United States Air and Radiation EPA420-R-00-028
Environmental Protection December 2000
Agency
vxEPA 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
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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
in 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 InputFiles 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 Rel ati ve Reducti on F actors 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
11
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E.
F.
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
Visibility Calculations 38
Need for FIDE 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.
in
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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 Vn 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
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.
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.
3For this analysis the term exposure is used to describe the number of people living in areas with
concentrations above various cut-points.
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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 HA. and n.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 HI 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 in). 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.
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Table II-l. Summer season daily State-level emissions (tons) for the 1996 Base.
State
Alabama
Arkansas
Connecticut
Delaware
DC
Florida1
Georgia
Illinois
Indiana
Iowa
Kansas1
Kentucky
Louisiana
Maine1
Maryland
Massachusetts
Michigan1
Minnesota1
Mississippi
Missouri
voc
1,254
709
476
167
61
2,791
1,715
2,428
1,536
785
782
998
1,274
323
601
901
2,427
1,263
934
1,158
NOX
1,971
935
603
243
60
3,443
2,255
3,187
2,652
1,216
1,661
2,276
2,562
273
1,078
958
2,420
1,511
1,109
1,761
CO
5,866
3,092
2,335
663
223
16,065
9,615
8,498
7,177
2,893
3,212
3,857
6,501
1,379
3,641
3,669
9,269
4,214
3,500
5,563
State
Nebraska1
Mew Hampshire
Mew Jersey
Mew York
Morth Carolina
North Dakota1
Ohio
Oklahoma1
Pennsylvania
Rhode Island
South Carolina
South Dakota1
Tennessee
Texas1
Vermont
Virginia
West Virginia
Wisconsin
Total
VOC
612
240
1,330
2,385
2,089
350
2,364
1,149
2,068
159
996
270
1,660
4,350
139
1,459
418
1,354
45,975
NOX
875
267
1,333
2,054
2,292
857
3,758
1,495
2,924
103
1,230
460
2,384
6,893
118
1,865
1,340
1,450
63,872
CO
2,750
1,011
4,785
9,589
8,140
1,096
11,977
6,510
10,112
635
4,412
1,069
5,915
17,932
602
6,560
1,931
4,720
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.
1996 Base
2007 Base
2020 Base
2020 Control
2030 Base
2030 Control
VOC
45,975
36,285
37,190
36,801
41,007
40,499
NOx
63,872
46,822
39,948
36,086
42,239
36,806
CO
200,978
195,401
230,507
228,481
261,829
259,186
Scenario Diff (%)
From 1996
From 2007 Base
From 2020 Base
From 2020 Base
From 2030 Base
VOC
-21.1
2.5
-1.0
10.2
-1.2
NOx
-26.7
-14.7
-9.7
5.7
-12.9
CO
-2.8
18.0
-0.9
13.6
-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-
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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.
1996 Base
2030 Base
2030 Control
Organic
Carbon
1,224,857
1,416,023
1,394,587
Elemental
Carbon
566,051
536,979
465,905
Gaseous
Sulfate
167,392
220,966
220,189
Primary
Nitrate
13,386
17,618
17,481
Other1
PM-2.5
2,210,692
2,611,202
2,615,144
Total
PM-2.5
4,182,378
4,802,789
4,713,306
1. Other PM-2.5 contains primarily crustal material.
1996 Base
2030 Base
2030 Control
voc
18,522,037
15,676,964
15,430,241
NOx
26,117,335
18,717,720
16,157,296
CO
98,637,147
120,491,650
119,211,301
S02
18,789,382
16,436,874
16,285,231
NH3
4,762,317
5,400,554
5,400,554
SOA
202,517
163,196
157,884
Table II-3b. The percent change in total national annual emissions for selected scenarios.
2030 Base vs 1996 Base
2030 Control vs 2030 Base
Organic
Carbon
15.6 %
- 1.5 %
Elemental
Carbon
-5.1%
- 13.2 %
Gaseous
Sulfate
32.0 %
- 0.4 %
Primary
Nitrate
31.6%
- 0.8 %
Other
PM-2.5
18.1%
0.1%
Total
PM-2.5
14.8 %
- 1.9 %
Ivoc
-15.4 %
-1.6%
NOx
-28.3 %
-13.7 %
CO
22.2 %
-1.1%
S02
-12.5 %
-0.9 %
NH3
13.4%
0.0 %
SOA
-19.4 %
-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
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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
4Generally, 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.
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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-l 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-l. 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).
6/12/95
6/13/95
6/14/95
6/15/95
6/16/95
6/17/95
6/18/95
6/19/95
6/20/95
6/21/95
6/22/95
6/23/95
6/24/95
AL
AR
CT
1
2
3
2
DE
3
2
DC
1
1
FL
GA
IL
4
IN
1
1
2
KY
LA
2
2
ME
MD
1
7
1
MA
2
MI
4
M
2
NH
NJ
4
3
NY
2
NC
OH
1
OK
PA
8
RI
1
SC
TN
TX
1
1
1
1
3
7
7
4
3
VA
1
W
WI
1
2
TOT
0
1
1
2
2
7
6
32
13
7
7
6
11
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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 IJJ-2 shows a State-by-State listing of daily exceedance counts during the July 1995
FIDE 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).
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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).
7/05/95
7/06/95
7/07/95
7/08/95
7/09/95
7/10/95
7/11/95
7/12/95
7/13/95
7/14/95
7/15/95
AL
1
1
1
AR
1
1
CT
5
7
3
DE
3
3
DC
FL
GA
1
4
3
IL
8
2
2
IN
1
1
2
2
KY
1
LA
1
1
ME
1
MD
5
2
4
10
MA
3
2
MI
7
6
1
M
1
3
4
6
NH
NJ
1
7
6
NY
1
5
3
NC
3
OH
3
4
3
OK
PA
1
3
5
RI
3
SC
TN
1
1
1
TX
2
2
4
1
5
5
6
1
VA
4
W
1
WI
7
TOT
0
0
2
2
5
6
15
30
46
53
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 IU-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.
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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).
8/07/95
8/08/95
8/09/95
8/10/95
8/11/95
8/12/95
8/13/95
8/14/95
8/15/95
8/16/95
8/17/95
8/18/95
8/19/95
8/20/95
8/21/95
AL
1
1
1
1
1
4
AR
CT
3
DE
3
DC
FL
GA
1
3
2
2
5
1
IL
4
1
IN
1
1
KY
3
2
LA
1
2
1
ME
1
MD
3
3
MA
2
2
MI
1
1
M
1
NH
1
NJ
1
2
NY
2
NC
1
1
OH
OK
PA
1
1
RI
1
SC
1
TN
1
TX
1
1
6
6
1
VA
1
2
W
WI
TOT
0
0
0
5
3
8
1
7
8
5
7
12
9
6
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 ffi-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.
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Table III-4. Summary of Comparing the Five Highest Daily Maxima to Recent Design Values.
Ranking of
Observation
within HDE Days
Highest ozone
2nd high ozone
3rd high ozone
4th high ozone
5th hiah ozone
# of cases in which the
observed was greater than the
design value by 1 5 ppb
32
10
2
0
0
# of cases in which the
observed was within 1 5 ppb of
the design value
70
80
71
57
45
# of cases in which the observed
was less than the design value
by 1 5 ppb
8
20
37
53
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 ffi-l) 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
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
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Figure III-l. 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,
11
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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 2S-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 in-5. For more detail on the meteorological model configuration, see
Lagouvardos et al. (1997).
6 The inner nests were modeled with 34 layers while the outer 108 km domain was
modeled with 28 layers.
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Table III-5. Summary of RAMS model settings and inputs.
Model Setting/Input File
Input- Topography
Input - Sea-surface temperature
Input - Vegetation type
Input - Initial conditions
Input - Soil moisture
Setting
Setting - Lateral boundary conditions
Setting - Horizontal diffusivity
Setting - Vertical diffusivity
Setting - Shortwave/Longwave radiation
Description
30 arc-second data from EROS Data Center.
Mean monthly climatological data from NCAR.
10 arc-minute data from NOAA/NGDC.
The model was initialized with gridded one-degree ECMWF data
Six layer soil model. Assumed deeper layers were more moist than
Non-hydro static
Klemp-Wilhelmson
Smagorinsky
Mellor and Yamada parameterization scheme
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,
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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.
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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 ni-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
Domain
Midwest
Northeast
Southeast
Southwest
Tier 2
June 95
-10
-11
-17
-4
+2
Tier 2
July 95
-6
-13
-9
+4
+8
Tier 2 August
95
+2
+7
-9
+7
+6
HDE
June 95
-13
-15
-20
-7
+1
HDE
July 95
-11
-16
-11
-3
+3
HDE
August 95
+5
+10
-15
+12
+11
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Mean Normalized Gross Error
Domain
Midwest
Northeast
Southeast
Southwest
Tier 2
June 95
24
24
27
20
24
Tier 2
July 95
24
26
22
24
27
Tier 2 August
95
23
22
24
22
24
HDE
June 95
22
22
27
18
22
HDE
July 95
23
24
23
21
24
HDE
August 95
24
22
24
25
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) FIDE 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 JJI-2. The primary statistics for each of the 47 subregions is
shown in Table JJI-7.
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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 JrtDE 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.
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Table III-7. HDE Base Year model performance for the 47 local regions.
Region
Dallas
Houston-Galveston
Beaumont-Port Arthur
Baton Rouge
New Orleans
St. Louis
Memphis
Alabama
Atlanta
Nashville
Eastern TN
Charlotte
Greensboro
Raleigh-Durham
Evansville-Owensboro
Indianapolis
Louisville
Cincinnati-Dayton
Columbus OH
West Virginia
Chicago
Milwaukee
Muskegon-Grand Rapids
Gary-South Bend
Detroit
Pittsburgh
Central PA
Norfolk
Richmond
Baltimore- Washington
Delaware
Philadelphia
New York City
Hartford
Boston
Maine
Longview-Shreveport
Kansas City
Western NY
Northeast OH
South Carolina
Gulf Coast
FL West Coast
FL East Coast
Jackson, MS
Central MI
Macon-Columbus AL
Domainwide
Unpaired Accuracy
-0.155
-0.128
0.078
0.055
0.266
0.002
0.102
0.052
0.235
0.172
-0.005
0.198
0.137
0.093
0.097
-0.045
0.159
-0.038
-0.039
0.150
0.048
0.141
0.057
-0.097
0.058
-0.027
0.120
0.236
0.203
0.029
0.083
-0.021
0.125
-0.008
0.122
0.116
0.014
-0.113
0.106
0.014
0.161
0.239
0.424
0.248
0.347
-0.016
0.273
Average Accuracy
of the Peak
-0.079
0.043
0.151
0.212
0.198
-0.015
-0.090
0.024
0.079
0.078
-0.159
0.039
0.031
-0.026
-0.025
-0.104
0.104
-0.077
-0.117
0.043
-0.156
-0.148
-0.126
-0.173
-0.119
-0.059
-0.040
-0.015
0.032
-0.045
-0.074
-0.114
-0.108
-0.134
-0.103
-0.135
-0.049
-0.178
-0.136
-0.060
0.060
0.167
0.337
0.137
0.084
-0.102
0.012
Mean Normalized
Bias
-0.102
0.032
0.167
0.254
0.212
-0.007
-0.078
0.047
0.079
0.071
-0.195
0.061
0.021
-0.036
0.002
-0.115
0.094
-0.057
-0.109
0.048
-0.228
-0.190
-0.153
-0.212
-0.196
-0.073
-0.069
-0.075
0.040
-0.074
-0.047
-0.191
-0.207
-0.144
-0.177
-0.187
-0.088
-0.197
-0.178
-0.081
0.053
0.216
0.299
0.133
0.084
-0.161
0.033
Mean Normalized
Gross Error
0.216
0.267
0.251
0.308
0.264
0.205
0.200
0.201
0.244
0.265
0.257
0.182
0.177
0.179
0.236
0.217
0.265
0.230
0.204
0.225
0.291
0.239
0.226
0.271
0.275
0.218
0.213
0.246
0.192
0.213
0.156
0.269
0.294
0.243
0.270
0.262
0.251
0.238
0.229
0.209
0.188
0.279
0.382
0.250
0.198
0.227
0.187
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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 in-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.
7 These 37 areas are listed in Appendix A, as described in Section ni.F..
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Table III-8. Model performance statistics for individual local areas in the western U.S.
Region
Albuquerque
Denver
El Paso
Phoenix
Portland
Salt Lake City
San Joaquin Valley
Seattle
San Francisco
Southern California
Unpaired Peak
Prediction Accuracy
-0.205
-0.182
-0.279
-0.245
0.021
-0.199
-0.236
0.144
-0.287
-0.320
Average Peak
Prediction Accuracy
-0.340
-0.327
-0.408
-0.398
-0.145
-0.311
-0.372
-0.155
-0.361
-0.571
Mean Normalized
Bias
-0.354
-0.351
-0.437
-0.456
-0.209
-0.347
-0.396
-0.252
-0.373
-0.585
Mean Normalized
Gross Error
0.354
0.352
0.437
0.459
0.251
0.353
0.403
0.359
0.375
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 CMS A and MS As 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
8This 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.
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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".
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(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 the 1-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-
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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 future-
year 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
23
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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 paniculate sulfate, while nitric acid is the gas phase precursor for paniculate 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.
9Hydrogen peroxide is formed from photochemical reactions within the mCB4
mechanism.
24
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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
84
120
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
25
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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
Control
Emissions
Initial and
boundary
concentrations
Meteorological
Surface
characteristics
Chemical rates
Files
CONTROL
PT SOURCE
EMISSIONS
AIRQUALITY
BOUNDARY
WIND
TEMPERATURE
PSURF
H2o
VDIFFUSION
RAIN
SURFACE
TERRAIN
CHEMPARAM
RATES
Description
Simulation control information
Elevated source emissions
Surface emissions
Initial concentrations
Lateral boundary concentrations
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
Gridded land use
Terrain heights
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. a/., 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
26
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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 OZ, 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)
D02
500
1500
2500
3500
4 5
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
0
1
MM5 Layer
0
1
9
3
4
Sigma
1.000
0.995
0.988
0.980
0.970
Approximate
Height(m)
0.0
38.0
91.5
152.9
230.3
Pressure(mb)
1000.0
995.5
989.2
982.0
973.0
27
-------
REMSAD
Layer
2
3
4
5
6
7
8
MM5 Layer
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Sigma
0.956
0.938
0.916
0.893
0.868
0.839
0.808
0.777
0.744
0.702
0.648
0.582
0.500
0.400
0.300
0.200
0.120
0.052
0.000
Approximate
Height(m)
339.5
481.6
658.1
845.8
1053.9
1300.7
1571.4
1849.6
2154.5
2556.6
3099.0
3805.8
4763.7
6082.5
7627.9
9510.5
11465.1
13750.2
16262.4
Pressure(mb)
960.4
944.2
924.4
903.7
881.2
855.1
827.2
799.3
769.6
731.8
683.2
623.8
550.0
460.0
370.0
280.0
208.0
146.0
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 s to 3.0 ' 10 s
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)
28
-------
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. PEL 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
FIDE 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 FIDE REMSAD
modeling.
Table IV-3. REMSAD Initial and Boundary Conditions (ppm)
Species
NO
NO2
03
S02
NH3
Layer 1
l.OOE-12
l.OOE-04
4.00E-02
7.00E-04
5.00E-04
Layer 2
l.OOE-12
l.OOE-04
4.00E-02
7.00E-04
5.00E-04
Layer 3
l.OOE-12
l.OOE-04
4.00E-02
7.00E-04
5.00E-04
Layer 4
l.OOE-12
l.OOE-04
4.00E-02
7.00E-04
5.00E-04
Layer 5
8.57E-13
8.57E-05
4.00E-02
6.00E-04
3.67E-04
Layer 6
5.71E-13
5.71E-05
4.00E-02
4.00E-04
1.63E-04
Layer 7
2.86E-13
2.86E-05
4.00E-02
2.00E-04
4.08E-05
Layer 8
7.14E-14
7.14E-06
4.00E-02
5.00E-05
2.55E-06
29
-------
voc
CARB
ISOP
CO
HNO3
PNO3
GSO4
AS04
NH4N
NH4S
SOA
POA
PEC
PMFINE
PMCOARS
Layer 1
2.00E-02
.OOE-07
.OOE-09
.OOE-01
.OOE-05
.OOE-05
.OOE-04
.OOE-12
.OOE-05
.OOE-04
.OOE-03
.OOE-03
5.00E-03
.OOE-03
.OOE-03
Layer 2
2.00E-02
1. OOE-07
1. OOE-09
1. OOE-01
1. OOE-05
1. OOE-05
1. OOE-04
l.OOE-12
1. OOE-05
1. OOE-04
1. OOE-03
1. OOE-03
5.00E-03
1. OOE-03
1. OOE-03
Layer 3
2.00E-02
.OOE-07
.OOE-09
.OOE-01
.OOE-05
.OOE-05
.OOE-04
.OOE-12
.OOE-05
.OOE-04
.OOE-03
.OOE-03
5. OOE-03
.OOE-03
.OOE-03
Layer 4
2.00E-02
.OOE-07
.OOE-09
.OOE-01
.OOE-05
.OOE-05
.OOE-04
.OOE-12
.OOE-05
.OOE-04
.OOE-03
.OOE-03
5.00E-03
.OOE-03
.OOE-03
Layer 5
1.71E-02
1. OOE-07
1. OOE-09
1. OOE-01
8.57E-06
7.35E-06
7.35E-05
8.57E-13
7.35E-06
7.35E-05
7.35E-04
7.35E-04
3.67E-03
7.35E-04
6.30E-04
Layer 6
1.14E-02
1. OOE-07
1. OOE-09
1. OOE-01
5.71E-06
3.27E-06
3.27E-05
5.71E-13
3.27E-06
3.27E-05
3.27E-04
3.27E-04
1.63E-03
3.27E-04
1.87E-04
Layer 7
5.71E-03
1. OOE-07
1. OOE-09
1. OOE-01
2.86E-06
8.16E-07
8.16E-06
2.86E-13
8.16E-07
8.16E-06
8.16E-05
8.16E-05
4.08E-04
8.16E-05
2.33E-05
Layer 8
1.43E-03
1. OOE-07
1. OOE-09
1. OOE-01
7.14E-07
5.10E-08
5.10E-07
7.14E-14
5.10E-08
5.10E-07
5.10E-06
5.10E-06
2.55E-05
5.10E-06
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
30
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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/NAMPMJine/Data/NAMPMJ'.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 PM25 species
that are necessary to evaluate models and to develop PM2 5 control strategies.
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.
-------
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.
1996 IMPROVE Monitoring Sites
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 PM25
and related species:
nThe dividing line between the West and East was defined as the 100th meridian.
32
-------
Sulfate Ion: TSO4 = ASO4 + GSO4
Nitrate Ion: PNO3
Organic aerosols: TOA = POA + SOA
Elemental Carbon: PEC
Crustal Material (soils): PMFINE
PM25: PM25 = 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 PM25). PM25 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.
OBS — Obs'
N 1 1 x'(
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.
1 N
PRED — Pred'
N 1 1 x'(
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.
33
-------
i N Pred11
RATIO — ^i-
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
BIAS-L (Pred't Obs't}
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
2 N (Pred' Obs'}
FBIAS — — 100
N ,i
can 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.
ERR — Pred1' Obs''
N 11 x'' x''
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.
34
-------
FERROR —
N
Pred'
Obs'
100
Predlxt
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 PM25 and PM25 species
concentrations at the IMPROVE sites is as follows:
a.
PM,, Performance
Table IV-4 lists the performance statistics for PM2 5 at the IMPROVE sites. For the full
domain, PM25 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.
National
East
West
No. of
Sites
59
17
42
Mean
REMSAD
Predictions
(ug/m3)
5.14
11.38
2.61
Mean
Observations
(ug/m3)
6.07
10.55
4.26
Ratio of
Means
(pred/obs)
0.77
1.07
0.65
Bias
(ug/m3)
-0.93
0.82
-1.64
Fractional
Bias (%)
-21.1
2.8
-30.7
Error
(ug/m3)
3.04
4.40
2.48
Fractional
Error (%)
58.2
41.8
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.
35
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Table IV-5. Annual mean sulfate ion performance at IMPROVE sites.
National
East
West
No. of
Sites
60
18
42
Mean
REMSAD
Predictions
(ug/m3)
1.87
4.71
0.65
Mean
Observations
(ug/m3)
1.63
3.81
0.70
Ratio of
Means
(pred/obs)
1.09
1.25
1.02
Bias
(ug/m3)
0.24
0.90
-0.05
Fractional
Bias (%)
4.4
9.0
2.4
Error
(ug/m3)
0.85
2.00
0.35
Fractional
Error (%)
51.5
47.6
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
National
East
West
No. of
Sites
48
16
32
Mean
REMSAD
Predictions
(ug/m3)
0.30
0.50
0.21
Mean
Observations
(ug/m3)
0.31
0.47
0.24
Ratio of
Means
(pred/obs)
1.10
1.26
1.02
Bias
(ug/m3)
-0.01
0.03
-0.03
Fractional
Bias (%)
10.5
14.8
8.3
Error
(ug/m3)
0.18
0.24
0.14
Fractional
Error (%)
56.2
50.8
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.
36
-------
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
National
East
West
No. of
Sites
48
16
32
Mean
REMSAD
Predictions
(ug/m3)
0.76
1.11
0.60
Mean
Observations
(ug/m3)
1.25
1.74
1.01
Ratio of
Means
(pred/obs)
0.67
0.68
0.67
Bias
(ug/m3)
-0.48
-0.63
-0.41
Fractional
Bias (%)
-44.1
-38.3
-47.0
Error
(ug/m3)
0.81
0.99
0.72
Fractional
Error (%)
74.8
64.5
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.
37
-------
Table IV-8. Annual mean nitrate ion performance at IMPROVE sites
National
East
West
No. of
Sites
51
17
33
Mean
REMSAD
Predictions
(ug/m3)
0.68
1.48
0.27
Mean
Observations
(ug/m3)
0.40
0.54
0.32
Ratio of
Means
(pred/obs)
1.64
2.80
1.04
Bias
(ug/m3)
0.29
0.94
-.05
Fractional
Bias (%)
-46.7
-1.1
-70.3
Error
(ug/m3)
0.63
1.19
0.35
Fractional
Error (%)
134.4
126.8
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
National
East
West
No. of
Sites
60
18
42
Mean
REMSAD
Predictions
(ug/m3)
0.96
1.76
0.62
Mean
Observations
(ug/m3)
0.63
0.52
0.68
Ratio of
Means
(pred/obs)
2.11
4.10
1.26
Bias
(ug/m3)
0.33
1.24
-0.06
Fractional
Bias (%)
47.1
106.1
21.8
Error
(ug/m3)
0.86
1.46
0.61
Fractional
Error (%)
96.0
118.1
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 (PM25), by -20%. PM25 in the Eastern U.S. is slightly overpredicted, while PM25 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,
38
-------
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 PM25 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 PM25 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)]
39
-------
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:
dv 10.0 In
10.0 Mm
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.
40
-------
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 in. 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.
41
-------
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, CJ. Tremback, W.A. Lyons, L.D. Grasso, M.E.
Nicholls, M.D. Moran, D.A. Wesley, TJ. 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
Atlanta, GA MSA
Barnstable, MA MSA
Baton Rouge, LA MSA
Beaumont-Port Arthur, TX MSA
Benton Harbor, MI MSA
Biloxi, MS MSA
Birmingham, AL MSA
Boston, MA CMSA
Charleston, WV MSA
Charlotte, NC MSA
Chicago, IL CMSA
Cincinnati, OH CMSA
Cleveland, OH CMSA
Detroit, MI CMSA
Grand Rapids, MI MSA
Hartford, CT MSA
Houma, LA MSA
Houston, TX CMSA
Huntington, WV MSA
Lake Charles, LA MSA
Louisville, KY MSA
Macon, GA MSA
Memphis, TN MSA
Milwaukee, WI CMSA
Nashville, TN MSA
New London, CT MSA
New Orleans, LA MSA
New York City, NY CMSA
Norfolk, VA MSA
Total Number of Grid Cells in Area
115
19
30
39
15
41
64
189
31
69
129
71
68
126
58
41
51
132
47
20
45
37
58
39
78
12
96
195
60
-------
Orlando, FL MSA
Pensacola, FL MSA
Philadelphia, PA CMSA
Providence, RI MSA
Richmond, VA MSA
St. Louis, MO MSA
Tampa, FL MSA
Washington, DC-Baltimore, MD CMSA
61
34
118
20
66
127
56
187
-------
Appendix C :
1-hour Ozone Metrics
-------
APPENDIX C
1-Hour Ozone Metrics
Total ppb Increase
Total ppb Increase | Total
"1996 Base"vs"2007 Base [ 56.9
2020 Base vs 2020 Control i 1 96.3
2030 Base vs 2030 Control [ 352.1
Increase, on Average (ppb) E Total
1 996 Base vs 2007 Base I 0.0
2020 Base vs 2020 Control [ 0.2
2030 Base vs Control i 0.3
Note: N.A. is used to denote that i
there are no exceedances in the i
Base Case or Control Case i
Atlanta
o
0
0
Atlanta
0.0
0.0
0.0
Barnstable, MA
o
0
0
Barnstable, MA
0.0
N.A.
N.A.
Baton Rouge
o
0
0
Baton Rouge
0.0
0.0
0.0
Beaumont
o
0
0
Beaumont
0.0
0.0
0.0
Benton Harbor, Ml
o
0
0
Benton Harbor, Ml
0.0
0.0
0.0
Blloxl
o
0
0
Blloxl
0.0
0.0
0.0
Birmingham
lag
0
0
Birmingham
0.3
0.0
0.0
-------
APPENDIX C
1-Hour Ozone Metrics
Total ppb Increase
Total ppb Increase
1996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs 2030 Control
Increase, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Boston
o
0
0
Boston
0.0
0.0
0.0
Charleston, WV
o
0
0
Charleston, WV
0.0
0.0
0.0
Charlotte
o
0
0
Charlotte
0.0
0.0
0.0
Chicago
o
11.4
36.3
Chicago
0.0
0.9
2.0
Cincinnati
5~8
0
0
Cincinnati
0.1
0.0
0.0
Cleveland
o
0
0
Cleveland
0.0
0.0
0.0
Detroit
16~3
35
68.8
Detroit
0.5
2.3
3.3
Grand Rapids
o
0
0
Grand Rapids
0.0
0.0
0.0
-------
APPENDIX C
1-Hour Ozone Metrics
Total ppb Increase
Total ppb Increase
1996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs 2030 Control
Increase, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Hartford
o
0
0
Hartford
0.0
0.0
0.0
Houma, LA
o
0
0
Houma, LA
0.0
0.0
0.0
Houston
o
0.6
1.9
Houston
0.0
0.0
0.0
Huntington, VW
o
0
0
Huntington, VW
0.0
0.0
0.0
Lake Charles, LA
o
0
0
Lake Charles, LA
0.0
0.0
N.A.
Louisville
o
4
5.5
Louisville
0.0
0.2
0.2
Macon, GA
o
0
0
Macon, GA
0.0
0.0
N.A.
Memphis
iT"2
0
0
Memphis
0.3
0.0
0.0
-------
APPENDIX C
1-Hour Ozone Metrics
Total ppb Increase
Total ppb Increase
1996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs 2030 Control
Increase, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Milwaukee
o
0
0
Milwaukee
0.0
0.0
0.0
Nashville
o
0
0
Nashville
0.0
0.0
0.0
New London, CT
o
0
0
New London, CT
0.0
0.0
0.0
New Orleans
o
3.9
6.3
New Orleans
0.0
0.0
0.0
New York City
as
141.3
221.1
New York City
0.0
0.8
1.1
Norfolk
o
0
0
Norfolk
0.0
0.0
N.A.
Orlando
o
0
0
Orlando
0.0
0.0
0.0
Pensacola
o
0
0
Pensacola
0.0
N.A.
N.A.
-------
APPENDIX C
1-Hour Ozone Metrics
Total ppb Increase
Total ppb Increase
1996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs 2030 Control
Increase, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Philadelphia
o
0
0
Philadelphia
0.0
0.0
0.0
Providence
o
0
0
Providence
0.0
0.0
0.0
Richmond
o
0
0
Richmond
0.0
0.0
0.0
St. Louis
o
0
0
St. Louis
0.0
0.0
0.0
Tampa
l"2
0
0
Tampa
0.0
0.0
0.0
Wash-Baltimore
o
0.1
12.2
Wash-Baltimore
0.0
0.0
0.2
-------
APPENDIX C
1-Hour Ozone Metics
Peak Ozone
Peak 1 -Hour Ozone (ppb)
1996 Base
2007 Base
2020 Base
2020 Control
2030 Base
2030 Control
Percent Change
1 996 vs 2007 Base
2007 Base vs 2020 Base
2020 Base vs 2020 Control
2020 Base vs 2030 Base
2030 Base vs 2030 Control
1996 vs2030 Control
Max i
219!
191 !
183!
171 1
191!
176J
Max |
-12.8%!
-42%!
-6.6%!
4.4%!
-7.9%j
-19.6%!
Mean
162
147
143
138
147
140
Mean
-9.6%
-2.9%
-3.4%
2.7%
-4.2%
-13.6%
Atlanta
219
191
183
171
191
176
Atlanta
-12.8%
-4.2%
-6.6%
4.4%
-7.9%
-1 9.6%
Barnstable, MA
160
134
124
116
128
118
Barnstable, MA
-16.2%
-7.5%
-6.5%
3.2%
-7.8%
-26.2%
Baton Rouge
154
148
144
142
148
145
Baton Rouge
-3.9%
-2.7%
-1 .4%
2.8%
-2.0%
-5.8%
Beaumont
135
134
129
127
132
130
Beaumont
-0.7%
-3.7%
-1 .6%
2.3%
-1 .5%
-3.7%
Benton Harbor, Ml !
160!
148
144
140
148!
143J
Benton Harbor, Ml j
-7.5% I
-27%!
-2.8%!
2.8%!
-3.4%j
-10.6%!
Blloxl |
144!
140!
136!
134!
138!
136|
Blloxl !
-2.8% I
-2.9%1
-1.5%!
1.5%!
-1.4% |
-5.6% |
Birmingham
153
135
132
126
135
128
Birmingham
-1 1 .8%
-2.2%'
-4.5%
2.3%
-5.2%
-16.3%
-------
APPENDIX C
1-Hour Ozone Metics
Peak Ozone
Peak 1 -Hour Ozone (ppb) E
1996 Base I
2007 Base I
2020 Base f
2020 Control I
2030 Base [
2030 Control i
Percent Change I
1 996 vs 2007 Base I
2007 Base vs 2020 Base I
2020 Base vs 2020 Control I
2020 Base vs 2030 Base [
2030 Base vs 2030 Control i
1996 vs 2030 Control i
Boston E
1671
145
138"T
1301
142!
132[
Boston j
-13.2% I
-48%T
-5.8% I
2.9% I
-7.0% i
-21.0% |
Charleston, WV i
1521
1341
l"29"i
1251
130!
124[
Charleston, WV i
-11.8%
:3;7%j
-3.1%
0.8% [
-4.6% [
-18.4% |
Charlotte E Chicago E Cincinnati E Cleveland E
1531 1691 1671 1441
141 1 1501 1351 1361
1 38 i 1 47 i 1 35 i 1 40 i
1321 1431 1301 1391
144! 151! 139! 143!
136[ 146[ 134[ 142[
Charlotte i Chicago i Cincinnati i Cleveland i
-7.8% I -11. 2% I -19.2% I -5.6% I
:2;T%j :2;0%j 0;'o%"i 2':'9%"!
-4.3% I -2.7% I -3.7% I -0.7% I
4.3% [ 2.7% [ 3.0% [ 2.1%[
-5.6% [ -3.3% [ -3.6% [ -0.7% i
-11.1% | -13.6% | -19.8% | -1.4% |
Detroit E
1601
1461
156j
1581
157!
161 [
Detroit E
-8.7% I
6"8%[
1.3% I
0.6%[
2.5% [
0.6% |
Grand Rapids
163
151
147
143
152
146
Grand Rapids
-7.4%
-276%
-2.7%
3.4%
-3.9%
-10.4%
-------
APPENDIX C
1-Hour Ozone Metics
Peak Ozone
Peak 1 -Hour Ozone (ppb) E
1996 Base i
2007 Base i
2020 Base I
2020 Control I
2030 Base [
2030 Control i
Percent Change E
1 996 vs 2007 Base I
2007 Base vs 2020 Base I
2020 Base vs 2020 Control I
2020 Base vs 2030 Base [
2030 Base vs 2030 Control i
1996 vs 2030 Control i
Hartford E Houma, LA E Houston E
1881 147| 165|
171 1 1431 1561
1661 140[ 155i"
1591 1381 1531
171! 143! 159!
162[ 140[ 157[
Hartford i Houma, LA i Houston i
-9.0%i -2.7% I -5.5% I
:2;9%j :2;T%j :0;6%j
-4.2% I -1.4% I -1.3% I
3.0% [ 2.1%[ 2.6%\
-5.3% [ -2.1% [ -1.3% [
-13.8% | -4.8% i -4.8% i
Huntington, WV i
1711
151 1
1501
1471
153!
150[
Huntington, WV i
-11. 7% I
:0;7%j
-2.0%
2.0% [
-2.0% [
-12.3% |
Lake Charles, LA E
1321
1291
1251
1241
128!
126[
Lake Charles, LA E
-2.3% I
:3;T%j
-0.8% I
2.4% I
-1.6% [
-4.5% |
Louisville E Macon, GA E
1721 1731
1491 1321
148"1 126"I
1471 1181
151! 129!
152[ 118[
Louisville j Macon, GA i
-13.4% I -23.7% I
:0;7%j :4;5%|
-0.7% I -6.3% I
2.0% [ 2.4%[
0.7% i -8.5% i
-11.6%i -31.8%i
Memphis
160
151
144
140
148
142
Memphis
-5.6%
-4.6%'
-2.8%
2.8%
-4.1%
-1 1 .2%
-------
APPENDIX C
1-Hour Ozone Metics
Peak Ozone
Peak 1 -Hour Ozone (ppb)
1996 Base
2007 Base
2020 Base
2020 Control
2030 Base
2030 Control
Percent Change
1 996 vs 2007 Base
2007 Base vs 2020 Base
2020 Base vs 2020 Control
2020 Base vs 2030 Base
2030 Base vs 2030 Control
1996 vs 2030 Control
Milwaukee E
148!
1301
125i
1251
130!
128[
Milwaukee E
-12.2% I
:3;8%j
0.0% I
4.0% i
-1.5% [
-13.5% |
Nashville E
166!
1541
1491"
1421
154!
145[
Nashville E
-7.2% I
-3"2%T
-4.7% I
3.4% i
-5.8% i
-12.7% |
New London, CT i
180!
1591
1521
1451
157!
148[
New London, CT E
-11.7%
-4T4%1
-4.6% I
3.3% i
-5.7% i
-17.8% |
New Orleans E
165!
1601
157i
1561
160!
158[
New Orleans E
-3.0%!
T9%[
-0.6% I
1.9% i
-1.2% [
-4.2% |
New York City i
192!
1781
175i
1681
180!
171 i
New York City i
-7.3% I
:1;7%J
-4.0%
2.9% i
-5.0% i
-10.9% |
Norfolk E Orlando E Pensacola
146! 145! 139
1271 1381 127
1261 1321 12?
1231 1251 115
130! 137E 124
126[ 127[ 116
Norfolk E Orlando E Pensacola
-13.0% I -4. 8% I -8.6%
-0.8% | -4. 3% | -4.7%
-2.4% I -5.3% I -5.0%
3.2% i 3.8% i 2.5%
-3.1% [ -7.3% i -6.5%
-13.7% | -12.4% | -16.5%
-------
APPENDIX C
1-Hour Ozone Metics
Peak Ozone
Peak 1 -Hour Ozone (ppb)
1996 Base
2007 Base
2020 Base
2020 Control
2030 Base
2030 Control
Percent Change
1 996 vs 2007 Base
2007 Base vs 2020 Base
2020 Base vs 2020 Control
2020 Base vs 2030 Base
2030 Base vs 2030 Control
1996 vs 2030 Control
E Philadelphia E Providence E
I 166! 173!
I 1421 1491
[ 135i 141 [
I 1271 1341
i 139E 146E
I 129I 136I
E Philadelphia E Providence E
I -14.5% I -13.9% I
[ -4.9% [ -5.4% [
I -5.9% I -5.0%l
i 3.0% i 3.5% i
I -7-2%l -6-8%l
| -22.3% i -21 .4% |
Richmond E
170!
1501
141 [
1371
145E
139[
Richmond E
-11.8%
-676%1
-2.8% I
2.8% i
-4.1% [
-18.2% |
St. Louis E
151!
141 1
1361
1281
140E
129[
St. Louis E
-6.6% I
-375%1
-5.9% 1
2.9% i
-7.9% i
-14.6%
Tampa E Wash-Baltimore
188! 172
1731 154
161 i 150
1501 143
166! 154
152[ 145
Tampa E Wash-Baltimore
-8.0%! -10.5%
-6.9% i -2.6%
-6.8% I -4.7%
3.1% i 2.7%
-8.4% i -5.8%
-19.1% | -15.7%
-------
APPENDIX C
1-Hour Ozone Metrics
Total Nonattainment
Total Nonattainment
(ppb>=125)
1996 Base
2007 Base
2020 Base
2020 Control
2030 Base
2030 Control
Percent Change
1 996 vs 2007 Base
2007 Base vs 2020 Base
2020 Base vs 2020 Control
2020 Base vs 2030 Base
2030 Base vs 2030 Control
Note: N.A. denotes predicted
exceedances in the 2030 Base,
but not in the 2020 Base
Total
39665.2
12743.4
8334.2
5288.3
l"2"T"2"a2
6841.1
Total
-67.9%
-34.6%
-36.5%
45.5%
-43.6%
Atlanta
7738.3
2604.9
1319.4
546.6
1945'T
636.9
Atlanta
-66.3%
-49.3%
-58.6%
47.4%
:67;3%
Barnstable, MA
192.8
11.6
0
0
3.7
0
Barnstable, MA
-94.0%
-100.0%
0.0%
N.A.
-10OO%
Baton Rouge
1176.9
687.4
389.8
257.9
635.4"
406
Baton Rouge
-41 .6%
-43.3%
-33.8%
63.0%
^"6"T%"
Beaumont
111.1
49.5
7.8
4
244
10.5
Beaumont
-55.4%
-84.2%
-48.7%
212.8%
"-576%"
Benton Harbor, Ml j Blloxl j Birmingham
205.6] 191.9] 534.2
65.5! 81.6! 36.7
45.4! 28.5! 10.7
36.1 1 13.8J 2
6l""l"] 59.81 l7"5
42.6! 29.2! 3.5
Benton Harbor, Ml ! Blloxl ! Birmingham
-68.1%) -57.5%| -93.1%
-30.7%! -65.1%! -70.8%
-20.5%! -51 .6%! -81 .3%
34.6% I 109.8%l 63.6%
^"63"%"] ^T2""%1 -8"6"6%"
-------
APPENDIX C
1-Hour Ozone Metrics
Total Nonattainment
Total Nonattainment !
(ppb>=125) ]
1996 Base ]
2007 Base !
2020 Base !
2020 Control I
2030 Base |
2030 Control !
Percent Change !
1 996 vs 2007 Base ]
2007 Base vs 2020 Base !
2020 Base vs 2020 Control !
2020 Base vs 2030 Base I
2030 Base vs 2030 Control ]
Note: N.A. denotes predicted !
exceedances in the 2030 Base,
but not in the 2020 Base |
Boston !
608.8]
95.8!
23.5!
5.5J
56:37
7.5]
Boston !
-84.3%)
-75.5%!
-76.6%!
139.6%l
-86"7%T
Charleston, WV ! Charlotte ! Chicago ! Cincinnati ! Cleveland !
288.2] 292] 374.9] 1025.1] 191.9]
9.5! 36.5! 125.8! 50.8! 15.4!
4.2! 22.6! 120! 33.6! 15.4!
0.2l 7.4l 10ll 14.5) 14.1 1
5~.3\ 4a3! 1747! 72.5\ 275!
o! 11. el 149.3] 28.el 17.3]
Charleston, WV ! Charlotte ! Chicago ! Cincinnati ! Cleveland !
-96.7%) -87.5%) -66.4%) -95.0%) -92.0%)
-55.8%! -38.1%! -4.6%! -33.9%! 0.0%!
-95.2%! -67.3%! -15.8%! -56.8%! -8.4%!
26.2%l 113.7%] 45.6%l 115.8%] 78.6%!
-i' 66.6% | -76.6% | -14.5% | -60.6% | -135.3% |
Detroit ! Grand Rapids
294.7] 1160.5
163.8! 491.8
212.2! 343.2
2121 214.5
2545! 472.8
263.9I 275.1
Detroit ! Grand Rapids
-44.4%) -57.6%
29.5%! -30.2%
-0.1% | -37.5%
19.9%l 37.8%
37%] ^4l"8%
-------
APPENDIX C
1-Hour Ozone Metrics
Total Nonattainment
Total Nonattainment !
(ppb>=125) ]
1996 Base ]
2007 Base !
2020 Base |
2020 Control I
2030 Base |
2030 Control !
Percent Change !
1 996 vs 2007 Base ]
2007 Base vs 2020 Base !
2020 Base vs 2020 Control |
2020 Base vs 2030 Base I
2030 Base vs 2030 Control ]
Note: N.A. denotes predicted \
exceedances in the 2030 Base,
but not in the 2020 Base |
Hartford I
649.6]
346.7!
303.5!
219.6!
3779!
263.9!
Hartford I
-46.6%)
-12.5% |
-27.6%!
24.5%!
-3b"2%|
Houma, LA !
586.6]
288.2!
165.3!
119.5!
285!
182.8!
Houma, LA !
-50.9%)
-42.6% |
-27.7%!
72.4%!
-35"9%|
Houston !
1597.5]
640.5!
437.4!
325.8!
663'^T
441 1
Houston !
-59.9%)
-31.7%!
-25.5%!
51.6%
-33"5%T
Huntington, VW I
1411.4]
71.7!
52.7!
36.1 1
68J9"I
44.5!
Huntington, VW I
-94.9%)
-26.5% |
-31.5%!
30.7%!
-35"4%|
Lake Charles, LA j
36.3]
12.7!
0.8!
ol
as!
2.3!
Lake Charles, LA j
-65.0%)
-93.7% |
-100.0%!
962.4%!
-72"9%|
Louisville ! Macon, GA !
1437.1] 491.8]
267.3! 7.6 1
277.3! 1.6 1
229.6! ol
347! 47!
272! ol
Louisville ! Macon, GA !
-81.4%) -98.5%)
3.7%! -78.9%!
-17.2%! -100.0%!
25.1%! 193.7%!
-2T6%| -Tbb"b%|
Memphis
284.1
89
72.5
47.7
10O5
63.8
Memphis
-68.7%
-18.5%
-34.2%
38.6%
^5%
-------
APPENDIX C
1-Hour Ozone Metrics
Total Nonattainment
Total Nonattainment ! Milwaukee !
(ppb>=125) ] ]
1996 Base ] 69.4]
2007 Base ! 5.3!
2020 Base ! 0.8!
2020 Control I 0.1 1
2030 Base | 11.9!
2030 Control ! 4.1!
Percent Change ! Milwaukee !
1 996 vs 2007 Base ] -92.4% ]
2007 Base vs 2020 Base ! -84.9%!
2020 Base vs 2020 Control ! -87.5% !
2020 Base vs 2030 Base I 1 387.3%]
2030 gase""vs"2Q3QQontro| | -657"5""%""|
Note: N.A. denotes predicted ! !
exceedances in the 2030 Base, !
but not in the 2020 Base | |
Nashville j
1263.1]
103.8!
53!
30.4!
7676!
36.6!
Nashville j
=^%|
-48.9% |
-42.6% |
44.5%l
-5272%T
New London, CT I
612.7]
259.6!
195.1!
113.g|
27875!
152.5!
New London, CT I
-57.6%)
-24.8% |
-41.6%!
42.7%l
-4572%|
New Orleans !
1857.5]
1108!
742.3!
581.21
ll'SOS'l
826.4!
New Orleans !
-40.3%)
-33.0%!
-21.7%!
52.3%l
-267"9%1
New York City !
5787.7]
2190.4!
1870.2!
1430.6!
2503J61
1778.2!
New York City I
-62.2%)
-14.6% |
-23.5%!
33.9%!
-2970%|
Norfolk j
92.6]
5.5!
1.3!
o!
872!
1.2!
Norfolk j
-94.1%)
-76.4% |
-100.0%!
530.7%!
-8574%|
Orlando !
100.7]
40.6!
11.6|
0.2!
33!
2.4!
Orlando !
-59.7%)
-71.4%!
-98.3%!
184.5%!
-927%|
Pensacola
33.6
2.3
0
0
o
0
Pensacola
-93.2%
-100.0%
0.0%
0.0%
"676%"
-------
APPENDIX C
1-Hour Ozone Metrics
Total Nonattainment
Total Nonattainment !
(ppb>=125) ]
1996 Base ]
2007 Base !
2020 Base !
2020 Control I
2030 Base |
2030 Control !
Percent Change !
1 996 vs 2007 Base ]
2007 Base vs 2020 Base !
2020 Base vs 2020 Control !
2020 Base vs 2030 Base I
2030 Base vs 2030 Control ]
Note: N.A. denotes predicted !
exceedances in the 2030 Base,
but not in the 2020 Base |
Philadelphia !
1588.3]
162.3!
68.7!
10.sl
iso'Jg'l
24.3!
Philadelphia !
-89.8%)
-57.7% |
-84.7% |
119.7%
-83"9%|
Providence |
512.6]
155.4!
79.4!
29.2J
133;6
43.3]
Providence |
-69.7%)
-48.9% |
-63.2% |
68.3% I
-67"6%|
Richmond ! St. Louis !
495.3] 591.5]
160.3 1 74.2!
85.3! 32.9!
33J 4.9J
12T.TI 6T.51
42.4! 9.9!
Richmond ! St. Louis !
-67.6%) -87.5%)
-46.8% | -55.7% |
-61.3%! -85.1%!
42.0%l 86.9% I
-65"6%| -83"9%|
Tampa |
2396.9]
1380.4!
803.8!
402.2J
1124'^T
464.8!
Tampa |
-42.4%)
-41.8%!
-50.0% |
39.9% I
-58"7%T
Wash-Baltimore
3382
845
502.4
244.2
78T2
302.2
Wash-Baltimore
-75.0%
-40.5%
-51 .4%
55.5%
:glT3%
-------
APPENDIX C
1-Hour Metrics
Total ppb Reduction
Total ppb Reduction
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Reduction, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Total | Atlanta j Barnstable, MA j Baton Rouge j Beaumont j Benton Harbor, Ml
478901"] 75845] 3T7~5\ 576! 83J9] 28l72
4161.8! 1098! O! 152.8! 4.8\ 13
7569.8! 2007.2! 10.5! 266! 24.2! 25.4
Total | Atlanta ! Barnstable, MA ! Baton Rouge ! Beaumont ! Benton Harbor, Ml
16.2! 20.2! 18.0! 4.6! 3.1 1 14.8
4.9! 12.2! N.A. | 2.4! 1.6! 3.3
6.5| 16.2| 10.5] 3.3| 2.4| 4.2
Blloxl
14675
20.7
40.6
Blloxl
3.8
1.9
2.5
Birmingham
133T.7
12.6
17
Birmingham
23.8
6.3
8.5
-------
APPENDIX C
1-Hour Metrics
Total ppb Reduction
Total ppb Reduction
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Reduction, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Boston
909.5"
31.9
87
Boston
16.5
8.0
10.9
Charleston, WV
1152
4
5.4
Charleston, WV
37.2
4.0
5.4
Charlotte
il'37.7"
32.1
47.7
Charlotte
25.3
6.4
8.0
Chicago
38276
36.5
56.7
Chicago
11.6
2.8
3.2
Cincinnati
2127.4"
28
61.6
Cincinnati
25.6
4.0
5.1
Cleveland
6l"i"."4"
1.3
13.9
Cleveland
15.3
1.3
3.5
Detroit
29l79
24.7
62.7
Detroit
8.6
1.6
3.0
Grand Rapids
93778
142.4
208.4
Grand Rapids
12.8
4.3
5.6
-------
APPENDIX C
1-Hour Metrics
Total ppb Reduction
Total ppb Reduction
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Reduction, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Hartford
432.3"
91.9
122.8
Hartford
12.7
5.1
6.5
Houma, LA
405.8"
52.4
128
Houma, LA
4.4
1.9
2.5
Houston
i"32"l"."8"
143.9
284.1
Houston
7.9
2.5
3.3
Huntington, VW
323T4"
19.6
30.3
Huntington, VW
35.1
3.3
4.3
Lake Charles, LA
4179
1.3
7.2
Lake Charles, LA
3.2
1.3
1.8
Louisville
25757
57.6
90.6
Louisville
23.8
2.4
3.4
Macon, GA
i'273.3"
7.8
10.8
Macon, GA
33.5
7.8
10.8
Memphis
4274
26.7
39.2
Memphis
11.9
3.3
3.9
-------
APPENDIX C
1-Hour Metrics
Total ppb Reduction
Total ppb Reduction
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Reduction, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Milwaukee
85.4"
6.4
12.8
Milwaukee
14.2
3.2
3.2
Nashville
3959"."9
37.5
50.2
Nashville
38.8
7.5
10.0
New London, CT
3907l
98
155.3
New London, CT
13.9
5.2
6.5
New Orleans
95379
195.6
344
New Orleans
4.4
1.8
2.3
New York City
4"625."2"
687.8
1156.1
New York City
14.0
4.1
5.6
Norfolk
27679
2.9
10.5
Norfolk
16.3
2.9
5.3
Orlando
773
21
55.3
Orlando
6.5
7.0
9.2
Pensacola
8674
0
0
Pensacola
10.8
N.A.
N.A.
-------
APPENDIX C
1-Hour Metrics
Total ppb Reduction
Total ppb Reduction
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Reduction, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Philadelphia
243T.8"
93.4
221.5
Philadelphia
19.6
6.2
7.9
Providence
5045
69.4
118.5
Providence
15.8
6.9
8.5
Richmond
537.3"
59
100.3
Richmond
17.9
6.6
9.1
St. Louis
Ti4i
43.6
64.6
St. Louis
16.8
7.3
9.2
Tampa
is'iTi"
460.1
862.1
Tampa
10.2
8.1
11.1
Wash-Baltimore
3867.8"
383.1
771.3
Wash-Baltimore
17.8
7.4
10.3
-------
APPENDIX C
1-Hour Ozone Metrics
Total ppb Increase
Total ppb Increase | Total
"1996 Base"vs"2007 Base [ 56.9
2020 Base vs 2020 Control i 1 96.3
2030 Base vs 2030 Control [ 352.1
Increase, on Average (ppb) E Total
1 996 Base vs 2007 Base I 0.0
2020 Base vs 2020 Control [ 0.2
2030 Base vs Control i 0.3
Note: N.A. is used to denote that i
there are no exceedances in the i
Base Case or Control Case i
Atlanta
o
0
0
Atlanta
0.0
0.0
0.0
Barnstable, MA
o
0
0
Barnstable, MA
0.0
N.A.
N.A.
Baton Rouge
o
0
0
Baton Rouge
0.0
0.0
0.0
Beaumont
o
0
0
Beaumont
0.0
0.0
0.0
Benton Harbor, Ml
o
0
0
Benton Harbor, Ml
0.0
0.0
0.0
Blloxl
o
0
0
Blloxl
0.0
0.0
0.0
Birmingham
lag
0
0
Birmingham
0.3
0.0
0.0
-------
APPENDIX C
1-Hour Ozone Metrics
Total ppb Increase
Total ppb Increase
1996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs 2030 Control
Increase, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Boston
o
0
0
Boston
0.0
0.0
0.0
Charleston, WV
o
0
0
Charleston, WV
0.0
0.0
0.0
Charlotte
o
0
0
Charlotte
0.0
0.0
0.0
Chicago
o
11.4
36.3
Chicago
0.0
0.9
2.0
Cincinnati
5~8
0
0
Cincinnati
0.1
0.0
0.0
Cleveland
o
0
0
Cleveland
0.0
0.0
0.0
Detroit
16~3
35
68.8
Detroit
0.5
2.3
3.3
Grand Rapids
o
0
0
Grand Rapids
0.0
0.0
0.0
-------
APPENDIX C
1-Hour Ozone Metrics
Total ppb Increase
Total ppb Increase
1996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs 2030 Control
Increase, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Hartford
o
0
0
Hartford
0.0
0.0
0.0
Houma, LA
o
0
0
Houma, LA
0.0
0.0
0.0
Houston
o
0.6
1.9
Houston
0.0
0.0
0.0
Huntington, VW
o
0
0
Huntington, VW
0.0
0.0
0.0
Lake Charles, LA
o
0
0
Lake Charles, LA
0.0
0.0
N.A.
Louisville
o
4
5.5
Louisville
0.0
0.2
0.2
Macon, GA
o
0
0
Macon, GA
0.0
0.0
N.A.
Memphis
iT"2
0
0
Memphis
0.3
0.0
0.0
-------
APPENDIX C
1-Hour Ozone Metrics
Total ppb Increase
Total ppb Increase
1996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs 2030 Control
Increase, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Milwaukee
o
0
0
Milwaukee
0.0
0.0
0.0
Nashville
o
0
0
Nashville
0.0
0.0
0.0
New London, CT
o
0
0
New London, CT
0.0
0.0
0.0
New Orleans
o
3.9
6.3
New Orleans
0.0
0.0
0.0
New York City
as
141.3
221.1
New York City
0.0
0.8
1.1
Norfolk
o
0
0
Norfolk
0.0
0.0
N.A.
Orlando
o
0
0
Orlando
0.0
0.0
0.0
Pensacola
o
0
0
Pensacola
0.0
N.A.
N.A.
-------
APPENDIX C
1-Hour Ozone Metrics
Total ppb Increase
Total ppb Increase
1996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs 2030 Control
Increase, on Average (ppb)
1 996 Base vs 2007 Base
2020 Base vs 2020 Control
2030 Base vs Control
Note: N.A. is used to denote that
there are no exceedances in the
Base Case or Control Case
Philadelphia
o
0
0
Philadelphia
0.0
0.0
0.0
Providence
o
0
0
Providence
0.0
0.0
0.0
Richmond
o
0
0
Richmond
0.0
0.0
0.0
St. Louis
o
0
0
St. Louis
0.0
0.0
0.0
Tampa
l"2
0
0
Tampa
0.0
0.0
0.0
Wash-Baltimore
o
0.1
12.2
Wash-Baltimore
0.0
0.0
0.2
-------
Appendix D:
8 Hour Relative Reduction Factors
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
010270001 AL
County
CLAY CO
010510001 AL ELMORECO
010731003 AL JEFFERSON CO
010731005 AL JEFFERSON CO
010732006 AL
010735002 AL
JEFFERSON CO
JEFFERSON CO
010736002 AL JEFFERSON CO
010790002'AL LAWRENCl CO
01 089001 4 AL MADISON CO
010970003 AL MOBILE CO
010970028 AL MOBILE CO
011011002 AL
01 1 1 70004 AL
MONTGOMERY CO
SHELBY CO
01 11 90002 AL SUMTERCO
050350005 AR CRITTENDEN CO
050970001 AR MONTGOMERY CO
051010002 AR NEWTON CO
051 190007 AR PULASKI CO
051191002 AR
090010017 CT
PULASKI CO
FAIRFIELDCO
090011123 CT FAIRFIELDCO
090013007 CT FAIRFIELD CO
09001 9003 CT FAIRFIELDCO
090031003 CT HARTFORD CO
090050006 CT LITCHFIELD CO
090070007 CT
090091123 CT
MIDDLESEX CO
NEW HAVEN CO
090093002 CT NEW HAVEN CO
090Ti0008 CT NEW LONDON CO
090131001 CT TOLLANDCO
100010002 DE KENT CO
100031003 DE NEWCASTLE CO
100031007 DE
100031010 DE
NEW CASTLE CO
NEW CASTLE CO
100051002 DE SUSSEX CO
iOOOSToOS'DE SUSSEX CO
110010025 DC WASHINGTON
110010041 DC WASHINGTON
110010043 DC WASHINGTON
120013011 FL
120030002 FL
ALACHUACO
BAKER CO
120094001 FL BREVARD CO
120095001 FL BREVARD CO
120310070 FL DUVAL CO
RRF 2007 RRF 2020 R[
Area Name Base Base C(
CLAY CO, AL 0.821 1 0.7747
MONTGOMERY, AL 0.8784! 0.8301
BIRMINGHAM, AL 0.8765 0.8145
BIRMINGHAM, AL 0.8541! 0.8001
BIRMINGHAM, AL 0.8734! 0.8133
BIRMINGHAM, AL 0.8634 0.8103
BIRMINGHAM, AL 0.8728! 0.8154
LAWRENCE CO,' AL 0.84281 0.8010
HUNTSVILLE, AL 0.8743! 0.8272
MOBILE, AL 0.9107 0.8786
MOBILE, AL 0.9035! 0.8711
MONTGOMERY, AL 0.8835! 0.8398
BIRMINGHAM, AL 0.8632 0.8069
SUMTERCO, AL 0.8460! 0.8266
MEMPHIS^ TN'-A'R'-M'S' o"J9027l assgg
MONTGOMERY CO, AR 0.8917! 0.8432
NEWTON CO, AR 0.8744 0.8421
LITTLE ROCK-NORTH LITTLE ROCK, i 0.9008! 0.8425
LITTLE ROCK-NORTH LITTLE ROCK, t 0.9008! 0.8426
NEW YORK CMSA 0.9458! 0.9508
NEW YORK CMSA 0.9323! 0.9324
^^^^^^ »--»-»-»-»| 0.9197
NEW YORK CMSA 0.9396! 0.9437
HARTFORD, CT 0.9059 0.8735
HARTFORD, CT 0.8993! 0.8672
HARTFORD, CT 0.9197! 0.8974
NEW YORK CMSA 0.9274! 0.9148
NEW YORK CMSA 0.9165! 0.8956
^^^^^^ ---------1 0.8929
HARTFORD, CT 0.8935! 0.8528
PHILADELPHIA CMSA 0.8729! 0.8377
PHILADELPHIA CMSA 0.9003! 0.8823
PHILADELPHIA CMSA 0.8726! 0.8441
PHILADELPHIA CMSA 0.8933! 0.8733
SUSSEX CO, DE 0.8759! 0.8388
SUSSEX'CO, DE 0.88091 0.8473
WASHINGTON, DC-MD-VA-WV 0.9282! 0.9206
WASHINGTON, DC-MD-VA-WV 0.9022 0.8828
WASHINGTON, DC-MD-VA-WV 0.9282! 0.9206
GAINESVILLE, FL 0.8972! 0.8472
BAKER CO, FL 0.8909 0.8416
MELBOURNE-TITUSVILLE-PALM BAY, 0.9393! 0.8850
.^._..__..^._..^^^^ _.^ o.9407l 0.8882
JACKSONVILLE, FL 0.9173J 0.8499
RF 2020 RRF 2030 RRF 2030
>ntrol Base Control
0.7277; 0.7953 0.7304
0.7836 0.8546 0.7903
0.7594 0.8385! 0.7623
0.7532: 0.8208! 0.7555
0.7592 0.8370! 0.7621
0.7612 0.8354 1 0.7679
0.7625 0.8389! 0.7662
07628 6782241 "67700
0.7817 0.8493 0.7872
0.8464 0.9007! 0.8566
0.8385 0.8932! 0.8485
0.7936 0.8667! 0.8033
0.7546 0.8300! 0.7578
0.7986 0.8497! 0.8113
678720 6"."9"i"6"2"1 6""8"8"57
0.8071 0.8614 0.8118
0.8164 0.8580! 0.8224
0.7992 0.8674! 0.8071
0.7994 0.8675! 0.8072
0.9392 0.9690! 0.9550
0.9135: 0.9516J 0.9288
6"8958| 0.9423"! 67gTi5
0.931 1 ; 0.9622 0.9474
0.8332; 0.8978! 0.8447
0.8306: 0.8921! 0.8437
0.8657; 0.9226! 0.8812
0.8899; 0.9376! 0.9064
0.8663: 0.9210! 0.8822
6"8612| 0.g"T84"I 678771
0.8173; 0.8767 0.8283
0.7971; 0.8595! 0.8040
0.8509: 0.9020! 0.8596
0.8033 0.8667! 0.8110
0.8418 0.8933! 0.8504
0.7973 0.8622J 0.8053
asm 6786861 678184
0.8933 0.9410 0.9070
0.8562 0.9018! 0.8685
0.8933: 0.9410! 0.9070
0.7903; 0.8736! 0.7935
0.7923; 0.8652! 0.7965
0.8357: 0.9145! 0.8446
6"8409| o.gTeel 6784"gg
0.7976; 0.8761 0.8025
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
120310077 FL
120330004 FL
120330018 FL
120330024 FL
120570081 FL
120571035 FL
120571065 FL
120590004 FL
120712001 FL
120713002 FL
120813002 FL
120814010 FL
120950008 FL
120952002 FL
120972002 FL
120990007 FL
120992004 FL
121012001 FL
121030004 FL
121030018 FL
121035002 FL
121056005 FL
121056006 FL
121111002 FL
121151002 FL
121151005 FL
121171002 FL
121272001 FL
121275002 FL
130210012 GA
130510021 GA
130850001 GA
130890002 GA
130893001 GA
130970004 GA
131110094 GA
131130001 GA
131210055 GA
131350002 GA
132150008 GA
132151003 GA
132230003 GA
132450091 GA
132470001 GA
County
DUVAL CO
ESCAMBIA CO
ESCAMBIA CO
ESCAMBIA CO
HILLSBOROUGH CO
HILLSBOROUGH CO
HILLSBOROUGH CO
HOLMES CO
LEE CO
LEE CO
MANATEE CO
MANATEE CO
ORANGE CO
ORANGE CO
OSCEOLA CO
PALM BEACH CO
PALM BEACH CO
PASCO CO
PINELLAS CO
PINELLAS CO
PINELLAS CO
POLK CO
POLK CO
ST LUCIE CO
SARASOTA CO
SARASOTA CO
SEMINOLECO
VOLUSIACO
VOLUSIACO
BIBB CO
CHATHAM CO
DAWSON CO
DE KALB CO
DE KALB CO
DOUGLAS CO
FANNIN CO
FAYETTE CO
FULTON CO
GWINNETT CO
MUSCOGEECO
MUSCOGEECO
PAULDINGCO
RICHMOND CO
ROCKDALE CO
RRF 2007
Area Name Base
JACKSONVILLE, FL 0.9117!
PENSACOLA, FL 0.9224!
^ENSACOLA^FL ^^PJ3??^
PENSACOLA, FL 0.9223!
TAMPA-ST. PETERSBURG-CLEARWA 0.9513i
TAMPA-ST. PETERSBURG-CLEARWA 0.9524!
TAMPA-ST. PETERSBURG-CLEARWA 0.9656!
HOLMES CO, FL 0.9059
FORT MYERS-CAPE CORAL, FL 0.9655!
fORIM^§RS^Ar^F^pr^Aj-LfL^^^^_^^a9^44|
SARASOTA-BRADENTON, FL 0.9590!
SARASOTA-BRADENTON, FL 0.9496
ORLANDO, FL 0.9370!
ORLANDO, FL 0.9368!
ORLANDO, FL 0.9328
MIAMI CMSA 0.9260!
MIAMI CMSA 0.9237!
TAMPA-ST. PETERSBURG-CLEARWA 0.9499
TAMPA-ST. PETERSBURG-CLEARWA 0.9688!
TAMPA-ST. PETERSBURG-CLEARWA 0.9726
TAMPA-ST. PETERSBURG-CLEARWA 0.9548!
LAKELAND-WINTER HAVEN, FL 0.9370
LAKELAND-WINTER HAVEN, FL 0.941 1 !
£°?LP!§RcJiPP^L§IJzycJE1£L_^^_^^a.9^i^|
SARASOTA-BRADENTON, FL 0.9459!
SARASOTA-BRADENTON, FL 0.9459
ORLANDO, FL 0.9297!
DAYTONA BEACH, FL 0.9150!
DAYTONA BEACH, FL 0.9108
MACON, GA 0.8144!
^AVANNAH^GA ^^PJ3?^.
DAWSON CO, GA 0.8365!
ATLANTA, GA 0.8898
ATLANTA, GA 0.9073!
ATLANTA, GA 0.8781 1
FANNIN CO, GA 0.8221
ATLANTA, GA 0.8700!
ATLANTAJ3A ^^PJ3??^
ATLANTA, GA 0.8766 !
COLUMBUS, GA-AL 0.8694
COLUMBUS, GA-AL 0.8694!
ATLANTA, GA 0.8432!
AUGUSTA-AIKEN, GA-SC 0.8531
ATLANTA, GA 0.8660!
RRF 2020 RRF 2020
Base Control
0.8570 0.8100
0.8859 0.8480
0.8857 0.8486
0.8857 0.8486
0.9105 0.8676
0.9090 0.8621
0.9336 0.8895
0.8648 0.8232
0.9179 0.8719
0.9146 0.8664
0.9292 0.8875
0.9108 0.8641
0.8892 0.8341
0.8889 0.8330
0.8846 0.8330
0.8553 0.8086
0.8512 0.8009
0.9020 0.8475
0.941 1 0.8979
0.9505 0.9093
0.9141 0.8642
0.8854 0.8374
0.8955 0.8491
0.9068 0.8640
0.9058 0.8600
0.9058 0.8600
0.8762 0.8184
0.8603 0.8061
0.8575 0.7999
0.7683 0.7188
0.8665 0.8282
0.7706 0.7007
0.8497 0.7965
0.8728 0.8190
0.8232 0.7641
0.7641 0.7038
0.8117 0.7458
0.8626 0.8075
0.8119 0.7360
0.8047 0.7473
0.8047 0.7473
0.7945 0.7399
0.7869 0.7367
0.8018 0.7275
RRF 2030
Base
0.8831
0.9086
0.9085
0.9085
0.9390
0.9372
0.9630
0.8880
0.9481
0.9468
0.9609
0.9445
0.9233
0.9236
0.9159
0.8868
0.8849
0.9320
0.9710
0.9808
0.9440
0.9121
0.9209
0.9340
0.9393
0.9393
0.9100
0.8876
0.8857
0.7903
0.8900
0.8022
0.8784
0.9018
0.8551
0.7892
0.8443
0.8925
0.8458
0.8313
0.8313
0.8206
0.8149
0.8328
RRF 2030
Control
0.8177
0.8564
0.8573
0.8573
0.8792
0.8720
0.9018
0.8303
0.8822
0.8780
0.9035
0.8803
0.8482
0.8477
0.8433
0.8212
0.8146
0.8563
0.9111
0.9238
0.8747
0.8447
0.8558
0.8733
0.8759
0.8759
0.8299
0.8115
0.8048
0.7222
0.8375
0.7056
0.8081
0.8318
0.7747
0.7057
0.7546
0.8200
0.7433
0.7526
0.7526
0.7458
0.7458
0.7310
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
132611001 GA
170010006 IL
170190004 IL
170310001 IL
170310032 IL
170310050 IL
170310063 IL
170310064 IL
170310072 IL
170311003 IL
170311601 IL
170314002 IL
170314006 IL
170314201 IL
170317002 IL
170318003 IL
170436001 IL
170491001 IL
170650001 IL
170831001 IL
170890005 IL
170970001 IL
170971002 IL
170971007 IL
170973001 IL
171110001 IL
171150013 IL
171170002 IL
171190008 IL
171191009 IL
171192007 IL
171193007 IL
171430024 IL
171431001 IL
171570001 IL
171610003 IL
171630010 IL
171670010 IL
171971008 IL
171971011 IL
172010009 IL
172012001 IL
180030002 IN
180030004 IN
RRF 2007 RR
County Area Name Base Bas
SUMTERCO {SUMTERCO, GA 0.8616!
ADAMS CO {ADAMS CO, IL 0.8903!
^HAMPAIGNKX) iCHAjyiPAJGN-URBMIAJL ^^^^§658^
COOK CO | CHICAGO CMSA 0.9265!
COOK co (CHICAGO CMSA 0.9111 i
COOK CO {CHICAGO CMSA 0.9111 1
COOK CO [CHICAGO CMSA 0.9163!
COOK co [CHICAGO CMSA o.giesi
COOK CO {CHICAGO CMSA 0.9363!
COOK CO {CHICAGO CMSA 0.9022!
COOK CO {CHICAGO CMSA 0.9292!
COOK co (CHICAGO CMSA 0.9114!
COOK CO (CHICAGO CMSA 0.9310(
COOK CO (CHICAGO CMSA 0.9171 1
COOK co (CHICAGO CMSA 0.9171!
COOK CO (CHICAGO CMSA ! 0.9030J
DU PAGE CO iCHICAGOCMSA | 0.9390!
EFFINGHAMCO iEFFINGHAM CO, IL ! 0.8431
HAMILTON CO {HAMILTON CO, IL | 0.8280!
JERSEY CO (ST. LOUIS, MO-IL ! 0.8936{
KANE co ICHICAGOCMSA o.94i7{
LAKE co ICHICAGOCMSA 0.91 93 1
LAKE CO {CHICAGO CMSA 0.9168{
LAKE CO {CHICAGO CMSA 0.9250{
LAKE CO {CHICAGO CMSA 0.9165{
MC HENRY CO iCHICAGOCMSA 0.9389(
MACONCO (DECATUR, IL 0.8580{
MACOUPINCO [ST. LOUIS, MO-IL 0.8536!
MADISON CO (ST. LOUIS, MO-IL 0.8922
MADISON CO (ST. LOUIS, MO-IL 0.8929{
jyiADisoNjX) (STj-oyisjyio-iL ^^PJ^^J^L
MADISON CO {ST. LOUIS, MO-IL 0.8992{
PEORIACO [PEORIA-PEKIN, IL 0.9056
PEORIACO {PEORIA-PEKIN, IL 0.9056{
RANDOLPH CO [RANDOLPH CO, IL 0.8538!
ROCK ISLAND CO I DAVENPORT-MOLINE-ROCK ISLAND, 0.9264
ST CLAIR CO {ST. LOUIS, MO-IL 0.9078{
J5ANGAMONKX) [SPRINGFIELDJL ^^PJ^^SJ^L
WILL CO {CHICAGO CMSA 0.9281 {
WILL co ICHICAGOCMSA o.sgosl
WINNEBAGOCO (ROCKFORD, IL 0.9129{
WINNEBAGOCO [ROCKFORD, IL 0.9059!
ALLENCO IFORTWAYNE, IN 0.3919
ALLEN CO {FORT WAYNE, IN 0.8954{
F2020 RRF 2020 RRF 2030 RRF 2030
>e Control Base Control
0.8145 0.7635! 0.8383! 0.7680
0.8575 0.8329 0.8745 { 0.8405
0.8318 0.8023 0.851 1{ 0.8105
0.9462 0.9537 0.961 0{ 0.9730
0.9071 0.8958 0.9240 1 0.9094
0.9071 0.8958: 0.9240 { 0.9094
0.9165 0.9087! 0.9325! 0.9226
0.9165 0.9087! 0.9325 1 0.9226
0.9444 0.9451! 0.9592{ 0.9607
0.8897 0.8963! 0.9046 { 0.9124
0.9254 0.9183! 0.9422 { 0.9333
0.8994 0.8883! 0.91661 0.9020
0.9423 0.9612 0.9534{ 0.9811
0.9268 0.9302 0.9408! 0.9472
0.9268 0.9302 0.9408 1 0.9472
0.9094! 0.9014! 0.9257 { 0.9158
0.9441 0.9391 0.9594 0.9559
0.8059 0.7762 0.8240 0.7828
0.7748 0.7501 0.7887 0.7545
0.8451 0.8014 0.8713 0.8117
0.9441 0.9427 0.9619! 0.9624
0.9200 0.9314 0.9339 1 0.9508
0.9044 0.8904 0.9237 { 0.9067
0.9226 0.9149 0.941 7 { 0.9327
0.9100 0.9014 0.9282 { 0.9181
0.9404 0.9368 0.9591 1 0.9563
0.8280 0.8011 0.8471 { 0.8097
0.8140 0.7763 0.8390! 0.7869
0.8501 0.8123 0.8732 1 0.8213
0.8521 0.8126 0.8745 { 0.8205
0.8570 0.8177 0.8809 { 0.8281
0.8570 0.8177 0.8809 { 0.8281
0.8803 0.8570 0.8970 1 0.8653
0.8803 0.8570! 0.8970 { 0.8653
0.8230 0.7939! 0.8400! 0.7996
0.8983 0.8772! 0.91 63 1 0.8876
0.8693 0.8298! 0.891 9 { 0.8381
0.8297 0.7961! 0.8509 { 0.8046
0.9268 0.9139 0.9449 { 0.9288
0.8722 0.8503 0.8903 1 0.8611
0.8875 0.8604 0.9095 { 0.8728
0.8783 0.8497 0.9005! 0.8620
0.8557 0.8268 0.8750 1 0.8349
0.8602 0.8306! 0.8797 { 0.8389
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
180190003 IN
180390002 IN
180431004 IN
180571001 IN
180590003 IN
180810002 IN
180890022 IN
180890024 IN
180892008 IN
180910005 IN
180910010 IN
180950010 IN
180970042 IN
180970050 IN
180970057 IN
180970073 IN
181090005 IN
181230008 IN
181270020 IN
181270024 IN
181270026 IN
181290003 IN
181410010 IN
181411007 IN
181411008 IN
181630012 IN
181630013 IN
181670018 IN
181730002 IN
181730008 IN
181730009 IN
190851101 IA
191130033 IA
191131015 IA
191530058 IA
191632011 IA
191690011 IA
191770004 I A
191810022 IA
201730001 KS
201730010 KS
202090001 KS
210130002 KY
210150003 KY
County
CLARK CO
ELKHART CO
FLOYD CO
HAMILTON CO
HANCOCK CO
JOHNSON CO
LAKE CO
LAKE CO
LAKE CO
LA PORTE CO
LA PORTE CO
MADISON CO
MARION CO
MARION CO
MARION CO
MARION CO
MORGAN CO
PERRY CO
PORTER CO
PORTER CO
PORTER CO
POSEY CO
ST JOSEPH CO
ST JOSEPH CO
ST JOSEPH CO
VANDERBURGH CO
VANDERBURGH CO
VIGO CO
WARRICKCO
WARRICKCO
WARRICKCO
HARRISON CO
LINN CO
LINN CO
POLK CO
SCOTT CO
STORY CO
VAN BUREN CO
WARREN CO
SEDGWICKCO
SEDGWICKCO
WYANDOTTE CO
BELL CO
BOONECO
RRF 2007 RRF 2020
Area Name Base Base
LOUISVILLE, KY-IN 0.8857! 0.8729
ELKHART-GOSHEN, IN 0.8810! 0.8471
J-OyiSVILLEJKY-IN i____0.891_4j 0.8859^
INDIANAPOLIS, IN 0.8918! 0.8620
INDIANAPOLIS, IN 0.8899 0.8621
INDIANAPOLIS, IN 0.8542! 0.8227
CHICAGO CMSA 0.9042! 0.8940
CHICAGO CMSA 0.8893i 0.8669
CHICAGO CMSA 0.9049! 0.9015
J_ALPORTEJX>JN i____0.918qj 0.9026^
LA PORTE CO, IN 0.9104! 0.8892
INDIANAPOLIS, IN 0.8833 0.8466
INDIANAPOLIS, IN 0.8865! 0.8648
INDIANAPOLIS, IN 0.8960! 0.8802
INDIANAPOLIS, IN 0.9062 0.9035
INDIANAPOLIS, IN ! 0.8960! 0.8802
INDIANAPOLIS, IN | 0.8688! 0.8431
PERRY CO, IN | 0.8203! 0.8058
CHICAGO CMSA ! 0.9161! 0.9077
CHICAGO CMSA ! 0.9042! 0.8940
CHICAGO CMSA 0.9246! 0.9113
EVANSVILLE-HENDERSON, IN-KY 0.8773 0.8506
SOUTH BEND, IN 0.8853! 0.8547
J5OUTHJ3§NDJN i____0.8889j 0.8620^
SOUTH BEND, IN 0.8889! 0.8622
EVANSVILLE-HENDERSON, IN-KY 0.8800 0.8529
EVANSVILLE-HENDERSON, IN-KY 0.8633! 0.8364
TERRE HAUTE, IN 0.8695! 0.8393
EVANSVILLE-HENDERSON, IN-KY 0.8336 0.8140
EVANSVILLE-HENDERSON, IN-KY 0.8315! 0.8109
JVA^SyiLLE-IHENDER^ 0.8188^
HARRISON CO, IA 0.9155! 0.8819
CEDAR RAPIDS, IA 0.9185 0.8886
CEDAR RAPIDS, IA 0.9183! 0.8919
DES MOINES, IA 0.9051 | 0.8646
DAVENPORT-MOLINE-ROCK ISLAND, 0.9283 0.9011
STORY CO, IA 0.9052! 0.8657
VANJ3URENJX)JA i____0.9032j 0.8725^
DES MOINES, IA 0.8980! 0.8596
WICHITA, KS 0.9406 0.8963
WICHITA, KS 0.9408! 0.8966
KANSAS CITY, MO-KS 0.9366 1 0.9083
BELL CO, KY 0.7997 0.7395
CINCINNATI CMSA 0.8478! 0.8205
RRF 2020 RRF 2030 RRF 2030
Control Base Control
0.8538! 0.8892! 0.8638
0.8189 0.8655! 0.8266
0.8731 0.9022! 0.8858
0.8332 0.8819! 0.8428
0.8342 0.8812! 0.8436
0.7932: 0.8402! 0.7997
0.8778! 0.9115! 0.8902
0.8434! 0.8841 I 0.8521
0.8881! 0.9191! 0.9018
0.8855! 0.9207! 0.8974
0.8677! 0.9070! 0.8781
0.8144! 0.8673! 0.8229
0.8411 0.8825! 0.8501
0.8603 0.8982! 0.8716
0.8873 0.9202! 0.8992
0.8603! 0.8982! 0.8716
0.81811 0.8602! 0.8260
0.7842! 0.8189! 0.7889
0.8943! 0.9251! 0.9069
0.8778! 0.9115! 0.8902
0.8938! 0.9281! 0.9045
0.8325! 0.8659! 0.8408
0.8278! 0.8725! 0.8358
0.8368! 0.8796! 0.8450
0.8377! 0.8801! 0.8467
0.8311! 0.8685! 0.8382
0.8140! 0.8511! 0.8201
0.8134 0.8550! 0.8191
0.7921 0.8275! 0.7969
0.7887 0.8243! 0.7935
0.7962 0.8329! 0.8015
0.8590 0.8980! 0.8662
0.8653 0.9069! 0.8749
0.8703! 0.9108! 0.8814
0.8360! 0.8839! 0.8445
0.8792! 0.9194! 0.8892
0.8377! 0.8847! 0.8460
0.8489! 0.8899! 0.8572
0.8316 0.8784! 0.8398
0.8669 0.9157! 0.8752
0.8673 0.9159! 0.8756
0.8848 0.9264! 0.8943
0.6929 0.7561! 0.6914
0.7965! 0.8383! 0.8055
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
210190015 KY
210290006 KY
210371001 KY
210430500 KY
210470006 KY
210590005 KY
210610501 KY
210670001 KY
210670012 KY
210830003 KY
210890007 KY
210910012 KY
210930005 KY
211010013 KY
211010014 KY
211110027 KY
211110051 KY
211111021 KY
211130001 KY
21 1 1 70007 KY
21 1 390003 KY
21 1 390004 KY
211451024 KY
211490001 KY
211850004 KY
21 1 930002 KY
21 1 950002 KY
211990003 KY
212090001 KY
212130004 KY
220050004 LA
220110002 LA
220150008 LA
220170001 LA
220190002 LA
220190008 LA
220190009 LA
220330003 LA
220330009 LA
220330013 LA
220331001 LA
220430001 LA
220470002 LA
220470007 LA
County
BOYD CO
BULLITTCO
CAMPBELL CO
CARTER CO
CHRISTIAN CO
DAVIESS CO
EDMONSONCO
FAYETTE CO
FAYETTE CO
GRAVES CO
GREENUPCO
HANCOCK CO
HARDIN CO
HENDERSON CO
HENDERSON CO
JEFFERSON CO
JEFFERSON CO
JEFFERSON CO
JESSAMINE CO
KENTON CO
LIVINGSTON CO
LIVINGSTON CO
MC CRACKEN CO
MC LEAN CO
OLDHAM CO
PERRY CO
PIKE CO
PULASKI CO
SCOTT CO
SIMPSON CO
ASCENSION PAR
BEAUREGARD PAR
BOSSIER PAR
CADDO PAR
CALCASIEU PAR
CALCASIEU PAR
CALCASIEU PAR
EAST BATON ROUGE PAR
EAST BATON ROUGE PAR
EAST BATON ROUGE PAR
EAST BATON ROUGE PAR
GRANT PAR
IBERVILLE PAR
IBERVILLE PAR
RRF 2007 RRF 2020
Area Name Base Base
HUNTINGTON-ASHLAND, WV-KY-OH 0.8522! 0.8276
LOUISVILLE, KY-IN 0.8603! 0.8479
CINCINNATI CMSA 0.8988! 0.8804
HUNTINGTON-ASHLAND, WV-KY-OH 0.8055! 0.7818
CHRISTIAN CO, KY 0.7743 0.7452
OWENSBORO, KY 0.8295! 0.8131
EDMONSON CO, KY 0.7963! 0.7700
LEXINGTON, KY 0.8567 0.8365
LEXINGTON, KY 0.8702! 0.8477
.GjRAVESJXUKY i____0.841_4j 07950^
HUNTINGTON-ASHLAND, WV-KY-OH 0.8454! 0.8238
OWENSBORO, KY 0.8107 0.7966
HARDIN CO, KY 0.8262! 0.8077
EVANSVILLE-HENDERSON, IN-KY 0.8737! 0.8523
EVANSVILLE-HENDERSON, IN-KY 0.8559 0.8375
LOUISVILLE, KY-IN ! 0.8918! 0.8929
LOUISVILLE, KY-IN | 0.8929! 0.8866
LOUISVILLE, KY-IN ! 0.8946! 0.8846
LEXINGTON, KY | 0.8684! 0.8485
CINCINNATI CMSA ! 0.8940! 0.8787
LIVINGSTON CO, KY 0.8319! 0.7814
LIVINGSTON CO, KY 0.8306 0.7834
MC CRACKEN CO, KY 0.8341 ! 0.7802
JVICJ-EANJXU
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
220470009 LA
220511001 LA
220550005 LA
220570002 LA
220630002 LA
220710012 LA
220730004 LA
220770001 LA
220870002 LA
220890003 LA
220930002 LA
220950002 LA
221010003 LA
221210001 LA
230052003 ME
230090102 ME
230090103 ME
230112005 ME
230130004 ME
230173001 ME
230194008 ME
230230003 ME
230312002 ME
230313002 ME
240030014 MD
240030019 MD
240051007 MD
240053001 MD
240090010 MD
240130001 MD
240150003 MD
240170010 MD
240210037 MD
240251001 MD
240259001 MD
240290002 MD
240313001 MD
240330002 MD
240338001 MD
245100051 MD
250010002 MA
250051002 MA
250051005 MA
250090005 MA
County
IBERVILLE PAR
JEFFERSON PAR
LAFAYETTE PAR
LAFOURCHE PAR
LIVINGSTON PAR
ORLEANS PAR
OUACHITA PAR
POINTECOUPEEPAR
ST BERNARD PAR
ST CHARLES PAR
ST JAMES PAR
ST JOHN THE BAPTIST PAR
ST MARY PAR
WEST BATON ROUGE PAR
CUMBERLAND CO
HANCOCK CO
HANCOCK CO
KENNEBEC CO
KNOX CO
OXFORD CO
PENOBSCOT CO
SAGADAHOC CO
YORK CO
YORK CO
ANNEARUNDELCO
ANNEARUNDELCO
BALTIMORE CO
BALTIMORE CO
CALVERT CO
CARROLL CO
CECIL CO
CHARLES CO
FREDERICK CO
HARFORD CO
HARFORD CO
KENT CO
MONTGOMERY CO
PRINCE GEORGES CO
PRINCE GEORGES CO
BALTIMORE
BARNSTABLE CO
BRISTOL CO
BRISTOL CO
ESSEX CO
RRF 2007 RRF 2020
Area Name Base Base
BATON ROUGE, LA 0.9659! 0.9372
NEWORLEANS, LA 0.9510! 0.9279
J-AFAYETTEJ.A i____0.9468j 0-9061
HOUMA, LA 0.9616! 0.9382
BATON ROUGE, LA 0.9626 0.9369
NEWORLEANS, LA 0.9469! 0.9409
MONROE, LA 0.9421! 0.9137
BATON ROUGE, LA 0.9466 0.9007
NEWORLEANS, LA 0.9534! 0.9396
J\IEVVORLEANSJ-A i___j0.9529j 0.9464^
BATON ROUGE, LA 0.9629! 0.9394
NEWORLEANS, LA 0.9592 0.9392
ST MARY PAR, LA 0.9656! 0.9448
BATON ROUGE, LA 0.9513! 0.9077
PORTLAND, ME 0.9038 0.8662
HANCOCK CO, ME ! 0.8928! 0.8448
HANCOCK CO, ME | 0.8928! 0.8448
LEWISTON-AUBURN, ME ! 0.8970! 0.8492
KNOX CO, ME | 0.8984! 0.8576
OXFORD CO, ME ! 0.9190! 0.8980
PENOBSCOT CO, ME 0.8873! 0.8379
SAGADAHOC CO, ME 0.9005 0.8652
PORTLAND, ME 0.9030! 0.8690
^ORTLANDJVIE i____0.9128j 0.8793^
BALTIMORE, MD 0.8853! 0.8518
BALTIMORE, MD 0.9012 0.8707
BALTIMORE, MD 0.9071! 0.8816
BALTIMORE, MD 0.9162! 0.8985
WASHINGTON, DC-MD-VA-WV 0.8663 0.8252
BALTIMORE, MD 0.8873! 0.8569
PHILADELPHIA CMSA 0.8783! 0.8488
WASHINGTON, DC-MD-VA-WV 0.8614! 0.8171
WASHINGTON, DC-MD-VA-WV 0.8822 0.8488
BALTIMORE, MD 0.9129! 0.8885
BALTIMORE, MD 0.9021 1 0.8734
PHILADELPHIA CMSA 0.8826i 0.8576
WASHINGTON, DC-MD-VA-WV 0.9097! 0.8907
VW^HINGTON^C-Mp-VA-W\,^^ 0.8707^
WASHINGTON, DC-MD-VA-WV 0.8922! 0.8731
BALTIMORE, MD 0.9084 0.8903
BOSTON CMSA 0.9026! 0.8622
BOSTON CMSA 0.8972! 0.8662
BOSTON CMSA 0.8983 i 0.8530
BOSTON CMSA 0.9128! 0.8863
RRF 2020 RRF 2030 RRF 2030
Control Base Control
0.9208! 0.9581 ! 0.9363
0.9107 0.9451! 0.9226
0.8814 0.9259! 0.8927
0.9211 0.9596! 0.9369
0.9185 0.9599! 0.9353
0.9303: 0.9527! 0.9390
0.8900! 0.9323! 0.8995
0.8719! 0.9226 1 0.8845
0.9260! 0.9551! 0.9371
0.9343! 0.9620! 0.9464
0.9221! 0.9613! 0.9385
0.9224! 0.9593! 0.9374
0.9307 0.9633! 0.9445
0.8809 0.9308! 0.8956
0.8281 0.891 5 1 0.8399
0.8011! 0.8716! 0.8123
0.80111 0.8716! 0.8123
0.8076! 0.8755! 0.8188
0.8163! 0.8834! 0.8276
0.8651! 0.9207! 0.8774
0.7965! 0.8641! 0.8075
0.8251 ! 0.8901 I 0.8366
0.8299! 0.8934! 0.8414
0.8453! 0.9020! 0.8566
0.8089! 0.8764! 0.8185
0.8293! 0.8953! 0.8407
0.8468! 0.9040! 0.8575
0.8667 0.9188! 0.8779
0.7814 0.8483 1 0.7883
0.8147 0.8802! 0.8249
0.8060 0.8726! 0.8141
0.7689 0.8403! 0.7746
0.8061 0.8731 1 0.8160
0.8543! 0.9099! 0.8642
0.8321! 0.8974! 0.8413
0.8199! 0.8795 1 0.8282
0.8535! 0.9149! 0.8671
0.8293! 0.8953! 0.8407
0.8464 0.8909! 0.8562
0.8602 0.91 101 0.8723
0.8214 0.8884! 0.8337
0.8283 0.8941 [ 0.8434
0.8097 0.8787 1 0.8205
0.8592! 0.9069! 0.8694
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
250092006 MA
250094004 MA
250130003 MA
250130008 MA
250150103 MA
250154002 MA
250171102 MA
250171801 MA
250174003 MA
250251003 MA
250270015 MA
260050003 Ml
260190003 Ml
260210014 Ml
260270003 Ml
260370001 Ml
260490021 Ml
260492001 Ml
260630007 Ml
260650012 Ml
260770008 Ml
260810020 Ml
260812001 Ml
260910007 Ml
260990009 Ml
260991003 Ml
261050007 Ml
261130001 Ml
261210039 Ml
261250001 Ml
261390005 Ml
261470005 Ml
261610007 Ml
261630001 Ml
261630016 Ml
261630019 Ml
270031001 MN
270031002 MN
270376018 MN
280010004 MS
280330002 MS
280450001 MS
280490010 MS
280590006 MS
County
ESSEX CO
ESSEX CO
HAMPDEN CO
HAMPDEN CO
HAMPSHIRE CO
HAMPSHIRE CO
MIDDLESEX CO
MIDDLESEX CO
MIDDLESEX CO
SUFFOLK CO
WORCESTER CO
ALLEGAN CO
BENZIE CO
BERRIENCO
CASS CO
CLINTON CO
GENESEECO
GENESEECO
HURON CO
INGHAM CO
KALAMAZOO CO
KENT CO
KENT CO
LENAWEE CO
MACOMB CO
MACOMB CO
MASON CO
MISSAUKEE CO
MUSKEGON CO
OAKLAND CO
OTTAWA CO
ST CLAIR CO
WASHTENAWCO
WAYNE CO
WAYNE CO
WAYNE CO
ANOKACO
ANOKACO
DAKOTA CO
ADAMS CO
DE SOTO CO
HANCOCK CO
HINDS CO
JACKSON CO
RRF 2007 RRF 2020
Area Name Base Base
BOSTON CMSA 0.9226! 0.8943
BOSTON CMSA 0.9180! 0.8802
^PRINGFIELDJVIA i____0.9100j 0.8820^
SPRINGFIELD, MA 0.9120! 0.8863
SPRINGFIELD, MA 0.9275 0.8991
SPRINGFIELD, MA 0.9262! 0.9033
BOSTON CMSA 0.9067! 0.8780
BOSTON CMSA 0.9067 i 0.8780
BOSTON CMSA 0.9148! 0.8875
BOSTON CMSA 0.9068! 0.8725
WORCESTER, MA-CT 0.9021 ! 0.8649
ALLEGAN CO, Ml 0.9196 0.9023
BENZIE CO, Ml 0.9080! 0.8779
BENTON HARBOR, Ml 0.9019! 0.8740
CASS CO, Ml 0.8844 0.8528
LANSING-EAST LANSING, Ml ! 0.9092! 0.8804
FLINT, Ml | 0.9051! 0.8759
FLINT, Ml | 0.9016! 0.8679
HURON CO, Ml | 0.9174! 0.8978
LANSING-EAST LANSING, Ml ! 0.9003! 0.8736
KALAMAZOO-BATTLE CREEK, Ml 0.8917! 0.8612
GRAND RAPIDS-MUSKEGON-HOLLAh 0.9041 i 0.8781
GRAND RAPIDS-MUSKEGON-HOLLAh 0.9040! 0.8726
J-ENAWEEJXljyil i____0.91_15j 0.8825^
DETROIT CMSA 0.9387! 0.9447
DETROIT CMSA 0.9480i 0.9580
MASON CO, Ml 0.9074! 0.8823
MISSAUKEE CO, Ml 0.8944! 0.8593
GRAND RAPIDS-MUSKEGON-HOLLAh 0.9185i 0.8984
DETROIT CMSA 0.9391 ! 0.9641
GRAND RAPIDS-MUSKEGON-HOLLAh 0.9123! 0.8891
DETROIT CMSA 0.9197! 0.9061
DETROIT CMSA 0.9230i 0.9048
DETROIT CMSA 0.9292! 0.9227
DETROIT CMSA 0.9305! 0.9388
DETROIT CMSA 0.9305i 0.9388
MINNEAPOLIS-ST. PAUL, MN-WI 0.9440! 0.9090
jyilNNEAPOUS-ST^^ 0.9258^
MINNEAPOLIS-ST. PAUL, MN-WI 0.9254! 0.9074
ADAMS CO, MS 0.9361 0.8952
MEMPHIS, TN-AR-MS 0.8943! 0.8534
BILOXI-GULFPORT-PASCAGOULA, Ml 0.9546! 0.9149
JACKSON, MS 0.9069 0.8556
BILOXI-GULFPORT-PASCAGOULA, Mj 0.9427! 0.9226
RRF 2020 RRF 2030 RRF 2030
Control Base Control
0.8622! 0.9145! 0.8726
0.8451 0.9041 1 0.8572
0.8491 0.9062! 0.8625
0.8577 0.9080! 0.8710
0.8706 0.9228! 0.8835
0.8739 0.9248! 0.8868
0.8453 0.8969! 0.8549
0.8453 0.8969! 0.8549
0.8553 0.9048! 0.8638
0.8409 0.8922! 0.8493
0.8264 0.8880! 0.8368
0.8821 0.9230 1 0.8968
0.8466 0.8991 ! 0.8570
0.8461 0.8953! 0.8573
0.8233 0.8721 1 0.8317
0.8507: 0.9012! 0.8609
0.8456! 0.8961! 0.8551
0.8396! 0.8875! 0.8495
0.8757! 0.9164! 0.8869
0.8474: 0.8933! 0.8578
0.8309: 0.8813! 0.8401
0.8515: 0.8989 1 0.8632
0.8422: 0.8941! 0.8525
0.8553! 0.9020! 0.8653
0.9392: 0.9606! 0.9546
0.9656: 0.9723! 0.9811
0.8545! 0.9044! 0.8668
0.8296 0.8783! 0.8375
0.8783 0.91 83 1 0.8910
0.9823 0.9757! 0.9997
0.8652 0.9097! 0.8783
0.8849 0.9263! 0.8990
0.8839 0.9229! 0.8958
0.9282! 0.9414! 0.9448
0.9479! 0.9508! 0.9634
0.9479! 0.9508! 0.9634
0.8822! 0.9305! 0.8950
0.9167! 0.9378! 0.9272
0.8926 0.9208! 0.9018
0.8702 0.91 71 I 0.8831
0.8233 0.8739! 0.8326
0.8908 0.9320! 0.8995
0.8124 0.8782! 0.8193
0.8978! 0.9436! 0.9098
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
280750003 MS
280810005 MS
280890002 MS
281490004 MS
290390001 MO
290470003 MO
290470005 MO
290470025 MO
290770026 MO
290770036 MO
290950036 MO
290990012 MO
291370001 MO
291650023 MO
291831002 MO
291831004 MO
291860005 MO
291890004 MO
291890006 MO
291893001 MO
291895001 MO
291897002 MO
295100007 MO
295100072 MO
295100080 MO
310550028 NE
310550032 NE
310550035 NE
311090016 NE
330012003 NH
330031002 NH
330050007 NH
330090008 NH
330110016 NH
330111010 NH
330130007 NH
330150009 NH
330150012 NH
330173002 NH
330190003 NH
340010005 NJ
340070003 NJ
340071001 NJ
340110007 NJ
County
LAUDERDALE CO
LEE CO
MADISON CO
WARREN CO
CEDAR CO
CLAY CO
CLAY CO
CLAY CO
GREENE CO
GREENE CO
JACKSON CO
JEFFERSON CO
MONROE CO
PLATTE CO
ST CHARLES CO
ST CHARLES CO
STE GENEVIEVE CO
ST LOUIS CO
ST LOUIS CO
ST LOUIS CO
ST LOUIS CO
ST LOUIS CO
ST LOUIS
ST LOUIS
ST LOUIS
DOUGLAS CO
DOUGLAS CO
DOUGLAS CO
LANCASTER CO
BELKNAP CO
CARROLL CO
CHESHIRE CO
GRAFTON CO
HILLSBOROUGHCO
HILLSBOROUGHCO
MERRIMACKCO
ROCKINGHAM CO
ROCKINGHAM CO
STRAFFORD CO
SULLIVAN CO
ATLANTIC CO
CAMDEN CO
CAMDEN CO
CUMBERLAND CO
RRF 2007 RRF 2020
Area Name Base Base
LAUDERDALE CO, MS 0.8624! 0.8265
LEE CO, MS 0.8347! 0.7886
^JACKSONJVIS i____0.9322j 0.9075
WARREN CO, MS 0.9453! 0.9220
CEDAR CO, MO 0.9287 0.8852
KANSAS CITY, MO-KS 0.9176! 0.8770
KANSAS CITY, MO-KS 0.9270! 0.8917
KANSAS CITY, MO-KS 0.9244 0.8905
SPRINGFIELD, MO 0.8574! 0.8097
^PRINGFIELDJVIO i____0.8567j 0.8091^
KANSAS CITY, MO-KS 0.9260! 0.8946
ST. LOUIS, MO-IL 0.8919 0.8405
MONROE CO, MO 0.8910! 0.8566
KANSAS CITY, MO-KS 0.9381 | 0.9096
ST. LOUIS, MO-IL 0.9032 0.8577
ST. LOUIS, MO-IL | 0.8825! 0.8206
STE GENEVIEVE CO, MO | 0.8689! 0.8299
ST. LOUIS, MO-IL | 0.9096! 0.8653
ST. LOUIS, MO-IL | 0.9126! 0.8719
ST. LOUIS, MO-IL | 0.9126! 0.8719
ST. LOUIS, MO-IL 0.9071 | 0.8630
ST. LOUIS, MO-IL 0.9093 0.8639
ST. LOUIS, MO-IL 0.9082! 0.8661
jSTJ-OUISjyiO-IL i____0.9147j 0.8753^
ST. LOUIS, MO-IL 0.9071 ! 0.8630
OMAHA, NE-IA 0.9204 0.8874
OMAHA, NE-IA 0.9196! 0.8843
OMAHA, NE-IA 0.9204! 0.8874
LINCOLN, NE 0.9190 0.8823
BELKNAP CO, NH 0.9263! 0.8963
^ARROLLCOJSIH i____0.9102j 0.881:3,
CHESHIRE CO, NH 0.8926! 0.8451
GRAFTON CO, NH 0.8885 0.8370
HILLSBOROUGH CO, NH 0.9073! 0.8712
BOSTON CMSA 0.9011! 0.8648
MANCHESTER, NH 0.9030 0.8638
PORTSMOUTH-ROCHESTER, NH-ME 0.9128! 0.8793
j'ORTSMOirm-ROCHES^ 0.8793^
PORTSMOUTH-ROCHESTER, NH-ME 0.9125! 0.8771
SULLIVAN CO, NH 0.9016 0.8476
ATLANTIC-CAPE MAY, NJ 0.8894! 0.8659
PHILADELPHIA CMSA 0.9193! 0.9126
PHILADELPHIA CMSA 0.9057! 0.8877
PHILADELPHIA CMSA 0.8747! 0.8463
RRF 2020 RRF 2030 RRF 2030
Control Base Control
0.7837! 0.8480! 0.7896
0.7505 0.8079! 0.7557
0.8865 0.9202! 0.8911
0.9026 0.9452! 0.9192
0.8523 0.9033! 0.8575
0.8458: 0.8966! 0.8532
0.8638! 0.9108! 0.8722
0.8635! 0.9094 1 0.8720
0.7578! 0.8341! 0.7610
0.7573! 0.8336! 0.7605
0.8689! 0.9132! 0.8780
0.7965! 0.8630 1 0.8013
0.8295 0.8743! 0.8365
0.8859 0.9277! 0.8953
0.8138 0.8824! 0.8229
0.7668! 0.8482! 0.7738
0.7930! 0.8495! 0.7985
0.8227! 0.8878! 0.8291
0.8265! 0.8958! 0.8348
0.8265! 0.8958! 0.8348
0.8195! 0.8856! 0.8272
0.8168! 0.8887 1 0.8248
0.8274! 0.8873! 0.8340
0.8331! 0.8986! 0.8415
0.8195! 0.8856! 0.8272
0.8626! 0.9055! 0.8709
0.8594! 0.9024! 0.8674
0.8626 0.9055! 0.8709
0.8523 0.9031! 0.8618
0.8594 0.9184! 0.8700
0.8452 0.9034! 0.8557
0.7992 0.8712! 0.8100
0.7895 0.8643! 0.7998
0.8364! 0.8929! 0.8469
0.8287! 0.8875! 0.8396
0.8230! 0.8876! 0.8328
0.8453! 0.9020! 0.8566
0.8453! 0.9020! 0.8566
0.8360 0.9028! 0.8487
0.8033 0.8759! 0.8163
0.8294 0.8894! 0.8398
0.8940 0.9307! 0.9062
0.8561 0.9085! 0.8657
0.8104! 0.8670! 0.8181
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
340130011 NJ
340150002 NJ
340170006 NJ
340190001 NJ
340210005 NJ
340230011 NJ
340250005 NJ
340273001 NJ
340290006 NJ
340315001 NJ
360010012 NY
360050080 NY
360050083 NY
360130011 NY
360150003 NY
360270007 NY
360290002 NY
360310002 NY
360310003 NY
360410005 NY
360430005 NY
360450002 NY
360530006 NY
360551004 NY
360610010 NY
360631006 NY
360650004 NY
360671015 NY
360715001 NY
360790005 NY
360810097 NY
360850067 NY
360910004 NY
360930003 NY
361030002 NY
361030004 NY
361111005 NY
361173001 NY
361192004 NY
370030003 NC
370210030 NC
370270003 NC
370290099 NC
370330001 NC
County
ESSEX CO
GLOUCESTER CO
HUDSON CO
HUNTERDONCO
MERCER CO
MIDDLESEX CO
MONMOUTHCO
MORRIS CO
OCEAN CO
PASSAIC CO
ALBANY CO
BRONX CO
BRONX CO
CHAUTAUQUA CO
CHEMUNGCO
DUTCHESS CO
ERIE CO
ESSEX CO
ESSEX CO
HAMILTON CO
HERKIMERCO
JEFFERSON CO
MADISON CO
MONROE CO
NEW YORK CO
NIAGARA CO
ONEIDACO
ONONDAGACO
ORANGE CO
PUTNAM CO
QUEENS CO
RICHMOND CO
SARATOGA CO
SCHENECTADY CO
SUFFOLK CO
SUFFOLK CO
ULSTER CO
WAYNE CO
WESTCHESTER CO
ALEXANDER CO
BUNCOMBE CO
CALDWELL CO
CAMDEN CO
CASWELL CO
RRF 2007 RRF 2020
Area Name Base Base
NEW YORK CMSA 0.9339! 0.9268
PHILADELPHIA CMSA 0.9024! 0.8862
NEW YORK CMSA 0.9339! 0.9268
NEW YORK CMSA 0.9270! 0.9062
PHILADELPHIA CMSA 0.9412! 0.9355
NEW YORK CMSA 0.9255! 0.9102
NEW YORK CMSA 0.9227! 0.9045
NEW YORK CMSA 0.9054! 0.8865
NEW YORK CMSA 0.9198! 0.9053
NEW YORK CMSA 0.9178! 0.9018
ALBANY-SCHENECTADY-TROY, NY 0.9095! 0.8673
NEW YORK CMSA 0.9626! 0.9847
NEW YORK CMSA 0.9626! 0.9847
JAMESTOWN, NY 0.8935! 0.8653
ELMIRA, NY 0.8853 0.8471
DUTCHESS COUNTY, NY ! 0.9030! 0.8816
BUFFALO CMSA | 0.9081! 0.8910
ESSEX CO, NY | 0.9070! 0.8786
ESSEX CO, NY | 0.9070! 0.8786
HAMILTON CO, NY ! 0.8987! 0.8677
HERKIMER CO, NY 0.9043! 0.8778
JEFFERSON CO, NY 0.9041 0.8802
SYRACUSE, NY 0.9047! 0.8625
JROCHESTER>IY i____0.9p42j 0.8773
NEWYORKCMSA 0.9143! 0.8968
BUFFALO CMSA 0.9006! 0.8667
UTICA-ROME, NY 0.8844! 0.8502
SYRACUSE, NY 0.8930! 0.8539
NEWBURGH, NY-PA 0.9119 0.9010
NEWYORKCMSA 0.9184! 0.9071
NEWYORKCMSA 0.9115! 0.8905
NEWYORKCMSA 0.9231! 0.9145
ALBANY-SCHENECTADY-TROY, NY 0.8735 0.8286
ALBANY-SCHENECTADY-TROY, NY 0.9028! 0.8771
NEWYORKCMSA 0.9081! 0.8907
NEWYORKCMSA 0.9197! 0.9129
ULSTER CO, NY 0.8898! 0.8608
JROCHESTER>IY i____0.9101j 0.8892^
NEWYORKCMSA 0.9343! 0.9357
HICKORY-MORGANTON, NC 0.8421 0.8006
ASHEVILLE, NC 0.8175! 0.7771
CALDWELL CO, NC 0.8307! 0.7869
CAMDEN CO, NC 0.9116 0.8866
CASWELL CO, NC 0.8497! 0.8179
RRF 2020 RRF 2030 RRF 2030
Control Base Control
0.9067! 0.9464! 0.9201
0.8594 0.9039! 0.8685
0.9067 0.9464! 0.9201
0.8741 0.9282! 0.8858
0.9107 0.9553! 0.9237
0.8778: 0.9312! 0.8896
0.8701! 0.9265! 0.8809
0.8571! 0.9062 1 0.8670
0.8781! 0.9264! 0.8907
0.8791! 0.9189! 0.8884
0.8271! 0.8910! 0.8359
0.9826! 0.9966! 0.9974
0.9826 0.9966! 0.9974
0.8410 0.8845! 0.8514
0.8118 0.8665I 0.8183
0.8508! 0.9028! 0.8626
0.8734! 0.9072! 0.8841
0.8577! 0.8926! 0.8637
0.8577! 0.8926! 0.8637
0.8410! 0.8847! 0.8484
0.8558! 0.8929! 0.8628
0.8581! 0.8966 1 0.8662
0.8305! 0.8799! 0.8363
0.8558! 0.8951! 0.8654
0.8709! 0.9167! 0.8827
0.8392! 0.8849! 0.8476
0.8199! 0.8700! 0.8284
0.8221 0.8746! 0.8310
0.8755 0.9200! 0.8869
0.8828 0.9275! 0.8962
0.8607 0.9122! 0.8720
0.8906 0.9350! 0.9032
0.7877 0.8521 I 0.7963
0.8431! 0.8987! 0.8545
0.8703! 0.9115! 0.8848
0.8927! 0.9337! 0.9077
0.8249! 0.8825! 0.8345
0.8667! 0.9092! 0.8787
0.9231 0.9536! 0.9374
0.7559 0.8238! 0.7609
0.7237 0.8010! 0.7269
0.7391 0.8093! 0.7427
0.8574 0.9077! 0.8681
0.7767! 0.8395! 0.7807
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
370370004 NC
370510008 NC
370511003 NC
370590002 NC
370610002 NC
370630013 NC
370650099 NC
370670022 NC
370670027 NC
370670028 NC
370671008 NC
370690001 NC
370770001 NC
370810011 NC
370870035 NC
370870036 NC
371010002 NC
371070004 NC
371090004 NC
371170001 NC
371190034 NC
371191005 NC
371191009 NC
371290002 NC
371310002 NC
371450003 NC
371470099 NC
371570099 NC
371590021 NC
371590022 NC
371730002 NC
371830014 NC
371830015 NC
371830016 NC
371830017 NC
371990003 NC
390030002 OH
390071001 OH
390170004 OH
390171004 OH
390230001 OH
390230003 OH
390250020 OH
390271002 OH
County
CHATHAM CO
CUMBERLAND CO
CUMBERLAND CO
DAVIE CO
DUPLIN CO
DURHAM CO
EDGECOMBE CO
FORSYTH CO
FORSYTH CO
FORSYTH CO
FORSYTH CO
FRANKLIN CO
GRANVILLECO
GUILFORDCO
HAYWOOD CO
HAYWOOD CO
JOHNSTON CO
LENOIRCO
LINCOLN CO
MARTIN CO
MECKLENBURG CO
MECKLENBURG CO
MECKLENBURG CO
NEW HANOVER CO
NORTHAMPTON CO
PERSON CO
PITT CO
ROCKINGHAM CO
ROWAN CO
ROWAN CO
SWAIN CO
WAKE CO
WAKE CO
WAKE CO
WAKE CO
YANCEY CO
ALLEN CO
ASHTABULACO
BUTLER CO
BUTLER CO
CLARK CO
CLARK CO
CLERMONTCO
CLINTON CO
Area Name
RALEIGH-DURHAM-CHAPEL HILL, NC
FAYETTEVILLE, NC
FAYETTEVILLE, NC
GREENSBORO-WINSTON-SALEM-H
WILMINGTON, NC
RALEIGH-DURHAM-CHAPEL HILL, NC
EDGECOMBE CO, NC
GREENSBORO-WINSTON-SALEM-H
GREENSBORO-WINSTON-SALEM-H
GREENSBORO-WINSTON-SALEM-H
GREENSBORO-WINSTON-SALEM-H
RALEIGH-DURHAM-CHAPEL HILL, NC
RALEIGH-DURHAM-CHAPEL HILL, NC
GREENSBORO-WINSTON-SALEM-H
HAYWOOD CO, NC
HAYWOOD CO, NC
RALEIGH-DURHAM-CHAPEL HILL, NC
LENOIRCO, NC
CHARLOTTE-GASTONIA-ROCK HILL,
MARTIN CO, NC
CHARLOTTE-GASTONIA-ROCK HILL,
CHARLOTTE-GASTONIA-ROCK HILL,
CHARLOTTE-GASTONIA-ROCK HILL,
WILMINGTON, NC
NORTHAMPTON CO, NC
RALEIGH-DURHAM-CHAPEL HILL, NC
PITT CO, NC
ROCKINGHAM CO, NC
CHARLOTTE-GASTONIA-ROCK HILL,
CHARLOTTE-GASTONIA-ROCK HILL,
SWAIN CO, NC
RALEIGH-DURHAM-CHAPEL HILL, NC
RALEIGH-DURHAM-CHAPEL HILL, NC
RALEIGH-DURHAM-CHAPEL HILL, NC
RALEIGH-DURHAM-CHAPEL HILL, NC
YANCEY CO, NC
LIMA, OH
CLEVELAND CMSA
CINCINNATI CMSA
CINCINNATI CMSA
DAYTON-SPRINGFIELD, OH
DAYTON-SPRINGFIELD, OH
CINCINNATI CMSA
CINCINNATI CMSA
RRF 2007
Base
0.8542
0.8644
0.8534
0.8328
0.8504
0.8734
0.8608
0.8534
0.8404
0.8537
0.8667
0.8556
0.8617
0.8629
0.8235
0.8106
0.8656
0.8578
0.8625
0.8834
0.8771
0.8917
0.8598
0.8793
0.8632
0.8516
0.8614
0.8528
0.8366
0.8552
0.8249
0.8886
0.8886
0.8792
0.8867
0.8241
0.8955
0.9007
0.8957
0.8816
0.8749
0.8748
0.8834
0.8560
RRF 2020
Base
0.8160
0.8169
0.8043
0.7742
0.8109
0.8282
0.8250
0.8042
0.7943
0.8030
0.8200
0.8121
0.8195
0.8224
0.7798
0.7710
0.8159
0.8185
0.8223
0.8517
0.8325
0.8569
0.8113
0.8465
0.8339
0.8232
0.8231
0.8056
0.7873
0.7987
0.7797
0.8464
0.8464
0.8257
0.8495
0.7819
0.8687
0.8771
0.8710
0.8507
0.8360
0.8399
0.8600
0.8191
RRF 2020
Control
0.7693
0.7617
0.7477
0.7221
0.7659
0.7802
0.7825
0.7504
0.7453
0.7482
0.7653
0.7614
0.7714
0.7735
0.7258
0.7207
0.7618
0.7730
0.7743
0.8147
0.7854
0.8161
0.7616
0.8080
0.7974
0.7833
0.7773
0.7570
0.7323
0.7513
0.7345
0.7923
0.7923
0.7681
0.7977
0.7329
0.8441
0.8528
0.8455
0.8225
0.8037
0.8098
0.8335
0.7877
RRF 2030
Base
0.8419
0.8440
0.8309
0.8004
0.8342
0.8565
0.8485
0.8314
0.8163
0.8288
0.8479
0.8393
0.8456
0.8474
0.8050
0.7945
0.8443
0.8418
0.8480
0.8738
0.8604
0.8846
0.8389
0.8734
0.8539
0.8442
0.8478
0.8300
0.8149
0.8250
0.7993
0.8761
0.8761
0.8567
0.8772
0.8052
0.8888
0.8974
0.8917
0.8711
0.8573
0.8606
0.8796
0.8384
RRF 2030
Control
0.7771
0.7667
0.7515
0.7276
0.7711
0.7883
0.7888
0.7564
0.7471
0.7517
0.7725
0.7685
0.7790
0.7797
0.7296
0.7249
0.7685
0.7783
0.7817
0.8227
0.7965
0.8299
0.7704
0.8210
0.8033
0.7879
0.7839
0.7625
0.7382
0.7586
0.7366
0.8021
0.8021
0.7757
0.8072
0.7373
0.8559
0.8648
0.8572
0.8325
0.8131
0.8194
0.8445
0.7953
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
390350034 OH
390350064 OH
390355002 OH
390410002 OH
390490004 OH
390490081 OH
390550004 OH
390570006 OH
390610006 OH
390610010 OH
390610037 OH
390830002 OH
390850003 OH
390853002 OH
390870006 OH
390870011 OH
390890005 OH
390911001 OH
390931003 OH
390950034 OH
390950081 OH
390970007 OH
390990009 OH
391030003 OH
391090005 OH
391130019 OH
391331001 OH
391351001 OH
391510016 OH
391510019 OH
391511009 OH
391514005 OH
391530020 OH
391550008 OH
391550009 OH
391591001 OH
391650006 OH
391670004 OH
391730003 OH
400270049 OK
400770440 OK
400870073 OK
401090033 OK
401091037 OK
County
CUYAHOGA CO
CUYAHOGA CO
CUYAHOGA CO
DELAWARE CO
FRANKLIN CO
FRANKLIN CO
GEAUGA CO
GREENE CO
HAMILTON CO
HAMILTON CO
HAMILTON CO
KNOX CO
LAKE CO
LAKE CO
LAWRENCE CO
LAWRENCE CO
LICKING CO
LOGAN CO
LORAIN CO
LUCAS CO
LUCAS CO
MADISON CO
MAHONING CO
MEDINA CO
MIAMI CO
MONTGOMERY CO
PORTAGE CO
PREBLE CO
STARK CO
STARK CO
STARK CO
STARK CO
SUMMIT CO
TRUMBULLCO
TRUMBULLCO
UNION CO
WARREN CO
WASHINGTON CO
WOOD CO
CLEVELAND CO
LATIMERCO
MC CLAIN CO
OKLAHOMA CO
OKLAHOMA CO
RRF 2007 RRF 2020
Area Name Base Base
CLEVELAND CMSA 0.8929! 0.8671
CLEVELAND CMSA 0.9165! 0.9039
CLEVELAND CMSA 0.8996! 0.8761
COLUMBUS, OH 0.8731 ! 0.8374
COLUMBUS, OH 0.8923 0.8780
COLUMBUS, OH 0.8884! 0.8715
CLEVELAND-LORAIN-ELYRIA, OH 0.8938! 0.8634
DAYTON-SPRINGFIELD, OH 0.8665 0.8333
CINCINNATI CMSA 0.8949! 0.8771
CINCINNATI CMSA 0.8826! 0.8749
CINCINNATI CMSA 0.9095! 0.8969
COLUMBUS, OH 0.8767 0.8526
CLEVELAND CMSA 0.9004! 0.8787
CLEVELAND CMSA 0.9019! 0.8777
HUNTINGTON-ASHLAND, WV-KY-OH 0.8522 0.8276
HUNTINGTON-ASHLAND, WV-KY-OH ! 0.8531! 0.8231
COLUMBUS, OH | 0.8679! 0.8412
LOGAN CO, OH ! 0.8771 ! 0.8389
CLEVELAND CMSA ! 0.9174! 0.9079
TOLEDO, OH | 0.9056! 0.8901
TOLEDO, OH 0.8990! 0.8793
COLUMBUS, OH 0.8742 0.8421
YOUNGSTOWN-WARREN, OH 0.8693! 0.8275
CLEVELAND CMSA 0.8814! 0.8510
DAYTON-SPRINGFIELD, OH 0.8756! 0.8400
DAYTON-SPRINGFIELD, OH 0.8786 0.8502
CLEVELAND CMSA 0.8838! 0.8510
DAYTON-SPRINGFIELD, OH 0.8594! 0.8209
CANTON-MASSILLON, OH 0.8734 0.8397
CANTON-MASSILLON, OH 0.8649! 0.8367
^A^TON-MA^SIU-ON^OH i____0.8736j 0-8388,
CANTON-MASSILLON, OH 0.8811! 0.8464
CLEVELAND CMSA 0.8951 i 0.8709
YOUNGSTOWN-WARREN, OH 0.8709! 0.8290
YOUNGSTOWN-WARREN, OH 0.8678! 0.8284
UNION CO, OH 0.8732 0.8356
CINCINNATI CMSA 0.8851! 0.8576
PA^KERSBURG-MA^IETTAWy-OhL^ 0.7896
TOLEDO, OH 0.9002! 0.8756
OKLAHOMA CITY, OK 0.9306 0.8694
LATIMERCO, OK 0.9519! 0.9187
OKLAHOMA CITY, OK 0.9320! 0.8711
OKLAHOMA CITY, OK 0.9362 0.8758
OKLAHOMA CITY, OK 0.9340! 0.8726
RRF 2020 RRF 2030 RRF 2030
Control Base Control
0.8465! 0.8876! 0.8604
0.8881 0.9218! 0.9020
0.8557 0.8970! 0.8693
0.8068 0.8574! 0.8154
0.8613 0.8958! 0.8751
0.8554: 0.8905! 0.8692
0.8358! 0.8849! 0.8478
0.8026! 0.8536 1 0.8116
0.8570! 0.8973! 0.8709
0.8654! 0.8915! 0.8796
0.8819! 0.9171! 0.8974
0.8261! 0.8722! 0.8363
0.8577 0.9002! 0.8723
0.8544 0.8995! 0.8685
0.8033 0.8430! 0.8104
0.7978! 0.8379! 0.8036
0.8127! 0.8608! 0.8221
0.8063! 0.8597! 0.8154
0.8933! 0.9250! 0.9063
0.8717! 0.9060! 0.8823
0.8590! 0.8919! 0.8659
0.8131! 0.8623! 0.8228
0.7903! 0.8479! 0.7978
0.8213! 0.8714! 0.8321
0.8097! 0.8615! 0.8202
0.8216! 0.8711 I 0.8325
0.8178! 0.8728! 0.8284
0.7886 0.8390! 0.7944
0.8054 0.8603! 0.8143
0.8055 0.8559! 0.8136
0.8043 0.8595! 0.8130
0.8126 0.8675! 0.8220
0.8431 0.8899! 0.8543
0.7916! 0.8500! 0.7997
0.7938! 0.8494! 0.8024
0.8037! 0.8551! 0.8116
0.8292! 0.8791! 0.8407
0.7627! 0.8033! 0.7665
0.8516 0.8935! 0.8615
0.8296 0.8904! 0.8351
0.8940 0.9348! 0.9011
0.8313 0.8914! 0.8360
0.8373 0.8971 1 0.8434
0.8342! 0.8933! 0.8399
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
401430127 OK
401430137 OK
401430174 OK
420030008 PA
420030010 PA
420030067 PA
420030088 PA
420031005 PA
420050001 PA
420070002 PA
420070005 PA
420070014 PA
420110001 PA
420110009 PA
420130801 PA
420170012 PA
420210011 PA
420274000 PA
420334000 PA
420430401 PA
420431100 PA
420450002 PA
420490003 PA
420550001 PA
420590002 PA
420690101 PA
420692006 PA
420710007 PA
420730015 PA
420770004 PA
420791 1 00 PA
420791101 PA
420810403 PA
420814000 PA
420850100 PA
420890001 PA
420910013 PA
420950025 PA
420950100 PA
420990301 PA
421010004 PA
421010014 PA
421010024 PA
421010136 PA
County
TULSA CO
TULSA CO
TULSA CO
ALLEGHENY CO
ALLEGHENY CO
ALLEGHENY CO
ALLEGHENY CO
ALLEGHENY CO
ARMSTRONG CO
BEAVER CO
BEAVER CO
BEAVER CO
BERKS CO
BERKS CO
BLAIR CO
BUCKS CO
CAMBRIA CO
CENTRE CO
CLEARFIELD CO
DAUPHIN CO
DAUPHIN CO
DELAWARE CO
ERIE CO
FRANKLIN CO
GREENE CO
LACKAWANNA CO
LACKAWANNA CO
LANCASTER CO
LAWRENCE CO
LEHIGH CO
LUZERNE CO
LUZERNE CO
LYCOMING CO
LYCOMING CO
MERCER CO
MONROE CO
MONTGOMERY CO
NORTHAMPTON CO
NORTHAMPTON CO
PERRY CO
PHILADELPHIA CO
PHILADELPHIA CO
PHILADELPHIA CO
PHILADELPHIA CO
Area Name
TULSA, OK
TULSA, OK
TULSA, OK
PITTSBURGH CMSA
PITTSBURGH CMSA
PITTSBURGH CMSA
PITTSBURGH CMSA
PITTSBURGH CMSA
ARMSTRONG CO, PA
PITTSBURGH CMSA
PITTSBURGH CMSA
PITTSBURGH CMSA
READING, PA
READING, PA
ALTOONA, PA
PHILADELPHIA CMSA
JOHNSTOWN, PA
STATE COLLEGE, PA
CLEARFIELD CO, PA
HARRISBURG-LEBANON-CARLISLE, F
HARRISBURG-LEBANON-CARLISLE, F
PHILADELPHIA CMSA
ERIE, PA
FRANKLIN CO, PA
GREENE CO, PA
SCRANTON-WILKES-BARRE-HAZLE
SCRANTON-WILKES-BARRE-HAZLE
LANCASTER, PA
LAWRENCE CO, PA
ALLENTOWN-BETHLEHEM-EASTON,
SCRANTON-WILKES-BARRE-HAZLE
SCRANTON-WILKES-BARRE-HAZLE
WILLIAMSPORT, PA
WILLIAMSPORT, PA
YOUNGSTOWN-WARREN, OH
MONROE CO, PA
PHILADELPHIA CMSA
ALLENTOWN-BETHLEHEM-EASTON,
ALLENTOWN-BETHLEHEM-EASTON,
HARRISBURG-LEBANON-CARLISLE, F
PHILADELPHIA CMSA
PHILADELPHIA CMSA
PHILADELPHIA CMSA
PHILADELPHIA CMSA
RRF 2007
Base
0.9375
0.9375
0.9327
0.9122
0.9120
0.8809
0.9028
0.9025
0.8708
0.8987
0.9088
0.9191
0.8819
0.8804
0.8482
0.9287
0.8770
0.8530
0.8516
0.8826
0.8867
0.8997
0.8956
0.8455
0.8087
0.8660
0.8647
0.8974
0.8774
0.8955
0.8487
0.8687
0.8636
0.8490
0.8627
0.8966
0.9060
0.8955
0.8992
0.8563
0.9310
0.9053
0.9310
0.9042
RRF 2020
Base
0.8825
0.8828
0.8781
0.8904
0.8961
0.8661
0.8824
0.8814
0.8395
0.8701
0.8812
0.8945
0.8375
0.8472
0.8213
0.9258
0.8582
0.8153
0.8177
0.8306
0.8355
0.8786
0.8703
0.8052
0.7774
0.8217
0.8232
0.8653
0.8387
0.8628
0.8060
0.8262
0.8265
0.8155
0.8208
0.8652
0.9055
0.8628
0.8635
0.8108
0.9286
0.8967
0.9286
0.8901
RRF 2020
Control
0.8433
0.8451
0.8390
0.8627
0.8739
0.8425
0.8540
0.8554
0.8093
0.8434
0.8566
0.8704
0.7969
0.8103
0.7900
0.9064
0.8314
0.7768
0.7840
0.7834
0.7883
0.8500
0.8461
0.7622
0.7506
0.7811
0.7845
0.8270
0.8041
0.8286
0.7677
0.7866
0.7892
0.7808
0.7831
0.8325
0.8898
0.8286
0.8297
0.7688
0.9091
0.8766
0.9091
0.8662
RRF 2030
Base
0.9066
0.9071
0.9003
0.9096
0.9145
0.8832
0.9013
0.8996
0.8568
0.8858
0.8982
0.9109
0.8584
0.8679
0.8361
0.9444
0.8725
0.8344
0.8353
0.8511
0.8562
0.8980
0.8902
0.8245
0.7907
0.8419
0.8438
0.8873
0.8583
0.8824
0.8243
0.8472
0.8447
0.8335
0.8412
0.8842
0.9224
0.8824
0.8833
0.8296
0.9471
0.9155
0.9471
0.9082
RRF 2030
Control
0.8534
0.8556
0.8467
0.8719
0.8850
0.8520
0.8629
0.8642
0.8154
0.8498
0.8645
0.8780
0.8030
0.8181
0.7932
0.9195
0.8356
0.7819
0.7890
0.7874
0.7928
0.8587
0.8573
0.7655
0.7539
0.7867
0.7913
0.8351
0.8110
0.8360
0.7723
0.7934
0.7937
0.7859
0.7902
0.8398
0.9026
0.8360
0.8371
0.7720
0.9218
0.8883
0.9218
0.8760
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
421250005 PA
421250200 PA
421255001 PA
421290006 PA
421330008 PA
440030002 Rl
440071010 Rl
440090007 Rl
450010001 SC
450030003 SC
450070003 SC
450110001 SC
450150002 SC
450190042 SC
450190046 SC
450210002 SC
450230002 SC
450290002 SC
450310003 SC
450370001 SC
450730001 SC
450770002 SC
450790007 SC
450791002 SC
450830009 SC
450870001 SC
450910006 SC
470010101 TN
470090101 TN
470090102 TN
470370011 TN
470370026 TN
470650028 TN
470651011 TN
470750003 TN
470890001 TN
470890002 TN
470930021 TN
470931020 TN
470990002 TN
471410004 TN
471490101 TN
471550101 TN
471550102 TN
County
WASHINGTON CO
WASHINGTON CO
WASHINGTON CO
WESTMORELAND CO
YORK CO
KENT CO
PROVIDENCE CO
WASHINGTON CO
ABBEVILLE CO
AIKEN CO
ANDERSON CO
BARNWELL CO
BERKELEY CO
CHARLESTON CO
CHARLESTON CO
CHEROKEE CO
CHESTER CO
COLLETON CO
DARLINGTON CO
EDGEFIELDCO
OCONEE CO
PICKENSCO
RICHLANDCO
RICHLANDCO
SPARTANBURG CO
UNION CO
YORK CO
ANDERSON CO
BLOUNTCO
BLOUNTCO
DAVIDSON CO
DAVIDSON CO
HAMILTON CO
HAMILTON CO
HAYWOOD CO
JEFFERSON CO
JEFFERSON CO
KNOX CO
KNOX CO
LAWRENCE CO
PUTNAM CO
RUTHERFORD CO
SEVIER CO
SEVIER CO
RRF 2007 RRF 2020
Area Name Base Base
PITTSBURGH CMSA 0.8648! 0.8394
PITTSBURGH CMSA 0.8394! 0.8073
PITTSBURGH CMSA 0.9044! 0.8816
PITTSBURGH CMSA 0.9147! 0.8947
YORK, PA 0.8871 0.8497
PROVIDENCE CMSA 0.9094! 0.8806
PROVIDENCE CMSA 0.9020! 0.8634
PROVIDENCE CMSA 0.8972! 0.8688
ABBEVILLE CO, SC 0.8424! 0.7909
A^JGUSTA-AIKEN^GA-SC i____0.8421j 07831^
GREENVILLE-SPARTANBURG-ANDEP 0.8518! 0.8015
BARNWELLCO.SC 0.8444 0.8072
CHARLESTON-NORTH CHARLESTON 0.8663! 0.8325
CHARLESTON-NORTH CHARLESTON 0.8663! 0.8325
CHARLESTON-NORTH CHARLESTON! 0.8573i 0.8275
CHEROKEECO.SC ! 0.8445! 0.8074
CHARLOTTE-GASTONIA-ROCKHILL,! 0.8719! 0.8389
COLLETONCO.SC ! 0.8500! 0.8127
DARLINGTON CO, SC | 0.8780! 0.8548
EDGEFIELDCO.SC ! 0.8422! 0.7963
OCONEE CO, SC 0.8253! 0.7763
GREENVILLE-SPARTANBURG-ANDEP 0.8424! 0.7888
COLUMBIA, SC 0.8607! 0.8134
^OLUMBIA^SC i____0.8653j 0.8168^
GREENVILLE-SPARTANBURG-ANDEP 0.8489! 0.8095
UNION CO, SC 0.8444 0.8070
CHARLOTTE-GASTONIA-ROCKHILL, 0.8749! 0.8404
KNOXVILLE, TN 0.8271 1 0.7622
KNOXVILLE, TN 0.8387 0.7780
KNOXVILLE, TN 0.8355! 0.7775
^ASHVILLE^™ i____0.8893j 0-8531 ,
NASHVILLE, TN 0.8853! 0.8474
CHATTANOOGA, TN-GA 0.8492 0.7851
CHATTANOOGA, TN-GA 0.8493! 0.7811
HAYWOOD CO, TN 0.8672! 0.8235
JEFFERSON CO, TN 0.8120 0.7519
JEFFERSON CO, TN 0.8120! 0.7519
J
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
471570021 TN
471571004 TN
471 632002 TN
471 632003 TN
471 650007 TN
471650101 TN
471870106 TN
471890103 TN
480391003 TX
480430101 TX
480850005 TX
481 1 30045 TX
481 1 30069 TX
481 1 30087 TX
481210034 TX
481210054 TX
481390015 TX
481670014 TX
481671002 TX
481830001 TX
482010024 TX
482010029 TX
482010046 TX
482010047 TX
482010051 TX
482010055 TX
482010062 TX
482010066 TX
482011034 TX
482011035 TX
482011037 TX
482011039 TX
482450009 TX
482450011 TX
483550025 TX
483550026 TX
483611001 TX
484230004 TX
484390057 TX
484391002 TX
484392003 TX
484530014 TX
484530020 TX
484690003 TX
RRF 2007 RR
County Area Name Base Bas
SHELBY CO {MEMPHIS, TN-AR-MS 0.9229!
SHELBY CO {MEMPHIS, TN-AR-MS 0.8980!
SULLIVAN CO {JOHNSON CITY-KINGSPORT-BRISTO 0.8353!
SULLIVAN CO {JOHNSON CITY-KINGSPORT-BRISTO 0.8357!
SUMNERCO [NASHVILLE, TN o.ssis
SUMNERCO {NASHVILLE, TN 0.8401!
WILLIAMSON CO [NASHVILLE, TN 0.8044[
WILSON co [NASHVILLE, TN 0.3433
BRAZORIACO {HOUSTON CMSA 0.9632!
J3REWSTERJX) [BREWSTER^CO^JX ] [__
COLLINCO | DALLAS CMSA 0.9301!
DALLAS co [DALLAS CMSA 0.9352!
DALLAS CO {DALLAS CMSA 0.9361 {
DALLAS CO [DALLAS CMSA 0.9381 [
DENTONCO I DALLAS CMSA 0.9271 1
DENTONCO {DALLAS CMSA ! 0.9309{
ELLIS co IDALLASCMSA | o.9268|
GALVESTONCO {HOUSTON CMSA ! 0.9524!
GALVESTONCO {HOUSTON CMSA ! 0.9508!
GREGG CO {LONGVIEW-MARSHALL, TX ! 0.9292{
HARRIS CO [HOUSTON CMSA 0.9485[
HARRIS CO [HOUSTON CMSA 0.9400[
HARRIS CO {HOUSTON CMSA 0.9491 {
HARRIS CO {HOUSTON CMSA 0.9472 {
HARRIS CO {HOUSTON CMSA 0.9501 {
HARRIS CO [HOUSTON CMSA 0.9494[
HARRIS CO {HOUSTON CMSA 0.9505 {
HARRIS CO [HOUSTON CMSA 0.9441 [
HARRIS CO [HOUSTON CMSA 0.9510 1
HARRIS CO {HOUSTON CMSA 0.9510J
HARRIS CO {HOUSTON CMSA 0.9494 {
HARRIS CO {HOUSTON CMSA 0.9526 {
JEFFERSON CO [BEAUMONT-PORT ARTHUR, TX 0.9685
JEFFERSON CO {BEAUMONT-PORT ARTHUR, TX 0.9679{
NUECESCO [CORPUS CHRISTI, TX [
NUECESCO [CORPUS CHRISTI, TX
ORANGE CO { BEAUMONT-PORT ARTHUR, TX 0.9718J
j5MITh[Cp IIYLEFV[X ^^PJ3270L
TARRANTCO {DALLAS CMSA 0.9373 {
TARRANTCO I DALLAS CMSA 0.9337[
TARRANTCO {DALLAS CMSA 0.931 2 {
TRAVIS CO [AUSTIN-SAN MARCOS, TX 0.9304[
TRAVIS co [AUSTIN-SAN MARCOS, TX 0.9290
VICTORIA CO {VICTORIA, TX 0.9438J
F2020 RRF 2020 RRF 2030 RRF 2030
>e Control Base Control
0.9125 0.8952! 0.9328! 0.9099
0.8596 0.8327 0.8783 { 0.8414
0.8043 0.7687 0.8237 { 0.7746
0.8041 0.7683 0.8236 { 0.7743
0.8441 0.8104 0.8645 1 0.8192
0.7961 0.7605: 0.81 54 { 0.7663
0.7662 0.7343! 0.7860 [ 0.7428
0.7980 0.7598! 0.81 68 1 0.7645
0.9339 0.9186: 0.951 2 { 0.9304
0.8867 0.8532! 0.91 17{ 0.8653
0.9097 0.8796! 0.93141 0.8909
0.9099 0.8782 0.931 8 { 0.8895
0.9131 0.8818 0.9348 [ 0.8931
0.8739 0.8360 0.900ol 0.8479
0.8946! 0.8617! 0.9184{ 0.8734
0.8632 0.8256 0.8866 0.8346
0.9423 0.9283 0.9600 0.9409
0.9406 0.9255 0.9588 0.9376
0.8772 0.8428 0.8943 0.8469
0.9679 0.9643 0.981 6 [ 0.9792
0.9064 0.8811 0.9278 1 0.8934
0.9782 0.9787 0.9907 { 0.9942
0.9727 0.9727 0.9858 { 0.9883
0.9598 0.9562 0.9747 { 0.9713
0.9819 0.9842 0.9938 1 1.0001
0.9636 0.9610 0.9781 { 0.9762
0.9486 0.9391 0.9651 [ 0.9545
0.9884 0.9922 1.00021 1.0084
0.9884 0.9922 1.0002{ 1.0084
0.9819 0.9842 0.9938 { 1.0001
0.9873 0.9922 0.9989 { 1.0082
0.9323 0.9158 0.9536 1 0.9310
0.9479 0.9358! 0.9675{ 0.9511
0.9415 0.9276! 0.9620 { 0.9431
0.8656 0.8275! 0.8851 { 0.8322
0.9059 0.8742 0.9286 { 0.8863
0.8883 0.8522 0.91 25 1 0.8645
0.8881 0.8527 0.91 20 { 0.8646
0.8831 0.8506 0.9067 [ 0.8619
0.8794 0.8448 0.9034 1 0.8557
0.9120 0.8905! 0.9290 { 0.8995
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
500030004 VT
500070007 VT
510130020 VA
510330001 VA
510360002 VA
510410004 VA
510590005 VA
510590018 VA
510590030 VA
510591004 VA
510595001 VA
510610002 VA
510690010 VA
510850001 VA
510870014 VA
511071005 VA
51 1 1 30003 VA
511530009 VA
511611004 VA
511790001 VA
511970002 VA
515100009 VA
51 6500004 VA
51 8000004 VA
51 8000005 VA
540110006 WV
540250003 WV
540291004 WV
540390004 WV
540690007 WV
541071002 WV
550090026 Wl
550210015 Wl
550250041 Wl
550270007 Wl
550290004 Wl
550370001 Wl
550390006 Wl
550550002 Wl
550590002 Wl
550590019 Wl
550590022 Wl
550610002 Wl
550710004 Wl
County
BENNINGTONCO
CHITTENDENCO
ARLINGTON CO
CAROLINE CO
CHARLES CITY CO
CHESTERFIELD CO
FAIRFAX CO
FAIRFAX CO
FAIRFAX CO
FAIRFAX CO
FAIRFAX CO
FAUQUIERCO
FREDERICK CO
HANOVER CO
HENRICOCO
LOUDOUN CO
MADISON CO
PRINCE WILLIAM CO
ROANOKE CO
STAFFORD CO
WYTHE CO
ALEXANDRIA
HAMPTON
SUFFOLK
SUFFOLK
CABELL CO
GREENBRIERCO
HANCOCK CO
KANAWHA CO
OHIO CO
WOOD CO
BROWN CO
COLUMBIA CO
DANE CO
DODGE CO
DOOR CO
FLORENCE CO
FOND DU LAC CO
JEFFERSON CO
Kenosha Co.
Kenosha Co.
Kenosha Co.
KEWAUNEE CO
MANITOWOC CO
Area Name
BENNINGTONCO, VT
CHITTENDENCO, VT
WASHINGTON, DC-MD-VA-WV
CAROLINE CO, VA
RICHMOND-PETERSBURG, VA
RICHMOND-PETERSBURG, VA
WASHINGTON, DC-MD-VA-WV
WASHINGTON, DC-MD-VA-WV
WASHINGTON, DC-MD-VA-WV
WASHINGTON, DC-MD-VA-WV
WASHINGTON, DC-MD-VA-WV
FAUQUIER CO, VA
FREDERICK CO, VA
RICHMOND-PETERSBURG, VA
RICHMOND-PETERSBURG, VA
WASHINGTON, DC-MD-VA-WV
CHARLOTTESVILLE, VA
WASHINGTON, DC-MD-VA-WV
ROANOKE, VA
WASHINGTON, DC-MD-VA-WV
WYTHE CO, VA
WASHINGTON, DC-MD-VA-WV
NORFOLK-VIRGINIA BEACH-NEWPOF
NORFOLK-VIRGINIA BEACH-NEWPOF
NORFOLK-VIRGINIA BEACH-NEWPOF
HUNTINGTON-ASHLAND, WV-KY-OH
GREENBRIERCO, WV
STEUBENVILLE-WEIRTON, OH-WV
CHARLESTON, WV
WHEELING, WV-OH
PARKERSBURG-MARIETTA, WV-OH
GREEN BAY, Wl
MADISON, Wl
MADISON, Wl
DODGE CO, Wl
DOOR CO, Wl
FLORENCE CO, Wl
FOND DU LAC CO, Wl
MILWAUKEE-RACINE CMSA
CHICAGO CMSA
CHICAGO CMSA
KEWAUNEE CO, Wl
MANITOWOC CO, Wl
RRF 2007
Base
0.8789
0.8847
0.9204
0.8694
0.8744
0.8772
0.8970
0.9012
0.9012
0.9204
0.9235
0.8588
0.8516
0.8683
0.8763
0.8969
0.8093
0.8793
0.8322
0.8774
0.7877
0.9204
0.9099
0.9042
0.8708
0.8605
0.7290
0.8802
0.8574
0.8564
0.8299
0.9074
0.8973
0.9122
0.9043
0.9160
0.9040
0.9075
0.9212
0.9250
0.9167
0.9085
0.9100
RRF 2020
Base
0.8257
0.8385
0.9124
0.8238
0.8440
0.8318
0.8677
0.8819
0.8819
0.9124
0.9157
0.8187
0.8181
0.8296
0.8325
0.8693
0.7706
0.8461
0.7865
0.8290
0.7462
0.9124
0.8864
0.8844
0.8375
0.8347
0.6921
0.8523
0.8209
0.8275
0.7943
0.8750
0.8606
0.8776
0.8710
0.8931
0.8696
0.8754
0.9167
0.9226
0.9048
0.8818
0.8843
RRF 2020
Control
0.7832
0.7993
0.8836
0.7760
0.8104
0.7853
0.8277
0.8562
0.8562
0.8836
0.8904
0.7742
0.7836
0.7857
0.7857
0.8301
0.7288
0.8047
0.7432
0.7781
0.7029
0.8836
0.8568
0.8585
0.8026
0.8099
0.6642
0.8244
0.7912
0.7999
0.7674
0.8458
0.8269
0.8430
0.8389
0.8688
0.8392
0.8449
0.9049
0.9149
0.8886
0.8561
0.8600
RRF 2030
Base
0.8502
0.8594
0.9318
0.8472
0.8642
0.8541
0.8915
0.9005
0.9005
0.9318
0.9357
0.8417
0.8366
0.8523
0.8563
0.8941
0.7898
0.8712
0.8076
0.8539
0.7632
0.9318
0.9089
0.9059
0.8588
0.8502
0.7034
0.8686
0.8329
0.8421
0.8074
0.8943
0.8809
0.8997
0.8921
0.9155
0.8886
0.8959
0.9366
0.9417
0.9254
0.9029
0.9046
RRF 2030
Control
0.7920
0.8055
0.8957
0.7821
0.8180
0.7906
0.8401
0.8671
0.8671
0.8957
0.9049
0.7815
0.7893
0.7920
0.7926
0.8437
0.7324
0.8165
0.7484
0.7851
0.7033
0.8957
0.8684
0.8696
0.8113
0.8170
0.6649
0.8303
0.7917
0.8043
0.7706
0.8543
0.8349
0.8531
0.8482
0.8827
0.8471
0.8543
0.9227
0.9327
0.9047
0.8684
0.8718
-------
APPENDIX D
8-Hnur Relative Reduction Factors
Site Id. State
550710007 Wl
550730012 Wl
550790041 Wl
550790044 Wl
550790048 Wl
550790085 Wl
550791025 Wl
550850004 Wl
550870009 Wl
550890008 Wl
550890009 Wl
551010017 Wl
551050021 Wl
551050024 Wl
551091002 Wl
551110007 Wl
551 1 70006 Wl
551230008 Wl
551250001 Wl
551270005 Wl
551310009 Wl
551330017 Wl
551390011 Wl
County
MANITOWOC CO
MARATHON CO
MILWAUKEE CO
MILWAUKEE CO
MILWAUKEE CO
MILWAUKEE CO
MILWAUKEE CO
ONEIDACO
OUTAGAMIE CO
OZAUKEE CO
OZAUKEE CO
RACINE CO
ROCK CO
ROCK CO
ST CROIX CO
SAUKCO
SHEBOYGAN CO
VERNON CO
Vilas Co.
WALWORTH CO
WASHINGTON CO
WAUKESHACO
WINNEBAGO CO
Area Name
MANITOWOC CO, Wl
WAUSAU, Wl
MILWAUKEE-RACINE CMSA
MILWAUKEE-RACINE CMSA
MILWAUKEE-RACINE CMSA
MILWAUKEE-RACINE CMSA
MILWAUKEE-RACINE CMSA
ONEIDACO, Wl
APPLETON-OSHKOSH-NEENAH, Wl
MILWAUKEE-RACINE CMSA
MILWAUKEE-RACINE CMSA
MILWAUKEE-RACINE CMSA
JANESVILLE-BELOIT, Wl
JANESVILLE-BELOIT, Wl
MINNEAPOLIS-ST. PAUL, MN-WI
SAUK CO, Wl
SHEBOYGAN, Wl
VERNON CO, Wl
MILWAUKEE-RACINE CMSA
MILWAUKEE-RACINE CMSA
MILWAUKEE-RACINE CMSA
APPLETON-OSHKOSH-NEENAH, Wl
RRF 2007
Base
0.9147
0.8919
0.9132
0.9186
0.9164
0.9026
0.9164
0.8905
0.9044
0.9091
0.9183
0.9214
0.9052
0.9017
0.9305
0.9007
0.9199
0.9021
0.8822
0.9089
0.9174
0.9176
0.8999
RRF 2020
Base
0.8929
0.8558
0.8979
0.9096
0.8994
0.8814
0.8994
0.8553
0.8722
0.8939
0.9064
0.9196
0.8758
0.8696
0.8886
0.8632
0.9080
0.8653
0.8482
0.8821
0.9026
0.9006
0.8661
RRF 2020
Control
0.8690
0.8245
0.8789
0.8965
0.8791
0.8609
0.8791
0.8238
0.8444
0.8740
0.8901
0.9126
0.8505
0.8408
0.8620
0.8314
0.8910
0.8354
0.8158
0.8555
0.8843
0.8786
0.8377
RRF 2030
Base
0.9143
0.8742
0.9189
0.9288
0.9217
0.9034
0.9217
0.8740
0.8897
0.9160
0.9275
0.9391
0.8959
0.8911
0.9065
0.8830
0.9292
0.8841
0.8676
0.9039
0.9231
0.9207
0.8834
RRF 2030
Control
0.8820
0.8315
0.8946
0.9125
0.8950
0.8758
0.8950
0.8309
0.8516
0.8897
0.9063
0.9319
0.8615
0.8516
0.8703
0.8393
0.9072
0.8426
0.8235
0.8679
0.8989
0.8923
0.8443
-------
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
MONITOR ID STATE
401 39990 {ARIZONA
401 39991 [ARIZONA
401 39992 {ARIZONA
401 39997 {ARIZONA
401 90011 {ARIZONA
401 91 028 [ARIZONA
60011001 CALIFORNIA
60070002 CALIFORNIA
60090001 [CALIFORNIA
60111002{CALIFORNIA
601 70011 {CALIFORNIA
601 70011 {CALIFORNIA
60190008{CALIFORNIA
60190008JCALIFORNIA
601 95001 {CALIFORNIA
60231002[CALIFORNIA
60250005{CALIFORNIA
60251003{CALIFORNIA
60290010JCALIFORNIA
60290014{CALIFORNIA
60310004{CALIFORNIA
60371002|CALIFORNIA
60371 IDSICALIFORNIA
60371201 {CALIFORNIA
60371301 {CALIFORNIA
60371601 {CALIFORNIA
60374002{CALIFORNIA
60379002 CALIFORNIA
60450006{CALIFORNIA
60490001 {CALIFORNIA
60570005{CALIFORNIA
60610006{CALIFORNIA
60651003JCALIFORNIA
60652002{CALIFORNIA
60658001 CALIFORNIA
60670010[CALIFORNIA
1999
COUNTY Ambient 2020
MARICOPA | 10.8J
MARICOPACO [ 13.0]
MARICOPA { 11.4!
MARICOPA { 12.6!
PIMACO i 9.7!
PIMA [ 8.8|
ALAMEDACO 13.9
BUTTECO 17.5
CALAVERAS I 11.1 1
COLUSA { 13.2!
EL DORADO CO | 8.3!
ELDORADO i 8.1 1
FRESNO CO 27.7
FRESNO 21.4
FRESNO 20.0
HUMBOLDT [ 9.o]
IMPERIAL { 15.4!
IMPERIAL | 11.5!
KERN i 26.2!
KERN 27.8
KINGS 22.2
LOS ANGELES , 22.8,
LOS ANGELES I 23.9!
LOS ANGELES CO i 17.5!
LOS ANGELES i 24.6!
LOS ANGELES i 25.9!
LOS ANGELES [ 21 .3]
LOS ANGELES ' ' 10.8'
MENDOCINO 8.7
MODOC 7.9
NEVADA { 7.6!
PLACER CO { 13.4!
RIVERSIDE CO i 27.1 1
RIVERSIDE { 12.8|
RIVERSIDE 30.2
SACRAMENTO { 16.5!
PM2.5 Concentrations
2020 2030
Base Control 2030 Base Control
14.6! 14.2J 16.1 1 15.4
17.6J 16.9] 19.4 18.5
15.51 14.9! 17.1 { 16.3
17.1 { 16.4! 18.9{ 18.0
12.0{ 11.8! 13.11 12.7
11.7J 11.3] 12.7J 12.2
15.3 14.4 16.6 15.3
18.6 18.1 1 19.3 18.6
12.il 11.s! 12.?l 11.8
13.4{ 13.0! 13.71 13.2
8.3{ 7.9! 8.8{ 8.1
8.1 { 7.6! 8.5{ 7.9
28.1 26.5 30.2 27.8
21.7 20.4 23.3 21.5
20.3 19.1 21.8 20.1
10.2J 10.1] 10.5 10.3
16.2{ 15.0! 17.21 15.5
12.4{ 11.5! 13.3{ 12.0
26.9{ 25.3! 28.3{ 26.0
28.4 26.8 30.0 27.6
21.9 20.4 23.1 20.9
27.5! 26.0J 30.0| 27.9
28.8J 27.2J 31 .4! 29.2
22.7{ 21.7! 24.4{ 23.0
31 .4{ 30.1! 34.2{ 32.3
31.2! 29.5! 34.0! 31.6
27.1 { 26.0] 29.6{ 28.0
11.2' 10.5' 11.8S 10.8
9.0 8.7 9.3 8.9
8.7 8.6 8.8 8.6
7.4{ 7.1 1 7.7{ 7.3
13.7| 13.1! 14.6{ 13.7
35.2{ 33.4! 38.5{ 36.0
15.2J 13.9] 16.7J 14.9
36.5 34.3 39.7 36.6
17.4{ 16.3i 18.8{ 17.2
-------
APPENDIX E
1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs
MONITOR ID STATE
60674001 {CALIFORNIA
60710025iCALIFORNIA
60712002iCALIFORNIA
6071 8001 [CALIFORNIA
60719004JCALIFORNIA
60730001 {CALIFORNIA
60730003JCALIFORNIA
60730006{CALIFORNIA
60731002iCALIFORNIA
60731007iCALIFORNIA
60771002|CALIFORNIA
60792002 CALIFORNIA
60798001 CALIFORNIA
60798001 (CALIFORNIA
6081 1001 {CALIFORNIA
60830010iCALIFORNIA
60890004iCALIFORNIA
60970003|CALIFORNIA
60990005iCALIFORNIA
61010003JCALIFORNIA
61072002[CALIFORNIA
61110007{CALIFORNIA
61110007iCALIFORNIA
61112002iCALIFORNIA
61 11 3001 [CALIFORNIA
61131003 CALIFORNIA
80010001 COLORADO
80130003lcOLORADO
801 3001 2 {COLORADO
80770003 {COLORADO
81 230006 {COLORADO
10001 0002 {DELAWARE
10001 0003 {DELAWARE
100031 003 {DELAWARE
100031 011 [DELAWARE
100032004 {DELAWARE
1999
COUNTY Ambient 2020
SACRAMENTO | 16.2!
SAN BERNARDINO CO | 25.4!
SAN BERNARDINO i 25.3!
SAN BERNARDINO [ 10.3]
SAN BERNARDINO 25.6
SAN DIEGO CO 14.7
SAN DIEGO , , 16.6,
SAN DIEGO { 13.7!
SAN DIEGO { 17.8!
SAN DIEGO i 17.5!
SANJOAQUINCO [ 19.8]
SAN LUIS OBISPO CO 8.2
SAN LUIS OBISPO 9.6
SAN LUIS OBISPO i Q.5\
SAN MATED CO i 12.1 1
SANTA BARBARA i 13.3!
SHASTA CO i 12.9!
SONOMA CO [ 11.7]
STANISLAUS CO I 24.4!
SUTTER [ 15.9!
TULARE CO [ 27.6]
VENTURA { 12.0!
VENTURA { 13.8!
VENTURA i 13.8!
VENTURA CO [ 12.1 1
YOLOCO 16.3
ADAMS CO 8.5
BOULDER CO I 8.3\
BOULDER { 6.9!
MESA \ 6.9!
WELD i 7.6!
KENT CO 11.6
KENT 12.4
NEWCASTLE 13.8
NEWCASTLE [ 13.3J
NEWCASTLE { 15.6!
PM2.5 Concentrations
2020 2030
Base Control 2030 Base Control
17.0{ 15.9! 18.3{ 16.9
31 .4{ 30.0! 33.9{ 31.9
31 .3l 29.9! 33.8{ 31.8
12.5[ 11.8| 13.7[ 12.6
30.9 29.1 33.6 31.0
17.9 17.1 19.5 18.3
21.1, 19.9, 23.2s 21.5
17.8{ 16.9! 19.6{ 18.3
22.2{ 20.5! 24.5{ 22.0
22.8{ 21.7! 25.2{ 23.5
20.5[ 19.1 1 22.0[ 20.1
8.5 8.1 1 8.9 8.4
9.8 9.5 10.3 9.7
9.7J 9.4J 10.2{ 9.6
13.4{ 12.8! 14.5{ 13.6
16.5{ 15.9! 17.4| 16.6
13.9! 13.6! 14.4! 13.9
11. 6[ 11.0] 12.2[ 11.4
24.6{ 22.7! 26.2{ 23.5
15.8[ 15.3J 16.4[ 15.6
28.4J 26.3] 30.3 27.2
15.51 14.8! 16.71 15.7
17.9| 17.2! 19.3{ 18.2
17.91 17.1 1 19.31 18.1
16.6[ 16.1] 17.7[ 16.9
16.7 16.0 17.7 16.6
11.6 11.3 12.6 12.2
10.2J 9.g] 10.8{ 10.5
8.5{ 8.3! 9.0{ 8.7
8.3{ 8.1 1 8.6{ 8.4
9.0{ 8.6! 9.5{ 9.1
11.4 10.9 12.0 11.3
12.5 12.0 13.2 12.5
15.0 14.5 16.0 15.3
13.8 13.3] 14.6 13.8
16.1 15.5! 17.0{ 16.2
-------
APPENDIX E
1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs
MONITOR ID STATE
100051 002! DELAWARE
110010041 {DISTRICT OF COLU
1999
COUNTY Ambient 2020
SUSSEX | 14.2!
WASHINGTON | 15.2!
110010043iDISTRICTOFCOLUiWASHINGTON I 14.9!
120111002|FLORIDA
120251016 FLORIDA
120256001 FLORIDA
120330004 {FLORIDA
120570030 {FLORIDA
120571075{FLORIDA
120710005JFLORIDA
120730012{FLORIDA
120814012 FLORIDA
120830003 FLORIDA
120951004JFLORIDA
120952002 {FLORIDA
120992003 {FLORIDA
121030018iFLORIDA
121031008{FLORIDA
121056006{FLORIDA
121111002JFLORIDA
121150013{FLORIDA
121171002{FLORIDA
121275002{FLORIDA
13021 0007 {GEORGIA
13021 0012{GEORGIA
130510017 GEORGIA
130590001 GEORGIA
130630091 [GEORGIA
130890002 {GEORGIA
130892001 {GEORGIA
131 150005 {GEORGIA
13121 0032{GEORGIA
131210039 GEORGIA
131211001 GEORGIA
131390003JGEORGIA
1321 50011 {GEORGIA
BROWARD CO [ 9.3|
DADE 12.1
DADE CO 8.6
ESCAMBIACO , , 14.8,
HILLSBOROUGH CO | 12.8!
HILLSBOROUGH | 13.0!
LEE CO I 10.2!
LEON CO { 14.0|
MANATEE CO 11.6
MARION CO 11.4
ORANGE CO I 11.3J
ORANGE { 11.4!
PALM BEACH CO | 9.3!
PINELLASCO i 11.91
PINELLAS i 11.8]
POLK CO I 11.1 1
ST LUCIE CO | 9.7!
SARASOTACO [ 10.6|
SEMINOLECO i 10.9!
VOLUSIACO { 11.4!
BIBB CO i 19.6!
BIBB { 17.8|
CHATHAM CO 18.2
CLARKE CO 17.9
CLAYTON CO I 20.9J
DEKALBCO | 21.0!
DEKALB \ 21.6!
FLOYD I 21.1 1
FULTON 20.3
FULTON 23.0
FULTON CO 18.9
HALL [ 17.9]
MUSCOGEE { 18.5!
PM2.5 Concentrations
2020 2030
Base Control 2030 Base Control
14.1 13.6! 14.81 14.0
16.1 15.5! 17.1 { 16.2
16.4 15.7! 17.71 16.6
11.9 11.7] 12.9J 12.5
15.3 14.9 16.5 16.0
9.6 9.4 10.1 9.9
15.6, 15.1, 16.4, 15.6
14.61 14.1 1 15.71 15.0
14.5| 13.8! 15.5| 14.5
11. 1| 10.8! 11.81 11.3
14.6{ 14.2] 15.3J 14.7
12.8 12.5 13.4 13.1
11.8 11.4 12.5 11.8
13.8J 13.31 14.81 14.1
13.91 13.5! 15.01 14.3
11.1| 10.8! 11.9| 11.5
13.6! 13.3! 14.7! 14.1
12.6{ 12.3] 13.5J 12.9
12.il 11. 6l 12.8{ 12.1
10.0! 9.9! 10.5J 10.2
10.9J 10.7] 11.3 11.1
12.21 11.8! 13.11 12.4
12.4| 12.0! 13.1 { 12.6
20.6{ 20.0! 21.71 20.9
18.7J 18.2] 19.7J 19.0
19.6 19.1 1 20.5 19.8
18.1 1 17.2 19.1 1 17.9
22.7J 21 .6l 24.4J 22.8
25.1 { 24.0! 27.5{ 25.8
25.8{ 24.6! 28.2{ 26.5
21.31 20.3! 22.6{ 21.2
24.3 23.2 26.6 25.0
27.5 26.2 30.1 28.3
20.6 19.5 22.1 20.6
17.1 16.0] 18.3 16.6
19.6 19.2! 20.5{ 19.8
-------
APPENDIX E
1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs
MONITOR ID STATE
132230003! GEORGIA
132450005 {GEORGIA
132450091! GEORGIA
133030001 [GEORGIA
160010017 IDAHO
160050006 IDAHO
160270004 {IDAHO
160270005 {IDAHO
170310014{ILLINOIS
170310022NLLINOIS
170310050{ILLINOIS
170311016 ILLINOIS
170311701 ILLINOIS
170313301 (ILLINOIS
170314006{ILLINOIS
17031 4201 {ILLINOIS
170434002IILLINOIS
171191 007{ILLINOIS
171430037{lLLINOIS
17161 0003JILLINOIS
171670012{lLLINOIS
171971002{ILLINOIS
180030004 {INDIANA
180190005NNDIANA
180431004{INDIANA
180890006 INDIANA
180890022 INDIANA
180891 OOSJINDIANA
180891 016{INDIANA
181270024{INDIANA
1901 30008 {IOWA
190450021 IOWA
191032001 IOWA
191130036 IOWA
191 130037 [lOWA
191 39001 6 {IOWA
1999
COUNTY Ambient 2020
PAULDINGCO | 18.5!
RICHMOND { 19.4!
RICHMOND CO I 19.9!
WASHINGTON [ 18.2]
ADA 8.0
BANNOCK 8.1
CANYON CO , , 8.7,
CANYON | 9.9!
COOK { 18.0!
COOK I 17.4!
COOK { 17.2]
COOK 21.8
COOK 18.2
COOK I 17.5J
COOK { 15.1 1
COOK { 15.5!
DU PAGE CO i 15.5!
MADISON i 17.2]
PEORIACO I 16.01
ROCK ISLAND CO | 16.4J
SANGAMONCO [ 15.9J
WILL | 15.5!
ALLEN CO { 12.3!
CLARK CO i 15.9!
FLOYD CO { 14.1]
LAKE 14.3
LAKE 15.5
LAKE I 13.61
LAKE | 15.4!
PORTER \ 12.0!
BLACK HAWK CO I 12.1 1
CLINTON 12.7
JOHNSON CO 12.3
LINN CO 11.8
LINN [ 11.7]
MUSCATINE { 12.9!
PM2.5 Concentrations
2020 2030
Base Control 2030 Base Control
18.71 17.9! 19.81 18.6
19.5| 18.7! 20.6{ 19.4
20.3{ 19.6! 21.41 20.3
18.8J 18.4] 19.6J 19.1
8.4 8.2 8.7 8.4
8.6 8.4 9.0 8.7
8.2r 8.0, 8.3, 8.0
9.3{ 9.1 1 9.4{ 9.1
20.1 { 19.5! 21.6| 20.8
19.41 18.8! 20.8{ 20.1
19.2{ 18.6] 20.6J 19.9
24.3 23.6 26.2 25.2
20.3 19.7 21.8 21.0
19.5J 18.9J 21. 0{ 20.2
16.61 16.1 1 17.71 17.0
17.0| 16.5! 18.1 { 17.4
17.2! 16.7! 18.3! 17.6
17.7{ 17.1] 18.9{ 18.0
16.41 16.ol 17.2{ 16.5
16.7J 16.2J 17.4J 16.7
15.5J 15.0| 16.2 15.5
16.61 16.1 1 17.51 16.9
12.4| 11.9! 13.1| 12.4
16.21 15.6! 17.1 { 16.4
14.4J 13.9] 15.2J 14.6
15.1 1 14.6 15.9 15.3
16.4 15.9 17.3 16.6
14.3J 13.9J 15.21 14.5
16.21 15.7! 17.1 { 16.4
12.7| 12.3! 13.4| 12.9
12.11 11.7! 12.61 12.0
12.8 12.3 13.3 12.7
12.3 11.9 12.8 12.2
11.8 11.4 12.3 11.7
11.7 11.3] 12.2 11.6
13.1 12.7! 13.71 13.1
-------
APPENDIX E
1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs
MONITOR ID STATE
191 532510! IOWA
191 532520! IOWA
191 63001 5 1 IOWA
191 93001 7 ! IOWA
200910008 KANSAS
200910009 KANSAS
201 730008 {KANSAS
201 730009! KANSAS
201 730010 {KANSAS
201 770010 {KANSAS
201 770011 [KANSAS
210190017 KENTUCKY
210290006 KENTUCKY
21 0370003 (KENTUCKY
21 0430500 {KENTUCKY
21 059001 4 {KENTUCKY
21 067001 2 {KENTUCKY
21 067001 4 [KENTUCKY
21 0730006 {KENTUCKY
21 11 10043 {KENTUCKY
21 11 10044 1 KENTUCKY
21 11 10048 {KENTUCKY
21 11 10051 {KENTUCKY
21 11 70007 {KENTUCKY
21 1451 004 { KENTUCKY
211950002 KENTUCKY
212270007 KENTUCKY
220171002ILOUISIANA
220190010{LOUISIANA
220290002 {LOUISIANA
220330002 {LOUISIANA
220330009 {LOUISIANA
220331001 LOUISIANA
220470005 LOUISIANA
220470009 [LOUISIANA
22051 1001 {LOUISIANA
1999
COUNTY Ambient 2020
POLK | 11.3!
POLK { 11.7|
SCOTT CO I 13.1 1
WOODBURYCO [ 9.9|
JOHNSON 12.3
JOHNSON 11.5
SEDGWICKCO , , 12.0,
SEDGWICK | 11.9!
SEDGWICK { 12.5!
SHAWNEECO I 12.3!
SHAWNEE { 12.5]
BOYDCO 14.9
BULLITTCO 15.4
CAMPBELL CO I 15.41
CARTER CO { 11.9!
DAVIESSCO { 15.4!
FAYETTE i 15.4!
FAYETTECO | 16.4]
FRANKLIN I 14.1 !
JEFFERSON CO | 17.5J
JEFFERSON [ 16.9J
JEFFERSON | 17.2!
JEFFERSON | 15.2!
KENTONCO I 15.7!
MCCRACKEN [ 15.7]
PIKE 17.7
WARREN 16.1 1
CADDOPAR I 14.2J
CALCASIEU | 13.0!
CONCORDIA \ 13.8!
EAST BATON ROUGE PAR! 15.3!
EAST BATON ROUGE 15.1
EAST BATON ROUGE 13.5
IBERVILLE 15.3
IBERVILLE [ 12.6]
JEFFERSON { 13.8!
PM2.5 Concentrations
2020 2030
Base Control 2030 Base Control
11.61 11.2! 12.11 11.6
12.1| 11.6! 12.6| 12.0
13.31 12.9! 13.91 13.3
10.4J 10.1] 10.8J 10.4
13.0 12.6 13.6 13.0
12.1 11.7 12.7 12.2
12.6, 12.3, 13.0, 12.6
12.51 12.2! 12.91 12.5
13.0| 12.7! 13.5| 13.0
12.71 12.3! 13.21 12.6
12.8{ 12.4] 13.3J 12.7
15.0 14.6 15.7 15.2
14.3 13.8 14.9 14.2
15.5J 14.9J 16.41 15.5
11.71 11.5! 12.11 11.8
14.8| 14.3! 15.5| 14.9
14.0! 13.4! 14.7! 13.8
15.3{ 14.7] 16.1 1 15.2
13.21 12.61 13.8{ 13.1
17.8J 17.2J 18.9J 18.1
17.2J 16.6| 18.2 17.5
17.51 16.9! 18.51 17.7
15.5| 15.0! 16.4| 15.7
16.01 15.4! 17.01 16.1
14.9J 14.5] 15.6J 15.0
17.6 17.3 18.3 17.8
14.7 14.1 1 15.3 14.6
15.31 14.9J 16.ol 15.4
14.71 14.4! 15.61 15.1
15.0| 14.7! 15.8| 15.4
18.1 { 17.8! 19.21 18.7
17.9 17.6 19.0 18.5
16.1 15.7 17.0 16.6
17.9 17.5 18.7 18.2
14.7J 14.4] 15.4 15.0
16.61 16.3! 17.61 17.1
-------
APPENDIX E
1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs
MONITOR ID STATE
22051 2001! LOUISIANA
220550005 {LOUISIANA
220710010JLOUISIANA
220710012|LOUISIANA
220730004 LOUISIANA
220790001 LOUISIANA
221 050001 {LOUISIANA
221 21 0001 {LOUISIANA
23001 0011 {MAINE
23001 0011 {MAINE
23003001 3 [MAINE
230031011 MAINE
230050027 MAINE
2301 72011 (MAINE
2301 90002 {MAINE
2301 94003 {MAINE
260050003! MICHIGAN
26021 0014{MICHIGAN
260490021 {MICHIGAN
260650012JMICHIGAN
260650012{MICHIGAN
260770008 {MICHIGAN
260770008 {MICHIGAN
260810020JMICHIGAN
260990009 { MICHIGAN
261210040 MICHIGAN
261250001 MICHIGAN
261390005JMICHIGAN
261450018{MICHIGAN
261470005{MICHIGAN
29021 0010JMISSOURI
290390001 {MISSOURI
290470005 MISSOURI
290470026 MISSOURI
290470041 {MISSOURI
290770032{MISSOURI
1999
COUNTY Ambient 2020
JEFFERSON PAR | 14.8!
LAFAYETTE PAR | 12.9!
ORLEANS PAR I 15.1 1
ORLEANS ! 15.0]
OUACHITAPAR 13.9
RAPIDESPAR 14.3
TANGIPAHOA , , 13.9,
WEST BATON ROUGE PAR 14.9!
ANDROSCOGGIN CO | 10.0!
ANDROSCOGGIN CO I 10.0!
AROOSTOOK { 10.6]
AROOSTOOK 8.1 1
CUMBERLAND CO 10.0
OXFORD I 10.2!
PENOBSCOTCO | 8.9!
PENOBSCOTCO | 8.6!
ALLEGANCO i 12.2!
BERRIENCO | 12.3]
GENESEECO I 12.ol
INGHAMCO | 12.6!
INGHAM [ 12.9]
KALAMAZOOCO | 14.9!
KALAMAZOO | 14.7!
KENT CO i 13.8!
MACOMBCO { 12.7]
MUSKEGONCO 12.2
OAKLAND CO 14.2
OTTAWA CO I 12.g!
SAGINAW | 10.4!
STCLAIRCO \ 13.2!
BUCHANAN CO I 12.5!
CEDAR 11.2
CLAY 11.3
CLAY CO 12.3
CLAY [ 11. 6|
GREENE CO { 12.2!
PM2.5 Concentrations
2020 2030
Base Control 2030 Base Control
18.8 18.4! 20.0{ 19.5
14.1 13.7! 14.7| 14.2
19.2 18.8! 20.4{ 19.9
19.1 18.7] 20.3J 19.7
15.3 15.0 16.0 15.6
15.2 14.9 15.9 15.4
16.2, 15.9, 17.0, 16.5
17.61 17.3! 18.71 18.2
13.1 { 13.0! 14.8| 14.6
13.1 { 13.0! 14.81 14.6
11. 0{ 11.0] 11. 4{ 11.4
9.2 9.1 1 9.8 9.8
12.7 12.6 14.3 14.1
11. el 11.5! 12.5! 12.4
11.51 11.4! 12.91 12.8
11.0| 10.9! 12.4| 12.3
12.2! 11.81 12.9! 12.2
12.3{ 11.8] 12.8J 12.2
12.ol 11.7! 12.6{ 12.1
12.4J 12.0J 13.0J 12.4
12.7J 12.2] 13.3 12.6
14.91 14.3! 15.61 14.8
14.7| 14.1! 15.4| 14.6
14.01 13.5! 14.81 14.0
12.8J 12.5] 13.3J 12.9
12.4 12.0 13.1 1 12.5
15.2 14.7 16.2 15.5
13.1 1 12.61 13.9{ 13.1
10.31 10.0! 10.81 10.4
13.2| 13.0! 13.6| 13.3
12.71 12.3! 13.21 12.6
11.0 10.6 11.4 10.9
11.4 11.0 11.9 11.4
13.1 12.7 13.8 13.3
11. 7\ 11.3] 12.3 11.7
12.21 11.8! 12.71 12.1
-------
APPENDIX E
1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs
MONITOR ID STATE
29091 OOOSiMISSOURI
290950036iMISSOURI
290952002JMISSOURI
290990012|MISSOURI
291370001 MISSOURI
291831002 MISSOURI
291860006JMISSOURI
291892003{MISSOURI
291 895001! MISSOURI
295100086JMISSOURI
300290039 i MONTANA
300490018 MONTANA
300530018 MONTANA
300630024 (MONTANA
300630031! MONTANA
300930005 {MONTANA
301111065iMONTANA
311090022|NEBRASKA
311090022{NEBRASKA
320030022 {NEVADA
320030560 1 NEVADA
32031 001 6 {NEVADA
32031 001 6 {NEVADA
340030003 {NEW JERSEY
3401 71 003 [NEW JERSEY
340210008 NEW JERSEY
340230006 NEW JERSEY
340292002JNEW JERSEY
340310005{NEW JERSEY
340390004{NEW JERSEY
340390006JNEW JERSEY
35001 0024 {NEW MEXICO
350050005 {NEW MEXICO
3501 3001 7 {NEW MEXICO
3501 31 006 {NEW MEXICO
3501 71 002 {NEW MEXICO
1999
COUNTY Ambient 2020
HOWELL | 13.1 1
JACKSON CO { 10.9!
JACKSON I 14.1 1
JEFFERSON CO [ 15.1 1
MONROE 10.9
ST CHARLES CO 14.1
STEGENEVIEVE , , 13.7,
ST LOUIS CO | 15.31
ST LOUIS { 14.6!
ST LOUIS (CITY) | 15.1 1
FLATHEAD { 10.5]
LEWIS AND CLARK 6.3
LINCOLN 15.9
MISSOULA i 9.o!
MISSOULA { 9.8!
SILVER BOW { 7.3!
YELLOWSTONE CO i 8.0!
LANCASTER CO | 10.6]
LANCASTER I 11. el
CLARK | 4.7J
CLARK [ 11.2]
WASHOE | 9.9!
WASHOE { 9.8!
BERGEN CO i 13.4!
HUDSON CO { 14.4]
MERCER CO 12.4
MIDDLESEX CO 10.9
OCEAN CO I 10.4!
PASSAICCO | 12.4!
UNION CO \ 14.7!
UNION I 13.5!
BERNALILLO 6.3
CHAVES 7.0
DONA ANA CO 11.2
DONA ANA [ 6.6]
GRANT { 5.6!
PM2.5 Concentrations
2020 2030
Base Control 2030 Base Control
13.31 13.1 1 13.91 13.6
11.4| 11.1! 12.0| 11.5
15.01 14.6! 15.81 15.2
15.8J 15.2] 16.8J 16.0
10.6 10.3 11.0 10.6
14.6 14.1 15.5 14.8
13.3, 12.9, 13.9, 13.3
16.01 15.4! 17.01 16.2
15.1 { 14.6! 16.0| 15.4
15.61 15.1 1 16.61 15.9
11. 3{ 11.1] 11.5J 11.3
6.9 6.9 7.1 1 7.0
18.3 18.1 1 18.6 18.3
10.6J 10.5J 10.81 10.6
11.21 11.0! 11.41 11.2
8.3{ 8.2! 8.6{ 8.4
8.8! 8.7! 9.1! 8.9
11. 2{ 10.9] 11.6J 11.2
12.21 11.9! 12.6{ 12.2
5.6{ 5.4J 5.9{ 5.6
17.3J 16.6| 19.3 18.2
11.31 10.7! 12.11 11.4
11.1| 10.6! 12.0| 11.2
14.91 14.5! 15.91 15.4
15.5J 15.1] 16.6J 16.0
13.2 12.8 14.0 13.4
11.4 11.1 12.2 11.8
10.9J 10.7J 11. 5{ 11.1
13.31 12.9! 14.31 13.7
15.5| 15.1! 16.5| 16.0
14.61 14.1 1 15.61 15.0
7.4 7.2 7.8 7.6
7.5 7.4 7.8 7.6
11.8 11.4 12.3 11.8
6.6J 6.4] 6.8 6.6
5.8{ 5.7! 6.1 6.0
-------
APPENDIX E
1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs
MONITOR ID STATE
350250007! NEW MEXICO
350431 003! NEW MEXICO
350450006! NEW MEXICO
350490020 ! NEW MEXICO
37021 0034! NORTH CAROLINA
370670022! NORTH CAROLINA
370670024 {NORTH CAROLINA
3801 50003! NORTH DAKOTA
3801 71 004 {NORTH DAKOTA
380350004 {NORTH DAKOTA
380570004 [NORTH DAKOTA
390090003 OHIO
390170003 OHIO
390350013(OHIO
390350027 {OHIO
390350038 {OHIO
390350060! OHIO
390350065 [OHIO
390350066 {OHIO
390351002JOHIO
390490024 1 OHIO
390490025 {OHIO
390490081 {OHIO
39061 0014JOHIO
39061 7001 [OHIO
390810016 OHIO
390811001 OHIO
390851 001 [OHIO
390932003 {OHIO
390950024 {OHIO
390990005 {OHIO
391130014 OHIO
391130031 OHIO
391330002 OHIO
391450013JOHIO
391510017{OHIO
1999
COUNTY Ambient 2020
LEA | 7.2!
SANDOVAL CO | 5.2!
SAN JUAN I 5.8!
SANTA FE CO [ 4.9]
BUNCOMBE 16.4
FORSYTHCO 16.4
FORSYTH , , 16.4,
BURLEIGH CO | 7.6!
CASS CO { 9.4!
GRAND FORKS CO I 10.2!
MERCER { 6.9]
ATHENS 13.7
BUTLER CO 18.7
CUYAHOGACO I 17.91
CUYAHOGA { 18.2!
CUYAHOGA { 20.9!
CUYAHOGA i 18.6!
CUYAHOGA | 17.6]
CUYAHOGA I 15.ol
CUYAHOGA | 15.3!
FRANKLIN CO [ 18.3J
FRANKLIN | 17.1 1
FRANKLIN | 17.0!
HAMILTON I 19.9!
HAMILTON [ 17.2]
JEFFERSON 19.3
JEFFERSON CO 18.3
LAKE CO I 13.81
LORAINCO | 14.4!
LUCAS CO \ 14.9!
MAHONINGCO I 16.9!
MONTGOMERY CO 17.6
MONTGOMERY 16.0
PORTAGE CO 15.0
SCIOTO [ 24.2]
STARK CO { 18.4!
PM2.5 Concentrations
2020 2030
Base Control 2030 Base Control
7.4{ 7.3! 7.6{ 7.5
6.1 { 6.0! 6.5{ 6.3
5.7{ 5.6! 5.9{ 5.8
6.6[ 6.5] 6.7{ 6.6
16.3 15.9 17.1 16.5
16.4 15.7 17.5 16.4
16.4, 15.7, 17.5, 16.4
8.2{ 8.1 1 8.4{ 8.3
10.1 { 9.9! 10.4| 10.1
11. 1| 10.9! 11.31 11.1
7.3{ 7.2] 7.4{ 7.3
12.6 12.4 13.1 1 12.7
18.4 17.7 19.4 18.4
19.0J 18.4J 20.1 i 19.4
19.31 18.8! 20.5{ 19.7
22.2{ 21.6! 23.5{ 22.7
19.7! 19.2! 20.9! 20.2
18.7{ 18.2] 19.8J 19.1
15.9{ 15.4! 16.8{ 16.2
16.3J 15.8J 17.2J 16.6
18.5J 17.9] 19.4 18.7
17.31 16.8! 18.21 17.4
17.0| 16.4! 17.8| 17.1
20.1 { 19.3! 21.21 20.1
17.3J 16.6] 18.3J 17.4
18.7 18.3 19.5 19.0
17.0 16.5 17.6 17.0
14.61 14.sl 15.31 14.9
14.51 14.1 1 15.1 { 14.6
15.4| 15.0! 16.3| 15.6
16.71 16.2! 17.51 16.9
17.9 17.2 18.9 18.0
16.3 15.6 17.2 16.3
15.1 14.7 15.9 15.3
23.0[ 22.4] 23.9 23.2
17.91 17.3! 18.81 17.9
-------
APPENDIX E
1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs
MONITOR ID STATE
391510020{OHIO
391530017iOHIO
391550007iOHIO
410030013|OREGON
410290133 OREGON
410291001 OREGON
41 0350004 {OREGON
41 0370001! OREGON
41 0390060 {OREGON
410392013iOREGON
410470040|OREGON
410510080 OREGON
410510244 OREGON
41 05901 21 (OREGON
4106701 11 {OREGON
420010001 {PENNSYLVANIA
420030064 i P E N N SYLVAN I A
420030064 { P E N N SYLVAN I A
4200301 16JPENNSYLVANIA
420110009JPENNSYLVANIA
420170012[PENNSYLVANIA
420210011 {PENNSYLVANIA
420430401 {PENNSYLVANIA
420450002 { P E N N SYLVAN I A
420692006 { P E N N SYLVAN I A
420710007 PENNSYLVANIA
420770004 P E N N SYLVAN I A
420791101 [PENNSYLVANIA
42091 0013{PENNSYLVANIA
420950025 { P E N N SYLVAN I A
421010004JPENNSYLVANIA
421250005{PENNSYLVANIA
421250200JPENNSYLVANIA
421255001 {PENNSYLVANIA
421290008JPENNSYLVANIA
421330008{PENNSYLVANIA
1999
COUNTY Ambient 2020
STARK | 17.4!
SUMMIT { 18.0!
TRUMBULLCO i 16.7!
BENTON { 7.1 1
JACKSON CO 11.8
JACKSON 6.4
KLAMATH , , 10.7,
LAKE | 8.7!
LANE { 8.5!
LANE I 12.8!
MARION CO { 7.5]
MULTNOMAH CO 8.8
MULTNOMAH 8.5
UMATILLA I 8.8\
WASHINGTON CO i 7.3!
ADAMS { 13.1!
ALLEGHENY i 18.8!
ALLEGHENY | 22.0|
ALLEGHENY I 16.4!
BERKS CO | 13.5!
BUCKS CO [ 12.0]
CAMBRIA CO | 14.8!
DAUPHIN CO { 14.4!
DELAWARE CO I 13.1 1
LACKAWANNACO [ 11. 0|
LANCASTER CO 15.6
LEHIGHCO 11.9
LUZERNECO I 12.sl
MONTGOMERY CO | 13.0!
NORTHAMPTON CO { 12.9!
PHILADELPHIA I 14.4!
WASHINGTON 15.4
WASHINGTON CO 14.6
WASHINGTON 13.0
WESTMORELAND CO [ 14.9J
YORK CO { 15.4!
PM2.5 Concentrations
2020 2030
Base Control 2030 Base Control
16.91 16.3! 17.71 16.9
18.1 { 17.6! 19.0| 18.3
16.41 16.0! 17.21 16.6
7.5[ 7.4] 7.7J 7.5
13.8 13.6 14.0 13.7
6.9 6.8 7.0 6.9
11.4, 11.2, 11.6, 11.3
9.3{ 9.2! 9.4{ 9.2
9.4{ 9.3! 9.6{ 9.4
14.41 14.3! 14.61 14.4
7.8{ 7.6] 8.0J 7.8
10.0 9.8 10.6 10.3
9.6 9.4 10.2 9.9
9.5J 9.4J 9.7l 9.5
8.3{ 8.1 1 8.8{ 8.5
12.5| 11.9! 13.2| 12.3
17.0! 16.4! 17.7! 16.8
19.9{ 19.1] 20.7J 19.7
16.31 15.81 17.1 [ 16.4
13.3! 12.7J 14.0J 13.2
12.8J 12.4] 13.6 13.0
13.81 13.4! 14.31 13.7
13.7| 13.0! 14.4| 13.4
14.31 13.8! 15.21 14.6
10.7J 10.4] 11. 1{ 10.7
14.5 13.7 15.3 14.1
12.4 12.0 13.1 1 12.5
12.sl 12.o! 12.9i 12.4
14.01 13.5! 14.91 14.3
13.4| 12.9! 14.2| 13.5
15.51 14.9! 16.51 15.7
14.0 13.4 14.5 13.8
13.6 13.2 14.1 13.6
12.9 12.5 13.5 13.0
13.5[ 12.9] 14.0 13.3
14.91 14.2! 15.71 14.7
-------
APPENDIX E
1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs
MONITOR ID STATE
4501 90049! SOUTH CAROLINA
45041 0002! SOUTH CAROLINA
450430009! SOUTH CAROLINA
450430009|SOUTH CAROLINA
450470003! SOUTH CAROLINA
450790007! SOUTH CAROLINA
45079001 9 {SOUTH CAROLINA
450830010! SOUTH CAROLINA
481 130050 {TEXAS
481 41 0037 {TEXAS
484393006 [TEXAS
490110001 UTAH
490350003 UTAH
49035001 2 (UTAH
490353006 {UTAH
490353007 {UTAH
490450002! UTAH
490490002 [UTAH
490494001 {UTAH
490495010 {UTAH
490570001 1 UTAH
490570007 {UTAH
500030005 {VERMONT
500070007 {VERMONT
50021 0002 [VERMONT
500230005 VERMONT
500230005 VERMONT
51013002olviRGINIA
510590030{VIRGINIA
510591 004{VIRGINIA
510870015JVIRGINIA
511071005{VIRGINIA
515200006 VIRGINIA
515500012 VIRGINIA
516500004[VIRGINIA
517000013{VIRGINIA
1999
COUNTY Ambient 2020
CHARLESTON | 13.1 1
FLORENCE CO | 14.3!
GEORGETOWN I 13.5!
GEORGETOWN [ 12.9]
GREENWOOD 15.5
RICHLAND 15.4
RICHLANDCO , , 15.9,
SPARTANBURG CO | 16.0!
DALLAS { 17.0!
EL PASO CO I 9.4!
TARRANT { 12.6|
DAVIS CO 7.9
SALT LAKE CO 10.9
SALT LAKE I 12.5J
SALT LAKE | 9.9!
SALT LAKE | 10.2!
TOOELE i 9.3!
UTAH CO | 9.4|
UTAH I 9.3!
UTAH | 7.7!
WEBER CO [ 9.9]
WEBER | 8.1 1
BENNINGTON | 9.9!
CHITTENDEN CO I 7.0!
RUTLAND [ 10.9|
WASHINGTON 10.8
WASHINGTON 10.6
ARLINGTON CO I 13.sl
FAIRFAX CO | 13.3!
FAIRFAX \ 14.5!
HENRICOCO I 13.3!
LOUDOUNCO 12.7
BRISTOL CITY 16.4
CHESAPEAKE CITY 12.9
HAMPTON CITY [ 12.1 ]
NEWPORT NEWS CITY { 12.5!
PM2.5 Concentrations
2020 2030
Base Control 2030 Base Control
13.91 13.7! 14.51 14.2
14.7| 14.3! 15.4| 14.8
14.01 13.8! 14.61 14.2
13.4J 13.1] 13.9J 13.6
15.8 15.3 16.5 15.7
15.9 15.4 16.7 16.0
16.5, 15.9, 17.3, 16.4
16.61 15.9! 17.51 16.5
20.3{ 19.6! 21.9| 20.9
9.8{ 9.6! 10.31 9.9
14.8{ 14.2] 15.9J 15.1
10.1 1 9.8 10.8 10.5
13.9 13.4 15.0 14.4
16.o! 15.61 17.31 16.7
12.71 12.4! 13.71 13.2
13.0| 12.6! 14.0| 13.5
10.9! 10.7! 11.61 11.2
11. 0{ 10.7] 11.8J 11.3
11. 9{ 11.5! 12.9{ 12.3
9.1 1 8.8J 9.8! 9.4
11.3[ 11. l| 11.9 11.5
9.2{ 9.1 1 9.7{ 9.4
9.9{ 9.7! 10.3| 10.0
6.8{ 6.7! 7.1 { 6.9
10.8J 10.6] 11.2J 10.9
10.5 10.4 10.9 10.6
10.3 10.1 1 10.6 10.4
15.21 14.sl 16.41 15.4
14.71 14.0! 15.81 14.9
15.9| 15.2! 17.2| 16.2
13.81 13.5! 14.51 14.0
13.4 12.8 14.3 13.4
15.4 14.9 16.1 15.4
14.9 14.5 15.8 15.2
13.7 13.5] 14.5 14.1
14.1 13.8! 14.91 14.4
-------
APPENDIX E
1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs
MONITOR ID STATE
517600020!VIRGINIA
517750010iVIRGINIA
518100008!VIRGINIA
530110013|WASHINGTON
530330021 {WASHINGTON
530330057!WASHINGTON
530330080!WASHINGTON
530332004!WASHINGTON
530530031 {WASHINGTON
530531018!WASHINGTON
53061 1007|WASHINGTON
530630016 WASHINGTON
530630047 WASHINGTON
530670013(WASHINGTON
530730015!WASHINGTON
540030003! WEST VIRGINIA
540090005! WEST VIRGINIA
5401 1 0006 i WEST VI RG I N IA
54029001 1 iWEST VIRGINIA
54029001 1 {WEST VIRGINIA
540291 004 1 WEST VIRGINIA
540330003! WEST VIRGINIA
540390009! WEST VIRGINIA
540391 005! WEST VIRGINIA
540391 005 i WEST VIRGINIA
540511002 WEST VIRGINIA
540610003 WEST VIRGINIA
540690008 1 WEST VIRGINIA
54081 0002! WEST VIRGINIA
540890001 !WEST VIRGINIA
541 071 002! WEST VIRGINIA
550090005IWISCONSIN
550090026JWISCONSIN
550250025!WISCONSIN
550250047JWISCONSIN
550310025!WISCONSIN
1999
COUNTY Ambient 2020
RICHMOND CITY | 14.5!
SALEM CITY ! 13.2!
VIRGINIA BEACH CITY I 13.5!
CLARK CO | 9.4|
KING 10.3
KING 11.5
KING , , 8.9,
KING | 10.9!
PIERCE | 11.1!
PIERCE I 9.7!
SNOHOMISH [ 10.0|
SPOKANE CO 10.3
SPOKANE 8.5
THURSTON CO i 9.3J
WHATCOMCO | 8.1 1
BERKEKEYCO i 16.1 1
BROOKE CO i 17.8!
CABELLCO i 18.2|
HANCOCK CO I 16.91
HANCOCK | 17.3!
HANCOCK [ 16.8]
HARRISON | 15.0!
KANAWHACO i 17.1!
KANAWHA I 18.3!
KANAWHA [ 19.6|
MARSHALL CO 17.1
MONONGALIA 14.9
OHIO CO I 15.9J
RALEIGH | 14.0!
SUMMERS | 11.8!
WOOD CO I 17.8!
BROWN CO 11.1
BROWN 10.6
DANE CO 13.1
DANE [ 13.4]
DOUGLAS CO | 8.6!
PM2.5 Concentrations
2020 2030
Base Control 2030 Base Control
15.9! 15.4! 16.8! 16.1
12.7! 12.3! 13.2! 12.7
15.6! 15.2! 16.5! 15.9
10.6| 10.4| 11.2! 10.9
12.8 12.4 13.9 13.4
14.3 13.9 15.5 14.9
11.1, 10.7, 12.0, 11.5
13.6! 13.1 1 14.7! 14.1
13.7! 13.2! 14.7! 14.1
11.9! 11.5! 12.8! 12.2
11.4| 11.1! 12.1J 11.7
13.1 1 12.9 13.4 13.1
10.7 10.5 11.0 10.8
10.7J 10.4J 11.4i 10.9
8.5! 8.4! 8.8! 8.6
15.3! 14.7! 16.0! 15.1
17.2! 16.9! 18.0! 17.5
18.2| 17.9] 19.0J 18.5
16.4i 16.o! 17.1 1 16.6
16.7J 16.4J 17.4! 16.9
16.3J 15.9] 16.9 16.5
14.1! 13.8! 14.6! 14.2
17.8! 17.4! 18.5! 18.1
19.0! 18.6! 19.8! 19.3
20.4| 20.0| 21.3J 20.7
15.9 15.6 16.5 16.1
13.9 13.6 14.3 13.9
14.8! 14.4! 15.4! 14.8
13.4! 13.1 1 13.9! 13.5
11.2! 11.0! 11.6! 11.3
17.3! 17.0! 18.0! 17.5
11.1 10.7 11.6 11.1
10.6 10.2 11.1 10.6
13.3 12.8 14.0 13.3
13.6[ 13.l] 14.4 13.6
9.7! 9.5! 10.3! 10.1
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APPENDIX E
1999 Annual Mean PM2.5 Values and Future-Year Predictions Based on RRFs
MONITOR ID
550550008
STATE
WISCONSIN
550790010|WISCONSIN
550790026JWISCONSIN
550790059iWISCONSIN
550870009!WISCONSIN
551050002
551330027
551330034
551390011
551410016
560210001
560330001
560330002
WISCONSIN
WISCONSIN
WISCONSIN
WISCONSIN
WISCONSIN
WYOMING
WYOMING
WYOMING
1999
COUNTY Ambient 2020
JEFFERSON I 13.5!
MILWAUKEE [ 14.5]
MILWAUKEE 13.8
MILWAUKEE 15.0
OUTAGAMIECO , 11.2,
ROCK CO | 14.3!
WAUKESHACO i 14.9!
WAUKESHA I 13.5!
WINNEBAGOCO [ 11. 6|
WOOD 11.2
LARAMIE CO 5.6
SHERIDAN I 8.5J
SHERIDAN | 9.5!
PM2.5 Concentrations
2020 2030
Base Control 2030 Base Control
13.6! 13.1 1 14.3! 13.6
15.9| 15.4] 17.0J 16.2
15.2 14.7 16.2 15.5
16.1 15.6 17.1 16.4
11.4, 11.0, 12.0, 11.4
14.6! 14.1 1 15.3! 14.6
15.8! 15.2! 16.7! 15.9
14.2! 13.7! 15.1 1 14.4
11.6| 11.2] 12.1J 11.6
10.9 10.6 11.4 10.9
6.6 6.4 6.9 6.7
9.3J 9.2! 9.5i 9.3
10.4! 10.2! 10.6! 10.4
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Appendix F:
IMPROVE Monitoring Sites used in the REMSAD Model Performance
Evaluation
IMPROVE
Site Code
ACAD1
BADL1
BAND1
BIBE1
BLIS1
BO WAI
BRCA1
BRID1
BRIG1
BRLA1
CANY1
CHAS1
CHIR1
CORI1
CRLA1
CRMO1
DEVA1
DOLA1
DOSO1
EVER1
GICL1
GLAC1
GRBA1
GRCA1
GRSA1
GRSM1
GUMO1
JARB1
JEFF1
LAVO1
LOPE1
LYBR1
MACA1
MEVE1
MOOS1
MORA1
MOZI1
OKEF1
PEFO1
PINN1
PORE1
Site Name
Acadia National Park
Badlands National Park
Bandelier National Monument
Big Bend National Park
Bliss State Park(TRPA)
Boundary Waters Canoe Area
Bryce Canyon National Park
Bridger Wilderness
Brigantine National Wildlife Refu
Brooklyn Lake
Canyonlands National Park
Chassahowitzka National Wildlife
Chiricahua National Monument
Columbia River Gorge
Crater Lake National Park
Craters of the MoonNM(US DOE)
Death Valley Monument
Dome Lands Wilderness
Dolly Sods /Otter Creek Wildernes
Everglades National Park
Gila Wilderness
Glacier National Park
Great Basin National Park
Grand Canyon NP- Hopi Point
Great Sand Dunes National Monument
Great Smoky Mountains National Park
Guadalupe Mountains National Park
Jarbidge Wilderness
Jefferson/James River Face Wildern
Lassen Volcanic National Park
Lone Peak Wilderness
Lye Brook Wilderness
Mammoth Cave National Park
Mesa Verde National Park
Moosehorn NWR
Mount Rainier National Park
Mount Zirkel Wilderness
Okefenokee National Wildlife Refu
Petrified Forest National Park
Pinnacles National Monument
Point Reyes National Seashore
State
Maine
South Dakota
New Mexico
Texas
California
Minnesota
Colorado
Wyoming
New Jersey
Wyoming
Utah
Florida
Arizona
Washington
Oregon
Idaho
California
California
West Virginia
Florida
New Mexico
Montana
Nevada
Arizona
Colorado
Tennessee
Texas
Nevada
Virginia
California
Utah
Vermont
Kentucky
Colorado
Maine
Washington
Colorado
Georgia
Arizona
California
California
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APPENDIX F
IMPROVE Monitoring Sites used in the REMSAD Performance Evaluation
(Continued)
IMPROVE
Site Code
PUSO1
REDW1
ROMA1
ROMO2
SAGO1
SALMI
SAWT1
SCOV1
SEQU1
SHEN1
SHRO1
SIPS1
SNPA1
SOLA1
SULA1
THSI1
TONT1
UPBU1
WASH1
Site Name
Puget Sound
Redwood National Park
Cape Remain National Wildlife Ref
Rocky Mountain National Park
San Gorgonio Wilderness
Salmon National Forest
Sawtooth National Forest
Scoville (US DOE)
Sequoia National Park
Shenandoah National Park
Shining Rock Wilderness
Sipsy Wilderness
Snoqualamie Pass, Snoqualamie N.F
South Lake Tahoe (TRPA)
Sula (Selway Bitteroot Wilderness)
Three Sisters Wilderness
Tonto National Monument
Upper Buffalo Wilderness
Washington D.C.
State
Washington
California
South Carolina
Colorado
California
Idaho
Idaho
Idaho
California
Virginia
North Carolina
Alabama
Washington
California
Montana
Idaho
Arizona
Arkansas
Washington D.C.
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