Air Quality Modeling Technical Support
Document: Heavy-Duty Vehicle
Greenhouse Gas Phase 2 Final Rule
£% United States
Environmental Protect
Agency
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Air Quality Modeling Technical Support
Document: Heavy-Duty Vehicle
Greenhouse Gas Phase 2 Final Rule
Air Quality Assessment Division
Office of Air Quality Planning and Standards
U.S. Environmental Protection Agency
and
Assessment and Standards Division
Office of Transportation and Air Quality
U.S. Environmental Protection Agency
4>EPA
Environmental Protection EPA.420-R-16-007
Agency August 2016
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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 HDGHG Phase 2 Standards on Future 8-Hour Ozone Levels 10
B. Impacts of HDGHG Phase 2 Standards on Future Annual PM2.5 Levels 11
C. Impacts of HDGHG Phase 2 Standards on Future 24-hour PM2.5 Levels 13
D. Impacts of HDGHG Phase 2 Standards on Future Nitrogen Dioxide Levels 14
E. Impacts of HDGHG Phase 2 Standards on Future Ambient Air Toxic
Concentrations 15
F. Impacts of HDGHG Phase 2 Standards on Future Annual Nitrogen and Sulfur
Deposition Levels 25
G. Impacts of HDGHG Phase 2 Standards on Future Visibility Levels 26
Appendices
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List of Appendices
Appendix A.
Model Performance Evaluation for the 2011-Based Air Quality Modeling Platform
Appendix B.
8-Hour Ozone Design Values for Air Quality Modeling Scenarios
Appendix C.
Annual PM2.5 Design Values for Air Quality Modeling Scenarios
Appendix D.
24-Hour PM2.5 Design Values for Air Quality Modeling Scenarios
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I. Introduction
This document describes the air quality modeling performed by EPA in support of the
Heavy-Duty Greenhouse Gas (HDGHG) Phase 2 motor vehicle emission and fuel standards. A
national scale air quality modeling analysis was performed to estimate the impact of the Phase 2
standards on future year annual and 24-hour PM2.5 concentrations, daily maximum 8-hour ozone
concentrations, annual nitrogen dioxide concentrations, annual nitrogen and sulfur deposition
levels, specific 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 are slightly different than the final emissions inventories. The standards in the air
quality modeling inventory are based on the Phase 2 proposal. As mentioned in Chapter 5.5.2.3
and 6.2.2.3 of the RIA, the air quality inventories and the final rule inventories are generally
consistent, however there are some important differences. For example, the air quality modeling
inventory predicted increases in downstream PM2.5 emissions that are not expected to occur. The
air quality modeling inventory also predicts larger reductions in NOx emissions than the final
inventory. The implications of these differences are noted in the following discussion of the air
quality modeling results.
Air quality modeling was performed for three emissions cases: a 2011 base year, a 2040
reference case projection without the HDGHG Phase 2 rule standards and a 2040 control case
projection with HDGHG Phase 2 standards in place. The year 2011 was selected for the
HDGHG Phase 2 base year because this is the most recent year for which EPA had a complete
national emissions inventory at the time of emissions 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 2040 to assess the impacts on air quality of the Phase 2 vehicle
standards. Information on the development of emissions inventories for the HDGHG Phase 2
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-2014-
0827; EPA-420-R-16-008). 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 2011-based CMAQ modeling platform was used as the basis for the air quality
modeling of the HDGHG Phase 2 final rule. This platform represents a structured system of
connected modeling-related tools and data that provide a consistent and transparent basis for
assessing the air quality response to projected changes in emissions. The base year of data used
to construct this platform includes emissions and meteorology for 2011. 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 5.1, which was an upcoming new community version
in late 2015, was most recently peer-reviewed in September of 2015 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 2011 multi-pollutant modeling platform used
the most recent multi-pollutant CMAQ code available at the time of air quality modeling
(CMAQ version 5.0.2; multipollutant version6). CMAQ v5.0.2 reflects updates to version 5.0 to
improve the underlying science algorithms which are detailed at http://www.cmascenter.org.7-8'9
2 Moran, M., Astitha, M., Barsanti, K.C., Brown, N.J., Kaduwela, A., McKeen, S.A., Pickering, K.E. (28 September
2015). Final Report: Fifth Peer Review of the CMAQ Model, NERL/ORD/EPA. U.S. EPA, Research Triangle
Park, NC. https://www.cmascenter.org/PDF/CMAO 5th peer review report.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, J., Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, J., et al. (2004). Simulating regional-scale
ozone climatology over the eastern United States: model evaluation results. Atmospheric Environment, 38(17),
2627-2638.
4 United States Environmental Protection Agency. (2008). Technical support document for the final
locomotive/marine rule: Air quality modeling analyses. Research Triangle Park, N.C.: U.S. Environmental
Protection Agency, Office of Air Quality Planning and Standards, Air Quality Assessment Division.
5 Lin, M., Oki, T., Holloway, T., Streets, D.G., Bengtsson, M., Kanae, S., (2008). Long range transport of acidifying
substances in East Asia Part I: Model evaluation and sensitivity studies. Atmospheric Environment, 42(24), 5939-
5955.
6 CMAQ version 5.0.2 was released on April 2014. 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.. RELEASENOTES
for CMAQv5.0 - February 2012.
8 Community Modeling and Analysis System (CMAS) website: http://www.cmascenter.org.. RELEASE NOTES
for CMAQv5.0.1 - July 2012.
9 Community Modeling and Analysis System (CMAS) website: http://www.cmascenter.org.. CMAQ version 5.0.2
(April 2014 release) Technical Documentation. - May 2014.
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B. Model Domains and Grid Resolution
The CMAQ modeling analyses were performed for a 12 kilometer (km) domain covering
the continental United States, as shown in Figure II-1. The model extends vertically from the
surface to 50 millibars (approximately 17,600 meters) using a sigma-pressure coordinate system
with 25 vertical layers. Table II-l provides some basic geographic information regarding the
CMAQ domains.
In addition to the CMAQ model, the HDGHG Phase 2 modeling platform includes (1)
emissions for the 2011 base year, 2040 reference and control case projections, (2) meteorology
for the year 2011, and (3) estimates of intercontinental transport (i.e., boundary concentrations)
for the year 2011 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, 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 2011 meteorological inputs and initial and boundary concentrations are
described below. The emissions inventories used in the HDGHG Phase 2 air quality modeling
are described in the EITSD found in the docket for this rule (EPA-420-R-16-008).
Table II-1. Geographic elements of domains used in HDGHG Phase 2 modeling,
CMAQ Modeling Configuration
Grid Resolution
12 km National Grid
Map Projection
Lambert Conformal Projection
Coordinate Center
97 deg W, 40 deg N
True Latitudes
33 deg N and 45 deg N
Dimensions
396 x246 x25
Vertical extent
25 Layers: Surface to 50 millibar level
(see Table II-3)
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12US2 domain 1 \
x,y origin: -2412000r|i, (>1621
col: 396 row:246 1 ,
Figure 11-1. Map of the CMAQ 12 km modeling domain (noted by the purple box).
C. Modeling Simulation Periods
The 12 km CMAQ modeling domain was modeled for the entire year of the 2011 base
year and 2040 reference and control scenarios. 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 12 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. The ramp-up period is not considered as part of the
output analyses.
For the 8-hour ozone results, we are only using modeling results from the period between
May I and September 30, 2011. 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 2011. Data from the entire year were utilized when looking at the estimation
of PM2.5, total nitrogen and sulfate deposition, nitrogen dioxide, 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 PM2.5 concentrations, 8-hour ozone concentrations, annual NO2
concentrations, annual and seasonal air toxics concentrations, annual total nitrogen and sulfur
deposition levels and visibility impairment for each of the following emissions scenarios:
2011 base year
2040 reference case projection without the HDGHG Phase 2 standards
2040 control case projection with the HDGHG Phase 2 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 2040 reference scenario (a
scenario without the vehicle standards) to a 2040 control scenario which includes the vehicle
standards. This is done by calculating the simulated air quality ratios between the 2040 future
year simulation and the 2011 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., 2009-2013). 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 2040 reference case and 2040 control case was
used to quantify the air quality benefits of the rule. Additionally, the differences in projected
annual average PM2.5 and seasonal average ozone were used to calculate monetized benefits by
the BenMAP model (see Section 8.6 of the RIA).
The design value projection methodology used here followed EPA guidance10 for such
analyses. For each monitoring site, all valid design values (up to 3) from the 2009-2013 period
were averaged together. Since 2011 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.
Concentrations of PM2.5 in 2040 were estimated by applying the modeled 201 l-to-2040
relative change in PM2.5 species to the 5 year weighted average (2009-2013) design values.
Monitoring sites were included in the analysis if they had at least one complete design value in
the 2009-2013 period. EPA followed the procedures recommended in the modeling guidance for
projecting PM2.5 by projecting individual PM2.5 component species and then summing these to
calculate the concentration of total PM2.5. The PM2.5 species are defined as sulfates, nitrates,
ammonium, organic carbon mass, elemental carbon, crustal mass, water, and blank mass (a fixed
value of 0.5 |ig/m3). EPA's Modeled Attainment Test Software (MATS) was used to calculate
10 U.S. EPA, 2014: Draft Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5,
and Regional Haze (Draft version of the updated Ozone, PM2 5, and Regional Haze modeling guidance document).
Office of Air Quality Planning and Standards, Research Triangle Park, NC.
https ://www3. epa.gov/ttn/scram/guidance/guide/Draft_03 -PM-RH_Modeling_Guidance -2014 .pdf
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the future year design values. The software (including documentation) is available at:
http://www.epa.gov/scram001/modelingapps mats.htm.
To calculate 24-hour PM2.5 design values, the measured 98th percentile concentrations
from the 2009-2013 period at each monitor are projected to the future. The procedures for
calculating the future year 24-hour PM2.5 design values have been updated. The updates are
intended to make the projection methodology more consistent with the procedures for calculating
ambient design values.
A basic assumption of the old projection methodology is that the distribution of high
measured days in the base period will be the same in the future. In other words, EPA assumed
that the 98th-percentile day could only be displaced "from below" in the instance that a different
day's future concentration exceeded the original 98th-percentile day's future concentration. This
sometimes resulted in overstatement of future-year design values for 24-hour PM2.5 at receptors
whose seasonal distribution of highest-concentration 24-hour PM2.5 days changed between the
2009-2013 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 2011 base case and the 2040 cases were used to project ambient design
values to 2040. The calculations used the base period 2009-2013 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 70 ppb11.
We also conducted an analysis to compare the absolute and percent differences between
the 2040 control case and the 2040 reference case for annual and seasonal nitrogen dioxide,
formaldehyde, acetaldehyde, benzene, 1,3-butadiene, acrolein, and naphthalene as well as annual
nitrate and sulfate deposition. These data were not compared in a relative sense due to the
limited observational data available.
11 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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E. Meteorological Input Data
The gridded meteorological input data for the entire year of 2011 were derived from
simulations of the Weather Research and Forecasting Model (WRF) version 3.4, Advanced
Research WRF (ARW) core12 for the entire year of 2011 over a model domain slightly larger
than that shown in Figure II-1. Meteorological model input fields were prepared for the 12 km
domain shown in Figure II-1. The WRF simulation was run on the same map projection as
CMAQ.
The selections for key WRF physics options are shown below13:
• Pleim-Xiu PBL and land surface schemes
• Asymmetric Convective Model version 2 planetary boundary layer scheme
• Kain-Fritsh cumulus parameterization utilizing the moisture-advection trigger
• Morrison double moment microphysics
• RRTMG longwave and shortwave radiation schemes
The WRF model was initialized using the 12km North American Model (12NAM)
analysis product provided by National Climatic Data Center (NCDC). Where 12NAM data was
unavailable, the 40km Eta Data Assimilation System (EDAS) analysis (ds609.2) from the
National Center for Atmospheric Research (NCAR) was used. 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.14 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 19 m 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
WRF
Layers
Sigma P
Approximate
Height (m)
25
35
0.0000
17,556
34
0.0500
14,780
24
33
0.1000
12,822
32
0.1500
11,282
12 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.
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.
14 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.
7
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23
31
0.2000
10,002
30
0.2500
8,901
22
29
0.3000
7,932
28
0.3500
7,064
21
27
0.4000
6,275
26
0.4500
5,553
20
25
0.5000
4,885
24
0.5500
4,264
19
23
0.6000
3,683
18
22
0.6500
3,136
17
21
0.7000
2,619
16
20
0.7400
2,226
15
19
0.7700
1,941
14
18
0.8000
1,665
13
17
0.8200
1,485
12
16
0.8400
1,308
11
15
0.8600
1,134
10
14
0.8800
964
9
13
0.9000
797
12
0.9100
714
8
11
0.9200
632
10
0.9300
551
7
9
0.9400
470
8
0.9500
390
6
7
0.9600
311
5
6
0.9700
232
4
5
0.9800
154
4
0.9850
115
3
3
0.9900
77
2
2
0.9950
38
1
1
0.9975
19
Surface
1.0000
0
The 2011 meteorological outputs from the 12km WRF simulation were processed to
create model-ready inputs for CMAQ using the Meteorology-Chemistry Interface Processor
(MCIP), version 4.1.3.15,16
15 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).
16 Otte, T.L., Pleim, J.E., 2010. The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling
system: updates through MCIPv3.4.1. 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 2011 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 (PBL) heights. The
operational evaluation included statistical comparisons of model/observed pairs (e.g., mean bias,
mean (gross) error, fractional bias, and fractional error17) 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.18 The results of these analyses indicate that the
bias and error values associated with all three sets of 2011 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-CHEM19 model (standard version 8-03-02 with version
8-02-03 chemistry). The global GEOS-CHEM model simulates atmospheric chemical and
physical processes driven by assimilated meteorological observations from the NASA's Goddard
Earth Observing System (GEOS-5; additional information available at:
http://gmao.gsfc.nasa.gov/GEOS/ and http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-
5). This model was run for 2011 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 and an initial concentration field for the CMAQ simulations.
A GEOS-Chem model evaluation was conducted for the purpose of validating the 2011
GEOS-Chem simulation outputs for their use as inputs to the CMAQ modeling system. This
evaluation included using satellite retrievals paired with GEOS-Chem grid cell concentrations.22
17Boylan, 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.
18 Misenis, Chris, Meteorological Model Performance Evaluation for Annual 2011 WRF v3.4 Simulation,
USEPA/OAQPS, November, 2014.
19 Yantosca, B. and Carouge, C., 2010, GEOS-Chem v8-03-01 User's Guide, Atmospheric Chemistry Modeling
Group, Harvard University, Cambridge, MA, http://acmg.seas.harvard.edu/geos/doc/archive/man.v8-03-
02/index.html
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.
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.
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More information is available about the GEOS-Chem model and other applications using this
tool at: http://www-as.harvard.edu/chemistry/trop/geos.
G. CMAQ Base Case Model Performance Evaluation
The CMAQ predictions for ozone, fine particulate matter, sulfate, nitrate, ammonium,
organic carbon, elemental carbon, nitrogen and sulfur deposition, and specific air toxics
(formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and acrolein) from the 2011 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 HDGHG Phase 2 standards. We looked at
impacts on future ambient levels of daily and annual PM2.5 concentrations, 8-hour maximum
ozone concentrations and annual NO2 concentrations, as well as changes in annual and seasonal
(summer and winter) ambient concentrations of the following air toxics: formaldehyde,
acetaldehyde, acrolein, benzene, 1,3-butadiene, and naphthalene . 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 2040 HDGHG Phase
2 control case relative to the 2040 reference case.
A. Impacts of HDGHG Phase 2 Standards on Future 8-Hour Ozone Levels
This section summarizes the results of our modeling of ozone air quality impacts in the
future with the HDGHG Phase 2 fuel and vehicle standards. Specifically, for the year 2040 we
compare a reference scenario (a scenario without the proposed HDGHG Phase 2 standards) to a
control scenario which includes the Phase 2 standards. Our modeling indicates that there will be
reductions in 8-hour maximum ozone across most of the country as a result of the HDGHG
Phase 2 standards. The decreases in 8-hour ozone design values (DV), max reduction of 1.7 ppb,
are likely due to the projected reductions in both upstream and downstream NOx and VOC
emissions. As described in the RIA Section 5.5.2.3, assumptions about the usage of diesel-
powered APUs differs between the air quality inventories and the final rule inventories. The air
quality inventories assumed more widespread usage of diesel-powered APUs than was assumed
for the final rule. The APU assumptions mean that the NOx reductions assumed in the air
quality inventories are larger than we expect to occur and reductions in 8-hour ozone are over-
estimated in the air quality modeling. The magnitude of the reductions in 8-hour ozone DV from
the final rule inventories is difficult to estimate due to the complex, non-linear chemistry
governing ozone formation. However, EPA does expect reductions in ambient ozone
concentrations due to these final standards.
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Figure III-1 presents the changes in 8-hour ozone design value concentrations between
the projected air quality modeling inventories for the 2040 reference case and the 2040 control
case. Appendix B details the state and county 8-hour maximum ozone design values for the
2011 ambient baseline and the 2040 future reference and control cases.
Legend Number of Counties
| >= -2,0 to < -1,50 ppb 2
>- -1.50 to <-1.0 14
| >= -1.0 to < -0.75 62
| >= -0.75 to < -0.5 271
>= -0.5 to < -0,25 267
_J >= -0.25 to < -0.1 55
~ >= -0.1 to <= 0.1 35
Difference in Ozone DV
2040ei_ldghgp2_ctl minus 2040ei_hdghgp2_ref
Figure III-l. Projected Change in 2040 8-hour Ozone Design Values Between the Reference Case and
Control Case Using Air Quality Modeling Inventories
B. Impacts of HDGHG Phase 2 Standards on Future Annual PM2.5 Levels
This section summarizes the results of our modeling of annual average PM2.5 air quality
impacts in the future due to the HDGHG Phase 2 fuel and vehicle standards. Specifically, for the
year of 2040 we compare a reference scenario (a scenario without the standards) to a control
scenario that includes the standards. Our modeling indicates that by 2040 annual PM2.5 design
values in the majority of the modeled counties would decrease due to the standards. The
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.
11
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decreases in annual PM2.5 DV, less than 0.05 |ig/m3, are likely due to the projected reductions in
upstream primary PM2.5 emissions, and reductions in both upstream and downstream NOx, SOx
and VOCs. As described in the RIA Section 5.5.3.2 and 6A2.1, the air quality modeling used
inventories that do not reflect the new requirements for controlling PM2.5 emissions from APUs
installed in new tractors and therefore show increases in downstream PM2.5 emissions that we
now do not expect to occur. 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. We do not expect to actually see increases in PM2.5 DV from the HDGHG Phase 2
program. In addition, assumptions about the usage of diesel-powered APUs also differs between
the air quality inventories and the final rule inventories. The air quality inventories assumed
more widespread usage of diesel-powered APUs than was assumed for the final rule. The APU
assumptions mean that the NOx reductions assumed in the air quality inventories are larger than
we expect to occur and reductions in ambient PM2.5 due to secondary nitrate formation are over-
estimated in the air quality modeling.
The magnitude of the reductions in PM2.5 DV from the HDGHG Phase 2 final rule
inventories is difficult to estimate due to the differences in the air quality inventories, namely
overestimation of nitrate reductions and underestimation of direct PM2.5 reductions. However,
EPA does expect reductions in ambient concentrations of PM2.5 due to these final standards.
Figure III-2 presents the projected impacts of the air quality modeling inventories on annual
PM2.5 design values in 2040.24 Appendix C details the state and county annual PM2.5 design
values for the ambient 2011 baseline and the 2040 future reference and control cases.
24 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
-------
I >= -0.01 to <= 0.0
I > 0.0 to <= 0.01
i > 0.01 to <= 0.05
I >0.05 to <=0.10
329
Difference in Annual PM2.5 DV
2040ei_hdghgp2_ctl minus 2040ei_hdghgp2_ref
Figure ELI-2. Projected Change in 2040 Annual PM2.5 Design Values Between the Reference Case and Control
Case Using Air Quality Modeling Inventories
C. Impacts of HDGHG Phase 2 Standards on Future 24-hour P1VI2.5 Levels
This section summarizes the results of our modeling of 24-hour PM2.5 air quality impacts
in the future due to the HDGHG Phase 2 final rule. Specifically, for the year 2040 we compare a
reference scenario (a scenario without the proposed standards) to a 2040 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 daily PM2.5 decreases, less
than 0.6 jig/m3, are likely due to the projected reductions in upstream primary PM2.5 emissions,
and reductions in both upstream and downstream NOx, SOx and VOCs. As described in Section
5.5.2.3 of the RIA, the air quality modeling used inventories that do not reflect the new
requirements for controlling PM2.5 emissions from APUs installed in new tractors and therefore
show increases in downstream PM2.5 emissions. 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. We do not expect to actually see increases in PM2.5 DV
from the Phase 2 program. In addition, assumptions about the usage of diesel-powered APUs
also differs between the air quality inventories and the final rule inventories. The air quality
13
-------
inventories assumed more widespread usage of diesel-powered APUs than was assumed for the
final rule. The APU assumptions mean that the NOx reductions assumed in the air quality
inventories are larger than we expect to occur and reductions in ambient PM2.5 due to secondary
nitrate formation are over-estimated in the air quality modeling.
The magnitude of the reductions in PM2.5 DV from the final rule inventories is difficult to
estimate due to the differences in the air quality inventories, namely overestimation of nitrate
reductions and underestimation of direct PM2.5 reductions. However, EPA does expect
reductions in ambient concentrations of PM2.5 due to these final standards. Figure III-3 shows
the projected impacts of the air quality inventories on 24-hour PM2.5 DVs.
Legend
Number of Counties
Difference in Daily PM2.5 DV
2040ei_ldghgp2_ctl minus 2040ei_hdghgp2_ref
Figure III-3. Projected Change in 2040 24-hour PM2.5 Design Values Between the Reference Case and the
Control Case Using Air Quality Modeling Inventories
D. Impacts of HDGHG Phase 2 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 HDGHG Phase 2 standards. Specifically,
we compare a 2040 reference scenario (a scenario without the HDGHG Phase 2 standards) to a
14
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2040 control scenario that includes the HDGHG Phase 2 standards. Figure III-4 presents the
changes in annual NO2 concentrations in 2040 based on percent changes and absolute changes.
Air quality modeling results indicate that annual average NO2 concentrations will be reduced
across the country. However, the magnitude of the reductions that will actually result from the
final standards is difficult to estimate because the air quality modeling inventories included
larger NOx emission reductions than we now expect to occur. As described in Section 5.5.2.3,
the air quality inventories and the final rule inventories make different assumptions about the
usage of diesel-powered APUs. The air quality inventories assumed more widespread usage of
diesel-powered APUs than was assumed for the final rule, and as a result the reductions in
ambient NO2 concentrations are overestimated in the air quality modeling.
l*- v
/ 'S'A
5*4=J:
Figure EEI-4. Projected Change in 2040 Annual NO2 Concentrations Between the Reference Case and
Control Case Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in ppb
(right)
E. Impacts of HDGHG Phase 2 Standards on Future Ambient Air Toxic Concentrations
This section summarize the results of our modeling of air toxics impacts in the future
from the HDGHG Phase 2 fuel and vehicle emission standards. Our modeling indicates that the
standards have relatively little impact on national average ambient concentrations of the modeled
air toxics. Annual absolute changes in ambient concentrations are generally less than 0.2 |ig/m5
for benzene, formaldehyde, and acetaldehyde and less than 0.005 ug/m ' for acrolein and 1,3-
butadiene. Naphthalene changes are in the range of 0.005 lig'nr' along major roadways and in
urban areas. Air toxics concentration maps are presented below in Figures 111-5 through 111-22
along with Table III-l showing the percent of the population experiencing changes in ambient
toxic concentrations.
15
-------
Figure III-5. Annual Changes in Acetaldehyde Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in jig/m3 (right)
Figure III-6. Winter Changes in Acetaldehyde Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in fig/m' (right)
16
-------
Figure III-7. Summer Changes in Acetaldehyde Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in jig/m3 (right)
Figure III-8. Changes in Formaldehyde Ambient Concentrations between the Reference Case and the Control
Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in (ig/m3
(right)
17
-------
Figure III-9. Winter Changes in Formaldehyde Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in jig/m3 (right)
Figure 111-10. Summer Changes in Formaldehyde Ambient Concentrations between the Reference Case and
the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute
Changes in fig/m3 (right)
18
-------
Figure III-ll. Annual Changes in Acrolein Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in jig/m3 (right)
Figure 111-12. Winter Changes in Acrolein Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in fig/m' (right)
19
-------
Figure 111-13. Summer Changes in Acrolein Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in jig/m3 (right)
_tt owl l
Figure 111-14. Annual Changes in Benzene Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in jig/m3 (right)
20
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Figure 111-15. Winter Changes in Benzene Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in jig/m3 (right)
Figure 111-16. Summer Changes in Benzene Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in fig/m' (right)
21
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Figure 111-17. Changes In 1,3-Butadiene Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in jig/m3 (right)
\ Tfr .'* / I
Lcgond \
¦i A
H '-'ab — -•* V
Si \
¦¦ ~JI»- l*«
¦B "B* ^ t-.J ne~- m nr, uromn >=»»
»OtOI XHOOOl
BB ' fteJH-iiaai
B MndalTMktvntvlvr f,J S*i«lwrr - WWlinn
Figure 111-18, Winter Changes in 1,3-Butadiene Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in fig/m' (right)
22
-------
Figure 111-19. Summer Changes in 1,3-Butadiene Ambient Concentrations between the Reference Case and
the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute
Changes in jig/m1 (right)
Figure 111-20. Changes in Naphthalene Ambient Concentrations between the Reference Case and the Control
Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in (ig/m3
(right)
23
-------
Figure 111-21. Winter Changes in Naphthalene Ambient Concentrations between the Reference Case and the
Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes
in jig/m3 (right)
Figure 111-22. Summer Changes in Naphthalene Ambient Concentrations between the Reference Case and
the Control Case in 2040 Using Air Quality Modeling Inventories: Percent Changes (left) and Absolute
Changes in fig/m3 (right)
24
-------
Table III-l. Percent of Total Population Experiencing Changes in Annual Ambient Concentrations of Toxic
Pollutants in 2040 as a Result of the HDGHG Phase 2 Standards
Percent Change
Acet aldehyde
Acrolein
Benzene
1,3-Butadiene
Formaldehyde
Naphthalene
<-50
0%
0%
> -50 to < -25
1%
4%
> -25 to <-10
8%
1%
20%
> -10 to < -5
0%
15%
0%
2%
24%
> -5 to < -2.5
0%
25%
1%
5%
21%
> -2.5 to <-1
3%
28%
5%
1%
18%
15%
> -1 to < 1
97%
23%
94%
99%
74%
15%
> 1 to < 2.5
0%
>2.5 to <5
> 5 to < 10
> 10 to < 25
> 25 to < 50
>50
F. Impacts of HDGHG Phase 2 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
(Figures 111-23 and 111-24). However, the magnitude of the reductions that will actually result from the
final standards is difficult to estimate because the air quality modeling inventories included larger NOx
emission reductions than we now expect to occur. As described in the RIA Section 5.5.2.3, the air quality
inventories and the final rule inventories make different assumptions about the usage of diesel-powered
APUs. The air quality inventories assumed more widespread usage of diesel-powered APLIs than was
assumed for the final rule, and as a result the reductions in ambient NOx deposition are overestimated in
the air quality modeling.
Figure III-23. Changes in Nitrogen Deposition between the Reference Case and the Control Case in 2040
using Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in kg/ha (right)
25
-------
Legend
Figure 111-24. Changes in Sulfur Deposition between the Reference Case and the Control Case in 2040 using
Air Quality Modeling Inventories: Percent Changes (left) and Absolute Changes in kg/ha (right)
G. Impacts of HDGHG Phase 2 Standards on Future Visibility Levels
Air quality modeling conducted for the HDGHG Phase 2 final rule was used to project
visibility conditions in 135 Mandatory Class I Federal areas across the U S in 2040. 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 2011 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 2009-2013 period23.
Visibility for the 2040 reference and control cases were calculated using the regional
haze methodology outlined in section 6 of the photochemical modeling guidance, which applies
modeling results in a relative sense, using base year ambient data. The PM2.5 and regional haze
modeling guidance recommends the calculation of future year changes in visibility in a similar
manner to the calculation of changes in PM2.5 design values
https://www3.epa.gOv/scram001/guidance/guide/Draft_03-PM-RH_Modeling_Guidance-
2014.pdf). 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 values26 are made up of
individual component species (sulfate, nitrate, organics, etc). The predicted change in visibility
(on the 20 percent worst days) is calculated as the modeled percent change in the mass for each
of the PM2.5 species (on the 20% worst observed days) multiplied by the observed
concentrations. The future mass is converted to extinction and then daily species extinction
25 Since the base case modeling used meteorology' for 2011, one of the complete years must be 2011.
26 Extinction coefficient is in units of inverse megameters (Mm1). It is a measure of how much light is absorbed or
scattered as it passes through a medium. Light extinction is commonly used as a measure of visibility impairment in
the regional haze program.
26
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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 2040 reference case from the
corresponding 2040 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, (lxl for 36km grid)
Start monitor year- 2009
End monitor year- 2013
Base model year 2011
Minimum years required for a valid monitor- 3
The "base model year" was chosen as 2011 because it is the base case meteorological
year for the HDGHG Phase 2 final rule modeling. The start and end years were chosen as 2009
and 2013 because that is the 5 year period which is centered on the base model year of 2011.
These choices are consistent with using a 5 year base period for regional haze calculations.
The results show that in 2040 all the modeled areas would continue to have annual
average deciview levels above background and the rule would improve visibility in the majority
of these areas.27 Table III-2 contains the full visibility results from 2040 for the 135 analyzed
areas.
Table III-2. Visibility Levels (in Deciviews) for Mandatory Class I Federal Areas on the 20
Percent Worst Days Using Air Quality Inventories (with and without HDGHG Phase 2
Rule)
Class 1 Area
(20% worst days)
State
2011
Baseline
Visibility
2040
Reference
2040
HDGHGP2
Control
Natural
Background
Sipsey Wilderness
Alabama
22.93
18.16
18.07
10.99
Mazatzal Wilderness
Arizona
12.03
11.40
11.38
6.68
Pine Mountain Wilderness
Arizona
12.03
11.40
11.38
6.68
Superstition Wilderness
Arizona
12.72
11.82
11.80
6.54
Chiricahua NM
Arizona
12.08
11.54
11.53
7.20
Chiricahua Wilderness
Arizona
12.08
11.54
11.53
7.20
27 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.
27
-------
Class 1 Area
(20% worst days)
State
2011
Baseline
Visibility
2040
Reference
2040
HDGHGP2
Control
Natural
Background
Galiuro Wilderness
Arizona
12.08
11.54
11.53
7.20
Grand Canyon NP
Arizona
10.92
10.53
10.52
7.04
Petrified Forest NP
Arizona
11.92
11.64
11.63
6.49
Sycamore Canyon Wilderness
Arizona
14.62
14.00
14.01
6.65
Caney Creek Wilderness
Arkansas
22.23
19.01
18.96
11.58
Upper Buffalo Wilderness
Arkansas
22.12
19.00
18.95
11.57
Joshua Tree NM
California
15.07
13.49
13.47
7.19
Kings Canyon NP
California
20.82
17.93
17.91
7.70
San Rafael Wilderness
California
16.46
14.51
14.49
7.57
San Gorgonio Wilderness
California
16.85
14.11
14.09
7.30
San Jacinto Wilderness
California
16.85
14.11
14.09
7.30
Sequoia NP
California
20.82
17.93
17.91
7.70
Agua Tibia Wilderness
California
18.44
15.66
15.65
7.64
Ansel Adams Wilderness (Minarets)
California
14.27
13.01
13.00
7.12
Desolation Wilderness
California
11.82
11.02
11.01
6.05
Dome Land Wilderness
California
17.23
15.93
15.92
7.46
Emigrant Wilderness
California
14.75
14.16
14.15
7.64
Hoover Wilderness
California
10.78
10.31
10.30
7.71
John Muir Wilderness
California
14.27
13.01
13.00
7.12
Kaiser Wilderness
California
14.27
13.01
13.00
7.12
Marble Mountain Wilderness
California
14.10
13.34
13.33
7.90
Mokelumne Wilderness
California
11.82
11.02
11.01
6.05
Pinnacles NM
California
16.15
14.42
14.41
7.99
Ventana Wilderness
California
16.15
14.42
14.41
7.99
Yolla Bolly Middle Eel Wilderness
California
14.10
13.34
13.33
7.90
Yosemite NP
California
14.75
14.16
14.15
7.64
Caribou Wilderness
California
13.49
12.83
12.83
7.31
Lava Beds NM
California
13.38
12.93
12.93
7.85
Lassen Volcanic NP
California
13.49
12.83
12.83
7.31
Point Reyes NS
California
20.98
19.93
19.93
15.77
Redwood NP
California
17.38
16.82
16.82
13.91
South Warner Wilderness
California
13.38
12.93
12.93
7.85
Thousand Lakes Wilderness
California
13.49
12.83
12.83
7.31
Rocky Mountain NP
Colorado
11.84
10.93
10.91
7.15
Black Canyon of the Gunnison NM
Colorado
9.88
9.71
9.70
6.21
La Garita Wilderness
Colorado
9.88
9.71
9.70
6.21
Weminuche Wilderness
Colorado
9.88
9.71
9.70
6.21
Eagles Nest Wilderness
Colorado
8.48
8.04
8.03
6.06
Flat Tops Wilderness
Colorado
8.48
8.04
8.03
6.06
Great Sand Dunes NM
Colorado
11.57
11.50
11.49
6.66
28
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Class 1 Area
(20% worst days)
State
2011
Baseline
Visibility
2040
Reference
2040
HDGHGP2
Control
Natural
Background
Maroon Bells-Snowmass Wilderness
Colorado
8.48
8.04
8.03
6.06
Mount Zirkel Wilderness
Colorado
9.11
8.70
8.69
6.08
Rawah Wilderness
Colorado
9.11
8.70
8.69
6.08
West Elk Wilderness
Colorado
8.48
8.04
8.03
6.06
Mesa Verde NP
Colorado
11.22
11.37
11.37
6.81
Chassahowitzka
Florida
21.34
18.21
18.17
11.03
St. Marks
Florida
22.23
18.74
18.70
11.67
Everglades NP
Florida
18.15
17.65
17.62
12.15
Cohutta Wilderness
Georgia
22.71
17.47
17.43
10.78
Okefenokee
Georgia
22.68
18.82
18.78
11.44
Wolf Island
Georgia
22.68
18.82
18.78
11.44
Craters of the Moon NM
Idaho
14.05
12.93
12.80
7.53
Sawtooth Wilderness
Idaho
15.64
15.44
15.44
6.42
Selway-Bitterroot Wilderness
Idaho
14.89
14.77
14.77
7.43
Mammoth Cave NP
Kentucky
25.09
19.83
19.75
11.08
Acadia NP
Maine
17.93
15.81
15.80
12.43
Moosehorn
Maine
16.83
15.27
15.26
12.01
Roosevelt Campobello International Park
Maine
16.83
15.27
15.26
12.01
Seney
Michigan
20.56
17.15
17.08
12.65
Isle Royale NP
Michigan
18.92
16.06
16.01
12.37
Boundary Waters Canoe Area
Minnesota
18.82
16.66
16.60
11.61
Hercules-Glades Wilderness
Missouri
22.89
19.57
19.51
11.30
Mingo
Missouri
24.31
20.91
20.86
11.62
Medicine Lake
Montana
17.98
17.07
17.06
7.89
Bob Marshall Wilderness
Montana
14.43
14.33
14.32
7.73
Cabinet Mountains Wilderness
Montana
12.73
12.24
12.23
7.52
Glacier NP
Montana
16.03
15.82
15.81
9.18
Mission Mountains Wilderness
Montana
14.43
14.33
14.32
7.73
Red Rock Lakes
Montana
11.98
11.73
11.72
6.44
Scapegoat Wilderness
Montana
14.43
14.33
14.32
7.73
UL Bend
Montana
14.11
13.77
13.76
8.16
Anaconda-Pintler Wilderness
Montana
14.89
14.77
14.77
7.43
Jarbidge Wilderness
Nevada
11.97
11.90
11.90
7.87
Great Gulf Wilderness
New Hampshire
16.66
13.61
13.60
11.99
Presidential Range-Dry River Wilderness
New Hampshire
16.66
13.61
13.60
11.99
Brigantine
New Jersey
23.75
19.64
19.61
12.24
Bosque del Apache
New Mexico
14.02
14.37
14.34
6.73
Salt Creek
New Mexico
17.42
18.32
18.30
6.81
Bandelier NM
New Mexico
11.92
12.22
12.21
6.26
Carlsbad Caverns NP
New Mexico
15.32
15.09
15.08
6.65
29
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Class 1 Area
(20% worst days)
State
2011
Baseline
Visibility
2040
Reference
2040
HDGHGP2
Control
Natural
Background
Pecos Wilderness
New Mexico
9.93
9.84
9.83
6.08
San Pedro Parks Wilderness
New Mexico
10.02
10.02
10.01
5.72
Wheeler Peak Wilderness
New Mexico
9.93
9.84
9.83
6.08
White Mountain Wilderness
New Mexico
14.19
14.56
14.56
6.80
Linville Gorge Wilderness
North Carolina
21.60
15.94
15.91
11.22
Swanquarter
North Carolina
21.77
16.75
16.73
11.55
Theodore Roosevelt NP
North Dakota
16.96
15.96
15.95
7.80
Wichita Mountains
Oklahoma
21.24
18.83
18.76
7.53
Hells Canyon Wilderness
Oregon
16.58
15.10
14.94
8.32
Eagle Cap Wilderness
Oregon
14.87
14.20
14.17
8.92
Strawberry Mountain Wilderness
Oregon
14.87
14.20
14.17
8.92
Kalmiopsis Wilderness
Oregon
15.01
14.52
14.51
9.44
Mount Hood Wilderness
Oregon
13.35
12.72
12.71
8.43
Mount Jefferson Wilderness
Oregon
15.77
15.52
15.51
8.79
Mount Washington Wilderness
Oregon
15.77
15.52
15.51
8.79
Three Sisters Wilderness
Oregon
15.77
15.52
15.51
8.79
Crater Lake NP
Oregon
11.64
11.33
11.33
7.62
Diamond Peak Wilderness
Oregon
11.64
11.33
11.33
7.62
Gearhart Mountain Wilderness
Oregon
11.64
11.33
11.33
7.62
Mountain Lakes Wilderness
Oregon
11.64
11.33
11.33
7.62
Cape Romain
South Carolina
23.17
19.02
18.99
12.12
Wind Cave NP
South Dakota
14.04
12.85
12.82
7.71
Badlands NP
South Dakota
15.67
14.32
14.30
8.06
Great Smoky Mountains NP
Tennessee
22.50
16.99
16.95
11.24
Joyce-Kilmer-Slickrock Wilderness
Tennessee
22.50
16.99
16.95
11.24
Guadalupe Mountains NP
Texas
15.32
15.09
15.08
6.65
Big Bend NP
Texas
16.30
16.54
16.54
7.16
Arches NP
Utah
10.83
10.53
10.50
6.43
Canyonlands NP
Utah
10.83
10.53
10.50
6.43
Capitol Reef NP
Utah
10.18
9.69
9.66
6.03
Bryce Canyon NP
Utah
10.61
10.21
10.19
6.80
Lye Brook Wilderness
Vermont
19.26
14.94
14.92
11.73
James River Face Wilderness
Virginia
22.55
17.28
17.24
11.13
Shenandoah NP
Virginia
21.82
15.20
15.16
11.35
Alpine Lake Wilderness
Washington
16.14
14.86
14.80
8.43
Mount Rainier NP
Washington
15.50
14.43
14.41
8.54
Olympic NP
Washington
14.10
13.50
13.48
8.44
Pasayten Wilderness
Washington
12.44
11.83
11.81
8.25
Glacier Peak Wilderness
Washington
13.51
12.82
12.81
8.39
Goat Rocks Wilderness
Washington
12.37
11.77
11.76
8.35
30
-------
Class 1 Area
(20% worst days)
State
2011
Baseline
Visibility
2040
Reference
2040
HDGHGP2
Control
Natural
Background
North Cascades NP
Washington
13.51
12.82
12.81
8.01
Mount Adams Wilderness
Washington
12.37
11.77
11.76
8.35
Dolly Sods Wilderness
West Virginia
22.40
16.06
16.03
10.39
Otter Creek Wilderness
West Virginia
22.40
16.06
16.03
10.39
Bridger Wilderness
Wyoming
10.25
9.91
9.90
6.45
Fitzpatrick Wilderness
Wyoming
10.25
9.91
9.90
6.45
Grand Teton NP
Wyoming
11.98
11.73
11.72
6.44
Teton Wilderness
Wyoming
11.98
11.73
11.72
6.44
Yellowstone NP
Wyoming
11.98
11.73
11.72
6.44
31
-------
Air Quality Modeling Technical Support Document:
Heavy-Duty Vehicle Greenhouse Gas Phase 2 Final Rule
Appendix A
Model Performance Evaluation for the 2011-Based
Air Quality Modeling Platform
A-l
-------
A.l. Introduction
An operational model performance evaluation for ozone, PM2.5 and its related speciated
components, specific air toxics (i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and
acrolein), as well as nitrate and sulfate deposition was conducted using 2011 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 domain (Figure A-l) \
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 are defined in Section A. 1.2). Statistics were calculated for individual
monitoring sites and for each of the nine National Oceanic and Atmospheric Administration
(NOAA) climate regions of the 12-km U.S. modeling domain (Figure A-2)2. The regions include
the Northeast, Ohio Valley, Upper Midwest, Southeast, South, Southwest, Northern Rockies,
Northwest and West3'4 as were originally identified in Karl and Koss (1984)5. The statistics for
each site and climate region were calculated by season ("winter" is defined as average of
December, January, and February; "spring" is defined as average of March, April, and May;
"summer" is defined as average of June, July, and August; and "fall" is defined as average of
September, October, and December). For 8-hour daily maximum ozone, we also calculated
performance statistics by region for the May through September ozone season6. In addition to
the performance statistics, we prepared several graphical presentations of model performance.
These graphical presentations include regional maps which show the mean bias, mean error,
normalized mean bias and normalized mean error calculated for each season at individual
monitoring sites.
1 See section 6 A. 1. of the RIA document (Figure 6 A-l for the description and map of the CMAQ modeling domain.
2 NOAA, National Centers for Environmental Information scientists have identified nine climatically consistent
regions within the contiguous U.S., http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php.
3 The nine climate regions are defined by States where: Northeast includes CT, DE, ME, MA, MD, NH, NJ, NY, PA,
RI, and VT; Ohio Valley includes IL, IN, KY, MO, OH, TN, and WV; Upper Midwest includes IA, MI, MN, and
WI; Southeast includes AL, FL, GA, NC, SC, and VA; South includes AR, KS, LA, MS, OK, and TX; Southwest
includes AZ, CO, NM, and UT; Northern Rockies includes MT, NE, ND, SD, WY; Northwest includes ID, OR, and
WA; and West includes CA and NV.
4 Note most monitoring sites in the West region are located in California (see Figure A-2), therefore statistics for the
West will be mostly representative of California ozone air quality.
5 Karl, T. R. and Koss, W. J., 1984: "Regional and National Monthly, Seasonal, and Annual Temperature Weighted
by Area, 1895-1983." Historical Climatology Series 4-3, National Climatic Data Center, Asheville, NC, 38 pp.
6 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
-------
12US2 domain
x.y origin: -2412C
col; 396 row:246
Figure A-l. Map of the CMAQ 12 km Modeling Domain Used for HDGHG Phase 2 rule
(noted by the purple box).
U.S. Climate Regions
Figure A-2. NOAA Nine Climate Regions (source: litti)://www.nc(lc.noaa.gov/monitoriiig-
referenccs/mai)s/us-climate-rcgions.i)hi)#references)
A-3
-------
A.l.l 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 2011 at
monitoring sites in the EPA Air Quality System (AQS) and the Clean Air Status and Trends
Network (CASTNet). The observed ozone data were measured and reported on an hourly basis.
The PM2.5 evaluation focuses on concentrations of PM2.5 total mass and its components including
sulfate (SO4), nitrate (NO3), total nitrate (TNO3), ammonium (NH4), elemental carbon (EC), and
organic carbon (OC) as well as wet deposition for nitrate and sulfate. The PM2.5 performance
statistics were calculated for each season (e.g., "winter" is defined as December, January, and
February). PM2.5 ambient measurements for 2011 were obtained from the following networks:
Chemical Speciation Network (CSN), Interagency Monitoring of PROtected Visual
Environments (IMPROVE), Clean Air Status and Trends Network (CASTNet), and National
Acid Deposition Program/National Trends (NADP/NTN). NADP/NTN collects and reports wet
deposition measurements as weekly average data. The pollutant species included in the
evaluation for each monitoring network are listed in Table A-l. For PM2.5 species that are
measured by more than one network, we calculated separate sets of statistics for each network.
The CSN and IMPROVE networks provide 24-hour average concentrations on a 1 in every 3 day,
or 1 in every 6 day sampling cycle. The PM2.5 species data at CASTNet sites are weekly
integrated samples. In this analysis we use the term "urban sites" to refer to CSN sites;
"suburban/rural sites" to refer to CASTNet sites; and "rural sites" to refer to IMPROVE sites.
Table A-l. PM2.5 monitoring networks and pollutants species included in the CMAQ
performance evaluation.
Ambient
Monitoring
Networks
Particulate
Species
Wet
Deposition
Species
PM2.5
Mass
S04
N03
TN03a
EC
OC
nh4
S04
NO3
IMPROVE
X
X
X
X
X
CASTNet
X
X
X
CSN
X
X
X
X
X
X
NADP
X
X
a TNO3 = (NO3 + HNO3)
The air toxics evaluation focuses on specific species relevant to the HDGHG Phase 2
standards and rulemaking, i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and acrolein.
Similar to the PM2.5 evaluation, the air toxics performance statistics were calculated for each
season to estimate the ability of the CMAQ modeling system to replicate the base year
concentrations for the 12 km continental U.S. domain. Toxic measurements for 2011 were
obtained from the air toxics archive, http://www.epa.g0v/ttn/amtic/t0xdat.html#data. While most
of the data in the archive are from the AQS database including the National Air Toxics Trends
Stations (NATTS) (downloaded in July 2014), additional data (e.g., special studies) are included
in the archive but not reported in the AQS.
A-4
-------
A.1.2 Model Performance Statistics
The Atmospheric Model Evaluation Tool (AMET) was used to conduct the evaluation
described in this document.7 There are various statistical metrics available and used by the
science community for model performance evaluation. For this evaluation of the 2011 CMAQ
modeling platform, we have selected the mean bias, mean error, normalized mean bias, and
normalized mean error to characterize model performance, statistics which are consistent with
the recommendations in Simon et al. (2012)8 and the draft photochemical modeling guidance9.
Mean bias (MB) is used as average of the difference (predicted - observed) divided by
the total number of replicates (n). Mean bias is given in units of ppb and is defined as:
MB = ~Hi(P ~ 0) , where P = predicted and O = observed concentrations.
Mean error (ME) calculates the absolute value of the difference (predicted - observed)
divided by the total number of replicates (n). Mean error is given in units of ppb and is defined as:
ME = i£I|P-0|
Normalized mean bias (NMB) is used as a normalization to facilitate a range of
concentration magnitudes. This statistic averages the difference (predicted - 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 given in percentage units and is defined as:
i(p-o)
NMB = *100
n
I(O)
1
Normalized mean error (NME) is also similar to NMB, where the performance statistic is
used as a normalization of the mean error. NME calculates the absolute value of the difference
(predicted - observed) over the sum of observed values. Normalized mean error is given in
percentage units and is defined as:
7 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/)
8 Simon, H., Baker, K., Phillips, S., 2012: Compilation and interpretation of photochemical model performance
statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139.
9 U.S. Environmental Protection Agency (US EPA), Draft Modeling Guidance for Demonstrating Attainment of Air
Quality Goals for Ozone, PM2 5, and Regional Haze. December 2014, U.S. EPA, Research Triangle Park, NC, 27711.
A-5
-------
YJ\p-o\
NME = *100
1(0)
The "acceptability" of model performance was judged by comparing our CMAQ 2011
performance results in light of the range of performance found in recent regional ozone model
applications.10'11'12'13'14'151617'18'19'20 These other modeling studies represent a wide range of
modeling analyses that cover various models, model configurations, domains, years and/or
episodes, chemical mechanisms, and aerosol modules. Overall, the ozone model performance
results for the 2011 CMAQ simulations are within the range found in other recent peer-reviewed
and regulatory applications. The model performance results, as described in this document,
demonstrate that that our applications of CMAQ using this 2011 modeling platform provide a
scientifically credible approach for assessing ozone and PM2.5 concentrations for the purposes of
the HDGHG Phase 2 final rule.
10 National Research Council (NRC), 2002. Estimating the Public Health Benefits of Proposed Air Pollution
Regulations, Washington, DC: National Academies Press.
11 Appel, K.W., Roselle, S.J., Gilliam, R.C., and Pleim, J.E, 2010: 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.
12 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., 2010: Incremental testing of the Community
multiscale air quality (CMAQ) modeling system version 4.7. Geoscientific Model Development, 3, 205-226.
13 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.
14 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007. Evaluation of 2002 Multi-pollutant Platform:
Air Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.
(http://www.cmascenter.org/conference/2008/agenda.cfm).
15 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.Org/10.1016/i.atmosenv.2012.07.012
16 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.
dittp://www.epa.gov/ttn/chief/conference/eil7/sessionl 1/strum pres.pdf)
17 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.
18 U.S. Environmental Protection Agency; Technical Support Document for the Final Clean Air Interstate Rule: Air
Quality Modeling; Office of Air Quality Planning and Standards; RTP, NC; March 2005 (CAIR Docket OAR-2005-
0053-2149).
19 U.S. Environmental Protection Agency, Proposal to Designate an Emissions Control Area for Nitrogen Oxides,
Sulfur Oxides, and Particulate Matter: Technical Support Document. EPA-420-R-007, 329pp., 2009.
(http://www.epa.gov/otaa/regs/nonroad/marine/ci/420r09007.pdf)
20 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-0AR-2009-0472-
11332. (http://www.epa.gov/oms/renewableluels/420rl0006.pdf)
A-6
-------
A.2. Evaluation for 8-hour Daily Maximum Ozone
The 8-hour ozone model performance bias and error statistics for each climate region, for
each season defined above and for each monitor network (AQS and CASTNet) are provided in
Table A-2. Spatial plots of the mean bias and error as well as the normalized mean bias and
error for individual monitors are shown in Figures A-la through A-lh. 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.
As indicated by the statistics in Table A-2, bias and error for 8-hour daily maximum
ozone are relatively low in each climate region. In general the winter shows under prediction
except at AQS sites in the Southeast and West and also at rural CASTNet sites in the Northeast.
Likewise, the model tends to under predict in the spring with the exception of slight over
predictions at AQS sites in the Ohio Valley, South and Southeast in addition to CASTNet sites in
the Southeast and Northwest. Model predictions for the summer season typically show slight
over predictions apart from rural CASTNet sites in the Upper Midwest, Southwest, Northern
Rockies, and West and at AQS sites in the Northwest and Southwest. Figures A-la and A-le
show MB for 8-hour ozone > 60 ppb during the ozone season in the range of ±10 ppb at the
majority of ozone AQS and CASTNet measurement sites. At both AQS and CASTNet sites,
NMB is within the range of ±20 percent (Figures A-lc and A-lg). Model error for 8-hour
maximum ozone > 60 ppb, as seen from Figure A-lb and A-lf, is 10 ppb or less at most of the
sites across the modeling domain.
Table A-2. Daily Maximum 8-hour Ozone Performance Statistics by Climate Region, by
Season, and by Monitoring Network for the 2011 CMAQ Model Simulation.
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(PPb)
(PPb)
(%)
(%)
Winter
8,109
-2.5
5.1
-8.3
16.7
AQS
Spring
15,432
-0.2
5.3
-0.4
12.4
Summer
17,223
1.4
7.1
3.0
14.8
Northeast
Fall
14,105
3.2
5.1
9.9
19.9
Winter
1,188
-3.2
4.7
-9.3
13.8
CASTNet
Spring
1,160
-1.2
5.0
-2.8
11.3
Summer
1,217
0.7
6.0
1.6
13.1
Fall
1,295
2.8
5.5
8.3
16.6
Winter
3,293
-1.4
5.0
-4.9
17.7
AQS
Spring
15,995
0.1
5.8
0.2
13.0
Summer
19,865
1.4
7.3
2.7
13.9
Ohio Valley
Fall
13,574
1.5
6.2
4.1
16.6
Winter
1,485
-1.1
4.8
-3.2
14.5
CASTNet
Spring
1,461
-0.7
5.3
-1.5
11.4
Summer
1,393
0.7
6.1
1.4
11.7
Fall
1,501
1.2
5.4
3.1
13.8
Winter
1,048
-3.3
5.3
-10.7
17.0
AQS
Spring
5,416
-0.3
4.9
-0.7
11.1
Upper
Summer
8,149
1.4
6.4
3.2
14.5
Midwest
Fall
4,727
2.8
5.7
7.9
16.3
CASTNet
Winter
442
-4.4
5.6
-12.6
15.9
Spring
432
-3.0
5.4
-6.6
11.9
A-7
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(PPb)
(PPb)
(%)
(%)
Summer
403
-1.7
5.5
-3.8
12.8
Fall
393
1.7
4.5
5.0
13.1
Winter
6,117
0.5
5.0
1.3
13.6
AQS
Spring
15,428
3.5
6.5
7.5
13.9
Summer
17,342
6.6
9.8
13.9
20.5
Southeast
Fall
898
2.8
5.4
6.8
13.4
Winter
851
-1.4
4.5
-3.8
11.8
CASTNet
Spring
910
1.2
5.4
2.4
11.2
Summer
892
5.1
8.0
10.6
16.6
Fall
14,169
4.1
6.6
10.6
16.9
Winter
11,863
-0.2
5.4
-0.8
16.7
AQS
Spring
13,954
2.7
6.5
6.0
14.4
Summer
14,054
7.4
11.7
15.3
24.2
South
Fall
13,407
1.6
6.4
3.5
14.1
Winter
566
-1.1
4.8
-3.0
13.1
CASTNet
Spring
549
-0.1
5.9
-0.3
12.3
Summer
547
1.1
6.9
2.0
13.1
Fall
551
-0.3
5.0
-0.5
10.9
Winter
9,010
-1.0
6.30
-2.7
15.8
AQS
Spring
10,867
-2.7
5.6
-5.0
10.4
Summer
11,989
-1.6
7.3
-2.8
12.7
Southwest
Fall
10,711
2.9
5.8
6.6
13.1
Winter
640
-3.2
4.9
-7.1
10.7
CASTNet
Spring
687
-4.8
6.2
-8.5
10.9
Summer
702
-3.0
6.8
-5.2
11.7
Fall
688
0.1
3.8
0.2
7.8
Winter
3,293
-6.2
7.5
-16.0
19.3
AQS
Spring
3,673
-2.0
6.3
-4.2
13.1
Summer
4,148
2.4
6.0
5.1
12.7
Northern
Fall
4,062
3.1
4.7
8.2
12.4
Rockies
Winter
423
-5.8
6.8
-13.9
16.2
CASTNet
Spring
403
-4.6
6.8
-8.9
13.1
Summer
421
-1.3
4.9
-2.6
9.5
Fall
386
1.9
4.3
4.4
10.3
Winter
654
-0.3
6.7
-0.9
23
AQS
Spring
1,522
-1.3
5.7
-3.2
13.5
Summer
2,784
-0.1
5.5
-0.3
15.1
Northwest
Fall
1,266
3.0
6.7
8.5
19.1
Winter
87
10.6
11.1
44.4
46.7
CASTNet
Spring
92
2.4
4.0
6.0
10.2
Summer
84
5.4
6.3
18.3
21.1
Fall
78
13.1
13.6
51.4
53.1
Winter
15,225
3.6
6.4
11.3
19.9
AQS
Spring
16,907
-1.7
6.0
-3.7
12.6
Summer
18,073
1.1
8.2
2.1
16.0
West
Fall
17,064
2.8
7.5
6.6
17.4
Winter
551
-0.8
5.1
-1.9
11.7
CASTNet
Spring
535
-5.2
6.9
-9.7
12.9
Summer
539
-6.7
8.9
-10.8
14.3
Fall
532
-1.7
6.6
-3.5
13.1
A-8
-------
03_8hrmax MB (ppb) for run2011ei_cb05v2_hdghgp2_12US2 lor 20110501 to 20110930
units = ppb
coverage limit = 75%
fl>25
W20
— 15
— 10
CIRCLE=AQS_Daily;
Figure A-la. Mean Bias (ppb) of 8-hour daily maximum ozone greater than 60 ppb over
the period May-September 2011 at AQS monitoring sites in the modeling domain.
03_8hrmax ME (ppb) lor run2011ei_cb05v2_hdghgp2_12US2 for 20110501 to 20110930
units = ppb
coverage limit = 75%
CIRCLE=AQS_Daily;
Figure A-lb. Mean Error (ppb) of 8-hour daily maximum ozone greater than 60 ppb over
the period May-September 2011 at AQS monitoring sites in the modeling domain.
A-9
-------
03_8hrmax NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for 20110501 to 20110930
units = %
coverage limit = 75%
CIRCLE=AQS_Daily;
Figure A-lc. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60
ppb over the period May-September AQS 2011 at monitoring sites in the modeling domain.
03_8hrmax NME (%) for run 2011ei_cb05v2_hdghgp2_12US2 for 20110501 to 20110930
units = %
coverage limit = 75%
CIRCLE=AQS_Daily;
Figure A-ld. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60
ppb over the period May-September AQS 2011 at monitoring sites in the modeling domain.
A-10
-------
03_8hrmax MB (ppb) for run2011ei_cb05v2_hdghgp2_12US2 for 20110501 to 20110930
units = ppb
. coverage limit = 75%
TRIANGLE=CASTNETJDaily;
Figure A-le. Mean Bias (ppb) of 8-hour daily maximum ozone greater than 60 ppb over
the period May-September 2011 at CASTNet monitoring sites in the modeling domain.
03_8hrmax ME (ppb) for run2011ei_cb05v2_hdghgp2_12US2for 20110501 to 20110930
units = ppb
coverage limit = 75%
TRIANGLE=CASTNET_Daily;
Figure A-lf. Mean Error (ppb) of 8-hour daily maximum ozone greater than 60 ppb over
the period May-September 2011 at CASTNet monitoring sites in the modeling domain.
A-11
-------
03_8hrmax NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for 20110501 to 20110930
^ units = %
aA** coverage limit = 75%
TRI ANGLE=CASTN ET_Daily;
Figure A-lg. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60
ppb over the period May-September CAST Net 2011 at monitoring sites in the modeling
domain.
03_8hrmax NME (%) for run 2011ei_cb05v2_hdghgp2_12US2for 20110501 to 20110930
^ "^j units = %
.1^ coverage limit = 75%
TRIANGLE=CASTNET_Daily;
Figure A-111. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60
ppb over the period May-September CAST Net 2011 at monitoring sites in the modeling
domain.
A-12
-------
A.3. Seasonal Evaluation of PM2.5 Component Species
The evaluation of 2011 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 climate region 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 nine NOAA climate regions. 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 mean bias and error and the normalized
mean bias and error by site, aggregated by season.
A.3.1. Seasonal Evaluation for Sulfate
The model performance bias and error statistics for sulfate for each climate region and
each season by monitor network 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 NOAA climate regions
throughout the entire year except for the following: (1) at Southeast IMPROVE sites during the
spring season, (2) at Northeast, Northern Rockies and Upper Midwest IMPROVE sites, as well
as Northeast, Northern Rockies, and South CSN sites during the fall season, (3) at Southwest and
West IMPROVE and CASTNet ozone sites in addition to CSN in the West, and (4) at Northwest
IMPROVE, CASTNet and CSN during all seasons except for the summer at CASTNet sites.
Table A-3. Sulfate Performance Statistics by Climate Region, by Season, and by
Monitoring Network for the 2011 CMAQ Model Simulation.
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
425
-0.2
0.3
-12.4
26.2
IMPROVE
Spring
475
0.2
0.5
18.2
40.6
Summer
422
-0.2
0.7
-9.7
41.7
Fall
418
0.1
0.4
5.9
34.7
Winter
679
-0.4
0.7
-19.0
35.2
Northeast
CSN
Spring
717
-0.2
0.5
-8.1
29.7
Summer
721
-0.4
0.9
-13.6
28.8
Fall
685
0.1
0.6
5.3
33.1
Winter
170
-0.6
0.6
-32.6
33.2
CASTNet
Spring
193
-0.3
0.4
-13.9
23.8
Summer
187
-0.7
0.7
-23.1
26.2
Fall
196
-0.2
0.3
-9.7
18.9
Winter
207
-0.5
0.7
-27.6
37.0
IMPROVE
Spring
235
-0.4
0.7
-19.1
31.5
Summer
211
-0.9
1.2
-24.5
33.3
Ohio Valley
Fall
226
-0.1
0.6
-7.3
34.1
Winter
588
-0.7
0.9
-31.5
39.0
CSN
Spring
624
-0.5
0.8
-18.8
31.0
Summer
645
-0.7
1.2
17.0
30.3
Fall
611
-0.2
0.6
-12.1
31.6
A-13
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
201
-0.9
1.0
-39.7
40.3
CASTNet
Spring
214
-0.7
0.7
-26.5
27.7
Summer
207
-1.2
1.3
-28.6
30.2
Fall
214
-0.4
0.5
-20.0
23.5
Winter
210
-0.3
0.4
-23.5
37.2
IMPROVE
Spring
205
0.0
0.4
-1.3
27.2
Summer
221
-0.2
0.5
-16.9
33.8
Fall
223
0.0
0.4
1.9
36.7
Winter
334
-0.4
0.6
-24.6
38.4
Upper
CSN
Spring
337
0.0
0.6
-0.1
30.5
Midwest
Summer
335
-0.4
0.8
-17.6
33.1
Fall
340
0.0
0.5
-1.2
31.3
Winter
56
-0.5
0.5
-33.4
33.8
CASTNet
Spring
62
-0.2
0.3
-12.5
18.3
Summer
65
-0.5
0.5
-24.7
25.7
Fall
62
-0.2
0.3
-12.4
19.0
Winter
329
-0.2
0.6
-11.0
34.7
IMPROVE
Spring
346
-0.4
0.7
-16.3
31.0
Summer
331
-0.7
0.9
-22.4
31.0
Fall
319
-0.1
0.5
-4.9
30.2
Winter
435
-0.2
0.6
-8.9
35.6
Southeast
CSN
Spring
454
-0.4
0.8
-16.4
32.6
Summer
471
-0.6
1.0
-17.5
29.0
Fall
442
0.0
0.5
-0.3
29.7
Winter
138
-0.6
0.6
-30.0
30.3
CASTNet
Spring
146
-0.8
0.9
-31.0
32.1
Summer
147
-1.2
1.2
-34.2
34.6
Fall
150
-0.4
0.5
-23.4
26.6
Winter
247
-0.2
0.5
-18.7
39.7
IMPROVE
Spring
269
-0.5
0.7
-28.0
37.1
Summer
279
-0.7
0.8
-33.4
36.8
Fall
252
-0.1
0.3
-8.7
27.3
Winter
222
-0.2
0.7
-10.1
38.1
South
CSN
Spring
248
-0.6
0.8
-23.9
33.0
Summer
253
-0.7
0.8
-26.4
33.7
Fall
238
0.0
0.5
1.9
30.1
Winter
70
-0.6
0.6
-35.7
36.5
CASTNet
Spring
85
-1.0
1.0
-39.9
40.0
Summer
88
-1.0
1.0
-40.6
42.0
Fall
76
-0.4
0.5
-22.3
27.2
Winter
904
0.1
0.2
39.5
60.6
IMPROVE
Spring
920
-0.1
0.3
-14.8
42.7
Summer
922
-0.4
0.4
-43.1
44.9
Fall
916
-0.1
0.2
-21.3
34.9
Winter
185
0.0
0.3
-4.0
48.0
Southwest
CSN
Spring
190
0.0
0.3
-3.5
37.5
Summer
192
-0.4
0.4
-39.8
43.9
Fall
186
-0.1
0.2
-14.8
32.6
Winter
94
0.1
0.1
23.0
33.4
CASTNet
Spring
102
-0.1
0.2
-18.3
31.9
Summer
102
-0.4
0.5
-39.0
45.9
A-14
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Fall
101
-0.2
0.2
-27.5
32.0
Winter
522
0.0
0.2
-5.4
49.1
IMPROVE
Spring
590
0.0
0.3
-5.5
38.5
Summer
580
-0.1
0.2
-14.8
31.8
Fall
551
0.1
0.2
22.5
40.1
Winter
66
-0.2
0.3
-24.0
31.9
Northern
CSN
Spring
70
-0.3
0.4
-17.2
29.7
Rockies
Summer
72
-0.2
0.4
-12.0
29.3
Fall
69
0.0
0.2
4.8
28.1
Winter
77
-0.1
0.2
-17.2
32.9
CASTNet
Spring
76
-0.2
0.2
-20.8
26.6
Summer
88
-0.3
0.3
33.1
33.9
Fall
89
-0.1
0.1
-9.1
20.6
Winter
422
0.2
0.2
65.0
90.
IMPROVE
Spring
500
0.2
0.2
45.2
58.3
Summer
438
0.0
0.3
6.9
39.3
Fall
450
0.2
0.3
41.5
62.4
Winter
166
0.3
0.5
44.4
70.3
Northwest
CSN
Spring
167
0.3
0.4
54.6
63.0
Summer
172
0.2
0.4
15.9
39.9
Fall
164
0.4
0.5
48.4
65.6
Winter
12
0.1
0.1
48.6
54.2
CASTNet
Spring
13
0.1
0.2
26.0
32.4
Summer
13
-0.1
0.2
-14.1
21.6
Fall
13
0.1
0.1
17.1
30.0
Winter
471
0.1
0.2
51.1
82.7
IMPROVE
Spring
513
0.0
0.3
-1.4
47.3
Summer
526
-0.4
0.5
-46.5
53.7
Fall
525
-0.1
0.3
-20.6
44.3
Winter
226
0.0
0.4
4.6
55.8
West
CSN
Spring
242
-0.1
0.4
-9.9
39.2
Summer
246
-0.9
0.9
-49.5
52.3
Fall
229
-0.5
0.7
-36.1
46.8
Winter
69
0.0
0.2
5.1
40.8
CASTNet
Spring
73
-0.2
0.3
-24.9
37.9
Summer
77
-0.6
0.6
-57.4
57.8
Fall
77
-0.3
0.4
-40.7
46.8
A-15
-------
S04 MB (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for December to February 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-3a. Mean Bias (ug/m3) of sulfate during winter 2011 at monitoring sites in the
modeling domain.
S04 ME (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for December to February 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-3b. Mean Error (ug/m3) of sulfate during winter 2011 at monitoring sites in the
modeling domain.
A-16
-------
S04 NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for December to February 2011
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-3c. Normalized Mean Bias (%) of sulfate during winter 2011 at monitoring sites
in the modeling domain.
S04 NME (%) for run 2011ei_cb05v2_hdghgp2 12US2 for December to February 2011
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-3d. Normalized Mean Error (%) of sulfate during winter 2011 at monitoring
sites in the modeling domain.
A-17
-------
S04 MB (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for March to May 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-4a. Mean Bias (ug/mJ) of sulfate during spring 2011 at monitoring sites in the
modeling domain.
S04 ME (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for March to May 2011
units = ug/m3
coverage limit = 75%
CIRCLEdMPROVE; TRIANGLE=CSN; SQUARE=CASTNET:
Figure A-4b. Mean Error (ug/m3) of sulfate during spring 2011 at monitoring sites in the
modeling domain.
A-18
-------
S04 NMB (%) for run 2011eLcb05v2_hdghgp2_12US2 for March to May 2011
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-4c. Normalized Mean Bias (%) of sulfate during spring 2011 at monitoring sites
in the modeling domain.
S04 NME (%) for run 2011ei_cb05v2_hdghgp2_12US2 for March to May 2011
units = %
coverage limit = 75%
CIRCLEdMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-4d. Normalized Mean Error (%) of sulfate during spring 2011 at monitoring
sites in the modeling domain.
A-19
-------
S04 MB (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for June to August 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-5a. Mean Bias (ug/m3) of sulfate during summer 2011 at monitoring sites in the
modeling domain.
S04 ME (ug/m3) for run2011ei_cb05v2Jidghgp2 12US21or June to August 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET:
Figure A-5b. Mean Error (ug/m3) of sulfate during summer 2011 at monitoring sites in the
modeling domain.
A-20
-------
S04 NMB (%) for run 2011ei_cb05v2 hdghgp2 12US2 for June to August 2011
units = %
coverage limit = 75%
' •• j im
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-5c. Normalized Mean Bias (%) of sulfate during summer 2011 at monitoring
sites in the modeling domain.
S04 NME {%) for run 2011ei_cb05v2_hdghgp2_12US2 for June to August 2011
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-5d. Normalized Mean Error (%) of sulfate during summer 2011 at monitoring
sites in the modeling domain.
A-21
-------
S04 MB (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for September to November 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-6a. Mean Bias (ug/m3) of sulfate during fall 2011 at monitoring sites in the
modeling domain.
S04 ME (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for September to November 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-6b. Mean Error (ug/mJ) of sulfate during fall 2011 at monitoring sites in the
modeling domain.
A-22
-------
S04 NMB (%) for run 2011ei_cb05v2_hdghgp2 12US2 for September to November 2011
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-6c. Normalized Mean Bias (%) of sulfate during fall 2011 at monitoring sites in
the modeling domain.
units = %
coverage limit = 75%
SQ4 NME (%) for run 2011ei Cb05v2 hdghgp2 12US2 for September to November 2011
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure A-6d. Normalized Mean Error (%) of sulfate during fall 2011 at monitoring sites in
the modeling domain.
> 100
90
80
70
60
50
40
30
20
10
A-23
-------
A.3.1. Seasonal Evaluation for Nitrate
The model performance bias and error statistics for nitrate for each climate region and
each season are provided in Table A-4. This table includes statistics for particulate nitrate as
measured at CSN and IMPROVE sites and total nitrate (NO3+HNO3) as measured at CASTNet
sites. Spatial plots of the mean bias and error as well as normalized mean bias and error by
season for individual monitors are shown in Figures A-7 through A-10. Overall, nitrate and total
nitrate performance are over predicted in the Northeast, Ohio Valley, Upper Midwest, Southeast,
South, Northern Rockies and Northwest U.S.; with the exception at the IMPROVE and CSN
sites in the Southwest where nitrate is under predicted in the winter. Likewise, the model tends
to over predict nitrate during the fall season except in the South and Southwest at CSN and
CASTNet and in the Southwest at all three monitoring networks. During the spring, nitrate and
total nitrate is also over predicted except in the South, Southeast and Southwest. Nitrate and
total nitrate performance during the summer season typically shows an under prediction in most
areas of the U.S. with the exception of the Northwest at urban CSN sites and the Ohio Valley
region at rural CASTNet sites. Model performance shows an under prediction in the West for all
of the seasonal assessments of nitrate.
Table A-4. Nitrate and Total Nitrate Performance Statistics by Climate Region, by Season,
and by Monitoring Network for the 2011 CMAQ Model Simulation.
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
425
0.9
1.0
154.0
163.0
IMPROVE
Spring
474
0.1
0.3
42.6
111.0
Summer
422
-0.1
0.2
-55.5
97.1
Fall
418
0.1
0.3
26.2
106.0
Winter
679
1.1
1.4
52.9
66.7
Northeast
CSN
Spring
717
0.1
0.6
6.5
58.8
Summer
721
-0.3
0.4
-54.1
72.5
Fall
685
0.1
0.5
16.0
63.8
Winter
170
0.8
0.9
38.7
40.9
CASTNet
Spring
193
0.2
0.4
11.0
30.3
Summer
187
0.0
0.3
-1.7
27.5
Fall
196
0.4
0.6
35.4
46.1
Winter
207
0.6
1.2
29.5
57.7
IMPROVE
Spring
235
0.6
0.9
70.5
110.0
Summer
211
-0.1
0.2
-62.5
81.3
Fall
226
0.2
0.4
38.9
83.0
Winter
588
1.0
1.4
35.0
49.0
Ohio Valley
CSN
Spring
624
0.7
1.1
43.0
68.5
Summer
645
-0.2
0.4
-33.0
69.4
Fall
611
0.3
0.1
38.0
67.0
Winter
201
0.6
0.9
16.1
25.4
CASTNet
Spring
214
0.3
0.7
14.7
33.1
Summer
207
0.0
0.5
0.7
28.4
Fall
214
0.5
0.6
33.6
38.1
Winter
210
0.6
1.0
30.2
49.9
Upper
IMPROVE
Spring
205
0.4
0.7
35.7
59.3
Midwest
Summer
221
-0.1
0.1
-33.2
69.0
Fall
222
0.5
0.6
74.9
88.7
A-24
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
334
0.8
1.3
22.7
36.8
CSN
Spring
337
0.7
1.1
30.5
50.8
Summer
335
-0.2
0.4
-38.1
72.1
Fall
340
0.6
0.8
47.5
62.9
Winter
56
0.5
0.7
18.6
23.4
CASTNet
Spring
62
0.4
0.6
24.1
38.6
Summer
65
-0.1
0.4
-10.7
29.6
Fall
62
0.7
0.7
43.1
47.3
Winter
329
0.4
0.7
70.9
119.0
IMPROVE
Spring
346
-0.1
0.4
-17.5
97.5
Summer
331
-0.2
0.2
-60.7
87.2
Fall
319
0.1
0.3
24.7
111.0
Winter
435
0.9
1.0
97.3
115.0
Southeast
CSN
Spring
454
0.0
0.4
-8.1
83.4
Summer
471
-0.1
0.2
-47.9
70.8
Fall
442
0.3
0.5
88.1
131.0
Winter
138
0.4
0.9
22.1
50.8
CASTNet
Spring
146
-0.4
0.6
-25.8
39.2
Summer
147
-0.3
0.4
-21.0
34.5
Fall
150
0.1
0.5
11.2
41.7
Winter
247
0.1
0.7
3.9
51.8
IMPROVE
Spring
269
0.0
0.5
-1.4
61.1
Summer
279
-0.2
0.3
-91.6
93.6
Fall
252
0.0
0.3
0.1
78.3
Winter
222
0.3
1.0
15.9
52.7
South
CSN
Spring
248
-0.1
0.6
-13.2
70.6
Summer
253
-0.3
0.3
-79.7
87.0
Fall
238
0.0
0.4
-1.0
65.2
Winter
70
0.3
0.7
11.7
27.7
CASTNet
Spring
85
-0.5
0.8
-27.0
40.0
Summer
88
-0.7
0.7
-39.4
40.3
Fall
76
-0.2
0.4
-12.3
31.0
Winter
903
-0.1
0.3
-37.5
74.2
IMPROVE
Spring
920
-0.1
0.2
-59.5
75.0
Summer
922
-0.1
0.2
-87.1
92.0
Fall
916
-0. 1
0.1
-49.6
93.0
Winter
185
-1.8
2.1
-51.9
62.6
Southwest
CSN
Spring
190
-0.2
0.4
-26.2
58.2
Summer
192
-0.1
0.3
-44.2
96.8
Fall
186
-0.2
0.6
-21.5
73.0
Winter
94
0.0
0.3
1.7
47.2
CASTNet
Spring
102
-0.2
0.2
-26.6
36.2
Summer
102
-0.4
0.4
-40.2
45.4
Fall
101
-0.1
0.2
-15.7
38.6
Winter
520
0.5
0.6
130.0
166.0
IMPROVE
Spring
588
0.3
0.4
67.1
101.0
Northern
Rockies
Summer
578
-0.1
0.1
-59.9
90.0
Fall
551
0.2
0.3
152
193
Winter
66.0
0.1
1.1
4.4
46.7
CSN
Spring
70.0
0.2
0.8
12.6
48.4
Summer
72.0
-0.2
0.2
-64.2
83.2
A-25
-------
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Fall
69.0
0.5
0.7
66.0
94.3
Winter
77
0.3
0.3
36.8
45.1
CASTNet
Spring
76
0.1
0.3
13.4
33.6
Summer
88
-0.2
0.2
-19.5
23.8
Fall
89
0.1
0.2
23.9
35.9
Winter
416
0.1
0.4
16.5
109.0
IMPROVE
Spring
498
0.1
0.1
37.3
103.0
Summer
436
0.0
0.1
-21.3
103.0
Fall
447
0.1
0.2
40.8
119.0
Winter
166
0.5
1.5
28.7
84.9
Northwest
CSN
Spring
167
0.2
0.4
49.6
84.6
Summer
172
0.1
0.3
40.5
113.0
Fall
164
0.4
0.6
61.1
99.6
Winter
-
-
-
-
-
CASTNet
Spring
-
-
-
-
-
Summer
-
-
-
-
-
Fall
-
-
-
-
-
Winter
460
-0.4
0.6
-44.3
74.1
IMPROVE
Spring
513
-0.2
0.3
-46.6
72.8
Summer
526
-0.3
0.3
-78.9
90.6
Fall
522
-0.1
0.4
-33.0
85.4
Winter
226
-2.5
2.9
-52.9
61.2
West
CSN
Spring
242
-0.7
1.0
-38.8
55.5
Summer
246
-1.5
1.5
-71.4
72.8
Fall
229
-2.0
2.3
-55.8
64.3
Winter
69
-0.2
0.6
-21.4
55.1
CASTNet
Spring
73
-0.3
0.4
-31.8
41.7
Summer
77
-0.6
0.6
-34.3
38.9
Fall
77
-0.4
0.6
-29.1
40.1
A-26
-------
N03 MB (ug/m3) lor run2011ei_cb05v2 hdghgp2 12US2 for December to February 2011
units = ug/m3
coverage limit = 75%
CIRCLEdMPROVE; TRIANGLE=CSN;
Figure A-7a. Mean Bias (ug/m3) for nitrate during winter 2011 at monitoring sites in the
modeling domain.
N03 ME (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for December to February 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-7b. Mean Error (ug/m3) for nitrate during winter 2011 at monitoring sites in the
modeling domain.
A-27
-------
TN03 MB (ug/m3) for run2011ei_cb05v2 hdghgp2_12US2 for December to February 2011
units = ug/m3
coverage limit = 75%
SQUARE=CASTNET;
Figure A-7c. Mean Bias (ug/mJ) for total nitrate during winter 2011 at monitoring sites in
the modeling domain.
TN03 ME (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for December to February 2011
units = ug/m3
coverage limit = 75%
SQUARE=CASTN ET;
Figure A-7d. Mean Error (ug/m3) for total nitrate during winter 2011 at monitoring sites in
the modeling domain.
A-28
-------
N03 NMB (%) lor run 2011ei_cb05v2_hdghgp2 12US2 for December to February 2011
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-7e. Normalized Mean Bias (%) for nitrate during winter 2011 at monitoring sites
in the modeling domain.
N03 NME (%) for run 2011 ei_cb05v2 hdghgp212US2 for December to February 2011
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-7f. Normalized Mean Error (%) for nitrate during winter 2011 at monitoring
sites in the modeling domain.
A-29
-------
TN03 NMB (%) for run 2011 ei_cb05v2 hdghgp2 12US2 for December to February 2011
units = %
coverage limit = 75%
> 100
<-100
SQUARE=CASTNET;
Figure A-7g. Normalized Mean Bias (%) for total nitrate during winter 2011 at monitoring
sites in the modeling domain.
units = %
coverage limit = 75%
TN03 NME (%) for run 2011ei_cb05v2 hdghgp2 12US2 for December to February 2011
> 100
90
80
70
60
50
40
30
20
10
0
Figure A-7h. Normalized Mean Error (%) for total nitrate during winter 2011 at
monitoring sites in the modeling domain.
SQUARE=CASTNET;
A-30
-------
N03 MB (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for March to May 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IIVIPROVE; TRIANGLE=CSN;
Figure A-8a. Mean Bias (ug/m3) for nitrate during spring 2011 at monitoring sites in the
modeling domain.
N03 ME (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for March to May 2011
units = ug/m3
coverage limit = 75%
f*
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-8b. Mean Error (ug/m3) for nitrate during spring 2011 at monitoring sites in the
modeling domain.
A-31
-------
TN03 MB (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for March to May 2011
units = ug/m3
coverage limit = 75%
SQUARE=CASTNET;
Figure A-8c. Mean Bias (ug/nr') for total nitrate during spring 2011 at monitoring sites in
the modeling domain.
TN03 ME (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 lor March to May 2011
units = ug/m3
coverage limit = 75%
SQUARE=CASTN ET;
Figure A-8d. Mean Error (ug/mJ) for total nitrate during spring 2011 at monitoring sites in
the modeling domain.
A-32
-------
N03 NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for March to May 2011
units = %
coverage limit = 75%
> 100
80
60
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-8e. Normalized Mean Bias (%) for nitrate during spring 2011 at monitoring sites
in the modeling domain.
units = %
coverage limit = 75%
NQ3 NME (%) for run 2011eLcb05v2_hdghgp2_12US2 for March to May 2011
>100
90
80
70
60
50
40
30
20
10
0
Figure A-8f. Normalized Mean Error (%) for nitrate during spring 2011 at monitoring
sites in the modeling domain.
CIRCLE=IMPROVE; TRIANGLE=CSN;
A-33
-------
TN03 NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for March to May 2011
units = %
coverage limit = 75%
SQUARE=CASTNET;
Figure A-8g. Normalized Mean Bias (%) for total nitrate during spring 2011 at monitoring
sites in the modeling domain.
TN03 NME (%) for run 2011ei_cb05v2_hdghgp2_12US2 lor March to May 2011
units = %
coverage limit = 75%
SQUARE=CASTNET;
Figure A-8h. Normalized Mean Error (%) for total nitrate during spring 2011 at
monitoring sites in the modeling domain.
A-34
-------
N03 MB (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for June to August 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-9a. Mean Bias (ug/m3) for nitrate during summer 2011 at monitoring sites in the
modeling domain.
N03 ME (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for June to August 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-9b. Mean Error (ug/m3) for nitrate during summer 2011 at monitoring sites in the
modeling domain.
A-35
-------
TN03 MB (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for June to August 2011
units = ug/m3
coverage limit = 75%
SQUARE=CASTNET;
Figure A-9c. Mean Bias (ug/m3) for total nitrate during summer 2011 at monitoring sites in
the modeling domain.
TN03 ME (ug/m3) for run2011ei_cb05v2_hdghgp2_12US2 for June to August 2011
units = ug/m3
coverage limit = 75%
SQUARE=CASTNET;
Figure A-9d. Mean Error (ug/m3) for total nitrate during summer 2011 at monitoring sites
in the modeling domain.
A-36
-------
N03 NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for June to August 2011
units = %
coverage limit = 75%
> 100
¦¦
80
60
40
n
¦
20
0
_
-20
-40
-60
-80
<-100
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-9e. Normalized Mean Bias (%) for nitrate during summer 2011 at monitoring
sites in the modeling domain.
NQ3 NME (%) for run 2011ei_cb05v2_hdghgp2_12US2 lor June to August 2011
units = %
coverage limit = 75%
> 100
90
80
70
60
50
40
30
20
10
0
Figure A-9f. Normalized Mean Error (%) for nitrate during summer 2011 at monitoring
sites in the modeling domain.
CIRCLE=IMPROVE; TRIANGLE=CSN;
A-37
-------
TN03 NMB (%) for run 2011ei_cb05v2 hdghgp2 12US2 for June to August 2011
units = %
coverage limit = 75%
SQUARE=CASTNET;
Figure A-9g. Normalized Mean Bias (%) for total nitrate during summer 2011 at
monitoring sites in the modeling domain.
units = %
coverage limit = 75%
TN03 NME (%) for run 2011ei_cb05v2 hdghgp2 12US2 for June to August 2011
! -<«H V I \ .
SQUARE=CASTNET;
Figure A-9h. Normalized Mean Error (%) for total nitrate during summer 2011 at
monitoring sites in the modeling domain.
A-38
-------
N03 MB (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for September to November 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN:
Figure A-lOa. Mean Bias (ug/m3) for nitrate during fall 2011 at monitoring sites in the
modeling domain.
N03 ME (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for September to November 2011
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-lOb. Mean Error (ug/m3) for nitrate during fall 2011 at monitoring sites in the
modeling domain.
A-39
-------
TN03 MB (ug/m3) for run2011ei_cb05v2 hdghgp2_12US2 for September to November 2011
units = ug/m3
coverage limit = 75%
SQUARE=CASTNET;
Figure A-lOc. Mean Bias (ug/m3) for total nitrate during fall 2011 at monitoring sites in the
modeling domain.
TN03 ME (ug/m3) for run2011ei_cb05v2 hdghgp2 12US2 for September to November 2011
units = ug/m3
coverage limit = 75%
SQUARE=CASTNET;
Figure A-lOd. Mean Error (ug/m3) for total nitrate during fall 2011 at monitoring sites in
the modeling domain.
A-40
-------
N03 NMB (%) for run 2011ei_cb05v2 hdghgp2_12US2 for September to November 2011
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-lOe. Normalized Mean Bias (%) for nitrate during fall 2011 at monitoring sites in
the modeling domain.
N03 NME (%) for run 2011ei_cb05v2 hdghgp2 12US2 for September to November 2011
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure A-lOf. Normalized Mean Error (%) for nitrate during fall 2011 at monitoring sites
in the modeling domain.
A-41
-------
TN03 NMB (%) for run 2011ei_cb05v2_hdghgp2_12US2 for September to November 2011
units = %
coverage limit = 75%
SQUAR E=CASTN ET;
Figure A-lOg. Normalized Mean Bias (%) for total nitrate during fall 2011 at monitoring
sites in the modeling domain.
TN03 NME (%) for run 2011 ei_cb05v2 hdghgp212US2 for September to November 2011
units ¦ %
coverage limit - 75%
SQUARE=CASTNET;
Figure A-lOh. Normalized Mean Error (%) for total nitrate during fall 2011 at monitoring
sites in the modeling domain.
A-42
-------
H. Seasonal Ammonium Performance
The model performance bias and error statistics for ammonium for each climate region
and season are provided in Table A-5. These statistics indicate model bias for ammonium is
generally over predicted in the spring, fall and winter seasons except for the following exclusions:
(1) the spring shows under predictions in the South, Southeast, and Southwest at both CSN and
CASTNet sites; (2) the fall has under predictions at rural CASTNet sites in the Ohio Valley,
Southeast, South, Southwest, and Northern Rockies as well as at the urban CSN sites in the
Southwest; and (3) the winter performance shows under predictions in the Ohio Valley,
Southeast, South, Southwest and Northern Rockies at CASTNet monitors in addition to CSN
sites at the Northern Rockies. Generally, the West (California and Nevada) show under
predictions of ammonia.
Table A-5. Ammonium Performance Statistics by Climate Region, by Season, and by
Monitoring Network for the 2011 CMAQ Model Simulation.
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
679
0.2
0.5
20.9
39.3
CSN
Spring
717
0.0
0.3
2.3
37.2
Summer
721
-0.1
0.3
-11.8
36.6
Northeast
Fall
685
0.2
0.3
36.2
56.3
Winter
170
0.1
0.2
6.7
25.3
CASTNet
Spring
193
0.0
0.2
-0.1
24.3
Summer
187
-0.3
0.3
-33.0
34.2
Fall
196
0.0
0.2
36.2
56.3
Winter
588
0.1
0.5
5.0
34.9
CSN
Spring
624
0.1
0.4
4.4
33.7
Summer
645
-0.1
0.4
-11.5
34.7
Ohio Valley
Fall
611
0.1
0.3
18.6
47.3
Winter
201
-0.2
0.3
-13.5
21.6
CASTNet
Spring
214
0.0
0.3
-2.9
28.6
Summer
207
-0.5
0.5
-34.1
35.2
Fall
214
-0.1
0.3
-6.6
32.9
Winter
334
0.1
0.6
00
00
38.5
CSN
Spring
337
0.2
0.4
17.8
35.2
Summer
335
-0.1
0.3
-11.0
43.2
Upper
Fall
340
0.3
0.4
38.8
53.1
Midwest
Winter
56
0.0
0.2
2.6
16.2
CASTNet
Spring
62
0.1
0.2
12.3
29.0
Summer
65
-0.2
0.2
-30.6
32.1
Fall
62
0.0
0.2
6.9
28.0
Winter
435
0.2
0.3
23.0
47.3
CSN
Spring
454
-0.1
0.3
-14.6
39.5
Summer
471
0.0
0.3
-4.0
35.9
Southeast
Fall
442
0.2
0.3
56.5
71.6
Winter
138
-0.1
0.2
-8.0
23.4
CASTNet
Spring
146
-0.2
0.2
-22.5
30.3
Summer
147
-0.3
0.4
-32.8
35.2
Fall
150
-0.1
0.2
-15.3
30.1
South
CSN
Winter
222
0.0
0.4
2.4
44.8
A-43
-------
Climate
Region
Monitor
Network
Season
No. of
Obs
MB
(ug/m3)
ME
(ug/m3)
NMB
(%)
NME
(%)
Spring
248
-0.2
0.4
-23.2
40.9
Summer
253
-0.1
0.3
-24.3
45.0
Fall
238
0.0
0.3
8.4
50.9
CASTNet
Winter
70
-0.1
0.3
-13.0
32.1
Spring
85
-0.2
0.3
-24.0
38.5
Summer
88
-0.3
0.3
-38.6
41.2
Fall
76
-0.1
0.2
-22.4
32.6
Southwest
CSN
Winter
185
-0.6
0.8
-53.9
65.2
Spring
190
-0.1
0.2
-44.5
68.9
Summer
471
0.0
0.3
-4.0
35.9
Fall
186
-0.2
0.2
-45.6
60.5
CASTNet
Winter
94
0.0
0.1
13.0
44.9
Spring
102
-0.1
0.1
-52.8
61.7
Summer
147
-0.3
.4
-32.8
35.2
Fall
101
-0.1
0.1
-40.7
45.0
Northern
Rockies
CSN
Winter
70
0.0
0.3
-1.5
33.1
Spring
66
0.1
0.4
5.9
40.5
Summer
72
0.0
0.1
1.8
39.8
Fall
69
0.2
0.3
57.9
86.4
CASTNet
Winter
76
0.0
0.1
-1.7
28.7
Spring
77
0.1
0.1
23.4
37.6
Summer
88
-0.1
0.1
-49.4
51.0
Fall
89
0.0
0.1
-4.4
42.1
Northwest
CSN
Winter
166
0.3
0.6
48.6
108.0
Spring
167
0.2
0.2
111.0
135.0
Summer
172
0.0
0.1
11.7
58.8
Fall
164
0.2
0.3
109.0
152.0
CASTNet
Winter
12
0.0
0.1
71.5
79.3
Spring
13
0.0
0.1
33.4
47.1
Summer
13
0.0
0.1
-18.3
25.0
Fall
13
0.0
0.1
27.3
50.6
West
CSN
Winter
226
-0.8
1.0
-49.9
62.9
Spring
242
-0.2
0.4
-32.0
60.0
Summer
246
-0.6
0.6
-66.8
68.6
Fall
229
-0.8
0.9
-56.0
66.0
CASTNet
Winter
69
-0.1
0.2
-19.0
64.9
Spring
73
-0.1
0.2
-44.2
63.9
Summer
77
-0.3
0.3
-74.9
74.9
Fall
77
-0.2
0.2
-52.9
64.1
A-44
-------
I. Seasonal Elemental Carbon Performance
The model performance bias and error statistics for elemental carbon for each of the nine
climate regions and each season are provided in Table A-6. The statistics show clear over
prediction at urban and rural sites in all climate regions with the exception of a slight under
prediction during the winter in the Northern Rockies urban sites. In the Northwest, issues in the
ambient data when compared to model predictions were found and thus removed from the
performance analysis.
Table A-6. Elemental Carbon Performance Statistics by Climate Region, by Season, and
by Monitoring Network for the 2011 CMAQ Model Simulation.
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
441
0.2
0.3
94.0
108.0
IMPROVE
Spring
480
0.1
0.1
52.4
79.3
Summer
446
0.0
0.1
6.0
39.8
Northeast
Fall
449
0.1
0.1
34.9
58.1
Winter
645
0.7
0.8
94.3
108.0
CSN
Spring
687
0.3
0.4
58.7
81.8
Summer
699
0.1
0.4
17.5
48.9
Fall
625
0.3
0.5
48.1
68.8
Winter
222
0.2
0.2
62.4
71.8
IMPROVE
Spring
238
0.0
0.1
15.8
44.0
Summer
225
0.0
0.1
6.0
31.9
Ohio Valley
Fall
236
0.1
0.1
28.3
47.8
Winter
575
0.6
0.7
109.0
117.0
CSN
Spring
604
0.3
0.4
50.2
67.8
Summer
662
0.3
0.4
33.9
52.8
Fall
611
0.4
0.5
55.4
70.3
Winter
222
0.1
0.2
88.5
102.0
IMPROVE
Spring
232
0.1
0.1
65.8
22.2
Summer
231
0.0
0.1
0.6
41.6
Upper
Fall
228
0.2
0.2
80.3
99.2
Midwest
Winter
326
0.6
0.6
153.0
155.0
CSN
Spring
330
0.4
0.4
96.3
103.0
Summer
333
0.2
0.3
37.7
53.4
Fall
340
0.4
0.4
78.1
82.3
Winter
345
0.2
0.3
51.0
66.8
IMPROVE
Spring
374
0.0
0.2
5.1
50.4
Summer
359
0.0
0.2
9.5
53.0
Southeast
Fall
350
0.1
0.2
26.0
49.6
Winter
417
0.4
0.5
52.8
74.5
CSN
Spring
430
0.3
0.4
44.0
68.2
Summer
460
0.3
0.5
53.4
80.6
Fall
423
0.4
0.5
61.2
81.5
Winter
267
0.2
0.2
54.5
76.2
IMPROVE
Spring
299
0.0
0.2
6.5
56.0
South
Summer
279
0.0
0.1
1.2
47.1
Fall
251
0.1
0.1
26.2
52.0
CSN
Winter
222
0.5
0.6
68.9
95.6
Spring
250
0.3
0.4
53.5
81.6
A-45
-------
Climate
Region
Monitor
Network
Season
No. of
Obs
MB
(ug/m3)
ME
(ug/m3)
NMB
(%)
NME
(%)
Summer
257
0.4
0.5
94.1
111.0
Fall
240
0.4
0.5
67.7
85.5
Southwest
IMPROVE
Winter
946
0.1
0.1
36.5
68.3
Spring
965
0.1
0.1
68.0
101.0
Summer
987
0.1
0.1
37.7
96.7
Fall
948
0.1
0.1
40.2
78.9
CSN
Winter
181
0.6
0.8
57.2
72.4
Spring
187
0.5
0.5
153.0
155.0
Summer
195
0.4
0.5
88.4
103.0
Fall
189
0.6
0.6
76.8
86.6
Northern
Rockies
IMPROVE
Winter
541
0.0
0.1
52.6
91.0
Spring
594
0.0
0.0
18.3
59.2
Summer
583
0.0
0.1
32.1
69.6
Fall
568
0.1
0.1
52.5
82.8
CSN
Winter
63
-0.1
0.9
-13.1
117.0
Spring
60
0.1
0.3
35.6
91.3
Summer
70
0.1
0.3
34.3
76.5
Fall
69
0.2
0.5
41.4
96.2
Northwest
IMPROVE
Winter
-
-
-
-
-
Spring
-
-
-
-
-
Summer
-
-
-
-
-
Fall
-
-
-
-
-
CSN
Winter
-
-
-
-
-
Spring
-
-
-
-
-
Summer
-
-
-
-
-
Fall
-
-
-
-
-
West
IMPROVE
Winter
552
0.0
0.1
11.6
50.9
Spring
555
0.0
0.1
25.2
67.0
Summer
566
0.0
0.1
32.6
69.8
Fall
588
0.1
0.1
26.0
72.0
CSN
Winter
226
0.1
0.6
9.5
41.2
Spring
237
0.5
0.5
100.0
105.0
Summer
244
0.4
0.4
77.0
80.3
Fall
226
0.4
0.5
39.0
52.7
A-46
-------
J. Seasonal Organic Carbon Performance
The model performance bias and error statistics for organic carbon for each climate
region and season are provided in Table A-7. The statistics in this table indicate a tendency for
the modeling platform to under predict observed organic carbon concentrations during the spring
and summer although over predict organic carbon during the fall and winter at urban and rural
locations with the exceptions of the following: (1) the spring shows over predictions in the
Northeast and Upper Midwest at MPROVE and CSN sites as well as in the Ohio Valley and
Southeast at CSN sites; (2) the summer has over predictions at urban CSN sites in the South,
Southeast and Southwest; (3) the fall under predicts at rural IMPROVE sites in the Ohio Valley,
South and Southeast; and (4) the winter under predicts in the Northern Rockies at urban sites. In
the West, organic carbon performance shows over predictions at urban sites during all seasons.
However, in the West performance shows under predictions at rural sites during the entire year.
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.
Similar to the elemental carbon performance, issues in the ambient data when compared to
model predictions were found in the Northwest as well as in the Southwest at urban sites and
thus removed from the performance analysis.
Table A-7. Organic Carbon Performance Statistics by Climate Region, by Season, and by
Monitoring Network for the 2011 CMAQ Model Simulation.
Climate
Monitor
Season
No. of
MB
ME
NMB
NME
Region
Network
Obs
(ug/m3)
(ug/m3)
(%)
(%)
Winter
440
1.3
1.4
133.0
140.0
IMPROVE
Spring
478
0.2
0.5
32.8
78.0
Summer
445
-0.7
0.8
-44.4
50.2
Northeast
Fall
448
0.1
0.5
12.1
51.5
Winter
639
3.4
3.4
211.0
213.0
CSN
Spring
682
0.9
1.2
84.0
106.0
Summer
698
-0.2
0.7
-10.9
36.1
Fall
622
1.1
1.2
74.3
84.3
Winter
222
0.6
0.9
45.7
65.9
IMPROVE
Spring
238
-0.3
0.6
-18.7
45.9
Summer
225
-0.6
0.6
-29.4
36.3
Ohio Valley
Fall
236
0.0
0.5
-0.9
42.6
Winter
570
1.9
2.0
121.0
126.0
CSN
Spring
600
0.4
0.8
26.9
52.0
Summer
662
-0.1
0.7
-4.4
29.0
Fall
610
0.6
0.8
39.3
54.2
Winter
221
0.6
0.7
95.5
106.0
IMPROVE
Spring
232
0.2
0.5
19.9
64.3
Summer
231
-0.7
0.7
-45.4
48.2
Upper
Fall
228
0.2
1.2
12.1
73.3
Midwest
Winter
324
2.4
2.4
186.0
190.0
CSN
Spring
330
1.1
1.2
98.3
110.0
Summer
332
-0.1
0.7
-3.3
37.3
Fall
333
1.0
1.1
72.1
80.1
A-47
-------
Climate
Region
Monitor
Network
Season
No. of
Obs
MB
(ug/m3)
ME
(ug/m3)
NMB
(%)
NME
(%)
Southeast
IMPROVE
Winter
345
0.5
1.0
33.5
63.9
Spring
374
-0.5
1.1
-26.4
56.8
Summer
361
-0.7
1.4
-31.9
61.6
Fall
349
-0.1
0.9
-6.1
61.1
CSN
Winter
415
1.3
1.7
62.3
80.5
Spring
429
0.4
1.0
19.9
52.6
Summer
458
0.4
1.6
16.4
60.0
Fall
421
0.8
1.2
47.0
68.4
South
IMPROVE
Winter
266
0.3
0.5
33.5
67.9
Spring
299
-0.5
0.9
-29.7
57.4
Summer
281
-0.4
0.6
-28.5
41.6
Fall
251
0.0
0.4
-1.7
45.7
CSN
Winter
220
1.2
1.7
61.7
89.3
Spring
250
-0.1
1.2
-7.0
57.6
Summer
257
0.5
1.0
27.6
55.8
Fall
239
0.7
1.1
42.5
64.1
Southwest
IMPROVE
Winter
930
0.1
0.4
18.3
67.8
Spring
962
0.0
0.2
-6.6
55.5
Summer
991
-0.3
0.6
-33.3
59.3
Fall
948
0.0
0.4
8.0
61.8
CSN
Winter
-
-
-
-
-
Spring
-
-
-
-
-
Summer
-
-
-
-
-
Fall
-
-
-
-
-
Northern
Rockies
IMPROVE
Winter
527
0.1
0.2
20.6
73.1
Spring
584
-0.1
0.2
-25.3
51.2
Summer
583
-0.3
0.7
-31.4
64.5
Fall
568
0.1
0.6
7.5
62.3
CSN
Winter
63
-1.2
3.2
-38.8
107.0
Spring
58
0.0
0.7
4.4
67.6
Summer
70
-0.4
0.6
-30.2
39.3
Fall
68
0.1
1.1
4.1
61.2
Northwest
IMPROVE
Winter
-
-
-
-
-
Spring
-
-
-
-
-
Summer
-
-
-
-
-
Fall
-
-
-
-
-
CSN
Winter
-
-
-
-
-
Spring
-
-
-
-
-
Summer
-
-
-
-
-
Fall
-
-
-
-
-
West
IMPROVE
Winter
538
0.0
0.3
-2.1
48.1
Spring
551
-0.1
0.3
-21.0
55.5
Summer
242
-0.3
0.6
-26.5
56.3
Fall
585
-0.2
0.8
-14.3
62.6
CSN
Winter
224
1.3
2.1
34.1
56.5
Spring
237
1.2
1.3
87.9
96.5
Summer
242
0.1
0.6
7.1
38.8
Fall
225
0.7
1.3
27.2
45.7
A-48
-------
K. Seasonal Hazardous Air Pollutants Performance
A seasonal operational model performance evaluation for specific hazardous air
pollutants (i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene and acrolein) 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 12 km modeling domain are presented below in Table A-8. Toxic
measurements included in the evaluation were taken from the 2011 air toxics archive,
http ://www. epa. gov/ttn/amtic/toxdat.html#data. While most of the data in the archive are from
the AQS database including the National Air Toxics Trends Stations (NATTS) (downloaded in
July 2014), additional data (e.g., special studies) are included in the archive but not reported in
the AQS. 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, benzene and 1,3 butadiene
showed relatively small to moderate bias and error percentages when compared to observations.
The model yielded larger bias and error results for 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)
ambient data below method detection limit (MDL); (4) commensurability issues between
measurements and model predictions; (5) emissions and science uncertainty issues may also
affect model performance; and (6) 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 2011 performance results to the
limited performance found in recent regional multi-pollutant model applications.21'22'23 Overall,
the mean bias and error (MB and ME), as well as the normalized mean bias and error (NMB and
NME) statistics shown below in Table A-8 indicate that CMAQ-predicted 2011 toxics (i.e.,
observation vs. model predictions) are within the range of recent regional modeling applications.
21 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform:
Air Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008.
22 Strum, M., Wesson, K., Phillips, S., Cook, R., Michaels, H., Brzezinski, D., Pollack, A., Jimenez, M., Shepard, S.
Impact of using in-line 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.
23 Wesson, K., N. Fann, and B. Timin, 2010: Draft Manuscript: Air Quality and Benefits Model Responsiveness to
Varying Horizontal Resolution in the Detroit Urban Area, Atmospheric Pollution Research, Special Issue: Air
Quality Modeling and Analysis.
A-49
-------
Table A-8. Hazardous Air Toxics Performance Statistics by Season for the 2011 CMAQ
odel Simulation.
Air Toxic Species
Season
No. of
Obs.
MB
(ug/m3)
ME
(ug/m3)
NMB
(%)
NME
(%)
Formaldehyde
Winter
1,070
-0.9
1.1
-50.8
59.9
Spring
1,067
-1.3
1.3
-59.1
62.4
Summer
1,044
-1.2
1.6
-32.8
41.8
Fall
1,055
-0.8
1.1
-41.3
42.9
Acetaldehyde
Winter
1,056
-0.4
0.7
-30.0
52.9
Spring
1,069
-0.2
0.7
-12.0
55.0
Summer
1,063
1.6
1.9
88.5
106.0
Fall
1,095
0.0
0.8
2.6
55.9
Benzene
Winter
2,498
0.2
0.7
25.5
76.4
Spring
2,386
-0.1
0.5
-11.0
64.7
Summer
2,504
-0.1
0.6
-14.0
79.0
Fall
2,448
0.0
0.6
-5.6
67.9
1,3-Butadiene
Winter
2,373
0.0
0.1
-8.2
114.0
Spring
2,254
0.0
0.1
-24.7
115.0
Summer
2,330
0.0
0.1
-15.3
116.0
Fall
2,327
0.0
0.1
-38.3
100.0
Acrolein
Winter
172
-0.4
0.4
-93.2
93.5
Spring
140
-0.3
0.3
-94.9
94.9
Summer
211
-0.5
0.5
-97.5
97.6
Fall
198
-0.4
0.4
-93.7
94.5
A-50
-------
L. Seasonal Nitrate and Sulfate Deposition Performance
Seasonal nitrate and sulfate wet deposition performance statistics for the 12 km
Continental U.S. domain are provided in Table A-9. The model predictions for seasonal nitrate
deposition generally show under predictions for the continental U.S. NADP sites (NMB values
range from -8% to -41%). Sulfate deposition performance shows the similar under predictions
(NMB values range from -15% to 25%). The errors for both annual nitrate and sulfate are
relatively moderate with values ranging from 51% to 69% which reflect scatter in the model
predictions versus observation comparison.
Table A-9. Nitrate and Sulfate Wet Deposition Performance Statistics by Season for the
2011 CMAQ Model Simulation.
Wet Deposition
Species
Season
No. of Obs.
MB
(kg/ha)
ME
(kg/ha)
NMB
(%)
NME
(%)
Nitrate
Winter
1,772
0.0
0.1
-15.4
58.4
Spring
2,006
-0.1
0.1
-26.7
50.9
Summer
1,892
-0.1
0.2
-40.8
64.1
Fall
1,934
0.0
0.1
-7.6
55.5
Sulfate
Winter
1,772
0.0
0.1
-24.7
53.0
Spring
2,006
-0.1
0.1
-22.8
53.5
Summer
1,892
0.0
0.2
-19.1
68.7
Fall
1,934
0.0
0.1
-15.0
56.1
A-51
-------
Air Quality Modeling Technical Support Document:
Heavy-Duty Vehicle Greenhouse Gas Phase 2 Final Rule
Appendix B
8-Hour Ozone Design Values for Air Quality Modeling
Scenarios
B-l
-------
Table B-l. 8-Hour Ozone Design Values for HDGHG Phase 2 Scenarios
(units are ppb)
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Alabama
Baldwin
70.0
48.94
48.42
Alabama
Colbert
65.0
42.70
42.29
Alabama
De Kalb
66.0
49.11
48.40
Alabama
Elmore
66.3
46.73
46.05
Alabama
Etowah
61.7
43.59
42.74
Alabama
Houston
63.7
47.11
46.60
Alabama
Jefferson
76.7
55.69
54.88
Alabama
Madison
70.7
50.92
50.31
Alabama
Mobile
73.0
50.79
50.33
Alabama
Montgomery
67.3
47.97
47.23
Alabama
Morgan
68.7
51.73
51.09
Alabama
Russell
66.0
48.06
47.58
Alabama
Shelby
73.3
50.70
49.93
Alabama
Sumter
61.0
47.57
47.01
Alabama
Tuscaloosa
58.7
43.54
42.93
Arizona
Cochise
72.0
66.64
66.38
Arizona
Coconino
71.0
62.68
62.48
Arizona
Gila
73.7
59.06
58.11
Arizona
La Paz
71.3
61.74
61.34
Arizona
Maricopa
79.7
63.29
62.15
Arizona
Navajo
68.7
61.14
60.83
Arizona
Pima
71.3
55.84
54.87
Arizona
Pinal
75.0
60.30
59.53
Arizona
Yavapai
68.0
61.76
61.46
Arizona
Yuma
75.3
60.91
60.55
Arkansas
Crittenden
77.3
57.18
56.39
Arkansas
Newton
68.0
53.10
52.55
Arkansas
Polk
72.3
57.34
56.68
Arkansas
Pulaski
75.7
52.21
50.93
Arkansas
Washington
71.0
56.87
56.37
California
Alameda
73.3
61.69
61.59
California
Amador
72.0
54.63
54.55
California
Butte
76.3
56.58
56.49
California
Calaveras
75.0
56.50
56.41
California
Colusa
61.0
49.03
48.89
California
Contra Costa
71.7
59.24
59.13
California
El Dorado
82.7
59.99
59.89
B-2
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
California
Fresno
97.0
75.88
75.80
California
Glenn
64.3
51.51
51.40
California
Imperial
81.0
72.19
72.06
California
Inyo
71.7
64.98
64.94
California
Kern
91.7
73.89
73.76
California
Kings
87.0
68.38
68.30
California
Lake
58.3
46.68
46.64
California
Los Angeles
97.3
79.22
79.14
California
Madera
85.0
68.04
67.95
California
Marin
52.3
47.87
47.81
California
Mariposa
77.3
64.73
64.67
California
Merced
82.7
65.69
65.60
California
Monterey
58.0
46.87
46.81
California
Napa
62.3
48.11
48.03
California
Nevada
77.7
56.30
56.20
California
Orange
72.0
62.51
62.45
California
Placer
84.0
60.93
60.84
California
Riverside
100.7
79.14
79.04
California
Sacramento
93.3
66.68
66.57
California
San Benito
70.0
56.83
56.75
California
San Bernardino
105.0
87.81
87.70
California
San Diego
81.0
60.49
60.45
California
San Joaquin
79.0
64.19
64.07
California
San Luis Obispo
78.0
62.78
62.70
California
Santa Barbara
68.3
58.74
58.69
California
Santa Clara
71.3
58.57
58.51
California
Santa Cruz
53.0
44.46
44.40
California
Shasta
68.0
54.14
54.08
California
Solano
68.0
53.93
53.82
California
Sonoma
48.0
36.06
36.02
California
Stanislaus
87.0
69.89
69.78
California
Sutter
65.0
49.49
49.38
California
Tehama
75.3
58.90
58.80
California
Tulare
94.7
74.01
73.94
California
Tuolumne
73.3
55.65
55.56
California
Ventura
81.0
63.90
63.81
California
Yolo
69.0
55.97
55.87
Colorado
Adams
76.0
64.84
64.32
Colorado
Arapahoe
76.7
64.64
64.21
Colorado
Boulder
74.7
65.21
64.85
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Colorado
Denver
71.0
60.57
60.09
Colorado
Douglas
80.7
66.77
66.33
Colorado
El Paso
72.7
61.40
61.10
Colorado
Garfield
65.0
63.21
63.06
Colorado
Jefferson
80.3
69.89
69.46
Colorado
La Plata
73.0
64.05
63.81
Colorado
Larimer
78.0
71.33
70.94
Colorado
Mesa
67.0
60.82
60.65
Colorado
Montezuma
68.3
59.24
59.02
Colorado
Rio Blanco
77.0
71.10
70.90
Colorado
Weld
74.7
68.41
68.07
Connecticut
Fairfield
84.3
77.78
77.32
Connecticut
Hartford
73.7
55.79
55.28
Connecticut
Litchfield
70.3
54.48
54.13
Connecticut
Middlesex
79.3
59.52
59.06
Connecticut
New Haven
85.7
67.33
66.86
Connecticut
New London
80.3
62.78
62.45
Connecticut
Tolland
75.3
56.94
56.45
Delaware
Kent
74.3
55.93
55.36
Delaware
New Castle
78.0
56.34
55.70
Delaware
Sussex
77.7
60.20
59.69
D.C.
Washington
80.7
56.67
56.19
Florida
Alachua
63.7
49.36
48.72
Florida
Baker
61.7
49.17
48.69
Florida
Bay
68.0
49.31
48.80
Florida
Brevard
64.0
50.68
50.25
Florida
Broward
59.3
48.20
48.05
Florida
Collier
59.5
45.95
45.64
Florida
Columbia
62.7
50.26
49.75
Florida
Duval
64.3
49.67
49.20
Florida
Escambia
72.0
50.77
50.17
Florida
Highlands
63.3
52.16
51.86
Florida
Hillsborough
71.7
55.26
55.00
Florida
Holmes
62.3
45.97
45.46
Florida
Indian River
65.0
51.03
50.64
Florida
Lake
65.7
51.28
51.00
Florida
Lee
63.7
48.17
47.80
Florida
Leon
64.3
46.19
45.72
Florida
Manatee
67.0
51.20
50.89
Florida
Marion
65.0
49.23
48.85
B-4
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Florida
Miami-Dade
64.0
52.53
52.32
Florida
Okaloosa
66.0
47.21
46.68
Florida
Orange
71.7
54.13
53.82
Florida
Osceola
66.0
48.71
48.31
Florida
Palm Beach
62.7
51.92
51.73
Florida
Pasco
66.7
51.56
51.31
Florida
Pinellas
66.7
56.12
55.91
Florida
Polk
68.3
51.87
51.61
Florida
Santa Rosa
71.7
50.44
49.81
Florida
Sarasota
71.3
53.31
52.91
Florida
Seminole
67.3
50.42
50.07
Florida
Volusia
63.3
48.91
48.41
Florida
Wakulla
63.7
48.30
47.78
Georgia
Bibb
72.3
48.55
47.79
Georgia
Chatham
63.3
48.07
47.53
Georgia
Chattooga
66.3
47.19
46.43
Georgia
Clarke
70.7
45.68
45.05
Georgia
Cobb
76.0
50.41
49.75
Georgia
Columbia
68.7
48.53
47.87
Georgia
Coweta
65.0
43.36
42.84
Georgia
Dawson
66.3
45.22
44.56
Georgia
De Kalb
77.3
50.19
49.47
Georgia
Douglas
73.3
48.53
47.85
Georgia
Fulton
81.0
54.12
53.44
Georgia
Glynn
60.0
45.73
45.04
Georgia
Gwinnett
76.7
49.02
48.42
Georgia
Henry
80.0
53.99
53.26
Georgia
Murray
70.3
49.42
48.33
Georgia
Muscogee
66.0
47.98
47.46
Georgia
Paulding
70.7
45.48
44.62
Georgia
Richmond
70.0
49.94
49.26
Georgia
Rockdale
77.0
49.96
49.22
Georgia
Sumter
64.7
49.50
49.00
Idaho
Ada
67.5
57.33
56.32
Idaho
Butte
62.3
58.82
58.65
Illinois
Adams
67.0
53.67
53.14
Illinois
Champaign
71.0
54.89
54.04
Illinois
Clark
66.0
52.80
52.19
Illinois
Cook
77.7
59.44
58.96
Illinois
Du Page
66.3
52.60
52.18
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Illinois
Effingham
68.3
53.12
52.16
Illinois
Hamilton
74.3
59.35
58.71
Illinois
Jersey
76.0
56.37
55.44
Illinois
Jo Daviess
68.0
54.59
54.18
Illinois
Kane
69.7
57.70
57.14
Illinois
Lake
79.3
51.44
51.14
Illinois
McHenry
69.7
57.84
57.26
Illinois
McLean
70.3
53.67
52.89
Illinois
Macon
71.3
54.33
53.57
Illinois
Macoupin
71.3
52.56
51.59
Illinois
Madison
78.3
57.18
56.10
Illinois
Peoria
70.7
53.98
53.39
Illinois
Randolph
67.7
53.90
53.01
Illinois
Rock Island
58.3
46.76
46.40
Illinois
St Clair
74.7
56.45
55.14
Illinois
Sangamon
72.0
54.39
53.50
Illinois
Will
64.0
52.23
51.62
Illinois
Winnebago
67.3
54.64
54.02
Indiana
Allen
69.3
54.26
53.73
Indiana
Boone
72.3
56.74
56.19
Indiana
Carroll
69.0
55.04
54.42
Indiana
Clark
78.0
60.20
59.31
Indiana
Delaware
68.7
52.08
51.47
Indiana
Elkhart
67.7
52.05
51.37
Indiana
Floyd
76.0
57.91
57.32
Indiana
Greene
77.0
65.65
65.24
Indiana
Hamilton
71.0
54.71
54.20
Indiana
Hancock
66.7
50.84
50.33
Indiana
Hendricks
67.0
52.23
51.77
Indiana
Huntington
65.0
51.86
51.34
Indiana
Jackson
66.0
54.92
54.41
Indiana
Johnson
69.0
54.93
54.29
Indiana
Knox
73.0
61.01
60.63
Indiana
Lake
69.7
54.06
53.55
Indiana
La Porte
79.3
64.26
64.00
Indiana
Madison
68.3
51.45
50.84
Indiana
Marion
72.7
55.62
55.08
Indiana
Morgan
69.0
53.98
53.40
Indiana
Perry
72.7
58.26
58.13
Indiana
Porter
70.3
55.09
54.75
B-6
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Indiana
Posey
70.3
57.59
57.22
Indiana
St Joseph
69.3
53.69
52.95
Indiana
Shelby
74.0
56.94
56.28
Indiana
Vanderburgh
74.0
61.24
60.87
Indiana
Vigo
65.7
52.03
51.52
Indiana
Warrick
71.0
59.41
59.04
Iowa
Bremer
64.0
50.54
50.02
Iowa
Clinton
66.7
53.05
52.63
Iowa
Harrison
67.7
54.06
53.60
Iowa
Linn
64.3
50.92
50.37
Iowa
Montgomery
65.3
53.56
53.08
Iowa
Palo Alto
66.7
54.62
54.14
Iowa
Polk
59.7
46.00
45.48
Iowa
Scott
66.0
52.93
52.52
Iowa
Story
61.3
47.85
47.32
Iowa
Van Buren
65.7
51.23
50.58
Iowa
Warren
63.7
49.72
49.16
Kansas
Johnson
72.7
56.24
55.72
Kansas
Leavenworth
72.0
54.95
54.33
Kansas
Linn
70.0
55.80
55.25
Kansas
Sedgwick
75.7
58.67
58.22
Kansas
Shawnee
71.7
54.64
54.08
Kansas
Sumner
76.0
61.85
61.31
Kansas
Trego
72.3
63.91
63.60
Kansas
Wyandotte
65.7
50.15
49.70
Kentucky
Bell
63.3
48.37
47.58
Kentucky
Boone
68.0
56.50
55.93
Kentucky
Boyd
70.0
56.02
55.53
Kentucky
Bullitt
72.3
57.48
56.53
Kentucky
Campbell
76.7
60.10
59.45
Kentucky
Carter
67.0
54.06
53.55
Kentucky
Christian
70.7
49.97
49.32
Kentucky
Daviess
76.3
60.56
60.14
Kentucky
Edmonson
72.0
53.82
52.61
Kentucky
Fayette
71.3
54.79
54.14
Kentucky
Greenup
69.7
56.82
56.31
Kentucky
Hancock
73.7
59.24
58.84
Kentucky
Hardin
70.3
53.80
52.97
Kentucky
Henderson
76.3
62.77
62.39
Kentucky
Jefferson
82.0
67.22
66.68
B-7
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Kentucky
Jessamine
70.0
51.87
51.23
Kentucky
Livingston
72.3
54.78
54.22
Kentucky
McCracken
73.7
55.29
54.79
Kentucky
Oldham
82.0
63.12
62.32
Kentucky
Perry
65.3
54.27
53.86
Kentucky
Pike
65.7
53.81
53.40
Kentucky
Pulaski
66.7
47.03
46.40
Kentucky
Simpson
69.3
49.79
48.95
Kentucky
Trigg
69.0
52.39
51.64
Kentucky
Warren
64.0
46.97
46.02
Kentucky
Washington
69.0
53.44
52.82
Louisiana
Ascension
74.7
59.74
59.26
Louisiana
Bossier
77.3
62.52
62.11
Louisiana
Caddo
74.7
59.76
59.34
Louisiana
Calcasieu
73.3
59.86
59.39
Louisiana
East Baton Rouge
78.7
63.68
63.21
Louisiana
Iberville
76.0
61.76
61.30
Louisiana
Jefferson
73.7
58.99
58.53
Louisiana
Lafayette
71.0
56.01
55.53
Louisiana
Lafourche
72.3
57.83
57.30
Louisiana
Livingston
74.0
58.43
57.94
Louisiana
Orleans
69.3
56.68
56.26
Louisiana
Ouachita
63.3
52.36
52.07
Louisiana
Pointe Coupee
75.3
58.17
57.73
Louisiana
St Bernard
69.0
55.24
54.85
Louisiana
St Charles
70.0
55.72
55.28
Louisiana
St James
68.0
54.75
54.28
Louisiana
St John the
Baptist
74.0
57.79
57.24
Louisiana
St Tammany
73.3
60.17
59.75
Louisiana
West Baton
Rouge
70.3
55.57
55.08
Maine
Androscoggin
61.0
47.87
47.36
Maine
Cumberland
69.3
53.82
53.38
Maine
Hancock
71.7
57.50
57.12
Maine
Kennebec
62.7
47.18
46.63
Maine
Knox
67.7
52.92
52.31
Maine
Oxford
54.3
44.20
43.79
Maine
Sagadahoc
61.0
47.78
47.28
Maine
Washington
58.3
46.94
46.64
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Maine
York
73.7
56.74
56.20
Maryland
Anne Arundel
83.0
57.66
57.07
Maryland
Baltimore
80.7
63.14
62.63
Maryland
Calvert
79.7
62.26
61.90
Maryland
Carroll
76.3
57.04
56.40
Maryland
Cecil
83.0
60.36
59.61
Maryland
Charles
79.0
55.03
54.58
Maryland
Dorchester
75.0
58.20
57.62
Maryland
Frederick
76.3
57.88
57.17
Maryland
Garrett
72.0
54.68
54.28
Maryland
Harford
90.0
68.70
68.06
Maryland
Kent
78.7
55.75
55.05
Maryland
Montgomery
75.7
55.07
54.45
Maryland
Prince Georges
82.3
56.71
56.17
Maryland
Washington
72.7
55.55
54.87
Maryland
Baltimore City
73.7
57.84
57.38
Massachusetts
Barnstable
73.0
58.28
57.83
Massachusetts
Berkshire
69.0
54.77
54.37
Massachusetts
Bristol
74.0
58.34
57.86
Massachusetts
Dukes
77.0
62.53
62.03
Massachusetts
Essex
71.0
55.74
55.28
Massachusetts
Hampden
73.7
55.62
55.12
Massachusetts
Hampshire
71.3
53.44
52.97
Massachusetts
Middlesex
67.3
51.31
50.83
Massachusetts
Norfolk
72.3
54.65
54.54
Massachusetts
Suffolk
68.3
49.82
49.61
Massachusetts
Worcester
69.0
51.55
51.09
Michigan
Allegan
82.7
65.91
65.20
Michigan
Benzie
73.0
58.45
57.66
Michigan
Berrien
79.7
61.82
61.07
Michigan
Cass
76.7
58.75
57.93
Michigan
Chippewa
63.5
56.16
55.84
Michigan
Clinton
69.3
53.41
52.70
Michigan
Genesee
73.0
57.23
56.58
Michigan
Huron
71.3
57.28
56.62
Michigan
Ingham
70.3
53.37
52.66
Michigan
Kalamazoo
73.7
57.25
56.46
Michigan
Kent
73.0
55.67
54.85
Michigan
Lenawee
75.5
58.06
57.38
Michigan
Macomb
77.3
63.92
63.36
B-9
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Michigan
Manistee
72.3
58.23
57.53
Michigan
Mason
73.3
58.53
57.87
Michigan
Missaukee
68.3
54.51
53.88
Michigan
Muskegon
79.7
63.98
63.42
Michigan
Oakland
76.3
61.56
60.94
Michigan
Ottawa
76.0
58.42
57.53
Michigan
St Clair
75.3
61.23
60.57
Michigan
Schoolcraft
71.7
56.74
55.90
Michigan
Washtenaw
73.3
57.32
56.71
Michigan
Wayne
78.7
64.84
64.25
Minnesota
Anoka
67.0
52.98
52.52
Minnesota
Crow Wing
62.0
49.64
49.20
Minnesota
Goodhue
62.5
50.51
50.10
Minnesota
Lyon
64.5
53.62
53.23
Minnesota
Mille Lacs
59.7
45.61
45.35
Minnesota
Olmsted
63.5
50.36
49.93
Minnesota
St Louis
61.3
42.83
42.71
Minnesota
Scott
63.5
51.03
50.67
Minnesota
Stearns
61.5
51.18
50.73
Minnesota
Wright
63.5
52.22
51.88
Mississippi
Bolivar
71.7
58.68
58.28
Mississippi
De Soto
72.3
53.58
52.75
Mississippi
Hancock
66.3
50.28
49.82
Mississippi
Harrison
72.3
51.65
51.07
Mississippi
Hinds
67.0
44.87
44.17
Mississippi
Jackson
71.7
54.60
53.99
Mississippi
Lauderdale
62.7
47.51
46.78
Mississippi
Lee
65.0
48.56
48.08
Mississippi
Yalobusha
63.0
50.49
50.08
Missouri
Andrew
73.3
56.14
55.53
Missouri
Boone
69.0
53.72
53.01
Missouri
Callaway
67.7
52.88
52.27
Missouri
Cass
70.0
53.85
53.36
Missouri
Cedar
71.7
57.05
56.50
Missouri
Clay
77.7
59.25
58.65
Missouri
Clinton
78.0
58.64
57.93
Missouri
Greene
71.7
54.92
54.44
Missouri
Jasper
76.7
60.36
59.77
Missouri
Jefferson
76.3
57.29
55.57
Missouri
Lincoln
77.0
58.68
57.65
B-10
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Missouri
Monroe
68.7
54.83
54.19
Missouri
Perry
74.3
57.73
56.91
Missouri
St Charles
82.3
60.36
59.24
Missouri
Ste Genevieve
72.3
57.18
56.30
Missouri
St Louis
79.0
59.55
58.23
Missouri
Taney
69.0
54.17
53.59
Missouri
St Louis City
75.7
56.93
55.57
Montana
Powder River
55.0
50.47
50.25
Montana
Rosebud
55.5
52.08
51.93
Nebraska
Douglas
67.0
54.55
54.08
Nebraska
Knox
68.0
59.37
59.03
Nebraska
Lancaster
53.3
45.22
44.92
Nevada
Churchill
56.7
50.48
50.36
Nevada
Clark
76.0
64.62
64.28
Nevada
Lyon
68.5
58.89
58.71
Nevada
Washoe
67.3
56.90
56.65
Nevada
White Pine
72.0
63.30
63.10
Nevada
Carson City
66.0
57.09
56.99
New Hampshire
Belknap
62.3
49.20
48.88
New Hampshire
Cheshire
62.3
47.79
47.39
New Hampshire
Coos
69.3
56.84
56.38
New Hampshire
Grafton
59.7
47.04
46.58
New Hampshire
Hillsborough
69.0
52.95
52.52
New Hampshire
Merrimack
64.7
49.83
49.34
New Hampshire
Rockingham
68.0
52.76
52.22
New Jersey
Atlantic
74.3
56.05
55.56
New Jersey
Bergen
77.0
58.86
58.37
New Jersey
Camden
82.7
62.81
62.25
New Jersey
Cumberland
72.0
53.98
53.45
New Jersey
Essex
78.0
60.44
59.93
New Jersey
Gloucester
84.3
62.41
61.75
New Jersey
Hudson
77.0
62.20
61.73
New Jersey
Hunterdon
78.0
57.77
57.27
New Jersey
Mercer
78.3
59.37
58.87
New Jersey
Middlesex
81.3
61.60
61.08
New Jersey
Monmouth
80.0
63.08
62.62
New Jersey
Morris
76.3
55.94
55.47
New Jersey
Ocean
82.0
60.92
60.40
New Jersey
Passaic
73.3
57.14
56.66
New Jersey
Warren
66.0
47.90
47.48
B-ll
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
New Mexico
Bernalillo
72.0
59.63
59.25
New Mexico
Dona Ana
71.0
59.08
58.70
New Mexico
Eddy
70.3
66.47
66.21
New Mexico
Grant
65.0
58.53
58.16
New Mexico
Lea
62.7
60.51
60.34
New Mexico
Luna
63.0
54.72
54.31
New Mexico
Sandoval
63.0
56.56
56.37
New Mexico
San Juan
71.0
60.79
60.61
New Mexico
Santa Fe
64.3
58.04
57.72
New Mexico
Valencia
68.5
56.21
55.70
New York
Albany
68.0
53.85
53.46
New York
Bronx
74.0
73.23
73.06
New York
Chautauqua
74.0
58.29
57.80
New York
Chemung
66.5
53.89
53.46
New York
Dutchess
72.0
54.42
54.05
New York
Erie
71.3
58.34
57.79
New York
Essex
70.3
58.12
57.70
New York
Hamilton
66.0
51.96
51.46
New York
Herkimer
62.0
49.79
49.32
New York
Jefferson
71.7
57.89
57.58
New York
Madison
67.0
53.93
53.45
New York
New York
73.3
70.61
70.39
New York
Niagara
72.3
62.56
62.24
New York
Oneida
61.5
49.39
48.98
New York
Onondaga
69.3
55.75
55.34
New York
Orange
67.0
52.41
52.01
New York
Oswego
68.0
54.68
54.37
New York
Putnam
70.0
52.36
51.93
New York
Queens
78.0
76.00
75.80
New York
Rensselaer
67.0
52.68
52.31
New York
Richmond
81.3
73.85
73.47
New York
Rockland
75.0
57.67
57.18
New York
Saratoga
67.0
52.18
51.77
New York
Steuben
65.3
53.43
53.01
New York
Suffolk
83.3
72.82
72.49
New York
Ulster
69.0
55.74
55.37
New York
Wayne
65.0
53.78
53.45
New York
Westchester
75.3
74.17
73.94
North Carolina
Alexander
66.7
47.39
46.95
North Carolina
Avery
63.3
48.76
48.19
B-12
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
North Carolina
Buncombe
66.7
48.11
47.55
North Carolina
Caldwell
66.0
47.68
47.24
North Carolina
Caswell
70.7
49.49
48.60
North Carolina
Chatham
64.0
45.06
44.37
North Carolina
Cumberland
70.7
50.75
50.11
North Carolina
Davie
71.0
47.93
47.17
North Carolina
Durham
70.0
47.10
46.27
North Carolina
Edgecombe
70.0
49.85
49.25
North Carolina
Forsyth
75.3
52.22
51.53
North Carolina
Franklin
69.3
48.12
47.36
North Carolina
Graham
70.3
52.47
51.83
North Carolina
Granville
70.7
49.49
48.62
North Carolina
Guilford
74.0
51.87
51.22
North Carolina
Haywood
67.7
52.83
52.27
North Carolina
Jackson
67.0
51.93
51.39
North Carolina
Johnston
71.7
49.65
49.08
North Carolina
Lenoir
67.7
51.44
50.84
North Carolina
Lincoln
72.7
49.35
48.76
North Carolina
Martin
66.3
48.67
48.16
North Carolina
Mecklenburg
80.0
54.96
54.15
North Carolina
Montgomery
66.0
46.25
45.50
North Carolina
New Hanover
63.0
45.18
44.79
North Carolina
Person
71.0
48.37
47.59
North Carolina
Pitt
69.7
51.59
50.94
North Carolina
Rockingham
71.0
50.07
49.30
North Carolina
Rowan
75.3
50.83
50.08
North Carolina
Swain
60.7
46.52
46.02
North Carolina
Union
71.0
47.47
46.81
North Carolina
Wake
73.0
51.61
51.06
North Carolina
Yancey
69.7
53.01
52.50
Ohio
Allen
73.0
56.88
56.34
Ohio
Ashtabula
77.3
58.00
57.44
Ohio
Athens
69.0
54.72
54.22
Ohio
Butler
79.7
62.26
61.56
Ohio
Clark
75.0
56.06
55.40
Ohio
Clermont
78.7
58.58
57.86
Ohio
Clinton
78.7
57.77
56.98
Ohio
Cuyahoga
77.7
57.10
56.83
Ohio
Delaware
73.0
54.45
53.80
Ohio
Fayette
72.0
52.14
51.55
B-13
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Ohio
Franklin
80.3
60.19
59.51
Ohio
Geauga
74.7
57.09
56.53
Ohio
Greene
73.0
53.98
53.28
Ohio
Hamilton
82.0
64.25
63.48
Ohio
Jefferson
70.3
58.34
57.96
Ohio
Knox
73.7
54.68
54.02
Ohio
Lake
80.0
56.20
55.83
Ohio
Lawrence
70.0
57.07
56.55
Ohio
Licking
74.3
54.27
53.48
Ohio
Lorain
71.7
52.72
52.39
Ohio
Lucas
74.3
55.56
55.13
Ohio
Madison
74.3
54.46
53.76
Ohio
Mahoning
70.7
54.12
53.63
Ohio
Medina
69.0
52.97
52.40
Ohio
Miami
73.3
56.85
56.33
Ohio
Montgomery
76.7
57.30
56.64
Ohio
Portage
68.3
51.55
51.05
Ohio
Preble
72.3
55.49
54.89
Ohio
Stark
76.7
57.34
56.72
Ohio
Summit
72.0
55.29
54.69
Ohio
Trumbull
76.3
57.53
57.01
Ohio
Warren
77.7
58.18
57.44
Ohio
Washington
71.3
55.56
55.07
Ohio
Wood
71.3
55.79
55.25
Oklahoma
Adair
73.7
58.39
57.81
Oklahoma
Caddo
74.7
59.54
59.02
Oklahoma
Canadian
75.7
56.27
55.68
Oklahoma
Cherokee
73.7
58.51
58.02
Oklahoma
Cleveland
75.0
59.25
58.71
Oklahoma
Comanche
74.7
62.71
62.19
Oklahoma
Creek
77.0
58.51
57.99
Oklahoma
Dewey
72.3
64.28
63.92
Oklahoma
Kay
73.0
59.16
58.61
Oklahoma
Mc Clain
74.0
59.15
58.60
Oklahoma
Mc Curtain
68.0
56.84
56.27
Oklahoma
Mayes
76.3
61.60
61.06
Oklahoma
Oklahoma
78.3
62.06
61.52
Oklahoma
Ottawa
74.0
57.69
57.13
Oklahoma
Pittsburg
73.3
62.38
61.79
Oklahoma
Sequoyah
72.0
58.49
57.90
B-14
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Oklahoma
Tulsa
79.0
60.94
60.42
Oregon
Clackamas
64.0
51.19
50.51
Oregon
Columbia
51.3
42.33
41.83
Oregon
Deschutes
58.5
52.28
52.14
Oregon
Jackson
61.7
53.21
52.87
Oregon
Lane
60.0
47.17
46.60
Oregon
Marion
59.3
47.20
46.68
Oregon
Multnomah
56.7
49.10
48.69
Oregon
Umatilla
61.3
50.07
49.52
Pennsylvania
Allegheny
80.7
67.30
66.98
Pennsylvania
Armstrong
74.3
62.54
62.26
Pennsylvania
Beaver
74.7
64.12
63.78
Pennsylvania
Berks
76.3
57.11
56.61
Pennsylvania
Blair
72.7
61.24
60.99
Pennsylvania
Bucks
80.3
60.12
59.58
Pennsylvania
Cambria
70.3
58.15
57.89
Pennsylvania
Centre
72.0
59.88
59.60
Pennsylvania
Chester
76.3
54.53
53.86
Pennsylvania
Clearfield
72.3
59.26
58.90
Pennsylvania
Dauphin
74.7
56.94
56.42
Pennsylvania
Delaware
75.7
55.55
54.96
Pennsylvania
Erie
74.0
57.49
56.98
Pennsylvania
Franklin
67.0
52.35
51.89
Pennsylvania
Greene
69.0
54.24
53.78
Pennsylvania
Indiana
75.7
63.39
63.10
Pennsylvania
Lackawanna
71.0
56.37
55.86
Pennsylvania
Lancaster
78.0
57.50
57.01
Pennsylvania
Lawrence
71.0
57.22
56.84
Pennsylvania
Lebanon
76.0
57.19
56.65
Pennsylvania
Lehigh
76.0
57.34
56.84
Pennsylvania
Luzerne
65.0
49.80
49.36
Pennsylvania
Lycoming
67.0
54.25
53.78
Pennsylvania
Mercer
76.3
58.40
57.89
Pennsylvania
Monroe
66.7
50.23
49.77
Pennsylvania
Montgomery
76.3
57.76
57.21
Pennsylvania
Northampton
76.0
57.19
56.73
Pennsylvania
Perry
68.3
56.16
55.87
Pennsylvania
Philadelphia
83.3
63.33
62.77
Pennsylvania
Somerset
65.0
49.75
49.44
Pennsylvania
Tioga
69.7
56.88
56.48
B-15
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Pennsylvania
Washington
70.7
59.40
59.02
Pennsylvania
Westmoreland
71.7
58.88
58.59
Pennsylvania
York
74.3
53.02
52.52
Rhode Island
Kent
73.7
57.09
56.61
Rhode Island
Providence
74.0
57.94
57.66
Rhode Island
Washington
76.3
60.16
59.75
South Carolina
Abbeville
62.0
43.88
43.22
South Carolina
Aiken
64.3
45.53
44.89
South Carolina
Anderson
70.0
49.12
48.50
South Carolina
Berkeley
62.3
46.89
46.45
South Carolina
Charleston
64.7
48.73
48.17
South Carolina
Chesterfield
64.3
46.32
45.82
South Carolina
Colleton
61.0
46.36
45.90
South Carolina
Darlington
68.0
49.75
49.27
South Carolina
Edgefield
61.3
43.48
42.91
South Carolina
Greenville
68.0
47.73
47.22
South Carolina
Pickens
69.7
49.75
49.10
South Carolina
Richland
71.7
51.14
50.34
South Carolina
Spartanburg
73.7
51.25
50.60
South Carolina
York
64.0
43.46
42.82
South Dakota
Brookings
63.3
53.08
52.66
South Dakota
Custer
61.7
56.23
56.00
South Dakota
Jackson
57.0
51.10
50.79
South Dakota
Meade
58.5
51.25
50.96
South Dakota
Minnehaha
66.0
54.45
53.97
South Dakota
Union
62.5
52.34
51.96
Tennessee
Anderson
70.7
51.11
50.06
Tennessee
Blount
76.7
56.35
55.53
Tennessee
Claiborne
62.0
46.70
45.83
Tennessee
Davidson
70.3
51.22
50.27
Tennessee
Hamilton
73.3
52.69
51.68
Tennessee
Jefferson
74.7
53.65
52.53
Tennessee
Knox
71.7
50.85
49.96
Tennessee
Loudon
72.3
53.18
51.65
Tennessee
Meigs
71.3
51.94
51.02
Tennessee
Rutherford
68.5
48.73
47.79
Tennessee
Sevier
74.3
55.42
54.52
Tennessee
Shelby
78.0
57.73
56.95
Tennessee
Sullivan
71.7
57.80
57.38
Tennessee
Sumner
76.7
54.48
53.51
B-16
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Tennessee
Williamson
70.3
50.51
49.63
Tennessee
Wilson
71.7
50.33
49.46
Texas
Bell
74.5
59.64
59.11
Texas
Bexar
78.7
61.17
60.68
Texas
Brazoria
88.0
68.78
68.15
Texas
Brewster
70.0
67.21
67.10
Texas
Cameron
62.7
55.55
55.29
Texas
Collin
82.7
62.63
61.92
Texas
Dallas
82.0
63.53
62.77
Texas
Denton
84.3
65.28
64.46
Texas
Ellis
75.7
61.15
60.37
Texas
El Paso
71.0
57.85
57.43
Texas
Galveston
77.3
64.97
64.41
Texas
Gregg
77.7
66.39
65.96
Texas
Harris
83.0
69.77
69.19
Texas
Harrison
72.7
60.39
59.91
Texas
Hidalgo
61.0
53.42
53.17
Texas
Hood
76.7
61.73
61.07
Texas
Hunt
71.7
56.31
55.81
Texas
Jefferson
78.0
63.33
62.71
Texas
Johnson
79.0
64.01
63.31
Texas
Kaufman
70.7
58.45
57.91
Texas
Mc Lennan
72.7
60.22
59.63
Texas
Montgomery
77.3
59.06
58.57
Texas
Navarro
71.0
61.12
60.64
Texas
Nueces
71.0
62.97
62.66
Texas
Orange
72.7
59.46
58.87
Texas
Parker
78.7
64.28
63.62
Texas
Rockwall
77.0
60.08
59.48
Texas
Smith
75.0
62.19
61.69
Texas
Tarrant
87.3
68.61
67.91
Texas
Travis
73.7
59.17
58.76
Texas
Victoria
68.7
59.23
58.82
Utah
Box Elder
67.7
58.53
57.96
Utah
Cache
64.3
55.98
55.61
Utah
Carbon
69.0
62.53
62.36
Utah
Davis
69.3
60.81
60.36
Utah
Duchesne
68.0
61.45
61.27
Utah
Salt Lake
76.0
65.58
65.10
Utah
San Juan
68.7
61.83
61.67
B-17
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Utah
Tooele
72.0
62.45
61.76
Utah
Utah
70.0
61.80
61.35
Utah
Washington
71.7
63.79
63.61
Utah
Weber
72.7
62.96
62.59
Vermont
Bennington
63.7
50.24
49.85
Virginia
Albemarle
66.7
50.83
50.17
Virginia
Arlington
81.7
58.02
57.43
Virginia
Caroline
71.7
51.54
50.97
Virginia
Charles City
75.7
56.19
55.53
Virginia
Chesterfield
72.0
53.39
52.90
Virginia
Fairfax
82.3
56.61
56.07
Virginia
Fauquier
62.7
47.85
47.35
Virginia
Frederick
66.7
51.57
51.05
Virginia
Giles
63.0
46.56
46.09
Virginia
Hanover
73.7
54.82
54.16
Virginia
Henrico
75.0
55.26
54.63
Virginia
Loudoun
73.0
56.88
56.30
Virginia
Madison
70.7
57.08
56.65
Virginia
Page
66.3
53.68
53.28
Virginia
Prince Edward
62.0
47.64
47.06
Virginia
Prince William
70.0
55.13
54.59
Virginia
Roanoke
67.3
51.97
51.27
Virginia
Rockbridge
62.3
51.41
50.99
Virginia
Rockingham
66.0
52.40
51.96
Virginia
Stafford
73.0
48.99
48.42
Virginia
Wythe
64.3
51.01
50.39
Virginia
Alexandria City
80.0
56.13
55.68
Virginia
Hampton City
74.0
57.97
57.49
Virginia
Suffolk City
71.3
58.23
57.78
Washington
Clark
56.0
47.54
47.14
Washington
Spokane
59.0
48.55
48.13
Washington
Whatcom
45.0
41.77
41.74
West Virginia
Berkeley
68.0
52.55
51.97
West Virginia
Cabell
69.3
55.39
54.85
West Virginia
Gilmer
60.0
50.28
49.92
West Virginia
Greenbrier
64.7
51.72
51.17
West Virginia
Hancock
73.0
60.87
60.48
West Virginia
Kanawha
72.3
61.84
61.35
West Virginia
Monongalia
69.7
57.92
57.55
West Virginia
Ohio
72.3
55.98
55.47
B-18
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
West Virginia
Wood
68.3
52.89
52.44
Wisconsin
Brown
68.3
53.20
52.70
Wisconsin
Columbia
67.0
53.51
52.88
Wisconsin
Dane
66.3
53.03
52.44
Wisconsin
Dodge
71.5
57.72
57.01
Wisconsin
Door
75.7
59.18
58.36
Wisconsin
Eau Claire
62.0
48.34
47.74
Wisconsin
Fond Du Lac
70.0
56.32
55.65
Wisconsin
Jefferson
68.5
54.83
54.28
Wisconsin
Kenosha
81.0
53.84
53.54
Wisconsin
Kewaunee
75.0
59.25
58.44
Wisconsin
La Crosse
63.3
50.41
49.91
Wisconsin
Manitowoc
78.7
62.03
61.33
Wisconsin
Marathon
63.3
50.06
49.44
Wisconsin
Milwaukee
80.0
61.95
61.48
Wisconsin
Outagamie
69.3
55.64
55.07
Wisconsin
Ozaukee
76.3
64.23
63.87
Wisconsin
Racine
77.7
55.40
55.15
Wisconsin
Rock
69.5
56.14
55.54
Wisconsin
Sauk
65.0
52.44
51.75
Wisconsin
Sheboygan
84.3
68.51
67.97
Wisconsin
Walworth
69.3
56.53
55.93
Wisconsin
Waukesha
66.7
54.29
53.77
Wyoming
Campbell
63.7
58.62
58.40
Wyoming
Carbon
63.0
58.18
58.02
Wyoming
Fremont
68.0
62.77
62.58
Wyoming
Laramie
68.0
61.36
61.06
Wyoming
Sublette
77.3
72.16
71.99
Wyoming
Sweetwater
66.0
59.28
59.00
Wyoming
Teton
65.3
61.61
61.44
Wyoming
Uinta
64.3
56.49
56.18
B-19
-------
Air Quality Modeling Technical Support Document:
Heavy-Duty Vehicle Greenhouse Gas Phase 2 Final Rule
Appendix C
Annual PM2.5 Design Values for Air Quality Modeling
Scenarios
c-i
-------
Table C-l. Annual PM2.5 Design Values for HDGHG Phase 2 Scenarios
(units are ug/m3)
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Alabama
Baldwin
9.49
7.94
7.92
Alabama
Clay
9.74
7.74
7.73
Alabama
Colbert
9.70
8.28
8.27
Alabama
DeKalb
10.41
8.38
8.37
Alabama
Etowah
10.70
8.74
8.73
Alabama
Houston
9.62
8.19
8.18
Alabama
Jefferson
12.59
11.03
11.03
Alabama
Madison
10.48
9.18
9.17
Alabama
Mobile
9.45
8.06
8.05
Alabama
Montgomery
10.88
9.49
9.49
Alabama
Morgan
10.02
8.74
8.73
Alabama
Russell
11.87
9.95
9.94
Alabama
Shelby
9.75
8.22
8.21
Alabama
Talladega
11.05
9.05
9.04
Alabama
Tuscaloosa
10.21
8.81
8.80
Alabama
Walker
10.84
9.03
9.02
Arizona
Cochise
6.77
7.21
7.21
Arizona
Coconino
5.47
5.34
5.35
Arizona
Maricopa
11.48
10.18
10.22
Arizona
Pima
5.52
4.89
4.89
Arizona
Pinal
9.36
8.61
8.64
Arizona
Santa Cruz
10.07
10.02
10.02
Arizona
Yavapai
4.14
4.10
4.10
Arizona
Yuma
7.70
7.29
7.28
Arkansas
Arkansas
10.51
8.71
8.70
Arkansas
Ashley
10.48
9.10
9.09
Arkansas
Crittenden
10.94
8.56
8.56
Arkansas
Faulkner
10.76
9.02
9.00
Arkansas
Garland
10.75
9.04
9.02
Arkansas
Jackson
10.00
8.11
8.10
Arkansas
Phillips
10.67
8.75
8.74
Arkansas
Polk
10.67
9.22
9.21
Arkansas
Pope
11.34
9.70
9.68
Arkansas
Pulaski
12.01
9.89
9.89
Arkansas
Union
11.07
9.59
9.57
Arkansas
Washington
10.67
9.11
9.10
Arkansas
White
11.26
9.52
9.51
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
California
Alameda
9.37
8.20
8.19
California
Butte
10.09
9.19
9.19
California
Calaveras
7.76
6.56
6.55
California
Colusa
6.56
5.95
5.95
California
Contra Costa
7.43
6.53
6.51
California
Fresno
16.44
13.86
13.84
California
Humboldt
6.21
6.01
6.01
California
Imperial
13.64
14.88
14.86
California
Inyo
7.38
7.06
7.05
California
Kern
17.02
13.75
13.73
California
Kings
16.33
13.87
13.85
California
Lake
3.51
3.25
3.25
California
Los Angeles
12.92
10.57
10.55
California
Madera
18.75
16.25
16.22
California
Marin
9.53
8.59
8.58
California
Mendocino
8.55
7.91
7.91
California
Merced
14.54
12.81
12.80
California
Monterey
6.15
5.43
5.42
California
Nevada
6.39
5.84
5.84
California
Orange
10.77
8.75
8.73
California
Placer
7.54
6.57
6.56
California
Plumas
9.59
8.92
8.91
California
Riverside
15.31
12.43
12.41
California
Sacramento
9.94
8.82
8.81
California
San Benito
5.51
4.74
4.74
California
San Bernardino
13.03
10.64
10.62
California
San Diego
10.79
9.48
9.48
California
San Francisco
9.51
8.36
8.34
California
San Joaquin
12.09
10.47
10.45
California
San Luis Obispo
11.33
9.91
9.90
California
San Mateo
8.80
7.73
7.72
California
Santa Barbara
9.59
8.61
8.61
California
Santa Clara
9.79
8.56
8.55
California
Santa Cruz
6.25
5.53
5.53
California
Shasta
5.42
5.10
5.09
California
Siskiyou
5.54
5.29
5.28
California
Solano
9.15
8.09
8.07
California
Sonoma
8.15
7.54
7.54
California
Stanislaus
15.27
13.12
13.10
California
Sutter
7.30
6.45
6.44
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
California
Tulare
15.54
13.06
13.04
California
Ventura
8.98
8.02
8.02
California
Yolo
6.87
5.99
5.98
Colorado
Adams
8.06
6.88
6.87
Colorado
Arapahoe
6.29
5.33
5.32
Colorado
Boulder
6.92
6.22
6.21
Colorado
Denver
7.63
6.51
6.50
Colorado
Douglas
5.68
4.85
4.84
Colorado
El Paso
5.87
5.08
5.08
Colorado
Larimer
6.32
5.66
5.66
Colorado
Mesa
8.60
7.79
7.79
Colorado
Montezuma
6.05
5.77
5.77
Colorado
Rio Blanco
9.55
9.50
9.50
Colorado
Weld
7.49
6.89
6.88
Connecticut
Fairfield
9.35
7.26
7.26
Connecticut
Hartford
8.78
7.23
7.23
Connecticut
Litchfield
5.63
4.48
4.48
Connecticut
New Haven
9.45
7.37
7.37
Connecticut
New London
8.19
6.51
6.51
Delaware
Kent
8.93
6.63
6.62
Delaware
New Castle
10.35
7.83
7.81
Delaware
Sussex
8.97
6.68
6.67
District Of Co
District of
Columbia
10.29
7.91
7.90
Florida
Palm Beach
7.37
6.79
6.79
Georgia
Bibb
12.78
10.61
10.60
Georgia
Chatham
10.70
8.57
8.57
Georgia
Clarke
10.35
7.87
7.86
Georgia
Clayton
11.97
9.02
9.02
Georgia
Cobb
11.10
8.26
8.25
Georgia
DeKalb
11.31
8.34
8.34
Georgia
Dougherty
12.05
10.29
10.28
Georgia
Floyd
11.72
9.22
9.20
Georgia
Fulton
13.08
9.87
9.87
Georgia
Hall
10.22
7.80
7.79
Georgia
Houston
10.45
8.57
8.57
Georgia
Muscogee
12.58
10.55
10.53
Georgia
Richmond
12.05
9.85
9.84
Georgia
Walker
10.16
7.73
7.73
Georgia
Wilkinson
12.27
10.19
10.18
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Idaho
Bannock
6.45
6.02
6.01
Idaho
Lemhi
11.59
11.09
11.09
Idaho
Shoshone
12.34
11.69
11.69
Indiana
Allen
10.51
7.78
7.75
Indiana
Clark
12.91
9.88
9.87
Indiana
Delaware
10.74
8.02
8.00
Indiana
Dubois
12.23
8.99
8.97
Indiana
Elkhart
11.10
8.39
8.35
Indiana
Floyd
11.60
8.71
8.69
Indiana
Gibson
11.43
8.51
8.49
Indiana
Greene
9.89
7.02
7.00
Indiana
Henry
10.43
7.66
7.63
Indiana
Howard
11.61
8.76
8.72
Indiana
Knox
11.70
8.68
8.66
Indiana
Lake
12.04
9.24
9.21
Indiana
LaPorte
9.96
7.41
7.38
Indiana
Madison
10.08
7.42
7.39
Indiana
Marion
12.57
9.34
9.32
Indiana
Monroe
10.14
7.32
7.29
Indiana
Porter
10.73
8.09
8.06
Indiana
St. Joseph
10.54
7.94
7.90
Indiana
Spencer
11.82
8.70
8.68
Indiana
Tippecanoe
10.51
7.80
7.77
Indiana
Vanderburgh
12.06
9.21
9.19
Indiana
Vigo
11.80
8.69
8.67
Indiana
Whitley
9.61
7.11
7.08
Iowa
Black Hawk
10.63
8.39
8.37
Iowa
Clinton
11.34
8.81
8.78
Iowa
Delaware
9.49
7.40
7.37
Iowa
Johnson
10.29
8.09
8.07
Iowa
Lee
11.14
8.90
8.87
Iowa
Linn
10.19
8.03
8.01
Iowa
Montgomery
9.10
7.38
7.34
Iowa
Muscatine
12.10
9.53
9.50
Iowa
Palo Alto
8.82
7.11
7.08
Iowa
Polk
9.52
7.29
7.24
Iowa
Pottawattamie
10.70
8.55
8.52
Iowa
Scott
11.42
8.81
8.77
Iowa
Van Buren
9.40
7.50
7.46
Iowa
Woodbury
9.65
7.88
7.85
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Kansas
Johnson
8.32
6.64
6.62
Kansas
Linn
9.08
7.47
7.44
Kansas
Sedgwick
9.24
7.79
7.77
Kansas
Shawnee
9.10
7.57
7.55
Kansas
Sumner
8.56
7.22
7.19
Kansas
Wyandotte
10.09
8.19
8.17
Kentucky
Bell
10.83
8.50
8.50
Kentucky
Boyd
10.44
7.82
7.81
Kentucky
Bullitt
12.18
9.67
9.66
Kentucky
Campbell
10.53
7.14
7.11
Kentucky
Carter
8.71
6.40
6.39
Kentucky
Christian
10.51
7.99
7.98
Kentucky
Daviess
11.73
8.78
8.76
Kentucky
Fayette
10.59
7.59
7.58
Kentucky
Hardin
11.08
8.07
8.06
Kentucky
Henderson
11.22
8.44
8.43
Kentucky
Jefferson
12.38
9.39
9.38
Kentucky
McCracken
10.84
8.19
8.17
Kentucky
Madison
9.37
6.58
6.56
Kentucky
Pike
9.42
7.08
7.07
Kentucky
Warren
11.03
8.45
8.45
Louisiana
Caddo
11.50
10.27
10.26
Louisiana
Calcasieu
8.80
7.61
7.58
Louisiana
East Baton Rouge
9.95
8.71
8.70
Louisiana
Iberville
9.78
8.74
8.74
Louisiana
Jefferson
9.03
7.40
7.39
Louisiana
Lafayette
8.89
7.87
7.87
Louisiana
Ouachita
9.14
7.91
7.91
Louisiana
Rapides
8.56
7.34
7.33
Louisiana
St. Bernard
10.23
8.42
8.41
Louisiana
Tangipahoa
8.80
7.26
7.25
Louisiana
Terrebonne
8.26
7.18
7.19
Louisiana
West Baton Rouge
10.50
9.26
9.25
Maine
Androscoggin
7.50
6.24
6.24
Maine
Aroostook
6.53
6.00
6.00
Maine
Cumberland
8.37
7.01
7.01
Maine
Hancock
4.59
4.06
4.06
Maine
Kennebec
7.16
5.99
5.99
Maine
Oxford
8.20
7.11
7.11
Maine
Penobscot
7.21
6.19
6.19
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Maryland
Anne Arundel
10.53
8.20
8.20
Maryland
Baltimore
10.79
8.27
8.27
Maryland
Cecil
10.27
7.72
7.70
Maryland
Garrett
8.93
6.90
6.90
Maryland
Harford
10.11
7.59
7.58
Maryland
Kent
10.16
7.75
7.74
Maryland
Montgomery
10.14
8.04
8.03
Maryland
Prince George's
10.53
8.44
8.44
Maryland
Washington
10.89
8.45
8.45
Maryland
Baltimore (City)
10.97
8.51
8.51
Massachusetts
Berkshire
8.68
7.12
7.11
Massachusetts
Bristol
7.58
6.00
6.00
Massachusetts
Essex
7.91
6.67
6.68
Massachusetts
Hampden
9.22
7.75
7.75
Massachusetts
Middlesex
7.49
6.28
6.29
Massachusetts
Plymouth
7.85
6.35
6.35
Massachusetts
Suffolk
9.87
8.10
8.10
Massachusetts
Worcester
8.71
7.29
7.29
Michigan
Allegan
8.42
6.40
6.38
Michigan
Bay
7.81
6.19
6.17
Michigan
Berrien
8.66
6.52
6.48
Michigan
Chippewa
6.23
5.62
5.62
Michigan
Genesee
8.35
6.48
6.45
Michigan
Ingham
8.65
6.76
6.74
Michigan
Kalamazoo
9.16
6.95
6.92
Michigan
Kent
9.53
7.44
7.41
Michigan
Lenawee
9.13
6.99
6.97
Michigan
Macomb
8.73
6.84
6.82
Michigan
Manistee
6.58
5.28
5.26
Michigan
Missaukee
5.96
4.86
4.85
Michigan
Monroe
9.72
7.27
7.24
Michigan
Muskegon
8.48
6.59
6.56
Michigan
Oakland
9.23
7.12
7.10
Michigan
Ottawa
8.99
6.89
6.87
Michigan
St. Clair
9.13
7.52
7.51
Michigan
Washtenaw
9.35
7.25
7.23
Michigan
Wayne
11.47
9.33
9.31
Minnesota
Anoka
8.44
7.28
7.25
Minnesota
Dakota
8.89
7.65
7.62
Minnesota
Hennepin
8.94
7.76
7.74
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Minnesota
Olmsted
8.96
7.20
7.17
Minnesota
Ramsey
9.86
8.57
8.54
Minnesota
Saint Louis
6.59
5.80
5.79
Minnesota
Scott
8.62
7.37
7.34
Minnesota
Stearns
8.34
7.09
7.07
Minnesota
Washington
9.21
7.84
7.81
Mississippi
DeSoto
9.76
7.78
7.78
Mississippi
Forrest
11.36
9.40
9.39
Mississippi
Grenada
9.41
7.56
7.56
Mississippi
Hancock
9.44
7.84
7.83
Mississippi
Harrison
9.66
7.93
7.91
Mississippi
Hinds
10.83
8.97
8.97
Mississippi
Jackson
9.38
7.64
7.63
Mississippi
Jones
11.67
9.68
9.66
Mississippi
Lauderdale
10.86
8.92
8.90
Mississippi
Lee
10.77
8.86
8.86
Missouri
Cass
10.65
8.81
8.79
Missouri
Cedar
10.48
8.87
8.85
Missouri
Clay
9.38
7.47
7.44
Missouri
Greene
10.15
8.46
8.45
Missouri
Jackson
10.25
8.28
8.26
Missouri
Jefferson
10.05
7.63
7.62
Missouri
Saint Louis
10.89
8.10
8.07
Missouri
St. Louis City
11.61
8.68
8.66
Montana
Lewis and Clark
8.45
8.10
8.10
Montana
Lincoln
11.43
10.97
10.97
Montana
Missoula
10.83
10.40
10.41
Montana
Powder River
5.83
5.72
5.72
Montana
Ravalli
10.00
9.76
9.76
Montana
Richland
6.81
6.53
6.53
Montana
Silver Bow
10.07
9.24
9.24
Nebraska
Douglas
10.34
8.32
8.29
Nebraska
Hall
7.24
5.98
5.96
Nebraska
Lancaster
8.57
6.97
6.94
Nebraska
Sarpy
11.26
9.06
9.03
Nebraska
Washington
9.09
7.34
7.30
Nevada
Clark
8.16
7.13
7.14
Nevada
Washoe
6.90
6.14
6.14
New Hampshire
Belknap
5.91
4.99
4.99
New Hampshire
Cheshire
9.27
7.75
7.75
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
New Hampshire
Grafton
6.75
5.52
5.53
New Hampshire
Hillsborough
7.78
6.59
6.60
New Hampshire
Merrimack
8.48
7.22
7.22
New Hampshire
Rockingham
7.49
6.35
6.35
New Jersey
Atlantic
8.91
6.83
6.82
New Jersey
Bergen
9.17
6.84
6.84
New Jersey
Camden
9.51
7.23
7.21
New Jersey
Essex
9.45
7.25
7.24
New Jersey
Gloucester
9.30
6.77
6.74
New Jersey
Hudson
11.10
8.53
8.53
New Jersey
Mercer
9.54
7.48
7.48
New Jersey
Middlesex
8.01
6.16
6.15
New Jersey
Morris
8.39
6.43
6.43
New Jersey
Ocean
8.48
6.48
6.47
New Jersey
Passaic
9.32
7.08
7.08
New Jersey
Union
11.24
8.45
8.44
New Jersey
Warren
9.24
7.18
7.17
New Mexico
Bernalillo
6.36
6.00
6.00
New Mexico
Dona Ana
5.78
5.98
5.98
New Mexico
Lea
8.02
8.30
8.30
New Mexico
San Juan
4.60
4.97
4.97
New Mexico
Santa Fe
4.55
4.63
4.63
New York
Albany
8.05
6.37
6.37
New York
Bronx
11.91
9.08
9.08
New York
Chautauqua
7.43
5.69
5.68
New York
Erie
9.43
7.42
7.41
New York
Essex
4.33
3.61
3.61
New York
Kings
9.98
7.57
7.56
New York
Nassau
8.88
6.73
6.72
New York
New York
11.75
9.08
9.08
New York
Onondaga
7.52
5.87
5.86
New York
Orange
8.04
6.20
6.19
New York
Queens
9.08
6.91
6.91
New York
Richmond
9.47
7.01
7.01
New York
Steuben
6.85
5.28
5.28
New York
Suffolk
8.31
6.14
6.14
New York
Westchester
9.09
6.83
6.83
North Carolina
Alamance
9.53
7.09
7.11
North Carolina
Buncombe
9.07
6.77
6.77
North Carolina
Caswell
8.66
6.30
6.30
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
North Carolina
Catawba
10.14
7.74
7.73
North Carolina
Chatham
8.08
5.93
5.93
North Carolina
Cumberland
9.78
7.58
7.57
North Carolina
Davidson
10.77
8.23
8.23
North Carolina
Duplin
8.57
6.52
6.51
North Carolina
Durham
9.12
6.76
6.75
North Carolina
Edgecombe
8.73
6.51
6.50
North Carolina
Forsyth
9.53
6.99
6.99
North Carolina
Gaston
10.00
7.54
7.53
North Carolina
Guilford
9.29
6.82
6.82
North Carolina
Haywood
9.65
7.82
7.82
North Carolina
Jackson
8.96
7.02
7.02
North Carolina
Johnston
8.76
6.55
6.55
North Carolina
Lenoir
8.88
6.74
6.73
North Carolina
McDowell
9.48
7.42
7.42
North Carolina
Martin
8.30
6.12
6.12
North Carolina
Mecklenburg
10.65
8.21
8.21
North Carolina
Mitchell
8.94
7.07
7.07
North Carolina
Montgomery
8.88
6.74
6.73
North Carolina
New Hanover
7.77
5.76
5.75
North Carolina
Pitt
8.27
6.13
6.12
North Carolina
Robeson
9.56
7.83
7.83
North Carolina
Rowan
9.97
7.66
7.66
North Carolina
Swain
9.36
7.37
7.36
North Carolina
Wake
9.97
7.64
7.63
North Carolina
Watauga
7.99
6.07
6.07
North Carolina
Wayne
9.51
7.40
7.40
North Dakota
Billings
4.38
4.11
4.10
North Dakota
Burke
6.76
6.43
6.43
North Dakota
Burleigh
6.60
5.98
5.97
North Dakota
Cass
7.70
6.80
6.79
North Dakota
McKenzie
6.46
6.18
6.17
North Dakota
Mercer
6.14
5.76
5.75
Ohio
Athens
8.80
6.21
6.20
Ohio
Butler
12.39
9.02
8.99
Ohio
Clark
11.83
8.49
8.46
Ohio
Clermont
11.34
7.87
7.84
Ohio
Cuyahoga
12.82
9.92
9.91
Ohio
Franklin
11.63
8.31
8.28
Ohio
Greene
11.18
7.90
7.87
C-10
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Ohio
Hamilton
13.17
9.52
9.50
Ohio
Jefferson
12.07
8.72
8.70
Ohio
Lake
9.54
7.01
7.00
Ohio
Lawrence
10.97
8.34
8.33
Ohio
Lorain
9.64
7.31
7.29
Ohio
Lucas
10.89
8.28
8.25
Ohio
Mahoning
11.14
8.45
8.43
Ohio
Montgomery
12.06
8.63
8.59
Ohio
Portage
10.26
7.45
7.43
Ohio
Preble
10.66
7.76
7.74
Ohio
Scioto
10.37
7.62
7.61
Ohio
Stark
12.85
9.89
9.87
Ohio
Summit
11.85
8.70
8.68
Ohio
Trumbull
10.57
7.92
7.90
Ohio
Warren
11.54
8.23
8.19
Oklahoma
Oklahoma
9.61
8.48
8.47
Oklahoma
Pittsburg
10.25
9.15
9.13
Oklahoma
Sequoyah
10.68
9.26
9.25
Oklahoma
Tulsa
10.46
9.10
9.08
Oregon
Crook
9.02
8.93
8.92
Oregon
Harney
9.05
8.72
8.71
Oregon
Jackson
9.43
9.19
9.19
Oregon
Josephine
7.76
7.59
7.59
Oregon
Klamath
10.67
10.20
10.20
Oregon
Lake
9.66
9.37
9.37
Oregon
Lane
9.32
8.94
8.93
Oregon
Multnomah
7.61
7.24
7.23
Oregon
Umatilla
7.41
7.02
7.02
Oregon
Washington
7.82
7.49
7.49
Pennsylvania
Adams
11.49
8.93
8.92
Pennsylvania
Allegheny
14.40
10.57
10.56
Pennsylvania
Armstrong
11.60
9.05
9.04
Pennsylvania
Beaver
12.00
9.36
9.35
Pennsylvania
Berks
10.88
8.31
8.29
Pennsylvania
Blair
11.89
8.66
8.65
Pennsylvania
Bucks
10.88
8.61
8.61
Pennsylvania
Cambria
12.34
9.35
9.34
Pennsylvania
Centre
9.36
6.94
6.93
Pennsylvania
Chester
12.33
9.56
9.54
Pennsylvania
Cumberland
11.00
8.32
8.31
C-ll
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Pennsylvania
Dauphin
11.97
9.18
9.16
Pennsylvania
Delaware
12.81
10.00
9.98
Pennsylvania
Erie
11.60
9.49
9.48
Pennsylvania
Lackawanna
9.16
7.13
7.13
Pennsylvania
Lancaster
12.01
9.05
9.03
Pennsylvania
Lebanon
12.56
9.59
9.57
Pennsylvania
Mercer
10.44
7.90
7.88
Pennsylvania
Monroe
7.90
5.99
5.98
Pennsylvania
Montgomery
9.90
7.68
7.67
Pennsylvania
Northampton
12.90
10.44
10.44
Pennsylvania
Philadelphia
11.15
8.40
8.37
Pennsylvania
Washington
11.81
8.70
8.68
Pennsylvania
Westmoreland
12.63
9.91
9.90
Pennsylvania
York
11.48
8.78
8.75
Rhode Island
Kent
6.15
4.75
4.75
Rhode Island
Providence
9.38
7.59
7.60
South Carolina
Charleston
8.89
7.02
7.02
South Carolina
Chesterfield
9.15
7.10
7.09
South Carolina
Edgefield
9.75
7.81
7.81
South Carolina
Florence
10.26
8.25
8.24
South Carolina
Greenville
10.74
8.44
8.44
South Carolina
Lexington
10.89
8.75
8.75
South Carolina
Richland
10.41
8.36
8.36
South Carolina
Spartanburg
10.53
8.28
8.27
South Dakota
Brookings
8.34
6.99
6.97
South Dakota
Brown
7.67
6.73
6.71
South Dakota
Codington
9.11
7.87
7.86
South Dakota
Custer
4.20
4.00
3.99
South Dakota
Jackson
3.96
3.66
3.66
South Dakota
Minnehaha
8.83
7.19
7.17
South Dakota
Pennington
5.89
5.58
5.58
South Dakota
Union
9.22
7.62
7.59
Tennessee
Hamilton
10.79
8.26
8.25
Texas
Bexar
9.03
8.74
8.73
Texas
Bowie
10.94
9.56
9.55
Texas
Dallas
10.07
8.89
8.89
Texas
El Paso
10.39
10.80
10.79
Texas
Harris
12.05
11.24
11.23
Texas
Harrison
10.65
9.32
9.31
Texas
Hidalgo
10.37
10.74
10.74
C-12
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
Texas
Nueces
10.28
9.79
9.78
Texas
Tarrant
10.59
9.70
9.69
Texas
Travis
10.01
9.51
9.50
Utah
Box Elder
8.03
6.86
6.82
Utah
Cache
9.40
8.03
8.00
Utah
Davis
8.65
7.24
7.19
Utah
Salt Lake
9.60
7.88
7.83
Utah
Tooele
6.48
5.55
5.50
Utah
Utah
8.71
7.19
7.14
Utah
Washington
4.63
4.51
4.51
Utah
Weber
9.38
7.80
7.75
Vermont
Bennington
6.83
5.62
5.61
Vermont
Chittenden
7.12
6.03
6.02
Vermont
Rutland
9.49
7.85
7.85
Virginia
Albemarle
8.40
6.40
6.40
Virginia
Charles
8.61
6.37
6.36
Virginia
Chesterfield
9.54
7.19
7.19
Virginia
Fairfax
9.23
7.03
7.02
Virginia
Frederick
10.04
7.81
7.80
Virginia
Henrico
9.22
6.97
6.96
Virginia
Loudoun
9.27
7.25
7.24
Virginia
Page
8.79
6.84
6.84
Virginia
Rockingham
9.66
7.67
7.67
Virginia
Alexandria City
10.74
8.34
8.34
Virginia
Bristol City
9.58
7.54
7.54
Virginia
Hampton City
7.85
5.78
5.78
Virginia
Lynchburg City
8.40
6.37
6.36
Virginia
Norfolk City
9.20
6.98
6.98
Virginia
Roanoke City
9.85
7.53
7.53
Virginia
Salem City
9.59
7.29
7.29
Virginia
Virginia Beach City
9.11
6.85
6.85
Washington
Clark
7.34
6.87
6.87
Washington
King
10.13
8.88
8.88
Washington
Pierce
7.88
7.00
7.00
Washington
Snohomish
7.62
6.88
6.88
Washington
Spokane
7.69
7.19
7.19
Washington
Yakima
8.91
7.77
7.74
West Virginia
Berkeley
11.38
8.90
8.90
West Virginia
Brooke
12.41
8.98
8.97
West Virginia
Cabell
11.36
8.54
8.53
C-13
-------
State
County
2011
Baseline DV
2040
Reference
DV
2040
HDGHGP2
Control DV
West Virginia
Hancock
11.17
8.16
8.15
West Virginia
Kanawha
11.76
8.80
8.80
West Virginia
Marion
11.34
8.71
8.70
West Virginia
Marshall
12.46
9.65
9.64
West Virginia
Monongalia
10.20
7.56
7.56
West Virginia
Ohio
11.35
8.23
8.21
West Virginia
Raleigh
9.06
6.60
6.60
West Virginia
Wood
11.51
8.69
8.68
Wisconsin
Ashland
5.32
4.58
4.56
Wisconsin
Brown
9.57
7.86
7.84
Wisconsin
Dane
10.07
7.97
7.94
Wisconsin
Dodge
8.99
7.08
7.05
Wisconsin
Forest
5.57
4.55
4.54
Wisconsin
Grant
10.04
7.83
7.80
Wisconsin
Kenosha
9.33
7.25
7.22
Wisconsin
La Crosse
8.98
7.32
7.29
Wisconsin
Milwaukee
10.82
8.52
8.50
Wisconsin
Outagamie
9.22
7.49
7.46
Wisconsin
Ozaukee
9.02
7.08
7.05
Wisconsin
Sauk
8.36
6.43
6.40
Wisconsin
Taylor
7.62
6.34
6.31
Wisconsin
Vilas
5.76
4.82
4.80
Wisconsin
Waukesha
11.26
8.84
8.81
Wyoming
Albany
4.97
4.64
4.64
Wyoming
Fremont
8.19
8.03
8.02
Wyoming
Laramie
4.54
4.21
4.21
Wyoming
Natrona
4.79
4.64
4.64
Wyoming
Park
4.55
4.53
4.52
Wyoming
Sheridan
8.04
7.80
7.80
Wyoming
Sublette
3.82
3.68
3.68
Wyoming
Sweetwater
5.77
5.17
5.16
Wyoming
Teton
4.94
4.71
4.71
C-14
-------
Air Quality Modeling Technical Support Document:
Heavy-Duty Vehicle Greenhouse Gas Phase 2 Final Rule
Appendix D
24-Hour PM2.5 Design Values for Air Quality Modeling
Scenarios
D-l
-------
Table D-l. 24-hour PM2.5 Design Values for HDGHG Phase 2 Scenarios
(units are ug/m3)
State
County
2011
2040
2040 HDGHGP2
Baseline DV
Reference DV
Control DV
Alabama
Baldwin
19.0
15.4
15.5
Alabama
Clay
20.7
16.8
16.8
Alabama
Colbert
19.5
16.7
16.7
Alabama
DeKalb
20.7
17.0
17.0
Alabama
Etowah
21.6
18.0
17.9
Alabama
Houston
19.3
17.0
16.9
Alabama
Jefferson
25.6
23.1
23.1
Alabama
Madison
21.0
19.4
19.4
Alabama
Mobile
20.0
16.3
16.3
Alabama
Montgomery
23.1
20.2
20.2
Alabama
Morgan
20.1
17.3
17.2
Alabama
Russell
26.2
22.9
22.9
Alabama
Shelby
20.1
16.4
16.4
Alabama
Talladega
21.4
18.3
18.3
Alabama
Tuscaloosa
22.9
19.4
19.4
Alabama
Walker
22.0
17.8
17.7
Arizona
Cochise
12.8
14.1
14.1
Arizona
Coconino
12.6
12.5
12.5
Arizona
Maricopa
27.2
23.0
23.2
Arizona
Pima
12.2
10.6
10.6
Arizona
Pinal
28.9
26.6
26.6
Arizona
Santa Cruz
28.1
27.9
27.9
Arizona
Yavapai
9.7
9.7
9.7
Arizona
Yuma
15.5
14.9
14.9
Arkansas
Arkansas
21.5
17.4
17.3
Arkansas
Ashley
22.5
18.6
18.6
Arkansas
Crittenden
22.7
16.9
17.0
Arkansas
Faulkner
20.1
16.4
16.4
Arkansas
Garland
21.4
17.7
17.6
Arkansas
Jackson
21.4
17.0
16.9
Arkansas
Phillips
20.6
16.7
16.7
Arkansas
Polk
22.0
18.8
18.8
Arkansas
Pope
22.8
19.3
19.3
Arkansas
Pulaski
25.2
20.0
20.1
Arkansas
Union
22.5
18.7
18.6
Arkansas
Washington
22.3
19.0
18.9
Arkansas
White
21.7
18.5
18.4
D-2
-------
State
County
2011
2040
2040 HDGHGP2
Baseline DV
Reference DV
Control DV
California
Alameda
27.5
22.0
21.9
California
Butte
34.6
30.5
30.4
California
Calaveras
19.0
14.1
14.1
California
Colusa
22.3
19.5
19.5
California
Contra Costa
27.0
21.6
21.6
California
Fresno
58.9
49.8
49.7
California
Humboldt
22.7
22.1
22.1
California
Imperial
30.8
30.8
30.7
California
Inyo
35.3
33.7
33.7
California
Kern
61.6
41.2
41.1
California
Kings
60.1
44.3
44.2
California
Lake
8.7
7.9
7.9
California
Los Angeles
31.1
26.3
26.2
California
Madera
52.3
41.0
41.0
California
Marin
24.3
21.3
21.2
California
Mendocino
19.2
16.7
16.7
California
Merced
41.7
35.7
35.7
California
Monterey
13.9
11.9
11.9
California
Nevada
17.5
15.3
15.3
California
Orange
26.6
21.0
21.0
California
Placer
19.9
16.4
16.4
California
Plumas
32.1
30.1
30.1
California
Riverside
36.7
26.0
25.9
California
Sacramento
34.0
29.5
29.5
California
San Benito
14.3
11.2
11.2
California
San Bernardino
29.5
26.8
26.7
California
San Diego
23.2
19.5
19.5
California
San Francisco
25.3
21.6
21.5
California
San Joaquin
39.8
34.3
34.2
California
San Luis Obispo
30.2
27.8
27.8
California
San Mateo
24.5
20.1
20.0
California
Santa Barbara
18.9
17.1
17.1
California
Santa Clara
32.1
26.6
26.5
California
Santa Cruz
13.0
11.5
11.5
California
Shasta
15.3
14.3
14.3
California
Siskiyou
18.4
17.8
17.8
California
Solano
28.5
24.8
24.8
California
Sonoma
22.4
19.6
19.6
California
Stanislaus
50.9
40.5
40.4
California
Sutter
27.2
23.1
23.0
D-3
-------
State
County
2011
2040
2040 HDGHGP2
Baseline DV
Reference DV
Control DV
California
Tulare
49.8
36.7
36.6
California
Ventura
20.2
17.0
16.9
California
Yolo
21.3
18.4
18.4
Colorado
Adams
21.1
17.6
17.5
Colorado
Arapahoe
14.5
12.4
12.3
Colorado
Boulder
20.2
17.6
17.5
Colorado
Denver
18.8
15.7
15.7
Colorado
Douglas
15.5
13.1
13.0
Colorado
El Paso
13.7
11.9
11.9
Colorado
Larimer
17.7
15.2
15.2
Colorado
Mesa
33.5
28.0
27.9
Colorado
Montezuma
13.6
13.3
13.3
Colorado
Rio Blanco
20.6
21.3
21.3
Colorado
Weld
22.8
19.6
19.6
Connecticut
Fairfield
24.8
19.9
19.9
Connecticut
Hartford
22.9
18.2
18.2
Connecticut
Litchfield
16.4
11.4
11.4
Connecticut
New Haven
25.5
19.3
19.3
Connecticut
New London
21.4
16.8
16.8
Delaware
Kent
22.9
17.5
17.4
Delaware
New Castle
25.7
20.5
20.3
Delaware
Sussex
23.6
16.2
16.2
District Of Co
District of Columbia
25.9
20.0
19.9
Florida
Alachua
20.1
19.3
19.3
Florida
Brevard
14.8
12.8
12.8
Florida
Broward
14.5
13.8
13.8
Florida
Citrus
17.0
14.3
14.3
Florida
Duval
20.9
18.3
18.3
Florida
Escambia
19.5
15.8
15.9
Florida
Hillsborough
16.1
14.1
14.1
Florida
Lee
14.0
12.3
12.3
Florida
Leon
23.8
21.3
21.3
Florida
Miami-Dade
15.2
13.9
13.9
Florida
Orange
15.6
13.9
13.9
Florida
Palm Beach
15.1
13.8
13.8
Florida
Pinellas
16.7
15.2
15.2
Florida
Polk
15.2
13.4
13.4
Florida
Sarasota
15.5
13.3
13.3
Florida
Seminole
17.4
14.9
14.9
Florida
Volusia
16.2
13.8
13.8
D-4
-------
State
County
2011
2040
2040 HDGHGP2
Baseline DV
Reference DV
Control DV
Georgia
Bibb
26.7
22.3
22.2
Georgia
Chatham
29.6
23.4
23.4
Georgia
Clarke
21.7
16.7
16.6
Georgia
Cobb
21.4
16.3
16.3
Georgia
DeKalb
22.0
16.5
16.5
Georgia
Dougherty
26.4
24.3
24.3
Georgia
Hall
21.1
15.9
15.9
Georgia
Houston
22.4
19.3
19.2
Georgia
Walker
22.0
17.1
17.1
Georgia
Wilkinson
22.8
20.0
20.0
Idaho
Ada
33.1
31.5
31.0
Idaho
Bannock
23.5
21.1
21.0
Idaho
Benewah
28.4
27.8
27.8
Idaho
Canyon
24.2
22.1
21.8
Idaho
Franklin
42.2
35.5
35.4
Idaho
Lemhi
37.0
35.3
35.3
Idaho
Shoshone
38.1
36.3
36.3
Indiana
Allen
24.9
18.0
17.8
Indiana
Clark
26.8
20.7
20.7
Indiana
Delaware
25.4
18.5
18.4
Indiana
Dubois
25.3
17.8
17.7
Indiana
Elkhart
29.2
21.9
21.7
Indiana
Floyd
24.5
17.9
17.8
Indiana
Gibson
25.3
17.3
17.3
Indiana
Henry
24.2
18.0
17.8
Indiana
Howard
26.0
18.9
18.8
Indiana
Knox
25.8
18.1
18.0
Indiana
Lake
30.0
23.9
23.6
Indiana
La Porte
24.1
18.1
18.0
Indiana
Madison
22.6
17.2
16.8
Indiana
Marion
28.1
20.8
20.7
Indiana
Monroe
21.9
15.1
15.0
Indiana
Porter
26.6
20.8
20.7
Indiana
St. Joseph
26.9
20.3
20.1
Indiana
Spencer
25.7
17.5
17.3
Indiana
Tippecanoe
23.8
17.7
17.5
Indiana
Vanderburgh
25.5
19.9
19.8
Indiana
Vigo
26.0
17.8
17.7
Indiana
Whitley
22.2
16.0
15.9
Iowa
Black Hawk
29.2
22.1
21.9
D-5
-------
State
County
2011
2040
2040 HDGHGP2
Baseline DV
Reference DV
Control DV
Iowa
Clinton
27.6
20.6
20.3
Iowa
Delaware
21.9
15.7
15.5
Iowa
Johnson
26.1
19.7
19.5
Iowa
Lee
25.2
18.9
18.7
Iowa
Linn
30.4
22.2
21.9
Iowa
Montgomery
22.1
16.4
16.2
Iowa
Muscatine
31.5
24.6
24.4
Iowa
Palo Alto
22.0
15.9
15.8
Iowa
Polk
24.3
17.8
17.5
Iowa
Pottawattamie
25.2
19.0
18.9
Iowa
Scott
28.7
20.8
20.6
Iowa
Van Buren
23.8
18.2
18.0
Iowa
Woodbury
25.9
19.4
19.1
Kansas
Johnson
18.2
14.2
14.1
Kansas
Linn
20.4
16.4
16.3
Kansas
Sedgwick
22.1
17.6
17.5
Kansas
Shawnee
20.0
18.3
18.2
Kansas
Sumner
20.8
17.5
17.5
Kansas
Wyandotte
22.4
18.1
18.0
Kentucky
Bell
23.7
20.1
20.0
Kentucky
Boyd
23.0
16.7
16.7
Kentucky
Campbell
23.7
15.4
15.3
Kentucky
Carter
18.3
14.1
14.1
Kentucky
Christian
21.5
14.8
14.7
Kentucky
Daviess
25.5
18.8
18.8
Kentucky
Fayette
21.9
15.5
15.5
Kentucky
Hardin
22.7
16.3
16.2
Kentucky
Henderson
23.9
17.2
17.2
Kentucky
Jefferson
26.0
20.5
20.5
Kentucky
McCracken
23.4
15.9
15.8
Kentucky
Madison
19.6
13.8
13.8
Kentucky
Pike
21.5
16.3
16.3
Kentucky
Warren
21.9
15.3
15.4
Louisiana
Caddo
22.0
19.2
19.1
Louisiana
Calcasieu
19.8
17.0
16.9
Louisiana
East Baton Rouge
21.2
18.5
18.7
Louisiana
Iberville
20.6
19.9
20.1
Louisiana
Jefferson
18.7
16.1
16.3
Louisiana
Lafayette
19.9
17.8
17.8
Louisiana
Ouachita
19.9
16.9
16.9
D-6
-------
State
County
2011
2040
2040 HDGHGP2
Baseline DV
Reference DV
Control DV
Louisiana
Rapides
19.8
16.8
16.8
Louisiana
St. Bernard
20.2
16.2
16.2
Louisiana
Tangipahoa
18.3
15.2
15.3
Louisiana
Terrebonne
17.5
14.4
14.6
Louisiana
West Baton Rouge
21.9
18.9
18.9
Maine
Androscoggin
20.9
16.0
16.0
Maine
Aroostook
18.6
16.3
16.3
Maine
Cumberland
20.4
16.1
16.1
Maine
Hancock
14.8
12.7
12.7
Maine
Kennebec
19.7
14.6
14.6
Maine
Oxford
26.4
21.6
21.6
Maine
Penobscot
19.5
15.4
15.4
Maryland
Anne Arundel
24.5
18.9
18.9
Maryland
Baltimore
27.0
21.6
21.6
Maryland
Cecil
26.7
19.9
19.8
Maryland
Garrett
19.5
14.2
14.2
Maryland
Harford
23.6
17.9
17.8
Maryland
Kent
24.1
18.9
18.8
Maryland
Montgomery
24.5
19.8
19.7
Maryland
Prince George's
24.4
19.9
19.8
Maryland
Washington
27.4
21.8
21.8
Maryland
Baltimore (City)
27.1
21.9
21.9
Massachusetts
Berkshire
24.1
18.6
18.5
Massachusetts
Bristol
19.5
13.9
13.9
Massachusetts
Essex
18.8
15.4
15.4
Massachusetts
Hampden
24.6
20.0
20.0
Massachusetts
Middlesex
19.3
15.0
15.1
Massachusetts
Plymouth
19.4
15.0
15.0
Massachusetts
Suffolk
22.5
17.8
17.8
Massachusetts
Worcester
21.6
17.1
17.1
Michigan
Allegan
23.9
17.5
17.3
Michigan
Bay
23.1
17.6
17.4
Michigan
Berrien
21.2
16.2
16.1
Michigan
Chippewa
16.2
13.6
13.5
Michigan
Genesee
21.8
16.1
16.0
Michigan
Ingham
22.4
17.1
17.1
Michigan
Kalamazoo
22.6
17.0
16.8
Michigan
Kent
24.2
18.5
18.3
Michigan
Lenawee
24.0
18.6
18.5
Michigan
Macomb
23.5
17.9
17.7
D-7
-------
State
County
2011
2040
2040 HDGHGP2
Baseline DV
Reference DV
Control DV
Michigan
Manistee
18.6
13.4
13.3
Michigan
Missaukee
17.1
13.4
13.4
Michigan
Monroe
24.4
18.0
17.9
Michigan
Muskegon
23.7
17.5
17.3
Michigan
Oakland
24.8
18.7
18.6
Michigan
Ottawa
23.8
18.1
17.9
Michigan
St. Clair
23.8
19.3
19.2
Michigan
Washtenaw
23.3
17.8
17.6
Michigan
Wayne
28.4
22.7
22.5
Minnesota
Anoka
22.6
17.7
17.6
Minnesota
Dakota
25.9
20.6
20.4
Minnesota
Hennepin
25.7
21.1
21.0
Minnesota
Olmsted
25.7
19.1
19.0
Minnesota
Ramsey
28.5
23.0
22.9
Minnesota
Saint Louis
21.2
17.7
17.6
Minnesota
Scott
24.8
19.4
19.2
Minnesota
Stearns
24.4
18.4
18.2
Mississippi
DeSoto
18.9
14.6
14.7
Mississippi
Forrest
21.7
18.3
18.4
Mississippi
Grenada
19.5
15.3
15.3
Mississippi
Hancock
19.2
16.4
16.3
Mississippi
Harrison
18.3
14.5
14.5
Mississippi
Hinds
21.2
17.5
17.5
Mississippi
Jackson
20.4
16.2
16.2
Mississippi
Jones
22.6
19.5
19.5
Mississippi
Lauderdale
21.0
17.4
17.3
Mississippi
Lee
21.1
16.3
16.3
Missouri
Cass
23.4
19.6
19.5
Missouri
Cedar
22.4
18.7
18.7
Missouri
Clay
21.6
17.6
17.4
Missouri
Greene
22.0
18.6
18.6
Missouri
Jackson
23.0
19.1
19.1
Missouri
Jefferson
22.9
17.6
17.5
Missouri
Saint Louis
25.4
20.2
20.0
Missouri
St. Louis City
25.3
20.0
19.7
Montana
Lewis and Clark
33.3
31.9
31.9
Montana
Missoula
31.5
31.2
31.1
Montana
Ravalli
51.3
50.3
50.3
Montana
Richland
15.7
15.2
15.2
Montana
Silver Bow
39.7
36.1
36.0
D-8
-------
State
County
2011
2040
2040 HDGHGP2
Baseline DV
Reference DV
Control DV
Nebraska
Douglas
22.7
17.3
17.3
Nebraska
Hall
18.9
14.4
14.2
Nebraska
Lancaster
21.3
16.3
16.2
Nebraska
Sarpy
25.7
19.8
19.5
Nebraska
Washington
22.7
17.1
16.9
Nevada
Clark
21.3
18.4
18.4
Nevada
Washoe
22.8
19.0
18.9
New Hampshire
Belknap
16.3
13.0
13.0
New Hampshire
Cheshire
28.4
23.0
23.0
New Hampshire
Grafton
19.0
14.4
14.4
New Hampshire
Hillsborough
20.5
16.5
16.5
New Hampshire
Merrimack
21.8
17.4
17.4
New Hampshire
Rockingham
22.8
18.2
18.3
New Jersey
Atlantic
23.2
17.0
17.0
New Jersey
Bergen
23.5
17.3
17.3
New Jersey
Camden
22.6
16.5
16.4
New Jersey
Essex
22.8
18.0
18.0
New Jersey
Gloucester
22.2
15.9
15.8
New Jersey
Hudson
26.8
20.1
20.1
New Jersey
Mercer
25.0
19.9
19.9
New Jersey
Middlesex
19.3
14.7
14.7
New Jersey
Morris
21.1
15.4
15.4
New Jersey
Ocean
22.7
16.3
16.3
New Jersey
Passaic
24.3
18.9
18.8
New Jersey
Union
29.4
22.3
22.2
New Jersey
Warren
25.3
19.1
19.0
New Mexico
Bernalillo
19.1
16.2
16.2
New Mexico
Dona Ana
12.7
14.3
14.3
New Mexico
Lea
19.4
20.3
20.3
New Mexico
San Juan
13.4
15.3
15.3
New Mexico
Santa Fe
9.9
10.1
10.1
New York
Albany
21.8
16.5
16.4
New York
Bronx
28.0
21.9
21.9
New York
Chautauqua
21.1
14.3
14.3
New York
Erie
24.5
18.5
18.5
New York
Essex
14.2
9.9
9.9
New York
Kings
24.1
18.1
18.1
New York
Nassau
23.0
17.2
17.1
New York
New York
25.7
20.3
20.3
New York
Onondaga
20.7
14.4
14.3
D-9
-------
State
County
2011
2040
2040 HDGHGP2
Baseline DV
Reference DV
Control DV
New York
Orange
21.7
16.5
16.4
New York
Queens
24.2
18.5
18.4
New York
Richmond
23.0
17.8
17.8
New York
Steuben
19.3
13.5
13.5
New York
Suffolk
21.9
16.6
16.6
New York
Westchester
25.4
18.4
18.4
North Carolina
Alamance
19.8
14.6
14.7
North Carolina
Buncombe
17.8
13.1
13.1
North Carolina
Caswell
17.8
12.3
12.2
North Carolina
Catawba
20.6
16.0
16.0
North Carolina
Chatham
18.1
12.7
12.7
North Carolina
Cumberland
21.0
16.8
16.8
North Carolina
Davidson
20.8
15.6
15.6
North Carolina
Duplin
19.3
14.5
14.5
North Carolina
Durham
18.7
13.4
13.3
North Carolina
Edgecombe
19.6
14.4
14.4
North Carolina
Forsyth
19.9
14.7
14.6
North Carolina
Gaston
21.6
16.2
16.1
North Carolina
Guilford
20.4
15.4
15.4
North Carolina
Haywood
21.1
18.6
18.6
North Carolina
Jackson
17.4
13.8
13.8
North Carolina
Johnston
18.9
13.8
13.8
North Carolina
Lenoir
21.4
15.4
15.4
North Carolina
McDowell
18.4
15.1
15.1
North Carolina
Martin
22.9
16.5
16.5
North Carolina
Mecklenburg
22.6
17.6
17.5
North Carolina
Mitchell
18.0
13.8
13.8
North Carolina
Montgomery
19.6
14.6
14.6
North Carolina
New Hanover
22.0
15.8
15.8
North Carolina
Pitt
20.6
15.0
15.0
North Carolina
Robeson
20.5
17.5
17.5
North Carolina
Rowan
19.3
14.8
14.8
North Carolina
Swain
19.4
15.4
15.4
North Carolina
Wake
21.9
16.9
16.9
North Carolina
Watauga
16.9
12.5
12.5
North Carolina
Wayne
20.3
15.6
15.6
North Dakota
Billings
10.9
9.7
9.7
North Dakota
Burke
14.7
13.9
13.9
North Dakota
Burleigh
15.7
14.1
14.0
North Dakota
Cass
20.2
17.1
17.0
D
-------
State
County
2011
Baseline DV
2040
Reference DV
2040 HDGHGP2
Control DV
North Dakota
McKenzie
15.2
14.5
14.5
North Dakota
Mercer
14.9
13.9
13.9
Oh
o
Athens
17.1
12.1
12.1
Oh
o
Butler
27.0
20.2
20.0
Oh
o
Clark
26.4
18.7
18.6
Oh
o
Clermont
26.6
18.1
18.0
Oh
o
Cuyahoga
29.4
22.7
22.7
Oh
o
Franklin
24.8
18.0
17.9
Oh
o
Greene
21.8
15.5
15.4
Oh
o
Hamilton
28.9
21.0
20.7
Oh
o
Jefferson
27.2
19.6
19.5
Oh
o
Lake
22.3
15.7
15.6
Oh
o
Lawrence
22.3
17.7
17.7
Oh
o
Lorain
22.7
16.2
16.2
Oh
o
Lucas
25.6
19.8
19.7
Oh
o
Mahoning
24.8
18.8
18.7
Oh
o
Montgomery
26.6
19.7
19.6
Oh
o
Portage
24.1
17.0
16.9
Oh
o
Preble
24.0
17.6
17.5
Oh
o
Scioto
21.1
14.8
14.8
Oh
o
Stark
27.9
21.9
21.9
Oh
o
Summit
26.5
18.5
18.5
Oh
o
Trumbull
23.9
17.4
17.3
Oh
o
Warren
26.3
18.8
18.7
Oklahoma
Oklahoma
19.6
17.1
17.1
Oklahoma
Pittsburg
20.2
18.1
18.0
Oklahoma
Sequoyah
22.2
18.8
18.7
Oklahoma
Tulsa
22.3
19.3
19.2
Oregon
Crook
33.5
33.2
33.2
Oregon
Harney
32.6
31.3
31.3
Oregon
Jackson
32.2
31.4
31.4
Oregon
Josephine
26.2
25.9
25.9
Oregon
Klamath
35.8
33.7
33.7
Oregon
Lake
41.8
40.4
40.4
Oregon
Lane
24.6
23.9
23.9
Oregon
Multnomah
27.2
26.2
26.2
Oregon
Umatilla
23.4
22.1
22.0
Oregon
Washington
28.9
27.9
27.9
Pennsylvania
Adams
28.8
21.2
21.1
Pennsylvania
Allegheny
41.4
33.7
33.6
D-ll
-------
State
County
2011
2040
2040 HDGHGP2
Baseline DV
Reference DV
Control DV
Pennsylvania
Armstrong
25.5
18.9
18.8
Pennsylvania
Beaver
27.4
21.6
21.6
Pennsylvania
Berks
27.4
22.2
22.1
Pennsylvania
Blair
29.8
22.5
22.5
Pennsylvania
Bucks
28.8
24.0
23.9
Pennsylvania
Cambria
30.2
23.0
23.0
Pennsylvania
Centre
25.0
18.2
18.2
Pennsylvania
Chester
29.6
23.1
23.0
Pennsylvania
Cumberland
30.9
24.1
24.1
Pennsylvania
Dauphin
31.5
25.3
25.2
Pennsylvania
Delaware
29.7
24.1
23.9
Pennsylvania
Erie
26.6
20.6
20.6
Pennsylvania
Lackawanna
23.6
17.6
17.5
Pennsylvania
Lancaster
30.9
24.6
24.5
Pennsylvania
Mercer
24.8
18.7
18.5
Pennsylvania
Monroe
20.4
14.7
14.6
Pennsylvania
Montgomery
25.8
19.9
19.8
Pennsylvania
Northampton
32.1
25.3
25.2
Pennsylvania
Philadelphia
30.4
22.5
22.5
Pennsylvania
Washington
26.4
19.0
18.9
Pennsylvania
York
28.6
22.6
22.5
Rhode Island
Kent
16.0
11.4
11.4
Rhode Island
Providence
23.3
17.6
17.6
South Carolina
Charleston
21.0
16.8
16.7
South Carolina
Chesterfield
19.5
17.3
17.3
South Carolina
Edgefield
20.3
16.4
16.3
South Carolina
Florence
21.9
17.4
17.4
South Carolina
Greenville
22.4
18.7
18.7
South Carolina
Lexington
22.8
19.4
19.4
South Carolina
Richland
22.8
18.9
18.9
South Carolina
Spartanburg
21.3
17.1
17.0
South Dakota
Brookings
21.8
16.2
16.0
South Dakota
Brown
20.9
15.2
15.1
South Dakota
Codington
21.1
15.8
15.7
South Dakota
Custer
12.6
11.7
11.7
South Dakota
Jackson
11.9
11.0
11.0
South Dakota
Minnehaha
22.8
16.5
16.3
South Dakota
Pennington
14.9
14.2
14.2
South Dakota
Union
23.1
17.6
17.5
Tennessee
Hamilton
22.5
18.0
18.0
D
-------
State
County
2011
2040
2040 HDGHGP2
Baseline DV
Reference DV
Control DV
Texas
Bexar
22.6
22.2
22.2
Texas
Bowie
21.8
18.5
18.5
Texas
Dallas
20.9
18.8
18.8
Texas
El Paso
29.2
32.0
32.0
Texas
Harris
23.7
21.4
21.4
Texas
Harrison
21.1
17.5
17.5
Texas
Nueces
27.8
26.4
26.3
Texas
Tarrant
22.3
21.0
21.0
Texas
Travis
22.3
21.1
21.1
Utah
Box Elder
38.1
31.1
30.7
Utah
Cache
41.7
32.7
32.5
Utah
Davis
36.5
29.0
28.6
Utah
Salt Lake
41.2
32.4
32.0
Utah
Tooele
26.4
20.4
19.9
Utah
Utah
42.5
32.7
32.1
Utah
Washington
10.8
10.6
10.6
Utah
Weber
39.4
31.7
31.3
Vermont
Bennington
18.2
13.2
13.2
Vermont
Chittenden
20.4
16.1
16.1
Vermont
Rutland
28.1
22.4
22.4
Virginia
Albemarle
18.6
12.7
12.7
Virginia
Charles
20.3
13.2
13.2
Virginia
Chesterfield
21.0
14.8
14.8
Virginia
Fairfax
23.0
17.7
17.6
Virginia
Frederick
23.4
18.2
18.2
Virginia
Henrico
21.4
15.6
15.5
Virginia
Loudoun
20.2
16.0
15.9
Virginia
Page
20.8
15.5
15.4
Virginia
Rockingham
21.8
17.4
17.4
Virginia
Bristol City
19.5
15.7
15.6
Virginia
Hampton City
20.6
14.7
14.7
Virginia
Lynchburg City
18.2
13.5
13.4
Virginia
Norfolk City
21.6
16.0
15.9
Virginia
Roanoke City
21.5
16.6
16.6
Virginia
Salem City
19.8
14.6
14.6
Virginia
Virginia Beach City
23.1
16.6
16.5
Washington
Clark
27.9
25.8
25.7
Washington
King
23.8
21.7
21.7
Washington
Pierce
31.8
28.5
28.5
Washington
Snohomish
28.4
26.7
26.7
D
-------
State
County
2011
2040
2040 HDGHGP2
Baseline DV
Reference DV
Control DV
Washington
Spokane
26.0
24.3
24.3
Washington
Yakima
32.7
27.3
27.1
West Virginia
Berkeley
29.1
22.4
22.4
West Virginia
Brooke
26.2
18.1
18.1
West Virginia
Cabell
23.3
17.3
17.3
West Virginia
Hancock
27.0
19.0
19.0
West Virginia
Kanawha
24.1
17.8
17.7
West Virginia
Marion
24.2
18.5
18.5
West Virginia
Marshall
27.6
22.4
22.4
West Virginia
Monongalia
23.6
16.4
16.4
West Virginia
Ohio
25.2
16.8
16.7
West Virginia
Raleigh
19.4
13.6
13.6
West Virginia
Wood
24.2
17.7
17.7
Wisconsin
Ashland
17.2
13.6
13.5
Wisconsin
Brown
28.5
21.7
21.4
Wisconsin
Dane
27.4
20.8
20.7
Wisconsin
Dodge
25.0
18.4
18.2
Wisconsin
Forest
19.5
14.2
14.0
Wisconsin
Grant
25.1
18.9
18.7
Wisconsin
Kenosha
25.5
19.3
19.0
Wisconsin
La Crosse
24.6
18.1
17.8
Wisconsin
Milwaukee
29.6
22.5
22.3
Wisconsin
Outagamie
27.2
19.9
19.7
Wisconsin
Ozaukee
23.6
17.6
17.4
Wisconsin
Sauk
24.3
18.1
18.0
Wisconsin
Taylor
23.8
17.6
17.4
Wisconsin
Vilas
17.5
12.3
12.2
Wisconsin
Waukesha
27.3
21.0
20.8
Wyoming
Albany
13.0
12.2
12.1
Wyoming
Fremont
29.6
30.0
30.0
Wyoming
Laramie
11.3
10.9
10.9
Wyoming
Natrona
14.1
14.1
14.1
Wyoming
Park
12.6
12.8
12.8
Wyoming
Sheridan
22.0
21.9
21.9
Wyoming
Sweetwater
15.6
14.0
14.0
Wyoming
Teton
14.1
13.6
13.6
D
------- |