SEPA
United State
Envircfirrwnlal Pratecboci
Agency
Air Quality Modeling Technical Support
Document: Light-Duty Vehicle Greenhouse Gas
Emission Standards Final Rule
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EP A 454/R-10-003
April 2010
Air Quality Modeling Technical Support Document:
Light-Duty Vehicle Greenhouse Gas Emission Standards
Final Rule
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC 27711
April 2010
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I. Introduction
This document describes the air quality modeling performed by EPA in support of the
Light-Duty Vehicle Greenhouse Gas Final Rule (hereafter referred to as LDGHG). A national
scale air quality modeling analysis was performed to estimate the impact of the vehicle standards
on future year: annual and 24-hour PM2.5 concentrations, daily maximum 8-hour ozone
concentrations, annual nitrogen and sulfur deposition levels, annual and seasonal ethanol levels
and select annual and seasonal air toxic concentrations (formaldehyde, acetaldehyde, benzene,
1,3-butadiene and acrolein) as well as visibility impairment. To model the air quality benefits of
this rule we used the Community Multiscale Air Quality (CMAQ)1 model. CMAQ simulates the
numerous physical and chemical processes involved in the formation, transport, and destruction
of ozone, particulate matter and air toxics. In addition to the CMAQ model, the modeling
platform includes the emissions, meteorology, and initial and boundary condition data which are
inputs to this model.
Emissions and air quality modeling decisions are made early in the analytical process.
For this reason, it is important to note that the inventories used in the air quality modeling and
the benefits modeling, which are presented in Section 5.8 of the RIA, are slightly different than
the final vehicle standard inventories presented in Section 5.5 of the RIA. However, the air
quality inventories and the final rule inventories are generally consistent, so the air quality
modeling adequately reflects the effects of the rule.
II. CMAQ Model Version, Inputs and Configuration
The 2005-based CMAQ modeling platform was used as the basis for the air quality
modeling of the LDGHG future baseline and the future control scenario for this final rule. This
platform represents a structured system of connected modeling-related tools and data that
provide a consistent and transparent basis for assessing the air quality response to projected
changes in emissions. The base year of data used to construct this platform includes emissions
and meteorology for 2005. The platform was developed by the U.S. EPA's Office of Air Quality
Planning and Standards in collaboration with the Office of Research and Development and is
intended to support a variety of regulatory and research model applications and analyses. This
modeling platform and analysis is fully described below.
A. Model version
CMAQ is a non-proprietary computer model that simulates the formation and fate of
photochemical oxidants, primary and secondary PM concentrations, acid deposition, and air
toxics, over regional and urban spatial scales for given input sets of meteorological conditions
and emissions. The CMAQ model version 4.7 was most recently peer-reviewed in February of
1 Byun, D.W., and K. L. Schere, 2006: Review of the Governing Equations, Computational Algorithms, and Other
Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Applied Mechanics
Reviews, Volume 59, Number 2 (March 2006), pp. 51-77.
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2009 for the U.S. EPA.2 The CMAQ model is a well-known and well-respected tool and has
been used in numerous national and international applications.3'4'5 CMAQ includes numerous
science modules that simulate the emission, production, decay, deposition and transport of
organic and inorganic gas-phase and particle-phase pollutants in the atmosphere. This 2005
multi-pollutant modeling platform used CMAQ version 4.7.16 with a minor internal change made
by the U.S. EPA CMAQ model developers intended to speed model runtimes when only a small
subset of toxics species are of interest. CMAQ v4.7.1 reflects updates to version 4.7 to improve
the underlying science which include aqueous chemistry mass conservation improvements,
improved vertical convective mixing and lowered Carbon Bond Mechanism-05 (CB-05)
mechanism unit yields for acrolein (from 1,3-butadiene tracer reactions which were updated to
be consistent with laboratory measurements).
B. Model domain and grid resolution
The CMAQ modeling analyses were performed for a domain covering the continental
United States, as shown in Figure II-l. This domain has a parent horizontal grid of 36 km with
two finer-scale 12 km grids over portions of the eastern and western U.S. The model extends
vertically from the surface to 100 millibars (approximately 15 km) using a sigma-pressure
coordinate system. Air quality conditions at the outer boundary of the 36 km domain were taken
from a global model and did not change over the simulations. In turn, the 36 km grid was only
used to establish the incoming air quality concentrations along the boundaries of the 12 km grids.
Only the finer grid data were used in determining the impacts of the LDGHG emission standard
program changes. Table II-l provides some basic geographic information regarding the CMAQ
domains.
2 Allen, D., Burns, D., Chock, D., Kumar, N., Lamb, B., Moran, M. (February 2009 Draft Version). Report on the
Peer Review of the Atmospheric Modeling and Analysis Division, NERL/ORD/EPA. U.S. EPA, Research Triangle
Park, NC. CMAQ version 4.7 was released on December, 2008. It is available from the Community Modeling and
Analysis System (CMAS) as well as previous peer-review reports at: http://www.cmascenter.org.
3 Hogrefe, C., Biswas, I, Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, I, et al. (2004). Simulating regional-scale
ozone climatology over the eastern United States: model evaluation results. Atmospheric Environment, 38(17),
2627-2638.
4 United States Environmental Protection Agency. (2008). Technical support document for the final
locomotive/marine rule: Air quality modeling analyses. Research Triangle Park, N.C.: U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards, Air Quality Assessment Division.
5 Lin, M., Oki, T., Holloway, T., Streets, D.G., Bengtsson, M., Kanae, S., (2008). Long range transport of acidifying
substances in East Asia Part I: Model evaluation and sensitivity studies. Atmospheric Environment, 42(24), 5939-
5955.
6 CMAQ version 4.7.1 model code is available from the Community Modeling and Analysis System (CMAS) at:
http://www.cmascenter.org as well as at EPA-HQ-OAR-0472-DRAFT-11662.
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Table II-l. Geographic elements of domains used in RFS2 modeling.
Map Projection
Grid Resolution
Coordinate Center
True Latitudes
Dimensions
Vertical extent
CMAQ Modeling Configuration
National Grid
Western U.S. Fine Grid
Eastern U.S. Fine Grid
Lambert Conformal Projection
36km
12km
12km
97degW,40degN
33 deg N and 45 deg N
148x112x14
213x192x14
279 x 240 x 14
14 Layers: Surface to 100 millibar level (see Table II-3)
Figure II-l. Map of the CMAQ modeling domain. The black outer box denotes the 36 km
national modeling domain; the red inner box is the 12 km western U.S. fine grid; and the
blue inner box is the 12 km eastern U.S. fine grid.
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C. Modeling Time-period
The 36 km and both 12 km CMAQ modeling domains were modeled for the entire year
of 2005.7 For the 8-hour ozone results, we are only using modeling results from the period
between May 1 and September 30, 2005. This 153-day period generally conforms to the ozone
season across most parts of the U.S. and contains the majority of days with observed high ozone
concentrations in 2005. Data from the entire year were utilized when looking at the estimation
of PM2.s, total nitrogen and sulfate deposition, visibility and toxics impacts from the regulation.
D. Model Inputs: Emissions, Meteorology and Boundary Conditions
The 2005-based CMAQ modeling platform was used for the air quality modeling of
future baseline emissions and control scenarios. As noted in the introduction, in addition to the
CMAQ model, the modeling platform also consists of the base- and future-year emissions
estimates (both anthropogenic and biogenic), meteorological fields, as well as initial and
boundary condition data which are all inputs to the air quality model.
1. Base Year and Future Baseline Emissions: The emissions modeling TSD, found in the
docket for this rule (EPA-420-R-10-011) contains a detailed discussion of the emissions inputs
used in our air quality modeling. We have provided a brief summary of the base year and future
baseline emissions used for the air quality modeling. The emissions data used in the base year
and future reference and control case are based on the 2005 v4 platform. The LDGHG cases use
some different emissions data than the official v4 platform for two reasons: (1) the LDGHG
Standard was evaluated in comparison to the modeling performed for the Revised annual
Renewable Fuel Standard (RFS2)8; therefore, RFS2-specific inputs were retained for LDGHG
and (2) the LDGHG modeling used data intended only for the rule development and not for
general use. Unlike the 2005 v4 platform, the configuration for LDGHG modeling included
additional hazardous air pollutants (HAPs) and used slightly older ancillary data. Both of these
differences are described in Section 2.1 of the emissions modeling TSD.
The 2030 reference case (projection without vehicle standards) is intended to represent
the emissions associated with use of the most likely volume of ethanol in the absence of the
LDGHG CC>2 reductions and RFS2 rule and Energy Independence and Security Act of 2007
(EISA) renewable fuel requirements. For this case, the ethanol volume was projected for 2030
using the Department of Energy, Energy Information Administration in the 2007 Annual Energy
Outlook (AEO) report. The US EGU point source emissions estimates for the future year
reference and control case are based on an Integrated Planning Model (IPM) run for criteria
pollutants, hydrochloric acid, and mercury in 2020 (though mercury was not modeled). The year
2020 was used since it was the closest readily year to the 2030 year used for LDGHG air quality
modeling. Both control and growth factors were applied to a subset of the 2005 non-EGU point
7 We also modeled 10 days at the end of December 2004 as a modeled "ramp up" period. These days are used to
minimize the effects of initial conditions and are not considered as part of the output analyses.
8 EPA 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. EPA-420-R-10-006. February
2010. Sections 3.4.2.1.2 and 3.4.3.3. Docket EPA-HQ-OAR-2009-0472-11332.
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and nonpoint to create the 2030 reference case. The 2002 v3.1 platform 2020 projection factors
were the starting point for most of the LDGHG year of 2030 SMOKE-based projections.
Ethanol plant replacements and additions were included in the 2005 base and 2030 reference
case as well as biodiesel additions and portable fuel containers.
It should be noted that the emission inventories used in the air quality and benefits
modeling are different from the final rule inventories due to the length of time required to
conduct the modeling. However, the air quality modeling inventories are generally consistent
with the final emission inventories, so the air quality modeling adequately reflects the effects of
the rule.
2. LDGHG Modeling Scenarios: As part of our analysis for this rulemaking, the CMAQ
modeling system was used to calculate daily and annual PM2 5 concentrations, 8-hour ozone
concentrations, annual and seasonal air toxics concentrations, annual total nitrogen and sulfur
deposition levels and visibility impairment for each of the following emissions scenarios:
2005 base year
2030 reference case projection without the vehicle standards
2030 control case projection with the vehicle standards
Model predictions are used in a relative sense to estimate scenario-specific, future-year
design values of PM2.5 and ozone. Specifically, we compare a 2030 reference scenario, a
scenario without the vehicle standards, to a 2030 control scenario which includes the vehicle
standards. This is done by calculating the simulated air quality ratios between any particular
future year simulation and the 2005 base. These predicted change ratios are then applied to
ambient base year design values. The design value projection methodology used here followed
EPA guidance9 for such analyses. Additionally, the raw model outputs are also used in a relative
sense as inputs to the health and welfare impact functions of the benefits analysis. Only model
predictions for air toxics as well as nitrogen and sulfur deposition were analyzed using absolute
model changes, although these parameters also considered percent changes between the control
case and two future baselines.
3. Meteorological Input Data: The gridded meteorological input data for the entire year
of 2005 were derived from simulations of the Pennsylvania State University /National Center for
Atmospheric Research Mesoscale Model. This model, commonly referred to as MM5, is a
limited-area, nonhydrostatic, terrain-following system that solves for the full set of physical and
thermodynamic equations which govern atmospheric motions.10 Meteorological model input
9 U. S. EPA, Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the 8-hour
Ozone NAAQS; EPA-454/R-05-002; Research Triangle Park, NC; October 2005.
10 Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth-Generation Perm State/NCAR Mesoscale
Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for Atmospheric Research, Boulder CO.
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fields were prepared separately for each of the three domains shown in Figure II-1 using MM5
version 3.7.4. The MM5 simulations were run on the same map projection as CMAQ.
All three meteorological model runs configured similarly. The selections for key MM5
physics options are shown below:
• Pleim-Xiu PEL and land surface schemes
• Kain-Fritsh 2 cumulus parameterization
• Reisner 2 mixed phase moisture scheme
• RRTM longwave radiation scheme
• Dudhia shortwave radiation scheme
Three dimensional analysis nudging for temperature and moisture was applied above the
boundary layer only. Analysis nudging for the wind field was applied above and below the
boundary layer. The 36 km domain nudging weighting factors were 3.0 x 104 for wind fields and
temperatures and 1.0 x 105 for moisture fields. The 12 km domain nudging weighting factors
were 1.0 x 104 for wind fields and temperatures and 1.0 x 105 for moisture fields.
All three sets of model runs were conducted in 5.5 day segments with 12 hours of overlap
for spin-up purposes. All three domains contained 34 vertical layers with an approximately 38m
deep surface layer and a 100 millibar top. The MM5 and CMAQ vertical structures are shown in
Table II-3 and do not vary by horizontal grid resolution.
Table II-3. Vertical layer structure for MM5 and CMAQ (heights are layer top).
CMAQ Layers
0
1
2
'J
A
6
1
8
Q
in
11
MM5 Layers
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Sigma P
1.000
0.995
0.990
0.985
0.980
0.970
0.960
0.950
0.940
0.930
0.920
0.910
0.900
0.880
0.860
0.840
0.820
0.800
0.770
0.740
0.700
0.650
0.600
Approximate
Height (m)
0
38
77
115
154
232
310
389
469
550
631
712
794
961
,130
,303
,478
,657
,930
2,212
2,600
3,108
3,644
Approximate
Pressure (mb)
1000
995
991
987
982
973
964
955
946
937
928
919
910
892
874
856
838
820
793
766
730
685
640
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12
13
14
23
24
25
26
27
28
29
30
31
32
33
34
0.550
0.500
0.450
0.400
0.350
0.300
0.250
0.200
0.150
0.100
0.050
0.000
4,212
4,816
5,461
6,153
6,903
7,720
8,621
9,625
10,764
12,085
13,670
15,674
595
550
505
460
415
370
325
280
235
190
145
100
The meteorological outputs from all three MM5 sets were processed to create model-
ready inputs for CMAQ using the Meteorology-Chemistry Interface Processor (MCIP), version
3.4 to derive the specific inputs to CMAQ.11
Before initiating the air quality simulations, it is important to identify the biases and
errors associated with the meteorological modeling inputs. The 2005 MM5 model performance
evaluations used an approach which included a combination of qualitative and quantitative
analyses to assess the adequacy of the MM5 simulated fields. The qualitative aspects involved
comparisons of the model-estimated synoptic patterns against observed patterns from historical
weather chart archives. Additionally, the evaluations compared spatial patterns of monthly
average rainfall and monthly maximum planetary boundary layer (PEL) heights. Qualitatively,
the model fields closely matched the observed synoptic patterns, which is not unexpected given
the use of nudging. The operational evaluation included statistical comparisons of
model/observed pairs (e.g., mean normalized bias, mean normalized error, index of agreement,
root mean square errors, etc.) for multiple meteorological parameters. For this portion of the
evaluation, five meteorological parameters were investigated: temperature, humidity, shortwave
downward radiation, wind speed, and wind direction. The three individual MM5 evaluations are
described elsewhere.12'13'14 It was ultimately determined that the bias and error values associated
with all three sets of 2005 meteorological data were generally within the range of past
meteorological modeling results that have been used for air quality applications.
4. Initial and Boundary Conditions: The lateral boundary and initial species
concentrations are provided by a three-dimensional global atmospheric chemistry model, the
11 Byun, D.W., and Ching, J.K.S., Eds, 1999. Science algorithms of EPA Models-3 Community Multiscale Air
Quality (CMAQ modeling system, EPA/600/R-99/030, Office of Research and Development).
12 Baker K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Eastern U.S.
12-km Domain Simulation, USEPA/OAQPS, February 2, 2009.
13 Baker K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Western U.S.
12-km Domain Simulation, USEPA/OAQPS, February 2, 2009.
14 Baker K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Continental
U.S. 36-km Domain Simulation, USEPA/OAQPS, February 2, 2009.
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GEOS-CHEM15 model (standard version 7-04-1116). The global GEOS-CHEM model simulates
atmospheric chemical and physical processes driven by assimilated meteorological observations
from the NASA's Goddard Earth Observing System (GEOS). This model was run for 2005 with
a grid resolution of 2.0 degree x 2.5 degree (latitude-longitude) and 30 vertical layers up to 100
mb. The predictions were used to provide one-way dynamic boundary conditions at three-hour
intervals and an initial concentration field for the 36-km CMAQ simulations. The future base
conditions from the 36 km coarse grid modeling were used as the initial/boundary state for all
subsequent 12 km finer grid modeling.
E. CMAQ Base Case Model Performance Evaluation
1. PM2.5'. An operational model performance evaluation for PM2.5 and its related
speciated components (e.g., sulfate, nitrate, elemental carbon, organic carbon, etc.) was
conducted using 2005 state/local monitoring data in order to estimate the ability of the CMAQ
modeling system to replicate base year concentrations. In summary, model performance
statistics were calculated for observed/predicted pairs of daily/monthly/seasonal/annual
concentrations. Statistics were generated for the following geographic groupings: domain wide,
Eastern vs. Western (divided along the 100th meridian), and each Regional Planning
Organization (RPO) region17. The "acceptability" of model performance was judged by
comparing our CMAQ 2005 performance results to the range of performance found in recent
regional PM2.5 model applications for other, non-EPA studies18. Overall, the fractional bias,
fractional error, normalized mean bias, and normalized mean error statistics shown in Table II-4
are within the range or close to that found by other groups in recent applications. The model
performance results give us confidence that our application of CMAQ using this modeling
platform provides a scientifically credible approach for assessing PM2.5 concentrations for the
purposes of the LDGHG vehicle standards assessment. A detailed summary of the 2005 CMAQ
model performance evaluation is available in Appendix D19.
15 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric Chemistry Modeling
Group, Harvard University, Cambridge, MA, October 15, 2004.
16 Henze, O.K., J.H. Seinfeld, N.L. Ng, J.H. Kroll, T-M. Fu, DJ. Jacob, C.L. Heald, 2008. Global modeling of
secondary organic aerosol formation from aromatic hydrocarbons: high-vs.low-yield pathways. Atmos. Chem. Phys.,
8, 2405-2420.
17 Regional Planning Organization regions include: Mid-Atlantic/Northeast Visibility Union (MANEVU), Midwest
Regional Planning Organization - Lake Michigan Air Directors Consortium (MWRPO-LADCO), Visibility
Improvement State and Tribal Association of the Southeast (VISTAS), Central States Regional Air Partnership
(CENRAP), and the Western Regional Air Partnership (WRAP).
18 These other modeling studies represent a wide range of modeling analyses which cover various models, model
configurations, domains, years and/or episodes, chemical mechanisms, and aerosol modules.
19 U.S. Environmental Protection Agency, Air Quality Modeling Technical Support Document: Light-Duty Vehicle
Greenhouse Gas Emission Standards Final Rule, Appendix D: CMAQ Model Performance Evaluation for Ozone,
Paniculate Matter and Toxics. April, 2010 (EPA-454/R-10-003).
10
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Table II-4. 2005 CMAQ annual PM2.s species model performance statistics.
CMAQ 2005 Annual PM2 5 species
PM25
Total Mass
Sulfate
Nitrate
STN
IMPROVE
STN
IMPROVE
CASTNet
STN
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
No. of
Obs.
11797
3440
2318
3020
3067
2523
2826
9321
10411
571
2339
1694
2376
8820
13897
3920
2495
3498
3882
3059
3157
9034
10002
531
2253
1685
2350
8496
3170
1142
615
786
1099
300
1041
12741
3655
2495
3499
NMB (%)
6.6
-10.0
16.6
19.5
-7.2
0.6
-10.9
1.6
-10.9
9.1
21.5
-10.5
-5.5
-13.3
-8.7
-17.0
-1.2
-3.9
-10.9
-19.8
-15.5
-11.4
-9.0
-9.5
-3.8
-13.8
-19.7
-4.7
-15.7
-19.8
-13.4
-10.3
-17.9
-29.1
-18.4
37.8
-41.7
41.0
48.9
NME (%)
39.5
45.0
38.1
45.6
34.1
41.7
46.1
43.4
44.8
39.4
52.9
37.2
42.4
45.0
32.6
42.3
33.4
31.7
30.3
36.5
45.8
33.8
39.9
31.8
34.2
31.5
35.8
41.7
23.1
31.1
21.7
21.3
22.7
32.0
31.6
78.6
65.3
73.3
83.1
FB (%)
4.0
-9.5
15.8
16.5
-7.6
-3.6
-10.6
0.0
-13.6
8
14.9
-9.7
-4.7
-14.5
-5.8
-7.8
4.1
-0.7
-8.6
-16.6
-6.7
-2.6
6.8
-1.9
3.0
-8.1
-12.2
9.6
-14.2
-10.5
-11.0
-7.7
-19.5
-29.3
-9.3
0.4
-70.8
24.0
11.5
FE (%)
39.2
44.4
35.1
40.7
29.1
44.7
45.0
44.0
46.8
38.5
46.8
42.3
46.0
47.2
35.2
42.8
34.1
33.3
32.9
40.8
44.0
38.4
43.5
34.1
37.1
35.4
39.8
44.4
25.8
32.6
22.9
23.0
25.6
35.2
32.8
79.4
97.5
66.8
75.6
11
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Total
Nitrate
(NO3 +
HNO3)
Ammonium
Elemental
Carbon
IMPROVE
CASTNet
STN
CASTNet
STN
IMPROVE
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
3882
1927
3139
9027
9987
531
2248
1685
2350
8480
3170
1142
615
786
1099
300
1041
13897
3893
2495
3498
3882
3059
3130
3170
1142
615
786
1099
300
1041
14038
3814
2502
3479
3877
3221
3015
8668
12851
602
2117
1584
2123
34.5
25.0
-47.3
52.8
-18.3
32.8
86.2
67.2
40.89
-35.7
41.0
5.4
37.4
55.8
41.8
23.6
4.8
13.0
-14.9
21.0
21.2
7.0
1.3
-20.5
5.7
-6.4
15.4
14.5
-7.5
3.3
-12.6
45.5
31.1
40.4
57.3
27.2
72.1
38.7
-14.6
43.4
-1.3
10.6
-36.7
-23.8
93.7
67.2
65.4
98.5
75.4
77.1
122.5
126.4
82.1
77.0
51.0
36.3
47.0
59.4
53.4
40.3
37.6
44.8
55.3
44.8
47.6
41.4
45.1
59.0
36.5
37.8
37.2
40.8
32.9
36.9
37.1
77.1
77.7
65.9
83.7
64.4
102.0
82.8
49.3
75.6
45.1
54.5
46.9
47.7
-19.4
4.8
-79.1
-21.0
-84.2
2.0
8.9
-24.5
-10.1
-91.7
31.7
13.1
35.1
44.4
29.0
16.8
14.7
17.2
7.1
27.0
29.5
11.4
4.22
5.8
7.1
-4.0
18.9
18.4
-7.4
5.5
-4.9
30.6
19.5
33.8
38.5
20.6
39.0
21.0
-18.2
29.6
-15.7
-3.7
-39.3
-21.5
91.4
75.0
99.9
101.4
120.6
83.8
97.3
104.4
95.8
124.2
44.0
40.6
40.6
50.2
45.6
36.2
41.6
46.8
55.0
43.8
48.2
43.4
51.1
57.2
36.8
37.6
35.2
38.7
36.2
40.1
37.5
58.3
62.5
53.4
59.6
51.0
69.5
65.1
53.3
57.8
48.1
54.1
56.3
53.4
12
-------
Organic
Carbon
STN
IMPROVE
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
8169
12619
3582
2380
3323
3802
2259
3060
8662
11586
601
2116
1587
2123
8165
5.8
-27.2
-32.1
-28.3
-7.6
-39.3
-31.4
-31.7
-21.7
-29.9
-22.7
3.5
-30.1
-36.9
-11.7
64.9
50.5
56.7
49.0
53.3
49.2
51.2
57.6
49.4
50.4
41.3
55.7
42.8
51.7
59.2
-8.6
-22.8
-28.2
-19.7
-4.2
-39.4
-28.3
-27.8
-25.6
-26.7
-27.7
-5.1
-37.8
-39.1
-19.3
61.0
60.1
61.3
58.1
58.6
61.0
63.2
61.4
55.8
60.6
48.3
53.1
54.1
61.0
61.0
2. Ozone: An operational model performance evaluation for hourly and eight-hour daily
maximum ozone was conducted in order to estimate the ability of the CMAQ modeling system
to replicate the base year concentrations for the 12-km Eastern and Western United States
domain shown in Figure II-1. Ozone measurements from 1194 sites (817 in the East and 377 in
the West) were included in the evaluation and were taken from the 2005 State/local monitoring
site data in the Air Quality System (AQS) Aerometric Information Retrieval System (AIRS).
The ozone metrics covered in this evaluation include one-hour daily maximum ozone
concentrations and eight-hour daily maximum ozone concentrations. The evaluation principally
consists of statistical assessments of model versus observed pairs that were paired in time and
space on an hourly and/or daily basis, depending on the sampling frequency of each
measurement site (measured data). This ozone model performance was limited to the ozone
season (May through September) that was modeled for the LDGHG final rule. Appendix D
contains a more detailed summary of ozone model performance over the 12km Eastern and
Western U.S. grid. A summary of the evaluation is presented here.
As with the national, annual PM2 5 CMAQ modeling, the "acceptability" of model
performance was judged by comparing our CMAQ 2005 performance results to the range of
performance found in recent regional ozone model applications (e.g., EPA's Renewable Fuel
Standards-2 Final Rule20, EPA's Proposal to Designate an Emissions Control Area for Nitrogen
Oxides, Sulfur Oxides, and Particulate Matter21 and the Clean Air Interstate Rule22). Overall,
U.S. Environmental Protection Agency, Air Quality Modeling Technical Support Document: Changes to
Renewable Fuel Standard Program, Appendix B: CMAQ Model Performance Evaluation for Ozone, Particulate
Matter and Toxics. January, 2010 (EPA-454/R-10-001A).
21 U.S. Environmental Protection Agency, Proposal to Designate an Emissions Control Area for Nitrogen Oxides,
Sulfur Oxides, and Particulate Matter: Technical Support Document. EPA-420-R-007, 329pp., 2009.
(http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09007.pdf)
13
-------
the normalized mean bias and error (NMB and NME), as well as the fractional bias and error (FB
and FE) statistics shown in Tables II-5 and II-6 indicate that CMAQ-predicted 2005 hourly and
eight-hour daily maximum ozone residuals (i.e., observation vs. model predictions) are within
the range of other recent regional modeling applications. The CMAQ model performance results
give us confidence that our applications of CMAQ using this modeling platform provide a
scientifically credible approach for assessing ozone concentration changes resulting from the
final LDGHG emission standard reductions.
Table II-5. 2005 CMAQ one-hour daily maximum ozone model performance statistics
calculated for a threshold of 40 ppb.
CMAQ 2005 One-Hour Maximum Ozone:
Threshold of 40 ppb
May
June
July
August
September
12-kmEUS
12-kmWUS
Midwest
Northeast
Southeast
Central U.S.
West
12-kmEUS
12-kmWUS
Midwest
Northeast
Southeast
Central U.S.
West
12-kmEUS
12-kmWUS
Midwest
Northeast
Southeast
Central U.S.
West
12-kmEUS
12-kmWUS
Midwest
Northeast
Southeast
Central U.S.
West
12-kmEUS
12-kmWUS
Midwest
Northeast
Southeast
Central U.S.
West
No.ofObs.
21394
9631
4418
4102
6424
4328
8294
19517
9056
4639
4148
4644
4062
7737
19692
9443
4923
4445
4733
3521
8168
19643
9562
4549
4139
5303
3589
8357
18085
8725
4002
3667
5259
3286
7530
NMB (%)
-1.6
-3.4
0.8
5.4
-3.6
-6.4
-3.5
-3.5
-3.7
-4.6
-1.0
-2.7
-6.2
-4.0
1.2
0.4
0.4
4.2
4.2
-3.8
0.2
0.1
-0.8
0.2
0.2
3.6
-4.1
-1.0
-2.2
-3.6
-3.6
-1.8
-0.1
-6.1
-4.1
NME (%)
11.5
12.8
10.0
11.8
11.3
13.4
12.9
12.8
13.0
12.3
14.1
12.5
13.2
13.1
14.2
16.0
12.7
15.2
15.1
14.8
16.2
13.9
15.5
12.2
13.2
14.9
16.2
15.7
12.0
14.1
10.7
11.3
12.1
14.5
14.3
FB (%)
-0.8
-2.8
1.0
5.9
-3.0
-5.5
-3.0
-2.8
-3.2
-4.0
-0.1
-2.2
-5.4
-3.6
1.8
1.0
0.9
4.8
4.6
-3.1
0.7
0.8
-0.6
1.0
1.2
3.9
-2.9
-1.0
-1.3
-3.2
-3.0
-0.7
0.8
-5.1
-3.8
FE (%)
11.6
12.7
10.2
11.7
11.5
13.4
12.8
12.9
13.0
12.4
14.2
12.6
13.3
13.1
14.1
15.8
12.6
14.9
14.8
14.9
16.0
13.8
15.5
12.3
13.1
14.5
16.1
15.7
12.0
14.3
10.8
11.3
12.1
14.5
14.4
22 U.S. Environmental Protection Agency; Technical Support Document for the Final Clean Air Interstate Rule: Air
Quality Modeling; Office of Air Quality Planning and Standards; Research Triangle Park, NC; March 2005.
14
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Seasonal Aggregate
(May - September)
12-kmEUS
12-kmWUS
Midwest
Northeast
Southeast
Central U.S.
West
98331
46417
22531
20501
26363
18786
40086
-1.2
-2.1
-1.4
1.4
0.1
-5.4
-2.3
12.9
14.3
11.7
13.3
13.1
14.4
14.5
-0.5
-1.7
-0.8
2.3
0.7
-4.4
-2.1
12.8
14.2
11.7
13.1
13.0
14.4
14.4
Table 11-6. 2005 CMAQ eight-hour daily maximum ozone model performance statistics
calculated for a threshold of 40 ppb.
CMAQ 2005 Eight-Hour Maximum
Ozone: Threshold of 40 ppb
May
June
July
August
September
Seasonal Aggregate
(May - September)
12-km BUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Midwest
No. of Obs.
19310
8445
3858
3528
6019
3927
7234
17404
8102
4324
3590
3924
3663
6889
17045
8556
4429
3856
3806
3057
7407
16953
8523
4027
3530
4447
3096
7469
15190
7465
3265
2856
4647
2798
6446
85902
41091
19903
NMB (%)
-1.0
-1.6
0.2
5.2
-2.1
-5.8
-1.8
-2.1
-1.9
-3.8
0.3
-0.3
-5.5
-2.2
3.3
3.7
1.8
6.6
7.4
-2.3
3.5
1.9
1.6
0.9
1.4
7.4
-3.4
1.4
-1.8
-2.4
-4.2
-2.3
1.5
-6.5
-2.9
0.1
0.0
-0.9
NME (%)
10.9
12.0
10.0
11.4
10.5
12.8
12.1
11.9
11.9
11.6
13.1
11.4
12.1
12.1
13.4
15.0
11.8
14.6
15.0
13.2
15.1
12.9
13.9
11.3
12.3
14.7
14.4
14.1
11.2
13.4
10.2
10.6
11.2
13.6
13.7
12.1
13.3
11.1
FB (%)
-0.4
-1.2
0.7
5.4
-1.6
-5.2
-1.5
-1.5
-1.6
-3.4
1.0
0.1
-5.0
-2.0
3.6
3.9
2.3
6.8
7.3
-2.1
3.6
2.2
1.5
1.4
2.0
7.2
-3.1
1.2
-1.3
-2.6
-4.0
-1.8
2.1
-6.1
-3.1
0.5
0.1
-0.5
FE (%)
11.0
12.0
10.2
11.2
10.6
13.0
12.0
12.0
11.9
11.8
13.2
11.5
12.3
12.1
13.3
14.7
11.8
14.3
14.5
13.5
14.9
12.9
13.9
11.4
12.2
14.1
14.8
14.0
11.3
13.9
10.4
10.7
11.2
14.0
14.1
12.1
13.3
11.2
15
-------
Northeast
Southeast
Central U.S.
West
17360
22843
16541
35445
2.4
2.3
-4.8
-0.2
12.6
12.3
13.2
13.5
2.9
2.6
-4.4
-0.3
12.4
12.2
13.4
13.5
3. Nitrate and Sulfate Deposition
Annual nitrate and sulfate deposition performance statistics are provided in Table II-7.
The model predictions for annual nitrate deposition generally show small under-predictions for
the Eastern and Western NADP sites (NMB values range from -3% to -18%). Sulfate deposition
performance in the EUS and WUS shows the similar over predictions (NMB values range from
3% to 14%), except for predicted under-prediction in the Central US (NMB = -9.9%). The errors
for both annual nitrate and sulfate are relatively moderate with values ranging from 54% to 87%
which reflect scatter in the model predictions to observation comparison. Similar to the national,
annual PM2.5 and ozone CMAQ modeling, the "acceptability" of model performance was judged
by comparing our CMAQ 2005 performance results to the model performance results found in
recent regional multi-pollutant model applications.
Table II-7. CMAQ 2005 annual model performance statistics for total nitrate and sulfate
deposition.
CMAQ 2005 Total Deposition
Nitrate
Sulfate
12-kmEUS
12-kmWUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
No. of Obs.
7381
2732
1391
1658
1980
1229
2257
7381
2732
1391
1658
1980
1229
2257
NMB (%)
-0.6
-11.6
4.9
10.1
4.9
-11.0
-9.8
7.8
5.6
16.4
12.6
8.6
-7.3
13.2
NME (%)
63.9
69.5
62.0
60.9
67.3
62.7
73.8
67.0
76.3
62.6
64.3
71.4
65.1
81.8
FB (%)
-7.8
-12.7
5.0
3.9
0.1
-11.0
-12.7
6.0
4.8
23.2
16.5
6.4
-1.2
6.5
FE (%)
74.0
83.4
67.8
66.5
71.3
78.3
85.0
75.3
86.5
70.4
67.3
73.8
80.3
87.9
4. Hazardous air pollutants
An operational model performance evaluation for daily, monthly, seasonal, and annual
specific air toxics (formaldehyde, acetaldehyde, benzene, acrolein, and 1,3-butadiene) was
conducted in order to estimate the ability of the CMAQ modeling system to replicate the base
year concentrations for the 12-km Eastern and Western United States domains. Toxic
measurements from 471 sites in the East and 135 sites in the West were included in the
evaluation and were taken from the 2005 State/local monitoring site data in the National Air
Toxics Trends Stations (NATTS). Similar to PM2 5 and ozone, the evaluation principally
16
-------
consists of statistical assessments of model versus observed pairs that were paired in time and
space on daily basis. Appendix D contains a more detailed summary of air toxics model
performance over the 12km Eastern and Western U.S. grid. A summary of the evaluation is
presented here.
Model predictions of annual formaldehyde, acetaldehyde and benzene showed relatively
small bias and error percentages when compared to observations. The model yielded larger bias
and error results for 1,3 butadiene and acrolein based on limited monitoring sites. As with the
national, annual PM2.5 and ozone CMAQ modeling, the "acceptability" of model performance
was judged by comparing our CMAQ 2005 performance results to the limited performance
found in recent regional multi-pollutant model applications.23'24'25 Overall, the normalized mean
bias and error (NMB and NME), as well as the fractional bias and error (FB and FE) statistics
shown in Table II-8 indicate that CMAQ-predicted 2005 toxics (i.e., observation vs. model
predictions) are within the range of recent regional modeling applications.
Table II-8. 2005 CMAQ annual toxics model performance statistics
CMAQ 2005 Annual
Formaldehyde
Acetaldehyde
Benzene
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
No. of Obs.
6365
1928
1982
771
1246
1815
1746
6094
1892
1969
703
1231
1640
1709
11615
3369
2589
1425
NMB (%)
-53.8
-24.5
-28.2
-75.9
-65.0
-41.4
-21.7
-0.9
-14.4
-6.6
-8.9
3.2
5.6
-15.6
-30.6
-34.9
26.5
-5.8
NME (%)
64.5
50.9
50.7
85.0
71.4
49.4
51.5
63.0
53.6
64.0
58.8
64.1
57.9
53.9
66.9
60.5
55.1
73.5
FB (%)
-36.6
-25.8
-26.3
-22.6
-48.8
-38.7
-22.0
-5.2
-14.7
-6.4
-8.7
-3.7
-0.9
-15.4
-10.4
-25.4
22.7
27.9
FE (%)
64.2
58.6
60.7
72.7
69.1
59.6
58.2
59.8
58.2
63.4
59.3
61.5
50.5
59.3
62.4
62.3
47.7
62.7
23 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform:
Air Toxics, Ozone, and Paniculate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.
24 Strum, M., Wesson, K., Phillips, S., Cook, R., Michaels, H., Brzezinski, D., Pollack, A., Jimenez, M., Shepard, S.
Impact of using link-level emissions on multi-pollutant air quality model predictions at regional and local scales.
17th Annual International Emission Inventory Conference, Portland, Oregon, June 2-5, 2008.
25 Wesson, K., N. Farm, and B. Timin, 2010: Draft Manuscript: Air Quality and Benefits Model Responsiveness to
Varying Horizontal Resolution in the Detroit Urban Area, Atmospheric Pollution Research, Special Issue: Air
Quality Modeling and Analysis.
17
-------
1,3-Butadiene
Acrolein
Southeast
Central U.S.
West
12-kmEUS
12-kmWUS
Northeast
Midwest
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
2426
4737
2333
8102
1976
1902
516
1226
4142
1082
1660
783
850
n/a
278
n/a
592
-38.3
-46.4
-26.1
-71.9
-46.5
-34.1
-74.8
-82.4
-63.8
-36.7
-93.8
-95.4
-89.5
n/a
-96.7
n/a
-95.8
69.4
67.6
61.2
84.9
83.1
53.0
85.5
84.0
86.1
78.4
94.5
95.5
90.9
n/a
96.7
n/a
95.8
-12.9
-30.9
-13.6
-37.5
-22.7
-42.5
-34.8
-93.4
-8.0
-36.6
-126.2
-164.5
-116.0
n/a
-152.8
n/a
-176.9
60.1
68.0
62.1
88.2
91.5
64.5
77.7
100.7
89.6
84.2
138.4
167.2
131.1
n/a
153.6
n/a
176.9
III. CMAQ Model Results
As described above, we performed a series of air quality modeling simulations for the
continental U.S in order to assess the impacts of the light-duty vehicle greenhouse gas rule. We
looked at impacts on future ambient PM2.5, ozone, ethanol and air toxics levels, as well as
nitrogen and sulfur deposition levels and visibility impairment. In this section, we present
information on current levels of pollution as well as model projected levels of pollution for 2030.
A. Impacts of LDGHG Standards on Future 8-Hour Ozone Levels
This section summarizes the results of our modeling of ozone air quality impacts in the
future with the vehicle standards. Specifically, we compare a 2030 reference scenario, a scenario
without the vehicle standards, to a 2030 control scenario which includes the vehicle standards.
Our modeling indicates ozone design value concentrations will increase in many areas of the
country and decrease in a few areas. The increases in ozone design values are related to our
assumptions about changes in fuel consumption and production that are not directly due to the
standards finalized in this rule. As discussed in Sections 5.3.2 and 5.3.3.5 of the RIA, the
decreased fuel consumption and production from this program is attributed to gasoline only,
while assuming constant ethanol volumes in our reference and control cases. Holding ethanol
volumes constant while decreasing gasoline volumes increases the market share of 10% ethanol
(E10) in the control case. However, the increased E10 market share is projected to occur
regardless of this rule, and the air quality impacts of this effect are included in our analyses for
the recent RFS2 rule. As the RFS2 analyses indicate, increasing usage of E10 fuels (when
compared with EO fuels) can increase NOx emissions and thereby increase ozone concentrations,
especially in NOx-limited areas where relatively small amounts of NOx enable ozone to form
18
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rapidly.26 Figure III-l presents the changes in 8-hour ozone design value concentration in 2030
between the reference case and the control case.27 Appendix A details the state and county 8-
hour maximum ozone design values for the ambient baseline and the future reference and control
cases.
Figure III-l. Projected Change in 2030 8-hour Ozone Design Values Between the
Reference Case and Control Case
Legend
^H -==-050 ppfc
^H
m > -0 30 to <= -0 20
^ > -0,20 10 <= -0.10
HI >-0.10to<0.10
>= 0 30 to < 0 50
D/fference in 8-ttour Ozone DV: 2030cp_idghg minus 2030cp
As can be seen in Figure III-l, the majority of the design value increases are less than 0.1
ppb. However, there are some counties that will see 8-hour ozone design value increases above
0.1 ppb; these counties are along the mid-Atlantic coast and in southern Arizona. The maximum
projected increase in an 8-hour ozone design value is 0.25 ppb in Richland County, South
Carolina. There are also some counties that are projected to see 8-hour ozone design value
decreases. The decreases in ambient ozone concentration are likely due to projected upstream
emissions decreases in NOx and VOCs from reduced gasoline production. The counties with
ozone design value decreases greater than 0.1 ppb are in California, Texas, Louisiana,
26 EPA 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. EPA-420-R-10-006.
February 2010. Sections 3.4.2.1.2 and 3.4.3.3. Docket EPA-HQ-OAR-2009-0472-11332.
27 An 8-hour ozone design value is the concentration that determines whether a monitoring site meets the 8-hour
ozone NAAQS. The full details involved in calculating an 8-hour ozone design value are given in appendix I of 40
CFR part 50.
19
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Mississippi, Kentucky, Ohio and West Virginia. The maximum decrease projected in an 8-hour
ozone design value is 0.22 ppb in Riverside, CA.
B. Impacts of LDGHG Standards on Future Annual PM2.s Levels
This section summarizes the results of our modeling of annual average PM2.5 air quality
impacts in the future due to the vehicle standards. Specifically, we compare a 2030 reference
scenario, a scenario without the vehicle standards, to a 2030 control scenario which includes the
vehicle standards. Our modeling indicates that the majority of the modeled counties will see
decreases of less than 0.05 |ig/m3 in their annual PM2.5 design values due to the vehicle
standards. Error! Reference source not found, presents the changes in annual PM2.5 design
values in 2030.28
Figure III-2. Projected Change in 2030 Annual PM2.s Design Values Between the
Reference Case and Control Case
Annual PM2.5 DV Projections: 2030cp_ldghg minus 2030cp
As shown in Figure III-2, six counties will see decreases of more than 0.05 |ig/m3. These
counties are in southern California, central North Dakota, eastern Missouri, southwest Louisiana
and the Houston area in Texas. The maximum projected decrease in an annual PM2.5 design
28 An annual PM2 5 design value is the concentration that determines whether a monitoring site meets the annual
NAAQS for PM2 5. The full details involved in calculating an annual PM2 5 design value are given in appendix N of
40 CFR part 50.
20
-------
value is 0.07 |ig/m3 in Harris County, Texas. The decreases in annual PM2.5 design values that
are modeled in some counties are likely due to emission reductions related to lower gasoline
production at existing oil refineries; reductions in direct PM2.5 emissions and PM2.5 precursor
emissions (NOX and SOX) contribute to reductions in ambient concentrations of both direct PM2.5
and secondarily-formed PM2.5. Additional information on the upstream emissions reductions that
are projected with this final rule is available in Section 5.5 of the RIA.
C. Impacts of LDGHG Standards on Future 24-hour PM2.5 Levels
This section summarizes the results of our modeling of 24-hour PM2.5 air quality impacts
in the future due to the vehicle standards. Specifically, we compare a 2030 reference scenario, a
scenario without the vehicle standards, to a 2030 control scenario which includes the vehicle
standards. Our modeling indicates that the majority of the modeled counties will see changes of
between -0.05 |ig/m3 and +0.05 |ig/m3 in their 24-hour PM2.5 design values. Figure III-3
presents the changes in 24-hour PM2 5 design values in 2030.29
Figure III-3. Projected Change in 2030 24-hour PM2.s Design Values Between the
Reference Case and the Control Case
Difference in 24-hr PM2.5 DV Projections: 2030cp_ldghg minus 2030cp
29 A 24-hour PM2 5 design value is the concentration that determines whether a monitoring site meets the 24-hour
NAAQS for PM2 5. The full details involved in calculating a 24-hour PM2 5 design value are given in appendix N of
40 CFR part 50.
21
-------
As shown in Figure III-3, 17 counties will see decreases of more than 0.05 |ig/m3. These
counties are in southern California, northern Utah, central North Dakota, eastern Missouri,
southern Arkansas, northern Oklahoma, southwest Louisiana and the Houston area in Texas.
The maximum projected decrease in a 24-hour PM2.5 design value is 0.21 |ig/m3 in Harris
County, Texas. The decreases in 24-hour PM2 5 design values that we see in some counties are
likely due to emission reductions related to lower gasoline production at existing oil refineries;
reductions in direct PM2.5 emissions and PM2.5 precursor emissions (NOX and SOX) contribute to
reductions in ambient concentrations of both direct PM2.5 and secondarily-formed PM2.5. There
are also some counties that will see small, less than 0.05 |ig/m3' design value increases. These
small increases in 24-hour PM2 5 design values are likely related to the same factors responsible
for the increases in annual PM2.5 design values (see Section III-B above). Appendix C details the
state and county annual PM2.5 design values for the ambient baseline and the future reference and
control cases.
D. Impacts of LDGHG Standards on Future Toxic Air Pollutant Levels
The following sections summarize the results of our modeling of air toxics impacts in the
future from this vehicle emission standards required by LDGHG. We focus on air toxics which
were identified as national and regional-scale cancer and noncancer risk drivers in past NATA
assessments and were also likely to be significantly impacted by the standards. These
compounds include benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein. Ethanol
impacts were also included in our analyses. Our modeling indicates that the GHG standards
have relatively little impact on national average ambient concentrations of the modeled air
toxics. Because overall impacts are small, we concluded that assessing exposure to ambient
concentrations and conducting a quantitative risk assessment of air toxic impacts was not
warranted. However, we did develop population metrics, including the population living in areas
with increases or decreases in concentrations of various magnitudes. We also estimated
aggregated populations above and below reference concentrations for noncancer effects.
1. Acetaldehyde
Overall, the air quality modeling does not show substantial nationwide impacts on
ambient concentrations of acetaldehyde as a result of the standards finalized in this rule. Annual
and seasonal percent changes in ambient concentrations of acetaldehyde are less than 1% across
the country (Figure III-4 through III-6). Decreases in ambient concentrations of acetaldehyde
seen in the much of the eastern half of the U.S. and parts of the West are generally less than 0.01
|ig/m3. Small increases of less than 0.01 |ig/m3 are noted in the in New York, Pennsylvania, and
Utah during the winter season (Figure III-5).
22
-------
Figure III-4. Changes in Annual Acetaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
Figure III-5. Changes in Winter Acetaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
23
-------
Figure III-6. Changes in Summer Acetaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
2. Formaldehyde
Our modeling projects that the standards finalized in this rule will not have a significant impact
on ambient formaldehyde concentrations. As shown in Figure III-7, annual percent changes in
ambient concentrations of formaldehyde are less than 1% across the country, with the exception
of a 1 to 5% decrease in a small area of southern Kansas and northern Oklahoma. Figure III-7
also shows that absolute changes in ambient concentrations of formaldehyde are generally less
than 0.1 |ig/m3. Also, increases in annual and seasonal ambient formaldehyde (Figures III-7
through III-9), which range from 0.001 to 0.1 |ig/m3, are a reflection of our ethanol volume
assumptions as discussed above in Section III-A and are not due to the standards finalized in this
rule.
24
-------
Figure III-7 Changes in Annual Formaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
Perceni Cfurrcre - !.l)iC p_ '•.._,'.;., minus 2036cp. far Formaldehyde
Figure III-8. Changes in Winter Formaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
25
-------
Figure III-9. Changes in Summer Formaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
3. Ethanol
Our modeling results do not show substantial impacts on ambient concentrations of
ethanol from the vehicle GHG standards. While Figure III-10 through III-12 show increases in
ambient ethanol concentrations ranging between 1 and 50% in some areas of the country, these
increases are a reflection of our ethanol volume assumptions as discussed above in Section III-A
and are not due to the standards finalized in this rule.
26
-------
Figure 111-10. Changes in Annual Ethanol Ambient Concentrations Between the Reference
Case and the Control Case in 2030: Percent Changes (left) and Absolute Changes in ug/m3
(right)
Figure III-ll. Changes in Winter Ethanol Ambient Concentrations Between the Reference
Case and the Control Case in 2030: Percent Changes (left) and Absolute Changes in ug/m3
(right)
uap ccton aa ra nftw o»
Col* m\ft ifJ gj-nmutu iray nffl
27
-------
Figure 111-12. Changes in Summer Ethanol Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
4. Benzene
Our air quality modeling projects that the standards finalized in this rule will not have a
significant impact on ambient benzene concentrations. Figure III-13, III-14, and III-15 show
decreases in annual and seasonal ambient benzene concentrations ranging between 1 and 10%
and between 0.001 and 0.1 |ig/m3. Because this rule will reduce consumption and production of
gasoline, some of these decreases in benzene concentrations are likely due to the vehicle GHG
standards. However, decreases in benzene concentrations may also be a reflection of our ethanol
volume assumptions as discussed above for ozone, ethanol and formaldehyde, and are not due to
the standards finalized in this rule. For example, the percent change map in Figure III-13 below
shows benzene decreases occurring in the same areas of the country as ozone, ethanol, and
formaldehyde increases.
28
-------
Figure 111-13. Changes in Annual Benzene Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
ZQlOcpJdgttg minus 1630cp. for Benzene
Figure 111-14. Changes in Winter Benzene Ambient Concentrations Between the Reference
Case and the Control Case in 2030: Percent Changes (left) and Absolute Changes in ug/m3
(right)
29
-------
Figure 111-15. Changes in Summer Benzene Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
5. 1,3-Butadiene
Our air quality modeling results do not show substantial impacts on ambient concentrations of
1,3-butadiene from the GHG standards. In the annual and winter, small decreases ranging from
1 to 10% occur in some southern areas of the country and increases ranging from 1 to over 100%
occur in some northern areas and areas with high altitudes (Figure III-16). Changes in absolute
concentrations of ambient 1,3-butadiene are less than 0.001 |ig/m3 except in some areas of the
Northeast and Utah (Figure III-16 and III-17). Annual increases in ambient concentrations of
1,3-butadiene are driven by wintertime rather than summertime changes (Figures 111-17 and III-
18). These increases appear in rural areas with cold winters and low ambient levels but high
contributions of emissions from snowmobiles, and a major reason for this modeled increase may
be deficiencies in available emissions test data used to estimate snowmobile 1,3-butadiene
emission inventories. These data were based on tests using only three engines, which showed
significantly higher 1,3-butadiene emissions with 10% ethanol. However, they may not have
been representative of real-world response of snowmobile engines to ethanol. Regardless, these
increases are a reflection of our ethanol volume assumptions and are not due to the standards
finalized in this rule.
30
-------
Figure 111-16. Changes in Annual 1,3-Butadiene Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
Figure 111-17. Changes in Winter 1,3-Butadiene Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
-------
Figure 111-18. Changes in Summer 1,3-Butadiene Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
1
6. Acrolein
Our air quality modeling results do not show substantial impacts on ambient
concentrations of acrolein from the standards finalized in this rule. Small decreases ranging
from 1.0 to 2.5% occur in a few areas of the country and increases ranging from 1 to 100% occur
in some northern areas and areas with high altitudes in the annual and winter (Figure III-19 and
111-20). Changes in annual absolute concentrations of acrolein are less than 0.001 |ig/m3 across
the country (Figure 111-19). Ambient acrolein increases are driven by wintertime changes rather
than summertime changes (Figures 111-20 and 111-21), and occur in the same areas of the country
that have wintertime rather than summertime increases in ambient 1,3-butadiene. 1,3-butadiene
is a precursor to acrolein, and these increases are likely associated with the same emission
inventory uncertainties in areas of high snowmobile usage seen for 1,3-butadiene. As described
above, these increases are a reflection of our ethanol volume assumptions and are not due to the
standards finalized in this rule.
32
-------
Figure 111-19. Changes in Annual Acrolein Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
'.hange - SOlScpJdgt'g minus 2Q30cp. it
WZQcp, iar Acrolew
Figure 111-20. Changes in Winter Acrolein Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
33
-------
Figure 111-21. Changes in Summer Acrolein Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
E. Impacts of LDGHG Standards on Future Annual Nitrogen and Sulfur Deposition
Levels
Our air quality modeling does not show substantial overall nationwide impacts on the
annual total sulfur and nitrogen deposition occurring across the U.S. as a result of the vehicle
standards required by this rule. Figure 111-22 shows that for sulfur deposition the vehicle
standards will result in annual percent decreases of 0.5% to more than 2% in locations with
refineries as a result of the lower output from refineries due to less gasoline usage. These
locations include the Texas and Louisiana portions of the Gulf Coast; the Washington D.C. area;
Chicago, IL; portions of Oklahoma and northern Texas; Bismarck, North Dakota; Billings,
Montana; Casper, Wyoming; Salt Lake City, Utah; Seattle, Washington; and San Francisco, Los
Angeles, and San Luis Obispo, California. The remainder of the country will see only minimal
changes in sulfur deposition, ranging from decreases of less than 0.5% to increases of less than
0.5%. The impacts of the vehicle standards on nitrogen deposition are minimal, ranging from
decreases of up to 0.5% to increases of up to 0.5%.
34
-------
Figure 111-22. Percent Change in Annual Total Sulfur over the U.S. Modeling Domain as a
Result of the Required Vehicle Standards
-20to<=-1 5
-1 5!o<=-1 0
-1.0!o<=-0.5
-0 5 to < 0 5
• 0 510 <: 1 0
= 1 0 to < 1 5
-1,5to<2.0
= 20
Percent Change InAnnttat Total Sulfur Deposition (2030cpjdghg minus 2030cp )
F. Impacts of LDGHG Standards on Future Visibility Levels
Air quality modeling conducted for this final rule was used to project visibility conditions
in 138 mandatory class I federal areas across the U.S. in 2030. As expected, the results show
that all the modeled areas will continue to have annual average deciview levels above
background in 2030.30 The results also indicate that the majority of the modeled mandatory class
I federal areas will see no change in their visibility, but some mandatory class I federal areas will
see improvements in visibility due to the vehicle standards and a few mandatory class I federal
areas will see visibility decreases. The average visibility at all modeled mandatory class I federal
areas on the 20% worst days is projected to improve by 0.002 deciviews, or 0.01%, in 2030. The
greatest improvement in visibilities will be seen in Bosque de Apache (New Mexico) and the San
The level of visibility impairment in an area is based on the light-extinction coefficient and a unitless visibility
index, called a "deciview", which is used in the valuation of visibility. The deciview metric provides a scale for
perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic conditions, the
average person can generally perceive a change of one deciview. The higher the deciview value, the worse the
visibility. Thus, an improvement in visibility is a decrease in deciview value.
35
-------
Gorgonio Wilderness (near Los Angeles, California). Bosque de Apache will see a 0.15%
improvement (0.02 DV) and the San Gorgonio Wilderness will see a 0.10% improvement (0.02
DV) in 2030 due to the vehicle standards. The following six areas will see a degradation of 0.01
DV in 2030 as a result of the vehicle standards: Hells Canyon Wilderness (Oregon), 0.06%
degradation; Kalmiopsis Wilderness (Oregon), 0.06% degradation; Strawberry Mountain
Wilderness (Oregon), 0.06% degradation; Petrified Forest National Park (Arizona), 0.08%
degradation; Rocky Mountain National Park (Colorado), 0.08% degradation; and Three Sisters
Wilderness (Oregon), 0.06% degradation. Section 7.2.2.6.2 of this RIA contains more detail on
the visibility portion of the air quality modeling. Table III-l contains the full visibility results
from 2030 for the 138 analyzed areas.
Table III-l. Visibility Levels in Deciviews for Individual U.S. Class I Areas on the 20%
Worst Days for Several Scenarios
CLASS 1
AREA
(20% WORST
DAYS)
Sipsey
Wilderness
Caney Creek
Wilderness
Upper Buffalo
Wilderness
Chiricahua NM
Chiricahua
Wilderness
Galiuro
Wilderness
Grand Canyon
NP
Mazatzal
Wilderness
Mount Baldy
Wilderness
Petrified Forest
NP
Pine Mountain
Wilderness
Saguaro NM
Sierra Ancha
Wilderness
Superstition
Wilderness
Sycamore
Canyon
Wilderness
Agua Tibia
Wilderness
Ansel Adams
Wilderness
(Minarets)
STATE
AL
AR
AR
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
CA
CA
2005
BASELINE
VISIBILITY
29.62
26.78
27.09
13.33
13.33
13.33
11.85
13.80
11.27
13.73
13.80
14.53
14.37
14.01
15.34
23.09
14.90
2030
BASE
23.41
22.52
23.06
13.28
13.27
13.20
11.58
13.10
11.10
13.31
13.12
14.04
13.82
13.46
15.04
24.56
14.78
2030
CONT-
ROL
23.41
22.51
23.05
13.28
13.27
13.20
11.58
13.10
11.10
13.32
13.12
14.04
13.82
13.46
15.04
24.55
14.77
NATURAL
BACKGROUND
11.39
11.33
11.28
6.92
6.91
6.88
6.95
6.91
6.95
6.97
6.92
6.84
6.92
6.88
6.96
7.17
7.12
36
-------
Caribou
Wilderness
Cucamonga
Wilderness
Desolation
Wilderness
Emigrant
Wilderness
Hoover
Wilderness
John Muir
Wilderness
Joshua Tree
NM
Kaiser
Wilderness
Kings Canyon
NP
Lassen
Volcanic NP
Lava Beds NM
Mokelumne
Wilderness
Pinnacles NM
Point Reyes NS
Redwood NP
San Gabriel
Wilderness
San Gorgonio
Wilderness
San Jacinto
Wilderness
San Rafael
Wilderness
Sequoia NP
South Warner
Wilderness
Thousand
Lakes
Wilderness
Ventana
Wilderness
Yosemite NP
Black Canyon
of the Gunnison
NM
Eagles Nest
Wilderness
Flat Tops
Wilderness
Great Sand
Dunes NM
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CO
CO
CO
CO
14.19
19.35
12.52
17.37
11.92
14.90
19.40
14.90
23.41
14.19
14.77
12.52
18.22
22.89
18.66
19.35
21.80
21.80
19.04
23.41
14.77
14.19
18.22
17.37
10.18
9.38
9.38
12.49
13.98
18.23
12.54
17.21
11.85
14.81
18.68
14.71
22.81
14.00
14.31
12.50
18.10
22.98
19.22
18.06
20.23
20.12
18.94
22.64
14.58
14.00
18.58
17.24
9.82
9.19
9.27
12.29
13.97
18.22
12.54
17.20
11.85
14.80
18.68
14.71
22.80
14.00
14.31
12.49
18.09
22.98
19.22
18.05
20.21
20.11
18.93
22.64
14.58
14.00
18.58
17.24
9.82
9.19
9.27
12.28
7.29
7.17
7.13
7.14
7.12
7.14
7.08
7.13
7.13
7.31
7.49
7.14
7.34
7.39
7.81
7.17
7.10
7.12
7.28
7.13
7.32
7.32
7.32
7.14
7.06
7.08
7.07
7.10
37
-------
La Garita
Wilderness
Maroon Bells-
Snowmass
Wilderness
Mesa Verde NP
Mount Zirkel
Wilderness
Rawah
Wilderness
Rocky Mountain
NP
Weminuche
Wilderness
West Elk
Wilderness
Everglades NP
Okefenokee
Wolf Island
Craters of the
Moon NM
Sawtooth
Wilderness
Mammoth Cave
NP
Acadia NP
Moosehorn
Roosevelt
Campobello
International
Park
Isle Royale NP
Seney
Voyageurs NP
Hercules-
Glades
Wilderness
Anaconda-
Pintler
Wilderness
Bob Marshall
Wilderness
Cabinet
Mountains
Wilderness
Gates of the
Mountains
Wilderness
Glacier NP
CO
CO
CO
CO
CO
CO
CO
CO
FL
GA
GA
ID
ID
KY
ME
ME
ME
Ml
Ml
MN
MO
MT
MT
MT
MT
MT
10.18
9.38
12.78
10.19
10.19
13.54
10.18
9.38
22.48
27.24
27.24
14.19
14.33
31.76
23.19
21.94
21.94
21.33
24.71
19.82
27.15
13.91
14.54
14.15
11.67
19.13
10.00
9.23
12.44
10.08
9.99
13.33
9.99
9.20
21.34
23.44
23.44
13.56
14.24
25.48
22.20
21.03
21.03
19.42
22.45
17.79
23.60
13.72
14.32
13.81
11.47
18.55
10.00
9.23
12.44
10.08
9.99
13.34
9.99
9.20
21.34
23.44
23.44
13.56
14.24
25.48
22.20
21.03
21.03
19.42
22.45
17.79
23.60
13.72
14.32
13.81
11.47
18.55
7.06
7.07
7.09
7.08
7.08
7.05
7.06
7.07
11.15
11.45
11.42
7.13
7.15
11.53
11.45
11.36
11.36
11.22
11.37
11.09
11.27
7.28
7.36
7.43
7.22
7.56
38
-------
Medicine Lake
Mission
Mountains
Wilderness
Scapegoat
Wilderness
Selway-
Bitterroot
Wilderness
UL Bend
Linville Gorge
Wilderness
Shining Rock
Wilderness
Lost wood
Theodore
Roosevelt NP
Great Gulf
Wilderness
Presidential
Range-Dry
River
Wilderness
Brigantine
Bandelier NM
Bosque del
Apache
Gila Wilderness
Pecos
Wilderness
Salt Creek
San Pedro
Parks
Wilderness
Wheeler Peak
Wilderness
White Mountain
Wilderness
Jarbidge
Wilderness
Wichita
Mountains
Crater Lake NP
Diamond Peak
Wilderness
Eagle Cap
Wilderness
Gearhart
Mountain
Wilderness
Hells Canyon
Wilderness
MT
MT
MT
MT
MT
NC
NC
ND
ND
NH
NH
NJ
NM
NM
NM
NM
NM
NM
NM
NM
NV
OK
OR
OR
OR
OR
OR
17.78
14.54
14.54
13.91
14.92
29.40
28.72
19.50
17.69
22.13
22.13
29.28
11.87
13.89
13.32
10.10
18.20
10.39
10.10
13.52
12.13
23.79
14.04
14.04
18.25
14.04
18.73
16.81
14.25
14.30
13.79
14.63
23.36
23.04
17.95
16.29
20.19
20.19
25.88
11.29
13.18
13.03
9.82
17.21
10.06
9.70
12.94
12.09
20.50
13.76
13.71
17.64
13.88
17.90
16.81
14.25
14.29
13.79
14.63
23.36
23.04
17.95
16.29
20.18
20.18
25.87
11.28
13.16
13.03
9.82
17.20
10.06
9.70
12.94
12.09
20.49
13.76
13.71
17.63
13.88
17.91
7.30
7.39
7.29
7.32
7.18
11.43
11.45
7.33
7.31
11.31
11.33
11.28
7.02
6.97
6.95
7.04
6.99
7.03
7.07
6.98
7.10
11.07
7.71
111
7.34
7.46
7.32
39
-------
Kalmiopsis
Wilderness
Mount Hood
Wilderness
Mount Jefferson
Wilderness
Mount
Washington
Wilderness
Mountain Lakes
Wilderness
Strawberry
Mountain
Wilderness
Three Sisters
Wilderness
Cape Romain
Badlands NP
Wind Cave NP
Great Smoky
Mountains NP
Joyce-Kilmer-
Slickrock
Wilderness
Big Bend NP
Carlsbad
Caverns NP
Guadalupe
Mountains NP
Arches NP
Bryce Canyon
NP
Canyonlands
NP
Capitol Reef NP
James River
Face
Wilderness
Shenandoah
NP
Lye Brook
Wilderness
Alpine Lake
Wilderness
Glacier Peak
Wilderness
Goat Rocks
Wilderness
Mount Adams
Wilderness
Mount Rainier
NP
OR
OR
OR
OR
OR
OR
OR
SC
SD
SD
TN
TN
TX
TX
TX
UT
UT
UT
UT
VA
VA
VT
WA
WA
WA
WA
WA
16.31
14.79
15.93
15.93
14.04
18.25
15.93
27.14
16.73
15.96
30.43
30.43
17.39
16.98
16.98
11.04
11.73
11.04
10.63
29.32
29.66
24.17
17.35
13.78
12.88
12.88
17.56
16.38
14.49
15.75
15.72
13.75
17.65
15.72
24.09
15.52
14.93
24.30
24.30
16.43
15.89
15.89
10.82
11.52
10.88
10.74
23.18
23.73
20.72
17.29
14.06
12.32
12.33
17.23
16.39
14.49
15.75
15.72
13.74
17.66
15.73
24.09
15.51
14.93
24.30
24.30
16.42
15.88
15.88
10.81
11.52
10.88
10.74
23.17
23.72
20.72
17.28
14.05
12.32
12.33
17.22
7.71
111
7.81
7.89
7.57
7.49
7.87
11.36
7.30
7.24
11.44
11.45
6.93
7.02
7.03
6.99
6.99
7.01
7.03
11.24
11.25
11.25
7.86
7.80
7.82
7.78
7.90
40
-------
North Cascades
NP
Olympic NP
Pasayten
Wilderness
Dolly Sods
Wilderness
Otter Creek
Wilderness
Bridger
Wilderness
Fitzpatrick
Wilderness
Grand Teton
NP
North Absaroka
Wilderness
Red Rock
Lakes
Teton
Wilderness
Washakie
Wilderness
Yellowstone NP
WA
WA
WA
WV
WV
WY
WY
WY
WY
WY
WY
WY
WY
13.78
16.14
15.39
29.73
29.73
10.93
10.93
10.94
11.12
10.94
10.94
11.12
10.94
14.20
16.35
14.99
23.14
23.14
10.80
10.80
10.61
10.98
10.68
10.70
10.98
10.66
14.19
16.35
14.99
23.14
23.14
10.80
10.80
10.61
10.98
10.68
10.70
10.98
10.66
7.78
7.88
111
11.32
11.33
7.08
7.09
7.09
7.09
7.14
7.09
7.09
7.12
41
-------
Appendix A: 8-Hour Ozone Design Values for LDGHG Scenarios (units are ppb)
State Name
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arkansas
Arkansas
Arkansas
Arkansas
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
County Name
Baldwin
Clay
Colbert
El mo re
Etowah
Houston
Jefferson
Lawrence
Madison
Mobile
Montgomery
Morgan
Russell
Shelby
Sumter
Talladega
Tuscaloosa
Cochise
Coconino
Gila
La Paz
Maricopa
Pima
Final
Yavapai
Yuma
Crittenden
Newton
Polk
Pulaski
Alameda
Amador
Butte
Calaveras
Colusa
Contra Costa
El Dorado
Fresno
Glenn
Imperial
Inyo
Kern
Kings
Lake
Los Angeles
Madera
Marin
Mariposa
Baseline
DV
77.30
74.00
72.00
70.70
71.70
71.00
83.70
72.00
77.30
76.70
69.30
77.30
71.30
85.70
64.00
72.00
73.30
71.30
73.00
80.30
72.00
83.00
76.00
79.30
72.00
75.00
87.30
72.70
75.00
79.70
78.30
83.00
83.70
91.30
67.00
73.30
96.00
98.30
65.50
85.00
82.30
110.00
85.70
60.70
114.00
79.30
49.70
86.30
2030
Reference
Case DV
62.12
54.56
49.56
50.66
53.50
54.06
60.25
54.59
57.70
62.00
49.81
61.83
53.80
61.15
50.66
52.05
51.92
60.19
61.38
60.96
58.91
68.25
60.34
61.31
59.01
59.62
64.30
56.17
61.52
57.57
68.54
67.88
64.96
77.10
54.40
67.60
74.21
82.26
52.97
71.17
67.61
94.06
69.61
50.74
99.78
65.08
45.02
71.56
2030
Control
Case DV
62.13
54.66
49.60
50.70
53.57
54.12
60.28
54.65
57.77
61.99
49.85
61.88
53.91
61.19
50.72
52.10
51.97
60.26
61.37
61.14
58.90
68.40
60.46
61.48
59.03
59.66
64.31
56.20
61.55
57.61
68.50
67.80
64.93
77.00
54.38
67.55
74.16
82.22
52.96
71.15
67.59
94.00
69.58
50.73
99.73
65.04
44.99
71.51
42
-------
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
D.C.
Delaware
Delaware
Delaware
Florida
Florida
Mendocino
Merced
Monterey
Napa
Nevada
Orange
Placer
Riverside
Sacramento
San Benito
San Bernardino
San Diego
San Francisco
San Joaquin
San Luis Obispo
San Mateo
Santa Barbara
Santa Clara
Santa Cruz
Shasta
Siskiyou
Solano
Sonoma
Stanislaus
Sutter
Tehama
Tulare
Tuolumne
Ventura
Yolo
Adams
Arapahoe
Boulder
Denver
Douglas
El Paso
Jefferson
La Plata
Larimer
Montezuma
Weld
Fairfield
Hartford
Litchfield
Middlesex
New Haven
New London
Tolland
Washington
Kent
New Castle
Sussex
Alachua
Baker
56.70
89.30
61.00
59.30
96.30
84.30
94.00
112.30
97.30
75.00
123.30
87.70
46.00
75.30
70.70
53.70
76.00
75.30
61.30
79.30
63.50
72.70
47.70
84.70
82.00
82.70
103.70
80.00
89.70
78.70
69.00
78.70
77.00
73.00
83.00
73.30
81.70
63.70
76.00
72.00
76.70
92.30
84.30
87.70
90.30
90.30
85.30
88.70
84.70
80.30
82.30
82.70
72.00
68.70
47.18
72.83
52.97
50.01
75.01
80.34
72.94
108.99
75.82
63.10
119.23
74.73
45.94
65.35
59.82
51.04
65.76
63.59
53.31
63.16
51.30
61.24
40.16
71.72
66.24
65.63
84.91
67.55
76.08
64.49
61.53
67.53
65.65
65.09
71.56
62.29
73.11
56.79
64.19
63.35
65.92
78.57
65.93
68.49
74.32
76.01
69.67
68.80
68.15
63.66
65.94
68.73
52.50
51.57
47.17
72.79
52.96
49.97
74.96
80.21
72.89
108.78
75.78
63.08
119.05
74.68
45.94
65.29
59.78
51.02
65.75
63.55
53.28
63.15
51.31
61.20
40.15
71.66
66.21
65.61
84.89
67.46
76.06
64.46
61.56
67.60
65.71
65.13
71.63
62.34
73.15
56.81
64.26
63.35
65.95
78.57
66.02
68.59
74.38
76.04
69.72
68.88
68.25
63.69
65.97
68.76
52.60
51.70
43
-------
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Idaho
Idaho
Idaho
Idaho
Illinois
Bay
Brevard
Broward
Collier
Columbia
Duval
Escambia
Highlands
Hillsbo rough
Holmes
Lake
Lee
Leon
Manatee
Marion
Miami-Dade
Orange
Osceola
Palm Beach
Pasco
Pinellas
Polk
St Lucie
Santa Rosa
Sarasota
Seminole
Volusia
Wakulla
Bibb
Chatham
Chattooga
Clarke
Cobb
Columbia
Coweta
Dawson
De Kalb
Douglas
Fayette
Fulton
Glynn
Gwinnett
Henry
Murray
Muscogee
Paulding
Richmond
Rockdale
Sumter
Ada
Canyon
Elmo re
Kootenai
Adams
78.70
71.30
65.00
68.30
72.00
77.70
82.70
72.30
80.70
70.30
76.70
70.30
71.00
77.30
73.00
71.30
79.30
72.00
65.00
76.30
72.70
74.70
66.50
80.00
77.30
76.00
68.30
71.30
81.00
68.30
75.00
80.70
82.70
73.00
82.00
76.30
88.70
87.30
85.70
91.70
67.00
88.70
89.70
78.00
75.70
80.30
80.30
90.00
72.30
76.00
66.00
63.00
67.00
70.00
60.32
56.10
53.85
51.32
55.63
62.06
65.40
59.63
63.51
54.06
58.47
55.42
51.79
59.10
51.54
66.00
62.64
53.23
58.40
58.77
54.92
54.96
55.23
63.28
57.58
57.58
51.29
54.29
63.22
56.28
55.13
54.77
58.69
55.80
62.93
51.47
69.21
63.01
65.17
71.55
51.68
65.14
65.47
59.29
55.33
55.75
60.26
64.04
54.58
68.16
57.50
56.42
56.09
57.37
60.40
56.17
53.86
51.40
55.74
62.10
65.44
59.69
63.53
54.14
58.61
55.49
51.91
59.15
51.65
65.98
62.78
53.37
58.42
58.84
54.99
55.04
55.29
63.34
57.64
57.71
51.38
54.37
63.33
56.41
55.23
54.88
58.82
55.91
63.01
51.58
69.31
63.14
65.27
71.65
51.90
65.26
65.58
59.37
55.47
55.86
60.38
64.16
54.68
68.19
57.53
56.44
56.13
57.37
44
-------
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Champaign
Clark
Cook
Du Page
Effingham
Hamilton
Jersey
Kane
Lake
McHenry
McLean
Macon
Macoupin
Madison
Peoria
Randolph
Rock Island
St Clair
Sangamon
Will
Winnebago
Allen
Boone
Carroll
Clark
Delaware
Elkhart
Floyd
Greene
Hamilton
Hancock
Hendricks
Huntington
Jackson
Johnson
Lake
La Porte
Madison
Marion
Morgan
Perry
Porter
Posey
St Joseph
Shelby
Vanderburgh
Vigo
Warrick
Bremer
Clinton
Harrison
Linn
Montgomery
Palo Alto
68.30
66.00
77.70
69.00
70.00
73.00
78.70
74.30
78.00
73.30
73.00
71.30
73.00
83.00
72.70
72.00
65.30
81.70
70.00
71.70
69.00
79.30
79.70
74.00
80.30
76.30
79.00
77.70
78.30
82.70
78.00
75.30
75.00
74.70
76.70
81.00
78.50
76.70
78.70
77.00
81.00
78.30
71.70
79.30
77.30
77.30
74.00
77.70
66.30
71.30
74.70
68.30
65.70
61.00
55.36
53.83
69.26
62.42
56.86
57.28
58.17
62.39
68.34
59.25
58.06
58.34
53.17
64.79
61.04
57.77
53.40
65.01
53.38
60.19
55.39
63.25
62.92
58.00
61.29
58.66
62.48
63.36
61.19
64.80
61.82
60.42
59.55
58.89
62.15
70.86
66.03
59.43
62.96
62.18
61.98
68.89
55.40
63.08
63.40
60.50
60.27
61.55
53.55
58.82
61.19
56.98
52.99
50.64
55.37
53.80
69.23
62.39
56.85
57.29
58.18
62.35
68.30
59.23
58.05
58.35
53.12
64.77
61.02
57.77
53.38
65.01
53.37
60.17
55.36
63.26
62.92
58.02
61.30
58.69
62.48
63.36
61.21
64.81
61.84
60.42
59.57
58.91
62.16
70.85
66.03
59.44
62.97
62.19
62.00
68.87
55.41
63.07
63.40
60.51
60.28
61.56
53.55
58.82
61.19
56.98
53.00
50.64
45
-------
Iowa
Iowa
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Polk
Scott
Story
Van Buren
Warren
Douglas
Johnson
Leavenworth
Linn
Sedgwick
Sumner
Trego
Wyandotte
Bell
Boone
Boyd
Bullitt
Campbell
Carter
Christian
Daviess
Edmonson
Fayette
Greenup
Hancock
Hardin
Henderson
Jefferson
Jessamine
Kenton
Livingston
McCracken
McLean
Oldham
Perry
Pike
Pulaski
Simpson
Trigg
Warren
Ascension
Beauregard
Bossier
Caddo
Calcasieu
East Baton Rouge
Grant
Iberville
Jefferson
Lafayette
Lafourche
Livingston
Orleans
Ouachita
63.00
72.00
61.00
69.00
64.50
73.00
75.30
75.00
73.30
71.30
71.70
70.70
75.30
71.70
75.70
77.30
74.00
75.00
71.00
78.00
75.70
73.70
70.30
76.70
74.00
74.70
75.30
78.30
73.30
78.70
73.70
73.30
73.00
83.00
72.30
66.70
70.30
75.70
70.00
72.00
82.00
75.00
78.00
79.00
82.00
92.00
73.00
85.00
83.00
82.00
79.30
78.30
70.00
75.30
49.82
58.08
48.15
56.48
50.26
57.98
59.77
62.21
59.10
56.85
57.13
61.71
62.79
53.22
59.43
63.21
58.62
61.20
56.12
58.91
59.81
57.55
55.38
62.96
56.96
59.20
59.99
64.65
57.33
62.06
59.14
60.47
57.79
62.16
57.12
52.53
56.82
57.29
53.84
56.36
69.22
64.45
60.36
61.30
70.59
77.68
60.23
72.73
70.23
67.35
67.93
66.09
60.37
59.90
49.83
58.08
48.16
56.48
50.27
57.99
59.79
62.21
59.12
56.85
57.14
61.72
62.78
53.27
59.45
63.07
58.62
61.20
56.03
58.94
59.82
57.57
55.38
62.84
56.97
59.21
60.01
64.64
57.34
62.08
59.17
60.49
57.80
62.17
57.15
52.55
56.84
57.32
53.87
56.38
69.13
64.38
60.36
61.32
70.52
77.57
60.22
72.64
70.18
67.30
67.79
66.01
60.33
59.94
46
-------
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Pointe Coupee
St Bernard
St Charles
St James
St John The Baptis
St Mary
West Baton Rouge
Cumberland
Hancock
Kennebec
Knox
Oxford
Penobscot
Sagadahoc
York
Anne Arundel
Baltimore
Calvert
Carroll
Cecil
Charles
Frederick
Garrett
Harford
Kent
Montgomery
Prince Georges
Washington
Barnstable
Berkshire
Bristol
Dukes
Essex
Hampden
Hampshire
Middlesex
Norfolk
Suffolk
Worcester
Allegan
Benzie
Berrien
Cass
Clinton
Genesee
Huron
Ingham
Kalamazoo
Kent
Leelanau
Lenawee
Macomb
Mason
Missaukee
83.70
78.00
77.30
76.30
79.00
76.00
84.30
72.00
82.00
69.70
75.30
61.00
67.00
68.50
74.00
89.70
85.30
81.00
83.30
90.70
86.00
80.30
75.50
92.70
82.00
83.00
91.00
78.30
84.70
79.70
82.70
83.00
83.30
87.30
85.00
79.00
84.70
80.30
80.00
90.00
81.70
82.30
80.70
75.70
79.30
75.70
76.00
75.30
81.00
75.70
78.70
86.00
79.70
73.70
72.55
65.84
65.95
65.50
68.56
63.79
71.70
58.52
66.90
56.26
60.91
50.62
56.68
55.64
60.50
68.30
74.70
63.53
62.87
69.07
64.72
61.49
59.59
79.90
62.52
64.86
70.29
60.78
70.72
62.69
70.01
71.02
70.86
68.12
65.66
63.28
67.57
65.84
60.98
74.82
66.95
68.23
64.33
58.96
63.07
62.68
60.47
59.39
63.46
62.73
63.47
70.72
64.06
60.01
72.46
65.79
65.90
65.37
68.44
63.68
71.60
58.60
66.99
56.34
60.99
50.67
56.75
55.70
60.57
68.41
74.75
63.63
62.99
69.16
64.81
61.59
59.64
79.96
62.58
64.98
70.40
60.89
70.76
62.78
70.05
71.04
70.91
68.22
65.76
63.34
67.62
65.89
61.06
74.76
66.91
68.20
64.33
58.96
63.08
62.68
60.47
59.39
63.44
62.71
63.47
70.71
64.03
59.98
47
-------
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Minnesota
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Montana
Nebraska
Nebraska
Nevada
Nevada
Nevada
Nevada
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
Muskegon
Oakland
Ottawa
St Clair
Schoolcraft
Washtenaw
Wayne
Anoka
St Louis
Adams
Bolivar
De Soto
Hancock
Harrison
Hinds
Jackson
Lauderdale
Lee
Cass
Cedar
Clay
Clinton
Greene
Jefferson
Lincoln
Monroe
Perry
Platte
St Charles
Ste Genevieve
St Louis
St Louis City
Yellowstone
Douglas
Lancaster
Clark
Washoe
White Pine
Carson City
Belknap
Cheshire
Coos
Grafton
Hillsbo rough
Merrimack
Rockingham
Sullivan
Atlantic
Bergen
Camden
Cumberland
Gloucester
Hudson
Hunterdon
85.00
78.00
81.70
82.30
79.30
78.30
82.00
67.70
65.00
74.70
74.30
82.70
79.00
83.00
71.30
80.30
74.30
73.70
74.70
75.70
84.30
83.00
73.00
82.30
87.00
71.70
77.50
77.00
87.00
79.70
88.00
84.00
59.00
68.70
56.00
83.70
70.70
72.30
65.00
71.30
70.70
77.00
67.00
78.70
71.70
75.00
70.00
79.30
86.00
89.30
83.30
87.00
85.70
89.00
70.50
66.05
65.82
65.96
64.42
64.01
67.60
62.22
54.11
60.66
58.09
61.60
64.72
67.11
48.15
66.23
55.73
53.00
58.86
59.98
67.18
65.61
57.62
67.04
68.52
56.96
60.83
63.46
66.95
64.64
71.39
67.73
53.77
57.84
45.40
72.85
57.13
61.46
52.27
53.91
55.40
61.95
53.73
62.91
55.55
61.31
55.05
65.33
74.04
72.01
65.99
70.30
74.89
68.12
70.45
66.04
65.79
65.97
64.40
64.02
67.59
62.19
54.11
60.65
58.13
61.60
64.66
67.01
48.17
66.11
55.82
53.08
58.86
60.00
67.18
65.62
57.63
67.01
68.50
56.96
60.85
63.46
66.93
64.60
71.35
67.72
53.73
57.83
45.41
72.88
57.12
61.47
52.23
53.98
55.46
62.03
53.80
62.98
55.62
61.39
55.13
65.35
74.04
71.99
66.01
70.30
74.84
68.19
48
-------
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
Mercer
Middlesex
Monmouth
Morris
Ocean
Passaic
Bernalillo
Dona Ana
Eddy
Grant
Lea
Sandoval
San Juan
Albany
Bronx
Chautauqua
Chemung
Dutch ess
Erie
Essex
Hamilton
Herkimer
Jefferson
Madison
Monroe
Niagara
Oneida
Onondaga
Orange
Oswego
Putnam
Queens
Rensselaer
Richmond
Saratoga
Schenectady
Suffolk
Ulster
Wayne
Westchester
Alexander
A very
Buncombe
Caldwell
Caswell
Chatham
Cumberland
Davie
Durham
Edgecombe
Forsyth
Franklin
Graham
Granville
88.00
88.30
87.30
83.30
93.00
81.00
73.70
75.30
69.00
66.00
69.50
73.30
71.30
73.70
74.70
86.70
68.70
75.70
85.00
77.00
71.70
68.30
78.00
72.00
75.00
82.70
68.30
73.70
82.00
78.00
84.30
80.00
77.30
88.30
79.70
70.00
90.30
77.30
68.00
87.70
77.00
70.00
74.00
74.30
76.30
73.30
81.70
81.30
77.00
77.00
80.00
78.70
78.30
82.00
70.81
70.53
73.06
65.36
74.05
65.90
60.10
64.16
63.01
58.00
64.11
59.78
65.82
58.31
67.12
73.47
55.85
59.06
71.88
64.30
59.84
58.12
64.85
56.92
63.07
72.43
56.98
61.50
64.50
67.43
66.58
69.37
61.18
74.46
63.14
56.13
83.36
60.44
58.07
75.32
56.20
56.72
57.37
54.80
57.06
55.22
59.46
60.28
55.71
58.27
61.01
58.29
58.90
60.30
70.85
70.55
73.06
65.44
74.07
65.96
60.11
64.19
63.01
58.03
64.11
59.78
65.82
58.40
67.11
73.50
55.89
59.14
71.88
64.33
59.88
58.14
64.81
57.00
63.10
72.45
57.02
61.54
64.59
67.46
66.66
69.34
61.26
74.45
63.23
56.22
83.34
60.53
58.10
75.32
56.35
56.78
57.48
54.94
57.19
55.36
59.66
60.44
55.87
58.40
61.12
58.46
58.97
60.47
49
-------
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Guilford
Haywood
Jackson
Johnston
Lenoir
Lincoln
Martin
Mecklenburg
New Hanover
Person
Pitt
Rockingham
Rowan
Swain
Union
Wake
Yancey
Billings
Burke
Cass
McKenzie
Oliver
Allen
Ashtabula
Butler
Clark
Clermont
Clinton
Cuyahoga
Delaware
Franklin
Geauga
Greene
Hamilton
Jefferson
Knox
Lake
Lawrence
Licking
Lorain
Lucas
Madison
Mahoning
Medina
Miami
Montgomery
Portage
Preble
Stark
Summit
Trumbull
Warren
Washington
Wood
82.00
78.30
76.00
77.30
75.30
81.00
75.00
89.30
72.30
77.30
76.30
77.00
86.70
66.30
79.30
80.30
76.00
61.50
57.50
60.00
61.30
57.70
78.70
89.00
83.30
81.00
81.00
82.30
79.70
78.30
86.30
79.30
80.30
84.70
78.00
77.70
86.30
70.70
78.00
76.70
81.30
79.70
78.70
80.30
76.70
74.00
83.70
73.00
81.00
83.70
84.30
87.70
82.70
80.00
60.11
61.08
57.67
55.60
58.61
59.12
61.07
67.24
62.14
58.86
57.55
57.60
63.62
48.49
56.19
58.70
59.00
55.15
51.48
49.25
54.96
52.43
63.23
74.40
65.42
62.17
65.24
61.79
66.25
61.86
67.88
61.14
62.22
67.23
60.93
59.90
70.00
58.04
60.04
63.35
66.46
60.41
60.02
63.53
58.74
57.59
65.22
56.44
62.34
66.01
64.41
67.73
67.24
64.12
60.30
61.18
57.74
55.80
58.72
59.29
61.20
67.36
62.24
58.92
57.68
57.70
63.79
48.54
56.39
58.89
59.06
55.15
51.48
49.26
54.97
52.43
63.21
74.41
65.44
62.19
65.24
61.81
66.23
61.89
67.90
61.16
62.24
67.23
60.95
59.92
69.98
57.92
60.07
63.35
66.44
60.44
60.06
63.53
58.76
57.61
65.24
56.47
62.35
66.02
64.45
67.74
67.27
64.12
50
-------
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Oregon
Oregon
Oregon
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Rhode Island
Rhode Island
Rhode Island
Adair
Canadian
Cherokee
Cleveland
Comanche
Creek
Dewey
Kay
Me Clain
Mayes
Oklahoma
Ottawa
Pittsburg
Tulsa
Clackamas
Jackson
Lane
Marion
Multnomah
Adams
Allegheny
Armstrong
Beaver
Berks
Blair
Bucks
Cambria
Centre
Chester
Clear-field
Dauphin
Delaware
Erie
Franklin
Greene
Indiana
Lackawanna
Lancaster
Lawrence
Lehigh
Luzerne
Lycoming
Mercer
Montgomery
Northampton
Perry
Philadelphia
Tioga
Washington
Westmoreland
York
Kent
Providence
Washington
75.70
76.00
75.70
74.70
77.50
76.70
72.70
78.00
72.00
78.50
80.00
78.00
72.00
79.30
66.30
68.00
69.30
65.70
56.30
76.30
83.70
83.00
83.00
76.00
74.30
88.00
74.70
78.30
86.00
78.30
79.30
83.30
81.30
72.30
80.00
80.00
75.30
83.30
72.30
83.30
76.30
77.30
82.00
85.70
84.30
77.00
90.30
77.70
78.30
79.00
82.00
84.30
82.30
86.00
63.64
59.87
64.95
60.04
62.08
61.61
58.90
62.49
58.00
68.29
62.86
64.01
59.61
66.10
61.35
52.47
56.38
56.16
68.82
58.87
65.49
63.64
66.42
58.57
57.20
73.37
58.80
61.29
65.39
61.50
63.45
66.79
68.63
55.39
62.27
61.30
57.70
64.64
55.73
64.77
58.61
61.59
62.29
69.23
65.16
59.69
75.25
62.11
61.80
61.36
64.36
69.16
66.90
72.74
63.64
59.90
64.94
60.04
62.09
61.59
58.91
62.48
58.01
68.28
62.89
64.01
59.60
66.11
61.36
52.52
56.43
56.21
68.81
58.96
65.54
63.70
66.43
58.63
57.28
73.37
58.84
61.37
65.48
61.54
63.50
66.81
68.65
55.49
62.30
61.34
57.79
64.72
55.76
64.82
58.71
61.74
62.32
69.25
65.21
59.78
75.26
62.21
61.84
61.40
64.44
69.20
66.97
72.78
51
-------
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Dakota
South Dakota
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Abbeville
Aiken
Anderson
Barnwell
Berkeley
Charleston
Cherokee
Chester
Chesterfield
Colleton
Darlington
Edgefield
Oconee
Pickens
Richland
Spartanburg
Union
Williamsburg
York
Custer
Jackson
Minnehaha
Anderson
Blount
Davidson
Hamilton
Jefferson
Knox
Loudon
Meigs
Rutherford
Sevier
Shelby
Sullivan
Sumner
Williamson
Wilson
Bexar
B razor! a
Brewster
Cameron
Collin
Dallas
Denton
Ellis
Galveston
Gregg
Harris
Harrison
Hidalgo
Hood
Hunt
Jefferson
Johnson
79.00
76.00
76.50
73.00
67.30
74.00
74.00
75.70
75.00
72.30
76.30
70.00
73.00
78.70
82.30
82.30
76.00
69.30
76.70
70.00
67.00
66.00
77.30
85.30
77.70
81.00
82.30
85.00
83.00
80.00
76.30
80.70
80.70
80.30
83.00
75.30
78.70
85.00
94.70
64.00
66.00
90.30
88.30
94.00
81.70
80.30
84.30
100.70
79.00
65.70
83.00
78.00
84.70
87.00
59.15
55.83
55.10
53.26
52.12
63.01
56.01
55.03
57.05
55.94
58.42
52.25
52.92
57.21
58.61
60.75
58.78
52.71
56.33
62.69
59.43
54.41
55.86
60.51
57.38
58.97
59.67
60.95
60.13
57.90
55.68
60.36
58.94
69.16
61.82
55.01
58.47
71.27
80.54
54.87
59.57
71.15
73.63
71.00
64.35
70.17
71.97
88.23
63.31
57.13
60.03
62.83
73.06
65.96
59.29
55.95
55.28
53.37
52.27
63.17
56.11
55.23
57.17
56.04
58.55
52.36
53.13
57.39
58.86
60.90
58.90
52.82
56.52
62.69
59.44
54.42
55.90
60.57
57.40
59.02
59.72
61.00
60.18
57.95
55.71
60.43
58.97
69.16
61.86
55.04
58.50
71.31
80.39
54.89
59.57
71.18
73.66
71.05
64.39
70.05
71.96
88.06
63.32
57.13
60.11
62.84
72.95
66.02
52
-------
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
West Virginia
West Virginia
West Virginia
Kaufman
Montgomery
Nueces
Orange
Parker
Rockwall
Smith
Tarrant
Travis
Victoria
Webb
El Paso
Box Elder
Cache
Davis
Salt Lake
San Juan
Tooele
Utah
Washington
Weber
Bennington
Chittenden
Arlington
Caroline
Charles City
Chesterfield
Fairfax
Fauquier
Frederick
Hanover
Henrico
Loudoun
Madison
Page
Prince William
Roanoke
Rockbridge
Stafford
Wythe
Alexandria City
Hampton City
Suffolk City
Clark
King
Klickitat
Pierce
Skagit
Spokane
Thurston
What com
Berkeley
Cabell
Greenbrier
74.70
85.00
72.30
78.00
88.70
79.70
81.00
95.30
81.30
72.30
61.30
77.70
76.00
68.70
81.30
81.00
70.30
78.00
76.70
78.50
80.30
72.00
69.70
86.70
80.00
80.30
76.70
90.00
72.70
72.30
81.30
82.00
80.70
77.70
74.00
78.70
74.70
69.70
81.70
72.70
81.70
76.70
76.70
59.50
72.30
64.50
68.70
46.00
68.30
65.00
57.00
75.00
78.70
69.70
59.69
70.30
63.26
65.65
64.23
64.29
67.68
72.45
65.57
62.12
53.70
64.98
65.44
58.73
69.94
69.42
62.60
65.58
69.40
64.53
68.68
56.33
57.57
70.80
59.66
65.43
61.41
71.19
56.47
56.38
63.37
65.02
60.81
61.03
57.96
60.78
58.05
56.44
62.52
57.76
64.62
67.99
71.64
60.99
66.81
58.02
61.53
45.94
55.77
56.03
55.79
58.60
64.31
57.31
59.72
70.24
63.25
65.54
64.30
64.32
67.68
72.50
65.63
62.07
53.68
65.02
65.48
58.79
69.98
69.46
62.62
65.63
69.43
64.54
68.73
56.39
57.66
70.90
59.76
65.48
61.49
71.30
56.55
56.47
63.47
65.08
60.92
61.11
58.04
60.87
58.16
56.50
62.61
57.83
64.72
68.02
71.66
60.99
66.78
58.04
61.51
45.93
55.81
56.05
55.81
58.69
64.21
57.31
53
-------
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wyoming
Wyoming
Wyoming
Hancock
Kanawha
Monongalia
Ohio
Wood
Ashland
Brown
Columbia
Dane
Dodge
Door
Florence
Fond Du Lac
Forest
Jefferson
Kenosha
Kewaunee
Manitowoc
Marathon
Milwaukee
Oneida
Outagamie
Ozaukee
Racine
Rock
St Croix
Sauk
Sheboygan
Vernon
Vilas
Walworth
Washington
Waukesha
Campbell
Sublette
Teton
75.70
77.30
75.30
78.30
79.00
61.50
73.70
72.70
72.00
74.70
88.70
66.30
73.70
69.50
74.30
84.70
82.70
85.00
70.00
82.70
69.00
74.00
83.30
80.30
74.00
69.00
69.70
88.00
69.70
68.70
75.70
72.30
75.00
67.30
70.00
62.70
60.17
61.53
57.03
62.29
63.28
51.68
61.42
57.64
57.79
60.55
72.01
55.01
60.79
57.69
59.34
75.11
68.15
70.80
58.52
70.97
57.89
60.72
71.82
70.19
59.40
56.81
56.22
73.81
55.62
57.33
60.32
59.91
62.21
63.38
63.41
55.86
60.19
61.55
57.08
62.30
63.30
51.68
61.40
57.64
57.79
60.54
71.96
55.02
60.77
57.69
59.34
75.06
68.10
70.75
58.52
70.92
57.90
60.71
71.76
70.15
59.39
56.80
56.22
73.75
55.62
57.34
60.30
59.89
62.18
63.39
63.42
55.87
54
-------
Appendix B: Annual PM2.s Design Values for LDGHG Scenarios (units are ug/m )
State Name
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
California
California
California
California
California
California
California
California
California
County Name
Clay
Colbert
DeKalb
Etowah
Houston
Jefferson
Madison
Montgomery
Morgan
Russell
Shelby
Sumter
Talladega
Walker
Baldwin
Escambia
Mobile
Tuscaloosa
Maricopa
Cochise
Coconino
Gila
Pima
Final
Santa Cruz
Arkansas
Ashley
Garland
Mississippi
Phillips
Polk
Pope
Pulaski
Crittenden
White
Faulkner
Union
Alameda
Colusa
Fresno
Imperial
Inyo
Kings
Lake
Mendocino
Monterey
Baseline
DV
13.21
12.67
14.09
14.80
12.86
18.48
13.73
14.14
13.23
15.63
14.28
11.92
14.51
13.77
11.44
13.12
12.90
13.44
12.59
7.00
6.49
8.94
6.04
7.77
12.94
12.45
12.83
12.40
12.61
12.08
11.65
12.79
14.05
13.27
12.57
12.79
12.86
9.34
7.39
17.17
12.71
5.25
17.28
4.62
6.46
6.96
2030
Reference
Case DV
10.25
9.88
10.67
11.26
10.78
14.62
10.58
11.78
10.44
12.46
11.10
9.64
11.00
10.71
9.47
11.15
10.59
10.61
11.01
6.91
6.27
8.58
5.50
7.42
12.56
10.46
11.14
10.44
10.06
9.81
9.88
10.90
11.57
10.39
10.60
10.71
11.12
10.24
7.24
15.90
12.74
5.21
16.12
4.66
6.28
7.25
2030
Control
Case DV
10.25
9.88
10.67
11.26
10.78
14.62
10.58
11.78
10.44
12.46
11.10
9.64
11.00
10.71
9.46
11.14
10.58
10.60
11.02
6.91
6.27
8.58
5.50
7.42
12.56
10.46
11.14
10.44
10.06
9.81
9.88
10.90
11.57
10.38
10.59
10.70
11.10
10.24
7.24
15.90
12.74
5.21
16.12
4.66
6.28
7.25
55
-------
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Delaware
Delaware
Delaware
District Of Columbia
Florida
Florida
Florida
Florida
Florida
Nevada
Placer
San Diego
San Francisco
San Luis Obispo
San Mateo
Santa Barbara
Santa Clara
Shasta
Sonoma
S utter
Yolo
Kern
Butte
Merced
Plumas
Sacramento
San Joaquin
Ventura
Calaveras
Stanislaus
Tulare
Contra Costa
Riverside
Solano
Orange
San Bernardino
Los Angeles
Mesa
Arapahoe
Boulder
Delta
Denver
Elbert
El Paso
Larimer
Pueblo
San Miguel
Weld
Adams
Fairfield
Hartford
Litchfield
New Haven
New London
Sussex
Kent
New Castle
District of
Columbia
Hillsborough
Alachua
Bay
Brevard
B reward
6.71
9.80
13.38
9.62
7.94
9.03
10.37
11.38
7.41
8.21
9.85
9.03
19.17
12.73
14.78
11.46
11.88
12.94
11.68
111
14.21
18.51
9.47
20.95
9.99
15.75
19.67
17.66
9.28
7.96
8.32
7.44
9.76
4.40
7.94
7.33
7.45
4.65
8.78
10.06
13.18
11.03
8.01
13.12
10.96
13.39
12.61
14.87
14.41
10.74
9.59
11.46
8.32
8.21
6.32
9.11
15.73
10.80
8.07
10.15
10.33
12.30
6.63
8.42
9.10
8.66
17.33
11.45
14.00
10.62
11.34
12.65
12.60
7.40
13.30
17.28
9.87
20.77
10.41
16.50
19.82
18.69
8.49
6.94
7.41
6.67
8.34
4.05
7.09
6.74
6.76
4.44
111
8.61
11.64
9.59
7.05
11.35
9.58
11.01
10.34
12.20
11.11
8.67
8.00
9.65
7.37
8.20
6.32
9.11
15.73
10.80
8.07
10.15
10.33
12.30
6.63
8.42
9.10
8.66
17.32
11.44
13.99
10.61
11.33
12.64
12.59
7.39
13.29
17.27
9.85
20.75
10.39
16.47
19.79
18.63
8.50
6.94
7.41
6.67
8.34
4.05
7.09
6.74
6.76
4.44
111
8.60
11.64
9.59
7.05
11.35
9.58
11.01
10.33
12.19
11.11
8.68
8.00
9.65
7.37
8.20
56
-------
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Citrus
Duval
Escambia
Lee
Leon
Manatee
Marion
Miami-Dade
Orange
Palm Beach
Pinellas
Polk
St. Lucie
Sarasota
Seminole
Volusia
Clarke
Cobb
Bibb
Chatham
Clayton
DeKalb
Dougherty
Floyd
Fulton
Glynn
Gwinnett
Hall
Houston
Lowndes
Muscogee
Paulding
Richmond
Walker
Washington
Wilkinson
Idaho
Ada
Bannock
Benewah
Canyon
Franklin
Shoshone
Adams
Cook
Kane
Lake
McLean
Macon
Winnebago
Champaign
DuPage
McHenry
Madison
9.00
10.44
11.72
8.36
12.56
8.81
10.11
9.45
9.61
7.70
9.82
9.55
8.34
8.77
9.51
9.27
14.90
16.09
16.47
13.88
16.47
15.33
14.35
16.10
17.43
12.18
16.07
14.12
13.99
12.49
15.16
14.08
15.68
15.49
15.14
15.23
9.58
8.41
7.66
9.59
8.46
7.70
12.08
12.50
15.75
14.34
11.81
12.39
13.24
13.57
12.53
13.82
12.40
16.72
7.30
8.92
9.83
7.39
10.54
7.16
8.43
9.39
8.07
7.41
8.02
7.91
7.57
7.34
7.99
7.93
11.67
12.06
13.10
12.37
12.01
11.15
11.94
12.38
12.81
10.59
12.04
10.88
11.09
10.68
12.07
10.25
12.93
11.89
12.37
12.16
9.18
7.76
7.14
9.11
7.62
6.97
11.38
10.30
12.52
11.66
9.82
10.08
10.76
11.15
10.08
11.25
10.15
13.31
7.30
8.92
9.83
7.39
10.54
7.16
8.43
9.39
8.07
7.41
8.02
7.91
7.57
7.34
7.99
7.93
11.68
12.07
13.10
12.37
12.01
11.15
11.94
12.38
12.81
10.59
12.04
10.88
11.09
10.68
12.07
10.25
12.93
11.89
12.37
12.16
9.19
7.76
7.14
9.11
7.62
6.97
11.38
10.30
12.52
11.66
9.82
10.08
10.76
11.15
10.07
11.24
10.14
13.30
57
-------
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Peoria
Randolph
Rock Island
Sangamon
Jersey
Saint Clair
Will
Allen
Clark
Dubois
Floyd
Henry
Howard
Knox
LaPorte
Madison
Marion
St. Joseph
Spencer
Tippecanoe
Vanderburgh
Delaware
Lake
Porter
Vigo
Black Hawk
Clinton
Johnson
Linn
Montgomery
Muscatine
Palo Alto
Polk
Pottawattamie
Scott
Van Buren
Wood bury
Wright
Johnson
Shawnee
Wyandotte
Linn
Sedgwick
Sumner
Bell
Bullitt
Campbell
Carter
Fayette
Franklin
Hardin
Henderson
Jefferson
Kenton
13.34
13.11
12.01
13.13
12.89
15.58
13.63
13.67
16.40
15.18
14.80
13.64
13.93
14.03
12.69
13.96
16.05
13.69
14.32
13.69
14.99
13.66
14.27
13.21
13.99
11.16
12.52
12.08
10.79
10.02
12.92
9.53
10.64
11.13
14.42
10.84
10.32
10.37
11.10
10.93
12.73
10.47
10.36
9.89
14.28
14.90
13.67
12.22
14.85
13.37
13.58
13.93
15.53
14.36
10.93
10.48
9.86
10.96
10.51
12.40
11.10
11.14
12.58
11.46
11.28
10.72
11.15
10.60
10.24
10.93
12.40
11.33
10.59
10.91
11.71
10.79
11.50
10.53
10.82
9.35
10.30
10.13
9.06
8.37
10.79
8.00
8.74
9.18
11.93
9.06
8.67
8.68
9.21
9.37
10.54
8.93
8.77
8.48
10.82
11.22
9.81
8.77
11.09
9.76
10.03
10.54
11.85
10.45
10.92
10.47
9.85
10.95
10.49
12.38
11.08
11.14
12.58
11.46
11.28
10.72
11.15
10.60
10.24
10.93
12.40
11.33
10.59
10.91
11.71
10.78
11.49
10.52
10.81
9.35
10.30
10.13
9.06
8.37
10.79
8.00
8.74
9.18
11.93
9.06
8.67
8.68
9.21
9.37
10.54
8.92
8.76
8.47
10.82
11.22
9.81
8.77
11.09
9.76
10.03
10.54
11.85
10.45
58
-------
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Laurel
McCracken
Madison
Warren
Boyd
Christian
Perry
Pike
Daviess
Caddo
Concordia
Ouachita
Rapid es
Terrebonne
Lafayette
West Baton Rouge
Tangipahoa
Iberville
East Baton Rouge
Jefferson
Calcasieu
Androscoggin
Aroostook
Cumberland
Hancock
Kennebec
Oxford
Penobscot
Anne Arundel
Baltimore
Harford
Montgomery
Washington
Baltimore (City)
Cecil
Prince George's
Suffolk
Berkshire
Bristol
Essex
Hampden
Plymouth
Worcester
Allegan
Bay
Berrien
Genesee
Ingham
Kalamazoo
Kent
Macomb
Missaukee
Monroe
Muskegon
12.55
13.38
13.61
13.83
14.49
13.20
13.06
13.46
14.10
12.53
11.42
11.97
11.03
10.74
11.08
13.51
12.03
12.90
13.38
11.52
11.07
9.90
9.74
11.13
5.76
9.99
10.13
9.12
14.82
14.76
12.51
12.47
13.70
15.76
12.68
13.03
13.07
10.65
9.58
9.58
12.17
9.87
11.29
11.84
10.93
11.72
11.61
12.23
12.84
12.89
12.70
8.26
13.92
11.61
9.26
10.36
9.94
10.32
10.77
9.98
9.79
9.90
10.38
10.62
9.58
10.31
9.50
9.33
9.55
11.34
10.14
11.08
11.23
9.85
9.81
8.85
9.44
9.87
5.35
8.91
9.25
8.23
11.87
11.68
9.72
9.59
10.41
12.51
10.06
10.13
11.22
9.88
8.26
8.24
10.69
8.51
9.72
9.64
8.94
9.57
9.33
9.82
10.40
10.25
10.34
7.10
11.02
9.55
9.26
10.36
9.94
10.32
10.76
9.97
9.78
9.89
10.37
10.61
9.57
10.30
9.49
9.32
9.54
11.32
10.12
11.05
11.20
9.81
9.75
8.86
9.44
9.87
5.35
8.91
9.25
8.23
11.87
11.68
9.72
9.59
10.41
12.51
10.05
10.12
11.23
9.88
8.26
8.24
10.69
8.51
9.72
9.64
8.94
9.57
9.33
9.82
10.40
10.25
10.34
7.10
11.02
9.55
59
-------
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Nebraska
Oakland
Ottawa
Saginaw
St. Clair
Washtenaw
Wayne
Cass
Dakota
Hennepin
Mille Lacs
Olmsted
Ramsey
Saint Louis
Scott
Stearns
Bolivar
DeSoto
Warren
Adams
Forrest
Harrison
Hinds
Jones
Lauderdale
Lowndes
Pearl River
Lee
Jackson
Boone
Buchanan
Cass
Cedar
Clay
Greene
Jackson
Monroe
Sainte Genevieve
Saint Louis
Jefferson
St. Louis City
Saint Charles
Cascade
Flathead
Gallatin
Lake
Lewis and Clark
Lincoln
Missoula
Ravalli
Sanders
Silver Bow
Rosebud
Yellowstone
Cass
13.78
12.55
10.61
13.34
13.88
17.50
5.70
9.30
9.76
6.54
10.13
11.32
7.51
9.00
8.58
12.36
12.43
12.32
11.29
13.62
12.20
12.56
14.39
13.07
12.79
12.14
12.57
12.04
11.84
12.80
10.67
11.12
11.03
11.75
12.78
10.87
13.34
13.46
13.79
14.56
13.29
5.57
9.87
4.25
9.06
7.96
14.93
10.20
8.56
6.69
9.86
6.58
8.14
9.99
11.02
10.02
8.68
11.35
10.99
14.18
5.13
7.84
8.15
5.73
8.56
9.56
6.55
7.61
7.40
10.43
9.76
10.11
9.44
11.32
10.42
10.39
11.90
10.58
10.24
10.28
9.88
10.01
10.03
10.84
8.87
9.46
9.27
9.83
10.52
8.95
10.84
10.67
11.10
11.58
10.80
5.15
8.97
4.15
8.35
7.41
13.47
9.34
7.92
6.33
9.06
6.38
7.45
8.31
11.02
10.02
8.68
11.35
10.99
14.18
5.13
7.84
8.15
5.73
8.56
9.56
6.55
7.61
7.40
10.43
9.76
10.11
9.43
11.31
10.41
10.38
11.89
10.57
10.23
10.27
9.87
9.98
10.03
10.84
8.87
9.46
9.27
9.83
10.52
8.95
10.84
10.67
11.09
11.56
10.75
5.15
8.97
4.15
8.35
7.41
13.47
9.34
7.92
6.33
9.06
6.37
7.44
8.31
60
-------
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nevada
Nevada
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Douglas
Hall
Lancaster
Lincoln
Sarpy
Scotts Bluff
Washington
Clark
Washoe
Hillsborough
Rockingham
Belknap
Cheshire
Coos
Grafton
Merrimack
Sullivan
Atlantic
Bergen
Essex
Hudson
Morris
Ocean
Passaic
Union
Warren
Camden
Mercer
Middlesex
Gloucester
Bernalillo
Grant
Sandoval
San Juan
Santa Fe
Chaves
Dona Ana
Kings
New York
Queens
Albany
Bronx
Chautauqua
Erie
Monroe
Nassau
Niagara
Onondaga
Orange
Richmond
St. Lawrence
Steuben
Suffolk
Westch ester
9.88
7.95
8.90
7.57
9.79
6.04
9.29
9.44
8.11
10.18
9.00
7.28
11.53
10.24
8.43
9.72
9.86
11.47
13.09
13.27
14.24
11.50
10.92
12.88
14.94
12.72
13.51
12.71
12.15
13.46
7.03
5.93
7.99
5.92
4.76
6.54
9.95
14.20
16.18
12.18
11.83
15.43
9.80
12.62
10.64
11.66
11.96
10.08
10.99
13.31
7.29
9.00
11.52
11.73
8.12
6.85
7.29
6.73
8.04
5.49
7.73
8.90
7.33
8.64
7.78
6.36
10.08
9.53
7.45
8.26
8.69
9.92
10.83
11.04
12.10
9.48
9.20
10.57
12.35
10.32
11.03
10.39
10.03
11.11
6.06
5.73
7.49
5.65
4.46
6.16
8.89
12.29
13.71
10.31
10.55
12.98
7.59
10.23
8.93
10.15
10.01
9.66
9.37
11.14
6.57
7.13
9.94
10.05
8.12
6.85
7.29
6.73
8.04
5.49
7.73
8.91
7.34
8.65
7.79
6.36
10.08
9.53
7.45
8.26
8.69
9.92
10.83
11.04
12.10
9.48
9.20
10.57
12.35
10.32
11.02
10.38
10.02
11.09
6.06
5.73
7.49
5.65
4.46
6.15
8.88
12.30
13.72
10.32
10.55
12.98
7.59
10.23
8.93
10.15
10.01
9.66
9.37
11.14
6.57
7.13
9.94
10.05
61
-------
New York
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Essex
Alamance
Buncombe
Caswell
Catawba
Cumberland
Davidson
Duplin
Durham
Edgecombe
Forsyth
Gaston
Guilford
Haywood
Jackson
Lenoir
McDowell
Martin
Mecklenburg
Mitchell
New Hanover
Onslow
Orange
Pitt
Robeson
Rowan
Swain
Wake
Watauga
Chatham
Montgomery
Wayne
Billings
Burke
Cass
McKenzie
Mercer
Burleigh
Cuyahoga
Butler
Clark
Clermont
Franklin
Greene
Hamilton
Lake
Lorain
Mahoning
Montgomery
Portage
Preble
Stark
Summit
Trumbull
5.94
13.94
12.60
13.19
15.31
13.73
15.17
11.30
13.57
12.37
14.28
14.26
13.79
12.98
12.09
11.12
14.24
10.86
15.31
12.75
9.96
10.98
13.12
11.59
12.78
14.02
12.65
13.54
12.05
11.99
12.24
12.96
4.61
5.90
7.72
5.01
6.04
6.61
17.37
15.36
14.64
14.15
15.27
13.36
17.54
13.02
13.87
15.12
15.54
13.37
13.70
16.15
15.17
14.53
5.24
10.60
9.63
9.85
11.34
11.12
11.53
9.42
11.12
10.12
10.52
10.83
10.47
10.29
9.24
9.21
10.92
8.85
12.37
9.63
8.35
9.10
10.20
9.56
10.67
10.75
9.66
11.24
8.85
9.23
9.44
10.87
4.31
5.70
6.76
4.72
5.30
5.92
13.38
11.86
11.38
10.31
11.52
10.12
12.97
10.23
10.68
11.50
11.95
10.26
10.66
12.34
11.80
11.17
5.23
10.60
9.63
9.85
11.34
11.12
11.53
9.42
11.12
10.12
10.52
10.83
10.47
10.29
9.24
9.21
10.92
8.85
12.37
9.63
8.35
9.10
10.20
9.56
10.67
10.75
9.66
11.24
8.85
9.22
9.43
10.86
4.31
5.70
6.76
4.72
5.30
5.87
13.39
11.86
11.38
10.31
11.52
10.12
12.97
10.23
10.68
11.50
11.95
10.26
10.66
12.34
11.80
11.17
62
-------
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Oregon
Oregon
Oregon
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Rhode Island
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
Athens
Jefferson
Lawrence
Lucas
Scioto
Caddo
Cherokee
Muskogee
Pittsburg
Sequoyah
Tulsa
Lincoln
Mayes
Oklahoma
Ottawa
Kay
Multnomah
Jackson
Klamath
Lane
Union
Adams
Allegheny
Beaver
Berks
Bucks
Cambria
Cumberland
Dauphin
Erie
Lackawanna
Lancaster
Lehigh
Luzerne
Mercer
Northampton
Washington
Westmoreland
York
Centre
Perry
Chester
Delaware
Philadelphia
Providence
Beaufort
Charleston
Edgefield
Florence
Georgetown
Greenville
Greenwood
Horry
Lexington
12.39
16.51
15.14
14.38
14.65
9.22
11.79
11.89
11.06
12.99
11.52
10.28
11.70
10.07
11.69
10.26
9.13
10.32
11.20
11.93
8.35
13.05
20.31
16.38
15.82
13.42
15.40
14.45
15.13
12.54
11.73
16.55
14.50
12.76
13.28
13.68
15.17
15.49
16.52
12.78
12.81
15.22
15.23
15.19
12.14
11.52
12.21
13.14
12.62
12.85
15.65
13.53
12.00
14.64
8.83
12.02
11.44
11.40
10.69
7.95
10.01
10.17
9.41
11.13
9.80
8.79
10.11
8.35
10.02
9.08
8.59
9.88
10.55
11.38
7.80
9.84
15.00
12.42
12.67
10.88
10.92
11.05
11.32
9.86
9.27
12.87
11.76
10.16
9.98
11.05
10.69
10.80
12.64
9.60
9.83
12.13
12.60
12.42
10.51
10.03
10.11
10.55
10.21
10.91
11.98
10.57
9.93
11.52
8.82
12.01
11.43
11.39
10.68
7.95
10.01
10.17
9.41
11.13
9.80
8.78
10.10
8.34
10.01
9.06
8.60
9.88
10.55
11.38
7.80
9.84
15.00
12.42
12.67
10.88
10.92
11.05
11.32
9.86
9.27
12.87
11.76
10.16
9.98
11.05
10.69
10.80
12.64
9.59
9.82
12.12
12.58
12.40
10.51
10.03
10.11
10.55
10.21
10.91
11.98
10.57
9.93
11.52
63
-------
South Carolina
South Carolina
South Carolina
South Carolina
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Richland
Spartanburg
Chesterfield
Oconee
Brookings
Brown
Codington
Custer
Jackson
Minnehaha
Pennington
Blount
Davidson
Hamilton
Knox
Lawrence
Loudon
McMinn
Montgomery
Putnam
Roane
Shelby
Sullivan
Sumner
Dyer
Maury
Dallas
El Paso
Hidalgo
Tarrant
Bowie
Harrison
Ector
Nueces
Orange
Jefferson
Harris
Box Elder
Weber
Cache
Salt Lake
Utah
Davis
Chittenden
Addison
Bennington
Rutland
Arlington
Charles
Fairfax
Henrico
Loudoun
Page
Bristol City
14.24
14.17
12.53
10.95
9.37
8.42
10.14
5.64
5.39
10.18
8.77
14.30
14.18
15.48
15.64
11.69
15.49
14.29
13.79
13.37
14.49
13.71
14.16
13.68
12.28
13.21
11.80
9.09
10.98
12.23
12.85
11.69
7.78
10.42
11.51
11.51
15.42
8.40
11.16
11.56
11.98
10.51
10.31
10.02
8.94
8.52
11.08
14.27
12.37
13.88
13.51
13.57
12.79
13.93
11.13
10.70
10.04
8.13
8.07
7.45
8.89
5.40
5.08
8.60
8.26
10.87
10.73
11.84
11.72
9.13
11.81
10.84
10.68
10.00
10.79
10.65
11.09
10.11
9.65
10.46
9.49
8.03
10.08
9.84
10.82
9.89
6.90
9.06
10.29
10.16
13.44
7.51
9.91
10.21
10.61
9.41
9.56
9.00
8.10
7.70
9.96
11.02
9.36
10.75
10.09
10.39
9.39
10.52
11.13
10.70
10.03
8.12
8.07
7.45
8.89
5.40
5.08
8.60
8.26
10.87
10.73
11.84
11.72
9.13
11.81
10.84
10.68
10.00
10.79
10.65
11.09
10.11
9.64
10.45
9.49
8.03
10.08
9.84
10.81
9.88
6.89
9.04
10.26
10.11
13.37
7.52
9.92
10.21
10.61
9.41
9.55
9.01
8.10
7.70
9.96
11.02
9.36
10.75
10.09
10.39
9.39
10.52
64
-------
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Washington
Washington
Washington
Washington
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Hampton City
Lynchburg City
Norfolk City
Roanoke City
Salem City
Virginia Beach City
Chesterfield
King
Pierce
Snohomish
Spokane
Berkeley
Brooke
Hancock
Harrison
Marion
Monongalia
Ohio
Raleigh
Wood
Cabell
Kanawha
Marshall
Dane
Milwaukee
Outagamie
Ashland
Brown
Dodge
Forest
Grant
Kenosha
Manitowoc
Ozaukee
St. Croix
Sauk
Taylor
Vilas
Waukesha
Campbell
Converse
Fremont
Laramie
Sheridan
12.17
12.84
12.78
14.27
14.69
12.40
13.44
11.24
10.55
9.91
9.97
15.93
16.52
15.76
13.99
15.03
14.35
14.58
12.90
15.40
16.30
16.52
15.19
12.20
14.08
10.96
6.07
11.39
11.04
7.41
11.79
11.98
10.20
11.60
10.09
10.22
8.24
6.78
13.91
6.29
3.52
8.17
4.48
9.70
9.71
9.55
10.24
10.58
11.04
10.04
10.13
10.72
9.93
9.63
8.51
12.30
12.04
11.56
10.19
10.87
9.85
10.14
9.52
11.45
12.37
12.18
10.72
10.47
11.89
9.53
5.40
9.94
9.33
6.53
9.99
10.00
8.86
9.85
8.60
8.58
7.19
5.99
11.87
6.10
3.40
7.61
4.10
9.05
9.71
9.55
10.24
10.58
11.04
10.04
10.12
10.73
9.93
9.63
8.51
12.30
12.04
11.56
10.19
10.87
9.85
10.14
9.52
11.45
12.36
12.17
10.71
10.48
11.90
9.54
5.40
9.94
9.33
6.53
9.99
10.00
8.86
9.85
8.60
8.58
7.19
5.99
11.87
6.10
3.40
7.61
4.10
9.05
65
-------
Appendix C: 24-hour PM2.s Design Values for LDGHG Scenarios (units are ug/m3)
State Name
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
California
California
California
California
California
California
California
California
California
California
County Name
Baldwin
Clay
Colbert
DeKalb
Escambia
Etowah
Houston
Jefferson
Madison
Mobile
Montgomery
Morgan
Russell
Shelby
Sumter
Talladega
Tuscaloosa
Walker
Cochise
Coconino
Gila
Maricopa
Pima
Final
Santa Cruz
Arkansas
Ashley
Crittenden
Faulkner
Garland
Mississippi
Phillips
Polk
Pope
Pulaski
Union
White
Alameda
Butte
Calaveras
Colusa
Contra Costa
Fresno
Imperial
Inyo
Kern
Kings
Baseline DV
26.20
31.80
30.40
32.00
29.00
35.10
28.60
44.00
33.50
30.00
32.00
31.50
35.50
32.00
28.90
33.40
29.80
32.80
16.60
17.10
22.10
31.40
12.20
17.50
36.00
29.10
28.90
35.00
29.80
29.20
30.30
29.10
26.10
28.30
31.90
28.70
29.90
32.50
52.50
20.50
26.10
34.70
60.20
40.20
16.60
64.50
58.00
2030
Reference
Case DV
20.80
22.07
20.24
21.30
22.35
22.06
22.02
33.06
21.80
22.16
23.97
18.97
28.05
22.02
20.26
22.41
21.54
21.70
16.38
16.59
21.09
27.23
10.88
15.42
34.33
21.93
23.21
22.79
23.33
22.41
22.92
21.79
19.02
24.34
25.84
23.38
22.79
28.25
41.83
16.97
23.63
31.22
49.22
36.78
15.81
53.63
48.18
2030 Control
Case DV
20.80
22.07
20.24
21.29
22.35
22.06
22.02
33.06
21.80
22.15
23.97
18.97
28.05
22.02
20.26
22.41
21.53
21.70
16.37
16.59
21.09
27.25
10.88
15.43
34.34
21.93
23.20
22.79
23.33
22.40
22.92
21.78
19.02
24.34
25.84
23.26
22.79
28.24
41.83
16.96
23.63
31.22
49.21
36.78
15.84
53.60
48.16
66
-------
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Delaware
Delaware
Delaware
District Of Co
Florida
Florida
Florida
Florida
Florida
Florida
Lake
Los Angeles
Mendocino
Merced
Monterey
Nevada
Orange
Placer
Plumas
Riverside
Sacramento
San Bernardino
San Diego
San Francisco
San Joaquin
San Luis Obispo
San Mateo
Santa Barbara
Santa Clara
Shasta
Solano
Sonoma
Stanislaus
Sutter
Tulare
Ventura
Yolo
Adams
Arapahoe
Boulder
Delta
Denver
Elbert
El Paso
Larimer
Mesa
Pueblo
San Miguel
Weld
Fairfield
Hartford
Litchfield
New Haven
New London
Kent
New Castle
Sussex
District of Columbia
Alachua
Bay
Brevard
Broward
Citrus
Duval
12.90
50.90
15.30
46.10
14.30
16.50
43.70
29.80
32.40
59.10
49.20
55.50
33.20
30.90
41.80
22.50
29.40
24.00
38.60
20.40
34.70
29.10
51.40
38.50
56.60
30.30
30.30
25.30
21.20
21.10
20.70
26.40
13.10
16.50
18.30
23.50
15.40
10.10
22.90
34.90
31.80
27.10
38.30
32.00
32.10
36.60
33.70
36.30
23.40
28.40
20.70
19.10
21.70
25.20
13.97
49.25
12.22
37.03
14.81
14.58
42.89
24.96
28.84
54.76
45.65
49.20
36.00
30.35
35.21
19.78
27.48
24.03
37.27
18.01
32.00
26.03
41.63
31.65
47.58
31.98
25.83
20.48
18.72
18.10
17.34
22.60
12.26
15.09
16.35
21.03
13.47
9.89
20.09
29.79
25.18
19.32
31.64
25.27
26.18
30.74
27.75
29.61
18.36
22.37
16.83
17.80
16.67
22.23
13.96
49.15
12.22
37.02
14.80
14.57
42.79
24.96
28.83
54.70
45.64
49.18
35.97
30.33
35.21
19.77
27.47
24.02
37.25
18.01
31.97
26.02
41.62
31.64
47.58
31.94
25.82
20.45
18.72
18.11
17.35
22.56
12.27
15.09
16.36
21.04
13.47
9.89
20.10
29.79
25.19
19.30
31.65
25.27
26.18
30.74
27.75
29.62
18.35
22.37
16.82
17.80
16.67
22.23
67
-------
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Escambia
Hillsbo rough
Lee
Leon
Manatee
Marion
Miami-Dade
Orange
Palm Beach
Pinellas
Polk
St. Lucie
Sarasota
Seminole
Volusia
Bibb
Chatham
Clayton
Cobb
DeKalb
Dougherty
Floyd
Fulton
Glynn
Gwinnett
Hall
Houston
Lowndes
Muscogee
Paulding
Richmond
Walker
Washington
Wilkinson
Ada
Bannock
Benewah
Canyon
Franklin
Idaho
Lemhi
Power
Shoshone
Adams
Champaign
Cook
DuPage
Hamilton
Jersey
Kane
Lake
La Salle
McHenry
McLean
28.80
23.60
17.80
29.00
19.90
23.20
19.40
21.70
18.90
21.90
19.50
18.70
19.80
22.80
22.60
33.50
28.40
35.80
35.00
33.90
34.10
35.10
37.60
26.10
32.80
30.60
29.60
25.90
31.30
33.00
32.70
30.90
30.80
33.10
28.30
27.00
32.90
31.80
36.70
28.40
36.50
33.30
38.10
31.40
30.00
43.00
34.60
31.60
32.10
34.80
33.00
28.90
31.50
33.40
23.34
19.71
14.88
23.41
14.32
18.60
21.14
17.64
17.88
17.97
15.89
15.59
15.24
16.66
16.00
25.08
23.58
24.31
24.86
25.98
27.21
25.52
26.06
21.55
23.72
23.87
21.70
20.97
25.78
22.48
26.34
21.92
21.99
25.54
24.94
24.40
30.33
27.31
31.82
27.20
34.20
30.05
34.99
23.38
22.77
34.31
30.70
20.93
23.92
29.09
26.29
23.73
27.75
24.71
23.34
19.70
14.88
23.41
14.32
18.60
21.14
17.65
17.88
17.98
15.89
15.58
15.24
16.66
16.00
25.09
23.59
24.33
24.87
26.00
27.21
25.52
26.08
21.55
23.73
23.87
21.70
20.97
25.78
22.48
26.34
21.93
21.99
25.55
24.95
24.41
30.34
27.32
31.85
27.20
34.20
30.06
34.99
23.38
22.77
34.32
30.72
20.92
23.89
29.09
26.28
23.73
27.75
24.68
68
-------
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Macon
Madison
Peoria
Randolph
Rock Island
Saint Clair
Sangamon
Will
Winnebago
Allen
Clark
Delaware
Dubois
Elkhart
Floyd
Henry
Howard
Knox
Lake
LaPorte
Madison
Marion
Porter
St. Joseph
Spencer
Tippecanoe
Vanderburgh
Vigo
Black Hawk
Clinton
Johnson
Linn
Montgomery
Muscatine
Palo Alto
Polk
Pottawattamie
Scott
Van Buren
Woodbury
Wright
Johnson
Linn
Sedgwick
Shawnee
Sumner
Wyandotte
Bell
Boyd
Bullitt
Campbell
Carter
Christian
Daviess
33.20
39.10
32.70
28.90
30.90
33.70
33.40
36.40
34.70
33.10
37.50
32.00
35.30
34.40
33.20
31.80
32.20
35.90
38.90
33.60
32.80
38.40
31.80
33.10
32.30
35.60
32.60
35.10
30.10
33.90
34.60
30.60
27.50
36.00
25.70
31.40
28.60
37.10
28.30
26.40
28.60
29.30
25.30
25.30
29.10
22.80
29.50
29.50
33.10
34.60
31.20
29.90
33.60
33.80
22.79
31.89
26.23
23.92
25.32
27.30
27.56
28.28
28.20
28.81
27.96
25.33
26.58
27.61
22.89
25.51
23.62
26.50
32.04
25.54
25.67
30.34
25.06
27.90
24.09
28.11
27.09
30.86
24.71
27.22
29.55
25.81
21.16
30.34
20.39
24.86
22.80
29.19
21.81
21.49
23.78
25.37
20.85
21.62
23.75
18.83
23.58
21.15
21.23
24.18
24.64
17.83
21.10
22.82
22.79
31.87
26.23
23.91
25.32
27.29
27.55
28.26
28.21
28.82
27.96
25.32
26.57
27.61
22.89
25.50
23.62
26.49
32.03
25.55
25.66
30.35
25.06
27.91
24.09
28.11
27.09
30.86
24.72
27.22
29.56
25.83
21.16
30.34
20.39
24.87
22.80
29.19
21.82
21.50
23.78
25.37
20.84
21.62
23.74
18.82
23.58
21.15
21.19
24.18
24.65
17.82
21.09
22.82
69
-------
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Fayette
Franklin
Hardin
Henderson
Jefferson
Kenton
Laurel
McCracken
Madison
Perry
Pike
Warren
Caddo
Calcasieu
Concordia
East Baton Rouge
Iberville
Jefferson
Lafayette
Ouachita
Rapides
Tangipahoa
Terrebonne
West Baton Rouge
Androscoggin
Aroostook
Cumberland
Hancock
Kennebec
Oxford
Penobscot
Anne Arundel
Baltimore
Cecil
Harford
Montgomery
Prince George's
Washington
Baltimore (City)
Berkshire
Bristol
Essex
Hampden
Plymouth
Suffolk
Worcester
Allegan
Bay
Berrien
Genesee
Ingham
Kalamazoo
Kent
Macomb
32.70
32.10
32.80
31.80
36.10
34.70
25.10
33.60
30.10
28.50
30.50
33.10
27.50
26.30
26.10
29.30
28.60
27.00
24.20
28.90
30.20
29.60
26.20
29.00
26.50
24.20
29.20
19.40
26.20
28.30
22.00
35.50
35.80
30.80
31.20
30.90
33.40
33.40
39.00
31.00
25.00
28.70
33.10
28.40
32.10
30.50
33.80
31.60
31.30
30.40
31.90
31.10
36.50
35.30
22.92
21.14
21.35
22.88
28.25
23.80
16.76
23.32
19.76
17.31
19.00
23.36
21.80
21.57
20.50
22.27
23.38
21.42
18.98
22.70
23.15
23.21
20.03
22.48
23.08
23.48
24.85
16.30
22.69
24.77
19.43
31.41
29.17
24.67
23.22
23.91
25.22
27.22
32.91
28.40
19.63
23.09
28.00
21.18
27.22
24.62
28.27
24.94
25.21
25.63
26.72
24.49
28.61
29.76
22.90
21.15
21.34
22.88
28.25
23.80
16.75
23.31
19.76
17.31
19.00
23.35
21.79
21.49
20.48
22.15
23.32
21.35
18.96
22.69
23.13
23.18
20.00
22.36
23.08
23.48
24.86
16.30
22.70
24.77
19.43
31.42
29.19
24.68
23.22
23.90
25.22
27.22
32.93
28.40
19.63
23.09
28.01
21.18
27.23
24.63
28.28
24.95
25.21
25.63
26.73
24.50
28.64
29.77
70
-------
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Missaukee
Monroe
Muskegon
Oakland
Ottawa
Saginaw
St. Clair
Washtenaw
Wayne
Cass
Dakota
Hennepin
Mille Lacs
Ramsey
Saint Louis
Scott
Stearns
Adams
Bolivar
DeSoto
Forrest
Harrison
Hinds
Jackson
Jones
Lauderdale
Lee
Lowndes
Pearl River
Warren
Boone
Buchanan
Cass
Cedar
Clay
Greene
Jackson
Jefferson
Monroe
Saint Charles
Sainte Genevieve
Saint Louis
St. Louis City
Cascade
Flathead
Gallatin
Lake
Lewis and Clark
Lincoln
Missoula
Ravalli
Rosebud
Sanders
Silver Bow
24.80
38.80
34.70
39.90
34.20
30.60
39.60
39.40
43.80
18.00
25.40
26.70
22.00
28.00
23.50
24.90
20.90
27.40
28.90
30.80
30.40
30.50
28.80
28.20
31.20
29.80
32.10
32.40
28.50
30.20
30.20
30.10
25.60
28.70
28.00
28.20
28.90
33.40
27.80
33.10
31.40
33.20
34.30
17.30
24.50
29.50
30.60
30.70
42.70
38.50
37.80
19.70
19.50
33.80
19.08
30.01
25.56
33.37
29.52
23.61
34.43
31.31
36.62
15.11
20.59
21.44
18.64
24.00
19.25
20.53
17.44
20.04
22.97
21.24
24.69
23.80
21.27
21.89
25.45
22.48
22.24
21.44
22.33
22.40
24.29
24.06
20.74
22.05
23.04
21.38
23.77
27.46
21.52
26.61
24.69
28.48
28.29
14.86
21.90
27.64
27.94
27.49
38.28
33.40
32.63
18.99
18.38
29.19
19.09
30.02
25.58
33.38
29.54
23.62
34.44
31.34
36.61
15.11
20.60
21.46
18.64
23.99
19.25
20.53
17.44
20.03
22.97
21.23
24.69
23.78
21.27
21.86
25.45
22.48
22.24
21.44
22.32
22.39
24.28
24.05
20.73
22.04
23.04
21.38
23.78
27.45
21.52
26.54
24.69
28.48
28.27
14.85
21.91
27.65
27.94
27.50
38.28
33.40
32.64
18.99
18.38
29.20
71
-------
Montana
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nevada
Nevada
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
Yellowstone
Douglas
Hall
Lancaster
Lincoln
Sarpy
Scotts Bluff
Washington
Clark
Washoe
Belknap
Cheshire
Coos
Grafton
Hillsbo rough
Merrimack
Rockingham
Sullivan
Bergen
Camden
Essex
Gloucester
Hudson
Mercer
Middlesex
Morris
Ocean
Passaic
Union
Warren
Bernalillo
C haves
Dona Ana
Grant
Sandoval
San Juan
Santa Fe
Albany
Bronx
Chautauqua
Erie
Essex
Kings
Monroe
Nassau
New York
Niagara
Onondaga
Orange
Queens
Richmond
St. Lawrence
Steuben
Suffolk
19.30
25.70
19.10
24.70
23.70
24.10
16.60
24.10
25.20
30.70
20.50
30.20
26.50
23.00
28.60
25.60
26.30
28.90
37.00
37.30
38.30
32.10
41.40
34.70
34.80
32.30
31.50
36.30
40.40
34.00
18.60
15.60
32.90
13.00
15.60
12.40
9.70
34.20
38.80
29.10
35.30
22.40
36.90
32.20
34.00
39.70
33.60
27.30
28.90
35.50
34.90
22.10
27.80
34.60
17.32
20.97
15.56
19.67
20.41
19.57
14.87
20.35
22.39
25.77
15.09
26.11
23.28
18.65
24.52
20.84
21.27
21.67
30.67
29.15
28.66
25.43
35.23
25.81
25.95
24.94
21.55
26.88
31.97
28.03
16.04
14.21
27.19
12.46
14.41
11.80
8.90
30.26
31.95
21.47
30.93
18.74
29.62
26.79
24.95
33.54
28.45
24.07
23.41
30.29
29.98
19.75
21.12
24.27
17.30
20.97
15.56
19.67
20.41
19.58
14.86
20.34
22.40
25.78
15.09
26.12
23.28
18.65
24.53
20.84
21.28
21.67
30.68
29.14
28.66
25.41
35.25
25.81
25.95
24.93
21.55
26.88
31.96
28.03
16.04
14.21
27.19
12.45
14.41
11.79
8.91
30.28
31.97
21.47
30.94
18.73
29.64
26.80
24.96
33.55
28.45
24.07
23.41
30.31
29.98
19.75
21.11
24.27
72
-------
New York
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Westchester
Alamance
Buncombe
Caswell
Catawba
Chatham
Cumberland
Davidson
Duplin
Durham
Edgecombe
Forsyth
Gaston
Guilford
Haywood
Jackson
Lenoir
McDowell
Martin
Mecklenburg
Mitchell
Montgomery
New Hanover
Onslow
Orange
Pitt
Robeson
Rowan
Swain
Wake
Watauga
Wayne
Billings
Burke
Burleigh
Cass
McKenzie
Mercer
Athens
Butler
Clark
Clermont
Cuyahoga
Franklin
Greene
Hamilton
Jefferson
Lake
Lawrence
Lorain
Lucas
Mahoning
Montgomery
Portage
33.50
31.70
30.00
29.40
34.50
26.90
30.70
31.30
28.30
31.00
26.70
31.90
30.80
30.60
27.70
25.50
25.20
31.50
24.80
32.30
30.20
28.20
24.00
24.60
29.30
26.20
29.90
30.20
27.30
31.60
30.40
29.70
13.00
16.70
17.60
21.20
11.90
16.90
32.30
39.20
35.30
34.40
42.10
38.50
33.00
40.60
41.90
37.10
33.70
31.50
36.30
36.80
37.80
34.30
26.06
22.16
20.91
20.15
23.59
20.36
23.62
23.16
21.91
22.87
22.10
24.85
21.30
23.44
21.09
18.04
20.43
22.14
20.95
28.92
21.55
19.95
17.88
19.37
20.67
22.98
21.38
22.09
20.42
24.74
20.12
22.89
11.97
15.92
15.61
17.74
11.21
13.68
20.73
29.27
25.99
23.60
33.04
31.07
24.73
28.83
28.87
29.71
22.01
22.97
29.22
28.03
28.90
25.96
26.05
22.16
20.91
20.15
23.59
20.36
23.62
23.16
21.91
22.88
22.09
24.86
21.30
23.44
21.09
18.03
20.43
22.13
20.95
28.92
21.54
19.95
17.87
19.37
20.67
22.97
21.38
22.08
20.42
24.75
20.12
22.88
11.97
15.91
15.47
17.74
11.21
13.67
20.72
29.27
25.99
23.61
33.05
31.10
24.74
28.84
28.87
29.72
22.00
22.97
29.23
28.04
28.93
25.96
73
-------
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Oregon
Oregon
Oregon
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Rhode Island
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
Preble
Scioto
Stark
Summit
Trumbull
Caddo
Cherokee
Kay
Lincoln
Mayes
Muskogee
Oklahoma
Ottawa
Pittsburg
Sequoyah
Tulsa
Jackson
Klamath
Lane
Multnomah
Union
Adams
Allegheny
Beaver
Berks
Bucks
Cambria
Centre
Chester
Cumberland
Dauphin
Delaware
Erie
Lackawanna
Lancaster
Lehigh
Luzerne
Mercer
Northampton
Perry
Philadelphia
Washington
Westmoreland
York
Providence
Beaufort
Charleston
Chesterfield
Edgefield
Florence
Georgetown
Greenville
Greenwood
Horry
32.80
34.50
36.90
38.00
36.20
23.90
27.50
31.80
27.80
28.70
29.50
27.10
29.10
26.30
31.40
30.30
33.70
44.00
48.90
29.80
27.30
34.90
64.20
43.40
37.70
34.00
39.00
36.20
36.70
38.00
38.00
35.20
34.40
31.50
40.80
36.40
32.40
36.30
36.70
30.40
36.50
38.10
37.10
38.20
30.60
30.20
27.90
28.70
32.20
28.80
29.20
32.10
30.00
28.60
25.89
22.57
27.46
31.04
28.29
19.09
22.73
27.68
22.20
24.98
24.86
21.64
23.48
21.28
25.67
25.65
32.10
40.83
45.99
27.26
25.15
26.74
50.29
28.51
32.22
27.86
23.83
29.34
28.24
31.65
32.61
27.93
29.92
25.05
33.71
31.47
26.19
28.27
30.01
25.54
30.06
25.26
22.48
32.46
25.59
22.52
23.12
21.07
22.23
22.15
22.88
25.86
21.42
21.85
25.89
22.57
27.47
31.05
28.30
19.08
22.73
27.59
22.19
24.96
24.85
21.64
23.47
21.27
25.66
25.63
32.10
40.83
45.99
27.26
25.15
26.75
50.33
28.51
32.23
27.86
23.84
29.34
28.26
31.65
32.62
27.90
29.92
25.06
33.73
31.48
26.20
28.29
30.02
25.55
30.06
25.26
22.49
32.47
25.60
22.52
23.12
21.08
22.24
22.15
22.88
25.86
21.42
21.85
74
-------
South Carolina
South Carolina
South Carolina
South Carolina
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Lexington
Oconee
Richland
Spartanburg
Brookings
Brown
Codington
Custer
Jackson
Minnehaha
Pennington
Blount
Davidson
Dyer
Hamilton
Knox
Lawrence
Loudon
McMinn
Maury
Montgomery
Putnam
Roane
Shelby
Sullivan
Sumner
Bowie
Dallas
Ector
El Paso
Harris
Harrison
Hidalgo
Jefferson
Nueces
Orange
Tarrant
Box Elder
Cache
Davis
Salt Lake
Tooele
Utah
Weber
Addison
Bennington
Chittenden
Rutland
Arlington
Charles
Chesterfield
Fairfax
Henrico
Loudoun
32.80
28.40
33.20
32.40
23.50
18.70
23.60
14.30
12.70
24.10
18.50
32.50
33.50
31.90
33.20
36.60
28.40
32.20
32.40
30.80
36.30
32.60
30.20
32.20
31.10
33.60
29.40
25.70
17.80
22.90
30.80
25.90
26.40
26.00
27.50
27.70
25.70
33.20
56.90
38.90
50.10
30.50
44.00
38.50
31.70
26.40
30.10
30.60
34.10
31.70
31.20
33.30
31.90
34.40
24.88
20.06
25.18
23.04
19.38
15.79
20.06
13.77
11.75
19.55
17.28
22.50
24.02
22.99
24.77
24.42
18.97
23.08
21.10
21.20
26.38
20.47
21.08
23.10
22.45
21.01
22.41
21.59
14.68
19.25
26.81
20.54
25.22
19.95
22.72
22.02
21.92
29.56
45.29
33.75
44.48
26.77
39.59
33.37
27.42
23.22
26.14
28.43
26.65
22.32
20.44
26.75
21.73
24.50
24.88
20.06
25.18
23.04
19.38
15.78
20.05
13.77
11.74
19.56
17.28
22.50
24.03
22.99
24.77
24.42
18.96
23.08
21.10
21.20
26.38
20.46
21.08
23.10
22.45
21.01
22.41
21.59
14.67
19.21
26.59
20.53
25.22
19.90
22.66
21.96
21.93
29.58
45.30
33.70
44.46
26.81
39.64
33.36
27.42
23.22
26.16
28.43
26.65
22.32
20.45
26.76
21.73
24.50
75
-------
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Washington
Washington
Washington
Washington
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Page
Bristol City
Hampton City
Lynchburg City
Norfolk City
Roanoke City
Salem City
Virginia Beach City
King
Pierce
Snohomish
Spokane
Berkeley
Brooke
Cabell
Hancock
Harrison
Kanawha
Marion
Marshall
Monongalia
Ohio
Raleigh
Summers
Wood
Ashland
Brown
Dane
Dodge
Forest
Grant
Kenosha
Manitowoc
Milwaukee
Outagamie
Ozaukee
St. Croix
Sauk
Taylor
Vilas
Waukesha
Campbell
Converse
Fremont
Laramie
Sheridan
30.00
30.20
29.00
30.70
29.60
32.70
34.00
30.00
29.10
41.80
34.30
29.70
34.50
43.90
35.10
40.60
33.50
36.90
33.60
33.90
35.60
32.00
30.60
31.20
35.40
18.60
36.50
35.50
31.80
25.20
34.30
32.70
29.70
38.60
32.80
32.50
26.60
28.60
25.30
22.60
35.40
18.60
10.00
29.80
11.90
30.80
20.65
20.45
22.52
19.39
23.58
22.41
24.22
25.13
27.47
38.07
32.87
24.34
29.43
35.53
22.48
25.52
20.55
24.17
20.36
22.66
18.86
23.26
19.27
18.84
22.22
14.96
32.31
29.68
27.06
20.80
29.04
28.96
26.17
35.73
28.94
28.11
21.85
24.38
21.72
19.03
30.20
17.71
9.72
25.89
10.91
28.42
20.66
20.45
22.52
19.38
23.59
22.41
24.22
25.13
27.49
38.10
32.87
24.35
29.44
35.53
22.47
25.52
20.54
24.16
20.36
22.66
18.86
23.26
19.26
18.83
22.22
14.96
32.27
29.70
27.07
20.81
29.05
28.95
26.18
35.73
28.96
28.11
21.85
24.39
21.73
19.03
30.21
17.71
9.72
25.90
10.88
28.43
76
-------
Appendix D: 2005 CMAQ Model Performance
Evaluation for Ozone, Particulate Matter and Air
Toxics
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC 27711
April 2010
-------
A. Introduction
An operational model performance evaluation for ozone, PM2.5 and its related speciated
components, and specific air toxics (i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene,
and acrolein) was conducted using 2005 State/local monitoring sites data in order to estimate the
ability of the CMAQ modeling system to replicate the base year concentrations for the 12-km
Eastern and Western United States domain1. This evaluation principally comprises statistical
assessments of model versus observed pairs that were paired in space and time on a daily or
weekly basis, depending on the sampling frequency of each network (measured data). For
certain time periods with missing ozone, PM25 and air toxic observations we excluded the
CMAQ predictions from those time periods in our calculations. It should be noted when pairing
model and observed data that each CMAQ concentration represents a grid-cell volume-averaged
value, while the ambient network measurements are made at specific locations. In conjunction
with the model performance statistics, we also provide spatial plots for individual monitors of the
calculated bias and error statistics (defined below). Statistics were generated for the 12-km
Eastern US domain (EUS), 12-km Western US domain (WUS), and five large subregions2:
Midwest, Northeast, Southeast, Central, and West U.S. The Atmospheric Model Evaluation
Tool (AMET) was used to conduct the evaluation described in this document.3
The ozone evaluation primarily focuses on observed and predicted one-hour daily maximum
ozone concentrations and eight-hour daily maximum ozone concentrations at a threshold of
40ppb. This ozone model performance was limited to the ozone season modeled for the Light-
Duty Vehicle Greenhouse Gas Final Rule (hereafter referred to as LDGHG): May, June, July,
August, and September. Ozone ambient measurements for 2005 were obtained from the Air
Quality System (AQS) Aerometric Information Retrieval System (AIRS). A total of 1194 ozone
measurement sites were included for evaluation. The ozone data were measured and reported on
an hourly basis.
The PM2.5 evaluation focuses on PM2.5 total mass and its components including sulfate (864),
nitrate (NO3), total nitrate (TNO3=NO3+HNO3), ammonium (NH4), elemental carbon (EC), and
organic carbon (OC). The PM2 5 performance statistics were calculated for each month and
season individually and for the entire year, as a whole. Seasons were defined as: winter
(December-January-February), spring (March-April-May), summer (June-July-August), and fall
(September-October-November). PM2 5 ambient measurements for 2002 were obtained from the
following networks for model evaluation: Speciation Trends Network (STN- total of 260 sites),
JSee Air Quality Modeling Technical Support Document, 2010 (EPA 454/R-10-001): Changes to the Renewable
Fuel Standard Program (Figure II-1) for the map of the CMAQ modeling domain.
2 The subregions are defined by States where: Midwest is IL, IN, MI, OH, and WI; Northeast is CT, DE,
MA, MD, ME, NH, NJ, NY, PA, RI, and VT; Southeast is AL, FL, GA, KY, MS, NC, SC, TN, VA, and
WV; Central is AR, IA, KS, LA, MN, MO, ME, OK, and TX; West is AK, CA, OR, WA, AZ, MM, CO, UT, WY,
SD,ND,MT, ID, andNV.
3 Gilliam, R. C., W. Appel, and S. Phillips. The Atmospheric Model Evaluation Tool (AMET): Meteorology
Module. Presented at 4th Annual CMAS Models-3 Users Conference, Chapel Hill, NC, September 26 - 28, 2005.
(http://www.cmascenter.org/)
-------
Interagency Monitoring of PROtected Visual Environments (IMPROVE- total of 204), Clean
Air Status and Trends Network (CASTNet- total of 93), and National Acid Deposition
Program/National Trends (NADP/NTN- toal of 297). The pollutant species included in the
evaluation for each network are listed in Table A-l. For PM2.5 species that are measured by
more than one network, we calculated separate sets of statistics for each network.
Table A-l. PM2.s monitoring networks and pollutants species included in the CMAQ
performance evaluation.
Ambient
Monitoring
Networks
IMPROVE
CASTNet
STN
NADP
Particulate
Species
PM2.5
Mass
X
X
SO4
X
X
X
N03
X
X
TNO3a
X
EC
X
X
NH4
X
X
X
oc
X
X
Wet
Deposition
Species
SO4
X
N03
X
a TNO3 = (NO3 + HNO3)
The air toxics evaluation focuses on specific species relevant to the LDGHG final rule, i.e.,
formaldehyde, acetaldehyde, benzene, 1,3-butadiene, acrolein, and naphthalene. Similar to the
PM2.s evaluation, the air toxics performance statistics were calculated for each month and season
individually and for the entire year, as a whole to estimate the ability of the CMAQ modeling
system to replicate the base year concentrations for the 12-km Eastern and Western United States
domains. As mentioned above, seasons were defined as: winter (December-January-February),
spring (March-April-May), summer (June-July-August), and fall (September-October-
November). Toxic measurements for 2005 were obtained from the National Air Toxics Trends
Stations (NATTS). Toxic measurements from 471 sites in the East and 135 sites in the West
were included in the evaluation for the 12km Eastern and Western U.S. grids, respectively.
There are various statistical metrics available and used by the science community for model
performance evaluation. For a robust evaluation, the principal evaluation statistics used to
evaluate CMAQ performance were two bias metrics, normalized mean bias and fractional bias;
and two error metrics, normalized mean error and fractional error.
Normalized mean bias (NMB) is used as a normalization to facilitate a range of concentration
magnitudes. This statistic averages the difference (model - observed) over the sum of observed
values. NMB is a useful model performance indicator because it avoids over inflating the
observed range of values, especially at low concentrations. Normalized mean bias is defined as:
-------
NMB =
Z(o)
*100
Normalized mean error (NME) is also similar to NMB, where the performance statistic is used as
a normalization of the mean error. NME calculates the absolute value of the difference (model -
observed) over the sum of observed values. Normalized mean error is defined as:
NME =
-*100
Fractional bias is defined as:
FB = -
n
*100, where P = predicted concentrations and O = observed
concentrations. FB is a useful model performance indicator because it has the advantage of
equally weighting positive and negative bias estimates. The single largest disadvantage in this
estimate of model performance is that the estimated concentration (i.e., prediction, P) is found in
both the numerator and denominator. Fractional error (FE) is similar to fractional bias except the
absolute value of the difference is used so that the error is always positive. Fractional error is
defined as:
n
V 1
*100
The "acceptability" of model performance was judged by comparing our CMAQ 2005
performance results to the range of performance found in recent regional ozone, PM2 5, and air
toxic4'5'6 model applications (e.g., Revised Renewable Fuel Standards Final Rule,7 Clean Air
4 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform:
Air Toxics, Ozone, and Paniculate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.
5 Strum, M., Wesson, K., Phillips, S., Cook, R., Michaels, H., Brzezinski, D., Pollack, A., Jimenez, M., Shepard, S.
Impact of using lin-level emissions on multi-pollutant air quality model predictions at regional and local scales. 17th
Annual International Emission Inventory Conference, Portland, Oregon, June 2-5, 2008.
6 Wesson, K., N. Farm, and B. Timin, 2010: Draft Manuscript: Air Quality and Benefits Model Responsiveness to
Varying Horizontal Resolution in the Detroit Urban Area, Atmospheric Pollution Research, Special Issue: Air
Quality Modeling and Analysis.
-------
Interstate Rule8, Final PM NAAQS Rule9, and EPA's Proposal to Designate an Emissions
Control Area for Nitrogen Oxides10). These other modeling studies represent a wide range of
modeling analyses which cover various models, model configurations, domains, years and/or
episodes, chemical mechanisms, and aerosol modules. Overall, the NMB, NME, FB, and FE
statistics shown in Sections B through P below for CMAQ predicted 2005 ozone, PM2.5, and air
toxics concentrations are within the range or close to that found in recent OAQPS applications.
The CMAQ model performance results give us confidence that our applications of CMAQ using
this 2005 modeling platform provide a scientifically credible approach for assessing ozone and
PM2.5 concentrations for the purposes of the LDGHG Final Rule. We discuss in the following
sections the bias and error results for the one-hour maximum ozone concentrations and eight-
hour daily maximum ozone concentrations evaluated at a threshold of 40 ppb, the annual and
seasonal PM2.s and its related speciated components as well as specific air toxic concentrations.
7 EPA 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. EPA-420-R-10-006. February
2010. Sections 3.4.2.1.2 and 3.4.3.3. Docket EPA-HQ-OAR-2009-0472-11332.
8 See: U.S. Environmental Protection Agency; Technical Support Document for the Final Clean Air Interstate Rule:
Air Quality Modeling; Office of Air Quality Planning and Standards; RTF, NC; March 2005 (CAIR Docket OAR-
2005-0053-2149).
9 U.S. Environmental Protection Agency, 2006. Technical Support Document for the Final PM NAAQS Rule:
Office of Air Quality Planning and Standards, Research Triangle Park, NC.
10 U.S. Environmental Protection Agency, Proposal to Designate an Emissions Control Area for Nitrogen Oxides,
Sulfur Oxides, and Paniculate Matter: Technical Support Document. EPA-420-R-007, 329pp., 2009.
(http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09007.pdf)
-------
B. One-Hour Daily Maximum Ozone Performance
Ozone Performance: Threshold of 40 ppb
Table B-l provides one-hour daily maximum ozone model performance statistics calculated for a
threshold of 40 ppb of observed and modeled concentrations, restricted to the ozone season
modeled for the 12-km Eastern and Western U.S. domain and the five subregions (Midwest,
Northeast, Southeast, Central and Western U.S.). Spatial plots of the NMB and NME statistics
(units of percent) for individual monitors are also provided as a complement to the tabular
statistical data (Figures B-l - B-24). Overall, one-hour daily maximum ozone model
performance is slightly under-predicted or near negligible in both the 12-km EUS and WUS
when applying a threshold of 40 ppb for the modeled ozone season (May-September). For the
12-km Eastern domain, the bias and error statistics are comparable for the aggregate of the ozone
season and for each individual ozone month modeled, with a NMB range of -1% to -5% and a
FB range of-0.5% to -4%, and a NME and FE range of 11% to 14%. Likewise, for the 12-km
Western domain, the bias and error statistics are similar between the ozone seasonal aggregate
and the individual months, with a NMB and FB approximately -2%, and a NME and FE
approximately 14%. Hourly ozone model performance when compared across the five
subregions shows slightly better performance in the Southeast. In general, the Northeast,
Midwest, Central and West U.S. exhibit similar bias and error statistics for the episodes modeled.
The month of August shows a slightly better bias and error model performance results, although
the results are spatially and temporally comparable across the months modeled.
Table B-l. 2005 CMAQ one-hour daily maximum ozone model performance statistics
calculated for a threshold of 40 ppb.
CMAQ 2005 One-Hour Maximum Ozone:
Threshold of 40 ppb
May
June
July
12-km EUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-km EUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-km EUS
12-km WUS
Midwest
Northeast
Southeast
No. of Obs.
21394
9631
4418
4102
6424
4328
8294
19517
9056
4639
4148
4644
4062
7737
19692
9443
4923
4445
4733
NMB (%)
-1.6
-3.4
0.8
5.4
-3.6
-6.4
-3.5
-3.5
-3.7
-4.6
-1.0
-2.7
-6.2
-4.0
1.2
0.4
0.4
4.2
4.2
NME (%)
11.5
12.8
10.0
11.8
11.3
13.4
12.9
12.8
13.0
12.3
14.1
12.5
13.2
13.1
14.2
16.0
12.7
15.2
15.1
FB (%)
-0.8
-2.8
1.0
5.9
-3.0
-5.5
-3.0
-2.8
-3.2
-4.0
-0.1
-2.2
-5.4
-3.6
1.8
1.0
0.9
4.8
4.6
FE (%)
11.6
12.7
10.2
11.7
11.5
13.4
12.8
12.9
13.0
12.4
14.2
12.6
13.3
13.1
14.1
15.8
12.6
14.9
14.8
-------
August
September
Seasonal Aggregate
Central U.S.
West
12-kmEUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
3521
8168
19643
9562
4549
4139
5303
3589
8357
18085
8725
4002
3667
5259
3286
7530
98331
46417
22531
20501
26363
18786
40086
-3.8
0.2
0.1
-0.8
0.2
0.2
3.6
-4.1
-1.0
-2.2
-3.6
-3.6
-1.8
-0.1
-6.1
-4.1
-1.2
-2.1
-1.4
1.4
0.1
-5.4
-2.3
14.8
16.2
13.9
15.5
12.2
13.2
14.9
16.2
15.7
12.0
14.1
10.7
11.3
12.1
14.5
14.3
12.9
14.3
11.7
13.3
13.1
14.4
14.5
-3.1
0.7
0.8
-0.6
1.0
1.2
3.9
-2.9
-1.0
-1.3
-3.2
-3.0
-0.7
0.8
-5.1
-3.8
-0.5
-1.7
-0.8
2.3
0.7
-4.4
-2.1
14.9
16.0
13.8
15.5
12.3
13.1
14.5
16.1
15.7
12.0
14.3
10.8
11.3
12.1
14.5
14.4
12.8
14.2
11.7
13.1
13.0
14.4
14.4
-------
O3 NMB (%) for run 2005ci_lox_05b_all_bc_v47_N1b_MP_12km for May
CIRCLE=AQS Ihrmax;
Figure B-l. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., May 2005.
O3 NME (%) for run 2005cLlox_05b_all_bc_v47_N1 b_MP_12km for May
CIRCLE=AQS_1hrmax;
Figure B-2. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., May 2005.
-------
O3 NMB (%) for run 2005cLtox 05b_alt_bc_v47 N1b MP 12km for June
CIRCLE=AQS_1hrmax;
Figure B-3. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., June 2005.
O3 NME (%) for run 2005cLtox 05b_alt bc_v47 N1b_MP_12km for June
CIRCLE=AQS_1hrmax;
Figure B-4. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., June 2005.
-------
O3 NMB (%) for run 2005cLlox_05b_alt_bc_v47_N1 b_MP_12km for July
CIRCLE=AQS_1hrmax;
Figure B-5. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., July 2005.
O3 NME (%) for run 2005cLlox_05b_all_bc_v47_N1b_MP_12l(m tor July
CIRCLE=AQS_1hrmax;
Figure B-6. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., July 2005.
-------
O3 NMB (%) for run 2005cMox 05b_alt_bc_v47 N1b_MP_12km for August
CIRCLE=AQS_1hrmax;
Figure B-7. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., August 2005.
O3 NME (%) for run 2005CJ tox 05b all be v47 N1b MP 12km for August
CIRCLE=AQS_1hrmax;
Figure B-8. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., August 2005.
-------
O3 NMB (%) for run 2005cLlo«_05b_all_bc_v47_N1b_MP_12kni for September
CIRCLE=AQS_1hrmax;
Figure B-9. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., September 2005.
O3 NME (%) for run 2005cLlox_05b_all_bc_v47_N1b_MP_12km for September
CIRCLE=AQS_1hrmax;
Figure B-10. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., September 2005.
-------
O3 NMB (%) for run 2005ci_tox 05b_alt_bc_v47 N1b_MP_12km for May to September
CIRCLE=AQS_1hrmax;
Figure B-ll. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., seasonal aggregate 2005.
O3 NME (%) for run 2005CJ tox 05b alt be v47 N1b MP 12km for May to September
CIRCLE=AQS_1hrmax;
Figure B-12. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., seasonal aggregate 2005.
-------
O3 NMB (%) for run 2005ci_tox 05b alt be v47 N1b MP W12km for May
An Atmospheric MotteKSBsluation (AMET) Prod
CIRCLE=AQS_1hrmax;
Figure B-13. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., May 2005.
O3 NME (%) for run 2005ci_tox_05b_alt_bc_v47_N1b_MP_W12km for May
An Atmosbheric Motelertifeluation (AMET) Prod
CIRCLE=AQS_1hrmax;
Figure B-14. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., May 2005.
-------
O3 NMB (%) for run 2005ci tox 05b_alt bc_v47_N1b MP W12km tor June
UiKULh=AUb_1 hrmax;
Figure B-15. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., June 2005.
O3 NME (%) tor run 2005ciJoxJ)5bj3lt_bc_v47_N1b_MP_W12km tor June
An Atmospheric MdclencphiuatiGn (AMET) Prodii
CIRCLE=AQS_1hrmax;
Figure B-16. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., June 2005.
-------
O3 NMB (%) for run 2005ci tox_05b alt_bc_v47_N1b_MP_W12km lor July
An Atmospheric MoMeKEaiuation (AMET) Produ
UiKULh=AUb_1 hrmax;
Figure B-17. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., July 2005.
O3 NME (%) for run 2005ci_tox_05b_alt_bc_v47_N1b_MP_W12km for July
An Atmospheric MotteKEWuation (AMET) Prod
CIRCLE=AQS_1hrmax;
Figure B-18. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., July 2005.
-------
03 NMB (%) for run 2005ci Jox_05b_alt_bc_v47_N1 b_MP_W12km for August
CIRCLE=AQS Ihrmax;
Figure B-19. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., August 2005.
O3 NME (%) tor run 2005ci_tox_05b_all_bc_v47_N1b_MP_W12kin for August
An Atmospheric MoHeftWuation (AMET) Pro
CiRCLE=AQS_1 hrmax;
Figure B-20. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., August 2005.
-------
03 HUB (%) tor run 2005ci_lo*_05b_altbc_v47_M1b_MP_W12km tor September
CIRCLE=AQS Ihrmax;
Figure B-21. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., September 2005.
O3 NME (%) for run 2005ci_tox_05b_all bc_v47_N1b_MP_W12km tor September
An Atmospheric MoseKtftlluatton (AMET) Produ
CIRCLE=AQS Ihrmax;
Figure B-22. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., September 2005.
-------
03 MMB (%) tor run 2005ci_tox_05b alt be v47_N1b_MPW12km tor May to September
An Atmospheric MoSeKEtSluation (AMET) Produ
UiKULh=AUb_1hrmax;
Figure B-23. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., seasonal aggregate 2005.
O3 NME (%) for run 20Q5ciJox_05b_alt_bc_v47,N1b_MP_W12km tor May to September
An Atmospheric MdclencphiuatiGn (AMET) Produ
CIRCLE=AQS_1hrmax;
Figure B-24. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., seasonal aggregate 2005.
C. Eight-hour Daily Maximum Ozone Performance
-------
Ozone Performance: Threshold of 40 ppb
Table C-l presents eight-hour daily maximum ozone model performance bias and error statistics
for the entire range of observed and modeled concentrations at a threshold of 40 ppb for the
ozone season modeled for the 12-km Eastern and Western U.S. domain and the corresponding
subregions defined above. Spatial plots of the NMB and NME statistics (units of percent) for
individual monitors based on the aggregate and the individual ozone months modeled
respectively are shown in Figures C-l through C-24. In general, CMAQ slightly under-predicts
eight-hour daily maximum ozone with a threshold of 40 ppb in the months of May, June and
August. Likewise, model predictions in the BUS and WUS are slightly over-predicted in the
months of July and August. For the 12-km Eastern domain, the bias statistics are within the
range of approximately -4% to 7%, while the error statistics range from 11% to 14% for the
aggregate of the ozone season and for most of the months modeled. For the 12-km Western
domain, the bias statistics are within the range of approximately 3% to -3%, while the error
statistics range from 11% to 13% for the aggregate of the ozone season and for the individual
months modeled. The five subregions show relatively similar eight-hour daily maximum ozone
performance.
Table C-l. 2005 CMAQ eight-hour daily maximum ozone model performance statistics
calculated for a threshold of 40 pbb.
CMAQ 2005 Eight-Hour Maximum
Ozone: Threshold of 40 ppb
May
June
July
12-km BUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
No. ofObs.
19310
8445
3858
3528
6019
3927
7234
17404
8102
4324
3590
3924
3663
6889
17045
8556
4429
3856
3806
3057
7407
NMB (%)
-1.0
-1.6
0.2
5.2
-2.1
-5.8
-1.8
-2.1
-1.9
-3.8
0.3
-0.3
-5.5
-2.2
3.3
3.7
1.8
6.6
7.4
-2.3
3.5
NME (%)
10.9
12.0
10.0
11.4
10.5
12.8
12.1
11.9
11.9
11.6
13.1
11.4
12.1
12.1
13.4
15.0
11.8
14.6
15.0
13.2
15.1
FB (%)
-0.4
-1.2
0.7
5.4
-1.6
-5.2
-1.5
-1.5
-1.6
-3.4
1.0
0.1
-5.0
-2.0
3.6
3.9
2.3
6.8
7.3
-2.1
3.6
FE (%)
11.0
12.0
10.2
11.2
10.6
13.0
12.0
12.0
11.9
11.8
13.2
11.5
12.3
12.1
13.3
14.7
11.8
14.3
14.5
13.5
14.9
-------
August
September
Seasonal Aggregate
(May - September)
12-kmEUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Midwest
Northeast
Southeast
Central U.S.
West
16953
8523
4027
3530
4447
3096
7469
15190
7465
3265
2856
4647
2798
6446
85902
41091
19903
17360
22843
16541
35445
1.9
1.6
0.9
1.4
7.4
-3.4
1.4
-1.8
-2.4
-4.2
-2.3
1.5
-6.5
-2.9
0.1
0.0
-0.9
2.4
2.3
-4.8
-0.2
12.9
13.9
11.3
12.3
14.7
14.4
14.1
11.2
13.4
10.2
10.6
11.2
13.6
13.7
12.1
13.3
11.1
12.6
12.3
13.2
13.5
2.2
1.5
1.4
2.0
7.2
-3.1
1.2
-1.3
-2.6
-4.0
-1.8
2.1
-6.1
-3.1
0.5
0.1
-0.5
2.9
2.6
-4.4
-0.3
12.9
13.9
11.4
12.2
14.1
14.8
14.0
11.3
13.9
10.4
10.7
11.2
14.0
14.1
12.1
13.3
11.2
12.4
12.2
13.4
13.5
-------
O3 NMB (%) for run 2005ci_tox 05b alt bc_v47 N1b_MP_12km for May
CIRCLE=AQS_8hrmax;
Figure C-l. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., May 2005.
O3 NME (%) for run 2005CJ tox 05b alt_bc_v47_N1 b_MP_12km for May
CIRCLE=AQS_8hrmax;
Figure C-2. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., May 2005.
-------
O3 NMB (%) for run 2005cLtox 05b_alt_bc_v47 N1b MP 12km for June
CIRCLE=AQS_8hrmax;
Figure C-3. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., June 2005.
O3 NME (%) for run 2005ci tox 05b alt be v47 N1b MP 12km for June
CIRCLE=AQS_8hrmax;
Figure C-4. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., June 2005.
-------
O3 NMB (%) for run 2005ci tox 05b_alt _bc_v47_N1 b_MP_12km for July
CIRCLE=AQS_8hrmax;
Figure C-5. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., July 2005.
O3 NME (%) for run 2005ci lox 05b alt be v47 N1b MP_12km for July
CIRCLE=AQS_8hrmax;
Figure C-6. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., July 2005.
-------
O3 NMB (%) for run 2u05ci_lox_05b_all_bc_v47_N1b_MP_l2l(in tor August
CIRCLE=AQS Shrmax;
Figure C-7. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., August 2005.
O3 NME (%) tor run 2005cLlox_05b_all_bc_v47_N1b_MP_12km for August
CIRCLE=AQS_8hrmax;
Figure C-8. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., August 2005.
-------
O3 NMB (%) for run 2005ci_tox 05b_alt_bc_v47 N1b_MP_12km for September
CIRCLE=AQS_8hrmax;
Figure C-9. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., September 2005.
O3 NME (%) for run 2005ci tox 05b alt be v47 Mb MP 12km for September
CIRCLE=AQS_8hrmax;
Figure C-10. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., September 2005.
-------
O3 NMB (%) for run 2005ci_tox 05b_alt_bc_v47 N1b_MP_12km for May to September
CIRCLE=AQS_8hrmax;
Figure C-ll. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., seasonal aggregate 2005.
O3 NME (%) for run 2005ci tox 05b_alt be v47_N1b.MP_12km for May to September
CIRCLE=AQS_8hrmax;
Figure C-12. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Eastern U.S., seasonal aggregate 2005.
-------
03 NMB (%) (or run 2005ci_tox_05b alt_bc_v47_N1b_MP_W12km tor May
CIRCLE=AQS_8hrmax;
Figure C-13. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., May 2005.
O3 NME (%) for run 2005ci_toxJ)5b,alt_bc_v47_N1b_MP__W12km tor May
CIRCLE=AQS_8hrmax;
Figure C-14. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., May 2005.
-------
O3 NMB (%) for run 2005ci_tox_05b_alt_bc_v47_N1b_MP_W12km for June
An Atmospheric MdsteVtvaluation (AMET) Prod
CIRCLE=AQS_8hrmax;
Figure C-15. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., June 2005.
O3 NME (%) for run 2005ci_tox_05b_alt_bc_v47_N1b_MP_W12km for June
An Atmospheric MdsteVtValuation (AMET) Prod
CIRCLE=AQS_8hrmax;
Figure C-16. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., June 2005.
-------
O3 NMB (%) for run 2005ci tox 05b alt bc_v47^N1bJ
-------
O3 NMB (%) for run 20Q5ci to> 05b alt_bc_v47_N1b_MPW12km tor August
An Atmospheric MoSeKEtSluation (AMET) Produ
UiKULh=AUb_8hrmax;
Figure C-19. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., August 2005.
O3 NME (%) for run 2005ci tox 05b alt_bc_v47_N1b_MP_W12km for August
An Atmospheric MotteKEWuation (AMET) Prod
CIRCLE=AQS_8hrmax;
Figure C-20. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., August 2005.
-------
O3 NMB (%) for run 2005ci_tox_05b_alt_bc_v47_N1b_MP_W12km lor September
CIRCLE=AQS Shrmax;
Figure C-21. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., September 2005.
O3 NME (%) for run 2005cLtox_05b_alt_bc_v47_N1b_MP_W12km for September
An Atmospheric MosLeKt&lluation (AMET) Pro
CiRCLE=AQS_8hrmax;
Figure C-22. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., September 2005.
-------
O3 NMB (%) for run 2005ci_tox_05b_alt_bc_v47_N1b_MP_W12km for May to September
An Atmospheric MoHrftwuation (AMET) Prod
CIRCLE=AQS_8hrmax;
Figure C-23. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., seasonal aggregate 2005.
O3 NME (%) for run 2005ci tQX_05b_alt_bc_v47_N1b_MP_W12km for May to September
CIRCLE=AQS_8hrmax;
Figure C-24. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb
threshold) by monitor for Western U.S., seasonal aggregate 2005.
-------
D. Annual PM2.s Species Evaluation
Table D-l provides annual model performance statistics for PM2.5 and its component species for
the 12-km Eastern domain, 12-km Western domain, and five subregions defined in Section A
(Midwest, Northeast, Southeast, Central, and West U.S.). Spatial plots of the NMB and NME
statistics (units of percent) for individual monitors are also provided as a complement to the
tabular statistical data (Figures D-l - D-28). In the East, annual total PM2.5 mass is under-
predicted when compared at STN and IMPROVE sites in the Southeast and Central U.S. In the
West, annual total PM2.5 mass is under-predicted when evaluated at STN sites and IMPROVE
sites, with slightly better performance at the STN network (bias ~ -10). Although not shown
here, the mean observed concentrations of PM2.5 are approximately twice as high at the STN
sites (EUS = ~13|ig m'3; WUS = ~1 l|ig m'3) as the IMPROVE sites (EUS = ~7|ig m'3; WUS =
~4|ig m"3), thus illustrating the statistical differences between the urban STN and rural
IMPROVE networks. Sulfate is consistently under-predicted at STN, IMPROVE, and CASTNet
sites, with NMB values ranging from -29% to -1%. Overall, sulfate performance is best in the
East at urban STN sites. Nitrate is over-predicted in the 12-km Eastern domain (NMB in the
range of 25% to 86%), while nitrate is under-predicted in the 12-km Western domain (NMB in
the range of-18% to -47%). Likewise, model performance of total nitrate at CASTNet sites
shows an over-prediction in the East (NMB ~ 40%) and in the West (NMB ~ 5%). Ammonium
model performance varies across the STN and CASTNet in the East and West, with a mix of
over and under-predictions in the Eastern domain and also an under-prediction in the West.
Elemental carbon is over-predicted at STN sites in the East and West with a bias of-30% and
error of-70%. Although, EC is under-predicted at IMPROVE sites in the East and over-
predicted in the West. Organic carbon is moderately under-predicted for all domains in the STN
and IMPROVE networks (bias - -30% and error - 50%. Differences in model predictions
between IMPROVE and STN networks could be attributed to both the rural versus urban
characteristics as well as differences in the measurement methodology between the two networks
(e.g. blank correction factors, and filter technology used).
Table D-l. 2005 CMAQ annual PM2.s species model performance statistics.
CMAQ 2005 Annual PM2.5 species
PM25
Total Mass
STN
IMPROVE
12-km EUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km EUS
12-km WUS
Northeast
Midwest
No. of
Obs.
11797
3440
2318
3020
3067
2523
2826
9321
10411
571
2339
NMB (%)
6.6
-10.0
16.6
19.5
-7.2
0.6
-10.9
1.6
-10.9
9.1
21.5
NME (%)
39.5
45.0
38.1
45.6
34.1
41.7
46.1
43.4
44.8
39.4
52.9
FB (%)
4.0
-9.5
15.8
16.5
-7.6
-3.6
-10.6
0.0
-13.6
8
14.9
FE (%)
39.2
44.4
35.1
40.7
29.1
44.7
45.0
44.0
46.8
38.5
46.8
-------
Sulfate
Nitrate
Total
Nitrate
(NO3 +
HNO3)
STN
IMPROVE
CASTNet
STN
IMPROVE
CASTNet
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
1694
2376
8820
13897
3920
2495
3498
3882
3059
3157
9034
10002
531
2253
1685
2350
8496
3170
1142
615
786
1099
300
1041
12741
3655
2495
3499
3882
1927
3139
9027
9987
531
2248
1685
2350
8480
3170
1142
615
786
1099
300
-10.5
-5.5
-13.3
-8.7
-17.0
-1.2
-3.9
-10.9
-19.8
-15.5
-11.4
-9.0
-9.5
-3.8
-13.8
-19.7
-4.7
-15.7
-19.8
-13.4
-10.3
-17.9
-29.1
-18.4
37.8
-41.7
41.0
48.9
34.5
25.0
-47.3
52.8
-18.3
32.8
86.2
67.2
40.89
-35.7
41.0
5.4
37.4
55.8
41.8
23.6
37.2
42.4
45.0
32.6
42.3
33.4
31.7
30.3
36.5
45.8
33.8
39.9
31.8
34.2
31.5
35.8
41.7
23.1
31.1
21.7
21.3
22.7
32.0
31.6
78.6
65.3
73.3
83.1
93.7
67.2
65.4
98.5
75.4
77.1
122.5
126.4
82.1
77.0
51.0
36.3
47.0
59.4
53.4
40.3
-9.7
-4.7
-14.5
-5.8
-7.8
4.1
-0.7
-8.6
-16.6
-6.7
-2.6
6.8
-1.9
3.0
-8.1
-12.2
9.6
-14.2
-10.5
-11.0
-7.7
-19.5
-29.3
-9.3
0.4
-70.8
24.0
11.5
-19.4
4.8
-79.1
-21.0
-84.2
2.0
8.9
-24.5
-10.1
-91.7
31.7
13.1
35.1
44.4
29.0
16.8
42.3
46.0
47.2
35.2
42.8
34.1
33.3
32.9
40.8
44.0
38.4
43.5
34.1
37.1
35.4
39.8
44.4
25.8
32.6
22.9
23.0
25.6
35.2
32.8
79.4
97.5
66.8
75.6
91.4
75.0
99.9
101.4
120.6
83.8
97.3
104.4
95.8
124.2
44.0
40.6
40.6
50.2
45.6
36.2
-------
Ammonium
Elemental
Carbon
Organic
Carbon
STN
CASTNet
STN
IMPROVE
STN
IMPROVE
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
1041
13897
3893
2495
3498
3882
3059
3130
3170
1142
615
786
1099
300
1041
14038
3814
2502
3479
3877
3221
3015
8668
12851
602
2117
1584
2123
8169
12619
3582
2380
3323
3802
2259
3060
8662
11586
601
2116
1587
2123
8165
4.8
13.0
-14.9
21.0
21.2
7.0
1.3
-20.5
5.7
-6.4
15.4
14.5
-7.5
3.3
-12.6
45.5
31.1
40.4
57.3
27.2
72.1
38.7
-14.6
43.4
-1.3
10.6
-36.7
-23.8
5.8
-27.2
-32.1
-28.3
-7.6
-39.3
-31.4
-31.7
-21.7
-29.9
-22.7
3.5
-30.1
-36.9
-11.7
37.6
44.8
55.3
44.8
47.6
41.4
45.1
59.0
36.5
37.8
37.2
40.8
32.9
36.9
37.1
77.1
111
65.9
83.7
64.4
102.0
82.8
49.3
75.6
45.1
54.5
46.9
47.7
64.9
50.5
56.7
49.0
53.3
49.2
51.2
57.6
49.4
50.4
41.3
55.7
42.8
51.7
59.2
14.7
17.2
7.1
27.0
29.5
11.4
4.22
5.8
7.1
-4.0
18.9
18.4
-7.4
5.5
-4.9
30.6
19.5
33.8
38.5
20.6
39.0
21.0
-18.2
29.6
-15.7
-3.7
-39.3
-21.5
-8.6
-22.8
-28.2
-19.7
-4.2
-39.4
-28.3
-27.8
-25.6
-26.7
-27.7
-5.1
-37.8
-39.1
-19.3
41.6
46.8
55.0
43.8
48.2
43.4
51.1
57.2
36.8
37.6
35.2
38.7
36.2
40.1
37.5
58.3
62.5
53.4
59.6
51.0
69.5
65.1
53.3
57.8
48.1
54.1
56.3
53.4
61.0
60.1
61.3
58.1
58.6
61.0
63.2
61.4
55.8
60.6
48.3
53.1
54.1
61.0
61.0
-------
PM25 NMB (%) lor run 2005cp_to»_05b_12km_EUS tor 2005
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure D-l. Normalized Mean Bias (%) of annual PMi.s by monitor for Eastern U.S., 2005.
PM25 NME (%) for run 2005cp_lo>_05bJ 2kiruEUS tor 2005
CIRCLE=IMPROVE; TRIAWGLE=STN;
Figure D-2. Normalized Mean Error (%) of annual PM2.s by monitor for Eastern U.S.,
2005.
-------
SOI NMB (%) lor run 2005cp_lox_05b_12hm_EUS tor 2005
C!RCLE=!MPROVE; TRIANGLE=STN; SQUARE=CASTNet;
Figure D-3. Normalized Mean Bias (%) of annual sulfate by monitor for Eastern U.S.,
2005.
S04 NME (%) for run 2005cp_tox_05b_12km_EUS for 2005
CIRCLE=!MPROVE; TRIANGLE=STN; SQUARE=CASTNet;
Figure D-4. Normalized Mean Error (%) of annual sulfate by monitor for Eastern U.S.,
2005.
-------
NO3 NMB (%) tor run 2005cp_tox_05b_12km_EUS tor 2005
CIRCLE=IMPROVE: TRIANGLE=STN:
Figure D-5. Normalized Mean Bias (%) of annual nitrate by monitor for Eastern U.S.,
2005.
N03 NME (%) lor run 2005cp_tO)l_05tlJ 2km EUS lor 2005
CIRCLE=IMPROVE; TRIANGLE=STN:
Figure D-6. Normalized Mean Error (%) of annual nitrate by monitor for Eastern U.S.,
2005.
-------
TNO3 NMB (%) for run 2005cp_tox_05b_12km_EUS tor 2005
CIRCI_E=CASTNet;
Figure D-7. Normalized Mean Bias (%) of annual total nitrate by monitor for Eastern
U.S., 2005.
TN03NME (%) for run 2005cp tox 05b 12km EUS tor 2005
CIRCLE=CASTNet;
Figure D-8. Normalized Mean Error (%) of annual total nitrate by monitor for Eastern
U.S., 2005.
-------
NH4 NMB (%) (or run 2005cp_tox_05b_12km_EUS for 2005
CIRCLE=STN; TRIANGLE=CASTNet;
Figure D-9. Normalized Mean Bias (%) of annual ammonium by monitor for Eastern U.S.,
2005.
NH4 NME (%) for run 2005cp_tox_05b_12km_EUS tor 2005
CIRCLE=STN; TRIANGLE=CASTNel;
Figure D-10. Normalized Mean Error (%) of annual ammonium by monitor for Eastern
U.S., 2005.
-------
EC NMB (%) lor run 2005cp_tox 05b 12km_EUS for 2005
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure D-ll. Normalized Mean Bias (%) of annual elemental carbon by monitor for
Eastern U.S., 2005.
EC NME (%) tor run 2005cpJoii_05l)J2Km EUS lor 2005
CIRCLE=IMPROVE; TRIANGLE=STN:
Figure D-12. Normalized Mean Error (%) of annual elemental carbon by monitor for
Eastern U.S., 2005.
-------
PC NMB (%) for run 2005cp_tQX_05b_1gKni_EllS for 2005
CIRCLEdMPROVE; TRIANGLE=STN;
Figure D-13. Normalized Mean Bias (%) of annual organic carbon by monitor for Eastern
U.S., 2005.
PC NME (%) lor run 2005cp JO»_05b_1ikm_EUS lor J005
CIRCLEdMPROVE; TRIANGLE=STM;
Figure D-14. Normalized Mean Error (%) of annual organic carbon by monitor for
Eastern U.S., 2005.
-------
PM25 NMB (%) tor run 2005cljox_05li_all_bc_v47 Nib_MPWl2km lof January to December
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure D-15. Normalized Mean Bias (%) of annual PM2.s by monitor for Western U.S.,
2005.
PM25 MME (%) for run 2005cl_tox_05b_alt bc_v47_m b_MP^Vl2km tor January to December
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure D-16. Normalized Mean Error (%) of annual PM2.s by monitor for Western U.S.,
2005.
-------
SO4 NMB (%) tor run 2005cl_lox 05te_all_6c_v47 Nib MP Wl2km lot January to December
CIRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNet;
Figure D-17. Normalized Mean Bias (%) of annual sulfate by monitor for Western U.S.,
2005.
SO4 NME (%) tor run 2005cl_tox_05b_att_bc_v47_Nlt>_MP_wi2km lor Janjary to Decembef
vr: l V
N*"~r/ \ »
>r-
CIRCLE=IMPROVE; TRIANGI_E=STN; SQUARE=CASTNet;
Figure D-18. Normalized Mean Error (%) of annual sulfate by monitor for Western U.S.,
2005.
-------
N03 NMB (%) for run 2005cl_tOK_05b_all_bc_v47 Nib MPW12km tor January to December
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure D-19. Normalized Mean Bias (%) of annual nitrate by monitor for Western U.S.,
2005.
NO3 NME (%) tor run 2005eUox_05b_aH_bc_v47_Nlb_MP_wi2l
-------
TNO3 NMB (%) tor run 2005CI tax OSD alt be v17 N1b_MP_W12km tor January to December
CIRCI_E=CASTNet;
Figure D-21. Normalized Mean Bias (%) of annual total nitrate by monitor for Western
U.S., 2005.
TM03 NME (%) tor run 2005ei_lox 05b_ali bc_v47 Nib_MP_Wl2km tor January to December
CIRCLE=CASTNet;
Figure D-22. Normalized Mean Error (%) of annual total nitrate by monitor for Western
U.S., 2005.
-------
NH4 NMB (%) for run 2005cl lox 05b all be v47 N1b MP W12km for January lo December
tf
f
^
3
*
*
..
*
*
*
*
*
•
eajy,
fXr
SO
50
30
20
10
0
-10
-20
-30
-40
-50
-W
-70
-80
-90
-100
T-1QO
units = %
coverage limit = 75%
CIRCLE=STN; TRIANGLE=CASTNet;
Figure D-23. Normalized Mean Bias (%) of annual ammonium by monitor for Western
U.S., 2005.
NH4 NME (%) tor run 2005cl JoxJ)5bJilt_bc_v47_N1b MP W12km (or January lo December
CIRCI_E=STN; TRIANGLE=CASTNet;
Figure D-24. Normalized Mean Error (%) of annual ammonium by monitor for Western
U.S., 2005.
-------
EC NMB (%) for run 2005cLtox_05b_MP_W12km_noPMcul for 20050101 to 20051231
its = %
awnplsleness - 75%
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure D-25. Normalized Mean Bias (%) of annual elemental carbon by monitor for
Western U.S., 2005.
EC NME (%) tor run 2005ci_tox 05b_MP W12km noPMcut tor 20050101 to 20051231
its = %
completeness = 75%
An AMET Product
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure D-26. Normalized Mean Error (%) of annual elemental carbon by monitor for
Western U.S., 2005.
-------
PC NMB (%) lor run 2005ciJo»_05b^E12km noPMcut for 20050101 to 20051231
^ •can^jletencss = 75%
4n WET Product
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure D-27. Normalized Mean Bias (%) of annual organic carbon by monitor for Western
U.S., 2005.
PC NME (%) for run 2005ci_tox_05b_MP_W12km_noPMcut tor 20050101 to 20051231
An AWET P'-ji!.';;!
G!RCLE=!MPROVE; TR!ANGLE=STN;
Figure D-28. Normalized Mean Error (%) of annual organic carbon by monitor for
Western U.S., 2005.
-------
E. Seasonal PM2.5 Total Mass Performance
Seasonal model performance statistics for PM2.5 total mass are shown in Table E-l. Spatial plots
of the NMB and NME statistics (units of percent) for individual monitors are also provided as a
complement to the tabular statistical data (Figures E-l - E-l6). Total PM2.5 mass is generally
over-predicted in the winter, fall, and spring seasons for both STN and IMPROVE networks. In
the fall season, PM2.5 is over-predicted for Eastern STN and IMPROVE sites with NMB values
ranging from 6% to 30% whereas PM2.5 is under-predicted at Western STN and IMPROVE sites.
In the winter season, PM2.5 is over-predicted for EUS and WUS STN and IMPROVE networks
with NMB values ranging from 5% to 58%. However, in the 12-km Western domain, PM2.5 is
under-predicted at STN in the winter (NMB in the range of -6% to -11%) and the fall (NMB in
the range of-7% to -9%). Note that for comparison of West versus East STN sites, the total
number of Western sites is usually less than a third of the Eastern sites. In the spring, PM2.s is
over-predicted in the East and West, although PM2.s at urban STN sites is over-predicted in the
East but under-predicted in the West. In the summer season, PM2.5 is under-predicted in the East
and West for STN and IMPROVE (NMB = ~ 25% and NME = -35%).
Table E-l. CMAQ 2005 seasonal model performance statistics for PM2.s total mass.
CMAQ 2005 PM2.5 total mass
Winter
Spring
STN
IMPROVE
STN
IMPROVE
12-km EUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km EUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km EUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km EUS
No. of Obs.
2866
895
716
542
762
635
739
2252
2532
573
143
406
574
2145
3159
964
795
612
798
752
773
2424
NMB (%)
20.5
-6.2
36.7
20.1
7.9
20.2
-10.9
31.5
11.8
58.6
21.5
17.5
19.6
5.4
22.6
0.5
44.2
49.7
3.1
1.0
3.2
14.7
NME (%)
42.4
54.7
48.7
36.7
38.1
47.1
55.0
51.8
51.6
68.4
39.2
43.7
46.3
51.1
46.9
43.2
60.1
59.8
34.2
38.3
45.5
46.2
FB (%)
14.8
-3.2
28.1
19.0
4.7
12.8
-8.7
23.9
7.1
40.8
16.8
11.4
17.7
5.5
15.2
-1.1
33.0
36.0
2.0
-2.6
-0.2
7.6
FE (%)
38.5
53.0
39.4
33.3
36.5
44.2
54.6
45.8
49.5
50.7
35.9
42.3
45.0
50.7
41.0
41.2
47.4
45.8
33.5
40.2
41.9
42.4
-------
Summer
Fall
STN
IMPROVE
STN
IMPROVE
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
2735
630
153
429
628
2308
2954
935
754
558
722
701
758
2334
2492
580
156
421
596
2114
2818
962
755
606
785
435
812
2311
2652
556
119
438
578
2253
-18.0
47.9
36.8
2.7
-3.7
-20.5
-19.3
-20.9
-15.0
-8.2
-28.2
-24.1
-18.2
-27.6
-20.1
-20.5
-18.8
-34.1
-32.2
-18.1
10.6
-6.9
28.6
12.4
-5.5
16.5
-9.1
6.0
-9.6
29.8
7.9
-9.7
5.6
-13.9
43.2
65.6
53.4
35.7
39.1
44.7
32.6
35.9
30.5
26.7
34.7
38.2
36.2
37.2
41.9
35.2
30.1
39.1
39.8
42.6
38.4
46.2
51.2
33.1
30.08
45.6
47.2
42.6
44.9
54.5
38.5
31.7
45.9
43.8
-22.8
28.4
27.3
4.1
-3.1
-23.9
-24.7
-21.0
-16.3
-7.9
-34.9
-33.3
-19.0
-33.8
-23.0
-27.7
-19.7
-45.5
-38.8
-21.5
10.6
-4.9
20.8
14.6
-4.1
18.4
-7.8
2.8
-14.9
17.1
8.9
-8.2
6.6
-17.3
46.4
49.3
44.8
36.1
42.1
47.0
39.8
40.3
33.9
28.4
43.1
50.3
40.3
45.6
45.3
42.1
34.2
53.6
50.2
45.1
37.4
45.3
41.7
31.9
32.8
44.2
45.8
42.2
46.1
45.0
39.2
37.4
46.7
46.0
-------
PM25 NMB (%) tor run 2005cp lox 05b_12km EUS tor December lo February
CIRCLE=IMPROVE: TRIANGLE=STN;
Figure E-l. Normalized Mean Bias (%) of PM2.s by monitor for 12-km Eastern U.S.
domain, Winter 2005.
PM25 NME (%) tor run 2005cpjox_05b J2km_EUS lor December to February
CIRCLE=IMPROVE; TRIANGLE=STN:
Figure E-2. Normalized Mean Error (%) of PM2.s by monitor for 12-km Eastern U.S.
domain, Winter 2005.
-------
PM25 NMB {%) for run 2005cpJox_05b_12km_EUS for March to May
CiRCLE=IMPROVE; TRIANGLE=STN;
Figure E-3. Normalized Mean Bias (%) of PM2.s by monitor for 12-km Eastern U.S.
domain, Spring 2005.
PM25 NME (%) for run 2005cp_tox_05b_12km_EUS for March to May
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-4. Normalized Mean Error (%) of PMi.s by monitor for 12-km Eastern U.S.
domain, Spring 2005.
-------
PM25 NMB (%) lor run 2005cpJox_05b_12km_EUS tor June to August
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-5. Normalized Mean Bias (%) of PM2.s by monitor for 12-km Eastern U.S.
domain, Summer 2005.
PM25 NME (%) tor run 2005cpJOK_05b_12km EPS lof June to August
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-6. Normalized Mean Error (%) of PM2.s by monitor for 12-km Eastern U.S.
domain., Summer 2005.
-------
PM25 NMB (%) for run 2005cp_tox_05b_12km_EUS for September to November
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-7. Normalized Mean Bias (%) of PM2.s by monitor for 12-km Eastern U.S.
domain, Fall 2005.
PM25 NME (%) tor run 2005cpjox_05b 12km EUS tor September to November
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-8. Normalized Mean Error (%) of PM2.s by monitor for 12-km Eastern U.S.
domain, Fall 2005.
-------
PM25 NMB (%) tor run 2005el lox Q5b all be v47 N1b MP W12km lor December lo Febrjary
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-9. Normalized Mean Bias (%) of PM2.s by monitor for 12-km Western U.S.
domain, Winter 2005.
PM25 NME (%) tor run 200SCI IPX 05b_alt-bc_v47_N1b MP W12Mn lor December lo February
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-10. Normalized Mean Error (%) of PMi.s by monitor for 12-km Western U.S.
domain, Winter 2005.
-------
PM25 NMB (%) lor run 2005ci_tox_05b_all_bc_v47_Nlb_MP_W12km tor March to May
An Atmospneric Model Ev^yation (AMET) Prod
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-ll. Normalized Mean Bias (%) of PM2.s by monitor for 12-km Western U.S.
domain, Spring 2005.
PM25 NME (%) lor run 2005cl_lox_05ti_alt_bc_v47_H1t)_MP_W12Km tor March lo May
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-12. Normalized Mean Error (%) of PM2.s by monitor for 12-km Western U.S.
domain, Spring 2005.
-------
PM25 NMB (%) for run 2005ci_tox_05b_alt_bc_v47_Nl b_MP_Wl 2km for June to August
An Atmospneric Model Elongation (AMET) Prod
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-13. Normalized Mean Bias (%) of PM2.s by monitor for 12-km Western U.S.
domain, Summer 2005.
PM25 HME (%) tor run 2005eMox_05b_all_bc_v47_N1 b_MP_W12km for June to August
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-14. Normalized Mean Error (%) of PM2.s by monitor for 12-km Western U.S.
domain, Summer 2005.
-------
PM25 NMB (%) for run 2005cl_n»i 05b all bc_v47 Nib_MP_wt2km lor September 10 November
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-15. Normalized Mean Bias (%) of PM2.s by monitor for 12-km Western U.S.
domain, Fall 2005.
PM25 NME (%) for run 2005eLtoxJ)5b^3lLbc_v47_N1b_MP_W12kin for September to November
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure E-16. Normalized Mean Error (%) of PM2.s by monitor for 12-km Western U.S.
domain, Fall 2005.
-------
F. Seasonal Sulfate Performance
As seen in Table F-l, CMAQ generally under-predicts sulfate in the 12-km Eastern and Western
domains throughout the entire year. Spatial plots of the NMB and NME statistics (units of
percent) for individual monitors are also provided in Figures F-l - F-l6. In the fall season,
sulfate predictions show NMB values ranging from -5% to -20%, across STN, IMPROVE, and
CASTNet networks in the East and West. In the spring and winter seasons, sulfate predictions
for the most part are under-predicted in the East and West, with NMB values ranging from -2%
to -32%. Sulfate predictions during the summer season are moderately under-predicted in the
East and West across the available monitoring data (NMB values range from -8% to -35%.
Table F-l. CMAQ 2005 seasonal model performance statistics for sulfate.
CMAQ 2005 Sulfate
Winter
Spring
STN
IMPROVE
CASTNet
STN
IMPROVE
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
No. of Obs.
3390
1033
828
598
963
766
830
2076
2428
502
129
386
539
2086
760
267
193
142
264
72
243
3626
1085
894
637
988
875
867
2435
2703
630
NMB (%)
-4.7
-7.3
-5.0
6.9
-5.1
-14.2
-10.3
-4.4
13.9
-3.5
6.6
-3.9
-15.5
23.8
-15.3
1.5
-12.4
-11.2
-17.9
-32.9
5.7
1.5
-12.3
11.8
24.0
-4.9
-15.4
-6.2
-1.7
-5.2
12.5
NME (%)
38.3
55.4
36.3
41.3
36.6
38.4
58.1
37.4
56.0
31.6
37.7
35.8
41.1
59.3
23.6
34.3
21.6
21.0
22.6
34.1
36.5
34.1
35.0
38.4
45.6
27.7
31.1
37.5
33.8
32.9
40.7
FB (%)
-6.8
-2.6
-10.7
-0.1
-4.1
-12.3
-3.5
4.7
30.9
-7.6
0.1
1.4
-9.7
34.7
-14.0
16.0
-16.3
-13.9
-17.1
-34.8
18.1
1.1
-3.4
8.2
19.2
-5.4
-11.2
0.3
1.4
3.1
9.8
FE (%)
38.4
53.7
34.5
39.2
36.7
41.6
54.9
42.4
56.0
32.7
35.2
36.7
44.1
57.2
27.7
37.3
24.4
25.5
23.6
37.0
38.4
33.4
35.8
35.6
38.9
29.2
32.8
36.7
34.2
35.0
39.3
-------
Summer
Fall
CASTNet
STN
IMPROVE
CASTNet
STN
IMPROVE
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
147
436
632
2305
832
287
206
155
292
78
262
3516
1075
874
621
941
847
853
2324
2394
590
158
427
601
2021
792
295
192
161
270
75
269
3365
1095
902
639
990
571
900
2199
2476
531
97
436
7.9
-4.7
-16.6
-2.4
-8.2
-18.5
2.7
-2.7
-12.6
-28.7
-17.6
-15.8
-35.3
-8.6
-9.9
-18.1
-30.5
-35.4
-19.5
-25.0
-11.2
-18.2
-22.7
-27.0
-22.9
-19.2
-34.1
-14.9
-15.7
-21.1
-33.3
-33.6
-10.5
-17.2
-8.3
-10.9
-10.9
-10.9
-14.6
-11.3
-4.8
-5.6
-19.1
-13.1
37.5
28.5
31.9
34.4
23.6
26.6
25.8
22.9
21.3
32.3
26.8
31.7
43.4
27.5
28.7
31.8
41.5
45.8
34.3
40.0
32.7
29.8
34.0
38.4
40.8
23.9
37.7
20.6
21.3
24.8
36.9
37.9
28.4
44.2
29.7
26.8
26.7
32.6
48.8
30.8
38.2
32.6
26.6
28.6
10.2
-2.4
-10.4
5.0
-7.6
-16.4
2.9
-1.1
-14.0
-24.5
-15.9
-15.0
-28.8
-2.5
0.1
-19.0
-35.7
-27.3
-16.2
-16.4
-1.0
-6.6
-23.9
-23.3
-14.0
-21.7
-34.4
-11.2
-13.2
-28.1
-38.9
-33.5
-2.5
-7.9
1.6
-3.1
-6.1
-2.4
-6.9
0.6
9.5
9.2
-15.3
-6.7
36.5
29.9
33.2
36.1
24.6
27.3
26.8
22.1
23.0
31.6
27.4
38.1
45.1
31.3
30.9
37.3
52.2
45.6
41.1
42.9
39.0
33.9
41.5
45.1
42.7
29.5
40.6
22.4
23.2
32.6
46.2
40.2
30.9
44.5
31.9
27.7
28.5
35.1
46.4
36.5
41.0
36.5
29.3
33.9
-------
CASTNet
Central
West
12-kmEUS
12-kmWUS
Northeast
Midwest
Southeast
Central
West
578
2084
786
293
195
157
273
75
267
-14.3
0.5
-17.5
-11.4
-12.3
-19.7
-18.6
-21.0
-9.2
32.0
41.2
21.0
24.3
18.4
21.9
21.3
23.6
24.9
-5.1
12.6
-14.0
-4.9
-7.2
-15.8
-19.4
-19.3
-3.5
37.5
42.5
21.7
25.3
18.2
20.9
23.4
26.3
25.7
-------
SO4 NMB (%) for run 2005cp to» 05b 12km EUS for December to February
An Atmospheric Model EvaluationTAfJET) Producl
CIRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNel;
Figure F-l. Normalized Mean Bias (%) of sulfate by monitor for 12-km Eastern U.S.
domain, Winter 2005.
SO4 NME (%) tor run 2005cp_tox_05b.12km_EUS lor December to February
.T ^TST:
CIRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNel;
Figure F-2. Normalized Mean Error (%) of sulfate by monitor for 12-km Eastern U.S.
domain, Winter 2005.
-------
SO4 NMB (%) for run 20OScp_toK_OSb_12km_EUS tor March to May
CIRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNet;
Figure F-3. Normalized Mean Bias (%) of sulfate by monitor for 12-km Eastern U.S.
domain, Spring 2005.
SOI NME (%) tor run 2005cp_tox_05b_1gkm_EUS tor March to May
CtRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNet;
Figure F-4. Normalized Mean Error (%) of sulfate by monitor for 12-km Eastern U.S.
domain, Spring 2005.
-------
SOI NMB (%) tor run 2005cp IPX 05b_12km_EUS tor June to August
An Atmospheric Model EvahjanonrAKitT) Produci
CIRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNel;
Figure F-5. Normalized Mean Bias (%) of sulfate by monitor for 12-km Eastern U.S.
domain, Summer 2005.
SO4 MME (%) lor run 2005cp_tox_05b_12km_EUS (or June to August
At'. Atmospheric Mode' EvalualionTAMETi p:;>:l.i:::
C!RCLE=!MPROVE; TR!ANGLE=STN; SQUARE-GASTNet;
Figure F-6. Normalized Mean Error (%) of sulfate by monitor for 12-km Eastern U.S.
domain, Summer 2005.
-------
SO4 NMB (%) tor run 2005cp tox 05b 12km EUS for September to November
An Atmospheric Model Evahjaiia.-i rAMET'i Pied
CIRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNel;
Figure F-7. Normalized Mean Bias (%) of sulfate by monitor for 12-km Eastern U.S.
domain, Fall 2005.
SO4 NME (%) lor run 2005cp tox 05b 12km EUS tor September lo November
At-. Atmospheric Mode1 Evalualio.-vrAMfcTi P:;>:l.i:::
C!RCLE=!MPROVE; TR!ANGLE=STN; SQUARE-GASTNet;
Figure F-8. Normalized Mean Error (%) of sulfate by monitor for 12-km Eastern U.S.
domain, Fall 2005.
-------
SO4 NMB (%) tor run 2005el tox_05b all hc_v47 Nib_MP_wi2hm tor December 10 February
CIRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNet;
Figure F-9. Normalized Mean Bias (%) of sulfate by monitor for 12-km Western U.S.
domain, Winter 2005.
SO4 NHE (%) for run 2005ci_tox_05b_alt_be_v47_N1b_MP_W12km (or December lo February
CIRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNet;
Figure F-10. Normalized Mean Error (%) of sulfate by monitor for 12-km Western U.S.
domain, Winter 2005.
-------
SO4 NMB (%) tor fun 2005eM°"_05b_all_6c_v47 Nlb_MP wi2Km lor March 10 May
CIRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNet;
Figure F-ll. Normalized Mean Bias (%) of sulfate by monitor for 12-km Western U.S.
domain, Spring 2005.
504 NME (%) for run 2005ci ton 05b all be v47 Nib MPW12km tor March to May
3IRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNet;
Figure F-12. Normalized Mean Error (%) of sulfate by monitor for 12-km Western U.S.
domain, Spring 2005.
-------
304 NMB {%) for fun 2005ciJOX_CI5b_3lLfaC_'"*7_N1fa_MP_W12km tor June '° A"9"St
C!RCl_E=!MPROVE; TR!ANG!_E=STN; SQUARE=CASTNet;
Figure F-13. Normalized Mean Bias (%) of sulfate by monitor for 12-km Western U.S.
domain, Summer 2005.
SOI NME (%) lor run 2005cMoii_05b_altJ>c_v47_Nlb^MP Wl2km tor June lo August
CIRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNet;
Figure F-14. Normalized Mean Error (%) of sulfate by monitor for 12-km Western U.S.
domain, Summer 2005.
-------
SO4 NMB (%) tor run 2005cijox 05b alt_bc v47 Nib MPJAM2km tor September to November
An Atmospheric Model B/^fyatiori (AMET) Prod
CIRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNet;
Figure F-15. Normalized Mean Bias (%) of sulfate by monitor for 12-km Western U.S.
domain, Fall 2005.
S04 NME (%) for run 2005clJoii_05b_alt_bc_v47_N1b_MP_W12km tor September to November
CIRCLE=IMPROVE; TRIANGLE=STN; SQUARE=CASTNet;
Figure F-16. Normalized Mean Error (%) of sulfate by monitor for 12-km Western U.S.
domain, Fall 2005.
-------
G. Seasonal Nitrate Performance
Table G-l provides the seasonal model performance statistics for nitrate and total nitrate for the
12-km Eastern and Western domains. Spatial plots of the NMB and NME statistics (units of
percent) for individual monitors are also provided as a complement to the tabular statistical data
(Figures G-l - G-32). Overall, nitrate and total nitrate performance is over-predicted in the BUS
and under-predicted in the WUS for all of the seasonal assessments except in the winter and fall
season, where total nitrate is over-predicted in the EUS and WUS and in the spring where nitrate
is over-predicted in the EUS. Likewise, in the East, nitrate and total nitrate are moderately over-
predicted during the spring and summer seasons (NMB values ranging from 10% to 100%). In
the winter season when nitrate is most abundant, nitrate is under-predicted in the East and West,
however total nitrate is over-predicted.
Table G-l. CMAQ 2005 seasonal model performance statistics for nitrate.
CMAQ 2005 Nitrate
Nitrate
(Winter)
Total
Nitrate
(Winter)
Nitrate
(Spring)
STN
IMPROVE
CASTNet
STN
12-km EUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km EUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km EUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km EUS
12-km WUS
Northeast
Midwest
Southeast
Central
No. of Obs.
3099
973
829
598
963
479
831
2076
2426
502
129
386
539
2084
760
267
193
142
264
72
243
3254
987
894
637
988
503
NMB (%)
-0.4
-42.8
17.2
-10.1
-2.1
-1.4
-46.9
15.8
-18.6
59.0
-18.5
24.5
6.9
-31.2
16.8
25.6
29.7
-4.0
26.9
26.3
29.8
65.7
-33.5
71.9
78.3
68.5
33.4
NME (%)
47.9
61.8
48.7
38.1
61.8
48.1
64.1
64.1
63.1
84.2
41.9
84.8
53.7
71.6
31.6
44.5
34.3
20.9
37.6
36.8
53.1
91.7
55.5
98.1
93.1
111.6
62.5
FB (%)
-6.9
-53.1
16.0
-7.3
-25.5
2.1
-60.5
-13.6
-71.4
40.6
-19.9
-24.6
-3.0
-77.7
24.1
35.2
35.9
3.1
22.5
26.1
37.9
29.9
-56.9
51.8
51.0
9.8
21.2
FE (%)
58.1
82.1
49.5
43.5
73.1
56.9
86.1
82.5
110.2
75.8
63.5
81.9
71.2
115.9
35.4
51.5
38.4
21.4
36.2
35.5
54.6
76.3
81.4
75.9
68.9
87.9
64.6
-------
Total
Nitrate
(Spring)
Nitrate
(Summer)
Total
Nitrate
(Summer)
Nitrate
(Fall)
IMPROVE
CASTNet
STN
IMPROVE
CASTNet
STN
IMPROVE
West
12-kmEUS
12-kmWUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
859
2435
2697
630
147
436
632
2299
832
287
206
155
292
78
262
3150
992
874
621
941
485
846
2324
2394
590
158
427
601
2020
792
295
192
161
270
75
269
3238
1048
902
639
990
460
896
2192
-38.4
70.7
-14.6
100.6
70.9
93.3
48.4
-29.5
39.9
-4.8
56.5
43.0
35.2
10.4
-4.5
31.1
-68.2
19.6
71.6
-4.1
36.5
-70.6
27.6
-59.8
40.6
51.5
29.8
36.5
-67.2
44.3
-7.4
61.1
59.7
34.5
2.1
-7.5
102.6
-42.0
110.6
91.8
112.9
114.3
-48.9
147.0
56.8
103.4
71.1
133.2
93.2
130.1
76.1
78.9
48.1
31.1
58.0
45.8
47.7
35.7
33.0
100.0
74.6
96.2
109.2
89.3
105.6
74.1
118.1
83.9
129.7
111.3
124.8
118.7
82.3
55.2
31.1
67.8
61.8
51.3
28.6
31.0
130.8
72.4
133.7
111.8
159.0
137.9
69.1
185.11
-63.8
4.1
-74.2
41.1
23.9
4.4
-1.2
-80.7
27.6
2.2
44.2
35.0
24.2
7.7
3.4
-36.9
-120.1
-33.5
10.1
-60.9
-31.2
-126.0
-69.6
-132.8
-47.4
-17.9
-58.9
-51.8
-139.2
25.2
-8.3
38.3
44.6
20.8
-4.8
-6.7
14.0
-52.3
10.9
40.0
-3.1
27.7
-63.2
-4.4
84.3
95.3
112.0
98.6
83.2
96.7
90.4
115.8
40.0
32.1
47.3
38.0
41.1
34.1
33.0
94.3
127.1
90.4
78.8
102.8
91.8
130.0
119
145.2
110.9
92.8
118.5
111.6
148.6
43.3
33.4
51.5
46.4
44.3
29.5
32.7
88.4
91.6
84.9
74.8
101.8
87.3
92.5
107.3
-------
Total
Nitrate
(Fall)
CASTNet
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
2470
526
97
436
578
2078
786
293
195
157
273
75
267
3.6
147.8
126.1
151.0
171.5
-29.0
72.7
17.2
93.5
79.3
79.1
57.3
14.2
99.6
179.7
157.1
212.3
199.3
79.9
82.7
42.2
95.6
79.6
86.6
63.8
40.8
-60.7
3.0
30.5
-19.8
16.8
-71.7
50.1
25.1
58.9
54.5
48.5
38.7
26.4
116.5
101.0
97.2
118.5
108.4
118.1
57.3
46.1
63.5
54.7
60.9
45.9
47.0
-------
HO3 NMB (%) for run 200Scp_toxJ)5tM2km^EUS for December to February
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-l. Normalized Mean Bias (%) of nitrate by monitor for 12-km Eastern U.S.
domain, Winter 2005.
N03 HME (%) for run 2005cp_lox JlSbJ 2krtuEUS tor December lo February
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-2. Normalized Mean Error (%) of nitrate by monitor for 12-km Eastern U.S.
domain, Winter 2005.
-------
TNO3 NMB (%) for run 2005cp tox_05b 12km_ EDS for December to February
CIRCLE=CASTNet;
Figure G-3. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Eastern U.S.
domain, Winter 2005.
TNO3 NME (%) for run 2005cp tox_05b 12km_EUS for December to February
CIRCLE=CASTNet:
Figure G-4. Normalized Mean Error (%) of total nitrate by monitor for 12-km Eastern
U.S. domain, Winter 2005.
-------
NO3 NMB (%) for run 2005cp_tox 05b 12km EUS for March to May
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-5. Normalized Mean Bias (%) of nitrate by monitor for 12-km Eastern U.S.
domain, Spring 2005.
N03 NME (%) lor run 2005cp_to»,OSb_12km_EUS tor March to May
CIRCLE=IMPROVE; TRIANGLE-STN;
Figure G-6. Normalized Mean Error (%) of nitrate by monitor for 12-km Eastern U.S.
domain, Spring 2005.
-------
TNO3 NMB (%) for run 2005cp_tox_05b_12km EDS for March to May
CIRCLE=CASTNet;
Figure G-7. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Eastern U.S.
domain, Spring 2005.
TNO3 NME (%) for run 2005cp_tox_05b_12km_EUS for March to May
CIRCLE=CASTNet;
Figure G-8 Normalized Mean Error (%) of total nitrate by monitor for 12-km Eastern
U.S. domain, Spring 2005.
-------
NO3 NMB (%) (or run 2005cp ton 05b 12km EUS for June lo August
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-9. Normalized Mean Bias (%) of nitrate by monitor for 12-km Eastern U.S.
domain, Summer 2005.
NO3 NME (%) lor run 2005cp Jox_05b_12km_EUS for June lo August
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-10. Normalized Mean Error (%) of nitrate by monitor for 12-km Eastern U.S.
domain, Summer 2005.
-------
TNO3 NMB (%)Jor run 2005cp_tox_05b_12km_EUS for June to August
CIRCLE=CASTNet;
Figure G-ll. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Eastern
U.S. domain, Summer 2005.
TNO3 NME (%) tor run 2005cp_tox_05b_12km_EUS tor June to August
*" •vw "7**
CIRCLE=CASTNet;
Figure G-12. Normalized Mean Error (%) of total nitrate by monitor for 12-km Eastern
U.S. domain, Summer 2005.
-------
NO3 NMB (%) for run 2005cp_tox_05b_12km_EUS (or September to November
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-13. Normalized Mean Bias (%) of nitrate by monitor for 12-km Eastern U.S.
domain, Fall 2005.
H03 NME (%) for run 2005cpJoxJISb^12km^EUS tor September to November
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-14. Normalized Mean Error (%) of nitrate by monitor for 12-km Eastern U.S.
domain, Fall 2005.
-------
TNO3 NMB (%) for run 2005cp_tox_05b_12km_EUS for September to November
CIRCLE=CASTNet;
Figure G-15. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Eastern
U.S. domain, Fall 2005.
TNO3 NME (%) for run 2005cp_tox_05b_12km JUS for September to November
CIRCLE=CASTNet;
Figure G-16. Normalized Mean Error (%) of total nitrate by monitor for 12-km Eastern
U.S. domain, Fall 2005.
-------
NO3 NMB (%) tor run 2005CI tox_05b_alt_bc_v47_N1b_MP_W12km tor December lo February
An Atmospneric Model Ev^yation (AMET) Praduc
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-17. Normalized Mean Bias (%) of nitrate by monitor for 12-km Western U.S.
domain, Winter 2005.
NO3 NME (%) tor run 2005ci_tox_05b_all_bc_v47_N1 b_MP_W12km tor December to February
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-18. Normalized Mean Error (%) of nitrate by monitor for 12-km Western U.S.
domain, Winter 2005.
-------
TN03 NMB (%) for run 2005ciJox_05b_altj3C_v47 N1b_MP_W12km for December to February
An Atmospheric MoSeP^tfaluation (AMET) Pro
« I
CIRCLE=CASTNet;
Figure G-19. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Western
U.S. domain, Winter 2005.
TNO3 NME (%) for run 2005cLtox_05b_alt_bc_v47_N1b_MP W12km for December lo February
An Atmospheric MoHeNEJ&lijation (AMET) Prorjuc
CIRCLE=CASTNet;
Figure G-20. Normalized Mean Error (%) of total nitrate by monitor for 12-km Western
U.S. domain, Winter 2005.
-------
NO3 NMB (%) tor run 2005ci_tox_05b_alt_bc_v47,N1b_MP.W12km tor March to May
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-21. Normalized Mean Bias (%) of nitrate by monitor for 12-km Western U.S.
domain, Spring 2005.
NO3 NME (%) tor run 2005CLIox_05b_all_bc_vl7_H1b_MP_W12l
-------
TN03 NMB (%) for run 2005ci_toxJ)5b_alt_bc_v47_N1 b_MP_W12km tor March to May
CIRCLE=CASTNet;
Figure G-23. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Western
U.S. domain, Spring 2005.
TN03 NME (%) for run 2005ci_tox_05b_alt_bc_v47_N1b_MP_W12km for March to May
An AtrrfosphericM°otsl^valuation (AMET) Product
CIRCLE=CASTNet;
Figure G-24. Normalized Mean Error (%) of total nitrate by monitor for 12-km Western
U.S. domain, Spring 2005.
-------
N03 NMB (%) for tan 2005el_IOi!_05b_all_bc_v47_Nl b_MP_Wl 2km tor June to August
) \ , '
T^
CIRCLE=IMPROVE;TRIANGLE=STN;
Figure G-25. Normalized Mean Bias (%) of nitrate by monitor for 12-km Western U.S.
domain, Summer 2005.
N03 NME (%) for fun 2005cMox_05b_all_bC_v47_N1b_MP W12km lor June to August
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-26. Normalized Mean Error (%) of nitrate by monitor for 12-km Western U.S.
domain, Summer 2005.
-------
TN03 NMB (%) tor run 2005ei tox 05b^all_bc_v47_N1 b_MP_W12km tor June to August
CIRCLE=CASTNet;
Figure G-27. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Western
U.S. domain, Summer 2005.
TNO3 NME (%) for run 2005cijox_05b_alt_bc_v47 N1b_MP W12km for June to August
An Atmospheric MpSqffivaluation (AMET) P
CIRCLE=CASTNet;
Figure G-28. Normalized Mean Error (%) of total nitrate by monitor for 12-km Western
U.S. domain, Summer 2005.
-------
NO3 NMB (%) for run 2005ci Jox_05b_alt_bc_v47_N1 b_MP_W12km tor September to November
An Atmospheric Modal B/aVation (AMET) Produ
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-29. Normalized Mean Bias (%) of nitrate by monitor for 12-km Western U.S.
domain, Fall 2005.
N03 NME (%) lor fun 2005ei_tox_05b_alt_bc_v47_N1b_MP_W12km tor September to November
An Atmospheric Model Et^njation (AMET) Produc
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure G-30. Normalized Mean Error (%) of nitrate by monitor for 12-km Western U.S.
domain, Fall 2005.
-------
TNO3 NMB (%) for run 2005ci_ tox_05b_3lt_bc_v47_N1 b_MP_W12km for September to November
An Atrrbspheric MoeCeW*valuation (AMET) Product
CiRCLE=CASTNet;
Figure G-31. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Western
U.S. domain, Fall 2005.
TN03 NME (%) for run 2005cUox_05b_alt_bc_v47_N1b_MP_W12km tor September to November
An Atmospheric MoKqMEValuation (AMET) Proouct
CIRCLE=CASTNet;
Figure G-32. Normalized Mean Error (%) of total nitrate by monitor for 12-km Western
U.S. domain, Fall 2005.
-------
H. Seasonal Ammonium Performance
Table H-l lists the performance statistics for ammonium PM at the STN and CASTNet sites.
Spatial plots of the NMB and NME statistics (units of percent) for individual monitors are also
provided in Figures H-l - H-16. In the winter and spring, ammonium performance at STN sites
shows an over-prediction in the BUS and under-prediction in the WUS. Model performance at
CASTNet sites show over-predictions at both EUS and WUS. Ammonium performance for the
summer season shows an under-prediction in the East and West. However, in the spring, model
predictions in the East are over-predicted, whereas ammonia predictions are under-predicted in
the West.
Table H-l. CMAQ 2005 seasonal model performance statistics for ammonium.
CMAQ 2005 Ammonium
Winter
Spring
Summer
STN
CASTNet
STN
CASTNet
STN
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
No. of Obs.
3390
1032
828
598
963
766
829
760
267
193
142
264
72
243
3626
1077
894
637
988
875
859
832
287
206
155
292
78
262
3516
1071
874
NMB (%)
6.6
-24.0
16.9
2.9
4.3
3.5
-31.0
7.7
4.4
33.4
-5.3
0.9
4.6
-0.5
34.3
-2.0
49.1
61.1
19.4
7.7
-2.5
33.1
-1.4
53.2
52.6
13.0
10.7
-7.1
-4.0
-31.6
-1.9
NME (%)
39.5
58.9
38.4
32.4
43.8
43.0
61.1
31.4
38.8
44.7
23.3
27.9
36.8
43.6
55.8
47.4
66.3
72.1
43.7
41.8
52.2
45.8
33.6
57.4
58.5
32.1
37.3
32.9
37.9
49.6
35.3
FB (%)
8.4
-8.0
18.4
7.8
5.0
5.4
-14.5
12.2
10.2
33.6
0.3
1.3
5.7
10.6
28.1
17.5
46.2
47.0
17.3
10.1
20.4
24.6
-1.4
40.1
41.9
11.1
10.3
-2.5
3.4
-8.9
12.9
FE (%)
42.0
63.4
36.0
32.8
44.4
50.0
66.0
33.3
40.8
40.8
24.1
28.7
41.7
42.1
48.3
48.7
57.3
56.1
41.4
42.9
51.4
37.6
32.1
43.5
45.4
31.0
34.6
32.5
46.8
51.7
43.1
-------
Fall
CASTNet
STN
CASTNet
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
621
941
847
849
792
295
192
161
270
75
269
3365
1081
902
639
990
571
886
786
293
195
157
273
75
267
8.1
-6.5
-18.9
-34.7
-18.5
-27.6
-19.5
-1.5
-29.3
-16.5
-31.0
19.2
-18.3
31.2
16.5
12.0
22.5
-24.9
10.5
11.0
16.2
25.5
-4.5
19.9
0.9
37.8
35.3
45.0
54.6
30.0
38.5
27.9
25.9
33.6
30.1
39.5
47.8
62.5
57.4
39.7
43.4
54.3
64.7
41.0
41.3
42.6
47.6
36.2
45.8
34.1
23.3
0.7
-19.9
-7.2
-23.5
-30.6
-21.5
1.6
-40.8
-20.5
-31.1
28.9
8.6
39.1
28.7
21.8
29.5
5.5
14.5
7.1
19.8
30.9
-2.4
26.4
4.9
42.6
41.8
58.7
54.2
36.5
43.1
32.1
27.3
45.5
37.2
43.3
50.0
59.8
55.3
42.9
46.0
54.1
61.2
39.6
34.4
38.2
43.1
39.8
47.4
32.5
-------
NH4 NMB (%) for run 2005cp_tox_05bJ2km EUS tor December to February
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-l. Normalized Mean Bias (%) of ammonium by monitor for 12-km Eastern U.S.
domain, Winter 2005.
NH4 NME (%) for run 2005cp_to>_05b_12km_EUS tor December to February
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-2. Normalized Mean Error (%) of ammonium by monitor for 12-km Eastern
U.S. domain, Winter 2005.
-------
NH4 NMB (%) for run 2005cp tpx 05b 12km EUS for March to May
An Atmospheric Model Evaluatisn-hJMET) Produc:
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-3. Normalized Mean Bias (%) of ammonium by monitor for 12-km Eastern U.S.
domain, Spring 2005.
NH4 NME (%) tor run 2005cpJOK_05b_12km_EUS tor March to May
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-4. Normalized Mean Error (%) of ammonium by monitor for 12-km Eastern
U.S. domain, Spring 2005.
-------
NH4 HMB (%) lor run 2005cp to* 05b 12km EUS tor June to August
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-5. Normalized Mean Bias (%) of ammonium by monitor for 12-km Eastern U.S.
domain, Summer 2005.
NH4 NME (%) tor run 2Q05cp tox_05b_12km EU5 (or June to August
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-6. Normalized Mean Error (%) of ammonium by monitor for 12-km Eastern
U.S. domain, Summer 2005.
-------
NH4 HMB (%) tor run 2005cpjox 05b_12km EUS tor September to November
An Atmospheric Model EvaluatiSrrMlftETj Produc:
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-7. Normalized Mean Bias (%) of ammonium by monitor for 12-km Eastern U.S.
domain, Fall 2005.
NH4 HME (%) tor run 2005cp tox 05b 12km EUS tor September to November
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-8. Normalized Mean Error (%) of ammonium by monitor for 12-km Eastern
U.S. domain, Fall 2005.
-------
NH4 NMB (%) for run 2005el_tox Q5b_alt_bc_v47 N1b_MP _W12km for December lo February
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-9. Normalized Mean Bias (%) of ammonium by monitor for 12-km Western U.S.
domain, Winter 2005.
NH4 NME (%) tor run 2005cl lox 05b alt be v47 N1b MP W12km lor December lo February
CIRCI_E=STN; TRIANGI_E=CASTNet;
Figure H-10. Normalized Mean Error (%) of ammonium by monitor for 12-km Western
U.S. domain, Winter 2005.
-------
NH4 NMB (%) for run 2005ci_lox_05b_alt_bc_v47_N1b_MP_W12km tor March to May
An Atmospheric Mtjd«$
-------
NH4 NMB (%) tor run 2005ci_tox_05b_alt_bc_v47_N1b_MP_W12km lor June to August
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-13. Normalized Mean Bias (%) of ammonium by monitor for 12-km Western
U.S. domain, Summer 2005.
NH4 NME (14) (or run 2005ciJoQ5b,alt_bc_v47_N1b MP W12km for June lo Augusl
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-14. Normalized Mean Error (%) of ammonium by monitor for 12-km Western
U.S. domain, Summer 2005.
-------
NH4 NMB (%) for run 2005cijox_05b_art_bc_v47 N1 b_MP_W12km for September to November
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-15. Normalized Mean Bias (%) of ammonium by monitor for 12-km Western
U.S. domain, Fall 2005.
NH4 NME (%) for run 2005ci_tox_05b_alt_bc_v47_N1b_MP_W12km tor September to November
CIRCLE=STN; TRIANGLE=CASTNet;
Figure H-16. Normalized Mean Error (%) of ammonium by monitor for 12-km Western
U.S. domain, Fall 2005.
-------
I. Seasonal Elemental Carbon Performance
Table 1-1 presents the seasonal performance statistics of elemental carbon for the urban and rural
2005 monitoring data. Spatial plots of the NMB and NME statistics (units of percent) for
individual monitors are also provided as a complement to the tabular statistical data (Figures II -
116). In the winter, elemental carbon performance is mixed across the STN and IMPROVE
networks in the EUS and WUS, with a moderate over-prediction at STN sites and a slight under-
prediction at the IMPROVE sites. In general, model performance at urban STN sites is over-
predicted, whereas model performance at rural IMPROVE sites show an under-prediction.
These biases and errors are not unexpected since there are known uncertainties among the
scientific community in carbonaceous emissions/measurements, transport, and deposition
processes.
Table 1-1. CMAQ 2005 seasonal model performance statistics for elemental carbon.
CMAQ 2005 Elemental Carbon
Winter
Spring
STN
IMPROVE
STN
IMPROVE
12-kmEUS
12-kmWUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
No.ofObs.
3441
657
831
602
964
811
520
2072
2279
522
166
386
474
1994
3672
1064
881
637
985
937
822
2296
2563
565
160
408
578
2191
NMB (%)
60.2
8.1
78.2
85.2
36.3
71.6
10.4
6.7
-2.8
34.7
36.2
-20.6
-4.4
-6.3
49.9
49.5
71.2
35.0
34.0
65.2
65.2
-13.9
3.9
17.0
0.6
-35.4
-27.3
10.1
NME (%)
89.1
71.3
93.8
104.6
68.5
109.3
71.0
54.5
61.4
63.6
59.6
43.3
52.2
61.0
79.0
86.4
93.6
62.9
67.6
94.7
97.3
49.6
61.1
56.0
47.0
46.0
49.9
63.8
FB (%)
38.6
5.4
49.7
56.1
21.7
45.4
0.4
-2.7
-20.6
21.0
14.2
-19.1
-0.3
-23.4
33.3
24.2
47.1
33.1
26.3
33.5
28.5
-10.4
-6.7
6.9
-17.3
-31.5
-17.6
-6.2
FE (%)
61.3
65.2
61.6
66.8
48.7
71.5
66.1
52.3
66.0
51.0
44.1
48.9
50.8
67.5
58.3
63.8
63.6
52.1
51.6
65.1
67.2
51.3
54.7
54.2
48.4
46.8
54.4
55.7
-------
Summer
Fall
STN
IMPROVE
STN
IMPROVE
12-kmEUS
12-kmWUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
3529
1030
866
621
940
871
806
2182
2301
512
160
384
561
1961
3396
1063
901
642
988
602
867
2118
2352
518
116
406
510
2023
45.5
62.1
47.0
21.2
35.7
85.3
78.9
-34.7
14.0
-29.0
-25.0
-51.3
-38.1
19.8
28.5
21.4
36.0
28.0
9.4
67.2
26.4
-13.7
-0.9
19.7
-18.4
-38.7
-19.6
-0.4
76.7
91.3
76.7
47.9
73.5
113.8
10.7
47.8
67.7
46.1
36.4
53.3
49.1
71.6
64.8
70.3
72.7
55.2
53.2
89.4
74.3
45.9
60.1
52.2
35.4
44.8
41.0
62.3
28.6
34.6
34.6
21.6
27.3
32.3
41.0
-38.8
8.9
-44.5
-42.1
-71.0
-45.5
12.9
21.6
9.0
23.4
25.2
7.6
48.4
7.6
-20.5
-16.7
0.0
-20.0
-36.2
-19.0
-17.6
61.3
62.5
59.5
47.6
58.0
76.5
65.8
59.8
57.8
61.5
52.0
78.4
61.5
59.1
52.1
59.6
53.8
47.9
45.8
63.4
61.8
49.9
60.3
49.6
48.0
51.9
46.0
62.4
-------
EC NMB (%) for run 200Scp_ton_05b_12km_EUS for December lo February
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure 1-1. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Eastern
U.S. domain, Winter 2005.
EC NME (%) for run 2005cp_tox_05b_12km_EUS for December to February
CIRCLE=IMPROVE: TRIANGLE=STN;
Figure 1-2. Normalized Mean Error (%) of elemental carbon by monitor for 12-km
Eastern U.S. domain, Winter 2005.
-------
EC NMB (%) for run 2005cp_tox_OSb_12km_EUS lor March to May
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure 1-3. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Eastern
U.S. domain, Spring 2005.
EC NME (%) tor run 2005cp_lox_05b_12km_EUS lor March to Hay
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure 1-4. Normalized Mean Error (%) of elemental carbon by monitor for 12-km
Eastern U.S. domain, Spring 2005.
-------
EC NMB (%) lor run 2005cpjox J)5tM2km_EUS lor June to August
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure 1-5. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Eastern
U.S. domain, Summer 2005.
EC NME (%) for run 2005cp_tox_05b_12km_EUS tor June to August
CIRCLE=IMPROVE; TRIAWGLE=STN;
Figure 1-6. Normalized Mean Error (%) of elemental carbon by monitor for 12-km
Eastern U.S. domain, Summer 2005.
-------
EC NMB 1%) for run 200ScpJo» _05b_12km_EUS for September to November
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure 1-7. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Eastern
U.S. domain, Fall 2005.
EC NME (%) tor run 20Q5cpJoxJ)5b_12km^ELJS for September to November
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure 1-8. Normalized Mean Error (%) of elemental carbon by monitor for 12-km
Eastern U.S. domain, Fall 2005.
-------
EC NMB (%) tor run 2005ci_tox 05b_MP W12km noPMcut tor December to February 2005
n AMET Protect
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure 1-9. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Eastern
U.S. domain, Winter 2005.
EC NME (%) for run 2005ci_tox 05b_MP_W12km noPMcut for December to February 2005
An AMET Pmducl
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure 1-10. Normalized Mean Error (%) of elemental carbon by monitor for 12-km
Western U.S. domain, Winter 2005.
-------
EC NMB (%} for run 2Q05ci tox 05b MP W12km noPMcut for March to May 2005
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure 1-11. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km
Western U.S. domain, Spring 2005.
EC NME (%) for run 2005CI tox 05b MP W12l
-------
EC NMB (%) tor run 2005ci_tox_05b_MP_W12km_noPMcjt for June to August 2005
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure 1-13. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km
Western U.S. domain, Summer 2005.
EC NME (%) for run 2005cUox_05b_MP_W12km_noPMcut for June to August 2005
CIRCLE=IMPROVE; TRIANGLE=STN;
its = %
compleloncss - 75%
All AMET Producl
Figure 1-14. Normalized Mean Error (%) of elemental carbon by monitor for 12-km
Western U.S. domain, Summer 2005.
-------
EC NMB (%) tor run 2005ci_tox 05b_MPW12km noPMcut tor September to November 2005
units = %
> 100
90
80
70
60
50
40
30
20
10
-10
-20
-30
-40
-SO
-60
-70
-80
-90
-100
<-100
AnAMETPiodud
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure 1-15. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km
Western U.S. domain, Fall 2005.
EC NME (%) tor run 2005ci_tox_05b_MP_W12km_noPMeul tor September to November 2005
An AMET Prottucl
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure 1-16. Normalized Mean Error (%) of elemental carbon by monitor for 12-km
Western U.S. domain, Fall 2005.
-------
J. Seasonal Organic Carbon Performance
Seasonal organic carbon performance statistics are provided in Table J-l. Spatial plots of the
NMB and NME statistics (units of percent) for individual monitors are also provided as a
complement to the tabular statistical data (Figures J-l - J-l6). The model predictions generally
show moderate under-predictions for all Eastern sites located in the urban STN sites and rural
IMPROVE sites. Organic carbon performance in the EUS and WUS shows the largest under
estimations during the summer season. These biases and errors reflect sampling artifacts among
each monitoring network. In addition, uncertainties exist for primary organic mass emissions
and secondary organic aerosol formation. Research efforts are ongoing to improve fire emission
estimates and understand the formation of semi-volatile compounds, and the partitioning of SOA
between the gas and paniculate phases.
Table J-l. CMAQ 2002 seasonal model performance statistics for organic carbon.
CMAQ 2002 Organic Carbon
Winter
Spring
STN
IMPROVE
STN
IMPROVE
12-kmEUS
12-kmWUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
No. of Obs.
3051
606
804
565
943
544
507
2071
2269
522
166
386
474
1981
3243
656
831
605
972
627
560
2290
2554
565
160
409
577
2183
NMB (%)
1.9
-25.6
31.7
9.0
-23.1
2.4
-24.2
11.1
-12.1
53.9
10.7
-16.8
-2.6
-16.3
-24.8
-19.8
3.8
-27.7
-35.7
-36.6
-15.9
-20.5
-26.3
8.1
-21.2
-22.1
-41.6
-22.1
NME (%)
53.2
62.0
61.1
54.5
44.9
56.3
63.0
54.0
59.0
72.7
40.4
40.5
51.9
58.4
48.9
63.4
51.5
45.5
48.1
50.8
65.9
47.1
53.0
48.0
35.0
41.0
54.8
52.2
FB (%)
13.1
-22.4
34.4
25.7
-11.9
17.7
-22.0
1.2
-21.7
37.0
7.1
-19.1
-2.8
-23.7
-15.0
-13.7
10.0
-15.5
-29.0
-32.9
-7.1
-18.3
-25.3
1.5
-26.2
-24.9
-35.9
-23.5
FE (%)
54.9
62.6
56.1
55.3
50.3
58.9
64.3
50.9
63.6
54.2
37.2
47.8
48.6
65.0
56.1
64.5
53.7
52.2
56.7
61.7
65.5
50.8
56.5
46.7
41.8
45.6
58.7
56.6
-------
Summer
Fall
STN
IMPROVE
STN
IMPROVE
12-kmEUS
12-kmWUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
3228
832
859
619
931
595
684
2183
2311
513
160
384
562
1970
3097
809
829
591
956
493
657
2118
2361
516
115
408
510
2031
-51.7
-55.4
-47.6
-53.1
-55.9
-50.3
-54.0
-41.6
-1.5
-47.5
-43.9
-43.6
-46.4
4.0
-26.1
-40.4
-3.9
-27.6
-39.2
-29.4
-41.0
-23.5
-22.7
16.3
-30.2
-35.1
-39.2
-21.3
54.3
60.0
52.1
54.3
57.1
53.2
59.3
50.7
63.3
52.2
46.8
48.2
51.6
65.2
45.0
57.9
47.6
40.7
45.7
45.2
59.9
46.1
56.0
48.4
40.3
41.3
48.9
57.0
-68.2
-76.2
-62.1
-69.5
-76.0
-68.0
-73.3
-53.0
-7.1
-60.2
-58.0
-66.3
-65.8
-2.3
-19.2
-38.7
4.1
-15.1
-41.3
-25.3
-39.6
-31.3
-29.0
32.6
-37.7
-41.6
-47.1
-27.1
74.2
83.6
70.2
72.8
79.8
74.7
81.8
66.7
61.0
67.6
62.2
71.3
73.5
60.2
54.7
63.4
53.7
51.6
57.5
56.1
66.8
54.7
62.7
-0.3
53.7
52.6
61.5
62.7
-------
PC NMB (%) tor run 2005cp_lox_05b_12km_EUS tor December to February
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure J-l. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Eastern
U.S. domain, Winter 2005.
PC NME (%) tor run 2005cp tox 05b_l2km EUS tor December to February
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure J-2. Normalized Mean Error (%) of organic carbon by monitor for 12-km Eastern
U.S. domain, Winter 2005.
-------
PC NMB (%) tor run 2005cp_lox_05b_12kin_EUS tor March to May
CIRCLE=IMPROVE: TRIANGLE=STN;
Figure J-3. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Eastern
U.S. domain, Spring 2005.
PC NME (%) tor run 2005cp Jo«_09b_12km EUS lor March to May
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure J-4. Normalized Mean Error (%) of organic carbon by monitor for 12-km Eastern
U.S. domain, Spring 2005.
-------
PC NMB (%) tor run ;005cp_tox_05b_12kin_EUS tor June to August
CIRCLE=IMPROVE: TRIANGLE=STN;
Figure J-5. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Eastern
U.S. domain, Summer 2005.
PC NME (%) tor run 2005cp_lo» 05b 12km_EUS tor June to August
CIRCLE=IMPROVE; TRIANGLE=STN:
Figure J-6. Normalized Mean Error (%) of organic carbon by monitor for 12-km Eastern
U.S. domain, Summer 2005.
-------
OC NMB (%) for run 2005cp_tox_05b_12km_EUS for September lo November
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure J-7. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Eastern
U.S. domain, Fall 2005.
OC NME (%) for run 2005cp lox OSb 12km EUS lor September to November
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure J-8. Normalized Mean Error (%) of organic carbon by monitor for 12-km Eastern
U.S. domain, Fall 2005.
-------
PC NMB (%) tor run 2002ae_12hm_WUS tor December lo February
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure J-9. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Western
U.S. domain, Winter 2005.
PC NME (%) (or run 2002ac_12linn_WUS (or December lo February
CIRCLE=IMPROVE;TRIANGLE=STN;
Figure J-10. Normalized Mean Error (%) of organic carbon by monitor for 12-km
Western U.S. domain, Winter 2005.
-------
PC NMB (%) for run 2002ac_12km_WUS for March to May
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure J-ll. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Western
U.S. domain, Spring 2005.
OC NME (%) for run 2002ac_12km_WUS for March to May
CIRCLE=IMPROVE;TRIANGLE=STN;
Figure J-12. Normalized Mean Error (%) of organic carbon by monitor for 12-km
Western U.S. domain, Spring 2005.
-------
PC NMB (%) tor fun 2002ac_12Km_wuS tor June lo August
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure J-13. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Western
U.S. domain, Summer 2005.
PC NME (%) tor run 2002ae_12kmJVUS tor June lo August
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure J-14. Normalized Mean Error (%) of organic carbon by monitor for 12-km
Western U.S. domain, Summer 2005.
-------
PC NMB (%) tor run 2002ac 12km WUS tor September to November
CIRCLE=IMPROVE; TRIANGLE=STN;
Figure J-15. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Western
U.S. domain, Fall 2005.
PC NME (%) tor run 2002ai:_12km_WLJS tor September lo November
CIRCLE=IMPROVE;TRIANGLE=STN;
Figure J-16. Normalized Mean Error (%) of organic carbon by monitor for 12-km
Western U.S. domain, Fall 2005.
-------
K. Annual Hazardous Air Pollutants Performance
An annual and seasonal operational model performance evaluation for specific hazardous air
pollutants (formaldehyde, acetaldehyde, benzene, acrolein, and 1,3-butadiene) was conducted in
order to estimate the ability of the CMAQ modeling system to replicate the base year
concentrations for the 12-km Eastern and Western United States domains. The annual model
performance results are presented in Table K-l below. Spatial plots of the NMB and NME
statistics (units of percent) for individual monitors are also provided as a complement to the
tabular statistical data (Figures K-l - K-24). The seasonal results follow in Sections L-P. Toxic
measurements from 471 sites in the East and 135 sites in the West were included in the
evaluation and were taken from the 2005 State/local monitoring site data in the National Air
Toxics Trends Stations (NATTS). Similar to PM2.5 and ozone, the evaluation principally
consists of statistical assessments of model versus observed pairs that were paired in time and
space on daily basis.
Model predictions of annual formaldehyde, acetaldehyde and benzene showed relatively small
bias and error percentages when compared to observations. The model yielded larger bias and
error results for 1,3 butadiene and acrolein based on limited monitoring sites. Model
performance for HAPs is not as good as model performance for ozone and PM2.5. Technical
issues in the HAPs data consist of (1) uncertainties in monitoring methods; (2) limited
measurements in time/space to characterize ambient concentrations ("local in nature"); (3)
commensurability issues between measurements and model predictions; (4) emissions and
science uncertainty issues may also affect model performance; and (5) limited data for estimating
intercontinental transport that effects the estimation of boundary conditions (i.e., boundary
estimates for some species are much higher than predicted values inside the domain).
As with the national, annual PM2.5 and ozone CMAQ modeling, the "acceptability" of model
performance was judged by comparing our CMAQ 2005 performance results to the limited
performance found in recent regional multi-pollutant model applications.1'2'3 Overall, the
normalized mean bias and error (NMB and NME), as well as the fractional bias and error (FB
and FE) statistics shown in Table J-l indicate that CMAQ-predicted 2005 toxics (i.e.,
observation vs. model predictions) are within the range of recent regional modeling applications.
1 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform:
Air Toxics, Ozone, and Paniculate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.
2 Strum, M., Wesson, K., Phillips, S., Cook, R., Michaels, H., Brzezinski, D., Pollack, A., Jimenez, M., Shepard, S.
Impact of using lin-level emissions on multi-pollutant air quality model predictions at regional and local scales. 17th
Annual International Emission Inventory Conference, Portland, Oregon, June 2-5, 2008.
3 Wesson, K., N. Farm, and B. Timin, 2010: Draft Manuscript: Air Quality and Benefits Model Responsiveness to
Varying Horizontal Resolution in the Detroit Urban Area, Atmospheric Pollution Research, Special Issue: Air
Quality Modeling and Analysis.
-------
Table K-l. 2005 CMAQ annual toxics model performance statistics
CMAQ 2005 Annual
Formaldehyde
Acetaldehyde
Benzene
1,3-Butadiene
Acrolein
12-kmEUS
12-kmWUS
Northeast
Midwest
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central U.S.
West
No. of Obs.
6365
1928
1982
771
1246
1815
1746
6094
1892
1969
703
1231
1640
1709
11615
3369
2589
1425
2426
4737
2333
8102
1976
1902
516
1226
4142
1082
1660
783
850
n/a
278
n/a
592
NMB (%)
-53.8
-24.5
-28.2
-75.9
-65.0
-41.4
-21.7
-0.9
-14.4
-6.6
-8.9
3.2
5.6
-15.6
-30.6
-34.9
26.5
-5.8
-38.3
-46.4
-26.1
-71.9
-46.5
-34.1
-74.8
-82.4
-63.8
-36.7
-93.8
-95.4
-89.5
n/a
-96.7
n/a
-95.8
NME (%)
64.5
50.9
50.7
85.0
71.4
49.4
51.5
63.0
53.6
64.0
58.8
64.1
57.9
53.9
66.9
60.5
55.1
73.5
69.4
67.6
61.2
84.9
83.1
53.0
85.5
84.0
86.1
78.4
94.5
95.5
90.9
n/a
96.7
n/a
95.8
FB (%)
-36.6
-25.8
-26.3
-22.6
-48.8
-38.7
-22.0
-5.2
-14.7
-6.4
-8.7
-3.7
-0.9
-15.4
-10.4
-25.4
22.7
27.9
-12.9
-30.9
-13.6
-37.5
-22.7
-42.5
-34.8
-93.4
-8.0
-36.6
-126.2
-164.5
-116.0
n/a
-152.8
n/a
-176.9
FE (%)
64.2
58.6
60.7
72.7
69.1
59.6
58.2
59.8
58.2
63.4
59.3
61.5
50.5
59.3
62.4
62.3
47.7
62.7
60.1
68.0
62.1
88.2
91.5
64.5
77.7
100.7
89.6
84.2
138.4
167.2
131.1
n/a
153.6
n/a
176.9
-------
FORM NMB (%) for run 2005cp_tox_05b_12km_EUS for 20050101 to 20051231
90
BO
70
60
90
40
90
20
10
-10
20
-30
10
-50
-GO
-70
-90
-90
-100
An AMET Producl
CIRCLE=Toxics;
Figure K-l. Normalized Mean Bias (%) of annual formaldehyde by monitor for Eastern
U.S., 2005.
FORM NME (%) for run 2005cp_tox_05b_12km_EUS for 20050101 to 20051231
An AMET ProdlKI
CIRCLE=Toxics;
Figure K-2. Normalized Mean Error (%) of annual formaldehyde by monitor for Eastern
U.S., 2005.
-------
ALD2 NMB (%) for run 2005cp_tox_05b_12km_EUS for 20050101 to 20051231
=• 100
90
BO
70
60
90
40
90
20
10
-10
20
-30
10
-50
-GO
-70
-90
-90
-100
<-too
An AMET ProdLd
CIRCLE=Toxics;
Figure K-3. Normalized Mean Bias (%) of annual acetaldehyde by monitor for Eastern
U.S., 2005.
ALD2 NME (%) for run 2005cp_tox_05bJ2km_EUS for 20050101 to 20051231
completeness - 7S%
An AMET Product
CIRCLE=Toxics;
Figure K-4. Normalized Mean Error (%) of annual acetaldehyde by monitor for Eastern
U.S., 2005.
-------
Benzene NMB (%) tor run 2005cp_lo«_05b_12km_EUS tor 20050101 lo 20051231
»too
90
80
70
60
50
40
30
20
10
-10
-20
-30
-40
-SO
-60
-70
-80
-90
-100
<-100
An AMET Product
CIRCLE=Toxics;
Figure K-5. Normalized Mean Bias (%) of annual benzene by monitor for Eastern U.S.,
2005.
Benzene NME (%) for run 2005cpJox_05b_12km_EUS tor 20050101 to 20051231
completeness - 75%
An AMET Praducl
CIRCLE=Toxics;
Figure K-6. Normalized Mean Error (%) of annual benzene by monitor for Eastern U.S.,
2005.
-------
Butadiene13 NMB (%) tor run 2005cp tox 05b 12km EUS tor 20050101 to 20051231
An AMET Product
CIRCLE=Toxics;
Figure K-7. Normalized Mean Bias (%) of annual 1,3-butadiene by monitor for Eastern
U.S., 2005.
Butadiene13NMB(%)torrun2005cp tox 05b 12km EUS tor 20050101 to 20051231
>100
90
80
70
60
50
40
30
20
10
-10
-20
-30
-40
-50
-60
-70
-80
-90
-100
<-100
An AMET Product
CIRCLE=Toxics;
Figure K-8. Normalized Mean Error (%) of annual 1,3-butadiene by monitor for Eastern
U.S., 2005.
-------
Acrolein NMB (%) for run 2005cp_tox_05b_12km_EUS for 20050101 to 20051231
CIRCLE=Toxics;
,n AMET Product
Figure K-9. Normalized Mean Bias (%) of annual acrolein by monitor for Eastern U.S.,
2005.
Acrolein NME (%) for run 2005cp_tox_05b_12km_EUS for 20050101 to 20051231
CIRCLE=Toxics;
,n AMET Product
Figure K-10. Normalized Mean Error (%) of annual acrolein by monitor for Eastern U.S.,
2005.
-------
FORM NMB (%) for run 2005cp_tox_05b_12km_WUS for 20050101 to 20051231
CIRCLE=Toxics;
Figure K-15. Normalized Mean Bias (%) of annual formaldehyde by monitor for Western
U.S., 2005.
FORM NME (%) for run 2005cp_tox_05b_12km_WUS tor 20050101 to 20051231
An AMET Product
CIRCLE=Toxics;
Figure K-16. Normalized Mean Error (%) of annual formaldehyde by monitor for
Western U.S., 2005.
-------
ALD2 NMB (%) for run 2005cp_tox_05b_12km_WUS for 20050101 to 20051231
jntts-100
90
SO
70
60
50
40
30
20
10
10
-20
-30
-40
SO
-60
-70
80
-90
-100
e-100
CIRCLE=Toxics;
Figure K-17. Normalized Mean Bias (%) of annual acetaldehyde by monitor for Western
U.S., 2005.
ALD2 NME (%) tor run 2005cp_tox_05b_12krn_WUS tor 20050101 to 20051231
complelenwfi • 75%
• >100
• 90
• 80
70
60
50
• 40
30
• 20
• 10
• 0
An AWET PrwJucI
CIRCLE=Toxics;
Figure K-18. Normalized Mean Error (%) of annual acetaldehyde by monitor for Western
U.S., 2005.
-------
Benzene NMB (%) tor run 2005cp_tox_05b_12km_WUS for 20050101 to 20051231
nits = %
completeness - 75*.'*
90
80
70
60
50
40
30
20
10
-10
-20
-30
-40
-50
-60
70
30
90
-100
<-100
An AMET Product
CIRCLE=Toxics;
Figure K-19. Normalized Mean Bias (%) of annual benzene by monitor for Western U.S.,
2005.
Benzene NME (%) for run 2005cp_tox_05b_12km_WUS for 20050101 to 20051231
\
compJmcrwss * 75%
• >100
• 90
• 80
70
60
50
• 40
30
• 20
• 10
• 0
An AMET Product
CIRCLE=Toxics;
Figure K-20. Normalized Mean Error (%) of annual benzene by monitor for Western U.S.,
2005.
-------
Biitadiene13 NMB (%) for run 2005cp_tox_05b_12km_WUS tor 20050101 to 20051231
units = %
completeness - 75*.'*
;-100
90
80
70
60
50
40
30
20
10
-10
-20
-30
-40
-50
-60
70
30
-90
-100
<-100
An AUET Product
CIRCLE=Toxics;
Figure K-21. Normalized Mean Bias (%) of annual 1,3-butadiene by monitor for Western
U.S., 2005.
Butadiene13 NME (%) for run 2005cp_tox_05b_12km_WUS for 20050101 to 20051231
completeness - 75%
An AUET Product
CIRCLE=Toxics;
Figure K-22. Normalized Mean Error (%) of annual 1,3-butadiene by monitor for
Western U.S., 2005.
-------
Acrolein NMB (%) for run 2005cp_tox_05b_12km_WUS for 20050101 to 20051231
\
>100
90
80
70
60
50
40
30
20
10
-10
• -20
-30
-40
-50
-60
-70
-80
-90
-100
<-100
An AMET Product
CIRCLE=Toxics;
Figure K-23. Normalized Mean Bias (%) of annual acrolein by monitor for Western U.S.,
2005.
Acrolein NME (%) for run 2005cp_tox_05b_12km_WUS for 20050101 to 20051231
ompleteness = 75%
• > 100
• 90
• 80
70
60
50
• 40
30
20
• 10
• 0
An AMET Product
CIRCLE=Toxios;
Figure K-24. Normalized Mean Error (%) of annual acrolein by monitor for Western U.S.,
2005.
-------
L. Annual Nitrate and Sulfate Deposition Performance
Annual nitrate and sulfate deposition performance statistics are provided in Table L-l. Spatial
plots of the NMB and NME statistics (units of percent) for individual monitors are also provided
as a complement to the tabular statistical data (Figures L-l - L-8). The model predictions for
annual nitrate deposition generally show small under-predictions for the Eastern and Western
NADP sites (NMB values range from 0% to -11%). Sulfate deposition performance in the EUS
and WUS shows the similar over predictions (NMB values range from 5% to 16%). The errors
for both annual nitrate and sulfate are relatively moderate with values ranging from 60% to 81%
which reflect scatter in the model predictions o observation comparison.
Table L-l. CMAQ 2005 annual model performance statistics for total nitrate and sulfate
deposition.
CMAQ 2005 Total Deposition
Nitrate
Sulfate
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
No. of Obs.
7381
2732
1391
1658
1980
1229
2257
7381
2732
1391
1658
1980
1229
2257
NMB (%)
-0.6
-11.6
4.9
10.1
4.9
-11.0
-9.8
7.8
5.6
16.4
12.6
8.6
-7.3
13.2
NME (%)
63.9
69.5
62.0
60.9
67.3
62.7
73.8
67.0
76.3
62.6
64.3
71.4
65.1
81.8
FB (%)
-7.8
-12.7
5.0
3.9
0.1
-11.0
-12.7
6.0
4.8
23.2
16.5
6.4
-1.2
6.5
FE (%)
74.0
83.4
67.8
66.5
71.3
78.3
85.0
75.3
86.5
70.4
67.3
73.8
80.3
87.9
-------
NO3 NMB (%) for run 2005cp_lQX_05b_12km_EUS tor 2005
Es^
>100
90
60
70
60
50
40
30
20
10
0
-10
-20
-30
-40
-50
-60
-70
-80
-90
-100
units • %
coverftge smil - 75%
CIRCI_E=NADP_dep;
Figure L-l. Normalized Mean Bias (%) of annual nitrate deposition by monitor for
Eastern U.S., 2005.
NO3 NME (%) tor run 2005cp_IQ»_05b_12km_EUS for 2005
CIRCLE=NADP_dep;
Figure L-2. Normalized Mean Error (%) of annual nitrate by monitor for Eastern U.S.,
2005.
-------
NO3 NMB (%) for run 2005cp_tox 05b_12km WUS lor 2005
CIRCLE=NADP_dep;
Figure L-3. Normalized Mean Bias (%) of annual nitrate deposition by monitor for
Western U.S., 2005.
N03 HME (%) for run 2005cpjox OSbJZkm WUS tor 2005
CIRCI_E=NADP_dep;
Figure L-4. Normalized Mean Error (%) of annual nitrate deposition by monitor for
Western U.S., 2005.
-------
S04 MMB (%) tor run 2005cp_lox_05bJ2km_EUS tor 2005
CIRCLE=NADP_dep;
Figure L-5. Normalized Mean Bias (%) of annual sulfate deposition by monitor for
Eastern U.S., 2005.
SO4 NME [%) lor run aOQ5cp_tox_05b_12km_EUS lor 2005
CIRCLE=NADP_dep;
Figure L-6. Normalized Mean Error (%) of annual sulfate deposition by monitor for
Eastern U.S., 2005.
-------
SO4 NMB (%) for run 2005cp_tOx_05b_1 gkm WU5 tor ZOOS
CIRCLE=NADP_dop;
Figure L-7. Normalized Mean Bias (%) of annual sulfate deposition by monitor for
Western U.S., 2005.
SOI NME (%) lor run ZODScp_lox_05b_mm_WUS for 2005
CIRCLE=NADP_dep;
Figure L-8. Normalized Mean Error (%) of annual sulfate deposition by monitor for
Western U.S., 2005.
-------
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-
Environmental Protection Air Quality Assessment Division 10-003
Agency Research Triangle Park, NC April, 2010
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