&EPA
United Sates
Enviroimental PlutoUiuii
Agency
Air Quality Modeling Technical Support
Document: 2017-2025 Light-Duty Vehicle
Greenhouse Gas Emission Standards Final Rule
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EPA-454/R-12-004
August 2012
Air Quality Modeling Technical Support Document:
2017-2025 Light-Duty Vehicle Greenhouse Gas Emission
Standards Final Rule
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC 27711
August 2012
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Table of Contents
I. Introduction 1
II. Air Quality Modeling Platform 2
A. Air Quality Model 2
B. Model domains and grid resolution 3
C. Modeling Simulation Periods 4
D. LD GHG 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 LD GHG Standards on Future 8-Hour Ozone Levels 9
B. Impacts of LD GHG Standards on Future Annual PM2.5 Levels 11
C. Impacts of LD GHG Standards on Future 24-hour PM2.5 Levels 12
D. Impacts of LD GHG Standards on Future Toxic Air Pollutant Levels 13
1. Acetaldehyde 13
2. Formaldehyde 15
3. Benzene 17
4. 1,3-Butadiene 19
5. Acrolein 20
E. Population Metrics 22
F. Impacts of LD GHG Standards on Future Annual Nitrogen and Sulfur Deposition. ..23
G. Impacts of LD GHG Standards on Future Visibility Levels 24
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
2017-2025 Light-Duty Vehicle Greenhouse Gas Final Rule (hereafter referred to as LD GHG).
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.l 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 6.2 and 6.3, respectively of the RIA, are slightly
different than the final vehicle standard inventories presented in Chapter 4 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 2017-2025 light-duty vehicle standards, and a 2030 control
case projection with 2017-2025 light-duty vehicle standards. The year 2005 was selected for the
LD GHG base year because this is the most recent year for which EPA had a complete national
emissions inventory at the time of emission and air quality modeling.
The remaining sections of the Air Quality Modeling 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. In Section III we present the results of
modeling performed for 2030 to assess the impacts on air quality of the vehicle standards.
Information on the development of emissions inventories for the LD GHG 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-HQ-OAR-2010-0799). The docket for
this final rulemaking also contains state/sector/pollutant emissions summaries for each of the
emissions scenarios modeled.
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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II. Air Quality Modeling Platform
The 2005-based CMAQ modeling platform was used as the basis for the air quality
modeling of the 2017-2025 LD GHG final rule. This platform represents a structured system of
connected modeling-related tools and data that provide a consistent and transparent basis for
assessing the air quality response to projected changes in emissions. The base year of data used
to construct this platform includes emissions and meteorology for 2005. The platform was
developed by the U.S. EPA's Office of Air Quality Planning and Standards in collaboration with
the Office of Research and Development and is intended to support a variety of regulatory and
research model applications and analyses. This modeling platform and analysis is fully described
below.
A. 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).
2 Allen, D., Burns, D., Chock, D., Kumar, N., Lamb, B., Moran, M. (February 2009 Draft Version). Report on the
Peer Review of the Atmospheric Modeling and Analysis Division, NERL/ORD/EPA. U.S. EPA, Research Triangle
Park, NC. CMAQ version 4.7 was released on December, 2008. It is available from the Community Modeling and
Analysis System (CMAS) as well as previous peer-review reports at: http://www.cmascenter.org.
3 Hogrefe, C., Biswas, I, Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, I, et al. (2004). Simulating regional-scale
ozone climatology over the eastern United States: model evaluation results. Atmospheric Environment, 38(17),
2627-2638.
4 United States Environmental Protection Agency. (2008). Technical support document for the final
locomotive/marine rule: Air quality modeling analyses. Research Triangle Park, N.C.: U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards, Air Quality Assessment Division.
5 Lin, M., Oki, T., Holloway, T., Streets, D.G., Bengtsson, M., Kanae, S., (2008). Long range transport of acidifying
substances in East Asia Part I: Model evaluation and sensitivity studies. Atmospheric Environment, 42(24), 5939-
5955.
6 CMAQ version 4.7.1 model code is available from the Community Modeling and Analysis System (CMAS) at:
http://www.cmascenter.org as well as at EPA-HQ-OAR-0472-DRAFT-l 1662.
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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-1. This domain has a parent horizontal grid of 36 km with
two finer-scale 12 km grids over portions of the eastern and western U.S. The model extends
vertically from the surface to 100 millibars (approximately 15 km) using a sigma-pressure
coordinate system. Air quality conditions at the outer boundary of the 36 km domain were taken
from a global model and did not change over the simulations. In turn, the 36 km grid was only
used to establish the incoming air quality concentrations along the boundaries of the 12 km grids.
Only the finer grid data were used in determining the impacts of the 2017-2025 LD GHG
emission standard program changes. Table II-1 provides some basic geographic information
regarding the CMAQ domains.
In addition to the CMAQ model, the LD GHG 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 LD GHG air quality modeling are described in the EITSD
found in the docket for this rule (EPA-HQ-OAR-2010-0799).
Table II-l. Geogra
Map Projection
Grid Resolution
Coordinate Center
True Latitudes
Dimensions
Vertical extent
phic elements of domains used in LD GHG modeling.
CMAQ Modeling Configuration
National Grid
Western U.S. Fine Grid
Eastern U.S. Fine Grid
Lambert Conformal Projection
36km
12km
12km
97 deg W, 40degN
33 deg N and 45 deg N
148x112x14
213x192x14
279 x 240 x 14
14 Layers: Surface to 100 millibar level (see Table II-3)
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Figure II-l. Map of the CMAQ modeling domain. The black outer box denotes the 36 km
national modeling domain; the red inner box is the 12 km western U.S. fine grid; and the
blue inner box is the 12 km eastern U.S. fine grid.
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. LD GHG 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 PM2.5 and ozone. Specifically, we compare a 2030 reference scenario, a
scenario without the vehicle standards, to a 2030 control scenario which includes the vehicle
standards. This is done by calculating the simulated air quality ratios between 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 6.3 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 PM2.5 in 2030 were estimated by applying the modeled 2005-to-2030
relative change in PM2.5 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 PM2.5 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 jig/m3). EPA's Modeled Attainment Test Software (MATS) was used to calculate
the future year design values. The software (including documentation) is available at:
http://www.epa.gov/scram001/modelingapps 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,
PM25, 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 PEL and land surface schemes
• Kain-Fritsh 2 cumulus parameterization
• Reisner 2 mixed phase moisture scheme
• RRTM longwave radiation scheme
• Dudhia shortwave radiation scheme
Three dimensional analysis nudging for temperature and moisture was applied above the
boundary layer only. Analysis nudging for the wind field was applied above and below the
boundary layer. The 36 km domain nudging weighting factors were 3.0 x 104 for wind fields and
temperatures and 1.0 x 105 for moisture fields. The 12 km domain nudging weighting factors
were 1.0 x 104 for wind fields and temperatures and 1.0 x 105 for moisture fields.
All three sets of model runs were conducted in 5.5 day segments with 12 hours of overlap
for spin-up purposes. All three meteorological modeling domains contained 34 vertical layers
with an approximately 38m deep surface layer and a 100 millibar top. The MM5 and CMAQ
vertical structures are shown in Table II-3 and do not vary by horizontal grid resolution.
Table II-3. Vertical layer structure for MM5 and CMAQ (heights are layer top).
CMAQ Layers
0
1
2
3
4
5
6
7
8
MM5 Layers
0
1
2
o
J
4
5
6
7
8
9
10
11
12
13
14
15
Sigma P
1.000
0.995
0.990
0.985
0.980
0.970
0.960
0.950
0.940
0.930
0.920
0.910
0.900
0.880
0.860
0.840
Approximate
Height (m)
0
38
77
115
154
232
310
389
469
550
631
712
794
961
1,130
1,303
Approximate
Pressure (mb)
1000
995
991
987
982
973
964
955
946
937
928
919
910
892
874
856
10 Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth-Generation Perm State/NCAR Mesoscale
Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for Atmospheric Research, Boulder CO.
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9
10
11
12
13
14
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
0.820
0.800
0.770
0.740
0.700
0.650
0.600
0.550
0.500
0.450
0.400
0.350
0.300
0.250
0.200
0.150
0.100
0.050
0.000
1,478
1,657
1,930
2,212
2,600
3,108
3,644
4,212
4,816
5,461
6,153
6,903
7,720
8,621
9,625
10,764
12,085
13,670
15,674
838
820
793
766
730
685
640
595
550
505
460
415
370
325
280
235
190
145
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 (PEL) heights. Qualitatively,
the model fields closely matched the observed synoptic patterns, which is not unexpected given
the use of nudging. The operational evaluation included statistical comparisons of
model/observed pairs (e.g., mean normalized bias, mean normalized error, index of agreement,
root mean square errors, etc.) for multiple meteorological parameters. For this portion of the
evaluation, five meteorological parameters were investigated: temperature, humidity, shortwave
downward radiation, wind speed, and wind direction. The three individual MM5 evaluations are
described elsewhere.12'13'14 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.
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 2017-2025 light-duty vehicle greenhouse
gas final 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 LD GHG control case relative to the 2030
reference case.
A. Impacts of LD GHG 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 2017-2025 LD GHG vehicle standards. Specifically, we compare a 2030 reference
scenario, a scenario without the 2017-2025 light-duty vehicle standards, to a 2030 control
scenario which includes the 2017-2025 light-duty vehicle standards. Our modeling indicates that
there will be very small changes in ozone across most of the country. In addition, ozone
15 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric Chemistry Modeling
Group, Harvard University, Cambridge, MA, October 15, 2004.
16 Henze, O.K., J.H. Seinfeld, N.L. Ng, J.H. Kroll, T-M. Fu, 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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concentrations in some areas will decrease and ozone concentrations in some other areas will
increase. The ozone impacts are related to downstream emissions changes from VMT rebound
and upstream emissions changes in electrical power generation and fuel production. In some
areas the ozone impact is a result of a combination of the various emissions changes but in other
areas the impact is likely mainly the result of one of the types of emissions changes. Some of the
ozone increases and decreases are related mainly to upstream emissions changes in electricity
generation. Some areas saw increases in ozone due mainly to increased demand for electricity
from electric vehicles (e.g. Las Vegas, Dayton, and Little Rock) while other areas saw decreases
in ozone due mainly to projected power plant closings (e.g. northeast West Virginia).17 Some of
the ozone decreases are mainly related to upstream emissions reductions from reduced refinery
demand as fuel production decreases (e.g. the Gulf Coast) and some of the ozone increases are
mainly related to increased emissions of NOx from the VMT rebound effect (e.g. Knoxville and
Atlanta). Figure III-l presents the changes in 8-hour ozone design value concentration in 2030
between the reference case and the control case.18 Appendix B details the state and county 8-
hour maximum ozone design values for the ambient baseline and the future reference and control
cases.
Difference in 8-hr Ozone DV- 2030ctjdghg_ctl2 minus 2030ctjdghg_ref
Figure III-l. Projected Change in 2030 8-hour Ozone Design Values Between the
Reference Case and Control Case
17 Section 4.7.3.1 has more information on the IPM modeling which was done to project future electricity demand
and plant locations.
18 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.
10
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As can be seen in Figure III-l, the majority of the ozone design value impacts are
between + 0.30 ppb and -0.030 ppb. However, there are two counties that will experience 8-hour
ozone design value decreases of more than 0.30 ppb; Garrett County, Maryland, and Harris
County, Texas. The maximum projected decrease in an 8-hour ozone design value is 0.47 ppb in
Garrett County, Maryland. There are also one county, Pulaski County in Arkansas, with a
projected design value increase greater than 0.30 ppb.
B. Impacts of LD GHG 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 2017-2025 LD GHG vehicle standards. We compare a 2030
reference scenario, a scenario without the vehicle standards, to a 2030 control scenario which
includes the vehicle standards. Our modeling indicates that the majority of the modeled counties
will experience small changes of between 0.05 |ig/m3 and -0.05 |ag/m3 in their annual PM2.5
design values due to the vehicle standards. Figure III-2 presents the changes in annual PM2.5
design values in 2030.19
Difference in Annual PM2.5 DV - 2030ctjdghg_ctl2 minus 2030ctJOghg_ret
Figure III-2. Projected Change in 2030 Annual PMi.s Design Values Between the
Reference Case and Control Case
19 An annual PM2 5 design value is the concentration that determines whether a monitoring site meets the annual
NAAQS for PM2 5. The full details involved in calculating an annual PM2 5 design value are given in appendix N of
40 CFR part 50.
11
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As shown in Figure III-2, eight counties will experience decreases larger than 0.05
|ig/m3. These counties are in the Gulf Coast and in Missouri. The maximum projected decrease
in an annual PM2.5 design value is 0.16 |ig/m3 in West Baton Rouge County, Louisiana. The
decreases in annual PM2.5 design values in the gulf coast are likely due to emission reductions
related to lower fuel production. Additional information on the emissions reductions that are
projected with this final action is available in Section 4.7of the RIA. Appendix C details the
state and county annual PM2.5 design values for the ambient baseline and the future reference and
control cases.
C. Impacts of LD GHG 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 2017-2025 light-duty vehicle standards. Specifically, we compare a 2030
reference scenario, a scenario without the vehicle standards, to a 2030 control scenario which
includes the vehicle standards. Our modeling indicates that the majority of the modeled counties
will experience changes of between -0.05 |ig/m3 and 0.05 |ig/m3 in their 24-hour PM2.5 design
values. Figure III-3 presents the changes in 24-hour PM2.5 design values in 2030.20
Legend Number of Counties
^H <= -0.50 ug/m3 2
j^H >-050 10 <=-0,25
^H > -0.25 to <=-0.15
I | > -0.1510 <= -0.05
^ > -0.05 to < 0.05
^J >=0.05to<0.15
^B >«0.15to < 0.25
^B >= 0.25 to-=0.50
^H =•= 0.50
2
3
16
540
6
0
0
0
Difference in Dally PM2.S DV- 2030ctjdghg_ctt2 minus 2030ctjdghg_ret
Figure III-3. Projected Change in 2030 24-hour PMi.s Design Values Between the
Reference Case and the Control Case
20 A 24-hour PM2 5 design value is the concentration that determines whether a monitoring site meets the 24-hour
NAAQS for PM2 5. The full details involved in calculating a 24-hour PM2 5 design value are given in appendix N of
40 CFR part 50.
12
-------
As shown in Figure III-3, design value concentrations will increase more than 0.05 |ig/m3
in six counties and design value concentrations will decrease more than 0.05 |ig/m3 in 23
counties. The increases in 24-hour PM2.5 design values in some counties are likely due to
increased emissions from the VMT rebound effect or increased electricity generation. The
maximum projected increase in a 24-hour PM2.5 design value is 0.14 |ig/m3 in El Paso County,
Colorado. The decreases in 24-hour PM2.5 design values in some counties are likely due to
emission reductions related to lower fuel production. The maximum projected decrease in a 24-
hour PM2.5 design value is 0.76 |ig/m3 in East Baton Rouge County, Louisiana. Additional
information on the emissions changes that are projected with this final action is available in
Section 4.7 of the RIA. 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 LD GHG 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 LD GHG. We focus on air toxics which
were identified as national and regional-scale cancer and noncancer risk drivers in the 2005
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 national average ambient concentrations of the modeled air toxics change
less than 1 percent across most of the country due to the final standards. Because overall
impacts are relatively small in future years, we concluded that assessing exposure to ambient
concentrations and conducting a quantitative risk assessment of air toxic impacts was not
warranted. However, we did develop population metrics, including the population living in areas
with changes in concentrations of various magnitudes.
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 between ±1
percent across the country with decrease up to 10 percent in a few urban areas (Figures III-4
through III-6). Annual and seasonal reductions in ambient acetaldehyde in 2030 range between
0.001 and 0.01 |ig/m3 across much of the country with decreases as high as 0.1 |ig/m3 in urban
areas; these changes are mainly associated with reductions from upstream sources including fuel
production, refining, storage and transport. Specifically, the winter season shows decreases of 1
percent to 10 percent in the Midwest as well as urban areas in the Northeast, Florida, Louisiana,
Texas, Colorado, Utah, and California (Figure III-5).
13
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Figure III-4. Changes in Annual Acetaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
Figure III-5. Changes in Winter Acetaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
14
-------
M changes Mtoeen iti* reference and control r,
• mtfcate Ihe sev-rrty cf exposure
m mooted trianjss Catneen me refBlmnB arc
Map tutors da not pnScflta Inn seventy of eiposi
Figure III-6. Changes in Summer Acetaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in jig/m3 (right)
2. Formaldehyde
Our modeling projects that the standards finalized in this rule do not show substantial
impacts on ambient formaldehyde concentrations. In 2030, annual and seasonal percent changes
in ambient concentrations of formaldehyde are less than 1 percent across much of the country,
with a decrease ranging from 2.5 to 10 percent in Oklahoma (Figures III-7 to III-8). Likwise,
ambient annual and seasonal formaldehyde reductions in 2030 generally range from 0.001 to 0.1
|ig/m3 and are associated with upstream reductions in fuel production, refining, storage and
transport. Decreases in Oklahoma are greater than 0.3 |ig/m3 and due to reductions in emissions
from refineries in that area. Increases in annual and seasonal ambient formaldehyde
concentrations range between 0.001 to 0.1 |ig/m3 in areas associated with increased emissions
from power plants.
15
-------
10 net indicate fit EBveirtv of e.pciure
Figure III-7. Changes in Annual Formaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
Figure III-8. Changes in Winter Formaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
16
-------
Figure III-9. Changes in Summer Formaldehyde Ambient Concentrations Between the
Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute
Changes in ug/m3 (right)
3. 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 percent
nationwide; with a few areas, mainly in the Gulf Coast region, are projected to have benzene
reductions from 1 to 10%, likely due to decreases in refinery emissions. Annual and seasonal
absolute changes in ambient benzene in 2030 are generally ± 0.001 |ig/m3 in the western half of
the U.S. with decreases up to 0.01 |ig/m3 across the eastern half of the U.S due to upstream
reductions in fuel production, refining, storage and transport.
17
-------
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 ug/m3 (right)
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 ug/m3
(right)
18
-------
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 ug/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 final standards. As shown in Figures 111-13 to 111-15,
annual and seasonal ambient concentrations of 1,3-butdiene are generally between ± 1 percent
across the country in 2030. Some areas in Texas, Nebraska and Utah have 1,3-butadiene
increases on the order of 1 to 5 percent; however, as shown in the map on the right, all changes
in annual and seasonal absolute concentrations are between ± 0.001 ug/m3 nationwide.
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 ug/m3 (right)
19
-------
M changes Mtoeen iti* reference and control r,
• mtfcate Ihe sev-rrty cf exposure
Mtbec-ir--,i =,[.- ' H
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)
le raises and mciemews rvs? not te comparable behwa t
•;-,-,,-:--- =r,.-/. -„:,;-- M -I,;,r;'i-, -.--.',.-tr I-.- rt-f-r,-- •- vi.-i --•-,
SC3W ranges and mcJemesilB may rwt te comparable between
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 ug/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. In 2030, annual and seasonal
percent changes in ambient acrolein concentrations are generally ± 1 percent nationwide (Figures
20
-------
Ill-16 to III-18). Parts of the Midwest, Texas, Arizona, New Mexico and Utah have decreases in
ambient acrolein concentrations generally between 1 and 10 percent and increases of similar
magnitude in a few urban areas; however, all absolute changes in ambient acrolein
concentrations are between ±0.001 ug/m3 in 2030.
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 ug/m3 (right)
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 ug/m3 (right)
21
-------
M changes Mtoeen iti* reference and control r,
• mtfcate Ihe sev-rrty cf exposure
Mtbec-ir--,i =,[.- ' H
Atso/ure Difference tofAcralein- Sai
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 jig/m3 (right)
E. Population Metrics
To assess the impact the rule's of projected changes in air quality, we developed
population metrics that show population experiencing changes in annual ambient concentrations
across the modeled air toxics. As shown in Table III-l, over 98 percent of the U.S. population is
projected to experience a less than one percent change in formaldehyde and 1,3-butadiene. Over
83 percent of the U.S. population is projected to experience a less than one percent change in
acetaldehyde, benzene and acrolein, and over 12 percent are projected to experience a 1 to 5
percent decrease in these pollutants.
Table III-l Percent of Total Population Experiencing Changes in Annual Ambient
Concentrations of Toxic Pollutants in 2030 as a Result of the Final Standards
Percent Change
<-100
> -100 to < -50
> -50 to < -10
>-10to<-5
> -5 to < -2.5
>-2.5to<-l
>-l to< 1
> 1 to < 2.5
> 2.5 to<5
> 5 to < 10
> 10 to < 50
> 50to< 100
> 100
Acetaldehyde
—
—
—
0.0%
1.5%
15.3%
83.1%
—
—
—
—
—
—
Formaldehyde
—
—
—
0.0%
0.1%
1.2%
98.7%
—
—
—
—
—
—
Benzene
—
—
—
0.8%
1.8%
13.0%
84.4%
0.0%
—
—
—
—
—
1,3 -Butadiene
—
—
—
—
0.0%
0.2%
99.2%
0.6%
0.0%
—
—
—
—
Acrolein
—
—
—
0.2%
2.0%
10.3%
86.1%
0.9%
0.0%
0.0%
—
—
—
22
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F. Impacts of LD GHG Standards on Future Annual Nitrogen and Sulfur Deposition
Levels
Our air quality modeling projects increases in nitrogen deposition in some localized areas
across the US along with a few areas of decreases in nitrogen deposition. Figure III-19 shows
that for nitrogen deposition the vehicle standards will result in annual percent increases of more
than 2% in some areas. The increases in nitrogen deposition are likely due to projected upstream
emissions increases in NOx from increased electricity generation and increased driving due to
the rebound effect. Figure III-19 Error! Reference source not found, also shows that for
nitrogen deposition the vehicle standards will result in annual percent decreases of more than 2%
in a few areas in West Virginia and New Mexico. The decreases in nitrogen deposition are likely
due to projected upstream emissions decreases in NOx from changes in the location of electricity
generation. The remainder of the country will experience only minimal changes in nitrogen
deposition, ranging from decreases of less than 0.5% to increases of less than 0.5%.
Figure III-19. Changes in Annual Total Nitrogen Deposition Between the Reference Case
and the Control Case in 2030: Percent Changes (left) and Absolute Changes in ug/m3
(right)
Our air quality modeling projects both increases and decreases in sulfur deposition in
localized areas across the U.S. Figure III-20Error! Reference source not found, shows that for
sulfur deposition the vehicle standards will result in annual percent decreases of more than 2% in
many areas. The decreases in sulfur deposition are likely due to projected upstream emissions
decreases from changes in the location of electricity generation and from reduced gasoline
production. Error! Reference source not found.Figure 111-20 also shows that for sulfur
deposition the vehicle standards will result in annual percent increases of more than 2% in some
areas. The increases in sulfur deposition are likely due to projected upstream emissions
increases from increased electricity generation. The remainder of the country will experience
only minimal changes in sulfur deposition, ranging from decreases of less than 0.5% to increases
of less than 0.5%.
23
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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 ug/m3 (right)
G. Impacts of LD GHG Standards on Future Visibility Levels
Air quality modeling conducted for this final rule was used to project visibility conditions
in 139 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
period21.
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/scramOO l/modelingapps_mats.htm)
In calculating visibility impairment, the extinction coefficient values22 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
21 Since the base case modeling used meteorology for 2005, one of the complete years must be 2005.
22 Extinction coefficient is in units of inverse megameters (Mm"1). It is a measure of how much light is absorbed or
scattered as it passes through a medium. Light extinction is commonly used as a measure of visibility impairment in
the regional haze program.
24
-------
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 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, (1x1 for 36km grid)
Start monitor year- 2003
End monitor year- 2007
Base model year 2005
Minimum years required for a valid monitor- 3
The "base model year" was chosen as 2005 because it is the base case meteorological
year for the final LD GHG Rule modeling. The start and end years were chosen as 2003 and
2007 because that is the 5 year period which is centered on the base model year of 2005. These
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
01
deciview levels above background in 2030. 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 light-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
0.003 deciviews, or 0.01%, in 2030. The greatest improvement in visibility will be seen at
Sipsey Wilderness, Alabama, Aqua Tibia Wilderness, California, and Wilderness Lake,
Washington with a 0.02 DV improvement due to the 2017-2025 light-duty standards. The
greatest degradation of visibility is projected to be seen at Wolf Island, Georgia with a
degradation of 0.03 DV in 2030 as a result of the 2017-2025 light-duty standards. Section
6.2.2.5 of the LD GHG final rule RIA contains more details on the visibility portion of the air
quality modeling. Table III-2 contains the full visibility results for the 20% worst days from
2030 for the 139 analyzed areas.
23 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.
25
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Table III-2. Visibility Levels in Deciviews for Individual U.S. Class I Areas on the 20%
Worst Days for Several Scenarios
Class 1 Area
(20% worst days)
Sipsey Wilderness
Caney Creek Wilderness
Upper Buffalo Wilderness
Chiricahua NM
Chiricahua Wilderness
Galiuro Wilderness
Grand Canyon NP
Mazatzal Wilderness
Mount Baldy Wilderness
Petrified Forest NP
Pine Mountain Wilderness
Saguaro NM
Sierra Ancha Wilderness
Superstition Wilderness
Sycamore Canyon Wilderness
Agua Tibia Wilderness
Ansel Adams Wilderness (Minarets)
Caribou Wilderness
Cucamonga Wilderness
Desolation Wilderness
Emigrant Wilderness
Hoover Wilderness
John Muir Wilderness
Joshua Tree NM
Kaiser Wilderness
Kings Canyon NP
Lassen Volcanic NP
Lava Beds NM
Mokelumne Wilderness
Pinnacles NM
Point Reyes NS
Redwood NP
San Gabriel Wilderness
San Gorgonio Wilderness
San Jacinto Wilderness
San Rafael Wilderness
Sequoia NP
South Warner Wilderness
Thousand Lakes Wilderness
Ventana Wilderness
Yosemite NP
Black Canyon of the Gunnison NM
Eagles Nest Wilderness
State
AL
AR
AR
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CO
CO
2005
Base
29.88
26.69
26.97
12.89
12.89
12.89
11.86
13.95
11.32
13.56
13.95
14.39
14.45
14.15
15.45
22.36
15.24
13.65
18.44
12.87
16.87
11.61
15.24
18.90
15.24
23.73
13.65
14.13
12.87
17.90
22.40
18.55
18.44
21.43
21.43
19.43
23.73
14.13
13.65
17.90
16.87
10.00
8.82
2030
LDGHG
Reference
20.54
19.84
20.17
12.08
12.08
12.09
10.92
12.46
10.74
12.65
12.42
13.43
13.28
12.85
14.67
18.41
14.39
12.68
15.64
12.10
15.94
11.07
14.34
16.39
14.11
22.19
12.66
13.19
12.08
15.42
21.00
17.66
15.54
19.27
18.10
17.40
21.68
13.31
12.65
16.37
15.95
9.21
8.05
2030
LDGHG
Control
20.52
19.84
20.18
12.07
12.07
12.09
10.91
12.45
10.74
12.65
12.41
13.43
13.28
12.85
14.67
18.39
14.39
12.67
15.64
12.09
15.94
11.06
14.34
16.41
14.10
22.19
12.66
13.19
12.07
15.42
21.00
17.66
15.53
19.28
18.11
17.39
21.67
13.31
12.64
16.37
15.95
9.21
8.05
Natural
Background
11.39
11.33
11.28
6.92
6.91
6.88
6.95
6.91
6.95
6.97
6.92
6.84
6.92
6.88
6.96
7.17
7.12
7.29
7.17
7.13
7.14
7.12
7.14
7.08
7.13
7.13
7.31
7.49
7.14
7.34
7.39
7.81
7.17
7.10
7.12
7.28
7.13
7.32
7.32
7.32
7.14
7.06
7.08
26
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Flat Tops Wilderness
Great Sand Dunes NM
La Garita Wilderness
Maroon Bells-Snowmass Wilderness
Mesa Verde NP
Mount Zirkel Wilderness
Rawah Wilderness
Rocky Mountain NP
Weminuche Wilderness
West Elk Wilderness
Everglades NP
Okefenokee
Wolf Island
Craters of the Moon NM
Sawtooth Wilderness
Mammoth Cave NP
Acadia NP
Moosehorn
Roosevelt Campobello International Park
Isle Royale NP
Seney
Boundary Waters Canoe Area
Voyageurs NP
Hercules-Glades Wilderness
Anaconda-Pintler Wilderness
Bob Marshall Wilderness
Cabinet Mountains Wilderness
Gates of the Mountains Wilderness
Glacier NP
Medicine Lake
Mission Mountains Wilderness
Red Rock Lakes
Scapegoat Wilderness
Selway-Bitterroot Wilderness
ULBend
Linville Gorge Wilderness
Shining Rock Wilderness
Lostwood
Theodore Roosevelt NP
Great Gulf Wilderness
Presidential Range-Dry River Wilderness
Brigantine
Bandelier NM
Bosque del Apache
Carlsbad Caverns NP
Gila Wilderness
Pecos Wilderness
Salt Creek
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
FL
GA
GA
ID
ID
KY
ME
ME
ME
Ml
Ml
MN
MN
MO
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
NC
NC
ND
ND
NH
NH
NJ
NM
NM
NM
NM
NM
NM
8.82
11.82
10.00
8.82
12.14
9.72
9.72
12.85
10.00
8.82
22.48
27.21
27.21
14.06
14.97
32.00
22.75
21.19
21.19
21.31
25.05
20.20
19.62
26.95
17.11
16.13
14.31
11.94
19.62
18.21
16.13
11.19
16.13
17.11
15.49
29.66
28.54
19.61
17.88
21.43
21.43
28.68
11.97
13.81
16.51
13.12
9.60
18.27
8.32
11.20
9.49
8.27
11.31
9.20
9.15
12.15
9.46
8.21
18.43
20.28
20.12
12.94
14.70
22.29
18.34
17.58
17.57
18.19
20.80
16.56
16.61
21.00
16.69
15.63
13.65
11.48
18.73
17.17
15.50
10.62
15.59
16.74
15.00
20.08
19.49
17.64
16.02
16.46
16.39
20.96
10.51
12.40
14.48
12.41
8.85
16.19
8.31
11.20
9.49
8.26
11.31
9.19
9.14
12.15
9.46
8.21
18.43
20.29
20.15
12.94
14.70
22.29
18.33
17.58
17.56
18.19
20.80
16.56
16.61
21.00
16.68
15.63
13.65
11.47
18.73
17.17
15.49
10.62
15.59
16.74
15.00
20.07
19.48
17.64
16.02
16.46
16.39
20.95
10.51
12.40
14.47
12.40
8.85
16.18
7.07
7.10
7.06
7.07
7.09
7.08
7.08
7.05
7.06
7.07
11.15
11.45
11.42
7.13
7.15
11.53
11.45
11.36
11.36
11.22
11.37
11.21
11.09
11.27
7.28
7.36
7.43
7.22
7.56
7.30
7.39
7.14
7.29
7.32
7.18
11.43
11.45
7.33
7.31
11.31
11.33
11.28
7.02
6.97
7.02
6.95
7.04
6.99
27
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San Pedro Parks Wilderness
Wheeler Peak Wilderness
White Mountain Wilderness
Jarbidge Wilderness
Wichita Mountains
Crater Lake NP
Diamond Peak Wilderness
Eagle Cap Wilderness
Gearhart Mountain Wilderness
Hells Canyon Wilderness
Kalmiopsis Wilderness
Mount Hood Wilderness
Mount Jefferson Wilderness
Mount Washington Wilderness
Mountain Lakes Wilderness
Strawberry Mountain Wilderness
Three Sisters Wilderness
Cape Romain
Badlands NP
Wind Cave NP
Great Smoky Mountains NP
Joyce- Kilmer-Slickrock Wilderness
Big Bend NP
Guadalupe Mountains NP
Arches NP
Bryce Canyon NP
Canyonlands NP
Capitol Reef NP
James River Face Wilderness
Shenandoah NP
Lye Brook Wilderness
Alpine Lake Wilderness
Glacier Peak Wilderness
Goat Rocks Wilderness
Mount Adams Wilderness
Mount Rainier NP
North Cascades NP
Olympic NP
Pasayten Wilderness
Dolly Sods Wilderness
Otter Creek Wilderness
Bridger Wilderness
Fitzpatrick Wilderness
Grand Teton NP
North Absaroka Wilderness
Teton Wilderness
Washakie Wilderness
Yellowstone NP
NM
NM
NM
NV
OK
OR
OR
OR
OR
OR
OR
OR
OR
OR
OR
OR
OR
SC
SD
SD
TN
TN
TX
TX
UT
UT
UT
UT
VA
VA
VT
WA
WA
WA
WA
WA
WA
WA
WA
WV
WV
WY
WY
WY
WY
WY
WY
WY
10.42
9.60
13.01
12.26
23.63
13.21
13.21
17.34
13.21
19.00
16.38
14.68
15.80
15.80
13.21
17.34
15.80
27.43
16.82
15.95
30.56
30.56
17.21
16.51
10.77
11.62
10.77
10.86
28.93
29.42
24.11
16.99
13.29
12.67
12.67
17.07
13.29
15.83
15.35
29.94
29.94
10.73
10.73
11.19
11.30
11.19
11.30
11.19
9.63
8.66
12.05
11.92
18.27
12.49
12.39
16.31
12.61
17.57
15.36
13.03
14.78
14.78
12.42
16.37
14.87
19.70
14.91
14.21
21.28
20.97
15.35
14.47
9.98
10.95
10.12
10.39
19.62
19.58
16.87
15.06
12.18
11.35
11.39
15.36
12.15
14.31
14.36
19.65
19.73
10.29
10.29
10.57
10.90
10.68
10.90
10.61
9.62
8.65
12.05
11.92
18.26
12.49
12.39
16.31
12.61
17.58
15.36
13.03
14.78
14.77
12.42
16.37
14.87
19.70
14.90
14.21
21.27
20.97
15.34
14.46
9.97
10.95
10.11
10.39
19.62
19.58
16.86
15.04
12.17
11.34
11.39
15.35
12.14
14.31
14.36
19.64
19.72
10.29
10.28
10.56
10.90
10.68
10.90
10.61
7.03
7.07
6.98
7.10
11.07
7.71
7.77
7.34
7.46
7.32
7.71
7.77
7.81
7.89
7.57
7.49
7.87
11.36
7.30
7.24
11.44
11.45
6.93
7.03
6.99
6.99
7.01
7.03
11.24
11.25
11.25
7.86
7.80
7.82
7.78
7.90
7.78
7.88
7.77
11.32
11.33
7.08
7.09
7.09
7.09
7.09
7.09
7.12
28
-------
29
-------
Air Quality Modeling Technical Support Document:
2017-2025 Light-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
August 2012
A-l
-------
A.I. 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 domainl.
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 (804), nitrate (NOs), total nitrate
(TNO3=NO3+HNO3), 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
: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, ME, OK, and TX; West is AK, CA, OR, WA, AZ, MM, 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
-------
obtained from the following 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.s monitoring networks and pollutants species included in the CMAQ
)enormance evaluation.
Ambient
Monitoring
Networks
IMPROVE
CASTNet
STN
NADP
Particulate
Species
PM2.5
Mass
X
X
SO4
X
X
X
NO3
X
X
TNO3a
X
EC
X
X
OC
X
X
NH4
X
X
Wet
Deposition
Species
SO4
X
NO3
X
aTNO3=(NO3+HNO3)
The air toxics evaluation focuses on specific species relevant to the 2017-2025 Light-
Duty Greenhouse Gas final rule (hereafter referred to as LD GHG), 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. 1 for evaluating meteorological and air quality models, Environ. Modell. Softw. ,26, 4,
434-443, 2011. (http://www.cmascenter.org/)
A-3
-------
evaluation statistics used to evaluate CMAQ performance were two bias metrics, normalized
mean bias and fractional bias; and two error metrics, normalized mean error and fractional error.
Normalized mean bias (NMB) is used as a normalization to facilitate a range of
concentration magnitudes. This statistic averages the difference (model - observed) over the sum
of observed values. NMB is a useful model performance indicator because it avoids over
inflating the observed range of values, especially at low concentrations.
Normalized mean bias is defined as:
NMB= -^— *100
Normalized mean error (NME) is also similar to NMB, where the performance statistic is used as
a normalization of the mean error. NME calculates the absolute value of the difference (model -
observed) over the sum of observed values.
Normalized mean error is defined as:
NME= *100
i
Fractional bias is defined as:
FB= -
n
-O
*100, where P = predicted and O = observed concentrations.
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:
:-0l
n
-O
*100
2 ^
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
-------
toxic model applications.5'6'7'8'9!lo'1112'13'14! 15'16 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 the 2017-2025 LD GHG final rule 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 2017-2025 LD GHG 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 - paniculate 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, IE.,: 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, IE., Otte, T.L., Mathur, R., Sarwar, G., Young, J.O.,
Gilliam, R.C., Nolle, C.G., Kelly, IT., Gilliland, A.B., and Bash, IO.,: 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 Paniculate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.
(http://www.cmascenter.org/conference/2008/agenda.cfm).
11 Simon, H., Baker, K.R., and Phillips, S., 2012. Compilation and interpretation of photochemical model
performance statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139.
http://dx.doi.0rg/10.1016/j.atmosenv.2012.07.012
12 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 l/strum_pres.pdf)
13 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.
14 U.S. Environmental Protection Agency; Technical Support Document for the Final Clean Air Interstate Rule: Air
Quality Modeling; Office of Air Quality Planning and Standards; RTF, NC; March 2005 (CAIR Docket OAR-2005-
0053-2149).
15 U.S. Environmental Protection Agency, Proposal to Designate an Emissions Control Area for Nitrogen Oxides,
Sulfur Oxides, and Paniculate Matter: Technical Support Document. EPA-420-R-007, 329pp., 2009.
(http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09007.pdf)
16 U.S. Environmental Protection Agency, 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/renewablefuels/420rl0006.pdf)
A-5
-------
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 over-predicts seasonal eight-hour daily maximum ozone for
the five subregions, with the exception of a slight under-prediction in the winter at the Midwest
and Northeast subregions (Table A-2). Model performance for 8-hour daily maximum ozone for
all subregions is typically better in the spring, summer, and fall months, where the bias statistics
are within the range of approximately 0.4 to 16.8 percent and the error statistics range from 13.8
to 22.7 percent 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
Central
U.S.
Midwest
Southeast
Northeast
West
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of
Obs
8,304
12,916
13,474
10,166
1,819
10,981
15,738
9,136
5,150
17,857
19,617
12,008
3,497
11,667
15,489
9,438
18,285
25,814
28,380
19,588
NMB (%)
8.5
0.4
3.9
2.3
-5.7
2.2
3.1
3.2
8.2
1.1
16.8
11.0
-9.7
1.9
8.6
4.3
27.2
2.2
5.4
5.7
NME (%)
24.6
13.8
17.5
19.0
23.2
14.4
13.6
16.4
17.4
11.9
22.7
18.0
22.7
14.7
17.7
17.9
33.1
14.1
17.0
18.6
FB (%)
8.0
1.6
7.0
4.6
-7.9
3.8
4.2
5.8
7.9
2.6
19.5
14.0
-12.5
2.5
10.6
7.3
27.4
2.9
6.0
7.5
FE (%)
27.3
14.7
19.2
20.5
28.2
15.3
14.1
18.9
18.5
12.6
24.2
20.6
29.1
15.7
18.6
21.3
33.9
14.5
17.3
19.8
A-6
-------
2005c1_ldghg2_05b_12EUS1 O3_8hrmax for AQS_Dally for 20050501 to 20050930
Q.
AQS^Daily
CMAQ
5231
92S2
2005_05 2005 06 2005J>7 2005 08 2005 09
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 = 75th/25th percentiles; top/bottom line = max/min values]
2005ct_ldghg2_05b_12EUS1 O3_8hrmax for AQS_Daily for 20050501 to 20050930
I
AQS_Daily
CMAQ
6495
2005_07
Months
Figure A-2. Distribution of observed and predicted 8-hour daily maximum ozone by
month for the period May through September 2005 for the Southeast subregion.
A-7
-------
2005c1_ldghg2_05b_12EUS1 O3_8hrmax for AQS_Dally for 20050501 to 20050930
Q.
AQS^Daily
CMAQ
S267
2005_07
Months
Figure A-3. Distribution of observed and predicted 8-hour daily maximum ozone by
month for the period May through September for the Midwest subregion.
2005c1Jdgfig2_05b_12EUS1 O3_8hrmax for AQS_Da!ly for 20050501 to 20050930
•—EI AQS_Daily
H---A CMAQ
= CENRAP
I
2005_07
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.
A-8
-------
2005ctJdghg2_05b_12WUS1 O3_Shrmax for AQS Daily for 20050501 to 20050930
Q.
AQS_Daily
CMAQ
2005_05 2005 06 2005J>7 2005 08 2005 09
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.
03 Shrmax NMB (%) for run 2005cl Idghg2_05b 12EUS1 for 20050501 10 20050930
units . %
coveiage limit. 75%
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=AQS_Daily;
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.
A-9
-------
03_8hrmax NME (%) (or run 2005ct_ldghg2_05b_12EUS1 tor 20050501 to 20050930
unlls«%
coveoge limit. 75%
>100
90
BO
70
60
50
40
30
20
10
0
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.
O3_8hrmax NMB (%) tor run 2005clJd9hg2_05b_12WUS1 tor 20050501 to 20050930
60
40
20
0
-20
-40
-«0
-SO
<-100
CIRCLE=AQS_Daily;
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.
A-10
-------
O3_8hrmax NME (%) tor fun 2005ctJdghg2_05D_12WUS1 tot 20050501 to 20050930
CIRCLE=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.3. Evaluation of PMi.s 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.
A-ll
-------
Table A-3. Sulfate performance statistics by subregion, by season for the 2005 CMAQ
model simulation.
Subregion
Central
U.S.
Midwest
Southeast
Northeast
Network
CSN
IMPROVE
CASTNet
CSN
IMPROVE
CASTNet
CSN
IMPROVE
CASTNet
CSN
IMPROVE
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
No. of
Obs.
771
875
851
587
608
722
688
622
72
77
72
75
598
637
621
639
143
171
182
126
142
155
161
157
888
918
866
911
469
525
500
496
264
292
268
273
828
894
874
902
561
NMB (%)
-16.0
-15.1
-30.4
-9.9
-19.4
-17.6
-28.1
-15.8
-33.2
-24.6
-33.3
-21.1
0.7
19.2
-10.7
-12.2
3.3
4.6
-18.7
-18.0
-14.1
-6.1
-16.6
-20.0
-4.8
-5.2
-18.0
-10.5
-1.7
-6.6
-24.1
-11.7
-18.6
-13.5
-21.3
-18.4
-9.1
8.2
-8.7
-9.0
-7.2
NME (%)
38.4
32.2
42.3
34.9
40.2
31.3
39.2
31.4
34.7
27.8
36.9
23.7
FB (%)
-14.3
-11.3
-37.3
-3.6
-14.2
-11.9
-25.7
-7.5
-35.4
-23.6
-38.2
-19.6
38.8
42.8
28.6
26.6
35.8
35.3
30.
1
26.7
22.0
22.4
21.9
22.6
37.
1
27.4
32.8
27.7
36.8
29.0
35.6
29.2
22.9
21.2
24.8
21.2
35.
1
37.2
27.2
28.8
31.2
-4.9
15.1
-0.9
-3.9
-0.4
6.6
-6.0
-7.2
-16.8
-4.6
-14.2
-16.1
-4.4
-6.1
-19.8
-5.8
0.5
-6.0
-30.8
-6.2
-17.9
-14.8
-28.4
-19.2
-13.9
4.3
-3.0
0.1
-11.1
FE (%)
41.8
33.8
54.2
36.7
43.6
32.4
46.2
37.1
37.9
29.6
45.9
26.4
38.9
36.8
30.8
27.4
34.4
35.1
36.0
31.3
26.8
21.7
23.9
21.8
37.0
29.4
39.0
29.4
37.5
31.7
47.0
34.4
24.0
22.9
32.7
23.3
34.8
34.9
30.9
31.0
33.3
A-12
-------
Subregion
West
Network
CASTNet
CSN
IMPROVE
CASTNet
Season
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of
Obs.
689
649
591
193
206
192
195
830
867
853
900
2343
2620
2281
2343
250
273
281
268
NMB (%)
7.05
-12.9
-6.8
-14.9
-0.3
-15.6
-12.3
-5.2
-3.8
-32.1
-7.6
22.3
-3.6
-24.7
-0.2
6.5
-18.4
-35.1
-10.7
NME (%) FB (%)
37.9
32.3
32.3
22.5
25.1
20.5
18.4
57.7
36.9
43.7
47.2
58.6
33.6
41.2
40.2
35.9
27.1
38.7
23.6
3.6
-4.5
7.7
-19.0
-1.4
-12.7
-7.3
1.8
0.0
-23.3
0.4
34.0
3.5
-16.4
11.5
17.9
-17.0
-36.0
-5.0
FE (%)
38.2
37.7
35.4
25.8
26.4
22.0
18.0
54.4
36.2
42.5
43.4
56.9
35.3
42.9
41.3
37.5
27.6
41.5
24.3
A-13
-------
2005ct_ldghg2_05b_12EUS1 SO4 for IMPROVE for 20050101 to 20051231
I o IMPROVE
I--& CMAQ
IPO = MANE-VU
25 -
CO
I
O 15-
\ I
»••**
201 176 219 22*5 335 2"t?
2005 01 2005 03 2005 05 200507 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
= 75th/25th percentiles; top/bottom line = max/min values]
2005ct_ldghg2_05b_12EUS1 SO4 for CSN for 20050101 to 20051231
CSN
--A CMAQ
IPO - MANE-VU
30 -
E
d>
S ,5-
295 293 296 3T3 287 255
2005_01 2005_03 2005_05 2005_07 2005_09 2005J1
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-14
-------
2005ct_ldghg2_05b_12EUS1 SO4 for CASTNET for 20050101 to 20051231
RPO = MANE-VU
30 -
25 -
3
5 -
CASTNET
-A CMAQ
65 67 76 65 77 59 54
61 54 60 47
2005_01 2005_03 2005_05 2005_07 2005J9 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.
2005ct_ldghg2_05b_12EUS1 SO4 for IMPROVE for 20050101 to 20051231
I B IMPROVE
I--A CMAQ
IPO = VISTAS
25 -
O 15 -
tn
2005_01 2005_03 2005_05 200507 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-15
-------
2005ct_ldghg2_05b_12EUS1 SO4 for CSN for 20050101 to 20051231
IPO = VISTAS
30 -
25 -
3
CSN
--& CMAQ
304 299 302 302 314
2^5 282 269
283 332 296 285
2005_01 2005_03 2005_05 2005_07 2005J9 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.
2005ct_ldghg2J)5bJ2EUS1 SO4 for CASTNET for 20050101 to 20051231
I o CASTNET
I---A CMAQ
RPO = VISTAS
25 -
O 15 -
tn
10 -
112 B9 110
79 101 83
2005_01 2005_03 2005 05 200507 2005 09 2005_11
Months
Figure A-9c. Distribution of observed and predicted weekly average sulfate by month for
2005 at CASTNet sites in the Southeast subregion.
A-16
-------
2005ct_ldghg2_05b_12EUS1 SO4 for IMPROVE for 20050101 to 20051231
IMPROVE
--A CMAQ
WO = LADCO
30 -
25 -
3
5 -
•un
51 50 63
2005_01 2005_03 2005_05 2005_07 2005J9 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.
2005ct_ldghg2_05b_12EUS1 SO4 for CSN for 20050101 to 20051231
I EI CSN
I---A CMAQ
IPO = LADCO
m
§
O 15 -
10 -
208 199 211 ZTT 215 207 208 206 202 236 201 191
I
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-17
-------
2005ct_ldghg2_05b_12EUS1 SO4 for CASTNET for 20050101 to 20051231
IPO = LADCO
30 -
25 -
3
5 -
CASTNET
--A CMAQ
2005_01 2005_03 2005_05 2005_07 2005J9 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.
2005ct_ldghg2_05b_12EUS1 SO4 for IMPROVE for 20050101 to 20051231
I EI IMPROVE
I--A CMAQ
IPO = CENRAP
m
cb
O 15-
1*1 it! K* Ml*! §JJ
i
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-18
-------
2005ct_ldghg2_05b_12EUS1 SO4 for CSN for 20050101 to 20051231
I Q CSN
I---A CMAQ
IPO = CENRAP
25 -
CO
I
O 15 -
10 -
T
0 ~ 2&5 289 278 3fe 295 2B7 2fe 278 194 203 T^D
2005JJ1 2005_03 2005 05 200507 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.
2005ct_ldghg2_05b_12EUS1 SO4 for CASTNET for 20050101 to 20051231
I Q CASTNET
I--A CMAQ
IPO = CENRAP
m
o>
O 15 -
10 -
0 - 24 24 30 24 29 22 21
22 23 30 IB
I
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-19
-------
2005ct_ldghg2_05b_12WUS1 SO4 for IMPROVE for 20050101 to 20051231
3
IMPROVE
--A CMAQ
0 - 837 7fB B42 S$7 92T 797 736 748 736 S49 75B 788
2005_01 2005_03 2005_05 2005_07 2005J9 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.
2005ctJdghg2_05b_12WUS1 SO4 for CSN for 20050101 to 20051231
SUUH
330 282 271
2005_01 2005_03 2005_05 200507 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-20
-------
2005ct_ldghg2_05b_12WUS1 SO4 for CASTNET for 20050101 to 20051231
8 -
I
3
CASTNET
--A CMAQ
87 B4 104 B5 101 87 69 109 82 S3 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.
SO4 NMB (%) for run 2005ct_ldghg2_05b_12EUS1 for Winter
CIRCLE=CSN: TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-13a. Normalized Mean Bias (%) of sulfate during winter 2005 at monitoring sites
in Eastern modeling domain.
A-21
-------
SO4 NME (%) for run 2005ct_ldghg2_05b_12EUSl for Winter
^ coverage limit = 75%
CIRCLE=CSN: TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-13b. Normalized Mean Error (%) of sulfate during winter 2005 at monitoring
sites in Eastern modeling domain.
SO4 NMB (%) for run 2005ct_ldghg2_u5b_12EUS1 for Spring
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-14a. Normalized Mean Bias (%) of sulfate during spring 2005 at monitoring sites
in Eastern modeling domain.
A-22
-------
SO4 NME (%) for run 2005ct_ldghg2_05b_12EUS1 for Spring
^ coverage limit = 75%
CIRCLE=CSN: TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-14b. Normalized Mean Error (%) of sulfate during spring 2005 at monitoring
sites in Eastern modeling domain.
SO4 NMB (%) for run 2005ct_ldghg2_05b_12EUS1 (or Summer
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-15a. Normalized Mean Bias (%) of sulfate during summer 2005 at monitoring
sites in Eastern modeling domain.
A-23
-------
SO4 NME (%} for run 2005ct_ldghg2_05b_12EUS1 for Summer
units = %
coverage \iff\n - 75%
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-15b. Normalized Mean Error (%) of sulfate during summer 2005 at monitoring
sites in Eastern modeling domain.
SO4 NMB (%) tor run 2005ct_ldghg2_05b_12EUS1 for Fai
__ cove-age limit * 75%
>100
80
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-16a. Normalized Mean Bias (%) of sulfate during fall 2005 at monitoring sites in
Eastern modeling domain.
A-24
-------
SO4 NME (%) for run 2005ct_ldghg2_05b_12EUS1 for Fall
^ coverage limit = 75%
CIRCLE=CSN: TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-16b. Normalized Mean Error (%) of sulfate during fall 2005 at monitoring sites
in Eastern modeling domain.
S04 NMB (%) for run 2005ctJdghg2_05b_12WUS1 for Winter
mils = %
coverage limit =
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-17a. Normalized Mean Bias (%) of sulfate during winter 2005 at monitoring sites
in Western modeling domain.
A-25
-------
S04 NME (%) for run 2Q05ctJdghg2_Q5b_12WUS1 for Winter
uniis = %
coverage limit = 75%
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure F-17b. Normalized Mean Error (%) of sulfate during fall 2005 at monitoring sites
in Western modeling domain.
S04 NMB (%) for run 2005cl_ldghg2_05b_12WUS1 tor Spring
c:''.••:n ;c-1 "'i i 7V
100
80
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-18a. Normalized Mean Bias (%) of sulfate during spring 2005 at monitoring sites
in Western modeling domain.
A-26
-------
S04 NME (%) for run 2005ct_ldghg2_05b_12WUS1 for Spring
uniis = %
coverage limit = 75%
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-18b. Normalized Mean Error (%) of sulfate during spring 2005 at monitoring
sites in Western modeling domain.
SO4 NMB (%) for run 2005clJdghg2_05b_12WUS1 lor Summer
coverage limit = 75%
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-19a. Normalized Mean Bias (%) of sulfate during summer 2005 at monitoring
sites in Western modeling domain.
A-27
-------
S04 NME (%) for run 2005ct_ldghg2_05b_12WUS1 for Summer
coverage limit = 75%
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-19b. Normalized Mean Error (%) of sulfate during summer 2005 at monitoring
sites in Western modeling domain.
SO4 NMB (%) for run 2005cl_ldghg2_05b_12WUS1 for Fall
>100
80
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-20a. Normalized Mean Bias (%) of sulfate during fall 2005 at monitoring sites in
Western modeling domain.
A-28
-------
SO4 NMIE (%) for run 2005ctJdghg2_Q5b_12WUS1 lor Fall
inils = %
coverage limit = 75%
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-20b. Normalized Mean Error (%) of sulfate during fall 2005 at monitoring sites
in Western modeling domain.
A-29
-------
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 paniculate 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 are over-predicted in the Northeast, Midwest, Southeast and Central U.S.; with the
exception at the urban monitors (CSN) where nitrate is under-predicted in the winter. Likewise,
nitrate is under-predicted at CSN sites during the summer in the Southeast and Northeast. Model
performance shows an under-prediction in the West for all of the seasonal assessments of nitrate
and total nitrate.
Table A-4. Nitrate performance statistics by subregion, by season for the 2005 CMAQ
model simulation.
Region
Central
U.S.
Midwest
Network
CSN
IMPROVE
CASTNet
CSN
IMPROVE
CASTNet
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of
Obs.
479
503
485
460
608
722
688
622
72
77
72
75
598
637
621
639
143
171
182
126
142
155
161
157
NMB (%)
-4.5
30.0
28.1
107.0
5.1
49.1
21.8
164.0
26.9
14.6
-0.2
52.8
-20.9
63.3
43.4
69.5
-27.3
54.8
25.4
108.0
6.9
38.4
53.4
73.6
NME (%)
48.9
62.1
102.0
133.0
54.9
78.6
112.0
193.0
38.9
34.3
26.3
60.1
40.5
83.4
98.2
98.1
47.7
87.6
100.0
141.0
21.4
42.1
56.0
74.0
FB (%)
-5.3
15.3
-41.4
19.4
-6.5
-3.8
-56.1
14.3
26.6
7.7
-6.1
35.9
-21.3
40.5
-10.8
24.2
-29.8
-3.5
-41.4
0.7
0.3
31.7
40.7
51.1
FE (%)
59.1
66.0
95.4
88.9
70.7
91.0
111.0
108.0
36.7
31.3
27.6
44.0
48.8
65.4
83.6
74.3
72.7
90.3
99.7
102.0
21.8
35.6
43.1
51.4
A-30
-------
Region
Southeast
Northeast
West
Network
CSN
IMPROVE
CASTNet
CSN
IMPROVE
CASTNet
CSN
IMPROVE
CASTNet
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of
Obs.
888
918
866
911
469
525
500
496
264
292
268
273
829
894
874
902
561
689
649
586
193
206
192
195
831
859
846
896
2,344
2,613
2,279
2,335
250
273
281
268
NMB (%)
-23.5
39.9
-26.8
78.6
-2.5
59.8
-14.2
105.0
24.4
31.7
28.8
73.8
-1.6
43.5
-5.7
76.3
42.1
74.1
11.2
116.0
23.6
49.0
53.6
85.3
-44.0
-37.2
-72.8
-47.7
-29.7
-38.4
-73.6
-31.8
34.6
-1.9
-6.8
15.9
NME (%)
61.7
98.0
85.6
141.0
82.4
117.0
112.0
184.0
35.9
44.9
47.2
81.9
43.9
77.6
89.9
109.0
77.5
113.0
115.0
157.0
30.7
51.4
61.3
87.8
63.8
58.2
76.4
69.8
77.5
76.5
83.9
82.0
52.9
32.7
31.2
40.5
FB (%)
-55.5
-10.8
-83.3
-28.1
-58.6
-29.3
-92.7
-46.8
20.8
21.8
17.0
45.8
-1.2
32.9
-58.3
-11.0
32.8
31.5
-61.5
-8.9
31.3
37.4
33.3
54.1
-60.1
-68.4
-132.0
-66.5
-83.2
-87.4
-144.0
-75.1
41.7
6.2
-5.8
28.2
FE (%)
85.6
92.2
115.0
108.0
98.8
108.0
136.0
125.0
35.2
39.6
42.6
58.9
49.7
68.5
101.0
86.2
76.0
93.2
113.0
100.0
35.4
42.4
49.5
60.5
87.1
89.0
137.0
95.5
121.0
118.0
152.0
121.0
54.6
32.4
33.0
46.6
A-31
-------
2005ct_ldghg2_05b_12EUS1 NO3 for IMPROVE for 20050101 to 20051231
I o IMPROVE
I--& CMAQ
IPO = MANE-VU
20 -
I
r
2005 01 2005 03 2005 05 200507 2005 09 2005_11
Months
Figure A-21a. 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 = 75th/25th percentiles; top/bottom line = max/min values]
2005ct_ldghg2_05b_12EUS1 NO3 for CSN for 20050101 to 20051231
CSN
--A CMAQ
IPO - MANE-VU
20 -
m
O
2005_01 2005_03 2005_05 2005_07 2005_09 2005J1
Months
Figure A-21b. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at CSN sites in the Northeast subregion.
A-32
-------
2005ct_ldghg2_05b_12EUS1 TNO3 for CASTNET for 20050101 to 20051231
• E CASTNET
B--A CMAQ
WO = MANE-VU
20 -
CO 15
E
61 54 80 47
2005_01 2005_03 2005_05 2005_07 2005J9 2005_11
Months
Figure A-21c. Distribution of observed and predicted weekly average total nitrate by
month for 2005 at CASTNet sites in the Northeast subregion.
2005cl_ldghg2_05b_12EUS1 NO3 for IMPROVE for 20050101 to 20051231
I EI IMPROVE
I--A CMAQ
IPO = VISTAS
i
2005 01 2005 03 2005 05 2005 07 2005 09 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-33
-------
2005ct_ldghg2_05b_12EUS1 NO3 for CSN for 20050101 to 20051231
• EI CSN
D---A CMAQ
WO = VISTAS
20 -
CO
I
304 299 302 302
296
2005_01 2005_03 2005_05 2005_07 2005J9 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.
2005ct_ldghg2_05b_12EUS1 TNO3 for CASTNET for 20050101 to 20051231
I o CASTNET
I--A CMAQ
IPO = VISTAS
to
O
1
110 83 79 101 83
2005_01 2005_03 2005 05 200507 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-34
-------
2005ct_ldghg2_05b_12EUS1 NO3 for IMPROVE for 20050101 to 20051231
0 IMPROVE
--A CMAQ
WO = LADCO
20 -
CO
I
2005_01 2005_03 2005_05 2005_07 2005J9 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.
2005ct_ldghg2_05b_12EUS1 NO3 for CSN for 20050101 to 20051231
I EI CSN
I---A CMAQ
IPO = LADCO
0 ~ 208 1^9 211 2tT 3t5
I
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-35
-------
2005ct_ldghg2_05b_12EUS1 TNO3 for CASTNET for 20050101 to 20051231
WO = LADCO
20 -
E CASTNET
CMAQ
46 47 57 49 62 48 49 63 49 5! 58 36
2005_01 2005_03 2005_05 2005_07 2005J9 2005_11
Months
Figure A-23c. Distribution of observed and predicted weekly average total nitrate by
month for 2005 at CASTNet sites in the Midwest subregion.
2005cl_ldghg2_05b_12EUS1 NO3 for IMPROVE for 20050101 to 20051231
I EI IMPROVE
I--A CMAQ
IPO = CENRAP
I
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-36
-------
2005ct_ldghg2_05b_12EUS1 NO3 for CSN for 20050101 to 20051231
20 -
CO
I
rts 7
2005_01 2005_03 2005_05 2005_07 2005J9 2005_11
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.
2005ct_ldghg2_05b_12EUS1 TNO3 for CASTNET for 20050101 to 20051231
I o CASTNET
I--A CMAQ
IPO = OENRAP
C? 15 -
-
to
O
24 24 30 24 29 22
29 22 23 30 IB
2005_01 2005_03 2005 05 200507 2005 09 2005_11
Months
Figure A-24c. Distribution of observed and predicted weekly average total nitrate by
month for 2005 at CASTNet sites in the Central states subregion.
A-37
-------
2005ct_ldghg2_05b_12WUS1 NO3 for IMPROVE for 20050101 to 20051231
5 -
CO
I
3- 3 -
IMPROVE
-A CMAQ
2005_01 2005_03 2005_05 2005_07 2005J9 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.
2005ct_ldghg2_05b_12WUS1 NO3 for CSN for 20050101 to 20051231
261 275 303 279 279 28& 287 326 2B3 272
2005_01 2005_03 2005_05 200507 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-38
-------
2005ct_ldghg2_05b_12WUS1 TNO3 for CASTNET for 20050101 to 20051231
• E CASTNET
•---A CMAQ
WO = WRAP
5 -
67 64 104 B5 101 87 B9
82 S3 101 60
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 CASTNet sites in the Western states subregion.
A-39
-------
NO3 NMB (%} for run 2005ct_ldghg2_05b_12EUS1 for Winter
units = %
coverage \iff\n - 75%
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-26a. Normalized Mean Bias (%) for nitrate during winter 2005 at monitoring
sites in Eastern modeling domain.
NO3 NME (%) tor run 2005ct_ldghg2_05b_12EUS1 for Winter
^ cove-age limit * 75%
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-26b. Normalized Mean Error (%) for nitrate during winter 2005 at monitoring
sites in Eastern modeling domain.
A-40
-------
TNQ3 NMB (%) (or run 20QScl_ldghg2_Q5b_l2EUS1 for Winter
coverage limit» 75%
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CASTNET;
Figure A-26c. Normalized Mean Bias (%) for total nitrate during winter 2005 at
monitoring sites in Eastern modeling domain.
TNO3 NME (%) for run 2005ct_ldghg2_05b_12EUS1 for Winter
units = %
coverage limit - 75%
CIRCLE=CASTNET:
Figure A-26d. Normalized Mean Error (%) for total nitrate during winter 2005 at
monitoring sites in Eastern modeling domain.
A-41
-------
NO3 NMB (%) for run 2005ct_ldghg2_05b_12EUS1 for Spring
__ coverage limit = 75%
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-27a. Normalized Mean Bias (%) for nitrate during spring 2005 at monitoring
sites in Eastern modeling domain.
NO3 NME (%) for run 2005ct_ldghg2_05b_12EUS1 for Spring
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-27b. Normalized Mean Error (%) for nitrate during spring 2005 at monitoring
sites in Eastern modeling domain.
A-42
-------
TNO3 NMB (%) for run 2005cl_ldghg2_05b_12EUS1 for Spring
coverage limit» 75%
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CASTNET;
Figure A-27c. Normalized Mean Bias (%) for total nitrate during spring 2005 at
monitoring sites in Eastern modeling domain.
TNO3 NME (%) for run 2005ctJdghg2_05b_12EUS1 for Spring
units = %
coverage limit - 75%
CIRCLE=CASTNET:
Figure A-27d. Normalized Mean Error (%) for total nitrate spring 2005 at monitoring
sites in Eastern modeling domain.
A-43
-------
NO3 NMB (%) tor run 2005cl_ldghg2 05b_12EUS1 tor Summer
unils - %
rage limit = 75%
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-28a. Normalized Mean Bias (%) for nitrate during summer 2005 at monitoring
sites in Eastern modeling domain.
NO3 NME (%) for run 2005ct_ldghg2_05b_12EUS1 lor Summer
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-28b. Normalized Mean Error (%) for nitrate during summer 2005 at monitoring
sites in Eastern modeling domain.
A-44
-------
TNQ3 NMB (%) for run 2005cl_ldghg2_05b_12EUS1 for Summer
coverage limit» 75%
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CASTNET;
Figure A-28c. Normalized Mean Bias (%) for total nitrate during summer 2005 at
monitoring sites in Eastern modeling domain.
TNO3 NME (%) for run 2005ct_ldghg2_05b_12EUS1 for Summer
units = %
coverage limit - 75%
CIRCLE=CASTNET:
Figure A-28d. Normalized Mean Error (%) for total nitrate summer 2005 at monitoring
sites in Eastern modeling domain.
A-45
-------
N03 NMB (%) tor run 2005ct_ldghg2_05b_12EUS1 for Fall
urwts = %
coverage limit = 75%
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-29a. Normalized Mean Bias (%) for nitrate during fall 2005 at monitoring sites
in Eastern modeling domain.
N03 NME (%) tor run 2005ctJdghg2_05b_12EUS1 tor Fall
units = %
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-29b. Normalized Mean Error (%) for nitrate during fall 2005 at monitoring sites
in Eastern modeling domain.
A-46
-------
TNO3 NMB (%) for run 2005cl_ldghg2_05b_12EUS1 for Fall
coverage limit» 75%
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CASTNET;
Figure A-29c. Normalized Mean Bias (%) for total nitrate during fall 2005 at monitoring
sites in Eastern modeling domain.
TNO3 NME (%) for run 2005ct_ldghg2_05b_12EUS1 for Fall
units = %
coverage limit - 75%
CIRCLE=CASTNET:
Figure A-29d. Normalized Mean Error (%) for total nitrate fall 2005 at monitoring sites in
Eastern modeling domain.
A-47
-------
N03 NMB (%) for run 2005ctJdghg2_05b_12WUS1 tor Winter
inits = %
Mvefage limit = 75%
> 100
80
60
40
20
0
-20
-40
-60
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-30a. Normalized Mean Bias (%) for nitrate during winter 2005 at monitoring
sites in Western modeling domain.
N03 NME (%) tor run 2005clJdghg2_05b_12WUS1 for Winter
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-30b. Normalized Mean Error (%) for nitrate during winter 2005 at monitoring
sites in Western modeling domain.
A-48
-------
TNO3 NMB (%) for run 20Q5ct_ldghg2_05b_12WUS1 for Winter
mils . %
coverage limit. 75%
100
80
60
40
20
0
-20
-40
-60
-BO
-100
CIRCLE=CASTNET;
Figure A-30c. Normalized Mean Bias (%) for total nitrate during winter 2005 at
monitoring sites in Western modeling domain.
TNO3 NME (%) for run 2005ct_ldghg2_05b_12WUS1 far Winter
ills . %
coverage limit = 75%
C!RCLE=CASTNET;
Figure A-30d. Normalized Mean Error (%) for total nitrate winter 2005 at monitoring
sites in Western modeling domain.
A-49
-------
N03 NMB {%) for run 2005ct_ldghg2_05b_12WUS1 for Spring
uniis = %
coverage limit = 75%
BO
40
20
0
-20
-40
-60
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-31a. Normalized Mean Bias (%) for nitrate during spring 2005 at monitoring
sites in Western modeling domain.
N03 NME (%) (or run 2Q05ctJdghg2_05b_12WUS1 tor Spring
coverage limii = 75%
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-31b. Normalized Mean Error (%) for nitrate during spring 2005 at monitoring
sites in Western modeling domain.
A-50
-------
TN03 NMB (%) for run 2005ctJdghg2_05bJ2WUS1 for Spring
units = %
coverage limit = 75%
60
40
20
0
-20
-40
-en
-80
<-100
CIRCLE=CASTNET;
Figure A-31c. Normalized Mean Bias (%) for total nitrate during spring 2005 at
monitoring sites in Western modeling domain.
TNO3 NME (%) tor run 200Scl_ldghg2_05b_12WUS1 lor Spring
units = %
coverage limit = 75%
CIRCLE=CASTNET;
Figure A-31d. Normalized Mean Error (%) for total nitrate spring 2005 at monitoring
sites in Western modeling domain.
A-51
-------
NO3 NMB (%) for run 2005clJdghg2_05b_12WUS1 tor Summer
coverage hmil - 75%
>100
80
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-32a. Normalized Mean Bias (%) for nitrate during summer 2005 at monitoring
sites in Western modeling domain.
NO3 NME (%)for run 2005ctJdghg2_05b_12WUSt tor Summer
'IIS %
coverage hmil - 75%
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-32b. Normalized Mean Error (%) for nitrate during summer 2005 at monitoring
sites in Western modeling domain.
A-52
-------
TN03 NMB (%) for run 2005clJdghg2J)5b_12WUS1 tor Summer
units = %
coverage limit = 75%
60
40
20
0
-20
-40
-en
-80
<-100
CIRCLE=CASTNET;
Figure A-32c. Normalized Mean Bias (%) for total nitrate during summer 2005 at
monitoring sites in Western modeling domain.
TN03 NME (%) lor run 2005ct_ld9hg2_05b_12WUS1 for Summer
units = %
coverage limit = 75%
CIRCLE=CASTNET;
Figure A-32d. Normalized Mean Error (%) for total nitrate summer 2005 at monitoring
sites in Western modeling domain.
A-53
-------
NO3 NMB (%) lor run 2005clJdghg2_05b_12WUS1 tor Fall
coverage limit = 75%
60
40
20
0
-20
-40
-60
-80
-100
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-33a. Normalized Mean Bias (%) for nitrate during fall 2005 at monitoring sites
in Western modeling domain.
N03 NME (%) for run 2005clJdghg2_05b_12WUS1 for Fall
coverage limit = 75%
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-33b. Normalized Mean Error (%) for nitrate during fall 2005 at monitoring sites
in Western modeling domain.
A-54
-------
TN03 NMB (%>for run 2005clJdghg2_p5b_12WUS1 tor Fall
units = %
coverage limit = 75%
60
40
20
0
-20
-40
-en
-80
<-100
CIRCLE=CASTNET;
Figure A-33c. Normalized Mean Bias (%) for total nitrate during fall 2005 at monitoring
sites in Western modeling domain.
TNO3 NME (%) for run 2005ctjdghg2_05b_12WUS1 for Fall
units = %
coverage limit = 75%
CIRCLE=CASTNET;
Figure A-33d. Normalized Mean Error (%) for total nitrate fall 2005 at monitoring sites in
Western modeling domain.
A-55
-------
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 to moderate under-prediction in the subregions for urban sub-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
Central
U.S.
Network
CSN
CASTNet
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of
Obs.
771
875
851
587
72
77
72
75
NMB (%)
-1.1
5.8
-20.9
18.5
3.8
17.3
-16.9
17.7
NME (%)
43.5
42.2
45.9
55.3
37.9
34.4
29.5
44.4
FB (%)
-0.2
8.1
-24.0
23.4
4.4
11.3
-19.6
24.8
FE (%)
50.8
43.4
60.8
55.8
42.6
32.5
35.7
46.5
Midwest
CSN
CASTNet
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
598
637
621
639
142
155
161
157
-8.2
49.6
0.4
8.2
-10.5
45.8
-4.8
20.9
31.9
63.6
37.1
37.7
24.3
53.2
25.9
45.6
-3.0
39.4
16.5
22.3
-4.8
37.5
-1.5
27.4
33.4
51.3
41.9
41.3
25.1
42.1
27.5
41.5
Southeast
CSN
CASTNet
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
888
918
866
911
264
292
268
273
-8.1
9.4
-13.7
4.1
-6.0
9.0
-31.8
-8.3
41.5
39.9
36.8
42.6
28.0
31.2
35.3
36.5
-8.0
9.9
-8.1
14.5
-6.4
7.3
-44.9
-6.8
44.1
40.4
44.2
45.6
29.5
30.8
48.6
40.9
Northeast
CSN
CASTNet
Winter
Spring
Summer
Fall
Winter
828
894
874
902
193
2.8
33.3
-10.5
18.8
23.3
34.5
54.7
36.1
50.6
38.7
6.8
35.5
4.5
30.1
27.2
34.3
50.4
43.9
51.2
37.5
A-56
-------
Region
Network
Season
Spring
Summer
Fall
No. of
Obs.
206
192
195
NMB (%)
43.4
-23.0
9.7
NME (%)
49.7
29.7
39.4
FB (%)
32.8
-26.2
14.4
FE (%)
38.9
34.5
36.4
West
CSN
CASTNet
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
829
859
849
886
250
273
281
268
-27.6
-0.3
-33.0
-21.3
-2.3
-8.8
-33.3
-3.1
60.7
52.7
53.0
63.6
41.0
32.1
40.2
32.1
-11.8
18.8
-4.7
9.5
7.6
-4.5
-34.4
1.7
65.5
51.3
51.6
58.6
39.3
31.7
44.6
31.4
A-57
-------
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 in the West.
Table A-6. Elemental Carbon performance statistics by subregion, by season for the 2005
CMAQ model simulation.
Subregion
Central
U.S.
Network
CSN
IMPROVE
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of
Obs.
816
938
875
618
589
716
701
620
NMB (%)
101.0
90.6
109.0
93.6
9.6
-9.4
-30.5
-17.2
NME (%)
132.0
114.0
132.0
111.0
54.5
55.8
46.8
34.8
FB (%)
56.5
45.5
41.9
57.5
4.9
-10.1
-38.3
-16.0
FE (%)
77.5
70.6
80.7
70.7
47.1
53.7
56.2
41.1
Midwest
CSN
IMPROVE
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
602
637
621
642
182
184
185
145
122.0
64.1
48.0
53.1
61.4
17.9
-13.8
-12.6
137.0
85.2
64.6
73.1
79.6
56.8
40.9
33.4
69.3
48.9
38.2
39.8
22.9
-11.8
-37.3
-19.2
76.5
61.5
54.4
55.6
45.9
51.1
53.8
48.2
Southeast
CSN
IMPROVE
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
889
914
866
909
491
530
493
481
37.3
37.1
39.9
12.0
-3.1
-17.1
-41.4
-27.0
61.4
62.6
68.9
45.7
44.4
44.9
48.4
39.0
30.2
36.6
37.7
18.2
-0.8
-11.4
-55.7
-22.9
49.3
54.1
61.1
45.6
48.6
45.2
71.4
45.6
Northeast
CSN
Winter
831
97.5
110.0
57.7
67.1
A-58
-------
Subregion
Network
IMPROVE
Season
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of
Obs.
881
866
901
603
658
596
591
NMB (%)
90.2
64.7
52.2
45.4
28.1
-20.6
30.9
NME (%)
107.0
87.8
82.5
72.9
63.0
45.6
57.3
FB (%)
57.0
45.3
34.6
22.7
11.3
-37.8
6.0
FE (%)
68.7
63.2
56.6
53.1
54.3
57.4
49.3
West
CSN
IMPROVE
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
808
822
806
867
2,315
2,567
2,285
2,348
43.6
99.5
112.0
52.1
0.2
17.3
28.8
7.4
84.7
123.0
126.0
86.6
63.5
68.2
76.7
66.0
21.4
44.0
57.5
26.3
-15.0
-1.7
18.2
-9.4
66.9
73.9
72.3
64.0
64.6
54.1
58.4
59.2
A-59
-------
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, except in the summer at rural sites. 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
Central U.S.
Network
CSN
IMPROVE
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of
Obs.
544
628
595
493
589
715
699
619
NMB (%)
0.2
-34.8
-51.4
-30.8
-8.1
-38.5
-50.1
-44.4
NME (%)
57.7
52.4
54.1
45.2
51.1
57.6
52.3
48.2
FB (%)
14.9
-32.0
-69.8
-28.0
-12.0
-38.1
-69.9
-54.4
FE (%)
59.7
63.3
76.3
56.7
47.9
61.1
74.3
62.3
Midwest
CSN
IMPROVE
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
566
605
619
595
182
184
185
144
4.3
-29.4
-53.1
-28.5
3.4
-25.9
-48.4
-35.0
53.2
45.9
54.6
41.3
38.4
36.4
51.4
43.6
21.8
-17.8
-69.7
-16.4
1.6
-32.9
-64.6
-43.8
54.2
52.8
73.2
52.0
37.1
44.6
68.9
61.5
Southeast
CSN
IMPROVE
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
871
901
857
880
491
529
492
481
-25.7
-35.6
-55.8
-39.9
-10.1
-9.2
-48.6
-33.8
45.4
48.7
57.7
46.0
45.1
49.1
54.2
41.2
-15.2
-28.8
-75.9
-42.8
-11.5
-15.1
-66.5
-41.7
50.6
57.0
80.7
57.3
50.9
50.3
75.0
53.2
Northeast
CSN
Winter
Spring
806
832
27.9
2.2
59.3
50.7
31.3
8.5
55.2
53.1
A-60
-------
Region
Network
IMPROVE
Season
Summer
Fall
Winter
Spring
Summer
Fall
No. of
Obs.
859
830
602
657
596
588
NMB (%)
-47.3
-4.4
48.2
3.8
-47.0
14.3
NME (%)
51.6
47.1
69.3
46.3
51.5
47.4
FB (%)
-61.0
3.7
31.6
-3.1
-59.3
-1.9
FE (%)
69.1
53.3
52.1
46.1
66.3
43.9
West
CSN
IMPROVE
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
803
823
840
881
2,273
2,529
2,268
2,171
-26.5
-12.2
-24.1
-28.3
-16.8
-23.1
4.4
-21.7
67.4
60.4
41.6
57.1
58.6
51.8
65.2
56.9
-20.2
-4.1
-28.7
-26.2
-22.6
-25.3
-1.3
-26.3
70.0
60.3
50.5
58.6
64.5
57.0
60.3
61.9
A-61
-------
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 to moderate bias and error percentages when compared to observations. The model
yielded larger bias and error results for 1,3 butadiene and acrolein based on limited monitoring
sites. Model performance for HAPs is not as good as model performance for ozone and PM2.5.
Technical issues in the HAPs data consist of (1) uncertainties in monitoring methods; (2) limited
measurements in time/space to characterize ambient concentrations ("local in nature"); (3)
commensurability issues between measurements and model predictions; (4) emissions and
science uncertainty issues may also affect model performance; and (5) limited data for estimating
intercontinental transport that effects the estimation of boundary conditions (i.e., boundary
estimates for some species are much higher than predicted values inside the domain).
As with the national, annual PM2.5 and ozone CMAQ modeling, the "acceptability" of
model performance was judged by comparing our CMAQ 2005 performance results to the
limited performance found in recent regional multi-pollutant model applications.17'18'19 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.
17 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform:
Air Toxics, Ozone, and Paniculate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.
18 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.
19 Wesson, K., N. Farm, and B. Timin, 2010: Draft Manuscript: Air Quality and Benefits Model Responsiveness to
Varying Horizontal Resolution in the Detroit Urban Area, Atmospheric Pollution Research, Special Issue: Air
Quality Modeling and Analysis.
A-62
-------
Table A-8. Air toxics performance statistics by season in the Eastern domain for the 2005
CMAQ model simulation.
Air Toxic Species
Formaldehyde
Acetaldehyde
Benzene
1,3-Butadiene
Acrolein
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of Obs.
,646
,545
,835
,932
,570
,486
,778
,881
3,182
3,099
3,270
3,433
2,649
2,726
2,782
2,877
612
430
834
1,002
NMB (%)
-51.7
-52.7
-52.3
-50.7
-39.5
-25.6
58.9
0.3
-32.0
-39.0
-38.4
-32.5
-63.1
-77.7
-73.4
-61.7
-90.4
-82.3
-95.9
-95.1
NME (%)
61.1
64.9
63.2
61.8
49.7
49.8
91.2
57.1
68.2
66.7
68.6
64.9
89.8
92.8
87.8
81.5
94.7
91.3
99.0
98.8
FB (%)
-45.4
-35.0
-28.9
-38.3
-40.3
-21.1
48.9
-5.4
-11.3
-25.6
-20.5
-18.3
-21.2
-48.2
-54.8
-49.9
-124.0
-117.0
-137.0
-149.0
FE (%)
67.8
67.1
57.9
59.9
56.0
54.0
68.4
55.4
58.5
63.5
66.4
59.6
87.0
92.4
87.6
85.8
135.0
127.0
154.0
153.0
Table A-9. Air toxics performance statistics by season in the Western domain for the 2005
CMAQ model simulation.
Air Toxic Species
Formaldehyde
Acetaldehyde
Benzene
1,3-Butadiene
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of Obs.
441
514
657
595
426
499
646
584
880
891
1,086
880
752
788
725
764
NMB (%)
-22.1
-30.0
-25.4
-26.2
-21.1
-24.5
-1.3
-17.6
-39.6
-31.9
-43.4
-39.5
-43.4
-18.3
-33.8
-45.7
NME (%)
68.4
57.4
38.5
43.1
68.5
56.1
46.4
51.4
58.4
56.1
65.0
58.4
98.4
91.4
81.8
88.1
FB (%)
-34.7
-22.1
-21.7
-27.5
-33.1
-23.6
7.9
-16.2
-37.9
-30.7
-25.5
-37.9
-28.4
-24.8
-35.9
-36.0
FE (%)
73.7
61.2
41.4
49.5
73.1
61.9
44.7
56.0
64.2
61.9
64.2
64.2
100.0
81.1
80.3
90.4
A-63
-------
Acrolein
Winter
Spring
Summer
Fall
201
190
316
295
-95.3
-95.8
-96.2
-96.9
95.3
95.8
98.8
98.2
-164.0
-167.0
-172.0
-173.0
165.0
169.0
178.0
175.0
A-64
-------
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
seasonal nitrate deposition generally show small under-predictions for the Eastern and Western
NADP sites (NMB values range from 1% to -27%). However, nitrate deposition is over
predicted in the East and West during the winter. 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 87% 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
Nitrate
Sulfate
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of Obs.
1,788
1,882
1,975
1,736
1,788
1,882
1,975
1,736
NMB (%)
31.4
-1.7
-23.2
7.0
33.6
6.4
3.2
-3.2
NME (%)
74.7
57.3
61.8
65.7
69.9
59.6
73.9
61.6
FB (%)
13.5
-2.9
-20.0
-5.8
24.1
12.4
6.4
-9.9
FE (%)
72.5
64.8
75.3
74.2
72.1
67.4
79.4
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
Nitrate
Sulfate
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of Obs.
649
768
641
674
649
768
641
674
NMB (%)
8.0
-0.9
-27.5
-4.9
24.9
16.6
-5.1
-8.7
NME (%)
82.1
67.1
63.5
75.9
86.7
73.0
73.8
76.7
FB (%)
5.3
2.5
-23.1
-6.5
25.6
18.2
-1.7
-5.0
FE (%)
83.3
73.6
79.6
84.4
88.8
77.3
81.6
86.7
A-65
-------
Air Quality Modeling Technical Support Document:
2017-2025 Light-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
August 2012
B-l
-------
Table B-l. 8-Hour Ozone Design Values for 2017-2025 LD GHG Scenarios
(units are ppb)
State
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arkansas
Arkansas
Arkansas
Arkansas
California
California
California
California
California
California
County
Baldwin
Clay
Colbert
Elmore
Etowah
Houston
Jefferson
Lawrence
Madison
Mobile
Montgomery
Morgan
Russell
Shelby
Sumter
Talladega
Tuscaloosa
Cochise
Coconino
Gila
Maricopa
Pima
Pinal
Yuma
Crittenden
Newton
Polk
Pulaski
Alameda
Amador
Butte
Calaveras
Colusa
Contra Costa
2005 Baseline
DV
77.3
74.0
72.0
70.7
71.7
71.0
83.7
72.0
77.3
76.7
69.3
77.3
71.3
85.7
64.0
72.0
73.3
71.3
73.0
80.3
83.0
76.0
79.3
75.0
87.3
72.7
75.0
79.7
78.3
83.0
83.7
91.3
67.0
73.3
2030 Reference
Case DV
59.13
51.06
48.97
50.76
50.99
52.37
59.36
52.91
55.43
60.19
49.72
60.10
51.69
59.51
52.55
51.63
51.81
59.66
59.85
56.91
63.23
56.53
55.87
57.61
62.32
54.59
60.03
55.12
65.74
64.63
65.08
73.83
53.56
65.53
2030 Control
Case DV
59.09
51.12
49.01
50.81
51.02
52.42
59.43
52.94
55.47
60.13
49.76
60.14
51.78
59.61
52.55
51.66
51.86
59.77
59.85
57.02
63.34
56.61
55.96
57.61
62.36
54.77
60.00
55.49
65.72
64.63
65.10
73.78
53.54
65.50
B-2
-------
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
El Dorado
Fresno
Glenn
Imperial
Inyo
Kern
Kings
Lake
Los Angeles
Madera
Marin
Mariposa
Mendocino
Merced
Monterey
Napa
Nevada
Orange
Placer
Riverside
Sacramento
San Benito
San Bernardino
San Diego
San Francisco
San Joaquin
San Luis Obispo
San Mateo
Santa Barbara
Santa Clara
Santa Cruz
Shasta
Siskiyou
Solano
Sonoma
Stanislaus
Sutter
Tehama
Tulare
96.0
98.3
67.0
85.0
82.3
110.0
85.7
60.7
114.0
79.3
49.7
86.3
56.7
89.3
61.0
59.3
96.3
84.3
94.0
112.3
97.3
75.0
123.3
87.7
46.0
75.3
70.7
53.7
76.0
75.3
61.3
79.3
63.5
73.5
47.7
84.7
82.0
82.7
103.7
71.88
79.79
53.58
68.83
66.04
92.09
67.66
49.08
97.20
63.51
42.32
69.67
45.49
70.68
50.09
48.27
72.85
80.72
70.60
109.58
73.60
59.51
119.53
70.43
45.98
62.16
56.89
49.60
60.97
59.81
52.02
64.28
50.83
58.80
37.96
68.68
66.17
65.60
82.27
71.93
79.74
53.57
68.81
66.04
92.14
67.63
49.09
97.07
63.48
42.31
69.66
45.49
70.67
50.09
48.26
72.88
80.59
70.64
109.34
73.65
59.51
119.32
70.40
45.96
62.12
56.87
49.57
60.92
59.83
52.00
64.28
50.83
58.79
37.96
68.65
66.13
65.60
82.25
-------
California
California
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
D.C.
Delaware
Delaware
Delaware
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Tuolumne
Ventura
Yolo
Adams
Arapahoe
Boulder
Denver
Douglas
El Paso
Jefferson
La Plata
Larimer
Montezuma
Weld
Fairfield
Hartford
Litchfield
Middlesex
New Haven
New London
Tolland
Washington
Kent
New Castle
Sussex
Alachua
Baker
Bay
Brevard
Broward
Collier
Columbia
Duval
Escambia
Highlands
Hillsborough
Holmes
Lake
Lee
80.0
89.7
78.7
69.0
78.7
77.0
73.0
83.7
73.3
81.7
72.0
76.0
72.0
76.7
92.3
84.3
87.7
90.3
90.3
85.3
88.7
84.7
80.3
82.3
82.7
72.0
68.7
78.7
71.3
65.0
68.3
72.0
77.7
82.7
72.3
80.7
70.3
76.7
70.3
64.22
71.06
62.11
57.71
63.21
61.57
61.06
67.12
61.27
67.84
62.18
61.13
64.43
66.46
74.33
61.34
63.75
69.06
70.34
64.15
64.58
64.05
59.37
63.48
61.41
48.57
48.71
57.74
53.64
53.85
48.66
52.08
56.62
60.30
56.15
60.36
53.29
57.11
52.87
64.17
71.04
62.11
57.76
63.26
61.61
61.11
67.21
61.31
67.90
62.20
61.16
64.43
66.48
74.31
61.37
63.79
69.06
70.34
64.15
64.62
64.10
59.38
63.50
61.41
48.62
48.74
57.78
53.68
53.88
48.73
52.10
56.64
60.33
56.19
60.37
53.31
57.21
52.94
B-4
-------
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Idaho
Idaho
Leon
Manatee
Marion
Miami-Dade
Orange
Osceola
Palm Beach
Pasco
Pinellas
Polk
St Lucie
Santa Rosa
Sarasota
Seminole
Volusia
Wakulla
Bibb
Chatham
Chattooga
Clarke
Cobb
Columbia
Coweta
Dawson
De Kalb
Douglas
Fayette
Fulton
Glynn
Gwinnett
Henry
Murray
Muscogee
Paulding
Richmond
Rockdale
Sumter
Ada
Canyon
71.0
77.3
73.0
71.3
79.3
72.0
65.0
76.3
72.7
74.7
66.5
80.0
77.3
76.0
68.3
71.3
81.0
68.3
75.0
80.7
82.7
73.0
82.0
76.3
88.7
87.3
85.7
91.7
67.0
88.7
89.7
78.0
75.7
80.3
80.3
90.0
72.3
76.0
66.0
50.43
56.26
48.09
61.23
61.11
51.09
54.19
55.94
52.55
52.79
51.67
59.30
54.05
55.52
47.69
51.67
53.73
50.92
52.56
51.47
54.85
53.34
57.39
48.52
64.39
57.10
61.58
66.57
48.40
60.34
61.28
56.27
52.48
52.43
58.47
59.45
50.72
66.53
54.67
50.47
56.28
48.16
61.22
61.21
51.17
54.23
55.98
52.59
52.79
51.71
59.33
54.10
55.64
47.75
51.70
53.83
50.93
52.63
51.63
55.08
53.39
57.46
48.64
64.55
57.22
61.69
66.73
48.45
60.57
61.40
56.34
52.59
52.54
58.53
59.61
50.74
66.55
54.71
B-5
-------
Idaho
Idaho
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Elmore
Kootenai
Adams
Champaign
Clark
Cook
Du Page
Effingham
Hamilton
Jersey
Kane
Lake
McHenry
McLean
Macon
Macoupin
Madison
Peoria
Randolph
Rock Island
StClair
Sangamon
Will
Winnebago
Allen
Boone
Carroll
Clark
Delaware
Elkhart
Floyd
Greene
Hamilton
Hancock
Hendricks
Huntington
Jackson
Johnson
Lake
63.0
67.0
70.0
68.3
66.0
77.7
69.0
70.0
73.0
78.7
74.3
78.0
73.3
73.0
71.3
73.0
83.0
72.7
72.0
65.3
81.7
70.0
71.7
69.0
79.3
79.7
74.0
80.3
76.3
79.0
77.7
78.3
82.7
78.0
75.3
75.0
74.7
76.7
81.0
54.12
54.29
56.18
54.48
52.53
67.55
60.21
55.67
55.44
58.03
60.21
65.99
57.28
56.35
56.21
51.83
63.51
58.58
55.95
50.89
64.00
52.13
58.16
52.95
60.57
61.52
56.38
60.65
56.82
60.41
62.17
62.02
62.86
59.39
59.04
58.11
57.95
60.17
68.67
54.14
54.34
56.18
54.49
52.52
67.48
60.21
55.62
55.46
58.17
60.22
65.98
57.29
56.36
56.24
51.92
63.64
58.57
55.99
50.89
64.17
52.18
58.16
52.95
60.66
61.55
56.46
60.72
56.94
60.45
62.25
62.05
63.08
59.67
59.03
58.17
58.01
60.25
68.67
B-6
-------
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
La Porte
Madison
Marion
Morgan
Perry
Porter
Posey
St Joseph
Shelby
Vanderburgh
Vigo
Warrick
Bremer
Clinton
Harrison
Linn
Montgomery
Palo Alto
Polk
Scott
Story
Van Buren
Warren
Douglas
Johnson
Leavenworth
Linn
Sedgwick
Sumner
Trego
Wyandotte
Bell
Boone
Boyd
Bullitt
Campbell
Carter
Christian
Daviess
78.5
76.7
78.7
77.0
81.0
78.3
71.7
79.3
77.3
77.3
74.0
77.7
66.3
71.3
74.7
68.3
65.7
61.0
63.0
72.0
61.0
69.0
64.5
73.0
75.3
75.0
73.3
71.3
71.7
70.7
75.3
71.7
75.7
77.3
74.0
83.0
71.0
78.0
75.7
63.51
57.12
61.14
60.28
63.04
64.98
55.10
60.89
61.53
59.47
57.58
58.39
52.29
55.52
58.90
53.79
50.65
49.66
48.18
55.38
46.63
54.25
48.33
54.12
56.91
58.09
55.51
54.45
54.60
59.74
59.32
51.02
58.31
60.49
59.55
68.33
54.61
55.65
59.21
63.51
57.31
61.31
60.36
63.07
64.98
55.14
60.91
61.65
59.55
57.57
58.51
52.32
55.54
58.92
53.79
50.68
49.65
48.20
55.39
46.64
54.26
48.35
54.15
56.97
58.11
55.52
54.45
54.59
59.74
59.33
51.12
58.32
60.27
59.59
68.39
54.51
55.69
59.26
B-7
-------
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Edmonson
Fayette
Greenup
Hancock
Hardin
Henderson
Jefferson
Jessamine
Kenton
Livingston
McCracken
McLean
Oldham
Perry
Pike
Pulaski
Simpson
Trigg
Warren
Ascension
Beauregard
Bossier
Caddo
Calcasieu
East Baton
Rouge
Iberville
Jefferson
Lafayette
Lafourche
Livingston
Ouachita
Pointe Coupee
St Bernard
St Charles
St James
St John The
Baptis
St Mary
73.7
70.3
76.7
74.0
74.7
75.3
78.3
73.3
78.7
73.7
73.3
73.0
83.0
72.3
66.7
70.3
75.7
70.0
72.0
82.0
75.0
78.0
79.0
82.0
92.0
85.0
83.0
82.0
79.3
78.3
75.3
83.7
78.0
77.3
76.3
79.0
76.0
56.07
52.92
60.54
57.11
58.02
57.29
63.93
56.22
62.11
56.24
57.56
56.61
61.51
54.44
50.93
55.53
55.60
50.86
54.48
66.46
63.89
58.58
59.83
68.56
74.34
69.51
67.45
63.97
64.04
63.02
57.31
69.42
63.14
62.48
62.29
65.98
60.36
56.12
53.00
60.32
57.15
58.07
57.40
64.04
56.28
62.16
56.27
57.56
56.70
61.71
54.45
50.93
55.55
55.64
50.88
54.53
66.27
63.73
58.50
59.74
68.40
74.13
69.30
67.27
63.84
63.81
62.84
57.33
69.21
63.03
62.31
62.07
65.75
60.17
-------
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
West Baton
Rouge
Cumberland
Hancock
Kennebec
Knox
Oxford
Penobscot
Sagadahoc
York
Anne Arundel
Baltimore
Calvert
Carroll
Cecil
Charles
Frederick
Garrett
Harford
Kent
Montgomery
Prince Georges
Washington
Barnstable
Berkshire
Bristol
Dukes
Essex
Hampden
Hampshire
Middlesex
Norfolk
Suffolk
Worcester
Allegan
Benzie
Berrien
Cass
Clinton
84.3
72.0
82.0
69.7
75.3
61.0
67.0
68.5
74.0
89.7
85.3
81.0
83.3
90.7
86.0
80.3
75.5
92.7
82.0
83.0
91.0
78.3
84.7
79.7
82.7
83.0
83.3
87.3
85.0
79.0
84.7
80.3
80.0
90.0
81.7
82.3
80.7
75.7
68.92
53.64
61.30
51.45
55.81
48.75
51.33
50.77
56.15
64.22
70.45
59.24
59.81
64.96
62.55
56.22
58.92
74.48
59.21
60.88
66.04
56.14
65.47
59.36
63.22
65.13
67.76
63.46
62.00
60.12
64.99
63.01
57.75
71.36
64.36
66.00
62.00
57.09
68.68
53.73
61.36
51.52
55.88
48.76
51.36
50.84
56.21
64.27
70.46
59.27
59.85
64.99
62.60
56.26
58.44
74.50
59.24
60.88
66.09
56.18
65.50
59.37
63.24
65.15
67.78
63.51
62.05
60.16
64.98
63.05
57.80
71.34
64.41
65.99
62.01
57.13
B-9
-------
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Minnesota
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Genesee
Huron
Ingham
Kalamazoo
Kent
Leelanau
Lenawee
Macomb
Mason
Missaukee
Muskegon
Oakland
Ottawa
StClair
Schoolcraft
Washtenaw
Wayne
Anoka
St Louis
Adams
Bolivar
De Soto
Hancock
Harrison
Hinds
Jackson
Lauderdale
Lee
Cass
Cedar
Clay
Clinton
Greene
Jefferson
Lincoln
Monroe
Perry
Platte
St Charles
79.3
75.7
76.0
75.3
81.0
75.7
78.7
86.0
79.7
73.7
85.0
78.0
81.7
82.3
79.3
78.3
82.0
67.7
65.0
74.7
74.3
82.7
79.0
83.0
71.3
80.3
74.3
73.7
74.7
75.7
84.7
83.0
73.0
82.3
87.0
71.7
77.5
77.0
87.0
61.51
60.74
58.48
58.06
60.96
60.51
61.98
68.68
61.09
57.84
66.94
64.86
63.03
63.85
62.09
62.38
66.12
60.84
52.66
58.83
56.22
60.11
61.43
63.17
47.22
62.39
56.43
51.12
56.30
56.99
64.63
61.73
54.11
66.78
67.64
55.13
58.54
59.85
65.78
61.54
60.77
58.50
58.05
60.96
60.50
61.98
68.67
61.10
57.83
66.94
64.83
63.02
63.91
62.13
62.39
66.11
60.83
52.65
58.80
56.26
60.13
61.30
62.95
47.30
62.12
56.44
51.17
56.33
56.99
64.66
61.78
54.17
66.85
67.64
55.15
58.57
59.88
65.87
B-10
-------
Missouri
Missouri
Missouri
Montana
Nebraska
Nebraska
Nevada
Nevada
Nevada
Nevada
Nevada
New
Hampshire
New
Hampshire
New
Hampshire
New
Hampshire
New
Hampshire
New
Hampshire
New
Hampshire
New
Hampshire
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Mexico
Ste Genevieve
St Louis
St Louis City
Yellowstone
Douglas
Lancaster
Churchill
Clark
Washoe
White Pine
Carson City
Belknap
Cheshire
Coos
Graf ton
Hillsborough
Merrimack
Rockingham
Sullivan
Atlantic
Bergen
Camden
Cumberland
Gloucester
Hudson
Hunterdon
Mercer
Middlesex
Monmouth
Morris
Ocean
Passaic
Bernalillo
79.7
88.0
84.0
59.0
68.7
56.0
64.0
83.7
70.7
72.3
65.0
71.3
70.7
77.0
67.0
78.7
71.7
77.0
70.0
79.3
86.0
89.3
83.3
87.0
85.7
89.0
88.0
88.3
87.3
83.3
93.0
81.0
77.0
64.65
70.47
66.95
52.60
56.30
43.93
51.68
69.55
55.64
59.06
50.19
51.70
51.45
59.25
51.88
59.56
52.32
58.42
53.13
59.84
72.25
68.01
60.16
66.65
75.11
65.53
68.24
68.26
69.36
61.99
70.23
63.42
61.34
64.66
70.48
67.11
52.62
56.33
43.92
51.69
69.59
55.64
59.09
50.20
51.73
51.49
59.27
51.91
59.63
52.36
58.48
53.16
59.84
72.18
68.01
60.17
66.65
75.01
65.53
68.25
68.26
69.34
61.98
70.24
63.41
61.39
B-ll
-------
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
Dona Ana
Eddy
Lea
Sandoval
San Juan
Albany
Bronx
Chautauqua
Chemung
Dutchess
Erie
Essex
Hamilton
Herkimer
Jefferson
Madison
Monroe
Niagara
Oneida
Onondaga
Orange
Oswego
Putnam
Queens
Rensselaer
Richmond
Saratoga
Schenectady
Suffolk
Ulster
Wayne
Westchester
Alexander
Avery
Buncombe
Caldwell
Caswell
Chatham
Cumberland
75.3
69.0
71.0
73.3
71.3
73.7
74.7
86.7
68.7
75.7
85.0
77.0
71.7
68.3
78.0
72.0
76.3
82.7
68.3
73.7
82.0
78.0
84.3
80.0
77.3
88.3
79.7
70.0
90.3
77.3
68.0
87.7
77.0
70.0
74.0
74.3
76.3
73.3
81.7
62.51
61.65
63.97
58.20
66.38
55.74
65.60
72.56
53.98
55.43
68.74
62.41
56.49
55.39
62.86
55.22
60.70
70.39
53.73
58.40
60.25
65.88
65.09
66.67
58.38
74.48
60.22
53.64
76.93
57.94
56.05
73.50
56.79
53.10
53.47
54.77
53.22
52.33
57.62
62.43
61.64
63.96
58.25
66.37
55.75
65.54
72.60
54.01
55.45
68.74
62.43
56.52
55.39
62.87
55.25
60.72
70.38
53.74
58.41
60.27
65.89
65.10
66.60
58.40
74.38
60.25
53.66
76.86
57.97
56.07
73.46
56.82
53.13
53.52
54.81
53.28
52.37
57.68
B-12
-------
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Davie
Durham
Edgecombe
Forsyth
Franklin
Graham
Granville
Guilford
Haywood
Jackson
Johnston
Lenoir
Lincoln
Martin
Mecklenburg
New Hanover
Person
Pitt
Rockingham
Rowan
Swain
Union
Wake
Yancey
Billings
Burke
Cass
McKenzie
Mercer
Oliver
Allen
Ashtabula
Butler
Clark
Clermont
Clinton
Cuyahoga
Delaware
Franklin
81.3
77.0
77.0
80.0
78.7
78.3
82.0
82.0
78.3
76.0
77.3
75.3
81.0
75.0
89.3
72.3
77.3
76.3
77.0
86.7
66.3
79.3
80.3
76.0
61.5
57.5
60.0
61.3
59.3
57.7
78.7
89.0
83.3
81.0
81.0
82.3
79.7
78.3
86.3
57.66
53.41
56.87
57.69
55.74
56.21
59.09
56.70
59.17
54.81
53.26
55.78
58.57
58.34
66.14
55.15
59.63
54.41
55.40
60.86
48.01
55.73
56.42
54.27
54.35
52.29
49.12
55.05
55.89
54.74
60.85
71.81
64.23
59.70
64.91
60.43
65.23
59.99
65.99
57.70
53.48
56.90
57.74
55.79
56.35
59.13
56.80
59.22
54.91
53.32
55.81
58.60
58.35
66.17
55.17
59.63
54.44
55.44
60.90
48.09
55.81
56.48
54.33
54.35
52.27
49.11
55.04
55.90
54.74
60.88
71.83
64.40
59.83
64.98
60.52
65.21
60.06
66.12
B-13
-------
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Geauga
Greene
Hamilton
Jefferson
Knox
Lake
Lawrence
Licking
Lorain
Lucas
Madison
Mahoning
Medina
Miami
Montgomery
Portage
Preble
Stark
Summit
Trumbull
Warren
Washington
Wood
Adair
Canadian
Cherokee
Cleveland
Comanche
Creek
Dewey
Kay
McClain
Mayes
Oklahoma
Ottawa
Pitts burg
Tulsa
Clackamas
Jackson
79.3
80.3
84.7
78.0
77.7
86.3
70.7
78.0
76.7
81.3
79.7
78.7
80.3
76.7
74.0
83.7
73.0
81.0
83.7
84.3
88.3
82.7
80.0
75.7
76.0
75.7
74.7
77.5
76.7
72.7
78.0
72.0
78.5
80.0
78.0
72.0
79.3
66.3
68.0
60.24
59.67
66.77
59.78
57.93
69.10
55.81
57.72
62.38
65.09
58.46
57.82
62.75
56.10
54.31
63.03
54.38
61.73
64.68
62.55
65.78
63.80
62.48
60.43
58.20
61.13
57.32
58.97
58.74
56.48
59.74
55.05
63.64
60.10
60.47
57.67
62.08
58.94
51.67
60.27
59.81
66.84
59.74
58.04
69.09
55.60
57.83
62.39
65.09
58.53
57.84
62.73
56.28
54.58
63.05
54.47
61.75
64.71
62.59
65.94
63.77
62.48
60.42
58.29
61.12
57.38
58.98
58.72
56.47
59.69
55.10
63.59
60.16
60.45
57.65
62.06
58.96
51.72
B-14
-------
Oregon
Oregon
Oregon
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Rhode Island
Rhode Island
Rhode Island
South Carolina
Lane
Marion
Multnomah
Adams
Allegheny
Armstrong
Beaver
Berks
Blair
Bucks
Cambria
Centre
Chester
Clearfield
Dauphin
Delaware
Erie
Franklin
Greene
Indiana
Lackawanna
Lancaster
Lawrence
Lehigh
Luzerne
Lycoming
Mercer
Montgomery
Northampton
Perry
Philadelphia
Tioga
Washington
Westmoreland
York
Kent
Providence
Washington
Abbeville
69.3
65.7
57.0
76.3
83.7
83.0
83.0
80.0
74.3
88.0
74.7
78.3
86.0
78.3
79.3
83.3
81.3
72.3
80.0
80.0
75.3
83.3
72.3
83.3
76.3
77.3
82.0
85.7
84.3
77.0
90.3
77.7
78.3
79.0
82.0
84.3
82.3
86.0
79.0
53.78
53.46
68.04
56.01
65.40
64.55
65.77
60.80
57.85
69.65
60.32
60.93
61.77
60.25
63.45
63.87
66.40
52.56
64.68
63.60
56.29
64.29
54.70
62.68
56.96
59.28
60.87
66.66
63.02
59.17
71.54
60.55
63.29
61.89
62.89
63.16
61.24
65.49
57.18
53.82
53.52
68.22
56.04
65.50
64.63
65.76
60.82
57.92
69.65
60.38
60.99
61.81
60.26
63.48
63.87
66.43
52.61
64.44
63.75
56.30
64.31
54.71
62.71
56.99
59.31
60.92
66.68
63.04
59.20
71.51
60.59
63.17
61.96
62.94
63.16
61.25
65.50
57.22
B-15
-------
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Dakota
South Dakota
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Texas
Texas
Texas
Aiken
Anderson
Barnwell
Berkeley
Charleston
Cherokee
Chester
Chesterfield
Colleton
Darlington
Edgefield
Oconee
Pickens
Richland
Spartanburg
Union
Williamsburg
York
Custer
Jackson
Minnehaha
Anderson
Blount
Davidson
Hamilton
Jefferson
Knox
Loudon
Meigs
Rutherford
Sevier
Shelby
Sullivan
Sumner
Williamson
Wilson
Bexar
Brazoria
Brewster
76.0
76.5
73.0
67.3
74.0
74.0
75.7
75.0
72.3
76.3
70.0
73.0
78.7
82.3
82.3
76.0
69.3
76.7
70.0
67.5
66.0
77.3
85.3
77.7
81.0
82.3
85.0
85.0
80.0
76.3
80.7
80.7
80.3
83.0
75.3
78.7
85.0
94.7
64.0
54.21
53.33
53.96
49.77
55.86
53.45
53.85
55.79
52.99
55.50
49.93
50.88
54.79
55.15
59.29
56.10
49.93
55.04
62.13
58.93
52.81
51.43
57.15
53.22
55.52
52.80
56.11
56.70
53.55
52.25
55.31
57.24
65.33
57.70
51.58
54.01
67.42
76.77
53.24
54.26
53.39
54.02
49.82
55.90
53.47
53.89
55.84
53.02
55.53
49.98
50.91
54.83
55.23
59.33
56.14
49.97
55.08
62.13
58.94
52.80
51.66
57.42
53.28
55.60
52.99
56.42
56.84
53.66
52.34
55.44
57.28
65.35
57.79
51.64
54.09
67.43
76.47
53.24
B-16
-------
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Virginia
Virginia
Cameron
Collin
Dallas
Denton
Ellis
Galveston
Gregg
Harris
Harrison
Hidalgo
Hood
Hunt
Jefferson
Johnson
Kaufman
Montgomery
Nueces
Orange
Parker
Rockwall
Smith
Tarrant
Travis
Victoria
Webb
El Paso
Box Elder
Cache
Davis
Salt Lake
San Juan
Tooele
Utah
Washington
Weber
Bennington
Chittenden
Arlington
Caroline
66.0
90.3
88.3
94.0
81.7
85.0
84.3
100.7
79.0
65.7
83.0
78.0
84.7
87.0
74.7
85.0
72.3
78.0
88.7
79.7
81.0
95.3
81.3
72.3
61.3
77.7
76.0
68.7
81.3
81.0
70.3
78.0
76.7
78.5
80.3
72.0
69.7
86.7
80.0
58.17
67.04
69.69
66.54
60.25
68.99
70.66
83.04
62.56
55.41
57.29
62.12
69.75
60.93
57.34
66.37
60.14
63.04
60.81
62.26
66.03
68.10
61.99
57.76
51.51
63.54
63.36
56.84
69.19
68.50
61.21
63.88
66.45
61.43
66.44
53.83
55.50
67.20
57.57
58.16
67.12
69.73
66.62
60.34
68.72
70.66
82.73
62.52
55.39
57.38
62.12
69.54
61.04
57.37
66.24
60.11
62.84
60.93
62.27
66.03
68.20
62.02
57.66
51.50
63.45
63.37
56.87
69.17
68.51
61.23
63.93
66.43
61.44
66.43
53.86
55.52
67.21
57.64
B-17
-------
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Washington
Washington
Washington
Washington
Washington
Washington
Washington
Washington
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Charles City
Chesterfield
Fairfax
Fauquier
Frederick
Hanover
Henrico
Loudoun
Madison
Page
Prince William
Roanoke
Rockbridge
Stafford
Wythe
Alexandria City
Hampton City
Suffolk City
Clark
King
Klickitat
Pierce
Skagit
Spokane
Thurston
Whatcom
Berkeley
Cabell
Greenbrier
Hancock
Kanawha
Monongalia
Ohio
Wood
Ashland
Brown
Columbia
Dane
Dodge
80.3
76.7
90.0
72.7
72.3
81.3
82.0
80.7
77.7
74.0
78.7
74.7
69.7
81.7
72.7
81.7
76.7
76.7
59.5
72.3
64.5
68.7
46.0
68.3
65.0
57.0
75.0
78.7
69.7
75.7
77.3
75.3
78.3
79.0
63.0
73.7
72.7
72.0
74.7
62.98
58.95
67.37
53.92
52.34
60.69
62.49
57.03
57.19
55.13
57.56
55.74
54.20
59.91
56.08
61.16
63.18
67.51
59.54
63.55
52.91
58.25
46.96
54.61
51.91
55.15
54.69
61.53
57.39
59.18
59.75
62.37
60.91
60.98
51.70
58.79
55.47
55.72
58.29
62.99
58.96
67.41
53.95
52.38
60.72
62.51
57.06
57.19
55.14
57.56
55.80
54.23
59.95
56.07
61.19
63.19
67.49
59.53
63.47
52.93
58.16
46.91
54.66
51.82
55.05
54.70
61.32
57.39
59.15
59.76
62.09
60.82
60.96
51.71
58.82
55.52
55.74
58.31
B-18
-------
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wyoming
Wyoming
Wyoming
Door
Florence
Fond Du Lac
Forest
Jefferson
Kenosha
Kewaunee
Manitowoc
Marathon
Milwaukee
Oneida
Outagamie
Ozaukee
Racine
Rock
St Croix
Sauk
Sheboygan
Vernon
Vilas
Walworth
Washington
Waukesha
Campbell
Sublette
Teton
88.7
66.3
73.7
69.5
74.3
84.7
82.7
85.0
70.0
82.7
69.0
74.0
83.3
80.3
74.0
69.0
69.7
88.0
69.7
68.7
75.7
72.3
75.0
67.3
70.0
62.7
68.95
53.77
58.35
55.94
57.56
72.61
65.20
67.72
56.09
68.79
55.88
59.02
69.01
69.09
57.25
55.37
54.14
70.64
53.39
55.69
57.75
57.91
60.12
62.13
65.07
54.90
68.99
53.78
58.36
55.95
57.60
72.58
65.23
67.75
56.09
68.83
55.89
59.01
69.04
69.05
57.26
55.38
54.16
70.67
53.41
55.70
57.80
57.94
60.17
62.13
65.06
54.90
B-19
-------
Air Quality Modeling Technical Support Document:
2017-2025 Light-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
August 2012
C-l
-------
Table C-l. Annual PM25 Design Values for 2017-2025 LD GHG Scenarios
(units are ug/m3)
State Name
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
County Name
Baldwin
Clay
Colbert
DeKalb
Escambia
Etowah
Houston
Jefferson
Madison
Mobile
Montgomery
Morgan
Russell
Shelby
Sumter
Talladega
Tuscaloosa
Walker
Cochise
Coconino
Gila
Maricopa
Pima
Pinal
Santa Cruz
Arkansas
Ashley
Crittenden
Faulkner
Garland
Mississippi
Phillips
Polk
Pope
Baseline DV
11.44
13.27
12.75
14.13
13.19
14.87
13.22
18.57
13.83
12.90
14.24
13.32
15.73
14.43
11.92
14.51
13.56
13.86
7.00
6.49
8.94
12.59
6.04
7.77
12.94
12.45
12.83
13.36
12.79
12.40
12.61
12.10
11.65
12.79
2030 Reference
Case DV
7.32
8.30
8.13
8.65
9.21
9.25
9.24
12.12
8.44
8.55
9.66
8.38
10.48
9.16
7.67
9.01
8.67
8.78
6.52
5.95
8.16
10.21
5.08
6.85
12.08
8.60
9.32
8.43
9.01
8.70
8.12
7.97
8.26
9.31
2030 Control Case
DV
7.31
8.30
8.12
8.64
9.20
9.25
9.24
12.12
8.44
8.54
9.66
8.37
10.48
9.15
7.66
9.00
8.66
8.77
6.51
5.95
8.16
10.20
5.08
6.85
12.08
8.59
9.31
8.42
9.01
8.69
8.11
7.97
8.26
9.30
C-2
-------
Arkansas
Arkansas
Arkansas
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
Pulaski
Union
White
Alameda
Butte
Calaveras
Colusa
Contra Costa
Fresno
Imperial
Inyo
Kern
Kings
Lake
Los Angeles
Mendocino
Merced
Monterey
Nevada
Orange
Placer
Plumas
Riverside
Sacramento
San Bernardino
San Diego
San Francisco
San Joaquin
San Luis Obispo
San Mateo
Santa Barbara
Santa Clara
Shasta
Solano
Sonoma
Stanislaus
Sutter
Tulare
Ventura
14.05
12.86
12.57
9.34
12.73
7.77
7.39
9.47
17.17
12.71
5.25
19.17
17.28
4.62
18.19
6.46
14.78
6.96
6.71
15.75
9.80
11.46
20.95
11.88
19.67
13.46
9.62
12.94
7.94
9.03
10.37
11.38
7.41
9.99
8.21
14.21
9.85
18.51
11.68
9.75
9.19
8.99
8.51
10.34
6.45
6.56
8.29
14.47
11.51
4.86
15.25
13.98
4.03
14.50
5.35
12.39
5.87
5.73
12.99
8.12
9.73
17.32
10.36
16.72
11.75
8.69
11.18
6.46
8.02
8.69
10.46
5.98
8.97
6.96
11.67
8.07
15.12
9.49
9.74
9.17
8.99
8.51
10.33
6.44
6.55
8.28
14.46
11.47
4.86
15.23
13.97
4.03
14.46
5.35
12.39
5.86
5.73
12.96
8.11
9.73
17.30
10.36
16.71
11.75
8.68
11.18
6.45
8.02
8.67
10.47
5.98
8.94
6.96
11.67
8.06
15.10
9.47
c-:
-------
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Delaware
Delaware
Delaware
District Of Columbia
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Yolo
Adams
Arapahoe
Boulder
Delta
Denver
Elbert
El Paso
Larimer
Mesa
Pueblo
San Miguel
Weld
Fairfield
Hartford
Litchfield
New Haven
New London
Kent
New Castle
Sussex
District of
Columbia
Alachua
Bay
Brevard
Broward
Citrus
Duval
Escambia
Hillsborough
Lee
Leon
Manatee
Marion
Miami-Dade
Orange
Palm Beach
Pinellas
9.03
10.06
7.96
8.32
7.44
9.76
4.40
7.94
7.33
9.28
7.45
4.65
8.78
13.21
11.03
8.01
13.12
10.96
12.52
14.87
13.39
14.16
9.59
11.46
8.32
8.22
9.00
10.44
11.72
10.74
8.36
12.56
8.81
10.11
9.45
9.61
7.84
9.82
7.77
7.95
6.32
6.97
6.18
7.69
3.76
6.48
6.42
7.90
6.26
4.19
7.34
8.99
7.71
5.13
8.92
7.63
7.79
9.44
8.22
8.90
6.29
7.84
5.30
5.68
5.55
7.38
8.27
7.14
5.57
8.85
5.37
6.73
6.26
6.22
5.56
6.47
7.77
7.96
6.33
6.96
6.17
7.70
3.76
6.49
6.42
7.90
6.26
4.19
7.34
8.99
7.71
5.13
8.92
7.63
7.78
9.42
8.22
8.90
6.30
7.83
5.29
5.68
5.55
7.39
8.26
7.13
5.56
8.85
5.37
6.73
6.26
6.22
5.56
6.46
C-4
-------
Florida
Florida
Florida
Florida
Florida
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Polk
St. Lucie
Sarasota
Seminole
Volusia
Bibb
Chatham
Clarke
Clayton
Cobb
DeKalb
Dougherty
Floyd
Fulton
Glynn
Gwinnett
Hall
Houston
Lowndes
Muscogee
Paulding
Richmond
Walker
Washington
Wilkinson
Ada
Bannock
Benewah
Canyon
Franklin
Idaho
Shoshone
Adams
Champaign
Cook
DuPage
Jersey
Kane
Lake
9.53
8.34
8.77
9.51
9.27
16.54
13.93
14.90
16.50
16.15
15.48
14.46
16.13
17.43
12.25
16.07
14.16
14.19
12.58
15.39
14.12
15.68
15.49
15.14
15.27
8.41
7.66
9.59
8.46
7.70
9.58
12.08
12.50
12.53
15.75
13.82
12.89
14.34
11.81
6.34
5.44
5.52
6.10
5.88
11.02
9.37
9.58
10.44
10.42
9.46
10.11
10.52
11.05
8.66
10.27
9.06
9.18
9.12
10.26
8.62
10.76
9.80
10.31
10.15
7.43
6.91
8.66
7.15
6.43
8.85
10.88
8.76
8.30
11.03
9.61
8.64
10.03
8.32
6.29
5.44
5.51
6.09
5.88
11.01
9.37
9.57
10.43
10.41
9.45
10.11
10.52
11.05
8.67
10.27
9.06
9.18
9.12
10.26
8.62
10.76
9.80
10.30
10.14
7.43
6.91
8.66
7.15
6.43
8.85
10.88
8.76
8.30
11.04
9.60
8.62
10.02
8.32
C-5
-------
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
McHenry
McLean
Macon
Madison
Peoria
Randolph
Rock Island
Saint Clair
Sangamon
Will
Winnebago
Allen
Clark
Delaware
Dubois
Floyd
Henry
Howard
Knox
Lake
La Porte
Madison
Marion
Porter
St. Joseph
Spencer
Tippecanoe
Vanderburgh
Vigo
Black Hawk
Clinton
Johnson
Linn
Montgomery
Muscatine
Palo Alto
Polk
Pottawattamie
Scott
12.40
12.39
13.24
16.72
13.34
13.11
12.01
15.58
13.13
13.63
13.57
13.67
16.44
13.69
15.19
14.85
13.64
13.93
14.03
14.33
12.69
13.97
16.05
13.21
13.69
14.32
13.70
14.99
13.99
11.16
12.52
12.08
10.79
10.02
12.92
9.53
10.64
11.13
14.42
8.62
8.42
9.06
11.33
9.27
8.60
8.49
10.43
9.28
9.33
9.73
9.69
10.11
9.00
9.22
8.93
8.93
9.44
8.65
10.20
8.80
9.19
10.57
9.18
10.13
8.43
9.21
9.90
8.82
8.08
8.90
8.80
7.71
7.15
9.36
7.08
7.69
8.23
10.50
8.62
8.42
9.06
11.31
9.26
8.59
8.48
10.41
9.28
9.32
9.73
9.69
10.10
8.99
9.22
8.92
8.93
9.43
8.65
10.19
8.79
9.19
10.57
9.18
10.12
8.43
9.21
9.90
8.82
8.08
8.90
8.80
7.70
7.15
9.35
7.08
7.69
8.23
10.49
C-6
-------
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Van Buren
Woodbury
Wright
Johnson
Linn
Sedgwick
Shawnee
Sumner
Wyandotte
Bell
Boyd
Bullitt
Campbell
Carter
Christian
Daviess
Fayette
Franklin
Hardin
Henderson
Jefferson
Kenton
Laurel
McCracken
Madison
Perry
Pike
Warren
Caddo
Calcasieu
Concordia
East Baton Rouge
Iberville
Jefferson
Lafayette
Ouachita
Rapides
Tangipahoa
Terrebonne
10.84
10.32
10.37
11.10
10.47
10.36
10.93
9.89
12.73
14.10
14.49
14.92
13.67
12.22
13.20
14.10
14.87
13.37
13.58
13.93
15.55
14.39
12.55
13.41
13.61
13.21
13.49
13.83
12.53
11.07
11.42
13.38
12.90
11.52
11.08
11.97
11.03
12.03
10.74
7.81
7.79
7.54
7.92
7.61
7.65
8.15
7.33
9.23
8.58
8.73
9.02
8.04
7.05
7.93
8.17
8.92
7.81
7.95
8.75
9.38
8.63
7.39
8.26
7.88
7.95
7.91
8.22
8.62
7.84
7.63
9.59
9.19
7.36
7.48
8.49
7.50
7.97
7.16
7.81
7.78
7.54
7.92
7.60
7.64
8.14
7.32
9.23
8.57
8.73
9.01
8.04
7.05
7.93
8.17
8.92
7.81
7.94
8.74
9.38
8.63
7.39
8.25
7.88
7.93
7.90
8.22
8.60
7.74
7.61
9.43
9.10
7.31
7.45
8.48
7.48
7.93
7.13
C-7
-------
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
West Baton Rouge
Androscoggin
Aroostook
Cumberland
Hancock
Kennebec
Oxford
Penobscot
Anne Arundel
Baltimore
Cecil
Harford
Montgomery
Prince George's
Washington
Baltimore (City)
Berkshire
Bristol
Essex
Hampden
Plymouth
Suffolk
Worcester
Allegan
Bay
Berrien
Genesee
Ingham
Kalamazoo
Kent
Macomb
Missaukee
Monroe
Muskegon
Oakland
Ottawa
Saginaw
St. Clair
Washtenaw
13.51
9.90
9.74
11.13
5.76
9.99
10.13
9.12
14.82
14.76
12.68
12.51
12.47
13.03
13.70
15.76
10.65
9.58
9.58
12.17
9.87
13.07
11.29
11.84
10.93
11.72
11.61
12.23
12.84
12.89
12.70
8.26
13.92
11.61
13.78
12.55
10.61
13.34
13.88
9.70
7.51
8.88
8.39
4.35
7.66
8.18
7.11
9.88
9.63
7.79
7.70
7.82
8.05
8.71
10.44
7.82
6.71
7.05
8.87
7.11
9.73
8.18
8.23
7.81
8.17
8.06
8.43
9.00
8.97
8.96
6.29
9.32
8.27
9.54
8.71
7.60
9.79
9.43
9.53
7.51
8.88
8.39
4.35
7.66
8.18
7.11
9.88
9.63
7.78
7.70
7.82
8.05
8.71
10.44
7.82
6.71
7.05
8.87
7.11
9.73
8.18
8.23
7.81
8.16
8.06
8.43
9.00
8.97
8.96
6.29
9.31
8.27
9.54
8.71
7.60
9.79
9.43
-------
Michigan
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Montana
Montana
Montana
Wayne
Cass
Dakota
Hennepin
Mille Lacs
Olmsted
Ramsey
Saint Louis
Scott
Stearns
Adams
Bolivar
DeSoto
Forrest
Harrison
Hinds
Jackson
Jones
Lauderdale
Lee
Lowndes
Pearl River
Warren
Boone
Buchanan
Cass
Cedar
Clay
Greene
Jackson
Jefferson
Monroe
Saint Charles
Sainte Genevieve
Saint Louis
St. Louis City
Cascade
Flathead
Gallatin
17.50
5.70
9.30
9.76
6.54
10.13
11.32
7.51
9.00
8.58
11.29
12.36
12.43
13.62
12.20
12.56
12.04
14.39
13.07
12.57
12.79
12.14
12.32
11.84
12.80
10.67
11.12
11.03
11.75
12.78
13.79
10.87
13.29
13.34
13.46
14.56
5.72
9.99
4.38
12.27
4.76
7.11
7.47
5.25
7.53
8.79
6.08
6.87
6.78
7.49
8.44
7.80
9.00
8.07
8.34
7.76
9.45
8.53
7.98
8.35
8.11
8.29
8.33
9.59
7.61
7.77
7.93
8.29
9.23
9.40
7.44
8.98
8.98
8.90
9.63
5.11
8.67
4.14
12.27
4.76
7.11
7.47
5.25
7.53
8.79
6.07
6.87
6.77
7.47
8.43
7.79
8.98
8.06
8.33
7.73
9.44
8.52
7.97
8.34
8.09
8.27
8.33
9.59
7.60
7.77
7.92
8.28
9.22
9.39
7.44
8.93
8.97
8.88
9.61
5.10
8.67
4.14
C-9
-------
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nevada
Nevada
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
Lake
Lewis and Clark
Lincoln
Missoula
Ravalli
Rosebud
Sanders
Silver Bow
Yellowstone
Cass
Douglas
Hall
Lancaster
Lincoln
Sarpy
Scotts Bluff
Washington
Clark
Washoe
Belknap
Cheshire
Coos
Graf ton
Hillsborough
Merrimack
Rockingham
Sullivan
Atlantic
Bergen
Camden
Essex
Gloucester
Hudson
Mercer
Middlesex
Morris
Ocean
Passaic
Union
9.06
8.20
14.93
10.52
9.01
6.58
6.75
10.14
8.14
9.99
9.88
7.95
8.90
7.57
9.79
6.04
9.29
9.44
8.11
7.28
11.53
10.24
8.43
10.18
9.72
9.00
9.86
11.47
13.09
13.31
13.27
13.46
14.24
12.71
12.15
11.50
10.92
12.88
14.94
7.99
7.33
12.84
9.16
7.97
6.11
6.13
8.94
7.07
7.29
7.21
5.98
6.35
6.28
7.13
5.11
6.90
8.19
6.80
5.34
8.63
8.37
6.42
7.44
7.08
6.61
7.47
7.24
8.71
8.53
8.53
8.48
9.42
8.17
7.88
7.39
6.81
8.41
9.66
7.99
7.33
12.84
9.16
7.97
6.12
6.13
8.93
7.05
7.29
7.21
5.97
6.35
6.27
7.13
5.10
6.90
8.18
6.80
5.34
8.63
8.37
6.42
7.44
7.08
6.61
7.47
7.23
8.71
8.50
8.52
8.44
9.42
8.17
7.87
7.39
6.81
8.41
9.66
C-10
-------
New Jersey
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
Warren
Bernalillo
Chaves
Dona Ana
Grant
Sandoval
San Juan
Santa Fe
Albany
Bronx
Chautauqua
Erie
Essex
Kings
Monroe
Nassau
New York
Niagara
Onondaga
Orange
Queens
Richmond
St. Lawrence
Steuben
Suffolk
Westchester
Alamance
Buncombe
Caswell
Catawba
Chatham
Cumberland
Davidson
Duplin
Durham
Edgecombe
Forsyth
Gaston
Guilford
12.72
7.03
6.54
9.95
5.93
7.99
5.92
4.76
11.83
15.43
9.80
12.62
5.94
14.20
10.64
11.66
16.18
11.96
10.08
10.99
12.18
13.31
7.29
9.00
11.52
11.73
13.94
12.60
13.19
15.31
11.99
13.73
15.17
11.30
13.57
12.37
14.28
14.26
13.79
8.23
5.69
5.65
8.53
5.49
7.15
5.25
4.19
9.15
10.79
6.39
8.74
4.52
9.43
7.72
7.55
10.98
8.58
7.19
7.33
8.02
8.46
5.82
6.00
7.41
7.56
8.36
7.69
7.72
9.13
7.06
8.75
8.91
6.97
8.27
7.76
8.29
8.36
8.15
8.23
5.69
5.64
8.53
5.48
7.15
5.25
4.19
9.15
10.79
6.39
8.74
4.52
9.43
7.72
7.55
10.99
8.58
7.20
7.33
8.02
8.46
5.82
6.00
7.42
7.56
8.36
7.68
7.71
9.13
7.05
8.74
8.91
6.97
8.27
7.75
8.29
8.36
8.15
C-ll
-------
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Haywood
Jackson
Lenoir
McDowell
Martin
Mecklenburg
Mitchell
Montgomery
New Hanover
Onslow
Orange
Pitt
Robeson
Rowan
Swain
Wake
Watauga
Wayne
Billings
Burke
Burleigh
Cass
McKenzie
Mercer
Athens
Butler
Clark
Clermont
Cuyahoga
Franklin
Greene
Hamilton
Jefferson
Lake
Lawrence
Lorain
Lucas
Mahoning
Montgomery
12.98
12.09
11.12
14.24
10.86
15.31
12.75
12.35
9.96
10.98
13.12
11.59
12.78
14.02
12.65
13.54
12.05
12.96
4.61
5.90
6.61
7.72
5.01
6.04
12.39
15.36
14.64
14.15
17.37
15.27
13.36
17.54
16.51
13.02
15.14
13.87
14.38
15.12
15.54
8.55
7.43
6.88
8.95
6.73
9.27
7.65
7.32
6.11
6.76
7.81
7.27
7.99
8.35
7.76
8.29
6.84
8.29
4.13
5.49
5.55
6.37
4.57
5.35
7.18
10.03
9.47
8.49
11.63
9.75
8.25
10.99
9.97
8.57
9.44
9.01
9.72
9.85
9.95
8.54
7.43
6.88
8.95
6.73
9.27
7.64
7.32
6.11
6.76
7.80
7.27
7.98
8.35
7.76
8.29
6.84
8.29
4.13
5.49
5.54
6.37
4.57
5.35
7.17
10.03
9.47
8.49
11.63
9.76
8.25
11.00
9.96
8.57
9.43
9.01
9.70
9.85
9.95
C-12
-------
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Oregon
Oregon
Oregon
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Portage
Preble
Scioto
Stark
Summit
Trumbull
Caddo
Cherokee
Kay
Lincoln
Mayes
Muskogee
Oklahoma
Ottawa
Pitts burg
Sequoyah
Tulsa
Jackson
Klamath
Lane
Multnomah
Union
Adams
Allegheny
Beaver
Berks
Bucks
Cambria
Centre
Chester
Cumberland
Dauphin
Delaware
Erie
Lackawanna
Lancaster
Lehigh
Luzerne
Mercer
13.37
13.70
14.65
16.26
15.17
14.53
9.22
11.79
10.26
10.28
11.70
11.89
10.07
11.69
11.09
12.99
11.52
10.32
11.20
11.93
9.13
8.35
13.05
20.31
16.38
15.82
13.42
15.40
12.78
15.22
14.45
15.13
15.23
12.54
11.73
16.55
14.50
12.76
13.28
8.62
8.70
8.86
10.41
10.09
9.50
6.78
8.48
7.74
7.41
8.54
8.73
7.07
8.55
7.94
9.57
8.33
9.23
9.98
10.72
7.77
7.17
8.19
12.68
10.52
10.48
8.53
9.51
8.03
9.61
9.34
9.43
9.79
8.40
7.58
10.55
9.64
8.43
8.42
8.62
8.70
8.86
10.40
10.09
9.50
6.78
8.47
7.71
7.40
8.53
8.71
7.06
8.55
7.94
9.56
8.31
9.23
9.98
10.72
7.77
7.17
8.19
12.67
10.51
10.48
8.53
9.49
8.02
9.60
9.34
9.42
9.75
8.40
7.57
10.54
9.64
8.43
8.42
C-13
-------
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Rhode Island
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Northampton
Perry
Philadelphia
Washington
Westmoreland
York
Providence
Beaufort
Charleston
Chesterfield
Edgefield
Florence
Georgetown
Greenville
Greenwood
Horry
Lexington
Oconee
Richland
Spartanburg
Brookings
Brown
Codington
Custer
Jackson
Minnehaha
Pennington
Blount
Davidson
Dyer
Hamilton
Knox
Lawrence
Loudon
McMinn
Maury
Montgomery
Putnam
Roane
13.68
12.81
15.19
15.17
15.49
16.52
12.14
11.52
12.21
12.56
13.17
12.65
12.85
15.65
13.53
12.04
14.64
10.95
14.24
14.17
9.37
8.42
10.14
5.64
5.39
10.18
8.77
14.30
14.21
12.28
15.67
15.64
11.69
15.49
14.29
13.21
13.80
13.37
14.49
8.96
8.27
9.91
8.82
9.25
10.57
8.87
7.19
7.80
7.85
8.44
7.98
8.38
9.73
8.35
7.63
9.21
6.42
8.84
8.56
7.41
6.96
8.28
4.98
4.63
7.79
7.79
9.02
8.75
7.69
9.86
9.64
7.34
9.99
8.91
8.30
8.56
7.99
8.89
8.95
8.27
9.87
8.82
9.24
10.57
8.88
7.19
7.80
7.84
8.44
7.98
8.37
9.73
8.35
7.63
9.21
6.41
8.84
8.56
7.41
6.96
8.28
4.98
4.62
7.79
7.79
9.02
8.75
7.68
9.86
9.64
7.33
9.98
8.90
8.29
8.55
7.99
8.88
C-14
-------
Tennessee
Tennessee
Tennessee
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Washington
Shelby
Sullivan
Sumner
Bowie
Dallas
Ector
El Paso
Harris
Harrison
Hidalgo
Jefferson
Nueces
Orange
Tarrant
Box Elder
Cache
Davis
Salt Lake
Utah
Weber
Addison
Bennington
Chittenden
Rutland
Arlington
Charles
Chesterfield
Fairfax
Henrico
Loudoun
Page
Bristol City
Hampton City
Lynchburg City
Norfolk City
Roanoke City
Salem City
Virginia Beach City
King
13.71
14.16
13.68
12.85
12.77
7.78
9.09
15.42
11.69
10.98
11.56
10.42
11.51
12.23
8.40
11.56
10.31
12.02
10.52
11.16
8.94
8.52
10.02
11.08
14.27
12.37
13.44
13.88
13.51
13.57
12.79
13.93
12.17
12.84
12.78
14.27
14.69
12.40
11.24
8.57
9.12
7.96
8.99
8.78
6.37
7.66
11.29
7.77
8.98
7.97
7.29
8.18
8.23
6.72
9.43
8.21
9.50
8.44
8.81
7.16
6.47
8.02
8.83
8.84
7.46
8.10
8.84
8.09
8.65
7.50
8.25
7.40
7.48
7.89
8.53
9.00
7.55
9.16
8.56
9.12
7.96
8.98
8.77
6.37
7.66
11.21
7.75
8.98
7.90
7.26
8.13
8.23
6.72
9.43
8.21
9.50
8.45
8.81
7.16
6.47
8.03
8.83
8.85
7.45
8.09
8.84
8.08
8.65
7.50
8.25
7.39
7.48
7.89
8.52
8.99
7.54
9.16
C-15
-------
Washington
Washington
Washington
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Pierce
Snohomish
Spokane
Berkeley
Brooke
Cabell
Hancock
Harrison
Kanawha
Marion
Marshall
Monongalia
Ohio
Raleigh
Wood
Ashland
Brown
Dane
Dodge
Forest
Grant
Kenosha
Manitowoc
Milwaukee
Outagamie
Ozaukee
St. Croix
Sauk
Taylor
Vilas
Waukesha
Campbell
Converse
Fremont
Laramie
Sheridan
10.55
9.91
9.97
15.93
16.52
16.30
15.76
13.99
16.52
15.03
15.19
14.35
14.58
12.90
15.40
6.07
11.39
12.20
11.04
7.41
11.79
11.98
10.20
14.08
10.96
11.60
10.09
10.22
8.24
6.78
13.91
6.29
3.58
8.17
4.48
9.70
9.09
8.71
7.80
10.48
10.02
10.23
9.60
8.40
9.94
9.03
8.82
8.12
8.30
7.37
9.65
4.93
9.40
8.95
8.12
5.87
8.56
8.53
7.99
10.39
8.71
8.51
7.82
7.34
6.44
5.42
10.44
5.92
3.27
7.29
3.78
8.73
9.10
8.71
7.80
10.48
10.01
10.22
9.60
8.39
9.94
9.02
8.81
8.11
8.29
7.37
9.65
4.93
9.38
8.96
8.11
5.86
8.56
8.52
7.98
10.40
8.70
8.51
7.82
7.33
6.44
5.42
10.45
5.91
3.26
7.29
3.78
8.73
C-16
-------
Air Quality Modeling Technical Support Document:
2017-2025 Light-Duty Vehicle Greenhouse Gas Emission
Standards Final Rule
Appendix D
24-Hour 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
August 2012
D-l
-------
Table D-l. 24-hour PM2 5 Design Values for 2017-2025 LD GHG Scenarios
(units are ug/m3)
State Name
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
County Name
Baldwin
Clay
Colbert
De Kalb
Escambia
Etowah
Houston
Jefferson
Madison
Mobile
Montgomery
Morgan
Russell
Shelby
Sumter
Talladega
Tuscaloosa
Walker
Cochise
Coconino
Gila
Maricopa
Pima
Pinal
Santa Cruz
Arkansas
Ashley
Crittenden
Faulkner
Garland
Baseline DV
26.21
31.88
30.43
32.08
29.03
35.18
28.66
44.06
33.58
30.03
32.05
31.58
35.55
32.05
28.90
33.46
29.80
32.82
16.62
17.11
22.12
32.80
12.27
17.55
36.08
29.16
28.91
35.06
29.87
29.27
2030 Reference
Case DV
16.06
16.81
15.30
16.77
18.65
18.54
18.02
28.17
16.72
18.25
18.51
14.71
22.87
18.14
15.93
18.21
16.52
16.86
15.62
15.75
19.97
24.39
9.65
14.37
33.78
17.87
20.62
18.16
18.86
18.43
2030 Control
Case DV
16.03
16.80
15.29
16.76
18.62
18.54
18.02
28.16
16.71
18.21
18.50
14.70
22.87
18.13
15.92
18.20
16.50
16.84
15.61
15.75
19.97
24.39
9.65
14.38
33.78
17.86
20.60
18.16
18.86
18.42
D-2
-------
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
Phillips
Polk
Pope
Pulaski
Union
White
Alameda
Butte
Calaveras
Colusa
Contra Costa
Fresno
Imperial
Inyo
Kern
Kings
Lake
Los Angeles
Mendocino
Merced
Monterey
Nevada
Orange
Placer
Plumas
Riverside
Sacramento
San Bernardino
San Diego
San Francisco
San Joaquin
San Luis Obispo
San Mateo
Santa Barbara
29.18
26.13
28.32
31.93
28.70
29.91
32.58
52.55
20.55
26.16
34.70
60.22
40.21
20.00
64.54
58.06
12.94
50.97
15.30
46.15
14.35
16.55
43.76
29.88
32.44
59.13
49.22
55.50
35.55
30.91
41.88
22.58
29.41
24.07
17.68
15.73
18.06
21.38
19.54
19.03
26.54
37.47
15.05
22.02
28.97
47.04
33.37
18.42
51.14
44.34
11.97
44.24
10.35
34.55
11.96
13.00
38.85
24.02
26.18
49.07
45.56
46.40
31.46
26.52
33.39
18.00
25.69
22.49
17.67
15.72
18.06
21.33
19.45
19.02
26.55
37.46
15.04
22.02
28.97
47.01
33.20
18.42
51.16
44.30
11.97
44.27
10.35
34.54
11.95
12.99
38.90
24.00
26.18
49.06
45.56
46.13
31.47
26.48
33.38
18.00
25.69
22.48
-------
California
California
California
California
California
California
California
California
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Delaware
Delaware
Delaware
District of Columbia
Florida
Florida
Florida
Florida
Santa Clara
Shasta
Solano
Sonoma
Stanislaus
Sutter
Tulare
Ventura
Yolo
Adams
Arapahoe
Boulder
Delta
Denver
Elbert
El Paso
Larimer
Mesa
Pueblo
San Miguel
Weld
Fairfield
Hartford
Litchfield
New Haven
New London
Kent
New Castle
Sussex
Washington
Alachua
Bay
Brevard
Broward
38.61
20.42
34.76
29.10
51.48
38.55
56.63
30.30
30.38
25.35
21.27
21.12
20.76
26.44
13.18
16.51
18.30
23.51
15.42
10.11
22.90
36.27
31.83
27.16
38.37
32.03
32.14
36.66
33.78
36.35
21.35
28.08
20.73
18.63
35.47
14.39
29.97
23.86
39.22
29.18
42.79
26.06
24.71
19.09
16.83
17.09
15.98
20.79
10.92
13.71
15.89
19.45
12.54
9.47
19.43
23.28
19.43
13.31
22.87
17.50
19.09
22.90
19.76
21.15
13.35
17.97
12.76
13.26
35.47
14.37
29.89
23.85
39.19
29.16
42.75
26.03
24.70
19.12
16.89
17.09
15.97
20.81
10.94
13.85
15.90
19.48
12.55
9.46
19.43
23.28
19.43
13.30
22.87
17.49
19.09
22.88
19.76
21.20
13.34
17.96
12.74
13.25
D-4
-------
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Citrus
Duval
Escambia
Hillsborough
Lee
Leon
Manatee
Marion
Miami-Dade
Orange
Palm Beach
Pinellas
Polk
St Lucie
Sarasota
Seminole
Volusia
Bibb
Chatham
Clayton
Cobb
De Kalb
Dougherty
Floyd
Fulton
Glynn
Gwinnett
Hall
Houston
Lowndes
Muscogee
Paulding
Richmond
Walker
21.22
24.35
28.80
23.44
17.70
27.03
19.57
22.56
19.13
21.83
18.22
21.73
19.30
18.18
19.22
22.08
22.00
33.56
28.45
35.88
35.04
33.92
34.15
35.12
37.66
26.13
32.81
30.11
29.63
25.68
34.58
33.02
32.70
30.98
11.68
17.79
20.67
14.81
11.92
18.20
11.20
13.16
12.42
12.74
13.36
14.47
12.49
11.20
11.90
12.04
12.42
21.21
18.67
20.80
19.57
19.30
23.36
20.67
22.61
18.02
18.06
19.25
17.38
16.65
22.07
18.28
23.22
18.24
11.68
17.77
20.67
14.79
11.91
18.19
11.19
13.18
12.42
12.74
13.35
14.47
12.47
11.18
11.89
12.04
12.41
21.20
18.67
20.79
19.56
19.30
23.36
20.66
22.61
18.01
18.06
19.25
17.37
16.65
22.06
18.27
23.22
18.24
D-5
-------
Georgia
Georgia
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Indiana
Washington
Wilkinson
Ada
Bannock
Benewah
Canyon
Franklin
Idaho
Lemhi
Power
Shoshone
Adams
Champaign
Cook
Du Page
Hamilton
Jersey
Kane
Lake
La Salle
McHenry
McLean
Macon
Madison
Peoria
Randolph
Rock Island
StClair
Sangamon
Will
Winnebago
Allen
Clark
Delaware
30.83
33.16
28.36
27.08
32.94
31.80
36.76
28.43
36.53
33.36
38.16
31.41
31.32
43.03
34.64
31.60
32.18
34.83
33.08
28.92
31.58
33.43
33.25
39.16
32.76
28.96
30.90
33.70
33.41
36.45
34.73
33.10
37.57
32.07
18.67
20.38
23.48
23.78
28.89
24.12
29.35
26.35
32.73
29.35
33.34
17.57
18.69
28.30
25.10
16.70
19.29
24.75
21.05
18.70
20.15
19.98
18.03
24.58
20.20
19.75
22.10
22.43
21.07
23.76
24.38
22.63
20.71
20.26
18.66
20.37
23.48
23.78
28.90
24.11
29.32
26.35
32.74
29.35
33.35
17.56
18.69
28.29
25.06
16.68
19.24
24.75
21.05
18.68
20.14
19.97
18.03
24.54
20.18
19.74
22.11
22.41
21.07
23.75
24.37
22.64
20.68
20.26
D-6
-------
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Dubois
Elkhart
Floyd
Henry
Howard
Knox
Lake
La Porte
Madison
Marion
Porter
St Joseph
Spencer
Tippecanoe
Vanderburgh
Vigo
Black Hawk
Clinton
Johnson
Linn
Montgomery
Muscatine
Palo Alto
Polk
Pottawattamie
Scott
Van Buren
Woodbury
Wright
Johnson
Linn
Sedgwick
Shawnee
Sumner
35.36
34.43
33.26
31.86
32.21
35.92
38.98
33.00
32.82
38.47
32.96
33.16
32.32
35.68
34.80
34.88
30.78
33.95
34.67
30.60
27.50
36.03
25.73
31.46
28.60
37.10
28.36
26.40
28.65
29.30
25.38
25.37
29.16
22.84
21.54
24.65
17.00
19.26
19.57
21.04
29.14
21.25
19.86
23.90
22.38
23.52
15.24
20.52
22.50
20.22
21.40
23.11
23.24
19.84
16.99
26.58
17.34
21.78
20.49
24.47
18.86
19.20
18.75
22.09
17.75
17.96
21.37
15.66
21.55
24.64
16.99
19.25
19.56
21.04
29.01
21.24
19.85
23.90
22.36
23.52
15.23
20.51
22.48
20.22
21.40
23.12
23.24
19.84
16.97
26.52
17.33
21.79
20.48
24.48
18.86
19.19
18.74
22.08
17.72
17.96
21.35
15.64
D-7
-------
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Wyandotte
Bell
Boyd
Bullitt
Campbell
Carter
Christian
Daviess
Fayette
Franklin
Hardin
Henderson
Jefferson
Kenton
Laurel
McCracken
Madison
Perry
Pike
Warren
Caddo
Calcasieu
Concordia
East Baton Rouge
Iberville
Jefferson
Lafayette
Ouachita
Rapides
Tangipahoa
Terrebonne
West Baton Rouge
Androscoggin
Aroostook
29.58
29.90
33.15
34.63
31.20
29.91
33.60
33.86
32.23
32.17
32.81
31.85
36.44
34.74
25.16
33.62
30.11
28.54
30.52
33.14
27.56
26.38
26.16
29.36
28.62
27.06
24.28
28.91
30.26
29.61
26.25
29.08
26.56
24.23
20.91
16.78
16.11
17.31
16.14
13.79
15.69
16.79
17.51
16.60
15.72
17.33
20.27
18.91
13.89
16.76
14.70
13.39
15.17
15.74
18.67
17.17
15.70
20.44
20.91
16.32
15.80
19.27
18.45
18.00
15.98
20.25
18.88
21.01
20.89
16.76
16.08
17.29
16.15
13.78
15.67
16.77
17.51
16.58
15.72
17.31
20.25
18.93
13.88
16.76
14.69
13.35
15.16
15.74
18.64
17.05
15.67
19.67
20.75
16.21
15.73
19.25
18.41
17.93
15.91
19.52
18.88
21.01
D-8
-------
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Cumberland
Hancock
Kennebec
Oxford
Penobscot
Anne Arundel
Baltimore
Cecil
Harford
Montgomery
Prince Georges
Washington
Baltimore City
Berkshire
Bristol
Essex
Hampden
Plymouth
Suffolk
Worcester
Allegan
Bay
Berrien
Genesee
Ingham
Kalamazoo
Kent
Macomb
Missaukee
Monroe
Muskegon
Oakland
Ottawa
Saginaw
29.20
19.43
26.21
28.36
22.03
36.16
35.84
30.82
31.21
30.93
33.46
33.43
39.01
31.06
25.07
28.72
33.13
28.48
32.17
30.66
33.82
31.68
31.32
30.46
31.96
31.17
36.53
35.32
24.83
38.88
34.71
39.94
34.24
30.66
19.36
11.95
18.36
21.29
15.71
25.14
22.88
19.28
17.50
17.26
17.62
20.44
27.46
21.51
16.12
19.16
23.10
17.61
21.61
20.44
23.40
20.43
20.50
21.23
21.72
20.31
23.18
26.66
15.46
23.40
22.48
24.02
24.81
19.93
19.36
11.95
18.36
21.28
15.71
25.17
22.89
19.28
17.51
17.27
17.63
20.44
27.49
21.52
16.12
19.17
23.10
17.61
21.61
20.45
23.40
20.41
20.50
21.23
21.72
20.30
23.18
26.68
15.45
23.38
22.48
24.05
24.79
19.92
D-9
-------
Michigan
Michigan
Michigan
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
StClair
Washtenaw
Wayne
Cass
Dakota
Hennepin
Mille Lacs
Ramsey
St Louis
Scott
Adams
Bolivar
De Soto
Forrest
Harrison
Hinds
Jackson
Jones
Lee
Lowndes
Warren
Boone
Buchanan
Cass
Cedar
Clay
Greene
Jackson
Jefferson
Monroe
St Charles
Ste Genevieve
St Louis
St Louis City
39.61
39.46
43.88
18.02
25.42
27.25
22.03
28.38
23.53
24.98
27.48
28.98
30.82
30.48
29.00
28.83
26.96
31.21
32.18
32.44
30.26
30.23
30.10
25.61
28.70
28.04
28.27
27.88
33.43
27.83
33.16
31.44
33.21
34.35
28.31
22.69
31.01
13.55
18.43
19.03
16.48
20.62
16.88
17.45
16.46
18.93
15.48
20.77
18.13
17.05
16.19
20.54
16.38
16.97
18.58
18.77
20.82
16.43
18.51
19.37
18.96
19.93
20.73
17.53
19.40
18.50
23.16
21.65
28.30
22.69
31.02
13.54
18.44
19.04
16.47
20.60
16.89
17.46
16.44
18.92
15.47
20.76
18.11
17.03
16.16
20.53
16.36
16.95
18.55
18.77
20.81
16.41
18.49
19.36
18.95
19.92
20.71
17.52
19.33
18.50
23.14
21.60
D-10
-------
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nevada
Nevada
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
Cascade
Flathead
Gallatin
Lake
Lewis And Clark
Lincoln
Missoula
Ravalli
Rosebud
Sanders
Silver Bow
Yellowstone
Cass
Douglas
Hall
Lancaster
Scotts Bluff
Washington
Clark
Washoe
Belknap
Cheshire
Coos
Graf ton
Hillsborough
Merrimack
Rockingham
Sullivan
Bergen
Camden
Essex
Hudson
Mercer
Middlesex
20.15
27.17
29.55
43.66
33.53
42.71
44.64
45.11
19.73
20.42
35.00
19.38
28.30
25.76
19.16
24.77
16.66
24.01
25.26
30.78
20.55
30.23
26.50
23.00
28.66
25.65
26.35
28.92
37.03
37.37
38.38
41.43
34.75
34.82
17.37
24.14
26.59
38.93
28.67
36.18
37.29
37.76
18.31
18.39
28.85
16.38
19.97
18.70
13.54
17.35
13.67
17.25
21.15
23.29
12.06
20.95
17.89
14.88
20.89
16.43
16.40
18.26
22.18
20.90
22.71
29.74
19.20
19.82
17.37
24.14
26.60
38.93
28.69
36.19
37.31
37.76
18.33
18.39
28.85
16.33
19.96
18.70
13.53
17.34
13.66
17.24
21.18
23.33
12.06
20.95
17.88
14.88
20.90
16.44
16.40
18.26
22.18
20.86
22.71
29.74
19.20
19.80
D-ll
-------
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
North Carolina
North Carolina
North Carolina
North Carolina
Morris
Ocean
Passaic
Union
Warren
Bernalillo
Chaves
Dona Ana
Grant
Sandoval
San Juan
Santa Fe
Albany
Bronx
Chautauqua
Erie
Essex
Kings
Monroe
Nassau
New York
Niagara
Onondaga
Orange
Queens
Richmond
St Lawrence
Steuben
Suffolk
Westchester
Alamance
Buncombe
Caswell
Catawba
32.32
31.56
36.30
40.47
34.06
18.60
15.68
32.95
13.00
15.68
12.40
9.78
34.26
38.87
29.15
35.35
22.45
36.94
32.20
34.01
39.70
33.87
27.35
28.92
35.56
34.93
22.05
27.81
34.66
33.51
31.72
30.05
29.45
34.53
18.53
15.97
21.40
24.58
20.65
14.66
12.36
26.58
12.09
13.60
10.94
8.47
26.70
26.13
16.34
25.99
14.01
22.59
20.00
19.15
26.00
22.25
17.88
18.99
22.26
20.75
16.73
15.39
18.35
19.10
17.97
15.77
15.97
18.93
18.52
15.97
21.42
24.57
20.64
14.67
12.35
26.59
12.09
13.59
10.93
8.47
26.72
26.13
16.34
26.01
14.01
22.60
20.00
19.14
26.03
22.24
17.90
19.01
22.27
20.74
16.73
15.39
18.37
19.09
17.96
15.76
15.96
18.92
D-12
-------
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
Ohio
Chatham
Cumberland
Davidson
Duplin
Durham
Edgecombe
Forsyth
Gaston
Guilford
Haywood
Jackson
Lenoir
McDowell
Martin
Mecklenburg
Mitchell
Montgomery
New Hanover
Onslow
Orange
Pitt
Robeson
Rowan
Swain
Wake
Watauga
Wayne
Billings
Burke
Burleigh
Cass
McKenzie
Mercer
Athens
26.94
30.78
31.35
28.30
31.02
26.78
31.92
30.86
30.63
27.74
24.96
25.20
31.55
24.83
32.33
30.25
28.21
25.40
24.61
29.35
26.21
29.92
30.23
27.34
31.63
30.43
29.72
13.07
16.73
17.62
21.22
11.96
16.98
32.32
13.67
17.65
18.41
15.30
16.40
16.68
18.32
16.10
17.66
16.40
13.83
15.68
17.36
14.92
18.41
15.44
14.75
13.80
14.57
15.34
16.28
16.44
17.35
15.07
17.00
15.65
17.04
11.48
14.99
14.27
15.92
10.52
14.44
16.16
13.66
17.65
18.40
15.30
16.39
16.68
18.32
16.09
17.65
16.39
13.82
15.68
17.35
14.91
18.41
15.43
14.75
13.80
14.56
15.33
16.28
16.43
17.34
15.07
17.00
15.64
17.03
11.48
14.99
14.24
15.91
10.52
14.44
16.15
D-13
-------
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Oregon
Butler
Clark
Clermont
Cuyahoga
Franklin
Greene
Hamilton
Jefferson
Lake
Lawrence
Lorain
Lucas
Mahoning
Montgomery
Portage
Preble
Scioto
Stark
Summit
Trumbull
Caddo
Cherokee
Kay
Lincoln
Mayes
Muskogee
Oklahoma
Ottawa
Pitts burg
Sequoyah
Tulsa
Jackson
Klamath
Lane
39.23
35.37
34.46
44.20
38.51
32.21
40.60
41.96
37.16
33.77
31.56
36.34
36.83
37.80
34.32
32.85
34.55
36.90
38.06
36.23
23.97
27.55
31.80
27.83
28.71
29.54
27.12
29.14
26.37
31.43
30.37
33.72
44.08
48.95
23.38
19.59
17.05
29.24
21.56
17.13
22.26
24.24
20.80
18.23
19.23
26.10
21.41
22.86
18.82
17.78
18.35
20.43
21.32
21.40
16.28
20.22
25.08
18.86
21.63
20.84
18.38
20.39
18.46
22.77
21.43
29.00
37.84
42.20
23.39
19.59
17.05
29.25
21.58
17.13
22.28
24.24
20.81
18.22
19.24
26.11
21.41
22.87
18.83
17.77
18.35
20.41
21.33
21.41
16.26
20.20
24.98
18.84
21.59
20.81
18.36
20.38
18.45
22.76
21.40
29.01
37.85
42.20
D-14
-------
Oregon
Oregon
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Rhode Island
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
Multnomah
Union
Adams
Allegheny
Beaver
Berks
Bucks
Cambria
Centre
Chester
Cumberland
Dauphin
Delaware
Erie
Lackawanna
Lancaster
Lehigh
Luzerne
Mercer
Northampton
Perry
Philadelphia
Washington
Westmoreland
York
Providence
Charleston
Chesterfield
Edgefield
Florence
Greenville
Greenwood
Horry
Lexington
29.88
27.38
34.93
64.27
43.42
37.71
34.01
39.04
36.28
36.70
38.00
38.04
35.24
34.46
31.55
40.83
36.40
32.46
36.30
36.72
30.46
37.30
38.14
37.12
38.24
30.62
27.93
28.77
32.23
28.81
32.55
30.01
28.30
32.86
25.17
23.15
20.14
40.42
23.50
27.05
21.33
19.85
21.23
22.60
25.91
25.99
21.55
20.05
18.06
30.18
24.35
20.68
20.46
23.22
20.38
22.09
19.91
18.87
28.30
20.31
15.73
15.90
17.07
16.43
19.07
16.09
16.60
19.13
25.18
23.15
20.14
40.41
23.48
27.06
21.32
19.82
21.22
22.60
25.92
25.99
21.44
20.06
18.09
30.19
24.36
20.69
20.48
23.20
20.38
21.92
19.90
18.86
28.35
20.33
15.73
15.89
17.06
16.42
19.07
16.08
16.59
19.14
D-15
-------
South Carolina
South Carolina
South Carolina
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Oconee
Richland
Spartanburg
Brookings
Brown
Codington
Custer
Jackson
Minnehaha
Pennington
Blount
Davidson
Dyer
Hamilton
Knox
Lawrence
Loudon
Me Minn
Maury
Montgomery
Putnam
Roane
Shelby
Sullivan
Sumner
Bowie
Dallas
Ector
El Paso
Harris
Harrison
Hidalgo
Nueces
Orange
27.98
33.20
32.46
23.54
18.73
23.67
14.36
12.73
24.17
18.58
32.54
33.50
31.92
33.53
36.66
28.48
32.20
32.73
30.96
36.30
32.66
30.24
33.50
31.13
33.66
29.42
27.44
17.81
22.93
30.81
25.95
26.42
27.55
27.78
14.51
18.70
17.61
16.73
14.06
17.46
11.63
10.22
17.31
16.32
18.44
17.86
17.49
20.65
20.01
14.78
19.75
17.40
16.50
17.63
16.11
15.46
16.54
18.69
15.02
18.71
17.79
13.39
18.95
20.24
17.24
22.15
18.24
18.19
14.52
18.70
17.61
16.73
14.05
17.46
11.63
10.21
17.32
16.32
18.43
17.85
17.49
20.65
20.01
14.78
19.74
17.40
16.48
17.62
16.10
15.45
16.54
18.69
15.01
18.69
17.79
13.39
18.94
19.91
17.22
22.15
18.19
18.08
D-16
-------
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Washington
Washington
Washington
Washington
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Tarrant
Box Elder
Cache
Davis
Salt Lake
Tooele
Utah
Weber
Addison
Bennington
Chittenden
Rutland
Arlington
Charles City
Chesterfield
Fairfax
Henrico
Loudoun
Page
Bristol City
Hampton City
Lynchburg City
Norfolk City
Roanoke City
Salem City
King
Pierce
Snohomish
Spokane
Berkeley
Brooke
Cabell
Hancock
Harrison
25.76
33.20
56.95
38.95
50.14
30.53
44.00
38.58
31.73
26.47
30.13
30.60
34.18
31.76
31.25
34.47
31.95
34.45
30.06
30.24
29.01
30.71
29.66
32.70
34.06
29.16
41.82
34.36
29.86
34.51
43.90
35.10
40.64
33.53
17.07
25.67
40.95
29.47
37.77
23.95
33.47
28.79
19.82
17.09
22.03
25.42
18.66
16.74
15.58
18.87
16.65
19.12
16.33
15.94
15.82
15.67
16.72
17.53
18.79
24.78
36.25
31.22
22.25
23.71
25.12
18.10
20.85
16.07
17.07
25.69
41.00
29.49
37.79
23.99
33.54
28.87
19.82
17.10
22.03
25.44
18.69
16.73
15.57
18.88
16.65
19.13
16.32
15.93
15.82
15.66
16.72
17.52
18.78
24.81
36.28
31.21
22.26
23.71
25.13
18.07
20.85
16.04
D-17
-------
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Kanawha
Marion
Marshall
Monongalia
Ohio
Raleigh
Summers
Wood
Ashland
Brown
Dane
Dodge
Forest
Grant
Kenosha
Manitowoc
Milwaukee
Outagamie
Ozaukee
St Croix
Sauk
Taylor
Vilas
Waukesha
Campbell
Converse
Fremont
Laramie
Sheridan
36.98
33.68
33.98
35.65
32.00
30.67
31.26
35.44
18.61
36.56
35.57
31.82
25.26
34.35
32.78
29.70
39.92
32.87
32.53
26.66
28.63
25.38
22.61
35.48
18.63
10.00
29.80
11.93
30.86
18.35
15.86
17.53
14.68
16.65
14.26
14.14
17.96
12.58
31.28
25.62
21.64
17.02
25.06
22.65
22.72
28.15
26.53
22.60
19.75
21.53
18.17
16.43
25.25
17.12
9.30
23.98
9.94
27.21
18.33
15.83
17.52
14.64
16.64
14.28
14.14
17.94
12.58
31.21
25.64
21.64
17.00
25.07
22.66
22.71
28.18
26.53
22.59
19.75
21.54
18.18
16.43
25.27
17.11
9.29
23.99
9.93
27.25
D-18
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-12-004
Environmental Protection Air Quality Assessment Division August, 2012
Agency Research Triangle Park, NC
------- |