United States

Environmental Protection

Agency

Air and Radiation EPA420-R-00-028

December 2000

Technical Support

Document for the

Heavy-Duty Engine and

Vehicle Standards and

Highway Diesel Fuel

Sulfur Control

Requirements:

Air Quality Modeling

Analyses

 Printed on Recycled Paper
i
ii

Table of Contents

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

II Emissions Inventory Estimates .......................................................................... 2

A. Ozone Precursors (Summer Season Day) ............................................... 2

B. Particulate Matter and Precursors (Annual) ............................................ 3

III Ozone Modeling over the Eastern U.S. ............................................................. 4

A. Episode Selection ................................................................................. 4

1. Episodic Meteorological Conditions and Ozone Levels ......... 5

2. General Representativeness of Episodic Ozone ..................... 9

B. Domain and Grid Configuration ............................................................ 10

C. Meteorological Modeling ...................................................................... 11

D. Development of Other UAM-V Input Files ............................................ 13

E. Model Performance Evaluation .............................................................. 13

1. Statistical Definitions ..................................................... 13

2. Domainwide and Regional Model Performance .............. 14

3. Local-scale Model Performance ...................................... 16

4. Model Performance over the Western U.S. Domain ........ 18

F. Ozone Modeling Results for Future-Year Scenarios ............................... 19

1. Future Year Model-Predicted Exceedances ..................... 19

2. Impacts of the HDE Rule on One Hour Ozone ................ 20

a. Definition of Areas for Analysis ....................... 20

b. Description of Ozone Metrics .......................... 20

3. Need for HDE Rule Based on Unhealthy 

8-Hour Ozone Concentrations ......................................... 21

4. One-Hour Ozone Relative Reduction Factors .................. 21

IV Particulate Matter Modeling over the Continental U.S. ...................................... 22

A. REMSAD Model Description.................................................................. 22

1. Gas Phase Chemistry .............................................................. 23

2. PM Chemistry ........................................................................ 23

B. REMSAD Modeling Domain .................................................................. 24

C. REMSAD Inputs .................................................................................... 24

1. Meteorological Data .............................................................. 25

2. Initial and Boundary Conditions, and Surface Characteristics .. 28

3. Emissions Inputs .................................................................... 30

D. Model Performance Evaluation ............................................................... 30

1. Statistical Definitions ............................................................. 32

2. Results of REMSAD Performance Evaluation ........................ 34

a. PM2.5 Performance ..................................................... 34

b. Sulfate Performance ..................................................... 35
iii

c. Elemental Carbon Performance .................................... 35

d. Organic Aerosol Performance ...................................... 35

e. Nitrate Performance ..................................................... 37

f. PMFINE-Other (crustal) Performance .......................... 37

g. Summary of Model Performance

 Results using IMPROVE Data .................................... 37

h. Comparisons to Other Observational Databases ............ 38

E. Visibility Calculations .............................................................................. 38

F. Need for HDE Rule Based on Unhealthy Annual Mean 

PM2.5 Concentrations ............................................................................. 39

V References ........................................................................................................ 40

Appendix A Areas in the East with Predicted Exceedances in 2007, 2020, and/or 2030 and 1-

Hour Design Values >=125 ppb or >=113 ppb.

Appendix B Number of 12km Grid Cells Assigned to Each CMSA/MSA.

Appendix C 1-hour Ozone Metrics.

Appendix D 8 Hour Relative Reduction Factors.

Appendix E 1999 Annual Mean PM2.5 Values and Future Year Predictions Based on RRfs.

Appendix F IMPROVE Monitoring Sites used in the REMSAD Model Performance

Evaluation.
1

As described in Section III , base year ozone predictions from the western model simulations seriously

underestimated observed concentrations to the extent that the results were not used for the HDE rulemaking.

2

PM modeling was performed for the 2020 and 2030 scenarios and ozone modeling was performed for all

three scenarios. The rationale for selecting these time periods is described in the preamble for this rule.

3

For this analysis the term exposure is used to describe the number of people living in areas with

concentrations above various cut-points.

1

I. Introduction

This document describes the procedures and results of the air quality modeling analyses

used to support the Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel (HDE)

final rulemaking. The air quality modeling was conducted to support several components of the

rulemaking including: 

(a) an assessment of the need for the HDE program, 

(b) an assessment of the costs and benefits associated with the rulemaking, and 

(c) an assessment of the expected impact of the program on ozone and PM levels.

The air quality model applications include episodic regional scale ozone modeling for the

eastern and western U.S. and annual particulate matter (PM) modeling on a continental scale

covering the 48 contiguous States. For both ozone and PM, 1996 base year simulations were

made to examine the ability of the modeling systems to replicate observed concentrations of

these pollutants.1

 This was followed by simulations for several future-year “base case” scenarios

(i.e., 2007, 2020, and 2030)2. The results of the future base case model runs were used to support

the need for the HDE emissions reductions to help mitigate unhealthy concentrations of ozone

and PM. In this regard, the predictions from these model runs were used to determine the extent

of future 1-hour ozone exceedances (i.e., 1-hour daily maximum ozone concentrations >=125

ppb) and the magnitude of “exposures”3

 to unhealthy concentrations of ozone and PM2.5 (i.e.,

particulates with a diameter <= 2.5 ug/m3). For 2020 and 2030 additional simulations were made

to examine the impacts of the HDE controls on air quality in these years. In addition, the outputs

of the 2030 base and control case model runs were used to calculate portions of the monetized

benefits of the rule as part of the cost-benefits analysis.

The air quality model simulations, associated input and output data sets, and model

performance statistics used to support the above analyses are described in this document. The

procedures for calculating the monetized benefits of the rule are described in Chapter VII of the

Regulatory Impact Assessment (RIA) document (EPA, 2000a). Also, in Chapter II of the RIA

are discussions of (1) how the projected future-year exposures to ozone and PM2.5 were

calculated along with the results of these analyses and (2) the impacts of the rule on future 1-hour

ozone exceedances. 

The remainder of this report includes a description of the overall magnitude of emissions
2

for each of the scenarios modeled, the ozone and PM modeling systems, the time periods

modeled, the base year model performance evaluations, and procedures for generating the results

of the modeling for subsequent use in various HDE analyses. All of the air quality modeling

input and output data sets can be obtained from the following ftp site:

ftp.epa.gov/modelingcenter/Heavy_Duty_Diesel

II. Emissions Inventory Estimates

In order to complete the requisite ozone and PM modeling, it was necessary to first

develop a national mass emissions inventory. This mass emissions inventory was then used as

the basis for developing component input files for the modeling. The development and details of

these inventories for each of the scenarios (i.e., 1996 base, 2007 base, 2020 base, 2020 control,

2030 base, and 2030 control) are more fully described elsewhere (EPA, 2000b, 2000c). 

The mass inventories are prepared at the county-level for on-highway mobile, electric

generating unit (EGU), non-EGU point, stationary area, and nonroad sources. The inventories

contain annual and typical summer season day (SSD) emissions for the following pollutants: 

oxides of nitrogen (NOx), volatile organic compounds (VOC), carbon monoxide (CO), oxides of

sulfur (SOx), primary particulate matter with an aerodynamic diameter less than or equal to 10

micrometers and 2.5 micrometers (PM10 and PM2.5), ammonia (NH3), and secondary organic

aerosols (SOA). The 2007, 2020, and 2030 Base Case inventories are prepared by applying

growth and control assumptions to the 1996 Base Year inventory. The 2007, 2020, and 2030

Control Case inventories are developed from the 2007, 2020, and 2030 Base Case inventories,

respectively, by applying HDE control and fuel measures to the on-highway vehicle and nonroad

emission source sectors. Section II.A. and II.B. below provide summaries of the emissions for a

summer season day and on an annual basis, respectively. The summer day emissions are

provided to give a general sense of the magnitude of emissions used in the ozone modeling. 

Similarly, the annual emissions give a general sense of what was used for modeling

concentrations of primary and secondary PM. The procedures for developing the model-ready

emissions inputs are described in Section III for ozone modeling and Section IV for PM

modeling.

A. Ozone Precursor Emissions (Summer Season Day)

Table II-1 displays the typical summer season day 1996 base year emissions for those

States within the Eastern U.S. ozone modeling domain (see Section III). Emissions are provided

for volatile organic compounds (VOC), nitrogen oxides (NOx), and carbon monoxide (CO)

which are the anthropogenic precursor emissions for ozone.

Table II-2 shows the total summer day emissions for all States in the East combined along

with the percent change between various emissions scenarios.
3

Table II-1. Summer season daily State-level emissions (tons) for the 1996 Base.

State VOC NOx CO State VOC NOx CO

Alabama 1,254 1,971 5,866 Nebraska1

612 875 2,750

Arkansas 709 935 3,092 New Hampshire 240 267 1,011

Connecticut 476 603 2,335 New Jersey 1,330 1,333 4,785

Delaware 167 243 663 New York 2,385 2,054 9,589

DC 61 60 223 North Carolina 2,089 2,292 8,140

Florida1

2,791 3,443 16,065 North Dakota1350 857 1,096

Georgia 1,715 2,255 9,615 Ohio 2,364 3,758 11,977

Illinois 2,428 3,187 8,498 Oklahoma1

1,149 1,495 6,510

Indiana 1,536 2,652 7,177 Pennsylvania 2,068 2,924 10,112

Iowa 785 1,216 2,893 Rhode Island 159 103 635

Kansas1

782 1,661 3,212 South Carolina 996 1,230 4,412

Kentucky 998 2,276 3,857 South Dakota1

270 460 1,069

Louisiana 1,274 2,562 6,501 Tennessee 1,660 2,384 5,915

Maine1

323 273 1,379 Texas14,350 6,893 17,932

Maryland 601 1,078 3,641 Vermont 139 118 602

Massachusetts 901 958 3,669 Virginia 1,459 1,865 6,560

Michigan1

2,427 2,420 9,269 West Virginia 418 1,340 1,931

Minnesota1

1,263 1,511 4,214 Wisconsin 1,354 1,450 4,720

Mississippi 934 1,109 3,500

Missouri 1,158 1,761 5,563 Total 45,975 63,872 200,978

1. State is partially outside the ozone modeling domain, but the emissions totals are provided for the entire State.

Table II-2. Total summer season daily emissions (tons) for the 37 States within the Eastern

modeling domain for each of the six modeling scenarios.

VOC NOx CO

1996 Base 45,975 63,872 200,978 Scenario Diff (%) VOC NOx CO

2007 Base 36,285 46,822 195,401 From 1996 -21.1 -26.7 -2.8

2020 Base 37,190 39,948 230,507 From 2007 Base 2.5 -14.7 18.0

2020 Control 36,801 36,086 228,481 From 2020 Base -1.0 -9.7 -0.9

2030 Base 41,007 42,239 261,829 From 2020 Base 10.2 5.7 13.6

2030 Control 40,499 36,806 259,186 From 2030 Base -1.2 -12.9 -1.0

B. Particulate Matter and Precursor Emissions (Annual)

Table II-3a shows the national annual emissions of primary PM and precursor species for

secondary PM for the 1996 base year, 2030 base case, and 2030 control case scenarios. Table II-
4

3b shows the percent change in emissions between several of these scenarios.

Table II-3a. Total national annual emissions (tons) for the 48 States included in the PM

modeling. 

Organic

Carbon

Elemental

Carbon

Gaseous

Sulfate

Primary

Nitrate

Other1

PM-2.5

Total 

PM-2.5

1996 Base 1,224,857 566,051 167,392 13,386 2,210,692 4,182,378

2030 Base 1,416,023 536,979 220,966 17,618 2,611,202 4,802,789

2030 Control 1,394,587 465,905 220,189 17,481 2,615,144 4,713,306

1. Other PM-2.5 contains primarily crustal material.

VOC NOx CO SO2 NH3 SOA

1996 Base 18,522,037 26,117,335 98,637,147 18,789,382 4,762,317 202,517

2030 Base 15,676,964 18,717,720 120,491,650 16,436,874 5,400,554 163,196

2030 Control 15,430,241 16,157,296 119,211,301 16,285,231 5,400,554 157,884

Table II-3b. The percent change in total national annual emissions for selected scenarios.

Organic

Carbon

Elemental

Carbon

Gaseous

Sulfate

Primary

Nitrate

Other 

PM-2.5

Total 

PM-2.5

2030 Base vs 1996 Base 15.6 % - 5.1 % 32.0 % 31.6 % 18.1 % 14.8 %

2030 Control vs 2030 Base - 1.5 % - 13.2 % - 0.4 % - 0.8 % 0.1 % - 1.9 %

VOC NOx CO SO2 NH3 SOA

2030 Base vs 1996 Base -15.4 % -28.3 % 22.2 % -12.5 % 13.4 % -19.4 %

2030 Control vs 2030 Base -1.6 % -13.7 % -1.1 % -0.9 % 0.0 % -3.3 %

III. Ozone Modeling over the Eastern United States

The Urban Airshed Model-Variable Grid (UAM-V), (SAI, 1996) was used as the tool for

simulating base year and future concentrations of ozone in support of the HDE air quality

assessments. UAM-V was designed for the expressed purpose of modeling regional ozone

episodes. The model contains a subgrid-scale plume model, allows for nested finer resolution

grids, and requires hourly meteorological fields. Model runs were made for the 1996 base year as

well as for a 2007 base, and 2020 and 2030 base and control scenarios. As described below, each

of these emissions scenarios was simulated for three meteorological datasets during the summer

of 1995.

A. Episode Selection

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

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

meteorological conditions leading to ambient ozone levels similar to an area’s 1 -hour design
4

Generally, the design value for a monitoring site is the 4th highest 1-hour daily maximum

concentration over a 3 year period. The design value for an area is the highest design value

among all sites in the area.

5

value4

. Ideally, the modeling time periods would be supported by large amounts of ambient data

that could be used in input development and model evaluation. The issue, in terms of regional

modeling, is how to meet these episode selection goals over a large number of individual ozone

non-attainment areas without having to model several entire ozone seasons (impossibly time

consuming and resource-intensive). It is inevitable that the chosen episodes will feature

observed ozone lower than the design value in some areas and greater than the design value in

other areas. For the HDE analyses, we simulated the same episodes during the summer of 1995

as used for the Tier 2 rule. These periods were selected because 1995 is a recent time period for

which we had model-ready meteorological inputs.

Based on a review of observed daily maximum ozone concentrations across the eastern

U.S. during June through August, three episodes were selected for ozone modeling: June 12-24,

July 5-15, and August 10-21. The start of each episode was chosen to correspond to days with no

ozone exceedances. Thirty episode days were modeled in all, not including the three ramp-up

days used in each episode to minimize the effects of initial conditions. The meteorological

conditions and ozone levels during each episode are described below.

1. Episodic Meteorological Conditions and Ozone Levels

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

the typical characteristics of ozone episodes. On a synoptic scale, these conditions usually result

from a combination of high pressure aloft (500 millibars) and at the surface. At a smaller scale,

the conditions that lead to local ozone exceedances can vary from location to location (based on

factors such as wind direction, sea/lake breezes, etc.) The meteorological and resultant ozone

patterns for the three 1995 modeling episodes are discussed in more detail below.

June 12-24, 1995

The initial stages of this episode were fairly typical from the standpoint of regional

meteorology. A 500-millibar ridge propagated into the eastern U.S. from the west. The ridge

was associated with a surface high that migrated south from Canada. A cold front passed

completely through the region by June 13 (Wednesday) allowing the modeling to start with a

clean set of initial conditions. Maximum temperatures during the June 15 - 17-period were

generally in the 80s and little precipitation was measured. By June 17, a strong (1028 mb)

surface high was anchored over the region.

The observed ozone fields in the early part of the episode were high (e.g., 125-130 ppb)

only in locations such as Houston, Beaumont, and Lake Michigan. It was not until June 17 that

concentrations exceeded 100 ppb over large parts of the domain (i.e., Midwest and Northeast
6

Corridor).

However, as the aloft pattern amplified, a cut off low developed over the southeastern

U.S. On the 19th and 20th, cooler temperatures and occasional rain prevailed in the Southeast. 

This resulted in a temperature pattern that featured maximums of 90-100 degrees F over the

northern tier of States and 75-85 degrees F in the south. Additionally, the strong cyclonic

circulation around this low resulted in aloft flow from east to west over the mid-Atlantic and

Ohio Valley States. Ozone continued to build throughout this period in the Northeast, peaking

on the 19th and 20th with values greater than 125 ppb common from Washington, D.C. to Boston.

The last four days of the episode were relatively clean in the Northeast due to the

combination of a “backdoor” cold front and the northward migration of the cut off low. 

Meanwhile ozone conducive conditions returned to the Texas Gulf Coast and Lake Michigan

areas. The highest value over the entire summer of 1995 (210 ppb) was recorded near Houston

on the 22nd. The episode came to an end on the 25th as a long-wave trough replaced the 500-mb

ridge over the eastern U.S. 

Table III-1 shows a State-by-State listing of daily exceedance counts during the June

1995 HDE episode. There were 85 exceedances of the ozone NAAQS during this period. The

peak day of the episode was June 19. Texas had the most exceedances (28).

Table III-1. Summary of exceedance days, by State/day, for the June 1995 HDE

episode. Dates in bold indicate episode days (i.e., non-ramp-up days).

AL AR CT DE DC FL GA IL IN KY LA ME MD MA MI M NH NJ NY NC OH OK PA RI SC TN TX VA W WI TOT

6/12/95 0

6/13/95 1 1

6/14/95 1 1

6/15/95 1 1 2

6/16/95 1 12

6/17/95 1 4 2 7

6/18/95 2 1 1 1 1 6

6/19/95 3 3 1 7 2 4 2 1 8 1 32

6/20/95 2 2 2 1 3 3 13

6/21/95 7 7

6/22/95 7 7

6/23/95 2 4 6

6/24/95 4 2 2 3 11
7

July 5-15, 1995

The mid-July episode, which covered most of the Ozone Transport Assessment Group

(OTAG) July 1995 episode, is much easier to characterize from a meteorological perspective. A

strong 500-mb ridge progressed from west to east across the eastern U.S. over the period. This

feature was centered over Colorado on the 8th, over Kansas on the 11th, over Illinois on the 13th,

and over Pennsylvania on the 15th. The ridge finally flattened out on the 16th allowing a surface

cold front to clean out the northern portions of the domain and less stable conditions to prevail

over the southern portions.

Excessively hot temperatures accompanied the core of this strong ridge. Temperatures in

the 90s and 100s were common throughout the episode. Rainfall was confined primarily to the

coastal regions in the south and southeast. Wind speeds were moderate and the mean transport

direction was southwest to northeast, especially over the northern half of the domain.

From the 8th through the 10th, ozone levels in the airmass over the eastern U.S. were

gradually increasing. Ozone hot spots occurred in urban areas like Houston, Dallas, and Atlanta. 

By the 11th, the area of regionally high ozone (roughly defined as the area where peak ozone was

greater than 75 ppb) had expanded to encompass most of the domain. On top of that

“background,” local contributions from urban emissions yielded ozone exceedances in places

like Kansas City, St. Louis, Birmingham, Dallas, Memphis, Atlanta, Baton Rouge, Evansville,

Louisville, Cincinnati, Chicago, Milwaukee, Columbus, and Baltimore/Washington on the 11th

and 12th. 

July 13 and 14 marked the highest regional ozone levels of the summer as most sites,

with the exception of those in the Southeast, exceeded 100 ppb. Almost all major metropolitan

areas in the northern two-thirds of the domain measured values greater than 125 ppb on this day. 

For the 14th and 15th, most of the ozone problem shifted east and south due to both transport and

the location of the aloft core of warm air. The Northeast Corridor, Charlotte, Greensboro,

Birmingham, and Atlanta all had exceedances of the standard on this day. The episode ended

abruptly on the 16th (Sunday) for most of the domain, although elevated ozone lingered over the

southern regions into the early part of the next week.

Table III-2 shows a State-by-State listing of daily exceedance counts during the July 1995

HDE episode. There were 199 exceedances of the ozone NAAQS during this period. The peak

day of the episode, in terms of exceedance monitors was July 14. Texas had the most

exceedances (26).
8

Table III-2. Summary of exceedance days, by State/day, for the July 1995 HDE episode. Dates

in bold indicate episode days (i.e., non-ramp-up days).

AL AR CT DE DC FL GA IL IN KY LA ME MD MA MI M NH NJ NY NC OH OK PA RI SC TN TX VA W WI TOT

7/05/95 0

7/06/95 0

7/07/95 2 2

7/08/95 2 2

7/09/95 1 4 5

7/10/95 4 1 1 6

7/11/95 1 1 3 1 3 1 5 15

7/12/95 1 1 1 1 5 4 1 3 1 5 7 30

7/13/95 1 5 8 1 2 3 7 6 1 4 1 6 1 46

7/14/95 7 3 2 2 1 4 2 6 7 5 3 3 3 3 1 1 53

7/15/95 1 3 3 2 2 10 1 6 3 5 4 40

August 7-21, 1995

A one-day ozone event occurred over New England on August 10, and a separate one-day

event occurred in the Lake Michigan region on the 12th. By the 14th, high pressure aloft and at

the surface dominated the eastern half of the U.S. Temperatures ranged from 90 to 100 degrees F

over most of the domain throughout this period. Ozone was highest over Georgia, Tennessee,

Kentucky, North Carolina, and Virginia during this period. Hurricane Felix brushed the East

Coast from the 16th – 18th, but appeared to have little effect on ozone levels or ozone transport

away from the immediate eastern seaboard.

A weak cold front, draped across the Great Lakes over most of the episode, moved slowly

southward over the eastern half of the Appalachians during the August 18-21 period. This front

initiated precipitation that helped keep ozone concentrations low in the upper Midwest. The 18th

featured high ozone across the South in cities such as: Atlanta, Charlotte, Birmingham, Augusta,

as well as St. Louis. On the 19th and 20th, as the front slid further south, ozone air quality

improved over this region as well. Only sites in Texas and Louisiana remain above 125 ppb. 

The 21st marked the fourth day that the same airmass has resided over the Northeast.

Table III-3 shows a State-by-State listing of daily exceedance counts during the August

1995 HDE episode. There were 90 exceedances of the ozone NAAQS during this period. The

peak day of the episode, in terms of exceedance monitors was August 21st.
9

Table III-3. Summary of exceedance days, by State/day, for the August 1995 HDE episode. 

Dates in bold indicate episode days (i.e., non-ramp-up days).

AL AR CT DE DC FL GA IL IN KY LA ME MD MA MI M NH NJ NY NC OH OK PA RI SC TN TX VA W WI TOT

8/07/95 0

8/08/95 0

8/09/95 0

8/10/95 1 1 2 1 5

8/11/95 1 1 1 3

8/12/95 1 4 1 1 1 8

8/13/95 1 1

8/14/95 1 3 1 1 1 7

8/15/95 1 3 1 1 2 8

8/16/95 2 3 5

8/17/95 2 2 1 1 1 7

8/18/95 4 5 1 1 1 12

8/19/95 1 2 6 9

8/20/95 6 6

8/21/95 3 3 1 3 2 2 2 1 1 1 19

2. General Representativeness of Episodic Ozone as Compared to Design Values

In order to examine the representativeness of ozone levels during the episodes selected

for modeling, a comparison was made between the daily maximum observed values to recent

design values. In this analysis, the magnitude of county-specific design values for 1996-1998

were compared to the highest through 5th highest concentrations measured in the county during

the three episodes. Counties with design values (DV) >120 ppb were selected for analysis in

order to focus on concentrations approaching and exceeding the NAAQS. As can be seen in

Table III-4, 70 of the 110 counties examined have design values within 15 ppb of the highest

observed ozone in the HDE episodes. Additionally, the second-high observed value yields more

values below the design value than above it. The results indicate that the selected episodes

contain measured ozone concentrations that are representative of design values over a large

portion of the eastern U.S.
5

 Model concentrations are not calculated for the outer periphery of the nested grid. Two

buffer rows and columns are needed to solve the advection portion of the mass balance equation.

10

Table III-4. Summary of Comparing the Five Highest Daily Maxima to Recent Design Values.

Ranking of

Observation

within HDE Days 

# of cases in which the

observed was greater than the

design value by 15 ppb

# of cases in which the

observed was within 15 ppb of

the design value

# of cases in which the observed

was less than the design value

by 15 ppb

Highest ozone 32 70 8

2nd high ozone 10 80 20

3rd high ozone 2 71 37

4th high ozone 0 57 53

5th high ozone 0 45 65

B. Domain and Grid Configuration

As with episode selection, there are also several considerations involved in selecting the

domain and grid configuration to be used in the ozone modeling analysis. The modeling domain

should encompass the area of intended analysis with an additional buffer of grid cells to

minimize the effects of uncertain boundary condition inputs. Grid resolution should be

equivalent to the resolution of the primary model inputs (emissions, winds, etc.) and equivalent

to the scale of the air quality issue being addressed. The regional/national HDE ozone analyses

used the previously established Tier 2 domain to model regional ozone over the eastern U.S. 

The HDE UAM-V modeling was completed using two grids of varying extent (shown in

Figure III-1) and resolution as described below.

Main Grid: Resolution: 1/2° longitude, 1/3° latitude (approximately 36 km)

East-West extent: -99 W to -67 W

North-South extent: 26 N to 47 N

Vertical extent: Surface to 4 km

Dimensions: 64 by 63 by 9 

Nested Grid5

: Resolution: 1/6° longitude, 1/9° latitude (approximately 12 km)

East-West extent: -92 W to -69.5 W

North-South extent: 32 N to 44 N

Vertical extent: Surface to 4 km

Dimensions: 137 by 110 by 9
11

Figure III-1. Map of the HDE Eastern U.S. modeling domain. The outer box denotes the entire

modeling domain (36 km) and the inner box indicates the fine grid location (12 km). 

The vertical layers were consistent between the two grids: 0-50, 50-100, 100-300, 300-

600, 600-1000, 1000-1500, 1500-2000, 2000-2500, 2500-4000. All model heights are in meters

above ground level. The number of vertical layers is greater than past regional-scale modeling 

applications (e.g., OTAG) and was intended to better capture the depth of the planetary boundary

layer.

This modeling domain allows for the calculation of residual future ozone exceedances

and the effects of HDE emissions reductions over most major metropolitan areas in the eastern

U.S. (The Dallas-Fort Worth area may be the exception given its proximity to the western

boundary.)

C. Meteorological Modeling

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

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

and destruction of pollutant material. In particular, the UAM-V model used in the HDE analyses

requires five meteorological input files: wind (u- and v-vector wind components), temperature,
6 The inner nests were modeled with 34 layers while the outer 108 km domain was

modeled with 28 layers.

12

water vapor mixing ratio, atmospheric air pressure, and vertical diffusion coefficient. Fine grid

values of wind and vertical diffusivity are used; the other fine grid meteorological inputs are

interpolated from the coarse grid files.

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

the New York Department of Environment and Conservation (NYDEC) using the Regional

Atmospheric Modeling System (RAMS), version 3b. RAMS (Pielke et. al., 1992) is a numerical

meteorological model that solves the full set of physical and thermodynamic equations which

govern atmospheric motions. The output data from RAMS, which is run in a polar stereographic

projection and a sigma-p coordinate system, are then mapped to the UAM-V grid. Two separate

meteorological UAM-V inputs, cloud fractions and rainfall rates, were developed based on

observed data.

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

12 km with 28-346

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

grids. The two finer grids were at least as large as their UAM-V counterparts. In order to keep

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

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

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

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

northeast U.S.

A summary of the settings and assorted input files employed in this RAMS application

are listed below in Table III-5. For more detail on the meteorological model configuration, see

Lagouvardos et al. (1997).
13

Table III-5. Summary of RAMS model settings and inputs.

Model Setting/Input File Description

Input- Topography 30 arc-second data from EROS Data Center.

Input - Sea-surface temperature Mean monthly climatological data from NCAR.

Input - Vegetation type 10 arc-minute data from NOAA/NGDC.

Input - Initial conditions The model was initialized with gridded one-degree ECMWF data

Input - Soil moisture Six layer soil model. Assumed deeper layers were more moist than

Setting Non-hydrostatic

Setting - Lateral boundary conditions Klemp-Wilhelmson

Setting - Horizontal diffusivity Smagorinsky

Setting - Vertical diffusivity Mellor and Yamada parameterization scheme

Setting - Shortwave/Longwave radiation Mahrer and Pielke

A limited model performance evaluation (Sistla, 1999) was completed for a portion of the

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

procedure were compared against modeled data at the surface and aloft. In general, there were no

widespread biases in temperatures and winds. Furthermore, the meteorological fields were

compared before and after being processed into UAM-V inputs. It was concluded that this

preprocessing did not distort the meteorological fields. 

D. Development of Other UAM-V Input Files

The hourly, gridded, model-ready anthropogenic emissions for the six modeling scenarios

were created using EMS-95 (Alpine Geophysics, 1994). As part of this processing, emissions for

stationary and nonroad sources were developed for typical summer weekday, Saturday, and

Sunday emissions levels and then used for the corresponding day-types that occurred during the

episodes. The exceptions to this are utility emissions which were adjusted to reflect differing

emissions levels during June, July, and August (EPA, 2000b). Hourly mobile source emissions

were developed using grid-specific temperature data. Biogenic emissions were developed using

the BEIS-2 model (Pierce et al., 1998). In addition, the photochemical grid model requires

several other types of input data. In general, most of these miscellaneous model files were taken

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

and as lateral and top boundary conditions as in Tier 2 (EPA, 1999b). 

The model requires information regarding land use type and surface albedo for all Layer 1

grid cells in the domain. Existing Tier 2/OTAG data were used for these non-day-specific files. 

Photolysis rates were developed using the JCALC portion of the UAM-V modeling system (SAI,
14

1996). Turbidity values were set equal to a constant thought to be representative of regional

conditions.

E. Model Performance Evaluation

The goal of the base year modeling was to reproduce the atmospheric processes resulting

in high ozone concentrations over the eastern United States during the three 1995 episodes

selected for modeling. Note that the base year of the emissions was 1996 while the episodes are

in 1995. The effects on model performance of using 1996 base year emissions for the 1995

episodes are unknown.

An operational model performance evaluation for surface ozone for the 1995 episodes

was performed in order to estimate the ability of the modeling system to replicate base year

ozone concentrations. This evaluation is comprised principally of statistical assessments of

model versus observed pairs. The robustness of an operational evaluation is directly proportional

to the amount and quality of the ambient data available for comparison.

1. Statistical Definitions

Below are the definitions of those statistics used for the evaluation. The format of all the

statistics is such that negative values indicate model ozone predictions that were less than their

observed counterparts. Positively-valued statistics indicate model overestimation of surface

ozone. Statistics were not generated for the first three days of an episode to avoid the

initialization period. The operational statistics were principally generated on a regional basis in

accordance with the primary purpose of the modeling which is to assess the need for, and impacts

of, a national mobile source emissions control program. However, a local assessment of model

performance was also completed to ensure that the model did not significantly overestimate the

need for controls in individual areas. The statistics were calculated for (a) the entire HDE

domain, (b) four quadrants (Midwest, Northeast, Southeast, Southwest), and (c) 47 local areas. 

The statistics that were calculated for each of these sets of areas are described below.

Domainwide unpaired peak prediction accuracy: This metric simply compares the peak

concentration modeled anywhere in the selected area against the peak ambient concentration

anywhere in the same area. The difference of the peaks (model - observed) is then normalized by

the peak observed concentration.

Peak prediction accuracy: This metric averages the paired peak prediction accuracy calculated for

each monitor in the subregion. It characterizes the capacity of the model to replicate peak

(afternoon) ozone over a subregion. The daily peak model versus daily peak observed residuals

are paired in space but not in time.

Mean normalized bias: This performance statistic averages the normalized (by observation)

difference (model - observed) over all pairs in which the observed values were greater than 60

ppb. A value of zero would indicate that the model over predictions and model under predictions

exactly cancel each other out.
15

Mean normalized gross error: The last metric used to assess the performance of the HDE base

cases is similar to the above statistic, except in this case it is the absolute value of the residual

which is normalized by the observation, and then averaged over all sites. A zero gross error

value would indicate that all model concentrations (in which their observed counterpart was

greater than 60 ppb) exactly matched the ambient values.

2. Domainwide and Regional Model Performance

As with previous regional photochemical modeling studies, the HDE base year

simulations are accurate representations of the historical ozone patterns at certain times and

locations and poor representations at other times and locations over this large modeling domain. 

From a qualitative standpoint, there appears to be considerable similarity on most days between

the observed and simulated ozone patterns. Additionally, where possible to discern, the model

appears to follow the day-to-day variations in synoptic-scale ozone fairly closely. Other relevant

observations, in terms of model performance, are listed below.

• Mean normalized bias and mean normalized gross error values are similar to the Tier 2

model performance statistics for the entire domain and the four quadrants as summarized

in Table III-6. In turn, the Tier 2 model performance was very similar to what was

observed in OTAG, as summarized in the Tier 2/Low Sulfur Technical Support

Document (TSD) (EPA, 1999b).

Table III-6. Tier 2 and HDE Base Year model performance for the entire grid and by quadrant.

Mean Normalized Bias Tier 2 

June 95

Tier 2

 July 95

Tier 2 August

95

HDE 

June 95

HDE

 July 95

HDE

August 95

Domain -10 -6 +2 -13 -11 +5

Midwest -11 -13 +7 -15 -16 +10

Northeast -17 -9 -9 -20 -11 -15

Southeast -4 +4 +7 -7 -3 +12

Southwest +2 +8 +6 +1 +3 +11
16

Mean Normalized Gross Error Tier 2 

June 95

Tier 2

 July 95

Tier 2 August

95

HDE 

June 95

HDE

 July 95

HDE

August 95

Domain 24 24 23 22 23 24

Midwest 24 26 22 22 24 22

Northeast 27 22 24 27 23 24

Southeast 20 24 22 18 21 25

Southwest 24 27 24 22 24 27

• In general, the model under predicts ozone for the June and July episodes (-13 and -11

percent, respectively). This underestimation bias generally occurs over the first half of an

episode. The latter portions of these episodes are generally unbiased.

• Mean normalized gross error ranges from 18 to 27 percent. Bias and errors are generally

lowest in the Southeast region.

• The model typically underestimates the peaks as well as the mean ozone, but not as

severely.

• Although the overall tendency (June/July episodes) is to underestimate the observed

ozone, there are several instances in which large overestimations occurred.

• The model is slightly biased toward overestimation in the August episode (5 percent). 

Only the Northeast quadrant is underestimated (-15 percent) in this episode.

• While there are no established statistical criteria for evaluating the adequacy of regional

modeling applications, the relatively low values of bias and error plus the OTAG and Tier

2 equivalent performance indicate the modeling is sufficient for a national assessment of

the need for (and impact of) HDE controls.

3. Local-scale Model Performance

The HDE modeling results were also evaluated at a “local” level. The purpose of this

analysis was to ensure that areas determined to need the HDE emissions reductions based on 1-

hour exceedances of the ozone standard were not unduly influenced by local overestimation of

ozone in the model base year. For this analysis, the modeling domain was broken up into 47

local subregions as shown in Figure III-2. The primary statistics for each of the 47 subregions is

shown in Table III-7.
17

If one were to compare the performance of the 1995 eastern base year modeling against

the performance criteria recommended in EPA’s ozone modeling guidance (EPA, 1996) for

accuracy (within +/- 20 percent), bias (within +/- 15 percent), and error (less than 35 percent), the

results indicate that 57% of the regions would meet these criteria for the June episode, 45% of

the regions would for the July episodes, and 55% of the regions would for the August episode. 

Most of the areas that did not meet the local-scale criteria exhibited an under prediction bias of

15 percent or more.

Figure III-2. Map of the 47 HDE local-scale evaluation zones.

The general tendency of the model, as discussed above, is to underestimate observed

ozone concentrations. Given that one of the primary uses of the model is to calculate potential

exceedance areas in the future that may require additional ozone precursor control, this model

tendency should lead to a conservative estimate of future-year air quality need. When the model

is used in a relative sense to assess potential impacts from the rulemaking, any model bias will be

in both the base and control simulations and should be canceled out as comparisons are made.
18

Table III-7. HDE Base Year model performance for the 47 local regions.

Region Domainwide

Unpaired Accuracy

Average Accuracy 

of the Peak

Mean Normalized 

Bias

Mean Normalized

Gross Error

Dallas -0.155 -0.079 -0.102 0.216

Houston-Galveston -0.128 0.043 0.032 0.267

Beaumont-Port Arthur 0.078 0.151 0.167 0.251

Baton Rouge 0.055 0.212 0.254 0.308

New Orleans 0.266 0.198 0.212 0.264

St. Louis 0.002 -0.015 -0.007 0.205

Memphis 0.102 -0.090 -0.078 0.200

Alabama 0.052 0.024 0.047 0.201

Atlanta 0.235 0.079 0.079 0.244

Nashville 0.172 0.078 0.071 0.265

Eastern TN -0.005 -0.159 -0.195 0.257

Charlotte 0.198 0.039 0.061 0.182

Greensboro 0.137 0.031 0.021 0.177

Raleigh-Durham 0.093 -0.026 -0.036 0.179

Evansville-Owensboro 0.097 -0.025 0.002 0.236

Indianapolis -0.045 -0.104 -0.115 0.217

Louisville 0.159 0.104 0.094 0.265

Cincinnati-Dayton -0.038 -0.077 -0.057 0.230

Columbus OH -0.039 -0.117 -0.109 0.204

West Virginia 0.150 0.043 0.048 0.225

Chicago 0.048 -0.156 -0.228 0.291

Milwaukee 0.141 -0.148 -0.190 0.239

Muskegon-Grand Rapids 0.057 -0.126 -0.153 0.226

Gary-South Bend -0.097 -0.173 -0.212 0.271

Detroit 0.058 -0.119 -0.196 0.275

Pittsburgh -0.027 -0.059 -0.073 0.218

Central PA 0.120 -0.040 -0.069 0.213

Norfolk 0.236 -0.015 -0.075 0.246

Richmond 0.203 0.032 0.040 0.192

Baltimore-Washington 0.029 -0.045 -0.074 0.213

Delaware 0.083 -0.074 -0.047 0.156

Philadelphia -0.021 -0.114 -0.191 0.269

New York City 0.125 -0.108 -0.207 0.294

Hartford -0.008 -0.134 -0.144 0.243

Boston 0.122 -0.103 -0.177 0.270

Maine 0.116 -0.135 -0.187 0.262

Longview-Shreveport 0.014 -0.049 -0.088 0.251

Kansas City -0.113 -0.178 -0.197 0.238

Western NY 0.106 -0.136 -0.178 0.229

Northeast OH 0.014 -0.060 -0.081 0.209

South Carolina 0.161 0.060 0.053 0.188

Gulf Coast 0.239 0.167 0.216 0.279

FL West Coast 0.424 0.337 0.299 0.382

FL East Coast 0.248 0.137 0.133 0.250

Jackson, MS 0.347 0.084 0.084 0.198

Central MI -0.016 -0.102 -0.161 0.227

Macon-Columbus AL 0.273 0.012 0.033 0.187
7

 These 37 areas are listed in Appendix A, as described in Section III.F..

19

Because one of the primary uses of the model is to determine areas at risk of having

exceedances in the future, it is important to determine how well the model is doing at estimating

peak ozone concentrations in the base year. Particularly, it is important to ensure that the highest

model ozone concentrations are not overestimated, which could lead to an exaggerated

assessment of potential future exceedance areas. As such, the domainwide peak prediction

accuracy was calculated for each day and area for which a model exceedance was predicted in the

future. If the model peak was more than 20 percent overestimated, then that day/area was

flagged as a possible performance issue. Of the 37 areas7

 determined to need additional controls

in the future based on HDE modeling projections of exceedances, 11 areas have an

overprediction of the peak on some exceedance days in the base year modeling: Charlotte,

Huntington KY, Macon, Nashville, Richmond, Charleston WV, Cincinnati, Cleveland, Norfolk,

Orlando, and Tampa-St. Petersburg. However, for Cincinnati and Richmond there were also

days with observed exceedances on which the modeling underpredicted ozone and therefore did

not identify any exceedances. 

4. Model Performance over the Western U.S. Domain

UAM-V modeling was also performed for the western U.S. using the domain and all of

the inputs, except anthropogenic emissions, which were used in the western modeling for Tier 2

(EPA, 1999b). Anthropogenic emissions developed for the HDE rule (EPA, 2000b) were used

for this modeling. An operational evaluation was performed for the western modeling using the

same procedures and statistics discussed in section III-E-1. Model performance measures were

calculated over the entire modeling domain, the 12 km fine grid, and 10 individual areas

(Albuquerque, Denver, El Paso, Phoenix, Portland, Salt Lake City, the San Joaquin Valley,

Seattle, San Francisco, and Southern California). Table III-8 contains the operational evaluation

statistics. Observations on the evaluation results are listed below.

• Mean normalized bias and mean gross error values indicate that the model almost

exclusively underestimates the amounts of ozone actually measured (where observed

ozone is greater than 60 ppb). The average under prediction bias is about 40 percent. 

• This large negative bias exists over both 1996 episodes (-0.423 for the 1st episode, -0.406

for the 2nd episode). There is a slight tendency for the underestimation bias to be worst in

the early stages of the episodes. As seen in the table, model performance is poorest in

southern California where there are a high number of monitors.

• There is a deterioration in the performance of the western U.S. HDE base case

simulations relative to the same simulations completed as part of the Tier 2 air quality

modeling exercise. Overall, the HDE base case exhibits even more underprediction

(about 2-3 percent), mostly due to model-observed pairs in southern California.
8

This is in contrast to the Tier 2 assessment which included emissions reductions from the

California Low Emissions Vehicle Program in the future-year baseline scenarios.

20

Table III-8. Model performance statistics for individual local areas in the western U.S.

Region Unpaired Peak 

Prediction Accuracy

Average Peak

Prediction Accuracy

Mean Normalized

Bias

Mean Normalized 

Gross Error

Albuquerque -0.205 -0.340 -0.354 0.354

Denver -0.182 -0.327 -0.351 0.352

El Paso -0.279 -0.408 -0.437 0.437

Phoenix -0.245 -0.398 -0.456 0.459

Portland 0.021 -0.145 -0.209 0.251

Salt Lake City -0.199 -0.311 -0.347 0.353

San Joaquin Valley -0.236 -0.372 -0.396 0.403

Seattle 0.144 -0.155 -0.252 0.359

San Francisco -0.287 -0.361 -0.373 0.375

Southern California -0.320 -0.571 -0.585 0.591

While model performance for ozone in the western U.S. for the HDE 1996 base is

roughly similar to the performance found in the Tier 2 modeling for this same region, it is the

different scope of the HDE rule that calls into question the use of these data in the HDE

rulemaking. One of the primary differences relative to California between Tier 2 and HDE is

that the HDE rule will provide additional emissions reductions in California8. Also, the HDE

analysis has given more consideration to longer term ozone exposure analyses, which will

certainly be compromised by inadequate model performance of this magnitude. The magnitude

of the underpredictions, especially for areas of California, calls into question the credibility of the

directional response of the model to controls. Also, considering the performance in the West

relative to the performance of the model for the eastern U.S. (biases within plus/minus 10

percent) and what is typically expected out of such regional modeling applications, it was

determined that this application of the model should not be used to support the air quality

assessments in this rule.

F. Ozone Modeling Results For Future-Year Scenarios

The HDE modeling output for the East was analyzed to provide information to (a)

support the determination of the need for HDE, and (b) examine the air quality impacts of the

rulemaking. The procedures and results of each of these analyses are described below.

1. Future-Year Model-Predicted Exceedances

To support the determination of the need for HDE, the modeling results were examined to

identify those CMSA and MSAs that have predicted exceedances of the 1-hour NAAQS in the

2007, 2020, and/or 2030 base scenarios. This determination was limited to those areas which
21

had ambient 1-hour design values above the standard (i.e., >= 125 ppb) or within 10 percent of

the standard (i.e., >= 113 ppb). A CMSA/MSA is determined to contain a predicted exceedance

if at least one of the grid cells assigned to the area has at least one exceedance during the

episodes modeled. The procedures for assigning grid cells to areas are defined below. The

CMSA/MSAs with predicted 2007, 2020, and/or 2030 base case exceedances are listed in

Appendix A.

2. Impacts of the HDE Rule on 1-Hour Ozone

a. Definition of Areas for Analysis

In order to analyze the impacts of the HDE emissions reductions, it was necessary to

"link" or assign the model’s grid cells to individual CMSA/MSAs. The rules for assigning grid

cells to CMSA/MSAs (i.e., areas) is as follows. The first step was to assign grid cells to States

based on the fraction of the grid cells’ area in a State. A grid cell was assigned to the State which

contains most of the cells’ area. Next, grid cells were assigned to an individual CMSA/MSAs if

(1) the grid is wholly contained within the CMSA/MSA or (2) partially within (i.e., overlapping)

the area, but not also partially within another CMSA/MSA. Grid cells that partially overlap two

or more CMSA/MSAs are assigned to the county, and thereby the corresponding CMSA/MSA, 

which contains the largest portion of the grid cell. Each grid cell in the "coarse" or 36 km grid

portion of the domain was divided into nine 12 km grids before applying the preceding

methodology. The number of grid cells assigned to each metric area is listed in Appendix B.

b. Description of Ozone Metrics

The impacts of HDE on ozone were quantified using a number of metrics (i.e., measures

of ozone concentrations). These metrics include:

(1) the peak 1-hour ozone concentrations, 

(2) the number of exceedances,

(3) the total amount of ozone >= 125 ppb,

(4) the decrease in ozone, on average, and 

(5) the increase in ozone, on average. 

(1) The peak 1-hour ozone represents the highest ozone prediction within the area (i.e., CMSA or

MSA) across all episodes modeled.

(2) The number of exceedances is the total number of grid cells with predicted exceedances in

the area across all days. This exceedance metric counts each grid cell every day there is a

predicted exceedance in that grid. Thus, an individual grid cell can be counted more than once if

there are multiple days with predicted exceedances in that grid.

(3) The total amount of ozone above 125 ppb in an area is determined by taking the difference

between the predicted daily maximum ozone concentration and 125 ppb (i.e., daily maximum -

125 ppb) in each grid cell and then summing this amount across all grid cells in the area and days

modeled. This metric is referred to as the “amount of nonattainment”. 
22

(4) The decrease, on average is determined by first summing all the reductions predicted in those

grid cells with daily maximum ozone >=125 ppb in the base case (i.e., base case exceedances). 

This total reduction is then divided by the number of base case exceedances in the area to yield

the "ppb" decrease that occurs, on average, for the exceedances predicted in the area.

(5) The increase, on average is determined by summing any increases in ozone that occur in

values already >= 125 ppb in the base case together with any increases that cause a value below

125 ppb in the base case to go above 125 ppb in the control case. This total increase is then

divided by the number of exceedances in the base case.

The impacts of HDE on 1-hour ozone exceedances were examined for the individual

CMSA/MSAs as well as by aggregating the metrics across all areas to obtain the overall impact

expected from the program. The values of the metrics are provided in Appendix C for 2007,

2020 and 2030.

3. Need for HDE Rule Based on Unhealthy 8-Hour Ozone Concentrations

One component of the analysis to support the need for this rule was the calculation of the

number of people living in metropolitan counties that experience 8-hour ozone concentrations

above certain concentration levels for different lengths of time. This “exposure” type analysis

was based on current 1997-1999 ambient 8-hour concentrations and projected future 8-hour

concentrations, based on modeling of the HDE emissions scenarios. To provide the future-year

estimates of 8-hour concentrations, 8-hour relative reduction factors (RRFs) were calculated then

applied to ambient 8-hour daily maximum concentrations. The procedures for determining the

RRFs are similar to those in EPA’s draft guidance for modeling for an 8-hour ozone standard

(EPA, 1999a). Hourly model predictions were processed to determine daily maximum 8-hour

concentrations for each grid cell for each non-ramp-up day modeled. The RRF for a monitoring

site was determined by first calculating the multi-day mean of the 8-hour daily maximum

predictions in the nine grid cells surrounding the site using only those predictions >= 70 ppb, as

recommended in the guidance. This calculations was performed for the base year scenario and

each future-year scenario. The RRF for a site is the ratio of the mean 8-hour prediction in the

future-year scenario to the mean 8-hour prediction in the base year scenario. This value was then

multiplied by the ambient 8-hour concentrations to provide estimates of future 8-hour

concentrations. These future concentrations were then used in the “exposure” analysis as

described in the HDE docket (Docket A-99-06, item IV-B-09). The 8-hour RRFs are provided

for each monitoring site in Appendix D.

4. One-Hour Ozone Relative Reduction Factors 

EPA received comments that recommended using relative reduction factors applied to

ambient design values as an approach to estimate which areas are expected to have a future

problem attaining the1-hour ozone standard. Specifically, the commenters recommended that

EPA follow draft guidance for demonstrating attainment of the 8-hour NAAQS for such an

analysis (EPA, 1999a). In response, we calculated relative reduction factors for the 2007, 2020,

and 2030 base case and control scenarios using the general methodology in this guidance. The

exceptions to this guidance is that we used a cut-off of 80 ppb as appropriate for considering 1-
23

hour model predictions as opposed to 70 ppb recommended in the guidance for 8-hour

concentrations (see the Tier 2 Air Quality Modeling TSD, 1999). The 1-hour monitor-specific

RRFs were applied to the ambient 1-hour design value (i.e., 4th highest 1-hour daily maximum

concentration at the monitor from 1997-1999) at each site with valid data. The resulting futureyear 1-hour design values were examined for all monitors in an area to select the highest value

for the area. These data can be found in Docket A-99-06; item IV-B-06.. Information on the use

of these data for this rule can be found in the Response to Comments Document.

IV. Particulate Matter Modeling over the Continental U.S.

A. REMSAD Model Description

The REgulatory Modeling System for Aerosols and Deposition (REMSAD), (ICF Kaiser,

1998) model was used as the tool for simulating base year and future concentrations of PM in

support of the HDE air quality assessments. Model runs were made for the 1996 base year as

well as for the 2020 and 2030 base and control scenarios. As described below, each of these

emissions scenarios was simulated using 1996 meteorological data in order to provide the annual

mean PM concentrations and estimates of visibility needed for the PM “exposure” analysis and

benefits calculations.

REMSAD was designed to calculate the concentrations of both inert and chemically

reactive pollutants by simulating the physical and chemical processes in the atmosphere that

affect pollutant concentrations. Version 4.1 of REMSAD was used for the HDE modeling. The

framework of this model is taken from version 1.23 of the UAM-V regional-scale photochemical

model, without Plume-in-Grid and with a modified Carbon Bond IV routine, as described below.

The UAM-V framework has been extended vertically to treat the entire troposphere and

converted to a sigma (terrain following) vertical coordinate. REMSAD includes a cumulus

convective parameterization scheme and a stratiform cloud parameterization scheme for the

distribution and removal of pollutant species.

The basis for REMSAD is the atmospheric diffusion equation (also called the species

continuity or advection/diffusion equation). This equation represents a mass balance in which all

of the relevant emissions, transport, diffusion, chemical reactions, and removal processes are

expressed in mathematical terms. REMSAD employs finite-difference numerical techniques for

the solution of the advection/diffusion equation. 

REMSAD uses a latitude/longitude horizontal grid structure in which the horizontal grids

are generally divided into areas of equal latitude and longitude. The vertical layer structure of

REMSAD is defined in terms of sigma-pressure coordinates. The top and bottom of the domain

are defined as 0 and 1 respectively. The vertical layers are defined as a percent of the

atmospheric pressure between the top and bottom of the domain. For example, a vertical layer of

0.50 sigma is exactly halfway between the top and bottom of the domain as defined by the local

atmospheric pressure. Usually, the vertical layers are defined to match the vertical layer structure
9

Hydrogen peroxide is formed from photochemical reactions within the mCB4

mechanism. 

24

of the meteorological model used to generate the REMSAD meteorological inputs.

1. Gas Phase Chemistry

REMSAD simulates gas phase chemistry using a reduced-form version of CB4 termed

“micro-CB4” (mCB4) which treats fewer VOC species compared to the full CB4 mechanism. 

The inorganic and radical parts of the reduced mechanism are identical to CB4. In this version of

mCB4 the organic portion is based on one primary species (VOC) and one primary and

secondary carbonyl species (CARB). The VOC species was incorporated with kinetics

representing an average anthropogenic hydrocarbon species. A second primary VOC species

representing biogenic emissions is also included with kinetic characteristics representing

isoprene. The intent of the mCB4 mechanism is to (a) provide a physically faithful

representation of the linkages between emissions of ozone precursor species and secondary PM

precursors species, (b) treat the oxidizing capacity of the troposphere, represented primarily by

the concentrations of radicals and hydrogen peroxide, and (c) simulate the rate of oxidation of the

nitrogen oxide (NOx) and sulfur dioxide (SO2) PM precursors. Box model testing of mCB4 has

found that it performs very closely to the full CBM4 that is contained in UAM-V (Whitten,

1999).

2. PM Chemistry

Primary PM emissions in REMSAD are treated as inert species. They are advected and

deposited without any chemical interaction with other species. Secondary PM species, such as

sulfate and nitrate are formed through chemical reactions within the model. SO2 is the gas phase

precursor for particulate sulfate, while nitric acid is the gas phase precursor for particulate nitrate. 

Several other gas phase species are also involved in the secondary reactions.

There are two pathways for sulfate formation; gas phase and aqueous phase. Aqueous

phase reactions take place within clouds, rain, and/or fog. In-cloud processes can account for the

majority of atmospheric sulfate formation in many areas. In REMSAD, aqueous SO2 reacts with

hydrogen peroxide (H2O2) to form sulfate9

. This reaction also occurs in the gas phase although

the gas phase reaction is much slower. SO2 also reacts with OH radicals in the gas phase to form

sulfate.

Particulate nitrate is calculated in an equilibrium reaction between nitric acid, sulfuric

acid, and ammonia. Nitric acid is a product of gas phase chemistry and is formed through the

mCB4 reactions. The acids are neutralized by ammonia with sulfuric acid reacting more quickly

than nitric acid. An equilibrium is established among ammonium sulfate and ammonium nitrate

which strongly favors ammonium sulfate unless the available ammonia exceeds twice the

available sulfate. Nitrate is then partitioned between particulate nitrate and gas phase nitric acid. 

The partitioning of nitrate depends on the availability of ammonia as well meteorological factors

such as temperature and relative humidity. 
25

B. REMSAD Modeling Domain

The modeling domain used for the HDE modeling was designed to provide air quality

predictions for the lower 48 States, as shown in Figure IV-1. The geographic characteristics of

the domain are as follows:

120 (E-W) X 84 (N-S) grid cells

Cell size (~36 km)

1/2 degree longitude (0.5)

1/3 degree latitude (0.3333)

E-W range: 66 degrees W - 126 degrees W

N-S range: 24 degrees N - 52 degrees N

Vertical extent: Ground to 16,200 meters (100mb) with 8 layers

Figure IV-1. REMSAD Modeling Domain. 

C. REMSAD Inputs

Input data for REMSAD can be classified into six categories: (1) simulation control, (2)

emissions, (3) initial and boundary concentrations, (4) meteorological, (5) surface characteristics,

and (6) chemical rates. The REMSAD predictions of pollutant concentrations are calculated

from the emissions, advection, and dispersion processes coupled with the formation and
26

deposition of secondary PM species within every grid cell of the modeling domain. To

adequately replicate the full three-dimensional structure of the atmosphere, the REMSAD

program requires hourly (or 3-hour average) input data for a number of variables. Table IV-1

lists the required REMSAD input files.

Table IV-1. List of REMSAD input files.

Data type Files Description

Control CONTROL Simulation control information

Emissions PTSOURCE

EMISSIONS

Elevated source emissions 

Surface emissions

Initial and

boundary

concentrations

AIRQUALITY

BOUNDARY 

Initial concentrations

Lateral boundary concentrations

Meteorological WIND

TEMPERATURE 

PSURF

H2O 

VDIFFUSION 

RAIN

X,Y-components of winds

3D array of temperature

2D array of surface pressure

3D array of water vapor

3D array of vertical turbulent diffusivity

coefficients

2D array of rainfall rates

Surface 

characteristics

SURFACE 

TERRAIN

Gridded land use

Terrain heights

Chemical rates CHEMPARAM 

RATES

Chemical reaction rates

Photolysis rates file

1. Meteorological Data

REMSAD requires input of winds (u- and v-vector wind components), temperatures,

surface pressure, specific humidity, vertical diffusion coefficients, and rainfall rates. The

meteorological input files were developed from a 1996 annual MM5 model run that was

developed for previous projects. MM5 is the Fifth-Generation NCAR / Penn State Mesoscale

Model. MM5 (Grell et. al., 1994) is a numerical meteorological model that solves the full set of

physical and thermodynamic equations which govern atmospheric motions. MM5 was run in a

nested-grid mode with 2 levels of resolution: 108 km, and 36km with 23 vertical layers sigma 
27

layers extending from the surface to the 100 mb pressure level. The model was simulated in five

day segments with an eight hour ramp-up period. The MM5 runs were started at 0Z, which is

7PM EST. The first eight hours of each five day period were removed before being input into

REMSAD. Figure IV-2 shows the MM5 and REMSAD 36km domain superimposed on each

other. Table IV-2 lists the vertical grid structures for the MM5 and REMSAD domains. Further

detailed information concerning the development of the 1996 MM5 datasets can be found in

(Olerud, 2000) 

0 500 1500 2500 3500 45

D01

D02

Figure IV-2. MM5 36km Domain (solid box) and REMSAD Domain (dashed lines).

Table IV-2. Vertical Grid Structure for 1996 MM5 and HDE REMSAD Domains. Layer heights

represent the top of each layer. The first layer is from the ground up to 153 meters.

REMSAD 

Layer MM5 Layer Sigma

Approximate

Height(m) Pressure(mb) 

0 0 1.000 0.0 1000.0

 1 0.995 38.0 995.5

 2 0.988 91.5 989.2

1 3 0.980 152.9 982.0

 4 0.970 230.3 973.0
REMSAD 

Layer MM5 Layer Sigma

Approximate

Height(m) Pressure(mb) 

28

 5 0.956 339.5 960.4

2 6 0.938 481.6 944.2

 7 0.916 658.1 924.4

 8 0.893 845.8 903.7

 9 0.868 1053.9 881.2

3 10 0.839 1300.7 855.1

 11 0.808 1571.4 827.2

 12 0.777 1849.6 799.3

 13 0.744 2154.5 769.6

4 14 0.702 2556.6 731.8

 15 0.648 3099.0 683.2

 16 0.582 3805.8 623.8

5 17 0.500 4763.7 550.0

 18 0.400 6082.5 460.0

6 19 0.300 7627.9 370.0

 20 0.200 9510.5 280.0

7 21 0.120 11465.1 208.0

 22 0.052 13750.2 146.0

8 23 0.000 16262.4 100.0

The physical options selected for this configuration of MM5 include the following:

1. One-way nested grids

2. Nonhydrostatic dynamics

3. Four-dimensional data assimilation (FDDA):

• Analysis nudging of wind, temperature, and mixing ratios

• Nudging coefficients range from 1.0  ́ 10-5 s

-1 to 3.0  ́ 10-4

 s

-1

4. Explicit moisture treatment:

• 3-D predictions of cloud and precipitation fields

• Simple ice microphysics

• Cloud effects on surface radiation

• Moist vertical diffusion in clouds

• Normal evaporative cooling

5. Boundary conditions:

• Time and inflow/outflow relaxation

6. Cumulus cloud parameterization schemes: 

• Anthes-Kuo (108-km grid)

• Kain-Fritsch (36-km grid)
29

7. No shallow convection

8. Full 3-dimensional Coriolis force

9. Drag coefficients vary with stability

10. Vertical mixing of momentum in mixed layer

11. Virtual temperature effects

12. PBL process parameterization: MRF scheme

13. Surface layer parameterization:

• Fluxes of momentum, sensible and latent heat 

• Ground temperature prediction using energy balance equation 

• 24 land use categories 

14. Atmospheric radiation schemes: 

• Simple cooling 

• Long- and short-wave radiation scheme 

15. Sea ice treatment: 

• Forced Great Lakes/Hudson Bay to permanent ice under very cold conditions

 • 36-km treatment keyed by observations of sea ice over the Great Lakes 

16. Snow cover: 

• Assumed no snow cover for July and August

• National Center for Environmental Prediction (NCEP) snow cover for January to

June, and for September to December 

The MM5 model output cannot be directly input into REMSAD due to differences in the

grid coordinate systems and file formats. A postprocessor called MM5REMSAD was developed

to convert the MM5 data into REMSAD format. This postprocessor was used to develop 3-hour

average meteorological input files from the MM5 output. Documentation of the MM5REMSAD

code and further details on the development of the input files is contained in (Mansell, 2000). 

2. Initial and Boundary Conditions, and Surface Characteristics

Application of the REMSAD modeling system requires data files specifying the initial

species concentration fields (AIRQUALITY) and lateral species concentrations (BOUNDARY). 

Due the extent of the proposed modeling domains and the regional-scale nature of the REMSAD

model, these inputs were developed based on “clean” background concentration values. The

HDE modeling used temporally and spatially (horizontal) invariant data for both initial and

boundary conditions. Species concentration values were allowed to decay vertically for most

species. Table IV-3 summarizes the initial and boundary conditions used in the HDE REMSAD

modeling.

Table IV-3. REMSAD Initial and Boundary Conditions (ppm)

Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Layer 6 Layer 7 Layer 8

Species

NO 1.00E-12 1.00E-12 1.00E-12 1.00E-12 8.57E-13 5.71E-13 2.86E-13 7.14E-14

NO2 1.00E-04 1.00E-04 1.00E-04 1.00E-04 8.57E-05 5.71E-05 2.86E-05 7.14E-06

O3 4.00E-02 4.00E-02 4.00E-02 4.00E-02 4.00E-02 4.00E-02 4.00E-02 4.00E-02

SO2 7.00E-04 7.00E-04 7.00E-04 7.00E-04 6.00E-04 4.00E-04 2.00E-04 5.00E-05

NH3 5.00E-04 5.00E-04 5.00E-04 5.00E-04 3.67E-04 1.63E-04 4.08E-05 2.55E-06
Layer 1 Layer 2 Layer 3 Layer 4 Layer 5 Layer 6 Layer 7 Layer 8

30

VOC 2.00E-02 2.00E-02 2.00E-02 2.00E-02 1.71E-02 1.14E-02 5.71E-03 1.43E-03

CARB 1.00E-07 1.00E-07 1.00E-07 1.00E-07 1.00E-07 1.00E-07 1.00E-07 1.00E-07

ISOP 1.00E-09 1.00E-09 1.00E-09 1.00E-09 1.00E-09 1.00E-09 1.00E-09 1.00E-09

CO 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01 1.00E-01

HNO3 1.00E-05 1.00E-05 1.00E-05 1.00E-05 8.57E-06 5.71E-06 2.86E-06 7.14E-07

PNO3 1.00E-05 1.00E-05 1.00E-05 1.00E-05 7.35E-06 3.27E-06 8.16E-07 5.10E-08

GSO4 1.00E-04 1.00E-04 1.00E-04 1.00E-04 7.35E-05 3.27E-05 8.16E-06 5.10E-07

ASO4 1.00E-12 1.00E-12 1.00E-12 1.00E-12 8.57E-13 5.71E-13 2.86E-13 7.14E-14

NH4N 1.00E-05 1.00E-05 1.00E-05 1.00E-05 7.35E-06 3.27E-06 8.16E-07 5.10E-08

NH4S 1.00E-04 1.00E-04 1.00E-04 1.00E-04 7.35E-05 3.27E-05 8.16E-06 5.10E-07

SOA 1.00E-03 1.00E-03 1.00E-03 1.00E-03 7.35E-04 3.27E-04 8.16E-05 5.10E-06

POA 1.00E-03 1.00E-03 1.00E-03 1.00E-03 7.35E-04 3.27E-04 8.16E-05 5.10E-06

PEC 5.00E-03 5.00E-03 5.00E-03 5.00E-03 3.67E-03 1.63E-03 4.08E-04 2.55E-05

PMFINE 1.00E-03 1.00E-03 1.00E-03 1.00E-03 7.35E-04 3.27E-04 8.16E-05 5.10E-06

PMCOARS 1.00E-03 1.00E-03 1.00E-03 1.00E-03 6.30E-04 1.87E-04 2.33E-05 3.64E-07

Application of the REMSAD model requires specification of gridded terrain elevations

(TERRAIN) and landuse characteristics (SURFACE). The SURFACE data files provides the

fraction of the 11 landuse categories recognized by REMSAD in each grid cell. Landuse

characteristics are used in the model for the calculation of deposition parameters. For this task, a

landuse/terrain processor, PROC_LUTERR, was developed based on the MM5 TERRAIN

preprocessor. Landuse data was obtained from the USGS Global 30 sec. vegetation database

which is the same database used in the 1996 MM5 models runs. This dataset provides 24

landuse categories, including urban. For the REMSAD application, the 10 min. (1/6 deg.)

datasets was utilized. The processor remapped the 24 USGS vegetation categories to those

required for application of REMSAD. It also aggregated the 10 min resolution data to the ~36

km horizontal resolution used for this REMSAD application.

For the TERRAIN input data files, a similar global terrain elevation dataset is also

available from NCAR and was used for this task. While it is possible to use the terrain

elevations obtained from the MM5 model output data files, it was deemed more appropriate to

begin with the USGS 10 min. resolution database due to the various map projections and

interpolations involved in developing the required data files for the geodetic coordinates used in

REMSAD. However, because proper application of REMSAD will require zero terrain

elevations, “dummy” terrain files (with all zeroes) were developed and provided for input to

REMSAD. 

3. Emissions Inputs

The REMSAD emissions input files were generated using the EPS2.5 emissions

preprocessing system. The annual county level HDE emissions inventory data was speciated,

temporally allocated and gridded to the REMSAD domain. The individual species contained in

these inventory files were oxides of nitrogen (NOx), volatile organic compounds (VOC), carbon

monoxide (CO), sulfur dioxide (SO2), ammonia (NH3), primary PM10, and primary PM2.5. The

primary PM emissions were further speciated into primary elemental carbon (PEC), primary
10The primary organic carbon emissions were multiplied by a factor of 1.2 to account for

the additional mass of oxygen and other compounds typically found attached to particulate

organic carbon.

31

organic aerosols10 (POA), primary sulfate (GSO4), primary nitrate (PNO3), crustal/fugitive

(PMFINE), and primary course particles in the 2.5-10 um range (PMCOARS). Secondary

organic aerosols (SOA) are estimated from the total anthropogenic VOC emissions. The yield of

SOA is calculated from the raw county level VOC inventory and the SOA emissions were input

into REMSAD in the same way as primary PM emissions (EPA, 2000b).

The annual emissions for stationary and nonroad sources were processed to generate

separate sets of emissions representing typical weekday, Saturday, and Sunday emissions for

each season. For mobile sources, monthly emissions were obtained from the mass emissions

files and processed to create emissions for each day-type for each month. Hourly emissions for

anthropogenic emissions were created by applying diurnal profile factors to the daily emissions. 

Hourly biogenic emissions were created by applying a typical diurnal pattern to monthly average

biogenic VOC emissions developed using the BEIS2 model. Biogenic emissions were not

altered for any of the scenarios modeled.

D. Model Performance Evaluation

The goal of the 1996 base year modeling was to reproduce the atmospheric processes

resulting in formation and dispersion of fine particulate matter across the U.S. An operational

model performance evaluation for PM2.5 and its related speciated components (e.g., sulfate,

nitrate, elemental carbon etc.) for 1996 was performed in order to estimate the ability of the

modeling system to replicate base year concentrations. All of the observational data used in this

analysis can be found at the CAPITA website:

http://capita.wustl.edu/datawarehouse/Datasets/CAPITA/NAMPM_fine/Data/NAMPM_f.html

This evaluation is comprised principally of statistical assessments of model versus

observed pairs. The robustness of any evaluation is directly proportional to the amount and

quality of the ambient data available for comparison. Unfortunately, there are few PM2.5

monitoring networks with available data for evaluation of the HDE PM modeling. Critical

limitations of the existing databases are a lack of urban monitoring sites with speciated

measurements and poor geographic representation of ambient concentration in the East. PM2.5

monitoring networks were recently expanded in 1999 to include more than 1000 Federal

Reference Method (FRM) monitoring sites. The purpose of this network is to monitor PM2.5

mass levels in urban areas. These monitors only measure total PM2.5 mass and do not measure

PM species. In the next 1-2 years a new network of ~300 urban oriented speciation monitor sites

will begin operation across the country. These monitors will collect a full range of PM2.5 species

that are necessary to evaluate models and to develop PM2.5 control strategies. 
11The dividing line between the West and East was defined as the 100th meridian.

32

The largest available ambient database for 1996 comes from the Interagency Monitoring

of PROtected Visual Environments (IMPROVE) network. IMPROVE is a cooperative visibility

monitoring effort between EPA, federal land management agencies, and state air agencies. Data

is collected at Class I areas across the United States mostly at National Parks, National

Wilderness Areas, and other protected pristine areas (IMPROVE 2000). There were

approximately 60 IMPROVE sites that had complete annual PM2.5 mass and/or PM2.5 species

data for 1996. Forty two sites were in the West11 and 18 sites were in the East. Figure IV-3

shows the locations of the IMPROVE monitoring sites used in this evaluation. IMPROVE data

is collected twice weekly (Wednesday and Saturday). Thus, there is a total of 104 possible

samples per year or 26 samples per season. For this analysis, a 50% completeness criteria was

used. That is, in order to be counted in the statistics a site had to have > 50% complete data in all

4 seasons. If any season was missing, an annual average was not calculated for the site. See

Appendix F for a list of the IMPROVE sites used in the evaluation.

Figure IV-3. Map of 1996 IMPROVE monitoring sites used in the REMSAD model

performance evaluation.

The observed IMPROVE data used for the performance evaluation was PM2.5 mass,

sulfate ion, nitrate ion, elemental carbon, organic aerosols, and crustal material (soils). The

REMSAD model output species were postprocessed in order to achieve compatibility with the

observation species. The following is the translation of the REMSAD output species into PM2.5

and related species: 
33

OBS 1

N

N

i 1

Obs i

x,t

PRED 1

N

N

i 1

Pred i

x,t

Sulfate Ion: TSO4 = ASO4 + GSO4

Nitrate Ion: PNO3

Organic aerosols: TOA = POA + SOA

Elemental Carbon: PEC

Crustal Material (soils): PMFINE

PM2.5: PM2.5 = PMFINE + 1.375 * (ASO4 + GSO4) +

1.29 * (PNO3) + POA + SOA + PEC

where, TSO4 is total sulfate ion, ASO4 is aqueous path sulfate, GSO4 is gaseous path sulfate,

PNO3 is nitrate ion, TOA is total organic aerosols, POA is primary organic aerosol, SOA is

secondary organic aerosol, PEC is primary elemental carbon, and PMFINE is primary fine

particles (other unspeciated primary PM2.5). PM2.5 is defined as the sum of the individual species. 

Sulfate ion is multiplied by 1.375 and nitrate ion is multiplied by 1.29 in order to account for

particulate ammonium. It is assumed that sulfate and nitrate exist in the atmosphere and in the

model as ammonium sulfate and ammonium nitrate respectively.

1. Statistical Definitions

Below are the definitions of statistics used for the evaluation. The statistics are similar to

those used for a previous REMSAD evaluation of a 1990 basecase (Wayland, 1999). The format

of all the statistics is such that negative values indicate model predictions that were less than their

observed counterparts. Positive statistics indicate model overestimation of observed PM. The

statistics were calculated for the entire REMSAD domain and separately for the East and West. 

The dividing line between East and West is the 100th meridian.

Mean Observation: The mean observed value (in ug/m3) averaged over all monitored days in

the year and then averaged over all sites in the region.

Mean REMSAD Prediction: The mean predicted value (in ug/m3) paired in time and space

with the observations and then averaged over all sites in the region.

Ratio of the Means: Ratio of the predicted over the observed values. A ratio of greater than 1

indicates on overprediction and a ratio of less than 1 indicates an underprediction.
34

RATIO 1

N

N

i 1

Pred i

x,t

Obs i

x,t

BIAS 1

N

N

i 1

(Pred i

x,t Obs ix,t)

FBIAS 2

N

N

i 1

(Pred i

x,t Obs ix,t

)

(Pred i

x,t Obs ix,t

)

100

ERR 1

N

N

i 1

Pred i

x,t Obs ix,t

Mean Bias (ug/m3): This performance statistic averages the difference (model - observed) over

all pairs in which the observed values were greater than zero. A mean bias of zero indicates that

the model over predictions and model under predictions exactly cancel each other out. Note that

the model bias is defined such that it is a positive quantity when model prediction exceeds the

observation, and vice versa. This model performance estimate is used to make statements about

the absolute or unnormalized bias in the model simulation

Mean Fractional Bias (percent): Normalized bias can become very large when a minimum

threshold is not used. Therefore fractional bias is used as a substitute. The fractional bias for

cases with factors of 2 under- and over-prediction are -67 and + 67 percent, respectively (as

opposed to -50 and +100 percent, when using normalized bias, which is not presented here). 

Fractional bias is a useful model performance indicator because it has the advantage of equally

weighting positive and negative bias estimates. The single largest disadvantage in this estimate of

model performance is that the estimated concentration (i.e., prediction, Pred) is found in both the

numerator and denominator. 

M

ean Error (ug/m3): This performance statistic averages the absolute value of the difference

(model - observed) over all pairs in which the observed values were greater than zero. It is

similar to mean bias except that the absolute value of the difference is used so that the error is

always positive. 

Mean Fractional Error: Normalized error can become very large when a minimum threshold is

not used. Therefore fractional error is used as a substitute. It is similar to the fractional bias

except the absolute value of the difference is used so that the error is always positive. 
35

FERROR 2

N

N

i 1

Pred i

x,t Obs ix,t

Pred i

x,t Obs ix,t

100

2. Results of REMSAD Performance Evaluation

The statistics described above are presented for the entire domain, the Eastern sites, and

the Western sites. The model’s ability to replicate annual average PM2.5 and PM2.5 species

concentrations at the IMPROVE sites is as follows:

a. PM2.5 Performance

Table IV-4 lists the performance statistics for PM2.5 at the IMPROVE sites. For the full

domain, PM2.5 is underpredicted ~25%. The ratio of the means is 0.77 with a bias of -0.93

ug/m3. It can be seen that most of this underprediction is due to the Western sites. The West is

underpredicted by ~35% while the East is overpredicted by ~10 %. The fractional bias is less

than 10% in the East, while the fractional error is ~40%. The fractional bias and error in the West

is 31% and 65% respectively. The observed PM2.5 concentrations in the East are relatively high

compared to the West. REMSAD displays an ability to differentiate between generally high and

low PM2.5 areas.

Table IV-4. Annual mean PM2.5 performance at IMPROVE sites.

No. of

Sites

Mean

REMSAD

Predictions

(ug/m3)

Mean

Observations

(ug/m3)

Ratio of

Means

(pred/obs)

 Bias

(ug/m3)

Fractional

Bias (%)

 Error

(ug/m3)

Fractional

Error (%)

National 59 5.14 6.07 0.77 -0.93 -21.1 3.04 58.2

East 17 11.38 10.55 1.07 0.82 2.8 4.40 41.8

West 42 2.61 4.26 0.65 -1.64 -30.7 2.48 64.9

b. Sulfate Performance

Table IV-5 lists the performance statistics for particulate sulfate at the IMPROVE sites. 

Domainwide, sulfate performance is better than PM2.5 with a slight overprediction of 9%. The

sulfate bias in the West is close to zero, while there is a ~25% overprediction of annual sulfate

levels in the East. The biases are relatively low, however the errors are considerably higher

indicating that some overpredicted values are canceling out some underpredicted values.
36

Table IV-5. Annual mean sulfate ion performance at IMPROVE sites.

No. of

Sites

Mean

REMSAD

Predictions

(ug/m3)

Mean

Observations

(ug/m3)

Ratio of

Means

(pred/obs)

 Bias

(ug/m3)

Fractional

Bias (%)

 Error

(ug/m3)

Fractional

Error (%)

National 60 1.87 1.63 1.09 0.24 4.4 0.85 51.5

East 18 4.71 3.81 1.25 0.90 9.0 2.00 47.6

West 42 0.65 0.70 1.02 -0.05 2.4 0.35 53.2

c. Elemental Carbon Performance

Table IV-6 lists the performance statistics for primary elemental carbon at the IMPROVE

sites. Performance for elemental carbon predictions is similar to that of sulfate with a slight

overprediction in the East and a slight underprediction in the West. Model performance between

the East and West was remarkably similar. The bias is very low, but the fractional error is ~50%

of the observed values. 

Table IV-6. Annual mean elemental carbon performance at IMPROVE sites

No. of

Sites

Mean

REMSAD

Predictions

(ug/m3)

Mean

Observations

(ug/m3)

Ratio of

Means

(pred/obs)

 Bias

(ug/m3)

Fractional

Bias (%)

 Error

(ug/m3)

Fractional

Error (%)

National 48 0.30 0.31 1.10 -0.01 10.5 0.18 56.2

East 16 0.50 0.47 1.26 0.03 14.8 0.24 50.8

West 32 0.21 0.24 1.02 -0.03 8.3 0.14 58.9

d. Organic Aerosol Performance

Table IV-7 lists the performance statistics for primary organic aerosols at the IMPROVE

sites. Organic aerosols are underpredicted nationwide. The East and West are equally

underpredicted by about 35%. Both the fractional bias and fractional errors are higher than for

PM2.5, sulfate, and elemental carbon. It is clear that the model is not accounting for all of the

organics that were observed. 

Currently REMSAD has a very crude accounting for secondarily formed organics (SOA). 

In the atmosphere, SOA is formed from both anthropogenic and biogenic VOC emissions. 

REMSAD accounts for anthropogenic SOA by estimating the SOA yield from anthropogenic

VOC emissions. Currently REMSAD does not account for biogenic SOA which mostly comes

from terpene emissions from coniferous trees. It is expected that in the IMPROVE Class I areas,

the majority of the SOA will be from biogenic emissions. This is a possible explanation for the 

modeled underprediction of measured organic aerosols.
37

Also, at some Class I areas, particularly in the West, wildfires account for a portion of the

annual observed organic aerosol measurements. The current emission inventory is lacking in

detailed representation of wildfires that occurred in 1996 which may be important for model

evaluation, but not necessarily for the HDE analysis.

Table IV-7. Annual mean organic aerosol performance at IMPROVE sites

No. of

Sites

Mean

REMSAD

Predictions

(ug/m3)

Mean

Observations

(ug/m3)

Ratio of

Means

(pred/obs)

 Bias

(ug/m3)

Fractional

Bias (%)

 Error

(ug/m3)

Fractional

Error (%)

National 48 0.76 1.25 0.67 -0.48 -44.1 0.81 74.8

East 16 1.11 1.74 0.68 -0.63 -38.3 0.99 64.5

West 32 0.60 1.01 0.67 -0.41 -47.0 0.72 79.9

e. Nitrate Performance

Table IV-8 lists the performance statistics for nitrate ion at the IMPROVE sites. Nitrate

is generally overpredicted in the East and somewhat underpredicted in the West. The ratio of the

means in the East is 2.80 indicating an overprediction. The fractional bias is close to zero, but

the fractional error is > 100%. This indicates that on a day to day basis the model is relatively

unbiased, but it does a poor job of predicting individual days (indicated by the high error). When

the model overpredicts, it overpredicts by a large margin (which causes the high overall ratio of

means). In the western United States, the overall ratio of the means is near unity, but the

fractional bias is strongly negative, which indicates an underprediction. And the fractional error

is slightly higher than in the East. Again, the model is not accurately predicting day to day

concentrations. 

It is important to consider these results in the context that the observed nitrate

concentrations at the IMPROVE sites are very low. The mean nationwide observations are only

0.40 ug/m3. It is often difficult for models to replicate very low concentrations of secondarily

formed pollutants. Nitrate is generally a small percentage of the measured PM2.5 at almost all of

the IMPROVE sites. Nitrate can be an important contributor to PM2.5 in some urban areas

(particularly in California) but performance for those areas could not be assessed due to the lack

of urban area speciated nitrate data for 1996. 
38

Table IV-8. Annual mean nitrate ion performance at IMPROVE sites

No. of

Sites

Mean

REMSAD

Predictions

(ug/m3)

Mean

Observations

(ug/m3)

Ratio of

Means

(pred/obs)

 Bias

(ug/m3)

Fractional

Bias (%)

 Error

(ug/m3)

Fractional

Error (%)

National 51 0.68 0.40 1.64 0.29 -46.7 0.63 134.4

East 17 1.48 0.54 2.80 0.94 -1.1 1.19 126.8

West 33 0.27 0.32 1.04 -.05 -70.3 0.35 138.3

f. PMFINE-Other (crustal) Performance

Table IV-9 lists the performance statistics for PMFINE-other or primary crustal

emissions. The observations show crustal PM2.5 to be generally higher in the West than in the

East. But REMSAD is predicting higher crustal concentrations in the East. The largest

categories of PMFINE-other are fugitive dust sources such as paved roads, unpaved roads,

construction, and animal feed lots. There is a large uncertainty in the handling of these emissions

in the inventory. It is apparent that too much fugitive dust is being emitted in the East. It is

evident from the performance statistics that further work needs to be done to study the magnitude

of these emissions and how they are emitted into the model.

Table IV-9. Annual mean PMFINE (crustal) performance at IMPROVE sites

No. of

Sites

Mean

REMSAD

Predictions

(ug/m3)

Mean

Observations

(ug/m3)

Ratio of

Means

(pred/obs)

 Bias

(ug/m3)

Fractional

Bias (%)

 Error

(ug/m3)

Fractional

Error (%)

National 60 0.96 0.63 2.11 0.33 47.1 0.86 96.0

East 18 1.76 0.52 4.10 1.24 106.1 1.46 118.1

West 42 0.62 0.68 1.26 -0.06 21.8 0.61 86.6

g. Summary of Model Performance Results Using Improve Data

The purpose of this model performance evaluation was to evaluate the capabilities of the

REMSAD modeling system in reproducing annual average concentrations for all IMPROVE

sites in the contiguous U.S. for fine particulate mass and its associated speciated components. 

When considering annual average statistics (e.g., predicted versus observed), which are

computed and aggregated over all sites and all days, REMSAD underpredicts fine particulate

mass (PM2.5), by ~20%. PM2.5 in the Eastern U.S. is slightly overpredicted, while PM2.5 in the

West is underpredicted by about 35%. Eastern sulfate and elemental carbon are slightly

overpredicted while nitrate and crustal are largely overpredicted. This is balanced by an

underprediction in organic aerosols. Overall the PM2.5 performance in the East is relatively

unbiased due to the dominance of sulfate in the observations. Western predictions of sulfate,
39

nitrate, elemental carbon, and crustal are all relatively unbiased, while organic aerosols are

underpredicted by ~30%. Since organic aerosols are the largest PM2.5 component in the West,

overall Western PM2.5 is underpredicted by ~35%. 

It should be noted that PM2.5 modeling is an evolving science. There have been few

regional or national scale model applications for primary and secondary PM. In fact, this is the

one of the first nationwide applications of a full chemistry Eulerian grid model for the purpose of

estimating annual average concentrations of PM2.5 and its component species. Also, unlike ozone

modeling, there is essentially no database of past performance statistics against which to measure

the performance of the HDE PM modeling. Given the state of the science relative to PM

modeling, it is inappropriate to judge PM model performance using criteria derived for other

pollutants, like ozone. Still, the performance of the HDE PM modeling is very encouraging,

especially considering that the results may be limited by our current knowledge of PM science

and chemistry, and by the emissions inventories for primary PM and secondary PM precursor

pollutants.

h. Comparisons to Other Observational Databases

Although IMPROVE was the largest and most complete nationwide fine particulate

network operating in 1996, there were several other smaller networks operating at the time that

can provide useful ambient data for comparison with REMSAD results. Among those networks

are the CASTNET Dry Deposition network and the California Air Resources Board (CARB)

PM2.5 monitoring network. There were 26 CASTNET sites which collected weekly average

data for several PM species and 16 CARB sites which collected PM2.5 mass and several

elemental species. 

Both datasets are inconsistent with the sampling methodologies and sampling frequency

of the IMPROVE sites. Further analysis needs to be completed to determine the reliability of

these data. A preliminary review of REMSAD model performance for these networks confirms

what was seen relative to the IMPROVE evaluation. Total nitrate values (particulate nitrate plus

nitric acid) at the CASTNET sites were overpredicted in the East and underpredicted in the West. 

At the CARB sites, PM2.5 mass was underpredicted similar to what was seen at the Western

IMPROVE sites. 

E. Visibility Calculations

Several visibility parameters were calculated from the REMSAD model output for use in

the benefits analysis. These included light extinction coefficient (bext) and deciviews. The

extinction coefficient values in units of inverse megameters (1/M) were calculated based on the

IMPROVE protocol (IMPROVE, 2000). The reconstructed bext values were calculated as

follows:

bext = 10.0 + [3.0 * f(RH) * (1.375 * (GSO4 + ASO4)) + 3.0 * f(RH) * (1.29 * PNO3)+

 4.0 * (SOA + POA) + 10.0 * PEC + 1.0 * (PMFINE) + 0.6 * (PMCOARS)]
40

dv 10.0 ln (bext)

10.0 Mm 1

The 10.0 initial value accounts for atmospheric background (i.e., Rayleigh) scattering. 

f(RH) refers to the relative humidity correction function as defined by IMPROVE (2000). The

relative humidity correction factor was calculated from the 3-hour average modeled relative

humidity at each grid cell for each time period. The 3-hour average bext was then calculated. All

of the hours in the day were then averaged to derive a daily average bext for each grid cell. The

daily average bext were averaged to derive the annual average bext. The annual average bext were

used to calculate the annual average deciviews (dv) using the following formula:

F. Need for HDE Rule Based on Unhealthy Annual Mean PM2.5

Concentrations

One component of the analysis to support the need for this rule was the calculation of the

number of people living in metropolitan counties that experience annual PM2.5 concentrations

above certain concentration levels. This “exposure” type analysis was based on 1999 ambient

annual mean PM2.5 concentrations and projected future PM2.5 concentrations, based on

modeling of the HDE emissions scenarios. To provide the future-year estimates of PM2.5

concentrations, relative reduction factors (RRFs) were calculated then applied to the ambient

data. The procedures for determining the RRFs are similar to those in EPA’s draft guidance for

demonstrating attainment of air quality goals for PM2.5 and regional haze (EPA, 2000d). One

aspect of the procedures in the guidance is to develop RRFs for each component species of

PM2.5 and then to apply these to the corresponding species measured at the monitoring site. 

However, the only extensive nationwide data base of ambient PM2.5 data available for this

analysis does not contain speciated data. Thus, the RRFs were calculated for PM2.5 and applied

to the monitoring data as described as follows. First, the REMSAD predictions of individual

PM2.5 component species were postprocessed to provide annual mean PM2.5 concentrations in

each grid cell for the 1996 base year and each future year scenario modeled (i.e., 2020 base and

control and 2030 base and control). The gridded data were used to determine RRFs at each

monitoring site with valid annual mean PM2.5 data. The RRFs were calculated as the ratio of

mean PM2.5 in the future-year scenario to the mean for the 1996 base year. This value was then

multiplied by the ambient PM2.5 concentration at the monitoring site to provide an estimate of

the future PM2.5 concentrations at that site. These future concentrations were then used in the

“exposure” analysis as described in the HDE docket (Docket A-99-06, item IV-B-01). The

annual mean PM2.5 data along with the corresponding future-year estimates, based on RRFs, are

provided in Appendix E.
41

V. References

Alpine Geophysics, 1994: Technical Formulation Document: SARMAP/LMOS Emissions

Modeling System (EMS-95), Pittsburgh, PA.

EPA, 1996: Guidance on Use of Modeled Results to Demonstrate Attainment, Office of Air

Quality Planning and Standards, EPA-454/B-95-007, Research Triangle Park, NC.

EPA, 1999a: Draft Guidance on the Use of Models and Other Analyses in Attainment

Demonstrations for the 8-Hour Ozone NAAQS, Office of Air Quality Planning and Standards,

Research Triangle Park, NC.

EPA, 1999b: Technical Support Document for the Tier 2/Gasoline Sulfur Ozone Modeling

Analyses, Office of Air Quality Planning and Standards, Research Triangle Park, NC.

EPA, 2000a: "Regulatory Impact Analysis for the Heavy-Duty Engine and Vehicle Standards and

Highway Diesel Fuel Sulfur Control.

EPA, 2000b: Procedures for Developing Base Year and Future Year Mass and Modeling

Inventories for the Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel (HDD)

Rulemaking, EPA420-R-00-020, Research Triangle Park, NC.

EPA, 2000c: Data Summaries of Base Year and Future Year Mass and Modeling Inventories for

the Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel (HDD) Rulemaking -

Detailed Report, EPA420-R-00-019, Research Triangle Park, NC.

EPA, 2000d: Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and

Regional Haze; Draft 1.1, Office of Air Quality Planning and Standards, Research Triangle Park,

NC.

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.

ICF Kaiser, 1998: User’s Guide to the Regulatory Modeling System for Aerosols and Deposition

(REMSAD), SYSAPP98-96/42r2, San Rafael, CA.

IMPROVE. 2000. Spatial and Seasonal Patterns and Temporal Variability of Haze and its

Constituents in the United States: Report III. Cooperative Institute for Research in the

Atmosphere, ISSN: 0737-5352-47.

Lagouvardos, K., Kallos, G., and V. Kotroni, 1997: Modeling and Analysis of Ozone and its

Precursors in the Northeast U.S.A. (Atmospheric Model Simulations), University of Athens,

Department of Physics, Laboratory of Meteorology, Athens.
Mansell, G., 2000: User’s Instructions for the Phase 2 REMSAD Preprocessors, Environ

International, Novato, CA.

Olerud, D., K. Alapaty, and N. Wheeler, 2000: Meteorological Modeling of 1996 for the United

States with MM5. MCNC-Environmental Programs. Research Triangle Park, NC. 

OTAG, 1997. “OTAG Technical Support Document, Chapter 2: Regional Scale Modeling

Workgroup,” Des Plaines, IL.

Pielke, R.A., W.R. Cotton, R.L. Walko, C.J. Tremback, W.A. Lyons, L.D. Grasso, M.E.

Nicholls, M.D. Moran, D.A. Wesley, T.J. Lee, and J.H. Copeland, 1992: A Comprehensive

Meteorological Modeling System - RAMS, Meteor. Atmos. Phys., 49. 69-91.

Pierce, T., C. Geron, L. Bender, R. Dennis, G. Tonnesen, and A. Guenther, 1998: Influence of

increased isoprene emissions on regional ozone modeling, J. Geophys. Res., 103. 25,611-25,629.

Sistla, Gopal, 1999: Personal communication.

Systems Applications International, 1996: User’s Guide to the Variable-Grid Urban Airshed

Model (UAM-V), SYSAPP-96-95/27r, San Rafael CA.

Wayland, Robert J., 1999: REMSAD- 1990 Base Case Simulation: Model Performance

evaluation- Annual Average statistics, EPA, Research Triangle Park, NC.

Whitten, Gary Z., 1999: Computer Efficient Photochemistry for Simultaneous Modeling of

Smog and Secondary Particulate Precursors, Systems Application International, San Rafael, CA.
Appendix A:

Areas in the East with Predicted Exceedances in 2007, 2020, and/or 2030 and

1-Hour Design Values >=125 ppb or >=113 ppb.

MSA/ CMSA / State

Atlanta, GA

Barnstable-Yarmouth, MA

Baton Rouge, LA

Benton Harbor, MI

Beaumont-Port Arthur, TX

Biloxi-Gulfport-Pascagoula, MS

Birmingham, AL

Boston-Worcester-Lawrence, MA-HN-ME-CT

Charleston, WV

Charlotte-Gastonia-Rock Hill, NC-SC

Chicago-Gary-Kenosha, IL-IN-WI

Cincinnati-Hamilton, OH-KY-IN

Cleveland-Akron, OH

Detroit-Ann Arbor-Flint, MI

Grand Rapids-Muskegon-Holland, MI

Hartford, CT

Houma, LA

Houston-Galveston-Brazoria, TX

Huntington-Ashland, WV-KY-OH

Lake Charles, LA

Louisville, KY-IN

Macon, GA MSA

Memphis, TN-AR-MS

Milwaukee-Racine, WI

Nashville, TN

New Orleans, LA

New London-Norwich, CT-RI

New York-Northern New Jersey-Long Island, NY-NJ-CT-PA

Norfolk-Virginia Beach-Newport News, VA-NC

Orlando, FL

Pensacola, FL

Philadelphia-Wilmington- Atlantic City, PA-NJ-DE-MD

Providence-Fall River-Warwick, RI-MA

Richmond-Petersburg, VA

St. Louis, MO-IL

Tampa-St. Petersburg-Clearwater, FL

Washington, DC-Baltimore, DC, MD, VA
Appendix B:

Number of 12km Grid Cells Assigned to Each CMSA/MSA

CMSA/MSAs Total Number of Grid Cells in Area

Atlanta, GA MSA 115

Barnstable, MA MSA 19

Baton Rouge, LA MSA 30

Beaumont-Port Arthur, TX MSA 39

Benton Harbor, MI MSA 15

Biloxi, MS MSA 41

Birmingham, AL MSA 64

Boston, MA CMSA 189

Charleston, WV MSA 31

Charlotte, NC MSA 69

Chicago, IL CMSA 129

Cincinnati, OH CMSA 71

Cleveland, OH CMSA 68

Detroit, MI CMSA 126

Grand Rapids, MI MSA 58

Hartford, CT MSA 41

Houma, LA MSA 51

Houston, TX CMSA 132

Huntington, WV MSA 47

Lake Charles, LA MSA 20

Louisville, KY MSA 45

Macon, GA MSA 37

Memphis, TN MSA 58

Milwaukee, WI CMSA 39

Nashville, TN MSA 78

New London, CT MSA 12

New Orleans, LA MSA 96

New York City, NY CMSA 195

Norfolk, VA MSA 60
Orlando, FL MSA 61

Pensacola, FL MSA 34

Philadelphia, PA CMSA 118

Providence, RI MSA 20

Richmond, VA MSA 66

St. Louis, MO MSA 127

Tampa, FL MSA 56

Washington, DC-Baltimore, MD CMSA 187

.
Appendix C :

1-hour Ozone Metrics
APPENDIX C

1-Hour Ozone Metrics

Total ppb Increase

Total ppb Increase Total Atlanta Barnstable, MA Baton Rouge Beaumont Benton Harbor, MI 

1996 Base vs 2007 Base 56.9 0000 0

2020 Base vs 2020 Control 196.3 0000 02030 Base vs 2030 Control 352.1 0000 0Increase, on Average (ppb) Total Atlanta Barnstable, MA Baton Rouge Beaumont Benton Harbor, MI 

1996 Base vs 2007 Base 0.0 0.0 0.0 0.0 0.0 0.0 2020 Base vs 2020 Control 0.2 0.0 N.A. 0.0 0.0 0.0 2030 Base vs Control 0.3 0.0 N.A. 0.0 0.0 0.0 Note: N.A. is used to denote that there are no exceedances in the Base Case or Control Case
APPENDIX C

1-Hour Ozone Metrics

Total ppb Increase

Total ppb Increase1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs 2030 ControlIncrease, on Average (ppb)1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlNote: N.A. is used to denote that there are no exceedances in the Base Case or Control Case

Boston Charleston, WV Charlotte Chicago Cincinnati Cleveland 

0 0 0 0 5.8 0 0 0 0 11.4 0 0 0 0 0 36.3 0 0 

Boston Charleston, WV Charlotte Chicago Cincinnati Cleveland 

0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.9 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 
APPENDIX C

1-Hour Ozone Metrics

Total ppb Increase

Total ppb Increase1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs 2030 ControlIncrease, on Average (ppb)1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlNote: N.A. is used to denote that there are no exceedances in the Base Case or Control Case

Hartford Houma, LA Houston Huntington, WV Lake Charles, LA Louisville M

0000000 0 0.6 0 0 4 0 0 1.9 0 0 5.5 

Hartford Houma, LA Houston Huntington, WV Lake Charles, LA Louisville M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 N.A. 0.2 
APPENDIX C

1-Hour Ozone Metrics

Total ppb Increase

Total ppb Increase1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs 2030 ControlIncrease, on Average (ppb)1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlNote: N.A. is used to denote that there are no exceedances in the Base Case or Control Case

Milwaukee Nashville New London, CT New Orleans New York City Norfolk 

0 0 0 0 3.5 0 0 0 0 3.9 141.3 0 0 0 0 6.3 221.1 0 

Milwaukee Nashville New London, CT New Orleans New York City Norfolk 

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.0 0.0 0.0 0.0 1.1 N.A. 
APPENDIX C

1-Hour Ozone Metrics

Total ppb Increase

Total ppb Increase1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs 2030 ControlIncrease, on Average (ppb)1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlNote: N.A. is used to denote that there are no exceedances in the Base Case or Control Case

Philadelphia Providence Richmond St. Louis Tampa Was

0 0 0 0 1.2 0 0 0 0 0 00000 

Philadelphia Providence Richmond St. Louis Tampa Was

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
APPENDIX C

1-Hour Ozone Metics

Peak Ozone

Peak 1-Hour Ozone (ppb) Max Mean Atlanta Barnstable, MA Baton Rouge Beaumont Benton Harbor, MI 

1996 Base 219 162 219 160 154 135 160 2007 Base 191 147 191 134 148 134 148 2020 Base 183 143 183 124 144 129 144 2020 Control 171 138 171 116 142 127 140 2030 Base 191 147 191 128 148 132 148 2030 Control 176 140 176 118 145 130 143 Percent Change Max Mean Atlanta Barnstable, MA Baton Rouge Beaumont Benton Harbor, MI 1996 vs 2007 Base -12.8% -9.6% -12.8% -16.2% -3.9% -0.7% -7.5% 2007 Base vs 2020 Base -4.2% -2.9% -4.2% -7.5% -2.7% -3.7% -2.7% 2020 Base vs 2020 Control -6.6% -3.4% -6.6% -6.5% -1.4% -1.6% -2.8% 2020 Base vs 2030 Base 4.4% 2.7% 4.4% 3.2% 2.8% 2.3% 2.8% 2030 Base vs 2030 Control -7.9% -4.2% -7.9% -7.8% -2.0% -1.5% -3.4% 1996 vs 2030 Control -19.6% -13.6% -19.6% -26.2% -5.8% -3.7% -10.6% 
APPENDIX C

1-Hour Ozone Metics

Peak Ozone

Peak 1-Hour Ozone (ppb)1996 Base2007 Base2020 Base2020 Control2030 Base2030 ControlPercent Change1996 vs 2007 Base2007 Base vs 2020 Base2020 Base vs 2020 Control2020 Base vs 2030 Base2030 Base vs 2030 Control1996 vs 2030 Control

Boston Charleston, WV Charlotte Chicago Cincinnati Cleveland 

167 152 153 169 167 144 145 134 141 150 135 136 138 129 138 147 135 140 130 125 132 143 130 139 142 130 144 151 139 143 132 124 136 146 134 142 

Boston Charleston, WV Charlotte Chicago Cincinnati Cleveland 

-13.2% -11.8% -7.8% -11.2% -19.2% -5.6% 

-4.8% -3.7% -2.1% -2.0% 0.0% 2.9% -5.8% -3.1% -4.3% -2.7% -3.7% -0.7% 

2.9% 0.8% 4.3% 2.7% 3.0% 2.1% 

-7.0% -4.6% -5.6% -3.3% -3.6% -0.7% 

-21.0% -18.4% -11.1% -13.6% -19.8% -1.4% 
APPENDIX C

1-Hour Ozone Metics

Peak Ozone

Peak 1-Hour Ozone (ppb)1996 Base2007 Base2020 Base2020 Control2030 Base2030 ControlPercent Change1996 vs 2007 Base2007 Base vs 2020 Base2020 Base vs 2020 Control2020 Base vs 2030 Base2030 Base vs 2030 Control1996 vs 2030 Control

Hartford Houma, LA Houston Huntington, WV Lake Charles, LA Louisville Ma

188 147 165 171 132 172 171 143 156 151 129 149 166 140 155 150 125 148 159 138 153 147 124 147 171 143 159 153 128 151 162 140 157 150 126 152 

Hartford Houma, LA Houston Huntington, WV Lake Charles, LA Louisville Ma

-9.0% -2.7% -5.5% -11.7% -2.3% -13.4% -2.9% -2.1% -0.6% -0.7% -3.1% -0.7% -4.2% -1.4% -1.3% -2.0% -0.8% -0.7% 

3.0% 2.1% 2.6% 2.0% 2.4% 2.0% 

-5.3% -2.1% -1.3% -2.0% -1.6% 0.7% 

-13.8% -4.8% -4.8% -12.3% -4.5% -11.6% 
APPENDIX C

1-Hour Ozone Metics

Peak Ozone

Peak 1-Hour Ozone (ppb)1996 Base2007 Base2020 Base2020 Control2030 Base2030 ControlPercent Change1996 vs 2007 Base2007 Base vs 2020 Base2020 Base vs 2020 Control2020 Base vs 2030 Base2030 Base vs 2030 Control1996 vs 2030 Control

Milwaukee Nashville New London, CT New Orleans New York City Norfolk O

148 166 180 165 192 146 130 154 159 160 178 127 125 149 152 157 175 126 125 142 145 156 168 123 130 154 157 160 180 130 128 145 148 158 171 126 

Milwaukee Nashville New London, CT New Orleans New York City Norfolk O

-12.2% -7.2% -11.7% -3.0% -7.3% -13.0% 

-3.8% -3.2% -4.4% -1.9% -1.7% -0.8% 

0.0% -4.7% -4.6% -0.6% -4.0% -2.4% 4.0% 3.4% 3.3% 1.9% 2.9% 3.2% 

-1.5% -5.8% -5.7% -1.2% -5.0% -3.1% 

-13.5% -12.7% -17.8% -4.2% -10.9% -13.7% 
APPENDIX C

1-Hour Ozone Metics

Peak Ozone

Peak 1-Hour Ozone (ppb)1996 Base2007 Base2020 Base2020 Control2030 Base2030 ControlPercent Change1996 vs 2007 Base2007 Base vs 2020 Base2020 Base vs 2020 Control2020 Base vs 2030 Base2030 Base vs 2030 Control1996 vs 2030 Control

Philadelphia Providence Richmond St. Louis Tampa Wash

166 173 170 151 188 142 149 150 141 173 135 141 141 136 161 127 134 137 128 150 139 146 145 140 166 129 136 139 129 152 

Philadelphia Providence Richmond St. Louis Tampa Wash

-14.5% -13.9% -11.8% -6.6% -8.0% 

-4.9% -5.4% -6.0% -3.5% -6.9% -5.9% -5.0% -2.8% -5.9% -6.8% 

3.0% 3.5% 2.8% 2.9% 3.1% 

-7.2% -6.8% -4.1% -7.9% -8.4% 

-22.3% -21.4% -18.2% -14.6% -19.1% 
APPENDIX C

1-Hour Ozone Metrics

Total Nonattainment

Total Nonattainment Total Atlanta Barnstable, MA Baton Rouge Beaumont Benton Harbor, MI 

(ppb >= 125)1996 Base 39665.2 7738.3 192.8 1176.9 111.1 205.6 2007 Base 12743.4 2604.9 11.6 687.4 49.5 65.5 2020 Base 8334.2 1319.4 0 389.8 7.8 45.4 2020 Control 5288.3 546.6 0 257.9 4 36.1 2030 Base 12129.2 1945.1 3.7 635.4 24.4 61.1 2030 Control 6841.1 636.9 0 406 10.5 42.6 Percent Change Total Atlanta Barnstable, MA Baton Rouge Beaumont Benton Harbor, MI 1996 vs 2007 Base -67.9% -66.3% -94.0% -41.6% -55.4% -68.1% 2007 Base vs 2020 Base -34.6% -49.3% -100.0% -43.3% -84.2% -30.7% 2020 Base vs 2020 Control -36.5% -58.6% 0.0% -33.8% -48.7% -20.5% 2020 Base vs 2030 Base 45.5% 47.4% N.A. 63.0% 212.8% 34.6% 2030 Base vs 2030 Control -43.6% -67.3% -100.0% -36.1% -57.0% -30.3% Note: N.A. denotes predicted exceedances in the 2030 Base, but not in the 2020 Base
APPENDIX C

1-Hour Ozone Metrics

Total Nonattainment

Total Nonattainment(ppb >= 125)1996 Base2007 Base2020 Base2020 Control2030 Base2030 ControlPercent Change 1996 vs 2007 Base2007 Base vs 2020 Base2020 Base vs 2020 Control2020 Base vs 2030 Base2030 Base vs 2030 ControlNote: N.A. denotes predicted exceedances in the 2030 Base, but not in the 2020 Base

Boston Charleston, WV Charlotte Chicago Cincinnati Cleveland D

608.8 288.2 292 374.9 1025.1 191.9 

95.8 9.5 36.5 125.8 50.8 15.4 23.5 4.2 22.6 120 33.6 15.4 

5.5 0.2 7.4 101 14.5 14.1 

56.3 5.3 48.3 174.7 72.5 27.5 

7.5 0 11.6 149.3 28.6 17.8 

Boston Charleston, WV Charlotte Chicago Cincinnati Cleveland D

-84.3% -96.7% -87.5% -66.4% -95.0% -92.0% -75.5% -55.8% -38.1% -4.6% -33.9% 0.0% -76.6% -95.2% -67.3% -15.8% -56.8% -8.4% 

139.6% 26.2% 113.7% 45.6% 115.8% 78.6% 

-86.7% -100.0% -76.0% -14.5% -60.6% -35.3% 
APPENDIX C

1-Hour Ozone Metrics

Total Nonattainment

Total Nonattainment(ppb >= 125)1996 Base2007 Base2020 Base2020 Control2030 Base2030 ControlPercent Change 1996 vs 2007 Base2007 Base vs 2020 Base2020 Base vs 2020 Control2020 Base vs 2030 Base2030 Base vs 2030 ControlNote: N.A. denotes predicted exceedances in the 2030 Base, but not in the 2020 Base

Hartford Houma, LA Houston Huntington, WV Lake Charles, LA Louisville Ma

649.6 586.6 1597.5 1411.4 36.3 1437.1 346.7 288.2 640.5 71.7 12.7 267.3 303.5 165.3 437.4 52.7 0.8 277.3 219.6 119.5 325.8 36.1 0 229.6 377.9 285 663.2 68.9 8.5 347 263.9 182.8 441 44.5 2.3 272 

Hartford Houma, LA Houston Huntington, WV Lake Charles, LA Louisville Ma

-46.6% -50.9% -59.9% -94.9% -65.0% -81.4% -12.5% -42.6% -31.7% -26.5% -93.7% 3.7% -27.6% -27.7% -25.5% -31.5% -100.0% -17.2% 

24.5% 72.4% 51.6% 30.7% 962.4% 25.1% 

-30.2% -35.9% -33.5% -35.4% -72.9% -21.6% 
APPENDIX C

1-Hour Ozone Metrics

Total Nonattainment

Total Nonattainment(ppb >= 125)1996 Base2007 Base2020 Base2020 Control2030 Base2030 ControlPercent Change 1996 vs 2007 Base2007 Base vs 2020 Base2020 Base vs 2020 Control2020 Base vs 2030 Base2030 Base vs 2030 ControlNote: N.A. denotes predicted exceedances in the 2030 Base, but not in the 2020 Base

Milwaukee Nashville New London, CT New Orleans New York City Norfolk O

69.4 1263.1 612.7 1857.5 5787.7 92.6 

5.3 103.8 259.6 1108 2190.4 5.5 0.8 53 195.1 742.3 1870.2 1.3 0.1 30.4 113.9 581.2 1430.6 0 

11.9 76.6 278.5 1130.3 2503.6 8.2 

4.1 36.6 152.5 826.4 1778.2 1.2 

Milwaukee Nashville New London, CT New Orleans New York City Norfolk O

-92.4% -91.8% -57.6% -40.3% -62.2% -94.1% -84.9% -48.9% -24.8% -33.0% -14.6% -76.4% -87.5% -42.6% -41.6% -21.7% -23.5% -100.0% 

1387.3% 44.5% 42.7% 52.3% 33.9% 530.7% 

-65.5% -52.2% -45.2% -26.9% -29.0% -85.4% 
APPENDIX C

1-Hour Ozone Metrics

Total Nonattainment

Total Nonattainment(ppb >= 125)1996 Base2007 Base2020 Base2020 Control2030 Base2030 ControlPercent Change 1996 vs 2007 Base2007 Base vs 2020 Base2020 Base vs 2020 Control2020 Base vs 2030 Base2030 Base vs 2030 ControlNote: N.A. denotes predicted exceedances in the 2030 Base, but not in the 2020 Base

Philadelphia Providence Richmond St. Louis Tampa Wash

1588.3 512.6 495.3 591.5 2396.9 

162.3 155.4 160.3 74.2 1380.4 

68.7 79.4 85.3 32.9 803.8 10.5 29.2 33 4.9 402.2 

150.9 133.6 121.1 61.5 1124.2 

24.3 43.3 42.4 9.9 464.8 

Philadelphia Providence Richmond St. Louis Tampa Wash

-89.8% -69.7% -67.6% -87.5% -42.4% -57.7% -48.9% -46.8% -55.7% -41.8% -84.7% -63.2% -61.3% -85.1% -50.0% 

119.7% 68.3% 42.0% 86.9% 39.9% 

-83.9% -67.6% -65.0% -83.9% -58.7% 
APPENDIX C

1-Hour Metrics

Total ppb Reduction

Total ppb Reduction Total Atlanta Barnstable, MA Baton Rouge Beaumont Benton Harbor, MI 

1996 Base vs 2007 Base 47890.1 7584.5 377.5 576 83.9 281.2 2020 Base vs 2020 Control 4161.8 1098 0 152.8 4.8 13 2030 Base vs Control 7569.8 2007.2 10.5 266 24.2 25.4 Reduction, on Average (ppb) Total Atlanta Barnstable, MA Baton Rouge Beaumont Benton Harbor, MI 1996 Base vs 2007 Base 16.2 20.2 18.0 4.6 3.1 14.8 2020 Base vs 2020 Control 4.9 12.2 N.A. 2.4 1.6 3.3 2030 Base vs Control 6.5 16.2 10.5 3.3 2.4 4.2 Note: N.A. is used to denote that there are no exceedances in the Base Case or Control Case
APPENDIX C

1-Hour Metrics

Total ppb Reduction

Total ppb Reduction1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlReduction, on Average (ppb)1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlNote: N.A. is used to denote that there are no exceedances in the Base Case or Control Case

Boston Charleston, WV Charlotte Chicago Cincinnati Cleveland 

909.5 1152 1137.7 382.6 2127.4 611.4 

31.9 4 32.1 36.5 28 1.3 

87 5.4 47.7 56.7 61.6 13.9 

Boston Charleston, WV Charlotte Chicago Cincinnati Cleveland 

16.5 37.2 25.3 11.6 25.6 15.3 

8.0 4.0 6.4 2.8 4.0 1.3 

10.9 5.4 8.0 3.2 5.1 3.5 
APPENDIX C

1-Hour Metrics

Total ppb Reduction

Total ppb Reduction1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlReduction, on Average (ppb)1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlNote: N.A. is used to denote that there are no exceedances in the Base Case or Control Case

Hartford Houma, LA Houston Huntington, WV Lake Charles, LA Louisville M

432.3 405.8 1321.8 3231.4 41.9 2575.7 

91.9 52.4 143.9 19.6 1.3 57.6 

122.8 128 284.1 30.3 7.2 90.6 

Hartford Houma, LA Houston Huntington, WV Lake Charles, LA Louisville M

12.7 4.4 7.9 35.1 3.2 23.8 

5.1 1.9 2.5 3.3 1.3 2.4 6.5 2.5 3.3 4.3 1.8 3.4 
APPENDIX C

1-Hour Metrics

Total ppb Reduction

Total ppb Reduction1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlReduction, on Average (ppb)1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlNote: N.A. is used to denote that there are no exceedances in the Base Case or Control Case

Milwaukee Nashville New London, CT New Orleans New York City Norfolk 

85.4 3959.9 390.1 953.9 4625.2 276.9 

6.4 37.5 98 195.6 687.8 2.9 

12.8 50.2 155.3 344 1156.1 10.5 

Milwaukee Nashville New London, CT New Orleans New York City Norfolk 

14.2 38.8 13.9 4.4 14.0 16.3 

3.2 7.5 5.2 1.8 4.1 2.9 3.2 10.0 6.5 2.3 5.6 5.3 
APPENDIX C

1-Hour Metrics

Total ppb Reduction

Total ppb Reduction1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlReduction, on Average (ppb)1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlNote: N.A. is used to denote that there are no exceedances in the Base Case or Control Case

Philadelphia Providence Richmond St. Louis Tampa Wa

2431.8 504.5 537.3 1141 1311.1 

93.4 69.4 59 43.6 460.1 

221.5 118.5 100.3 64.6 862.1 

Philadelphia Providence Richmond St. Louis Tampa Wa

19.6 15.8 17.9 16.8 10.2 

6.2 6.9 6.6 7.3 8.1 7.9 8.5 9.1 9.2 11.1 
APPENDIX C

1-Hour Ozone Metrics

Total ppb Increase

Total ppb Increase Total Atlanta Barnstable, MA Baton Rouge Beaumont Benton Harbor, MI 

1996 Base vs 2007 Base 56.9 0000 0

2020 Base vs 2020 Control 196.3 0000 02030 Base vs 2030 Control 352.1 0000 0Increase, on Average (ppb) Total Atlanta Barnstable, MA Baton Rouge Beaumont Benton Harbor, MI 

1996 Base vs 2007 Base 0.0 0.0 0.0 0.0 0.0 0.0 2020 Base vs 2020 Control 0.2 0.0 N.A. 0.0 0.0 0.0 2030 Base vs Control 0.3 0.0 N.A. 0.0 0.0 0.0 Note: N.A. is used to denote that there are no exceedances in the Base Case or Control Case
APPENDIX C

1-Hour Ozone Metrics

Total ppb Increase

Total ppb Increase1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs 2030 ControlIncrease, on Average (ppb)1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlNote: N.A. is used to denote that there are no exceedances in the Base Case or Control Case

Boston Charleston, WV Charlotte Chicago Cincinnati Cleveland 

0 0 0 0 5.8 0 0 0 0 11.4 0 0 0 0 0 36.3 0 0 

Boston Charleston, WV Charlotte Chicago Cincinnati Cleveland 

0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.9 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 
APPENDIX C

1-Hour Ozone Metrics

Total ppb Increase

Total ppb Increase1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs 2030 ControlIncrease, on Average (ppb)1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlNote: N.A. is used to denote that there are no exceedances in the Base Case or Control Case

Hartford Houma, LA Houston Huntington, WV Lake Charles, LA Louisville M

0000000 0 0.6 0 0 4 0 0 1.9 0 0 5.5 

Hartford Houma, LA Houston Huntington, WV Lake Charles, LA Louisville M

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.0 N.A. 0.2 
APPENDIX C

1-Hour Ozone Metrics

Total ppb Increase

Total ppb Increase1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs 2030 ControlIncrease, on Average (ppb)1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlNote: N.A. is used to denote that there are no exceedances in the Base Case or Control Case

Milwaukee Nashville New London, CT New Orleans New York City Norfolk 

0 0 0 0 3.5 0 0 0 0 3.9 141.3 0 0 0 0 6.3 221.1 0 

Milwaukee Nashville New London, CT New Orleans New York City Norfolk 

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8 0.0 0.0 0.0 0.0 0.0 1.1 N.A. 
APPENDIX C

1-Hour Ozone Metrics

Total ppb Increase

Total ppb Increase1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs 2030 ControlIncrease, on Average (ppb)1996 Base vs 2007 Base2020 Base vs 2020 Control2030 Base vs ControlNote: N.A. is used to denote that there are no exceedances in the Base Case or Control Case

Philadelphia Providence Richmond St. Louis Tampa Was

0 0 0 0 1.2 0 0 0 0 0 00000 

Philadelphia Providence Richmond St. Louis Tampa Was

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 
Appendix D:

8 Hour Relative Reduction Factors
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

010270001 AL CLAY CO CLAY CO, AL 0.8211 0.7747 0.7277 010510001 AL ELMORE CO MONTGOMERY, AL 0.8784 0.8301 0.7836 010731003 AL JEFFERSON CO BIRMINGHAM, AL 0.8765 0.8145 0.7594 010731005 AL JEFFERSON CO BIRMINGHAM, AL 0.8541 0.8001 0.7532 010732006 AL JEFFERSON CO BIRMINGHAM, AL 0.8734 0.8133 0.7592 010735002 AL JEFFERSON CO BIRMINGHAM, AL 0.8634 0.8103 0.7612 010736002 AL JEFFERSON CO BIRMINGHAM, AL 0.8728 0.8154 0.7625 010790002 AL LAWRENCE CO LAWRENCE CO, AL 0.8428 0.8010 0.7628 

010890014 AL MADISON CO HUNTSVILLE, AL 0.8743 0.8272 0.7817 010970003 AL MOBILE CO MOBILE, AL 0.9107 0.8786 0.8464 010970028 AL MOBILE CO MOBILE, AL 0.9035 0.8711 0.8385 011011002 AL MONTGOMERY CO MONTGOMERY, AL 0.8835 0.8398 0.7936 011170004 AL SHELBY CO BIRMINGHAM, AL 0.8632 0.8069 0.7546 011190002 AL SUMTER CO SUMTER CO, AL 0.8460 0.8266 0.7986 050350005 AR CRITTENDEN CO MEMPHIS, TN-AR-MS 0.9027 0.8899 0.8720 050970001 AR MONTGOMERY CO MONTGOMERY CO, AR 0.8917 0.8432 0.8071 051010002 AR NEWTON CO NEWTON CO, AR 0.8744 0.8421 0.8164 

051190007 AR PULASKI CO LITTLE ROCK-NORTH LITTLE ROCK, A 0.9008 0.8425 0.7992 

051191002 AR PULASKI CO LITTLE ROCK-NORTH LITTLE ROCK, A 0.9008 0.8426 0.7994 090010017 CT FAIRFIELD CO NEW YORK CMSA 0.9458 0.9508 0.9392 

090011123 CT FAIRFIELD CO NEW YORK CMSA 0.9323 0.9324 0.9135 

090013007 CT FAIRFIELD CO NEW YORK CMSA 0.9329 0.9197 0.8958 

090019003 CT FAIRFIELD CO NEW YORK CMSA 0.9396 0.9437 0.9311 

090031003 CT HARTFORD CO HARTFORD, CT 0.9059 0.8735 0.8332 090050006 CT LITCHFIELD CO HARTFORD, CT 0.8993 0.8672 0.8306 090070007 CT MIDDLESEX CO HARTFORD, CT 0.9197 0.8974 0.8657 090091123 CT NEW HAVEN CO NEW YORK CMSA 0.9274 0.9148 0.8899 

090093002 CT NEW HAVEN CO NEW YORK CMSA 0.9165 0.8956 0.8663 

090110008 CT NEW LONDON CO NEW YORK CMSA 0.9130 0.8929 0.8612 

090131001 CT TOLLAND CO HARTFORD, CT 0.8935 0.8528 0.8173 100010002 DE KENT CO PHILADELPHIA CMSA 0.8729 0.8377 0.7971 100031003 DE NEW CASTLE CO PHILADELPHIA CMSA 0.9003 0.8823 0.8509 

100031007 DE NEW CASTLE CO PHILADELPHIA CMSA 0.8726 0.8441 0.8033 100031010 DE NEW CASTLE CO PHILADELPHIA CMSA 0.8933 0.8733 0.8418 100051002 DE SUSSEX CO SUSSEX CO, DE 0.8759 0.8388 0.7973 100051003 DE SUSSEX CO SUSSEX CO, DE 0.8809 0.8473 0.8111 110010025 DC WASHINGTON WASHINGTON, DC-MD-VA-WV 0.9282 0.9206 0.8933 

110010041 DC WASHINGTON WASHINGTON, DC-MD-VA-WV 0.9022 0.8828 0.8562 

110010043 DC WASHINGTON WASHINGTON, DC-MD-VA-WV 0.9282 0.9206 0.8933 

120013011 FL ALACHUA CO GAINESVILLE, FL 0.8972 0.8472 0.7903 120030002 FL BAKER CO BAKER CO, FL 0.8909 0.8416 0.7923 120094001 FL BREVARD CO MELBOURNE-TITUSVILLE-PALM BAY, 0.9393 0.8850 0.8357 

120095001 FL BREVARD CO MELBOURNE-TITUSVILLE-PALM BAY, 0.9407 0.8882 0.8409 120310070 FL DUVAL CO JACKSONVILLE, FL 0.9173 0.8499 0.7976 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

120310077 FL DUVAL CO JACKSONVILLE, FL 0.9117 0.8570 0.8100 120330004 FL ESCAMBIA CO PENSACOLA, FL 0.9224 0.8859 0.8480 120330018 FL ESCAMBIA CO PENSACOLA, FL 0.9223 0.8857 0.8486 120330024 FL ESCAMBIA CO PENSACOLA, FL 0.9223 0.8857 0.8486 120570081 FL HILLSBOROUGH CO TAMPA-ST. PETERSBURG-CLEARWA 0.9513 0.9105 0.8676 

120571035 FL HILLSBOROUGH CO TAMPA-ST. PETERSBURG-CLEARWA 0.9524 0.9090 0.8621 120571065 FL HILLSBOROUGH CO TAMPA-ST. PETERSBURG-CLEARWA 0.9656 0.9336 0.8895 120590004 FL HOLMES CO HOLMES CO, FL 0.9059 0.8648 0.8232 120712001 FL LEE CO FORT MYERS-CAPE CORAL, FL 0.9655 0.9179 0.8719 120713002 FL LEE CO FORT MYERS-CAPE CORAL, FL 0.9644 0.9146 0.8664 120813002 FL MANATEE CO SARASOTA-BRADENTON, FL 0.9590 0.9292 0.8875 120814010 FL MANATEE CO SARASOTA-BRADENTON, FL 0.9496 0.9108 0.8641 120950008 FL ORANGE CO ORLANDO, FL 0.9370 0.8892 0.8341 120952002 FL ORANGE CO ORLANDO, FL 0.9368 0.8889 0.8330 120972002 FL OSCEOLA CO ORLANDO, FL 0.9328 0.8846 0.8330 120990007 FL PALM BEACH CO MIAMI CMSA 0.9260 0.8553 0.8086 120992004 FL PALM BEACH CO MIAMI CMSA 0.9237 0.8512 0.8009 121012001 FL PASCO CO TAMPA-ST. PETERSBURG-CLEARWA 0.9499 0.9020 0.8475 121030004 FL PINELLAS CO TAMPA-ST. PETERSBURG-CLEARWA 0.9688 0.9411 0.8979 121030018 FL PINELLAS CO TAMPA-ST. PETERSBURG-CLEARWA 0.9726 0.9505 0.9093 121035002 FL PINELLAS CO TAMPA-ST. PETERSBURG-CLEARWA 0.9548 0.9141 0.8642 121056005 FL POLK CO LAKELAND-WINTER HAVEN, FL 0.9370 0.8854 0.8374 121056006 FL POLK CO LAKELAND-WINTER HAVEN, FL 0.9411 0.8955 0.8491 121111002 FL ST LUCIE CO FORT PIERCE-PORT ST. LUCIE, FL 0.9516 0.9068 0.8640 121151002 FL SARASOTA CO SARASOTA-BRADENTON, FL 0.9459 0.9058 0.8600 121151005 FL SARASOTA CO SARASOTA-BRADENTON, FL 0.9459 0.9058 0.8600 121171002 FL SEMINOLE CO ORLANDO, FL 0.9297 0.8762 0.8184 121272001 FL VOLUSIA CO DAYTONA BEACH, FL 0.9150 0.8603 0.8061 121275002 FL VOLUSIA CO DAYTONA BEACH, FL 0.9108 0.8575 0.7999 130210012 GA BIBB CO MACON, GA 0.8144 0.7683 0.7188 130510021 GA CHATHAM CO SAVANNAH, GA 0.8960 0.8665 0.8282 130850001 GA DAWSON CO DAWSON CO, GA 0.8365 0.7706 0.7007 

130890002 GA DE KALB CO ATLANTA, GA 0.8898 0.8497 0.7965 130893001 GA DE KALB CO ATLANTA, GA 0.9073 0.8728 0.8190 130970004 GA DOUGLAS CO ATLANTA, GA 0.8781 0.8232 0.7641 131110094 GA FANNIN CO FANNIN CO, GA 0.8221 0.7641 0.7038 131130001 GA FAYETTE CO ATLANTA, GA 0.8700 0.8117 0.7458 131210055 GA FULTON CO ATLANTA, GA 0.8992 0.8626 0.8075 131350002 GA GWINNETT CO ATLANTA, GA 0.8766 0.8119 0.7360 132150008 GA MUSCOGEE CO COLUMBUS, GA-AL 0.8694 0.8047 0.7473 132151003 GA MUSCOGEE CO COLUMBUS, GA-AL 0.8694 0.8047 0.7473 132230003 GA PAULDING CO ATLANTA, GA 0.8432 0.7945 0.7399 132450091 GA RICHMOND CO AUGUSTA-AIKEN, GA-SC 0.8531 0.7869 0.7367 132470001 GA ROCKDALE CO ATLANTA, GA 0.8660 0.8018 0.7275 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

132611001 GA SUMTER CO SUMTER CO, GA 0.8616 0.8145 0.7635 170010006 IL ADAMS CO ADAMS CO, IL 0.8903 0.8575 0.8329 170190004 IL CHAMPAIGN CO CHAMPAIGN-URBANA, IL 0.8658 0.8318 0.8023 170310001 IL COOK CO CHICAGO CMSA 0.9265 0.9462 0.9537 170310032 IL COOK CO CHICAGO CMSA 0.9111 0.9071 0.8958 170310050 IL COOK CO CHICAGO CMSA 0.9111 0.9071 0.8958 170310063 IL COOK CO CHICAGO CMSA 0.9163 0.9165 0.9087 170310064 IL COOK CO CHICAGO CMSA 0.9163 0.9165 0.9087 170310072 IL COOK CO CHICAGO CMSA 0.9363 0.9444 0.9451 170311003 IL COOK CO CHICAGO CMSA 0.9022 0.8897 0.8963 170311601 IL COOK CO CHICAGO CMSA 0.9292 0.9254 0.9183 170314002 IL COOK CO CHICAGO CMSA 0.9114 0.8994 0.8883 170314006 IL COOK CO CHICAGO CMSA 0.9310 0.9423 0.9612 170314201 IL COOK CO CHICAGO CMSA 0.9171 0.9268 0.9302 170317002 IL COOK CO CHICAGO CMSA 0.9171 0.9268 0.9302 170318003 IL COOK CO CHICAGO CMSA 0.9030 0.9094 0.9014 170436001 IL DU PAGE CO CHICAGO CMSA 0.9390 0.9441 0.9391 170491001 IL EFFINGHAM CO EFFINGHAM CO, IL 0.8431 0.8059 0.7762 170650001 IL HAMILTON CO HAMILTON CO, IL 0.8280 0.7748 0.7501 170831001 IL JERSEY CO ST. LOUIS, MO-IL 0.8936 0.8451 0.8014 170890005 IL KANE CO CHICAGO CMSA 0.9417 0.9441 0.9427 170970001 IL LAKE CO CHICAGO CMSA 0.9193 0.9200 0.9314 170971002 IL LAKE CO CHICAGO CMSA 0.9168 0.9044 0.8904 170971007 IL LAKE CO CHICAGO CMSA 0.9250 0.9226 0.9149 170973001 IL LAKE CO CHICAGO CMSA 0.9165 0.9100 0.9014 171110001 IL MC HENRY CO CHICAGO CMSA 0.9389 0.9404 0.9368 171150013 IL MACON CO DECATUR, IL 0.8580 0.8280 0.8011 171170002 IL MACOUPIN CO ST. LOUIS, MO-IL 0.8536 0.8140 0.7763 171190008 IL MADISON CO ST. LOUIS, MO-IL 0.8922 0.8501 0.8123 171191009 IL MADISON CO ST. LOUIS, MO-IL 0.8929 0.8521 0.8126 171192007 IL MADISON CO ST. LOUIS, MO-IL 0.8992 0.8570 0.8177 171193007 IL MADISON CO ST. LOUIS, MO-IL 0.8992 0.8570 0.8177 171430024 IL PEORIA CO PEORIA-PEKIN, IL 0.9056 0.8803 0.8570 171431001 IL PEORIA CO PEORIA-PEKIN, IL 0.9056 0.8803 0.8570 171570001 IL RANDOLPH CO RANDOLPH CO, IL 0.8538 0.8230 0.7939 171610003 IL ROCK ISLAND CO DAVENPORT-MOLINE-ROCK ISLAND, 0.9264 0.8983 0.8772 171630010 IL ST CLAIR CO ST. LOUIS, MO-IL 0.9078 0.8693 0.8298 171670010 IL SANGAMON CO SPRINGFIELD, IL 0.8632 0.8297 0.7961 171971008 IL WILL CO CHICAGO CMSA 0.9281 0.9268 0.9139 

171971011 IL WILL CO CHICAGO CMSA 0.8908 0.8722 0.8503 172010009 IL WINNEBAGO CO ROCKFORD, IL 0.9129 0.8875 0.8604 172012001 IL WINNEBAGO CO ROCKFORD, IL 0.9059 0.8783 0.8497 180030002 IN ALLEN CO FORT WAYNE, IN 0.8919 0.8557 0.8268 180030004 IN ALLEN CO FORT WAYNE, IN 0.8954 0.8602 0.8306 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

180190003 IN CLARK CO LOUISVILLE, KY-IN 0.8857 0.8729 0.8538 180390002 IN ELKHART CO ELKHART-GOSHEN, IN 0.8810 0.8471 0.8189 180431004 IN FLOYD CO LOUISVILLE, KY-IN 0.8914 0.8859 0.8731 180571001 IN HAMILTON CO INDIANAPOLIS, IN 0.8918 0.8620 0.8332 180590003 IN HANCOCK CO INDIANAPOLIS, IN 0.8899 0.8621 0.8342 180810002 IN JOHNSON CO INDIANAPOLIS, IN 0.8542 0.8227 0.7932 180890022 IN LAKE CO CHICAGO CMSA 0.9042 0.8940 0.8778 180890024 IN LAKE CO CHICAGO CMSA 0.8893 0.8669 0.8434 180892008 IN LAKE CO CHICAGO CMSA 0.9049 0.9015 0.8881 180910005 IN LA PORTE CO LA PORTE CO, IN 0.9180 0.9026 0.8855 180910010 IN LA PORTE CO LA PORTE CO, IN 0.9104 0.8892 0.8677 180950010 IN MADISON CO INDIANAPOLIS, IN 0.8833 0.8466 0.8144 180970042 IN MARION CO INDIANAPOLIS, IN 0.8865 0.8648 0.8411 180970050 IN MARION CO INDIANAPOLIS, IN 0.8960 0.8802 0.8603 180970057 IN MARION CO INDIANAPOLIS, IN 0.9062 0.9035 0.8873 180970073 IN MARION CO INDIANAPOLIS, IN 0.8960 0.8802 0.8603 181090005 IN MORGAN CO INDIANAPOLIS, IN 0.8688 0.8431 0.8181 181230008 IN PERRY CO PERRY CO, IN 0.8203 0.8058 0.7842 181270020 IN PORTER CO CHICAGO CMSA 0.9161 0.9077 0.8943 181270024 IN PORTER CO CHICAGO CMSA 0.9042 0.8940 0.8778 181270026 IN PORTER CO CHICAGO CMSA 0.9246 0.9113 0.8938 181290003 IN POSEY CO EVANSVILLE-HENDERSON, IN-KY 0.8773 0.8506 0.8325 181410010 IN ST JOSEPH CO SOUTH BEND, IN 0.8853 0.8547 0.8278 181411007 IN ST JOSEPH CO SOUTH BEND, IN 0.8889 0.8620 0.8368 181411008 IN ST JOSEPH CO SOUTH BEND, IN 0.8889 0.8622 0.8377 181630012 IN VANDERBURGH CO EVANSVILLE-HENDERSON, IN-KY 0.8800 0.8529 0.8311 181630013 IN VANDERBURGH CO EVANSVILLE-HENDERSON, IN-KY 0.8633 0.8364 0.8140 181670018 IN VIGO CO TERRE HAUTE, IN 0.8695 0.8393 0.8134 181730002 IN WARRICK CO EVANSVILLE-HENDERSON, IN-KY 0.8336 0.8140 0.7921 181730008 IN WARRICK CO EVANSVILLE-HENDERSON, IN-KY 0.8315 0.8109 0.7887 181730009 IN WARRICK CO EVANSVILLE-HENDERSON, IN-KY 0.8424 0.8188 0.7962 190851101 IA HARRISON CO HARRISON CO, IA 0.9155 0.8819 0.8590 191130033 IA LINN CO CEDAR RAPIDS, IA 0.9185 0.8886 0.8653 191131015 IA LINN CO CEDAR RAPIDS, IA 0.9183 0.8919 0.8703 191530058 IA POLK CO DES MOINES, IA 0.9051 0.8646 0.8360 191632011 IA SCOTT CO DAVENPORT-MOLINE-ROCK ISLAND, 0.9283 0.9011 0.8792 191690011 IA STORY CO STORY CO, IA 0.9052 0.8657 0.8377 191770004 IA VAN BUREN CO VAN BUREN CO, IA 0.9032 0.8725 0.8489 191810022 IA WARREN CO DES MOINES, IA 0.8980 0.8596 0.8316 

201730001 KS SEDGWICK CO WICHITA, KS 0.9406 0.8963 0.8669 

201730010 KS SEDGWICK CO WICHITA, KS 0.9408 0.8966 0.8673 

202090001 KS WYANDOTTE CO KANSAS CITY, MO-KS 0.9366 0.9083 0.8848 210130002 KY BELL CO BELL CO, KY 0.7997 0.7395 0.6929 210150003 KY BOONE CO CINCINNATI CMSA 0.8478 0.8205 0.7965 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

210190015 KY BOYD CO HUNTINGTON-ASHLAND, WV-KY-OH 0.8522 0.8276 0.8033 210290006 KY BULLITT CO LOUISVILLE, KY-IN 0.8603 0.8479 0.8287 210371001 KY CAMPBELL CO CINCINNATI CMSA 0.8988 0.8804 0.8628 210430500 KY CARTER CO HUNTINGTON-ASHLAND, WV-KY-OH 0.8055 0.7818 0.7572 210470006 KY CHRISTIAN CO CHRISTIAN CO, KY 0.7743 0.7452 0.7214 210590005 KY DAVIESS CO OWENSBORO, KY 0.8295 0.8131 0.7919 

210610501 KY EDMONSON CO EDMONSON CO, KY 0.7963 0.7700 0.7441 210670001 KY FAYETTE CO LEXINGTON, KY 0.8567 0.8365 0.8094 210670012 KY FAYETTE CO LEXINGTON, KY 0.8702 0.8477 0.8192 210830003 KY GRAVES CO GRAVES CO, KY 0.8414 0.7950 0.7699 210890007 KY GREENUP CO HUNTINGTON-ASHLAND, WV-KY-OH 0.8454 0.8238 0.8015 210910012 KY HANCOCK CO OWENSBORO, KY 0.8107 0.7966 0.7752 

210930005 KY HARDIN CO HARDIN CO, KY 0.8262 0.8077 0.7844 211010013 KY HENDERSON CO EVANSVILLE-HENDERSON, IN-KY 0.8737 0.8523 0.8319 211010014 KY HENDERSON CO EVANSVILLE-HENDERSON, IN-KY 0.8559 0.8375 0.8179 211110027 KY JEFFERSON CO LOUISVILLE, KY-IN 0.8918 0.8929 0.8832 211110051 KY JEFFERSON CO LOUISVILLE, KY-IN 0.8929 0.8866 0.8713 211111021 KY JEFFERSON CO LOUISVILLE, KY-IN 0.8946 0.8846 0.8703 211130001 KY JESSAMINE CO LEXINGTON, KY 0.8684 0.8485 0.8198 211170007 KY KENTON CO CINCINNATI CMSA 0.8940 0.8787 0.8611 211390003 KY LIVINGSTON CO LIVINGSTON CO, KY 0.8319 0.7814 0.7572 211390004 KY LIVINGSTON CO LIVINGSTON CO, KY 0.8306 0.7834 0.7592 211451024 KY MC CRACKEN CO MC CRACKEN CO, KY 0.8341 0.7802 0.7566 211490001 KY MC LEAN CO MC LEAN CO, KY 0.8412 0.8261 0.8045 211850004 KY OLDHAM CO LOUISVILLE, KY-IN 0.8678 0.8494 0.8296 211930002 KY PERRY CO PERRY CO, KY 0.7778 0.7387 0.7039 211950002 KY PIKE CO PIKE CO, KY 0.7805 0.7392 0.6984 211990003 KY PULASKI CO PULASKI CO, KY 0.8096 0.7977 0.7643 212090001 KY SCOTT CO LEXINGTON, KY 0.8302 0.8027 0.7734 212130004 KY SIMPSON CO SIMPSON CO, KY 0.8091 0.7747 0.7456 220050004 LA ASCENSION PAR BATON ROUGE, LA 0.9647 0.9364 0.9171 220110002 LA BEAUREGARD PAR LAKE CHARLES, LA 0.9552 0.9230 0.9023 220150008 LA BOSSIER PAR SHREVEPORT-BOSSIER CITY, LA 0.9307 0.8880 0.8550 220170001 LA CADDO PAR SHREVEPORT-BOSSIER CITY, LA 0.9329 0.8899 0.8587 220190002 LA CALCASIEU PAR LAKE CHARLES, LA 0.9680 0.9377 0.9220 220190008 LA CALCASIEU PAR LAKE CHARLES, LA 0.9638 0.9348 0.9175 220190009 LA CALCASIEU PAR LAKE CHARLES, LA 0.9708 0.9369 0.9212 220330003 LA EAST BATON ROUGE PAR BATON ROUGE, LA 0.9565 0.9173 0.8938 220330009 LA EAST BATON ROUGE PAR BATON ROUGE, LA 0.9513 0.9077 0.8809 220330013 LA EAST BATON ROUGE PAR BATON ROUGE, LA 0.9471 0.9013 0.8727 220331001 LA EAST BATON ROUGE PAR BATON ROUGE, LA 0.9472 0.9007 0.8713 220430001 LA GRANT PAR ALEXANDRIA, LA 0.9443 0.8978 0.8695 220470002 LA IBERVILLE PAR BATON ROUGE, LA 0.9663 0.9395 0.9230 220470007 LA IBERVILLE PAR BATON ROUGE, LA 0.9565 0.9166 0.8932 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

220470009 LA IBERVILLE PAR BATON ROUGE, LA 0.9659 0.9372 0.9208 220511001 LA JEFFERSON PAR NEW ORLEANS, LA 0.9510 0.9279 0.9107 220550005 LA LAFAYETTE PAR LAFAYETTE, LA 0.9468 0.9061 0.8814 220570002 LA LAFOURCHE PAR HOUMA, LA 0.9616 0.9382 0.9211 220630002 LA LIVINGSTON PAR BATON ROUGE, LA 0.9626 0.9369 0.9185 220710012 LA ORLEANS PAR NEW ORLEANS, LA 0.9469 0.9409 0.9303 220730004 LA OUACHITA PAR MONROE, LA 0.9421 0.9137 0.8900 220770001 LA POINTE COUPEE PAR BATON ROUGE, LA 0.9466 0.9007 0.8719 220870002 LA ST BERNARD PAR NEW ORLEANS, LA 0.9534 0.9396 0.9260 220890003 LA ST CHARLES PAR NEW ORLEANS, LA 0.9529 0.9464 0.9343 220930002 LA ST JAMES PAR BATON ROUGE, LA 0.9629 0.9394 0.9221 220950002 LA ST JOHN THE BAPTIST PAR NEW ORLEANS, LA 0.9592 0.9392 0.9224 221010003 LA ST MARY PAR ST MARY PAR, LA 0.9656 0.9448 0.9307 221210001 LA WEST BATON ROUGE PAR BATON ROUGE, LA 0.9513 0.9077 0.8809 230052003 ME CUMBERLAND CO PORTLAND, ME 0.9038 0.8662 0.8281 230090102 ME HANCOCK CO HANCOCK CO, ME 0.8928 0.8448 0.8011 230090103 ME HANCOCK CO HANCOCK CO, ME 0.8928 0.8448 0.8011 230112005 ME KENNEBEC CO LEWISTON-AUBURN, ME 0.8970 0.8492 0.8076 230130004 ME KNOX CO KNOX CO, ME 0.8984 0.8576 0.8163 230173001 ME OXFORD CO OXFORD CO, ME 0.9190 0.8980 0.8651 230194008 ME PENOBSCOT CO PENOBSCOT CO, ME 0.8873 0.8379 0.7965 230230003 ME SAGADAHOC CO SAGADAHOC CO, ME 0.9005 0.8652 0.8251 230312002 ME YORK CO PORTLAND, ME 0.9030 0.8690 0.8299 230313002 ME YORK CO PORTLAND, ME 0.9128 0.8793 0.8453 240030014 MD ANNE ARUNDEL CO BALTIMORE, MD 0.8853 0.8518 0.8089 240030019 MD ANNE ARUNDEL CO BALTIMORE, MD 0.9012 0.8707 0.8293 240051007 MD BALTIMORE CO BALTIMORE, MD 0.9071 0.8816 0.8468 240053001 MD BALTIMORE CO BALTIMORE, MD 0.9162 0.8985 0.8667 240090010 MD CALVERT CO WASHINGTON, DC-MD-VA-WV 0.8663 0.8252 0.7814 

240130001 MD CARROLL CO BALTIMORE, MD 0.8873 0.8569 0.8147 240150003 MD CECIL CO PHILADELPHIA CMSA 0.8783 0.8488 0.8060 240170010 MD CHARLES CO WASHINGTON, DC-MD-VA-WV 0.8614 0.8171 0.7689 

240210037 MD FREDERICK CO WASHINGTON, DC-MD-VA-WV 0.8822 0.8488 0.8061 

240251001 MD HARFORD CO BALTIMORE, MD 0.9129 0.8885 0.8543 240259001 MD HARFORD CO BALTIMORE, MD 0.9021 0.8734 0.8321 240290002 MD KENT CO PHILADELPHIA CMSA 0.8826 0.8576 0.8199 240313001 MD MONTGOMERY CO WASHINGTON, DC-MD-VA-WV 0.9097 0.8907 0.8535 

240330002 MD PRINCE GEORGES CO WASHINGTON, DC-MD-VA-WV 0.9012 0.8707 0.8293 

240338001 MD PRINCE GEORGES CO WASHINGTON, DC-MD-VA-WV 0.8922 0.8731 0.8464 

245100051 MD BALTIMORE BALTIMORE, MD 0.9084 0.8903 0.8602 250010002 MA BARNSTABLE CO BOSTON CMSA 0.9026 0.8622 0.8214 250051002 MA BRISTOL CO BOSTON CMSA 0.8972 0.8662 0.8283 250051005 MA BRISTOL CO BOSTON CMSA 0.8983 0.8530 0.8097 250090005 MA ESSEX CO BOSTON CMSA 0.9128 0.8863 0.8592 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

250092006 MA ESSEX CO BOSTON CMSA 0.9226 0.8943 0.8622 250094004 MA ESSEX CO BOSTON CMSA 0.9180 0.8802 0.8451 250130003 MA HAMPDEN CO SPRINGFIELD, MA 0.9100 0.8820 0.8491 250130008 MA HAMPDEN CO SPRINGFIELD, MA 0.9120 0.8863 0.8577 250150103 MA HAMPSHIRE CO SPRINGFIELD, MA 0.9275 0.8991 0.8706 250154002 MA HAMPSHIRE CO SPRINGFIELD, MA 0.9262 0.9033 0.8739 250171102 MA MIDDLESEX CO BOSTON CMSA 0.9067 0.8780 0.8453 250171801 MA MIDDLESEX CO BOSTON CMSA 0.9067 0.8780 0.8453 250174003 MA MIDDLESEX CO BOSTON CMSA 0.9148 0.8875 0.8553 250251003 MA SUFFOLK CO BOSTON CMSA 0.9068 0.8725 0.8409 250270015 MA WORCESTER CO WORCESTER, MA-CT 0.9021 0.8649 0.8264 

260050003 MI ALLEGAN CO ALLEGAN CO, MI 0.9196 0.9023 0.8821 260190003 MI BENZIE CO BENZIE CO, MI 0.9080 0.8779 0.8466 260210014 MI BERRIEN CO BENTON HARBOR, MI 0.9019 0.8740 0.8461 260270003 MI CASS CO CASS CO, MI 0.8844 0.8528 0.8233 260370001 MI CLINTON CO LANSING-EAST LANSING, MI 0.9092 0.8804 0.8507 260490021 MI GENESEE CO FLINT, MI 0.9051 0.8759 0.8456 260492001 MI GENESEE CO FLINT, MI 0.9016 0.8679 0.8396 260630007 MI HURON CO HURON CO, MI 0.9174 0.8978 0.8757 260650012 MI INGHAM CO LANSING-EAST LANSING, MI 0.9003 0.8736 0.8474 260770008 MI KALAMAZOO CO KALAMAZOO-BATTLE CREEK, MI 0.8917 0.8612 0.8309 260810020 MI KENT CO GRAND RAPIDS-MUSKEGON-HOLLAN 0.9041 0.8781 0.8515 

260812001 MI KENT CO GRAND RAPIDS-MUSKEGON-HOLLAN 0.9040 0.8726 0.8422 260910007 MI LENAWEE CO LENAWEE CO, MI 0.9115 0.8825 0.8553 

260990009 MI MACOMB CO DETROIT CMSA 0.9387 0.9447 0.9392 260991003 MI MACOMB CO DETROIT CMSA 0.9480 0.9580 0.9656 261050007 MI MASON CO MASON CO, MI 0.9074 0.8823 0.8545 261130001 MI MISSAUKEE CO MISSAUKEE CO, MI 0.8944 0.8593 0.8296 261210039 MI MUSKEGON CO GRAND RAPIDS-MUSKEGON-HOLLAN 0.9185 0.8984 0.8783 

261250001 MI OAKLAND CO DETROIT CMSA 0.9391 0.9641 0.9823 261390005 MI OTTAWA CO GRAND RAPIDS-MUSKEGON-HOLLAN 0.9123 0.8891 0.8652 261470005 MI ST CLAIR CO DETROIT CMSA 0.9197 0.9061 0.8849 261610007 MI WASHTENAW CO DETROIT CMSA 0.9230 0.9048 0.8839 

261630001 MI WAYNE CO DETROIT CMSA 0.9292 0.9227 0.9282 261630016 MI WAYNE CO DETROIT CMSA 0.9305 0.9388 0.9479 261630019 MI WAYNE CO DETROIT CMSA 0.9305 0.9388 0.9479 270031001 MN ANOKA CO MINNEAPOLIS-ST. PAUL, MN-WI 0.9440 0.9090 0.8822 270031002 MN ANOKA CO MINNEAPOLIS-ST. PAUL, MN-WI 0.9324 0.9258 0.9167 270376018 MN DAKOTA CO MINNEAPOLIS-ST. PAUL, MN-WI 0.9254 0.9074 0.8926 280010004 MS ADAMS CO ADAMS CO, MS 0.9361 0.8952 0.8702 280330002 MS DE SOTO CO MEMPHIS, TN-AR-MS 0.8943 0.8534 0.8233 280450001 MS HANCOCK CO BILOXI-GULFPORT-PASCAGOULA, MS 0.9546 0.9149 0.8908 

280490010 MS HINDS CO JACKSON, MS 0.9069 0.8556 0.8124 280590006 MS JACKSON CO BILOXI-GULFPORT-PASCAGOULA, MS 0.9427 0.9226 0.8978 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

280750003 MS LAUDERDALE CO LAUDERDALE CO, MS 0.8624 0.8265 0.7837 280810005 MS LEE CO LEE CO, MS 0.8347 0.7886 0.7505 280890002 MS MADISON CO JACKSON, MS 0.9322 0.9075 0.8865 281490004 MS WARREN CO WARREN CO, MS 0.9453 0.9220 0.9026 

290390001 MO CEDAR CO CEDAR CO, MO 0.9287 0.8852 0.8523 290470003 MO CLAY CO KANSAS CITY, MO-KS 0.9176 0.8770 0.8458 290470005 MO CLAY CO KANSAS CITY, MO-KS 0.9270 0.8917 0.8638 290470025 MO CLAY CO KANSAS CITY, MO-KS 0.9244 0.8905 0.8635 290770026 MO GREENE CO SPRINGFIELD, MO 0.8574 0.8097 0.7578 290770036 MO GREENE CO SPRINGFIELD, MO 0.8567 0.8091 0.7573 290950036 MO JACKSON CO KANSAS CITY, MO-KS 0.9260 0.8946 0.8689 290990012 MO JEFFERSON CO ST. LOUIS, MO-IL 0.8919 0.8405 0.7965 291370001 MO MONROE CO MONROE CO, MO 0.8910 0.8566 0.8295 291650023 MO PLATTE CO KANSAS CITY, MO-KS 0.9381 0.9096 0.8859 291831002 MO ST CHARLES CO ST. LOUIS, MO-IL 0.9032 0.8577 0.8138 291831004 MO ST CHARLES CO ST. LOUIS, MO-IL 0.8825 0.8206 0.7668 291860005 MO STE GENEVIEVE CO STE GENEVIEVE CO, MO 0.8689 0.8299 0.7930 291890004 MO ST LOUIS CO ST. LOUIS, MO-IL 0.9096 0.8653 0.8227 291890006 MO ST LOUIS CO ST. LOUIS, MO-IL 0.9126 0.8719 0.8265 291893001 MO ST LOUIS CO ST. LOUIS, MO-IL 0.9126 0.8719 0.8265 291895001 MO ST LOUIS CO ST. LOUIS, MO-IL 0.9071 0.8630 0.8195 291897002 MO ST LOUIS CO ST. LOUIS, MO-IL 0.9093 0.8639 0.8168 295100007 MO ST LOUIS ST. LOUIS, MO-IL 0.9082 0.8661 0.8274 295100072 MO ST LOUIS ST. LOUIS, MO-IL 0.9147 0.8753 0.8331 295100080 MO ST LOUIS ST. LOUIS, MO-IL 0.9071 0.8630 0.8195 310550028 NE DOUGLAS CO OMAHA, NE-IA 0.9204 0.8874 0.8626 310550032 NE DOUGLAS CO OMAHA, NE-IA 0.9196 0.8843 0.8594 310550035 NE DOUGLAS CO OMAHA, NE-IA 0.9204 0.8874 0.8626 311090016 NE LANCASTER CO LINCOLN, NE 0.9190 0.8823 0.8523 330012003 NH BELKNAP CO BELKNAP CO, NH 0.9263 0.8963 0.8594 330031002 NH CARROLL CO CARROLL CO, NH 0.9102 0.8813 0.8452 330050007 NH CHESHIRE CO CHESHIRE CO, NH 0.8926 0.8451 0.7992 330090008 NH GRAFTON CO GRAFTON CO, NH 0.8885 0.8370 0.7895 330110016 NH HILLSBOROUGH CO HILLSBOROUGH CO, NH 0.9073 0.8712 0.8364 330111010 NH HILLSBOROUGH CO BOSTON CMSA 0.9011 0.8648 0.8287 330130007 NH MERRIMACK CO MANCHESTER, NH 0.9030 0.8638 0.8230 330150009 NH ROCKINGHAM CO PORTSMOUTH-ROCHESTER, NH-ME 0.9128 0.8793 0.8453 330150012 NH ROCKINGHAM CO PORTSMOUTH-ROCHESTER, NH-ME 0.9128 0.8793 0.8453 330173002 NH STRAFFORD CO PORTSMOUTH-ROCHESTER, NH-ME 0.9125 0.8771 0.8360 330190003 NH SULLIVAN CO SULLIVAN CO, NH 0.9016 0.8476 0.8033 340010005 NJ ATLANTIC CO ATLANTIC-CAPE MAY, NJ 0.8894 0.8659 0.8294 340070003 NJ CAMDEN CO PHILADELPHIA CMSA 0.9193 0.9126 0.8940 340071001 NJ CAMDEN CO PHILADELPHIA CMSA 0.9057 0.8877 0.8561 340110007 NJ CUMBERLAND CO PHILADELPHIA CMSA 0.8747 0.8463 0.8104 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

340130011 NJ ESSEX CO NEW YORK CMSA 0.9339 0.9268 0.9067 

340150002 NJ GLOUCESTER CO PHILADELPHIA CMSA 0.9024 0.8862 0.8594 340170006 NJ HUDSON CO NEW YORK CMSA 0.9339 0.9268 0.9067 

340190001 NJ HUNTERDON CO NEW YORK CMSA 0.9270 0.9062 0.8741 

340210005 NJ MERCER CO PHILADELPHIA CMSA 0.9412 0.9355 0.9107 340230011 NJ MIDDLESEX CO NEW YORK CMSA 0.9255 0.9102 0.8778 

340250005 NJ MONMOUTH CO NEW YORK CMSA 0.9227 0.9045 0.8701 

340273001 NJ MORRIS CO NEW YORK CMSA 0.9054 0.8865 0.8571 

340290006 NJ OCEAN CO NEW YORK CMSA 0.9198 0.9053 0.8781 

340315001 NJ PASSAIC CO NEW YORK CMSA 0.9178 0.9018 0.8791 

360010012 NY ALBANY CO ALBANY-SCHENECTADY-TROY, NY 0.9095 0.8673 0.8271 360050080 NY BRONX CO NEW YORK CMSA 0.9626 0.9847 0.9826 

360050083 NY BRONX CO NEW YORK CMSA 0.9626 0.9847 0.9826 

360130011 NY CHAUTAUQUA CO JAMESTOWN, NY 0.8935 0.8653 0.8410 

360150003 NY CHEMUNG CO ELMIRA, NY 0.8853 0.8471 0.8118 360270007 NY DUTCHESS CO DUTCHESS COUNTY, NY 0.9030 0.8816 0.8508 360290002 NY ERIE CO BUFFALO CMSA 0.9081 0.8910 0.8734 360310002 NY ESSEX CO ESSEX CO, NY 0.9070 0.8786 0.8577 360310003 NY ESSEX CO ESSEX CO, NY 0.9070 0.8786 0.8577 360410005 NY HAMILTON CO HAMILTON CO, NY 0.8987 0.8677 0.8410 360430005 NY HERKIMER CO HERKIMER CO, NY 0.9043 0.8778 0.8558 360450002 NY JEFFERSON CO JEFFERSON CO, NY 0.9041 0.8802 0.8581 360530006 NY MADISON CO SYRACUSE, NY 0.9047 0.8625 0.8305 360551004 NY MONROE CO ROCHESTER, NY 0.9042 0.8773 0.8558 360610010 NY NEW YORK CO NEW YORK CMSA 0.9143 0.8968 0.8709 

360631006 NY NIAGARA CO BUFFALO CMSA 0.9006 0.8667 0.8392 360650004 NY ONEIDA CO UTICA-ROME, NY 0.8844 0.8502 0.8199 360671015 NY ONONDAGA CO SYRACUSE, NY 0.8930 0.8539 0.8221 360715001 NY ORANGE CO NEWBURGH, NY-PA 0.9119 0.9010 0.8755 

360790005 NY PUTNAM CO NEW YORK CMSA 0.9184 0.9071 0.8828 

360810097 NY QUEENS CO NEW YORK CMSA 0.9115 0.8905 0.8607 

360850067 NY RICHMOND CO NEW YORK CMSA 0.9231 0.9145 0.8906 

360910004 NY SARATOGA CO ALBANY-SCHENECTADY-TROY, NY 0.8735 0.8286 0.7877 360930003 NY SCHENECTADY CO ALBANY-SCHENECTADY-TROY, NY 0.9028 0.8771 0.8431 361030002 NY SUFFOLK CO NEW YORK CMSA 0.9081 0.8907 0.8703 

361030004 NY SUFFOLK CO NEW YORK CMSA 0.9197 0.9129 0.8927 

361111005 NY ULSTER CO ULSTER CO, NY 0.8898 0.8608 0.8249 361173001 NY WAYNE CO ROCHESTER, NY 0.9101 0.8892 0.8667 

361192004 NY WESTCHESTER CO NEW YORK CMSA 0.9343 0.9357 0.9231 

370030003 NC ALEXANDER CO HICKORY-MORGANTON, NC 0.8421 0.8006 0.7559 370210030 NC BUNCOMBE CO ASHEVILLE, NC 0.8175 0.7771 0.7237 370270003 NC CALDWELL CO CALDWELL CO, NC 0.8307 0.7869 0.7391 370290099 NC CAMDEN CO CAMDEN CO, NC 0.9116 0.8866 0.8574 370330001 NC CASWELL CO CASWELL CO, NC 0.8497 0.8179 0.7767 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

370370004 NC CHATHAM CO RALEIGH-DURHAM-CHAPEL HILL, NC 0.8542 0.8160 0.7693 

370510008 NC CUMBERLAND CO FAYETTEVILLE, NC 0.8644 0.8169 0.7617 370511003 NC CUMBERLAND CO FAYETTEVILLE, NC 0.8534 0.8043 0.7477 370590002 NC DAVIE CO GREENSBORO--WINSTON-SALEM--H 0.8328 0.7742 0.7221 370610002 NC DUPLIN CO WILMINGTON, NC 0.8504 0.8109 0.7659 370630013 NC DURHAM CO RALEIGH-DURHAM-CHAPEL HILL, NC 0.8734 0.8282 0.7802 370650099 NC EDGECOMBE CO EDGECOMBE CO, NC 0.8608 0.8250 0.7825 370670022 NC FORSYTH CO GREENSBORO--WINSTON-SALEM--H 0.8534 0.8042 0.7504 370670027 NC FORSYTH CO GREENSBORO--WINSTON-SALEM--H 0.8404 0.7943 0.7453 370670028 NC FORSYTH CO GREENSBORO--WINSTON-SALEM--H 0.8537 0.8030 0.7482 370671008 NC FORSYTH CO GREENSBORO--WINSTON-SALEM--H 0.8667 0.8200 0.7653 370690001 NC FRANKLIN CO RALEIGH-DURHAM-CHAPEL HILL, NC 0.8556 0.8121 0.7614 370770001 NC GRANVILLE CO RALEIGH-DURHAM-CHAPEL HILL, NC 0.8617 0.8195 0.7714 370810011 NC GUILFORD CO GREENSBORO--WINSTON-SALEM--H 0.8629 0.8224 0.7735 370870035 NC HAYWOOD CO HAYWOOD CO, NC 0.8235 0.7798 0.7258 

370870036 NC HAYWOOD CO HAYWOOD CO, NC 0.8106 0.7710 0.7207 

371010002 NC JOHNSTON CO RALEIGH-DURHAM-CHAPEL HILL, NC 0.8656 0.8159 0.7618 

371070004 NC LENOIR CO LENOIR CO, NC 0.8578 0.8185 0.7730 371090004 NC LINCOLN CO CHARLOTTE-GASTONIA-ROCK HILL, 0.8625 0.8223 0.7743 371170001 NC MARTIN CO MARTIN CO, NC 0.8834 0.8517 0.8147 371190034 NC MECKLENBURG CO CHARLOTTE-GASTONIA-ROCK HILL, 0.8771 0.8325 0.7854 371191005 NC MECKLENBURG CO CHARLOTTE-GASTONIA-ROCK HILL, 0.8917 0.8569 0.8161 371191009 NC MECKLENBURG CO CHARLOTTE-GASTONIA-ROCK HILL, 0.8598 0.8113 0.7616 371290002 NC NEW HANOVER CO WILMINGTON, NC 0.8793 0.8465 0.8080 

371310002 NC NORTHAMPTON CO NORTHAMPTON CO, NC 0.8632 0.8339 0.7974 371450003 NC PERSON CO RALEIGH-DURHAM-CHAPEL HILL, NC 0.8516 0.8232 0.7833 

371470099 NC PITT CO PITT CO, NC 0.8614 0.8231 0.7773 371570099 NC ROCKINGHAM CO ROCKINGHAM CO, NC 0.8528 0.8056 0.7570 371590021 NC ROWAN CO CHARLOTTE-GASTONIA-ROCK HILL, 0.8366 0.7873 0.7323 

371590022 NC ROWAN CO CHARLOTTE-GASTONIA-ROCK HILL, 0.8552 0.7987 0.7513 371730002 NC SWAIN CO SWAIN CO, NC 0.8249 0.7797 0.7345 

371830014 NC WAKE CO RALEIGH-DURHAM-CHAPEL HILL, NC 0.8886 0.8464 0.7923 

371830015 NC WAKE CO RALEIGH-DURHAM-CHAPEL HILL, NC 0.8886 0.8464 0.7923 371830016 NC WAKE CO RALEIGH-DURHAM-CHAPEL HILL, NC 0.8792 0.8257 0.7681 371830017 NC WAKE CO RALEIGH-DURHAM-CHAPEL HILL, NC 0.8867 0.8495 0.7977 371990003 NC YANCEY CO YANCEY CO, NC 0.8241 0.7819 0.7329 390030002 OH ALLEN CO LIMA, OH 0.8955 0.8687 0.8441 390071001 OH ASHTABULA CO CLEVELAND CMSA 0.9007 0.8771 0.8528 390170004 OH BUTLER CO CINCINNATI CMSA 0.8957 0.8710 0.8455 390171004 OH BUTLER CO CINCINNATI CMSA 0.8816 0.8507 0.8225 390230001 OH CLARK CO DAYTON-SPRINGFIELD, OH 0.8749 0.8360 0.8037 390230003 OH CLARK CO DAYTON-SPRINGFIELD, OH 0.8748 0.8399 0.8098 390250020 OH CLERMONT CO CINCINNATI CMSA 0.8834 0.8600 0.8335 390271002 OH CLINTON CO CINCINNATI CMSA 0.8560 0.8191 0.7877 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

390350034 OH CUYAHOGA CO CLEVELAND CMSA 0.8929 0.8671 0.8465 390350064 OH CUYAHOGA CO CLEVELAND CMSA 0.9165 0.9039 0.8881 390355002 OH CUYAHOGA CO CLEVELAND CMSA 0.8996 0.8761 0.8557 390410002 OH DELAWARE CO COLUMBUS, OH 0.8731 0.8374 0.8068 

390490004 OH FRANKLIN CO COLUMBUS, OH 0.8923 0.8780 0.8613 390490081 OH FRANKLIN CO COLUMBUS, OH 0.8884 0.8715 0.8554 390550004 OH GEAUGA CO CLEVELAND-LORAIN-ELYRIA, OH 0.8938 0.8634 0.8358 390570006 OH GREENE CO DAYTON-SPRINGFIELD, OH 0.8665 0.8333 0.8026 390610006 OH HAMILTON CO CINCINNATI CMSA 0.8949 0.8771 0.8570 390610010 OH HAMILTON CO CINCINNATI CMSA 0.8826 0.8749 0.8654 390610037 OH HAMILTON CO CINCINNATI CMSA 0.9095 0.8969 0.8819 390830002 OH KNOX CO COLUMBUS, OH 0.8767 0.8526 0.8261 390850003 OH LAKE CO CLEVELAND CMSA 0.9004 0.8787 0.8577 390853002 OH LAKE CO CLEVELAND CMSA 0.9019 0.8777 0.8544 390870006 OH LAWRENCE CO HUNTINGTON-ASHLAND, WV-KY-OH 0.8522 0.8276 0.8033 

390870011 OH LAWRENCE CO HUNTINGTON-ASHLAND, WV-KY-OH 0.8531 0.8231 0.7978 

390890005 OH LICKING CO COLUMBUS, OH 0.8679 0.8412 0.8127 390911001 OH LOGAN CO LOGAN CO, OH 0.8771 0.8389 0.8063 390931003 OH LORAIN CO CLEVELAND CMSA 0.9174 0.9079 0.8933 390950034 OH LUCAS CO TOLEDO, OH 0.9056 0.8901 0.8717 390950081 OH LUCAS CO TOLEDO, OH 0.8990 0.8793 0.8590 390970007 OH MADISON CO COLUMBUS, OH 0.8742 0.8421 0.8131 390990009 OH MAHONING CO YOUNGSTOWN-WARREN, OH 0.8693 0.8275 0.7903 

391030003 OH MEDINA CO CLEVELAND CMSA 0.8814 0.8510 0.8213 391090005 OH MIAMI CO DAYTON-SPRINGFIELD, OH 0.8756 0.8400 0.8097 391130019 OH MONTGOMERY CO DAYTON-SPRINGFIELD, OH 0.8786 0.8502 0.8216 391331001 OH PORTAGE CO CLEVELAND CMSA 0.8838 0.8510 0.8178 391351001 OH PREBLE CO DAYTON-SPRINGFIELD, OH 0.8594 0.8209 0.7886 391510016 OH STARK CO CANTON-MASSILLON, OH 0.8734 0.8397 0.8054 391510019 OH STARK CO CANTON-MASSILLON, OH 0.8649 0.8367 0.8055 391511009 OH STARK CO CANTON-MASSILLON, OH 0.8736 0.8388 0.8043 391514005 OH STARK CO CANTON-MASSILLON, OH 0.8811 0.8464 0.8126 391530020 OH SUMMIT CO CLEVELAND CMSA 0.8951 0.8709 0.8431 391550008 OH TRUMBULL CO YOUNGSTOWN-WARREN, OH 0.8709 0.8290 0.7916 

391550009 OH TRUMBULL CO YOUNGSTOWN-WARREN, OH 0.8678 0.8284 0.7938 

391591001 OH UNION CO UNION CO, OH 0.8732 0.8356 0.8037 391650006 OH WARREN CO CINCINNATI CMSA 0.8851 0.8576 0.8292 

391670004 OH WASHINGTON CO PARKERSBURG-MARIETTA, WV-OH 0.8203 0.7896 0.7627 391730003 OH WOOD CO TOLEDO, OH 0.9002 0.8756 0.8516 400270049 OK CLEVELAND CO OKLAHOMA CITY, OK 0.9306 0.8694 0.8296 400770440 OK LATIMER CO LATIMER CO, OK 0.9519 0.9187 0.8940 400870073 OK MC CLAIN CO OKLAHOMA CITY, OK 0.9320 0.8711 0.8313 401090033 OK OKLAHOMA CO OKLAHOMA CITY, OK 0.9362 0.8758 0.8373 401091037 OK OKLAHOMA CO OKLAHOMA CITY, OK 0.9340 0.8726 0.8342 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

401430127 OK TULSA CO TULSA, OK 0.9375 0.8825 0.8433 401430137 OK TULSA CO TULSA, OK 0.9375 0.8828 0.8451 401430174 OK TULSA CO TULSA, OK 0.9327 0.8781 0.8390 420030008 PA ALLEGHENY CO PITTSBURGH CMSA 0.9122 0.8904 0.8627 420030010 PA ALLEGHENY CO PITTSBURGH CMSA 0.9120 0.8961 0.8739 420030067 PA ALLEGHENY CO PITTSBURGH CMSA 0.8809 0.8661 0.8425 420030088 PA ALLEGHENY CO PITTSBURGH CMSA 0.9028 0.8824 0.8540 420031005 PA ALLEGHENY CO PITTSBURGH CMSA 0.9025 0.8814 0.8554 420050001 PA ARMSTRONG CO ARMSTRONG CO, PA 0.8708 0.8395 0.8093 420070002 PA BEAVER CO PITTSBURGH CMSA 0.8987 0.8701 0.8434 420070005 PA BEAVER CO PITTSBURGH CMSA 0.9088 0.8812 0.8566 420070014 PA BEAVER CO PITTSBURGH CMSA 0.9191 0.8945 0.8704 420110001 PA BERKS CO READING, PA 0.8819 0.8375 0.7969 420110009 PA BERKS CO READING, PA 0.8804 0.8472 0.8103 420130801 PA BLAIR CO ALTOONA, PA 0.8482 0.8213 0.7900 420170012 PA BUCKS CO PHILADELPHIA CMSA 0.9287 0.9258 0.9064 420210011 PA CAMBRIA CO JOHNSTOWN, PA 0.8770 0.8582 0.8314 

420274000 PA CENTRE CO STATE COLLEGE, PA 0.8530 0.8153 0.7768 420334000 PA CLEARFIELD CO CLEARFIELD CO, PA 0.8516 0.8177 0.7840 420430401 PA DAUPHIN CO HARRISBURG-LEBANON-CARLISLE, P 0.8826 0.8306 0.7834 

420431100 PA DAUPHIN CO HARRISBURG-LEBANON-CARLISLE, P 0.8867 0.8355 0.7883 420450002 PA DELAWARE CO PHILADELPHIA CMSA 0.8997 0.8786 0.8500 

420490003 PA ERIE CO ERIE, PA 0.8956 0.8703 0.8461 420550001 PA FRANKLIN CO FRANKLIN CO, PA 0.8455 0.8052 0.7622 420590002 PA GREENE CO GREENE CO, PA 0.8087 0.7774 0.7506 420690101 PA LACKAWANNA CO SCRANTON--WILKES-BARRE--HAZLE 0.8660 0.8217 0.7811 

420692006 PA LACKAWANNA CO SCRANTON--WILKES-BARRE--HAZLE 0.8647 0.8232 0.7845 420710007 PA LANCASTER CO LANCASTER, PA 0.8974 0.8653 0.8270 420730015 PA LAWRENCE CO LAWRENCE CO, PA 0.8774 0.8387 0.8041 

420770004 PA LEHIGH CO ALLENTOWN-BETHLEHEM-EASTON, 0.8955 0.8628 0.8286 420791100 PA LUZERNE CO SCRANTON--WILKES-BARRE--HAZLE 0.8487 0.8060 0.7677 

420791101 PA LUZERNE CO SCRANTON--WILKES-BARRE--HAZLE 0.8687 0.8262 0.7866 420810403 PA LYCOMING CO WILLIAMSPORT, PA 0.8636 0.8265 0.7892 420814000 PA LYCOMING CO WILLIAMSPORT, PA 0.8490 0.8155 0.7808 420850100 PA MERCER CO YOUNGSTOWN-WARREN, OH 0.8627 0.8208 0.7831 

420890001 PA MONROE CO MONROE CO, PA 0.8966 0.8652 0.8325 420910013 PA MONTGOMERY CO PHILADELPHIA CMSA 0.9060 0.9055 0.8898 420950025 PA NORTHAMPTON CO ALLENTOWN-BETHLEHEM-EASTON, 0.8955 0.8628 0.8286 420950100 PA NORTHAMPTON CO ALLENTOWN-BETHLEHEM-EASTON, 0.8992 0.8635 0.8297 420990301 PA PERRY CO HARRISBURG-LEBANON-CARLISLE, P 0.8563 0.8108 0.7688 

421010004 PA PHILADELPHIA CO PHILADELPHIA CMSA 0.9310 0.9286 0.9091 421010014 PA PHILADELPHIA CO PHILADELPHIA CMSA 0.9053 0.8967 0.8766 421010024 PA PHILADELPHIA CO PHILADELPHIA CMSA 0.9310 0.9286 0.9091 421010136 PA PHILADELPHIA CO PHILADELPHIA CMSA 0.9042 0.8901 0.8662 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

421250005 PA WASHINGTON CO PITTSBURGH CMSA 0.8648 0.8394 0.8141 421250200 PA WASHINGTON CO PITTSBURGH CMSA 0.8394 0.8073 0.7783 421255001 PA WASHINGTON CO PITTSBURGH CMSA 0.9044 0.8816 0.8582 421290006 PA WESTMORELAND CO PITTSBURGH CMSA 0.9147 0.8947 0.8671 421330008 PA YORK CO YORK, PA 0.8871 0.8497 0.8098 440030002 RI KENT CO PROVIDENCE CMSA 0.9094 0.8806 0.8440 440071010 RI PROVIDENCE CO PROVIDENCE CMSA 0.9020 0.8634 0.8228 440090007 RI WASHINGTON CO PROVIDENCE CMSA 0.8972 0.8688 0.8322 450010001 SC ABBEVILLE CO ABBEVILLE CO, SC 0.8424 0.7909 0.7296 450030003 SC AIKEN CO AUGUSTA-AIKEN, GA-SC 0.8421 0.7831 0.7367 450070003 SC ANDERSON CO GREENVILLE-SPARTANBURG-ANDER 0.8518 0.8015 0.7469 

450110001 SC BARNWELL CO BARNWELL CO, SC 0.8444 0.8072 0.7620 

450150002 SC BERKELEY CO CHARLESTON-NORTH CHARLESTON 0.8663 0.8325 0.7894 

450190042 SC CHARLESTON CO CHARLESTON-NORTH CHARLESTON 0.8663 0.8325 0.7894 450190046 SC CHARLESTON CO CHARLESTON-NORTH CHARLESTON 0.8573 0.8275 0.7886 450210002 SC CHEROKEE CO CHEROKEE CO, SC 0.8445 0.8074 0.7562 450230002 SC CHESTER CO CHARLOTTE-GASTONIA-ROCK HILL, 0.8719 0.8389 0.7945 450290002 SC COLLETON CO COLLETON CO, SC 0.8500 0.8127 0.7666 450310003 SC DARLINGTON CO DARLINGTON CO, SC 0.8780 0.8548 0.8126 450370001 SC EDGEFIELD CO EDGEFIELD CO, SC 0.8422 0.7963 0.7446 450730001 SC OCONEE CO OCONEE CO, SC 0.8253 0.7763 0.7205 450770002 SC PICKENS CO GREENVILLE-SPARTANBURG-ANDER 0.8424 0.7888 0.7285 450790007 SC RICHLAND CO COLUMBIA, SC 0.8607 0.8134 0.7526 450791002 SC RICHLAND CO COLUMBIA, SC 0.8653 0.8168 0.7570 450830009 SC SPARTANBURG CO GREENVILLE-SPARTANBURG-ANDER 0.8489 0.8095 0.7601 450870001 SC UNION CO UNION CO, SC 0.8444 0.8070 0.7592 450910006 SC YORK CO CHARLOTTE-GASTONIA-ROCK HILL, 0.8749 0.8404 0.7953 470010101 TN ANDERSON CO KNOXVILLE, TN 0.8271 0.7622 0.7126 470090101 TN BLOUNT CO KNOXVILLE, TN 0.8387 0.7780 0.7244 470090102 TN BLOUNT CO KNOXVILLE, TN 0.8355 0.7775 0.7264 470370011 TN DAVIDSON CO NASHVILLE, TN 0.8893 0.8531 0.8192 470370026 TN DAVIDSON CO NASHVILLE, TN 0.8853 0.8474 0.8142 470650028 TN HAMILTON CO CHATTANOOGA, TN-GA 0.8492 0.7851 0.7248 470651011 TN HAMILTON CO CHATTANOOGA, TN-GA 0.8493 0.7811 0.7185 470750003 TN HAYWOOD CO HAYWOOD CO, TN 0.8672 0.8235 0.7949 

470890001 TN JEFFERSON CO JEFFERSON CO, TN 0.8120 0.7519 0.7057 470890002 TN JEFFERSON CO JEFFERSON CO, TN 0.8120 0.7519 0.7057 470930021 TN KNOX CO KNOXVILLE, TN 0.8472 0.7900 0.7406 470931020 TN KNOX CO KNOXVILLE, TN 0.8450 0.7854 0.7344 470990002 TN LAWRENCE CO LAWRENCE CO, TN 0.7858 0.7505 0.7183 

471410004 TN PUTNAM CO PUTNAM CO, TN 0.8160 0.7610 0.7201 471490101 TN RUTHERFORD CO NASHVILLE, TN 0.8625 0.8140 0.7721 471550101 TN SEVIER CO KNOXVILLE, TN 0.8176 0.7634 0.7162 471550102 TN SEVIER CO KNOXVILLE, TN 0.7977 0.7537 0.7106 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

471570021 TN SHELBY CO MEMPHIS, TN-AR-MS 0.9229 0.9125 0.8952 471571004 TN SHELBY CO MEMPHIS, TN-AR-MS 0.8980 0.8596 0.8327 471632002 TN SULLIVAN CO JOHNSON CITY-KINGSPORT-BRISTO 0.8353 0.8043 0.7687 471632003 TN SULLIVAN CO JOHNSON CITY-KINGSPORT-BRISTO 0.8357 0.8041 0.7683 471650007 TN SUMNER CO NASHVILLE, TN 0.8813 0.8441 0.8104 471650101 TN SUMNER CO NASHVILLE, TN 0.8401 0.7961 0.7605 471870106 TN WILLIAMSON CO NASHVILLE, TN 0.8044 0.7662 0.7343 471890103 TN WILSON CO NASHVILLE, TN 0.8433 0.7980 0.7598 

480391003 TX BRAZORIA CO HOUSTON CMSA 0.9632 0.9339 0.9186 480430101 TX BREWSTER CO BREWSTER CO, TX480850005 TX COLLIN CO DALLAS CMSA 0.9301 0.8867 0.8532 481130045 TX DALLAS CO DALLAS CMSA 0.9352 0.9097 0.8796 481130069 TX DALLAS CO DALLAS CMSA 0.9361 0.9099 0.8782 481130087 TX DALLAS CO DALLAS CMSA 0.9381 0.9131 0.8818 481210034 TX DENTON CO DALLAS CMSA 0.9271 0.8739 0.8360 481210054 TX DENTON CO DALLAS CMSA 0.9309 0.8946 0.8617 481390015 TX ELLIS CO DALLAS CMSA 0.9268 0.8632 0.8256 481670014 TX GALVESTON CO HOUSTON CMSA 0.9524 0.9423 0.9283 481671002 TX GALVESTON CO HOUSTON CMSA 0.9508 0.9406 0.9255 481830001 TX GREGG CO LONGVIEW-MARSHALL, TX 0.9292 0.8772 0.8428 482010024 TX HARRIS CO HOUSTON CMSA 0.9485 0.9679 0.9643 482010029 TX HARRIS CO HOUSTON CMSA 0.9400 0.9064 0.8811 482010046 TX HARRIS CO HOUSTON CMSA 0.9491 0.9782 0.9787 482010047 TX HARRIS CO HOUSTON CMSA 0.9472 0.9727 0.9727 482010051 TX HARRIS CO HOUSTON CMSA 0.9501 0.9598 0.9562 482010055 TX HARRIS CO HOUSTON CMSA 0.9494 0.9819 0.9842 482010062 TX HARRIS CO HOUSTON CMSA 0.9505 0.9636 0.9610 482010066 TX HARRIS CO HOUSTON CMSA 0.9441 0.9486 0.9391 482011034 TX HARRIS CO HOUSTON CMSA 0.9510 0.9884 0.9922 482011035 TX HARRIS CO HOUSTON CMSA 0.9510 0.9884 0.9922 482011037 TX HARRIS CO HOUSTON CMSA 0.9494 0.9819 0.9842 482011039 TX HARRIS CO HOUSTON CMSA 0.9526 0.9873 0.9922 482450009 TX JEFFERSON CO BEAUMONT-PORT ARTHUR, TX 0.9685 0.9323 0.9158 482450011 TX JEFFERSON CO BEAUMONT-PORT ARTHUR, TX 0.9679 0.9479 0.9358 483550025 TX NUECES CO CORPUS CHRISTI, TX483550026 TX NUECES CO CORPUS CHRISTI, TX483611001 TX ORANGE CO BEAUMONT-PORT ARTHUR, TX 0.9718 0.9415 0.9276 484230004 TX SMITH CO TYLER, TX 0.9270 0.8656 0.8275 484390057 TX TARRANT CO DALLAS CMSA 0.9373 0.9059 0.8742 484391002 TX TARRANT CO DALLAS CMSA 0.9337 0.8883 0.8522 484392003 TX TARRANT CO DALLAS CMSA 0.9312 0.8881 0.8527 484530014 TX TRAVIS CO AUSTIN-SAN MARCOS, TX 0.9304 0.8831 0.8506 484530020 TX TRAVIS CO AUSTIN-SAN MARCOS, TX 0.9290 0.8794 0.8448 484690003 TX VICTORIA CO VICTORIA, TX 0.9438 0.9120 0.8905 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

500030004 VT BENNINGTON CO BENNINGTON CO, VT 0.8789 0.8257 0.7832 500070007 VT CHITTENDEN CO CHITTENDEN CO, VT 0.8847 0.8385 0.7993 510130020 VA ARLINGTON CO WASHINGTON, DC-MD-VA-WV 0.9204 0.9124 0.8836 

510330001 VA CAROLINE CO CAROLINE CO, VA 0.8694 0.8238 0.7760 510360002 VA CHARLES CITY CO RICHMOND-PETERSBURG, VA 0.8744 0.8440 0.8104 510410004 VA CHESTERFIELD CO RICHMOND-PETERSBURG, VA 0.8772 0.8318 0.7853 510590005 VA FAIRFAX CO WASHINGTON, DC-MD-VA-WV 0.8970 0.8677 0.8277 

510590018 VA FAIRFAX CO WASHINGTON, DC-MD-VA-WV 0.9012 0.8819 0.8562 

510590030 VA FAIRFAX CO WASHINGTON, DC-MD-VA-WV 0.9012 0.8819 0.8562 

510591004 VA FAIRFAX CO WASHINGTON, DC-MD-VA-WV 0.9204 0.9124 0.8836 

510595001 VA FAIRFAX CO WASHINGTON, DC-MD-VA-WV 0.9235 0.9157 0.8904 

510610002 VA FAUQUIER CO FAUQUIER CO, VA 0.8588 0.8187 0.7742 510690010 VA FREDERICK CO FREDERICK CO, VA 0.8516 0.8181 0.7836 510850001 VA HANOVER CO RICHMOND-PETERSBURG, VA 0.8683 0.8296 0.7857 510870014 VA HENRICO CO RICHMOND-PETERSBURG, VA 0.8763 0.8325 0.7857 511071005 VA LOUDOUN CO WASHINGTON, DC-MD-VA-WV 0.8969 0.8693 0.8301 

511130003 VA MADISON CO CHARLOTTESVILLE, VA 0.8093 0.7706 0.7288 511530009 VA PRINCE WILLIAM CO WASHINGTON, DC-MD-VA-WV 0.8793 0.8461 0.8047 

511611004 VA ROANOKE CO ROANOKE, VA 0.8322 0.7865 0.7432 511790001 VA STAFFORD CO WASHINGTON, DC-MD-VA-WV 0.8774 0.8290 0.7781 

511970002 VA WYTHE CO WYTHE CO, VA 0.7877 0.7462 0.7029 

515100009 VA ALEXANDRIA WASHINGTON, DC-MD-VA-WV 0.9204 0.9124 0.8836 

516500004 VA HAMPTON NORFOLK-VIRGINIA BEACH-NEWPOR 0.9099 0.8864 0.8568 

518000004 VA SUFFOLK NORFOLK-VIRGINIA BEACH-NEWPOR 0.9042 0.8844 0.8585 518000005 VA SUFFOLK NORFOLK-VIRGINIA BEACH-NEWPOR 0.8708 0.8375 0.8026 540110006 WV CABELL CO HUNTINGTON-ASHLAND, WV-KY-OH 0.8605 0.8347 0.8099 540250003 WV GREENBRIER CO GREENBRIER CO, WV 0.7290 0.6921 0.6642 540291004 WV HANCOCK CO STEUBENVILLE-WEIRTON, OH-WV 0.8802 0.8523 0.8244 540390004 WV KANAWHA CO CHARLESTON, WV 0.8574 0.8209 0.7912 

540690007 WV OHIO CO WHEELING, WV-OH 0.8564 0.8275 0.7999 

541071002 WV WOOD CO PARKERSBURG-MARIETTA, WV-OH 0.8299 0.7943 0.7674 550090026 WI BROWN CO GREEN BAY, WI 0.9074 0.8750 0.8458 

550210015 WI COLUMBIA CO MADISON, WI 0.8973 0.8606 0.8269 

550250041 WI DANE CO MADISON, WI 0.9122 0.8776 0.8430 

550270007 WI DODGE CO DODGE CO, WI 0.9043 0.8710 0.8389 

550290004 WI DOOR CO DOOR CO, WI 0.9160 0.8931 0.8688 

550370001 WI FLORENCE CO FLORENCE CO, WI550390006 WI FOND DU LAC CO FOND DU LAC CO, WI 0.9040 0.8696 0.8392 

550550002 WI JEFFERSON CO MILWAUKEE-RACINE CMSA 0.9075 0.8754 0.8449 550590002 WI Kenosha Co. CHICAGO CMSA 0.9212 0.9167 0.9049 550590019 WI Kenosha Co. CHICAGO CMSA 0.9250 0.9226 0.9149 550590022 WI Kenosha Co. 0.9167 0.9048 0.8886 550610002 WI KEWAUNEE CO KEWAUNEE CO, WI 0.9085 0.8818 0.8561 

550710004 WI MANITOWOC CO MANITOWOC CO, WI 0.9100 0.8843 0.8600 
APPENDIX D

8-Hour Relative Reduction Factors

Site Id. State County Area Name

RRF 2007 Base

RRF 2020 Base

RRF 2020 Control

550710007 WI MANITOWOC CO MANITOWOC CO, WI 0.9147 0.8929 0.8690 

550730012 WI MARATHON CO WAUSAU, WI 0.8919 0.8558 0.8245 

550790041 WI MILWAUKEE CO MILWAUKEE-RACINE CMSA 0.9132 0.8979 0.8789 550790044 WI MILWAUKEE CO MILWAUKEE-RACINE CMSA 0.9186 0.9096 0.8965 550790048 WI MILWAUKEE CO MILWAUKEE-RACINE CMSA 0.9164 0.8994 0.8791 550790085 WI MILWAUKEE CO MILWAUKEE-RACINE CMSA 0.9026 0.8814 0.8609 550791025 WI MILWAUKEE CO MILWAUKEE-RACINE CMSA 0.9164 0.8994 0.8791 550850004 WI ONEIDA CO ONEIDA CO, WI 0.8905 0.8553 0.8238 

550870009 WI OUTAGAMIE CO APPLETON-OSHKOSH-NEENAH, WI 0.9044 0.8722 0.8444 550890008 WI OZAUKEE CO MILWAUKEE-RACINE CMSA 0.9091 0.8939 0.8740 550890009 WI OZAUKEE CO MILWAUKEE-RACINE CMSA 0.9183 0.9064 0.8901 551010017 WI RACINE CO MILWAUKEE-RACINE CMSA 0.9214 0.9196 0.9126 551050021 WI ROCK CO JANESVILLE-BELOIT, WI 0.9052 0.8758 0.8505 551050024 WI ROCK CO JANESVILLE-BELOIT, WI 0.9017 0.8696 0.8408 551091002 WI ST CROIX CO MINNEAPOLIS-ST. PAUL, MN-WI 0.9305 0.8886 0.8620 551110007 WI SAUK CO SAUK CO, WI 0.9007 0.8632 0.8314 

551170006 WI SHEBOYGAN CO SHEBOYGAN, WI 0.9199 0.9080 0.8910 551230008 WI VERNON CO VERNON CO, WI 0.9021 0.8653 0.8354 

551250001 WI Vilas Co. 0.8822 0.8482 0.8158 551270005 WI WALWORTH CO MILWAUKEE-RACINE CMSA 0.9089 0.8821 0.8555 

551310009 WI WASHINGTON CO MILWAUKEE-RACINE CMSA 0.9174 0.9026 0.8843 551330017 WI WAUKESHA CO MILWAUKEE-RACINE CMSA 0.9176 0.9006 0.8786 

551390011 WI WINNEBAGO CO APPLETON-OSHKOSH-NEENAH, WI 0.8999 0.8661 0.8377 
Appendix E:

1999 Annual Mean PM2.5 Values and Future Year Predictions Based on

RRfs
APPENDIX E

1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs

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APPENDIX E

1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs

021,725 ,' 67$7( &2817<



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APPENDIX E

1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs

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APPENDIX E

1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs

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APPENDIX E

1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs

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APPENDIX E

1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs

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APPENDIX E

1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs

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APPENDIX E

1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs

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APPENDIX E

1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs

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APPENDIX E

1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs

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APPENDIX E

1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs

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APPENDIX E

1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs

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Appendix F:

IMPROVE Monitoring Sites used in the REMSAD Model Performance

Evaluation

IMPROVE

Site Code

Site Name State

ACAD1 Acadia National Park Maine

BADL1 Badlands National Park South Dakota

BAND1 Bandelier National Monument New Mexico

BIBE1 Big Bend National Park Texas

BLIS1 Bliss State Park(TRPA) California

BOWA1 Boundary Waters Canoe Area Minnesota

BRCA1 Bryce Canyon National Park Colorado

BRID1 Bridger Wilderness Wyoming

BRIG1 Brigantine National Wildlife Refu New Jersey

BRLA1 Brooklyn Lake Wyoming

CANY1 Canyonlands National Park Utah

CHAS1 Chassahowitzka National Wildlife Florida

CHIR1 Chiricahua National Monument Arizona

CORI1 Columbia River Gorge Washington

CRLA1 Crater Lake National Park Oregon

CRMO1 Craters of the Moon NM(US DOE) Idaho

DEVA1 Death Valley Monument California

DOLA1 Dome Lands Wilderness California

DOSO1 Dolly Sods /Otter Creek Wildernes West Virginia

EVER1 Everglades National Park Florida

GICL1 Gila Wilderness New Mexico

GLAC1 Glacier National Park Montana

GRBA1 Great Basin National Park Nevada

GRCA1 Grand Canyon NP- Hopi Point Arizona

GRSA1 Great Sand Dunes National Monument Colorado

GRSM1 Great Smoky Mountains National Park Tennessee

GUMO1 Guadalupe Mountains National Park Texas

JARB1 Jarbidge Wilderness Nevada

JEFF1 Jefferson/James River Face Wildern Virginia

LAVO1 Lassen Volcanic National Park California

LOPE1 Lone Peak Wilderness Utah

LYBR1 Lye Brook Wilderness Vermont

MACA1 Mammoth Cave National Park Kentucky

MEVE1 Mesa Verde National Park Colorado

MOOS1 Moosehorn NWR Maine

MORA1 Mount Rainier National Park Washington

MOZI1 Mount Zirkel Wilderness Colorado

OKEF1 Okefenokee National Wildlife Refu Georgia

PEFO1 Petrified Forest National Park Arizona

PINN1 Pinnacles National Monument California

PORE1 Point Reyes National Seashore California
APPENDIX F

IMPROVE Monitoring Sites used in the REMSAD Performance Evaluation

(Continued)

IMPROVE

Site Code

Site Name State

PUSO1 Puget Sound Washington

REDW1 Redwood National Park California

ROMA1 Cape Romain National Wildlife Ref South Carolina

ROMO2 Rocky Mountain National Park Colorado

SAGO1 San Gorgonio Wilderness California

SALM1 Salmon National Forest Idaho

SAWT1 Sawtooth National Forest Idaho

SCOV1 Scoville (US DOE) Idaho

SEQU1 Sequoia National Park California

SHEN1 Shenandoah National Park Virginia

SHRO1 Shining Rock Wilderness North Carolina

SIPS1 Sipsy Wilderness Alabama

SNPA1 Snoqualamie Pass, Snoqualamie N.F Washington

SOLA1 South Lake Tahoe (TRPA) California

SULA1 Sula (Selway Bitteroot Wilderness) Montana

THSI1 Three Sisters Wilderness Idaho

TONT1 Tonto National Monument Arizona

UPBU1 Upper Buffalo Wilderness Arkansas

WASH1 Washington D.C. Washington D.C.