Air Quality Modeling Technical Support
Document: Heavy-Duty Vehicle
Greenhouse Gas Emission Standards
Final Rule

£%	United States
Environmental Protect
Agency

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Air Quality Modeling Technical Support
Document: Heavy-Duty Vehicle
Greenhouse Gas Emission Standards
Final Rule
Air Quality Assessment Division
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
United States
Environmental Protection
^1	Agency
EPA-454-R-11-004
August 2011

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Table of Contents
I.	Introduction	1
II.	Air Quality Modeling Platform	1
A.	Air Quality Model	2
B.	Model domains and grid resolution	2
C.	Modeling Simulation Periods	4
D.	HDGHG Modeling Scenarios	4
E.	Meteorological Input Data	6
F.	Initial and Boundary Conditions	9
G.	CMAQ Base Case Model Performance Evaluation	9
III.	CMAQ Model Results	9
A.	Impacts of HDGHG Standards on Future 8-Hour Ozone Levels	9
B.	Impacts of HDGHG Standards on Future Annual PM2.5 Levels	10
C.	Impacts of HDGHG Standards on Future 24-hour PM2.5 Levels	11
D.	Impacts of HDGHG Standards on Future Toxic Air Pollutant Levels	12
1.	Acetaldehyde	13
2.	Formaldehyde	14
3.	Benzene	16
4.	1,3-Butadiene	17
5.	Acrolein	19
E.	Impacts of HDGHG Standards on Future Annual Nitrogen and Sulfur Deposition....20
F.	Impacts of HDGHG Standards on Future Visibility Levels	22
Appendices
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List of Appendices
Appendix A.
Model Performance Evaluation for the 2005-Based Air Quality Modeling Platform
Appendix B.
8-Hour Ozone Design Values for Air Quality Modeling Scenarios
Appendix C.
Annual PM2.5 Design Values for Air Quality Modeling Scenarios
Appendix D.
24-Hour PM2.5 Design Values for Air Quality Modeling Scenarios
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I. Introduction
This document describes the air quality modeling performed by EPA in support of the
Heavy-Duty Vehicle Greenhouse Gas Final Rule (hereafter referred to as HDGHG). 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, 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) model.1 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 to
allow for sufficient time required to conduct emissions and air quality modeling. 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 8.2 and 8.3, respectively of the RIA, are slightly
different than the final vehicle standard inventories presented in Chapter 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.
Air quality modeling was performed for three emissions cases: a 2005 base year, a 2030
reference case projection without vehicle standards, and a 2030 control case projection with
vehicle standards. The year 2005 was selected for the HDGHG base year because this is the
most recent year for which EPA has a complete national emissions inventory.
The remaining sections of the Air Quality Modeling Final Rule TSD are as follows.
Section II describes the air quality modeling platform and the evaluation of model predictions of
PM2.5 and ozone using corresponding ambient measurements. Section III we present the results
of modeling performed for 2030 to assess the impacts on air quality of the vehicle standards
expected from this rule. Information on the development of emissions inventories for the
HDGHG Rule and the steps and data used in creating emissions inputs for air quality modeling
can be found in the Emissions Inventory for Air Quality Modeling TSD (EITSD; EPA-420-R-
11-008). The docket for this final rulemaking (EPA-HQ-OAR-2010-0162) also contains
state/sector/pollutant emissions summaries for each of the emissions scenarios modeled.
II. Air Quality Modeling Platform
The 2005-based CMAQ modeling platform was used as the basis for the air quality
modeling of the HDGHG 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
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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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.	Air Quality Model
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
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 domains 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
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, J., Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, J., 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-l 1662.
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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 HDGHG emission standard
program changes. Table II-l provides some basic geographic information regarding the CMAQ
domains.
In addition to the CMAQ model, the HDGHG modeling platform includes (1) emissions
for the 2005 base year, 2030 reference case projection, 2030 control case projection, (2)
meteorology for the year 2005, and (3) estimates of intercontinental transport (i.e., boundary
concentrations) from a global photochemical model. Using these input data, CMAQ was run to
generate hourly predictions of ozone, PM2.5 component species, nitrogen and sulfate deposition,
and a subset of air toxics (formaldehyde, acetaldehyde, acrolein, benzene, and 1,3-butadiene)
concentrations for each grid cell in the modeling domains. The development of 2005
meteorological inputs and initial and boundary concentrations are described below. The
emissions inventories used in the HDGHG air quality modeling are described in the EITSD
found in the docket for this rule (EPA-420-R-11-008).
Table II-l. Geographic elements of domains used in HDGHG modeling.

CMAQ Modeling Configuration

National Grid
Western U.S. Fine Grid
Eastern U.S. Fine Grid
Map Projection
Lambert Conformal Projection
Grid Resolution
36 km
12 km
12 km
Coordinate Center
97 deg W, 40 deg N
True Latitudes
33 deg N and 45 deg N
Dimensions
148x112x14
213 x 192 x 14
279 x 240 x 14
Vertical extent
14 Layers: Surface to 100 millibar level (see Table II-3)
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| 36km Domain Boundary
| 12km East Domain Boundary
| 12km West Domain Boundary |'
Figure II-1. 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.
C. Modeling Simulation Periods
The 36 km and both 12 km CMAQ modeling domains were modeled for the entire year
of 2005. These annual simulations were performed in quarterly segments (i.e., January through
March, April through June, July through September, and October through December) for each
emissions scenario. With this approach to segmenting an annual simulation we were able to
model several quarters at the same time and, thus, reduce the overall throughput time for an
annual simulation. The 36 km domain simulations included a "ramp-up" period, comprised of
10 days before the beginning of each quarter, to mitigate the effects of initial concentrations. For
the 12 km Eastern domain simulations we used a 3-day ramp-up period for each quarter, the
ramp-up periods are not considered as part of the output analyses. Fewer ramp-up days were
used for the 12 km simulations because the initial concentrations were derived from the parent
36 km simulations.
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 PM2.5, total nitrogen and sulfate deposition, visibility and toxics impacts from this final
rulemaking.
D. HDGHG 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
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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 PM25 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 (relative percent change)
between the 2030 future year simulation and the 2005 base. These predicted change ratios are
then applied to ambient base year design values. The ambient air quality observations are
average conditions, on a site-by-site basis, for a period centered around the model base year (i.e.,
2003-2007). The raw model outputs are also used in a relative sense as inputs to the health and
welfare impact functions of the benefits analysis. The difference between the 2030 reference
case and 2030 control case was used to quantify the air quality benefits of the rule. Additionally,
the differences in projected annual average PM2.5 and seasonal average ozone were used to
calculate monetized benefits by the BenMAP model (see Section 8.3 of the RIA).
The design value projection methodology used here followed EPA guidance7 for such
analyses. For each monitoring site, all valid design values (up to 3) from the 2003-2007 period
were averaged together. Since 2005 is included in all three design value periods, this has the
effect of creating a 5-year weighted average, where the middle year is weighted 3 times, the 2nd
and 4th years are weighted twice, and the 1st and 5th years are weighted once. We refer to this
as the 5-year weighted average value. The 5-year weighted average values were then projected
to the future years that were analyzed for the final rule.
Concentrations of PM25 in 2030 were estimated by applying the modeled 2005-to-2030
relative change in PM25 species to the 5 year weighted average (2003-2007) design values.
Monitoring sites were included in the analysis if they had at least one complete design value in
the 2003-2007 period. EPA followed the procedures recommended in the modeling guidance for
projecting PM25 by projecting individual PM2 5 component species and then summing these to
calculate the concentration of total PM2 5. The PM2 5 species are defined as sulfates, nitrates,
ammonium, organic carbon mass, elemental carbon, crustal mass, water, and blank mass (a fixed
value of 0.5 |ig/m3). EPA's Modeled Attainment Test Software (MATS) was used to calculate
the future year design values. The software (including documentation) is available at:
httD://www.eDa.gov/scrani001/niodelingaDDS mats.htm. For this latest analysis, several
datasets and techniques were updated. These changes are fully described within the technical
support document for the Final Transport Rule AQM TSD.8
7	U.S. EPA, 2007: Guidance on the Use of Models and Other Analyses for Demonstrating Attainment for Ozone,
PM2 5, and Regional Haze, Office of Air Quality Planning and Standards, Research Triangle Park, NC.
8	U.S. EPA, 2011: Cross-State Air Pollution Rule (Final Transport Rule) Air Quality Modeling Final RuleTechnical
Support Document, Docket EPA-HQ-OAR-2009-0491-4140.
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To calculate 24-hour PM2.5 design values, the measured 98th percentile concentrations
from the 2003-2007 period at each monitor are projected to the future. The procedures for
calculating the future year 24-hour PM2.5 design values have been updated for the final rule. The
updates are intended to make the projection methodology more consistent with the procedures
for calculating ambient design values.
A basic assumption of the old projection methodology is that the distribution of high
measured days in the base period will be the same in the future. In other words, EPA assumed
that the 98th-percentile day could only be displaced "from below" in the instance that a different
day's future concentration exceeded the original 98th-percentile day's future concentration. This
sometimes resulted in overstatement of future-year design values for 24-hour PM2.5 at receptors
whose seasonal distribution of highest-concentration 24-hour PM2.5 days changed between the
2003-2007 period and the future year modeling.
In the revised methodology, we do not assume that the seasonal distribution of high days
in the base period years and future years will remain the same. We project a larger set of ambient
days from the base period to the future and then re-rank the entire set of days to find the new
future 98th percentile value (for each year). More specifically, we project the highest 8 days per
quarter (32 days per year) to the future and then re-rank the 32 days to derive the future year 98th
percentile concentrations. More details on the methodology can be found in a guidance memo
titled "Update to the 24 Hour PM2.5 NAAQS Modeled Attainment Test" which can be found
here: http://www.epa.gov/ttn/scram/guidance/guide/Update to the 24-
hour PM25 Modeled Attainment Test.pdf.
The future year 8-hour average ozone design values were calculated in a similar manner
as the PM2.5 design values. The May-to-September daily maximum 8-hour average
concentrations from the 2005 base case and the 2030 cases were used to project ambient design
values to 2030. The calculations used the base period 2003-2007 ambient ozone design value
data for projecting future year design values. Relative response factors (RRF) for each
monitoring site were calculated as the percent change in ozone on days with modeled ozone
greater than 85 ppb9.
We also conducted an analysis to compare the absolute and percent differences between
the 2030 control case and the 2030 reference cases for annual and seasonal formaldehyde,
acetaldehyde, benzene, 1,3-butadiene, and acrolein, as well as annual nitrate and sulfate
deposition. These data were not compared in a relative sense due to the limited observational
data available.
E. 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
9
As specified in the attainment demonstration modeling guidance, if there are less than 10 modeled days > 85 ppb,
then the threshold is lowered in 1 ppb increments (to as low as 70 ppb) until there are 10 days. If there are less than
5 days > 70 ppb, then an RRF calculation is not completed for that site.
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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 fields were prepared
separately for each of the three domains shown in Figure II-l 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 PBL and land surface schemes
•	Kain-Fritsh 2 cumulus parameterization
•	Reisner 2 mixed phase moisture scheme
•	RRTM longwave radiation scheme
•	Dudhia shortwave radiation scheme
Three dimensional analysis nudging 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 meteorological modeling domains contained 34 vertical layers
with an approximately 38 m 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
MM5 Layers
Sigma P
Approximate
Height (m)
Approximate
Pressure (mb)
0
0
1.000
0
1000
1
1
0.995
38
995
2
2
0.990
77
991
3
3
0.985
115
987
4
0.980
154
982
4
5
0.970
232
973
6
0.960
310
964
5
7
0.950
389
955
8
0.940
469
946

9
0.930
550
937
6
10
0.920
631
928

11
0.910
712
919

12
0.900
794
910
7
13
0.880
961
892

14
0.860
1,130
874
8
15
0.840
1,303
856
10 Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth-Generation Penn State/NCAR Mesoscale
Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for Atmospheric Research, Boulder CO.
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16
0.820
1,478
838
17
0.800
1,657
820
9
18
0.770
1,930
793
19
0.740
2,212
766
10
20
0.700
2,600
730
21
0.650
3,108
685
11
22
0.600
3,644
640
23
0.550
4,212
595
12
24
0.500
4,816
550
25
0.450
5,461
505
26
0.400
6,153
460
13
27
0.350
6,903
415
28
0.300
7,720
370
29
0.250
8,621
325
30
0.200
9,625
280
14
31
0.150
10,764
235
32
0.100
12,085
190
33
0.050
13,670
145
34
0.000
15,674
100
The 2005 meteorological outputs from all three MM5 sets were processed to create
model-ready inputs for CMAQ using the Meteorology-Chemistry Interface Processor (MCIP),
version 3.4.11
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 (PBL) 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 The results of these analyses indicate 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.
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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F.	Initial and Boundary Conditions
The lateral boundary and initial species concentrations are provided by a three-
dimensional global atmospheric chemistry model, the 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 to develop the initial/boundary concentrations for the
subsequent 12 km Eastern and Western domain model simulations.
G.	CMAQ Base Case Model Performance Evaluation
The CMAQ predictions for ozone, fine particulate matter, sulfate, nitrate, ammonium,
organic carbon, elemental carbon, a selected subset of toxics, and nitrogen and sulfur deposition
from the 2005 base year evaluation case were compared to measured concentrations in order to
evaluate the performance of the modeling platform for replicating observed concentrations. This
evaluation was comprised of statistical and graphical comparisons of paired modeled and
observed data. Details on the model performance evaluation including a description of the
methodology, the model performance statistics, and results are provided in Appendix A.
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 heavy-duty vehicle greenhouse gas rule. We
looked at impacts on future ambient PM2.5, ozone, and air toxics levels, as well as nitrogen and
sulfur deposition levels and visibility impairment. In this section, we present the air quality
modeling results for the 2030 HDGHG control case relative to the 2030 reference case.
A. Impacts of HDGHG 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 HDGHG 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 decrease in many
areas of the country as a result of this action. The decreases in ozone design values are likely
due to projected tailpipe reductions in NOx and projected upstream emissions decreases in NOx
15	Yantosca, B., 2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric Chemistry Modeling
Group, Harvard University, Cambridge, MA, October 15, 2004.
16	Henze, D.K., J.H. Seinfeld, N.L. Ng, J.H. Kroll, T-M. Fu, D.J. Jacob, C.L. Heald, 2008. Global modeling of
secondary organic aerosol formation from aromatic hydrocarbons: high-vs.low-yield pathways. Atmos. Chem. Phys.,
8, 2405-2420.
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and VOCs from reduced fuel production. Figure III-l presents the changes in 8-hour ozone
design value concentration in 2030 between the reference case and the control case 1' Appendix
B details the state and county 8-hour maximum ozone design values for the ambient baseline and
the future reference and control cases.
Legend
Number of counties
24
Difference in 8-hr Oznne DV: 2030r.K_hrlghg_r.fl
2030a_hrighg_ref
Figure III-l. Projected Change in 2030 8-hour Ozone Design Values Between the
Reference Case and Control Case
As can be seen in Figure III-l, the majority of the design value decreases are less than 1
ppb. However, there are 24 counties that will see 8-hour ozone design value decreases above 1
ppb; these counties are in southern Arizona, and the Midwest. The maximum projected decrease
in an 8-hour ozone design value is 1.57 ppb in Jefferson County, Tennessee.
B. Impacts of HDGHG 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 HDGHG vehicle standards. We compare a 2030 reference
scenario, a scenario without the heavy-duty vehicle standards, to a 2030 control scenario which
includes the heavy-duty vehicle standards. Our modeling indicates that the majority of the
modeled counties will see decreases of less than 0.01 ng/rn3 in their annual PM2.5 design values
1 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.
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due to the vehicle standards. Figure III-2 presents the changes in annual PM2.5 design values in
2030.18
Legend
I < -0.1 uQ/m3
| >¦ -0.110 < -0.05
| >= -0O51o< -0.01
>= -0.0110 <=0O
>0.0 to <= 0.01
>0 01 to <=0 05
Dtfferonco in Annual PM2.S DV: 2020cs_Mghg_ctl minus 2020cs_hdghg_ref
Figure 111-2. Projected Change in 2030 Annual PM2 5 Design Values Between the
Reference Case and Control Case
As shown in Figure III-2, 27 counties will see decreases between 0.01 ug/nr and 0.05
jag/m3. These counties are in the upper Midwest, Utah, Idaho and Missouri. The maximum
projected decrease in an annual PM2.5 design value is 0.03 ug/m3 in Allen County, Indiana and
Canyon County, Idaho. The decreases in annual PM2.5 design values that are modeled in some
counties are likely due to emission reductions related to lower fuel production at existing oil
refineries and/or reductions in PM2.5 precursor emissions (NOx,SOx, and VOCs) due to
improvements in road load. Additional information on the emissions reductions that are
projected with this final rule is available in Section 5.5 of the RIA. Appendix C details the state
and county annual PM25 design values for the ambient baseline and the future reference and
control cases.
C. Impacts of HDGHG 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 heavy-duty vehicle standards. Specifically, we compare a 2030 reference
scenario, a scenario without the vehicle standards, to a 2030 control scenario which includes the
18 An annual PM2 S 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 PM: 5 design value are given in appendix N of
40 CFR part 50.
n

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vehicle standards. Our modeling indicates that the majority of the modeled counties will see
changes of between -0.05 ug/m' and 0.0 ug/m in their 24-hour PM2.5 design values. Figure III-3
presents the changes in 24-hour PM2.5 design values in 2030.19
Legend
Numtwr of Counties
DifforBnce In Dally PM2.S DV: 2030cs_Hdghg_ctl minus 2030cs_hdghg_rct
Figure 111-3. Projected Change in 2030 24-hour PM2.5 Design Values Between the
Reference Case and the Control Case
As shown in Figure III-3, 39 counties will see decreases of more than 0.1 |ig/m'\ These
counties are in Idaho, Montana, northern Utah, and the upper Midwest. The maximum projected
decrease in a 24-hour PM2.5 design value is 0.27 jig/m3 in Canyon County, Idaho. The decreases
in 24-hour PM2.5 design values that we see in some counties are likely due to emission reductions
related to lower fuel production at existing oil refineries and/or reductions in PM2.5 precursor
emissions (NOx,SOx, and VOCs) due to improvements in road load. Appendix D details the
state and county 24-hour PM2.5 design values for the ambient baseline and the future reference
and control cases.
D. Impacts of HDGHG Standards on Future Toxic Air Pollutant Levels
The following sections summarize the results of our modeling of air toxics impacts in the
future from the vehicle emission standards required by HDGHG. We focus on air toxics which
were identified as national and regional-scale cancer and noncancer risk drivers in the 2005
19 A 24-hour PM2 S 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.
12

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NATA assessment and were also likely to be significantly impacted by the standards. These
compounds include benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein. Our
modeling indicates that the HDGHG 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.
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 typically less than
1% across the country (Figure III-4 through III-6). The summer season shows decreases of 5%
to 10% in certain urban areas of the Midwest. Likewise, small increases in ambient
concentrations of acetaldehyde of less than 0.01 ug/m3 are noted across most of the nation during
the summer season (Figure III-6). Decreases in ambient concentrations of acetaldehyde seen in
urban areas during the winter and summer are generally between 0.001 ug/m3 and 0.1 |ig/m3.
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)
13

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Legend
Figure 111-5. Changes in Winter Acetaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ju,g/in3 (right)
lAjfi crttfl to I
Pwrctnt Cit jog* tar AcaWtMiyd* • Suninwr Smton
ZOJOcj	minus 20J0cs../idgl>g.r«f
i noJfc«.
Figure 111-6. Changes in Summer Acetaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ng/m3 (right)
2. Formaldehyde
Our modeling projects that the standards finalized in this rule will generally decrease
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
14

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of a 1 to 5% decrease in the Midwest. Figure III-7 also shows that annual absolute changes in
ambient concentrations of formaldehyde are generally less than -0.1 ug/rn3. Also, decreases are
shown in seasonal ambient formaldehyde (Figures III-8 through III-9), which range from 0.01 to
0.1 |ig/m3.
Legend
Figure 111-7. Changes in Annual Formaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in jug/nr' (right)
Figure 111-8. Changes in Winter Formaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in jig/m3 (right)
15

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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 ju,g/m3 (right)
3. Benzene
Our air quality modeling projects that the standards finalized in this rule will not have a
significant impact on ambient benzene concentrations. Figures III-10, III-l 1, and III-12 show
decreases in annual and seasonal ambient benzene concentrations ranging between 1 and 10%
and between 0.001 and 0.1 jag/m3. The decreases are noted in urban areas in the Midwest,
Tennessee, Arkansas, Georgia, Mississippi, Louisiana, Texas, Arizona, and Pennsylvania.
Figure 111-10. Changes in Annual Benzene Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in jig/nr' (right)
16

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Legend
Figure III-ll. Changes in Winter Benzene Ambient Concentrations Between the Reference
Case and the Control Case In 2030: Percent Changes (left) and Absolute Changes in ^g/in3
(right)
P»rc#/rt Chan?* ft* Brmrn* • SuuinMr Snaon
20J0cs-.Adgfrg.crJ minus 20J0cs_/idgfrfl.,r»r
Figure 111-12. Changes in Summer Benzene Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ]ng/m3 (right)
4. 1,3-Butadiene
Our air quality modeling results do not show substantial impacts on ambient
concentrations of 1,3-butadiene from the HDGHG standards. As shown in Figure 111-13, annual
percent changes in ambient concentrations of 1,3-butadiene are less than 1% across the country,
with the exception of a small increase of 1 to 2.5% in Texas. Annual increases in ambient
17

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concentrations of 1,3-butadiene are driven by summertime changes (Figures III-15). In the
winter, small decreases ranging from 1 to 2.5% occur in Indiana (Figure 111-14). Changes in
absolute concentrations of ambient 1,3-butadiene are negligible, ± 0.001 (ig/m3.
Figure 111-13. 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 jtig/nr' (right)
Figure 111-14. 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 jig/m3 (right)
Pwrwrt Cfwnu» lot M-BuUdw/i».
18

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Figure 111-15. 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 ju,g/m3 (right)
5. Acrolein
Our air quality modeling results do not show substantial impacts on ambient
concentrations of acrolein from the standards finalized in this rule. Decreases ranging from 1 to
100% occur across the country (Figures III-16, III-17 and III-18). However, changes in annual
and seasonal absolute concentrations of acrolein are less than 0.003 |ig/m3 across the country.
Figure 111-16. Changes in Annual Acrolein Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in jig/m3 (right)
19

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P*rt*nf Chtagm for Acrolein • Wtntu Smioit
2030csJ\dgt*a-Cti minus HOOcs^lidgtpa.rrt
Figure 111-17. Changes in Winter Acrolein Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in jig/in-' (right)
Pwcwtf Change far Acroltm • Summer SMian
ZQJOc s lidgtxi ctl minus MJCc5_Jidgftfl„rief
Figure 111-18. Changes in Summer Acrolein Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ng/m3 (right)
E. Impacts of I1DGHG Standards on Future Annual Nitrogen and Sulfur Deposition
Levels
Our air quality modeling projects decreases in nitrogen deposition, especially in the upper Midwest.
Figure III-19 shows that for nitrogen deposition the heavy-duty standards will result in annual percent
decreases of more than 2% in some areas. The decreases in nitrogen deposition are likely due to projected
20

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tailpipe reductions in NOx and projected upstream emissions decreases in NOx from reduced gasoline
production. The remainder of the country will see only minimal changes in nitrogen deposition, ranging
from decreases of less than 0.5% to increases of less than 0.5%.
Percent Change in Annual Nitrogen Deposition -
203Qcs hdghg ctl minus 2030cs Mghgref
Difference in Annual Nitrogen Deposition -
2030cs.hdghg ctl minus 2030cs_ Mghg rvf
Figure 111-19. Changes in Annual Total Nitrogen Deposition Between the Reference Case
and the Control Case in 2030: Percent Changes (left) and Absolute Changes in jig/m3
(right)
Our air quality modeling does not show substantial overall nationwide impacts on the
annual total sulfur deposition occurring across the U.S. as a result of the heavy-duty standards
required by this final action. Figure 111-20 shows the impacts of the heavy-duty standards on
sulfur deposition are minimal.
Percent Change in Annual Sulfur Deposition -
2030cs^MghgrcH minus 2030cs_Mghg rttf
Difference in Annual Sutfur Deposition -
2030cs_Mghg ctl minus 2030cs_ Mghg^rvt
Figure 111-20. Changes in Annual Total Sulfur Deposition Between the Reference Case and
the Control Case in 2030: Percent Changes (left) and Absolute Changes in ng/ni3 (right)
21

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F. Impacts of HDGHG 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. The impacts of this action were
examined in terms of the projected improvements in visibility on the 20 percent worst visibility
days at Class I areas. We quantified visibility impacts at the Class I areas which have complete
IMPROVE ambient data for 2005 or are represented by IMPROVE monitors with complete data.
Sites were used in this analysis if they had at least 3 years of complete data for the 2003-2007
period20.
Visibility for the 2030 reference case and 2030 control case was calculated using the
regional haze methodology outlined in section 6 of the photochemical modeling guidance, which
applies modeling results in a relative sense, using base year ambient data. The PM2.5 and
regional haze modeling guidance recommends the calculation of future year changes in visibility
in a similar manner to the calculation of changes in PM2.5 design values. The regional haze
methodology for calculating future year visibility impairment is included in MATS
(http://www.epa.gov/scram001/modelingapps mats.htm)
In calculating visibility impairment, the extinction coefficient values21 are made up of
individual component species (sulfate, nitrate, organics, etc). The predicted change in visibility
(on the 20 percent worst days) is calculated as the modeled percent change in the mass for each
of the PM2.5 species (on the 20% worst observed days) multiplied by the observed
concentrations. The future mass is converted to extinction and then daily species extinction
coefficients are summed to get a daily total extinction value (including Rayleigh scattering). The
daily extinction coefficients are converted to deciviews and averaged across all 20 percent worst
days. In this way, we calculate an average change in deciviews from the base case to a future
case at each IMPROVE site. Subtracting the 2030 reference case from the corresponding 2030
reference case deciview values gives an estimate of the visibility benefits in Class I areas that are
expected to occur from the HD GHG rule.
The following options were chosen in MATS for calculating the future year visibility
values for the rule:
New IMPROVE algorithm
Use model grid cells at (IMPROVE) monitor
Temporal adjustment at monitor- 3x3 for 12km grid, (lxl for 36km grid)
Start monitor year- 2003
End monitor year- 2007
Base model year 2005
Minimum years required for a valid monitor- 3
The "base model year" was chosen as 2005 because it is the base case meteorological
year for the final HDGHG Rule modeling. The start and end years were chosen as 2003 and
20	Since the base case modeling used meteorology for 2005, one of the complete years must be 2005.
21	Extinction coefficient is in units of inverse megameters (Mm1). It is a measure of how much light is absorbed or
scattered as it passes through a medium. Light extinction is commonly used as a measure of visibility impairment in
the regional haze program.
22

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2007 because that is the 5 year period which is centered on the base model year of 2005. These
choices are consistent with using a 5 year base period for regional haze calculations.
The results show that all the modeled areas will continue to have annual average
deciview levels above background in 2030.22 The results also indicate that the majority of the
modeled mandatory class I federal areas will see very little change in their visibility. Some
mandatory class I federal areas will see improvements in visibility due to the heavy-duty
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 0.01 deciviews, or 0.06%, in 2030. The greatest improvement in visibility will be
seen at Craters of the Moon (New Mexico) and the Hells Canyon Wilderness (Oregon). Craters
of the Moon will see a 0.46% improvement (0.06 DV) and the Hells Canyon Wilderness will see
a 0.40%) improvement (0.07 DV) in 2030 due to the heavy-duty standards. The following four
areas will see a degradation of 0.01 DV in 2030 as a result of the heavy-duty standards:
Chiricahua (New Mexico), 0.08%> degradation; San Gabriel Wilderness (California), 0.06%>
degradation; San Jacinto Wilderness (California), 0.05% degradation; and Roosevelt Campobello
International Park (Maine), 0.05% degradation. Section 8.2.3.5 of the HD GHG final rule RIA
contains more details on the visibility portion of the air quality modeling. Table III-l contains
the full visibility results for the 20% worst days 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
STATE
2005
BASELINE
VISIBILITY
2030
2030
NATURAL
(20% WORST DAYS)
BASELINE
HD GHG
BACKGROUND
SIPSEY WILDERNESS
AL
29.62
21.78
21.76
11.39
CANEY CREEK WILDERNESS
AR
26.78
20.91
20.88
11.33
UPPER BUFFALO WILDERNESS
AR
27.09
21.33
21.30
11.28
CHIRICAHUA NM
AZ
13.33
12.84
12.85
6.92
CHIRICAHUA WILDERNESS
AZ
13.33
12.86
12.86
6.91
GALIURO WILDERNESS
AZ
13.33
12.72
12.71
6.88
GRAND CANYON NP
AZ
11.85
11.04
11.04
6.95
MAZATZAL WILDERNESS
AZ
13.80
12.55
12.53
6.91
MOUNT BALDY WILDERNESS
AZ
11.27
10.77
10.77
6.95
PETRIFIED FOREST NP
AZ
13.73
12.93
12.93
6.97
PINE MOUNTAIN WILDERNESS
AZ
13.80
12.53
12.52
6.92
SAGUARO NM
AZ
14.53
13.67
13.67
6.84
SIERRA ANCHA WILDERNESS
AZ
14.37
13.33
13.32
6.92
SUPERSTITION WILDERNESS
AZ
14.01
12.83
12.81
6.88
SYCAMORE CANYON WILDERNESS
AZ
15.34
14.60
14.59
6.96
22 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.
23

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AGUATIBIA WILDERNESS
CA
23.09
19.37
19.37
7.17
ANSEL ADAMS WILDERNESS (MINARETS)
CA
14.90
14.10
14.10
7.12
CARIBOU WILDERNESS
CA
14.19
13.30
13.29
7.29
CUCAMONGA WILDERNESS
CA
19.35
16.64
16.64
7.17
DESOLATION WILDERNESS
CA
12.52
11.90
11.90
7.13
EMIGRANT WILDERNESS
CA
17.37
16.60
16.60
7.14
HOOVER WILDERNESS
CA
11.92
11.38
11.37
7.12
JOHN MUIR WILDERNESS
CA
14.90
14.00
14.00
7.14
JOSHUA TREE NM
CA
19.40
17.06
17.04
7.08
KAISER WILDERNESS
CA
14.90
13.78
13.78
7.13
KINGS CANYON NP
CA
23.41
22.03
22.02
7.13
LASSEN VOLCANIC NP
CA
14.19
13.29
13.29
7.31
LAVA BEDS NM
CA
14.77
13.78
13.78
7.49
MOKELUMNE WILDERNESS
CA
12.52
11.88
11.88
7.14
PINNACLES NM
CA
18.22
15.93
15.93
7.34
POINT REYES NS
CA
22.89
21.49
21.49
7.39
REDWOOD NP
CA
18.66
17.81
17.79
7.81
SAN GABRIEL WILDERNESS
CA
19.35
16.60
16.61
7.17
SAN GORGONIO WILDERNESS
CA
21.80
19.59
19.58
7.10
SAN JACINTO WILDERNESS
CA
21.80
18.43
18.44
7.12
SAN RAFAEL WILDERNESS
CA
19.04
17.11
17.11
7.28
SEQUOIA NP
CA
23.41
21.55
21.55
7.13
SOUTH WARNER WILDERNESS
CA
14.77
14.00
14.00
7.32
THOUSAND LAKES WILDERNESS
CA
14.19
13.27
13.27
7.32
VENTANA WILDERNESS
CA
18.22
16.73
16.72
7.32
YOSEMITE NP
CA
17.37
16.61
16.61
7.14
BLACK CANYON OF THE GUNNISON NM
CO
10.18
9.48
9.48
7.06
EAGLES NEST WILDERNESS
CO
9.38
8.76
8.76
7.08
FLATTOPS WILDERNESS
CO
9.38
8.96
8.95
7.07
GREAT SAND DUNES NM
CO
12.49
11.98
11.98
7.10
LA GARITA WILDERNESS
CO
10.18
9.73
9.72
7.06
MAROON BELLS-SNOWMASS WILDERNESS
CO
9.38
8.93
8.93
7.07
MESA VERDE NP
CO
12.78
12.18
12.18
7.09
MOUNT ZIRKEL WILDERNESS
CO
10.19
9.74
9.74
7.08
RAWAH WILDERNESS
CO
10.19
9.72
9.71
7.08
ROCKY MOUNTAIN NP
CO
13.54
12.99
12.98
7.05
WEMINUCHE WILDERNESS
CO
10.18
9.70
9.70
7.06
WEST ELK WILDERNESS
CO
9.38
8.89
8.89
7.07
EVERGLADES NP
FL
22.48
19.02
19.02
11.15
OKEFENOKEE
GA
27.24
21.77
21.75
11.45
WOLF ISLAND
GA
27.24
21.39
21.38
11.42
CRATERS OF THE MOON NM
ID
14.19
13.18
13.12
7.13
SAWTOOTH WILDERNESS
ID
14.33
14.13
14.13
7.15
MAMMOTH CAVE NP
KY
31.76
23.02
22.99
11.53
24

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ACADIA NP
ME
23.19
19.42
19.42
11.45
MOOSEHORN
ME
21.94
18.79
18.79
11.36
ROOSEVELT CAMPOBELLO INTERNATIONAL
PARK
ME
21.94
18.78
18.79
11.36
ISLE ROYALE NP
Ml
21.33
18.74
18.72
11.22
SENEY
Ml
24.71
21.00
20.96
11.37
VOYAGEURS NP
MN
19.82
17.22
17.20
11.09
HERCULES-GLADES WILDERNESS
MO
27.15
22.25
22.22
11.27
ANACONDA-PINTLER WILDERNESS
MT
13.91
13.59
13.58
7.28
BOB MARSHALL WILDERNESS
MT
14.54
14.16
14.16
7.36
CABINET MOUNTAINS WILDERNESS
MT
14.15
13.61
13.61
7.43
GATES OF THE MOUNTAINS WILDERNESS
MT
11.67
11.31
11.31
7.22
GLACIER NP
MT
19.13
18.29
18.29
7.56
MEDICINE LAKE
MT
17.78
17.09
17.08
7.30
MISSION MOUNTAINS WILDERNESS
MT
14.54
14.04
14.04
7.39
RED ROCK LAKES
MT
10.94
10.50
10.49
7.14
SCAPEGOAT WILDERNESS
MT
14.54
14.13
14.13
7.29
SELWAY-BITTERROOT WILDERNESS
MT
13.91
13.64
13.64
7.32
UL BEND
MT
14.92
14.54
14.54
7.18
LINVILLE GORGE WILDERNESS
NC
29.40
21.21
21.20
11.43
SHINING ROCK WILDERNESS
NC
28.72
21.03
21.01
11.45
LOSTWOOD
ND
19.50
18.14
18.13
7.33
THEODORE ROOSEVELT NP
ND
17.69
16.35
16.34
7.31
GREAT GULF WILDERNESS
NH
22.13
17.78
17.78
11.31
PRESIDENTIAL RANGE-DRY RIVER
WILDERNESS
NH
22.13
17.74
17.74
11.33
BRIGANTINE
NJ
29.28
22.53
22.52
11.28
BANDELIER NM
NM
11.87
10.89
10.88
7.02
BOSQUE DEL APACHE
NM
13.89
12.75
12.73
6.97
CARLSBAD CAVERNS NP
NM
16.98
15.35
15.34
7.02
GILA WILDERNESS
NM
13.32
12.78
12.78
6.95
PECOS WILDERNESS
NM
10.10
9.55
9.55
7.04
SALT CREEK
NM
18.20
16.71
16.70
6.99
SAN PEDRO PARKS WILDERNESS
NM
10.39
9.80
9.79
7.03
WHEELER PEAK WILDERNESS
NM
10.10
9.36
9.35
7.07
WHITE MOUNTAIN WILDERNESS
NM
13.52
12.61
12.61
6.98
JARBIDGE WILDERNESS
NV
12.13
11.86
11.86
7.10
WICHITA MOUNTAINS
OK
23.79
19.42
19.37
11.07
CRATER LAKE NP
OR
14.04
13.41
13.41
7.71
DIAMOND PEAK WILDERNESS
OR
14.04
13.34
13.33
7.77
EAGLE CAP WILDERNESS
OR
18.25
17.31
17.28
7.34
GEARHART MOUNTAIN WILDERNESS
OR
14.04
13.53
13.53
7.46
HELLS CANYON WILDERNESS
OR
18.73
17.40
17.33
7.32
KALMIOPSIS WILDERNESS
OR
16.31
15.52
15.51
7.71
MOUNT HOOD WILDERNESS
OR
14.79
13.53
13.50
7.77
25

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MOUNTJEFFERSON WILDERNESS
OR
15.93
15.19
15.18
7.81
MOUNT WASHINGTON WILDERNESS
OR
15.93
15.19
15.18
7.89
MOUNTAIN LAKES WILDERNESS
OR
14.04
13.35
13.34
7.57
STRAWBERRY MOUNTAIN WILDERNESS
OR
18.25
17.34
17.30
7.49
THREE SISTERS WILDERNESS
OR
15.93
15.25
15.24
7.87
CAPE ROMAIN
SC
27.14
20.67
20.66
11.36
BADLANDS NP
SD
16.73
15.40
15.40
7.30
WIND CAVE NP
SD
15.96
14.76
14.75
7.24
GREAT SMOKY MOUNTAINS NP
TN
30.43
22.57
22.54
11.44
JOYCE-KILMER-SLICKROCK WILDERNESS
TN
30.43
22.29
22.26
11.45
BIG BEND NP
TX
17.39
15.75
15.74
6.93
GUADALUPE MOUNTAINS NP
TX
16.98
15.30
15.29
7.03
ARCHES NP
UT
11.04
10.43
10.42
6.99
BRYCE CANYON NP
UT
11.73
11.18
11.18
6.99
CANYONLANDS NP
UT
11.04
10.53
10.51
7.01
CAPITOL REEF NP
UT
10.63
10.27
10.27
7.03
JAMES RIVER FACE WILDERNESS
VA
29.32
21.02
21.00
11.24
SHENANDOAH NP
VA
29.66
21.27
21.27
11.25
LYE BROOK WILDERNESS
VT
24.17
18.05
18.04
11.25
ALPINE LAKE WILDERNESS
WA
17.35
15.65
15.62
7.86
GLACIER PEAK WILDERNESS
WA
13.78
12.72
12.72
7.80
GOAT ROCKS WILDERNESS
WA
12.88
11.73
11.72
7.82
MOUNT ADAMS WILDERNESS
WA
12.88
11.78
11.77
7.78
MOUNT RAINIER NP
WA
17.56
16.18
16.17
7.90
NORTH CASCADES NP
WA
13.78
12.71
12.70
7.78
OLYMPIC NP
WA
16.14
14.96
14.95
7.88
PASAYTEN WILDERNESS
WA
15.39
14.51
14.51
7.77
DOLLY SODS WILDERNESS
WV
29.73
20.82
20.81
11.32
OTTER CREEK WILDERNESS
WV
29.73
20.93
20.92
11.33
BRIDGER WILDERNESS
WY
10.93
10.60
10.60
7.08
FITZPATRICK WILDERNESS
WY
10.93
10.60
10.60
7.09
GRAND TETON NP
WY
10.94
10.45
10.44
7.09
NORTH ABSAROKA WILDERNESS
WY
11.12
10.81
10.81
7.09
TETON WILDERNESS
WY
10.94
10.55
10.54
7.09
WASHAKIE WILDERNESS
WY
11.12
10.82
10.82
7.09
YELLOWSTONE NP
WY
10.94
10.47
10.46
7.12
26

-------
Air Quality Modeling
Technical Support Document: Heavy-Duty Vehicle
Greenhouse Gas Emission Standards Final Rule
Appendix A
Model Performance Evaluation for the 2005-Based
Air Quality Modeling Platform
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC 27711
July 2011

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A.l. Introduction
An operational model performance evaluation for ozone, PM2.5 and its related speciated
components, specific air toxics (i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and
acrolein), as well as nitrate and sulfate deposition 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.
Included in this evaluation are statistical measures 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, PM2.5, air toxic
observations and nitrate and sulfate deposition 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.
Model performance statistics were calculated for several spatial scales and temporal
periods. 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 statistics for each site and subregion were calculated by season (e.g., "winter" is
defined as December, January, and February). For 8-hour daily maximum ozone, we also
calculated performance statistics by subregion for the May through September ozone season3. In
addition to the performance statistics, we prepared several graphical presentations of model
performance. These graphical presentations include:
(1)	regional maps which show the normalized mean bias and error calculated for each
season at individual monitoring sites, and
(2)	bar and whisker plots which show the distribution of the predicted and observed data
by month by subregion.
A. 1.1 Monitoring Networks
The model evaluation for ozone was based upon comparisons of model predicted 8-hour daily
maximum concentrations to the corresponding ambient measurements for 2005 at monitoring
sites in the EPA Air Quality System (AQS). The observed ozone data were measured and
reported on an hourly basis. The PM2.5 evaluation focuses on concentrations of PM2 5 total mass
and its components including sulfate (S04), nitrate (N03), total nitrate (TN03=N03+HN03),
ammonium (NH4), elemental carbon (EC), and organic carbon (OC) as well as wet deposition for
nitrate and sulfate. The PM2.5 performance statistics were calculated for each season and for the
entire year, as a whole. PM2.5 ambient measurements for 2005 were obtained from the following
1	See section II.B. of the main document (Figure II-l) for the description and map of the CMAQ modeling domains.
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, NE, OK, and TX; West is AK, CA, OR, WA, AZ, NM, CO, UT, WY,
SD, ND, MT, ID, and NV.
3	In calculating the ozone season statistics we limited the data to those observed and predicted pairs with
observations that exceeded 60 ppb in order to focus on concentrations at the upper portion of the distribution of
values.
A-2

-------
networks: Chemical Speciation Network (CSN), Interagency Monitoring of PROtected Visual
Environments (IMPROVE), Clean Air Status and Trends Network (CASTNet), and National
Acid Deposition Program/National Trends (NADP/NTN). NADP/NTN collects and reports wet
deposition measurements as weekly average data. 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. The CSN and
IMPROVE networks provide 24-hour average concentrations on a 1 in every 3 day, or 1 in every
6 day sampling cycle. The PM2.5 species data at CASTNet sites are weekly integrated samples.
In this analysis we use the term "urban sites" to refer to CSN sites; "suburban/rural sites" to refer
to CASTNet sites; and "rural sites" to refer to IMPROVE sites.
Table A-l. PM2.5 monitoring networks and pollutants species included in the CMAQ
performance evaluation.
Ambient
Monitoring
Networks
Particulate
Species
Wet
Deposition
Species
PM2.5
Mass
S04
N03
TN03a
EC
OC
nh4
S04
N03
IMPROVE
X
X
X

X
X



CASTNet

X

X


X


STN
X
X
X

X
X
X


NADP







X
X
a TNO3 = (N03 + HNO3)
The air toxics evaluation focuses on specific species relevant to the Heavy-Duty
Greenhouse Gas final rule (hereafter referred to as HDGHG), i.e., formaldehyde, acetaldehyde,
benzene, 1,3-butadiene, and acrolein. Similar to the PM2.5 evaluation, the air toxics performance
statistics were calculated for each season 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).
A.1.2 Model Performance Statistics
The Atmospheric Model Evaluation Tool (AMET) was used to conduct the evaluation
described in this document.4 There are various statistical metrics available and used by the
science community for model performance evaluation. For a robust evaluation, the principal
4 Appel, K.W., Gilliam, R.C., Davis, N., Zubrow, A., and Howard, S.C.: Overview of the Atmospheric Model
Evaluation Tool (AMET) vl.l for evaluating meteorological and air quality models, Environ. Modell. Softw.,26, 4,
434-443, 2011. (http://www.cmascenter.org/)
A-3

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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:
i(p-o)
NMB = 		*100
n
I(O)
1
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:
±\p-0i
NME =
1(0)
= 100
Fractional bias is defined as:
f n	\
l(p-o)
FB =
1
I
(/• i >n
:100, where P = predicted and O = observed concentrations.
V , v 2
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:
f n	\
t\P-o\
FE =
1
n
1
V 1
(P+O)
= 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
A-4

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toxic model applications.5'6'7'8'9'10'1112'13'14'15 These other modeling studies represent a wide range
of modeling analyses which cover various models, model configurations, domains, years and/or
episodes, chemical mechanisms, and aerosol modules. Overall, the ozone, PM2.5, air toxics
concentrations and nitrate and sulfate deposition model performance results for the 2005 CMAQ
simulations performed for HDGHG are within the range or close to that found in other recent
applications. The model performance results, as described in this report, 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 HDGHG Final
Rule.
5	Appel, K.W., Bhave, P.V., Gilliland, A.B., Sarwar, G., and Roselle, S.J.: evaluation of the community multiscale
air quality (CMAQ) model version 4.5: sensitivities impacting model performance: Part II - particulate matter.
Atmospheric Environment 42, 6057-6066, 2008.
6	Appel, K.W., Gilliland, A.B., Sarwar, G., Gilliam, R.C., 2007. Evaluation of the community multiscale air quality
(CMAQ) model version 4.5: sensitivities impacting model performance: Part I - ozone. Atmospheric Environment
41, 9603-9615.
7	Appel, K.W., Roselle, S.J., Gilliam, R.C., and Pleim, J.E.,: Sensitivity of the Community Multiscale Air Quality
(CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers. Geoscientific
Model Development, 3, 169-188, 2010.
8	Foley, K.M., Roselle, S.J., Appel, K.W., Bhave, P.V., Pleim, J.E., Otte, T.L., Mathur, R., Sarwar, G., Young, J.O.,
Gilliam, R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., and Bash, J.O.,: Incremental testing of the Community
multiscale air quality (CMAQ) modeling system version 4.7. Geoscientific Model Development, 3, 205-226, 2010.
9	Hogrefe, G., Civeroio, K.L., Hao, W., Ku, J-Y., Zalewsky, E.E., and Sistla, G., Rethinking the Assessment of
Photochemical Modeling Systems in Air Quality Planning Applications. Air & Waste Management Assoc.,
58:1086-1099, 2008.
10	Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform:
Air Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.
(http://www.cmascenter.org/conference/2008/agenda.cfm).
11	Strum, M., Wesson, K., Phillips, S.,Pollack, A., Shepard, S., Jimenez, M., M., Beidler, A., Wilson, M., Ensley, D.,
Cook, R., Michaels H., and Brzezinski, D. Link Based vs NEI Onroad Emissions Impact on Air Quality Model
Predictions. 17th Annual International Emission Inventory Conference, Portland, Oregon, June 2-5, 2008.
(http://www.epa.gov/ttn/chief/conference/eil7/sessionl 1/strum pres.pdf)
12	Tesche, T.W., Morris, R., Tonnesen, G., McNally, D., Boylan, J., Brewer, P., 2006. CMAQ/CAMx annual 2002
performance evaluation over the eastern United States. Atmospheric Environment 40, 4906-4919.
13	U.S. Environmental Protection Agency; Technical Support Document for the Final Clean Air Interstate Rule: Air
Quality Modeling; Office of Air Quality Planning and Standards; RTP, NC; March 2005 (CAIR Docket OAR-2005-
0053-2149).
14	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/n:gs/nonroad/marinc/ci/420r09(K)7.pdl~)
15	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.
(http://www.epa.gov/oms/renewableluels/420rl0006.pdf)
A-5

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A.2. Evaluation for 8-hour Daily Maximum Ozone
The 8-hour ozone model performance bias and error statistics for each subregion and
each season are provided in Table A-2. The distributions of observed and predicted 8-hour
ozone by month in the 5-month ozone season for each subregion are shown in Figures A-l
through A-5. Spatial plots of the normalized mean bias and error for individual monitors are
shown in Figures A-6 through A-7. The statistics shown in these two figures were calculated
over the ozone season using data pairs on days with observed 8-hour ozone of > 60 ppb.
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
EUS 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 A-2. Daily maximum 8-hour ozone performance statistics by subregion, by season.
Subregion

No. of
Obs
NMB (%)
NME (%)
FB (%)
FE (%)
Central
States
Winter
8304
8.5
24.6
8.0
27.3
Spring
12811
0.4
13.9
1.6
14.8
Summer
13414
3.9
19.17.6
7.0
19.2
Fall
10166
2.3
19.0
4.6
20.5







Midwest
Winter
1819
-5.8
23.2
-8.1
28.2
Spring
10981
2.2
14.5
3.7
15.3
Summer
15738
3.1
13.6
4.2
14.1
Fall
9136
3.2
16.4
5.8
18.9







Southeast
Winter
5150
8.2
17.4
7.9
18.5
Spring
17823
1.0
11.9
2.6
12.6
Summer
19423
14.6
22.6
16.1
23.8
Fall
11978
11.0
18.0
14.0
20.6







Northeast
Winter
3497
-9.7
22.7
-12.5
29.2
Spring
11667
1.8
14.7
2.5
15.8
Summer
15489
8.6
17.7
10.5
18.6
Fall
9438
4.3
17.9
7.3
21.3







West
Winter
18259
27.3
33.1
27.5
33.9
Spring
25665
2.3
14.1
2.9
14.6
Summer
28156
5.5
17.0
6.0
17.3
A-6

-------
Subregion

No. of
Obs
NMB (%)
NME (%)
FB (%)
FE (%)

Fall
19492
5.7
18.6
7.5
19.9
2005cs_hdghg_05b_12EUS1 03_8hrmax for AQS_Dally for 20050501 to 20050931
AQS Daily
CMAQ
MANE-VU
5231
	1	
200507
Months
Figure A-l. Distribution of observed and predicted 8-hour daily maximum ozone by
month for the period May through September for the Northeast subregion. [symbol =
median; top/bottom of box = 75lh/25lh percentiles; top/bottom line = max/min values]
A-7

-------
2005cs_hdghg_05b_12EUS1 03_8hrmax for AQS_ Dally lor 20050501 to 20050931
AOS Daily
*--A CMAQ
= VISTAS

...J**—
2005 07
Months
Figure A-2. Distribution of observed and predicted 8-hour daily maximum ozone
month for the period May through September 2005 for the Southeast subregion.
2005cs hdghg 05b_12EUS1 03 8hrmax for AQS Dally for 20050501 to 20050931
IS—~ AQS Daily
Q - -A CMAQ
= LADCO
0.15 -
0.10 -
0.05 -
0.00 -
2005 05	2005 06	2005 07	2005 08	2005 09
Months
Figure A-3. Distribution of observed and predicted 8-hour daily maximum ozone
month for the period May through September for the Midwest subregion.

-------
2005cs_hdghg_05b_12EUS1 03_8hrmax for AQS_Dally for 20050501 to 20050931
AOS Daily
B--A CMAQ
=» CENRAP
4800
	1	
¦1408
	1	
4375
	1	
200507
Months
Figure A-4. Distribution of observed and predicted 8-hour daily maximum ozone by
month for the period May through September for the Central states subregion.
2005cs hdghg 05b 12WUS1 03_8hrmax for AOS Daily for 20050501 to 20050931
0.15 -
0.10 -
CO
0.05 -
0.00 -
IS—~ AQS Daily
EJ - -A CMAQ
= WRAP
200507
Months
Figure A-5. Distribution of observed and predicted 8-hour daily maximum ozone by
month for the period May through September for the Western states subregion.
A-9

-------
03 8hrmax NMB (%) for run 2005cs hdghg 05b_12EUS1 for 20050501 to 20050931
coverage hrr« <¦ 75%
> 100

< -100
\_/i nwLL^ttuo	i_/ciuy
Figure A-6a. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60
ppb over the period May-September 2005 at monitoring sites in Eastern modeling domain.
03 8hrmax NME (%) for run 2005cs hdghg 05b 12EUS1 tor 20050501 lo 20050931
CIRCLE=AQS_Daily;
Figure A-6b. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60
ppb over the period May-September 2005 at monitoring sites in Eastern modeling domain.
A-10

-------
03_8hrmax NMB (%) for run 2005cs_hdghg_05b_12WUS1 for 20050501 to 20050931
CI RCLE=AQS_Daily;
-%
coverage limit - 75%
Figure A-7a. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60
ppb over the period May-September 2005 at monitoring sites in Western modeling domain.
03_8hrmax NME (%) for run 2005cs_hdghg_05b_12WUS1 for 20050501 to 20050931
*
unrts - %
coverage HmM • 75%
CI RCLE=AQS_Daily;
Figure A-7b. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60
ppb over the period May-September 2005 at monitoring sites in Western modeling domain.
A-ll

-------
A.3. Evaluation of PM2.5 Component Species
The evaluation of 2005 model predictions for PM2.5 covers the performance for the
individual PM2.5 component species (i.e., sulfate, nitrate, organic carbon, elemental carbon, and
ammonium). Performance results are provided for each PM2.5 species. As indicated above, for
each species we present tabular summaries of bias and error statistics by subregion for each
season. These statistics are based on the set of observed-predicted pairs of data for the particular
quarter at monitoring sites within the subregion. Separate statistics are provided for each
monitoring network, as applicable for the particular species measured. For sulfate and nitrate we
also provide a more refined temporal and spatial analysis of model performance that includes (1)
graphics of the distribution of 24-hour average concentrations and predictions by month for each
subregion, and (2) spatial maps which show the normalized mean bias and error by site,
aggregated by season.
A.3.1. Evaluation for Sulfate
The model performance bias and error statistics for sulfate for each subregion and each
season are provided in Table A-3. The distributions of observed and predicted suflate by month
for each subregion are shown in Figures A-8 through A-12. Spatial plots of the normalized mean
bias and error by season for individual monitors are shown in Figures A-3 through A-20. As
seen in Table A-3, CMAQ generally under-predicts sulfate in the five U.S. subregions
throughout the entire year. 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. Sulfate predictions during the summer season are moderately under-predicted in the
East and West across the available monitoring data (NMB values range from -12% to -35%.)
Table A-3. Sulfate performance statistics by subregion, by season for the 2005 CMAQ
model simulation.
Subregion
Network
Season
No. of
Obs.
NMB
(%)
NME
(%)
FB (%)
FE (%)


Winter
771
-15.7
38.4
-14.1
41.7

CSN
Spring
875
-14.9
32.2
-11.0
33.8

Summer
851
-30.0
42.2
-37.0
54.2


Fall
587
-9.6
34.8
-3.3
36.6
CENRAP

Winter
608
-19.2
40.1
14.0
43.5

IMPROVE
Spring
722
-17.4
31.3
-11.7
32.3

Summer
688
-27.7
39.1
-25.3
46.1


Fall
622
-15.5
31.3
-7.3
36.9

CASTNet
Winter
72
-33.1
34.6
-35.3
37.8
A-12

-------
Subregion
Network
Season
Spring
No. of
Obs.
77
NMB
(%)
-24.4
NME
(%)
FB (%)
FE (%)
27.7
-23.4
29.5
Summer
72
-33.0
36.8
-38.0
45.8
Fall
75
-20.9
23.5
-19.3
26.2

MWRPO
CSN
Winter
598
1.2
38.8
-4.4
38.7
Spring
637
19.6
42.9
15.6
36.9
Summer
621
-10.3
28.7
-0.3
30.8
Fall
639
-11.8
26.5
-3.4
27.3
IMPROVE
Winter
143
3.7
36.0
0.0
34.5
Spring
171
5.0
35.5
7.2
35.3
Summer
182
-18.4
30.0
-5.6
36.1
Fall
126
-17.8
26.9
-6.7
31.6
CASTNet
Winter
142
-13.6
21.8
-16.1
26.4
Spring
155
-5.6
22.4
-4.0
21.7
Summer
161
-16.1
21.7
-13.6
23.6
Fall
157
-19.6
22.3
-15.5
21.4








VISTAS
CSN
Winter
949
-5.1
37.1
-4.5
37.2
Spring
973
-4.5
28.0
-5.2
29.7
Summer
926
-17.4
32.0
-18.8
38.0
Fall
975
-9.9
27.0
-5.7
29.0
IMPROVE
Winter
469
-1.6
36.8
0.6
37.5
Spring
525
-6.4
29.1
-5.8
31.7
Summer
500
-24.0
35.6
-30.7
47.0
Fall
496
-11.6
29.1
-6.1
34.4
CASTNet
Winter
264
-18.5
22.8
-17.8
23.9
Spring
292
-13.2
21.2
-14.5
22.9
Summer
268
-21.1
24.8
-28.3
32.7
Fall
273
-18.3
21.1
-19.1
23.2

MANEVU
CSN
Winter
828
-9.1
35.1
-13.9
34.8
Spring
894
8.4
37.4
4.4
35.0
Summer
874
-8.6
27.1
-2.9
30.9
Fall
902
-9.0
28.8
0.1
30.9
A-13

-------
Subregion
Network
Season
No. of
Obs.
NMB
(%)
NME
(%)
FB (%)
FE (%)


Winter
561
-7.1
31.2
-11.0
33.3

IMPROVE
Spring
689
7.2
38.0
3.7
38.2

Summer
649
-12.9
32.3
-4.4
37.6


Fall
591
-6.8
32.2
7.7
35.4


Winter
193
-14.8
22.4
-19.0
25.8

CASTNet
Spring
206
-0.2
25.3
-1.2
26.5

Summer
192
-15.5
20.4
-12.6
22.0


Fall
195
-12.2
18.4
-7.3
18.0



Winter
830
-5.2
57.7
1.8
54.4

CSN
Spring
867
-3.8
37.0
0.0
36.2

Summer
853
-32.1
43.6
-23.3
42.5


Fall
900
-7.6
47.2
0.4
43.4


Winter
2373
22.3
58.4
33.8
56.6
WRAP
IMPROVE
Spring
2650
-3.6
33.5
3.5
35.1
Summer
2307
-24.8
41.1
-16.5
42.8


Fall
2365
-0.4
40.0
11.2
41.2


Winter
250
6.5
35.9
17.9
37.5

CASTNet
Spring
273
-18.4
27.1
-17.0
27.6

Summer
281
-35.1
38.7
-36.0
41.5


Fall
268
-10.7
23.5
-5.0
24.3
A-14

-------
2005cs_hdghg_05b_12EUS1 S04 for IMPROVE for 20050101 to 20051231
IMPROVE
h--A CMAQ
IPO = MANE-VU
CO
E
o>
^3
8


o ~ 201 176 219 225 sts 2l7 215 219 194 T*5 198 184
	1	1	1	1	1	1	1	1	1	1	1	T~
2005.01 2005 03 2005_05 2005_07 2005 09 2005_11
Months
Figure A-8a. Distribution of observed and predicted 24-hour average sulfate by month for
2005 at IMPROVE sites in the Northeast subregion. [symbol = median; top/bottom of box
= 75' 725" percentiles; top/bottom line = max/min values]
2005cs_hdghg_05b_12EUS1 S04 for CSN for 20050101 to 20051231
I—~ CSN
I- A CMAQ
*PO = MANE-VU
Q 15-
C/3
10 -

S|4WF!
Z$T	284 3tT 2fe 295 233 as 5T5 287 255
	1	1	1	1	1	1	1	1	1	1	1	r
2005_01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-8b. Distribution of observed and predicted 24-hour average sulfate by month for
2005 at CSN sites in the Northeast subregion.
A-15

-------
2005cs_hdghg_05b_12EUS1 S04 for CASTNET for 20050101 to 20051231
CASTNET
-A CMAQ
*PO = MANE-VU
CO 9.
E 21
O)
3
S 15 -
CO
~i	1	1	1	1	r
2005_01 2005_03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-8c. Distribution of observed and predicted weekly average sulfate by month for
2005 at CASTNet sites in the Northeast subregion.
2005cs hdghg 05b_12EUS1 S04 for IMPROVE for 20050101 to 20051231
I—~ IMPROVE
& A CMAQ
3PO = VISTAS
O 15 ¦
Cfl
o "1 174 TsT 16& 174 183 T/B 167 (63 172 Ttg Tft 144
	1	1	1	1	1	1	1	1	1	1	1	r~
2005_01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-9a. Distribution of observed and predicted 24-hour average sulfate by month for
2005 at IMPROVE sites in the Southeast subregion.
A-16

-------
2005cs_hdghg_05b_12EUS1 S04 for CSN for 20050101 to 20051231
CSN
A CMAQ
IPO = VISTAS
321 333 3*5 302 36?
~i	1	1	1	1	1	1	1	r
2005_01 2005_03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-9b. Distribution of observed and predicted 24-hour average sulfate by month for
2005 at CSN sites in the Southeast subregion.
2005cs_hdghg_05b_12EUS1 S04 for CASTNET for 20050101 to 20051231
I—~ CASTNET
& A CMAQ
3PO = VISTAS
O 15 ¦
Cfl
o n 69 91 112 89 110 88 79 101 83 84 106 65
	1	1	1	1	1	1	1	1	1	1	1	r~
2005_01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-9c. Distribution of observed and predicted weekly average sulfate by month for
2005 at CAST Net sites in the Southeast subregion.
A-17

-------
2005cs_hdghg_05b_12EUS1 S04 for IMPROVE for 20050101 to 20051231
¦—a IMPROVE


©--A CMAQ
3PO = LAD CO

-j-
lit!inn
¦ I
-4- T
a] -
raklRiflBu^
Hi

51 50 63 60 48 "o5" 64
19
40 tT 43 42
200501 2005_03 2005_05 2005 07 2005_09 2005_11
Months
Figure A-lOa. Distribution of observed and predicted 24-hour average sulfate by month
for 2005 at IMPROVE sites in the Midwest subregion.
2005cs hdghg 05b_12EUS1 S04 for CSN for 20050101 to 20051231
CSN
--A CMAQ
3PO . LAD CO
3
H M jfj
199 211 2tT 215 207 255
202 230 201 191
	1	1	1	1	1	1	1	1	1	1	1	r
2005_01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-lOb. Distribution of observed and predicted 24-hour average sulfate by month
for 2005 at CSN sites in the Midwest subregion.
A-18

-------
2005cs_hdghg_05b_12EUS1 S04 for CASTNET for 20050101 to 20051231
¦—0 CASTNET
H--A CMAQ
?PO » LADCO



T

-7

t t -i-


ai«i|*r^'LJT x _
"*B" 47 57 49 62 49 63 49 61 58 36
200501 2005_03 2005_05 2005 07 2005_09 2005_11
Months
Figure A-lOc. Distribution of observed and predicted weekly average sulfate by month for
2005 at CASTNet sites in the Midwest subregion.
2005cs hdghg 05b 12EUS1 S04 lor IMPROVE lor 20050101 to 20051231
35 -
30 -
25 -
O)
3
10 -
5 -
¦—0 IMPROVE
&--A CMAQ
IPO = CENRAP
	1	1	1	1	1	1	1	1	1	1	1	T
2005 01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-lla. Distribution of observed and predicted 24-hour average sulfate by month
for 2005 at IMPROVE sites in the Central states subregion.
A-19

-------
2005cs hdghg 05b 12EUS1 S04 for CSN for 20050101 to 20051231
35 - ¦	~ CSN
IB--A CMAQ
*PO = CENRAP
30 -
25 -
	1	1	1	1	1	1	1	1	1	1	1	1	
2005 01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-llb. Distribution of observed and predicted 24-hour average sulfate by month
for 2005 at CSN sites in the Central states subregion.
2005cs hdghg 05b_12EUS1 S04 for CASTNET for 20050101 to 20051231
CASTNET
CMAQ
IPO = CENRAP
3
24 24 30 24 29 22 21
22 23 30 18
	1	1	1	1	1	1	1	1	1	1	1	r
2005_01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-llc. Distribution of observed and predicted weekly average sulfate by month for
2005 at CASTNet sites in the Central states subregion.
A-20

-------
2005cs_hdghg_05b_12WUS1 S04 for IMPROVE for 20050101 to 20051231
IMPROVE
-A CMAQ
*PO = WRAP
CO
E
o>
3
LiiLJ

53T BOB 745 756 742
bJui
	1	1	1	1	1	1	1	1	1	1	1	r
2005_01 2005_03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-12a. Distribution of observed and predicted 24-hour average sulfate by month
for 2005 at IMPROVE sites in the Western states subregion.
2005cs_hdghg_05b_12WUS1 S04 for CSN for 20050101 to 20051231
I—~ CSN
&--A CMAQ
3PO = WRAP _
O
4
0 ~"| 7*33	2B3 Z78 306 280 284 2B9 ^5 330 282 271
	1	1	1	1	1	1	1	1	1	1	1	1	
2005 01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-12b. Distribution of observed and predicted 24-hour average sulfate by month
for 2005 at CSN sites in the Western states subregion.
A-21

-------
2005cs_hdghg_05b_12WUS1 S04 for CASTNET for 20050101 to 20051231
10
"
¦—a CASTNET
E3---A CMAQ
\PO = WRAP





8
-






6







4







2
-
-T- T
-y
-y ;
{




rr-
i






H




"I"


, , ¦. ¦ '
¦-A-1



___
0

87 M IW IB
101
87 89 109 82
83
101 60
2005_01 2005_03 2005_05 2005 07 2005_09 2005_11
Months
Figure A-12c. Distribution of observed and predicted weekly average sulfate by month for
2005 at CASTNet sites in the Western states subregion.
A-22

-------
SQ4 NMB (%) for run 20D5cs hdghg 05b 12EUS1 for December to February 2005
units » %
coverage linrti! = 75%
V

CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-13a. Normalized Mean Bias (%) of sulfate during winter 2005 at monitoring sites
in Eastern modeling domain.
S04 NME {%) for run 2005cs hdghg 05b 12EUS1 for December to February 2005
< 00
1
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-13b. Normalized Mean Error (%) of sulfate during winter 2005 at monitoring
sites in Eastern modeling domain.
A-23

-------
S04 NMB (%) tor run 2005CS hdghg 05b12EUS1 for March to May 2005
units » %
coverage linrti! = 75%
* \


CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-14a. Normalized Mean Bias (%) of sulfate during spring 2005 at monitoring sites
in Eastern modeling domain.
SQ4 NME (%) for run 2005CS hdghg 05b 12EUS1 for March to May 2005
coverage Until » 75%
< 100

CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-14b. Normalized Mean Error (%) of sulfate during spring 2005 at monitoring
sites in Eastern modeling domain.
A-24

-------
-%
age limit = 75%
>100
80
60
40
20
0
-20
-40
-60
-80
<-100
Figure A-15a. Normalized Mean Bias (%) of sulfate during summer 2005 at monitoring
sites in Eastern modeling domain.
-%
age ifflM» 75%
>100
80
60
40
20
0
-20
-40
-60
-80
<-100
Figure A-15b. Normalized Mean Error (%) of sulfate during summer 2005 at monitoring
sites in Eastern modeling domain.
S04 NMB (%) for run 2005cs hdghg 05b 12EUS1 for June to August 2005
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
SQ4NMB {%) for run 2005CS hdghg 05b 12EUS1 for June to August 2005

:
• y	i

CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
A-2 5

-------
S04 NMB (%) (or run 2005cs hdghg 05b 12EUS1 for September to November 2005
units » %
coverage linrti! = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-16a. Normalized Mean Bias (%) of sulfate during fall 2005 at monitoring sites in
Eastern modeling domain.
SQ4 MME (%) for run 2005cs hdghg 05b 12EUS1 for September to November 2005
coverage Until » 75%
< 100

CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-16b. Normalized Mean Error (%) of sulfate during fall 2005 at monitoring sites
in Eastern modeling domain.
A-26

-------
S04 NMB (%) for run 2005CS_hdghg_05b_12WUS1 for December to February 2005
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-17a. Normalized Mean Bias (%) of sulfate during winter 2005 at monitoring sites
in Western modeling domain.
2005cs_hdghg 05b_12WUS1 for December to February 2005
-%
rage lirnil ¦ 75%
< 100
90
80
70
60
50
40
30
20
10
0
Figure F-17b. Normalized Mean Error (%) of sulfate during fall 2005 at monitoring sites
in Western modeling domain.
A-27

-------
S04 NMB (%) for run 2005cS hdghg 05b 12WUS1 for March to May 2005
CIRCLE=IMPROVE; TR!ANGLE=CSN; SQUARE=CASTNET;
Figure A-18a. Normalized Mean Bias (%) of sulfate during spring 2005 at monitoring sites
in Western modeling domain.
S04 NME (%) for run 2005CS_hdghg 05b_12WUS1 for March to May 2005
C I D/"M C IUDDry\/C. TDIAKiril C-^CM. CT\\ I A DC	/""A OTKICT •
' 11 i vli——iivii liuvL., i i uni mvjli_=woin , o^uni ii_=unu nii—i,
Figure A-18b. Normalized Mean Error (%) of sulfate during spring 2005 at monitoring
sites in Western modeling domain.
A-28

-------
S04 NMB (%) for run 2005CS hdghg 05b_12WUS1 for June to August 2005
CIRCLE=IMPROVE; TR!ANGLE=CSN; SQUARE=CASTNET;
Figure A-19a. Normalized Mean Bias (%) of sulfate during summer 2005 at monitoring
sites in Western modeling domain.
S04 NME (%) for run 2005cs_hdghg 05b 12WUS1 for June to August 2005
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-19b. Normalized Mean Error (%) of sulfate during summer 2005 at monitoring
sites in Western modeling domain.
A-29

-------
S04 NMB (%) for run 2005c$Jidghg 05b 12WUS1 for September to November 2005
riQn c_niiDDn\/c- tdiakipi c_row- cm iadc	r*aotwct-
Wll iULL-11VII I ivy V l_ , I I 1I/-V1NVJI_L_ = ^01,<, VJWUAI ll_=W/*\vJ I I N I— I ,
Figure A-20a. Normalized Mean Bias (%) of sulfate during fall 2005 at monitoring sites in
Western modeling domain.
S04 NME (%) for run 2005cs hdghg 05b 12WUS1 for September to November 2005
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-20b. Normalized Mean Error (%) of sulfate during fall 2005 at monitoring sites
in Western modeling domain.
A-30

-------
A.3.1. Evaluation for Nitrate
The model performance bias and error statistics for nitrate for each subregion and each
season are provided in Table A-4. This table includes statistics for particulate nitrate, as
measured at CSN and IMPROVE sites, and statistics for total nitrate, as measured at CASTNet
sites. The distributions of observed and predicted nitrate by month for each subregion are shown
in Figures A-21 through A-25. Spatial plots of the normalized mean bias and error by season for
individual monitors are shown in Figures A-26 through A-33. Overall, nitrate and total nitrate
performance is over-predicted in the EUS and under-predicted in the WUS for all of the seasonal
assessments except in the winter and summer 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 A-4. Nitrate performance statistics by subregion, by season for the 2005 CMAQ
model simulation.
Region
Network
Season
No. of
Obs.
NMB
(%)
NME
(%)
FB (%)
FE (%)
CENRAP
CSN
Winter
479
-4.7
49.0
-5.5
59.3
Spring
503
30.0
62.2
15.1
66.0
Summer
485
28.3
102
-41.5
95.5
Fall
460
107.0
133.0
19.3
89.0
IMPROVE
Winter
608
5.1
55.0
-6.5
70.7
Spring
722
49.2
78.7
-3.7
90.9
Summer
688
21.8
112.0
-56.1
111.0
Fall
622
164.0
193.0
14.5
107.0
CASTNet
Winter
72
27.0
38.9
26.6
36.7
Spring
77
14.7
34.3
7.7
31.3
Summer
72
-0.1
26.3
-6.0
27.6
Fall
75
53.0
60.2
36.0
44.0

MWRPO
CSN
Winter
598
-21.2
40.7
-21.7
49.0
Spring
637
63.2
83.3
40.4
65.5
Summer
621
43.4
98.2
-10.9
83.6
Fall
639
69.5
98.1
24.1
74.3
IMPROVE
Winter
143
-27.6
47.7
-29.9
72.9
Spring
171
54.5
87.7
-3.5
90.0
A-31

-------
Region
Network
Season
No. of
Obs.
NMB
(%)
NME
(%)
FB (%)
FE (%)
Summer
182
25.1
100.0
-41.4
99.7
Fall
126
108.0
141.0
0.2
102.0
CASTNet
Winter
142
-7.1
21.4
0.1
21.8
Spring
155
38.5
42.2
31.8
35.7
Summer
161
53.8
56.3
41.0
43.3
Fall
157
73.8
74.1
51.2
51.5








VISTAS
CSN
Winter
949
-25.8
60.7
-54.7
84.1
Spring
973
47.6
100.0
-7.0
91.2
Summer
926
-24.6
84.3
-80.0
113.0
Fall
975
78.2
137.0
-25.1
106.0
IMPROVE
Winter
469
-2.6
82.4
-58.7
98.9
Spring
525
59.6
116.0
-29.4
108.0
Summer
500
-14.2
112
-92.7
136.0
Fall
496
105.0
184.0
-46.7
125.0
CASTNet
Winter
264
24.3
35.8
20.8
35.2
Spring
292
31.8
45.0
21.9
39.6
Summer
268
28.9
47.3
17.0
42.7
Fall
273
73.9
82.0
45.9
59.0

MANEVU
CSN
Winter
829
-1.8
43.9
-1.5
49.8
Spring
894
43.2
77.4
32.6
68.5
Summer
874
-5.8
89.9
-58.4
101.0
Fall
902
75.9
109.0
-11.3
86.3
IMPROVE
Winter
561
41.9
77.3
32.7
75.9
Spring
689
73.8
113.0
31.3
93.2
Summer
649
11.2
115.0
-61.5
112.0
Fall
586
115.0
156.0
-11.3
86.3
CASTNet
Winter
193
23.4
30.7
31.2
35.4
A-32

-------
Region
Network
Season
No. of
Obs.
NMB
(%)
NME
(%)
FB (%)
FE (%)
Spring
206
48.9
51.3
37.4
42.4
Summer
192
53.6
61.3
33.4
49.5
Fall
195
85.2
87.7
54.0
60.5

West
CSN
Winter
831
-44.2
63.8
-60.4
87.2
Spring
859
-37.5
58.3
-68.8
89.2
Summer
846
-72.8
76.5
-133.0
137.0
Fall
896
-47.9
69.9
-66.9
95.7
IMPROVE
Winter
2374
-30.0
77.6
-84.2
121.0
Spring
2643
-38.7
76.6
-88.1
119.0
Summer
2305
-73.7
83.9
-144.0
152.0
Fall
2357
-31.9
82.0
-75.3
121.0
CASTNet
Winter
250
34.6
52.9
41.6
54.6
Spring
273
-1.9
32.7
6.2
32.4
Summer
281
-6.9
31.2
-5.8
33.0
Fall
268
15.9
40.6
28.2
46.6
A-33

-------
2005cs_hdghg_05b_12EUS1 N03 for IMPROVE for 20050101 to 20051231
IMPROVE
h--A CMAQ
IPO = MANE-VU
CO
E
CO
O
2 10 -
	1	1	1	1	1	1	1	1	1	1	1	1~
2005 01 2005_03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-21 a. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at IMPROVE sites in the Northeast subregion. [symbol = median; top/bottom of
box = 75,h/25f percentiles; top/bottom line = max/inin values]
2005cs_hdghg_05b_12EUS1 N03 for CSN for 20050101 to 20051231
CSN
-A CMAQ
^PO = MANE-VU
~i	1	1	1	1	1	1	1	1	1	1	r
2005_01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-21 b. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at CSN sites in the Northeast subregion.
A-34

-------
2005cs_hdghg_05b 12EUS1 TN03 for CASTNET for 20050101 to 20051231
I—~ CASTNET
I- A CMAQ
IPO = MANE-VU
CO 15 -
E
o>
Z3
CO
o
? 10 H
65 67 78
•	-A
1^1.Av*
77 59 54 81 61 54 80 47
	1	1	1	1	1	1	1	1	1	1	1	T~
2005_01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-21 c. Distribution of observed and predicted weekly average total nitrate by
month for 2005 at CASTNet sites in the Northeast subregion.
2005cs_hdghg_05b_12EUS1 N03 for IMPROVE for 20050101 to 20051231
I	B IMPROVE
I--A CMAQ
*PO = VISTAS
E
CO
O
2 10 -
f~i TTT TOJ ' r i" *$1* '"Wr " "if- ' - -Mi1 - "tV
	1	1	1	1	1	1	1	1	1	1	1	1	
2005_01 2005 03 2005_05 2005_07 2005JD9 2005 _11
Months
Figure A-22a. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at IMPROVE sites in the Southeast subregion.
A-35

-------
2005cs_hdghg_05b_12EUS1 N03 for CSN for 20050101 to 20051231
25 -
¦	E3 CSN
Q--A CMAQ
*PO = VISTAS






20 -







15 -







10 -
i —






5 -
J -y



T



[IUJlai

	
-j-
A
r*A ¦
0 -
325 319 321 333 375



356 316
305
2005 01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-22b. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at CSN sites in the Southeast subregion.
2005cs hdghg 05b 12EUS1 TN03 for CASTNET for 20050101 to 20051231
25 -
M—0 CASTNET
CMAQ
IPO * VISTAS






20 -







15 -







10 -







5 -
T 1 i T
¦y







til
£:A# ¦ vv .
«*¦
W
n

0 -
89 91 112 OS
110
mm
SB *79
H
101
mm
83
8-1
106 65
2005 01 2005 03 2005 05 2005_07 2005_09 2005_11
Months
Figure A-22c. Distribution of observed and predicted weekly average total nitrate by
month for 2005 at CASTNet sites in the Southeast subregion.
A-36

-------
2005cs hdghg 05b_12EUS1 N03 for IMPROVE for 20050101 to 20051231
I—~ IMPROVE
CMAQ
3PO = LADCO
CO
!>
CO
O
2
	1	1	1	1	1	1	1	1	1	1	1	r~
2005 01 2005 03 2005_05 2005_ 07 2005 09 2005_11
Months
Figure A-23a. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at IMPROVE sites in the Midwest subregion.
2005cs_hdghg_05b_12EUS1 N03 for CSN for 20050101 to 20051231
I—B CSN
I -A CMAQ
3PO = LADCO
E
CO
O
2 10 -
~i	1	1	1	1	1	1	1	1	1	1	r
2005_01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-23b. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at CSN sites in the Midwest subregion.
A-37

-------
2005cs_hdghg_05b 12EUS1 TN03 for CASTNET for 20050101 to 20051231
¦—~ CASTNET
EJ--A CMAQ
*PO = LADCO
Months
Figure A-23c. Distribution of observed and predicted weekly average total nitrate by
month for 2005 at CASTNet sites in the Midwest subregion.
2005cs_hdghg_05b_12EUS1 N03 for IMPROVE for 20050101 to 20051231
¦—~ IMPROVE
Q--A CMAQ
3PO = C EN RAP





I | -r-

I ^ -y
1 I-aI
1 Jl I I U I
HIS T95 25 332

T T : _*_¦*,|
#5 219 T95 TH5
2005_01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-24a. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at IMPROVE sites in the Central states subregion.
A-38

-------
2005cs_hdghg_05b_12EUS1 N03 for CSN for 20050101 to 20051231
25 -
20 -
15 -
CO
o
2 10 -
ITT itT TPT
T75
2005_01
2005 03
2005.05
2005_07
200509
200511
Months
Figure A-24b. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at CSN sites in the Central states subregion.
2005cs hdghg 05b 12EUS1 TN03 for CASTNET for 20050101 to 20051231
¦—~
CASTNET




&--A
CMAQ




3PO » CENRAP




•=F-





1 A I
1 ^ 1
I •&.. ~r_
j.X ¦
A

A


	 — —.



24
24 30 24 29
22 21 29
22 23
30
18
2005 01 2005 03 2005 05 2005_07 2005_09 2005_11
Months
Figure A-24c. Distribution of observed and predicted weekly average total nitrate by
month for 2005 at CAST Net sites in the Central states subregion.
A-39

-------
2005cs_hdghg_05b_12WUS1 N03 for IMPROVE for 20050101 to 20051231
IMPROVE
-A CMAQ
*PO = WRAP
E
3- 3 "
CO
O
z
	1	1	1	1	1	1	1	1	1	1	1	r
2005_01 2005_03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-25a. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at IMPROVE sites in the Western states subregion.
2005CS hdghg 05b_12WUS1 N03 for CSN for 20050101 to 20051231
I—a CSN
&--A CMAQ
3PO = WRAP
CO
!>
3- 3
CO
O
2
293 266 281 275 303 279 '27$ "2BIF 287 32B 283 272
	1	1	1	1	1	1	1	1	1	1	1	r~
2005_01 2005 03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-25b. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at CSN sites in the Western states subregion.
A-40

-------
2005cs_hdghg_05b_1 2WUS1 TN03 for CASTNET for 20050101 to 20051231
CASTNET
-A CMAQ
*PO = WRAP
CO
E
CO
O
z
1(X 85 101 87
-~i	r
109 82
~v
101 60
T
	1	1	1	1	1	1	1	1	1	1	1	r
2005_01 2005_03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-25c. Distribution of observed and predicted weekly average total nitrate by
month for 2005 at CAST Net sites in the Western states subregion.
A-41

-------
N03 NMB (%) for run 2005cs hdghg_05b 12EUS1 for December to February 2005
ttrv&ragG l.ftW - 75%
c-iMDDnvc- toiam/ii c.rcw-
Figure A-26a. Normalized Mean Bias (%) for nitrate during winter 2005 at monitoring
sites in Eastern modeling domain.
N03 NME (%) for run 2005cs hdghg 05b 12EUS1 for December to February 2005
coverage Uimt - 75%
*
< 100
I A'
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-26b. Normalized Mean Error (%) for nitrate during winter 2005 at monitoring
sites in Eastern modeling domain.
A-42

-------
TN03 NMB (%) for run 2005cs_hdghg_05b_12EUS1 for December to February 2005
units-%
coverage limit« 75%
CI RCLE=CASTN ET;
Figure A-26c. Normalized Mean Bias (%) for total nitrate during winter 2005 at
monitoring sites in Eastern modeling domain.
TNQ3 NME (%) for run 2005cs_hdghg 05b_12EUS1 for December to February 2005
coverage limit • 76%
< 130
CIRGLE=CASTNET:
Figure A-26d. Normalized Mean Error (%) for total nitrate during winter 2005 at
monitoring sites in Eastern modeling domain.
A-43

-------
N03 NMB (%) tor run 2005cs hdghg 05b 12EUS1 for March to May 2005
&5v6r,1gd lifrtil » 75%
CIRCLE=IMPROVE; TRiANGLE=CSN;
Figure A-27a. Normalized Mean Bias (%) for nitrate during spring 2005 at monitoring
sites in Eastern modeling domain.
NQ3 NMB (%) tor run 2005cs hdghg 05b 12EUS1 for March to May 2005
units - %
coverage limit • 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-27b. Normalized Mean Error (%) for nitrate during spring 2005 at monitoring
sites in Eastern modeling domain.
A-44

-------
Figure A-27c. Normalized Mean Bias (%) for total nitrate during spring 2005 at
monitoring sites in Eastern modeling domain.
TN03 NMB (%) for run 2005cs hdghg 05b 12EUS1 for March to May 2005
CIRCLE=CASTNET;
2005cs_hdghg_05b_12EUS1
coverage limit
TN03 NMB (%) for run
for March to May 2005
CIRGLE=CASTNET;
Figure A-27d. Normalized Mean Error (%) for total nitrate spring 2005 at monitoring
sites in Eastern modeling domain.
A

-------
N03 NMB (%) for run 2005cs hdghg 05b 12EUS1 tor June to August 2005
ttrvgrjige lirtl » 75%
CIRCLE=IMPROVE; TRiANGLE=CSN;
Figure A-28a. Normalized Mean Bias (%) for nitrate during summer 2005 at monitoring
sites in Eastern modeling domain.
NQ3 NME (%) for run 2005cs hdghg 05b 12EUS1 for June to August 2005
"'^1* "k
units - %
COwiS'iigit lifrifl • 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-28b. Normalized Mean Error (%) for nitrate during summer 2005 at monitoring
sites in Eastern modeling domain.
A-46

-------
TN03 NMB {%) for run 2005cs_hdghg_05b_12EUS1 for June to August 2005
coverage limit« 75%
> 140
120
100
60
40
20
0
-20
-40
-60
-80
-100
120
< -140
CIRCLE=CASTNET;
Figure A-28c. Normalized Mean Bias (%) for total nitrate during summer 2005 at
monitoring sites in Eastern modeling domain.
2005cs_hdghg_05b_12EUS1
coverage limn
TN03 NMB (%) for run
for June to August 2005
CIRCLE=CASTNET;
Figure A-28d. Normalized Mean Error (%) for total nitrate summer 2005 at monitoring
sites in Eastern modeling domain.
A-47

-------
NH4 NMB (%) for run 2005cs_hdghg 05b_12EUS1 for September to November 2005
75%
CIRCLE=CSN; TRIANGLE=CASTNET;
Figure A-29a. Normalized Mean Bias (%) for nitrate during fall 2005 at monitoring sites
in Eastern modeling domain.
NH4 NME (%) for run 20Q5cs_hdghg_05b_12EUS1 for September to November 2005
coverage limn « 75%
CIRCLE=CSN; TRIANGLE=CASTNET;
Figure A-29b. Normalized Mean Error (%) for nitrate during fall 2005 at monitoring sites
in Eastern modeling domain.
A-48

-------
TNQ3 NMB (%) for run 2005cs_hdghg_05b_12EUS1 for September to November 2005
coverage limit« 75%
> 250
2 CO
<-250
CIRCLE=CASTNET;
Figure A-29c. Normalized Mean Bias (%) for total nitrate during fall 2005 at monitoring
sites in Eastern modeling domain.
TNQ3 NME (%) for run 2005cs_hdghg_05b_12EUS1 for September to November 2005
coverage limit • 76%
<240
CIRCLE=GASTNET;
Figure A-29d. Normalized Mean Error (%) for total nitrate fall 2005 at monitoring sites in
Eastern modeling domain.
A-49

-------
N03 NMB (%) for run 2005CS hdghg 05b 12WUS1 for December to February 2005
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-30a. Normalized Mean Bias (%) for nitrate during winter 2005 at monitoring
sites in Western modeling domain.
N03 NME (%) for run 2005OS hdghg 05b 12WUS1 for June to August 2005
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-30b. Normalized Mean Error (%) for nitrate during winter 2005 at monitoring
sites in Western modeling domain.
A-50

-------
age linrrt! = 75%
> 100
80
60
40
20
0
-20
-40
-60
-80
<-100
Figure A-30c. Normalized Mean Bias (%) for total nitrate during winter 2005 at
monitoring sites in Western modeling domain.
TN03 NMB (%) lor run 2005cs hdghg 05b 12WUS1 for December to February 2005
\kN	 f	
CIRCLE=CASTNET;
TNQ3 NMB (%) lor run 2005cs_hdghg 05b 12WUS1 for December to February 2005
units = %
coverage liirtl - 75%
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CASTNET;
Figure A-30d. Normalized Mean Error (%) for total nitrate winter 2005 at monitoring
sites in Western modeling domain.
A-

-------
N03 NMB (%) for run 2005CSJldghg 05b J 2WUS1 for March to May 2005
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-31 a. Normalized Mean Bias (%) for nitrate during spring 2005 at monitoring
sites in Western modeling domain.
N03 NME (%) for run 2005fiSjldghg_05b_12WUS1 for March to May 2005
< 100
40
10
CIRCLE=IMPROVE; TR!ANGLE=CSN;
Figure A-31 b. Normalized Mean Error (%) for nitrate during spring 2005 at monitoring
sites in Western modeling domain.
A-52

-------
TN03 NMB (%) for run 2005cs hdqhq 05b 12WUS1 for March to May 2005
CIRCLE=CASTNET;
Figure A-31c. Normalized Mean Bias (%) for total nitrate during spring 2005 at
monitoring sites in Western modeling domain.
TN03 NMB (%) for run 2005cs_hdghg_05b_12WUS1 for March to May 2005
> 100
20
-20
-40
-60
-80
CIRCLE=CASTNET;
Figure A-31d. Normalized Mean Error (%) for total nitrate spring 2005 at monitoring
sites in Western modeling domain.
A

-------
N03 NMB (%) for run 2005CS hdghg 05b 12WUS1 for June to August 2005
CIRCLE=IMPROVE, TRIANGLE=CSN;
Figure A-32a. Normalized Mean Bias (%) for nitrate during summer 2005 at monitoring
sites in Western modeling domain.
N03 NMB (%) for run 2005CS hdghg 05b 12WUS1 for June to August 2005
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-32b. Normalized Mean Error (%) for nitrate during summer 2005 at monitoring
sites in Western modeling domain.
A-54

-------
TN03 NMB (%) for run 2005cs_hdghg_05b_12WUS1 for June to August 2005
CIRCLE=CASTNET;
Figure A-32c. Normalized Mean Bias (%) for total nitrate during summer 2005 at
monitoring sites in Western modeling domain.
TN03 NME (%) for run 2005cs_hdghg_05b_12WUS1 for June to August 2005
CIRCLE=CASTNET;
Figure A-32d. Normalized Mean Error (%) for total nitrate summer 2005 at monitoring
sites in Western modeling domain.
A-55

-------
N03 NMB (%) for run 2005CS hdghg 05b 12WUS1 for September to November 2005
CIRCLE=IMPROVE, TRIANGLE=CSN;
Figure A-33a. Normalized Mean Bias (%) for nitrate during fall 2005 at monitoring sites
in Western modeling domain.
N03 NME (%) for run 2005CS hdghg_05b_12WUS1 for September to November 2005
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-33b. Normalized Mean Error (%) for nitrate during fall 2005 at monitoring sites
in Western modeling domain.
A-56

-------
TN03 NMB (%) for run 2005cs hdqhg 05b 12WUS1 for September to November 2005
CIRCLE=CASTNET;
Figure A-33c. Normalized Mean Bias (%) for total nitrate during fall 2005 at monitoring
sites in Western modeling domain.
TN03 NME (%) for run 2005cs_hdghg_05b_12WUS1 for September to November 2005
CIRCLE=CASTNET;
Figure A-33d. Normalized Mean Error (%) for total nitrate fall 2005 at monitoring sites in
Western modeling domain.
A-57

-------
H. Seasonal Ammonium Performance
The model performance bias and error statistics for ammonium for each subregion and
each season are provided in Table A-5. These statistics indicate model bias for ammonium is
generally + 40 percent or less for all seasons in each subregion. During the summer, there is
slight under prediction with a low bias in the subregions for urban locations. In other times of
the year ammonium tends to be somewhat over predicted with a bias of 19 percent, on average
across the subregions for urban locations.
Table A-5. Ammonium performance statistics by subregion, by season for the 2005 CMAQ
model simulation.
Region
Network
Season
No. of
Obs.
NMB
(%)
NME
(%)
FB (%)
FE (%)
CENRAP
CSN
Winter
771
-1.0
43.5
-0.1
50.7
Spring
875
6.0
42.3
8.2
43.5
Summer
851
-20.6
46.0
-23.7
60.9
Fall
587
18.7
55.4
23.6
55.8
CASTNet
Winter
72
3.9
37.9
4.4
42.7
Spring
77
17.5
34.6
11.5
32.6
Summer
72
-16.6
29.4
-19.3
35.8
Fall
75
18.0
44.5
25.0
46.5

MWRPO
CSN
Winter
598
-8.1
31.9
-3.0
33.4
Spring
637
49.9
63.8
39.6
51.4
Summer
621
0.7
37.3
16.9
42.1
Fall
639
8.5
37.8
22.6
41.5
CASTNet
Winter
142
-10.4
24.2
-4.8
25.1
Spring
155
46.2
53.6
37.8
42.4
Summer
161
-4.4
25.9
-1.0
27.5
Fall
157
21.3
45.8
27.7
41.7

VISTAS
CSN
Winter
949
-9.4
41.1
-8.5
44.1
Spring
973
12.0
41.5
10.9
41.0
Summer
926
-13.7
35.9
-8.0
43.3
Fall
975
3.8
41.5
14.5
45.0
CASTNet
Winter
264
-6.0
28.0
-6.3
29.4
Spring
292
9.2
31.2
7.5
30.9
Summer
268
-31.7
35.3
-44.8
48.6
Fall
273
-8.2
36.5
-6.7
40.8
A-5 8

-------
Region
Network
Season
No. of
Obs.
NMB
(%)
NME
(%)
FB (%)
FE (%)

MANEVU
CSN
Winter
828
2.8
34.5
6.7
34.2
Spring
894
33.4
54.8
35.5
50.4
Summer
874
-10.4
36.1
4.6
43.9
Fall
902
18.8
50.5
30.1
51.2
CASTNet
Winter
193
23.2
38.6
27.2
37.5
Spring
206
43.5
49.8
32.8
38.9
Summer
192
-22.9
29.7
-26.1
34.4
Fall
195
9.7
39.4
14.4
36.4

WRAP
CSN
Winter
829
-27.7
60.7
-12.0
65.5
Spring
859
-0.5
52.7
18.7
51.2
Summer
849
-33.0
53.1
-4.7
51.6
Fall
886
-21.4
63.6
9.4
58.6
CASTNet
Winter
250
-2.4
41.0
7.6
39.3
Spring
273
1
OO
be
32.1
-4.5
31.7
Summer
281
-33.3
40.3
-34.4
44.6
Fall
268
-3.1
32.1
1.7

A-59

-------
I. Seasonal Elemental Carbon Performance
The model performance bias and error statistics for elemental carbon for each subregion
and each season are provided in Table A-6. The statistics show clear over prediction at urban
sites in all subregions. For example, NMBs typically range between 50 and 100 percent at urban
sites in the Midwest, Northeast, and Central subregions with only slightly less over prediction at
urban sites in the Southeast. Rural sites show much less over prediction than at urban sites with
under predictions occurring in the spring, summer, and fall at rural sites in the Southeast,
Midwest and Central subregions. In the West, the model tends to over predict at both urban and
rural sites during all seasons. In addition, the predictions for urban sites have greater error than
the predictions for rural locations.
Table A-6. Elemental Carbon performance statistics by subregion, by season for the 2005
CMAQ model simulation.
Subregion
Network
Season
No. of
Obs.
NMB
(%)
NME
(%)
FB (%)
FE (%)
CENRAP
CSN
Winter
816
101.0
132.0
56.5
77.5
Spring
938
90.6
114.0
45.5
70.6
Summer
875
109.0
132.0
41.9
80.7
Fall
618
93.6
111.0
57.5
70.7
IMPROVE
Winter
589
9.7
54.5
4.9
47.1
Spring
716
-9.4
55.8
-10.1
53.8
Summer
701
-30.5
46.8
-38.2
56.2
Fall
620
-17.2
34.8
-16.0
41.1

MWRPO
CSN
Winter
602
122.0
137.0
69.3
76.5
Spring
637
64.2
85.3
49.0
61.5
Summer
621
48.1
64.6
38.2
54.4
Fall
642
53.2
73.2
39.9
55.6
IMPROVE
Winter
182
61.4
79.6
22.9
46.0
Spring
184
17.9
56.8
-11.8
51.1
Summer
185
-13.8
40.9
-37.3
53.9
Fall
145
-12.8
33.6
-19.3
48.2

VISTAS
CSN
Winter
950
40.4
63.6
31.6
49.8
Spring
970
37.6
62.6
35.6
53.4
Summer
925
41.4
69.5
38.2
61.1
Fall
973
13.6
46.2
18.5
45.5
IMPROVE
Winter
491
-3.0
44.4
-0.7
48.6
A-60

-------
Subregion
Network
Season
No. of
Obs.
NMB
(%)
NME
(%)
FB (%)
FE (%)
Spring
530
-17.0
44.9
-11.3
45.1
Summer
493
-41.3
48.4
-55.6
71.4
Fall
481
-26.9
38.9
-22.8
45.6

MANEVU
CSN
Winter
831
97.5
110.0
57.7
67.1
Spring
881
90.3
107.0
57.0
68.7
Summer
866
64.8
87.8
45.3
63.2
Fall
901
52.2
82.5
34.6
56.6
IMPROVE
Winter
603
45.4
72.9
22.8
53.1
Spring
658
28.1
63.0
11.3
54.3
Summer
596
-20.6
45.6
-37.8
57.4
Fall
591
30.9
57.3
6.0
49.3

WRAP
CSN
Winter
808
43.6
84.7
21.4
66.9
Spring
822
99.5
123.0
44.0
73.9
Summer
806
112.0
126.0
575
72.3
Fall
867
52.1
86.6
26.3
64.0
IMPROVE
Winter
2338
0.0
63.5
-15.5
64.6
Spring
2597
17.3
68.0
-2.0
53.9
Summer
2314
28.4
76.4
17.9
58.2
Fall
2372
7.0
66.0
-10.1
59.4
A-61

-------
J. Seasonal Organic Carbon Performance
The model performance bias and error statistics for organic carbon for each subregion
and each season are provided in Table A-7. The statistics in this table indicate a tendency for the
modeling platform to somewhat under predict observed organic carbon concentrations during the
spring, summer, and fall at urban and rural locations across the Eastern subregions. Likewise,
the modeling platform under predicts organic carbon during all seasons at urban and rural
locations in the Western subregion. 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 particulate phases.
Table A-7. Organic Carbon performance statistics by subregion, by season for the 2005
CMAQ model simulation.
Region
Network
Season
No. of
Obs.
NMB
(%)
NME
(%)
FB (%)
FE (%)
CENRAP
CSN
Winter
544
0.2
57.7
14.9
59.7
Spring
628
-34.8
52.4
-32.0
63.4
Summer
595
-51.4
54.1
-69.8
76.3
Fall
493
-30.8
45.2
-28.0
56.7
IMPROVE
Winter
589
-8.1
51.1
-12.0
47.9
Spring
715
-38.5
57.6
-38.1
61.1
Summer
699
-50.1
52.3
-69.9
74.2
Fall
619
-44.4
48.2
-54.4
62.3

MWRPO
CSN
Winter
566
4.3
53.2
21.8
54.2
Spring
605
-29.4
45.9
-17.8
52.8
Summer
619
-53.1
54.6
-69.6
73.2
Fall
595
-28.5
41.3
-16.4
52.0
IMPROVE
Winter
182
3.4
38.5
1.6
37.2
Spring
184
-26.0
36.5
-32.9
44.7
Summer
185
-48.3
51.5
-64.6
68.9
Fall
144
-35.1
43.7
-43.9
61.8

VISTAS
CSN
Winter
932
-24.4
45.4
-13.2
50.4
Spring
957
-35.3
48.6
-28.7
56.9
Summer
916
-55.5
57.5
-75.3
80.1
Fall
942
-39.3
45.8
-41.1
57.5
A-62

-------
Region
Network
Season
No. of
Obs.
NMB
(%)
NME
(%)
FB (%)
FE (%)
IMPROVE
Winter
491
-10.1
45.1
-11.4
50.9
Spring
529
-9.2
49.1
-15.1
50.3
Summer
492
-48.5
54.2
-66.4
75.0
Fall
481
-33.8
41.2
-41.6
53.2

MANEVU
CSN
Winter
806
27.9
59.3
31.3
55.2
Spring
832
2.2
50.7
8.5
53.1
Summer
859
-47.3
51.7
-61.1
69.2
Fall
830
-4.5
47.1
3.7
53.3
IMPROVE
Winter
602
48.2
69.3
31.6
52.1
Spring
657
3.7
46.3
-3.1
46.1
Summer
596
-47.0
51.5
-59.4
66.4
Fall
588
14.2
47.4
-1.9
43.9

WRAP
CSN
Winter
803
-26.5
67.4
-20.2
70.0
Spring
823
-12.3
60.4
-4.1
60.3
Summer
840
-24.1
41.6
-28.8
50.5
Fall
881
-28.4
57.1
-26.2
58.6
IMPROVE
Winter
2296
-17.0
58.6
-23.1
64.6
Spring
2559
-23.2
51.7
-25.4
56.8
Summer
2297
4.2
65.1
-1.2
60.1
Fall
2350
-21.9
56.9
-26.9
62.1
A-63

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K. Seasonal Hazardous Air Pollutants Performance
A 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 seasonal model
performance results for the East and West are presented below in Tables A-8 and A-9,
respectively. 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 PM25. 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 PM25 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.16'17'18 Overall,
the normalized mean bias and error (NMB and NME), as well as the fractional bias and error (FB
and FE) statistics shown below indicate that CMAQ-predicted 2005 toxics (i.e., observation vs.
model predictions) are within the range of recent regional modeling applications.
16	Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform:
Air Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.
17	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.
18	Wesson, K., N. Fann, 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.
A-64

-------
Table A-8. Air toxics performance statistics by season in the Eastern domain for the 2005
CMAQ model simulation.	
Air Toxic Species
Season
No. of Obs.
NMB (%)
NME (%)
FB (%)
FE (%)

Winter
1625
-52.2
61.5
-46.0
68.2
Formaldehyde
Spring
1530
-53.2
65.2
-35.6
67.6

Summer
1809
-52.9
63.5
-29.5
58.2

Fall
1901
-51.6
62.2
36.0
60.6

Winter
1549
-41.4
50.6
-42.9
57.4
Acetaldehyde
Spring
1471
-27.5
50.3
-23.7
55.0
Summer
1752
57.0
89.6
47.7
67.9

Fall
1850
-2.5
56.8
-8.3
56.1

Winter
3107
-30.7
68.3
-9.5
58.2
Benzene
Spring
3085
-38.0
66.7
-25.0
63.2

Summer
3242
-37.0
69.6
-18.9
66.1

Fall
3387
-30.3
64.8
-16.2
59.0

Winter
2629
-63.0
89.8
-20.4
86.8
1,3-Butadiene
Spring
2712
-77.7
92.7
-47.8
92.2

Summer
2758
-73.4
87.9
-54.4
87.5

Fall
2487
-61.4
81.5
-49.0
85.4

Winter
602
-90.1
94.6
-123.0
134.0
Acrolein
Spring
430
-82.4
91.4
-117.0
128.0

Summer
814
-95.9
99.0
-136.0
154.0

Fall
992
-95.0
98.7
-148.0
153.0
Table A-9. Air toxics performance statistics by season in the Western domain for the 2005
CMAQ model simulation.





Air Toxic Species
Season
No. of Obs.
NMB (%)
NME (%)
FB (%)
FE (%)

Winter
426
-21.1
68.5
-33.1
73.1
Formaldehyde
Spring
499
-30.1
57.7
-21.3
61.1

Summer
641
-25.1
38.0
-21.6
40.5

Fall
579
-25.8
42.8
-26.5
48.7

Winter
425
-23.9
71.0
-37.0
75.8
Acetaldehyde
Spring
484
-24.3
55.6
-23.0
61.0
Summer
630
-1.5
46.0
7.4
44.3

Fall
568
-18.2
51.3
-16.2
55.7

Winter
820
-39.5
58.3
-38.3
64.0
Benzene
Spring
835
-31.8
55.8
-29.9
61.2

Summer
1033
-44.1
64.7
-26.2
63.6

Fall
959
-58.0
71.6
-37.3
69.6
1,3-Butadiene
Winter
693
-45.4
95.5
-26.1
99.4
A-65

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Spring
732
-21.0
87.4
-23.6
80.6

Summer
676
-35.9
78.1
-34.8
80.1

Fall
708
-46.1
86.5
-32.9
89.4

Winter
196
-95.1
95.2
-163.0
165.0
Acrolein
Spring
190
-95.8
95.9
-167.0
169.0

Summer
305
-96.2
98.8
-171.0
178.0

Fall
279
-96.8
98.1
-172.0
174.0
A-66

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L. Seasonal Nitrate and Sulfate Deposition Performance
Seasonal nitrate and sulfate deposition performance statistics for the 12-km Eastern and
Western domains are provided in Tables A-10 and A-l 1, respectively. 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 East
and West shows the similar predictions (NMB values range from -3% to 33%). The errors for
both annual nitrate and sulfate are relatively moderate with values ranging from 60% to 77%
which reflect scatter in the model predictions versus observation comparison.
Table A-10. Nitrate and sulfate wet deposition performance statistics by season in the
Eastern domain for the 2005 CMAQ model simulation.
Wet Deposition
Species
Season
No. of Obs.
NMB (%)
NME (%)
FB (%)
FE (%)
Nitrate
Winter
1788
31.4
74.7
13.5
72.5
Spring
1882
-1.7
57.3
-2.9
64.8
Summer
1975
-23.1
61.9
-20.0
75.3
Fall
1736
7.1
65.7
-5.8
74.2
Sulfate
Winter
1788
33.8
70.0
24.3
72.2
Spring
1882
6.6
59.7
12.6
67.5
Summer
1975
3.4
74.0
6.7
79.4
Fall
1736
-3.0
61.6
-9.7
74.2
Table A-ll. Nitrate and sulfate wet deposition performance statistics by season in the
Western domain for the 2005 CMAQ model simulation.
Wet Deposition
Species
Season
No. of Obs.
NMB (%)
NME (%)
FB (%)
FE (%)
Nitrate
Winter
649
8.0
82.1
5.3
83.3
Spring
768
-0.9
67.1
2.5
73.6
Summer
641
-27.5
63.5
-23.1
79.6
Fall
674
-4.8
76.0
-6.5
84.4
Sulfate
Winter
649
25.0
86.8
25.6
88.8
Spring
768
16.7
73.0
18.3
77.3
Summer
641
-5.0
73.9
-1.6
81.6
Fall
674
-8.6
76.7
-4.9
86.7
A-67

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Air Quality Modeling
Technical Support Document: Heavy-Duty Vehicle
Greenhouse Gas Emission Standards Final Rule
Appendix B
8-Hour Ozone Design Values for Air Quality Modeling
Scenarios
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC 27711
July 2011
B-l

-------
Table B-l. 8-Hour Ozone Design Values for HDGHG Scenarios (units are ppb)
State
County
2005 Baseline
2030 Reference
2030 Control
DV
Case DV
Case DV
Alabama
Baldwin
77.3
58.67
58.30
Alabama
Clay
74.0
51.11
50.58
Alabama
Colbert
72.0
56.62
56.29
Alabama
Elmore
70.7
50.16
49.74
Alabama
Etowah
71.7
51.30
50.74
Alabama
Houston
71.0
52.45
51.92
Alabama
Jefferson
83.7
59.20
58.70
Alabama
Lawrence
72.0
52.76
52.24
Alabama
Madison
77.3
55.76
54.95
Alabama
Mobile
76.7
58.96
58.60
Alabama
Montgomery
69.3
49.33
48.92
Alabama
Morgan
77.3
59.10
58.60
Alabama
Russell
71.3
50.89
50.33
Alabama
Shelby
85.7
59.65
59.09
Alabama
Sumter
64.0
52.12
51.78
Alabama
Talladega
72.0
51.40
50.88
Alabama
Tuscaloosa
73.3
51.66
51.21
Arizona
Cochise
71.3
60.46
59.92
Arizona
Coconino
73.0
59.60
59.58
Arizona
Gila
80.3
58.21
57.17
Arizona
Maricopa
83.0
64.54
63.53
Arizona
Pima
76.0
57.76
57.29
Arizona
Pinal
79.3
57.31
56.19
Arizona
Yuma
75.0
57.46
57.30
Arkansas
Crittenden
87.3
62.64
61.92
Arkansas
Newton
72.7
54.42
53.87
Arkansas
Polk
75.0
60.06
59.59
Arkansas
Pulaski
79.7
54.70
53.74
California
Alameda
78.3
65.62
65.59
California
Amador
83.0
64.41
64.38
California
Butte
83.7
63.29
63.26
California
Calaveras
91.3
73.75
73.71
California
Colusa
67.0
52.91
52.89
California
Contra Costa
73.3
65.43
65.40
California
El Dorado
96.0
71.35
71.32
California
Fresno
98.3
79.16
79.14
B-2

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California
Glenn
67.0
53.34
53.32
California
Imperial
85.0
68.46
68.43
California
Inyo
82.3
65.70
65.67
California
Kern
110.0
90.82
90.80
California
Kings
85.7
67.32
67.30
California
Lake
60.7
49.11
49.09
California
Los Angeles
114.0
96.95
96.92
California
Madera
79.3
63.30
63.28
California
Marin
49.7
42.23
42.20
California
Mariposa
86.3
69.38
69.35
California
Mendocino
56.7
45.45
45.42
California
Merced
89.3
70.43
70.41
California
Monterey
61.0
49.90
49.87
California
Napa
59.3
48.10
48.08
California
Nevada
96.3
72.10
72.08
California
Orange
84.3
80.68
80.65
California
Placer
94.0
70.03
70.00
California
Riverside
112.3
109.51
109.49
California
Sacramento
97.3
73.25
73.22
California
San Benito
75.0
59.29
59.28
California
San Bernardino
123.3
119.34
119.31
California
San Diego
87.7
70.17
70.16
California
San Francisco
46.0
45.31
45.30
California
San Joaquin
75.3
62.44
62.42
California
San Luis Obispo
70.7
56.75
56.73
California
San Mateo
53.7
49.32
49.30
California
Santa Barbara
76.0
60.38
60.37
California
Santa Clara
75.3
58.77
58.75
California
Santa Cruz
61.3
51.87
51.86
California
Shasta
79.3
62.71
62.67
California
Siskiyou
63.5
50.46
50.34
California
Solano
73.5
58.68
58.65
California
Sonoma
47.7
37.83
37.80
California
Stanislaus
84.7
68.66
68.64
California
Sutter
82.0
64.00
63.97
California
Tehama
82.7
64.70
64.67
California
Tulare
103.7
81.64
81.62
California
Tuolumne
80.0
64.23
64.20
California
Ventura
89.7
70.59
70.57
California
Yolo
78.7
61.77
61.74

-------
Colorado
Adams
69.0
60.18
59.94
Colorado
Arapahoe
78.7
65.54
65.25
Colorado
Boulder
77.0
64.86
64.55
Colorado
Denver
73.0
63.67
63.42
Colorado
Douglas
83.7
69.83
69.50
Colorado
El Paso
73.3
62.13
61.92
Colorado
Jefferson
81.7
71.90
71.61
Colorado
La Plata
72.0
63.40
63.30
Colorado
Larimer
76.0
63.23
62.93
Colorado
Montezuma
72.0
65.30
65.19
Colorado
Weld
76.7
67.28
67.11
Connecticut
Fairfield
92.3
74.60
74.36
Connecticut
Hartford
84.3
61.43
61.10
Connecticut
Litchfield
87.7
63.89
63.59
Connecticut
Middlesex
90.3
69.13
68.85
Connecticut
New Haven
90.3
70.55
70.28
Connecticut
New London
85.3
64.44
64.23
Connecticut
Tolland
88.7
64.86
64.48
D.C.
Washington
84.7
65.11
64.82
Delaware
Kent
80.3
60.25
59.99
Delaware
New Castle
82.3
64.35
64.07
Delaware
Sussex
82.7
62.37
62.12
Florida
Alachua
72.0
51.05
50.69
Florida
Baker
68.7
50.19
49.88
Florida
Bay
78.7
57.91
57.59
Florida
Brevard
71.3
53.46
53.19
Florida
Broward
65.0
54.39
54.23
Florida
Collier
68.3
49.31
49.07
Florida
Columbia
72.0
53.23
52.91
Florida
Duval
77.7
59.12
58.90
Florida
Escambia
82.7
60.32
59.86
Florida
Highlands
72.3
56.66
56.39
Florida
Hillsborough
80.7
61.30
60.99
Florida
Holmes
70.3
53.44
53.02
Florida
Lake
76.7
57.44
57.10
Florida
Lee
70.3
53.38
53.14
Florida
Leon
71.0
50.48
50.09
Florida
Manatee
77.3
56.97
56.67
Florida
Marion
73.0
49.14
48.74
Florida
Miami-Dade
71.3
61.54
61.31
B-4

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Florida
Orange
79.3
61.13
60.75
Florida
Osceola
72.0
51.23
50.88
Florida
Palm Beach
65.0
53.96
53.76
Florida
Pasco
76.3
57.33
57.07
Florida
Pinellas
72.7
54.16
53.90
Florida
Polk
74.7
56.01
55.78
Florida
St Lucie
66.5
52.05
51.82
Florida
Santa Rosa
80.0
59.17
58.76
Florida
Sarasota
77.3
55.33
55.05
Florida
Seminole
76.0
55.99
55.62
Florida
Volusia
68.3
48.35
48.06
Florida
Wakulla
71.3
51.73
51.27
Georgia
Bibb
81.0
53.55
52.99
Georgia
Chatham
68.3
51.53
51.23
Georgia
Chattooga
75.0
52.54
52.00
Georgia
Clarke
80.7
51.99
51.21
Georgia
Cobb
82.7
55.91
54.92
Georgia
Columbia
73.0
52.53
52.09
Georgia
Coweta
82.0
58.26
57.67
Georgia
Dawson
76.3
49.07
48.38
Georgia
De Kalb
88.7
65.47
64.58
Georgia
Douglas
87.3
58.31
57.43
Georgia
Fayette
85.7
62.34
61.59
Georgia
Fulton
91.7
67.69
66.76
Georgia
Glynn
67.0
49.60
49.37
Georgia
Gwinnett
88.7
61.23
60.33
Georgia
Henry
89.7
62.26
61.42
Georgia
Murray
78.0
56.30
55.72
Georgia
Muscogee
75.7
51.68
51.08
Georgia
Paulding
80.3
52.28
51.56
Georgia
Richmond
80.3
57.43
56.94
Georgia
Rockdale
90.0
60.39
59.50
Georgia
Sumter
72.3
50.85
50.40
Idaho
Ada
76.0
67.03
66.72
Idaho
Canyon
66.0
55.32
55.03
Idaho
Elmore
63.0
54.57
54.33
Idaho
Kootenai
67.0
54.73
54.35
Illinois
Adams
70.0
56.01
55.55
Illinois
Champaign
68.3
54.22
53.75
Illinois
Clark
66.0
52.56
52.11

-------
Illinois
Cook
77.7
67.96
67.52
Illinois
Du Page
69.0
60.77
60.15
Illinois
Effingham
70.0
55.37
54.84
Illinois
Hamilton
73.0
55.85
55.39
Illinois
Jersey
78.7
57.76
56.75
Illinois
Kane
74.3
60.82
60.16
Illinois
Lake
78.0
66.67
66.17
Illinois
McHenry
73.3
57.83
57.19
Illinois
McLean
73.0
56.46
55.96
Illinois
Macon
71.3
57.04
56.56
Illinois
Macoupin
73.0
51.66
50.85
Illinois
Madison
83.0
63.51
62.47
Illinois
Peoria
72.7
58.96
58.53
Illinois
Randolph
72.0
55.99
55.48
Illinois
Rock Island
65.3
51.08
50.64
Illinois
St Clair
81.7
64.26
63.26
Illinois
Sangamon
70.0
51.82
51.25
Illinois
Will
71.7
58.77
58.17
Illinois
Winnebago
69.0
53.29
52.74
Indiana
Allen
79.3
61.84
60.87
Indiana
Boone
79.7
62.45
61.16
Indiana
Carroll
74.0
56.64
55.75
Indiana
Clark
80.3
62.60
61.38
Indiana
Delaware
76.3
57.11
56.18
Indiana
Elkhart
79.0
61.18
60.44
Indiana
Floyd
77.7
63.72
62.63
Indiana
Greene
78.3
62.19
61.65
Indiana
Hamilton
82.7
64.04
62.60
Indiana
Hancock
78.0
60.72
59.23
Indiana
Hendricks
75.3
59.83
58.65
Indiana
Huntington
75.0
58.32
57.48
Indiana
Jackson
74.7
57.75
57.10
Indiana
Johnson
76.7
60.76
59.89
Indiana
Lake
81.0
69.04
68.59
Indiana
La Porte
78.5
63.94
63.49
Indiana
Madison
76.7
58.20
56.87
Indiana
Marion
78.7
62.27
60.91
Indiana
Morgan
77.0
60.94
59.90
Indiana
Perry
81.0
63.90
63.24
Indiana
Porter
78.3
65.32
65.09
B-6

-------
Indiana
Posey
71.7
54.56
53.95
Indiana
St Joseph
79.3
61.81
61.07
Indiana
Shelby
77.3
62.07
61.06
Indiana
Vanderburgh
77.3
59.74
59.18
Indiana
Vigo
74.0
58.85
58.20
Indiana
Warrick
77.7
60.55
60.13
Iowa
Bremer
66.3
52.46
52.03
Iowa
Clinton
71.3
56.25
55.82
Iowa
Harrison
74.7
58.98
58.66
Iowa
Linn
68.3
54.46
54.05
Iowa
Montgomery
65.7
50.90
50.52
Iowa
Palo Alto
61.0
49.32
49.05
Iowa
Polk
63.0
48.16
47.69
Iowa
Scott
72.0
55.64
55.15
Iowa
Story
61.0
46.60
46.15
Iowa
Van Buren
69.0
53.94
53.53
Iowa
Warren
64.5
48.27
47.80
Kansas
Douglas
73.0
54.64
54.15
Kansas
Johnson
75.3
57.37
56.84
Kansas
Leavenworth
75.0
59.14
58.60
Kansas
Linn
73.3
55.56
55.13
Kansas
Sedgwick
71.3
54.72
54.28
Kansas
Sumner
71.7
54.91
54.47
Kansas
Trego
70.7
59.87
59.58
Kansas
Wyandotte
75.3
60.61
60.03
Kentucky
Bell
71.7
50.82
50.15
Kentucky
Boone
75.7
58.82
58.23
Kentucky
Boyd
77.3
60.37
59.94
Kentucky
Bullitt
74.0
60.42
59.54
Kentucky
Campbell
83.0
68.61
67.91
Kentucky
Carter
71.0
53.54
53.12
Kentucky
Christian
78.0
56.75
56.25
Kentucky
Daviess
75.7
59.55
59.16
Kentucky
Edmonson
73.7
56.80
56.32
Kentucky
Fayette
70.3
53.28
52.67
Kentucky
Greenup
76.7
60.24
59.82
Kentucky
Hancock
74.0
57.48
57.02
Kentucky
Hardin
74.7
58.81
57.96
Kentucky
Henderson
75.3
59.10
58.71
Kentucky
Jefferson
78.3
65.64
64.55
B-7

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Kentucky
Jessamine
73.3
60.05
59.52
Kentucky
Kenton
78.7
62.17
61.48
Kentucky
Livingston
73.7
57.96
57.55
Kentucky
McCracken
73.3
59.03
58.67
Kentucky
McLean
73.0
57.25
56.83
Kentucky
Oldham
83.0
63.44
62.05
Kentucky
Perry
72.3
54.87
54.37
Kentucky
Pike
66.7
50.81
50.37
Kentucky
Pulaski
70.3
55.44
54.98
Kentucky
Simpson
75.7
56.39
55.88
Kentucky
Trigg
70.0
51.90
51.40
Kentucky
Warren
72.0
55.28
54.82
Louisiana
Ascension
82.0
66.93
66.63
Louisiana
Beauregard
75.0
63.56
63.30
Louisiana
Bossier
78.0
58.87
58.28
Louisiana
Caddo
79.0
60.03
59.46
Louisiana
Calcasieu
82.0
68.60
68.26
Louisiana
East Baton Rouge
92.0
74.53
74.07
Louisiana
Iberville
85.0
69.97
69.67
Louisiana
Jefferson
83.0
68.67
68.29
Louisiana
Lafayette
82.0
64.18
63.76
Louisiana
Lafourche
79.3
64.64
64.35
Louisiana
Livingston
78.3
63.16
62.81
Louisiana
Ouachita
75.3
57.44
56.94
Louisiana
Pointe Coupee
83.7
69.51
69.18
Louisiana
St Bernard
78.0
63.14
62.68
Louisiana
St Charles
77.3
63.64
63.28
Louisiana
St James
76.3
62.87
62.58
Louisiana
St John The Baptis
79.0
66.59
66.28
Louisiana
St Mary
76.0
60.75
60.51
Louisiana
West Baton Rouge
84.3
68.74
68.37
Maine
Cumberland
72.0
53.59
53.26
Maine
Hancock
82.0
61.23
60.90
Maine
Kennebec
69.7
51.42
51.14
Maine
Knox
75.3
55.72
55.39
Maine
Oxford
61.0
48.98
48.76
Maine
Penobscot
67.0
51.40
51.18
Maine
Sagadahoc
68.5
50.70
50.39
Maine
York
74.0
56.06
55.75
Maryland
Anne Arundel
89.7
64.74
64.43

-------
Maryland
Baltimore
85.3
70.35
70.15
Maryland
Calvert
81.0
58.96
58.74
Maryland
Carroll
83.3
60.25
59.95
Maryland
Cecil
90.7
66.57
66.27
Maryland
Charles
86.0
62.59
62.30
Maryland
Frederick
80.3
57.63
57.34
Maryland
Garrett
75.5
59.57
59.26
Maryland
Harford
92.7
75.26
75.03
Maryland
Kent
82.0
60.68
60.41
Maryland
Montgomery
83.0
61.66
61.35
Maryland
Prince Georges
91.0
66.83
66.53
Maryland
Washington
78.3
57.43
57.12
Massachusetts
Barnstable
84.7
65.67
65.37
Massachusetts
Berkshire
79.7
59.58
59.23
Massachusetts
Bristol
82.7
63.14
62.92
Massachusetts
Dukes
83.0
65.13
64.95
Massachusetts
Essex
83.3
67.78
67.43
Massachusetts
Hampden
87.3
63.60
63.21
Massachusetts
Hampshire
85.0
62.21
61.82
Massachusetts
Middlesex
79.0
60.39
59.98
Massachusetts
Norfolk
84.7
65.21
64.91
Massachusetts
Suffolk
80.3
61.94
61.59
Massachusetts
Worcester
80.0
58.04
57.65
Michigan
Allegan
90.0
72.53
71.92
Michigan
Benzie
81.7
64.83
64.29
Michigan
Berrien
82.3
67.01
66.44
Michigan
Cass
80.7
62.65
62.06
Michigan
Clinton
75.7
57.36
56.87
Michigan
Genesee
79.3
61.79
61.28
Michigan
Huron
75.7
60.86
60.43
Michigan
Ingham
76.0
58.63
58.17
Michigan
Kalamazoo
75.3
58.07
57.56
Michigan
Kent
81.0
61.81
61.26
Michigan
Leelanau
75.7
60.72
60.25
Michigan
Lenawee
78.7
62.39
61.99
Michigan
Macomb
86.0
69.20
68.62
Michigan
Mason
79.7
61.99
61.39
Michigan
Missaukee
73.7
58.46
57.97
Michigan
Muskegon
85.0
68.50
67.90
Michigan
Oakland
78.0
65.43
64.99
B-9

-------
Michigan
Ottawa
81.7
64.20
63.66
Michigan
St Clair
82.3
64.24
63.68
Michigan
Schoolcraft
79.3
62.45
61.86
Michigan
Washtenaw
78.3
62.78
62.31
Michigan
Wayne
82.0
66.45
65.96
Minnesota
Anoka
67.7
60.76
60.51
Minnesota
St Louis
65.0
52.61
52.29
Mississippi
Adams
74.7
58.90
58.52
Mississippi
Bolivar
74.3
56.30
55.82
Mississippi
De Soto
82.7
60.13
59.46
Mississippi
Hancock
79.0
61.40
60.95
Mississippi
Harrison
83.0
62.54
62.12
Mississippi
Hinds
71.3
47.69
46.90
Mississippi
Jackson
80.3
61.53
61.16
Mississippi
Lauderdale
74.3
56.77
56.34
Mississippi
Lee
73.7
51.61
51.00
Missouri
Cass
74.7
56.50
56.01
Missouri
Cedar
75.7
57.14
56.66
Missouri
Clay
84.7
65.40
64.68
Missouri
Clinton
83.0
62.83
62.15
Missouri
Greene
73.0
56.18
55.66
Missouri
Jefferson
82.3
67.07
66.08
Missouri
Lincoln
87.0
67.72
66.76
Missouri
Monroe
71.7
55.12
54.54
Missouri
Perry
77.5
58.97
58.52
Missouri
Platte
77.0
61.24
60.65
Missouri
St Charles
87.0
66.15
65.06
Missouri
Ste Genevieve
79.7
64.94
64.35
Missouri
St Louis
88.0
70.76
69.59
Missouri
St Louis City
84.0
67.30
66.20
Montana
Yellowstone
59.0
53.16
52.99
Nebraska
Douglas
68.7
56.54
56.29
Nebraska
Lancaster
56.0
44.11
43.75
Nevada
Churchill
64.0
51.53
51.47
Nevada
Clark
83.7
69.35
69.22
Nevada
Washoe
70.7
55.42
55.33
Nevada
White Pine
72.3
58.82
58.80
Nevada
Carson City
65.0
49.85
49.82
New Hampshire
Belknap
71.3
51.52
51.23
New Hampshire
Cheshire
70.7
51.60
51.28
B-10

-------
New Hampshire
Coos
77.0
59.96
59.62
New Hampshire
Grafton
67.0
52.03
51.74
New Hampshire
Hillsborough
78.7
59.77
59.36
New Hampshire
Merrimack
71.7
52.24
51.91
New Hampshire
Rockingham
77.0
58.34
58.01
New Hampshire
Sullivan
70.0
53.19
52.88
New Jersey
Atlantic
79.3
60.79
60.58
New Jersey
Bergen
86.0
71.98
71.74
New Jersey
Camden
89.3
69.45
69.13
New Jersey
Cumberland
83.3
61.33
61.03
New Jersey
Gloucester
87.0
68.01
67.70
New Jersey
Hudson
85.7
75.41
75.18
New Jersey
Hunterdon
89.0
66.16
65.74
New Jersey
Mercer
88.0
69.07
68.68
New Jersey
Middlesex
88.3
68.68
68.34
New Jersey
Monmouth
87.3
69.90
69.67
New Jersey
Morris
83.3
62.10
61.79
New Jersey
Ocean
93.0
70.88
70.55
New Jersey
Passaic
81.0
63.20
62.92
New Mexico
Bernalillo
77.0
62.44
61.69
New Mexico
Dona Ana
75.3
62.92
62.75
New Mexico
Eddy
69.0
62.00
61.81
New Mexico
Lea
71.0
64.28
64.08
New Mexico
Sandoval
73.3
59.24
58.50
New Mexico
San Juan
71.3
66.78
66.69
New York
Albany
73.7
55.88
55.60
New York
Bronx
74.7
65.54
65.34
New York
Chautauqua
86.7
72.60
72.22
New York
Chemung
68.7
54.29
53.89
New York
Dutchess
75.7
55.54
55.29
New York
Erie
85.0
68.76
68.33
New York
Essex
77.0
62.51
62.13
New York
Hamilton
71.7
56.56
56.26
New York
Herkimer
68.3
55.22
54.94
New York
Jefferson
78.0
63.28
63.02
New York
Madison
72.0
55.04
54.64
New York
Monroe
76.3
60.69
60.43
New York
Niagara
82.7
69.88
69.63
New York
Oneida
68.3
54.18
53.88
New York
Onondaga
73.7
58.20
57.87
B-ll

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New York
Orange
82.0
60.35
60.06
New York
Oswego
78.0
65.89
65.67
New York
Putnam
84.3
64.01
63.74
New York
Queens
80.0
66.66
66.47
New York
Rensselaer
77.3
58.53
58.22
New York
Richmond
88.3
73.24
73.00
New York
Saratoga
79.7
60.38
60.06
New York
Schenectady
70.0
53.79
53.50
New York
Suffolk
90.3
77.36
77.17
New York
Ulster
77.3
58.19
57.86
New York
Wayne
68.0
55.97
55.76
New York
Westchester
87.7
73.48
73.23
North Carolina
Alexander
77.0
54.96
54.58
North Carolina
Avery
70.0
52.19
51.76
North Carolina
Buncombe
74.0
53.42
52.98
North Carolina
Caldwell
74.3
53.73
53.33
North Carolina
Caswell
76.3
53.04
52.61
North Carolina
Chatham
73.3
52.46
52.07
North Carolina
Cumberland
81.7
56.95
56.46
North Carolina
Davie
81.3
56.58
56.14
North Carolina
Durham
77.0
53.43
52.98
North Carolina
Edgecombe
77.0
56.94
56.58
North Carolina
Forsyth
80.0
56.67
56.20
North Carolina
Franklin
78.7
55.72
55.30
North Carolina
Graham
78.3
56.66
55.91
North Carolina
Granville
82.0
58.64
58.22
North Carolina
Guilford
82.0
56.27
55.71
North Carolina
Haywood
78.3
59.19
58.70
North Carolina
Jackson
76.0
55.19
54.54
North Carolina
Johnston
77.3
53.58
53.10
North Carolina
Lenoir
75.3
55.83
55.45
North Carolina
Lincoln
81.0
56.91
56.47
North Carolina
Martin
75.0
58.38
58.13
North Carolina
Mecklenburg
89.3
64.48
64.02
North Carolina
New Hanover
72.3
55.30
54.99
North Carolina
Person
77.3
57.87
57.62
North Carolina
Pitt
76.3
54.48
54.08
North Carolina
Rockingham
77.0
53.69
53.26
North Carolina
Rowan
86.7
59.71
59.23
North Carolina
Swain
66.3
48.33
47.78
B-12

-------
North Carolina
Union
79.3
54.49
54.01
North Carolina
Wake
80.3
56.90
56.43
North Carolina
Yancey
76.0
54.82
54.31
North Dakota
Billings
61.5
54.60
54.51
North Dakota
Burke
57.5
52.15
52.08
North Dakota
Cass
60.0
48.83
48.58
North Dakota
McKenzie
61.3
55.21
55.12
North Dakota
Mercer
59.3
56.66
56.63
North Dakota
Oliver
57.7
54.59
54.54
Ohio
Allen
78.7
61.46
60.83
Ohio
Ashtabula
89.0
72.07
71.54
Ohio
Butler
83.3
64.79
64.01
Ohio
Clark
81.0
60.61
59.87
Ohio
Clermont
81.0
65.19
64.54
Ohio
Clinton
82.3
61.09
60.35
Ohio
Cuyahoga
79.7
65.67
65.26
Ohio
Delaware
78.3
60.43
59.75
Ohio
Franklin
86.3
66.86
66.07
Ohio
Geauga
79.3
61.18
60.64
Ohio
Greene
80.3
60.60
59.83
Ohio
Hamilton
84.7
67.14
66.42
Ohio
Jefferson
78.0
59.65
59.26
Ohio
Knox
77.7
58.53
57.83
Ohio
Lake
86.3
69.58
69.11
Ohio
Lawrence
70.7
55.52
55.14
Ohio
Licking
78.0
58.41
57.72
Ohio
Lorain
76.7
62.68
62.28
Ohio
Lucas
81.3
65.52
65.18
Ohio
Madison
79.7
59.19
58.47
Ohio
Mahoning
78.7
59.43
58.82
Ohio
Medina
80.3
63.16
62.60
Ohio
Miami
76.7
56.98
56.24
Ohio
Montgomery
74.0
55.53
54.82
Ohio
Portage
83.7
64.25
63.57
Ohio
Preble
73.0
54.86
54.26
Ohio
Stark
81.0
61.75
61.13
Ohio
Summit
83.7
65.03
64.29
Ohio
Trumbull
84.3
64.07
63.42
Ohio
Warren
88.3
66.83
65.92
Ohio
Washington
82.7
66.35
66.04
B-13

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Ohio
Wood
80.0
62.93
62.43
Oklahoma
Adair
75.7
59.90
59.56
Oklahoma
Canadian
76.0
59.72
59.07
Oklahoma
Cherokee
75.7
60.60
60.33
Oklahoma
Cleveland
74.7
57.83
57.26
Oklahoma
Comanche
77.5
59.45
58.91
Oklahoma
Creek
76.7
58.64
58.10
Oklahoma
Dewey
72.7
56.78
56.34
Oklahoma
Kay
78.0
60.20
59.74
Oklahoma
Mc Clain
72.0
56.01
55.49
Oklahoma
Mayes
78.5
62.99
62.68
Oklahoma
Oklahoma
80.0
60.68
60.02
Oklahoma
Ottawa
78.0
60.21
59.81
Oklahoma
Pittsburg
72.0
57.88
57.49
Oklahoma
Tulsa
79.3
62.24
61.77
Oregon
Clackamas
66.3
59.79
59.40
Oregon
Jackson
68.0
51.65
51.28
Oregon
Lane
69.3
54.35
53.94
Oregon
Marion
65.7
54.11
53.64
Oregon
Multnomah
57.0
68.63
68.02
Pennsylvania
Adams
76.3
56.37
56.05
Pennsylvania
Allegheny
83.7
65.04
64.67
Pennsylvania
Armstrong
83.0
63.91
63.53
Pennsylvania
Beaver
83.0
65.17
64.77
Pennsylvania
Berks
80.0
60.87
60.44
Pennsylvania
Blair
74.3
57.57
57.23
Pennsylvania
Bucks
88.0
70.90
70.55
Pennsylvania
Cambria
74.7
60.17
59.87
Pennsylvania
Centre
78.3
61.01
60.59
Pennsylvania
Chester
86.0
63.27
62.96
Pennsylvania
Clearfield
78.3
60.22
59.75
Pennsylvania
Dauphin
79.3
63.31
62.98
Pennsylvania
Delaware
83.3
64.72
64.40
Pennsylvania
Erie
81.3
66.47
66.04
Pennsylvania
Franklin
72.3
52.98
52.70
Pennsylvania
Greene
80.0
65.33
65.03
Pennsylvania
Indiana
80.0
63.22
62.88
Pennsylvania
Lackawanna
75.3
56.42
55.96
Pennsylvania
Lancaster
83.3
64.22
63.86
Pennsylvania
Lawrence
72.3
55.41
54.91
B-14

-------
Pennsylvania
Lehigh
83.3
62.89
62.46
Pennsylvania
Luzerne
76.3
57.12
56.66
Pennsylvania
Lycoming
77.3
60.47
60.05
Pennsylvania
Mercer
82.0
62.27
61.61
Pennsylvania
Montgomery
85.7
67.65
67.24
Pennsylvania
Northampton
84.3
63.40
62.96
Pennsylvania
Perry
77.0
59.02
58.60
Pennsylvania
Philadelphia
90.3
72.70
72.33
Pennsylvania
Tioga
77.7
60.79
60.37
Pennsylvania
Washington
78.3
64.27
63.99
Pennsylvania
Westmoreland
79.0
62.13
61.75
Pennsylvania
York
82.0
63.20
62.90
Rhode Island
Kent
84.3
63.64
63.40
Rhode Island
Providence
82.3
61.68
61.42
Rhode Island
Washington
86.0
65.84
65.61
South Carolina
Abbeville
79.0
57.12
56.56
South Carolina
Aiken
76.0
53.62
53.10
South Carolina
Anderson
76.5
53.00
52.53
South Carolina
Barnwell
73.0
53.83
53.35
South Carolina
Berkeley
67.3
49.60
49.26
South Carolina
Charleston
74.0
56.30
55.92
South Carolina
Cherokee
74.0
52.56
52.17
South Carolina
Chester
75.7
52.36
51.92
South Carolina
Chesterfield
75.0
55.35
54.87
South Carolina
Colleton
72.3
53.16
52.69
South Carolina
Darlington
76.3
55.06
54.57
South Carolina
Edgefield
70.0
49.23
48.74
South Carolina
Oconee
73.0
50.55
50.09
South Carolina
Pickens
78.7
54.46
53.95
South Carolina
Richland
82.3
55.19
54.48
South Carolina
Spartanburg
82.3
58.39
57.94
South Carolina
Union
76.0
55.82
55.37
South Carolina
Williamsburg
69.3
49.70
49.24
South Carolina
York
76.7
53.72
53.27
South Dakota
Custer
70.0
62.61
62.50
South Dakota
Jackson
67.5
59.46
59.32
South Dakota
Minnehaha
66.0
52.80
52.47
Tennessee
Anderson
77.3
51.69
50.61
Tennessee
Blount
85.3
56.66
55.42
Tennessee
Davidson
77.7
54.66
54.08
B-15

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Tennessee
Hamilton
81.0
56.14
55.58
Tennessee
Jefferson
82.3
53.93
52.35
Tennessee
Knox
85.0
57.19
55.64
Tennessee
Loudon
85.0
56.07
55.23
Tennessee
Meigs
80.0
53.89
53.22
Tennessee
Rutherford
76.3
53.74
53.08
Tennessee
Sevier
80.7
56.19
55.27
Tennessee
Shelby
80.7
57.49
56.84
Tennessee
Sullivan
80.3
65.72
65.36
Tennessee
Sumner
83.0
59.18
58.53
Tennessee
Williamson
75.3
52.90
52.30
Tennessee
Wilson
78.7
56.46
55.89
Texas
Bexar
85.0
68.14
67.35
Texas
Brazoria
94.7
77.34
76.79
Texas
Brewster
64.0
53.48
53.15
Texas
Cameron
66.0
58.24
58.00
Texas
Collin
90.3
67.77
67.07
Texas
Dallas
88.3
69.97
69.20
Texas
Denton
94.0
67.33
66.63
Texas
Ellis
81.7
60.68
60.05
Texas
Galveston
85.0
69.04
68.73
Texas
Gregg
84.3
70.44
70.09
Texas
Harris
100.7
83.71
83.04
Texas
Harrison
79.0
62.59
62.15
Texas
Hidalgo
65.7
55.50
55.22
Texas
Hood
83.0
57.66
57.04
Texas
Hunt
78.0
62.53
62.03
Texas
Jefferson
84.7
69.78
69.44
Texas
Johnson
87.0
61.37
60.76
Texas
Kaufman
74.7
57.52
56.95
Texas
Montgomery
85.0
67.47
66.75
Texas
Nueces
72.3
60.49
60.20
Texas
Orange
78.0
62.98
62.62
Texas
Parker
88.7
61.35
60.69
Texas
Rockwall
79.7
62.46
61.90
Texas
Smith
81.0
66.10
65.64
Texas
Tarrant
95.3
69.01
68.34
Texas
Travis
81.3
62.83
61.88
Texas
Victoria
72.3
58.70
58.35
Texas
Webb
61.3
51.83
51.40
B-16

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Texas
El Paso
77.7
64.02
63.82
Utah
Box Elder
76.0
63.76
63.68
Utah
Cache
68.7
57.27
57.01
Utah
Davis
81.3
69.69
69.61
Utah
Salt Lake
81.0
69.43
69.33
Utah
San Juan
70.3
61.77
61.70
Utah
Tooele
78.0
64.78
64.63
Utah
Utah
76.7
66.18
66.08
Utah
Washington
78.5
61.33
61.19
Utah
Weber
80.3
67.01
66.94
Vermont
Bennington
72.0
53.98
53.66
Vermont
Chittenden
69.7
55.64
55.36
Virginia
Arlington
86.7
68.35
68.03
Virginia
Caroline
80.0
57.81
57.51
Virginia
Charles City
80.3
62.36
62.07
Virginia
Chesterfield
76.7
58.48
58.18
Virginia
Fairfax
90.0
68.41
68.10
Virginia
Fauquier
72.7
54.40
54.13
Virginia
Frederick
72.3
53.08
52.77
Virginia
Hanover
81.3
60.34
60.01
Virginia
Henrico
82.0
61.95
61.65
Virginia
Loudoun
80.7
57.41
57.11
Virginia
Madison
77.7
57.46
57.14
Virginia
Page
74.0
55.38
55.04
Virginia
Prince William
78.7
57.90
57.63
Virginia
Roanoke
74.7
56.25
55.77
Virginia
Rockbridge
69.7
54.32
53.93
Virginia
Stafford
81.7
60.37
60.04
Virginia
Wythe
72.7
56.20
55.80
Virginia
Alexandria City
81.7
62.10
61.82
Virginia
Hampton City
76.7
63.25
63.08
Virginia
Suffolk City
76.7
67.66
67.51
Washington
Clark
59.5
60.26
60.10
Washington
King
72.3
64.78
64.50
Washington
Klickitat
64.5
56.28
56.08
Washington
Pierce
68.7
58.47
58.05
Washington
Skagit
46.0
46.99
46.97
Washington
Spokane
68.3
55.02
54.72
Washington
Thurston
65.0
52.03
51.61
Washington
Whatcom
57.0
55.20
55.17
B-17

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West Virginia
Berkeley
75.0
55.58
55.29
West Virginia
Cabell
78.7
61.35
60.92
West Virginia
Greenbrier
69.7
57.31
56.99
West Virginia
Hancock
75.7
58.80
58.43
West Virginia
Kanawha
77.3
59.39
59.04
West Virginia
Monongalia
75.3
63.49
63.25
West Virginia
Ohio
78.3
59.45
59.08
West Virginia
Wood
79.0
62.74
62.43
Wisconsin
Ashland
63.0
51.22
50.91
Wisconsin
Brown
73.7
59.42
59.00
Wisconsin
Columbia
72.7
55.75
55.32
Wisconsin
Dane
72.0
56.08
55.63
Wisconsin
Dodge
74.7
58.54
58.07
Wisconsin
Door
88.7
69.81
69.15
Wisconsin
Florence
66.3
53.94
53.61
Wisconsin
Fond Du Lac
73.7
58.61
58.15
Wisconsin
Forest
69.5
56.22
55.87
Wisconsin
Jefferson
74.3
57.51
57.04
Wisconsin
Kenosha
84.7
73.40
72.88
Wisconsin
Kewaunee
82.7
66.06
65.49
Wisconsin
Manitowoc
85.0
68.68
68.08
Wisconsin
Marathon
70.0
56.60
56.27
Wisconsin
Milwaukee
82.7
69.79
69.24
Wisconsin
Oneida
69.0
56.18
55.85
Wisconsin
Outagamie
74.0
59.05
58.66
Wisconsin
Ozaukee
83.3
70.08
69.58
Wisconsin
Racine
80.3
69.95
69.47
Wisconsin
Rock
74.0
57.42
56.85
Wisconsin
St Croix
69.0
55.48
55.20
Wisconsin
Sauk
69.7
54.12
53.71
Wisconsin
Sheboygan
88.0
71.71
71.10
Wisconsin
Vernon
69.7
53.79
53.33
Wisconsin
Vilas
68.7
55.94
55.61
Wisconsin
Walworth
75.7
58.11
57.53
Wisconsin
Washington
72.3
58.44
57.99
Wisconsin
Waukesha
75.0
60.78
60.27
Wyoming
Campbell
67.3
62.28
62.20
Wyoming
Sublette
70.0
64.98
64.91
Wyoming
Teton
62.7
55.00
54.94
B-18

-------
Air Quality Modeling
Technical Support Document: Heavy-Duty Vehicle
Greenhouse Gas Emission Standards Final Rule
Appendix C
Annual PM2.s Design Values for Air Quality Modeling
Scenarios
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC 27711
July 2011
C-l

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Table C-l. Annual PM25 Design Values for HDGHG Scenarios (units are ug/m3)
State Name
County Name
Baseline DV
2030
Reference
Case DV
2030 Control
Case DV
Alabama
Baldwin
11.44
8.34
8.34
Alabama
Clay
13.27
9.25
9.25
Alabama
Colbert
12.75
8.95
8.95
Alabama
DeKalb
14.13
9.58
9.58
Alabama
Escambia
13.19
10.23
10.23
Alabama
Etowah
14.87
10.21
10.21
Alabama
Houston
13.22
10.13
10.13
Alabama
Jefferson
18.57
13.06
13.05
Alabama
Madison
13.83
9.31
9.31
Alabama
Mobile
12.90
9.51
9.51
Alabama
Montgomery
14.24
10.52
10.53
Alabama
Morgan
13.32
9.19
9.19
Alabama
Russell
15.73
11.32
11.32
Alabama
Shelby
14.43
10.18
10.17
Alabama
Sumter
11.92
8.59
8.59
Alabama
Talladega
14.51
10.23
10.23
Alabama
Tuscaloosa
13.56
9.66
9.66
Alabama
Walker
13.86
9.71
9.71
Arizona
Cochise
7.00
6.63
6.63
Arizona
Coconino
6.49
6.02
6.02
Arizona
Gila
8.94
8.27
8.27
Arizona
Maricopa
12.59
10.28
10.29
Arizona
Pima
6.04
5.17
5.17
Arizona
Pinal
7.77
6.95
6.95
Arizona
Santa Cruz
12.94
12.21
12.22
Arkansas
Arkansas
12.45
9.46
9.46
Arkansas
Ashley
12.83
10.18
10.18
Arkansas
Crittenden
13.36
9.24
9.24
Arkansas
Faulkner
12.79
9.84
9.83
Arkansas
Garland
12.40
9.60
9.59
Arkansas
Mississippi
12.61
8.90
8.90
Arkansas
Phillips
12.10
8.76
8.76
Arkansas
Polk
11.65
9.14
9.15
Arkansas
Pope
12.79
10.17
10.17
Arkansas
Pulaski
14.05
10.62
10.62
C-2

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Arkansas
Union
12.86
10.07
10.07
Arkansas
White
12.57
9.77
9.77
California
Alameda
9.34
8.59
8.59
California
Butte
12.73
10.34
10.34
California
Calaveras
7.77
6.49
6.49
California
Colusa
7.39
6.59
6.59
California
Contra Costa
9.47
8.40
8.39
California
Fresno
17.17
14.67
14.67
California
Imperial
12.71
11.68
11.68
California
Inyo
5.25
4.92
4.92
California
Kern
19.17
15.61
15.60
California
Kings
17.28
14.44
14.44
California
Lake
4.62
4.06
4.06
California
Los Angeles
18.19
14.72
14.71
California
Mendocino
6.46
5.40
5.39
California
Merced
14.78
12.68
12.68
California
Monterey
6.96
5.97
5.97
California
Nevada
6.71
5.78
5.78
California
Orange
15.75
13.05
13.04
California
Placer
9.80
8.15
8.15
California
Plumas
11.46
9.80
9.80
California
Riverside
20.95
17.48
17.47
California
Sacramento
11.88
10.43
10.43
California
San Bernardino
19.67
16.85
16.84
California
San Diego
13.46
11.94
11.94
California
San Francisco
9.62
8.80
8.80
California
San Joaquin
12.94
11.33
11.33
California
San Luis Obispo
7.94
6.59
6.59
California
San Mateo
9.03
8.12
8.12
California
Santa Barbara
10.37
8.78
8.78
California
Santa Clara
11.38
10.56
10.56
California
Shasta
7.41
5.99
5.99
California
Solano
9.99
9.08
9.08
California
Sonoma
8.21
7.07
7.07
California
Stanislaus
14.21
11.87
11.87
California
Sutter
9.85
8.09
8.09
California
Tulare
18.51
15.44
15.44
California
Ventura
11.68
9.72
9.72
California
Yolo
9.03
7.82
7.82
Colorado
Adams
10.06
8.50
8.49

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Colorado
Arapahoe
7.96
6.73
6.73
Colorado
Boulder
8.32
7.36
7.36
Colorado
Delta
7.44
6.31
6.30
Colorado
Denver
9.76
8.23
8.22
Colorado
Elbert
4.40
3.93
3.93
Colorado
El Paso
7.94
6.81
6.81
Colorado
Larimer
7.33
6.69
6.69
Colorado
Mesa
9.28
8.03
8.03
Colorado
Pueblo
7.45
6.50
6.49
Colorado
San Miguel
4.65
4.26
4.26
Colorado
Weld
8.78
7.78
7.77
Connecticut
Fairfield
13.21
9.38
9.38
Connecticut
Hartford
11.03
8.00
8.00
Connecticut
Litchfield
8.01
5.50
5.50
Connecticut
New Haven
13.12
9.29
9.29
Connecticut
New London
10.96
7.95
7.95
Delaware
Kent
12.61
8.25
8.25
Delaware
New Castle
14.87
10.00
9.98
Delaware
Sussex
13.39
8.77
8.77
District Of Co
District of Columbia
14.16
9.41
9.41
Florida
Alachua
9.59
6.94
6.94
Florida
Bay
11.46
8.83
8.83
Florida
Brevard
8.32
5.75
5.75
Florida
Broward
8.22
5.89
5.89
Florida
Citrus
9.00
6.52
6.52
Florida
Duval
10.44
7.85
7.85
Florida
Escambia
11.72
9.30
9.30
Florida
Hillsborough
10.74
7.56
7.56
Florida
Lee
8.36
5.94
5.94
Florida
Leon
12.56
9.62
9.62
Florida
Manatee
8.81
5.85
5.85
Florida
Marion
10.11
7.43
7.43
Florida
Miami-Dade
9.45
6.51
6.51
Florida
Orange
9.61
6.69
6.69
Florida
Palm Beach
7.84
5.74
5.74
Florida
Pinellas
9.82
6.94
6.94
Florida
Polk
9.53
6.74
6.74
Florida
St. Lucie
8.34
5.84
5.84
Florida
Sarasota
8.77
6.00
6.00
Florida
Seminole
9.51
6.60
6.59

-------
Florida
Volusia
9.27
6.45
6.45
Georgia
Bibb
16.54
11.84
11.85
Georgia
Chatham
13.93
10.10
10.10
Georgia
Clarke
14.90
10.45
10.45
Georgia
Clayton
16.50
11.17
11.17
Georgia
Cobb
16.15
11.13
11.13
Georgia
DeKalb
15.48
10.15
10.15
Georgia
Dougherty
14.46
10.91
10.91
Georgia
Floyd
16.13
11.34
11.34
Georgia
Fulton
17.43
11.75
11.74
Georgia
Glynn
12.25
9.26
9.26
Georgia
Gwinnett
16.07
10.99
10.99
Georgia
Hall
14.16
9.90
9.90
Georgia
Houston
14.19
9.99
9.99
Georgia
Lowndes
12.58
9.77
9.77
Georgia
Muscogee
15.39
11.09
11.09
Georgia
Paulding
14.12
9.40
9.40
Georgia
Richmond
15.68
11.64
11.65
Georgia
Walker
15.49
10.57
10.57
Georgia
Washington
15.14
11.12
11.12
Georgia
Wilkinson
15.27
10.94
10.94
Idaho
Ada
8.41
7.52
7.49
Idaho
Bannock
7.66
7.00
7.00
Idaho
Benewah
9.59
8.80
8.80
Idaho
Canyon
8.46
7.31
7.28
Idaho
Franklin
7.70
6.63
6.61
Idaho
Idaho
9.58
8.99
8.99
Idaho
Shoshone
12.08
11.03
11.03
Illinois
Adams
12.50
9.37
9.36
Illinois
Champaign
12.53
8.87
8.86
Illinois
Cook
15.75
11.39
11.37
Illinois
DuPage
13.82
10.01
9.99
Illinois
Jersey
12.89
9.37
9.35
Illinois
Kane
14.34
10.47
10.44
Illinois
Lake
11.81
8.71
8.69
Illinois
McHenry
12.40
9.05
9.03
Illinois
McLean
12.39
9.00
8.99
Illinois
Macon
13.24
9.69
9.67
Illinois
Madison
16.72
12.03
12.02
Illinois
Peoria
13.34
9.84
9.83

-------
Illinois
Randolph
13.11
9.34
9.33
Illinois
Rock Island
12.01
9.00
8.99
Illinois
Saint Clair
15.58
11.12
11.11
Illinois
Sangamon
13.13
9.90
9.88
Illinois
Will
13.63
9.76
9.74
Illinois
Winnebago
13.57
10.21
10.19
Indiana
Allen
13.67
10.12
10.08
Indiana
Clark
16.44
10.71
10.70
Indiana
Delaware
13.69
9.53
9.52
Indiana
Dubois
15.19
9.94
9.92
Indiana
Floyd
14.85
9.54
9.53
Indiana
Henry
13.64
9.49
9.47
Indiana
Howard
13.93
9.98
9.96
Indiana
Knox
14.03
9.31
9.29
Indiana
Lake
14.33
10.64
10.62
Indiana
LaPorte
12.69
9.29
9.27
Indiana
Madison
13.97
9.76
9.74
Indiana
Marion
16.05
11.19
11.17
Indiana
Porter
13.21
9.65
9.63
Indiana
St. Joseph
13.69
10.51
10.48
Indiana
Spencer
14.32
9.09
9.07
Indiana
Tippecanoe
13.70
9.75
9.73
Indiana
Vanderburgh
14.99
10.47
10.46
Indiana
Vigo
13.99
9.40
9.38
Iowa
Black Hawk
11.16
8.61
8.59
Iowa
Clinton
12.52
9.46
9.45
Iowa
Johnson
12.08
9.32
9.30
Iowa
Linn
10.79
8.20
8.19
Iowa
Montgomery
10.02
7.73
7.72
Iowa
Muscatine
12.92
9.93
9.92
Iowa
Palo Alto
9.53
7.56
7.55
Iowa
Polk
10.64
8.15
8.13
Iowa
Pottawattamie
11.13
8.68
8.67
Iowa
Scott
14.42
11.03
11.02
Iowa
Van Buren
10.84
8.31
8.30
Iowa
Woodbury
10.32
8.26
8.26
Iowa
Wright
10.37
8.06
8.05
Kansas
Johnson
11.10
8.56
8.55
Kansas
Linn
10.47
8.34
8.34
Kansas
Sedgwick
10.36
8.29
8.28

-------
Kansas
Shawnee
10.93
8.78
8.77
Kansas
Sumner
9.89
8.02
8.02
Kansas
Wyandotte
12.73
9.86
9.85
Kentucky
Bell
14.10
9.30
9.30
Kentucky
Boyd
14.49
9.40
9.40
Kentucky
Bullitt
14.92
9.64
9.63
Kentucky
Campbell
13.67
8.59
8.58
Kentucky
Carter
12.22
7.64
7.64
Kentucky
Christian
13.20
8.60
8.60
Kentucky
Daviess
14.10
8.77
8.76
Kentucky
Fayette
14.87
9.55
9.53
Kentucky
Franklin
13.37
8.41
8.39
Kentucky
Hardin
13.58
8.60
8.59
Kentucky
Henderson
13.93
9.31
9.30
Kentucky
Jefferson
15.55
10.00
9.99
Kentucky
Kenton
14.39
9.20
9.19
Kentucky
Laurel
12.55
8.05
8.05
Kentucky
McCracken
13.41
9.06
9.06
Kentucky
Madison
13.61
8.59
8.57
Kentucky
Perry
13.21
8.63
8.63
Kentucky
Pike
13.49
8.62
8.62
Kentucky
Warren
13.83
8.95
8.95
Louisiana
Caddo
12.53
9.56
9.56
Louisiana
Calcasieu
11.07
8.64
8.63
Louisiana
Concordia
11.42
8.50
8.50
Louisiana
East Baton Rouge
13.38
10.38
10.37
Louisiana
Iberville
12.90
9.89
9.88
Louisiana
Jefferson
11.52
8.08
8.07
Louisiana
Lafayette
11.08
8.25
8.25
Louisiana
Ouachita
11.97
9.32
9.32
Louisiana
Rapides
11.03
8.33
8.33
Louisiana
Tangipahoa
12.03
8.79
8.79
Louisiana
Terrebonne
10.74
7.89
7.89
Louisiana
West Baton Rouge
13.51
10.49
10.47
Maine
Androscoggin
9.90
7.72
7.72
Maine
Aroostook
9.74
9.02
9.02
Maine
Cumberland
11.13
8.61
8.62
Maine
Hancock
5.76
4.53
4.54
Maine
Kennebec
9.99
7.85
7.85
Maine
Oxford
10.13
8.38
8.39

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Maine
Penobscot
9.12
7.25
7.25
Maryland
Anne Arundel
14.82
10.43
10.43
Maryland
Baltimore
14.76
10.30
10.29
Maryland
Cecil
12.68
8.31
8.30
Maryland
Harford
12.51
8.29
8.28
Maryland
Montgomery
12.47
8.28
8.28
Maryland
Prince George's
13.03
8.54
8.53
Maryland
Washington
13.70
9.26
9.26
Maryland
Baltimore (City)
15.76
11.07
11.07
Massachusetts
Berkshire
10.65
8.16
8.16
Massachusetts
Bristol
9.58
6.99
6.99
Massachusetts
Essex
9.58
7.27
7.27
Massachusetts
Hampden
12.17
9.15
9.15
Massachusetts
Plymouth
9.87
7.38
7.38
Massachusetts
Suffolk
13.07
9.99
9.99
Massachusetts
Worcester
11.29
8.45
8.45
Michigan
Allegan
11.84
8.65
8.63
Michigan
Bay
10.93
8.08
8.07
Michigan
Berrien
11.72
8.60
8.57
Michigan
Genesee
11.61
8.37
8.36
Michigan
Ingham
12.23
8.82
8.81
Michigan
Kalamazoo
12.84
9.42
9.40
Michigan
Kent
12.89
9.31
9.29
Michigan
Macomb
12.70
9.28
9.27
Michigan
Missaukee
8.26
6.54
6.54
Michigan
Monroe
13.92
9.78
9.76
Michigan
Muskegon
11.61
8.59
8.57
Michigan
Oakland
13.78
9.84
9.82
Michigan
Ottawa
12.55
9.07
9.05
Michigan
Saginaw
10.61
7.86
7.85
Michigan
St. Clair
13.34
10.14
10.14
Michigan
Washtenaw
13.88
9.76
9.75
Michigan
Wayne
17.50
12.65
12.64
Minnesota
Cass
5.70
4.94
4.93
Minnesota
Dakota
9.30
7.40
7.39
Minnesota
Hennepin
9.76
7.70
7.69
Minnesota
Mille Lacs
6.54
5.48
5.47
Minnesota
Olmsted
10.13
8.01
7.99
Minnesota
Ramsey
11.32
9.09
9.08
Minnesota
Saint Louis
7.51
6.22
6.22

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Minnesota
Scott
9.00
7.19
7.18
Minnesota
Stearns
8.58
7.06
7.05
Mississippi
Adams
11.29
8.37
8.37
Mississippi
Bolivar
12.36
9.26
9.26
Mississippi
DeSoto
12.43
8.55
8.55
Mississippi
Forrest
13.62
10.08
10.09
Mississippi
Harrison
12.20
9.00
9.00
Mississippi
Hinds
12.56
9.14
9.14
Mississippi
Jackson
12.04
8.80
8.79
Mississippi
Jones
14.39
10.59
10.59
Mississippi
Lauderdale
13.07
9.49
9.49
Mississippi
Lee
12.57
8.78
8.77
Mississippi
Lowndes
12.79
9.23
9.23
Mississippi
Pearl River
12.14
8.99
8.99
Mississippi
Warren
12.32
9.11
9.11
Missouri
Boone
11.84
9.02
9.00
Missouri
Buchanan
12.80
10.17
10.17
Missouri
Cass
10.67
8.25
8.24
Missouri
Cedar
11.12
8.60
8.59
Missouri
Clay
11.03
8.49
8.48
Missouri
Greene
11.75
9.04
9.04
Missouri
Jackson
12.78
9.82
9.81
Missouri
Jefferson
13.79
10.06
10.05
Missouri
Monroe
10.87
8.09
8.08
Missouri
Saint Charles
13.29
9.69
9.66
Missouri
Sainte Genevieve
13.34
9.69
9.68
Missouri
Saint Louis
13.46
9.53
9.52
Missouri
St. Louis City
14.56
10.32
10.30
Montana
Cascade
5.72
5.15
5.15
Montana
Flathead
9.99
8.72
8.72
Montana
Gallatin
4.38
4.20
4.20
Montana
Lake
9.06
8.06
8.06
Montana
Lewis and Clark
8.20
7.39
7.39
Montana
Lincoln
14.93
12.93
12.93
Montana
Missoula
10.52
9.26
9.25
Montana
Ravalli
9.01
8.05
8.05
Montana
Rosebud
6.58
6.18
6.18
Montana
Sanders
6.75
6.21
6.21
Montana
Silver Bow
10.14
9.01
9.01
Montana
Yellowstone
8.14
7.18
7.17

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Nebraska
Cass
9.99
7.81
7.80
Nebraska
Douglas
9.88
7.66
7.65
Nebraska
Hall
7.95
6.53
6.52
Nebraska
Lancaster
8.90
6.81
6.80
Nebraska
Lincoln
7.57
6.61
6.61
Nebraska
Sarpy
9.79
7.58
7.57
Nebraska
Scotts Bluff
6.04
5.39
5.39
Nebraska
Washington
9.29
7.31
7.30
Nevada
Clark
9.44
8.24
8.24
Nevada
Washoe
8.11
6.83
6.83
New Hampshire
Belknap
7.28
5.52
5.52
New Hampshire
Cheshire
11.53
8.88
8.88
New Hampshire
Coos
10.24
8.59
8.59
New Hampshire
Grafton
8.43
6.62
6.62
New Hampshire
Hillsborough
10.18
7.64
7.64
New Hampshire
Merrimack
9.72
7.26
7.26
New Hampshire
Rockingham
9.00
6.89
6.89
New Hampshire
Sullivan
9.86
7.70
7.70
New Jersey
Atlantic
11.47
7.75
7.75
New Jersey
Bergen
13.09
8.93
8.93
New Jersey
Camden
13.31
9.01
9.00
New Jersey
Essex
13.27
8.85
8.84
New Jersey
Gloucester
13.46
9.02
9.01
New Jersey
Hudson
14.24
9.72
9.72
New Jersey
Mercer
12.71
8.65
8.65
New Jersey
Middlesex
12.15
8.31
8.31
New Jersey
Morris
11.50
7.83
7.83
New Jersey
Ocean
10.92
7.24
7.24
New Jersey
Passaic
12.88
8.66
8.66
New Jersey
Union
14.94
10.05
10.05
New Jersey
Warren
12.72
8.78
8.78
New Mexico
Bernalillo
7.03
5.78
5.78
New Mexico
Chaves
6.54
5.91
5.91
New Mexico
Dona Ana
9.95
8.67
8.67
New Mexico
Grant
5.93
5.57
5.57
New Mexico
Sandoval
7.99
7.25
7.25
New Mexico
San Juan
5.92
5.34
5.34
New Mexico
Santa Fe
4.76
4.28
4.28
New York
Albany
11.83
9.48
9.49
New York
Bronx
15.43
11.01
11.00
C-10

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New York
Chautauqua
9.80
6.73
6.72
New York
Erie
12.62
9.09
9.09
New York
Essex
5.94
4.74
4.74
New York
Kings
14.20
9.73
9.73
New York
Monroe
10.64
7.99
7.99
New York
Nassau
11.66
7.91
7.91
New York
New York
16.18
11.26
11.26
New York
Niagara
11.96
8.95
8.94
New York
Onondaga
10.08
7.46
7.46
New York
Orange
10.99
7.80
7.80
New York
Queens
12.18
8.34
8.34
New York
Richmond
13.31
8.81
8.81
New York
St. Lawrence
7.29
6.04
6.04
New York
Steuben
9.00
6.37
6.37
New York
Suffolk
11.52
7.77
7.77
New York
Westchester
11.73
7.89
7.89
North Carolina
Alamance
13.94
9.17
9.17
North Carolina
Buncombe
12.60
8.39
8.39
North Carolina
Caswell
13.19
8.52
8.52
North Carolina
Catawba
15.31
9.94
9.94
North Carolina
Chatham
11.99
7.78
7.78
North Carolina
Cumberland
13.73
9.49
9.49
North Carolina
Davidson
15.17
9.95
9.95
North Carolina
Duplin
11.30
7.72
7.73
North Carolina
Durham
13.57
8.98
8.98
North Carolina
Edgecombe
12.37
8.49
8.50
North Carolina
Forsyth
14.28
9.09
9.09
North Carolina
Gaston
14.26
9.26
9.26
North Carolina
Guilford
13.79
8.94
8.94
North Carolina
Haywood
12.98
9.23
9.23
North Carolina
Jackson
12.09
8.15
8.15
North Carolina
Lenoir
11.12
7.59
7.59
North Carolina
McDowell
14.24
9.68
9.68
North Carolina
Martin
10.86
7.31
7.31
North Carolina
Mecklenburg
15.31
10.20
10.20
North Carolina
Mitchell
12.75
8.38
8.38
North Carolina
Montgomery
12.35
8.15
8.15
North Carolina
New Hanover
9.96
6.70
6.70
North Carolina
Onslow
10.98
7.43
7.43
North Carolina
Orange
13.12
8.54
8.54
C-ll

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North Carolina
Pitt
11.59
7.94
7.94
North Carolina
Robeson
12.78
8.73
8.73
North Carolina
Rowan
14.02
9.34
9.34
North Carolina
Swain
12.65
8.52
8.52
North Carolina
Wake
13.54
8.99
8.99
North Carolina
Watauga
12.05
7.59
7.59
North Carolina
Wayne
12.96
9.12
9.13
North Dakota
Billings
4.61
4.29
4.29
North Dakota
Burke
5.90
5.63
5.63
North Dakota
Burleigh
6.61
5.93
5.92
North Dakota
Cass
7.72
6.57
6.57
North Dakota
McKenzie
5.01
4.73
4.73
North Dakota
Mercer
6.04
5.67
5.66
Ohio
Athens
12.39
7.75
7.75
Ohio
Butler
15.36
10.60
10.59
Ohio
Clark
14.64
10.03
10.02
Ohio
Clermont
14.15
9.09
9.08
Ohio
Cuyahoga
17.37
12.16
12.15
Ohio
Franklin
15.27
10.31
10.29
Ohio
Greene
13.36
8.79
8.78
Ohio
Hamilton
17.54
11.59
11.58
Ohio
Jefferson
16.51
10.46
10.46
Ohio
Lake
13.02
9.00
8.99
Ohio
Lawrence
15.14
10.07
10.08
Ohio
Lorain
13.87
9.54
9.52
Ohio
Lucas
14.38
10.18
10.16
Ohio
Mahoning
15.12
10.30
10.30
Ohio
Montgomery
15.54
10.50
10.49
Ohio
Portage
13.37
9.06
9.05
Ohio
Preble
13.70
9.29
9.28
Ohio
Scioto
14.65
9.46
9.46
Ohio
Stark
16.26
10.84
10.83
Ohio
Summit
15.17
10.50
10.49
Ohio
Trumbull
14.53
9.96
9.96
Oklahoma
Caddo
9.22
7.45
7.45
Oklahoma
Cherokee
11.79
9.41
9.41
Oklahoma
Kay
10.26
8.54
8.53
Oklahoma
Lincoln
10.28
8.22
8.22
Oklahoma
Mayes
11.70
9.51
9.51
Oklahoma
Muskogee
11.89
9.71
9.71
C-12

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Oklahoma
Oklahoma
10.07
7.80
7.79
Oklahoma
Ottawa
11.69
9.40
9.40
Oklahoma
Pittsburg
11.09
8.85
8.85
Oklahoma
Sequoyah
12.99
10.47
10.47
Oklahoma
Tulsa
11.52
9.23
9.23
Oregon
Jackson
10.32
9.26
9.26
Oregon
Klamath
11.20
10.03
10.02
Oregon
Lane
11.93
10.78
10.78
Oregon
Multnomah
9.13
7.87
7.87
Oregon
Union
8.35
7.31
7.30
Pennsylvania
Adams
13.05
8.61
8.60
Pennsylvania
Allegheny
20.31
13.19
13.20
Pennsylvania
Beaver
16.38
10.92
10.93
Pennsylvania
Berks
15.82
11.27
11.26
Pennsylvania
Bucks
13.42
9.01
9.00
Pennsylvania
Cambria
15.40
9.96
9.96
Pennsylvania
Centre
12.78
8.54
8.54
Pennsylvania
Chester
15.22
10.21
10.19
Pennsylvania
Cumberland
14.45
9.82
9.82
Pennsylvania
Dauphin
15.13
9.93
9.91
Pennsylvania
Delaware
15.23
10.35
10.35
Pennsylvania
Erie
12.54
8.79
8.79
Pennsylvania
Lackawanna
11.73
8.05
8.05
Pennsylvania
Lancaster
16.55
11.26
11.24
Pennsylvania
Lehigh
14.50
10.21
10.21
Pennsylvania
Luzerne
12.76
8.93
8.93
Pennsylvania
Mercer
13.28
8.85
8.85
Pennsylvania
Northampton
13.68
9.51
9.51
Pennsylvania
Perry
12.81
8.73
8.73
Pennsylvania
Philadelphia
15.19
10.42
10.41
Pennsylvania
Washington
15.17
9.31
9.31
Pennsylvania
Westmoreland
15.49
9.70
9.70
Pennsylvania
York
16.52
11.13
11.11
Rhode Island
Providence
12.14
9.15
9.15
South Carolina
Beaufort
11.52
7.95
7.95
South Carolina
Charleston
12.21
8.56
8.56
South Carolina
Chesterfield
12.56
8.73
8.73
South Carolina
Edgefield
13.17
9.33
9.33
South Carolina
Florence
12.65
8.80
8.80
South Carolina
Georgetown
12.85
9.11
9.12
C-13

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South Carolina
Greenville
15.65
10.55
10.55
South Carolina
Greenwood
13.53
9.22
9.22
South Carolina
Horry
12.04
8.42
8.42
South Carolina
Lexington
14.64
10.15
10.16
South Carolina
Oconee
10.95
7.15
7.15
South Carolina
Richland
14.24
9.75
9.75
South Carolina
Spartanburg
14.17
9.35
9.35
South Dakota
Brookings
9.37
7.83
7.83
South Dakota
Brown
8.42
7.29
7.29
South Dakota
Codington
10.14
8.67
8.67
South Dakota
Custer
5.64
5.25
5.25
South Dakota
Jackson
5.39
4.97
4.97
South Dakota
Minnehaha
10.18
8.21
8.20
South Dakota
Pennington
8.77
7.93
7.93
Tennessee
Blount
14.30
9.68
9.68
Tennessee
Davidson
14.21
9.46
9.45
Tennessee
Dyer
12.28
8.47
8.47
Tennessee
Hamilton
15.67
10.63
10.64
Tennessee
Knox
15.64
10.33
10.33
Tennessee
Lawrence
11.69
8.09
8.09
Tennessee
Loudon
15.49
10.53
10.53
Tennessee
McMinn
14.29
9.62
9.62
Tennessee
Maury
13.21
9.06
9.06
Tennessee
Montgomery
13.80
9.26
9.25
Tennessee
Putnam
13.37
8.75
8.75
Tennessee
Roane
14.49
9.55
9.55
Tennessee
Shelby
13.71
9.34
9.34
Tennessee
Sullivan
14.16
9.84
9.84
Tennessee
Sumner
13.68
8.75
8.75
Texas
Bowie
12.85
9.91
9.91
Texas
Dallas
12.77
9.56
9.56
Texas
Ector
7.78
6.67
6.67
Texas
El Paso
9.09
7.82
7.82
Texas
Harris
15.42
11.94
11.93
Texas
Harrison
11.69
8.74
8.74
Texas
Hidalgo
10.98
9.31
9.32
Texas
Jefferson
11.56
8.68
8.67
Texas
Nueces
10.42
7.86
7.86
Texas
Orange
11.51
8.95
8.95
Texas
Tarrant
12.23
9.04
9.04
C-14

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Utah
Box Elder
8.40
6.94
6.91
Utah
Cache
11.56
9.68
9.65
Utah
Davis
10.31
8.42
8.40
Utah
Salt Lake
12.02
9.77
9.75
Utah
Utah
10.52
8.61
8.60
Utah
Weber
11.16
9.04
9.01
Vermont
Addison
8.94
7.41
7.42
Vermont
Bennington
8.52
6.75
6.75
Vermont
Chittenden
10.02
8.26
8.26
Vermont
Rutland
11.08
9.09
9.09
Virginia
Arlington
14.27
9.38
9.37
Virginia
Charles
12.37
7.84
7.84
Virginia
Chesterfield
13.44
8.52
8.52
Virginia
Fairfax
13.88
9.33
9.33
Virginia
Henrico
13.51
8.55
8.55
Virginia
Loudoun
13.57
9.15
9.15
Virginia
Page
12.79
8.11
8.10
Virginia
Bristol City
13.93
9.02
9.02
Virginia
Hampton City
12.17
7.90
7.90
Virginia
Lynchburg City
12.84
8.08
8.09
Virginia
Norfolk City
12.78
8.51
8.51
Virginia
Roanoke City
14.27
9.22
9.22
Virginia
Salem City
14.69
9.70
9.70
Virginia
Virginia Beach City
12.40
8.17
8.18
Washington
King
11.24
9.34
9.34
Washington
Pierce
10.55
9.30
9.30
Washington
Snohomish
9.91
8.84
8.84
Washington
Spokane
9.97
7.90
7.90
West Virginia
Berkeley
15.93
11.06
11.06
West Virginia
Brooke
16.52
10.51
10.51
West Virginia
Cabell
16.30
10.93
10.93
West Virginia
Hancock
15.76
10.06
10.07
West Virginia
Harrison
13.99
9.01
9.02
West Virginia
Kanawha
16.52
10.72
10.73
West Virginia
Marion
15.03
9.69
9.69
West Virginia
Marshall
15.19
9.42
9.42
West Virginia
Monongalia
14.35
8.69
8.69
West Virginia
Ohio
14.58
8.81
8.81
West Virginia
Raleigh
12.90
8.13
8.13
West Virginia
Wood
15.40
10.35
10.35
C-15

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Wisconsin
Ashland
6.07
5.13
5.12
Wisconsin
Brown
11.39
9.61
9.60
Wisconsin
Dane
12.20
9.59
9.58
Wisconsin
Dodge
11.04
8.57
8.56
Wisconsin
Forest
7.41
6.13
6.13
Wisconsin
Grant
11.79
9.07
9.06
Wisconsin
Kenosha
11.98
8.93
8.90
Wisconsin
Manitowoc
10.20
8.27
8.26
Wisconsin
Milwaukee
14.08
10.82
10.79
Wisconsin
Outagamie
10.96
9.01
9.01
Wisconsin
Ozaukee
11.60
8.93
8.92
Wisconsin
St. Croix
10.09
8.17
8.16
Wisconsin
Sauk
10.22
7.83
7.82
Wisconsin
Taylor
8.24
6.78
6.77
Wisconsin
Vilas
6.78
5.66
5.66
Wisconsin
Waukesha
13.91
10.86
10.84
Wyoming
Campbell
6.29
6.02
6.02
Wyoming
Converse
3.58
3.38
3.38
Wyoming
Fremont
8.17
7.36
7.36
Wyoming
Laramie
4.48
3.96
3.96
Wyoming
Sheridan
9.70
8.81
8.81
C-16

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Air Quality Modeling
Technical Support Document: Heavy-Duty Vehicle
Greenhouse Gas Emission Standards Final Rule
Appendix D
24-Hour PM2.5 Design Values for Air Quality Modeling
Scenarios
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC 27711
July 2011

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Table D-l. 24-hour PM25 Design Values for HDGHG Scenarios (units are
ug/m3)
State Name
County Name
Baseline
DV
2030
Reference
Case DV
2030 Control
Case DV
Alabama
Baldwin
26.21
18.26
18.27
Alabama
Clay
31.88
18.55
18.54
Alabama
Colbert
30.43
17.06
17.04
Alabama
De Kalb
32.08
18.45
18.44
Alabama
Escambia
29.03
20.78
20.77
Alabama
Etowah
35.18
20.75
20.72
Alabama
Houston
28.66
19.51
19.51
Alabama
Jefferson
44.06
29.33
29.33
Alabama
Madison
33.58
18.14
18.11
Alabama
Mobile
30.03
20.02
20.01
Alabama
Montgomery
32.05
20.09
20.07
Alabama
Morgan
31.58
16.15
16.13
Alabama
Russell
35.55
24.13
24.12
Alabama
Shelby
32.05
19.60
19.58
Alabama
Sumter
28.90
17.48
17.48
Alabama
Talladega
33.46
20.92
20.91
Alabama
Tuscaloosa
29.80
18.33
18.32
Alabama
Walker
32.82
18.89
18.87
Arizona
Cochise
16.62
15.87
15.88
Arizona
Coconino
17.11
15.87
15.88
Arizona
Gila
22.12
20.29
20.30
Arizona
Maricopa
32.80
24.87
24.87
Arizona
Pima
12.27
9.81
9.80
Arizona
Pinal
17.55
14.67
14.68
Arizona
Santa Cruz
36.08
33.96
33.98
Arkansas
Arkansas
29.16
19.25
19.26
Arkansas
Ashley
28.91
21.88
21.86
Arkansas
Crittenden
35.06
19.54
19.55
Arkansas
Faulkner
29.87
20.11
20.10
Arkansas
Garland
29.27
19.88
19.86
Arkansas
Phillips
29.18
18.83
18.84
Arkansas
Polk
26.13
16.99
17.00
Arkansas
Pope
28.32
19.40
19.41
D-2

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Arkansas
Pulaski
31.93
22.69
22.69
Arkansas
Union
28.70
21.03
21.00
Arkansas
White
29.91
20.37
20.36
California
Alameda
32.58
26.91
26.91
California
Butte
52.55
37.41
37.41
California
Calaveras
20.55
14.96
14.96
California
Colusa
26.16
22.24
22.24
California
Contra Costa
34.70
29.54
29.54
California
Fresno
60.22
47.36
47.35
California
Imperial
40.21
34.61
34.60
California
Inyo
20.00
18.80
18.81
California
Kern
64.54
51.55
51.53
California
Kings
58.06
45.99
45.98
California
Lake
12.94
12.10
12.10
California
Los Angeles
50.97
45.57
45.55
California
Mendocino
15.30
10.39
10.39
California
Merced
46.15
35.26
35.26
California
Monterey
14.35
12.36
12.36
California
Nevada
16.55
13.18
13.18
California
Orange
43.76
38.74
38.71
California
Placer
29.88
23.79
23.79
California
Plumas
32.44
26.18
26.18
California
Riverside
59.13
49.27
49.26
California
Sacramento
49.22
45.78
45.78
California
San Bernardino
55.50
49.10
49.09
California
San Diego
35.55
32.37
32.37
California
San Francisco
30.91
26.83
26.82
California
San Joaquin
41.88
33.70
33.69
California
San Luis Obispo
22.58
18.53
18.52
California
San Mateo
29.41
26.15
26.15
California
Santa Barbara
24.07
22.79
22.79
California
Santa Clara
38.61
35.70
35.69
California
Shasta
20.42
14.35
14.35
California
Solano
34.76
30.44
30.42
California
Sonoma
29.10
24.08
24.08
California
Stanislaus
51.48
39.82
39.82
California
Sutter
38.55
28.98
28.98
California
Tulare
56.63
43.66
43.65
D-3

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.61
~09~
In
.10
37~
.18
to"
23
.62
io~
32
.79
57~
.84
IT
~80~
.67
74~
~98~
.57
30~
.77
ir
.83
!02~
^99~
.67
45~
.69
31
^8~
.37
42~
!oT
.34
!oT
.31
Ventura
30.30
26.61
Yolo
30.38
25.09
Adams
Arapahoe
Boulder
25.35
21.27
21.12
19.73
17.13
18.39
Delta
20.76
16.20
Denver
Elbert
26.44
13.18
21.76
11.25
El Paso
16.51
13.63
Larimer
18.30
16.61
Mesa
23.51
19.57
Pueblo
San Miguel
Weld
Fairfield
Hartford
Litchfield
15.42
10.11
22.90
36.27
31.83
27.16
12.79
9.57
20.87
24.52
19.79
14.66
New Haven
38.37
23.75
New London
32.03
18.97
Kent
32.14
19.58
New Castle
36.66
23.33
Sussex
33.78
20.78
Washington
36.35
22.34
Alachua
21.35
14.64
Bay
28.08
19.83
Brevard
20.73
14.02
Broward
18.63
13.99
Citrus
21.22
13.66
Duval
24.35
18.46
Escambia
28.80
22.70
Hillsborough
23.44
16.31
Lee
17.70
12.99
Leon
27.03
19.36
Manatee
19.57
12.42
Marion
22.56
15.01
Miami-Dade
19.13
13.34
Orange
21.83
14.00
Palm Beach
18.22
14.31

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Florida
Pinellas
21.73
15.33
15.33
Florida
Polk
19.30
13.53
13.53
Florida
St Lucie
18.18
12.31
12.31
Florida
Sarasota
19.22
12.99
12.99
Florida
Seminole
22.08
13.49
13.48
Florida
Volusia
22.00
13.69
13.69
Georgia
Bibb
33.56
22.40
22.41
Georgia
Chatham
28.45
19.41
19.42
Georgia
Clayton
35.88
22.81
22.80
Georgia
Cobb
35.04
20.92
20.89
Georgia
De Kalb
33.92
20.84
20.83
Georgia
Dougherty
34.15
24.59
24.59
Georgia
Floyd
35.12
22.22
22.20
Georgia
Fulton
37.66
23.84
23.84
Georgia
Glynn
26.13
19.25
19.27
Georgia
Gwinnett
32.81
19.11
19.12
Georgia
Hall
30.11
19.69
19.70
Georgia
Houston
29.63
19.02
19.02
Georgia
Lowndes
25.68
18.17
18.17
Georgia
Muscogee
34.58
23.58
23.58
Georgia
Paulding
33.02
19.72
19.71
Georgia
Richmond
32.70
23.60
23.61
Georgia
Walker
30.98
19.46
19.45
Georgia
Washington
30.83
20.31
20.30
Georgia
Wilkinson
33.16
21.63
21.62
Idaho
Ada
28.36
24.05
23.83
Idaho
Bannock
27.08
23.92
23.87
Idaho
Benewah
32.94
29.32
29.30
Idaho
Canyon
31.80
25.22
24.95
Idaho
Franklin
36.76
30.59
30.44
Idaho
Idaho
28.43
26.61
26.58
Idaho
Lemhi
36.53
33.06
33.07
Idaho
Power
33.36
29.48
29.43
Idaho
Shoshone
38.16
33.85
33.83
Illinois
Adams
31.41
18.31
18.19
Illinois
Champaign
31.32
20.21
20.14
Illinois
Cook
43.03
29.90
29.74
Illinois
Du Page
34.64
26.39
26.26
D-5

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Illinois
Hamilton
31.60
17.67
17.63
Illinois
Jersey
32.18
20.64
20.54
Illinois
Kane
34.83
25.70
25.53
Illinois
Lake
33.08
22.45
22.36
Illinois
La Salle
28.92
20.00
19.91
Illinois
McHenry
31.58
21.97
21.81
Illinois
McLean
33.43
21.50
21.38
Illinois
Macon
33.25
19.37
19.29
Illinois
Madison
39.16
25.21
25.15
Illinois
Peoria
32.76
21.31
21.19
Illinois
Randolph
28.96
20.36
20.30
Illinois
Rock Island
30.90
22.59
22.49
Illinois
St Clair
33.70
23.04
22.94
Illinois
Sangamon
33.41
22.72
22.69
Illinois
Will
36.45
24.64
24.44
Illinois
Winnebago
34.73
25.41
25.27
Indiana
Allen
33.10
23.51
23.47
Indiana
Clark
37.57
21.71
21.67
Indiana
Delaware
32.07
20.63
20.53
Indiana
Dubois
35.36
21.63
21.52
Indiana
Elkhart
34.43
25.42
25.29
Indiana
Floyd
33.26
18.26
18.22
Indiana
Henry
31.86
19.62
19.49
Indiana
Howard
32.21
20.56
20.43
Indiana
Knox
35.92
21.66
21.63
Indiana
Lake
38.98
29.92
29.88
Indiana
La Porte
33.00
22.26
22.14
Indiana
Madison
32.82
20.28
20.20
Indiana
Marion
38.47
24.97
24.90
Indiana
Porter
32.96
22.94
22.89
Indiana
St Joseph
33.16
24.47
24.39
Indiana
Spencer
32.32
15.75
15.72
Indiana
Tippecanoe
35.68
21.84
21.80
Indiana
Vanderburgh
34.80
23.26
23.19
Indiana
Vigo
34.88
20.78
20.66
Iowa
Black Hawk
30.78
21.97
21.88
Iowa
Clinton
33.95
23.86
23.78
Iowa
Johnson
34.67
23.97
23.82
D-6

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Iowa
Linn
30.60
20.82
20.68
Iowa
Montgomery
27.50
18.78
18.76
Iowa
Muscatine
36.03
27.51
27.38
Iowa
Palo Alto
25.73
18.41
18.38
Iowa
Polk
31.46
22.15
22.10
Iowa
Pottawattamie
28.60
21.35
21.33
Iowa
Scott
37.10
25.60
25.56
Iowa
Van Buren
28.36
19.76
19.66
Iowa
Woodbury
26.40
20.17
20.15
Iowa
Wright
28.65
20.21
20.18
Kansas
Johnson
29.30
23.05
23.00
Kansas
Linn
25.38
19.10
19.08
Kansas
Sedgwick
25.37
19.02
18.98
Kansas
Shawnee
29.16
22.74
22.69
Kansas
Sumner
22.84
16.73
16.68
Kansas
Wyandotte
29.58
22.35
22.32
Kentucky
Bell
29.90
18.03
18.01
Kentucky
Boyd
33.15
17.01
17.02
Kentucky
Bullitt
34.63
18.30
18.27
Kentucky
Campbell
31.20
16.56
16.52
Kentucky
Carter
29.91
14.59
14.59
Kentucky
Christian
33.60
17.38
17.36
Kentucky
Daviess
33.86
17.83
17.81
Kentucky
Fayette
32.23
17.75
17.65
Kentucky
Franklin
32.17
17.06
16.93
Kentucky
Hardin
32.81
16.81
16.80
Kentucky
Henderson
31.85
18.03
18.01
Kentucky
Jefferson
36.44
21.32
21.21
Kentucky
Kenton
34.74
19.31
19.26
Kentucky
Laurel
25.16
14.83
14.85
Kentucky
McCracken
33.62
18.16
18.14
Kentucky
Madison
30.11
15.38
15.30
Kentucky
Perry
28.54
14.64
14.63
Kentucky
Pike
30.52
16.70
16.70
Kentucky
Warren
33.14
17.36
17.34
Louisiana
Caddo
27.56
20.12
20.12
Louisiana
Calcasieu
26.38
19.38
19.36
Louisiana
Concordia
26.16
17.31
17.31
D-7

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Louisiana
East Baton Rouge
29.36
22.36
22.24
Louisiana
Iberville
28.62
21.87
21.85
Louisiana
Jefferson
27.06
17.90
17.89
Louisiana
Lafayette
24.28
17.30
17.31
Louisiana
Ouachita
28.91
20.52
20.53
Louisiana
Rapides
30.26
19.98
19.98
Louisiana
Tangipahoa
29.61
19.80
19.79
Louisiana
Terrebonne
26.25
17.42
17.42
Louisiana
West Baton Rouge
29.08
22.19
22.08
Maine
Androscoggin
26.56
19.35
19.37
Maine
Aroostook
24.23
21.48
21.52
Maine
Cumberland
29.20
20.35
20.36
Maine
Hancock
19.43
12.70
12.70
Maine
Kennebec
26.21
19.11
19.14
Maine
Oxford
28.36
21.78
21.80
Maine
Penobscot
22.03
15.96
15.96
Maryland
Anne Arundel
36.16
26.15
26.13
Maryland
Baltimore
35.84
24.70
24.68
Maryland
Cecil
30.82
19.67
19.63
Maryland
Harford
31.21
18.65
18.62
Maryland
Montgomery
30.93
18.34
18.31
Maryland
Prince Georges
33.46
18.90
18.90
Maryland
Washington
33.43
21.56
21.53
Maryland
Baltimore City
39.01
28.06
28.04
Massachusetts
Berkshire
31.06
22.35
22.37
Massachusetts
Bristol
25.07
16.83
16.83
Massachusetts
Essex
28.72
19.62
19.61
Massachusetts
Hampden
33.13
23.61
23.63
Massachusetts
Plymouth
28.48
18.21
18.21
Massachusetts
Suffolk
32.17
22.40
22.40
Massachusetts
Worcester
30.66
20.90
20.92
Michigan
Allegan
33.82
24.43
24.22
Michigan
Bay
31.68
20.90
20.80
Michigan
Berrien
31.32
21.26
21.15
Michigan
Genesee
30.46
21.59
21.46
Michigan
Ingham
31.96
23.11
22.99
Michigan
Kalamazoo
31.17
21.73
21.67
Michigan
Kent
36.53
24.19
24.13

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Michigan
Macomb
35.32
26.37
26.29
Michigan
Missaukee
24.83
16.46
16.47
Michigan
Monroe
38.88
24.46
24.43
Michigan
Muskegon
34.71
22.89
22.68
Michigan
Oakland
39.94
24.67
24.59
Michigan
Ottawa
34.24
25.78
25.63
Michigan
Saginaw
30.66
20.98
20.90
Michigan
St Clair
39.61
29.11
28.99
Michigan
Washtenaw
39.46
23.80
23.74
Michigan
Wayne
43.88
31.43
31.42
Minnesota
Cass
18.02
14.24
14.21
Minnesota
Dakota
25.42
19.23
19.17
Minnesota
Hennepin
27.25
19.38
19.31
Minnesota
Mille Lacs
22.03
17.46
17.45
Minnesota
Ramsey
28.38
21.32
21.31
Minnesota
St Louis
23.53
17.63
17.62
Minnesota
Scott
24.98
18.28
18.24
Mississippi
Adams
27.48
17.94
17.93
Mississippi
Bolivar
28.98
20.16
20.17
Mississippi
De Soto
30.82
17.28
17.26
Mississippi
Forrest
30.48
22.18
22.19
Mississippi
Harrison
29.00
19.92
19.91
Mississippi
Hinds
28.83
18.68
18.69
Mississippi
Jackson
26.96
18.25
18.25
Mississippi
Jones
31.21
22.29
22.29
Mississippi
Lee
32.18
17.93
17.90
Mississippi
Lowndes
32.44
18.92
18.89
Mississippi
Warren
30.26
20.04
20.02
Missouri
Boone
30.23
20.20
20.18
Missouri
Buchanan
30.10
21.94
21.93
Missouri
Cass
25.61
17.65
17.62
Missouri
Cedar
28.70
19.80
19.79
Missouri
Clay
28.04
20.87
20.79
Missouri
Greene
28.27
19.98
20.00
Missouri
Jackson
27.88
21.09
21.07
Missouri
Jefferson
33.43
21.83
21.78
Missouri
Monroe
27.83
18.90
18.83
Missouri
St Charles
33.16
20.90
20.84
D-9

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Missouri
Ste Genevieve
31.44
19.57
19.55
Missouri
St Louis
33.21
23.36
23.26
Missouri
St Louis City
34.35
22.17
22.11
Montana
Cascade
20.15
17.40
17.40
Montana
Flathead
27.17
24.30
24.29
Montana
Gallatin
29.55
26.85
26.84
Montana
Lake
43.66
39.29
39.28
Montana
Lewis And Clark
33.53
28.81
28.82
Montana
Lincoln
42.71
36.38
36.36
Montana
Missoula
44.64
37.54
37.40
Montana
Ravalli
45.11
38.13
38.09
Montana
Rosebud
19.73
18.50
18.50
Montana
Sanders
20.42
18.63
18.63
Montana
Silver Bow
35.00
29.07
29.06
Montana
Yellowstone
19.38
16.62
16.61
Nebraska
Cass
28.30
21.11
21.04
Nebraska
Douglas
25.76
19.40
19.38
Nebraska
Hall
19.16
14.52
14.47
Nebraska
Lancaster
24.77
18.35
18.33
Nebraska
Scotts Bluff
16.66
14.56
14.56
Nebraska
Washington
24.01
18.44
18.42
Nevada
Clark
25.26
21.16
21.17
Nevada
Washoe
30.78
23.43
23.39
New Hampshire
Belknap
20.55
12.56
12.57
New Hampshire
Cheshire
30.23
21.33
21.34
New Hampshire
Coos
26.50
18.77
18.79
New Hampshire
Grafton
23.00
16.18
16.18
New Hampshire
Hillsborough
28.66
20.98
20.97
New Hampshire
Merrimack
25.65
16.60
16.59
New Hampshire
Rockingham
26.35
18.05
18.04
New Hampshire
Sullivan
28.92
18.48
18.49
New Jersey
Bergen
37.03
23.13
23.11
New Jersey
Camden
37.37
22.22
22.20
New Jersey
Essex
38.38
23.76
23.75
New Jersey
Hudson
41.43
29.96
29.93
New Jersey
Mercer
34.75
20.25
20.25
New Jersey
Middlesex
34.82
21.30
21.31
New Jersey
Morris
32.32
19.53
19.53
D-10

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New Jersey
Ocean
31.56
17.74
17.75
New Jersey
Passaic
36.30
21.58
21.56
New Jersey
Union
40.47
25.61
25.60
New Jersey
Warren
34.06
22.74
22.73
New Mexico
Bernalillo
18.60
14.80
14.80
New Mexico
Chaves
15.68
13.26
13.26
New Mexico
Dona Ana
32.95
26.85
26.85
New Mexico
Grant
13.00
12.23
12.24
New Mexico
Sandoval
15.68
13.82
13.82
New Mexico
San Juan
12.40
11.16
11.16
New Mexico
Santa Fe
9.78
8.68
8.68
New York
Albany
34.26
26.84
26.86
New York
Bronx
38.87
26.20
26.18
New York
Chautauqua
29.15
17.17
17.19
New York
Erie
35.35
25.86
25.83
New York
Essex
22.45
14.79
14.80
New York
Kings
36.94
23.45
23.43
New York
Monroe
32.20
20.08
20.06
New York
Nassau
34.01
20.55
20.54
New York
New York
39.70
26.63
26.62
New York
Niagara
33.87
23.22
23.18
New York
Onondaga
27.35
18.07
18.08
New York
Orange
28.92
20.16
20.16
New York
Queens
35.56
23.06
23.05
New York
Richmond
34.93
21.65
21.65
New York
St Lawrence
22.05
17.41
17.43
New York
Steuben
27.81
16.31
16.33
New York
Suffolk
34.66
18.74
18.73
New York
Westchester
33.51
19.91
19.89
North Carolina
Alamance
31.72
19.53
19.53
North Carolina
Buncombe
30.05
17.14
17.13
North Carolina
Caswell
29.45
17.50
17.50
North Carolina
Catawba
34.53
20.53
20.51
North Carolina
Chatham
26.94
15.83
15.83
North Carolina
Cumberland
30.78
19.22
19.25
North Carolina
Davidson
31.35
20.00
20.00
North Carolina
Duplin
28.30
17.05
17.07
North Carolina
Durham
31.02
18.04
18.04
D-ll

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North Carolina
Edgecombe
26.78
18.01
18.01
North Carolina
Forsyth
31.92
19.84
19.83
North Carolina
Gaston
30.86
17.75
17.75
North Carolina
Guilford
30.63
19.49
19.48
North Carolina
Haywood
27.74
17.73
17.72
North Carolina
Jackson
24.96
15.09
15.08
North Carolina
Lenoir
25.20
17.28
17.32
North Carolina
McDowell
31.55
18.67
18.68
North Carolina
Martin
24.83
16.23
16.25
North Carolina
Mecklenburg
32.33
20.69
20.68
North Carolina
Mitchell
30.25
16.79
16.80
North Carolina
Montgomery
28.21
16.73
16.72
North Carolina
New Hanover
25.40
15.22
15.24
North Carolina
Onslow
24.61
15.97
15.99
North Carolina
Orange
29.35
17.24
17.24
North Carolina
Pitt
26.21
17.87
17.89
North Carolina
Robeson
29.92
17.71
17.71
North Carolina
Rowan
30.23
19.03
19.03
North Carolina
Swain
27.34
16.19
16.19
North Carolina
Wake
31.63
19.08
19.08
North Carolina
Watauga
30.43
17.09
17.09
North Carolina
Wayne
29.72
19.32
19.35
North Dakota
Billings
13.07
12.01
12.01
North Dakota
Burke
16.73
15.74
15.74
North Dakota
Burleigh
17.62
15.51
15.51
North Dakota
Cass
21.22
16.89
16.88
North Dakota
McKenzie
11.96
11.32
11.33
North Dakota
Mercer
16.98
15.52
15.51
Ohio
Athens
32.32
16.87
16.87
Ohio
Butler
39.23
24.13
24.03
Ohio
Clark
35.37
20.06
20.04
Ohio
Clermont
34.46
17.71
17.68
Ohio
Cuyahoga
44.20
28.72
28.62
Ohio
Franklin
38.51
21.56
21.49
Ohio
Greene
32.21
17.48
17.44
Ohio
Hamilton
40.60
22.65
22.62
Ohio
Jefferson
41.96
24.65
24.67
Ohio
Lake
37.16
21.82
21.79
D-12

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Ohio
Lawrence
33.77
19.18
19.18
Ohio
Lorain
31.56
19.65
19.70
Ohio
Lucas
36.34
25.53
25.42
Ohio
Mahoning
36.83
22.36
22.35
Ohio
Montgomery
37.80
23.11
23.03
Ohio
Portage
34.32
19.82
19.80
Ohio
Preble
32.85
17.90
17.83
Ohio
Scioto
34.55
19.03
19.05
Ohio
Stark
36.90
20.80
20.83
Ohio
Summit
38.06
22.14
22.09
Ohio
Trumbull
36.23
22.50
22.44
Oklahoma
Caddo
23.97
17.69
17.67
Oklahoma
Cherokee
27.55
21.32
21.32
Oklahoma
Kay
31.80
26.22
26.16
Oklahoma
Lincoln
27.83
20.27
20.27
Oklahoma
Mayes
28.71
23.34
23.32
Oklahoma
Muskogee
29.54
21.91
21.91
Oklahoma
Oklahoma
27.12
19.51
19.44
Oklahoma
Ottawa
29.14
22.13
22.13
Oklahoma
Pittsburg
26.37
19.26
19.24
Oklahoma
Sequoyah
31.43
24.13
24.14
Oklahoma
Tulsa
30.37
23.02
23.01
Oregon
Jackson
33.72
29.07
29.01
Oregon
Klamath
44.08
37.94
37.92
Oregon
Lane
48.95
42.48
42.45
Oregon
Multnomah
29.88
25.29
25.25
Oregon
Union
27.38
23.47
23.42
Pennsylvania
Adams
34.93
21.28
21.28
Pennsylvania
Allegheny
64.27
40.92
40.98
Pennsylvania
Beaver
43.42
24.23
24.25
Pennsylvania
Berks
37.71
27.57
27.49
Pennsylvania
Bucks
34.01
22.01
21.97
Pennsylvania
Cambria
39.04
20.63
20.65
Pennsylvania
Centre
36.28
22.09
22.10
Pennsylvania
Chester
36.70
23.65
23.65
Pennsylvania
Cumberland
38.00
26.17
26.12
Pennsylvania
Dauphin
38.04
27.11
27.03
Pennsylvania
Delaware
35.24
22.51
22.46
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Pennsylvania
Erie
34.46
21.50
21.48
Pennsylvania
Lackawanna
31.55
18.45
18.45
Pennsylvania
Lancaster
40.83
30.44
30.35
Pennsylvania
Lehigh
36.40
25.51
25.51
Pennsylvania
Luzerne
32.46
20.72
20.70
Pennsylvania
Mercer
36.30
20.76
20.71
Pennsylvania
Northampton
36.72
24.37
24.35
Pennsylvania
Perry
30.46
21.20
21.15
Pennsylvania
Philadelphia
37.30
23.21
23.17
Pennsylvania
Washington
38.14
20.24
20.27
Pennsylvania
Westmoreland
37.12
19.47
19.48
Pennsylvania
York
38.24
28.37
28.31
Rhode Island
Providence
30.62
20.72
20.72
South Carolina
Charleston
27.93
17.43
17.46
South Carolina
Chesterfield
28.77
17.78
17.77
South Carolina
Edgefield
32.23
19.35
19.33
South Carolina
Florence
28.81
18.09
18.08
South Carolina
Greenville
32.55
20.20
20.21
South Carolina
Greenwood
30.01
17.63
17.62
South Carolina
Horry
28.30
18.10
18.10
South Carolina
Lexington
32.86
20.91
20.90
South Carolina
Oconee
27.98
16.09
16.08
South Carolina
Richland
33.20
20.78
20.77
South Carolina
Spartanburg
32.46
19.34
19.32
South Dakota
Brookings
23.54
17.62
17.58
South Dakota
Brown
18.73
15.09
15.09
South Dakota
Codington
23.67
18.49
18.47
South Dakota
Custer
14.36
12.49
12.46
South Dakota
Jackson
12.73
11.15
11.16
South Dakota
Minnehaha
24.17
17.94
17.91
South Dakota
Pennington
18.58
16.65
16.64
Tennessee
Blount
32.54
19.79
19.77
Tennessee
Davidson
33.50
19.17
19.15
Tennessee
Dyer
31.92
18.69
18.69
Tennessee
Hamilton
33.53
21.75
21.75
Tennessee
Knox
36.66
21.31
21.29
Tennessee
Lawrence
28.48
16.11
16.10
Tennessee
Loudon
32.20
20.15
20.14
D-14

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Tennessee
Mc Minn
32.73
18.79
18.78
Tennessee
Maury
30.96
17.97
17.96
Tennessee
Montgomery
36.30
18.95
18.93
Tennessee
Putnam
32.66
17.66
17.66
Tennessee
Roane
30.24
16.48
16.45
Tennessee
Shelby
33.50
18.36
18.34
Tennessee
Sullivan
31.13
20.20
20.19
Tennessee
Sumner
33.66
16.87
16.86
Texas
Bowie
29.42
20.15
20.15
Texas
Dallas
27.44
19.38
19.29
Texas
Ector
17.81
14.26
14.25
Texas
El Paso
22.93
19.18
19.17
Texas
Harris
30.81
22.11
22.06
Texas
Harrison
25.95
18.54
18.57
Texas
Hidalgo
26.42
22.72
22.73
Texas
Nueces
27.55
19.97
19.98
Texas
Orange
27.78
20.08
20.06
Texas
Tarrant
25.76
18.26
18.26
Utah
Box Elder
33.20
25.93
25.82
Utah
Cache
56.95
41.82
41.68
Utah
Davis
38.95
30.14
30.02
Utah
Salt Lake
50.14
38.49
38.44
Utah
Tooele
30.53
25.31
25.26
Utah
Utah
44.00
33.73
33.67
Utah
Weber
38.58
28.87
28.80
Vermont
Addison
31.73
21.00
21.02
Vermont
Bennington
26.47
18.09
18.10
Vermont
Chittenden
30.13
22.64
22.64
Vermont
Rutland
30.60
25.51
25.52
Virginia
Arlington
34.18
19.66
19.64
Virginia
Charles City
31.76
17.95
17.95
Virginia
Chesterfield
31.25
16.22
16.22
Virginia
Fairfax
34.47
20.68
20.65
Virginia
Henrico
31.95
17.66
17.66
Virginia
Loudoun
34.45
20.14
20.12
Virginia
Page
30.06
17.35
17.33
Virginia
Bristol City
30.24
17.25
17.24
Virginia
Hampton City
29.01
17.06
17.06
D-15

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Virginia
Lynchburg City
30.71
16.73
16.72
Virginia
Norfolk City
29.66
17.96
17.96
Virginia
Roanoke City
32.70
18.75
18.74
Virginia
Salem City
34.06
20.23
20.21
Washington
King
29.16
24.93
24.92
Washington
Pierce
41.82
36.22
36.22
Washington
Snohomish
34.36
31.34
31.34
Washington
Spokane
29.86
22.39
22.38
West Virginia
Berkeley
34.51
24.65
24.62
West Virginia
Brooke
43.90
26.15
26.16
West Virginia
Cabell
35.10
19.43
19.43
West Virginia
Hancock
40.64
21.14
21.18
West Virginia
Harrison
33.53
17.07
17.07
West Virginia
Kanawha
36.98
20.15
20.12
West Virginia
Marion
33.68
17.06
17.08
West Virginia
Marshall
33.98
18.13
18.16
West Virginia
Monongalia
35.65
15.12
15.12
West Virginia
Ohio
32.00
17.36
17.38
West Virginia
Raleigh
30.67
15.83
15.82
West Virginia
Summers
31.26
15.82
15.81
West Virginia
Wood
35.44
18.82
18.85
Wisconsin
Ashland
18.61
13.41
13.41
Wisconsin
Brown
36.56
31.46
31.42
Wisconsin
Dane
35.57
26.32
26.19
Wisconsin
Dodge
31.82
22.83
22.64
Wisconsin
Forest
25.26
18.43
18.40
Wisconsin
Grant
34.35
25.20
25.09
Wisconsin
Kenosha
32.78
23.32
23.13
Wisconsin
Manitowoc
29.70
22.96
22.92
Wisconsin
Milwaukee
39.92
29.18
29.09
Wisconsin
Outagamie
32.87
26.98
26.87
Wisconsin
Ozaukee
32.53
24.07
23.91
Wisconsin
St Croix
26.66
20.69
20.68
Wisconsin
Sauk
28.63
22.15
22.01
Wisconsin
Taylor
25.38
19.24
19.18
Wisconsin
Vilas
22.61
17.22
17.19
Wisconsin
Waukesha
35.48
26.77
26.63
Wyoming
Campbell
18.63
17.50
17.49
D-16

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Wyoming
Converse
10.00
9.56
9.56
Wyoming
Fremont
29.80
24.10
24.08
Wyoming
Laramie
11.93
10.63
10.63
Wyoming
Sheridan
30.86
27.26
27.26
D-17

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