Air Quality Modeling Technical Support
Document:
Proposed Tier 3 Emission Standards
&EPA
United States
Environmental Protection
Agency
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Air Quality Modeling Technical Support
Document:
Proposed Tier 3 Emission Standards
Air Quality Assessment Division
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
NOTICE
This technical report does not necessarily represent final EPA decisions or
positions. It is intended to present technical analysis of issues using data
that are currently available. The purpose in the release of such reports is to
facilitate the exchange of technical information and to inform the public of
technical developments.
&EPA
United States
Environmental Protection
Agency
ERA-454/R-13-001
March 2013
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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. Tier 3 Modeling Scenarios 4
E. Meteorological Input Data 7
F. Initial and Boundary Conditions 9
G. CMAQ Base Case Model Performance Evaluation 9
III. CMAQ Model Results 9
A. Impacts of Proposed Tier 3 Standards on Future 8-Hour Ozone Levels 10
B. Impacts of Proposed Tier 3 Standards on Future Annual PM2.5 Levels 12
C. Impacts of Proposed Tier 3 Standards on Future 24-hour PM2.5 Levels 14
D. Impacts of Proposed Tier 3 Standards on Future Nitrogen Dioxide Levels 17
E. Impacts of Proposed Tier 3 Standards on Future Toxic Air Pollutant Level 18
1. Acetaldehyde 19
2. Formaldehyde 20
3. Benzene 21
4. 1,3-Butadiene 22
5. Acrolein 23
6. Ethanol 24
F. Population Metrics 25
G. Impacts of Proposed Tier 3 Standards on Future Annual Nitrogen and Sulfur
Deposition 26
H. Impacts of Proposed Tier 3 Standards on Future Visibility Levels 28
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
proposed Tier 3 standards. A national scale air quality modeling analysis was performed to
estimate the impact of the proposed fuel and vehicle standards on future year levels of annual
and 24-hour PM2.5 concentrations, daily maximum 8-hour ozone concentrations, annual nitrogen
dioxide, annual nitrogen and sulfur deposition, annual ethanol 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 7.2.1 of the DRIA, are slightly different than the
proposed fuel and vehicle standard inventories presented in Chapter 7 of the DRIA. However,
the air quality inventories and the proposed rule inventories are generally consistent, so the air
quality modeling adequately reflects the effects of the rule.
Air quality modeling was performed for five emissions cases: a 2005 base year, a 2017
reference case projection without the Tier 3 rule standards and a 2017 control case projection
with Tier 3 standards in place, as well as a 2030 reference case projection without the Tier 3 rule
standards and a 2017 control case projection with Tier 3 standards in place. The year 2005 was
selected for the Tier 3 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 2017 and 2030 to assess the impacts on air quality of the fuel and
vehicle standards. Information on the development of emissions inventories for the proposed
Tier 3 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-
2011-0135). The docket for this proposed 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 proposed Tier 3 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).
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 proposed Tier 3 emission
standard program changes. Table II-1 provides some basic geographic information regarding the
CMAQ domains.
In addition to the CMAQ model, the Tier 3 modeling platform includes (1) emissions for
the 2005 base year, 2017 reference and control case projection, 2030 reference and 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, nitrogen dioxide, ethanol 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 Tier 3 air quality modeling are described
in the EITSD found in the docket for this rule (EPA-HQ-OAR-2011-0135).
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
97degW, 40degN
33 deg N and 45 deg N
148x112x14
213x192x14
279 x 240 x 14
14 Layers: Surface to 100 millibar level (see Table II-3)
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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, nitrogen dioxide, ethanol, toxics and visibility
impacts from this proposed rulemaking.
D. 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 NC>2
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concentrations, annual and seasonal air toxics concentrations, annual total nitrogen and sulfur
deposition levels and visibility impairment for each of the following emissions scenarios:
2005 base year
2017 reference case projection without the Tier 3 fuel and vehicle standards
2017 control case projection with the Tier 3 fuel and vehicle standards
2030 reference case projection without the Tier 3 fuel and vehicle standards
2030 control case projection with the Tier 3 fuel and vehicle standards
Model predictions are used in a relative sense to estimate scenario-specific, future-year
design values of PM2.5 and ozone. For example, 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 8.1.2 of the DRIA).
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 proposed rule.
Concentrations of PM2.5 in 2017 and 2030 were estimated by applying the modeled 2005-
to-2017 and 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 |ig/m3). EPA's Modeled
Attainment Test Software (MATS) was used to calculate the future year design values. The
software (including documentation) is available at:
7 U.S. EPA, 2007: Guidance on the Use of Models and Other Analyses for Demonstrating Attainment for Ozone,
PM2 5, and Regional Haze, Office of Air Quality Planning and Standards, Research Triangle Park, NC.
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http://www.epa.gov/scratnOOl/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
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. 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 2017 and 2030 cases were used to project
ambient design values to 2017 and 2030 respectively. 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 2017 control case and the 2017 reference case as well as the 2030 control case and the 2030
reference case for annual and seasonal nitrogen dioxide, ethanol, 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.
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.
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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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
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-1 using MM5 version 3.7.4. The
MM5 simulations were run on the same map projection as CMAQ.
All three meteorological model runs configured similarly. The selections for key MM5
physics options are shown below:
• Pleim-Xiu PEL and land surface schemes
• Kain-Fritsh 2 cumulus parameterization
• Reisner 2 mixed phase moisture scheme
• RRTM longwave radiation scheme
• Dudhia shortwave radiation scheme
Three dimensional analysis nudging for temperature and moisture was applied above the
boundary layer only. Analysis nudging for the wind field was applied above and below the
boundary layer. The 36 km domain nudging weighting factors were 3.0 x 104 for wind fields and
temperatures and 1.0 x 105 for moisture fields. The 12 km domain nudging weighting factors
were 1.0 x 104 for wind fields and temperatures and 1.0 x 105 for moisture fields.
All three sets of model runs were conducted in 5.5 day segments with 12 hours of overlap
for spin-up purposes. All three 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
MM5 Layers
0
1
2
3
4
5
6
7
8
9
10
Sigma P
1.000
0.995
0.990
0.985
0.980
0.970
0.960
0.950
0.940
0.930
0.920
Approximate
Height (m)
0
38
77
115
154
232
310
389
469
550
631
Approximate
Pressure (mb)
1000
995
991
987
982
973
964
955
946
937
928
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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CMAQ Layers
7
8
1 f\
12
i "3
1A
MM5 Layers
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
Sigma P
0.910
0.900
0.880
0.860
0.840
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
Approximate
Height (m)
712
794
961
,130
,303
,478
,657
,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
Approximate
Pressure (mb)
919
910
892
874
856
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
11 Byun, D.W., and Ching, J.K.S., Eds, 1999. Science algorithms of EPA Models-3 Community Multiscale Air
Quality (CMAQ modeling system, EPA/600/R-99/030, Office of Research and Development).
12 Baker K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Eastern U.S.
12-km Domain Simulation, USEPA/OAQPS, February 2, 2009.
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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.
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 Tier 3 proposed rule emission standards.
We looked at impacts on future ambient levels of PM2.5, ozone and NO2, as well as changes in
ambient concentrations of ethanol and the following air toxics: acetaldehyde, acrolein, benzene,
1,3-butadiene, and formaldehyde. The air quality modeling results also include impacts in
deposition of nitrogen and sulfur and in visibility levels due to this proposed rule. In this section,
we present the air quality modeling results for the 2017 Tier 3 control case relative to the 2017
reference case as well as the 2030 Tier 3 control case relative to the 2030 reference case.
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.
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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A. Impacts of Proposed Tier 3 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 proposed Tier 3 fuel and vehicle standards. Specifically, for the years 2017 and
2030 we compare a reference scenario (a scenario without the proposed Tier 3 standards) to a
control scenario which includes the proposed Tier 3 standards. Our modeling indicates that there
will be substantial decreases in ozone across most of the country as a result of the proposed Tier
3 standards.
Figure III-l and Figure III-2 present the changes in 8-hour ozone design value
concentrations between the reference case and the control case in 2017 and 2030 respectively.17
Note that the projected results for 2017 do not include California, while the projected results for
2030 do.18 This issue does not have a significant impact on the AQ modeling results for the rest
of the country. Appendix B details the state and county 8-hour maximum ozone design values
for the ambient baseline and the 2017 and 2030 future reference and control cases.
17 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.
18 The processing of control case sulfur levels nationwide introduced an error in California counties. Control case
fuels were set to 10 ppm in all California counties, whereas the reference case sulfur levels ranged from 8-19 ppm.
This led to small changes in emissions due to fuel changes between reference and control, whereas in reality we
expect no change in California fuel. The error had a negligible effect on national emission totals, though the 2017
air quality modeling results captured regional California impacts associated with the error that we judged not valid.
10
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^H >i] ;nini-• o a
^H :• [ .>:• a
* in • Jv Drniv IW - PM7rt_cilfl_CJI-Jii^w(«l mvxtt 3
Figure III-l. Projected Change in 2017 8-hour Ozone Design Values Between the Reference Case and
Control Case
»D,l
Figure III-2. Projected Change in 2030 8-hour Ozone Design Values Between the Reference Case and Control
Case
11
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As can be seen in Figure III-l, the majority of the design value decreases in 2017 are
between 0.5 and 1.0 ppb. There are also seven counties with projected 8-hour ozone design
value decreases of more than 1 ppb; these counties are in Arizona, Texas and Tennessee. The
maximum projected decrease in an 8-hour ozone design value in 2017 is 1.09 ppb in Tarrant
County, Texas near Dallas, which is projected to be above the ozone standard. Figure III-2
presents the ozone design value changes for 2030. In 2030, the ozone design value decreases are
larger than in 2017; most decreases are projected to be between 1.0 and 1.5 ppb, and over 200
counties have design values with projected decreases greater than 1.5 ppb. The maximum
projected decrease in an 8-hour ozone design value in 2030 is 3.2 ppb in Maricopa County,
Arizona, where Phoenix is located.
B. Impacts of Proposed Tier 3 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 proposed Tier 3 fuel and vehicle standards. Specifically, for the
years 2017 and 2030 we compare a reference scenario (a scenario without the proposed
standards) to a control scenario that includes the proposed standards. Our modeling indicates
that by 2030 annual PM2.5 design values in the majority of the modeled counties would decrease
due to the proposed standards. The decreases in annual PM2.5 design values are likely due to the
projected reductions in primary PM2s, NOx, SOx and VOC emissions (see Section 7.2.1 in the
DRIA).
It is important to note that, the control scenario emissions inventory prepared for air
quality modeling included direct PM2 5 vehicle emissions increases that we do not expect to
occur in reality (discussed in Section 7.1.5 and 7.2.1.1 of the DRIA). These increases resulted
from a series of conservative assumptions and uncertainties related to fuel parameters in 2017,
and also an emissions processing issue which erroneously increased direct PM emissions in
about one third of modeled counties (see Section 7.2.1.1.2 of the DRIA for more details).
Because our air quality modeling assumes this increase, our air quality results overestimate
ambient PM and underestimate the reductions that would result from the proposed Tier 3
standards. Appendix C details the state and county annual PM2.5 design values for the ambient
baseline and the 2017 and 2030 future reference and control cases.
Figure III-3 and III-4 presents the changes in annual PM2 5 design values in 2017 and
2030 respectively.19 Note that the projected results for 2017 do not include California, while the
projected results for 2030 do.20 This issue does not have a significant impact on the AQ
modeling results for the rest of the country.
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.
20 The processing of control case sulfur levels nationwide introduced an error in California counties. Control case
fuels were set to 10 ppm in all California counties, whereas the reference case sulfur levels ranged from 8-19 ppm.
This led to small changes in emissions due to fuel changes between reference and control, whereas in reality we
expect no change in California fuel. The error had a negligible effect on national emission totals, though the 2017
air quality modeling results captured regional California impacts associated with the error that we judged not valid.
12
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Figure III-3. Projected Change in 2017 Annual PM2.5 Design Values Between the Reference Case and Control
Case
ell mitiuh ZlUIkr r«T
Figure III-4. Projected Change in 2030 Annual PM2.5 Design Values Between the Reference Case and Control
Case
13
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The projected population-weighted average design value concentration without the
proposed rule is 9.3 |ig/m3 in 2017. As shown in Figure III-3, we project that in 2017 seven
counties will have design value decreases of between 0.01 |ig/m3 and 0.05 |ig/m3. These
counties are in Utah, Pennsylvania and Wisconsin. The maximum projected decrease in a 2017
annual PM2.5 design value is 0.03 |ig/m3 in Weber County, Utah. As mentioned above the
decreases in ambient annual PM2.5 concentrations are due to reductions in NOx, SOx and VOCs
and the subsequent reductions in secondarily formed PM due to this proposed rule in 2017,
which offset the small increases in direct PM emissions that were modeled but we do not expect
to occur (see Section 7.1.5 and Section 7.2.1.1.2 of the DRIA for more details). As a result, the
projected decreases in design values are underestimates of the actual effects of the proposed rule.
There are a few counties with projected small increases in annual PM2.5 in 2017, but as
explained, we do not expect that these localized small increases will actually happen.
The projected population-weighted average design value concentration without the
proposed rule is 9.5 |ig/m3 in 2030. Figure III-4 presents the annual PM2.5 design value changes
in 2030. In 2030 all the modeled counties have decreases in annual PM2 5 design values. The
annual PM2 5 design value decreases in 2030 are larger than the decreases in 2017; most design
values are projected to decrease between 0.01 and 0.05 |ig/m3 and over 100 additional counties
have projected design value decreases greater than 0.05 |ig/m3. The maximum projected
decrease in an annual PM2 5 design value in 2030 is 0.20 |ig/m3 in Tulare County, California.
C. Impacts of Proposed Tier 3 Standards on Future 24-hour PMi.s Levels
This section summarizes the results of our modeling of 24-hour PM2 5 air quality impacts
in the future due to the proposed Tier 3 rule. Specifically, for the years 2017 and 2030 we
compare a reference scenario (a scenario without the proposed standards) to a 2030 control
scenario that includes the proposed standards. Our modeling indicates that by 2030 24-hour
PM2.s design values in the majority of the modeled counties would decrease due to the proposed
standards. The decreases in 24-hour PM2 5 design values are likely due to the projected
reductions in primary PM2.5, NOx, SOx and VOCs. Additional information on the emissions
reductions that are projected with this proposed action is available in Section 7.2.1 of the DRIA.
It is important to note that, as discussed in Section 7.1.5 and 7.2.1.1 of the DRIA, the
control scenario emissions inventory prepared for air quality modeling included direct PM2.5
vehicle emissions increases that we do not expect to occur in reality. These increases resulted
from a series of conservative assumptions and uncertainties related to fuel parameters in 2017,
and also an emissions processing issue which erroneously increased direct PM emissions in
about one third of modeled counties (see Section 7.2.1.1.2). Because our air quality modeling
assumes this increase, our air quality results overestimate ambient PM and underestimate the
reductions that would result from the proposed Tier 3 standards.
Figure III-5 and Figure III-6 present the changes in 24-hour PM2 5 design values in 2017
and 2030 respectively.21 Note that the projected results for 2017 do not include California, while
21 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
40CFRpart50.
14
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22
the projected results for 2030 do. * This issue does not have a significant impact on the AQ
modeling results for the rest of the country.
ir Li.il,-flWI-5 £'V-
Figure III-5. Projected Change in 2017 24-hour PM2.5 Design Values Between the Reference Case and the
Control Case
The processing of control case sulfur levels nationwide introduced an error in California counties. Control case
fuels were set to 10 ppm in all California counties, whereas the reference case sulfur levels ranged from 8-19 ppm.
This led to small changes in emissions due to fuel changes between reference and control, whereas in reality we
expect no change in California fuel. The error had a negligible effect on national emission totals, though the 2017
air quality modeling results captured regional California impacts associated with the error that we judged not valid.
15
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-02510-=-0,15
-0.15 lo *=-COS
-0 05 lo < 0.05
= 005!o<0 15
>=0.15tO<0.25
>= 0.25 » < 0.5
nD«iVy PM2.5DV-
Figure III-6. Projected Change in 2030 24-hour PM2.5 Design Values Between the Reference Case and the
Control Case
The projected population-weighted average design value concentration without the
proposed rule is 23.4 |ig/m3 in 2017. As shown in Figure III-5, in 2017 there are 72 counties
with projected 24-hour PM2.5 design value decreases greater than 0.05 |ig/m3. These counties are
in Utah, Pennsylvania and scattered throughout the Midwest. The maximum projected decrease
in a 2017 24-hour PM2.5 design value is 0.20 |ig/m3 in Tooele County, Utah. As mentioned
above, the decreases in ambient annual PM2.5 concentrations are due to reductions in NOx, SOx
and VOCs and the subsequent reductions in secondarily formed PM due to this proposed rule in
2017, which offset the small increases in direct PM emissions that were modeled but we do not
expect to occur (see Section 7.1.5 and Section 7.2.1.1.2 of the DRIA for more details). As a
result, the projected decreases in design values are underestimates of the actual effects of the
proposed rule. There are some counties with projected small increases in 24-hour PM2.5 in 2017,
but as explained, we do not expect that these localized small increases will actually happen.
The projected population-weighted average design value concentration without the
proposed rule is 24.3 |ig/m3 in 2030. Figure III-6 presents the 24-hour PM2.5 design value
changes in 2030. In 2030, the 24-hour PM2.5 design value decreases are larger; most design
values are projected to decrease between 0.05 and 0.15 |ig/m3 and over 200 counties have
projected design value decreases greater than 0.15 |ig/m3 The maximum projected decrease in a
24-hour PM2.5 design value in 2030 is 1.28 |ig/m3 in Kings County, California. As shown in
Figure III-6, design values in 93 counties would decrease by more than 0.25 |ig/m3. These
counties are in Idaho, Nevada, California, Montana, Louisiana, northern Utah, and the upper
Midwest. The decreases in 24-hour PM2.5 design values that are projected in some counties are
likely due to emission reductions related to reductions in PM2.5 precursor emissions (NOx, SOx,
16
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and VOCs). There is one county, Richmond County, Georgia, with a projected 24-hour PM2.5
design value increase of less than 0.15 |ig/m3.
Additional information on the emissions reductions that are projected with this proposed
action is available in Section 7.2.1 of the DRIA. 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 Proposed Tier 3 Standards on Future Nitrogen Dioxide Levels
This section summarizes the results of our modeling of annual average nitrogen dioxide
(NC>2) air quality impacts in the future due to the proposed standards. Specifically, for the years
2017 and 2030 we compare a reference scenario (a scenario without the proposed standards) to a
control scenario that includes the proposed standards. Figure III-7 and Figure III-8 present the
changes in annual NC>2 concentrations in 2017 and 2030 respectively.
Figure III-7. Projected Change in 2017 Annual NO2 Concentrations Between the Reference Case and
Control Case
17
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Deference In Annual Totat NO2 Concentration
2010ct_ctt minus 203oct_ret
Figure III-8. Projected Change in 2030 Annual NO2 Concentrations Between the Reference Case and
Control Case
As shown in Figure III-8, our modeling indicates that by 2030 annual NC>2 concentrations
in the majority of the country would decrease less than 0.1 ppb due to this proposal. However,
decreases in annual NC>2 concentrations are greater than 0.3 ppb in most urban areas. These
emissions reductions would also likely decrease 1-hour NC>2 concentrations and help any
potential nonattainment areas to attain and maintain the standard. Note that the projected results
for 2017 do not include California, while the projected results for 2030 do. 23 This issue does not
have a significant impact on the AQ modeling results for the rest of the country.
E. Impacts of Proposed Tier 3 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 fuel and vehicle emission standards proposed by Tier 3. 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.
Impacts on ethanol concentrations were also included in our analyses. Our modeling indicates
that the impacts of the proposed standards include generally small decreases in ambient
concentrations of air toxics, with the greatest reductions in urban areas. Air toxics pollutants
The processing of control case sulfur levels nationwide introduced an error in California counties. Control case
fuels were set to lOppm in all California counties, whereas the reference case sulfur levels ranged from 8-19ppm.
This led to small changes in emissions due to fuel changes between reference and control, whereas in reality we
expect no change in California fuel. The error had a negligible effect on national emission totals, though the 2017
air quality modeling results captured regional California impacts associated with the error that we judged not valid.
18
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dominated by primary emissions (or a decay product of a directly emitted pollutant), such as
benzene and 1,3-butadiene, have the largest impacts. Air toxics that primarily result from
photochemical transformation, such as formaldehyde and acetaldehyde, are not impacted as
much as those dominated by direct emissions. Our modeling shows decreases in ambient air
toxics concentrations for both 2017 and 2030. Reductions are greater in 2030, when Tier 3 cars
and trucks would contribute nearly 90 percent of fleet-wide vehicle miles travelled, than in 2017,
which is the first year of the proposed program. However, our modeling projects there would be
small immediate reductions in ambient concentrations of air toxics due to the proposed sulfur
controls in 2017. Furthermore, the full reduction of the vehicle program would be realized after
2030, when the fleet has fully turned over to Tier 3 vehicles. 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
Air quality modeling shows annual percent changes in ambient concentrations of
acetaldehyde of generally less than 1 percent across the U.S., although the proposal may
decrease acetaldehyde concentrations in some urban areas by 1 to 2.5 percent in 2030 (Figure III-
10). Changes in ambient concentrations of acetaldehyde are generally in the range of 0.01 |ig/m3
to -0.01 |ig/m3 with decreases happening in the more populated areas and increases happening in
more rural areas (Figure III-10).
The complex photochemistry associated with NOx emissions and acetaldehyde formation
appears to be the explanation for the split between increased rural concentrations and decreased
urban concentrations. In the atmosphere, acetaldehyde precursors react with NOx to form
peroxyacylnitrate (PAN). Reducing NOx allows acetaldehyde precursors to be available to form
acetaldehyde instead. This phenomenon is more prevalent in rural areas where NOx is low. The
chemistry involved is further described by a recent study done by EPA's Office of Research and
Development and Region 3 evaluating the complex effects of reducing multiple emissions on
reactive air toxics and criteria pollutants.24
24 Luecken, D,J, Clmorel, A.J. 2008. Codependencies of Reactive Air Toxic and Criteria Pollutants on Emission
Reductions. J. Air & Waste Manage. Assoc. 58:693-701. DOI: 10.3155/1047-3289.58.5.693
19
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Figure III-9. Changes in Annual Acetaldehyde Ambient Concentrations Between the Reference Case and the
Control Case in 2017: Percent Changes (left) and Absolute Changes in jig/m3 (right)
Figure 111-10. Changes in Annual 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 formaldehyde concentrations would slightly decrease in parts
of the country (mainly urban areas) as a result of the Tier 3 proposal. As shown in Figure III-l 1
and Figure III-12, annual percent changes in ambient concentrations of formaldehyde are less
than 1 percent across much of the country for 2017 but are on the order of 1 to 5 percent in 2030
in some urban areas as a result of the proposal. Figure III-l 1 and Figure III-12 also show that
absolute changes in ambient concentrations of formaldehyde are generally between 0.001 and
0.01 |ig/m3 in both years, with some areas as high as 0.1 |ig/m3 in 2030.
20
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Figure III-ll. Changes in Formaldehyde Ambient Concentrations Between the Reference Case and the Control
Case in 2017: Percent Changes (left) and Absolute Changes in jig/m3 (right)
Figure 111-12. Changes in Formaldehyde Ambient Concentrations Between the Reference Case and the Control
Case in 2030: Percent Changes (left) and Absolute Changes in jig/m3 (right)
3. Benzene
Our air quality modeling projects that the proposed standards would have a notable
impact on ambient benzene concentrations. In 2017, the first year the proposed Tier 3 standards
take effect, ambient benzene reductions are generally between 0.001 and 0.01 |ig/m3, or between
1 and 2.5 percent in some areas (Figure 111-13). In 2030, our modeling projects that the proposal
would decrease ambient benzene concentrations across much of the country on the order of 1 to
5 percent, with reductions ranging from 10 to 25 percent in some urban areas (Figure 111-14).
21
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Absolute decreases in ambient concentrations of benzene are generally between 0.001 and 0.01
|ig/m3 in rural areas and as much as 0.1 |ig/m3 in urban areas (Figure III-14).
Figure 111-13. Changes in Benzene Ambient Concentrations Between the Reference Case and the Control Case in
2017: Percent Changes (left) and Absolute Changes in jig/m3 (right)
Figure 111-14. Changes in Benzene Ambient Concentrations Between the Reference Case and the Control Case in
2030: Percent Changes (left) and Absolute Changes in jig/m3 (right)
4. 1,3-Butadiene
Our modeling also shows reductions of ambient 1,3-butadiene concentrations in 2017 and
2030. Figure III-15 shows that in 2017, ambient concentrations of 1,3-butdiene generally
decrease between 1 and 5 percent across the country, corresponding to small decreases in
absolute concentrations (less than 0.001 ug/m3). In 2030, reductions of 1,3-butadiene
22
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concentrations range between 1 and 25 percent, with decreases of at least 0.005 ug/m3 in urban
areas (Figure III-16).
Figure 111-15. Changes in 1,3-Butadiene Ambient Concentrations Between the Reference Case and the Control
Case in 2017: Percent Changes (left) and Absolute Changes in jig/m3 (right)
Figure 111-16. Changes in 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)
5. Acrolein
Our modeling indicates the proposed standards would reduce ambient concentrations of
acrolein in 2017 and 2030. Figure 111-17 shows decreases in ambient concentrations of acrolein
generally between 1 and 2.5 percent across the parts of the country in 2017, corresponding to
small decreases in absolute concentrations (less than 0.001 ug/m3). Reductions of acrolein
23
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concentrations in 2030 range between 1 and 25 percent, with decreases as high as 0.003 ug/m3 in
a few urban areas (Figure III-18).
Figure 111-17. Changes in Acrolein Ambient Concentrations Between the Reference Case and the Control Case
in 2017: Percent Changes (left) and Absolute Changes in jig/m3 (right)
Figure 111-18. Changes in Acrolein Ambient Concentrations Between the Reference Case and the Control Case
in 2030: Percent Changes (left) and Absolute Changes in jig/m3 (right)
6. Ethanol
Our modeling projects that the proposed standards would slightly decrease ambient
ethanol concentrations in 2030, with negligible impact in 2017. As shown in Figure 111-19, in
2017, annual percent changes in ambient concentrations of ethanol are less than 1 percent across
the country, with absolute concentrations of ± 0.01 ppb. In 2030, some parts of the country,
24
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especially urban areas, are projected to have reductions in ethanol concentrations on the order of
1 to 5 percent as a result of the proposal (Figure 111-20). Figure 111-20 also shows that absolute
decreases in ambient concentrations of ethanol are generally between 0.001 and 0.1 ppb in 2030
with decreases in a few urban areas as high as 0.2 ppb.
Figure 111-19. Changes in Ethanol Ambient Concentrations Between the Reference Case and the Control
Case in 2017: Percent Changes (left) and Absolute Changes in jig/m3 (right)
Figure 111-20. Changes in Ethanol Ambient Concentrations Between the Reference Case and the Control Case in
2030: Percent Changes (left) and Absolute Changes in jig/m3 (right)
F. Population Metrics
To assess the impact of the proposed Tier 3 rule on projected changes in air quality, we
developed population metrics that show population experiencing changes in annual ambient
concentrations across the modeled air toxics. Although the reductions in ambient air toxics
concentrations expected from the proposed Tier 3 standards are generally small, they are
25
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projected to benefit the majority of the U.S. population. As shown in Table III-l, over 80
percent of the total U.S. population is projected to experience a decrease in ambient benzene and
acrolein concentrations of at least 2.5 percent. More than 85 percent of the population is
projected to experience decrease in 1,3-butadiene concentrations of at least 5 percent. Table
7-38 also shows that over 80 percent of the U.S population is projected to experience at least a 1
percent decrease in ambient ethanol concentrations, and over 60 percent would experience a
similar decrease in ambient formaldehyde concentrations with the proposed standards.
Table III-l. Percent of Total Population Experiencing Changes in Annual Ambient Concentrations of Toxic
Pollutants in 2030 as a Result of the Proposed Standards
Percent Change
<-50
> -50 to < -25
> -25 to < -10
>-10to<-5
> -5 to < -2.5
>-2.5to<-l
>-l to< 1
> lto<2.5
>2.5 to<5
> 5 to < 10
> 10 to < 25
> 25 to < 50
>50
Benzene
2.8%
23.7%
54.5%
17.7%
1.4%
Acrolein
0.7%
36.8%
43.7%
15.3%
3.5%
1,3 -Butadiene
0.1%
56.8%
30.8%
7.1%
3.4%
1.7%
0.0%
Formaldehyde
1.2%
63.2%
35.6%
Ethanol
33.0%
55.3%
11.6%
Acetaldehyde
0.3%
25.1%
74.6%
G. Impacts of Proposed Tier 3 Standards on Future Annual Nitrogen and Sulfur
Deposition Levels
Our air quality modeling projects decreases in both nitrogen and sulfur deposition due to this
proposed rule. Figure 111-21 shows that for nitrogen deposition by 2030 the proposed standards would
result in annual percent decreases of more than 5 percent in most urban areas with decreases of more than 7
percent in urban areas in Nevada, Arizona and Florida. In addition, smaller decreases, in the 1 to 1.5
percent range, would occur over most of the rest of the country.
26
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Figure 111-21. Percent Changes in Annual Total Nitrogen Deposition Between the
Reference Case and the Control Case in 2017 (left) and 2030 (right)
Figure 111-22 shows that for sulfur deposition the proposed standards will result in annual
percent decreases of more than 2 percent in some areas in 2030. The decreases in sulfur
deposition are likely due to projected reductions in the sulfur level in fuel. Minimal changes in
sulfur deposition, ranging from decreases of less than 0.5 percent to no change, are projected for
the rest of the country.
Figure 111-22. Percent Changes in Annual Total Sulfur Deposition Between the Reference
Case and the Control Case in 2017 (left) and 2030 (right)
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H. Impacts of Proposed Tier 3 Standards on Future Visibility Levels
Air quality modeling conducted for the proposed Tier 3 rule was used to project visibility
conditions in 139 mandatory class I Federal areas across the U.S. in 2017 and 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 period25.
Visibility for the 2017 and 2030 reference and control cases were 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 values26 are made up of
individual component species (sulfate, nitrate, organics, etc). The predicted change in visibility
(on the 20 percent worst days) is calculated as the modeled percent change in the mass for each
of the PM2.5 species (on the 20% worst observed days) multiplied by the observed
concentrations. The future mass is converted to extinction and then daily species extinction
coefficients are summed to get a daily total extinction value (including Rayleigh scattering). The
daily extinction coefficients are converted to deciviews and averaged across all 20 percent worst
days. In this way, we calculate an average change in deciviews from the base case to a future
case at each IMPROVE site. For example, 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
25 Since the base case modeling used meteorology for 2005, one of the complete years must be 2005.
26 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.
28
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2007 because that is the 5 year period which is centered on the base model year of 2005. These
choices are consistent with using a 5 year base period for regional haze calculations.
The results show that in 2030 all the modeled areas would continue to have annual
average deciview levels above background and the proposed rule would improve visibility in all
these areas.27 The average visibility on the 20 percent worst days at all modeled Mandatory
Class I Federal areas is projected to improve by 0.04 deciviews, or 0.28 percent, in 2030. The
greatest improvement in visibilities will be seen in Joshua Tree National Monument, where
visibility is projected to improve by 0.99 percent (0.16 DV) in 2030 due to the proposed
standards. Table III-2 contains the full visibility results for the 20% worst days from 2017 and
2030 for the 139 analyzed areas.
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
State
AL
AR
AR
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
CA
CA
CA
CA
CA
2005
Baseline
Visibility
29.03
26.36
26.27
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
2017
Reference
21.67
21.00
21.24
12.29
12.27
12.37
11.03
12.87
10.91
12.90
12.81
13.72
13.55
13.15
14.83
18.87
14.48
12.75
15.83
11.89
2017
TierS
Control
21.80
21.03
21.30
12.28
12.27
12.35
11.02
12.84
10.91
12.89
12.78
13.71
13.53
13.13
14.81
18.87
14.48
12.75
15.83
11.88
2030
Reference
21.84
21.10
21.35
12.23
12.22
12.21
10.89
12.61
10.85
12.75
12.54
13.57
13.33
12.99
14.70
18.19
14.29
12.61
15.42
11.76
2030
Tier3
Control
21.76
21.02
21.28
12.21
12.20
12.15
10.86
12.55
10.84
12.72
12.48
13.55
13.28
12.93
14.67
18.09
14.25
12.57
15.32
11.73
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
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.
29
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Class 1 Area
(20% worst days)
Emigrant Wilderness
Hoover Wilderness
John Muir Wilderness
Joshua Tree NM
Kaiser Wilderness
Kings Canyon NP
Lassen Volcanic NP
Lava Beds NM
Mokelumne Wilderness
Pinnacles NM
Point Reyes NS
Redwood NP
San Gabriel Wilderness
San Gorgonio Wilderness
San Jacinto Wilderness
San Rafael Wilderness
Sequoia NP
South Warner
Wilderness
Thousand Lakes
Wilderness
Ventana Wilderness
Yosemite NP
Black Canyon of the
Gunnison NM
Eagles Nest Wilderness
Flat Tops Wilderness
Great Sand Dunes NM
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
Selway-Bitterroot
Wilderness
Mammoth Cave NP
State
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
FL
GA
GA
ID
ID
ID
KY
2005
Baseline
Visibility
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
8.82
11.82
10.00
8.82
12.14
9.72
9.72
12.85
10.00
8.82
22.31
27.13
27.13
14.06
14.97
17.11
31.37
2017
Reference
15.86
11.05
14.41
16.74
14.21
22.29
12.78
13.14
12.04
15.64
20.89
17.99
15.86
19.50
18.70
17.63
21.90
13.34
12.76
16.48
15.87
9.32
8.27
8.43
11.34
9.59
8.38
11.46
9.29
9.29
12.37
9.58
8.35
19.30
21.29
21.10
13.30
14.75
16.83
22.87
2017
TierS
Control
15.85
11.05
14.42
16.72
14.21
22.29
12.77
13.13
12.03
15.61
20.87
17.97
15.86
19.50
18.71
17.61
21.88
13.33
12.75
16.45
15.86
9.32
8.26
8.43
11.34
9.59
8.38
11.45
9.29
9.29
12.36
9.58
8.35
19.06
21.44
21.12
13.30
14.74
16.83
23.09
2030
Reference
15.69
10.95
14.25
16.14
13.99
21.99
12.61
13.20
11.91
15.43
21.08
17.77
15.37
19.01
17.67
17.30
21.52
13.30
12.60
16.21
15.71
9.29
8.22
8.39
11.31
9.54
8.36
11.48
9.28
9.26
12.34
9.51
8.33
19.10
21.47
21.18
13.10
14.75
16.86
23.14
2030
Tier3
Control
15.65
10.94
14.22
15.98
13.95
21.91
12.56
13.17
11.88
15.31
20.99
17.73
15.26
18.89
17.52
17.21
21.42
13.27
12.55
16.07
15.67
9.28
8.20
8.38
11.30
9.54
8.35
11.46
9.27
9.25
12.32
9.51
8.32
19.04
21.40
21.12
13.05
14.75
16.85
23.07
Natural
Background
7.14
7.12
7.14
7.08
7.13
7.13
7.31
7.49
7.14
7.34
7.39
7.81
7.17
7.10
7.12
7.28
7.13
7.32
7.32
7.32
7.14
7.06
7.08
7.07
7.10
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
7.32
11.53
30
-------
Class 1 Area
(20% worst days)
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
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
San Pedro Parks
Wilderness
Wheeler Peak
Wilderness
White Mountain
Wilderness
State
ME
ME
ME
Ml
Ml
MN
MN
MO
MT
MT
MT
MT
MT
MT
MT
MT
MT
MT
NC
NC
ND
ND
NH
NH
NJ
NM
NM
NM
NM
NM
NM
NM
NM
NM
2005
Baseline
Visibility
22.89
21.72
21.72
20.74
24.16
20.20
19.27
26.75
17.11
16.13
14.31
11.94
19.62
18.21
16.13
11.19
16.13
15.49
28.77
28.54
19.57
17.74
22.82
22.82
29.01
11.97
13.81
17.19
13.12
9.60
18.27
10.42
9.60
13.01
2017
Reference
18.51
17.81
17.68
18.69
21.32
17.13
17.03
21.92
16.73
15.71
13.74
11.56
18.81
17.65
15.57
10.78
15.68
15.17
20.85
20.47
18.48
16.71
16.73
16.68
21.56
10.89
12.78
14.93
12.57
9.08
16.70
9.87
8.92
12.16
2017
TierS
Control
18.80
18.01
17.95
18.64
21.33
17.05
16.95
21.99
16.72
15.71
13.74
11.56
18.82
17.65
15.57
10.78
15.68
15.17
21.23
20.78
18.28
16.51
17.01
16.97
21.88
10.88
12.76
15.00
12.56
9.07
16.70
9.87
8.92
12.16
2030
Reference
18.83
18.03
17.96
18.74
21.44
17.16
17.05
22.04
16.77
15.74
13.79
11.57
18.81
17.58
15.62
10.74
15.70
15.13
21.24
20.81
18.39
16.61
17.05
17.00
21.93
10.77
12.63
15.03
12.53
9.01
16.67
9.78
8.82
12.20
2030
Tier3
Control
18.80
18.01
17.94
18.68
21.35
17.10
16.99
21.97
16.77
15.73
13.78
11.57
18.81
17.57
15.61
10.72
15.70
15.12
21.19
20.73
18.36
16.58
17.01
16.96
21.84
10.75
12.61
15.00
12.52
8.99
16.64
9.77
8.80
12.19
Natural
Background
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
7.18
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
7.03
7.07
6.98
31
-------
Class 1 Area
(20% worst days)
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
State
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
2005
Baseline
Visibility
12.26
23.81
13.21
13.21
17.34
13.21
19.00
16.38
14.68
15.80
15.80
13.21
17.34
15.80
26.48
17.14
15.84
30.28
30.28
17.30
17.19
10.77
11.62
10.77
10.86
29.12
29.31
24.45
16.99
13.29
12.67
12.67
17.07
13.29
15.83
2017
Reference
11.98
19.38
12.52
12.45
16.36
12.69
18.00
15.48
13.33
14.96
14.95
12.44
16.66
15.02
20.61
15.56
14.81
22.32
22.03
15.76
14.95
10.13
10.95
10.15
10.46
20.45
20.24
17.72
15.59
12.26
11.54
11.57
15.77
12.24
14.63
2017
TierS
Control
11.98
19.18
12.51
12.44
16.36
12.68
18.00
15.46
13.30
14.95
14.93
12.43
16.65
15.01
20.72
15.50
14.72
22.57
22.29
15.71
15.03
10.13
10.95
10.13
10.46
20.61
20.67
17.75
15.55
12.25
11.52
11.56
15.75
12.23
14.61
2030
Reference
11.98
19.29
12.62
12.56
16.51
12.68
17.82
15.60
13.53
15.12
15.12
12.57
16.55
15.18
20.76
15.56
14.77
22.62
22.34
15.75
15.06
10.20
10.93
10.24
10.53
20.65
20.69
17.80
15.35
12.26
11.60
11.65
15.80
12.18
14.71
2030
Tier3
Control
11.97
19.18
12.60
12.54
16.48
12.66
17.76
15.56
13.46
15.09
15.09
12.55
16.51
15.15
20.69
15.54
14.75
22.52
22.25
15.72
15.03
10.18
10.93
10.21
10.52
20.56
20.61
17.67
15.22
12.24
11.56
11.61
15.75
12.17
14.65
Natural
Background
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
32
-------
Class 1 Area
(20% worst days)
Pasayten Wilderness
Dolly Sods Wilderness
Otter Creek Wilderness
Bridger Wilderness
Fitzpatrick Wilderness
Grand Teton NP
North Absaroka
Wilderness
Teton Wilderness
Washakie Wilderness
Yellowstone NP
State
WA
WV
WV
WY
WY
WY
WY
WY
WY
WY
2005
Baseline
Visibility
15.35
29.05
29.05
10.73
10.73
11.19
11.30
11.19
11.30
11.19
2017
Reference
14.34
20.23
20.34
10.38
10.38
10.73
10.99
10.81
10.99
10.76
2017
TierS
Control
14.32
20.82
20.90
10.38
10.38
10.72
10.99
10.80
10.99
10.76
2030
Reference
14.48
20.84
20.93
10.39
10.38
10.68
10.97
10.77
10.97
10.70
2030
Tier3
Control
14.46
20.79
20.87
10.39
10.38
10.66
10.97
10.75
10.96
10.69
Natural
Background
7.77
11.32
11.33
7.08
7.09
7.09
7.09
7.09
7.09
7.12
33
-------
Air Quality Modeling Technical Support Document:
Proposed Tier 3 Emission Standards
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
March 2013
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 (864), nitrate (NOs), total nitrate
(TNO3=NO3+HNO3), ammonium (NH/t), 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
performance 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
Appel, K.W., Gilliam, R.C., Davis, N., Zubrow, A., and Howard, S.C.: Overview of the Atmospheric Model
Evaluation Tool (AMET) vl.l for evaluating meteorological and air quality models, Environ. Modell. Softw.,26, 4,
434-443, 2011. (http://www.cmascenter.org/)
A-3
-------
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:
i\p-o\
NME =
(o)
-*100
Fractional bias is defined as
n
(P+O)
*100, where P = predicted and O = observed concentrations.
FB is a useful model performance indicator because it has the advantage of equally weighting
positive and negative bias estimates. The single largest disadvantage in this estimate of model
performance is that the estimated concentration (i.e., prediction, P) is found in both the
numerator and denominator. Fractional error (FE) is similar to fractional bias except the
absolute value of the difference is used so that the error is always positive.
Fractional error is defined as:
*100
The "acceptability" of model performance was judged by comparing our CMAQ 2005
performance results to the range of performance found in recent regional ozone, PM2.5, and air
P"Tn —
1
n
\
n (
A
^. i V
(P+O)}
2 JJ
A-4
-------
toxic model applications.5'6'7'8'9'10'1112'13'14'15'16 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., Nolte, 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 0.2 to 18.0 percent and the error statistics range from 13.5
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 (%)
9.4
0.2
3.4
2.2
-3.8
2.5
2.7
3.3
9.1
0.6
15.8
10.2
-7.8
2.2
7.7
4.4
27.6
2.2
5.1
6.0
NME (%)
24.8
13.8
17.4
18.9
23.1
14.4
13.5
16.2
17.5
12.0
22.1
17.7
22.1
14.6
17.3
17.8
33.4
13.9
16.9
18.3
FB (%)
9.0
1.5
6.6
4.7
-5.2
4.2
4.0
6.2
8.9
2.3
18.7
13.5
-9.7
2.9
9.8
7.7
27.9
2.8
5.7
7.9
FE (%)
27.4
14.7
19.1
20.3
27.4
15.2
14.0
18.8
18.5
12.7
23.8
20.4
28.0
15.6
18.3
21.1
34.1
14.4
17.1
19.5
A-6
-------
2005ct_05b_12EUS1 O3_8hrmax tor AQS_Dally (or 20050501 to 20050930
AQS_Daily
CMAQ
RPO-MANE VU
K31
2005_05
2005_06
2005_07
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_05b_12EUS1 O3_8hrmax (or AQS_Dally (or 20050501 to 20050930
3
I
m E AQS_Daily
D---A CMAQ
RPO - VISTAS
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
-------
2005ct_05b_12EUS1 O3_8hrmax tor AQS_Dally (or 20050501 to 20050930
~ o.io
n
O
AQS_Daily
CMAQ
RPO - LADCO
5267
5E6
2005_05 2O05_06 2005_07 2005_08 2005 09
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.
2005ct_05b_12EUS1 O3_8hrmax tor AQS_Dally tor 20050501 to 20050930
CL
5
m E AQS_Daily
M---& CMAQ
RPO . CENRAP
*06 4375
2005 07 2005_08
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
-------
2005ct_05b_12WUS1 O3_8hrmax for AQS_Dally for 20050501 to 20050930
n
O
AQS_Daily
CMAQ
2005_05 2005_06 2005_07
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.
O3_8hrmax NMB (%) tor run 2005cl_05b_12EUS1 for 20050501 to 20050930
coverage limit = 75%
60
40
20
0
-20
-40
-60
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
-------
O3_ahrmax NME (%) for run 2005Ct_05b 12EUS1 for 20050501 to 20050930
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 2005ct_05b_12WUS1 lor 20050501 Lo 20050930
•• ,
10D
80
eo
40
20
0
-20
-40
-60
-BO
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 (%) lor run 2005ct_05b_12WUS1 lor 20050501 to 20050930
> 100
90
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 PM2.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 (%)
-15.8
-15.2
-30.4
-10.1
-18.9
-17.7
-28.2
-15.9
-32.8
-24.6
-33.4
-21.3
0.7
19.5
-10.8
-12.4
3.5
4.7
-18.8
-18.2
-13.8
-5.9
-16.7
-20.1
-4.3
-5.3
-18.2
-10.6
-1.0
-6.6
-24.3
-11.9
-18.1
-13.4
-21.7
-18.6
-9.1
8.2
-8.9
-9.1
-6.8
NME (%)
38.3
32.2
42.3
34.9
40.0
31.4
39.3
31.5
34.3
27.8
37.0
23.8
38.6
43.0
28.7
26.7
35.8
35.5
30.2
27.1
21.8
22.4
22.0
22.7
37.1
27.4
32.8
27.8
36.9
29.0
35.7
29.3
22.6
21.3
24.9
21.3
34.9
37.2
27.2
28.9
31.1
FB (%)
-14.1
-11.3
-37.4
-3.7
-13.7
-11.9
-25.8
-7.6
-34.8
-23.6
-38.4
-19.7
-4.8
15.3
-0.9
-4.0
-0.1
6.8
-6.2
-7.2
-16.4
-4.4
-14.4
-16.1
-3.9
-6.1
-20.0
-6.0
1.1
-6.0
-31.0
-6.3
-17.2
-14.7
-28.6
-19.3
-13.0
4.3
-3.1
0.0
-10.7
FE (%)
41.7
33.8
54.3
36.8
43.4
32.4
46.2
37.1
37.4
29.6
46.0
26.4
38.7
36.9
30.8
27.5
34.4
35.2
36.2
31.7
26.6
21.7
24.0
21.8
37.0
29.4
39.1
29.5
37.5
31.7
47.1
34.5
23.6
22.9
32.9
23.3
34.6
34.9
31.0
31.0
33.2
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
2373
2650
2307
2365
250
273
281
268
NMB (%)
7.05
-13.1
-6.7
-14.5
-0.3
-15.7
-12.3
-5.5
-3.8
-32.3
-7.7
22.4
-3.6
-25.0
-0.6
6.6
-18.5
-35.3
-10.9
NME (%)
37.9
32.3
32.3
22.2
25.1
20.6
18.5
57.3
36.9
43.7
47.0
58.3
33.5
41.2
40.0
35.9
27.1
-36.2
23.6
FB (%)
3.6
-4.6
7.8
-18.6
-1.4
-12.9
-7.2
1.7
0.0
-23.5
0.3
33.8
3.4
-16.8
11.1
17.9
-17.1
-36.2
-5.1
FE (%)
38.2
37.7
35.5
25.5
26.4
22.1
18.1
54.3
36.1
42.6
43.3
56.6
35.2
42.9
41.2
37.4
27.7
41.7
24.3
A-13
-------
2005ct 05b 12EUS1 SO4 for IMPROVE for 20050101 to 20051231
25 -
0>
O 15 -
10 -
-B IMPROVE
-& CMAQ
0 - 2ci 176 219 535 245 27? ?T5 219 IM '93 13& IE
I
200501 2005_03 2005_05 2005_07 2005_09 2005J1
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 OSb 12EUS1 SO4 for IMPROVE for 20050101 to 20051231
30 -
25 -
n
i
s -
m—E IMPROVE
13---A CMAQ
2C1 176 219 53B £»5 2T? 513 219 194 *33 19Q 1M
I I I I I 1 \
2005 01 2005 03 2005 05 2005 07 2005 09 2005 11
Months
Figure A-8b. Distribution of observed and predicted 24-hour average sulfate by month for
2005 at CSN sites in the Northeast subregion.
A-14
-------
2005ct 05b 12EUS1 SO4 lor CASTNET tor 20050101 to 20051231
25 -
o>
O 15 -
10 -
CASTNET
CMAQ
0 - 65 57 7G 65 77 59 5* SI 61 54 BO 47
I
200501 2005_03 2005_05 2005_07 2005_09 2005J1
Months
Figure A-8c. Distribution of observed and predicted weekly average sulfate by month for
2005 at CASTNet sites in the Northeast subregion.
2005ct OSb 12EUS1 SO4 for IMPROVE for 200S0101 1020051231
CO
I
s
10 -
• B IMPROVE
a--A CMAQ
2005JJ1 2005_03 2005_05 2005_07 2005_Q9 2005J1
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 05b 12EUS1 SO4 Tor CSN (or 20050101 1o 20051231
25 -
0>
O 15 -
CSN
CMAQ
SM 302 302 314 Z$5 292 269 283 332 296 2BS
I
200501 2005_03 2005_05 2005_07 2005_09 2005J1
Months
Figure A-9b. Distribution of observed and predicted 24-hour average sulfate by month for
2005 at CSN sites in the Southeast subregion.
2005C1 05b 12EU51 SO4 for CASTNET tor 20050101 to 20051231
CO
I
s
10 -
• E CASTNET
H---& CMAQ
2005JJ1 2005_03 2005_05 2005_07 2Q05_Q9 2005J1
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 05b 12EUS1 SO4 for IMPROVE for 20050101 to 20051231
25 -
0>
O 15 -
10 -
-B IMPROVE
-& CMAQ
51 50 63 60 48
I
200501 2005_03 2005_05 2005_07 2005_09 2005J1
Months
Figure A-lOa. Distribution of observed and predicted 24-hour average sulfate by month
for 2005 at IMPROVE sites in the Midwest subregion.
200Sct 05b 12EUS1 SO4 for CSN for 20050101 1O 20051231
30 -
20 -
10 -
• E CSN
O- - -& CMAQ
0 - 5rjg 199 211 Btf £1S 207 SOS 336
200S_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 05b 12EUS1 SO4 lor CASTNET tor 20050101 to 20051231
25 -
o>
O 15 -
10 -
CASTNET
CMAQ
T
47 S7 49 «2
49 S3 43 51 M 36
I
200501 2005_03 2005_05 2005_07 2005_09 2005J1
Months
Figure A-lOc. Distribution of observed and predicted weekly average sulfate by month for
2005 at CASTNet sites in the Midwest subregion.
2M5CI 05b 12EUS1 SO4 for IMPROVE for 20050101 to 20051231
30 -
20 -
10 -
E IMPROVE
£ CUAQ
220 199 238 232 152 228 226
204 2!3 13S rt3
200S_01 200S_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 Q5b 12EUS1 SO4 for CSN tor 20090101 10 20051231
30 -
25
m
I
10 -
• E CSN
EJ--A CMAQ
RPO - CEMRl
'
3?8 1&4 M3 T9C fl?
2005_01 2005_03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-llb. Distribution of observed and predicted 24-hour average sulfate by month
for 2005 at CSN sites in the Central states subregion.
2005C1 05b 12EUS1 SO4 for CASTNET tor 20050101 to 20051231
30 -
20 -
10 -
B CASTNET
£ CUAQ
24 24 30 24 29 22 2' 29 22 23 30
200S_01 200S_03 2005_05 2005_07 2005_09 2005_11
Months
Figure A-lie. Distribution of observed and predicted weekly average sulfate by month for
2005 at CASTNet sites in the Central states subregion.
A-19
-------
2005ct 05b 12WUS1 SO4 for IMPROVE tor 20050101 to 20051231
0>
o
» 4 -
IMPROVE
CMAQ
RPO - WRAP
?45 756 7*2 8S3
I
200501 2005_03 2005_05 2005_07 2005_09 2005J1
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.
2QO5C1 05b 12WUS1 SO4 tor CSN (Or 20050101 1020051231
CO
"I
• E CSN
El- -A CMAQ
|gJl£|T»fgn-J
RPO - WRAP
283 27fl 3M 280 Sfl4
2005JJ1 2005_03 2005_05 2005_07 2Q05_M 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 05b 12WUS1 SO4 tor CASTNET for 20050101 to 20051231
o>
O
» 4 -
CASTNET
CMAQ
RPO - WRAP
97 34 1M B5 10- G7 89 1M 62 83 101 60
I
200501 2005_03 2005_05 2005_07 2005_09 2005J1
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 2005cl_05b_12EUS1 tor Winter
> 100
BO
60
40
20
0
-20
-40
-60
-80
<-100
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
-------
S04 NME (%) for run 2005ct_05b_12EUS1 for Winter
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_05b_12EUS1 for Spring
coverage limit = 75%
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 2005cl_05bJ2EllS1 tor Spring
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-14b. Normalized Mean Error (%) of sulfate during spring 2005 at monitoring
sites in Eastern modeling domain.
S04 NMB (%) for run 2005ct_OSb_12EUS1 tor Summer
coverage llmil ^ 75%
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 2005cl_05b_12EUS1 tor Summer
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-15b. Normalized Mean Error (%) of sulfate during summer 2005 at monitoring
sites in Eastern modeling domain.
S04 NMB (%) lor run 2005ct_05b_12ELJS1 for Fall
coverage llmil ^ 75%
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
-------
S04 NME (%) for run 2005ct_05b_12EUS1 for Fall
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure A-16b. Normalized Mean Error (%) of sulfate during fall 2005 at monitoring sites
in Eastern modeling domain.
SO4 NMB (%) for run 2005ct_05b_12WUS1 for Winter
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
-------
SO4 NME (%) tor fun 2005ct_05b_12WUS1 tor Winter
coverage limit = 75%
<100
90
80
70
60
50
40
30
20
10
0
CIRCLE=CSN; TRIANGLE=IMPROVE; SQUARE=CASTNET;
Figure F-17b. Normalized Mean Error (%) of sulfate during winter 2005 at monitoring
sites in Western modeling domain.
SO4 NMB (%) (or run 2005ct_05bJ2WUS1 tor Spring
coverage limit = 75%
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
-------
SO4 NME (%) for run 2005ct_05b_12WUS1 tor Spring
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.
S04 NMB (%) tor run 2005ct_05b_12WUS1 (or Summer
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
-------
SO4 NME (%) for run 200Sct_05b_12WUS1 tor 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 2005ct_05b_12WUS1 for Fall
coverage limit = 75%
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 NME (%) lor run 2005ct_05b_12WUS1 for Fall
coverage limit = 75%
<100
90
80
70
60
50
40
30
20
10
0
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 (%)
-7.6
26.9
23.7
101.0
2.6
46.1
17.7
158.0
23.5
12.0
-2.9
48.9
-23.7
59.1
38.0
64.8
-30.1
50.4
20.3
104.0
-8.7
34.5
47.4
68.6
NME (%)
48.7
60.3
99.1
129.0
54.0
76.5
109.0
188.0
37.0
2204
25.9
57.8
41.4
80.3
94.3
94.9
49.0
85.1
96.7
138.0
21.4
39.2
50.8
69.2
FB (%)
-9.1
12.6
-44.1
16.0
-8.5
-5.4
-58.1
12.4
23.9
5.4
-8.6
33.2
-25.3
38.0
-13.8
21.0
-33.0
-5.8
-43.8
-1.5
-1.6
29.0
37.2
48.5
FE (%)
59.8
65.6
95.9
89.1
70.6
90.7
112.0
107.0
35.5
31.1
27.9
43.1
50.6
64.6
83.3
74.0
74.3
89.9
99.8
102.0
21.7
33.8
40.4
48.9
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
2,374
2,643
2,305
2,357
250
273
281
268
NMB (%)
-29.3
34.4
-31.1
71.3
-7.3
54.9
-18.3
98.7
20.3
26.9
22.7
66.9
-6.4
37.5
-11.2
68.5
35.5
67.2
5.0
108.0
20.2
43.3
44.5
77.1
-47.8
-38.9
-73.1
-49.7
-33.1
-40.3
-74.6
-34.2
28.2
-4.8
-10.2
12.3
NME (%)
61.6
94.7
83.5
136.0
81.3
113.0
109.0
179.0
33.2
41.5
43.4
76.1
43.4
74.0
87.5
104.0
74.4
108.0
111.0
151.0
28.9
46.3
53.6
80.0
64.8
59.1
76.8
70.7
78.3
76.4
84.1
82.3
49.2
32.2
31.2
40.0
FB (%)
-62.9
-14.6
-86.4
-32.4
-63.8
-32.1
-95.0
-49.5
17.6
17.9
12.6
41.7
-6.6
28.5
-62.7
-16.2
28.5
28.3
-64.9
-12.4
28.6
33.8
27.9
50.1
-65.4
-70.9
-134.0
-69.8
-88.0
-89.9
-145.0
-77.2
37.7
3.2
-9.1
25.1
FE (%)
89.1
92.4
115.0
109.0
101.0
108.0
136.0
126.0
33.4
37.6
41.0
56.2
50.6
67.5
103.0
87.1
76.0
92.4
113.0
100.0
33.9
39.6
46.1
57.2
89.7
90.6
138.0
97.5
123.0
119.0
153.0
122.0
52.9
32.3
33.5
46.2
A-31
-------
200Sct 05b 12EUS1 NO3 lor IMPROVE for 20050101 to 20051231
15 -
I
3. 10 -
8
-B IMPROVE
-& CMAQ
200501 2tX)5_03 2005_05 2005_07 2005_09 2005J1
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]
2005CI 05b 12EUS1 NO3 for CSN for 20050101 to 20051 231
15 -
I
3 10 -
5 -
o - & aa
I I I I I 1 \
200S 01 2005 03 2005 05 2005 07 2005 09 2005 11
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
-------
2005C1 05b I2EUS1 TNO3 for CASTNET for 20050101 to 20051231
CASTNET
CMAQ
15 -
i5 67 76 65 77 59 54 81 61 Sfl BO 47
I
200501 2005_03 2005_05 2005_07 2005_09 2005J1
Months
Figure A-21c. Distribution of observed and predicted weekly average total nitrate by
month for 2005 at CASTNet sites in the Northeast subregion.
2005CI 05b 12EUS1 NO3 for IMPROVE for 20050101 to 20051231
15 -
1 -0-
• E IMPROVE
E3---& CUAQ
-*--'•*•'-
200S_01 200S_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
-------
2005CI 05b 12EUS1 NO3 for CSN for 20050101 1020051231
15 -
I
3, lo
8
CSN
CMAQ
I
200501 2tX)5_03 2005_05 2005_07 2005_09 2005J1
Months
Figure A-22b. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at CSN sites in the Southeast subregion.
2005C1 05b 12EUS1 TNO3 for CASTNET for 2O0501O1 1020051231
15 -
•g)
8
• E CASTNET
H---A CMAQ
2005_01 2005_03 2005JJ5 2005_07 2Q05_Q9 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
-------
200Sct 05b 12EUS1 NO3 lor IMPROVE for 20050101 to 20051231
15 -
I
3. 10 -
8
-B IMPROVE
-& CMAQ
i
200501 2005_03 2005_05 2005_07 2005_09 2005J1
Months
Figure A-23a. Distribution of observed and predicted 24-hour average nitrate by month
for 2005 at IMPROVE sites in the Midwest subregion.
2005CI 05b 12EUS1 NO3 for CSN for 20050101 lo 20051 231
IS -
• E CSN
D- - -& CMAQ
200S_01 2005_03 2O05_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
-------
2005C1 05b I2EUS1 TNO3 for CASTNET for 20050101 to 20051231
I
~di
t- 10 -
CASTNET
CMAQ
4C. 47 57 49 62
43 63 49 51 M 36
I
200501 2005_03 2005_05 2005_07 2005_09 2005J1
Months
Figure A-23c. Distribution of observed and predicted weekly average total nitrate by
month for 2005 at CASTNet sites in the Midwest subregion.
2005CI 05b 12EUS1 NO3 for IMPROVE for 20050101 to 20051231
E IMPROVE
£ CUAQ
_ ,.
«,
200S_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
-------
2005CI 05b 12EUS1 NO3 for CSN for 20050101 1020051231
8
CSN
CMAQ
r>B Ite it! J7
I
200501 2005_03 2005_05 2005_07 2005_09 2005J1
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.
2005C1 05b 12EUS1 TNO3 for CASTNET for 2O0501O1 1020051231
15 -
•g)
8
• E CASTNET
H---& CMAQ
RPO-CENRl
i
2005JJ1 2005_03 2005_05 2005_07 2Q05_Q9 2005J1
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
-------
2005CI 05b 12WUS1 NO3 for IMPROVE for 20050101 to 20051231
I
8
IMPROVE
CMAQ
RPO-WRAP
I
200501 2005_03 2005_05 2005_07 2005_09 2005J1
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.
2005C1 05b 12WUS1 NO3 for CSN for 20050101 1020051231
I 3
RPO - WRAP
-3
2005_01 2005_03 2005_05 2Q05_07 2Q05_M 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
-------
ZOOSCt 05b 12WUS1 TNO3 for CASTNET for 20050101 to 20051231
RPO-WRAP
I
200501 2005_03 2005_05 2005_07 2005_09 2005J1
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 (%) tor run 2005ct_05b_l2EUS1 for Winter
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=IMPROVE:
Figure A-26a. Normalized Mean Bias (%) for nitrate during winter 2005 at monitoring
sites in Eastern modeling domain.
N03 NME (%) for run 2005ct_05b_12EUS1 for Winter
^ coverage limit = 75%
< 100
90
80
70
60
50
40
30
20
10
0
CIRCLE=CSN: TRIANGLE=IMPROVE;
Figure A-26b. Normalized Mean Error (%) for nitrate during winter 2005 at monitoring
sites in Eastern modeling domain.
A-40
-------
TN03 NMB (%) for run 2005cl_05b_12EUS1 lor Winter
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 (%) tor run 2005ct_05b_12EUS1 tor Winter
units =%
covsraga 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_05b_12EUS1 tor Sprim
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.
N03 NME (%) for run 2005ct_05b_12EUS1 for Spring
coverage llmil ^ 75%
< 100
90
80
70
60
50
40
30
20
10
0
CIRCLE=CSN: TRIANGLE=IMPROVE;
Figure A-27b. Normalized Mean Error (%) for nitrate during spring 2005 at monitoring
sites in Eastern modeling domain.
A-42
-------
TN03 NMB (%) for run 2005ct_05b_12EUS1 for Spring
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 2005ctJ)5b_12EUS1 for Spring
units =%
covsraga 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_05b_12EUS1 for Summer
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.
N03 NME (%) for run 2005ct_OSb_12EUS1 tor Summer
coverage llmil ^ 75%
< 100
90
80
70
60
50
40
30
20
10
0
CIRCLE=CSN: TRIANGLE=IMPROVE;
Figure A-28b. Normalized Mean Error (%) for nitrate during summer 2005 at monitoring
sites in Eastern modeling domain.
A-44
-------
TN03 NMB (%) tor run 20Q5ct_05b_12EUS1 for Summer
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 (%> tor run 2005ct_05b_12EUS1 tor Summer
units =%
covsraga limit = 75%
CIRCLE=CASTNET:
Figure A-28d. Normalized Mean Error (%) for total nitrate summer 2005 at monitoring
sites in Eastern modeling domain.
A-45
-------
NO3 NMB (%) for run 2005ct_05b_12EUS1 for Fall
coverage limit = 75%
> 100
80
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.
NO3 NME (%) tor run 2005cl_05b_12EUS1 for Fall
coverage limit-75%
< 100
90
80
70
60
50
40
30
20
10
0
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-29b. Normalized Mean Error (%) for nitrate during fall 2005 at monitoring sites
in Eastern modeling domain.
A-46
-------
TN03 NMB (%) tor run 2005cl_05b_12EUS1 for Fall
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_05b_12EUS1 for Fall
units =%
covsraga limit = 75%
CIRCLE=CASTNET:
Figure A-29d. Normalized Mean Error (%) for total nitrate fall 2005 at monitoring sites in
Eastern modeling domain.
A-47
-------
NO3 NMB (%) for run 2005ct_05b_12WUS1 tor Winter
coverage limit = 75%
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN: TRIANGLE=IMPROVE:
Figure A-30a. Normalized Mean Bias (%) for nitrate during winter 2005 at monitoring
sites in Western modeling domain.
NO3 NME (%) for run 2005ct_05b_12WUS1 tor 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 (%) lor run 2005ct_05b_12WUS1 lor Winter
mils = %
•average limit = 75%
60
40
20
0
-20
-40
-60
-80
<-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_05b_12WUS1 tor Winter
unils = %
coverage limit = 75%
CIRCLE=CASTNET;
Figure A-30d. Normalized Mean Error (%) for total nitrate winter 2005 at monitoring
sites in Western modeling domain.
A-49
-------
NO3 NMB (%) for run 2005ct_05b_12WUS1 lor Spring
coverage limit = 75%
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=CSN: TRIANGLE=IMPROVE:
Figure A-31a. Normalized Mean Bias (%) for nitrate during spring 2005 at monitoring
sites in Western modeling domain.
NO3 NME (%) for fun 2005ct_05b_12WUS1 for Spring
CIRCLE=CSN; TRIANGLE=IMPROVE;
Figure A-31b. Normalized Mean Error (%) for nitrate during spring 2005 at monitoring
sites in Western modeling domain.
A-50
-------
TNO3 NMB (%) (or run 2005ct_05b_12WUS1 for Spring
units = %
coverage limit = 75%
20
0
-20
-40
-60
-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 (%) for run 2005ctj)5bjl 2WUS1 tor Spring
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 2005ct 05b 12WUS1 (or Summer
coverage limit ~ 75%
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 2005ctJ)5b_12WUS1 for Summer
units = %
coverage limit - 75%
< 100
90
SO
70
60
50
40
30
20
10
0
CIRCLE=CSN: TRIANGLE=IMPROVE;
Figure A-32b. Normalized Mean Error (%) for nitrate during summer 2005 at monitoring
sites in Western modeling domain.
A-52
-------
TNO3 NMB (%) lor run 2005ct_05b_12WUS1 for Summer
units = %
coverage limit = 75%
20
0
-20
-40
-60
-80
<-100
CIRCLE=CASTNET;
Figure A-32c. Normalized Mean Bias (%) for total nitrate during summer 2005 at
monitoring sites in Western modeling domain.
TNO3 NME (%) for run 2005ctJ)5b_12WUS1 for Summer
CIRCLE=CASTNET;
Figure A-32d. Normalized Mean Error (%) for total nitrate summer 2005 at monitoring
sites in Western modeling domain.
A-53
-------
NO3 NMB (%) for run 2005ct_05b_12WUS1 for 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.
NO3 NME (%) for run 2005cl_05b_12WUS1 for Fall
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 2005cl_05b_12WUS1 tor Fall
units = %
coverage limit = 75%
20
0
-20
-40
-60
-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 2005ct_05b_12WUS1 for Fall
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 (%)
-2.9
4.8
-21.4
17.1
2.9
16.6
-17.1
16.9
NME (%)
43.3
41.9
45.9
54.8
37.6
33.9
29.5
44.1
FB (%)
-1.9
7.3
-24.4
22.5
3.7
10.9
-19.8
24.3
FE (%)
50.7
43.2
60.9
55.6
42.5
32.2
35.8
46.3
Midwest
CSN
CASTNet
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
598
637
621
639
142
155
161
157
-10.2
47.7
-0.50
6.8
-11.5
44.2
-5.4
19.9
32.2
62.2
36.9
37.5
24.5
51.9
25.7
45.1
-5.1
38.3
15.8
21.2
-6.0
36.5
-2.1
26.7
33.9
50.6
41.8
41.1
25.4
41.4
27.4
41.1
Southeast
CSN
CASTNet
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
888
918
866
911
264
292
268
273
-10.9
8.0
-14.4
2.5
-7.1
8.2
-32.0
-9.0
41.2
39.4
36.8
42.2
28.0
30.9
35.4
36.4
-11.0
7.9
-9.1
13.1
-7.6
6.6
-45.2
-7.5
44.5
40.2
44.4
45.5
29.7
30.7
48.8
41.0
Northeast
CSN
CASTNet
Winter
Spring
Summer
Fall
Winter
828
894
874
902
193
0.1
31.1
-11.5
16.6
21.3
34.1
53.2
36.1
49.4
37.6
4.2
34.0
3.6
28.4
25.9
34.3
49.5
44.0
50.6
36.8
A-56
-------
Region
Network
Season
Spring
Summer
Fall
No. of
Obs.
206
192
195
NMB (%)
42.0
-23.5
8.7
NME (%)
48.5
29.8
39.0
FB (%)
32.0
-26.7
13.6
FE (%)
38.3
34.7
36.2
West
CSN
CASTNet
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
829
859
849
886
250
273
281
268
-30.8
-1.5
-33.3
-22.9
-4.0
-9.6
-33.7
-4.1
60.8
52.6
53.1
63.6
40.8
32.0
40.5
31.8
-15.1
17.8
-5.1
8.1
6.2
-5.2
-34.9
0.9
65.9
51.2
51.7
58.4
39.3
31.7
44.9
31.2
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 (%)
103.0
94.0
113.0
96.8
9.4
-9.0
-30.3
-17.1
NME (%)
136.0
117.0
136.0
115.0
54.5
56.0
46.8
34.8
FB (%)
56.8
46.3
43.0
58.0
4.4
-9.9
-38.2
-16.0
FE (%)
78.1
71.2
81.2
71.8
47.1
53.8
56.2
41.1
Midwest
CSN
IMPROVE
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
602
637
621
642
182
184
185
145
121.0
65.0
49.3
53.8
61.6
19.0
-13.1
-12.7
136.0
86.1
65.7
73.8
80.0
57.8
41.3
33.6
68.6
49.2
38.7
40.1
22.6
-11.4
-36.9
-19.2
76.0
61.8
54.8
55.9
45.9
51.3
53.9
48.2
Southeast
CSN
IMPROVE
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
889
914
866
909
491
530
493
481
38.5
38.7
41.4
13.3
-3.0
-16.5
-40.9
-26.5
62.4
63.7
69.8
46.4
44.5
44.9
48.2
38.8
30.7
37.4
38.4
19.1
-1.0
-11.0
-55.5
-22.5
49.6
54.6
61.4
46.0
48.7
45.1
71.5
45.5
Northeast
CSN
Winter
831
98.5
111.0
57.6
67.0
A-58
-------
Subregion
Network
IMPROVE
Season
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of
Obs.
881
866
901
603
658
596
591
NMB (%)
92.6
66.9
54.3
46.1
29.2
-19.7
32.9
NME (%)
109.0
89.6
84.2
73.8
64.0
45.8
59.1
FB (%)
57.8
46.2
35.6
22.3
11.7
-37.2
6.7
FE (%)
69.3
63.8
57.1
53.1
54.6
57.3
49.7
West
CSN
IMPROVE
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
808
822
806
867
2,338
2,597
2,314
2,372
50.2
111.0
121.0
58.8
1.8
19.4
30.0
9.0
89.1
134.0
134.0
91.4
65.1
69.7
77.9
67.4
24.3
47.8
60.3
29.6
-15.8
-1.5
18.4
-9.5
67.6
76.7
74.4
65.9
64.8
54.2
58.6
59.6
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 (%)
-2.0
-35.3
-51.9
-31.7
-9.0
-38.7
-50.3
-44.7
NME (%)
57.1
52.6
54.5
45.6
51.2
57.7
52.6
48.4
FB (%)
12.9
-32.8
-70.7
-29.2
-13.0
-38.4
-70.3
-54.8
FE (%)
59.6
63.7
77.1
57.2
48.1
61.3
74.6
62.7
Midwest
CSN
IMPROVE
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
566
605
619
595
182
184
185
144
1.1
-29.4
-53.8
-29.7
0.9
-25.9
-49.0
-35.6
52.3
45.9
55.1
41.7
37.7
36.4
52.0
44.0
19.1
-17.8
-70.8
-17.9
0.0
-32.9
-65.7
-44.5
53.5
52.8
74.2
52.5
37.2
44.6
69.8
62.2
Southeast
CSN
IMPROVE
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
871
901
857
880
491
529
492
481
-26.8
-36.0
-56.2
-40.5
-11.0
-9.6
-49.0
-34.4
45.7
48.9
58.1
46.4
45.1
49.2
54.5
41.5
-16.5
-29.4
-76.7
-43.7
-12.5
-15.6
-67.2
-42.3
51.0
57.3
81.4
57.9
51.2
50.5
75.6
53.6
Northeast
CSN
Winter
Spring
806
832
25.8
1.9
58.4
50.8
29.7
8.1
54.8
53.1
A-60
-------
Region
Network
IMPROVE
Season
Summer
Fall
Winter
Spring
Summer
Fall
No. of
Obs.
859
830
602
657
596
588
NMB (%)
-47.4
-4.9
46.4
3.1
-47.2
13.9
NME (%)
51.8
47.3
68.1
46.1
51.6
47.4
FB (%)
-61.4
3.2
30.6
-3.6
-59.7
-2.3
FE (%)
69.5
53.3
51.7
46.1
66.6
44.0
West
CSN
IMPROVE
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
803
823
840
881
2,296
2,559
2,297
2,350
25.2
-9.2
-22.3
-26.5
-17.0
-22.6
4.7
-21.4
67.4
60.3
41.3
56.5
58.9
51.5
65.2
56.8
-19.3
-1.0
-26.4
-24.2
-23.2
-24.8
-0.9
-26.5
70.0
60.3
49.9
58.0
64.7
56.6
60.1
62.1
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.
1,646
1,545
1,835
1,932
1,570
1,486
1,778
1,881
3,128
3,099
3,270
3,433
2,649
2,726
2,782
2,877
612
430
834
1,022
NMB (%)
-52.7
-53.0
-52.6
-51.1
-40.8
-25.4
59.2
0.8
-32.9
-40.6
-39.5
-34.3
-64.7
-78.1
-73.6
-62.4
-90.8
-82.8
-96.1
-95.2
NME (%)
62.2
65.3
63.4
62.0
50.9
49.9
91.5
57.3
68.2
66.5
68.2
64.7
89.4
92.7
87.9
81.8
94.8
91.6
99.0
98.8
FB (%)
-47.5
-35.6
-29.5
38.9
-42.1
-20.9
48.9
-4.9
-12.5
-28.8
-22.5
-21.0
-27.0
-51.6
-57.9
-53.5
-126.0
-119.0
-138.0
-150.0
FE (%)
69.9
67.8
58.3
60.4
57.8
54.1
68.5
55.4
58.7
63.6
66.2
59.7
86.5
92.6
89.4
87.6
136.0
129.0
155.0
154.0
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
440
499
646
584
880
891
1,086
880
752
788
725
764
NMB (%)
-26.6
-32.6
-27.0
-28.6
-26.1
-24.2
-0.3
-16.4
-39.6
-31.9
-43.4
-38.2
-44.7
-19.3
-32.0
-45.7
NME (%)
68.1
57.6
38.2
43.2
71.1
56.3
46.4
51.6
58.4
56.1
65.0
57.5
98.1
92.3
83.2
89.0
FB (%)
-39.9
-25.0
-23.5
-30.1
-40.5
-23.6
9.1
-15.2
-37.9
-30.7
-25.5
-36.0
-29.1
-26.2
-36.6
-37.7
FE (%)
74.7
61.6
41.2
50.0
77.3
62.2
44.7
56.1
64.2
61.9
64.2
63.2
101.0
83.1
80.2
91.7
A-63
-------
Acrolein
Winter
Spring
Summer
Fall
201
190
316
295
-95.5
-95.9
-96.1
-96.8
95.6
95.9
98.9
98.2
-166.0
-168.0
-172.0
-174.0
167.0
169.0
179.0
176.0
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 under-predictions for the Eastern and Western NADP
sites (NMB values range from 1% to -30%). 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 34%). 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 (%)
26.5
-5.3
-26.1
3.0
33.9
6.5
3.1
-3.2
NME (%)
71.6
56.2
61.5
63.9
70.1
59.7
73.9
61.6
FB (%)
10.6
-5.6
22.9
-8.6
24.4
12.4
6.4
-9.9
FE (%)
71.7
64.7
75.7
73.9
72.2
67.4
79.3
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 (%)
4.1
-3.4
-29.5
-7.1
25.0
16.5
-5.2
-8.7
NME (%)
80.1
66.3
63.5
75.3
86.8
73.0
73.8
76.7
FB (%)
2.3
0.5
-24.9
-8.3
25.6
18.2
-1.7
-5.0
FE (%)
82.9
73.5
80.0
84.5
88.8
77.3
81.6
86.7
A-65
-------
Air Quality Modeling Technical Support Document:
Proposed Tier 3 Emission Standards
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
March 2013
B-l
-------
Table B-l. 8-Hour Ozone Design Values for Proposed Tier3 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
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
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
2017
Reference
DV
65.55
57.97
53.22
55.99
56.20
58.54
66.19
58.61
62.22
65.14
54.89
64.91
57.36
67.27
56.05
57.70
58.29
62.84
64.34
63.97
69.88
64.27
63.82
63.09
69.60
60.23
64.21
61.87
70.93
71.06
71.15
80.24
58.44
2017
TierS
Control
DV
64.95
57.32
52.72
55.40
55.56
58.02
65.42
58.05
61.42
64.59
54.30
64.38
56.75
66.46
55.64
57.13
57.65
62.24
64.34
62.94
68.97
63.57
62.75
62.87
68.80
59.75
63.80
61.11
70.93
71.06
71.15
80.24
58.44
2030
Reference
DV
59.90
52.24
57.39
50.88
51.96
54.33
59.64
54.29
56.93
59.97
50.20
61.90
52.76
60.38
52.57
52.92
52.90
58.85
59.80
58.18
64.63
60.26
57.97
57.25
62.28
55.57
60.23
54.79
66.14
65.03
65.05
74.29
54.33
2030
Tier3
Control
DV
58.33
50.60
56.50
49.56
50.62
53.16
57.99
53.05
55.31
58.85
48.96
60.75
51.30
58.61
51.59
51.64
51.52
56.88
59.13
55.02
61.42
58.29
55.06
55.95
60.34
54.51
59.25
53.09
65.10
64.05
64.18
73.34
53.75
B-2
-------
State
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
County
Contra Costa
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
2005
Baseline
DV
73.3
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
2017
Reference
DV
69.20
80.34
83.67
58.14
73.11
71.83
96.17
73.65
52.54
103.32
68.49
44.93
75.33
48.30
76.72
54.16
52.10
80.91
84.22
78.88
110.06
81.64
65.66
121.24
74.91
45.35
66.45
61.91
52.41
67.34
65.39
56.08
68.76
55.21
64.54
2017
TierS
Control
DV
69.20
80.34
83.67
58.14
73.11
71.83
96.17
73.65
52.54
103.32
68.49
44.93
75.33
48.30
76.72
54.16
52.10
80.91
84.22
78.88
110.06
81.64
65.66
121.24
74.91
45.35
66.45
61.91
52.41
67.34
65.39
56.08
68.76
55.21
64.54
2030
Reference
DV
66.02
72.17
77.65
54.49
67.24
66.30
89.54
68.52
49.02
95.82
63.65
42.69
70.47
44.74
71.08
50.21
48.49
72.75
83.98
70.87
108.70
73.70
60.47
118.03
68.28
45.47
62.19
57.25
51.42
60.31
58.53
52.79
64.16
51.89
59.63
2030
Tier3
Control
DV
65.28
70.72
76.50
53.90
66.28
65.49
88.53
67.65
48.42
94.14
62.79
42.28
69.69
44.17
70.07
49.57
47.90
71.45
82.84
69.53
107.01
72.24
59.65
116.12
66.85
45.36
61.37
56.53
51.00
59.79
57.29
52.02
63.46
51.23
58.85
-------
State
California
California
California
California
California
California
California
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Delaware
Delaware
Delaware
D.C.
Florida
Florida
Florida
Florida
Florida
County
Sonoma
Stanislaus
Sutter
Tehama
Tulare
Tuolumne
Ventura
Yolo
Adams
Arapahoe
Boulder
Denver
Douglas
El Paso
Jefferson
La Plata
Larimer
Montezuma
Weld
Fairfield
Hartford
Litchfield
Middlesex
New Haven
New London
Tolland
Kent
New Castle
Sussex
Washington
Alachua
Baker
Bay
Brevard
Broward
2005
Baseline
DV
47.7
84.7
82.0
82.7
103.7
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
80.3
82.3
82.7
84.7
72.0
68.7
78.7
71.3
65.0
2017
Reference
DV
41.23
73.97
72.55
71.07
87.45
69.68
78.38
68.35
62.76
69.04
68.18
66.40
73.83
65.66
74.90
59.90
67.41
67.08
70.47
79.91
70.39
73.22
77.01
78.58
73.23
73.49
67.42
69.83
69.91
72.78
54.86
55.86
63.38
60.16
58.63
2017
TierS
Control
DV
41.23
73.97
72.55
71.07
87.45
69.68
78.38
68.35
62.38
68.51
67.69
65.99
73.27
65.31
74.42
59.83
66.95
66.98
70.18
79.35
69.56
72.30
76.29
77.94
72.58
72.65
66.86
69.18
69.38
71.94
54.18
55.22
62.82
59.64
58.19
2030
Reference
DV
38.07
68.67
65.77
66.00
81.20
64.58
70.36
63.12
60.36
65.96
65.63
63.86
70.45
63.30
72.19
58.85
64.18
65.95
68.33
75.42
64.48
66.91
71.42
73.31
67.68
67.52
62.85
65.61
64.93
67.40
52.76
52.22
59.37
55.85
55.01
2030
Tier3
Control
DV
37.50
67.68
65.00
65.24
80.20
63.76
69.18
62.24
59.11
64.55
64.11
62.54
68.85
62.40
70.58
58.50
62.66
65.57
67.55
73.58
62.33
64.57
69.40
71.35
65.95
65.41
61.46
64.05
63.61
64.93
51.40
50.81
58.08
54.50
53.74
B-4
-------
State
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
County
Collier
Columbia
Duval
Escambia
Highlands
Hillsborough
Holmes
Lake
Lee
Leon
Manatee
Marion
Miami-Dade
Orange
Osceola
Palm Beach
Pasco
Pinellas
Polk
St Lucie
Santa Rosa
Sarasota
Seminole
Volusia
Wakulla
Bibb
Chatham
Chattooga
Clarke
Cobb
Columbia
Coweta
Dawson
De Kalb
Douglas
2005
Baseline
DV
68.3
72.0
77.7
82.7
72.3
80.7
70.3
76.7
70.3
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
2017
Reference
DV
57.17
58.91
64.52
67.70
61.79
68.09
58.04
63.57
59.77
55.58
63.67
57.64
65.13
66.96
58.12
58.07
61.96
59.85
60.67
57.09
66.60
62.52
63.02
54.68
57.77
60.93
57.20
58.98
61.26
62.62
58.98
65.67
57.96
71.15
65.89
2017
TierS
Control
DV
56.64
58.27
63.90
67.00
61.37
67.39
57.50
62.75
59.24
54.95
63.07
56.97
64.73
66.17
57.29
57.66
61.25
59.22
59.97
56.64
65.88
61.88
62.18
54.05
57.20
60.21
56.73
58.37
60.45
61.76
58.26
64.96
57.14
70.30
64.96
2030
Reference
DV
53.29
55.04
60.51
62.19
58.52
62.50
53.80
57.64
55.76
50.76
58.64
54.03
62.50
61.40
52.19
55.01
58.64
55.80
57.21
53.33
61.24
57.09
57.10
49.97
53.08
54.15
52.42
53.76
54.17
55.46
54.32
59.66
50.77
64.10
58.61
2030
Tier3
Control
DV
51.95
53.71
59.06
60.22
57.52
60.48
52.57
55.24
54.37
49.34
56.76
52.50
61.25
59.07
49.98
53.84
57.12
54.14
55.74
52.22
59.43
55.40
54.62
48.44
51.75
52.32
51.28
52.26
51.92
52.87
52.77
57.74
48.43
61.38
55.97
B-5
-------
State
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Idaho
Idaho
Idaho
Idaho
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
County
Fayette
Fulton
Glynn
Gwinnett
Henry
Murray
Muscogee
Paulding
Richmond
Rockdale
Sumter
Ada
Canyon
Elmore
Kootenai
Adams
Champaign
Clark
Cook
Du Page
Effingham
Hamilton
Jersey
Kane
Lake
McHenry
McLean
Macon
Macoupin
Madison
Peoria
Randolph
Rock Island
StClair
Sangamon
2005
Baseline
DV
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
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
2017
Reference
DV
68.56
73.56
54.69
67.74
69.50
62.52
59.28
60.05
64.61
68.05
58.68
70.01
58.98
57.60
58.28
60.15
58.51
55.50
71.01
63.92
59.54
60.25
64.59
65.59
72.06
63.66
60.61
60.42
58.21
70.45
61.66
60.81
54.93
69.40
57.00
2017
TierS
Control
DV
67.78
72.68
54.09
66.87
68.63
61.88
58.54
59.34
63.83
67.14
58.14
69.78
58.77
57.41
57.92
59.78
58.12
55.16
70.71
63.49
59.12
59.81
63.90
65.08
71.78
62.95
60.13
60.01
57.56
69.76
61.24
60.38
54.51
68.71
56.52
2030
Reference
DV
62.75
66.27
51.42
60.02
62.96
57.15
53.80
53.27
59.79
60.72
54.37
67.48
55.97
55.18
55.31
56.68
55.67
53.13
68.60
60.81
55.97
56.21
58.50
61.60
69.34
59.15
56.91
56.71
52.50
64.48
58.90
56.82
51.63
63.40
52.63
2030
Tier3
Control
DV
60.48
63.46
50.10
57.33
60.50
55.69
52.08
51.24
58.17
58.15
53.16
66.81
55.35
54.58
54.53
55.82
54.78
52.21
67.42
59.27
54.95
55.22
56.85
59.87
68.23
57.31
55.76
55.74
50.80
62.57
57.95
55.84
50.65
61.65
51.46
B-6
-------
State
Illinois
Illinois
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
County
Will
Winnebago
Allen
Boone
Carroll
Clark
Delaware
Elkhart
Floyd
Greene
Hamilton
Hancock
Hendricks
Huntington
Jackson
Johnson
Lake
La Porte
Madison
Marion
Morgan
Perry
Porter
Posey
St Joseph
Shelby
Vanderburgh
Vigo
Warrick
Bremer
Clinton
Harrison
Linn
Montgomery
Palo Alto
2005
Baseline
DV
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
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
2017
Reference
DV
62.87
58.03
66.71
67.21
61.57
66.29
63.00
66.45
66.31
66.33
68.56
64.74
63.81
63.17
62.18
65.15
73.60
68.03
62.82
66.28
65.56
68.98
68.55
59.89
66.68
66.41
64.97
61.50
65.85
56.58
59.93
63.17
57.62
54.71
53.10
2017
TierS
Control
DV
62.37
57.51
66.16
66.57
61.05
65.88
62.45
65.90
65.94
65.91
67.90
64.09
63.22
62.66
61.79
64.62
73.19
67.62
62.19
65.62
64.95
68.59
68.23
59.49
66.13
65.87
64.53
61.04
65.49
56.22
59.49
62.76
57.23
54.32
52.81
2030
Reference
DV
59.07
54.24
61.97
62.34
57.19
62.44
58.39
62.12
62.63
62.89
63.51
59.61
59.41
58.82
57.90
61.09
70.72
64.62
57.93
61.46
61.35
64.79
66.11
55.33
62.31
62.10
60.18
59.51
61.74
53.70
56.43
59.96
55.12
51.30
50.56
2030
Tier3
Control
DV
57.48
52.94
60.62
60.72
55.98
61.33
57.14
60.90
61.56
61.93
61.90
57.97
57.92
57.68
56.96
59.75
69.50
63.53
56.45
59.95
59.78
63.84
65.20
54.34
61.07
60.65
59.21
58.47
60.88
52.84
55.37
59.01
54.19
50.39
49.91
B-7
-------
State
Iowa
Iowa
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
County
Polk
Scott
Story
Van Buren
Warren
Douglas
Johnson
Leavenworth
Linn
Sedgwick
Sumner
Trego
Wyandotte
Bell
Boone
Boyd
Bullitt
Campbell
Carter
Christian
Daviess
Edmonson
Fayette
Greenup
Hancock
Hardin
Henderson
Jefferson
Jessamine
Kenton
Livingston
McCracken
McLean
Oldham
Perry
2005
Baseline
DV
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
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
2017
Reference
DV
52.31
59.99
50.67
58.61
52.83
59.71
62.28
63.68
60.04
59.66
60.02
63.14
63.95
55.27
63.33
64.99
63.75
72.06
58.38
61.35
63.67
61.03
58.09
64.64
61.79
63.27
63.67
67.89
62.66
66.51
61.08
61.75
60.97
67.57
58.87
2017
TierS
Control
DV
51.82
59.50
50.23
58.24
52.33
59.14
61.75
63.11
59.57
59.17
59.53
62.89
63.46
54.64
62.94
64.56
63.45
71.62
57.98
60.76
63.31
60.61
57.49
64.23
61.43
62.92
63.32
67.55
62.23
66.05
60.65
61.36
60.64
67.17
58.40
2030
Reference
DV
48.32
56.29
46.89
55.21
48.79
55.32
58.05
59.51
56.18
56.15
56.54
60.63
60.13
50.18
59.29
60.55
60.24
67.96
54.08
57.47
59.95
57.44
53.93
60.46
57.84
59.60
59.57
64.53
61.11
62.22
57.97
59.51
57.12
62.96
55.28
2030
Tier3
Control
DV
47.11
55.18
45.85
54.37
47.65
53.98
56.71
58.11
55.06
55.01
55.43
60.01
58.86
48.75
58.25
59.25
59.34
66.73
53.05
56.23
59.12
56.55
52.59
59.20
57.00
58.68
58.76
63.49
60.19
61.02
57.03
58.66
56.34
61.81
54.23
-------
State
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
County
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
West Baton
Rouge
Cumberland
Hancock
Kennebec
Knox
Oxford
Penobscot
Sagadahoc
York
Anne Arundel
Baltimore
2005
Baseline
DV
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
84.3
72.0
82.0
69.7
75.3
61.0
67.0
68.5
74.0
89.7
85.3
2017
Reference
DV
54.96
59.78
60.93
56.12
59.29
72.41
67.71
64.17
65.57
73.20
81.25
75.48
72.74
70.44
70.36
69.21
63.10
74.97
68.17
67.71
67.91
70.83
66.79
74.97
60.66
69.07
58.38
63.19
52.44
57.54
57.60
62.99
74.28
77.62
2017
TierS
Control
DV
54.52
59.38
60.37
55.67
58.84
72.11
67.47
63.61
65.06
72.92
80.85
75.18
72.38
70.06
70.09
68.92
62.58
74.69
67.80
67.37
67.67
70.56
66.53
74.64
60.00
68.37
57.77
62.51
51.97
57.03
56.99
62.36
73.28
77.11
2030
Reference
DV
51.25
56.05
56.79
52.08
55.82
67.71
64.20
59.59
61.08
69.33
75.47
70.73
69.22
65.08
65.11
64.22
58.81
70.40
63.87
64.18
63.46
66.85
61.52
69.74
55.74
63.82
53.79
58.14
49.50
53.51
53.00
58.40
67.78
73.05
2030
Tier3
Control
DV
50.29
55.18
55.53
51.12
54.87
66.72
63.42
58.33
60.00
68.46
74.16
69.68
68.06
63.99
63.97
63.21
57.73
69.41
62.71
63.08
62.40
65.72
60.53
68.56
54.03
62.06
52.24
56.45
48.42
52.24
51.44
56.77
65.22
71.43
B-9
-------
State
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
County
Calvert
Carroll
Cecil
Charles
Frederick
Garrett
Harford
Kent
Montgomery
Prince Georges
Washington
Barnstable
Berkshire
Bristol
Dukes
Essex
Hampden
Hampshire
Middlesex
Norfolk
Suffolk
Worcester
Allegan
Benzie
Berrien
Cass
Clinton
Genesee
Huron
Ingham
Kalamazoo
Kent
Leelanau
Lenawee
Macomb
2005
Baseline
DV
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
79.3
75.7
76.0
75.3
81.0
75.7
78.7
86.0
2017
Reference
DV
66.97
69.16
74.14
71.30
66.40
64.83
82.78
67.63
70.94
75.68
65.24
72.37
66.86
71.22
72.69
73.31
72.52
70.70
67.10
71.85
68.44
65.88
77.61
69.42
72.02
67.63
62.38
66.70
65.15
63.23
62.98
66.50
65.17
66.87
74.02
2017
TierS
Control
DV
66.19
68.23
73.23
70.47
65.48
64.34
82.15
66.87
70.02
74.68
64.44
71.84
66.18
70.61
72.15
72.84
71.63
69.88
66.34
71.33
67.99
65.08
77.13
68.93
71.57
67.08
61.76
66.09
64.72
62.66
62.43
65.88
64.72
66.40
73.48
2030
Reference
DV
61.40
63.18
69.24
65.83
60.53
60.59
77.93
63.36
65.22
69.56
60.20
67.27
61.73
66.20
67.76
69.63
66.35
64.88
62.37
67.40
64.14
60.47
73.53
65.62
68.11
63.34
58.28
62.42
61.61
59.63
59.09
62.52
61.68
63.26
69.81
2030
Tier3
Control
DV
59.59
60.72
67.06
63.76
58.24
59.56
76.01
61.54
62.54
66.99
58.16
65.73
60.06
64.57
66.10
68.04
64.18
62.85
60.51
65.93
62.66
58.59
72.25
64.34
66.86
62.11
56.83
60.97
60.50
58.34
57.86
61.05
60.53
62.17
68.22
B-10
-------
State
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
Missouri
Missouri
Missouri
Montana
County
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
Ste Genevieve
St Louis
St Louis City
Yellowstone
2005
Baseline
DV
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
79.7
88.0
84.0
59.0
2017
Reference
DV
66.97
62.42
72.85
68.99
68.61
68.88
67.23
68.19
71.09
63.37
56.17
64.67
61.52
67.40
68.06
69.52
53.28
68.05
62.76
57.60
60.69
61.16
70.64
68.01
59.31
71.47
74.24
60.18
64.09
64.74
72.37
69.50
76.25
72.05
55.49
2017
TierS
Control
DV
66.43
61.94
72.37
68.54
68.05
68.31
66.75
67.64
70.61
63.09
55.84
64.34
61.03
66.68
67.61
69.07
52.51
67.63
62.19
56.93
60.20
60.67
70.02
67.33
58.73
70.89
73.50
59.72
63.64
64.21
71.58
69.03
75.56
71.32
55.34
2030
Reference
DV
63.05
59.14
69.30
65.78
64.95
64.70
63.13
64.38
67.23
60.97
53.34
60.17
57.09
60.71
62.66
63.35
47.17
62.19
58.53
52.68
56.62
57.33
65.54
63.07
56.86
66.54
68.86
56.04
59.55
60.79
66.55
65.64
70.91
65.86
53.98
2030
Tier3
Control
DV
61.71
58.00
68.05
64.55
63.66
63.33
61.94
62.92
65.87
60.11
52.60
59.32
56.02
58.93
61.26
61.66
45.32
60.53
57.20
51.14
55.43
56.19
63.99
61.46
55.66
65.06
67.12
54.94
58.55
59.44
64.64
64.58
69.06
64.03
53.62
B-ll
-------
State
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
New Mexico
County
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
Dona Ana
2005
Baseline
DV
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
75.3
2017
Reference
DV
59.50
46.59
55.65
74.34
61.04
63.54
55.44
58.67
58.95
65.31
57.18
67.00
59.43
65.55
59.33
67.97
76.93
75.18
68.36
74.13
77.37
73.54
75.33
75.16
75.81
70.36
78.51
70.23
65.78
66.79
2017
TierS
Control
DV
59.21
46.23
55.60
73.99
60.93
63.45
55.49
58.02
58.29
64.68
56.56
66.24
58.71
64.89
58.68
67.48
76.44
74.46
67.68
73.41
77.10
72.72
74.67
74.53
75.28
69.59
77.79
69.54
65.26
66.45
2030
Reference
DV
57.24
44.17
52.26
69.88
56.64
59.60
50.40
53.26
54.04
61.60
53.05
61.86
54.49
60.76
54.74
63.82
73.59
70.52
63.30
70.24
76.83
68.10
70.63
70.68
71.66
64.93
73.04
65.22
62.28
64.32
2030
Tier3
Control
DV
56.44
43.33
51.66
68.07
55.83
58.85
49.58
51.65
52.40
60.11
51.54
59.93
52.72
59.07
53.17
62.43
72.22
68.64
61.70
68.46
75.95
66.10
68.75
68.70
69.98
62.87
70.99
63.25
61.06
63.52
B-12
-------
State
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
County
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
2005
Baseline
DV
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
2017
Reference
DV
63.88
66.04
62.17
68.24
62.80
69.23
77.02
59.72
63.09
73.52
67.05
63.02
60.06
67.89
61.76
66.54
74.85
58.79
62.88
69.75
69.69
72.48
71.11
65.73
78.41
68.03
60.04
81.82
65.49
60.33
78.92
61.97
57.65
58.71
59.50
2017
TierS
Control
DV
63.72
65.90
61.67
68.15
62.15
69.03
76.66
59.32
62.36
73.07
66.60
62.56
59.63
67.47
61.28
66.04
74.49
58.31
62.43
68.94
69.30
71.77
70.76
65.07
77.95
67.35
59.44
81.42
64.87
59.96
78.51
61.37
57.16
58.14
58.86
2030
Reference
DV
62.42
64.75
58.83
67.12
58.23
67.05
73.67
56.51
58.16
69.96
63.47
59.46
56.54
64.60
57.87
62.54
71.15
55.09
59.49
64.23
66.56
67.13
68.01
61.11
75.90
63.30
55.81
78.20
60.98
57.29
75.41
56.01
53.66
54.16
53.87
2030
Tier3
Control
DV
62.05
64.43
57.65
66.79
56.63
65.99
72.77
55.55
56.24
68.90
62.43
58.33
55.48
63.59
56.77
61.30
70.31
53.95
58.42
62.04
65.64
65.09
66.81
59.49
74.94
61.58
54.31
76.87
59.49
56.38
73.79
54.62
52.54
53.09
52.43
B-13
-------
State
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
Ohio
Ohio
County
Caswell
Chatham
Cumberland
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
2005
Baseline
DV
76.3
73.3
81.7
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
2017
Reference
DV
59.90
58.91
64.46
63.99
60.87
61.70
64.06
62.87
62.67
65.65
64.06
64.41
60.72
61.10
62.06
64.58
64.30
70.93
60.89
62.93
61.10
60.64
68.02
52.69
61.95
64.79
59.48
56.85
53.99
52.41
57.50
57.05
55.99
65.92
76.92
2017
TierS
Control
DV
59.14
58.22
63.67
63.20
60.02
60.98
63.32
62.11
62.03
64.96
63.20
63.88
60.06
60.27
61.52
63.83
63.81
70.25
60.45
62.59
60.43
59.96
67.11
52.11
61.26
64.01
58.81
56.72
53.91
52.14
57.38
57.02
55.94
65.42
76.47
2030
Reference
DV
54.41
53.89
59.15
57.91
54.67
56.29
58.30
57.20
57.86
60.00
57.63
60.25
56.11
55.04
57.61
58.07
60.46
65.11
56.43
59.08
55.89
54.27
61.04
48.51
55.46
59.26
55.34
55.23
52.50
49.88
55.74
56.82
54.81
62.32
72.75
2030
Tier3
Control
DV
52.87
52.27
57.41
56.09
52.62
54.61
56.63
55.38
56.51
58.37
55.57
59.21
54.71
52.92
56.31
56.37
59.35
63.33
55.43
58.33
54.35
52.72
58.90
47.30
53.72
57.26
54.15
54.90
52.30
49.30
55.44
56.73
54.68
61.09
71.56
B-14
-------
State
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
County
Butler
Clark
Clermont
Clinton
Cuyahoga
Delaware
Franklin
Geauga
Greene
Hamilton
Jefferson
Knox
Lake
Lawrence
Licking
Lorain
Lucas
Madison
Mahoning
Medina
Miami
Montgomery
Portage
Preble
Stark
Summit
Trumbull
Warren
Washington
Wood
Adair
Canadian
Cherokee
Cleveland
Comanche
2005
Baseline
DV
83.3
81.0
81.0
82.3
79.7
78.3
86.3
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
2017
Reference
DV
69.72
65.47
68.98
66.66
68.95
64.99
71.43
65.97
65.76
71.07
64.51
63.19
73.69
59.58
63.14
66.02
69.82
65.12
64.34
67.82
61.55
58.97
69.52
60.10
67.63
70.14
69.19
72.12
67.20
67.58
63.87
63.29
64.04
63.14
65.13
2017
TierS
Control
DV
69.18
64.79
68.58
66.13
68.64
64.40
70.74
65.44
65.13
70.52
64.04
62.54
73.32
59.21
62.48
65.75
69.47
64.46
63.71
67.27
60.91
58.37
68.83
59.54
67.08
69.51
68.54
71.47
66.82
67.12
63.51
62.55
63.75
62.48
64.61
2030
Reference
DV
64.95
60.98
64.69
61.50
65.89
60.53
66.69
61.98
61.10
66.21
60.42
58.71
69.85
55.73
58.56
62.59
66.00
60.38
60.34
63.73
57.31
55.49
64.92
56.22
62.90
65.47
64.74
67.19
66.87
64.06
61.02
59.23
61.72
58.91
61.10
2030
Tier3
Control
DV
63.69
59.45
63.54
60.22
64.87
59.12
64.91
60.77
59.65
64.83
59.38
57.19
68.72
54.57
57.03
61.61
65.07
58.79
58.94
62.45
55.82
54.11
63.36
55.01
61.52
63.97
63.29
65.63
66.08
62.96
60.17
57.58
61.01
57.32
59.86
B-15
-------
State
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
County
Creek
Dewey
Kay
Me Clain
Mayes
Oklahoma
Ottawa
Pitts burg
Tulsa
Clackamas
Columbia
Jackson
Lane
Marion
Multnomah
Adams
Allegheny
Armstrong
Beaver
Berks
Blair
Bucks
Cambria
Centre
Chester
Clearfield
Dauphin
Delaware
Erie
Franklin
Greene
Indiana
Lackawanna
Lancaster
Lawrence
2005
Baseline
DV
76.7
72.7
78.0
72.0
78.5
80.0
78.0
72.0
79.3
66.3
58.7
68.0
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
2017
Reference
DV
63.71
61.84
64.49
60.64
67.16
65.96
64.75
61.97
66.64
62.73
55.55
58.39
60.44
59.13
62.65
63.77
71.47
70.63
70.59
67.42
63.48
76.35
64.75
67.03
70.42
65.14
68.91
70.68
70.94
60.59
68.55
68.85
62.17
70.78
60.70
2017
TierS
Control
DV
63.06
61.34
63.93
60.07
66.82
65.20
64.31
61.57
66.08
62.43
55.22
57.88
59.93
58.66
62.90
63.00
70.95
70.12
70.15
66.77
63.00
75.68
64.37
66.51
69.54
64.65
68.38
70.01
70.55
59.85
68.15
68.43
61.55
70.13
60.22
2030
Reference
DV
59.72
58.18
60.81
56.66
64.12
61.05
61.10
58.67
62.82
59.88
52.32
54.12
56.04
54.87
64.68
58.72
65.86
65.03
66.29
62.63
58.76
71.84
61.02
62.32
65.72
60.60
64.55
66.56
67.23
55.64
66.15
64.11
57.80
65.90
56.56
2030
Tier3
Control
DV
58.12
57.00
59.45
55.23
63.32
59.32
60.09
57.71
61.53
58.76
51.66
53.01
54.86
53.62
63.83
56.87
64.58
63.83
65.25
61.12
57.64
69.93
60.11
61.15
63.66
59.46
63.31
64.94
66.21
53.79
65.17
63.05
56.45
64.33
55.41
B-16
-------
State
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Rhode Island
Rhode Island
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 Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Carolina
South Dakota
County
Lehigh
Luzerne
Lycoming
Mercer
Montgomery
Northampton
Perry
Philadelphia
Tioga
Washington
Westmoreland
York
Kent
Providence
Washington
Abbeville
Aiken
Anderson
Barnwell
Berkeley
Charleston
Cherokee
Chester
Chesterfield
Colleton
Darlington
Edgefield
Oconee
Pickens
Richland
Spartanburg
Union
Williamsburg
York
Custer
2005
Baseline
DV
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
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
2017
Reference
DV
69.76
63.17
64.59
67.42
73.64
70.44
65.30
78.62
66.61
68.45
67.47
70.04
71.61
69.99
74.37
63.57
60.50
59.67
59.81
54.79
62.58
58.91
59.80
61.51
58.73
61.38
55.71
58.23
61.60
62.15
65.21
61.90
56.36
60.63
65.04
2017
TierS
Control
DV
69.04
62.54
63.98
66.77
72.95
69.71
64.68
78.01
66.06
68.03
67.00
69.37
70.94
69.28
73.68
62.85
59.75
58.96
59.18
54.32
62.11
58.27
59.06
60.99
58.17
60.77
55.00
57.58
60.84
61.17
64.49
61.30
55.83
59.89
64.89
2030
Reference
DV
64.77
58.69
61.83
63.11
69.21
65.57
60.51
74.67
62.78
65.34
63.12
65.42
66.38
64.74
68.80
58.13
55.65
54.50
55.28
50.25
57.33
53.96
53.78
56.70
54.40
56.33
51.13
53.04
55.86
55.23
60.14
57.00
51.76
54.48
63.24
2030
Tier3
Control
DV
63.07
57.29
60.52
61.65
67.47
63.83
58.98
72.82
61.52
64.34
61.95
63.68
64.59
62.97
67.01
56.51
54.05
52.93
53.82
49.14
56.18
52.56
52.02
55.43
53.14
54.95
49.60
51.61
54.24
52.96
58.58
55.61
50.62
52.73
62.86
B-17
-------
State
South Dakota
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
County
Jackson
Minnehaha
Anderson
Blount
Davidson
Hamilton
Jefferson
Knox
Loudon
Meigs
Rutherford
Sevier
Shelby
Sullivan
Sumner
Williamson
Wilson
Bexar
Brazoria
Brewster
Cameron
Collin
Dallas
Denton
Ellis
El Paso
Galveston
Gregg
Harris
Harrison
Hidalgo
Hood
Hunt
Jefferson
Johnson
2005
Baseline
DV
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
66.0
90.3
88.3
94.0
81.7
77.7
85.0
84.3
100.7
79.0
65.7
83.0
78.0
84.7
87.0
2017
Reference
DV
62.03
56.71
56.94
64.86
59.81
62.16
60.77
63.40
63.01
60.28
58.67
61.25
64.26
69.27
65.10
58.41
60.28
72.36
83.33
56.68
60.33
75.35
76.92
75.80
67.53
68.37
75.38
74.18
90.22
66.93
57.31
65.82
66.40
75.14
68.73
2017
TierS
Control
DV
61.86
56.39
56.12
63.93
58.98
61.37
59.69
62.40
62.25
59.49
57.87
60.33
63.48
68.84
64.22
57.55
59.52
71.76
82.80
56.40
60.10
74.46
76.02
74.73
66.67
67.95
75.12
73.88
89.68
66.51
56.97
64.80
65.94
74.86
67.85
2030
Reference
DV
60.12
54.02
50.59
57.81
54.71
55.96
53.33
56.15
56.43
54.49
53.82
55.21
57.41
66.24
59.66
53.30
56.67
67.88
77.29
54.18
58.99
69.58
71.16
69.20
62.02
65.44
69.80
71.21
83.94
63.26
54.64
59.79
62.85
70.47
63.08
2030
Tier3
Control
DV
59.65
53.28
48.73
55.64
52.92
54.27
50.80
53.77
54.73
52.72
52.01
53.29
55.58
65.27
57.58
51.44
55.00
66.34
75.33
53.50
58.50
67.14
68.63
66.58
59.72
64.57
68.69
70.51
81.88
62.39
53.81
57.31
61.73
69.47
60.86
B-18
-------
State
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
County
Kaufman
Montgomery
Nueces
Orange
Parker
Rockwall
Smith
Tarrant
Travis
Victoria
Webb
Box Elder
Cache
Davis
Salt Lake
San Juan
Tooele
Utah
Washington
Weber
Bennington
Chittenden
Arlington
Caroline
Charles City
Chesterfield
Fairfax
Fauquier
Frederick
Hanover
Henrico
Loudoun
Madison
Page
Prince William
2005
Baseline
DV
74.7
85.0
72.3
78.0
88.7
79.7
81.0
95.3
81.3
72.3
61.3
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
80.3
76.7
90.0
72.7
72.3
81.3
82.0
80.7
77.7
74.0
78.7
2017
Reference
DV
63.93
73.00
64.68
68.09
69.66
67.66
69.81
77.34
67.77
63.68
54.58
68.79
61.69
75.28
74.99
64.16
70.23
71.25
66.97
73.83
60.93
61.23
76.27
65.84
68.84
64.88
77.06
61.04
59.62
68.24
69.33
66.63
63.88
61.19
65.74
2017
TierS
Control
DV
63.48
72.41
64.41
67.80
68.65
67.12
69.44
76.25
67.06
63.39
54.33
68.43
61.37
74.80
74.53
64.04
69.74
70.90
66.96
73.26
60.29
60.72
75.46
65.04
68.30
64.33
76.13
60.35
58.91
67.55
68.68
65.68
63.21
60.56
64.94
2030
Reference
DV
60.41
67.71
61.13
63.71
63.42
63.57
66.63
70.74
62.83
60.32
52.10
65.39
58.66
71.53
71.46
62.29
66.25
67.28
62.34
69.37
56.51
57.94
70.87
60.25
63.94
60.08
71.02
56.51
55.01
62.70
63.79
60.58
59.30
56.88
60.61
2030
Tier3
Control
DV
59.31
65.78
60.40
62.68
60.97
62.18
65.82
68.09
61.06
59.40
51.45
64.39
57.82
70.06
70.20
61.80
64.90
65.45
61.55
67.44
55.00
56.73
68.22
58.31
62.63
58.72
68.34
54.67
53.29
61.09
62.36
58.07
57.70
55.38
58.49
B-19
-------
State
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
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
County
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
Door
Florence
Fond Du Lac
Forest
Jefferson
Kenosha
Kewaunee
2005
Baseline
DV
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
88.7
66.3
73.7
69.5
74.3
84.7
82.7
2017
Reference
DV
61.70
59.60
68.77
60.92
69.95
68.20
70.24
61.06
68.17
60.10
63.36
46.37
59.47
58.01
56.12
62.41
65.87
60.16
63.63
64.05
66.26
63.80
65.12
54.41
63.41
60.47
60.66
63.24
75.43
56.89
63.05
59.83
62.48
78.31
70.99
2017
TierS
Control
DV
61.11
59.17
68.00
60.42
69.11
67.73
70.11
60.96
67.80
59.81
63.08
46.38
59.01
57.57
56.10
61.68
65.44
59.82
63.18
63.63
65.94
63.39
64.72
54.10
63.00
59.94
60.14
62.71
74.85
56.51
62.59
59.45
61.94
78.01
70.51
2030
Reference
DV
57.16
55.98
63.30
57.01
64.47
65.36
69.91
59.97
64.88
57.10
59.35
46.92
55.88
53.62
55.05
57.78
61.55
57.09
59.54
59.93
64.28
60.75
63.23
51.79
59.89
56.66
56.95
59.37
70.80
54.16
59.57
57.24
58.53
75.20
66.83
2030
Tier3
Control
DV
55.93
55.00
60.94
55.92
62.04
64.30
68.81
59.34
63.54
56.36
58.09
46.88
54.88
52.34
54.98
55.96
60.23
56.26
58.53
58.94
63.48
59.76
62.39
51.07
58.80
55.41
55.72
58.19
69.24
53.29
58.39
56.38
57.25
73.91
65.47
B-20
-------
State
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wyoming
Wyoming
Wyoming
County
Manitowoc
Marathon
Milwaukee
Oneida
Outagamie
Ozaukee
Racine
Rock
St Croix
Sauk
Sheboygan
Vernon
Vilas
Walworth
Washington
Waukesha
Campbell
Sublette
Teton
2005
Baseline
DV
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
2017
Reference
DV
73.52
59.78
73.77
59.49
63.04
73.50
73.48
62.19
59.06
58.61
76.59
58.14
59.48
64.64
62.51
64.87
63.86
67.29
57.94
2017
TierS
Control
DV
73.03
59.39
73.37
59.12
62.55
73.17
73.19
61.69
58.67
58.13
76.09
57.60
59.13
64.04
62.06
64.43
63.78
67.18
57.81
2030
Reference
DV
69.30
57.35
70.06
56.93
59.95
70.26
70.45
58.34
56.22
54.91
72.30
54.49
56.97
60.36
59.05
61.80
62.56
65.49
55.96
2030
Tier3
Control
DV
67.84
56.49
68.64
56.09
58.81
69.01
69.21
57.08
55.23
53.80
70.82
53.23
56.16
58.82
57.90
60.66
62.29
65.13
55.52
B-21
-------
Air Quality Modeling Technical Support Document:
Proposed Tier 3 Emission Standards
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
March 2013
C-l
-------
Table C-l. Annual PM2.s Design Values for Proposed Tier 3 Scenarios
(units are ug/m3)
State
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
County
Baldwin
Clay
Colbert
DeKalb
Escambia
Etowah
Houston
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Jefferson
Madison
Mobile
Mobile
Mobile
Montgomery
Morgan
Russell
Shelby
Sumter
Talladega
Tuscaloosa
Walker
Cochise
Coconino
Gila
Maricopa
Maricopa
Maricopa
2005
Baseline
DV
11.44
13.27
12.75
14.13
13.19
14.87
13.22
18.57
15.46
13.52
15.89
17.15
15.10
14.42
14.53
13.83
12.90
12.36
11.51
14.24
13.32
15.73
14.43
11.92
14.51
13.56
13.86
7.00
6.49
8.94
12.17
12.59
9.97
2017
Reference
DV
7.99
9.04
8.62
9.29
9.82
9.97
9.80
12.92
10.97
9.36
10.71
12.26
10.57
9.58
9.82
9.04
9.38
8.91
8.05
10.33
8.90
11.16
9.99
8.35
10.08
9.40
9.42
6.61
6.05
8.15
9.68
10.28
8.05
2017 Tier 3
Control DV1
7.99
9.03
8.61
9.28
9.81
9.96
9.79
12.91
10.96
9.35
10.70
12.25
10.56
9.57
9.81
9.03
9.38
8.90
8.04
10.33
8.89
11.15
9.98
8.35
10.08
9.39
9.41
6.61
6.05
8.15
9.68
10.28
8.05
2030
Reference
DV
8.18
9.09
8.81
9.36
10.09
10.03
9.97
12.89
11.02
9.47
10.70
12.34
10.59
9.58
9.88
9.18
9.39
8.92
8.18
10.40
9.06
11.23
10.04
8.45
10.16
9.51
9.53
6.58
6.04
8.18
9.60
10.22
8.00
2030 Tier 3
Control DV
8.16
9.05
8.77
9.32
10.07
9.99
9.94
12.84
10.98
9.44
10.65
12.29
10.55
9.53
9.84
9.14
9.35
8.89
8.15
10.36
9.02
11.18
9.99
8.42
10.12
9.47
9.50
6.58
6.02
8.15
9.53
10.16
7.95
C-2
-------
State
Arizona
Arizona
Arizona
Arizona
Arizona
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
County
Pima
Pima
Pinal
Pinal
Santa Cruz
Arkansas
Ashley
Crittenden
Faulkner
Garland
Mississippi
Phillips
Polk
Pope
Pulaski
Pulaski
Pulaski
Union
White
Alameda
Alameda
Butte
Calaveras
Colusa
Contra Costa
Fresno
Fresno
Fresno
Imperial
Imperial
Imperial
Inyo
Kern
Kern
Kern
Kings
2005
Baseline
DV
6.04
5.85
7.77
5.71
12.94
12.45
12.83
13.36
12.79
12.40
12.61
12.10
11.65
12.79
13.17
14.05
13.59
12.86
12.57
9.44
9.34
12.73
7.77
7.39
9.47
16.99
16.38
17.17
12.71
8.39
9.20
5.25
18.94
18.68
19.17
17.28
2017
Reference
DV
5.18
5.01
6.97
5.08
12.11
9.28
10.00
9.08
9.67
9.37
8.77
8.61
8.91
9.89
9.66
10.47
10.08
9.90
9.60
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
2017 Tier 3
Control DV1
5.18
5.01
6.97
5.08
12.12
9.27
10.00
9.07
9.66
9.37
8.76
8.60
8.91
9.88
9.65
10.47
10.08
9.90
9.60
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
2030
Reference
DV
5.18
5.00
6.87
5.05
12.13
9.28
10.00
9.12
9.70
9.45
8.75
8.62
9.01
9.97
9.68
10.48
10.11
9.91
9.63
7.98
8.01
10.14
6.21
6.45
7.91
13.65
13.20
13.90
11.16
7.36
8.13
4.82
14.89
14.63
15.19
13.55
2030 Tier 3
Control DV
5.16
4.98
6.85
5.03
12.10
9.25
9.97
9.07
9.66
9.42
8.72
8.59
8.98
9.93
9.64
10.44
10.07
9.87
9.59
7.89
7.94
10.10
6.16
6.43
7.83
13.48
13.04
13.73
11.12
7.34
8.11
4.81
14.69
14.43
14.99
13.37
C-3
-------
State
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
County
Lake
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Los Angeles
Mendocino
Merced
Monterey
Nevada
Nevada
Orange
Orange
Placer
Plumas
Plumas
Riverside
Riverside
Riverside
Sacramento
Sacramento
Sacramento
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Bernardino
San Diego
San Diego
San Diego
San Diego
2005
Baseline
DV
4.62
17.03
18.19
18.00
15.35
17.66
17.92
15.36
16.62
15.21
8.42
6.46
14.78
6.96
5.16
6.71
15.75
11.33
9.80
9.75
11.46
18.91
10.31
20.95
11.88
11.44
10.53
19.67
10.29
19.14
10.77
19.01
11.92
12.27
10.59
12.79
2017
Reference
DV
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
2017 Tier 3
Control DV1
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
2030
Reference
DV
3.94
12.95
13.35
13.14
11.59
13.02
13.27
11.34
12.16
11.10
6.89
5.32
12.00
5.68
3.99
5.46
12.13
9.47
7.93
8.09
9.37
14.99
8.77
16.62
9.97
9.47
8.66
15.68
8.33
15.17
9.35
15.46
9.29
9.92
8.25
10.64
2030 Tier 3
Control DV
3.93
12.87
13.25
13.06
11.51
12.95
13.19
11.27
12.09
11.04
6.81
5.29
11.88
5.64
3.98
5.43
12.07
9.39
7.86
8.07
9.33
14.85
8.73
16.47
9.89
9.37
8.56
15.57
8.28
15.05
9.31
15.34
9.24
9.86
8.20
10.57
C-4
-------
State
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
County
San Diego
San Francisco
San Joaquin
San Luis Obispo
San Luis Obispo
San Mateo
Santa Barbara
Santa Clara
Santa Clara
Shasta
Solano
Sonoma
Stanislaus
Sutter
Tulare
Ventura
Ventura
Ventura
Ventura
Yolo
Adams
Arapahoe
Boulder
Boulder
Delta
Denver
Denver
Elbert
El Paso
El Paso
Larimer
Mesa
Pueblo
San Miguel
Weld
Weld
2005
Baseline
DV
13.46
9.62
12.94
6.92
7.94
9.03
10.37
11.38
10.32
7.41
9.99
8.21
14.21
9.85
18.51
10.68
9.74
11.68
10.69
9.03
10.06
7.96
8.32
6.96
7.44
9.37
9.76
4.40
6.73
7.94
7.33
9.28
7.45
4.65
8.19
8.78
2017
Reference
DV
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
8.10
6.40
7.03
5.96
5.81
7.59
7.89
3.63
5.14
5.97
6.18
7.41
5.93
4.13
6.93
7.44
2017 Tier 3
Control DV1
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
8.13
6.42
7.05
5.97
5.80
7.62
7.91
3.64
5.14
5.97
6.18
7.40
5.93
4.13
6.94
7.46
2030
Reference
DV
10.66
7.95
10.67
5.56
6.33
7.55
8.34
9.87
8.86
5.87
8.43
6.75
11.40
7.89
14.52
8.08
7.71
9.01
7.95
7.53
8.42
6.64
7.33
6.11
6.31
7.84
8.16
3.92
5.72
6.78
6.69
8.03
6.49
4.22
7.29
7.76
2030 Tier 3
Control DV
10.60
7.89
10.55
5.51
6.26
7.49
8.31
9.79
8.79
5.86
8.36
6.72
11.24
7.84
14.32
8.02
7.66
8.95
7.91
7.47
8.39
6.61
7.30
6.10
6.29
7.81
8.13
3.91
5.70
6.76
6.65
7.99
6.47
4.21
7.24
7.73
C-5
-------
State
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Delaware
Delaware
Delaware
Delaware
Delaware
Delaware
Delaware
District Of
Columbia
District Of
Columbia
District Of
Columbia
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
County
Fairfield
Fairfield
Fairfield
Fairfield
Hartford
Litchfield
New Haven
New Haven
New Haven
New Haven
New Haven
New London
Kent
Kent
New Castle
New Castle
New Castle
New Castle
Sussex
Washington
Washington
Washington
Alachua
Alachua
Bay
Brevard
Broward
Broward
Broward
Citrus
Duval
Duval
Escambia
Hillsborough
2005
Baseline
DV
13.21
12.49
12.43
11.48
11.03
8.01
12.12
12.45
13.12
11.17
12.74
10.96
12.61
12.52
13.73
12.92
13.69
14.87
13.39
14.16
14.41
13.99
9.32
9.59
11.46
8.32
8.22
8.18
8.21
9.00
9.90
10.44
11.72
10.74
2017
Reference
DV
8.96
8.42
8.32
7.67
7.42
5.17
8.10
8.27
8.79
7.41
8.48
7.43
7.85
7.90
8.78
8.14
8.77
9.63
8.31
9.10
8.97
8.74
6.63
6.87
8.54
5.82
6.06
5.92
5.92
6.32
7.21
7.80
8.79
7.62
2017 Tier 3
Control DV1
8.96
8.43
8.32
7.67
7.42
5.17
8.10
8.27
8.79
7.40
8.48
7.43
7.85
7.89
8.77
8.13
8.76
9.62
8.31
9.10
8.97
8.74
6.62
6.87
8.54
5.82
6.06
5.92
5.92
6.31
7.21
7.80
8.78
7.62
2030
Reference
DV
9.30
8.85
8.56
7.85
7.92
5.40
8.41
8.67
9.23
7.71
9.08
7.87
8.14
8.20
9.06
8.41
9.04
9.94
8.61
9.40
9.26
9.05
6.56
6.82
8.66
5.63
5.82
5.67
5.61
6.40
7.14
7.74
9.19
7.48
2030 Tier 3
Control DV
9.25
8.80
8.52
7.82
7.88
5.39
8.38
8.63
9.18
7.68
9.03
7.84
8.09
8.15
9.00
8.35
8.97
9.87
8.56
9.35
9.22
9.01
6.54
6.81
8.64
5.62
5.81
5.66
5.60
6.39
7.12
7.72
9.16
7.45
C-6
-------
State
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
County
Hillsborough
Lee
Leon
Manatee
Marion
Miami-Dade
Miami-Dade
Orange
Orange
Palm Beach
Palm Beach
Pinellas
Pinellas
Polk
St. Lucie
Sarasota
Seminole
Volusia
Bibb
Bibb
Chatham
Chatham
Clarke
Clayton
Cobb
Cobb
DeKalb
DeKalb
Dougherty
Floyd
Fulton
Fulton
Glynn
Gwinnett
Hall
Houston
2005
Baseline
DV
10.52
8.36
12.56
8.81
10.11
9.45
8.14
9.61
9.50
7.84
7.70
9.82
9.52
9.53
8.34
8.77
9.51
9.27
16.54
13.94
13.74
13.93
14.90
16.50
16.15
15.42
15.48
15.37
14.46
16.13
15.84
17.43
12.25
16.07
14.16
14.19
2017
Reference
DV
7.50
6.11
9.42
5.92
7.37
6.86
6.56
6.72
6.58
5.99
5.85
6.91
6.68
6.84
5.99
6.05
6.63
6.41
11.68
9.53
9.75
9.99
10.08
11.06
10.98
10.27
9.93
9.92
10.67
11.09
10.20
11.49
9.09
10.81
9.52
9.80
2017 Tier 3
Control DV1
7.50
6.11
9.42
5.92
7.37
6.86
6.56
6.71
6.58
5.99
5.85
6.91
6.68
6.84
5.99
6.05
6.63
6.41
11.68
9.53
9.75
9.99
10.07
11.06
10.98
10.27
9.93
9.92
10.67
11.08
10.20
11.49
9.09
10.81
9.52
9.80
2030
Reference
DV
7.32
5.89
9.47
5.76
7.34
6.39
6.17
6.62
6.49
5.76
5.60
6.84
6.62
6.64
5.74
5.90
6.52
6.32
11.70
9.56
9.72
9.96
10.24
11.12
11.08
10.35
10.03
10.03
10.74
11.15
10.29
11.60
9.11
10.94
9.69
9.83
2030 Tier 3
Control DV
7.29
5.88
9.44
5.75
7.32
6.38
6.15
6.59
6.47
5.75
5.59
6.82
6.60
6.63
5.72
5.88
6.49
6.31
11.65
9.52
9.69
9.93
10.18
11.04
11.00
10.28
9.96
9.97
10.71
11.10
10.22
11.52
9.09
10.86
9.65
9.80
C-7
-------
State
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
County
Lowndes
Muscogee
Muscogee
Muscogee
Paulding
Richmond
Richmond
Walker
Washington
Wilkinson
Ada
Bannock
Benewah
Canyon
Franklin
Idaho
Shoshone
Adams
Champaign
Champaign
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
Cook
DuPage
Jersey
Kane
Kane
Lake
McHenry
2005
Baseline
DV
12.58
14.94
15.39
14.16
14.12
15.61
15.68
15.49
15.14
15.27
8.41
7.66
9.59
8.46
7.70
9.58
12.08
12.50
12.50
12.53
15.21
14.81
15.75
15.03
14.89
14.77
15.24
12.78
12.76
15.48
13.82
12.89
13.32
14.34
11.81
12.40
2017
Reference
DV
9.61
10.47
10.93
10.06
9.21
11.26
11.34
10.31
10.84
10.72
7.62
6.99
8.60
7.49
6.76
8.82
10.67
9.29
8.71
8.73
11.35
10.90
11.37
10.76
10.71
10.98
10.99
9.15
9.12
11.08
10.09
9.25
9.78
10.51
8.59
9.12
2017 Tier 3
Control DV1
9.60
10.47
10.92
10.05
9.21
11.25
11.34
10.30
10.84
10.72
7.62
7.00
8.61
7.49
6.75
8.83
10.69
9.28
8.70
8.72
11.35
10.89
11.36
10.75
10.72
10.98
11.00
9.13
9.10
11.08
10.08
9.24
9.77
10.51
8.57
9.11
2030
Reference
DV
9.61
10.55
10.99
10.12
9.26
11.49
11.55
10.40
10.91
10.75
7.54
7.01
8.81
7.32
6.67
8.97
11.05
9.31
8.78
8.81
11.19
10.72
11.30
10.64
10.56
10.80
10.83
9.16
9.14
10.96
10.06
9.33
9.76
10.50
8.74
9.15
2030 Tier 3
Control DV
9.59
10.51
10.95
10.09
9.22
11.46
11.52
10.35
10.87
10.72
7.51
7.00
8.79
7.28
6.61
8.96
11.04
9.25
8.72
8.75
11.11
10.64
11.20
10.55
10.47
10.71
10.73
9.09
9.07
10.87
9.96
9.24
9.66
10.40
8.67
9.06
C-8
-------
State
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
County
McLean
Macon
Madison
Madison
Madison
Peoria
Randolph
Rock Island
Saint Clair
Saint Clair
Sangamon
Will
Will
Winnebago
Allen
Allen
Clark
Delaware
Dubois
Floyd
Henry
Howard
Knox
Lake
Lake
Lake
Lake
Lake
La Porte
La Porte
Madison
Marion
Marion
Marion
Marion
Marion
2005
Baseline
DV
12.39
13.24
16.72
14.01
14.32
13.34
13.11
12.01
15.58
14.29
13.13
13.63
11.52
13.57
13.67
13.55
16.44
13.69
15.19
14.85
13.64
13.93
14.03
14.33
13.83
14.02
14.05
13.89
12.49
12.69
13.97
14.24
15.26
14.71
16.05
15.90
2017
Reference
DV
8.91
9.55
12.01
10.16
10.42
9.73
9.15
8.92
11.09
10.12
9.73
9.95
8.24
10.04
10.13
10.06
10.49
9.39
9.68
9.32
9.34
9.90
9.09
10.70
10.33
10.62
10.53
10.36
9.17
9.32
9.63
9.60
10.47
10.00
11.12
10.97
2017 Tier 3
Control DV1
8.89
9.54
12.00
10.14
10.41
9.71
9.14
8.91
11.08
10.11
9.72
9.95
8.23
10.03
10.12
10.05
10.48
9.39
9.66
9.31
9.33
9.89
9.08
10.69
10.32
10.61
10.52
10.35
9.16
9.31
9.62
9.59
10.46
10.00
11.12
10.97
2030
Reference
DV
8.94
9.60
12.04
10.28
10.54
9.74
9.27
8.95
11.13
10.24
9.83
9.87
8.19
10.20
10.07
9.99
10.61
9.48
9.80
9.43
9.43
9.91
9.20
10.54
10.19
10.48
10.39
10.20
9.07
9.22
9.70
9.66
10.50
10.04
11.14
11.00
2030 Tier 3
Control DV
8.87
9.54
11.93
10.11
10.37
9.67
9.21
8.89
11.02
10.16
9.76
9.78
8.13
10.11
9.98
9.90
10.55
9.42
9.74
9.38
9.37
9.84
9.15
10.47
10.12
10.41
10.32
10.13
9.01
9.16
9.63
9.59
10.43
9.97
11.06
10.92
C-9
-------
State
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
Iowa
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
County
Porter
Porter
St. Joseph
St. Joseph
St. Joseph
Spencer
Tippecanoe
Vanderburgh
Vanderburgh
Vanderburgh
Vigo
Vigo
Black Hawk
Clinton
Johnson
Linn
Montgomery
Muscatine
Palo Alto
Polk
Polk
Polk
Pottawattamie
Scott
Scott
Scott
Van Buren
Woodbury
Wright
Johnson
Johnson
Johnson
Linn
Sedgwick
Sedgwick
Sedgwick
2005
Baseline
DV
12.66
13.21
13.29
13.69
12.82
14.32
13.70
14.69
14.82
14.99
13.99
13.46
11.16
12.52
12.08
10.79
10.02
12.92
9.53
10.41
9.95
10.64
11.13
11.86
11.64
14.42
10.84
10.32
10.37
10.59
11.10
9.68
10.47
10.26
10.29
10.36
2017
Reference
DV
9.27
9.70
10.23
10.56
9.85
8.87
9.70
10.07
10.15
10.31
9.25
8.82
8.49
9.33
9.28
8.20
7.78
9.77
7.54
8.01
7.67
8.17
8.83
8.82
8.65
10.93
8.30
8.29
8.03
8.07
8.49
7.39
8.22
7.97
7.99
8.06
2017 Tier 3
Control DV1
9.26
9.69
10.22
10.55
9.84
8.85
9.69
10.06
10.14
10.30
9.25
8.81
8.47
9.32
9.26
8.18
7.77
9.76
7.53
8.00
7.66
8.16
8.82
8.81
8.64
10.92
8.29
8.28
8.02
8.06
8.48
7.38
8.21
7.97
7.99
8.05
2030
Reference
DV
9.15
9.56
10.13
10.46
9.74
8.96
9.69
10.16
10.22
10.40
9.33
8.88
8.61
9.37
9.28
8.20
7.72
9.82
7.54
7.99
7.64
8.16
8.73
8.85
8.68
10.97
8.27
8.27
8.03
8.13
8.53
7.43
8.25
8.14
8.17
8.23
2030 Tier 3
Control DV
9.09
9.50
10.05
10.37
9.66
8.91
9.63
10.10
10.16
10.35
9.27
8.83
8.55
9.31
9.22
8.14
7.68
9.76
7.50
7.93
7.59
8.10
8.68
8.79
8.62
10.89
8.22
8.23
7.98
8.08
8.48
7.39
8.21
8.10
8.12
8.19
C-10
-------
State
Kansas
Kansas
Kansas
Kansas
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
County
Shawnee
Shawnee
Sumner
Wyandotte
Wyandotte
Bell
Boyd
Bullitt
Campbell
Carter
Christian
Daviess
Fayette
Fayette
Franklin
Hardin
Henderson
Jefferson
Jefferson
Jefferson
Jefferson
Kenton
Laurel
McCracken
Madison
Perry
Pike
Warren
Caddo
Calcasieu
Calcasieu
Concordia
East Baton Rouge
East Baton Rouge
Iberville
Iberville
2005
Baseline
DV
10.79
10.93
9.89
12.73
10.93
14.10
14.49
14.92
13.67
12.22
13.20
14.10
14.36
14.87
13.37
13.58
13.93
15.55
15.35
15.31
14.74
14.39
12.55
13.41
13.61
13.21
13.49
13.83
12.53
10.58
11.07
11.42
13.38
12.08
12.90
11.02
2017
Reference
DV
8.49
8.66
7.85
9.79
8.33
8.92
9.05
9.41
8.46
7.32
8.36
8.57
8.93
9.34
8.21
8.34
9.16
9.74
9.59
9.56
9.12
9.05
7.73
8.83
8.31
8.28
8.25
8.63
9.46
8.19
8.55
8.48
10.36
9.36
9.86
8.19
2017 Tier 3
Control DV1
8.48
8.66
7.85
9.78
8.32
8.92
9.04
9.40
8.45
7.32
8.35
8.56
8.91
9.33
8.19
8.33
9.15
9.74
9.58
9.54
9.11
9.04
7.73
8.82
8.30
8.28
8.25
8.62
9.46
8.18
8.55
8.48
10.35
9.35
9.86
8.19
2030
Reference
DV
8.52
8.70
7.95
9.84
8.39
9.14
9.27
9.52
8.54
7.52
8.50
8.67
9.04
9.47
8.31
8.47
9.24
9.88
9.71
9.65
9.24
9.14
7.92
8.95
8.48
8.46
8.46
8.81
9.42
8.10
8.41
8.38
10.08
9.06
9.67
8.07
2030 Tier 3
Control DV
8.48
8.66
7.91
9.78
8.34
9.11
9.24
9.47
8.49
7.49
8.46
8.63
8.98
9.41
8.26
8.43
9.19
9.83
9.66
9.60
9.19
9.09
7.89
8.91
8.44
8.44
8.43
8.78
9.37
8.03
8.32
8.35
9.99
8.98
9.62
8.04
C-ll
-------
State
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
County
Jefferson
Lafayette
Ouachita
Rapides
Tangipahoa
Terrebonne
West Baton
Rouge
Androscoggin
Aroostook
Aroostook
Cumberland
Cumberland
Hancock
Kennebec
Oxford
Penobscot
Anne Arundel
Anne Arundel
Anne Arundel
Baltimore
Baltimore
Cecil
Harford
Montgomery
Prince George's
Prince George's
Washington
Baltimore (City)
Baltimore (City)
Baltimore (City)
Baltimore (City)
Berkshire
Bristol
Essex
Essex
2005
Baseline
DV
11.52
11.08
11.97
11.03
12.03
10.74
13.51
9.90
9.74
8.27
11.06
11.13
5.76
9.99
10.13
9.12
11.91
14.82
14.57
13.77
14.76
12.68
12.51
12.47
12.24
13.03
13.70
14.12
14.38
15.76
15.63
10.65
9.58
9.03
9.10
2017
Reference
DV
7.97
8.22
9.19
8.27
8.67
7.79
10.47
6.93
8.77
6.88
7.60
7.71
4.32
7.08
7.74
6.75
7.51
9.87
9.68
8.82
9.70
8.04
7.86
7.99
7.82
8.27
8.86
9.23
9.31
10.39
10.37
7.46
6.73
6.50
6.56
2017 Tier 3
Control DV1
7.97
8.21
9.19
8.27
8.66
7.78
10.46
6.93
8.77
6.89
7.61
7.71
4.32
7.08
7.74
6.75
7.50
9.87
9.68
8.81
9.70
8.02
7.85
7.98
7.81
8.27
8.85
9.22
9.30
10.39
10.36
7.46
6.73
6.49
6.55
2030
Reference
DV
7.96
8.18
9.16
8.22
8.65
7.78
10.18
7.64
8.96
7.21
8.45
8.53
4.46
7.78
8.31
7.22
7.80
10.40
10.22
9.16
10.28
8.29
8.27
8.29
8.16
8.57
9.20
9.72
9.72
11.02
10.99
8.06
6.89
6.70
6.86
2030 Tier 3
Control DV
7.87
8.15
9.13
8.19
8.59
7.74
10.09
7.61
8.95
7.20
8.42
8.50
4.45
7.76
8.29
7.21
7.76
10.34
10.16
9.11
10.23
8.22
8.22
8.25
8.12
8.52
9.15
9.67
9.68
10.96
10.94
8.02
6.86
6.68
6.83
C-12
-------
State
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Minnesota
County
Essex
Hampden
Hampden
Hampden
Plymouth
Suffolk
Suffolk
Suffolk
Suffolk
Worcester
Worcester
Allegan
Bay
Berrien
Genesee
Ingham
Kalamazoo
Kent
Macomb
Missaukee
Monroe
Muskegon
Oakland
Ottawa
Saginaw
St. Clair
Washtenaw
Washtenaw
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Wayne
Cass
2005
Baseline
DV
9.58
9.85
12.17
11.85
9.87
12.34
11.86
10.88
13.07
10.55
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
12.30
13.88
14.52
15.88
14.57
14.32
13.39
17.50
14.67
5.70
2017
Reference
DV
6.87
6.85
8.41
8.20
7.06
8.94
8.50
7.85
9.45
7.31
7.81
8.53
8.13
8.57
8.48
8.89
9.42
9.35
9.33
6.46
9.74
8.55
9.83
9.06
7.92
10.08
8.79
10.03
10.52
11.42
10.56
10.51
9.50
12.61
10.65
4.89
2017 Tier 3
Control DV1
6.86
6.84
8.41
8.20
7.06
8.94
8.49
7.85
9.45
7.31
7.81
8.52
8.12
8.56
8.47
8.88
9.40
9.34
9.33
6.45
9.73
8.53
9.82
9.05
7.91
10.07
8.78
10.02
10.51
11.41
10.56
10.50
9.49
12.61
10.64
4.89
2030
Reference
DV
7.21
7.23
9.05
8.81
7.31
9.30
8.85
8.11
9.87
7.76
8.36
8.63
8.12
8.56
8.46
8.85
9.43
9.37
9.32
6.51
9.75
8.60
9.79
9.11
7.90
10.11
8.75
9.96
10.39
11.39
10.53
10.49
9.46
12.58
10.57
4.91
2030 Tier 3
Control DV
7.18
7.20
9.01
8.77
7.28
9.25
8.81
8.08
9.82
7.73
8.32
8.56
8.07
8.49
8.39
8.78
9.35
9.28
9.25
6.47
9.66
8.53
9.72
9.02
7.85
10.06
8.68
9.85
10.29
11.32
10.46
10.42
9.38
12.49
10.48
4.89
C-13
-------
State
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
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
County
Dakota
Hennepin
Hennepin
Hennepin
Hennepin
Hennepin
Hennepin
Mille Lacs
Olmsted
Ramsey
Ramsey
Ramsey
Saint Louis
Saint Louis
Saint Louis
Scott
Stearns
Adams
Bolivar
DeSoto
Forrest
Harrison
Hinds
Jackson
Jones
Lauderdale
Lee
Lowndes
Pearl River
Warren
Boone
Buchanan
Cass
Cedar
Clay
Greene
2005
Baseline
DV
9.30
9.76
9.14
9.59
9.54
9.56
9.33
6.54
10.13
11.32
11.02
9.63
6.10
6.19
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
2017
Reference
DV
7.28
7.56
7.10
7.42
7.39
7.42
7.25
5.43
7.87
8.85
8.49
7.52
5.12
5.06
6.09
7.12
6.97
8.34
9.05
8.39
9.79
8.74
8.95
8.42
10.29
9.23
8.51
8.94
8.79
9.08
8.89
10.15
8.20
8.44
8.50
8.83
2017 Tier 3
Control DV1
7.27
7.55
7.09
7.40
7.38
7.41
7.24
5.43
7.85
8.84
8.49
7.51
5.11
5.06
6.08
7.11
6.96
8.33
9.05
8.39
9.78
8.74
8.95
8.41
10.28
9.23
8.50
8.93
8.79
9.07
8.88
10.14
8.19
8.43
8.49
8.82
2030
Reference
DV
7.39
7.69
7.21
7.54
7.52
7.54
7.36
5.44
7.97
9.08
8.72
7.65
5.22
5.15
6.23
7.19
7.02
8.24
9.10
8.44
9.95
8.83
9.02
8.62
10.40
9.34
8.63
9.08
8.83
8.97
8.95
10.13
8.20
8.49
8.51
8.94
2030 Tier 3
Control DV
7.34
7.64
7.16
7.48
7.47
7.48
7.31
5.42
7.92
8.99
8.63
7.59
5.21
5.13
6.21
7.14
6.98
8.20
9.07
8.40
9.91
8.79
8.98
8.57
10.36
9.31
8.60
9.04
8.78
8.94
8.90
10.07
8.16
8.45
8.45
8.90
C-14
-------
State
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nevada
Nevada
County
Jackson
Jefferson
Monroe
Saint Charles
Sainte Genevieve
Saint Louis
Saint Louis
St. Louis City
St. Louis City
St. Louis City
St. Louis City
Cascade
Flathead
Flathead
Gallatin
Lake
Lake
Lewis and Clark
Lincoln
Missoula
Ravalli
Rosebud
Sanders
Silver Bow
Yellowstone
Cass
Douglas
Douglas
Hall
Lancaster
Lincoln
Sarpy
Scotts Bluff
Washington
Clark
Clark
2005
Baseline
DV
12.78
13.79
10.87
13.29
13.34
13.04
13.46
14.27
14.36
13.44
14.56
5.72
9.99
8.58
4.38
9.06
9.00
8.20
14.93
10.52
9.01
6.58
6.75
10.14
8.14
9.99
9.88
9.85
7.95
8.90
7.57
9.79
6.04
9.29
4.02
5.75
2017
Reference
DV
9.75
10.04
8.02
9.58
9.51
9.37
9.54
10.20
10.18
9.49
10.31
5.02
8.54
7.29
4.15
7.82
7.72
7.20
12.62
9.15
7.91
6.23
6.05
8.71
6.93
7.87
7.81
7.79
6.47
6.88
6.57
7.71
5.37
7.49
3.66
4.98
2017 Tier 3
Control DV1
9.74
10.03
8.01
9.57
9.50
9.36
9.53
10.20
10.17
9.48
10.30
5.03
8.55
7.30
4.16
7.83
7.74
7.22
12.63
9.15
7.92
6.24
6.06
8.74
6.93
7.86
7.81
7.78
6.46
6.88
6.57
7.70
5.38
7.48
3.66
4.99
2030
Reference
DV
9.80
10.06
8.04
9.68
9.60
9.37
9.54
10.23
10.19
9.50
10.32
5.15
8.76
7.51
4.17
8.07
8.01
7.45
13.02
9.32
8.13
6.17
6.22
9.01
7.14
7.82
7.71
7.70
6.46
6.89
6.61
7.63
5.35
7.42
3.63
5.00
2030 Tier 3
Control DV
9.74
9.99
7.99
9.53
9.55
9.30
9.47
10.15
10.09
9.41
10.23
5.15
8.75
7.50
4.18
8.06
8.00
7.44
13.00
9.28
8.11
6.17
6.21
9.01
7.12
7.78
7.67
7.66
6.44
6.85
6.59
7.58
5.34
7.38
3.61
4.96
C-15
-------
State
Nevada
Nevada
Nevada
Nevada
New Hampshire
New Hampshire
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 Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Mexico
New Mexico
New Mexico
New Mexico
County
Clark
Clark
Clark
Washoe
Belknap
Cheshire
Coos
Graf ton
Hillsborough
Hillsborough
Hillsborough
Merrimack
Rockingham
Sullivan
Atlantic
Bergen
Camden
Camden
Essex
Gloucester
Hudson
Mercer
Mercer
Middlesex
Morris
Morris
Ocean
Passaic
Union
Union
Union
Warren
Bernalillo
Bernalillo
Chaves
Dona Ana
2005
Baseline
DV
9.44
3.67
8.49
8.11
7.28
11.53
10.24
8.43
10.18
10.01
6.27
9.72
9.00
9.86
11.47
13.09
13.31
13.51
13.27
13.46
14.24
12.71
11.14
12.15
11.50
10.21
10.92
12.88
14.94
13.32
13.06
12.72
7.03
6.64
6.54
9.95
2017
Reference
DV
8.13
3.31
7.31
6.40
5.22
8.07
8.08
6.16
7.21
7.13
4.38
6.87
6.42
7.10
7.13
9.05
8.67
8.71
8.90
8.65
9.81
8.32
7.17
8.08
7.60
6.73
7.01
8.76
10.04
8.88
8.61
8.41
5.82
5.49
5.84
8.78
2017 Tier 3
Control DV1
8.14
3.31
7.32
6.40
5.22
8.07
8.08
6.16
7.21
7.13
4.38
6.87
6.42
7.10
7.14
9.05
8.66
8.71
8.90
8.64
9.81
8.32
7.17
8.08
7.60
6.73
7.01
8.76
10.05
8.89
8.61
8.40
5.82
5.49
5.84
8.78
2030
Reference
DV
8.01
3.30
7.26
6.68
5.46
8.81
8.50
6.60
7.58
7.55
4.60
7.21
6.82
7.66
7.61
8.87
8.90
8.92
8.84
8.91
9.71
8.58
7.38
8.28
7.80
7.01
7.17
8.65
10.01
8.81
8.61
8.72
5.80
5.47
5.86
8.71
2030 Tier 3
Control DV
7.95
3.29
7.21
6.64
5.45
8.77
8.48
6.57
7.56
7.52
4.59
7.18
6.80
7.62
7.59
8.82
8.85
8.86
8.79
8.85
9.65
8.53
7.34
8.23
7.76
6.98
7.13
8.60
9.95
8.75
8.56
8.67
5.78
5.45
5.85
8.68
C-16
-------
State
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
County
Dona Ana
Grant
Sandoval
Sandoval
San Juan
Santa Fe
Albany
Bronx
Bronx
Bronx
Chautauqua
Erie
Erie
Essex
Kings
Monroe
Nassau
New York
New York
New York
New York
Niagara
Onondaga
Orange
Queens
Richmond
Richmond
St. Lawrence
Steuben
Suffolk
Westchester
Alamance
Buncombe
Caswell
Catawba
Chatham
2005
Baseline
DV
6.31
5.93
5.00
7.99
5.92
4.76
11.83
15.43
13.09
13.45
9.80
12.62
12.64
5.94
14.20
10.64
11.66
16.18
14.80
13.61
15.41
11.96
10.08
10.99
12.18
13.31
11.59
7.29
9.00
11.52
11.73
13.94
12.60
13.19
15.31
11.99
2017
Reference
DV
5.64
5.56
4.19
7.23
5.30
4.29
8.54
11.00
8.83
9.54
6.47
8.83
8.80
4.51
9.82
7.73
7.84
11.32
10.22
9.60
10.75
8.69
6.94
7.43
8.29
8.87
7.71
5.67
5.97
7.66
7.85
8.71
8.02
8.05
9.52
7.39
2017 Tier 3
Control DV1
5.64
5.56
4.19
7.23
5.30
4.29
8.55
11.01
8.83
9.54
6.46
8.83
8.79
4.51
9.82
7.73
7.84
11.33
10.22
9.61
10.76
8.69
6.94
7.43
8.29
8.87
7.71
5.67
5.97
7.66
7.85
8.71
8.02
8.05
9.51
7.39
2030
Reference
DV
5.56
5.54
4.17
7.23
5.34
4.28
9.40
10.87
8.72
9.36
6.69
9.09
9.05
4.67
9.71
7.96
7.86
11.19
10.12
9.43
10.64
8.89
7.40
7.75
8.31
8.78
7.66
5.97
6.28
7.70
7.85
8.97
8.19
8.34
9.74
7.58
2030 Tier 3
Control DV
5.55
5.53
4.15
7.21
5.32
4.27
9.35
10.80
8.67
9.30
6.66
9.04
9.00
4.66
9.65
7.91
7.82
11.12
10.06
9.37
10.58
8.85
7.37
7.71
8.27
8.73
7.61
5.96
6.26
7.66
7.81
8.93
8.16
8.31
9.70
7.56
C-17
-------
State
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
Ohio
County
Cumberland
Davidson
Duplin
Durham
Edgecombe
Forsyth
Gaston
Guilford
Haywood
Jackson
Lenoir
McDowell
Martin
Mecklenburg
Mecklenburg
Mecklenburg
Mitchell
Montgomery
New Hanover
Onslow
Orange
Pitt
Robeson
Rowan
Swain
Wake
Watauga
Wayne
Billings
Burke
Burke
Burleigh
Cass
McKenzie
Mercer
Athens
2005
Baseline
DV
13.73
15.17
11.30
13.57
12.37
14.28
14.26
13.79
12.98
12.09
11.12
14.24
10.86
15.31
14.74
14.80
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
5.78
6.61
7.72
5.01
6.04
12.39
2017
Reference
DV
9.15
9.32
7.29
8.62
8.05
8.62
8.78
8.49
8.83
7.75
7.18
9.23
6.98
9.63
9.16
9.22
7.93
7.72
6.43
7.08
8.18
7.54
8.43
8.75
8.11
8.61
7.18
8.61
4.32
5.63
5.48
5.92
6.58
4.73
5.66
7.39
2017 Tier 3
Control DV1
9.15
9.32
7.29
8.62
8.04
8.62
8.78
8.49
8.83
7.75
7.18
9.23
6.98
9.63
9.16
9.21
7.93
7.72
6.42
7.08
8.18
7.54
8.43
8.75
8.11
8.61
7.18
8.61
4.32
5.63
5.48
5.92
6.58
4.73
5.66
7.38
2030
Reference
DV
9.31
9.78
7.43
8.81
8.24
8.94
9.11
8.78
9.02
7.96
7.33
9.47
7.14
10.04
9.54
9.59
8.17
7.94
6.54
7.22
8.36
7.71
8.46
9.15
8.30
8.81
7.39
8.75
4.30
5.63
5.48
5.94
6.57
4.73
5.67
7.67
2030 Tier 3
Control DV
9.28
9.74
7.41
8.77
8.22
8.90
9.07
8.74
8.99
7.93
7.31
9.44
7.13
9.99
9.50
9.55
8.15
7.92
6.52
7.20
8.32
7.69
8.43
9.11
8.27
8.77
7.37
8.73
4.29
5.63
5.48
5.93
6.55
4.73
5.67
7.64
C-18
-------
State
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
County
Butler
Butler
Butler
Clark
Clermont
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Cuyahoga
Franklin
Franklin
Franklin
Greene
Hamilton
Hamilton
Hamilton
Hamilton
Hamilton
Hamilton
Hamilton
Jefferson
Jefferson
Lake
Lawrence
Lorain
Lorain
Lucas
Lucas
Lucas
Mahoning
Mahoning
Montgomery
Montgomery
2005
Baseline
DV
15.74
15.36
14.90
14.64
14.15
15.46
13.76
17.37
16.47
17.11
15.97
14.14
15.27
15.08
14.33
13.36
14.84
17.29
15.50
16.85
15.55
16.17
17.54
15.41
16.51
13.02
15.14
13.87
12.78
14.38
13.95
14.08
14.68
15.12
14.58
15.54
2017
Reference
DV
10.22
10.37
9.93
9.83
8.92
10.59
9.36
12.10
11.35
11.82
10.95
9.73
10.08
9.96
9.44
8.62
9.50
11.15
9.79
10.96
10.07
10.33
11.38
9.56
10.21
8.91
9.79
9.41
8.98
10.15
9.80
9.99
9.70
10.13
9.59
10.29
2017 Tier 3
Control DV1
10.21
10.36
9.92
9.82
8.90
10.59
9.35
12.10
11.35
11.82
10.94
9.73
10.07
9.95
9.42
8.61
9.49
11.14
9.78
10.96
10.07
10.32
11.37
9.57
10.21
8.91
9.78
9.40
8.98
10.14
9.78
9.98
9.69
10.13
9.58
10.28
2030
Reference
DV
10.32
10.53
10.08
9.96
9.04
10.60
9.37
12.13
11.36
11.81
10.96
9.75
10.22
10.11
9.56
8.75
9.61
11.27
9.89
11.06
10.19
10.43
11.50
9.73
10.40
8.95
9.95
9.52
9.06
10.14
9.79
9.98
9.84
10.28
9.73
10.44
2030 Tier 3
Control DV
10.25
10.46
10.01
9.90
8.98
10.54
9.32
12.05
11.30
11.74
10.90
9.70
10.13
10.02
9.48
8.69
9.54
11.19
9.82
10.99
10.11
10.36
11.41
9.69
10.36
8.91
9.91
9.46
9.01
10.04
9.70
9.89
9.79
10.22
9.66
10.36
C-19
-------
State
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
County
Portage
Preble
Scioto
Stark
Stark
Summit
Summit
Trumbull
Caddo
Cherokee
Kay
Lincoln
Mayes
Mayes
Muskogee
Oklahoma
Oklahoma
Ottawa
Pitts burg
Sequoyah
Tulsa
Tulsa
Jackson
Jackson
Klamath
Lane
Lane
Lane
Lane
Multnomah
Multnomah
Union
Adams
Allegheny
Allegheny
Allegheny
2005
Baseline
DV
13.37
13.70
14.65
16.26
15.23
15.17
14.26
14.53
9.22
11.79
10.26
10.28
11.70
11.44
11.89
10.07
9.86
11.69
11.09
12.99
11.52
11.37
10.32
5.41
11.20
8.64
6.35
7.56
11.93
9.13
8.35
8.35
13.05
15.24
14.66
20.31
2017
Reference
DV
8.96
9.09
9.16
10.65
10.26
10.39
9.78
9.77
7.30
9.19
8.37
8.08
9.31
9.04
9.57
7.72
7.56
9.19
8.68
10.25
9.07
9.00
7.69
4.25
8.56
6.40
4.90
5.76
9.44
6.25
5.83
6.76
8.38
9.95
9.46
13.18
2017 Tier 3
Control DV1
8.95
9.08
9.16
10.64
10.25
10.39
9.78
9.77
7.29
9.19
8.36
8.07
9.30
9.03
9.57
7.72
7.56
9.18
8.68
10.25
9.07
9.00
7.68
4.24
8.55
6.40
4.89
5.76
9.44
6.25
5.83
6.76
8.37
9.96
9.46
13.20
2030
Reference
DV
9.03
9.23
9.37
10.77
10.34
10.48
9.84
9.92
7.36
9.27
8.45
8.13
9.37
9.10
9.61
7.75
7.59
9.27
8.73
10.32
9.13
9.07
9.33
4.96
10.04
7.86
5.74
6.89
10.78
7.92
7.23
7.31
8.54
9.99
9.48
13.13
2030 Tier 3
Control DV
8.98
9.17
9.34
10.71
10.26
10.41
9.77
9.87
7.33
9.24
8.38
8.10
9.34
9.07
9.58
7.70
7.54
9.24
8.70
10.28
9.08
9.02
9.30
4.95
10.02
7.83
5.72
6.87
10.75
7.91
7.21
7.28
8.49
9.94
9.43
13.07
C-20
-------
State
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
Rhode Island
South Carolina
South Carolina
County
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Allegheny
Beaver
Berks
Bucks
Cambria
Centre
Chester
Cumberland
Dauphin
Delaware
Erie
Lackawanna
Lancaster
Lehigh
Luzerne
Mercer
Northampton
Perry
Philadelphia
Washington
Washington
Washington
Westmoreland
York
Providence
Providence
Providence
Providence
Beaufort
Charleston
2005
Baseline
DV
13.07
13.84
15.36
15.25
16.26
15.30
14.44
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
13.68
12.81
15.19
15.17
14.92
13.37
15.49
16.52
10.07
12.14
10.82
9.93
11.52
12.21
2017
Reference
DV
8.00
8.68
9.96
9.57
10.28
9.65
9.05
10.72
10.94
8.64
9.66
8.23
9.89
9.52
9.76
9.94
8.59
7.74
11.02
9.91
8.61
8.69
9.18
8.41
10.07
9.20
9.00
8.38
9.49
10.91
7.14
8.58
7.71
7.01
7.69
8.31
2017 Tier 3
Control DV1
8.00
8.69
9.97
9.57
10.29
9.66
9.06
10.73
10.93
8.63
9.67
8.22
9.87
9.52
9.75
9.93
8.59
7.73
11.00
9.90
8.60
8.69
9.17
8.40
10.06
9.20
9.00
8.39
9.50
10.89
7.15
8.58
7.71
7.01
7.69
8.31
2030
Reference
DV
8.09
8.75
10.01
9.64
10.25
9.61
9.08
10.91
11.19
8.93
9.91
8.49
10.18
9.75
9.90
10.26
8.77
7.96
11.21
10.15
8.85
8.83
9.46
8.64
10.30
9.26
9.14
8.58
9.68
11.10
7.42
9.01
8.02
7.27
7.76
8.43
2030 Tier 3
Control DV
8.05
8.71
9.96
9.60
10.21
9.58
9.04
10.86
11.12
8.87
9.86
8.45
10.09
9.68
9.82
10.18
8.73
7.93
11.11
10.09
8.81
8.78
9.40
8.59
10.23
9.22
9.10
8.54
9.63
11.02
7.39
8.97
7.98
7.24
7.75
8.40
C-21
-------
State
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
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
County
Charleston
Chesterfield
Edgefield
Florence
Georgetown
Greenville
Greenville
Greenwood
Horry
Lexington
Oconee
Richland
Richland
Spartanburg
Brookings
Brown
Codington
Custer
Jackson
Minnehaha
Minnehaha
Pennington
Pennington
Pennington
Blount
Davidson
Davidson
Davidson
Dyer
Hamilton
Hamilton
Hamilton
Knox
Knox
Knox
Lawrence
2005
Baseline
DV
11.60
12.56
13.17
12.65
12.85
15.65
14.66
13.53
12.04
14.64
10.95
13.59
14.24
14.17
9.37
8.42
10.14
5.64
5.39
10.18
9.58
7.48
8.77
7.32
14.30
14.21
13.99
12.97
12.28
15.67
13.73
15.16
15.47
15.64
15.18
11.69
2017
Reference
DV
7.53
8.33
9.00
8.47
8.77
10.05
9.26
8.84
8.07
9.83
6.78
8.88
9.44
8.94
7.82
7.28
8.63
5.24
4.92
8.13
7.67
6.67
7.82
6.55
9.41
9.21
8.99
8.20
8.26
10.37
8.63
9.84
9.98
10.10
9.57
7.78
2017 Tier 3
Control DV1
7.54
8.33
9.00
8.47
8.77
10.04
9.26
8.83
8.07
9.83
6.78
8.88
9.44
8.93
7.81
7.28
8.63
5.24
4.92
8.13
7.67
6.67
7.83
6.55
9.40
9.21
8.99
8.20
8.25
10.36
8.62
9.84
9.98
10.10
9.57
7.78
2030
Reference
DV
7.64
8.53
9.22
8.63
8.91
10.43
9.63
9.05
8.23
10.04
6.98
9.05
9.64
9.25
7.81
7.28
8.63
5.23
4.94
8.19
7.70
6.76
7.94
6.63
9.58
9.36
9.15
8.36
8.35
10.47
8.73
9.94
10.13
10.24
9.71
7.96
2030 Tier 3
Control DV
7.62
8.50
9.19
8.60
8.89
10.39
9.58
9.01
8.20
10.00
6.95
9.02
9.61
9.21
7.78
7.27
8.60
5.23
4.94
8.15
7.66
6.75
7.92
6.62
9.54
9.31
9.10
8.31
8.32
10.43
8.70
9.90
10.07
10.18
9.65
7.93
C-22
-------
State
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Utah
County
Loudon
McMinn
Maury
Montgomery
Putnam
Roane
Shelby
Shelby
Shelby
Shelby
Sullivan
Sumner
Bowie
Dallas
Dallas
Dallas
Ector
El Paso
Harris
Harris
Harrison
Hidalgo
Jefferson
Nueces
Nueces
Orange
Tarrant
Tarrant
Box Elder
Cache
Davis
Salt Lake
Salt Lake
Salt Lake
Salt Lake
Salt Lake
2005
Baseline
DV
15.49
14.29
13.21
13.80
13.37
14.49
13.71
13.43
13.68
12.04
14.16
13.68
12.85
12.77
11.80
11.15
7.78
9.09
11.77
15.42
11.69
10.98
11.56
10.42
9.63
11.51
11.41
12.23
8.40
11.56
10.31
11.68
9.21
11.30
12.02
8.33
2017
Reference
DV
10.33
9.29
8.77
8.99
8.43
9.28
9.18
8.92
9.07
7.96
9.46
8.43
9.81
9.47
8.66
8.06
6.71
8.02
9.05
12.01
8.59
9.38
8.71
8.03
7.37
8.87
8.27
8.92
7.25
9.87
8.67
9.39
7.88
9.14
9.85
6.99
2017 Tier 3
Control DV1
10.32
9.28
8.76
8.99
8.42
9.27
9.17
8.91
9.06
7.96
9.45
8.42
9.80
9.47
8.66
8.05
6.71
8.02
9.05
12.01
8.59
9.38
8.71
8.03
7.37
8.86
8.27
8.92
7.22
9.85
8.65
9.37
7.86
9.13
9.83
6.98
2030
Reference
DV
10.50
9.46
8.93
9.14
8.60
9.42
9.23
8.97
9.17
7.99
9.66
8.67
9.85
9.51
8.71
8.10
6.70
7.96
8.92
11.79
8.61
9.30
8.57
7.83
7.20
8.78
8.35
8.99
7.03
9.81
8.60
9.38
7.78
9.14
9.82
6.92
2030 Tier 3
Control DV
10.46
9.42
8.89
9.10
8.57
9.38
9.19
8.92
9.12
7.95
9.63
8.63
9.81
9.45
8.66
8.06
6.69
7.94
8.82
11.66
8.57
9.28
8.44
7.77
7.13
8.70
8.30
8.94
6.95
9.71
8.49
9.26
7.69
9.02
9.70
6.85
C-23
-------
State
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Vermont
Vermont
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Washington
Washington
Washington
Washington
Washington
County
Utah
Utah
Utah
Utah
Weber
Weber
Weber
Addison
Addison
Bennington
Chittenden
Chittenden
Rutland
Arlington
Charles
Chesterfield
Fairfax
Fairfax
Fairfax
Henrico
Henrico
Loudoun
Page
Bristol City
Hampton City
Lynchburg City
Norfolk City
Roanoke City
Salem City
Virginia Beach
City
King
King
King
Pierce
Snohomish
2005
Baseline
DV
10.00
10.52
8.88
8.78
11.16
9.28
9.36
8.94
8.91
8.52
9.27
10.02
11.08
14.27
12.37
13.44
13.33
13.62
13.88
13.51
12.93
13.57
12.79
13.93
12.17
12.84
12.78
14.27
14.69
12.40
9.15
11.24
8.13
10.55
9.91
2017
Reference
DV
8.39
8.81
7.49
7.44
9.32
7.80
7.89
6.83
6.79
6.11
7.24
7.84
8.24
9.11
7.66
8.32
8.56
8.77
9.06
8.31
7.91
8.83
7.74
8.67
7.69
7.75
8.14
8.91
9.41
7.78
6.98
8.40
6.23
8.18
7.82
2017 Tier 3
Control DV1
8.37
8.79
7.47
7.41
9.29
7.77
7.86
6.83
6.79
6.11
7.24
7.84
8.24
9.13
7.66
8.32
8.58
8.78
9.07
8.31
7.91
8.84
7.74
8.67
7.70
7.75
8.15
8.91
9.41
7.79
6.99
8.40
6.23
8.18
7.82
2030
Reference
DV
8.26
8.72
7.39
7.27
9.14
7.60
7.69
7.33
7.31
6.67
7.57
8.23
9.04
9.38
7.72
8.40
8.78
8.99
9.31
8.44
8.09
9.09
7.99
8.87
7.82
7.97
8.39
9.17
9.64
8.04
7.82
9.32
6.95
9.37
8.89
2030 Tier 3
Control DV
8.15
8.60
7.30
7.17
9.02
7.50
7.59
7.30
7.28
6.64
7.53
8.19
8.99
9.34
7.69
8.37
8.75
8.95
9.27
8.40
8.06
9.05
7.94
8.83
7.78
7.94
8.36
9.13
9.60
8.01
7.78
9.28
6.92
9.31
8.84
C-24
-------
State
Washington
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
County
Spokane
Berkeley
Brooke
Brooke
Cabell
Hancock
Harrison
Kanawha
Kanawha
Kanawha
Marion
Marshall
Monongalia
Ohio
Raleigh
Wood
Ashland
Brown
Dane
Dodge
Forest
Grant
Kenosha
Manitowoc
Milwaukee
Milwaukee
Milwaukee
Milwaukee
Milwaukee
Outagamie
Ozaukee
St. Croix
Sauk
Taylor
Vilas
Waukesha
2005
Baseline
DV
9.97
15.93
16.52
16.04
16.30
15.76
13.99
15.15
13.17
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
13.32
12.88
14.08
13.68
13.54
10.96
11.60
10.09
10.22
8.24
6.78
13.91
2017
Reference
DV
7.20
10.60
10.26
9.85
10.52
9.83
8.58
9.19
7.89
10.23
9.20
9.03
8.33
8.54
7.70
9.85
4.96
8.62
8.86
8.18
5.95
8.79
8.72
7.81
9.51
9.11
10.04
9.78
9.61
8.28
8.61
7.99
7.54
6.48
5.48
10.07
2017 Tier 3
Control DV1
7.20
10.60
10.26
9.85
10.52
9.84
8.58
9.20
7.89
10.23
9.20
9.03
8.33
8.54
7.70
9.85
4.96
8.61
8.85
8.16
5.95
8.78
8.70
7.80
9.50
9.10
10.03
9.77
9.59
8.27
8.59
7.97
7.52
6.47
5.48
10.06
2030
Reference
DV
7.93
10.96
10.45
10.04
10.78
10.01
8.91
9.49
8.19
10.54
9.59
9.37
8.62
8.76
7.94
10.24
5.09
9.50
9.54
8.50
6.07
9.00
8.95
8.20
10.18
9.75
10.80
10.50
10.32
8.93
8.87
8.12
7.78
6.69
5.60
10.81
2030 Tier 3
Control DV
7.90
10.91
10.41
10.00
10.74
9.97
8.88
9.47
8.16
10.51
9.56
9.31
8.59
8.72
7.92
10.20
5.07
9.44
9.46
8.43
6.04
8.94
8.87
8.15
10.10
9.68
10.71
10.42
10.25
8.87
8.80
8.06
7.72
6.65
5.58
10.73
C-25
-------
State
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
County
Campbell
Campbell
Campbell
Converse
Fremont
Laramie
Sheridan
2005
Baseline
DV
6.29
5.11
5.26
3.58
8.17
4.48
9.70
2017
Reference
DV
6.03
4.89
4.97
3.38
7.29
3.97
8.73
2017 Tier 3
Control DV1
6.03
4.89
4.97
3.38
7.30
3.97
8.75
2030
Reference
DV
6.01
4.89
4.97
3.37
7.40
3.96
8.84
2030 Tier 3
Control DV
6.01
4.89
4.96
3.36
7.39
3.95
8.83
1 Note that the projected results for 2017 do not include California, while the projected results for 2030 do. The
processing of control case sulfur levels nationwide introduced an error in California counties. Control case fuels
were set to 10 ppm in all California counties, whereas the reference case sulfur levels ranged from 8-19 ppm. This
led to small changes in emissions due to fuel changes between reference and control, whereas in reality we expect
no change in California fuel. The error had a negligible effect on national emission totals, though the 2017 air
quality modeling results captured regional California impacts associated with the error that we judged not valid.
This issue does not have a significant impact on the AQ modeling results for the rest of the country.
C-26
-------
Air Quality Modeling Technical Support Document:
Proposed Tier 3 Emission Standards
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
March 2013
D-l
-------
Table D-l. 24-hour PM2.s Design Values for Proposed Tier 3 Scenarios
(units are ug/m3)
State
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
County
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
Phillips
Polk
Pope
2005
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
29.18
26.13
28.32
2017
Reference
DV
17.38
17.98
16.12
17.69
19.95
19.94
18.92
29.11
17.75
19.94
19.61
15.65
24.01
19.43
16.97
20.90
17.97
18.14
15.80
16.25
19.79
24.40
9.74
14.58
33.85
18.63
21.42
19.26
19.73
19.17
18.63
16.46
18.66
2017 Tier 3
Control
DV1
17.39
18.00
16.15
17.72
19.97
19.98
18.93
29.13
17.79
19.95
19.63
15.68
24.03
19.45
16.99
20.92
18.01
18.17
15.80
16.25
19.80
24.40
9.74
14.60
33.84
18.64
21.44
19.26
19.74
19.19
18.63
16.47
18.66
2030
Reference
DV
17.66
17.89
16.42
17.70
20.29
19.75
18.98
28.90
17.86
19.69
19.71
15.71
24.11
19.38
17.07
20.79
18.06
18.16
15.81
16.17
19.72
24.15
9.88
14.31
33.90
18.66
21.23
19.23
19.63
19.13
18.48
16.74
19.04
2030 Tier 3
Control DV
17.74
17.99
16.52
17.81
20.39
19.92
19.05
28.99
17.99
19.81
19.79
15.82
24.21
19.48
17.15
20.90
18.15
18.27
15.83
16.23
19.82
24.36
9.92
14.44
34.02
18.72
21.31
19.31
19.73
19.22
18.54
16.80
19.11
D-2
-------
State
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
County
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
2005
Baseline
DV
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
38.61
20.42
34.76
29.10
51.48
2017
Reference
DV
22.09
20.49
19.87
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
2017 Tier 3
Control
DV1
22.09
20.51
19.88
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
2030
Reference
DV
21.94
20.28
19.88
26.50
36.71
14.86
21.64
28.35
45.63
31.68
18.13
49.05
43.20
11.97
42.10
10.33
34.33
11.66
12.65
35.91
24.26
25.29
49.21
43.75
47.61
30.34
25.48
32.74
17.90
25.47
21.61
34.39
14.33
29.30
23.40
39.15
2030 Tier 3
Control DV
22.03
20.39
19.97
26.06
36.44
14.57
21.47
27.8
44.57
31.39
18.07
47.82
41.92
11.96
42.09
10.22
33.61
11.56
12.57
35.78
23.88
25.08
48.61
43.31
47.25
29.95
25.09
32.05
17.47
25.02
21.49
34.0
14.30
28.82
23.12
38.06
D-3
-------
State
California
California
California
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Delaware
Delaware
Delaware
D.C.
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
County
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
Citrus
Duval
Escambia
Hillsborough
Lee
Leon
Manatee
2005
Baseline
DV
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
21.22
24.35
28.80
23.44
17.70
27.03
19.57
2017
Reference
DV
N/A
N/A
N/A
N/A
18.78
15.86
17.51
14.12
21.08
10.42
10.65
14.62
17.03
11.24
9.38
18.47
22.77
18.10
13.70
22.17
17.26
18.54
22.89
19.72
21.40
14.56
19.36
13.69
13.80
12.92
18.42
21.58
15.72
12.85
19.33
12.13
2017 Tier 3
Control
DV1
N/A
N/A
N/A
N/A
18.68
15.78
17.40
14.16
21.00
10.41
10.65
14.59
17.06
11.25
9.38
18.45
22.78
18.09
13.70
22.14
17.28
18.57
22.90
19.75
21.40
14.57
19.38
13.69
13.80
12.93
18.42
21.60
15.72
12.86
19.35
12.14
2030
Reference
DV
28.66
40.70
23.82
24.22
18.97
16.51
18.12
16.54
21.19
11.20
13.35
16.49
19.69
12.76
9.42
20.65
23.89
19.79
14.54
23.71
18.23
19.22
23.37
20.29
22.49
14.55
19.42
13.76
13.80
13.21
18.35
22.32
15.59
12.73
19.36
11.98
2030 Tier 3
Control DV
28.33
39.45
23.63
23.70
19.10
16.57
18.21
16.71
21.30
11.26
13.43
16.66
19.91
12.82
9.44
20.85
24.06
19.91
14.58
23.87
18.31
19.36
23.58
20.51
22.62
14.59
19.49
13.81
13.84
13.25
18.39
22.39
15.65
12.77
19.45
12.03
D-4
-------
State
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
County
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
Washington
Wilkinson
Ada
Bannock
Benewah
Canyon
Franklin
Idaho
Lemhi
2005
Baseline
DV
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
30.83
33.16
28.36
27.08
32.94
31.80
36.76
28.43
36.53
2017
Reference
DV
14.46
13.34
13.91
14.07
15.07
13.61
12.30
13.00
13.34
13.66
22.51
19.43
22.13
20.55
20.29
24.15
21.84
23.67
18.80
18.92
19.32
18.70
17.81
23.29
19.45
23.92
18.98
19.65
21.48
25.13
24.21
28.50
26.37
30.68
26.40
31.54
2017 Tier 3
Control
DV1
14.48
13.34
13.92
14.08
15.08
13.61
12.30
13.00
13.35
13.67
22.51
19.42
22.14
20.57
20.30
24.14
21.86
23.67
18.78
18.92
19.30
18.71
17.83
23.31
19.46
23.92
18.98
19.68
21.49
25.14
24.20
28.48
26.42
30.77
26.38
31.46
2030
Reference
DV
14.55
13.28
13.70
14.09
15.03
13.30
12.16
12.84
13.21
13.54
22.33
19.55
22.50
20.31
20.34
24.36
21.57
23.55
19.12
19.08
20.17
18.76
17.93
23.36
19.29
24.33
18.94
19.59
21.43
24.06
24.13
29.32
24.60
30.29
26.50
32.99
2030 Tier 3
Control DV
14.62
13.33
13.76
14.12
15.08
13.34
12.19
12.87
13.27
13.58
22.43
19.60
22.66
20.51
20.49
24.43
21.71
23.71
19.16
19.21
20.22
18.81
18.00
23.44
19.45
24.20
19.05
19.69
21.51
24.30
24.23
29.40
24.93
30.72
26.55
33.03
D-5
-------
State
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
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
County
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
Dubois
Elkhart
Floyd
Henry
Howard
Knox
Lake
La Porte
Madison
Marion
Porter
2005
Baseline
DV
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
35.36
34.43
33.26
31.86
32.21
35.92
38.98
33.00
32.82
38.47
32.96
2017
Reference
DV
29.78
32.05
18.60
19.56
29.59
26.06
17.51
20.60
25.49
21.97
20.06
21.33
21.54
19.09
25.89
21.51
20.75
22.97
23.33
22.60
25.29
24.89
23.44
21.30
20.60
22.25
25.48
17.86
19.54
20.50
21.76
30.05
22.48
20.36
24.57
23.34
2017 Tier 3
Control
DV1
29.76
32.00
18.69
19.61
29.55
26.10
17.55
20.67
25.55
22.01
20.15
21.42
21.62
19.15
25.96
21.59
20.78
23.03
23.37
22.64
25.32
24.97
23.52
21.33
20.62
22.31
25.55
17.89
19.57
20.56
21.82
30.06
22.54
20.41
24.60
23.36
2030
Reference
DV
29.78
33.62
18.08
19.54
29.11
25.96
17.52
20.40
25.04
22.09
19.56
21.05
20.91
18.81
25.41
20.92
20.98
22.49
23.20
22.56
24.49
25.03
23.03
21.29
20.70
22.10
25.04
17.74
19.62
20.25
21.66
29.08
21.90
20.35
24.50
22.51
2030 Tier 3
Control DV
29.89
33.67
18.32
19.72
29.37
26.29
17.66
20.65
25.45
22.35
19.79
21.37
21.19
18.99
25.72
21.18
21.14
22.76
23.44
22.77
24.87
25.39
23.27
21.42
20.91
22.30
25.30
17.87
19.81
20.45
21.84
29.31
22.15
20.54
24.70
22.70
D-6
-------
State
Indiana
Indiana
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
County
St Joseph
Spencer
Tippecanoe
Vanderburgh
Vigo
Black Hawk
Clinton
Johnson
Linn
Montgomery
Muscatine
Palo Alto
Polk
Pottawattamie
Scott
Van Buren
Woodbury
Wright
Johnson
Linn
Sedgwick
Shawnee
Sumner
Wyandotte
Bell
Boyd
Bullitt
Campbell
Carter
Christian
Daviess
Fayette
Franklin
Hardin
Henderson
Jefferson
2005
Baseline
DV
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
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
2017
Reference
DV
24.94
15.83
21.43
23.33
20.91
22.21
24.37
24.66
20.95
18.57
27.63
18.55
22.46
21.62
25.56
20.32
20.39
19.85
23.08
18.62
18.62
22.50
16.73
21.93
17.42
16.56
17.87
16.62
13.89
16.52
17.47
17.93
17.34
16.29
18.02
20.71
2017 Tier 3
Control
DV1
25.08
15.86
21.44
23.36
20.94
22.28
24.43
24.75
21.06
18.59
27.70
18.61
22.50
21.64
25.62
20.41
20.41
19.89
23.13
18.64
18.68
22.55
16.79
21.95
17.43
16.56
17.89
16.65
13.91
16.56
17.50
18.00
17.41
16.31
18.05
20.75
2030
Reference
DV
23.62
15.94
21.52
23.05
20.76
22.20
23.91
23.84
20.70
18.38
27.16
18.30
21.98
21.10
25.17
19.49
20.08
19.73
22.91
18.49
18.68
22.31
16.63
21.87
17.60
17.06
18.02
16.63
14.43
16.83
17.68
17.57
17.04
16.58
17.92
20.77
2030 Tier 3
Control DV
24.04
16.04
21.68
23.22
20.96
22.46
24.18
24.22
20.90
18.49
27.44
18.48
22.23
21.26
25.44
19.73
20.22
19.88
23.10
18.58
18.83
22.47
16.77
22.03
17.69
17.13
18.12
16.76
14.47
16.93
17.78
17.83
17.20
16.66
18.04
20.89
D-7
-------
State
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
County
Kenton
Laurel
McCracken
Madison
Perry
Pike
Warren
Caddo
Calcasieu
Concordia
East Baton
Rouge
Iberville
Jefferson
Lafayette
Ouachita
Rapides
Tangipahoa
Terrebonne
West Baton
Rouge
Androscoggin
Aroostook
Cumberland
Hancock
Kennebec
Oxford
Penobscot
Anne Arundel
Baltimore
Cecil
Harford
Montgomery
Prince Georges
Washington
Baltimore City
2005
Baseline
DV
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
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
2017
Reference
DV
19.64
14.40
17.90
15.46
13.95
15.85
16.53
19.76
18.60
16.92
21.76
21.97
17.23
16.82
20.33
19.65
19.25
17.01
21.58
17.07
20.36
17.79
12.13
16.12
19.28
14.47
24.15
22.29
19.35
17.72
17.66
18.53
20.59
26.47
2017 Tier 3
Control
DV1
19.67
14.41
17.93
15.51
13.96
15.87
16.55
19.77
18.61
16.94
21.76
21.97
17.23
16.83
20.34
19.66
19.26
17.02
21.59
17.06
20.35
17.79
12.14
16.12
19.26
14.47
24.16
22.30
19.37
17.75
17.69
18.56
20.61
26.46
2030
Reference
DV
19.67
14.75
18.21
15.54
14.32
16.21
16.93
19.71
18.44
16.90
20.45
21.33
17.27
16.92
20.16
19.57
19.29
17.14
20.39
19.50
21.37
19.77
12.41
19.00
21.78
15.92
26.13
24.55
19.89
18.52
18.50
19.01
21.60
28.21
2030 Tier 3
Control DV
19.78
14.82
18.32
15.68
14.39
16.29
17.02
19.80
18.62
16.98
20.77
21.42
17.44
17.01
20.24
19.67
19.39
17.25
20.68
19.56
21.39
19.85
12.45
19.09
21.86
15.98
26.31
24.70
20.07
18.66
18.62
19.16
21.77
28.41
D-8
-------
State
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
County
Berkshire
Bristol
Essex
Hampden
Plymouth
Suffolk
Worcester
Allegan
Bay
Berrien
Genesee
Ingham
Kalamazoo
Kent
Macomb
Missaukee
Monroe
Muskegon
Oakland
Ottawa
Saginaw
StClair
Washtenaw
Wayne
Cass
Dakota
Hennepin
Mille Lacs
Ramsey
St Louis
Scott
Adams
Bolivar
De Soto
Forrest
Harrison
2005
Baseline
DV
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
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
2017
Reference
DV
20.08
16.06
18.32
21.17
16.99
21.24
18.83
24.37
22.00
21.64
22.60
23.30
21.62
24.01
27.28
16.09
24.32
23.84
24.75
25.70
21.34
29.15
24.13
31.96
14.31
18.71
19.38
17.21
20.95
16.94
18.17
17.56
19.79
16.65
21.42
19.41
2017 Tier 3
Control
DV1
20.05
16.03
18.32
21.15
16.98
21.25
18.83
24.46
22.09
21.73
22.67
23.33
21.67
24.03
27.32
16.10
24.33
23.98
24.78
25.74
21.39
29.23
24.16
32.01
14.34
18.77
19.46
17.24
20.99
16.96
18.24
17.57
19.81
16.68
21.44
19.42
2030
Reference
DV
22.03
16.81
19.41
23.49
18.04
22.12
20.70
24.06
21.08
20.99
21.98
23.27
21.28
23.99
26.33
16.33
24.35
23.02
24.45
25.75
20.93
28.37
23.89
31.44
14.16
18.80
18.97
16.96
20.88
17.60
17.97
17.60
19.79
16.82
21.68
19.51
2030 Tier 3
Control DV
22.15
16.86
19.50
23.62
18.13
22.22
20.80
24.53
21.31
21.26
22.32
23.49
21.46
24.32
26.67
16.43
24.58
23.45
24.67
26.03
21.18
28.72
24.18
31.67
14.28
19.01
19.18
17.08
21.13
17.70
18.18
17.69
19.87
16.93
21.76
19.60
D-9
-------
State
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
County
Hinds
Jackson
Jones
Lee
Lowndes
Warren
Boone
Buchanan
Cass
Cedar
Clay
Greene
Jackson
Jefferson
Monroe
St Charles
Ste Genevieve
St Louis
St Louis City
Cascade
Flathead
Gallatin
Lake
Lewis And Clark
Lincoln
Missoula
Ravalli
Rosebud
Sanders
Silver Bow
Yellowstone
Cass
Douglas
Hall
Lancaster
Scotts Bluff
2005
Baseline
DV
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
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
2017
Reference
DV
18.14
17.38
21.81
17.40
18.19
20.04
19.58
22.36
17.61
19.73
21.01
19.53
21.08
21.89
18.75
20.81
19.41
24.02
22.67
17.14
24.30
26.41
38.51
28.49
35.46
37.46
37.49
18.75
18.28
28.29
16.01
21.41
19.44
14.87
18.54
14.40
2017 Tier 3
Control
DV1
18.15
17.39
21.83
17.43
18.23
20.07
19.61
22.42
17.64
19.75
21.06
19.52
21.09
21.92
18.80
20.88
19.43
24.08
22.73
17.11
24.30
26.34
38.47
28.37
35.41
37.50
37.39
18.73
18.26
28.22
16.00
21.46
19.46
14.92
18.56
14.39
2030
Reference
DV
18.52
17.57
21.79
17.50
18.46
19.59
19.66
21.70
17.39
19.73
20.76
19.90
20.86
21.86
18.65
20.62
19.70
23.78
22.23
17.47
24.41
26.99
39.55
29.77
36.88
38.03
38.95
18.46
18.61
29.19
16.49
20.89
19.13
14.34
18.43
14.31
2030 Tier 3
Control DV
18.57
17.66
21.91
17.62
18.57
19.68
19.80
21.93
17.50
19.82
20.96
20.00
20.99
22.05
18.80
20.93
19.83
24.02
22.47
17.55
24.47
27.05
39.62
30.02
37.01
38.31
39.15
18.46
18.64
29.24
16.58
21.08
19.28
14.49
18.58
14.33
D-10
-------
State
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 Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New York
County
Washington
Clark
Washoe
Belknap
Cheshire
Coos
Graf ton
Hillsborough
Merrimack
Rockingham
Sullivan
Bergen
Camden
Essex
Hudson
Mercer
Middlesex
Morris
Ocean
Passaic
Union
Warren
Bernalillo
Chaves
Dona Ana
Grant
Sandoval
San Juan
Santa Fe
Albany
2005
Baseline
DV
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
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
2017
Reference
DV
18.37
19.46
20.83
11.62
19.12
17.41
15.01
19.18
15.34
16.42
16.64
23.10
21.44
23.06
29.99
19.12
20.27
18.81
16.72
21.51
25.14
21.36
14.75
12.93
27.10
12.30
13.92
11.05
8.73
22.72
2017 Tier 3
Control
DV1
18.38
19.42
20.88
11.62
19.12
17.40
15.02
19.16
15.34
16.43
16.64
23.10
21.45
23.06
29.97
19.12
20.24
18.80
16.73
21.52
25.10
21.37
14.75
12.93
27.07
12.30
13.92
11.05
8.73
22.68
2030
Reference
DV
18.13
19.42
22.53
12.57
21.31
18.44
15.80
21.11
16.24
17.46
18.63
22.85
21.91
23.53
29.71
19.92
20.92
19.60
17.14
21.55
25.49
22.31
14.82
13.07
26.91
12.23
13.83
11.03
8.69
26.58
2030 Tier 3
Control DV
18.25
19.74
22.84
12.62
21.40
18.50
15.90
21.21
16.34
17.57
18.72
22.99
22.06
23.71
29.99
20.05
21.07
19.71
17.24
21.73
25.69
22.43
14.92
13.09
27.08
12.26
13.88
11.07
8.75
26.83
D-ll
-------
State
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
County
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
Haywood
Jackson
Lenoir
McDowell
Martin
Mecklenburg
2005
Baseline
DV
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
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
2017
Reference
DV
26.32
16.44
25.61
14.13
23.34
19.44
19.72
26.84
22.34
16.86
19.11
22.86
21.76
15.50
15.35
18.39
19.92
18.82
16.35
16.72
19.76
14.73
18.40
19.07
16.09
17.30
17.32
19.05
16.94
18.41
16.94
14.57
16.43
17.91
15.46
19.23
2017 Tier 3
Control
DV1
26.31
16.44
25.65
14.13
23.35
19.46
19.73
26.83
22.38
16.86
19.10
22.86
21.73
15.50
15.36
18.38
19.92
18.83
16.37
16.74
19.78
14.75
18.39
19.08
16.10
17.30
17.31
19.07
16.94
18.41
16.95
14.58
16.42
17.91
15.46
19.23
2030
Reference
DV
26.01
17.26
26.11
14.73
23.26
20.17
20.20
26.64
22.95
18.12
20.05
23.14
21.69
17.26
15.99
18.74
19.95
19.11
16.65
17.05
19.95
15.11
19.03
19.75
16.38
17.62
17.78
19.49
17.43
18.80
17.26
14.85
16.66
18.34
15.71
20.00
2030 Tier 3
Control DV
26.24
17.32
26.33
14.79
23.45
20.39
20.33
26.75
23.16
18.24
20.19
23.32
21.82
17.31
16.04
18.87
20.09
19.20
16.73
17.13
20.08
15.19
19.09
19.84
16.45
17.73
17.84
19.60
17.53
18.91
17.33
14.90
16.71
18.42
15.76
20.10
D-12
-------
State
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
North Dakota
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
County
Mitchell
Montgomery
New Hanover
Onslow
Orange
Pitt
Robeson
Rowan
Swain
Wake
Watauga
Wayne
Billings
Burke
Burleigh
Cass
McKenzie
Mercer
Athens
Butler
Clark
Clermont
Cuyahoga
Franklin
Greene
Hamilton
Jefferson
Lake
Lawrence
Lorain
Lucas
Mahoning
Montgomery
Portage
Preble
Scioto
2005
Baseline
DV
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
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
2017
Reference
DV
15.87
15.81
14.44
15.20
16.38
16.86
17.08
18.29
15.73
17.82
16.52
17.95
11.94
15.60
15.38
16.67
10.97
15.41
16.31
23.93
19.69
17.38
30.05
21.23
17.24
22.49
24.43
21.69
18.70
19.52
26.14
22.12
23.00
19.50
17.79
18.78
2017 Tier 3
Control
DV1
15.87
15.84
14.44
15.19
16.40
16.86
17.08
18.31
15.74
17.84
16.53
17.95
11.94
15.60
15.37
16.69
10.96
15.41
16.32
23.99
19.70
17.40
30.10
21.25
17.26
22.49
24.41
21.71
18.70
19.47
26.18
22.13
23.01
19.52
17.81
18.78
2030
Reference
DV
16.42
16.09
14.77
15.51
16.68
17.13
17.16
18.33
16.12
18.42
16.77
18.18
11.88
15.59
15.46
16.58
11.12
15.51
17.03
23.91
19.93
17.59
29.33
20.84
17.40
22.52
24.68
21.64
19.00
19.81
25.55
22.48
22.80
19.91
18.09
19.30
2030 Tier 3
Control DV
16.47
16.18
14.81
15.56
16.75
17.18
17.19
18.46
16.18
18.54
16.83
18.25
11.89
15.61
15.49
16.66
11.12
15.55
17.12
24.12
20.11
17.74
29.62
21.16
17.59
22.72
24.76
21.80
19.06
19.97
25.86
22.71
23.14
20.02
18.27
19.38
D-13
-------
State
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
County
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
2005
Baseline
DV
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
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
2017
Reference
DV
20.64
21.95
22.02
17.40
20.88
26.21
20.17
22.96
21.86
19.87
21.50
19.41
23.73
22.70
23.48
30.87
34.05
19.11
22.65
20.46
41.72
23.75
27.70
20.92
20.05
21.53
22.81
25.58
26.77
21.50
20.86
17.82
30.91
24.31
20.25
21.26
2017 Tier 3
Control
DV1
20.65
21.99
22.04
17.43
20.89
26.26
20.20
22.98
21.87
19.96
21.51
19.44
23.74
22.73
23.52
30.90
34.09
19.13
22.66
20.50
41.65
23.72
27.75
20.94
20.05
21.50
22.83
25.62
26.85
21.49
20.88
17.82
30.99
24.32
20.25
21.29
2030
Reference
DV
21.00
22.00
22.34
17.32
21.17
25.89
20.04
22.92
21.90
19.43
21.62
19.34
23.70
22.62
29.50
37.98
42.38
25.50
23.44
20.77
41.22
24.05
28.05
21.83
20.53
22.26
23.38
26.17
26.64
22.25
21.21
18.45
31.20
25.02
21.07
21.05
2030 Tier 3
Control DV
21.15
22.19
22.50
17.43
21.27
26.22
20.15
23.06
21.98
19.59
21.71
19.46
23.78
22.76
29.67
38.12
42.51
25.57
23.55
20.94
41.33
24.23
28.34
22.03
20.62
22.38
23.61
26.48
27.05
22.43
21.36
18.58
31.65
25.19
21.25
21.32
D-14
-------
State
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 Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
County
Northampton
Perry
Philadelphia
Washington
Westmoreland
York
Providence
Charleston
Chesterfield
Edgefield
Florence
Greenville
Greenwood
Horry
Lexington
Oconee
Richland
Spartanburg
Brookings
Brown
Codington
Custer
Jackson
Minnehaha
Pennington
Blount
Davidson
Dyer
Hamilton
Knox
Lawrence
Loudon
Me Minn
Maury
Montgomery
Putnam
2005
Baseline
DV
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
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
2017
Reference
DV
23.34
20.39
22.12
20.35
19.24
28.50
19.17
16.42
16.86
18.24
17.36
19.31
17.06
17.31
19.97
15.24
19.93
18.52
17.75
14.86
18.32
12.61
10.89
17.95
16.48
19.34
18.40
18.31
21.16
20.98
15.45
20.48
18.36
17.29
18.40
16.95
2017 Tier 3
Control
DV1
23.35
20.43
22.12
20.31
19.23
28.54
19.15
16.41
16.88
18.27
17.36
19.32
17.08
17.31
19.98
15.25
19.95
18.55
17.80
14.88
18.35
12.64
10.90
18.02
16.48
19.36
18.41
18.32
21.16
21.00
15.47
20.50
18.40
17.31
18.44
16.97
2030
Reference
DV
24.23
20.86
22.52
20.43
19.46
29.25
20.46
16.97
17.05
18.35
17.68
20.03
17.24
17.74
20.31
15.57
20.13
18.63
17.57
14.85
18.22
12.42
11.01
17.83
16.67
19.49
18.49
18.47
21.43
20.95
15.80
20.65
18.41
17.53
18.72
17.33
2030 Tier 3
Control DV
24.47
21.06
22.68
20.50
19.58
29.44
20.56
17.03
17.13
18.46
17.75
20.11
17.33
17.80
20.39
15.64
20.25
18.75
17.73
14.93
18.34
12.51
11.02
18.02
16.73
19.60
18.61
18.54
21.52
21.12
15.88
20.75
18.56
17.64
18.84
17.41
D-15
-------
State
Tennessee
Tennessee
Tennessee
Tennessee
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
County
Roane
Shelby
Sullivan
Sumner
Bowie
Dallas
Ector
El Paso
Harris
Harrison
Hidalgo
Nueces
Orange
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
2005
Baseline
DV
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
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
2017
Reference
DV
16.44
17.68
19.41
15.67
20.07
18.61
14.37
19.57
22.00
18.28
22.59
19.43
19.61
17.76
27.65
42.83
31.19
36.74
26.40
33.58
29.74
19.08
16.50
21.72
22.88
19.06
17.30
15.78
19.94
17.06
19.61
17.14
16.72
16.48
16.18
17.53
2017 Tier 3
Control
DV1
16.48
17.71
19.44
15.69
20.09
18.62
14.37
19.56
22.01
18.27
22.59
19.43
19.62
17.77
27.74
42.93
31.34
36.74
26.61
33.61
29.86
19.09
16.49
21.74
22.87
19.04
17.31
15.77
19.93
17.07
19.61
17.16
16.75
16.47
16.20
17.50
2030
Reference
DV
16.29
17.89
19.62
16.32
19.98
18.86
14.23
19.27
20.99
18.36
22.66
19.49
19.44
17.94
26.56
43.31
30.52
37.34
25.19
33.49
28.87
20.40
17.82
22.50
25.68
19.86
17.41
16.15
20.61
17.29
20.29
17.48
17.03
16.84
16.65
17.65
2030 Tier 3
Control DV
16.43
18.01
19.70
16.43
20.06
18.95
14.28
19.34
21.23
18.43
22.69
19.61
19.61
18.04
27.07
44.13
31.09
37.93
25.72
34.11
29.45
20.46
17.90
22.64
25.88
19.95
17.48
16.23
20.76
17.38
20.44
17.63
17.10
16.92
16.73
17.71
D-16
-------
State
Virginia
Virginia
Washington
Washington
Washington
Washington
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wyoming
County
Roanoke City
Salem City
King
Pierce
Snohomish
Spokane
Berkeley
Brooke
Cabell
Hancock
Harrison
Kanawha
Marion
Marshall
Monongalia
Ohio
Raleigh
Summers
Wood
Ashland
Brown
Dane
Dodge
Forest
Grant
Kenosha
Manitowoc
Milwaukee
Outagamie
Ozaukee
St Croix
Sauk
Taylor
Vilas
Waukesha
Campbell
2005
Baseline
DV
32.70
34.06
29.16
41.82
34.36
29.86
34.51
43.90
35.10
40.64
33.53
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
2017
Reference
DV
18.41
19.87
20.82
31.07
27.06
19.19
23.85
25.76
18.50
20.95
16.24
18.97
16.01
17.42
14.96
17.02
15.01
14.90
18.23
12.96
25.55
24.44
21.99
17.79
25.24
23.23
21.51
26.65
23.60
23.34
20.04
21.93
18.51
16.61
25.11
17.35
2017 Tier 3
Control
DV1
18.43
19.90
20.81
31.03
27.04
19.17
23.86
25.77
18.51
20.92
16.23
18.98
16.00
17.38
14.95
17.01
15.03
14.92
18.23
12.97
25.63
24.54
22.13
17.82
25.31
23.35
21.55
26.73
23.68
23.42
20.08
22.00
18.59
16.65
25.18
17.34
2030
Reference
DV
18.66
19.99
24.52
36.16
31.17
22.33
24.60
26.00
18.98
21.36
17.10
19.63
17.14
18.24
15.31
17.40
15.49
15.26
19.40
13.29
30.73
25.97
22.16
17.92
25.36
23.28
22.95
28.54
26.68
23.78
20.39
21.88
18.87
17.10
25.97
17.37
2030 Tier 3
Control DV
18.78
20.12
24.70
36.40
31.37
22.42
24.78
26.13
19.04
21.43
17.14
19.74
17.18
18.32
15.37
17.48
15.57
15.35
19.43
13.35
31.06
26.29
22.59
18.04
25.63
23.66
23.14
28.81
27.03
24.06
20.57
22.18
19.12
17.24
26.29
17.38
D-17
-------
State
Wyoming
Wyoming
Wyoming
Wyoming
County
Converse
Fremont
Laramie
Sheridan
2005
Baseline
DV
10.00
29.80
11.93
30.86
2017
Reference
DV
9.51
23.96
10.69
27.59
2017 Tier 3
Control
DV1
9.51
23.88
10.69
27.42
2030
Reference
DV
9.49
24.65
10.64
27.67
2030 Tier 3
Control DV
9.49
24.82
10.67
27.69
1 Note that the projected results for 2017 do not include California, while the projected results for 2030 do. The
processing of control case sulfur levels nationwide introduced an error in California counties. Control case fuels
were set to 10 ppm in all California counties, whereas the reference case sulfur levels ranged from 8-19 ppm. This
led to small changes in emissions due to fuel changes between reference and control, whereas in reality we expect
no change in California fuel. The error had a negligible effect on national emission totals, though the 2017 air
quality modeling results captured regional California impacts associated with the error that we judged not valid.
This issue does not have a significant impact on the AQ modeling results for the rest of the country.
D-18
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-13-001
Environmental Protection Air Quality Assessment Division March, 2013
Agency Research Triangle Park, NC
------- |