# 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

# (This page intentionally left blank)

# 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 B oundary Concentrations .........................................................10

F. Model Performance Evaluation for Ozone and PM 2.5 

# ......................................11

III. Projection of Future Nonattainment and Maintenance for Annual PM 2.5 

# , 24-Hour PM 2.5 

# , 8-Hour and Ozone.............................................................12

A. Procedures for Processing Ambient Ozone and PM 2.5 

# Data................................13

B. Projection of Future Design Va lues and Determination of

Nonattainment and Maintenance for Annual and 24-Hour PM 2.5 

# .....................14

1. Methodology for Projecting Future Annual PM 2.5



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

2. Methodology for Projecting Future 24-Hour PM 2.5



# 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 PM 2.5



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

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

C. Approach to Calculating Cont ributions for Annual and 24-Hour PM 2.5 

# ...............35

D. Approach to Calculating C ontributions for 8-Hour Ozone .............................38

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

1. Contributions to Annual PM 2.5 

# Nonattainment and Maintenance............42

2. Contributions to 24-Hour PM 2.5 

# Nonattainment a nd Maintenance.........43

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

F. Analysis of Contri butions Captured by Various Thresholds.............................47

V. Air Quality Impacts of the Transport Rule SO 2 

# and NOx Emissions Reductions ......50

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

B. Impacts on Projected PM 2.5 

# Design Value Concentrations...............................54

C. Impacts on Annual and 24-Hour PM 2.5 

# 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 PM 2.5 

# Contribution Calculation Examples

Appendix D.

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

Ozone, Annual PM 2.5 

# , and 24-Hour PM 2.5



# Appendix E.

Upwind States to Downwind Nonattainment and Maintenance Linkages

Appendix F.

Analysis of Contribution Thresholds

Appendix G.

Summary of Aggregated Emissions of SO 2 

# and NOx

Appendix H.

Concentration and Difference Maps for Annual PM 2.5 

# , 24-Hour PM 2.5 

# , and 8-Hour Ozone

# 1 

# I. Introduction

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

performed to support the Fi nal Transport Rule (TR) 1 

# . For this rule the Environmental

Protection Agency (EPA) used air quality modeling to:

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

for annual (average) PM 2.5 

# , 24-hour PM 2.5 

# , and/or 8-hour ozone for the analytic

years chosen for the TR,

(2) quantify the impacts (i.e., ai r quality contributions) of SO 2 

# and NO X 

# emissions

from upwind states on downwind annual and 24-hour PM 2.5 

# concentrations at

monitoring sites projected to be nonattainme nt or have maintenance problems in

2012 for the 1997 annual and 2006 24-hour PM 2.5 

# NAAQS 2 

# , respectively,

(3) quantify the contributions of NO X 

# emissions on downwind 8-hour ozone

concentrations at monitoring sites pr ojected 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 pr edictions compared to measured concentrations,

• the procedures and results of projecting ozone and PM 2.5 

# concentrations for future

year scenarios,

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

PM 2.5 

# , and 8-hour ozone,

• the results of evaluating the intersta te 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 SO 2 

# and NO X 

# emissions reducti ons 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, an d two emissions sensitivity scenarios.

There were two separate annual mode l 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-s pecific 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 y ear 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 PM 2.5 

# , 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. Th e 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 qual ity modeling platform described in this

TSD to simulate a 2014 emissions scenario de veloped to calibrate AQAT. This scenario

is referred to as the AQAT calibration s cenario 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 scenar ios and as data points valuable to

calibrating AQAT 3 

# . For more information on AQAT and how the AQAT calibration was

3 

# The 2014 AQAT calibration scenario was designe d 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 th e proposed Group 2 states were modeled using

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

included in the proposed Transport Rule for contributions to PM 2.5 

# 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 SO 2 

# in the final

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

AQAT calibration scenario were merged with th e 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 scenar io 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 SO 2 

# emissions on concentrations of PM 2.5 

# . The

sensitivity scenarios included (1) a simula tion 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 SO 2 

# emissions are reduced (“nitrate replacement”). The NOx

zero-out simulation was performed to quan tify 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 docket 4



# .

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

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

model predictions of PM 2.5 

# and ozone using corresponding ambient measurements.

Section III defines the procedures for projecting future case PM 2.5 

# and ozone

concentrations and the approach for determ ining locations with projected nonattainment

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

contribution) modeling, (2) the procedures fo r quantifying contributi ons to nonattainment

and/or maintenance, (3) the evaluation of c ontributions 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 re sults of modeling performed for 2014 to assess

model predictions from the AQAT calibration scen ario were used to characterize the seasonspecific response of sulfate to the SO 2 

# and NO

x 

# 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 PM 2.5 

# component species

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

sulfate estimates from AQAT to estimate total PM 2.5 

# concentrations at the cost threshold levels

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

quality modeling results to calibrate AQAT, and the AQAT PM 2.5 

# concentration estimates are

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

4 

# “Technical Analyses in Support of the Need fo r 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 emissi ons 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 Extension 5 

# (CAMx) version

5.3 to simulate ozone and PM 2.5 

# 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 inte rstate transport of ozone and PM 2.5 

# . CAMx is a

three-dimensional Eulerian grid-based phot ochemical model designed to simulate the

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

directly emitted PM 2.5 

# , and their precursor species on natio nal, regional, and local scales

for short-term episodes up to annual time period s. 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. Meteorologi cal model outputs of parameters such as

wind speed, wind direction, temper ature, pressure, and precipit ation are important inputs

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

concentrations of pollution were simulate d at 1-hour interval s 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) exte nds from Texas northward to North Dakota

and eastward to the East Coast. Thirty seve n 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 whic h 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 millibar s (approximately 15 km) using a sigmapressure coordinate system. Predictions from the 36 km Continental U.S. (CONUS)

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

12 km domain. The 2005 base year scenar ios, 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 Exte nsions Version 5.3 User’s Guide. Environ

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

# 6 However, as described below, the source a pportionment modeling wa s performed for the

37 states wholly within the 12 km Easter n 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 spec ifications 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) estima tes of intercontinen tal transport (i.e.,

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

CAMx was run to generate hourly predictions of ozone and PM 2.5 

# component species

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

meteorological inputs and in itial and boundary concentratio ns for the CONUS domain

are described below. The emissions inventor ies used in the TR ai r 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 quart ers at the same time and, thus, reduce the

overall throughput time for an annual simula tion. 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 concen trations. 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 in itial 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 Atmo spheric 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 m odel 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 nudging 7 

# for temperature and moisture was applied

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

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

10 4 

# for wind fields and temperatures and 1.0 x 10 5 

# for moisture fields. The 12 km domain

nudging weighting factors were 1.0 x 10 4 

# for wind fields and temperatures and 1.0 x 10 5



# 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 ar e shown in Table II-2 and do not vary by

horizontal grid resolution. Th e meteorological outputs from the MM5 simulations were

processed to create model -ready inputs for CAMx usi ng 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 meteorologi cal 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 fo r 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

10 0.920 631 928 6

11 0.910 712 919

12 0.900 794 910

13 0.880 961 892 7

14 0.860 1,130 874

15 0.840 1,303 856

16 0.820 1,478 838 8

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

25 0.450 5,461 505 12

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 performan ce evaluation to assess the adequacy of the

MM5 simulated fields. The qualitative as pects of this evaluation included the

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

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

patterns of monthly average rainfall and monthly maximum planetary boundary layer

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

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

for 2005 was performed using statistical comp arisons 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 investig ated: temperature, humidity, shortwave

downward radiation, wind speed, and wind di rection. 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 we re generally within

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

applications 11



# .

E. Initial and Boundary Concentrations

The lateral boundary and init ial species concentrations are provided by a threedimensional global atmospheric chem istry model, the GEOS-CHEM model 12 

# (standard

version 7-04-11 13 

# ). The global GEOS-CHEM model simulates atmospheric chemical and

physical processes driven by assimilated me teorological 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 de gree (latitude-longitude ) and 30 vertical

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

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

simulations. The concentrations from th e 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 Simu lation, 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, 6 th 

# 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. Kro ll, T-M. Fu, D.J. Jacob, C.L. Heald, 2008. Global

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

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

# 11 F. Model Performance Evaluation for Ozone and PM 2.5



# The predictions from CAMx for ozone a nd fine particulate sulfate, nitrate,

ammonium, organic carbon, elemental carbon, an d 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 re plicating observed concentrations. This

evaluation was comprised of statistical and gr aphical comparisons of paired modeled and

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

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

Appendix A.

# 12 

# III. Projection of Future Nonatta inment and Maintenance for Annual

PM 2.5 

# , 24-Hour PM 2.5 

# , and 8-Hour Ozone

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

ozone and PM 2.5 

# to identify locations that are exp ected 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 PM 2.5 

# monitoring sites. The nonattainment and

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

contributions of emissions in upwind stat es to nonattainment and maintenance in

downwind locations. Future year concentr ations 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 PM 2.5 

# air quality.

For this analysis we are us ing air quality modeling result s in a “relative” sense to

project future concentrations of ozone and PM 2.5 

# . Rather than use the absolute modelpredicted future year ozone and PM 2.5 

# concentrations, the base year and future year

predictions are used to ca lculate a (relative) percen t change in ozone and PM 2.5



# 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 adju st (2003-2007) ambient

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

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

constrain the future year design value pr edictions, even if the absolute model

concentrations are over-pred icted or under-predicted. Fo r example, assume that a

monitoring site has a measured sulfate concentration of 6.0 μ g/m 3 

# in the 2003-2007 base

period and a modeled con centration (in 2005) of 4.0 μ g/m 3 

# . In the 2012 base case, the

modeled concentration is 3.2 μ g/m 3 

# . Therefore the relative response factor is 0.80 or a

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

value would be 6 μ g/m 3 

# multiplied by the relative response factor of 0.80, or 4.8 μ g/m 3



# .

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

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

PM 2.5 

# and 8-hour ozone conform to the methodologi es contained in the final guidance for

attainment demonstration modeling 14 

# (referred to below as the “modeling guidance”).

14 

# U.S. EPA, 2007: Guidance on the Use of M odels and Other Analyses for Demonstrating

Attainment of Air Quality Goals for Ozone, PM 2.5 

# , and Regional Haze; Office of Air Quality

# 13 A. Procedures for Processing Ambient Ozone and PM 2.5 

# Data

In this analysis we use measurements of ambient ozone and PM 2.5 

# 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) PM 2.5 

# sites in the

Eastern U.S.. In addition, speciated PM 2.5 

# data from the Chemical Speciation Network

(CSN) and IMPROVE network are used to estimate PM 2.5 

# 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 O 3 

# NAAQS is 0.08 ppm. The 8-hour ozone st andard is not me t if the 3-year

average of the annual 4th highest daily maximum 8-hour O 3 

# 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 PM 2.5 

# ambient data were processed consis tent with the formats associated

with the NAAQS for PM 2.5 

# . For PM 2.5 

# , we evaluated concentra tions of both the annual

PM 2.5 

# NAAQS and the 24-hour PM 2.5 

# NAAQS. The annual PM 2.5 

# standard is not met if

the 3-year average of the annual mean concentration is greater than 15.0 μg/m 3 

# (15.05

μg/m 3 

# 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 98 th



# percentile PM 2.5 

# concentration is greater than 35 μg/m 3 

# (35.5 μg/m 3 

# or greater when

rounded up). The 3-year average mean 98 th 

# percentile concentration is computed at each

site by averaging the 3 individual annual 98 th 

# percentile values at each site. The 3-year

average 98 th 

# percentile concentration is referr ed 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 r ecommends 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 modeli ng, we used the design value for 2003-2005, 2004-

2006, and 2005-2007 to represent the base period ozone and PM 2.5 

# concentrations.

Specifically, we used ambient ozone and annual and 24-hour PM 2.5 

# design values for the

periods 2003-2005, 2004-2006, and 2005-2007 as the st arting 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 accessibl e 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 complete 16 

# design value in the 2003-2007 five-year period. 17 

# There

were 723 monitoring sites in the 12 km Easter n modeling domain whic h had at least one

complete design value period for the annual PM 2.5 

# NAAQS, and 722 sites which met this

criteria for the 24-hour NAAQS 18 

# 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 PM 2.5



# As noted above, the projection methodology for PM 2.5 

# involves using the model

predictions in a relative sense to estimate the change in PM 2.5 

# 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 fu ture year design value for that location.

The procedure for calculating fu ture year annual and 24-hour PM 2.5 

# design values is

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

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

16 

# Design value completeness was determined acco rding to the monitoring rules in CFR 40 Part

50 Appendix I (8-hour ozone) and A ppendix 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 PM 2.5 

# NAAQS are

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

# 15 MATS 19 

# . The software (including doc umentation) is available at:

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

.

As described below, design values of PM 2.5 

# in 2012 and 2014 were estimated by

applying the 2005 to 2012 or 2005 to 2014 rela tive change in model-predicted PM 2.5



# species concentrations to the measured (2003-2007) PM 2.5 

# species concentrations. The

PM 2.5 

# species include sulfate, nitrate, amm onium, particle bound wa ter, elemental carbon,

salt, other primary PM 2.5 

# , and organic aerosol mass (by difference). Organic aerosol

mass by difference is defined as the difference between FRM PM 2.5 

# and the sum of the

other components.

For each FRM PM 2.5 

# 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 cr eating a 5-year weighted average. The middle

year (2005) is weighted 3 times, the 2 nd 

# and 4 th 

# years (2004 and 2006) are weighted

twice, and the 1 st 

# and 5 th 

# years (2003 and 2007) are weighted once. We refer to this as

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

valid design values, the “avera ge” of all valid design valu es 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 desi gn value periods (i .e., 2003-2005, 2004-2006, and

2003-2007). The projection of design values fo r these individual peri ods was used in the

interstate contribution analysis to determ ine sites expected to have maintenance

problems, as described below.

1. Methodology for Projecting Future Annual PM 2.5 

# Nonattainment and

Maintenance

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

PM 2.5 

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

MATS documentation.

19 

# U.S. EPA, 2009: Modeled Attainme nt 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 si nce these quality assured measurements are

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

information on PM 2.5 

# speciation is needed for the location of each FRM monitoring site.

Since co-located PM 2.5 

# speciation data are only availa ble at ~15 percent of FRM

monitoring sites, spatial interpolation t echniques 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 Aver aging (VNA) technique (using MATS). Additional

information on the VNA interpolation techniqu es and data handling procedures can be

found in the MATS User’s Guide 20 

# . After the species fracti ons 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 PM 2.5 

# (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 concen tration of FRM-derived total PM 2.5 

# by the monitored

fractional composition of PM 2.5 

# species for each quarter averaged over 3 years 21 

# (e.g., 20

percent sulfate fraction multiplied by 15 μg/m 3 

# PM 2.5 

# equals 3 μg/m 3 

# sulfate).

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

predictions for sulfate, nitrate, elemen tal carbon, organic carbon, and other primary

PM2.5. The result is a set of species-specifi c quarterly average re lative response factors

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

location is 10.0 μg/m 3 

# and the 2012 future concentration is 8.0 μg/m 3 

# , then the RRF for

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

over the nine grid cells 22 

# centered at the location of the monitor.

Step 3 : For each quarter and each of the specie s, 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 futu re year quarterly mean concentration for each

20 

# Available at http://www.epa.gov/ttn/scr am/guidance/guide/MATS-2-3-1_manual.pdf

21 

# For this analysis, species fractions were calcu lated 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 PM 2.5 

# RRFs using a 3 X 3 grid cell

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

# 17 species (e.g., 3 μ g/m 3 

# sulfate multiplied by the 0.8 RRF equals a future sulfate

concentration of 2.4 μ g/m 3 

# ).

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

calculated 23 

# . The future year ammonium is calculate d based on the calculated future year

sulfate and nitrate concentrations, using a c onstant value for the de gree of neutralization

of sulfate 24 

# (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 fu ture year annual design value for PM 2.5



# .

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

three base design value periods (200 3-2005, 2004-2006, and 2005-2007) separately. The

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

values are used to determine future year maintenance sites.

The preceding procedures for determining future year PM 2.5 

# concentrations were

applied for each FRM site . The calculated annual PM 2.5 

# design values are truncated after

the second decimal place 26 

# . This is consistent with the truncation and rounding

procedures for the annual PM 2.5 

# NAAQS. Any value that is gr eater than or equal to 15.05

μg/m 3 

# is rounded to 15.1 μg/m 3 

# and is considered to be violating the NAAQS. Thus,

sites with future year annual PM 2.5 

# design values of 15.05 μg/m 3 

# or greater, based on the

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

sites. Sites with future year maximum design values of 15.05 μg/m 3 

# or greater are

predicted to be maintenance sites. Note th at 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 eas e of reference we use the term “nonattainment

23 

# All of the calculations and assumptions are c onsistent with the default MATS settings (as

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

we did not explicitly model salt and therefore th e 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 c oncentration 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 pr ojected to exceed the NAAQS based on both the

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

based on the average design value but are pr ojected to exceed the NAAQS based on the

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

project to be nonattainment and/or maintenance for the annual PM 2.5 

# NAAQS in the 2012

base case are used as the nonattainment/m aintenance receptors for assessing the

contribution of emissions in upwind states to downwind nonattainment and maintenance

of the annual PM 2.5 

# NAAQS (see section IV, below) . The 2003-2007 base period and

projected annual PM 2.5 

# design values for each site for the 2012 and 2014 scenarios are

provided in Appendix B.

2. Methodology for Projecting Future 24-Hour PM 2.5 

# Nonattainment and

Maintenance

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

hour PM 2.5 

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

MATS documentation. The procedures for calculating the future year 24-hour PM 2.5



# design values have been updated for the final rule 27 

# . The updates are intended to make

the projection methodology more consistent w ith the procedures for calculating ambient

design values. In the 24-hr attainment te st used for the proposed TR, for each PM 2.5



# monitor, we projected the measured 98 th 

# percentile concentra tions from the 2003-2007

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

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

98 th 

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

# percentile did not switch

seasons in the future year compared to th e 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. Examinati on 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 PM 2.5 

# episodes.

27 

# See “Update to the 24-hour PM 2.5 

# 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 PM 2.5 

# 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 ba se and future periods will remain the same.

We project a larger set of ambient days fromthe base period to the future and then rerank the entire set of days to find the new future 98 th 

# percentile value (for each year).

Similar to the annual PM 2.5 

# calculations, we are usi ng the 2003-2007 base period

FRM data for projecting future y ear design values. The 24-hour PM 2.5 

# calculations are

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

the base period “high day” 24-hour PM 2.5 

# concentrations are projected to the future year,

instead of the annual average concentrations. Also, the PM 2.5 

# species fractions and

relative response factors are calcul ated from observed and modeled high concentration

days, instead of quarterly average data.

Both the annual PM 2.5 

# and 24-hour PM 2.5 

# calculations are performed on a calendar

quarter basis. Since all years and quarters are averaged together in the annual PM 2.5



# calculations, the individual years can be aver aged together early in the calculations.

However, in the 24-hour PM 2.5 

# calculations, the 98 th 

# percentile value from each year is

used in the final calculations. Since the 98 th 

# percentile value can come from any day in

the year, all potential 98 th 

# percentile days in the four qua rters must be carried through to

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



# percentile for each year is identified and the n, three yearly values from consecutive years

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

2003-2007 time period, as was done for the annual PM 2.5 

# 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 averag e of the three design values

is a five year weighted average of the 98 th 

# percentile values for each site.

In the revised 24-hour attainment test methodology, the 8 highest ambient PM 2.5



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

species specific quarterly rela tive response factors. After al l 32 days are projected to the

future, the days are re-ranked to determine the future year 98 th 

# percentile day. The 98 th



# percentile is selected based on the rank of the 98 th 

# percentile day in the base year ambient

data. For example, at site A, if the observed 98 th 

# percentile value in year 1 was the 3 rd



# high day for the year, then the future year 98 th 

# percentile is selected as the 3 rd 

# high future

day for the year.

# 20 This update to the methodology ensures that the 98 th 

# percentile day is not overestimated or under-estimated due to changes in the temporal dist ribution 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 98 th 

# percentile values and therefore lower

projected future 24-hour PM 2.5 

# design values.

Table III-1 and Table III-2 show an exam ple 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 PM 2.5 

# concentration. At this site for

2003, the 98 th 

# percentile day is the 3 rd 

# high for the year (based on the number of samples

collected in 2003). Therefore the 3 rd 

# high day is flagged (futur e year concentration of

20.5718 μ g/m 3 

# ) and carried through to the final desi gn value calculation. Note that the

3 rd 

# high day in the base year is on a diffe rent day (20030524), which had a concentration

of 29.1 μ g/m 3



# 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 98 th 

# percentile values for each of the five years (2003-

2007). The future year 98 th 

# percentile values from all five years of the 2003-2007 period

(the 3 rd 

# high future year day in each year) are used to calculate the final five year

weighted design value.

Table III-2. Example design valu e 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 fo r calculating the future year 24-hour

PM 2.5 

# design values:

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

PM 2.5 

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

rank of the official 98 th 

# percentile value based on the number of collected ambient

samples (e.g., the 3 rd 

# highest day of the year). This re sults 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 quarte rly ambient species fractions on “high” 28



# days for each of the major component species of PM 2.5 

# (sulfate, nitrate, ammonium,

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

calculation is performed by multiplying the m onitored concentrations of FRM-derived

total PM 2.5 

# mass on the 10 percent of highest PM 2.5 

# days at each site for each quarter, by

the average monitored species composition 29 

# on the 10 percent highest PM 2.5 

# days for

each quarter, averaged over 3 years 30



# . 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 su lfate, nitrate, elemental carbon, organic

carbon, and other primary PM 2.5 

# for the average of the t op 10 percent of modeled days 31



# based on predicted concen trations of 24-hour PM 2.5 

# . The result is a single set of speciesspecific “high day” relative response factor s (RRF), one for each quarter (e.g., assume

that 2005 predicted sulfate on the 10 percent highest PM 2.5 

# days for a quarter for a

particular location is 20 μg/m 3 

# and the 2012 base case concentration is 16 μg/m 3 

# , then the

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

the single 32 

# grid cell where the monitor is located.

28 

# High ambient data and model days were define d as the top 10 percent days in each quarter

based on 24-hour average concentrations of PM 2.5 

# mass.

29 

# Similar to the annual average calculations, the qua rterly species fractions ar e calculated at each

FRM site using interpolated species data (usi ng 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 calcu lated 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 indi cated 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 calcu lations) 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 quarterly 33 

# 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/m 3 

# 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/m 3 

# ).

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

calculated 34 

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

calculated future year sulfate and nitrate c oncentrations, using a c onstant 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

model 35 

# . The inputs to the formula are the calc ulated future year concentrations of

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

Step 7 : Sum the species concentrations to obtain total PM 2.5 

# 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 PM 2.5 

# concentration. For each year , the previously identified 98 th 

# percentile

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

# percentile concentr ation for each year.

For example, if the base year 98 th 

# percentile value for year 1 was the 3 rd 

# high

concentration, then the future year 98 th 

# percentile concentration is the future year 3 rd 

# high

PM 2.5 

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

# percentile

concentration is identified for each year (2003-2 007) that has been projected to the future

year.

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

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

values (e.g., the 98 th 

# percentile values from 2003, 2004, and 2005 are averaged. Then the

98 th 

# 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 th e 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, th e 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 th e “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 weight ed average for that monitoring site.

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

three projected base design valu e 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 PM 2.5 

# maintenance

receptors.

The preceding procedures for determining future year 24-hour PM 2.5



# concentrations were applied for each FRM site. The 24-hour PM 2.5 

# design values are

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

and rounding procedures for the 24-hour PM 2.5 

# NAAQS. Any value that is greater than

or equal to 35.5 μg/m 3 

# is rounded to 36 μg/m 3 

# and is violating the NAAQS. Sites with

future year 5 year weighted average design values of 35.5 μg/m 3 

# 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/m 3 

# or greater, based on a

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

nonattainment sites for the 24-hour NAAQS ar e also maintenance sites because the

maximum design value is at a site is always gr eater 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 ex ceed the NAAQS based on both the average and

maximum design values. Those sites that ar e 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 PM 2.5 

# NAAQS in the 2012 base case

are used as the nonattainment/maintenance re ceptors for assessing the contribution of

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

PM 2.5 

# NAAQS (see section IV, below). Th e 2003-2007 base period and projected 24-

hour PM 2.5 

# 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 futu re year 8-hour ozone calculations.

Additional details are provided in the m odeling guidance and MATS documentation.

Base period 2003-2007 ambient ozone design valu e 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 relative ly simple (compared to the PM 2.5 

# projection

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

ozone design values and gridded, modeled oz one 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 res ponse factors. The methodology we followed is

consistent with the attainment demonstr ation 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 cal culate 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 ppb 36 

# . 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 ba se 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 cas e, 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 fo r 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 weight ed 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 site s. Note that, as described

previously for the annual and 24-hour PM 2.5 

# NAAQS, nonattainment sites for the ozone

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

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

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

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

be attainment based on the average desi gn value but exceed the NAAQS based on the

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

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

case are the nonattainment/maintenance receptor s used for assessing the contribution of

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

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

for all sites for the 2012 and 2014 scen arios are provided in Appendix B 37



# .

37 

# Note that there are a number of ozone monitori ng 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 PM 2.5 

# , 24-hour PM 2.5 

# ,

and 8-hour ozone concentrations at proj ected 2012 nonattainment and maintenance

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

contribute to ozone, and/or annual PM 2.5 

# , and/or 24-hour PM 2.5 

# nonattainment and/or

maintenance problems in other states 38 

# in amounts at or above thresholds based on 1

percent of the corresponding NAAQS (see section V of the pr eamble 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 PM 2.5 

# at individual receptor locations. The strength

of the photochemical model source apportionm ent technique is that all modeled ozone

and/or PM 2.5 

# 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 atmo spheric chemical relationships within the

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

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

contributions that are asso ciated with ozone and PM 2.5 

# , as estimated by the model. More

details on the implementation of source a pportionment in CAMx can be found in the

CAMx user’s guide 39



# .

38 

# In this assessment we refer to source stat es 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 Exte nsions Version 5 User’s Guide. Environ

International Corporation. Novato, CA. March 2009.

# 28 A. Nonattainment and Maintenance Receptors for the Assessment of PM 2.5 

# and Ozone

Contributions

As described above in section III, we iden tified monitoring site s 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 PM 2.5 

# standards and the 1997 8-hour

standard 40 

# . These 2012 nonattainment and/or mainte nance sites are used as the receptors

in the analysis of interstate contributi ons. For each of the NAAQS, the projected

nonattainment and maintenance sites are liste d, 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 coun ties 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

PM 2.5 

# design values and the corresponding 2012 base case average and maximum design

values for sites projected to be nonattainment of the annual PM 2.5 

# NAAQS in 2012. Table

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

There are 12 sites in 7 counties projected to be nonattainment for the annual PM 2.5



# NAAQS in 2012. There are 4 sites in 3 coun ties projected to have maintenance only

problems for the annual PM 2.5 

# NAAQS in 2012. The 2012 base case annual PM 2.5



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

Table IV-1. Average and maximu m 2003-2007 and 2012 base case annual PM 2.5 

# design

values (μg/m 3 

# ) 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 va lues 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 maximu m 2003-2007 and 2012 base case annual PM 2.5 

# design

values (μg/m 3 

# ) 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 PM 2.5 

# nonattainment and/or maintenance

sites in the East.

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

PM 2.5 

# design values and the 2012 base case aver age and maximum design values for sites

projected to be 2012 nonattainment of the 24-hour PM 2.5 

# NAAQS in 2012. Table IV-4

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

are 20 sites in 12 counties projected to be nonattainment for the 24-hour PM 2.5 

# NAAQS

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

for the 24-hour PM 2.5 

# NAAQS in 2012. The 2012 base case 24-hour PM 2.5



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

Table IV-3. Average and maximu m 2003-2007 and 2012 base case 24-hour PM 2.5 

# design

values (μg/m 3 

# ) 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 maximu m 2003-2007 and 2012 base case 24-hour PM 2.5 

# design

values (μg/m 3 

# ) 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 PM 2.5 

# 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 av erage and maximum design values for sites

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

contains this same information for projecte d 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 coun ties 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 PM 2.5 

# was performed using

the 2012 base case emissions. In this modeling we tracked ozone and PM 2.5 

# 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 th e emissions in these

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

receptors. The states included in the s ource apportionment modeling for ozone and PM 2.5



# are: Alabama, Arkansas, Connecticut , Delaware, the District of Columbia 41 

# , Florida,

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

Massachusetts, Michigan, Minnesota, Mississipp i, Missouri, Nebraska, New Hampshire,

New Jersey, New York, North Carolina, Nort h 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 c ontributions from Maryland and the District of

Columbia as a single entity in our contribution anal ysis 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 onl y partially contained within the 12 km

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

track the emissions or assess th e 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 Cana da 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) a nd initial and boundary concen trations (IC/BCs). The

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

in the analysis to determine upwind st ate contributions to downwind areas.

C. Approach to Calculating Contributions for Annual and 24-Hour PM 2.5



# We used the CAMx Particulate Sour ce Apportionment Technique (PSAT) to

calculate contributions to annual and 24-hour PM 2.5 

# nonattainment and maintenance. The

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

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

emissions of NO X 

# , SO 2 

# , and primary PM 2.5 

# 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 organi c aerosols (SOA), and the relatively large

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

In the PSAT simulation, NO X 

# emissions are tracked to c ontributions to particulate

nitrate concentrations, SO 2 

# emissions are tracked to contri butions to particulate sulfate

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

unspeciated PM 2.5 

# ) are tracked as contributions to prim ary particulates. We combined the

contributions of nitrate and sulfate in calculating the net contributions to PM 2.5 

# for the

purpose of evaluating the intersta te contributions against spec ific threshold criteria, as

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

plus sulfate and excluding primary PM 2.5 

# 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 PM 2.5 

# 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 pro cedures for calculating contributions for

annual PM 2.5. 

# These procedures were applied se parately for each source state.

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

source apportionment to calculate contributi ons to each monitoring site. Particularly

important monitoring sites included the recepto r sites identified in the 2012 base case

modeling as being nonattainment a nd/or maintenance receptors.

2. Contributions for each of the PM 2.5 

# species from each state, as predicted by PSAT, are

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

model output files for each source region (state).

3. Daily, 24-hr average PM 2.5 

# species are calculated for th e “standard” model output files

and newly generated source co ntribution output files.

4. The relative response factor s (RRFs) for each of the PM 2.5 

# 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 (g enerated 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 PM 2.5 

# species contributions in

μ g/m 3 

# from each species for each source-tagged state 42 

# . [The actual calculation is

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

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

each source state are combined to ca lculate the nitrate plus sulfate PM 2.5 

# contribution

used in the TR analysis 43



# .

42 

# The calculations in step 5 use MATS out puts and additional post-processing software

developed for this rule. More details on the pos t-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 concentrati ons, using a constant value for the degree of

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

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

# 37 7. Annual PM 2.5 

# (i.e., nitrate plus sulf ate) contributions are expressed in units of μg/m 3



# .

Values of annual PM 2.5 

# contribution are truncated after two places to the right of the

decimal (e.g., a contribution of 0.149 μg/m 3 

# is truncated to 0.14 μg/m 3 

# ).

The 24-hour PM 2.5 

# contributions were calculate d in a manner similar to the

procedures for annual PM 2.5 

# . 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 PM 2.5 

# , the contributions are ca lculated as the five

year average contributions to the 98 th 

# percentile concentrations at each site.

The following is a description of the pro cedures for calculating contributions for

24-hour PM 2.5. 

# These procedures were applied separa tely for each source-tagged state.

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

source apportionment to calculate contributi ons to each monitoring site. Particularly

important monitoring sites included the recepto r sites identified in the 2012 base case

modeling as being nonattainment a nd/or maintenance for 24-hour PM 2.5



# .

2. Contributions for each of the PM 2.5 

# species from each state, as predicted by PSAT, are

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

output files for each source region.

3. Daily, 24-hr average PM 2.5 

# species model output files are generated for the “standard”

2012 base case model output files and newly ge nerated source contribution output files.

4. Relative response factors (RRFs) are calculated for each of the PM 2.5 

# species 44 

# for each

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

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

Here, the “high” concentration days ar e based on the highest 10% of modeled PM 2.5



# days 45 

# (in each grid cell) in the 2012 base cas e. 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 contri bution region model output file (from step 3).

44 

# As in the design value calculations in secti on III, the ammonium contribution is calculated

based on the calculated sulfate and nitrate contribu tion concentrations, using a constant value for

the degree of neutralization of sulfate (from th e 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 mode led 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 multiplied 46 

# by the previously identified 2012 98 th 

# percentile day

species concentrations for each site for each year (2003-2007) projected to the 2012 base

case. [The actual calculation is (species co ncentration – (RRF*speci es 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 98 th 

# percentile value (in 2012)

occurred. For example, if the projected 98 th 

# percentile value for 2003 occurs in January,

then the modeled RRFs for quarter 1 will be appl ied to the species concentrations for that

particular day. The result of these calculati ons are the contribution of each of the species

from the upwind source to each of the 98 th 

# percentile days, for up to 5 years.

6. For each receptor, the contributions of each species for the 98 th 

# percentile days (2003-

2007 projected to the 2012 base case) are averaged 47 

# together. This represents the

species contributions to 24-hour PM 2.5 

# concentrations.

7. The 24-hour contributions of sulfate ion, nitrate ion, a mmonium, and water 48 

# for each

source state are combined to calculate the total PM 2.5 

# contribution used in the TR

analysis, as in the calculations for annual PM 2.5 

# , described above..

8. 24-hour PM 2.5 

# contributions are expr essed in units of μg/m 3 

# . For the contribution

assessment, values of 24-hour PM 2.5 

# contribution are truncated after two places to the

right of the decimal (e.g ., a contribution of 0.349 μg/m 3 

# is truncated to 0.34 μg/m 3 

# ).

D. Approach to Calculating Contributions for 8-Hour Ozone

We used the CAMx Anthropogenic Precursor Culpability Assessment (APCA)

source apportionment technique to calcul ate the contributions to 8-hour ozone

nonattainment and maintenance in the 2012 base case. APCA tracks the formation of

ozone from NO X 

# and VOC emissions. Through emissions pre-processing procedures, we

tagged all of the anthropogenic NO X 

# 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 98 th 

# 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 fo r 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 wa s 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 NO X 

# and VOC were tracked in

the APCA simulation. Several post-processi ng 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 PM 2.5 

# , except that the APCA model

outputs were processed using MATS in stead of the PSAT outputs used for PM 2.5.



# The following is a description of the pro cedures for calculating contributions for

8-hour ozone. These procedures were appl ied separately for each source state.

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

source apportionment to calculate contributi ons to each monitoring site. Particularly

important monitoring sites included the recepto r 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 genera ted 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 allowabl e criterion of 70 ppb. A minimum number of

5 days at or above the 85 ppb criterion or 5 days of modeled oz one greater than the

minimum allowable criteri on (70 ppb) are required 50 

# 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 concentratio ns 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 fact or (from step 4) is multiplied by the 2012 base case design

value to estimate ozone contributio n in ppb from the source state 51 

# [The actual

calculation is (ozone concentrati on – (RRF*ozone concentration))]

7. Ozone contributions are e xpressed in units of ppb. Oz one 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 pro cedures for calculating 8-hour ozone and

annual and 24-hour PM 2.5 

# contributions to the nonattain ment and maintenance receptor

sites 52 

# . The contributions to each 2012 nonatta inment receptor and each maintenance

receptor are provided in Appendix D 53 

# . For annual PM 2.5 

# 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 PM 2.5



# NAAQS in the 2012 base case. For 24-hour PM 2.5 

# , 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 ar e projected to have maintenance problems for the 24-hour

PM 2.5 

# NAAQS in the 2012 base case. Similarl y, 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 po st-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 b ase 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 6 54 

# sites that are projected to have ma intenance 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 contribu tions to downwind nonattainment and/or

maintenance are at or above specific contribu tion 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 PM 2.5 

# NAAQS, 2006 24-

hour PM 2.5 

# NAAQS, and 1997 8-hour ozone NAAQ S are shown in Table IV-7.

Table IV-7. Threshold concentrations used for evaluating intersta te contributions to

annual and 24-hour PM 2.5 

# and 8-hour ozone.

Pollutant Level of the NAAQS 1 Percent Threshold

Annual 

# PM 2.5 

# 15.0 μg/m 3 

# 0.15 μg/m 3



# 24-Hour 

# PM 2.5 

# 35 μg/m 3 

# 0.35 μg/m 3



# 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 recept or and each maintenance only receptor for

the three NAAQS and noted the magnitude of the contribution relativ e to the threshold.

States that contribute amount s at or above the threshol d to at least one downwind

nonattainment receptor or one maintenance receptor for the annual PM 2.5 

# NAAQS or the

24-hour PM 2.5 

# NAAQS are included in the TR for annual SO 2 

# and NO X 

# emissions

budgets. States that contribute amounts that ar e at or above the threshold to at least one

8-hour ozone downwind nonattainment or mainte nance receptor are included in the TR

for ozone season (i.e., May through September) NO X 

# 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 PM 2.5 

# Nonattainment and Maintenance

The largest contribution from each state to annual PM 2.5 

# across all downwind

nonattainment receptors is provided in Table IV-8. The largest contribution from each

State to annual PM 2.5 

# across all downwind maintenance-only receptors is also provided in

Table IV-8.

Table IV-8. Largest contri bution to downwind annual PM 2.5 

# (μg/m 3 

# ) nonattainment and

maintenance for each of 37 states.

Upwind State Largest Downwind

Contribution to Nonattainment

for Annual PM 2.5 

# (μg/m 3 

# ) Largest Downwind Contribution

to Maintenance

for Annual PM 2.5 

# (μg/m 3 

# )

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 PM 2.5 

# (μg/m 3 

# ) Largest Downwind Contribution

to Maintenance

for Annual PM 2.5 

# (μg/m 3 

# )

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 contributi on analysis, there are 18 states which

contribute 0.15 μg/m 3 

# or more to downwind annual PM 2.5 

# 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 nonattainme nt sites to which each

upwind state contributes 0.15 μg/m 3 

# or more (i.e., the upwind state-to-downwind

nonattainment “linkages”) are provided in Appendix E.

There are 12 states which contribut e 0.15 μg/m3 or more to downwind annual

PM 2.5 

# maintenance. These states are: Alabam a, Illinois, Indiana, Kentucky, Michigan,

Missouri, New York, Ohio, Pennsylvania, Tenn essee, 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-dow nwind maintenance “li nkages”) are provided

in Appendix E.

2. Contributions to 24-Hour PM 2.5 

# Nonattainment and Maintenance

The largest contribution from each state to 24-hour PM 2.5 

# across all downwind

nonattainment receptors is provided in Table IV-9. The largest contribution from each

state to 24-hour PM 2.5 

# across all downwind maintenance-on ly receptors is also provided

in Table IV-9.

# 44 Table IV-9. Largest contri bution to downwind 24-hour PM 2.5 

# (μg/m 3 

# ) nonattainment and

maintenance for each of 37 states.

Upwind State Largest Downwind

Contribution to

Nonattainment

for 24-hour PM 2.5 

# (μg/m 3 

# ) Largest Downwind

Contribution to Maintenance

for 24-hour PM 2.5 

# (μg/m 3 

# )

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 PM 2.5 

# (μg/m 3 

# ) Largest Downwind

Contribution to Maintenance

for 24-hour PM 2.5 

# (μg/m 3 

# )

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 contributi on analysis, there are 21 states which

contribute 0.35 μg/m3 or more to downwind 24-hour PM 2.5 

# nonattainment. These states

are: Alabama, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Maryland, Michigan,

Minnesota, Missouri, New Jersey, New Yor k, 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 contribut e (individually) 0.35 μg/m3 or more to

downwind 24-hour PM 2.5 

# maintenance. These states are: Alabama, Georgia, Illinois,

Indiana, Iowa, Kansas, Kentucky, Maryland, Mi chigan, Minnesota, Missouri, Nebraska,

New Jersey, New York, North Carolina, Ohi o, Pennsylvania, Tenness ee, 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 stat e 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 a ll downwind maintenance-only r eceptors is also provided in

Table IV-10.

# 46 Table IV-10. Largest cont ribution 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, I ndiana, Kentucky, Louisiana, Mississippi,

Missouri, Tennessee, and Texas. A list of the downwind nonattainment counties to

which each upwind state contri butes 0.8 ppb or more (i.e., th e 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, Ar kansas, Florida, Georgi a, Illinois, Indiana,

Iowa, Kansas, Kentucky, Louisiana, Mary land, Michigan, Mississi ppi, 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 st ate contributes 0.8 ppb or more (i.e., the

upwind state-to-downwind nona ttainment “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 pr oposed 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 per cent, 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/m 3 

# ) 0.075 0.15 0.75

24-Hour PM2.5 (μg/m 3 

# ) 0.175 0.35 1.75



# For the analysis of thresholds we calcula te several “metrics” (i.e., measures of

contribution) for each downwind receptor for the 8-hour ozone, annual PM 2.5 

# , and 24-

hour PM 2.5 

# NAAQS, individually. These metrics are lis ted 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 Percen t 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 th e 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 tota l upwind contribution (met ric 2), expressed as

a percent

.



# 4. Number of Upwind States that Contri bute 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 Stat es using a “x” Percent Threshold

- Sum of contributions from all upwind states the individually contri bute at or above the

given threshold.

6. Percent of Upwind Contribution Captured with “x” Percent Threshold

- Total contribution using an “x” percent threshold (met ric 5) divided by the total

contribution from all upwind states (m etric 2), expressed as a percent.

The data for each of the above contribu tion metrics for each 2012 nonattainment and

maintenance receptor are provided in Appe ndix F Tables F-1, F-2, and F-3 for 8-hour

ozone, annual PM 2.5 

# and 24-hour PM 2.5 

# , 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 PM 2.5 

# and 24-hour PM 2.5 

# nonattainment and maintenance sites. For

ozone, more than 90 percent of the concentr ation 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 concen tration. For annual PM 2.5 

# , 60 to 70 percent of the concentration

is due to upwind transport at all recept ors, except for Jefferson County, AL where

transport is somewhat less, but still substant ial at 45 to 50 percent. The amount of PM 2.5



# due to transport is 60 to 80 pe rcent of the concentration at a majority of the 24-hour PM 2.5



# receptors. The interpretation of the contribution summaries presented in Tables F-1

through F-3 with respect to deci sions 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 SO 2 

# and NO X 

# Emissions

Reductions

In this section we present th e air quality impacts of the SO 2 

# and NO X 

# emissions

reductions in the 2014 remedy scenario in co mparison to air quality predicted for the

2012 base case and 2014 base case scenarios 55 

# . We first describe the magnitude and

spatial patterns in emissions reductions (and in creases) in the East. Then we quantify the

impacts of these emissions changes on proj ected design value concentrations for annual

PM 2.5 

# , 24-hour PM 2.5 

# , and 8-hour ozone at monitoring sites in the Eastern modeling

domain 56 

# . We also provide the impacts on visibi lity 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 reme dy scenario is described in the TR

preamble. The methods for projecting emissions for the 2012 and 2014 base case

scenarios, including the baseline contro l programs, are presented in the EITSD 57 

# . The 23

states that are included in the TR for annual SO 2 

# and NO X 

# emissions controls to reduce

interstate contributions of PM 2.5 

# are shown on the map in Figure V-1. These states are

divided into two groups with different levels of SO 2 

# emissions reductions. The states in

Group 1 have deeper reducti ons than those in Group 2 58 

# . The 20 states subject to ozone

season NO X 

# controls to reduce interstate transp ort 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 NO X 

# trading program. The aggregate emissions of annual SO 2 

# , annual

55 

# Note that the comparisons between the 2012 b ase 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 additi on 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 criteri a, 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 SO 2 

# 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 NO X 

# , and ozone season NO X 

# for these states are provi ded in Appendix G for the 2014

base case and remedy scenarios.

Figure V-1. States modeled for annual SO 2 

# and NO X 

# emission reductions.

Figure V-2. States modeled for ozone season NO X 

# emission reductions.

For the Group 1 states as a whole, annual SO 2 

# emissions from electric generating

units (EGUs) are projected to be 67 percent lo wer in 2014 with the TR controls compared

to those in the 2014 base case. The reducti on in EGU emissions yi elds a net 52 percent

# 52 reduction in total SO 2 

# for the Group 1 states. Overall, for both Group 1 and Group 2

states, total SO 2 

# emissions across all source categorie s are 47 percent lower in the 2014

remedy scenario, compared to the 2014 base case.

The difference in annual total county-level SO 2 

# emissions between the 2014 base

and remedy scenarios is shown in Figure V-3. This figure reveals that substantial SO 2



# 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 SO 2 

# emissions are noted in a few states

not included in the TR for annual SO 2 

# reductions, most notably in several counties in

Arkansas, Colorado, Florida, Loui siana, Montana, and Wyoming.

Figure V-3. Change in county total annual EGU SO 2 

# emissions between the 2014 base

and control scenarios.

Annual emissions of NO X 

# from EGUs are lower by 11 percent in the remedy

scenario compared to the 2014 ba se case for the states that we re modeled as part of the

TR for annual NO X 

# emissions reductions. The reduction in EGU NO X 

# produces a 2

percent net reduction in total NO X 

# for these states. For the states included in the air

# 53 quality modeling of ozone season NO X 

# controls, ozone season EGU NO X 

# emission are 11

percent lower in the 2014 remedy scenari o, compared to the 2014 base case.

The changes in winter and ozone season NO X 

# emissions between the 2014

remedy and 2014 base case at i ndividual EGU facilities are pr esented in Figures V-4 and

V-5, respectively. As is evident from Fi gure V-4, the largest reductions in winter NO X



# are at facilities located in the area exte nding from Texas, Oklahoma, Kansas, and

Nebraska eastward across the Midwest and Oh io Valley into Pennsylvania. Increases in

EGU NO X 

# emissions are seen in locations scatte red across the East. Comparing Figures

V-4 and V-5 indicates that the spatial pattern in NO X 

# emissions reductions in the ozone

season is similar to that in the winter.

Figure V-4. Change in winter season county total EGU NO X 

# emissions between the 2014

base and control scenarios at individual facilities.

# 54 Figure V-5. Change in ozone season county total EGU NO X 

# emissions between the 2014

base and control scenarios at individual facilities.

B. Impacts on Projected PM 2.5 

# Design Value Concentrations

The projected annual and 24-hour PM 2.5 

# design values for the 2014 base case and

2014 remedy scenario at indivi dual monitoring sites are prov ided in Appendix B. The

impacts of the annual SO 2 

# and NO X 

# emissions reductions on annual and 24-hour PM 2.5



# design values are also provided in Appendix B. The impacts in 2014 on annual and 24-

hour PM 2.5 

# design values for each monitored county are shown in Figures V-6 and V-7,

respectively 60,61 

# . The results, as shown in these fi gures indicate that the emissions

reductions from the TR will substantially lower annual and 24-hour PM 2.5 

# concentrations

at most locations in the Eastern U.S, co mpared to the 2014 base case. Annual PM 2.5 

# is

60 

# These maps, which show the difference betw een the 2014 remedy scenario and the 2014 base

case, are based upon 5-year weighted average d esign 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 PM 2.5 

# are provided in Appendix H. Maps showing the predicted

change in annual and 24-hour PM 2.5 

# 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 μ g/m 3 

# 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 PM 2.5 

# concentrations are

lower by 1 to 2 μ g/m 3



# .

As shown in Figure V-7, the pr edicted reductions in 24-hour PM 2.5 

# are greater

than those for annual PM 2.5 

# concentrations. Daily PM 2.5 

# concentrations in the remedy

scenario are lower than in the 2014 base case by 2 to 6 μ g/m 3 

# 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 PM 2.5 

# concentrations

that are 6 to 10 μ g/m 3 

# lower than those in the 2014 base case.

Figure V-6. Impacts on annual average PM 2.5 

# concentrations ( μ g/m 3 

# ) resulting from the

emissions reductions in the 2014 remedy s cenario compared to the 2014 base case

scenario.

# 56 Figure V-7. Impacts on 24-hour PM 2.5 

# concentrations ( μ g/m 3 

# ) resulting from the

emissions reductions in the 2014 remedy s cenario compared to the 2014 base case

scenario.

The spatial patterns of the magnitude of reduction in annual and 24-hour PM 2.5



# across the East are generally consistent with the patterns in annual SO 2 

# and winter NO X



# emissions reductions from EGUs, as evid ent by comparing Figures V-6 and V-7 to

Figures V-3 and V-4, respectively. Th ese figures also indicate that PM 2.5 

# concentrations

are lower in the remedy scenario in those stat es in the East not included in the TR for

annual SO 2 

# and NO X 

# reductions, like Arkansas, Louisi ana, Mississippi, Oklahoma, and

several northern New England. In Arkansas, Florida, and Louisiana, the reductions in

transported PM 2.5 

# are large enough to more than offset the forecasted increases in local

emissions of SO 2 

# in these states.

There are a few locations ma inly in the West where SO 2 

# emissions and, therefore,

PM 2.5 

# is predicted to be higher in the 2014 re medy scenario compared to the 2014 base

case. Locations where annual PM 2.5 

# levels are higher in the remedy scenario are evident

in Colorado and Wyoming.

# 57 C. Impacts on Annual and 24-Hour PM 2.5 

# 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 PM 2.5 

# and 24-hour PM 2.5 

# are provided in Table V-1. The percent difference in

the number of nonattainment and maintenan ce 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 PM 2.5 

# and 24-hour PM 2.5 

# concentrations for those

sites with 2012 nonattainment and/or mainte nance problems are provided in Table V-2 62



# .

The annual PM 2.5 

# nonattainment and/or maintenance sites in the East for the 2014 base

case are shown in Figure V-8. There are no annual PM 2.5 

# nonattainment and/or

maintenance sites projected for the 2014 reme dy scenario. Maps s howing the location of

24-hour PM 2.5 

# nonattainment and/or maintenance s ites for the 2014 base case and 2014

remedy scenario are provided in Fi gures V-9 and V-10, respectively.

Table V-1. Percent reduction in annual and 24-hour PM 2.5 

# 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 

# PM 2.5



# Nonattainment Sites a 

# 103 12 7 0 100 percent 100 percent

Annual 

# PM 2.5 

# Maintenance-Only

Sites 22 4 3 0 100 percent 100 percent

Daily 

# PM 2.5



# Nonattainment Sites 151 20 10 1 95 percent 90 percent

Daily 

# PM 2.5 

# 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 concentra tions are calculated using the projected average

design values.

# 58 Table V-2. Average and peak reductions in annual PM 2.5 

# and 24-hour PM 2.5 

# for the 2012

base case nonattainment and/or maintenan ce 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 PM 2.5 

# Nonattainment Sites 2.73 μg/m 3 

# 3.32 μg/m 3 

# 2.18 μg/m 3 

# 2.69 μg/m 3



# Annual PM 2.5 

# Maintenance-Only Sites 2.99 μg/m 3 

# 3.26 μg/m 3 

# 2.49 μg/m3 2.69 μg/m3

24-Hour PM 2.5 

# Nonattainment Sites 6.8 μg/m 3 

# 11.7 μg/m 3 

# 5.3 μg/m 3 

# 9.1 μg/m 3



# 24-Hour PM 2.5 

# Maintenance-Only Sites 6.5 μg/m 3 

# 11.0 μg/m 3 

# 5.1 μg/m 3 

# 9.3 μg/m 3



# The information in Table V-1 shows th at the extent of nonattainment and

maintenance problems for annual PM 2.5 

# and 24-hour PM 2.5 

# is significantly lower in the

2014 remedy scenario compared to both the 201 2 base case and the 201 4 base case. All

annual PM 2.5 

# nonattainment and maintenance problem s are projected to be resolved by

2014 with the remedy coupled with other ex isting control programs. Annual PM 2.5



# concentrations are, on average, more than 2 μg/m 3 

# lower across the 12 sites that were

nonattainment in the 2012 base case. The impacts on annual PM 2.5 

# at maintenance-only

sites are comparable to those at the nonattainment sites.

For 24-hour PM 2.5 

# , there are significantly fewer nonattainment and/or

maintenance sites in the 2014 remedy scenar io compared to both the 2012 and 2014 base

cases. There is only one remaining nonattainment site 63 

# and 4 remaining maintenance

sites 64 

# for 24-hour PM 2.5 

# with the 2014 remedy scenario.. The average reduction in 24-

hour PM 2.5 

# across the 20 2012 nonattainment sites is 6.8 μg/m 3 

# and the peak reduction at

an individual nonattainment site is 11.7 μg/m 3 

# . Comparable reductions are projected at

24-hour PM 2.5 

# maintenance-only sites.

63 

# Allegheny Co, PA site 420030064

64 

# Cook Co, IL site 170311016, Lake Co, IN s ite 180890022, Wayne Co, MI site 261630019, and

Lancaster Co, PA site 420710007

# 59 Figure V-8. Projected annual PM 2.5 

# nonattainment and/or maintenance sites in the East

for the 2014 base case.

Figure V-9. Projected 24-hour PM 2.5 

# nonattainment and/or mainte nance sites in the East

for the 2014 base case.

# 60 Figure V-10. Projected 24-hour PM 2.5 

# 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 valu es for the 2014 base case and 2014

remedy scenario at individual monitoring site s are provided in Appendix B. The impacts

of the ozone season NO X 

# emissions reductions on 8-hour ozone design values for

monitored counties in the East ern 12 km modeling domain ar e also provided in Appendix

B. The impacts are shown in Figure V-11 65 

# . 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 th e 2014 remedy scenario in portions of the

Southeast, Midwest, and Northeast. Reducti ons in ozone of 1 ppb or more are predicted

at sites in 23 counties in por tions Florida, Georgia, Iowa, Kansas, Oklahoma, Ohio, West

Virginia, and Pennsylvania.

65 

# These maps, which show the difference betw een the 2014 remedy scenario and the 2014 base

case, are based upon 5-year weighted average d esign 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 predic ted 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 nonatt ainment 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 w ith 2012 nonattainment and/or maintenance

problems are provided in Table V-4. The lo cation of 8-hour ozone nonattainment and/or

maintenance sites in the East for the 2014 ba se 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 percen t 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 mainte nance problems in the 2014 remedy scenario,

the predicted ozone reductions provide 5 percen t of the amount needed for these sites to

attain and/or maintain the oz one standard. Of these 12 si tes, 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 locate d in Houston. One main tenance site is in

Baton Rouge.

# 63 Figure V-12. Projected 8-hour ozone nonattainment and/or main tenance sites in the East

for the 2014 base case.

Figure V-13. Projected 8-hour ozone nonattainment and/or main tenance sites in the East

for the 2014 remedy scenario.

# 64 F. Impacts on Visibility in Class 1 Areas

The impacts of the TR regional SO 2 

# and NO X 

# emissions reductions were

examined in terms of the projected improveme nts in visibility on the 20 percent best and

worst visibility days at Class I areas 66 

# . We quantified visibility impacts at the 60 Class I

areas in the Eastern 12km m odeling domain which have complete IMPROVE ambient

data for 2005 or are represen ted by IMPROVE monitors with complete data. Sites were

used in this analysis if th ey had at least 3 years of complete data for the 2003-2007

period 67



# .

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 applie s modeling results in a relative sense,

using base year ambient data. The PM 2.5 

# and regional haze modeling guidance

recommends the calculation of future year ch anges in visibility in a similar manner to the

calculation of changes in PM 2.5 

# 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 values 68 

# are made

up of individual component species (sulfate, ni trate, organics, etc). The predicted change

in visibility (on the 20 percent best and worst days) is calc ulated as the modeled percent

change in the mass for each of the PM 2.5 

# species (on the 20% best and worst observed

days) multiplied by the observed concentratio ns. The future mass is converted to

extinction and then daily species extinction co efficients are summed to get a daily total

extinction value (including Rayleigh scattering) . The daily extinction coefficients are

converted to deciviews and aver aged 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 th e 2014 Transport rule remedy scenario deciview values

66 

# The focus of this analysis is on visibility im pacts at Class I areas within our 12 km Eastern

modeling domain. We have also calculated visib ility impacts for Class I areas farther to the west

using results of the modeling for the 36 km m odeling domain. These results can be found in

Appendix I.

67 

# Since the base case modeling used meteorology fo r 2005, one of the complete years must be

2005.

68 

# Extinction coefficient is in un its 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 va lues gives an estimate of the visibility

benefits in Class I areas that ar e 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- 3x 3 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 Ru le modeling. The star t 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 choi ces are consistent with usin g a 5 year base period for

regional haze calculations. The predicted vi sibility 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 ar ea 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, S outh Dakota. Note that the modeling did

not include Best Available Retr ofit Technology (BART) controls 69



# .

69 

# All known enforceable controls are included in the modeling. EPA does not know of any

specific state BART rules that are currently in pl ace. We did not attempt to apply generic BART

emissions reductions for any sources.

# 66 Table V-5. Visibility (in deci views) on the 20 percent worst da ys 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 deci views) on the 20 percent best da ys 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 ab ility of the Transport Rule ai r quality modeling platform to

replicate the magnitude and spatial and temporal variability of measured (i.e., observed) ozone

and PM2.5 component species conc entrations 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 spac e and time on a daily or weekly basis, depending

on the sampling frequency of the particular m onitoring network, as described below.

Model performance statistics were calculated for several spatial scales and temporal

periods. Statistics were calculated for indi vidual monitoring sites and for each of four

subregions of the Eastern mode ling domain. The subregions in clude 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 defi ned as January through March). For 8-hour daily

maximum ozone, we also calculated performance statistics by subregion for the May through

September ozone season 4 

# . In addition to the performan ce statistics, we prepared several

graphical presentations of m odel performance for 8-hour dail y 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 calcula ted for each season at

individual monitoring sites,

(2) bar and whisker plots which show the dist ribution of the predicted and observed data by

month by subregion, and

(3) time series plots of observed and predic ted concentrations for those monitoring sites

predicted to be nonattainment or have main tenance 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 prescrib ed fires. These data were used for the 200 5 evaluation case in order to provide more

resolved year-specific emissions data fo r 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: Midwes t 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 correspondi ng observed data at monitoring sites in the EPA

Air Quality System (AQS). The PM 2.5 

# evaluation focused on PM 2.5 

# component species

including sulfate (SO 4 

# ), nitrate (NO 3 

# ), total nitrate (TNO 3 

# =NO 3 

# +HNO 3 

# ), ammonium (NH 4 

# ),

elemental carbon (EC), organic carbon (O C), and crustal material (CR). PM 2.5 

# observed data for

2005 were obtained from the following networks : Chemical Speciation Network (CSN),

Interagency Monitoring of PROt ected Visual Environments (I MPROVE) 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 PM 2.5 

# species that are measured by more than one

network, we calculated separate sets of statistics for each ne twork. 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 PM 2.5 

# species data at CASTNet sites are w eekly integrated samples. In this

analysis we use the term “urban sites” to refer to CSN sites; “suburban/ru ral sites” to refer to

CASTNet sites; and “rural sites” to refer to IMPROVE sites.

Table A-1. PM 2.5 

# monitoring networks and pollutants species included in the evaluation of the

2005-based modeling platform.

Particulate Species Ambient

Monitoring

Networks

SO 4 

# NO 3 

# TNO 3 a 

# EC NH 4 

# OC

IMPROVE X X X X

CASTNet X X X

CSN X X X X X

a 

# TNO 3 

# = (NO 3 

# + HNO 3 

# )

A.1.2 Model Performance Statistics

The Atmospheric Model Evaluation Tool (A MET) was used to calculate the model

performance statistics used in this document 5 

# . Some of the statis tics 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, fractiona l 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 cal culated the performance statistics by season. In

5 

# Gilliam, R. C., W. Appel, and S. Phillips. The At mospheric Model Evaluation Tool (AMET): Meteorology

Module. Presented at 4th Annual CMAS Models-3 Users Co nference, 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” include s 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 Percen t Normalized Mean Bias

Median Predicted Percent Normalized Mean Error

Standard Deviation of Observed Normalized Median Bias

Standard Deviation of Predicti ons 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 averag es 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 performanc e statistic is used as

a normalization of the mean error. NME calcula tes 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 b ecause it has the advantag e of equally weighting

positive and negative bias estimates. The single larg est disadvantage in this estimate of model

performance is that the estimated concentra tion (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 Mo del Evaluation Results

In this section we present a summary of the model performance results for 8-hour daily

maximum ozone and each of the PM 2.5 

# component species included in th is 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 statisti cs indicate that the 8-hour daily maximum ozone

concentrations predicted by th e 2005 CAMx modeling platform cl osely 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-hou r 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 closes t to observations during the late spring and

summer when observed sulfate c oncentrations are highest. Av erage 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 su lfate is generally low, the modeling platform

shows more moderate over predic tion of nearly 60 percent, on average across the subregions at

both urban and rural locations.

● Nitrate predictions best match the observati ons during the fall and winter when observations

are at their highest levels. The average low bias across the subregions du ring the fall and winter

is smaller than 17 percent for urban locations an d 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 th e 30 percent average low bias at both urban and

rural locations for the spring and summer seasons.

● The modeling platform closel y replicates the short-term va riability, 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 PM 2.5 

# 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 correc t timing of the observed peak

episode days for sulfate and for nitrate.

Ammonium, Elemental Carbon, Organic Carbon, and Crustal Material

● The model performance statis tics indicate that pr edictions of ammonium are close to the

observations. During the summer, th ere is slight under pr ediction 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 w ith 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 PM 2.5 

# 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 whic h may affect the comparability of observations

to model predictions 6



# .

6 

# Ambient measurements of EC and OC are based on sp eciation 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 somewh at under predicted duri ng 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 so mewhat over predicted wi th an average positive

bias of 27 percent at urban locations.

● Crustal material is over predicted by a cons iderable amount at both urban and rural locations

in all seasons and subregions with an averag e 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 applications 13 

# and may be associated with pr oblems in properly characterizing

the magnitude of emissions inventories for directly emitted PM 2.5 

# 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

PM 2.5 

# 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 cove r various models, model configur ations, domains, years and/or

episodes, chemical mechanisms, and aeros ol modules. Overall, the ozone and PM 2.5 

# model

performance results for the 2005 CAMx simulations performed for the Transport Rule are within

the range, or close to that f ound in other recent applications. The model performance results, as

described in this report, demonstrate that th e predictions from the Transport Rule modeling

platform closely replicate the corresponding observe d concentrations in te rms 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., Plei m, J.E., Otte, T.L., Mathur, R., Sarwar, G., Young, J.O.,

Gilliam, R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., an d 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 Planni ng 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, 7 th 

# Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.

13 

# Strum, M., Wesson, K., Phillips, S.,P ollack, A., Shepard, S., Jimenez, M. , et.al.. Link Based vs NEI Onroad

Emissions Impact on Air Quality Model Predictions. 17 th 

# 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. Atmosphe ric 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 St andards; 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 polluta nts for the Transport Rule. In addition, the modeling platform

captures the general magnitude and seasonal va riations in ammonium and organic carbon, two

other components of PM 2.5 

# . As noted above, model prediction s of elemental carbon and crustal

material are over predicted, most likely due to pr oblems 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 Tr ansport Rule in a relati ve manner for projecting

future concentrations and for calculating interstate contributions of ozone and PM 2.5 

# . The

National Research Council 18 

# states that using air quality mode ling 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 va lues) to estimate future pollutan t levels”. Thus, the results of

this evaluation together with the manner in wh ich we are applying model predictions gives us

confidence that our air quality model applic ations using the CAMx 2005 modeling platform

provides a scientifically credible approach for assessing ozone and PM 2.5 

# 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-Ho ur Daily Maximum Ozone

The 8-hour ozone model performance bias a nd error statistics fo r each subregion and

each season are provided in Table A-3. The di stributions of observe d and predicted 8-hour

ozone by month in the 5-month ozone season fo r each subregion are shown in Figures A-1

through A-4. Spatial plots of th e normalized mean bias and error for individual monitors are

shown in Figures A-5 and A-6, respectively. Th e statistics shown in th ese two figures were

calculated over the ozone season using data pa irs on days with obser ved 8-hour ozone of > 60

ppb. Time series plots of observed and predic ted 8-hour ozone during the ozone season at

monitoring sites identified as nonattainment or maintenance in the Transport Rule 2012 base

case 19 

# are provided in Figure A-7a-h 20 

# . 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 statistic s indicate that the 8-hour daily maximum

ozone concentrations predicted by the 2005 CA Mx 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 sta tistics 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. Spec ifically, 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 pr ojected 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 i ndicated by the graphics in Figures

A-1 through A-4. Specifically, the predicted con centrations tend to be close to, or somewhat

greater than, the observed medi an and 75th percentile values for each subregion. Model

estimates in the Midwest and Cent ral states capture the entire 25 th 

# to 75 th 

# percentile range of the

observed concentrations.

Model bias at individual site s 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 oz one 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 concentr ations greater than 60 ppb are

under predicted generally in the ra nge of 20 to 40 percent. Looking at the map of bias in Figure

A-5 indicates that the low bias at these sites is no t 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 indicativ e of a systematic problem in the modeling

platform.

Model error, as seen from Figure A-6, is 14 pe rcent or less at most of the sites across the

Eastern modeling domain. Somewh at greater error is evident at sites in several areas most

notably along portions of the Northeast Corridor a nd 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 da y fluctuations in obser ved 8-hour daily maximum

concentrations at those monito ring sites predicted to be nonattainment or have maintenance

problems for the 8-hour ozone NAAQS in the Tran sport 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 th at the modeling platform replicat es the day-to-day variability

in ozone during this time period. This is particular ly true for the three sites in the Northeast (i.e.,

Harford County, MD, New Haven County, CT, a nd Fairfield County, CT) and for Allegan

County, MI. Looking across all the s ites indicates that the modeling platform is able to capture

the site to site differences in the short-term va riability 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 Bato n 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 obs erved and predicted 8-hour daily maximum ozone by month for

the period May through September for the Nort heast subregion. [symbol = median; top/bottom

of box = 75 th 

# /25 th 

# percentiles; top/bottom line = max/min values]

# A-12 Figure A-2. Distribution of obs erved 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 obs erved and predicted 8-hour daily maximum ozone by month for

the period May through Septembe r for the Midwest subregion.

# A-13 Figure A-4. Distribution of obs erved and predicted 8-hour daily maximum ozone by month for

the period May through September fo r 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 PM 2.5 

# Component Species

The evaluation of 2005 model predictions for PM2.5 covers the performance for the

individual PM2.5 component specie s (i.e., sulfate, nitr ate, organic carbon, elemental carbon, and

crustal material). Performance results are prov ided for each species with a focus on sulfate and

nitrate, which are the key PM2.5 sp ecies for the Transport Rule. As indicated above, for each

species we present tabular summaries of bias a nd error statistics by s ubregion for each season.

These statistics are based on the set of observed-pr edicted pairs of data fo r the particular quarter

at monitoring sites within the subregion. Se parate statistics are pr ovided for each monitoring

network, as applicable for the pa rticular species measured. Fo r 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 norma lized mean bias and er ror 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 NAAQS 21



# , 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 sulf ate 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 c ounty 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

PM 2.5 

# 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 c ounty nor in adjacent counties in

West Virginia

Milwaukee Wisconsin 550790026



# A.3.1. Evaluation for Sulfate

The model performance bias and error statis tics 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 th rough 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 pr edicted 24-hour average sulfate during the ozone season at CSN

monitoring sites in count ies identified as nonattainment or ma intenance for the annual and/or 24-

hour PM2.5 NAAQS are provi ded in Figure A-16a-x 23



# .

The results of this analysis indicate that sulfate predictions from our modeling platform

correspond closest to observati ons during the late spring and summer when observed sulfate

concentrations are highest. During the fall a nd winter, when sulfate is generally low, the

modeling platform tends to over pr edict observed concentr ations. The bias a nd 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 si tes in the Midwest, Southeast, a nd Central subregions. In these

subregions NMB is generally + 15 percent or less. In the North east subregion the bias is in this

same range during the summer, but there is a so mewhat stronger signal of over prediction in the

spring when NMB and FB are in the range of 30 to 50 percen t. 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 ove r 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 PM 2.5 

# 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 si tes in these two subregions. The tendency for

over prediction during the cool se asons is less evident in the No rtheast and Central subregions

where NMBs are generally less than 40 percent. On average across the subregions for both urban

and rural locations, the over predic tion bias nearly 60 percent.

The close relationship between observations an d 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 th e 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 pe rformance 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 ther e is no clear regional

trend in error for these two seasons. During the spring (Fig ure 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/m 3 

# 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 ra nge 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 magnit ude and short-term fluctuations in observed 24-

hour average sulfate concentrations at CSN monitoring sites in c ounties 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 observe d and predicted 24-hour

average concentrations for the 24 CSN sites in these counties are provid ed in Figures A-16a

through x. These figures indicate that the modeling platform clos ely replicates the short-term

variability, the general magnitude, and site-to- site differences in sulfate concentrations

throughout much of the year in counties predicte d by our modeling to have future nonattainment

and/or maintenance problems for PM2.5. The mo deling platform shows considerable skill at

distinguishing between high and lo w days at each site. The ove r prediction during winter and

fall, as seen in the performance statistics, is ev ident 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 exhib it considerable short-term vari ability, particularly during the

summer. As evident from this figure, the m odel predictions closely track these temporal

fluctuations and the magnitude of the observati ons in each season. Notable over predictions are

limited to a few days in January, April, and Octobe r. Time series for all of the sites examined

# A-21 indicates that the model consistently captures the correct timing of th e 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 pr edicted 24-hour average sulfate by month for 2005

at IMPROVE sites in the Nort heast subregion. [symbol = me dian; top/bottom of box = 75 th 

# /25 th



# percentiles; top/bottom line = max/min values]

Figure A-8b. Distribution of observed and pr edicted 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 th e Northeast subregion.

Figure A-9a. Distribution of observed and pr edicted 24-hour average sulfate by month for 2005

at IMPROVE sites in th e Southeast subregion.

# A-25 Figure A-9b. Distribution of observed and pr edicted 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 th e Southeast subregion.

# A-26 Figure A-10a. Distribution of observed and pr edicted 24-hour average sulfate by month for 2005

at IMPROVE sites in the Midwest subregion.

Figure A-10b. Distribution of observed and pr edicted 24-hour average sulfate by month for

2005 at CSN sites in the Midwest subregion.

# A-27 Figure A-10c. Distribution of observed and pr edicted weekly average sulfate by month for 2005

at CASTNet sites in the Midwest subregion.

Figure A-11a. Distribution of observed and pr edicted 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 pr edicted 24-hour average sulfate by month for

2005 at CSN sites in the Ce ntral states subregion.

Figure A-11c. Distribution of observed and pr edicted 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) a nd 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-h our 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) a nd 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-h our average sulfate for

2005 at site 170310076 in Cook County, IL.

Figure A-16f. Time series of observed (black) and predicted (blue) 24-h our average sulfate for

2005 at site 170314201 in Cook County, IL.

# A-35 Figure A-16g. Time series of observed (black) a nd predicted (blue) 24-hour average sulfate for

2005 at site 1711192009 in Madison County, IL.

Figure A-16h. Time series of observed (black) a nd 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-h our 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-h our average sulfate for

2005 at site 180970078 in Marion County, IN.

Figure A-16k. Time series of observed (black) a nd 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-h our 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-h our average sulfate for

2005 at site 261630033 in Wayne County, MI.

Figure A-16n. Time series of observed (black) a nd predicted (blue) 24-hour average sulfate for

2005 at site 390171004 in Butler County, OH.

Figure A-16o. Time series of observed (black) a nd 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) a nd predicted (blue) 24-hour average sulfate for

2005 at site 390350060 in Cuyahoga County, OH.

Figure A-16q. Time series of observed (black) a nd 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-h our 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-h our average sulfate for

2005 at site 391130031 in Montgomery County, OH.

Figure A-16t. Time series of observed (black) and predicted (blue) 24-h our average sulfate for

2005 at site 420030008 in Allegheny County, PA.

Figure A-16u. Time series of observed (black) a nd 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) a nd 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-h our average sulfate for

2005 at site 421330008 in York County, PA.

Figure A-16x. Time series of observed (black) a nd 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 sta tistics for nitrate for each subregion and each

season are provided in Table A-6. This table in cludes statistics for pa rticulate nitrate, as

measured at CSN and IMPROVE sites, and statisti cs for total nitrate, as measured at CASTNet

sites. The distributions of obs erved and predicted nitrate by m onth for each subregion are shown

in Figures A-17 through A-20. Spatial plots of th e 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 coun ties identified as nonattainment

or maintenance for the annual and/or 24-hour PM 2.5 

# NAAQS in the 2012 Transport Rule base

case are provided in Figure A-25a-x 24



# .

The nitrate predictions from our modeli ng platform correspond well with the

observations, particularly during th e fall and winter when observations are at their highest levels.

The bias and error statistics in Table A-6 indicate that model perf ormance 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 rura l 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, wh en nitrate concentrations are low in all

subregions, the under prediction bi as is larger than in fall a nd winter, as indicated by the 30

percent average bias at both urban and rural lo cations for the spring and summer seasons. Model

error for nitrate is somewhat greater in all s easons 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 concen trations 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 betw een urban, suburban, and ru ral 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 tende ncy for under prediction is evident in the 75 th



# percentile concentrations dur ing 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 PM 2.5 

# 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 indivi dual sites indicates mainly und er prediction biases of less

than 40 percent at most sites duri ng the winter and fall, as indicat ed in Figures A-21a and A-24a.

The exception to this is the Southeast and ex treme western portion of the modeling domain

where there appears to be a grea ter number of sites with under pr ediction in the range of 40 to 60

percent. During the spring and summer, when partic le nitrate concentrations are very low, most

sites across the modeling domain show under predic tion 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 stat es 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 concentrati ons for the 24 CSN sites in these counties are

provided in Figures A-25a through x. These figures indicate th at the short-term variability,

general magnitude, and site -to-site differences in nitrate obs ervations are captured by the model

predictions in counties predicted to have future nonattainment and/or maintenance problems. In

addition, the modeling platform shows excellent sk ill at replicating the ti ming 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 se ries for site 262610008 in Washtenaw County, MI

(Figure A-25k) shows that the model predictions not only closel y 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 concentra tions in the transition months of April, May,

and October, as well as the very low concentratio ns during July and August. At most sites there

are a few peak days that are unde r predicted and others that are over predicted by the model. For

example, at site 170310057 in Cook County, IL (F igure 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 hi gh nitrate episodes is

accurately captured by the model. Overall, the model predictions of nitrate track the temporal

variability in the observations throughout the ye ar 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 pr edicted 24-hour average nitrate by month for 2005

at IMPROVE sites in the Nort heast subregion. [symbol = me dian; top/bottom of box = 75 th 

# /25 th



# percentiles; top/bottom line = max/min values]

Figure A-17b. Distribution of observed and pr edicted 24-hour average nitrate by month for 2005

at CSN sites in the Northeast subregion.

# A-46 Figure A-17c. Distribution of observed and pred icted weekly average to tal nitrate by month for

2005 at CASTNet sites in the Northeast subregion.

Figure A-18a. Distribution of observed and pr edicted 24-hour average nitrate by month for 2005

at IMPROVE sites in th e Southeast subregion.

# A-47 Figure A-18b. Distribution of observed and pr edicted 24-hour average nitrate by month for 2005

at CSN sites in the Southeast subregion.

Figure A-18c. Distribution of observed and pred icted weekly average to tal nitrate by month for

2005 at CASTNet sites in the Southeast subregion.

# A-48 Figure A-19a. Distribution of observed and pr edicted 24-hour average nitrate by month for 2005

at IMPROVE sites in the Midwest subregion.

Figure A-19b. Distribution of observed and pr edicted 24-hour average nitrate by month for 2005

at CSN sites in the Midwest subregion.

# A-49 Figure A-19c. Distribution of observed and pred icted weekly average to tal nitrate by month for

2005 at CASTNet sites in the Midwest subregion.

Figure A-20a. Distribution of observed and pr edicted 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 pr edicted 24-hour average nitrate by month for 2005

at CSN sites in the Central states subregion.

Figure A-20c. Distribution of observed and pred icted weekly average to tal 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 (%) fo r nitrate during January through March 2005 at

monitoring sites in Eastern modeling domain.

# A-52 Figure A-22a. Normalized Mean Bias (%) fo r nitrate during April through June 2005 at

monitoring sites in Eastern modeling domain.

Figure A-22b. Normalized Mean Error (%) fo r nitrate during April through June 2005 at

monitoring sites in Eastern modeling domain.

# A-53 Figure A-23a. Normalized Mean Bias (%) fo r 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 (%) fo r nitrate during October through December 2005 at

monitoring sites in Eastern modeling domain.

Figure A-24b. Normalized Mean Error (%) for nitrate during Oct ober through December 2005

at monitoring sites in Eastern modeling domain.

# A-55 Figure A-25a. Time series of observed (black) and predicted (blue) 24-h our average nitrate for

2005 at site 010730023 in Jefferson County, AL.

Figure A-25b. Time series of observed (black) a nd 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-h our 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) a nd 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-h our average nitrate for

2005 at site 170310076 in Cook County, IL.

Figure A-25f. Time series of observed (black) and predicted (blue) 24-h our average nitrate for

2005 at site 170314201 in Cook County, IL.

# A-57 Figure A-25g. Time series of observed (black) a nd predicted (blue) 24-hour average nitrate for

2005 at site 1711192009 in Madison County, IL.

Figure A-25h. Time series of observed (black) a nd 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-h our 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-h our average nitrate for

2005 at site 180970078 in Marion County, IN.

Figure A-25k. Time series of observed (black) a nd 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-h our 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-h our average nitrate for

2005 at site 261630033 in Wayne County, MI.

Figure A-25n. Time series of observed (black) a nd predicted (blue) 24-hour average nitrate for

2005 at site 390171004 in Butler County, OH.

Figure A-25o. Time series of observed (black) a nd 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) a nd predicted (blue) 24-hour average nitrate for

2005 at site 390350060 in Cuyahoga County, OH.

Figure A-25q. Time series of observed (black) a nd 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-h our 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-h our average nitrate for

2005 at site 391130031 in Montgomery County, OH.

Figure A-25t. Time series of observed (black) and predicted (blue) 24-h our average nitrate for

2005 at site 420030008 in Allegheny County, PA.

Figure A-25u. Time series of observed (black) a nd 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) a nd 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-h our average nitrate for

2005 at site 421330008 in York County, PA.

Figure A-25x. Time series of observed (black) a nd 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 sta tistics indicate model bias for ammonium is

generally + 40 percent or less for all s easons 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 rura l sites in the error statistics for

ammonium.

Table A-7. Ammonium performance statisti cs 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 sta tistics 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 ar e greater than 100 percent at urban sites in the

Midwest, Northeast, and Central subregions with only slightly less over pr ediction 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 Southeas t 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 st atistics 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 sta tistics for organic car bon 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 obse rved organic carbon con centrations during the

spring and summer at urban locations with an NM B of -15 percent, on average at urban locations

across the four subregions. In contrast, duri ng the winter organic ca rbon is somewhat over

predicted with an average NMB of 27 percen t at urban locations. Organic carbon at rural

locations in the spring and summer tends to be sl ightly under predicted in some subregions and

over predicted in others with NMBs generally within + 30 percent. At ru ral locations in the

winter organic carbon is genera lly under predicted with NMBs generally smaller than -20

percent.

# A-66 Table A-9. Organic Carbon perf ormance 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 statis tics 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 observe d 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 sta tistics 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

Winter 963 897.0 906.0 159.0 160.0 VISTAS CSN

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 th e 5-year weighted average and maximum

design values for 8-hour ozone, a nnual PM2.5, and 24- hour PM2.5 for

monitoring sites in the 12 km Eastern U.S. modeling domain 1 

# . Design values

are provided for the 2003-2007 base pe riod, 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 th at increases and

decreases reported as + 0.1 ppb for ozone, + 0.01 μg/m 3 

# for annual PM2.5, and

+ 0.1 μg/m 3 

# 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 site s which meet the data completeness and

projection criteria described in Section III of the TSD.

# B-3 This page intentionally left blankB-48-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues010030010AlabamaBaldwin77.378.069.670.267.668.267.468.0010270001AlabamaClay74.076.063.865.561.262.860.962.5010331002AlabamaColbert72.074.064.065.862.364.061.563.2010510001AlabamaElmore70.771.062.262.560.160.459.960.1010550011AlabamaEtowah71.773.061.863.059.860.859.560.5010730023AlabamaJefferson78.786.068.775.066.472.566.172.3010731003AlabamaJefferson79.084.068.272.565.970.165.769.9010731005AlabamaJefferson81.086.070.074.367.371.567.171.2010731009AlabamaJefferson77.082.066.270.564.168.263.767.8010731010AlabamaJefferson73.075.062.564.260.261.860.061.6010732006AlabamaJefferson83.789.072.977.570.374.770.174.5010735002AlabamaJefferson74.077.064.967.563.165.662.865.4010735003AlabamaJefferson77.082.066.971.264.969.164.668.8010736002AlabamaJefferson81.389.071.778.469.676.169.375.9010790002AlabamaLawrence72.074.063.765.461.663.461.563.2010890014AlabamaMadison77.378.068.068.765.566.165.466.0010970003AlabamaMobile76.778.069.270.467.568.667.168.2010972005AlabamaMobile76.777.069.169.467.267.567.067.2011011002AlabamaMontgomery69.374.060.464.558.362.258.062.0011030011AlabamaMorgan77.379.069.370.867.468.967.268.7011130002AlabamaRussell71.375.064.668.062.265.562.165.4011170004AlabamaShelby85.788.074.176.171.373.271.173.0011190002AlabamaSumter64.066.057.559.358.059.857.959.7011210003AlabamaTalladega72.072.062.462.460.560.560.060.0011250010AlabamaTuscaloosa73.377.063.666.861.364.461.064.1B-58-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues050350005ArkansasCrittenden87.389.077.178.674.475.974.475.8051190007ArkansasPulaski78.380.067.969.465.566.965.566.9051191002ArkansasPulaski79.783.069.572.467.269.967.169.9051191005ArkansasPulaski74.075.064.465.262.162.962.062.9080013001ColoradoAdams69.070.065.166.164.165.164.165.1080050002ColoradoArapahoe78.781.073.875.972.574.672.574.6080130011ColoradoBoulder77.081.071.875.570.474.070.474.0080310014ColoradoDenver73.075.068.970.867.869.767.969.7080350004ColoradoDouglas83.785.078.880.077.578.777.578.7080590002ColoradoJefferson76.379.072.575.171.574.071.574.0080590005ColoradoJefferson70.375.066.470.865.469.865.469.8080590006ColoradoJefferson81.785.076.779.875.478.475.478.4080590011ColoradoJefferson80.782.076.777.975.676.875.676.8080690007ColoradoLarimer76.078.070.572.368.770.568.770.5080691004ColoradoLarimer72.374.066.768.365.266.765.266.7081230009ColoradoWeld76.778.071.572.770.171.370.171.3090010017ConnecticutFairfield88.090.081.182.979.581.379.481.2090011123ConnecticutFairfield92.394.083.985.582.083.581.883.3090013007ConnecticutFairfield90.092.082.984.781.483.281.383.1090019003ConnecticutFairfield87.789.080.982.179.480.679.380.5090031003ConnecticutHartford84.390.074.379.372.377.272.177.0090050005ConnecticutLitchfield87.789.078.279.476.177.276.077.1090070007ConnecticutMiddlesex90.392.080.982.478.980.478.880.3090090027ConnecticutNew Haven79.381.072.874.471.573.071.472.9090093002ConnecticutNew Haven90.393.082.785.181.083.480.983.3090110008ConnecticutNew London85.388.076.779.274.977.374.877.2B-68-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues090131001ConnecticutTolland88.791.078.480.576.378.376.278.1100010002DelawareKent80.381.071.271.869.069.668.969.5100031007DelawareNew Castle80.082.070.372.068.169.868.069.7100031010DelawareNew Castle82.383.073.674.271.672.271.572.1100031013DelawareNew Castle81.382.072.172.769.970.569.770.3100051002DelawareSussex81.382.071.271.869.069.668.969.5100051003DelawareSussex82.784.074.175.272.273.472.173.3110010025D.C.Washington79.781.072.773.970.972.170.871.9110010041D.C.Washington80.385.072.977.271.175.370.975.0110010043D.C.Washington84.787.076.979.075.077.074.876.8120010025FloridaAlachua72.072.062.362.360.560.559.359.3120030002FloridaBaker68.770.059.760.857.758.857.158.2120050006FloridaBay78.781.069.671.667.969.966.768.6120090007FloridaBrevard69.069.061.961.960.560.560.360.3120094001FloridaBrevard71.372.063.764.462.363.062.162.7120112003FloridaBroward61.363.056.958.555.957.555.757.2120118002FloridaBroward65.067.060.862.759.861.759.661.4120210004FloridaCollier68.369.059.259.857.257.857.157.7120310077FloridaDuval77.077.069.169.167.967.964.364.3120310100FloridaDuval77.779.069.770.868.269.365.366.4120330004FloridaEscambia79.781.072.473.671.072.269.670.7120330018FloridaEscambia82.783.073.874.071.972.270.370.6120330024FloridaEscambia80.781.072.072.370.270.468.668.9120570081FloridaHillsborough78.780.073.674.972.173.372.073.2120570110FloridaHillsborough76.076.067.467.465.865.865.765.7120571035FloridaHillsborough74.076.069.371.267.969.867.769.6B-78-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues120571065FloridaHillsborough80.781.076.676.975.175.474.975.1120573002FloridaHillsborough77.578.070.070.468.468.868.268.6120574004FloridaHillsborough74.074.066.466.464.764.764.464.4120690002FloridaLake76.777.068.869.166.967.166.767.0120712002FloridaLee70.372.062.063.560.161.660.061.5120713002FloridaLee70.371.061.562.159.560.159.359.9120730012FloridaLeon70.070.060.860.858.358.358.258.2120730013FloridaLeon71.072.061.662.558.959.858.859.7120813002FloridaManatee77.379.069.971.568.369.868.169.6120814012FloridaManatee76.377.067.468.065.666.265.566.1120814013FloridaManatee72.773.064.464.662.763.062.662.8120830003FloridaMarion71.772.062.662.860.760.959.759.9120830004FloridaMarion73.073.063.263.261.361.360.360.3120950008FloridaOrange79.381.072.073.670.572.070.471.9120952002FloridaOrange79.380.072.372.970.571.170.471.0120972002FloridaOsceola72.073.064.465.362.763.562.463.2120990020FloridaPalm Beach65.065.059.759.758.658.658.458.4121010005FloridaPasco76.377.067.067.666.066.664.965.5121012001FloridaPasco73.375.064.766.263.464.862.163.6121030004FloridaPinellas72.373.064.765.363.263.862.863.4121030018FloridaPinellas69.771.061.762.960.261.360.061.1121035002FloridaPinellas72.774.065.166.363.864.963.264.4121056005FloridaPolk73.375.065.667.164.165.662.964.4121056006FloridaPolk74.775.066.466.765.065.263.663.9121111002FloridaSt Lucie66.568.059.761.158.359.758.059.3121130015FloridaSanta Rosa80.080.071.971.970.170.168.968.9B-88-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues121151005FloridaSarasota77.378.067.368.065.466.065.365.9121151006FloridaSarasota73.374.064.465.062.563.162.563.1121152002FloridaSarasota73.574.064.064.562.262.662.062.4121171002FloridaSeminole76.077.068.869.766.967.866.867.7121272001FloridaVolusia68.369.059.660.257.658.257.357.9121275002FloridaVolusia68.369.059.359.957.357.957.157.7121290001FloridaWakulla71.374.063.165.461.163.460.963.2130210012GeorgiaBibb81.083.071.273.066.568.266.468.0130210013GeorgiaBibb81.081.071.571.567.067.066.866.8130510021GeorgiaChatham68.369.061.762.459.860.559.059.7130550001GeorgiaChattooga75.076.064.965.762.563.362.363.2130590002GeorgiaClarke80.783.068.970.865.867.665.667.5130670003GeorgiaCobb82.787.072.576.269.773.369.673.2130730001GeorgiaColumbia73.073.064.064.061.361.361.261.2130770002GeorgiaCoweta82.085.071.974.668.470.968.270.7130850001GeorgiaDawson76.379.066.368.663.565.863.565.7130890002GeorgiaDe Kalb88.793.079.983.777.581.277.381.1130893001GeorgiaDe Kalb87.588.079.580.077.778.277.678.0130970004GeorgiaDouglas87.390.076.879.273.876.173.776.0131130001GeorgiaFayette85.789.076.279.173.576.373.476.2131210055GeorgiaFulton91.794.082.684.680.182.180.082.0131270006GeorgiaGlynn67.068.059.460.358.058.956.457.2131350002GeorgiaGwinnett88.790.078.079.274.875.974.675.7131510002GeorgiaHenry89.795.078.983.675.880.375.780.1132130003GeorgiaMurray78.079.068.269.165.666.465.466.3132150008GeorgiaMuscogee75.780.066.670.463.867.463.767.3B-98-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues132151003GeorgiaMuscogee70.773.062.164.159.561.459.461.3132230003GeorgiaPaulding80.383.068.170.465.467.665.367.5132450091GeorgiaRichmond80.381.070.270.867.468.067.367.9132470001GeorgiaRockdale90.095.078.382.774.778.974.678.8132611001GeorgiaSumter72.374.064.165.661.462.861.362.8170010006IllinoisAdams70.070.063.863.862.762.762.362.3170190004IllinoisChampaign68.370.061.763.360.562.060.361.8170230001IllinoisClark66.066.059.259.257.857.857.757.7170310001IllinoisCook77.382.072.276.671.175.471.075.3170310032IllinoisCook74.377.068.971.467.870.367.670.1170310064IllinoisCook71.377.066.171.465.170.364.970.1170310072IllinoisCook71.073.066.368.265.567.465.467.2170310076IllinoisCook77.079.072.374.271.373.271.273.1170311003IllinoisCook76.379.072.274.771.373.871.173.7170311601IllinoisCook76.780.071.774.870.673.670.673.6170314002IllinoisCook66.368.063.465.062.964.562.864.4170314007IllinoisCook71.774.066.969.065.867.965.767.8170314201IllinoisCook74.376.069.370.968.269.868.169.6170317002IllinoisCook77.779.072.974.171.973.271.873.0170436001IllinoisDu Page69.070.065.166.164.365.364.365.2170491001IllinoisEffingham70.072.063.465.261.763.561.663.3170650002IllinoisHamilton73.073.066.366.364.764.764.764.7170831001IllinoisJersey78.780.070.571.768.369.568.169.2170890005IllinoisKane74.377.068.070.566.568.966.468.8170971002IllinoisLake76.779.071.974.170.973.070.772.8170971007IllinoisLake78.079.073.174.172.173.071.972.8B-108-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues171110001IllinoisMcHenry73.378.066.871.065.269.465.169.2171132003IllinoisMcLean73.074.065.366.263.964.863.864.6171150013IllinoisMacon71.374.064.767.263.565.963.465.8171170002IllinoisMacoupin73.074.064.465.362.463.362.263.0171190008IllinoisMadison82.784.074.775.972.673.772.373.5171191009IllinoisMadison83.084.075.776.673.874.773.574.4171193007IllinoisMadison81.083.073.275.071.172.970.872.6171430024IllinoisPeoria68.371.061.463.960.362.760.062.3171431001IllinoisPeoria72.776.065.468.464.267.163.866.7171570001IllinoisRandolph72.075.065.868.564.667.364.667.3171613002IllinoisRock Island65.368.059.562.058.260.657.960.3171630010IllinoisSt Clair81.782.075.575.873.774.073.473.7171670010IllinoisSangamon70.071.061.662.560.561.360.461.2171971011IllinoisWill71.772.066.066.264.765.064.664.9172010009IllinoisWinnebago69.071.061.963.760.462.260.262.0172012001IllinoisWinnebago68.370.061.362.859.861.359.661.1180030002IndianaAllen79.383.071.474.769.873.069.672.9180030004IndianaAllen74.376.066.868.465.366.865.266.7180110001IndianaBoone79.781.072.273.470.571.770.171.2180150002IndianaCarroll74.075.066.467.364.965.764.765.5180190008IndianaClark80.383.072.174.570.072.469.672.0180350010IndianaDelaware76.378.067.969.566.267.765.967.4180390007IndianaElkhart79.083.070.874.469.272.769.072.5180431004IndianaFloyd77.779.071.072.269.370.568.970.0180550001IndianaGreene78.380.070.171.668.469.968.169.6180570005IndianaHamilton82.787.075.078.973.377.172.876.6B-118-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues180590003IndianaHancock78.081.070.673.469.071.668.571.1180630004IndianaHendricks75.376.068.268.966.867.466.366.9180690002IndianaHuntington75.076.067.668.566.066.965.966.8180710001IndianaJackson74.776.066.667.765.066.264.765.8180810002IndianaJohnson76.778.069.670.768.269.367.868.9180890022IndianaLake77.782.072.176.171.275.170.974.8180890030IndianaLake81.085.075.879.574.978.574.778.4180892008IndianaLake77.779.072.773.971.873.071.772.9180910005IndianaLa Porte77.078.071.272.170.171.069.970.8180910010IndianaLa Porte78.579.071.571.970.270.769.970.3180950010IndianaMadison76.780.068.871.867.170.066.869.6180970042IndianaMarion72.776.066.769.765.468.464.967.9180970050IndianaMarion78.781.072.274.370.772.870.372.3180970057IndianaMarion75.077.069.170.967.869.667.469.2180970073IndianaMarion75.777.069.370.567.969.167.568.7181090005IndianaMorgan77.079.069.971.768.470.267.869.6181230009IndianaPerry81.081.075.175.173.173.172.672.6181270024IndianaPorter78.381.071.974.470.973.370.673.1181270026IndianaPorter75.377.068.469.967.168.666.968.4181290003IndianaPosey71.775.064.967.963.266.162.965.8181410010IndianaSt Joseph74.777.067.869.966.568.566.368.3181410015IndianaSt Joseph75.079.067.170.765.769.265.569.0181411007IndianaSt Joseph79.382.071.073.469.571.969.371.6181450001IndianaShelby77.380.070.973.469.672.069.171.5181630013IndianaVanderburgh67.775.061.167.759.565.959.365.7181630021IndianaVanderburgh77.380.070.472.868.570.968.370.6B-128-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues181670018IndianaVigo63.067.056.059.555.158.654.658.0181670024IndianaVigo74.076.065.567.264.666.463.965.6181730008IndianaWarrick77.780.071.373.469.671.669.271.3181730009IndianaWarrick72.775.065.367.463.665.763.365.3181730011IndianaWarrick76.777.070.670.868.769.068.568.7190450021IowaClinton71.373.064.165.662.664.162.263.7190851101IowaHarrison74.775.067.667.865.966.164.564.8191130028IowaLinn67.771.060.963.959.562.559.262.0191530058IowaPolk63.066.056.959.655.658.255.357.9191630014IowaScott70.374.063.466.861.865.061.564.8191632011IowaScott72.072.065.065.063.463.463.163.1191690011IowaStory61.066.054.959.453.758.153.557.9200450004KansasDouglas73.073.065.365.363.863.862.962.9200910010KansasJohnson75.376.067.968.566.266.865.666.2201030003KansasLeavenworth75.077.067.469.165.867.565.166.9201070002KansasLinn73.374.064.865.463.363.962.262.8201730001KansasSedgwick64.366.057.258.755.557.055.256.7202090021KansasWyandotte75.377.068.770.367.268.766.768.2210130002KentuckyBell71.774.062.064.060.061.959.961.8210150003KentuckyBoone75.777.068.169.366.167.365.866.9210190017KentuckyBoyd77.379.069.771.368.269.768.069.5210290006KentuckyBullitt74.076.067.769.566.067.865.467.2210370003KentuckyCampbell83.083.076.576.574.874.873.873.8210430500KentuckyCarter71.072.062.963.861.362.261.162.0210470006KentuckyChristian78.081.068.671.266.569.066.168.6210590005KentuckyDaviess75.781.069.974.868.072.867.872.6B-138-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues210610501KentuckyEdmonson73.776.065.267.363.365.363.165.0210670001KentuckyFayette62.765.056.658.755.157.154.656.6210670012KentuckyFayette70.373.063.565.961.864.161.363.6210890007KentuckyGreenup76.778.069.070.267.568.667.268.4210910012KentuckyHancock74.076.068.770.566.768.566.568.3210930006KentuckyHardin74.778.068.071.066.269.265.768.6211010014KentuckyHenderson75.378.069.171.667.469.967.069.5211110027KentuckyJefferson75.379.068.672.066.970.166.469.7211110051KentuckyJefferson78.382.072.175.570.473.869.973.2211111021KentuckyJefferson71.073.064.666.462.864.662.564.2211130001KentuckyJessamine73.377.067.070.464.968.264.467.7211170007KentuckyKenton78.781.072.274.370.572.669.871.8211390003KentuckyLivingston73.775.066.968.165.166.364.365.5211451024KentuckyMcCracken73.376.067.469.966.068.465.367.8211490001KentuckyMcLean73.073.065.965.964.264.263.963.9211850004KentuckyOldham83.085.074.175.871.973.671.473.1211990003KentuckyPulaski70.372.065.166.761.963.461.563.0212130004KentuckySimpson75.778.066.768.864.666.664.466.3212210013KentuckyTrigg70.070.062.662.660.660.660.460.4212270008KentuckyWarren72.073.063.664.461.662.561.462.3220050004LouisianaAscension82.084.075.977.874.576.374.476.3220110002LouisianaBeauregard75.075.070.470.469.469.469.469.4220150008LouisianaBossier78.079.069.870.767.368.267.368.2220170001LouisianaCaddo79.080.070.471.368.068.967.968.8220190002LouisianaCalcasieu82.083.076.677.575.476.375.376.2220190008LouisianaCalcasieu70.071.065.266.164.165.064.165.0B-148-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues220190009LouisianaCalcasieu78.780.072.473.671.072.270.972.1220330003LouisianaEast BatonRouge92.096.085.689.384.187.784.087.7220330009LouisianaEast BatonRouge80.381.074.875.473.474.073.374.0220330013LouisianaEast BatonRouge81.382.075.476.073.874.573.874.4220331001LouisianaEast BatonRouge86.087.080.581.479.079.979.079.9220470007LouisianaIberville84.086.077.279.175.677.475.577.3220470009LouisianaIberville80.081.073.574.472.072.971.972.8220470012LouisianaIberville85.086.078.979.977.578.477.478.3220511001LouisianaJefferson83.084.076.677.574.975.874.975.8220550005LouisianaLafayette82.082.074.674.672.972.972.772.7220570004LouisianaLafourche79.380.072.973.671.672.271.472.0220630002LouisianaLivingston78.380.072.373.870.872.470.872.3220730004LouisianaOuachita75.377.067.268.765.066.565.066.5220770001LouisianaPointe Coupee83.786.077.880.076.478.576.478.5220870002LouisianaSt Bernard78.078.071.371.369.869.869.769.7220890003LouisianaSt Charles77.378.071.472.069.870.469.870.4220930002LouisianaSt James76.377.070.771.369.370.069.369.9220950002LouisianaSt John TheBaptis79.080.073.174.071.872.771.772.6221010003LouisianaSt Mary76.076.069.569.568.268.268.068.0221210001LouisianaWest BatonRouge84.385.078.579.177.077.677.077.6230050027MaineCumberland59.059.052.252.250.850.850.850.8230052003MaineCumberland72.075.063.766.362.164.662.064.6B-158-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues230090102MaineHancock82.083.072.573.470.871.770.871.6230090103MaineHancock73.074.064.665.563.063.963.063.8230090301MaineHancock68.770.060.661.859.160.259.160.2230112005MaineKennebec69.771.061.062.259.560.659.460.6230130004MaineKnox75.377.066.367.864.766.264.666.1230194008MainePenobscot67.068.059.660.558.359.258.359.1230230004MaineSagadahoc68.570.060.361.658.760.058.760.0230310038MaineYork73.775.065.166.263.464.563.464.5230312002MaineYork74.074.066.066.064.364.364.364.3230313002MaineYork74.077.066.368.964.667.264.667.2240030014MarylandAnne Arundel89.790.080.080.277.677.977.377.6240051007MarylandBaltimore77.378.070.070.668.368.968.268.8240053001MarylandBaltimore85.387.078.780.377.378.877.178.7240090011MarylandCalvert81.081.071.371.369.469.469.169.1240130001MarylandCarroll83.386.074.276.672.274.572.074.3240150003MarylandCecil90.793.079.481.477.079.076.978.8240170010MarylandCharles86.088.075.176.873.074.772.674.3240210037MarylandFrederick80.383.071.373.769.371.669.271.5240230002MarylandGarrett75.576.069.469.968.168.567.668.0240251001MarylandHarford92.794.084.485.682.583.782.383.5240259001MarylandHarford88.391.078.681.076.478.776.278.6240290002MarylandKent82.083.072.072.969.970.769.770.5240313001MarylandMontgomery83.086.075.378.073.476.173.275.9240330030MarylandPrince Georges85.085.077.077.075.075.074.874.8240338003MarylandPrince Georges91.091.080.980.978.678.678.378.3240430009MarylandWashington78.379.069.970.568.268.867.968.5B-168-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues250010002MassachusettsBarnstable84.786.075.676.773.975.073.875.0250034002MassachusettsBerkshire79.781.071.272.369.370.569.270.3250051002MassachusettsBristol82.786.073.576.471.974.871.974.8250070001MassachusettsDukes83.084.075.075.973.474.373.474.3250092006MassachusettsEssex83.384.075.676.373.974.673.974.5250094004MassachusettsEssex77.779.069.971.168.369.468.269.3250095005MassachusettsEssex80.080.071.071.069.069.069.069.0250130008MassachusettsHampden87.392.076.880.974.578.674.578.5250150103MassachusettsHampshire71.777.063.367.961.466.061.365.9250154002MassachusettsHampshire85.087.075.176.973.174.873.074.7250170009MassachusettsMiddlesex79.079.070.570.568.668.668.568.5250171102MassachusettsMiddlesex77.381.069.572.867.871.067.670.9250213003MassachusettsNorfolk84.786.075.376.573.574.773.474.5250250041MassachusettsSuffolk80.381.072.673.271.271.871.171.7250250042MassachusettsSuffolk67.368.061.361.960.160.760.060.7250270015MassachusettsWorcester80.083.071.073.769.271.869.171.7260050003MichiganAllegan90.093.082.485.180.983.680.483.1260190003MichiganBenzie81.783.074.175.372.673.872.473.5260210014MichiganBerrien82.384.075.476.973.975.473.675.2260270003MichiganCass80.784.072.074.970.273.169.972.7260370001MichiganClinton75.778.067.069.165.267.264.966.9260490021MichiganGenesee77.380.069.171.567.369.767.069.4260492001MichiganGenesee79.382.071.173.569.471.869.171.5260630007MichiganHuron75.778.068.670.767.369.467.169.1260650012MichiganIngham76.078.067.669.365.867.665.567.2260770008MichiganKalamazoo75.378.067.870.266.268.665.968.3B-178-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues260810020MichiganKent79.383.070.774.069.072.268.571.7260810022MichiganKent81.083.071.973.770.071.869.671.3260890001MichiganLeelanau75.777.069.270.467.868.967.568.7260910007MichiganLenawee78.781.070.672.769.271.268.670.6260990009MichiganMacomb86.090.078.081.676.780.376.480.0260991003MichiganMacomb84.087.076.879.675.678.375.378.0261050007MichiganMason79.781.071.772.970.271.369.870.9261130001MichiganMissaukee73.775.066.167.364.865.964.465.6261210039MichiganMuskegon85.088.077.079.775.578.175.077.6261250001MichiganOakland78.081.071.774.570.673.470.473.1261390005MichiganOttawa81.785.073.276.171.574.470.973.8261470005MichiganSt Clair82.385.073.976.372.374.772.074.4261530001MichiganSchoolcraft79.382.071.473.869.872.269.672.0261610008MichiganWashtenaw78.381.070.873.369.471.869.171.5261630001MichiganWayne73.075.066.268.065.066.864.766.5261630016MichiganWayne73.576.066.268.564.967.164.666.8261630019MichiganWayne82.083.075.576.474.275.173.974.8270031002MinnesotaAnoka67.769.065.466.764.866.164.365.6280330002MississippiDe Soto82.785.073.375.370.872.870.772.7280450001MississippiHancock79.079.071.371.369.769.769.569.5280470008MississippiHarrison83.083.074.074.072.072.071.871.8280470009MississippiHarrison77.077.068.568.567.067.066.866.8280490010MississippiHinds71.373.062.664.159.961.459.961.3280590006MississippiJackson80.381.072.473.170.871.470.671.2280590007MississippiJackson74.074.066.466.465.265.265.065.0280750003MississippiLauderdale74.376.066.568.065.667.165.567.0B-188-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues280810005MississippiLee73.775.064.665.862.263.362.063.1290370003MissouriCass74.777.066.468.464.866.864.066.0290470003MissouriClay79.081.070.472.268.670.367.969.7290470005MissouriClay84.787.076.278.274.376.373.775.8290470006MissouriClay83.087.074.277.872.375.871.775.2290490001MissouriClinton83.085.074.376.172.474.171.873.5290770036MissouriGreene73.077.064.968.463.466.963.066.4290990012MissouriJefferson82.386.076.079.474.978.274.577.9291130003MissouriLincoln87.087.078.978.976.876.876.576.5291370001MissouriMonroe71.775.064.467.463.066.062.665.4291570001MissouriPerry77.580.069.872.068.470.668.270.4291650023MissouriPlatte77.077.069.369.367.567.567.067.0291831002MissouriSt Charles86.389.078.380.876.378.776.078.4291831004MissouriSt Charles87.089.079.281.077.178.976.978.6291860005MissouriSte Genevieve79.783.072.375.371.874.871.674.5291890004MissouriSt Louis82.386.076.680.175.378.775.078.4291890005MissouriSt Louis83.083.076.176.174.474.474.174.1291890006MissouriSt Louis78.078.072.172.170.570.570.270.2291890014MissouriSt Louis88.088.081.481.479.679.679.379.3295100085MissouriSt Louis City84.084.077.877.876.176.175.975.9295100086MissouriSt Louis City83.086.076.979.775.277.975.077.7310550028NebraskaDouglas68.770.063.364.562.163.261.362.4310550032NebraskaDouglas64.766.059.360.558.259.457.458.6310550035NebraskaDouglas66.368.061.162.659.861.459.060.5330012004NewHampshireBelknap71.373.063.364.961.562.961.462.9330050007New Cheshire70.771.063.163.461.561.861.461.7B-198-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValuesHampshire330074001NewHampshireCoos77.077.069.069.067.367.367.367.3330074002NewHampshireCoos65.067.058.360.056.858.556.858.5330090010NewHampshireGrafton67.067.059.159.157.557.557.457.4330110020NewHampshireHillsborough70.371.061.962.560.160.760.060.6330111011NewHampshireHillsborough78.780.069.871.067.868.967.768.8330115001NewHampshireHillsborough78.380.069.871.368.069.467.969.4330131007NewHampshireMerrimack71.772.063.163.361.361.661.261.5330150014NewHampshireRockingham75.377.067.468.965.867.265.767.2330150016NewHampshireRockingham77.079.068.970.767.269.067.269.0330190003NewHampshireSullivan70.072.061.763.560.061.759.961.6340010005New JerseyAtlantic79.382.071.173.569.371.669.271.5340030005New JerseyBergen86.086.079.479.477.877.877.577.5340070003New JerseyCamden85.788.076.778.874.876.874.676.6340071001New JerseyCamden89.391.079.981.477.879.377.679.0340110007New JerseyCumberland83.384.073.474.071.271.871.071.6340150002New JerseyGloucester87.088.077.578.475.476.375.276.1340170006New JerseyHudson85.789.079.882.978.981.978.881.8340190001New JerseyHunterdon89.090.079.780.677.478.277.278.1340210005New JerseyMercer88.091.079.782.477.780.377.580.1B-208-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues340230011New JerseyMiddlesex88.391.079.281.677.179.476.879.2340250005New JerseyMonmouth87.389.079.781.278.179.678.079.5340273001New JerseyMorris83.386.074.777.172.775.172.574.8340290006New JerseyOcean93.094.083.184.080.981.880.781.6340315001New JerseyPassaic81.083.073.475.271.673.471.473.2360010012New YorkAlbany73.776.066.068.164.466.564.366.3360050110New YorkBronx74.076.068.169.967.168.966.968.8360050133New YorkBronx74.775.070.070.369.169.469.069.3360130006New YorkChautauqua86.789.079.281.377.779.777.179.2360130011New YorkChautauqua77.379.071.072.569.671.169.170.6360270007New YorkDutchess75.779.067.670.565.968.865.868.7360290002New YorkErie85.086.077.478.376.177.075.776.6360410005New YorkHamilton71.773.064.966.163.764.863.664.8360430005New YorkHerkimer68.370.062.464.061.262.861.262.7360450002New YorkJefferson78.080.071.273.069.871.669.771.5360551007New YorkMonroe76.380.069.673.068.271.568.071.3360631006New YorkNiagara82.786.077.380.476.279.276.179.1360650004New YorkOneida68.372.062.065.460.764.060.563.8360671015New YorkOnondaga73.776.066.068.164.566.564.366.3360715001New YorkOrange82.084.074.476.272.774.572.774.4360750003New YorkOswego78.082.071.174.769.773.369.573.1360790005New YorkPutnam84.386.076.277.874.475.974.275.7360810098New YorkQueens69.069.063.563.562.662.662.462.4360810124New YorkQueens80.082.073.775.572.474.272.274.0360830004New YorkRensselaer77.380.069.271.667.669.967.569.8360850067New YorkRichmond88.389.082.583.281.482.081.281.9B-218-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues360910004New YorkSaratoga79.782.071.273.269.471.569.371.3360930003New YorkSchenectady70.074.062.966.561.565.061.464.9361030002New YorkSuffolk90.091.083.083.981.882.781.682.5361030004New YorkSuffolk84.790.077.982.776.381.176.281.0361030009New YorkSuffolk90.391.083.283.881.782.481.682.3361111005New YorkUlster77.379.069.070.567.268.767.068.5361173001New YorkWayne68.071.061.764.560.563.160.363.0361192004New YorkWestchester87.790.081.183.279.781.879.581.6370030004NorthCarolinaAlexander77.079.066.968.664.966.664.666.3370110002NorthCarolinaAvery70.072.061.162.958.660.358.460.1370210030NorthCarolinaBuncombe74.074.062.762.760.860.860.760.7370270003NorthCarolinaCaldwell74.376.064.465.962.363.762.163.5370330001NorthCarolinaCaswell76.377.066.567.264.264.864.164.7370370004NorthCarolinaChatham73.374.063.764.361.862.361.361.9370510008NorthCarolinaCumberland78.080.067.769.465.467.165.066.7370511003NorthCarolinaCumberland81.783.070.771.868.469.568.169.2370590002NorthCarolinaDavie81.383.070.972.468.870.268.069.4370630013NorthCarolinaDurham77.078.067.468.365.366.265.266.0370650099NorthCarolinaEdgecombe77.079.067.669.465.567.265.266.9B-228-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues370670022NorthCarolinaForsyth78.079.068.469.266.367.266.167.0370670028NorthCarolinaForsyth73.074.062.663.560.961.860.761.6370670030NorthCarolinaForsyth76.076.066.666.664.464.464.264.2370671008NorthCarolinaForsyth80.081.069.670.567.568.367.368.1370690001NorthCarolinaFranklin78.781.069.071.066.768.766.568.4370750001NorthCarolinaGraham78.379.067.267.864.865.464.665.2370770001NorthCarolinaGranville82.085.070.973.568.871.368.671.1370810011NorthCarolinaGuilford77.077.067.467.464.964.964.864.8370810013NorthCarolinaGuilford82.082.071.471.468.968.968.768.7370870036NorthCarolinaHaywood77.378.067.768.465.466.065.265.8370990005NorthCarolinaJackson76.076.066.166.163.763.763.463.4371010002NorthCarolinaJohnston77.379.067.569.065.266.764.966.3371070004NorthCarolinaLenoir75.377.066.067.563.364.863.164.6371090004NorthCarolinaLincoln81.083.070.772.468.670.368.269.9371170001NorthCarolinaMartin75.077.067.269.065.567.265.467.1371190041NorthCarolinaMecklenburg88.090.077.779.476.077.875.377.0B-238-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues371191005NorthCarolinaMecklenburg80.383.070.873.269.371.668.670.9371191009NorthCarolinaMecklenburg89.393.078.081.375.878.975.278.3371290002NorthCarolinaNew Hanover72.373.065.466.063.063.662.863.4371450003NorthCarolinaPerson77.379.064.866.264.265.764.165.5371470099NorthCarolinaPitt76.377.066.567.163.864.463.564.1371570099NorthCarolinaRockingham77.078.065.666.463.564.463.464.2371590021NorthCarolinaRowan86.789.075.377.372.974.872.374.3371590022NorthCarolinaRowan86.790.075.678.473.276.072.775.5371730002NorthCarolinaSwain66.368.057.458.955.356.855.156.6371790003NorthCarolinaUnion79.381.068.269.765.867.265.466.8371830014NorthCarolinaWake80.382.071.072.568.870.268.569.9371830016NorthCarolinaWake80.083.070.172.767.770.367.469.9390030009OhioAllen78.781.070.272.368.470.468.270.2390071001OhioAshtabula89.091.081.483.279.781.579.080.7390170004OhioButler83.385.075.376.973.274.672.874.3390171004OhioButler82.385.074.276.772.174.571.774.1390230001OhioClark81.083.071.973.769.771.569.471.1390230003OhioClark77.078.069.170.067.268.166.967.7B-248-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues390250022OhioClermont81.083.074.476.372.774.571.873.6390271002OhioClinton82.385.073.275.770.873.170.372.6390350034OhioCuyahoga71.076.064.769.363.467.962.967.3390350064OhioCuyahoga73.076.067.870.666.869.566.369.0390355002OhioCuyahoga79.781.072.974.171.572.770.972.1390410002OhioDelaware78.381.069.772.167.870.167.569.8390490028OhioFranklin80.382.071.973.470.071.569.671.1390490029OhioFranklin86.388.077.278.775.176.674.876.2390490037OhioFranklin80.381.072.673.370.971.570.671.2390490081OhioFranklin79.780.071.471.769.669.869.269.5390550004OhioGeauga79.386.071.077.069.475.368.874.6390570006OhioGreene80.383.071.573.969.371.769.171.4390610006OhioHamilton84.786.077.478.675.576.674.976.1390610010OhioHamilton82.084.075.176.973.275.072.574.3390610040OhioHamilton81.783.075.977.174.375.573.474.6390810017OhioJefferson78.080.069.170.967.669.466.968.6390830002OhioKnox77.779.068.970.167.068.166.767.8390850003OhioLake86.389.078.781.177.179.576.578.9390850007OhioLake80.781.073.573.871.972.271.271.5390870006OhioLawrence70.773.063.665.762.264.262.064.0390870011OhioLawrence63.369.055.260.253.858.753.558.3390890005OhioLicking78.081.069.271.867.269.866.969.5390930018OhioLorain76.780.069.472.467.970.867.570.4390950024OhioLucas76.378.069.971.568.570.067.969.4390950027OhioLucas77.782.071.075.069.573.469.072.9390950034OhioLucas81.386.073.878.172.276.471.775.9B-258-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues390950081OhioLucas80.784.073.977.072.475.471.874.8390970007OhioMadison79.782.070.572.568.470.468.170.1390990013OhioMahoning78.780.070.271.468.669.767.969.0391030003OhioMedina80.384.071.975.270.273.469.672.9391090005OhioMiami76.780.068.070.965.868.765.568.3391130037OhioMontgomery74.075.065.666.563.664.563.264.1391331001OhioPortage83.788.074.778.572.976.672.376.0391351001OhioPreble73.075.064.966.763.164.862.864.5391510016OhioStark78.079.070.070.968.369.268.068.9391510022OhioStark77.380.069.371.767.770.067.369.7391514005OhioStark81.082.072.072.970.371.169.970.8391530020OhioSummit83.785.075.276.473.574.673.174.2391550009OhioTrumbull80.083.071.373.969.572.168.871.4391550011OhioTrumbull84.386.075.176.673.374.872.574.0391650007OhioWarren88.391.079.381.776.979.376.578.9391670004OhioWashington82.785.075.377.473.275.369.371.2391730003OhioWood80.083.072.274.970.573.270.172.7400170101OklahomaCanadian76.076.068.768.766.866.866.566.5400219002OklahomaCherokee75.776.069.069.267.968.264.564.7400270049OklahomaCleveland74.775.067.067.265.165.364.564.8400370144OklahomaCreek76.777.068.168.466.566.765.765.9400719010OklahomaKay78.079.070.070.968.168.966.667.5400871073OklahomaMc Clain72.072.064.564.562.662.662.262.2400979014OklahomaMayes78.579.071.672.170.671.167.868.2401090033OklahomaOklahoma77.077.069.869.868.068.067.667.6401090096OklahomaOklahoma76.378.069.170.667.468.966.968.4B-268-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues401091037OklahomaOklahoma80.081.071.572.469.470.269.270.0401159004OklahomaOttawa78.079.069.470.367.968.766.267.1401430137OklahomaTulsa79.380.070.971.669.169.768.268.8401430174OklahomaTulsa74.076.066.668.465.166.964.165.8401430178OklahomaTulsa79.380.071.972.570.370.969.069.7401431127OklahomaTulsa77.078.069.069.967.268.166.767.6420010002PennsylvaniaAdams76.378.067.869.465.967.465.867.3420030008PennsylvaniaAllegheny79.381.072.574.171.272.770.672.1420030010PennsylvaniaAllegheny82.384.075.376.873.975.473.274.8420030067PennsylvaniaAllegheny80.382.072.774.271.272.770.772.2420031005PennsylvaniaAllegheny83.787.076.279.274.577.473.976.8420050001PennsylvaniaArmstrong83.084.074.575.472.773.672.273.1420070002PennsylvaniaBeaver83.084.074.375.272.773.672.273.1420070005PennsylvaniaBeaver79.381.071.773.370.271.769.671.1420070014PennsylvaniaBeaver74.075.067.067.965.566.465.065.9420110009PennsylvaniaBerks80.080.071.971.969.869.869.669.6420130801PennsylvaniaBlair74.377.068.070.466.669.166.168.5420170012PennsylvaniaBucks88.092.080.283.978.281.878.081.6420210011PennsylvaniaCambria74.777.068.170.266.868.866.268.3420270100PennsylvaniaCentre78.379.070.971.669.369.969.069.6420290100PennsylvaniaChester86.087.075.676.573.474.273.274.1420334000PennsylvaniaClearfield78.382.069.973.268.371.667.770.9420430401PennsylvaniaDauphin79.381.071.973.470.271.770.071.5420431100PennsylvaniaDauphin79.381.072.373.870.672.170.471.9420450002PennsylvaniaDelaware83.385.074.576.072.573.972.373.7420490003PennsylvaniaErie81.383.074.375.972.874.372.273.7B-278-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues420550001PennsylvaniaFranklin72.375.064.466.862.664.962.464.7420590002PennsylvaniaGreene80.081.073.974.872.573.471.071.9420630004PennsylvaniaIndiana80.080.072.872.871.371.370.770.7420690101PennsylvaniaLackawanna74.375.066.266.964.565.164.364.9420692006PennsylvaniaLackawanna75.376.067.167.865.566.165.265.8420710007PennsylvaniaLancaster83.384.074.875.572.973.572.773.3420730015PennsylvaniaLawrence72.373.064.765.363.263.862.763.3420770004PennsylvaniaLehigh83.385.075.076.573.074.572.874.3420791100PennsylvaniaLuzerne69.373.061.865.160.363.659.963.1420791101PennsylvaniaLuzerne76.377.067.968.566.166.765.966.5420810100PennsylvaniaLycoming77.379.069.771.368.369.867.268.7420850100PennsylvaniaMercer82.083.073.174.071.472.370.571.4420910013PennsylvaniaMontgomery85.786.077.477.675.475.675.275.4420950025PennsylvaniaNorthampton84.387.075.978.473.976.373.876.1420958000PennsylvaniaNorthampton80.082.071.773.569.871.569.671.3420990301PennsylvaniaPerry77.078.069.270.167.468.267.268.1421010004PennsylvaniaPhiladelphia64.368.058.862.157.460.857.360.6421010014PennsylvaniaPhiladelphia79.781.072.673.770.972.170.871.9421010024PennsylvaniaPhiladelphia90.391.081.882.479.880.479.680.2421010136PennsylvaniaPhiladelphia77.077.069.469.467.667.667.567.5421174000PennsylvaniaTioga77.781.069.972.968.471.368.070.8421250005PennsylvaniaWashington78.380.071.973.570.672.269.971.4421250200PennsylvaniaWashington77.381.070.573.969.172.468.271.4421255001PennsylvaniaWashington78.078.069.769.768.268.267.567.5421290006PennsylvaniaWestmoreland78.380.071.372.969.971.469.470.9421290008PennsylvaniaWestmoreland79.082.071.974.670.473.069.872.4B-288-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues421330008PennsylvaniaYork82.083.073.874.772.173.071.972.8440030002Rhode IslandKent84.386.074.976.473.174.573.074.4440071010Rhode IslandProvidence82.384.073.575.071.773.271.673.1440090007Rhode IslandWashington86.089.077.279.975.478.075.378.0450010001SouthCarolinaAbbeville79.081.068.570.266.267.866.067.6450030003SouthCarolinaAiken76.077.066.167.063.464.263.264.0450070003SouthCarolinaAnderson76.578.065.767.063.264.463.064.2450110001SouthCarolinaBarnwell73.073.064.064.061.761.761.561.5450150002SouthCarolinaBerkeley67.370.060.062.458.560.858.160.4450190046SouthCarolinaCharleston74.075.067.268.165.466.364.965.8450210002SouthCarolinaCherokee74.075.064.164.961.862.661.662.4450230002SouthCarolinaChester75.776.066.366.564.464.764.064.2450250001SouthCarolinaChesterfield75.075.065.765.763.863.863.463.4450290002SouthCarolinaColleton72.374.063.565.061.162.560.862.3450310003SouthCarolinaDarlington76.377.066.467.064.364.964.164.6450370001SouthCarolinaEdgefield70.070.061.061.058.458.458.358.3450730001SouthCarolinaOconee73.076.062.965.560.663.160.563.0450770002South Pickens78.781.066.968.964.266.164.266.0B-298-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValuesCarolina450790007SouthCarolinaRichland80.382.068.970.465.867.265.566.9450790021SouthCarolinaRichland72.773.062.262.459.459.759.259.5450791001SouthCarolinaRichland82.383.070.671.267.468.067.167.7450830009SouthCarolinaSpartanburg82.383.071.271.868.368.968.268.8450870001SouthCarolinaUnion76.077.066.767.664.665.564.365.1450890001SouthCarolinaWilliamsburg69.370.061.361.959.660.259.259.8450910006SouthCarolinaYork76.779.066.968.964.966.864.366.3470010101TennesseeAnderson77.380.065.868.163.665.863.465.6470090101TennesseeBlount85.386.074.274.871.572.171.371.9470090102TennesseeBlount68.570.059.861.157.658.957.558.7470370011TennesseeDavidson68.772.060.463.358.060.857.660.4470370026TennesseeDavidson77.779.067.668.864.865.964.465.4470651011TennesseeHamilton80.083.071.173.768.370.868.170.7470654003TennesseeHamilton81.084.071.774.468.871.468.771.2470890002TennesseeJefferson82.384.072.373.868.670.068.369.8470930021TennesseeKnox78.781.068.970.966.268.266.168.0470931020TennesseeKnox85.088.074.477.071.674.171.574.0471050109TennesseeLoudon85.087.071.373.069.070.768.970.5471210104TennesseeMeigs80.081.070.371.267.568.367.368.1471490101TennesseeRutherford76.380.066.469.663.666.763.266.2471550101TennesseeSevier79.082.068.671.265.968.465.668.1B-308-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues471550102TennesseeSevier80.783.070.572.567.969.867.669.6471570021TennesseeShelby80.082.070.972.668.470.168.370.0471571004TennesseeShelby80.781.071.271.468.668.968.668.8471632002TennesseeSullivan80.383.073.576.071.573.971.473.8471632003TennesseeSullivan80.083.073.476.271.574.271.474.1471650007TennesseeSumner83.084.072.773.569.870.669.370.2471650101TennesseeSumner79.082.069.472.066.769.366.268.7471870106TennesseeWilliamson75.377.065.266.762.664.062.463.8471890103TennesseeWilson78.782.068.771.665.968.765.468.2480290032TexasBexar82.085.074.877.572.875.572.775.4480391004TexasBrazoria94.797.086.788.884.786.784.486.5480391016TexasBrazoria78.079.071.972.870.371.270.271.1480850005TexasCollin90.392.079.981.476.678.076.577.9481130069TexasDallas87.090.080.783.478.080.777.980.6481130075TexasDallas88.390.080.481.977.478.977.378.8481130087TexasDallas87.088.081.382.378.879.778.779.6481133003TexasDallas84.084.076.776.774.174.173.973.9481210034TexasDenton94.095.081.782.577.978.777.978.7481390015TexasEllis81.784.071.773.768.270.168.170.0481670014TexasGalveston85.087.079.080.877.579.377.479.2481830001TexasGregg84.385.078.078.675.976.575.876.4482010024TexasHarris88.092.083.487.282.286.082.185.9482010026TexasHarris85.789.079.482.477.880.877.780.7482010029TexasHarris91.793.084.285.482.483.682.383.5482010046TexasHarris78.782.074.577.773.576.673.476.5482010047TexasHarris78.780.074.175.472.974.172.974.1B-318-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues482010051TexasHarris93.098.086.190.884.388.884.188.6482010055TexasHarris100.7103.093.395.491.393.491.193.2482010062TexasHarris95.799.088.891.887.090.086.989.9482010066TexasHarris92.396.087.190.685.588.985.488.8482010070TexasHarris84.388.078.882.277.380.777.280.5482010075TexasHarris83.388.077.882.276.480.776.280.5482011015TexasHarris89.096.082.488.980.887.280.787.1482011034TexasHarris82.787.076.680.675.179.074.978.8482011035TexasHarris86.395.079.988.078.486.378.286.1482011039TexasHarris96.3100.088.892.286.890.286.890.1482011050TexasHarris89.392.082.885.481.283.781.183.6482030002TexasHarrison79.080.071.672.569.370.269.270.1482210001TexasHood83.084.071.071.867.668.467.568.3482450009TexasJefferson81.782.074.775.073.273.573.173.4482450011TexasJefferson80.081.073.874.772.373.272.273.1482450018TexasJefferson83.384.076.877.575.476.075.375.9482450022TexasJefferson79.781.072.773.871.072.270.972.1482450101TexasJefferson84.788.078.481.476.879.876.779.7482450628TexasJefferson78.078.071.971.970.570.570.470.4482510003TexasJohnson87.089.075.276.971.473.171.372.9483390078TexasMontgomery85.086.078.078.976.076.975.976.8483611001TexasOrange78.080.071.473.269.871.669.771.5483611100TexasOrange73.074.067.067.965.766.665.666.5483670081TexasParker88.791.076.178.172.574.372.474.3484230007TexasSmith81.082.074.074.971.972.771.772.6484390075TexasTarrant95.396.082.483.078.579.078.479.0B-328-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues484391002TexasTarrant93.395.082.283.778.579.978.479.8484392003TexasTarrant93.795.081.883.078.179.278.079.1484393009TexasTarrant92.793.081.381.677.878.177.778.0484393011TexasTarrant86.087.077.178.073.974.773.874.6484530014TexasTravis81.382.072.673.270.571.170.370.9484530020TexasTravis79.381.071.272.869.470.869.370.7484530613TexasTravis76.076.068.768.766.866.866.666.6500030004VermontBennington72.073.064.365.262.763.662.663.4510130020VirginiaArlington86.787.079.179.377.177.476.977.2510330001VirginiaCaroline80.081.071.071.968.969.868.769.6510360002VirginiaCharles City80.382.072.373.970.571.970.271.7510410004VirginiaChesterfield76.777.068.668.966.967.266.666.9510590005VirginiaFairfax78.379.070.571.268.569.168.368.9510590018VirginiaFairfax90.091.082.483.380.481.380.181.0510590030VirginiaFairfax88.089.080.281.178.279.078.078.8510591005VirginiaFairfax85.787.078.179.376.177.375.977.1510595001VirginiaFairfax82.084.074.376.172.274.072.173.8510610002VirginiaFauquier72.773.064.865.163.263.463.063.3510690010VirginiaFrederick72.373.063.864.462.463.062.362.9510850003VirginiaHanover81.382.072.673.370.571.270.370.9510870014VirginiaHenrico82.085.073.476.171.373.971.173.7511071005VirginiaLoudoun80.782.072.173.369.971.069.870.9511130003VirginiaMadison77.780.069.771.768.070.167.969.9511390004VirginiaPage74.076.066.368.164.766.564.666.4511530009VirginiaPrince William78.779.070.270.468.268.568.168.4511611004VirginiaRoanoke74.776.067.868.965.967.165.766.9B-338-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues511630003VirginiaRockbridge69.771.062.263.460.561.660.361.4511790001VirginiaStafford81.785.074.077.072.074.971.874.7515100009VirginiaAlexandria City81.783.074.876.073.074.172.773.9516500004VirginiaHampton City76.778.070.771.969.670.869.570.7518000004VirginiaSuffolk City76.778.071.472.670.671.870.571.7518000005VirginiaSuffolk City75.377.066.968.465.166.665.066.4540030003West VirginiaBerkeley75.076.066.967.865.366.265.065.9540110006West VirginiaCabell78.784.070.975.769.374.069.173.8540291004West VirginiaHancock75.777.067.568.666.167.265.466.6540390010West VirginiaKanawha77.379.068.169.666.467.966.367.8540610003West VirginiaMonongalia75.378.072.074.670.873.369.371.8540690010West VirginiaOhio78.382.069.172.467.670.866.569.6541071002West VirginiaWood79.082.071.173.868.471.067.269.7550090026WisconsinBrown73.775.066.968.065.566.665.266.4550210015WisconsinColumbia72.774.065.166.363.564.763.364.5550250041WisconsinDane72.074.064.866.663.465.163.264.9550270007WisconsinDodge74.777.067.669.766.068.165.867.8550290004WisconsinDoor88.790.080.381.478.579.678.279.4550390006WisconsinFond Du Lac73.775.068.169.366.868.066.667.7550550002WisconsinJefferson74.376.066.768.265.066.564.866.2550590019WisconsinKenosha84.786.079.881.178.880.078.679.8550610002WisconsinKewaunee82.786.075.378.373.776.673.476.4550710007WisconsinManitowoc85.087.078.079.876.478.276.278.0550790010WisconsinMilwaukee69.070.065.065.964.165.164.064.9550790026WisconsinMilwaukee76.077.071.672.570.671.670.471.4550790041WisconsinMilwaukee81.383.076.678.275.677.175.476.9B-348-Hour Ozone (ppb)Monitor IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues550790044WisconsinMilwaukee71.071.066.666.665.865.865.565.5550790085WisconsinMilwaukee82.786.077.780.876.679.776.479.4550870009WisconsinOutagamie74.075.067.168.065.866.665.566.4550890008WisconsinOzaukee81.385.076.179.675.178.574.978.3550890009WisconsinOzaukee83.388.077.982.376.781.076.480.8551010017WisconsinRacine80.382.075.677.274.776.374.576.1551050024WisconsinRock74.076.066.468.264.866.664.666.4551091002WisconsinSt Croix69.071.061.463.260.262.059.661.4551110007WisconsinSauk69.771.062.563.661.062.260.962.0551170006WisconsinSheboygan88.089.081.682.580.181.079.880.8551270005WisconsinWalworth75.777.068.870.067.168.266.868.0551310009WisconsinWashington72.375.065.768.164.366.764.066.4551330017WisconsinWaukesha75.075.068.768.767.367.367.267.2551330027WisconsinWaukesha71.572.065.565.964.264.664.064.5B-35Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues010030010AlabamaBaldwin11.4411.759.8710.159.589.858.408.63010270001AlabamaClay13.2713.3211.7711.8211.2511.319.689.73010331002AlabamaColbert12.7513.1111.6511.9711.1911.509.379.62010491003AlabamaDeKalb14.1314.4212.4412.7011.9312.1910.0610.28010530002AlabamaEscambia13.1913.3711.7111.8811.4211.5910.2410.40010550010AlabamaEtowah14.8715.2213.1013.4012.5912.8810.8211.07010690003AlabamaHouston13.2213.2211.6911.6911.4411.4410.2710.27010730023AlabamaJefferson18.5718.9416.1516.4615.5815.8913.9414.21010731005AlabamaJefferson15.4615.8913.8114.2013.3313.7111.6712.00010731009AlabamaJefferson13.5214.0512.2112.7011.7812.2510.0310.44010731010AlabamaJefferson15.8916.1913.8314.1013.2913.5511.5611.79010732003AlabamaJefferson17.1517.6915.1615.6414.6915.1613.1113.53010732006AlabamaJefferson15.1015.4213.2113.4912.7513.0111.2311.46010735002AlabamaJefferson14.4214.8512.4812.8511.9812.3410.3610.67010735003AlabamaJefferson14.5314.8812.8613.1712.3712.6810.6410.90010890014AlabamaMadison13.8313.9712.3412.4611.8011.919.709.78010970002AlabamaMobile12.9012.9011.4111.4111.0311.039.859.85010970003AlabamaMobile12.3612.4910.8810.9910.5110.619.349.43010972005AlabamaMobile11.5111.929.9610.319.639.978.478.77011010007AlabamaMontgomery14.2414.4112.7912.9512.3512.5110.9211.06011030011AlabamaMorgan13.3213.7411.9512.3111.4611.819.569.85011130001AlabamaRussell15.7315.8313.9113.9913.3613.4411.8511.92011170006AlabamaShelby14.4314.6112.7512.9112.2612.4210.5710.71011190002AlabamaSumter11.9211.9210.7110.7110.3110.318.778.77B-36Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues011210002AlabamaTalladega14.5114.5112.9712.9712.4512.4510.5410.54011250004AlabamaTuscaloosa13.5613.7912.1912.3911.7311.939.9910.17011270002AlabamaWalker13.8614.3012.4612.8611.9712.3610.1610.49050010011ArkansasArkansas12.4512.5411.5411.6211.1111.199.759.82050030005ArkansasAshley12.8313.3011.9212.3511.5611.9810.3910.76050350005ArkansasCrittenden13.3613.3612.1512.1511.5811.589.869.86050450002ArkansasFaulkner12.7913.1011.8712.1611.4411.7210.1810.43050510003ArkansasGarland12.4012.6711.5011.7411.0711.319.8410.06050930007ArkansasMississippi12.6112.6111.6011.6011.0811.089.409.40051070001ArkansasPhillips12.1012.4011.1311.4010.6710.929.179.38051130002ArkansasPolk11.6512.0410.7911.1610.4510.819.379.70051150003ArkansasPope12.7913.1411.9412.2611.5511.8610.4010.68051190007ArkansasPulaski13.1713.2712.0712.1511.5911.6710.2510.32051191004ArkansasPulaski14.0514.2312.9413.1112.4612.6211.1211.26051191005ArkansasPulaski13.5913.6212.4912.5212.0212.0510.7010.73051390006ArkansasUnion12.8613.0911.8812.0911.5111.7110.3210.51051450001ArkansasWhite12.5712.7411.7211.8811.3311.4810.1510.27080010006ColoradoAdams10.0610.169.109.198.898.978.949.03080050005ColoradoArapahoe7.968.107.197.327.037.167.077.19080130003ColoradoBoulder8.328.547.747.957.627.837.657.86080130012ColoradoBoulder6.967.066.426.516.326.416.366.45080290004ColoradoDelta7.447.626.826.996.716.886.706.87080310002ColoradoDenver9.379.748.458.788.258.578.308.63080310023ColoradoDenver9.769.768.818.818.608.608.668.66080390001ColoradoElbert4.404.544.134.264.084.224.094.23080410008ColoradoEl Paso6.736.736.136.136.016.026.036.04B-37Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues080410011ColoradoEl Paso7.948.117.277.427.157.307.187.33080690009ColoradoLarimer7.337.406.886.946.816.886.836.89080770017ColoradoMesa9.289.488.478.658.318.488.308.47081010012ColoradoPueblo7.457.656.907.096.806.996.796.98081130004ColoradoSan Miguel4.654.814.414.564.394.544.384.53081230006ColoradoWeld8.198.357.547.707.457.617.467.62081230008ColoradoWeld8.789.288.148.598.038.488.068.51090010010ConnecticutFairfield13.2113.2711.3011.3410.9611.0010.1310.16090011123ConnecticutFairfield12.4912.5910.7110.7810.3910.469.559.63090013005ConnecticutFairfield12.4312.7210.5010.7410.1610.389.289.48090019003ConnecticutFairfield11.4811.739.669.859.349.528.498.64090031003ConnecticutHartford11.0311.369.549.829.309.578.648.89090050005ConnecticutLitchfield8.018.016.726.726.526.525.725.72090090026ConnecticutNew Haven12.1212.1310.3010.319.9910.019.179.19090090027ConnecticutNew Haven12.4512.5710.6110.7110.3010.409.479.56090091123ConnecticutNew Haven13.1213.5411.2211.5710.9011.2410.1010.41090092008ConnecticutNew Haven11.1711.229.459.489.169.198.388.40090092123ConnecticutNew Haven12.7412.9110.9811.1210.6910.829.9210.03090113002ConnecticutNew London10.9611.339.469.779.259.558.588.86100010002DelawareKent12.6112.8410.5010.7010.2110.418.859.03100010003DelawareKent12.5212.6610.4510.5910.1710.308.889.01100031003DelawareNew Castle13.7314.3311.4811.9711.1611.649.8110.22100031007DelawareNew Castle12.9213.4110.7411.1610.4510.869.119.48100031012DelawareNew Castle13.6913.8811.4611.6211.1511.309.839.95100032004DelawareNew Castle14.8715.1112.5112.7212.1712.3710.7910.97100051002DelawareSussex13.3913.4511.1411.1810.8210.869.449.47B-38Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues110010041District Of Columbia14.1614.3811.9612.1511.5711.7510.1010.26110010042District Of Columbia14.4114.5212.0112.1011.5911.689.9310.01110010043District Of Columbia13.9914.4411.6912.0811.2911.669.649.96120010023FloridaAlachua9.329.517.837.987.677.826.846.97120010024FloridaAlachua9.599.648.088.137.927.977.117.15120051004FloridaBay11.4611.699.9710.159.9210.108.989.14120090007FloridaBrevard8.328.646.957.226.797.056.136.36120111002FloridaBroward8.228.306.997.066.876.946.496.56120112004FloridaBroward8.188.286.906.986.776.856.386.45120113002FloridaBroward8.218.216.916.916.796.796.406.40120170005FloridaCitrus9.009.147.317.427.177.276.496.59120310098FloridaDuval9.909.998.478.578.288.387.457.55120310099FloridaDuval10.4410.539.059.128.858.928.068.11120330004FloridaEscambia11.7211.9110.2910.4410.0610.219.129.24120570030FloridaHillsborough10.7410.958.828.998.638.808.078.23120573002FloridaHillsborough10.5210.758.708.908.518.717.938.11120710005FloridaLee8.368.416.967.006.826.866.266.30120730012FloridaLeon12.5612.7110.9311.0510.7410.869.809.90120814012FloridaManatee8.818.916.987.066.826.906.226.29120830003FloridaMarion10.1110.208.538.608.368.437.627.69120861016FloridaMiami-Dade9.459.547.958.027.767.837.377.44120866001FloridaMiami-Dade8.148.287.137.257.047.166.656.77120951004FloridaOrange9.619.767.998.127.807.927.067.18120952002FloridaOrange9.509.607.877.957.677.756.947.02120990009FloridaPalm Beach7.847.846.756.756.636.636.206.20120992005FloridaPalm Beach7.707.816.596.686.476.566.056.13B-39Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues121030018FloridaPinellas9.829.857.957.967.787.807.237.24121031009FloridaPinellas9.529.697.707.847.547.686.997.12121056006FloridaPolk9.539.627.867.947.717.787.107.17121111002FloridaSt. Lucie8.348.477.037.136.876.976.276.35121150013FloridaSarasota8.778.867.077.166.927.006.326.39121171002FloridaSeminole9.519.647.908.007.717.806.967.05121275002FloridaVolusia9.279.347.737.787.547.596.716.75130210007GeorgiaBibb16.5416.7814.5814.7913.9514.1512.5212.70130210012GeorgiaBibb13.9414.1712.2012.4011.5711.7510.1910.35130510017GeorgiaChatham13.7413.8712.0012.1011.6511.7510.4010.49130510091GeorgiaChatham13.9314.1112.2312.3911.8712.0310.6210.75130590002GeorgiaClarke14.9014.9013.1513.1512.4812.4810.9610.96130630091GeorgiaClayton16.5016.7114.3014.4813.6613.8312.2112.36130670003GeorgiaCobb16.1516.2114.0514.1013.5013.5412.0312.07130670004GeorgiaCobb15.4215.5013.3013.3712.7512.8111.3111.36130890002GeorgiaDeKalb15.4815.6613.2513.4012.5912.7411.1511.28130892001GeorgiaDeKalb15.3715.5613.2013.3612.5612.7311.1111.26130950007GeorgiaDougherty14.4614.8312.7313.0712.4112.7511.2411.55131150005GeorgiaFloyd16.1316.1814.2114.2513.7013.7512.0112.05131210032GeorgiaFulton15.8416.0213.5613.7212.9313.0811.5011.63131210039GeorgiaFulton17.4317.4715.0715.1014.4214.4512.9913.02131270006GeorgiaGlynn12.2512.3010.7310.7810.4910.549.519.56131350002GeorgiaGwinnett16.0716.3614.0114.2613.3813.6311.9312.15131390003GeorgiaHall14.1614.3712.4312.6211.8512.0310.3010.47131530001GeorgiaHouston14.1914.5812.3512.6911.8212.1410.4210.69131850003GeorgiaLowndes12.5812.8311.1411.3610.9111.139.9710.17B-40Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues132150001GeorgiaMuscogee14.9415.1413.1613.3312.6312.8011.1211.26132150008GeorgiaMuscogee15.3915.3913.6513.6513.1113.1111.6211.62132150011GeorgiaMuscogee14.1614.4812.5212.8112.0412.3210.6610.92132230003GeorgiaPaulding14.1214.4312.1312.3911.6411.8910.0710.28132450005GeorgiaRichmond15.6115.8213.9614.1513.4313.6111.9912.15132450091GeorgiaRichmond15.6815.8814.0414.2113.5013.6712.0512.20132950002GeorgiaWalker15.4915.7513.7714.0013.1913.4011.2211.40133030001GeorgiaWashington15.1415.5413.6413.9912.9313.2611.5511.83133190001GeorgiaWilkinson15.2715.5013.5913.7912.8613.0511.4711.63170010006IllinoisAdams12.5012.8711.4811.8111.0811.4010.0810.35170190004IllinoisChampaign12.5012.8911.4111.7710.9411.299.489.82170191001IllinoisChampaign12.5312.9211.4611.8310.9811.349.509.89170310022IllinoisCook15.2115.5513.7314.0513.2513.5512.2412.52170310050IllinoisCook14.8115.2213.2913.6612.7713.1311.8512.19170310052IllinoisCook15.7516.0214.0914.3313.5013.7412.5912.83170310057IllinoisCook15.0315.3413.3813.6612.8013.0711.9012.16170310076IllinoisCook14.8915.1813.3013.5612.7412.9811.8312.07170312001IllinoisCook14.7715.1413.3013.6312.8213.1411.9512.26170313301IllinoisCook15.2415.5913.6213.9313.0513.3512.1712.46170314007IllinoisCook12.7813.1511.4711.8010.9511.2610.0010.29170314201IllinoisCook12.7613.1811.4611.8410.9311.299.9810.29170316005IllinoisCook15.4816.0713.8014.3213.2113.7112.3112.78170434002IllinoisDuPage13.8214.0112.5212.6911.9612.1311.0111.16170831001IllinoisJersey12.8913.2012.0212.3211.4911.7710.0110.26170890003IllinoisKane13.3213.5412.0612.2611.5511.7410.5610.74170890007IllinoisKane14.3414.3413.0313.0312.5012.5011.5411.54B-41Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues170971007IllinoisLake11.8112.1010.7511.0110.2910.549.369.60171110001IllinoisMcHenry12.4012.4711.2611.3310.8010.869.869.93171132003IllinoisMcLean12.3912.3911.3711.3710.8710.879.659.65171150013IllinoisMacon13.2413.5712.2112.5111.6911.9810.3310.60171191007IllinoisMadison16.7217.0115.4615.7314.7615.0213.2813.51171192009IllinoisMadison14.0114.6613.1613.7712.5713.1511.0711.58171193007IllinoisMadison14.3214.4513.4513.5812.8612.9811.3911.49171430037IllinoisPeoria13.3413.6612.2412.5411.7512.0310.6910.95171570001IllinoisRandolph13.1113.6412.1612.6411.5712.0310.0410.43171613002IllinoisRock Island12.0112.3110.9711.2510.5910.859.709.92171630010IllinoisSaint Clair15.5815.7414.4014.5413.7113.8512.2112.35171634001IllinoisSaint Clair14.2914.4913.2313.4112.5812.7511.1411.28171670012IllinoisSangamon13.1313.2912.2312.3811.7111.8510.4310.53171971002IllinoisWill13.6314.0512.3412.7211.7712.1210.7311.06171971011IllinoisWill11.5211.7810.4710.7010.0010.218.869.02172010013IllinoisWinnebago13.5713.5712.3812.3811.9611.9611.1211.12180030004IndianaAllen13.6714.0812.5112.8912.1612.5310.9011.25180030014IndianaAllen13.5513.9112.4112.7412.0612.3810.8111.11180190006IndianaClark16.4416.6714.8315.0314.2014.3911.6911.83180350006IndianaDelaware13.6913.8612.6212.7812.1612.3110.2110.33180372001IndianaDubois15.1915.6813.9014.3513.2913.7210.6611.04180431004IndianaFloyd14.8514.9913.4313.5612.8412.9610.3010.42180650003IndianaHenry13.6413.6412.5912.5912.1712.1710.1010.10180670003IndianaHoward13.9314.2912.8213.1512.4012.7310.6510.95180830004IndianaKnox14.0314.2112.7712.9212.1812.3210.0210.11180890006IndianaLake14.3314.5112.9513.1112.4712.6311.4211.57B-42Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues180890027IndianaLake13.8314.1312.5312.8012.0712.3310.9711.22180891003IndianaLake14.0214.2512.7312.9412.2912.4911.2311.42180892004IndianaLake14.0514.4112.7113.0412.2512.5711.2111.52180892010IndianaLake13.8914.1012.5412.7312.0712.2611.0611.25180910011IndianaLaPorte12.4912.8411.2911.6010.9111.219.8110.10180910012IndianaLaPorte12.6913.0711.5011.8411.1211.459.9710.29180950009IndianaMadison13.9714.3712.9013.2712.4712.8310.4410.75180970042IndianaMarion14.2414.5113.2013.4512.7312.9710.3410.56180970078IndianaMarion15.2615.4314.1214.2813.6413.7911.2911.42180970079IndianaMarion14.7114.9913.5613.8113.0813.3310.8111.04180970081IndianaMarion16.0516.3614.8615.1614.3614.6512.0112.27180970083IndianaMarion15.9016.2714.7115.0614.2114.5511.8612.16181270020IndianaPorter12.6613.0111.4211.7411.0111.329.9010.19181270024IndianaPorter13.2113.3911.9212.0911.4911.6510.3610.48181410014IndianaSt. Joseph13.2913.6512.2112.5411.8912.2110.9911.29181411008IndianaSt. Joseph13.6913.6912.5712.5712.2312.2311.3711.37181412004IndianaSt. Joseph12.8213.2211.7712.1411.4511.8110.5710.90181470009IndianaSpencer14.3214.5513.0613.2712.4712.669.829.95181570008IndianaTippecanoe13.7014.0612.5412.8712.1012.4210.4110.70181630006IndianaVanderburgh14.6914.8913.5313.7113.0113.1910.9611.13181630012IndianaVanderburgh14.8215.0013.6113.7813.0813.2511.0811.23181630016IndianaVanderburgh14.9915.1413.8113.9513.2913.4311.2311.36181670018IndianaVigo13.9914.1712.6512.8212.1212.2710.1710.29181670023IndianaVigo13.4613.6912.1612.3711.6311.839.669.82190130008IowaBlack Hawk11.1611.5210.2010.549.9110.239.229.51190450021IowaClinton12.5212.6311.4411.5411.0711.1610.2010.28B-43Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues191032001IowaJohnson12.0812.2611.0411.2010.7310.889.9710.11191130037IowaLinn10.7910.959.789.929.489.638.758.88191370002IowaMontgomery10.0210.339.099.378.849.118.138.37191390015IowaMuscatine12.9213.2911.8512.2011.5011.8310.6610.96191471002IowaPalo Alto9.539.538.728.728.518.517.997.99191530030IowaPolk10.4110.549.449.559.179.298.528.63191532510IowaPolk9.9510.149.019.198.768.938.138.31191532520IowaPolk10.6410.649.659.659.389.388.758.75191550009IowaPottawattamie11.1311.5210.0810.449.8210.179.259.57191630015IowaScott11.8612.0610.8411.0310.4710.649.599.75191630018IowaScott11.6411.8910.6410.8710.2710.489.389.58191630019IowaScott14.4214.4213.2713.2712.8612.8612.0112.01191770006IowaVan Buren10.8410.849.949.949.659.658.928.92191930017IowaWoodbury10.3210.539.479.669.279.468.808.98191970004IowaWright10.3710.519.479.599.229.348.598.70200910007KansasJohnson10.5910.869.609.849.339.568.598.81200910009KansasJohnson11.1011.1010.0810.089.799.798.978.97200910010KansasJohnson9.689.748.818.868.568.617.827.86201070002KansasLinn10.4710.629.669.799.419.538.558.65201730008KansasSedgwick10.2610.859.289.819.059.578.398.89201730009KansasSedgwick10.2910.959.319.899.079.648.428.95201730010KansasSedgwick10.3610.969.379.929.149.678.498.99201770010KansasShawnee10.7910.799.869.869.619.618.878.87201770011KansasShawnee10.9310.9310.0010.009.769.769.069.06201910002KansasSumner9.8910.319.019.398.809.178.138.47202090021KansasWyandotte12.7313.3711.5412.1211.2311.8010.4410.97B-44Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues202090022KansasWyandotte10.9311.259.9010.189.619.908.869.14210130002KentuckyBell14.1014.1812.5212.5811.9311.999.849.88210190017KentuckyBoyd14.4914.7012.9913.1712.3612.549.859.98210290006KentuckyBullitt14.9215.2713.5713.8912.9613.2610.4110.64210370003KentuckyCampbell13.6713.6712.4612.4611.9411.949.339.33210430500KentuckyCarter12.2212.6110.8511.1910.2910.628.098.33210470006KentuckyChristian13.2013.5512.1612.4711.5911.889.139.35210590005KentuckyDaviess14.1014.1013.0313.0312.4212.429.409.40210670012KentuckyFayette14.3614.6012.8713.0812.3012.519.829.97210670014KentuckyFayette14.8715.1313.3613.5912.7913.0110.3010.52210730006KentuckyFranklin13.3713.5212.0412.1711.4911.629.049.12210930006KentuckyHardin13.5814.0412.3712.7811.7812.179.189.47211010014KentuckyHenderson13.9314.2812.6913.0012.1812.479.9910.22211110043KentuckyJefferson15.5515.7714.0414.2413.4213.6210.8811.05211110044KentuckyJefferson15.3515.5213.7913.9413.1813.3210.7010.79211110048KentuckyJefferson15.3115.3313.7413.7613.1213.1410.6310.65211110051KentuckyJefferson14.7415.2813.3013.7912.6913.1510.1310.50211170007KentuckyKenton14.3914.5213.1213.2412.5712.709.9410.06211250004KentuckyLaurel12.5512.5511.1911.1910.6710.678.588.58211451004KentuckyMcCracken13.4113.9112.3312.7811.7312.169.639.95211510003KentuckyMadison13.6113.7012.1712.2611.6211.719.169.25211930003KentuckyPerry13.2113.8811.7612.3611.2111.789.129.58211950002KentuckyPike13.4913.9811.9412.3711.3711.779.059.37212270007KentuckyWarren13.8313.9212.7112.7812.1112.199.449.49220171002LouisianaCaddo12.5312.5311.4911.4911.0311.039.909.90220190009LouisianaCalcasieu10.5810.789.559.749.319.498.478.64B-45Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues220190010LouisianaCalcasieu11.0711.2410.0010.159.759.908.899.03220290003LouisianaConcordia11.4211.4210.3210.329.989.988.798.79220330009LouisianaEast BatonRouge13.3813.5712.0712.2411.7511.9110.8711.03220331001LouisianaEast BatonRouge12.0812.1810.8710.9510.5910.669.729.78220470005LouisianaIberville12.9012.9811.4711.5411.1511.2210.2810.34220470009LouisianaIberville11.0211.169.779.899.479.598.458.55220511001LouisianaJefferson11.5211.689.9210.059.599.718.558.66220550006LouisianaLafayette11.0811.189.859.959.569.658.458.54220730004LouisianaOuachita11.9712.2111.0211.2310.6810.899.579.75220790002LouisianaRapides11.0311.049.929.949.629.648.558.57221050001LouisianaTangipahoa12.0312.3910.6110.9210.2710.589.129.39221090001LouisianaTerrebonne10.7410.839.409.479.119.188.068.12221210001LouisianaWest BatonRouge13.5113.6812.1912.3511.8712.0310.9911.13230010011MaineAndroscoggin9.9010.628.929.588.759.398.439.05230030013MaineAroostook9.7410.279.359.879.289.789.039.52230031011MaineAroostook8.278.927.808.417.718.327.498.07230050015MaineCumberland11.0611.689.8810.449.7010.249.379.90230050027MaineCumberland11.1311.539.9510.329.7710.129.449.78230090103MaineHancock5.766.175.025.384.955.314.654.98230110016MaineKennebec9.9910.709.029.678.869.498.559.16230172011MaineOxford10.1310.389.349.579.219.438.889.09230190002MainePenobscot9.129.658.208.678.058.527.728.17240030014MarylandAnne Arundel11.9111.919.919.919.599.598.288.28B-46Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues240031003MarylandAnne Arundel14.8215.2612.5912.9612.2312.5910.9511.27240032002MarylandAnne Arundel14.5714.5712.3212.3211.9811.9810.7210.72240051007MarylandBaltimore13.7714.0411.5811.8011.2411.459.739.91240053001MarylandBaltimore14.7615.0512.4212.6712.0812.3310.6910.92240150003MarylandCecil12.6812.8210.5810.6910.2910.408.989.06240251001MarylandHarford12.5112.6210.3610.4510.0710.168.738.81240313001MarylandMontgomery12.4712.7010.5710.7610.2310.418.758.91240330030MarylandPrinceGeorge's12.2412.2410.3410.3410.0210.028.618.61240338003MarylandPrinceGeorge's13.0313.2310.8811.0610.5410.729.179.33240430009MarylandWashington13.7014.1412.0012.3811.6412.019.8110.12245100006MarylandBaltimoreCity14.1214.3711.8612.0711.5211.7210.1310.31245100007MarylandBaltimoreCity14.3815.0112.1512.6711.8012.2910.3310.74245100008MarylandBaltimoreCity15.7615.8713.3413.4312.9613.0511.5811.66245100049MarylandBaltimoreCity15.6315.6313.2713.2712.9012.9011.5711.57250035001MassachusettsBerkshire10.6511.159.449.909.249.688.558.97250051004MassachusettsBristol9.5810.168.068.567.998.487.477.92250092006MassachusettsEssex9.039.097.717.767.587.637.097.14250095005MassachusettsEssex9.109.327.898.077.767.947.297.46250096001MassachusettsEssex9.589.748.328.468.188.317.697.82250130008MassachusettsHampden9.8510.148.568.828.378.627.747.97B-47Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues250130016MassachusettsHampden12.1712.5210.7011.0010.4710.769.8410.12250132009MassachusettsHampden11.8511.9010.4110.4510.1910.239.579.60250230004MassachusettsPlymouth9.8710.148.398.638.308.537.787.99250250002MassachusettsSuffolk12.3413.0010.6811.2410.5011.059.9810.51250250027MassachusettsSuffolk11.8612.2710.2110.5710.0310.399.519.86250250042MassachusettsSuffolk10.8811.369.329.749.179.578.659.03250250043MassachusettsSuffolk13.0713.8811.3412.0511.1511.8410.6311.30250270016MassachusettsWorcester10.5510.709.179.308.989.108.378.48250270023MassachusettsWorcester11.2911.329.869.879.669.679.069.07260050003MichiganAllegan11.8411.9910.7610.8910.4110.539.199.27260170014MichiganBay10.9311.059.8910.019.619.728.608.69260210014MichiganBerrien11.7211.9110.6310.8110.2710.449.189.30260490021MichiganGenesee11.6111.7910.4710.6310.1410.298.939.06260650012MichiganIngham12.2312.5111.0711.3210.7310.989.499.71260770008MichiganKalamazoo12.8413.0611.6811.8811.3211.5110.1310.30260810020MichiganKent12.8913.0911.5611.7411.1811.3510.1410.30260990009MichiganMacomb12.7013.0411.5311.8311.1911.489.8510.07261130001MichiganMissaukee8.268.367.687.777.467.556.866.94261150005MichiganMonroe13.9214.1312.5512.7412.1712.3610.5310.68261210040MichiganMuskegon11.6111.7010.4810.5610.1310.209.169.20261250001MichiganOakland13.7814.2612.2912.7311.8912.3110.5610.93261390005MichiganOttawa12.5512.6711.2811.3810.9111.019.829.89261450018MichiganSaginaw10.6110.619.619.619.339.338.348.34261470005MichiganSt. Clair13.3413.7512.2712.6411.9512.3210.5810.88261610005MichiganWashtenaw12.3012.3011.0411.0410.6910.699.359.35261610008MichiganWashtenaw13.8814.2912.4112.7812.0212.3710.7011.01B-48Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues261630001MichiganWayne14.5215.1112.9413.4712.5213.0311.3111.77261630015MichiganWayne15.8816.4014.2614.7213.8114.2712.2412.62261630016MichiganWayne14.5715.1713.0913.6312.6913.2111.2911.76261630019MichiganWayne14.3214.7712.9613.3712.5912.9811.2011.53261630025MichiganWayne13.3913.8811.9312.3611.5211.9410.1410.47261630033MichiganWayne17.5018.1615.7316.3215.2515.8213.5914.08261630036MichiganWayne14.6715.4413.1713.8612.7813.4411.4412.00270210001MinnesotaCass5.705.805.375.475.295.385.065.15270370470MinnesotaDakota9.309.558.498.728.278.497.868.07270530050MinnesotaHennepin9.769.828.878.928.628.678.248.29270530961MinnesotaHennepin9.149.278.308.428.078.187.687.78270530963MinnesotaHennepin9.599.818.698.898.448.638.058.23270530965MinnesotaHennepin9.549.588.678.718.428.468.058.09270531007MinnesotaHennepin9.569.758.688.868.448.618.058.21270532006MinnesotaHennepin9.339.398.488.528.248.287.857.89270953051MinnesotaMille Lacs6.546.676.096.225.986.115.725.84271095008MinnesotaOlmsted10.1310.139.269.269.049.048.488.48271230866MinnesotaRamsey11.3211.6110.4110.6710.1610.429.7810.03271230868MinnesotaRamsey11.0211.3110.0610.329.7910.049.439.68271230871MinnesotaRamsey9.639.808.778.938.548.698.188.32271377001MinnesotaSaint Louis6.106.155.735.785.655.705.455.49271377550MinnesotaSaint Louis6.196.255.785.845.675.735.445.49271377551MinnesotaSaint Louis7.517.627.027.126.907.006.666.75271390505MinnesotaScott9.009.168.248.398.048.177.627.74271453052MinnesotaStearns8.588.807.968.177.818.017.467.66280010004MississippiAdams11.2911.4610.1710.339.849.998.638.77B-49Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues280110001MississippiBolivar12.3612.5811.3911.6010.9711.169.559.72280330002MississippiDeSoto12.4312.5111.2411.3110.7010.769.089.14280350004MississippiForrest13.6213.9912.0812.4111.6912.0010.2810.55280470008MississippiHarrison12.2012.3410.6910.8110.3810.509.269.37280490010MississippiHinds12.5612.6611.2911.3810.8710.959.589.66280590006MississippiJackson12.0412.1610.4210.5410.1010.218.999.09280670002MississippiJones14.3914.5912.7912.9612.3512.5210.8511.00280750003MississippiLauderdale13.0713.0911.7611.7811.3211.349.769.78280810005MississippiLee12.5712.8311.4211.6510.8911.119.199.37280870001MississippiLowndes12.7913.1111.6611.9411.1711.449.529.75281090001MississippiPearl River12.1412.1410.7210.7210.4010.409.249.24281490004MississippiWarren12.3212.3211.2211.2210.8310.839.569.56290190004MissouriBoone11.8411.8410.9110.9110.5510.559.529.52290210005MissouriBuchanan12.8012.8011.7811.7811.5211.5210.7910.79290370003MissouriCass10.6710.839.749.899.469.608.628.73290390001MissouriCedar11.1211.1210.2410.249.949.948.858.85290470005MissouriClay11.0311.2510.0310.249.769.968.949.10290770032MissouriGreene11.7511.8710.9211.0310.5710.689.499.60290950034MissouriJackson12.7813.1911.5611.9311.2411.6010.4410.78290990012MissouriJefferson13.7913.9712.8112.9812.2712.4310.9611.12291370001MissouriMonroe10.8710.879.999.999.629.628.578.57291831002MissouriSaint Charles13.2913.7012.4412.8311.8812.2510.3810.71291860006MissouriSainteGenevieve13.3413.6612.3812.6711.8612.1310.4410.68291890004MissouriSaint Louis13.0413.0412.0612.0611.5111.5110.1610.16291892003MissouriSaint Louis13.4613.7912.3912.6911.7912.0810.3210.58B-50Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues295100007MissouriSt. Louis City14.2714.4513.1713.3412.5612.7211.1511.30295100085MissouriSt. Louis City14.3614.4513.2113.2912.5612.6411.0711.14295100086MissouriSt. Louis City13.4413.6012.3712.5211.7411.8910.2710.40295100087MissouriSt. Louis City14.5614.7013.4413.5712.7712.9011.2611.38300870307MontanaRosebud6.586.586.346.346.326.326.396.39301111065MontanaYellowstone8.148.187.767.797.697.727.707.73310250002NebraskaCass9.999.999.039.038.808.808.238.23310550019NebraskaDouglas9.8810.068.919.088.678.848.108.25310550052NebraskaDouglas9.8510.188.889.178.648.928.088.36310790004NebraskaHall7.957.957.227.227.067.066.666.66311090022NebraskaLancaster8.909.287.998.337.778.107.257.57311111002NebraskaLincoln7.577.577.047.046.956.956.776.77311530007NebraskaSarpy9.799.868.838.898.598.658.018.08311570003NebraskaScotts Bluff6.046.045.645.645.605.605.575.57311770002NebraskaWashington9.299.358.428.478.218.267.677.73330012004NewHampshireBelknap7.287.336.396.436.276.315.825.86330050007NewHampshireCheshire11.5311.6110.2910.3610.1110.179.559.60330070014NewHampshireCoos10.2410.399.499.639.369.508.979.09330090010NewHampshireGrafton8.438.437.597.597.457.456.996.99330110020NewHampshireHillsborough10.1810.188.868.868.718.718.198.19330111015NewHampshireHillsborough10.0110.018.758.758.598.598.078.07330115001NewHampshireHillsborough6.276.275.445.445.345.344.944.94B-51Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues330131006NewHampshireMerrimack9.729.738.468.478.318.327.807.81330150014NewHampshireRockingham9.009.207.848.027.747.917.327.48330190003NewHampshireSullivan9.8610.178.869.128.708.968.188.41340011006New JerseyAtlantic11.4711.479.619.619.399.398.288.28340030003New JerseyBergen13.0913.2911.0511.2110.5810.739.729.84340070003New JerseyCamden13.3113.4011.2011.2810.8910.969.729.79340071007New JerseyCamden13.5113.7811.3111.5210.9611.179.759.93340130015New JerseyEssex13.2713.2911.0911.1110.6610.689.719.73340155001New JerseyGloucester13.4613.4611.2811.2810.9710.979.719.71340171003New JerseyHudson14.2414.6612.0412.3911.5511.8810.6310.92340210008New JerseyMercer12.7112.9710.8010.9910.4810.679.329.46340218001New JerseyMercer11.1411.669.379.809.099.507.998.34340230006New JerseyMiddlesex12.1512.5210.3210.6310.0110.308.939.19340270004New JerseyMorris11.5011.929.7310.079.419.748.368.64340273001New JerseyMorris10.2110.598.739.048.478.787.457.71340292002New JerseyOcean10.9211.279.179.458.909.177.878.11340310005New JerseyPassaic12.8813.0910.7810.9410.3710.519.419.52340390004New JerseyUnion14.9415.5312.5713.0812.1012.5911.1111.56340390006New JerseyUnion13.3213.6411.1011.3510.6610.919.729.93340392003New JerseyUnion13.0613.3210.9011.1010.4810.689.519.68340410006New JerseyWarren12.7213.1110.8711.1910.5610.869.349.60350010023New MexicoBernalillo7.037.136.416.496.286.376.216.30350010024New MexicoBernalillo6.646.816.036.205.926.085.846.01350050005New MexicoChaves6.546.586.136.176.066.115.845.88B-52Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues350130017New MexicoDona Ana9.9510.409.429.849.349.769.189.59350130025New MexicoDona Ana6.316.355.895.935.815.855.665.70350431003New MexicoSandoval5.005.024.554.584.474.494.414.43350439011New MexicoSandoval7.998.347.617.947.557.887.497.81350450006New MexicoSan Juan5.926.085.615.765.595.745.585.73350490020New MexicoSanta Fe4.764.814.494.544.454.504.404.44360010005New YorkAlbany11.8311.8310.7110.7110.4810.489.789.78360050080New YorkBronx15.4315.7213.3013.5412.7913.0212.0112.22360050083New YorkBronx13.0913.3510.9811.1810.4710.679.549.71360050110New YorkBronx13.4514.0611.5112.0411.0411.5410.3210.79360130011New YorkChautauqua9.8010.098.738.988.478.716.977.16360290005New YorkErie12.6212.7911.3011.4510.9811.139.539.66360291007New YorkErie12.6413.0011.2911.6210.9711.299.479.77360310003New YorkEssex5.945.985.445.485.335.364.774.80360470122New YorkKings14.2014.6412.0412.4211.5411.9010.6410.97360551007New YorkMonroe10.6410.649.529.529.289.288.338.33360590008New YorkNassau11.6612.079.7910.139.439.758.568.85360610056New YorkNew York16.1817.0213.8014.4913.2313.8912.3012.89360610062New YorkNew York14.8015.3112.5312.9612.0212.4311.0711.44360610079New YorkNew York13.6114.0011.6211.9511.1411.4510.3910.67360610128New YorkNew York15.4115.8013.1413.4612.6112.9111.6911.97360632008New YorkNiagara11.9612.2910.8411.1310.5610.859.179.41360671015New YorkOnondaga10.0810.468.989.328.719.037.647.90360710002New YorkOrange10.9911.439.389.749.079.428.158.45360810124New YorkQueens12.1812.6910.3010.749.8510.278.969.34360850055New YorkRichmond13.3113.3711.1311.1810.6810.739.769.81B-53Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues360850067New YorkRichmond11.5911.829.709.909.329.518.518.69360893001New YorkSt. Lawrence7.297.716.767.146.647.016.026.35361010003New YorkSteuben9.009.387.968.297.768.086.556.81361030001New YorkSuffolk11.5211.529.639.639.269.268.388.38361191002New YorkWestchester11.7311.949.839.999.429.578.558.68370010002North CarolinaAlamance13.9414.0711.9012.0111.4211.549.559.65370210034North CarolinaBuncombe12.6012.6510.9110.9510.4410.488.768.79370330001North CarolinaCaswell13.1913.3311.1811.3010.7010.818.848.93370350004North CarolinaCatawba15.3115.3712.9212.9712.3812.4310.4610.51370370004North CarolinaChatham11.9912.1510.2310.379.839.978.068.18370510009North CarolinaCumberland13.7313.8512.0112.1211.5311.639.9410.03370570002North CarolinaDavidson15.1715.2412.8612.9212.3912.4510.3210.37370610002North CarolinaDuplin11.3011.549.739.949.329.537.898.06370630001North CarolinaDurham13.5713.6711.6711.7511.2311.329.469.54370650004North CarolinaEdgecombe12.3712.3810.6910.6910.2410.248.668.66370670022North CarolinaForsyth14.2814.4512.0212.1611.5011.639.519.63370710016North CarolinaGaston14.2614.4012.0112.1211.5311.649.719.80370810013North CarolinaGuilford13.7913.7911.7311.7311.2211.229.409.40370870010North CarolinaHaywood12.9813.0011.4411.4611.0011.029.569.57370990006North CarolinaJackson12.0912.4310.5410.8510.0610.358.458.70371070004North CarolinaLenoir11.1211.299.579.739.179.327.767.90371110004North CarolinaMcDowell14.2414.3512.3412.4311.8611.9510.1110.18371170001North CarolinaMartin10.8611.129.279.508.919.147.527.71371190010North CarolinaMecklenburg15.3115.3112.9612.9612.5012.5010.7010.70371190041North CarolinaMecklenburg14.7414.9412.4312.6011.9712.1410.1410.29371190042North CarolinaMecklenburg14.8014.9312.4912.5912.0312.1310.2010.29B-54Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues371210001North CarolinaMitchell12.7513.0010.9511.1710.4810.688.678.84371230001North CarolinaMontgomery12.3512.5210.5810.7210.1610.308.408.51371290002North CarolinaNew Hanover9.9610.188.548.728.178.346.947.08371330005North CarolinaOnslow10.9811.269.409.669.039.287.667.88371350007North CarolinaOrange13.1213.2511.2111.3210.7810.888.969.05371470005North CarolinaPitt11.5911.909.9910.269.579.848.138.36371550005North CarolinaRobeson12.7812.7911.1811.2110.6510.689.099.12371590021North CarolinaRowan14.0214.0211.9311.9311.5211.529.619.61371730002North CarolinaSwain12.6512.8211.0611.2110.5410.688.848.96371830014North CarolinaWake13.5413.5711.6411.6711.1911.229.469.49371890003North CarolinaWatauga12.0512.0610.2410.259.759.767.827.83371910005North CarolinaWayne12.9613.1411.3311.4910.7810.939.319.44380070002North DakotaBillings4.614.684.424.494.404.464.374.44380130002North DakotaBurke5.905.905.745.745.725.725.695.69380130003North DakotaBurke5.785.785.615.615.585.585.555.55380150003North DakotaBurleigh6.616.716.246.336.186.276.116.19380171004North DakotaCass7.727.757.227.257.107.136.916.94380530002North DakotaMcKenzie5.015.094.854.934.824.914.804.88380570004North DakotaMercer6.046.155.805.905.765.875.715.82390090003OhioAthens12.3912.6710.8811.1310.3910.638.148.33390170016OhioButler15.7416.1114.3414.6813.7814.1111.2811.55390170017OhioButler15.3615.3614.1114.1113.6313.6311.3811.38390171004OhioButler14.9015.1413.6713.8913.1913.4010.8511.03390230005OhioClark14.6414.7713.3513.4712.9013.0210.7410.84390250022OhioClermont14.1514.1512.9412.9412.4312.439.829.82390350027OhioCuyahoga15.4616.1314.2214.8313.8014.4011.2811.76B-55Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues390350034OhioCuyahoga13.7614.0712.7513.0412.3712.659.9810.21390350038OhioCuyahoga17.3718.1015.9916.6615.5416.2012.9913.54390350045OhioCuyahoga16.4716.9815.1415.6114.7015.1612.1512.53390350060OhioCuyahoga17.1117.6615.6716.1815.2115.7112.7013.14390350065OhioCuyahoga15.9716.4414.6715.1014.2514.6711.6912.03390351002OhioCuyahoga14.1414.6412.9513.4112.5813.0310.3810.75390490024OhioFranklin15.2715.9513.6214.2313.1213.7111.1211.63390490025OhioFranklin15.0815.4513.4513.7812.9513.2710.9511.23390490081OhioFranklin14.3314.3312.8012.8012.3312.3310.3510.35390570005OhioGreene13.3613.5512.0712.2411.6111.789.439.57390610006OhioHamilton14.8414.8413.5013.5012.9712.9710.4710.47390610014OhioHamilton17.2917.5315.7615.9815.1615.3712.4712.63390610040OhioHamilton15.5015.8814.1214.4613.5513.8910.8611.14390610042OhioHamilton16.8517.2515.4015.7714.8115.1712.1612.47390610043OhioHamilton15.5515.8214.1614.4113.6113.8611.1311.35390617001OhioHamilton16.1716.5614.7415.1014.1714.5211.4811.80390618001OhioHamilton17.5417.9016.0116.3315.4115.7212.7312.99390810017OhioJefferson15.4115.4613.3713.4112.9112.9510.5710.60390811001OhioJefferson16.5117.1714.3114.8813.8214.3711.2811.71390851001OhioLake13.0213.0212.1512.1511.8111.819.539.53390870010OhioLawrence15.1415.4413.6613.9213.0713.3210.7110.91390930016OhioLorain13.8714.1312.8013.0412.4312.6710.1510.33390933002OhioLorain12.7812.9811.8812.0611.5711.759.729.88390950024OhioLucas14.3814.6813.0113.2812.6212.8810.9811.22390950025OhioLucas13.9514.3712.6213.0012.2412.6110.5710.90390950026OhioLucas14.0814.3012.7812.9712.3812.5810.7510.93B-56Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues390990005OhioMahoning14.6814.9913.1313.4112.7413.0210.6010.82390990014OhioMahoning15.1215.5313.6113.9913.2213.5811.0711.39391130031OhioMontgomery14.5814.5813.2213.2212.7612.7610.5810.58391130032OhioMontgomery15.5415.9214.1014.4413.6213.9511.3811.66391330002OhioPortage13.3713.5612.0612.2211.6911.849.669.79391351001OhioPreble13.7013.8812.6812.8412.2512.419.9210.05391450013OhioScioto14.6514.8413.1013.2612.5512.7110.1210.25391510017OhioStark16.2616.6914.4714.8714.0214.4111.7012.02391510020OhioStark15.2315.2313.6113.6113.2113.2111.3111.31391530017OhioSummit15.1715.6113.7014.1013.3113.7011.3311.67391530023OhioSummit14.2614.5812.8813.1812.5212.8110.5910.85391550007OhioTrumbull14.5314.7313.1613.3512.7812.9610.6610.82400159008OklahomaCaddo9.229.778.378.878.198.677.568.00400219002OklahomaCherokee11.7912.2810.9211.3710.6311.069.6210.02400719010OklahomaKay10.2610.629.479.819.269.598.538.84400819005OklahomaLincoln10.2810.289.409.409.179.178.358.35400970186OklahomaMayes11.7011.8310.8711.0010.6110.739.699.81400979014OklahomaMayes11.4411.6210.6210.7910.3510.519.419.56401010169OklahomaMuskogee11.8912.1011.0811.2810.8011.009.8510.04401090035OklahomaOklahoma10.0710.239.049.198.798.948.058.18401091037OklahomaOklahoma9.8610.078.869.048.618.797.878.03401159004OklahomaOttawa11.6911.9510.8411.0710.5710.799.639.81401210415OklahomaPittsburg11.0911.3610.2310.489.9710.219.019.22401359015OklahomaSequoyah12.9912.9912.0912.0911.7811.7810.7810.78401430110OklahomaTulsa11.5211.5210.6110.6110.3010.309.439.43401431127OklahomaTulsa11.3711.5610.4710.6410.1810.349.329.46B-57Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues420010001PennsylvaniaAdams13.0513.5911.2511.7210.9211.389.229.63420030008PennsylvaniaAllegheny15.2415.4813.5113.7313.0513.2710.9711.17420030021PennsylvaniaAllegheny14.6614.6612.9912.9912.5412.5410.3810.38420030064PennsylvaniaAllegheny20.3120.7517.9418.3317.3017.6814.6214.95420030067PennsylvaniaAllegheny13.0713.4811.3811.7410.9711.318.809.08420030095PennsylvaniaAllegheny13.8414.5412.1212.7311.7012.299.529.99420030116PennsylvaniaAllegheny15.3615.3613.5413.5413.0913.0910.9710.97420031008PennsylvaniaAllegheny15.2515.4913.4813.7013.0313.2410.5310.70420031301PennsylvaniaAllegheny16.2616.5714.3214.5913.7914.0511.3311.54420033007PennsylvaniaAllegheny15.3015.7213.4413.8112.9413.3010.6510.95420039002PennsylvaniaAllegheny14.4414.4412.7212.7212.2612.269.949.94420070014PennsylvaniaBeaver16.3816.4514.4414.5114.0314.0911.8511.93420110011PennsylvaniaBerks15.8216.1913.7414.0613.4213.7412.0112.30420170012PennsylvaniaBucks13.4213.9311.3211.7410.9811.389.7710.12420210011PennsylvaniaCambria15.4015.5513.6113.7413.2213.3410.7510.82420270100PennsylvaniaCentre12.7813.4211.1711.7310.8511.419.019.49420290100PennsylvaniaChester15.2215.2212.8812.8812.5412.5411.1211.12420410101PennsylvaniaCumberland14.4515.1212.5213.1112.2012.7710.6211.12420430401PennsylvaniaDauphin15.1315.7812.7913.3512.4412.9810.8311.31420450002PennsylvaniaDelaware15.2315.6912.8513.2312.5112.8811.1711.49420490003PennsylvaniaErie12.5412.5711.3311.3611.0111.049.279.30420692006PennsylvaniaLackawanna11.7312.1710.0510.439.7610.128.538.85420710007PennsylvaniaLancaster16.5517.4614.0614.8313.7014.4512.2112.86420770004PennsylvaniaLehigh14.5014.5012.4812.4812.1312.1310.8910.89420791101PennsylvaniaLuzerne12.7612.7610.9910.9910.6910.699.489.48420850100PennsylvaniaMercer13.2813.7411.9112.3211.5511.959.479.80B-58Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues420950025PennsylvaniaNorthampton13.6814.0711.7212.0611.3811.7210.1610.47420990301PennsylvaniaPerry12.8112.8111.1311.1310.8610.869.419.41421010047PennsylvaniaPhiladelphia15.1915.1912.9012.9012.5212.5211.3611.36421250005PennsylvaniaWashington15.1715.4813.2613.5312.7613.0210.1610.38421250200PennsylvaniaWashington14.9214.9213.0013.0012.5212.529.949.94421255001PennsylvaniaWashington13.3713.6111.6711.8911.3011.519.249.41421290008PennsylvaniaWestmoreland15.4915.6813.6513.8213.2013.3610.5410.68421330008PennsylvaniaYork16.5217.2514.0114.6313.6414.2412.0512.58440070022Rhode IslandProvidence10.0710.078.688.688.548.548.008.00440070026Rhode IslandProvidence12.1412.4110.5510.7910.3710.619.8010.02440070028Rhode IslandProvidence10.8211.109.379.619.219.458.658.88440071010Rhode IslandProvidence9.9310.238.538.788.388.637.818.04450130007South CarolinaBeaufort11.5211.719.8910.059.579.738.238.37450190048South CarolinaCharleston12.2112.4810.6310.8710.3110.558.959.15450190049South CarolinaCharleston11.6011.839.8910.089.569.748.228.38450250001South CarolinaChesterfield12.5612.7511.0311.1910.6310.799.049.17450370001South CarolinaEdgefield13.1713.3811.6411.8311.1411.329.699.84450410002South CarolinaFlorence12.6512.7311.0811.1510.7010.769.149.20450430009South CarolinaGeorgetown12.8512.9011.1011.1310.7810.819.459.47450450008South CarolinaGreenville15.6515.9213.5413.7813.0113.2411.3311.53450450009South CarolinaGreenville14.6614.8212.6112.7512.0912.2210.3910.51450470003South CarolinaGreenwood13.5313.7011.8111.9611.2511.389.659.77450510002South CarolinaHorry12.0412.1610.4410.5310.1210.218.748.81450630008South CarolinaLexington14.6415.0712.9113.2812.3812.7510.7811.10450730001South CarolinaOconee10.9511.249.379.628.939.177.417.60450790007South CarolinaRichland13.5913.7911.9012.0811.3911.579.739.88B-59Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues450790019South CarolinaRichland14.2414.4512.5112.7011.9912.1710.3610.51450830010South CarolinaSpartanburg14.1714.3312.1212.2511.6311.769.9310.04460110002South DakotaBrookings9.379.778.689.048.528.878.178.51460130003South DakotaBrown8.428.487.887.947.777.837.577.63460290002South DakotaCodington10.1410.349.489.669.339.529.079.25460330132South DakotaCuster5.645.645.335.335.315.315.285.28460710001South DakotaJackson5.395.455.085.145.065.114.955.01460990006South DakotaMinnehaha10.1810.399.349.539.149.328.758.93460990007South DakotaMinnehaha9.589.828.799.008.608.808.208.41461030016South DakotaPennington7.487.597.067.167.017.106.997.09461030020South DakotaPennington8.778.838.318.368.248.298.238.28461031001South DakotaPennington7.327.356.926.956.876.906.856.88470090011TennesseeBlount14.3014.7112.6112.9612.0312.3710.2710.55470370023TennesseeDavidson14.2114.3213.0713.1812.4912.5910.1710.24470370025TennesseeDavidson13.9914.1912.8213.0012.2412.409.9010.03470370036TennesseeDavidson12.9712.9811.8411.8511.2711.278.989.00470450004TennesseeDyer12.2812.5711.2311.4910.6910.938.818.98470650031TennesseeHamilton15.6716.0513.9114.2513.3113.6411.2911.57470651011TennesseeHamilton13.7313.8912.0912.2311.5011.649.409.51470654002TennesseeHamilton15.1615.4313.4213.6512.8113.0310.7510.92470930028TennesseeKnox15.4715.7413.5313.7612.9013.1211.0011.18470931017TennesseeKnox15.6415.7113.7113.7613.0413.0911.1511.19470931020TennesseeKnox15.1815.2613.2513.3212.5812.6410.6410.69470990002TennesseeLawrence11.6912.1310.6911.0910.2610.648.448.75471050108TennesseeLoudon15.4915.7213.6713.8713.1113.3011.2811.44471071002TennesseeMcMinn14.2914.7012.6313.0012.0812.4310.2010.50B-60Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues471192007TennesseeMaury13.2113.5112.1412.4011.6111.879.509.71471251009TennesseeMontgomery13.8013.9112.7212.8212.1412.239.799.86471410001TennesseePutnam13.3713.3712.0612.0611.5111.519.269.26471450004TennesseeRoane14.4914.7712.7312.9912.1812.4210.2410.44471570014TennesseeShelby13.7113.7912.3612.4311.7611.8310.0310.11471570038TennesseeShelby13.4313.5712.0912.2111.4911.609.759.83471570047TennesseeShelby13.6813.7912.3612.4611.7411.859.9110.02471571004TennesseeShelby12.0412.0810.8810.9210.3210.368.608.66471631007TennesseeSullivan14.1614.4612.5112.7811.9612.2210.0110.22471650007TennesseeSumner13.6813.8312.6412.7712.0212.149.229.30480370004TexasBowie12.8512.9511.8011.8811.3911.4610.2510.32481130050TexasDallas12.7712.8911.2211.3310.9211.029.9710.07481130069TexasDallas11.8012.2610.3210.7210.0310.429.069.42481130087TexasDallas11.1511.619.7310.139.439.828.448.78481350003TexasEctor7.787.997.267.457.157.346.827.00481410037TexasEl Paso9.099.098.608.608.538.538.388.38482010058TexasHarris11.7712.3910.5411.0810.2610.799.5510.04482011035TexasHarris15.4215.8413.9314.3113.5713.9412.8913.24482030002TexasHarrison11.6911.7910.6710.7510.1810.269.029.09482150043TexasHidalgo10.9811.1610.2310.4010.0510.229.489.64482450021TexasJefferson11.5611.6610.3810.4810.0910.189.239.32483550032TexasNueces10.4210.879.319.719.049.438.318.67483550034TexasNueces9.6310.248.589.128.338.857.618.08483611001TexasOrange11.5111.7210.3810.5810.1210.319.249.41484391002TexasTarrant11.4111.799.9210.259.639.958.708.99484391006TexasTarrant12.2312.8110.6611.1610.3610.859.439.87B-61Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues500010002VermontAddison8.948.948.228.228.088.087.557.55500010003VermontAddison8.918.918.208.208.058.057.527.52500030004VermontBennington8.529.007.668.087.507.926.917.29500070012VermontChittenden9.279.568.538.798.388.647.858.10500070014VermontChittenden10.0210.029.239.239.089.088.548.54500210002VermontRutland11.0811.1310.1810.2210.0210.069.489.52510130020VirginiaArlington14.2714.6212.0712.3711.6511.9410.0710.33510360002VirginiaCharles12.3712.4810.3210.429.9610.068.438.52510410003VirginiaChesterfield13.4413.5911.2511.3910.8510.989.209.31510590030VirginiaFairfax13.3313.5911.3011.5410.9211.149.469.67510591005VirginiaFairfax13.6213.7711.5611.7011.1611.309.689.81510595001VirginiaFairfax13.8814.1211.9512.1611.5511.7510.0210.20510870014VirginiaHenrico13.5113.8311.3111.5810.9011.169.249.47510870015VirginiaHenrico12.9312.9910.9611.0210.5110.578.808.84511071005VirginiaLoudoun13.5713.9011.8212.1011.4311.719.8310.06511390004VirginiaPage12.7912.8511.0711.1110.6510.698.678.69515200006VirginiaBristol City13.9314.0012.0212.0811.4311.489.329.36516500004VirginiaHampton City12.1712.3510.1110.289.779.928.428.56516800015VirginiaLynchburgCity12.8413.0010.9611.1010.4810.618.608.71517100024VirginiaNorfolk City12.7813.0110.6910.8810.3310.519.029.18517700014VirginiaRoanoke City14.2714.4812.4812.6711.8812.079.819.98517750010VirginiaSalem City14.6914.6912.9412.9412.3512.3510.3510.35518100008VirginiaVirginiaBeach City12.4012.5710.3010.459.9510.098.618.74540030003West VirginiaBerkeley15.9316.1914.0914.3213.7013.9211.8111.99B-62Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues540090005West VirginiaBrooke16.5216.8014.3314.5813.8414.0911.3411.55540090011West VirginiaBrooke16.0416.3713.8714.1513.3913.6610.8911.13540110006West VirginiaCabell16.3016.5714.7114.9414.0514.2811.5711.75540291004West VirginiaHancock15.7616.6413.7314.5113.2814.0310.8111.44540330003West VirginiaHarrison13.9914.1913.1713.3612.7112.909.369.49540390010West VirginiaKanawha15.1515.3813.4013.6012.7812.9710.1310.28540390011West VirginiaKanawha13.1713.1711.6711.6711.1311.138.678.67540391005West VirginiaKanawha16.5216.5914.6514.7114.0114.0611.3011.35540490006West VirginiaMarion15.0315.2514.2214.4313.7613.9510.1610.30540511002West VirginiaMarshall15.1915.3313.2513.3812.7512.8710.0710.17540610003West VirginiaMonongalia14.3514.4712.8913.0112.4112.539.289.40540690010West VirginiaOhio14.5814.5812.5312.5312.0512.059.519.51540810002West VirginiaRaleigh12.9013.0311.2211.3310.7010.808.398.47541071002West VirginiaWood15.4015.4413.7413.7713.1613.2010.8410.87550030010WisconsinAshland6.076.215.665.795.575.695.295.41550090005WisconsinBrown11.3911.8610.6011.0310.3610.789.8510.27550250047WisconsinDane12.2012.6511.1311.5410.8011.2010.0510.42550270007WisconsinDodge11.0411.2610.1310.349.8210.029.119.29550410007WisconsinForest7.417.416.906.906.756.756.366.36550430009WisconsinGrant11.7912.2410.7511.1610.4510.859.7010.07550590019WisconsinKenosha11.9812.9310.9411.8010.4911.329.5810.35550710007WisconsinManitowoc10.2010.899.5010.139.249.858.719.30550790010WisconsinMilwaukee13.3213.9412.1612.7211.7212.2610.8911.41550790026WisconsinMilwaukee12.8813.8311.7312.5911.3012.1210.4711.24550790043WisconsinMilwaukee14.0814.6212.8713.3612.4112.8911.5712.02550790059WisconsinMilwaukee13.6814.8712.5113.5812.0613.1011.2412.22B-63Annual PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues550790099WisconsinMilwaukee13.5414.4012.3613.1411.9212.6811.1011.81550870009WisconsinOutagamie10.9611.4510.1910.639.9510.399.419.83550890009WisconsinOzaukee11.6012.2210.6611.2310.3010.859.5310.05551091002WisconsinSt. Croix10.0910.099.319.319.119.118.708.70551110007WisconsinSauk10.2210.579.359.679.069.378.328.61551198001WisconsinTaylor8.248.437.677.857.517.697.077.24551250001WisconsinVilas6.786.876.336.426.216.305.855.92551330027WisconsinWaukesha13.9114.3412.7713.1712.3612.7511.6111.98560050877WyomingCampbell6.296.296.126.126.106.106.116.11560050892WyomingCampbell5.115.334.965.174.955.164.965.17560050899WyomingCampbell5.265.495.075.305.065.285.075.29560090819WyomingConverse3.583.683.433.533.423.523.433.53560131003WyomingFremont8.178.507.818.127.748.057.758.05560210001WyomingLaramie4.484.694.154.344.114.314.124.31560330002WyomingSheridan9.709.849.329.459.259.389.269.39B-6424-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues010030010AlabamaBaldwin26.227.522.723.821.722.818.219.1010270001AlabamaClay31.832.426.427.124.925.619.720.2010331002AlabamaColbert30.430.426.226.324.925.018.118.2010491003AlabamaDe Kalb32.032.626.026.924.725.619.620.3010530002AlabamaEscambia29.029.724.825.424.024.621.021.5010550010AlabamaEtowah35.135.729.329.528.128.422.422.5010690003AlabamaHouston28.628.624.624.623.723.720.320.3010730023AlabamaJefferson44.044.236.937.335.636.031.131.6010731005AlabamaJefferson34.835.530.130.628.829.324.224.6010731009AlabamaJefferson34.534.928.829.027.627.820.621.2010731010AlabamaJefferson34.134.328.128.326.626.820.821.0010732003AlabamaJefferson40.340.835.335.934.234.930.531.3010732006AlabamaJefferson33.134.127.928.626.927.521.421.7010735002AlabamaJefferson33.034.827.929.326.527.920.721.7010735003AlabamaJefferson35.836.529.229.727.928.422.322.9010890014AlabamaMadison33.534.127.928.026.526.619.019.3010970002AlabamaMobile30.030.026.226.224.924.921.621.6010970003AlabamaMobile28.529.424.825.523.624.320.220.8011010007AlabamaMontgomery32.033.527.128.526.027.321.221.6011030011AlabamaMorgan31.531.925.125.423.824.017.318.0011130001AlabamaRussell35.536.931.032.129.630.525.225.5011170006AlabamaShelby32.032.827.427.626.326.420.721.1011190002AlabamaSumter28.928.924.824.823.823.818.918.9011210002AlabamaTalladega33.433.428.928.927.827.822.122.1011250004AlabamaTuscaloosa29.830.125.525.524.424.419.519.6B-6524-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues011270002AlabamaWalker32.833.326.527.625.226.319.720.6050010011ArkansasArkansas29.129.627.027.425.826.220.420.7050030005ArkansasAshley28.929.926.627.125.626.122.523.1050350005ArkansasCrittenden35.035.030.930.929.029.020.920.9050450002ArkansasFaulkner29.830.627.227.826.026.621.121.6050510003ArkansasGarland29.229.326.326.425.025.120.420.5051070001ArkansasPhillips29.130.226.627.625.226.220.121.4051130002ArkansasPolk26.127.722.924.021.622.617.818.4051150003ArkansasPope28.329.125.926.225.025.219.920.7051190007ArkansasPulaski31.131.428.428.727.027.322.422.9051191004ArkansasPulaski31.932.429.429.928.528.924.524.8051191005ArkansasPulaski31.932.329.529.928.228.724.024.4051390006ArkansasUnion28.729.626.126.725.225.721.822.4051450001ArkansasWhite29.931.127.428.526.227.321.722.3080010006ColoradoAdams25.329.221.925.421.024.421.124.5080050005ColoradoArapahoe21.222.218.319.117.618.317.518.2080130003ColoradoBoulder21.122.319.320.419.020.119.120.2080130012ColoradoBoulder18.719.716.617.516.217.016.217.0080290004ColoradoDelta20.722.318.419.817.919.217.819.1080310002ColoradoDenver26.427.623.324.322.523.522.623.6080310023ColoradoDenver26.326.323.523.522.922.923.123.1080390001ColoradoElbert13.115.111.713.411.313.111.213.0080410008ColoradoEl Paso16.416.714.815.014.414.614.414.5080410011ColoradoEl Paso16.517.414.815.614.415.214.415.2080690009ColoradoLarimer18.318.816.817.316.517.016.617.1080770017ColoradoMesa23.524.621.622.520.821.620.721.5B-6624-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues081010012ColoradoPueblo15.415.814.014.313.714.113.714.1081130004ColoradoSan Miguel10.111.39.710.99.610.89.610.8081230006ColoradoWeld22.923.920.821.920.621.620.821.8081230008ColoradoWeld18.318.916.717.116.416.716.416.8090010010ConnecticutFairfield36.237.329.930.628.629.325.726.7090011123ConnecticutFairfield32.232.728.228.627.227.624.324.7090013005ConnecticutFairfield34.935.628.829.227.528.022.322.9090019003ConnecticutFairfield33.636.727.028.325.726.920.620.9090031003ConnecticutHartford31.832.626.026.424.925.221.922.1090050005ConnecticutLitchfield27.127.120.220.219.219.215.015.0090090026ConnecticutNew Haven35.637.928.329.427.128.023.423.8090090027ConnecticutNew Haven35.536.028.929.327.828.123.723.7090091123ConnecticutNew Haven38.340.330.831.529.429.925.826.4090092008ConnecticutNew Haven33.636.427.128.226.026.921.621.6090092123ConnecticutNew Haven34.434.728.328.627.327.523.924.0090113002ConnecticutNew London32.034.425.426.424.525.420.120.2100010002DelawareKent32.132.224.826.024.025.221.022.1100010003DelawareKent31.532.424.726.424.025.820.222.2100031003DelawareNew Castle34.334.328.028.027.127.124.524.5100031007DelawareNew Castle32.634.524.625.623.524.319.320.1100031012DelawareNew Castle33.533.527.727.726.826.825.525.5100032004DelawareNew Castle36.636.729.529.728.428.625.726.5100051002DelawareSussex33.734.325.626.524.925.822.523.4110010041District Of Columbia36.337.830.331.529.130.323.424.9110010042District Of Columbia34.937.029.231.427.930.222.625.8110010043District Of Columbia34.134.828.729.427.528.222.123.1B-6724-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues120010023FloridaAlachua21.321.517.117.416.416.713.513.8120010024FloridaAlachua20.922.217.418.817.118.514.715.9120051004FloridaBay28.029.224.124.923.524.320.821.6120090007FloridaBrevard20.722.017.218.316.517.514.315.4120111002FloridaBroward18.318.915.716.215.315.714.114.5120112004FloridaBroward18.619.016.216.515.716.014.414.6120113002FloridaBroward15.915.913.613.613.113.112.012.0120170005FloridaCitrus21.221.917.118.016.617.413.414.1120310098FloridaDuval23.724.119.620.319.220.117.618.3120310099FloridaDuval24.324.521.121.520.721.219.119.6120330004FloridaEscambia28.830.625.226.424.525.521.822.3120570030FloridaHillsborough23.424.019.219.818.719.216.917.3120573002FloridaHillsborough22.222.517.618.017.017.414.915.4120710005FloridaLee17.717.914.514.814.014.312.713.1120730012FloridaLeon27.028.223.524.722.723.820.120.8120814012FloridaManatee19.520.215.515.914.915.312.913.2120830003FloridaMarion22.522.518.318.317.717.715.015.0120861016FloridaMiami-Dade19.119.115.215.214.814.813.513.5120866001FloridaMiami-Dade18.619.115.816.315.315.713.914.3120952002FloridaOrange21.821.817.117.116.516.514.514.5120990009FloridaPalm Beach17.717.715.815.815.515.514.614.6120992005FloridaPalm Beach18.218.415.215.414.715.013.614.0121030018FloridaPinellas21.722.418.118.217.617.816.316.6121031009FloridaPinellas20.821.116.717.016.216.615.115.2121056006FloridaPolk19.319.915.916.415.616.114.214.8121111002FloridaSt Lucie18.118.315.115.314.614.912.713.2B-6824-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues121150013FloridaSarasota19.219.916.016.615.416.013.413.9121171002FloridaSeminole22.023.017.317.916.517.113.914.4121275002FloridaVolusia22.023.917.218.916.518.114.015.2130210007GeorgiaBibb33.533.628.629.027.127.724.325.1130210012GeorgiaBibb30.731.425.926.424.324.720.020.4130510017GeorgiaChatham28.428.924.424.723.523.921.121.6130510091GeorgiaChatham27.928.624.525.323.724.520.921.2130630091GeorgiaClayton35.837.730.031.428.729.824.725.1130670003GeorgiaCobb35.036.329.029.627.728.123.223.4130670004GeorgiaCobb34.135.728.029.126.327.222.723.3130890002GeorgiaDe Kalb33.433.727.527.825.926.222.322.8130892001GeorgiaDe Kalb33.935.428.029.226.527.622.823.7130950007GeorgiaDougherty34.135.229.830.828.929.925.727.5131150005GeorgiaFloyd35.136.429.831.028.629.724.325.0131210032GeorgiaFulton34.135.028.229.627.028.623.424.9131210039GeorgiaFulton37.637.631.831.830.230.227.227.2131270006GeorgiaGlynn26.126.723.323.522.622.820.520.6131350002GeorgiaGwinnett32.835.027.127.725.426.021.221.5131390003GeorgiaHall30.130.325.525.924.124.619.920.8131530001GeorgiaHouston29.630.625.427.123.925.619.421.5131850003GeorgiaLowndes25.626.221.622.220.721.318.218.5132150001GeorgiaMuscogee31.332.927.628.626.427.323.523.9132150008GeorgiaMuscogee34.537.629.231.427.929.924.625.0132150011GeorgiaMuscogee30.230.926.226.225.025.322.123.7132230003GeorgiaPaulding33.033.326.626.925.425.721.221.3132450005GeorgiaRichmond32.733.430.030.628.929.326.226.4B-6924-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues132450091GeorgiaRichmond31.932.628.428.827.528.224.224.5132950002GeorgiaWalker30.932.526.027.424.826.121.322.6133030001GeorgiaWashington30.831.426.526.924.825.321.021.3133190001GeorgiaWilkinson33.133.729.429.927.728.123.224.2170010006IllinoisAdams31.432.328.028.826.326.821.321.5170190004IllinoisChampaign31.332.528.229.926.628.422.924.8170191001IllinoisChampaign30.031.727.228.526.127.222.423.7170310022IllinoisCook36.638.633.234.932.434.130.231.9170310050IllinoisCook36.138.031.533.130.031.427.929.6170310052IllinoisCook40.241.434.936.033.234.129.530.0170310057IllinoisCook37.338.632.633.730.932.027.428.4170310076IllinoisCook38.039.133.034.031.532.328.929.5170311016IllinoisCook43.046.337.540.435.838.732.636.1170312001IllinoisCook37.740.633.636.132.434.929.432.0170313103IllinoisCook39.640.335.035.433.533.930.330.7170313301IllinoisCook40.243.334.937.633.436.130.232.5170314007IllinoisCook34.336.430.532.328.830.525.027.3170314201IllinoisCook32.033.827.729.626.027.824.225.5170316005IllinoisCook39.141.834.136.432.734.931.934.3170434002IllinoisDu Page34.635.931.032.329.731.127.728.3170650002IllinoisHamilton31.631.629.629.627.827.821.221.2170831001IllinoisJersey32.133.229.630.527.828.623.323.7170890003IllinoisKane33.835.429.931.228.229.426.727.3170890007IllinoisKane34.834.831.231.229.529.528.528.5170971007IllinoisLake33.035.029.931.527.829.623.525.0170990007IllinoisLa Salle28.929.925.826.624.625.322.323.0B-7024-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues171110001IllinoisMcHenry31.532.628.329.326.927.923.825.4171132003IllinoisMcLean33.433.430.230.228.828.824.324.3171150013IllinoisMacon33.234.430.331.528.329.222.223.7171190023IllinoisMadison37.338.135.135.832.833.628.329.3171191007IllinoisMadison39.140.136.536.834.635.329.230.5171192009IllinoisMadison34.935.933.334.331.532.526.027.4171193007IllinoisMadison34.034.632.032.730.631.223.323.6171430037IllinoisPeoria32.734.129.330.627.528.624.525.1171570001IllinoisRandolph28.929.927.328.325.626.623.725.9171613002IllinoisRock Island30.931.428.228.927.428.126.026.7171630010IllinoisSt Clair33.734.131.732.329.730.025.526.4171634001IllinoisSt Clair31.932.930.131.228.229.226.726.9171670012IllinoisSangamon33.434.130.430.929.229.724.825.2171971002IllinoisWill36.437.132.332.930.631.426.928.2171971011IllinoisWill30.731.727.127.925.426.219.920.4172010013IllinoisWinnebago34.734.731.331.329.929.927.927.9180030004IndianaAllen33.134.630.732.030.031.426.729.2180030014IndianaAllen30.531.128.228.827.628.223.723.8180190006IndianaClark37.539.433.134.531.432.624.324.8180350006IndianaDelaware32.033.629.030.327.628.722.823.6180372001IndianaDubois35.336.933.234.831.833.426.429.1180390003IndianaElkhart34.436.331.132.829.931.427.529.0180431004IndianaFloyd33.234.530.231.228.529.320.721.2180650003IndianaHenry31.831.828.528.527.227.221.721.7180670003IndianaHoward32.232.928.929.727.428.123.224.5180830004IndianaKnox35.936.333.033.231.631.825.926.9B-7124-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues180890006IndianaLake34.935.531.331.830.330.827.228.2180890022IndianaLake38.944.034.939.533.738.332.136.1180890026IndianaLake38.441.334.037.032.835.830.333.2180890027IndianaLake32.634.229.130.828.129.725.626.8180890031IndianaLake34.034.030.630.629.229.224.424.4180891003IndianaLake32.733.529.530.328.629.526.928.3180892004IndianaLake32.933.929.630.628.629.727.428.9180892010IndianaLake34.235.630.731.929.631.026.828.7180910011IndianaLa Porte33.033.628.529.127.227.923.724.6180910012IndianaLa Porte30.631.827.228.126.327.324.026.3180950009IndianaMadison32.834.029.830.828.529.322.623.2180970042IndianaMarion34.235.332.233.530.832.123.623.8180970043IndianaMarion38.439.935.737.134.335.726.627.4180970066IndianaMarion38.339.635.736.934.235.327.628.6180970078IndianaMarion36.637.634.134.932.433.225.225.9180970079IndianaMarion35.636.732.533.430.731.724.124.3180970081IndianaMarion38.239.235.836.934.135.126.927.0180970083IndianaMarion36.637.034.034.532.432.725.825.9181270020IndianaPorter32.934.628.730.627.429.224.225.5181270024IndianaPorter31.832.428.128.826.927.724.725.8181410014IndianaSt Joseph32.433.929.831.429.030.426.228.3181411008IndianaSt Joseph33.133.129.529.528.828.828.328.3181412004IndianaSt Joseph30.031.427.228.426.527.825.927.9181470009IndianaSpencer32.333.130.431.328.929.519.920.3181570008IndianaTippecanoe35.636.732.833.931.332.123.824.6181630006IndianaVanderburgh34.835.531.932.530.831.426.326.9B-7224-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues181630012IndianaVanderburgh33.234.431.032.230.031.025.826.5181630016IndianaVanderburgh32.633.730.331.329.430.326.426.9181670018IndianaVigo34.635.132.132.630.831.723.724.0181670023IndianaVigo34.836.132.233.530.431.522.522.7190130008IowaBlack Hawk30.732.027.929.327.028.426.026.9190450021IowaClinton33.935.930.331.929.330.727.428.2191032001IowaJohnson34.635.230.931.629.430.128.329.1191130037IowaLinn30.633.327.530.026.629.023.224.8191370002IowaMontgomery27.527.724.424.823.323.919.720.6191390015IowaMuscatine36.037.733.234.532.233.331.032.7191471002IowaPalo Alto25.725.723.523.522.622.620.220.2191530030IowaPolk28.429.025.726.224.725.222.823.3191532510IowaPolk27.229.524.326.023.325.121.124.0191532520IowaPolk31.431.427.927.927.127.125.425.4191550009IowaPottawattamie28.628.925.625.824.825.023.323.8191630015IowaScott31.032.127.728.626.727.724.725.6191630018IowaScott32.333.329.731.028.830.026.827.4191630019IowaScott37.137.133.233.231.531.529.429.4191770006IowaVan Buren28.328.325.525.524.724.724.224.2191930017IowaWoodbury26.428.423.625.423.024.821.623.1191970004IowaWright28.628.825.625.824.925.022.122.3200910007KansasJohnson25.326.523.024.122.123.119.421.0200910009KansasJohnson29.329.326.326.325.525.523.823.8200910010KansasJohnson23.523.620.921.020.020.216.417.0201070002KansasLinn25.326.223.023.721.922.519.120.1201730008KansasSedgwick23.725.320.822.320.021.417.619.2B-7324-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues201730009KansasSedgwick25.026.422.223.521.522.618.920.3201730010KansasSedgwick25.327.022.523.921.923.320.021.2201770010KansasShawnee29.129.126.126.125.225.223.723.7201910002KansasSumner22.823.720.120.919.320.117.818.8202090021KansasWyandotte29.532.126.328.925.127.623.325.7202090022KansasWyandotte26.628.624.226.023.525.320.622.8210130002KentuckyBell29.931.025.426.124.224.719.619.7210190017KentuckyBoyd33.134.428.729.826.928.018.719.4210290006KentuckyBullitt34.635.830.932.029.130.120.420.8210370003KentuckyCampbell31.231.227.327.326.126.118.618.6210430500KentuckyCarter29.931.225.426.623.724.815.816.9210470006KentuckyChristian33.633.629.629.627.827.818.718.7210590005KentuckyDaviess33.833.832.032.030.130.119.719.7210670012KentuckyFayette31.933.227.928.925.826.819.019.4210670014KentuckyFayette32.233.228.429.226.527.420.522.2210730006KentuckyFranklin32.133.827.729.025.827.019.420.0210930006KentuckyHardin32.835.129.031.127.129.018.319.1211010014KentuckyHenderson31.832.728.529.427.428.220.821.2211110043KentuckyJefferson35.436.131.432.229.830.722.122.6211110044KentuckyJefferson36.136.631.832.230.030.422.222.4211110048KentuckyJefferson36.437.231.532.329.830.524.224.5211110051KentuckyJefferson32.433.828.429.726.627.818.018.3211170007KentuckyKenton34.735.430.231.328.829.722.523.0211250004KentuckyLaurel25.125.121.521.520.420.415.915.9211451004KentuckyMcCracken33.635.930.532.528.530.420.621.7211510003KentuckyMadison30.130.924.525.423.124.217.217.6B-7424-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues211930003KentuckyPerry28.529.824.425.522.823.916.116.9211950002KentuckyPike30.531.426.026.824.625.318.119.1212270007KentuckyWarren33.135.129.231.027.329.118.519.8220171002LouisianaCaddo27.527.524.524.523.323.320.820.8220190009LouisianaCalcasieu24.225.822.023.621.422.918.519.4220190010LouisianaCalcasieu26.327.223.724.822.923.919.119.8220290003LouisianaConcordia26.126.123.823.822.622.617.917.9220330009LouisianaEast BatonRouge29.330.525.927.025.026.222.923.9220331001LouisianaEast BatonRouge25.426.121.922.621.221.819.419.9220470005LouisianaIberville28.629.325.225.924.425.223.123.9220470009LouisianaIberville26.127.822.823.921.923.018.319.2220511001LouisianaJefferson27.027.922.322.921.622.318.819.3220550006LouisianaLafayette24.224.421.722.020.921.117.317.6220730004LouisianaOuachita28.930.626.127.525.026.221.722.6220790002LouisianaRapides30.234.326.330.325.128.920.623.8221050001LouisianaTangipahoa29.630.325.125.724.024.720.921.4221090001LouisianaTerrebonne26.226.422.622.721.721.818.218.8221210001LouisianaWest BatonRouge29.029.825.626.524.825.622.923.8230010011MaineAndroscoggin26.529.023.625.222.924.521.222.6230030013MaineAroostook24.225.422.523.622.223.321.322.3230031011MaineAroostook22.924.320.822.220.421.819.520.9230050015MaineCumberland27.730.123.826.523.225.822.324.8230050027MaineCumberland29.231.124.526.023.825.422.323.5230090103MaineHancock19.419.713.513.713.413.511.912.1B-7524-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues230110016MaineKennebec26.228.123.125.122.424.420.822.5230172011MaineOxford28.330.425.626.625.026.023.825.0230190002MainePenobscot22.024.318.720.618.120.017.018.8240030014MarylandAnne Arundel33.233.227.527.526.626.620.420.4240031003MarylandAnne Arundel35.537.429.530.928.329.724.025.6240032002MarylandAnne Arundel36.136.131.431.430.430.426.826.8240051007MarylandBaltimore33.334.127.628.226.226.822.022.7240053001MarylandBaltimore35.837.029.830.728.929.824.825.8240150003MarylandCecil30.832.525.326.624.425.621.923.4240251001MarylandHarford31.231.424.024.323.123.419.619.6240313001MarylandMontgomery30.931.825.826.324.725.118.619.7240330030MarylandPrinceGeorges31.731.726.226.225.125.119.419.4240338003MarylandPrinceGeorges33.434.627.728.626.727.620.220.8240430009MarylandWashington33.435.529.130.628.229.622.824.5245100006MarylandBaltimoreCity33.333.827.928.126.826.924.325.1245100007MarylandBaltimoreCity34.736.128.729.527.528.124.725.2245100008MarylandBaltimoreCity37.237.331.332.030.030.727.628.8245100035MarylandBaltimoreCity37.739.232.233.031.131.827.629.7245100040MarylandBaltimoreCity39.040.933.034.431.833.128.630.2B-7624-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues245100049MarylandBaltimoreCity38.138.133.233.232.132.129.829.8250035001MassachusettsBerkshire31.033.527.128.826.227.823.123.7250051004MassachusettsBristol25.026.819.320.219.320.217.818.6250092006MassachusettsEssex28.733.423.227.022.726.520.724.5250095005MassachusettsEssex26.827.120.720.820.320.317.717.7250096001MassachusettsEssex27.827.923.323.722.823.121.121.6250130008MassachusettsHampden27.227.922.823.322.122.620.220.7250130016MassachusettsHampden32.333.528.129.327.428.425.225.3250132009MassachusettsHampden33.133.328.628.727.927.926.126.2250230004MassachusettsPlymouth28.428.821.722.221.421.819.119.7250250002MassachusettsSuffolk29.429.724.324.423.723.822.822.8250250027MassachusettsSuffolk29.229.523.623.623.023.122.122.2250250042MassachusettsSuffolk28.629.123.723.723.123.121.621.9250250043MassachusettsSuffolk32.133.926.127.825.627.323.124.8250270016MassachusettsWorcester30.030.023.924.023.223.321.221.5250270023MassachusettsWorcester30.630.625.225.224.624.622.622.6260050003MichiganAllegan33.834.030.130.329.129.327.127.7260170014MichiganBay31.632.128.328.827.428.025.225.9260210014MichiganBerrien31.332.327.828.526.527.324.125.3260490021MichiganGenesee30.432.027.428.826.527.824.825.6260650012MichiganIngham31.932.129.329.628.428.826.527.4260770008MichiganKalamazoo31.132.828.329.927.428.924.325.3260810020MichiganKent36.537.132.232.730.931.328.328.7260990009MichiganMacomb35.335.932.032.731.031.729.630.6261130001MichiganMissaukee24.825.222.022.220.921.017.417.4B-7724-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues261150005MichiganMonroe38.839.634.434.832.633.126.927.0261210040MichiganMuskegon34.736.630.532.129.030.426.727.9261250001MichiganOakland39.940.435.035.433.433.927.829.0261390005MichiganOttawa34.234.731.031.430.230.628.629.3261450018MichiganSaginaw30.630.627.227.226.126.124.024.0261470005MichiganSt Clair39.640.636.237.135.036.032.232.9261610005MichiganWashtenaw33.633.629.829.828.728.726.626.6261610008MichiganWashtenaw39.440.835.036.333.935.028.429.2261630001MichiganWayne37.840.132.935.131.333.528.930.0261630015MichiganWayne40.140.635.536.034.134.630.831.6261630016MichiganWayne42.945.438.941.237.940.133.735.4261630019MichiganWayne40.941.437.337.836.336.634.735.5261630025MichiganWayne35.136.830.831.929.330.226.426.8261630033MichiganWayne43.844.239.439.838.238.534.334.5261630036MichiganWayne37.137.934.034.933.033.729.129.7261630039MichiganWayne37.037.032.932.931.831.829.429.4270210001MinnesotaCass18.019.116.417.415.916.915.116.1270370470MinnesotaDakota25.425.623.023.222.022.120.721.0270530050MinnesotaHennepin27.228.024.024.723.023.721.522.2270530961MinnesotaHennepin25.526.222.923.522.222.820.521.3270530963MinnesotaHennepin26.027.823.024.622.123.520.821.9270530965MinnesotaHennepin24.725.521.822.521.121.720.120.7270531007MinnesotaHennepin25.425.722.422.721.521.820.420.7270532006MinnesotaHennepin26.727.524.024.723.023.620.921.5270953051MinnesotaMille Lacs22.022.220.520.819.920.218.518.9271230866MinnesotaRamsey28.029.425.526.824.725.922.823.1B-7824-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues271230868MinnesotaRamsey28.330.525.427.124.426.022.924.5271230871MinnesotaRamsey26.326.823.424.022.322.721.221.3271377001MinnesotaSt Louis20.321.218.519.418.118.917.218.2271377550MinnesotaSt Louis19.520.117.918.217.217.515.716.0271377551MinnesotaSt Louis23.523.821.221.420.720.818.518.6271390505MinnesotaScott24.925.822.723.422.122.820.121.2280010004MississippiAdams27.427.824.324.823.223.718.719.1280110001MississippiBolivar28.929.926.427.225.126.120.921.8280330002MississippiDe Soto30.831.327.127.325.425.718.419.5280350004MississippiForrest30.431.127.027.626.026.523.524.3280470008MississippiHarrison29.029.025.625.624.624.621.221.2280490010MississippiHinds28.829.125.425.524.224.320.220.3280590006MississippiJackson26.926.923.423.422.422.419.219.2280670002MississippiJones31.231.627.828.326.727.223.523.8280810005MississippiLee32.133.427.728.526.226.819.420.0280870001MississippiLowndes32.433.027.528.026.026.619.921.3281490004MississippiWarren30.230.226.726.725.625.621.821.8290190004MissouriBoone30.230.227.027.025.825.821.621.6290210005MissouriBuchanan30.130.126.926.926.226.223.623.6290370003MissouriCass25.626.123.023.321.721.918.118.2290390001MissouriCedar28.728.725.725.724.124.120.720.7290470005MissouriClay28.028.624.925.624.124.822.323.7290770032MissouriGreene28.229.425.926.924.325.121.021.4290950034MissouriJackson27.827.925.225.224.024.122.422.4290990012MissouriJefferson33.434.231.231.929.429.924.224.8291370001MissouriMonroe27.827.824.924.923.623.621.021.0B-7924-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues291831002MissouriSt Charles33.134.731.633.030.031.323.524.4291860006MissouriSte Genevieve31.432.028.629.327.127.622.223.2291890004MissouriSt Louis32.032.029.529.527.527.524.124.1291892003MissouriSt Louis33.233.830.130.929.029.527.528.6295100007MissouriSt Louis City33.133.531.231.728.829.323.624.4295100085MissouriSt Louis City33.233.830.731.428.929.424.525.0295100086MissouriSt Louis City32.532.630.130.328.228.325.926.9295100087MissouriSt Louis City34.334.732.232.730.030.425.526.0300870307MontanaRosebud19.719.718.918.918.718.718.918.9301111065MontanaYellowstone19.319.418.218.518.118.418.118.4310250002NebraskaCass28.328.325.025.024.324.322.922.9310550019NebraskaDouglas25.725.722.722.722.122.120.920.9310550052NebraskaDouglas25.726.823.124.022.323.320.521.7310790004NebraskaHall19.119.116.716.716.316.315.615.6311090022NebraskaLancaster24.726.622.223.821.523.220.122.3311570003NebraskaScotts Bluff16.616.615.415.415.215.215.015.0311770002NebraskaWashington24.024.821.822.721.322.119.119.7330012004New HampshireBelknap20.521.415.616.215.115.712.913.3330050007New HampshireCheshire30.231.026.226.425.525.623.323.4330070014New HampshireCoos26.526.922.722.822.022.019.520.0330090010New HampshireGrafton23.023.018.818.818.218.216.116.1330110020New HampshireHillsborough28.628.624.924.924.224.222.422.4330111015New HampshireHillsborough27.327.324.624.623.923.922.622.6330115001New HampshireHillsborough25.925.920.020.019.419.415.415.4330131006New HampshireMerrimack25.626.120.721.020.120.418.218.3330150014New HampshireRockingham26.327.821.522.621.122.218.819.3B-8024-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues330190003New HampshireSullivan28.930.822.724.121.823.119.620.6340030003New JerseyBergen37.037.730.531.028.929.424.424.8340070003New JerseyCamden36.538.629.331.228.130.123.525.5340071007New JerseyCamden37.338.829.730.828.329.323.724.7340130015New JerseyEssex38.338.430.230.728.729.225.426.0340171003New JerseyHudson39.040.532.232.730.731.228.128.6340172002New JerseyHudson41.441.435.435.433.733.732.732.7340210008New JerseyMercer34.735.828.228.426.927.021.421.7340230006New JerseyMiddlesex34.838.127.630.026.528.922.223.8340270004New JerseyMorris32.333.626.527.425.326.120.420.8340273001New JerseyMorris31.532.925.626.824.425.618.418.9340292002New JerseyOcean31.533.924.526.123.424.918.319.5340310005New JerseyPassaic36.337.129.630.528.129.123.124.9340390004New JerseyUnion40.441.432.333.430.831.927.528.8340390006New JerseyUnion37.337.730.731.529.129.823.824.4340392003New JerseyUnion36.837.429.429.928.028.524.325.0340410006New JerseyWarren34.034.729.129.928.229.023.324.2350010023New MexicoBernalillo18.618.816.917.216.316.716.116.4350010024New MexicoBernalillo16.417.114.815.414.414.914.114.7350050005New MexicoChaves15.616.514.315.014.014.612.913.3350130017New MexicoDona Ana32.935.531.133.530.833.230.432.8350130025New MexicoDona Ana13.814.112.713.112.512.911.812.2350431003New MexicoSandoval10.310.99.39.99.19.68.79.3350439011New MexicoSandoval15.616.214.915.614.715.414.515.2350450006New MexicoSan Juan12.412.611.712.011.711.911.611.8350490020New MexicoSanta Fe9.710.29.19.69.09.48.89.2B-8124-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues360010005New YorkAlbany34.234.230.330.329.529.527.627.6360050080New YorkBronx38.840.233.033.431.231.628.429.1360050083New YorkBronx34.735.329.430.227.428.222.923.4360050110New YorkBronx36.136.631.332.429.530.526.927.7360130011New YorkChautauqua29.130.124.425.123.424.017.218.1360290005New YorkErie35.337.031.633.230.832.327.729.4360291007New YorkErie33.635.230.132.229.331.425.728.3360310003New YorkEssex22.422.819.719.918.819.015.115.2360470122New YorkKings36.938.030.431.028.729.325.025.7360551007New YorkMonroe32.233.027.227.826.226.622.222.7360590008New YorkNassau34.035.026.927.625.526.121.222.2360610056New YorkNew York39.740.633.334.031.331.927.828.0360610062New YorkNew York38.841.631.232.129.830.525.726.4360610079New YorkNew York37.940.231.633.529.831.626.928.3360610128New YorkNew York39.441.832.433.830.832.027.828.7360632008New YorkNiagara33.834.329.830.729.030.024.525.5360671015New YorkOnondaga27.328.523.624.122.723.018.319.4360710002New YorkOrange28.929.423.924.423.023.420.921.3360810124New YorkQueens35.535.529.629.628.028.024.024.0360850055New YorkRichmond34.937.029.130.827.729.323.224.7360850067New YorkRichmond32.433.025.426.024.024.519.720.9360893001New YorkSt Lawrence22.022.519.520.119.019.617.118.4361010003New YorkSteuben27.828.223.323.622.422.717.417.8361030001New YorkSuffolk34.634.626.726.725.325.319.919.9361191002New YorkWestchester33.534.325.826.524.325.021.121.3370010002North CarolinaAlamance31.732.126.626.825.225.520.621.0B-8224-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues370210034North CarolinaBuncombe30.030.224.524.723.523.618.218.3370330001North CarolinaCaswell29.429.524.024.222.923.118.718.8370350004North CarolinaCatawba34.535.527.127.926.126.821.722.0370370004North CarolinaChatham26.927.422.223.021.322.016.216.5370510009North CarolinaCumberland30.731.225.826.624.725.520.621.0370570002North CarolinaDavidson31.332.125.726.424.825.521.521.8370610002North CarolinaDuplin28.329.622.823.521.722.417.918.6370630001North CarolinaDurham31.032.024.825.423.724.318.719.0370650004North CarolinaEdgecombe26.726.923.824.022.622.818.719.0370670022North CarolinaForsyth31.932.226.126.325.125.421.021.4370710016North CarolinaGaston30.830.924.124.323.123.318.518.7370810013North CarolinaGuilford30.630.626.126.124.924.920.420.4370870010North CarolinaHaywood27.728.523.624.022.622.818.518.9370990006North CarolinaJackson24.925.420.620.719.419.515.616.0371070004North CarolinaLenoir25.225.521.221.420.120.317.517.9371110004North CarolinaMcDowell31.532.025.325.824.424.919.820.2371170001North CarolinaMartin24.826.221.322.320.221.217.017.3371190010North CarolinaMecklenburg32.332.326.226.225.225.221.421.4371190041North CarolinaMecklenburg31.732.124.624.923.623.919.619.8371190042North CarolinaMecklenburg30.731.423.523.822.522.918.719.0371210001North CarolinaMitchell30.231.624.126.022.924.817.419.4371230001North CarolinaMontgomery28.229.023.123.922.323.017.217.6371290002North CarolinaNew Hanover25.425.420.620.619.319.315.715.7371330005North CarolinaOnslow24.625.121.421.920.420.916.917.1371350007North CarolinaOrange29.329.823.524.022.322.817.818.2371470005North CarolinaPitt26.226.922.522.721.321.518.118.4B-8324-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues371550005North CarolinaRobeson29.930.424.724.923.123.318.819.1371590021North CarolinaRowan30.230.224.424.423.623.619.819.8371730002North CarolinaSwain27.328.322.022.720.921.616.717.8371830014North CarolinaWake31.632.225.926.524.825.419.119.6371890003North CarolinaWatauga30.430.524.624.823.423.517.918.1371910005North CarolinaWayne29.730.424.525.422.623.419.620.1380070002North DakotaBillings13.013.612.412.912.312.812.212.7380130003North DakotaBurke16.716.716.116.116.016.015.815.8380150003North DakotaBurleigh17.618.716.717.616.417.415.916.9380171004North DakotaCass21.222.519.520.618.920.017.719.1380530002North DakotaMcKenzie11.913.011.412.411.312.311.112.0380570004North DakotaMercer16.917.916.117.016.016.915.616.5390090003OhioAthens32.333.226.827.325.125.517.718.1390170003OhioButler39.241.134.436.533.035.426.126.9390170016OhioButler37.137.732.333.230.431.322.823.0390170017OhioButler37.937.933.433.432.032.022.822.8390171004OhioButler37.138.132.633.830.932.021.922.7390230005OhioClark35.336.431.732.630.231.022.523.2390250022OhioClermont34.434.429.929.928.528.519.519.5390350027OhioCuyahoga36.638.833.435.332.434.327.128.7390350034OhioCuyahoga36.537.932.334.031.433.223.424.3390350038OhioCuyahoga44.247.039.441.838.240.532.634.5390350045OhioCuyahoga38.541.534.738.133.737.025.526.6390350060OhioCuyahoga42.145.737.740.836.739.729.832.2390350065OhioCuyahoga38.641.034.937.633.736.525.125.9390351002OhioCuyahoga34.234.830.631.529.830.923.725.4B-8424-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues390490024OhioFranklin38.539.733.134.331.232.824.525.5390490025OhioFranklin38.439.133.133.731.231.822.923.5390490081OhioFranklin34.134.128.428.426.626.621.321.3390570005OhioGreene32.233.029.029.827.027.919.719.8390610006OhioHamilton37.637.633.133.131.631.621.221.2390610014OhioHamilton38.239.433.234.131.332.222.523.0390610040OhioHamilton36.737.731.532.629.730.721.522.4390610042OhioHamilton37.338.232.633.331.231.724.826.4390610043OhioHamilton35.936.232.232.831.131.521.922.3390617001OhioHamilton38.839.633.634.232.232.923.223.9390618001OhioHamilton40.640.935.235.833.533.925.626.2390810017OhioJefferson40.742.433.434.931.933.426.128.2390811001OhioJefferson41.945.534.537.833.136.425.127.3390851001OhioLake37.137.134.334.332.932.922.922.9390870010OhioLawrence33.734.828.429.426.727.620.120.9390933002OhioLorain31.532.129.029.528.128.621.922.3390950024OhioLucas36.338.632.835.131.933.926.428.4390950025OhioLucas35.136.831.733.230.832.328.830.8390950026OhioLucas34.936.731.433.130.331.925.827.5390990005OhioMahoning35.135.729.830.428.829.522.623.6390990014OhioMahoning36.838.231.832.930.731.624.025.2391130031OhioMontgomery35.737.132.233.231.032.026.927.3391130032OhioMontgomery37.840.033.635.632.133.922.824.2391330002OhioPortage34.334.529.830.028.929.121.121.4391351001OhioPreble32.833.930.431.628.930.120.020.4391450013OhioScioto34.536.129.430.927.829.220.622.2B-8524-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues391510017OhioStark36.937.632.333.031.232.022.924.2391530017OhioSummit38.039.633.134.731.933.524.626.1391530023OhioSummit35.837.532.133.830.832.523.625.1391550007OhioTrumbull36.237.832.133.431.132.224.125.1400159008OklahomaCaddo23.926.021.322.920.622.118.219.3400219002OklahomaCherokee27.528.024.625.223.624.622.323.4400710602OklahomaKay31.831.828.928.928.228.227.027.0400719010OklahomaKay27.932.225.329.324.628.522.626.3400819005OklahomaLincoln27.827.825.025.024.124.121.421.4400970186OklahomaMayes28.729.126.226.825.426.123.925.0400979014OklahomaMayes26.126.323.323.322.322.419.720.5401010169OklahomaMuskogee29.530.527.428.026.727.222.723.8401090035OklahomaOklahoma23.424.020.621.119.720.318.519.2401091037OklahomaOklahoma27.128.223.724.922.823.921.122.4401159004OklahomaOttawa29.129.626.027.125.226.322.423.8401210415OklahomaPittsburg26.327.223.925.022.924.220.121.4401359015OklahomaSequoyah31.431.429.129.128.228.225.025.0401430110OklahomaTulsa28.428.425.825.824.724.722.822.8401431127OklahomaTulsa30.331.027.728.626.627.723.624.9420010001PennsylvaniaAdams34.936.228.830.127.929.322.824.5420030008PennsylvaniaAllegheny39.439.933.834.232.632.925.926.6420030021PennsylvaniaAllegheny35.135.130.130.128.928.922.622.6420030064PennsylvaniaAllegheny64.268.256.759.954.157.545.048.0420030067PennsylvaniaAllegheny36.439.429.732.228.230.419.321.6420030093PennsylvaniaAllegheny45.651.539.144.337.542.529.433.7420030095PennsylvaniaAllegheny38.740.732.835.131.333.724.327.2B-8624-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues420030116PennsylvaniaAllegheny42.542.535.535.533.833.825.625.6420030133PennsylvaniaAllegheny39.239.234.334.332.832.827.527.5420031008PennsylvaniaAllegheny41.342.835.036.333.234.424.024.8420031301PennsylvaniaAllegheny40.342.433.935.632.333.824.525.4420033007PennsylvaniaAllegheny37.543.132.337.330.935.724.528.3420039002PennsylvaniaAllegheny37.837.832.232.230.630.622.822.8420070014PennsylvaniaBeaver43.444.636.237.434.535.727.028.0420110011PennsylvaniaBerks37.739.133.735.332.934.530.332.7420170012PennsylvaniaBucks34.034.828.529.027.027.424.524.7420210011PennsylvaniaCambria39.039.434.334.732.833.322.823.1420270100PennsylvaniaCentre36.237.630.131.229.330.322.724.2420290100PennsylvaniaChester36.736.731.031.030.030.026.326.3420410101PennsylvaniaCumberland38.040.231.432.730.932.228.830.1420430401PennsylvaniaDauphin38.039.032.934.132.233.429.831.4420450002PennsylvaniaDelaware35.235.929.330.028.329.123.625.6420490003PennsylvaniaErie34.434.430.130.128.828.821.921.9420692006PennsylvaniaLackawanna31.532.626.527.225.426.020.020.1420710007PennsylvaniaLancaster40.844.035.938.335.337.634.736.9420770004PennsylvaniaLehigh36.436.431.431.430.430.427.027.0420791101PennsylvaniaLuzerne32.432.427.127.125.825.822.322.3420850100PennsylvaniaMercer36.336.330.530.529.629.623.123.1420950025PennsylvaniaNorthampton36.737.430.631.129.630.325.626.2420990301PennsylvaniaPerry30.430.427.127.126.426.422.822.8421010004PennsylvaniaPhiladelphia36.536.729.730.028.528.924.926.1421010024PennsylvaniaPhiladelphia35.935.928.928.927.327.322.122.1421010047PennsylvaniaPhiladelphia37.337.730.230.629.229.524.725.2B-8724-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues421250005PennsylvaniaWashington35.536.329.530.328.028.821.521.6421250200PennsylvaniaWashington33.533.527.127.125.925.920.120.1421255001PennsylvaniaWashington38.139.931.032.429.631.020.320.6421290008PennsylvaniaWestmoreland37.137.532.132.230.730.921.922.7421330008PennsylvaniaYork38.240.733.336.032.635.330.833.6440070022Rhode IslandProvidence29.429.423.123.122.622.620.020.0440070026Rhode IslandProvidence30.632.225.526.124.825.421.822.1440070028Rhode IslandProvidence28.128.123.323.322.622.620.420.4440071010Rhode IslandProvidence28.829.722.923.122.322.520.020.2450190049South CarolinaCharleston27.927.922.622.621.821.818.518.6450250001South CarolinaChesterfield28.729.923.924.722.923.618.318.7450370001South CarolinaEdgefield32.233.726.827.925.226.220.421.2450410002South CarolinaFlorence28.828.924.324.623.223.519.019.3450450008South CarolinaGreenville31.831.827.027.025.825.822.022.0450450009South CarolinaGreenville32.533.526.727.325.426.121.321.6450470003South CarolinaGreenwood30.031.024.925.523.424.018.819.2450510002South CarolinaHorry28.329.024.024.722.923.719.119.5450630008South CarolinaLexington32.833.428.028.826.627.522.423.1450730001South CarolinaOconee27.928.223.423.422.122.117.117.4450790007South CarolinaRichland31.332.026.026.424.624.920.020.3450790019South CarolinaRichland33.233.827.828.226.326.721.521.9450830010South CarolinaSpartanburg32.432.426.326.325.125.120.420.4460110002South DakotaBrookings23.524.920.922.020.321.418.920.1460130003South DakotaBrown18.719.217.017.316.616.915.916.3460290002South DakotaCodington23.624.021.922.421.321.719.419.6460330132South DakotaCuster14.314.313.513.513.313.313.013.0B-8824-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues460710001South DakotaJackson12.713.411.812.411.612.210.911.4460990006South DakotaMinnehaha24.125.521.723.021.022.219.720.7460990007South DakotaMinnehaha23.925.121.322.120.621.418.920.0461030016South DakotaPennington17.217.215.916.015.715.715.615.7461030020South DakotaPennington18.519.217.518.117.317.817.317.8461031001South DakotaPennington15.916.614.815.614.515.414.415.3470090011TennesseeBlount32.534.228.129.526.728.021.122.2470370023TennesseeDavidson33.534.330.731.429.330.021.321.8470370025TennesseeDavidson30.931.528.128.626.827.420.020.6470370036TennesseeDavidson32.733.529.630.428.028.719.119.4470450004TennesseeDyer31.933.328.829.827.028.019.619.9470650031TennesseeHamilton33.235.228.129.026.827.523.824.1470651011TennesseeHamilton29.731.324.225.122.923.617.217.6470654002TennesseeHamilton33.534.327.528.026.126.620.921.2470930028TennesseeKnox36.636.630.830.829.329.323.623.6470931017TennesseeKnox33.433.928.929.227.627.922.422.6470990002TennesseeLawrence28.431.725.228.223.926.717.419.3471050108TennesseeLoudon32.233.327.828.626.527.321.321.9471071002TennesseeMc Minn32.733.927.228.025.726.520.221.0471192007TennesseeMaury30.931.728.429.127.127.919.820.7471251009TennesseeMontgomery36.337.533.034.231.232.321.021.7471410001TennesseePutnam32.632.628.528.526.826.818.218.2471450004TennesseeRoane30.231.425.026.123.624.618.219.0471570014TennesseeShelby32.233.528.028.926.227.219.820.4471570038TennesseeShelby32.533.528.228.826.226.719.219.7471570047TennesseeShelby33.533.828.829.426.827.219.420.1B-8924-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues471571004TennesseeShelby29.830.426.927.125.325.518.019.1471631007TennesseeSullivan31.132.025.926.724.825.520.420.5471650007TennesseeSumner33.634.531.031.929.330.318.418.9480370004TexasBowie29.430.026.226.925.326.021.121.9481130050TexasDallas27.429.923.325.922.124.719.621.5481130069TexasDallas25.727.722.223.821.222.718.519.8481130087TexasDallas24.227.020.422.719.521.816.818.8481350003TexasEctor17.818.216.516.916.116.514.715.2481410037TexasEl Paso22.922.921.621.621.521.521.021.0482011035TexasHarris30.831.427.527.926.426.823.924.1482030002TexasHarrison25.926.223.623.922.623.019.520.0482150043TexasHidalgo26.427.924.826.224.125.622.023.4483550032TexasNueces27.530.224.126.823.125.720.122.5483550034TexasNueces20.722.217.718.716.717.713.814.5483611001TexasOrange27.728.725.125.824.124.720.421.0484391002TexasTarrant25.326.821.623.320.822.518.320.0484391006TexasTarrant25.727.922.324.321.623.518.920.3500010002VermontAddison28.228.223.823.822.822.819.219.2500010003VermontAddison31.731.726.926.925.825.821.121.1500030004VermontBennington26.428.522.324.421.723.918.420.2500070012VermontChittenden29.830.325.325.824.425.020.622.7500070014VermontChittenden30.130.126.426.425.825.823.323.3500210002VermontRutland30.631.328.028.127.527.626.327.0510130020VirginiaArlington34.136.328.630.627.229.120.421.7510360002VirginiaCharles City31.732.125.626.024.725.018.919.4510410003VirginiaChesterfield31.232.623.724.622.723.618.018.5B-9024-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues510590030VirginiaFairfax34.435.028.729.127.627.921.121.3510591005VirginiaFairfax33.735.228.229.926.928.520.221.5510595001VirginiaFairfax33.333.728.729.027.427.721.221.9510870014VirginiaHenrico31.932.925.526.524.625.519.019.8510870015VirginiaHenrico29.129.623.523.822.422.716.416.9511071005VirginiaLoudoun34.435.729.630.828.529.721.722.4511390004VirginiaPage30.030.926.227.225.126.219.621.7515200006VirginiaBristol City30.230.524.724.923.323.418.218.3516500004VirginiaHampton City29.029.223.824.622.823.618.319.2516800015VirginiaLynchburgCity30.731.125.926.224.624.918.118.3517100024VirginiaNorfolk City29.629.924.024.322.923.319.219.7517700014VirginiaRoanoke City32.733.227.227.525.826.020.020.3517750010VirginiaSalem City34.034.028.728.727.327.322.022.0540030003West VirginiaBerkeley34.535.830.631.129.830.325.927.2540090005West VirginiaBrooke39.441.531.933.430.431.624.525.5540090011West VirginiaBrooke43.944.937.538.336.137.028.329.1540110006West VirginiaCabell35.136.630.932.229.330.721.022.2540291004West VirginiaHancock40.641.232.532.730.731.122.523.3540330003West VirginiaHarrison33.534.631.332.230.131.018.518.8540390010West VirginiaKanawha34.735.529.830.428.429.019.920.9540390011West VirginiaKanawha33.133.128.428.427.127.119.819.8540391005West VirginiaKanawha36.937.732.032.630.531.022.423.2540490006West VirginiaMarion33.633.730.931.029.729.718.418.5540511002West VirginiaMarshall33.934.828.128.327.027.219.319.6540610003West VirginiaMonongalia35.636.230.331.028.328.917.217.9B-9124-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues540690010West VirginiaOhio32.032.026.326.325.125.118.118.1540810002West VirginiaRaleigh30.631.325.726.324.525.017.217.4540890001West VirginiaSummers31.231.226.426.425.125.117.417.4541071002West VirginiaWood35.436.730.131.227.528.520.221.2550030010WisconsinAshland18.620.516.818.516.217.913.814.6550090005WisconsinBrown36.537.033.734.132.833.330.831.7550090009WisconsinBrown35.835.833.133.132.632.631.931.9550250047WisconsinDane35.536.932.533.831.332.529.730.3550270007WisconsinDodge31.833.129.230.428.229.326.727.5550410007WisconsinForest25.225.222.822.822.222.219.819.8550430009WisconsinGrant34.335.031.231.730.430.828.829.3550590019WisconsinKenosha32.734.029.330.327.828.926.327.2550710007WisconsinManitowoc29.731.627.529.326.728.724.526.4550790010WisconsinMilwaukee38.640.035.436.734.035.430.733.0550790026WisconsinMilwaukee37.341.333.637.232.335.929.932.9550790043WisconsinMilwaukee39.940.836.237.134.535.431.633.8550790059WisconsinMilwaukee35.537.032.333.630.431.728.229.8550790099WisconsinMilwaukee37.738.734.035.032.533.830.632.0550870009WisconsinOutagamie32.834.430.231.729.731.229.231.0550890009WisconsinOzaukee32.534.030.031.529.030.527.128.6551091002WisconsinSt Croix26.626.624.224.223.423.422.222.2551110007WisconsinSauk28.628.826.226.425.525.824.925.5551198001WisconsinTaylor25.326.023.023.722.422.921.421.7551250001WisconsinVilas22.623.820.421.419.820.718.319.0551330027WisconsinWaukesha35.436.232.433.131.031.729.729.9560050877WyomingCampbell18.618.617.817.817.717.717.717.7B-9224-Hour PM2.5Design Values (μg/m3)Site IDStateCounty2003-2007AverageAmbientValues2003-2007MaximumAmbientValues2012 BaseCaseAverageValues2012 BaseCaseMaximumValues2014 BaseCaseAverageValues2014 BaseCaseMaximumValues2014RemedyAverageValues2014RemedyMaximumValues560050892WyomingCampbell12.513.312.213.012.213.012.213.0560050899WyomingCampbell12.613.512.413.212.313.212.413.2560090819WyomingConverse9.910.59.610.19.610.19.610.1560131003WyomingFremont29.831.928.030.027.729.627.729.6560210001WyomingLaramie11.912.711.111.811.011.711.011.7560330002WyomingSheridan30.832.529.631.129.430.829.430.8

# 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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 PM 2.5 

# ( μ g/m 3 

# )

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 ExamplesC-2As described in Section IV, the contribution of ozone and PM2.5 from upwindstates to downwind nonattainment andmaintenance receptors was calculated using a combination of MATSand other post-processing software. This appendix describes thecalculations 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 values werecalculated by running MATS using the CAMx 2005 base year and 2012future base case model outputs. The contribution metricswere calculated as described in Section IV.D. There are several pieces of information neededto calculate the upwind ozonecontributions to downwind ozone receptor sites.1)The 2012 base case 8-hr designvalues at each receptor.2)The relative response factors (RRFs) from each “source apportionment” MATS run. The source apportionment MATS run isdescribed in Section IV.B steps 4 and 5.The source apportionment MATS runs specify the “base” year model file as the 2012CAMx base case and the “future” year model file as the 2012 CAMx source apportionment case. A separate sourceapportionment 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 output file“scenario_name- Ozone Monitors -- monitor data, temporally adjusted 2012.csv”)_idlatLongreferencecellf_o3_DV_state_name_county_name1033100234.76056-87.650615509164.5AlabamaColbert1073100333.48556-86.91516208069.7AlabamaJefferson1073100533.33111-87.003616207871AlabamaJefferson1073100933.45972-87.305615907966.4AlabamaJeffersonThese values are multiplied by the RRFs calculated from each source apportionment MATS run (for each upwind state). The valuesin the orange column (below) represent the relative ozone contribution from an upwind state toeach downwind receptor. Forexample, an RRF value of 0.9569 for Colbert County means thatthe upwind state contributes4.31% [(1 – 0.9569)* 100)] of the ozonein Colbert County (averaged across all high1ozone days)1The high ozone day threshold for the MATS ozone contribution runs are defined in Section IV.D. step 5.C-3_idlatlongreferencecellf_o3_DV_state_name_county_nameSA_rrf1033100234.76056-87.650615509164.5AlabamaColbert0.95691073100333.48556-86.91516208069.7AlabamaJefferson0.99441073100533.33111-87.003616207871AlabamaJefferson0.99791073100933.45972-87.305615907966.4AlabamaJefferson0.9986The 2012 future year design value (f_o3_DV) is multiplied by theRRF and then subtracted fromthe 2012 design value to get thetotalupwind 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 accomplished with additionalpost-processing software (using the formula above). The final 8-hr ozone contributions(in ppb) are truncatedto the tenths digit._idlatLongreferencecellf_o3_DV_state_name_county_nameSA_rrfContribution1033100234.76056-87.650615509164.5AlabamaColbert0.95692.71073100333.48556-86.91516208069.7AlabamaJefferson0.99440.31073100533.33111-87.003616207871AlabamaJefferson0.99790.11073100933.45972-87.305615907966.4AlabamaJefferson0.99860.0C-4Annual Average PM2.5[All example concentrations are in ug/m3]The 2012 future year base case annual average PM2.5 design valuecalculations are described inSection III.B. The 2012 designvalues were calculated by running MATS using the CAMx 2005 base year and 2012 futurebase case model outputs. The annualPM2.5 contribution metrics were calculated asdescribed in Section IV.C. The information needed to calculate the upwind annualPM2.5 contributions to downwind annualPM2.5 receptor sitesis as follows:1) The 2012 future base case annual average PM2.5 design valuesandcomponent species concentrations at each receptor.2)The relative response factors (RRFs) from each “source apportionment” MATS run. The source apportionment MATS run isdescribed in Section IV.A step 4. Thesource apportionment MATS runs specify the“base” year model file as the 2012 CAMxbase case and the “future” year model file as the 2012 CAMx source apportionmentcase. A separate source apportionmentMATS case is run for each upwind state.The 2012 future year annual average PM2.5 design values are shownin the example below as the variable “f_pm25_ann_DV” (fromthe MATS output file “scenario_nameAnnual PM25 Point.csv”)._id_STATE_NAME_COUNTY_NAMEmonitor_latmonitor_longmonitor_gridcellb_pm25_ann_DVf_pm25_ann_DV10030010AlabamaBaldwin30.498-87.881415805111.4410.8110270001AlabamaClay33.28126-85.802217107913.211210331002AlabamaColbert34.76056-87.650615509112.6712.1310491003AlabamaDeKalb34.28763-85.968316908814.0912.75The 2012 future design value is comprisedof a number of component PM2.5 species.The following table shows the 2012 speciesconcentrations._id_STATE_NAME_COUNTY_NAMEf_blank_massf_crustal_massf_EC_massf_NH4_massf_Ocmb_massf_SO4_massf_NO3_massf_water_massf_salt_mass10030010AlabamaBaldwin0.50.48380.45641.06123.23194.00030.00421.04890.031510270001AlabamaClay0.50.70340.59731.11214.36533.55290.06751.08880.0210331002AlabamaColbert0.50.39020.31371.26834.27284.03960.15371.19840.002410491003AlabamaDeKalb0.50.65740.47131.34234.20964.09670.14441.3140.0163C-5The source apportionment RRFs (in orange below) are derived fromthe source apportionment MATSruns. They represent therelative PM2.5 species contribution from anupwind state to each downwind receptor. For example, an RRF value of 0.9656 forBaldwin County for sulfate means that the upwind state contributes 3.44% [(1 – 0.9656)*100)] of the sulfate ion in Baldwin County(averaged across all days)._id_STATE_NAME_COUNTY_NAMESA_rrf_crustalSA_rrf_ecSA_rrf_nh4SA_rrf_ocSA_rrf_so4SA_rrf_no3SA_rrf_water10030010AlabamaBaldwin0.99490.99470.9640.99850.96560.99960.963410270001AlabamaClay0.98950.98970.95350.99720.95290.99290.951410331002AlabamaColbert0.98630.98710.93220.99540.9280.98990.929210491003AlabamaDeKalb0.98620.98590.93710.99610.93640.99130.9357The annual RRFs from the source apportionmentMATS run are multiplied by the 2012 speciesconcentrations and then subtractedfrom the 2012 species concentrationsto 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 species2which have an RRF. The yellow and orange columns are taken directly fromMATS output files.The species contributions from upwind state “A” to the example downwind receptors are shown in blue below. The blue columns arethe final species contributions which are calculated usingadditional post-processing software (using the formula above)._id_STATE_NAME_COUNTY_NAMEcrustal_mass_SAEC_mass_SANH4_mass_SAOcmb_mass_SASO4_mass_SANO3_mass_SAwater_mass_SA10030010AlabamaBaldwin0.00250.00240.03820.00480.13760.00000.038410270001AlabamaClay0.00740.00620.05170.01220.16730.00050.052910331002AlabamaColbert0.00530.00400.08600.01970.29090.00160.084810491003AlabamaDeKalb0.00910.00660.08440.01640.26060.00130.08452The RRF for salt is not used because it is always equal to 1.C-6For the final transport rule contributions, the sulfate and nitrate and related species (ammoniumand water) contributions weresummed to get the total PM2.5 contribution usedin the proposed rule. These are the values in the blue column below. The finalannual average PM2.5 contributions (in ug/m3)are truncated to the hundredths digit._id_STATE_NAME_COUNTY_NAMETotal AnnualPM2.5 MassContribution10030010AlabamaBaldwin0.2010270001AlabamaClay0.2610331002AlabamaColbert0.4310491003AlabamaDeKalb0.40C-724-Hour Average PM2.5[All example concentrations are in ug/m3]The 2012 future year base case 24 hr. average PM2.5design value calculations are described in Section III.B. The 2012 design valueswere calculated by running MATS usingthe CAMx 2005 base year and 2012 futurebase case model outputs. The 24-hr. PM2.5contribution metrics were calculated as described in Section IV.C. The 24-hr. PM2.5contribution calculationsare considerably morecomplicated than the annual average calculations. High 24-hr valuescan occur at different timesof the year at each receptor.Therefore, we must calculate the contributions to the downwind receptor for each of the quarters and years. The final contribution is a5 year average of thecontribution to the 98thpercentile day for each year3. The 98thpercentile future days vary by receptor and areidentified in the “New Daily All Years High Quarters PM25scenario_name.csv” file4.The information needed tocalculate the upwind 24-hr. PM2.5contributions to downwind 24-hr. PM2.5receptor sites is as follows:1) The 2012 future base case 24-hour average PM2.5and component species concentrations for each 98thpercentile day for (up to)five years at each receptor.3)The relative response factors (RRFs) from each “source apportionment” MATS run. The source apportionment MATS run isdescribed in Section IV.A step 4. Thesource apportionment MATS runs specify the“base” year model file as the 2012 CAMxfuture base case and the “future” year model file asthe 2012 CAMx source apportionment case. A separate sourceapportionment MATS case is run for each upwind state.The 2012 future year 24-hr. 98thpercentile PM2.5values are shown in the example below as the variable “f_pm25__d_q_conc” (fromthe file “New Daily All Years High Quarters PM25scenario_name.csv”). For the design value calculations, the future year 98thpercentile values (in bold) are averaged together to create a 5 year weighted average (year 1 is weighted once, year 2 twice, year 3three times, year 4 twice, and year 5 once).3The contribution on the 98thpercentile day represents the contribution to the particular quarter of the year when the future year 98thpercentile dayoccurred. The quarter in which the future year 98thpercentile day occurs varies across the 5 years.4This is a file that was created in the post-processing of standard MATS outputs. It is not afile directly output from MATS.C-8_id_state_name_county_namemonitor_latmonitor_lonyearb_high_quarterb_pm25_d_q_concf_high_quarterf_pm25_d_q_conc10030010AlabamaBaldwin30.498001‐87.8814122003229.1225.36282110030010AlabamaBaldwin30.498001‐87.8814122004325.5123.90060210030010AlabamaBaldwin30.498001‐87.8814122005223.7322.3015110030010AlabamaBaldwin30.498001‐87.8814122006325.6222.3967310030010AlabamaBaldwin30.498001‐87.8814122007222.4219.605115The 2012 future 24-hr PM2.5design value is comprised ofa number of component PM2.5species. The following table shows the 2012species concentrations for each of the 98thpercentile value days in Baldwin County._id_state_name_county_nameyearf_high_quarterf_blank_mass_qf_crustal_mass_qf_EC_mass_qf_NH4_mass_qf_Ocmb_mass_qf_SO4_mass_qf_NO3_mass_qf_water_mass_qf_salt_mass_q10030010AlabamaBaldwin200320.50.656430.561792.2629111.087568.30357101.9817970.00875810030010AlabamaBaldwin200410.50.541680.9524192.2892810.3653966.76625502.4774960.00807310030010AlabamaBaldwin200530.50.849540.5887021.959048.58552978.26926701.5435050.00592710030010AlabamaBaldwin200620.50.578120.4947691.992959.76483397.3129701.7453720.00771310030010AlabamaBaldwin200720.50.504420.4316911.738878.51991496.38063901.5228550.00673The source apportionment RRFs5(in orange below) are derived from the sourceapportionment MATS runs. They represent therelative PM2.5species contribution from an upwind stateto each downwind receptor in each quarter6. For example, an RRF value of0.9656 for Baldwin County for quarter 2 sulfate means that theupwind state contributes 3.44% [(1 – 0.9656)* 100)] of the sulfate ion5One additional step is needed to calculate the water RRF. Since the MATS output file does not contain the quarterly water RRF, it must be backcalculated from the base and future year water concentrations (f_water_mass_q / b_water_mass_q)6There is a single species RRF for each of the 4 quarters.C-9in Baldwin County in quarter2 (averaged across all high7days in each quarter). The Source apportionment MATS run producesspecies specific RRFs for each of the fourquarters. The quarters that contain the 98thpercentile values for the receptor site werepreviously identified in the first step. The future year 98thpercentile quarters for the 5 years at the example receptor site (Baldwin)are:Year 1 (2003) quarter 2Year 2 (2004) quarter 1Year 3 (2005) quarter 3Year 4 (2006) quarter 2Year 5 (2007) quarter 2The table below shows the source apportionment RRFs for each year and quarter. The rows which contain the RRFs for the 98thpercentile quarters are in bold below:_id_state_name_county_nameyearquarterSA_rrf_crustal_qSA_rrf_ec_qSA_rrf_nh4_qSA_rrf_oc_qSA_rrf_so4_qSA_rrf_no3_qSA_rrf_water_q10030010AlabamaBaldwin200310.96630.97720.99650.98520.88810.99580.888210030010AlabamaBaldwin200320.99120.99340.99960.99780.96560.99850.965510030010AlabamaBaldwin200330.98970.99140.99930.99850.94210.941910030010AlabamaBaldwin200340.97150.9710.99810.99180.90820.99810.908210030010AlabamaBaldwin200410.96630.97720.99650.98520.88810.99580.888210030010AlabamaBaldwin200420.99120.99340.99960.99780.96560.99850.965510030010AlabamaBaldwin200430.98970.99140.99930.99850.94210.941910030010AlabamaBaldwin200440.97150.9710.99810.99180.90820.99810.908210030010AlabamaBaldwin200510.96630.97720.99650.98520.88810.99580.888210030010AlabamaBaldwin200520.99120.99340.99960.99780.96560.99850.965510030010AlabamaBaldwin200530.98970.99140.99930.99850.94210.941910030010AlabamaBaldwin200540.97150.9710.99810.99180.90820.99810.908310030010AlabamaBaldwin200610.96630.97720.99650.98520.88810.99580.888210030010AlabamaBaldwin200620.99120.99340.99960.99780.96560.99850.965510030010AlabamaBaldwin200630.98970.99140.99930.99850.94210.94197Consistent with the 24-hrPM2.5design value calculations, the “high” days are defined as the 10% highest modeledPM2.5concentration days foreach quarter at each receptor.C-1010030010AlabamaBaldwin200640.97150.9710.99810.99180.90820.99810.908310030010AlabamaBaldwin200710.96630.97720.99650.98520.88810.99580.888210030010AlabamaBaldwin200720.99120.99340.99960.99780.96560.99850.965510030010AlabamaBaldwin200730.98970.99140.99930.99850.94210.941910030010AlabamaBaldwin200740.97150.9710.99810.99180.90820.99810.9082The quarterly RRFs from the source apportionment MATS run which correspond to the quarter when the 98thpercentile value occursare multiplied by the 2012 species concentrations for the 98thpercentile days in each year. Theresult is then subtracted from the 2012species concentrations to get species contributions for each year. Here is the formula:98thpercentile 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 species8which have an RRF.The species contributions from upwind state “A” to the example downwind receptor is shown in blue below. The blue columns arethe species contributions which are calculated using additional post-processing software (using the contribution formula above)._id_state_name_county_nameyearquartercrustal_mass_SAEC_mass_SANH4_mass_SAOcmb_mass_SASO4_mass_SANO3_mass_SAwater_mass_SA10030010AlabamaBaldwin200320.00580.00370.00090.02440.285600.068410030010AlabamaBaldwin200410.01830.02170.00800.15340.757100.277010030010AlabamaBaldwin200530.00880.00510.00140.01290.479600.089710030010AlabamaBaldwin200620.00510.00330.00080.02150.251600.060210030010AlabamaBaldwin200720.00440.00280.00070.01870.219500.0525The 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 averaged9across the five 98thpercentile days with the followingresult:8The RRF for salt is ignored because it is always equal to 1.C-11crustal_mass_SAEC_mass_SANH4_mass_SAOcmb_mass_SASO4_mass_SANO3_mass_SAwater_mass_SA10030010AlabamaBaldwin0.00800.00750.00250.04810.38350.00000.1114For the final transport rule contributions, the sulfate and nitrate and related species (ammoniumand water) contributions weresummed to get the total PM2.5contribution used in the final rule. The sum of these values is shown as the 24-hr PM2.5contribution inthe blue column below. The final 24 hr. PM2.5contributions (in ug/m3) are truncated to the hundredths digit._id_STATE_NAME_COUNTY_NAMETotal 24 Hr. PM2.5Mass Contribution10030010AlabamaBaldwin0.519The 5 year average for thecontributioncalculations 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-S tate Contributions to

Nonattainment and Maintenance for

8-Hour Ozone, Annual PM 2.5 

# , and 24-Hour PM 2.5

# D-2 

# This Appendix provides tabular summaries of the state-by-state

contributions to 8- hour ozone, annual PM 2.5, 

# and 24-hour PM 2.5 

# 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 us ed 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 pr ovided 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 PM 2.5 

# Threshold: 0.15 μg/m 3



# 24-Hour PM 2.5 

# Threshold: 0.35 μg/m 3



# In-state contributions (i.e., the cont ribution from a state to nonattainment or

maintenance within the State itself) are provided in the tables in this

Appendix for completeness. The in-sta te contributions were not used to

identify states to be covere d by the Transport Rule.

The tabular summaries are provided by pollutant starting with 8-hour ozone,

then annual PM2.5, and fi nally 24-hour PM2.5. For each pollutant the

contributions to nonattainment site s are provided in “Part 1” and the

contributions to maintenance sites are pr ovided in “Part 2”. Note that some

counties have multiple sites that are pr ojected to be nonatta inment as well as

other sites that are projected to have a maintenance problem.D-3State-by-State Contributions to 8-Hour Ozone (ppb)NonattainmentReceptorsPart 1a.Source States: Alabama through MissouriReceptorMonitorIDReceptorStateReceptorCountySourceState =>ALARCTDEFLGAILINIAKSKYLAMEMDMAMIMNMSMO220330003LouisianaEastBatonRouge2.82.10.00.00.31.60.20.20.10.50.339.70.00.00.00.00.04.00.5480391004TexasBrazoria3.11.10.00.00.51.21.41.00.60.21.37.90.00.00.00.00.33.21.1482010051TexasHarris3.70.30.00.00.41.21.81.20.50.11.57.30.00.00.00.00.13.10.9482010055TexasHarris4.00.40.00.00.41.31.91.30.60.11.68.00.00.00.00.00.13.41.0LargestContributionto DownwindNonattainment4.02.10.00.00.51.61.91.30.60.51.68.00.00.00.00.00.34.01.1D-4State-by-State Contributions to 8-Hour Ozone (ppb)NonattainmentReceptorsPart 1b.Source States: Nebraska through WisconsinReceptorMonitorIDReceptorStateReceptorCountySourceState =>NENHNJNYNCNDOHOKPARISCSDTNTXVTVAWVWI220330003LouisianaEast BatonRouge0.10.00.00.00.50.20.10.30.10.00.40.11.03.90.00.20.00.0480391004TexasBrazoria0.20.00.00.00.20.10.10.30.00.00.30.11.635.20.00.10.00.2482010051TexasHarris0.20.00.00.00.20.10.10.10.00.00.30.12.027.20.00.00.00.2482010055TexasHarris0.20.00.00.00.20.10.10.10.00.00.30.12.229.50.00.10.00.2LargestContributionto DownwindNonattainment0.20.00.00.00.50.20.10.30.10.00.40.12.23.90.00.20.00.2D-5State-by-State Contributions to 8-Hour Ozone (ppb)MaintenanceReceptorsPart 2a.Source States: Alabama through MissouriReceptorMonitorIDReceptorStateReceptorCountySourceState =>ALARCTDEFLGAILINIAKSKYLAMEMDMAMIMNMSMO90011123ConnecticutFairfield0.40.55.70.40.00.30.80.90.20.21.60.10.02.30.60.70.20.50.690093002ConnecticutNewHaven0.10.14.40.60.10.00.71.10.10.11.40.10.02.70.10.70.10.10.4240251001MarylandHarford0.30.10.20.10.00.10.71.00.10.11.40.00.034.50.30.90.20.20.4260050003MichiganAllegan0.32.00.00.00.00.326.89.40.91.00.60.30.00.00.04.30.10.34.8482010029TexasHarris2.70.20.00.03.62.80.20.20.00.00.35.80.00.00.00.10.02.00.0482011050TexasHarris2.80.50.00.00.91.90.80.60.30.10.811.10.00.20.00.10.13.30.5LargestContributionto DownwindMaintenance2.82.00.20.63.62.826.89.40.91.01.611.10.02.70.60.90.23.34.8D-6State-by-State Contributions to 8-Hour Ozone (ppb)MaintenanceReceptorsPart 2b.Source States: Nebraska through WisconsinReceptorMonitorIDReceptorStateReceptorCountySourceState =>NENHNJNYNCNDOHOKPARISCSDTNTXVTVAWVWI90011123ConnecticutFairfield0.10.111.518.00.60.02.20.26.40.00.10.00.90.30.02.61.60.290093002ConnecticutNewHaven0.00.010.418.81.30.03.20.18.20.00.10.00.40.10.04.52.10.2240251001MarylandHarford0.10.00.61.01.00.03.10.15.60.00.10.01.00.20.08.22.80.3260050003MichiganAllegan0.20.00.00.00.30.00.22.80.00.00.10.00.61.90.00.10.02.2482010029TexasHarris0.00.00.00.00.70.00.20.00.10.00.90.00.635.00.00.30.10.0482011050TexasHarris0.10.00.10.20.60.10.10.10.50.00.70.11.133.10.00.40.00.1LargestContributionto DownwindMaintenance0.20.111.518.81.30.13.22.88.20.00.90.11.11.90.08.22.82.2D-7State-by-State Contributions to Annual PM2.5(μg/m3)NonattainmentReceptorsPart 1a.Source States: Alabama through MissouriReceptorMonitorIDReceptorStateReceptorCountySourceState =>ALARCTDEFLGAILINIAKSKYLAMEMDMAMIMNMSMO10730023AlabamaJefferson3.110.030.000.000.070.460.140.280.030.020.220.090.000.020.000.050.010.050.1210732003AlabamaJefferson2.580.030.000.000.080.460.150.300.040.020.240.090.000.020.000.050.010.050.12131210039GeorgiaFulton0.510.030.000.000.081.360.160.310.040.020.250.060.000.040.000.070.020.020.13171191007IllinoisMadison0.120.100.000.000.010.081.510.700.260.090.330.070.000.010.000.260.140.011.22261630033MichiganWayne0.080.020.000.000.000.060.420.690.100.020.250.020.000.030.002.270.090.000.14390350038OhioCuyahoga0.090.020.000.000.010.070.370.650.090.020.280.020.000.060.000.630.070.000.15390350045OhioCuyahoga0.090.020.000.000.010.080.370.650.090.020.290.020.000.060.000.640.070.000.15390350060OhioCuyahoga0.100.020.000.000.010.080.370.650.090.020.290.020.000.060.000.640.070.000.15390610014OhioHamilton0.190.030.000.000.010.130.501.280.100.030.810.030.000.040.000.350.070.010.22390610042OhioHamilton0.200.040.000.000.010.130.491.340.100.020.940.030.000.040.000.330.060.010.22390618001OhioHamilton0.190.030.000.000.010.130.501.270.100.020.800.030.000.040.000.350.060.010.22420030064PennsylvaniaAllegheny0.080.020.000.000.010.070.260.510.060.010.270.010.000.150.000.330.040.000.12LargestContributionto DownwindNonattainment0.510.100.000.000.080.460.501.340.260.090.940.090.000.150.000.640.140.051.22D-8State-by-State Contributions to Annual PM2.5(μg/m3)NonattainmentReceptorsPart 1b.Source States: Nebraska through WisconsinReceptorMonitorIDReceptorStateReceptorCountySourceState =>NENHNJNYNCNDOHOKPARISCSDTNTXVTVAWVWI10730023AlabamaJefferson0.010.000.000.010.090.010.240.020.100.000.110.000.250.070.000.050.140.0310732003AlabamaJefferson0.010.000.000.020.090.010.260.020.110.000.110.000.260.080.000.050.140.03131210039GeorgiaFulton0.010.000.010.030.200.010.320.020.170.000.240.000.290.070.000.090.190.04171191007IllinoisMadison0.060.000.000.020.030.060.420.080.110.000.030.030.190.180.000.020.130.16261630033MichiganWayne0.020.000.000.130.030.040.990.030.290.000.010.010.110.040.000.040.240.22390350038OhioCuyahoga0.020.000.010.210.040.042.620.020.530.000.020.010.130.040.000.060.400.18390350045OhioCuyahoga0.020.000.010.210.040.042.640.020.540.000.020.010.130.040.000.060.400.18390350060OhioCuyahoga0.020.000.010.210.040.042.630.020.540.000.020.010.130.040.000.060.400.18390610014OhioHamilton0.020.000.000.090.060.032.120.030.380.000.040.010.320.070.000.060.370.15390610042OhioHamilton0.020.000.000.080.060.032.030.030.370.000.040.010.320.070.000.060.360.14390618001OhioHamilton0.020.000.000.090.060.032.110.030.380.000.040.010.320.070.000.060.370.15420030064PennsylvaniaAllegheny0.010.000.020.170.080.031.340.012.140.000.040.010.130.040.000.120.950.10LargestContributionto DownwindNonattainment0.060.000.020.210.200.061.340.080.540.000.240.030.320.180.000.120.950.22D-9State-by-State Contributions to Annual PM2.5(μg/m3)MaintenanceReceptorsPart 2a.Source States: Alabama through MissouriReceptorMonitorIDReceptorStateReceptorCountySourceState =>ALARCTDEFLGAILINIAKSKYLAMEMDMAMIMNMSMO180970081IndianaMarion0.150.040.000.000.010.090.652.940.140.040.630.030.000.030.000.410.090.010.26180970083IndianaMarion0.150.040.000.000.010.090.652.940.140.040.630.030.000.030.000.410.090.010.27390350065OhioCuyahoga0.090.020.000.000.010.080.370.650.090.020.290.020.000.060.000.640.070.000.15390617001OhioHamilton0.190.030.000.000.010.130.501.270.100.030.810.030.000.040.000.350.070.010.22LargestContributionto DownwindMaintenance0.190.040.000.000.010.130.651.270.140.040.810.030.000.060.000.640.090.010.27D-10State-by-State Contributions to Annual PM2.5(μg/m3)MaintenanceReceptorsPart 2b.Source States: Nebraska through WisconsinReceptorMonitorIDReceptorStateReceptorCountySourceState =>NENHNJNYNCNDOHOKPARISCSDTNTXVTVAWVWI180970081IndianaMarion0.030.000.000.070.050.040.940.030.230.000.040.010.250.070.000.040.210.19180970083IndianaMarion0.030.000.000.070.050.040.940.030.230.000.040.010.250.070.000.040.210.19390350065OhioCuyahoga0.020.000.010.210.040.042.630.020.540.000.020.010.130.040.000.060.400.18390617001OhioHamilton0.020.000.000.090.060.032.120.030.380.000.040.010.320.070.000.060.370.15LargestContributionto DownwindMaintenance0.030.000.010.210.060.040.940.030.540.000.040.010.320.070.000.060.400.19D-11State-by-State Contributions to 24-Hour PM2.5(μg/m3)NonattainmentReceptorsPart 1a.Source States: Alabama through MissouriReceptorMonitorIDReceptorStateReceptorCountySourceState =>ALARCTDEFLGAILINIAKSKYLAMEMDMAMIMNMSMO10730023AlabamaJefferson6.810.020.000.000.071.100.290.650.040.010.600.090.000.120.000.130.000.060.18170311016IllinoisCook0.080.160.000.000.000.084.753.290.700.110.720.070.000.060.001.370.210.030.75171191007IllinoisMadison0.280.240.000.000.000.174.033.020.450.371.710.070.000.050.000.990.250.023.73180970043IndianaMarion0.380.070.000.000.000.161.679.150.160.024.380.020.000.110.000.250.020.010.88180970066IndianaMarion0.480.090.000.000.000.271.408.390.120.033.930.030.000.120.000.220.020.020.86180970081IndianaMarion0.510.090.000.000.010.301.358.290.110.033.860.030.000.120.000.210.020.020.86261470005MichiganSt Clair0.310.050.000.010.000.201.072.440.080.021.250.060.000.200.001.970.030.030.38261630015MichiganWayne0.340.070.000.000.020.191.062.740.110.041.280.110.000.130.004.350.060.040.46261630016MichiganWayne0.120.010.000.010.000.081.502.750.170.060.670.000.000.170.005.220.030.000.23261630019MichiganWayne0.110.010.000.010.000.071.472.790.150.050.640.000.000.160.003.580.030.000.21261630033MichiganWayne0.330.050.000.000.020.211.022.590.110.041.030.100.000.120.005.030.100.040.32390350038OhioCuyahoga0.290.030.000.000.000.200.701.840.110.010.890.010.000.360.011.760.030.010.22390350060OhioCuyahoga0.170.020.000.000.000.130.761.670.140.020.570.010.000.270.011.860.060.000.17420030064PennsylvaniaAllegheny0.060.040.000.000.000.070.671.710.090.030.860.010.000.170.000.880.060.000.36420030093PennsylvaniaAllegheny0.200.070.000.010.010.140.551.580.060.021.090.060.000.260.000.400.020.020.34420030116PennsylvaniaAllegheny0.160.070.000.010.000.140.631.720.070.031.190.060.000.260.000.320.010.020.41420070014PennsylvaniaBeaver0.240.080.010.010.010.130.701.730.120.041.020.070.000.300.010.630.060.030.40420710007PennsylvaniaLancaster0.230.040.100.220.040.290.160.310.050.020.260.040.062.840.190.200.020.020.11540090011WestVirginiaBrooke0.060.030.010.010.000.060.751.710.150.030.850.010.000.330.010.900.100.000.32550790043WisconsinMilwaukee0.160.040.000.000.000.153.723.560.820.301.150.020.000.060.001.060.610.010.88LargestContributionto DownwindNonattainment0.510.240.100.220.071.103.723.560.820.374.380.110.062.840.191.860.610.063.73D-12State-by-State Contributions to 24-Hour PM2.5(μg/m3)NonattainmentReceptorsPart 1b.Source States: Nebraska through WisconsinReceptorMonitorIDReceptorStateReceptorCountySourceState =>NENHNJNYNCNDOHOKPARISCSDTNTXVTVAWVWI10730023AlabamaJefferson0.010.000.020.060.280.011.180.010.650.000.290.000.700.040.000.201.040.02170311016IllinoisCook0.110.000.000.230.040.071.730.090.650.000.020.050.240.270.000.070.660.69171191007IllinoisMadison0.100.000.000.120.050.102.400.170.690.000.030.060.750.370.000.060.660.15180970043IndianaMarion0.010.000.010.090.090.012.660.030.620.000.050.001.160.040.000.121.100.07180970066IndianaMarion0.010.000.010.100.110.022.540.020.670.000.070.001.330.050.000.121.050.06180970081IndianaMarion0.010.000.010.100.110.022.530.020.690.000.070.001.380.050.000.121.040.06261470005MichiganSt Clair0.020.000.030.410.100.014.510.021.410.000.050.000.620.110.000.161.560.15261630015MichiganWayne0.020.000.020.340.090.033.670.041.000.000.030.010.510.090.000.131.080.26261630016MichiganWayne0.060.000.030.830.040.013.420.011.360.000.010.000.240.020.000.100.970.30261630019MichiganWayne0.050.000.030.730.040.013.320.011.320.000.010.000.230.010.000.100.950.38261630033MichiganWayne0.030.000.020.460.080.053.830.030.960.000.040.010.450.090.000.130.980.35390350038OhioCuyahoga0.010.000.020.250.230.038.490.001.910.000.070.010.440.010.000.312.940.17390350060OhioCuyahoga0.040.000.020.820.130.048.960.012.160.000.040.010.260.010.000.201.760.23420030064PennsylvaniaAllegheny0.030.000.020.130.130.074.810.035.530.000.050.010.200.040.000.224.020.16420030093PennsylvaniaAllegheny0.010.000.040.120.150.023.070.033.210.000.060.000.340.060.000.302.960.06420030116PennsylvaniaAllegheny0.010.000.040.120.170.023.480.032.960.000.070.000.350.060.000.323.360.06420070014PennsylvaniaBeaver0.030.000.040.210.120.044.000.042.610.000.040.010.340.080.000.242.570.16420710007PennsylvaniaLancaster0.010.050.680.660.400.010.760.036.690.020.140.000.260.090.031.210.840.07540090011WestVirginiaBrooke0.020.000.040.310.080.065.850.022.850.000.030.020.190.030.000.223.280.28550790043WisconsinMilwaukee0.240.000.000.110.070.212.410.110.690.000.060.100.260.110.000.080.753.00LargestContributionto DownwindNonattainment0.240.050.680.830.400.215.850.172.850.020.290.101.380.370.031.214.020.69D-13State-by-State Contributions to 24-Hour PM2.5(μg/m3)MaintenanceReceptorsPart 2a.Source States: Alabama through MissouriReceptorMonitorIDReceptorStateReceptorCountySourceState =>ALARCTDEFLGAILINIAKSKYLAMEMDMAMIMNMSMO10732003AlabamaJefferson4.610.020.000.000.020.920.310.720.030.010.640.060.000.100.000.140.000.030.18170310052IllinoisCook0.090.060.000.000.000.126.132.840.980.360.660.010.000.070.001.490.310.001.07170312001IllinoisCook0.080.110.000.000.000.073.964.870.410.230.670.010.000.070.001.830.090.000.82170313301IllinoisCook0.030.090.000.000.000.025.103.390.670.260.550.030.000.060.001.780.180.010.86170316005IllinoisCook0.110.160.000.000.000.156.152.491.030.360.400.060.000.020.001.360.190.011.10171190023IllinoisMadison0.320.230.000.000.000.193.873.050.400.281.760.050.000.060.000.850.190.023.71180890022IndianaLake0.070.020.000.000.000.063.095.770.520.200.540.000.000.040.001.410.140.000.62180890026IndianaLake0.120.030.000.000.000.102.854.520.420.180.730.020.000.090.001.830.140.010.60261610008MichiganWashtenaw0.400.090.000.010.010.251.092.960.140.061.180.130.000.140.002.670.030.070.39390170003OhioButler0.420.060.000.010.000.281.375.150.140.033.580.040.000.260.000.820.040.020.55390350045OhioCuyahoga0.180.020.000.000.000.120.701.660.110.010.680.010.000.310.002.030.050.000.17390350065OhioCuyahoga0.200.060.000.000.000.140.811.820.110.020.870.020.000.320.001.640.040.010.33390618001OhioHamilton0.240.040.000.010.010.141.043.590.170.032.230.020.000.320.000.510.030.010.35390811001OhioJefferson0.070.040.000.010.000.070.551.620.080.021.200.000.000.200.000.680.050.000.28391130032OhioMontgomery0.400.080.000.010.000.251.023.950.090.033.100.040.000.210.000.600.040.020.52420031008PennsylvaniaAllegheny0.130.080.000.010.000.090.712.140.090.031.230.050.000.200.000.770.030.020.37420031301PennsylvaniaAllegheny0.070.020.000.000.000.060.541.610.070.020.690.010.000.190.000.850.030.000.23420033007PennsylvaniaAllegheny0.040.020.000.010.000.040.491.400.050.010.670.000.000.190.000.590.010.000.23421330008PennsylvaniaYork0.170.020.180.200.030.270.370.730.110.020.420.050.102.120.300.520.060.010.17550790010WisconsinMilwaukee0.070.020.000.000.000.075.702.901.550.810.850.010.000.030.001.470.660.001.20550790026WisconsinMilwaukee0.060.040.000.000.000.064.141.961.470.480.580.010.000.020.001.101.010.001.01LargestContributionto DownwindMaintenance0.420.230.180.200.030.925.705.151.550.813.580.130.102.120.302.031.010.073.71D-14State-by-State Contributions to 24-Hour PM2.5(μg/m3)MaintenanceReceptorsPart 2b.Source States: Nebraska through WisconsinReceptorMonitorIDReceptorStateReceptorCountySourceState =>NENHNJNYNCNDOHOKPARISCSDTNTXVTVAWVWI10732003AlabamaJefferson0.000.000.020.050.220.010.990.000.500.000.250.000.680.030.000.150.780.02170310052IllinoisCook0.220.000.000.100.060.091.450.150.560.000.050.060.180.080.000.060.580.97170312001IllinoisCook0.120.000.000.140.040.021.710.080.670.000.020.020.200.080.000.060.700.45170313301IllinoisCook0.150.000.000.160.020.051.530.110.640.000.000.040.120.160.000.050.610.66170316005IllinoisCook0.230.000.000.200.060.060.850.200.310.000.040.060.240.330.000.050.250.83171190023IllinoisMadison0.070.000.000.090.060.082.380.140.670.000.030.050.810.290.000.060.680.14180890022IndianaLake0.110.000.000.130.030.051.120.070.390.000.010.030.120.040.000.040.410.58180890026IndianaLake0.100.000.000.180.050.051.710.030.740.000.020.030.180.050.000.080.770.54261610008MichiganWashtenaw0.030.000.030.410.080.014.410.051.010.000.050.000.590.220.000.131.020.19390170003OhioButler0.020.000.030.300.100.025.900.031.580.000.050.011.220.050.000.181.560.11390350045OhioCuyahoga0.020.000.020.890.150.029.040.001.870.000.040.010.290.010.000.232.040.18390350065OhioCuyahoga0.020.000.020.620.160.028.510.022.200.000.040.000.350.040.000.242.260.15390618001OhioHamilton0.020.000.040.210.110.035.880.041.650.000.030.000.780.050.000.222.150.13390811001OhioJefferson0.010.000.030.180.090.035.090.022.290.000.050.010.290.030.000.183.160.12391130032OhioMontgomery0.010.000.040.300.120.024.930.041.140.000.050.011.300.070.000.231.390.11420031008PennsylvaniaAllegheny0.020.000.030.220.070.044.740.034.230.000.030.000.340.070.000.182.960.12420031301PennsylvaniaAllegheny0.010.000.030.190.070.024.220.024.310.000.030.000.160.030.000.152.870.10420033007PennsylvaniaAllegheny0.010.000.030.130.100.023.640.014.260.000.040.000.140.020.000.183.330.07421330008PennsylvaniaYork0.020.100.751.340.380.031.550.028.980.030.170.010.190.060.051.011.470.23550790010WisconsinMilwaukee0.520.000.000.060.030.191.190.170.390.000.020.090.160.060.000.030.374.26550790026WisconsinMilwaukee0.430.000.000.100.020.331.030.170.350.000.020.170.130.130.000.030.324.03LargestContributionto DownwindMaintenance0.520.100.751.340.380.334.740.202.290.030.250.171.300.330.051.013.330.97

# 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 Nona ttainment Site “Linkages” for Annual PM 2.5



# Upwind State Annual PM 2.5 

# 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 PM 2.5 

# 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 Main tenance Site “Linkages” for Annual PM 2.5



# Upwind State Annual PM 2.5 

# 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 Nonatt ainment Site “Linkages” for 24-hour PM 2.5



# Upwind State 24-Hour PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 Main tenance Site “Linkages” for 24-hour PM 2.5



# Upwind State 24-Hour PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 PM 2.5 

# 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 Nona ttainment “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 ThresholdsF-2Table F-1a. Contribution metrics for 8-hourozone 2012 nonattainment receptors – part 1.MonitoringSiteStateCounty2012 Base CaseIn-StateContribution(ppb)2012 TotalContributionfrom All UpwindStates(ppb)UpwindContribution asa Percent ofReceptorConcentration# StatesContributing Ator Above0.5% Threshold# StatesContributing Ator Above1% Threshold# StatesContributing Ator Above5% Threshold220330003LAEast BatonRouge39.8 21.0 35%1061480391004TXBrazoria35.3 27.6 44%12101482010051TXHarris27.3 26.9 50%1191482010055TXHarris29.6 29.1 50%1292Table F-1b. Contribution metrics for 8-hourozone 2012 nonattainment receptors – part 2.MonitoringSiteStateCountyTransport(ppb) Using0.5% ThresholdTransport(ppb) Using1% ThresholdTransport(ppb) Using5% Threshold% of TotalTransportCaptured with0.5% Threshold% of TotalTransportCaptured with1% Threshold% of TotalTransportCaptured with5% Threshold220330003LAEast BatonRouge17.815.74.085%75%19%480391004TXBrazoria24.523.37.989%84%29%482010051TXHarris24.223.27.390%86%27%482010055TXHarris26.625.112.191%86%42%F-3Table F-1c. Contribution metrics for 8-hour ozone 2012 maintenance receptors – part 1.MonitoringSiteStateCounty2012 Base CaseIn-StateContribution(ppb)2012 TotalContributionfrom AllUpwind States(ppb)UpwindContributionas a Percent ofReceptorConcentration# StatesContributing Ator Above0.5% Threshold# StatesContributing Ator Above1% Threshold# StatesContributingAt or Above5% Threshold90011123CTFairfield5.7 56.9 91%1911390093002CTNewHaven4.4 59.6 93%15104240251001MDHarford33.7 32.7 49%13102260050003MIAllegan4.3 56.9 93%1193482010029TXHarris35.0 21.9 38%861482011050TXHarris33.1 29.7 47%1581Table F-1d. Contribution metrics for 8-hour ozone 2012 maintenance receptors – part 2.MonitoringSiteStateCountyTransport(ppb) Using0.5% ThresholdTransport(ppb) Using1% ThresholdTransport(ppb) Using5% Threshold% of TotalTransportCaptured with0.5% Threshold% of TotalTransportCaptured with1% Threshold% of TotalTransportCaptured with5% Threshold90011123CTFairfield53.949.336.095%87%63%90093002CTNewHaven57.354.242.096%91%71%240251001MDHarford28.226.413.886%81%42%260050003MIAllegan53.752.341.194%92%72%482010029TXHarris19.418.05.889%82%27%482011050TXHarris27.122.911.191%77%37%F-4Table F-2a. Contribution metrics for annual PM2.5 2012 nonattainment receptors – part 1.MonitoringSiteStateCounty2012 BaseCase In-StateContribution(μg/m3)2012 TotalContributionfrom AllUpwind States(μg/m3)UpwindContributionas a Percent ofReceptorConcentration# StatesContributing Ator Above0.5% Threshold# StatesContributingAt or Above1% Threshold# StatesContributing Ator Above5% Threshold10730023ALJefferson3.12 2.72 47%125010732003ALJefferson2.58 2.85 52%1460131210039GAFulton1.37 3.43 72%13100171191007ILMadison1.51 4.92 77%1791261630033MIWayne2.27 4.12 64%1371390350038OHCuyahoga2.62 4.24 62%1290390350045OHCuyahoga2.64 4.28 62%1390390350060OHCuyahoga2.64 4.29 62%1390390610014OHHamilton2.13 5.43 72%13102390610042OHHamilton2.04 5.55 73%1392390618001OHHamilton2.11 5.39 72%13102420030064PAAllegheny2.14 4.99 70%1482F-5Table F-2b. Contribution metrics for annualPM2.5 2012 nonattainment receptors – part 2.MonitoringSiteStateCountyTransport(μg/m3) Using0.5% ThresholdTransport(μg/m3) Using1% ThresholdTransport(μg/m3) Using5% Threshold% of TotalTransportCaptured with0.5% Threshold% of TotalTransportCaptured with1% Threshold% of TotalTransportCaptured with5% Threshold10730023ALJefferson2.241.450.0082%53%0%10732003ALJefferson2.491.670.0087%59%0%131210039GAFulton2.942.640.0086%77%0%171191007ILMadison4.573.721.2293%76%25%261630033MIWayne3.753.100.9991%75%24%390350038OHCuyahoga3.713.400.0088%80%0%390350045OHCuyahoga3.823.430.0089%80%0%390350060OHCuyahoga3.833.430.0089%80%0%390610014OHHamilton4.894.572.0990%84%39%390610042OHHamilton5.024.572.2891%82%41%390618001OHHamilton4.874.552.0790%84%38%420030064PAAllegheny4.613.982.2992%80%46%Table F-2c. Contribution metrics for annual PM2.5 2012 maintenance receptors – part 1.MonitoringSiteStateCounty2012 BaseCase In-StateContribution(μg/m3)2012 TotalContributionfrom All UpwindStates (μg/m3)UpwindContributionas a Percent ofReceptorConcentration# StatesContributing Ator Above0.5% Threshold# StatesContributingAt or Above1% Threshold# StatesContributingAt or Above5% Threshold180970081INMarion2.94 4.78 62%13101180970083INMarion2.94 4.79 62%13101390350065OHCuyahoga2.64 4.28 62%1390390617001OHHamilton2.12 5.42 72%13102F-6Table F-2d. Contribution metrics for annualPM2.5 2012 maintenance receptors – part 2.MonitoringSiteStateCountyTransport(μg/m3) Using0.5% ThresholdTransport(μg/m3) Using1% ThresholdTransport(μg/m3) Using5% Threshold% of TotalTransportCaptured with0.5% Threshold% of TotalTransportCaptured with1% Threshold% of TotalTransportCaptured with5% Threshold180970081INMarion4.243.920.9489%82%20%180970083INMarion4.253.930.9489%82%20%390350065OHCuyahoga3.823.430.0089%80%0%390617001OHHamilton4.884.562.0890%84%38%F-7Table F-3a. Contribution metrics for 24-hourPM2.5 2012 nonattainment receptors – part 1.MonitoringSiteStateCounty2012 BaseCase In-StateContribution(μg/m3)2012 TotalContributionfrom AllUpwind States(μg/m3)UpwindContribution asa Percent ofReceptorConcentration# StatesContributing Ator Above0.5% Threshold# StatesContributingAt or Above1% Threshold# StatesContributing Ator Above5% Threshold10730023ALJefferson6.81 7.87 54% 1270170311016 IL Cook4.75 12.55 73% 1391171191007ILMadison4.03 17.06 81% 14113180970043INMarion9.15 14.19 61% 982180970066INMarion8.39 13.75 62% 1082180970081INMarion8.29 13.73 62% 1082261470005MISt Clair1.97 15.28 89% 1292261630015MIWayne4.35 13.97 76% 1282261630016 MI Wayne5.22 13.20 72% 1072261630019 MI Wayne3.58 12.89 78% 1082261630033 MI Wayne5.03 13.59 73% 1292390350038 OH Cuyahoga8.49 12.86 60% 1484390350060 OH Cuyahoga8.96 11.58 56% 1173420030064 PA Allegheny5.53 14.93 73% 972420030093 PA Allegheny3.21 12.05 79% 1162420030116 PA Allegheny2.96 13.19 82% 1072420070014 PA Beaver2.61 13.51 84% 1272420710007 PA Lancaster6.69 10.45 61% 1571540090011 WV Brooke3.28 15.33 82% 1262550790043 WI Milwaukee3.00 17.74 86% 14103F-8Table F-3b. Contribution metrics for 24-hourPM2.5 2012 nonattainment receptors – part 2.MonitoringSiteStateCountyTransport(μg/m3) Using0.5% ThresholdTransport(μg/m3) Using1% ThresholdTransport(μg/m3) Using5% Threshold% of TotalTransportCaptured with0.5% Threshold% of TotalTransportCaptured with1% Threshold% of TotalTransportCaptured with5% Threshold10730023ALJefferson7.165.920.0091%75%0%170311016 IL Cook11.5110.563.2992%84%26%171191007ILMadison15.9115.149.1593%89%54%180970043INMarion13.1012.857.0492%91%50%180970066INMarion12.7512.266.4793%89%47%180970081INMarion12.7312.226.3993%89%47%261470005MISt Clair14.3513.656.9594%89%46%261630015MIWayne12.9311.806.4193%85%46%261630016 MI Wayne12.2711.506.1793%87%47%261630019 MI Wayne12.0411.606.1193%90%47%261630033 MI Wayne12.5311.676.4292%86%47%390350038 OH Cuyahoga12.3310.838.4596%84%66%390350060 OH Cuyahoga10.559.605.7891%83%50%420030064 PA Allegheny13.7313.318.8392%89%59%420030093 PA Allegheny11.099.656.0392%80%50%420030116 PA Allegheny12.0411.146.8491%85%52%420070014 PA Beaver12.3811.056.5792%82%49%420710007 PA Lancaster9.347.382.8389%71%27%540090011 WV Brooke14.5612.918.7095%84%57%550790043 WI Milwaukee16.6615.659.6994%88%55%F-9Table F-3c. Contribution metrics for 24-hour PM2.5 2012 maintenance receptors – part 1.MonitoringSite State County2012 BaseCase In-StateContribution(μg/m3)2012 TotalContributionfrom AllUpwind States(μg/m3)UpwindContribution asa Percent ofReceptorConcentration# StatesContributing Ator Above0.5% Threshold# StatesContributing Ator Above1% Threshold# StatesContributing Ator Above5% Threshold10732003 AL Jefferson4.61 6.88 60% 1170170310052 IL Cook6.13 12.66 67% 13101170312001 IL Cook3.96 13.57 77% 1192170313301 IL Cook5.10 12.23 71% 1192170316005 IL Cook6.15 11.15 65% 1681171190023 IL Madison3.87 16.65 81% 1594180890022 IN Lake5.77 9.84 63% 1091180890026 IN Lake4.52 11.65 72% 1292261610008MIWashtenaw2.67 15.18 85% 13102390170003OHButler5.90 18.03 75% 1392390350045OHCuyahoga9.04 11.81 57% 1273390350065OHCuyahoga8.51 12.53 60% 1283390618001OHHamilton5.88 14.19 71% 1283390811001OHJefferson5.09 11.36 69% 1162391130032OHMontgomery4.93 15.19 76% 1392420031008PAAllegheny4.23 14.80 78% 1173420031301PAAllegheny4.31 12.29 74% 962420033007PAAllegheny4.26 11.46 73% 962421330008PAYork8.98 13.25 60% 17111550790010WIMilwaukee4.26 18.62 81% 13122550790026WIMilwaukee4.03 15.17 79% 13112F-10Table F-3d. Contribution metrics for 24-hourPM2.5 2012 maintenancereceptors – part 2.MonitoringSiteState CountyTransport(μg/m3) Using0.5% ThresholdTransport(μg/m3) Using1% ThresholdTransport(μg/m3) Using5% Threshold% of TotalTransportCaptured with0.5% Threshold% of TotalTransportCaptured with1% Threshold% of TotalTransportCaptured with5% Threshold10732003 AL Jefferson6.195.230.0090%76%0%170310052 IL Cook11.6710.962.8492%87%22%170312001 IL Cook12.5612.136.7093%89%49%170313301 IL Cook11.1310.695.1791%87%42%170316005 IL Cook10.378.422.4993%76%22%171190023 IL Madison15.8114.3110.9095%86%66%180890022 IN Lake8.888.683.0990%88%31%180890026 IN Lake10.7310.194.6892%88%40%261610008MIWashtenaw14.1213.467.3793%89%49%390170003OHButler17.2716.258.7396%90%48%390350045OHCuyahoga11.059.875.9494%84%50%390350065OHCuyahoga11.6610.576.2893%84%50%390618001OHHamilton13.2812.307.9794%87%56%390811001OHJefferson10.639.505.4594%84%48%391130032OHMontgomery14.4113.427.0595%88%46%420031008PAAllegheny13.8612.929.8494%87%67%420031301PAAllegheny11.3910.787.0993%88%58%420033007PAAllegheny10.7110.126.9794%88%61%421330008PAYork12.0210.652.1191%80%16%550790010WIMilwaukee17.8017.618.6096%95%46%550790026WIMilwaukee14.2113.566.1094%89%40%

# Air Quality Modeling Final Rule

Technical Support Document

Appendix G

Summary of Aggregated Em issions of SO2 and NOx

For States Included in the Transport RuleG-2TableG-1. Group1 and Group 2 States NOXTotal Emissions for each Transport Rule Modeling Case.2005 BaseYear2012 BaseCase2014 BaseCase2014Remedy2014Remedy -2012 BaseCasePercentChange:2014Remedy vs2012 BaseCase2014Remedy- 2014BaseCasePercentChange:2014Remedyvs 2014Base CaseAnnual Total NOXEmissions for States inGroup 1 8,942,956 5,998,929 5,592,557 5,490,517 -508,412 -8.5% -102,039 -1.8%Annual Total NOXEmissions for States inGroup 2 4,626,321 3,351,169 3,083,373 3,030,042 -321,127 -9.6% -53,331 -1.7%Annual Total NOXfor all States included forPM 13,569,277 9,350,098 8,675,929 8,520,559 -829,539 -8.9% -155,370 -1.8%Annual Total NOXEmissions for All StatesFully within the Eastern Modeling Domain 17,265,033 12,013,803 11,173,286 10,974,018 -1,039,784 -8.7% -199,268 -1.8%Annual Total NOXEmissions for All WesternStates 3,887,276 2,959,396 2,751,224 2,751,659 -207,737 -7.0% 435 0.0%Total NOX21,152,309 14,973,199 13,924,510 13,725,678 -1,247,521 -8.3% -198,832 -1.4%G-3Table G-2.Group1 and Group 2 States SO2Total Emissions for each Transport Rule Case.2005 BaseYear2012 BaseCase2014 BaseCase2014Remedy2014Remedy -2012 BaseCasePercentChange:2014Remedyvs 2012Base Case2014Remedy -2014 BaseCasePercentChange:2014Remedyvs 2014Base CaseAnnual Total SO2 Emissions for States inGroup 1 8,695,431 6,841,869 6,198,185 2,999,641 -3,842,228 -56.2% -3,198,544 -51.6%Annual Total SO2 Emissions for States inGroup 2 2,989,533 2,343,536 2,086,522 1,419,361 -924,175 -39.4% -667,161 -32.0%Annual Total SO2 for all States included forPM 11,684,964 9,185,405 8,284,707 4,419,002 -4,766,403 -51.9% -3,865,705 -46.7%Annual Total SO2 Emissions for All StatesFully within the Eastern Modeling Domain 13,545,837 10,361,804 9,512,351 5,680,977 -4,680,826 -45.2% -3,831,374 -40.3%Annual Total SO2 Emissions for All WesternStates 808,533 567,085 566,435 594,818 27,733 4.9% 28,383 5.0%Total SO214,354,370 10,928,889 10,078,786 6,275,795 -4,653,094 -42.6% -3,802,991 -37.7%G-4Table G-3.Group1 and Group 2 States NOXEGU Sector Emissions for each Transport Rule Case.2005 BaseYear2012 BaseCase2014 BaseCase2014Remedy2014Remedy -2012 BaseCasePercentChange:2014Remedy vs2012 BaseCase2014Remedy -2014 BaseCasePercentChange:2014Remedyvs 2014Base CaseAnnual EGU NOXEmissions for States inGroup 1 1,927,858 938,824 940,211 838,171 -100,653 -10.7% -102,039 -10.9%Annual EGU NOXEmissions for States inGroup 2 700,110 444,377 425,686 372,355 -72,022 -16.2% -53,331 -12.5%Annual EGU NOXfor all States included forPM 2,627,967 1,383,201 1,365,897 1,210,527 -172,674 -12.5% -155,370 -11.4%Annual EGU NOXEmissions for All StatesFully within the Eastern Modeling Domain 3,224,300 1,715,510 1,718,178 1,518,910 -196,600 -11.5% -199,268 -11.6%Annual EGU NOXEmissions for All WesternStates 504,861 369,180 371,244 371,680 2,500 0.7% 435 0.1%Total EGU NOX3,729,161 2,084,689 2,089,422 1,890,590 -194,099 -9.3% -198,832 -9.5%G-5Table G-4.Group1 and Group 2 States SO2EGU Sector Emissions for each Transport Rule Modeling Case.2005 BaseYear2012 BaseCase2014 BaseCase2014Remedy2014Remedy -2012 BaseCasePercentChange:2014Remedy vs2012 BaseCase2014Remedy -2014 BaseCasePercentChange:2014Remedyvs 2014Base CaseAnnual EGU SO2 Emissions for States inGroup 1 6,764,335 5,313,000 4,752,537 1,553,993 -3,759,007 -70.8% -3,198,544 -67.3%Annual EGU SO2 Emissions for States inGroup 2 2,143,069 1,702,727 1,468,071 800,910 -901,816 -53.0% -667,161 -45.4%Annual EGU SO2 for all States included forPM 8,907,403 7,015,726 6,220,608 2,354,903 -4,660,823 -66.4% -3,865,705 -62.1%Annual EGU SO2 Emissions for All StatesFully within the Eastern Modeling Domain 10,019,270 7,635,785 6,912,529 3,081,155 -4,554,630 -59.6% -3,831,374 -55.4%Annual EGU SO2 Emissions for All WesternStates 361,613 224,026 247,039 275,422 51,397 22.9% 28,383 11.5%EGU SO210,380,883 7,859,810 7,159,569 3,356,577 -4,503,233 -57.3% -3,802,991 -53.1%G-6Table G-5.26-State1Total and EGU Sector Ozone Season NOXEmissions for each Modeling Scenario.2005 BaseYear2012BaseCase2014BaseCase2014Remedy2014Remedy- 2012BaseCasePercentChange:2014Remedyvs 2012Base Case2014Remedy- 2014BaseCasePercentChange:2014Remedyvs 2014Base CaseOzone Season EGU NOxEmissions for StatesIncluded for Ozone 1,001,600 671,939 668,513 593,833 -78,106 -11.6% -74,680 -11.2%Ozone Season Total NOxEmissions for StatesIncluded for Ozone 6,153,473 4,455,600 4,128,792 4,054,111 -401,489 -9.0% -74,680 -1.8%1Includes the 20 states covered by the final Transport Rule for ozone season NOx budgets and the 6 states covered for ozone season NOx budgets in the SupplementalNotice.

# H-1 

# Air Quality Modeling Final Rule

Technical Support Document

Appendix H

Concentration and Difference Maps for

Annual PM 2.5 

# , 24-Hour PM 2.5 

# , and 8-Hour Ozone

# H-2 

# Annual PM 2.5 

# Maps

# H-3 

# 2003-2007 Average Ambient Annual PM 2.5 

# Concentrations

2012 Base Case Annual PM 2.5



# Concentrations

# H-4 

# 2014 Base Case Annual PM 2.5 

# Concentrations

2014 Remedy Annual PM 2.5



# Concentrations

# H-5 

# 2014 Remedy – 2012 Base Case Difference in Annual PM 2.5 

# Concentrations

2014 Remedy – 2014 Base Case Difference in Annual PM 2.5



# Concentrations

# H-6 

# 24-Hour PM 2.5 

# Maps

# H-7 

# 2003-2007 Average Ambient 24-Hour PM 2.5 

# Concentrations

2012 Base Case 24-Hour PM 2.5 

# Concentrations

# H-8 

# 2014 Base Case 24-Hour PM 2.5 

# Concentrations

2014 Remedy 24-Hour PM 2.5



# Concentrations

# H-9 

# 2014 Remedy – 2012 Base Case Difference in 24-Hour PM 2.5 

# Concentrations

2014 Remedy – 2014 Base Case Difference in 24-Hour PM 2.5 

# Concentrations

# H-10 

# 8-Hour Ozone Maps

# H-11 

# 2003-2007 Average Ambient 8-Ho ur 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 Differen ce in 8-Hour Ozone Concentrations

2014 Remedy – 2014 Base Case Differen ce in 8-Hour Ozone Concentrations