Bayesian space-time downscaling fusion model 

(downscaler) -Derived Estimates of Air Quality 

for 2010 
EPA-454/S-14-001 

November 2014 

Bayesian space-time downscaling fusion model (downscaler) -Derived 

Estimates of Air Quality for 2010

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Research Triangle Park, NC 
ii

Authors: 

Adam Reff (EPA/OAR) 

Sharon Phillips (EPA/OAR) 

Alison Eyth (EPA/OAR) 

David Mintz (EPA/OAR) 

Acknowledgements 

The following people served as reviewers of this document and provided valuable comments that 

were included: Liz Naess (EPA/OAR), Tyler Fox (EPA/OAR), and Dennis Doll (EPA/OAR).
1 

Contents 

Contents .......................................................................................................................................... 1

1.0 Introduction .......................................................................................................................... 2

2.0 Air Quality Data ................................................................................................................... 5

2.1 Introduction to Air Quality Impacts in the United States ................................................ 5

2.2 Ambient Air Quality Monitoring in the United States ..................................................... 7

2.3 Air Quality Indicators Developed for the EPHT Network ............................................. 11

3.0 Emissions Data................................................................................................................... 13

3.1 Introduction to Emissions Data Development ............................................................... 13

3.2 2010 Emission Inventories and Approaches .................................................................. 13

3.3 Emissions Modeling Summary ...................................................................................... 36

4.0 CMAQ Air Quality Model Estimates ................................................................................ 60

4.1 Introduction to the CMAQ Modeling Platform .............................................................. 60

4.2 CMAQ Model Version, Inputs and Configuration ......................................................... 62

4.3 CMAQ Model Performance Evaluation ......................................................................... 67

5.0 Bayesian space-time downscaling fusion model (downscaler) -Derived Air Quality 

Estimates ....................................................................................................................................... 84

5.1 Introduction .................................................................................................................... 84

5.2 Downscaler Model ......................................................................................................... 84

5.3 Downscaler Concentration Predictions .......................................................................... 85

5.4 Downscaler Uncertainties .............................................................................................. 90

5.5 Summary and Conclusions ............................................................................................. 92

Appendix A - Acronyms ............................................................................................................... 93

 
2 

1.0 Introduction 

This report describes estimates of daily ozone (maximum 8-hour average) and PM2.5 (24-hour 

average) concentrations throughout the contiguous United States during the 2010 calendar 

year generated by EPA's recently developed data fusion method termed the "downscaler 

model" (DS). Air quality monitoring data from the National Air Monitoring Stations/State and 

Local Air Monitoring Stations (NAMS/SLAMS) and numerical output from the Community 

Multiscale Air Quality (CMAQ) model were both input to DS to predict concentrations at the 

2010 US census tract centroids encompassed by the CMAQ modeling domain. Information on 

EPA's air quality monitors, CMAQ model, and downscaler model is included to provide the 

background and context for understanding the data output presented in this report. These 

estimates are intended for use by statisticians and environmental scientists interested in the 

daily spatial distribution of ozone and PM2.5. 

DS essentially operates by calibrating CMAQ data to the observational data, and then uses the 

resulting relationship to predict "observed" concentrations at new spatial points in the domain. 

Although similar in principle to a linear regression, spatial modeling aspects have been 

incorporated for improving the model fit, and a Bayesian1

approaching to fitting is used to 

generate an uncertainty value associated with each concentration prediction. The uncertainties 

that DS produces are a major distinguishing feature from earlier fusion methods previously 

used by EPA such as the "Hierarchical Bayesian" (HB) model (McMillan et al, 2009). The 

term "downscaler" refers to the fact that DS takes grid-averaged data (CMAQ) for input and 

produces point-based estimates, thus "scaling down" the area of data representation. Although 

this allows air pollution concentration estimates to be made at points where no observations 

exist, caution is needed when interpreting any within-gridcell spatial gradients generated by 

DS since they may not exist in the input datasets. The theory, development, and initial 

evaluation of DS can be found in the earlier papers of Berrocal, Gelfand, and Holland (2009, 

2010, and 2011).

The data contained in this report are an outgrowth of a collaborative research partnership 

between EPA scientists from the Office of Research and Development’s (ORD) National 

Exposure Research Laboratory (NERL) and personnel from EPA’s Office of Air and 

Radiation’s (OAR) Office of Air Quality Planning and Standards (OAQPS). NERL’s Human 

Exposure and Atmospheric Sciences Division (HEASD), Atmospheric Modeling Division 

(AMD), and Environmental Sciences Division (ESD), in conjunction with OAQPS, work 

together to provide air quality monitoring data and model estimates to the Centers for Disease 

Control and Prevention (CDC) for use in their Environmental Public Health Tracking (EPHT) 

Network. 

 

1 Bayesian statistical modeling refers to methods that are based on Bayes’ theorem, and model the world in terms 

of probabilities based on previously acquired knowledge. 
3 

CDC’s EPHT Network supports linkage of air quality data with human health outcome data 

for use by various public health agencies throughout the U.S. The EPHT Network Program is 

a multidisciplinary collaboration that involves the ongoing collection, integration, analysis, 

interpretation, and dissemination of data from: environmental hazard monitoring activities; 

human exposure assessment information; and surveillance of noninfectious health conditions. 

As part of the National EPHT Program efforts, the CDC led the initiative to build the National 

EPHT Network (http:// www.cdc.gov/nceh/tracking/default.htm). The National EPHT 

Program, with the EPHT Network as its cornerstone, is the CDC’s response to requests calling 

for improved understanding of how the environment affects human health. The EPHT 

Network is designed to provide the means to identify, access, and organize hazard, exposure, 

and health data from a variety of sources and to examine, analyze and interpret those data 

based on their spatial and temporal characteristics. 

Since 2002, EPA has collaborated with the CDC on the development of the EPHT Network. 

On September 30, 2003, the Secretary of Health and Human Services (HHS) and the 

Administrator of EPA signed a joint Memorandum of Understanding (MOU) with the 

objective of advancing efforts to achieve mutual environmental public health goals2

. HHS, 

acting through the CDC and the Agency for Toxic Substances and Disease Registry 

(ATSDR), and EPA agreed to expand their cooperative activities in support of the CDC 

EPHT Network and EPA’s Central Data Exchange Node on the Environmental Information 

Exchange Network in the following areas: 

 Collecting, analyzing and interpreting environmental and health data from both 

agencies (HHS and EPA). 

 Collaborating on emerging information technology practices related to building, 

supporting, and operating the CDC EPHT Network and the Environmental 

Information Exchange Network. 

 Developing and validating additional environmental public health indicators. 

 Sharing reliable environmental and public health data between their respective 

networks in an efficient and effective manner. 

 Consulting and informing each other about dissemination of results obtained through 

work carried out under the MOU and the associated Interagency Agreement (IAG) 

between EPA and CDC. 

 

2HHS and EPA agreed to extend the duration of the MOU, effective since 2002 and renewed in 2007, until June 29, 

2017. The MOU is available at www.cdc.gov/nceh/tracking/partners/epa_mou_2007.htm. 
4 

The best available statistical fusion model, air quality data, and CMAQ numerical model 

output were used to develop the 2010 estimates. Fusion results can vary with different inputs 

and fusion modeling approaches. As new and improved statistical models become available, 

EPA will provide updates. 

Although these data have been processed on a computer system at the Environmental Protection 

Agency, no warranty expressed or implied is made regarding the accuracy or utility of the data on 

any other system or for general or scientific purposes, nor shall the act of distribution of the data 

constitute any such warranty. It is also strongly recommended that careful attention be paid to the 

contents of the metadata file associated with these data to evaluate data set limitations, restrictions 

or intended use. The U.S. Environmental Protection Agency shall not be held liable for improper 

or incorrect use of the data described and/or contained herein. 

The four remaining sections and one appendix in the report are as follows. 

 Section 2 describes the air quality data obtained from EPA’s nationwide monitoring 

network and the importance of the monitoring data in determining health potential 

health risks. 

 Section 3 details the emissions inventory data, how it is obtained and its role as a key 

input into the CMAQ air quality computer model. 

 Section 4 describes the CMAQ computer model and its role in providing estimates of 

pollutant concentrations across the U.S. based on 12-km grid cells over the contiguous 

U.S. 

 Section 5 explains the downscaler model used to statistically combine air quality 

monitoring data and air quality estimates from the CMAQ model to provide daily air 

quality estimates for the 2010 US census tract centroid locations within the contiguous 

U.S. 

 The appendix provides a description of acronyms used in this report.
5 

2.0 Air Quality Data 

To compare health outcomes with air quality measures, it is important to understand the origins 

of those measures and the methods for obtaining them. This section provides a brief overview of 

the origins and process of air quality regulation in this country. It provides a detailed discussion 

of ozone (O3) and particulate matter (PM). The EPHT program has focused on these two 

pollutants, since numerous studies have found them to be most pervasive and harmful to public 

health and the environment, and there are extensive monitoring and modeling data available. 

2.1 Introduction to Air Quality Impacts in the United States 

2.1.1 The Clean Air Act 

In 1970, the Clean Air Act (CAA) was signed into law. Under this law, EPA sets limits on how 

much of a pollutant can be in the air anywhere in the United States. This ensures that all 

Americans have the same basic health and environmental protections. The CAA has been 

amended several times to keep pace with new information. For more information on the CAA, 

go to http://www.epa.gov/oar/caa/.

Under the CAA, the U.S. EPA has established standards or limits for six air pollutants, known as 

the criteria air pollutants: carbon monoxide (CO), lead (Pb), nitrogen dioxide (NO2), sulfur 

dioxide (SO2), ozone (O3), and particulate matter (PM). These standards, called the National 

Ambient Air Quality Standards (NAAQS), are designed to protect public health and the 

environment. The CAA established two types of air quality standards. Primary standards set 

limits to protect public health, including the health of “sensitive” populations such as asthmatics, 

children, and the elderly. Secondary standards set limits to protect public welfare, including 

protection against decreased visibility, damage to animals, crops, vegetation, and buildings. The 

law requires EPA to review periodically these standards. For more specific information on the 

NAAQS, go to www.epa.gov/air/criteria.html. For general information on the criteria pollutants, 

go to http://www.epa.gov/air/urbanair/6poll.html.

When these standards are not met, the area is designated as a nonattainment area. States must 

develop state implementation plans (SIPs) that explain the regulations and controls it will use to 

clean up the nonattainment areas. States with an EPA-approved SIP can request that the area be 

designated from nonattainment to attainment by providing three consecutive years of data 

showing NAAQS compliance. The state must also provide a maintenance plan to demonstrate 

how it will continue to comply with the NAAQS and demonstrate compliance over a 10-year 

period, and what corrective actions it will take should a NAAQS violation occur after 

designation. EPA must review and approve the NAAQS compliance data and the maintenance 

plan before designating the area; thus, a person may live in an area designated as non- attainment 

even though no NAAQS violation has been observed for quite some time. For more information 

on designations, go to http://www.epa.gov/ozonedesignations/ and

http://www.epa.gov/pmdesignations.
6 

2.1.2 Ozone 

Ozone is a colorless gas composed of three oxygen atoms. Ground level ozone is formed when 

pollutants released from cars, power plants, and other sources react in the presence of heat and 

sunlight. It is the prime ingredient of what is commonly called “smog.” When inhaled, ozone can 

cause acute respiratory problems, aggravate asthma, cause inflammation of lung tissue, and even 

temporarily decrease the lung capacity of healthy adults. Repeated exposure may permanently scar 

lung tissue. Toxicological, human exposure, and epidemiological studies were integrated by EPA 

in “Air Quality Criteria for Ozone and Related Photochemical Oxidants.” It is available at 

http://www.epa.gov/ttn/naaqs/standards/ozone/s_o3_index.html. The current (as of October 2008) 

NAAQS for ozone is a daily maximum 8-hour average of 0.075 parts per million [ppm] (for details, 

see http://www.epa.gov/air/criteria.html. The Clean Air Act requires EPA to review the NAAQS at 

least every five years and revise them as appropriate in accordance with Section 108 and Section 

109 of the Act. 

2.1.3 Particulate Matter 

PM air pollution is a complex mixture of small and large particles of varying origin that can 

contain hundreds of different chemicals, including cancer-causing agents like polycyclic aromatic 

hydrocarbons (PAH), as well as heavy metals such as arsenic and cadmium. PM air pollution 

results from direct emissions of particles as well as particles formed through chemical 

transformations of gaseous air pollutants. The characteristics, sources, and potential health effects 

of particulate matter depend on its source, the season, and atmospheric conditions. 

As practical convention, PM is divided by sizes into classes with differing health concerns and 

potential sources3. Particles less than 10 micrometers in diameter (PM10) pose a health concern 

because they can be inhaled into and accumulate in the respiratory system. Particles less than 2.5 

micrometers in diameter (PM2.5) are referred to as “fine” particles. Because of their small size, fine 

particles can lodge deeply into the lungs. Sources of fine particles include all types of combustion 

(motor vehicles, power plants, wood burning, etc.) and some industrial processes. Particles with 

diameters between 2.5 and 10 micrometers (PM10-2.5) are referred to as “coarse” or PMc. Sources 

of PMc include crushing or grinding operations and dust from paved or unpaved roads. The 

distribution of PM10, PM2.5 and PMc varies from the Eastern U.S. to arid western areas. 

Particle pollution - especially fine particles - contains microscopic solids and liquid droplets that 

are so small that they can get deep into the lungs and cause serious health problems. Numerous 

scientific studies have linked particle pollution exposure to a variety of problems, including 

premature death in people with heart or lung disease, nonfatal heart attacks, irregular heartbeat, 

aggravated asthma, decreased lung function, and increased respiratory symptoms, such as irritation 

of airways, coughing or difficulty breathing. Additional information on the health effects of 

particle pollution and other technical documents related to PM standards are available at 

http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_index.html.

 

3

 The measure used to classify PM into sizes is the aerodynamic diameter. The measurement instruments used for PM 

are designed and operated to separate large particles from the smaller particles. For example, the PM2.5 instrument only 

captures and thus measures particles with an aerodynamic diameter less than 2.5 micrometers. The EPA method to 

measure PMc is designed around taking the mathematical difference between measurements for PM10 and PM2.5.
7 

The current NAAQS for PM2.5 includes both a 24-hour standard to protect against short-term

effects, and an annual standard to protect against long-term effects. The annual average PM2.5

concentration must not exceed 12.0 micrograms per cubic meter (ug/m3) based on the annual 

mean concentration averaged over three years, and the 24-hr average concentration must not 

exceed 35 ug/m3based on the 98th percentile 24-hour average concentration averaged over three 

years. More information is available at http://www.epa.gov/air/criteria.html and 

http://www.epa.gov/oar/particlepollution/. The standards for PM2.5 values are shown in Table 2-

1.

Table 2-1. PM2.5 Standards 

Micrograms Per Cubic Meter: 

Measurement - (ug/m3)

1997 2006 2012

Annual Average 15.0 15.0 12.0

24-Hour Average 65 35 35

2.2 Ambient Air Quality Monitoring in the United States 

2.2.1 Monitoring Networks 

The Clean Air Act (Section 319) requires establishment of an air quality monitoring system 

throughout the U.S. The monitoring stations in this network have been called the State and Local 

Air Monitoring Stations (SLAMS). The SLAMS network consists of approximately 4,000 

monitoring sites set up and operated by state and local air pollution agencies according to 

specifications prescribed by EPA for monitoring methods and network design. All ambient 

monitoring networks selected for use in SLAMS are tested periodically to assess the quality of 

the SLAMS data being produced. Measurement accuracy and precision are estimated for both 

automated and manual methods. The individual results of these tests for each method or 

analyzer are reported to EPA. Then, EPA calculates quarterly integrated estimates of precision 

and accuracy for the SLAMS data. 

The SLAMS network experienced accelerated growth throughout the 1970s. The networks were 

further expanded in 1999 based on the establishment of separate NAAQSs for fine particles 

(PM2.5) in 1997. The NAAQSs for PM2.5 were established based on their link to serious health 

problems ranging from increased symptoms, hospital admissions, and emergency room visits, to 

premature death in people with heart or lung disease. While most of the monitors in these 

networks are located in populated areas of the country, “background” and rural monitors are an 

important part of these networks. For more information on SLAMS, as well as EPA’s other air 

monitoring networks go to www.epa.gov/ttn/amtic.

In 2009, approximately 43 percent of the US population was living within 10 kilometers of 

ozone and PM2.5 monitoring sites. In terms of US Census Bureau tract locations, 31,341 out of 

72,283 census tract centroids were within 10 kilometers of ozone monitoring sites. Highly 

populated Eastern US and California coasts are well covered by both ozone and PM2.5

monitoring network (Figure 2-1).
8 

Figure 2-1. Distances from US Census Tract centroids to the nearest monitoring site, 2009. 
9 

In summary, state and local agencies and tribes implement a quality-assured monitoring network 

to measure air quality across the United States. EPA provides guidance to ensure a thorough 

understanding of the quality of the data produced by these networks. These monitoring data 

have been used to characterize the status of the nation's air quality and the trends across the U.S. 

(see www.epa.gov/airtrends). 

2.2.2 Air Quality System Database 

EPA’s Air Quality System (AQS) database contains ambient air monitoring data collected by 

EPA, state, local, and tribal air pollution control agencies from thousands of monitoring stations. 

AQS also contains meteorological data, descriptive information about each monitoring station 

(including its geographic location and its operator), and data quality assurance and quality 

control information. State and local agencies are required to submit their air quality monitoring 

data into AQS within 90 days following the end of the quarter in which the data were collected. 

This ensures timely submission of these data for use by state, local, and tribal agencies, EPA, and 

the public. EPA’s Office of Air Quality Planning and Standards and other AQS users rely upon 

the data in AQS to assess air quality, assist in compliance with the NAAQS, evaluate SIPs, 

perform modeling for permit review analysis, and perform other air quality management 

functions. For more details, including how users can retrieve data, go to 

http://www.epa.gov/ttn/airs/airsaqs/index.htm.

2.2.3 Advantages and Limitations of the Air Quality Monitoring and Reporting System 

Air quality data is required to assess public health outcomes that are affected by poor air quality. 

The challenge is to get surrogates for air quality on time and spatial scales that are useful for 

Environmental Public Health Tracking activities.

The advantage of using ambient data from EPA monitoring networks for comparing with health 

outcomes is that these measurements of pollution concentrations are the best characterization of 

the concentration of a given pollutant at a given time and location. Furthermore, the data are 

supported by a comprehensive quality assurance program, ensuring data of known quality. One 

disadvantage of using the ambient data is that it is usually out of spatial and temporal alignment 

with health outcomes. This spatial and temporal ‘misalignment’ between air quality monitoring 

data and health outcomes is influenced by the following key factors: the living and/or working 

locations (microenvironments) where a person spends their time not being co-located with an air 

quality monitor; time(s)/date(s) when a patient experiences a health outcome/symptom (e.g., 

asthma attack) not coinciding with time(s)/date(s) when an air quality monitor records ambient 

concentrations of a pollutant high enough to affect the symptom (e.g., asthma attack either during 

or shortly after a high PM2.5 day). To compare/correlate ambient concentrations with acute 

health effects, daily local air quality data is needed4. Spatial gaps exist in the air quality 

monitoring network, especially in rural areas, since the air quality monitoring network is 

designed to focus on measurement of pollutant concentrations in high population density areas. 

Temporal limits also exist. Hourly ozone measurements are aggregated to daily values (the daily 

max 8-hour average is relevant to the ozone standard). Ozone is typically monitored during the 

ozone season (the warmer months, approximately April through October). However, year-long 

 

4 EPA uses exposure models to evaluate the health risks and environmental effects associated with exposure. 

These models are limited by the availability of air quality estimates. http://www.epa.gov/ttn/fera/index.html. 
10

data is available in many areas and is extremely useful to evaluate whether ozone is a factor in 

health outcomes during the non-ozone seasons. PM2.5 is generally measured year-round. Most 

Federal Reference Method (FRM) PM2.5 monitors collect data one day in every three days, due in 

part to the time and costs involved in collecting and analyzing the samples. However, over the 

past several years, continuous monitors, which can automatically collect, analyze, and report 

PM2.5 measurements on an hourly basis, have been introduced. These monitors are available in 

most of the major metropolitan areas. Some of these continuous monitors have been determined 

to be equivalent to the FRM monitors for regulatory purposes and are called FEM (Federal 

Equivalent Methods). 

2.2.4 Use of Air Quality Monitoring Data 

Air quality monitoring data has been used to provide the information for the following situations: 

(1) Assessing effectiveness of SIPs in addressing NAAQS nonattainment areas 

(2) Characterizing local, state, and national air quality status and trends 

(3) Associating health and environmental damage with air quality levels/concentrations 

For the EPHT effort, EPA is providing air quality data to support efforts associated with (2), and 

(3) above. Data supporting (3) is generated by EPA through the use of its air quality data and its 

downscaler model. 

Most studies that associate air quality with health outcomes use air monitoring as a surrogate for 

exposure to the air pollutants being investigated. Many studies have used the monitoring 

networks operated by state and federal agencies. Some studies perform special monitoring that 

can better represent exposure to the air pollutants: community monitoring, near residences, inhouse or work place monitoring, and personal monitoring. For the EPHT program, special 

monitoring is generally not supported, though it could be used on a case-by-case basis. 

From proximity based exposure estimates to statistical interpolation, many approaches are 

developed for estimating exposures to air pollutants using ambient monitoring data (Jerrett et al., 

2005). Depending upon the approach and the spatial and temporal distribution of ambient 

monitoring data, exposure estimates to air pollutants may vary greatly in areas further apart from 

monitors (Bravo et al., 2012). Factors like limited temporal coverage (i.e., PM2.5 monitors do not 

operate continuously such as recording every third day or ozone monitors operate only certain 

part of the year) and limited spatial coverage (i. e., most monitors are located in urban areas and 

rural coverage is limited) hinder the ability of most of the interpolation techniques that use 

monitoring data alone as the input. If we look at the example of Voronoi Neighbor Averaging 

(VNA) (referred as the Nearest Neighbor Averaging in most literature), rural estimates would 

be biased towards the urban estimates. To further explain this point, assume the scenario of two 

cities with monitors and no monitors in the rural areas between, which is very plausible. , Since 

exposure estimates are guaranteed to be within the range of monitors in VNA, estimates for the 

rural areas would be higher according to this scenario. 

Air quality models may overcome some of the limitations that monitoring networks possess. 

Models such as the Community Multi-Scale Air Quality (CMAQ) modeling systems can 

estimate concentrations in reasonable temporal and spatial resolutions. However these 
11

sophisticated air quality models are prune to systematic biases since they depend upon so many 

variables (i.e., metrological models and emission models) and complex chemical and physical 

process simulations. 

Combining monitoring data with air quality models (via fusion or regression) may provide the 

best results in terms of estimating ambient air concentrations in space and time. EPA’s eVNA5 

is an example of an earlier approach for merging air quality monitor data with CMAQ model 

predictions. The downscaler model attempts to address some of the shortcomings in these earlier 

attempts to statistically combine monitor and model predicted data, see published paper 

referenced in section 1 for more information about the downscaler model. As discussed in the 

next section, there are two methods used in EPHT to provide estimates of ambient concentrations 

of air pollutants: air quality monitoring data and the downscaler model estimate, which is a 

statistical ‘combination’ of air quality monitor data and photochemical air quality model 

predictions (e.g., CMAQ).

2.3 Air Quality Indicators Developed for the EPHT Network 

Air quality indicators have been developed for use in the Environmental Public Health Tracking 

Network by CDC using the ozone and PM2.5 data from EPA. The approach used divides

“indicators” into two categories. First, basic air quality measures were developed to compare air 

quality levels over space and time within a public health context (e.g., using the NAAQS as a 

benchmark). Next, indicators were developed that mathematically link air quality data to public 

health tracking data (e.g., daily PM2.5 levels and hospitalization data for acute myocardial 

infarction). Table 2-3 and Table 2-4 describe the issues impacting calculation of basic air quality 

indicators. 

Table 2-2. Public Health Surveillance Goals and Current Status 

Goal Status

1) Air data sets and metadata required for air quality 

indicators are available to EPHT state Grantees.

AQS data are available through state agencies and EPA’s 

Air Quality System (AQS). EPA and CDC developed an 

interagency agreement, where EPA provides air quality 

data along with statistically combined AQS and 

Community Multiscale Air Quality (CMAQ) Model 

data, associated metadata, and technical reports that are

delivered to CDC.

2) Estimate the linkage or association of PM2.5 and ozone on 

health to:

a) Identify populations that may have higher risk of adverse 

health effects due to PM2.5 and ozone,

b) Generate hypothesis for further research, and

c) Provide information to support prevention and pollution 

control strategies.

Regular discussions have been held on health-air linked 

indicators and CDC/HFI/EPA convened a workshop 

January 2008. CDC has collaborated on a health impact 

assessment (HIA) with Emory University, EPA, and 

state grantees that can be used to facilitate greater 

understanding of these linkages.

3) Produce and disseminate basic indicators and other 

findings in electronic and print formats to provide the 

public, environmental health professionals, and 

policymakers, with current and easy-to-use information 

about air pollution and the impact on public health.

Templates and “how to” guides for PM2.5 and ozone 

have been developed for routine indicators. Calculation 

techniques and presentations for the indicators have been 

developed.

 

5 eVNA is described in the “Regulatory Impact Analysis for the Final Clean Air Interstate Rule”, EPA-452/R-05-002, 

March 2005, http://www.epa.gov/cair/pdfs/finaltech08.pdf, Appendix F. 
12

Table 2-3. Basic Air Quality Indicators used in EPHT, derived from the EPA data 

delivered to CDC 

Ozone (daily 8-hr period with maximum concentration—ppm—by Federal Reference Method (FRM))

 Number of days with maximum ozone concentration over the NAAQS (or other relevant benchmarks (by county 

and MSA)

 Number of person-days with maximum 8-hr average ozone concentration over the NAAQS & other relevant 

benchmarks (by county and MSA)

PM2.5 (daily 24-hr integrated samples –ug/m3-by FRM)

 Average ambient concentrations of particulate matter (< 2.5 microns in diameter) and compared to annual PM2.5 

NAAQS (by state).

 % population exceeding annual PM2.5 NAAQS (by state).

 % of days with PM2.5 concentration over the daily NAAQS (or other relevant benchmarks (by county and MSA)

 Number of person-days with PM2.5 concentration over the daily NAAQS & other relevant benchmarks (by 

county and MSA)

2.3.1 Rationale for the Air Quality Indicators 

The CDC EPHT Network is initially focusing on ozone and PM2.5. These air quality indicators 

are based mainly around the NAAQS health findings and program-based measures 

(measurement, data and analysis methodologies). The indicators will allow comparisons across 

space and time for EPHT actions. They are in the context of health-based benchmarks. By 

bringing population into the measures, they roughly distinguish between potential exposures (at 

broad scale). 

2.3.2 Air Quality Data Sources 

The air quality data will be available in the US EPA Air Quality System (AQS) database based 

on the state/federal air program’s data collection and processing. The AQS database contains 

ambient air pollution data collected by EPA, state, local, and tribal air pollution control agencies 

from thousands of monitoring stations (SLAMS and NAMS). 

2.3.3 Use of Air Quality Indicators for Public Health Practice 

The basic indicators will be used to inform policymakers and the public regarding the degree of 

hazard within a state and across states (national). For example, the number of days per year that 

ozone is above the NAAQS can be used to communicate to sensitive populations (such as 

asthmatics) the number of days that they may be exposed to unhealthy levels of ozone. This is 

the same level used in the Air Quality Alerts that inform these sensitive populations when and 

how to reduce their exposure. These indicators, however, are not a surrogate measure of 

exposure and therefore will not be linked with health data. 
13

3.0 Emissions Data 

3.1 Introduction to Emissions Data Development 

The U.S. Environmental Protection Agency (EPA) developed an air quality modeling platform 

based primarily on the 2008 National Emissions Inventory (NEI), Version 2 to process year 2010

emission data for this project. This section provides a summary of the emissions inventory and 

emissions modeling techniques applied to Criteria Air Pollutants (CAPs) and the following select 

Hazardous Air Pollutants (HAPs): chlorine (Cl), hydrogen chloride (HCl), benzene, 

acetaldehyde, formaldehyde and methanol. This section also describes the approach and data 

used to produce emissions inputs to the air quality model. The air quality modeling, 

meteorological inputs and boundary conditions are described in a separate section. 

The Community Multiscale Air Quality (CMAQ) model (http://www.epa.gov/AMD/CMAQ/) is 

used to model ozone (O3) and particulate matter (PM) for this project. CMAQ requires hourly 

and gridded emissions of the following inventory pollutants: carbon monoxide (CO),nitrogen 

oxides (NOX), volatile organic compounds (VOC), sulfur dioxide (SO2), ammonia (NH3), 

particulate matter less than or equal to10 microns (PM10), and individual component species for 

particulate matter less than or equal to 2.5 microns (PM2.5). In addition, the CMAQ CB05 with 

chlorine chemistry used here allows for explicit treatment of the VOC HAPs benzene, 

acetaldehyde, formaldehyde and methanol (BAFM) and includes anthropogenic HAP emissions 

of HCl and Cl. 

The effort to create the 2010 emission inputs for this study included development of emission 

inventories for a 2010 model evaluation case, and application of emissions modeling tools to 

convert the inventories into the format and resolution needed by CMAQ. An evaluation case 

uses year-specific fire and continuous emission monitoring (CEM) data for electric generating 

units (EGUs), whereas other types of modeling cases use averages for these sources. The 

primary emissions modeling tool used to create the CMAQ model-ready emissions was the 

Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system. SMOKE version 3.1 was 

used to create emissions files for a 12-km national grid. Additional information about SMOKE is 

available from http://www.smoke-model.org. 

This chapter contains two additional sections. Section 3.2 describes the inventories input to 

SMOKE and the ancillary files used along with the emission inventories. Section 3.3 describes 

the emissions modeling performed to convert the inventories into the format and resolution 

needed by CMAQ. 

3.2 2010 Emission Inventories and Approaches 

This section describes the emissions inventories created for input to SMOKE. The 2008 NEI is 

the primary basis for the inputs to SMOKE and includes five main categories of source sectors: 

a) nonpoint (formerly called “stationary area”) sources; b) point sources; c) nonroad mobile 

sources; d) onroad mobile sources; and e) fires. For CAPs, the NEI data are largely compiled 

from data submitted by state, local and tribal (S/L/T) agencies. HAP emissions data are often 

augmented by EPA when they are not provided by S/L/T agencies because they are a voluntarily 
14

submitted to the NEI. The 2008 NEI was compiled using the Emissions Inventory System (EIS). 

EIS includes hundreds of automated QA checks to help improve data quality, and also supports 

release point (stack) coordinates separately from facility coordinates. Improved EPA 

collaboration with S/L/T agencies prevented duplication between point and nonpoint source 

categories such as industrial boilers. Documentation for the 2008 NEI is available at 

http://www.epa.gov/ttn/chief/net/2008inventory.html#inventorydoc. 

2010-specific data submitted by S/L/T agencies was used for point sources that were large 

enough to be required to submit emissions to the NEI every year. These are sources with the 

potential to emit 2500 tons per year of SO2, NOx, or CO, or 250 tons per year of the other CAPs 

besides lead. 2010 continuous emissions monitoring (CEM) data was used where it was 

available. For fires, EPA used the SMARTFIRE2 system to develop 2010 emissions. 

SMARTFIRE2 was the first system to categorize all fires as either prescribed burning or wildfire 

categories, and it also includes improved emission factor estimates for prescribed burning. 2010-

specific data were also developed for onroad, nonroad, and large commercial marine sources. 

Some data obtained from regional planning organizations (RPOs) was substituted for NEI data in 

cases for which the RPO data was more recently collected. California-provided mobile source 

emissions were used. Canadian emissions reflect year 2006, Mexico emissions reflect year 2008 

as projected from the 1999 inventory, and offshore emissions reflect year 2008. 

The methods used to process emissions for this project are very similar to those documented for 

EPA’s Version 5, 2007 Emissions Modeling Platform. A technical support document (TSD) for 

this platform is available at EPA’s emissions modeling clearinghouse (EMCH): 

http://www.epa.gov/ttn/chief/emch/index.html#pmnaaqs. Electronic copies of inventories similar 

to those used for this project are available in the same section of the EMCH. 

The emissions modeling process, performed using SMOKE v3.1 apportions the emissions 

inventories into the grid cells used by CMAQ and temporalizes the emissions into hourly values. 

In addition, the pollutants in the inventories (e.g., NOx and VOC) are split into the chemical 

species needed by CMAQ. For the purposes of preparing the CMAQ- ready emissions, the 

broader NEI emissions inventories are split into emissions modeling “platform” sectors; and 

biogenic emissions are added along with emissions from other sources other than the NEI, such 

as the Canadian, Mexican, and offshore inventories. The significance of an emissions sector for 

the emissions modeling platform is that emissions for that sector are run through all of the 

SMOKE programs, except the final merge, independently from emissions in the other sectors. 

The final merge program called Mrggrid combines the sector-specific gridded, speciated and 

temporalized emissions to create the final CMAQ-ready emissions inputs. 

Table 3-1 presents the sectors in the emissions modeling platform used to develop 2010

emissions for this project. The sector abbreviations are provided in italics; these abbreviations 

are used in the SMOKE modeling scripts, the inventory file names, and throughout the remainder 

of this section. Annual 2010 emission summaries for the U.S. anthropogenic sectors are shown in 

Table 3-2 (i.e., biogenic emissions are excluded). Table 3-3 provides a summary of emissions for 

the anthropogenic sectors containing Canadian, Mexican and offshore sources. State total 

emissions for each sector are provided in Appendix B, a workbook entitled 

“Appendix_B_2010_emissions_totals_by_sector.xlsx”.
15

Table 3-1. Platform Sectors Used in the Emissions Modeling Process 

2010 Platform Sector 

(Abbrev)

2010 NEI 

Sector

Description and resolution of the data input 

to SMOKE

IPM (ptipm) Point

2010 NEI point source EGUs that can be 

mapped to the Integrated Planning Model (IPM) 

model. NEI values are replaced with year 2010

hourly continuous emission monitoring (CEM) 

NOX and SO2 emissions, where available. Other 

pollutants are scaled from 2008 NEI using heat 

input.

Point non-IPM (ptnonipm) Point

A mix of 2008 NEI point source emissions with 

some 2010 records where data was provided by 

states and locals plus 2006 WRAP oil and gas 

data; these are emissions not matched to the 

ptipm sector, annual resolution, including all 

aircraft emissions

Point source fire (ptfire) Fires Point source day-specific wildfires and 

prescribed fires for 2010.

Agricultural (ag) Nonpoint

2008 NEI nonpoint NH3 emissions from 

livestock and fertilizer application; county and 

annual resolution with some 2007 monthly 

resolution data provided by the Midwest.

Area fugitive dust (afdust) Nonpoint

2008 NEI nonpoint PM10 and PM2.5 from 

fugitive dust sources (e.g., building construction, 

road construction, paved roads, unpaved roads, 

agricultural dust), county and annual resolution. 

A land use-based transport fraction and 2010-

based precipitation zero-out is applied.

Remaining nonpoint 

(nonpt)

Nonpoint

Primarily 2008 NEI nonpoint for sources not 

included in other sectors, plus 2006 WRAP oil 

and gas data, county and annual resolution.

Nonroad (nonroad) Nonroad

Year 2010 monthly nonroad emissions from the 

National Mobile Inventory Model (NMIM) plus 

California-provided data; county and annual 

resolution.

C1 and C2 marine and 

locomotive (c1c2rail)

Nonroad

Year 2008 non-rail maintenance locomotives, 

and category 1 and category 2 commercial 

marine vessel (CMV) emissions sources; county 

and annual resolution; year 2010 for California.

C3 commercial marine 

(c3marine)

Nonroad

Non-NEI, year 2010 category 3 (C3) CMV 

emissions projected from year 2002. Developed 

for the rule “Control of Emissions from New 

Marine Compression-Ignition Engines at or 

Above 30 Liters per Cylinder”, also known as 

the Emissions Control Area-International 
16

Maritime Organization (ECA-IMO) study: 

www.epa.gov/otaq/ oceanvessels.htm. (EPA420-F-10-041, August 2010). Annual resolution, 

treated as point sources.

Onroad (onroad) Onroad

Year 2010 gridded hourly emissions from 

onroad mobile gasoline and diesel vehicles from 

parking lots and moving vehicles including 

exhaust, evaporative, permeation, and brake and 

tire wear. Generated using MOVES 2010b 

emission factors, 2010 VMT and vehicle 

population data, and 2010 gridded met. data. In 

California, adjusted to match CA-provided 

emissions.

Onroad Refueling 

(onroad_rfl) Onroad

Year 2010 gridded hourly refueling emissions 

from onroad mobile gasoline and diesel vehicles 

from parking lots and moving vehicles. 

Generated using MOVES 2010b, emission 

actors, 2010 VMT and vehicle population data, 

and 2010 gridded met. data. Spatially allocated 

to gasoline station locations.

Biogenic (beis) Biogenic

Hour- and grid cell-specific emissions for 2010

generated from the BEIS 3.14 model, including 

emissions in Canada and Mexico.

Other point sources 

(othpt)

N/A

Point sources not from the NEI, including 

Canada's 2006 inventory and a 2008 projection 

of Mexico's Phase III 1999 inventory; annual 

resolution. Also includes 2008 offshore oil point 

source emissions for the U.S. from the 2008 

NEI.

Other nonpoint and 

nonroad (othar)

N/A

Nonpoint and nonroad sources not from the NEI, 

including annual 2006 Canada sources at 

province resolution and a 2008 projection of 

annual 1999 Mexico sources at municipio 

resolution.

Other onroad sources 

(othon)

N/A

Onroad sources not from the NEI, including 

annual 2006 Canada sources at province 

resolution and a 2008 projection of 1999 Mexico 

sources at municipio resolution.
17

Table 3-2. 2010 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.) 

Sector CO NH3 NOx PM10 PM2.5 SO2 VOC

afdust 6,211,274 874,619

ag 3,595,429

c1c2rail 216,862 559 1,321,691 43,248 40,467 48,112 60,353

nonpt 4,336,565 155,317 1,230,624 767,225 676,243 402,633 6,456,455

nonroad 14,497,993 2,025 1,720,692 169,509 160,950 16,426 2,177,810

onroad (inc. rfl) 28,044,484 119,366 5,665,524 276,929 197,366 36,764 2,747,883

ptfire 13,170,780 216,518 197,824 1,356,023 1,149,172 104,516 3,112,451

ptipm 732,585 24,191 2,092,202 303,455 212,912 5,279,849 34,383

ptnonipm 2,684,750 75,288 1,928,047 532,559 378,408 1,372,508 1,116,417

c3marine 15,296 155,779 5,132 4,674 46,151 5,934

Con.US Total 63,699,315 4,188,694 14,312,384 9,665,354 3,694,812 7,306,960 15,711,685

Table 3-3. 2010 Non-US Emissions by Sector within Modeling Domain (tons/yr for Canada, 

Mexico, Offshore) 

Sector CO NH3 NOX PM10 PM2.5 SO2 VOC

Canada othar

3,746,95

4

537,91

1

719,026 1,422,50

3

393,80

4

97,714 1,267,37

0

Canada othon 4,514,63

2

21,814 537,706 15,004 10,634 5,430 277,915

Canada othpt 1,148,01

4

21,138 861,258 117,254 68,114 1,762,35

0

425,792

Canada 

Subtotal

9,409,60

0

580,86

3

2,117,99

0

1,554,76

1

472,55

3

1,865,49

3

1,971,07

7

Mexico othar 477,952 132,91

3

199,048 88,354 56,824 56,418 510,965

Mexico othon 659,796 2,972 93,849 7,937 7,349 5,740 96,253

Mexico othpt 101,309 0 344,896 122,654 90,304 740,235 78,465

Mexico Subtotal 1,239,05

7

135,88

5

637,793 218,944 154,47

7

802,393 685,684

Offshore othpt 82,133 0 74,277 780 769 1,021 60,756

Canada c3marine 13,930 157,046 4,708 4,283 38,030 5,919

Offshore 

c3marine 83,610 960,546 49,509 45,403 380,001 35,509

2010 TOTAL 179,673 0

1,191,86

9

54,997 50,455 419,052 102,184
18

3.2.1 Point Sources (ptipm and ptnonipm) 

Point sources are sources of emissions for which specific geographic coordinates (e.g., 

latitude/longitude) are specified, as in the case of an individual facility. A facility may have 

multiple emission points, which may be characterized as units such as boilers, reactors, spray 

booths, kilns, etc. A unit may have multiple processes (e.g., a boiler that sometimes burns 

residual oil and sometimes burns natural gas). The point sources used for this study include a 

limited set of emissions data for 2010 collected via the NEI process, with 2008 NEI data for any 

sources that did not report in 2010. Note that only large sources are required to report annually 

as opposed to triennially. This section describes NEI point sources within the contiguous United 

States. The offshore oil (othpt sector), fires (ptfire) and category 3 CMV emissions (c3marine 

sector) are point source formatted inventories discussed later in this section. Full documentation 

for the development of the 2008 NEI (EPA, 2012), is posted at: 

http://www.epa.gov/ttn/chief/net/2008inventory.html#inventorydoc. 

After removing offshore oil platforms into the othpt sector, two platform sectors were created 

from the remaining point sources: the EGU sector – also called the IPM sector (i.e., ptipm) and 

the non-EGU sector – also called the non-IPM sector (i.e., ptnonipm). This split facilitates the 

use of different SMOKE temporal processing and future-year projection techniques for each of 

these sectors. The inventory pollutants processed through SMOKE for both the ptipm and 

ptnonipm sectors were: CO, NOX, VOC, SO2, NH3, PM10, and PM2.5 and the following 

HAPs: HCl (pollutant code = 7647010), and Cl (code = 7782505). BAFM from these sectors 

was not utilized because VOC was speciated without the use (i.e., integration) of VOC HAP 

pollutants from the inventory. Integration is discussed in detail in Section 3.3.4). 

In the 2010 model evaluation case used in this study, for ptipm sector sources with CEM data that 

could be matched to the NEI, 2010 hourly SO2 and NOx emissions were used alongside annual 

emissions of all other pollutants allocated down to each hour. The hourly electric generating unit 

(EGU) emissions were obtained for SO2 and NOx emissions and heat input from EPA’s Acid Rain 

Program. This data also contained heat input, which was used to allocate the annual emissions for 

other pollutants (e.g., VOC, PM2.5, HCl) to hourly values. For unmatched EGU units, annual 

emissions were temporalized to days using multi-year averages and to hours using state-specific 

averages for that year. 

The Non-EGU Stationary Point Sources (ptnonipm) emissions were input to SMOKE as annual 

emissions. The full description of how the 2008 NEI emissions were developed is provided in the 

NEI documentation, but a summary of their development follows: 

a. 2008 CAP and HAP data were provided by States, locals and tribes under the 

Consolidated Emissions Reporting Rule 

b. EPA corrected known issues and filled PM data gaps. 

c. EPA added HAP data from the Toxic Release Inventory (TRI) where corresponding data 

was not already provided by states/locals.

d. EPA provided data for airports and rail yards. 

e. Off-shore platform data was added from Mineral Management Services (MMS). 
19

Note that some sources were large enough to require emissions to be reported to the NEI for 

2009. The 2009 emissions were used, where available. The changes made to the NEI point 

sources prior to modeling with SMOKE are as follows:

 The tribal data, which do not use state/county Federal Information Processing Standards 

(FIPS) codes in the NEI, but rather use the tribal code, were assigned a state/county FIPS 

code of 88XXX, where XXX is the3-digit tribal code in the NEI. This change was made 

because SMOKE requires the state/county FIPS code.

 Stack parameters for some point sources were defaulted when modeling in SMOKE. 

SMOKE uses an ancillary file, called the PSTK file, which provides default stack 

parameters by SCC code to either gap fill stack parameters if they are missing in the NEI 

or to correct stack parameters if they are outside the ranges specified. 

 Replaced stack parameters with values from the 2008 NEI where 2008 values were 

determined to be more realistic. 

 Replaced facility emissions with 2008 NEI values where the 2010 NEI contained 

questionable values. 

3.2.1.1 IPM Sector (ptipm) 

The ptipm sector contains emissions from EGUs in the 2010 NEI point inventory that could be

matched to the units found in the NEEDS database, version 4.10 

(http://www.epa.gov/airmarkets/progsregs/epa-ipm/ index.html). IPM provides future year 

emission inventories for the universe of EGUs contained in the NEEDS database. As described 

below, matching with NEEDS was done (1) to provide consistency between the 2010 EGU 

sources and future year EGU emissions for sources which are forecasted by IPM, and (2) to 

avoid double counting when projecting point source emissions. 

The 2010 NEI point source inventory contains emissions estimates for both EGU and non-EGU 

sources. When future years are modeled, IPM is used to predict the future year emissions for the 

EGU sources. The remaining non-EGU point sources are projected by applying projection and 

control factors to the base year emissions. It was therefore necessary to identify and separate into 

two sectors: (1) sources that are projected via IPM (i.e., the “ptipm” sector) and (2) sources that 

are not (i.e., “the “ptnonipm” sector). The two sectors are modeled separately in the base year as 

well as the future years. 

A primary reason the ptipm sources were separated from the other point sources was due to the 

difference in the temporal resolution of the data input to SMOKE. The ptipm sector uses the 

available hourly CEM data via a method first implemented in the 2002 platform and still used for 

the 2010 platform. Hourly CEM data for 2010 were obtained from the CAMD Data and Maps 

website3. For sources and pollutants with CEM data, the actual year 2010 hourly CEM data were 

used. The SMOKE modeling system matches the ORIS Facility and Boiler IDs in the NEI 

SMOKE-ready file to the same fields in the CEM data, thereby allowing the hourly SO2 and NOx

CEM emissions to be read directly from the CEM data file. The heat input from the hourly CEM 

data was used to allocate the NEI annual values to hourly values for all other pollutants from 
20

CEM sources, because CEMs are not used to measure emissions of these pollutants. 

For this project, the point source inventory was reviewed to determine whether additional 

matches needed to be made. Newly identified matches for CEM and NEEDS IDs were loaded 

into the Emissions Inventory System (EIS) so they could then be written into the modeling files. 

Some matches were made outside of EIS when IDs were not mapped one to one between the 

systems.

Emissions were scaled from 2008 levels to 2010 levels where possible based on CEM data. For 

sources not matching the CEM data (“non-CEM” sources), daily emissions were computed from 

the NEI annual emissions using a structured query language (SQL) program and state-average 

CEM data. To allocate annual emissions to each month, state-specific, three-year averages of 

2008-2010 CEM data were created. These average annual- to-month factors were assigned to 

non-CEM sources by state. To allocate the monthly emissions to each day, the 2010 CEM data 

were used to compute state-specific month- to-day factors, which were then averaged across all 

units in each state. The resulting daily emissions were input into SMOKE. The daily-to-hourly 

allocation was performed in SMOKE using diurnal profiles. The development of these diurnal 

ptipm-specific profiles, considered ancillary data for SMOKE, is described in a later section. 

3.2.1.2 Non-IPM Sector (ptnonipm) 

The non-IPM (ptnonipm) sector contains all NEI point sources not included in the IPM (ptipm) 

sector except for the offshore oil and day-specific fire emissions. For the most part, the ptnonipm 

sector reflects the non-EGU component of the NEI point inventory; however, as previously 

discussed, it is likely that some small low-emitting EGUs that are not reflected in the CEMs 

database are present in the ptnonipm sector. The ptnonipm sector contains a small amount of 

fugitive dust PM emissions from vehicular traffic on paved or unpaved roads at industrial 

facilities or coal handling at coal mines. In previous versions of the platform, these emissions 

were reduced prior to input to SMOKE. However, in this platform the reduction is not made 

because of a new methodology used to reduce PM dust based on gridded meteorological data. 

For some geographic areas, some of the sources in the ptnonipm sector belong to source 

categories that are contained in other sectors. This occurs in the inventory when states, tribes or 

local programs report certain inventory emissions as point sources because they have specific 

geographic coordinates for these sources. They may use point source SCCs (8-digit) or they may 

use non- point, onroad or nonroad (10-digit) SCCs. In the 2008 NEI, examples of these types of 

sources include: aircraft and ground support emissions, livestock (i.e., cattle feedlots) in 

California, and rail yards. 

Some adjustments were made to the point inventory prior to its processing with SMOKE. These 

include:

 Removing sources with state county codes ending in ‘777’. These are used for ‘portable’ 

point sources like asphalt plants.

 Removing sources with SCCs not typically used for modeling. 

 Adjusting latitude-longitude coordinates for sources identified to be substantially outside 
21

the county in which they reside.

 Removing offshore oil records as reflected by FIPS=85000 because these sources are 

processed in the othpt sector.

 Added 2008 ethanol facilities provided by EPA’s OTAQ that were not already included 

in the 2008 NEI.

 Correcting stack parameters for some units with missing or invalid parameter 

assignments.

 Adding South Dakota emissions because they did not submit to the 2008 NEI.

 Adding MeadWestVaco facility in Covington, VA because it was missing in the 2008 

NEI.

 Adding oil and gas emissions that were not otherwise included in the NEI from the 

Western Regional Air Partnership (WRAP) RPO created year 2006 “Phase III” oil and 

gas inventory project.

 Removing onroad refueling emissions that some states included in the point sector 

because these are modeled nationwide using MOVES2010b.

3.2.2 Nonpoint Sources (afdust, ag, nonpt) 

The nonpoint emissions sources used in this study are primarily from the 2008 NEI. 

Documentation for the 2008 NEI is available at 

http://www.epa.gov/ttn/chief/net/2008inventory.html#inventorydoc. Prior to processing with 

SMOKE, the nonpoint portion of the 2008 NEI was divided into the following sectors for which 

the data is processed in consistent ways: area fugitive dust (afdust), agricultural ammonia (ag), 

and the other nonpoint sources (nonpt). This section describes stationary nonpoint sources only. 

Class 1 & Class 2 (c1c2) and Class 3 (c3) commercial marine vessels and locomotives are also in 

the 2008 NEI nonpoint data category, but these sources are included in the mobile source portion 

of this documentation. Nonpoint tribal-submitted emissions were removed to prevent possible 

double counting with county-level emissions. Because the tribal nonpoint emissions are small, 

these omissions should not impact results at the 12-km scale used for modeling. This omission 

also eliminated the need to develop costly spatial surrogate data to allocate tribal data to grid cells 

during the SMOKE processing. Some specific types of nonpoint sources were not included in 

the modeling due to one of the following reasons: 1) the sources are only reported by a few 

states or agencies, 2) the sources are ‘atypical’ and small, and/or 3) there are other data available 

that appears to be more accurate. Additional details on nonpoint source processing can be found 

in the Version 5, 2007 Emissions Modeling Platform documentation discussed earlier. 

In the rest of this section, each of the platform sectors into which the 2008 nonpoint NEI was 

divided is described, along with any changes made to these data. 

3.2.2.1 Area Fugitive Dust Sector (afdust) 

The area-source fugitive dust (afdust) sector contains PM emission estimates for 2008 NEI 

nonpoint SCCs identified by EPA staff as fugitive dust sources. Categories included in this 

sector are paved roads, unpaved roads and airstrips, construction (residential, industrial, road and 

total), agriculture production and all of the mining 10-digit SCCs beginning with the digits 
22

3 3

3

3

“2325.” It does not include fugitive dust from grain elevators because these are elevated point 

sources. 

This sector is separated from other nonpoint sectors to allow for the application of “transport 

fraction,” and meteorology/precipitation-based reductions. These adjustments are applied via 

sector-specific scripts and make use of land use-based gridded transport fractions. The land use 

data used to reduce the NEI emissions explains the amount of emissions that are subject to 

transport. This methodology is discussed in (Pouliot, et. al., 2010), 

http://www.epa.gov/ttn/chief/conference/ei19/session9/pouliot_pres.pdf, and in Fugitive Dust 

Modeling for the 2008 Emissions Modeling Platform (Adelman, 2012). The precipitation 

adjustment is then applied to remove all emissions for days on which measureable rain occurs or 

there is snow on the ground. Both the transport fraction and meteorological adjustments are 

based on the gridded meteorological data; therefore, different emissions could result from 

different grid resolutions. Application of the transport fraction and meteorological adjustments 

reduces the overestimation of fugitive dust impacts in the grid modeling as compared to ambient 

samples. 

3.2.2.2 Agricultural Ammonia Sector (ag) 

The agricultural NH3 “ag” sector is comprised of livestock and agricultural fertilizer application 

emissions from the nonpoint sector of the 2008 NEI. The livestock and fertilizer emissions were 

extracted based on SCC. The “ag” sector includes all of the NH3 emissions from fertilizer 

contained in the NEI. However, the “ag” sector does not include all of the livestock ammonia 

emissions, as there are also some NH3 emissions from feedlot livestock in the point source 

inventory. To prevent double-counting, emissions were not included in the nonpoint ag inventory 

for counties in which they were in the point source inventory. A significant error in the 2008 NEI 

was corrected in the modeling platform ag sector. A fertilizer application source “N-P-K (multigrade nutrient fertilizers)” (SCC=2801700010) in Luna county New Mexico (FIPS=35025), was 

6,953 tons of NH3 in the 2008 NEI. This source was corrected by a factor of 1,000 to be 6.953 

tons in the modeling platform. 

Monthly NH3 emissions provided by the Lake Michigan Air Directors Consortium were used to 

replace NEI ag sector emissions in that region due to the improved temporal resolution. 2008 

NEI (annual) ag sector emissions were used in all other states. A new temporal allocation 

methodology for animal NH3 was implemented for this modeling platform that allocates 

monthly emissions down to the hourly level by taking into account temperature and wind speed. 

This method is discussed in more detail in the emission modeling portion of this chapter. 

3.2.2.3 Other Nonpoint Sources (nonpt) 

Stationary nonpoint sources that were not included in the afdust, ag or nonpt sectors were 

assigned to the “nonpt” sector. In preparing the nonpt sector, catastrophic releases were excluded

since these emissions were dominated by tire burning, which is an episodic, location-specific

emissions category. Tire burning accounts for significant emissions of particulate matter in some 

parts of the country. Because such sources are reported by a very small number of states, and are 

inventoried as county/annual totals without the information needed to temporally and spatially 

allocate the emissions to the time and location where the event occurred, catastrophic releases 

were excluded. All fire emissions, including agricultural, wildfire, and prescribed burning, were 
23

removed and substituted with SMARTFIRE emissions (see the “ptfire” sector). Locomotives and 

CMV mobile sources from the 2008 NEI nonpoint inventory are described in the mobile sources 

section. 

The nonpt sector includes emission estimates for Portable Fuel Containers (PFCs), also known as 

“gas cans.” The PFC inventory consists of five distinct sources of PFC emissions, further 

distinguished by residential or commercial use. The five sources are: (1) displacement of the 

vapor within the can; (2) spillage of gasoline while filling the can; (3) spillage of gasoline during 

transport; (4) emissions due to evaporation (i.e., diurnal emissions); and (5) emissions due to

permeation. Note that spillage and vapor displacement associated with using PFCs to refuel 

nonroad equipment are included in the nonroad inventory. 

Some adjustments to the 2008 NEI nonpoint data were made using data from regional planning 

organizations (RPOs) as follows: 

 Replaced 2008 NEI oil and gas emissions (SCCs beginning with “23100”) with year 

2006 Phase III oil and gas emissions for several basins in the WRAP RPO states. These 

WRAP Phase III emissions contain point and nonpoint formatted data are discussed in 

greater detail at: http://www.wrapair2.org/PhaseIII.aspx. These changes were made only 

in counties for which there was WRAP data. 

 Replaced 2008 NEI nonpoint agriculture burning emissions with year 2008 SMARTFIRE 

day-specific county-based emissions aggregated to monthly totals. 

 Replaced open burning “land clearing” (SCC=2610000500) emissions in Florida and 

Georgia with SESARM-provided daily point data, but aggregated to county and monthly 

resolution. 

 Replaced open burning data (SCCs beginning with 261000x) in MARAMA states with 

RPO-proved data. 

 Removed industrial coal combustion emissions (SCC=2102002000) in Tennessee. 

 Replaced, removed and modified much of the residential wood combustion (RWC) 

emissions in the MARAMA, MWRPO and SESARM states with RPO data and non-RPO 

corrections, modified the outdoor hydronic heater (OHH) emissions in all states and 

indoor furnaces in MWRPO states. 

 Removed EPA-estimated commercial cooking (SCCs 2302002100 and 2302002200) 

duplicate PM emissions in California. 

 Removed duplicate “Industrial Processes; Food and Kindred Products; Total” source 

(SCC=23020000000) in Maricopa county Arizona (FIPS=04013). 

The oil and gas changes were already discussed in the ptnonipm section. Other significant 

changes are discussed below. 

Ag burning 

2008 NEI agricultural burning estimates were replaced with more specific data from the Fire 

Characteristic Classification System (FCCS) module fuel loadings map in the BlueSky 
24

Framework (http://blueskyframework.org/modules/fuel-loading/fccs). Year 2008-specific fire 

locations from SMARTFIRE version 1 (Sullivan, et al., 2008) were read into the FCCS module 

and intersected with the FCCS fuel-loading dataset. The module assigned an FCCS code to each 

fire record that reflects the ecosystem geography and potential natural vegetation based on 

remote sensing data. Prescribed or unclassified fires having an FCCS code equal to zero (0) 

were assumed to be agricultural fires. ArcGIS was used to categorize the fires as occurring on 

rangeland, cropland or other land use via USGS 2006 National Land Cover Database (NLCD). 

Activity data were analyzed to restrict to cropland fires and assign state and crop-specific 

emission factors. Emissions were then appropriately weighted based on known statistics about 

each state’s crop mix. 

These SMARTFIRE-based ag burning emissions were provided in at 1km point source and dayspecific resolution. State-county FIPS codes were assigned using GIS. The emissions were 

aggregated to county and monthly resolution and converted to SMOKE nonpoint FF10 format. 

This SMARTFIRE-based ag burning dataset includes emissions for all but these 7 of the lower 

48 states: CT, DC, MA, ME, NH, RI and VT. These 7 states did not contain any cropland 

burning estimates for year 2008 based on this SMARTFIRE approach. 

Open burning RPO data 

All 2008 NEI open burning emissions (CAPs only) were replaced in the MARAMA states with 

the 2007 MARAMA open burning inventory. These MARAMA open burning emissions include 

estimates for household waste (SCC=2610030000), land clearing (2610000500) and yard waste 

leaf and brush (2610000100 and 2610000400 respectively). 

The 2008 NEI land clearing emissions in Georgia and Florida were replaced with SESARMbased year-2007 data. The SESARM land clearing emissions are based on daily point emissions 

from the CONSUME v3.0 model (SESARM, 2012a). These daily point-format emissions were 

aggregated to county and monthly resolution as a separate FF10 nonpoint monthly inventory. 

TN coal combustion 

Tennessee nonpoint industrial coal combustion (SCC=2102002000) emissions are significantly 

overestimated in the 2008 NEI because of incorrect reconciliation with the point source 

inventory. Nonpoint industrial coal combustion emissions were estimated by subtracting point 

source emissions rather than activity. By not accounting for controlled sources, the remaining 

activity for nonpoint coal combustion is significantly overestimated. EPA NEI experts 

determined that it would be more appropriate to completely remove the nonpoint component of 

this sector than to leave the values as they were. The reality for TN industrial coal combustion 

nonpoint sector emissions is likely much closer to zero than the value in the 2008 NEI because 

these emissions are accounted for in the point source inventory. 

Residential Wood Combustion 

There were many modifications to the RWC emissions data. First, all RWC outdoor wood 

burning devices such as “fire pits and chimneys” (SCC=2104008700) were removed because 

they were only reported in a couple of states, RPO inventories did not include them for most 

states and emissions were generally insignificant. A market research report (Frost and Sullivan, 

2010) developed in support of the potential RWC New Source Performance Standard (NSPS) 

indicated slower sales of outdoor hydronic heaters compared to what was assumed for growth 
25

estimates in the 2008 NEI. Therefore, outdoor hydronic heater appliance counts and emissions 

estimates (SCC=2104008610) were recomputed for all states, resulting in a 51% reduction to 

outdoor hydronic heater emissions for all states.

In addition, all emissions in the SESARM states (i.e., AL, FL, GA, KY, MS, NC, SC, TN, VA, 

WV), including Virginia, were replaced with the SESARM year-2007 inventory (SESARM, 

2012b). Urban area RWC were lower than the NEI estimates partially because of the 

assumptions about greater penetration of natural gas fireplaces, less access to inexpensive wood 

supplies and a lower proportion of housing units with wood burning appliances as primary 

heating units than rural areas. Overall, the SESARM RWC estimates are considerably lower than 

the 2008 NEI estimates for several states, particularly for “uncertified” and “general” wood 

stoves and insert categories: FL, KY, NC, TN, VA and WV. However, emissions in Mississippi 

are only slightly reduced and emissions in AL, GA and SC are very similar to those in the 

2008NEIv2. 

The Midwest RPO (LADCO) states (i.e., IL, IL, MI, OH, WI, MN) year-2007 RWC inventory 

was similar to the 2008 NEI for most source types. However, the pellet stoves 

(SCC=2104008400), indoor furnaces (2104008510), and outdoor hydronic heater (OHH, 

SCC=2104008610) estimates were updated to reallocate the indoor furnaces and OHHs to nonMSA counties (LADCO, 2012) for several urban areas. Some double counting of appliances 

was also fixed in Wisconsin and Michigan. Overall, the MWRPO states totals are very similar to 

the 2008 NEI; however, emissions are spatially redistributed from urban to rural areas. 

Therefore, for the MWRPO states, the 2008 NEI emissions were used for all RWC sources 

except the three aforementioned SCCs that use the 2007 MWRPO data. 

Emissions from indoor wood fired furnaces (SCC=2104008510) in several MWRPO states based 

were also recomputed based on newer, improved survey data from Minnesota. The 2008 NEI for 

these sources started with an assumption of year 2002 Minnesota wood burning survey data of 

38 indoor furnaces per 100 woodstoves for Illinois, Indiana, Michigan, Ohio, and Wisconsin. 

More recent year 2007 MN survey data resulted in the much lower ratio of 7.3 indoor furnaces 

per 100 wood stove units. Thus, for the other five MWRPO states previously listed, the indoor 

furnace emissions are normalized by setting the indoor furnace count ratio to wood stoves to 

match the 7.6% reported value in Minnesota. The resulting adjustment factors reduce the indoor 

furnace emissions in these states by 67% (Wisconsin) to as much as 83% in Ohio. 

The MARAMA states (i.e., CT, DE, DC, ME, MD, MA, NH, NJ, NY, PA, RI, VT) year 2007 

RWC inventory was either unchanged from the 2008 NEI, or was missing for most states. The 

exceptions were New York and Pennsylvania which includes significantly revised RWC 

estimates compared to the 2008 NEI. For New York, the MARAMA estimates were not split out 

into the refined set of 10 RWC appliance types/SCCs in the NEI. New York only reported 

“general” fireplaces (SCC=2104008100) and “EPA certified, non-catalytic” woodstoves 

(SCC=2104008320). However, similar to the SESARM and MWRPO improvements, the 

MARAMA NY RWC estimates were spatially reallocated from urban to more rural areas and 

were also lower state-wide than the NEI. For Pennsylvania, MARAMA RWC estimates were 

not much different state-wide on the aggregate, but were refined by SCC and spatially compared 

to the 2008 NEI. Therefore, the MARAMA 2007 RWC data is used for New York and 
26

Pennsylvania and the 2008 NEI emissions are used for all RWC sources in the rest of the 

MARAMA states. 

The uniform temporalization from month to day was modified to be day-of-year specific as 

discussed in more detail in the emissions modeling section. In short, the SMOKE program 

(GenTPRO) is used to distribute annual RWC emissions to the coldest days of the year, using 

maximum temperature thresholds by-state and/or by-county. On days where the low temperature 

does not drop below this threshold, RWC emissions are zero. Conversely, the program 

temporally allocates the most relative emissions to the coldest days. This meteorological-based 

temporal allocation can have a substantial impact on the amount of RWC emissions in an area on 

any given day, particularly in the winter. 

3.2.4 Day-Specific Point Source Fires (ptfire) 

Wildfire and prescribed burning emissions are contained in the ptfire sector. The ptfire sector has 

emissions provided at geographic coordinates (point locations) and has daily estimates of the 

emissions from each fires value. The ptfire sector for the 2010 Platform excludes agricultural

burning and other open burning sources, which are included in the nonpt sector. The agricultural 

burning and other open burning sources are in the nonpt sector because these categories were not 

factored into the development of the ptfire sector. Additionally, their year-to-year impacts are not 

as variable as wildfires and non-agricultural prescribed/managed burns. 

The ptfire sector includes a satellite derived latitude/longitude of the fire’s origin and other 

parameters associated with the emissions such as acres-burned and fuel load, which allow 

estimation of plume rise. Note that agricultural burning is not included in the ptfire sector but is 

included in the nonpt sector. The point source day-specific emission estimates for 2010 fires rely 

on the Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation Version 2 

(SMARTFIRE2) system (Raffuse, et al., 2012). Activity data was used from the Monitoring 

Trends in Burn Severity (MTBS) project, Incident Command Summary Reports (ICS-209), and 

the National Oceanic and Atmospheric Administration’s (NOAA’s) Hazard Mapping System 

(HMS). 

The method involves the reconciliation of ICS-209 reports (Incident Status Summary Reports) 

with satellite-based fire detections to determine spatial and temporal information about the fires. 

The ICS-209 reports for each large wildfire are created daily to enable fire incident commanders 

to track the status and resources assigned to each large fire (100 acre timber fire or 300 acre 

rangeland fire). The SMARTFIRE system of reconciliation with ICS-209 reports is described in 

an Air and Waste Management Association report (Raffuse, et al., 2007). Once the fire 

reconciliation process is completed, the emissions are calculated using the U.S. Forest Service’s 

CONSUMEv3.0 fuel consumption model and the FCCS fuel-loading database in the BlueSky 

Framework (Ottmar, et. al., 2007). The detection of fires with this method is satellite-based. 

Additional sources of information used in the fire classification process included MODIS 

satellite and fuel moistures derived from fire weather observational data. 

The ICS-209 reports for each large wildfire are created daily to enable fire incident commanders

to track the status and resources assigned to each large fire (100 acre timber fire or 300 acre 

rangeland fire). Note that the distinction between wildfire and prescribed burn is not as precise as 
27

with ground-based methods. The fire size was based on the number of satellite pixels and a 

nominal fire size of 100 acres/pixel was assumed for a significant number of fire detections when 

the first detections were not matched to ICS 209 reports, so the fire size information is not as 

precise as ground-based methods. 

The activity data and other information were used within the BlueSky Framework to model 

vegetation distribution, fuel consumption, and emission rates, respectively. Latitude and 

longitude locations were incorporated as a post processing step. The method to classify fires as 

WF, WFU, RX (FCCS > 0), and unclassified (FCCS > 0) involves the reconciliation of ICS-209 

reports (Incident Status Summary Reports) with satellite-based fire detections to determine spatial 

and temporal information about the fires. 

Because the HMS satellite product from NOAA is based on daily detections, the emission 

inventory represents a time-integrated emission estimate. For example, a large smoldering fire 

will show up on satellite for many days and would count as acres burned on a daily basis; 

whereas a ground-based method would count the area burned only once even it burns over many 

days. 

The SMOKE-ready “ORL” inventory files created from the raw daily fires contain both CAPs 

and HAPs. The BAFM HAP emissions from the inventory were obtained using VOC speciation 

profiles (i.e., a “no-integrate noHAP” use case). The BEIS3.14 model creates gridded, hourly,

model-species emissions from vegetation and soils. It estimates CO, VOC, and NOX emissions 

for the U.S., Mexico, and Canada. The BEIS3.14 model is described further in

http://www.cmascenter.org/conference/2008/slides/pouliot_tale_two_ cmas08.ppt. Additional 

references for this method are provided in (McKenzie, et al., 2007), (Ottmar, et al., 2003), 

(Ottmar, et al., 2006), and (Anderson et al., 2004). 

3.2.5 Biogenic Sources (beis) 

For CMAQ, biogenic emissions were computed with the BEIS3.14 model within SMOKE using 

2010 meteorological data. The BEIS3.14 model creates gridded, hourly, model-species emissions 

from vegetation and soils. It estimates CO, VOC (most notably isoprene, terpine, and sesquiterpene), 

and NO emissions for the U.S., Mexico, and Canada. The BEIS3.14 model is described further in: 

http://www.cmascenter.org/conference/2008/slides/pouliot_tale_two_cmas08.ppt. 

The inputs to BEIS include:

 Temperature data at 2 meters from the CMAQ meteorological input files,

 Land-use data from the Biogenic Emissions Landuse Database, version 3 (BELD3) that 

provides data on the 230 vegetation classes at 1-km resolution over most of North

America. 

3.2.6 Mobile Sources (onroad, onroad_rfl, nonroad, c1c2rail, c3marine) 

The 2010 onroad emissions are broken out into two sectors: “onroad” and “onroad_rfl”. Aircraft 

emissions are in the nonEGU point inventory. The locomotive and commercial marine emissions 

are divided into two sectors: “c1c2rail” and “c3marine”, and the “nonroad” sector contains the 
28

remaining nonroad emissions. Note that the 2008 NEI includes state-submitted emissions data 

for nonroad, but the modeling performed for this platform does not incorporate state-submitted 

emissions for the onroad or nonroad sectors, except for California. All tribal data from the 

mobile sectors have been dropped because we do not have spatial surrogate data, and the 

emissions are small. 

The onroad and onroad_rfl sectors are processed separately to allow for different spatial 

allocation to be applied to onroad refueling via a gas station surrogate, versus onroad vehicles 

that are spatially allocated based on roads and population. Except for California, all onroad and 

onroad refueling emissions are generated using the SMOKE-MOVES emissions modeling 

framework that leverages MOVES2010b-generated outputs 

(http://www.epa.gov/otaq/models/moves/index.htm) and gridded hourly meteorological data. 

Emissions for onroad (including refueling), nonroad and c1c2rail sources in California were 

provided by the California Air Resources Board (CARB). 

The nonroad sector is based on NMIM except for California which uses data provided by the 

California Air Resources Board (CARB). NMIM (EPA, 2005) creates the nonroad emissions on 

a month-specific basis that accounts for temperature, fuel types, and other variables that vary by 

month. The 2010 NMIM nonroad emissions were generated using activity (e.g., fuels, vehicle 

population, etc) data that represent 2010. All nonroad emissions are compiled at the county/SCC 

level. Detailed inventory documentation for the 2008 NEI nonroad sectors is available at 

http://www.epa.gov/ttn/chief/net/2008inventory.html#inventorydoc. Neither NMIM nor MOVES 

generates tribal data. 

The locomotive and commercial marine vessel (CMV) emissions are divided into two nonroad 

sectors: “c1c2rail” and “c3marine”. The c1c2rail sector includes all railway and most rail yard 

emissions as well as the gasoline and diesel-fueled Class 1 and Class 2 CMV emissions. The 

c3marine sector emissions contain the larger residual fueled ocean-going vessel Class 3 CMV 

emissions and are treated as point emissions with an elevated release component; all other 

nonroad emissions are treated as county-specific low-level emissions (i.e., are in model layer 1). 

The 2008 NEI c3marine emissions were replaced with a set of approximately 4-km resolution 

point source format emissions. These data are used for all states, including California, as well as 

offshore and international emissions within our air quality modeling domain, and are modeled 

separately as point sources in the “c3marine” sector. 

3.2.7 Onroad non-refueling (onroad) 

For the Version 5 modeling platform, EPA estimated emissions for every county in the 

continental U.S. except for California using similar methods as for the 2008 NEI Versions 2 and 

3. The modeling framework took into account the temperature sensitivity of the onroad 

emissions. Specifically, county-specific inputs and tools were used that integrated the MOVES 

model with the SMOKE emission inventory model to take advantage of the gridded hourly 

temperature information available from meteorology modeling used for air quality modeling. 

This integrated “SMOKE-MOVES” tool was developed by EPA in 2010 and is in use by states 

and regional planning organizations for regional air quality modeling. SMOKE-MOVES 

requires emission rate “lookup” tables generated by MOVES that differentiate emissions by 

process (running, start, vapor venting, etc.), vehicle type, road type, temperature, speed, hour of 
29

day, etc. 

To generate the MOVES emission rates that could be applied across the U.S., EPA used an 

automated process to run MOVES to produce emission factors by temperature and speed for 146 

“representative counties,” to which every other county could be mapped as detailed below. 

Using the MOVES emission rates, SMOKE selected appropriate emissions rates for each county, 

hourly temperature, SCC, and speed bin and multiplied the emission rate by 2010-specific 

activity (i.e., VMT (vehicle miles travelled) or vehicle population) to produce emissions. These 

calculations were done for every county, grid cell, and hour in the continental United States. 

SMOKE-MOVES can be used with different versions of the MOVES model. For the Version 5 

modeling platform, EPA used the latest publically released version: MOVES2010b 

(http://www.epa.gov/otaq/models/moves/index.htm). Fuels representative of 2010 were used, 

with temperature and humidity values from 2010. 

The steps to apply SMOKE-MOVES to create emissions for modeling requires numerous steps, 

as follows: 

 Determine which counties will be used to represent other counties in the MOVES runs. 

 Determine which months will be used to represent other month’s fuel characteristics. 

 Create MOVES inputs needed only for MOVES runs. MOVES requires county-specific 

information on vehicle populations, age distributions, and inspection-maintenance 

programs for each of the representative counties. 

 Create inputs needed both by MOVES and by SMOKE, including a list of year-specific 

temperatures and activity data. 

 Run MOVES to create emission factor tables using year-specific fuel information. 

 Run SMOKE to apply the emission factors to activities to calculate emissions. 

 Aggregate the results at the county-SCC level for summaries and quality assurance. 

Some data used in the SMOKE-MOVES process is year-specific. When MOVES was run to 

generate the emission factors, gasoline and diesel properties for representing counties were based 

on 2010 fuel information (i.e., RegionalFuels_2010_20120802. The temperature and humidity 

inputs were also based on 2010 values. The VMT used by SMOKE-MOVES was generated by 

taking 2010 VMT by state and freeway/non-freeway from FHWA VM-2 tables and allocating to 

county and month and roadtype using the 2008 NEI VMT. The VMT was allocated to vehicle 

type using FHWA’s VM-4 table and to MOVES sourcetype using ratios from MOVES. Vehicle 

populations were then generated by applying VMT/vehicle default ratios from MOVES to the 

VMT. The same speed data used for the 2008 NEI were also used for this study. 

The California emissions were post-processed to incorporate both CARB supplied inventories 

and the shape of the meteorologically-based SMOKE-MOVES results by scaling the SMOKEMOVES generated totals to match CARB-provided totals. Because CARB provide 2007 and 

2011 emissions data, the data for 2010 were linearly interpolated between 2007 and 2011 levels. 

For more details on this process, see the Version 5 platform documentation. 
30

3.2.8 Onroad Refueling (onroad_rfl) 

Onroad refueling was modeled very similarly to the other onroad emissions. MOVES2010b was 

used produce emission factors (EFs) for refueling. These EFs are at the resolution of the onroad 

SCC and were run separately from the other onroad mobile sources to allow for different spatial 

allocation. To facilitate this, the EFs were separated into refueling and non-refueling tables. 

SMOKE-MOVES was then run using these EF tables as inputs and the results spatially allocated 

based on a gas stations spatial surrogate. For California, the SMOKE-MOVES generated 

emissions were used for onroad refueling without any adjustments because there were no CARBsupplied refueling emissions. 

3.2.9 Nonroad Mobile Sources — NMIM-Based (nonroad) 

The nonroad sector includes monthly exhaust, evaporative and refueling emissions from nonroad 

engines (not including commercial marine, aircraft, and locomotives) that are derived from 

NMIM for all states except California. NMIM was run using 2010 meteorological and fuel data 

to create county-SCC emissions by month for the 2010 nonroad mobile CAP and HAP sources. 

This version of NMIM ran the NR08a version of NONROAD. The run incorporated Bond rule 

revisions to some of the base case inputs and the Bond Rule controls did not take effect until 

future years. NMIM provides nonroad emissions for VOC by three emission modes: exhaust, 

evaporative and refueling. Unlike the onroad sector, refueling emissions from nonroad sources 

are not separated into a different sector. 

EPA default inputs were replaced by state inputs where such data were provided via the 2008 NEI 

process. The 2008 NEI documentation describes this and other details of the NMIM nonroad 

emissions development. CAPs and only the necessary HAPs for the nonroad sector (i.e., BAFM, 

butadiene, and naphthalene) were included. For this study, NMIM was run separately for each 

county. To aid with the processing by SMOKE, the mode was appended to the pollutant name 

and the California NMIM data was replaced with state-supplied data. 

For California, year 2010 nonroad emissions values were interpolated between the 2007 and 

2011 emissions provided by CARB. The CARB-supplied nonroad annual inventory to monthly 

emissions values by using the aforementioned EPA NMIM monthly inventories to compute 

monthly ratios by pollutant and SCC. Some adjustments to the CARB inventory were needed to 

convert the provided total organic gas (TOG) to the VOC that was needed by SMOKE. 

3.2.10 Nonroad Mobile Sources: Commercial Marine C1, C2, and Locomotive (c1c2rail) 

The c1c2rail sector contains CAP and HAP emissions from locomotive and commercial marine 

sources, except for the category 3/residual-fuel (C3) commercial marine vessels (CMV) found in 

the c3marine sector. The “c1c2” portion of this sector name refers to the Class I/II CMV 

emissions, not the railway emissions. Railway maintenance emissions are included in the 

nonroad sector because these are included in the nonroad NMIM monthly inventories. The C3 

CMV emissions are in the c3marine sector. Except for California, the emissions in the c1c2rail 

sector are year 2008 and are composed of the following SCCs: 2280002100 (CMV diesel, 

ports), 2280002200 (CMV diesel, underway), 2285002006 (locomotives diesel line haul Class I), 

2285002007 (locomotives diesel line haul Class II/III), 2285002008 (locomotives diesel line haul 
31

passenger trains), 2285002009 (locomotives diesel line haul commuter lines), and 2285002010 

(locomotives diesel, yard). 

The 2008 NEI Version 2 was the starting point for this sector, but several adjustments were 

made. First, the 2008 NEI point inventory contains rail yard emissions for several states and 

counties. The NEI point and nonpoint inventories were reviewed for counties with significant 

rail yard emissions in both inventories. It was assumed that the point inventory contained more 

accurate information when both inventories contained rail yard emissions. Therefore, nonpoint 

rail yards were removed from the c1c2rail sector for certain counties in California, Maryland, 

Oregon and Arizona. For more information, see the Version 5 2007 platform documentation. 

Analysis of the total rail emissions in the 2008 NEI showed what appeared to be missing rail line 

emissions in Texas. It was determined that line haul emissions from Texas were essentially zero 

in the 2008 NEI. Therefore, all line haul emissions from the 2008 NEI were removed and 

information from an EPA default dataset of Texas line haul emissions was added. These EPA 

line haul emissions are restricted to the Class I and Class II/III operations and add approximately 

52,000 tons of NOX to Texas that would otherwise be missing. 

For several Texas counties, the C1/C2 CMV emissions in the 2008 NEI included EPA gap filled 

values where shape IDs were not populated in the state submittal. The intended Texas submittal 

was often much smaller than the EPA-estimated default value for several counties. An example 

of this is Harris county (FIPS=48201) where the Texas submittal was approximately 1,200 tons 

of NOX for port and underway emissions but not all shape IDs were included. The NEI 

methodology used EPA emissions where Texas did not provide estimates and the resulting 

double count and overestimate of this top-down method resulted in over 49,000 tons of NOX in 

the 2008 NEI in Harris County, Texas. Therefore, the modeling platform used the original Texas 

submittal, did not append any EPA emissions, and summed up port and underway for the 

modeling files to the county level. Similar corrections to these may have been included in 

Version 3 of the 2008 NEI. Other states were impacted by a similar error in the 2008 NEI 

Version 2, but for many of these states alternative data were used as discussed below. 

For California, the California Air Resources Board (CARB) provided year 2007 and 2011 

emissions for all mobile sources, including C1/C2 CMV and rail. These emissions are 

documented in a staff report available at: 

http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf. The modeling platform uses 

2010 emissions interpolated between the 2007 and 2011 emissions. The C1/C2 CMV emissions 

were obtained from the CARB nonroad mobile dataset and include the regulations to reduce 

emissions from diesel engines on commercial harbor craft operated within California waters and 

24 nautical miles of the California baseline. These emissions were developed using Version 1 of 

the CEPAM that supports various California off-road regulations. The locomotive emissions 

were obtained from the CARB trains dataset “ARMJ_RF#2002_ANNUAL_TRAINS.txt”. 

Documentation of the CARB offroad mobile methodology, including c1c2rail sector data, is 

provided here: http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles. The CARB 

inventory TOG emissions were converted to VOC by dividing the inventory TOG by the 

available VOC-to-TOG speciation factor. 
32

Year-2007 inventories provided by MARAMA, SESARM and the MWRPO were used for the 

c1c2rail sector emissions in their respective states. Emissions data from MARAMA rather than 

SESARM was used for Virginia because the SESARM data included some rather large 

emissions for Commuter Lines (SCC=2285002009) that were not reflected in the 2008 NEI nor 

the MARAMA dataset. The MWRPO year-2007 c1c2rail data were obtained from a subset of 

their version 7 emissions modeling file “nrinv.mwrpo_alm.baseCv7.annual.orl.txt”, where 

MWRPO NEI Inventory Format (NIF)-formatted data were converted to SMOKE ORL format. 

The MARAMA dataset was obtained from a subset of their version 3.3 January 27, 2012 vintage 

file “ARINV_2007_MAR_Jan2012.txt”. The SESARM dataset was obtained from a subset of 

the file “nrinv.alm.semap.base07.v093010.orl.txt” developed for the Southeastern Modeling, 

Analysis, and Planning (SEMAP) project. All RPO datasets were edited to remove non-c1c2rail 

sources. 

3.2.11 Nonroad mobile sources: C3 commercial marine (c3marine) 

The c3marine sector emissions data were developed based on a 4-km resolution ASCII raster 

format dataset used since the Emissions Control Area-International Marine Organization (ECAIMO) project began in 2005, then known as the Sulfur Emissions Control Area (SECA). These 

emissions consist of large marine diesel engines (at or above 30 liters/cylinder) that until very 

recently, were allowed to meet relatively modest emission requirements, often burning residual 

fuel. The emissions in this sector are comprised of primarily foreign-flagged ocean-going 

vessels, referred to as Category 3 (C3) CMV ships. 

The c3marine inventory includes these ships in several intra-port modes (cruising, hoteling, 

reduced speed zone, maneuvering, and idling) and underway mode and includes near-port 

auxiliary engines. An overview of the C3 ECA Proposal to the International Maritime 

Organization (EPA-420-F-10-041, August 2010) project and future-year goals for reduction of 

NOX, SO2, and PM C3 emissions can be found at: 

http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09019.pdf. The resulting ECA-IMO 

coordinated strategy, including emission standards under the Clean Air Act for new marine 

diesel engines with per-cylinder displacement at or above 30 liters, and the establishment of 

Emission Control Areas is at: http://www.epa.gov/oms/oceanvessels.htm. 

The ECA-IMO emissions data were converted to SMOKE point-source ORL input format as 

described in http://www.epa.gov/ttn/chief/conference/ei17/session6/mason.pdf, thereby allowing 

for the emissions to be allocated to modeling layers above the surface layer. As described in the 

paper, the ASCII raster dataset was converted to latitude-longitude, mapped to state/county FIPS 

codes that extended up to 200 nautical miles (nm) from the coast, assigned stack parameters, and 

monthly ASCII raster dataset emissions were used to create monthly temporal profiles. Counties 

were assigned as extending up to 200nm from the coast because this was the distance to the edge 

of the U.S. Exclusive Economic Zone (EEZ), a distance that defines the outer limits of ECAIMO controls for these vessels. All non-US emissions (i.e., in waters considered outside of the 

200nm EEZ, and hence out of the U.S. territory) are assigned a dummy state/county FIPS 

code=98001. The SMOKE-ready data were cropped from the original ECA-IMO data to cover 

only the 36-km CMAQ domain, which is the largest domain used for this effort, and larger than 

the 12km domain used in this project. 
33

The base year ECA inventory is 2002 and consists of these CAPs: PM10, PM2.5, CO, CO2, 

NH3, NOX, SOX (assumed to be SO2), and Hydrocarbons (assumed to be VOC). The EPA 

developed regional growth (activity-based) factors that we applied to create the 2007v5 

inventory from the 2002 data. These growth factors are provided in Table 3-4. The East Coast 

and Gulf Coast regions were divided along a line roughly through Key Largo (longitude 80° 26’ 

West). 
34

Table 3-4. Growth factors to project the 2002 ECA inventory to 2010 

Region EEZ FIPS NOX PM10 PM2.5 VOC CO SO2

East Coast (EC) 85004 1.258 0.478 0.475 1.436 1.436 0.513

Gulf Coast (GC) 85003 1.096 0.415 0.411 1.251 1.252 0.448

North Pacific (NP) 85001 1.158 0.452 0.444 1.310 1.309 0.508

South Pacific (SP) 85002 1.314 0.499 0.495 1.489 1.486 0.580

Great Lakes (GL) n/a 1.061 0.387 0.384 1.157 1.156 0.408

Outside ECA 98001 1.300 1.396 1.396 1.396 1.396 1.396

A modification to the original ECA-IMO c3marine dataset include updating the state of 

Delaware county total emissions to reflect comments received during the Cross-State Air 

Pollution Rule (CSAPR) emissions modeling platform development: 

http://www.epa.gov/ttn/chief/emch/index.html#final. The original ECA-IMO inventory also did 

not delineate between ports and underway (or other C3 modes such as hoteling, maneuvering, 

reduced-speed zone, and idling) emissions; however, we used a U.S. ports spatial surrogate 

dataset to assign the ECA-IMO emissions to ports and underway SCCs - 2280003100 and 

2280003200, respectively. This has no effect on temporal allocation or speciation because all C3 

emissions, unclassified/total, port and underway, share the same temporal and speciation 

profiles. 

Canadian near-shore emissions were assigned to province-level FIPS codes and paired those to 

region classifications for British Columbia (North Pacific), Ontario (Great Lakes) and Nova 

Scotia (East Coast). The assignment of U.S. FIPS was also restricted to state-federal water 

boundaries data from the Mineral Management Service (MMS) that extended only 

(approximately) 3 to 10 miles off shore. Emissions outside the 3 to 10 mile MMS boundary but 

within the approximately 200 nm EEZ boundary in Figure 2 8 were projected to year 2010 using 

the same regional adjustment factors as the U.S. emissions; however, the FIPS codes were 

assigned as “EEZ” FIPS. Note that state boundaries in the Great Lakes are an exception, 

extending through the middle of each lake such that all emissions in the Great Lakes are assigned 

to a U.S. county or Ontario. The classification of emissions to U.S. and Canadian FIPS codes is 

primarily needed only for inventory summaries and is irrelevant for air quality modeling except 

potentially for source apportionment of states contributions to transport. 

Factors were applied to compute HAP emissions (based on emissions ratios) to VOC to obtain

HAP emissions values. Table 3-5 below shows these factors. Because HAPs were computed 

directly from the CAP inventory and the calculations are therefore consistent, the entire c3marine 

sector utilizes CAP-HAP VOC integration to use the VOC HAP species directly, rather than 

VOC speciation profiles. 
35

Table 3-5. HAP emission ratios for generation of HAP emissions from criteria emissions for 

C3 commercial marine vessels 

Pollutant Apply to

Pollutant 

Code Factor

Acetaldehyde VOC 75070 0.0002286

Benzene VOC 71432 9.80E-06

Formaldehyde VOC 50000 0.0015672

3.2.12 Emissions from Canada, Mexico and Offshore Drilling Platforms (othpt, othar, othon) 

The emissions from Canada, Mexico, and offshore drilling platforms are included as part of three 

emissions modeling sectors: othpt, othar, and othon. The “oth” refers to the fact that these 

emissions are usually “other” than those in the U.S. state-county geographic FIPS code, and the 

third and fourth characters provide the SMOKE source types: “pt” for point, “ar” for “area and 

nonroad mobile”, and “on” for onroad mobile. All “oth” emissions are CAP-only inventories. 

For Canada, year-2006 Canadian emissions were used but several modifications were applied to 

the inventories: 

1. Wildfires or prescribed burning were not included because Canada does not include these 

inventory data in their modeling. 

2. In-flight aircraft emissions were not included because we do not include these for the 

U.S. and we do not have a finalized approach to include in our modeling. 

3. A 75% reduction (“transport fraction”) was applied to PM for the road dust, agricultural, 

and construction emissions in the Canadian “afdust” inventory. This approach is more 

simplistic than the county-specific approach used for the U.S., but a comparable approach 

was not available for Canada. 

4. Speciated VOC emissions from the ADOM chemical mechanism were not included 

because we use speciated emissions from the CB5 chemical mechanism that Canada also 

provided.

5. Residual fuel CMV (C3) SCCs (22800030X0) were removed because these emissions are 

included in the c3marine sector, which covers not only emissions close to Canada but 

also emissions far at sea. Canada was involved in the inventory development of the 

c3marine sector emissions. 

6. Wind erosion (SCC=2730100000) and cigarette smoke (SCC=2810060000) emissions 

were removed from the nonpoint (nonpt) inventory; these emissions are also absent from 

our U.S. inventory. 

7. Quebec PM2.5 emissions (2,000 tons/yr) were removed for one SCC (2305070000) for 

Industrial Processes, Mineral Processes, Gypsum, and Plaster Products due to corrupt 
36

fields after conversion to SMOKE input format. This error should be corrected in a 

future inventory. 

8. Excessively high CO emissions were removed from Babine Forest Products Ltd (British 

Columbia SMOKE plantid=’5188’) in the point inventory. 

9. The county part of the state/county FIPS code field in the SMOKE inputs were modified 

in the point inventory from “000” to “001” to enable matching to existing temporal 

profiles. 

For Mexico, year 2008 emissions were used that are projections of their 1999 inventory 

originally developed by Eastern Research Group Inc., (ERG, 2006) as part of a partnership 

between Mexico's Secretariat of the Environment and Natural Resources (Secretaría de Medio 

Ambiente y Recursos Naturales-SEMARNAT) and National Institute of Ecology (Instituto 

Nacional de Ecología-INE), the U.S. EPA, the Western Governors' Association (WGA), and the 

North American Commission for Environmental Cooperation (CEC). This inventory includes 

emissions from all states in Mexico. A background on the development of year-2008 Mexico 

emissions from the 1999 inventory is available at: 

http://www.wrapair.org/forums/ef/inventories/MNEI/index.html. 

The offshore emissions include point source offshore oil and gas drilling platforms. We used 

emissions from the 2008 NEI point source inventory. The offshore sources were provided by the 

Mineral Management Services (MMS). 

3.2.13 SMOKE-ready non-anthropogenic chlorine inventory 

The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cl2) 

concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 36 km and 12 km 

resolution were available and were not modified other than the name “CHLORINE” was 

changed to “CL2” because that is the name required by the CMAQ model. The same data was 

used as in the CAP and HAP 2002-based Platform was used. See 

ftp://ftp.epa.gov/EmisInventory/2002v3CAPHAP/ documentation for additional details.

3.3 Emissions Modeling Summary 

CMAQ requires emissions data to be input as hourly rates of specific gas and particle species for 

the horizontal and vertical grid cells contained within the modeled region (i.e., modeling 

domain). To provide emissions in the form and format required by the model, it is necessary to 

“pre-process” the “raw” emissions (i.e., emissions input to SMOKE) for the sectors described 

above. In brief, the process of emissions modeling transforms the emissions inventories from 

their original temporal resolution, pollutant resolution, and spatial resolution into the hourly, 

speciated, gridded resolution required by the air quality model. The pre-processing steps 

involving temporal allocation, spatial allocation, pollutant speciation, and vertical allocation of 

point sources are referred to as emissions modeling. 

The temporal resolution of the emissions inventories input to SMOKE for the modeling platform 

varies across sectors, and may be hourly, monthly, or annual total emissions. The spatial 

resolution, which also can be different for different sectors, may be at the level of individual 
37

point sources, county totals, province totals for Canada, or municipio totals for Mexico. This 

section provides some basic information about the tools and data files used for emissions 

modeling as part of the Version 5 platform. The emissions inventories were discussed in detail 

earlier. Therefore, the descriptions of data in this section are limited to the ancillary data used by 

SMOKE to perform the emissions modeling steps. 

3.3.1 The SMOKE Modeling System 

For this study, emission inventories were processed into CMAQ-ready inputs using SMOKE 

version 3.1. SMOKE executables and source code are available from the Community Multiscale 

Analysis System (CMAS) Center at http://www.cmascenter.org. Additional information about 

SMOKE is available from http://www.smoke-model.org. For sectors that have plume rise, the inline emissions capability of CMAQ was used, and therefore source-based emissions files were 

created rather than the much larger three-dimensional files. For quality assurance purposes, 

emissions totals by specie for the entire model domain are output as reports that are then 

compared to inventory level reports generated by SMOKE to ensure mass is not lost or gained 

during this conversion process. 

3.3.2 Key Emissions Modeling Settings 

When preparing emissions for the air quality model, emissions for each sector are processed 

separately through SMOKE. Then, the final merge program (Mrggrid) is run to combine the 

model-ready, sector-specific emissions across sectors. The SMOKE settings in the run scripts 

and the data in the SMOKE ancillary files control the approaches used for the individual 

SMOKE programs for each sector. Table 3-6 summarizes the major processing steps of each 

platform sector. The “Spatial” column shows the spatial approach: “point” indicates that 

SMOKE maps the source from a point location (i.e., latitude and longitude) to a grid cell; 

“surrogates” indicates that some or all of the sources use spatial surrogates to allocate county 

emissions to grid cells; and “area-to-point” indicates that some of the sources use the SMOKE 

area-to-point feature to grid the emissions. The “Speciation” column indicates that all sectors 

use the SMOKE speciation step, though biogenics speciation is done within BEIS3 and not as a 

separate SMOKE step. The “Inventory resolution” column shows the inventory temporal 

resolution from which SMOKE needs to calculate hourly emissions. Note that for some sectors 

(e.g., onroad, beis), there is no input inventory. Instead activity data and emission factors are 

used in combination with meteorological data to compute hourly emissions. 

Finally, the “plume rise” column indicates the sectors for which the “in-line” approach is used. 

These sectors are the only ones which will have emissions in aloft layers, based on plume rise. 

The term “in-line” means that the plume rise calculations are done inside of the air quality model 

instead of being computed by SMOKE. The air quality model computes the plume rise using the 

stack data and the hourly air quality model inputs found in the SMOKE output files for each 

model-ready emissions sector. The height of the plume rise determines the model layer into 

which the emissions are placed. The c3marine and ptfire sectors are the only sectors with only 

“in-line” emissions, meaning that all of the emissions are placed in aloft layers and thus there are 

no emissions for those sectors in the two-dimensional, layer-1 files created by SMOKE. In 

addition to the other settings, no grouping of stacks was performed using the PELVCONFIG file 

because grouping done for “in-line” processing will not give identical results as “offline” (i.e., 

processing whereby SMOKE creates 3-dimensional files). The only way to get the same results 
38

between in-line and offline is to choose to have no grouping. 

Table 3-6. Key emissions modeling steps by sector 

Platform sector

Spatial Speciation

Inventory 

resolution Plume rise

Ptipm Point Yes daily & hourly in-line

Ptnonipm Point Yes annual in-line

Ptfire Point Yes Daily in-line

Othpt Point Yes annual in-line

c3marine Point Yes annual in-line

Ag Surrogates Yes annual & monthly

Afdust Surrogates Yes annual

Beis pre-gridded landuse in BEIS computed hourly

c1c2rail Surrogates Yes annual

Nonpt surrogates & areato-point Yes

annual & monthly for 

ag burning and 

SESARM open 

burning

Nonroad surrogates & areato-point Yes monthly

Onroad Surrogates Yes computed hourly

onroad_rfl Surrogates Yes computed hourly

Othar Surrogates Yes annual

Othon Surrogates Yes annual

3.3.3 Spatial Configuration 

For this study, SMOKE and CMAQ were run for a 12-km modeling domain shown in Figure 3-1 

(12US1). The grid used a Lambert-Conformal projection, with Alpha = 33, Beta = 45 and

Gamma = -97, with a center of X = -97 and Y = 40. Later sections provide details on the spatial 

surrogates and area-to-point data used to accomplish spatial allocation with SMOKE.
39

Figure 3-1. CMAQ Modeling Domain

3.3.4 Chemical Speciation Configuration 

The emissions modeling step for chemical speciation creates “model species” needed by the air 

quality model for a specific chemical mechanism. These model species are either individual 

chemical compounds or groups of species, called “model species.” The chemical mechanism used 

for this study is the Carbon Bond 05 (CB05) mechanism (Yarwood, 2005) with secondary 

organic aerosol (SOA) and HONO enhancements as described in

http://www.cmascenter.org/help/model_docs/cmaq/4.7/ RELEASE_NOTES.txt. The mapping of 

inventory pollutants to model species is shown in Table 3-7. From the perspective of emissions 

preparation, the CB05 with SOA mechanism is the same as was used in the 2005 platform. It 

should be noted that the BENZENE model species is not part of CB05 in that the concentrations 

of BENZENE do not provide any feedback into the chemical reactions (i.e., it is not “inside” the 

chemical mechanism). Rather, benzene is used as a reactive tracer and as such is impacted by 

the CB05 chemistry. BENZENE, along with several reactive CB05 species (such as TOL and 

XYL) plays a role in SOA formation in CMAQ. 
40

Table 3-7. Model Species Produced by SMOKE for CB05 

Inventory Pollutant Model Species Model Species Description

CO CO Carbon monoxide

NOX NO Nitrogen oxide

NO2 Nitrogen dioxide

SO2 SO2 Sulfur dioxide

SULF Sulfuric acid vapor

NH3 NH3 Ammonia

VOC ALD2 Acetaldehyde

ALDX Propionaldehyde and higher aldehydes

ETH Ethene

ETHA Ethane

ETOH Ethanol

FORM Formaldehyde

IOLE Internal olefin carbon bond (R-C=C-R)

ISOP Isoprene

MEOH Methanol

OLE Terminal olefin carbon bond (R-C=C)

PAR Paraffin carbon bond

TOL Toluene and other monoalkyl aromatics

XYL Xylene and other polyalkyl aromatics

Various additional VOC 

species from the biogenics 

model which do not map to 

the above model species

TERP Terpenes

PM10 PMC Coarse PM > 2.5 microns and  10 microns

PM2.5 PEC Particulate elemental carbon  2.5 microns

PNO3 Particulate nitrate  2.5 microns

POC Particulate organic carbon (carbon only)  2.5 

microns

PSO4 Particulate sulfate  2.5 microns

PMFINE Other particulate matter  2.5 microns

The approach for speciating PM2.5 emissions supports both CMAQ 4.7.1 with five species (i.e., 

AE5) and CMAQ 5.0 and includes speciation of PM2.5 into 17 PM model species (i.e., AE6). 

The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach 

were developed from the SPECIATE4.3 database 

(http://www.epa.gov/ttn/chief/software/speciate) and is the EPA's repository of TOG and PM 

speciation profiles of air pollution sources. A few of the profiles used in the v5 platform will be 

published in later versions of the SPECIATE database. The SPECIATE database development 

and maintenance is a collaboration involving the EPA’s ORD, OTAQ, and the Office of Air 

Quality Planning and Standards (OAQPS), and Environment Canada (EPA, 2006a). The 

SPECIATE database contains speciation profiles for TOG, speciated into individual chemical 

compounds, VOC-to-TOG conversion factors associated with the TOG profiles, and speciation 

profiles for PM2.5. The database also contains the PM2.5, speciated into both individual chemical 
41

compounds (e.g., zinc, potassium, manganese, lead) and into the “simplified” PM2.5 components 

used in the air quality model. These simplified components for AE5 are: 

 PSO4 : primary particulate sulfate

 PNO3: primary particulate nitrate

 PEC: primary particulate elemental carbon

 POC: primary particulate organic carbon

 PMFINE: other primary particulate, less than 2.5 micrograms in diameter

NOX can be speciated into NO, NO2, and/or HONO. For the non-mobile sources, a single 

profile is used “NHONO” to split NOX into NO and NO2 with 10% NO2 and 90% NO. For the 

mobile sources except for onroad (including nonroad, c1c2rail, c3marine, othon sectors) and for 

specific SCCs in othar and ptnonipm, the profile “HONO” splits NOX into NO, NO2, and 

HONO with 90% NO, 9.2% NO2 and 0.8% HONO. The onroad sector does not use the 

“HONO” profile to speciate NOX. Instead, MOVES2010b produces speciated NO, NO2, and 

HONO by source, including emission factors for these species in the emission factor tables used 

by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOX. The 

NO fraction varies by heavy duty versus light duty, fuel type, and model year. The NO2 fraction 

= 1 – NO – HONO. For more details on the NOX fractions within MOVES, see 

http://www.epa.gov/otaq/models/moves/documents/420r12022.pdf. The SMOKE-MOVES 

system is configured to model these species directly without further speciation. 

The approach for speciating VOC emissions from non-biogenic sources has the following 

characteristics: 1) for some sources, HAP emissions are used in the speciation process to allow 

integration of VOC and HAP emissions in the NEI; and, 2) for some mobile sources, 

“combination” profiles are specified by county and month and emission mode (e.g., exhaust, 

evaporative). SMOKE computes the resultant profile on-the-fly given the fraction of each specific 

profile specified for the particular county, month and emission mode. The SMOKE feature called 

the GSPRO_COMBO file supports this approach. 

The VOC speciation approach for the 2010 Platform includes HAP emissions from the NEI in the 

speciation process for some sectors. That is instead of speciating VOC to generate all of the 

species needed by the model, emissions of the 4 HAPs, benzene, acetaldehyde, formaldehyde and

methanol (BAFM) from the NEI were integrated with the NEI VOC. The integration process 

combines the BAFM HAPs with the VOC in a way that does not double-count emissions and

uses the BAFM directly in the speciation process. Generally, the HAP emissions from the NEI 

are believed to be more representative of emissions of these compounds than their generation via 

VOC speciation. 

The BAFM HAPs were chosen for this special treatment because, with the exception of 

BENZENE, they are the only explicit VOC HAPs in the base version of CMAQ 4.7 model. By

“explicit VOC HAPs,” we mean model species that participate in the modeled chemistry using 

the CB05 chemical mechanism. The use of these HAP emission estimates along with VOC is 

called “HAP-CAP integration”. BENZENE was chosen because it was added as a model species 

in the base version of CMAQ 4.7, and there was a desire to keep its emissions consistent between

multi- pollutant and base versions of CMAQ. 
42

For specific sources, especially within the onroad and onroad_rfl sectors, we included ethanol in 

our integration. To differentiate when a source was integrating BAFM versus EBAFM (ethanol 

in addition to BAFM), the speciation profiles which do not include ethanol are referred to as an 

“E-profile”, for example E10 headspace gasoline evaporative speciation profile 8763 where 

ethanol is speciated from VOC, versus 8763E where ethanol is obtained directly from the 

inventory. The specific profiles used in 2010 are the same as used for the 2007 platform (see 

2007 speciation in Table 3-6 in the 2007v5 TSD). The only differences between 2010 and 2007 

are the GSPRO_COMBOs, which represent a different mixture of E0 and E10 by county 

between the two modeling years. 

The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory 

formats other than PTDAY (the format used for the ptfire sector). SMOKE allows the user to 

specify the particular HAPs to integrate and the particular sources to integrate. The HAPs to 

integrate are specified in the INVTABLE file, and the sources to integrate are based on the 

NHAPEXCLUDE file (which lists the sources that are excluded from integration). For the 

“integrate” sources, SMOKE subtracts the “integrate ” HAPs from the VOC (at the source level) 

to compute emissions for the new pollutant “NONHAPVOC.” The user provides 

NONHAPVOC-to-NONHAPTOG factors and NONHAPTOG speciation profiles. SMOKE 

computes NONHAPTOG and then applies the speciation profiles to allocate the NONHAPTOG 

to the other CMAQ VOC species not including the integrated HAPs. 

CAP-HAP integration was considered for all sectors and “integration criteria” were developed for 

some of those. Table 3-8 summarizes the integration approach for each platform sector. For the 

c1c2rail sector, the integration criteria were (1) that the source had to have at least one of the 4 

HAPs and (2) that the sum of BAFM could not exceed the VOC emissions. For the nonpt sector, 

the following integration criteria were used to determine the sources to integrate:

1. Any source for which the sum of B, A, F, or M is greater than the VOC was not 

integrated, since this clearly identifies sources for which there is an inconsistency between 

VOC and VOC HAPs. 

2. For some source categories (those that comprised 80% of the VOC emissions), sources 

were selected for integration in the category per specific criteria. For most of these source 

categories, sources may be integrated if they had the minimum combination of B, A, F, 

and M. For some source categories, all sources were designated as “no-integrate”. 

3. For source categories that do not comprise the top 80% of VOC emissions, as long as the 

source has emissions of one of the B, F, A or M pollutants, then it can be integrated. 
43

Table 3-8. Integration status of benzene, acetaldehyde, formaldehyde and methanol 

(BAFM) for each platform sector 

Platform

Sector

Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A), Formaldehyde (F) and 

Methanol (M)

Ptipm No integration because emissions of BAFM are relatively small for this sector

Ptnonipm No integration because emissions of BAFM are relatively small for this sector and it is not 

expected that criteria for integration would be met by a significant number of sources

Ptfire No integration.

Ag N/A—sector contains no VOC

Afdust N/A—sector contains no VOC

Biog N/A—sector contains no inventory pollutant “VOC”; but rather specific VOC species

C1c2rail Partial integration

C3marine Full integration

Nonpt Partial integration

Nonroad

Partial integration—did not integrate California emissions, CNG or LPG sources (SCCs 

beginning with 2268 or 2267) because NMIM computed only VOC and not any HAPs for 

these SCCs.

Onroad Full integration

Othar No integration—not the NEI

Othon No integration—not the NEI

Othpt No integration—not the NEI

The SMOKE feature to compute speciation profiles from mixtures of other profiles in userspecified proportions was used in this project. The combinations are specified in the 

GSPRO_COMBO ancillary file by pollutant (including pollutant mode, e.g., EXH VOC), state 

and county (i.e., state/county FIPS code) and time period (i.e., month). This feature was used for 

onroad and nonroad mobile and gasoline-related related stationary sources. Since the ethanol

content varies spatially (e.g., by state or sources use fuels with varying ethanol content, and

therefore the speciation profiles require different combinations of gasoline and E10 profiles by 

county), temporally (e.g., by month) and by modeling year (i.e., future years have more thanol) 

the combo feature allows combinations to be specified at various levels for different years. 

The INVTABLE and NHAPEXCLUDE SMOKE input files have a critical function in the VOC 

speciation process for emissions modeling cases utilizing HAP-CAP integration, as is done for 

the 2010 Platform. Two different types of INVTABLE files were developed to use with different 

sectors of the platform. For sectors in which we chose no integration across the entire sector a 

“no HAP use” INVTABLE was developed in which the “KEEP” flag is set to “N” for BAFM 

pollutants. Thus, any BAFM pollutants in the inventory input into SMOKE are dropped. This 

both avoids double-counting of these species and assumes that the VOC speciation is the best 

available approach for these species for the sectors using the approach. The second INVTABLE 

is used for sectors in which one or more sources are integrated and causes SMOKE to keep the 

BAFM pollutants and indicates that they are to be integrated with VOC (by setting the “VOC or 

TOG component” field to “V” for all four HAP pollutants. This integrate INVTABLE is further 

differentiated into sectors that integrate BAFM versus those that integrate EBAFM (e.g., the 

onroad and onroad_rfl sectors). 
44

Unlike other sectors, the onroad sector has pre-speciated PM. This speciated PM comes from the 

MOVES model and is processed through the SMOKE-MOVES system. Unfortunately, the 

MOVES2010b speciated PM does not map 1-to-1 to either the AE5 or AE6 species. Table 3-9 

shows the relationship between MOVES2010b exhaust PM2.5 related species and CMAQ AE5 

PM species. 

Table 3-9. MOVES exhaust PM species versus AE5 species 

MOVES2010b Pollutant Name

Variable 

name for 

Equations

Relation to AE5 model species

Primary Exhaust PM2.5 – Total PM25_TOTAL

Primary PM2.5 - Organic Carbon PM25OM Sum of POC, PNO3 and 

PMFINE

Primary PM2.5 - Elemental 

Carbon

PM25EC PEC

Primary PM2.5 - Sulfate 

Particulate

PM25SO4 PSO4

MOVES species are related as follows: 

PM25_TOTAL = PM25EC + PM25OM + PSO4 

The five CMAQ AE5 species also sum to total PM2.5: 

PM2.5 = POC+PEC+PNO3+PSO4+PMFINE 

The basic problem is to differentiate MOVES species “PM25OM” into the component AE5 

species (POC, PNO3 and PMFINE). The Moves2smkEF post-processor script takes the 

MOVES2010b species (EF tables) and calculates the appropriate AE5 PM2.5 species and 

converts them into a format that is appropriate for SMOKE (see http://www.smokemodel.org/version3.1/html/ch05s02s04.html for details on the Moves2smkEF script). 

For brake wear and tire wear PM, total PM2.5 (not speciated) comes directly from MOVES2010b. 

These PM modes are speciated by SMOKE. PMFINE from onroad exhaust is further speciated 

by SMOKE into the component AE6 species. 

Speciation profiles for use with BEIS are not included in SPECIATE. The 2010 Platform uses 

BEIS3.14 and includes a species (SESQ) that was not in BEIS3.13 (the version used for the 2002 

Platform). This species was mapped to the CMAQ species SESQT. The profile code associated 

with BEIS3.14 profiles for use with CB05 was “B10C5.”

3.3.4 Temporal Processing Configuration 

Temporal allocation or temporalization is the process of distributing aggregated emissions to a 

finer temporal resolution, such as converting annual emissions to hourly emissions. While the 

total emissions are important, the timing of the occurrence of emissions is also essential for 

accurately simulating ozone, PM, and other pollutant concentrations in the atmosphere. 
45

Typically, emissions inventories are annual or monthly in nature. Temporalization takes these 

annual emissions and distributes them to the month, the monthly emissions to the day, and the 

daily emissions to the hour. This process is typically done by applying temporal profiles—

monthly, day of the week, and diurnal—to the inventories. 

The monthly, weekly, and diurnal temporal profiles and associated cross references used to 

create the hourly emissions inputs for the air quality model were similar to those used for the 

2005v4.3 platform. Some new methodologies are introduced in this platform and updated 

profiles are discussed. Temporal factors are typically applied to the inventory by some 

combination of country, state, county, SCC, and pollutant. 

Table 3-10 summarizes the temporal aspect of the emissions processing configuration. It

compares the key approaches used for temporal processing across the sectors. The temporal 

aspects of SMOKE processing are controlled through (a) the scripts T_TYPE (Temporal type) 

and M_TYPE (Mergetype) settings and (b) ancillary data files. In the table, “Daily temporal 

approach” refers to the temporal approach for getting daily emissions from the inventory using 

the Temporal program. The “Merge processing approach” refers to the days used to represent 

other days in the month for the merge step. If not “all”, then the SMOKE merge step runs only 

for representative days, which could include holidays as indicated by the right-most column. In 

addition to the resolution, temporal processing includes a ramp-up period for several days prior to

January 1, 2010, intended to mitigate the effects of initial condition concentrations. The ramp up 

period for the national 12km grid was 10 days. For most sectors, the emissions from late 

December of 2009 were used to provide emissions for the end of December, 2010.

The Flat File 2010 format (FF10) is a new inventory format for SMOKE that provides a more 

consolidated format for monthly, daily, and hourly emissions inventories. Previously, 12 

separate inventory files would be required to process monthly inventory data. With the FF10 

format, a single inventory file can contain emissions for all 12 months and the annual emissions 

in a single record. This helps simplify the management of numerous inventories. Similarly, 

individual records contain data for all days in a month and all hours in a day in the daily and 

hourly FF10 inventories, respectively. 

SMOKE 3.1 prevents the application of temporal profiles on top of the “native” resolution of the 

inventory. For example, a monthly inventory should not have annual to month temporalization 

applied; rather, it should only have month to day and diurnal temporalization. This is

particularly important when sectors have a mix of annual, monthly, daily, and/or hourly 

inventories (e.g. the nonpt sector). The flags that control temporalization for a mixed set of 

inventories are discussed in the SMOKE documentation.
46

Table 3-10. Temporal Settings Used for the Platform Sectors in SMOKE 

Platform sector

Inventory 

resolution

Monthly 

profiles 

used?

Daily 

temporal 

approach 

1,2

Merge 

processing 

approach 1,3

Process 

Holidays as 

separate 

days?

Ptipm daily & hourly All all yes

Ptnonipm annual yes Mwdss all yes

Ptfire Daily All all yes

Ag annual & monthly yes all all yes

Afdust annual yes Week all yes

Beis hourly n/a all yes

c3marine annual yes Aveday aveday

c1c2rail annual yes Mwdss mwdss

Nonpt annual & monthly yes All all yes

Nonroad monthly Mwdss mwdss yes

Onroad annual & monthlyaall all yes

onroad_rfl annual & monthlya All all yes

Othar annual yes Week week

Othon annual yes Week week

Othpt annual yes Mwdss mwdss

1 Definitions for processing resolution:

all = hourly emissions computed for every day of the year

week = hourly emissions computed for all days in one “representative” week, representing all weeks for each month, which 

means emissions have day-of-week variation, but not week-to-week variation within the month

mwdss = hourly emissions for one representative Monday, representative weekday, representative Saturday and representative 

Sunday for each month, which means emissions have variation between Mondays, other weekdays, Saturdays and Sundays 

within the month, but not week-to-week variation within the month. Also Tuesdays, Wednesdays and Thursdays are treated the 

same.

aveday = hourly emissions computed for one representative day of each month, which means emissions for all days of each 

month are the same.

2 Daily temporal approach refers to the temporal approach for getting daily emissions from the inventory using the Temporal 

program. The values given are the values of the L_TYPE setting.

3 Merge processing approach refers to the days used to represent other days in the month for the merge step. If not “all”, then the 

SMOKE merge step just run for representative days, which could include holidays as indicated by the rightmost column. The values 

given are the values of the M_TYPE setting.

a For onroad and onroad_rfl, the annual and monthly refers to activity data (VMT and VPOP). Emissions are computed on an 

hourly basis.

For the EGU emissions in the ptipm sector, hourly CEM NOX and SO2 data were used directly 

for sources that match CEMs. For other pollutants, hourly CEM heat input data were used to 

allocate the NEI annual values. For sources not matching CEM data (“non-CEM” sources), daily 

emissions were computed from the NEI annual emissions using a structured query language 

(SQL) program and state-average CEM data. To allocate annual emissions to each month, statespecific three-year averages of 2008-2010 CEM data were created. These average annual-tomonth factors were assigned to non-CEM sources within each state. To allocate the monthly 

emissions to each day, the 2010 CEM data to compute state-specific month-to-day factors, 

averaged across all units in each state. These daily emissions were calculated outside of 

SMOKE and the resulting daily inventory is used as an input into SMOKE. 
47

The daily-to-hourly allocation was performed in SMOKE using diurnal profiles. The statespecific and pollutant-specific diurnal profiles for use in allocating the day-specific emissions for 

non-CEM sources in the ptipm sector were updated. The 2010 CEM data was used to create 

state-specific, day-to-hour factors, averaged over the whole year and all units in each state. 

Diurnal factors were calculated using CEM SO2 and NOX emissions and heat input. SO2 and 

NOX-specific factors were computed from the CEM data for these pollutants. All other 

pollutants used factors created from the hourly heat input data. The resulting profiles were 

assigned by state and pollutant. 

Two updated diurnal temporal profiles were incorporated into the 2010 modeling platform. For 

all agricultural burning, we used a diurnal temporal profile (McCarty et al., 2009) that puts more 

of the emissions during the actual work day and suppresses the emissions during the middle of 

the night was used. Note that all states used a uniform day of week profile for all agricultural 

burning emissions, except for the following states that for which state-specific day of week 

profiles were used: Arkansas, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma, 

and Texas. For residential wood combustion, a profile was used that placed more of the 

emissions in the morning and the evening when people are typically using these sources. This 

profile is based on an average of 2004 MANE-VU survey based temporal profiles (see 

http://www.marama.org/publications_folder/ResWoodCombustion/ Final_report.pdf). When this 

profile was compared to a concentration-based analysis of aethalometer measurements in 

Rochester, NY (Wang et al. 2011) for various seasons and day of the week it was found that the 

updated RWC profile generally tracked the concentration based temporal patterns. 

The temporal profile assignments for the Canadian 2006 inventory were provided by 

Environment Canada along with the inventory. They provided profile assignments that rely on 

the existing set of temporal profiles in the 2002 Platform. For point sources, they provided 

profile assignments by PLANTID. 

3.3.5 Meteorological-based Temporal Profiles 

A significant improvement over previous platforms is the introduction of meteorologically-based 

temporalization. We recognize that there are many factors that impact the timing of when 

emissions occur. The benefits of utilizing meteorology as method of temporalizing are: (1) a 

consistent meteorological dataset as is used by the AQ model (e.g. WRF) is available; (2) the 

meteorological model data is highly resolved in terms of spatial resolution; and (3) the 

meteorological variables vary at hourly resolution which can translate to hour-specific 

temporalization. 

The SMOKE program GenTPRO provides a method for developing meteorologically-based 

temporalization. Currently, the program can utilize three types of temporal algorithms: RWC, 

agricultural livestock ammonia, and a generic meteorology based algorithm. For the 2007 

platform, we used the RWC and ag NH3 GenTPRO generated profiles. GenTPRO reads in 

gridded meteorology data (MCIP) and spatial surrogates and uses the specified algorithm to 

produce a new temporal profile that can be input into SMOKE. The meteorological variables 

and the resolution of the generated temporal profile (hourly, daily, etc.) depend on the algorithm 

and the run parameters. For more details on the development of these algorithms and running 

GenTPRO, see the GenTPRO documentation http://www.smoke-
48

model.org/version3.1/GenTPRO_TechnicalSummary_Aug2012_Final.pdf and the SMOKE 

manual section http://www.smoke-model.org/version3.1/html/ch05s03s07.html. 

For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the 

temporal allocation of emissions to days. GenTPRO was run to create an annual-to-day temporal 

profile for the RWC sources within the nonpt sector. These generated profiles distribute annual 

RWC emissions to the coldest days of the year. On days where the minimum temperature does 

not drop below a user-defined threshold, RWC emissions are zero. Conversely, the program 

temporally allocates the largest percentage of emissions to the coldest days. Similar to other 

temporal allocation profiles, the total annual emissions do not change, just the distribution of the 

emissions within the year. Initially, the RWC algorithm used a default temperature threshold of 

50  ̊F. For most of the country, this produced a reasonable distribution of emissions, but for a 

few Southern counties all of the emissions were compressed into a few days creating excessively 

high daily emissions. GenTPRO was then modified to accept an optional input that defines a 

county/state specific alternative temperature threshold. In addition, an alternative RWC 

algorithm was created to avoid negative RWC emissions when the daily minimum temperature 

was greater than 53.3  ̊F. For the v5 platform, the alternative RWC algorithm was used for the 

whole country, with the default 50  ̊F threshold for the majority of the states, and a 60  ̊F 

threshold for the following states: Alabama, Arizona, California, Florida, Georgia, Louisiana, 

Mississippi, South Carolina, and Texas. 

For the agricultural livestock NH3 algorithm, GenTPRO algorithm is based on the Russel and 

Cass (1986) equation. This algorithm uses county-average hourly temperature and wind speed to 

calculate the temporal profile. GenTPRO was run to create month-to-hour temporal profiles for 

these sources. Because these profiles distribute to the hour based on monthly emissions, the 

emissions will either come from a monthly inventory or from an annual inventory that has been 

temporalized already to the month. 

For the onroad and onroad_rfl sectors, meteorology is not used in the development of the 

temporal profiles; rather, but meteorology impacts the calculation of the hourly emissions 

through the program Movesmrg. The result is that the emissions will vary at the hourly level by 

grid cell. More specifically, the on-network (RPD) and the off-network (RPV) exhaust, 

evaporative, and evaporative permeation modes use the gridded meteorology (MCIP) directly. 

Movesmrg determines the temperature for each hour and grid cell and uses it to select the 

appropriate EF for that SCC/pollutant/mode. For the off-network rate per profile (RPP) 

emissions, Movesmrg uses the Met4moves output for SMOKE (daily minimum and maximum 

temperatures by county) to determine the appropriate EF for that hour and SCC/pollutant. The 

result is that the emissions will vary hourly by county. The combination of these three processes 

(RPD, RPV, and RPP) is the total onroad emissions, while the combination of the two processes 

(RPD, RPV) for the refueling mode only is the total onroad_rfl emissions. Both sectors will 

show a strong meteorological influence on their temporal patterns. 

3.3.6 Vertical Allocation of Emissions 

Table 3-6 specifies the sectors for which plume rise is calculated. If there is no plume rise for a 

sector, the emissions are placed into layer 1 of the air quality model. Vertical plume rise was 

performed in-line within CMAQ for all of the SMOKE point-source sectors (i.e., ptipm, 
49

ptnonipm, ptfire, othpt, and c3marine). The in-line plume rise computed within CMAQ is nearly 

identical to the plume rise that would be calculated within SMOKE using the Laypoint program. 

See http://www.smoke-model.org/version2.7/html/ch06s07.html for full documentation of 

Laypoint. The selection of point sources for plume rise is pre-determined in SMOKE using the 

Elevpoint program (http://www.smoke-model.org/ version2.7/html/ch06s03.html). The 

calculation is done in conjunction with the CMAQ model time steps with interpolated 

meteorological data and is therefore more temporally resolved than when it is done in SMOKE.

Also, the calculation of the location of the point source is slightly different than the one used in 

SMOKE and this can result in slightly different placement of point sources near grid cell 

boundaries. 

For point sources, the stack parameters are used as inputs to the Briggs algorithm, but point fires 

do not have stack parameters. However, the ptfire inventory does contain data on the acres burned 

(acres per day) and fuel consumption (tons fuel per acre) for each day. CMAQ uses these 

additional parameters to estimate the plume rise of emissions into layers above the surface model 

layer. Specifically, these data are used to calculate heat flux, which is then used to estimate plume 

rise. In addition to the acres burned and fuel consumption, heat content of the fuel is needed to

compute heat flux. The heat content was assumed to be 8000 Btu/lb of fuel for all fires because 

specific data on the fuels were unavailable in the inventory. The plume rise algorithm applied to

the fires is a modification of the Briggs algorithm with a stack height of zero. 

CMAQ uses the Briggs algorithm to determine the plume top and bottom, and then computes the 

plumes’ distributions into the vertical layers that the plumes intersect. The pressure difference 

across each layer divided by the pressure difference across the entire plume is used as a 

weighting factor to assign the emissions to layers. This approach gives plume fractions by layer 

and source. 

3.3.7 Emissions Modeling Ancillary Files 

The methods used to perform spatial allocation for the 2007 platform are summarized in this 

section. For the 2007 platform, spatial factors are typically applied by country and SCC. As 

described earlier, spatial allocation was performed for a national 12-km domain. To accomplish 

this, SMOKE used national 12-km spatial surrogates and a SMOKE area-to-point data file. For 

the U.S., the spatial surrogates used 2010-based data (e.g., population) wherever possible. For 

Mexico, the same spatial surrogates were used in the 2005 platform. For Canada we used a set 

of Canadian surrogates provided by Environment Canada, also unchanged from the 2005v4.3 

platform. The U.S., Mexican, and Canadian 12-km surrogates cover the entire CONUS domain 

12US1 shown in Figure 3-1. The remainder of this subsection provides further details on the 

origin of the data used for the spatial surrogates and the area-to-point data. 

The SMOKE ancillary data files, particularly the cross-reference files, provide the specific 

inventory resolution at which spatial, speciation, and temporal factors are applied. For the 2010

Platform, spatial factors were generally applied by country/SCC, speciation factors by 

pollutant/SCC or (for combination profiles) state/ county FIPS code and month, and temporal 

factors by some combination of country, state, county, SCC, and pollutant. 
50

3.3.7.1 Surrogates for U.S. Emissions 

More than sixty spatial surrogates were used to spatially allocate U.S. county-level emissions to

the CMAQ 12-km grid cells. The Surrogate Tool was used to generate all of the surrogates. The 

shapefiles input to the Surrogate Tool are provided and documented at

http://www.epa.gov/ttn/chief/emch/spatial/spatialsurrogate.html. The tool and updated 

documentation for it is available at http://www.ie.unc.edu/cempd/projects/mims/spatial/ and 

http://www.cmascenter.org/help/documentation.cfm?MODEL=spatial_allocator&VERSION=3.

6&temp_id=99999. The detailed steps in developing the county boundaries for the surrogates are 

documented at ftp://ftp.epa.gov/EmisInventory/ emiss_shp2006/us/metadata_for_2002_ 

county_boundary_shapefiles_rev.pdf. 

Table 3-11 lists the codes and descriptions of the surrogates. The surrogates in bold have been 

updated with 2010-based data, including 2010 census data at the block group level, 2010 

American Community Survey Data for heating fuels, 2010 TIGER/Line data for railroads and 

roads, and 2010 National Transportation Atlas Data for ports and navigable waterways. For this 

project “Version 3” of the 2010-baed spatial surrogates was used. Not all of the available 

surrogates are used to spatially allocate sources in the 2007 platform; that is, some surrogates 

shown in Table 3-11 were not assigned to any SCCs. An area-to-point approach overrides the use 

of surrogates for some airport-related sources. 

Alternative surrogates for ports (801) and shipping lanes (802) were developed from the 2008 

NEI shapefiles: Ports_032310_wrf and ShippingLanes_111309FINAL_wrf. These surrogates 

were used for c1 and c2 commercial marine emissions instead of the standard 800 and 810 

surrogates, respectively. For the onroad sector, the on-network (RPD) emissions were spatially 

allocated to roadways, which the off-network (RPP and RPV) emissions were allocated to 

parking areas. For the onroad_rfl sector, the emissions were spatially allocated to gas station 

locations. 

For the oil and gas sources in the nonpt sector, the WRAP Phase III sources have detailed basinspecific spatial surrogates shown in Table 3-12. The remaining oil and gas sources used the 

2005-based surrogate “Oil & Gas Wells, IHS Energy, Inc. and USGS” (680) developed for oil 

and gas SCCs. The surrogates in Table 3-12 were applied for the counties listed in Table 3-13. 

3.3.7.3 Allocation Method for Airport-Related Sources in the U.S. 

There are numerous airport-related emission sources in the 2005 NEI, such as aircraft, airport 

ground support equipment, and jet refueling. In the 2002 platform most of these emissions were 

contained in sectors with county-level resolution — alm (aircraft), nonroad (airport ground 

support) and nonpt (jet refueling), but in the 2005 and 2008 platforms aircraft emissions are 

included as point sources as part of the ptnonipm sector. 

For the 2010 platform, the SMOKE “area-to-point” approach was used for airport ground support 

equipment (nonroad sector), and jet refueling (nonpt sector). The approach is described in detail 

in the 2002 Platform documentation: http://www.epa.gov/scram001/reports/ 

Emissions%20TSD%20Vol1_02-28-08.pdf. 

Nearly the same ARTOPNT file was used to implement the area-to-point approach as was done 
51

for the CAP and HAP-2002-based Platform. This was slightly updated from the CAP-only 2002 

Platform by further allocating the Detroit-area airports into multiple sets of geographic 

coordinates to support finer scale modeling. The updated file was retained for the 2010 Platform. 

3.3.7.4 Surrogates for Canada and Mexico Emission Inventories 

The Mexican single surrogate (population) was the same as was used in the 2002 and 2005 

Platforms. For Canada, surrogates provided by Environment Canada with the 2006 emissions 

were used to spatially allocate the 2006 Canadian emissions for the 2005 and 2010 Platforms. 

The Canadian surrogate data described in Table 3-14 came from Environment Canada. They 

provided both the surrogates and cross references; the surrogates were outputs from the Surrogate 

Tool (previously referenced). Per Environment Canada, the surrogates are based on 2001 

Canadian census data. The cross-references that Canada originally provided were updated as 

follows: all assignments to surrogate ‘978’ (manufacturing industries) were changed to ‘906’ 

(manufacturing services), and all assignments to ‘985’ (construction and mining) and ‘984’ 

(construction industries) were changed to ‘907’ (construction services) because the surrogate 

fractions in 984, 978 and 985 did not sum to 1. Codes for surrogates other than population that 

did not begin with the digit “9” were also changed. 

Table 3-11. U.S. Surrogates Available for the 2010 Platform 

Code Surrogate Description Code Surrogate Description

N/A Area-to-point approach (see 3.3.1.2) 520 Commercial plus Industrial plus 

Institutional 100 Population

525

Golf Courses + Institutional +Industrial + 

Commercial

110 Housing 527 Single Family Residential

120 Urban Population 530 Residential - High Density

130 Rural Population

535

Residential + Commercial + Industrial + 

Institutional

+ Government 137 Housing Change 540 Retail Trade

140 Housing Change and Population 545 Personal Repair

150 Residential Heating - Natural Gas 550 Retail Trade plus Personal Repair

160 Residential Heating – Wood 555 Professional/Technical plus General 

Government

165 0.5 Residential Heating - Wood plus 0.5 Low

Intensity Residential

560 Hospital

170 Residential Heating - Distillate Oil 565 Medical Office/Clinic

180 Residential Heating – Coal 570 Heavy and High Tech Industrial

190 Residential Heating - LP Gas 575 Light and High Tech Industrial

200 Urban Primary Road Miles 580 Food, Drug, Chemical Industrial

210 Rural Primary Road Miles 585 Metals and Minerals Industrial

220 Urban Secondary Road Miles 590 Heavy Industrial

230 Rural Secondary Road Miles 595 Light Industrial

240 Total Road Miles 596 Industrial plus Institutional plus Hospitals
52

250 Urban Primary plus Rural Primary 600 Gas Stations

255 0.75 Total Roadway Miles plus 0.25 Population 650 Refineries and Tank Farms

260 Total Railroad Miles 675 Refineries and Tank Farms and Gas Stations

270 Class 1 Railroad Miles 680 Oil and Gas 

280 Class 2 and 3 Railroad Miles 700 Airport Areas

300 Low Intensity Residential 710 Airport Points

310 Total Agriculture 720 Military Airports

312 Orchards/Vineyards 800 Marine Ports

320 Forest Land 801 NEI Ports

330 Strip Mines/Quarries 802 NEI Shipping Lanes 

340 Land 807 Navigable Waterway Miles

350 Water 810 Navigable Waterway Activity

400 Rural Land Area 850 Golf Courses

500 Commercial Land 860 Mines

505 Industrial Land 870 Wastewater Treatment Facilities

510 Commercial plus Industrial 880 Drycleaners

515 Commercial plus Institutional Land 890 Commercial Timber

Table 3-12. Spatial Surrogates for WRAP Oil and Gas Data 

Country Code Surrogate Description

USA 699 Gas production at CBM wells

USA 698 Well count - gas wells

USA 697 Oil production at gas wells

USA 696 Gas production at gas wells

USA 695 Well count - oil wells

USA 694 Oil production at Oil wells

USA 693 Well count - all wells

USA 692 Spud count

USA 691 Well count - CBM wells

USA 690 Oil production at all wells

USA 689 Gas production at all wells

Table 3-13. Counties included in the WRAP Dataset 

FIPS State County FIPS State County

8001 Colorado Adams 30075 Montana Powder River 

8005 Colorado Arapahoe 35031 New Mexico Mc Kinley 

8007 Colorado Archuleta 35039 New Mexico Rio Arriba 

8013 Colorado Boulder 35043 New Mexico Sandoval 

8014 Colorado Broomfield 35045 New Mexico San Juan 

8029 Colorado Delta 49007 Utah Carbon 

8031 Colorado Denver 49009 Utah Daggett 
53

8039 Colorado Elbert 49013 Utah Duchesne 

8043 Colorado Fremont 49015 Utah Emery 

8045 Colorado Garfield 49019 Utah Grand 

8051 Colorado Gunnison 49043 Utah Summit 

8063 Colorado Kit Carson 49047 Utah Uintah 

8067 Colorado La Plata 56001 Wyoming Albany 

8069 Colorado Larimer 56005 Wyoming Campbell 

8073 Colorado Lincoln 56007 Wyoming Carbon 

8075 Colorado Logan 56009 Wyoming Converse 

8077 Colorado Mesa 56011 Wyoming Crook 

8081 Colorado Moffat 56013 Wyoming Fremont 

8087 Colorado Morgan 56019 Wyoming Johnson 

8095 Colorado Phillips 56023 Wyoming Lincoln 

8103 Colorado Rio Blanco 56025 Wyoming Natrona 

8107 Colorado Routt 56027 Wyoming Niobrara 

8115 Colorado Sedgwick 56033 Wyoming Sheridan 

8121 Colorado Washington 56035 Wyoming Sublette 

8123 Colorado Weld 56037 Wyoming Sweetwater 

8125 Colorado Yuma 56041 Wyoming Uinta 

30003 Montana Big Horn 56045 Wyoming Weston 

Table 3-14. Canadian Spatial Surrogates for Canadian Emissions 

Code Description Code Description

9100 Population 9493 Warehousing and storage

9101 Total dwelling 9494 Total Transport and warehouse

9102 Urban dwelling 9511 Publishing and information services

9103 Rural dwelling 9512 Motion picture and sound recording 

industries

9104 Total Employment 9513 Broadcasting and 

telecommunications

9106 ALL_INDUST 9514 Data processing services

9111 Farms 9516 Total Info and culture

9113 Forestry and logging 9521 Monetary authorities - central bank

9114 Fishing hunting and trapping 9522 Credit intermediation activities

9115 Agriculture and forestry activities 9523 Securities commodity contracts and 

other financial investment activities

9116 Total Resources 9524 Insurance carriers and related 

activities

9211 Oil and Gas Extraction 9526 Funds and other financial vehicles

9212 Mining except oil and gas 9528 Total Banks

9213 Mining and Oil and Gas Extract 

activities

9531 Real estate
54

Code Description Code Description

9219 Mining-unspecified 9532 Rental and leasing services

9221 Total Mining 9533 Lessors of non-financial intangible 

assets (except copyrighted works)

9222 Utilities 9534 Total Real estate

9231 Construction except land subdivision 

and land development

9541 Professional scientific and technical 

services

9232 Land subdivision and land 

development

9551 Management of companies and 

enterprises

9233 Total Land Development 9561 Administrative and support services

9308 Food manufacturing 9562 Waste management and remediation 

services

9309 Beverage and tobacco product 

manufacturing

9611 Education Services

9313 Textile mills 9621 Ambulatory health care services

9314 Textile product mills 9622 Hospitals

9315 Clothing manufacturing 9623 Nursing and residential care 

facilities

9316 Leather and allied product 

manufacturing

9624 Social assistance

9321 Wood product manufacturing 9625 Total Service

9322 Paper manufacturing 9711 Performing arts spectator sports and 

related industries

9323 Printing and related support activities 9712 Heritage institutions

9324 Petroleum and coal products 

manufacturing

9713 Amusement gambling and 

recreation industries

9325 Chemical manufacturing 9721 Accommodation services

9326 Plastics and rubber products 

manufacturing

9722 Food services and drinking places

9327 Non-metallic mineral product 

manufacturing

9723 Total Tourism

9331 Primary Metal Manufacturing 9811 Repair and maintenance

9332 Fabricated metal product 

manufacturing

9812 Personal and laundry services

9333 Machinery manufacturing 9813 Religious grant-making civic and 

professional and similar 

organizations

9334 Computer and Electronic 

manufacturing

9814 Private households

9335 Electrical equipment appliance and 

component manufacturing

9815 Total other services

9336 Transportation equipment 

manufacturing

9911 Federal government public 

administration
55

Code Description Code Description

9337 Furniture and related product 

manufacturing

9912 Provincial and territorial public 

administration (9121 to 9129)

9338 Miscellaneous manufacturing 9913 Local municipal and regional public 

administration (9131 to 9139)

9339 Total Manufacturing 9914 Aboriginal public administration

9411 Farm product wholesaler-distributors 9919 International and other extraterritorial public administration

9412 Petroleum product wholesalerdistributors9920 Total Government

9413 Food beverage and tobacco 

wholesaler-distributors

9921 Commercial Fuel Combustion

9414 Personal and household goods 

wholesaler-distributors

9922 TOTAL DISTRIBUTION AND 

RETAIL

9415 Motor vehicle and parts wholesalerdistributors9923 TOTAL INSTITUTIONAL AND 

GOVERNEMNT

9416 Building material and supplies 

wholesaler-distributors

9924 Primary Industry

9417 Machinery equipment and supplies 

wholesaler-distributors

9925 Manufacturing and Assembly

9418 Miscellaneous wholesaler-distributors 9926 Distribution and Retail (no 

petroleum)

9419 Wholesale agents and brokers 9927 Commercial Services

9420 Total Wholesale 9928 Commercial Meat cooking

9441 Motor vehicle and parts dealers 9929 HIGHJET

9442 Furniture and home furnishings stores 9930 LOWMEDJET

9443 Electronics and appliance stores 9931 OTHERJET

9444 Building material and garden 

equipment and supplies dealers

9932 CANRAIL

9445 Food and beverage stores 9933 Forest fires

9446 Health and personal care stores 9941 PAVED ROADS

9447 Gasoline stations 9942 UNPAVED ROADS

9448 clothing and clothing accessories 

stores

9943 HIGHWAY

9451 Sporting goods hobby book and 

music stores

9944 ROAD

9452 General Merchandise stores 9945 Commercial Marine Vessels

9453 Miscellaneous store retailers 9946 Construction and mining

9454 Non-store retailers 9947 Agriculture Construction and 

mining

9455 Total Retail 9950 Intersection of Forest and Housing

9481 Air transportation 9960 TOTBEEF

9482 Rail transportation 9970 TOTPOUL
56

Code Description Code Description

9483 Water Transportation 9980 TOTSWIN

9484 Truck transportation 9990 TOTFERT

9485 Transit and ground passenger 

transportation

9993 Trail

9486 Pipeline transportation 9994 ALLROADS

9487 Scenic and sightseeing transportation 9995 30UNPAVED_70trail

9488 Support activities for transportation 9996 Urban area

9491 Postal service 9997 CHBOISQC

9492 Couriers and messengers 9991 Traffic
57

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Ottmar, R.D.; Sandberg, D.V.; Riccardi, C.L.; Prichard, S.J. 2007. An Overview of the Fuel 

Characteristic Classification System – Quantifying, Classifying, and Creating Fuelbeds for 

Resource Planning. Canadian Journal of Forest Research. 37(12): 2383-2393. FCCS is available 

at: http://www.fs.fed.us/pnw/fera/fccs/index.shtml

Pouliot, G., H. Simon, P. Bhave, D. Tong, D. Mobley, T. Pace, and T. Pierce . (2010) “Assessing the 

Anthropogenic Fugitive Dust Emission Inventory and Temporal Allocation Using an Updated 

Speciation of Particulate Matter.” International Emission Inventory Conference, San Antonio, 

TX. Available at http://www.epa.gov/ttn/chief/conference/ei19/session9/pouliot.pdf

Raffuse, S., N. Larkin, P. Lahm, Y. Du, 2012. Development of Version 2 of the Wildland Fire Portion 

of the [2011] National Emissions Inventory. International Emission Inventory Conference, 

Tampa, FL. Available at: http://www.epa.gov/ttn/chief/conference/ei20/session2/sraffuse.pdf 

Raffuse, S., D. Sullivan, L. Chinkin, S. Larkin, R. Solomon, A. Soja, 2007. Integration of SatelliteDetected and Incident Command Reported Wildfire Information into BlueSky, June 27, 2007. 

Available at: http://getbluesky.org/smartfire/docs.cfm

Russell, A.G. and G.R. Cass, 1986. Verification of a Mathematical Model for Aerosol Nitrate and Nitric 

Acid Formation and Its Use for Control Measure Evaluation, Atmospheric Environment, 20: 

2011-2025.

SESARM, 2012a. “Development of the 2007 Base Year and Typical Year Fire Emission Inventory for 

the Southeastern States”, Air Resources Managers, Inc., Fire Methodology, AMEC Environment 

and Infrastructure, Inc. AMEC Project No.: 6066090326, April, 2012 

SESARM, 2012b. “Area and Nonroad 2007 Base Year Inventories. Revised Final Report”, Contract No. S-2009-

06-01, Prepared by Transystems Corporation, January 2012. Available at:

http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&cad=rja&ved=0CDAQFjAC&

url=ftp%3A%2F%2Fwsip-70-164-45-

196.dc.dc.cox.net%2Fpublic%2FSESARM%2FRevised%2520Final%2FSESARM%2520Base%2520Yea

r%2520Revised%2520Final%2520Report_Jan2012.docx&ei=xUAUPu1F4WA0AHC5YHYCg&usg=AFQjCNFhjgx3EjhbfYmMUP4zGI_HBiqZA&sig2=hWWN0m3WYPSO28QSzn5BIA. 

Skamarock, W., J. Klemp, J. Dudhia, D. Gill, D. Barker, M. Duda, X. Huang, W. Wang, J. Powers, 

2008. A Description of the Advanced Research WRF Version 3. NCAR Technical Note. 

National Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division, 

Boulder, CO. June 2008. Available at: http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf

Sullivan D.C., Raffuse S.M., Pryden D.A., Craig K.J., Reid S.B., Wheeler N.J.M., Chinkin L.R., Larkin 

N.K., Solomon R., and Strand T. (2008) Development and applications of systems for modeling 

emissions and smoke from fires: the BlueSky smoke modeling framework and SMARTFIRE: 

17th International Emissions Inventory Conference, Portland, OR, June 2-5. Available at: 

http://www.epa.gov/ttn/chief/conferences.html
59

Wang, Y., P. Hopke, O. V. Rattigan, X. Xia, D. C. Chalupa, M. J. Utell. (2011) “Characterization of 

Residential Wood Combustion Particles Using the Two-Wavelength Aethalometer”, Environ. 

Sci. Technol., 45 (17), pp 7387–7393

Yarwood, G., S. Rao, M. Yocke, and G. Whitten, 2005: Updates to the Carbon Bond Chemical 

Mechanism: CB05. Final Report to the US EPA, RT-0400675. Available at 

http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf.
60

4.0 CMAQ Air Quality Model Estimates 

4.1 Introduction to the CMAQ Modeling Platform 

The Clean Air Act (CAA) provides a mandate to assess and manage air pollution levels to protect 

human health and the environment. EPA has established National Ambient Air Quality Standards

(NAAQS), requiring the development of effective emissions control strategies for such pollutants 

as ozone and particulate matter. Air quality models are used to develop these emission control 

strategies to achieve the objectives of the CAA.

Historically, air quality models have addressed individual pollutant issues separately. However, 

many of the same precursor chemicals are involved in both ozone and aerosol (particulate matter) 

chemistry; therefore, the chemical transformation pathways are dependent. Thus, modeled 

abatement strategies of pollutant precursors, such as volatile organic compounds (VOC) and NOx 

to reduce ozone levels, may exacerbate other air pollutants such as particulate matter. To meet 

the need to address the complex relationships between pollutants, EPA developed the Community 

Multiscale Air Quality (CMAQ) modeling system. The primary goals for CMAQ are to:

• Improve the environmental management community’s ability to evaluate the impact of air 

quality management practices for multiple pollutants at multiple scales.

• Improve the scientist’s ability to better probe, understand, and simulate chemical and

physical interactions in the atmosphere.

The CMAQ modeling system brings together key physical and chemical functions associated 

with the dispersion and transformations of air pollution at various scales. It was designed to

approach air quality as a whole by including state-of-the-science capabilities for modeling 

multiple air quality issues, including tropospheric ozone, fine particles, toxics, acid deposition, 

and visibility degradation. CMAQ relies on emission estimates from various sources, including 

the U.S. EPA Office of Air Quality Planning and Standards’ current emission inventories, 

observed emission from major utility stacks, and model estimates of natural emissions from 

biogenic and agricultural sources. CMAQ also relies on meteorological predictions that include 

assimilation of meteorological observations as constraints. Emissions and meteorology data are 

fed into CMAQ and run through various algorithms that simulate the physical and chemical 

processes in the atmosphere to provide estimated concentrations of the pollutants. Traditionally,

the model has been used to predict air quality across a regional or national domain and then to

simulate the effects of various changes in emission levels for policymaking purposes. For health 

studies, the model can also be used to provide supplemental information about air quality in areas 

where no monitors exist. 

CMAQ was also designed to have multi-scale capabilities so that separate models were not 

needed for urban and regional scale air quality modeling. The grid spatial resolutions in past 

annual CMAQ runs have been 36 km x 36 km per grid for the “parent” domain, and nested within 

that domain are 12 km x 12 km grid resolution domains. The parent domain typically covered the 
61

continental United States, and the nested 12 km x 12 km domain covered the Eastern or Western

United States. The CMAQ simulation performed for this 2010 assessment used a single domain 

that covers the entire continental U.S. (CONUS) and large portions of Canada and Mexico using 

12 km by 12 km horizontal grid spacing. Currently, 12 km x 12 km resolution is recommended 

for most applications as the highest resolution. With the temporal flexibility of the model, 

simulations can be performed to evaluate longer term (annual to multi-year) pollutant 

climatologies as well as short-term (weeks to months) transport from localized sources. By 

making CMAQ a modeling system that addresses multiple pollutants and different temporal and 

spatial scales, CMAQ has a “one atmosphere” perspective that combines the efforts of the 

scientific community. Improvements will be made to the CMAQ modeling system as the 

scientific community further develops the state-of-the-science.

For more information on CMAQ, go to http://www.epa.gov/asmdnerl/CMAQ or 

http://www.cmascenter.org.

4.1.1 Advantages and Limitations of the CMAQ Air Quality Model 

An advantage of using the CMAQ model output for characterizing air quality for use in 

comparing with health outcomes is that it provides a complete spatial and temporal coverage 

across the U.S. CMAQ is a three-dimensional Eulerian photochemical air quality model that 

simulates the numerous physical and chemical processes involved in the formation, transport, 

and destruction of ozone, particulate matter and air toxics for given input sets of initial and 

boundary conditions, meteorological conditions and emissions. The CMAQ model includes 

state-of-the-science capabilities for conducting urban to regional scale simulations of multiple air 

quality issues, including tropospheric ozone, fine particles, toxics, acid deposition and visibility 

degredation. However, CMAQ is resource intensive, requiring significant data inputs and 

computing resources. 

An uncertainty of using the CMAQ model includes structural uncertainties, representation of 

physical and chemical processes in the model. These consist of: choice of chemical mechanism 

used to characterize reactions in the atmosphere, choice of land surface model and choice of 

planetary boundary layer. Another uncertainty in the CMAQ model is based on parametric 

uncertainties, which includes uncertainties in the model inputs: hourly meteorological fields, 

hourly 3-D gridded emissions, initial conditions, and boundary conditions. Uncertainties due to 

initial conditions are minimized by using a 10 day ramp-up period from which model results are 

not used in the aggregation and analysis of model outputs. Evaluations of models against 

observed pollutant concentrations build confidence that the model performs with reasonable 

accuracy despite the uncertainties listed above. A detailed model evaluation for ozone and 

PM2.5 species provided in Section 4.3 shows generally acceptable model performance which is 

equivalent or better than typical state-of-the-science regional modeling simulations as 

summarized in Simon et al., 20126.

 

6 Heather Simon, Kirk R. Baker, Sharon Phillips. (2012) Compilation and interpretation of photochemical model 

performance statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139. 

Online publication date: 1-Dec-2012.
62

4.2 CMAQ Model Version, Inputs and Configuration 

This section describes the air quality modeling platform used for the 2010 CMAQ simulation. A 

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

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

emissions and/or meteorology. A platform typically consists of a specific air quality model, 

emissions estimates, a set of meteorological inputs, and estimates of “boundary conditions” 

representing pollutant transport from source areas outside the region modeled. We used the 

CMAQ7 model as part of the 2010 Platform to provide a national scale air quality modeling 

analysis. The CMAQ model simulates the multiple physical and chemical processes involved in

the formation, transport, and destruction of ozone and fine particulate matter (PM2.5).

This section provides a description of each of the main components of the 2010 CMAQ 

simulation along with the results of a model performance evaluation in which the 2010 model 

predictions are compared to corresponding measured concentrations. 

4.2.1 Model Version

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

photochemical oxidants, including PM2.5 and ozone, for given input sets of meteorological

conditions and emissions. As mentioned previously, CMAQ includes numerous science modules 

that simulate the emission, production, decay, deposition and transport of organic and inorganic 

gas-phase and pollutants in the atmosphere. This 2010 analysis employed CMAQ version 4.7.18

which reflects updates to version 4.7 to improve the underlying science which include aqueous 

chemistry mass conservation improvements and improved vertical convective mixing. . The 

CMAQ model version 4.7 was most recently peer-reviewed in February of 2009 for the U.S. 

EPA.9 The model enhancements in version 4.7.1 also include: 

1. Aqueous chemistry 

 Mass conservation improvements 

- Imposed one second minimum timestep for remainder of the cloud lifetime after 100 

‘iterations’ in the solver 

- Force mass balance for the last timestep in the cloud by limiting oxidized amount to 

mass available 

 

7 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.

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

http://www.cmascenter.org.

9 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.
63

 Implemented steady state assumption for OH 

 Only allow sulfur oxidation to control the aqueous chemistry solver timestep (previously, 

reactions of OH, GLY, MGLY, and Hg for multipollutant model also controlled the 

timestep) 

2. Advection

 Added additional divergence-based constraint on advection timestep 

 Vertical advection in the Yamo module is now represented with the PPM scheme to limit 

numerical diffusion 

3. Model time step determination

 Fixed a potential advection time step error 

- The sum of the advection steps for a given layer time step might not equal the output 

time step duration in some extreme cases

- Ensured that the advection steps sum up to the synchronization step

4. Horizontal diffusion 

 Fixed a potential error 

- Concentration data may not be correctly initialized if multiple sub-cycle time steps 

are required 

- Fix to initialize concentrations with values calculated in the previous sub-time step 

5. Emissions

 Bug fix in EMIS_DEFN.F to include point source layer 1 NH3 emissions 

 Bug fix to calculate soil NO “pulse” emissions in BEIS 

 Remove excessive logging of cases where ambient air temperature exceeds 315.0 Kelvin. 

When this occurs, the values are just slightly over 315

 Bug fix for parallel decomposition errors in plume rise emissions

6. Photolysis

 JPROC/phot_table and phot_sat options

- Expanded lookup tables to facilitate applications across the globe and vertical 

extent to 20km

- Updated temperature adjustments for absorption cross sections and quantum 

yields

- Revised algorithm that processes TOMS datasets for OMI data format

 In-line option

- Asymmetry factor calculation updated using values from Mie theory integrated 

over log normal particle distribution; added special treatment for large particles in

asymmetry factor algorithm to avoid numerical instabilities

4.2.2 Model Domain and Grid Resolution

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

States, as shown in Figure 4-1. This single domain covers the entire continental U.S. (CONUS) 

and large portions of Canada and Mexico using 12 km by 12 km horizontal grid spacing. The 
64

model extends vertically from the surface to 50 millibars (approximately 19 km) using a sigmapressure coordinate system. Air quality conditions at the outer boundary of the 12 km domain

were taken from a global model. Table 4-1 provides some basic geographic information 

regarding the 12 km CMAQ domain.

Table 4-1. Geographic Information for 12 km Modeling Domain

National 12 km CMAQ Modeling Configuration

Map Projection Lambert Conformal Projection

Grid Resolution 12 km

Coordinate Center 97 W, 40 N

True Latitudes 33 and 45 N

Dimensions 459 x 299 x 25

Vertical Extent 25 Layers: Surface to 50 mb level (see Table 4-2)

Figure 4-1. Map of the CMAQ Modeling Domain. The blue box denotes the 12 km national 

modeling domain. (Same as Figure 3-3.) 
65

4.2.3 Modeling Period / Ozone Episodes

The 12 km CMAQ modeling domain was modeled for the entire year of 2010. The 2010 annual 

simulation was performed in two half-year segments (i.e., January through June, and July through 

December) for each emissions scenario. With this approach to segmenting an annual simulation 

we were able to reduce the overall throughput time for an annual simulation. The annual 

simulation included a “ramp-up” period, comprised of 10 days before the beginning of each halfyear segment, to mitigate the effects of initial concentrations. All 365 model days were used in 

the annual average levels of PM2.5. For the 8-hour ozone, we used modeling results from the 

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

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

concentrations. 

4.2.4 Model Inputs: Emissions, Meteorology and Boundary Conditions 

2010 Emissions: The emissions inventories used in the 2010 air quality modeling are described 

in Section 3, above. 

Meteorological Input Data: The gridded meteorological data for the entire year of 2010 at the 

12 km continental United States scale domain was derived from version 3.4 of the Weather 

Research and Forecasting Model (WRF), Advanced Research WRF (ARW) core.[1] The WRF 

Model is a state-of-the-science mesoscale numerical weather prediction system developed for 

both operational forecasting and atmospheric research applications (http://wrf-model.org). The 

2010 WRF simulation included the physics options of the Pleim-Xiu land surface model (LSM), 

Asymmetric Convective Model version 2 planetary boundary layer (PBL) scheme, Morrison 

double moment microphysics, Kain- Fritsch cumulus parameterization scheme and the RRTMG 

long-wave and shortwave radiation (LWR/SWR) scheme.[2]

The WRF meteorological outputs were processed using the Meteorology-Chemistry Interface 

Processor (MCIP) package[3], version 4.1.2, to derive the specific inputs to CMAQ: horizontal 

wind components (i.e., speed and direction), temperature, moisture, and its related speciated 

components was conducted for vertical diffusion rates, and rainfall rates for each grid cell in 

each vertical layer. The WRF simulation used the same CMAQ map projection, a Lambert 

Conformal projection centered at (-97, 40) with true latitudes at 33 and 45 degrees north. The 12 

km WRF domain consisted of 459 by 299 grid cells and 35 vertical layers with a surface layer of 

approximately 38 meters. Table 4-2 shows the vertical layer structure used in WRF and the layer 

 

[1] Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang, W., 

Powers, J.G., 2008. A Description of the Advanced Research WRF Version 3. 

[2] Gilliam, R.C., Pleim, J.E., 2010. Performance Assessment of New Land Surface and Planetary Boundary Layer 

Physics in the WRF-ARW. Journal of Applied Meteorology and Climatology 49, 760-774. 

[3] Otte T.L., Pleim, J.E., 2010. The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling 

system: updates through v3.4.1. Geoscientific Model Development 3, 243-256.
66

collapsing approach to generate the CMAQ meteorological inputs. CMAQ resolved the vertical 

atmosphere with 25 layers, preserving greater resolution in the PBL. 

In terms of the 2010 WRF meteorological model performance evaluation, a combination of 

qualitative and quantitative analyses was used to assess the adequacy of the WRF simulated 

fields. The qualitative aspects involved comparisons of the model-estimated synoptic patterns 

against observed patterns from historical weather chart archives. Additionally, the evaluations 

compared spatial patterns of monthly average rainfall and monthly maximum planetary boundary 

layer (PBL) heights. The statistical portion of the evaluation examined the model bias and error 

for temperature, water vapor mixing ratio, solar radiation, and wind fields. These statistical 

values were calculated on a monthly basis. 

Table 4-2. Vertical layer structure for 2010 WRF and CMAQ simulations (heights are layer 

top). 

 

CMAQ 

Layers

WRF 

Layers Sigma P Approximate Height (m)

25 35 0.0000 17,556

34 0.0500 14,780

24 33 0.1000 12,822

32 0.1500 11,282

23 31 0.2000 10,002

30 0.2500 8,901

22 29 0.3000 7,932

28 0.3500 7,064

21 27 0.4000 6,275

26 0.4500 5,553

20 25 0.5000 4,885

24 0.5500 4,264

19 23 0.6000 3,683

18 22 0.6500 3,136

17 21 0.7000 2,619

16 20 0.7400 2,226

15 19 0.7700 1,941

14 18 0.8000 1,665

13 17 0.8200 1,485

12 16 0.8400 1,308

11 15 0.8600 1,134

10 14 0.8800 964

9 13 0.9000 797

12 0.9100 714

8 11 0.9200 632

10 0.9300 551
67

7 9 0.9400 470

8 0.9500 390

6 7 0.9600 311

5 6 0.9700 232

4 5 0.9800 154

4 0.9850 115

3 3 0.9900 77

2 2 0.9950 38

1 1 0.9975 19

0 0 1.0000 0

Initial and Boundary Conditions: The lateral boundary and initial species concentrations are 

provided by a three- dimensional global atmospheric chemistry model, the GEOS-CHEM10 

model (standard version 8-03-02 with 8-02-03 chemistry). The global GEOS-CHEM model 

simulates atmospheric chemical and physical processes driven by assimilated meteorological 

observations from the NASA’s Goddard Earth Observing System (GEOS). This model was run 

for 2010 with a grid resolution of 2.0 degrees x 2.5 degrees (latitude-longitude) and 46 vertical 

layers up to 0.01 hPa. The predictions were processed using the GEOS-2-CMAQ tool and used 

to provide one-way dynamic boundary conditions at one-hour intervals.11 Ozone was evaluated 

from these GEOS-Chem runs by comparing to satellite vertical profiles and ground-based 

measurements and found acceptable model performance. More information is available about the 

GEOS-CHEM model and other applications using this tool at: http://wwwas.harvard.edu/chemistry/trop/geos.

4.3 CMAQ Model Performance Evaluation 

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

components was conducted for the 20010 simulation using state/local monitoring sites data in 

order to estimate the ability of the CMAQ modeling system to replicate the 2010 base year 

concentrations for the 12 km continental U.S. domain.

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 

 

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

University, Cambridge, MA, October 15, 2004. 

11 Akhtar, F., Henderson, B., Appel, W., Napelenok, S., Hutzell, B., Pye, H., Foley, K., 2012. Multiyear Boundary 

Conditions for CMAQ 5.0 from GEOS-Chem with Secondary Organic Aerosol Extensions, 11th Annual Community 

Modeling and Analysis System conference, Chapel Hill, NC, October 2012. 
68

averages the difference (model - observed) over the sum of observed values. NMB is a useful 

model performance indicator because it avoids overinflating the observed range of values, 

especially at low concentrations. Normalized mean bias is defined as: 

(NMB = 

 

 

P O

O

n

n

 



1

1

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

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

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 

In addition to the performance statistics, regional maps which show the normalized mean bias 

and error were prepared for the ozone season, May through September, at individual monitoring 

sites as well as on an annual basis for PM2.5 and its component species. 

Evaluation for 8-hour Daily Maximum Ozone: The operational model performance evaluation 

for eight-hour daily maximum ozone was conducted using the statistics defined above. Ozone 

measurements for 2010 in the continental U.S. were included in the evaluation and were taken 

from the 2010 State/local monitoring site data in the Air Quality System (AQS) Aerometric 

Information Retrieval System (AIRS). The performance statistics were calculated using 

predicted and observed data that were paired in time and space on an 8-hour basis. Statistics 
69

were generated for the following geographic groupings in the 12-km continental U.S. domain12: 

five large subregions: Midwest, Northeast, Southeast, Central and Western U.S. 

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

are provided in Table 4-4. Seasons were defined as: winter (December-January- February), 

spring (March-April-May), summer (June, July, August), and fall (September-OctoberNovember). Spatial plots of the normalized mean bias and error for individual monitors are 

shown in Figures 4-2 through 4-3. The statistics shown in these two figures were calculated over 

the ozone season, May through September, using data pairs on days with observed 8-hour ozone 

of greater than or equal to 60 ppb.

In general, the model performance statistics indicate that the 8-hour daily maximum ozone 

concentrations predicted by the 2010 CMAQ simulation closely reflect the corresponding 8-hour 

observed ozone concentrations in space and time in each subregion of the 12 km modeling 

domain. As indicated by the statistics in Table 4-4, bias and error for 8-hour daily maximum 

ozone are relatively low in each subregion, not only in the summer when concentrations are 

highest, but also during other times of the year. Specifically, 8-hour ozone in the summer is

slightly over predicted with the greatest over prediction in Central U.S. (NMB is 20.6 percent). 

Ozone performance in spring shows better performance with slight over predictions in most of 

the subregions except in the Northeast (slight under prediction of 2.2). In the winter, when 

concentrations are generally low, the model slightly under predicts 8-hour ozone with the 

exception of the West (NMB is 5.6). In the fall, when concentrations are also relatively low, 

ozone is also slightly over predicted; with NMBs less than 7 percent in each subregion. 

Model bias at individual sites during the ozone season is similar to that seen on a subregional 

basis for the summer. The information in Figure 4-2 indicates that the bias for days with 

observed 8-hour daily maximum ozone greater than 60 ppb is within ± 20 percent at the vast 

majority of monitoring sites across the U.S. domain. The exceptions are sites along the California 

coast and in Seattle, WA. At these sites observed concentrations greater than 60 ppb are 

generally predicted in the range of ±20 to 60 percent. Looking at the map of bias, Figure 4-2 

indicates that the higher or lower bias at these sites is not evident at other sites in these same 

areas. This suggests that the under prediction at these sites is likely due to very local features 

(e.g., meteorology and/or emissions) and not indicative of a systematic problem in the modeling 

platform. Model error, as seen from Figure 4-3, is 20 percent or less at most of the sites across 

the U.S. modeling domain. Somewhat greater error is evident at sites in several areas most 

notably along portions of the California coastline and Maine, Baton Rouge, LA, Cleveland, OH, 

and Seattle, WA. 

 

12 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.
70

Table 4-4. Summary of CMAQ 20010 8-Hour Daily Maximum Ozone Model Performance 

Statistics by Subregion, by Season.

Subregion Season No. of 

Obs

NMB 

(%)

NME 

(%) FB (%) FE (%)

Northeast Winter 6,015 -20.3 22.9 -23.0 26.7

Spring 13,825 -2.2 12.2 -2.0 13.1

Summer 16,359 3.3 14.1 3.9 14.6

Fall 11,373 4.1 16.2 6.1 17.5

Midwest Winter 2,720 -23.6 27.7 -27.0 32.7

Spring 12,066 3.9 12.0 4.6 12.5

Summer 16,389 8.3 16.3 7.8 16.1

Fall 9,666 4.0 14.2 5.5 15.2

Central States Winter 11,505 -4.6 15.8 -5.0 17.5

Spring 16,330 4.1 13.2 4.9 13.7

Summer 17,956 20.6 26.7 20.6 26.2

Fall 14,781 3.4 16.8 4.9 17.3

Southeast Winter 7,112 -4.7 12.5 -4.6 13.5

Spring 20,567 4.9 12.8 6.1 13.5

Summer 22,162 17.4 22.4 18.1 22.4

Fall 16,908 5.3 15.3 6.7 15.9

West Winter 22,696 5.6 20.9 8.2 23.0

Spring 27,849 1.4 11.6 2.1 12.1

Summer 30,279 7.2 16.0 7.6 16.7

Fall 22,984 6.9 15.6 7.7 16.2
71

Figure 4-2. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 ppb over the period MaySeptember 2010 at monitoring sites in the continental U.S. modeling domain.

Figure 4-3. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 

ppb over the period May-September 2010 at monitoring sites in the continental U.S. 

modeling domain. 
72

Evaluation for Annual PM2.5: The PM 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 bias and error performance statistics were calculated on an annual basis for each 

subregion (Table 4-5). PM2.5 measurements for 2010 were obtained from the following networks 

for model evaluation: Chemical Speciation Network (CSN, 24 hour average), Interagency 

Monitoring of Protected Visual Environments (IMPROVE, 24 hour average, and Clean Air 

Status and Trends Network (CASTNet, weekly average). For PM2.5 species that are measured by 

more than one network, we calculated separate sets of statistics for each network by subregion. 

For brevity, Table 4-5 provides annual model performance statistics for PM2.5 and its component 

species for the five sub-regions in the 12 km continental U.S. domain defined above (Northeast, 

Midwest, Southeast, Central, and West). In addition to the tabular summaries of bias and error 

statistics, annual spatial maps which show the normalized mean bias and error by site for each 

PM2.5 species are provided in Figures 4-4 through 4-17. 

As indicated by the statistics in Table 4-5, annual CMAQ PM2.5 for 2009 shows over predictions 

at rural IMPROVE monitoring sites and urban CSN monitoring sites in each subregion except in 

the Southeast at CSN sites (which shows an under prediction in NMB of 17 percent) and in the 

Southeast, Central and West at IMPROVE sites (which shows a slight under prediction in NMB 

of 2 to 11 percent). Although not shown here, the mean observed concentrations of PM2.5 are 

more than twice as high at the CSN sites (~11μg m-3) as the IMPROVE sites (~5 μg m-3), thus 

illustrating the statistical differences between the urban CSN and rural IMPROVE networks. 

Annual average sulfate is consistently under predicted at CSN, IMPROVE, and CASTNet 

monitoring sites across the modeling domain, with NMB values ranging from near negligible to -

29 percent. Sulfate performance shows moderate error, ranging from 24 to 45 percent. Figures 

4-6 and 4-7, suggest spatial patterns vary by region. The model bias for most of the Southeast, 

Central and Southwest states are within 0 to -30 percent. The model bias appears to be

slightly greater in the Northwest with over predictions up to 80 percent at individual monitors. 

Model error also shows a spatial trend by region, where much of the Eastern states are 20 to 40 

percent, the Western and Central U.S. states are 30 to 70 percent.

Annual average nitrate is under predicted at the urban CSN monitoring sites in most of the 

subregions (NMB in the range of near negligible to -41 percent), except in the Southeast 

where nitrate is over predicted on average by 17.5 percent. At IMPROVE rural sites, annual 

average nitrate is over predicted at all subregions (NMB in the range of 3 to 40 

percent), except in the West where nitrate is under predicted on average by 26 

percent. The bias statistics indicate that the model performance for nitrate is generally best at

the urban CSN monitoring sites. Model performance of total nitrate at sub-urban CASTNet 

monitoring sites shows an over prediction across all subregions (NMB in the range of 2 to 27

percent). Model error for nitrate is somewhat greater for each subregion as compared to 

sulfate. Model bias at individual sites indicates mainly over prediction of greater than 20 

percent at most monitoring sites in the Eastern half of the U.S. as indicated in Figure 4-8. The 
73

exception to this is in the Southern Florida and the Southwest of the modeling domain where 

there appears to be a greater number of sites with under prediction of nitrate of 20 to 80

percent. Model error for annual nitrate, as shown in Figure 4-9, is least at sites in portions of 

the Midwest and extending eastward to the Northeast corridor. Nitrate concentrations are 

typically higher in these areas than in other portions of the modeling domain.

Annual average ammonium model performance as indicated in Table 4-5 has a tendency for 

the model to under predict across the CASTNet sites (ranging from near negligible to -7 

percent) and over predict across the urban CSN sites (ranging from 6 to 13 percent). There is not 

a large variation from subregion to subregion or at urban versus rural sites in the error statistics 

for ammonium. The spatial variation of ammonium across the majority of individual monitoring 

sites shows bias within ±30 percent.

Annual average elemental carbon is over predicted in all subregions at urban and rural sites. 

Similar to ammonium error there is not a large variation from subregion to subregion or at urban 

versus rural sites.

Annual average organic carbon is under predicted across most subregions in rural IMPROVE 

areas (NMB ranging from -12 to -17 percent), except in the Northeast where the bias on average 

is 30.5 percent. However, the model over predicted annual average organic carbon in most 

subregions at urban CSN sites (NMB ranging from 1 to 34 percent), except in the Southeast 

where the bias on average is -5.7 percent. Similar to ammonium and elemental carbon, error 

model performance does not show a large variation from subregion to subregion or at urban

versus rural sites (42 to 81 percent). 

Table 4-5. Summary of CMAQ 2010 Annual PM Species Model Performance Statistic 

Pollutant Monitor 

Network Subregion No. of Obs

NMB 

(%)

NME 

(%) FB (%) FE (%)

PM2.5

CSN Northeast 2,882 0.7 39.9 1.1 39.4

Midwest 2,210 9.5 36.6 6.2 35.6

Southeast 2,441 -17.0 35.6 -23.5 39.7

Central 1,671 3.9 39.0 -0.1 38.4

West 1,297 0.6 58.7 -3.0 54.0

IMPROVE Northeast 2,101 9.0 46.2 6.0 44.2

Midwest 562 0.4 38.2 -1.4 40.0

Southeast 1,940 -10.5 38.4 -17.9 43.3

Central 2,429 -2.4 40.9 -7.2 42.3

West 10,060 -8.7 49.4 -7.7 48.3

Sulfate

CSN Northeast 2,885 -10.9 30.3 -0.1 30.7

Midwest 2,226 -6.6 32.1 -0.5 32.4
74

Pollutant Monitor 

Network Subregion No. of Obs

NMB 

(%)

NME 

(%) FB (%) FE (%)

Southeast 2,453 -20.5 30.9 -22.8 35.4

Central 1,789 -11.9 37.4 -7.9 39.3

West 1,308 1.0 43.1 5.6 42.2

IMPROVE Northeast 2,035 0.1 32.4 13.6 36.5

Midwest 583 -12.2 32.8 1.9 37.3

Southeast 1,919 -17.5 30.3 -17.5 35.3

Central 2,261 -14.0 35.2 -6.7 37.6

West 9,832 -0.1 45.1 15.6 48.7

CASTNet Northeast 758 -15.7 22.3 -9.3 22.4

Midwest 618 -20.4 23.7 -18.8 24.7

Southeast 1,095 -24.5 26.7 -28.3 31.5

Central 378 -28.9 31.4 -29.1 34.3

West 1,017 -17.5 32.9 -9.8 36.3

Nitrate

CSN Northeast 2,885 -0.5 57.2 -37.4 81.8

Midwest 2,226 -0.3 49.3 -21.6 67.8

Southeast 2,453 17.5 81.1 -50.0 101.0

Central 1,690 -4.1 53.4 -45.0 88.7

West 1,308 -40.6 71.6 -66.8 94.5

IMPROVE Northeast 2,031 40.3 89.2 -29.8 97.5

Midwest 580 2.6 64.4 -40.8 92.6

Southeast 1,919 22.2 95.2 -59.7 116.0

Central 2,261 15.4 62.4 -32.0 92.9

West 9,765 -26.4 80.0 -82.3 121.0

Total Nitrate 

(NO3 + HNO3)

CASTNet Northeast 758 27.2 37.9 20.3 38.8

Midwest 618 7.6 33.3 13.8 30.0

Southeast 1,095 18.9 42.3 13.8 42.4

Central 378 4.7 31.3 2.0 30.7

West 1,016 1.6 34.8 12.5 39.3

Ammonium CSN Northeast 2,885 7.9 40.8 34.3 44.2
75

Pollutant Monitor 

Network Subregion No. of Obs

NMB 

(%)

NME 

(%) FB (%) FE (%)

Midwest 2,226 10.0 38.1 21.6 41.5

Southeast 2,453 9.6 37.2 12.9 38.0

Central 1,789 13.0 44.6 20.7 47.1

West 1,308 5.8 77.6 30.3 65.2

CASTNet Northeast 758 0.0 27.7 2.0 28.0

Midwest 618 -2.2 27.3 3.7 27.0

Southeast 1,095 -6.7 25.9 -7.5 28.8

Central 378 -0.3 30.7 -0.5 32.9

West 1,017 -6.8 38.3 -4.4 40.9

Elemental 

Carbon

CSN Northeast 2,638 32.9 59.2 29.4 48.5

Midwest 2,191 49.6 68.4 35.1 52.5

Southeast 2,403 9.6 47.6 12.1 45.6

Central 1,629 74.8 91.8 46.6 61.2

West 1,227 50.9 87.3 32.8 65.7

IMPROVE Northeast 2,117 24.6 53.4 11.5 46.8

Midwest 583 13.0 46.0 2.7 48.1

Southeast 1,924 4.0 46.9 -1.3 45.6

Central 2,506 17.1 52.5 7.7 43.4

West 10,118 22.3 72.7 8.0 56.8

Organic Carbon

CSN Northeast 2,112 1.0 61.1 -6.4 56.3

Midwest 2,180 16.7 54.7 12.3 49.1

Southeast 2,390 -5.7 42.6 -10.2 44.0

Central 1,624 23.0 59.2 13.9 48.7

West 1,218 34.0 80.6 27.9 63.9

IMPROVE Northeast 2,624 30.5 65.9 22.7 55.6

Midwest 575 -11.6 48.6 -17.5 54.8

Southeast 1,931 -16.3 47.7 -33.3 54.6

Central 2,502 -17.2 51.5 -30.5 55.5

West 9,919 -15.1 59.9 -12.9 58.1
76

Figure 4-4. Normalized Mean Bias (%) of annual PM2.5 mass at monitoring sites in

the continental U.S. modeling domain.
77

Figure 4-5. Normalized Mean Error (%) of annual PM2.5 mass at monitoring sites in the continental U.S. 

modeling domain.

Figure 4-6. Normalized Mean Bias (%) of annual sulfate at monitoring sites in the 

continental U.S. modeling domain. 
78

Figure 4-7. Normalized Mean Error (%) of annual sulfate at monitoring sites in the 

continental U.S. modeling domain.

Figure 4-8. Normalized Mean Bias (%) of annual nitrate at monitoring sites in the 

continental U.S. modeling domain.
79

Figure 4-9. Normalized Mean Error (%) of annual nitrate at monitoring sites in the 

continental U.S. modeling domain.

Figure 4-10. Normalized Mean Bias (%) of annual total nitrate at monitoring sites in the 

continental U.S. modeling domain.
80

Figure 4-11. Normalized Mean Error (%) of annual total nitrate at monitoring sites in the 

continental U.S. modeling domain. 

Figure 4-12. Normalized Mean Error (%) of annual ammonium at monitoring sites in the continental U.S. 

modeling domain.
81

Figure 4-13. Normalized Mean Error (%) of annual ammonium at monitoring sites in the 

continental U.S. modeling domain. 

Figure 4-14. Normalized Mean Bias (%) of annual elemental carbon at monitoring sites in 

the continental U.S. modeling domain.
82

Figure 4-15. Normalized Mean Error (%) of annual elemental carbon at monitoring sites in 

the continental U.S. modeling domain.

Figure 4-16. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the 

continental U.S. modeling domain.
83

Figure 4-17. Normalized Mean Error (%) of annual organic carbon at monitoring sites in 

the continental U.S. modeling domain.
84

5.0 Bayesian space-time downscaling fusion model (downscaler) -

Derived Air Quality Estimates 

5.1 Introduction 

The need for greater spatial coverage of air pollution concentration estimates has grown in recent 

years as epidemiology and exposure studies that link air pollution concentrations to health effects 

have become more robust and as regulatory needs have increased. Direct measurement of 

concentrations is the ideal way of generating such data, but prohibitive logistics and costs limit 

the possible spatial coverage and temporal resolution of such a database. Numerical methods 

that extend the spatial coverage of existing air pollution networks with a high degree of 

confidence are thus a topic of current investigation by researchers. The downscaler model (DS) 

is the result of the latest research efforts by EPA for performing such predictions. DS utilizes 

both monitoring and CMAQ data as inputs, and attempts to take advantage of the measurement 

data’s accuracy and CMAQ’s spatial coverage to produce new spatial predictions. This chapter 

describes methods and results of the DS application that accompany this report, which utilized 

ozone and PM2.5 data from AQS and CMAQ to produce predictions to continental U.S. 2010 

census tract centroids for the year 2010. 

5.2 Downscaler Model 

DS develops a relationship between observed and modeled concentrations, and then uses that 

relationship to spatially predict what measurements would be at new locations in the spatial 

domain based on the input data. This process is separately applied for each time step (daily in 

this work) of data, and for each of the pollutants under study (ozone and PM2.5). In its most 

general form, the model can be expressed in an equation similar to that of linear regression: 

Y(s, t) = ~β0(s, t) + β1(s, t) ∗ ~x(s, t) + ε(s, t) (Equation 1) 

Where: 

Y(s,t) is the observed concentration at point s and time t.

~x(s,t) is the CMAQ concentration at time t. This value is a weighted average of both the 

gridcell containing the monitor and neighboring gridcells. 

~β0(s,t) is the intercept, and is composed of both a global and a local component. 

β1(t) is the global slope; local components of the slope are contained in the ~x(s,t) term. 

ε(s,t) is the model error. 

DS has additional properties that differentiate it from linear regression: 

1) Rather than just finding a single optimal solution to Equation 1, DS uses a Bayesian approach 

so that uncertainties can be generated along with each concentration prediction. This involves 

drawing random samples of model parameters from built-in "prior" distributions and assessing 

their fit on the data on the order of thousands of times. After each iteration, properties of the 

prior distributions are adjusted to try to improve the fit of the next iteration. The resulting 

collection of ~β0 and β1 values at each space-time point are the "posterior" distributions, and the 
85

means and standard distributions of these are used to predict concentrations and associated 

uncertainties at new spatial points. 

2) The model is "heirarchical" in structure, meaning that the top level parameters in Equation 1 

(ie ~β0(s,t), β1(t), ~x(s,t)) are actually defined in terms of further parameters and sub-parameters 

in the DS code. For example, the overall slope and intercept is defined to be the sum of a global 

(one value for the entire spatial domain) and local (values specific to each spatial point) 

component. This gives more flexibility in fitting a model to the data to optimize the fit (i.e. 

minimize ε(s,t)).

Further information about the development and inner workings of the current version of DS can 

be found in Berrocal, Gelfand and Holland (2011) and references therein. The DS outputs that 

accompany this report are described below, along with some additional analyses that include 

assessing the accuracy of the DS predictions. Results are then summarized, and caveats are 

provided for interpreting them in the context of air quality management activities. 

5.3 Downscaler Concentration Predictions

In this application, DS was used to predict daily concentration and associated uncertainty values 

at the 2010 US census tract centroids across the continental U.S. using 2010 measurement and 

CMAQ data as inputs. For ozone, the concentration unit is the daily maximum 8-hour average in 

ppb and for PM2.5 the concentration unit is the 24-hour average in g/m3

.

5.3.1 Summary of 8-hour Ozone Results 

Figure 5-1 summarizes the AQS, CMAQ and DS ozone data over the year 2010. It shows the 4th

max daily maximum 8-hour average ozone for AQS observations, CMAQ model predictions and 

DS model results. The DS model estimated that for 2010 about half of the US Census tracts 

(36206 out of 72283) experienced at least one day with an ozone value above the NAAQS of 75 

ppb. 
86

Figure 5-1. Annual 4th max (daily max 8-hour ozone concentrations) derived from AQS, 

CMAQ and DS data. 
87

5.3.2 Summary of PM2.5 Results 

Figures 5-2 and 5-3 summarize the AQS, CMAQ and DS PM2.5 data over the year 2010. Figure 

5-2 shows annual means and Figure 5-3 shows 98’th percentiles of 24-hour PM2.5 

concentrations for AQS observations, CMAQ model predictions and DS model results. The DS 

model estimated that for 2010 about 33% of the US Census tracts (23547 out of 72283) 

experienced at least one day with a PM2.5 value above the 24-hour NAAQS of 35 ug/m3. 
88

Figure 5-2. Annual mean PM2.5 concentrations derived from AQS, CMAQ and DS data. 
89

Figure 5-3. 98th percentile 24-hour average PM2.5 concentrations derived from AQS, 

CMAQ and DS data. 
90

5.4 Downscaler Uncertainties

5.4.1 Standard Errors 

As mentioned above, the DS model works by drawing random samples from built-in 

distributions during its parameter estimation. The standard errors associated with each of these 

populations provide a measure of uncertainty associated with each concentration prediction. 

Figure 5-4 shows the percent errors resulting from dividing the DS standard errors by the 

associated DS prediction. The black dots on the maps show the location of EPA sampling 

network monitors whose data was input to DS via the AQS datasets (Chapter 2). The maps show 

that, in general, errors are relatively smaller in regions with more densely situation monitors (ie 

the eastern US), and larger in regions with more sparse monitoring networks (ie western states). 

These standard errors could potentially be used to estimate the probability of an exceedance for a 

given point estimate of a pollutant concentration. 
91

Figure 5-4. Annual mean relative errors (standard errors divided by predictions) from the 

DS 2010 runs. The black dots show the locations of monitors that generated the AQS data 

used as input to the DS model. 
92

5.4.2 Cross Validation 

To check the quality of its spatial predictions, DS can be set to perform “cross-validation” (CV), 

which involves leaving a subset of AQS data out of the model run and predicting the 

concentrations of those left out points. The predicted values are then compared to the actual leftout values to generate statistics that provide an indicator of the predictive ability. In the DS runs 

associated with this report, 10% of the data was chosen randomly by the DS model to be used for 

the CV process. The resulting CV statistics are shown below in Table 5-1.

Pollutant # Monitors Mean Bias RMSE Mean Coverage

PM2.5 901 6.20e-3 2.88 0.96

O3 1239 1.35e-2 5.12 0.96

Table 5-1. Cross-validation statistics associated with the 2010 DS runs. 

The statistics indicated by the columns of Table 5-1 are as follows: 

- Mean Bias: The bias of each prediction is the DS prediction minus the AQS value. This column is 

the mean of all biases across the CV cases. 

- Root Mean Squared Error (RMSE): The bias is squared for each CV prediction, then the square 

root of the mean of all squared biases across all CV predictions is obtained. 

- Mean Coverage: A value of 1 is assigned if the measured AQS value lies in the 95% confidence 

interval of the DS prediction (the DS prediction +/- the DS standard error), and 0 otherwise. This 

column is the mean of all those 0’s and 1’s.

5.5 Summary and Conclusions

The results presented in this report are from an application of the DS fusion model for 

characterizing national air quality for Ozone and PM2.5. DS provided spatial predictions of daily 

ozone and PM2.5 at 2010 U.S. census tract centroids by utilizing monitoring data and CMAQ 

output for 2010. Large-scale spatial and temporal patterns of concentration predictions are 

generally consistent with those seen in ambient monitoring data. Both ozone and PM2.5 were 

predicted with lower error in the eastern versus the western U.S., presumably due to the greater 

monitoring density in the east. 

An additional caution that warrants mentioning is related to the capability of DS to provide 

predictions at multiple spatial points within a single CMAQ gridcell. Care needs to be taken not 

to over-interpret any within-gridcell gradients that might be produced by a user. Fine-scale 

emission sources in CMAQ are diluted into the gridcell averages, but a given source within a 

gridcell might or might not affect every spatial point contained therein equally. Therefore DSgenerated fine-scale gradients are not expected to represent actual fine-scale atmospheric 

concentration gradients, unless possibly multiple monitors are present in the gridcell. 
93

Appendix A - Acronyms 

Acronyms 

ARW Advanced Research WRF core model 

BEIS Biogenic Emissions Inventory System 

BlueSky Emissions modeling framework 

CAIR Clean Air Interstate Rule 

CAMD EPA’s Clean Air Markets Division 

CAP Criteria Air Pollutant 

CAR Conditional Auto Regressive spatial covariance structure (model) 

CARB California Air Resources Board 

CEM Continuous Emissions Monitoring 

CHIEF Clearinghouse for Inventories and Emissions Factors 

CMAQ Community Multiscale Air Quality model 

CMV Commercial marine vessel 

CO Carbon monoxide 

CSN Chemical Speciation Network 

DQO Data Quality Objectives 

EGU Electric Generating Units 

Emission Inventory Listing of elements contributing to atmospheric release of pollutant 

 substances 

EPA Environmental Protection Agency 

EMFAC Emission Factor (California’s onroad mobile model) 

FAA Federal Aviation Administration 

FDDA Four Dimensional Data Assimilation 

FIPS Federal Information Processing Standards 

HAP Hazardous Air Pollutant 

HMS Hazard Mapping System 

ICS-209 Incident Status Summary form 

IPM Integrated Planning Model 

ITN Itinerant 

LSM Land Surface Model 

MOBILE OTAQ’s model for estimation of onroad mobile emissions factors 

MODIS Moderate Resolution Imaging Spectroradiometer 

MOVES Motor Vehicle Emission Simulator 

NEEDS National Electric Energy Database System 

NEI National Emission Inventory 

NERL National Exposure Research Laboratory 

NESHAP National Emission Standards for Hazardous Air Pollutants 

NH Ammonia 

NMIM National Mobile Inventory Model 

NONROAD OTAQ’s model for estimation of nonroad mobile emissions 

NO Nitrogen oxides 

OAQPS EPA’s Office of Air Quality Planning and Standards 

OAR EPA’s Office of Air and Radiation 
94

ORD EPA’s Office of Research and Development 

ORIS Office of Regulatory Information Systems (code) - is a 4 or 5 digit 

number assigned by the Department of Energy’s (DOE) Energy 

 Information Agency (EIA) to facilities that generate electricity 

ORL One Record per Line 

OTAQ EPA’s Office of Transportation and Air Quality 

PAH Polycyclic Aromatic Hydrocarbon 

PFC Portable Fuel Container 

PM2.5 Particulate matter less than or equal to 2.5 microns 

PM10 Particulate matter less than or equal to 10 microns 

PMc Particulate matter greater than 2.5 microns and less than 10 

microns 

Prescribed Fire Intentionally set fire to clear vegetation 

RIA Regulatory Impact Analysis 

RPO Regional Planning Organization 

RRTM Rapid Radiative Transfer Model 

SCC Source Classification Code 

SMARTFIRE Satellite Mapping Automatic Reanalysis Tool for Fire Incident 

Reconciliation 

SMOKE Sparse Matrix Operator Kernel Emissions 

TCEQ Texas Commission on Environmental Quality 

TSD Technical support document 

VOC Volatile organic compounds 

VMT Vehicle miles traveled 

Wildfire Uncontrolled forest fire 

WRAP Western Regional Air Partnership 

WRF Weather Research and Forecasting Model 
United States 

Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Health and Environmental Impacts Division 

Research Triangle Park, NC 

Publication N