Bayesian space-time downscaling fusion model 

(downscaler) -Derived Estimates of Air Quality 

for 2011 
EPA-454/S-15-001 

April 2015

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

Estimates of Air Quality for 2011

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 2011 Emission Inventories and Approaches .................................................................. 13

3.3 Emissions Modeling Summary ...................................................................................... 35

3.4 Emissions References ..................................................................................................... 62

4.0 CMAQ Air Quality Model Estimates ................................................................................ 66

4.1 Introduction to the CMAQ Modeling Platform .............................................................. 66

4.2 CMAQ Model Version, Inputs and Configuration ......................................................... 68

4.3 CMAQ Model Performance Evaluation ......................................................................... 73

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

Estimates ....................................................................................................................................... 94

5.1 Introduction .................................................................................................................... 94

5.2 Downscaler Model ......................................................................................................... 94

5.3 Downscaler Concentration Predictions .......................................................................... 95

5.4 Downscaler Uncertainties ............................................................................................ 100

5.5 Summary and Conclusions ........................................................................................... 102

Appendix A - Acronyms ............................................................................................................. 103

 
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 2011 calendar 

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

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

(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. 

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 

 

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 

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. 

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

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

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

will provide updates. 

 

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 

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 nonattainment 

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 NAAQS for ozone 

(last revised in 2008) 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 (last revised in 2012) 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 NAAQS for fine particles 

(PM2.5) in 1997. The NAAQS 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 to 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). 

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 2011 National Emissions Inventory (NEI), Version 1 to process year 2011

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 to 10 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 2011 emission inputs for this study included development of emission 

inventories for a 2011 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 can 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 3.5.1 

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 2011 Emission Inventories and Approaches 

This section describes the emissions inventories created for input to SMOKE. The 2011 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 voluntarily submitted to the NEI by S/L/T agencies. The 
14 

2011 NEI was compiled using the Emissions Inventory System (EIS). EIS includes hundreds of 

automated QA checks to improve data quality, and it also supports release point (stack) 

coordinates separately from facility coordinates. EPA collaboration with S/L/T agencies helped 

preveny duplication between point and nonpoint source categories such as industrial boilers. 

Documentation for the 2011 NEI is available at 

http://www.epa.gov/ttn/chief/net/2011inventory.html#inventorydoc. 

EPA used the SMARTFIRE2 system to develop 2011 fire emissions. SMARTFIRE2 categorizes 

all fires as either prescribed burning or wildfire categories, and includes improved emission 

factor estimates for prescribed burning. Onroad mobile source emissions in the 2011NEIv1 were 

developed using MOVES2010b; however, the 2011 emissions modeling platform used a 

different version of MOVES, hence forth referred to as “MOVESTier3FRM” that facilitated the 

representation of the final Tier 3 standards in future years. When given the same inputs, these 

two versions of MOVES produce similar emissions estimates for the year 2011. Canadian 

emissions reflect year 2006, as those were the latest available at the time of the modeling. 

Mexican emissions reflect year 2012 as projected from their 1999 inventory, and offshore 

emissions reflect year 2008 because 2011 data were not yet available at the time of the modeling. 

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

EPA’s Version 6.1, 2011 Emissions Modeling Platform that was also used for the proposed 

Ozone National Ambient Air Quality Standards (NAAQS). 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#2011 (EPA, 2014a) and includes additional 

details regarding some aspects of the data preparation and emissions modeling. Electronic 

copies of the main inventories and ancillary data used for this project are available from the 

version 6.1 section of the EMCH. 

The emissions modeling process, performed using SMOKE v3.5.1 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 the 2011 

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 2011 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 
15

“Appendix_B_2011_emissions_totals_by_sector.xlsx”. 

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

2011 Platform Sector 

(Abbrev)

2011 NEI 

Sector

Description and resolution of the data input to 

SMOKE

EGU non-peaking units 

(ptegu) Point

2011 NEI point source EGUs determined to operate 

as non-peaking units. The 2011NEIv1 emissions are 

replaced with hourly 2011 CEMS values for NOX 

and SO2, where the units are matched to the NEI. 

Annual resolution for non-CEMS sources, hourly for 

sources matched to CEMS.

EGU peaking units 

(ptegu_pk) Point

Same as ptegu sector, but limited to EGUs 

determined to operate as peaking units. All sources 

in this sector have CEMS data for 2011 and are 

therefore hourly.

Point source oil and gas 

(pt_oilgas) Point 2011NEIv1 point sources with oil and gas production emissions processes.

Remaining non-EGU point 

(ptnonipm)

Point

All 2011NEIv1 point source records not matched to 

the ptegu, ptegu_pk, and pt_oilgas sectors, except for 

offshore point sources that are in the othpt sector. 

Includes all aircraft emissions and some rail yard 

emissions. Annual resolution.

Point source fire (ptfire) Fires

Point source day-specific wildfires and prescribed 

fires for 2011 computed using SMARTFIRE 2, 

except for Georgia-submitted emissions. Consistent 

with 2011NEIv1.

Agricultural (ag) Nonpoint

NH3 emissions from 2011NEIv1 nonpoint livestock 

and fertilizer application, county and annual 

resolution. 

Area fugitive dust (afdust) Nonpoint

PM10 and PM2.5 from fugitive dust sources in the 

2011NEIv1 nonpoint inventory, including building 

construction, road construction, agricultural dust, and 

road dust; however, unpaved and paved road dust 

emissions differ from the NEI in that they do not 

have a precipitation adjustment. Instead, the 

emissions modeling adjustment applies a transport

fraction and a meteorology-based (precipitation and 

snow/ice cover) zero-out. County and annual 

resolution. 

Nonpoint source oil and gas 

(np_oilgas) Nonpoint 2011NEIv1 nonpoint sources from oil and gasrelated processes. County and annual resolution.

Residential Wood 

Combustion (rwc)

Nonpoint

2011NEIv1 NEI nonpoint sources with Residential 

Wood Combustion (RWC) processes. County and 

annual resolution.

Remaining nonpoint (nonpt) Nonpoint 2011NEIv1 nonpoint sources not included in other 

platform sectors; county and annual resolution.

C3 commercial marine 

(c3marine)

Nonpoint

Category 3 (C3) CMV emissions projected to 2011 

from year 2002 values. These emissions are not from 

the NEI, but rather were developed for the rule called 

“Control of Emissions from New Marine 
16

Compression-Ignition Engines at or Above 30 Liters 

per Cylinder”, usually described as the Emissions 

Control Area- International Maritime Organization 

(ECA-IMO) study: 

http://www.epa.gov/otaq/oceanvessels.htm. (EPA420-F-10-041, August 2010). U.S. states-only 

emissions (zero in Midwest); see othpt sector for all 

non-U.S. emissions. Treated as point sources to 

reflect shipping lanes, annual resolution.

C1 and C2 marine and 

locomotive (c1c2rail)

Nonpoint

Locomotives and primarily category 1 (C1) and 

category 2 (C2) commercial marine vessel (CMV) 

emissions sources from the 2011NEIv1 nonpoint 

inventory. Midwestern states’ CMV emissions, 

including Class 3 sources, are from a separate year 

2010 emissions inventory. County and annual 

resolution.

Nonroad (nonroad) Nonroad

2011NEIv1 nonroad equipment emissions developed 

with the National Mobile Inventory Model (NMIM) 

using NONROAD2008 version NR08a. NMIM was 

used for all states except California and Texas, which 

submitted their own emissions to the 2011NEIv1. 

County and monthly resolution.

Onroad non-refueling 

(onroad)

Onroad

2011 onroad mobile source gasoline and diesel 

vehicles from parking lots and moving vehicles. 

Includes the following modes: exhaust, extended 

idle, evaporative, permeation, and brake and tire 

wear. For all states except California and Texas, 

based on monthly MOVES emissions tables from 

MOVESTier3FRM. Texas emissions are from the 

2011NEIv1 and are based on MOVES 2010b, and 

California emissions are based on Emission Factor 

(EMFAC). MOVES-based emissions computed for 

each hour and model grid cell using monthly and 

annual activity data (e.g., VMT, vehicle population).

Onroad Refueling 

(onroad_rfl) Onroad

2011NEIv1 onroad mobile gasoline and diesel 

vehicle refueling emissions for all states. Based on 

MOVESTier3FRM emissions tables. Computed 

hourly based on temperature and for each model grid 

cell.

Biogenic (beis) Biogenic

Hour- and grid cell-specific emissions for 2011 

generated from the BEIS 3.14 model, including 

emissions in Canada and Mexico.

Other point sources not from 

the NEI (othpt)

N/A

Point sources from Canada’s 2006 inventory and 

Mexico’s 2012 inventory grown from year 1999 

(ERG, 2009; Wolf, 2009). Also includes all nonU.S. C3 CMV and U.S. offshore oil production, 

which are unchanged from the 2008 NEI point source 

annual emissions.
17

Other nonpoint and nonroad 

(othar)

N/A

Annual year 2006 Canada (province resolution) and 

year 2012 (grown from 1999) Mexico Phase III 

(municipio resolution) nonpoint and nonroad mobile

inventories.

Other onroad sources 

(othon)

N/A

Year 2006 Canada (province resolution) and year 

2012 (grown from 1999) Mexico Phase III 

(municipio resolution) onroad mobile inventories, 

annual resolution.
18

Table 3-2. 2011 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.) 

Sector CO NH3 NOx PM10 PM2.5 SO2 VOC

afdust 18,502,317 2,487,403

ag 3,517,371

c1c2rail 173,437 481 1,046,095 34,670 32,367 17,651 47,714

nonpt 3,046,375 142,323 832,166 715,709 533,248 392,638 3,792,612

np_oilgas 642,182 0 653,219 21,756 17,200 17,195 2,273,214

nonroad 13,952,389 2,627 1,630,409 162,420 154,660 4,031 2,024,633

onroad_adj 25,230,444 118,130 5,591,695 287,540 207,517 28,475 2,576,504

onroad_rfl 161,415

c3marine 12,425 124,725 4,279 3,909 38,645 4,954

ptfire 22,580,113 362,910 347,103 2,362,132 2,005,142 177,107 5,174,593

ptegu 719,414 21,644 1,925,742 259,011 188,811 4,596,656 32,288

ptegu_pk 8,662 425 21,941 2,159 1,886 28,476 783

ptnonipm 2,565,936 74,841 1,767,748 491,837 338,447 1,071,950 872,433

pt_oilgas 20,579 112 17,026 1,833 1,810 55,142 87,842

rwc 2,578,229 20,343 35,672 389,019 388,288 8,986 446,972

Con.US Total 71,530,185 4,261,207 13,993,540 23,234,681 6,360,688 6,436,952 17,495,956

Table 3-3. 2011 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 2,810,350 386,147 462,996 810,747 248,907 61,179 932,322

Canada othon 3,303,239 17,572 392,209 11,075 7,712 4,046 199,939

Canada othpt 560,661 15,543 369,993 65,782 39,828 825,675 157,170

Canada Subtotal 6,674,250 419,263 1,225,198 887,604 296,447 890,900 1,289,431

Mexico othar 439,901 109,861 189,592 69,523 45,923 26,559 499,145

Mexico othon 423,978 3,247 76,880 7,593 6,970 1,413 73,888

Mexico othpt 116,609 0 414,399 137,512 101,884 828,418 83,838

Mexico Subtotal 980,488 113,108 680,871 214,628 154,778 856,390 656,872

Offshore to EEZ 130,419 0 610,644 16,961 15,525 133,606 81,286

Non-US c3marine 17,168 0 202,516 17,199 15,823 127,563 7,297

Offshore Subtotal 147,587 0 813,159 34,160 31,348 261,168 88,583

2011 TOTAL 7,802,325 532,371 2,719,229 1,136,392 482,573 2,008,459 2,034,886
19

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 release 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). With a couple of minor exceptions, this section 

describes only NEI point sources within the contiguous United States. The offshore oil platform 

(othpt sector) and category 3 CMV emissions (c3marine and othpt sectors) are processed by 

SMOKE as point source inventories and are discussed later in this section. Full documentation 

for the development of the 2011 NEI (EPA, 2014b), is posted at: 

http://www.epa.gov/ttn/chief/net/2011inventory.html#inventorydoc. 

After moving offshore oil platforms into the othpt sector, and dropping sources without specific 

locations (i.e., the FIPS code ends in 777), initial versions of the other four platform point source 

sectors were created from the remaining 2011NEIv1 point sources. The point sectors are: the 

EGU sector for non-peaking units (ptegu), the EGU sector for peaking units (ptegu_pk), point 

source oil and gas extraction -related emissions (pt_oilgas) and the remaining non-EGU sector 

also called the non-IPM (ptnonipm) sector. The EGU emissions are split out from the other 

sources to facilitate the use of distinct SMOKE temporal processing and future-year projection 

techniques. The EGU sectors are further split into “peaking” (ptegu_pk) and non-peaking units 

to allow for better analysis of the impact of peaking units. The oil and gas sector emissions 

(pt_oilgas) were processed separately for summary tracking purposes and distinct future-year 

projection techniques from the remaining non-EGU emissions (ptnonipm). 

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. 

The ptnonipm and pt_oilgas sector emissions were provided to SMOKE as annual emissions. 

For 2011NEIv1 sources in the ptegu and ptegu_pk sectors that could be matched to 2011 CEMS 

data, hourly CEMS NOX and SO2 emissions for 2011 from EPA’s Acid Rain Program were used 

rather than NEI emissions. For all other pollutants (e.g., VOC, PM2.5, HCl), annual emissions 

were used as-is from the NEI, but were allocated to hourly values using heat input from the 

CEMS data. For the unmatched units in the ptegu and ptegu_pk sectors, annual emissions were 

allocated to daily values using IPM region- and pollutant-specific profiles, and similarly, regionand pollutant-specific diurnal profiles were applied to create hourly emissions. 

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

emissions. The full description of how the 2011 NEI emissions were developed is provided in the 

NEI documentation, but a brief summary of their development follows: 

a. 2011 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. 
20

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 were added from Mineral Management Services (MMS), 

although in 2011NEIv1, these data were still from 2008 because the 2011 data were not 

yet 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 all sources to have a state/county FIPS code. 

 Sources that did not have specific counties assigned (i.e., the county code ends in 777) 

were not included in the modeling because it was only possible to know the state in 

which the sources resided, but no more specific details related to the location of the 

sources were available. 

 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. 

3.2.1.1 EGU sector (ptegu) 

The ptegu and ptegu_pk sectors contain emissions from EGUs in the 2011NEIv1 point inventory 

that could be matched to units found in the NEEDS v5.13 database 

(http://www.epa.gov/powersectormodeling/BaseCasev513.html). It was necessary to put these 

EGUs into separate sectors in the platform because IPM projects future emissions for the EGUs 

defined in the NEEDS database, and emissions for sources in the ptegu and ptegu_pk sectors are 

replaced with IPM outputs in the future year modeling case. Sources not matched to units found 

in NEEDS are placed into the pt_oilgas or ptnonipm sectors and are projected to the future year 

using projection and control factors appropriate for their source categories. It is important that 

the matching between the NEI and NEEDS database be as complete as possible because there 

can be double-counting of emissions in the future year if emissions for units are projected by 

IPM are not properly matched to the units in the NEI. 

Some units in the ptegu and ptegu_pk sectors are matched to CEMS data via ORIS facility codes 

and boiler ID. For these units, SMOKE replaces the 2011 emissions of NOX and SO2 with the 

CEMS emissions, thereby ignoring the annual values specified in the NEI. For other pollutants, 

the hourly CEMS heat input data are used to allocate the NEI annual emissions to hourly values. 

All stack parameters, stack locations, and SCC codes for these sources come from the NEI. 

Because these attributes are obtained from the NEI, the chemical speciation of VOC and PM2.5 
21

for the sources is selected based on the SCC or in some cases, based on unit-specific data. If 

CEMS data exists for a unit, but the unit is not matched to the NEI, the CEMS data for that unit 

is not used in the modeling platform. However, if the source exists in the NEI and is just not 

matched to a CEMS unit, the emissions from that source would be modeled using the annual 

emission value in the NEI and would be allocated to daily values using region-, fuel- and 

pollutant-specific average profiles. EIS stores many matches from EIS units to the ORIS facility 

codes and boiler IDs used to reference the CEMS data. Some additional matches were made at 

the release point level in the modeling platform.

For sources not matched to CEMS data (i.e., “non-CEMS” sources), daily emissions were 

computed from the NEI annual emissions using average CEMS data profiles specific to fuel 

type, pollutant, and IPM region. To allocate emissions to each hour of the day, diurnal profiles 

were created using average CEMS data for heat input specific to fuel type and IPM region. For 

future-year scenarios, there are no CEMS data available for specific units, but the shape of the 

CEMS profiles is preserved for sources that are carried into the future year. This method keeps 

the temporal behavior of the base and future year cases as consistent as possible. 

3.2.1.2 EGU Peaking Unit Sector (ptegu_pk) 

The ptegu_pk sector includes sources identified by EPA as peaking units. The units were 

separated into this sector to facilitate analyses of the impact of peaking units. Aside from their 

inclusion in this sector, in all other ways they are treated in the same way as CEMS sources in 

the ptegu sector because all of them are matched to CEMS data. To identify units for inclusion 

in this sector, EPA required them to satisfy two tests: (1) the capacity factor was less than 10% 

over a 3 year average (2010-2012), and (2) the capacity factor was less than 20% in each of the 3 

years. Here, “capacity factor” means either: (1) The ratio of a unit's actual annual electric output 

(expressed in MWe/hr) to the unit's nameplate capacity (or maximum observed hourly gross load 

(in MWe/hr) if greater than the nameplate capacity) times 8760 hours; or (2) The ratio of a unit's 

annual heat input (in million BTUs or equivalent units of measure) to the unit's maximum rated 

hourly heat input rate (in million BTUs per hour or equivalent units of measure) times 8,760 

hours. The list of units in the ptegu_pk sector is provided in the file 

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/2011_emissions/Peakers_CAMD_20

11.080213_NEI_IPM_match.xls).

3.2.1.3 Non-IPM Sector (pt_oilgas) 

The pt_oilgas sector includes sources with the SCCs identified as oil and gas sources. The 

emissions and other source characteristics in the pt_oilgas sector are submitted by states, while 

EPA developed a dataset of nonpoint oil and gas emissions for each county in the U.S. with oil 

and gas activity that was available for states to use. The nonpoint emissions can be found in the 

np_oilgas sector. More information on the development of the 2011 oil and gas emissions can be 

found in Section 3.21 of the 2011NEIv1 TSD. 

3.2.1.4 Non-IPM Sector (ptnonipm) 

Except for some minor exceptions, the non-IPM (ptnonipm) sector contains the 2011NEIv1 point 

sources that are not in the ptegu, ptegu_pk, or pt_oilgas sectors. For the most part, the ptnonipm 
22

sector reflects the non-EGU sources of the NEI point inventory; however, it is likely that some 

small low-emitting EGUs not matched to the NEEDS database or to CEMS data are present in 

the ptnonipm sector. The sector also includes some ethanol plants that have been identified by 

EPA but are not in 2011NEIv1. The ptnonipm sector contains a small amount of fugitive dust 

PM emissions from vehicular traffic on paved or unpaved roads at industrial facilities, coal 

handling at coal mines, and grain elevators. Some point sources in the 2011NEIv1 that are not 

included in any modeling sectors are sources with state/county FIPS code ending with “777”. 

These sources represent mobile (temporary) asphalt plants that are only reported for some states, 

and are generally in a fixed location for only a part of the year and are therefore difficult to 

allocate to specific places and days for modeling. Therefore, these sources are dropped from the 

point-based sectors in the modeling platform. 

Another difference between the 2011NEIv1 data and the modeling platform is the addition of 

some ethanol production facilities identified by EPA but were not found in the NEI. For some 

rule development work, EPA developed a list of corn ethanol facilities for 2011. Many of these 

ethanol facilities were included in the 2011NEIv1, but those that were not matched were added 

to the ptnonipm sector in a separate inventory data file. Locations and FIPS codes for these 

ethanol plants were verified using web searches and Google Earth. EPA believes that some of 

these sources are not included in the NEI as point sources because they do not meet the 100 

ton/year potential-to-emit threshold for NEI point sources. In other cases, EPA is following up 

with states to evaluate whether the state data should include these point sources. Emission rates 

for the ethanol plants were obtained from EPA’s updated spreadsheet model for upstream 

impacts developed for the Renewable Fuel Standard (RFS2) rule. 

3.2.2 Nonpoint Sources (afdust, ag, nonpt, np_oilgas, rwc) 

Several modeling platform sectors were created from the 2011NEIv1 nonpoint inventory. This 

section describes the stationary nonpoint sources. Note that locomotives, C1 and C2 CMV, and 

C3 CMV are also included the 2011NEIv1 nonpoint data category, but are mobile sources and 

are placed into the c1c2rail and c3marine sectors, respectively. The 2011NEIv1 TSD available 

from http://www.epa.gov/ttn/chief/net/2011inventory.html includes documentation for the 

nonpoint sector of the 2011NEIv1. 

Nonpoint tribal-submitted emissions are dropped during spatial processing with SMOKE 

because the spatial surrogates are available at the county, but not the tribal level. In addition, 

possible double-counting with county-level emissions is prevented. These omissions are not 

expected to have an impact on the results of the air quality modeling at the 12-km scales used for 

this platform. 

The emissions modeling sector inventories start with the NEI data. Several source categories 

were not included in the modeling platform inventories for the following reasons: 1) these 

sources are only reported by a small number of states or agencies, 2) these sources are ‘atypical’ 

and have small emissions, and/or 3) EPA has have other data the Agency believes to be more 

accurate. The following subsections describe how the remaining sources in the 2011NEIv1 

nonpoint inventory were separated into 2011 modeling platform sectors, along with any data that 

were updated replaced with non-NEI data. 
23

3 3

In the rest of this section, each of the platform sectors into which the 2011 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 PM10 and PM2.5 emission estimates for 

nonpoint SCCs identified by EPA staff as dust sources. Categories included in the afdust sector 

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

agriculture production, and mining and quarrying. It does not include fugitive dust from grain 

elevators, coal handling at coal mines, or vehicular traffic on paved or unpaved roads at 

industrial facilities because these are treated as point sources so they are properly located. 

The afdust sector is separated from other nonpoint sectors to allow for the application of a 

“transport fraction,” and meteorological/precipitation-based reductions. These adjustments are 

applied with a script that applies land use-based gridded transport fractions followed by another 

script that zeroes out emissions for days on which at least 0.01 inches of precipitation occurs or 

there is snow cover on the ground. The land use data used to reduce the NEI emissions 

determines 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 purpose of 

applying the transport fraction and meteorological adjustments is to reduce the overestimation of 

fugitive dust in the grid modeling as compared to ambient observations. Both the transport 

fraction and meteorological adjustments are based on the gridded resolution of the platform (e.g., 

12km grid cells); therefore, different emissions will result if the process were applied to different 

grid resolutions. A limitation of the transport fraction approach is the lack of monthly variability 

that would be expected with seasonal changes in vegetative cover. While wind speed and 

direction are not accounted for in the emissions processing, the hourly variability due to soil 

moisture, snow cover and precipitation is accounted for in the subsequent meteorological 

adjustment. 

The dust emissions in the modeling platform are not the same as the 2011NEIv1 emissions 

because the NEI paved and unpaved road dust emissions include a built-in precipitation 

reduction that is based on average meteorological data, which is at a coarser temporal and spatial 

resolution than the modeling platform meteorological adjustment. Due to this, in the platform 

paved and unpaved road emissions data was used that did not include any precipitation-based 

reduction. This allows the entire sector to be processed consistently so that the same grid-specific 

transport fractions and meteorological adjustments can be applied. Where states submitted afdust 

data, it was assumed that the state-submitted data were not met-adjusted and therefore the 

meteorological adjustments were still applied. Thus, it is possible that these sources may have 

been adjusted twice. Even with that possibility, air quality modeling shows that in general, dust 

is frequently overestimated in the air quality modeling results. 

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 2011 NEI. The livestock and fertilizer emissions were 

extracted based on SCC. The “ag” sector includes all of the NH3 emissions from fertilizer 
24

3

3

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 in California (175 tons) and Wisconsin (125 tons). To prevent double-counting, 

emissions were not included in the nonpoint ag inventory for counties in which they were in the 

point source inventory. 

3.2.2.3 Nonpoint Oil-gas Sector (np_oilgas) 

The nonpoint oil and gas (np_oilgas) sector contains onshore and offshore oil and gas emissions. 

EPA estimated emissions for all counties with 2011 oil and gas activity data with the Oil and Gas 

Tool, and many S/L/T agencies also submitted nonpoint oil and gas data. The types of sources 

covered include drill rigs, workover rigs, artificial lift, hydraulic fracturing engines, pneumatic 

pumps and other devices, storage tanks, flares, truck loading, compressor engines, and 

dehydrators. For more information on the development of the oil and gas emissions in the 

2011NEIv1, see Section 3.21 of the 2011NEIv1 TSD. See the pt_oilgas sector of this document 

for more information on point source oil and gas sources. 

3.2.2.4 Residential Wood Combustion Sector (rwc) 

The residential wood combustion (rwc) sector includes residential wood burning devices such as 

fireplaces, fireplaces with inserts (inserts), free standing woodstoves, pellet stoves, outdoor 

hydronic heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in 

firepots and chimeneas. Free standing woodstoves and inserts are further differentiated into three 

categories: conventional (not EPA certified); EPA certified, catalytic; and EPA certified, 

noncatalytic. Generally speaking, the conventional units were constructed prior to 1988. Units 

constructed after 1988 had to meet EPA emission standards and they are either catalytic or noncatalytic. For more information on the development of the residential wood combustion 

emissions, see Section 3.14 of the 2011NEIv1 TSD. 

3.2.2.5 Other Nonpoint Sources (nonpt) 

Stationary nonpoint sources that were not subdivided into the afdust, ag, np_oilgas, or rwc 

sectors were assigned to the “nonpt” sector. Locomotives and CMV mobile sources from the 

2011NEIv1 nonpoint inventory were assigned to the c1c2rail sector. The types of sources in the 

nonpt sector include: 

 stationary source fuel combustion, including industrial, commercial, and residential; 

 chemical manufacturing; 

 industrial processes such as commercial cooking, metal production, mineral processes, 

petroleum refining, wood products, fabricated metals, and refrigeration; 

 solvent utilization for surface coatings such as architectural coatings, auto refinishing, 

traffic marking, textile production, furniture finishing, and coating of paper, plastic, 

metal, appliances, and motor vehicles; 

 solvent utilization for degreasing of furniture, metals, auto repair, electronics, and 

manufacturing; 

 solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal 
25

care products, household products, adhesives and sealants; 

 solvent utilization for asphalt application and roofing, and pesticide application; 

 storage and transport of petroleum for uses such as portable gas cans, bulk terminals, 

gasoline service stations, aviation, and marine vessels; 

 storage and transport of chemicals; 

 waste disposal, treatment, and recovery via incineration, open burning, landfills, and 

composting; 

 agricultural burning and orchard heating; 

 miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive 

repair shops. 

Most sources in this sector have annual emissions that are temporally allocated to hourly values 

using temporal profiles, but the annual agricultural burning estimates are treated as monthly 

values. The annual values in the 2011NEIv1 were split into monthly emissions by aggregating 

the data up to monthly values from daily estimates of emissions. 

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. 

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 emissions values. The ptfire 

sector excludes agricultural burning and other open burning sources that are included in the nonpt sector. 

Emissions are day-specific and include 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. The point source fire emissions are consistent with the fires stored in the Events data category 

of the 2011NEIv1. For more information on the development of the 2011NEIv1 fire inventory, see 

Section 5.1 of the 2011NEIv1 TSD. 

The point source day-specific emission estimates for 2011 fires rely on SMARTFIRE 2 

(Sullivan, et al., 2008), which uses the National Oceanic and Atmospheric Administration’s 

(NOAA’s) Hazard Mapping System (HMS) fire location information as input. Additional inputs 

include the CONSUMEv3.0 software application (Joint Fire Science Program, 2009) and the 

Fuel Characteristic Classification System (FCCS) fuel-loading database to estimate fire 

emissions from wildfires and prescribed burns on a daily basis. 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. A functional diagram 

of the SMARTFIRE 2 process of reconciling fires with ICS-209 reports is available in the 

documentation (Raffuse, et al., 2007). Once the fire reconciliation process is completed, the 
26

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). 

SMARTFIRE 2 estimates were used directly for all states except Georgia and Florida. For 

Georgia, the satellite-derived emissions were removed from the ptfire inventory and replaced 

with a separate state-supplied ptfire inventory. Adjustments were also made to Florida as 

described in Section 5.1.4 of the 2011NEIv1 TSD. These changes made the data in the ptfire 

inventory consistent with the data in the 2011NEIv1.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). 

3.2.5 Biogenic Sources (beis) 

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

2011 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. 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). 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) 

Onroad mobile sources include emissions from motorized vehicles that are normally operated on 

public roadways. These include passenger cars, motorcycles, minivans, sport-utility vehicles, 

light-duty trucks, heavy-duty trucks, and buses. The sources are further divided between diesel 

and gasoline vehicles. The sector characterizes emissions from off-network processes (e.g. 

starts, hot soak, and extended idle) and on-network processes (i.e., from vehicles moving along 

the roads). 

For the 2011 platform, the 2011 onroad emissions are separated into two sectors: (1) “onroad” 

and (2) “onroad_rfl”. The onroad and onroad_rfl sectors are processed separately to allow for 

different spatial allocation to be applied to onroad refueling, which is allocated using a gas 

station surrogate, versus onroad vehicles, which are allocated using surrogates based on roads 

and population. Except for California and Texas, all onroad and onroad refueling emissions are 

generated using the SMOKE-MOVES emissions modeling framework that leverages MOVES 

generated outputs (http://www.epa.gov/otaq/models/moves/index.htm) and hourly meteorology. 

All tribal data from the mobile sectors have been dropped because the (1) emissions are small, 

(2) the emissions could be double-counted with state-provided onroad emissions, (3) all tribal 

data was developed using the older model MOBILE6, and (4) because spatial surrogate data at 

the tribal level is not currently available. Emissions for onroad (including refueling), nonroad 
27

and c1c2rail sources in California were provided by the California Air Resources Board 

(CARB). 

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 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) 

Onroad mobile sources include emissions from motorized vehicles that are normally operate on 

public roadways. These include passenger cars, motorcycles, minivans, sport-utility vehicles, 

light-duty trucks, heavy-duty trucks, and buses. The sources are further divided between diesel 

and gasoline vehicles. The sector characterizes emissions from off-network processes (e.g. 

starts, hot soak, and extended idle) as well as from on-network processes (i.e., from vehicles 

moving along the roads). For the 2011v6.1 platform, the 2011 onroad emissions are separated 

into two sectors: (1) “onroad” and (2) “onroad_rfl”. The onroad and onroad_rfl sectors are 

processed separately to allow for different spatial allocation to be applied to onroad refueling, 

which is allocated using a gas station surrogate, versus onroad vehicles, which are allocated 

using surrogates based on roads and population. Except for California and Texas, all onroad and 

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

framework that leverages MOVES generated outputs 

(http://www.epa.gov/otaq/models/moves/index.htm) and hourly meteorology. All tribal data 

from the mobile sectors have been dropped because the emissions are small, the emissions could 

be double-counted with state-provided onroad emissions, all tribal data was developed using the 

older model MOBILE6, and because spatial surrogate data is not currently available. 

For the continental U.S., EPA used a modeling framework that took into account the temperature 

sensitivity of the on-road emissions. Specifically, EPA used MOVES inputs for representative 

counties, vehicle miles traveled (VMT) and vehicle population (VPOP) data for all counties, 

along with tools that integrated the MOVES model with SMOKE. In this way, it was possible to 

take advantage of the gridded hourly temperature information available from meteorology 

modeling used for air quality modeling. The “SMOKE-MOVES” integration tool was developed 

by EPA in 2010 and is in use by states and regional planning organizations for regional air 

quality modeling of onroad mobile sources. SMOKE-MOVES requires that emission rate 

“lookup” tables be generated by MOVES which differentiate emissions by process (i.e., running, 

start, vapor venting, etc.), vehicle type, road type, temperature, speed, hour of 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 a 

series of “representative counties,” to which every other county was mapped. Using the MOVES 
28

emission rates, SMOKE selects appropriate emissions rates for each county, hourly temperature, 

SCC, and speed bin and multiplied the emission rate by activity (VMT (vehicle miles travelled) 

or VPOP (vehicle population)) to produce emissions. These calculations were done for every 

county and grid cell, in the continental U.S. for each hour of the year. 

Using SMOKE-MOVES for creating the model-ready emissions requires numerous steps: 

1) Determine which counties will be used to represent other counties in the MOVES runs.

2) Determine which months will be used to represent other month’s fuel characteristics.

3) Create MOVES inputs needed only by MOVES. MOVES requires county-specific 

information on vehicle populations, age distributions, and inspection-maintenance 

programs for each of the representative counties. 

4) Create inputs needed both by MOVES and by SMOKE, including a list of temperatures 

and activity data. 

5) Run MOVES to create emission factor tables using the MOVESTier3FRM model 

(specifically, model “Moves 20121002f” with default database 

“movesdb20121002l_truncatedgfre”)

6) Run SMOKE to apply the emission factors to activity data (VMT and VPOP) to calculate 

emissions 

7) Aggregate the results to the county-SCC level for summaries and quality assurance 

The California and Texas onroad emissions were created through a hybrid approach of 

combining state-supplied annual emissions (from the 2011NEIv1) with EPA developed 

SMOKE-MOVES runs. Through this approach, the platform was able to reflect California’s 

unique rules and Texas’ detailed modeling, while leveraging the more detailed SCCs and the 

highly resolved spatial patterns, temporal patterns, and speciation from SMOKE-MOVES. The 

basic steps involved in temporally allocating California’s and Texas’ onroad emissions based on 

SMOKE-MOVES results were: 

1) Run CA/TX using EPA inputs through SMOKE-MOVES to produce hourly 2011 

emissions hereafter known as “EPA estimates”. These EPA estimates for CA/TX are run 

in a separate sector called “onroad_catx”. 

2) Calculate ratios between state-supplied emissions and EPA estimates6. For Texas, these 

ratios were calculated for each county/SCC7 (fuel and vehicle type)/pollutant 

combination. For California, these were calculated for each county/SCC3 (fuel 

type)/pollutant combination. These were not calculated at a greater resolution because 

California’s emissions did not provide data for all vehicle types. 

3) Create an adjustment factor file (CFPRO) that includes EPA-to-state estimate ratios. For 

extended idle adjustments, each specific state ratio (county/SCC Group (7 or 3)/pollutant) 

 

6

 These ratios were created for all matching pollutants. These ratios were duplicated for all appropriate modeling 

species. For example, EPA used the NOX ratio for NO, NO2, HONO and used the PM2.5 ratio for PEC, PNO3, POC, 

PSO4, and PMFINE (For more details on NOX and PM speciation, see Sections 3.2.3 and Error! Reference source not 

ound.). For VOC model-species, if there was an exact match (e.g., BENZENE), EPA used that HAP pollutant ratio. 

For other VOC-based model-species that didn’t exist in the NEI inventory, EPA used VOC ratios. 
29

was multiplied by the extended idle adjustment factor (see the 2011NEIv1 TSD for 

details).

4) Rerun CA/TX through SMOKE-MOVES using EPA inputs and the new adjustment 

factor file. 

Through this process, adjusted model-ready files were created that sum to California’s and 

Texas’ annual totals, but have the temporal and spatial patterns reflecting the highly resolved 

meteorology and SMOKE-MOVES. After adjusting the emissions, this sector is called 

“onroad_catx_adj”. Note that in emission summaries, the emissions from the “onroad” and 

“onroad_catx_adj” sectors are summed and designated as the emissions for the onroad sector. 

3.2.8 Onroad Refueling (onroad_rfl) 

Onroad refueling is modeled very similarly to other onroad emissions, and were generated using 

MOVESTier3FRM. The onroad_rfl emissions are spatially allocated to gas station locations. 

Because the refueling emission factors use the same SCCs as the other onroad models, refueling 

was run in a separate sector from the other onroad mobile sources to allow for the different 

spatial allocation. To facilitate this, the refueling EFs were separated from the other emission 

factors into rate-per-distance (RPD) refueling and rate-per-vehicle (RPV) refueling tables7. 

SMOKE-MOVES was run using these EF tables as inputs, and spatially allocated using a gas 

stations spatial surrogate. Lastly, the SMOKE program Mrggrid combined RPD refueling and 

RPV refueling into a single onroad_rfl model-ready output for final processing with the other 

sectors prior to use in CMAQ. 

EPA SMOKE-MOVES generated emissions for onroad refueling were used without any 

adjustments for all states, including California and Texas. These emissions were used instead of 

state submissions to provide a consistent approach nationwide and also because most states did 

not submit refueling emissions for diesel fuel. Since the 2011NEIv1 includes the state-submitted 

emissions, the platform and the NEI refueling emissions in the nonpoint category are inconsistent 

for states that submitted refueling emissions. For states that didn’t submit emissions, the 

approaches are similar but not identical because of differences in the MOVES version, 

specifically 2010b for the NEI and Tier3FRM for the modeling platform. 

3.2.9 Nonroad Mobile Sources — NMIM-Based (nonroad) 

The nonroad equipment emissions are equivalent to the emissions in the nonroad data category 

of the 2011NEIv1, with the exception that the modeling platform emissions also include monthly 

totals. All nonroad emissions are compiled at the county/SCC level. 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 nonroad sector includes monthly exhaust, 

evaporative and refueling emissions from nonroad engines (not including commercial marine, 

aircraft, and locomotives) that EPA derived from NMIM for all states except California and 

Texas. Additional details on the development of the 2011NEIv1 nonroad emissions are available 

in Section 4.5 the 2011NEIv1 TSD. 

 

7 The Moves2smk post-processing script has command line arguments that will either consolidate or split out the 

refueling EF. 
30

California year 2011 nonroad emissions were submitted to the 2011NEIv1 and are also 

documented in a staff report (ARB, 2010a). The nonroad sector emissions in California were 

developed using a modular approach and include all rulemakings and updates in place by 

December 2010. These emissions were developed using Version 1 of the CEPAM which 

supports various California off-road regulations such as in-use diesel retrofits (ARB, 2007), 

Diesel Risk-Reduction Plan (ARB, 2000) and 2007 State Implementation Plans (SIPS) for the 

South Coast and San Joaquin Valley air basins (ARB, 2010b). 

The CARB-supplied 2011NEIv1 nonroad annual inventory emissions values were converted to 

monthly values by using the aforementioned EPA NMIM monthly inventories to compute 

monthly ratios by county, SCC7 (fuel, engine type, and equipment type group), mode, and 

pollutant. SCC7 ratios were used because the SCCs in the CARB inventory did not align with 

many of the SCCs in EPA NMIM inventory. By aggregating up to SCC7, the two inventories 

had a more consistent coverage of sources. Some VOC emissions were added to California to 

account for situations when VOC HAP emissions were included in the inventory, but there were 

no VOC emissions. These additional VOC emissions were computed by summing benzene, 

acetaldehyde, and formaldehyde for the specific sources. 

Texas year 2011 nonroad emissions were also submitted to the NEI. The 2011NEIv1 nonroad 

annual inventory emissions values were converted to monthly values by using EPA’s NMIM 

monthly inventories to compute monthly ratios by county, SCC7, mode, and poll. 

3.2.10 Nonroad Mobile Sources: Commercial Marine C1, C2, and Locomotive (c1c2rail) 

The c1c2rail sector contains locomotive and smaller CMV sources, except for railway 

maintenance locomotives and C3 CMV sources outside of the Midwest states. The “c1c2” 

portion of this sector name refers to the Class 1 and 2 CMV emissions, not the railway 

emissions. Railway maintenance emissions are included in the nonroad sector. The C3 CMV 

emissions are in the c3marine sector. All emissions in this sector are annual and at the countySCC resolution. 

The starting point for the c1c2rail sector is the 2011NEIv1 nonpoint inventory for all but specific 

Midwest states, which are instead derived from the Great Lakes 2010 CMV inventory. The 

modeling platform emissions for the c1c2rail SCCs were extracted from the NEI nonpoint 

inventory. For more information on CMV sources in the NEI, see Section 4.3 of the 2011NEIv1 

TSD. For more information on locomotives, see Section 4.4 of the 2011NEIv1 TSD. 

The difference between the 2011NEIv1 and the modeling platform for this sector is due to the 

availability of alternative data from the Midwest RPO. Year-2010 emissions were received from 

the Lake Michigan Air Directors Consortium for tug boats, Great Lakes vessels (“Lakers”) and 

inland waterways for states within the Midwest RPO and Minnesota, hereafter simply referred to 

as “MWRPO” (http://www.ladco.org/). The states in the MWRPO are: Illinois, Indiana, 

Michigan, Minnesota, Ohio and Wisconsin. These MWRPO CMV emissions include coverage 

for bordering states/counties along the inland waterways such as the Mississippi and Ohio rivers 

in Iowa, Missouri, Kentucky, West Virginia, Pennsylvania and New York. The LADCO 2010 

inventory was used to replace EPA-estimated CMV emissions in the MWRPO states, but was 
31

not used to replace the 2011NEIv1 emissions in the bordering non-MWRPO states. 

Some modifications to the MWRPO CMV data were made prior to SMOKE processing: 

 Emissions provided at the level of NEI Shape IDs were aggregated to county-level. 

 The 2011NEIv1 was used to determine which counties had ports; for those counties that 

had ports, 90% of emissions in the MWPRO inventory were assigned as underway 

(SCC=2280002200) and 10% were assigned as port emissions (SCC=2280002100). 

 Emissions were converted to short tons and PM2.5 was added by assuming it is equal to 

92% of PM10 at the suggestion of the MWRPO. 

 Tugs were assigned a unique SCC (2280002021) to allow for unique spatial allocation 

(see Section 3.4.1). 

 Tugs were assigned from MWRPO total to counties based on 2011NEIv1 county-level 

activity information for tug vessels. 

Because the Great Lakes vessels include all CMV activity on the Great Lakes, EPA-estimated 

C3 CMV (c3marine) sector emissions (discussed in the following section) in the MWRPO states 

were removed to avoid potential double-counting of C3 CMV with the LADCO inventory in the 

MWRPO states. 

3.2.11 Nonroad mobile sources: C3 commercial marine (c3marine) 

The U.S. C3 CMV inventory was developed based on a 4-km resolution ASCII raster format 

dataset used since the Emissions Control Area-International Marine Organization (ECA-IMO) 

project began in 2005, then known as the Sulfur Emissions Control Area (SECA). The ECAIMO data are used instead of the 2011NEIv1 data for the modeling platform because 

accompanying estimates of emission projections for future years are available. In addition, the 

inventory preserves shipping lanes in federal waters while these are not stored within the NEI 

data. Keeping the sources in this sector separate from smaller CMV sources allows for the 

emissions to be elevated above the surface layer within the AQ model. The ECA-IMO data are 

used for all states with C3 CMV emissions. For the MWPRO states, the ECA-IMO C3 CMV 

emissions in the Great Lakes are assumed to be misclassified as C3 vessels for which emissions 

are included in the c1c2rail sector as part of the LADCO inventory, therefore the ECA-IMO 

emissions are not included in the c3marine sector. 

The development of this ECA-IMO-based C3 CMV inventory is discussed below; however, all 

non-U.S. emissions (Canadian emissions and emissions farther offshore than U.S. waters) are 

processed in the “othpt” sector. This splitting of the C3 CMV emissions from the farther 

offshore emissions allows for easier summaries of U.S.-only and state or county total emissions. 

The ECA-IMO emissions consist of large marine diesel engines (at or above 30 liters/cylinder) 

that until recently, were allowed to meet relatively modest emission requirements, and often burn 

residual fuel. The emissions in this sector are comprised of primarily foreign-flagged oceangoing vessels, referred to as C3 CMV ships. The c3marine inventory includes these ships in 

several intra-port modes (i.e., cruising, hoteling, reduced speed zone, maneuvering, and idling) 

and an underway mode, and includes near-port auxiliary engine emissions. An overview of the 

C3 ECA Proposal to the International Maritime Organization (EPA-420-F-10-041, August 2010) 
32

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. 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 ECA-IMO controls for these vessels. 

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). EPA developed 

regional growth (activity-based) factors that were applied to create the 2011 inventory from the 

2002 data. These growth factors are provided in Table 3-4. The emissions were converted to 

SMOKE point source inventory format, allowing for the emissions to be allocated to modeling 

layers above the surface layer. All non-US, non-EEZ emissions (i.e., in waters considered 

outside of the 200 nm EEZ, and hence out of the U.S. and Canadian ECA-IMO controllable 

domain) were simply assigned a dummy state/county FIPS code=98001, and were projected to 

year 2011 using the “Outside ECA” factors in Table 3-4. The SMOKE-ready data have been 

cropped from the original ECA-IMO entire northwestern quarter of the globe to cover only the 

large continental U.S. 36-km “36US1” air quality model domain, the largest domain used by 

EPA in recent years. 

For California, the ECA-IMO 2011 emissions were scaled by county to match those provided by 

CARB for year 2011 because CARB has had distinct projection and control approaches for this 

sector since 2002. These CARB C3 CMV emissions are documented in a staff report available 

at: http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf. The CMV emissions 

obtained from the CARB nonroad mobile dataset include the 2011 regulations to reduce 

emissions from diesel engines on commercial harbor craft operated within California waters and 

24 nautical miles of the California shoreline. These emissions were developed using Version 1 

of the California Emissions Projection Analysis Model (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 at: 

http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles. 

The geographic regions listed in the table are shown in Figure 3-1. The East Coast and Gulf 

Coast regions were divided along a line roughly through Key Largo (longitude 80°26’ West). 

The 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). 
33

Table 3-4. Growth factors to project the 2002 ECA inventory to 2011 

* Technically, these are not really “FIPS” state-county codes, but are treated as such in the 

inventory and emissions processing. 

Figure 3-1. Illustration of regional modeling domains in ECA-IMO study

The assignment of U.S. state/county FIPS codes was restricted to state-federal water boundaries 

data from the Mineral Management Service (MMS) that extend approximately 3 to 10 nautical 

miles (nm) off shore. Emissions outside the 3 to 10 mile MMS boundary, but within the 

approximately 200 nm EEZ boundaries, were projected to year 2011 using the same regional 

adjustment factors as the U.S. emissions; however, the state/county FIPS codes were assigned as 

“EEZ” codes and these emissions processed in the “othpt” sector. 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. This holds true for 

MWRPO states and other states such as Pennsylvania and New York. The classification of 

emissions to U.S. and Canadian FIPS codes is needed to avoid double-counting of C3 CMV U.S. 

emissions in the Great Lakes because, as discussed in the previous section, all CMV emissions in 

the Midwest RPO are processed in the “c1c2rail” sector. 

The emissions were converted to SMOKE point source inventory format, allowing for the 

emissions to be allocated to modeling layers above the surface layer. All non-US, non-EEZ 

Region

EEZ 

FIPS NOX PM10 PM2.5

VOC 

(HC) CO SO2

East Coast (EC) 85004 1.301 0.500 0.496 1.501 1.501 0.536

Gulf Coast (GC) 85003 1.114 0.428 0.423 1.288 1.288 0.461

North Pacific (NP) 85001 1.183 0.467 0.458 1.353 1.353 0.524

South Pacific (SP) 85002 1.367 0.525 0.521 1.565 1.562 0.611

Great Lakes (GL) n/a 1.072 0.394 0.390 1.177 1.176 0.415

Outside ECA 98001 1.341 1.457 1.457 1.457 1.457 1.457
34

emissions (i.e., in waters considered outside of the 200 nm EEZ, and hence out of the U.S. and 

Canadian ECA-IMO controllable domain) were simply assigned a dummy state/county FIPS 

code=98001, and were projected to year 2011 using the “Outside ECA” factors in Table 3-4. 

The SMOKE-ready data have been cropped from the original ECA-IMO entire northwestern 

quarter of the globe to cover only the large continental U.S. 36-km “36US1” air quality model 

domain, the largest domain used by EPA in recent years8. 

The original ECA-IMO inventory did not delineate between ports and underway emissions (or 

other C3 modes such as hoteling, maneuvering, reduced-speed zone, and idling). However, a 

U.S. ports spatial surrogate dataset was used to assign the ECA-IMO emissions to ports and 

underway SCCs 2280003100 and 2280003200, respectively. This had no effect on temporal 

allocation or speciation because all C3 CMV emissions, unclassified/total, port and underway, 

share the same temporal and speciation profiles. 

3.2.12 Emissions from Canada, Mexico and Offshore Drilling Platforms (othpt, othar, othon) 

The emissions from Canada, Mexico, and non-U.S. offshore Class 3 Commercial Marine Vessels 

(C3 CMV) and 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, 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. 

The ECA-IMO-based C3 CMV emissions for non-U.S. states are processed in the othpt sector. 

These C3 CMV emissions include those assigned to Canada, those assigned to the Exclusive 

Economic Zone (EEZ, defined as those emissions just beyond U.S. waters approximately 3-10 

miles offshore, extending to about 200 nautical miles from the U.S. coastline), and all other 

offshore emissions –far offshore and non-U.S. These emissions are included in the othpt sector 

for simplicity of creating U.S.-only emissions summaries. Otherwise, these emissions are 

developed in the same way as the U.S. C3 CMV emissions in the c3marine sector. 

The othpt sector also includes point source offshore oil and gas drilling platforms that are beyond 

U.S. state-county boundaries in the Gulf of Mexico. For these offshore emissions, the 2008 NEI 

version 3 point source inventory data were used because the 2011 data were not yet available. 

This is consistent with the 2011NEIv1. Updated offshore oil and gas drilling emissions are 

expected to be incorporated into version 2 of the 2011 NEI. The 2008-based offshore emission 

sources were provided by the Mineral Management Services (MMS).

For Canada, year-2006 Canadian emissions were the latest available at the time the modeling 

was performed. These were the starting point with the addition of several modifications to these 

inventories. The SCCs in these inventories were changed to the generic 39999999 and the 

industrial code information was removed to preserve confidentiality. The Canadian point sources 

were split into three inventory files: 

 

8

 The extent of the “36US1” domain is similar to the full geographic region shown in Figure 3-1. Note that this 

domain is not specifically used in this 2011 platform, although spatial surrogates that can be used with it are 

provided. 
35

 ptinv_canada_point_2006_orl_13aug2013_v3_orl.txt: contains point sources for all 

pollutants except VOC; 

 ptinv_canada_point_cb5_2006_orl_13aug2013_v1_orl.txt: contains VOC emissions split 

into CB05 species; 

 ptinv_canada_point_uog_2006_orl_02mar2009_v0_orl.txt: contains oil and gas-related 

sources. 

The year-2006 nonpoint emissions provided by Canada were unchanged from EPA 2007 

platform. Inventory files were provided for area fugitive dust, agricultural, commercial marine, 

railroad, nonroad, aircraft, and other area sources. Canadian onroad emissions are also 

unchanged from the EPA 2007 platform. 

For Mexico, point, nonpoint, and onroad emissions for year 2012 are projections of their 1999 

inventory originally developed by Eastern Research Group Inc., (ERG, 2006; ERG, 2009; Wolf, 

2009) 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-2012 Mexico emissions from the 1999 inventory is available at: 

http://www.wrapair.org/forums/ef/inventories/MNEI/index.html.

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 hourly emissions 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 

emission inventories (i.e., emissions input to SMOKE) for the sectors as 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. Emissions modeling includes temporal 

allocation, spatial allocation, and pollutant speciation. In some cases, emissions modeling also 

includes the vertical allocation of point sources, but many air quality models also perform this 

task because it greatly reduces the size of the input emissions files if the vertical distribution of 

the sources does not need to be provided as an input. 

The temporal resolutions of the emissions inventories input to SMOKE vary across sectors, and 
36

may be hourly, daily, monthly, or annual total emissions, or even emission factors and activity 

data. The spatial resolution also varies: it may be individual point sources, 

county/province/municipio totals, or gridded emissions. This section provides some basic 

information about the tools and data files used for emissions modeling as part of the modeling 

platform. 

3.3.1 The SMOKE Modeling System 

SMOKE version 3.5.1 was used to pre-process the raw emissions inventories into emissions 

inputs for CMAQ. 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 in-line emissions capability of the air quality models was used, which allows the 

creation of source-based and two-dimensional gridded emissions files that are much smaller than 

full three-dimensional gridded emissions files. For quality assurance of the emissions modeling 

steps, emissions totals by specie for the entire model domain are output as reports that are then 

compared to reports generated by SMOKE on the input inventories to ensure that mass is not lost 

or gained during the emissions modeling 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, and 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 by the individual SMOKE 

programs for each sector. Table 3-5 summarizes the major processing steps of each platform 

sector. The “Spatial” column shows the spatial approach used: here “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 the Tmpbeis3 

program 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 with emissions in aloft layers based on plume rise. The term “inline” 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, othpt, and ptfire sectors are the only sectors with only “inline” emissions, meaning that all of the emissions are placed in aloft layers and there are no 

emissions for those sectors in the two-dimensional, layer-1 files created by SMOKE. 
37

Table 3-5. Key emissions modeling steps by sector 

Platform sector Spatial Speciation

Inventory

resolution Plume rise

Afdust Surrogates Yes annual

Ag Surrogates Yes annual

(some monthly)

Beis Pre-gridded 

land use in BEIS3.14 computed hourly

c1c2rail Surrogates Yes annual

c3marine Point Yes annual in-line

Nonpt Surrogates &

area-to-point Yes annual(some monthly)

Nonroad Surrogates &

area-to-point Yes monthly

np_oilgas Surrogates Yes annual

Onroad Surrogates Yes monthly activity, 

computed hourly

onroad_rfl Surrogates Yes monthly activity, 

computed hourly

Othar Surrogates Yes annual

Othon Surrogates Yes annual

Othpt Point Yes annual in-line

pt_oilgas Point Yes annual in-line

ptegu Point Yes daily & hourly in-line

ptegu_pk Point Yes daily & hourly in-line

ptfire Point Yes daily in-line

ptnonipm Point Yes annual in-line

rwc Surrogates Yes annual

SMOKE has the option of grouping sources so that they are treated as a single stack when 

computing plume rise. For the 2011 platform, no grouping was performed because grouping 

combined with “in-line” processing will not give identical results as “offline” processing (i.e., 

when SMOKE creates 3-dimensional files). This occurs when stacks with different stack 

parameters or lat/lons are grouped, thereby changing the parameters of one or more sources. The 

most straightforward way to get the same results between in-line and offline is to avoid the use 

of grouping. 

3.3.3 Spatial Configuration 

For this study, SMOKE was run for the smaller 12-km CONtinental United States “CONUS” 

modeling domain (12US2) shown in Figure 3-2 and boundary conditions were obtained from a 

2011 run of GEOS-Chem. 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 
38

spatial surrogates and area-to-point data used to accomplish spatial allocation with SMOKE.

Figure 3-2. CMAQ Modeling Domain

3.3.4 Chemical Speciation Configuration 

The emissions modeling step for chemical speciation creates the “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 the 2011 platform is the CB05 mechanism (Yarwood, 2005). The same base chemical 

mechanism is used within both CMAQ and CAMX, but the implementation differs slightly 

between the two models. The specific versions of CMAQ and CAMx used in applications of this 

platform include secondary organic aerosol (SOA) and HONO enhancements. 

From the perspective of emissions preparation, the CB05 with SOA mechanism is the same as 

was used in the 2007 platform. Table 3-6 lists the model species produced by SMOKE for use in 

CMAQ and CAMX. 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. 
39

The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach 

were developed from the SPECIATE 4.3 database 

(http://www.epa.gov/ttn/chief/software/speciate), which is EPA's repository of TOG and PM 

speciation profiles of air pollution sources. However, a few of the profiles used in the v6 

platform will be published in later versions of the SPECIATE database after the release of this 

documentation. The SPECIATE database development and maintenance is a collaboration 

involving EPA’s ORD, OTAQ, and the Office of Air Quality Planning and Standards (OAQPS), 

in cooperation with 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. 

Speciation profiles and cross-references for 2011v6 platform are available in spreadsheet form 

from ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/speciation_profiles/. The 

profiles are in the Excel files “gspro_2011.xlsx” and “gspro_combo_2011.xlsx, 

gsref_2011.xlsx”. The cross reference information is in “gsref_2011.xlsx. A spreadsheet 

showing emission totals for each speciation profile for the 2011ed case by modeling sector is 

available in the file “2011ed_speciation_profile_CAPs_feb112014.xlsx”. Note that the 

emissions totals differ slightly from the 2011ef case, as do some of the VOC to TOG conversion 

factors. However, the reports still convey the relative importance of each speciation profile in 

terms of emissions affected. 
40

Table 3-6. Emission model species produced for CB05 with SOA for CMAQ 5.0.1

Inventory Pollutant Model Species Model species description

Cl2 CL2 Atomic gas-phase chlorine

HCl HCL Hydrogen Chloride (hydrochloric acid) gas

CO CO Carbon monoxide

NOX NO Nitrogen oxide

NO2 Nitrogen dioxide

HONO Nitrous acid

SO2 SO2 Sulfur dioxide

SULF Sulfuric acid vapor

NH3 NH3 Ammonia

VOC ALD2 Acetaldehyde

ALDX Propionaldehyde and higher aldehydes

BENZENE Benzene (not part of CB05)

CH4 Methane9

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

VOC species from the biogenics 

model that do not map to model 

species above

SESQ Sesquiterpenes

TERP Terpenes

PM10 PMC Coarse PM > 2.5 microns and  10 microns

PM2.5

10 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

Sea-salt species (non –

anthropogenic) 11

PCL Particulate chloride

PNA Particulate sodium

*The same species names are used for the CAMX model with exceptions as follows:

1. CL2 is not used in CAMX

2. CAMX particulate sodium is NA (in CMAQ it is PNA)

3. CAMX uses different names for species that are both in CBO5 and SOA for the following: TOLA=TOL, XYLA=XYL, 

ISP=ISOP, TRP=TERP. They are duplicate species in CAMX that are used in the SOA chemistry. CMAQ uses the same 

names in CB05 and SOA for these species.

4. CAMX uses a different name for sesquiterpenes: CMAQ SESQ = CAMX SQT

5. CAMX particulate species have different names for organic carbon, coarse particulate matter and other particulate mass: 

CMAQ uses POC, PMC, PMFINE, and PMOTHR, while CAMX uses POA, CPRM, FCRS, and FPRM, respectively.

 

9 Technically, CH4 is not a VOC but part of TOG. Although emissions of CH4 are derived, the AQ models do not use 
41

The speciation of VOC includes HAP emissions from the 2011NEIv1 in the speciation process. 

Instead of speciating VOC to generate all of the species listed in Table 3-6, emissions of four 

specific HAPs: benzene, acetaldehyde, formaldehyde and methanol (collectively known as 

“BAFM”) from the NEI were “integrated” with the NEI VOC. The integration process 

(described in more detail below) combines these HAPs with the VOC in a way that does not 

double count emissions and uses the HAP inventory directly in the speciation process. The basic 

process is to subtract the specified HAPs emissions mass from VOC emissions mass and to then 

use a special “integrated” profile to speciate the remainder of VOC to the model species 

excluding the specific HAPs. EPA believes that generally, the HAP emissions from the NEI are 

more representative of emissions of these compounds than their generation via VOC speciation. 

The BAFM HAPs (benzene, acetaldehyde, formaldehyde and methanol) were chosen because, 

with the exception of BENZENE, they are the only explicit VOC HAPs in the base version of 

CMAQ 5.0.1 (CAPs only with chlorine chemistry) model. Explicit means that they are not 

lumped chemical groups like the other CB05 species. These “explicit VOC HAPs” are 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 is a model species in the base version of CMAQ 5.0.1, and there was a 

desire to keep its emissions consistent between multi-pollutant and base versions of CMAQ. 

For specific sources, especially within the onroad and onroad_rfl sectors, the integration included 

ethanol. To differentiate when a source was integrating BAFM versus EBAFM (ethanol in 

addition to BAFM), the speciation profiles that do not include ethanol are referred to as an “Eprofile” (to be used when the ethanol comes from the inventory pollutant). For example, use E10 

headspace gasoline evaporative speciation profile 8763 when ethanol is speciated from VOC, but 

use 8763E when ethanol is obtained directly from the inventory. 

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 both the particular HAPs to integrate via the INVTABLE and the particular sources to 

integrate via the NHAPEXCLUDE file (which actually provides the sources to be excluded from 

integration12). For the “integrated” sources, SMOKE subtracts the “integrated” 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 

profiles13. SMOKE computes NONHAPTOG and then applies the speciation profiles to allocate 

the NONHAPTOG to the other air quality model VOC species not including the integrated 

HAPs. After determining if a sector is to be integrated, if all sources have the appropriate HAP 

 

these emissions because the anthropogenic emissions are dwarfed by the CH4 already in the atmosphere. 

10 For CMAQ 5.0, PM2.5 is speciated into a finer set of PM components. Listed in this table are the AE5 species 

11 These emissions are created outside of SMOKE 

12 In SMOKE version 3.5.1, the options to specify sources for integration are expanded so that a user can specify the 

particular sources to include or exclude from integration, and there are settings to include or exclude all sources 

within a sector. In addition, the error checking is significantly stricter for integrated sources. If a source is 

supposed to be integrated, but it is missing BAFM or VOC, SMOKE will now raise an error. 

13 These ratios and profiles are typically generated from the Speciation Tool when it is run with integration of a 

specified list of pollutants, for example BAFM. 
42

emissions, then the sector is considered fully integrated and does not need a NHAPEXCLUDE 

file. If on the other hand, certain sources do not have the necessary HAPs, then an 

NHAPEXCLUDE file must be provided based on the evaluation of each source’s pollutant mix. 

EPA considered CAP-HAP integration for all sectors and developed “integration criteria” for 

some of them. 

The process of partial integration for BAFM means that the BAFM records in the input 

inventories do not need to be removed from any sources in a partially integrated sector because 

SMOKE does this automatically using the INVTABLE configuration. For EBAFM integration, 

this process is identical to that shown in the figure except for the addition of ethanol (E) to the 

list of subtracted HAP pollutants. For full integration, the process would be very similar except 

that the NHAPEXCLUDE file would not be used and all sources in the sector would be 

integrated. 

In SMOKE, the INVTABLE allows the user to specify both the particular HAPs to integrate. 

Two different types of INVTABLE files are included for use with different sectors of the 

platform. For sectors that had no integration across the entire sector (see Error! Reference 

ource not found.7), EPA created a “no HAP use” INVTABLE in which the “KEEP” flag is set 

to “N” for BAFM pollutants. Thus, any BAFM pollutants in the inventory input into SMOKE 

are automatically dropped. This approach both avoids double-counting of these species and 

assumes that the VOC speciation is the best available approach for these species for sectors using 

this approach. The second INVTABLE, used for sectors in which one or more sources are 

integrated, causes SMOKE to keep the inventory BAFM pollutants and indicates that they are to 

be integrated with VOC. This is done by setting the “VOC or TOG component” field to “V” for 

all four HAP pollutants. This type of INVTABLE is further differentiated into a version for 

those sectors that integrate BAFM and another for those that integrate EBAFM, such as the 

onroad and onroad_rfl sectors. 

Table 3-7. 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), Methanol (M), and Ethanol (E)

ptegu No integration 

ptegu_pk No integration 

ptnonipm No integration

ptfire No integration 

othar No integration

othon 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

nonpt Partial integration (BAFM and EBAFM)

np_oilgas Partial integration (BAFM)

pt_oilgas Partial integration (BAFM)

rwc Partial integration (BAFM)

nonroad Partial integration (BAFM)
43

Platform 

Sector

Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A), 

Formaldehyde (F), Methanol (M), and Ethanol (E)

c1c2rail Partial integration (BAFM)

othpt Partial integration (BAFM)

c3marine Full integration (BAFM)

onroad Full integration (EBAFM and BAFM)

onroad_rfl Full integration (EBAFM and BAFM)

SMOKE can compute speciation profiles from mixtures of other profiles in user-specified 

proportions. 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 to speciate onroad and nonroad mobile and 

gasoline-related related stationary sources that use fuels with varying ethanol content. In these 

cases, the speciation profiles require different combinations of gasoline profiles, e.g. E0 and E10 

profiles. Since the ethanol content varies spatially (e.g., by state or county), temporally (e.g., by 

month) and by modeling year (future years have more ethanol) the GSPRO_COMBO feature 

allows combinations to be specified at various levels for different years. SMOKE computes the 

resultant profile using the fraction of each specific profile assigned by county, month and 

emission mode. 

The GSREF file indicates that a specific source uses a combination file with the profile code 

“COMBO”. Because the GSPRO_COMBO file does not differentiate by SCC and there are 

various levels of integration across sectors, sector specific GSPRO_COMBO files are used. For 

the onroad and onroad_rfl sectors, the GSPRO_COMBO uses E-profiles (i.e. there is EBAFM 

integration). Different profile combinations are specified by the mode (e.g. exhaust, evaporative, 

refueling, etc.) by changing the pollutant name (e.g. EXH__NONHAPTOG, 

EVP__NONHAPTOG, RFL__NONHAPTOG). For the nonpt sector, a combination of BAFM 

and EBAFM integration is used. Due to the lack of SCC-specificity in the GSPRO_COMBO, 

the only way to differentiate the sources that should use BAFM integrated profiles versus Eprofiles is by changing the pollutant name. For example, EPA changed the pollutant name for 

the PFC future year inventory so the integration would use EVP__NONHAPVOC to correctly 

select the E-profile combinations, while other sources used NONHAPVOC to select the typical 

BAFM profiles. 

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 specie SESQT. The profile code associated 

with BEIS3.14 profiles for use with CB05 was “B10C5.” For additional sector-specific details on 

VOC speciation for a variety of sectors, see Section 3.2.1.3 of the TSD Preparation of Emission 

Inventories for the Version 6.1 Emissions Modeling Platform (EPA, 2014a). 

In addition to VOC profiles, the SPECIATE database also contains the PM2.5 speciated into both 

individual chemical compounds (e.g., zinc, potassium, manganese, lead), and into the 

“simplified” PM2.5 components used in the air quality model. For CMAQ 4.7.1 modeling, these 

“simplified” components (AE5) are all that is needed. For CMAQ 5.0.1, there is a new 

thermodynamic equilibrium aerosol modeling tool (ISORROPIA) v2 mechanism that needs 

additional PM components (AE6), which are further subsets of PMFINE (see Table 3-8). EPA 
44

speciated PM2.5 so that it included both AE5 and AE6 PM model species without causing any 

double counting. Therefore, emissions from this platform can be used with either CMAQ 4.7.1 

or CMAQ 5.0.1. The majority of the 2011 platform PM profiles come from the 911XX series 

which include updated AE6 speciation14. 

Table 3-8. PM model species: AE5 versus AE6

species name species description AE5 AE6

POC organic carbon Y Y

PEC elemental carbon Y Y

PSO4 Sulfate Y Y

PNO3 Nitrate Y Y

PMFINE unspeciated PM2.5 Y N

PNH4 Ammonium N Y

PNCOM non-carbon organic matter N Y

PFE Iron N Y

PAL Aluminum N Y

PSI Silica N Y

PTI Titanium N Y

PCA Calcium N Y

PMG Magnesium N Y

PK Potassium N Y

PMN Manganese N Y

PNA Sodium N Y

PCL Chloride N Y

PH2O Water N Y

PMOTHR PM2.5 not in other AE6 species N Y

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 

MOVES speciated PM does not map 1-to-1 to the AE5 speciation (nor the AE6 speciation) 

needed for CMAQ modeling. For additional details on PM speciation, see Section 3.2.2 of the 

Version 6.1 platform TSD (EPA, 2014a). 

NOX can be speciated into NO, NO2, and/or HONO. For the non-mobile sources, EPA used a 

single profile “NHONO” to split NOX into NO and NO2. 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. The onroad sector does 

not use the “HONO” profile to speciate NOX. 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 

 

14 The exceptions are 5674 (Marine Vessel – Marine Engine – Heavy Fuel Oil) used for c3marine and 92018 (Draft 

Cigarette Smoke – Simplified) used in nonpt. 
45

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. HONO is not calculated 

directly by the Tier 3 proposal version of MOVES. For these EF tables, the calculation of 

HONO and the NO2 fraction are calculated externally by the moves2smk script15. The SMOKEMOVES system then models these species directly without further speciation.

3.3.4 Temporal Processing Configuration 

Temporal allocation (i.e., temporalization) is the process of distributing aggregated emissions to 

a finer temporal resolution, thereby 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. Many 

emissions inventories are annual or monthly in nature. Temporalization takes these aggregated 

emissions and if needed distributes them to the month, and then distributes the monthly 

emissions to the day and the daily emissions to the hour. This process is typically done by 

applying temporal profiles to the inventories in this order: monthly, day of the week, and diurnal. 

The temporal profiles and associated cross references used to create the hourly emissions inputs 

for the 2011 air quality modeling platform were similar to those used for the 2007 platform. The 

temporal factors applied to the inventory are selected using some combination of country, state, 

county, SCC, and pollutant. Table 3-9 summarizes the temporal aspects of emissions modeling 

by comparing the key approaches used for temporal processing across the sectors. In the table, 

“Daily temporal approach” refers to the temporal approach for getting daily emissions from the 

inventory using the SMOKE Temporal program. The values given are the values of the SMOKE 

L_TYPE setting. The “Merge processing approach” refers to the days used to represent other 

days in the month for the merge step. If this is not “all”, then the SMOKE merge step runs only 

for representative days, which could include holidays as indicated by the right-most column. 

The values given are those used for the SMOKE M_TYPE setting (see below for more 

information).

Table 3-9. Temporal Settings Used for the Platform Sectors in SMOKE 

Platform 

Sector

Inventory 

resolutions

Monthly 

profiles 

used?

Daily 

temporal 

approach

Merge 

processing 

approach

Process 

Holidays as 

separate days

ptegu Daily & hourly all all Yes

ptegu_pk Daily & hourly all all Yes

ptnonipm Annual yes mwdss mwdss Yes

pt_oilgas Annual yes mwdss mwdss Yes

ptfire Daily all all Yes

othpt Annual yes mwdss mwdss

nonroad Monthly mwdss mwdss Yes

 

15A specific version of the moves2smk script was developed to do this calculation of HONO. The typical version 

assumes that HONO was calculated directly by MOVES2010b. 
46

Platform 

Sector

Inventory 

resolutions

Monthly 

profiles 

used?

Daily 

temporal 

approach

Merge 

processing 

approach

Process 

Holidays as 

separate days

othar Annual yes week week

c1c2rail Annual yes mwdss mwdss

c3marine Annual yes aveday aveday

onroad Annual & monthly1all all Yes

onroad_rfl Annual & monthly2all all Yes

othon Annual yes week week

nonpt Annual & monthly yes all all Yes

np_oilgas Annual yes mwdss mwdss Yes

rwc Annual no met-based All Yes

ag Annual yes all all Yes

afdust_adj Annual yes week all Yes

beis Hourly n/a all Yes

1. Note the annual and monthly “inventory” actually refers to the activity data (VMT and VPOP) for 

onroad. The actual emissions are computed on an hourly basis. 

2. Note the annual and monthly “inventory” actually refers to the activity data (VMT and VPOP) for 

onroad_rfl. The actual emissions are computed on an hourly basis.

The following values are used in the table: The value “all” means that hourly emissions 

computed for every day of the year and that emissions potentially have day-of-year variation. 

The value “week” means that hourly emissions computed for all days in one “representative” 

week, representing all weeks for each month. This means emissions have day-of-week variation, 

but not week-to-week variation within the month. The value “mwdss” means hourly emissions 

for one representative Monday, representative weekday (Tuesday through Friday), representative 

Saturday, and representative Sunday for each month. This means emissions have variation 

between Mondays, other weekdays, Saturdays and Sundays within the month, but not week-toweek variation within the month. The value “aveday” means hourly emissions computed for one 

representative day of each month, meaning emissions for all days within a month are the same. 

Special situations with respect to temporalization are described in the following subsections. 

In addition to the resolution, temporal processing includes a ramp-up period for several days 

prior to January 1, 2011, which is intended to mitigate the effects of initial condition 

concentrations. The ramp-up period was 10 days (December 22-31, 2010). For most sectors, 

emissions from December 2011 were used to fill in surrogate emissions for the end of December 

2010. In particular, December 2011 emissions (representative days) were used for December 

2010. For biogenic emissions, December 2010 emissions were processed using 2010 

meteorology. 

The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated 

format for monthly, daily, and hourly emissions inventories than previous formats supported. 

Previously, to process monthly inventory data required the use of 12 separate inventory files. 
47

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, daily and hourly FF10 inventories contain individual records with data 

for all days in a month and all hours in a day, respectively. 

SMOKE 3.5.1 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 to it; rather, it should only have month-to-day and diurnal 

temporalization. This becomes particularly important when specific 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. The 

modeling platform sectors that make use of monthly values in the FF10 files are nonroad, 

onroad, and the agricultural burning inventory within the nonpt sector. 

The 2011NEIv1 annual EGU emissions are allocated to hourly emissions using the following 3-

step methodology: annual value to month, month to day, and day to hour. The temporal 

allocation procedure is differentiated by whether or not the source could be directly matched to a 

CEMS unit via ORIS facility code and boiler ID. Prior to temporal allocation, as many sources 

as possible were matched to CEMS data via ORIS facility code and boiler ID. EIS stores a base 

set of previously matched units via alternate facility and unit IDs. For any units not yet matched, 

reports were generated by unit to identify potential matches with the NEI. The reports included 

FIPS state/county code, facility name, and NOx and SO2 emissions. Units were considered 

matches if the FIPS state/county code matched, the facility name was similar, and the NOx and 

SO2 emissions were similar. 

For sources not matched to CEMS measurements, the first two steps of the allocation are done 

outside of SMOKE. For sources in the ptegu and ptegu_pk sectors that are matched to CEMS 

data, annual totals of the emissions may be different than the annual values in 2011NEIv1 

because the CEMS data actually replaces the inventory data. All units in the ptegu_pk sector 

with non-zero emissions for 2011 were matched to CEMS data. 

For units not matched to CEMS data, the allocation of the inventory annual emissions to months 

is done using average fuel-specific season-to-month factors generated for each of the 64 IPM 

regions shown in Figure 3-3. These factors are based 2011 CEMS data only. In each region, 

separate factors were developed for the fuels coal, natural gas, and “other”, where the types of 

fuels included in “other” vary by region. Separate profiles were computed for NOx and SO2, and 

heat input. An overall composite profile was also computed and was used in a few cases in which 

the fuel-specific profile was too irregular, or there were no CEMS units with the specified fuel in 

the region containing the unit. For both CEMS and non-CEMS matched units, NOx and SO2

CEMS data are used to allocate NOx and SO2 emissions, while CEMS heat input data is used to 

allocate all other pollutants. 

For the c1c2rail and c3marine sectors, emissions are allocated with flat monthly and day of week 

profiles, and most emissions are also allocated with flat hourly profiles. 

For the nonpt sector, most the inventories are annual except for the agricultural burning (SCC 
48

2801500000) inventory which was allocated to months by adding up the available values for 

each day of the month. For all agricultural burning, the diurnal temporal profile used reflected 

the fact that burning occurs during the daylight hours - see Figure 3-4 (McCarty et al., 2009). 

This puts most of the emissions during the work day and suppresses the emissions during the 

middle of the night. A uniform profile was used for each day of the week for all agricultural 

burning emissions in all states except for the following, for which state-specific day of week 

profiles were used: Arkansas, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma, 

and Texas. 

Figure 3-3. IPM Regions for EPA Base Case v5.13 
49

Figure 3-4. Agricultural burning diurnal temporal profile

For the ptfire sector, the inventories are in the daily point fire format ORL PTDAY. The ptfire 

sector is used in model evaluation cases. The 2007 and earlier platforms had additional 

regulatory cases that used averaged fires and temporally averaged EGU emissions, but the 2011 

platform uses base year-specific (i.e., 2011) data for all cases. 

For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses 

monthly inventories from output from NMIM. For California, a monthly inventory was created 

from CARB’s annual inventory using EPA-estimated NMIM monthly results to compute 

monthly ratios by pollutant and SCC7 and these ratios were applied to the CARB inventory to 

create a monthly inventory. 

3.3.5 Meteorological-based Temporal Profiles 

There are many factors that impact the timing of when emissions occur, and for some sectors this 

includes meteorology. The benefits of utilizing meteorology as method for temporalization are: 

(1) a meteorological dataset consistent with that used by the AQ model is available (e.g., outputs 

from WRF); (2) the meteorological model data is highly resolved in terms of spatial resolution; 

and (3) the meteorological variables vary at hourly resolution and can therefore be translated into 

hour-specific temporalization. 

The SMOKE program GenTPRO provides a method for developing meteorology-based 

temporalization. Currently, the program can utilize three types of temporal algorithms: annualto-day temporalization for residential wood combustion (RWC), month-to-hour temporalization 

for agricultural livestock ammonia, and a generic meteorology-based algorithm for other 

situations. For the 2011 platform, meteorological-based temporalization was used for portions of 

the rwc sector and for livestock within the ag sector. 
50

GenTPRO reads in gridded meteorological data (output from MCIP) along with 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 selected algorithm and the run parameters. For more details 

on the development of these algorithms and running GenTPRO, see the GenTPRO 

documentation and the SMOKE documentation at 

http://www.cmascenter.org/smoke/documentation/3.1/GenTPRO_TechnicalSummary_Aug2012

_Final.pdf and http://www.cmascenter.org/smoke/documentation/3.5.1/html/ch05s03s07.html, 

respectively.

For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the 

temporal allocation of emissions to days. GenTPRO was used to create an annual-to-day 

temporal profile for the RWC sources. 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 for most sources in the sector 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, 

only the distribution of the emissions within the year is affected. The temperature threshold for 

rwc emissions was 50  ̊F for most of the country, and 60  ̊F for the following states: Alabama, 

Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and Texas. 

Figure 3-5 illustrates the impact of changing the temperature threshold for a warm climate 

county. The plot shows the temporal fraction by day for Duval County, Florida for the first four 

months of 2007. The default 50  ̊F threshold creates large spikes on a few days, while the 60  ̊F 

threshold dampens these spikes and distributes a small amount of emissions to the days that have 

a minimum temperature between 50 and 60  ̊F. 

Figure 3-5. Example of RWC temporalization in 2007 using a 50 versus 60  ̊F threshold 

The diurnal profile for used for most RWC sources places more of the RWC emissions in the 

morning and the evening when people are typically using these sources. This profile is based on 

a 2004 MANE-VU survey based temporal profiles (see 

http://www.marama.org/publications_folder/ResWoodCombustion/Final_report.pdf). This 

profile was created by averaging three indoor and three RWC outdoor temporal profiles from 

counties in Delaware and aggregating them into a single RWC diurnal profile. This new profile 
51

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 and found that the new RWC profile 

generally tracked the concentration based temporal patterns. Additional details regarding 

temporalization of RWC sources can be found in Section 3.3.3 of the 2011v6.1 Platform TSD. 

For the agricultural livestock NH3 algorithm, the GenTPRO algorithm is based on an equation 

derived by Jesse Bash of EPA ORD based on the Zhu, Henze, et al. (2013) empirical equation. 

This equation is based on observations from the TES satellite instrument with the GEOS-Chem 

model and its adjoint to estimate diurnal NH3 emission variations from livestock as a function of 

ambient temperature, aerodynamic resistance, and wind speed. The equations are: 

Ei,h = [161500/Ti,h x e(-1380/Ti,h)] x ARi,h

PEi,h = Ei,h / Sum(Ei,h) 

where 

 PEi,h = Percentage of emissions in county i on hour h

 Ei,h = Emission rate in county i on hour h

 Ti,h = Ambient temperature (Kelvin) in county i on hour h

 Vi,h = Wind speed (meter/sec) in county i (minimum wind speed is 0.1 meter/sec) 

 ARi,h = Aerodynamic resistance in county i

GenTPRO was run using the “BASH_NH3” profile method to create month-to-hour temporal 

profiles for these sources. Because these profiles distribute to the hour based on monthly 

emissions, the monthly emissions are obtained from a monthly inventory, or from an annual 

inventory that has been temporalized to the month. 

Figure 3-6 compares the daily emissions for Minnesota from the “old” approach (uniform 

monthly profile) with the “new” approach (GenTPRO generated month-to-hour profiles). 

Although the GenTPRO profiles show daily (and hourly variability), the monthly total emissions 

are the same between the two approaches. 

Figure 3-6. Example of new animal NH3 emissions temporalization approach, summed to 

daily emissions 

For the onroad and onroad_rfl sectors, the temporal distribution of emissions is a combination of 

more traditional temporal profiles and the influence of meteorology. This section discusses both 

the meteorological influences and the updates to the diurnal temporal profiles for the 2011 
52

platform. 

Meteorology is not used in the development of the temporal profiles, but rather it impacts the 

calculation of the hourly emissions through the program Movesmrg. The result is that the 

emissions vary at the hourly level by grid cell. More specifically, the on-network (RPD) and the 

off-network (RPV and RPP) processes use the gridded meteorology (MCIP) directly. Movesmrg 

determines the temperature for each hour and grid cell and uses that information to select the 

appropriate emission factor (EF) for the specified SCC/pollutant/mode combination. In the 

previous platform, RPP used county level minimum and maximum temperature ranges for the 

day to determine the appropriate EF. This potentially overestimated the temperature range for 

any particular grid cell, which would result in increased emissions for vapor-venting. In the 

2011 platform (and the 2011NEIv1), RPP was updated to use the gridded minimum and 

maximum temperature for the day. This more spatially resolved temperature range produces 

more accurate emissions for each grid cell. The combination of these three processes (RPD, 

RPV, and RPP) is the total onroad sector emissions, while the combination of the two processes 

(RPD, RPV) for the refueling mode only is the total onroad_rfl sector emissions. Both sectors 

show a strong meteorological influence on their temporal patterns (see the 2011NEIv1 TSD for 

more details). 

Figure 3-7 illustrates the difference between temporalization of the onroad sector used in the 

2005 and earlier platforms and the meteorological influence via SMOKE-MOVES. In the plot, 

the “MOVES” inventory is a monthly inventory that is temporalized by SCC to day-of-week and 

hour. Similar temporalization is done for the VMT in SMOKE-MOVES, but the 

meteorologically varying EFs add an additional variation on top of the temporalization. Note, 

the SMOKE-MOVES run is based on the 2005 platform and previous temporalization of VMT to 

facilitate the comparison of the results. In the figure, the MOVES emissions have a repeating 

pattern within the month, while the SMOKE-MOVES shows day-to-day (and hour-to-hour) 

variability. In addition, the MOVES emissions have an artificial jump between months which is 

due to the inventory providing new emissions for each month that are then temporalized within 

the month but not between months. The SMOKE-MOVES emissions have a smoother transition 

between the months. 
53

Figure 3-7. Example of SMOKE-MOVES temporal variability of NOX emissions 

For the onroad and onroad_rfl sectors, the “inventories” actually consist of activity data. For 

RPP and RPV processes, the VPOP inventory is annual and does not need temporalization. For 

RPD, the VMT inventory is monthly and was temporalized to days of the week and then to 

hourly VMT through temporal profiles. The RPD processes require a speed profile (SPDPRO) 

that consists of vehicle speed by hour for a typical weekday and weekend day. Unlike other 

sectors, the temporal profiles and SPDPRO will impact not only the distribution of emissions 

through time but also the total emissions. Because SMOKE-MOVES’ process RPD calculates 

emissions from VMT, speed and meteorology, if one shifted the VMT or speed to different 

hours, it would align with different temperatures and hence different EF. In other words, two 

SMOKE-MOVES runs with identical annual VMT, meteorology, and MOVES EF, will have 

different total emissions if the temporalization of VMT changes. 

In previous platforms, the diurnal profile for VMT16 varied by road type but not by vehicle type. 

These profiles were used throughout the nation. EPA wanted to create new diurnal profiles that 

could differentiate by vehicle type as well as by road type and would potentially vary over 

geography. The 2011NEIv1 process provided an opportunity to update the diurnal profile with 

information submitted by states. States submitted MOVES county databases (CDBs) that 

included information on the distribution of VMT by hour of day and by day of week17 (see the 

2011NEIv1 TSD for details on the submittal process for onroad). EPA decided not to update the 

day of week profile because MOVES only differentiated weekday versus weekend while the 

default SMOKE profiles differentiated each of the 7 days. EPA mined the state submitted 

MOVES CDBs for non-default diurnal profiles18. The list of potential diurnal profiles was then 

 

16 These same profiles were used for onroad emissions in the 2005 platform. 

17 The MOVES tables are the hourvmtfraction and the dayvmtfraction. 

18 Further QA was done to remove duplicates and profiles that were missing two or more hours. If they were 

missing a single hour, the missing hour could be calculated by subtracting all other hours fractions from 1. 
54

analyzed to see whether the profiles varied by vehicle type, road type, weekday vs. weekend, and 

by county within a state. 

EPA attempted to maximize the use of state and/or county specific diurnal profiles. If a specific 

state or county’s profiles varied by vehicle type or/and road type, then the submitted profile was 

used. If the profile had less variability than the old SMOKE defaults (i.e. neither varied by 

vehicle type nor road type), then a new default profile would be used (see below for description 

of new profiles). This analysis was done separately for weekdays and for weekends, therefore 

some areas had submitted profiles for weekdays but defaults for weekends. The result was a set 

of profiles that varied geographically depending on whether or not the profile was submitted and 

the characteristics of the profiles. 

A new set of diurnal profiles was developed from the submitted profiles that varied by both 

vehicle type and road type. Before developing the national profiles, there needs to be a mapping 

between MOVES road types and SMOKE road types (i.e., the last three digits of the SCC) and 

between MOVES source types and SMOKE vehicle types. The mapping between road types is 

relatively straight forward. Basically the road types are consolidated into 4 types in MOVES, 

therefore the new profiles will not differentiate at the level of the SMOKE road type. For 

example, the SMOKE “urban interstate” (SCCLAST3=230) will have the same profile as the 

SMOKE “urban other freeways and expressways” (SCCLAST3=250). The mapping between 

MOVES source type and SMOKE vehicle type is more complicated; it is a many-to-many 

mapping. Figure 3-8 Illustrates the difference between the profiles for the light-duty gas vehicles 

versus the heavy-duty diesel vehicles. For additional details on the updated onroad mobile 

temporal profiles, see Section 3.5.5 of the 2011v6.1 Platform TSD. 

For the afdust sector, meteorology is not used in the development of the temporal profiles, but it 

is used to reduce the total emissions based on meteorological conditions. These adjustments are 

applied through sector-specific scripts, beginning with the application of land use-based gridded 

transport fractions and then subsequent zero-outs for hours during which precipitation occurs or 

there is snow cover on the ground. 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 applied to remove all emissions for days where measureable rain 

occurs. Therefore, the afdust emissions vary day-to-day based on the precipitation and/or snow 

cover for that grid cell and day. Both the transport fraction and meteorological adjustments are 

based on the gridded resolution of the platform; therefore, somewhat different emissions will 

result from different grid resolutions. Application of the transport fraction and meteorological 

adjustments prevents the overestimation of fugitive dust impacts in the grid modeling as 

compared to ambient samples. 

Biogenic emissions in the beis sector vary by every day of the year because they are developed 

using meteorological data including temperature, surface pressure, and radiation/cloud data. The 

emissions are computed using appropriate emission factors according to the vegetation in each 

model grid cell, while taking the meteorological data into account. 
55

Figure 3-8. Updated national default profiles for LDGV (top) vs. HHDDV (bottom), urban 

restricted weekday

3.3.6 Vertical Allocation of Emissions 

Table 3-5 specifies the sectors for which plume rise is calculated. If there is no plume rise for a 
56

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, 

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. 

The selection of point sources for plume rise is pre-determined in SMOKE using the Elevpoint.

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 Spatial Allocation 

The methods used to perform spatial allocation are summarized in this section. For the modeling 

platform, spatial factors are typically applied by county and SCC. Spatial allocation was 

performed for the national 12-km domain “12US2” (see Figure 3-2). To accomplish this, 

SMOKE used national 12-km spatial surrogates and a SMOKE area-to-point data file. For the 

U.S., EPA updated surrogates to use circa 2010-2011 data wherever possible. For Mexico, 

updated spatial surrogates were used as described below. For Canada, surrogates provided by 

Environment Canada were used and are unchanged from the 2007 platform. The U.S., Mexican, 

and Canadian 12-km surrogates cover the entire CONUS domain 12US1 shown in Figure 3-2. 

The remainder of this subsection provides further detail on the origin of the data used for the 

spatial surrogates and the area-to-point data. 

Additional documentation on the 2011 spatial surrogates is available at 

ftp://ftp.epa.gov/EmisInventory/2011v6/v1platform/reports/spatial_surrogates/ in the files 

US_SpatialSurrogate_Documentation_v091113.pdf and 

US_SpatialSurrogate_Workbook_v093013.xlsx. The spatial cross reference file is in 

gsref_2011.xlsx. Plots of the spatial surrogates are available in 

all_surrogate_maps_2011platform_12US1_v2.pdf. Note that these are plots of the surrogate 

fractions summed by grid cell, so grid cells that overlap multiple counties can show values 
57

greater than one. These maps are only to give an idea of the spatial distribution of the surrogates. 

Allocations of CAP emissions to each of the surrogate codes is given in 

2011ed_spatial_surrogate_CAPs_feb112014.xlsx. One noteworthy change between the 

2011v6.0 and 2011v6.1 platforms is the update of the oil and gas surrogates.

3.3.7.1 Surrogates for U.S. Emissions

There are more than 70 spatial surrogates available for spatially allocating U.S. county-level 

emissions to the 12-km grid cells used by the air quality model. An area-to-point approach 

overrides the use of surrogates for some sources. Table 3-10 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, the 2006 National Land Cover Database, 2011 

gas station and dry cleaner data, and the 2012 National Transportation Atlas Data for rail-lines, 

ports and navigable waterways. Surrogates for ports (801) and shipping lanes (802) were 

developed based on the 2011NEIv1 shapefiles: Ports_032310_wrf and 

ShippingLanes_111309FINAL_wrf, but also included shipping lane data in the Great Lakes and 

support vessel activity data in the Gulf of Mexico. 

The creation of surrogates and shapefiles for the U.S. was generated via the Surrogate Tool. The 

tool and 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. 

Table 3-10. U.S. Surrogates available for the 2011 modeling 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
58

Code Surrogate Description Code Surrogate Description

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 & Gas Wells, IHS Energy, Inc. and

USGS

261 NTAD Total Railroad Density 700 Airport Areas

271 NTAD Class 1, 2, 3 Railroad Density 710 Airport Points

280 Class 2 and 3 Railroad Miles 720 Military Airports

300 Low Intensity Residential 800 Marine Ports

310 Total Agriculture 801 NEI Ports

312 Orchards/Vineyards 802 NEI Shipping Lanes 

320 Forest Land 807 Navigable Waterway Miles

330 Strip Mines/Quarries 808 Gulf Tug Zone Area

340 Land 810 Navigable Waterway Activity

350 Water 812 Midwest Shipping Lanes

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

For the onroad sector, the on-network (RPD) emissions were spatially allocated to roadways, and 

the off-network (RPP and RPV) emissions were allocated to population. For the onroad_rfl 

sector, the emissions were spatially allocated to gas station locations. For the oil and gas sources 

in the np_oilgas sector, the spatial surrogates were updated to those shown in Table 3-11 using 

2011 data consistent with what was used to develop the 2011NEI nonpoint oil and gas emissions. 

Note that the “Oil & Gas Wells, IHS Energy, Inc. and USGS” (680) is older and based on circa2005 data. These surrogates were based on the same GIS data of well locations and related 

attributes as was used to develop the 2011NEIv1 data for the oil and gas sector. The data 

sources included Drilling Info (DI) Desktop’s HPDI database (Drilling Info, 2012) aggregated to 

grid cell levels, along with data from Oil and Gas Commission (OGC) websites. Well completion 

data from HPDI was supplemented by implementing the methodology for counting oil and gas 

well completions developed for the U.S. National Greenhouse Gas Inventory. Under that 

methodology, both completion date and date of first production from HPDI were used to identify 

wells completed during 2011. In total, over 1.08 million unique well locations were compiled 

from the various data sources. The well locations cover 33 states and 1,193 counties (ERG, 

2014). 

Table 3-11. Spatial Surrogates for Oil and Gas Sources

Surrogate 

Code Surrogate Description

681 Spud count - Oil Wells

682 Spud count - Horizontally-drilled wells

683 Produced Water at all wells

684 Completions at Gas and CBM Wells
59

685 Completions at Oil Wells

686 Completions at all wells

687 Feet drilled at all wells

688 Spud count - Gas and CBM Wells

689 Gas production at all wells

692 Spud count - All Wells

693 Well count - all wells

694 Oil production at oil wells

695 Well count - oil wells

697 Oil production at Gas and CBM Wells

698 Well counts - Gas and CBM Wells

Not all of the available surrogates are used to spatially allocate sources in the modeling platform; 

that is, some surrogates shown in Table 3-10 were not assigned to any SCCs, although many of 

the “unused” surrogates are actually used to “gap fill” other surrogates that are assigned. When 

the source data for a surrogate has no values for a particular county, gap filling is used to provide 

values for the surrogate in those counties to ensure that no emissions are dropped when the 

spatial surrogates are applied to the emission inventories. Additional information on U.S. Spatial 

Surrogates, including total CAP emissions for each spatial surrogate, is available in Section 3.4.1 

of the 2011v6.1 Platform TSD (EPA, 2014a). 

3.3.7.3 Allocation Method for Airport-Related Sources in the U.S. 

There are numerous airport-related emission sources in the NEI, such as aircraft, airport ground 

support equipment, and jet refueling. The modeling platform includes the aircraft emissions as 

point sources. For the modeling platform, EPA used the SMOKE “area-to-point” approach for 

only 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.The 

ARTOPNT file that lists the nonpoint sources to locate using point data was unchanged from the 

2005-based platform. 

3.3.7.4 Surrogates for Canada and Mexico Emission Inventories 

The surrogates for Canada to spatially allocate the 2006 Canadian emissions are unchanged from 

the 2007 platform. The spatial surrogate data came from Environment Canada, along with cross 

references. Over 100 surrogates were provided and were outputs from the Surrogate Tool 

(previously referenced), although only about 40 were used in the modeling platform. Per 

Environment Canada, the surrogates are based on 2001 Canadian census data. 

The 2011 platform uses about 20 surrogates for Mexico became. The surrogates are circa 1999 

and 2000 and were based on data obtained from the Sistema Municpal de Bases de Datos 

(SIMBAD) de INEGI and the Bases de datos del Censo Economico 1999. Most of the CAPs 

allocated to the Mexico and Canada surrogates are shown in Table 3-12. The entries in this table 

are for the othar sector except for the MEX Total Road Miles and The CAN traffic rows, which 
60

are for the othon sector. 

Table 3-12. CAPs Allocated to Mexican and Canadian Spatial Surrogates 

Srg 

code Description NH3 NOX PM 2_5 SO2 VOC

22 MEX Total Road Miles 15,965

370,86

7 34,396

13,71

3

375,27

6

10 MEX Population 0 0 0 0

431,23

1

12 MEX Housing 0

161,01

3 17,483 2,123

452,68

5

14 MEX Residential Heating - Wood 0 20,093

211,52

5 2,859

380,57

2

16 MEX Residential Heating - Distillate Oil 0 38 0 11 2

20 MEX Residential Heating - LP Gas 0 25,303 787 63 614

22 MEX Total Road Miles 0 0 0 0 3,513

24 MEX Total Railroads Miles 0 74,969 1,669 663 2,824

26 MEX Total Agriculture

679,21

2

164,14

4 72,372 2,127 43,958

28 MEX Forest Land 0 16,224 67,683 660 79,018

32 MEX Commercial Land 0

125,21

1 7,726 0

286,98

2

34 MEX Industrial Land 0 45,831 5,684

59,20

1

133,44

0

36 MEX Commercial plus Industrial Land 0 0 0 0

332,49

5

38

MEX Commercial plus Institutional 

Land 0 6,400 216 84 28,293

40

Residential (RES1-

4)+Commercial+Industrial+Institutional

+ Government 0 8 20 0

241,71

0

42 MEX Personal Repair (COM3) 0 0 0 0 33,616

44 MEX Airports Area 0 14,639 0 1,149 6,857

46 MEX Marine Ports 0

124,95

1 2,991 1,482 1,099

48 Brick Kilns – Mexico 0 776 6,691 0 10,244

50

Mobile sources - Border Crossing -

Mexico 0 454 0 0 2,668

9100 CAN Population 603 0 276 0 304

9101 CAN total dwelling 643 46,256 12,783

14,69

8 32,944

9106 CAN ALL_INDUST 133 21,526 381 3,921 2

9113 CAN Forestry and logging 1,582 8,561 28,622 1,809 36,114

9115 CAN Agriculture and forestry activities 160

239,55

3 25,318 9,092 26,526

9116 CAN Total Resources 0 17 0 0 5
61

Srg 

code Description NH3 NOX PM 2_5 SO2 VOC

9212 CAN Mining except oil and gas 0 0 5,391 0 0

9221 CAN Total Mining 42 2,292 45,374 728 26

9222 CAN Utilities 189 14,882 369 1,124 255

9233 CAN Total Land Development 17 20,789 1,928 981 2,551

9308 CAN Food manufacturing 0 0 0 0 4,535

9323

CAN Printing and related support 

activities 0 0 0 0 25,203

9324

CAN Petroleum and coal products 

manufacturing 0 0 2,402 0 0

9327

CAN Non-metallic mineral product 

manufacturing 0 238 7,708 2,941 1,218

9331 CAN Primary Metal Manufacturing 0 98 5,062 12 6

9412

CAN Petroleum product wholesalerdistributors 0 0 0 0 70,125

9416

CAN Building material and supplies 

wholesaler-distributors 2 0 1,461 3,259 560

9448

CAN clothing and clothing accessories 

stores 0 0 0 0 328

9562

CAN Waste management and 

remediation services 165 893 1,596 1,998 16,551

9921 CAN Commercial Fuel Combustion 494 33,816 2,750

35,47

1 850

9924 CAN Primary Industry 0 0 0 0

219,28

2

9925 CAN Manufacturing and Assembly 0 0 0 0

139,22

7

9931 CAN OTHERJET 9 14,388 548 1,139 7,629

9932 CAN CANRAIL 109

122,69

4 4,093 5,737 3,304

9942 CAN UNPAVED ROADS 40 3,462 3,499 48

152,67

4

9945 CAN Commercial Marine Vessels 28 45,454 6,404

14,32

5 61,139

9946 CAN Construction and mining 247

156,77

0 10,070 5,667 17,180

9947

CAN Agriculture Construction and 

mining 19 37,452 536 26 32,683

9950 CAN Intersection of Forest and Housing 1,053 11,700

120,04

5 1,671

173,13

0

9960 CAN TOTBEEF

176,15

6 0 7,420 0

317,39

4

9970 CAN TOTPOUL 74,204 0 2 0 264

9980 CAN TOTSWIN

122,09

4 0 996 0 3,186
62

Srg 

code Description NH3 NOX PM 2_5 SO2 VOC

9990 CAN TOTFERT

178,79

1 0 9,279 0 0

9991 CAN traffic 22,294

550,89

6 10,888 5,548

285,10

4

9994 CAN ALLROADS 0 0 55,468 0 0

9995 CAN 30UNPAVED_70trail 0 0

106,70

7 0 0

9996 CAN urban_area 0 0 284 0 0

3.4 Emissions References 

Adelman, Z. 2012. Memorandum: Fugitive Dust Modeling for the 2008 Emissions Modeling 

Platform. UNC Institute for the Environment, Chapel Hill, NC. September, 28, 2012 

ARB, 2000. “Risk Reduction Plan to Reduce Particulate Matter Emissions from Diesel-Fueled 

Engines and Vehicles”. California Environmental Protection Agency Air Resources 

Board, Mobile Source Control Division, Sacramento, CA. October, 2000. Available at: 

http://www.arb.ca.gov/diesel/documents/rrpFinal.pdf. 

ARB, 2007. “Proposed Regulation for In-Use Off-Road Diesel Vehicles”. California 

Environmental Protection Agency Air Resources Board, Mobile Source Control Division, 

Sacramento, CA. April, 2007. Available at: 

http://www.arb.ca.gov/regact/2007/ordiesl07/isor.pdf 

ARB, 2010a. “Proposed Amendments to the Regulation for In-Use Off-Road Diesel-Fueled 

Fleets and the Off-Road Large Spark-Ignition Fleet Requirements”. California 

Environmental Protection Agency Air Resources Board, Mobile Source Control Division, 

Sacramento, CA. October, 2010. Available at: 

http://www.arb.ca.gov/regact/2010/offroadlsi10/offroadisor.pdf. 

ARB, 2010b. “Estimate of Premature Deaths Associated with Fine Particle Pollution (PM2.5) in 

California Using a U.S. Environmental Protection Agency Methodology”. California 

Environmental Protection Agency Air Resources Board, Mobile Source Control Division, 

Sacramento, CA. August, 2010. Available at: 

http://www.arb.ca.gov/research/health/pm-mort/pm-report_2010.pdf.Adelman, Z. 2012. 

Memorandum: Fugitive Dust Modeling for the 2008 Emissions Modeling Platform. 

UNC Institute for the Environment, Chapel Hill, NC. September, 28, 2012. 

Anderson, G.K.; Sandberg, D.V; Norheim, R.A., 2004. Fire Emission Production Simulator 

(FEPS) User's Guide. Available at 

http://www.fs.fed.us/pnw/fera/feps/FEPS_users_guide.pdf

Bullock Jr., R, and K. A. Brehme (2002) “Atmospheric mercury simulation using the CMAQ 

model: formulation description and analysis of wet deposition results.” Atmospheric 

Environment 36, pp 2135–2146.

ERG, 2006. Mexico National Emissions Inventory, 1999: Final, prepared by Eastern Research 

Group for Secratariat of the Environment and Natural Resources and the National 

Institute of Ecology, Mexico, October 11, 2006. Available at: 
63

http://www.epa.gov/ttn/chief/net/mexico/1999_mexico_nei_final_report.pdf

ERG, 2009. Development of Mexico National Emissions Inventory Projecions for 2008, 2012, 

and 2023. Available at: http://www.azdeq.gov/environ/air/plan/download/dmneip.pdf

ERG, 2014. “Technical Memorandum: Modeling Allocation Factors for the 2011 NEI”. 

Environ Corp. 2008. Emission Profiles for EPA SPECIATE Database, Part 2: EPAct Fuels 

(Evaporative Emissions). Prepared for U. S. EPA, Office of Transportation and Air 

Quality, September 30, 2008. 

EPA, 2005. EPA’s National Inventory Model (NMIM), A Consolidated Emissions Modeling 

System for MOBILE6 and NONROAD, U.S. Environmental Protection Agency, Office of 

Transportation and Air Quality, Assessment and Standards Division. Ann Arbor, MI

48105, EPA420-R-05-024, December 2005. Available at 

http://www.epa.gov/otaq/models/nmim/420r05024.pdf.

EPA 2006a. SPECIATE 4.0, Speciation Database Development Document, Final Report, U.S. 

Environmental Protection Agency, Office of Research and Development, National Risk 

Management Research Laboratory, Research Triangle Park, NC 27711, EPA600-R-06-

161, February 2006. Available at 

http://www.epa.gov/ttn/chief/software/speciate/speciate4/documentation/speciatedoc_120

6.pdf.

EPA, 2012a. 2008 National Emissions Inventory, version 2 Technical Support Document. 

Office of Air Quality Planning and Standards, Air Quality Assessment Division, 

Research Triangle Park, NC. Available at: 

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

EPA, 2014a. 2011 Technical Support Document (TSD) Preparation of Emissions Inventories for 

the Version 6.1, 2011 Emissions Modeling Platform. Office of Air Quality Planning and

Standards, Air Quality Assessment Division, Research Triangle Park, NC. Available at 

http://www.epa.gov/ttn/chief/emch/index.html#2011.

EPA, 2014b. 2011 National Emissions Inventory, version 1 Technical Support Document. 

Office of Air Quality Planning and Standards, Air Quality Assessment Division, 

Research Triangle Park, NC. Available at 

http://www.epa.gov/ttn/chief/net/2011inventory.html#inventorydoc.

Frost & Sullivan, 2010. “Project: Market Research and Report on North American Residential 

Wood Heaters, Fireplaces, and Hearth Heating Products Market (P.O. # PO1-IMP403-

F&S). Final Report April 26, 2010”. Prepared by Frost & Sullivan, Mountain View, CA 

94041.

Joint Fire Science Program, 2009. Consume 3.0--a software tool for computing fuel 

consumption. Fire Science Brief. 66, June 2009. Consume 3.0 is available at: 

http://www.fs.fed.us/pnw/fera/research/smoke/consume/index.shtml

Kochera, A., 1997. “Residential Use of Fireplaces,” Housing Economics, March 1997, 10-11. 

Also see: http://www.epa.gov/ttnchie1/conference/ei10/area/houck.pdf. 

LADCO, 2012. “Regional Air Quality Analyses for Ozone, PM2.5, and Regional Haze: Base C 

Emissions Inventory (September 12, 2011)”. Lake Michigan Air Directors Consortium, 
64

Rosemont, IL 60018. Available at: 

http://www.ladco.org/tech/emis/basecv8/Base_C_Emissions_Documentation_Sept_12.pd

f

McCarty, J.L., Korontzi, S., Jutice, C.O., and T. Loboda. 2009. The spatial and temporal 

distribution of crop residue burning in the contiguous United States. Science of the Total 

Environment, 407 (21): 5701-5712.

McKenzie, D.; Raymond, C.L.; Kellogg, L.-K.B.; Norheim, R.A; Andreu, A.G.; Bayard, A.C.; 

Kopper, K.E.; Elman. E. 2007. Mapping fuels at multiple scales: landscape application of 

the Fuel Characteristic Classification System. Canadian Journal of Forest Research. 

37:2421-2437. Oak Ridge National Laboratory, 2009. Analysis of Fuel Ethanol 

Transportation Activity and Potential Distribution Constraints. U.S. Department of 

Energy, March 2009. Docket No. EPA–HQ–OAR–2010–0133.

Ottmar, R.D.; Sandberg, D.V.; Bluhm, A. 2003. Biomass consumption and carbon pools. Poster. 

In: Galley, K.E.M., Klinger, R.C.; Sugihara, N.G. (eds.) Proceedings of Fire Ecology, 

Prevention, and Management. Misc. Pub. 13, Tallahassee, FL: Tall Timbers Research 

Station. 

Ottmar, R.D.; Prichard, S.J.; Vihnanek, R.E.; Sandberg, D.V. 2006. Modification and validation 

of fuel consumption models for shrub and forested lands in the Southwest, Pacific 

Northwest, Rockes, Midwest, Southeast, and Alaska. Final report, JFSP Project 98-1-9-

06. 

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 

Satellite-Detected 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, 
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SESARM, 2012b. “Area and Nonroad 2007 Base Year Inventories. Revised Final Report”, 

Contract No. S-2009-06-01, Prepared by Transystems Corporation, January 2012. . 

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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

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

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Years of 2008, 2012, and 2030. Available at: 

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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.

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Observations and the GEOS-Chem adjoint model”, Journal of Geophysical Research: 

Atmospheres, 118: 1-14. 
66

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 system19. 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 

 

19 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.
67

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 

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 2011 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 sufficient as the 

highest resolution for most regional-scale air quality model applications and assessments.20 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/Research/RIA/cmaq.html

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 

 

20 U.S. EPA (2014), Draft Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, 

PM2.5, and Regional Haze, pp 214. http://www.epa.gov/ttn/scram/guidance/guide/Draft_O3-PMRH_Modeling_Guidance-2014.pdf. 
68

summarized in Simon et al., 201221.

4.2 CMAQ Model Version, Inputs and Configuration 

This section describes the air quality modeling platform used for the 2011 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 

CMAQ model as part of the 2011 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 2011 CMAQ 

simulation along with the results of a model performance evaluation in which the 2011 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 2011 analysis employed CMAQ version 5.0.2

22

which reflects updates to version 5.0.1 which include several changes to the science algorithms 

to improve the underlying science. The CMAQ simulation included parameterizations to 

estimate the vertical distribution nitrogen oxide emissions generated due to lightning as well as 

to estimate bi-directional ammonia flux. The CMAQ model version 5.0 was most recently peerreviewed in June of 2011 for the U.S. EPA.23

 The model enhancements in version 5.0.2 include: 

1. SOA yield update 

2. Gas-phase chemistry

3. Sulfate inhibition effect in aqueous chemistry 

4. CSQY_DATA files

5. WRF land use options

 

21 Simon, H., Baker, K.R., and Phillips, S. (2012) Compilation and interpretation of photochemical model 

performance statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139. 

22 CMAQ version 5.0.2 model code is available from the Community Modeling and Analysis System (CMAS) at: 

http://www.cmascenter.org.

23 Brown, N.J., Allen, D.T., Amar, P., Kallos, G., McNider, R., Russell, A.G., Stockwell, W.R. (September 2011). 

Final Report: Fourth Peer Review of the CMAQ Model, 

http://www.epa.gov/AMD/Reviews/2011_CMAQ_Review_FinalReport.pdf. CMAQ version 5.0 was released on 

February, 2012. It is available from the Community Modeling and Analysis System (CMAS) as well as previous 

peer-review reports at: http://www.cmascenter.org.
69

6. Ammonia bidirectional exchange and dry deposition change 

7. M3DRY backward compatibility with MCIP for wind staggering 

8. Vertical advection time step 

9. Aerosol updates 

10. ACONC bug fix 

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 

model extends vertically from the surface to 50 millibars (approximately 17,600 meters) using a 

sigma-pressure 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 396 x 246 x 25

Vertical Extent 25 Layers: Surface to 50 mb level (see Table 4-2)
70

Figure 4-1. Map of the CMAQ Modeling Domain. The purple box denotes the 12 km 

national modeling domain. (Same as Figure 3-3.) 

4.2.3 Modeling Period / Ozone Episodes

The 12 km CMAQ modeling domain was modeled for the entire year of 2011. The 2011 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 

2011 Emissions: The emissions inventories used in the 2011 air quality modeling are described 

in Section 3, above. 

Meteorological Input Data: The gridded meteorological data for the entire year of 2011 at the 

12 km continental United States scale domain was derived from version 3.424 of the Weather 

Research and Forecasting Model (WRF), Advanced Research WRF (ARW) core.25 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 

2011 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 utilizing the 

moisture-advection trigger26 and the RRTMG long-wave and shortwave radiation (LWR/SWR) 

scheme.27 In addition, the Group for High Resolution Sea Surface Temperatures (GHRSST)28

1km SST data was used for SST information to provide more resolved information compared to 

the more coarse data in the NAM analysis. Landuse and land cover data are based on the 

National Land Cover Database 2006.29

The WRF meteorological outputs were processed using the Meteorology-Chemistry Interface 

 

24 Version 3.4 was the current version of WRF at the time the 2011 meteorological model simulation was performed. 

25 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. 

26 Ma, L-M. and Tan, Z-M, 2009. Improving the behavior of the Cumulus Parameterization for Tropical Cyclone 

Prediction: Convection Trigger. Atmospheric Research 92 Issue 2, 190-211. 

http://www.sciencedirect.com/science/article/pii/S0169809508002585 

27 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.

28 Stammer, D., F.J. Wentz, and C.L. Gentemann, 2003, Validation of Microwave Sea Surface Temperature 

Measurements for Climate Purposes, J. Climate, 16, 73-87.

29 Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J., 2011. 

Completion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 

77(9):858-864. 
71

Processor (MCIP) package30, version 4.1.3, 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 396 by 246 grid cells and 35 vertical layers up to 50 mb. Table 4-2 

shows the vertical layer structure used in WRF and the layer 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 2011 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 2011 WRF and CMAQ simulations (heights are layer 

top). 

CMAQ 

Layers

WRF 

Layers Sigma P Pressure (mb)

Approximate 

Height (m)

25 35 0 5000 17,556

34 0.05 9750 14,780

24 33 0.1 14500 12,822

32 0.15 19250 11,282

23 31 0.2 24000 10,002

30 0.25 28750 8,901

22 29 0.3 33500 7,932

28 0.35 38250 7,064

21 27 0.4 43000 6,275

26 0.45 47750 5,553

20 25 0.5 52500 4,885

24 0.55 57250 4,264

19 23 0.6 62000 3,683

18 22 0.65 66750 3,136

 

30 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.
72

17 21 0.7 71500 2,619

16 20 0.74 75300 2,226

15 19 0.77 78150 1,941

14 18 0.8 81000 1,665

13 17 0.82 82900 1,485

12 16 0.84 84800 1,308

11 15 0.86 86700 1,134

10 14 0.88 88600 964

9

13 0.9 90500 797

12 0.91 91450 714

8

11 0.92 92400 632

10 0.93 93350 551

7

9 0.94 94300 470

8 0.95 95250 390

6 7 0.96 96200 311

5 6 0.97 97150 232

4 5 0.98 98100 154

3

4 0.985 98575 115

3 0.99 99050 77

2 2 0.995 99525 38

1 1 0.9975 99763 19

0 0 1 100000 0

Initial and Boundary Conditions: The lateral boundary and initial species concentrations are 

provided by a three-dimensional global atmospheric chemistry model, the GEOS-CHEM31

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-5). This model was run 

for 2011 with a grid resolution of 2.0 degrees x 2.5 degrees (latitude-longitude). The predictions 

were processed using the GEOS-2-CMAQ tool and used to provide one-way dynamic boundary 

conditions at one-hour intervals.32 A GEOS-Chem evaluation was conducted for the purpose of 

validating the 2011 GEOS-Chem simulation for predicting selected measurements relevant to 

their use as boundary conditions for CMAQ. This evaluation included using satellite retrievals 

paired with GEOS-Chem grid cells.33 More information is available about the GEOS-CHEM 

 

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

University, Cambridge, MA, October 15, 2004. 

32Akhtar, 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. 

33 Henderson, B.H., Akhtar, F., Pye, H.O.T., Napelenok, S.L., and Hutzell, W.T. (2014) A database and tool for 

boundary conditions for regional air quality modeling: description and evaluation, Geosci. Model Dev., 7, 339-360.
73

model and other applications using this tool at: http://gmao.gsfc.nasa.gov/GEOS/ and 

http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-5. 

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 2011 simulation using state/local monitoring sites data in 

order to estimate the ability of the CMAQ modeling system to replicate the 2011 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, mean bias and normalized mean bias; and 

two error metrics, mean error and normalized mean error. 

Mean bias (MB) is used as average of the difference (predicted – observed) divided by the total 

number of replicates (n). Mean bias is defined as: 

MB = 1

n

∑ (P − O)

n

1

, where P = predicted and O = observed concentrations. 

Mean error (ME) calculates the absolute value of the difference (predicted - observed) divided by 

the total number of replicates (n). Mean error is defined as: 

ME = 1

n

∑ |P − O| n

1

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

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

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

In addition to the performance statistics, regional maps which show the MB, ME, NMB, and 

NME were prepared for the ozone season, May through September, at individual monitoring sites 
74

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 2011 in the continental U.S. were included in the evaluation and were taken 

from the 2011 State/local monitoring site data in the EPA Air Quality System (AQS) and the 

Clean Air Status and Trends Network (CASTNet). The performance statistics were calculated 

using predicted and observed data that were paired in time and space on an 8-hour basis. 

Statistics were generated for the following geographic groupings in the 12-km continental U.S. 

domain34: 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 mean bias, mean error, normalized mean bias and error for 

individual monitors are shown in Figures 4-2 through 4-5. 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 2011 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. Generally, 8-hour ozone in the summer is slightly 

over predicted with the greatest over prediction in Central U.S. (NMB is 15.7 percent) at AQS 

and CASTNet sites. However, 8-hour ozone is slightly underpredicted in the West at CASTNet 

sites (NMB is -4.5 percent). Ozone performance in spring shows better performance with slight 

under predictions in most of the subregions except in the Southeast (slight over prediction of 5.4 at 

AQS and 0.8 at CASTNet sites). In the winter, when concentrations are generally low, the model

slightly under predicts 8-hour ozone with the exception of the West at AQS sites (NMB is 6.3). In 

the fall, when concentrations are also relatively low, ozone is slightly over predicted; with NMBs 

less than 11 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. Figure 4-2 shows the mean bias for 8-hour daily maximum ozone greater 

than 60 ppb is generally ±4 ppb across the AQS and CASTNet sites. Likewise, the information in

Figure 4-4 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, St. Paul, WI, and Cleveland, OH. 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 4-4 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 

 

34The 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.
75

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 Figures 

4-3 and 4-5, is 14 ppb and 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, Northeast coastline, Great Lakes coastline, Seattle, WA, Salem, OR, and 

North Dakota. 

Table 4-4. Summary of CMAQ 2011 8-Hour Daily Maximum Ozone Model Performance

Statistics by Subregion, by Season and Monitoring Network.

Subregion

Monitor 

Network Season

No. of 

Obs

MB 

(ppb)

ME 

(ppb)

NMB

(%)

NME 

(%)

Northeast

AQS Winter 7,445 -2.8 5.4 -9.1 17.6

Spring 14,741 -0.5 5.5 -1.2 12.9

Summer 15,835 1.7 7.4 3.5 15.4

Fall 12,697 6.7 3.4 10.9 20.9

CASTNet Winter 1,188 -3.1 4.8 -9.2 14.1

Spring 1,160 -1.3 5.2 -2.9 11.6

Summer 1,217 1.5 6.4 3.2 14.0

Fall 1,295 3.2 5.7 9.5 17.1

Midwest

AQS Winter 2,894 -1.6 5.0 -5.7 17.6

Spring 11,329 0.2 5.8 0.5 13.5

Summer 15,887 2.3 7.8 4.7 15.6

Fall 9,482 2.9 6.6 6.1 14.2

CASTNet Winter 1,015 -1.6 4.6 -4.8 14.2

Spring 962 -1.0 4.8 -2.2 10.6

Summer 905 0.0 6.0 0.1 11.9

Fall 979 2.2 5.2 6.1 14.2

Central States

AQS Winter 11,632 -0.2 5.4 -0.7 16.7

Spring 16,667 1.8 6.2 -3.7 12.9

Summer 17,459 7.6 11.3 15.7 23.5

Fall 15,496 1.7 6.4 3.9 14.6

CASTNet Winter 660 -2.8 5.7 -7.6 15.6

Spring 634 -1.8 6.2 -3.7 12.9

Summer 645 1.7 7.5 3.3 14.7

Fall 612 -0.2 4.82 -0.6 11.0

Southeast AQS Winter 5,741 -0.1 5.2 -0.4 14.1

Spring 19,791 2.5 6.4 5.4 13.8
76

Subregion

Monitor 

Network Season

No. of 

Obs

MB 

(ppb)

ME 

(ppb)

NMB

(%)

NME 

(%)

Summer 22,093 6.7 10.2 13.5 20.7

Fall 17,050 3.6 6.7 9.0 16.9

CASTNet Winter 1,756 -0.9 4.6 -2.5 12.7

Spring 1,824 0.4 5.6 0.8 11.9

Summer 1,777 4.4 8.0 9.0 16.1

Fall 1,827 1.9 5.5 4.8 13.7

West

AQS Winter 25,735 2.2 6.7 6.3 19.4

Spring 29,951 -1.8 5.9 -3.6 12.0

Summer 32,485 1.0 7.6 1.9 14.8

Fall 28,901 3.4 7.0 8.0 16.5

CASTNet Winter 1,614 -1.6 5.6 -3.8 12.9

Spring 1,649 -4.5 6.5 -8.4 12.2

Summer 1,654 -2.6 7.2 -4.5 12.7

Fall 1,609 0.6 5.6 1.3 11.9

Figure 4-2. Mean Bias (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the 

period May-September 2011 at AQS and CASTNet monitoring sites in the continental U.S. 

modeling domain. 
77

Figure 4-4. Mean Error (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the 

period May-September 2011 at AQS and CASTNet monitoring sites in the continental U.S. 

modeling domain.

Figure 4-3. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 

ppb over the period May-September 2011 at AQS and CASTNet monitoring sites in the 

continental U.S. modeling domain.
78

Figure 4-5. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 

ppb over the period May-September 2011 at AQS and CASTNet monitoring sites in the 

continental U.S. modeling domain. 

Evaluation for Annual PM2.5 components: The PM evaluation focuses on PM2.5 components 

including sulfate (SO4), nitrate (NO3), total nitrate (TNO3 = NO3 + HNO3), ammonium (NH4), 

elemental carbon (EC), and organic carbon (OC). The bias and error performance statistics were 

calculated on an annual basis for each subregion (Table 4-5). PM2.5 measurements for 2011 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 the PM2.5 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 

mean bias, mean error, normalized mean bias and normalized mean error by site for each PM2.5

species are provided in Figures 4-6 through 4-29. 

As indicated by the statistics in Table 4-5, annual average sulfate is consistently under predicted 

at CSN, IMPROVE, and CASTNet monitoring sites across the modeling domain, with MBB 

values ranging from -0.1 to -0.7 μgm-3 and NMB values ranging from near negligible to -31 

percent. Sulfate performance shows moderate error, ranging from 23 to 47 percent. Figures 4-6 

through 4-9, 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 80 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 
79

average nitrate is over predicted at all subregions, except in the Central and Western 

U.S. where nitrate is under predicted. Model performance of total nitrate at sub-urban

CASTNet monitoring sites also shows an over prediction across all subregions (NMB in the 

range of near negligible to 27 percent), except in the Central and Western U.S. (NMB 

on average is underpredicted by 23 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 4-12. The 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 

Figures 4-11 and 4-13, 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 -34 

percent). Ammonium performance across the urban CSN sites shows an over prediction 

(ranging from 5 to 24 percent) in the Northeast, Midwest, and Southeast, and an under prediction 

(ranging from 4 to 29 percent in the Central and Western U.S. 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 in the Eastern 

U.S. shows bias within ±30 percent. A larger bias is seen in the Southwest, bias on average 60-

70 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 over predicted across most subregions in rural IMPROVE areas 

(NMB ranging from 4 to 39 percent), except in the Central and Southeast U.S. where the bias is 

2 to 19 percent respectively. The model over predicted annual average organic carbon in all 

subregions at urban CSN sites. 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.

Table 4-5. Summary of CMAQ 2011 Annual PM Species Model Performance Statistics by 

Subregion, by Monitoring Network. 

Pollutant

Monitor 

Network Subregion

No. of 

Obs

MB 

(μgm-3)

ME 

(μgm-3)

NMB 

(%)

NME 

(%)

Sulfate

CSN Northeast 2,898 -0.1 0.7 -4.5 30.8

Midwest 2,448 -0.3 0.8 -13.1 33.1

Southeast 2,584 -0.4 0.8 -14.1 30.5

Central 1,643 -0.2 0.7 -10.8 34.3

West 2,544 -0.1 0.4 -8.4 45.5
80

Pollutant

Monitor 

Network Subregion

No. of 

Obs

MB 

(μgm-3)

ME 

(μgm-3)

NMB 

(%)

NME 

(%)

IMPROVE Northeast 1,840 0.1 0.5 4.4 35.9

Midwest 464 -0.2 0.6 -12.0 32.9

Southeast 1,675 -0.4 0.7 -16.4 31.5

Central 2,203 -0.3 0.5 -16.5 34.6

West 9,518 0.0 0.3 -2.1 46.7

CASTNet Northeast 746 -0.3 0.5 -15.3 22.7

Midwest 566 -0.6 0.7 -25.5 27.8

Southeast 1,094 -0.7 .08 -26.6 29.0

Central 369 -0.6 0.6 -31.0 34.0

West 1,028 -0.1 0.2 -20.3 36.5

Nitrate

CSN Northeast 2,898 0.4 0.8 40.4 73.4

Midwest 2,448 0.3 0.8 47.4 6.8

Southeast 2,584 0.3 0.6 56.5 99.8

Central 1,643 0.0 .5 -3.6 53.1

West 2,544 -0.5 1.2 -29.0 67.0

IMPROVE Northeast 1,839 0.4 0.5 113.0 155.0

Midwest 464 0.1 0.5 11.7 64.1

Southeast 1,675 0.2 0.4 42.6 112.0

Central 1,643 0.0 0.7 -0.2 47.1

West 9,484 0.0 0.2 -13.2 97.5

Total Nitrate 

(NO3 + HNO3)

CASTNet Northeast 746 0.4 0.6 26.5 39.7

Midwest 566 0.0 0.6 1.6 23.5

Southeast 1,094 0.0 0.6 0.7 39.1

Central 369 -0.4 0.6 -20.6 34.3

West 1,028 -0.2 0.4 -26.2 47.4

Ammonium

CSN Northeast 2,898 0.2 0.4 24.3 46.8

Midwest 2,448 0.1 .4 5.3 38.7

Southeast 2,584 0.1 0.3 14.5 44.6

Central 1,643 0.0 0.4 -4.0 42.1
81

Pollutant

Monitor 

Network Subregion

No. of 

Obs

MB 

(μgm-3)

ME 

(μgm-3)

NMB 

(%)

NME 

(%)

West 2,544 -0.2 0.4 -29.2 67.5

CASTNet Northeast 746 0.0 0.2 0.9 27.5

Midwest 566 -0.2 0.3 -15.9 27.1

Southeast 1,094 -0.1 0.2 -14.9 27.0

Central 369 0.1 0.2 -20.3 34.5

West 1,028 -0.1 0.1 -33.6 54.5

Elemental 

Carbon

CSN Northeast 2,750 0.5 0.6 73.2 90.6

Midwest 2,426 0.5 .6 80.3 91.4

Southeast 2,513 0.3 0.5 54.5 74.2

Central 1,625 0.6 0.7 121.0 129.0

West 2,510 0.7 0.8 92.8 111.0

IMPROVE Northeast 1,916 0.2 0.2 70.9 87.0

Midwest 474 0.1 0.1 40.6 57.8

Southeast 1,784 0.1 0.2 26.6 55.7

Central 2,331 0.1 0.2 44.2 68.5

West 10,058 0.1 0.2 80.2 117.0

Organic Carbon

CSN Northeast 2,735 0.4 0.9 38.9 84.1

Midwest 2,412 0.8 1.1 53.9 70.9

Southeast 2,500 0.5 1.1 25.2 54.1

Central 1,622 1.2 1.6 71.5 95.8

West 2,493 1.9 2.4 93.7 118.0

IMPROVE Northeast 1,911 0.4 0.9 38.9 84.1

Midwest 473 0.1 0.7 12.3 61.2

Southeast 1,785 -0.3 0.9 -18.7 49.4

Central 2,332 0.0 0.7 -2.2 55.4

West 9,921 0.0 0.5 4.3 73.5
82

Figure 4-6. Mean Bias (μgm-3) of annual sulfate at monitoring sites in the continental 

U.S. modeling domain. 

Figure 4-7. Mean Error (μgm-3) of annual sulfate at monitoring sites in the continental 

U.S. modeling domain. 
83

Figure 4-8. Normalized Mean Bias (%) of annual sulfate at monitoring sites in the 

continental U.S. modeling domain. 

Figure 4-9. Normalized Mean Error (%) of annual sulfate at monitoring sites in the 

continental U.S. modeling domain. 
84

Figure 4-10. Mean Bias (μgm-3) of annual nitrate at monitoring sites in the continental U.S. 

modeling domain.

Figure 4-11. Mean Error (μgm-3) of annual nitrate at monitoring sites in the continental 

U.S. modeling domain. 
85

Figure 4-12. Normalized Mean Bias (%) of annual nitrate at monitoring sites in the 

continental U.S. modeling domain. 

Figure 4-13. Normalized Mean Error (%) of annual nitrate at monitoring sites in the 

continental U.S. modeling domain. 
86

Figure 4-14. Mean Bias (μgm-3) of annual total nitrate at monitoring sites in the continental 

U.S. modeling domain.

Figure 4-15. Mean Error (μgm-3) of annual total nitrate at monitoring sites in the 

continental U.S. modeling domain. 
87

Figure 4-16. Normalized Mean Bias (%) of annual total nitrate at monitoring sites in the 

continental U.S. modeling domain. 

Figure 4-17. Normalized Mean Error (%) of annual total nitrate at monitoring sites in the 

continental U.S. modeling domain.
88

Figure 4-18. Mean Bias (μgm-3) of annual ammonium at monitoring sites in the continental 

U.S. modeling domain. 

Figure 4-19. Mean Error (μgm-3) of annual ammonium at monitoring sites in the 

continental U.S. modeling domain. 
89

Figure 4-20. Normalized Mean Bias (%) of annual ammonium at monitoring sites in the 

continental U.S. modeling domain. 

Figure 4-21. Normalized Mean Error (%) of annual ammonium at monitoring sites in the 

continental U.S. modeling domain. 
90

Figure 4-22. Mean Bias (μgm-3) of annual elemental carbon at monitoring sites in the 

continental U.S. modeling domain.

Figure 4-23. Mean Error (μgm-3) of annual elemental carbon at monitoring sites in the 

continental U.S. modeling domain. 
91

Figure 4-24. Normalized Mean Bias (%) of annual elemental carbon at monitoring sites in 

the continental U.S. modeling domain.

Figure 4-25. Normalized Mean Error (%) of annual elemental carbon at monitoring sites in 

the continental U.S. modeling domain.
92

Figure 4-26. Mean Bias (μgm-3) of annual organic carbon at monitoring sites in the 

continental U.S. modeling domain.

Figure 4-27. Mean Error (μgm-3) of annual organic carbon at monitoring sites in the 

continental U.S. modeling domain. 
93

Figure 4-28. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the 

continental U.S. modeling domain. 

Figure 4-29. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the 

continental U.S. modeling domain. 
94

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. 2011 

census tract centroids for the year 2011. 

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 
95

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 2011 US census tract centroids across the continental U.S. using 2011 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 2011. 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 2011 over half of the US Census tracts 

(42455 out of 72283) experienced at least one day with an ozone value above the NAAQS of 75 

ppb. 
96

Figure 5-1. Annual 4th max (daily max 8-hour ozone concentrations) derived from AQS, 

CMAQ and DS data. 
97

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 2011. Figure 

5-2 shows annual means and Figure 5-3 shows 98th percentiles of 24-hour PM2.5 concentrations 

for AQS observations, CMAQ model predictions and DS model results. The DS model 

estimated that for 2011 about 30% of the US Census tracts (21336 out of 72283) experienced at 

least one day with a PM2.5 value above the 24-hour NAAQS of 35 ug/m3. 
98

Figure 5-2. Annual mean PM2.5 concentrations derived from AQS, CMAQ and DS data. 
99

Figure 5-3. 98th percentile 24-hour average PM2.5 concentrations derived from AQS, 

CMAQ and DS data. 
100 

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. 
101 

Figure 5-4. Annual mean relative errors (standard errors divided by predictions) from the 

DS 2011 runs. The black dots show the locations of monitors that generated the AQS data 

used as input to the DS model. 
102 

5.4.2 Cross Validation 

To check the quality of its spatial predictions, DS can be set to perform “crossvalidation” (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 left-out 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

PM2.5 830 3.933e-2 2.82 0.96

O3 1290 1.096e-2 4.87 0.95

Table 5-1. Cross-validation statistics associated with the 2011 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 2011 U.S. census tract centroids by utilizing monitoring data and CMAQ 

output for 2011. 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. 
103 

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 
104 

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

Air Quality Assessment Division

Research Triangle Park, NC

Publication No. EPA-454/S-15-001

April, 2015