Air quality modeling technical support document : ozone source apportionment application in support of the designation process for the ozone NAAQS.

Air Quality Modeling Technical Support Document: 

Ozone Source Apportionment Application in Support of 

the Designation Process for the Ozone NAAQS 
EPA-454/R-10-005 

November 2010 

Air Quality Modeling Technical Support Document: 

Ozone Source Apportionment Application in Support of 

the Designation Process for the Ozone NAAQS 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 27711 
1 

Contents 

1 Background 

2 Limitations 

3 Derivation of Contribution Estimate 

 3.1 Primary Standard 

 3.2 Secondary Standard 

4 Methodology Details 

4.1 Identifying Violating Monitors 

4.2 Selection of Sources: Counties 

4.3 Photochemical Model Application 

4.4 Emissions 

4.5 Meteorological Inputs 

4.6 Initial and Boundary Conditions 

5 Operational Model Performance Description 

6 References 

Appendix A Emissions Totals 

Appendix B Model Performance
2 

1 Background

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

most recent ozone designations process. A national scale air quality modeling analysis 

was performed to estimate the impact of county specific anthropogenic NOX and VOC 

emissions on model estimated ozone concentrations. Source contribution is estimated for 

the primary and secondary forms of the ozone National Ambient Air Quality Standard 

(NAAQS). Air quality impacts are estimated with the Comprehensive Air Quality Model 

with Extensions (CAMx) model. CAMx 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 CAMx model, the modeling platform includes the 

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

model. 

Photochemical grid models use state of the science numerical algorithms to estimate 

pollutant formation, transport, and deposition over a variety of spatial scales that range 

from urban to continental. Emissions of precursor species are injected into the model 

where they react to form secondary species such as ozone and then transport around the 

modeling domain before ultimately being removed by deposition or chemical reaction. 

Photochemical model source apportionment tracks the formation and transport of ozone 

from emissions sources and allows the estimation of contributions at receptors. This type 

of emissions apportionment is useful to understand what types of sources or regions are 

contributing to ozone estimated by photochemical grid models. 

Source apportionment is an alternative approach to zero-out modeling and has the 

advantage of being much more efficient with computational resources. For instance, to 

estimate the contribution from 20 source regions a total of 20 individual zero-out 

simulations would be needed compared to a single source apportionment simulation. The 

incremental run-time associated with the additional source region tracking is less than 

performing numerous iterative zero-out simulations. 

The Comprehensive Air-Quality Model with extensions is a three-dimensional Eularian 

“one-atmosphere” photochemical transport model that uses state of the science routines 

to model ozone and particulate matter formation and removal processes (Nobel, 

McDonald-Buller et al. 2001; ENVIRON 2008). CAMx contains a variety of ozone 

source apportionment tools, including the original ozone source apportionment tool 

(OSAT) and the anthropogenic pre-cursor culpability assessment (APCA) tool 

(ENVIRON 2008). 

Ozone source apportionment in CAMx tracks the contributions to each grid cell from 

emissions source groups, emissions source regions, initial conditions, and boundary 

conditions with reactive tracer species. Source apportionment tracers are treated using the 

standard model algorithms for vertical advection, vertical diffusion, and horizontal 

diffusion. Horizontal advective fluxes for each of the regular model species that make up 

nitrogen oxides (NOX) and volatile organic compounds (VOC) are combined and 
3 

normalized by a concentration based weighted mean. The estimated normalized fluxes 

are used to advect the tracer species rather than solving them with the standard model 

formulation to improve consistency between tracer and regular model concentrations 

(ENVIRON 2008). The deposition velocities for NOX tracers are the concentration 

weighted average of the deposition velocities for NO and NO2. The deposition velocities 

for VOC tracers are the concentration weighted average (done on a ppmC basis) of the 

deposition velocities for each of the CB05 species. Ozone tracers use the same deposition 

velocities as ozone (ENVIRON 2008). 

Separate ozone tracers are used in CAMx to track ozone formation that happens under 

NOX and VOC limited conditions. The ozone production regime indicator used in CAMx 

compares well with sensitivity to NOX and VOC estimated using the Decoupled Direct 

Method (DDM) (Dunker, Yarwood et al. 2002). The source apportionment methodology 

in CAMx compared favorably to sensitivity runs done using the Urban Airshed Model 

and DDM, particularly for higher estimated ozone concentrations (Yarwood 1996; 

Dunker, Yarwood et al. 2002). CAMx source apportionment compared well against the 

sensitivity simulations in terms of spatial impacts of emissions from source regions and 

the relative contributions for each emission regions (tags) to ozone. 

The APCA tool provides information that is most policy relevant when assessing regional 

and emission sector contribution to ozone formation compared to OSAT. When ozone is 

formed under VOC limited conditions due to biogenic VOC + anthropogenic NOX then 

OSAT attributes it to the biogenic VOC sources. When ozone is formed under NOXlimited conditions due to biogenic VOC + anthropogenic NOX then OSAT attributes it to 

the anthropogenic NOX sources. APCA is designed to provide more control strategy 

relevant information and recognizes that there are source categories such as biogenics 

that can not be controlled so the model only attributes ozone to biogenics when it is due 

to the interaction of biogenic VOC + biogenic NOX. In the case where ozone formed to 

biogenic VOC + anthropogenic NOX under VOC-limited conditions, OSAT attributes it 

to biogenic VOC, but APCA redirects the attribution to anthropogenic NOX. In NOXlimited conditions both OSAT and APCA attribute the ozone to anthropogenic NOX

(ENVIRON 2008). 
4 

2 Limitations 

Source apportionment estimates are as good as the inputs to the photochemical model. 

Any deficiencies with the emissions or meteorological inputs may lead to source 

contribution estimates that may not fully characterize the source contribution mix at a 

receptor location. This application used a minimum of a complete year of meteorology to 

capture the variety of ozone formation regimes. However, it is possible that the 

meteorology used for these model applications may not represent all ozone formation 

regimes at every individual receptor location in the continental United States. 
5 

3 Derivation of Contribution Estimate 

3.1 Primary Standard 

The steps used to estimate the source contribution at each receptor location for the 

primary 8-hr ozone NAAQS are shown below. More details on the model application 

methodology are available in the next subsection of this document. The policy relevant 

threshold is defined as the level of the new 8-hr ozone NAAQS. 

1. Receptor locations are defined as ozone monitors in the photochemical modeling 

domain with a 2006-2008 design value exceeding the new 8-hr ozone NAAQS 

2. 8-hr averaged source contributions related to the 8-hr average daily maximum 

model estimate at these receptor locations are extracted from model output files 

3. Identify high modeled days at each receptor: predicted 8-hr max ozone > policy 

relevant threshold 

4. Average daily 8-hr maximum contributions over all days with modeled ozone > 

policy relevant threshold at each receptor 

5. The final contribution estimate is an 8-hr average with units of parts per billion 

(ppb) 

3.2 Secondary Standard 

The steps used to estimate the final source contribution at each receptor location for the 

secondary ozone NAAQS are shown below. 

1. Receptor locations are defined as ozone monitors in the photochemical modeling 

domain that were in operation during the time period modeled. 

2. Daily index, monthly index, and the 3 month sum W126 ozone estimates at these 

receptor locations are extracted from standard model output files 
6 

3. “Pseudo-output” files are generated for each source region (or tag) by subtracting 

the source region hourly ozone contribution estimate from the standard model 

hourly ozone estimate 

4. Daily index, monthly index, and the 3 month sum W126 ozone estimates at these 

receptor locations are extracted from “pseudo” model output files 

5. Identify the highest 3 month sum based on standard model output at each monitor 

location 

6. The source area (tag) contribution is the difference between the highest 3 month 

sum estimated from the standard model output and the corresponding 3 month 

sum estimated from the “pseudo” model output for that source area 

7. The final contribution estimate is a W126 weighted 3 month sum in units of ppmhours 
7 

4 Methodology Details 

Additional information is provided in this section regarding the final source contribution 

estimate from each source area to each receptor. Also, information is given about the 

selection of source areas, emission preparation, photochemical model application, 

meteorological inputs, and photochemical model performance evaluation. 

4.1 Identifying Violating Monitors (Receptors) 

Receptors are defined as individual model grid cells that contain a monitor with a design 

value for the 2006 to 2008 time period that is greater than the level of the 8-hr ozone 

NAAQS. Design values are estimated using methods described in 40 CFR part 50 

Appendix P for a 75 ppb 8-hr ozone NAAQS and the proposed revision to Appendix P 

for alternative lower levels of the 8-hr ozone NAAQS. 

4.2 Selection of Sources: Counties 

The sources selected for tracking with source apportionment include emissions from 

specific selected counties. Specific counties were selected if they comprised part of a 

CSA or CBSA that had a violating monitor for the new 8-hr ozone NAAQS. Counties 

tracked with source apportionment are shown in Figure 1. Each county is color coded to 

correspond to the modeling domain used for tracking that particular source area. 

Figure 1. Sources selected for tracking with source apportionment 
8 

Additional counties were identified for tracking with source apportionment by U.S. EPA 

Regional offices. The emissions tracked with source apportionment for each county 

include all VOC and NOX emissions except wildfires, prescribed fires, and biogenics. 

4.3 Photochemical Model Application 

The CAMx photochemical model is applied for at least one entire calendar year using 

multiple model domains to track source group emissions. Source group emissions are 

tracked for ozone contribution using the APCA approach (ENVIRON 2008). CAMx 

version 5.01 is a freely available computer model that simulates the formation and fate of 

photochemical oxidants, primary and secondary PM concentrations, and air toxics, over 

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

emissions (Nobel, McDonald-Buller et al. 2001; Baker and Scheff 2007; Russell 2008). 

CAMx 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. CAMx is applied with Regional Acid Deposition Model (RADM) 

aqueous phase chemistry (Chang, Brost et al. 1987), and Carbon Bond 05 (CB05) gasphase chemistry module (Gery, Whitten et al. 1989; ENVIRON 2008). 

All model domains (Table 1, Figure 1) were applied for the entire year of 2005 except for 

12WUS1 and 12SEATTLE1, which wer only applied for 2006. The 12WUS2 domain 

was applied for both 2006 and 2005. All model domains are applied with a Lambert 

projection centered at (-97, 40) and true latitudes at 33 and 45. The specifications for 

each of the modeling domains are given in Table 1 and shown in Figure 1. Each domain 

is applied with square 12 km sized grid cells. 

Table 1. Model domain specifications (Cell size, X, and Y origins in units of km). 

Domain Name X origin Y origin # X cells # Y cells Cell size 

12FLORIDA2 900 -1620 75 75 12

12SOUTH1 -444 -1620 140 90 12

12NE2 432 -288 156 108 12

12SE2 540 -972 120 72 12

12MW3 -288 -792 93 72 12

12WUS2 -2412 -972 170 150 12

12SEATTLE1 -2196 852 53 40 12

12OV1 468 -648 75 75 12

12UPMW1 -180 -36 114 75 12

12EUS1 -1008 -1620 279 240 12

12WUS1 -2412 -972 213 192 12

4.4 Emissions 

The emissions used for the photochemical modeling are based on the 2005 National 

Emission Inventory version 2 (modeling inventory scenario = 2005cm_05b) for 

stationary point, onroad and nonroad sources. Day specific biogenic emissions are 
9 

estimated using hourly gridded day-specific meteorology. Other area sources were grown 

from the 2002 National Emission Inventory to approximate the level of emissions 

expected in 2005 (Strum 2008). Average day fire emissions are used for this modeling, 

but were not tracked as part of any source area (county). Fire emissions are included as 

part of the non-tagged emissions in the source apportionment modeling. Oil and gas 

related emissions in the NEI were replaced by more recent inventories made available by 

the Western Regional Air Partnership for certain States in the western United States (BarIlan 2007). 

Table 2. Sectors Used in the Emissions Modeling Platform 

Platform Sector Tagged? 2005 NEI 

Sector 

Description and resolution of the data input to 

SMOKE 

IPM sector: 

ptipm 

Y Point 2005v2 NEI point source EGUs mapped to the 

Integrated Planning Model (IPM) model using the 

National Electric Energy Database System (NEEDS, 

2006 version 3.02) database. Hourly files for 

continuous emission monitoring (CEM) sources are 

included only for the 2005 evaluation case. Dayspecific emissions for non-CEM sources created for 

input into SMOKE. 

Non-IPM 

sector: 

ptnonipm 

Y Point All 2005v2 NEI point source records not matched to 

the ptipm sector, annual resolution. Includes all 

aircraft emissions. 

Point source fire 

sector: avgfire 

N Fires Average wildfires and prescribed fires. Fire 

emissions based on average of 1996-2002. 

Agricultural 

sector: ag 

N Nonpoint NH3 emissions from NEI nonpoint livestock and 

fertilizer application, county and annual resolution. 

Area fugitive 

dust sector: 

afdust 

N Nonpoint PM10 and PM2.5 from fugitive dust sources from the 

NEI nonpoint inventory (e.g., building construction, 

road construction, paved roads, unpaved roads, 

agricultural dust), county and annual resolution. 

Remaining 

nonpoint sector: 

nonpt 

Y Nonpoint Primarily 2002 NEI nonpoint sources not otherwise 

included in other SMOKE sectors, county and annual 

resolution. Also includes updated Residential Wood 

Combustion emissions and year 2005 non-California 

Western Regional Air Partnership (WRAP) oil and 

gas “Phase II” inventory. Includes portable fuel 

container emissions from OTAQ. 

Nonroad sector: 

nonroad 

Y Mobile: 

Nonroad 

Monthly nonroad emissions from the National 

Mobile Inventory Model (NMIM) using 

NONROAD2005 version nr05c-BondBase for all 

states except California. Monthly emissions for 

California created from annual emissions submitted 

by the California Air Resources Board (CARB) for 

the 2005v2 NEI. 

locomotive, and 

non-C3 

Y Mobile: 

Nonroad 

Year 2002 non-rail maintenance locomotives, and 

category 1 and category 2 commercial marine vessel 
10 

Platform Sector Tagged? 2005 NEI 

Sector 

Description and resolution of the data input to 

SMOKE 

commercial 

marine: 

alm_no_c3 

(CMV) emissions sources, county and annual 

resolution. Unlike prior platforms, aircraft emissions 

are now included in the ptnonipm sector and 

category 3 CMV emissions are now contained in the 

seca_c3 sector 

C3 commercial 

marine: 

seca_c3 

Y Mobile : 

Nonroad 

Annual point source formatted year 2005 category 3 

(C3) CMV emissions, developed for the EPA rule 

called “Control of Emissions from New Marine 

Compression-Ignition Engines at or Above 30 Liters 

per Cylinder”, usually described as the Area (ECA) 

study, originally called SO2 (“S”) ECA. 

Onroad 

(NMIM-based)

Y Mobile: 

onroad 

Three, monthly, county-level components: 

1) Onroad emissions from NMIM using 

MOBILE6.2, other than for California. 

2) California onroad, created using annual 

emissions submitted by CARB for the 

2005v2 NEI. 

Biogenic: biog N N/A Hour-specific, grid cell-specific emissions generated 

from the BEIS3.14 model -includes emissions in 

Canada and Mexico. 

Other point 

sources not 

from the NEI: 

othpt 

N N/A Point sources from Canada’s 2006 inventory and 

Mexico’s Phase III 1999 inventory, annual 

resolution. Also includes annual U.S. offshore oil 

2005v2 NEI point source emissions. 

Other nonpoint 

and nonroad 

not from the 

NEI: othar 

N N/A Annual year 2006 Canada (province resolution) and 

year 1999 Mexico Phase III (municipio resolution) 

nonpoint and nonroad mobile inventories, annual 

resolution. 

Other onroad 

sources not 

from the NEI: 

othon 

N N/A Year 2006 Canada (province resolution) and year 

1999 Mexico Phase III (municipio resolution) onroad 

mobile inventories, annual resolution. 

Daily hour-specific biogenic emissions based on 2005 and 2006 meteorology data are 

generated using the BEIS 3.14 model. The BEIS model creates gridded, hourly emissions 

of CO, VOC, and NOX from vegetation and soils for the United States, Mexico, and 

Canada (Guenther, Geron et al. 2000). The inputs to BEIS include shortwave downward 

solar radiation and temperature data at 10 meters which were obtained from the CMAQ 

meteorological input files and land-use data from the Biogenic Emissions Landuse 

Database, version 3 (BELD3). BELD3 data provides data on the 230 vegetation classes at 

1 km resolution over most of North America (Kinnee, Geron et al. 1997). 

All emissions were processed using the latest version of the Sparse Matrix Operator 

Kernel Emissions (SMOKE) Modeling System (Houyoux, Vukovich et al. 2000; UNC 

2007). SMOKE has been enhanced with a feature that allows county or state specific 

emissions to be processed through the emissions model as specific unique pollutants. For 
11 

example, rather than run SMOKE for specific counties or states then merge the output 

files into a model ready emissions file, a new input file called the ‘GSTAG’ file matches 

specific emissions based on FIPS codes to additional emissions species that are tracked in 

the source apportionment photochemical model. This type of approach to source 

apportionment emissions processing is favorable because it is efficient in terms of 

processing time (compared to running SMOKE for each specific source group 

individually) and results in precursor emissions specific to the source group. The 

alternative is using a gridded “mask” field to define source groups, which has much less 

specificity about allocating emissions to the correct county when multiple county 

boundaries fall within the same grid cell. 

Total county emissions of NOX and VOC are presented in tabular form in the Appendix. 

Emission totals are only shown for counties selected to be tracked with source 

apportionment for contribution to receptor locations of interest. 

4.5 Meteorological Inputs 

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

thermodynamic equations which govern atmospheric motions. Meteorological model 

input fields were prepared separately for each of the three domains shown in Figure 2 

using MM5 version 3.7.4. Meteorological model output for the 36 km continental U.S. 

domain and 12 km eastern U.S. and 12 km western U.S. are translated to photochemical 

modeling domains shown in Figure 2 and Table 3. Meteorological modeling domains are 

several grid cells larger in the X and Y domain than the photochemical model domains. 

The photochemical model inputs for these domains were windowed to match the smaller 

12 km domains listed in Table 1. 

Table 3. Geographic elements of domains used in photochemical modeling. 

Photochemical 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) 
12 

Figure 2. Map of the photochemical modeling domain. The black outer box denotes the 

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

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

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

spin-up purposes. All domains contained 34 vertical layers with an approximately 38 m 

deep surface layer and a 100 millibar top. The MM5 and CAMx vertical structures are 

shown in Table 4 and do not vary by horizontal grid resolution. The meteorological 

outputs from all three MM5 sets were processed to create model-ready inputs for CAMx 

using the MM5CAMx processor to derive the specific inputs. 
13 

Table 4. Vertical layer structure (heights are layer top). 

CAMx Layers MM5 Layers Sigma P Approximate 

Height (m)

Approximate 

Pressure (mb)

0 0 1.000 0 1000 

1 1 0.995 38 995 

2 2 0.990 77 991 

3 3 0.985 115 987 

4 0.980 154 982 

4 5 0.970 232 973 

6 0.960 310 964 

5 7 0.950 389 955 

8 0.940 469 946 

6 

9 0.930 550 937 

10 0.920 631 928 

11 0.910 712 919 

7 

12 0.900 794 910 

13 0.880 961 892 

14 0.860 1,130 874 

8 

15 0.840 1,303 856 

16 0.820 1,478 838 

17 0.800 1,657 820 

9 18 0.770 1,930 793 

19 0.740 2,212 766 

10 20 0.700 2,600 730 

21 0.650 3,108 685 

11 22 0.600 3,644 640 

23 0.550 4,212 595 

12 

24 0.500 4,816 550 

25 0.450 5,461 505 

26 0.400 6,153 460 

13 

27 0.350 6,903 415 

28 0.300 7,720 370 

29 0.250 8,621 325 

30 0.200 9,625 280 

14 

31 0.150 10,764 235 

32 0.100 12,085 190 

33 0.050 13,670 145 

34 0.000 15,674 100 

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 estimated to observed monthly average rainfall 

and checked maximum planetary boundary layer (PBL) heights for reasonableness. 

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 
14 

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 (Baker 

2009; Baker 2009; Baker 2009). 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. 

Meteorology generated from a prognostic meteorological model is used as input to the 

photochemical model used to track emissions for source contribution. Most areas in the 

continental United States have several observed days above 70 ppb in 2005 and 2006 

(Figure 3). The photochemical model is applied for the entire year of 2005 for the central 

and eastern United States and for the entire years of 2005 and 2006 for the western 

United States. This is done to capture meteorological conditions conducive of ozone 

formation and account for meteorological factors that drive elevated ozone 

concentrations. 

Figure 3. Number of days in each county with monitored ozone over 70 ppb 

4.6 Initial and Boundary Conditions 

Annual continental United States simulations using 36 km sized grid cells (see Figure 2) 

for calendar years of 2005 and 2006 are used to supply hourly boundary condition 

information for each of the 12 km modeling domains. The lateral boundary and initial 

species concentrations are provided by a three-dimensional global atmospheric chemistry 

model, the GEOS-CHEM model (standard version 7-04-11). 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 CAMx simulations. The 36 km coarse grid modeling 

was used as the initial/boundary state for all subsequent 12 km grid modeling scenarios. 
15 

5 Operational Model Performance Description

Model estimates are compared to observations of ozone collected during 2005 and 2006. 

Ozone data from the AIRS Network is compared to model predictions to estimate 

operational model performance. Metrics used to describe model performance include 

mean bias and gross error (Boylan and Russell 2006). The bias and error metrics describe 

performance in terms of measured concentration units. The best possible performance is 

when the metrics approach 0. Bias is estimated as prediction-observation meaning a 

positive number might suggest a tendency toward over-prediction and a negative value 

might suggest a tendency toward under-prediction. 

Scatter-plots of all daily 8-hr ozone maximum prediction-observation pairs and spatial 

plots of average bias for model predicted ozone greater than 70 ppb are shown for each 

modeling domain in Appendix B. 
16 

6 References 

Baker, K., Dolwick, P. (2009). Meteorological Modeling Performance Evaluation for the Annual 2005 

Continental U.S. 36-km Domain Simulation, US Environmental Protection Agency OAQPS. 

Baker, K., Dolwick, P. (2009). Meteorological Modeling Performance Evaluation for the Annual 2005 

Eastern U.S. 12-km Domain Simulation. RTP, US Environmental Protection Agency 

OAQPS. 

Baker, K., Dolwick, P. (2009). Meteorological Modeling Performance Evaluation for the Annual 2005 

Western U.S. 12-km Domain Simulation. U. EPA, US Environmental Protection Agency 

OAQPS. 

Bar-Ilan, A., Friesen, R., Pollack, A., Hoats, A. (2007). WRAP area source emissions inventory 

projections and control strategy evaluation phase II. C. P. Western Governor's Association, 

Suite 200, Denver, CO 80202, ENVIRON International Corporation. 

Boylan, J. W. and A. G. Russell (2006). "PM and light extinction model performance metrics, goals, 

and criteria for three-dimensional air quality models." Atmospheric Environment 40(26): 

4946-4959. 

Chang, J. S., R. A. Brost, et al. (1987). "A 3-DIMENSIONAL EULERIAN ACID DEPOSITION 

MODEL - PHYSICAL CONCEPTS AND FORMULATION." Journal of Geophysical 

Research-Atmospheres 92(D12): 14681-14700. 

Dunker, A. M., G. Yarwood, et al. (2002). "Comparison of source apportionment and source 

sensitivity of ozone in a three-dimensional air quality model." Environmental Science & 

Technology 36(13): 2953-2964. 

ENVIRON (2008). User's Guide Comprehensive Air Quality Model with Extensions. Novato, 

ENVIRON International Corporation. 

Gery, M. W., G. Z. Whitten, et al. (1989). "A PHOTOCHEMICAL KINETICS MECHANISM FOR 

URBAN AND REGIONAL SCALE COMPUTER MODELING." Journal of Geophysical 

Research-Atmospheres 94(D10): 12925-12956. 

Guenther, A., C. Geron, et al. (2000). "Natural emissions of non-methane volatile organic 

compounds; carbon monoxide, and oxides of nitrogen from North America." Atmospheric 

Environment 34(12-14): 2205-2230. 

Houyoux, M. R., J. M. Vukovich, et al. (2000). "Emission inventory development and processing for 

the Seasonal Model for Regional Air Quality (SMRAQ) project." Journal of Geophysical 

Research-Atmospheres 105(D7): 9079-9090. 

Kinnee, E., C. Geron, et al. (1997). "United States land use inventory for estimating biogenic ozone 

precursor emissions." Ecological Applications 7(1): 46-58. 
17 

Nobel, C. E., E. C. McDonald-Buller, et al. (2001). "Accounting for spatial variation of ozone 

productivity in NOx emission trading." Environmental Science & Technology 35(22): 4397-

4407. 

Strum, M., Houyoux, M., Mason, R. (2008). Technical Support Document: Preparation of Emissions 

Inventories For the 2002-based Platform, Version 3, Criteria Air Pollutants. U. S. E. P. 

Agency. Research Triangle Park, NC. 

UNC (2007). SMOKE v2.3.2 User's Manual. Chapel Hill, University of North Carolina Institute of 

the Environment. 

Yarwood, G., Stoeckenius, T.E., W ilson, G., Morris, R.E., Yocke, M.A. (1996). Development of a 

methodology for source apportionment of ozone concentration estimates from a 

photochemical grid model. 98th AWMA Annual Meeting, Nashville, TN. 
APPENDIX A 

SOURCE AREA EMISSIONS 
FIPS State County NOX(TPY) VOC(TPY)

1001 AL Autauga 5,057 1,631

1003 AL Baldwin 9,362 10,925

1007 AL Bibb 941 821

1009 AL Blount 2,499 2,005

1017 AL Chambers 1,838 1,539

1021 AL Chilton 2,594 2,011

1027 AL Clay 911 580

1033 AL Colbert 20,171 2,986

1037 AL Coosa 796 830

1043 AL Cullman 3,508 3,819

1051 AL Elmore 2,504 3,380

1055 AL Etowah 6,180 4,107

1061 AL Geneva 1,170 1,197

1063 AL Greene 9,043 1,021

1065 AL Hale 1,000 912

1067 AL Henry 1,044 1,039

1069 AL Houston 4,311 4,585

1073 AL Jefferson 57,582 28,053

1077 AL Lauderdale 4,120 4,153

1079 AL Lawrence 4,985 2,000

1081 AL Lee 4,468 3,730

1083 AL Limestone 4,542 3,927

1085 AL Lowndes 1,626 1,124

1087 AL Macon 1,639 1,304

1089 AL Madison 11,414 10,310

1095 AL Marshall 3,519 5,160

1097 AL Mobile 42,420 14,815

1101 AL Montgomery 11,536 9,704

1103 AL Morgan 8,514 4,496

1113 AL Russell 4,814 2,032

1115 AL St Clair 6,306 3,199

1117 AL Shelby 38,427 7,393

1119 AL Sumter 2,471 1,082

1121 AL Talladega 4,289 3,815

1125 AL Tuscaloosa 9,871 7,699

1127 AL Walker 16,553 3,043

1133 AL Winston 1,263 1,467

4001 AZ Apache 26,352 3,354

4003 AZ Cochise 15,989 6,434

4005 AZ Coconino 45,933 7,505

4007 AZ Gila 1,856 4,404

4009 AZ Graham 996 1,613

4011 AZ Greenlee 469 199

4012 AZ La Paz 3,003 2,407

4013 AZ Maricopa 99,446 88,995

4015 AZ Mohave 12,716 12,241

4017 AZ Navajo 24,950 5,008

4019 AZ Pima 30,349 29,506

4021 AZ Pinal 12,425 7,395

4023 AZ Santa Cruz 1,496 1,427

4025 AZ Yavapai 14,238 7,086

4027 AZ Yuma 9,211 6,800

5007 AR Benton 11,161 6,282

5009 AR Boone 1,282 1,636

5025 AR Cleveland 777 416

5035 AR Crittenden 5,824 3,824

5045 AR Faulkner 3,829 4,714
FIPS State County NOX(TPY) VOC(TPY)

5053 AR Grant 783 799

5069 AR Jefferson 23,253 3,053

5079 AR Lincoln 991 594

5085 AR Lonoke 3,416 2,621

5101 AR Newton 245 824

5105 AR Perry 347 640

5113 AR Polk 871 1,088

5119 AR Pulaski 18,407 18,551

5125 AR Saline 2,950 2,471

5131 AR Sebastian 5,285 4,141

5143 AR Washington 7,318 6,684

5145 AR White 6,196 2,965

6001 CA Alameda 43,316 26,321

6003 CA Alpine 194 219

6005 CA Amador 2,016 1,744

6007 CA Butte 9,199 7,004

6009 CA Calaveras 1,873 3,125

6011 CA Colusa 4,036 1,643

6013 CA Contrasta 37,566 19,608

6015 CA Del Norte 2,510 1,279

6017 CA El Dorado 3,843 6,001

6019 CA Fresno 41,167 23,906

6021 CA Glenn 4,081 1,776

6023 CA Humboldt 15,011 5,281

6025 CA Imperial 13,554 6,949

6027 CA Inyo 1,857 1,650

6029 CA Kern 89,387 32,413

6031 CA Kings 10,565 3,634

6033 CA Lake 2,693 4,158

6035 CA Lassen 2,165 2,079

6037 CA Los Angeles 287,426 175,408

6039 CA Madera 12,180 4,684

6041 CA Marin 6,863 6,227

6043 CA Mariposa 713 1,737

6045 CA Mendocino 15,058 4,592

6047 CA Merced 20,261 7,360

6049 CA Modoc 1,348 1,043

6051 CA Mono 1,088 923

6053 CA Monterey 16,655 12,966

6055 CA Napa 4,230 3,889

6057 CA Nevada 5,027 4,541

6059 CA Orange 64,995 56,760

6061 CA Placer 14,130 9,597

6063 CA Plumas 11,422 2,592

6065 CA Riverside 67,696 33,171

6067 CA Sacramento 30,405 25,275

6069 CA San Benito 4,577 1,704

6071 CA San Bernardino 121,128 45,581

6073 CA San Diego 78,452 66,071

6075 CA San Francisco 32,180 13,083

6077 CA San Joaquin 38,612 15,970

6079 CA San Luis Obispo 16,025 8,428

6081 CA San Mateo 19,342 13,466

6083 CA Santa Barbara 53,349 14,680

6085 CA Santa Clara 38,142 31,503

6087 CA Santa Cruz 8,028 7,917

6089 CA Shasta 13,646 8,011
FIPS State County NOX(TPY) VOC(TPY)

6091 CA Sierra 371 1,133

6093 CA Siskiyou 7,191 3,422

6095 CA Solano 17,204 9,494

6097 CA Sonoma 14,128 11,506

6099 CA Stanislaus 20,311 11,390

6101 CA Sutter 7,141 3,417

6103 CA Tehama 6,900 2,816

6105 CA Trinity 1,093 1,197

6107 CA Tulare 17,430 11,580

6109 CA Tuolumne 3,079 4,114

6111 CA Ventura 32,980 19,917

6113 CA Yolo 9,903 5,366

6115 CA Yuba 2,838 2,432

8001 CO Adams 23,905 10,642

8005 CO Arapahoe 12,935 14,482

8007 CO Archuleta 529 760

8013 CO Boulder 12,829 9,485

8014 CO Broomfield 1,643 1,103

8019 CO Clear Creek 1,801 1,508

8031 CO Denver 21,058 18,279

8035 CO Douglas 7,661 6,610

8039 CO Elbert 1,218 978

8041 CO El Paso 22,309 20,644

8043 CO Fremont 3,380 1,228

8045 CO Garfield 9,738 3,040

8047 CO Gilpin 588 313

8051 CO Gunnison 922 1,454

8059 CO Jefferson 15,801 14,684

8067 CO La Plata 7,812 2,142

8069 CO Larimer 11,765 9,534

8077 CO Mesa 6,641 4,643

8081 CO Moffat 19,248 918

8083 CO Montezuma 1,174 1,448

8087 CO Morgan 7,561 1,334

8093 CO Park 563 1,178

8101 CO Pueblo 13,939 5,099

8103 CO Rio Blanco 3,773 657

8107 CO Routt 9,543 1,395

8119 CO Teller 559 1,149

8123 CO Weld 24,969 9,949

9001 CT Fairfield 26,532 26,655

9003 CT Hartford 23,700 22,776

9005 CT Litchfield 4,385 9,171

9007 CT Middlesex 6,883 6,921

9009 CT New Haven 21,665 24,411

9011 CT New London 11,029 14,040

9013 CT Tolland 4,059 5,133

9015 CT Windham 3,638 4,975

10001 DE Kent 8,329 4,921

10003 DE New Castle 25,493 13,358

10005 DE Sussex 20,993 8,598

11001 DC Washington 14,588 9,898

12001 FL Alachua 12,879 11,204

12003 FL Baker 2,083 1,427

12005 FL Bay 13,732 9,718

12009 FL Brevard 39,784 28,544

12011 FL Broward 65,830 49,993
FIPS State County NOX(TPY) VOC(TPY)

12015 FL Charlotte 6,219 9,695

12019 FL Clay 4,749 6,559

12021 FL Collier 12,784 17,035

12023 FL Columbia 4,960 4,141

12031 FL Duval 60,455 37,372

12033 FL Escambia 22,503 15,727

12035 FL Flagler 4,844 3,980

12039 FL Gadsden 4,468 3,043

12041 FL Gilchrist 623 849

12053 FL Hernando 6,844 6,571

12055 FL Highlands 4,550 6,466

12057 FL Hillsborough 71,783 46,252

12059 FL Holmes 1,422 1,297

12061 FL Indian River 7,005 6,896

12065 FL Jefferson 1,906 1,475

12069 FL Lake 6,364 12,178

12071 FL Lee 26,038 29,382

12073 FL Leon 8,969 10,954

12081 FL Manatee 22,380 13,052

12083 FL Marion 12,936 15,296

12085 FL Martin 16,244 10,385

12086 FL Miami-Dade 79,694 83,167

12089 FL Nassau 8,396 3,760

12095 FL Orange 39,994 42,368

12097 FL Osceola 5,669 9,185

12099 FL Palm Beach 45,408 47,568

12101 FL Pasco 22,415 14,385

12103 FL Pinellas 35,221 35,961

12105 FL Polk 26,753 26,571

12109 FL St Johns 9,232 7,213

12111 FL St Lucie 9,746 10,477

12113 FL Santa Rosa 6,605 8,373

12115 FL Sarasota 13,108 15,940

12117 FL Seminole 11,723 18,389

12119 FL Sumter 4,176 4,116

12127 FL Volusia 21,257 22,539

12129 FL Wakulla 1,367 2,170

13013 GA Barrow 2,533 2,068

13015 GA Bartow 32,666 5,238

13021 GA Bibb 7,894 7,280

13025 GA Brantley 791 642

13029 GA Bryan 1,842 1,812

13033 GA Burke 1,310 1,243

13035 GA Butts 1,389 1,211

13045 GA Carroll 4,247 4,048

13047 GA Catoosa 2,822 2,658

13051 GA Chatham 29,315 12,805

13053 GA Chattahoochee 288 341

13055 GA Chattooga 1,746 1,469

13057 GA Cherokee 6,214 5,712

13059 GA Clarke 3,920 4,075

13063 GA Clayton 12,318 6,958

13067 GA Cobb 25,971 21,411

13073 GA Columbia 3,526 3,960

13077 GA Coweta 16,881 3,323

13079 GA Crawford 490 473

13083 GA Dade 1,431 922
FIPS State County NOX(TPY) VOC(TPY)

13085 GA Dawson 715 895

13089 GA De Kalb 22,497 23,062

13097 GA Douglas 4,282 3,205

13103 GA Effingham 5,944 1,941

13113 GA Fayette 3,559 3,116

13117 GA Forsyth 4,753 4,654

13121 GA Fulton 39,064 35,206

13127 GA Glynn 8,178 4,757

13135 GA Gwinnett 22,648 24,947

13139 GA Hall 6,795 8,244

13143 GA Haralson 1,325 1,290

13145 GA Harris 1,542 1,642

13149 GA Heard 15,184 838

13151 GA Henry 8,919 5,062

13153 GA Houston 6,566 4,465

13159 GA Jasper 675 636

13169 GA Jones 1,276 956

13171 GA Lamar 775 827

13189 GA Mc Duffie 1,347 1,270

13191 GA Mc Intosh 2,306 2,554

13195 GA Madison 3,181 1,340

13197 GA Marion 366 388

13199 GA Meriwether 1,445 1,120

13207 GA Monroe 20,794 2,010

13213 GA Murray 1,656 1,447

13215 GA Muscogee 5,751 6,248

13217 GA Newton 3,333 3,168

13219 GA Oconee 1,438 1,413

13221 GA Oglethorpe 503 666

13223 GA Paulding 3,196 2,496

13225 GA Peach 1,697 1,548

13227 GA Pickens 1,073 1,243

13231 GA Pike 499 566

13233 GA Polk 1,648 1,740

13245 GA Richmond 13,061 7,970

13247 GA Rockdale 3,123 3,075

13249 GA Schley 190 276

13255 GA Spalding 2,215 3,100

13261 GA Sumter 1,739 1,291

13285 GA Troup 3,348 4,000

13289 GA Twiggs 867 752

13293 GA Upson 1,086 983

13295 GA Walker 2,125 2,247

13297 GA Walton 2,590 3,080

13313 GA Whitfield 7,092 6,071

16001 ID Ada 18,576 10,280

16015 ID Boise 719 1,212

16023 ID Butte 343 270

16027 ID Canyon 7,348 4,493

16041 ID Franklin 694 940

16045 ID Gem 1,243 636

16055 ID Kootenai 7,519 5,952

16073 ID Owyhee 652 610

16075 ID Payette 1,252 962

16081 ID Teton 401 982

17005 IL Bond 1,446 1,159

17007 IL Boone 2,331 3,012
FIPS State County NOX(TPY) VOC(TPY)

17013 IL Calhoun 916 439

17019 IL Champaign 8,919 6,996

17027 IL Clinton 3,455 2,003

17031 IL Cook 174,358 121,322

17037 IL De Kalb 3,888 4,001

17043 IL Du Page 37,034 25,338

17049 IL Effingham 2,827 2,270

17053 IL Ford 1,205 1,120

17057 IL Fulton 3,814 1,915

17063 IL Grundy 3,873 2,418

17065 IL Hamilton 703 721

17073 IL Henry 7,122 2,787

17081 IL Jefferson 3,383 2,466

17083 IL Jersey 1,479 1,070

17089 IL Kane 16,659 12,338

17091 IL Kankakee 7,317 5,160

17093 IL Kendall 4,416 3,138

17097 IL Lake 29,527 29,437

17111 IL Mc Henry 9,583 7,925

17113 IL Mc Lean 9,648 5,887

17115 IL Macon 12,313 4,300

17117 IL Macoupin 2,083 2,017

17119 IL Madison 20,676 7,591

17123 IL Marshall 2,203 1,372

17127 IL Massac 12,034 2,014

17129 IL Menard 567 720

17131 IL Mercer 1,040 1,130

17133 IL Monroe 2,939 1,511

17143 IL Peoria 16,440 6,608

17147 IL Piatt 4,042 1,074

17157 IL Randolph 8,923 1,526

17161 IL Rock Island 6,053 5,371

17163 IL St Clair 10,184 8,303

17167 IL Sangamon 16,756 7,778

17175 IL Stark 490 579

17179 IL Tazewell 37,744 5,169

17197 IL Will 46,106 14,878

17201 IL Winnebago 10,624 10,330

17203 IL Woodford 2,201 2,184

18001 IN Adams 2,092 1,786

18003 IN Allen 16,173 13,482

18005 IN Bartholomew 3,058 2,455

18007 IN Benton 1,077 702

18011 IN Boone 3,505 2,692

18013 IN Brown 695 1,219

18015 IN Carroll 1,391 1,237

18019 IN Clark 5,749 3,203

18021 IN Clay 1,667 1,527

18029 IN Dearborn 13,242 1,836

18033 IN De Kalb 5,289 2,627

18035 IN Delaware 5,182 4,569

18039 IN Elkhart 10,210 7,566

18043 IN Floyd 8,147 2,191

18047 IN Franklin 1,220 1,300

18051 IN Gibson 32,699 1,970

18055 IN Greene 1,559 1,569

18057 IN Hamilton 7,956 7,263
FIPS State County NOX(TPY) VOC(TPY)

18059 IN Hancock 3,140 2,469

18061 IN Harrison 3,172 1,612

18063 IN Hendricks 6,093 3,925

18065 IN Henry 3,373 2,399

18069 IN Huntington 3,089 2,327

18071 IN Jackson 3,429 2,495

18073 IN Jasper 20,104 2,461

18079 IN Jennings 6,368 1,353

18081 IN Johnson 4,701 4,186

18089 IN Lake 42,940 14,882

18091 IN La Porte 12,017 4,412

18095 IN Madison 5,447 4,996

18097 IN Marion 40,646 27,723

18099 IN Marshall 3,079 2,671

18105 IN Monroe 3,828 4,364

18107 IN Montgomery 3,804 2,324

18109 IN Morgan 6,594 2,925

18111 IN Newton 1,145 1,862

18113 IN Noble 3,643 2,884

18115 IN Ohio 551 249

18119 IN Owen 1,105 1,237

18123 IN Perry 2,699 1,253

18127 IN Porter 27,291 6,981

18129 IN Posey 11,763 1,721

18133 IN Putnam 4,703 2,070

18141 IN St Joseph 10,649 8,312

18143 IN Scott 1,355 1,355

18145 IN Shelby 3,603 1,999

18153 IN Sullivan 11,335 1,170

18157 IN Tippecanoe 8,677 5,153

18163 IN Vanderburgh 6,757 5,471

18165 IN Vermillion 13,902 1,102

18167 IN Vigo 17,609 4,744

18173 IN Warrick 18,055 2,446

18175 IN Washington 1,356 1,291

18179 IN Wells 1,529 1,398

18183 IN Whitley 2,355 1,952

19011 IA Benton 2,467 1,422

19013 IA Black Hawk 6,755 5,610

19015 IA Boone 4,182 1,574

19017 IA Bremer 1,365 1,508

19031 IA Cedar 3,076 1,513

19045 IA Clinton 10,640 3,251

19049 IA Dallas 3,099 2,312

19061 IA Dubuque 6,122 4,204

19075 IA Grundy 1,047 999

19077 IA Guthrie 746 600

19085 IA Harrison 2,563 1,414

19103 IA Johnson 5,881 5,980

19105 IA Jones 1,103 984

19113 IA Linn 16,168 7,903

19115 IA Louisa 6,763 791

19121 IA Madison 791 921

19127 IA Marshall 6,012 1,677

19129 IA Mills 4,028 785

19137 IA Montgomery 1,921 736

19139 IA Muscatine 10,419 2,451
FIPS State County NOX(TPY) VOC(TPY)

19153 IA Polk 17,804 17,248

19155 IA Pottawattamie 23,399 5,501

19163 IA Scott 10,337 6,877

19169 IA Story 6,573 3,734

19177 IA Van Buren 512 468

19179 IA Wapello 8,563 1,193

19181 IA Warren 4,696 1,719

20005 KS Atchison 1,124 610

20015 KS Butler 10,444 2,944

20035 KS Cowley 4,305 1,334

20045 KS Douglas 8,943 4,579

20059 KS Franklin 2,805 1,224

20079 KS Harvey 2,519 1,378

20085 KS Jackson 1,336 584

20087 KS Jefferson 1,486 1,293

20091 KS Johnson 19,471 21,510

20103 KS Leavenworth 2,847 2,142

20107 KS Linn 32,793 622

20121 KS Miami 7,535 1,267

20149 KS Pottawatomie 35,337 1,155

20155 KS Reno 6,628 3,325

20173 KS Sedgwick 20,896 18,185

20177 KS Shawnee 10,780 6,378

20191 KS Sumner 4,470 1,447

20195 KS Trego 1,432 601

20209 KS Wyandotte 14,691 7,213

21005 KY Anderson 858 615

21007 KY Ballard 2,546 880

21011 KY Bath 1,049 684

21013 KY Bell 889 1,053

21015 KY Boone 10,641 4,189

21017 KY Bourbon 1,274 809

21019 KY Boyd 8,035 2,223

21023 KY Bracken 493 433

21029 KY Bullitt 3,140 2,851

21037 KY Campbell 4,052 2,554

21043 KY Carter 2,375 1,503

21047 KY Christian 3,937 3,009

21049 KY Clark 6,453 2,033

21059 KY Daviess 11,704 3,635

21061 KY Edmonson 571 912

21067 KY Fayette 9,140 9,838

21073 KY Franklin 2,133 1,687

21077 KY Gallatin 2,122 690

21081 KY Grant 2,746 1,260

21083 KY Graves 1,699 1,521

21089 KY Greenup 3,772 1,431

21091 KY Hancock 8,006 523

21093 KY Hardin 4,432 3,437

21101 KY Henderson 5,015 2,342

21103 KY Henry 1,313 969

21111 KY Jefferson 57,954 27,868

21113 KY Jessamine 1,931 2,221

21117 KY Kenton 6,235 4,834

21123 KY Larue 804 522

21139 KY Livingston 1,854 974

21145 KY Mc Cracken 24,187 5,298
FIPS State County NOX(TPY) VOC(TPY)

21149 KY Mc Lean 476 386

21151 KY Madison 4,872 3,731

21163 KY Meade 3,021 1,963

21165 KY Menifee 165 261

21173 KY Montgomery 1,313 1,266

21179 KY Nelson 1,744 1,679

21185 KY Oldham 3,040 1,548

21191 KY Pendleton 3,482 611

21193 KY Perry 1,496 1,069

21195 KY Pike 3,564 2,521

21199 KY Pulaski 8,012 2,896

21203 KY Rockcastle 1,902 995

21209 KY Scott 3,773 1,895

21211 KY Shelby 2,664 1,901

21213 KY Simpson 1,461 990

21215 KY Spencer 416 534

21221 KY Trigg 1,285 1,417

21223 KY Trimble 4,940 443

21227 KY Warren 3,722 3,688

21233 KY Webster 12,021 557

21239 KY Woodford 3,105 1,625

22001 LA Acadia Par 7,200 2,700

22005 LA Ascension Par 14,288 7,324

22007 LA Assumption Par 1,841 797

22015 LA Bossier Par 5,301 3,386

22017 LA Caddo Par 11,505 10,705

22019 LA Calcasieu Par 52,705 15,219

22023 LA Cameron Par 8,087 3,574

22031 LA De Soto Par 18,084 2,022

22033 LA East Baton Roug 40,779 16,581

22037 LA East Feliciana 1,229 923

22045 LA Iberia Par 6,001 3,032

22047 LA Iberville Par 28,347 5,683

22051 LA Jefferson Par 24,170 16,262

22053 LA Jefferson Davis 3,201 2,327

22055 LA Lafayette Par 8,051 7,865

22057 LA Lafourche Par 5,794 4,753

22063 LA Livingston Par 3,441 3,729

22071 LA Orleans Par 38,986 11,934

22073 LA Ouachita Par 13,902 8,026

22075 LA Plaquemines Par 52,834 7,458

22077 LA Pointeupee Par 19,242 1,007

22087 LA St Bernard Par 8,058 3,520

22089 LA St Charles Par 21,868 6,057

22091 LA St Helena Par 1,722 668

22093 LA St James Par 8,992 2,233

22095 LA St John The Bap 64,272 3,951

22097 LA St Landry Par 7,277 3,816

22099 LA St Martin Par 3,563 3,103

22103 LA St Tammany Par 6,167 9,220

22109 LA Terrebonne Par 36,658 7,639

22111 LA Union Par 1,166 1,405

22113 LA Vermilion Par 12,700 3,684

22117 LA Washington Par 4,986 1,933

22119 LA Webster Par 3,707 2,164

22121 LA West Baton Roug 5,380 1,886

22125 LA West Feliciana 4,605 770
FIPS State County NOX(TPY) VOC(TPY)

23001 ME Androscoggin 4,026 4,055

23005 ME Cumberland 13,668 13,195

23009 ME Hancock 4,100 5,839

23011 ME Kennebec 4,943 6,404

23013 ME Knox 5,409 3,965

23017 ME Oxford 3,981 5,237

23019 ME Penobscot 9,071 8,272

23023 ME Sagadahoc 1,847 3,143

23029 ME Washington 3,790 4,816

23031 ME York 8,618 9,208

24001 MD Allegany 7,915 3,058

24003 MD Anne Arundel 35,280 14,857

24005 MD Baltimore 36,605 23,555

24009 MD Calvert 2,696 3,265

24013 MD Carroll 6,669 4,750

24015 MD Cecil 4,488 4,438

24017 MD Charles 17,511 4,964

24021 MD Frederick 11,480 9,175

24023 MD Garrett 2,318 2,572

24025 MD Harford 7,730 7,686

24027 MD Howard 11,097 8,610

24029 MD Kent 1,073 1,927

24031 MD Montgomery 30,189 24,905

24033 MD Prince Georges 36,041 23,120

24035 MD Queen Annes 2,643 2,922

24043 MD Washington 9,135 6,096

24047 MD Worcester 16,457 5,850

24510 MD Baltimore 19,656 14,265

25001 MA Barnstable 22,564 15,087

25003 MA Berkshire 6,123 6,698

25005 MA Bristol 23,812 13,978

25007 MA Dukes 4,454 2,302

25009 MA Essex 22,551 19,477

25011 MA Franklin 3,971 5,267

25013 MA Hampden 15,783 11,970

25015 MA Hampshire 4,338 5,314

25017 MA Middlesex 43,611 41,006

25021 MA Norfolk 25,071 23,095

25023 MA Plymouth 11,244 14,316

25025 MA Suffolk 20,096 13,794

25027 MA Worcester 28,051 26,710

26005 MI Allegan 5,654 7,832

26015 MI Barry 1,882 3,238

26019 MI Benzie 1,004 2,521

26021 MI Berrien 8,968 8,097

26027 MI Cass 2,131 2,820

26037 MI Clinton 4,101 3,817

26043 MI Dickinson 3,620 1,938

26045 MI Eaton 6,222 4,118

26049 MI Genesee 17,560 15,604

26055 MI Grand Traverse 3,584 4,375

26063 MI Huron 3,978 2,712

26065 MI Ingham 14,591 8,990

26067 MI Ionia 2,626 2,796

26077 MI Kalamazoo 11,047 12,396

26079 MI Kalkaska 1,840 1,943

26081 MI Kent 24,363 24,922
FIPS State County NOX(TPY) VOC(TPY)

26087 MI Lapeer 3,609 4,424

26089 MI Leelanau 1,695 3,092

26091 MI Lenawee 3,934 4,433

26093 MI Livingston 7,902 7,866

26099 MI Macomb 26,473 26,283

26101 MI Manistee 5,472 3,254

26105 MI Mason 1,896 2,865

26113 MI Missaukee 795 1,932

26115 MI Monroe 50,212 7,486

26121 MI Muskegon 11,842 7,407

26123 MI Newaygo 1,500 2,715

26125 MI Oakland 48,594 49,464

26139 MI Ottawa 27,318 10,347

26147 MI St Clair 30,127 7,200

26153 MI Schoolcraft 1,182 3,040

26155 MI Shiawassee 3,296 3,213

26159 MI Van Buren 3,627 4,247

26161 MI Washtenaw 15,202 12,789

26163 MI Wayne 91,246 65,955

26165 MI Wexford 1,594 2,253

27003 MN Anoka 12,250 13,242

27019 MN Carver 3,245 3,550

27025 MN Chisago 2,957 3,535

27037 MN Dakota 26,466 14,432

27053 MN Hennepin 72,542 40,466

27059 MN Isanti 1,845 2,261

27123 MN Ramsey 29,370 17,481

27139 MN Scott 5,982 5,117

27141 MN Sherburne 28,270 4,472

27163 MN Washington 22,184 9,044

27171 MN Wright 6,698 7,000

28001 MS Adams 3,380 2,352

28011 MS Bolivar 4,487 3,250

28023 MS Clarke 1,690 842

28029 MS Copiah 2,002 1,815

28033 MS De Soto 7,874 5,627

28039 MS George 719 799

28045 MS Hancock 3,457 2,894

28047 MS Harrison 21,493 10,839

28049 MS Hinds 12,854 12,182

28057 MS Itawamba 981 1,116

28059 MS Jackson 31,668 8,001

28069 MS Kemper 572 564

28075 MS Lauderdale 4,549 4,721

28081 MS Lee 4,712 5,055

28089 MS Madison 3,977 3,789

28093 MS Marshall 2,168 1,881

28115 MS Pontotoc 1,389 1,487

28121 MS Rankin 7,420 7,106

28127 MS Simpson 1,251 1,566

28131 MS Stone 864 677

28137 MS Tate 3,228 1,534

28143 MS Tunica 2,737 1,575

29013 MO Bates 1,853 1,158

29019 MO Boone 8,858 5,112

29021 MO Buchanan 7,424 3,337

29025 MO Caldwell 981 601
FIPS State County NOX(TPY) VOC(TPY)

29031 MO Cape Girardeau 6,493 3,387

29037 MO Cass 4,025 3,283

29039 MO Cedar 604 1,249

29043 MO Christian 2,294 2,267

29047 MO Clay 8,547 7,407

29049 MO Clinton 1,073 1,013

29059 MO Dallas 612 811

29071 MO Franklin 15,570 4,243

29077 MO Greene 17,088 9,325

29083 MO Henry 7,935 2,388

29095 MO Jackson 38,847 22,640

29099 MO Jefferson 16,584 6,502

29101 MO Johnson 2,321 1,929

29107 MO Lafayette 3,456 2,114

29113 MO Lincoln 2,715 2,122

29133 MO Mississippi 3,777 1,223

29137 MO Monroe 1,125 1,247

29143 MO New Madrid 36,195 2,384

29157 MO Perry 2,615 1,377

29163 MO Pike 9,123 1,623

29165 MO Platte 14,154 3,938

29167 MO Polk 1,275 1,462

29175 MO Randolph 18,265 1,215

29177 MO Ray 2,960 1,166

29183 MO St Charles 20,679 9,509

29186 MO Ste Genevieve 6,327 1,277

29187 MO St Francois 2,142 2,048

29189 MO St Louis 55,163 42,728

29201 MO Scott 6,983 1,896

29209 MO Stone 1,060 3,033

29213 MO Taney 1,688 3,535

29219 MO Warren 1,849 1,833

29221 MO Washington 688 1,093

29225 MO Webster 3,066 2,228

29510 MO St Louis 22,268 12,988

30017 MT Custer 1,779 613

30021 MT Dawson 2,294 655

30025 MT Fallon 2,174 187

30083 MT Richland 3,700 592

30085 MT Roosevelt 2,306 456

30087 MT Rosebud 39,036 646

30109 MT Wibaux 986 316

31025 NE Cass 6,843 1,508

31053 NE Dodge 4,261 1,662

31055 NE Douglas 21,699 16,972

31109 NE Lancaster 28,975 10,181

31131 NE Otoe 12,027 902

31153 NE Sarpy 4,530 4,493

31155 NE Saunders 2,070 1,016

31177 NE Washington 1,696 1,032

32001 NV Churchill 2,007 1,607

32003 NV Clark 72,938 37,438

32005 NV Douglas 2,052 2,068

32007 NV Elko 8,294 2,928

32009 NV Esmeralda 99 138

32011 NV Eureka 1,493 362

32013 NV Humboldt 12,297 1,097
FIPS State County NOX(TPY) VOC(TPY)

32015 NV Lander 1,085 540

32017 NV Lincoln 1,286 281

32019 NV Lyon 8,779 2,120

32021 NV Mineral 190 439

32023 NV Nye 1,052 1,214

32027 NV Pershing 2,296 1,109

32029 NV Storey 1,997 173

32031 NV Washoe 13,261 13,061

32033 NV White Pine 487 520

32510 NV Carson City 1,414 1,390

33001 NH Belknap 2,935 5,685

33005 NH Cheshire 2,550 4,524

33007 NH Coos 1,675 4,154

33009 NH Grafton 6,000 6,820

33011 NH Hillsborough 12,960 12,803

33013 NH Merrimack 12,716 6,909

33015 NH Rockingham 16,598 12,738

33017 NH Strafford 2,332 3,067

33019 NH Sullivan 2,138 3,442

34001 NJ Atlantic 12,145 11,561

34003 NJ Bergen 23,591 22,155

34005 NJ Burlington 15,322 11,542

34007 NJ Camden 15,371 9,288

34011 NJ Cumberland 5,837 4,868

34013 NJ Essex 21,311 12,309

34015 NJ Gloucester 12,133 7,021

34017 NJ Hudson 28,960 7,987

34019 NJ Hunterdon 3,819 3,791

34021 NJ Mercer 16,771 6,539

34023 NJ Middlesex 28,059 16,739

34025 NJ Monmouth 18,919 15,823

34027 NJ Morris 13,645 13,868

34029 NJ Ocean 16,113 18,741

34031 NJ Passaic 8,774 8,663

34033 NJ Salem 7,328 3,012

34035 NJ Somerset 7,845 7,328

34037 NJ Sussex 2,754 5,108

34039 NJ Union 20,202 10,526

34041 NJ Warren 5,351 3,932

35001 NM Bernalillo 23,199 18,248

35013 NM Dona Ana 9,634 6,822

35015 NM Eddy 14,772 2,923

35017 NM Grant 2,288 1,513

35025 NM Lea 31,794 2,254

35031 NM Mc Kinley 13,448 4,292

35039 NM Rio Arriba 18,855 2,295

35043 NM Sandoval 4,627 3,334

35045 NM San Juan 103,288 3,950

35049 NM Santa Fe 5,774 6,183

35057 NM Torrance 4,607 1,501

35061 NM Valencia 7,167 2,559

36001 NY Albany 17,534 12,854

36005 NY Bronx 14,061 21,882

36009 NY Cattaraugus 3,031 4,152

36011 NY Cayuga 3,757 4,259

36013 NY Chautauqua 18,693 7,130

36015 NY Chemung 3,413 3,577
FIPS State County NOX(TPY) VOC(TPY)

36021 NY Columbia 2,436 3,291

36027 NY Dutchess 8,044 9,405

36029 NY Erie 32,876 35,630

36031 NY Essex 2,807 5,370

36035 NY Fulton 1,217 3,178

36037 NY Genesee 4,188 3,389

36039 NY Greene 5,640 3,141

36041 NY Hamilton 485 6,763

36043 NY Herkimer 3,575 4,573

36045 NY Jefferson 5,668 8,097

36047 NY Kings 25,287 47,314

36051 NY Livingston 2,521 3,341

36053 NY Madison 2,841 2,981

36055 NY Monroe 38,906 31,846

36057 NY Montgomery 3,591 3,139

36059 NY Nassau 34,137 38,869

36061 NY New York 34,443 39,053

36063 NY Niagara 11,030 9,844

36065 NY Oneida 7,535 10,521

36067 NY Onondaga 16,822 19,294

36069 NY Ontario 5,012 5,174

36071 NY Orange 18,555 12,821

36073 NY Orleans 1,406 2,064

36075 NY Oswego 5,324 6,390

36079 NY Putnam 5,322 6,462

36081 NY Queens 40,551 38,665

36083 NY Rensselaer 4,163 5,952

36085 NY Richmond 8,862 12,185

36087 NY Rockland 12,693 11,291

36091 NY Saratoga 6,261 9,521

36093 NY Schenectady 4,735 5,320

36095 NY Schoharie 1,601 2,280

36099 NY Seneca 1,525 3,544

36101 NY Steuben 5,235 4,473

36103 NY Suffolk 56,042 60,310

36111 NY Ulster 5,941 7,500

36113 NY Warren 4,807 5,696

36115 NY Washington 2,468 2,779

36117 NY Wayne 3,863 3,659

36119 NY Westchester 25,275 30,153

37001 NC Alamance 4,711 5,592

37003 NC Alexander 835 1,264

37007 NC Anson 1,394 1,358

37011 NC Avery 649 954

37021 NC Buncombe 12,517 8,560

37023 NC Burke 3,308 3,181

37025 NC Cabarrus 6,092 5,140

37027 NC Caldwell 2,512 2,687

37033 NC Caswell 730 693

37035 NC Catawba 23,480 5,635

37037 NC Chatham 5,915 3,005

37045 NC Cleveland 3,439 5,798

37051 NC Cumberland 9,091 9,332

37053 NC Currituck 4,239 2,711

37057 NC Davidson 6,408 5,317

37059 NC Davie 1,716 1,583

37063 NC Durham 8,309 9,444
FIPS State County NOX(TPY) VOC(TPY)

37065 NC Edgecombe 2,253 2,289

37067 NC Forsyth 10,794 10,271

37069 NC Franklin 1,326 2,045

37071 NC Gaston 17,147 8,389

37075 NC Graham 212 351

37077 NC Granville 2,349 3,740

37079 NC Greene 701 682

37081 NC Guilford 14,826 21,834

37085 NC Harnett 3,167 3,315

37087 NC Haywood 6,694 2,725

37089 NC Henderson 3,085 4,139

37093 NC Hoke 951 1,341

37097 NC Iredell 8,433 8,224

37099 NC Jackson 1,602 1,730

37101 NC Johnston 7,437 6,689

37107 NC Lenoir 2,128 3,478

37109 NC Lincoln 1,946 2,402

37115 NC Madison 911 861

37117 NC Martin 7,232 1,131

37119 NC Mecklenburg 26,493 28,808

37127 NC Nash 4,647 5,866

37129 NC New Hanover 17,258 7,342

37135 NC Orange 5,295 3,913

37145 NC Person 27,566 1,291

37147 NC Pitt 4,101 5,362

37151 NC Randolph 4,498 5,056

37157 NC Rockingham 6,891 3,721

37159 NC Rowan 7,838 5,894

37167 NC Stanly 2,109 2,129

37169 NC Stokes 21,539 1,531

37171 NC Surry 3,264 3,193

37173 NC Swain 580 1,490

37175 NC Transylvania 958 2,971

37179 NC Union 4,650 5,409

37183 NC Wake 21,421 25,488

37197 NC Yadkin 1,655 1,375

37199 NC Yancey 804 828

38007 ND Billings 1,826 421

38053 ND Mc Kenzie 2,548 612

38055 ND Mc Lean 13,318 1,059

38057 ND Mercer 44,476 569

38065 ND Oliver 24,027 246

38089 ND Stark 3,365 1,268

38105 ND Williams 5,633 1,179

39003 OH Allen 7,788 5,329

39007 OH Ashtabula 14,228 8,135

39011 OH Auglaize 2,687 2,164

39013 OH Belmont 9,825 3,880

39015 OH Brown 2,403 1,744

39017 OH Butler 16,538 10,024

39019 OH Carroll 1,519 1,329

39021 OH Champaign 1,567 1,735

39023 OH Clark 5,544 5,102

39025 OH Clermont 35,403 6,071

39027 OH Clinton 2,732 2,113

39029 OH Columbiana 4,441 4,409

39035 OH Cuyahoga 49,165 49,648
FIPS State County NOX(TPY) VOC(TPY)

39037 OH Darke 2,437 2,401

39041 OH Delaware 6,804 5,832

39045 OH Fairfield 5,764 4,415

39047 OH Fayette 2,240 1,503

39049 OH Franklin 37,577 36,689

39051 OH Fulton 4,471 2,736

39055 OH Geauga 3,070 4,815

39057 OH Greene 8,532 5,250

39061 OH Hamilton 48,326 31,486

39081 OH Jefferson 45,828 3,039

39083 OH Knox 2,168 2,802

39085 OH Lake 22,702 10,874

39087 OH Lawrence 3,465 2,768

39089 OH Licking 7,774 6,345

39093 OH Lorain 22,833 12,869

39095 OH Lucas 31,318 16,799

39097 OH Madison 3,125 2,772

39099 OH Mahoning 10,141 8,781

39101 OH Marion 3,593 2,244

39103 OH Medina 6,897 6,519

39109 OH Miami 3,981 4,145

39113 OH Montgomery 21,298 17,137

39117 OH Morrow 2,491 2,084

39123 OH Ottawa 5,137 4,484

39129 OH Pickaway 3,689 2,129

39133 OH Portage 7,364 7,367

39135 OH Preble 2,739 2,429

39141 OH Ross 6,731 2,854

39143 OH Sandusky 7,738 3,599

39151 OH Stark 12,637 14,152

39153 OH Summit 17,401 18,862

39155 OH Trumbull 13,455 7,528

39159 OH Union 2,401 2,315

39161 OH Van Wert 1,644 1,292

39165 OH Warren 7,370 6,156

39167 OH Washington 24,330 3,596

39173 OH Wood 9,619 5,272

40001 OK Adair 1,168 833

40017 OK Canadian 8,032 4,338

40021 OK Cherokee 1,090 1,952

40027 OK Cleveland 4,969 6,708

40031 OK Comanche 6,310 4,371

40037 OK Creek 5,385 3,272

40043 OK Dewey 3,059 350

40047 OK Garfield 7,734 1,913

40051 OK Grady 8,804 2,517

40071 OK Kay 6,542 2,347

40081 OK Lincoln 3,381 1,678

40083 OK Logan 4,145 1,481

40087 OK Mc Clain 3,905 1,557

40097 OK Mayes 19,969 2,612

40101 OK Muskogee 24,731 3,015

40103 OK Noble 16,755 1,059

40109 OK Oklahoma 27,777 27,110

40111 OK Okmulgee 3,006 1,607

40113 OK Osage 5,799 2,337

40115 OK Ottawa 2,153 2,023
FIPS State County NOX(TPY) VOC(TPY)

40117 OK Pawnee 1,377 915

40121 OK Pittsburg 6,536 2,352

40125 OK Pottawatomie 3,063 2,779

40131 OK Rogers 25,690 3,894

40135 OK Sequoyah 4,293 2,595

40143 OK Tulsa 31,587 26,128

40145 OK Wagoner 3,339 2,915

40147 OK Washington 2,106 1,639

41005 OR Clackamas 12,077 16,254

41009 OR Columbia 4,275 2,221

41019 OR Douglas 7,677 7,795

41029 OR Jackson 8,099 10,608

41039 OR Lane 14,653 19,007

41047 OR Marion 11,103 14,314

41051 OR Multnomah 42,322 34,689

41053 OR Polk 2,224 3,017

41065 OR Wasco 2,907 2,800

41067 OR Washington 12,438 21,452

41071 OR Yamhill 4,603 3,972

42001 PA Adams 3,389 3,495

42003 PA Allegheny 57,115 28,969

42005 PA Armstrong 20,333 2,566

42007 PA Beaver 32,385 4,544

42011 PA Berks 18,147 11,257

42013 PA Blair 5,486 3,407

42017 PA Bucks 16,321 16,169

42019 PA Butler 7,562 5,985

42021 PA Cambria 6,427 3,935

42025 PA Carbon 3,188 3,449

42027 PA Centre 7,431 4,457

42029 PA Chester 16,701 13,089

42033 PA Clearfield 11,404 3,461

42035 PA Clinton 3,024 2,620

42039 PA Crawford 4,971 4,432

42041 PA Cumberland 13,722 6,987

42043 PA Dauphin 11,504 9,488

42045 PA Delaware 33,005 12,316

42049 PA Erie 12,667 8,478

42051 PA Fayette 4,516 4,315

42055 PA Franklin 5,809 4,850

42059 PA Greene 20,544 1,667

42063 PA Indiana 43,100 3,479

42069 PA Lackawanna 6,556 5,357

42071 PA Lancaster 17,412 16,450

42073 PA Lawrence 9,106 2,731

42075 PA Lebanon 5,994 4,464

42077 PA Lehigh 10,890 8,666

42079 PA Luzerne 10,513 9,434

42081 PA Lycoming 4,236 4,792

42085 PA Mercer 5,803 4,403

42089 PA Monroe 5,574 6,512

42091 PA Montgomery 23,215 24,252

42093 PA Montour 14,042 1,496

42095 PA Northampton 23,850 6,549

42099 PA Perry 2,838 1,994

42101 PA Philadelphia 36,639 19,587

42103 PA Pike 2,508 3,394
FIPS State County NOX(TPY) VOC(TPY)

42107 PA Schuylkill 6,486 4,769

42111 PA Somerset 4,940 4,281

42117 PA Tioga 2,354 2,395

42125 PA Washington 16,235 6,499

42129 PA Westmoreland 16,323 9,970

42131 PA Wyoming 1,888 2,106

42133 PA York 33,697 9,848

44001 RI Bristol 857 1,337

44003 RI Kent 3,978 5,553

44005 RI Newport 2,014 3,452

44007 RI Providence 13,977 14,363

44009 RI Washington 5,633 4,259

45001 SC Abbeville 1,064 1,264

45003 SC Aiken 7,329 6,353

45007 SC Anderson 9,358 8,480

45015 SC Berkeley 21,360 8,164

45017 SC Calhoun 1,394 1,422

45019 SC Charleston 35,383 16,024

45021 SC Cherokee 3,377 2,682

45023 SC Chester 4,065 2,100

45025 SC Chesterfield 1,643 2,681

45029 SC Colleton 7,838 3,147

45031 SC Darlington 6,526 3,088

45035 SC Dorchester 7,564 3,813

45037 SC Edgefield 925 1,026

45039 SC Fairfield 1,883 2,065

45041 SC Florence 7,026 6,230

45045 SC Greenville 13,847 16,041

45055 SC Kershaw 2,422 3,118

45057 SC Lancaster 3,212 2,940

45059 SC Laurens 3,211 3,405

45063 SC Lexington 12,622 11,487

45071 SC Newberry 2,514 2,705

45073 SC Oconee 2,808 4,361

45077 SC Pickens 3,643 5,304

45079 SC Richland 21,510 13,244

45081 SC Saluda 803 1,087

45083 SC Spartanburg 13,650 12,233

45087 SC Union 1,536 1,326

45091 SC York 8,393 7,573

46033 SD Custer 1,118 583

46071 SD Jackson 893 505

46083 SD Lincoln 1,826 1,285

46087 SD Mcok 967 471

46099 SD Minnehaha 5,867 5,323

46103 SD Pennington 9,560 3,910

46125 SD Turner 685 407

47001 TN Anderson 16,342 9,174

47009 TN Blount 4,117 5,155

47011 TN Bradley 4,475 4,888

47013 TN Campbell 3,483 2,165

47015 TN Cannon 357 514

47019 TN Carter 1,474 2,139

47021 TN Cheatham 2,284 1,955

47029 TN Cocke 2,357 2,586

47037 TN Davidson 28,579 24,112

47043 TN Dickson 3,450 2,519
FIPS State County NOX(TPY) VOC(TPY)

47047 TN Fayette 2,993 1,683

47057 TN Grainger 794 1,478

47063 TN Hamblen 5,086 4,198

47065 TN Hamilton 17,447 18,113

47073 TN Hawkins 15,664 3,350

47075 TN Haywood 3,300 1,144

47081 TN Hickman 2,517 1,114

47089 TN Jefferson 3,882 3,447

47093 TN Knox 19,852 19,374

47099 TN Lawrence 1,257 1,739

47105 TN Loudon 5,341 2,983

47107 TN Mc Minn 9,893 3,122

47111 TN Macon 1,629 698

47115 TN Marion 3,575 2,222

47119 TN Maury 5,132 3,896

47121 TN Meigs 704 914

47125 TN Montgomery 5,411 5,378

47139 TN Polk 738 930

47141 TN Putnam 4,412 3,180

47145 TN Roane 18,289 3,228

47147 TN Robertson 4,253 2,870

47149 TN Rutherford 9,474 7,209

47153 TN Sequatchie 509 524

47155 TN Sevier 3,129 6,218

47157 TN Shelby 50,255 32,469

47159 TN Smith 2,003 1,236

47161 TN Stewart 28,571 1,327

47163 TN Sullivan 21,426 13,036

47165 TN Sumner 13,726 4,211

47167 TN Tipton 3,541 2,244

47169 TN Trousdale 430 362

47171 TN Unicoi 915 969

47173 TN Union 848 1,121

47179 TN Washington 4,136 4,911

47187 TN Williamson 6,555 4,582

47189 TN Wilson 5,097 3,932

48013 TX Atascosa 6,170 1,781

48015 TX Austin 3,209 1,487

48019 TX Bandera 1,477 1,143

48021 TX Bastrop 2,915 2,302

48029 TX Bexar 56,084 42,317

48039 TX Brazoria 38,604 8,035

48043 TX Brewster 756 447

48055 TX Caldwell 2,704 4,304

48057 TX Calhoun 9,955 5,167

48061 TX Cameron 12,239 13,324

48071 TX Chambers 7,359 2,384

48073 TX Cherokee 2,309 2,081

48085 TX Collin 17,069 13,789

48091 TX Comal 7,780 3,865

48097 TX Cooke 2,973 4,391

48113 TX Dallas 69,930 56,198

48121 TX Denton 16,787 12,028

48139 TX Ellis 18,573 4,263

48141 TX El Paso 21,921 16,090

48147 TX Fannin 1,149 1,241

48149 TX Fayette 12,217 2,834
FIPS State County NOX(TPY) VOC(TPY)

48157 TX Fort Bend 15,696 8,481

48161 TX Freestone 17,850 3,699

48167 TX Galveston 40,120 9,283

48175 TX Goliad 5,817 1,230

48181 TX Grayson 6,291 5,410

48183 TX Gregg 10,412 4,735

48187 TX Guadalupe 6,558 3,455

48199 TX Hardin 2,972 2,480

48201 TX Harris 171,697 96,358

48203 TX Harrison 16,043 2,980

48209 TX Hays 6,281 3,341

48213 TX Henderson 4,850 4,103

48215 TX Hidalgo 24,122 20,921

48221 TX Hood 2,140 1,529

48231 TX Hunt 3,614 3,193

48245 TX Jefferson 56,084 15,183

48251 TX Johnson 7,068 3,676

48257 TX Kaufman 4,933 3,481

48259 TX Kendall 1,076 1,384

48291 TX Liberty 4,736 3,268

48293 TX Limestone 14,704 2,242

48309 TX Mc Lennan 11,784 6,876

48321 TX Matagorda 12,595 5,609

48325 TX Medina 2,566 1,659

48331 TX Milam 14,744 2,454

48339 TX Montgomery 11,575 8,787

48349 TX Navarro 4,971 3,517

48355 TX Nueces 46,509 17,401

48361 TX Orange 13,114 3,059

48363 TX Palo Pinto 3,004 3,442

48365 TX Panola 13,545 7,585

48367 TX Parker 5,560 4,445

48397 TX Rockwall 1,905 1,698

48401 TX Rusk 22,953 1,913

48407 TX San Jacinto 1,245 1,309

48409 TX San Patricio 6,250 3,437

48423 TX Smith 9,809 6,763

48425 TX Somervell 271 522

48439 TX Tarrant 51,540 34,372

48449 TX Titus 26,543 1,876

48453 TX Travis 25,760 25,510

48459 TX Upshur 4,829 1,304

48469 TX Victoria 9,547 4,062

48471 TX Walker 3,254 2,276

48473 TX Waller 2,375 1,724

48491 TX Williamson 8,306 7,272

48493 TX Wilson 1,176 971

48497 TX Wise 11,060 7,066

49003 UT Box Elder 6,533 5,235

49005 UT Cache 3,236 3,513

49007 UT Carbon 5,832 1,278

49011 UT Davis 9,584 7,449

49013 UT Duchesne 2,123 1,173

49015 UT Emery 30,385 1,196

49021 UT Iron 4,155 2,310

49023 UT Juab 4,467 1,256

49027 UT Millard 29,923 2,052
FIPS State County NOX(TPY) VOC(TPY)

49029 UT Morgan 3,356 764

49035 UT Salt Lake 32,265 25,327

49037 UT San Juan 1,381 1,369

49043 UT Summit 5,073 2,165

49045 UT Tooele 6,310 3,474

49047 UT Uintah 13,363 1,382

49049 UT Utah 12,054 11,877

49051 UT Wasatch 1,051 1,037

49053 UT Washington 4,787 4,471

49057 UT Weber 5,895 5,923

50003 VT Bennington 1,116 1,871

50005 VT Caledonia 1,352 1,601

50007 VT Chittenden 4,759 6,256

50009 VT Essex 370 1,616

50011 VT Franklin 1,529 2,247

50013 VT Grand Isle 284 2,280

50017 VT Orange 1,206 1,724

50027 VT Windsor 2,500 3,061

51003 VA Albemarle 4,838 3,549

51007 VA Amelia 778 654

51013 VA Arlington 5,235 3,276

51023 VA Botetourt 6,049 2,050

51033 VA Caroline 2,620 1,816

51036 VA Charles City 697 695

51041 VA Chesterfield 20,939 8,823

51043 VA Clarke 1,034 862

51045 VA Craig 120 231

51049 VA Cumberland 297 355

51053 VA Dinwiddie 2,440 1,376

51059 VA Fairfax 22,429 20,709

51061 VA Fauquier 4,577 3,535

51065 VA Fluvanna 4,326 736

51067 VA Franklin 2,178 2,174

51069 VA Frederick 4,969 5,160

51071 VA Giles 9,214 1,716

51073 VA Gloucester 1,280 2,113

51075 VA Goochland 2,161 1,423

51079 VA Greene 722 550

51083 VA Halifax 11,197 2,163

51085 VA Hanover 6,071 4,178

51087 VA Henrico 11,079 7,525

51093 VA Isle Of Wight 5,478 1,715

51095 VA James City 2,851 1,972

51097 VA King And Queen 436 605

51101 VA King William 3,381 644

51107 VA Loudoun 6,901 5,975

51109 VA Louisa 1,885 1,594

51113 VA Madison 698 797

51115 VA Mathews 450 866

51121 VA Montgomery 5,660 3,319

51127 VA New Kent 1,857 1,392

51131 VA Northampton 6,039 3,911

51139 VA Page 1,059 1,195

51143 VA Pittsylvania 3,976 2,638

51145 VA Powhatan 938 949

51149 VA Prince George 2,189 1,751

51153 VA Prince William 10,224 6,860
FIPS State County NOX(TPY) VOC(TPY)

51161 VA Roanoke 5,153 6,688

51163 VA Rockbridge 2,714 1,885

51165 VA Rockingham 4,598 3,380

51167 VA Russell 8,706 1,094

51169 VA Scott 1,076 994

51177 VA Spotsylvania 4,223 3,212

51179 VA Stafford 4,308 2,594

51181 VA Surry 552 598

51183 VA Sussex 1,603 900

51187 VA Warren 1,612 1,823

51191 VA Washington 3,244 2,553

51197 VA Wythe 2,867 1,962

51199 VA York 10,158 2,462

51510 VA Alexandria 5,701 1,877

51520 VA Bristol 1,187 881

51540 VA Charlottesville 1,412 1,478

51550 VA Chesapeake 13,284 5,795

51570 VA Colonial Height 623 620

51590 VA Danville 2,585 1,489

51600 VA Fairfax 369 599

51610 VA Falls Church 189 252

51630 VA Fredericksburg 1,163 1,344

51650 VA Hampton 4,001 3,696

51660 VA Harrisonburg 1,768 1,266

51670 VA Hopewell 12,875 1,470

51683 VA Manassas 1,016 650

51685 VA Manassas Park 284 212

51700 VA Newport News 11,162 4,274

51710 VA Norfolk 18,926 5,593

51730 VA Petersburg 1,406 1,500

51735 VA Poquoson 292 359

51740 VA Portsmouth 7,430 2,085

51760 VA Richmond 8,113 6,926

51770 VA Roanoke 3,443 3,440

51775 VA Salem 1,213 896

51800 VA Suffolk 2,962 2,690

51810 VA Virginia Beach 11,216 9,169

51830 VA Williamsburg 369 311

51840 VA Winchester 1,202 828

53009 WA Clallam 17,760 4,072

53011 WA Clark 13,180 15,784

53015 WA Cowlitz 10,030 6,268

53029 WA Island 4,740 3,060

53031 WA Jefferson 4,774 2,097

53033 WA King 76,818 76,825

53035 WA Kitsap 6,901 8,030

53041 WA Lewis 21,481 4,893

53045 WA Mason 1,603 2,807

53053 WA Pierce 32,155 27,917

53057 WA Skagit 9,741 6,434

53059 WA Skamania 1,515 1,185

53061 WA Snohomish 22,697 24,226

53063 WA Spokane 16,380 19,477

53067 WA Thurston 8,289 10,769

53073 WA Whatcom 11,417 8,587

54003 WV Berkeley 7,828 3,053

54005 WV Boone 1,322 1,001
FIPS State County NOX(TPY) VOC(TPY)

54009 WV Brooke 2,074 1,025

54011 WV Cabell 5,239 3,778

54015 WV Clay 325 394

54023 WV Grant 24,312 876

54025 WV Greenbrier 2,136 2,068

54027 WV Hampshire 831 2,096

54029 WV Hancock 3,845 1,053

54033 WV Harrison 23,291 2,593

54035 WV Jackson 2,876 1,620

54037 WV Jefferson 2,709 1,671

54039 WV Kanawha 23,091 9,410

54043 WV Lincoln 576 647

54049 WV Marion 2,616 1,802

54051 WV Marshall 36,079 1,091

54053 WV Mason 24,204 1,293

54061 WV Monongalia 12,593 3,495

54065 WV Morgan 1,054 540

54069 WV Ohio 2,970 1,955

54073 WV Pleasants 14,121 567

54077 WV Preston 3,990 1,332

54079 WV Putnam 37,461 1,923

54099 WV Wayne 6,732 1,534

54105 WV Wirt 142 416

54107 WV Wood 5,331 3,553

55003 WI Ashland 3,014 1,907

55009 WI Brown 24,023 11,499

55015 WI Calumet 2,117 2,091

55021 WI Columbia 11,509 4,369

55025 WI Dane 18,896 19,321

55027 WI Dodge 3,861 4,000

55029 WI Door 1,877 3,419

55037 WI Florence 240 3,028

55039 WI Fond Du Lac 4,441 4,218

55041 WI Forest 754 2,422

55049 WI Iowa 1,021 1,723

55055 WI Jefferson 4,770 4,416

55059 WI Kenosha 16,167 4,795

55061 WI Kewaunee 941 1,697

55071 WI Manitowoc 5,691 3,898

55073 WI Marathon 15,895 6,622

55079 WI Milwaukee 35,493 29,271

55083 WI Oconto 1,538 2,920

55085 WI Oneida 3,456 5,008

55087 WI Outagamie 9,650 7,119

55089 WI Ozaukee 4,485 3,354

55093 WI Pierce 2,219 1,940

55101 WI Racine 5,415 7,402

55105 WI Rock 6,982 6,873

55109 WI St Croix 4,609 3,538

55111 WI Sauk 2,843 3,863

55117 WI Sheboygan 10,860 5,508

55123 WI Vernon 5,807 2,048

55125 WI Vilas 1,087 5,909

55127 WI Walworth 4,219 5,159

55131 WI Washington 4,127 4,765

55133 WI Waukesha 12,087 15,487

56001 WY Albany 5,719 1,872
FIPS State County NOX(TPY) VOC(TPY)

56005 WY Campbell 14,563 2,207

56007 WY Carbon 9,317 1,761

56009 WY Converse 21,371 1,216

56011 WY Crook 2,399 1,083

56019 WY Johnson 4,182 1,379

56021 WY Laramie 8,541 3,934

56023 WY Lincoln 22,856 1,481

56025 WY Natrona 7,766 3,389

56027 WY Niobrara 1,972 976

56031 WY Platte 23,234 1,153

56033 WY Sheridan 3,405 1,800

56035 WY Sublette 9,893 1,098

56037 WY Sweetwater 59,386 3,513

56039 WY Teton 1,083 2,165

56041 WY Uinta 5,678 1,345

56045 WY Weston 3,918 666
APPENDIX B 

MODEL PERFORMANCE 
Northeast Domain (12NE2) 
South domain (12SOUTH1) 
Southeast domain (12SE2) 
Florida domain (12FLORIDA2) 
Ohio Valley (12OV1) 
Midwest domain (12MW3) 
Midwest domain (12MW3) 
Western domain (12WUS1) – 2005 simulation; 12WUS1 includes both 12WUS2 and 

12SEATTLE1 
Western domain (12WUS1) – 2006 simulation; 12WUS1 includes both 12WUS2 and 

12SEATTLE1
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 

Publication N