Air Quality Modeling Technical Support 

Document: Light-Duty Vehicle Greenhouse Gas 

Emission Standards Final Rule 
 2 

EPA 454/R-10-003 

April 2010 

Air Quality Modeling Technical Support Document: 

Light-Duty Vehicle Greenhouse Gas Emission Standards 

Final Rule 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711 

April 2010 
 3 

I. Introduction

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

Light-Duty Vehicle Greenhouse Gas Final Rule (hereafter referred to as LDGHG). 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, annual and seasonal ethanol 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)1

 model. 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. 

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 5.8 of the RIA, are slightly different than 

the final vehicle standard inventories presented in Section 5.5 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. 

II. CMAQ Model Version, Inputs and Configuration 

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

modeling of the LDGHG 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 

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. Model version 

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 

 

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. 
 4 

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 domain and grid resolution 

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

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

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

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

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

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

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

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

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

domains. 

 

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. 
 5 

Table II-1. Geographic elements of domains used in RFS2 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) 

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. 
 6 

C. Modeling Time-period 

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

of 2005.7

 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 the regulation. 

D. Model Inputs: Emissions, Meteorology and Boundary Conditions 

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

future baseline emissions and control scenarios. As noted in the introduction, in addition to the 

CMAQ model, the modeling platform also consists of the base- and future-year emissions 

estimates (both anthropogenic and biogenic), meteorological fields, as well as initial and 

boundary condition data which are all inputs to the air quality model. 

1. Base Year and Future Baseline Emissions: The emissions modeling TSD, found in the 

docket for this rule (EPA-420-R-10-011) contains a detailed discussion of the emissions inputs 

used in our air quality modeling. We have provided a brief summary of the base year and future 

baseline emissions used for the air quality modeling. The emissions data used in the base year 

and future reference and control case are based on the 2005 v4 platform. The LDGHG cases use 

some different emissions data than the official v4 platform for two reasons: (1) the LDGHG 

Standard was evaluated in comparison to the modeling performed for the Revised annual 

Renewable Fuel Standard (RFS2)8

; therefore, RFS2-specific inputs were retained for LDGHG 

and (2) the LDGHG modeling used data intended only for the rule development and not for 

general use. Unlike the 2005 v4 platform, the configuration for LDGHG modeling included 

additional hazardous air pollutants (HAPs) and used slightly older ancillary data. Both of these 

differences are described in Section 2.1 of the emissions modeling TSD. 

The 2030 reference case (projection without vehicle standards) is intended to represent 

the emissions associated with use of the most likely volume of ethanol in the absence of the 

LDGHG CO2 reductions and RFS2 rule and Energy Independence and Security Act of 2007 

(EISA) renewable fuel requirements. For this case, the ethanol volume was projected for 2030 

using the Department of Energy, Energy Information Administration in the 2007 Annual Energy 

Outlook (AEO) report. The US EGU point source emissions estimates for the future year 

reference and control case are based on an Integrated Planning Model (IPM) run for criteria 

pollutants, hydrochloric acid, and mercury in 2020 (though mercury was not modeled). The year 

2020 was used since it was the closest readily year to the 2030 year used for LDGHG air quality 

modeling. Both control and growth factors were applied to a subset of the 2005 non-EGU point 

 

7

 We also modeled 10 days at the end of December 2004 as a modeled "ramp up" period. These days are used to 

minimize the effects of initial conditions and are not considered as part of the output analyses. 

8

 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. 
 7 

and nonpoint to create the 2030 reference case. The 2002 v3.1 platform 2020 projection factors 

were the starting point for most of the LDGHG year of 2030 SMOKE-based projections. 

Ethanol plant replacements and additions were included in the 2005 base and 2030 reference 

case as well as biodiesel additions and portable fuel containers. 

It should be noted that the emission inventories used in the air quality and benefits 

modeling are different from the final rule inventories due to the length of time required to 

conduct the modeling. However, the air quality modeling inventories are generally consistent 

with the final emission inventories, so the air quality modeling adequately reflects the effects of 

the rule. 

2. LDGHG 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 seasonal air toxics concentrations, annual total nitrogen and sulfur 

deposition levels and visibility impairment for each of the following emissions scenarios: 

2005 base year 

2030 reference case projection without the vehicle standards 

2030 control case projection with the vehicle standards 

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

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

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

standards. This is done by calculating the simulated air quality ratios between any particular 

future year simulation and the 2005 base. These predicted change ratios are then applied to 

ambient base year design values. The design value projection methodology used here followed 

EPA guidance9

 for such analyses. Additionally, the raw model outputs are also used in a relative 

sense as inputs to the health and welfare impact functions of the benefits analysis. Only model 

predictions for air toxics as well as nitrogen and sulfur deposition were analyzed using absolute 

model changes, although these parameters also considered percent changes between the control 

case and two future baselines. 

3. Meteorological Input Data: The gridded meteorological input data for the entire year 

of 2005 were derived from simulations of the Pennsylvania State University / National Center for 

Atmospheric Research Mesoscale Model. This model, commonly referred to as MM5, is a 

limited-area, nonhydrostatic, terrain-following system that solves for the full set of physical and 

thermodynamic equations which govern atmospheric motions.10 Meteorological model input 

 

9

 U.S. EPA, Guidance on the Use of Models and Other Analyses in Attainment Demonstrations for the 8-hour 

Ozone NAAQS; EPA-454/R-05-002; Research Triangle Park, NC; October 2005. 

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 

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 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 0.985 115 987 3 4 0.980 154 982 

5 0.970 232 973 4 6 0.960 310 964 

7 0.950 389 955 5 8 0.940 469 946 

9 0.930 550 937 

6 10 0.920 631 928 

11 0.910 712 919 

12 0.900 794 910 

7 13 0.880 961 892 

14 0.860 1,130 874 

15 0.840 1,303 856 

8 16 0.820 1,478 838 

17 0.800 1,657 820 

18 0.770 1,930 793 9 19 0.740 2,212 766 

20 0.700 2,600 730 10 21 0.650 3,108 685 

11 22 0.600 3,644 640 
 9 

23 0.550 4,212 595 

24 0.500 4,816 550 

12 25 0.450 5,461 505 

26 0.400 6,153 460 

27 0.350 6,903 415 

28 0.300 7,720 370 

29 0.250 8,621 325 13 

30 0.200 9,625 280 

31 0.150 10,764 235 

32 0.100 12,085 190 

33 0.050 13,670 145 14 

34 0.000 15,674 100 

The meteorological outputs from all three MM5 sets were processed to create modelready inputs for CMAQ using the Meteorology-Chemistry Interface Processor (MCIP), version 

3.4 to derive the specific inputs to CMAQ.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 It was ultimately determined 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. 

4. Initial and Boundary Conditions: The lateral boundary and initial species 

concentrations are provided by a three-dimensional global atmospheric chemistry model, the 

 

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. 
 10 

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 as the initial/boundary state for all 

subsequent 12 km finer grid modeling. 

E. CMAQ Base Case Model Performance Evaluation 

1. PM2.5: An operational model performance evaluation for PM2.5 and its related 

speciated components (e.g., sulfate, nitrate, elemental carbon, organic carbon, etc.) was 

conducted using 2005 state/local monitoring data in order to estimate the ability of the CMAQ 

modeling system to replicate base year concentrations. In summary, model performance 

statistics were calculated for observed/predicted pairs of daily/monthly/seasonal/annual 

concentrations. Statistics were generated for the following geographic groupings: domain wide, 

Eastern vs. Western (divided along the 100th meridian), and each Regional Planning 

Organization (RPO) region17. The “acceptability” of model performance was judged by 

comparing our CMAQ 2005 performance results to the range of performance found in recent 

regional PM2.5 model applications for other, non-EPA studies18. Overall, the fractional bias, 

fractional error, normalized mean bias, and normalized mean error statistics shown in Table II-4 

are within the range or close to that found by other groups in recent applications. The model 

performance results give us confidence that our application of CMAQ using this modeling 

platform provides a scientifically credible approach for assessing PM2.5 concentrations for the 

purposes of the LDGHG vehicle standards assessment. A detailed summary of the 2005 CMAQ 

model performance evaluation is available in Appendix D19. 

 

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. 

17 Regional Planning Organization regions include: Mid-Atlantic/Northeast Visibility Union (MANEVU), Midwest 

Regional Planning Organization – Lake Michigan Air Directors Consortium (MWRPO-LADCO), Visibility 

Improvement State and Tribal Association of the Southeast (VISTAS), Central States Regional Air Partnership 

(CENRAP), and the Western Regional Air Partnership (WRAP). 

18 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. 

19 U.S. Environmental Protection Agency, Air Quality Modeling Technical Support Document: Light-Duty Vehicle 

Greenhouse Gas Emission Standards Final Rule, Appendix D: CMAQ Model Performance Evaluation for Ozone, 

Particulate Matter and Toxics. April, 2010 (EPA-454/R-10-003). 
 11 

Table II-4. 2005 CMAQ annual PM2.5 species model performance statistics. 

CMAQ 2005 Annual PM2.5 species No. of 

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

12-km EUS 11797 6.6 39.5 4.0 39.2 

12-km WUS 3440 -10.0 45.0 -9.5 44.4 

Northeast 2318 16.6 38.1 15.8 35.1 

Midwest 3020 19.5 45.6 16.5 40.7 

Southeast 3067 -7.2 34.1 -7.6 29.1 

Central U.S. 2523 0.6 41.7 -3.6 44.7 

STN 

West 2826 -10.9 46.1 -10.6 45.0 

12-km EUS 9321 1.6 43.4 0.0 44.0 

12-km WUS 10411 -10.9 44.8 -13.6 46.8 

Northeast 571 9.1 39.4 8 38.5 

Midwest 2339 21.5 52.9 14.9 46.8 

Southeast 1694 -10.5 37.2 -9.7 42.3 

Central U.S. 2376 -5.5 42.4 -4.7 46.0 

PM2.5 

Total Mass 

IMPROVE 

West 8820 -13.3 45.0 -14.5 47.2 

12-km EUS 13897 -8.7 32.6 -5.8 35.2 

12-km WUS 3920 -17.0 42.3 -7.8 42.8 

Northeast 2495 -1.2 33.4 4.1 34.1 

Midwest 3498 -3.9 31.7 -0.7 33.3 

Southeast 3882 -10.9 30.3 -8.6 32.9 

Central U.S. 3059 -19.8 36.5 -16.6 40.8 

STN 

West 3157 -15.5 45.8 -6.7 44.0 

12-km EUS 9034 -11.4 33.8 -2.6 38.4 

12-km WUS 10002 -9.0 39.9 6.8 43.5 

Northeast 531 -9.5 31.8 -1.9 34.1 

Midwest 2253 -3.8 34.2 3.0 37.1 

Southeast 1685 -13.8 31.5 -8.1 35.4 

Central U.S. 2350 -19.7 35.8 -12.2 39.8 

IMPROVE 

West 8496 -4.7 41.7 9.6 44.4 

12-km EUS 3170 -15.7 23.1 -14.2 25.8 

12-km WUS 1142 -19.8 31.1 -10.5 32.6 

Northeast 615 -13.4 21.7 -11.0 22.9 

Midwest 786 -10.3 21.3 -7.7 23.0 

Southeast 1099 -17.9 22.7 -19.5 25.6 

Central U.S. 300 -29.1 32.0 -29.3 35.2 

Sulfate 

CASTNet 

West 1041 -18.4 31.6 -9.3 32.8 

12-km EUS 12741 37.8 78.6 0.4 79.4 

12-km WUS 3655 -41.7 65.3 -70.8 97.5 

Northeast 2495 41.0 73.3 24.0 66.8 

Nitrate STN 

Midwest 3499 48.9 83.1 11.5 75.6 
 12 

Southeast 3882 34.5 93.7 -19.4 91.4 

Central U.S. 1927 25.0 67.2 4.8 75.0 

West 3139 -47.3 65.4 -79.1 99.9 

12-km EUS 9027 52.8 98.5 -21.0 101.4 

12-km WUS 9987 -18.3 75.4 -84.2 120.6 

Northeast 531 32.8 77.1 2.0 83.8 

Midwest 2248 86.2 122.5 8.9 97.3 

Southeast 1685 67.2 126.4 -24.5 104.4 

Central U.S. 2350 40.89 82.1 -10.1 95.8 

IMPROVE 

West 8480 -35.7 77.0 -91.7 124.2 

12-km EUS 3170 41.0 51.0 31.7 44.0 

12-km WUS 1142 5.4 36.3 13.1 40.6 

Northeast 615 37.4 47.0 35.1 40.6 

Midwest 786 55.8 59.4 44.4 50.2 

Southeast 1099 41.8 53.4 29.0 45.6 

Central U.S. 300 23.6 40.3 16.8 36.2 

Total 

Nitrate 

(NO3 + 

HNO3) 

CASTNet 

West 1041 4.8 37.6 14.7 41.6 

12-km EUS 13897 13.0 44.8 17.2 46.8 

12-km WUS 3893 -14.9 55.3 7.1 55.0 

Northeast 2495 21.0 44.8 27.0 43.8 

Midwest 3498 21.2 47.6 29.5 48.2 

Southeast 3882 7.0 41.4 11.4 43.4 

Central U.S. 3059 1.3 45.1 4.22 51.1 

STN 

West 3130 -20.5 59.0 5.8 57.2 

12-km EUS 3170 5.7 36.5 7.1 36.8 

12-km WUS 1142 -6.4 37.8 -4.0 37.6 

Northeast 615 15.4 37.2 18.9 35.2 

Midwest 786 14.5 40.8 18.4 38.7 

Southeast 1099 -7.5 32.9 -7.4 36.2 

Central U.S. 300 3.3 36.9 5.5 40.1 

Ammonium 

CASTNet 

West 1041 -12.6 37.1 -4.9 37.5 

12-km EUS 14038 45.5 77.1 30.6 58.3 

12-km WUS 3814 31.1 77.7 19.5 62.5 

Northeast 2502 40.4 65.9 33.8 53.4 

Midwest 3479 57.3 83.7 38.5 59.6 

Southeast 3877 27.2 64.4 20.6 51.0 

Central U.S. 3221 72.1 102.0 39.0 69.5 

STN 

West 3015 38.7 82.8 21.0 65.1 

12-km EUS 8668 -14.6 49.3 -18.2 53.3 

12-km WUS 12851 43.4 75.6 29.6 57.8 

Northeast 602 -1.3 45.1 -15.7 48.1 

Midwest 2117 10.6 54.5 -3.7 54.1 

Southeast 1584 -36.7 46.9 -39.3 56.3 

Elemental 

Carbon 

IMPROVE 

Central U.S. 2123 -23.8 47.7 -21.5 53.4 
 13 

West 8169 5.8 64.9 -8.6 61.0 

12-km EUS 12619 -27.2 50.5 -22.8 60.1 

12-km WUS 3582 -32.1 56.7 -28.2 61.3 

Northeast 2380 -28.3 49.0 -19.7 58.1 

Midwest 3323 -7.6 53.3 -4.2 58.6 

Southeast 3802 -39.3 49.2 -39.4 61.0 

Central U.S. 2259 -31.4 51.2 -28.3 63.2 

STN 

West 3060 -31.7 57.6 -27.8 61.4 

12-km EUS 8662 -21.7 49.4 -25.6 55.8 

12-km WUS 11586 -29.9 50.4 -26.7 60.6 

Northeast 601 -22.7 41.3 -27.7 48.3 

Midwest 2116 3.5 55.7 -5.1 53.1 

Southeast 1587 -30.1 42.8 -37.8 54.1 

Central U.S. 2123 -36.9 51.7 -39.1 61.0 

Organic 

Carbon 

IMPROVE 

West 8165 -11.7 59.2 -19.3 61.0 

2. Ozone: An operational model performance evaluation for hourly and eight-hour daily 

maximum ozone 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 

domain shown in Figure II-1. Ozone measurements from 1194 sites (817 in the East and 377 in 

the West) were included in the evaluation and were taken from the 2005 State/local monitoring 

site data in the Air Quality System (AQS) Aerometric Information Retrieval System (AIRS). 

The ozone metrics covered in this evaluation include one-hour daily maximum ozone 

concentrations and eight-hour daily maximum ozone concentrations. The evaluation principally 

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

space on an hourly and/or daily basis, depending on the sampling frequency of each 

measurement site (measured data). This ozone model performance was limited to the ozone 

season (May through September) that was modeled for the LDGHG final rule. Appendix D 

contains a more detailed summary of ozone model performance over the 12km Eastern and 

Western U.S. grid. A summary of the evaluation is presented here. 

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

performance was judged by comparing our CMAQ 2005 performance results to the range of 

performance found in recent regional ozone model applications (e.g., EPA’s Renewable Fuel 

Standards-2 Final Rule20, EPA’s Proposal to Designate an Emissions Control Area for Nitrogen 

Oxides, Sulfur Oxides, and Particulate Matter 21 and the Clean Air Interstate Rule22). Overall, 

 

20 U.S. Environmental Protection Agency, Air Quality Modeling Technical Support Document: Changes to 

Renewable Fuel Standard Program, Appendix B: CMAQ Model Performance Evaluation for Ozone, Particulate 

Matter and Toxics. January, 2010 (EPA-454/R-10-001A). 

21 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) 
 14 

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

and FE) statistics shown in Tables II-5 and II-6 indicate that CMAQ-predicted 2005 hourly and 

eight-hour daily maximum ozone residuals (i.e., observation vs. model predictions) are within 

the range of other recent regional modeling applications. The CMAQ model performance results 

give us confidence that our applications of CMAQ using this modeling platform provide a 

scientifically credible approach for assessing ozone concentration changes resulting from the 

final LDGHG emission standard reductions. 

Table II-5. 2005 CMAQ one-hour daily maximum ozone model performance statistics 

calculated for a threshold of 40 ppb. 

CMAQ 2005 One-Hour Maximum Ozone: 

Threshold of 40 ppb No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 21394 -1.6 11.5 -0.8 11.6

12-km WUS 9631 -3.4 12.8 -2.8 12.7

Midwest 4418 0.8 10.0 1.0 10.2

Northeast 4102 5.4 11.8 5.9 11.7

Southeast 6424 -3.6 11.3 -3.0 11.5

Central U.S. 4328 -6.4 13.4 -5.5 13.4

May 

West 8294 -3.5 12.9 -3.0 12.8

12-km EUS 19517 -3.5 12.8 -2.8 12.9

12-km WUS 9056 -3.7 13.0 -3.2 13.0

Midwest 4639 -4.6 12.3 -4.0 12.4

Northeast 4148 -1.0 14.1 -0.1 14.2

Southeast 4644 -2.7 12.5 -2.2 12.6

Central U.S. 4062 -6.2 13.2 -5.4 13.3

June 

West 7737 -4.0 13.1 -3.6 13.1

12-km EUS 19692 1.2 14.2 1.8 14.1

12-km WUS 9443 0.4 16.0 1.0 15.8

Midwest 4923 0.4 12.7 0.9 12.6

Northeast 4445 4.2 15.2 4.8 14.9

Southeast 4733 4.2 15.1 4.6 14.8

Central U.S. 3521 -3.8 14.8 -3.1 14.9

July 

West 8168 0.2 16.2 0.7 16.0

12-km EUS 19643 0.1 13.9 0.8 13.8

12-km WUS 9562 -0.8 15.5 -0.6 15.5

Midwest 4549 0.2 12.2 1.0 12.3

Northeast 4139 0.2 13.2 1.2 13.1

Southeast 5303 3.6 14.9 3.9 14.5

Central U.S. 3589 -4.1 16.2 -2.9 16.1

August 

West 8357 -1.0 15.7 -1.0 15.7

12-km EUS 18085 -2.2 12.0 -1.3 12.0

12-km WUS 8725 -3.6 14.1 -3.2 14.3

Midwest 4002 -3.6 10.7 -3.0 10.8

Northeast 3667 -1.8 11.3 -0.7 11.3

Southeast 5259 -0.1 12.1 0.8 12.1

Central U.S. 3286 -6.1 14.5 -5.1 14.5

September 

West 7530 -4.1 14.3 -3.8 14.4

 22 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; Research Triangle Park, NC; March 2005. 
 15 

12-km EUS 98331 -1.2 12.9 -0.5 12.8

12-km WUS 46417 -2.1 14.3 -1.7 14.2

Midwest 22531 -1.4 11.7 -0.8 11.7

Northeast 20501 1.4 13.3 2.3 13.1

Southeast 26363 0.1 13.1 0.7 13.0

Central U.S. 18786 -5.4 14.4 -4.4 14.4

Seasonal Aggregate 

(May – September) 

West 40086 -2.3 14.5 -2.1 14.4

Table II-6. 2005 CMAQ eight-hour daily maximum ozone model performance statistics 

calculated for a threshold of 40 ppb. 

CMAQ 2005 Eight-Hour Maximum 

Ozone: Threshold of 40 ppb No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 19310 -1.0 10.9 -0.4 11.0 

12-km WUS 8445 -1.6 12.0 -1.2 12.0 

Midwest 3858 0.2 10.0 0.7 10.2 

Northeast 3528 5.2 11.4 5.4 11.2 

Southeast 6019 -2.1 10.5 -1.6 10.6 

Central U.S. 3927 -5.8 12.8 -5.2 13.0 

May 

West 7234 -1.8 12.1 -1.5 12.0 

12-km EUS 17404 -2.1 11.9 -1.5 12.0 

12-km WUS 8102 -1.9 11.9 -1.6 11.9 

Midwest 4324 -3.8 11.6 -3.4 11.8 

Northeast 3590 0.3 13.1 1.0 13.2 

Southeast 3924 -0.3 11.4 0.1 11.5 

Central U.S. 3663 -5.5 12.1 -5.0 12.3 

June 

West 6889 -2.2 12.1 -2.0 12.1 

12-km EUS 17045 3.3 13.4 3.6 13.3 

12-km WUS 8556 3.7 15.0 3.9 14.7 

Midwest 4429 1.8 11.8 2.3 11.8 

Northeast 3856 6.6 14.6 6.8 14.3 

Southeast 3806 7.4 15.0 7.3 14.5 

Central U.S. 3057 -2.3 13.2 -2.1 13.5 

July 

West 7407 3.5 15.1 3.6 14.9 

12-km EUS 16953 1.9 12.9 2.2 12.9 

12-km WUS 8523 1.6 13.9 1.5 13.9 

Midwest 4027 0.9 11.3 1.4 11.4 

Northeast 3530 1.4 12.3 2.0 12.2 

Southeast 4447 7.4 14.7 7.2 14.1 

Central U.S. 3096 -3.4 14.4 -3.1 14.8 

August 

West 7469 1.4 14.1 1.2 14.0 

12-km EUS 15190 -1.8 11.2 -1.3 11.3 

12-km WUS 7465 -2.4 13.4 -2.6 13.9 

Midwest 3265 -4.2 10.2 -4.0 10.4 

Northeast 2856 -2.3 10.6 -1.8 10.7 

Southeast 4647 1.5 11.2 2.1 11.2 

Central U.S. 2798 -6.5 13.6 -6.1 14.0 

September 

West 6446 -2.9 13.7 -3.1 14.1 

12-km EUS 85902 0.1 12.1 0.5 12.1 

12-km WUS 41091 0.0 13.3 0.1 13.3 

Seasonal Aggregate 

(May – September) 

Midwest 19903 -0.9 11.1 -0.5 11.2 
 16 

Northeast 17360 2.4 12.6 2.9 12.4 

Southeast 22843 2.3 12.3 2.6 12.2 

Central U.S. 16541 -4.8 13.2 -4.4 13.4 

West 35445 -0.2 13.5 -0.3 13.5 

3. Nitrate and Sulfate Deposition

 Annual nitrate and sulfate deposition performance statistics are provided in Table II-7. 

The model predictions for annual nitrate deposition generally show small under-predictions for 

the Eastern and Western NADP sites (NMB values range from -3% to -18%). Sulfate deposition 

performance in the EUS and WUS shows the similar over predictions (NMB values range from 

3% to 14%), except for predicted under-prediction in the Central US (NMB = -9.9%). The errors 

for both annual nitrate and sulfate are relatively moderate with values ranging from 54% to 87% 

which reflect scatter in the model predictions to observation comparison. Similar to 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 model performance results found in 

recent regional multi-pollutant model applications. 

Table II-7. CMAQ 2005 annual model performance statistics for total nitrate and sulfate 

deposition. 

CMAQ 2005 Total Deposition No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 7381 -0.6 63.9 -7.8 74.0 

12-km WUS 2732 -11.6 69.5 -12.7 83.4 

Northeast 1391 4.9 62.0 5.0 67.8 

Midwest 1658 10.1 60.9 3.9 66.5 

Southeast 1980 4.9 67.3 0.1 71.3 

Central 1229 -11.0 62.7 -11.0 78.3 

Nitrate 

West 2257 -9.8 73.8 -12.7 85.0 

12-km EUS 7381 7.8 67.0 6.0 75.3 

12-km WUS 2732 5.6 76.3 4.8 86.5 

Northeast 1391 16.4 62.6 23.2 70.4 

Midwest 1658 12.6 64.3 16.5 67.3 

Southeast 1980 8.6 71.4 6.4 73.8 

Central 1229 -7.3 65.1 -1.2 80.3 

Sulfate 

West 2257 13.2 81.8 6.5 87.9 

4. Hazardous air pollutants

An operational model performance evaluation for daily, monthly, seasonal, and annual 

specific air toxics (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. 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 
 17 

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

space on daily basis. Appendix D contains a more detailed summary of air toxics model 

performance over the 12km Eastern and Western U.S. grid. A summary of the evaluation is 

presented here. 

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. 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.23,24,25 Overall, the normalized mean 

bias and error (NMB and NME), as well as the fractional bias and error (FB and FE) statistics 

shown in Table II-8 indicate that CMAQ-predicted 2005 toxics (i.e., observation vs. model 

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

Table II-8. 2005 CMAQ annual toxics model performance statistics 

CMAQ 2005 Annual No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 6365 -53.8 64.5 -36.6 64.2 

12-km WUS 1928 -24.5 50.9 -25.8 58.6 

Northeast 1982 -28.2 50.7 -26.3 60.7 

Midwest 771 -75.9 85.0 -22.6 72.7 

Southeast 1246 -65.0 71.4 -48.8 69.1 

Central U.S. 1815 -41.4 49.4 -38.7 59.6 

Formaldehyde 

West 1746 -21.7 51.5 -22.0 58.2 

12-km EUS 6094 -0.9 63.0 -5.2 59.8 

12-km WUS 1892 -14.4 53.6 -14.7 58.2 

Northeast 1969 -6.6 64.0 -6.4 63.4 

Midwest 703 -8.9 58.8 -8.7 59.3 

Southeast 1231 3.2 64.1 -3.7 61.5 

Central U.S. 1640 5.6 57.9 -0.9 50.5 

Acetaldehyde 

West 1709 -15.6 53.9 -15.4 59.3 

12-km EUS 11615 -30.6 66.9 -10.4 62.4 

12-km WUS 3369 -34.9 60.5 -25.4 62.3 

Northeast 2589 26.5 55.1 22.7 47.7 

Benzene 

Midwest 1425 -5.8 73.5 27.9 62.7 

 

23 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. 

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

Impact of using link-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. 

 

25 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.
 18 

Southeast 2426 -38.3 69.4 -12.9 60.1 

Central U.S. 4737 -46.4 67.6 -30.9 68.0 

West 2333 -26.1 61.2 -13.6 62.1 

12-km EUS 8102 -71.9 84.9 -37.5 88.2 

12-km WUS 1976 -46.5 83.1 -22.7 91.5 

Northeast 1902 -34.1 53.0 -42.5 64.5 

Midwest 516 -74.8 85.5 -34.8 77.7 

Southeast 1226 -82.4 84.0 -93.4 100.7 

Central U.S. 4142 -63.8 86.1 -8.0 89.6 

1,3-Butadiene 

West 1082 -36.7 78.4 -36.6 84.2 

12-km EUS 1660 -93.8 94.5 -126.2 138.4 

12-km WUS 783 -95.4 95.5 -164.5 167.2 

Northeast 850 -89.5 90.9 -116.0 131.1 

Midwest n/a n/a n/a n/a n/a 

Southeast 278 -96.7 96.7 -152.8 153.6 

Central U.S. n/a n/a n/a n/a n/a 

Acrolein 

West 592 -95.8 95.8 -176.9 176.9 

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 light-duty vehicle greenhouse gas rule. We 

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

nitrogen and sulfur deposition levels and visibility impairment. In this section, we present 

information on current levels of pollution as well as model projected levels of pollution for 2030. 

A. Impacts of LDGHG 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 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 increase in many areas of the 

country and decrease in a few areas. The increases in ozone design values are related to our 

assumptions about changes in fuel consumption and production that are not directly due to the 

standards finalized in this rule. As discussed in Sections 5.3.2 and 5.3.3.5 of the RIA, the 

decreased fuel consumption and production from this program is attributed to gasoline only, 

while assuming constant ethanol volumes in our reference and control cases. Holding ethanol 

volumes constant while decreasing gasoline volumes increases the market share of 10% ethanol 

(E10) in the control case. However, the increased E10 market share is projected to occur 

regardless of this rule, and the air quality impacts of this effect are included in our analyses for 

the recent RFS2 rule. As the RFS2 analyses indicate, increasing usage of E10 fuels (when 

compared with E0 fuels) can increase NOX emissions and thereby increase ozone concentrations, 

especially in NOX-limited areas where relatively small amounts of NOX enable ozone to form 
 19 

rapidly.26 Figure III-1 presents the changes in 8-hour ozone design value concentration in 2030 

between the reference case and the control case.27 Appendix A 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 increases are less than 0.1 

ppb. However, there are some counties that will see 8-hour ozone design value increases above 

0.1 ppb; these counties are along the mid-Atlantic coast and in southern Arizona. The maximum 

projected increase in an 8-hour ozone design value is 0.25 ppb in Richland County, South 

Carolina. There are also some counties that are projected to see 8-hour ozone design value 

decreases. The decreases in ambient ozone concentration are likely due to projected upstream 

emissions decreases in NOX and VOCs from reduced gasoline production. The counties with 

ozone design value decreases greater than 0.1 ppb are in California, Texas, Louisiana, 

 

26 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. 

27 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. 
 20 

Mississippi, Kentucky, Ohio and West Virginia. The maximum decrease projected in an 8-hour 

ozone design value is 0.22 ppb in Riverside, CA. 

B. Impacts of LDGHG 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 vehicle standards. Specifically, we compare a 2030 reference 

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

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

decreases of less than 0.05 μg/m3

 in their annual PM2.5 design values due to the vehicle 

standards. Error! Reference source not found. presents the changes in annual PM2.5 design 

values in 2030.28 

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, six counties will see decreases of more than 0.05 μg/m3. These 

counties are in southern California, central North Dakota, eastern Missouri, southwest Louisiana 

and the Houston area in Texas. The maximum projected decrease in an annual PM2.5 design 

 

28 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. 
 21 

value is 0.07 μg/m3 in Harris County, Texas. The decreases in annual PM2.5 design values that 

are modeled in some counties are likely due to emission reductions related to lower gasoline 

production at existing oil refineries; reductions in direct PM2.5 emissions and PM2.5 precursor 

emissions (NOX and SOX) contribute to reductions in ambient concentrations of both direct PM2.5

and secondarily-formed PM2.5. Additional information on the upstream emissions reductions that 

are projected with this final rule is available in Section 5.5 of the RIA. 

 

C. Impacts of LDGHG 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 vehicle standards. Specifically, we compare a 2030 reference scenario, a 

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

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

between -0.05 μg/m3 and +0.05 μg/m3 in their 24-hour PM2.5 design values. Figure III-3 

presents the changes in 24-hour PM2.5 design values in 2030.29 

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

Reference Case and the Control Case

 

29 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. 
 22 

As shown in Figure III-3, 17 counties will see decreases of more than 0.05 μg/m3

. These 

counties are in southern California, northern Utah, central North Dakota, eastern Missouri, 

southern Arkansas, northern Oklahoma, southwest Louisiana and the Houston area in Texas. 

The maximum projected decrease in a 24-hour PM2.5 design value is 0.21 μg/m3

 in Harris 

County, Texas. The decreases in 24-hour PM2.5 design values that we see in some counties are 

likely due to emission reductions related to lower gasoline production at existing oil refineries; 

reductions in direct PM2.5 emissions and PM2.5 precursor emissions (NOX and SOX) contribute to 

reductions in ambient concentrations of both direct PM2.5 and secondarily-formed PM2.5. There 

are also some counties that will see small, less than 0.05 μg/m3, design value increases. These 

small increases in 24-hour PM2.5 design values are likely related to the same factors responsible 

for the increases in annual PM2.5 design values (see Section III-B above). Appendix C details the 

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

control cases. 

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

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

future from this vehicle emission standards required by LDGHG. We focus on air toxics which 

were identified as national and regional-scale cancer and noncancer risk drivers in past NATA 

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

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

impacts were also included in our analyses. Our modeling indicates that the GHG 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. However, we did develop population metrics, including the population living in areas 

with increases or decreases in concentrations of various magnitudes. We also estimated 

aggregated populations above and below reference concentrations for noncancer effects.

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 less than 1% across 

the country (Figure III-4 through III-6). Decreases in ambient concentrations of acetaldehyde 

seen in the much of the eastern half of the U.S. and parts of the West are generally less than 0.01 

μg/m3. Small increases of less than 0.01 μg/m3 are noted in the in New York, Pennsylvania, and 

Utah during the winter season (Figure III-5). 
 23 

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) 

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) 
 24 

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 not have a significant impact 

on 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 

of a 1 to 5% decrease in a small area of southern Kansas and northern Oklahoma. Figure III-7 

also shows that absolute changes in ambient concentrations of formaldehyde are generally less 

than 0.1 μg/m3. Also, increases in annual and seasonal ambient formaldehyde (Figures III-7 

through III-9), which range from 0.001 to 0.1 μg/m3, are a reflection of our ethanol volume 

assumptions as discussed above in Section III-A and are not due to the standards finalized in this 

rule. 
 25 

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) 
 26 

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. Ethanol 

Our modeling results do not show substantial impacts on ambient concentrations of 

ethanol from the vehicle GHG standards. While Figure III-10 through III-12 show increases in 

ambient ethanol concentrations ranging between 1 and 50% in some areas of the country, these 

increases are a reflection of our ethanol volume assumptions as discussed above in Section III-A 

and are not due to the standards finalized in this rule. 
 27 

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

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

(right) 

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

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

(right) 
 28 

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

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

Changes in μg/m3 (right) 

4. Benzene 

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

significant impact on ambient benzene concentrations. Figure III-13, III-14, and III-15 show 

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

and between 0.001 and 0.1 μg/m3. Because this rule will reduce consumption and production of 

gasoline, some of these decreases in benzene concentrations are likely due to the vehicle GHG 

standards. However, decreases in benzene concentrations may also be a reflection of our ethanol 

volume assumptions as discussed above for ozone, ethanol and formaldehyde, and are not due to 

the standards finalized in this rule. For example, the percent change map in Figure III-13 below 

shows benzene decreases occurring in the same areas of the country as ozone, ethanol, and 

formaldehyde increases. 
 29 

Figure III-13. 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) 

Figure III-14. 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) 
 30 

Figure III-15. 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) 

5. 1,3-Butadiene 

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

1,3-butadiene from the GHG standards. In the annual and winter, small decreases ranging from 

1 to 10% occur in some southern areas of the country and increases ranging from 1 to over 100% 

occur in some northern areas and areas with high altitudes (Figure III-16). Changes in absolute 

concentrations of ambient 1,3-butadiene are less than 0.001 μg/m3 except in some areas of the 

Northeast and Utah (Figure III-16 and III-17). Annual increases in ambient concentrations of 

1,3-butadiene are driven by wintertime rather than summertime changes (Figures III-17 and III18). These increases appear in rural areas with cold winters and low ambient levels but high 

contributions of emissions from snowmobiles, and a major reason for this modeled increase may 

be deficiencies in available emissions test data used to estimate snowmobile 1,3-butadiene 

emission inventories. These data were based on tests using only three engines, which showed 

significantly higher 1,3-butadiene emissions with 10% ethanol. However, they may not have 

been representative of real-world response of snowmobile engines to ethanol. Regardless, these 

increases are a reflection of our ethanol volume assumptions and are not due to the standards 

finalized in this rule. 
 31 

Figure III-16. 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-17. 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) 
 32 

Figure III-18. 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) 

6. Acrolein 

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

concentrations of acrolein from the standards finalized in this rule. Small decreases ranging 

from 1.0 to 2.5% occur in a few areas of the country and increases ranging from 1 to 100% occur 

in some northern areas and areas with high altitudes in the annual and winter (Figure III-19 and 

III-20). Changes in annual absolute concentrations of acrolein are less than 0.001 μg/m3 across 

the country (Figure III-19). Ambient acrolein increases are driven by wintertime changes rather 

than summertime changes (Figures III-20 and III-21), and occur in the same areas of the country 

that have wintertime rather than summertime increases in ambient 1,3-butadiene. 1,3-butadiene 

is a precursor to acrolein, and these increases are likely associated with the same emission 

inventory uncertainties in areas of high snowmobile usage seen for 1,3-butadiene. As described 

above, these increases are a reflection of our ethanol volume assumptions and are not due to the 

standards finalized in this rule. 
 33 

Figure III-19. 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) 

Figure III-20. 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) 
 34 

Figure III-21. 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 LDGHG Standards on Future Annual Nitrogen and Sulfur Deposition 

Levels 

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

annual total sulfur and nitrogen deposition occurring across the U.S. as a result of the vehicle 

standards required by this rule. Figure III-22 shows that for sulfur deposition the vehicle 

standards will result in annual percent decreases of 0.5% to more than 2% in locations with 

refineries as a result of the lower output from refineries due to less gasoline usage. These 

locations include the Texas and Louisiana portions of the Gulf Coast; the Washington D.C. area; 

Chicago, IL; portions of Oklahoma and northern Texas; Bismarck, North Dakota; Billings, 

Montana; Casper, Wyoming; Salt Lake City, Utah; Seattle, Washington; and San Francisco, Los 

Angeles, and San Luis Obispo, California. The remainder of the country will see only minimal 

changes in sulfur deposition, ranging from decreases of less than 0.5% to increases of less than 

0.5%. The impacts of the vehicle standards on nitrogen deposition are minimal, ranging from 

decreases of up to 0.5% to increases of up to 0.5%. 
 35 

Figure III-22. Percent Change in Annual Total Sulfur over the U.S. Modeling Domain as a 

Result of the Required Vehicle Standards 

F. Impacts of LDGHG 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. As expected, the results show 

that all the modeled areas will continue to have annual average deciview levels above 

background in 2030.30 The results also indicate that the majority of the modeled mandatory class 

I federal areas will see no change in their visibility, but some mandatory class I federal areas will 

see improvements in visibility due to the vehicle 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 0.002 deciviews, or 0.01%, in 2030. The 

greatest improvement in visibilities will be seen in Bosque de Apache (New Mexico) and the San 

 

30 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. 
 36 

Gorgonio Wilderness (near Los Angeles, California). Bosque de Apache will see a 0.15% 

improvement (0.02 DV) and the San Gorgonio Wilderness will see a 0.10% improvement (0.02 

DV) in 2030 due to the vehicle standards. The following six areas will see a degradation of 0.01 

DV in 2030 as a result of the vehicle standards: Hells Canyon Wilderness (Oregon), 0.06% 

degradation; Kalmiopsis Wilderness (Oregon), 0.06% degradation; Strawberry Mountain 

Wilderness (Oregon), 0.06% degradation; Petrified Forest National Park (Arizona), 0.08% 

degradation; Rocky Mountain National Park (Colorado), 0.08% degradation; and Three Sisters 

Wilderness (Oregon), 0.06% degradation. Section 7.2.2.6.2 of this RIA contains more detail on 

the visibility portion of the air quality modeling. Table III-1 contains the full visibility results 

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 

BASE 

2030 

CONTROL 

NATURAL 

BACKGROUND 

Sipsey 

Wilderness 

AL 29.62 23.41 23.41 11.39 

Caney Creek 

Wilderness 

AR 26.78 22.52 22.51 11.33 

Upper Buffalo 

Wilderness 

AR 27.09 23.06 23.05 11.28 

Chiricahua NM AZ 13.33 13.28 13.28 6.92 

Chiricahua 

Wilderness 

AZ 13.33 13.27 13.27 6.91 

Galiuro 

Wilderness 

AZ 13.33 13.20 13.20 6.88 

Grand Canyon 

NP 

AZ 11.85 11.58 11.58 6.95 

Mazatzal 

Wilderness 

AZ 13.80 13.10 13.10 6.91 

Mount Baldy 

Wilderness 

AZ 11.27 11.10 11.10 6.95 

Petrified Forest 

NP 

AZ 13.73 13.31 13.32 6.97 

Pine Mountain 

Wilderness 

AZ 13.80 13.12 13.12 6.92 

Saguaro NM AZ 14.53 14.04 14.04 6.84 

Sierra Ancha 

Wilderness 

AZ 14.37 13.82 13.82 6.92 

Superstition 

Wilderness 

AZ 14.01 13.46 13.46 6.88 

Sycamore 

Canyon 

Wilderness 

AZ 15.34 15.04 15.04 6.96 

Agua Tibia 

Wilderness 

CA 23.09 24.56 24.55 7.17 

Ansel Adams 

Wilderness 

(Minarets) 

CA 14.90 14.78 14.77 7.12 
 37 

Caribou 

Wilderness 

CA 14.19 13.98 13.97 7.29 

Cucamonga 

Wilderness 

CA 19.35 18.23 18.22 7.17 

Desolation 

Wilderness 

CA 12.52 12.54 12.54 7.13 

Emigrant 

Wilderness 

CA 17.37 17.21 17.20 7.14 

Hoover 

Wilderness 

CA 11.92 11.85 11.85 7.12 

John Muir 

Wilderness 

CA 14.90 14.81 14.80 7.14 

Joshua Tree 

NM 

CA 19.40 18.68 18.68 7.08 

Kaiser 

Wilderness 

CA 14.90 14.71 14.71 7.13 

Kings Canyon 

NP 

CA 23.41 22.81 22.80 7.13 

Lassen 

Volcanic NP 

CA 14.19 14.00 14.00 7.31 

Lava Beds NM CA 14.77 14.31 14.31 7.49 

Mokelumne 

Wilderness 

CA 12.52 12.50 12.49 7.14 

Pinnacles NM CA 18.22 18.10 18.09 7.34 

Point Reyes NS CA 22.89 22.98 22.98 7.39 

Redwood NP CA 18.66 19.22 19.22 7.81 

San Gabriel 

Wilderness 

CA 19.35 18.06 18.05 7.17 

San Gorgonio 

Wilderness 

CA 21.80 20.23 20.21 7.10 

San Jacinto 

Wilderness 

CA 21.80 20.12 20.11 7.12 

San Rafael 

Wilderness 

CA 19.04 18.94 18.93 7.28 

Sequoia NP CA 23.41 22.64 22.64 7.13 

South Warner 

Wilderness 

CA 14.77 14.58 14.58 7.32 

Thousand 

Lakes 

Wilderness 

CA 14.19 14.00 14.00 7.32 

Ventana 

Wilderness 

CA 18.22 18.58 18.58 7.32 

Yosemite NP CA 17.37 17.24 17.24 7.14 

Black Canyon 

of the Gunnison 

NM 

CO 10.18 9.82 9.82 7.06 

Eagles Nest 

Wilderness 

CO 9.38 9.19 9.19 7.08 

Flat Tops 

Wilderness 

CO 9.38 9.27 9.27 7.07 

Great Sand 

Dunes NM 

CO 12.49 12.29 12.28 7.10 
 38 

La Garita 

Wilderness 

CO 10.18 10.00 10.00 7.06 

Maroon BellsSnowmass 

Wilderness 

CO 9.38 9.23 9.23 7.07 

Mesa Verde NP CO 12.78 12.44 12.44 7.09 

Mount Zirkel 

Wilderness 

CO 10.19 10.08 10.08 7.08 

Rawah 

Wilderness 

CO 10.19 9.99 9.99 7.08 

Rocky Mountain 

NP 

CO 13.54 13.33 13.34 7.05 

Weminuche 

Wilderness 

CO 10.18 9.99 9.99 7.06 

West Elk 

Wilderness 

CO 9.38 9.20 9.20 7.07 

Everglades NP FL 22.48 21.34 21.34 11.15 

Okefenokee GA 27.24 23.44 23.44 11.45 

Wolf Island GA 27.24 23.44 23.44 11.42 

Craters of the 

Moon NM 

ID 14.19 13.56 13.56 7.13 

Sawtooth 

Wilderness 

ID 14.33 14.24 14.24 7.15 

Mammoth Cave 

NP 

KY 31.76 25.48 25.48 11.53 

Acadia NP ME 23.19 22.20 22.20 11.45 

Moosehorn ME 21.94 21.03 21.03 11.36 

Roosevelt 

Campobello 

International 

Park 

ME 21.94 21.03 21.03 11.36 

Isle Royale NP MI 21.33 19.42 19.42 11.22 

Seney MI 24.71 22.45 22.45 11.37 

Voyageurs NP MN 19.82 17.79 17.79 11.09 

HerculesGlades 

Wilderness 

MO 27.15 23.60 23.60 11.27 

AnacondaPintler 

Wilderness 

MT 13.91 13.72 13.72 7.28 

Bob Marshall 

Wilderness 

MT 14.54 14.32 14.32 7.36 

Cabinet 

Mountains 

Wilderness 

MT 14.15 13.81 13.81 7.43 

Gates of the 

Mountains 

Wilderness 

MT 11.67 11.47 11.47 7.22 

Glacier NP MT 19.13 18.55 18.55 7.56 
 39 

Medicine Lake MT 17.78 16.81 16.81 7.30 

Mission 

Mountains 

Wilderness 

MT 14.54 14.25 14.25 7.39 

Scapegoat 

Wilderness 

MT 14.54 14.30 14.29 7.29 

SelwayBitterroot 

Wilderness 

MT 13.91 13.79 13.79 7.32 

UL Bend MT 14.92 14.63 14.63 7.18 

Linville Gorge 

Wilderness 

NC 29.40 23.36 23.36 11.43 

Shining Rock 

Wilderness 

NC 28.72 23.04 23.04 11.45 

Lostwood ND 19.50 17.95 17.95 7.33 

Theodore 

Roosevelt NP 

ND 17.69 16.29 16.29 7.31 

Great Gulf 

Wilderness 

NH 22.13 20.19 20.18 11.31 

Presidential 

Range-Dry 

River 

Wilderness 

NH 22.13 20.19 20.18 11.33 

Brigantine NJ 29.28 25.88 25.87 11.28 

Bandelier NM NM 11.87 11.29 11.28 7.02 

Bosque del 

Apache 

NM 13.89 13.18 13.16 6.97 

Gila Wilderness NM 13.32 13.03 13.03 6.95 

Pecos 

Wilderness 

NM 10.10 9.82 9.82 7.04 

Salt Creek NM 18.20 17.21 17.20 6.99 

San Pedro 

Parks 

Wilderness 

NM 10.39 10.06 10.06 7.03 

Wheeler Peak 

Wilderness 

NM 10.10 9.70 9.70 7.07 

White Mountain 

Wilderness 

NM 13.52 12.94 12.94 6.98 

Jarbidge 

Wilderness 

NV 12.13 12.09 12.09 7.10 

Wichita 

Mountains 

OK 23.79 20.50 20.49 11.07 

Crater Lake NP OR 14.04 13.76 13.76 7.71 

Diamond Peak 

Wilderness 

OR 14.04 13.71 13.71 7.77 

Eagle Cap 

Wilderness 

OR 18.25 17.64 17.63 7.34 

Gearhart 

Mountain 

Wilderness 

OR 14.04 13.88 13.88 7.46 

Hells Canyon 

Wilderness 

OR 18.73 17.90 17.91 7.32 
 40 

Kalmiopsis 

Wilderness 

OR 16.31 16.38 16.39 7.71 

Mount Hood 

Wilderness 

OR 14.79 14.49 14.49 7.77 

Mount Jefferson 

Wilderness 

OR 15.93 15.75 15.75 7.81 

Mount 

Washington 

Wilderness 

OR 15.93 15.72 15.72 7.89 

Mountain Lakes 

Wilderness 

OR 14.04 13.75 13.74 7.57 

Strawberry 

Mountain 

Wilderness 

OR 18.25 17.65 17.66 7.49 

Three Sisters 

Wilderness 

OR 15.93 15.72 15.73 7.87 

Cape Romain SC 27.14 24.09 24.09 11.36 

Badlands NP SD 16.73 15.52 15.51 7.30 

Wind Cave NP SD 15.96 14.93 14.93 7.24 

Great Smoky 

Mountains NP 

TN 30.43 24.30 24.30 11.44 

Joyce-KilmerSlickrock 

Wilderness 

TN 30.43 24.30 24.30 11.45 

Big Bend NP TX 17.39 16.43 16.42 6.93 

Carlsbad 

Caverns NP 

TX 16.98 15.89 15.88 7.02 

Guadalupe 

Mountains NP 

TX 16.98 15.89 15.88 7.03 

Arches NP UT 11.04 10.82 10.81 6.99 

Bryce Canyon 

NP 

UT 11.73 11.52 11.52 6.99 

Canyonlands 

NP 

UT 11.04 10.88 10.88 7.01 

Capitol Reef NP UT 10.63 10.74 10.74 7.03 

James River 

Face 

Wilderness 

VA 29.32 23.18 23.17 11.24 

Shenandoah 

NP 

VA 29.66 23.73 23.72 11.25 

Lye Brook 

Wilderness 

VT 24.17 20.72 20.72 11.25 

Alpine Lake 

Wilderness 

WA 17.35 17.29 17.28 7.86 

Glacier Peak 

Wilderness 

WA 13.78 14.06 14.05 7.80 

Goat Rocks 

Wilderness 

WA 12.88 12.32 12.32 7.82 

Mount Adams 

Wilderness 

WA 12.88 12.33 12.33 7.78 

Mount Rainier 

NP 

WA 17.56 17.23 17.22 7.90 
 41 

North Cascades 

NP 

WA 13.78 14.20 14.19 7.78 

Olympic NP WA 16.14 16.35 16.35 7.88 

Pasayten 

Wilderness 

WA 15.39 14.99 14.99 7.77 

Dolly Sods 

Wilderness 

WV 29.73 23.14 23.14 11.32 

Otter Creek 

Wilderness 

WV 29.73 23.14 23.14 11.33 

Bridger 

Wilderness 

WY 10.93 10.80 10.80 7.08 

Fitzpatrick 

Wilderness 

WY 10.93 10.80 10.80 7.09 

Grand Teton 

NP 

WY 10.94 10.61 10.61 7.09 

North Absaroka 

Wilderness 

WY 11.12 10.98 10.98 7.09 

Red Rock 

Lakes 

WY 10.94 10.68 10.68 7.14 

Teton 

Wilderness 

WY 10.94 10.70 10.70 7.09 

Washakie 

Wilderness 

WY 11.12 10.98 10.98 7.09 

Yellowstone NP WY 10.94 10.66 10.66 7.12 
 42 

Appendix A: 8-Hour Ozone Design Values for LDGHG Scenarios (units are ppb) 

State Name County Name Baseline 

DV 

2030 

Reference 

Case DV 

2030 

Control 

Case DV 

Alabama Baldwin 77.30 62.12 62.13

Alabama Clay 74.00 54.56 54.66

Alabama Colbert 72.00 49.56 49.60

Alabama Elmore 70.70 50.66 50.70

Alabama Etowah 71.70 53.50 53.57

Alabama Houston 71.00 54.06 54.12

Alabama Jefferson 83.70 60.25 60.28

Alabama Lawrence 72.00 54.59 54.65

Alabama Madison 77.30 57.70 57.77

Alabama Mobile 76.70 62.00 61.99

Alabama Montgomery 69.30 49.81 49.85

Alabama Morgan 77.30 61.83 61.88

Alabama Russell 71.30 53.80 53.91

Alabama Shelby 85.70 61.15 61.19

Alabama Sumter 64.00 50.66 50.72

Alabama Talladega 72.00 52.05 52.10

Alabama Tuscaloosa 73.30 51.92 51.97

Arizona Cochise 71.30 60.19 60.26

Arizona Coconino 73.00 61.38 61.37

Arizona Gila 80.30 60.96 61.14

Arizona La Paz 72.00 58.91 58.90

Arizona Maricopa 83.00 68.25 68.40

Arizona Pima 76.00 60.34 60.46

Arizona Pinal 79.30 61.31 61.48

Arizona Yavapai 72.00 59.01 59.03

Arizona Yuma 75.00 59.62 59.66

Arkansas Crittenden 87.30 64.30 64.31

Arkansas Newton 72.70 56.17 56.20

Arkansas Polk 75.00 61.52 61.55

Arkansas Pulaski 79.70 57.57 57.61

California Alameda 78.30 68.54 68.50

California Amador 83.00 67.88 67.80

California Butte 83.70 64.96 64.93

California Calaveras 91.30 77.10 77.00

California Colusa 67.00 54.40 54.38

California Contra Costa 73.30 67.60 67.55

California El Dorado 96.00 74.21 74.16

California Fresno 98.30 82.26 82.22

California Glenn 65.50 52.97 52.96

California Imperial 85.00 71.17 71.15

California Inyo 82.30 67.61 67.59

California Kern 110.00 94.06 94.00

California Kings 85.70 69.61 69.58

California Lake 60.70 50.74 50.73

California Los Angeles 114.00 99.78 99.73

California Madera 79.30 65.08 65.04

California Marin 49.70 45.02 44.99

California Mariposa 86.30 71.56 71.51
 43 

California Mendocino 56.70 47.18 47.17

California Merced 89.30 72.83 72.79

California Monterey 61.00 52.97 52.96

California Napa 59.30 50.01 49.97

California Nevada 96.30 75.01 74.96

California Orange 84.30 80.34 80.21

California Placer 94.00 72.94 72.89

California Riverside 112.30 108.99 108.78

California Sacramento 97.30 75.82 75.78

California San Benito 75.00 63.10 63.08

California San Bernardino 123.30 119.23 119.05

California San Diego 87.70 74.73 74.68

California San Francisco 46.00 45.94 45.94

California San Joaquin 75.30 65.35 65.29

California San Luis Obispo 70.70 59.82 59.78

California San Mateo 53.70 51.04 51.02

California Santa Barbara 76.00 65.76 65.75

California Santa Clara 75.30 63.59 63.55

California Santa Cruz 61.30 53.31 53.28

California Shasta 79.30 63.16 63.15

California Siskiyou 63.50 51.30 51.31

California Solano 72.70 61.24 61.20

California Sonoma 47.70 40.16 40.15

California Stanislaus 84.70 71.72 71.66

California Sutter 82.00 66.24 66.21

California Tehama 82.70 65.63 65.61

California Tulare 103.70 84.91 84.89

California Tuolumne 80.00 67.55 67.46

California Ventura 89.70 76.08 76.06

California Yolo 78.70 64.49 64.46

Colorado Adams 69.00 61.53 61.56

Colorado Arapahoe 78.70 67.53 67.60

Colorado Boulder 77.00 65.65 65.71

Colorado Denver 73.00 65.09 65.13

Colorado Douglas 83.00 71.56 71.63

Colorado El Paso 73.30 62.29 62.34

Colorado Jefferson 81.70 73.11 73.15

Colorado La Plata 63.70 56.79 56.81

Colorado Larimer 76.00 64.19 64.26

Colorado Montezuma 72.00 63.35 63.35

Colorado Weld 76.70 65.92 65.95

Connecticut Fairfield 92.30 78.57 78.57

Connecticut Hartford 84.30 65.93 66.02

Connecticut Litchfield 87.70 68.49 68.59

Connecticut Middlesex 90.30 74.32 74.38

Connecticut New Haven 90.30 76.01 76.04

Connecticut New London 85.30 69.67 69.72

Connecticut Tolland 88.70 68.80 68.88

D.C. Washington 84.70 68.15 68.25

Delaware Kent 80.30 63.66 63.69

Delaware New Castle 82.30 65.94 65.97

Delaware Sussex 82.70 68.73 68.76

Florida Alachua 72.00 52.50 52.60

Florida Baker 68.70 51.57 51.70
 44 

Florida Bay 78.70 60.32 60.40

Florida Brevard 71.30 56.10 56.17

Florida Broward 65.00 53.85 53.86

Florida Collier 68.30 51.32 51.40

Florida Columbia 72.00 55.63 55.74

Florida Duval 77.70 62.06 62.10

Florida Escambia 82.70 65.40 65.44

Florida Highlands 72.30 59.63 59.69

Florida Hillsborough 80.70 63.51 63.53

Florida Holmes 70.30 54.06 54.14

Florida Lake 76.70 58.47 58.61

Florida Lee 70.30 55.42 55.49

Florida Leon 71.00 51.79 51.91

Florida Manatee 77.30 59.10 59.15

Florida Marion 73.00 51.54 51.65

Florida Miami-Dade 71.30 66.00 65.98

Florida Orange 79.30 62.64 62.78

Florida Osceola 72.00 53.23 53.37

Florida Palm Beach 65.00 58.40 58.42

Florida Pasco 76.30 58.77 58.84

Florida Pinellas 72.70 54.92 54.99

Florida Polk 74.70 54.96 55.04

Florida St Lucie 66.50 55.23 55.29

Florida Santa Rosa 80.00 63.28 63.34

Florida Sarasota 77.30 57.58 57.64

Florida Seminole 76.00 57.58 57.71

Florida Volusia 68.30 51.29 51.38

Florida Wakulla 71.30 54.29 54.37

Georgia Bibb 81.00 63.22 63.33

Georgia Chatham 68.30 56.28 56.41

Georgia Chattooga 75.00 55.13 55.23

Georgia Clarke 80.70 54.77 54.88

Georgia Cobb 82.70 58.69 58.82

Georgia Columbia 73.00 55.80 55.91

Georgia Coweta 82.00 62.93 63.01

Georgia Dawson 76.30 51.47 51.58

Georgia De Kalb 88.70 69.21 69.31

Georgia Douglas 87.30 63.01 63.14

Georgia Fayette 85.70 65.17 65.27

Georgia Fulton 91.70 71.55 71.65

Georgia Glynn 67.00 51.68 51.90

Georgia Gwinnett 88.70 65.14 65.26

Georgia Henry 89.70 65.47 65.58

Georgia Murray 78.00 59.29 59.37

Georgia Muscogee 75.70 55.33 55.47

Georgia Paulding 80.30 55.75 55.86

Georgia Richmond 80.30 60.26 60.38

Georgia Rockdale 90.00 64.04 64.16

Georgia Sumter 72.30 54.58 54.68

Idaho Ada 76.00 68.16 68.19

Idaho Canyon 66.00 57.50 57.53

Idaho Elmore 63.00 56.42 56.44

Idaho Kootenai 67.00 56.09 56.13

Illinois Adams 70.00 57.37 57.37
 45 

Illinois Champaign 68.30 55.36 55.37

Illinois Clark 66.00 53.83 53.80

Illinois Cook 77.70 69.26 69.23

Illinois Du Page 69.00 62.42 62.39

Illinois Effingham 70.00 56.86 56.85

Illinois Hamilton 73.00 57.28 57.29

Illinois Jersey 78.70 58.17 58.18

Illinois Kane 74.30 62.39 62.35

Illinois Lake 78.00 68.34 68.30

Illinois McHenry 73.30 59.25 59.23

Illinois McLean 73.00 58.06 58.05

Illinois Macon 71.30 58.34 58.35

Illinois Macoupin 73.00 53.17 53.12

Illinois Madison 83.00 64.79 64.77

Illinois Peoria 72.70 61.04 61.02

Illinois Randolph 72.00 57.77 57.77

Illinois Rock Island 65.30 53.40 53.38

Illinois St Clair 81.70 65.01 65.01

Illinois Sangamon 70.00 53.38 53.37

Illinois Will 71.70 60.19 60.17

Illinois Winnebago 69.00 55.39 55.36

Indiana Allen 79.30 63.25 63.26

Indiana Boone 79.70 62.92 62.92

Indiana Carroll 74.00 58.00 58.02

Indiana Clark 80.30 61.29 61.30

Indiana Delaware 76.30 58.66 58.69

Indiana Elkhart 79.00 62.48 62.48

Indiana Floyd 77.70 63.36 63.36

Indiana Greene 78.30 61.19 61.21

Indiana Hamilton 82.70 64.80 64.81

Indiana Hancock 78.00 61.82 61.84

Indiana Hendricks 75.30 60.42 60.42

Indiana Huntington 75.00 59.55 59.57

Indiana Jackson 74.70 58.89 58.91

Indiana Johnson 76.70 62.15 62.16

Indiana Lake 81.00 70.86 70.85

Indiana La Porte 78.50 66.03 66.03

Indiana Madison 76.70 59.43 59.44

Indiana Marion 78.70 62.96 62.97

Indiana Morgan 77.00 62.18 62.19

Indiana Perry 81.00 61.98 62.00

Indiana Porter 78.30 68.89 68.87

Indiana Posey 71.70 55.40 55.41

Indiana St Joseph 79.30 63.08 63.07

Indiana Shelby 77.30 63.40 63.40

Indiana Vanderburgh 77.30 60.50 60.51

Indiana Vigo 74.00 60.27 60.28

Indiana Warrick 77.70 61.55 61.56

Iowa Bremer 66.30 53.55 53.55

Iowa Clinton 71.30 58.82 58.82

Iowa Harrison 74.70 61.19 61.19

Iowa Linn 68.30 56.98 56.98

Iowa Montgomery 65.70 52.99 53.00

Iowa Palo Alto 61.00 50.64 50.64
 46 

Iowa Polk 63.00 49.82 49.83

Iowa Scott 72.00 58.08 58.08

Iowa Story 61.00 48.15 48.16

Iowa Van Buren 69.00 56.48 56.48

Iowa Warren 64.50 50.26 50.27

Kansas Douglas 73.00 57.98 57.99

Kansas Johnson 75.30 59.77 59.79

Kansas Leavenworth 75.00 62.21 62.21

Kansas Linn 73.30 59.10 59.12

Kansas Sedgwick 71.30 56.85 56.85

Kansas Sumner 71.70 57.13 57.14

Kansas Trego 70.70 61.71 61.72

Kansas Wyandotte 75.30 62.79 62.78

Kentucky Bell 71.70 53.22 53.27

Kentucky Boone 75.70 59.43 59.45

Kentucky Boyd 77.30 63.21 63.07

Kentucky Bullitt 74.00 58.62 58.62

Kentucky Campbell 75.00 61.20 61.20

Kentucky Carter 71.00 56.12 56.03

Kentucky Christian 78.00 58.91 58.94

Kentucky Daviess 75.70 59.81 59.82

Kentucky Edmonson 73.70 57.55 57.57

Kentucky Fayette 70.30 55.38 55.38

Kentucky Greenup 76.70 62.96 62.84

Kentucky Hancock 74.00 56.96 56.97

Kentucky Hardin 74.70 59.20 59.21

Kentucky Henderson 75.30 59.99 60.01

Kentucky Jefferson 78.30 64.65 64.64

Kentucky Jessamine 73.30 57.33 57.34

Kentucky Kenton 78.70 62.06 62.08

Kentucky Livingston 73.70 59.14 59.17

Kentucky McCracken 73.30 60.47 60.49

Kentucky McLean 73.00 57.79 57.80

Kentucky Oldham 83.00 62.16 62.17

Kentucky Perry 72.30 57.12 57.15

Kentucky Pike 66.70 52.53 52.55

Kentucky Pulaski 70.30 56.82 56.84

Kentucky Simpson 75.70 57.29 57.32

Kentucky Trigg 70.00 53.84 53.87

Kentucky Warren 72.00 56.36 56.38

Louisiana Ascension 82.00 69.22 69.13

Louisiana Beauregard 75.00 64.45 64.38

Louisiana Bossier 78.00 60.36 60.36

Louisiana Caddo 79.00 61.30 61.32

Louisiana Calcasieu 82.00 70.59 70.52

Louisiana East Baton Rouge 92.00 77.68 77.57

Louisiana Grant 73.00 60.23 60.22

Louisiana Iberville 85.00 72.73 72.64

Louisiana Jefferson 83.00 70.23 70.18

Louisiana Lafayette 82.00 67.35 67.30

Louisiana Lafourche 79.30 67.93 67.79

Louisiana Livingston 78.30 66.09 66.01

Louisiana Orleans 70.00 60.37 60.33

Louisiana Ouachita 75.30 59.90 59.94
 47 

Louisiana Pointe Coupee 83.70 72.55 72.46

Louisiana St Bernard 78.00 65.84 65.79

Louisiana St Charles 77.30 65.95 65.90

Louisiana St James 76.30 65.50 65.37

Louisiana St John The Baptis 79.00 68.56 68.44

Louisiana St Mary 76.00 63.79 63.68

Louisiana West Baton Rouge 84.30 71.70 71.60

Maine Cumberland 72.00 58.52 58.60

Maine Hancock 82.00 66.90 66.99

Maine Kennebec 69.70 56.26 56.34

Maine Knox 75.30 60.91 60.99

Maine Oxford 61.00 50.62 50.67

Maine Penobscot 67.00 56.68 56.75

Maine Sagadahoc 68.50 55.64 55.70

Maine York 74.00 60.50 60.57

Maryland Anne Arundel 89.70 68.30 68.41

Maryland Baltimore 85.30 74.70 74.75

Maryland Calvert 81.00 63.53 63.63

Maryland Carroll 83.30 62.87 62.99

Maryland Cecil 90.70 69.07 69.16

Maryland Charles 86.00 64.72 64.81

Maryland Frederick 80.30 61.49 61.59

Maryland Garrett 75.50 59.59 59.64

Maryland Harford 92.70 79.90 79.96

Maryland Kent 82.00 62.52 62.58

Maryland Montgomery 83.00 64.86 64.98

Maryland Prince Georges 91.00 70.29 70.40

Maryland Washington 78.30 60.78 60.89

Massachusetts Barnstable 84.70 70.72 70.76

Massachusetts Berkshire 79.70 62.69 62.78

Massachusetts Bristol 82.70 70.01 70.05

Massachusetts Dukes 83.00 71.02 71.04

Massachusetts Essex 83.30 70.86 70.91

Massachusetts Hampden 87.30 68.12 68.22

Massachusetts Hampshire 85.00 65.66 65.76

Massachusetts Middlesex 79.00 63.28 63.34

Massachusetts Norfolk 84.70 67.57 67.62

Massachusetts Suffolk 80.30 65.84 65.89

Massachusetts Worcester 80.00 60.98 61.06

Michigan Allegan 90.00 74.82 74.76

Michigan Benzie 81.70 66.95 66.91

Michigan Berrien 82.30 68.23 68.20

Michigan Cass 80.70 64.33 64.33

Michigan Clinton 75.70 58.96 58.96

Michigan Genesee 79.30 63.07 63.08

Michigan Huron 75.70 62.68 62.68

Michigan Ingham 76.00 60.47 60.47

Michigan Kalamazoo 75.30 59.39 59.39

Michigan Kent 81.00 63.46 63.44

Michigan Leelanau 75.70 62.73 62.71

Michigan Lenawee 78.70 63.47 63.47

Michigan Macomb 86.00 70.72 70.71

Michigan Mason 79.70 64.06 64.03

Michigan Missaukee 73.70 60.01 59.98
 48 

Michigan Muskegon 85.00 70.50 70.45

Michigan Oakland 78.00 66.05 66.04

Michigan Ottawa 81.70 65.82 65.79

Michigan St Clair 82.30 65.96 65.97

Michigan Schoolcraft 79.30 64.42 64.40

Michigan Washtenaw 78.30 64.01 64.02

Michigan Wayne 82.00 67.60 67.59

Minnesota Anoka 67.70 62.22 62.19

Minnesota St Louis 65.00 54.11 54.11

Mississippi Adams 74.70 60.66 60.65

Mississippi Bolivar 74.30 58.09 58.13

Mississippi De Soto 82.70 61.60 61.60

Mississippi Hancock 79.00 64.72 64.66

Mississippi Harrison 83.00 67.11 67.01

Mississippi Hinds 71.30 48.15 48.17

Mississippi Jackson 80.30 66.23 66.11

Mississippi Lauderdale 74.30 55.73 55.82

Mississippi Lee 73.70 53.00 53.08

Missouri Cass 74.70 58.86 58.86

Missouri Cedar 75.70 59.98 60.00

Missouri Clay 84.30 67.18 67.18

Missouri Clinton 83.00 65.61 65.62

Missouri Greene 73.00 57.62 57.63

Missouri Jefferson 82.30 67.04 67.01

Missouri Lincoln 87.00 68.52 68.50

Missouri Monroe 71.70 56.96 56.96

Missouri Perry 77.50 60.83 60.85

Missouri Platte 77.00 63.46 63.46

Missouri St Charles 87.00 66.95 66.93

Missouri Ste Genevieve 79.70 64.64 64.60

Missouri St Louis 88.00 71.39 71.35

Missouri St Louis City 84.00 67.73 67.72

Montana Yellowstone 59.00 53.77 53.73

Nebraska Douglas 68.70 57.84 57.83

Nebraska Lancaster 56.00 45.40 45.41

Nevada Clark 83.70 72.85 72.88

Nevada Washoe 70.70 57.13 57.12

Nevada White Pine 72.30 61.46 61.47

Nevada Carson City 65.00 52.27 52.23

New Hampshire Belknap 71.30 53.91 53.98

New Hampshire Cheshire 70.70 55.40 55.46

New Hampshire Coos 77.00 61.95 62.03

New Hampshire Grafton 67.00 53.73 53.80

New Hampshire Hillsborough 78.70 62.91 62.98

New Hampshire Merrimack 71.70 55.55 55.62

New Hampshire Rockingham 75.00 61.31 61.39

New Hampshire Sullivan 70.00 55.05 55.13

New Jersey Atlantic 79.30 65.33 65.35

New Jersey Bergen 86.00 74.04 74.04

New Jersey Camden 89.30 72.01 71.99

New Jersey Cumberland 83.30 65.99 66.01

New Jersey Gloucester 87.00 70.30 70.30

New Jersey Hudson 85.70 74.89 74.84

New Jersey Hunterdon 89.00 68.12 68.19
 49 

New Jersey Mercer 88.00 70.81 70.85

New Jersey Middlesex 88.30 70.53 70.55

New Jersey Monmouth 87.30 73.06 73.06

New Jersey Morris 83.30 65.36 65.44

New Jersey Ocean 93.00 74.05 74.07

New Jersey Passaic 81.00 65.90 65.96

New Mexico Bernalillo 73.70 60.10 60.11

New Mexico Dona Ana 75.30 64.16 64.19

New Mexico Eddy 69.00 63.01 63.01

New Mexico Grant 66.00 58.00 58.03

New Mexico Lea 69.50 64.11 64.11

New Mexico Sandoval 73.30 59.78 59.78

New Mexico San Juan 71.30 65.82 65.82

New York Albany 73.70 58.31 58.40

New York Bronx 74.70 67.12 67.11

New York Chautauqua 86.70 73.47 73.50

New York Chemung 68.70 55.85 55.89

New York Dutchess 75.70 59.06 59.14

New York Erie 85.00 71.88 71.88

New York Essex 77.00 64.30 64.33

New York Hamilton 71.70 59.84 59.88

New York Herkimer 68.30 58.12 58.14

New York Jefferson 78.00 64.85 64.81

New York Madison 72.00 56.92 57.00

New York Monroe 75.00 63.07 63.10

New York Niagara 82.70 72.43 72.45

New York Oneida 68.30 56.98 57.02

New York Onondaga 73.70 61.50 61.54

New York Orange 82.00 64.50 64.59

New York Oswego 78.00 67.43 67.46

New York Putnam 84.30 66.58 66.66

New York Queens 80.00 69.37 69.34

New York Rensselaer 77.30 61.18 61.26

New York Richmond 88.30 74.46 74.45

New York Saratoga 79.70 63.14 63.23

New York Schenectady 70.00 56.13 56.22

New York Suffolk 90.30 83.36 83.34

New York Ulster 77.30 60.44 60.53

New York Wayne 68.00 58.07 58.10

New York Westchester 87.70 75.32 75.32

North Carolina Alexander 77.00 56.20 56.35

North Carolina Avery 70.00 56.72 56.78

North Carolina Buncombe 74.00 57.37 57.48

North Carolina Caldwell 74.30 54.80 54.94

North Carolina Caswell 76.30 57.06 57.19

North Carolina Chatham 73.30 55.22 55.36

North Carolina Cumberland 81.70 59.46 59.66

North Carolina Davie 81.30 60.28 60.44

North Carolina Durham 77.00 55.71 55.87

North Carolina Edgecombe 77.00 58.27 58.40

North Carolina Forsyth 80.00 61.01 61.12

North Carolina Franklin 78.70 58.29 58.46

North Carolina Graham 78.30 58.90 58.97

North Carolina Granville 82.00 60.30 60.47
 50 

North Carolina Guilford 82.00 60.11 60.30

North Carolina Haywood 78.30 61.08 61.18

North Carolina Jackson 76.00 57.67 57.74

North Carolina Johnston 77.30 55.60 55.80

North Carolina Lenoir 75.30 58.61 58.72

North Carolina Lincoln 81.00 59.12 59.29

North Carolina Martin 75.00 61.07 61.20

North Carolina Mecklenburg 89.30 67.24 67.36

North Carolina New Hanover 72.30 62.14 62.24

North Carolina Person 77.30 58.86 58.92

North Carolina Pitt 76.30 57.55 57.68

North Carolina Rockingham 77.00 57.60 57.70

North Carolina Rowan 86.70 63.62 63.79

North Carolina Swain 66.30 48.49 48.54

North Carolina Union 79.30 56.19 56.39

North Carolina Wake 80.30 58.70 58.89

North Carolina Yancey 76.00 59.00 59.06

North Dakota Billings 61.50 55.15 55.15

North Dakota Burke 57.50 51.48 51.48

North Dakota Cass 60.00 49.25 49.26

North Dakota McKenzie 61.30 54.96 54.97

North Dakota Oliver 57.70 52.43 52.43

Ohio Allen 78.70 63.23 63.21

Ohio Ashtabula 89.00 74.40 74.41

Ohio Butler 83.30 65.42 65.44

Ohio Clark 81.00 62.17 62.19

Ohio Clermont 81.00 65.24 65.24

Ohio Clinton 82.30 61.79 61.81

Ohio Cuyahoga 79.70 66.25 66.23

Ohio Delaware 78.30 61.86 61.89

Ohio Franklin 86.30 67.88 67.90

Ohio Geauga 79.30 61.14 61.16

Ohio Greene 80.30 62.22 62.24

Ohio Hamilton 84.70 67.23 67.23

Ohio Jefferson 78.00 60.93 60.95

Ohio Knox 77.70 59.90 59.92

Ohio Lake 86.30 70.00 69.98

Ohio Lawrence 70.70 58.04 57.92

Ohio Licking 78.00 60.04 60.07

Ohio Lorain 76.70 63.35 63.35

Ohio Lucas 81.30 66.46 66.44

Ohio Madison 79.70 60.41 60.44

Ohio Mahoning 78.70 60.02 60.06

Ohio Medina 80.30 63.53 63.53

Ohio Miami 76.70 58.74 58.76

Ohio Montgomery 74.00 57.59 57.61

Ohio Portage 83.70 65.22 65.24

Ohio Preble 73.00 56.44 56.47

Ohio Stark 81.00 62.34 62.35

Ohio Summit 83.70 66.01 66.02

Ohio Trumbull 84.30 64.41 64.45

Ohio Warren 87.70 67.73 67.74

Ohio Washington 82.70 67.24 67.27

Ohio Wood 80.00 64.12 64.12
 51 

Oklahoma Adair 75.70 63.64 63.64

Oklahoma Canadian 76.00 59.87 59.90

Oklahoma Cherokee 75.70 64.95 64.94

Oklahoma Cleveland 74.70 60.04 60.04

Oklahoma Comanche 77.50 62.08 62.09

Oklahoma Creek 76.70 61.61 61.59

Oklahoma Dewey 72.70 58.90 58.91

Oklahoma Kay 78.00 62.49 62.48

Oklahoma Mc Clain 72.00 58.00 58.01

Oklahoma Mayes 78.50 68.29 68.28

Oklahoma Oklahoma 80.00 62.86 62.89

Oklahoma Ottawa 78.00 64.01 64.01

Oklahoma Pittsburg 72.00 59.61 59.60

Oklahoma Tulsa 79.30 66.10 66.11

Oregon Clackamas 66.30 61.35 61.36

Oregon Jackson 68.00 52.47 52.52

Oregon Lane 69.30 56.38 56.43

Oregon Marion 65.70 56.16 56.21

Oregon Multnomah 56.30 68.82 68.81

Pennsylvania Adams 76.30 58.87 58.96

Pennsylvania Allegheny 83.70 65.49 65.54

Pennsylvania Armstrong 83.00 63.64 63.70

Pennsylvania Beaver 83.00 66.42 66.43

Pennsylvania Berks 76.00 58.57 58.63

Pennsylvania Blair 74.30 57.20 57.28

Pennsylvania Bucks 88.00 73.37 73.37

Pennsylvania Cambria 74.70 58.80 58.84

Pennsylvania Centre 78.30 61.29 61.37

Pennsylvania Chester 86.00 65.39 65.48

Pennsylvania Clearfield 78.30 61.50 61.54

Pennsylvania Dauphin 79.30 63.45 63.50

Pennsylvania Delaware 83.30 66.79 66.81

Pennsylvania Erie 81.30 68.63 68.65

Pennsylvania Franklin 72.30 55.39 55.49

Pennsylvania Greene 80.00 62.27 62.30

Pennsylvania Indiana 80.00 61.30 61.34

Pennsylvania Lackawanna 75.30 57.70 57.79

Pennsylvania Lancaster 83.30 64.64 64.72

Pennsylvania Lawrence 72.30 55.73 55.76

Pennsylvania Lehigh 83.30 64.77 64.82

Pennsylvania Luzerne 76.30 58.61 58.71

Pennsylvania Lycoming 77.30 61.59 61.74

Pennsylvania Mercer 82.00 62.29 62.32

Pennsylvania Montgomery 85.70 69.23 69.25

Pennsylvania Northampton 84.30 65.16 65.21

Pennsylvania Perry 77.00 59.69 59.78

Pennsylvania Philadelphia 90.30 75.25 75.26

Pennsylvania Tioga 77.70 62.11 62.21

Pennsylvania Washington 78.30 61.80 61.84

Pennsylvania Westmoreland 79.00 61.36 61.40

Pennsylvania York 82.00 64.36 64.44

Rhode Island Kent 84.30 69.16 69.20

Rhode Island Providence 82.30 66.90 66.97

Rhode Island Washington 86.00 72.74 72.78
 52 

South Carolina Abbeville 79.00 59.15 59.29

South Carolina Aiken 76.00 55.83 55.95

South Carolina Anderson 76.50 55.10 55.28

South Carolina Barnwell 73.00 53.26 53.37

South Carolina Berkeley 67.30 52.12 52.27

South Carolina Charleston 74.00 63.01 63.17

South Carolina Cherokee 74.00 56.01 56.11

South Carolina Chester 75.70 55.03 55.23

South Carolina Chesterfield 75.00 57.05 57.17

South Carolina Colleton 72.30 55.94 56.04

South Carolina Darlington 76.30 58.42 58.55

South Carolina Edgefield 70.00 52.25 52.36

South Carolina Oconee 73.00 52.92 53.13

South Carolina Pickens 78.70 57.21 57.39

South Carolina Richland 82.30 58.61 58.86

South Carolina Spartanburg 82.30 60.75 60.90

South Carolina Union 76.00 58.78 58.90

South Carolina Williamsburg 69.30 52.71 52.82

South Carolina York 76.70 56.33 56.52

South Dakota Custer 70.00 62.69 62.69

South Dakota Jackson 67.00 59.43 59.44

South Dakota Minnehaha 66.00 54.41 54.42

Tennessee Anderson 77.30 55.86 55.90

Tennessee Blount 85.30 60.51 60.57

Tennessee Davidson 77.70 57.38 57.40

Tennessee Hamilton 81.00 58.97 59.02

Tennessee Jefferson 82.30 59.67 59.72

Tennessee Knox 85.00 60.95 61.00

Tennessee Loudon 83.00 60.13 60.18

Tennessee Meigs 80.00 57.90 57.95

Tennessee Rutherford 76.30 55.68 55.71

Tennessee Sevier 80.70 60.36 60.43

Tennessee Shelby 80.70 58.94 58.97

Tennessee Sullivan 80.30 69.16 69.16

Tennessee Sumner 83.00 61.82 61.86

Tennessee Williamson 75.30 55.01 55.04

Tennessee Wilson 78.70 58.47 58.50

Texas Bexar 85.00 71.27 71.31

Texas Brazoria 94.70 80.54 80.39

Texas Brewster 64.00 54.87 54.89

Texas Cameron 66.00 59.57 59.57

Texas Collin 90.30 71.15 71.18

Texas Dallas 88.30 73.63 73.66

Texas Denton 94.00 71.00 71.05

Texas Ellis 81.70 64.35 64.39

Texas Galveston 80.30 70.17 70.05

Texas Gregg 84.30 71.97 71.96

Texas Harris 100.70 88.23 88.06

Texas Harrison 79.00 63.31 63.32

Texas Hidalgo 65.70 57.13 57.13

Texas Hood 83.00 60.03 60.11

Texas Hunt 78.00 62.83 62.84

Texas Jefferson 84.70 73.06 72.95

Texas Johnson 87.00 65.96 66.02
 53 

Texas Kaufman 74.70 59.69 59.72

Texas Montgomery 85.00 70.30 70.24

Texas Nueces 72.30 63.26 63.25

Texas Orange 78.00 65.65 65.54

Texas Parker 88.70 64.23 64.30

Texas Rockwall 79.70 64.29 64.32

Texas Smith 81.00 67.68 67.68

Texas Tarrant 95.30 72.45 72.50

Texas Travis 81.30 65.57 65.63

Texas Victoria 72.30 62.12 62.07

Texas Webb 61.30 53.70 53.68

Texas El Paso 77.70 64.98 65.02

Utah Box Elder 76.00 65.44 65.48

Utah Cache 68.70 58.73 58.79

Utah Davis 81.30 69.94 69.98

Utah Salt Lake 81.00 69.42 69.46

Utah San Juan 70.30 62.60 62.62

Utah Tooele 78.00 65.58 65.63

Utah Utah 76.70 69.40 69.43

Utah Washington 78.50 64.53 64.54

Utah Weber 80.30 68.68 68.73

Vermont Bennington 72.00 56.33 56.39

Vermont Chittenden 69.70 57.57 57.66

Virginia Arlington 86.70 70.80 70.90

Virginia Caroline 80.00 59.66 59.76

Virginia Charles City 80.30 65.43 65.48

Virginia Chesterfield 76.70 61.41 61.49

Virginia Fairfax 90.00 71.19 71.30

Virginia Fauquier 72.70 56.47 56.55

Virginia Frederick 72.30 56.38 56.47

Virginia Hanover 81.30 63.37 63.47

Virginia Henrico 82.00 65.02 65.08

Virginia Loudoun 80.70 60.81 60.92

Virginia Madison 77.70 61.03 61.11

Virginia Page 74.00 57.96 58.04

Virginia Prince William 78.70 60.78 60.87

Virginia Roanoke 74.70 58.05 58.16

Virginia Rockbridge 69.70 56.44 56.50

Virginia Stafford 81.70 62.52 62.61

Virginia Wythe 72.70 57.76 57.83

Virginia Alexandria City 81.70 64.62 64.72

Virginia Hampton City 76.70 67.99 68.02

Virginia Suffolk City 76.70 71.64 71.66

Washington Clark 59.50 60.99 60.99

Washington King 72.30 66.81 66.78

Washington Klickitat 64.50 58.02 58.04

Washington Pierce 68.70 61.53 61.51

Washington Skagit 46.00 45.94 45.93

Washington Spokane 68.30 55.77 55.81

Washington Thurston 65.00 56.03 56.05

Washington Whatcom 57.00 55.79 55.81

West Virginia Berkeley 75.00 58.60 58.69

West Virginia Cabell 78.70 64.31 64.21

West Virginia Greenbrier 69.70 57.31 57.31
 54 

West Virginia Hancock 75.70 60.17 60.19

West Virginia Kanawha 77.30 61.53 61.55

West Virginia Monongalia 75.30 57.03 57.08

West Virginia Ohio 78.30 62.29 62.30

West Virginia Wood 79.00 63.28 63.30

Wisconsin Ashland 61.50 51.68 51.68

Wisconsin Brown 73.70 61.42 61.40

Wisconsin Columbia 72.70 57.64 57.64

Wisconsin Dane 72.00 57.79 57.79

Wisconsin Dodge 74.70 60.55 60.54

Wisconsin Door 88.70 72.01 71.96

Wisconsin Florence 66.30 55.01 55.02

Wisconsin Fond Du Lac 73.70 60.79 60.77

Wisconsin Forest 69.50 57.69 57.69

Wisconsin Jefferson 74.30 59.34 59.34

Wisconsin Kenosha 84.70 75.11 75.06

Wisconsin Kewaunee 82.70 68.15 68.10

Wisconsin Manitowoc 85.00 70.80 70.75

Wisconsin Marathon 70.00 58.52 58.52

Wisconsin Milwaukee 82.70 70.97 70.92

Wisconsin Oneida 69.00 57.89 57.90

Wisconsin Outagamie 74.00 60.72 60.71

Wisconsin Ozaukee 83.30 71.82 71.76

Wisconsin Racine 80.30 70.19 70.15

Wisconsin Rock 74.00 59.40 59.39

Wisconsin St Croix 69.00 56.81 56.80

Wisconsin Sauk 69.70 56.22 56.22

Wisconsin Sheboygan 88.00 73.81 73.75

Wisconsin Vernon 69.70 55.62 55.62

Wisconsin Vilas 68.70 57.33 57.34

Wisconsin Walworth 75.70 60.32 60.30

Wisconsin Washington 72.30 59.91 59.89

Wisconsin Waukesha 75.00 62.21 62.18

Wyoming Campbell 67.30 63.38 63.39

Wyoming Sublette 70.00 63.41 63.42

Wyoming Teton 62.70 55.86 55.87
 55 

Appendix B: Annual PM2.5 Design Values for LDGHG Scenarios (units are ug/m3

) 

State Name County Name Baseline 

DV 

2030 

Reference 

Case DV 

2030 

Control 

Case DV 

Alabama Clay 13.21 10.25 10.25

Alabama Colbert 12.67 9.88 9.88

Alabama DeKalb 14.09 10.67 10.67

Alabama Etowah 14.80 11.26 11.26

Alabama Houston 12.86 10.78 10.78

Alabama Jefferson 18.48 14.62 14.62

Alabama Madison 13.73 10.58 10.58

Alabama Montgomery 14.14 11.78 11.78

Alabama Morgan 13.23 10.44 10.44

Alabama Russell 15.63 12.46 12.46

Alabama Shelby 14.28 11.10 11.10

Alabama Sumter 11.92 9.64 9.64

Alabama Talladega 14.51 11.00 11.00

Alabama Walker 13.77 10.71 10.71

Alabama Baldwin 11.44 9.47 9.46

Alabama Escambia 13.12 11.15 11.14

Alabama Mobile 12.90 10.59 10.58

Alabama Tuscaloosa 13.44 10.61 10.60

Arizona Maricopa 12.59 11.01 11.02

Arizona Cochise 7.00 6.91 6.91

Arizona Coconino 6.49 6.27 6.27

Arizona Gila 8.94 8.58 8.58

Arizona Pima 6.04 5.50 5.50

Arizona Pinal 7.77 7.42 7.42

Arizona Santa Cruz 12.94 12.56 12.56

Arkansas Arkansas 12.45 10.46 10.46

Arkansas Ashley 12.83 11.14 11.14

Arkansas Garland 12.40 10.44 10.44

Arkansas Mississippi 12.61 10.06 10.06

Arkansas Phillips 12.08 9.81 9.81

Arkansas Polk 11.65 9.88 9.88

Arkansas Pope 12.79 10.90 10.90

Arkansas Pulaski 14.05 11.57 11.57

Arkansas Crittenden 13.27 10.39 10.38

Arkansas White 12.57 10.60 10.59

Arkansas Faulkner 12.79 10.71 10.70

Arkansas Union 12.86 11.12 11.10

California Alameda 9.34 10.24 10.24

California Colusa 7.39 7.24 7.24

California Fresno 17.17 15.90 15.90

California Imperial 12.71 12.74 12.74

California Inyo 5.25 5.21 5.21

California Kings 17.28 16.12 16.12

California Lake 4.62 4.66 4.66

California Mendocino 6.46 6.28 6.28

California Monterey 6.96 7.25 7.25
 56 

California Nevada 6.71 6.32 6.32

California Placer 9.80 9.11 9.11

California San Diego 13.38 15.73 15.73

California San Francisco 9.62 10.80 10.80

California San Luis Obispo 7.94 8.07 8.07

California San Mateo 9.03 10.15 10.15

California Santa Barbara 10.37 10.33 10.33

California Santa Clara 11.38 12.30 12.30

California Shasta 7.41 6.63 6.63

California Sonoma 8.21 8.42 8.42

California Sutter 9.85 9.10 9.10

California Yolo 9.03 8.66 8.66

California Kern 19.17 17.33 17.32

California Butte 12.73 11.45 11.44

California Merced 14.78 14.00 13.99

California Plumas 11.46 10.62 10.61

California Sacramento 11.88 11.34 11.33

California San Joaquin 12.94 12.65 12.64

California Ventura 11.68 12.60 12.59

California Calaveras 7.77 7.40 7.39

California Stanislaus 14.21 13.30 13.29

California Tulare 18.51 17.28 17.27

California Contra Costa 9.47 9.87 9.85

California Riverside 20.95 20.77 20.75

California Solano 9.99 10.41 10.39

California Orange 15.75 16.50 16.47

California San Bernardino 19.67 19.82 19.79

California Los Angeles 17.66 18.69 18.63

Colorado Mesa 9.28 8.49 8.50

Colorado Arapahoe 7.96 6.94 6.94

Colorado Boulder 8.32 7.41 7.41

Colorado Delta 7.44 6.67 6.67

Colorado Denver 9.76 8.34 8.34

Colorado Elbert 4.40 4.05 4.05

Colorado El Paso 7.94 7.09 7.09

Colorado Larimer 7.33 6.74 6.74

Colorado Pueblo 7.45 6.76 6.76

Colorado San Miguel 4.65 4.44 4.44

Colorado Weld 8.78 7.77 7.77

Colorado Adams 10.06 8.61 8.60

Connecticut Fairfield 13.18 11.64 11.64

Connecticut Hartford 11.03 9.59 9.59

Connecticut Litchfield 8.01 7.05 7.05

Connecticut New Haven 13.12 11.35 11.35

Connecticut New London 10.96 9.58 9.58

Delaware Sussex 13.39 11.01 11.01

Delaware Kent 12.61 10.34 10.33

Delaware New Castle 14.87 12.20 12.19

District Of Columbia 

District of 

Columbia 14.41 11.11 11.11

Florida Hillsborough 10.74 8.67 8.68

Florida Alachua 9.59 8.00 8.00

Florida Bay 11.46 9.65 9.65

Florida Brevard 8.32 7.37 7.37

Florida Broward 8.21 8.20 8.20
 57 

Florida Citrus 9.00 7.30 7.30

Florida Duval 10.44 8.92 8.92

Florida Escambia 11.72 9.83 9.83

Florida Lee 8.36 7.39 7.39

Florida Leon 12.56 10.54 10.54

Florida Manatee 8.81 7.16 7.16

Florida Marion 10.11 8.43 8.43

Florida Miami-Dade 9.45 9.39 9.39

Florida Orange 9.61 8.07 8.07

Florida Palm Beach 7.70 7.41 7.41

Florida Pinellas 9.82 8.02 8.02

Florida Polk 9.55 7.91 7.91

Florida St. Lucie 8.34 7.57 7.57

Florida Sarasota 8.77 7.34 7.34

Florida Seminole 9.51 7.99 7.99

Florida Volusia 9.27 7.93 7.93

Georgia Clarke 14.90 11.67 11.68

Georgia Cobb 16.09 12.06 12.07

Georgia Bibb 16.47 13.10 13.10

Georgia Chatham 13.88 12.37 12.37

Georgia Clayton 16.47 12.01 12.01

Georgia DeKalb 15.33 11.15 11.15

Georgia Dougherty 14.35 11.94 11.94

Georgia Floyd 16.10 12.38 12.38

Georgia Fulton 17.43 12.81 12.81

Georgia Glynn 12.18 10.59 10.59

Georgia Gwinnett 16.07 12.04 12.04

Georgia Hall 14.12 10.88 10.88

Georgia Houston 13.99 11.09 11.09

Georgia Lowndes 12.49 10.68 10.68

Georgia Muscogee 15.16 12.07 12.07

Georgia Paulding 14.08 10.25 10.25

Georgia Richmond 15.68 12.93 12.93

Georgia Walker 15.49 11.89 11.89

Georgia Washington 15.14 12.37 12.37

Georgia Wilkinson 15.23 12.16 12.16

Idaho Idaho 9.58 9.18 9.19

Idaho Ada 8.41 7.76 7.76

Idaho Bannock 7.66 7.14 7.14

Idaho Benewah 9.59 9.11 9.11

Idaho Canyon 8.46 7.62 7.62

Idaho Franklin 7.70 6.97 6.97

Idaho Shoshone 12.08 11.38 11.38

Illinois Adams 12.50 10.30 10.30

Illinois Cook 15.75 12.52 12.52

Illinois Kane 14.34 11.66 11.66

Illinois Lake 11.81 9.82 9.82

Illinois McLean 12.39 10.08 10.08

Illinois Macon 13.24 10.76 10.76

Illinois Winnebago 13.57 11.15 11.15

Illinois Champaign 12.53 10.08 10.07

Illinois DuPage 13.82 11.25 11.24

Illinois McHenry 12.40 10.15 10.14

Illinois Madison 16.72 13.31 13.30
 58 

Illinois Peoria 13.34 10.93 10.92

Illinois Randolph 13.11 10.48 10.47

Illinois Rock Island 12.01 9.86 9.85

Illinois Sangamon 13.13 10.96 10.95

Illinois Jersey 12.89 10.51 10.49

Illinois Saint Clair 15.58 12.40 12.38

Illinois Will 13.63 11.10 11.08

Indiana Allen 13.67 11.14 11.14

Indiana Clark 16.40 12.58 12.58

Indiana Dubois 15.18 11.46 11.46

Indiana Floyd 14.80 11.28 11.28

Indiana Henry 13.64 10.72 10.72

Indiana Howard 13.93 11.15 11.15

Indiana Knox 14.03 10.60 10.60

Indiana LaPorte 12.69 10.24 10.24

Indiana Madison 13.96 10.93 10.93

Indiana Marion 16.05 12.40 12.40

Indiana St. Joseph 13.69 11.33 11.33

Indiana Spencer 14.32 10.59 10.59

Indiana Tippecanoe 13.69 10.91 10.91

Indiana Vanderburgh 14.99 11.71 11.71

Indiana Delaware 13.66 10.79 10.78

Indiana Lake 14.27 11.50 11.49

Indiana Porter 13.21 10.53 10.52

Indiana Vigo 13.99 10.82 10.81

Iowa Black Hawk 11.16 9.35 9.35

Iowa Clinton 12.52 10.30 10.30

Iowa Johnson 12.08 10.13 10.13

Iowa Linn 10.79 9.06 9.06

Iowa Montgomery 10.02 8.37 8.37

Iowa Muscatine 12.92 10.79 10.79

Iowa Palo Alto 9.53 8.00 8.00

Iowa Polk 10.64 8.74 8.74

Iowa Pottawattamie 11.13 9.18 9.18

Iowa Scott 14.42 11.93 11.93

Iowa Van Buren 10.84 9.06 9.06

Iowa Woodbury 10.32 8.67 8.67

Iowa Wright 10.37 8.68 8.68

Kansas Johnson 11.10 9.21 9.21

Kansas Shawnee 10.93 9.37 9.37

Kansas Wyandotte 12.73 10.54 10.54

Kansas Linn 10.47 8.93 8.92

Kansas Sedgwick 10.36 8.77 8.76

Kansas Sumner 9.89 8.48 8.47

Kentucky Bell 14.28 10.82 10.82

Kentucky Bullitt 14.90 11.22 11.22

Kentucky Campbell 13.67 9.81 9.81

Kentucky Carter 12.22 8.77 8.77

Kentucky Fayette 14.85 11.09 11.09

Kentucky Franklin 13.37 9.76 9.76

Kentucky Hardin 13.58 10.03 10.03

Kentucky Henderson 13.93 10.54 10.54

Kentucky Jefferson 15.53 11.85 11.85

Kentucky Kenton 14.36 10.45 10.45
 59 

Kentucky Laurel 12.55 9.26 9.26

Kentucky McCracken 13.38 10.36 10.36

Kentucky Madison 13.61 9.94 9.94

Kentucky Warren 13.83 10.32 10.32

Kentucky Boyd 14.49 10.77 10.76

Kentucky Christian 13.20 9.98 9.97

Kentucky Perry 13.06 9.79 9.78

Kentucky Pike 13.46 9.90 9.89

Kentucky Daviess 14.10 10.38 10.37

Louisiana Caddo 12.53 10.62 10.61

Louisiana Concordia 11.42 9.58 9.57

Louisiana Ouachita 11.97 10.31 10.30

Louisiana Rapides 11.03 9.50 9.49

Louisiana Terrebonne 10.74 9.33 9.32

Louisiana Lafayette 11.08 9.55 9.54

Louisiana West Baton Rouge 13.51 11.34 11.32

Louisiana Tangipahoa 12.03 10.14 10.12

Louisiana Iberville 12.90 11.08 11.05

Louisiana East Baton Rouge 13.38 11.23 11.20

Louisiana Jefferson 11.52 9.85 9.81

Louisiana Calcasieu 11.07 9.81 9.75

Maine Androscoggin 9.90 8.85 8.86

Maine Aroostook 9.74 9.44 9.44

Maine Cumberland 11.13 9.87 9.87

Maine Hancock 5.76 5.35 5.35

Maine Kennebec 9.99 8.91 8.91

Maine Oxford 10.13 9.25 9.25

Maine Penobscot 9.12 8.23 8.23

Maryland Anne Arundel 14.82 11.87 11.87

Maryland Baltimore 14.76 11.68 11.68

Maryland Harford 12.51 9.72 9.72

Maryland Montgomery 12.47 9.59 9.59

Maryland Washington 13.70 10.41 10.41

Maryland Baltimore (City) 15.76 12.51 12.51

Maryland Cecil 12.68 10.06 10.05

Maryland Prince George's 13.03 10.13 10.12

Massachusetts Suffolk 13.07 11.22 11.23

Massachusetts Berkshire 10.65 9.88 9.88

Massachusetts Bristol 9.58 8.26 8.26

Massachusetts Essex 9.58 8.24 8.24

Massachusetts Hampden 12.17 10.69 10.69

Massachusetts Plymouth 9.87 8.51 8.51

Massachusetts Worcester 11.29 9.72 9.72

Michigan Allegan 11.84 9.64 9.64

Michigan Bay 10.93 8.94 8.94

Michigan Berrien 11.72 9.57 9.57

Michigan Genesee 11.61 9.33 9.33

Michigan Ingham 12.23 9.82 9.82

Michigan Kalamazoo 12.84 10.40 10.40

Michigan Kent 12.89 10.25 10.25

Michigan Macomb 12.70 10.34 10.34

Michigan Missaukee 8.26 7.10 7.10

Michigan Monroe 13.92 11.02 11.02

Michigan Muskegon 11.61 9.55 9.55
 60 

Michigan Oakland 13.78 11.02 11.02

Michigan Ottawa 12.55 10.02 10.02

Michigan Saginaw 10.61 8.68 8.68

Michigan St. Clair 13.34 11.35 11.35

Michigan Washtenaw 13.88 10.99 10.99

Michigan Wayne 17.50 14.18 14.18

Minnesota Cass 5.70 5.13 5.13

Minnesota Dakota 9.30 7.84 7.84

Minnesota Hennepin 9.76 8.15 8.15

Minnesota Mille Lacs 6.54 5.73 5.73

Minnesota Olmsted 10.13 8.56 8.56

Minnesota Ramsey 11.32 9.56 9.56

Minnesota Saint Louis 7.51 6.55 6.55

Minnesota Scott 9.00 7.61 7.61

Minnesota Stearns 8.58 7.40 7.40

Mississippi Bolivar 12.36 10.43 10.43

Mississippi DeSoto 12.43 9.76 9.76

Mississippi Warren 12.32 10.11 10.11

Mississippi Adams 11.29 9.44 9.43

Mississippi Forrest 13.62 11.32 11.31

Mississippi Harrison 12.20 10.42 10.41

Mississippi Hinds 12.56 10.39 10.38

Mississippi Jones 14.39 11.90 11.89

Mississippi Lauderdale 13.07 10.58 10.57

Mississippi Lowndes 12.79 10.24 10.23

Mississippi Pearl River 12.14 10.28 10.27

Mississippi Lee 12.57 9.88 9.87

Mississippi Jackson 12.04 10.01 9.98

Missouri Boone 11.84 10.03 10.03

Missouri Buchanan 12.80 10.84 10.84

Missouri Cass 10.67 8.87 8.87

Missouri Cedar 11.12 9.46 9.46

Missouri Clay 11.03 9.27 9.27

Missouri Greene 11.75 9.83 9.83

Missouri Jackson 12.78 10.52 10.52

Missouri Monroe 10.87 8.95 8.95

Missouri Sainte Genevieve 13.34 10.84 10.84

Missouri Saint Louis 13.46 10.67 10.67

Missouri Jefferson 13.79 11.10 11.09

Missouri St. Louis City 14.56 11.58 11.56

Missouri Saint Charles 13.29 10.80 10.75

Montana Cascade 5.57 5.15 5.15

Montana Flathead 9.87 8.97 8.97

Montana Gallatin 4.25 4.15 4.15

Montana Lake 9.06 8.35 8.35

Montana Lewis and Clark 7.96 7.41 7.41

Montana Lincoln 14.93 13.47 13.47

Montana Missoula 10.20 9.34 9.34

Montana Ravalli 8.56 7.92 7.92

Montana Sanders 6.69 6.33 6.33

Montana Silver Bow 9.86 9.06 9.06

Montana Rosebud 6.58 6.38 6.37

Montana Yellowstone 8.14 7.45 7.44

Nebraska Cass 9.99 8.31 8.31
 61 

Nebraska Douglas 9.88 8.12 8.12

Nebraska Hall 7.95 6.85 6.85

Nebraska Lancaster 8.90 7.29 7.29

Nebraska Lincoln 7.57 6.73 6.73

Nebraska Sarpy 9.79 8.04 8.04

Nebraska Scotts Bluff 6.04 5.49 5.49

Nebraska Washington 9.29 7.73 7.73

Nevada Clark 9.44 8.90 8.91

Nevada Washoe 8.11 7.33 7.34

New Hampshire Hillsborough 10.18 8.64 8.65

New Hampshire Rockingham 9.00 7.78 7.79

New Hampshire Belknap 7.28 6.36 6.36

New Hampshire Cheshire 11.53 10.08 10.08

New Hampshire Coos 10.24 9.53 9.53

New Hampshire Grafton 8.43 7.45 7.45

New Hampshire Merrimack 9.72 8.26 8.26

New Hampshire Sullivan 9.86 8.69 8.69

New Jersey Atlantic 11.47 9.92 9.92

New Jersey Bergen 13.09 10.83 10.83

New Jersey Essex 13.27 11.04 11.04

New Jersey Hudson 14.24 12.10 12.10

New Jersey Morris 11.50 9.48 9.48

New Jersey Ocean 10.92 9.20 9.20

New Jersey Passaic 12.88 10.57 10.57

New Jersey Union 14.94 12.35 12.35

New Jersey Warren 12.72 10.32 10.32

New Jersey Camden 13.51 11.03 11.02

New Jersey Mercer 12.71 10.39 10.38

New Jersey Middlesex 12.15 10.03 10.02

New Jersey Gloucester 13.46 11.11 11.09

New Mexico Bernalillo 7.03 6.06 6.06

New Mexico Grant 5.93 5.73 5.73

New Mexico Sandoval 7.99 7.49 7.49

New Mexico San Juan 5.92 5.65 5.65

New Mexico Santa Fe 4.76 4.46 4.46

New Mexico Chaves 6.54 6.16 6.15

New Mexico Dona Ana 9.95 8.89 8.88

New York Kings 14.20 12.29 12.30

New York New York 16.18 13.71 13.72

New York Queens 12.18 10.31 10.32

New York Albany 11.83 10.55 10.55

New York Bronx 15.43 12.98 12.98

New York Chautauqua 9.80 7.59 7.59

New York Erie 12.62 10.23 10.23

New York Monroe 10.64 8.93 8.93

New York Nassau 11.66 10.15 10.15

New York Niagara 11.96 10.01 10.01

New York Onondaga 10.08 9.66 9.66

New York Orange 10.99 9.37 9.37

New York Richmond 13.31 11.14 11.14

New York St. Lawrence 7.29 6.57 6.57

New York Steuben 9.00 7.13 7.13

New York Suffolk 11.52 9.94 9.94

New York Westchester 11.73 10.05 10.05
 62 

New York Essex 5.94 5.24 5.23

North Carolina Alamance 13.94 10.60 10.60

North Carolina Buncombe 12.60 9.63 9.63

North Carolina Caswell 13.19 9.85 9.85

North Carolina Catawba 15.31 11.34 11.34

North Carolina Cumberland 13.73 11.12 11.12

North Carolina Davidson 15.17 11.53 11.53

North Carolina Duplin 11.30 9.42 9.42

North Carolina Durham 13.57 11.12 11.12

North Carolina Edgecombe 12.37 10.12 10.12

North Carolina Forsyth 14.28 10.52 10.52

North Carolina Gaston 14.26 10.83 10.83

North Carolina Guilford 13.79 10.47 10.47

North Carolina Haywood 12.98 10.29 10.29

North Carolina Jackson 12.09 9.24 9.24

North Carolina Lenoir 11.12 9.21 9.21

North Carolina McDowell 14.24 10.92 10.92

North Carolina Martin 10.86 8.85 8.85

North Carolina Mecklenburg 15.31 12.37 12.37

North Carolina Mitchell 12.75 9.63 9.63

North Carolina New Hanover 9.96 8.35 8.35

North Carolina Onslow 10.98 9.10 9.10

North Carolina Orange 13.12 10.20 10.20

North Carolina Pitt 11.59 9.56 9.56

North Carolina Robeson 12.78 10.67 10.67

North Carolina Rowan 14.02 10.75 10.75

North Carolina Swain 12.65 9.66 9.66

North Carolina Wake 13.54 11.24 11.24

North Carolina Watauga 12.05 8.85 8.85

North Carolina Chatham 11.99 9.23 9.22

North Carolina Montgomery 12.24 9.44 9.43

North Carolina Wayne 12.96 10.87 10.86

North Dakota Billings 4.61 4.31 4.31

North Dakota Burke 5.90 5.70 5.70

North Dakota Cass 7.72 6.76 6.76

North Dakota McKenzie 5.01 4.72 4.72

North Dakota Mercer 6.04 5.30 5.30

North Dakota Burleigh 6.61 5.92 5.87

Ohio Cuyahoga 17.37 13.38 13.39

Ohio Butler 15.36 11.86 11.86

Ohio Clark 14.64 11.38 11.38

Ohio Clermont 14.15 10.31 10.31

Ohio Franklin 15.27 11.52 11.52

Ohio Greene 13.36 10.12 10.12

Ohio Hamilton 17.54 12.97 12.97

Ohio Lake 13.02 10.23 10.23

Ohio Lorain 13.87 10.68 10.68

Ohio Mahoning 15.12 11.50 11.50

Ohio Montgomery 15.54 11.95 11.95

Ohio Portage 13.37 10.26 10.26

Ohio Preble 13.70 10.66 10.66

Ohio Stark 16.15 12.34 12.34

Ohio Summit 15.17 11.80 11.80

Ohio Trumbull 14.53 11.17 11.17
 63 

Ohio Athens 12.39 8.83 8.82

Ohio Jefferson 16.51 12.02 12.01

Ohio Lawrence 15.14 11.44 11.43

Ohio Lucas 14.38 11.40 11.39

Ohio Scioto 14.65 10.69 10.68

Oklahoma Caddo 9.22 7.95 7.95

Oklahoma Cherokee 11.79 10.01 10.01

Oklahoma Muskogee 11.89 10.17 10.17

Oklahoma Pittsburg 11.06 9.41 9.41

Oklahoma Sequoyah 12.99 11.13 11.13

Oklahoma Tulsa 11.52 9.80 9.80

Oklahoma Lincoln 10.28 8.79 8.78

Oklahoma Mayes 11.70 10.11 10.10

Oklahoma Oklahoma 10.07 8.35 8.34

Oklahoma Ottawa 11.69 10.02 10.01

Oklahoma Kay 10.26 9.08 9.06

Oregon Multnomah 9.13 8.59 8.60

Oregon Jackson 10.32 9.88 9.88

Oregon Klamath 11.20 10.55 10.55

Oregon Lane 11.93 11.38 11.38

Oregon Union 8.35 7.80 7.80

Pennsylvania Adams 13.05 9.84 9.84

Pennsylvania Allegheny 20.31 15.00 15.00

Pennsylvania Beaver 16.38 12.42 12.42

Pennsylvania Berks 15.82 12.67 12.67

Pennsylvania Bucks 13.42 10.88 10.88

Pennsylvania Cambria 15.40 10.92 10.92

Pennsylvania Cumberland 14.45 11.05 11.05

Pennsylvania Dauphin 15.13 11.32 11.32

Pennsylvania Erie 12.54 9.86 9.86

Pennsylvania Lackawanna 11.73 9.27 9.27

Pennsylvania Lancaster 16.55 12.87 12.87

Pennsylvania Lehigh 14.50 11.76 11.76

Pennsylvania Luzerne 12.76 10.16 10.16

Pennsylvania Mercer 13.28 9.98 9.98

Pennsylvania Northampton 13.68 11.05 11.05

Pennsylvania Washington 15.17 10.69 10.69

Pennsylvania Westmoreland 15.49 10.80 10.80

Pennsylvania York 16.52 12.64 12.64

Pennsylvania Centre 12.78 9.60 9.59

Pennsylvania Perry 12.81 9.83 9.82

Pennsylvania Chester 15.22 12.13 12.12

Pennsylvania Delaware 15.23 12.60 12.58

Pennsylvania Philadelphia 15.19 12.42 12.40

Rhode Island Providence 12.14 10.51 10.51

South Carolina Beaufort 11.52 10.03 10.03

South Carolina Charleston 12.21 10.11 10.11

South Carolina Edgefield 13.14 10.55 10.55

South Carolina Florence 12.62 10.21 10.21

South Carolina Georgetown 12.85 10.91 10.91

South Carolina Greenville 15.65 11.98 11.98

South Carolina Greenwood 13.53 10.57 10.57

South Carolina Horry 12.00 9.93 9.93

South Carolina Lexington 14.64 11.52 11.52
 64 

South Carolina Richland 14.24 11.13 11.13

South Carolina Spartanburg 14.17 10.70 10.70

South Carolina Chesterfield 12.53 10.04 10.03

South Carolina Oconee 10.95 8.13 8.12

South Dakota Brookings 9.37 8.07 8.07

South Dakota Brown 8.42 7.45 7.45

South Dakota Codington 10.14 8.89 8.89

South Dakota Custer 5.64 5.40 5.40

South Dakota Jackson 5.39 5.08 5.08

South Dakota Minnehaha 10.18 8.60 8.60

South Dakota Pennington 8.77 8.26 8.26

Tennessee Blount 14.30 10.87 10.87

Tennessee Davidson 14.18 10.73 10.73

Tennessee Hamilton 15.48 11.84 11.84

Tennessee Knox 15.64 11.72 11.72

Tennessee Lawrence 11.69 9.13 9.13

Tennessee Loudon 15.49 11.81 11.81

Tennessee McMinn 14.29 10.84 10.84

Tennessee Montgomery 13.79 10.68 10.68

Tennessee Putnam 13.37 10.00 10.00

Tennessee Roane 14.49 10.79 10.79

Tennessee Shelby 13.71 10.65 10.65

Tennessee Sullivan 14.16 11.09 11.09

Tennessee Sumner 13.68 10.11 10.11

Tennessee Dyer 12.28 9.65 9.64

Tennessee Maury 13.21 10.46 10.45

Texas Dallas 11.80 9.49 9.49

Texas El Paso 9.09 8.03 8.03

Texas Hidalgo 10.98 10.08 10.08

Texas Tarrant 12.23 9.84 9.84

Texas Bowie 12.85 10.82 10.81

Texas Harrison 11.69 9.89 9.88

Texas Ector 7.78 6.90 6.89

Texas Nueces 10.42 9.06 9.04

Texas Orange 11.51 10.29 10.26

Texas Jefferson 11.51 10.16 10.11

Texas Harris 15.42 13.44 13.37

Utah Box Elder 8.40 7.51 7.52

Utah Weber 11.16 9.91 9.92

Utah Cache 11.56 10.21 10.21

Utah Salt Lake 11.98 10.61 10.61

Utah Utah 10.51 9.41 9.41

Utah Davis 10.31 9.56 9.55

Vermont Chittenden 10.02 9.00 9.01

Vermont Addison 8.94 8.10 8.10

Vermont Bennington 8.52 7.70 7.70

Vermont Rutland 11.08 9.96 9.96

Virginia Arlington 14.27 11.02 11.02

Virginia Charles 12.37 9.36 9.36

Virginia Fairfax 13.88 10.75 10.75

Virginia Henrico 13.51 10.09 10.09

Virginia Loudoun 13.57 10.39 10.39

Virginia Page 12.79 9.39 9.39

Virginia Bristol City 13.93 10.52 10.52
 65 

Virginia Hampton City 12.17 9.71 9.71

Virginia Lynchburg City 12.84 9.55 9.55

Virginia Norfolk City 12.78 10.24 10.24

Virginia Roanoke City 14.27 10.58 10.58

Virginia Salem City 14.69 11.04 11.04

Virginia Virginia Beach City 12.40 10.04 10.04

Virginia Chesterfield 13.44 10.13 10.12

Washington King 11.24 10.72 10.73

Washington Pierce 10.55 9.93 9.93

Washington Snohomish 9.91 9.63 9.63

Washington Spokane 9.97 8.51 8.51

West Virginia Berkeley 15.93 12.30 12.30

West Virginia Brooke 16.52 12.04 12.04

West Virginia Hancock 15.76 11.56 11.56

West Virginia Harrison 13.99 10.19 10.19

West Virginia Marion 15.03 10.87 10.87

West Virginia Monongalia 14.35 9.85 9.85

West Virginia Ohio 14.58 10.14 10.14

West Virginia Raleigh 12.90 9.52 9.52

West Virginia Wood 15.40 11.45 11.45

West Virginia Cabell 16.30 12.37 12.36

West Virginia Kanawha 16.52 12.18 12.17

West Virginia Marshall 15.19 10.72 10.71

Wisconsin Dane 12.20 10.47 10.48

Wisconsin Milwaukee 14.08 11.89 11.90

Wisconsin Outagamie 10.96 9.53 9.54

Wisconsin Ashland 6.07 5.40 5.40

Wisconsin Brown 11.39 9.94 9.94

Wisconsin Dodge 11.04 9.33 9.33

Wisconsin Forest 7.41 6.53 6.53

Wisconsin Grant 11.79 9.99 9.99

Wisconsin Kenosha 11.98 10.00 10.00

Wisconsin Manitowoc 10.20 8.86 8.86

Wisconsin Ozaukee 11.60 9.85 9.85

Wisconsin St. Croix 10.09 8.60 8.60

Wisconsin Sauk 10.22 8.58 8.58

Wisconsin Taylor 8.24 7.19 7.19

Wisconsin Vilas 6.78 5.99 5.99

Wisconsin Waukesha 13.91 11.87 11.87

Wyoming Campbell 6.29 6.10 6.10

Wyoming Converse 3.52 3.40 3.40

Wyoming Fremont 8.17 7.61 7.61

Wyoming Laramie 4.48 4.10 4.10

Wyoming Sheridan 9.70 9.05 9.05
 66 

Appendix C: 24-hour PM2.5 Design Values for LDGHG Scenarios (units are ug/m3) 

State Name County Name Baseline DV 

2030 

Reference 

Case DV 

2030 Control 

Case DV 

Alabama Baldwin 26.20 20.80 20.80

Alabama Clay 31.80 22.07 22.07

Alabama Colbert 30.40 20.24 20.24

Alabama DeKalb 32.00 21.30 21.29

Alabama Escambia 29.00 22.35 22.35

Alabama Etowah 35.10 22.06 22.06

Alabama Houston 28.60 22.02 22.02

Alabama Jefferson 44.00 33.06 33.06

Alabama Madison 33.50 21.80 21.80

Alabama Mobile 30.00 22.16 22.15

Alabama Montgomery 32.00 23.97 23.97

Alabama Morgan 31.50 18.97 18.97

Alabama Russell 35.50 28.05 28.05

Alabama Shelby 32.00 22.02 22.02

Alabama Sumter 28.90 20.26 20.26

Alabama Talladega 33.40 22.41 22.41

Alabama Tuscaloosa 29.80 21.54 21.53

Alabama Walker 32.80 21.70 21.70

Arizona Cochise 16.60 16.38 16.37

Arizona Coconino 17.10 16.59 16.59

Arizona Gila 22.10 21.09 21.09

Arizona Maricopa 31.40 27.23 27.25

Arizona Pima 12.20 10.88 10.88

Arizona Pinal 17.50 15.42 15.43

Arizona Santa Cruz 36.00 34.33 34.34

Arkansas Arkansas 29.10 21.93 21.93

Arkansas Ashley 28.90 23.21 23.20

Arkansas Crittenden 35.00 22.79 22.79

Arkansas Faulkner 29.80 23.33 23.33

Arkansas Garland 29.20 22.41 22.40

Arkansas Mississippi 30.30 22.92 22.92

Arkansas Phillips 29.10 21.79 21.78

Arkansas Polk 26.10 19.02 19.02

Arkansas Pope 28.30 24.34 24.34

Arkansas Pulaski 31.90 25.84 25.84

Arkansas Union 28.70 23.38 23.26

Arkansas White 29.90 22.79 22.79

California Alameda 32.50 28.25 28.24

California Butte 52.50 41.83 41.83

California Calaveras 20.50 16.97 16.96

California Colusa 26.10 23.63 23.63

California Contra Costa 34.70 31.22 31.22

California Fresno 60.20 49.22 49.21

California Imperial 40.20 36.78 36.78

California Inyo 16.60 15.81 15.84

California Kern 64.50 53.63 53.60

California Kings 58.00 48.18 48.16
 67 

California Lake 12.90 13.97 13.96

California Los Angeles 50.90 49.25 49.15

California Mendocino 15.30 12.22 12.22

California Merced 46.10 37.03 37.02

California Monterey 14.30 14.81 14.80

California Nevada 16.50 14.58 14.57

California Orange 43.70 42.89 42.79

California Placer 29.80 24.96 24.96

California Plumas 32.40 28.84 28.83

California Riverside 59.10 54.76 54.70

California Sacramento 49.20 45.65 45.64

California San Bernardino 55.50 49.20 49.18

California San Diego 33.20 36.00 35.97

California San Francisco 30.90 30.35 30.33

California San Joaquin 41.80 35.21 35.21

California San Luis Obispo 22.50 19.78 19.77

California San Mateo 29.40 27.48 27.47

California Santa Barbara 24.00 24.03 24.02

California Santa Clara 38.60 37.27 37.25

California Shasta 20.40 18.01 18.01

California Solano 34.70 32.00 31.97

California Sonoma 29.10 26.03 26.02

California Stanislaus 51.40 41.63 41.62

California Sutter 38.50 31.65 31.64

California Tulare 56.60 47.58 47.58

California Ventura 30.30 31.98 31.94

California Yolo 30.30 25.83 25.82

Colorado Adams 25.30 20.48 20.45

Colorado Arapahoe 21.20 18.72 18.72

Colorado Boulder 21.10 18.10 18.11

Colorado Delta 20.70 17.34 17.35

Colorado Denver 26.40 22.60 22.56

Colorado Elbert 13.10 12.26 12.27

Colorado El Paso 16.50 15.09 15.09

Colorado Larimer 18.30 16.35 16.36

Colorado Mesa 23.50 21.03 21.04

Colorado Pueblo 15.40 13.47 13.47

Colorado San Miguel 10.10 9.89 9.89

Colorado Weld 22.90 20.09 20.10

Connecticut Fairfield 34.90 29.79 29.79

Connecticut Hartford 31.80 25.18 25.19

Connecticut Litchfield 27.10 19.32 19.30

Connecticut New Haven 38.30 31.64 31.65

Connecticut New London 32.00 25.27 25.27

Delaware Kent 32.10 26.18 26.18

Delaware New Castle 36.60 30.74 30.74

Delaware Sussex 33.70 27.75 27.75

District Of Co District of Columbia 36.30 29.61 29.62

Florida Alachua 23.40 18.36 18.35

Florida Bay 28.40 22.37 22.37

Florida Brevard 20.70 16.83 16.82

Florida Broward 19.10 17.80 17.80

Florida Citrus 21.70 16.67 16.67

Florida Duval 25.20 22.23 22.23
 68 

Florida Escambia 28.80 23.34 23.34

Florida Hillsborough 23.60 19.71 19.70

Florida Lee 17.80 14.88 14.88

Florida Leon 29.00 23.41 23.41

Florida Manatee 19.90 14.32 14.32

Florida Marion 23.20 18.60 18.60

Florida Miami-Dade 19.40 21.14 21.14

Florida Orange 21.70 17.64 17.65

Florida Palm Beach 18.90 17.88 17.88

Florida Pinellas 21.90 17.97 17.98

Florida Polk 19.50 15.89 15.89

Florida St. Lucie 18.70 15.59 15.58

Florida Sarasota 19.80 15.24 15.24

Florida Seminole 22.80 16.66 16.66

Florida Volusia 22.60 16.00 16.00

Georgia Bibb 33.50 25.08 25.09

Georgia Chatham 28.40 23.58 23.59

Georgia Clayton 35.80 24.31 24.33

Georgia Cobb 35.00 24.86 24.87

Georgia DeKalb 33.90 25.98 26.00

Georgia Dougherty 34.10 27.21 27.21

Georgia Floyd 35.10 25.52 25.52

Georgia Fulton 37.60 26.06 26.08

Georgia Glynn 26.10 21.55 21.55

Georgia Gwinnett 32.80 23.72 23.73

Georgia Hall 30.60 23.87 23.87

Georgia Houston 29.60 21.70 21.70

Georgia Lowndes 25.90 20.97 20.97

Georgia Muscogee 31.30 25.78 25.78

Georgia Paulding 33.00 22.48 22.48

Georgia Richmond 32.70 26.34 26.34

Georgia Walker 30.90 21.92 21.93

Georgia Washington 30.80 21.99 21.99

Georgia Wilkinson 33.10 25.54 25.55

Idaho Ada 28.30 24.94 24.95

Idaho Bannock 27.00 24.40 24.41

Idaho Benewah 32.90 30.33 30.34

Idaho Canyon 31.80 27.31 27.32

Idaho Franklin 36.70 31.82 31.85

Idaho Idaho 28.40 27.20 27.20

Idaho Lemhi 36.50 34.20 34.20

Idaho Power 33.30 30.05 30.06

Idaho Shoshone 38.10 34.99 34.99

Illinois Adams 31.40 23.38 23.38

Illinois Champaign 30.00 22.77 22.77

Illinois Cook 43.00 34.31 34.32

Illinois DuPage 34.60 30.70 30.72

Illinois Hamilton 31.60 20.93 20.92

Illinois Jersey 32.10 23.92 23.89

Illinois Kane 34.80 29.09 29.09

Illinois Lake 33.00 26.29 26.28

Illinois La Salle 28.90 23.73 23.73

Illinois McHenry 31.50 27.75 27.75

Illinois McLean 33.40 24.71 24.68
 69 

Illinois Macon 33.20 22.79 22.79

Illinois Madison 39.10 31.89 31.87

Illinois Peoria 32.70 26.23 26.23

Illinois Randolph 28.90 23.92 23.91

Illinois Rock Island 30.90 25.32 25.32

Illinois Saint Clair 33.70 27.30 27.29

Illinois Sangamon 33.40 27.56 27.55

Illinois Will 36.40 28.28 28.26

Illinois Winnebago 34.70 28.20 28.21

Indiana Allen 33.10 28.81 28.82

Indiana Clark 37.50 27.96 27.96

Indiana Delaware 32.00 25.33 25.32

Indiana Dubois 35.30 26.58 26.57

Indiana Elkhart 34.40 27.61 27.61

Indiana Floyd 33.20 22.89 22.89

Indiana Henry 31.80 25.51 25.50

Indiana Howard 32.20 23.62 23.62

Indiana Knox 35.90 26.50 26.49

Indiana Lake 38.90 32.04 32.03

Indiana LaPorte 33.60 25.54 25.55

Indiana Madison 32.80 25.67 25.66

Indiana Marion 38.40 30.34 30.35

Indiana Porter 31.80 25.06 25.06

Indiana St. Joseph 33.10 27.90 27.91

Indiana Spencer 32.30 24.09 24.09

Indiana Tippecanoe 35.60 28.11 28.11

Indiana Vanderburgh 32.60 27.09 27.09

Indiana Vigo 35.10 30.86 30.86

Iowa Black Hawk 30.10 24.71 24.72

Iowa Clinton 33.90 27.22 27.22

Iowa Johnson 34.60 29.55 29.56

Iowa Linn 30.60 25.81 25.83

Iowa Montgomery 27.50 21.16 21.16

Iowa Muscatine 36.00 30.34 30.34

Iowa Palo Alto 25.70 20.39 20.39

Iowa Polk 31.40 24.86 24.87

Iowa Pottawattamie 28.60 22.80 22.80

Iowa Scott 37.10 29.19 29.19

Iowa Van Buren 28.30 21.81 21.82

Iowa Woodbury 26.40 21.49 21.50

Iowa Wright 28.60 23.78 23.78

Kansas Johnson 29.30 25.37 25.37

Kansas Linn 25.30 20.85 20.84

Kansas Sedgwick 25.30 21.62 21.62

Kansas Shawnee 29.10 23.75 23.74

Kansas Sumner 22.80 18.83 18.82

Kansas Wyandotte 29.50 23.58 23.58

Kentucky Bell 29.50 21.15 21.15

Kentucky Boyd 33.10 21.23 21.19

Kentucky Bullitt 34.60 24.18 24.18

Kentucky Campbell 31.20 24.64 24.65

Kentucky Carter 29.90 17.83 17.82

Kentucky Christian 33.60 21.10 21.09

Kentucky Daviess 33.80 22.82 22.82
 70 

Kentucky Fayette 32.70 22.92 22.90

Kentucky Franklin 32.10 21.14 21.15

Kentucky Hardin 32.80 21.35 21.34

Kentucky Henderson 31.80 22.88 22.88

Kentucky Jefferson 36.10 28.25 28.25

Kentucky Kenton 34.70 23.80 23.80

Kentucky Laurel 25.10 16.76 16.75

Kentucky McCracken 33.60 23.32 23.31

Kentucky Madison 30.10 19.76 19.76

Kentucky Perry 28.50 17.31 17.31

Kentucky Pike 30.50 19.00 19.00

Kentucky Warren 33.10 23.36 23.35

Louisiana Caddo 27.50 21.80 21.79

Louisiana Calcasieu 26.30 21.57 21.49

Louisiana Concordia 26.10 20.50 20.48

Louisiana East Baton Rouge 29.30 22.27 22.15

Louisiana Iberville 28.60 23.38 23.32

Louisiana Jefferson 27.00 21.42 21.35

Louisiana Lafayette 24.20 18.98 18.96

Louisiana Ouachita 28.90 22.70 22.69

Louisiana Rapides 30.20 23.15 23.13

Louisiana Tangipahoa 29.60 23.21 23.18

Louisiana Terrebonne 26.20 20.03 20.00

Louisiana West Baton Rouge 29.00 22.48 22.36

Maine Androscoggin 26.50 23.08 23.08

Maine Aroostook 24.20 23.48 23.48

Maine Cumberland 29.20 24.85 24.86

Maine Hancock 19.40 16.30 16.30

Maine Kennebec 26.20 22.69 22.70

Maine Oxford 28.30 24.77 24.77

Maine Penobscot 22.00 19.43 19.43

Maryland Anne Arundel 35.50 31.41 31.42

Maryland Baltimore 35.80 29.17 29.19

Maryland Cecil 30.80 24.67 24.68

Maryland Harford 31.20 23.22 23.22

Maryland Montgomery 30.90 23.91 23.90

Maryland Prince George's 33.40 25.22 25.22

Maryland Washington 33.40 27.22 27.22

Maryland Baltimore (City) 39.00 32.91 32.93

Massachusetts Berkshire 31.00 28.40 28.40

Massachusetts Bristol 25.00 19.63 19.63

Massachusetts Essex 28.70 23.09 23.09

Massachusetts Hampden 33.10 28.00 28.01

Massachusetts Plymouth 28.40 21.18 21.18

Massachusetts Suffolk 32.10 27.22 27.23

Massachusetts Worcester 30.50 24.62 24.63

Michigan Allegan 33.80 28.27 28.28

Michigan Bay 31.60 24.94 24.95

Michigan Berrien 31.30 25.21 25.21

Michigan Genesee 30.40 25.63 25.63

Michigan Ingham 31.90 26.72 26.73

Michigan Kalamazoo 31.10 24.49 24.50

Michigan Kent 36.50 28.61 28.64

Michigan Macomb 35.30 29.76 29.77
 71 

Michigan Missaukee 24.80 19.08 19.09

Michigan Monroe 38.80 30.01 30.02

Michigan Muskegon 34.70 25.56 25.58

Michigan Oakland 39.90 33.37 33.38

Michigan Ottawa 34.20 29.52 29.54

Michigan Saginaw 30.60 23.61 23.62

Michigan St. Clair 39.60 34.43 34.44

Michigan Washtenaw 39.40 31.31 31.34

Michigan Wayne 43.80 36.62 36.61

Minnesota Cass 18.00 15.11 15.11

Minnesota Dakota 25.40 20.59 20.60

Minnesota Hennepin 26.70 21.44 21.46

Minnesota Mille Lacs 22.00 18.64 18.64

Minnesota Ramsey 28.00 24.00 23.99

Minnesota Saint Louis 23.50 19.25 19.25

Minnesota Scott 24.90 20.53 20.53

Minnesota Stearns 20.90 17.44 17.44

Mississippi Adams 27.40 20.04 20.03

Mississippi Bolivar 28.90 22.97 22.97

Mississippi DeSoto 30.80 21.24 21.23

Mississippi Forrest 30.40 24.69 24.69

Mississippi Harrison 30.50 23.80 23.78

Mississippi Hinds 28.80 21.27 21.27

Mississippi Jackson 28.20 21.89 21.86

Mississippi Jones 31.20 25.45 25.45

Mississippi Lauderdale 29.80 22.48 22.48

Mississippi Lee 32.10 22.24 22.24

Mississippi Lowndes 32.40 21.44 21.44

Mississippi Pearl River 28.50 22.33 22.32

Mississippi Warren 30.20 22.40 22.39

Missouri Boone 30.20 24.29 24.28

Missouri Buchanan 30.10 24.06 24.05

Missouri Cass 25.60 20.74 20.73

Missouri Cedar 28.70 22.05 22.04

Missouri Clay 28.00 23.04 23.04

Missouri Greene 28.20 21.38 21.38

Missouri Jackson 28.90 23.77 23.78

Missouri Jefferson 33.40 27.46 27.45

Missouri Monroe 27.80 21.52 21.52

Missouri Saint Charles 33.10 26.61 26.54

Missouri Sainte Genevieve 31.40 24.69 24.69

Missouri Saint Louis 33.20 28.48 28.48

Missouri St. Louis City 34.30 28.29 28.27

Montana Cascade 17.30 14.86 14.85

Montana Flathead 24.50 21.90 21.91

Montana Gallatin 29.50 27.64 27.65

Montana Lake 30.60 27.94 27.94

Montana Lewis and Clark 30.70 27.49 27.50

Montana Lincoln 42.70 38.28 38.28

Montana Missoula 38.50 33.40 33.40

Montana Ravalli 37.80 32.63 32.64

Montana Rosebud 19.70 18.99 18.99

Montana Sanders 19.50 18.38 18.38

Montana Silver Bow 33.80 29.19 29.20
 72 

Montana Yellowstone 19.30 17.32 17.30

Nebraska Douglas 25.70 20.97 20.97

Nebraska Hall 19.10 15.56 15.56

Nebraska Lancaster 24.70 19.67 19.67

Nebraska Lincoln 23.70 20.41 20.41

Nebraska Sarpy 24.10 19.57 19.58

Nebraska Scotts Bluff 16.60 14.87 14.86

Nebraska Washington 24.10 20.35 20.34

Nevada Clark 25.20 22.39 22.40

Nevada Washoe 30.70 25.77 25.78

New Hampshire Belknap 20.50 15.09 15.09

New Hampshire Cheshire 30.20 26.11 26.12

New Hampshire Coos 26.50 23.28 23.28

New Hampshire Grafton 23.00 18.65 18.65

New Hampshire Hillsborough 28.60 24.52 24.53

New Hampshire Merrimack 25.60 20.84 20.84

New Hampshire Rockingham 26.30 21.27 21.28

New Hampshire Sullivan 28.90 21.67 21.67

New Jersey Bergen 37.00 30.67 30.68

New Jersey Camden 37.30 29.15 29.14

New Jersey Essex 38.30 28.66 28.66

New Jersey Gloucester 32.10 25.43 25.41

New Jersey Hudson 41.40 35.23 35.25

New Jersey Mercer 34.70 25.81 25.81

New Jersey Middlesex 34.80 25.95 25.95

New Jersey Morris 32.30 24.94 24.93

New Jersey Ocean 31.50 21.55 21.55

New Jersey Passaic 36.30 26.88 26.88

New Jersey Union 40.40 31.97 31.96

New Jersey Warren 34.00 28.03 28.03

New Mexico Bernalillo 18.60 16.04 16.04

New Mexico Chaves 15.60 14.21 14.21

New Mexico Dona Ana 32.90 27.19 27.19

New Mexico Grant 13.00 12.46 12.45

New Mexico Sandoval 15.60 14.41 14.41

New Mexico San Juan 12.40 11.80 11.79

New Mexico Santa Fe 9.70 8.90 8.91

New York Albany 34.20 30.26 30.28

New York Bronx 38.80 31.95 31.97

New York Chautauqua 29.10 21.47 21.47

New York Erie 35.30 30.93 30.94

New York Essex 22.40 18.74 18.73

New York Kings 36.90 29.62 29.64

New York Monroe 32.20 26.79 26.80

New York Nassau 34.00 24.95 24.96

New York New York 39.70 33.54 33.55

New York Niagara 33.60 28.45 28.45

New York Onondaga 27.30 24.07 24.07

New York Orange 28.90 23.41 23.41

New York Queens 35.50 30.29 30.31

New York Richmond 34.90 29.98 29.98

New York St. Lawrence 22.10 19.75 19.75

New York Steuben 27.80 21.12 21.11

New York Suffolk 34.60 24.27 24.27
 73 

New York Westchester 33.50 26.06 26.05

North Carolina Alamance 31.70 22.16 22.16

North Carolina Buncombe 30.00 20.91 20.91

North Carolina Caswell 29.40 20.15 20.15

North Carolina Catawba 34.50 23.59 23.59

North Carolina Chatham 26.90 20.36 20.36

North Carolina Cumberland 30.70 23.62 23.62

North Carolina Davidson 31.30 23.16 23.16

North Carolina Duplin 28.30 21.91 21.91

North Carolina Durham 31.00 22.87 22.88

North Carolina Edgecombe 26.70 22.10 22.09

North Carolina Forsyth 31.90 24.85 24.86

North Carolina Gaston 30.80 21.30 21.30

North Carolina Guilford 30.60 23.44 23.44

North Carolina Haywood 27.70 21.09 21.09

North Carolina Jackson 25.50 18.04 18.03

North Carolina Lenoir 25.20 20.43 20.43

North Carolina McDowell 31.50 22.14 22.13

North Carolina Martin 24.80 20.95 20.95

North Carolina Mecklenburg 32.30 28.92 28.92

North Carolina Mitchell 30.20 21.55 21.54

North Carolina Montgomery 28.20 19.95 19.95

North Carolina New Hanover 24.00 17.88 17.87

North Carolina Onslow 24.60 19.37 19.37

North Carolina Orange 29.30 20.67 20.67

North Carolina Pitt 26.20 22.98 22.97

North Carolina Robeson 29.90 21.38 21.38

North Carolina Rowan 30.20 22.09 22.08

North Carolina Swain 27.30 20.42 20.42

North Carolina Wake 31.60 24.74 24.75

North Carolina Watauga 30.40 20.12 20.12

North Carolina Wayne 29.70 22.89 22.88

North Dakota Billings 13.00 11.97 11.97

North Dakota Burke 16.70 15.92 15.91

North Dakota Burleigh 17.60 15.61 15.47

North Dakota Cass 21.20 17.74 17.74

North Dakota McKenzie 11.90 11.21 11.21

North Dakota Mercer 16.90 13.68 13.67

Ohio Athens 32.30 20.73 20.72

Ohio Butler 39.20 29.27 29.27

Ohio Clark 35.30 25.99 25.99

Ohio Clermont 34.40 23.60 23.61

Ohio Cuyahoga 42.10 33.04 33.05

Ohio Franklin 38.50 31.07 31.10

Ohio Greene 33.00 24.73 24.74

Ohio Hamilton 40.60 28.83 28.84

Ohio Jefferson 41.90 28.87 28.87

Ohio Lake 37.10 29.71 29.72

Ohio Lawrence 33.70 22.01 22.00

Ohio Lorain 31.50 22.97 22.97

Ohio Lucas 36.30 29.22 29.23

Ohio Mahoning 36.80 28.03 28.04

Ohio Montgomery 37.80 28.90 28.93

Ohio Portage 34.30 25.96 25.96
 74 

Ohio Preble 32.80 25.89 25.89

Ohio Scioto 34.50 22.57 22.57

Ohio Stark 36.90 27.46 27.47

Ohio Summit 38.00 31.04 31.05

Ohio Trumbull 36.20 28.29 28.30

Oklahoma Caddo 23.90 19.09 19.08

Oklahoma Cherokee 27.50 22.73 22.73

Oklahoma Kay 31.80 27.68 27.59

Oklahoma Lincoln 27.80 22.20 22.19

Oklahoma Mayes 28.70 24.98 24.96

Oklahoma Muskogee 29.50 24.86 24.85

Oklahoma Oklahoma 27.10 21.64 21.64

Oklahoma Ottawa 29.10 23.48 23.47

Oklahoma Pittsburg 26.30 21.28 21.27

Oklahoma Sequoyah 31.40 25.67 25.66

Oklahoma Tulsa 30.30 25.65 25.63

Oregon Jackson 33.70 32.10 32.10

Oregon Klamath 44.00 40.83 40.83

Oregon Lane 48.90 45.99 45.99

Oregon Multnomah 29.80 27.26 27.26

Oregon Union 27.30 25.15 25.15

Pennsylvania Adams 34.90 26.74 26.75

Pennsylvania Allegheny 64.20 50.29 50.33

Pennsylvania Beaver 43.40 28.51 28.51

Pennsylvania Berks 37.70 32.22 32.23

Pennsylvania Bucks 34.00 27.86 27.86

Pennsylvania Cambria 39.00 23.83 23.84

Pennsylvania Centre 36.20 29.34 29.34

Pennsylvania Chester 36.70 28.24 28.26

Pennsylvania Cumberland 38.00 31.65 31.65

Pennsylvania Dauphin 38.00 32.61 32.62

Pennsylvania Delaware 35.20 27.93 27.90

Pennsylvania Erie 34.40 29.92 29.92

Pennsylvania Lackawanna 31.50 25.05 25.06

Pennsylvania Lancaster 40.80 33.71 33.73

Pennsylvania Lehigh 36.40 31.47 31.48

Pennsylvania Luzerne 32.40 26.19 26.20

Pennsylvania Mercer 36.30 28.27 28.29

Pennsylvania Northampton 36.70 30.01 30.02

Pennsylvania Perry 30.40 25.54 25.55

Pennsylvania Philadelphia 36.50 30.06 30.06

Pennsylvania Washington 38.10 25.26 25.26

Pennsylvania Westmoreland 37.10 22.48 22.49

Pennsylvania York 38.20 32.46 32.47

Rhode Island Providence 30.60 25.59 25.60

South Carolina Beaufort 30.20 22.52 22.52

South Carolina Charleston 27.90 23.12 23.12

South Carolina Chesterfield 28.70 21.07 21.08

South Carolina Edgefield 32.20 22.23 22.24

South Carolina Florence 28.80 22.15 22.15

South Carolina Georgetown 29.20 22.88 22.88

South Carolina Greenville 32.10 25.86 25.86

South Carolina Greenwood 30.00 21.42 21.42

South Carolina Horry 28.60 21.85 21.85
 75 

South Carolina Lexington 32.80 24.88 24.88

South Carolina Oconee 28.40 20.06 20.06

South Carolina Richland 33.20 25.18 25.18

South Carolina Spartanburg 32.40 23.04 23.04

South Dakota Brookings 23.50 19.38 19.38

South Dakota Brown 18.70 15.79 15.78

South Dakota Codington 23.60 20.06 20.05

South Dakota Custer 14.30 13.77 13.77

South Dakota Jackson 12.70 11.75 11.74

South Dakota Minnehaha 24.10 19.55 19.56

South Dakota Pennington 18.50 17.28 17.28

Tennessee Blount 32.50 22.50 22.50

Tennessee Davidson 33.50 24.02 24.03

Tennessee Dyer 31.90 22.99 22.99

Tennessee Hamilton 33.20 24.77 24.77

Tennessee Knox 36.60 24.42 24.42

Tennessee Lawrence 28.40 18.97 18.96

Tennessee Loudon 32.20 23.08 23.08

Tennessee McMinn 32.40 21.10 21.10

Tennessee Maury 30.80 21.20 21.20

Tennessee Montgomery 36.30 26.38 26.38

Tennessee Putnam 32.60 20.47 20.46

Tennessee Roane 30.20 21.08 21.08

Tennessee Shelby 32.20 23.10 23.10

Tennessee Sullivan 31.10 22.45 22.45

Tennessee Sumner 33.60 21.01 21.01

Texas Bowie 29.40 22.41 22.41

Texas Dallas 25.70 21.59 21.59

Texas Ector 17.80 14.68 14.67

Texas El Paso 22.90 19.25 19.21

Texas Harris 30.80 26.81 26.59

Texas Harrison 25.90 20.54 20.53

Texas Hidalgo 26.40 25.22 25.22

Texas Jefferson 26.00 19.95 19.90

Texas Nueces 27.50 22.72 22.66

Texas Orange 27.70 22.02 21.96

Texas Tarrant 25.70 21.92 21.93

Utah Box Elder 33.20 29.56 29.58

Utah Cache 56.90 45.29 45.30

Utah Davis 38.90 33.75 33.70

Utah Salt Lake 50.10 44.48 44.46

Utah Tooele 30.50 26.77 26.81

Utah Utah 44.00 39.59 39.64

Utah Weber 38.50 33.37 33.36

Vermont Addison 31.70 27.42 27.42

Vermont Bennington 26.40 23.22 23.22

Vermont Chittenden 30.10 26.14 26.16

Vermont Rutland 30.60 28.43 28.43

Virginia Arlington 34.10 26.65 26.65

Virginia Charles 31.70 22.32 22.32

Virginia Chesterfield 31.20 20.44 20.45

Virginia Fairfax 33.30 26.75 26.76

Virginia Henrico 31.90 21.73 21.73

Virginia Loudoun 34.40 24.50 24.50
 76 

Virginia Page 30.00 20.65 20.66

Virginia Bristol City 30.20 20.45 20.45

Virginia Hampton City 29.00 22.52 22.52

Virginia Lynchburg City 30.70 19.39 19.38

Virginia Norfolk City 29.60 23.58 23.59

Virginia Roanoke City 32.70 22.41 22.41

Virginia Salem City 34.00 24.22 24.22

Virginia Virginia Beach City 30.00 25.13 25.13

Washington King 29.10 27.47 27.49

Washington Pierce 41.80 38.07 38.10

Washington Snohomish 34.30 32.87 32.87

Washington Spokane 29.70 24.34 24.35

West Virginia Berkeley 34.50 29.43 29.44

West Virginia Brooke 43.90 35.53 35.53

West Virginia Cabell 35.10 22.48 22.47

West Virginia Hancock 40.60 25.52 25.52

West Virginia Harrison 33.50 20.55 20.54

West Virginia Kanawha 36.90 24.17 24.16

West Virginia Marion 33.60 20.36 20.36

West Virginia Marshall 33.90 22.66 22.66

West Virginia Monongalia 35.60 18.86 18.86

West Virginia Ohio 32.00 23.26 23.26

West Virginia Raleigh 30.60 19.27 19.26

West Virginia Summers 31.20 18.84 18.83

West Virginia Wood 35.40 22.22 22.22

Wisconsin Ashland 18.60 14.96 14.96

Wisconsin Brown 36.50 32.31 32.27

Wisconsin Dane 35.50 29.68 29.70

Wisconsin Dodge 31.80 27.06 27.07

Wisconsin Forest 25.20 20.80 20.81

Wisconsin Grant 34.30 29.04 29.05

Wisconsin Kenosha 32.70 28.96 28.95

Wisconsin Manitowoc 29.70 26.17 26.18

Wisconsin Milwaukee 38.60 35.73 35.73

Wisconsin Outagamie 32.80 28.94 28.96

Wisconsin Ozaukee 32.50 28.11 28.11

Wisconsin St. Croix 26.60 21.85 21.85

Wisconsin Sauk 28.60 24.38 24.39

Wisconsin Taylor 25.30 21.72 21.73

Wisconsin Vilas 22.60 19.03 19.03

Wisconsin Waukesha 35.40 30.20 30.21

Wyoming Campbell 18.60 17.71 17.71

Wyoming Converse 10.00 9.72 9.72

Wyoming Fremont 29.80 25.89 25.90

Wyoming Laramie 11.90 10.91 10.88

Wyoming Sheridan 30.80 28.42 28.43
Appendix D: 2005 CMAQ Model Performance 

Evaluation for Ozone, Particulate Matter and Air 

Toxics

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711 

April 2010 
A. Introduction 

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

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

and acrolein) 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. This evaluation principally comprises statistical 

assessments 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, PM25 and air toxic observations 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. In conjunction 

with the model performance statistics, we also provide spatial plots for individual monitors of the 

calculated bias and error statistics (defined below). 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 Atmospheric Model Evaluation 

Tool (AMET) was used to conduct the evaluation described in this document.3

The ozone evaluation primarily focuses on observed and predicted one-hour daily maximum 

ozone concentrations and eight-hour daily maximum ozone concentrations at a threshold of 

40ppb. This ozone model performance was limited to the ozone season modeled for the LightDuty Vehicle Greenhouse Gas Final Rule (hereafter referred to as LDGHG): May, June, July, 

August, and September. Ozone ambient measurements for 2005 were obtained from the Air 

Quality System (AQS) Aerometric Information Retrieval System (AIRS). A total of 1194 ozone 

measurement sites were included for evaluation. The ozone data were measured and reported on 

an hourly basis. 

The PM2.5 evaluation focuses on 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). The PM2.5 performance statistics were calculated for each month and 

season individually and for the entire year, as a whole. Seasons were defined as: winter 

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

(September-October-November). PM2.5 ambient measurements for 2002 were obtained from the 

following networks for model evaluation: Speciation Trends Network (STN- total of 260 sites), 

 

1

See Air Quality Modeling Technical Support Document, 2010 (EPA 454/R-10-001): Changes to the Renewable 

Fuel Standard Program (Figure II-1) for the map of the CMAQ modeling domain. 

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

 Gilliam, R. C., W. Appel, and S. Phillips. The Atmospheric Model Evaluation Tool (AMET): Meteorology 

Module. Presented at 4th Annual CMAS Models-3 Users Conference, Chapel Hill, NC, September 26 - 28, 2005. 

(http://www.cmascenter.org/) 
Interagency Monitoring of PROtected Visual Environments (IMPROVE- total of 204), Clean 

Air Status and Trends Network (CASTNet- total of 93), and National Acid Deposition 

Program/National Trends (NADP/NTN- toal of 297). 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. 

 

Table A-1. PM2.5 monitoring networks and pollutants species included in the CMAQ 

performance evaluation. 

Particulate 

Species

Wet 

Deposition 

Species

Ambient 

Monitoring 

Networks PM2.5

Mass SO4 NO3 TNO3

a EC NH4 OC SO4 NO3

IMPROVE X 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 LDGHG final rule, i.e., 

formaldehyde, acetaldehyde, benzene, 1,3-butadiene, acrolein, and naphthalene. Similar to the 

PM2.5 evaluation, the air toxics performance statistics were calculated for each month and season 

individually 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-OctoberNovember). Toxic measurements for 2005 were obtained from the National Air Toxics Trends 

Stations (NATTS). Toxic measurements from 471 sites in the East and 135 sites in the West 

were included in the evaluation for the 12km Eastern and Western U.S. grids, respectively. 

There are various statistical metrics available and used by the science community for model 

performance evaluation. For a robust evaluation, the principal evaluation statistics used to 

evaluate CMAQ performance were two bias metrics, normalized mean bias and fractional bias; 

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

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 concentrations 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 

toxic4,5,6 model applications (e.g., Revised Renewable Fuel Standards Final Rule,7 Clean Air 

 

4

 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. 

5

 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. 

6

 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. 
Interstate Rule8

, Final PM NAAQS Rule9, and EPA’s Proposal to Designate an Emissions 

Control Area for Nitrogen Oxides10). 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 NMB, NME, FB, and FE 

statistics shown in Sections B through P below for CMAQ predicted 2005 ozone, PM2.5, and air 

toxics concentrations are within the range or close to that found in recent OAQPS applications. 

The CMAQ model performance results 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 LDGHG Final Rule. We discuss in the following 

sections the bias and error results for the one-hour maximum ozone concentrations and eighthour daily maximum ozone concentrations evaluated at a threshold of 40 ppb, the annual and 

seasonal PM2.5 and its related speciated components as well as specific air toxic concentrations. 

 7

 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. 

8

 See: 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 OAR2005-0053-2149). 

9

 U.S. Environmental Protection Agency, 2006. Technical Support Document for the Final PM NAAQS Rule: 

Office of Air Quality Planning and Standards, Research Triangle Park, NC. 

10 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) 
B. One-Hour Daily Maximum Ozone Performance 

Ozone Performance: Threshold of 40 ppb 

Table B-1 provides one-hour daily maximum ozone model performance statistics calculated for a 

threshold of 40 ppb of observed and modeled concentrations, restricted to the ozone season 

modeled for the 12-km Eastern and Western U.S. domain and the five subregions (Midwest, 

Northeast, Southeast, Central and Western U.S.). Spatial plots of the NMB and NME statistics 

(units of percent) for individual monitors are also provided as a complement to the tabular 

statistical data (Figures B-1 – B-24). Overall, one-hour daily maximum ozone model 

performance is slightly under-predicted or near negligible in both the 12-km EUS and WUS 

when applying a threshold of 40 ppb for the modeled ozone season (May-September). For the 

12-km Eastern domain, the bias and error statistics are comparable for the aggregate of the ozone 

season and for each individual ozone month modeled, with a NMB range of -1% to -5% and a 

FB range of -0.5% to -4%, and a NME and FE range of 11% to 14%. Likewise, for the 12-km 

Western domain, the bias and error statistics are similar between the ozone seasonal aggregate 

and the individual months, with a NMB and FB approximately -2%, and a NME and FE 

approximately 14%. Hourly ozone model performance when compared across the five 

subregions shows slightly better performance in the Southeast. In general, the Northeast, 

Midwest, Central and West U.S. exhibit similar bias and error statistics for the episodes modeled. 

The month of August shows a slightly better bias and error model performance results, although 

the results are spatially and temporally comparable across the months modeled. 

Table B-1. 2005 CMAQ one-hour daily maximum ozone model performance statistics 

calculated for a threshold of 40 ppb. 

CMAQ 2005 One-Hour Maximum Ozone: 

Threshold of 40 ppb No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 21394 -1.6 11.5 -0.8 11.6 

12-km WUS 9631 -3.4 12.8 -2.8 12.7 

Midwest 4418 0.8 10.0 1.0 10.2 

Northeast 4102 5.4 11.8 5.9 11.7 

Southeast 6424 -3.6 11.3 -3.0 11.5 

Central U.S. 4328 -6.4 13.4 -5.5 13.4 

May 

West 8294 -3.5 12.9 -3.0 12.8 

12-km EUS 19517 -3.5 12.8 -2.8 12.9 

12-km WUS 9056 -3.7 13.0 -3.2 13.0 

Midwest 4639 -4.6 12.3 -4.0 12.4 

Northeast 4148 -1.0 14.1 -0.1 14.2 

Southeast 4644 -2.7 12.5 -2.2 12.6 

Central U.S. 4062 -6.2 13.2 -5.4 13.3 

June 

West 7737 -4.0 13.1 -3.6 13.1 

12-km EUS 19692 1.2 14.2 1.8 14.1 

12-km WUS 9443 0.4 16.0 1.0 15.8 

Midwest 4923 0.4 12.7 0.9 12.6 

Northeast 4445 4.2 15.2 4.8 14.9 

July 

Southeast 4733 4.2 15.1 4.6 14.8 
Central U.S. 3521 -3.8 14.8 -3.1 14.9 

West 8168 0.2 16.2 0.7 16.0 

12-km EUS 19643 0.1 13.9 0.8 13.8 

12-km WUS 9562 -0.8 15.5 -0.6 15.5 

Midwest 4549 0.2 12.2 1.0 12.3 

Northeast 4139 0.2 13.2 1.2 13.1 

Southeast 5303 3.6 14.9 3.9 14.5 

Central U.S. 3589 -4.1 16.2 -2.9 16.1 

August 

West 8357 -1.0 15.7 -1.0 15.7 

12-km EUS 18085 -2.2 12.0 -1.3 12.0 

12-km WUS 8725 -3.6 14.1 -3.2 14.3 

Midwest 4002 -3.6 10.7 -3.0 10.8 

Northeast 3667 -1.8 11.3 -0.7 11.3 

Southeast 5259 -0.1 12.1 0.8 12.1 

Central U.S. 3286 -6.1 14.5 -5.1 14.5 

September 

West 7530 -4.1 14.3 -3.8 14.4 

12-km EUS 98331 -1.2 12.9 -0.5 12.8 

12-km WUS 46417 -2.1 14.3 -1.7 14.2 

Midwest 22531 -1.4 11.7 -0.8 11.7 

Northeast 20501 1.4 13.3 2.3 13.1 

Southeast 26363 0.1 13.1 0.7 13.0 

Central U.S. 18786 -5.4 14.4 -4.4 14.4 

Seasonal Aggregate 

(May – September) 

West 40086 -2.3 14.5 -2.1 14.4 
Figure B-1. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., May 2005. 

Figure B-2. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., May 2005. 
Figure B-3. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., June 2005. 

Figure B-4. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., June 2005. 
Figure B-5. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., July 2005. 

Figure B-6. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., July 2005. 
Figure B-7. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., August 2005. 

Figure B-8. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., August 2005. 
Figure B-9. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., September 2005. 

Figure B-10. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., September 2005. 
Figure B-11. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., seasonal aggregate 2005. 

Figure B-12. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., seasonal aggregate 2005. 
Figure B-13. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., May 2005. 

Figure B-14. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., May 2005. 
Figure B-15. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., June 2005. 

Figure B-16. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., June 2005. 
Figure B-17. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., July 2005. 

Figure B-18. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., July 2005. 
Figure B-19. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., August 2005. 

Figure B-20. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., August 2005. 
Figure B-21. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., September 2005. 

Figure B-22. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., September 2005. 
Figure B-23. Normalized Mean Bias (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., seasonal aggregate 2005. 

Figure B-24. Normalized Mean Error (%) of one-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., seasonal aggregate 2005. 

C. Eight-hour Daily Maximum Ozone Performance 
Ozone Performance: Threshold of 40 ppb 

Table C-1 presents eight-hour daily maximum ozone model performance bias and error statistics 

for the entire range of observed and modeled concentrations at a threshold of 40 ppb for the 

ozone season modeled for the 12-km Eastern and Western U.S. domain and the corresponding 

subregions defined above. Spatial plots of the NMB and NME statistics (units of percent) for 

individual monitors based on the aggregate and the individual ozone months modeled 

respectively are shown in Figures C-1 through C-24. 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 C-1. 2005 CMAQ eight-hour daily maximum ozone model performance statistics 

calculated for a threshold of 40 pbb. 

CMAQ 2005 Eight-Hour Maximum 

Ozone: Threshold of 40 ppb No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 19310 -1.0 10.9 -0.4 11.0 

12-km WUS 8445 -1.6 12.0 -1.2 12.0 May 

Midwest 3858 0.2 10.0 0.7 10.2 

Northeast 3528 5.2 11.4 5.4 11.2 

Southeast 6019 -2.1 10.5 -1.6 10.6 

Central U.S. 3927 -5.8 12.8 -5.2 13.0 

West 7234 -1.8 12.1 -1.5 12.0 

12-km EUS 17404 -2.1 11.9 -1.5 12.0 

12-km WUS 8102 -1.9 11.9 -1.6 11.9 

Midwest 4324 -3.8 11.6 -3.4 11.8 

Northeast 3590 0.3 13.1 1.0 13.2 

Southeast 3924 -0.3 11.4 0.1 11.5 

Central U.S. 3663 -5.5 12.1 -5.0 12.3 

June 

West 6889 -2.2 12.1 -2.0 12.1 

12-km EUS 17045 3.3 13.4 3.6 13.3 

12-km WUS 8556 3.7 15.0 3.9 14.7 

Midwest 4429 1.8 11.8 2.3 11.8 

Northeast 3856 6.6 14.6 6.8 14.3 

Southeast 3806 7.4 15.0 7.3 14.5 

Central U.S. 3057 -2.3 13.2 -2.1 13.5 

July 

West 7407 3.5 15.1 3.6 14.9 
12-km EUS 16953 1.9 12.9 2.2 12.9 

12-km WUS 8523 1.6 13.9 1.5 13.9 

Midwest 4027 0.9 11.3 1.4 11.4 

Northeast 3530 1.4 12.3 2.0 12.2 

Southeast 4447 7.4 14.7 7.2 14.1 

Central U.S. 3096 -3.4 14.4 -3.1 14.8 

August 

West 7469 1.4 14.1 1.2 14.0 

12-km EUS 15190 -1.8 11.2 -1.3 11.3 

12-km WUS 7465 -2.4 13.4 -2.6 13.9 

Midwest 3265 -4.2 10.2 -4.0 10.4 

Northeast 2856 -2.3 10.6 -1.8 10.7 

Southeast 4647 1.5 11.2 2.1 11.2 

Central U.S. 2798 -6.5 13.6 -6.1 14.0 

September 

West 6446 -2.9 13.7 -3.1 14.1 

12-km EUS 85902 0.1 12.1 0.5 12.1 

12-km WUS 41091 0.0 13.3 0.1 13.3 

Midwest 19903 -0.9 11.1 -0.5 11.2 

Northeast 17360 2.4 12.6 2.9 12.4 

Southeast 22843 2.3 12.3 2.6 12.2 

Central U.S. 16541 -4.8 13.2 -4.4 13.4 

Seasonal Aggregate 

(May – September) 

West 35445 -0.2 13.5 -0.3 13.5 
Figure C-1. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., May 2005. 

Figure C-2. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., May 2005. 
Figure C-3. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., June 2005. 

Figure C-4. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., June 2005. 
Figure C-5. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., July 2005. 

Figure C-6. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., July 2005. 
Figure C-7. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., August 2005. 

Figure C-8. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., August 2005. 
Figure C-9. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., September 2005. 

Figure C-10. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., September 2005. 
Figure C-11. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., seasonal aggregate 2005. 

Figure C-12. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Eastern U.S., seasonal aggregate 2005. 
Figure C-13. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., May 2005. 

Figure C-14. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., May 2005. 
Figure C-15. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., June 2005. 

Figure C-16. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., June 2005. 
Figure C-17. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., July 2005. 

Figure C-18. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., July 2005. 
Figure C-19. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., August 2005. 

Figure C-20. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., August 2005. 
Figure C-21. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., September 2005. 

Figure C-22. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., September 2005. 
Figure C-23. Normalized Mean Bias (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., seasonal aggregate 2005. 

Figure C-24. Normalized Mean Error (%) of eight-hour daily maximum ozone (40 ppb 

threshold) by monitor for Western U.S., seasonal aggregate 2005. 
D. Annual PM2.5 Species Evaluation 

Table D-1 provides annual model performance statistics for PM2.5 and its component species for 

the 12-km Eastern domain, 12-km Western domain, and five subregions defined in Section A 

(Midwest, Northeast, Southeast, Central, and West U.S.). Spatial plots of the NMB and NME 

statistics (units of percent) for individual monitors are also provided as a complement to the 

tabular statistical data (Figures D-1 – D-28). In the East, annual total PM2.5 mass is underpredicted when compared at STN and IMPROVE sites in the Southeast and Central U.S. In the 

West, annual total PM2.5 mass is under-predicted when evaluated at STN sites and IMPROVE 

sites, with slightly better performance at the STN network (bias ~ -10). Although not shown 

here, the mean observed concentrations of PM2.5 are approximately twice as high at the STN 

sites (EUS = ~13μg m-3; WUS = ~11μg m-3) as the IMPROVE sites (EUS = ~7μg m-3; WUS = 

~4μg m-3), thus illustrating the statistical differences between the urban STN and rural 

IMPROVE networks. Sulfate is consistently under-predicted at STN, IMPROVE, and CASTNet 

sites, with NMB values ranging from -29% to -1%. Overall, sulfate performance is best in the 

East at urban STN sites. Nitrate is over-predicted in the 12-km Eastern domain (NMB in the 

range of 25% to 86%), while nitrate is under-predicted in the 12-km Western domain (NMB in 

the range of -18% to -47%). Likewise, model performance of total nitrate at CASTNet sites 

shows an over-prediction in the East (NMB ~ 40%) and in the West (NMB ~ 5%). Ammonium 

model performance varies across the STN and CASTNet in the East and West, with a mix of 

over and under-predictions in the Eastern domain and also an under-prediction in the West. 

Elemental carbon is over-predicted at STN sites in the East and West with a bias of ~30% and 

error of ~70%. Although, EC is under-predicted at IMPROVE sites in the East and overpredicted in the West. Organic carbon is moderately under-predicted for all domains in the STN 

and IMPROVE networks (bias ~ -30% and error ~ 50%. Differences in model predictions 

between IMPROVE and STN networks could be attributed to both the rural versus urban 

characteristics as well as differences in the measurement methodology between the two networks 

(e.g. blank correction factors, and filter technology used). 

Table D-1. 2005 CMAQ annual PM2.5 species model performance statistics. 

CMAQ 2005 Annual PM2.5 species No. of 

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

12-km EUS 11797 6.6 39.5 4.0 39.2 

12-km WUS 3440 -10.0 45.0 -9.5 44.4 

Northeast 2318 16.6 38.1 15.8 35.1 

Midwest 3020 19.5 45.6 16.5 40.7 

Southeast 3067 -7.2 34.1 -7.6 29.1 

Central U.S. 2523 0.6 41.7 -3.6 44.7 

STN 

West 2826 -10.9 46.1 -10.6 45.0 

12-km EUS 9321 1.6 43.4 0.0 44.0 

12-km WUS 10411 -10.9 44.8 -13.6 46.8 

Northeast 571 9.1 39.4 8 38.5 

PM2.5 

Total Mass 

IMPROVE 

Midwest 2339 21.5 52.9 14.9 46.8 
Southeast 1694 -10.5 37.2 -9.7 42.3 

Central U.S. 2376 -5.5 42.4 -4.7 46.0 

West 8820 -13.3 45.0 -14.5 47.2 

12-km EUS 13897 -8.7 32.6 -5.8 35.2 

12-km WUS 3920 -17.0 42.3 -7.8 42.8 

Northeast 2495 -1.2 33.4 4.1 34.1 

Midwest 3498 -3.9 31.7 -0.7 33.3 

Southeast 3882 -10.9 30.3 -8.6 32.9 

Central U.S. 3059 -19.8 36.5 -16.6 40.8 

STN 

West 3157 -15.5 45.8 -6.7 44.0 

12-km EUS 9034 -11.4 33.8 -2.6 38.4 

12-km WUS 10002 -9.0 39.9 6.8 43.5 

Northeast 531 -9.5 31.8 -1.9 34.1 

Midwest 2253 -3.8 34.2 3.0 37.1 

Southeast 1685 -13.8 31.5 -8.1 35.4 

Central U.S. 2350 -19.7 35.8 -12.2 39.8 

IMPROVE 

West 8496 -4.7 41.7 9.6 44.4 

12-km EUS 3170 -15.7 23.1 -14.2 25.8 

12-km WUS 1142 -19.8 31.1 -10.5 32.6 

Northeast 615 -13.4 21.7 -11.0 22.9 

Midwest 786 -10.3 21.3 -7.7 23.0 

Southeast 1099 -17.9 22.7 -19.5 25.6 

Central U.S. 300 -29.1 32.0 -29.3 35.2 

Sulfate 

CASTNet 

West 1041 -18.4 31.6 -9.3 32.8 

12-km EUS 12741 37.8 78.6 0.4 79.4 

12-km WUS 3655 -41.7 65.3 -70.8 97.5 

Northeast 2495 41.0 73.3 24.0 66.8 

Midwest 3499 48.9 83.1 11.5 75.6 

Southeast 3882 34.5 93.7 -19.4 91.4 

Central U.S. 1927 25.0 67.2 4.8 75.0 

STN 

West 3139 -47.3 65.4 -79.1 99.9 

12-km EUS 9027 52.8 98.5 -21.0 101.4 

12-km WUS 9987 -18.3 75.4 -84.2 120.6 

Northeast 531 32.8 77.1 2.0 83.8 

Midwest 2248 86.2 122.5 8.9 97.3 

Southeast 1685 67.2 126.4 -24.5 104.4 

Central U.S. 2350 40.89 82.1 -10.1 95.8 

Nitrate 

IMPROVE 

West 8480 -35.7 77.0 -91.7 124.2 

12-km EUS 3170 41.0 51.0 31.7 44.0 

12-km WUS 1142 5.4 36.3 13.1 40.6 

Northeast 615 37.4 47.0 35.1 40.6 

Midwest 786 55.8 59.4 44.4 50.2 

Southeast 1099 41.8 53.4 29.0 45.6 

Total 

Nitrate 

(NO3 + 

HNO3) 

CASTNet 

Central U.S. 300 23.6 40.3 16.8 36.2 
West 1041 4.8 37.6 14.7 41.6 

12-km EUS 13897 13.0 44.8 17.2 46.8 

12-km WUS 3893 -14.9 55.3 7.1 55.0 

Northeast 2495 21.0 44.8 27.0 43.8 

Midwest 3498 21.2 47.6 29.5 48.2 

Southeast 3882 7.0 41.4 11.4 43.4 

Central U.S. 3059 1.3 45.1 4.22 51.1 

STN 

West 3130 -20.5 59.0 5.8 57.2 

12-km EUS 3170 5.7 36.5 7.1 36.8 

12-km WUS 1142 -6.4 37.8 -4.0 37.6 

Northeast 615 15.4 37.2 18.9 35.2 

Midwest 786 14.5 40.8 18.4 38.7 

Southeast 1099 -7.5 32.9 -7.4 36.2 

Central U.S. 300 3.3 36.9 5.5 40.1 

Ammonium 

CASTNet 

West 1041 -12.6 37.1 -4.9 37.5 

12-km EUS 14038 45.5 77.1 30.6 58.3 

12-km WUS 3814 31.1 77.7 19.5 62.5 

Northeast 2502 40.4 65.9 33.8 53.4 

Midwest 3479 57.3 83.7 38.5 59.6 

Southeast 3877 27.2 64.4 20.6 51.0 

Central U.S. 3221 72.1 102.0 39.0 69.5 

STN 

West 3015 38.7 82.8 21.0 65.1 

12-km EUS 8668 -14.6 49.3 -18.2 53.3 

12-km WUS 12851 43.4 75.6 29.6 57.8 

Northeast 602 -1.3 45.1 -15.7 48.1 

Midwest 2117 10.6 54.5 -3.7 54.1 

Southeast 1584 -36.7 46.9 -39.3 56.3 

Central U.S. 2123 -23.8 47.7 -21.5 53.4 

Elemental 

Carbon 

IMPROVE 

West 8169 5.8 64.9 -8.6 61.0 

12-km EUS 12619 -27.2 50.5 -22.8 60.1 

12-km WUS 3582 -32.1 56.7 -28.2 61.3 

Northeast 2380 -28.3 49.0 -19.7 58.1 

Midwest 3323 -7.6 53.3 -4.2 58.6 

Southeast 3802 -39.3 49.2 -39.4 61.0 

Central U.S. 2259 -31.4 51.2 -28.3 63.2 

STN 

West 3060 -31.7 57.6 -27.8 61.4 

12-km EUS 8662 -21.7 49.4 -25.6 55.8 

12-km WUS 11586 -29.9 50.4 -26.7 60.6 

Northeast 601 -22.7 41.3 -27.7 48.3 

Midwest 2116 3.5 55.7 -5.1 53.1 

Southeast 1587 -30.1 42.8 -37.8 54.1 

Central U.S. 2123 -36.9 51.7 -39.1 61.0 

Organic 

Carbon 

IMPROVE 

West 8165 -11.7 59.2 -19.3 61.0 
Figure D-1. Normalized Mean Bias (%) of annual PM2.5 by monitor for Eastern U.S., 2005. 

Figure D-2. Normalized Mean Error (%) of annual PM2.5 by monitor for Eastern U.S., 

2005. 
Figure D-3. Normalized Mean Bias (%) of annual sulfate by monitor for Eastern U.S., 

2005. 

Figure D-4. Normalized Mean Error (%) of annual sulfate by monitor for Eastern U.S., 

2005. 
Figure D-5. Normalized Mean Bias (%) of annual nitrate by monitor for Eastern U.S., 

2005. 

Figure D-6. Normalized Mean Error (%) of annual nitrate by monitor for Eastern U.S., 

2005. 
Figure D-7. Normalized Mean Bias (%) of annual total nitrate by monitor for Eastern 

U.S., 2005. 

Figure D-8. Normalized Mean Error (%) of annual total nitrate by monitor for Eastern 

U.S., 2005. 
Figure D-9. Normalized Mean Bias (%) of annual ammonium by monitor for Eastern U.S., 

2005. 

Figure D-10. Normalized Mean Error (%) of annual ammonium by monitor for Eastern 

U.S., 2005. 
Figure D-11. Normalized Mean Bias (%) of annual elemental carbon by monitor for 

Eastern U.S., 2005. 

Figure D-12. Normalized Mean Error (%) of annual elemental carbon by monitor for 

Eastern U.S., 2005. 
Figure D-13. Normalized Mean Bias (%) of annual organic carbon by monitor for Eastern 

U.S., 2005. 

Figure D-14. Normalized Mean Error (%) of annual organic carbon by monitor for 

Eastern U.S., 2005. 
Figure D-15. Normalized Mean Bias (%) of annual PM2.5 by monitor for Western U.S., 

2005. 

Figure D-16. Normalized Mean Error (%) of annual PM2.5 by monitor for Western U.S., 

2005. 
Figure D-17. Normalized Mean Bias (%) of annual sulfate by monitor for Western U.S., 

2005. 

Figure D-18. Normalized Mean Error (%) of annual sulfate by monitor for Western U.S., 

2005. 
Figure D-19. Normalized Mean Bias (%) of annual nitrate by monitor for Western U.S., 

2005. 

Figure D-20. Normalized Mean Error (%) of annual nitrate by monitor for Western U.S., 

2005. 
Figure D-21. Normalized Mean Bias (%) of annual total nitrate by monitor for Western 

U.S., 2005. 

Figure D-22. Normalized Mean Error (%) of annual total nitrate by monitor for Western 

U.S., 2005. 
Figure D-23. Normalized Mean Bias (%) of annual ammonium by monitor for Western 

U.S., 2005. 

Figure D-24. Normalized Mean Error (%) of annual ammonium by monitor for Western 

U.S., 2005. 
Figure D-25. Normalized Mean Bias (%) of annual elemental carbon by monitor for 

Western U.S., 2005. 

Figure D-26. Normalized Mean Error (%) of annual elemental carbon by monitor for 

Western U.S., 2005. 
Figure D-27. Normalized Mean Bias (%) of annual organic carbon by monitor for Western 

U.S., 2005. 

Figure D-28. Normalized Mean Error (%) of annual organic carbon by monitor for 

Western U.S., 2005. 
E. Seasonal PM2.5 Total Mass Performance 

Seasonal model performance statistics for PM2.5 total mass are shown in Table E-1. Spatial plots 

of the NMB and NME statistics (units of percent) for individual monitors are also provided as a 

complement to the tabular statistical data (Figures E-1 – E-16). Total PM2.5 mass is generally 

over-predicted in the winter, fall, and spring seasons for both STN and IMPROVE networks. In 

the fall season, PM2.5 is over-predicted for Eastern STN and IMPROVE sites with NMB values 

ranging from 6% to 30% whereas PM2.5 is under-predicted at Western STN and IMPROVE sites. 

In the winter season, PM2.5 is over-predicted for EUS and WUS STN and IMPROVE networks 

with NMB values ranging from 5% to 58%. However, in the 12-km Western domain, PM2.5 is 

under-predicted at STN in the winter (NMB in the range of -6% to -11%) and the fall (NMB in 

the range of -7% to -9%). Note that for comparison of West versus East STN sites, the total 

number of Western sites is usually less than a third of the Eastern sites. In the spring, PM2.5 is 

over-predicted in the East and West, although PM2.5 at urban STN sites is over-predicted in the 

East but under-predicted in the West. In the summer season, PM2.5 is under-predicted in the East 

and West for STN and IMPROVE (NMB = ~ 25% and NME = ~35%). 

Table E-1. CMAQ 2005 seasonal model performance statistics for PM2.5 total mass. 

CMAQ 2005 PM2.5 total mass No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 2866 20.5 42.4 14.8 38.5 

12-km WUS 895 -6.2 54.7 -3.2 53.0 

Northeast 716 36.7 48.7 28.1 39.4 

Midwest 542 20.1 36.7 19.0 33.3 

Southeast 762 7.9 38.1 4.7 36.5 

Central 635 20.2 47.1 12.8 44.2 

STN 

West 739 -10.9 55.0 -8.7 54.6 

12-km EUS 2252 31.5 51.8 23.9 45.8 

12-km WUS 2532 11.8 51.6 7.1 49.5 

Northeast 573 58.6 68.4 40.8 50.7 

Midwest 143 21.5 39.2 16.8 35.9 

Southeast 406 17.5 43.7 11.4 42.3 

Central 574 19.6 46.3 17.7 45.0 

Winter 

IMPROVE 

West 2145 5.4 51.1 5.5 50.7 

12-km EUS 3159 22.6 46.9 15.2 41.0 

12-km WUS 964 0.5 43.2 -1.1 41.2 

Northeast 795 44.2 60.1 33.0 47.4 

Midwest 612 49.7 59.8 36.0 45.8 

Southeast 798 3.1 34.2 2.0 33.5 

Central 752 1.0 38.3 -2.6 40.2 

STN 

West 773 3.2 45.5 -0.2 41.9 

Spring 

IMPROVE 12-km EUS 2424 14.7 46.2 7.6 42.4 
12-km WUS 2735 -18.0 43.2 -22.8 46.4 

Northeast 630 47.9 65.6 28.4 49.3 

Midwest 153 36.8 53.4 27.3 44.8 

Southeast 429 2.7 35.7 4.1 36.1 

Central 628 -3.7 39.1 -3.1 42.1 

West 2308 -20.5 44.7 -23.9 47.0 

12-km EUS 2954 -19.3 32.6 -24.7 39.8 

12-km WUS 935 -20.9 35.9 -21.0 40.3 

Northeast 754 -15.0 30.5 -16.3 33.9 

Midwest 558 -8.2 26.7 -7.9 28.4 

Southeast 722 -28.2 34.7 -34.9 43.1 

Central 701 -24.1 38.2 -33.3 50.3 

STN 

West 758 -18.2 36.2 -19.0 40.3 

12-km EUS 2334 -27.6 37.2 -33.8 45.6 

12-km WUS 2492 -20.1 41.9 -23.0 45.3 

Northeast 580 -20.5 35.2 -27.7 42.1 

Midwest 156 -18.8 30.1 -19.7 34.2 

Southeast 421 -34.1 39.1 -45.5 53.6 

Central 596 -32.2 39.8 -38.8 50.2 

Summer 

IMPROVE 

West 2114 -18.1 42.6 -21.5 45.1 

12-km EUS 2818 10.6 38.4 10.6 37.4 

12-km WUS 962 -6.9 46.2 -4.9 45.3 

Northeast 755 28.6 51.2 20.8 41.7 

Midwest 606 12.4 33.1 14.6 31.9 

Southeast 785 -5.5 30.08 -4.1 32.8 

Central 435 16.5 45.6 18.4 44.2 

STN 

West 812 -9.1 47.2 -7.8 45.8 

12-km EUS 2311 6.0 42.6 2.8 42.2 

12-km WUS 2652 -9.6 44.9 -14.9 46.1 

Northeast 556 29.8 54.5 17.1 45.0 

Midwest 119 7.9 38.5 8.9 39.2 

Southeast 438 -9.7 31.7 -8.2 37.4 

Central 578 5.6 45.9 6.6 46.7 

Fall 

IMPROVE 

West 2253 -13.9 43.8 -17.3 46.0 
Figure E-1. Normalized Mean Bias (%) of PM2.5 by monitor for 12-km Eastern U.S. 

domain, Winter 2005.

Figure E-2. Normalized Mean Error (%) of PM2.5 by monitor for 12-km Eastern U.S. 

domain, Winter 2005. 
Figure E-3. Normalized Mean Bias (%) of PM2.5 by monitor for 12-km Eastern U.S. 

domain, Spring 2005.

Figure E-4. Normalized Mean Error (%) of PM2.5 by monitor for 12-km Eastern U.S. 

domain, Spring 2005. 
Figure E-5. Normalized Mean Bias (%) of PM2.5 by monitor for 12-km Eastern U.S. 

domain, Summer 2005.

Figure E-6. Normalized Mean Error (%) of PM2.5 by monitor for 12-km Eastern U.S. 

domain., Summer 2005. 
Figure E-7. Normalized Mean Bias (%) of PM2.5 by monitor for 12-km Eastern U.S. 

domain, Fall 2005.

Figure E-8. Normalized Mean Error (%) of PM2.5 by monitor for 12-km Eastern U.S. 

domain, Fall 2005. 
Figure E-9. Normalized Mean Bias (%) of PM2.5 by monitor for 12-km Western U.S. 

domain, Winter 2005. 

Figure E-10. Normalized Mean Error (%) of PM2.5 by monitor for 12-km Western U.S. 

domain, Winter 2005. 
Figure E-11. Normalized Mean Bias (%) of PM2.5 by monitor for 12-km Western U.S. 

domain, Spring 2005. 

Figure E-12. Normalized Mean Error (%) of PM2.5 by monitor for 12-km Western U.S. 

domain, Spring 2005. 
Figure E-13. Normalized Mean Bias (%) of PM2.5 by monitor for 12-km Western U.S. 

domain, Summer 2005. 

Figure E-14. Normalized Mean Error (%) of PM2.5 by monitor for 12-km Western U.S. 

domain, Summer 2005. 
Figure E-15. Normalized Mean Bias (%) of PM2.5 by monitor for 12-km Western U.S. 

domain, Fall 2005. 

Figure E-16. Normalized Mean Error (%) of PM2.5 by monitor for 12-km Western U.S. 

domain, Fall 2005. 
F. Seasonal Sulfate Performance 

As seen in Table F-1, CMAQ generally under-predicts sulfate in the 12-km Eastern and Western 

domains throughout the entire year. Spatial plots of the NMB and NME statistics (units of 

percent) for individual monitors are also provided in Figures F-1 – F-16. 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, with NMB values ranging from -2% 

to -32%. Sulfate predictions during the summer season are moderately under-predicted in the 

East and West across the available monitoring data (NMB values range from -8% to -35%. 

Table F-1. CMAQ 2005 seasonal model performance statistics for sulfate. 

CMAQ 2005 Sulfate No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 3390 -4.7 38.3 -6.8 38.4 

12-km WUS 1033 -7.3 55.4 -2.6 53.7 

Northeast 828 -5.0 36.3 -10.7 34.5 

Midwest 598 6.9 41.3 -0.1 39.2 

Southeast 963 -5.1 36.6 -4.1 36.7 

Central 766 -14.2 38.4 -12.3 41.6 

STN 

West 830 -10.3 58.1 -3.5 54.9 

12-km EUS 2076 -4.4 37.4 4.7 42.4 

12-km WUS 2428 13.9 56.0 30.9 56.0 

Northeast 502 -3.5 31.6 -7.6 32.7 

Midwest 129 6.6 37.7 0.1 35.2 

Southeast 386 -3.9 35.8 1.4 36.7 

Central 539 -15.5 41.1 -9.7 44.1 

IMPROVE 

West 2086 23.8 59.3 34.7 57.2 

12-km EUS 760 -15.3 23.6 -14.0 27.7 

12-km WUS 267 1.5 34.3 16.0 37.3 

Northeast 193 -12.4 21.6 -16.3 24.4 

Midwest 142 -11.2 21.0 -13.9 25.5 

Southeast 264 -17.9 22.6 -17.1 23.6 

Central 72 -32.9 34.1 -34.8 37.0 

Winter 

CASTNet 

West 243 5.7 36.5 18.1 38.4 

12-km EUS 3626 1.5 34.1 1.1 33.4 

12-km WUS 1085 -12.3 35.0 -3.4 35.8 

Northeast 894 11.8 38.4 8.2 35.6 

Midwest 637 24.0 45.6 19.2 38.9 

Southeast 988 -4.9 27.7 -5.4 29.2 

Central 875 -15.4 31.1 -11.2 32.8 

STN 

West 867 -6.2 37.5 0.3 36.7 

12-km EUS 2435 -1.7 33.8 1.4 34.2 

12-km WUS 2703 -5.2 32.9 3.1 35.0 

Spring 

IMPROVE 

Northeast 630 12.5 40.7 9.8 39.3 
Midwest 147 7.9 37.5 10.2 36.5 

Southeast 436 -4.7 28.5 -2.4 29.9 

Central 632 -16.6 31.9 -10.4 33.2 

West 2305 -2.4 34.4 5.0 36.1 

12-km EUS 832 -8.2 23.6 -7.6 24.6 

12-km WUS 287 -18.5 26.6 -16.4 27.3 

Northeast 206 2.7 25.8 2.9 26.8 

Midwest 155 -2.7 22.9 -1.1 22.1 

Southeast 292 -12.6 21.3 -14.0 23.0 

Central 78 -28.7 32.3 -24.5 31.6 

CASTNet 

West 262 -17.6 26.8 -15.9 27.4 

12-km EUS 3516 -15.8 31.7 -15.0 38.1 

12-km WUS 1075 -35.3 43.4 -28.8 45.1 

Northeast 874 -8.6 27.5 -2.5 31.3 

Midwest 621 -9.9 28.7 0.1 30.9 

Southeast 941 -18.1 31.8 -19.0 37.3 

Central 847 -30.5 41.5 -35.7 52.2 

STN 

West 853 -35.4 45.8 -27.3 45.6 

12-km EUS 2324 -19.5 34.3 -16.2 41.1 

12-km WUS 2394 -25.0 40.0 -16.4 42.9 

Northeast 590 -11.2 32.7 -1.0 39.0 

Midwest 158 -18.2 29.8 -6.6 33.9 

Southeast 427 -22.7 34.0 -23.9 41.5 

Central 601 -27.0 38.4 -23.3 45.1 

IMPROVE 

West 2021 -22.9 40.8 -14.0 42.7 

12-km EUS 792 -19.2 23.9 -21.7 29.5 

12-km WUS 295 -34.1 37.7 -34.4 40.6 

Northeast 192 -14.9 20.6 -11.2 22.4 

Midwest 161 -15.7 21.3 -13.2 23.2 

Southeast 270 -21.1 24.8 -28.1 32.6 

Central 75 -33.3 36.9 -38.9 46.2 

Summer 

CASTNet 

West 269 -33.6 37.9 -33.5 40.2 

12-km EUS 3365 -10.5 28.4 -2.5 30.9 

12-km WUS 1095 -17.2 44.2 -7.9 44.5 

Northeast 902 -8.3 29.7 1.6 31.9 

Midwest 639 -10.9 26.8 -3.1 27.7 

Southeast 990 -10.9 26.7 -6.1 28.5 

Central 571 -10.9 32.6 -2.4 35.1 

STN 

West 900 -14.6 48.8 -6.9 46.4 

12-km EUS 2199 -11.3 30.8 0.6 36.5 

12-km WUS 2476 -4.8 38.2 9.5 41.0 

Northeast 531 -5.6 32.6 9.2 36.5 

Midwest 97 -19.1 26.6 -15.3 29.3 

Fall 

IMPROVE 

Southeast 436 -13.1 28.6 -6.7 33.9 
Central 578 -14.3 32.0 -5.1 37.5 

West 2084 0.5 41.2 12.6 42.5 

12-km EUS 786 -17.5 21.0 -14.0 21.7 

12-km WUS 293 -11.4 24.3 -4.9 25.3 

Northeast 195 -12.3 18.4 -7.2 18.2 

Midwest 157 -19.7 21.9 -15.8 20.9 

Southeast 273 -18.6 21.3 -19.4 23.4 

Central 75 -21.0 23.6 -19.3 26.3 

CASTNet 

West 267 -9.2 24.9 -3.5 25.7 
Figure F-1. Normalized Mean Bias (%) of sulfate by monitor for 12-km Eastern U.S. 

domain, Winter 2005. 

Figure F-2. Normalized Mean Error (%) of sulfate by monitor for 12-km Eastern U.S. 

domain, Winter 2005.
Figure F-3. Normalized Mean Bias (%) of sulfate by monitor for 12-km Eastern U.S. 

domain, Spring 2005. 

Figure F-4. Normalized Mean Error (%) of sulfate by monitor for 12-km Eastern U.S. 

domain, Spring 2005.
Figure F-5. Normalized Mean Bias (%) of sulfate by monitor for 12-km Eastern U.S. 

domain, Summer 2005. 

Figure F-6. Normalized Mean Error (%) of sulfate by monitor for 12-km Eastern U.S. 

domain, Summer 2005.
Figure F-7. Normalized Mean Bias (%) of sulfate by monitor for 12-km Eastern U.S. 

domain, Fall 2005. 

Figure F-8. Normalized Mean Error (%) of sulfate by monitor for 12-km Eastern U.S. 

domain, Fall 2005.
Figure F-9. Normalized Mean Bias (%) of sulfate by monitor for 12-km Western U.S. 

domain, Winter 2005. 

Figure F-10. Normalized Mean Error (%) of sulfate by monitor for 12-km Western U.S. 

domain, Winter 2005.
Figure F-11. Normalized Mean Bias (%) of sulfate by monitor for 12-km Western U.S. 

domain, Spring 2005. 

Figure F-12. Normalized Mean Error (%) of sulfate by monitor for 12-km Western U.S. 

domain, Spring 2005.
Figure F-13. Normalized Mean Bias (%) of sulfate by monitor for 12-km Western U.S. 

domain, Summer 2005. 

Figure F-14. Normalized Mean Error (%) of sulfate by monitor for 12-km Western U.S. 

domain, Summer 2005.
Figure F-15. Normalized Mean Bias (%) of sulfate by monitor for 12-km Western U.S. 

domain, Fall 2005. 

Figure F-16. Normalized Mean Error (%) of sulfate by monitor for 12-km Western U.S. 

domain, Fall 2005.
G. Seasonal Nitrate Performance 

Table G-1 provides the seasonal model performance statistics for nitrate and total nitrate for the 

12-km Eastern and Western domains. Spatial plots of the NMB and NME statistics (units of 

percent) for individual monitors are also provided as a complement to the tabular statistical data 

(Figures G-1 – G-32). 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 fall 

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 overpredicted 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 G-1. CMAQ 2005 seasonal model performance statistics for nitrate. 

CMAQ 2005 Nitrate No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 3099 -0.4 47.9 -6.9 58.1 

12-km WUS 973 -42.8 61.8 -53.1 82.1 

Northeast 829 17.2 48.7 16.0 49.5 

Midwest 598 -10.1 38.1 -7.3 43.5 

Southeast 963 -2.1 61.8 -25.5 73.1 

Central 479 -1.4 48.1 2.1 56.9 

STN 

West 831 -46.9 64.1 -60.5 86.1 

12-km EUS 2076 15.8 64.1 -13.6 82.5 

12-km WUS 2426 -18.6 63.1 -71.4 110.2 

Northeast 502 59.0 84.2 40.6 75.8 

Midwest 129 -18.5 41.9 -19.9 63.5 

Southeast 386 24.5 84.8 -24.6 81.9 

Central 539 6.9 53.7 -3.0 71.2 

Nitrate 

(Winter) 

IMPROVE 

West 2084 -31.2 71.6 -77.7 115.9 

12-km EUS 760 16.8 31.6 24.1 35.4 

12-km WUS 267 25.6 44.5 35.2 51.5 

Northeast 193 29.7 34.3 35.9 38.4 

Midwest 142 -4.0 20.9 3.1 21.4 

Southeast 264 26.9 37.6 22.5 36.2 

Central 72 26.3 36.8 26.1 35.5 

Total 

Nitrate 

(Winter) 

CASTNet 

West 243 29.8 53.1 37.9 54.6 

12-km EUS 3254 65.7 91.7 29.9 76.3 

12-km WUS 987 -33.5 55.5 -56.9 81.4 

Northeast 894 71.9 98.1 51.8 75.9 

Midwest 637 78.3 93.1 51.0 68.9 

Southeast 988 68.5 111.6 9.8 87.9 

Nitrate 

(Spring) 

STN 

Central 503 33.4 62.5 21.2 64.6 
West 859 -38.4 56.8 -63.8 84.3 

12-km EUS 2435 70.7 103.4 4.1 95.3 

12-km WUS 2697 -14.6 71.1 -74.2 112.0 

Northeast 630 100.6 133.2 41.1 98.6 

Midwest 147 70.9 93.2 23.9 83.2 

Southeast 436 93.3 130.1 4.4 96.7 

Central 632 48.4 76.1 -1.2 90.4 

IMPROVE 

West 2299 -29.5 78.9 -80.7 115.8 

12-km EUS 832 39.9 48.1 27.6 40.0 

12-km WUS 287 -4.8 31.1 2.2 32.1 

Northeast 206 56.5 58.0 44.2 47.3 

Midwest 155 43.0 45.8 35.0 38.0 

Southeast 292 35.2 47.7 24.2 41.1 

Central 78 10.4 35.7 7.7 34.1 

Total 

Nitrate 

(Spring) 

CASTNet 

West 262 -4.5 33.0 3.4 33.0 

12-km EUS 3150 31.1 100.0 -36.9 94.3 

12-km WUS 992 -68.2 74.6 -120.1 127.1 

Northeast 874 19.6 96.2 -33.5 90.4 

Midwest 621 71.6 109.2 10.1 78.8 

Southeast 941 -4.1 89.3 -60.9 102.8 

Central 485 36.5 105.6 -31.2 91.8 

STN 

West 846 -70.6 74.1 -126.0 130.0 

12-km EUS 2324 27.6 118.1 -69.6 119 

12-km WUS 2394 -59.8 83.9 -132.8 145.2 

Northeast 590 40.6 129.7 -47.4 110.9 

Midwest 158 51.5 111.3 -17.9 92.8 

Southeast 427 29.8 124.8 -58.9 118.5 

Central 601 36.5 118.7 -51.8 111.6 

Nitrate 

(Summer) 

IMPROVE 

West 2020 -67.2 82.3 -139.2 148.6 

12-km EUS 792 44.3 55.2 25.2 43.3 

12-km WUS 295 -7.4 31.1 -8.3 33.4 

Northeast 192 61.1 67.8 38.3 51.5 

Midwest 161 59.7 61.8 44.6 46.4 

Southeast 270 34.5 51.3 20.8 44.3 

Central 75 2.1 28.6 -4.8 29.5 

Total 

Nitrate 

(Summer) 

CASTNet 

West 269 -7.5 31.0 -6.7 32.7 

12-km EUS 3238 102.6 130.8 14.0 88.4 

12-km WUS 1048 -42.0 72.4 -52.3 91.6 

Northeast 902 110.6 133.7 10.9 84.9 

Midwest 639 91.8 111.8 40.0 74.8 

Southeast 990 112.9 159.0 -3.1 101.8 

Central 460 114.3 137.9 27.7 87.3 

STN 

West 896 -48.9 69.1 -63.2 92.5 

Nitrate 

(Fall) 

IMPROVE 12-km EUS 2192 147.0 185.11 -4.4 107.3 
12-km WUS 2470 3.6 99.6 -60.7 116.5 

Northeast 526 147.8 179.7 3.0 101.0 

Midwest 97 126.1 157.1 30.5 97.2 

Southeast 436 151.0 212.3 -19.8 118.5 

Central 578 171.5 199.3 16.8 108.4 

West 2078 -29.0 79.9 -71.7 118.1 

12-km EUS 786 72.7 82.7 50.1 57.3 

12-km WUS 293 17.2 42.2 25.1 46.1 

Northeast 195 93.5 95.6 58.9 63.5 

Midwest 157 79.3 79.6 54.5 54.7 

Southeast 273 79.1 86.6 48.5 60.9 

Central 75 57.3 63.8 38.7 45.9 

Total 

Nitrate 

(Fall) 

CASTNet 

West 267 14.2 40.8 26.4 47.0 
Figure G-1. Normalized Mean Bias (%) of nitrate by monitor for 12-km Eastern U.S. 

domain, Winter 2005. 

Figure G-2. Normalized Mean Error (%) of nitrate by monitor for 12-km Eastern U.S. 

domain, Winter 2005.
Figure G-3. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Eastern U.S. 

domain, Winter 2005. 

Figure G-4. Normalized Mean Error (%) of total nitrate by monitor for 12-km Eastern 

U.S. domain, Winter 2005.
Figure G-5. Normalized Mean Bias (%) of nitrate by monitor for 12-km Eastern U.S. 

domain, Spring 2005. 

Figure G-6. Normalized Mean Error (%) of nitrate by monitor for 12-km Eastern U.S. 

domain, Spring 2005.
Figure G-7. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Eastern U.S. 

domain, Spring 2005. 

Figure G-8 Normalized Mean Error (%) of total nitrate by monitor for 12-km Eastern 

U.S. domain, Spring 2005.
Figure G-9. Normalized Mean Bias (%) of nitrate by monitor for 12-km Eastern U.S. 

domain, Summer 2005. 

Figure G-10. Normalized Mean Error (%) of nitrate by monitor for 12-km Eastern U.S. 

domain, Summer 2005. 
Figure G-11. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Eastern 

U.S. domain, Summer 2005. 

Figure G-12. Normalized Mean Error (%) of total nitrate by monitor for 12-km Eastern 

U.S. domain, Summer 2005.
Figure G-13. Normalized Mean Bias (%) of nitrate by monitor for 12-km Eastern U.S. 

domain, Fall 2005. 

Figure G-14. Normalized Mean Error (%) of nitrate by monitor for 12-km Eastern U.S. 

domain, Fall 2005. 
Figure G-15. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Eastern 

U.S. domain, Fall 2005. 

Figure G-16. Normalized Mean Error (%) of total nitrate by monitor for 12-km Eastern 

U.S. domain, Fall 2005.
Figure G-17. Normalized Mean Bias (%) of nitrate by monitor for 12-km Western U.S. 

domain, Winter 2005. 

Figure G-18. Normalized Mean Error (%) of nitrate by monitor for 12-km Western U.S. 

domain, Winter 2005. 
Figure G-19. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Western 

U.S. domain, Winter 2005. 

Figure G-20. Normalized Mean Error (%) of total nitrate by monitor for 12-km Western 

U.S. domain, Winter 2005.
Figure G-21. Normalized Mean Bias (%) of nitrate by monitor for 12-km Western U.S. 

domain, Spring 2005. 

Figure G-22. Normalized Mean Error (%) of nitrate by monitor for 12-km Western U.S. 

domain, Spring 2005. 
Figure G-23. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Western 

U.S. domain, Spring 2005. 

Figure G-24. Normalized Mean Error (%) of total nitrate by monitor for 12-km Western 

U.S. domain, Spring 2005.
Figure G-25. Normalized Mean Bias (%) of nitrate by monitor for 12-km Western U.S. 

domain, Summer 2005. 

Figure G-26. Normalized Mean Error (%) of nitrate by monitor for 12-km Western U.S. 

domain, Summer 2005. 
Figure G-27. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Western 

U.S. domain, Summer 2005. 

Figure G-28. Normalized Mean Error (%) of total nitrate by monitor for 12-km Western 

U.S. domain, Summer 2005.
Figure G-29. Normalized Mean Bias (%) of nitrate by monitor for 12-km Western U.S. 

domain, Fall 2005. 

Figure G-30. Normalized Mean Error (%) of nitrate by monitor for 12-km Western U.S. 

domain, Fall 2005.
Figure G-31. Normalized Mean Bias (%) of total nitrate by monitor for 12-km Western 

U.S. domain, Fall 2005. 

Figure G-32. Normalized Mean Error (%) of total nitrate by monitor for 12-km Western 

U.S. domain, Fall 2005.
H. Seasonal Ammonium Performance 

Table H-1 lists the performance statistics for ammonium PM at the STN and CASTNet sites. 

Spatial plots of the NMB and NME statistics (units of percent) for individual monitors are also 

provided in Figures H-1 – H-16. In the winter and spring, ammonium performance at STN sites 

shows an over-prediction in the EUS and under-prediction in the WUS. Model performance at 

CASTNet sites show over-predictions at both EUS and WUS. Ammonium performance for the 

summer season shows an under-prediction in the East and West. However, in the spring, model 

predictions in the East are over-predicted, whereas ammonia predictions are under-predicted in 

the West. 

Table H-1. CMAQ 2005 seasonal model performance statistics for ammonium. 

CMAQ 2005 Ammonium No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 3390 6.6 39.5 8.4 42.0 

12-km WUS 1032 -24.0 58.9 -8.0 63.4 

Northeast 828 16.9 38.4 18.4 36.0 

Midwest 598 2.9 32.4 7.8 32.8 

Southeast 963 4.3 43.8 5.0 44.4 

Central 766 3.5 43.0 5.4 50.0 

STN 

West 829 -31.0 61.1 -14.5 66.0 

12-km EUS 760 7.7 31.4 12.2 33.3 

12-km WUS 267 4.4 38.8 10.2 40.8 

Northeast 193 33.4 44.7 33.6 40.8 

Midwest 142 -5.3 23.3 0.3 24.1 

Southeast 264 0.9 27.9 1.3 28.7 

Central 72 4.6 36.8 5.7 41.7 

Winter 

CASTNet 

West 243 -0.5 43.6 10.6 42.1 

12-km EUS 3626 34.3 55.8 28.1 48.3 

12-km WUS 1077 -2.0 47.4 17.5 48.7 

Northeast 894 49.1 66.3 46.2 57.3 

Midwest 637 61.1 72.1 47.0 56.1 

Southeast 988 19.4 43.7 17.3 41.4 

Central 875 7.7 41.8 10.1 42.9 

STN 

West 859 -2.5 52.2 20.4 51.4 

12-km EUS 832 33.1 45.8 24.6 37.6 

12-km WUS 287 -1.4 33.6 -1.4 32.1 

Northeast 206 53.2 57.4 40.1 43.5 

Midwest 155 52.6 58.5 41.9 45.4 

Southeast 292 13.0 32.1 11.1 31.0 

Central 78 10.7 37.3 10.3 34.6 

Spring 

CASTNet 

West 262 -7.1 32.9 -2.5 32.5 

12-km EUS 3516 -4.0 37.9 3.4 46.8 

12-km WUS 1071 -31.6 49.6 -8.9 51.7 

Summer STN 

Northeast 874 -1.9 35.3 12.9 43.1 
Midwest 621 8.1 37.8 23.3 42.6 

Southeast 941 -6.5 35.3 0.7 41.8 

Central 847 -18.9 45.0 -19.9 58.7 

West 849 -34.7 54.6 -7.2 54.2 

12-km EUS 792 -18.5 30.0 -23.5 36.5 

12-km WUS 295 -27.6 38.5 -30.6 43.1 

Northeast 192 -19.5 27.9 -21.5 32.1 

Midwest 161 -1.5 25.9 1.6 27.3 

Southeast 270 -29.3 33.6 -40.8 45.5 

Central 75 -16.5 30.1 -20.5 37.2 

CASTNet 

West 269 -31.0 39.5 -31.1 43.3 

12-km EUS 3365 19.2 47.8 28.9 50.0 

12-km WUS 1081 -18.3 62.5 8.6 59.8 

Northeast 902 31.2 57.4 39.1 55.3 

Midwest 639 16.5 39.7 28.7 42.9 

Southeast 990 12.0 43.4 21.8 46.0 

Central 571 22.5 54.3 29.5 54.1 

STN 

West 886 -24.9 64.7 5.5 61.2 

12-km EUS 786 10.5 41.0 14.5 39.6 

12-km WUS 293 11.0 41.3 7.1 34.4 

Northeast 195 16.2 42.6 19.8 38.2 

Midwest 157 25.5 47.6 30.9 43.1 

Southeast 273 -4.5 36.2 -2.4 39.8 

Central 75 19.9 45.8 26.4 47.4 

Fall 

CASTNet 

West 267 0.9 34.1 4.9 32.5 
Figure H-1. Normalized Mean Bias (%) of ammonium by monitor for 12-km Eastern U.S. 

domain, Winter 2005.

Figure H-2. Normalized Mean Error (%) of ammonium by monitor for 12-km Eastern 

U.S. domain, Winter 2005. 
Figure H-3. Normalized Mean Bias (%) of ammonium by monitor for 12-km Eastern U.S. 

domain, Spring 2005.

Figure H-4. Normalized Mean Error (%) of ammonium by monitor for 12-km Eastern 

U.S. domain, Spring 2005. 
Figure H-5. Normalized Mean Bias (%) of ammonium by monitor for 12-km Eastern U.S. 

domain, Summer 2005.

Figure H-6. Normalized Mean Error (%) of ammonium by monitor for 12-km Eastern 

U.S. domain, Summer 2005. 
Figure H-7. Normalized Mean Bias (%) of ammonium by monitor for 12-km Eastern U.S. 

domain, Fall 2005.

Figure H-8. Normalized Mean Error (%) of ammonium by monitor for 12-km Eastern 

U.S. domain, Fall 2005. 
Figure H-9. Normalized Mean Bias (%) of ammonium by monitor for 12-km Western U.S. 

domain, Winter 2005. 

Figure H-10. Normalized Mean Error (%) of ammonium by monitor for 12-km Western 

U.S. domain, Winter 2005. 
Figure H-11. Normalized Mean Bias (%) of ammonium by monitor for 12-km Western 

U.S. domain, Spring 2005. 

Figure H-12. Normalized Mean Error (%) of ammonium by monitor for 12-km Western 

U.S. domain, Spring 2005. 
Figure H-13. Normalized Mean Bias (%) of ammonium by monitor for 12-km Western 

U.S. domain, Summer 2005. 

Figure H-14. Normalized Mean Error (%) of ammonium by monitor for 12-km Western 

U.S. domain, Summer 2005. 
Figure H-15. Normalized Mean Bias (%) of ammonium by monitor for 12-km Western 

U.S. domain, Fall 2005. 

Figure H-16. Normalized Mean Error (%) of ammonium by monitor for 12-km Western 

U.S. domain, Fall 2005. 
I. Seasonal Elemental Carbon Performance 

Table I-1 presents the seasonal performance statistics of elemental carbon for the urban and rural 

2005 monitoring data. Spatial plots of the NMB and NME statistics (units of percent) for 

individual monitors are also provided as a complement to the tabular statistical data (Figures I1 – 

I16). In the winter, elemental carbon performance is mixed across the STN and IMPROVE 

networks in the EUS and WUS, with a moderate over-prediction at STN sites and a slight underprediction at the IMPROVE sites. In general, model performance at urban STN sites is overpredicted, whereas model performance at rural IMPROVE sites show an under-prediction. 

These biases and errors are not unexpected since there are known uncertainties among the 

scientific community in carbonaceous emissions/measurements, transport, and deposition 

processes. 

Table I-1. CMAQ 2005 seasonal model performance statistics for elemental carbon. 

CMAQ 2005 Elemental Carbon No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 3441 60.2 89.1 38.6 61.3 

12-km WUS 657 8.1 71.3 5.4 65.2 

Northeast 831 78.2 93.8 49.7 61.6 

Midwest 602 85.2 104.6 56.1 66.8 

Southeast 964 36.3 68.5 21.7 48.7 

Central 811 71.6 109.3 45.4 71.5 

STN 

West 520 10.4 71.0 0.4 66.1 

12-km EUS 2072 6.7 54.5 -2.7 52.3 

12-km WUS 2279 -2.8 61.4 -20.6 66.0 

Northeast 522 34.7 63.6 21.0 51.0 

Midwest 166 36.2 59.6 14.2 44.1 

Southeast 386 -20.6 43.3 -19.1 48.9 

Central 474 -4.4 52.2 -0.3 50.8 

Winter 

IMPROVE 

West 1994 -6.3 61.0 -23.4 67.5 

12-km EUS 3672 49.9 79.0 33.3 58.3 

12-km WUS 1064 49.5 86.4 24.2 63.8 

Northeast 881 71.2 93.6 47.1 63.6 

Midwest 637 35.0 62.9 33.1 52.1 

Southeast 985 34.0 67.6 26.3 51.6 

Central 937 65.2 94.7 33.5 65.1 

STN 

West 822 65.2 97.3 28.5 67.2 

12-km EUS 2296 -13.9 49.6 -10.4 51.3 

12-km WUS 2563 3.9 61.1 -6.7 54.7 

Northeast 565 17.0 56.0 6.9 54.2 

Midwest 160 0.6 47.0 -17.3 48.4 

Southeast 408 -35.4 46.0 -31.5 46.8 

Central 578 -27.3 49.9 -17.6 54.4 

Spring 

IMPROVE 

West 2191 10.1 63.8 -6.2 55.7 
12-km EUS 3529 45.5 76.7 28.6 61.3 

12-km WUS 1030 62.1 91.3 34.6 62.5 

Northeast 866 47.0 76.7 34.6 59.5 

Midwest 621 21.2 47.9 21.6 47.6 

Southeast 940 35.7 73.5 27.3 58.0 

Central 871 85.3 113.8 32.3 76.5 

STN 

West 806 78.9 10.7 41.0 65.8 

12-km EUS 2182 -34.7 47.8 -38.8 59.8 

12-km WUS 2301 14.0 67.7 8.9 57.8 

Northeast 512 -29.0 46.1 -44.5 61.5 

Midwest 160 -25.0 36.4 -42.1 52.0 

Southeast 384 -51.3 53.3 -71.0 78.4 

Central 561 -38.1 49.1 -45.5 61.5 

Summer 

IMPROVE 

West 1961 19.8 71.6 12.9 59.1 

12-km EUS 3396 28.5 64.8 21.6 52.1 

12-km WUS 1063 21.4 70.3 9.0 59.6 

Northeast 901 36.0 72.7 23.4 53.8 

Midwest 642 28.0 55.2 25.2 47.9 

Southeast 988 9.4 53.2 7.6 45.8 

Central 602 67.2 89.4 48.4 63.4 

STN 

West 867 26.4 74.3 7.6 61.8 

12-km EUS 2118 -13.7 45.9 -20.5 49.9 

12-km WUS 2352 -0.9 60.1 -16.7 60.3 

Northeast 518 19.7 52.2 0.0 49.6 

Midwest 116 -18.4 35.4 -20.0 48.0 

Southeast 406 -38.7 44.8 -36.2 51.9 

Central 510 -19.6 41.0 -19.0 46.0 

Fall 

IMPROVE 

West 2023 -0.4 62.3 -17.6 62.4 
Figure I-1. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Eastern 

U.S. domain, Winter 2005. 

Figure I-2. Normalized Mean Error (%) of elemental carbon by monitor for 12-km 

Eastern U.S. domain, Winter 2005.
Figure I-3. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Eastern 

U.S. domain, Spring 2005. 

Figure I-4. Normalized Mean Error (%) of elemental carbon by monitor for 12-km 

Eastern U.S. domain, Spring 2005.
Figure I-5. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Eastern 

U.S. domain, Summer 2005. 

Figure I-6. Normalized Mean Error (%) of elemental carbon by monitor for 12-km 

Eastern U.S. domain, Summer 2005.
Figure I-7. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Eastern 

U.S. domain, Fall 2005. 

Figure I-8. Normalized Mean Error (%) of elemental carbon by monitor for 12-km 

Eastern U.S. domain, Fall 2005.
Figure I-9. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km Eastern 

U.S. domain, Winter 2005. 

Figure I-10. Normalized Mean Error (%) of elemental carbon by monitor for 12-km 

Western U.S. domain, Winter 2005.
Figure I-11. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km 

Western U.S. domain, Spring 2005. 

Figure I-12. Normalized Mean Error (%) of elemental carbon by monitor for 12-km 

Western U.S. domain, Spring 2005.
Figure I-13. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km 

Western U.S. domain, Summer 2005. 

Figure I-14. Normalized Mean Error (%) of elemental carbon by monitor for 12-km 

Western U.S. domain, Summer 2005.
Figure I-15. Normalized Mean Bias (%) of elemental carbon by monitor for 12-km 

Western U.S. domain, Fall 2005. 

Figure I-16. Normalized Mean Error (%) of elemental carbon by monitor for 12-km 

Western U.S. domain, Fall 2005.
J. Seasonal Organic Carbon Performance 

Seasonal organic carbon performance statistics are provided in Table J-1. Spatial plots of the 

NMB and NME statistics (units of percent) for individual monitors are also provided as a 

complement to the tabular statistical data (Figures J-1 – J-16). The model predictions generally 

show moderate under-predictions for all Eastern sites located in the urban STN sites and rural 

IMPROVE sites. Organic carbon performance in the EUS and WUS shows the largest under 

estimations during the summer season. 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 J-1. CMAQ 2002 seasonal model performance statistics for organic carbon. 

CMAQ 2002 Organic Carbon No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 3051 1.9 53.2 13.1 54.9 

12-km WUS 606 -25.6 62.0 -22.4 62.6 

Northeast 804 31.7 61.1 34.4 56.1 

Midwest 565 9.0 54.5 25.7 55.3 

Southeast 943 -23.1 44.9 -11.9 50.3 

Central 544 2.4 56.3 17.7 58.9 

STN 

West 507 -24.2 63.0 -22.0 64.3 

12-km EUS 2071 11.1 54.0 1.2 50.9 

12-km WUS 2269 -12.1 59.0 -21.7 63.6 

Northeast 522 53.9 72.7 37.0 54.2 

Midwest 166 10.7 40.4 7.1 37.2 

Southeast 386 -16.8 40.5 -19.1 47.8 

Central 474 -2.6 51.9 -2.8 48.6 

Winter 

IMPROVE 

West 1981 -16.3 58.4 -23.7 65.0 

12-km EUS 3243 -24.8 48.9 -15.0 56.1 

12-km WUS 656 -19.8 63.4 -13.7 64.5 

Northeast 831 3.8 51.5 10.0 53.7 

Midwest 605 -27.7 45.5 -15.5 52.2 

Southeast 972 -35.7 48.1 -29.0 56.7 

Central 627 -36.6 50.8 -32.9 61.7 

STN 

West 560 -15.9 65.9 -7.1 65.5 

12-km EUS 2290 -20.5 47.1 -18.3 50.8 

12-km WUS 2554 -26.3 53.0 -25.3 56.5 

Northeast 565 8.1 48.0 1.5 46.7 

Midwest 160 -21.2 35.0 -26.2 41.8 

Southeast 409 -22.1 41.0 -24.9 45.6 

Central 577 -41.6 54.8 -35.9 58.7 

Spring 

IMPROVE 

West 2183 -22.1 52.2 -23.5 56.6 
12-km EUS 3228 -51.7 54.3 -68.2 74.2 

12-km WUS 832 -55.4 60.0 -76.2 83.6 

Northeast 859 -47.6 52.1 -62.1 70.2 

Midwest 619 -53.1 54.3 -69.5 72.8 

Southeast 931 -55.9 57.1 -76.0 79.8 

Central 595 -50.3 53.2 -68.0 74.7 

STN 

West 684 -54.0 59.3 -73.3 81.8 

12-km EUS 2183 -41.6 50.7 -53.0 66.7 

12-km WUS 2311 -1.5 63.3 -7.1 61.0 

Northeast 513 -47.5 52.2 -60.2 67.6 

Midwest 160 -43.9 46.8 -58.0 62.2 

Southeast 384 -43.6 48.2 -66.3 71.3 

Central 562 -46.4 51.6 -65.8 73.5 

Summer 

IMPROVE 

West 1970 4.0 65.2 -2.3 60.2 

12-km EUS 3097 -26.1 45.0 -19.2 54.7 

12-km WUS 809 -40.4 57.9 -38.7 63.4 

Northeast 829 -3.9 47.6 4.1 53.7 

Midwest 591 -27.6 40.7 -15.1 51.6 

Southeast 956 -39.2 45.7 -41.3 57.5 

Central 493 -29.4 45.2 -25.3 56.1 

STN 

West 657 -41.0 59.9 -39.6 66.8 

12-km EUS 2118 -23.5 46.1 -31.3 54.7 

12-km WUS 2361 -22.7 56.0 -29.0 62.7 

Northeast 516 16.3 48.4 32.6 -0.3 

Midwest 115 -30.2 40.3 -37.7 53.7 

Southeast 408 -35.1 41.3 -41.6 52.6 

Central 510 -39.2 48.9 -47.1 61.5 

Fall 

IMPROVE 

West 2031 -21.3 57.0 -27.1 62.7 
Figure J-1. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Eastern 

U.S. domain, Winter 2005. 

Figure J-2. Normalized Mean Error (%) of organic carbon by monitor for 12-km Eastern 

U.S. domain, Winter 2005.
Figure J-3. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Eastern 

U.S. domain, Spring 2005. 

Figure J-4. Normalized Mean Error (%) of organic carbon by monitor for 12-km Eastern 

U.S. domain, Spring 2005.
Figure J-5. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Eastern 

U.S. domain, Summer 2005. 

Figure J-6. Normalized Mean Error (%) of organic carbon by monitor for 12-km Eastern 

U.S. domain, Summer 2005. 
Figure J-7. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Eastern 

U.S. domain, Fall 2005. 

Figure J-8. Normalized Mean Error (%) of organic carbon by monitor for 12-km Eastern 

U.S. domain, Fall 2005. 
Figure J-9. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Western 

U.S. domain, Winter 2005. 

Figure J-10. Normalized Mean Error (%) of organic carbon by monitor for 12-km 

Western U.S. domain, Winter 2005. 
Figure J-11. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Western 

U.S. domain, Spring 2005. 

Figure J-12. Normalized Mean Error (%) of organic carbon by monitor for 12-km 

Western U.S. domain, Spring 2005. 
Figure J-13. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Western 

U.S. domain, Summer 2005. 

Figure J-14. Normalized Mean Error (%) of organic carbon by monitor for 12-km 

Western U.S. domain, Summer 2005. 
Figure J-15. Normalized Mean Bias (%) of organic carbon by monitor for 12-km Western 

U.S. domain, Fall 2005. 

Figure J-16. Normalized Mean Error (%) of organic carbon by monitor for 12-km 

Western U.S. domain, Fall 2005.
K. Annual Hazardous Air Pollutants Performance 

An annual and 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 annual model 

performance results are presented in Table K-1 below. Spatial plots of the NMB and NME 

statistics (units of percent) for individual monitors are also provided as a complement to the 

tabular statistical data (Figures K-1 – K-24). The seasonal results follow in Sections L-P. 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.1,2,3 Overall, the 

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

and FE) statistics shown in Table J-1 indicate that CMAQ-predicted 2005 toxics (i.e., 

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

 

1

 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. 

2

 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. 

 

3

 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.
Table K-1. 2005 CMAQ annual toxics model performance statistics 

CMAQ 2005 Annual No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 6365 -53.8 64.5 -36.6 64.2 

12-km WUS 1928 -24.5 50.9 -25.8 58.6 

Northeast 1982 -28.2 50.7 -26.3 60.7 

Midwest 771 -75.9 85.0 -22.6 72.7 

Southeast 1246 -65.0 71.4 -48.8 69.1 

Central U.S. 1815 -41.4 49.4 -38.7 59.6 

Formaldehyde 

West 1746 -21.7 51.5 -22.0 58.2 

12-km EUS 6094 -0.9 63.0 -5.2 59.8 

12-km WUS 1892 -14.4 53.6 -14.7 58.2 

Northeast 1969 -6.6 64.0 -6.4 63.4 

Midwest 703 -8.9 58.8 -8.7 59.3 

Southeast 1231 3.2 64.1 -3.7 61.5 

Central U.S. 1640 5.6 57.9 -0.9 50.5 

Acetaldehyde 

West 1709 -15.6 53.9 -15.4 59.3 

12-km EUS 11615 -30.6 66.9 -10.4 62.4 

12-km WUS 3369 -34.9 60.5 -25.4 62.3 

Northeast 2589 26.5 55.1 22.7 47.7 

Midwest 1425 -5.8 73.5 27.9 62.7 

Southeast 2426 -38.3 69.4 -12.9 60.1 

Central U.S. 4737 -46.4 67.6 -30.9 68.0 

Benzene 

West 2333 -26.1 61.2 -13.6 62.1 

12-km EUS 8102 -71.9 84.9 -37.5 88.2 

12-km WUS 1976 -46.5 83.1 -22.7 91.5 

Northeast 1902 -34.1 53.0 -42.5 64.5 

Midwest 516 -74.8 85.5 -34.8 77.7 

Southeast 1226 -82.4 84.0 -93.4 100.7 

Central U.S. 4142 -63.8 86.1 -8.0 89.6 

1,3-Butadiene 

West 1082 -36.7 78.4 -36.6 84.2 

12-km EUS 1660 -93.8 94.5 -126.2 138.4 

12-km WUS 783 -95.4 95.5 -164.5 167.2 

Northeast 850 -89.5 90.9 -116.0 131.1 

Midwest n/a n/a n/a n/a n/a 

Southeast 278 -96.7 96.7 -152.8 153.6 

Central U.S. n/a n/a n/a n/a n/a 

Acrolein 

West 592 -95.8 95.8 -176.9 176.9 
Figure K-1. Normalized Mean Bias (%) of annual formaldehyde by monitor for Eastern 

U.S., 2005.

Figure K-2. Normalized Mean Error (%) of annual formaldehyde by monitor for Eastern 

U.S., 2005. 
Figure K-3. Normalized Mean Bias (%) of annual acetaldehyde by monitor for Eastern 

U.S., 2005.

Figure K-4. Normalized Mean Error (%) of annual acetaldehyde by monitor for Eastern 

U.S., 2005. 
Figure K-5. Normalized Mean Bias (%) of annual benzene by monitor for Eastern U.S., 

2005.

Figure K-6. Normalized Mean Error (%) of annual benzene by monitor for Eastern U.S., 

2005. 
Figure K-7. Normalized Mean Bias (%) of annual 1,3-butadiene by monitor for Eastern 

U.S., 2005.

Figure K-8. Normalized Mean Error (%) of annual 1,3-butadiene by monitor for Eastern 

U.S., 2005. 
Figure K-9. Normalized Mean Bias (%) of annual acrolein by monitor for Eastern U.S., 

2005.

Figure K-10. Normalized Mean Error (%) of annual acrolein by monitor for Eastern U.S., 

2005. 
Figure K-15. Normalized Mean Bias (%) of annual formaldehyde by monitor for Western 

U.S., 2005.

Figure K-16. Normalized Mean Error (%) of annual formaldehyde by monitor for 

Western U.S., 2005. 
Figure K-17. Normalized Mean Bias (%) of annual acetaldehyde by monitor for Western 

U.S., 2005.

Figure K-18. Normalized Mean Error (%) of annual acetaldehyde by monitor for Western 

U.S., 2005. 
Figure K-19. Normalized Mean Bias (%) of annual benzene by monitor for Western U.S., 

2005.

Figure K-20. Normalized Mean Error (%) of annual benzene by monitor for Western U.S., 

2005. 
Figure K-21. Normalized Mean Bias (%) of annual 1,3-butadiene by monitor for Western 

U.S., 2005.

Figure K-22. Normalized Mean Error (%) of annual 1,3-butadiene by monitor for 

Western U.S., 2005. 
Figure K-23. Normalized Mean Bias (%) of annual acrolein by monitor for Western U.S., 

2005.

Figure K-24. Normalized Mean Error (%) of annual acrolein by monitor for Western U.S., 

2005. 
L. Annual Nitrate and Sulfate Deposition Performance 

Annual nitrate and sulfate deposition performance statistics are provided in Table L-1. Spatial 

plots of the NMB and NME statistics (units of percent) for individual monitors are also provided 

as a complement to the tabular statistical data (Figures L-1 – L-8). 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 EUS 

and WUS shows the similar over predictions (NMB values range from 5% to 16%). The errors 

for both annual nitrate and sulfate are relatively moderate with values ranging from 60% to 81% 

which reflect scatter in the model predictions o observation comparison. 

Table L-1. CMAQ 2005 annual model performance statistics for total nitrate and sulfate 

deposition. 

CMAQ 2005 Total Deposition No. of Obs. NMB (%) NME (%) FB (%) FE (%) 

12-km EUS 7381 -0.6 63.9 -7.8 74.0 

12-km WUS 2732 -11.6 69.5 -12.7 83.4 

Northeast 1391 4.9 62.0 5.0 67.8 

Midwest 1658 10.1 60.9 3.9 66.5 

Southeast 1980 4.9 67.3 0.1 71.3 

Central 1229 -11.0 62.7 -11.0 78.3 

Nitrate 

West 2257 -9.8 73.8 -12.7 85.0 

12-km EUS 7381 7.8 67.0 6.0 75.3 

12-km WUS 2732 5.6 76.3 4.8 86.5 

Northeast 1391 16.4 62.6 23.2 70.4 

Midwest 1658 12.6 64.3 16.5 67.3 

Southeast 1980 8.6 71.4 6.4 73.8 

Central 1229 -7.3 65.1 -1.2 80.3 

Sulfate 

West 2257 13.2 81.8 6.5 87.9 
Figure L-1. Normalized Mean Bias (%) of annual nitrate deposition by monitor for 

Eastern U.S., 2005. 

Figure L-2. Normalized Mean Error (%) of annual nitrate by monitor for Eastern U.S., 

2005. 
Figure L-3. Normalized Mean Bias (%) of annual nitrate deposition by monitor for 

Western U.S., 2005. 

Figure L-4. Normalized Mean Error (%) of annual nitrate deposition by monitor for 

Western U.S., 2005. 
Figure L-5. Normalized Mean Bias (%) of annual sulfate deposition by monitor for 

Eastern U.S., 2005. 

Figure L-6. Normalized Mean Error (%) of annual sulfate deposition by monitor for 

Eastern U.S., 2005. 
Figure L-7. Normalized Mean Bias (%) of annual sulfate deposition by monitor for 

Western U.S., 2005. 

Figure L-8. Normalized Mean Error (%) of annual sulfate deposition by monitor for 

Western U.S., 2005. 

United States 

Environmental Protection 

Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 

Publication No. EPA-454/R10-003

April, 2010