Air Quality Modeling Technical Support 

Document: Heavy-Duty Vehicle Greenhouse 

Gas Emission Standards Final Rule 
EPA-454/R-11-004 

July 2011 

Air Quality Modeling Technical Support Document: 

Heavy-Duty Vehicle Greenhouse Gas Emission Standards 

Final Rule 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711 

July 2011 
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i 

Table of Contents 

I. Introduction...........................................................................................1 

II. Air Quality Modeling Platform.....................................................................1 

A. Air Quality Model...............................................................................2 

B. Model domains and grid resolution..................................................................2 

C. Modeling Simulation Periods...................................................................4 

D. HDGHG Modeling Scenarios...................................................................4 

E. Meteorological Input Data......................................................................6 

F. Initial and Boundary Conditions...............................................................9 

G. CMAQ Base Case Model Performance Evaluation..........................................9 

III. CMAQ Model Results...............................................................................9 

A. Impacts of HDGHG Standards on Future 8-Hour Ozone Levels..........................9 

B. Impacts of HDGHG Standards on Future Annual PM2.5 Levels..........................10 

C. Impacts of HDGHG Standards on Future 24-hour PM2.5 Levels..........................11 

D. Impacts of HDGHG Standards on Future Toxic Air Pollutant Levels..................12 

1. Acetaldehyde................................................................................13 

2. Formaldehyde...........................................................................14 

3. Benzene.......................................................................................16 

4. 1,3-Butadiene.................................................................................17 

5. Acrolein.......................................................................................19 

E. Impacts of HDGHG Standards on Future Annual Nitrogen and Sulfur Deposition....20 

F. Impacts of HDGHG Standards on Future Visibility Levels..............................22 

Appendices 

 
ii 

List of Appendices 

Appendix A. 

Model Performance Evaluation for the 2005-Based Air Quality Modeling Platform 

Appendix B. 

8-Hour Ozone Design Values for Air Quality Modeling Scenarios 

Appendix C. 

Annual PM2.5 Design Values for Air Quality Modeling Scenarios 

Appendix D. 

24-Hour PM2.5 Design Values for Air Quality Modeling Scenarios 
 1 

I. Introduction

This document describes the air quality modeling performed by EPA in support of the 

Heavy-Duty Vehicle Greenhouse Gas Final Rule (hereafter referred to as HDGHG). A national 

scale air quality modeling analysis was performed to estimate the impact of the vehicle standards 

on future year: annual and 24-hour PM2.5 concentrations, daily maximum 8-hour ozone 

concentrations, annual nitrogen and sulfur deposition levels, and select annual and seasonal air 

toxic concentrations (formaldehyde, acetaldehyde, benzene, 1,3-butadiene and acrolein) as well 

as visibility impairment. To model the air quality benefits of this rule we used the Community 

Multiscale Air Quality (CMAQ) model.1

 CMAQ simulates the numerous physical and chemical 

processes involved in the formation, transport, and destruction of ozone, particulate matter and 

air toxics. In addition to the CMAQ model, the modeling platform includes the emissions, 

meteorology, and initial and boundary condition data which are inputs to this model. 

 Emissions and air quality modeling decisions are made early in the analytical process to 

allow for sufficient time required to conduct emissions and air quality modeling. For this reason, 

it is important to note that the inventories used in the air quality modeling and the benefits 

modeling, which are presented in Section 8.2 and 8.3, respectively of the RIA, are slightly 

different than the final vehicle standard inventories presented in Chapter 5 of the RIA. However, 

the air quality inventories and the final rule inventories are generally consistent, so the air quality 

modeling adequately reflects the effects of the rule. 

Air quality modeling was performed for three emissions cases: a 2005 base year, a 2030 

reference case projection without vehicle standards, and a 2030 control case projection with 

vehicle standards. The year 2005 was selected for the HDGHG base year because this is the 

most recent year for which EPA has a complete national emissions inventory. 

The remaining sections of the Air Quality Modeling Final Rule TSD are as follows. 

Section II describes the air quality modeling platform and the evaluation of model predictions of 

PM2.5 and ozone using corresponding ambient measurements. Section III we present the results 

of modeling performed for 2030 to assess the impacts on air quality of the vehicle standards 

expected from this rule. Information on the development of emissions inventories for the 

HDGHG Rule and the steps and data used in creating emissions inputs for air quality modeling 

can be found in the Emissions Inventory for Air Quality Modeling TSD (EITSD; EPA-420-R11-008). The docket for this final rulemaking (EPA-HQ-OAR-2010-0162) also contains 

state/sector/pollutant emissions summaries for each of the emissions scenarios modeled. 

II. Air Quality Modeling Platform 

The 2005-based CMAQ modeling platform was used as the basis for the air quality 

modeling of the HDGHG future baseline and the future control scenario for this final rule. This 

platform represents a structured system of connected modeling-related tools and data that 

 

1

 Byun, D.W., and K. L. Schere, 2006: Review of the Governing Equations, Computational Algorithms, and Other 

Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Applied Mechanics 

Reviews, Volume 59, Number 2 (March 2006), pp. 51-77. 
 2 

provide a consistent and transparent basis for assessing the air quality response to projected 

changes in emissions. The base year of data used to construct this platform includes emissions 

and meteorology for 2005. The platform was developed by the U.S. EPA’s Office of Air Quality 

Planning and Standards in collaboration with the Office of Research and Development and is 

intended to support a variety of regulatory and research model applications and analyses. This 

modeling platform and analysis is fully described below. 

A. Air Quality Model 

CMAQ is a non-proprietary computer model that simulates the formation and fate of 

photochemical oxidants, primary and secondary PM concentrations, acid deposition, and air 

toxics, over regional and urban spatial scales for given input sets of meteorological conditions 

and emissions. The CMAQ model version 4.7 was most recently peer-reviewed in February of 

2009 for the U.S. EPA.2

 The CMAQ model is a well-known and well-respected tool and has 

been used in numerous national and international applications.3,4,5 CMAQ includes numerous 

science modules that simulate the emission, production, decay, deposition and transport of 

organic and inorganic gas-phase and particle-phase pollutants in the atmosphere. This 2005 

multi-pollutant modeling platform used CMAQ version 4.7.16

 with a minor internal change made 

by the U.S. EPA CMAQ model developers intended to speed model runtimes when only a small 

subset of toxics species are of interest. CMAQ v4.7.1 reflects updates to version 4.7 to improve 

the underlying science which include aqueous chemistry mass conservation improvements, 

improved vertical convective mixing and lowered Carbon Bond Mechanism-05 (CB-05) 

mechanism unit yields for acrolein (from 1,3-butadiene tracer reactions which were updated to 

be consistent with laboratory measurements). 

B. Model domains and grid resolution 

The CMAQ modeling analyses were performed for a domain covering the continental 

United States, as shown in Figure II-1. This domain has a parent horizontal grid of 36 km with 

two finer-scale 12 km grids over portions of the eastern and western U.S. The model extends 

vertically from the surface to 100 millibars (approximately 15 km) using a sigma-pressure 

coordinate system. Air quality conditions at the outer boundary of the 36 km domain were taken 

 

2

 Allen, D., Burns, D., Chock, D., Kumar, N., Lamb, B., Moran, M. (February 2009 Draft Version). Report on the 

Peer Review of the Atmospheric Modeling and Analysis Division, NERL/ORD/EPA. U.S. EPA, Research Triangle 

Park, NC. CMAQ version 4.7 was released on December, 2008. It is available from the Community Modeling and 

Analysis System (CMAS) as well as previous peer-review reports at: http://www.cmascenter.org. 3

 Hogrefe, C., Biswas, J., Lynn, B., Civerolo, K., Ku, J.Y., Rosenthal, J., et al. (2004). Simulating regional-scale 

ozone climatology over the eastern United States: model evaluation results. Atmospheric Environment, 38(17),

2627-2638. 

4

 United States Environmental Protection Agency. (2008). Technical support document for the final 

locomotive/marine rule: Air quality modeling analyses. Research Triangle Park, N.C.: U.S. Environmental 

Protection Agency, Office of Air Quality Planning and Standards, Air Quality Assessment Division. 5

 Lin, M., Oki, T., Holloway, T., Streets, D.G., Bengtsson, M., Kanae, S., (2008). Long range transport of acidifying 

substances in East Asia Part I: Model evaluation and sensitivity studies. Atmospheric Environment, 42(24), 5939-

5955. 

6

 CMAQ version 4.7.1 model code is available from the Community Modeling and Analysis System (CMAS) at: 

http://www.cmascenter.org as well as at EPA-HQ-OAR-0472-DRAFT-11662. 
 3 

from a global model and did not change over the simulations. In turn, the 36 km grid was only 

used to establish the incoming air quality concentrations along the boundaries of the 12 km grids. 

Only the finer grid data were used in determining the impacts of the HDGHG emission standard 

program changes. Table II-1 provides some basic geographic information regarding the CMAQ 

domains. 

In addition to the CMAQ model, the HDGHG modeling platform includes (1) emissions 

for the 2005 base year, 2030 reference case projection, 2030 control case projection, (2) 

meteorology for the year 2005, and (3) estimates of intercontinental transport (i.e., boundary 

concentrations) from a global photochemical model. Using these input data, CMAQ was run to 

generate hourly predictions of ozone, PM2.5 component species, nitrogen and sulfate deposition, 

and a subset of air toxics (formaldehyde, acetaldehyde, acrolein, benzene, and 1,3-butadiene) 

concentrations for each grid cell in the modeling domains. The development of 2005 

meteorological inputs and initial and boundary concentrations are described below. The 

emissions inventories used in the HDGHG air quality modeling are described in the EITSD 

found in the docket for this rule (EPA-420-R-11-008). 

Table II-1. Geographic elements of domains used in HDGHG modeling. 

CMAQ Modeling Configuration 

National Grid Western U.S. Fine Grid Eastern U.S. Fine Grid 

Map Projection Lambert Conformal Projection 

Grid Resolution 36 km 12 km 12 km 

Coordinate Center 97 deg W, 40 deg N 

True Latitudes 33 deg N and 45 deg N 

Dimensions 148 x 112 x 14 213 x 192 x 14 279 x 240 x 14 

Vertical extent 14 Layers: Surface to 100 millibar level (see Table II-3) 
 4 

36km Domain Boundary

12km East Domain Boundary

12km West Domain Boundary

Figure II-1. Map of the CMAQ modeling domain. The black outer box denotes the 36 km 

national modeling domain; the red inner box is the 12 km western U.S. fine grid; and the 

blue inner box is the 12 km eastern U.S. fine grid. 

C. Modeling Simulation Periods 

 The 36 km and both 12 km CMAQ modeling domains were modeled for the entire year 

of 2005. These annual simulations were performed in quarterly segments (i.e., January through 

March, April through June, July through September, and October through December) for each 

emissions scenario. With this approach to segmenting an annual simulation we were able to 

model several quarters at the same time and, thus, reduce the overall throughput time for an 

annual simulation. The 36 km domain simulations included a “ramp-up” period, comprised of 

10 days before the beginning of each quarter, to mitigate the effects of initial concentrations. For 

the 12 km Eastern domain simulations we used a 3-day ramp-up period for each quarter, the 

ramp-up periods are not considered as part of the output analyses. Fewer ramp-up days were 

used for the 12 km simulations because the initial concentrations were derived from the parent 

36 km simulations. 

For the 8-hour ozone results, we are only using modeling results from the period between 

May 1 and September 30, 2005. This 153-day period generally conforms to the ozone season 

across most parts of the U.S. and contains the majority of days with observed high ozone 

concentrations in 2005. Data from the entire year were utilized when looking at the estimation 

of PM2.5, total nitrogen and sulfate deposition, visibility and toxics impacts from this final 

rulemaking. 

D. HDGHG Modeling Scenarios 

As part of our analysis for this rulemaking, the CMAQ modeling system was used to 

calculate daily and annual PM2.5 concentrations, 8-hour ozone concentrations, annual and 
 5 

seasonal air toxics concentrations, annual total nitrogen and sulfur deposition levels and visibility 

impairment for each of the following emissions scenarios: 

2005 base year 

2030 reference case projection without the vehicle standards 

2030 control case projection with the vehicle standards 

Model predictions are used in a relative sense to estimate scenario-specific, future-year 

design values of PM2.5 and ozone. Specifically, we compare a 2030 reference scenario, a 

scenario without the vehicle standards, to a 2030 control scenario which includes the vehicle 

standards. This is done by calculating the simulated air quality ratios (relative percent change) 

between the 2030 future year simulation and the 2005 base. These predicted change ratios are 

then applied to ambient base year design values. The ambient air quality observations are 

average conditions, on a site-by-site basis, for a period centered around the model base year (i.e., 

2003-2007). The raw model outputs are also used in a relative sense as inputs to the health and 

welfare impact functions of the benefits analysis. The difference between the 2030 reference 

case and 2030 control case was used to quantify the air quality benefits of the rule. Additionally, 

the differences in projected annual average PM2.5 and seasonal average ozone were used to 

calculate monetized benefits by the BenMAP model (see Section 8.3 of the RIA). 

The design value projection methodology used here followed EPA guidance7

 for such 

analyses. For each monitoring site, all valid design values (up to 3) from the 2003-2007 period 

were averaged together. Since 2005 is included in all three design value periods, this has the 

effect of creating a 5-year weighted average, where the middle year is weighted 3 times, the 2nd 

and 4th years are weighted twice, and the 1st and 5th years are weighted once. We refer to this 

as the 5-year weighted average value. The 5-year weighted average values were then projected 

to the future years that were analyzed for the final rule. 

Concentrations of PM2.5 in 2030 were estimated by applying the modeled 2005-to-2030 

relative change in PM2.5 species to the 5 year weighted average (2003-2007) design values. 

Monitoring sites were included in the analysis if they had at least one complete design value in 

the 2003-2007 period. EPA followed the procedures recommended in the modeling guidance for 

projecting PM2.5 by projecting individual PM2.5 component species and then summing these to 

calculate the concentration of total PM2.5. The PM2.5 species are defined as sulfates, nitrates, 

ammonium, organic carbon mass, elemental carbon, crustal mass, water, and blank mass (a fixed 

value of 0.5 μg/m3

). EPA’s Modeled Attainment Test Software (MATS) was used to calculate 

the future year design values. The software (including documentation) is available at:

http://www.epa.gov/scram001/modelingapps_mats.htm. For this latest analysis, several 

datasets and techniques were updated. These changes are fully described within the technical 

support document for the Final Transport Rule AQM TSD.8

 

7

 U.S. EPA, 2007: Guidance on the Use of Models and Other Analyses for Demonstrating Attainment for Ozone, 

PM2.5, and Regional Haze, Office of Air Quality Planning and Standards, Research Triangle Park, NC. 8

 U.S. EPA, 2011: Cross-State Air Pollution Rule (Final Transport Rule) Air Quality Modeling Final RuleTechnical 

Support Document, Docket EPA-HQ-OAR-2009-0491-4140. 
 6 

To calculate 24-hour PM2.5 design values, the measured 98th percentile concentrations 

from the 2003-2007 period at each monitor are projected to the future. The procedures for 

calculating the future year 24-hour PM2.5 design values have been updated for the final rule. The 

updates are intended to make the projection methodology more consistent with the procedures 

for calculating ambient design values. 

A basic assumption of the old projection methodology is that the distribution of high 

measured days in the base period will be the same in the future. In other words, EPA assumed 

that the 98th-percentile day could only be displaced “from below” in the instance that a different 

day’s future concentration exceeded the original 98th-percentile day’s future concentration. This 

sometimes resulted in overstatement of future-year design values for 24-hour PM2.5 at receptors 

whose seasonal distribution of highest-concentration 24-hour PM2.5 days changed between the 

2003-2007 period and the future year modeling. 

In the revised methodology, we do not assume that the seasonal distribution of high days 

in the base period years and future years will remain the same. We project a larger set of ambient 

days from the base period to the future and then re-rank the entire set of days to find the new 

future 98th percentile value (for each year). More specifically, we project the highest 8 days per 

quarter (32 days per year) to the future and then re-rank the 32 days to derive the future year 98th 

percentile concentrations. More details on the methodology can be found in a guidance memo 

titled “Update to the 24 Hour PM2.5 NAAQS Modeled Attainment Test” which can be found 

here: http://www.epa.gov/ttn/scram/guidance/guide/Update_to_the_24-

hour_PM25_Modeled_Attainment_Test.pdf. 

The future year 8-hour average ozone design values were calculated in a similar manner 

as the PM2.5 design values. The May-to-September daily maximum 8-hour average 

concentrations from the 2005 base case and the 2030 cases were used to project ambient design 

values to 2030. The calculations used the base period 2003-2007 ambient ozone design value 

data for projecting future year design values. Relative response factors (RRF) for each 

monitoring site were calculated as the percent change in ozone on days with modeled ozone 

greater than 85 ppb9

. 

We also conducted an analysis to compare the absolute and percent differences between 

the 2030 control case and the 2030 reference cases for annual and seasonal formaldehyde, 

acetaldehyde, benzene, 1,3-butadiene, and acrolein, as well as annual nitrate and sulfate 

deposition. These data were not compared in a relative sense due to the limited observational 

data available. 

E. Meteorological Input Data

The gridded meteorological input data for the entire year of 2005 were derived from 

simulations of the Pennsylvania State University / National Center for Atmospheric Research 

 

9 As specified in the attainment demonstration modeling guidance, if there are less than 10 modeled days > 85 ppb, 

then the threshold is lowered in 1 ppb increments (to as low as 70 ppb) until there are 10 days. If there are less than 

5 days > 70 ppb, then an RRF calculation is not completed for that site.
 7 

Mesoscale Model. This model, commonly referred to as MM5, is a limited-area, nonhydrostatic, 

terrain-following system that solves for the full set of physical and thermodynamic equations 

which govern atmospheric motions.10 Meteorological model input fields were prepared 

separately for each of the three domains shown in Figure II-1 using MM5 version 3.7.4. The 

MM5 simulations were run on the same map projection as CMAQ. 

All three meteorological model runs configured similarly. The selections for key MM5 

physics options are shown below: 

• Pleim-Xiu PBL and land surface schemes 

• Kain-Fritsh 2 cumulus parameterization 

• Reisner 2 mixed phase moisture scheme 

• RRTM longwave radiation scheme 

• Dudhia shortwave radiation scheme 

Three dimensional analysis nudging for temperature and moisture was applied above the 

boundary layer only. Analysis nudging for the wind field was applied above and below the 

boundary layer. The 36 km domain nudging weighting factors were 3.0 x 104

 for wind fields and 

temperatures and 1.0 x 105

 for moisture fields. The 12 km domain nudging weighting factors 

were 1.0 x 104 for wind fields and temperatures and 1.0 x 105 for moisture fields. 

All three sets of model runs were conducted in 5.5 day segments with 12 hours of overlap 

for spin-up purposes. All three meteorological modeling domains contained 34 vertical layers 

with an approximately 38 m deep surface layer and a 100 millibar top. The MM5 and CMAQ 

vertical structures are shown in Table II-3 and do not vary by horizontal grid resolution. 

Table II-3. Vertical layer structure for MM5 and CMAQ (heights are layer top). 

CMAQ Layers MM5 Layers Sigma P Approximate 

Height (m)

Approximate 

Pressure (mb)

0 0 1.000 0 1000 

1 1 0.995 38 995 

2 2 0.990 77 991 

3 3 0.985 115 987 

4 0.980 154 982 

4 5 0.970 232 973 

6 0.960 310 964 

5 7 0.950 389 955 

8 0.940 469 946 

6 

9 0.930 550 937 

10 0.920 631 928 

11 0.910 712 919 

7 

12 0.900 794 910 

13 0.880 961 892 

14 0.860 1,130 874 

8 15 0.840 1,303 856 

 

10 Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth-Generation Penn State/NCAR Mesoscale 

Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for Atmospheric Research, Boulder CO. 
 8 

16 0.820 1,478 838 

17 0.800 1,657 820 

9 18 0.770 1,930 793 

19 0.740 2,212 766 

10 20 0.700 2,600 730 

21 0.650 3,108 685 

11 22 0.600 3,644 640 

23 0.550 4,212 595 

12 

24 0.500 4,816 550 

25 0.450 5,461 505 

26 0.400 6,153 460 

13 

27 0.350 6,903 415 

28 0.300 7,720 370 

29 0.250 8,621 325 

30 0.200 9,625 280 

14 

31 0.150 10,764 235 

32 0.100 12,085 190 

33 0.050 13,670 145 

34 0.000 15,674 100 

The 2005 meteorological outputs from all three MM5 sets were processed to create 

model-ready inputs for CMAQ using the Meteorology-Chemistry Interface Processor (MCIP), 

version 3.4.11

Before initiating the air quality simulations, it is important to identify the biases and 

errors associated with the meteorological modeling inputs. The 2005 MM5 model performance 

evaluations used an approach which included a combination of qualitative and quantitative 

analyses to assess the adequacy of the MM5 simulated fields. The qualitative aspects involved 

comparisons of the model-estimated synoptic patterns against observed patterns from historical 

weather chart archives. Additionally, the evaluations compared spatial patterns of monthly 

average rainfall and monthly maximum planetary boundary layer (PBL) heights. Qualitatively, 

the model fields closely matched the observed synoptic patterns, which is not unexpected given 

the use of nudging. The operational evaluation included statistical comparisons of 

model/observed pairs (e.g., mean normalized bias, mean normalized error, index of agreement, 

root mean square errors, etc.) for multiple meteorological parameters. For this portion of the 

evaluation, five meteorological parameters were investigated: temperature, humidity, shortwave 

downward radiation, wind speed, and wind direction. The three individual MM5 evaluations are 

described elsewhere.12,13,14 The results of these analyses indicate that the bias and error values 

associated with all three sets of 2005 meteorological data were generally within the range of past 

meteorological modeling results that have been used for air quality applications. 

 

11 Byun, D.W., and Ching, J.K.S., Eds, 1999. Science algorithms of EPA Models-3 Community Multiscale Air 

Quality (CMAQ modeling system, EPA/600/R-99/030, Office of Research and Development). 12 Baker K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Eastern U.S. 

12-km Domain Simulation, USEPA/OAQPS, February 2, 2009. 

13 Baker K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Western U.S. 

12-km Domain Simulation, USEPA/OAQPS, February 2, 2009. 

14 Baker K. and P. Dolwick. Meteorological Modeling Performance Evaluation for the Annual 2005 Continental 

U.S. 36-km Domain Simulation, USEPA/OAQPS, February 2, 2009. 
 9 

F. Initial and Boundary Conditions

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry model, the GEOS-CHEM15 model (standard version 

7-04-1116). The global GEOS-CHEM model simulates atmospheric chemical and physical 

processes driven by assimilated meteorological observations from the NASA’s Goddard Earth 

Observing System (GEOS). This model was run for 2005 with a grid resolution of 2.0 degree x 

2.5 degree (latitude-longitude) and 30 vertical layers up to 100 mb. The predictions were used to 

provide one-way dynamic boundary conditions at three-hour intervals and an initial 

concentration field for the 36-km CMAQ simulations. The future base conditions from the 36 

km coarse grid modeling were used to develop the initial/boundary concentrations for the 

subsequent 12 km Eastern and Western domain model simulations. 

G. CMAQ Base Case Model Performance Evaluation 

The CMAQ predictions for ozone, fine particulate matter, sulfate, nitrate, ammonium, 

organic carbon, elemental carbon, a selected subset of toxics, and nitrogen and sulfur deposition 

from the 2005 base year evaluation case were compared to measured concentrations in order to 

evaluate the performance of the modeling platform for replicating observed concentrations. This 

evaluation was comprised of statistical and graphical comparisons of paired modeled and 

observed data. Details on the model performance evaluation including a description of the 

methodology, the model performance statistics, and results are provided in Appendix A. 

III. CMAQ Model Results 

As described above, we performed a series of air quality modeling simulations for the 

continental U.S in order to assess the impacts of the heavy-duty vehicle greenhouse gas rule. We 

looked at impacts on future ambient PM2.5, ozone, and air toxics levels, as well as nitrogen and 

sulfur deposition levels and visibility impairment. In this section, we present the air quality 

modeling results for the 2030 HDGHG control case relative to the 2030 reference case. 

A. Impacts of HDGHG Standards on Future 8-Hour Ozone Levels 

This section summarizes the results of our modeling of ozone air quality impacts in the 

future with the HDGHG vehicle standards. Specifically, we compare a 2030 reference scenario, 

a scenario without the vehicle standards, to a 2030 control scenario which includes the vehicle 

standards. Our modeling indicates ozone design value concentrations will decrease in many 

areas of the country as a result of this action. The decreases in ozone design values are likely 

due to projected tailpipe reductions in NOx and projected upstream emissions decreases in NOX

 

15 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry Modeling 

Group, Harvard University, Cambridge, MA, October 15, 2004. 

16 Henze, D.K., J.H. Seinfeld, N.L. Ng, J.H. Kroll, T-M. Fu, D.J. Jacob, C.L. Heald, 2008. Global modeling of 

secondary organic aerosol formation from aromatic hydrocarbons: high-vs.low-yield pathways. Atmos. Chem. Phys., 

8, 2405-2420. 
 10 

and VOCs from reduced fuel production. Figure III-1 presents the changes in 8-hour ozone 

design value concentration in 2030 between the reference case and the control case.17 Appendix 

B details the state and county 8-hour maximum ozone design values for the ambient baseline and 

the future reference and control cases. 

Figure III-1. Projected Change in 2030 8-hour Ozone Design Values Between the 

Reference Case and Control Case 

As can be seen in Figure III-1, the majority of the design value decreases are less than 1 

ppb. However, there are 24 counties that will see 8-hour ozone design value decreases above 1 

ppb; these counties are in southern Arizona, and the Midwest. The maximum projected decrease 

in an 8-hour ozone design value is 1.57 ppb in Jefferson County, Tennessee. 

B. Impacts of HDGHG Standards on Future Annual PM2.5 Levels 

This section summarizes the results of our modeling of annual average PM2.5 air quality 

impacts in the future due to the HDGHG vehicle standards. We compare a 2030 reference 

scenario, a scenario without the heavy-duty vehicle standards, to a 2030 control scenario which 

includes the heavy-duty vehicle standards. Our modeling indicates that the majority of the 

modeled counties will see decreases of less than 0.01 μg/m3 in their annual PM2.5 design values 

 

17 An 8-hour ozone design value is the concentration that determines whether a monitoring site meets the 8-hour 

ozone NAAQS. The full details involved in calculating an 8-hour ozone design value are given in appendix I of 40 

CFR part 50. 
 11 

due to the vehicle standards. Figure III-2 presents the changes in annual PM2.5 design values in 

2030.18 

Figure III-2. Projected Change in 2030 Annual PM2.5 Design Values Between the 

Reference Case and Control Case 

As shown in Figure III-2, 27 counties will see decreases between 0.01 ug/m3 and 0.05 

μg/m3. These counties are in the upper Midwest, Utah, Idaho and Missouri. The maximum 

projected decrease in an annual PM2.5 design value is 0.03 μg/m3

 in Allen County, Indiana and 

Canyon County, Idaho. The decreases in annual PM2.5 design values that are modeled in some 

counties are likely due to emission reductions related to lower fuel production at existing oil 

refineries and/or reductions in PM2.5 precursor emissions (NOX,SOX, and VOCs) due to 

improvements in road load. Additional information on the emissions reductions that are 

projected with this final rule is available in Section 5.5 of the RIA. Appendix C details the state 

and county annual PM2.5 design values for the ambient baseline and the future reference and 

control cases. 

C. Impacts of HDGHG Standards on Future 24-hour PM2.5 Levels 

This section summarizes the results of our modeling of 24-hour PM2.5 air quality impacts 

in the future due to the heavy-duty vehicle standards. Specifically, we compare a 2030 reference 

scenario, a scenario without the vehicle standards, to a 2030 control scenario which includes the 

 

18 An annual PM2.5 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 

vehicle standards. Our modeling indicates that the majority of the modeled counties will see 

changes of between -0.05 μg/m3

 and 0.0 μg/m3 in their 24-hour PM2.5 design values. Figure III-3 

presents the changes in 24-hour PM2.5 design values in 2030.19 

Figure III-3. Projected Change in 2030 24-hour PM2.5 Design Values Between the 

Reference Case and the Control Case

As shown in Figure III-3, 39 counties will see decreases of more than 0.1 μg/m3. These 

counties are in Idaho, Montana, northern Utah, and the upper Midwest. The maximum projected 

decrease in a 24-hour PM2.5 design value is 0.27 μg/m3

 in Canyon County, Idaho. The decreases 

in 24-hour PM2.5 design values that we see in some counties are likely due to emission reductions 

related to lower fuel production at existing oil refineries and/or reductions in PM2.5 precursor 

emissions (NOX,SOX, and VOCs) due to improvements in road load. Appendix D details the 

state and county 24-hour PM2.5 design values for the ambient baseline and the future reference 

and control cases. 

D. Impacts of HDGHG Standards on Future Toxic Air Pollutant Levels 

The following sections summarize the results of our modeling of air toxics impacts in the 

future from the vehicle emission standards required by HDGHG. We focus on air toxics which 

were identified as national and regional-scale cancer and noncancer risk drivers in the 2005 

 

19 A 24-hour PM2.5 design value is the concentration that determines whether a monitoring site meets the 24-hour 

NAAQS for PM2.5. The full details involved in calculating a 24-hour PM2.5 design value are given in appendix N of 

40 CFR part 50. 
 13 

NATA assessment and were also likely to be significantly impacted by the standards. These 

compounds include benzene, 1,3-butadiene, formaldehyde, acetaldehyde, and acrolein. Our 

modeling indicates that the HDGHG standards have relatively little impact on national average 

ambient concentrations of the modeled air toxics. Because overall impacts are small, we 

concluded that assessing exposure to ambient concentrations and conducting a quantitative risk 

assessment of air toxic impacts was not warranted. 

1. Acetaldehyde 

Overall, the air quality modeling does not show substantial nationwide impacts on 

ambient concentrations of acetaldehyde as a result of the standards finalized in this rule. Annual 

and seasonal percent changes in ambient concentrations of acetaldehyde are typically less than 

1% across the country (Figure III-4 through III-6). The summer season shows decreases of 5% 

to 10% in certain urban areas of the Midwest. Likewise, small increases in ambient 

concentrations of acetaldehyde of less than 0.01 μg/m3 are noted across most of the nation during 

the summer season (Figure III-6). Decreases in ambient concentrations of acetaldehyde seen in 

urban areas during the winter and summer are generally between 0.001 μg/m3 and 0.1 μg/m3. 

Figure III-4. Changes in Annual Acetaldehyde Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 
 14 

Figure III-5. Changes in Winter Acetaldehyde Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 

Figure III-6. Changes in Summer Acetaldehyde Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 

2. Formaldehyde 

Our modeling projects that the standards finalized in this rule will generally decrease 

ambient formaldehyde concentrations. As shown in Figure III-7, annual percent changes in 

ambient concentrations of formaldehyde are less than 1% across the country, with the exception 
 15 

of a 1 to 5% decrease in the Midwest. Figure III-7 also shows that annual absolute changes in 

ambient concentrations of formaldehyde are generally less than -0.1 μg/m3. Also, decreases are 

shown in seasonal ambient formaldehyde (Figures III-8 through III-9), which range from 0.01 to 

0.1 μg/m3. 

Figure III-7. Changes in Annual Formaldehyde Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 

Figure III-8. Changes in Winter Formaldehyde Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 
 16 

Figure III-9. Changes in Summer Formaldehyde Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 

3. Benzene 

Our air quality modeling projects that the standards finalized in this rule will not have a 

significant impact on ambient benzene concentrations. Figures III-10, III-11, and III-12 show 

decreases in annual and seasonal ambient benzene concentrations ranging between 1 and 10% 

and between 0.001 and 0.1 μg/m3. The decreases are noted in urban areas in the Midwest, 

Tennessee, Arkansas, Georgia, Mississippi, Louisiana, Texas, Arizona, and Pennsylvania. 

Figure III-10. Changes in Annual Benzene Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 
 17 

Figure III-11. Changes in Winter Benzene Ambient Concentrations Between the Reference 

Case and the Control Case in 2030: Percent Changes (left) and Absolute Changes in μg/m3 

(right) 

Figure III-12. Changes in Summer Benzene Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 

4. 1,3-Butadiene 

Our air quality modeling results do not show substantial impacts on ambient 

concentrations of 1,3-butadiene from the HDGHG standards. As shown in Figure III-13, annual 

percent changes in ambient concentrations of 1,3-butadiene are less than 1% across the country, 

with the exception of a small increase of 1 to 2.5% in Texas. Annual increases in ambient 
 18 

concentrations of 1,3-butadiene are driven by summertime changes (Figures III-15). In the 

winter, small decreases ranging from 1 to 2.5% occur in Indiana (Figure III-14). Changes in 

absolute concentrations of ambient 1,3-butadiene are negligible, ± 0.001 μg/m3. 

Figure III-13. Changes in Annual 1,3-Butadiene Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 

Figure III-14. Changes in Winter 1,3-Butadiene Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 
 19 

Figure III-15. Changes in Summer 1,3-Butadiene Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 

5. Acrolein 

Our air quality modeling results do not show substantial impacts on ambient 

concentrations of acrolein from the standards finalized in this rule. Decreases ranging from 1 to 

100% occur across the country (Figures III-16, III-17 and III-18). However, changes in annual 

and seasonal absolute concentrations of acrolein are less than 0.003 μg/m3 across the country. 

Figure III-16. Changes in Annual Acrolein Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 
 20 

Figure III-17. Changes in Winter Acrolein Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 

Figure III-18. Changes in Summer Acrolein Ambient Concentrations Between the 

Reference Case and the Control Case in 2030: Percent Changes (left) and Absolute 

Changes in μg/m3 (right) 

E. Impacts of HDGHG Standards on Future Annual Nitrogen and Sulfur Deposition 

Levels 

Our air quality modeling projects decreases in nitrogen deposition, especially in the upper Midwest. 

Figure III-19 shows that for nitrogen deposition the heavy-duty standards will result in annual percent 

decreases of more than 2% in some areas. The decreases in nitrogen deposition are likely due to projected 
 21 

tailpipe reductions in NOx and projected upstream emissions decreases in NOX from reduced gasoline 

production. The remainder of the country will see only minimal changes in nitrogen deposition, ranging 

from decreases of less than 0.5% to increases of less than 0.5%. 

Figure III-19. Changes in Annual Total Nitrogen Deposition Between the Reference Case 

and the Control Case in 2030: Percent Changes (left) and Absolute Changes in μg/m3 

(right) 

Our air quality modeling does not show substantial overall nationwide impacts on the 

annual total sulfur deposition occurring across the U.S. as a result of the heavy-duty standards 

required by this final action. Figure III-20 shows the impacts of the heavy-duty standards on 

sulfur deposition are minimal. 

Figure III-20. Changes in Annual Total Sulfur Deposition Between the Reference Case and 

the Control Case in 2030: Percent Changes (left) and Absolute Changes in μg/m3 (right) 
 22 

F. Impacts of HDGHG Standards on Future Visibility Levels 

Air quality modeling conducted for this final rule was used to project visibility conditions 

in 138 mandatory class I federal areas across the U.S. in 2030. The impacts of this action were 

examined in terms of the projected improvements in visibility on the 20 percent worst visibility 

days at Class I areas. We quantified visibility impacts at the Class I areas which have complete 

IMPROVE ambient data for 2005 or are represented by IMPROVE monitors with complete data. 

Sites were used in this analysis if they had at least 3 years of complete data for the 2003-2007 

period20. 

Visibility for the 2030 reference case and 2030 control case was calculated using the 

regional haze methodology outlined in section 6 of the photochemical modeling guidance, which 

applies modeling results in a relative sense, using base year ambient data. The PM2.5 and 

regional haze modeling guidance recommends the calculation of future year changes in visibility 

in a similar manner to the calculation of changes in PM2.5 design values. The regional haze 

methodology for calculating future year visibility impairment is included in MATS 

(http://www.epa.gov/scram001/modelingapps_mats.htm) 

 In calculating visibility impairment, the extinction coefficient values21 are made up of 

individual component species (sulfate, nitrate, organics, etc). The predicted change in visibility 

(on the 20 percent worst days) is calculated as the modeled percent change in the mass for each 

of the PM2.5 species (on the 20% worst observed days) multiplied by the observed 

concentrations. The future mass is converted to extinction and then daily species extinction 

coefficients are summed to get a daily total extinction value (including Rayleigh scattering). The 

daily extinction coefficients are converted to deciviews and averaged across all 20 percent worst 

days. In this way, we calculate an average change in deciviews from the base case to a future 

case at each IMPROVE site. Subtracting the 2030 reference case from the corresponding 2030 

reference case deciview values gives an estimate of the visibility benefits in Class I areas that are 

expected to occur from the HD GHG rule. 

The following options were chosen in MATS for calculating the future year visibility 

values for the rule: 

New IMPROVE algorithm 

Use model grid cells at (IMPROVE) monitor 

Temporal adjustment at monitor- 3x3 for 12km grid, (1x1 for 36km grid) 

Start monitor year- 2003 

End monitor year- 2007 

Base model year 2005 

Minimum years required for a valid monitor- 3 

The “base model year” was chosen as 2005 because it is the base case meteorological 

year for the final HDGHG Rule modeling. The start and end years were chosen as 2003 and 

 

20 Since the base case modeling used meteorology for 2005, one of the complete years must be 2005. 

21 Extinction coefficient is in units of inverse megameters (Mm-1). It is a measure of how much light is absorbed or 

scattered as it passes through a medium. Light extinction is commonly used as a measure of visibility impairment in 

the regional haze program. 
 23 

2007 because that is the 5 year period which is centered on the base model year of 2005. These 

choices are consistent with using a 5 year base period for regional haze calculations. 

The results show that all the modeled areas will continue to have annual average 

deciview levels above background in 2030.22 The results also indicate that the majority of the 

modeled mandatory class I federal areas will see very little change in their visibility. Some 

mandatory class I federal areas will see improvements in visibility due to the heavy-duty 

standards and a few mandatory class I federal areas will see visibility decreases. The average 

visibility at all modeled mandatory class I federal areas on the 20% worst days is projected to 

improve by 0.01 deciviews, or 0.06%, in 2030. The greatest improvement in visibility will be 

seen at Craters of the Moon (New Mexico) and the Hells Canyon Wilderness (Oregon). Craters 

of the Moon will see a 0.46% improvement (0.06 DV) and the Hells Canyon Wilderness will see 

a 0.40% improvement (0.07 DV) in 2030 due to the heavy-duty standards. The following four 

areas will see a degradation of 0.01 DV in 2030 as a result of the heavy-duty standards: 

Chiricahua (New Mexico), 0.08% degradation; San Gabriel Wilderness (California), 0.06% 

degradation; San Jacinto Wilderness (California), 0.05% degradation; and Roosevelt Campobello 

International Park (Maine), 0.05% degradation. Section 8.2.3.5 of the HD GHG final rule RIA 

contains more details on the visibility portion of the air quality modeling. Table III-1 contains 

the full visibility results for the 20% worst days from 2030 for the 138 analyzed areas. 

Table III-1. Visibility Levels in Deciviews for Individual U.S. Class I Areas on the 20% 

Worst Days for Several Scenarios 

CLASS 1 AREA

(20% WORST DAYS) STATE

2005

BASELINE

VISIBILITY

2030

BASELINE

2030

HD GHG

NATURAL

BACKGROUND

SIPSEY WILDERNESS AL 29.62 21.78 21.76 11.39

CANEY CREEK WILDERNESS AR 26.78 20.91 20.88 11.33

UPPER BUFFALO WILDERNESS AR 27.09 21.33 21.30 11.28

CHIRICAHUA NM AZ 13.33 12.84 12.85 6.92

CHIRICAHUA WILDERNESS AZ 13.33 12.86 12.86 6.91

GALIURO WILDERNESS AZ 13.33 12.72 12.71 6.88

GRAND CANYON NP AZ 11.85 11.04 11.04 6.95

MAZATZAL WILDERNESS AZ 13.80 12.55 12.53 6.91

MOUNT BALDY WILDERNESS AZ 11.27 10.77 10.77 6.95

PETRIFIED FOREST NP AZ 13.73 12.93 12.93 6.97

PINE MOUNTAIN WILDERNESS AZ 13.80 12.53 12.52 6.92

SAGUARO NM AZ 14.53 13.67 13.67 6.84

SIERRA ANCHA WILDERNESS AZ 14.37 13.33 13.32 6.92

SUPERSTITION WILDERNESS AZ 14.01 12.83 12.81 6.88

SYCAMORE CANYON WILDERNESS AZ 15.34 14.60 14.59 6.96

 

22 The level of visibility impairment in an area is based on the light-extinction coefficient and a unitless visibility 

index, called a “deciview”, which is used in the valuation of visibility. The deciview metric provides a scale for 

perceived visual changes over the entire range of conditions, from clear to hazy. Under many scenic conditions, the 

average person can generally perceive a change of one deciview. The higher the deciview value, the worse the 

visibility. Thus, an improvement in visibility is a decrease in deciview value. 
 24 

AGUA TIBIA WILDERNESS CA 23.09 19.37 19.37 7.17

ANSEL ADAMS WILDERNESS (MINARETS) CA 14.90 14.10 14.10 7.12

CARIBOU WILDERNESS CA 14.19 13.30 13.29 7.29

CUCAMONGA WILDERNESS CA 19.35 16.64 16.64 7.17

DESOLATION WILDERNESS CA 12.52 11.90 11.90 7.13

EMIGRANT WILDERNESS CA 17.37 16.60 16.60 7.14

HOOVER WILDERNESS CA 11.92 11.38 11.37 7.12

JOHN MUIR WILDERNESS CA 14.90 14.00 14.00 7.14

JOSHUA TREE NM CA 19.40 17.06 17.04 7.08

KAISER WILDERNESS CA 14.90 13.78 13.78 7.13

KINGS CANYON NP CA 23.41 22.03 22.02 7.13

LASSEN VOLCANIC NP CA 14.19 13.29 13.29 7.31

LAVA BEDS NM CA 14.77 13.78 13.78 7.49

MOKELUMNE WILDERNESS CA 12.52 11.88 11.88 7.14

PINNACLES NM CA 18.22 15.93 15.93 7.34

POINT REYES NS CA 22.89 21.49 21.49 7.39

REDWOOD NP CA 18.66 17.81 17.79 7.81

SAN GABRIEL WILDERNESS CA 19.35 16.60 16.61 7.17

SAN GORGONIO WILDERNESS CA 21.80 19.59 19.58 7.10

SAN JACINTO WILDERNESS CA 21.80 18.43 18.44 7.12

SAN RAFAEL WILDERNESS CA 19.04 17.11 17.11 7.28

SEQUOIA NP CA 23.41 21.55 21.55 7.13

SOUTH WARNER WILDERNESS CA 14.77 14.00 14.00 7.32

THOUSAND LAKES WILDERNESS CA 14.19 13.27 13.27 7.32

VENTANA WILDERNESS CA 18.22 16.73 16.72 7.32

YOSEMITE NP CA 17.37 16.61 16.61 7.14

BLACK CANYON OF THE GUNNISON NM CO 10.18 9.48 9.48 7.06

EAGLES NEST WILDERNESS CO 9.38 8.76 8.76 7.08

FLAT TOPS WILDERNESS CO 9.38 8.96 8.95 7.07

GREAT SAND DUNES NM CO 12.49 11.98 11.98 7.10

LA GARITA WILDERNESS CO 10.18 9.73 9.72 7.06

MAROON BELLS‐SNOWMASS WILDERNESS CO 9.38 8.93 8.93 7.07

MESA VERDE NP CO 12.78 12.18 12.18 7.09

MOUNT ZIRKEL WILDERNESS CO 10.19 9.74 9.74 7.08

RAWAH WILDERNESS CO 10.19 9.72 9.71 7.08

ROCKY MOUNTAIN NP CO 13.54 12.99 12.98 7.05

WEMINUCHE WILDERNESS CO 10.18 9.70 9.70 7.06

WEST ELK WILDERNESS CO 9.38 8.89 8.89 7.07

EVERGLADES NP FL 22.48 19.02 19.02 11.15

OKEFENOKEE GA 27.24 21.77 21.75 11.45

WOLF ISLAND GA 27.24 21.39 21.38 11.42

CRATERS OF THE MOON NM ID 14.19 13.18 13.12 7.13

SAWTOOTH WILDERNESS ID 14.33 14.13 14.13 7.15

MAMMOTH CAVE NP KY 31.76 23.02 22.99 11.53
 25 

ACADIA NP ME 23.19 19.42 19.42 11.45

MOOSEHORN ME 21.94 18.79 18.79 11.36

ROOSEVELT CAMPOBELLO INTERNATIONAL

PARK ME 21.94 18.78 18.79 11.36

ISLE ROYALE NP MI 21.33 18.74 18.72 11.22

SENEY MI 24.71 21.00 20.96 11.37

VOYAGEURS NP MN 19.82 17.22 17.20 11.09

HERCULES‐GLADES WILDERNESS MO 27.15 22.25 22.22 11.27

ANACONDA‐PINTLER WILDERNESS MT 13.91 13.59 13.58 7.28

BOB MARSHALL WILDERNESS MT 14.54 14.16 14.16 7.36

CABINET MOUNTAINS WILDERNESS MT 14.15 13.61 13.61 7.43

GATES OF THE MOUNTAINS WILDERNESS MT 11.67 11.31 11.31 7.22

GLACIER NP MT 19.13 18.29 18.29 7.56

MEDICINE LAKE MT 17.78 17.09 17.08 7.30

MISSION MOUNTAINS WILDERNESS MT 14.54 14.04 14.04 7.39

RED ROCK LAKES MT 10.94 10.50 10.49 7.14

SCAPEGOAT WILDERNESS MT 14.54 14.13 14.13 7.29

SELWAY‐BITTERROOT WILDERNESS MT 13.91 13.64 13.64 7.32

UL BEND MT 14.92 14.54 14.54 7.18

LINVILLE GORGE WILDERNESS NC 29.40 21.21 21.20 11.43

SHINING ROCK WILDERNESS NC 28.72 21.03 21.01 11.45

LOSTWOOD ND 19.50 18.14 18.13 7.33

THEODORE ROOSEVELT NP ND 17.69 16.35 16.34 7.31

GREAT GULF WILDERNESS NH 22.13 17.78 17.78 11.31

PRESIDENTIAL RANGE‐DRY RIVER

WILDERNESS NH 22.13 17.74 17.74 11.33

BRIGANTINE NJ 29.28 22.53 22.52 11.28

BANDELIER NM NM 11.87 10.89 10.88 7.02

BOSQUE DEL APACHE NM 13.89 12.75 12.73 6.97

CARLSBAD CAVERNS NP NM 16.98 15.35 15.34 7.02

GILA WILDERNESS NM 13.32 12.78 12.78 6.95

PECOS WILDERNESS NM 10.10 9.55 9.55 7.04

SALT CREEK NM 18.20 16.71 16.70 6.99

SAN PEDRO PARKS WILDERNESS NM 10.39 9.80 9.79 7.03

WHEELER PEAK WILDERNESS NM 10.10 9.36 9.35 7.07

WHITE MOUNTAIN WILDERNESS NM 13.52 12.61 12.61 6.98

JARBIDGE WILDERNESS NV 12.13 11.86 11.86 7.10

WICHITA MOUNTAINS OK 23.79 19.42 19.37 11.07

CRATER LAKE NP OR 14.04 13.41 13.41 7.71

DIAMOND PEAK WILDERNESS OR 14.04 13.34 13.33 7.77

EAGLE CAP WILDERNESS OR 18.25 17.31 17.28 7.34

GEARHART MOUNTAIN WILDERNESS OR 14.04 13.53 13.53 7.46

HELLS CANYON WILDERNESS OR 18.73 17.40 17.33 7.32

KALMIOPSIS WILDERNESS OR 16.31 15.52 15.51 7.71

MOUNT HOOD WILDERNESS OR 14.79 13.53 13.50 7.77
 26 

MOUNT JEFFERSON WILDERNESS OR 15.93 15.19 15.18 7.81

MOUNT WASHINGTON WILDERNESS OR 15.93 15.19 15.18 7.89

MOUNTAIN LAKES WILDERNESS OR 14.04 13.35 13.34 7.57

STRAWBERRY MOUNTAIN WILDERNESS OR 18.25 17.34 17.30 7.49

THREE SISTERS WILDERNESS OR 15.93 15.25 15.24 7.87

CAPE ROMAIN SC 27.14 20.67 20.66 11.36

BADLANDS NP SD 16.73 15.40 15.40 7.30

WIND CAVE NP SD 15.96 14.76 14.75 7.24

GREAT SMOKY MOUNTAINS NP TN 30.43 22.57 22.54 11.44

JOYCE‐KILMER‐SLICKROCK WILDERNESS TN 30.43 22.29 22.26 11.45

BIG BEND NP TX 17.39 15.75 15.74 6.93

GUADALUPE MOUNTAINS NP TX 16.98 15.30 15.29 7.03

ARCHES NP UT 11.04 10.43 10.42 6.99

BRYCE CANYON NP UT 11.73 11.18 11.18 6.99

CANYONLANDS NP UT 11.04 10.53 10.51 7.01

CAPITOL REEF NP UT 10.63 10.27 10.27 7.03

JAMES RIVER FACE WILDERNESS VA 29.32 21.02 21.00 11.24

SHENANDOAH NP VA 29.66 21.27 21.27 11.25

LYE BROOK WILDERNESS VT 24.17 18.05 18.04 11.25

ALPINE LAKE WILDERNESS WA 17.35 15.65 15.62 7.86

GLACIER PEAK WILDERNESS WA 13.78 12.72 12.72 7.80

GOAT ROCKS WILDERNESS WA 12.88 11.73 11.72 7.82

MOUNT ADAMS WILDERNESS WA 12.88 11.78 11.77 7.78

MOUNT RAINIER NP WA 17.56 16.18 16.17 7.90

NORTH CASCADES NP WA 13.78 12.71 12.70 7.78

OLYMPIC NP WA 16.14 14.96 14.95 7.88

PASAYTEN WILDERNESS WA 15.39 14.51 14.51 7.77

DOLLY SODS WILDERNESS WV 29.73 20.82 20.81 11.32

OTTER CREEK WILDERNESS WV 29.73 20.93 20.92 11.33

BRIDGER WILDERNESS WY 10.93 10.60 10.60 7.08

FITZPATRICK WILDERNESS WY 10.93 10.60 10.60 7.09

GRAND TETON NP WY 10.94 10.45 10.44 7.09

NORTH ABSAROKA WILDERNESS WY 11.12 10.81 10.81 7.09

TETON WILDERNESS WY 10.94 10.55 10.54 7.09

WASHAKIE WILDERNESS WY 11.12 10.82 10.82 7.09

YELLOWSTONE NP WY 10.94 10.47 10.46 7.12
A-1 

Air Quality Modeling 

Technical Support Document: Heavy-Duty Vehicle 

Greenhouse Gas Emission Standards Final Rule 

Appendix A 

Model Performance Evaluation for the 2005-Based 

Air Quality Modeling Platform

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711 

July 2011 
A-2 

A.1. Introduction 

An operational model performance evaluation for ozone, PM2.5 and its related speciated 

components, specific air toxics (i.e., formaldehyde, acetaldehyde, benzene, 1,3-butadiene, and 

acrolein), as well as nitrate and sulfate deposition was conducted using 2005 State/local 

monitoring sites data in order to estimate the ability of the CMAQ modeling system to replicate 

the base year concentrations for the 12-km Eastern and Western United States domain 1. 

Included in this evaluation are statistical measures of model versus observed pairs that were 

paired in space and time on a daily or weekly basis, depending on the sampling frequency of 

each network (measured data). For certain time periods with missing ozone, PM2.5, air toxic 

observations and nitrate and sulfate deposition we excluded the CMAQ predictions from those 

time periods in our calculations. It should be noted when pairing model and observed data that 

each CMAQ concentration represents a grid-cell volume-averaged value, while the ambient 

network measurements are made at specific locations. 

Model performance statistics were calculated for several spatial scales and temporal 

periods. Statistics were generated for the 12-km Eastern US domain (EUS), 12-km Western US 

domain (WUS), and five large subregions2

: Midwest, Northeast, Southeast, Central, and West 

U.S. The statistics for each site and subregion were calculated by season (e.g., “winter” is 

defined as December, January, and February). For 8-hour daily maximum ozone, we also 

calculated performance statistics by subregion for the May through September ozone season3. In 

addition to the performance statistics, we prepared several graphical presentations of model 

performance. These graphical presentations include: 

(1) regional maps which show the normalized mean bias and error calculated for each 

season at individual monitoring sites, and 

(2) bar and whisker plots which show the distribution of the predicted and observed data 

by month by subregion. 

A.1.1 Monitoring Networks 

The model evaluation for ozone was based upon comparisons of model predicted 8-hour daily 

maximum concentrations to the corresponding ambient measurements for 2005 at monitoring 

sites in the EPA Air Quality System (AQS). The observed ozone data were measured and 

reported on an hourly basis. The PM2.5 evaluation focuses on concentrations of PM2.5 total mass 

and its components including sulfate (SO4), nitrate (NO3), total nitrate (TNO3=NO3+HNO3), 

ammonium (NH4), elemental carbon (EC), and organic carbon (OC) as well as wet deposition for 

nitrate and sulfate. The PM2.5 performance statistics were calculated for each season and for the 

entire year, as a whole. PM2.5 ambient measurements for 2005 were obtained from the following 

 

1

See section II.B. of the main document (Figure II-1) for the description and map of the CMAQ modeling domains. 

2

 The subregions are defined by States where: Midwest is IL, IN, MI, OH, and WI; Northeast is CT, DE, 

MA, MD, ME, NH, NJ, NY, PA, RI, and VT; Southeast is AL, FL, GA, KY, MS, NC, SC, TN, VA, and 

WV; Central is AR, IA, KS, LA, MN, MO, NE, OK, and TX; West is AK, CA, OR, WA, AZ, NM, CO, UT, WY, 

SD, ND, MT, ID, and NV. 

3

 In calculating the ozone season statistics we limited the data to those observed and predicted pairs with 

observations that exceeded 60 ppb in order to focus on concentrations at the upper portion of the distribution of 

values. 
A-3 

networks: Chemical Speciation Network (CSN), Interagency Monitoring of PROtected Visual 

Environments (IMPROVE), Clean Air Status and Trends Network (CASTNet), and National 

Acid Deposition Program/National Trends (NADP/NTN). NADP/NTN collects and reports wet 

deposition measurements as weekly average data. The pollutant species included in the 

evaluation for each network are listed in Table A-1. 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-1. PM2.5 monitoring networks and pollutants species included in the CMAQ 

performance evaluation. 

Ambient 

Monitoring 

Networks

Particulate 

Species

Wet 

Deposition 

Species

PM2.5

Mass SO4 NO3 TNO3

a EC OC NH4 SO4 NO3

IMPROVE X X X X X 

CASTNet X X X 

STN X X X X X X 

NADP X X 

a

 TNO3 = (NO3 + HNO3) 

The air toxics evaluation focuses on specific species relevant to the Heavy-Duty 

Greenhouse Gas final rule (hereafter referred to as HDGHG), i.e., formaldehyde, acetaldehyde, 

benzene, 1,3-butadiene, and acrolein. Similar to the PM2.5 evaluation, the air toxics performance 

statistics were calculated for each season and for the entire year, as a whole to estimate the 

ability of the CMAQ modeling system to replicate the base year concentrations for the 12-km 

Eastern and Western United States domains. As mentioned above, seasons were defined as: 

winter (December-January-February), spring (March-April-May), summer (June-July-August), 

and fall (September-October-November). Toxic measurements for 2005 were obtained from the 

National Air Toxics Trends Stations (NATTS). 

A.1.2 Model Performance Statistics 

The Atmospheric Model Evaluation Tool (AMET) was used to conduct the evaluation 

described in this document.4 There are various statistical metrics available and used by the 

science community for model performance evaluation. For a robust evaluation, the principal 

 

4

 Appel, K.W., Gilliam, R.C., Davis, N., Zubrow, A., and Howard, S.C.: Overview of the Atmospheric Model 

Evaluation Tool (AMET) v1.1 for evaluating meteorological and air quality models, Environ. Modell. Softw.,26, 4, 

434-443, 2011. (http://www.cmascenter.org/) 
A-4 

evaluation statistics used to evaluate CMAQ performance were two bias metrics, normalized 

mean bias and fractional bias; and two error metrics, normalized mean error and fractional error. 

 Normalized mean bias (NMB) is used as a normalization to facilitate a range of 

concentration magnitudes. This statistic averages the difference (model - observed) over the sum 

of observed values. NMB is a useful model performance indicator because it avoids over 

inflating the observed range of values, especially at low concentrations. 

Normalized mean bias is defined as: 

NMB = 

( )

( )

P O

O

n

n

∑ −

∑

1

1

*100 

Normalized mean error (NME) is also similar to NMB, where the performance statistic is used as 

a normalization of the mean error. NME calculates the absolute value of the difference (model - 

observed) over the sum of observed values. 

Normalized mean error is defined as: 

NME = 

( )

P O

O

n

n

∑ −

∑

1

1

*100 

Fractional bias is defined as: 

FB = 

( )

( )

1

2

1

1

n

P O

P O

n

n

−

⎛ +

⎝

⎜ ⎞

⎠

⎟

⎛

⎝

⎜

⎜

⎜

⎜

⎞

⎠

⎟

⎟

⎟

⎟

∑

∑

*100, where P = predicted and O = observed concentrations. 

FB is a useful model performance indicator because it has the advantage of equally weighting 

positive and negative bias estimates. The single largest disadvantage in this estimate of model 

performance is that the estimated concentration (i.e., prediction, P) is found in both the 

numerator and denominator. Fractional error (FE) is similar to fractional bias except the 

absolute value of the difference is used so that the error is always positive. 

Fractional error is defined as: 

FE = ( )

1

2

1

1

n

P O

P O

n

n

−

⎛ +

⎝

⎜ ⎞

⎠

⎟

⎛

⎝

⎜

⎜

⎜

⎜

⎞

⎠

⎟

⎟

⎟

⎟

∑

∑

*100 

 The “acceptability” of model performance was judged by comparing our CMAQ 2005 

performance results to the range of performance found in recent regional ozone, PM2.5, and air 
A-5 

toxic model applications.5,6,7,8,9,10,11,12,13, 14,15 These other modeling studies represent a wide range 

of modeling analyses which cover various models, model configurations, domains, years and/or 

episodes, chemical mechanisms, and aerosol modules. Overall, the ozone, PM2.5, air toxics 

concentrations and nitrate and sulfate deposition model performance results for the 2005 CMAQ 

simulations performed for HDGHG are within the range or close to that found in other recent 

applications. The model performance results, as described in this report, give us confidence that 

our applications of CMAQ using this 2005 modeling platform provide a scientifically credible 

approach for assessing ozone and PM2.5 concentrations for the purposes of the HDGHG Final 

Rule. 

 

5 Appel, K.W., Bhave, P.V., Gilliland, A.B., Sarwar, G., and Roselle, S.J.: evaluation of the community multiscale 

air quality (CMAQ) model version 4.5: sensitivities impacting model performance: Part II – particulate matter. 

Atmospheric Environment 42, 6057-6066, 2008.

6 Appel, K.W., Gilliland, A.B., Sarwar, G., Gilliam, R.C., 2007. Evaluation of the community multiscale air quality 

(CMAQ) model version 4.5: sensitivities impacting model performance: Part I – ozone. Atmospheric Environment 

41, 9603-9615.

7 Appel, K.W., Roselle, S.J., Gilliam, R.C., and Pleim, J.E.,: Sensitivity of the Community Multiscale Air Quality 

(CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers. Geoscientific 

Model Development, 3, 169-188, 2010.

8 Foley, K.M., Roselle, S.J., Appel, K.W., Bhave, P.V., Pleim, J.E., Otte, T.L., Mathur, R., Sarwar, G., Young, J.O., 

Gilliam, R.C., Nolte, C.G., Kelly, J.T., Gilliland, A.B., and Bash, J.O.,: Incremental testing of the Community 

multiscale air quality (CMAQ) modeling system version 4.7. Geoscientific Model Development, 3, 205-226, 2010. 9 Hogrefe, G., Civeroio, K.L., Hao, W., Ku, J-Y., Zalewsky, E.E., and Sistla, G., Rethinking the Assessment of 

Photochemical Modeling Systems in Air Quality Planning Applications. Air & Waste Management Assoc., 

58:1086-1099, 2008.

10 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform: 

Air Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008. 

(http://www.cmascenter.org/conference/2008/agenda.cfm). 11 Strum, M., Wesson, K., Phillips, S.,Pollack, A., Shepard, S., Jimenez, M., M., Beidler, A., Wilson, M., Ensley, D., 

Cook, R., Michaels H., and Brzezinski, D. Link Based vs NEI Onroad Emissions Impact on Air Quality Model 

Predictions. 17th Annual International Emission Inventory Conference, Portland, Oregon, June 2-5, 2008. 

(http://www.epa.gov/ttn/chief/conference/ei17/session11/strum_pres.pdf) 12 Tesche, T.W., Morris, R., Tonnesen, G., McNally, D., Boylan, J., Brewer, P., 2006. CMAQ/CAMx annual 2002 

performance evaluation over the eastern United States. Atmospheric Environment 40, 4906-4919. 13 U.S. Environmental Protection Agency; Technical Support Document for the Final Clean Air Interstate Rule: Air 

Quality Modeling; Office of Air Quality Planning and Standards; RTP, NC; March 2005 (CAIR Docket OAR-2005-

0053-2149). 

14 U.S. Environmental Protection Agency, Proposal to Designate an Emissions Control Area for Nitrogen Oxides, 

Sulfur Oxides, and Particulate Matter: Technical Support Document. EPA-420-R-007, 329pp., 2009. 

(http://www.epa.gov/otaq/regs/nonroad/marine/ci/420r09007.pdf) 15 EPA 2010, Renewable Fuel Standard Program (RFS2) Regulatory Impact Analysis. EPA-420-R-10-006. 

February 2010. Sections 3.4.2.1.2 and 3.4.3.3. Docket EPA-HQ-OAR-2009-0472-11332. 

(http://www.epa.gov/oms/renewablefuels/420r10006.pdf) 
A-6 

A.2. Evaluation for 8-hour Daily Maximum Ozone 

 The 8-hour ozone model performance bias and error statistics for each subregion and 

each season are provided in Table A-2. The distributions of observed and predicted 8-hour 

ozone by month in the 5-month ozone season for each subregion are shown in Figures A-1 

through A-5. Spatial plots of the normalized mean bias and error for individual monitors are 

shown in Figures A-6 through A-7. The statistics shown in these two figures were calculated 

over the ozone season using data pairs on days with observed 8-hour ozone of > 60 ppb. 

 In general, CMAQ slightly under-predicts eight-hour daily maximum ozone with a 

threshold of 40 ppb in the months of May, June and August. Likewise, model predictions in the 

EUS and WUS are slightly over-predicted in the months of July and August. For the 12-km 

Eastern domain, the bias statistics are within the range of approximately -4% to 7%, while the 

error statistics range from 11% to 14% for the aggregate of the ozone season and for most of the 

months modeled. For the 12-km Western domain, the bias statistics are within the range of 

approximately 3% to -3%, while the error statistics range from 11% to 13% for the aggregate of 

the ozone season and for the individual months modeled. The five subregions show relatively 

similar eight-hour daily maximum ozone performance. 

Table A-2. Daily maximum 8-hour ozone performance statistics by subregion, by season. 

Subregion

No. of 

Obs NMB (%) NME (%) FB (%) FE (%)

Central

States

Winter 8304 8.5 24.6 8.0 27.3

Spring 12811 0.4 13.9 1.6 14.8

Summer 13414 3.9 19.17.6 7.0 19.2

Fall 10166 2.3 19.0 4.6 20.5 

Midwest

Winter 1819 -5.8 23.2 -8.1 28.2

Spring 10981 2.2 14.5 3.7 15.3

Summer 15738 3.1 13.6 4.2 14.1

Fall 9136 3.2 16.4 5.8 18.9 

Southeast

Winter 5150 8.2 17.4 7.9 18.5

Spring 17823 1.0 11.9 2.6 12.6

Summer 19423 14.6 22.6 16.1 23.8

Fall 11978 11.0 18.0 14.0 20.6 

Northeast

Winter 3497 -9.7 22.7 -12.5 29.2

Spring 11667 1.8 14.7 2.5 15.8

Summer 15489 8.6 17.7 10.5 18.6

Fall 9438 4.3 17.9 7.3 21.3 

West 

Winter 18259 27.3 33.1 27.5 33.9 

Spring 25665 2.3 14.1 2.9 14.6 

Summer 28156 5.5 17.0 6.0 17.3 
A-7 

Subregion

No. of 

Obs NMB (%) NME (%) FB (%) FE (%)

Fall 19492 5.7 18.6 7.5 19.9 

Figure A-1. Distribution of observed and predicted 8-hour daily maximum ozone by 

month for the period May through September for the Northeast subregion. [symbol = 

median; top/bottom of box = 75th/25th percentiles; top/bottom line = max/min values] 
A-8 

Figure A-2. Distribution of observed and predicted 8-hour daily maximum ozone by 

month for the period May through September 2005 for the Southeast subregion. 

Figure A-3. Distribution of observed and predicted 8-hour daily maximum ozone by 

month for the period May through September for the Midwest subregion. 
A-9 

Figure A-4. Distribution of observed and predicted 8-hour daily maximum ozone by 

month for the period May through September for the Central states subregion. 

Figure A-5. Distribution of observed and predicted 8-hour daily maximum ozone by 

month for the period May through September for the Western states subregion.
A-10 

Figure A-6a. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 

ppb over the period May-September 2005 at monitoring sites in Eastern modeling domain. 

Figure A-6b. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 

ppb over the period May-September 2005 at monitoring sites in Eastern modeling domain. 
A-11 

Figure A-7a. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 

ppb over the period May-September 2005 at monitoring sites in Western modeling domain. 

Figure A-7b. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 

ppb over the period May-September 2005 at monitoring sites in Western modeling domain. 
A-12 

A.3. Evaluation of PM2.5 Component Species 

 The evaluation of 2005 model predictions for PM2.5 covers the performance for the 

individual PM2.5 component species (i.e., sulfate, nitrate, organic carbon, elemental carbon, and 

ammonium). Performance results are provided for each PM2.5 species. As indicated above, for 

each species we present tabular summaries of bias and error statistics by subregion for each 

season. These statistics are based on the set of observed-predicted pairs of data for the particular 

quarter at monitoring sites within the subregion. Separate statistics are provided for each 

monitoring network, as applicable for the particular species measured. For sulfate and nitrate we 

also provide a more refined temporal and spatial analysis of model performance that includes (1) 

graphics of the distribution of 24-hour average concentrations and predictions by month for each 

subregion, and (2) spatial maps which show the normalized mean bias and error by site, 

aggregated by season. 

A.3.1. Evaluation for Sulfate 

 The model performance bias and error statistics for sulfate for each subregion and each 

season are provided in Table A-3. The distributions of observed and predicted suflate by month 

for each subregion are shown in Figures A-8 through A-12. Spatial plots of the normalized mean 

bias and error by season for individual monitors are shown in Figures A-3 through A-20. As 

seen in Table A-3, CMAQ generally under-predicts sulfate in the five U.S. subregions 

throughout the entire year. In the fall season, sulfate predictions show NMB values ranging from 

-5% to -20%, across STN, IMPROVE, and CASTNet networks in the East and West. In the 

spring and winter seasons, sulfate predictions for the most part are under-predicted in the East 

and West. Sulfate predictions during the summer season are moderately under-predicted in the 

East and West across the available monitoring data (NMB values range from -12% to -35%.) 

Table A-3. Sulfate performance statistics by subregion, by season for the 2005 CMAQ 

model simulation.

Subregion Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

CENRAP 

CSN 

Winter 771 -15.7 38.4 -14.1 41.7

Spring 875 -14.9 32.2 -11.0 33.8

Summer 851 -30.0 42.2 -37.0 54.2

Fall 587 -9.6 34.8 -3.3 36.6

IMPROVE 

Winter 608 -19.2 40.1 14.0 43.5

Spring 722 -17.4 31.3 -11.7 32.3

Summer 688 -27.7 39.1 -25.3 46.1

Fall 622 -15.5 31.3 -7.3 36.9

CASTNet Winter 72 -33.1 34.6 -35.3 37.8
A-13 

Subregion Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Spring 77 -24.4 27.7 -23.4 29.5

Summer 72 -33.0 36.8 -38.0 45.8

Fall 75 -20.9 23.5 -19.3 26.2

MWRPO 

CSN 

Winter 598 1.2 38.8 -4.4 38.7

Spring 637 19.6 42.9 15.6 36.9

Summer 621 -10.3 28.7 -0.3 30.8

Fall 639 -11.8 26.5 -3.4 27.3

IMPROVE 

Winter 143 3.7 36.0 0.0 34.5

Spring 171 5.0 35.5 7.2 35.3

Summer 182 -18.4 30.0 -5.6 36.1

Fall 126 -17.8 26.9 -6.7 31.6

CASTNet 

Winter 142 -13.6 21.8 -16.1 26.4

Spring 155 -5.6 22.4 -4.0 21.7

Summer 161 -16.1 21.7 -13.6 23.6

Fall 157 -19.6 22.3 -15.5 21.4

 

VISTAS 

CSN 

Winter 949 -5.1 37.1 -4.5 37.2

Spring 973 -4.5 28.0 -5.2 29.7

Summer 926 -17.4 32.0 -18.8 38.0

Fall 975 -9.9 27.0 -5.7 29.0

IMPROVE 

Winter 469 -1.6 36.8 0.6 37.5

Spring 525 -6.4 29.1 -5.8 31.7

Summer 500 -24.0 35.6 -30.7 47.0

Fall 496 -11.6 29.1 -6.1 34.4

CASTNet 

Winter 264 -18.5 22.8 -17.8 23.9

Spring 292 -13.2 21.2 -14.5 22.9

Summer 268 -21.1 24.8 -28.3 32.7

Fall 273 -18.3 21.1 -19.1 23.2

MANEVU CSN 

Winter 828 -9.1 35.1 -13.9 34.8

Spring 894 8.4 37.4 4.4 35.0

Summer 874 -8.6 27.1 -2.9 30.9

Fall 902 -9.0 28.8 0.1 30.9
A-14 

Subregion Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

IMPROVE 

Winter 561 -7.1 31.2 -11.0 33.3

Spring 689 7.2 38.0 3.7 38.2

Summer 649 -12.9 32.3 -4.4 37.6

Fall 591 -6.8 32.2 7.7 35.4

CASTNet 

Winter 193 -14.8 22.4 -19.0 25.8

Spring 206 -0.2 25.3 -1.2 26.5

Summer 192 -15.5 20.4 -12.6 22.0

Fall 195 -12.2 18.4 -7.3 18.0

WRAP 

CSN 

Winter 830 -5.2 57.7 1.8 54.4

Spring 867 -3.8 37.0 0.0 36.2

Summer 853 -32.1 43.6 -23.3 42.5

Fall 900 -7.6 47.2 0.4 43.4

IMPROVE 

Winter 2373 22.3 58.4 33.8 56.6

Spring 2650 -3.6 33.5 3.5 35.1

Summer 2307 -24.8 41.1 -16.5 42.8

Fall 2365 -0.4 40.0 11.2 41.2

CASTNet 

Winter 250 6.5 35.9 17.9 37.5

Spring 273 -18.4 27.1 -17.0 27.6

Summer 281 -35.1 38.7 -36.0 41.5

Fall 268 -10.7 23.5 -5.0 24.3

 
A-15 

Figure A-8a. Distribution of observed and predicted 24-hour average sulfate by month for 

2005 at IMPROVE sites in the Northeast subregion. [symbol = median; top/bottom of box 

= 75th/25th percentiles; top/bottom line = max/min values] 

Figure A-8b. Distribution of observed and predicted 24-hour average sulfate by month for 

2005 at CSN sites in the Northeast subregion. 
A-16 

Figure A-8c. Distribution of observed and predicted weekly average sulfate by month for 

2005 at CASTNet sites in the Northeast subregion. 

Figure A-9a. Distribution of observed and predicted 24-hour average sulfate by month for 

2005 at IMPROVE sites in the Southeast subregion. 
A-17 

Figure A-9b. Distribution of observed and predicted 24-hour average sulfate by month for 

2005 at CSN sites in the Southeast subregion. 

Figure A-9c. Distribution of observed and predicted weekly average sulfate by month for 

2005 at CASTNet sites in the Southeast subregion. 
A-18 

Figure A-10a. Distribution of observed and predicted 24-hour average sulfate by month 

for 2005 at IMPROVE sites in the Midwest subregion. 

Figure A-10b. Distribution of observed and predicted 24-hour average sulfate by month 

for 2005 at CSN sites in the Midwest subregion. 
A-19 

Figure A-10c. Distribution of observed and predicted weekly average sulfate by month for 

2005 at CASTNet sites in the Midwest subregion. 

Figure A-11a. Distribution of observed and predicted 24-hour average sulfate by month 

for 2005 at IMPROVE sites in the Central states subregion. 
A-20 

Figure A-11b. Distribution of observed and predicted 24-hour average sulfate by month 

for 2005 at CSN sites in the Central states subregion. 

Figure A-11c. Distribution of observed and predicted weekly average sulfate by month for 

2005 at CASTNet sites in the Central states subregion. 
A-21 

Figure A-12a. Distribution of observed and predicted 24-hour average sulfate by month 

for 2005 at IMPROVE sites in the Western states subregion. 

Figure A-12b. Distribution of observed and predicted 24-hour average sulfate by month 

for 2005 at CSN sites in the Western states subregion. 
A-22 

Figure A-12c. Distribution of observed and predicted weekly average sulfate by month for 

2005 at CASTNet sites in the Western states subregion. 
A-23 

Figure A-13a. Normalized Mean Bias (%) of sulfate during winter 2005 at monitoring sites 

in Eastern modeling domain. 

Figure A-13b. Normalized Mean Error (%) of sulfate during winter 2005 at monitoring 

sites in Eastern modeling domain. 
A-24 

Figure A-14a. Normalized Mean Bias (%) of sulfate during spring 2005 at monitoring sites 

in Eastern modeling domain. 

Figure A-14b. Normalized Mean Error (%) of sulfate during spring 2005 at monitoring 

sites in Eastern modeling domain. 
A-25 

Figure A-15a. Normalized Mean Bias (%) of sulfate during summer 2005 at monitoring 

sites in Eastern modeling domain.

Figure A-15b. Normalized Mean Error (%) of sulfate during summer 2005 at monitoring 

sites in Eastern modeling domain. 
A-26 

Figure A-16a. Normalized Mean Bias (%) of sulfate during fall 2005 at monitoring sites in 

Eastern modeling domain. 

Figure A-16b. Normalized Mean Error (%) of sulfate during fall 2005 at monitoring sites 

in Eastern modeling domain.
A-27 

Figure A-17a. Normalized Mean Bias (%) of sulfate during winter 2005 at monitoring sites 

in Western modeling domain.

Figure F-17b. Normalized Mean Error (%) of sulfate during fall 2005 at monitoring sites 

in Western modeling domain.
A-28 

Figure A-18a. Normalized Mean Bias (%) of sulfate during spring 2005 at monitoring sites 

in Western modeling domain.

Figure A-18b. Normalized Mean Error (%) of sulfate during spring 2005 at monitoring 

sites in Western modeling domain.
A-29 

Figure A-19a. Normalized Mean Bias (%) of sulfate during summer 2005 at monitoring 

sites in Western modeling domain.

Figure A-19b. Normalized Mean Error (%) of sulfate during summer 2005 at monitoring 

sites in Western modeling domain.
A-30 

Figure A-20a. Normalized Mean Bias (%) of sulfate during fall 2005 at monitoring sites in 

Western modeling domain.

Figure A-20b. Normalized Mean Error (%) of sulfate during fall 2005 at monitoring sites 

in Western modeling domain.
A-31 

A.3.1. Evaluation for Nitrate 

 The model performance bias and error statistics for nitrate for each subregion and each 

season are provided in Table A-4. This table includes statistics for particulate nitrate, as 

measured at CSN and IMPROVE sites, and statistics for total nitrate, as measured at CASTNet 

sites. The distributions of observed and predicted nitrate by month for each subregion are shown 

in Figures A-21 through A-25. Spatial plots of the normalized mean bias and error by season for 

individual monitors are shown in Figures A-26 through A-33. Overall, nitrate and total nitrate 

performance is over-predicted in the EUS and under-predicted in the WUS for all of the seasonal 

assessments except in the winter and summer season, where total nitrate is over-predicted in the 

EUS and WUS and in the spring where nitrate is over-predicted in the EUS. Likewise, in the 

East, nitrate and total nitrate are moderately over-predicted during the spring and summer 

seasons (NMB values ranging from 10% to 100%). In the winter season when nitrate is most 

abundant, nitrate is under-predicted in the East and West, however total nitrate is over-predicted. 

Table A-4. Nitrate performance statistics by subregion, by season for the 2005 CMAQ 

model simulation. 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

CENRAP 

CSN 

Winter 479 -4.7 49.0 -5.5 59.3

Spring 503 30.0 62.2 15.1 66.0

Summer 485 28.3 102 -41.5 95.5

Fall 460 107.0 133.0 19.3 89.0

IMPROVE 

Winter 608 5.1 55.0 -6.5 70.7

Spring 722 49.2 78.7 -3.7 90.9

Summer 688 21.8 112.0 -56.1 111.0

Fall 622 164.0 193.0 14.5 107.0

CASTNet 

Winter 72 27.0 38.9 26.6 36.7

Spring 77 14.7 34.3 7.7 31.3

Summer 72 -0.1 26.3 -6.0 27.6

Fall 75 53.0 60.2 36.0 44.0

MWRPO 

CSN 

Winter 598 -21.2 40.7 -21.7 49.0

Spring 637 63.2 83.3 40.4 65.5

Summer 621 43.4 98.2 -10.9 83.6

Fall 639 69.5 98.1 24.1 74.3

IMPROVE 

Winter 143 -27.6 47.7 -29.9 72.9

Spring 171 54.5 87.7 -3.5 90.0
A-32 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Summer 182 25.1 100.0 -41.4 99.7

Fall 126 108.0 141.0 0.2 102.0

CASTNet 

Winter 142 -7.1 21.4 0.1 21.8

Spring 155 38.5 42.2 31.8 35.7

Summer 161 53.8 56.3 41.0 43.3

Fall 157 73.8 74.1 51.2 51.5

 

VISTAS 

CSN 

Winter 949 -25.8 60.7 -54.7 84.1

Spring 973 47.6 100.0 -7.0 91.2

Summer 926 -24.6 84.3 -80.0 113.0

Fall 975 78.2 137.0 -25.1 106.0

IMPROVE 

Winter 469 -2.6 82.4 -58.7 98.9

Spring 525 59.6 116.0 -29.4 108.0

Summer 500 -14.2 112 -92.7 136.0

Fall 496 105.0 184.0 -46.7 125.0

CASTNet 

Winter 264 24.3 35.8 20.8 35.2

Spring 292 31.8 45.0 21.9 39.6

Summer 268 28.9 47.3 17.0 42.7

Fall 273 73.9 82.0 45.9 59.0

MANEVU 

CSN 

Winter 829 -1.8 43.9 -1.5 49.8

Spring 894 43.2 77.4 32.6 68.5

Summer 874 -5.8 89.9 -58.4 101.0

Fall 902 75.9 109.0 -11.3 86.3

IMPROVE 

Winter 561 41.9 77.3 32.7 75.9

Spring 689 73.8 113.0 31.3 93.2

Summer 649 11.2 115.0 -61.5 112.0

Fall 586 115.0 156.0 -11.3 86.3

CASTNet Winter 193 23.4 30.7 31.2 35.4
A-33 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Spring 206 48.9 51.3 37.4 42.4

Summer 192 53.6 61.3 33.4 49.5

Fall 195 85.2 87.7 54.0 60.5

West 

CSN 

Winter 831 -44.2 63.8 -60.4 87.2

Spring 859 -37.5 58.3 -68.8 89.2

Summer 846 -72.8 76.5 -133.0 137.0

Fall 896 -47.9 69.9 -66.9 95.7

IMPROVE 

Winter 2374 -30.0 77.6 -84.2 121.0

Spring 2643 -38.7 76.6 -88.1 119.0

Summer 2305 -73.7 83.9 -144.0 152.0

Fall 2357 -31.9 82.0 -75.3 121.0

CASTNet 

Winter 250 34.6 52.9 41.6 54.6

Spring 273 -1.9 32.7 6.2 32.4

Summer 281 -6.9 31.2 -5.8 33.0

Fall 268 15.9 40.6 28.2 46.6

 
A-34 

Figure A-21a. Distribution of observed and predicted 24-hour average nitrate by month 

for 2005 at IMPROVE sites in the Northeast subregion. [symbol = median; top/bottom of 

box = 75th/25th percentiles; top/bottom line = max/min values] 

Figure A-21b. Distribution of observed and predicted 24-hour average nitrate by month 

for 2005 at CSN sites in the Northeast subregion. 
A-35 

Figure A-21c. Distribution of observed and predicted weekly average total nitrate by 

month for 2005 at CASTNet sites in the Northeast subregion. 

Figure A-22a. Distribution of observed and predicted 24-hour average nitrate by month 

for 2005 at IMPROVE sites in the Southeast subregion. 
A-36 

Figure A-22b. Distribution of observed and predicted 24-hour average nitrate by month 

for 2005 at CSN sites in the Southeast subregion. 

Figure A-22c. Distribution of observed and predicted weekly average total nitrate by 

month for 2005 at CASTNet sites in the Southeast subregion. 
A-37 

Figure A-23a. Distribution of observed and predicted 24-hour average nitrate by month 

for 2005 at IMPROVE sites in the Midwest subregion. 

Figure A-23b. Distribution of observed and predicted 24-hour average nitrate by month 

for 2005 at CSN sites in the Midwest subregion. 
A-38 

Figure A-23c. Distribution of observed and predicted weekly average total nitrate by 

month for 2005 at CASTNet sites in the Midwest subregion. 

Figure A-24a. Distribution of observed and predicted 24-hour average nitrate by month 

for 2005 at IMPROVE sites in the Central states subregion. 
A-39 

Figure A-24b. Distribution of observed and predicted 24-hour average nitrate by month 

for 2005 at CSN sites in the Central states subregion. 

Figure A-24c. Distribution of observed and predicted weekly average total nitrate by 

month for 2005 at CASTNet sites in the Central states subregion. 
A-40 

Figure A-25a. Distribution of observed and predicted 24-hour average nitrate by month 

for 2005 at IMPROVE sites in the Western states subregion. 

Figure A-25b. Distribution of observed and predicted 24-hour average nitrate by month 

for 2005 at CSN sites in the Western states subregion. 
A-41 

Figure A-25c. Distribution of observed and predicted weekly average total nitrate by 

month for 2005 at CASTNet sites in the Western states subregion. 
A-42 

Figure A-26a. Normalized Mean Bias (%) for nitrate during winter 2005 at monitoring 

sites in Eastern modeling domain. 

Figure A-26b. Normalized Mean Error (%) for nitrate during winter 2005 at monitoring 

sites in Eastern modeling domain. 
A-43 

Figure A-26c. Normalized Mean Bias (%) for total nitrate during winter 2005 at 

monitoring sites in Eastern modeling domain. 

Figure A-26d. Normalized Mean Error (%) for total nitrate during winter 2005 at 

monitoring sites in Eastern modeling domain. 
A-44 

Figure A-27a. Normalized Mean Bias (%) for nitrate during spring 2005 at monitoring 

sites in Eastern modeling domain. 

Figure A-27b. Normalized Mean Error (%) for nitrate during spring 2005 at monitoring 

sites in Eastern modeling domain. 
A-45 

Figure A-27c. Normalized Mean Bias (%) for total nitrate during spring 2005 at 

monitoring sites in Eastern modeling domain. 

Figure A-27d. Normalized Mean Error (%) for total nitrate spring 2005 at monitoring 

sites in Eastern modeling domain. 
A-46 

Figure A-28a. Normalized Mean Bias (%) for nitrate during summer 2005 at monitoring 

sites in Eastern modeling domain. 

Figure A-28b. Normalized Mean Error (%) for nitrate during summer 2005 at monitoring 

sites in Eastern modeling domain. 
A-47 

Figure A-28c. Normalized Mean Bias (%) for total nitrate during summer 2005 at 

monitoring sites in Eastern modeling domain. 

Figure A-28d. Normalized Mean Error (%) for total nitrate summer 2005 at monitoring 

sites in Eastern modeling domain. 
A-48 

Figure A-29a. Normalized Mean Bias (%) for nitrate during fall 2005 at monitoring sites 

in Eastern modeling domain. 

Figure A-29b. Normalized Mean Error (%) for nitrate during fall 2005 at monitoring sites 

in Eastern modeling domain. 
A-49 

Figure A-29c. Normalized Mean Bias (%) for total nitrate during fall 2005 at monitoring 

sites in Eastern modeling domain. 

Figure A-29d. Normalized Mean Error (%) for total nitrate fall 2005 at monitoring sites in 

Eastern modeling domain. 
A-50 

Figure A-30a. Normalized Mean Bias (%) for nitrate during winter 2005 at monitoring 

sites in Western modeling domain. 

Figure A-30b. Normalized Mean Error (%) for nitrate during winter 2005 at monitoring 

sites in Western modeling domain. 
A-51 

Figure A-30c. Normalized Mean Bias (%) for total nitrate during winter 2005 at 

monitoring sites in Western modeling domain. 

Figure A-30d. Normalized Mean Error (%) for total nitrate winter 2005 at monitoring 

sites in Western modeling domain. 
A-52 

Figure A-31a. Normalized Mean Bias (%) for nitrate during spring 2005 at monitoring 

sites in Western modeling domain. 

Figure A-31b. Normalized Mean Error (%) for nitrate during spring 2005 at monitoring 

sites in Western modeling domain. 
A-53 

Figure A-31c. Normalized Mean Bias (%) for total nitrate during spring 2005 at 

monitoring sites in Western modeling domain. 

Figure A-31d. Normalized Mean Error (%) for total nitrate spring 2005 at monitoring 

sites in Western modeling domain. 
A-54 

Figure A-32a. Normalized Mean Bias (%) for nitrate during summer 2005 at monitoring 

sites in Western modeling domain. 

Figure A-32b. Normalized Mean Error (%) for nitrate during summer 2005 at monitoring 

sites in Western modeling domain. 
A-55 

Figure A-32c. Normalized Mean Bias (%) for total nitrate during summer 2005 at 

monitoring sites in Western modeling domain. 

Figure A-32d. Normalized Mean Error (%) for total nitrate summer 2005 at monitoring 

sites in Western modeling domain. 
A-56 

Figure A-33a. Normalized Mean Bias (%) for nitrate during fall 2005 at monitoring sites 

in Western modeling domain. 

Figure A-33b. Normalized Mean Error (%) for nitrate during fall 2005 at monitoring sites 

in Western modeling domain. 
A-57 

Figure A-33c. Normalized Mean Bias (%) for total nitrate during fall 2005 at monitoring 

sites in Western modeling domain. 

Figure A-33d. Normalized Mean Error (%) for total nitrate fall 2005 at monitoring sites in 

Western modeling domain. 
A-58 

H. Seasonal Ammonium Performance 

The model performance bias and error statistics for ammonium for each subregion and 

each season are provided in Table A-5. These statistics indicate model bias for ammonium is 

generally + 40 percent or less for all seasons in each subregion. During the summer, there is 

slight under prediction with a low bias in the subregions for urban locations. In other times of 

the year ammonium tends to be somewhat over predicted with a bias of 19 percent, on average 

across the subregions for urban locations. 

Table A-5. Ammonium performance statistics by subregion, by season for the 2005 CMAQ 

model simulation.

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

CENRAP 

CSN 

Winter 771 -1.0 43.5 -0.1 50.7

Spring 875 6.0 42.3 8.2 43.5

Summer 851 -20.6 46.0 -23.7 60.9

Fall 587 18.7 55.4 23.6 55.8

CASTNet 

Winter 72 3.9 37.9 4.4 42.7

Spring 77 17.5 34.6 11.5 32.6

Summer 72 -16.6 29.4 -19.3 35.8

Fall 75 18.0 44.5 25.0 46.5

MWRPO 

CSN 

Winter 598 -8.1 31.9 -3.0 33.4

Spring 637 49.9 63.8 39.6 51.4

Summer 621 0.7 37.3 16.9 42.1

Fall 639 8.5 37.8 22.6 41.5

CASTNet 

Winter 142 -10.4 24.2 -4.8 25.1

Spring 155 46.2 53.6 37.8 42.4

Summer 161 -4.4 25.9 -1.0 27.5

Fall 157 21.3 45.8 27.7 41.7

 

VISTAS 

CSN 

Winter 949 -9.4 41.1 -8.5 44.1

Spring 973 12.0 41.5 10.9 41.0

Summer 926 -13.7 35.9 -8.0 43.3

Fall 975 3.8 41.5 14.5 45.0

CASTNet 

Winter 264 -6.0 28.0 -6.3 29.4

Spring 292 9.2 31.2 7.5 30.9

Summer 268 -31.7 35.3 -44.8 48.6

Fall 273 -8.2 36.5 -6.7 40.8
A-59 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

MANEVU 

CSN 

Winter 828 2.8 34.5 6.7 34.2

Spring 894 33.4 54.8 35.5 50.4

Summer 874 -10.4 36.1 4.6 43.9

Fall 902 18.8 50.5 30.1 51.2

CASTNet 

Winter 193 23.2 38.6 27.2 37.5

Spring 206 43.5 49.8 32.8 38.9

Summer 192 -22.9 29.7 -26.1 34.4

Fall 195 9.7 39.4 14.4 36.4

WRAP 

CSN 

Winter 829 -27.7 60.7 -12.0 65.5

Spring 859 -0.5 52.7 18.7 51.2

Summer 849 -33.0 53.1 -4.7 51.6

Fall 886 -21.4 63.6 9.4 58.6

CASTNet 

Winter 250 -2.4 41.0 7.6 39.3

Spring 273 -8.8 32.1 -4.5 31.7

Summer 281 -33.3 40.3 -34.4 44.6

Fall 268 -3.1 32.1 1.7 
A-60 

I. Seasonal Elemental Carbon Performance 

 The model performance bias and error statistics for elemental carbon for each subregion 

and each season are provided in Table A-6. The statistics show clear over prediction at urban 

sites in all subregions. For example, NMBs typically range between 50 and 100 percent at urban 

sites in the Midwest, Northeast, and Central subregions with only slightly less over prediction at 

urban sites in the Southeast. Rural sites show much less over prediction than at urban sites with 

under predictions occurring in the spring, summer, and fall at rural sites in the Southeast, 

Midwest and Central subregions. In the West, the model tends to over predict at both urban and 

rural sites during all seasons. In addition, the predictions for urban sites have greater error than 

the predictions for rural locations. 

Table A-6. Elemental Carbon performance statistics by subregion, by season for the 2005 

CMAQ model simulation. 

Subregion Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

CENRAP 

CSN 

Winter 816 101.0 132.0 56.5 77.5

Spring 938 90.6 114.0 45.5 70.6

Summer 875 109.0 132.0 41.9 80.7

Fall 618 93.6 111.0 57.5 70.7

IMPROVE 

Winter 589 9.7 54.5 4.9 47.1

Spring 716 -9.4 55.8 -10.1 53.8

Summer 701 -30.5 46.8 -38.2 56.2

Fall 620 -17.2 34.8 -16.0 41.1

MWRPO 

CSN 

Winter 602 122.0 137.0 69.3 76.5

Spring 637 64.2 85.3 49.0 61.5

Summer 621 48.1 64.6 38.2 54.4

Fall 642 53.2 73.2 39.9 55.6

IMPROVE 

Winter 182 61.4 79.6 22.9 46.0

Spring 184 17.9 56.8 -11.8 51.1

Summer 185 -13.8 40.9 -37.3 53.9

Fall 145 -12.8 33.6 -19.3 48.2

 

VISTAS 

CSN 

Winter 950 40.4 63.6 31.6 49.8

Spring 970 37.6 62.6 35.6 53.4

Summer 925 41.4 69.5 38.2 61.1

Fall 973 13.6 46.2 18.5 45.5

IMPROVE Winter 491 -3.0 44.4 -0.7 48.6
A-61 

Subregion Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

Spring 530 -17.0 44.9 -11.3 45.1

Summer 493 -41.3 48.4 -55.6 71.4

Fall 481 -26.9 38.9 -22.8 45.6

MANEVU 

CSN 

Winter 831 97.5 110.0 57.7 67.1

Spring 881 90.3 107.0 57.0 68.7

Summer 866 64.8 87.8 45.3 63.2

Fall 901 52.2 82.5 34.6 56.6

IMPROVE 

Winter 603 45.4 72.9 22.8 53.1

Spring 658 28.1 63.0 11.3 54.3

Summer 596 -20.6 45.6 -37.8 57.4

Fall 591 30.9 57.3 6.0 49.3

WRAP 

CSN 

Winter 808 43.6 84.7 21.4 66.9

Spring 822 99.5 123.0 44.0 73.9

Summer 806 112.0 126.0 575 72.3

Fall 867 52.1 86.6 26.3 64.0

IMPROVE 

Winter 2338 0.0 63.5 -15.5 64.6

Spring 2597 17.3 68.0 -2.0 53.9

Summer 2314 28.4 76.4 17.9 58.2

Fall 2372 7.0 66.0 -10.1 59.4
A-62 

J. Seasonal Organic Carbon Performance 

 The model performance bias and error statistics for organic carbon for each subregion 

and each season are provided in Table A-7. The statistics in this table indicate a tendency for the 

modeling platform to somewhat under predict observed organic carbon concentrations during the 

spring, summer, and fall at urban and rural locations across the Eastern subregions. Likewise, 

the modeling platform under predicts organic carbon during all seasons at urban and rural 

locations in the Western subregion. These biases and errors reflect sampling artifacts among 

each monitoring network. In addition, uncertainties exist for primary organic mass emissions 

and secondary organic aerosol formation. Research efforts are ongoing to improve fire emission 

estimates and understand the formation of semi-volatile compounds, and the partitioning of SOA 

between the gas and particulate phases. 

Table A-7. Organic Carbon performance statistics by subregion, by season for the 2005 

CMAQ model simulation. 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

CENRAP 

CSN 

Winter 544 0.2 57.7 14.9 59.7

Spring 628 -34.8 52.4 -32.0 63.4

Summer 595 -51.4 54.1 -69.8 76.3

Fall 493 -30.8 45.2 -28.0 56.7

IMPROVE 

Winter 589 -8.1 51.1 -12.0 47.9

Spring 715 -38.5 57.6 -38.1 61.1

Summer 699 -50.1 52.3 -69.9 74.2

Fall 619 -44.4 48.2 -54.4 62.3

MWRPO 

CSN 

Winter 566 4.3 53.2 21.8 54.2

Spring 605 -29.4 45.9 -17.8 52.8

Summer 619 -53.1 54.6 -69.6 73.2

Fall 595 -28.5 41.3 -16.4 52.0

IMPROVE 

Winter 182 3.4 38.5 1.6 37.2

Spring 184 -26.0 36.5 -32.9 44.7

Summer 185 -48.3 51.5 -64.6 68.9

Fall 144 -35.1 43.7 -43.9 61.8

VISTAS CSN 

Winter 932 -24.4 45.4 -13.2 50.4

Spring 957 -35.3 48.6 -28.7 56.9

Summer 916 -55.5 57.5 -75.3 80.1

Fall 942 -39.3 45.8 -41.1 57.5
A-63 

Region Network Season 

No. of 

Obs. 

NMB 

(%) 

NME 

(%) FB (%) FE (%)

IMPROVE 

Winter 491 -10.1 45.1 -11.4 50.9

Spring 529 -9.2 49.1 -15.1 50.3

Summer 492 -48.5 54.2 -66.4 75.0

Fall 481 -33.8 41.2 -41.6 53.2

MANEVU 

CSN 

Winter 806 27.9 59.3 31.3 55.2

Spring 832 2.2 50.7 8.5 53.1

Summer 859 -47.3 51.7 -61.1 69.2

Fall 830 -4.5 47.1 3.7 53.3

IMPROVE 

Winter 602 48.2 69.3 31.6 52.1

Spring 657 3.7 46.3 -3.1 46.1

Summer 596 -47.0 51.5 -59.4 66.4

Fall 588 14.2 47.4 -1.9 43.9

WRAP 

CSN 

Winter 803 -26.5 67.4 -20.2 70.0

Spring 823 -12.3 60.4 -4.1 60.3

Summer 840 -24.1 41.6 -28.8 50.5

Fall 881 -28.4 57.1 -26.2 58.6

IMPROVE 

Winter 2296 -17.0 58.6 -23.1 64.6

Spring 2559 -23.2 51.7 -25.4 56.8

Summer 2297 4.2 65.1 -1.2 60.1

Fall 2350 -21.9 56.9 -26.9 62.1
A-64 

K. Seasonal Hazardous Air Pollutants Performance 

A seasonal operational model performance evaluation for specific hazardous air 

pollutants (formaldehyde, acetaldehyde, benzene, acrolein, and 1,3-butadiene) was conducted in 

order to estimate the ability of the CMAQ modeling system to replicate the base year 

concentrations for the 12-km Eastern and Western United States domains. The seasonal model 

performance results for the East and West are presented below in Tables A-8 and A-9, 

respectively. Toxic measurements from 471 sites in the East and 135 sites in the West were 

included in the evaluation and were taken from the 2005 State/local monitoring site data in the 

National Air Toxics Trends Stations (NATTS). Similar to PM2.5 and ozone, the evaluation 

principally consists of statistical assessments of model versus observed pairs that were paired in 

time and space on daily basis. 

Model predictions of annual formaldehyde, acetaldehyde and benzene showed relatively 

small bias and error percentages when compared to observations. The model yielded larger bias 

and error results for 1,3 butadiene and acrolein based on limited monitoring sites. Model 

performance for HAPs is not as good as model performance for ozone and PM2.5. Technical 

issues in the HAPs data consist of (1) uncertainties in monitoring methods; (2) limited 

measurements in time/space to characterize ambient concentrations (“local in nature”); (3) 

commensurability issues between measurements and model predictions; (4) emissions and 

science uncertainty issues may also affect model performance; and (5) limited data for estimating 

intercontinental transport that effects the estimation of boundary conditions (i.e., boundary 

estimates for some species are much higher than predicted values inside the domain). 

As with the national, annual PM2.5 and ozone CMAQ modeling, the “acceptability” of 

model performance was judged by comparing our CMAQ 2005 performance results to the 

limited performance found in recent regional multi-pollutant model applications.16,17,18 Overall, 

the normalized mean bias and error (NMB and NME), as well as the fractional bias and error (FB 

and FE) statistics shown below indicate that CMAQ-predicted 2005 toxics (i.e., observation vs. 

model predictions) are within the range of recent regional modeling applications. 

 

16 Phillips, S., K. Wang, C. Jang, N. Possiel, M. Strum, T. Fox, 2007: Evaluation of 2002 Multi-pollutant Platform: 

Air Toxics, Ozone, and Particulate Matter, 7th Annual CMAS Conference, Chapel Hill, NC, October 6-8, 2008. 

17 Strum, M., Wesson, K., Phillips, S., Cook, R., Michaels, H., Brzezinski, D., Pollack, A., Jimenez, M., Shepard, S. 

Impact of using lin-level emissions on multi-pollutant air quality model predictions at regional and local scales. 17th

Annual International Emission Inventory Conference, Portland, Oregon , June 2-5, 2008. 

 

18 Wesson, K., N. Fann, and B. Timin, 2010: Draft Manuscript: Air Quality and Benefits Model Responsiveness to 

Varying Horizontal Resolution in the Detroit Urban Area, Atmospheric Pollution Research, Special Issue: Air 

Quality Modeling and Analysis.
A-65 

Table A-8. Air toxics performance statistics by season in the Eastern domain for the 2005 

CMAQ model simulation. 

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

Formaldehyde 

Winter 1625 -52.2 61.5 -46.0 68.2

Spring 1530 -53.2 65.2 -35.6 67.6

Summer 1809 -52.9 63.5 -29.5 58.2

Fall 1901 -51.6 62.2 36.0 60.6

Acetaldehyde 

Winter 1549 -41.4 50.6 -42.9 57.4

Spring 1471 -27.5 50.3 -23.7 55.0

Summer 1752 57.0 89.6 47.7 67.9

Fall 1850 -2.5 56.8 -8.3 56.1

Benzene 

Winter 3107 -30.7 68.3 -9.5 58.2

Spring 3085 -38.0 66.7 -25.0 63.2

Summer 3242 -37.0 69.6 -18.9 66.1

Fall 3387 -30.3 64.8 -16.2 59.0

1,3-Butadiene 

Winter 2629 -63.0 89.8 -20.4 86.8

Spring 2712 -77.7 92.7 -47.8 92.2

Summer 2758 -73.4 87.9 -54.4 87.5

Fall 2487 -61.4 81.5 -49.0 85.4

Acrolein 

Winter 602 -90.1 94.6 -123.0 134.0

Spring 430 -82.4 91.4 -117.0 128.0

Summer 814 -95.9 99.0 -136.0 154.0

Fall 992 -95.0 98.7 -148.0 153.0

Table A-9. Air toxics performance statistics by season in the Western domain for the 2005 

CMAQ model simulation. 

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

Formaldehyde 

Winter 426 -21.1 68.5 -33.1 73.1

Spring 499 -30.1 57.7 -21.3 61.1

Summer 641 -25.1 38.0 -21.6 40.5

Fall 579 -25.8 42.8 -26.5 48.7

Acetaldehyde 

Winter 425 -23.9 71.0 -37.0 75.8

Spring 484 -24.3 55.6 -23.0 61.0

Summer 630 -1.5 46.0 7.4 44.3

Fall 568 -18.2 51.3 -16.2 55.7

Benzene 

Winter 820 -39.5 58.3 -38.3 64.0

Spring 835 -31.8 55.8 -29.9 61.2

Summer 1033 -44.1 64.7 -26.2 63.6

Fall 959 -58.0 71.6 -37.3 69.6

1,3-Butadiene Winter 693 -45.4 95.5 -26.1 99.4
A-66 

Spring 732 -21.0 87.4 -23.6 80.6

Summer 676 -35.9 78.1 -34.8 80.1

Fall 708 -46.1 86.5 -32.9 89.4

Acrolein 

Winter 196 -95.1 95.2 -163.0 165.0

Spring 190 -95.8 95.9 -167.0 169.0

Summer 305 -96.2 98.8 -171.0 178.0

Fall 279 -96.8 98.1 -172.0 174.0
A-67 

L. Seasonal Nitrate and Sulfate Deposition Performance 

 Seasonal nitrate and sulfate deposition performance statistics for the 12-km Eastern and 

Western domains are provided in Tables A-10 and A-11, respectively. The model predictions for 

annual nitrate deposition generally show small under-predictions for the Eastern and Western 

NADP sites (NMB values range from 0% to -11%). Sulfate deposition performance in the East 

and West shows the similar predictions (NMB values range from -3% to 33%). The errors for 

both annual nitrate and sulfate are relatively moderate with values ranging from 60% to 77% 

which reflect scatter in the model predictions versus observation comparison. 

Table A-10. Nitrate and sulfate wet deposition performance statistics by season in the 

Eastern domain for the 2005 CMAQ model simulation. 

Wet Deposition 

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

Nitrate 

Winter 1788 31.4 74.7 13.5 72.5 

Spring 1882 -1.7 57.3 -2.9 64.8 

Summer 1975 -23.1 61.9 -20.0 75.3 

Fall 1736 7.1 65.7 -5.8 74.2 

Sulfate 

Winter 1788 33.8 70.0 24.3 72.2 

Spring 1882 6.6 59.7 12.6 67.5 

Summer 1975 3.4 74.0 6.7 79.4 

Fall 1736 -3.0 61.6 -9.7 74.2 

Table A-11. Nitrate and sulfate wet deposition performance statistics by season in the 

Western domain for the 2005 CMAQ model simulation. 

Wet Deposition 

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

Nitrate 

Winter 649 8.0 82.1 5.3 83.3 

Spring 768 -0.9 67.1 2.5 73.6 

Summer 641 -27.5 63.5 -23.1 79.6 

Fall 674 -4.8 76.0 -6.5 84.4 

Sulfate 

Winter 649 25.0 86.8 25.6 88.8 

Spring 768 16.7 73.0 18.3 77.3 

Summer 641 -5.0 73.9 -1.6 81.6 

Fall 674 -8.6 76.7 -4.9 86.7 
B-1 

Air Quality Modeling 

Technical Support Document: Heavy-Duty Vehicle 

Greenhouse Gas Emission Standards Final Rule 

Appendix B 

8-Hour Ozone Design Values for Air Quality Modeling 

Scenarios

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711 

July 2011 

 
B-2 

Table B-1. 8-Hour Ozone Design Values for HDGHG Scenarios (units are ppb) 

State County 2005 Baseline

DV

2030 Reference

Case DV

2030 Control

Case DV

Alabama Baldwin 77.3 58.67 58.30

Alabama Clay 74.0 51.11 50.58

Alabama Colbert 72.0 56.62 56.29

Alabama Elmore 70.7 50.16 49.74

Alabama Etowah 71.7 51.30 50.74

Alabama Houston 71.0 52.45 51.92

Alabama Jefferson 83.7 59.20 58.70

Alabama Lawrence 72.0 52.76 52.24

Alabama Madison 77.3 55.76 54.95

Alabama Mobile 76.7 58.96 58.60

Alabama Montgomery 69.3 49.33 48.92

Alabama Morgan 77.3 59.10 58.60

Alabama Russell 71.3 50.89 50.33

Alabama Shelby 85.7 59.65 59.09

Alabama Sumter 64.0 52.12 51.78

Alabama Talladega 72.0 51.40 50.88

Alabama Tuscaloosa 73.3 51.66 51.21

Arizona Cochise 71.3 60.46 59.92

Arizona Coconino 73.0 59.60 59.58

Arizona Gila 80.3 58.21 57.17

Arizona Maricopa 83.0 64.54 63.53

Arizona Pima 76.0 57.76 57.29

Arizona Pinal 79.3 57.31 56.19

Arizona Yuma 75.0 57.46 57.30

Arkansas Crittenden 87.3 62.64 61.92

Arkansas Newton 72.7 54.42 53.87

Arkansas Polk 75.0 60.06 59.59

Arkansas Pulaski 79.7 54.70 53.74

California Alameda 78.3 65.62 65.59

California Amador 83.0 64.41 64.38

California Butte 83.7 63.29 63.26

California Calaveras 91.3 73.75 73.71

California Colusa 67.0 52.91 52.89

California Contra Costa 73.3 65.43 65.40

California El Dorado 96.0 71.35 71.32

California Fresno 98.3 79.16 79.14
B-3 

California Glenn 67.0 53.34 53.32

California Imperial 85.0 68.46 68.43

California Inyo 82.3 65.70 65.67

California Kern 110.0 90.82 90.80

California Kings 85.7 67.32 67.30

California Lake 60.7 49.11 49.09

California Los Angeles 114.0 96.95 96.92

California Madera 79.3 63.30 63.28

California Marin 49.7 42.23 42.20

California Mariposa 86.3 69.38 69.35

California Mendocino 56.7 45.45 45.42

California Merced 89.3 70.43 70.41

California Monterey 61.0 49.90 49.87

California Napa 59.3 48.10 48.08

California Nevada 96.3 72.10 72.08

California Orange 84.3 80.68 80.65

California Placer 94.0 70.03 70.00

California Riverside 112.3 109.51 109.49

California Sacramento 97.3 73.25 73.22

California San Benito 75.0 59.29 59.28

California San Bernardino 123.3 119.34 119.31

California San Diego 87.7 70.17 70.16

California San Francisco 46.0 45.31 45.30

California San Joaquin 75.3 62.44 62.42

California San Luis Obispo 70.7 56.75 56.73

California San Mateo 53.7 49.32 49.30

California Santa Barbara 76.0 60.38 60.37

California Santa Clara 75.3 58.77 58.75

California Santa Cruz 61.3 51.87 51.86

California Shasta 79.3 62.71 62.67

California Siskiyou 63.5 50.46 50.34

California Solano 73.5 58.68 58.65

California Sonoma 47.7 37.83 37.80

California Stanislaus 84.7 68.66 68.64

California Sutter 82.0 64.00 63.97

California Tehama 82.7 64.70 64.67

California Tulare 103.7 81.64 81.62

California Tuolumne 80.0 64.23 64.20

California Ventura 89.7 70.59 70.57

California Yolo 78.7 61.77 61.74
B-4 

Colorado Adams 69.0 60.18 59.94

Colorado Arapahoe 78.7 65.54 65.25

Colorado Boulder 77.0 64.86 64.55

Colorado Denver 73.0 63.67 63.42

Colorado Douglas 83.7 69.83 69.50

Colorado El Paso 73.3 62.13 61.92

Colorado Jefferson 81.7 71.90 71.61

Colorado La Plata 72.0 63.40 63.30

Colorado Larimer 76.0 63.23 62.93

Colorado Montezuma 72.0 65.30 65.19

Colorado Weld 76.7 67.28 67.11

Connecticut Fairfield 92.3 74.60 74.36

Connecticut Hartford 84.3 61.43 61.10

Connecticut Litchfield 87.7 63.89 63.59

Connecticut Middlesex 90.3 69.13 68.85

Connecticut New Haven 90.3 70.55 70.28

Connecticut New London 85.3 64.44 64.23

Connecticut Tolland 88.7 64.86 64.48

D.C. Washington 84.7 65.11 64.82

Delaware Kent 80.3 60.25 59.99

Delaware New Castle 82.3 64.35 64.07

Delaware Sussex 82.7 62.37 62.12

Florida Alachua 72.0 51.05 50.69

Florida Baker 68.7 50.19 49.88

Florida Bay 78.7 57.91 57.59

Florida Brevard 71.3 53.46 53.19

Florida Broward 65.0 54.39 54.23

Florida Collier 68.3 49.31 49.07

Florida Columbia 72.0 53.23 52.91

Florida Duval 77.7 59.12 58.90

Florida Escambia 82.7 60.32 59.86

Florida Highlands 72.3 56.66 56.39

Florida Hillsborough 80.7 61.30 60.99

Florida Holmes 70.3 53.44 53.02

Florida Lake 76.7 57.44 57.10

Florida Lee 70.3 53.38 53.14

Florida Leon 71.0 50.48 50.09

Florida Manatee 77.3 56.97 56.67

Florida Marion 73.0 49.14 48.74

Florida Miami‐Dade 71.3 61.54 61.31
B-5 

Florida Orange 79.3 61.13 60.75

Florida Osceola 72.0 51.23 50.88

Florida Palm Beach 65.0 53.96 53.76

Florida Pasco 76.3 57.33 57.07

Florida Pinellas 72.7 54.16 53.90

Florida Polk 74.7 56.01 55.78

Florida St Lucie 66.5 52.05 51.82

Florida Santa Rosa 80.0 59.17 58.76

Florida Sarasota 77.3 55.33 55.05

Florida Seminole 76.0 55.99 55.62

Florida Volusia 68.3 48.35 48.06

Florida Wakulla 71.3 51.73 51.27

Georgia Bibb 81.0 53.55 52.99

Georgia Chatham 68.3 51.53 51.23

Georgia Chattooga 75.0 52.54 52.00

Georgia Clarke 80.7 51.99 51.21

Georgia Cobb 82.7 55.91 54.92

Georgia Columbia 73.0 52.53 52.09

Georgia Coweta 82.0 58.26 57.67

Georgia Dawson 76.3 49.07 48.38

Georgia De Kalb 88.7 65.47 64.58

Georgia Douglas 87.3 58.31 57.43

Georgia Fayette 85.7 62.34 61.59

Georgia Fulton 91.7 67.69 66.76

Georgia Glynn 67.0 49.60 49.37

Georgia Gwinnett 88.7 61.23 60.33

Georgia Henry 89.7 62.26 61.42

Georgia Murray 78.0 56.30 55.72

Georgia Muscogee 75.7 51.68 51.08

Georgia Paulding 80.3 52.28 51.56

Georgia Richmond 80.3 57.43 56.94

Georgia Rockdale 90.0 60.39 59.50

Georgia Sumter 72.3 50.85 50.40

Idaho Ada 76.0 67.03 66.72

Idaho Canyon 66.0 55.32 55.03

Idaho Elmore 63.0 54.57 54.33

Idaho Kootenai 67.0 54.73 54.35

Illinois Adams 70.0 56.01 55.55

Illinois Champaign 68.3 54.22 53.75

Illinois Clark 66.0 52.56 52.11
B-6 

Illinois Cook 77.7 67.96 67.52

Illinois Du Page 69.0 60.77 60.15

Illinois Effingham 70.0 55.37 54.84

Illinois Hamilton 73.0 55.85 55.39

Illinois Jersey 78.7 57.76 56.75

Illinois Kane 74.3 60.82 60.16

Illinois Lake 78.0 66.67 66.17

Illinois McHenry 73.3 57.83 57.19

Illinois McLean 73.0 56.46 55.96

Illinois Macon 71.3 57.04 56.56

Illinois Macoupin 73.0 51.66 50.85

Illinois Madison 83.0 63.51 62.47

Illinois Peoria 72.7 58.96 58.53

Illinois Randolph 72.0 55.99 55.48

Illinois Rock Island 65.3 51.08 50.64

Illinois St Clair 81.7 64.26 63.26

Illinois Sangamon 70.0 51.82 51.25

Illinois Will 71.7 58.77 58.17

Illinois Winnebago 69.0 53.29 52.74

Indiana Allen 79.3 61.84 60.87

Indiana Boone 79.7 62.45 61.16

Indiana Carroll 74.0 56.64 55.75

Indiana Clark 80.3 62.60 61.38

Indiana Delaware 76.3 57.11 56.18

Indiana Elkhart 79.0 61.18 60.44

Indiana Floyd 77.7 63.72 62.63

Indiana Greene 78.3 62.19 61.65

Indiana Hamilton 82.7 64.04 62.60

Indiana Hancock 78.0 60.72 59.23

Indiana Hendricks 75.3 59.83 58.65

Indiana Huntington 75.0 58.32 57.48

Indiana Jackson 74.7 57.75 57.10

Indiana Johnson 76.7 60.76 59.89

Indiana Lake 81.0 69.04 68.59

Indiana La Porte 78.5 63.94 63.49

Indiana Madison 76.7 58.20 56.87

Indiana Marion 78.7 62.27 60.91

Indiana Morgan 77.0 60.94 59.90

Indiana Perry 81.0 63.90 63.24

Indiana Porter 78.3 65.32 65.09
B-7 

Indiana Posey 71.7 54.56 53.95

Indiana St Joseph 79.3 61.81 61.07

Indiana Shelby 77.3 62.07 61.06

Indiana Vanderburgh 77.3 59.74 59.18

Indiana Vigo 74.0 58.85 58.20

Indiana Warrick 77.7 60.55 60.13

Iowa Bremer 66.3 52.46 52.03

Iowa Clinton 71.3 56.25 55.82

Iowa Harrison 74.7 58.98 58.66

Iowa Linn 68.3 54.46 54.05

Iowa Montgomery 65.7 50.90 50.52

Iowa Palo Alto 61.0 49.32 49.05

Iowa Polk 63.0 48.16 47.69

Iowa Scott 72.0 55.64 55.15

Iowa Story 61.0 46.60 46.15

Iowa Van Buren 69.0 53.94 53.53

Iowa Warren 64.5 48.27 47.80

Kansas Douglas 73.0 54.64 54.15

Kansas Johnson 75.3 57.37 56.84

Kansas Leavenworth 75.0 59.14 58.60

Kansas Linn 73.3 55.56 55.13

Kansas Sedgwick 71.3 54.72 54.28

Kansas Sumner 71.7 54.91 54.47

Kansas Trego 70.7 59.87 59.58

Kansas Wyandotte 75.3 60.61 60.03

Kentucky Bell 71.7 50.82 50.15

Kentucky Boone 75.7 58.82 58.23

Kentucky Boyd 77.3 60.37 59.94

Kentucky Bullitt 74.0 60.42 59.54

Kentucky Campbell 83.0 68.61 67.91

Kentucky Carter 71.0 53.54 53.12

Kentucky Christian 78.0 56.75 56.25

Kentucky Daviess 75.7 59.55 59.16

Kentucky Edmonson 73.7 56.80 56.32

Kentucky Fayette 70.3 53.28 52.67

Kentucky Greenup 76.7 60.24 59.82

Kentucky Hancock 74.0 57.48 57.02

Kentucky Hardin 74.7 58.81 57.96

Kentucky Henderson 75.3 59.10 58.71

Kentucky Jefferson 78.3 65.64 64.55
B-8 

Kentucky Jessamine 73.3 60.05 59.52

Kentucky Kenton 78.7 62.17 61.48

Kentucky Livingston 73.7 57.96 57.55

Kentucky McCracken 73.3 59.03 58.67

Kentucky McLean 73.0 57.25 56.83

Kentucky Oldham 83.0 63.44 62.05

Kentucky Perry 72.3 54.87 54.37

Kentucky Pike 66.7 50.81 50.37

Kentucky Pulaski 70.3 55.44 54.98

Kentucky Simpson 75.7 56.39 55.88

Kentucky Trigg 70.0 51.90 51.40

Kentucky Warren 72.0 55.28 54.82

Louisiana Ascension 82.0 66.93 66.63

Louisiana Beauregard 75.0 63.56 63.30

Louisiana Bossier 78.0 58.87 58.28

Louisiana Caddo 79.0 60.03 59.46

Louisiana Calcasieu 82.0 68.60 68.26

Louisiana East Baton Rouge 92.0 74.53 74.07

Louisiana Iberville 85.0 69.97 69.67

Louisiana Jefferson 83.0 68.67 68.29

Louisiana Lafayette 82.0 64.18 63.76

Louisiana Lafourche 79.3 64.64 64.35

Louisiana Livingston 78.3 63.16 62.81

Louisiana Ouachita 75.3 57.44 56.94

Louisiana Pointe Coupee 83.7 69.51 69.18

Louisiana St Bernard 78.0 63.14 62.68

Louisiana St Charles 77.3 63.64 63.28

Louisiana St James 76.3 62.87 62.58

Louisiana St John The Baptis 79.0 66.59 66.28

Louisiana St Mary 76.0 60.75 60.51

Louisiana West Baton Rouge 84.3 68.74 68.37

Maine Cumberland 72.0 53.59 53.26

Maine Hancock 82.0 61.23 60.90

Maine Kennebec 69.7 51.42 51.14

Maine Knox 75.3 55.72 55.39

Maine Oxford 61.0 48.98 48.76

Maine Penobscot 67.0 51.40 51.18

Maine Sagadahoc 68.5 50.70 50.39

Maine York 74.0 56.06 55.75

Maryland Anne Arundel 89.7 64.74 64.43
B-9 

Maryland Baltimore 85.3 70.35 70.15

Maryland Calvert 81.0 58.96 58.74

Maryland Carroll 83.3 60.25 59.95

Maryland Cecil 90.7 66.57 66.27

Maryland Charles 86.0 62.59 62.30

Maryland Frederick 80.3 57.63 57.34

Maryland Garrett 75.5 59.57 59.26

Maryland Harford 92.7 75.26 75.03

Maryland Kent 82.0 60.68 60.41

Maryland Montgomery 83.0 61.66 61.35

Maryland Prince Georges 91.0 66.83 66.53

Maryland Washington 78.3 57.43 57.12

Massachusetts Barnstable 84.7 65.67 65.37

Massachusetts Berkshire 79.7 59.58 59.23

Massachusetts Bristol 82.7 63.14 62.92

Massachusetts Dukes 83.0 65.13 64.95

Massachusetts Essex 83.3 67.78 67.43

Massachusetts Hampden 87.3 63.60 63.21

Massachusetts Hampshire 85.0 62.21 61.82

Massachusetts Middlesex 79.0 60.39 59.98

Massachusetts Norfolk 84.7 65.21 64.91

Massachusetts Suffolk 80.3 61.94 61.59

Massachusetts Worcester 80.0 58.04 57.65

Michigan Allegan 90.0 72.53 71.92

Michigan Benzie 81.7 64.83 64.29

Michigan Berrien 82.3 67.01 66.44

Michigan Cass 80.7 62.65 62.06

Michigan Clinton 75.7 57.36 56.87

Michigan Genesee 79.3 61.79 61.28

Michigan Huron 75.7 60.86 60.43

Michigan Ingham 76.0 58.63 58.17

Michigan Kalamazoo 75.3 58.07 57.56

Michigan Kent 81.0 61.81 61.26

Michigan Leelanau 75.7 60.72 60.25

Michigan Lenawee 78.7 62.39 61.99

Michigan Macomb 86.0 69.20 68.62

Michigan Mason 79.7 61.99 61.39

Michigan Missaukee 73.7 58.46 57.97

Michigan Muskegon 85.0 68.50 67.90

Michigan Oakland 78.0 65.43 64.99
B-10 

Michigan Ottawa 81.7 64.20 63.66

Michigan St Clair 82.3 64.24 63.68

Michigan Schoolcraft 79.3 62.45 61.86

Michigan Washtenaw 78.3 62.78 62.31

Michigan Wayne 82.0 66.45 65.96

Minnesota Anoka 67.7 60.76 60.51

Minnesota St Louis 65.0 52.61 52.29

Mississippi Adams 74.7 58.90 58.52

Mississippi Bolivar 74.3 56.30 55.82

Mississippi De Soto 82.7 60.13 59.46

Mississippi Hancock 79.0 61.40 60.95

Mississippi Harrison 83.0 62.54 62.12

Mississippi Hinds 71.3 47.69 46.90

Mississippi Jackson 80.3 61.53 61.16

Mississippi Lauderdale 74.3 56.77 56.34

Mississippi Lee 73.7 51.61 51.00

Missouri Cass 74.7 56.50 56.01

Missouri Cedar 75.7 57.14 56.66

Missouri Clay 84.7 65.40 64.68

Missouri Clinton 83.0 62.83 62.15

Missouri Greene 73.0 56.18 55.66

Missouri Jefferson 82.3 67.07 66.08

Missouri Lincoln 87.0 67.72 66.76

Missouri Monroe 71.7 55.12 54.54

Missouri Perry 77.5 58.97 58.52

Missouri Platte 77.0 61.24 60.65

Missouri St Charles 87.0 66.15 65.06

Missouri Ste Genevieve 79.7 64.94 64.35

Missouri St Louis 88.0 70.76 69.59

Missouri St Louis City 84.0 67.30 66.20

Montana Yellowstone 59.0 53.16 52.99

Nebraska Douglas 68.7 56.54 56.29

Nebraska Lancaster 56.0 44.11 43.75

Nevada Churchill 64.0 51.53 51.47

Nevada Clark 83.7 69.35 69.22

Nevada Washoe 70.7 55.42 55.33

Nevada White Pine 72.3 58.82 58.80

Nevada Carson City 65.0 49.85 49.82

New Hampshire Belknap 71.3 51.52 51.23

New Hampshire Cheshire 70.7 51.60 51.28
B-11 

New Hampshire Coos 77.0 59.96 59.62

New Hampshire Grafton 67.0 52.03 51.74

New Hampshire Hillsborough 78.7 59.77 59.36

New Hampshire Merrimack 71.7 52.24 51.91

New Hampshire Rockingham 77.0 58.34 58.01

New Hampshire Sullivan 70.0 53.19 52.88

New Jersey Atlantic 79.3 60.79 60.58

New Jersey Bergen 86.0 71.98 71.74

New Jersey Camden 89.3 69.45 69.13

New Jersey Cumberland 83.3 61.33 61.03

New Jersey Gloucester 87.0 68.01 67.70

New Jersey Hudson 85.7 75.41 75.18

New Jersey Hunterdon 89.0 66.16 65.74

New Jersey Mercer 88.0 69.07 68.68

New Jersey Middlesex 88.3 68.68 68.34

New Jersey Monmouth 87.3 69.90 69.67

New Jersey Morris 83.3 62.10 61.79

New Jersey Ocean 93.0 70.88 70.55

New Jersey Passaic 81.0 63.20 62.92

New Mexico Bernalillo 77.0 62.44 61.69

New Mexico Dona Ana 75.3 62.92 62.75

New Mexico Eddy 69.0 62.00 61.81

New Mexico Lea 71.0 64.28 64.08

New Mexico Sandoval 73.3 59.24 58.50

New Mexico San Juan 71.3 66.78 66.69

New York Albany 73.7 55.88 55.60

New York Bronx 74.7 65.54 65.34

New York Chautauqua 86.7 72.60 72.22

New York Chemung 68.7 54.29 53.89

New York Dutchess 75.7 55.54 55.29

New York Erie 85.0 68.76 68.33

New York Essex 77.0 62.51 62.13

New York Hamilton 71.7 56.56 56.26

New York Herkimer 68.3 55.22 54.94

New York Jefferson 78.0 63.28 63.02

New York Madison 72.0 55.04 54.64

New York Monroe 76.3 60.69 60.43

New York Niagara 82.7 69.88 69.63

New York Oneida 68.3 54.18 53.88

New York Onondaga 73.7 58.20 57.87
B-12 

New York Orange 82.0 60.35 60.06

New York Oswego 78.0 65.89 65.67

New York Putnam 84.3 64.01 63.74

New York Queens 80.0 66.66 66.47

New York Rensselaer 77.3 58.53 58.22

New York Richmond 88.3 73.24 73.00

New York Saratoga 79.7 60.38 60.06

New York Schenectady 70.0 53.79 53.50

New York Suffolk 90.3 77.36 77.17

New York Ulster 77.3 58.19 57.86

New York Wayne 68.0 55.97 55.76

New York Westchester 87.7 73.48 73.23

North Carolina Alexander 77.0 54.96 54.58

North Carolina Avery 70.0 52.19 51.76

North Carolina Buncombe 74.0 53.42 52.98

North Carolina Caldwell 74.3 53.73 53.33

North Carolina Caswell 76.3 53.04 52.61

North Carolina Chatham 73.3 52.46 52.07

North Carolina Cumberland 81.7 56.95 56.46

North Carolina Davie 81.3 56.58 56.14

North Carolina Durham 77.0 53.43 52.98

North Carolina Edgecombe 77.0 56.94 56.58

North Carolina Forsyth 80.0 56.67 56.20

North Carolina Franklin 78.7 55.72 55.30

North Carolina Graham 78.3 56.66 55.91

North Carolina Granville 82.0 58.64 58.22

North Carolina Guilford 82.0 56.27 55.71

North Carolina Haywood 78.3 59.19 58.70

North Carolina Jackson 76.0 55.19 54.54

North Carolina Johnston 77.3 53.58 53.10

North Carolina Lenoir 75.3 55.83 55.45

North Carolina Lincoln 81.0 56.91 56.47

North Carolina Martin 75.0 58.38 58.13

North Carolina Mecklenburg 89.3 64.48 64.02

North Carolina New Hanover 72.3 55.30 54.99

North Carolina Person 77.3 57.87 57.62

North Carolina Pitt 76.3 54.48 54.08

North Carolina Rockingham 77.0 53.69 53.26

North Carolina Rowan 86.7 59.71 59.23

North Carolina Swain 66.3 48.33 47.78
B-13 

North Carolina Union 79.3 54.49 54.01

North Carolina Wake 80.3 56.90 56.43

North Carolina Yancey 76.0 54.82 54.31

North Dakota Billings 61.5 54.60 54.51

North Dakota Burke 57.5 52.15 52.08

North Dakota Cass 60.0 48.83 48.58

North Dakota McKenzie 61.3 55.21 55.12

North Dakota Mercer 59.3 56.66 56.63

North Dakota Oliver 57.7 54.59 54.54

Ohio Allen 78.7 61.46 60.83

Ohio Ashtabula 89.0 72.07 71.54

Ohio Butler 83.3 64.79 64.01

Ohio Clark 81.0 60.61 59.87

Ohio Clermont 81.0 65.19 64.54

Ohio Clinton 82.3 61.09 60.35

Ohio Cuyahoga 79.7 65.67 65.26

Ohio Delaware 78.3 60.43 59.75

Ohio Franklin 86.3 66.86 66.07

Ohio Geauga 79.3 61.18 60.64

Ohio Greene 80.3 60.60 59.83

Ohio Hamilton 84.7 67.14 66.42

Ohio Jefferson 78.0 59.65 59.26

Ohio Knox 77.7 58.53 57.83

Ohio Lake 86.3 69.58 69.11

Ohio Lawrence 70.7 55.52 55.14

Ohio Licking 78.0 58.41 57.72

Ohio Lorain 76.7 62.68 62.28

Ohio Lucas 81.3 65.52 65.18

Ohio Madison 79.7 59.19 58.47

Ohio Mahoning 78.7 59.43 58.82

Ohio Medina 80.3 63.16 62.60

Ohio Miami 76.7 56.98 56.24

Ohio Montgomery 74.0 55.53 54.82

Ohio Portage 83.7 64.25 63.57

Ohio Preble 73.0 54.86 54.26

Ohio Stark 81.0 61.75 61.13

Ohio Summit 83.7 65.03 64.29

Ohio Trumbull 84.3 64.07 63.42

Ohio Warren 88.3 66.83 65.92

Ohio Washington 82.7 66.35 66.04
B-14 

Ohio Wood 80.0 62.93 62.43

Oklahoma Adair 75.7 59.90 59.56

Oklahoma Canadian 76.0 59.72 59.07

Oklahoma Cherokee 75.7 60.60 60.33

Oklahoma Cleveland 74.7 57.83 57.26

Oklahoma Comanche 77.5 59.45 58.91

Oklahoma Creek 76.7 58.64 58.10

Oklahoma Dewey 72.7 56.78 56.34

Oklahoma Kay 78.0 60.20 59.74

Oklahoma Mc Clain 72.0 56.01 55.49

Oklahoma Mayes 78.5 62.99 62.68

Oklahoma Oklahoma 80.0 60.68 60.02

Oklahoma Ottawa 78.0 60.21 59.81

Oklahoma Pittsburg 72.0 57.88 57.49

Oklahoma Tulsa 79.3 62.24 61.77

Oregon Clackamas 66.3 59.79 59.40

Oregon Jackson 68.0 51.65 51.28

Oregon Lane 69.3 54.35 53.94

Oregon Marion 65.7 54.11 53.64

Oregon Multnomah 57.0 68.63 68.02

Pennsylvania Adams 76.3 56.37 56.05

Pennsylvania Allegheny 83.7 65.04 64.67

Pennsylvania Armstrong 83.0 63.91 63.53

Pennsylvania Beaver 83.0 65.17 64.77

Pennsylvania Berks 80.0 60.87 60.44

Pennsylvania Blair 74.3 57.57 57.23

Pennsylvania Bucks 88.0 70.90 70.55

Pennsylvania Cambria 74.7 60.17 59.87

Pennsylvania Centre 78.3 61.01 60.59

Pennsylvania Chester 86.0 63.27 62.96

Pennsylvania Clearfield 78.3 60.22 59.75

Pennsylvania Dauphin 79.3 63.31 62.98

Pennsylvania Delaware 83.3 64.72 64.40

Pennsylvania Erie 81.3 66.47 66.04

Pennsylvania Franklin 72.3 52.98 52.70

Pennsylvania Greene 80.0 65.33 65.03

Pennsylvania Indiana 80.0 63.22 62.88

Pennsylvania Lackawanna 75.3 56.42 55.96

Pennsylvania Lancaster 83.3 64.22 63.86

Pennsylvania Lawrence 72.3 55.41 54.91
B-15 

Pennsylvania Lehigh 83.3 62.89 62.46

Pennsylvania Luzerne 76.3 57.12 56.66

Pennsylvania Lycoming 77.3 60.47 60.05

Pennsylvania Mercer 82.0 62.27 61.61

Pennsylvania Montgomery 85.7 67.65 67.24

Pennsylvania Northampton 84.3 63.40 62.96

Pennsylvania Perry 77.0 59.02 58.60

Pennsylvania Philadelphia 90.3 72.70 72.33

Pennsylvania Tioga 77.7 60.79 60.37

Pennsylvania Washington 78.3 64.27 63.99

Pennsylvania Westmoreland 79.0 62.13 61.75

Pennsylvania York 82.0 63.20 62.90

Rhode Island Kent 84.3 63.64 63.40

Rhode Island Providence 82.3 61.68 61.42

Rhode Island Washington 86.0 65.84 65.61

South Carolina Abbeville 79.0 57.12 56.56

South Carolina Aiken 76.0 53.62 53.10

South Carolina Anderson 76.5 53.00 52.53

South Carolina Barnwell 73.0 53.83 53.35

South Carolina Berkeley 67.3 49.60 49.26

South Carolina Charleston 74.0 56.30 55.92

South Carolina Cherokee 74.0 52.56 52.17

South Carolina Chester 75.7 52.36 51.92

South Carolina Chesterfield 75.0 55.35 54.87

South Carolina Colleton 72.3 53.16 52.69

South Carolina Darlington 76.3 55.06 54.57

South Carolina Edgefield 70.0 49.23 48.74

South Carolina Oconee 73.0 50.55 50.09

South Carolina Pickens 78.7 54.46 53.95

South Carolina Richland 82.3 55.19 54.48

South Carolina Spartanburg 82.3 58.39 57.94

South Carolina Union 76.0 55.82 55.37

South Carolina Williamsburg 69.3 49.70 49.24

South Carolina York 76.7 53.72 53.27

South Dakota Custer 70.0 62.61 62.50

South Dakota Jackson 67.5 59.46 59.32

South Dakota Minnehaha 66.0 52.80 52.47

Tennessee Anderson 77.3 51.69 50.61

Tennessee Blount 85.3 56.66 55.42

Tennessee Davidson 77.7 54.66 54.08
B-16 

Tennessee Hamilton 81.0 56.14 55.58

Tennessee Jefferson 82.3 53.93 52.35

Tennessee Knox 85.0 57.19 55.64

Tennessee Loudon 85.0 56.07 55.23

Tennessee Meigs 80.0 53.89 53.22

Tennessee Rutherford 76.3 53.74 53.08

Tennessee Sevier 80.7 56.19 55.27

Tennessee Shelby 80.7 57.49 56.84

Tennessee Sullivan 80.3 65.72 65.36

Tennessee Sumner 83.0 59.18 58.53

Tennessee Williamson 75.3 52.90 52.30

Tennessee Wilson 78.7 56.46 55.89

Texas Bexar 85.0 68.14 67.35

Texas Brazoria 94.7 77.34 76.79

Texas Brewster 64.0 53.48 53.15

Texas Cameron 66.0 58.24 58.00

Texas Collin 90.3 67.77 67.07

Texas Dallas 88.3 69.97 69.20

Texas Denton 94.0 67.33 66.63

Texas Ellis 81.7 60.68 60.05

Texas Galveston 85.0 69.04 68.73

Texas Gregg 84.3 70.44 70.09

Texas Harris 100.7 83.71 83.04

Texas Harrison 79.0 62.59 62.15

Texas Hidalgo 65.7 55.50 55.22

Texas Hood 83.0 57.66 57.04

Texas Hunt 78.0 62.53 62.03

Texas Jefferson 84.7 69.78 69.44

Texas Johnson 87.0 61.37 60.76

Texas Kaufman 74.7 57.52 56.95

Texas Montgomery 85.0 67.47 66.75

Texas Nueces 72.3 60.49 60.20

Texas Orange 78.0 62.98 62.62

Texas Parker 88.7 61.35 60.69

Texas Rockwall 79.7 62.46 61.90

Texas Smith 81.0 66.10 65.64

Texas Tarrant 95.3 69.01 68.34

Texas Travis 81.3 62.83 61.88

Texas Victoria 72.3 58.70 58.35

Texas Webb 61.3 51.83 51.40
B-17 

Texas El Paso 77.7 64.02 63.82

Utah Box Elder 76.0 63.76 63.68

Utah Cache 68.7 57.27 57.01

Utah Davis 81.3 69.69 69.61

Utah Salt Lake 81.0 69.43 69.33

Utah San Juan 70.3 61.77 61.70

Utah Tooele 78.0 64.78 64.63

Utah Utah 76.7 66.18 66.08

Utah Washington 78.5 61.33 61.19

Utah Weber 80.3 67.01 66.94

Vermont Bennington 72.0 53.98 53.66

Vermont Chittenden 69.7 55.64 55.36

Virginia Arlington 86.7 68.35 68.03

Virginia Caroline 80.0 57.81 57.51

Virginia Charles City 80.3 62.36 62.07

Virginia Chesterfield 76.7 58.48 58.18

Virginia Fairfax 90.0 68.41 68.10

Virginia Fauquier 72.7 54.40 54.13

Virginia Frederick 72.3 53.08 52.77

Virginia Hanover 81.3 60.34 60.01

Virginia Henrico 82.0 61.95 61.65

Virginia Loudoun 80.7 57.41 57.11

Virginia Madison 77.7 57.46 57.14

Virginia Page 74.0 55.38 55.04

Virginia Prince William 78.7 57.90 57.63

Virginia Roanoke 74.7 56.25 55.77

Virginia Rockbridge 69.7 54.32 53.93

Virginia Stafford 81.7 60.37 60.04

Virginia Wythe 72.7 56.20 55.80

Virginia Alexandria City 81.7 62.10 61.82

Virginia Hampton City 76.7 63.25 63.08

Virginia Suffolk City 76.7 67.66 67.51

Washington Clark 59.5 60.26 60.10

Washington King 72.3 64.78 64.50

Washington Klickitat 64.5 56.28 56.08

Washington Pierce 68.7 58.47 58.05

Washington Skagit 46.0 46.99 46.97

Washington Spokane 68.3 55.02 54.72

Washington Thurston 65.0 52.03 51.61

Washington Whatcom 57.0 55.20 55.17
B-18 

West Virginia Berkeley 75.0 55.58 55.29

West Virginia Cabell 78.7 61.35 60.92

West Virginia Greenbrier 69.7 57.31 56.99

West Virginia Hancock 75.7 58.80 58.43

West Virginia Kanawha 77.3 59.39 59.04

West Virginia Monongalia 75.3 63.49 63.25

West Virginia Ohio 78.3 59.45 59.08

West Virginia Wood 79.0 62.74 62.43

Wisconsin Ashland 63.0 51.22 50.91

Wisconsin Brown 73.7 59.42 59.00

Wisconsin Columbia 72.7 55.75 55.32

Wisconsin Dane 72.0 56.08 55.63

Wisconsin Dodge 74.7 58.54 58.07

Wisconsin Door 88.7 69.81 69.15

Wisconsin Florence 66.3 53.94 53.61

Wisconsin Fond Du Lac 73.7 58.61 58.15

Wisconsin Forest 69.5 56.22 55.87

Wisconsin Jefferson 74.3 57.51 57.04

Wisconsin Kenosha 84.7 73.40 72.88

Wisconsin Kewaunee 82.7 66.06 65.49

Wisconsin Manitowoc 85.0 68.68 68.08

Wisconsin Marathon 70.0 56.60 56.27

Wisconsin Milwaukee 82.7 69.79 69.24

Wisconsin Oneida 69.0 56.18 55.85

Wisconsin Outagamie 74.0 59.05 58.66

Wisconsin Ozaukee 83.3 70.08 69.58

Wisconsin Racine 80.3 69.95 69.47

Wisconsin Rock 74.0 57.42 56.85

Wisconsin St Croix 69.0 55.48 55.20

Wisconsin Sauk 69.7 54.12 53.71

Wisconsin Sheboygan 88.0 71.71 71.10

Wisconsin Vernon 69.7 53.79 53.33

Wisconsin Vilas 68.7 55.94 55.61

Wisconsin Walworth 75.7 58.11 57.53

Wisconsin Washington 72.3 58.44 57.99

Wisconsin Waukesha 75.0 60.78 60.27

Wyoming Campbell 67.3 62.28 62.20

Wyoming Sublette 70.0 64.98 64.91

Wyoming Teton 62.7 55.00 54.94
C-1 

Air Quality Modeling 

Technical Support Document: Heavy-Duty Vehicle 

Greenhouse Gas Emission Standards Final Rule 

Appendix C 

Annual PM2.5 Design Values for Air Quality Modeling 

Scenarios

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711 

July 2011 

 
C-2 

Table C-1. Annual PM2.5 Design Values for HDGHG Scenarios (units are ug/m3) 

 

State Name County Name Baseline DV

2030

Reference

Case DV

2030 Control

Case DV

Alabama Baldwin 11.44 8.34 8.34

Alabama Clay 13.27 9.25 9.25

Alabama Colbert 12.75 8.95 8.95

Alabama DeKalb 14.13 9.58 9.58

Alabama Escambia 13.19 10.23 10.23

Alabama Etowah 14.87 10.21 10.21

Alabama Houston 13.22 10.13 10.13

Alabama Jefferson 18.57 13.06 13.05

Alabama Madison 13.83 9.31 9.31

Alabama Mobile 12.90 9.51 9.51

Alabama Montgomery 14.24 10.52 10.53

Alabama Morgan 13.32 9.19 9.19

Alabama Russell 15.73 11.32 11.32

Alabama Shelby 14.43 10.18 10.17

Alabama Sumter 11.92 8.59 8.59

Alabama Talladega 14.51 10.23 10.23

Alabama Tuscaloosa 13.56 9.66 9.66

Alabama Walker 13.86 9.71 9.71

Arizona Cochise 7.00 6.63 6.63

Arizona Coconino 6.49 6.02 6.02

Arizona Gila 8.94 8.27 8.27

Arizona Maricopa 12.59 10.28 10.29

Arizona Pima 6.04 5.17 5.17

Arizona Pinal 7.77 6.95 6.95

Arizona Santa Cruz 12.94 12.21 12.22

Arkansas Arkansas 12.45 9.46 9.46

Arkansas Ashley 12.83 10.18 10.18

Arkansas Crittenden 13.36 9.24 9.24

Arkansas Faulkner 12.79 9.84 9.83

Arkansas Garland 12.40 9.60 9.59

Arkansas Mississippi 12.61 8.90 8.90

Arkansas Phillips 12.10 8.76 8.76

Arkansas Polk 11.65 9.14 9.15

Arkansas Pope 12.79 10.17 10.17

Arkansas Pulaski 14.05 10.62 10.62
C-3 

Arkansas Union 12.86 10.07 10.07

Arkansas White 12.57 9.77 9.77

California Alameda 9.34 8.59 8.59

California Butte 12.73 10.34 10.34

California Calaveras 7.77 6.49 6.49

California Colusa 7.39 6.59 6.59

California Contra Costa 9.47 8.40 8.39

California Fresno 17.17 14.67 14.67

California Imperial 12.71 11.68 11.68

California Inyo 5.25 4.92 4.92

California Kern 19.17 15.61 15.60

California Kings 17.28 14.44 14.44

California Lake 4.62 4.06 4.06

California Los Angeles 18.19 14.72 14.71

California Mendocino 6.46 5.40 5.39

California Merced 14.78 12.68 12.68

California Monterey 6.96 5.97 5.97

California Nevada 6.71 5.78 5.78

California Orange 15.75 13.05 13.04

California Placer 9.80 8.15 8.15

California Plumas 11.46 9.80 9.80

California Riverside 20.95 17.48 17.47

California Sacramento 11.88 10.43 10.43

California San Bernardino 19.67 16.85 16.84

California San Diego 13.46 11.94 11.94

California San Francisco 9.62 8.80 8.80

California San Joaquin 12.94 11.33 11.33

California San Luis Obispo 7.94 6.59 6.59

California San Mateo 9.03 8.12 8.12

California Santa Barbara 10.37 8.78 8.78

California Santa Clara 11.38 10.56 10.56

California Shasta 7.41 5.99 5.99

California Solano 9.99 9.08 9.08

California Sonoma 8.21 7.07 7.07

California Stanislaus 14.21 11.87 11.87

California Sutter 9.85 8.09 8.09

California Tulare 18.51 15.44 15.44

California Ventura 11.68 9.72 9.72

California Yolo 9.03 7.82 7.82

Colorado Adams 10.06 8.50 8.49
C-4 

Colorado Arapahoe 7.96 6.73 6.73

Colorado Boulder 8.32 7.36 7.36

Colorado Delta 7.44 6.31 6.30

Colorado Denver 9.76 8.23 8.22

Colorado Elbert 4.40 3.93 3.93

Colorado El Paso 7.94 6.81 6.81

Colorado Larimer 7.33 6.69 6.69

Colorado Mesa 9.28 8.03 8.03

Colorado Pueblo 7.45 6.50 6.49

Colorado San Miguel 4.65 4.26 4.26

Colorado Weld 8.78 7.78 7.77

Connecticut Fairfield 13.21 9.38 9.38

Connecticut Hartford 11.03 8.00 8.00

Connecticut Litchfield 8.01 5.50 5.50

Connecticut New Haven 13.12 9.29 9.29

Connecticut New London 10.96 7.95 7.95

Delaware Kent 12.61 8.25 8.25

Delaware New Castle 14.87 10.00 9.98

Delaware Sussex 13.39 8.77 8.77

District Of Co District of Columbia 14.16 9.41 9.41

Florida Alachua 9.59 6.94 6.94

Florida Bay 11.46 8.83 8.83

Florida Brevard 8.32 5.75 5.75

Florida Broward 8.22 5.89 5.89

Florida Citrus 9.00 6.52 6.52

Florida Duval 10.44 7.85 7.85

Florida Escambia 11.72 9.30 9.30

Florida Hillsborough 10.74 7.56 7.56

Florida Lee 8.36 5.94 5.94

Florida Leon 12.56 9.62 9.62

Florida Manatee 8.81 5.85 5.85

Florida Marion 10.11 7.43 7.43

Florida Miami‐Dade 9.45 6.51 6.51

Florida Orange 9.61 6.69 6.69

Florida Palm Beach 7.84 5.74 5.74

Florida Pinellas 9.82 6.94 6.94

Florida Polk 9.53 6.74 6.74

Florida St. Lucie 8.34 5.84 5.84

Florida Sarasota 8.77 6.00 6.00

Florida Seminole 9.51 6.60 6.59
C-5 

Florida Volusia 9.27 6.45 6.45

Georgia Bibb 16.54 11.84 11.85

Georgia Chatham 13.93 10.10 10.10

Georgia Clarke 14.90 10.45 10.45

Georgia Clayton 16.50 11.17 11.17

Georgia Cobb 16.15 11.13 11.13

Georgia DeKalb 15.48 10.15 10.15

Georgia Dougherty 14.46 10.91 10.91

Georgia Floyd 16.13 11.34 11.34

Georgia Fulton 17.43 11.75 11.74

Georgia Glynn 12.25 9.26 9.26

Georgia Gwinnett 16.07 10.99 10.99

Georgia Hall 14.16 9.90 9.90

Georgia Houston 14.19 9.99 9.99

Georgia Lowndes 12.58 9.77 9.77

Georgia Muscogee 15.39 11.09 11.09

Georgia Paulding 14.12 9.40 9.40

Georgia Richmond 15.68 11.64 11.65

Georgia Walker 15.49 10.57 10.57

Georgia Washington 15.14 11.12 11.12

Georgia Wilkinson 15.27 10.94 10.94

Idaho Ada 8.41 7.52 7.49

Idaho Bannock 7.66 7.00 7.00

Idaho Benewah 9.59 8.80 8.80

Idaho Canyon 8.46 7.31 7.28

Idaho Franklin 7.70 6.63 6.61

Idaho Idaho 9.58 8.99 8.99

Idaho Shoshone 12.08 11.03 11.03

Illinois Adams 12.50 9.37 9.36

Illinois Champaign 12.53 8.87 8.86

Illinois Cook 15.75 11.39 11.37

Illinois DuPage 13.82 10.01 9.99

Illinois Jersey 12.89 9.37 9.35

Illinois Kane 14.34 10.47 10.44

Illinois Lake 11.81 8.71 8.69

Illinois McHenry 12.40 9.05 9.03

Illinois McLean 12.39 9.00 8.99

Illinois Macon 13.24 9.69 9.67

Illinois Madison 16.72 12.03 12.02

Illinois Peoria 13.34 9.84 9.83
C-6 

Illinois Randolph 13.11 9.34 9.33

Illinois Rock Island 12.01 9.00 8.99

Illinois Saint Clair 15.58 11.12 11.11

Illinois Sangamon 13.13 9.90 9.88

Illinois Will 13.63 9.76 9.74

Illinois Winnebago 13.57 10.21 10.19

Indiana Allen 13.67 10.12 10.08

Indiana Clark 16.44 10.71 10.70

Indiana Delaware 13.69 9.53 9.52

Indiana Dubois 15.19 9.94 9.92

Indiana Floyd 14.85 9.54 9.53

Indiana Henry 13.64 9.49 9.47

Indiana Howard 13.93 9.98 9.96

Indiana Knox 14.03 9.31 9.29

Indiana Lake 14.33 10.64 10.62

Indiana LaPorte 12.69 9.29 9.27

Indiana Madison 13.97 9.76 9.74

Indiana Marion 16.05 11.19 11.17

Indiana Porter 13.21 9.65 9.63

Indiana St. Joseph 13.69 10.51 10.48

Indiana Spencer 14.32 9.09 9.07

Indiana Tippecanoe 13.70 9.75 9.73

Indiana Vanderburgh 14.99 10.47 10.46

Indiana Vigo 13.99 9.40 9.38

Iowa Black Hawk 11.16 8.61 8.59

Iowa Clinton 12.52 9.46 9.45

Iowa Johnson 12.08 9.32 9.30

Iowa Linn 10.79 8.20 8.19

Iowa Montgomery 10.02 7.73 7.72

Iowa Muscatine 12.92 9.93 9.92

Iowa Palo Alto 9.53 7.56 7.55

Iowa Polk 10.64 8.15 8.13

Iowa Pottawattamie 11.13 8.68 8.67

Iowa Scott 14.42 11.03 11.02

Iowa Van Buren 10.84 8.31 8.30

Iowa Woodbury 10.32 8.26 8.26

Iowa Wright 10.37 8.06 8.05

Kansas Johnson 11.10 8.56 8.55

Kansas Linn 10.47 8.34 8.34

Kansas Sedgwick 10.36 8.29 8.28
C-7 

Kansas Shawnee 10.93 8.78 8.77

Kansas Sumner 9.89 8.02 8.02

Kansas Wyandotte 12.73 9.86 9.85

Kentucky Bell 14.10 9.30 9.30

Kentucky Boyd 14.49 9.40 9.40

Kentucky Bullitt 14.92 9.64 9.63

Kentucky Campbell 13.67 8.59 8.58

Kentucky Carter 12.22 7.64 7.64

Kentucky Christian 13.20 8.60 8.60

Kentucky Daviess 14.10 8.77 8.76

Kentucky Fayette 14.87 9.55 9.53

Kentucky Franklin 13.37 8.41 8.39

Kentucky Hardin 13.58 8.60 8.59

Kentucky Henderson 13.93 9.31 9.30

Kentucky Jefferson 15.55 10.00 9.99

Kentucky Kenton 14.39 9.20 9.19

Kentucky Laurel 12.55 8.05 8.05

Kentucky McCracken 13.41 9.06 9.06

Kentucky Madison 13.61 8.59 8.57

Kentucky Perry 13.21 8.63 8.63

Kentucky Pike 13.49 8.62 8.62

Kentucky Warren 13.83 8.95 8.95

Louisiana Caddo 12.53 9.56 9.56

Louisiana Calcasieu 11.07 8.64 8.63

Louisiana Concordia 11.42 8.50 8.50

Louisiana East Baton Rouge 13.38 10.38 10.37

Louisiana Iberville 12.90 9.89 9.88

Louisiana Jefferson 11.52 8.08 8.07

Louisiana Lafayette 11.08 8.25 8.25

Louisiana Ouachita 11.97 9.32 9.32

Louisiana Rapides 11.03 8.33 8.33

Louisiana Tangipahoa 12.03 8.79 8.79

Louisiana Terrebonne 10.74 7.89 7.89

Louisiana West Baton Rouge 13.51 10.49 10.47

Maine Androscoggin 9.90 7.72 7.72

Maine Aroostook 9.74 9.02 9.02

Maine Cumberland 11.13 8.61 8.62

Maine Hancock 5.76 4.53 4.54

Maine Kennebec 9.99 7.85 7.85

Maine Oxford 10.13 8.38 8.39
C-8 

Maine Penobscot 9.12 7.25 7.25

Maryland Anne Arundel 14.82 10.43 10.43

Maryland Baltimore 14.76 10.30 10.29

Maryland Cecil 12.68 8.31 8.30

Maryland Harford 12.51 8.29 8.28

Maryland Montgomery 12.47 8.28 8.28

Maryland Prince George's 13.03 8.54 8.53

Maryland Washington 13.70 9.26 9.26

Maryland Baltimore (City) 15.76 11.07 11.07

Massachusetts Berkshire 10.65 8.16 8.16

Massachusetts Bristol 9.58 6.99 6.99

Massachusetts Essex 9.58 7.27 7.27

Massachusetts Hampden 12.17 9.15 9.15

Massachusetts Plymouth 9.87 7.38 7.38

Massachusetts Suffolk 13.07 9.99 9.99

Massachusetts Worcester 11.29 8.45 8.45

Michigan Allegan 11.84 8.65 8.63

Michigan Bay 10.93 8.08 8.07

Michigan Berrien 11.72 8.60 8.57

Michigan Genesee 11.61 8.37 8.36

Michigan Ingham 12.23 8.82 8.81

Michigan Kalamazoo 12.84 9.42 9.40

Michigan Kent 12.89 9.31 9.29

Michigan Macomb 12.70 9.28 9.27

Michigan Missaukee 8.26 6.54 6.54

Michigan Monroe 13.92 9.78 9.76

Michigan Muskegon 11.61 8.59 8.57

Michigan Oakland 13.78 9.84 9.82

Michigan Ottawa 12.55 9.07 9.05

Michigan Saginaw 10.61 7.86 7.85

Michigan St. Clair 13.34 10.14 10.14

Michigan Washtenaw 13.88 9.76 9.75

Michigan Wayne 17.50 12.65 12.64

Minnesota Cass 5.70 4.94 4.93

Minnesota Dakota 9.30 7.40 7.39

Minnesota Hennepin 9.76 7.70 7.69

Minnesota Mille Lacs 6.54 5.48 5.47

Minnesota Olmsted 10.13 8.01 7.99

Minnesota Ramsey 11.32 9.09 9.08

Minnesota Saint Louis 7.51 6.22 6.22
C-9 

Minnesota Scott 9.00 7.19 7.18

Minnesota Stearns 8.58 7.06 7.05

Mississippi Adams 11.29 8.37 8.37

Mississippi Bolivar 12.36 9.26 9.26

Mississippi DeSoto 12.43 8.55 8.55

Mississippi Forrest 13.62 10.08 10.09

Mississippi Harrison 12.20 9.00 9.00

Mississippi Hinds 12.56 9.14 9.14

Mississippi Jackson 12.04 8.80 8.79

Mississippi Jones 14.39 10.59 10.59

Mississippi Lauderdale 13.07 9.49 9.49

Mississippi Lee 12.57 8.78 8.77

Mississippi Lowndes 12.79 9.23 9.23

Mississippi Pearl River 12.14 8.99 8.99

Mississippi Warren 12.32 9.11 9.11

Missouri Boone 11.84 9.02 9.00

Missouri Buchanan 12.80 10.17 10.17

Missouri Cass 10.67 8.25 8.24

Missouri Cedar 11.12 8.60 8.59

Missouri Clay 11.03 8.49 8.48

Missouri Greene 11.75 9.04 9.04

Missouri Jackson 12.78 9.82 9.81

Missouri Jefferson 13.79 10.06 10.05

Missouri Monroe 10.87 8.09 8.08

Missouri Saint Charles 13.29 9.69 9.66

Missouri Sainte Genevieve 13.34 9.69 9.68

Missouri Saint Louis 13.46 9.53 9.52

Missouri St. Louis City 14.56 10.32 10.30

Montana Cascade 5.72 5.15 5.15

Montana Flathead 9.99 8.72 8.72

Montana Gallatin 4.38 4.20 4.20

Montana Lake 9.06 8.06 8.06

Montana Lewis and Clark 8.20 7.39 7.39

Montana Lincoln 14.93 12.93 12.93

Montana Missoula 10.52 9.26 9.25

Montana Ravalli 9.01 8.05 8.05

Montana Rosebud 6.58 6.18 6.18

Montana Sanders 6.75 6.21 6.21

Montana Silver Bow 10.14 9.01 9.01

Montana Yellowstone 8.14 7.18 7.17
C-10 

Nebraska Cass 9.99 7.81 7.80

Nebraska Douglas 9.88 7.66 7.65

Nebraska Hall 7.95 6.53 6.52

Nebraska Lancaster 8.90 6.81 6.80

Nebraska Lincoln 7.57 6.61 6.61

Nebraska Sarpy 9.79 7.58 7.57

Nebraska Scotts Bluff 6.04 5.39 5.39

Nebraska Washington 9.29 7.31 7.30

Nevada Clark 9.44 8.24 8.24

Nevada Washoe 8.11 6.83 6.83

New Hampshire Belknap 7.28 5.52 5.52

New Hampshire Cheshire 11.53 8.88 8.88

New Hampshire Coos 10.24 8.59 8.59

New Hampshire Grafton 8.43 6.62 6.62

New Hampshire Hillsborough 10.18 7.64 7.64

New Hampshire Merrimack 9.72 7.26 7.26

New Hampshire Rockingham 9.00 6.89 6.89

New Hampshire Sullivan 9.86 7.70 7.70

New Jersey Atlantic 11.47 7.75 7.75

New Jersey Bergen 13.09 8.93 8.93

New Jersey Camden 13.31 9.01 9.00

New Jersey Essex 13.27 8.85 8.84

New Jersey Gloucester 13.46 9.02 9.01

New Jersey Hudson 14.24 9.72 9.72

New Jersey Mercer 12.71 8.65 8.65

New Jersey Middlesex 12.15 8.31 8.31

New Jersey Morris 11.50 7.83 7.83

New Jersey Ocean 10.92 7.24 7.24

New Jersey Passaic 12.88 8.66 8.66

New Jersey Union 14.94 10.05 10.05

New Jersey Warren 12.72 8.78 8.78

New Mexico Bernalillo 7.03 5.78 5.78

New Mexico Chaves 6.54 5.91 5.91

New Mexico Dona Ana 9.95 8.67 8.67

New Mexico Grant 5.93 5.57 5.57

New Mexico Sandoval 7.99 7.25 7.25

New Mexico San Juan 5.92 5.34 5.34

New Mexico Santa Fe 4.76 4.28 4.28

New York Albany 11.83 9.48 9.49

New York Bronx 15.43 11.01 11.00
C-11 

New York Chautauqua 9.80 6.73 6.72

New York Erie 12.62 9.09 9.09

New York Essex 5.94 4.74 4.74

New York Kings 14.20 9.73 9.73

New York Monroe 10.64 7.99 7.99

New York Nassau 11.66 7.91 7.91

New York New York 16.18 11.26 11.26

New York Niagara 11.96 8.95 8.94

New York Onondaga 10.08 7.46 7.46

New York Orange 10.99 7.80 7.80

New York Queens 12.18 8.34 8.34

New York Richmond 13.31 8.81 8.81

New York St. Lawrence 7.29 6.04 6.04

New York Steuben 9.00 6.37 6.37

New York Suffolk 11.52 7.77 7.77

New York Westchester 11.73 7.89 7.89

North Carolina Alamance 13.94 9.17 9.17

North Carolina Buncombe 12.60 8.39 8.39

North Carolina Caswell 13.19 8.52 8.52

North Carolina Catawba 15.31 9.94 9.94

North Carolina Chatham 11.99 7.78 7.78

North Carolina Cumberland 13.73 9.49 9.49

North Carolina Davidson 15.17 9.95 9.95

North Carolina Duplin 11.30 7.72 7.73

North Carolina Durham 13.57 8.98 8.98

North Carolina Edgecombe 12.37 8.49 8.50

North Carolina Forsyth 14.28 9.09 9.09

North Carolina Gaston 14.26 9.26 9.26

North Carolina Guilford 13.79 8.94 8.94

North Carolina Haywood 12.98 9.23 9.23

North Carolina Jackson 12.09 8.15 8.15

North Carolina Lenoir 11.12 7.59 7.59

North Carolina McDowell 14.24 9.68 9.68

North Carolina Martin 10.86 7.31 7.31

North Carolina Mecklenburg 15.31 10.20 10.20

North Carolina Mitchell 12.75 8.38 8.38

North Carolina Montgomery 12.35 8.15 8.15

North Carolina New Hanover 9.96 6.70 6.70

North Carolina Onslow 10.98 7.43 7.43

North Carolina Orange 13.12 8.54 8.54
C-12 

North Carolina Pitt 11.59 7.94 7.94

North Carolina Robeson 12.78 8.73 8.73

North Carolina Rowan 14.02 9.34 9.34

North Carolina Swain 12.65 8.52 8.52

North Carolina Wake 13.54 8.99 8.99

North Carolina Watauga 12.05 7.59 7.59

North Carolina Wayne 12.96 9.12 9.13

North Dakota Billings 4.61 4.29 4.29

North Dakota Burke 5.90 5.63 5.63

North Dakota Burleigh 6.61 5.93 5.92

North Dakota Cass 7.72 6.57 6.57

North Dakota McKenzie 5.01 4.73 4.73

North Dakota Mercer 6.04 5.67 5.66

Ohio Athens 12.39 7.75 7.75

Ohio Butler 15.36 10.60 10.59

Ohio Clark 14.64 10.03 10.02

Ohio Clermont 14.15 9.09 9.08

Ohio Cuyahoga 17.37 12.16 12.15

Ohio Franklin 15.27 10.31 10.29

Ohio Greene 13.36 8.79 8.78

Ohio Hamilton 17.54 11.59 11.58

Ohio Jefferson 16.51 10.46 10.46

Ohio Lake 13.02 9.00 8.99

Ohio Lawrence 15.14 10.07 10.08

Ohio Lorain 13.87 9.54 9.52

Ohio Lucas 14.38 10.18 10.16

Ohio Mahoning 15.12 10.30 10.30

Ohio Montgomery 15.54 10.50 10.49

Ohio Portage 13.37 9.06 9.05

Ohio Preble 13.70 9.29 9.28

Ohio Scioto 14.65 9.46 9.46

Ohio Stark 16.26 10.84 10.83

Ohio Summit 15.17 10.50 10.49

Ohio Trumbull 14.53 9.96 9.96

Oklahoma Caddo 9.22 7.45 7.45

Oklahoma Cherokee 11.79 9.41 9.41

Oklahoma Kay 10.26 8.54 8.53

Oklahoma Lincoln 10.28 8.22 8.22

Oklahoma Mayes 11.70 9.51 9.51

Oklahoma Muskogee 11.89 9.71 9.71
C-13 

Oklahoma Oklahoma 10.07 7.80 7.79

Oklahoma Ottawa 11.69 9.40 9.40

Oklahoma Pittsburg 11.09 8.85 8.85

Oklahoma Sequoyah 12.99 10.47 10.47

Oklahoma Tulsa 11.52 9.23 9.23

Oregon Jackson 10.32 9.26 9.26

Oregon Klamath 11.20 10.03 10.02

Oregon Lane 11.93 10.78 10.78

Oregon Multnomah 9.13 7.87 7.87

Oregon Union 8.35 7.31 7.30

Pennsylvania Adams 13.05 8.61 8.60

Pennsylvania Allegheny 20.31 13.19 13.20

Pennsylvania Beaver 16.38 10.92 10.93

Pennsylvania Berks 15.82 11.27 11.26

Pennsylvania Bucks 13.42 9.01 9.00

Pennsylvania Cambria 15.40 9.96 9.96

Pennsylvania Centre 12.78 8.54 8.54

Pennsylvania Chester 15.22 10.21 10.19

Pennsylvania Cumberland 14.45 9.82 9.82

Pennsylvania Dauphin 15.13 9.93 9.91

Pennsylvania Delaware 15.23 10.35 10.35

Pennsylvania Erie 12.54 8.79 8.79

Pennsylvania Lackawanna 11.73 8.05 8.05

Pennsylvania Lancaster 16.55 11.26 11.24

Pennsylvania Lehigh 14.50 10.21 10.21

Pennsylvania Luzerne 12.76 8.93 8.93

Pennsylvania Mercer 13.28 8.85 8.85

Pennsylvania Northampton 13.68 9.51 9.51

Pennsylvania Perry 12.81 8.73 8.73

Pennsylvania Philadelphia 15.19 10.42 10.41

Pennsylvania Washington 15.17 9.31 9.31

Pennsylvania Westmoreland 15.49 9.70 9.70

Pennsylvania York 16.52 11.13 11.11

Rhode Island Providence 12.14 9.15 9.15

South Carolina Beaufort 11.52 7.95 7.95

South Carolina Charleston 12.21 8.56 8.56

South Carolina Chesterfield 12.56 8.73 8.73

South Carolina Edgefield 13.17 9.33 9.33

South Carolina Florence 12.65 8.80 8.80

South Carolina Georgetown 12.85 9.11 9.12
C-14 

South Carolina Greenville 15.65 10.55 10.55

South Carolina Greenwood 13.53 9.22 9.22

South Carolina Horry 12.04 8.42 8.42

South Carolina Lexington 14.64 10.15 10.16

South Carolina Oconee 10.95 7.15 7.15

South Carolina Richland 14.24 9.75 9.75

South Carolina Spartanburg 14.17 9.35 9.35

South Dakota Brookings 9.37 7.83 7.83

South Dakota Brown 8.42 7.29 7.29

South Dakota Codington 10.14 8.67 8.67

South Dakota Custer 5.64 5.25 5.25

South Dakota Jackson 5.39 4.97 4.97

South Dakota Minnehaha 10.18 8.21 8.20

South Dakota Pennington 8.77 7.93 7.93

Tennessee Blount 14.30 9.68 9.68

Tennessee Davidson 14.21 9.46 9.45

Tennessee Dyer 12.28 8.47 8.47

Tennessee Hamilton 15.67 10.63 10.64

Tennessee Knox 15.64 10.33 10.33

Tennessee Lawrence 11.69 8.09 8.09

Tennessee Loudon 15.49 10.53 10.53

Tennessee McMinn 14.29 9.62 9.62

Tennessee Maury 13.21 9.06 9.06

Tennessee Montgomery 13.80 9.26 9.25

Tennessee Putnam 13.37 8.75 8.75

Tennessee Roane 14.49 9.55 9.55

Tennessee Shelby 13.71 9.34 9.34

Tennessee Sullivan 14.16 9.84 9.84

Tennessee Sumner 13.68 8.75 8.75

Texas Bowie 12.85 9.91 9.91

Texas Dallas 12.77 9.56 9.56

Texas Ector 7.78 6.67 6.67

Texas El Paso 9.09 7.82 7.82

Texas Harris 15.42 11.94 11.93

Texas Harrison 11.69 8.74 8.74

Texas Hidalgo 10.98 9.31 9.32

Texas Jefferson 11.56 8.68 8.67

Texas Nueces 10.42 7.86 7.86

Texas Orange 11.51 8.95 8.95

Texas Tarrant 12.23 9.04 9.04
C-15 

Utah Box Elder 8.40 6.94 6.91

Utah Cache 11.56 9.68 9.65

Utah Davis 10.31 8.42 8.40

Utah Salt Lake 12.02 9.77 9.75

Utah Utah 10.52 8.61 8.60

Utah Weber 11.16 9.04 9.01

Vermont Addison 8.94 7.41 7.42

Vermont Bennington 8.52 6.75 6.75

Vermont Chittenden 10.02 8.26 8.26

Vermont Rutland 11.08 9.09 9.09

Virginia Arlington 14.27 9.38 9.37

Virginia Charles 12.37 7.84 7.84

Virginia Chesterfield 13.44 8.52 8.52

Virginia Fairfax 13.88 9.33 9.33

Virginia Henrico 13.51 8.55 8.55

Virginia Loudoun 13.57 9.15 9.15

Virginia Page 12.79 8.11 8.10

Virginia Bristol City 13.93 9.02 9.02

Virginia Hampton City 12.17 7.90 7.90

Virginia Lynchburg City 12.84 8.08 8.09

Virginia Norfolk City 12.78 8.51 8.51

Virginia Roanoke City 14.27 9.22 9.22

Virginia Salem City 14.69 9.70 9.70

Virginia Virginia Beach City 12.40 8.17 8.18

Washington King 11.24 9.34 9.34

Washington Pierce 10.55 9.30 9.30

Washington Snohomish 9.91 8.84 8.84

Washington Spokane 9.97 7.90 7.90

West Virginia Berkeley 15.93 11.06 11.06

West Virginia Brooke 16.52 10.51 10.51

West Virginia Cabell 16.30 10.93 10.93

West Virginia Hancock 15.76 10.06 10.07

West Virginia Harrison 13.99 9.01 9.02

West Virginia Kanawha 16.52 10.72 10.73

West Virginia Marion 15.03 9.69 9.69

West Virginia Marshall 15.19 9.42 9.42

West Virginia Monongalia 14.35 8.69 8.69

West Virginia Ohio 14.58 8.81 8.81

West Virginia Raleigh 12.90 8.13 8.13

West Virginia Wood 15.40 10.35 10.35
C-16 

Wisconsin Ashland 6.07 5.13 5.12

Wisconsin Brown 11.39 9.61 9.60

Wisconsin Dane 12.20 9.59 9.58

Wisconsin Dodge 11.04 8.57 8.56

Wisconsin Forest 7.41 6.13 6.13

Wisconsin Grant 11.79 9.07 9.06

Wisconsin Kenosha 11.98 8.93 8.90

Wisconsin Manitowoc 10.20 8.27 8.26

Wisconsin Milwaukee 14.08 10.82 10.79

Wisconsin Outagamie 10.96 9.01 9.01

Wisconsin Ozaukee 11.60 8.93 8.92

Wisconsin St. Croix 10.09 8.17 8.16

Wisconsin Sauk 10.22 7.83 7.82

Wisconsin Taylor 8.24 6.78 6.77

Wisconsin Vilas 6.78 5.66 5.66

Wisconsin Waukesha 13.91 10.86 10.84

Wyoming Campbell 6.29 6.02 6.02

Wyoming Converse 3.58 3.38 3.38

Wyoming Fremont 8.17 7.36 7.36

Wyoming Laramie 4.48 3.96 3.96

Wyoming Sheridan 9.70 8.81 8.81
D-1 

Air Quality Modeling 

Technical Support Document: Heavy-Duty Vehicle 

Greenhouse Gas Emission Standards Final Rule 

Appendix D 

24-Hour PM2.5 Design Values for Air Quality Modeling 

Scenarios

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711 

July 2011 

 
D-2 

Table D-1. 24-hour PM2.5 Design Values for HDGHG Scenarios (units are 

ug/m3

) 

State Name County Name Baseline

DV

2030

Reference

Case DV

2030 Control

Case DV

Alabama Baldwin 26.21 18.26 18.27

Alabama Clay 31.88 18.55 18.54

Alabama Colbert 30.43 17.06 17.04

Alabama De Kalb 32.08 18.45 18.44

Alabama Escambia 29.03 20.78 20.77

Alabama Etowah 35.18 20.75 20.72

Alabama Houston 28.66 19.51 19.51

Alabama Jefferson 44.06 29.33 29.33

Alabama Madison 33.58 18.14 18.11

Alabama Mobile 30.03 20.02 20.01

Alabama Montgomery 32.05 20.09 20.07

Alabama Morgan 31.58 16.15 16.13

Alabama Russell 35.55 24.13 24.12

Alabama Shelby 32.05 19.60 19.58

Alabama Sumter 28.90 17.48 17.48

Alabama Talladega 33.46 20.92 20.91

Alabama Tuscaloosa 29.80 18.33 18.32

Alabama Walker 32.82 18.89 18.87

Arizona Cochise 16.62 15.87 15.88

Arizona Coconino 17.11 15.87 15.88

Arizona Gila 22.12 20.29 20.30

Arizona Maricopa 32.80 24.87 24.87

Arizona Pima 12.27 9.81 9.80

Arizona Pinal 17.55 14.67 14.68

Arizona Santa Cruz 36.08 33.96 33.98

Arkansas Arkansas 29.16 19.25 19.26

Arkansas Ashley 28.91 21.88 21.86

Arkansas Crittenden 35.06 19.54 19.55

Arkansas Faulkner 29.87 20.11 20.10

Arkansas Garland 29.27 19.88 19.86

Arkansas Phillips 29.18 18.83 18.84

Arkansas Polk 26.13 16.99 17.00

Arkansas Pope 28.32 19.40 19.41
D-3 

Arkansas Pulaski 31.93 22.69 22.69

Arkansas Union 28.70 21.03 21.00

Arkansas White 29.91 20.37 20.36

California Alameda 32.58 26.91 26.91

California Butte 52.55 37.41 37.41

California Calaveras 20.55 14.96 14.96

California Colusa 26.16 22.24 22.24

California Contra Costa 34.70 29.54 29.54

California Fresno 60.22 47.36 47.35

California Imperial 40.21 34.61 34.60

California Inyo 20.00 18.80 18.81

California Kern 64.54 51.55 51.53

California Kings 58.06 45.99 45.98

California Lake 12.94 12.10 12.10

California Los Angeles 50.97 45.57 45.55

California Mendocino 15.30 10.39 10.39

California Merced 46.15 35.26 35.26

California Monterey 14.35 12.36 12.36

California Nevada 16.55 13.18 13.18

California Orange 43.76 38.74 38.71

California Placer 29.88 23.79 23.79

California Plumas 32.44 26.18 26.18

California Riverside 59.13 49.27 49.26

California Sacramento 49.22 45.78 45.78

California San Bernardino 55.50 49.10 49.09

California San Diego 35.55 32.37 32.37

California San Francisco 30.91 26.83 26.82

California San Joaquin 41.88 33.70 33.69

California San Luis Obispo 22.58 18.53 18.52

California San Mateo 29.41 26.15 26.15

California Santa Barbara 24.07 22.79 22.79

California Santa Clara 38.61 35.70 35.69

California Shasta 20.42 14.35 14.35

California Solano 34.76 30.44 30.42

California Sonoma 29.10 24.08 24.08

California Stanislaus 51.48 39.82 39.82

California Sutter 38.55 28.98 28.98

California Tulare 56.63 43.66 43.65
D-4 

California Ventura 30.30 26.61 26.61

California Yolo 30.38 25.09 25.09

Colorado Adams 25.35 19.73 19.67

Colorado Arapahoe 21.27 17.13 17.10

Colorado Boulder 21.12 18.39 18.37

Colorado Delta 20.76 16.20 16.18

Colorado Denver 26.44 21.76 21.70

Colorado Elbert 13.18 11.25 11.23

Colorado El Paso 16.51 13.63 13.62

Colorado Larimer 18.30 16.61 16.60

Colorado Mesa 23.51 19.57 19.52

Colorado Pueblo 15.42 12.79 12.79

Colorado San Miguel 10.11 9.57 9.57

Colorado Weld 22.90 20.87 20.84

Connecticut Fairfield 36.27 24.52 24.51

Connecticut Hartford 31.83 19.79 19.80

Connecticut Litchfield 27.16 14.66 14.67

Connecticut New Haven 38.37 23.75 23.74

Connecticut New London 32.03 18.97 18.98

Delaware Kent 32.14 19.58 19.57

Delaware New Castle 36.66 23.33 23.30

Delaware Sussex 33.78 20.78 20.77

D.C. Washington 36.35 22.34 22.31

Florida Alachua 21.35 14.64 14.64

Florida Bay 28.08 19.83 19.83

Florida Brevard 20.73 14.02 14.02

Florida Broward 18.63 13.99 13.99

Florida Citrus 21.22 13.66 13.67

Florida Duval 24.35 18.46 18.45

Florida Escambia 28.80 22.70 22.69

Florida Hillsborough 23.44 16.31 16.31

Florida Lee 17.70 12.99 12.98

Florida Leon 27.03 19.36 19.37

Florida Manatee 19.57 12.42 12.42

Florida Marion 22.56 15.01 15.01

Florida Miami‐Dade 19.13 13.34 13.34

Florida Orange 21.83 14.00 14.01

Florida Palm Beach 18.22 14.31 14.31
D-5 

Florida Pinellas 21.73 15.33 15.33

Florida Polk 19.30 13.53 13.53

Florida St Lucie 18.18 12.31 12.31

Florida Sarasota 19.22 12.99 12.99

Florida Seminole 22.08 13.49 13.48

Florida Volusia 22.00 13.69 13.69

Georgia Bibb 33.56 22.40 22.41

Georgia Chatham 28.45 19.41 19.42

Georgia Clayton 35.88 22.81 22.80

Georgia Cobb 35.04 20.92 20.89

Georgia De Kalb 33.92 20.84 20.83

Georgia Dougherty 34.15 24.59 24.59

Georgia Floyd 35.12 22.22 22.20

Georgia Fulton 37.66 23.84 23.84

Georgia Glynn 26.13 19.25 19.27

Georgia Gwinnett 32.81 19.11 19.12

Georgia Hall 30.11 19.69 19.70

Georgia Houston 29.63 19.02 19.02

Georgia Lowndes 25.68 18.17 18.17

Georgia Muscogee 34.58 23.58 23.58

Georgia Paulding 33.02 19.72 19.71

Georgia Richmond 32.70 23.60 23.61

Georgia Walker 30.98 19.46 19.45

Georgia Washington 30.83 20.31 20.30

Georgia Wilkinson 33.16 21.63 21.62

Idaho Ada 28.36 24.05 23.83

Idaho Bannock 27.08 23.92 23.87

Idaho Benewah 32.94 29.32 29.30

Idaho Canyon 31.80 25.22 24.95

Idaho Franklin 36.76 30.59 30.44

Idaho Idaho 28.43 26.61 26.58

Idaho Lemhi 36.53 33.06 33.07

Idaho Power 33.36 29.48 29.43

Idaho Shoshone 38.16 33.85 33.83

Illinois Adams 31.41 18.31 18.19

Illinois Champaign 31.32 20.21 20.14

Illinois Cook 43.03 29.90 29.74

Illinois Du Page 34.64 26.39 26.26
D-6 

Illinois Hamilton 31.60 17.67 17.63

Illinois Jersey 32.18 20.64 20.54

Illinois Kane 34.83 25.70 25.53

Illinois Lake 33.08 22.45 22.36

Illinois La Salle 28.92 20.00 19.91

Illinois McHenry 31.58 21.97 21.81

Illinois McLean 33.43 21.50 21.38

Illinois Macon 33.25 19.37 19.29

Illinois Madison 39.16 25.21 25.15

Illinois Peoria 32.76 21.31 21.19

Illinois Randolph 28.96 20.36 20.30

Illinois Rock Island 30.90 22.59 22.49

Illinois St Clair 33.70 23.04 22.94

Illinois Sangamon 33.41 22.72 22.69

Illinois Will 36.45 24.64 24.44

Illinois Winnebago 34.73 25.41 25.27

Indiana Allen 33.10 23.51 23.47

Indiana Clark 37.57 21.71 21.67

Indiana Delaware 32.07 20.63 20.53

Indiana Dubois 35.36 21.63 21.52

Indiana Elkhart 34.43 25.42 25.29

Indiana Floyd 33.26 18.26 18.22

Indiana Henry 31.86 19.62 19.49

Indiana Howard 32.21 20.56 20.43

Indiana Knox 35.92 21.66 21.63

Indiana Lake 38.98 29.92 29.88

Indiana La Porte 33.00 22.26 22.14

Indiana Madison 32.82 20.28 20.20

Indiana Marion 38.47 24.97 24.90

Indiana Porter 32.96 22.94 22.89

Indiana St Joseph 33.16 24.47 24.39

Indiana Spencer 32.32 15.75 15.72

Indiana Tippecanoe 35.68 21.84 21.80

Indiana Vanderburgh 34.80 23.26 23.19

Indiana Vigo 34.88 20.78 20.66

Iowa Black Hawk 30.78 21.97 21.88

Iowa Clinton 33.95 23.86 23.78

Iowa Johnson 34.67 23.97 23.82
D-7 

Iowa Linn 30.60 20.82 20.68

Iowa Montgomery 27.50 18.78 18.76

Iowa Muscatine 36.03 27.51 27.38

Iowa Palo Alto 25.73 18.41 18.38

Iowa Polk 31.46 22.15 22.10

Iowa Pottawattamie 28.60 21.35 21.33

Iowa Scott 37.10 25.60 25.56

Iowa Van Buren 28.36 19.76 19.66

Iowa Woodbury 26.40 20.17 20.15

Iowa Wright 28.65 20.21 20.18

Kansas Johnson 29.30 23.05 23.00

Kansas Linn 25.38 19.10 19.08

Kansas Sedgwick 25.37 19.02 18.98

Kansas Shawnee 29.16 22.74 22.69

Kansas Sumner 22.84 16.73 16.68

Kansas Wyandotte 29.58 22.35 22.32

Kentucky Bell 29.90 18.03 18.01

Kentucky Boyd 33.15 17.01 17.02

Kentucky Bullitt 34.63 18.30 18.27

Kentucky Campbell 31.20 16.56 16.52

Kentucky Carter 29.91 14.59 14.59

Kentucky Christian 33.60 17.38 17.36

Kentucky Daviess 33.86 17.83 17.81

Kentucky Fayette 32.23 17.75 17.65

Kentucky Franklin 32.17 17.06 16.93

Kentucky Hardin 32.81 16.81 16.80

Kentucky Henderson 31.85 18.03 18.01

Kentucky Jefferson 36.44 21.32 21.21

Kentucky Kenton 34.74 19.31 19.26

Kentucky Laurel 25.16 14.83 14.85

Kentucky McCracken 33.62 18.16 18.14

Kentucky Madison 30.11 15.38 15.30

Kentucky Perry 28.54 14.64 14.63

Kentucky Pike 30.52 16.70 16.70

Kentucky Warren 33.14 17.36 17.34

Louisiana Caddo 27.56 20.12 20.12

Louisiana Calcasieu 26.38 19.38 19.36

Louisiana Concordia 26.16 17.31 17.31
D-8 

Louisiana East Baton Rouge 29.36 22.36 22.24

Louisiana Iberville 28.62 21.87 21.85

Louisiana Jefferson 27.06 17.90 17.89

Louisiana Lafayette 24.28 17.30 17.31

Louisiana Ouachita 28.91 20.52 20.53

Louisiana Rapides 30.26 19.98 19.98

Louisiana Tangipahoa 29.61 19.80 19.79

Louisiana Terrebonne 26.25 17.42 17.42

Louisiana West Baton Rouge 29.08 22.19 22.08

Maine Androscoggin 26.56 19.35 19.37

Maine Aroostook 24.23 21.48 21.52

Maine Cumberland 29.20 20.35 20.36

Maine Hancock 19.43 12.70 12.70

Maine Kennebec 26.21 19.11 19.14

Maine Oxford 28.36 21.78 21.80

Maine Penobscot 22.03 15.96 15.96

Maryland Anne Arundel 36.16 26.15 26.13

Maryland Baltimore 35.84 24.70 24.68

Maryland Cecil 30.82 19.67 19.63

Maryland Harford 31.21 18.65 18.62

Maryland Montgomery 30.93 18.34 18.31

Maryland Prince Georges 33.46 18.90 18.90

Maryland Washington 33.43 21.56 21.53

Maryland Baltimore City 39.01 28.06 28.04

Massachusetts Berkshire 31.06 22.35 22.37

Massachusetts Bristol 25.07 16.83 16.83

Massachusetts Essex 28.72 19.62 19.61

Massachusetts Hampden 33.13 23.61 23.63

Massachusetts Plymouth 28.48 18.21 18.21

Massachusetts Suffolk 32.17 22.40 22.40

Massachusetts Worcester 30.66 20.90 20.92

Michigan Allegan 33.82 24.43 24.22

Michigan Bay 31.68 20.90 20.80

Michigan Berrien 31.32 21.26 21.15

Michigan Genesee 30.46 21.59 21.46

Michigan Ingham 31.96 23.11 22.99

Michigan Kalamazoo 31.17 21.73 21.67

Michigan Kent 36.53 24.19 24.13
D-9 

Michigan Macomb 35.32 26.37 26.29

Michigan Missaukee 24.83 16.46 16.47

Michigan Monroe 38.88 24.46 24.43

Michigan Muskegon 34.71 22.89 22.68

Michigan Oakland 39.94 24.67 24.59

Michigan Ottawa 34.24 25.78 25.63

Michigan Saginaw 30.66 20.98 20.90

Michigan St Clair 39.61 29.11 28.99

Michigan Washtenaw 39.46 23.80 23.74

Michigan Wayne 43.88 31.43 31.42

Minnesota Cass 18.02 14.24 14.21

Minnesota Dakota 25.42 19.23 19.17

Minnesota Hennepin 27.25 19.38 19.31

Minnesota Mille Lacs 22.03 17.46 17.45

Minnesota Ramsey 28.38 21.32 21.31

Minnesota St Louis 23.53 17.63 17.62

Minnesota Scott 24.98 18.28 18.24

Mississippi Adams 27.48 17.94 17.93

Mississippi Bolivar 28.98 20.16 20.17

Mississippi De Soto 30.82 17.28 17.26

Mississippi Forrest 30.48 22.18 22.19

Mississippi Harrison 29.00 19.92 19.91

Mississippi Hinds 28.83 18.68 18.69

Mississippi Jackson 26.96 18.25 18.25

Mississippi Jones 31.21 22.29 22.29

Mississippi Lee 32.18 17.93 17.90

Mississippi Lowndes 32.44 18.92 18.89

Mississippi Warren 30.26 20.04 20.02

Missouri Boone 30.23 20.20 20.18

Missouri Buchanan 30.10 21.94 21.93

Missouri Cass 25.61 17.65 17.62

Missouri Cedar 28.70 19.80 19.79

Missouri Clay 28.04 20.87 20.79

Missouri Greene 28.27 19.98 20.00

Missouri Jackson 27.88 21.09 21.07

Missouri Jefferson 33.43 21.83 21.78

Missouri Monroe 27.83 18.90 18.83

Missouri St Charles 33.16 20.90 20.84
D-10 

Missouri Ste Genevieve 31.44 19.57 19.55

Missouri St Louis 33.21 23.36 23.26

Missouri St Louis City 34.35 22.17 22.11

Montana Cascade 20.15 17.40 17.40

Montana Flathead 27.17 24.30 24.29

Montana Gallatin 29.55 26.85 26.84

Montana Lake 43.66 39.29 39.28

Montana Lewis And Clark 33.53 28.81 28.82

Montana Lincoln 42.71 36.38 36.36

Montana Missoula 44.64 37.54 37.40

Montana Ravalli 45.11 38.13 38.09

Montana Rosebud 19.73 18.50 18.50

Montana Sanders 20.42 18.63 18.63

Montana Silver Bow 35.00 29.07 29.06

Montana Yellowstone 19.38 16.62 16.61

Nebraska Cass 28.30 21.11 21.04

Nebraska Douglas 25.76 19.40 19.38

Nebraska Hall 19.16 14.52 14.47

Nebraska Lancaster 24.77 18.35 18.33

Nebraska Scotts Bluff 16.66 14.56 14.56

Nebraska Washington 24.01 18.44 18.42

Nevada Clark 25.26 21.16 21.17

Nevada Washoe 30.78 23.43 23.39

New Hampshire Belknap 20.55 12.56 12.57

New Hampshire Cheshire 30.23 21.33 21.34

New Hampshire Coos 26.50 18.77 18.79

New Hampshire Grafton 23.00 16.18 16.18

New Hampshire Hillsborough 28.66 20.98 20.97

New Hampshire Merrimack 25.65 16.60 16.59

New Hampshire Rockingham 26.35 18.05 18.04

New Hampshire Sullivan 28.92 18.48 18.49

New Jersey Bergen 37.03 23.13 23.11

New Jersey Camden 37.37 22.22 22.20

New Jersey Essex 38.38 23.76 23.75

New Jersey Hudson 41.43 29.96 29.93

New Jersey Mercer 34.75 20.25 20.25

New Jersey Middlesex 34.82 21.30 21.31

New Jersey Morris 32.32 19.53 19.53
D-11 

New Jersey Ocean 31.56 17.74 17.75

New Jersey Passaic 36.30 21.58 21.56

New Jersey Union 40.47 25.61 25.60

New Jersey Warren 34.06 22.74 22.73

New Mexico Bernalillo 18.60 14.80 14.80

New Mexico Chaves 15.68 13.26 13.26

New Mexico Dona Ana 32.95 26.85 26.85

New Mexico Grant 13.00 12.23 12.24

New Mexico Sandoval 15.68 13.82 13.82

New Mexico San Juan 12.40 11.16 11.16

New Mexico Santa Fe 9.78 8.68 8.68

New York Albany 34.26 26.84 26.86

New York Bronx 38.87 26.20 26.18

New York Chautauqua 29.15 17.17 17.19

New York Erie 35.35 25.86 25.83

New York Essex 22.45 14.79 14.80

New York Kings 36.94 23.45 23.43

New York Monroe 32.20 20.08 20.06

New York Nassau 34.01 20.55 20.54

New York New York 39.70 26.63 26.62

New York Niagara 33.87 23.22 23.18

New York Onondaga 27.35 18.07 18.08

New York Orange 28.92 20.16 20.16

New York Queens 35.56 23.06 23.05

New York Richmond 34.93 21.65 21.65

New York St Lawrence 22.05 17.41 17.43

New York Steuben 27.81 16.31 16.33

New York Suffolk 34.66 18.74 18.73

New York Westchester 33.51 19.91 19.89

North Carolina Alamance 31.72 19.53 19.53

North Carolina Buncombe 30.05 17.14 17.13

North Carolina Caswell 29.45 17.50 17.50

North Carolina Catawba 34.53 20.53 20.51

North Carolina Chatham 26.94 15.83 15.83

North Carolina Cumberland 30.78 19.22 19.25

North Carolina Davidson 31.35 20.00 20.00

North Carolina Duplin 28.30 17.05 17.07

North Carolina Durham 31.02 18.04 18.04
D-12 

North Carolina Edgecombe 26.78 18.01 18.01

North Carolina Forsyth 31.92 19.84 19.83

North Carolina Gaston 30.86 17.75 17.75

North Carolina Guilford 30.63 19.49 19.48

North Carolina Haywood 27.74 17.73 17.72

North Carolina Jackson 24.96 15.09 15.08

North Carolina Lenoir 25.20 17.28 17.32

North Carolina McDowell 31.55 18.67 18.68

North Carolina Martin 24.83 16.23 16.25

North Carolina Mecklenburg 32.33 20.69 20.68

North Carolina Mitchell 30.25 16.79 16.80

North Carolina Montgomery 28.21 16.73 16.72

North Carolina New Hanover 25.40 15.22 15.24

North Carolina Onslow 24.61 15.97 15.99

North Carolina Orange 29.35 17.24 17.24

North Carolina Pitt 26.21 17.87 17.89

North Carolina Robeson 29.92 17.71 17.71

North Carolina Rowan 30.23 19.03 19.03

North Carolina Swain 27.34 16.19 16.19

North Carolina Wake 31.63 19.08 19.08

North Carolina Watauga 30.43 17.09 17.09

North Carolina Wayne 29.72 19.32 19.35

North Dakota Billings 13.07 12.01 12.01

North Dakota Burke 16.73 15.74 15.74

North Dakota Burleigh 17.62 15.51 15.51

North Dakota Cass 21.22 16.89 16.88

North Dakota McKenzie 11.96 11.32 11.33

North Dakota Mercer 16.98 15.52 15.51

Ohio Athens 32.32 16.87 16.87

Ohio Butler 39.23 24.13 24.03

Ohio Clark 35.37 20.06 20.04

Ohio Clermont 34.46 17.71 17.68

Ohio Cuyahoga 44.20 28.72 28.62

Ohio Franklin 38.51 21.56 21.49

Ohio Greene 32.21 17.48 17.44

Ohio Hamilton 40.60 22.65 22.62

Ohio Jefferson 41.96 24.65 24.67

Ohio Lake 37.16 21.82 21.79
D-13 

Ohio Lawrence 33.77 19.18 19.18

Ohio Lorain 31.56 19.65 19.70

Ohio Lucas 36.34 25.53 25.42

Ohio Mahoning 36.83 22.36 22.35

Ohio Montgomery 37.80 23.11 23.03

Ohio Portage 34.32 19.82 19.80

Ohio Preble 32.85 17.90 17.83

Ohio Scioto 34.55 19.03 19.05

Ohio Stark 36.90 20.80 20.83

Ohio Summit 38.06 22.14 22.09

Ohio Trumbull 36.23 22.50 22.44

Oklahoma Caddo 23.97 17.69 17.67

Oklahoma Cherokee 27.55 21.32 21.32

Oklahoma Kay 31.80 26.22 26.16

Oklahoma Lincoln 27.83 20.27 20.27

Oklahoma Mayes 28.71 23.34 23.32

Oklahoma Muskogee 29.54 21.91 21.91

Oklahoma Oklahoma 27.12 19.51 19.44

Oklahoma Ottawa 29.14 22.13 22.13

Oklahoma Pittsburg 26.37 19.26 19.24

Oklahoma Sequoyah 31.43 24.13 24.14

Oklahoma Tulsa 30.37 23.02 23.01

Oregon Jackson 33.72 29.07 29.01

Oregon Klamath 44.08 37.94 37.92

Oregon Lane 48.95 42.48 42.45

Oregon Multnomah 29.88 25.29 25.25

Oregon Union 27.38 23.47 23.42

Pennsylvania Adams 34.93 21.28 21.28

Pennsylvania Allegheny 64.27 40.92 40.98

Pennsylvania Beaver 43.42 24.23 24.25

Pennsylvania Berks 37.71 27.57 27.49

Pennsylvania Bucks 34.01 22.01 21.97

Pennsylvania Cambria 39.04 20.63 20.65

Pennsylvania Centre 36.28 22.09 22.10

Pennsylvania Chester 36.70 23.65 23.65

Pennsylvania Cumberland 38.00 26.17 26.12

Pennsylvania Dauphin 38.04 27.11 27.03

Pennsylvania Delaware 35.24 22.51 22.46
D-14 

Pennsylvania Erie 34.46 21.50 21.48

Pennsylvania Lackawanna 31.55 18.45 18.45

Pennsylvania Lancaster 40.83 30.44 30.35

Pennsylvania Lehigh 36.40 25.51 25.51

Pennsylvania Luzerne 32.46 20.72 20.70

Pennsylvania Mercer 36.30 20.76 20.71

Pennsylvania Northampton 36.72 24.37 24.35

Pennsylvania Perry 30.46 21.20 21.15

Pennsylvania Philadelphia 37.30 23.21 23.17

Pennsylvania Washington 38.14 20.24 20.27

Pennsylvania Westmoreland 37.12 19.47 19.48

Pennsylvania York 38.24 28.37 28.31

Rhode Island Providence 30.62 20.72 20.72

South Carolina Charleston 27.93 17.43 17.46

South Carolina Chesterfield 28.77 17.78 17.77

South Carolina Edgefield 32.23 19.35 19.33

South Carolina Florence 28.81 18.09 18.08

South Carolina Greenville 32.55 20.20 20.21

South Carolina Greenwood 30.01 17.63 17.62

South Carolina Horry 28.30 18.10 18.10

South Carolina Lexington 32.86 20.91 20.90

South Carolina Oconee 27.98 16.09 16.08

South Carolina Richland 33.20 20.78 20.77

South Carolina Spartanburg 32.46 19.34 19.32

South Dakota Brookings 23.54 17.62 17.58

South Dakota Brown 18.73 15.09 15.09

South Dakota Codington 23.67 18.49 18.47

South Dakota Custer 14.36 12.49 12.46

South Dakota Jackson 12.73 11.15 11.16

South Dakota Minnehaha 24.17 17.94 17.91

South Dakota Pennington 18.58 16.65 16.64

Tennessee Blount 32.54 19.79 19.77

Tennessee Davidson 33.50 19.17 19.15

Tennessee Dyer 31.92 18.69 18.69

Tennessee Hamilton 33.53 21.75 21.75

Tennessee Knox 36.66 21.31 21.29

Tennessee Lawrence 28.48 16.11 16.10

Tennessee Loudon 32.20 20.15 20.14
D-15 

Tennessee Mc Minn 32.73 18.79 18.78

Tennessee Maury 30.96 17.97 17.96

Tennessee Montgomery 36.30 18.95 18.93

Tennessee Putnam 32.66 17.66 17.66

Tennessee Roane 30.24 16.48 16.45

Tennessee Shelby 33.50 18.36 18.34

Tennessee Sullivan 31.13 20.20 20.19

Tennessee Sumner 33.66 16.87 16.86

Texas Bowie 29.42 20.15 20.15

Texas Dallas 27.44 19.38 19.29

Texas Ector 17.81 14.26 14.25

Texas El Paso 22.93 19.18 19.17

Texas Harris 30.81 22.11 22.06

Texas Harrison 25.95 18.54 18.57

Texas Hidalgo 26.42 22.72 22.73

Texas Nueces 27.55 19.97 19.98

Texas Orange 27.78 20.08 20.06

Texas Tarrant 25.76 18.26 18.26

Utah Box Elder 33.20 25.93 25.82

Utah Cache 56.95 41.82 41.68

Utah Davis 38.95 30.14 30.02

Utah Salt Lake 50.14 38.49 38.44

Utah Tooele 30.53 25.31 25.26

Utah Utah 44.00 33.73 33.67

Utah Weber 38.58 28.87 28.80

Vermont Addison 31.73 21.00 21.02

Vermont Bennington 26.47 18.09 18.10

Vermont Chittenden 30.13 22.64 22.64

Vermont Rutland 30.60 25.51 25.52

Virginia Arlington 34.18 19.66 19.64

Virginia Charles City 31.76 17.95 17.95

Virginia Chesterfield 31.25 16.22 16.22

Virginia Fairfax 34.47 20.68 20.65

Virginia Henrico 31.95 17.66 17.66

Virginia Loudoun 34.45 20.14 20.12

Virginia Page 30.06 17.35 17.33

Virginia Bristol City 30.24 17.25 17.24

Virginia Hampton City 29.01 17.06 17.06
D-16 

Virginia Lynchburg City 30.71 16.73 16.72

Virginia Norfolk City 29.66 17.96 17.96

Virginia Roanoke City 32.70 18.75 18.74

Virginia Salem City 34.06 20.23 20.21

Washington King 29.16 24.93 24.92

Washington Pierce 41.82 36.22 36.22

Washington Snohomish 34.36 31.34 31.34

Washington Spokane 29.86 22.39 22.38

West Virginia Berkeley 34.51 24.65 24.62

West Virginia Brooke 43.90 26.15 26.16

West Virginia Cabell 35.10 19.43 19.43

West Virginia Hancock 40.64 21.14 21.18

West Virginia Harrison 33.53 17.07 17.07

West Virginia Kanawha 36.98 20.15 20.12

West Virginia Marion 33.68 17.06 17.08

West Virginia Marshall 33.98 18.13 18.16

West Virginia Monongalia 35.65 15.12 15.12

West Virginia Ohio 32.00 17.36 17.38

West Virginia Raleigh 30.67 15.83 15.82

West Virginia Summers 31.26 15.82 15.81

West Virginia Wood 35.44 18.82 18.85

Wisconsin Ashland 18.61 13.41 13.41

Wisconsin Brown 36.56 31.46 31.42

Wisconsin Dane 35.57 26.32 26.19

Wisconsin Dodge 31.82 22.83 22.64

Wisconsin Forest 25.26 18.43 18.40

Wisconsin Grant 34.35 25.20 25.09

Wisconsin Kenosha 32.78 23.32 23.13

Wisconsin Manitowoc 29.70 22.96 22.92

Wisconsin Milwaukee 39.92 29.18 29.09

Wisconsin Outagamie 32.87 26.98 26.87

Wisconsin Ozaukee 32.53 24.07 23.91

Wisconsin St Croix 26.66 20.69 20.68

Wisconsin Sauk 28.63 22.15 22.01

Wisconsin Taylor 25.38 19.24 19.18

Wisconsin Vilas 22.61 17.22 17.19

Wisconsin Waukesha 35.48 26.77 26.63

Wyoming Campbell 18.63 17.50 17.49
D-17 

Wyoming Converse 10.00 9.56 9.56

Wyoming Fremont 29.80 24.10 24.08

Wyoming Laramie 11.93 10.63 10.63

Wyoming Sheridan 30.86 27.26 27.26