Bayesian space-time downscaling fusion model 

(downscaler) - Derived Estimates of Air Quality 

for 2012
ii

EPA-450/R-16-001

March 2016

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

Estimates of Air Quality for 2012

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Research Triangle Park, NC
iii

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

3.3 Emissions Modeling Summary .....................................................................................................31

3.4 Emissions References....................................................................................................................65

4.0 CMAQ Air Quality Model Estimates ....................................................................................................68

4.1 Introduction to the CMAQ Modeling Platform.............................................................................68

4.2 CMAQ Model Version, Inputs and Configuration ........................................................................69

4.3 CMAQ Model Performance Evaluation ........................................................................................74

5.0 Bayesian space-time downscaling fusion model (downscaler) -Derived Air Quality Estimates......96

5.1 Introduction ...................................................................................................................................96

5.2 Downscaler Model ........................................................................................................................96

5.3 Downscaler Concentration Predictions .........................................................................................97

5.4 Downscaler Uncertainties............................................................................................................102

5.5 Summary and Conclusions..........................................................................................................104

Appendix A - Acronyms............................................................................................................................105
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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 Bayesian1approaching 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 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 

 

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

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.

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 

 

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

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/clean-air-act-overview. 

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 https://www.epa.gov/criteria-air-pollutants/naaqs-table. For 

general information on the criteria pollutants, go to https://www.epa.gov/criteria-air-pollutants. . 

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 https://www.epa.gov/ozone-designations and

https://www.epa.gov/particle-pollution-designations. 

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 
6

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 https://www.epa.gov/naaqs/ozone-o3-air-qualitystandards. The current NAAQS for ozone (last revised in 2015) is a daily maximum 8-hour average of 

0.070 parts per million [ppm] (for details, see https://www.epa.gov/ozone-pollution/setting-andreviewing-standards-control-ozone-pollution#standards. 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. The standards for ozone are shown in Table 2-1.

Table 2-1. Ozone Standards

Parts Per Million: Measurement – (ppm) 1997 2008 2015

4

th Highest Daily Max 8-hour average 0.08 0.075 0.070

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 

sources4. 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 https://www.epa.gov/pm-pollution. 

 

4 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/m

3

) based on the annual mean 

concentration averaged over three years, and the 24-hr average concentration must not exceed 35 ug/m

3

based on the 98th percentile 24-hour average concentration averaged over three years. More information is 

available at https://www.epa.gov/pm-pollution/setting-and-reviewing-standards-control-particulatematter-pm-pollution#standards. The standards for PM2.5 are shown in Table 2-2.

Table 2-2. 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 http://www3.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 

https://www.epa.gov/air-trends).

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 https://www.epa.gov/aqs. 

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 needed5. 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 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, 

 

5 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

analyze, and report PM2.5measurements 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, in-house 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 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 eVNA6 is an example of an 

 

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

2005, Appendix F.
11

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.
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 2 to process year 2012 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/) was 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 Carbon bond 2005 (CB05) with chlorine chemistry used here within 

CMAQ 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 2012 emission inputs for this study included development of emission inventories 

for input to a 2012 modeling case, along with application of emissions modeling tools to convert the 

inventories into the format and resolution needed by CMAQ. Year-specific fire and continuous emission

monitoring (CEM) data for electric generating units (EGUs) were used. 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 3.6.5 was used to create CMAQ-ready emissions files for a 

12-km national grid. Additional information about SMOKE is available from 

http://cmascenter.org/smoke. 

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 Emission Inventories and Approaches 

This section describes the emissions inventories created for input to SMOKE. The 2011 NEI, version 2

with updates for the year 2012 is the primary basis for the inputs to SMOKE. The NEI 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 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 
14

coordinates. EPA collaboration with S/L/T agencies helped prevent duplication between point and 

nonpoint source categories such as industrial boilers. Documentation for the 2011 NEI is available at

http://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventory-nei-documentation.

Point source data for the year 2012 as submitted to EIS were used for this study. EPA used the 

SMARTFIRE2 system to develop 2012 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 for year 2012 were developed using MOVES2014. Nonroad 

mobile source emissions for year 2012 were developed using the National Mobile Inventory Model 

(NMIM). Canadian emissions reflect year 2010 and Mexican emissions reflect year 2008, as those were 

the latest data available at the time of the modeling. 

The methods used to process emissions for this study are very similar to those documented for EPA’s 

Version 6.2, 2011 Emissions Modeling Platform that was also used for the final Ozone National Ambient 

Air Quality Standards (NAAQS) and for the proposed Cross-State Air Pollution Rule Update. A 

technical support document (TSD) for this platform is available at EPA’s emissions modeling 

clearinghouse (EMCH): https://www.epa.gov/air-emissions-modeling/2011-version-62-platform (EPA, 

2015a) and includes additional details regarding the data preparation and emissions modeling. 

The emissions modeling process, performed using SMOKE v3.6.5, 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 year 2012

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 2012 emission summaries for the U.S. anthropogenic sectors are shown in Table 3-2 (i.e., 

biogenic emissions are excluded). Table 3-3 provides a summary of emissions for the anthropogenic 

sectors containing Canadian, Mexican and offshore sources. State total emissions for each sector are 

provided in Appendix B, a workbook entitled “Appendix_B_2012_emissions_totals_by_sector.xlsx”.
15

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

2012 Platform Sector (Abbrev) NEI Categry Description and resolution of the data input to SMOKE

EGUs (ptegu) Point

2012 point source EGUs submitted to EIS and gapfilled 

with 2011NEIv2 emissions. Replaced with hourly 2012

CEMS values for NOX and SO2, where the units are 

matched to the inventory. Annual resolution for nonCEMS sources, hourly for sources matched to CEMS.

Point source oil and gas 

(pt_oilgas) Point 2012 point sources with oil and gas production NAICS codes, gapfilled with 2011NEIv2 oil and gas sources.

Remaining non-EGU point 

(ptnonipm)

Point

2012 point source records not matched to the ptegu or 

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 

2012 computed using SMARTFIRE 2. Fires over 20,000 

acres on a single day allocated to overlapping grid cells.

Agricultural Fires (agfire) Nonpoint Agricultural fires from 2011NEIv2

Agricultural (ag) Nonpoint

NH3 emissions from 2011NEIv2 nonpoint livestock and 

fertilizer application, county and annual resolution but 

temporalized using 2012 meteorological data. 

Area fugitive dust (afdust_adj) Nonpoint

PM10 and PM2.5 from fugitive dust sources in the 

2011NEIv2 nonpoint inventory, including building 

construction, road construction, agricultural dust, and road 

dust. Unpaved and paved road dust emissions differ from 

the NEI in that the NEI uses an average meteorological 

adjustment while the modeling uses gridded, hourly 2012 

meteorological data to reduce emissions via a transport 

fraction based on land use, plus emissions are zeroed out 

during periods of precipitation and snow/ice cover. County 

and annual resolution. 

Nonpoint source oil and gas 

(np_oilgas) Nonpoint

2011NEIv2 nonpoint sources from oil and gas-related 

processes with updates for five Utah counties. County and 

annual resolution.

Residential Wood Combustion 

(rwc)

Nonpoint

2011NEIv2 NEI nonpoint sources for Residential Wood 

Combustion (RWC) processes but temporalized using 

2012 meteorological data. County and annual resolution.

Remaining nonpoint (nonpt) Nonpoint 2011NEIv2 nonpoint sources not included in other 

platform sectors; county and annual resolution.

C3 commercial marine 

(c3marine)

Nonpoint 2011NEIv2 Category 3 (C3) commercial marine vessel 

(CMV) emissions. County and annual resolution.

C1 and C2 marine and 

locomotive (c1c2rail)

Nonpoint

2011NEIv2 locomotives and primarily category 1 (C1) and 

category 2 (C2) commercial marine vessel (CMV) 

emissions sources. County and annual resolution.
16

2012 Platform Sector (Abbrev) NEI Categry Description and resolution of the data input to SMOKE

Nonroad (nonroad) Nonroad

2012 nonroad equipment emissions developed with the 

National Mobile Inventory Model (NMIM) using 

NONROAD2008 version NR08a. NMIM was used for all 

states except California-submitted 2011 data is used as is

and Texas emissions for 2011 are projected to 2012 using 

data from EPA trends. County and monthly resolution.

Onroad (onroad) Onroad

2012 onroad mobile source gasoline and diesel vehicles 

from parking lots and moving vehicles. Includes the 

following modes: exhaust, extended idle, auxiliary power 

units, evaporative, permeation, refueling and brake and tire 

wear. For all states except California, based on monthly 

MOVES emissions tables from MOVES2014. California 

emissions are based on Emission Factor (EMFAC) and 

were projected to 2012 using factors specific to each air 

basin. MOVES-based emissions computed for each hour 

and model grid cell using monthly and annual activity data 

(e.g., VMT, vehicle population).

Biogenic (beis) Biogenic

Hour- and grid cell-specific emissions for 2012 generated 

from the BEIS 3.61 model, including emissions in Canada 

and Mexico.

Other area fugitive dust sources N/A

Area fugitive dust sources from Canada 2010 inventory 

with transport fraction and snow/ice adjustments based on 

2012 meteorological data. Annual and province 

resolution.

Other point sources not from 

the NEI (othpt)

N/A

Point sources from Canada’s 2010 inventory and Mexico’s 

2008 inventory. Also includes all non-U.S. and nonCanada C3 CMV and U.S. offshore oil production from 

2011NEIv2. Annual resolution.

Other nonpoint and nonroad 

(othar)

N/A

Year 2010 Canada (province resolution) and year 2008

(municipio resolution) nonpoint and nonroad mobile 

inventories, annual resolution.

Other onroad sources (othon) N/A

Year 2010 Canada (province resolution) and year 2008 

Mexico (municipio resolution) onroad mobile inventories, 

annual resolution.
17

Table 3-2. 2012 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.)

Sector CO NH3 NOx PM10 PM2.5 SO2 VOC

Sector CO NH3 NOX PM10 PM2_5 SO2 VOC

afdust_adj 7,156,383 986,032

ag 3,515,198

agfire 956,243 3,321 42,767 140,728 93,959 16,224 74,783

c1c2rail 180,579 511 1,075,217 35,359 33,019 12,609 48,281

nonpt 1,645,989 94,242 720,454 491,825 404,258 275,655 3,672,249

np_oilgas 627,764 0 641,611 16,850 15,395 18,338 2,548,410

nonroad 13,266,383 2,668 1,546,449 154,415 146,950 2,779 1,869,401

onroad 23,707,819 112,443 5,180,893 346,279 175,360 29,309 2,448,957

c3marine 12,532 68 131,382 10,168 9,043 86,373 5,149

ptfire 30,696,364 502,685 361,109 3,071,146 2,602,666 213,027 7,226,100

ptegu 803,584 26,954 1,829,235 226,421 171,851 3,416,894 36,968

ptnonipm 2,226,713 58,339 1,210,541 475,089 312,253 976,937 824,521

pt_oilgas 220,304 5,373 469,889 13,850 13,295 66,731 157,224

rwc 2,517,844 19,693 34,436 381,476 381,252 8,954 442,541

Continental U.S. 76,861,119 4,341,496 13,243,984 12,519,989 5,345,333 5,123,830 19,354,584

Table 3-3. 2012 Non-US Emissions by Sector within Modeling Domain (tons/yr for Canada, Mexico, 

Offshore)

Sector CO NH3 NOX PM10 PM2.5 SO2 VOC

Canada othafdust 821,220 120,724

Canada othar 3,023,643 327,439 362,798 159,741 131,777 70,475 889,624

Canada othon 3,039,490 18,700 346,500 17,670 12,245 1,706 178,868

Canada othpt 497,442 13,110 267,633 70,196 29,243 545,999 129,473

Canada Subtotal 6,560,576 359,249 976,931 1,068,827 293,989 618,179 1,197,964

Mexico othar 278,932 163,416 183,435 99,120 50,343 10,707 412,293

Mexico othon 3,369,179 7,997 244,293 2,431 1,628 4,931 320,121

Mexico othpt 153,467 3,715 287,064 55,320 42,222 454,837 53,958

Mexico Subtotal 3,801,578 175,128 714,791 156,871 94,192 470,476 786,373

Offshore to EEZ 175,833 185 902,448 26,320 24,611 139,550 81,825

Non-US SECA C3 17,231 0 202,987 17,253 15,871 127,928 7,314

Total non-U.S. 10,555,218 534,562 2,797,158 1,269,271 428,664 1,356,132 2,073,476
18

3.2.1 Point Sources (ptegu, pt_oilgas 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 outside of state 

waters (othpt sector) are processed by SMOKE as point source inventories and are discussed later in this

section. Full documentation for the development of the 2011NEIv2 (EPA, 2015b), is posted at: 

http://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventory-nei-documentation.

A complete NEI is developed every three years, with 2011 being the most recently finished complete NEI. 

In years other than the triennial NEI years, including 2012, states are only required to submit emissions 

for Type A point sources. Type A point sources have the potential to emit more than 2500 tons of CO, 

NOx, or SO2, or more than 250 tons of VOC, PM10, PM2.5 or NH3. Due to this potential incompleteness in 

the submitted sources for 2012, non-submitted but still operating sources were pulled forward from the 

2011NEIv2 inventory. 

In preparation for modeling, the complete set of point sources was exported from EIS and split into 

several sectors for modeling. 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 three point 

source sectors were created from the remaining 2012 point sources. The point sectors are: EGUs (ptegu), 

point source oil and gas extraction-related sources (pt_oilgas) and the remaining non-EGUs (ptnonipm). 

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 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 sources 

in the ptegu sector that could be matched to 2012 CEMS data, hourly CEMS NOX and SO2 emissions for 

2012 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, 

region- and pollutant-specific diurnal profiles were applied to create hourly emissions. 

The non-EGU stationary point source (ptnonipm) emissions were input to SMOKE as annual emissions. 

The full description of how the NEI emissions were developed is provided in the NEI documentation, but 

a brief summary of their development follows:

a. CAP and HAP data were provided by States, locals and tribes under the Air Emissions Reporting 

Rule (AERR)
19

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

c. EPA added HAP data from the Toxic Release Inventory (TRI) where corresponding data was not 

already provided by states/locals.

d. EPA provided data for airports and rail yards. 

e. Off-shore platform data were added from the Bureau of Ocean Energy Management (BOEM).

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 

default stack parameters by SCC code to gap fill stack parameters if they are missing in the input 

inventories. Also, SMOKE adjusts any stack parameter values that are outside of the SMOKEdefined range of acceptable values.

3.2.1.1 EGU sector (ptegu)

The ptegu sector contains emissions from EGUs in the 2012 point source inventory that could be matched 

to units found in the National Electric Energy Database System (NEEDS) v5.14 that is used by the 

Integrated Planning Model (IPM) to develop future year EGU emissions. It was necessary to put these 

EGUs into a separate sector in the platform because IPM projects future emissions for the EGUs defined 

in the NEEDS database. In future year modeling cases, emissions for sources in the ptegu sector are fully 

replaced with IPM outputs. 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 future year modeling scenarios

if emissions for units are projected by IPM are not properly matched to the units in the point source 

inventory.

Some units in the ptegu sector are matched to CEMS data via ORIS facility codes and boiler ID. For these 

units, SMOKE replaces the emissions of NOX and SO2 with the CEMS emissions, thereby ignoring the 

annual values specified in the point source inventory. For other pollutants, the hourly CEMS heat input 

data are used to allocate the ptegu inventory annual emissions to hourly values. All stack parameters, 

stack locations, and SCC codes for these sources come from the point source inventory. Because these 

attributes are obtained from the inventory, the chemical speciation of VOC and PM2.5 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 inventory, the CEMS data for that unit is not used in the modeling 

platform. However, if the source exists in the inventory and is not matched to a CEMS unit, the emissions 

from that source would be modeled using the annual emission value in the inventory and would be 

allocated to daily values using region-, fuel- and pollutant-specific average profiles. EIS stores many 
20

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 emissions modeling platform.

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

the annual emissions using average CEMS data profiles specific to fuel type and IPM region and based on 

input. 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. 

3.2.1.2 Non-IPM Sector (pt_oilgas)

The pt_oilgas sector was separated from the ptnonipm sector by selecting sources with specific North 

American Industry Classification System (NAICS) codes shown in Table 3-4. 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. Nonpoint oil and gas 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 3.20 of the 2011NEIv2 TSD.

Table 3-4. Point source oil and gas sector NAICS Codes

NAICS NAICS description

2111 Oil and Gas Extraction 

2212 Natural Gas Distribution 

4862 Pipeline Transportation of Natural Gas 

21111 Oil and Gas Extraction 

22121 Natural Gas Distribution 

48611 Pipeline Transportation of Crude Oil 

48621 Pipeline Transportation of Natural Gas 

211111 Crude Petroleum and Natural Gas Extraction 

211112 Natural Gas Liquid Extraction 

213111 Drilling Oil and Gas Wells 

213112 Support Activities for Oil and Gas Operations

221210 Natural Gas Distribution 

486110 Pipeline Transportation of Crude Oil 

486210 Pipeline Transportation of Natural Gas 

3.2.1.3 Non-IPM Sector (ptnonipm)

Except for some minor exceptions, the non-IPM (ptnonipm) sector contains the point sources that are not 

in the ptegu or pt_oilgas sectors. For the most part, the ptnonipm 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 in the ptnonipm sector. The sector also includes some ethanol 

plants that have been identified by EPA and require special treatment in the future cases as they are 

impacted by mobile source rules.

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. Sources 
21

with state/county FIPS code ending with “777” are in the NEI but are not included in any modeling 

sectors. These sources typically 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 as is needed for modeling. Therefore, these sources are dropped from 

the point-based sectors in the modeling platform.

3.2.2 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 agfire sector. Emissions are dayspecific 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 day-specific emission estimates for 2012 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) and GeoMAC 

Shapefiles 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 emissions are calculated using the U.S. Forest Service’s CONSUMEv3.0 fuel consumption model and 

the FCCS fuel-loading database in the BlueSky Framework (Ottmar, et. al., 2007).

The 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 “nointegrate noHAP” use case). For the 2012 modeling, fires over 20,000 acres in a single day were split into 

the respective grid cells that they overlapped. The idea of this was to prevent all emissions from a very 

large fire going into a single grid cell, when in reality the fire was more dispersed than a single point. The 

large fires were each projected as a circle over the area centered on the specified latitude and longitude, 

and then apportioned into the grid cells they overlapped. The area of each of the “subfires” was computed 

in proportion to the overlap with that grid cell. These “subfires” were given new names that were the 

same as the original, but with “_a”, “_b”, “_c”, and “_d” appended as needed. 

3.2.3 Nonpoint Sources (afdust, ag, agfire, nonpt, np_oilgas, rwc)

Many modeling platform sectors were created from the 2011NEIv2 nonpoint category. This section 

describes the stationary nonpoint sources. Note that locomotives, C1 and C2 CMV, and C3 CMV are also 

included the 2011NEIv2 nonpoint data category, but are mobile sources and are placed into the c1c2rail 

and c3marine sectors, respectively. The 2011NEIv2 TSD includes documentation for the nonpoint data 

category of the 2011NEIv2. 

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. 
22

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 for this study.

3.2.3.1 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 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), 

https://www3.epa.gov/ttn/chief/conference/ei19/session9/pouliot_pres.pdf, and in “Fugitive Dust 

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

3.2.3.2 Agricultural Ammonia Sector (ag)

The agricultural NH3 (ag) sector includes livestock and agricultural fertilizer application emissions from 

the 2011NEIv2 nonpoint inventory. The livestock and fertilizer emissions in this sector are based only on 

the SCCs starting with 2805 and 2801. The livestock SCCs are related to beef and dairy cattle, poultry 

production and waste, swine production, waste from horses and ponies, and production and waste for 

sheep, lambs, and goats. The fertilizer SCCs consist of 15 specific types of ammonia-based fertilizer and 

one for miscellaneous fertilizers. The “ag” sector includes all of the NH3 emissions from fertilizer from 

the NEI. However, the “ag” sector does not include all of the livestock ammonia emissions, as there are 

also a small amount of NH3 emissions from livestock feedlots in the ptnonipm inventory (as point 

sources) in California (175 tons) and Wisconsin (125 tons). The annual agricultural burning estimates are 

treated as monthly values for modeling. The annual values in the 2011NEIv2 were split into monthly 

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

3.2.3.3 Agricultural fires (agfire)

The agricultural fire (agfire) sector contains emissions from agricultural fires. These emissions were 

placed into the sector based on their SCC code. All SCCs starting with 28015 were included. The first 

three levels of descriptions for these SCCs are: Fires - Agricultural Field Burning; Miscellaneous Area 

Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole field set on 

fire. The SCC 2801500000 does not specify the crop type or burn method, while the more specific SCCs 
23

specify field or orchard crops, and in some cases the specific crop being grown. For more information on 

how emissions for agricultural fires were developed in the 2011NEIv2, see Section 5.2 of the 2011NEIv2 

TSD.

3.2.3.4 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 2011NEIv2, see Section 3.20 of the 2011NEIv2 TSD. 

3.2.3.5 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 

chimneas. 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 non-catalytic. For more information on the 

development of the residential wood combustion emissions, see Section 3.14 of the 2011NEIv2 TSD. 

3.2.3.6 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. 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 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;
24

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

shops.

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 Biogenic Sources (beis)

Biogenic emissions were developed using the Biogenic Emission Inventory System, version 3.61 

(BEIS3.61) within SMOKE. This was an update from the emissions in the 2011 modeling platform that 

used BEIS 3.14, and from the 2011NEIv2 that used BEIS 3.60. Like BEIS 3.14, BEIS3.61 creates 

gridded, hourly, model-species emissions from vegetation and soils. It estimates CO, VOC (most notably 

isoprene, terpene, and sesquiterpene), and NO emissions for the contiguous U.S. and for portions of 

Mexico and Canada. 

Updates in BEIS3.61 include the incorporation of Version 4 of the Biogenic Emissions Land use 

Database (BELD4), and the incorporation of a canopy model to estimate leaf-level temperatures (Pouliot 

and Bash, 2015). BELD4 is based on an updated version of the USDA-USFS Forest Inventory and 

Analysis (FIA) vegetation speciation based data from 2002 to 2013 from the Forest Inventory and 

Analysis version 5.1. Canopy coverage is based on the Landsat satellite NLCD product from 2001, since 

no canopy product was developed for the 2006 NLCD. The FIA includes approximately 250,000 

representative plots of species fraction data that are within approximately 75 km of one another in areas 

identified as forest by the NLCD canopy coverage. The 2006 NLCD provides land cover information with

a native data grid spacing of 30 meters. For land areas outside the conterminous United States, 500 meter 

grid spacing land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) is used.

BEIS version 3.61 includes a two-layer canopy model. Layer structure varies with light intensity and solar 

zenith angle. Both layers of the canopy model include estimates of sunlit and shaded leaf area based on 

solar zenith angle and light intensity, direct and diffuse solar radiation, and leaf temperature (Bash et al., 

2015). The new algorithm requires additional meteorological variables over previous versions of BEIS. 

The variables output from the Meteorology-Chemistry Interface Processor (MCIP) that are used to 

convert WRF outputs to CMAQ inputs are shown in Table 3-5.

Table 3-5. Meteorological variables required by BEIS 3.61

Variable Description

LAI leaf-area index 

PRSFC surface pressure

Q2 mixing ratio at 2 m

RC convective precipitation per met TSTEP

RGRND solar rad reaching sfc

RN nonconvective precipitation per met TSTEP

RSTOMI inverse of bulk stomatal resistance 
25

Variable Description

SLYTP soil texture type by USDA category

SOIM1 volumetric soil moisture in top cm 

SOIT1 soil temperature in top cm

TEMPG skin temperature at ground

USTAR cell averaged friction velocity

RADYNI inverse of aerodynamic resistance

TEMP2 temperature at 2 m

3.2.5 Mobile Sources (onroad, onroad_ca_adj, 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). 

The 2012 onroad emissions are in the onroad sector. Except for California, all onroad emissions are 

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

outputs (http://www.epa.gov/otaq/models/moves/) and hourly meteorology. All tribal data from the 

mobile sectors have been dropped because the (1) emissions are small, (2) the emissions could be doublecounted 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 (excluding refueling), nonroad and c1c2rail sources in California are based on data 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 that are within state waters, 

while those outside of state waters are in the othpt sector. All nonroad emissions are treated as countyspecific low-level emissions (i.e., they are released into model layer 1). 

3.2.5.1 Onroad (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). 

To develop the onroad mobile source emissions 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. 
26

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 year 2012-specific emission factors 

by temperature and speed for a series of “representative counties,” to which every other county was 

mapped. Using the MOVES 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), VPOP (vehicle population)), or HOTELING (hours of extended idle) to produce emissions. 

These calculations were done for every county and grid cell, in the continental U.S. for each hour of the 

year. 

The SMOKE-MOVES process for creating the model-ready emissions consists of the following 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 temperatures and activity data.

5) Run MOVES to create emission factor tables for the temperatures found in each county.

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

calculate emissions based on the gridded hourly temperatures in the meteorological data.

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

To generate the 2012 onroad mobile source emissions, SMOKE-MOVES was run using 2011 activity 

data and 2012 emission factors. This decision was made after an analysis of VMT trends from 2011 to 

2012 showed that there was very little change between the two years. Onroad emissions for California 

were created through a hybrid approach of combining state-supplied annual emissions (from the 

2011NEIv2) with EPA developed SMOKE-MOVES data. Through this approach, the platform was able 

to reflect California’s unique rules, while leveraging the more detailed SCCs and the highly resolved 

spatial patterns, temporal patterns, and speciation from SMOKE-MOVES. The California-provided year 

2011 emissions were adjusted to a 2012 inventory using the ratio of 2012 to 2011 factors retrieved from 

the EMFAC 2011 web data base (http://www.arb.ca.gov/emfac/). Factors were applied by air basin, fuel, 

and pollutant. NH3 used CO ratios (CARB did not provide NH3); VOC and all VOC HAPs used TOG 

ratios. Some counties are in multiple air basins, but for simplicity, each county was assigned to a single 

air basin for the projections. There are 15 air basins in California.

3.2.5.2 NMIM-Based Nonroad Mobile Sources (nonroad)

The nonroad equipment emissions were developed by obtaining monthly totals based on a run of NMIM 

(EPA, 2005) for year 2012. All nonroad emissions are compiled at the county/SCC level. NMIM 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 2011NEIv2 nonroad emissions, which provided the starting databases for the 2012 run, are available 

in Section 4.5 the 2011NEIv2 TSD.
27

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. They were then projected to 2012 using 

trend lines based on the 2012 NMIM run as compared to EPA 2011 NMIM run. 

California year 2011 nonroad emissions were submitted to the 2011NEIv2 and are also documented in a 

staff report (ARB, 2010a) and are used to represent year 2012 emissions. 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 2011NEIv2 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. 

3.2.5.3 Category 1 and 2 Commercial Marine 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 county-SCC resolution. The emissions include the offshore portion of the 

C1 and C2 commercial marine sources, including fishing vessels and oil rig support vessels in the Gulf of 

Mexico. Emissions that occur outside of state waters are not assigned to states. The emissions for c1c2rail 

sector are equivalent to those in the 2011NEIv2 nonpoint inventory. For more information on CMV 

sources in the NEI, see Section 4.3 of the 2011NEIv2 TSD. For more information on locomotives, see 

Section 4.4 of the 2011NEIv2 TSD. 

3.2.5.4 Category 3 commercial marine (c3marine)

The Category 3 (C3) CMV sources in the c3marine sector of the 2011v6.2 platform run on residual oil 

and use the SCCs 2280003100 and 2280003200 for port and underway emissions, respectively, and are 

consistent with the 2011NEIv2. Emissions for this sector use state-submitted values and EPA-developed 

emissions in areas where states did not submit. This sector only includes emissions in state-waters and 

the emissions are treated as nonpoint sources. Thus, the c3marine emissions are placed in layer 1 and 

allocated to grid cells using spatial surrogates. C3 CMV emissions outside of state waters, and non-U.S. 

emissions farther offshore than U.S. waters are processed in the “othpt” sector. C3 emissions for Canada 

were provided by Environment Canada for year 2010 and are found in the othar inventory.
28

The EPA-estimated C3 CMV emissions for year 2011 were developed based on a 4-km resolution ASCII 

raster format dataset that preserves shipping lanes. This dataset has been used since the Emissions 

Control Area-International Marine Organization (ECA-IMO) project began in 2005, although it was then 

known as the Sulfur Emissions Control Area (SECA). 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 as a result these ships would often burn residual fuel in that region. The 

emissions in this sector are comprised of primarily foreign-flagged ocean-going 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) 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 

available from https://www.epa.gov/regulations-emissions-vehicles-and-engines/international-standardsreduce-emissions-marine-diesel. The base year for the 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 2012 

inventory from the 2002 data. 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). Technically, the EEZ FIPS are not really “FIPS” state-county codes, but are treated as such in 

the inventory and emissions processing. The Canadian near-shore emissions were assigned to provincelevel FIPS codes and paired those to region classifications for British Columbia (North Pacific), Ontario 

(Great Lakes) and Nova Scotia (East Coast). 

* 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
29

The emissions were converted to SMOKE point source inventory format as described in 

https://www3.epa.gov/ttn/chief/conference/ei17/session6/mason.pdf, which allows for the emissions to be 

allocated to modeling layers above the surface layer. As described in the paper, the ASCII raster dataset 

was converted to latitude-longitude, mapped to state/county FIPS codes that extended up to 200 nautical 

miles (nm) from the coast, assigned stack parameters, and monthly ASCII raster dataset emissions were 

used to create monthly temporal profiles. 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. 

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 

in Figure 3-1, were projected to year 2012 using the same regional adjustment factors as the U.S. 

emissions; however, the state/county FIPS codes were assigned as “EEZ” codes and those 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 Midwest states and other states such as Pennsylvania and New York. The 

classification of emissions to U.S. and Canadian FIPS codes was 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 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. 

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. 

For California, the ECA-IMO 2011 emissions were scaled 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.
30

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

othafdust)

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 four emissions modeling sectors: othpt, othar, othon, 

and othafdust. The “oth” refers to the fact that these emissions are usually “other” than those in the U.S. 

state-county geographic FIPS, and the remaining 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 were processed in the othpt sector. These C3 CMV emissions include 

those assigned to U.S. federal waters, those assigned to the Exclusive Economic Zone (EEZ; defined as 

those emissions beyond the U.S. Federal waters approximately 3-10 miles offshore, and extending to 

about 200 nautical miles from the U.S. coastline), along with any other offshore emissions. These 

emissions are developed in the same way as the EPA-dataset described in the c3marine sector described 

above. Emissions in U.S. waters are aggregated into large regions and included in the 2011NEIv2 using 

special FIPS codes. Because these emissions are treat as point sources, shipping lane routes can be 

preserved and they may be allocated to air quality model layers higher than layer 1.

For Canadian point sources, 2010 emissions provided by Environment Canada were used. Note that VOC 

was not provided for Canadian point sources, but any VOC emissions were speciated into CB05 species. 

Temporal profiles and speciated emissions were also provided. Point sources in Mexico were compiled 

based on the Inventario Nacional de Emisiones de Mexico, 2008 (ERG, 2014a). The point source 

emissions in the 2008 inventory were converted to English units and into the FF10 format that could be 

read by SMOKE, missing stack parameters were gapfilled using SCC-based defaults, and latitude and 

longitude coordinates were verified and adjusted if they were not consistent with the reported 

municipality. Note that there are no explicit HAP emissions in this inventory.

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, data from the 2011NEIv2 

were used. 

For Canada area, nonroad mobile, and onroad mobile sources, year-2010 emissions provided by 

Environment Canada were used, including C3 CMV emissions. The Canadian inventory included fugitive 

dust emissions that do not incorporate either a transportable fraction or meteorological-based adjustments. 

To properly account for these issues, a separate sector called othafdust was created and modeled using the 

same adjustments as are done for U.S. sources. Updated Shapefiles for creating spatial surrogates for 

Canada were also provided. 

Area and nonroad mobile sources in Mexico were compiled from the Inventario Nacional de Emisiones 

de Mexico, 2008 (ERG, 2014a). The 2008 emissions were quality assured for completeness, SCC 

assignments were made when needed, the pollutants expected for the various processes were reviewed, 

and adjustments were made to ensure that PM10 was greater than or equal to PM2.5. The resulting 

inventory was written using English units to the nonpoint FF10 format that could be read by SMOKE.

Note that unlike the U.S. inventories, there are no explicit HAPs in the nonpoint or nonroad inventories 

for Canada and Mexico, and therefore all HAPs are created from speciation.

Onroad mobile sources in Mexico were compiled from the Inventario Nacional de Emisiones de Mexico, 

2008 (ERG, 2014a). SCCs compatible with the 2011NEIv2 were assigned to the 2008 onroad mobile 
31

source emissions in Mexico, and it was enforced that PM10 be greater than or equal to PM2.5. Quality 

assurance of the onroad mobile source emissions data revealed that Baja California, Michoacán, and 

Nuevo León had significantly high per capita emissions for all pollutants and should be considered to be 

outliers. The emissions for these states were replaced with values computed based on the average per 

capita emissions for the remaining states. The data were written using English units to the nonpoint FF10 

format that could be read by SMOKE. Note that unlike the U.S. inventories, there are no explicit HAPs in 

the onroad mobile inventories for Canada and Mexico, and therefore all HAPs are created from 

speciation.

3.2.7 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. 

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 CMAQ, it is necessary to “pre-process” the emission inventories for each of the 

sectors described above. In brief, the process of emissions modeling transforms the emissions inventories 

from their original temporal resolution, pollutant resolution, and spatial resolution into the hourly, 

speciated, gridded resolution required by the air quality model. Thus, 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 may be 

hourly, daily, monthly, or annual total emissions, or it could instead be 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 3.6.5 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.
32

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, sectorspecific 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 3-6 

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 “in-line” means 

that the plume rise calculations are done inside of the air quality model instead of being computed by 

SMOKE. The air quality model computes the plume rise using the stack data and the hourly air quality 

model inputs found in the SMOKE output files for each model-ready emissions sector. The height of the 

plume rise determines the model layer into which the emissions are placed. The othpt sector has only “inline” emissions, meaning that all of the emissions are treated as elevated sources and there are no 

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

fires are treated separately for CMAQ modeling in that fire plume rise is done within CMAQ itself. After 

plume rise is applied, there will be emissions in every layer from the ground up to the top of the plume.

Table 3-6. Key emissions modeling steps by sector

Platform sector Spatial Speciation

Inventory

resolution Plume rise

afdust Surrogates Yes annual

ag Surrogates Yes annual

agfire Surrogates Yes monthly

beis Pre-gridded 

land use in BEIS computed hourly

c1c2rail Surrogates Yes annual

c3marine Surrogates Yes annual

nonpt Surrogates &

area-to-point Yes annual

nonroad Surrogates &

area-to-point Yes monthly

np_oilgas Surrogates Yes annual

onroad Surrogates Yes monthly activity, 

computed hourly

othafdust Surrogates Yes annual

othar Surrogates Yes annual
33

Platform sector Spatial Speciation

Inventory

resolution Plume rise

othon Surrogates Yes annual

othpt Point Yes annual in-line

pt_oilgas Point Yes annual in-line

ptegu 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 2012 modeling case, 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.
34

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 GEOSChem. The grid used a Lambert-Conformal projection, with Alpha = 33, Beta = 45 and Gamma = -97, 

with a center of X = -97 and Y = 40. Later sections provide details on the spatial surrogates and area-topoint 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 this study is 

the CB05 mechanism (Yarwood, 2005). The versions of CMAQ used for this study include secondary 

organic aerosol (SOA) and HONO enhancements. The PM2.5 model species are those associated with the 

CMAQ Aerosol Module, version 6 (AE6). This modeling case uses CB05 with speciation profile 

mappings that were updated in February, 2015. Table 3-7 lists the model species produced by SMOKE 

for use in CMAQ. Speciation profiles and cross-references are available in the SMOKE input files for the 

2011v6.2 emissions modeling platform. 
35

Table 3-7. Emission model species produced for CB05 with SOA for CMAQ 5.0.2

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

NH3_FERT Ammonia from fertilizer

VOC ALD2 Acetaldehyde

ALDX Propionaldehyde and higher aldehydes

BENZENE Benzene (not part of CB05)

CH4 Methane

ETH Ethene

ETHA Ethane

ETOH Ethanol

FORM Formaldehyde

IOLE Internal olefin carbon bond (R-C=C-R)

ISOP Isoprene

MEOH Methanol

NVOL Non-volatile compounds

OLE Terminal olefin carbon bond (R-C=C)

PAR Paraffin carbon bond

SESQ Sequiterpenes (from biogenics only)

TERP Terpenes

TOL Toluene and other monoalkyl aromatics

UNR Unreactive 

XYL Xylene and other polyalkyl aromatics

PM10 PMC Coarse PM > 2.5 microns and  10 microns

PM2.5 PEC Particulate elemental carbon  2.5 microns

PNO3 Particulate nitrate  2.5 microns

POC Particulate organic carbon (carbon only)  2.5 microns

PSO4 Particulate Sulfate  2.5 microns

PMFINE7 Other particulate matter  2.5 microns

Sea-salt species (non –

anthropogenic) 8

PCL Particulate chloride

PNA Particulate sodium

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.

 

7

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

8 These emissions are created outside of SMOKE
36

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

developed from the SPECIATE 4.4 database (https://www.epa.gov/air-emissions-modeling/speciateversion-45-through-40), which is EPA's repository of TOG and PM speciation profiles of air pollution 

sources. However, a few of the profiles used in the v6.2 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.

The speciation of VOC includes HAP emissions from the 2011NEIv2 in the speciation process. Instead 

of speciating VOC to generate all of the species listed in Table 3-7, 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 

inventory 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 5.0.2 (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 5.0.2, and there was a desire to keep its emissions consistent between multi-pollutant 

and base versions of CMAQ. 

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 integration9). 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-toNONHAPTOG factors and NONHAPTOG speciation profiles10. 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 emissions, then the sector is considered fully integrated and does not need a 

 

9

In SMOKE version 3.6.5, 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.

10 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.
37

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 is illustrated in Figure 3-3 and 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.

Figure 3-3. Process of integrating BAFM with VOC for use in VOC Speciation

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 Table 3-8), 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.
38

Table 3-8. Integration status of benzene, acetaldehyde, formaldehyde and methanol (BAFM) for 

each platform sector

Platform 

Sector

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

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

ptegu No integration 

ptnonipm No integration

ptfire No integration 

othar No integration

othon No integration 

ag N/A – sector contains no VOC 

agfire Partial integration (BAFM)

afdust N/A – sector contains no VOC

biog N/A – sector contains no inventory pollutant "VOC"; but rather specific VOC species

c1c2rail Partial integration (BAFM)

c3marine Partial integration (BAFM)

nonpt Partial integration (BAFM)

nonroad Partial integration (BAFM)

np_oilgas Partial integration (BAFM)

othpt Partial integration (BAFM)

pt_oilgas Partial integration (BAFM)

rwc Partial integration (BAFM)

onroad Full integration (benzene, 1,3 butadiene, formaldehyde, acetaldehyde, naphthalene, 

acrolein, ethyl benzene, 2,2,4-Trimethylpentane, hexane, propionaldehyde, 

styrene, toluene, xylene, MTBE)

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 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. 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, BAFM 

integration is used. 

Speciation profiles for use with BEIS are not included in SPECIATE. BEIS3.61 includes a species (SESQ) 

that was mapped to the CMAQ specie SESQT. The profile code associated with BEIS 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 2011v6.2 TSD (EPA, 2015a).
39

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. Starting with CMAQ 5.0.1, a new thermodynamic equilibrium aerosol 

modeling tool (ISORROPIA) v2 mechanism was added that needs additional PM components (AE6), 

which are further subsets of PMFINE (see Table 3-9). The majority of the 2011 platform PM profiles 

come from the 911XX series which include updated AE6 speciation11. 

Table 3-9. 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 one-to-one 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 2011v6.2 platform TSD 

(EPA, 2015a).

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. Table 3-10 gives the split factor for these two profiles. 

The onroad sector does not use the “HONO” profile to speciate NOX. MOVES2014 produces speciated 

 

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

Smoke – Simplified) used in nonpt.
40

NO, NO2, and HONO by source, including emission factors for these species in the emission factor tables 

used by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOX. The NO 

fraction varies by heavy duty versus light duty, fuel type, and model year and equals 1 – NO – HONO. 

For more details on the NOX fractions within MOVES, see 

http://www.epa.gov/otaq/models/moves/documents/420r12022.pdf. 

Table 3-10. NOX speciation profiles

Profile pollutant species split factor

HONO NOX NO2 0.092

HONO NOX NO 0.9

HONO NOX HONO 0.008

NHONO NOX NO2 0.1

NHONO NOX NO 0.9

3.3.5 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.

In SMOKE 3.6.5 and in the 2011v6.2 platform, more readable and flexible file formats are used for 

temporal profiles and cross references. The profiles and cross references were initially created by 

converting the 2011v6.1 platform temporal profiles into the new formats, and then any specific 

adjustments for the 2011v6.2 platform were made. The temporal factors applied to the inventory are 

selected using some combination of country, state, county, SCC, and pollutant. Table 3-11 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).
41

Table 3-11. Temporal Settings Used for the Platform Sectors in SMOKE

Platform sector 

short name

Inventory 

resolutions

Monthly 

profiles 

used?

Daily 

temporal 

approach

Merge 

processing 

approach

Process Holidays 

as separate days

afdust_adj Annual Yes week all Yes

ag Annual Yes met-based all Yes

agfire Monthly week week Yes

beis Hourly n/a all Yes

c1c2rail Annual Yes mwdss mwdss

c3marine Annual Yes aveday aveday

nonpt Annual Yes week week Yes

nonroad Monthly mwdss Mwdss Yes

np_oilgas Annual yes week week Yes

onroad Annual & monthly1all all Yes

onroad_ca_adj Annual & monthly1all all Yes

othafdust_adj Annual yes week week

othar Annual yes week week

othon Annual yes week week

othpt Annual yes mwdss mwdss

pt_oilgas Annual yes mwdss mwdss Yes

ptegu Daily & hourly all all Yes

ptnonipm Annual yes mwdss mwdss Yes

ptprescfire Daily all all Yes

ptwildfire Daily all all Yes

rwc Annual no met-based 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.

The following values are used in the table: The value “all” means that hourly emissions are 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-to-week 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, 2012, which is intended to mitigate the effects of initial condition concentrations. The ramp-up 

period was 10 days (December 22-31, 2011). For most sectors, emissions from December 2012 were 

used to fill in surrogate emissions for the end of December 2011. In particular, December 2012 emissions 
42

(representative days) were used for December 2011. For biogenic emissions, December 2011 emissions 

were processed using 2011 meteorology.

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

monthly, daily, and hourly emissions inventories than prior formats supported. Previously, processing 

monthly inventory data required the use of 12 separate inventory files. 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 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. 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 agfire, nonroad, onroad 

(for activity data), and ptegu. 

3.3.5.1 Standard Temporal Profiles

Some sectors use straightforward temporal profiles not based on meteorology or other factors. For the 

agfire sector, the emissions were 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. This puts most of the emissions during the work day and suppresses the 

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

agricultural burning emissions in all states, except for the following states that for which EPA used statespecific day of week profiles: Arkansas, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma, 

and Texas. 

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 ptwildfire and ptprescfire sectors, 

the inventories are in the daily point fire format ORL PTDAY, so temporal profiles are only used to go 

from day-specific to hourly emissions. 

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.

Updates were made to temporal profiles for the ptnonipm sector in the 2011v6.2 platform based on 

comments and data review by EPA staff. Temporal profiles for small airports (i.e., non-commercial) were 

updated to eliminate emissions between 10pm and 6am due to a lack of tower operations. Industrial 

process that are not likely to shut down on Sundays such as those at cement plants were assigned to other 

more realistic profiles that included emissions on Sundays. This also affected emissions on holidays 

because Sunday emissions are also used on holidays. Some cross reference updates for temporalization of 

the np_oilgas sector were made in the 2011v6.2 platform based on comments received - np_oilgas sources 

were assigned to profiles that were 24 hours per day, 7 days a week.
43

3.3.5.2 Temporal Profiles for EGUs

The 2011NEIv2 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. Several updates were made to EGU 

temporalization in the 2011v6.2 platform. First, the CEMS data were processed using a tool that 

reviewed the data quality flags that indicate the data were not measured. Unmeasured data can cause 

erroneously high values to appear in the CEMS data. If the data were not measured at specific hours, and 

those values were found to be more the 3 times the annual mean for that unit, the data for those hours 

were replaced with annual mean values (Adelman, et al., 2012). These adjusted CEMS data were then 

used for the remainder of the temporalization process described below (see Figure 3-4 for an example).

Another update in the 2011v6.2 platform was the incorporation of winter and summer seasons into the 

development of the diurnal profiles as opposed to using data for the entire year. Analysis of the hourly 

CEMS data revealed that there were different diurnal patterns in winter versus summer in many areas. 

Typically a single mid-day peak is visible in the summer, while there are morning and evening peaks in 

the winter as shown in Figure 3-5. Finally, we identified specific CEMS sources as partial year reporters 

(e.g., those that only run in the summer) and allocated the difference between the CEMS and the annual 

emissions to months that did not have any CEMS data available.

Figure 3-4. Eliminating unmeasured spikes in CEMS data
44

Figure 3-5. Seasonal diurnal profiles for EGU emissions in a Virginia Region

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. Units were 

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

and SO2 emissions were similar. EIS stores a base set of previously matched units via alternate facility 

and unit IDs. Additions to these matches were made for this study. For any units that are matched, the 

ORIS facility and boiler ID columns of the point FF10 inventory files are filled with the information on 

the rows for the corresponding NEI unit. Note that for units matched to CEMS data, annual totals of their 

emissions may be different than the annual values in the inventory because the CEMS data actually 

replaces the inventory data for the seasons in which the CEMS are operating. If a CEMS-matched unit is 

determined to be a partial year reporter, as can happen for sources that run CEMS only in the summer, 

emissions totaling the difference between the annual emissions and the total CEMS emissions are 

allocated to the non-summer months.

For sources not matched to CEMS units, the allocation of annual emissions to months and then days are 

done outside of SMOKE and then daily emissions are output to day-specific inventory files. For these 

units, 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 3-6. 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, SO2, and heat input. An overall composite profile was also computed and was used 

when there were no CEMS units with the specified fuel in the region containing the unit. For both CEMSmatched units and units not matched to CEMS, NOx and SO2 CEMS data are used to allocate NOx and 
45

SO2 emissions to daily emissions, respectively, while heat input data are used to allocate emissions of all 

other pollutants. 

Daily temporal allocation of units matched to CEMS was performed using a procedure similar to the 

approach to allocate emissions to months in that the CEMS data replaces the inventory data for each 

pollutant. For units without CEMS data, emissions were allocated from month to day using IPM-region 

and fuel-specific average month-to-day factors based on the 2011 CEMS data. Separate month-to-day 

allocation factors were computed for each month of the year using heat input for the fuels coal, natural 

gas, and “other” in each region. 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 are used to allocate all other 

pollutants. An example of month-to-day profiles for gas, coal, and an overall composite for a region in 

western Texas is shown in Figure 3-7.

For units matched to CEMS data, hourly emissions use the hourly CEMS values for NOx and SO2, while 

other pollutants are allocated according to heat input values. For units not matched to CEMS data, 

temporal profiles from days to hours are computed based on the season-, region- and fuel-specific average 

day-to-hour factors derived from the CEMS data for those fuels and regions using the appropriate subset 

of data. For the unmatched units, CEMS heat input data are used to allocate all pollutants (including NOx

and SO2) because the heat input data was generally found to be more complete than the pollutant-specific 

data. SMOKE then allocates the daily emissions data to hours using the temporal profiles obtained from 

the CEMS data for the analysis base year (i.e., 2011 in this case).

Figure 3-6. IPM Regions for EPA Base Case v5.13
46

Figure 3-7. Month-to-day profiles for different fuels in a West Texas Region

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.

3.3.5.3 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 are 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: annual-to-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, meteorologicalbased temporalization was used for portions of the rwc sector and for livestock within the ag sector. 

GenTPRO reads in gridded meteorological data (output from MCIP) along with spatial surrogates, and 
47

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.pd

f and http://www.cmascenter.org/smoke/documentation/3.5.1/html/ch05s03s07.html, respectively.

In the 2011v6.2 platform and in SMOKE 3.6.5, the temporal profile format has been updated. GenTPRO 

now produces separate files including the monthly temporal profiles (ATPRO_MONTHLY) and day-ofmonth temporal profiles (ATPRO_DAILY), instead of a single ATPRO_DAILY with day-of-year 

temporal profiles as it did in SMOKE 3.5. The results are the same either way, so the temporal profiles 

themselves are effectively the same in 2011v6.2 as they were in 2011v6.0 since the meteorology is the 

same, but they are formatted differently.

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 3-8 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 3-8. 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 MANEVU survey based temporal profiles (see 
48

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 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.

The temporalization for “Outdoor Hydronic Heaters” (i.e.,“OHH”, SCC=2104008610) and “Outdoor 

wood burning device, NEC (fire-pits, chimneas, etc.)” (i.e., “recreational RWC”, SCC=21040087000) 

were updated because the meteorological-based temporalization used for the rest of the rwc sector did not 

agree with observations for how these appliances are used. For OHH, the annual-to-month, day-of-week 

and diurnal profiles were modified based on information in the New York State Energy Research and 

Development Authority (NYSERDA) “Environmental, Energy Market, and Health Characterization of 

Wood-Fired Hydronic Heater Technologies, Final Report” (NYSERDA, 2012) as well as a Northeast 

States for Coordinated Air Use Management (NESCAUM) report “Assessment of Outdoor Wood-fired 

Boilers” (NESCAUM, 2006). A Minnesota 2008 Residential Fuelwood Assessment Survey of individual 

household responses (MDNR, 2008) provided additional annual-to-month, day-of-week and diurnal 

activity information for OHH as well as recreational RWC usage.

The diurnal profile for OHH, shown in Figure 3-9 is based on a conventional single-stage heat load unit 

burning red oak in Syracuse, New York. The NESCAUM report describes how for individual units, OHH 

are highly variable day-to-day but that in the aggregate, these emissions have no day-of-week variation. 

In contrast, the day-of-week profile for recreational RWC follows a typical “recreational” profile with 

emissions peaked on weekends. Annual-to-month temporalization for OHH as well as recreational RWC 

were computed from the MN DNR survey (MDNR, 2008) and are illustrated in Figure 3-10. OHH 

emissions still exhibit strong seasonal variability, but do not drop to zero because many units operate year 

round for water and pool heating. In contrast to all other RWC appliances, recreational RWC emissions 

are used far more frequently during the warm season.

Figure 3-9. Diurnal profile for OHH, based on heat load (BTU/hr)

0

10,000

20,000

30,000

40,000

50,000

12am

1am2am3am4am5am6am7am8am9am

10am11am

12pm

1pm2pm3pm4pm5pm6pm7pm8pm9pm

10pm11pm

12am

Heat Load (BTU/hr)
49

Figure 3-10. Annual-to-month temporal profiles for OHH and recreational RWC

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 3-11 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.

0

10

20

30

40

50

60

70

80

90

100

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Millions

Monthly Temporal Activity for OHH & Recreational RWC

Fire Pit/Chimenea

Outdoor Hydronic Heater
50

Figure 3-11. Example of new animal NH3 emissions temporalization approach, summed to daily 

emissions

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, 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.

3.3.5.4 Temporal Profiles for Onroad Mobile Sources

For the onroad sector, 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 2011v6.2 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 parked vehicle 

(RPV, RPH, 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 2011 platform (and the 

2011NEIv2), 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 four processes (RPD, RPV, RPH, and RPP) is the total onroad sector emissions. 

The onroad sector show a strong meteorological influence on their temporal patterns (see the 2011NEIv2 

TSD for more details).
51

Figure 3-12 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.

Figure 3-12. Example of SMOKE-MOVES temporal variability of NOX emissions

For the onroad sector, the “inventories” referred to in Table 3-11 actually consist of activity data, not 

emissions. 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 (for 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. For RPH, the 

HOTELING inventory is monthly and was temporalized to days of the week and to hour of the day 

through temporal profiles. This is an analogous process to RPD except that speed is not included in the 

calculation of RPH.
52

In previous platforms, the diurnal profile for VMT varied by road type but not by vehicle type and these 

profiles were used throughout the nation. Diurnal profiles that could differentiate by vehicle type as well 

as by road type and would potentially vary over geography were desired. In the development of the 

2011v6.0 platform, the EPA updated these profiles to include information submitted by states in their 

MOVES county databases (CDBs). The development of the 2011NEIv2 and the 2011v6.2 platform 

provided an opportunity to update these diurnal profile with new information submitted by states, to 

supplement the data with additional sources, and to refine the methodology. 

States submitted MOVES county databases (CDBs) that included information on the distribution of VMT 

by hour of day and by day of week (see the 2011NEIv2 TSD for details on the submittal process for 

onroad). EPA mined the state submitted MOVES CDBs for non-default diurnal profiles. 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. The list 

of potential diurnal profiles was then analyzed to see whether the profiles varied by vehicle type, road 

type, weekday vs. weekend, and by county within a state. 

For the MOVES diurnal profiles, EPA only considered the state profiles that varied significantly by both 

vehicle and road types. Only those profiles that passed these criteria were used in that state or used in 

developing default temporal profiles. The Vehicle Travel Information System (VTRIS) is a repository for 

reported traffic count data to the Federal Highway Administration (FHWA). EPA used 2012 VTRIS data 

to create additional temporal profiles for states that did not submit temporal information in their CDBs or 

where those profiles did not pass the variance criteria. The VTRIS data were used to create state specific 

diurnal profiles by HPMS vehicle and road type. EPA created distinct diurnal profiles for weekdays, 

Saturday and Sunday along with day of the week profiles. Note that the day of the week profiles (ie. 

Monday vs Tuesday vs etc) are only from the VTRIS data. The MOVES CDBs only have weekday vs 

weekend profiles so they were not included in calculating a new national default day of the week profile

EPA attempted to maximize the use of state and/or county specific diurnal profiles (either from MOVES 

or VTRIS). Where there was no MOVES or VTRIS data, 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 the source of the profile and the 

characteristics of the profiles (see Figure 3-13).
53

Figure 3-13. Use of submitted versus new national default profiles

A new set of diurnal profiles was developed from the submitted profiles that varied by both vehicle type 

and road type. For the purposes of constructing the national default diurnal profiles, EPA created 

individual profiles for each state (averaging over the counties within) to create a single profile by state, 

vehicle type, road type, and the day (i.e. weekday vs Saturday vs Sunday). The source of the underlying 

profiles was either MOVES or VTRIS data. The states individual profiles were averaged together to 

create a new default profile. Figure 3-14 shows two new national default profiles for light duty gas 

vehicles (LDGV, SCC6 220121) and combination long-haul diesel trucks (HHDDV, SCC6 220262) on 

restricted urban roadways (interstates and freeways). The blue lines indicate the weekday profile, the 

green the Saturday profile, and the red the Sunday profile. In comparison, the new default profiles for 

weekdays places more LDGV VMT (upper plot) in the rush hours while placing HHDDV VMT (lower 

plot) predominately in the middle of the day with a longer tail into the evening hours and early morning. 

In addition to creating diurnal profiles, 

EPA also developed day of week profiles using the VTRIS data. The creation of the state and national

profiles was similar to that of the diurnal profiles (described above). Figure 3-15 shows a set of national 

default profiles for rural restricted roads (top plot) and urban unrestricted roads (lower plot). Each vehicle 

type is a different color on the plots.
54

Figure 3-14. Updated national default profiles for LDGV vs. HHDDV, urban restricted 
55

Figure 3-15. Updated national default profiles for day of week (rural restricted top and 

urban restricted bottom)

In addition to creating diurnal profiles for VMT, EPA developed a national profile for hoteling. EPA 

averaged all the combination long-haul truck profiles on restricted roads (urban and rural) for weekdays to 

create a single national restricted profile (blue line in Figure 3-16). This was then inverted to create a 
56

profile for hoteling (green line in Figure 3-16). This single national profile was used for hoteling 

irrespective of location.

Figure 3-16. Combination long-haul truck restricted and hoteling profile

For California, CARB supplied diurnal profiles that varied by vehicle type, day of the week (Monday, 

Tuesday-Thursday, Saturday, and Sunday), and air basin. These CARB specific profiles were used in 

developing EPA estimates for California. Although EPA adjusted the total emissions to match 

California’s submittal to the 2011NEIv2, the temporalization of these emissions took into account both 

the state-specific VMT profiles and the SMOKE-MOVES process of incorporating meteorology. For 

more details on the adjustments to California’s onroad emissions, see the 2011NEIv2 TSD.

3.3.6 Vertical Allocation of Emissions

Table 3-5 specifiesthe sectors for which plume rise is calculated. If there is no plume rise for a sector, the

emissions are placed into layer 1 of the air quality model. Vertical plume rise was performed in-line within

CMAQ for all of the SMOKE point-source sectors (i.e., ptipm, 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 predetermined 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)
57

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 a 

national 12-km domain. To accomplish this, SMOKE used national 12-km spatial surrogates and a 

SMOKE area-to-point data file. For the U.S., 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 details regarding how the 2011v6.2 platform surrogates were created are available from 

ftp://newftp.epa.gov/Air/emismod/2011/v2platform/spatial_surrogates/ in the files 

US_SpatialSurrogate_Workbook_v072115.xlsx and US_SpatialSurrogate_Documentation_v070115.pdf, 

and SurrogateTools_Scripts_2014.zip available. 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.

3.3.7.1 Surrogates for U.S. Emissions

There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions 

to the 12-km grid cells used by the air quality model. As described in Section 3.4.2, an area-to-point 

approach overrides the use of surrogates for a limited set of sources. Table 3-12 lists the codes and 

descriptions of the surrogates. Surrogate names and codes listed in italics are not directly assigned to any 

sources for the 2011v6.2 platform, but they are sometimes used to gapfill other surrogates, or as an input 

for merging two surrogates to create a new surrogate that is used. 

Many surrogates use circa 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 2011NEIv2 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 is available from http://cmascenter.org and documentation for 

it is available at https://www.cmascenter.org/sa-tools/documentation/4.2/html/srgtool/ 
58

SurrogateToolUserGuide_4_2.htm. 

Table 3-12. 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) 507 Heavy Light Construction Industrial Land

100 Population 510 Commercial plus Industrial

110 Housing 515 Commercial plus Institutional Land

120 Urban Population 520 Commercial plus Industrial plus Institutional

130 Rural Population 525

Golf Courses + Institutional +Industrial + 

Commercial

137 Housing Change 526 Residential Non-Institutional

140 Housing Change and Population 527 Single Family Residential

150 Residential Heating - Natural Gas 530 Residential - High Density

160 Residential Heating – Wood 535

Residential + Commercial + Industrial + 

Institutional + Government

165

0.5 Residential Heating - Wood plus 0.5 Low 

Intensity Residential 540 Retail Trade 

170 Residential Heating - Distillate Oil 545 Personal Repair 

180 Residential Heating – Coal 550 Retail Trade plus Personal Repair 

190 Residential Heating - LP Gas 555

Professional/Technical plus General 

Government 

200 Urban Primary Road Miles 560 Hospitals

205 Extended Idle Locations 565 Medical Offices/Clinics

210 Rural Primary Road Miles 570 Heavy and High Tech Industrial 

220 Urban Secondary Road Miles 575 Light and High Tech Industrial 

221 Urban Unrestricted Roads 580 Food, Drug, Chemical Industrial

230 Rural Secondary Road Miles 585 Metals and Minerals Industrial

231 Rural Unrestricted Roads 590 Heavy Industrial 

240 Total Road Miles 595 Light Industrial 

250 Urban Primary plus Rural Primary 596 Industrial plus Institutional plus Hospitals

255 0.75 Total Roadway Miles plus 0.25 Population 600 Gas Stations

256 Off-Network Short-Haul Trucks 650 Refineries and Tank Farms

257 Off-Network Long-Haul Trucks 675 Refineries and Tank Farms and Gas Stations

258 Intercity Bus Terminals 680

Oil & Gas Wells, IHS Energy, Inc. and 

USGS (see updated surrogates in Table 3-19)

259 Transit Bus Terminals 700 Airport Areas

260 Total Railroad Miles 710 Airport Points

261 NTAD Total Railroad Density 720 Military Airports

270 Class 1 Railroad Miles 800 Marine Ports

271 NTAD Class 1, 2, 3 Railroad Density 801 NEI Ports

280 Class 2 and 3 Railroad Miles 802 NEI Shipping Lanes 

300 Low Intensity Residential 806 Offshore Shipping NEI NOx

310 Total Agriculture 807 Navigable Waterway Miles

312 Orchards/Vineyards 808 Gulf Tug Zone Area

320 Forest Land 810 Navigable Waterway Activity

330 Strip Mines/Quarries 812 Midwest Shipping Lanes

340 Land 820 Ports NEI NOx

350 Water 850 Golf Courses
59

Code Surrogate Description Code Surrogate Description

400 Rural Land Area 860 Mines

500 Commercial Land 870 Wastewater Treatment Facilities

505 Industrial Land 880 Drycleaners

506 Education 890 Commercial Timber

For the onroad sector, the on-network (RPD) emissions were spatially allocated to roadways, and the offnetwork (RPP and RPV) emissions were allocated according to the mapping in Table 3-13. The refueling 

emissions were spatially allocated to gas station locations (surrogate 600). On-network (i.e., on-roadway) 

mobile source emissions were assigned to the following surrogates: rural restricted access to rural 

primary road miles (210), rural unrestricted access to 231, urban restricted access to urban primary road 

miles (200), and urban unrestricted access to 221. In the 2011v6.2 platform, emissions from the extended 

(i.e., overnight) idling of trucks were assigned to a new surrogate 205 that is based on locations of 

overnight truck parking spaces.

Table 3-13. Off-Network Mobile Source Surrogates

Source type Source Type name Surrogate ID

11 Motorcycle 535

21 Passenger Car 535

31 Passenger Truck 535

32 Light Commercial Truck 510

41 Intercity Bus 258

42 Transit Bus 259

43 School Bus 506

51 Refuse Truck 507

52 Single Unit Short-haul Truck 256

53 Single Unit Long-haul Truck 257

54 Motor Home 526

61 Combination Short-haul Truck 256

62 Combination Long-haul Truck 257

For the oil and gas sources in the np_oilgas sector, the spatial surrogates were updated to those shown in 

Table 3-14 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 

circa-2005 data. These surrogates were based on the same GIS data of well locations and related 

attributes as was used to develop the 2011NEIv2 data for the oil and gas sector. The data sources include 

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, 2014b). Although basically the same surrogates were used, some minor updates to 

the oil and gas surrogates were made in the 2011v6.2 platform to correct some mis-located emissions.
60

Table 3-14. 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

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

Some spatial surrogate cross reference updates were made between the 2011v6.1 platform and the 

2011v6.2 platform aside from the reworking of the onroad mobile source surrogates described above. 

These updates included the following: 

 Nonroad SCCs using spatial surrogate 525 (50% commercial + industrial + institutional, 50% golf 

courses) were changed to 520 (100% commercial + industrial + institutional). The golf course 

surrogate 850, upon which 525 is partially based, is incomplete and subject to hot spots;

 Some nonroad SCCs for commercial equipment in New York County had assignments updated to 

surrogate 340;

 Commercial lawn and garden equipment was updated to use surrogate 520; and

 Some county-specific assignments for RWC were updated to use surrogate 300.

Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is, 

some surrogates shown in Table were not assigned to any SCCs, although many of the “unused” 

surrogates are actually used to “gap fill” other surrogates that are used. 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. 

3.3.7.2 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 and airport ground support 
61

equipment emissions as point sources. For the modeling platform, EPA used the SMOKE “area-to-point” 

approach for only jet refueling in the nonpt sector. The following SCCs use this approach: 2501080050 

and 2501080100 (petroleum storage at airports), and 2810040000 (aircraft/rocket engine firing and 

testing). The ARTOPNT approach is described in detail in the 2002 platform documentation: 

https://www3.epa.gov/scram001/reports/Emissions%20TSD%20Vol1_02-28-08.pdf. The ARTOPNT file 

that lists the nonpoint sources to locate using point data were unchanged from the 2005-based platform. 

3.3.7.3 Surrogates for Canada and Mexico Emission Inventories

The surrogates for Canada to spatially allocate the 2010 Canadian emissions have been updated in the 

2011v6.2 platform. The spatial surrogate data came from Environment Canada, along with cross 

references. The surrogates they provided were outputs from the Surrogate Tool (previously referenced). 

The Canadian surrogates used for this platform are listed in Table 3-15. The leading “9” was added to the 

surrogate codes to avoid duplicate surrogate numbers with U.S. surrogates. Surrogates for Mexico 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 3-16. The entries in this table are for the othar 

sector except for the “MEX Total Road Miles” and the “CAN traffic” rows, which are for the othon 

sector.

Table 3-15. Canadian Spatial Surrogates 

Code Canadian Surrogate Description Code Description

9100 Population 92424 BARLEY

9101 total dwelling 92425 BUCWHT

9103 rural dwelling 92426 CANARY

9106 ALL_INDUST 92427 CANOLA

9111 Farms 92428 CHICPEA

9113 Forestry and logging 92429 CORNGR

9211 Oil and Gas Extraction 92425 BUCWHT

9212 Mining except oil and gas 92430 CORNSI

9221 Total Mining 92431 DFPEAS

9222 Utilities 92432 FLAXSD

9233 Total Land Development 92433 FORAGE

9308 Food manufacturing 92434 LENTIL

9321 Wood product manufacturing 92435 MUSTSD

9323 Printing and related support activities 92436 MXDGRN

9324 Petroleum and coal products manufacturing 92437 OATS

9327 Non-metallic mineral product manufacturing 92438 ODFBNS

9331 Primary Metal Manufacturing 92439 OTTAME

9412 Petroleum product wholesaler-distributors 92440 POTATS

9416

Building material and supplies wholesalerdistributors 92441 RYEFAL

9447 Gasoline stations 92442 RYESPG

9448 clothing and clothing accessories stores 92443 SOYBNS

9481 Air transportation 92444 SUGARB

9482 Rail transportation 92445 SUNFLS
62

Code Canadian Surrogate Description Code Description

9562 Waste management and remediation services 92446 TOBACO

9921 Commercial Fuel Combustion 92447 TRITCL

9924 Primary Industry 92448 WHITBN

9925 Manufacturing and Assembly 92449 WHTDUR

9932 CANRAIL 92450 WHTSPG

9941 PAVED ROADS 92451 WHTWIN

9942 UNPAVED ROADS 92452 BEANS

9945 Commercial Marine Vessels 92453 CARROT

9946 Construction and mining 92454 GRPEAS

9948 Forest 92455 OTHVEG

9950 Combination of Forest and Dwelling 92456 SWCORN

9955 UNPAVED_ROADS_AND_TRAILS 92457 TOMATO

9960 TOTBEEF 92430 CORNSI

9970 TOTPOUL 92431 DFPEAS

9980 TOTSWIN 92432 FLAXSD

9990 TOTFERT 92433 FORAGE

9996 urban_area 92434 LENTIL

9997 CHBOISQC 92435 MUSTSD

91201 traffic_bcw 92436 MXDGRN

92401 BULLS 92437 OATS

92402 BFCOWS 92438 ODFBNS

92403 BFHEIF 92439 OTTAME

92404 CALFU1 92440 POTATS

92405 FDHEIF 92441 RYEFAL

92406 STEERS 92442 RYESPG

92407 MLKCOW 92443 SOYBNS

92408 MLKHEIF 92444 SUGARB

92409 MBULLS 92445 SUNFLS

92410 MCALFU1 92446 TOBACO

92412 BROILER 92447 TRITCL

92413 LAYHEN 92448 WHITBN

92414 TURKEY 92449 WHTDUR

92416 BOARS 92450 WHTSPG

92417 GRWPIG 92451 WHTWIN

92418 NURPIG 92452 BEANS

92419 SOWS 92453 CARROT

92421 IMPAST 92454 GRPEAS

92422 UNIMPAST 92455 OTHVEG

92423 ALFALFA 92456 SWCORN

92457 TOMATO
63

Table 3-16. CAPs Allocated to Mexican and Canadian Spatial Surrogates 

Code Mexican or Canadian Surrogate Description NH3 NOX PM 2_5 SO2 VOC

22 MEX Total Road Miles 15,965 370,867 34,396 13,713 375,276

10 MEX Population 0 0 0 0 431,231

12 MEX Housing 0 161,013 17,483 2,123 452,685

14 MEX Residential Heating - Wood 0 20,093 211,525 2,859 380,572

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 679,212 164,144 72,372 2,127 43,958

28 MEX Forest Land 0 16,224 67,683 660 79,018

32 MEX Commercial Land 0 125,211 7,726 0 286,982

34 MEX Industrial Land 0 45,831 5,684 59,201 133,440

36 MEX Commercial plus Industrial Land 0 0 0 0 332,495

38 MEX Commercial plus Institutional Land 0 6,400 216 84 28,293

40

Residential (RES1-4)+Commercial+Industrial+

Institutional+ Government 0 8 20 0 241,710

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 0 124,951 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 14,698 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 160 239,553 25,318 9,092 26,526

9116 CAN Total Resources 0 17 0 0 5

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 wholesaler-distributors 0 0 0 0 70,125
64

Code Mexican or Canadian Surrogate Description NH3 NOX PM 2_5 SO2 VOC

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 35,471 850

9924 CAN Primary Industry 0 0 0 0 219,282

9925 CAN Manufacturing and Assembly 0 0 0 0 139,227

9931 CAN OTHERJET 9 14,388 548 1,139 7,629

9932 CAN CANRAIL 109 122,694 4,093 5,737 3,304

9942 CAN UNPAVED ROADS 40 3,462 3,499 48 152,674

9945 CAN Commercial Marine Vessels 28 45,454 6,404 14,325 61,139

9946 CAN Construction and mining 247 156,770 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 11,700 120,045 1,671 173,130

9960 CAN TOTBEEF 176,156 0 7,420 0 317,394

9970 CAN TOTPOUL 74,204 0 2 0 264

9980 CAN TOTSWIN 122,094 0 996 0 3,186

9990 CAN TOTFERT 178,791 0 9,279 0 0

9991 CAN traffic 22,294 550,896 10,888 5,548 285,104

9994 CAN ALLROADS 0 0 55,468 0 0

9995 CAN 30UNPAVED_70trail 0 0 106,707 0 0

9996 CAN urban_area 0 0 284 0 0
65

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

Adelman, Z., M. Omary, Q. He, J. Zhao and D. Yang, J. Boylan, 2012. “A Detailed Approach for 

Improving Continuous Emissions Monitoring Data for Regulatory Air Quality Modeling.” 

Presented at the 2012 International Emission Inventory Conference, Tampa, Florida. Available 

from https://www3.epa.gov/ttn/chief/conference/ei20/index.html#ses-5.

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

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/pmmort/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.

Bash, J.O., Baker, K.R., Beaver, M.R., Park, J.-H., Goldstein, A.H., 2015. Evaluation of improved land 

use and canopy representation in BEIS with biogenic VOC measurements in California (in 

preparation)

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.

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 https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P10023FZ.pdf.
66

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 https://www.epa.gov/air-emissions-modeling/speciate-version-45-

through-40. 

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: : https://www.epa.gov/air-emissions-inventories/2008-national-emissions-inventorynei-documentation-draft.

EPA, 2015a. 2011 Technical Support Document (TSD) Preparation of Emissions Inventories for the 

Version 6.2, 2011 Emissions Modeling Platform. Office of Air Quality Planning and Standards, 

Air Quality Assessment Division, Research Triangle Park, NC. Available at

https://www.epa.gov/air-emissions-modeling/2011-version-62-platform.

EPA, 2015b. 2011 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 https://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventorynei-technical-support-document.

ERG, 2014a. Develop Mexico Future Year Emissions Final Report. Available at 

ftp://newftp.epa.gov/air/emismod/2011/v2platform/2011emissions/Mexico_Emissions_WA%204-

09_final_report_121814.pdf

ERG, 2014b. “Technical Memorandum: Modeling Allocation Factors for the 2011 NEI”.

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

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. 

NYSERDA, 2012; “Environmental, Energy Market, and Health Characterization of Wood-Fired 

Hydronic Heater Technologies, Final Report”. New York State Energy Research and 

Development Authority (NYSERDA). Available from: 

http://www.nyserda.ny.gov/Publications/Case-Studies/-

/media/Files/Publications/Research/Environmental/Wood-Fired-Hydronic-Heater-Tech.ashx.

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
67

Pouliot, G. and J. Bash, 2015. Updates to Version 3.61 of the Biogenic Emission Inventory System 

(BEIS). Presented at Air and Waste Management Association conference, Raleigh, NC, 2015.

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., D. Sullivan, L. Chinkin, S. Larkin, R. Solomon, A. Soja, 2007. Integration of SatelliteDetected and Incident Command Reported Wildfire Information into BlueSky, June 27, 2007. 

Available at: https://www.airfire.org/smartfire. 

Skamarock, W., J. Klemp, J. Dudhia, D. Gill, D. Barker, M. Duda, X. Huang, W. Wang, J. Powers, 2008. 

A Description of the Advanced Research WRF Version 3. NCAR Technical Note. National 

Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division, Boulder, CO. 

June 2008. Available at: http://www.mmm.ucar.edu/wrf/users/docs/arw_v3.pdf

Sullivan D.C., Raffuse S.M., Pryden D.A., Craig K.J., Reid S.B., Wheeler N.J.M., Chinkin L.R., Larkin 

N.K., Solomon R., and Strand T. (2008) Development and applications of systems for modeling 

emissions and smoke from fires: the BlueSky smoke modeling framework and SMARTFIRE: 17th 

International Emissions Inventory Conference, Portland, OR, June 2-5. 

Wang, Y., P. Hopke, O. V. Rattigan, X. Xia, D. C. Chalupa, M. J. Utell. (2011) “Characterization of 

Residential Wood Combustion Particles Using the Two-Wavelength Aethalometer”, Environ. Sci. 

Technol., 45 (17), pp 7387–7393

Yarwood, G., S. Rao, M. Yocke, and G. Whitten, 2005: Updates to the Carbon Bond Chemical 

Mechanism: CB05. Final Report to the US EPA, RT-0400675. Available at

http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf.
68

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 system11. The primary goals for CMAQ are to:

• Improve the environmental management community’s ability to evaluate the impact of air quality

management practices for multiple pollutants at multiple scales.

• Improve the scientist’s ability to better probe, understand, and simulate chemical and physical 

interactions in the atmosphere.

The CMAQ modeling system brings together key physical and chemical functions associated with the

dispersion and transformations of air pollution at various scales. It was designed to approach air quality as 

a whole by including state-of-the-science capabilities for modeling multiple air quality issues, including

tropospheric ozone, fine particles, toxics, acid deposition, and visibility degradation. CMAQ relies on 

emission estimates from various sources, including the U.S. EPA Office of Air Quality Planning and 

Standards’ current emission inventories, observed emission from major utility stacks, and model estimates

of natural emissions from biogenic and agricultural sources. CMAQ also relies on meteorological

predictions that include assimilation of meteorological observations as constraints. Emissions and

meteorology data are fed into CMAQ and run through various algorithms that simulate the physical and 

chemical processes in the atmosphere to provide estimated concentrations of the pollutants. Traditionally,

the model has been used to predict air quality across a regional or national domain and then to simulate the

effects of various changes in emission levels for policymaking purposes. For health studies, the model can

also be used to provide supplemental information about air quality in areas where no monitors exist.

CMAQ was also designed to have multi-scale capabilities so that separate models were not needed for 

urban and regional scale air quality modeling. The grid spatial resolutions in past annual CMAQ runs 

have been 36 km x 36 km per grid for the “parent” domain, and nested within that domain are 12 km x 12 

km grid resolution domains. The parent domain typically covered the continental United States, and the

 

11 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.
69

nested 12 km x 12 km domain covered the Eastern or Western United States. The CMAQ simulation

performed for this 2012 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.

12 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 https://www.epa.gov/cmaq or http://www.cmascenter.org.

4.1.1 Advantages and Limitations of the CMAQ Air Quality Model

An advantage of using the CMAQ model output for characterizing air quality for use in comparing with

health outcomes is that it provides a complete spatial and temporal coverage across the U.S. CMAQ is a 

three-dimensional Eulerian photochemical air quality model that simulates the numerous physical and 

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

air toxics for given input sets of initial and boundary conditions, meteorological conditions and emissions. 

The CMAQ model includes state-of-the-science capabilities for conducting urban to regional scale 

simulations of multiple air quality issues, including tropospheric ozone, fine particles, toxics, acid 

deposition and visibility degredation. However, CMAQ is resource intensive, requiring significant data 

inputs and computing resources.

An uncertainty of using the CMAQ model includes structural uncertainties, representation of physical and 

chemical processes in the model. These consist of: choice of chemical mechanism used to characterize 

reactions in the atmosphere, choice of land surface model and choice of planetary boundary layer. 

Another uncertainty in the CMAQ model is based on parametric uncertainties, which includes 

uncertainties in the model inputs: hourly meteorological fields, hourly 3-D gridded emissions, initial 

conditions, and boundary conditions. Uncertainties due to initial conditions are minimized by using a 10 

day ramp-up period from which model results are not used in the aggregation and analysis of model 

outputs. Evaluations of models against observed pollutant concentrations build confidence that the model 

performs with reasonable accuracy despite the uncertainties listed above. A detailed model evaluation for 

ozone and PM2.5 species provided in Section 4.3 shows generally acceptable model performance which is 

equivalent or better than typical state-of-the-science regional modeling simulations as summarized in 

Simon et al., 201213.

4.2 CMAQ Model Version, Inputs and Configuration 

This section describes the air quality modeling platform used for the 2012 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

 

12 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-PM-RH_Modeling_Guidance-2014.pdf. 

13 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. 
70

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 2012 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 2012 CMAQ simulation along 

with the results of a model performance evaluation in which the 2012 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 

2012 analysis employed CMAQ version 5.0.2

14 which reflects updates to version 5.0.1 which include 

several changes to the science algorithms to improve the underlying science. The CMAQ model version 

5.0 was most recently peer-reviewed in June of 2011 for the U.S. EPA.15 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

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 

 

14 CMAQ version 5.0.2 model code is available from the Community Modeling and Analysis System (CMAS) at: 

http://www.cmascenter.org.

15 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, https://www.epa.gov/sites/production/files/2016-

11/documents/cmaq_fifth_review_final_report_2015.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.
71

provides some basic geographic information regarding the 12 km CMAQ domain.

Figure 4-1. Map of the CMAQ Modeling Domain. The purple box denotes the 12 km national

modeling domain. (Same as Figure 3-3.)

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)

4.2.3 Modeling Period / Ozone Episodes

The 12 km CMAQ modeling domain was modeled for the entire year of 2012. The 2012 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 half-year 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 
72

of days that observed high ozone concentrations.

4.2.4 Model Inputs: Emissions, Meteorology and Boundary Conditions

2012 Emissions: The emissions inventories used in the 2012 air quality modeling are described in Section

3, above.

Meteorological Input Data: The gridded meteorological data for the entire year of 2012 at the 12 km 

continental United States scale domain was derived from version 3.6.116 of the Weather Research and 

Forecasting Model (WRF), Advanced Research WRF (ARW) core.17 The WRF Model is a state-of-thescience mesoscale numerical weather prediction system developed for both operational forecasting and 

atmospheric research applications (http://wrf-model.org). The 2012 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 trigger18 and the RRTMG long-wave and 

shortwave radiation (LWR/SWR) scheme.19 In addition, the Group for High Resolution Sea Surface 

Temperatures (GHRSST)20 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 2006.21

The WRF meteorological outputs were processed using the Meteorology-Chemistry Interface Processor 

(MCIP) package22, version 4.2, to derive the specific inputs to CMAQ: horizontal wind components (i.e., 

speed and direction), temperature, moisture, and its related speciated components was conducted for 

vertical diffusion rates, and rainfall rates for each grid cell in each vertical layer. The WRF simulation 

used the same CMAQ map projection, a Lambert Conformal projection centered at (-97, 40) with true 

latitudes at 33 and 45 degrees north. The 12 km WRF domain consisted of 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 2012 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 

 

16 Version 3.6.1 was the current version of WRF at the time the 2012 meteorological model simulation was performed.

17 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.

18 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

19 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.

20 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.

21 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.

22 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.
73

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 2012 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

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
74

CMAQ 

Layers

WRF 

Layers Sigma P Pressure (mb)

Approximate 

Height (m)

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-CHEM23 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 2012 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.24 A GEOS-Chem evaluation was 

conducted for the purpose of validating the 2012 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.

25 More information is available about the GEOSCHEM 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 2012 simulation using state/local monitoring sites data in order to estimate the

ability of the CMAQ modeling system to replicate the 2012 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. 

 

23 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User’s Guide, Atmospheric Chemistry Modeling Group, Harvard University, 

Cambridge, MA, October 15, 2004.

24Akhtar, 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.

25 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.
75

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 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 eighthour daily maximum ozone was conducted using the statistics defined above. Ozone measurements for 

2012 in the continental U.S. were included in the evaluation and were taken from the 2012 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. domain26: 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 (MarchApril-May),summer (June, July, August), and fall (September-October-November). 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

 

26The 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.
76

predicted by the 2012 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 under predicted with the greatest under prediction in the 

Midwest U.S. at AQS and CASTNet sites (NMB is -6.7 percent and -10.3 percent, respectively). 

However, 8-hour ozone is slightly over predicted in Central U.S. at AQS sites (NMB is 4.0 percent) and 

in the Southeast at AQS and CASTNet sites (NMB is 7.2 percent and 2.0 percent, respectively). Ozone 

performance in spring shows better performance with a mix of slight under and over predictions in the 

U.S. at AQS and CASTNet sites. In the winter, when concentrations are generally low, the model slightly 

over predicts 8-hour maximum ozone with the exception of the Northeast at CASNet sites (NMB is -1.2 

percent). In the fall, when concentrations are also relatively low, ozone is slightly over predicted (with

NMBs less than 11 percent in each subregion); except for under prediction at AQS sites in the Central and 

the Western U.S (NMB is -2.7 percent and -1.0 percent, respectively).

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 ±6 ppb across the AQS and CASTNet sites. For some areas in the Northeast, Midwest, Central 

and California mean bias is under predicted by 8 to 12 ppb. 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. Model error, as seen from

Figures 4-3 and 4-5, is generally 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, and North Dakota.

Table 4-4. Summary of CMAQ 2012 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 11,007 0.7 4.3 2.4 14.9

Spring 16,122 1.2 5.7 2.6 12.6

Summer 17,696 -0.3 7.4 -0.6 14.9

Fall 14,473 2.5 6.2 7.5 18.7

CASTNet Winter 1,248 -0.4 3.7 -1.2 11.5

Spring 1,309 0.1 5.0 0.2 10.8

Summer 1,238 -1.4 6.6 -2.9 13.9

Fall 1,106 2.1 5.6 6.2 16.3
77

Subregion

Monitor 

Network Season

No. of 

Obs

MB 

(ppb)

ME 

(ppb)

NMB

(%)

NME 

(%)

Midwest

AQS Winter 4,192 2.5 4.6 9.6 17.5

Spring 12,777 0.7 5.6 1.5 11.6

Summer 16,772 -3.7 7.5 -6.7 13.6

Fall 10,055 0.3 6.0 0.8 16.9

CASTNet Winter 1,037 1.8 4.0 6.2 13.5

Spring 992 -0.8 5.0 -1.5 10.2

Summer 952 -5.8 7.4 -10.3 13.1

Fall 980 0.0 5.1 14.2 0.9

Central States

AQS Winter 12,455 2.0 5.1 6.1 16.0

Spring 16,394 2.7 6.8 5.7 14.5

Summer 17,814 2.0 9.7 4.0 19.7

Fall 16,391 0.7 6.5 1.6 15.9

CASTNet Winter 655 0.2 3.9 0.5 11.2

Spring 688 -0.8 5.7 -1.6 11.6

Summer 645 -3.3 7.7 -6.3 14.5

Fall 652 -1.1 5.8 -2.7 13.9

Southeast

AQS Winter 7,623 2.5 5.4 7.0 15.3

Spring 21,864 3.4 6.5 7.2 13.9

Summer 24,106 4.2 8.6 9.0 18.8

Fall 18,706 3.7 6.7 9.6 17.3

CASTNet Winter 1,904 1.7 4.4 4.9 12.5

Spring 1,901 0.8 5.3 1.6 10.9

Summer 1,864 1.0 7.2 2.0 14.5

Fall 1,832 1.5 5.4 3.9 13.6

West

AQS Winter 29,291 2.7 5.5 7.8 16.0

Spring 32,626 -0.7 5.5 -1.5 10.8

Summer 35,804 -0.9 7.4 -1.7 14.0

Fall 32,898 1.3 6.3 3.1 14.8

CASTNet Winter 1,634 0.0 4.3 0.0 10.3

Spring 1,675 -3.5 5.7 -6.4 10.3

Summer 1,639 -3.4 7.4 -6.0 13.0

Fall 1,599 -0.4 4.9 -1.0 10.8
78

Figure 4-2. Mean Bias (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period

May-September 2012 at AQS and CASTNet monitoring sites in the continental U.S. modeling

domain.

Figure 4-4. Mean Error (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period

May-September 2012 at AQS and CASTNet monitoring sites in the continental U.S. modeling

domain.
79

Figure 4-3. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 ppb over 

the period May-September 2012 at AQS and CASTNet monitoring sites in the continental U.S.

modeling domain.

Figure 4-5. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 ppb over 

the period May-September 2012 at AQS and CASTNet monitoring sites in the continental U.S.

modeling domain.
80

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 2012 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 MB values ranging from 

0.0 to -0.6 μgm-3 and NMB values ranging from near negligible to -30.3 percent. Sulfate performance 

shows moderate error, ranging from 24 to 51.4 percent. Figures 4-6 through 4-9, suggest spatial patterns

vary by region. The model bias for most of the Northeast, Southeast, Central and Southwest states are

within ±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 the Northeast, 

Midwest, and Southeast (NMBin the range of 7.7 to 19.9 percent), except in the Central and 

Western U.S. where nitrate is under predicted (NMB is -7.4 percent and -36.5 percent, respectively).

At IMPROVE rural sites, annual average nitrate is over predicted at all subregions, except in 

the Western U.S. where nitrate is under predicted by -25.8 percent. Model performance of total 

nitrate at sub-urban CASTNet monitoring sites also shows an over prediction across all subregions

(NMBin the range of 3.7 to 19 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 7 to -40 percent). Ammonium performance 

across the urban CSN sites shows an over prediction (ranging from 7 to 28 percent) in the Northeast, 

Midwest, Southeast, and Central U.S., and an under prediction of approximately 25 percent in the 

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 
81

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 3 to 56 percent), except in the Western U.S. where the bias is approximately -2 percent. 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 2012 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 2,054 -0.1 0.6 -5.3 29.7

Midwest 1,747 -0.3 0.7 -12.5 30.9

Southeast 1,813 -0.1 0.6 -7.2 29.5

Central 1,150 -0.2 0.6 -10.8 32.2

West 1,624 0.0 0.4 -5.3 44.6

IMPROVE Northeast 1,824 0.2 0.4 14.7 39.5

Midwest 442 -0.1 0.5 -6.1 34.4

Southeast 1,750 -0.2 0.6 -11.4 31.9

Central 2,253 -0.2 0.5 -13.7 34.4

West 9,636 0.0 0.3 -3.7 51.4

CASTNet Northeast 788 -0.3 0.4 -15.6 24.0

Midwest 592 -0.5 0.6 -24.2 26.3

Southeast 1,134 -0.6 0.6 -26.3 28.7

Central 386 -0.5 0.6 -30.3 33.6

West 1,039 -0.1 0.3 -21.7 40.4

Nitrate

CSN Northeast 2,054 0.1 0.7 7.7 62.4

Midwest 1,747 0.2 0.7 11.0 51.2

Southeast 1,813 0.1 0.4 19.9 85.0

Central 1,150 -0.1 0.6 -7.4 60.0

West 1,624 -0.5 0.9 -36.5 68.0
82

Pollutant

Monitor 

Network Subregion

No. of 

Obs

MB 

(μgm-3)

ME 

(μgm-3)

NMB 

(%)

NME 

(%)

IMPROVE Northeast 1,824 0.2 0.4 67.6 118.0

Midwest 442 0.2 0.5 38.5 74.2

Southeast 1,749 0.1 0.4 20.2 106.0

Central 2,252 0.1 0.5 14.9 73.7

West 9,603 -0.1 0.2 -25.8 89.6

Total Nitrate 

(NO3 + HNO3)

CASTNet Northeast 788 0.3 0.5 19.0 36.6

Midwest 592 0.7 0.1 7.7 27.4

Southeast 1,134 0.1 0.6 3.7 41.8

Central 386 -0.3 0.6 -17.4 38.2

West 1,038 -0.2 0.4 -27.6 45.7

Ammonium

CSN Northeast 2,054 0.1 0.3 18.3 43.7

Midwest 1,747 0.1 0.3 7.2 37.8

Southeast 1,813 0.1 0.3 27.9 52.0

Central 1,150 0.0 0.3 6.7 50.1

West 1,624 -0.2 0.3 -25.4 75.1

CASTNet Northeast 788 0.0 0.2 -6.9 26.6

Midwest 592 -0.1 0.3 -7.1 27.0

Southeast 1,134 -0.1 0.2 -17.3 32.1

Central 386 -0.1 0.2 -14.0 36.5

West 1,039 -0.1 0.1 -39.9 56.5

Elemental 

Carbon

CSN Northeast 1,979 0.3 0.5 67.8 84.6

Midwest 1,668 0.3 0.4 56.2 71.0

Southeast 1,775 0.3 0.4 46.4 71.1

Central 1,144 0.5 0.5 94.1 106.0

West 1,582 0.6 0.7 99.0 117.0

IMPROVE Northeast 1,929 0.2 0.2 67.8 84.6

Midwest 473 0.1 0.1 40.5 63.7

Southeast 1,806 0.1 0.2 40.1 64.1

Central 2,304 0.1 0.2 38.9 61.7

West 10,366 0.1 0.2 70.9 108.0
83

Pollutant

Monitor 

Network Subregion

No. of 

Obs

MB 

(μgm-3)

ME 

(μgm-3)

NMB 

(%)

NME 

(%)

Organic Carbon

CSN Northeast 1,966 1.2 1.6 75.3 95.1

Midwest 1,629 0.6 1.0 38.3 60.0

Southeast 1,775 0.6 1.2 32.6 61.6

Central 1,139 0.8 1.2 52.5 78.4

West 1,574 1.8 2.2 101.0 123.0

IMPROVE Northeast 1,927 0.5 0.8 55.6 86.8

Midwest 472 0.4 0.7 39.4 76.8

Southeast 1,817 0.1 0.8 9.3 60.8

Central 2,307 0.0 0.5 3.0 53.2

West 10,124 0.0 0.6 -1.8 66.2
84

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.
85

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.
86

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.
87

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.
88

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.
89

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.
90

Figure 4-18. Mean Bias (μgm-3) of annual ammoniumat 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.
91

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.
92

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.
93

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.
94

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.
95

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.
96

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

Derived Air Quality Estimates

5.1 Introduction 

The need for greater spatial coverage of air pollution concentration estimates has grown in recent years as 

epidemiology and exposure studies that link air pollution concentrations to health effects have become 

more robust and as regulatory needs have increased. Direct measurement of concentrations is the ideal 

way of generating such data, but prohibitive logistics and costs limit the possible spatial coverage and 

temporal resolution of such a database. Numerical methods that extend the spatial coverage of existing 

air pollution networks with a high degree of confidence are thus a topic of current investigation by 

researchers. The downscaler model (DS) is the result of the latest research efforts by EPA for performing 

such predictions. DS utilizes both monitoring and CMAQ data as inputs, and attempts to take advantage 

of the measurement data’s accuracy and CMAQ’s spatial coverage to produce new spatial predictions. 

This chapter describes methods and results of the DS application that accompany this report, which 

utilized ozone and PM2.5 data from AQS and CMAQ to produce predictions to continental U.S. 2010 

census tract centroids for the year 2012. 

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 means and standard distributions of these are used to 

predict concentrations and associated uncertainties at new spatial points. 
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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

2012 US census tract centroids across the continental U.S. using 2012 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 2012. 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 2012, about 75% of the US Census tracts (54633 out of 72283) 

experienced at least one day with an ozone value above the NAAQS of 75 ppb. 
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Figure 5-2. Annual 4th max (daily max 8-hour ozone concentrations) derived from AQS, CMAQ 

and DS data.
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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 2012. Figure 5-2 

shows annual means and Figure 5-3 shows 98’th percentiles of 24-hour PM2.5 concentrations for AQS 

observations, CMAQ model predictions and DS model results. The DS model estimated that for 2012 

about 39% of the US Census tracts (28402 out of 72283) experienced at least one day with a PM2.5 value 

above the 24-hour NAAQS of 35 ug/m3. 
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Figure 5-2. Annual mean PM2.5 concentrations derived from AQS, CMAQ and DS data.
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Figure 5-3. 98th percentile 24-hour average PM2.5 concentrations derived from AQS, CMAQ and 

DS data.
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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.
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Figure 5-4. Annual mean relative errors (standard errors divided by predictions) from the DS 2012 

runs. The black dots show the locations of monitors that generated the AQS data used as input to 

the DS model.
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5.4.2 Cross Validation 

To check the quality of its spatial predictions, DS can be set to perform “cross-validation” (CV), 

which involves leaving a subset of AQS data out of the model run and predicting the concentrations of 

those left out points. The predicted values are then compared to the actual 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

PM2.5 815 0.17 2.93 0.96

O3 1311 -0.01 4.58 0.95

Table 5-1. Cross-validation statistics associated with the 2012 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 

2012 U.S. census tract centroids by utilizing monitoring data and CMAQ output for 2012. 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, DS-generated fine-scale gradients are not expected to 

represent actual fine-scale atmospheric concentration gradients, unless possibly multiple monitors are 

present in the gridcell.
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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

ORD EPA’s Office of Research and Development

ORIS Office of Regulatory Information Systems (code) - is a 4 or 5 digit
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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
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United States

Environmental Protection

Agency

Office of Air Quality Planning and 

Standards

Air Quality Assessment Division

Research Triangle Park, NC

Publication No. EPA450R16001

March 2016