio Cuyahoga -6.3 -5.3 

390350034 Ohio Cuyahoga -8.9 -8.0 

390350038 Ohio Cuyahoga -6.8 -5.6 

390350045 Ohio Cuyahoga -9.2 -8.2 

390350060 Ohio Cuyahoga -7.9 -6.9 

390350065 Ohio Cuyahoga -9.8 -8.6 

390351002 Ohio Cuyahoga -6.9 -6.1 

390490024 Ohio Franklin -8.6 -6.7 

390490025 Ohio Franklin -10.2 -8.3 

390490081 Ohio Franklin -7.1 -5.3 

390570005 Ohio Greene -9.3 -7.3 

390610006 Ohio Hamilton -11.9 -10.4 

390610014 Ohio Hamilton -10.7 -8.8 

390610040 Ohio Hamilton -10.0 -8.2 

390610042 Ohio Hamilton -7.8 -6.4 
B-149 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

390610043 Ohio Hamilton -10.3 -9.2 

390617001 Ohio Hamilton -10.4 -9.0 

390618001 Ohio Hamilton -9.6 -7.9 

390810017 Ohio Jefferson -7.3 -5.8 

390811001 Ohio Jefferson -9.4 -8.0 

390851001 Ohio Lake -11.4 -10.0 

390870010 Ohio Lawrence -8.3 -6.6 

390933002 Ohio Lorain -7.1 -6.2 

390950024 Ohio Lucas -6.4 -5.5 

390950025 Ohio Lucas -2.9 -2.0 

390950026 Ohio Lucas -5.6 -4.5 

390990005 Ohio Mahoning -7.2 -6.2 

390990014 Ohio Mahoning -7.8 -6.7 

391130031 Ohio Montgomery -5.3 -4.1 

391130032 Ohio Montgomery -10.8 -9.3 

391330002 Ohio Portage -8.7 -7.8 

391351001 Ohio Preble -10.4 -8.9 

391450013 Ohio Scioto -8.8 -7.2 

391510017 Ohio Stark -9.4 -8.3 

391530017 Ohio Summit -8.5 -7.3 

391530023 Ohio Summit -8.5 -7.2 

391550007 Ohio Trumbull -8.0 -7.0 

400159008 Oklahoma Caddo -3.1 -2.4 

400219002 Oklahoma Cherokee -2.3 -1.3 

400710602 Oklahoma Kay -1.9 -1.2 

400719010 Oklahoma Kay -2.7 -2.0 

400819005 Oklahoma Lincoln -3.6 -2.7 

400970186 Oklahoma Mayes -2.3 -1.5 

400979014 Oklahoma Mayes -3.6 -2.6 

401010169 Oklahoma Muskogee -4.7 -4.0 

401090035 Oklahoma Oklahoma -2.1 -1.2 

401091037 Oklahoma Oklahoma -2.6 -1.7 

401159004 Oklahoma Ottawa -3.6 -2.8 

401210415 Oklahoma Pittsburg -3.8 -2.8 

401359015 Oklahoma Sequoyah -4.1 -3.2 

401430110 Oklahoma Tulsa -3.0 -1.9 

401431127 Oklahoma Tulsa -4.1 -3.0 
B-150 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

420010001 Pennsylvania Adams -6.0 -5.1 

420030008 Pennsylvania Allegheny -7.9 -6.7 

420030021 Pennsylvania Allegheny -7.5 -6.3 

420030064 Pennsylvania Allegheny -11.7 -9.1 

420030067 Pennsylvania Allegheny -10.4 -8.9 

420030093 Pennsylvania Allegheny -9.7 -8.1 

420030095 Pennsylvania Allegheny -8.5 -7.0 

420030116 Pennsylvania Allegheny -9.9 -8.2 

420030133 Pennsylvania Allegheny -6.8 -5.3 

420031008 Pennsylvania Allegheny -11.0 -9.2 

420031301 Pennsylvania Allegheny -9.4 -7.8 

420033007 Pennsylvania Allegheny -7.8 -6.4 

420039002 Pennsylvania Allegheny -9.4 -7.8 

420070014 Pennsylvania Beaver -9.2 -7.5 

420110011 Pennsylvania Berks -3.4 -2.6 

420170012 Pennsylvania Bucks -4.0 -2.5 

420210011 Pennsylvania Cambria -11.5 -10.0 

420270100 Pennsylvania Centre -7.4 -6.6 

420290100 Pennsylvania Chester -4.7 -3.7 

420410101 Pennsylvania Cumberland -2.6 -2.1 

420430401 Pennsylvania Dauphin -3.1 -2.4 

420450002 Pennsylvania Delaware -5.7 -4.7 

420490003 Pennsylvania Erie -8.2 -6.9 

420692006 Pennsylvania Lackawanna -6.5 -5.4 

420710007 Pennsylvania Lancaster -1.2 -0.6 

420770004 Pennsylvania Lehigh -4.4 -3.4 

420791101 Pennsylvania Luzerne -4.8 -3.5 

420850100 Pennsylvania Mercer -7.4 -6.5 

420950025 Pennsylvania Northampton -5.0 -4.0 

420990301 Pennsylvania Perry -4.3 -3.6 

421010004 Pennsylvania Philadelphia -4.8 -3.6 

421010024 Pennsylvania Philadelphia -6.8 -5.2 

421010047 Pennsylvania Philadelphia -5.5 -4.5 

421250005 Pennsylvania Washington -8.0 -6.5 

421250200 Pennsylvania Washington -7.0 -5.8 

421255001 Pennsylvania Washington -10.7 -9.3 

421290008 Pennsylvania Westmoreland -10.2 -8.8 
B-151 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

421330008 Pennsylvania York -2.5 -1.8 

440070022 Rhode Island Providence -3.1 -2.6 

440070026 Rhode Island Providence -3.7 -3.0 

440070028 Rhode Island Providence -2.9 -2.2 

440071010 Rhode Island Providence -2.9 -2.3 

450190049 South Carolina Charleston -4.1 -3.3 

450250001 South Carolina Chesterfield -5.6 -4.6 

450370001 South Carolina Edgefield -6.4 -4.8 

450410002 South Carolina Florence -5.3 -4.2 

450450008 South Carolina Greenville -5.0 -3.8 

450450009 South Carolina Greenville -5.4 -4.1 

450470003 South Carolina Greenwood -6.1 -4.6 

450510002 South Carolina Horry -4.9 -3.8 

450630008 South Carolina Lexington -5.6 -4.2 

450730001 South Carolina Oconee -6.3 -5.0 

450790007 South Carolina Richland -6.0 -4.6 

450790019 South Carolina Richland -6.3 -4.8 

450830010 South Carolina Spartanburg -5.9 -4.7 

460110002 South Dakota Brookings -2.0 -1.4 

460130003 South Dakota Brown -1.1 -0.7 

460290002 South Dakota Codington -2.5 -1.9 

460330132 South Dakota Custer -0.5 -0.3 

460710001 South Dakota Jackson -0.9 -0.7 

460990006 South Dakota Minnehaha -2.0 -1.3 

460990007 South Dakota Minnehaha -2.4 -1.7 

461030016 South Dakota Pennington -0.3 -0.1 

461030020 South Dakota Pennington -0.2 0.0 

461031001 South Dakota Pennington -0.4 -0.1 

470090011 Tennessee Blount -7.0 -5.6 

470370023 Tennessee Davidson -9.4 -8.0 

470370025 Tennessee Davidson -8.1 -6.8 

470370036 Tennessee Davidson -10.5 -8.9 

470450004 Tennessee Dyer -9.2 -7.4 

470650031 Tennessee Hamilton -4.3 -3.0 

470651011 Tennessee Hamilton -7.0 -5.7 

470654002 Tennessee Hamilton -6.6 -5.2 

470930028 Tennessee Knox -7.2 -5.7 
B-152 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

470931017 Tennessee Knox -6.5 -5.2 

470990002 Tennessee Lawrence -7.8 -6.5 

471050108 Tennessee Loudon -6.5 -5.2 

471071002 Tennessee Mc Minn -7.0 -5.5 

471192007 Tennessee Maury -8.6 -7.3 

471251009 Tennessee Montgomery -12.0 -10.2 

471410001 Tennessee Putnam -10.3 -8.6 

471450004 Tennessee Roane -6.8 -5.4 

471570014 Tennessee Shelby -8.2 -6.4 

471570038 Tennessee Shelby -9.0 -7.0 

471570047 Tennessee Shelby -9.4 -7.4 

471571004 Tennessee Shelby -8.9 -7.3 

471631007 Tennessee Sullivan -5.5 -4.4 

471650007 Tennessee Sumner -12.6 -10.9 

480370004 Texas Bowie -5.1 -4.2 

481130050 Texas Dallas -3.7 -2.5 

481130069 Texas Dallas -3.7 -2.7 

481130087 Texas Dallas -3.6 -2.7 

481350003 Texas Ector -1.8 -1.4 

481410037 Texas El Paso -0.6 -0.5 

482011035 Texas Harris -3.6 -2.5 

482030002 Texas Harrison -4.1 -3.1 

482150043 Texas Hidalgo -2.8 -2.1 

483550032 Texas Nueces -4.0 -3.0 

483550034 Texas Nueces -3.9 -2.9 

483611001 Texas Orange -4.7 -3.7 

484391002 Texas Tarrant -3.3 -2.5 

484391006 Texas Tarrant -3.4 -2.7 

500010002 Vermont Addison -4.6 -3.6 

500010003 Vermont Addison -5.8 -4.7 

500030004 Vermont Bennington -3.9 -3.3 

500070012 Vermont Chittenden -4.7 -3.8 

500070014 Vermont Chittenden -3.1 -2.5 

500210002 Vermont Rutland -1.7 -1.2 

510130020 Virginia Arlington -8.2 -6.8 

510360002 Virginia Charles City -6.7 -5.8 

510410003 Virginia Chesterfield -5.7 -4.7 
B-153 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

510590030 Virginia Fairfax -7.6 -6.5 

510591005 Virginia Fairfax -8.0 -6.7 

510595001 Virginia Fairfax -7.5 -6.2 

510870014 Virginia Henrico -6.5 -5.6 

510870015 Virginia Henrico -7.1 -6.0 

511071005 Virginia Loudoun -7.9 -6.8 

511390004 Virginia Page -6.6 -5.5 

515200006 Virginia Bristol City -6.5 -5.1 

516500004 Virginia Hampton City -5.5 -4.5 

516800015 Virginia Lynchburg City -7.8 -6.5 

517100024 Virginia Norfolk City -4.8 -3.7 

517700014 Virginia Roanoke City -7.2 -5.8 

517750010 Virginia Salem City -6.7 -5.3 

540030003 West Virginia Berkeley -4.7 -3.9 

540090005 West Virginia Brooke -7.4 -5.9 

540090011 West Virginia Brooke -9.2 -7.8 

540110006 West Virginia Cabell -9.9 -8.3 

540291004 West Virginia Hancock -10.0 -8.2 

540330003 West Virginia Harrison -12.8 -11.6 

540390010 West Virginia Kanawha -9.9 -8.5 

540390011 West Virginia Kanawha -8.6 -7.3 

540391005 West Virginia Kanawha -9.6 -8.1 

540490006 West Virginia Marion -12.5 -11.3 

540511002 West Virginia Marshall -8.8 -7.7 

540610003 West Virginia Monongalia -13.1 -11.1 

540690010 West Virginia Ohio -8.2 -7.0 

540810002 West Virginia Raleigh -8.5 -7.3 

540890001 West Virginia Summers -9.0 -7.7 

541071002 West Virginia Wood -9.9 -7.3 

550030010 Wisconsin Ashland -3.0 -2.4 

550090005 Wisconsin Brown -2.9 -2.0 

550090009 Wisconsin Brown -1.2 -0.7 

550250047 Wisconsin Dane -2.8 -1.6 

550270007 Wisconsin Dodge -2.5 -1.5 

550410007 Wisconsin Forest -3.0 -2.4 

550430009 Wisconsin Grant -2.4 -1.6 

550590019 Wisconsin Kenosha -3.0 -1.5 
B-154 

24-Hour PM2.5 (μg/m3

) 

Monitor ID State County 

Difference: 

2014 Remedy - 

2012 Base Case 

Difference: 

2014 Remedy - 

2014 Base Case 

550710007 Wisconsin Manitowoc -3.0 -2.2 

550790010 Wisconsin Milwaukee -4.7 -3.3 

550790026 Wisconsin Milwaukee -3.7 -2.4 

550790043 Wisconsin Milwaukee -4.6 -2.9 

550790059 Wisconsin Milwaukee -4.1 -2.2 

550790099 Wisconsin Milwaukee -3.4 -1.9 

550870009 Wisconsin Outagamie -1.0 -0.5 

550890009 Wisconsin Ozaukee -2.9 -1.9 

551091002 Wisconsin St Croix -2.0 -1.2 

551110007 Wisconsin Sauk -1.3 -0.6 

551198001 Wisconsin Taylor -1.6 -1.0 

551250001 Wisconsin Vilas -2.1 -1.5 

551330027 Wisconsin Waukesha -2.7 -1.3 

560050877 Wyoming Campbell -0.1 0.0 

560050892 Wyoming Campbell 0.0 0.0 

560050899 Wyoming Campbell 0.0 0.1 

560090819 Wyoming Converse 0.0 0.0 

560131003 Wyoming Fremont -0.3 0.0 

560210001 Wyoming Laramie -0.1 0.0 

560330002 Wyoming Sheridan -0.2 0.0 
C-1 

Air Quality Modeling Final Rule 

Technical Support Document 

Appendix C 

Ozone and PM2.5 Contribution Calculation Examples 
C-2 

As described in Section IV, the contribution of ozone and PM2.5 from upwind states to downwind nonatt

maintenance receptors was calculated using a combination of MATS and other post-processing software. This ap

calculations in more detail. 

8-Hour Ozone [All example concentrations are in ppb]The 2012 future year base case ozone design value calculations are described in Section III.C. The 2012 design calculated by running MATS using the CAMx 2005 base year and 2012 future base case model outputs. The con

were calculated as described in Section IV.D. There are several pieces of information needed to calculate the up

contributions to downwind ozone receptor sites. 

1) The 2012 base case 8-hr design values at each receptor. 2) The relative response factors (RRFs) from each “source apportionment” MATS run. The source apportio

described in Section IV.B steps 4 and 5. The source apportionment MATS runs specify the “base” year mCAMx base case and the “future” year model file as the 2012 CAMx source apportionment case. A sepaapportionment MATS case is run for each upwind state. 

The 2012 future year design values are shown in the example below as the variable “f_o3_DV” (from the MATS “scenario_name - Ozone Monitors -- monitor data, temporally adjusted 2012.csv”) 

_id lat Long referencecell f_o3_DV _state_name _county_name 

10331002 34.76056 -87.6506 155091 64.5 Alabama Colbert 10731003 33.48556 -86.915 162080 69.7 Alabama Jefferson 10731005 33.33111 -87.0036 162078 71 Alabama Jefferson 10731009 33.45972 -87.3056 159079 66.4 Alabama Jefferson 

These values are multiplied by the RRFs calculated from each source apportionment MATS run (for each upwinin the orange column (below) represent the relative ozone contribution from an upwind state to each downwind rexample, an RRF value of 0.9569 for Colbert County means that the upwind state contributes 4.31% [(1 – 0.9569

in Colbert County (averaged across all high1 ozone days) 

1 The high ozone day threshold for the MATS ozone contribution runs are defined in Section IV.D. step 5.
C-3 

_id lat long referencecell f_o3_DV _state_name _county_name SA_rrf 

10331002 34.76056 -87.6506 155091 64.5 Alabama Colbert 0.9569 10731003 33.48556 -86.915 162080 69.7 Alabama Jefferson 0.9944 10731005 33.33111 -87.0036 162078 71 Alabama Jefferson 0.9979 10731009 33.45972 -87.3056 159079 66.4 Alabama Jefferson 0.9986 

The 2012 future year design value (f_o3_DV) is multiplied by the RRF and then subtracted from the 2012 design 

upwind contribution in ppb (the values in blue below). Here is the formula: Ozone Contribution = f_o3_dv – (f_o3_dv * RRF) The future design values and RRFs were derived from MATS. The calculation of the contribution was accomplipost-processing software (using the formula above). The final 8-hr ozone contributions (in ppb) are truncated to 

_id lat Long referencecell f_o3_DV _state_name _county_name SA_rrf C

10331002 34.76056 -87.6506 155091 64.5 Alabama Colbert 0.9569 10731003 33.48556 -86.915 162080 69.7 Alabama Jefferson 0.9944 10731005 33.33111 -87.0036 162078 71 Alabama Jefferson 0.9979 10731009 33.45972 -87.3056 159079 66.4 Alabama Jefferson 0.9986 
C-4 

Annual Average PM2.5 [All example concentrations are in ug/m3] The 2012 future year base case annual average PM2.5 design value calculations are described in Section III.B. Tvalues were calculated by running MATS using the CAMx 2005 base year and 2012 future base case model outpPM2.5 contribution metrics were calculated as described in Section IV.C. The information needed to calculate thPM2.5 contributions to downwind annual PM2.5 receptor sites is as follows: 

1) The 2012 future base case annual average PM2.5 design values and component species concentrations at 2) The relative response factors (RRFs) from each “source apportionment” MATS run. The source apportio

described in Section IV.A step 4. The source apportionment MATS runs specify the “base” year model fbase case and the “future” year model file as the 2012 CAMx source apportionment case. A separate souMATS case is run for each upwind state. 

The 2012 future year annual average PM2.5 design values are shown in the example below as the variable “f_pmthe MATS output file “scenario_name Annual PM25 Point.csv”). 

_id 

_STATE_NAME 

_COUNTY_NAME monitor_lat monitor_long 

monitor_gridcell 

b_pm25_ann_DV 

f_pm25_ann_DV 

10030010 Alabama Baldwin 30.498 -87.8814 158051 11.44 10.81 10270001 Alabama Clay 33.28126 -85.8022 171079 13.21 12 10331002 Alabama Colbert 34.76056 -87.6506 155091 12.67 12.13 10491003 Alabama DeKalb 34.28763 -85.9683 169088 14.09 12.75 

The 2012 future design value is comprised of a number of component PM2.5 species. The following table show

concentrations. 

_id _STATE_NAME 

_COUNTY_NAME 

f_blank_mass 

f_crustal_mass 

f_EC_mass 

f_NH4_mass 

f_Ocmb_mass 

f_SO4_mass 

f_NO3_mass 

f_water_mass 

f_salt_mass 

10030010 Alabama Baldwin 0.5 0.4838 0.4564 1.0612 3.2319 4.0003 0.0042 1.0489 0.0315 10270001 Alabama Clay 0.5 0.7034 0.5973 1.1121 4.3653 3.5529 0.0675 1.0888 0.02 10331002 Alabama Colbert 0.5 0.3902 0.3137 1.2683 4.2728 4.0396 0.1537 1.1984 0.0024 10491003 Alabama DeKalb 0.5 0.6574 0.4713 1.3423 4.2096 4.0967 0.1444 1.314 0.0163 
C-5 

The source apportionment RRFs (in orange below) are derived from the source apportionment MATS runs. The

relative PM2.5 species contribution from an upwind state to each downwind receptor. For example, an RRF valuBaldwin County for sulfate means that the upwind state contributes 3.44% [(1 – 0.9656)* 100)] of the sulfate ion 

(averaged across all days). 

_id _STATE_NAME _COUNTY_NAME SA_rrf_crustal SA_rrf_ec SA_rrf_nh4 SA_rrf_oc SA_rrf_so4 SA_rrf_no3 SA_r

10030010 Alabama Baldwin 0.9949 0.9947 0.964 0.9985 0.9656 0.9996 10270001 Alabama Clay 0.9895 0.9897 0.9535 0.9972 0.9529 0.9929 10331002 Alabama Colbert 0.9863 0.9871 0.9322 0.9954 0.928 0.9899 10491003 Alabama DeKalb 0.9862 0.9859 0.9371 0.9961 0.9364 0.9913 

The annual RRFs from the source apportionment MATS run are multiplied by the 2012 species concentrations an

from the 2012 species concentrations to get species contributions. Here is the formula: Annual PM2.5 Contribution (SO4) = f_SO4_mass – (f_SO4_mass * SA_rrf_SO4) The calculation is completed for each of the 7 species2 which have an RRF. The yellow and orange columns are 

MATS output files. The species contributions from upwind state “A” to the example downwind receptors are shown in blue below. Tthe final species contributions which are calculated using additional post-processing software (using the formula 

_id _STATE_NAME 

_COUNTY_NAME 

crustal_mass_SA 

EC_mass_SA 

NH4_mass_SA 

Ocmb_mass_SA 

SO4_mass_SA 

NO3_mass_SA 

water__SA 

10030010 Alabama Baldwin 0.0025 0.0024 0.0382 0.0048 0.1376 0.0000 010270001 Alabama Clay 0.0074 0.0062 0.0517 0.0122 0.1673 0.0005 010331002 Alabama Colbert 0.0053 0.0040 0.0860 0.0197 0.2909 0.0016 010491003 Alabama DeKalb 0.0091 0.0066 0.0844 0.0164 0.2606 0.0013 0

2 The RRF for salt is not used because it is always equal to 1.
C-6 

For the final transport rule contributions, the sulfate and nitrate and related species (ammonium and water) contrisummed to get the total PM2.5 contribution used in the proposed rule. These are the values in the blue column bannual average PM2.5 contributions (in ug/m3) are truncated to the hundredths digit. 

_id _STATE_NAME _COUNTY_NAME 

Total Annual PM2.5 Mass Contribution 

10030010 Alabama Baldwin 0.20 10270001 Alabama Clay 0.26 10331002 Alabama Colbert 0.43 10491003 Alabama DeKalb 0.40 
C-7 

24-Hour Average PM2.5 [All example concentrations are in ug/m3]

The 2012 future year base case 24 hr. average PM2.5 design value calculations are described in Section III.B. The 

were calculated by running MATS using the CAMx 2005 base year and 2012 future base case model outputs. Th

contribution metrics were calculated as described in Section IV.C. The 24-hr. PM2.5 contribution calculations are complicated than the annual average calculations. High 24-hr values can occur at different times of the year at eTherefore, we must calculate the contributions to the downwind receptor for each of the quarters and years. The 5 year average of the contribution to the 98th percentile day for each year3. The 98th percentile future days vary b

identified in the “New Daily All Years High Quarters PM25 scenario_name .csv” file4. The information needed to calculate the upwind 24-hr. PM2.5 contributions to downwind 24-hr. PM2.5 receptor sit

1) The 2012 future base case 24-hour average PM2.5 and component species concentrations for each 98th per

five years at each receptor. 

3) The relative response factors (RRFs) from each “source apportionment” MATS run. The source apportio

described in Section IV.A step 4. The source apportionment MATS runs specify the “base” year model ffuture base case and the “future” year model file as the 2012 CAMx source apportionment case. A separaapportionment MATS case is run for each upwind state. 

The 2012 future year 24-hr. 98th percentile PM2.5 values are shown in the example below as the variable “f_pm25

the file “New Daily All Years High Quarters PM25 scenario_name .csv”). For the design value calculations, the 

percentile values (in bold) are averaged together to create a 5 year weighted average (year 1 is weighted once, ye

three times, year 4 twice, and year 5 once). 

3 The contribution on the 98th percentile day represents the contribution to the particular quarter of the year when the future 

occurred. The quarter in which the future year 98th percentile day occurs varies across the 5 years.

4 This is a file that was created in the post-processing of standard MATS outputs. It is not a file directly output from MAT
C-8 

_id _state_name _county_name monitor_lat monitor_lon year

b_high_quarter b_pm25_d_q_conc

f_high_quarter f_pm

10030010 Alabama Baldwin 30.498001 ‐87.881412 2003 2 29.1 2 10030010 Alabama Baldwin 30.498001 ‐87.881412 2004 3 25.5 1 10030010 Alabama Baldwin 30.498001 ‐87.881412 2005 2 23.7 3 10030010 Alabama Baldwin 30.498001 ‐87.881412 2006 3 25.6 2 10030010 Alabama Baldwin 30.498001 ‐87.881412 2007 2 22.4 2 

The 2012 future 24-hr PM2.5 design value is comprised of a number of component PM2.5 species. The following 

species concentrations for each of the 98th percentile value days in Baldwin County. 

_id

_state_name

_county_name year

f_high_quarter

f_blank_mass_q

f_crustal_mass_q

f_EC_mass_q

f_NH4_mass_q

f_Ocmb_mass_q

f_SO4_mass_q

10030010 Alabama Baldwin 2003 2 0.5 0.65643 0.56179 2.26291 11.08756 8.303571 10030010 Alabama Baldwin 2004 1 0.5 0.54168 0.952419 2.28928 10.365396 6.766255 10030010 Alabama Baldwin 2005 3 0.5 0.84954 0.588702 1.95904 8.5855297 8.269267 10030010 Alabama Baldwin 2006 2 0.5 0.57812 0.494769 1.99295 9.7648339 7.31297 10030010 Alabama Baldwin 2007 2 0.5 0.50442 0.431691 1.73887 8.5199149 6.380639 

The source apportionment RRFs5 (in orange below) are derived from the source apportionment MATS runs. Therelative PM2.5 species contribution from an upwind state to each downwind receptor in each quarter6. For examp

0.9656 for Baldwin County for quarter 2 sulfate means that the upwind state contributes 3.44% [(1 – 0.9656)* 10

5 One additional step is needed to calculate the water RRF. Since the MATS output file does not contain the quarterly wate

calculated from the base and future year water concentrations (f_water_mass_q / b_water_mass_q)

6 There is a single species RRF for each of the 4 quarters. 
C-9 

in Baldwin County in quarter 2 (averaged across all high7 days in each quarter). The Source apportionment MATspecies specific RRFs for each of the four quarters. The quarters that contain the 98th percentile values for the re

previously identified in the first step. The future year 98th percentile quarters for the 5 years at the example recep

are: Year 1 (2003) quarter 2 Year 2 (2004) quarter 1 Year 3 (2005) quarter 3 Year 4 (2006) quarter 2 Year 5 (2007) quarter 2 The table below shows the source apportionment RRFs for each year and quarter. The rows which contain the R

percentile quarters are in bold below: 

_id _state_name _county_name year quarter SA_rrf_crustal_q SA_rrf_ec_q SA_rrf_nh4_q SA_rrf_oc_q SA_rrf_so

10030010 Alabama Baldwin 2003 1 0.9663 0.9772 0.9965 0.9852 0.8

10030010 Alabama Baldwin 2003 2 0.9912 0.9934 0.9996 0.9978 0.9

10030010 Alabama Baldwin 2003 3 0.9897 0.9914 0.9993 0.9985 010030010 Alabama Baldwin 2003 4 0.9715 0.971 0.9981 0.9918 0.9

10030010 Alabama Baldwin 2004 1 0.9663 0.9772 0.9965 0.9852 0.8

10030010 Alabama Baldwin 2004 2 0.9912 0.9934 0.9996 0.9978 0.910030010 Alabama Baldwin 2004 3 0.9897 0.9914 0.9993 0.9985 010030010 Alabama Baldwin 2004 4 0.9715 0.971 0.9981 0.9918 0.910030010 Alabama Baldwin 2005 1 0.9663 0.9772 0.9965 0.9852 0.810030010 Alabama Baldwin 2005 2 0.9912 0.9934 0.9996 0.9978 0.9

10030010 Alabama Baldwin 2005 3 0.9897 0.9914 0.9993 0.9985 0.

10030010 Alabama Baldwin 2005 4 0.9715 0.971 0.9981 0.9918 0.910030010 Alabama Baldwin 2006 1 0.9663 0.9772 0.9965 0.9852 0.8

10030010 Alabama Baldwin 2006 2 0.9912 0.9934 0.9996 0.9978 0.9

10030010 Alabama Baldwin 2006 3 0.9897 0.9914 0.9993 0.9985 0

7 Consistent with the 24-hr PM2.5 design value calculations, the “high” days are defined as the 10% highest modeled PM2.5 

each quarter at each receptor.
C-10 

10030010 Alabama Baldwin 2006 4 0.9715 0.971 0.9981 0.9918 0.910030010 Alabama Baldwin 2007 1 0.9663 0.9772 0.9965 0.9852 0.8

10030010 Alabama Baldwin 2007 2 0.9912 0.9934 0.9996 0.9978 0.9

10030010 Alabama Baldwin 2007 3 0.9897 0.9914 0.9993 0.9985 010030010 Alabama Baldwin 2007 4 0.9715 0.971 0.9981 0.9918 0.9

The quarterly RRFs from the source apportionment MATS run which correspond to the quarter when the 98th per

are multiplied by the 2012 species concentrations for the 98th percentile days in each year. The result is then sub

species concentrations to get species contributions for each year. Here is the formula: 98th percentile PM2.5 Contribution (SO4) = f_SO4_mass_q – (f_SO4_mass_q * SA_rrf_SO4_q) 

The calculation is completed for each year for each of the 7 species8 which have an RRF. 

The species contributions from upwind state “A” to the example downwind receptor is shown in blue below. Ththe species contributions which are calculated using additional post-processing software (using the contribution f

_id _state_name _county_name year quarter 

crustal_mass_SA 

EC_mass_SA 

NH4_mass_SA 

Ocmb_mass_SA 

SO4_mass_SA 

NO3SA 

10030010 Alabama Baldwin 2003 2 0.0058 0.0037 0.0009 0.0244 0.2856 10030010 Alabama Baldwin 2004 1 0.0183 0.0217 0.0080 0.1534 0.7571 10030010 Alabama Baldwin 2005 3 0.0088 0.0051 0.0014 0.0129 0.4796 10030010 Alabama Baldwin 2006 2 0.0051 0.0033 0.0008 0.0215 0.2516 10030010 Alabama Baldwin 2007 2 0.0044 0.0028 0.0007 0.0187 0.2195 

The next step is to average the contributions for the each of the 5 years. The contributions (from the blue columns in the table above) are averaged9 across the five 98th percentile days w

result: 

8 The RRF for salt is ignored because it is always equal to 1.
C-11 

crustal_mass_SA EC_mass_SA NH4_mass_SA Ocmb_mass_SA SO4_mass_SA NO3_mass_SA 

10030010 Alabama Baldwin 0.0080 0.0075 0.0025 0.0481 0.3835 0.0000 

For the final transport rule contributions, the sulfate and nitrate and related species (ammonium and water) contrisummed to get the total PM2.5 contribution used in the final rule. The sum of these values is shown as the 24-hr Pthe blue column below. The final 24 hr. PM2.5 contributions (in ug/m3) are truncated to the hundredths digit. 

_id _STATE_NAME _COUNTY_NAME 

Total 24 Hr. PM2.5 Mass Contribution 

10030010 Alabama Baldwin 0.51 

9 The 5 year average for the contribution calculations is a “straight” 5 year average, not a weighted average.
Air Quality Modeling Final Rule 

Technical Support Document 

Appendix D 

2012 Base Case State-by-State Contributions to 

Nonattainment and Maintenance for 

8-Hour Ozone, Annual PM2.5, and 24-Hour PM2.5
D-2

This Appendix provides tabular summaries of the state-by-state 

contributions to 8-hour ozone, annual PM2.5, and 24-hour PM2.5 at projected 

2012 base case nonattainment and maintenance receptors. 

The contributions from a state to nonattainment and/or maintenance in 

another (i.e., “downwind”) state were used to identify states to be covered by 

the Transport Rule. 

The largest contribution from each state to nonattainment and/or 

maintenance in a downwind state is provided at the bottom of the table. 

Contributions that are at or above the following thresholds are shown in red. 

Ozone Threshold: 0.8 ppb 

Annual PM2.5 Threshold: 0.15 μg/m3

24-Hour PM2.5 Threshold: 0.35 μg/m3 

In-state contributions (i.e., the contribution from a state to nonattainment or 

maintenance within the State itself) are provided in the tables in this 

Appendix for completeness. The in-state contributions were not used to 

identify states to be covered by the Transport Rule. 

The tabular summaries are provided by pollutant starting with 8-hour ozone, 

then annual PM2.5, and finally 24-hour PM2.5. For each pollutant the 

contributions to nonattainment sites are provided in “Part 1” and the 

contributions to maintenance sites are provided in “Part 2”. Note that some 

counties have multiple sites that are projected to be nonattainment as well as 

other sites that are projected to have a maintenance problem.
D-3

State-by-State Contributions to 8-Hour Ozone (ppb) Nonattainment Recept

Part 1a. 

Source States: Alabama through Missouri

Receptor 

Monitor 

ID 

Receptor 

State 

Receptor 

County 

Source 

State => AL AR CT DE FL GA IL IN IA KS KY LA ME M

220330003 Louisiana 

East Baton Rouge 2.8 2.1 0.0 0.0 0.3 1.6 0.2 0.2 0.1 0.5 0.3 39.7 0.0 0

480391004 Texas Brazoria 3.1 1.1 0.0 0.0 0.5 1.2 1.4 1.0 0.6 0.2 1.3 7.9 0.0 0482010051 Texas Harris 3.7 0.3 0.0 0.0 0.4 1.2 1.8 1.2 0.5 0.1 1.5 7.3 0.0 0482010055 Texas Harris 4.0 0.4 0.0 0.0 0.4 1.3 1.9 1.3 0.6 0.1 1.6 8.0 0.0 0

Largest Contribution to Downwind Nonattainment 4.0 2.1 0.0 0.0 0.5 1.6 1.9 1.3 0.6 0.5 1.6 8.0 0.0 0
D-4

State-by-State Contributions to 8-Hour Ozone (ppb) Nonattainment Recept

Part 1b. 

Source States: Nebraska through Wisconsin 

Receptor 

Monitor 

ID 

Receptor 

State 

Receptor 

County 

Source 

State => NE NH NJ NY NC ND OH OK PA RI SC SD TN 

220330003 Louisiana 

East Baton Rouge 0.1 0.0 0.0 0.0 0.5 0.2 0.1 0.3 0.1 0.0 0.4 0.1 1.0 

480391004 Texas Brazoria 0.2 0.0 0.0 0.0 0.2 0.1 0.1 0.3 0.0 0.0 0.3 0.1 1.6 482010051 Texas Harris 0.2 0.0 0.0 0.0 0.2 0.1 0.1 0.1 0.0 0.0 0.3 0.1 2.0 482010055 Texas Harris 0.2 0.0 0.0 0.0 0.2 0.1 0.1 0.1 0.0 0.0 0.3 0.1 2.2 

Largest Contribution to Downwind Nonattainment 0.2 0.0 0.0 0.0 0.5 0.2 0.1 0.3 0.1 0.0 0.4 0.1 2.2 
D-5

State-by-State Contributions to 8-Hour Ozone (ppb) Maintenance Recepto

Part 2a. 

Source States: Alabama through Missouri 

Receptor 

Monitor 

ID 

Receptor 

State 

Receptor 

County 

Source 

State => AL AR CT DE FL GA IL IN IA KS KY LA ME M

90011123 Connecticut Fairfield 0.4 0.5 5.7 0.4 0.0 0.3 0.8 0.9 0.2 0.2 1.6 0.1 0.0 90093002 Connecticut 

New Haven 0.1 0.1 4.4 0.6 0.1 0.0 0.7 1.1 0.1 0.1 1.4 0.1 0.0 

240251001 Maryland Harford 0.3 0.1 0.2 0.1 0.0 0.1 0.7 1.0 0.1 0.1 1.4 0.0 0.0 3260050003 Michigan Allegan 0.3 2.0 0.0 0.0 0.0 0.3 26.8 9.4 0.9 1.0 0.6 0.3 0.0 482010029 Texas Harris 2.7 0.2 0.0 0.0 3.6 2.8 0.2 0.2 0.0 0.0 0.3 5.8 0.0 482011050 Texas Harris 2.8 0.5 0.0 0.0 0.9 1.9 0.8 0.6 0.3 0.1 0.8 11.1 0.0 

Largest Contribution to Downwind Maintenance 2.8 2.0 0.2 0.6 3.6 2.8 26.8 9.4 0.9 1.0 1.6 11.1 0.0 
D-6

State-by-State Contributions to 8-Hour Ozone (ppb) Maintenance Recepto

Part 2b. 

Source States: Nebraska through Wisconsin 

Receptor 

Monitor 

ID 

Receptor 

State 

Receptor 

County 

Source 

State => NE NH NJ NY NC ND OH OK PA RI SC SD TN 

90011123 Connecticut Fairfield 0.1 0.1 11.5 18.0 0.6 0.0 2.2 0.2 6.4 0.0 0.1 0.0 0.90093002 Connecticut 

New Haven 0.0 0.0 10.4 18.8 1.3 0.0 3.2 0.1 8.2 0.0 0.1 0.0 0.

240251001 Maryland Harford 0.1 0.0 0.6 1.0 1.0 0.0 3.1 0.1 5.6 0.0 0.1 0.0 1.260050003 Michigan Allegan 0.2 0.0 0.0 0.0 0.3 0.0 0.2 2.8 0.0 0.0 0.1 0.0 0.482010029 Texas Harris 0.0 0.0 0.0 0.0 0.7 0.0 0.2 0.0 0.1 0.0 0.9 0.0 0.482011050 Texas Harris 0.1 0.0 0.1 0.2 0.6 0.1 0.1 0.1 0.5 0.0 0.7 0.1 1.

Largest Contribution to Downwind Maintenance 0.2 0.1 11.5 18.8 1.3 0.1 3.2 2.8 8.2 0.0 0.9 0.1 1.
D-7

State-by-State Contributions to Annual PM2.5 (μg/m3) Nonattainment Recep

Part 1a. 

Source States: Alabama through Missouri 

Receptor 

Monitor 

ID 

Receptor 

State 

Receptor 

County 

Source 

State => AL AR CT DE FL GA IL IN IA KS KY LA ME M

10730023 Alabama Jefferson 3.11 0.03 0.00 0.00 0.07 0.46 0.14 0.28 0.03 0.02 0.22 0.09 0.00 010732003 Alabama Jefferson 2.58 0.03 0.00 0.00 0.08 0.46 0.15 0.30 0.04 0.02 0.24 0.09 0.00 0

131210039 Georgia Fulton 0.51 0.03 0.00 0.00 0.08 1.36 0.16 0.31 0.04 0.02 0.25 0.06 0.00 0171191007 Illinois Madison 0.12 0.10 0.00 0.00 0.01 0.08 1.51 0.70 0.26 0.09 0.33 0.07 0.00 0261630033 Michigan Wayne 0.08 0.02 0.00 0.00 0.00 0.06 0.42 0.69 0.10 0.02 0.25 0.02 0.00 0390350038 Ohio Cuyahoga 0.09 0.02 0.00 0.00 0.01 0.07 0.37 0.65 0.09 0.02 0.28 0.02 0.00 0390350045 Ohio Cuyahoga 0.09 0.02 0.00 0.00 0.01 0.08 0.37 0.65 0.09 0.02 0.29 0.02 0.00 0390350060 Ohio Cuyahoga 0.10 0.02 0.00 0.00 0.01 0.08 0.37 0.65 0.09 0.02 0.29 0.02 0.00 0390610014 Ohio Hamilton 0.19 0.03 0.00 0.00 0.01 0.13 0.50 1.28 0.10 0.03 0.81 0.03 0.00 0390610042 Ohio Hamilton 0.20 0.04 0.00 0.00 0.01 0.13 0.49 1.34 0.10 0.02 0.94 0.03 0.00 0390618001 Ohio Hamilton 0.19 0.03 0.00 0.00 0.01 0.13 0.50 1.27 0.10 0.02 0.80 0.03 0.00 0420030064 Pennsylvania Allegheny 0.08 0.02 0.00 0.00 0.01 0.07 0.26 0.51 0.06 0.01 0.27 0.01 0.00 0

Largest Contribution to Downwind Nonattainment 0.51 0.10 0.00 0.00 0.08 0.46 0.50 1.34 0.26 0.09 0.94 0.09 0.00 0
D-8

State-by-State Contributions to Annual PM2.5 (μg/m3) Nonattainment Recep

Part 1b. 

Source States: Nebraska through Wisconsin 

Receptor 

Monitor 

ID 

Receptor 

State 

Receptor 

County 

Source 

State => NE NH NJ NY NC ND OH OK PA RI SC SD 

10730023 Alabama Jefferson 0.01 0.00 0.00 0.01 0.09 0.01 0.24 0.02 0.10 0.00 0.11 0.00 10732003 Alabama Jefferson 0.01 0.00 0.00 0.02 0.09 0.01 0.26 0.02 0.11 0.00 0.11 0.00 

131210039 Georgia Fulton 0.01 0.00 0.01 0.03 0.20 0.01 0.32 0.02 0.17 0.00 0.24 0.00 171191007 Illinois Madison 0.06 0.00 0.00 0.02 0.03 0.06 0.42 0.08 0.11 0.00 0.03 0.03 261630033 Michigan Wayne 0.02 0.00 0.00 0.13 0.03 0.04 0.99 0.03 0.29 0.00 0.01 0.01 390350038 Ohio Cuyahoga 0.02 0.00 0.01 0.21 0.04 0.04 2.62 0.02 0.53 0.00 0.02 0.01 390350045 Ohio Cuyahoga 0.02 0.00 0.01 0.21 0.04 0.04 2.64 0.02 0.54 0.00 0.02 0.01 390350060 Ohio Cuyahoga 0.02 0.00 0.01 0.21 0.04 0.04 2.63 0.02 0.54 0.00 0.02 0.01 390610014 Ohio Hamilton 0.02 0.00 0.00 0.09 0.06 0.03 2.12 0.03 0.38 0.00 0.04 0.01 390610042 Ohio Hamilton 0.02 0.00 0.00 0.08 0.06 0.03 2.03 0.03 0.37 0.00 0.04 0.01 390618001 Ohio Hamilton 0.02 0.00 0.00 0.09 0.06 0.03 2.11 0.03 0.38 0.00 0.04 0.01 420030064 Pennsylvania Allegheny 0.01 0.00 0.02 0.17 0.08 0.03 1.34 0.01 2.14 0.00 0.04 0.01 

Largest Contribution to Downwind Nonattainment 0.06 0.00 0.02 0.21 0.20 0.06 1.34 0.08 0.54 0.00 0.24 0.03 
D-9

State-by-State Contributions to Annual PM2.5 (μg/m3) Maintenance Recept

Part 2a. 

Source States: Alabama through Missouri 

Receptor 

Monitor 

ID 

Receptor 

State 

Receptor 

County 

Source 

State => AL AR CT DE FL GA IL IN IA KS KY LA ME M

180970081 Indiana Marion 0.15 0.04 0.00 0.00 0.01 0.09 0.65 2.94 0.14 0.04 0.63 0.03 0.00 0.180970083 Indiana Marion 0.15 0.04 0.00 0.00 0.01 0.09 0.65 2.94 0.14 0.04 0.63 0.03 0.00 0.390350065 Ohio Cuyahoga 0.09 0.02 0.00 0.00 0.01 0.08 0.37 0.65 0.09 0.02 0.29 0.02 0.00 0.390617001 Ohio Hamilton 0.19 0.03 0.00 0.00 0.01 0.13 0.50 1.27 0.10 0.03 0.81 0.03 0.00 0.

Largest Contribution to Downwind Maintenance 0.19 0.04 0.00 0.00 0.01 0.13 0.65 1.27 0.14 0.04 0.81 0.03 0.00 0.
D-10

State-by-State Contributions to Annual PM2.5 (μg/m3) Maintenance Recept

Part 2b. 

Source States: Nebraska through Wisconsin 

Receptor 

Monitor 

ID 

Receptor 

State 

Receptor 

County 

Source 

State => NE NH NJ NY NC ND OH OK PA RI SC SD TN 

180970081 Indiana Marion 0.03 0.00 0.00 0.07 0.05 0.04 0.94 0.03 0.23 0.00 0.04 0.01 0.25 180970083 Indiana Marion 0.03 0.00 0.00 0.07 0.05 0.04 0.94 0.03 0.23 0.00 0.04 0.01 0.25 390350065 Ohio Cuyahoga 0.02 0.00 0.01 0.21 0.04 0.04 2.63 0.02 0.54 0.00 0.02 0.01 0.13 390617001 Ohio Hamilton 0.02 0.00 0.00 0.09 0.06 0.03 2.12 0.03 0.38 0.00 0.04 0.01 0.32 

Largest Contributionto Downwind Maintenance 0.03 0.00 0.01 0.21 0.06 0.04 0.94 0.03 0.54 0.00 0.04 0.01 0.32 
D-11

State-by-State Contributions to 24-Hour PM2.5 (μg/m3) Nonattainment Recep

Part 1a. 

Source States: Alabama through Missouri 

Receptor 

Monitor 

ID 

Receptor 

State 

Receptor 

County 

Source 

State => AL AR CT DE FL GA IL IN IA KS KY LA ME 

10730023 Alabama Jefferson 6.81 0.02 0.00 0.00 0.07 1.10 0.29 0.65 0.04 0.01 0.60 0.09 0.00 170311016 Illinois Cook 0.08 0.16 0.00 0.00 0.00 0.08 4.75 3.29 0.70 0.11 0.72 0.07 0.00 171191007 Illinois Madison 0.28 0.24 0.00 0.00 0.00 0.17 4.03 3.02 0.45 0.37 1.71 0.07 0.00 180970043 Indiana Marion 0.38 0.07 0.00 0.00 0.00 0.16 1.67 9.15 0.16 0.02 4.38 0.02 0.00 180970066 Indiana Marion 0.48 0.09 0.00 0.00 0.00 0.27 1.40 8.39 0.12 0.03 3.93 0.03 0.00 180970081 Indiana Marion 0.51 0.09 0.00 0.00 0.01 0.30 1.35 8.29 0.11 0.03 3.86 0.03 0.00 261470005 Michigan St Clair 0.31 0.05 0.00 0.01 0.00 0.20 1.07 2.44 0.08 0.02 1.25 0.06 0.00 261630015 Michigan Wayne 0.34 0.07 0.00 0.00 0.02 0.19 1.06 2.74 0.11 0.04 1.28 0.11 0.00 261630016 Michigan Wayne 0.12 0.01 0.00 0.01 0.00 0.08 1.50 2.75 0.17 0.06 0.67 0.00 0.00 261630019 Michigan Wayne 0.11 0.01 0.00 0.01 0.00 0.07 1.47 2.79 0.15 0.05 0.64 0.00 0.00 261630033 Michigan Wayne 0.33 0.05 0.00 0.00 0.02 0.21 1.02 2.59 0.11 0.04 1.03 0.10 0.00 390350038 Ohio Cuyahoga 0.29 0.03 0.00 0.00 0.00 0.20 0.70 1.84 0.11 0.01 0.89 0.01 0.00 390350060 Ohio Cuyahoga 0.17 0.02 0.00 0.00 0.00 0.13 0.76 1.67 0.14 0.02 0.57 0.01 0.00 420030064 Pennsylvania Allegheny 0.06 0.04 0.00 0.00 0.00 0.07 0.67 1.71 0.09 0.03 0.86 0.01 0.00 420030093 Pennsylvania Allegheny 0.20 0.07 0.00 0.01 0.01 0.14 0.55 1.58 0.06 0.02 1.09 0.06 0.00 420030116 Pennsylvania Allegheny 0.16 0.07 0.00 0.01 0.00 0.14 0.63 1.72 0.07 0.03 1.19 0.06 0.00 420070014 Pennsylvania Beaver 0.24 0.08 0.01 0.01 0.01 0.13 0.70 1.73 0.12 0.04 1.02 0.07 0.00 420710007 Pennsylvania Lancaster 0.23 0.04 0.10 0.22 0.04 0.29 0.16 0.31 0.05 0.02 0.26 0.04 0.06 540090011 

West Virginia Brooke 0.06 0.03 0.01 0.01 0.00 0.06 0.75 1.71 0.15 0.03 0.85 0.01 0.00 

550790043 Wisconsin Milwaukee 0.16 0.04 0.00 0.00 0.00 0.15 3.72 3.56 0.82 0.30 1.15 0.02 0.00 

Largest Contribution to Downwind Nonattainment 0.51 0.24 0.10 0.22 0.07 1.10 3.72 3.56 0.82 0.37 4.38 0.11 0.06 
D-12

State-by-State Contributions to 24-Hour PM2.5 (μg/m3) Nonattainment Recep

Part 1b. 

Source States: Nebraska through Wisconsin 

Receptor 

Monitor 

ID 

Receptor 

State 

Receptor 

County 

Source 

State => NE NH NJ NY NC ND OH OK PA RI SC SD TN 

10730023 Alabama Jefferson 0.01 0.00 0.02 0.06 0.28 0.01 1.18 0.01 0.65 0.00 0.29 0.00 0.70 170311016 Illinois Cook 0.11 0.00 0.00 0.23 0.04 0.07 1.73 0.09 0.65 0.00 0.02 0.05 0.24 171191007 Illinois Madison 0.10 0.00 0.00 0.12 0.05 0.10 2.40 0.17 0.69 0.00 0.03 0.06 0.75 180970043 Indiana Marion 0.01 0.00 0.01 0.09 0.09 0.01 2.66 0.03 0.62 0.00 0.05 0.00 1.16 180970066 Indiana Marion 0.01 0.00 0.01 0.10 0.11 0.02 2.54 0.02 0.67 0.00 0.07 0.00 1.33 180970081 Indiana Marion 0.01 0.00 0.01 0.10 0.11 0.02 2.53 0.02 0.69 0.00 0.07 0.00 1.38 261470005 Michigan St Clair 0.02 0.00 0.03 0.41 0.10 0.01 4.51 0.02 1.41 0.00 0.05 0.00 0.62 261630015 Michigan Wayne 0.02 0.00 0.02 0.34 0.09 0.03 3.67 0.04 1.00 0.00 0.03 0.01 0.51 261630016 Michigan Wayne 0.06 0.00 0.03 0.83 0.04 0.01 3.42 0.01 1.36 0.00 0.01 0.00 0.24 261630019 Michigan Wayne 0.05 0.00 0.03 0.73 0.04 0.01 3.32 0.01 1.32 0.00 0.01 0.00 0.23 261630033 Michigan Wayne 0.03 0.00 0.02 0.46 0.08 0.05 3.83 0.03 0.96 0.00 0.04 0.01 0.45 390350038 Ohio Cuyahoga 0.01 0.00 0.02 0.25 0.23 0.03 8.49 0.00 1.91 0.00 0.07 0.01 0.44 390350060 Ohio Cuyahoga 0.04 0.00 0.02 0.82 0.13 0.04 8.96 0.01 2.16 0.00 0.04 0.01 0.26 420030064 Pennsylvania Allegheny 0.03 0.00 0.02 0.13 0.13 0.07 4.81 0.03 5.53 0.00 0.05 0.01 0.20 420030093 Pennsylvania Allegheny 0.01 0.00 0.04 0.12 0.15 0.02 3.07 0.03 3.21 0.00 0.06 0.00 0.34 420030116 Pennsylvania Allegheny 0.01 0.00 0.04 0.12 0.17 0.02 3.48 0.03 2.96 0.00 0.07 0.00 0.35 420070014 Pennsylvania Beaver 0.03 0.00 0.04 0.21 0.12 0.04 4.00 0.04 2.61 0.00 0.04 0.01 0.34 420710007 Pennsylvania Lancaster 0.01 0.05 0.68 0.66 0.40 0.01 0.76 0.03 6.69 0.02 0.14 0.00 0.26 540090011 

West Virginia Brooke 0.02 0.00 0.04 0.31 0.08 0.06 5.85 0.02 2.85 0.00 0.03 0.02 0.19 

550790043 Wisconsin Milwaukee 0.24 0.00 0.00 0.11 0.07 0.21 2.41 0.11 0.69 0.00 0.06 0.10 0.26 

Largest Contribution to Downwind Nonattainment 0.24 0.05 0.68 0.83 0.40 0.21 5.85 0.17 2.85 0.02 0.29 0.10 1.38 
D-13

State-by-State Contributions to 24-Hour PM2.5 (μg/m3) Maintenance Recept

Part 2a. 

Source States: Alabama through Missouri 

Receptor 

Monitor 

ID 

Receptor 

State 

Receptor 

County 

Source 

State => AL AR CT DE FL GA IL IN IA KS KY LA ME 

10732003 Alabama Jefferson 4.61 0.02 0.00 0.00 0.02 0.92 0.31 0.72 0.03 0.01 0.64 0.06 0.00 170310052 Illinois Cook 0.09 0.06 0.00 0.00 0.00 0.12 6.13 2.84 0.98 0.36 0.66 0.01 0.00 170312001 Illinois Cook 0.08 0.11 0.00 0.00 0.00 0.07 3.96 4.87 0.41 0.23 0.67 0.01 0.00 170313301 Illinois Cook 0.03 0.09 0.00 0.00 0.00 0.02 5.10 3.39 0.67 0.26 0.55 0.03 0.00 170316005 Illinois Cook 0.11 0.16 0.00 0.00 0.00 0.15 6.15 2.49 1.03 0.36 0.40 0.06 0.00 171190023 Illinois Madison 0.32 0.23 0.00 0.00 0.00 0.19 3.87 3.05 0.40 0.28 1.76 0.05 0.00 180890022 Indiana Lake 0.07 0.02 0.00 0.00 0.00 0.06 3.09 5.77 0.52 0.20 0.54 0.00 0.00 180890026 Indiana Lake 0.12 0.03 0.00 0.00 0.00 0.10 2.85 4.52 0.42 0.18 0.73 0.02 0.00 261610008 Michigan Washtenaw 0.40 0.09 0.00 0.01 0.01 0.25 1.09 2.96 0.14 0.06 1.18 0.13 0.00 390170003 Ohio Butler 0.42 0.06 0.00 0.01 0.00 0.28 1.37 5.15 0.14 0.03 3.58 0.04 0.00 390350045 Ohio Cuyahoga 0.18 0.02 0.00 0.00 0.00 0.12 0.70 1.66 0.11 0.01 0.68 0.01 0.00 390350065 Ohio Cuyahoga 0.20 0.06 0.00 0.00 0.00 0.14 0.81 1.82 0.11 0.02 0.87 0.02 0.00 390618001 Ohio Hamilton 0.24 0.04 0.00 0.01 0.01 0.14 1.04 3.59 0.17 0.03 2.23 0.02 0.00 390811001 Ohio Jefferson 0.07 0.04 0.00 0.01 0.00 0.07 0.55 1.62 0.08 0.02 1.20 0.00 0.00 391130032 Ohio Montgomery 0.40 0.08 0.00 0.01 0.00 0.25 1.02 3.95 0.09 0.03 3.10 0.04 0.00 420031008 Pennsylvania Allegheny 0.13 0.08 0.00 0.01 0.00 0.09 0.71 2.14 0.09 0.03 1.23 0.05 0.00 420031301 Pennsylvania Allegheny 0.07 0.02 0.00 0.00 0.00 0.06 0.54 1.61 0.07 0.02 0.69 0.01 0.00 420033007 Pennsylvania Allegheny 0.04 0.02 0.00 0.01 0.00 0.04 0.49 1.40 0.05 0.01 0.67 0.00 0.00 421330008 Pennsylvania York 0.17 0.02 0.18 0.20 0.03 0.27 0.37 0.73 0.11 0.02 0.42 0.05 0.10 550790010 Wisconsin Milwaukee 0.07 0.02 0.00 0.00 0.00 0.07 5.70 2.90 1.55 0.81 0.85 0.01 0.00 550790026 Wisconsin Milwaukee 0.06 0.04 0.00 0.00 0.00 0.06 4.14 1.96 1.47 0.48 0.58 0.01 0.00 

Largest Contribution to Downwind Maintenance 0.42 0.23 0.18 0.20 0.03 0.92 5.70 5.15 1.55 0.81 3.58 0.13 0.10 
D-14

State-by-State Contributions to 24-Hour PM2.5 (μg/m3) Maintenance Recept

Part 2b. 

Source States: Nebraska through Wisconsin 

Receptor 

Monitor 

ID 

Receptor 

State 

Receptor 

County 

Source 

State => NE NH NJ NY NC ND OH OK PA RI SC SD T

10732003 Alabama Jefferson 0.00 0.00 0.02 0.05 0.22 0.01 0.99 0.00 0.50 0.00 0.25 0.00 0170310052 Illinois Cook 0.22 0.00 0.00 0.10 0.06 0.09 1.45 0.15 0.56 0.00 0.05 0.06 0170312001 Illinois Cook 0.12 0.00 0.00 0.14 0.04 0.02 1.71 0.08 0.67 0.00 0.02 0.02 0170313301 Illinois Cook 0.15 0.00 0.00 0.16 0.02 0.05 1.53 0.11 0.64 0.00 0.00 0.04 0170316005 Illinois Cook 0.23 0.00 0.00 0.20 0.06 0.06 0.85 0.20 0.31 0.00 0.04 0.06 0171190023 Illinois Madison 0.07 0.00 0.00 0.09 0.06 0.08 2.38 0.14 0.67 0.00 0.03 0.05 0180890022 Indiana Lake 0.11 0.00 0.00 0.13 0.03 0.05 1.12 0.07 0.39 0.00 0.01 0.03 0180890026 Indiana Lake 0.10 0.00 0.00 0.18 0.05 0.05 1.71 0.03 0.74 0.00 0.02 0.03 0261610008 Michigan Washtenaw 0.03 0.00 0.03 0.41 0.08 0.01 4.41 0.05 1.01 0.00 0.05 0.00 0390170003 Ohio Butler 0.02 0.00 0.03 0.30 0.10 0.02 5.90 0.03 1.58 0.00 0.05 0.01 1390350045 Ohio Cuyahoga 0.02 0.00 0.02 0.89 0.15 0.02 9.04 0.00 1.87 0.00 0.04 0.01 0390350065 Ohio Cuyahoga 0.02 0.00 0.02 0.62 0.16 0.02 8.51 0.02 2.20 0.00 0.04 0.00 0390618001 Ohio Hamilton 0.02 0.00 0.04 0.21 0.11 0.03 5.88 0.04 1.65 0.00 0.03 0.00 0390811001 Ohio Jefferson 0.01 0.00 0.03 0.18 0.09 0.03 5.09 0.02 2.29 0.00 0.05 0.01 0391130032 Ohio Montgomery 0.01 0.00 0.04 0.30 0.12 0.02 4.93 0.04 1.14 0.00 0.05 0.01 1420031008 Pennsylvania Allegheny 0.02 0.00 0.03 0.22 0.07 0.04 4.74 0.03 4.23 0.00 0.03 0.00 0420031301 Pennsylvania Allegheny 0.01 0.00 0.03 0.19 0.07 0.02 4.22 0.02 4.31 0.00 0.03 0.00 0420033007 Pennsylvania Allegheny 0.01 0.00 0.03 0.13 0.10 0.02 3.64 0.01 4.26 0.00 0.04 0.00 0421330008 Pennsylvania York 0.02 0.10 0.75 1.34 0.38 0.03 1.55 0.02 8.98 0.03 0.17 0.01 0550790010 Wisconsin Milwaukee 0.52 0.00 0.00 0.06 0.03 0.19 1.19 0.17 0.39 0.00 0.02 0.09 0550790026 Wisconsin Milwaukee 0.43 0.00 0.00 0.10 0.02 0.33 1.03 0.17 0.35 0.00 0.02 0.17 0

Largest Contribution to Downwind Maintenance 0.52 0.10 0.75 1.34 0.38 0.33 4.74 0.20 2.29 0.03 0.25 0.17 1
E-1 

Air Quality Modeling Final Rule 

Technical Support Document 

Appendix E 

Upwind States to Downwind Nonattainment and 

Maintenance Linkages 
E-2 

Table E-1. Upwind State to Downwind Nonattainment Site “Linkages” for Annual PM2.5 

Upwind State Annual PM2.5 Downwind Nonattainment Receptor Sites 

Alabama 

Fulton, GA 

(131210039) 

Hamilton, OH 

(390610014) 

Hamilton, OH 

(390610042) 

Hamilton, OH 

(390618001) 

Georgia 

Jefferson, AL 

(10730023) 

Jefferson, AL 

(10732003) 

Jefferson, AL 

(10732003) 

Fulton, GA 

(131210039) 

Wayne, MI 

(261630033) 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350060) 

Hamilton, OH 

(390610014) 

Hamilton, OH 

(390610042) 

Illinois 

Hamilton, OH 

(390618001) 

Allegheny, PA 

(420030064) 

Jefferson, AL 

(10730023) 

Jefferson, AL 

(10732003) 

Fulton, GA 

(131210039) 

Madison, IL 

(171191007) 

Wayne, MI 

(261630033) 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350060) 

Indiana 

Hamilton, OH 

(390610014) 

Hamilton, OH 

(390610042) 

Hamilton, OH 

(390618001) 

Allegheny, PA 

(420030064) 

Iowa 

Madison, IL 

(171191007) 

Jefferson, AL 

(10730023) 

Jefferson, AL 

(10732003) 

Fulton, GA 

(131210039) 

Madison, IL 

(171191007) 

Wayne, MI 

(261630033) 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350060) Kentucky 

Hamilton, OH 

(390610014) 

Hamilton, OH 

(390610042) 

Hamilton, OH 

(390618001) 

Allegheny, PA 

(420030064) 

Maryland 

Allegheny, PA 

(420030064) 

Madison, IL 

(171191007) 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350060) Michigan 

Hamilton, OH 

(390610014) 

Hamilton, OH 

(390610042) 

Hamilton, OH 

(390618001) 

Allegheny, PA 

(420030064) 

Missouri Madison, IL 

(171191007) 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350060) 
E-3 

Upwind State Annual PM2.5 Downwind Nonattainment Receptor Sites 

Hamilton, OH 

(390610014) 

Hamilton, OH 

(390610042) 

Hamilton, OH 

(390618001) 

New York 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350060) 

Allegheny, PA 

(420030064) 

North Carolina 

Fulton, GA 

(131210039) 

Jefferson, AL 

(10730023) 

Jefferson, AL 

(10732003) 

Fulton, GA 

(131210039) 

Madison, IL 

(171191007) Ohio 

Wayne, MI 

(261630033) 

Allegheny, PA 

(420030064) 

Fulton, GA 

(131210039) 

Wayne, MI 

(261630033) 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350045) Pennsylvania 

Cuyahoga, OH 

(390350060) 

Hamilton, OH 

(390610014) 

Hamilton, OH 

(390610042) 

Hamilton, OH 

(390618001) 

South Carolina 

Fulton, GA 

(131210039) 

Jefferson, AL 

(10730023) 

Jefferson, AL 

(10732003) 

Fulton, GA 

(131210039) 

Madison, IL 

(171191007) Tennessee 

Hamilton, OH 

(390610014) 

Hamilton, OH 

(390610042) 

Hamilton, OH 

(390618001) 

Texas 

Madison, IL 

(171191007) 

Fulton, GA 

(131210039) 

Wayne, MI 

(261630033) 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350060) 

Hamilton, OH 

(390610014) 

Hamilton, OH 

(390610042) 

Hamilton, OH 

(390618001) 

West Virginia 

Allegheny, PA 

(420030064) 

Madison, IL 

(171191007) 

Wayne, MI 

(261630033) 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350045) 

Wisconsin 

Cuyahoga, OH 

(390350060) 

Hamilton, OH 

(390610014) 

Hamilton, OH 

(390618001) 
E-4 

Table E-2. Upwind State to Downwind Maintenance Site “Linkages” for Annual PM2.5 

Upwind State Annual PM2.5 Downwind Maintenance Receptor Sites 

Alabama 

Marion, IN 

(180970081) 

Marion, IN 

(180970083) 

Hamilton, OH 

(390617001) 

Illinois 

Marion, IN 

(180970081) 

Marion, IN 

(180970083) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390617001) 

Indiana 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390617001) 

Kentucky 

Marion, IN 

(180970081) 

Marion, IN 

(180970083) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390617001) 

Michigan 

Marion, IN 

(180970081) 

Marion, IN 

(180970083) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390617001) 

Missouri 

Marion, IN 

(180970081) 

Marion, IN 

(180970083) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390617001) 

New York 

Cuyahoga, OH 

(390350065) 

Ohio 

Marion, IN 

(180970081) 

Marion, IN 

(180970083) 

Pennsylvania 

Marion, IN 

(180970081) 

Marion, IN 

(180970083) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390617001) 

Tennessee 

Marion, IN 

(180970081) 

Marion, IN 

(180970083) 

Hamilton, OH 

(390617001) 

West Virginia 

Marion, IN 

(180970081) 

Marion, IN 

(180970083) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390617001) 

Wisconsin 

Marion, IN 

(180970081) 

Marion, IN 

(180970083) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390617001) 
E-5 

Table E-3. Upwind State to Downwind Nonattainment Site “Linkages” for 24-hour PM2.5 

Upwind State 24-Hour PM2.5 Downwind Nonattainment Receptor Sites 

Alabama 

Marion, IN 

(180970043) 

Marion, IN 

(180970066) 

Marion, IN 

(180970081) 

Georgia 

Jefferson, AL 

(10730023) 

Marion, IN 

(180970043) 

Marion, IN 

(180970066) 

Marion, IN 

(180970081) 

St Clair, MI 

(261470005) 

Wayne, MI 

(261630015) 

Wayne, MI 

(261630016) 

Wayne, MI 

(261630019) 

Wayne, MI 

(261630033) 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350060) 

Allegheny, PA 

(420030064) 

Allegheny, PA 

(420030093) 

Illinois 

Allegheny, PA 

(420030116) 

Beaver, PA 

(420070014) 

Brooke, WV 

(540090011) 

Milwaukee, WI 

(550790043) 

Jefferson, AL 

(10730023) 

Cook, IL 

(170311016) 

Madison, IL 

(171191007) 

St Clair, MI 

(261470005) 

Wayne, MI 

(261630015) 

Wayne, MI 

(261630016) 

Wayne, MI 

(261630019) 

Wayne, MI 

(261630033) 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350060) 

Allegheny, PA 

(420030064) 

Allegheny, PA 

(420030093) 

Indiana 

Allegheny, PA 

(420030116) 

Beaver, PA 

(420070014) 

Brooke, WV 

(540090011) 

Milwaukee, WI 

(550790043) 

Iowa 

Cook, IL 

(170311016) 

Madison, IL 

(171191007) 

Milwaukee, WI 

(550790043) 

Kansas 

Madison, IL 

(171191007) 

Jefferson, AL 

(10730023) 

Cook, IL 

(170311016) 

Madison, IL 

(171191007) 

Marion, IN 

(180970043) 

Marion, IN 

(180970066) 

Marion, IN 

(180970081) 

St Clair, MI 

(261470005) 

Wayne, MI 

(261630015) 

Wayne, MI 

(261630016) 

Wayne, MI 

(261630019) 

Wayne, MI 

(261630033) 

Cuyahoga, OH 

(390350038) 

Kentucky 

Cuyahoga, OH 

(390350060) 

Allegheny, PA 

(420030064) 

Allegheny, PA 

(420030093) 

Allegheny, PA 

(420030116) 
E-6 

Upwind State 24-Hour PM2.5 Downwind Nonattainment Receptor Sites 

Beaver, PA 

(420070014) 

Brooke, WV 

(540090011) 

Milwaukee, WI 

(550790043) 

Maryland 

Cuyahoga, OH 

(390350038) 

Lancaster, PA 

(420710007) 

Cook, IL 

(170311016) 

Madison, IL 

(171191007) 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350060) 

Allegheny, PA 

(420030064) 

Allegheny, PA 

(420030093) 

Beaver, PA 

(420070014) 

Brooke, WV 

(540090011) 

Michigan 

Milwaukee, WI 

(550790043) 

Minnesota 

Milwaukee, WI 

(550790043) 

Cook, IL 

(170311016) 

Madison, IL 

(171191007) 

Marion, IN 

(180970043) 

Marion, IN 

(180970066) 

Marion, IN 

(180970081) 

St Clair, MI 

(261470005) 

Wayne, MI 

(261630015) 

Allegheny, PA 

(420030064) 

Missouri 

Allegheny, PA 

(420030116) 

Beaver, PA 

(420070014) 

Milwaukee, WI 

(550790043) 

New Jersey 

Lancaster, PA 

(420710007) 

St Clair, MI 

(261470005) 

Wayne, MI 

(261630016) 

Wayne, MI 

(261630019) 

Wayne, MI 

(261630033) New York 

Cuyahoga, OH 

(390350060) 

Lancaster, PA 

(420710007) 

North Carolina 

Lancaster, PA 

(420710007) 

Jefferson, AL 

(10730023) 

Cook, IL 

(170311016) 

Madison, IL 

(171191007) 

Marion, IN 

(180970043) 

Marion, IN 

(180970066) 

Marion, IN 

(180970081) 

St Clair, MI 

(261470005) 

Wayne, MI 

(261630015) 

Wayne, MI 

(261630016) 

Wayne, MI 

(261630019) 

Wayne, MI 

(261630033) 

Allegheny, PA 

(420030064) 

Ohio 

Allegheny, PA 

(420030093) 

Allegheny, PA 

(420030116) 

Beaver, PA 

(420070014) 

Lancaster, PA 

(420710007) 
E-7 

Upwind State 24-Hour PM2.5 Downwind Nonattainment Receptor Sites 

Brooke, WV 

(540090011) 

Milwaukee, WI 

(550790043) 

Jefferson, AL 

(10730023) 

Cook, IL 

(170311016) 

Madison, IL 

(171191007) 

Marion, IN 

(180970043) 

Marion, IN 

(180970066) 

Marion, IN 

(180970081) 

St Clair, MI 

(261470005) 

Wayne, MI 

(261630015) 

Wayne, MI 

(261630016) 

Wayne, MI 

(261630019) 

Wayne, MI 

(261630033) 

Cuyahoga, OH 

(390350038) 

Pennsylvania 

Cuyahoga, OH 

(390350060) 

Brooke, WV 

(540090011) 

Milwaukee, WI 

(550790043) 

Jefferson, AL 

(10730023) 

Madison, IL 

(171191007) 

Marion, IN 

(180970043) 

Marion, IN 

(180970066) 

Marion, IN 

(180970081) 

St Clair, MI 

(261470005) 

Wayne, MI 

(261630015) 

Wayne, MI 

(261630033) 

Tennessee 

Cuyahoga, OH 

(390350038) 

Allegheny, PA 

(420030116) 

Texas 

Madison, IL 

(171191007) 

Virginia 

Lancaster, PA 

(420710007) 

Jefferson, AL 

(10730023) 

Cook, IL 

(170311016) 

Madison, IL 

(171191007) 

Marion, IN 

(180970043) 

Marion, IN 

(180970066) 

Marion, IN 

(180970081) 

St Clair, MI 

(261470005) 

Wayne, MI 

(261630015) 

Wayne, MI 

(261630016) 

Wayne, MI 

(261630019) 

Wayne, MI 

(261630033) 

Cuyahoga, OH 

(390350038) 

Cuyahoga, OH 

(390350060) 

Allegheny, PA 

(420030064) 

Allegheny, PA 

(420030093) 

Allegheny, PA 

(420030116) 

West Virginia 

Beaver, PA 

(420070014) 

Lancaster, PA 

(420710007) 

Milwaukee, WI 

(550790043) 

Wisconsin 

Cook, IL 

(170311016) 

Wayne, MI 

(261630019) 

Wayne, MI 

(261630033) 
E-8 

Table E-4. Upwind State to Downwind Maintenance Site “Linkages” for 24-hour PM2.5 

Upwind State 24-Hour PM2.5 Downwind Maintenance Receptor Sites 

Alabama 

Washtenaw, MI 

(261610008) 

Butler, OH 

(390170003) 

Montgomery, OH 

(391130032) 

Georgia 

Jefferson, AL 

(10732003) 

Lake, IN 

(180890022) 

Lake, IN 

(180890026) 

Washtenaw, MI 

(261610008) 

Butler, OH 

(390170003) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390618001) 

Jefferson, OH 

(390811001) 

Montgomery, OH 

(391130032) 

Allegheny, PA 

(420031008) 

Allegheny, PA 

(420031301) 

Allegheny, PA 

(420033007) 

Illinois 

York, PA 

(421330008) 

Milwaukee, WI 

(550790010) 

Milwaukee, WI 

(550790026) 

Jefferson, AL 

(10732003) 

Cook, IL 

(170310052) 

Cook, IL 

(170312001) 

Cook, IL 

(170313301) 

Cook, IL 

(170316005) 

Madison, IL 

(171190023) 

Washtenaw, MI 

(261610008) 

Butler, OH 

(390170003) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390618001) 

Jefferson, OH 

(390811001) 

Montgomery, OH 

(391130032) 

Allegheny, PA 

(420031008) 

Allegheny, PA 

(420031301) 

Allegheny, PA 

(420033007) 

Indiana 

York, PA 

(421330008) 

Milwaukee, WI 

(550790010) 

Milwaukee, WI 

(550790026) 

Cook, IL 

(170310052) 

Cook, IL 

(170312001) 

Cook, IL 

(170313301) 

Cook, IL 

(170316005) 

Madison, IL 

(171190023) 

Lake, IN 

(180890022) 

Lake, IN 

(180890026) 

Milwaukee, WI 

(550790010) 

Iowa 

Milwaukee, WI 

(550790026) 

Kansas 

Cook, IL 

(170310052) 

Cook, IL 

(170316005) 

Milwaukee, WI 

(550790010) 

Milwaukee, WI 

(550790026) 

Kentucky Jefferson, AL 

(10732003) 

Cook, IL 

(170310052) 

Cook, IL 

(170312001) 

Cook, IL 

(170313301) 
E-9 

Upwind State 24-Hour PM2.5 Downwind Maintenance Receptor Sites 

Cook, IL 

(170316005) 

Madison, IL 

(171190023) 

Lake, IN 

(180890022) 

Lake, IN 

(180890026) 

Washtenaw, MI 

(261610008) 

Butler, OH 

(390170003) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390618001) 

Jefferson, OH 

(390811001) 

Montgomery, OH 

(391130032) 

Allegheny, PA 

(420031008) 

Allegheny, PA 

(420031301) 

Allegheny, PA 

(420033007) 

York, PA 

(421330008) 

Milwaukee, WI 

(550790010) 

Milwaukee, WI 

(550790026) 

Maryland 

York, PA 

(421330008) 

Cook, IL 

(170310052) 

Cook, IL 

(170312001) 

Cook, IL 

(170313301) 

Cook, IL 

(170316005) 

Madison, IL 

(171190023) 

Lake, IN 

(180890022) 

Lake, IN 

(180890026) 

Butler, OH 

(390170003) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390618001) 

Jefferson, OH 

(390811001) 

Montgomery, OH 

(391130032) 

Allegheny, PA 

(420031008) 

Allegheny, PA 

(420031301) 

Allegheny, PA 

(420033007) 

Michigan 

York, PA 

(421330008) 

Milwaukee, WI 

(550790010) 

Milwaukee, WI 

(550790026) 

Minnesota 

Milwaukee, WI 

(550790010) 

Milwaukee, WI 

(550790026) 

Cook, IL 

(170310052) 

Cook, IL 

(170312001) 

Cook, IL 

(170313301) 

Cook, IL 

(170316005) 

Madison, IL 

(171190023) 

Lake, IN 

(180890022) 

Lake, IN 

(180890026) 

Washtenaw, MI 

(261610008) 

Butler, OH 

(390170003) 

Hamilton, OH 

(390618001) 

Montgomery, OH 

(391130032) 

Allegheny, PA 

(420031008) 

Missouri 

Milwaukee, WI 

(550790010) 

Milwaukee, WI 

(550790026) 

Nebraska 

Milwaukee, WI 

(550790010) 

Milwaukee, WI 

(550790026) 
E-10 

Upwind State 24-Hour PM2.5 Downwind Maintenance Receptor Sites 

New Jersey 

York, PA 

(421330008) 

New York 

Washtenaw, MI 

(261610008) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350065) 

York, PA 

(421330008) 

North Carolina 

York, PA 

(421330008) 

Jefferson, AL 

(10732003) 

Cook, IL 

(170310052) 

Cook, IL 

(170312001) 

Cook, IL 

(170313301) 

Cook, IL 

(170316005) 

Madison, IL 

(171190023) 

Lake, IN 

(180890022) 

Lake, IN 

(180890026) 

Washtenaw, MI 

(261610008) 

Allegheny, PA 

(420031008) 

Allegheny, PA 

(420031301) 

Allegheny, PA 

(420033007) 

Ohio 

York, PA 

(421330008) 

Milwaukee, WI 

(550790010) 

Milwaukee, WI 

(550790026) 

Jefferson, AL 

(10732003) 

Cook, IL 

(170310052) 

Cook, IL 

(170312001) 

Cook, IL 

(170313301) 

Madison, IL 

(171190023) 

Lake, IN 

(180890022) 

Lake, IN 

(180890026) 

Washtenaw, MI 

(261610008) 

Butler, OH 

(390170003) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390618001) 

Pennsylvania 

Jefferson, OH 

(390811001) 

Montgomery, OH 

(391130032) 

Milwaukee, WI 

(550790010) 

Milwaukee, WI 

(550790026) 

Jefferson, AL 

(10732003) 

Madison, IL 

(171190023) 

Washtenaw, MI 

(261610008) 

Butler, OH 

(390170003) Tennessee 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390618001) 

Montgomery, OH 

(391130032) 

Virginia 

York, PA 

(421330008) 

Jefferson, AL 

(10732003) 

Cook, IL 

(170310052) 

Cook, IL 

(170312001) 

Cook, IL 

(170313301) 

Madison, IL 

(171190023) 

Lake, IN 

(180890022) 

Lake, IN 

(180890026) 

Washtenaw, MI 

(261610008) 

West Virginia 

Butler, OH 

(390170003) 

Cuyahoga, OH 

(390350045) 

Cuyahoga, OH 

(390350065) 

Hamilton, OH 

(390618001) 
E-11 

Upwind State 24-Hour PM2.5 Downwind Maintenance Receptor Sites 

Jefferson, OH 

(390811001) 

Montgomery, OH 

(391130032) 

Allegheny, PA 

(420031008) 

Allegheny, PA 

(420031301) 

Allegheny, PA 

(420033007) 

York, PA 

(421330008) 

Milwaukee, WI 

(550790010) 

Cook, IL 

(170310052) 

Cook, IL 

(170312001) 

Cook, IL 

(170313301) 

Cook, IL 

(170316005) Wisconsin 

Lake, IN 

(180890022) 

Lake, IN 

(180890026) 
E-12 

Table E-5. Upwind State to Downwind Nonattainment “Linkages” for 8-hour Ozone 

Upwind State 8-Hour Ozone Downwind Nonattainment Receptor Sites 

Alabama 

East Baton Rouge, 

LA (220330003) 

Brazoria, TX 

(480391004) 

Harris, TX 

(482010051) 

Harris, TX 

(482010055) 

Arkansas 

East Baton Rouge, 

LA (220330003) 

Brazoria, TX 

(480391004) 

Georgia 

East Baton Rouge, 

LA (220330003) 

Brazoria, TX 

(480391004) 

Harris, TX 

(482010051) 

Harris, TX 

(482010055) 

Illinois 

Brazoria, TX 

(480391004) 

Harris, TX 

(482010051) 

Harris, TX 

(482010055) 

Indiana 

Brazoria, TX 

(480391004) 

Harris, TX 

(482010051) 

Harris, TX 

(482010055) 

Kentucky 

Brazoria, TX 

(480391004) 

Harris, TX 

(482010051) 

Harris, TX 

(482010055) 

Louisiana 

Brazoria, TX 

(480391004) 

Harris, TX 

(482010051) 

Harris, TX 

(482010055) 

Mississippi 

East Baton Rouge, 

LA (220330003) 

Brazoria, TX 

(480391004) 

Harris, TX 

(482010051) 

Harris, TX 

(482010055) 

Missouri 

Brazoria, TX 

(480391004) 

Harris, TX 

(482010051) 

Harris, TX 

(482010055) 

Tennessee 

East Baton Rouge, 

LA (220330003) 

Brazoria, TX 

(480391004) 

Harris, TX 

(482010051) 

Harris, TX 

(482010055) 

Texas 

East Baton Rouge, 

LA (220330003) 
E-13 

Table E-6. Upwind State to Downwind Maintenance “Linkages” for 8-Hour Ozone 

Upwind State 8-Hour Ozone Downwind Maintenance Receptor Sites 

Alabama 

Harris, TX 

(482010029) 

Harris, TX 

(482011050) 

Arkansas 

Allegan, MI 

(260050003) 

Florida 

Harris, TX 

(482010029) 

Harris, TX 

(482011050) 

Georgia 

Harris, TX 

(482010029) 

Harris, TX 

(482011050) 

Illinois 

Fairfield, CT 

(90011123) 

Allegan, MI 

(260050003) 

Harris, TX 

(482011050) 

Indiana 

Fairfield, CT 

(90011123) 

New Haven, CT 

(90093002) 

Harford, MD 

(240251001) 

Allegan, MI 

(260050003) 

Iowa 

Allegan, MI 

(260050003) 

Kansas 

Allegan, MI 

(260050003) 

Kentucky 

Fairfield, CT 

(90011123) 

New Haven, CT 

(90093002) 

Harford, MD 

(240251001) 

Harris, TX 

(482011050) 

Louisiana 

Harris, TX 

(482010029) 

Harris, TX 

(482011050) 

Maryland 

Fairfield, CT 

(90011123) 

New Haven, CT 

(90093002) 

Michigan 

Harford, MD 

(240251001) 

Mississippi 

Harris, TX 

(482010029) 

Harris, TX 

(482011050) 

Missouri 

Allegan, MI 

(260050003) 

New Jersey 

Fairfield, CT 

(90011123) 

New Haven, CT 

(90093002) 

New York 

Fairfield, CT 

(90011123) 

New Haven, CT 

(90093002) 

Harford, MD 

(240251001) 

North Carolina 

New Haven, CT 

(90093002) 

Harford, MD 

(240251001) 

Ohio 

Fairfield, CT 

(90011123) 

New Haven, CT 

(90093002) 

Harford, MD 

(240251001) 
E-14 

Upwind State 8-Hour Ozone Downwind Maintenance Receptor Sites 

Oklahoma 

Allegan, MI 

(260050003) 

Pennsylvania 

Fairfield, CT 

(90011123) 

New Haven, CT 

(90093002) 

Harford, MD 

(240251001) 

South Carolina 

Harris, TX 

(482010029) 

Tennessee 

Fairfield, CT 

(90011123) 

Harford, MD 

(240251001) 

Harris, TX 

(482011050) 

Texas 

Allegan, MI 

(260050003) 

Virginia 

Fairfield, CT 

(90011123) 

New Haven, CT 

(90093002) 

Harford, MD 

(240251001) 

West Virginia 

Fairfield, CT 

(90011123) 

New Haven, CT 

(90093002) 

Harford, MD 

(240251001) 

Wisconsin 

Allegan, MI 

(260050003) 
Air Quality Modeling Final Rule 

Technical Support Document 

Appendix F 

Analysis of Contribution Thresholds 
F-2

Table F-1a. Contribution metrics for 8-hour ozone 2012 nonattainment receptors – part 1. 

Monitoring Site State County 

2012 Base Case 

In-State 

Contribution 

(ppb) 

2012 Total 

Contribution 

from All Upwind 

States 

(ppb) 

Upwind 

Contribution as 

a Percent of 

Receptor 

Concentration

# States 

Contributing At 

or Above 

0.5% Threshold 

# States 

Contributing 

or Above 

1% Thresh

220330003 LA 

East Baton Rouge 39.8 21.0 35% 10 6 

480391004 TX Brazoria 35.3 27.6 44% 12 10 482010051 TX Harris 27.3 26.9 50% 11 9 482010055 TX Harris 29.6 29.1 50% 12 9 

Table F-1b. Contribution metrics for 8-hour ozone 2012 nonattainment receptors – part 2. 

Monitoring Site State County 

Transport 

(ppb) Using 

0.5% Threshold

Transport 

(ppb) Using 

1% Threshold 

Transport 

(ppb) Using 

5% Threshold 

% of Total 

Transport 

Captured with 

0.5% Threshold

% of Tota

Transpor

Captured w

1% Thresho

220330003 LA 

East Baton Rouge 17.8 15.7 4.0 85% 75% 

480391004 TX Brazoria 24.5 23.3 7.9 89% 84% 482010051 TX Harris 24.2 23.2 7.3 90% 86% 482010055 TX Harris 26.6 25.1 12.1 91% 86% 
F-3

Table F-1c. Contribution metrics for 8-hour ozone 2012 maintenance receptors – part 1. 

Monitoring Site State County 

2012 Base Case 

In-State 

Contribution 

(ppb) 

2012 Total 

Contribution 

from All 

Upwind States 

(ppb) 

Upwind 

Contribution 

as a Percent of 

Receptor 

Concentration

# States 

Contributing At 

or Above 

0.5% Threshold 

# States 

Contributing 

or Above 

 1% Thresho

90011123 CT Fairfield 5.7 56.9 91% 19 11 90093002 CT 

New Haven 4.4 59.6 93% 15 10 

240251001 MD Harford 33.7 32.7 49% 13 10 260050003 MI Allegan 4.3 56.9 93% 11 9 482010029 TX Harris 35.0 21.9 38% 8 6 482011050 TX Harris 33.1 29.7 47% 15 8 

Table F-1d. Contribution metrics for 8-hour ozone 2012 maintenance receptors – part 2. 

Monitoring Site State County 

Transport 

(ppb) Using 

 0.5% Threshold 

Transport 

(ppb) Using 

1% Threshold 

Transport 

(ppb) Using 

5% Threshold 

% of Total 

Transport 

Captured with 

0.5% Threshold 

% of To

Transpo

Captured 

1% Thres

90011123 CT Fairfield 53.9 49.3 36.0 95% 87% 90093002 CT 

New Haven 57.3 54.2 42.0 96% 91% 

240251001 MD Harford 28.2 26.4 13.8 86% 81% 260050003 MI Allegan 53.7 52.3 41.1 94% 92% 482010029 TX Harris 19.4 18.0 5.8 89% 82% 482011050 TX Harris 27.1 22.9 11.1 91% 77% 
F-4

Table F-2a. Contribution metrics for annual PM2.5 2012 nonattainment receptors – part 1. 

Monitoring Site State County 

2012 Base 

Case In-State 

Contribution

(μg/m3) 

2012 Total 

Contribution 

from All 

Upwind States 

(μg/m3) 

Upwind 

Contribution 

as a Percent of 

Receptor 

Concentration 

# States 

Contributing At 

or Above 

0.5% Threshold

# States 

Contributin

At or Abov

 1% Thresh

10730023 AL Jefferson 3.12 2.72 47% 12 5 10732003 AL Jefferson 2.58 2.85 52% 14 6 

131210039 GA Fulton 1.37 3.43 72% 13 10 171191007 IL Madison 1.51 4.92 77% 17 9 261630033 MI Wayne 2.27 4.12 64% 13 7 390350038 OH Cuyahoga 2.62 4.24 62% 12 9 390350045 OH Cuyahoga 2.64 4.28 62% 13 9 390350060 OH Cuyahoga 2.64 4.29 62% 13 9 390610014 OH Hamilton 2.13 5.43 72% 13 10 390610042 OH Hamilton 2.04 5.55 73% 13 9 390618001 OH Hamilton 2.11 5.39 72% 13 10 420030064 PA Allegheny 2.14 4.99 70% 14 8 
F-5

Table F-2b. Contribution metrics for annual PM2.5 2012 nonattainment receptors – part 2. 

Monitoring Site State County 

Transport 

(μg/m3) Using 

0.5% Threshold

Transport 

(μg/m3) Using 

1% Threshold 

Transport 

(μg/m3) Using 

5% Threshold 

% of Total 

Transport 

Captured with 

0.5% Threshold 

% of T

Trans

Capture

1% Thr

10730023 AL Jefferson 2.24 1.45 0.00 82% 53% 10732003 AL Jefferson 2.49 1.67 0.00 87% 59% 

131210039 GA Fulton 2.94 2.64 0.00 86% 77% 171191007 IL Madison 4.57 3.72 1.22 93% 76% 261630033 MI Wayne 3.75 3.10 0.99 91% 75% 390350038 OH Cuyahoga 3.71 3.40 0.00 88% 80% 390350045 OH Cuyahoga 3.82 3.43 0.00 89% 80% 390350060 OH Cuyahoga 3.83 3.43 0.00 89% 80% 390610014 OH Hamilton 4.89 4.57 2.09 90% 84% 390610042 OH Hamilton 5.02 4.57 2.28 91% 82% 390618001 OH Hamilton 4.87 4.55 2.07 90% 84% 420030064 PA Allegheny 4.61 3.98 2.29 92% 80% 

Table F-2c. Contribution metrics for annual PM2.5 2012 maintenance receptors – part 1. 

Monitoring Site State County 

2012 Base 

Case In-State 

Contribution

(μg/m3) 

2012 Total 

Contribution 

from All Upwind 

States (μg/m3) 

Upwind 

Contribution 

as a Percent of 

Receptor 

Concentration 

# States 

Contributing At 

or Above 

0.5% Threshold

# State

Contribu

At or Ab

1% Thres

180970081 IN Marion 2.94 4.78 62% 13 10 180970083 IN Marion 2.94 4.79 62% 13 10 390350065 OH Cuyahoga 2.64 4.28 62% 13 9 390617001 OH Hamilton 2.12 5.42 72% 13 10 
F-6

Table F-2d. Contribution metrics for annual PM2.5 2012 maintenance receptors – part 2. 

Monitoring Site State County 

Transport 

(μg/m3) Using 

0.5% Threshold 

Transport 

(μg/m3) Using 

1% Threshold 

Transport 

(μg/m3) Using 

5% Threshold 

% of Total 

Transport 

Captured with 

0.5% Threshold 

% of 

Tran

Captur

1% Thr

180970081 IN Marion 4.24 3.92 0.94 89% 82180970083 IN Marion 4.25 3.93 0.94 89% 82390350065 OH Cuyahoga 3.82 3.43 0.00 89% 80390617001 OH Hamilton 4.88 4.56 2.08 90% 84
F-7

Table F-3a. Contribution metrics for 24-hour PM2.5 2012 nonattainment receptors – part 1. 

Monitoring Site State County 

2012 Base 

Case In-State 

Contribution 

(μg/m3) 

2012 Total 

Contribution 

from All 

Upwind States 

(μg/m3) 

Upwind 

Contribution as 

a Percent of 

Receptor 

Concentration 

# States 

Contributing At 

or Above 

0.5% Threshold 

# Sta

Contrib

At or A

1% Thre

10730023 AL Jefferson 6.81 7.87 54% 12 7 

170311016 IL Cook 4.75 12.55 73% 13 9 171191007 IL Madison 4.03 17.06 81% 14 11 180970043 IN Marion 9.15 14.19 61% 9 8 180970066 IN Marion 8.39 13.75 62% 10 8 180970081 IN Marion 8.29 13.73 62% 10 8 261470005 MI St Clair 1.97 15.28 89% 12 9 261630015 MI Wayne 4.35 13.97 76% 12 8 261630016 MI Wayne 5.22 13.20 72% 10 7 261630019 MI Wayne 3.58 12.89 78% 10 8 261630033 MI Wayne 5.03 13.59 73% 12 9 390350038 OH Cuyahoga 8.49 12.86 60% 14 8 390350060 OH Cuyahoga 8.96 11.58 56% 11 7 420030064 PA Allegheny 5.53 14.93 73% 9 7 420030093 PA Allegheny 3.21 12.05 79% 11 6 420030116 PA Allegheny 2.96 13.19 82% 10 7 420070014 PA Beaver 2.61 13.51 84% 12 7 420710007 PA Lancaster 6.69 10.45 61% 15 7 540090011 WV Brooke 3.28 15.33 82% 12 6 550790043 WI Milwaukee 3.00 17.74 86% 14 10 
F-8

Table F-3b. Contribution metrics for 24-hour PM2.5 2012 nonattainment receptors – part 2. 

Monitoring Site State County 

Transport 

(μg/m3) Using 

0.5% Threshold 

Transport 

(μg/m3) Using 

1% Threshold 

Transport 

(μg/m3) Using 

5% Threshold 

% of Total 

Transport 

Captured with 

0.5% Threshold 

% o

Tra

Captu

1% T

10730023 AL Jefferson 7.16 5.92 0.00 91% 

170311016 IL Cook 11.51 10.56 3.29 92% 171191007 IL Madison 15.91 15.14 9.15 93% 180970043 IN Marion 13.10 12.85 7.04 92% 180970066 IN Marion 12.75 12.26 6.47 93% 180970081 IN Marion 12.73 12.22 6.39 93% 261470005 MI St Clair 14.35 13.65 6.95 94% 261630015 MI Wayne 12.93 11.80 6.41 93% 261630016 MI Wayne 12.27 11.50 6.17 93% 261630019 MI Wayne 12.04 11.60 6.11 93% 261630033 MI Wayne 12.53 11.67 6.42 92% 390350038 OH Cuyahoga 12.33 10.83 8.45 96% 390350060 OH Cuyahoga 10.55 9.60 5.78 91% 420030064 PA Allegheny 13.73 13.31 8.83 92% 420030093 PA Allegheny 11.09 9.65 6.03 92% 420030116 PA Allegheny 12.04 11.14 6.84 91% 420070014 PA Beaver 12.38 11.05 6.57 92% 420710007 PA Lancaster 9.34 7.38 2.83 89% 540090011 WV Brooke 14.56 12.91 8.70 95% 550790043 WI Milwaukee 16.66 15.65 9.69 94% 
F-9

Table F-3c. Contribution metrics for 24-hour PM2.5 2012 maintenance receptors – part 1. 

Monitoring Site State County 

2012 Base 

Case In-State 

Contribution

(μg/m3) 

2012 Total 

Contribution 

from All 

Upwind States 

(μg/m3) 

Upwind 

Contribution as 

a Percent of 

Receptor 

Concentration 

# States 

Contributing At 

or Above 

0.5% Threshold 

# St

Contrib

or A

1% Th

10732003 AL Jefferson 4.61 6.88 60% 11 7 

170310052 IL Cook 6.13 12.66 67% 13 1170312001 IL Cook 3.96 13.57 77% 11 9 170313301 IL Cook 5.10 12.23 71% 11 9 170316005 IL Cook 6.15 11.15 65% 16 8 171190023 IL Madison 3.87 16.65 81% 15 9 180890022 IN Lake 5.77 9.84 63% 10 9 180890026 IN Lake 4.52 11.65 72% 12 9 261610008 MI Washtenaw 2.67 15.18 85% 13 1390170003 OH Butler 5.90 18.03 75% 13 9 390350045 OH Cuyahoga 9.04 11.81 57% 12 7 390350065 OH Cuyahoga 8.51 12.53 60% 12 8 390618001 OH Hamilton 5.88 14.19 71% 12 8 390811001 OH Jefferson 5.09 11.36 69% 11 6 391130032 OH Montgomery 4.93 15.19 76% 13 9 420031008 PA Allegheny 4.23 14.80 78% 11 7 420031301 PA Allegheny 4.31 12.29 74% 9 6 420033007 PA Allegheny 4.26 11.46 73% 9 6 421330008 PA York 8.98 13.25 60% 17 1550790010 WI Milwaukee 4.26 18.62 81% 13 1550790026 WI Milwaukee 4.03 15.17 79% 13 1
F-10

Table F-3d. Contribution metrics for 24-hour PM2.5 2012 maintenance receptors – part 2. 

Monitoring Site State County 

Transport 

(μg/m3) Using 

0.5% Threshold 

Transport 

(μg/m3) Using 

1% Threshold 

Transport 

(μg/m3) Using 

5% Threshold 

% of Total 

Transport 

Captured with 

0.5% Threshold 

% of 

Tran

Captur

1% Th

10732003 AL Jefferson 6.19 5.23 0.00 90% 76

170310052 IL Cook 11.67 10.96 2.84 92% 87170312001 IL Cook 12.56 12.13 6.70 93% 89170313301 IL Cook 11.13 10.69 5.17 91% 87170316005 IL Cook 10.37 8.42 2.49 93% 76171190023 IL Madison 15.81 14.31 10.90 95% 86180890022 IN Lake 8.88 8.68 3.09 90% 88180890026 IN Lake 10.73 10.19 4.68 92% 88261610008 MI Washtenaw 14.12 13.46 7.37 93% 89390170003 OH Butler 17.27 16.25 8.73 96% 90390350045 OH Cuyahoga 11.05 9.87 5.94 94% 84390350065 OH Cuyahoga 11.66 10.57 6.28 93% 84390618001 OH Hamilton 13.28 12.30 7.97 94% 87390811001 OH Jefferson 10.63 9.50 5.45 94% 84391130032 OH Montgomery 14.41 13.42 7.05 95% 88420031008 PA Allegheny 13.86 12.92 9.84 94% 87420031301 PA Allegheny 11.39 10.78 7.09 93% 88420033007 PA Allegheny 10.71 10.12 6.97 94% 88421330008 PA York 12.02 10.65 2.11 91% 80550790010 WI Milwaukee 17.80 17.61 8.60 96% 95550790026 WI Milwaukee 14.21 13.56 6.10 94% 89
Air Quality Modeling Final Rule 

Technical Support Document 

Appendix G 

Summary of Aggregated Emissions of SO2 and NOx 

For States Included in the Transport Rule 
G-2

Table G-1. Group1 and Group 2 States NOX Total Emissions for each Transport Rule Modeling 

2005 Base 

Year 

2012 Base 

Case 

2014 Base 

Case 

2014 

Remedy 

2014 

Remedy - 

2012 Base 

Case 

Percen

Change

2014 

Remedy 

2012 Ba

Case 

Annual Total NOX Emissions for States in 

Group 1 8,942,956 5,998,929 5,592,557 5,490,517 -508,412 -8.

Annual Total NOX Emissions for States in 

Group 2 4,626,321 3,351,169 3,083,373 3,030,042 -321,127 -9.

Annual Total NOX for all States included for 

PM 13,569,277 9,350,098 8,675,929 8,520,559 -829,539 -8.

Annual Total NOX Emissions for All States 

Fully within the Eastern Modeling Domain 17,265,033 12,013,803 11,173,286 10,974,018 -1,039,784 -8.

Annual Total NOX Emissions for All Western 

States 3,887,276 2,959,396 2,751,224 2,751,659 -207,737 -7.

Total NOX 21,152,309 14,973,199 13,924,510 13,725,678 -1,247,521 -8.
G-3

Table G-2. Group1 and Group 2 States SO2 Total Emissions for each Transport Rule Case

2005 Base 

Year 

2012 Base 

Case 

2014 Base 

Case 

2014 

Remedy 

2014 

Remedy - 

2012 Base 

Case 

Percent 

Change

2014 

Remedy 

vs 2012 

Base Cas

Annual Total SO2 Emissions for States in Group 1 8,695,431 6,841,869 6,198,185 2,999,641 -3,842,228 -56.2% Annual Total SO2 Emissions for States in Group 2 2,989,533 2,343,536 2,086,522 1,419,361 -924,175 -39.4% Annual Total SO2 for all States included for PM 11,684,964 9,185,405 8,284,707 4,419,002 -4,766,403 -51.9% Annual Total SO2 Emissions for All States Fully within the Eastern Modeling Domain 13,545,837 10,361,804 9,512,351 5,680,977 -4,680,826 -45.2% Annual Total SO2 Emissions for All Western States 808,533 567,085 566,435 594,818 27,733 4.9% Total SO2 14,354,370 10,928,889 10,078,786 6,275,795 -4,653,094 -42.6% 
G-4

Table G-3. Group1 and Group 2 States NOX EGU Sector Emissions for each Transport Rule C

2005 Base 

Year 

2012 Base 

Case 

2014 Base 

Case 

2014 

Remedy 

2014 

Remedy - 

2012 Base 

Case 

Percent 

Change: 

2014 

Remedy v

2012 Bas

Case 

Annual EGU NOX Emissions for States in 

Group 1 1,927,858 938,824 940,211 838,171 -100,653 -10.7% 

Annual EGU NOX Emissions for States in 

Group 2 700,110 444,377 425,686 372,355 -72,022 -16.2% 

Annual EGU NOX for all States included for 

PM 2,627,967 1,383,201 1,365,897 1,210,527 -172,674 -12.5% 

Annual EGU NOX Emissions for All States 

Fully within the Eastern Modeling Domain 3,224,300 1,715,510 1,718,178 1,518,910 -196,600 -11.5% 

Annual EGU NOX Emissions for All Western 

States 504,861 369,180 371,244 371,680 2,500 0.7% 

Total EGU NOX 3,729,161 2,084,689 2,089,422 1,890,590 -194,099 -9.3% 
G-5

Table G-4. Group1 and Group 2 States SO2 EGU Sector Emissions for each Transport Rule Model

2005 Base 

Year 

2012 Base 

Case 

2014 Base 

Case 

2014 

Remedy 

2014 

Remedy - 

2012 Base 

Case 

Percent 

Change: 

2014 

Remedy v

2012 Bas

Case 

Annual EGU SO2 Emissions for States in Group 1 6,764,335 5,313,000 4,752,537 1,553,993 -3,759,007 -70.8% Annual EGU SO2 Emissions for States in Group 2 2,143,069 1,702,727 1,468,071 800,910 -901,816 -53.0% Annual EGU SO2 for all States included for PM 8,907,403 7,015,726 6,220,608 2,354,903 -4,660,823 -66.4% Annual EGU SO2 Emissions for All States Fully within the Eastern Modeling Domain 10,019,270 7,635,785 6,912,529 3,081,155 -4,554,630 -59.6% Annual EGU SO2 Emissions for All Western States 361,613 224,026 247,039 275,422 51,397 22.9% EGU SO2 10,380,883 7,859,810 7,159,569 3,356,577 -4,503,233 -57.3% 
G-6

Table G-5. 26-State1 Total and EGU Sector Ozone Season NOX Emissions for each Modeling Sc

2005 Base 

Year 

2012 

Base 

Case 

2014 Base 

Case 

2014 

Remedy 

2014 

Remedy 

- 2012 

Base 

Case 

Perc

Chan

201

Reme

vs 20

Base C

Ozone Season EGU NOx Emissions for States 

Included for Ozone 1,001,600 671,939 668,513 593,833 -78,106 -1

Ozone Season Total NOx Emissions for States 

Included for Ozone 6,153,473 4,455,600 4,128,792 4,054,111 -401,489 -9

1 Includes the 20 states covered by the final Transport Rule for ozone season NOx budgets and the 6 states covered for ozone season NOx bud

Notice. 
H-1 

Air Quality Modeling Final Rule 

Technical Support Document 

Appendix H 

Concentration and Difference Maps for 

Annual PM2.5, 24-Hour PM2.5, and 8-Hour Ozone 
H-2 

Annual PM2.5 Maps 
H-3 

2003-2007 Average Ambient Annual PM2.5 Concentrations

2012 Base Case Annual PM2.5

Concentrations
H-4 

2014 Base Case Annual PM2.5 Concentrations

2014 Remedy Annual PM2.5

Concentrations
H-5 

2014 Remedy – 2012 Base Case Difference in Annual PM2.5 Concentrations

2014 Remedy – 2014 Base Case Difference in Annual PM2.5

Concentrations
H-6 

24-Hour PM2.5 Maps 
H-7 

2003-2007 Average Ambient 24-Hour PM2.5 Concentrations 

2012 Base Case 24-Hour PM2.5 Concentrations 
H-8 

2014 Base Case 24-Hour PM2.5 Concentrations 

2014 Remedy 24-Hour PM2.5

Concentrations
H-9 

2014 Remedy – 2012 Base Case Difference in 24-Hour PM2.5 Concentrations

2014 Remedy – 2014 Base Case Difference in 24-Hour PM2.5 Concentrations
H-10 

8-Hour Ozone Maps 
H-11 

2003-2007 Average Ambient 8-Hour Ozone Concentrations 

2012 Base Case 8-Hour Ozone 

Concentrations
H-12 

2014 Base Case 8-Hour Ozone Concentrations 

2014 Remedy 8-Hour Ozone 

Concentrations
H-13 

2014 Remedy – 2012 Base Case Difference in 8-Hour Ozone Concentrations 

2014 Remedy – 2014 Base Case Difference in 8-Hour Ozone Concentrations