Air Quality Modeling Technical Support
Document: Tier 3 Motor Vehicle
Emission and Standards
&EPA
United States
Environmental Protection
Agency
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Air Quality Modeling Technical Support
Document: Tier 3 Motor Vehicle
Emission and Standards
Air Quality Assessment Division
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC
&EPA
United States
Environmental Protection
Agency
ERA-454/R-14-002
February 2014
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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. Modeling Scenarios 5
E. Meteorological Input Data 7
F. Initial and Boundary Conditions 9
G. CMAQ Base Case Model Performance Evaluation 10
III. CMAQ Model Results 10
A. Impacts of Tier 3 Standards on Future 8-Hour Ozone Levels 10
B. Impacts of Tier 3 Standards on Future Annual PM2.5 Levels 12
C. Impacts of Tier 3 Standards on Future 24-hour PM2.s Levels 15
D. Impacts of Tier 3 Standards on Future Nitrogen Dioxide Levels 17
E. Impacts of Tier 3 Standards on Future Ambient Air Toxic Concentrations 18
1. Acetaldehyde 19
2. Formaldehyde 20
3. Benzene 21
4. 1,3-Butadiene 22
5. Acrolein 23
6. Ethanol 25
7. Naphthalene 26
F. Air Toxics Population Metrics 27
G. Impacts of Tier 3 Standards on Future Annual Nitrogen and Sulfur Deposition
Levels 27
H. Impacts of Tier 3 Standards on Future Visibility Levels 29
Appendices
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List of Appendices
Appendix A.
Model Performance Evaluation for the 2007-Based Air Quality Modeling Platform
Appendix B.
8-Hour Ozone Design Values for Air Quality Modeling Scenarios
Appendix C.
Annual PM2.s Design Values for Air Quality Modeling Scenarios
Appendix D.
24-Hour PM2.5 Design Values for Air Quality Modeling Scenarios
11
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I. Introduction
This document describes the air quality modeling performed by EPA in support of the
Tier 3 motor vehicle emission and fuel standards. A national scale air quality modeling analysis
was performed to estimate the impact of the Tier 3 standards on future year annual and 24-hour
PM2.s concentrations, daily maximum 8-hour ozone concentrations, annual nitrogen dioxide
concentrations, annual nitrogen and sulfur deposition levels, annual ethanol and select annual
and seasonal air toxic concentrations (formaldehyde, acetaldehyde, benzene, 1,3-butadiene,
acrolein and naphthalene) as well as visibility impairment. To model the air quality benefits of
this rule we used the Community Multiscale Air Quality (CMAQ) model.1 CMAQ simulates the
numerous physical and chemical processes involved in the formation, transport, and destruction
of ozone, particulate matter and air toxics. In addition to the CMAQ model, the modeling
platform includes the emissions, meteorology, and initial and boundary condition data which are
inputs to this model.
Emissions and air quality modeling decisions are made early in the analytical process to
allow for sufficient time required to conduct emissions and air quality modeling. For this reason,
it is important to note that the inventories used in the air quality modeling and the benefits
modeling, which are presented in Section 7.1 of the RIA, are slightly different than the final fuel
and vehicle standard inventories presented in Section 7.2 of the RIA. However, the air quality
inventories and the final rule inventories are generally consistent, so the air quality modeling
adequately reflects the effects of the rule.
Air quality modeling was performed for five emissions cases: a 2007 base year, a 2018
reference case projection without the Tier 3 rule standards and a 2018 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 2018 control case projection with Tier 3 standards in place. The year 2007 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 2018 and 2030 to assess the impacts on air quality of the fuel and
vehicle standards. Information on the development of emissions inventories for the 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;
EPA-454/R-14-003). The docket for this 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 2007-based CMAQ modeling platform was used as the basis for the air quality
modeling of the 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 2007. 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 5.0 was most recently peer-reviewed in September of
2011 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 2007
multi-pollutant modeling platform used the most recent multi-pollutant CMAQ code available at
the time of air quality modeling (CMAQ version 5.0.1; multipollutant version6). CMAQ vS.0.1
reflects updates to version 4.7 to improve the underlying science which are detailed at
http://www.cmascenter.org.7'8
2 Brown, N., Allen, D., Amar, P., Kallos, G., McNider, R., Russell,, A., Stockwell, W. (September 2011). Final
Report: Fourth Peer Review of the CMAQ Model, NERL/ORD/EPA. U.S. EPA, Research Triangle Park, NC.,
http://www.epa.gov/asmdnerl/Reviews/2011 CMAQ Review FinalReport.pdf. 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 5.0.1 was released on July 2012. It is available from the Community Modeling and Analysis
System (CMAS) website: http://www.cmascenter.org.
7 Community Modeling and Analysis System (CMAS) website: http://www.cmascenter.org.. RELEASE_NOTES
for CMAQvS.O - February 2012.
8 Community Modeling and Analysis System (CMAS) website: http://www.cmascenter.org.. RELEASE_NOTES
for CMAQvS.O.l - July 2012.
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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 kilometer
(km) with a finer-scale 12 km grid. The model extends vertically from the surface to 50
millibars (approximately 17,600 meters) using a sigma-pressure coordinate system with 25
vertical layers. 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 grid.
Only the finer grid data were used in determining the impacts of the Tier 3 standards. 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 2007 base year, 2018 reference and control case projection, 2030 reference and control case
projection, (2) meteorology for the year 2007, 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, 1,3-butadiene, and naphthalene) concentrations for each grid cell in the
modeling domains. The development of 2007 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-1. Geographic elements of domains used in Tier 3 modeling.
Grid Resolution
Map Projection
Coordinate Center
True Latitudes
Dimensions
Vertical extent
CMAQ Modeling Configuration
36 km National Grid
12 km National Grid
Lambert Conformal Projection
97degW, 40degN
33degNand45degN
148x112x14
396x246x25
25 Layers: Surface to 50 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 purple inner box is the 12 km national fine grid modeling
domain.
C. Modeling Simulation Periods
The 36 km and 12 km CMAQ modeling domains were modeled for the entire year of
2007. These annual simulations were performed in two half-year segments (i.e., January through
June, July through December) for each emissions scenario. With this approach to segmenting an
annual simulation we were able to 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 half-year segment, to mitigate the effects of initial concentrations. For the 12
km domain simulations we used a 3-day ramp-up period for each half-year segment. 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, 2007. 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 2007. 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 rulemaking.
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D. Modeling Scenarios
As part of our analysis for this rulemaking, the CMAQ modeling system was used to
calculate daily and annual PIVb.s concentrations, 8-hour ozone concentrations, annual NC>2
concentrations, annual and seasonal air toxics concentrations, annual total nitrogen and sulfur
deposition levels and visibility impairment for each of the following emissions scenarios:
2007 base year
2018 reference case projection without the Tier 3 fuel and vehicle standards
2018 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 2007 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., 2005-2009). 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 PIVb.s and seasonal average ozone were used to calculate monetized benefits by
the BenMAP model (see Section 8.1.2 of the RIA).
The design value projection methodology used here followed EPA guidance9 for such
analyses. For each monitoring site, all valid design values (up to 3) from the 2005-2009 period
were averaged together. Since 2007 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 2018 and 2030 were estimated by applying the modeled 2007-
to-2018 and the modeled 2007-to-2030 relative change in PM2.5 species to the 5 year weighted
average (2005-2009) design values. Monitoring sites were included in the analysis if they had at
least one complete design value in the 2005-2009 period. EPA followed the procedures
recommended in the modeling guidance for projecting PM2.s by projecting individual PM2.s
9 U.S. EPA, 2007: Guidance on the Use of Models and Other Analyses for Demonstrating Attainment for Ozone,
PM25, and Regional Haze, Office of Air Quality Planning and Standards, Research Triangle Park, NC (EPA -454/B-
07-002).
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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:
http://www.epa.gov/scramOO l/modelingapps_mats.htm.
To calculate 24-hour PM2.5 design values, the measured 98th percentile concentrations
from the 2005-2009 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.s days changed between the
2005-2009 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 2007 base case and the 2018 and 2030 cases were used to project
ambient design values to 2018 and 2030 respectively. The calculations used the base period
2005-2009 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 ppb10.
We also conducted an analysis to compare the absolute and percent differences between
the 2018 control case and the 2018 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, acrolein, and naphthalene as well as annual nitrate and sulfate
If there are less than 5 days > 70 ppb for a site, then the threshold is lowered in 1 ppb increments to as low as 60
ppb. If there are not 5 days > 60 ppb, then the site is excluded. If a county has no sites that meet the 70 ppb
threshold, then the county design value is calculated from the sites that meet the 60 ppb threshold.
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deposition. These data were not compared in a relative sense due to the limited observational
data available.
E. Meteorological Input Data
The gridded meteorological input data for the entire year of 2007 were derived from
simulations of the Weather Research and Forecasting Model (WRF) version 3.3, Advanced
Research WRF (ARW) core11 for the entire year of 2007 over model domains that are slightly
larger than those shown in Figure II-1. Meteorological model input fields were prepared
separately for the 36 km and 12 km domains shown in Figure II-1. The WRF simulations were
run on the same map projection as CMAQ.
The 36 km and 12 km meteorological model runs configured similarly. The selections
for key WRF physics options are shown below
12
Pleim-Xiu PEL and land surface schemes
Asymmetric Convective Model version 2 planetary boundary layer scheme
Kain-Fritsh cumulus parameterization
Morrison double moment microphysics
RRTMG longwave and shortwave radiation schemes
Three dimensional analysis nudging for temperature, wind, and moisture was applied above the
boundary layer only. The meteorological simulations were conducted in 5.5 day blocks with soil
moisture and temperature carried from one block to the next via the ipxwrf program.13 Landuse
and land cover data are based on the U.S. Geological Survey (USGS) data. The 36km and 12km
meteorological modeling domains contained 35 vertical layers with an approximately 19m deep
surface layer and a 50 millibar top. The WRF 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 WRF and CMAQ (heights are layer top).
CMAQ
Layers
25
24
23
WRF
Layers
35
34
33
32
31
Sigma P
0.0000
0.0500
0.1000
0.1500
0.2000
Approximate
Height (m)
17,556
14,780
12,822
11,282
10,002
11 Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang, W., Powers,
J.G., 2008. A Description of the Advanced Research WRF Version 3.
12 Gilliam, R.C., Pleim, J.E., 2010. Performance Assessment of New Land Surface and Planetary Boundary Layer
Physics in the WRF-ARW. Journal of Applied Meteorology and Climatology 49, 760-774.
13 Gilliam, R.C., Pleim, J.E., 2010. Performance Assessment of New Land Surface and Planetary Boundary Layer
Physics in the WRF-ARW. Journal of Applied Meteorology and Climatology 49, 760-774.
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22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
30
29
28
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
0.2500
0.3000
0.3500
0.4000
0.4500
0.5000
0.5500
0.6000
0.6500
0.7000
0.7400
0.7700
0.8000
0.8200
0.8400
0.8600
0.8800
0.9000
0.9100
0.9200
0.9300
0.9400
0.9500
0.9600
0.9700
0.9800
0.9850
0.9900
0.9950
0.9975
1.0000
8,901
7,932
7,064
6,275
5,553
4,885
4,264
3,683
3,136
2,619
2,226
1,941
1,665
1,485
1,308
1,134
964
797
714
632
551
470
390
311
232
154
115
77
38
19
0
The 2007 meteorological outputs from the 36km and 12km WRF sets were processed to
create model-ready inputs for CMAQ using the Meteorology-Chemistry Interface Processor
(MCIP), version 4.1.2.14'15
14 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).
15 Otte, T.L., Pleim, J.E., 2010. The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling
system: updates through MCIPvS.4.1. Geoscientific Model Development 3, 243-256.
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Before initiating the air quality simulations, it is important to identify the biases and
errors associated with the meteorological modeling inputs. The 2007 WRF model performance
evaluations used an approach which included a combination of qualitative and quantitative
analyses to assess the adequacy of the WRF 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. The
operational evaluation included statistical comparisons of model/observed pairs (e.g., mean bias,
mean (gross) error, fractional bias, and fractional error16) 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 36 km and 12
km WRF evaluations are described elsewhere.17 The results of these analyses indicate that the
bias and error values associated with all three sets of 2007 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 concentrations are provided by a three-dimensional global
atmospheric chemistry model, the GEOS-CFIEM18'19 model (standard version 8-03-02 with
version 8-02-03 chemistry). The global GEOS-CFIEM model simulates atmospheric chemical
and physical processes driven by assimilated meteorological observations from the NASA's
Goddard Earth Observing System (GEOS-5). This model was run for 2007 with a grid
resolution of 2.0 degree x 2.5 degree (latitude-longitude) and 46 vertical layers up to 0.01 hPa.
The predictions were processed using the GEOS-2-CMAQ tool20'21 and used to provide one-way
dynamic boundary conditions at one-hour intervals. The ozone from these GEOS-Chem runs
was evaluated by comparing to satellite vertical profiles and ground-based measurements and
found acceptable model performance.
Initial conditions were extracted from a slightly older model simulation using GEOS-
CHEM version 8-02-03. The model simulation from which the initial conditions were extracted
was also run with a grid resolution of 2.0 of 2.0 degree x 2.5 degree (latitude-longitude) and 46
vertical layers. A GEOS-Chem evaluation was conducted for the purpose of validating the 2007
GEOS-Chem simulation outputs for their use as inputs to the CMAQ modeling system. This
Boylan, J.W., Russell, A.G., 2006. PM and light extinction model performance metrics, goals, and criteria for
three-dimensional air quality models. Atmospheric Environment 40, 4946-4959.
17
Misenis, Chris Meteorological Model Performance Evaluation for the Annual 2007 Simulation WRF v3.3,
USEPA/OAQPS, July 15, 2012.
18 Yantosca, B.,2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric Chemistry Modeling Group, Harvard
University, Cambridge, MA, October 15, 2004.
19 Le Sager, P. Yantosca, B., Carouge, C. (2008). GEOS-CHEM v8-01-02 User's Guide, Atmospheric Chemistry
Modeling Group, Harvard University, Cambridge, MA, December 18, 2008.
20 Akhtar, F., Henderson, B., Appel, W., Napelenok, S., Hutzell, B., Pye, H., Foley, K.,2012. Multiyear Boundary
Conditions for CMAQ 5.0 from GEOS-Chem with Secondary Organic Aerosol Extensions, 11th annual Community
Modeling and Analysis System conference, Chapel Hill, NC, October 2012.
21 Henderson, B.H., Akhtar, F., Pye, H.O.T., Napelenok, S.L., Hutzell, W.T., 2013. A database and tool for
boundary conditions for regional air quality modeling: description and evaluation, Geoscientific Model
Development Discussions, 6, 4665-4704.
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evaluation included reproducing GEOS-Chem evaluation plots reported in the literature for
previous versions of the model.22
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 2007 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 standards. We looked at impacts on
future ambient levels of PM2.5, ozone and NC>2, as well as changes in ambient concentrations of
ethanol and the following air toxics: acetaldehyde, acrolein, benzene, 1,3-butadiene, naphthalene
and formaldehyde. The air quality modeling results also include impacts on deposition of
nitrogen and sulfur and on visibility levels due to this rule. In this section, we present the air
quality modeling results for the 2018 Tier 3 control case relative to the 2018 reference case as
well as the 2030 Tier 3 control case relative to the 2030 reference case.
A. Impacts of 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 Tier 3 fuel and vehicle standards. Specifically, for the years 2018 and 2030 we
compare a reference scenario (a scenario without the proposed Tier 3 standards) to a control
scenario which includes the Tier 3 standards. Our modeling indicates that there will be
substantial decreases in ozone across most of the country as a result of the 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 2018 and 2030 respectively.23
Appendix B details the state and county 8-hour maximum ozone design values for the ambient
baseline and the 2018 and 2030 future reference and control cases.
22 Lam, Y.F., Fu, J.S., Jacob, D.J., Jang, C, Dolwick, P., 2010 2006-2008 GEOS-Chem for CMAQ Initial and
Boundary Conditions. 9th Annual CMAS Conference, October 11-13, 2010, Chapel Hill, NC.
23 An 8-hour ozone design value is the concentration that determines whether a monitoring site meets the 8-hour
ozone NAAQS. The full details involved in calculating an 8-hour ozone design value are given in Appendix I of 40
CFR part 50.
10
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l_6Q&nd Number oi Counties
;-10ppb 33
>= -1.0 to < -0.5 303
HI "~ -05 to < -0 25 228
I [ >= -0.251Q< -0-1 38
>=-0.1lo<=0.1 56
I [> 0.1 to <=0.25 0
HI > 0.25 to <= 0-5 0
HI > 0 5 to <= 1 0 0
>IO 0
Difference in 8-hr Ozone OV - 2Qierg_ctl minus 2018rg_rst
Figure III-l. Projected Change in 2018 8-hour Ozone Design Values Between the Reference Case and
Control Case
Legend Number of Counties
Bl >=-2,5to<-20 16
Bl *=-2.0to <-1 5 55
Bl >=-1.5to<-1.0 156
HI >=-1 Oto<-05 301
I! >=-0.510 «-0.25 34
| | >=-0.25 to <-0.1 10
I |>=-0.1tO<=0.1 48
>0,1 0
Difference in 8-hr Ozone DV - 2030rg_ctf minus 2030ra_ret
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 2018 are
between 0.5 and 1.0 ppb. There are also 33 counties with projected 8-hour ozone design value
decreases of more than 1 ppb; these counties are generally in urban areas in states that have not
adopted California LEV III standards. The maximum projected decrease in an 8-hour ozone
design value in 2018 is 1.56 ppb in Henry County, Georgia near Atlanta. Figure III-2 presents
the ozone design value changes for 2030. In 2030 the ozone design value decreases are larger
than in 2018; most decreases are projected to be between 0.5 and 1.0 ppb, but over 250 more
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 2.8 ppb in Gwinnett County,
Georgia, the northeastern part of the Atlanta metropolitan area.
B. Impacts of 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 Tier 3 fuel and vehicle standards. Specifically, for the years
2018 and 2030 we compare a reference scenario (a scenario without the standards) to a control
scenario that includes the standards. Our modeling indicates that by 2030 annual PM2.5 design
values in the majority of the modeled counties would decrease due to the standards. The
decreases in annual PlVb.s design values are likely due to the projected reductions in primary
PM2.5, NOX, SOX and VOC emissions (see Section 7.2.1 in the RIA). Note that the air quality
modeling used inventories that included an increase in direct PM2.5 emissions in the West and
Pacific Northwest that is an artifact of a difference in fuel properties that isn't real.24 Although in
most areas this direct PM2.5 increase is outweighed by reductions in secondary PM2.5, the air
quality modeling does predict ambient PM2.5 increases in a few places in the West and Pacific
Northwest. These modeled increases are a result of the inventory issue, and we do not expect
them to actually occur. Appendix C details the state and county annual PM2.5 design values for
the ambient baseline and the 2018 and 2030 future reference and control cases.
Figure III-3 and III-4 presents the changes in annual PM2.5 design values in 2018 and
2030 respectively.25 As shown in Figure III-3, we project that in 2018 over 200 counties will
have design value decreases of between 0.01 |ig/m3 and 0.05 |ig/m3. These counties tend to be in
urban areas in states that have not adopted California LEV III standards. The maximum
projected decrease in a 2018 annual PM2.5 design value is 0.04 |ig/m3 in Waukesha County,
Wisconsin and Cook County, Illinois. There are two counties with very small projected
increases in their annual PM2.5 design values in 2018: Lewis & Clark County, Montana, and
Gallatin County, Montana. These projected increases are a result of the issue with the air quality
modeling inventories discussed in Section 7.2.1.1 of the RIA, and we do not expect these
increases will occur.
24 The issue is with the way that some of the fuel property data, specifically E200/E300 and T50/T90, matched up in
the fuel compliance database in the West and Pacific Northwest, see Section 7.2.1.1 for additional information.
25 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.
12
-------
Legend
^H <-0.1 ug/m3
^Hl >= ' ' < -0.05
j^H >= -0.05 to < -0.01
~ >=-0.01 to <= 0.01
^J > 0.01 to <= 0 05
' > 0.05 to <= 0.1
Difference in Annual PM2.5 DV ~ 2030rg_ctl minus 2030rg_ref
Figure III-4 presents the annual PM2.5 design value changes in 2030. The annual PM2.5
design value decreases in 2030 are larger than the decreases in 2018; most design values are
projected to decrease between 0.01 and 0.05 |ig/m3 and over 140 additional counties have
projected design value decreases greater than 0.05 ug/m3. The maximum projected decrease in
an annual PM2.5 design value in 2030 is 0.15 |ig/m in Milwaukee County, Wisconsin.
13
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Legend Number of Counties
j^H < -0.1 ug/m3 0
H >=-0.1 to < -0.05 0
^J >= -0.01 to <= 0.01 364
|~ 1 > 0.01 to <= 0.05 0
^|>0.05to<=0.1 0
>0.1 0
Difference in Annual PM2.5 DV- 2Q13rg_ctl minus 2018rg_ref2
Figure III-3. Projected Change in 2018 Annual PM2.5 Design Values Between the Reference Case and Control
Case
Legend Number of Counties
^H ---01 ug/m3 9
^^ >=-0,1 10 <-0.05 139
{Hi >= -° °5 '" ' -° Q1 3Z6
^ >=-0.0110 <= 0.01 100
^ > 0.01 to <= 0.05 0
, ' > 0.05 to <= 0,1 0
Difference in Annual PM2.5 DV~ 2030rg_cll minus 2030rg_ret
Figure III-4. Projected Change in 2030 Annual PM2.5 Design Values Between the Reference Case and Control
Case
14
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C. Impacts of Tier 3 Standards on Future 24-hour PMi.s Levels
This section summarizes the results of our modeling of 24-hour PM2.s air quality impacts
in the future due to the Tier 3 rule. Specifically, for the years 2018 and 2030 we compare a
reference scenario (a scenario without the proposed standards) to a 2030 control scenario that
includes the standards. Our modeling indicates that 24-hour PM2.5 design values in the majority
of the modeled counties would decrease due to the 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.
As described in Section 7.2.1.1 of the RIA, the air quality modeling used inventories that include
an increase in direct PM2.5 emissions in the West and Pacific Northwest that is an artifact of a
difference in fuel properties that isn't real.26 Although in most areas this direct PM2.5 increase is
outweighed by reductions in secondary PM2.5, the air quality modeling does predict ambient
PM2.5 increases in a few places in the West and Pacific Northwest. These modeled increases are
a result of the inventory issue, and we do not expect them to actually occur. Ambient PM2.5
projections are discussed in more detail below. Figures III-5 and III-6 present the changes in 24-
hour PM2.5 design values in 2018 and 2030 respectively.27 Appendix D details the state and
county 24-hour PM2.5 design values for the ambient baseline and the future reference and control
cases.
Number of Countii
^B <= -05 ug/m3 0
^H > -0.5 to <= -0.25 t
^B >-0.25 to <=-0.15 15
l>-0,15lo<=-0,05
I I >-0.05 to < 0.05
I >=0.0510<0.15
H >=0.15to<0.25
^H - 0.25 to < 0.5
= 05
Difference in Daily PM2.5 DV-- 2018rg_ctl minus 2018rg_ref2
Figure III-5. Projected Change in 2018 24-hour PM2.5 Design Values Between the Reference Case and the
Control Case
26 The issue is with the way that some of the fuel property data, specifically E200/E300 and T50/T90, matched up in
the fuel compliance database in the West and Pacific Northwest, see Section 7.2.1.1 for additional information.
27 A 24-hour PM2 5 design value is the concentration that determines whether a monitoring site meets the 24-hour
NAAQS for PM2 5. The full details involved in calculating a 24-hour PM2 5 design value are given in appendix N of
40 CFR part 50.
15
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^H <= -0,5 ug
^H .. >; -i r _:
^H > -0.25 10 <= -0.15 116
I > -0.15 to <= -0.05 243
I I > -0.05 10 < 0.05
I I >= 0.05 10 < 015
139
= 0.25 t
< 0 5
Difference in Daily PM2.S DV- 2030rg_ctl minus 2030rg_rel
Figure III-6. Projected Change in 2030 24-hour PM2.S Design Values Between the Reference Case and the
Control Case
As shown in Figure III-5, in 2018 there are 16 counties with projected 24-hour PM2.5
design value decreases greater than 0.15 |ig/m3. These counties are in urban areas in states that
have not adopted California LEV III standards. The maximum projected decrease in a 2018 24-
hour PM2.5 design value is 0.30 |ig/m3 in Utah County, Utah. There are three counties with
projected increases in their 24-hour PM2.5 design values in 2018: Washington County, Oregon;
King County, Washington; and Sheridan County, Wyoming. These projected increases are a
result of the issue with the air quality modeling emissions inventories discussed in Section
7.2.1.1 of the Tier 3 RIA, and we do not expect these increases will occur. 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 50 counties have projected design value decreases greater than 0.25 |ig/m3. The
maximum projected decrease in a 24-hour PM2.5 design value in 2030 is 0.8 |ig/m3 in Salt Lake
County, Utah. As shown in Figure III-6, design values in 9 counties are projected to decrease by
more than 0.5 |ig/m3. These counties are in Utah, Idaho, Colorado and Wisconsin. There are
two counties with projected increases in their 24-hour PM2.5 design values in 2030: King County,
Washington, and Pierce County, Washington. These projected increases are a result of the issue
with the air quality modeling emissions inventories discussed in Section 7.2.1.1 of the RIA and
we do not expect these increases will occur.
16
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D. Impacts of Tier 3 Standards on Future Nitrogen Dioxide Levels
This section summarizes the results of our modeling of annual average nitrogen dioxide
(NO2) air quality impacts in the future due to the final Tier 3 standards. Specifically, for the
years 2018 and 2030 we compare a reference scenario (a scenario without the Tier 3 standards)
to a control scenario that includes the Tier 3 standards. Figure III-7 and Figure III-8 present the
changes in annual NC>2 concentrations in 2018 and 2030 respectively.
Legend
= -03 ppbV
-0.3 to <=-0.2
-0.2 to <=-0.1
-0.1 to <=-0.01
-0.01 to < 0.01
= 0.01 to < 0,1
= 0.1 to < 0.2
= 0.2 to < 0.3
0.3
Difference in Annual Total HO2 Concentration
2018rg_ctl minus 20iarg_ref2
Figure III-7. Projected Change in 2018 Annual NO2 Concentrations Between the Reference Case and
Control Case
17
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Annual Total NO2 Concentration
2Q30rg_ctt minus 2030rg_ref
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 rule. 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 NO2 concentrations and help any
potential nonattainment areas to attain and maintain the standard.
E. Impacts of Tier 3 Standards on Future Ambient Air Toxic Concentrations
The following sections summarize the results of our modeling of air toxics impacts in the future
from the Tier 3 fuel and vehicle emission standards. We focus on air toxics which were
identified as national and regional-scale cancer and noncancer risk drivers in the 2005 National-
9R
Scale Air Toxics Assessment (NATA) and were also likely to be significantly impacted by the
standards. These compounds include benzene, 1,3-butadiene, formaldehyde, acetaldehyde,
naphthalene, and acrolein. Impacts on ethanol concentrations were also included in our analyses.
Our modeling indicates that the impacts of the standards include generally small decreases in
ambient concentrations of air toxics, with the greatest reductions in urban areas. Air toxics
pollutants 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
28 U.S. EPA. (2011) 2005 National-Scale Air Toxics Assessment, http://www.epa.gov/ttn/atw/nata2005/. Docket
EP A-HQ-O AR-2011-0135.
18
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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 2018 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 2018.
However, our 2018 modeling projects there would be small immediate reductions in ambient
concentrations of air toxics due to the sulfur controls that take effect 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 both 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 increases or decreases 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. 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.
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
9Q
reactive air toxics and criteria pollutants.
29 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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M* show mod*l*d chws** b*tw*»n On wtortnn and ««*.
Percent Cfwig* for Acvtaldertiyctt Am
*( thn" mM*»d changes b*tw**n Itv* refemnc* and centr
Scale ranges and increrrerrts may not b» conearabW between t
Figure III-9. Changes in Annual Acetaldehyde Ambient Concentrations Between the Reference Case and the
Control Case in 2018: Percent Changes (left) and Absolute Changes in jig/m3 (right)
Scales 6h
-------
Figure III-ll. Changes in Formaldehyde Ambient Concentrations Between the Reference Case and the Control
Case in 2018: 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 2018, soon after the 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 III-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 111-14).
Figure 111-13. Changes in Benzene Ambient Concentrations Between the Reference Case and the Control Case in
2018: Percent Changes (left) and Absolute Changes in jig/m3 (right)
may not be compare
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 2018 and
2030. Figure 111-15 shows that in 2018, ambient concentrations of 1,3-butadiene generally
decrease between 1 and 5 percent across the country, corresponding to small decreases in
22
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absolute concentrations (less than 0.001 ug/m3). In 2030, reductions of 1,3-butadiene
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 2018: 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 2018 and 2030. Figure 111-17 shows decreases in ambient concentrations of acrolein
23
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generally between 1 and 2.5 percent across the parts of the country in 2018, corresponding to
small decreases in absolute concentrations (less than 0.001 ug/m3). Reductions of acrolein
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 2018: Percent Changes (left) and Absolute Changes in jig/m3 (right)
MW colon Oo not mdicaw me wvemy d ei
Scale ranpe
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)
24
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6. Ethanol
Our modeling projects that the proposed standards would slightly decrease ambient
ethanol concentrations in 2018 and 2030. As shown in Figure 111-19, in 2018, annual percent
changes in ambient concentrations of ethanol are less than 1 percent across the country, with
absolute concentrations of up to 0.1 ppb in some places. In 2030, some parts of the country,
especially urban areas, are projected to have reductions in ethanol concentrations on the order of
1 to 10 percent as a result of the rule (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.
Map colors do not irotsaW the s»ity
Seals range
Figure 111-19. Changes in Ethanol Ambient Concentrations Between the Reference Case and the Control
Case in 2018: 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)
25
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7. Naphthalene
Our modeling projects reductions in naphthalene concentrations in 2018 and 2030. As
shown in Figure 111-21 and Figure 111-22, annual percent changes in ambient concentrations of
naphthalene are between 1 and 2.5 percent across much of the country for 2018, with small
decreases in absolute concentrations (less than 0.001 ug/m3). In 2030, reductions of naphthalene
concentrations generally range between 1 and 10 percent but are as high as 25 percent in some
areas of the Southeast, with corresponding absolute decreases in urban areas of up to 0.005
|ig/m3.
Figure 111-21. Changes in Naphthalene Ambient Concentrations Between the Reference Case and the Control
Case in 2018: Percent Changes (left) and Absolute Changes in jig/m3 (right)
Figure 111-22. Changes in Naphthalene Ambient Concentrations Between the Reference Case and the Control
Case in 2030: Percent Changes (left) and Absolute Changes in jig/m3 (right)
26
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F. Air Toxics Population Metrics
To assess the impact of the 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 Tier 3 standards are generally small, they are projected to benefit the majority
of the U.S. population. As shown in
Table III-l, over 75 percent of the total U.S. population is projected to experience a decrease in
ambient benzene and 1,3-butadiene concentrations of at least 1 percent.
Table III-l also shows that over 60 percent of the U.S population is projected to experience at
least a 1 percent decrease in ambient ethanol and acrolein concentrations, and over 35 percent
would experience a similar decrease in ambient formaldehyde concentrations with the standards.
Table III-l. Percent of Total Population Experiencing Changes in Annual Ambient Concentrations of Toxic
Pollutants in 2030 as a Result of the Tier 3 Standards
Percent Change
<-50
> -50 to < -25
> -25 to < -10
>-10to<-5
> -5 to < -2.5
>-2.5to<-l
> -1 to < 1
> 1 to < 2.5
>2.5 to<5
>5to< 10
> 10 to < 25
> 25 to < 50
>50
Benzene
2.29%
20.63%
27.50%
28.60%
20.97%
Acrolein
0.75%
12.72%
25.17%
24.62%
36.74%
1,3 -Butadiene
19.07%
27.29%
15.37%
18.33%
19.93%
Formaldehyde
0.60%
35.34%
64.06%
Ethanol
5.39%
24.08%
34.10%
36.43%
Acetaldehyde
11.77%
88.23%
Naphthalene
10.74%
31.56%
20.58%
14.98%
22.14%
G. Impacts of 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 rule.
Figure 111-23 shows that for nitrogen deposition by 2030 the proposed standards would result in annual
percent decreases of more than 2.5 percent in most urban areas with decreases of more than 5 percent in
urban areas in Nevada, Florida, Georgia and Virginia. In addition, smaller decreases, in the 1 to 1.5 percent
range, would occur over most of the rest of the country.
27
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Legend
M
^|
j^B
^|
^H
11
= -5.0%
-5.0 to
-2.5 to
-2.0 to
-1.510
-1.010
-0.5 to
0.5 to
1.0 to
1.5 to
= -2.5
= -2.0
= -1.5
= -1,0
= -0.5
= 0.5
= 1.0
= 1.5
= 2.0
2.0
Percent Change in Annual Total Nitrogen Deposition -- 2030rg_ctl minus 2030rg_ref
Figure 111-23. Percent Change in Annual Total Nitrogen Deposition over the U.S.
Modeling Domain as a Result of the Tier 3 Standards in 2030
Figure 111-24 shows that for sulfur deposition the Tier 3 standards will result in annual
percent decreases of more than 2 percent in some urban 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.
28
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Legend
^H
^H
^H
^|
^|
= -2.5%
-2.5(0
-2.0 to
-1.510
-1.0 to
-0.5(0
0.5(0
1.0 (0
1.5(0
2.0(0
= -2.0
= -1.5
= -1.0
= -0.5
= 0.5
= 1.0
= 1.5
= 2.0
= 2.5
2.5
Percent Change in Annual Total Sulfur Deposition - 2030rg_ctl minus 2030rg_ref
Figure 111-24. Percent Changes in Annual Total Sulfur Deposition over the U.S. Modeling
Domain as a Result of the Tier 3 Standards in 2030
H. Impacts of Tier 3 Standards on Future Visibility Levels
Air quality modeling conducted for the Tier 3 rule was used to project visibility
conditions in 137 Mandatory Class I Federal areas across the U.S. in 2018 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 2007 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 2005-2009 period30.
Visibility for the 2018 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.s and
30
Since the base case modeling used meteorology for 2007, one of the complete years must be 2007.
29
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regional haze modeling guidance recommends the calculation of future year changes in visibility
in a similar manner to the calculation of changes in PM2.5 design values. The regional haze
methodology for calculating future year visibility impairment is included in MATS
(http://www.epa.gov/scram001/modelingapps_mats.htm)
In calculating visibility impairment, the extinction coefficient values31 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- 2005
End monitor year- 2009
Base model year 2007
Minimum years required for a valid monitor- 3
The "base model year" was chosen as 2007 because it is the base case meteorological
year for the Tier 3 final rule modeling. The start and end years were chosen as 2005 and 2009
because that is the 5 year period which is centered on the base model year of 2007. 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 rule would improve visibility in all these
areas. 2 The average visibility on the 20 percent worst days at all modeled Mandatory Class I
Federal areas is projected to improve by 0.02 deciviews, or 0.16 percent, in 2030. The greatest
improvement in visibilities will be seen in Craters of the Moon National Monument, where
visibility is projected to improve by 0.7 percent (0.09 DV) in 2030 due to the standards. Table
III-2 contains the full visibility results from 2018 and 2030 for the 137 analyzed areas.
31 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.
32 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.
30
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Table III-2. Visibility Levels in Deciviews for Mandatory Class I Federal Areas on the 20
Percent Worst Days with and without Tier 3 Rule
Class 1 Area
(20% worst days)
Sipsey 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
Superstition Wilderness
Sycamore Canyon
Wilderness
Agua Tibia Wilderness
Ansel Adams Wilderness
(Minarets)
Caribou Wilderness
Cucamonga Wilderness
Desolation Wilderness
Dome Land Wilderness
Emigrant Wilderness
Hoover Wilderness
John Muir Wilderness
Joshua Tree NM
Kaiser Wilderness
Kings Canyon NP
Lassen Volcanic NP
Lava Beds NM
Marble Mountain
Wilderness
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
State
AL
AR
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
AZ
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
CA
2007
Baseline
Visibility
(dv)a
28.32
25.86
12.22
12.22
12.22
11.97
13.40
11.79
13.02
13.40
13.63
13.81
15.18
20.92
15.72
15.99
18.03
13.62
19.23
16.87
12.19
15.72
17.83
15.72
23.39
15.99
14.17
17.34
13.62
18.37
22.03
19.14
18.03
20.48
20.48
19.20
23.39
14.17
2018
Reference
20.59
20.01
11.82
11.83
11.99
11.21
12.65
10.98
12.24
12.69
13.02
13.18
14.94
17.67
14.57
15.54
15.37
12.89
17.89
15.84
11.49
14.76
15.75
14.80
21.56
15.52
13.78
17.02
12.88
16.44
21.04
18.72
15.71
17.68
17.76
17.46
21.28
13.60
2018
TierS
Control
20.55
19.98
11.82
11.82
11.98
11.20
12.65
10.98
12.23
12.69
13.00
13.18
14.94
17.66
14.57
15.54
15.36
12.89
17.89
15.84
11.48
14.76
15.75
14.80
21.55
15.52
13.78
17.01
12.88
16.43
21.03
18.70
15.71
17.68
17.76
17.46
21.28
13.60
2030
Reference
20.43
19.93
12.38
12.38
12.41
11.31
12.88
11.24
12.37
12.93
13.04
13.38
15.03
16.85
14.38
15.48
14.91
12.76
17.60
15.67
11.41
14.60
15.33
14.59
21.06
15.45
13.68
16.91
12.75
16.05
20.71
18.43
15.31
16.94
16.95
17.10
20.74
13.49
2030
Tier3
Control
20.37
19.88
12.37
12.37
12.40
11.30
12.85
11.22
12.35
12.91
12.99
13.34
15.02
16.85
14.38
15.48
14.90
12.75
17.60
15.66
11.41
14.60
15.32
14.59
21.05
15.45
13.67
16.91
12.75
16.05
20.71
18.42
15.30
16.93
16.95
17.10
20.73
13.49
Natural
Background
10.99
11.57
7.20
7.20
7.20
7.04
6.68
6.24
6.49
6.68
6.46
6.54
6.65
7.64
7.12
7.31
6.99
6.05
7.46
7.64
7.71
7.12
7.19
7.12
7.70
7.31
7.85
7.90
6.05
7.99
15.77
13.91
6.99
7.30
7.30
7.57
7.70
7.85
31
-------
Class 1 Area
(20% worst days)
Thousand Lakes
Wilderness
Ventana Wilderness
Yolla Bolly Middle Eel
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
Chassahowitzka
Everglades NP
St. Marks
Cohutta Wilderness
Okefenokee
Wolf Island
Craters of the Moon NM
Sawtooth Wilderness
Mammoth Cave NP
Acadia NP
Moosehorn
Roosevelt Campobello
International Park
Isle Royale NP
Seney
Boundary Waters Canoe
Area
Voyageurs NP
Hercules-Glades
Wilderness
Mingo
Bob Marshall Wilderness
Cabinet Mountains
Wilderness
Glacier NP
Medicine Lake
State
CA
CA
CA
CA
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
CO
FL
FL
FL
GA
GA
GA
ID
ID
KY
ME
ME
ME
Ml
Ml
MN
MN
MO
MO
MT
MT
MT
MT
2007
Baseline
Visibility
(dv)a
15.99
18.37
17.34
16.87
10.04
8.94
8.94
11.44
10.04
8.94
11.28
9.72
9.72
12.62
10.04
8.94
23.68
20.41
25.58
28.01
26.00
26.00
13.63
14.76
30.68
21.45
19.92
19.92
21.76
24.21
20.05
19.78
26.05
27.08
15.32
13.47
18.70
18.02
2018
Reference
15.53
16.79
17.06
15.98
9.21
7.98
8.26
10.57
9.36
8.15
10.48
9.12
8.92
11.66
9.38
8.12
18.63
17.43
20.07
18.77
21.32
20.53
12.91
14.61
21.59
17.41
16.23
16.45
18.49
20.30
17.05
17.60
20.36
21.09
15.13
13.16
18.39
16.67
2018
TierS
Control
15.53
16.79
17.06
15.98
9.20
7.97
8.26
10.56
9.35
8.14
10.47
9.11
8.91
11.64
9.37
8.11
18.59
17.42
20.04
18.73
21.30
20.51
12.86
14.61
21.55
17.38
16.21
16.43
18.45
20.26
17.01
17.57
20.32
21.06
15.13
13.15
18.38
16.66
2030
Reference
15.46
16.50
16.99
15.85
9.26
7.97
8.28
10.59
9.44
8.18
10.57
9.10
8.88
11.55
9.45
8.18
18.38
17.28
19.86
18.59
21.33
20.45
12.63
14.58
21.47
17.22
16.14
16.34
18.21
20.17
16.77
17.35
20.21
20.88
15.06
13.01
18.23
16.47
2030
Tier3
Control
15.45
16.49
16.99
15.84
9.24
7.93
8.27
10.57
9.43
8.17
10.55
9.08
8.86
11.50
9.44
8.16
18.31
17.25
19.81
18.52
21.31
20.41
12.54
14.57
21.41
17.19
16.12
16.32
18.13
20.09
16.70
17.29
20.14
20.83
15.05
13.00
18.21
16.45
Natural
Background
7.31
7.99
7.90
7.64
6.21
6.06
6.06
6.66
6.21
6.06
6.81
6.08
6.08
7.15
6.21
6.06
11.03
12.15
11.67
10.78
11.44
11.44
7.53
6.42
11.08
12.43
12.01
12.01
12.37
12.65
11.61
12.06
11.30
11.62
7.73
7.52
9.18
7.89
32
-------
Class 1 Area
(20% worst days)
Mission Mountains
Wilderness
Red Rock Lakes
Scapegoat Wilderness
ULBend
Linville Gorge Wilderness
Shining Rock Wilderness
Lostwood
Great Gulf Wilderness
Presidential Range-Dry
River Wilderness
Brigantine
Bandelier NM
Bosquedel Apache
Carlsbad Caverns NP
Gila Wilderness
Pecos Wilderness
San Pedro Parks
Wilderness
Wheeler Peak Wilderness
White Mountain
Wilderness
Jarbidge Wilderness
Wichita Mountains
Crater Lake NP
Diamond Peak Wilderness
Eagle Cap Wilderness
Gearhart Mountain
Wilderness
Hells Canyon Wilderness
Kalmiopsis Wilderness
Mount Hood Wilderness
Mount Jefferson
Wilderness
Mount Washington
Wilderness
Mountain Lakes
Wilderness
Strawberry Mountain
Wilderness
Three Sisters Wilderness
Cape Romain
Badlands NP
Wind Cave NP
Great Smoky Mountains
NP
State
MT
MT
MT
MT
NC
NC
ND
NH
NH
NJ
NM
NM
NM
NM
NM
NM
NM
NM
NV
OK
OR
OR
OR
OR
OR
OR
OR
OR
OR
OR
OR
OR
SC
SD
SD
TN
2007
Baseline
Visibility
(dv)a
15.32
11.53
15.32
14.86
27.39
26.60
19.56
20.19
20.19
27.32
11.84
13.40
15.85
12.49
9.13
9.89
9.13
13.20
12.42
22.97
13.79
13.79
16.23
13.79
18.15
16.45
13.72
16.18
16.18
13.79
16.23
16.18
26.45
16.55
15.50
28.50
2018
Reference
15.08
11.20
15.17
14.41
18.40
18.17
18.58
15.15
15.05
20.66
10.81
12.32
15.19
11.94
8.19
9.06
8.13
12.34
12.17
19.63
13.33
13.23
15.61
13.35
17.54
15.82
12.71
15.58
15.57
13.28
15.37
15.63
19.75
15.25
14.41
19.57
2018
TierS
Control
15.07
11.19
15.17
14.41
18.37
18.13
18.57
15.13
15.03
20.63
10.79
12.30
15.18
11.94
8.18
9.05
8.13
12.33
12.16
19.60
13.32
13.22
15.59
13.35
17.50
15.81
12.68
15.57
15.55
13.28
15.34
15.61
19.72
15.24
14.39
19.52
2030
Reference
14.98
11.13
15.12
14.37
18.33
18.04
18.45
15.08
14.97
20.59
10.89
12.54
15.88
12.40
8.34
9.28
8.25
12.74
12.13
19.52
13.22
13.07
15.22
13.27
17.20
15.63
12.25
15.33
15.32
13.16
15.00
15.45
19.61
15.19
14.26
19.44
2030
Tier3
Control
14.97
11.11
15.11
14.36
18.28
17.98
18.44
15.05
14.94
20.55
10.85
12.50
15.86
12.39
8.32
9.27
8.23
12.73
12.12
19.45
13.22
13.07
15.20
13.27
17.16
15.62
12.23
15.31
15.31
13.16
14.97
15.44
19.56
15.17
14.24
19.38
Natural
Background
7.73
6.44
7.73
8.16
11.22
11.47
8.00
11.99
11.99
12.24
6.26
6.73
6.65
6.66
6.08
5.72
6.08
6.80
7.87
7.53
7.62
7.62
8.92
7.62
8.32
9.44
8.43
8.79
8.79
7.62
8.92
8.79
12.12
8.06
7.71
11.24
33
-------
Class 1 Area
(20% worst days)
Joyce- Kilmer-Slickrock
Wilderness
Big Bend NP
Guadalupe Mountains NP
Arches NP
Bryce Canyon NP
Canyonlands NP
Capitol Reef NP
James River Face
Wilderness
Shenandoah NP
Lye Brook Wilderness
Alpine Lake Wilderness
Glacier Peak Wilderness
Goat Rocks Wilderness
Mount Adams Wilderness
Mount Rainier NP
North Cascades NP
Olympic NP
Pasayten Wilderness
Dolly Sods Wilderness
Otter Creek Wilderness
Bridger Wilderness
Fitzpatrick Wilderness
Grand Teton NP
Teton Wilderness
Yellowstone NP
State
TN
TX
TX
UT
UT
UT
UT
VA
VA
VT
WA
WA
WA
WA
WA
WA
WA
WA
WV
WV
WY
WY
WY
WY
WY
2007
Baseline
Visibility
(dv)a
28.50
16.69
15.85
11.02
11.88
11.02
11.30
27.29
27.26
23.01
16.09
13.72
12.66
12.66
16.38
13.72
15.20
14.09
27.55
27.55
10.68
10.68
11.53
11.53
11.53
2018
Reference
19.65
16.39
15.23
10.33
11.40
10.50
10.73
19.05
17.67
16.74
14.87
12.78
11.92
12.04
15.53
12.87
14.30
13.51
17.97
18.11
10.23
10.21
11.14
11.18
11.26
2018
TierS
Control
19.61
16.38
15.22
10.32
11.40
10.48
10.72
19.02
17.63
16.70
14.84
12.77
11.90
12.02
15.52
12.86
14.28
13.50
17.94
18.07
10.22
10.21
11.13
11.18
11.26
2030
Reference
19.52
17.32
15.94
10.30
11.39
10.57
10.74
18.89
17.60
16.58
14.22
12.56
11.66
11.77
15.25
12.71
13.94
13.26
17.99
18.08
10.20
10.18
11.09
11.15
11.23
2030
Tier3
Control
19.46
17.31
15.92
10.27
11.37
10.55
10.72
18.83
17.54
16.53
14.17
12.54
11.64
11.75
15.24
12.70
13.92
13.25
17.95
18.04
10.19
10.17
11.07
11.14
11.22
Natural
Background
11.24
7.16
6.65
6.43
6.80
6.43
6.03
11.13
11.35
11.73
8.43
8.39
8.35
8.35
8.54
8.01
8.44
8.25
10.39
10.39
6.45
6.45
6.44
6.44
6.44
34
-------
Air Quality Modeling Technical Support Document:
Tier 3 Motor Vehicle Emission and Fuel Standards
Appendix A
Model Performance Evaluation for the 2007-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
February 2014
A-l
-------
A.I. Introduction
An operational model performance evaluation for ozone, PM2.s 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 2007 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 Continental United States domain1. Included in this
evaluation are statistical measures of model versus observed pairs that were paired in space and
time on a daily or weekly basis, depending on the sampling frequency of each network
(measured data). For certain time periods with missing ozone, PM2.5, air toxic observations and
nitrate and sulfate deposition we excluded the CMAQ predictions from those time periods in our
calculations. It should be noted when pairing model and observed data that each CMAQ
concentration represents a grid-cell volume-averaged value, while the ambient network
measurements are made at specific locations.
Model performance statistics were calculated for several spatial scales and temporal
periods. Statistics were generated for 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 2007 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.s total mass and its components including sulfate (864), nitrate (NOs), total nitrate
(TNO3=NO3+HNO3), ammonium (NH/i), 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 2007 were
obtained from the following networks: Chemical Speciation Network (CSN), Interagency
: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
-------
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.s 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. PMi.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 Tier 3 standards and
rulemaking, i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and acrolein. Similar to
the PM2.s 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 Continental United States domain. 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
2007 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
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.
4 Appel, K.W., Gilliam, R.C., Davis, N., Zubrow, A., and Howard, S.C.: Overview of the Atmospheric Model
Evaluation Tool (AMET) vl.l for evaluating meteorological and air quality models, Environ. Modell. Softw.,26, 4,
434-443, 2011. (http://www.cmascenter.org/)
A-3
-------
Normalized mean bias (NMB) is used as a normalization to facilitate a range of
concentration magnitudes. This statistic averages the difference (model - observed) over the sum
of observed values. NMB is a useful model performance indicator because it avoids over
inflating the observed range of values, especially at low concentrations.
Normalized mean bias is defined as:
NMB= ^- *100
Normalized mean error (NME) is also similar to NMB, where the performance statistic is used as
a normalization of the mean error. NME calculates the absolute value of the difference (model -
observed) over the sum of observed values.
Normalized mean error is defined as:
NME= _ - *100
i
Fractional bias is defined as:
FB= -
n
-0
*100, where P = predicted and O = observed concentrations.
2 ^
FB is a useful model performance indicator because it has the advantage of equally weighting
positive and negative bias estimates. The single largest disadvantage in this estimate of model
performance is that the estimated concentration (i.e., prediction, P) is found in both the
numerator and denominator. Fractional error (FE) is similar to fractional bias except the
absolute value of the difference is used so that the error is always positive.
Fractional error is defined as:
FE=!
n
-O
*100
The "acceptability" of model performance was judged by comparing our CMAQ 2007
performance results to the range of performance found in recent regional ozone, PM2.5, and air
A-4
-------
toxic model applications.5'6'7'8'9'10'1112'13'14'15'16 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 2007 CMAQ
simulations performed for the Tier 3 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 2007 modeling platform provide a
scientifically credible approach for assessing ozone and PM2.5 concentrations for the purposes of
the Tier 3 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., andPleim, 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, J.E., Otte, T.L., Mathur, R., Sarwar, G., Young, J.O.,
Gilliam, R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., and Bash, J.O.,: Incremental testing of the Community
multiscale air quality (CMAQ) modeling system version 4.7. Geoscientific Model Development, 3, 205-226, 2010.
9 Hogrefe, G., Civeroio, K.L., Hao, W., Ku, J-Y., Zalewsky, E.E., and Sistla, G., Rethinking the Assessment of
Photochemical Modeling Systems in Air Quality Planning Applications. Air & Waste Management Assoc.,
58:1086-1099,2008.
10 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform:
Air Toxics, Ozone, and 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/i.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/sessionll/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. Spatial plots of the normalized mean bias and error for
individual monitors are shown in Figures A-la through A-lb. The statistics shown in these two
figures were calculated over the ozone season using data pairs on days with observed 8-hour
ozone of > 60 ppb.
In general, CMAQ slightly under-predicts seasonal eight-hour daily maximum ozone for
the five subregions, with the exception of a slight over-prediction in the summer and fall at the
Central, Southeast and West 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 -0.7 to 12.0 percent and the error
statistics range from 12.6 to 23.9 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
for the 2007 CMAQ model simulation.
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
11,194
15,222
16,730
14,711
2,884
12,028
17,012
9,911
6,549
21,249
23,418
17,819
5,216
12,468
16,455
11,429
24,485
28,684
32,295
28,984
NMB (%)
-8.1
-3.2
11.9
4.5
-20.9
-8.3
-3.3
-2.0
-5.3
-7.0
3.5
5.9
-19.7
-9.2
-0.7
0.7
-0.7
-4.3
7.1
5.5
NME (%)
20.3
15.0
23.9
18.6
25.1
14.5
15.2
16.4
14.8
12.8
17.1
17.6
23.3
15.3
15.6
16.9
18.2
12.6
18.3
17.8
FB (%)
-8.4
-2.4
13.0
5.7
-26.0
-8.4
-3.4
0.2
-4.0
-7.1
5.4
7.6
-23.6
-9.5
-0.4
2.5
0.2
-4.2
6.0
5.9
FE (%)
24.0
16.2
25.5
20.0
31.4
16.1
16.3
18.1
18.6
13.9
18.5
18.9
29.8
16.9
16.4
18.4
20.8
13.5
18.5
18.9
A-6
-------
O3_8hrmax NMB (%) for run 2007rg_v5J)7e_25L_cb05tump_12US2 for 20070501 to 20070930
CIRCLE=AQS_Daily;
units = %
coverage limit = 75%
l>100
80
60
40
20
0
-20
-40
-60
-80
<-100
Figure A-la. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60
ppb over the period May-September 2007 at monitoring sites in the modeling domain.
A-7
-------
O3_8hrmax NME (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for 20070501 to 20070930
units = %
coverage limit = 75%
> 100
90
80
70
60
50
40
30
120
10
'o
CIRCLE=AQS_Daily;
Figure A-lb. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60
ppb over the period May-September 2007 at monitoring sites in the modeling domain.
A.3. Evaluation of PMi.s Component Species
The evaluation of 2007 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
spatial maps which show the normalized mean bias and error by site, aggregated by season.
A-8
-------
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. Spatial plots of the normalized mean bias and error by season
for individual monitors are shown in Figures A-3 through A-6. As seen in Table A-3, CMAQ
generally under-predicts sulfate in the five U.S. subregions throughout the entire year.
Table A-3. Sulfate performance statistics by subregion, by season for the 2007 CMAQ
model simulation.
Subregion
Central
U.S.
Midwest
Southeast
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
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
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
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
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
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
A-9
-------
Subregion
Northeast
West
Network
CSN
IMPROVE
CASTNet
CSN
IMPROVE
CASTNet
Season
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.
268
273
828
894
874
902
561
689
649
591
193
206
192
195
830
867
853
900
2373
2650
2307
2365
250
273
281
268
NMB (%)
-21.7
-18.6
-9.1
8.2
-8.9
-9.1
-6.8
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 (%)
24.9
21.3
34.9
37.2
27.2
28.9
31.1
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 (%)
-28.6
-19.3
-13.0
4.3
-3.1
0.0
-10.7
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 (%)
32.9
23.3
34.6
34.9
31.0
31.0
33.2
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-10
-------
SO4 NMB (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for December to February 2007
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
units = %
coverage limit = 75%
>100
80
60
40
20
0
-20
I-40
-60
-80
Figure A-3a. Normalized Mean Bias (%) of sulfate during winter 2007 at monitoring sites
in the modeling domain.
A-ll
-------
SO4 NME (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for December to February 2007
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
units = %
coverage limit = 75%
|>100
90
80
70
60
50
140
30
20
10
'o
Figure A-3b. Normalized Mean Error (%) of sulfate during winter 2007 at monitoring
sites in the modeling domain.
A-12
-------
SO4 NMB (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for March to May 2007
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
units = %
coverage limit = 75%
>100
80
60
40
20
0
-20
-40
-60
-80
<-100
Figure A-4a. Normalized Mean Bias (%) of sulfate during spring 2007 at monitoring sites
in the modeling domain.
A-13
-------
SO4 NME (%) for run 2007rg_v5_07e_25L_cbQ5tump_12US2 for March to May 2007
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
units = %
coverage limit = 75%
> 100
90
80
70
60
50
40
30
20
10
0
Figure A-4b. Normalized Mean Error (%) of sulfate during spring 2007 at monitoring
sites in the modeling domain.
A-14
-------
SO4 NMB (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for June to August 2007
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
units = %
coverage limit = 75%
>100
80
60
40
20
0
-20
-40
-60
-80
<-100
Figure A-5a. Normalized Mean Bias (%) of sulfate during summer 2007 at monitoring
sites in the modeling domain.
A-15
-------
SO4 NME (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for June to August 2007
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
units = %
coverage limit = 75%
> 100
90
80
70
60
50
40
30
20
10
'o
Figure A-5b. Normalized Mean Error (%) of sulfate during summer 2007 at monitoring
sites in the modeling domain.
A-16
-------
SO4 NMB (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for September to November 2007
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
units = %
coverage limit = 75%
>100
80
60
40
20
0
-20
-40
-60
-80
<-100
Figure A-6a. Normalized Mean Bias (%) of sulfate during fall 2007 at monitoring sites in
the modeling domain.
A-17
-------
SO4 NME (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for September to November 2007
units = %
coverage limit = 75%
> 100
90
80
70
60
50
40
30
20
10
'o
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-6b. Normalized Mean Error (%) of sulfate during fall 2007 at monitoring sites in
the modeling domain.
A.3.1. Evaluation for Nitrate
The model performance bias and error statistics for nitrate for each subregion and each
season are provided in Table A-4. This table includes statistics for particulate nitrate, as
measured at CSN and IMPROVE sites. Spatial plots of the normalized mean bias and error by
season for individual monitors are shown in Figures A-7 through A-10. Overall, 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.
A-18
-------
Table A-4. Nitrate performance statistics by subregion, by season for the 2007 CMAQ
model simulation.
Region
Central
U.S.
Midwest
Southeast
Northeast
West
Network
CSN
IMPROVE
CSN
IMPROVE
CSN
IMPROVE
CSN
IMPROVE
CSN
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
Fall
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
Winter
Spring
No. of
Obs.
479
503
485
460
608
722
688
622
598
637
621
639
143
171
182
126
888
918
866
911
469
525
500
496
273
829
894
874
902
561
689
649
586
831
859
NMB (%)
-7.6
26.9
23.7
101.0
2.6
46.1
17.7
158.0
-23.7
59.1
38.0
64.8
-30.1
50.4
20.3
104.0
-29.3
34.4
-31.1
71.3
-7.3
54.9
-18.3
98.7
66.9
-6.4
37.5
-11.2
68.5
35.5
67.2
5.0
108.0
-47.8
-38.9
NME (%)
48.7
60.3
99.1
129.0
54.0
76.5
109.0
188.0
41.4
80.3
94.3
94.9
49.0
85.1
96.7
138.0
61.6
94.7
83.5
136.0
81.3
113.0
109.0
179.0
76.1
43.4
74.0
87.5
104.0
74.4
108.0
111.0
151.0
64.8
59.1
FB (%)
-9.1
12.6
-44.1
16.0
-8.5
-5.4
-58.1
12.4
-25.3
38.0
-13.8
21.0
-33.0
-5.8
-43.8
-1.5
-62.9
-14.6
-86.4
-32.4
-63.8
-32.1
-95.0
-49.5
41.7
-6.6
28.5
-62.7
-16.2
28.5
28.3
-64.9
-12.4
-65.4
-70.9
FE (%)
59.8
65.6
95.9
89.1
70.6
90.7
112.0
107.0
50.6
64.6
83.3
74.0
74.3
89.9
99.8
102.0
89.1
92.4
115.0
109.0
101.0
108.0
136.0
126.0
56.2
50.6
67.5
103.0
87.1
76.0
92.4
113.0
100.0
89.7
90.6
A-19
-------
Region
Network
IMPROVE
Season
Summer
Fall
Winter
Spring
Summer
Fall
No. of
Obs.
846
896
2,374
2,643
2,305
2,357
NMB (%)
-73.1
-49.7
-33.1
-40.3
-74.6
-34.2
NME (%)
76.8
70.7
78.3
76.4
84.1
82.3
FB (%)
-134.0
-69.8
-88.0
-89.9
-145.0
-77.2
FE (%)
138.0
97.5
123.0
119.0
153.0
122.0
NO3 NMB (%) tor run 2007rg_v5_07e_25L_cb05tump_12US2 tor December to February 2007
CIRCLE=IMPROVE; TRIANGLE=CSN;
units = %
coverage limit = 75%
> 100
80
60
40
20
0
-20
I-40
-60
-80
<-100
Figure A-7a. Normalized Mean Bias (%) for nitrate during winter 2007 at monitoring sites
in the modeling domain.
A-20
-------
NO3 NME (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for December to February 2007
CIRCLE=IMPROVE; TRIANGLE=CSN;
units = %
coverage limit = 75%
|>100
90
80
70
60
50
140
30
120
10
'o
Figure A-7b. Normalized Mean Error (%) for nitrate during winter 2007 at monitoring
sites in the modeling domain.
A-21
-------
NO3 NMB (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for March to May 2007
CIRCLE=IMPROVE; TRIANGLE=CSN;
units = %
coverage limit = 75%
>100
80
60
40
20
0
-20
I-40
-60
-80
Figure A-8a. Normalized Mean Bias (%) for nitrate during spring 2007 at monitoring sites
in the modeling domain.
A-22
-------
NO3 NME (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for March to May 2007
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-8b. Normalized Mean Error (%) for nitrate during spring 2007 at monitoring
sites in the modeling domain.
A-23
-------
NO3 NMB (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for June to August 2007
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-9a. Normalized Mean Bias (%) for nitrate during summer 2007 at monitoring
sites in the modeling domain.
A-24
-------
NO3 NME (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for June to August 2007
^TX-
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-9b. Normalized Mean Error (%) for nitrate during summer 2007 at monitoring
sites in the modeling domain.
A-25
-------
NO3 NMB (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for September to November 2007
CIRCLE=IMPROVE; TRIANGLE=CSN;
units = %
coverage limit = 75%
Figure A-lOa. Normalized Mean Bias (%) for nitrate during fall 2007 at monitoring sites
in the modeling domain.
A-26
-------
NO3 NME (%) for run 2007rg_v5_07e_25L_cb05tump_12US2 for September to November 2007
W*^
CIRCLE=IMPROVE; TRIANGLE=CSN;
units = %
coverage limit = 75%
|>100
90
80
70
60
50
140
30
120
10
'o
Figure A-lOb. Normalized Mean Error (%) for nitrate during fall 2007 at monitoring sites
in the modeling domain.
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 2007 CMAQ
model simulation.
Region
Central
U.S.
Network
CSN
Season
Winter
Spring
Summer
No. of
Obs.
771
875
851
NMB (%)
-2.9
4.8
-21.4
NME (%)
43.3
41.9
45.9
FB (%)
-1.9
7.3
-24.4
FE (%)
50.7
43.2
60.9
A-27
-------
Region
Network
CASTNet
Season
Fall
Winter
Spring
Summer
Fall
No. of
Obs.
587
72
77
72
75
NMB (%)
17.1
2.9
16.6
-17.1
16.9
NME (%)
54.8
37.6
33.9
29.5
44.1
FB (%)
22.5
3.7
10.9
-19.8
24.3
FE (%)
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
Spring
Summer
Fall
828
894
874
902
193
206
192
195
0.1
31.1
-11.5
16.6
21.3
42.0
-23.5
8.7
34.1
53.2
36.1
49.4
37.6
48.5
29.8
39.0
4.2
34.0
3.6
28.4
25.9
32.0
-26.7
13.6
34.3
49.5
44.0
50.6
36.8
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-28
-------
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 2007
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-29
-------
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-30
-------
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 2007
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-31
-------
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-32
-------
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 Continental United States domain. The seasonal model
performance results for the East and West are presented below in Tables A-8 and A-9,
respectively. Toxic measurements included in the evaluation were taken from the 2007
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 2007 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 2007 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-33
-------
Table A-8. Air toxics performance statistics by season for the 2007 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.
613
435
745
622
577
387
421
455
,507
,122
,038
938
,385
,033
,522
,257
559
416
685
951
NMB (%)
-48.6
-51.0
-51.6
-51.1
-31.0
-14.6
-32.0
-22.8
8.4
1.4
15.5
25.6
-30.4
-41.6
-51.2
-33.4
-91.5
-93.6
-94.1
-95.2
NME (%)
60.5
62.0
65.4
62.0
48.2
45.2
53.3
57.3
65.2
63.1
68.2
64.7
91.2
88.4
88.9
81.8
93.3
94.9
99.0
98.8
FB (%)
-53.0
-51.5
-31.0
-39.0
-31.0
-13.9
-28.1
-21.8
12.5
3.4
12.3
19.2
9.5
-17.6
-47.3
-21.2
-156.0
-169.0
-155.0
-150.0
FE (%)
68.0
74.6
57.3
60.4
54.0
49.2
58.0
55.4
53.1
54.6
66.2
59.7
85.1
74.4
89.4
87.6
156.0
169.0
155.0
154.0
A-34
-------
L. Seasonal Nitrate and Sulfate Deposition Performance
Seasonal nitrate and sulfate deposition performance statistics for the 12 km Continental
U.S. domain are provided in Table A-10. The model predictions for seasonal nitrate deposition
generally show under-predictions for the continental U.S. NADP sites (NMB values range from -
6% to -34%). Sulfate deposition performance shows the similar predictions (NMB values range
from -12% to 28%). The errors for both annual nitrate and sulfate are relatively moderate with
values ranging from 51% to 70% which reflect scatter in the model predictions versus
observation comparison.
Table A-10. Nitrate and sulfate wet deposition performance statistics by season for the
2007 CMAQ model simulation.
Wet Deposition
Species
Nitrate
Sulfate
Season
Winter
Spring
Summer
Fall
Winter
Spring
Summer
Fall
No. of Obs.
1,992
2,013
2,147
2,037
1,992
2,013
2,147
2,037
NMB (%)
-5.9
-21.6
-34.3
-14.8
-27.5
-17.6
-11.5
-21.1
NME (%)
60.7
51.3
60.9
57.0
53.5
53.1
69.8
59.2
FB (%)
-21.0
-27.4
-34.6
-25.0
-30.1
-13.0
-5.7
-28.2
FE (%)
75.5
70.5
77.4
74.8
76.2
70.7
78.7
78.0
A-35
-------
Air Quality Modeling Technical Support Document:
Tier 3 Motor Vehicle Emission and Fuel 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
February 2014
B-l
-------
Table B-l. 8-Hour Ozone Design Values for Tier3 Scenarios
(units are ppb)
State
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
California
California
California
California
California
California
California
County
Baldwin
Clay
Colbert
Elmore
Etowah
Houston
Jefferson
Lawrence
Madison
Mobile
Montgomery
Morgan
Russell
Shelby
Sumter
Tuscaloosa
Cochise
Coconino
Gila
La Paz
Maricopa
Pima
Pinal
Yuma
Crittenden
Newton
Polk
Pulaski
Washington
Alameda
Amador
Butte
Calaveras
Colusa
Contra Costa
El Dorado
2007
Baseline
DV
75.7
76.0
71.0
69.7
70.0
68.7
85.3
74.0
76.3
76.7
73.0
74.7
73.0
85.3
64.0
73.0
68.7
70.0
77.7
72.5
79.7
73.7
77.3
74.5
82.3
70.3
73.3
78.7
64.0
78.7
82.3
83.7
87.0
68.0
75.0
95.7
2018
Reference
DV
64.05
60.88
55.99
55.92
55.18
56.30
69.27
60.85
60.91
66.13
58.63
60.70
57.31
67.04
53.97
59.65
60.43
64.28
63.80
63.00
67.47
61.33
64.24
62.98
69.40
59.83
62.34
64.53
52.03
67.38
67.30
69.01
71.89
57.33
68.97
77.00
2018 Tier 3
Control DV
63.67
60.16
55.43
55.18
54.50
55.68
68.61
60.30
60.21
65.76
57.86
60.09
56.45
66.07
53.52
59.06
60.33
64.22
63.72
62.96
67.36
60.64
64.14
62.66
68.75
59.39
61.90
63.81
51.34
67.35
67.28
68.99
71.87
57.30
68.94
76.97
2030
Reference
DV
59.05
57.29
52.73
52.26
51.73
52.97
65.75
57.51
56.84
61.42
54.74
57.07
52.76
62.29
51.21
56.34
58.86
62.49
60.27
60.38
64.13
57.1
60.8
59.8
65.62
57.24
59.55
60.54
48.68
62.13
60.25
62.11
64.94
52.2
64.94
68.38
2030 Tier 3
Control DV
58.25
56.19
51.82
51.14
50.65
52.01
64.7
56.61
55.71
60.73
53.58
56.1
51.56
60.66
50.48
55.41
58.37
62.4
59.24
60.24
62.86
55.51
59.75
59.21
64.35
56.51
58.82
59.3
47.39
62.1
60.22
62.08
64.91
52.16
64.89
68.35
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
California
Colorado
Colorado
County
Fresno
Glenn
Imperial
Inyo
Kern
Kings
Lake
Los Angeles
Madera
Mariposa
Merced
Monterey
Napa
Nevada
Orange
Placer
Riverside
Sacramento
San Benito
San Bernardino
San Diego
San Joaquin
San Luis Obispo
Santa Barbara
Santa Clara
Santa Cruz
Shasta
Solano
Sonoma
Stanislaus
Sutter
Tehama
Tulare
Tuolumne
Ventura
Yolo
Adams
Arapahoe
2007
Baseline
DV
99.7
69.0
82.7
80.7
106.7
90.5
61.3
105.7
81.7
86.3
89.3
58.3
59.7
91.0
85.0
89.3
104.5
100.0
76.7
119.7
90.0
85.0
84.0
74.0
74.3
60.0
76.0
73.7
57.0
87.3
81.7
83.3
103.7
84.7
87.7
77.7
71.0
78.0
2018
Reference
DV
83.66
58.42
69.74
69.47
88.37
74.67
51.25
100.93
69.85
71.74
73.89
51.16
50.41
73.80
76.10
71.70
95.72
80.17
63.86
109.19
73.83
70.06
69.43
65.46
61.34
53.61
64.22
61.76
43.02
73.48
69.52
69.22
83.33
71.18
76.33
66.06
63.19
68.09
2018 Tier 3
Control DV
83.65
58.38
69.72
69.45
88.35
74.65
51.22
100.91
69.83
71.72
73.86
51.15
50.37
73.77
76.08
71.69
95.70
80.15
63.83
109.16
73.81
70.04
69.41
65.45
61.32
53.59
64.18
61.73
42.98
73.44
69.49
69.17
83.32
71.16
76.31
66.02
62.57
67.32
2030
Reference
DV
76.1
53.5
70.72
65.61
82.43
67.48
47.37
93.96
63.38
66.33
66.58
48.44
45.95
65.99
72.75
63.11
90.46
70.46
58.99
102.19
67.09
63.78
64.01
60.81
57.04
50.54
59.34
56.26
37.53
66.87
63.44
63.21
75.19
65.26
68.94
59.87
60.73
65.26
2030 Tier 3
Control DV
76.09
53.46
70.68
65.56
82.41
67.46
47.34
93.86
63.36
66.32
66.54
48.42
45.91
65.97
72.65
63.09
90.36
70.44
58.96
102.09
67.05
63.76
63.99
60.79
57
50.5
59.31
56.22
37.5
66.83
63.4
63.16
75.17
65.24
68.91
59.84
58.96
63.39
B-3
-------
State
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
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
County
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
Collier
Columbia
Duval
Escambia
Highlands
Hillsborough
Holmes
Lake
Lee
Leon
Manatee
Marion
Miami-Dade
2007
Baseline
DV
80.0
72.3
81.0
72.0
84.3
70.3
80.0
71.0
75.0
88.7
86.0
83.3
87.0
87.3
88.0
86.0
79.0
81.3
79.7
84.7
70.5
66.7
75.0
69.7
66.0
69.0
69.0
74.0
78.7
71.0
80.0
69.3
73.7
67.7
70.3
77.0
70.7
72.0
2018
Reference
DV
69.40
64.35
70.95
64.64
74.76
62.65
70.89
66.97
68.28
76.03
71.01
67.35
74.68
72.78
70.21
70.82
66.08
68.57
65.40
69.21
54.26
53.64
63.09
57.22
59.30
56.06
55.59
59.50
64.63
61.73
69.69
57.29
61.82
55.23
54.96
63.48
55.69
64.38
2018 Tier 3
Control DV
68.69
63.72
70.13
64.31
73.99
62.55
70.48
66.86
67.98
75.16
70.03
66.42
73.91
72.28
69.82
69.90
65.57
67.85
64.70
68.03
53.49
53.05
62.64
56.65
58.91
55.21
54.95
58.76
63.95
61.32
69.01
56.72
61.04
54.53
54.04
62.60
54.95
63.95
2030
Reference
DV
66.41
61.84
67.88
63
71.54
61.88
68.54
66.28
66.6
72.92
65.28
62.11
69.72
68.77
66.23
65.35
60.41
63.84
61.05
65.47
50.23
50.14
59.54
53.29
56.25
51.54
51.81
54.7
59.69
58.85
65.11
53.96
58.23
51.05
50.31
57.6
52.03
60.81
2030 Tier 3
Control DV
64.96
60.04
65.82
62.37
69.39
61.72
67.7
66.07
65.96
72.36
64.28
61.13
68.81
68.09
65.68
64.37
59.7
62.87
60.04
62.74
48.9
49.17
58.79
52.24
54.78
49.94
50.78
53.36
58.42
58.16
63.75
53.09
56.69
49.77
48.76
55.67
50.93
59.42
B-4
-------
State
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Idaho
Idaho
Idaho
Illinois
Illinois
County
Orange
Osceola
Palm Beach
Pasco
Pinellas
Polk
St Lucie
Santa Rosa
Sarasota
Seminole
Volusia
Wakulla
Bibb
Chatham
Chattooga
Clarke
Cobb
Columbia
Coweta
Dawson
De Kalb
Douglas
Fayette
Fulton
Glynn
Gwinnett
Henry
Murray
Muscogee
Paulding
Richmond
Rockdale
Sumter
Ada
Butte
Kootenai
Adams
Champaign
2007
Baseline
DV
75.7
71.3
66.0
74.7
70.3
73.7
64.3
79.3
75.3
71.0
65.7
69.7
80.5
66.0
73.3
80.0
84.0
72.7
82.0
76.3
90.7
85.3
87.5
90.3
64.3
86.0
92.0
77.7
77.0
79.0
77.7
91.7
71.7
77.0
64.0
63.5
67.0
66.3
2018
Reference
DV
64.29
59.67
58.15
61.97
59.77
58.82
55.29
64.00
60.98
59.50
52.50
59.27
62.14
54.24
57.06
59.18
65.75
58.86
65.13
58.89
71.78
65.73
67.51
71.01
51.27
65.06
69.43
58.67
60.42
60.79
62.99
69.15
57.94
65.56
60.10
54.18
56.68
57.44
2018 Tier 3
Control DV
63.53
58.86
57.65
61.29
59.22
57.95
54.83
63.33
60.11
58.67
51.75
58.84
61.15
53.65
56.22
58.00
64.47
58.02
64.19
57.79
70.35
64.46
66.12
69.52
50.64
63.55
67.87
57.68
59.45
59.73
62.09
67.62
57.25
64.41
59.98
53.61
56.24
57.06
2030
Reference
DV
60.86
55.93
54.68
58.49
56.16
54.21
52.09
58.85
54.84
55.52
48.62
56.19
57.09
49.62
52.07
53.2
59.61
54
61.23
53.14
65.29
59.1
61.53
64.24
46.88
57.43
62.76
52.46
55.16
55.18
57.75
62.32
53.87
60.96
58.73
48.2
53.56
55.03
2030 Tier 3
Control DV
59.28
54.15
53.44
57.32
55.05
52.59
51.15
57.6
53.02
53.83
47.44
55.46
55.71
48.67
50.66
51.29
57.26
52.65
59.82
51.22
62.55
57.02
59.08
61.51
45.86
54.61
60.22
50.91
53.8
53.35
56.37
59.79
52.87
58.7
58.53
47.4
52.76
54.33
B-5
-------
State
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
County
Clark
Cook
Du Page
Effingham
Hamilton
Jersey
Kane
Lake
McHenry
McLean
Macon
Macoupin
Madison
Peoria
Randolph
Rock Island
St Clair
Sangamon
Will
Winnebago
Allen
Boone
Carroll
Delaware
Elkhart
Floyd
Greene
Hancock
Hendricks
Huntington
Jackson
Johnson
Lake
La Porte
Madison
Marion
Morgan
Perry
2007
Baseline
DV
66.0
77.0
65.0
70.0
70.0
73.7
69.3
75.0
68.3
72.0
71.7
70.7
79.7
73.3
71.3
65.3
74.7
66.7
67.3
69.0
73.0
78.0
70.0
72.3
73.3
76.3
76.7
76.0
73.7
70.7
73.3
75.3
77.5
73.0
72.0
78.0
76.3
76.7
2018
Reference
DV
57.00
71.02
60.28
60.04
59.82
63.53
60.02
60.03
59.11
61.00
61.22
59.51
67.59
62.92
61.56
56.11
66.01
56.72
58.75
56.45
60.81
66.93
59.90
60.00
62.23
66.69
66.36
65.26
62.84
59.81
61.41
63.71
67.73
64.26
60.51
68.10
65.41
66.96
2018 Tier 3
Control DV
56.62
70.67
59.92
59.64
59.39
63.02
59.48
59.85
58.65
60.55
60.80
58.99
66.89
62.43
61.11
55.74
65.48
56.30
58.29
55.81
60.23
66.36
59.43
59.43
61.72
66.28
65.91
64.69
62.27
59.28
60.90
63.20
67.58
64.00
59.96
67.56
64.89
66.58
2030
Reference
DV
54.56
68.95
58.6
57.4
56.95
60.41
57.16
57.1
56.56
58.31
58.67
56.03
63.94
60.31
58.43
53.53
62.79
54.03
56.26
52.61
57.21
63.61
56.92
56.53
58.98
64.25
63.38
61.93
59.54
56.44
58.31
60.47
65.23
61.8
57.24
65.01
62.42
64.19
2030 Tier 3
Control DV
53.88
67.86
57.57
56.7
56.2
59.2
55.98
56.51
55.54
57.5
57.91
55.01
62.82
59.43
57.57
52.82
61.56
53.23
55.24
51.37
56.15
62.47
56.05
55.51
57.94
63.48
62.59
60.82
58.48
55.4
57.42
59.58
64.5
60.99
56.16
63.92
61.49
63.5
B-6
-------
State
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
County
Porter
Posey
St Joseph
Shelby
Vanderburgh
Vigo
Warrick
Bremer
Clinton
Harrison
Linn
Montgomery
Palo Alto
Polk
Scott
Story
Van Buren
Warren
Johnson
Leavenworth
Linn
Sedgwick
Shawnee
Sumner
Trego
Wyandotte
Bell
Boone
Boyd
Bullitt
Campbell
Carter
Christian
Daviess
Edmonson
Fayette
Greenup
Hancock
2007
Baseline
DV
76.0
71.0
74.3
76.0
79.0
69.7
76.3
65.3
68.7
68.3
68.3
65.7
59.3
61.0
66.7
64.0
66.3
65.0
70.0
72.7
69.7
67.0
66.0
72.7
68.7
71.7
69.0
72.0
73.7
72.7
76.0
70.0
81.0
77.7
74.0
71.3
75.3
75.3
2018
Reference
DV
66.73
61.72
64.38
65.31
68.33
60.56
66.61
56.28
59.08
59.57
59.16
56.19
51.95
52.12
56.17
53.67
56.29
54.89
60.71
61.84
59.27
58.09
56.21
62.84
63.14
62.60
56.02
61.47
62.35
63.60
65.12
58.94
67.52
68.32
61.98
59.38
64.13
65.70
2018 Tier 3
Control DV
66.56
61.37
63.91
64.75
67.93
60.11
66.25
55.90
58.67
59.22
58.80
55.82
51.66
51.73
55.71
53.19
55.90
54.47
60.26
61.28
58.87
57.63
55.84
62.39
62.92
62.11
55.46
61.04
61.90
63.21
64.53
58.49
67.10
67.96
61.56
58.79
63.68
65.34
2030
Reference
DV
64.26
58.92
61.26
62.04
65.23
57.83
62.19
53.52
56.13
56.83
56.7
53.34
49.44
48.97
53.27
50.25
53.13
51.52
57.62
58.03
56.72
55.67
53.63
60.4
61.79
59.22
52.73
58.51
58.93
61.08
61.78
55.68
64.58
65.19
59.19
55.69
60.51
62.32
2030 Tier 3
Control DV
63.55
58.28
60.27
60.96
64.53
56.98
61.51
52.82
55.38
56.15
55.99
52.68
48.91
48.23
52.44
49.36
52.42
50.76
56.51
56.9
55.94
54.76
52.92
59.56
61.4
58.06
51.81
57.72
58.14
60.26
60.57
54.95
63.86
64.56
58.45
54.54
59.72
61.69
B-7
-------
State
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
County
Hardin
Henderson
Jefferson
Jessamine
Livingston
McCracken
Oldham
Perry
Pike
Pulaski
Simpson
Trigg
Warren
Ascension
Bossier
Caddo
Calcasieu
East Baton
Rouge
Iberville
Jefferson
Lafayette
Lafourche
Livingston
Ouachita
Pointe Coupee
St Bernard
St Charles
St James
St John The
Baptis
West Baton
Rouge
Androscoggin
Cumberland
Hancock
Kennebec
Knox
Oxford
2007
Baseline
DV
76.3
77.0
80.0
73.7
71.3
74.3
80.7
72.3
70.3
68.7
75.3
75.0
70.3
81.7
75.0
75.3
76.7
83.0
81.3
79.3
75.0
76.0
79.0
67.0
82.0
70.0
74.0
74.0
78.0
78.0
72.0
74.3
80.3
70.7
72.3
62.7
2018
Reference
DV
65.23
67.34
69.68
61.85
61.87
64.81
67.15
61.65
58.77
59.03
61.92
62.40
58.27
73.74
63.26
64.76
68.87
74.50
73.06
70.38
65.01
66.81
70.68
55.69
74.93
61.57
65.21
66.66
69.59
70.32
61.32
61.51
69.70
58.68
61.54
54.62
2018 Tier 3
Control DV
64.74
66.98
69.25
61.35
61.49
64.46
66.48
61.15
58.19
58.63
61.35
61.95
57.86
73.47
62.67
64.31
68.60
74.17
72.75
70.10
64.60
66.51
70.34
55.18
74.66
61.28
64.93
66.41
69.31
70.01
60.75
60.81
69.12
58.01
60.88
54.24
2030
Reference
DV
62.32
63.2
66.98
58
58.99
61.87
63.24
58.57
55.4
56.26
58.18
59.23
55.22
70.38
59.82
61.97
66.13
71.55
69.23
66.03
61.65
62.51
67.36
52.52
72.37
57.68
60.94
62.99
65.53
67.51
57.08
56.8
65.03
54.53
57.14
52.18
2030 Tier 3
Control DV
61.42
62.53
66.15
57.12
58.34
61.22
61.97
57.73
54.48
55.55
57.16
58.44
54.48
69.82
58.68
61.14
65.63
70.87
68.61
65.5
60.93
61.95
66.69
51.59
71.86
57.12
60.39
62.48
65.01
66.93
56.43
55.97
64.28
53.79
56.35
51.64
B-8
-------
State
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
County
Penobscot
Washington
York
Anne Arundel
Baltimore
Calvert
Carroll
Cecil
Charles
Frederick
Garrett
Harford
Kent
Montgomery
Prince Georges
Washington
Baltimore City
Barnstable
Berkshire
Bristol
Dukes
Essex
Hampden
Hampshire
Middlesex
Norfolk
Suffolk
Worcester
Allegan
Benzie
Berrien
Cass
Clinton
Genesee
Huron
Ingham
Kalamazoo
Kent
2007
Baseline
DV
66.0
64.5
75.0
85.7
83.3
78.0
82.3
89.0
80.7
80.3
73.3
90.7
81.3
82.7
85.3
76.7
67.0
79.7
76.3
78.0
81.3
81.3
88.0
83.7
78.7
82.0
75.3
82.3
86.7
76.7
79.3
75.0
73.3
76.3
74.7
74.3
74.3
78.7
2018
Reference
DV
58.21
56.31
61.47
69.42
71.97
64.47
65.89
73.70
64.67
63.69
64.57
76.59
67.08
66.92
69.05
63.30
60.40
64.87
62.47
63.27
69.84
65.32
72.01
68.35
65.13
66.35
61.71
67.03
74.90
67.98
70.10
62.79
61.30
63.75
64.69
62.20
62.21
65.90
2018 Tier 3
Control DV
57.79
55.81
60.82
68.43
71.52
63.83
64.82
72.82
63.80
62.69
64.22
75.87
66.25
65.63
68.01
62.58
60.01
64.57
61.68
62.84
69.36
64.85
71.00
67.37
64.29
65.91
61.39
66.10
74.44
67.53
69.74
62.23
60.69
63.13
64.27
61.60
61.66
65.35
2030
Reference
DV
55.03
52.4
56.62
64.77
68.57
59.56
61.19
68.24
60.61
59.6
62.57
71.07
62.12
62.14
64.5
59.17
57.88
60.52
57.98
58.88
65.04
61.99
66.44
62.99
60.48
63.73
58.5
61.79
71.42
64.81
67.2
59.37
57.92
59.85
61.75
58.86
58.71
62.64
2030 Tier 3
Control DV
54.5
51.78
55.83
63.22
67.72
58.58
59.37
67.02
59.33
57.75
62.01
69.99
60.94
59.74
62.63
58.17
57.19
60.12
57.23
58.35
64.56
61.34
65.44
62.03
59.65
63.1
58.03
60.86
70.33
63.66
66.22
58.28
56.78
58.59
60.92
57.7
57.65
61.54
B-9
-------
State
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Montana
Nebraska
Nebraska
County
Leelanau
Lenawee
Macomb
Manistee
Mason
Missaukee
Muskegon
Oakland
Ottawa
St Clair
Schoolcraft
Washtenaw
Wayne
An oka
Adams
Bolivar
De Soto
Harrison
Hinds
Jackson
Lauderdale
Lee
Cass
Cedar
Clay
Clinton
Greene
Jefferson
Lincoln
Monroe
Perry
St Charles
Ste Genevieve
St Louis
St Louis City
Yellowstone
Douglas
Lancaster
2007
Baseline
DV
74.0
75.7
82.0
74.5
76.3
71.3
82.3
77.3
79.7
79.3
75.7
74.0
81.7
66.0
71.0
72.7
80.7
81.0
70.3
77.7
70.3
71.7
72.0
71.7
81.3
80.0
73.0
86.0
81.0
71.0
77.0
84.0
79.3
82.3
83.5
59.0
64.3
53.3
2018
Reference
DV
62.33
64.91
75.17
66.11
67.70
60.10
72.30
69.63
67.31
69.08
65.27
64.29
74.58
58.57
62.01
61.59
66.27
70.12
55.24
66.66
57.29
57.79
59.66
60.32
69.26
67.28
60.99
75.66
69.07
60.74
65.55
73.15
67.57
73.89
73.58
52.91
55.74
47.09
2018 Tier 3
Control DV
61.96
64.45
74.65
65.70
67.27
59.63
72.01
69.24
66.72
68.60
64.82
63.81
74.13
58.13
61.66
61.11
65.50
69.81
54.41
66.30
56.65
57.17
59.13
59.89
68.62
66.62
60.36
74.95
68.50
60.29
65.06
72.56
67.02
73.27
72.99
52.66
55.43
46.85
2030
Reference
DV
57.76
61.85
71.61
63.07
64.87
57.13
69.51
66.83
64.02
65.85
62.16
61.22
71.4
55.55
58.78
58.23
62.26
66.08
50.63
62.68
53.69
54.34
56.13
57.51
65.02
62.98
57.34
71.78
65.38
57.72
62.09
69.72
63.97
70.3
69.93
50.78
53.13
45.11
2030 Tier 3
Control DV
56.89
60.9
70.24
62.04
63.8
56.28
68.63
65.73
62.92
64.83
61.18
60.22
70.27
54.56
58.19
57.39
60.89
65.47
49.15
62.02
52.66
53.39
55.08
56.75
63.68
61.64
56.15
70.22
64.31
56.94
61.23
68.4
62.92
68.88
68.45
50.33
52.53
44.67
B-10
-------
State
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 Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
County
Churchill
Clark
Washoe
White Pine
Carson City
Belknap
Cheshire
Coos
Graf ton
Hillsborough
Merrimack
Rockingham
Sullivan
Atlantic
Camden
Cumberland
Gloucester
Hudson
Hunterdon
Mercer
Middlesex
Monmouth
Morris
Ocean
Passaic
Bernalillo
Dona Ana
Eddy
Grant
Lea
Luna
Sandoval
2007
Baseline
DV
65.7
82.0
72.3
72.0
66.0
70.0
69.0
76.3
66.7
78.0
70.0
78.0
68.5
77.0
87.5
80.7
85.7
85.0
85.3
86.3
86.3
85.0
83.7
86.3
79.3
72.0
75.0
68.0
62.5
67.3
59.0
71.5
2018
Reference
DV
58.40
71.20
60.96
64.00
54.52
60.04
56.75
64.73
55.58
62.04
59.09
64.10
56.48
65.68
74.75
66.00
71.82
75.61
70.43
74.67
73.34
70.49
68.89
72.24
68.71
60.24
66.66
64.00
55.51
63.20
52.86
61.68
2018 Tier 3
Control DV
58.33
70.45
60.77
63.85
54.50
59.70
56.04
64.09
54.96
61.27
58.36
63.47
55.80
65.38
74.00
65.31
71.13
75.31
69.64
73.94
72.53
70.02
68.03
71.31
68.15
59.61
66.29
63.85
55.40
63.03
52.65
61.38
2030
Reference
DV
56.19
68.11
57.23
61.44
49.92
57.24
52.63
60.72
51.99
57.56
55.18
59.33
52.52
61.16
70.28
60.95
67.43
74.28
65.47
70.1
68.41
65.52
64.03
66.93
65.41
57.39
66.79
63.34
54.66
62.56
53.06
59.98
2030 Tier 3
Control DV
56.1
66.37
56.71
61.17
49.9
56.51
51.86
59.88
51.18
56.68
54.13
58.52
51.67
60.62
69.51
60.1
66.62
73.73
64.55
69.07
67.4
64.87
63.13
65.95
64.65
56.27
66.1
63.06
54.27
62.25
52.61
59.45
B-ll
-------
State
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
New York
New York
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
County
San Juan
Albany
Bronx
Chautauqua
Chemung
Dutchess
Erie
Essex
Hamilton
Herkimer
Jefferson
Madison
Monroe
New York
Niagara
Oneida
Onondaga
Orange
Oswego
Putnam
Queens
Rensselaer
Richmond
Saratoga
Schenectady
Steuben
Suffolk
Ulster
Wayne
Westchester
Alexander
Avery
Buncombe
Caldwell
Caswell
Chatham
Cumberland
Davie
2007
Baseline
DV
70.0
72.3
73.3
83.0
69.0
74.3
81.0
76.7
70.7
70.0
76.0
72.0
77.3
76.0
77.0
66.0
73.3
79.3
73.7
80.3
76.7
74.3
80.7
77.0
67.0
69.5
88.0
72.3
70.0
86.3
75.0
67.0
71.3
74.0
77.3
71.7
77.7
81.0
2018
Reference
DV
66.29
58.54
66.10
75.01
58.53
61.08
72.72
64.79
61.24
60.57
61.74
61.41
67.26
68.53
70.13
55.91
61.30
65.13
64.17
69.80
68.21
59.63
71.03
60.84
53.36
59.74
79.00
61.36
62.58
78.92
59.96
53.98
57.97
57.69
60.21
56.25
61.77
65.79
2018 Tier 3
Control DV
66.20
57.80
65.77
74.63
58.04
60.19
72.38
64.25
60.78
60.10
61.58
60.89
66.79
68.19
69.91
55.42
60.66
64.19
63.89
69.06
67.75
58.83
70.51
59.91
52.58
59.29
78.52
60.79
62.34
78.55
59.10
53.35
57.24
56.72
59.25
55.41
60.82
64.95
2030
Reference
DV
65.45
53.99
63.61
73.61
55.36
56.33
70.29
61.41
58.28
57.71
59.85
58.16
64.15
65.96
68.25
53.02
57.65
60.3
61.53
65.5
65.04
54.87
67.15
55.4
48.75
56.83
74.98
57.76
60.28
76.69
55.65
50.53
53.77
52.7
55.33
51.77
56.45
61.42
2030 Tier 3
Control DV
65.28
53.16
63.05
72.83
54.64
55.39
69.58
60.74
57.64
57.08
59.6
57.42
63.46
65.37
67.84
52.37
56.8
59.26
61.14
64.57
64.42
54
66.44
54.43
47.9
56.15
74.35
57.06
59.94
76.05
54.25
49.57
52.53
51.03
53.71
50.42
54.87
59.94
B-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 Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
County
Durham
Edgecombe
Forsyth
Franklin
Graham
Granville
Guilford
Haywood
Jackson
Johnston
Lenoir
Lincoln
Martin
Mecklenburg
New Hanover
Person
Pitt
Rockingham
Rowan
Swain
Union
Wake
Yancey
Allen
Ashtabula
Athens
Butler
Clark
Clermont
Clinton
Cuyahoga
Delaware
Franklin
Geauga
Greene
Hamilton
Jefferson
Knox
2007
Baseline
DV
74.0
75.3
79.0
76.3
78.0
79.3
81.0
77.0
76.0
75.0
73.7
80.3
72.7
91.0
72.0
76.0
77.0
78.7
87.0
65.0
79.0
79.0
77.0
75.5
84.7
72.0
83.0
76.7
78.3
79.0
79.0
76.3
84.0
73.3
76.7
84.3
76.3
76.0
2018
Reference
DV
57.66
59.23
62.77
57.98
62.60
61.40
62.71
61.44
60.07
57.75
58.87
64.60
60.39
73.45
59.76
61.90
61.29
63.04
69.88
52.61
62.52
61.12
61.98
63.41
70.12
59.37
70.94
64.06
66.17
65.90
65.54
64.03
71.46
63.22
64.17
72.27
65.64
62.73
2018 Tier 3
Control DV
56.68
58.34
61.81
56.90
61.86
60.33
61.56
60.77
59.33
56.69
58.11
63.71
59.73
72.36
59.16
61.18
60.52
62.14
68.87
52.04
61.50
60.03
61.22
62.81
69.87
58.89
70.29
63.45
65.50
65.26
65.57
63.36
70.71
62.81
63.53
71.57
65.24
61.99
2030
Reference
DV
52.54
54.48
58.03
52.89
58.25
55.89
57.31
57.47
55.89
52.6
54.41
60.33
56.41
69.2
55.94
58.03
56.84
58.56
65.51
49.3
58.22
55.98
58.08
59.66
66.48
55.87
67.37
60.52
62.71
62.13
63.69
59.84
66.83
60.47
60.54
68.35
63.09
58.2
2030 Tier 3
Control DV
50.9
53.02
56.48
50.93
56.97
54.04
55.36
56.42
54.67
50.74
53.27
58.77
55.4
67.13
55.08
56.84
55.62
57.23
63.86
48.39
56.3
53.93
57.11
58.61
65.81
55.09
66.18
59.34
61.55
60.96
63.66
58.5
65.26
59.62
59.36
66.93
62.43
56.8
B-13
-------
State
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Oregon
Oregon
Oregon
Pennsylvania
County
Lake
Lawrence
Licking
Lorain
Lucas
Madison
Mahoning
Medina
Miami
Montgomery
Portage
Preble
Stark
Summit
Trumbull
Warren
Washington
Wood
Adair
Canadian
Cherokee
Cleveland
Creek
Dewey
Kay
McClain
Mayes
Oklahoma
Ottawa
Pitts burg
Sequoyah
Tulsa
Clackamas
Jackson
Lane
Marion
Umatilla
Adams
2007
Baseline
DV
78.7
74.0
74.7
73.0
79.0
76.7
75.0
73.0
72.7
75.0
76.0
72.0
78.7
82.3
80.3
85.0
81.0
76.3
72.3
73.7
72.3
72.3
74.3
70.0
74.0
70.0
73.0
78.0
72.0
70.7
66.0
77.7
64.3
67.3
63.7
66.0
63.0
76.0
2018
Reference
DV
63.75
63.02
62.41
59.77
67.31
63.21
63.22
62.10
60.14
63.88
63.86
60.86
65.21
69.79
67.55
71.63
68.33
65.04
62.30
62.58
59.41
63.06
62.05
62.30
62.51
60.48
61.56
64.45
60.73
61.26
56.85
66.44
59.43
55.71
55.58
57.69
57.60
63.18
2018 Tier 3
Control DV
63.67
62.58
61.69
59.53
66.98
62.56
62.61
61.58
59.48
63.30
63.23
60.33
64.56
69.03
66.90
70.91
67.92
64.51
61.89
61.89
59.03
62.56
61.38
61.95
62.05
60.00
61.22
63.68
60.32
60.85
56.44
65.84
59.06
55.04
55.06
57.16
57.31
62.43
2030
Reference
DV
61.93
59.47
58.15
57.17
64.47
59.28
59.99
58.71
56.42
60.56
60.14
57.49
61.32
65.45
63.99
67.73
65.53
61.41
59.79
60.37
57.29
60.65
59.27
60.44
60.12
58.16
59.75
61.41
58.43
58.93
54.45
63.81
54.02
48.4
47.97
50.47
50.14
59.24
2030 Tier 3
Control DV
61.45
58.69
56.74
56.75
63.73
58.16
58.95
57.8
55.28
59.47
58.91
56.52
60.13
63.96
62.83
66.44
64.85
60.44
59.06
59.02
56.59
59.58
58.12
59.8
59.25
57.23
59.07
60.09
57.68
58.15
53.69
62.52
53.52
47.85
47.34
49.85
49.82
58.07
B-14
-------
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
Pennsylvania
Pennsylvania
Rhode Island
Rhode Island
Rhode Island
South Carolina
South Carolina
South Carolina
County
Allegheny
Armstrong
Beaver
Berks
Blair
Bucks
Cambria
Centre
Chester
Clearfield
Dauphin
Delaware
Erie
Franklin
Greene
Indiana
Lackawanna
Lancaster
Lawrence
Lehigh
Luzerne
Ly com ing
Mercer
Monroe
Montgomery
Northampton
Perry
Philadelphia
Tioga
Washington
Westmoreland
York
Kent
Providence
Washington
Abbeville
Aiken
Barnwell
2007
Baseline
DV
85.0
80.0
77.7
79.0
71.7
90.7
70.3
74.3
81.3
73.3
78.0
81.7
78.3
71.0
76.0
76.3
73.7
81.0
71.0
79.3
73.7
76.0
80.0
72.5
83.0
78.3
75.3
88.0
72.7
75.3
75.3
80.0
81.0
81.0
80.7
77.0
76.0
73.0
2018
Reference
DV
73.38
68.00
68.77
66.91
62.23
78.71
60.49
64.18
66.53
62.42
67.84
68.49
69.42
59.17
63.66
66.08
61.51
69.20
59.63
66.86
62.28
64.56
67.26
59.70
71.66
66.45
63.70
78.12
61.69
66.33
65.31
67.56
67.79
65.97
67.65
61.41
60.99
59.71
2018 Tier 3
Control DV
72.91
67.48
68.39
66.29
61.84
77.87
60.13
63.71
65.69
61.98
67.32
67.78
69.09
58.51
63.22
65.67
60.92
68.63
59.09
66.21
61.78
63.98
66.61
59.04
70.99
65.83
63.16
77.32
61.16
65.95
64.91
66.94
67.14
65.63
67.19
60.56
60.09
58.91
2030
Reference
DV
70.29
64.34
66.34
62.87
59.43
73.39
58.32
61.07
61.19
59.51
64.39
63.89
66.48
55.38
60.94
63.43
57.79
65.39
56.7
62.63
58.87
60.29
63.88
55.58
67.52
62.48
59.99
73.02
58.09
63.92
62.62
63.9
63.19
61.43
62.92
56.83
55.85
55.16
2030 Tier 3
Control DV
69.58
63.65
65.71
62.08
58.84
72.34
57.78
60.43
60.01
58.9
63.73
62.98
65.74
54.37
60.27
62.87
57.08
64.69
55.84
61.85
58.25
59.6
62.75
54.77
66.57
61.69
59.25
71.99
57.42
63.33
61.99
63.14
62.49
60.93
62.38
55.48
54.49
53.96
B-15
-------
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 Dakota
South Dakota
South Dakota
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Texas
Texas
Texas
Texas
Texas
Texas
County
Berkeley
Charleston
Chester
Chesterfield
Colleton
Darlington
Edgefield
Pickens
Richland
Spartanburg
Union
Williamsburg
York
Custer
Jackson
Meade
Minnehaha
Anderson
Blount
Davidson
Hamilton
Jefferson
Knox
Loudon
Meigs
Rutherford
Sevier
Shelby
Sullivan
Sumner
Williamson
Wilson
Bexar
Brazoria
Brewster
Cameron
Collin
Dallas
2007
Baseline
DV
62.3
71.0
76.0
72.7
71.3
74.0
69.7
78.7
78.7
81.7
77.0
70.0
76.0
66.7
68.0
56.0
66.0
76.3
83.3
75.0
82.3
80.3
86.0
77.0
78.0
77.3
82.0
80.7
80.0
82.0
74.7
79.3
77.7
86.7
66.0
63.0
83.3
82.3
2018
Reference
DV
51.05
58.48
62.67
58.71
57.64
59.59
55.04
60.95
61.97
63.04
61.94
57.58
60.69
61.78
63.46
51.50
57.88
58.76
64.84
58.87
64.02
62.21
65.21
60.16
61.15
61.48
65.76
67.86
68.29
65.02
60.87
62.43
67.12
74.37
60.76
56.74
68.69
70.71
2018 Tier 3
Control DV
50.43
57.84
61.90
58.02
56.98
58.87
54.26
59.98
61.00
61.92
61.17
57.00
59.78
61.62
63.32
51.32
57.53
57.88
63.86
57.99
62.91
61.30
64.05
59.39
60.27
60.68
64.96
67.23
67.69
64.09
60.14
61.59
66.41
73.55
60.54
56.45
67.77
69.90
2030
Reference
DV
48.25
54.03
59.21
55.18
54.02
55.68
50.55
55.94
57.25
57.36
58.1
54.63
56.63
59.81
61.76
49.92
55.05
53.84
59.7
53.72
57.79
57.22
59.16
55.67
55.74
56.71
61.24
64.44
64.76
59.74
56.64
57.23
63.35
70.09
60.08
54.57
64.56
67.29
2030 Tier 3
Control DV
47.22
53
57.84
54.04
52.95
54.61
49.34
54.35
55.53
55.4
56.82
53.71
55.02
59.5
61.49
49.58
54.37
52.25
58
52.08
55.91
55.63
57.14
54.4
54.21
55.22
60.01
63.22
63.81
58.05
55.3
55.67
61.71
68.04
59.71
54.11
62.43
65.23
B-16
-------
State
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
County
Denton
Ellis
El Paso
Galveston
Gregg
Harris
Harrison
Hidalgo
Hood
Hunt
Jefferson
Johnson
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
2007
Baseline
DV
90.0
78.0
76.3
77.0
79.0
90.3
72.3
63.0
80.7
70.7
80.3
83.7
73.0
78.3
69.7
72.3
85.3
76.0
77.0
90.0
77.3
66.7
57.7
75.0
70.0
80.7
81.0
70.3
75.0
75.0
73.0
81.0
71.3
69.7
83.7
78.7
80.7
76.3
2018
Reference
DV
76.04
66.01
69.02
69.06
71.24
78.35
63.86
55.80
69.97
60.34
71.34
72.14
63.78
67.98
61.94
63.23
72.87
65.10
64.93
77.04
65.77
58.00
50.82
67.08
62.35
73.03
73.94
64.81
68.64
66.33
64.59
74.64
56.88
58.29
70.55
64.15
69.63
65.28
2018 Tier 3
Control DV
75.25
65.43
68.66
68.73
70.91
77.89
63.54
55.46
69.47
59.92
71.10
71.63
63.32
67.34
61.62
62.98
72.26
64.52
64.37
76.31
65.14
57.62
50.53
66.82
62.00
72.65
73.54
64.73
68.37
65.72
64.48
74.25
56.06
57.66
69.43
63.31
69.07
64.64
2030
Reference
DV
72.94
63.11
70.17
65.01
69.97
75.03
62.01
53.95
67.57
57.54
67.26
69.53
60.97
64.66
58.63
60.18
70.26
62.17
61.51
74.19
63.03
55.34
49.25
64.83
59.99
69.91
70.96
63.71
65.77
63.53
62
71.53
52.06
54.42
66.88
60.66
66.56
62.07
2030 Tier 3
Control DV
71.26
61.93
69.46
64.33
69.4
73.31
61.48
53.37
66.52
56.73
66.78
68.48
60.08
63.19
58.01
59.7
68.91
60.93
60.52
72.5
61.71
54.64
48.76
64.37
59.36
69
70.17
63.57
65.2
62.35
61.83
70.47
51.18
53.68
64.18
58.94
65.5
60.75
B-17
-------
State
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Washington
Washington
Washington
Washington
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
County
Fairfax
Fauquier
Frederick
Hanover
Henrico
Loudoun
Madison
Page
Prince William
Roanoke
Rockbridge
Rockingham
Stafford
Wythe
Alexandria City
Hampton City
Suffolk City
King
Pierce
Skagit
Spokane
Berkeley
Cabell
Greenbrier
Hancock
Kanawha
Monongalia
Ohio
Wood
Ashland
Brown
Columbia
Dane
Dodge
Door
Florence
Fond Du Lac
Forest
2007
Baseline
DV
85.3
69.7
71.7
78.7
82.7
80.7
74.7
69.7
75.7
73.3
66.3
67.0
79.3
69.7
79.7
76.5
74.7
73.7
67.3
46.0
62.7
73.0
79.0
70.0
76.0
76.7
73.3
75.5
77.3
61.3
71.7
70.0
70.3
70.0
82.7
65.3
69.3
68.0
2018
Reference
DV
70.40
57.88
56.92
66.41
70.83
64.81
63.86
59.86
62.14
60.08
54.92
57.44
62.67
58.03
65.78
66.39
65.70
68.34
61.55
44.80
53.79
58.45
66.38
59.91
66.86
62.84
64.22
62.92
64.54
53.51
64.75
59.23
59.52
59.45
71.33
55.87
59.75
58.52
2018 Tier 3
Control DV
69.25
57.25
56.39
65.75
70.17
63.74
63.31
59.36
61.33
59.27
54.37
56.97
61.63
57.51
64.70
65.95
65.29
68.06
61.18
44.78
53.21
57.83
65.87
59.51
66.48
62.43
63.92
62.52
64.14
53.16
64.58
58.66
58.89
58.92
70.84
55.45
59.25
58.11
2030
Reference
DV
66.86
54.51
53.42
63.03
67.45
60.71
60.49
56.79
58.51
55.25
51.51
54.54
58.83
54.88
62.47
62.18
62.16
62.51
54.99
43.8
47.18
54.65
62.51
56.98
64.36
59.22
61.98
60.49
61.74
50.73
61.75
56.3
56.42
56.35
68.13
53.48
57.08
55.76
2030 Tier 3
Control DV
64.18
53.4
52.61
61.76
66.11
58.66
59.67
56.01
56.85
53.86
50.6
53.81
56.35
54.05
59.97
61.26
61.39
61.82
54.37
43.77
46.54
53.67
61.65
56.32
63.71
58.49
61.52
59.81
61.08
50.11
61.14
55.24
55.15
55.28
66.92
52.69
56.03
54.97
B-18
-------
State
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
County
Jefferson
Kenosha
Kewaunee
Manitowoc
Marathon
Milwaukee
Outagamie
Ozaukee
Racine
Rock
St Croix
Sauk
Sheboygan
Vernon
Walworth
Washington
Waukesha
Campbell
Sublette
Sweetwater
Teton
Uinta
2007
Baseline
DV
71.3
79.7
77.0
78.7
67.7
77.3
69.7
76.7
74.3
70.7
68.3
66.3
83.3
67.7
71.7
67.3
67.0
68.3
79.0
64.0
64.7
64.0
2018
Reference
DV
60.76
64.28
67.71
68.03
58.87
66.19
60.20
66.32
61.16
60.08
58.61
57.11
71.93
57.76
61.23
59.31
58.97
63.49
73.41
59.28
60.31
56.83
2018 Tier 3
Control DV
60.22
64.08
67.35
67.59
58.47
65.96
59.73
66.07
61.00
59.55
58.12
56.67
71.57
57.35
60.72
58.90
58.55
63.32
73.27
59.14
60.19
56.58
2030
Reference
DV
57.73
61.15
65.07
64.82
56.33
63.29
57.33
63.79
58.19
56.69
55.12
54.43
69.02
54.93
58.24
56.89
56.49
61.9
71.88
58.06
58.92
54.85
2030 Tier 3
Control DV
56.58
60.53
64.03
63.67
55.58
62.58
56.37
62.99
57.63
55.65
54.23
53.59
67.97
54.11
57.1
55.89
55.51
61.62
71.62
57.82
58.73
54.43
B-19
-------
Air Quality Modeling Technical Support Document:
Tier 3 Motor Vehicle Emission and Fuel 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
February 2014
C-l
-------
Table C-l. Annual PM2.s Design Values for Tier 3 Scenarios
(units are ug/m3)
State
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
County
Baldwin
Clay
Colbert
DeKalb
Escambia
Etowah
Houston
Jefferson
Madison
Mobile
Montgomery
Morgan
Russell
Shelby
Tuscaloosa
Walker
Cochise
Coconino
Gila
Maricopa
Pima
Pinal
Santa Cruz
Arkansas
Ashley
Crittenden
Faulkner
Garland
Jackson
Phillips
Polk
Pope
Pulaski
Sebastian
Union
2007
Baseline
DV
10.80
12.04
12.05
12.79
13.18
13.87
11.89
17.01
12.80
11.39
13.70
12.59
14.29
13.11
12.59
13.06
6.83
6.93
8.93
11.98
5.79
9.33
12.67
11.82
12.03
12.53
11.82
11.79
11.19
11.68
11.38
12.30
12.85
11.42
12.02
2018
Reference
DV
7.52
8.23
8.51
8.20
9.80
9.23
8.70
11.89
8.35
8.19
9.95
8.45
10.44
9.03
8.91
8.99
6.87
6.67
8.59
10.86
5.44
8.74
12.55
8.92
9.37
8.71
9.12
9.15
8.42
8.30
8.93
9.81
9.93
9.05
9.41
2018 Tier 3
Control DV
7.51
8.22
8.49
8.18
9.79
9.21
8.68
11.87
8.33
8.18
9.94
8.43
10.43
9.02
8.90
8.98
6.86
6.67
8.58
10.86
5.43
8.74
12.54
8.91
9.36
8.70
9.11
9.14
8.41
8.29
8.92
9.80
9.92
9.03
9.40
2030
Reference
DV
7.42
8.16
8.41
8.10
9.70
9.11
8.61
11.65
8.24
8.01
9.84
8.35
10.33
8.91
8.83
8.88
7.36
6.76
8.71
10.88
5.51
8.84
13.24
8.80
9.30
8.46
9.03
9.06
8.32
8.13
8.86
9.73
9.82
8.96
9.33
2030 Tier 3
Control DV
7.39
8.13
8.37
8.06
9.67
9.06
8.58
11.57
8.19
7.98
9.80
8.31
10.29
8.87
8.80
8.85
7.35
6.73
8.68
10.80
5.49
8.79
13.21
8.78
9.27
8.41
8.99
9.03
8.30
8.11
8.84
9.70
9.77
8.93
9.31
C-2
-------
State
Arkansas
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
California
County
White
Alameda
Butte
Calaveras
Colusa
Contra Costa
Fresno
Humboldt
Imperial
Inyo
Kern
Kings
Lake
Los Angeles
Mendocino
Merced
Monterey
Nevada
Orange
Placer
Plumas
Riverside
Sacramento
San Benito
San Bernardino
San Diego
San Francisco
San Joaquin
San Luis Obispo
San Mateo
Santa Barbara
Santa Clara
Santa Cruz
Shasta
Solano
Sonoma
Stanislaus
Sutter
2007
Baseline
DV
11.54
9.43
11.65
7.90
7.89
8.87
16.88
7.38
12.90
6.14
21.20
17.28
4.84
16.23
6.81
14.70
6.90
6.91
13.18
9.43
11.48
19.20
12.12
6.24
17.17
12.97
9.35
12.73
8.12
8.87
9.98
10.95
6.47
6.88
9.81
8.24
14.46
9.16
2018
Reference
DV
8.77
8.21
10.97
7.00
7.33
7.80
14.20
6.98
13.06
5.56
16.98
14.20
4.56
12.52
6.28
13.14
6.01
6.56
10.12
8.48
11.12
15.40
11.04
5.29
13.88
10.73
7.99
11.44
6.48
7.54
8.98
9.68
5.74
6.66
8.82
7.57
12.81
8.34
2018 Tier 3
Control DV
8.76
8.20
10.97
7.00
7.33
7.80
14.19
6.98
13.06
5.56
16.98
14.20
4.56
12.52
6.28
13.14
6.01
6.56
10.12
8.48
11.12
15.39
11.04
5.28
13.88
10.73
7.99
11.44
6.48
7.54
8.98
9.68
5.74
6.66
8.82
7.57
12.81
8.34
2030
Reference
DV
8.68
8.01
10.82
6.81
7.22
7.68
13.39
6.96
14.92
5.50
15.85
13.19
4.53
12.24
6.21
12.64
5.94
6.49
9.83
8.25
11.02
14.72
10.75
5.17
13.37
10.67
7.88
11.06
6.20
7.41
8.93
9.49
5.68
6.63
8.66
7.47
12.27
8.12
2030 Tier 3
Control DV
8.66
8.01
10.82
6.81
7.21
7.67
13.39
6.96
14.91
5.50
15.84
13.19
4.53
12.24
6.21
12.64
5.94
6.49
9.83
8.25
11.02
14.71
10.75
5.16
13.36
10.67
7.88
11.06
6.20
7.41
8.93
9.49
5.68
6.63
8.66
7.47
12.27
8.12
C-3
-------
State
California
California
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Delaware
Delaware
Delaware
District of Co
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
County
Tulare
Ventura
Yolo
Adams
Arapahoe
Boulder
Denver
Douglas
Elbert
El Paso
Larimer
Mesa
Pueblo
Weld
Fairfield
Hartford
Litchfield
New Haven
New London
Kent
New Castle
Sussex
District of
Columbia
Alachua
Bay
Brevard
Broward
Citrus
Duval
Escambia
Hillsborough
Lee
Leon
Manatee
Marion
Miami-Dade
Orange
2007
Baseline
DV
19.07
10.94
8.75
9.86
7.61
8.13
9.19
6.17
4.44
7.70
7.28
9.34
7.69
9.08
12.28
10.00
8.83
11.84
10.12
11.65
13.95
12.59
13.12
8.66
10.55
7.72
7.83
8.18
9.60
10.45
9.56
7.67
11.11
8.68
9.59
8.64
8.47
2018
Reference
DV
15.82
8.47
7.88
8.67
6.54
7.42
7.99
5.38
4.05
7.11
6.76
8.71
7.21
8.35
8.89
7.67
6.54
9.10
7.59
7.33
9.38
8.13
8.28
6.30
7.75
5.60
5.89
5.66
7.11
7.36
6.69
5.63
8.49
6.01
7.01
6.63
6.02
2018 Tier 3
Control DV
15.81
8.47
7.88
8.67
6.54
7.42
7.99
5.38
4.05
7.10
6.76
8.70
7.20
8.34
8.88
7.66
6.54
9.09
7.58
7.31
9.37
8.11
8.26
6.29
7.74
5.60
5.88
5.65
7.10
7.35
6.68
5.62
8.48
6.01
7.00
6.62
6.01
2030
Reference
DV
14.80
8.32
7.69
8.49
6.40
7.30
7.81
5.29
4.01
7.03
6.69
8.61
7.14
8.23
8.71
7.57
6.46
8.97
7.50
7.21
9.17
8.01
8.17
6.24
7.66
5.45
5.60
5.57
7.01
7.27
6.48
5.45
8.39
5.83
6.89
6.31
5.90
2030 Tier 3
Control DV
14.79
8.32
7.69
8.37
6.32
7.24
7.70
5.23
3.99
6.99
6.63
8.57
7.11
8.16
8.69
7.56
6.45
8.95
7.49
7.18
9.14
7.98
8.12
6.21
7.63
5.43
5.56
5.56
6.97
7.23
6.43
5.43
8.35
5.80
6.86
6.25
5.86
C-4
-------
State
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
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
County
Palm Beach
Pinellas
Polk
St. Lucie
Sarasota
Seminole
Volusia
Bibb
Chatham
Clarke
Clayton
Cobb
DeKalb
Dougherty
Floyd
Fulton
Glynn
Gwinnett
Hall
Houston
Lowndes
Muscogee
Paulding
Richmond
Washington
Wilkinson
Ada
Benewah
Canyon
Franklin
Idaho
Shoshone
Champaign
Cook
DuPage
Hamilton
Jersey
Kane
2007
Baseline
DV
7.03
8.90
8.63
7.90
7.79
8.50
9.25
15.06
13.68
14.90
14.98
14.83
14.25
13.72
14.71
15.64
11.13
14.30
12.92
12.31
11.44
14.15
13.23
14.67
13.94
15.20
6.88
9.63
8.15
7.70
9.58
11.85
11.94
15.12
12.74
12.15
11.97
12.82
2018
Reference
DV
5.28
6.08
6.15
5.79
5.41
6.05
6.69
11.26
10.04
10.26
9.88
9.78
9.11
10.67
9.79
10.02
8.34
9.34
8.57
9.00
8.93
10.29
8.41
10.45
10.12
11.17
6.43
9.31
7.65
6.87
9.32
11.54
9.14
11.87
9.94
8.69
8.91
10.05
2018 Tier 3
Control DV
5.27
6.07
6.15
5.78
5.40
6.04
6.68
11.24
10.03
10.23
9.86
9.76
9.08
10.66
9.77
10.00
8.33
9.31
8.56
8.99
8.92
10.28
8.39
10.43
10.10
11.15
6.42
9.31
7.64
6.85
9.31
11.53
9.12
11.83
9.91
8.67
8.89
10.02
2030
Reference
DV
5.07
5.92
5.98
5.57
5.25
5.92
6.56
11.09
9.87
10.08
9.67
9.56
8.89
10.57
9.65
9.80
8.23
9.13
8.41
8.87
8.86
10.17
8.25
10.31
10.00
11.03
6.27
9.13
7.46
6.65
9.15
11.33
9.01
11.59
9.72
8.59
8.74
9.82
2030 Tier 3
Control DV
5.05
5.88
5.95
5.55
5.23
5.88
6.52
11.04
9.83
10.01
9.57
9.46
8.79
10.54
9.60
9.68
8.19
9.03
8.35
8.83
8.83
10.13
8.20
10.26
9.97
10.99
6.22
9.11
7.40
6.58
9.14
11.31
8.95
11.46
9.62
8.54
8.69
9.72
C-5
-------
State
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
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
County
Lake
McHenry
McLean
Macon
Madison
Peoria
Randolph
Rock Island
St. Clair
Sangamon
Will
Winnebago
Allen
Clark
Delaware
Dubois
Floyd
Henry
Howard
Knox
Lake
La Porte
Madison
Marion
Porter
St. Joseph
Spencer
Tippecanoe
Vanderburgh
Vigo
Black Hawk
Clinton
Johnson
Lee
Linn
Montgomery
Muscatine
Palo Alto
2007
Baseline
DV
10.91
11.33
11.65
12.87
15.43
12.31
12.36
11.31
14.41
12.21
13.03
12.10
13.46
15.55
12.73
14.94
13.87
11.74
12.79
13.10
14.09
12.52
12.97
15.00
12.68
12.74
13.39
12.61
14.25
13.36
11.18
12.73
11.56
11.41
10.53
9.72
13.08
9.19
2018
Reference
DV
8.37
8.79
8.88
9.76
11.95
9.65
9.12
9.28
10.92
9.32
10.16
9.74
10.08
10.37
9.19
10.18
9.03
8.42
9.44
9.10
11.24
9.46
9.31
10.78
9.71
9.77
9.00
9.21
9.97
9.64
9.35
10.92
9.55
9.36
8.52
7.79
11.04
7.42
2018 Tier 3
Control DV
8.34
8.76
8.86
9.73
11.93
9.62
9.11
9.26
10.89
9.29
10.13
9.71
10.05
10.35
9.16
10.16
9.02
8.40
9.42
9.08
11.21
9.43
9.29
10.75
9.69
9.74
8.98
9.19
9.95
9.62
9.33
10.90
9.53
9.34
8.50
7.77
11.02
7.40
2030
Reference
DV
8.19
8.59
8.75
9.57
11.70
9.49
8.98
9.11
10.68
9.14
9.92
9.54
9.91
10.29
9.07
10.07
8.97
8.33
9.31
8.99
11.01
9.25
9.20
10.63
9.50
9.58
8.90
9.05
9.88
9.51
9.16
10.69
9.34
9.16
8.31
7.60
10.83
7.21
2030 Tier 3
Control DV
8.12
8.52
8.69
9.50
11.61
9.42
8.93
9.04
10.59
9.08
9.82
9.46
9.83
10.22
9.01
10.01
8.92
8.27
9.24
8.94
10.93
9.19
9.13
10.53
9.44
9.51
8.85
8.99
9.82
9.45
9.09
10.62
9.27
9.10
8.25
7.56
10.76
7.17
C-6
-------
State
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
County
Polk
Pottawattamie
Scott
Van Buren
Woodbury
Wright
Johnson
Linn
Sedgwick
Shawnee
Sumner
Wyandotte
Bell
Boyd
Bullitt
Carter
Christian
Daviess
Fayette
Franklin
Hardin
Henderson
Jefferson
Kenton
McCracken
Madison
Ohio
Perry
Pike
Caddo Parish
Calcasieu Parish
East Baton Rouge
Parish
Iberville Parish
Jefferson Parish
Lafayette Parish
Ouachita Parish
Rapides Parish
2007
Baseline
DV
10.18
10.95
13.97
10.17
10.40
10.06
9.92
10.14
9.66
9.96
9.29
11.41
13.73
13.51
14.17
11.58
13.19
13.28
13.48
12.60
13.27
13.36
14.68
13.27
13.11
12.26
12.78
13.42
12.61
11.89
9.99
12.27
12.07
10.42
10.17
10.95
10.08
2018
Reference
DV
8.06
8.82
11.81
8.18
8.77
8.04
7.70
7.97
7.93
8.15
7.59
8.92
9.29
8.75
9.31
7.73
8.91
8.93
8.40
7.81
8.57
9.17
9.51
8.60
9.16
7.35
8.68
8.90
8.25
9.50
7.78
9.36
9.03
7.61
7.58
8.34
7.59
2018 Tier 3
Control DV
8.04
8.80
11.79
8.16
8.75
8.02
7.69
7.95
7.92
8.14
7.58
8.90
9.28
8.74
9.29
7.72
8.89
8.91
8.38
7.79
8.55
9.15
9.50
8.58
9.14
7.33
8.67
8.89
8.24
9.49
7.78
9.35
9.03
7.60
7.57
8.33
7.58
2030
Reference
DV
7.83
8.62
11.59
8.00
8.59
7.80
7.56
7.84
7.84
8.03
7.50
8.74
9.21
8.64
9.21
7.66
8.82
8.85
8.29
7.72
8.48
9.08
9.44
8.48
9.01
7.26
8.61
8.86
8.22
9.38
7.66
9.12
8.86
7.42
7.49
8.27
7.53
2030 Tier 3
Control DV
7.76
8.55
11.50
7.95
8.53
7.75
7.50
7.81
7.79
7.98
7.46
8.67
9.18
8.61
9.17
7.63
8.78
8.80
8.23
7.68
8.44
9.03
9.38
8.42
8.96
7.23
8.57
8.84
8.20
9.34
7.65
9.09
8.83
7.40
7.47
8.24
7.51
C-7
-------
State
Louisiana
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
County
St. Bernard
Parish
Tangipahoa
Parish
Terrebonne
Parish
West Baton
Rouge Parish
Androscoggin
Aroostook
Cumberland
Hancock
Kennebec
Oxford
Penobscot
Piscataquis
Anne Arundel
Baltimore
Cecil
Harford
Montgomery
Prince George's
Washington
Baltimore city
Berkshire
Bristol
Essex
Hampden
Middlesex
Plymouth
Suffolk
Worcester
Allegan
Bay
Berrien
Genesee
Ingham
Kalamazoo
Kent
2007
Baseline
DV
10.90
11.18
9.87
12.71
8.79
9.22
9.82
5.11
8.79
9.24
8.36
5.55
13.29
13.54
11.79
11.69
11.45
12.40
12.28
14.16
9.87
8.87
9.18
11.42
8.64
9.39
11.59
10.77
10.93
9.90
10.90
10.68
11.07
12.05
11.78
2018
Reference
DV
8.02
8.10
7.27
9.80
7.79
8.77
8.58
4.20
7.84
8.41
7.32
4.73
8.65
8.80
7.53
7.32
7.06
8.03
7.73
9.29
7.50
6.53
7.12
9.17
6.59
6.87
8.80
8.52
8.51
7.48
8.32
8.25
8.43
9.42
9.26
2018 Tier 3
Control DV
8.02
8.09
7.26
9.79
7.79
8.77
8.57
4.20
7.83
8.41
7.31
4.73
8.64
8.78
7.51
7.31
7.04
8.01
7.71
9.27
7.49
6.53
7.11
9.16
6.58
6.87
8.78
8.51
8.48
7.46
8.30
8.23
8.40
9.39
9.23
2030
Reference
DV
7.82
8.00
7.17
9.55
7.70
8.73
8.43
4.16
7.74
8.33
7.22
4.70
8.52
8.65
7.39
7.22
6.99
7.92
7.63
9.15
7.38
6.47
7.03
9.05
6.51
6.77
8.60
8.38
8.35
7.36
8.16
8.13
8.27
9.23
9.07
2030 Tier 3
Control DV
7.80
7.97
7.16
9.52
7.69
8.73
8.42
4.16
7.74
8.32
7.22
4.69
8.49
8.63
7.36
7.19
6.96
7.88
7.60
9.12
7.37
6.46
7.01
9.04
6.49
6.76
8.58
8.36
8.29
7.31
8.09
8.07
8.20
9.15
8.98
C-8
-------
State
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
County
Macomb
Manistee
Missaukee
Monroe
Muskegon
Oakland
Ottawa
St. Clair
Washtenaw
Wayne
Cass
Dakota
Hennepin
Mille Lacs
Olmsted
Ramsey
St. Louis
Scott
Stearns
Adams
Bolivar
DeSoto
Forrest
Grenada
Harrison
Hinds
Jackson
Jones
Lauderdale
Lee
Lowndes
Buchanan
Cass
Clay
Greene
Jackson
Jefferson
St. Charles
2007
Baseline
DV
11.50
7.41
7.50
12.60
10.57
12.38
11.54
11.08
12.40
15.57
5.74
9.47
9.99
6.67
10.01
11.06
7.57
9.25
8.50
10.79
11.80
11.92
13.49
10.46
10.93
12.27
10.95
13.89
12.51
12.31
12.38
12.08
10.38
10.63
11.19
12.00
13.89
13.30
2018
Reference
DV
8.85
6.07
6.25
9.58
8.15
9.34
8.99
8.89
9.69
11.89
5.10
7.94
8.44
5.68
8.15
9.51
6.83
7.78
7.27
7.89
8.65
8.22
10.35
7.33
7.93
9.02
7.89
10.68
9.18
8.70
8.84
9.92
8.06
8.29
8.91
9.48
10.51
10.02
2018 Tier 3
Control DV
8.83
6.06
6.24
9.55
8.13
9.32
8.97
8.88
9.67
11.87
5.09
7.92
8.42
5.66
8.13
9.49
6.82
7.76
7.25
7.88
8.64
8.21
10.33
7.32
7.92
9.00
7.88
10.66
9.17
8.69
8.83
9.91
8.04
8.27
8.90
9.46
10.48
10.00
2030
Reference
DV
8.73
5.99
6.19
9.39
7.99
9.18
8.81
8.80
9.53
11.69
5.04
7.72
8.21
5.56
7.91
9.22
6.69
7.56
7.09
7.78
8.54
8.05
10.25
7.26
7.84
8.91
7.74
10.58
9.13
8.61
8.78
9.74
7.90
8.15
8.82
9.30
10.36
9.78
2030 Tier 3
Control DV
8.67
5.95
6.17
9.32
7.93
9.10
8.73
8.76
9.45
11.59
5.02
7.65
8.13
5.53
7.85
9.13
6.66
7.50
7.04
7.75
8.52
8.01
10.21
7.23
7.81
8.86
7.71
10.54
9.10
8.57
8.75
9.68
7.86
8.09
8.79
9.22
10.28
9.72
C-9
-------
State
Missouri
Missouri
Missouri
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nevada
Nevada
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Hampshire
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
County
Ste. Genevieve
St. Louis
St. Louis city
Cascade
Flathead
Gallatin
Lewis and Clark
Lincoln
Missoula
Ravalli
Sanders
Silver Bow
Yellowstone
Douglas
Hall
Lancaster
Sarpy
Scotts Bluff
Washington
Clark
Washoe
Belknap
Cheshire
Graf ton
Hillsborough
Merrimack
Rockingham
Sullivan
Atlantic
Bergen
Camden
Essex
Gloucester
Hudson
Mercer
Middlesex
Morris
Ocean
2007
Baseline
DV
12.75
12.85
14.08
6.02
9.71
8.63
8.42
13.53
9.82
9.10
7.07
11.14
7.68
9.59
7.81
8.26
9.46
6.29
8.77
9.43
8.49
6.77
11.02
7.80
9.57
9.28
8.45
9.31
10.82
12.24
13.40
13.29
11.38
13.57
11.74
11.27
10.43
10.14
2018
Reference
DV
9.65
9.56
10.53
5.92
9.39
8.40
8.29
13.12
9.37
8.92
6.92
10.87
7.53
7.51
6.48
6.47
7.42
5.74
6.91
8.75
7.82
5.46
9.61
6.79
7.84
7.82
7.18
8.25
7.53
8.11
9.27
8.79
7.54
9.43
8.07
7.68
6.87
6.74
2018 Tier 3
Control DV
9.64
9.54
10.51
5.92
9.39
8.41
8.30
13.12
9.36
8.92
6.92
10.87
7.53
7.49
6.47
6.46
7.40
5.74
6.89
8.75
7.82
5.46
9.61
6.78
7.83
7.82
7.18
8.25
7.53
8.09
9.26
8.77
7.53
9.41
8.06
7.66
6.85
6.72
2030
Reference
DV
9.53
9.41
10.29
5.87
9.27
8.32
8.24
12.82
9.19
8.84
6.84
10.78
7.45
7.33
6.36
6.34
7.24
5.61
6.72
8.60
7.66
5.40
9.52
6.73
7.76
7.75
7.09
8.18
7.48
7.85
9.10
8.48
7.38
9.14
7.93
7.52
6.74
6.63
2030 Tier 3
Control DV
9.49
9.34
10.21
5.86
9.24
8.29
8.22
12.80
9.15
8.83
6.83
10.75
7.43
7.28
6.33
6.30
7.19
5.59
6.69
8.54
7.61
5.38
9.50
6.71
7.73
7.71
7.07
8.17
7.46
7.82
9.07
8.45
7.36
9.11
7.91
7.50
6.72
6.62
C-10
-------
State
New Jersey
New Jersey
New Jersey
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
County
Passaic
Union
Warren
Bernalillo
Chaves
Dona Ana
Grant
Sandoval
San Juan
Santa Fe
Albany
Bronx
Chautauqua
Erie
Essex
Kings
Monroe
Nassau
New York
Niagara
Onondaga
Orange
Queens
Richmond
St. Lawrence
Steuben
Suffolk
Westchester
Alamance
Buncombe
Caswell
Catawba
Chatham
Cumberland
Davidson
Duplin
Durham
Edgecombe
2007
Baseline
DV
12.17
13.56
11.81
6.61
6.47
10.36
5.01
7.81
5.82
4.62
9.26
14.58
8.88
11.43
5.27
13.01
9.64
10.86
15.86
10.62
9.03
10.03
11.25
12.43
6.22
8.15
10.06
11.16
12.73
11.22
12.01
13.98
11.24
12.74
14.15
10.31
13.39
11.55
2018
Reference
DV
8.06
9.01
8.17
6.02
6.15
10.11
4.87
7.36
5.60
4.30
6.98
10.06
6.10
8.31
4.07
8.90
6.74
7.31
11.25
7.92
6.65
6.82
7.62
8.33
4.88
5.56
6.65
7.34
7.74
7.27
7.16
9.03
6.93
8.41
9.03
6.55
8.69
7.47
2018 Tier 3
Control DV
8.04
8.99
8.16
6.01
6.14
10.11
4.86
7.36
5.60
4.29
6.97
10.04
6.09
8.30
4.07
8.88
6.73
7.30
11.23
7.91
6.64
6.81
7.60
8.31
4.87
5.55
6.64
7.33
7.72
7.26
7.15
9.01
6.92
8.40
9.01
6.54
8.68
7.46
2030
Reference
DV
7.81
8.67
8.02
6.03
6.43
11.21
5.09
7.42
5.63
4.36
6.82
9.74
6.01
8.15
4.05
8.64
6.63
7.15
10.92
7.83
6.57
6.70
7.46
8.01
4.84
5.53
6.52
7.16
7.66
7.17
7.09
8.90
6.86
8.32
8.92
6.52
8.56
7.39
2030 Tier 3
Control DV
7.78
8.65
8.00
5.99
6.42
11.15
5.08
7.39
5.61
4.33
6.81
9.71
5.99
8.13
4.05
8.62
6.61
7.13
10.89
7.81
6.56
6.68
7.44
7.99
4.83
5.52
6.50
7.14
7.61
7.13
7.06
8.84
6.82
8.28
8.86
6.50
8.50
7.36
C-ll
-------
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 Dakota
North Dakota
North Dakota
North Dakota
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
County
Forsyth
Gaston
Guilford
Haywood
Jackson
Lenoir
McDowell
Martin
Mecklenburg
Mitchell
Montgomery
New Hanover
Onslow
Orange
Pitt
Robeson
Rowan
Swain
Wake
Watauga
Wayne
Billings
Burleigh
Cass
Mercer
Athens
Butler
Clark
Clermont
Cuyahoga
Franklin
Greene
Hamilton
Jefferson
Lake
Lawrence
Lorain
Lucas
2007
Baseline
DV
13.02
13.14
11.28
13.00
11.47
10.33
12.92
10.14
13.73
11.90
11.59
9.68
10.48
12.90
11.18
12.09
13.28
11.98
12.46
10.75
11.97
4.66
6.77
7.85
6.28
11.78
14.96
13.83
13.07
15.86
13.84
12.65
16.00
14.80
12.28
15.44
12.10
13.88
2018
Reference
DV
7.98
8.36
6.67
9.30
7.76
6.54
8.77
6.62
8.86
7.98
7.34
6.19
6.63
7.99
7.28
8.08
8.58
8.12
8.16
6.54
7.98
4.34
6.16
6.94
5.82
7.28
10.32
9.86
8.61
11.18
9.58
8.59
11.02
9.42
8.43
10.02
8.68
10.46
2018 Tier 3
Control DV
7.97
8.35
6.66
9.29
7.75
6.53
8.76
6.61
8.85
7.97
7.33
6.19
6.62
7.98
7.27
8.07
8.57
8.11
8.15
6.53
7.97
4.33
6.15
6.93
5.82
7.27
10.30
9.83
8.59
11.16
9.55
8.57
11.00
9.41
8.42
10.01
8.67
10.44
2030
Reference
DV
7.88
8.25
6.60
9.22
7.69
6.51
8.68
6.60
8.73
7.93
7.29
6.15
6.61
7.88
7.24
8.02
8.50
8.05
8.06
6.49
7.91
4.28
6.03
6.79
5.73
7.23
10.19
9.70
8.48
11.02
9.43
8.45
10.83
9.34
8.31
9.90
8.59
10.24
2030 Tier 3
Control DV
7.82
8.20
6.56
9.17
7.66
6.49
8.64
6.58
8.65
7.90
7.26
6.13
6.59
7.82
7.21
7.98
8.45
8.01
7.99
6.46
7.88
4.27
6.01
6.75
5.72
7.21
10.12
9.63
8.42
10.93
9.35
8.39
10.73
9.30
8.27
9.87
8.54
10.16
C-12
-------
State
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
County
Mahoning
Medina
Montgomery
Portage
Preble
Scioto
Stark
Summit
Trumbull
Warren
Caddo
Cherokee
Kay
Mayes
Muskogee
Oklahoma
Ottawa
Pitts burg
Sequoyah
Tulsa
Harney
Jackson
Josephine
Klamath
Lake
Lane
Multnomah
Umatilla
Union
Washington
Adams
Allegheny
Beaver
Berks
Bucks
Cambria
Centre
Cumberland
2007
Baseline
DV
13.79
11.94
14.48
12.82
12.92
13.55
16.11
14.22
13.90
12.53
8.60
12.28
10.29
11.62
11.68
10.21
11.26
11.16
12.07
11.47
9.68
9.96
8.69
11.49
9.99
11.15
8.60
7.97
7.54
8.59
12.00
18.36
15.19
13.06
12.65
14.35
11.42
13.24
2018
Reference
DV
9.50
8.27
9.92
8.69
8.92
8.83
11.14
10.03
9.61
8.46
7.04
9.92
8.70
9.23
9.32
8.06
8.99
8.93
9.67
9.09
9.80
9.84
8.69
11.62
9.90
10.93
8.06
7.78
7.37
8.32
7.54
12.05
10.52
9.07
8.77
9.48
7.28
8.83
2018 Tier 3
Control DV
9.48
8.25
9.90
8.67
8.89
8.81
11.12
10.01
9.59
8.44
7.03
9.90
8.69
9.22
9.30
8.04
8.98
8.92
9.65
9.08
9.80
9.83
8.68
11.62
9.90
10.92
8.06
7.77
7.37
8.32
7.53
12.04
10.51
9.05
8.75
9.47
7.27
8.81
2030
Reference
DV
9.38
8.16
9.76
8.57
8.79
8.75
10.99
9.88
9.49
8.34
7.08
9.83
8.64
9.11
9.21
8.02
8.87
8.85
9.57
9.00
9.68
9.63
8.55
11.47
9.80
10.78
7.74
7.53
7.19
8.08
7.46
11.75
10.42
8.90
8.63
9.39
7.21
8.67
2030 Tier 3
Control DV
9.32
8.12
9.68
8.52
8.73
8.71
10.92
9.80
9.44
8.28
7.05
9.80
8.61
9.08
9.18
7.97
8.83
8.82
9.54
8.95
9.67
9.62
8.55
11.46
9.80
10.77
7.74
7.52
7.18
8.08
7.43
11.73
10.38
8.86
8.61
9.36
7.19
8.64
C-13
-------
State
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
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 Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
County
Dauphin
Delaware
Erie
Lackawanna
Lancaster
Mercer
Montgomery
Northampton
Philadelphia
Washington
Westmoreland
York
Kent
Providence
Beaufort
Charleston
Chesterfield
Edgefield
Florence
Greenville
Greenwood
Horry
Lexington
Oconee
Richland
Spartanburg
Brookings
Brown
Codington
Custer
Jackson
Minnehaha
Pennington
Blount
Davidson
Dyer
Hamilton
Knox
2007
Baseline
DV
13.86
14.24
11.57
10.77
14.73
12.31
11.99
12.89
12.97
14.52
14.45
14.77
7.54
11.27
11.39
10.99
11.75
12.30
12.32
14.74
13.52
11.92
13.46
10.32
13.38
13.08
8.66
8.07
9.45
5.55
5.22
9.64
8.19
13.89
14.04
11.57
13.95
15.71
2018
Reference
DV
9.17
9.79
8.27
7.46
9.88
8.29
7.98
9.11
8.98
8.94
8.86
9.84
5.39
8.91
7.71
7.45
7.91
8.39
8.17
10.14
9.09
8.02
8.94
6.60
8.76
8.59
7.24
7.10
8.21
5.27
4.85
8.00
7.85
9.69
9.54
7.97
9.27
10.54
2018 Tier 3
Control DV
9.15
9.78
8.26
7.45
9.85
8.28
7.96
9.10
8.97
8.93
8.85
9.82
5.38
8.90
7.70
7.45
7.90
8.37
8.16
10.12
9.07
8.01
8.93
6.59
8.75
8.58
7.23
7.09
8.19
5.27
4.85
7.98
7.84
9.68
9.52
7.96
9.25
10.53
2030
Reference
DV
8.98
9.59
8.17
7.35
9.68
8.20
7.83
8.94
8.81
8.78
8.77
9.65
5.35
8.80
7.64
7.39
7.86
8.30
8.10
9.93
8.96
7.95
8.84
6.52
8.65
8.46
7.06
6.94
8.03
5.25
4.83
7.77
7.81
9.57
9.37
7.84
9.11
10.33
2030 Tier 3
Control DV
8.95
9.57
8.14
7.33
9.64
8.16
7.81
8.92
8.79
8.76
8.75
9.62
5.34
8.79
7.62
7.36
7.83
8.26
8.06
9.85
8.92
7.93
8.78
6.49
8.60
8.40
7.02
6.92
7.99
5.25
4.83
7.71
7.78
9.50
9.29
7.81
9.04
10.25
C-14
-------
State
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Tennessee
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Texas
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
Virginia
County
Lawrence
Loudon
McMinn
Madison
Maury
Montgomery
Putnam
Roane
Shelby
Sullivan
Sumner
Bowie
Dallas
Ector
El Paso
Harris
Harrison
Hidalgo
Nueces
Orange
Potter
Tarrant
Travis
Box Elder
Cache
Davis
Salt Lake
Tooele
Utah
Weber
Bennington
Chittenden
Rutland
Arlington
Charles City
Chesterfield
Fairfax
Henrico
2007
Baseline
DV
11.18
14.76
13.89
11.17
12.22
12.67
11.26
13.86
13.57
13.24
12.65
12.19
10.99
8.13
11.21
15.04
11.01
10.94
10.71
11.29
6.17
11.32
9.06
8.28
9.79
10.25
11.69
6.84
10.42
10.54
7.67
8.39
10.67
12.93
11.35
12.30
13.47
12.03
2018
Reference
DV
7.67
10.37
9.39
7.63
8.15
8.46
7.18
9.28
9.40
8.40
8.33
9.57
8.34
7.69
11.22
11.99
8.46
9.57
8.66
8.93
5.45
8.76
7.05
7.34
8.84
9.28
10.66
6.24
9.26
9.49
5.84
7.15
9.66
8.14
7.23
7.78
8.35
7.57
2018 Tier 3
Control DV
7.66
10.36
9.38
7.62
8.13
8.45
7.16
9.26
9.39
8.39
8.31
9.56
8.32
7.69
11.21
11.97
8.45
9.57
8.66
8.93
5.44
8.74
7.04
7.31
8.81
9.26
10.64
6.22
9.23
9.47
5.84
7.14
9.65
8.13
7.21
7.77
8.34
7.56
2030
Reference
DV
7.61
10.22
9.26
7.54
8.05
8.38
7.10
9.13
9.15
8.33
8.19
9.43
8.29
8.12
12.76
11.93
8.36
9.67
8.58
8.86
5.55
8.73
7.05
7.08
8.57
9.05
10.41
6.08
8.97
9.19
5.78
7.06
9.57
8.06
7.16
7.72
8.28
7.51
2030 Tier 3
Control DV
7.58
10.16
9.21
7.51
8.01
8.34
7.07
9.08
9.08
8.30
8.14
9.40
8.20
8.10
12.70
11.84
8.33
9.64
8.55
8.85
5.53
8.64
7.02
7.01
8.48
8.95
10.29
6.04
8.86
9.09
5.77
7.05
9.56
8.01
7.12
7.66
8.22
7.46
C-15
-------
State
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Washington
Washington
Washington
Washington
Washington
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
County
Loudoun
Page
Rockingham
Bristol city
Hampton city
Lynch burg city
Norfolk city
Roanoke city
Virginia Beach
city
King
Pierce
Snohomish
Spokane
Yakima
Berkeley
Brooke
Cabell
Hancock
Harrison
Kanawha
Marion
Marshall
Monongalia
Ohio
Raleigh
Wood
Ashland
Brown
Dane
Dodge
Forest
Grant
Kenosha
La Crosse
Manitowoc
Milwaukee
Outagamie
2007
Baseline
DV
12.17
11.71
11.66
12.60
11.64
11.78
12.13
13.96
11.56
9.27
9.89
9.06
9.56
9.70
14.90
15.40
15.35
14.31
13.37
15.46
14.44
14.27
13.58
13.81
12.00
14.58
6.16
11.73
12.57
11.00
7.09
12.27
12.62
11.76
10.67
14.69
11.25
2018
Reference
DV
7.57
7.08
7.76
7.84
7.40
7.69
8.04
9.32
7.55
8.19
9.23
8.47
9.11
9.03
10.27
9.65
10.34
8.92
8.43
9.78
9.34
8.76
8.06
8.23
7.40
9.63
5.42
10.01
10.66
9.23
6.00
10.45
10.14
10.32
9.02
12.22
9.49
2018 Tier 3
Control DV
7.56
7.06
7.75
7.83
7.39
7.68
8.03
9.31
7.54
8.19
9.23
8.47
9.10
9.00
10.25
9.64
10.33
8.91
8.42
9.77
9.33
8.75
8.05
8.22
7.39
9.62
5.42
9.99
10.64
9.21
5.99
10.43
10.11
10.30
8.99
12.19
9.46
2030
Reference
DV
7.50
7.04
7.71
7.78
7.30
7.62
7.94
9.22
7.47
7.88
8.95
8.26
8.70
8.44
10.13
9.55
10.20
8.84
8.38
9.65
9.27
8.71
7.99
8.16
7.35
9.54
5.36
10.14
10.49
9.08
5.93
10.23
9.94
10.11
8.96
12.04
9.40
2030 Tier 3
Control DV
7.46
7.01
7.68
7.74
7.26
7.58
7.89
9.17
7.43
7.88
8.95
8.26
8.69
8.41
10.09
9.51
10.16
8.81
8.34
9.61
9.23
8.67
7.96
8.13
7.33
9.50
5.34
10.05
10.39
9.00
5.90
10.16
9.84
10.03
8.89
11.89
9.33
C-16
-------
State
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
County
Ozaukee
St. Croix
Sauk
Taylor
Vilas
Waukesha
Campbell
Converse
Fremont
La ramie
Sheridan
Sublette
2007
Baseline
DV
11.84
10.28
10.50
8.73
6.78
13.82
5.52
3.73
7.72
4.28
9.07
6.49
2018
Reference
DV
9.71
8.88
8.65
7.65
5.88
11.49
5.27
3.48
7.43
3.82
8.84
6.28
2018 Tier 3
Control DV
9.68
8.86
8.62
7.63
5.87
11.45
5.27
3.48
7.42
3.82
8.84
6.28
2030
Reference
DV
9.55
8.65
8.48
7.52
5.80
11.28
5.22
3.45
7.37
3.76
8.75
6.27
2030 Tier 3
Control DV
9.46
8.58
8.41
7.47
5.77
11.15
5.22
3.44
7.35
3.75
8.72
6.26
C-17
-------
Air Quality Modeling Technical Support Document:
Tier 3 Motor Vehicle Emission and Fuel 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
February 2014
D-l
-------
Table D-l. 24-hour PM2.s Design Values for Tier 3 Scenarios
(units are ug/m3)
State
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Alabama
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arizona
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
Arkansas
County
Baldwin
Clay
Colbert
DeKalb
Escambia
Houston
Jefferson
Madison
Mobile
Montgomery
Morgan
Russell
Shelby
Tuscaloosa
Walker
Cochise
Coconino
Gila
Maricopa
Pima
Pinal
Santa Cruz
Arkansas
Ashley
Crittenden
Faulkner
Garland
Jackson
Phillips
Polk
Pope
Pulaski
Sebastian
Union
White
2007
Baseline
DV
23.7
27.2
28.1
28.4
28.2
25.5
36.7
29.5
24.1
29.1
28.9
30.3
28.4
26.9
29.6
12.9
18.7
22.7
29.0
12.1
43.6
29.2
27.3
25.9
31.0
26.0
26.1
26.1
26.9
25.5
26.6
30.3
24.5
25.7
27.9
2018
Reference
DV
15.2
15.9
15.9
16.0
20.6
18.6
26.7
17.0
15.7
21.5
15.9
22.8
19.7
16.9
18.1
13.1
18.2
21.7
25.3
11.5
40.8
29.3
17.8
18.2
17.4
18.8
18.0
17.5
16.8
18.0
20.1
21.0
17.9
18.8
19.4
2018 Tier 3
Control DV
15.1
15.8
15.9
15.9
20.6
18.6
26.6
16.9
15.7
21.5
15.9
22.8
19.7
16.9
18.0
13.1
18.2
21.7
25.2
11.5
40.8
29.3
17.8
18.2
17.4
18.8
17.9
17.5
16.7
18.0
20.1
21.0
17.9
18.8
19.3
2030
Reference
DV
15.0
15.7
15.8
15.8
20.3
18.4
26.4
16.8
15.2
21.2
15.7
22.6
19.6
16.7
17.8
13.8
18.4
21.8
24.9
11.4
40.4
30.7
17.7
18.0
17.0
18.7
17.8
17.3
16.5
18.0
20.0
20.8
17.8
18.7
19.1
2030 Tier 3
Control DV
15.0
15.7
15.7
15.7
20.2
18.3
26.2
16.6
15.2
21.0
15.6
22.6
19.5
16.6
17.8
13.8
18.3
21.7
24.6
11.4
40.2
30.5
17.6
18.0
16.9
18.6
17.8
17.2
16.4
18.0
20.0
20.7
17.7
18.6
19.0
D-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
California
California
California
County
Alameda
Butte
Calaveras
Contra Costa
Fresno
Humboldt
Imperial
Inyo
Kern
Kings
Lake
Los Angeles
Mendocino
Merced
Monterey
Nevada
Orange
Placer
Plumas
Riverside
Sacramento
San Benito
San Bernardino
San Diego
San Francisco
San Joaquin
San Luis Obispo
San Mateo
Santa Barbara
Santa Clara
Santa Cruz
Shasta
Solano
Sonoma
Stanislaus
Sutter
Tulare
Ventura
2007
Baseline
DV
42.0
48.0
27.0
36.1
57.7
24.7
39.0
30.8
69.6
59.2
22.9
43.3
19.0
51.6
14.2
27.1
38.8
28.3
32.5
50.7
55.1
17.0
51.7
32.7
32.7
45.4
22.7
31.0
22.4
40.3
13.4
22.1
40.0
30.4
53.8
33.9
56.5
27.6
2018
Reference
DV
36.8
46.3
23.7
31.3
47.1
23.9
38.2
28.2
49.1
48.3
22.3
35.2
18.1
45.6
11.7
25.9
29.2
25.5
31.1
38.2
51.3
14.0
38.9
29.7
27.5
41.3
16.6
25.6
20.1
34.4
12.0
21.5
36.7
27.7
46.7
30.6
43.2
20.6
2018 Tier 3
Control DV
36.8
46.3
23.7
31.3
47.0
23.9
38.2
28.2
49.1
48.3
22.3
35.1
18.1
45.6
11.7
25.9
29.2
25.5
31.1
38.2
51.3
14.0
38.9
29.7
27.5
41.3
16.6
25.6
20.1
34.3
12.0
21.5
36.7
27.7
46.7
30.6
43.2
20.6
2030
Reference
DV
34.9
45.6
22.8
29.7
42.8
23.8
43.3
27.9
42.4
42.5
22.3
33.1
17.8
42.7
11.5
25.6
27.5
24.3
30.7
34.9
48.9
13.7
35.3
28.8
26.1
39.3
14.9
24.3
19.7
32.8
11.8
21.4
35.4
26.8
43.1
29.3
38.6
19.8
2030 Tier 3
Control DV
34.9
45.6
22.8
29.7
42.7
23.8
43.2
27.9
42.4
42.5
22.3
33.1
17.8
42.7
11.5
25.6
27.5
24.3
30.7
34.9
48.9
13.6
35.3
28.8
26.1
39.2
14.9
24.2
19.7
32.8
11.8
21.4
35.4
26.8
43.1
29.3
38.6
19.8
D-3
-------
State
California
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Connecticut
Connecticut
Connecticut
Connecticut
Connecticut
Delaware
Delaware
Delaware
District of Co
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
Florida
County
Yolo
Adams
Arapahoe
Boulder
Denver
Douglas
Elbert
El Paso
Larimer
Mesa
Pueblo
Weld
Fairfield
Hartford
Litchfield
New Haven
New London
Kent
New Castle
Sussex
District of
Columbia
Alachua
Bay
Brevard
Broward
Citrus
Duval
Escambia
Hillsborough
Lee
Leon
Manatee
Marion
Miami-Dade
Orange
Palm Beach
Pinellas
2007
Baseline
DV
33.1
29.4
19.4
22.8
25.1
16.6
13.5
15.8
18.8
26.1
15.6
24.1
32.9
28.6
24.0
32.2
27.9
29.4
34.8
30.3
31.2
20.8
24.2
20.5
19.0
18.6
22.1
24.0
20.0
16.4
23.5
19.2
22.5
19.2
19.6
17.8
20.0
2018
Reference
DV
31.1
25.8
16.9
20.7
21.9
14.4
12.4
15.6
18.0
25.1
15.7
22.5
22.9
21.8
14.9
23.9
19.6
17.8
23.3
18.3
20.4
15.3
16.8
15.0
14.3
11.8
15.9
16.2
14.4
11.7
18.4
12.8
15.9
13.1
13.2
12.0
13.9
2018 Tier 3
Control DV
31.1
25.8
16.9
20.6
21.9
14.4
12.4
15.6
18.0
25.1
15.7
22.4
22.9
21.8
14.9
23.9
19.6
17.7
23.2
18.2
20.4
15.3
16.8
15.0
14.3
11.8
15.9
16.2
14.4
11.7
18.4
12.8
15.8
13.1
13.2
12.0
13.9
2030
Reference
DV
30.0
25.1
16.5
20.3
21.3
14.1
12.1
15.5
17.8
24.7
15.6
22.0
22.5
21.6
14.8
23.8
19.4
17.5
22.9
18.0
20.3
15.2
16.5
14.9
14.0
11.6
15.7
16.0
14.1
11.7
18.2
12.4
15.5
12.8
13.0
11.9
13.6
2030 Tier 3
Control DV
30.0
24.6
16.3
20.1
20.9
13.9
12.0
15.4
17.6
24.5
15.6
21.7
22.5
21.6
14.8
23.8
19.4
17.4
22.8
18.0
20.3
15.1
16.4
14.8
14.0
11.6
15.6
15.8
14.0
11.7
18.2
12.4
15.3
12.7
12.9
11.9
13.5
D-4
-------
State
Florida
Florida
Florida
Florida
Florida
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Georgia
Idaho
Idaho
Idaho
Idaho
Idaho
Idaho
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
County
Polk
St. Lucie
Sarasota
Seminole
Volusia
Bibb
Chatham
Clayton
Cobb
DeKalb
Dougherty
Floyd
Fulton
Glynn
Gwinnett
Hall
Houston
Lowndes
Muscogee
Paulding
Richmond
Washington
Wilkinson
Ada
Benewah
Canyon
Franklin
Idaho
Shoshone
Champaign
Cook
DuPage
Hamilton
Jersey
Kane
Lake
LaSalle
McHenry
2007
Baseline
DV
17.0
17.7
17.4
19.0
23.9
33.6
26.7
30.3
32.2
30.9
33.6
34.9
33.6
25.0
28.4
28.4
30.1
25.9
29.5
32.3
30.8
29.4
32.3
22.3
28.6
28.2
36.7
28.4
35.0
29.2
38.9
32.8
28.6
28.0
31.1
29.3
27.5
28.7
2018
Reference
DV
12.4
12.4
12.7
13.1
16.7
25.6
21.1
19.5
19.5
18.5
29.0
22.4
19.6
18.5
18.0
17.0
23.6
20.7
25.0
18.7
23.0
19.6
24.9
20.4
27.7
25.4
32.2
27.5
33.9
21.8
31.3
25.9
21.5
20.1
25.9
22.1
20.9
21.7
2018 Tier 3
Control DV
12.4
12.4
12.7
13.1
16.7
25.6
21.0
19.5
19.5
18.4
29.0
22.3
19.6
18.5
18.0
17.0
23.5
20.7
24.9
18.6
23.0
19.5
24.9
20.3
27.7
25.3
32.0
27.5
33.8
21.8
31.1
25.7
21.4
20.0
25.7
22.0
20.8
21.6
2030
Reference
DV
12.2
12.2
12.6
12.9
16.5
25.3
20.8
19.3
19.2
18.2
28.8
22.1
19.2
18.2
17.6
16.6
23.1
20.6
24.9
18.2
22.8
19.3
24.7
19.6
27.2
24.2
30.2
27.0
33.2
21.6
30.8
25.2
21.2
19.7
25.1
21.7
20.3
21.1
2030 Tier 3
Control DV
12.1
12.2
12.5
12.8
16.4
25.2
20.7
19.1
18.9
18.0
28.8
21.9
18.9
18.1
17.4
16.4
22.9
20.5
24.8
18.0
22.6
19.2
24.6
19.2
27.1
23.8
29.5
27.0
33.1
21.4
30.4
24.8
21.1
19.6
24.7
21.5
20.1
20.9
D-5
-------
State
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Illinois
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Indiana
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
Iowa
County
McLean
Macon
Madison
Peoria
Randolph
Rock Island
St. Clair
Sangamon
Will
Winnebago
Allen
Clark
Delaware
Dubois
Floyd
Henry
Howard
Knox
Lake
La Porte
Madison
Marion
Porter
St. Joseph
Spencer
Tippecanoe
Vanderburgh
Vigo
Black Hawk
Clinton
Johnson
Lee
Linn
Montgomery
Muscatine
Palo Alto
Polk
Pottawattamie
2007
Baseline
DV
29.0
30.6
34.8
30.2
26.8
26.7
30.0
29.7
33.5
30.6
32.7
35.6
28.6
34.9
31.1
26.0
32.9
30.7
32.8
30.7
30.0
37.0
30.3
30.0
28.8
30.5
30.3
34.5
29.1
33.0
30.6
26.0
27.2
23.7
36.2
24.3
26.2
26.3
2018
Reference
DV
21.9
22.7
25.5
23.4
21.4
21.0
22.8
21.2
25.6
25.4
24.8
21.4
19.4
21.0
17.6
17.5
21.5
20.7
27.2
23.0
19.3
24.7
24.4
23.8
18.3
20.8
20.8
22.6
25.6
29.1
24.7
22.0
22.5
18.1
32.7
18.6
21.3
22.6
2018 Tier 3
Control DV
21.8
22.6
25.4
23.3
21.4
21.0
22.7
21.2
25.4
25.3
24.7
21.4
19.3
20.9
17.6
17.4
21.4
20.6
27.0
22.9
19.2
24.6
24.3
23.7
18.2
20.7
20.8
22.5
25.5
29.0
24.6
21.9
22.4
18.0
32.7
18.5
21.2
22.5
2030
Reference
DV
21.5
22.3
24.9
22.8
21.1
20.7
22.4
20.8
24.9
24.9
24.2
21.2
18.9
20.7
17.4
17.1
20.9
20.4
26.6
22.6
18.7
24.1
24.0
23.1
18.0
20.3
20.5
22.2
24.8
28.5
24.0
21.6
21.9
17.3
32.2
17.8
20.6
21.9
2030 Tier 3
Control DV
21.3
22.1
24.7
22.5
20.9
20.5
22.2
20.6
24.6
24.7
23.9
21.0
18.7
20.5
17.3
16.9
20.6
20.3
26.2
22.4
18.5
23.8
23.8
22.9
17.9
20.0
20.4
21.9
24.5
28.2
23.8
21.4
21.6
17.1
31.9
17.6
20.3
21.5
D-6
-------
State
Iowa
Iowa
Iowa
Kansas
Kansas
Kansas
Kansas
Kansas
Kansas
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Kentucky
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
Louisiana
County
Scott
Van Buren
Woodbury
Johnson
Linn
Sedgwick
Shawnee
Sumner
Wyandotte
Bell
Boyd
Bullitt
Carter
Christian
Daviess
Fayette
Franklin
Hardin
Henderson
Jefferson
Kenton
McCracken
Madison
Ohio
Perry
Pike
Warren
Caddo Parish
Calcasieu Parish
East Baton
Rouge Parish
Iberville Parish
Jefferson Parish
Lafayette Parish
Ouachita Parish
Rapides Parish
St. Bernard
Parish
2007
Baseline
DV
34.6
26.2
28.3
22.5
22.5
23.1
22.8
21.6
24.2
26.9
31.2
31.6
27.0
32.3
30.6
29.5
29.5
31.8
29.2
35.1
30.6
31.9
27.8
29.6
29.8
28.4
29.0
24.7
22.9
26.1
25.8
23.2
22.3
25.8
22.5
22.0
2018
Reference
DV
28.7
20.5
22.6
16.5
15.9
17.3
17.2
16.1
18.5
19.7
15.5
17.4
14.8
17.8
18.4
18.8
16.6
18.1
18.7
19.7
17.7
18.7
16.4
16.0
16.8
17.6
15.3
19.1
18.0
19.0
19.6
16.7
16.1
18.2
16.0
15.8
2018 Tier 3
Control DV
28.7
20.5
22.6
16.4
15.9
17.2
17.2
16.0
18.5
19.6
15.5
17.4
14.7
17.7
18.4
18.8
16.6
18.0
18.7
19.7
17.6
18.7
16.4
16.0
16.7
17.6
15.2
19.1
18.0
19.0
19.6
16.7
16.0
18.1
16.0
15.8
2030
Reference
DV
28.3
20.3
22.0
16.1
15.6
16.9
16.9
15.7
18.1
19.4
15.3
17.2
14.6
17.5
18.3
18.4
16.3
17.9
18.5
19.6
17.4
18.5
16.1
15.9
16.9
17.7
15.1
18.8
17.8
18.4
19.6
16.4
16.0
17.9
15.9
15.4
2030 Tier 3
Control DV
28.0
20.2
21.8
15.9
15.6
16.8
16.8
15.5
17.9
19.3
15.2
17.1
14.6
17.4
18.2
18.1
16.2
17.8
18.4
19.4
17.3
18.3
16.0
15.8
16.8
17.7
15.0
18.7
17.8
18.3
19.5
16.3
15.9
17.8
15.9
15.4
D-7
-------
State
Louisiana
Louisiana
Louisiana
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maine
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Maryland
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Massachusetts
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
County
Tangipahoa
Parish
Terrebonne
Parish
West Baton
Rouge Parish
Androscoggin
Aroostook
Cumberland
Hancock
Kennebec
Oxford
Penobscot
Piscataquis
Anne Arundel
Baltimore
Cecil
Harford
Montgomery
Prince George's
Washington
Baltimore city
Berkshire
Bristol
Essex
Hampden
Middlesex
Plymouth
Suffolk
Worcester
Allegan
Bay
Berrien
Genesee
Ingham
Kalamazoo
Kent
Macomb
Manistee
2007
Baseline
DV
25.7
22.8
26.0
23.8
22.3
21.7
20.5
21.4
22.5
21.4
17.2
33.1
33.0
27.8
28.6
28.0
27.5
29.1
34.0
27.9
24.1
26.2
30.8
21.7
27.0
29.2
28.2
30.4
26.9
28.8
26.9
28.5
28.9
31.1
31.2
22.5
2018
Reference
DV
18.0
16.3
19.6
21.0
21.1
18.1
12.3
18.9
20.2
16.6
12.4
20.3
23.2
18.5
17.2
16.4
17.2
17.5
25.1
20.9
16.8
19.1
24.3
13.9
18.1
21.4
20.1
22.8
20.7
21.3
21.6
22.3
23.7
25.1
23.6
17.1
2018 Tier 3
Control DV
18.0
16.3
19.6
21.0
21.1
18.1
12.2
18.9
20.2
16.6
12.4
20.2
23.1
18.5
17.1
16.3
17.1
17.5
25.1
20.9
16.7
19.0
24.3
13.9
18.1
21.3
20.0
22.8
20.6
21.3
21.5
22.3
23.6
25.0
23.6
17.0
2030
Reference
DV
17.9
16.2
19.0
20.8
21.1
17.8
12.1
18.7
20.0
16.3
12.3
20.1
23.1
18.2
17.1
16.3
17.1
17.2
24.6
20.6
16.6
18.9
24.2
13.8
17.9
20.9
19.8
22.6
20.4
21.1
21.4
22.0
23.3
24.7
23.6
16.8
2030 Tier 3
Control DV
17.9
16.2
18.9
20.8
21.1
17.8
12.1
18.7
20.0
16.3
12.3
20.0
23.0
18.1
17.0
16.2
17.0
17.1
24.5
20.6
16.6
18.8
24.2
13.8
17.9
20.9
19.7
22.4
20.2
20.9
21.2
21.8
23.2
24.4
23.4
16.6
D-8
-------
State
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Michigan
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Minnesota
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Mississippi
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
Missouri
County
Missaukee
Monroe
Muskegon
Oakland
Ottawa
St. Clair
Washtenaw
Wayne
Cass
Dakota
Hennepin
Mille Lacs
Olmsted
Ramsey
St. Louis
Scott
Stearns
Washington
Adams
Bolivar
DeSoto
Forrest
Grenada
Harrison
Hinds
Jackson
Jones
Lauderdale
Lee
Lowndes
Buchanan
Cass
Clay
Greene
Jackson
Jefferson
St. Charles
Ste. Genevieve
2007
Baseline
DV
22.5
32.4
29.4
35.0
29.7
35.5
33.6
38.3
17.9
25.7
27.2
22.2
29.7
29.8
23.6
24.5
22.1
30.2
24.0
26.4
26.9
28.4
22.8
24.5
26.2
24.7
28.5
26.4
29.8
28.1
27.0
24.6
24.7
25.7
26.6
34.2
32.8
29.8
2018
Reference
DV
16.2
23.5
21.4
25.0
24.3
26.0
25.1
30.9
14.9
22.9
24.5
16.6
25.9
26.7
21.1
20.7
19.9
28.8
16.5
18.3
15.2
21.5
14.2
16.9
17.4
16.6
21.9
18.3
16.8
18.9
22.2
17.5
18.0
18.9
21.8
23.8
22.9
20.4
2018 Tier 3
Control DV
16.2
23.4
21.3
24.9
24.2
26.0
25.0
30.9
14.8
22.8
24.4
16.5
25.7
26.6
21.0
20.6
19.8
28.8
16.5
18.2
15.2
21.5
14.2
16.9
17.3
16.5
21.8
18.2
16.7
18.9
22.1
17.5
18.0
18.8
21.7
23.8
22.8
20.4
2030
Reference
DV
16.1
22.9
20.9
24.6
23.9
25.9
24.4
30.6
14.5
22.2
23.7
16.2
25.0
25.7
20.6
19.9
19.2
28.0
16.3
18.0
14.9
21.2
14.0
16.7
17.1
16.3
21.6
18.0
16.6
18.8
21.6
17.1
17.6
18.6
21.2
23.7
22.4
20.2
2030 Tier 3
Control DV
16.0
22.7
20.7
24.4
23.7
25.7
24.1
30.3
14.4
21.9
23.3
16.1
24.7
25.4
20.4
19.6
19.0
27.7
16.2
18.0
14.8
21.2
14.0
16.6
16.9
16.2
21.5
17.9
16.5
18.7
21.4
17.0
17.5
18.4
21.0
23.5
22.2
20.1
D-9
-------
State
Missouri
Missouri
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Montana
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nebraska
Nevada
Nevada
New
Hampshire
New
Hampshire
New
Hampshire
New
Hampshire
New
Hampshire
New
Hampshire
New
Hampshire
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
County
St. Louis
St. Louis city
Cascade
Flathead
Gallatin
Lewis and Clark
Lincoln
Missoula
Sanders
Silver Bow
Yellowstone
Douglas
Hall
Lancaster
Sarpy
Scotts Bluff
Washington
Clark
Washoe
Belknap
Cheshire
Grafton
Hillsborough
Merrimack
Rockingham
Sullivan
Atlantic
Bergen
Camden
Essex
Gloucester
Hudson
Mercer
2007
Baseline
DV
30.9
32.4
17.3
22.7
27.0
29.5
35.6
29.8
20.1
32.8
18.3
24.3
18.3
18.9
22.9
17.6
20.8
23.0
34.9
17.9
28.9
20.5
26.5
24.6
23.7
23.3
27.4
34.6
33.2
38.4
25.7
39.6
32.0
2018
Reference
DV
23.5
23.6
17.1
22.2
26.2
29.4
34.8
28.7
19.6
32.4
18.3
20.0
15.0
14.4
17.5
16.3
15.9
21.4
31.5
11.2
26.2
15.7
22.4
20.3
18.7
19.1
15.9
19.7
22.4
24.9
17.1
27.4
22.0
2018 Tier 3
Control DV
23.5
23.5
17.1
22.2
26.2
29.4
34.8
28.6
19.6
32.4
18.3
19.9
14.9
14.3
17.5
16.3
15.8
21.4
31.4
11.2
26.2
15.7
22.4
20.3
18.7
19.1
15.9
19.5
22.3
24.9
17.1
27.3
22.0
2030
Reference
DV
23.3
23.1
17.0
22.0
25.9
29.3
33.8
28.2
19.3
32.2
18.2
19.3
14.5
14.0
17.1
15.9
15.3
20.8
30.8
11.1
26.0
15.6
22.3
20.2
18.4
19.0
15.8
19.1
22.0
24.3
16.8
26.4
21.7
2030 Tier 3
Control DV
23.0
22.8
16.9
21.9
25.8
29.2
33.7
28.0
19.3
32.0
18.1
19.0
14.4
13.9
16.9
15.8
15.1
20.5
30.4
11.0
26.0
15.6
22.2
20.1
18.3
19.0
15.7
18.9
21.9
24.1
16.7
26.2
21.6
D-10
-------
State
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Jersey
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New Mexico
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
New York
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
North Carolina
County
Middlesex
Morris
Ocean
Passaic
Union
Warren
Bernalillo
Chaves
Dona Ana
Grant
Sandoval
San Juan
Santa Fe
Albany
Bronx
Chautauqua
Erie
Essex
Kings
Monroe
New York
Niagara
Onondaga
Orange
Queens
Richmond
St. Lawrence
Steuben
Suffolk
Westchester
Alamance
Buncombe
Caswell
Catawba
Chatham
Cumberland
Davidson
Duplin
2007
Baseline
DV
29.9
28.9
28.0
33.3
37.6
33.6
16.9
16.2
29.4
10.1
15.4
12.5
9.1
26.5
35.3
26.5
30.4
17.5
33.1
27.9
38.0
28.7
25.8
27.6
30.7
31.4
20.3
24.6
27.4
31.2
28.5
26.7
26.6
29.5
25.0
27.5
28.5
24.1
2018
Reference
DV
18.8
17.8
16.9
20.7
24.3
24.3
15.3
13.2
26.9
9.9
14.4
12.4
8.4
19.5
25.1
13.9
20.8
9.9
21.8
17.8
27.3
18.4
15.1
17.9
21.2
20.1
12.8
14.0
15.8
18.5
18.6
15.1
16.4
17.4
15.5
17.7
16.6
13.4
2018 Tier 3
Control DV
18.8
17.7
16.9
20.6
24.3
24.2
15.3
13.2
26.9
9.9
14.4
12.4
8.4
19.5
25.0
13.9
20.7
9.8
21.8
17.8
27.3
18.3
15.1
17.8
21.1
20.1
12.7
14.0
15.7
18.4
18.6
15.0
16.3
17.4
15.4
17.7
16.5
13.4
2030
Reference
DV
18.5
17.4
16.8
20.2
23.4
23.8
15.2
13.8
28.1
10.5
14.5
12.4
8.5
19.1
24.2
13.8
20.4
9.8
21.4
17.5
26.6
18.1
15.1
17.5
20.7
19.5
12.7
13.9
15.6
18.1
18.2
14.8
15.9
17.1
15.2
17.4
16.3
13.4
2030 Tier 3
Control DV
18.4
17.4
16.7
20.1
23.4
23.7
15.0
13.8
27.7
10.5
14.4
12.4
8.4
19.0
24.1
13.8
20.4
9.8
21.3
17.4
26.5
18.0
15.0
17.5
20.6
19.5
12.7
13.8
15.5
18.0
18.0
14.7
15.8
16.9
15.1
17.3
16.1
13.4
D-ll
-------
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 Dakota
North Dakota
North Dakota
North Dakota
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
County
Durham
Edgecombe
Forsyth
Gaston
Guilford
Haywood
Lenoir
McDowell
Martin
Mecklenburg
Mitchell
Montgomery
New Hanover
Onslow
Orange
Pitt
Robeson
Rowan
Swain
Wake
Watauga
Wayne
Billings
Burleigh
Cass
Mercer
Athens
Butler
Clark
Clermont
Cuyahoga
Franklin
Greene
Hamilton
Jefferson
Lake
Lawrence
Lorain
2007
Baseline
DV
30.0
24.6
28.4
27.5
24.1
28.5
23.0
28.0
22.1
28.9
27.3
25.4
25.4
24.7
29.0
24.7
26.8
27.5
26.0
29.1
25.2
27.2
12.8
16.1
19.1
15.1
30.8
33.3
33.1
30.1
39.0
33.3
29.9
34.6
37.0
31.7
34.8
30.7
2018
Reference
DV
17.4
16.0
17.5
15.4
15.9
19.3
13.3
16.9
15.2
17.2
17.9
14.5
15.3
14.6
16.9
15.9
18.2
17.0
16.5
18.0
13.7
16.0
11.8
14.6
16.2
13.5
15.6
22.3
22.8
17.1
28.2
22.2
18.9
24.5
22.7
17.5
20.9
20.9
2018 Tier 3
Control DV
17.4
16.0
17.5
15.4
15.9
19.2
13.3
16.8
15.2
17.2
17.8
14.5
15.2
14.6
16.9
15.9
18.2
16.9
16.5
17.9
13.7
15.9
11.8
14.6
16.2
13.5
15.6
22.3
22.8
17.1
28.1
22.1
18.9
24.4
Tin
17.5
20.9
20.9
2030
Reference
DV
17.0
15.8
17.2
15.2
15.5
19.1
13.3
16.6
15.2
16.9
17.7
14.3
15.2
14.5
16.4
15.8
18.0
16.7
16.3
17.7
13.6
15.8
11.7
14.3
15.6
13.2
15.4
22.0
22.4
16.9
28.2
21.8
18.6
23.9
22.5
17.4
20.6
20.7
2030 Tier 3
Control DV
16.8
15.7
17.0
15.1
15.4
19.0
13.2
16.6
15.2
16.8
17.6
14.3
15.2
14.5
16.3
15.7
17.9
16.6
16.3
17.5
13.5
15.8
11.6
14.3
15.5
13.2
15.4
21.8
22.2
16.7
28.0
21.6
18.4
23.6
22.4
17.3
20.5
20.6
D-12
-------
State
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Ohio
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oklahoma
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Oregon
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
County
Lucas
Mahoning
Medina
Montgomery
Portage
Preble
Scioto
Stark
Summit
Trumbull
Warren
Caddo
Cherokee
Kay
Mayes
Muskogee
Oklahoma
Ottawa
Pitts burg
Sequoyah
Tulsa
Harney
Jackson
Josephine
Klamath
Lake
Lane
Multnomah
Umatilla
Union
Washington
Adams
Allegheny
Beaver
Berks
Bucks
Cambria
Centre
2007
Baseline
DV
34.7
32.8
28.7
33.7
30.9
29.8
31.6
36.0
34.7
33.2
27.1
26.2
27.4
26.8
25.4
27.5
24.2
24.9
24.8
27.3
27.4
33.0
33.2
30.6
46.1
41.4
42.4
29.1
24.7
21.7
31.6
31.4
54.4
37.0
34.1
32.9
35.3
31.6
2018
Reference
DV
24.9
21.6
19.6
21.0
19.5
20.8
19.9
23.3
23.6
20.9
18.4
20.3
20.5
21.9
18.1
20.8
18.7
18.1
18.4
22.0
20.8
35.6
33.2
31.8
46.6
42.8
41.1
28.5
24.7
21.2
32.7
19.3
40.1
25.0
27.2
23.1
19.5
19.0
2018 Tier 3
Control DV
24.8
21.5
19.6
20.9
19.5
20.8
19.9
23.2
23.5
20.9
18.4
20.3
20.4
21.8
18.1
20.8
18.7
18.1
18.4
22.0
20.8
35.6
33.1
31.7
46.5
42.8
41.0
28.5
24.7
21.2
32.8
19.2
40.1
25.0
27.1
23.1
19.4
19.0
2030
Reference
DV
24.3
21.3
19.4
20.7
19.3
20.5
19.7
23.0
23.1
20.6
18.2
20.1
20.3
21.6
17.8
20.7
18.7
17.9
18.4
22.0
20.7
35.0
32.1
31.0
45.4
42.2
39.9
27.4
23.6
20.4
31.9
19.0
39.5
24.8
26.6
22.8
19.2
18.9
2030 Tier 3
Control DV
24.0
21.0
19.2
20.5
19.2
20.3
19.6
22.8
22.9
20.4
18.0
19.9
20.2
21.5
17.8
20.6
18.5
17.7
18.3
21.9
20.5
34.9
32.0
31.0
45.3
42.2
39.8
27.4
23.5
20.4
31.9
18.9
39.5
24.8
26.4
22.7
19.2
18.8
D-13
-------
State
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
Pennsylvania
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 Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
South Dakota
Tennessee
Tennessee
Tennessee
Tennessee
County
Chester
Cumberland
Dauphin
Delaware
Erie
Lackawanna
Lancaster
Mercer
Montgomery
Northampton
Philadelphia
Washington
Westmoreland
York
Kent
Providence
Charleston
Chesterfield
Edgefield
Florence
Greenville
Greenwood
Horry
Lexington
Oconee
Richland
Spartanburg
Brookings
Brown
Codington
Custer
Jackson
Minnehaha
Pennington
Blount
Davidson
Dyer
Hamilton
2007
Baseline
DV
36.4
34.4
35.8
33.0
30.9
29.4
37.0
29.8
28.5
35.8
36.6
33.9
35.2
34.6
23.1
28.2
23.1
24.9
26.8
26.7
30.4
29.3
29.2
28.4
23.3
28.5
28.5
21.6
17.5
23.9
14.1
12.4
25.5
17.4
31.0
31.5
28.9
31.3
2018
Reference
DV
24.1
24.9
26.5
22.6
19.2
19.9
31.1
19.1
19.9
26.3
25.0
19.3
20.1
27.0
13.7
21.8
15.7
16.2
18.0
17.1
21.3
18.4
21.0
18.6
13.3
18.4
17.3
16.7
14.6
18.6
13.2
11.6
19.8
16.4
20.3
21.3
16.7
19.6
2018 Tier 3
Control DV
24.1
24.9
26.4
22.5
19.2
19.8
31.1
19.1
19.8
26.2
25.0
19.3
20.1
27.0
13.7
21.8
15.7
16.2
18.0
17.0
21.3
18.3
21.0
18.5
13.2
18.3
17.3
16.6
14.5
18.5
13.2
11.6
19.6
16.4
20.2
21.3
16.7
19.6
2030
Reference
DV
23.7
24.2
25.6
22.0
19.1
19.5
30.6
18.8
19.6
25.8
24.5
19.0
19.9
26.5
13.6
21.6
15.7
16.0
17.8
16.8
20.9
18.1
20.9
18.1
13.1
18.1
17.0
16.1
14.1
17.8
13.2
11.6
18.5
16.4
20.0
20.9
16.4
19.1
2030 Tier 3
Control DV
23.6
24.1
25.4
22.0
19.0
19.4
30.3
18.7
19.5
25.7
24.4
18.9
19.8
26.4
13.6
21.6
15.7
15.9
17.8
16.7
20.7
18.0
20.8
18.0
13.0
18.0
16.9
16.0
14.0
17.7
13.2
11.6
18.2
16.3
19.8
20.7
16.3
19.0
D-14
-------
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
Utah
Utah
Utah
Utah
Utah
Utah
Utah
Vermont
Vermont
Vermont
Virginia
Virginia
Virginia
Virginia
County
Knox
Lawrence
Loudon
McMinn
Madison
Maury
Montgomery
Putnam
Roane
Shelby
Sullivan
Sumner
Bowie
Dallas
Ector
El Paso
Harris
Harrison
Hidalgo
Nueces
Orange
Potter
Tarrant
Travis
Box Elder
Cache
Davis
Salt Lake
Tooele
Utah
Weber
Bennington
Chittenden
Rutland
Arlington
Charles City
Chesterfield
Fairfax
2007
Baseline
DV
32.6
29.6
31.0
32.8
28.1
28.1
32.6
25.5
29.0
33.5
29.4
29.6
27.2
23.6
17.4
27.1
29.8
23.4
24.3
27.8
28.7
14.8
24.5
20.9
33.8
39.3
37.1
47.5
25.1
46.1
37.5
23.2
25.9
28.9
29.6
28.1
27.7
31.1
2018
Reference
DV
20.0
18.1
20.3
19.7
16.4
16.5
18.4
15.5
17.6
19.4
17.4
19.4
19.1
16.9
15.6
26.9
22.4
17.4
21.4
21.0
22.4
13.2
17.7
15.4
29.7
34.0
32.8
43.0
22.0
41.0
32.9
14.6
20.4
29.2
17.8
15.4
15.4
17.8
2018 Tier 3
Control DV
20.0
18.0
20.3
19.7
16.3
16.5
18.3
15.5
17.6
19.4
17.4
19.4
19.1
16.8
15.5
26.9
22.3
17.4
21.4
21.0
22.3
13.2
17.7
15.3
29.5
33.9
32.7
42.8
21.9
40.7
32.7
14.6
20.4
29.2
17.8
15.4
15.3
17.8
2030
Reference
DV
19.5
17.9
20.0
19.4
16.2
16.2
18.2
15.3
17.2
19.0
17.1
19.0
18.9
16.8
16.2
30.8
22.2
17.0
21.6
21.1
22.4
13.5
17.8
15.2
28.2
32.3
31.5
41.5
21.0
39.0
31.1
14.4
20.3
29.0
17.7
15.2
15.2
17.9
2030 Tier 3
Control DV
19.2
17.8
19.9
19.3
16.1
16.1
18.1
15.2
17.1
18.9
17.0
18.8
18.8
16.6
16.1
30.7
22.1
17.0
21.6
21.0
22.4
13.4
17.5
15.1
27.6
31.6
30.9
40.7
20.7
38.4
30.5
14.4
20.2
28.9
17.5
15.1
15.1
17.7
D-15
-------
State
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Virginia
Washington
Washington
Washington
Washington
Washington
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
West Virginia
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
County
Henrico
Loudoun
Page
Rockingham
Bristol city
Hampton city
Lynchburg city
Norfolk city
Roanoke city
Virginia Beach
City
King
Pierce
Snohomish
Spokane
Yakima
Berkeley
Brooke
Cabell
Hancock
Harrison
Kanawha
Marion
Marshall
Monongalia
Ohio
Raleigh
Wood
Ashland
Brown
Dane
Dodge
Forest
Grant
Kenosha
La Crosse
Manitowoc
Milwaukee
2007
Baseline
DV
29.0
29.0
27.7
26.1
27.5
29.0
27.9
28.0
31.0
31.1
31.0
44.2
34.2
30.1
37.2
31.2
40.4
32.9
38.0
30.2
35.2
31.1
33.2
33.3
30.6
27.0
33.9
19.0
35.4
34.7
28.7
20.9
34.5
32.1
32.1
29.6
37.2
2018
Reference
DV
15.7
15.7
14.6
15.8
16.6
16.5
16.0
18.6
18.2
19.4
31.0
44.1
33.7
29.3
35.8
22.1
23.9
18.1
19.8
14.2
17.3
16.0
19.0
13.3
17.0
13.4
19.0
14.6
31.3
30.7
25.8
16.3
31.4
26.4
30.0
25.4
32.2
2018 Tier 3
Control DV
15.6
15.6
14.5
15.8
16.5
16.5
16.0
18.6
18.2
19.4
31.1
44.1
33.7
29.3
35.7
22.1
23.8
18.1
19.8
14.2
17.3
15.9
19.0
13.3
17.0
13.4
19.0
14.6
31.1
30.6
25.7
16.2
31.2
26.3
29.9
25.3
32.1
2030
Reference
DV
15.5
15.6
14.4
15.5
16.4
16.4
15.8
18.3
17.9
19.3
30.5
43.2
33.2
28.1
32.5
21.9
23.6
17.9
19.6
14.1
17.1
15.8
19.0
13.3
16.9
13.3
18.9
14.5
32.0
30.1
25.5
16.0
30.4
25.7
29.2
25.2
31.9
2030 Tier 3
Control DV
15.4
15.5
14.4
15.4
16.3
16.3
15.7
18.2
17.7
19.2
30.6
43.3
33.2
28.0
32.4
21.7
23.5
17.8
19.5
14.0
17.0
15.7
18.9
13.2
16.8
13.3
18.8
14.4
31.6
29.8
25.3
15.9
30.0
25.3
28.8
25.0
31.4
D-16
-------
State
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wisconsin
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
County
Outagamie
Ozaukee
St. Croix
Sauk
Taylor
Vilas
Waukesha
Campbell
Converse
Fremont
Laramie
Sheridan
2007
Baseline
DV
32.8
31.7
26.7
28.1
27.7
26.5
32.3
14.0
9.8
26.2
10.2
25.7
2018
Reference
DV
28.4
27.3
23.5
25.1
22.7
21.5
27.7
13.5
9.2
25.2
9.1
25.0
2018 Tier 3
Control DV
28.2
27.2
23.5
25.0
22.6
21.4
27.6
13.5
9.2
25.2
9.1
25.1
2030
Reference
DV
27.9
26.6
22.8
24.5
22.0
20.9
27.1
13.3
9.1
25.0
9.0
24.8
2030 Tier 3
Control DV
27.5
26.3
22.5
24.3
21.7
20.7
26.7
13.3
9.1
24.9
9.0
24.7
D-17
-------
United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-14-002
Environmental Protection Air Quality Assessment Division February, 2014
Agency Research Triangle Park, NC
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