Bayesian Space-time Downscaling Fusion 

Model (Downscaler) - Derived Estimates of Air 

Quality for 2014
ii
iii

EPA-454/R-18-008

October 2018

Bayesian Space-time Downscaling Fusion Model (Downscaler) -Derived 

Estimates of Air Quality for 2014

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Research Triangle Park, NC
iv

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: Liz Naess (EPA/OAR) and 

Elizabeth Mannshardt (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 .....................................................................................................34

3.4 Emissions References....................................................................................................................73

4.0 CMAQ Air Quality Model Estimates ....................................................................................................77

4.1 Introduction to the CMAQ Modeling Platform.............................................................................77

4.2 CMAQ Model Version, Inputs and Configuration ........................................................................78

5.0 Bayesian space-time downscaling fusion model (downscaler) -Derived Air Quality Estimates....106

5.1 Introduction .................................................................................................................................106

5.2 Downscaler Model ......................................................................................................................106

5.3 Downscaler Concentration Predictions .......................................................................................107

5.4 Downscaler Uncertainties............................................................................................................112

5.5 Summary and Conclusions..........................................................................................................114

Appendix A - Acronyms............................................................................................................................115
2

 

1.0 Introduction

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

concentrations throughout the contiguous United States during the 2014 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).

EPA’s Office of Air and Radiation’s (OAR) Office of Air Quality Planning and Standards (OAQPS)

provides 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 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 

 

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

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 

contained herein. 

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

 

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

• 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 https://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 CAA requires EPA to review these standards at least every five 

years. For more specific information on the NAAQS, go to https://www.epa.gov/criteria-airpollutants/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. EPA’s Integrated 

Science Assessments and Risk and Exposure documents are available at 

https://www.epa.gov/naaqs/ozone-o3-air-quality-standards. 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-and-reviewing-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 https://www.epa.gov/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 

 

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. https://www.epa.gov/technical-air-pollution-resources.
10

samples. However, over the past several years, continuous monitors, which can automatically collect, 

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 prone 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 
11

in terms of estimating ambient air concentrations in space and time. EPA’s eVNA6 is an example of an 

earlier approach for merging air quality monitor data with CMAQ model predictions. The downscaler 

model attempts to address some of the shortcomings in these earlier attempts to statistically combine 

monitor and model predicted data, see published paper referenced in section 1 for more information about 

the downscaler model. As discussed in the next section, there are two methods used in EPHT to provide 

estimates of ambient concentrations of air pollutants: air quality monitoring data and the downscaler

model estimate, which is a statistical ‘combination’ of air quality monitor data and photochemical air 

quality model predictions (e.g., CMAQ).

2.3 Air Quality Indicators Developed for the EPHT Network 

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

by CDC using the ozone and PM2.5 data from EPA. The approach used divides “indicators” into two 

categories. First, basic air quality measures were developed to compare air quality levels over space and 

time within a public health context (e.g., using the NAAQS as a benchmark). Next, indicators were 

developed that mathematically link air quality data to public health tracking data (e.g., daily PM2.5 levels 

and hospitalization data for acute myocardial infarction). Table 2-3 and Table 2-4 describe the issues 

impacting calculation of basic air quality indicators.

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

Goal Status

1) Air data sets and metadata required for air quality 

indicators are available to EPHT state Grantees.

AQS data are available through state agencies and EPA’s 

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

interagency agreement, where EPA provides air quality 

data along with statistically combined AQS and 

Community Multiscale Air Quality (CMAQ) Model 

data, associated metadata, and technical reports that are 

delivered to CDC.

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

health to: 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.

2) 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.

 

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.
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 2014 National Emissions Inventory (NEI), Version 1 to process year 2014 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) included in the modeling platform: chlorine (Cl), hydrogen chloride (HCl), benzene, 

acetaldehyde, formaldehyde, napthalene 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 version 6 (CB6) with chlorine chemistry used here within 

CMAQ allows for explicit treatment of the VOC HAPs naphthalene, benzene, acetaldehyde, 

formaldehyde and methanol (NBAFM) and includes anthropogenic HAP emissions of HCl and Cl.

The effort to create the 2014 emission inputs for this study included development of emission inventories 

for input to a 2014 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 4.5 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 2014 NEI, version 1 

with some updates is the primary basis for the inputs to SMOKE. The NEI includes five main data 

categories: 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 
14

Emissions Inventory System (EIS). EIS includes hundreds of automated QA checks to improve data 

quality, and it also supports release point (stack) coordinates separately from facility coordinates. EPA 

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

categories such as industrial boilers. The 2014 NEI Technical Support Document is available at

https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventory-nei-technical-supportdocument-tsd (EPA, 2016). 

Point source data for the year 2014 as submitted to EIS were used for this study. EPA used the 

SMARTFIRE2 system to develop 2014 fire emissions. SMARTFIRE2 categorizes all fires as either 

prescribed burning or wildfire categories, and includes improved emission factor estimates for prescribed 

burning. Onroad and nonroad mobile source emissions for year 2014 were developed using 

MOVES2014a. Canadian emissions reflect year 2013 and Mexican emissions reflect year 2014. 

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

7, 2014 Emissions Modeling Platform that was also used for the preliminary version of the 2014 National 

Air Toxics Assessment (NATA), with the exception that many fewer HAPs are included in this platform. 

A technical support document (TSD) for the 2014v7 platform is available here https://www.epa.gov/airemissions-modeling/2014-version-70-technical-support-document-tsd (EPA, 2017a) and includes 

additional details regarding the data preparation and emissions modeling with the exception of the HAP 

speciation. 

The emissions modeling process, performed using SMOKE v4.5, 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, PM and VOC) are split into the chemical species needed by 

CMAQ. For the purposes of preparing the CMAQ- ready emissions, the NEI emissions inventories by 

data category are split into emissions modeling “platform” sectors; and emissions from sources other than 

the NEI are added, such as the Canadian, Mexican, and offshore inventories. Emissions sectors within the 

emissions modeling platform are separated out from each other when the 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. Biogenic emissions are 

computed and used by CMAQ as it runs.

Table 3-1 presents the sectors in the emissions modeling platform used to develop the year 2014 

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 2014 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_2014_emissions_totals_by_sector.xlsx”.
15

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

2014 Platform Sector (Abbrev) NEI Category Description and resolution of the data input to SMOKE

EGUs (ptegu) Point

2014 point source EGUs. Replaced with hourly 2014 

Continuous Emissions Monitoring System (CEMS) values 

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

Emissions for all sources not matched to CEMS data come 

from 2014NEIv1. Annual resolution for sources not 

matched to CEMS data, hourly for CEMS sources.

Point source oil and gas 

(pt_oilgas) Point

2014NEIv1 point sources that include oil and gas 

production emissions processes based on facilities with the 

following NAICS: 211* (Oil and Gas Extraction), 213111 

(Drilling Oil and Gas Wells) 213112 (Support Activities for 

Oil and Gas Operations), 4861* (Pipeline Transportation of 

Crude Oil), 4862* (Pipeline Transportation of Natural Gas). 

Includes U.S. offshore oil production. Annual resolution.

Remaining non-EGU point 

(ptnonipm)

Point

All 2014NEIv1 point source records not matched to the 

ptegu or pt_oilgas sectors. Includes all aircraft and airport 

ground support emissions and some rail yard emissions. 

Annual resolution.

Point source fire (ptfire) Fires

Point source day-specific wildfires and prescribed fires for 

2014 computed using SMARTFIRE 2. Fires over 20,000 

acres on a single day allocated to overlapping grid cells.

Point Source agricultural fires 

(ptagfire) Nonpoint

Agricultural fire sources that were developed by EPA as 

point and day-specific emissions; they were put into the 

nonpoint data category of the NEI, but in the platform, they 

are treated as point sources. 

Agricultural (ag) Nonpoint

Ammonia emissions from 2014NEIv2 nonpoint livestock 

and 2014NEIv1 fertilizer application; county and annual 

resolution.

Area fugitive dust (afdust_adj) Nonpoint

PM10 and PM2.5 fugitive dust sources from the 2014NEIv1 

nonpoint inventory; including building construction, road 

construction, agricultural dust, and road dust. The 

emissions modeling adjustment applies a transport fraction 

and a meteorology-based (precipitation and snow/ice cover) 

zero-out. County and annual resolution. 

Biogenic (beis) Nonpoint

Biogenic emissions were left out of the CMAQ-ready 

merged emissions, in favor of inline biogenics produced 

during the CMAQ model run itself.

C1 and C2 commercial marine 

(cmv_c1c2)

Nonpoint

2014NEIv1 Category 1 (C1) and Category 2 (C2), 

commercial marine vessel (CMV) emissions. County and 

annual resolution.

C3 commercial marine (cmv_c3) Nonpoint

Within state waters, 2014NEIv1 Category 3 commercial 

marine vessel (CMV) emissions. Outside of state waters 

emissions are based on the Emissions Control Area (ECA) 

inventory. Point (to allow for plume rise) and annual 

resolution.

Remaining nonpoint (nonpt) Nonpoint 2014NEIv1 nonpoint sources not included in other platform 

sectors with adjustments to remove chromium from fugitive 
16

2014 Platform Sector (Abbrev) NEI Category Description and resolution of the data input to SMOKE

dust categories (paved and unpaved roads, construction and 

crops and livestock). County and annual resolution.

Nonpoint source oil and gas 

(np_oilgas) Nonpoint

2014NEIv1 nonpoint sources from oil and gas-related 

processes with specific adjustment in four unitah basin 

counties in Utah to correct EPA augmented benzene, 

toluene, ethylbenzene and xylenes. County and annual 

resolution.

Locomotive (rail) Nonpoint Rail locomotives emissions from the 2014NEIv1. County 

and annual resolution.

Residential Wood Combustion 

(rwc)

Nonpoint

2014NEIv1 nonpoint sources with residential wood 

combustion (RWC) processes. County and annual 

resolution.

Nonroad (nonroad) Nonroad

2014NEIv1 nonroad equipment emissions developed with 

the MOVES2014a using NONROAD2008 version NR08a 

and new HAP emission factors than had been used in the 

2011NEI. MOVES was used for all states except 

California, which submitted their own emissions for the 

2014NEIv1. County and monthly resolution.

Onroad (onroad) Onroad

2014 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, developed using 

winter and summer MOVES emission factors tables 

produced by MOVES2014a. 

Onroad California 

(onroad_ca_adj)

Onroad

2014 California-provided CAP and metal HAP onroad 

mobile source gasoline and diesel vehicles from parking lots 

and moving vehicles based on Emission Factor (EMFAC), 

gridded and temporalized using MOVES2014a. Volatile 

organic compound (VOC) HAP emissions derived from 

California-provided VOC emissions and MOVES-based 

speciation.

Onroad Canada (onroad_can) Non-US Monthly year 2013 Canada (province resolution) onroad 

mobile inventory.

Onroad Mexico (onroad_mex) Non-US Monthly year 2014 Mexico (municipio resolution) onroad 

mobile inventory.

Other area fugitive dust sources Non-US

Area fugitive dust sources from Canada 2013 inventory 

with transport fraction and snow/ice adjustments based on 

2014 meteorological data. Annual and province resolution.
17

2014 Platform Sector (Abbrev) NEI Category Description and resolution of the data input to SMOKE

Other nonpoint and nonroad 

(othar)

Non-US

Year 2013 Canada (province resolution) and projected year 

2014 Mexico (municipio resolution) nonpoint and nonroad 

mobile inventories, annual resolution.

Other point sources not from the 

NEI (othpt)

Non-US Point sources from Canada’s 2013 inventory and Mexico 

point sources projected to 2014. Annual resolution.

Point fires in Mexico and 

Canada (ptfire_mxca) Non-US

Point source day-specific wildfires and prescribed fires for 

2014 provided by Environment Canada with data for 

missing months and for Mexico filled in using fires from the 

Fire INventory (FINN) from National Center for 

Atmospheric Research (NCAR) fires (NCAR, 2016 and 

Wiedinmyer, C., 2011). 
18

Table 32. 2014 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.)

Sector CO NH3 NOx PM10 PM2.5 SO2 VOC

afdust_adj 6,991,664 975,147

ag 2,867,904

cmv_c1c2 44,808 109 227,954 5,978 5,737 3,387 4,488

cmv_c3 11,300 28 112,333 4,221 3,809 38,721 5,248

nonpt 2,894,351 114,049 794,416 712,611 569,249 300,871 3,571,099

np_oilgas 820,021 15 793,601 20,628 20,196 37,794 3,104,473

nonroad 12,425,532 2,244 1,392,082 140,863 133,362 3,163 1,630,321

onroad 21,548,865 103,333 4,622,945 307,113 157,997 28,324 2,170,529

ptagfire 426,253 55,639 12,897 69,528 49,398 3,870 25,816

ptfire 16,949,926 273,912 246,873 1,746,224 1,481,934 130,085 3,921,240

ptegu 735,826 25,933 1,758,567 236,027 183,029 3,241,498 35,523

ptnonipm 2,055,540 64,809 1,193,886 525,313 292,816 880,822 828,083

pt_oilgas 190,142 333 398,535 11,652 11,165 43,576 132,875

rail 119,252 364 779,801 25,094 23,166 6,986 39,857

rwc 2,156,051 16,221 32,174 333,219 332,700 8,087 351,696

Continental 

U.S. 60,377,867 3,524,893 12,366,065 11,130,134 4,239,702 4,727,182 15,821,249

Table 33. 2014 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 1,725,731 338,480

Canada othar 2,809,336 496,889 615,888 414,236 228,545 62,379 1,101,710

Canada onroad_can 2,105,867 8,446 474,813 28,204 21,474 1,593 191,505

Canada othpt 1,102,908 17,323 646,981 91,095 47,323 999,891 782,440

Canada ptfire_mxca 6,701,372 4,574 150,552 730,230 618,866 66,034 1,629,916

Canada Subtotal 12,719,483 527,233 1,888,235 2,989,497 1,254,688 1,129,897 3,705,570

Mexico othar 232,017 206,491 212,636 114,414 53,378 7,628 503,968

Mexico onroad_mex 1,823,639 2,660 432,368 14,716 10,649 5,849 158,524

Mexico othpt 188,253 4,669 465,960 72,872 57,479 543,591 66,392

Mexico ptfire_mxca 251,658 4,875 11,048 29,482 24,900 1,813 86,215

Mexico Subtotal 2,495,567 218,695 1,122,012 231,483 146,406 558,882 815,100

Offshore to EEZ 109,269 183 873,194 26,989 25,142 148,445 27,864

Non-US SECA C3 32,807 0 386,133 32,865 30,234 243,088 13,904

2014 Total non-U.S. 15,357,126 746,111 4,269,573 3,280,833 1,456,471 2,080,312 4,562,438
19

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 

that 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 

U.S. The offshore oil platform (pt_oilgas sector) and category 3 CMV emissions (cmv_c3 sector) are 

processed by SMOKE as point source inventories and are discussed later in this section. A complete NEI 

is developed every three years, with 2014 being the most recently finished complete NEI. A 

comprehensive description about the development of the 2014NEIv1 is available in the 2014NEIv1 TSD 

(EPA, 2016a). 

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

Flat File 2010 (FF10) format that is compatible with SMOKE (see 

https://www.cmascenter.org/smoke/documentation/4.0/html/ch08s02s08.html) and was then 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 inventories for the 

other three point source sectors were created from the remaining 2014 point sources. The point sectors 

are: EGUs (ptegu), point source oil and gas extraction-related sources (pt_oilgas) and the remaining nonEGUs (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). NBAFM pollutants from the point sectors were 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 2014 CEMS data, hourly CEMS NOX and SO2 emissions for 

2014 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 sector, 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)

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 
20

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 the 3-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 point sources missing this information were filled in prior to modeling in

SMOKE. 

Each of the point sectors is processed separately through SMOKE as described in the following 

subsections.

3.2.1.1 EGU sector (ptegu)

The ptegu sector contains emissions from EGUs in the 2014 point source inventory that could be matched 

to units found in the National Electric Energy Database System (NEEDS) v5.16 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 EGUs use different temporal profiles than other 

sources in the point sector and it is useful to segregate these emissions from the rest of the point sources 

to facilitate summaries of the data. Sources not matched to units found in NEEDS are placed into the 

pt_oilgas or ptnonipm sectors. For studies with future year cases, the sources in the ptegu sector are fully 

replaced with the emissions output from IPM. It is therefore 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 

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. 
21

For sources not matched to CEMS data, daily emissions were computed from the NEI annual emissions 

using average CEMS data profiles specific to fuel type, pollutant (i.e., NOx, SO2, and other), and IPM 

region. Note that pollutants other than NOx, SO2 are allocated based on heat 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. Sources identified as municipal waste combustors and co-generation units were 

temporally allocated using the same emissions for each hour of the year because these sources are 

assumed to operate at the same level continuously.

3.2.1.2 Point Oil and Gas 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 2014 oil and gas emissions can be found in Section 4.16 of the 2014NEIv1 TSD. 

The pt_oilgas sector includes emissions from offshore oil platforms.

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

NAICS NAICS description

2111 Oil and Gas Extraction 

4862 Pipeline Transportation of Natural Gas 

21111 Oil and Gas Extraction 

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

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 low-emitting EGUs not matched to the NEEDS 

database or to CEMS data are in the ptnonipm sector. 

The ptnonipm sector contains a small amount of fugitive dust PM emissions from vehicular traffic on 

paved or unpaved roads at industrial facilities, coal handling at coal mines, and grain elevators. Sources 

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.
22

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 ptagfire 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 2014 fires were developed using SMARTFIRE 2 

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

Hazard Mapping System (HMS) fire location information and for 2014 also uses state/local/tribal data 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). More information is available in the 

2014NEIv1 TSD.

A difference between the fires for this study and those in the NEI is that the proportion of emissions 

allocated to flaming versus smoldering SCCs were adjusted. Flaming fractions were calculated for each 

fire based on the flaming and smoldering consumption divided by the total consumption. Smoldering 

fractions were calculated by dividing the residual consumption by the total consumption. The fractions 

were then applied to the fire emissions in the 2014NEIv1 to obtained revised emissions for the flaming 

and smoldering SCCs. The total emissions by state were unchanged, but they were reapportioned to the 

flaming and smoldering SCCs to facilitate a more realistic plume rise for fires. Washington state 

emissions were adjusted according to emissions supplied by the state. Monthly factors of state data to NEI 

data by county, SCC, and pollutant were calculated then applied to the existing daily and annual 

emissions data in the FF10 for Washington state only.

Large fires of more than 20,000 acres in a single day were split using GeoMAC 

(https://www.geomac.gov/) fire shapes, where available, or otherwise using a circle centered on the detect 

lat/lon based on 12US2 grid cell overlap. The resulting split fires have emissions and area apportioned 

from the original fire into the grid cells based on fraction of area overlap between the fire shape and the 

cell. The idea is to prevent all of the emissions from a very large fire from going into a single grid cell, 

when in reality the fire emissions were more dispersed than a single point. 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. 

The SMOKE-ready 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). 
23

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

Several modeling platform sectors were created from the 2014NEIv1 nonpoint inventory. This section 

describes the stationary nonpoint sources. Locomotives, C1 and C2 CMV, and C3 CMV are also 

included the 2014NEIv1 nonpoint data category, but are mobile sources and are described in a later 

section. The 2014NEIv1 TSD includes documentation for the nonpoint data. 

The nonpoint tribal-submitted emissions are dropped during spatial processing with SMOKE due to the 

configuration of the spatial surrogates, which are available by county, but not at 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. 

In the rest of this section, each of the platform sectors into which the 2014 nonpoint NEI was divided is

described, along with any data that were updated or replaced with non-NEI data.

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.

Where states submitted afdust data, it was assumed that the state-submitted data were not met-adjusted 

and therefore the meteorological adjustments were applied. Thus, if states submitted data that were metadjusted, these sources would have been adjusted for meteorology twice. Even with that possibility, air 

quality modeling shows that, in general, dust is frequently overestimated in the air quality modeling 

results. 
24

3.2.3.2 Agricultural Ammonia Sector (ag)

The agricultural NH3 (ag) sector includes livestock emissions from the 2014NEIv2 nonpoint inventory 

and agricultural fertilizer application emissions from the 2014NEIv1 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 NH3 emissions, as there is a very small amount of NH3 emissions from livestock in the 

ptnonipm inventory (as point sources) in California (883 tons; less than 0.5 percent of state total) and 

Wisconsin (356 tons; about 1 percent of state total). 

The agricultural NH3 emissions in the NEI are a mix of state-submitted data and EPA estimates. For 

2014, the EPA estimates used new methodologies for both livestock and fertilizer emissions. Livestock 

emissions were estimated based on daily emission factors by animal and county from a model developed 

by Carnegie Mellon University (CMU) (Pinder, 2004, McQuilling, 2015) and 2012 and 2014 U.S. 

Department of Agriculture (USDA) agricultural census data. The annual 2014NEIv1 estimates for 

livestock were updated with revised animal counts for 2014NEIv2 and the resulting 2014NEIv2 numbers 

for NH3 were used in this study. Although the 2014NEIv2 also includes VOC for livestock, those 

emission were not used in this study because the numbers had not yet been fully evaluated. For 

California, state-provided emissions were used for counties and SCCs for which state data were available, 

while any county-SCC combinations that used EPA data in 2014NEIv1 were updated to use the data from 

2014NEIv2. Double-counts between EPA and California data were removed where different SCCs for the 

same animal type (e.g., beef and swine) were used, with preference given to the California data. 

Annual fertilizer emissions were submitted by three states for all or part of the sector as shown in 

parentheses: California (68 percent), Illinois (100 percent) and Georgia (58 percent). The remainder, 

estimated by EPA, employed a methodology that uses the bidirectional (bi-di) version of CMAQ and the 

Fertilizer Emissions Scenario Tool for CMAQ FEST-C (v1.2). This is described in Section 4.4 of the 

2014 NEIv1 TSD. These data were used at annual resolution.

3.2.3.3 Agricultural fires (ptagfire)

In the NEI, agricultural fires are stored as county-annual emissions and are part of the nonpoint data 

category. For this study agricultural fires are modeled as day specific fires derived from satellite data in a 

similar way to the emissions in ptfire, and state-provided agricultural fire data in AZ, CA, FL, GA, HI, 

ID, IL, IN, IA, NJ, SC and WA are not used in this study. The first three levels of descriptions for the 

agricultural burning SCCs are: 1) Fires - Agricultural Field Burning; Miscellaneous Area Sources; 

2) Agriculture Production - Crops - as nonpoint; and 3) 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 

specify field or orchard crops and, in some cases, the specific crop being grown. New agricultural field 

burning SCCs were added to the NEI for 2014 to account for grass/pasture burning (also known as 

rangeland burning) which is included the agriculture field burning sector of the NEI. The EPA’s 

estimation methods were improved from those used in the 2011 NEI and are documented in Section 4.11 

of the 2014NEIv1 TSD. Improvements include use of multiple satellite detection database and crop level 

land use information. 
25

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. The 

EPA estimated emissions for all counties with 2014 oil and gas activity data with the Oil and Gas Tool, 

and many S/L/T agencies also submitted nonpoint oil and gas data. Where S/L/T submitted nonpoint 

CAPS but no HAPs, the EPA augmented the HAPs using HAP augmentation factors (county and SCC 

level) created from the Oil and Gas Tool. 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. 

An error was discovered in the 2014NEIv1 for nonpoint oil and gas emissions of benzene, ethyl benzene, 

xylenes and toluene for four counties in the Uinta basin. To compensate for this, emissions from VOC 

species were genterated using basin specific speciation profiles instead of using the emissions from the 

inventory. The updates affected the following SCCs: 2310010200 (Oil Well Tanks - Flashing & 

Standing/Working/Breathing); 2310011201 (Tank Truck/Railcar Loading: Crude Oil); 2310021010 

(Storage Tanks: Condensate); and 2310021030 (Tank Truck/Railcar Loading: Condensate), and generally 

reduced these HAPs from the 2 condensate-related SCCs and increased benzene by a factor of 3 forthe 

two oil tank-related SCCs. Overall, the np_oilgas emissions in Utah in the platform are 31 tons lower for 

benzene, 29 tons lower for ethylbenzene, 213 tons lower for toluene, and 335 tons lower for xylenes than 

the 2014NEIv1.

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: 

1) conventional (not EPA certified); 2) EPA certified, catalytic; and 3) EPA certified, noncatalytic. 

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

have to meet EPA emission standards and they are either catalytic or non-catalytic. As with the other 

nonpoint categories, a mix of S/L and EPA estimates were used. The EPA’s estimates use updated 

methodologies for activity data and some changes to emission factors. For more information on the 

development of the residential wood combustion emissions, see Section 4.14 of the 2014NEIv1 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. Locomotives and CMV mobile sources from the 2014NEIv1 nonpoint 

inventory are described with the mobile sources. The types of sources in the nonpt sector include:

• stationary source fuel combustion, including industrial, commercial, and residential and orchard 

heaters; 

• 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; 
26

• 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;

• 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 computed based on the same 14j version of the 2014 meteorology data used for 

the air quality modeling, and were developed using the Biogenic Emission Inventory System version 3.61 

(BEIS3.61) within SMOKE. The 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. In the BEIS 3.61 two-layer 

canopy model, the layer structure varies with light intensity and solar zenith angle (Pouliot and Bash, 

2015). Both layers 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 

SLYTP soil texture type by USDA category

SOIM1 volumetric soil moisture in top cm 

SOIT1 soil temperature in top cm
27

Variable Description

TEMPG skin temperature at ground

USTAR cell averaged friction velocity

RADYNI inverse of aerodynamic resistance

TEMP2 temperature at 2 m

The BEIS3.61 was used in conjunction with Version 4.1 of the Biogenic Emissions Landuse Database 

(BELD4.1). The BELD version 4.1 is based on an updated version of the USDA-USFS Forest Inventory 

and Analysis (FIA) vegetation speciation based data from 2001 to 2014 from the FIA version 5.1. 

Canopy coverage is based on the Landsat satellite National Land Cover Database (NLCD) product from 

2011. 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 

2011 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. BELDv4.1 also incorporates the following:

• 30 meter NASA's Shuttle Radar Topography Mission (SRTM) elevation data 

(http://www2.jpl.nasa.gov/srtm/) to more accurately define the elevation ranges of the vegetation 

species than in previous versions; and 

• 2011 30 meter USDA Cropland Data Layer (CDL) data 

(http://www.nass.usda.gov/research/Cropland/Release/).

Biogenic emissions computed with BEIS version 3.61 were left out of the CMAQ-ready merged 

emissions, in favor of inline biogenics produced during the CMAQ model run itself.

3.2.5 Mobile Sources (onroad, onroad_ca_adj, nonroad, cmv_c1c2, cmv_c3, rail)

Mobile sources are emissions from vehicles that move and include several sectors. Onroad mobile source 

emissions result 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. Nonroad mobile source emissions are from vehicles that do not operate on roads such as tractors, 

construction equipment, lawnmowers, and recreational marine vessels. All nonroad emissions are treated 

as county-specific low-level emissions (i.e., they are released into model layer 1). 

Commercial marine vessel (CMV) emissions are split into two sectors: emissions from Category 1 and 

Category 2 vessels are in the cmv_c1c2 sector, and emissions from the larger Category 3 vessels are in the 

cmv_c3 sector where they are treated as point sources with plume rise. Locomotive emissions are in the 

rail sector. Having the emissions split into these sectors facilitates separating them in summaries and also 

allows for the largest vessels to be modeled with plume rise. 

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, gasoline, E-85, and 

compressed natural gas (CNG) vehicles. The sector characterizes emissions from parked vehicle 

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

vehicles moving along the roads).
28

The onroad SCCs in the modeling platform are more finely resolved than those in the NEI, because the 

NEI SCCs distinguish vehicles and fuels, but in the platform they also distinguish between emissions on 

roadways, off-network, extended idle, and the various MOVES road-types. For more details on the 

approach and for a summary of the inputs submitted by states, see the section 6.4.1 of the 2014NEIv1 

TSD. 

Except for California, onroad emissions are generated using the SMOKE-MOVES interface that leverages 

MOVES generated emission factors (http://www.epa.gov/otaq/models/moves/index.htm), county and 

SCC-specific activity data, and hourly meteorological data. SMOKE-MOVES takes into account the 

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

representative counties, vehicle miles traveled (VMT), vehicle population (VPOP), and hoteling hours 

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 data available from meteorological 

modeling that are also used for air quality modeling. 

SMOKE-MOVES makes use of emission rate “lookup” tables generated by MOVES that 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 2014-specific emission factors by temperature and 

speed for a series of “representative counties,” to which every other county was mapped. The 

representative counties for which emission factors are generated are selected according to their state, 

elevation, fuels, age distribution, ramp fraction, and inspection and maintenance programs. Each county 

is then mapped to a representative county based on its similarity to the representative county with respect 

to those attributes. For this study, there are 297 representative counties. A detailed discussion of the 

representative counties is in the 2014NEIv1 TSD, Section 6.6.2.

Once representative counties have been identified, emission factors are generated with MOVES for each 

representative county and for two “fuel months” – January to represent winter months, and July to 

represent summer months – due to the different types of fuels used. SMOKE selects the appropriate 

MOVES emissions rates for each county, hourly temperature, SCC, and speed bin and multiplies the 

emission rate by appropriate activity data. For on-roadway emissions, vehicle miles travelled (VMT) is 

the activity data, vehicle population (VPOP) is used for many off-network processes, and hoteling hours 

are used to develop emissions for extended idling of combination long-haul trucks. These calculations are 

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 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.
29

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.

The onroad emissions are processed in four processing streams that are merged together into the onroad 

sector emissions after each of the four streams have been processed: 

• rate-per-distance (RPD) uses VMT as the activity data plus speed and speed profile information to 

compute on-network emissions from exhaust, evaporative, permeation, refueling, and brake and 

tire wear processes;

• rate-per-vehicle (RPV) uses VPOP activity data to compute off-network emissions from exhaust, 

evaporative, permeation, and refueling processes; 

• rate-per-profile (RPP) uses VPOP activity data to compute off-network emissions from 

evaporative fuel vapor venting, including hot soak (immediately after a trip) and diurnal (vehicle 

parked for a long period) emissions; and

• rate-per-hour (RPH) uses hoteling hours activity data to compute off-network emissions for idling 

of long-haul trucks from extended idling and auxiliary power unit process.

The onroad emissions inputs for the platform are the same as for the emissions in the onroad data category 

of the 2014NEIv1, described in more detail in Sections 6.4 and 6.5 of the 2014NEIv1 TSD. These inputs 

are:

• MOVES County databases (CDBs) including Low Emission Vehicle (LEV) table 

• Representative counties 

• Fuel months

• Meteorology

• Activity data (VMT, VPOP, speed, HOTELING)

An additional step was taken for the refueling emissions. Colorado submitted point emissions for 

refueling for some counties7. For these counties, the EPA zeroed out the onroad estimates of refueling 

(i.e., SCCs =220xxxxx62) so that the states’ point emissions would take precedence. The onroad 

refueling emissions were zeroed out using the adjustment factor file (CFPRO) and Movesmrg.

For more detailed information on the methods used to develop the 2014 onroad mobile source emissions 

and the input data sets, see Section 6.6 of the 2014NEIv1 TSD.

California is the only state agency for which submitted onroad emissions were used in the 2014 NEI v1 

and 2014v7.0 platform. California uses their own emission model, EMFAC, which uses emission 

inventory codes (EICs) to characterize the emission processes instead of SCCs. The EPA and California 

worked together to develop a code mapping to better match EMFAC’s EICs to EPA MOVES’ detailed set 

of SCCs that distinguish between off-network and on-network and brake and tire wear emissions. This 

detail is needed for modeling but not for the NEI. This code mapping is provided in 

“2014v1_EICtoEPA_SCCmapping.xlsx.” California then provided their CAP and HAP emissions by 

county using EPA SCCs after applying the mapping. There was one change made after the mapping: the 

vehicle/fuel type combination gas intercity buses (first 6 digits of the SCC = 220141), that is not 

 

7 There were 52 counties in Colorado that had point emissions for refueling. Outside Colorado, it was determined that 

refueling emissions in the 2014 NEIv1 point did not significantly duplicate the refueling emissions in onroad.
30

generated using MOVES, was changed to gasoline single unit short-haul trucks (220152) for consistency 

with the modeling inventory. 

The California onroad mobile source emissions were created through a hybrid approach of combining 

state-supplied annual emissions with EPA-developed SMOKE-MOVES runs. Through this approach, the 

platform was able to reflect the unique rules in California, while leveraging the more detailed SCCs and 

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

steps involved in temporally allocating onroad emissions from California based on SMOKE-MOVES 

results were:

1) Run CA using EPA inputs through SMOKE-MOVES to produce hourly 2014 emissions hereafter 

known as “EPA estimates.” These EPA estimates for CA are run in a separate sector called 

“onroad_ca.”

2) Calculate ratios between state-supplied emissions and EPA estimates. These were calculated for 

each county/SCC/pollutant combination. Unlike in previous platforms, the California data 

separated off and on-network emissions and extended idling. However, the on-network did not 

provide specific road types, and California’s emissions did not include information for vehicles 

fueled by E-85, so these differentiations were obtained using MOVES.

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

4) Rerun CA through SMOKE-MOVES using EPA inputs and the new adjustment factor file.

Through this process, adjusted model-ready files were created that sum to annual totals from California, 

but have the temporal and spatial patterns reflecting the highly resolved meteorology and SMOKEMOVES. After adjusting the emissions, this sector is called “onroad_ca_adj.” Note that in emission 

summaries, the emissions from the “onroad” and “onroad_ca_adj” sectors are summed and designated as 

the emissions for the onroad sector.

3.2.5.2 MOVES-based Nonroad Mobile Sources (nonroad)

The nonroad equipment emissions in the platform and the NEI result primarily from running the 

MOVES2014a model, which incorporates the NONROAD2008 model. MOVES2014a replaces NMIM, 

which was used for 2011 and earlier NEIs. MOVES2014a provides a complete set of HAPs and 

incorporates updated nonroad emission factors for HAPs. MOVES2014a was used for all states other 

than California, which uses their own model. Additional details on the development of the 2014NEIv1 

nonroad emissions are available in Section 4.5 the 2014NEIv1 TSD. 

The magnitude of the annual emissions in the nonroad platform are equivalent to the emissions in the 

nonroad data category of the 2014NEIv1. However, the platform has monthly emission totals, which are 

provided by MOVES2014a and contain additional pollutants used in the emissions modeling. The 

emissions in the modeling platform include NONHAPTOG and ETHANOL, which are not included in 

the NEI. NONHAPTOG is the difference between total organic gases (TOG) and explicit species that are 

estimated separately such as benzene, toluene, styrene, ethanol, and numerous other compounds and are 

integrated into the chemical speciation process. MOVES2014a provides estimates of NONHAPTOG 

along with the speciation profile code for the NONHAPTOG emission source. This is accomplished by 

using NONHAPTOG#### as the pollutant code in the FF10 inventory file, where #### is a speciation 

profile code. Since speciation profiles are applied by SCC and pollutant, no changes to SMOKE were 

needed in order to use the FF10 with this profile information. This approach is not used for California, 
31

because their model provides VOC and traditional speciation is performed in SMOKE instead.

Nonroad emissions for California submitted to NEI were developed using the California Emissions 

Projection Analysis Model (CEPAM) that supports various California off-road regulations. 

Documentation of the CARB offroad mobile methodology, including CMV sector data, is provided at: 

http://www.arb.ca.gov/msei/categories.htm#offroad_motor_vehicles. The CARB-supplied nonroad annual 

inventory emissions values were temporalized to monthly values using monthly temporal profiles applied 

in SMOKE by SCC. Some VOC emissions were added to California to account for situations when VOC 

HAP emissions were included in the inventory, but VOC emissions were either less than the sum of the 

VOC HAP emissions, or were missing entirely. These additional VOC emissions were computed by 

summing benzene, acetaldehyde, formaldehyde, and naphthalene for the specific sources. 

3.2.5.3 Locomotive (rail)

The rail sector includes all locomotives in the NEI nonpoint data category. This sector excludes railway 

maintenance locomotives and point source yard locomotives. Railway maintenance emissions are 

included in the nonroad sector. The point source yard locomotives are included in the ptnonipm sector. 

The nonpoint rail data are a mix of S/L and EPA data. For 2014NEIv1, the EPA data were carried 

forward from the 2011 NEI. For more information on locomotive sources in the NEI, see Section 4.20 of 

the 2014NEIv1 TSD. 

3.2.5.4 Category 1, 2, and 3 commercial marine vessels (cmv_c1c2 and cmv_3)

The cmv_c1c2 sector contains Category 1 and 2 CMV emissions from the 2014 NEIv1. Category 1 and 2 

vessels use diesel fuel. All emissions in this sector are annual and at county-SCC resolution; however, in 

the NEI they are provided at the sub-county level (port or underway shape ids) and by SCC and emission 

type (e.g., hoteling, maneuvering). This sub-county data in the NEI are used to create spatial surrogates. 

The emissions for the CMV sector are equivalent to those in the 2014NEIv1 nonpoint inventory. For more 

information on CMV sources in the NEI, see Section 4.3 of the 2014NEIv1 TSD. C1 and C2 emissions 

that occur outside of state waters are not assigned to states. All CMV emissions in the cmv_c1c2 sector 

are treated as nonpoint sources, and are placed in layer 1 and allocated to grid cells using spatial 

surrogates. 

The Category 3 CMV vessels in the cmv_c3 sector use residual oil. The cmv_c3 sector uses 2014NEIv1 

emissions in state waters but excludes NEI C3 emissions in Federal Waters (FIPS codes beginning with 

85). Instead, more spatially resolved emissions from the Emissions Control Area-International Marine 

Organization (ECA-IMO)-based C3 CMV are used. The C3 CMV emissions are treated as point sources 

and were developed based on a 4-km resolution ASCII raster format dataset that preserves shipping lanes 

and extends within and beyond the federal waters. The treatment of these emissions as point sources 

allows for them to have plume rise when modeled by SMOKE and CMAQ. 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 C3 portion of the CMV inventory includes these ships in several intraport modes (i.e., cruising, hoteling, reduced speed zone, maneuvering, and idling) and an underway mode, 

and includes near-port auxiliary engine emissions. 
32

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 31. 

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

emissions processing.

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

The 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 

province-level FIPS codes and paired those to region classifications for British Columbia (North Pacific), 

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

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. Note that the year 2011 emissions were used for this 2014 study.
33

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 2011 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 all CMV emissions in the Midwest RPO are classified 

as C1 or C2 sources in the CMV inventory.

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 2014 emissions were scaled to match those provided by CARB for the 

2014NEIv1. Note that 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 

CARB include the 2014 regulations to reduce emissions from diesel engines on commercial harbor craft 

operated within California waters and 24 nautical miles of the California shoreline. 

3.2.6 Emissions from Canada, Mexico (othpt, othar, othafdust, onroad_can, onroad_mex, 

ptfire_mxca)

The emissions from Canada, Mexico, and non-U.S. offshore Category 3 CMV (C3 CMV) and drilling 

platforms are included as part of the emissions modeling sectors: othpt, othar, othafdust, onroad_can, and 

onroad_mex. 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, “afdust” for area fugitive dust (Canada only). The onroad 

emissions for Canada and Mexico are in the onroad_can and onroad_mex sectors, respectively.

For Canadian point sources, 2013 emissions provided by Environment Canada were used. These included 

VOC emissions and CB6 speciation for VOCs although the CB6 VOCs differed slightly from the version 

of CB6 in CMAQ. Airport emissions were provided by month. Temporal profiles were provided for all 

source categories. Point sources in Mexico were compiled based on a year 2014 inventory projected from 

the the Inventario Nacional de Emisiones de Mexico, 2008 (ERG, 2014a; ERG, 2016a). The point source 

emissions in the 2014 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.
34

For Canadian area and nonroad sources, year-2013 emissions provided by Environment Canada were 

used, including 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 

this, a separate sector called othafdust was created and modeled using the same adjustments as are done 

for U.S. sources. Updated Shapefiles used for creating spatial surrogates for Canada were also provided. 

For Canada nonroad mobile sources, the provided 2013 monthly emissions were used.

For Canada onroad emissions, month-specific year-2013 emissions provided by Environment Canada 

were used. Note that unlike the U.S. and Mexico inventories, there are no explicit HAPs in the onroad 

inventories for Canada and, therefore, NBAFM HAPs are created from speciation. For Mexico onroad 

emissiosn, a version of the MOVES model for Mexico was run that provided the same VOC HAPs and 

speciated VOCs as for the U.S. MOVES model (ERG, 2016a). This includes NBAFM plus several other 

VOC HAPs such as toluene, xylene, ethylbenzene and others. Except for VOC HAPs that are part of the 

speciation, no other HAPs are included in the Mexico onroad inventory (such as particulate HAPs nor 

diesel particulate matter).

Annual 2014 wildland emissions for Mexico and Canada are in the ptfire_mxca sector. They were 

developed from a combination of Fire Inventory from NCAR (FINN) daily fire emissions and fire data 

provided by Environment Canada for the months of June through November and FINN fire emissions 

were used to fill in the annual gaps from January through May and December. The FINN fire emissions 

come from an updated FINN v1.5 data set that differs from previous 2014 platforms largely in the 

inclusion of grassland and cropland coverage. Only CAP emissions are provided in the Canada and 

Mexico fire inventories. For FINN fires, listed vegetation type codes of 1 and 9 are defined as agricultural 

burning, all other fire detections and assumed to be wildfires. All wildland fires that are not defined as 

agricultural are assumed to be wild fires rather than prescribed. FINN fire detects less than 50 square 

meters (0.012 acres) are removed from the inventory. The locations of FINN fires are geocoded from 

latitude and longitude to FIPS code.

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 

The CMAQ model requires hourly emissions of specific gas and particle species for the horizontal and 

vertical grid cells contained within the modeled region (i.e., modeling domain). To provide emissions in 

the form and format required by the model, it is necessary to “pre-process” the “raw” emissions (i.e., 

emissions input to SMOKE) for the sectors described above. In brief, the process of emissions modeling 

transforms the emissions inventories from their original temporal resolution, pollutant resolution, and 

spatial resolution into the hourly, speciated, gridded resolution required by the air quality model. 

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 

layers of the sources are not included. 
35

As previously discussed, the temporal resolutions of the emissions inventories input to SMOKE vary 

across sectors and may be hourly, daily, monthly, or annual total emissions. The spatial resolution, may 

be individual point sources, county/province/municipio totals, or gridded emissions and varies by sector. 

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 4.5 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.

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.

Table 3-6. Key emissions modeling steps by sector

Platform sector Spatial Speciation

Inventory

resolution Plume rise

afdust_adj Surrogates Yes annual

ag Surrogates Yes annual 

beis Pre-gridded 

land use in BEIS computed hourly

cmv_c1c2 Surrogates Yes annual

cmv_c3 Point Yes annual in-line

nonpt Surrogates &

area-to-point Yes annual

nonroad Surrogates &

area-to-point Yes monthly
36

Platform sector Spatial Speciation

Inventory

resolution Plume rise

np_oilgas Surrogates Yes annual

onroad Surrogates Yes monthly activity, 

computed hourly

onroad_ca_adj Surrogates Yes monthly activity, 

computed hourly

onroad_can Surrogates Yes Monthly

onroad_mex Surrogates Yes Monthly

othafdust Surrogates Yes Annual

othar Surrogates Yes annual & 

monthly

othpt Point Yes annual & 

monthly

in-line

ptagfire Point Yes Daily none

pt_oilgas Point Yes Annual in-line

ptegu Point Yes daily & hourly in-line

ptfire Point Yes Daily in-line

ptfire_mxca Point Yes Daily in-line

ptnonipm Point Yes Annual in-line

rail Surrogates Yes Annual

rwc Surrogates Yes Annual

Biogenic emissions can be modeled two different ways in the CMAQ model. The BEIS model in SMOKE 

can produce gridded biogenic emissions that are then included in the gridded CMAQ-ready emissions 

inputs, or alternatively, CMAQ can be configured to create “in-line” biogenic emissions within CMAQ 

itself. For this study, the in-line biogenic emissions option was used, and so biogenic emissions from 

BEIS were not included in the gridded CMAQ-ready emissions.

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 cmv_c3, othpt, ptfire, and 

ptfire_mxca sectors have only “in-line” 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. For the ptagfire sector, all emissions were allocated to layer 1 and 

output to gridded 2-D emissions files.

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

plume rise. For the 2014 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, 
37

thereby changing the parameters of one or more sources. The most straightforward way to get the same 

results between in-line and offline is to avoid the use of grouping.

3.3.3 Spatial Configuration

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

domain (12US1) shown in Figure 3-2 and boundary conditions were obtained from a 2014 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
38

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 (i.e., “explicit species”) or groups of species (i.e., “lumped species”). The chemical 

mechanism used for the 2014 platform is the CB6 mechanism (Yarwood, 2010). We used a particular 

version of CB6 that we refer to as “CMAQ CB6” that breaks out naphthalene from XYL as an explicit 

model species, resulting in model species NAPH and XYLMN instead of XYL and uses SOAALK. This 

platform generates the PM2.5 model species associated with the CMAQ Aerosol Module version 6 (AE6). 

Table 3-7 lists the model species produced by SMOKE in the platform used for this study.
39

Table 3-7. Emission model species produced for CB6 for CMAQ

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 ACET Acetone

ALD2 Acetaldehyde

ALDX Propionaldehyde and higher aldehydes

BENZ Benzene (not part of CB05)

CH4 Methane

ETH Ethene

ETHA Ethane

ETHY Ethyne

ETOH Ethanol

FORM Formaldehyde

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

ISOP Isoprene

KET Ketone Groups

MEOH Methanol

NAPH Naphthalene

NVOL Non-volatile compounds

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

PAR Paraffin carbon bond

PRPA Propane

SESQ Sequiterpenes (from biogenics only)

SOAALK Secondary Organic Aerosol (SOA) tracer

TERP Terpenes (from biogenics only)

TOL Toluene and other monoalkyl aromatics

UNR Unreactive 

XYLMN Xylene and other polyalkyl aromatics, minus 

naphthalene

Naphthalene NAPH Naphthalene from inventory

Benzene BENZ Benzene from the inventory

Acetaldehyde ALD2 Acetaldehyde from inventory

Formaldehyde FORM Formaldehyde from inventory

Methanol MEOH Methanol from inventory

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

PAL Aluminum
40

Inventory Pollutant Model Species Model species description

PCA Calcium

PCL Chloride

PFE Iron

PK Potassium

PH2O Water

PMG Magnesium

PMN Manganese

PMOTHR PM2.5 not in other AE6 species

PNA Sodium

PNCOM Non-carbon organic matter

PNH4 Ammonium

PSI Silica

PTI Titanium

Sea-salt species (non –

anthropogenic) 8

PCL Particulate chloride

PNA Particulate sodium

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

developed from the SPECIATE 4.5 database (https://www.epa.gov/air-emissions-modeling/speciateversion-45-through-40), which is the EPA's repository of TOG and PM speciation profiles of air pollution 

sources. The SPECIATE database development and maintenance is a collaboration involving the EPA’s 

Office of Research and Development (ORD), Office of Transportation and Air Quality (OTAQ), and the 

Office of Air Quality Planning and Standards (OAQPS), in cooperation with Environment Canada (EPA, 

2016). 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. 

Some key features and updates to speciation from previous platforms include the following (the 

subsections below contain more details on the specific changes):

• VOC speciation profile cross reference assignments for point and nonpoint oil and gas sources 

were updated to (1) make corrections to cross references, (2) use new and revised profiles that 

were added to SPECIATE4.5 and (3) account for the portion of VOC estimated to come from 

flares, based on data from the Oil and Gas estimation tool used to estimate emissions for the NEI. 

The new/revised profiles included oil and gas operations in specific regions of the country and a 

national profile for natural gas flares;

• Two new PM2.5 profiles from SPECIATE4.5 are used for brake and tirewear;

• speciation profiles developed by the Western Regional Air Partnership (WRAP) are used for the 

np_oilgas sector were revised;

• VOC speciation for nonroad mobile has been updated to include a different speciation profile 

assignment method for VOC (profiles are assigned to SCCs within MOVES2014a which outputs 

the emissions with those assignments) and updated profiles;

 

8 These emissions are created outside of SMOKE
41

• VOC and PM speciation for onroad mobile sources occurs within MOVES2014a;

• Speciation for onroad mobile sources in Mexico is done within MOVES and is more consistent 

with that used in the United States; and

• The 2013 Canadian point source inventories were provided from Environment Canada with CB6 

speciation.

Speciation profiles and cross-references for the 2014 platform are available in the SMOKE input files for 

the 2014 platform. Emissions of VOC and PM2.5 emissions by county, sector and profile for all sectors 

other than onroad mobile can be found in the sector summaries for the case. Totals of each model species 

by state and sector can be found in the Appendix B state-sector totals workbook for this case. 

The speciation of VOC includes HAP emissions from the 2014NEIv1 in the speciation process. Instead 

of speciating VOC to generate all of the species listed in Table 3-7, emissions of five specific HAPs: 

naphthalene, benzene, acetaldehyde, formaldehyde and methanol (collectively known as “NBAFM”) from 

the NEI were “integrated” with the NEI VOC. The integration 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 the 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. The EPA believes that the HAP emissions in the NEI are often more representative of 

emissions than HAP emissions generated via VOC speciation, although this varies by sector.

The NBAFM HAPs were chosen for integration because they are the only explicit VOC HAPs in the 

CMAQ version 5.2. Explicit means that they are not lumped chemical groups like PAR, IOLE and 

several other CB6 model species. These “explicit VOC HAPs” are model species that participate in the 

modeled chemistry using the CB6 chemical mechanism. The use of inventory HAP emissions along with 

VOC is called “HAP-CAP integration.” 

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. This is done by setting the “VOC or TOG component” 

field to “V” for all HAP pollutants chosen for integration. SMOKE allows the user to also choose 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-to-NONHAPTOG 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 NHAPEXCLUDE file. If, on the other hand, certain sources do not have 

 

9 Since SMOKE version 3.7, 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 NBAFM 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 NBAFM.
42

the necessary HAPs, then an NHAPEXCLUDE file must be provided based on the evaluation of each 

source’s pollutant mix. The EPA considered CAP-HAP integration for all sectors in determining whether 

sectors would have full, no or partial integration (see Figure 3-3). For sectors with partial integration, all 

sources are integrated other than those that have either the sum of NBAFM > VOC or the sum of 

NBAFM = 0. 

In this platform, we create NBAFM species from the no-integrate source VOC emissions using speciation 

profiles. Figure 3-3 illustrates the integrate and no-integrate processes for U.S. Sources. Since Canada 

and Mexico inventories do not contain HAPs, we use the 2011v6.3 approach of generating the HAPs via 

speciation, except for Mexico onroad mobile sources where emissions for integrate HAPs were available.

It should be noted that even though NBAFM were removed from the SPECIATE profiles used to create 

the GSPRO for both the NONHAPTOG and no-integrate TOG profiles, there still may be small fractions 

for “BENZ”, “FORM”, “ALD2”, and “MEOH” present. This is because these model species may have 

come from species in SPECIATE that are mixtures. The quantity of these model species is expected to be 

very small compared to the BAFM in the NEI. These profiles are listed in Appendix E. There are no 

NONHAPTOG profiles that produce “NAPH.”

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

INVTABLE files are used for 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 NBAFM pollutants. Thus, any NBAFM 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 NBAFM 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. Note for the onroad 

sector, “full integration” includes the integration of benzene, 1,3 butadiene, formaldehyde, acetaldehyde, 

naphthalene, acrolein, ethyl benzene, 2,2,4-Trimethylpentane, hexane, propionaldehyde, styrene, toluene, 

xylene, and MTBE.
43

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

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

each platform sector

Platform 

Sector

Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B), 

Acetaldehyde (A), Formaldehyde (F) and Methanol (M)

ptegu No integration, create NBAFM from VOC speciation 

ptnonipm No integration, create NBAFM from VOC speciation 

ptfire No integration, create NBAFM from VOC speciation

ptfire_mxca No integration, create NBAFM from VOC speciation

ptagfire No integration, create NBAFM from VOC speciation

ag N/A – sector contains no VOC 

afdust N/A – sector contains no VOC

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

cmv_c1c2 Full integration (NBAFM)

cmv_c3 Full integration (NBAFM)

rail Partial integration (NBAFM)

nonpt Partial integration (NBAFM)

nonroad Full integration (NBAFM in California, internal to MOVES elsewhere) 

np_oilgas Partial integration (NBAFM)

othpt No integration, create NBAFM from VOC speciation

pt_oilgas No integration, create NBAFM from VOC speciation

rwc Partial integration (NBAFM)

onroad Full integration (internal to MOVES)

onroad_can No integration, no NBAFM in inventory, create NBAFM from speciation 

onroad_mex Full integration (internal to MOVES-Mexico); however, MOVES-MEXICO speciation 
44

Platform 

Sector

Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B), 

Acetaldehyde (A), Formaldehyde (F) and Methanol (M)

was CB6-CAMx, not CB6-CMAQ, so post-SMOKE emissions were converted to CB6-

CMAQ

othafdust N/A – sector contains no VOC

othar No integration, create NBAFM from VOC speciation

Integration for the mobile sources estimated from MOVES (onroad and nonroad sectors, other than for 

California) is done differently. Briefly there are three major differences: 1) for these sources integration 

is done using more than just NBAFM, 2) all sources from the MOVES model are integrated and 3) 

integration is done fully or partially within MOVES. For onroad mobile, speciation is done fully within 

MOVES2014a such that the MOVES model outputs emission factors for individual VOC model species 

along with the HAPs. This requires MOVES to be run for a specific chemical mechanism. MOVES was 

run for the CB6-CAMx mechanism rather than CB6-CMAQ, so post-SMOKE onroad emissions were 

converted to CB6-CMAQ. For nonroad mobile, speciation is partially done within MOVES such that it 

does not need to be run for a specific chemical mechanism. For nonroad, MOVES outputs emissions of 

HAPs and NONHAPTOG split by speciation profile. Taking into account that integrated species were 

subtracted out by MOVES already, the appropriate speciation profiles are then applied in SMOKE to get 

the VOC model species. HAP integration for nonroad uses the same additional HAPs and ethanol as for 

onroad. 

In previous platforms, the GSPRO_COMBO feature was used to speciate nonroad mobile and gasolinerelated 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. For the 2014v7.0 platform, GSPRO_COMBO is still used for nonroad 

sources in California and for certain gasoline-related stationary sources nationwide. The feature is also 

used to combine exhaust and evaporative profiles to use with Mexican nonroad sources, which do not 

include the mode in the SCC or pollutant. GSPRO_COMBO is no longer needed for nonroad sources in 

the US outside of California because nonroad emissions within MOVES have the speciation profiles built 

into the results, so there is no need to assign them via the GSREF or GSPRO_COMBO feature.

A new method to combine multiple profiles is available in SMOKE4.5. It allows multiple profiles to be 

combined by by pollutant, state and county (i.e., state/county FIPS code) and SCC. This was used 

specifically for the oil and gas sectors (pt_oilgas and np_oilgas) because SCCs include both controlled 

and uncontrolled oil and gas operations which use different profiles.

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

CB6 was “B10C6.” 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).

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), 
45

which are further subsets of PMFINE (see Table 3-9). The majority of the 2014 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, cmv_c1c2, cmv_c3, rail, onroad_can, onroad_mex 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 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 

 

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

Smoke – Simplified) used in nonpt.
46

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

Additional details on speciation for onroad, nonroad, and oil and gas sources, and new PM profiles used 

are discussed in the 2014v7.0 TSD (EPA, 2017a).

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 hours of 

each day. This process is typically done by applying temporal profiles to the inventories in this order: 

monthly, day of the week, and diurnal. 

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). 

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

beis Hourly n/a all Yes

cmv_c1c2 Annual Yes aveday aveday

cmv_c3 Annual Yes aveday aveday

nonpt Annual Yes week week Yes

nonroad Monthly mwdss Mwdss Yes
47

Platform sector 

short name

Inventory 

resolutions

Monthly 

profiles 

used?

Daily 

temporal 

approach

Merge 

processing 

approach

Process Holidays 

as separate days

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 & monthly yes week week

onroad_can Monthly week week

onroad_mex Monthly week week

othpt Annual & monthly yes mwdss mwdss

ptagfire Daily all all Yes

pt_oilgas Annual yes mwdss mwdss Yes

ptegu Daily & hourly all all Yes

ptnonipm Annual yes mwdss mwdss Yes

ptfire Daily all all Yes

ptfire_mxca Daily all all Yes

rail Annual yes aveday aveday

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 Table 3-11: 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, 2014, which is intended to mitigate the effects of initial condition concentrations. The ramp-up 

period was 10 days (December 22-31, 2013). For all anthropogenic sectors, emissions from December 

2014 were used to fill in surrogate emissions for the end of December 2013. In particular, December 

2014 emissions (representative days) were used for December 2013. For biogenic emissions, December 

2013 emissions were processed using 2013 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 
48

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 nonroad, onroad (for 

activity data), onroad_can, onroad_mex, othar, othpt, and ptegu. 

3.3.5.1 Standard Temporal Profiles

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

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

from day-specific to hourly emissions. 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. This diurnal profile was used for each 

day of the week for all agricultural burning emissions in all states. 

For the cmv_c1c2 and cmv_c3 sectors, emissions are allocated with flat day of week and flat hourly 

profiles. The C1 and C2 emissions are allocated with a flat monthly profile, except in the Great Lakes, 

where the profiles vary by month. C3 emissions are allocated with monthly profiles developed 

specifically for C3, including in the Great Lakes. 

For the rail sector, new monthly profiles were developed for the 2014 platform. Monthly temporalization 

for rail freight emissions is based on AAR Rail Traffic Data, Total Carloads and Intermodal, for 2014. 

For passenger trains, monthly temporalization is based on rail passenger miles data for 2014 from the 

Bureau of Transportation Statistics. Rail emissions are allocated with flat day of week profiles, and most 

emissions are allocated with flat hourly profiles.

For the ptfire sector, the inventories are in the daily point fire format, 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, the 

nonroad inventory is annual only, and monthly temporal profiles are applied in SMOKE. 

Diurnal, weekly, and monthly temporal profiles for aviation-related sources were updated in the 2014v7.0 

platform based on aviation metrics. Details on these new profiles are available in the 2014v7.0 TSD. 

Temporal profiles for small airports (i.e., non-commercial) do not have any emissions between 10pm and 

6am due to a lack of tower operations. Industrial processes that are not likely to shut down on Sundays 

such as those at cement plants are 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. 

For oil and gas sources, monthly profiles were applied based on monthly activity data computed from the 

data sources used to develop the 2014 NEI. The profiles were specific to each FIPS and SCC and were 

applied as part of the cross reference. For states that used non-standard SCCs not in the EPA data set, flat 

profiles were used. Many np_oilgas sources use profiles that represent 24 hours per day, 7 days a week.

For agricultural livestock, annual-to-month profiles were developed based on daily emissions data output 
49

from the CMU model by state and SCC. These profiles were used to temporally allocate 2014NEIv2 ag 

livestock emissions to monthly emissions, which are further temporally allocated to hours as described 

below in section 3.3.5.3. 

3.3.5.2 Temporal Profiles for EGUs

The 2014NEIv1 annual EGU emissions not matched to CEMS sources use region/fuel specific profiles 

based on average hourly emissions for the region and fuel. Peaking units were removed during the 

averaging to minimize the spikes generated by those units. The non-matched units are allocated to hourly 

emissions using the following 3-step methodology: annual value to month, month to day, and day to 

hour. 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 in the CEMS data. If 

the data were not measured at specific hours, and those values were found to be more than three 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 Figure 3-for an example). 

Winter and summer seasons are included in the development of the diurnal profiles as opposed to using 

data for the entire year because 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.

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. Note that for units matched to CEMS data, 

annual totals of their emissions may be different than the annual values in 2014NEIv1 because the CEMS 

data actually replaces the inventory data for the seasons in which the CEMS are operating. If a CEMSmatched 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.
50

Figure 3-4. Eliminating unmeasured spikes in CEMS data

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

For sources not matched to CEMS units, temporal profiles are calculated that are used by SMOKE to 

allocate the annual emissions to hourly values. For these units, the allocation of the inventory annual 
51

emissions to months is done using average fuel-specific annual-to-month factors generated for each of the 

64 IPM regions shown in in Figure 3-6. These factors are based on 2014 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 used when there were no CEMS units with the 

specified fuel in the region containing the unit. For both CEMS-matched units and units not matched to 

CEMS, NOX and SO2 CEMS data are used to allocate NOX and SO2 emissions to monthly emissions, 

respectively, while heat input data are used to allocate emissions of all other from monthly to daily 

emissions.

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 2014 CEMS heat data. Separate month-today 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 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., 2014 in this case).

Certain sources without CEMS data, such as specific municipal waste combustors (MWCs) and 

cogeneration facilities (cogens), were assigned a flat temporal profile by source. The emissions for these 

sources have an equal value for each day of the year.
52

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

Figure 3-7. Month-to-day profiles for different fuels in a West Texas Region
53

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 2014 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 

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.

As of the 2011v6.2 platform and in SMOKE 3.6.5, the temporal profile format was updated to support 

more flexibility in profile application. The corresponding version of GenTPRO produces separate files 

including the monthly temporal profiles (ATPRO_MONTHLY) and day-of-month temporal profiles 

(ATPRO_DAILY), instead of a single ATPRO_DAILY with day-of-year temporal profiles as it did in 

SMOKE 3.5. The new and old temporal allocation results are equivalent when given the same inputs.

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.
54

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 

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.
55

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

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. (2014) 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
56

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.

Figure 3-11. Example of animal NH3 emissions temporalization approaches, 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 2014 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 
57

(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 2014 platform (and the 

2014NEIv1), 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 2014NEIv1 

TSD for more details).

Figure 3-12 illustrates the difference between temporalization of the onroad sector and the meteorological 

influence via SMOKE-MOVES. Similar temporalization is done for the VMT in SMOKE-MOVES, but 

the meteorologically varying emission factors add variation on top of the temporalization. 

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

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 annual for some sources and monthly for other sources, 

depending on the source of the data. Sources without monthly VMT were temporalized from annual to 

month through temporal profiles. VMT was also temporalized from month to day of the week, and then 

to hourly 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 emission factors. In other words, two SMOKE-MOVES runs with 

identical annual VMT, meteorology, and MOVES emission factors, will have different total emissions if 

the temporalization of VMT changes. For RPH, the HOTELING inventory is annual and was 

temporalized to month, day of the week, and 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.

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 
58

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 2014NEIv1 provided an opportunity to 

update these diurnal profiles with 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 week12 (see the 2011NEIv2 TSD for details on the submittal process for 

onroad). The EPA mined the state submitted MOVES CDBs for non-default diurnal profiles13. The list 

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

type, weekday versus weekend, and by county within a state. For the MOVES diurnal profiles, the 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). The 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. The EPA created distinct diurnal profiles for weekdays, Saturday and 

Sunday along with day of the week profiles14. In comparison to the temporal profiles from the 2011 

emissions modeling platform, the profiles for the 2014 platform include the same 2012 VTRIS data, but 

updated data from MOVES CDBs for 2014.

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

MOVES or VTRIS). Where there were 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 and, 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).

 

12 The MOVES tables are the hourvmtfraction and the dayvmtfraction.

13 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.

14 Note, the day of the week profiles (i.e., Monday vs Tuesday vs etc) are only from the VTRIS data. The MOVES CDBs only 

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

profile.
59

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.
60

Figure 314. Updated national default profiles for LDGV vs. HHDDV, urban restricted 

The gray lines of Figure 3-14 indicate the weekday profile, the blue the Saturday profile, and the orange 

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

national profiles was similar to 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.
61

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

bottom: urban restricted)
62

The EPA also developed a national profile for hoteling by averaging all the combination long-haul truck 

profiles on restricted roads (urban and rural) for weekdays to create a single national restricted profile 

(orange line in Figure 3-16). This was then inverted to create a profile for hoteling (blue 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 week15, and air 

basin. These CARB-specific profiles were used in developing EPA estimates for California. Although 

the EPA adjusted the total emissions to match California’s submittal to the 2014NEIv1, 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 2014v7.0 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., ptegu, ptnonipm, ptfire, ptagfire, othpt, and

cmv_c3). The in-line plume rise computed within CMAQ is nearly identical to the plume rise that would 

be calculated within SMOKE using the Laypoint program. The selection of point sources for plume rise is

pre-determined in SMOKE using the Elevpoint program. 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 sources 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)

 

15 California’s diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles and 

Tuesday, Wednesday, Thursday had the same profile.
63

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 2010-2014 data 

wherever possible. For Mexico, updated spatial surrogates were used as described below. For Canada, 

shapefiles for generating new surrogates were provided by Environment Canada for use with their 2013 

inventories. The U.S., Mexican, and Canadian 12-km surrogates cover the entire CONUS domain 12US1 

shown in Figure 3-2. 

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. Note that 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 2014v7.0 

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 were updated or newly developed for use in the 2014v7.0 platform (Adelman, 2016). 

They include the use of the 2011 National Land Cover Database (the previous platform used 2006) and 

development of various development density levels such as open, low, medium high and various 

combinations of these. These landuse surrogates largely replaced the FEMA category surrogates that 

were used in the 2011 platform. Additionally, onroad surrogates were developed using average annual 

daily traffic counts from the highway monitoring performance system (HPMS). Previously, the “activity” 

for the onroad surrogates was length of road miles. This and other surrogates are described in the 

reference Adelman, 2016.

Similar to 2011, the Surrogates for ports (801) and shipping lanes (802) were developed based on the 

shapes in the NEI; however they were updated using 2014NEIv1 shapefiles and activity data. The 

creation of surrogates and shapefiles for the U.S. was generated via the Surrogate Tool. The tool and 

documentation for it is available at https://www.cmascenter.org/satools/documentation/4.2/SurrogateToolUserGuide_4_2.pdf. 
64

Table 3-12. U.S. Surrogates available for the 2014 modeling platform

Code Surrogate Description Code Surrogate Description

N/A Area-to-point approach (see 3.3.1.2) 505 Industrial Land

100 Population 506 Education

110 Housing 507 Heavy Light Construction Industrial Land

131 urban Housing 510 Commercial plus Industrial

132 Suburban Housing 515 Commercial plus Institutional Land

134 Rural Housing 520 Commercial plus Industrial plus Institutional

137 Housing Change 525

Golf Courses plus Institutional plus 

Industrial plus Commercial

140 Housing Change and Population 526 Residential - Non-Institutional

150 Residential Heating - Natural Gas 527 Single Family Residential

160 Residential Heating - Wood 535

Residential + Commercial + Industrial + 

Institutional + Government

170 Residential Heating - Distillate Oil 540 Retail Trade (COM1)

180 Residential Heating - Coal 545 Personal Repair (COM3)

190 Residential Heating - LP Gas 555

Professional/Technical (COM4) plus General 

Government (GOV1)

201 Urban Restricted Road Miles 560 Hospital (COM6)

202 Urban Restricted AADT 575

Light and High Tech Industrial (IND2 + 

IND5)

205 Extended Idle Locations 580 Food Drug Chemical Industrial (IND3)

211 Rural Restricted Road Miles 585 Metals and Minerals Industrial (IND4)

212 Rural Restricted AADT 590 Heavy Industrial (IND1)

221 Urban Unrestricted Road Miles 595 Light Industrial (IND2)

222 Urban Unrestricted AADT 596 Industrial plus Institutional plus Hospitals

231 Rural Unrestricted Road Miles 650 Refineries and Tank Farms

232 Rural Unrestricted AADT 670 Spud Count - CBM Wells

239 Total Road AADT 671 Spud Count - Gas Wells

240 Total Road Miles 672 Gas Production at Oil Wells

241 Total Restricted Road Miles 673 Oil Production at CBM Wells

242 All Restricted AADT 674 Unconventional Well Completion Counts

243 Total Unrestricted Road Miles 676 Well Count - All Producing

244 All Unrestricted AADT 677 Well Count - All Exploratory

258 Intercity Bus Terminals 678 Completions at Gas Wells

259 Transit Bus Terminals 679 Completions at CBM Wells

260 Total Railroad Miles 681 Spud Count - Oil Wells

261 NTAD Total Railroad Density 683 Produced Water at All Wells

271 NTAD Class 1 2 3 Railroad Density 685 Completions at Oil Wells

272 NTAD Amtrak Railroad Density 686 Completions at All Wells

273 NTAD Commuter Railroad Density 687 Feet Drilled at All Wells

275 ERTAC Rail Yards 691 Well Counts - CBM Wells

280 Class 2 and 3 Railroad Miles 692 Spud Count - All Wells

300 NLCD Low Intensity Development 693 Well Count - All Wells

301 NLCD Med Intensity Development 694 Oil Production at Oil Wells

302 NLCD High Intensity Development 695 Well Count - Oil Wells

303 NLCD Open Space 696 Gas Production at Gas Wells

304 NLCD Open + Low 697 Oil Production at Gas Wells

305 NLCD Low + Med 698 Well Count - Gas Wells
65

Code Surrogate Description Code Surrogate Description

306 NLCD Med + High 699 Gas Production at CBM Wells

307 NLCD All Development 710 Airport Points

308 NLCD Low + Med + High 711 Airport Areas

309 NLCD Open + Low + Med 801 Port Areas

310 NLCD Total Agriculture 802 Shipping Lanes

318 NLCD Pasture Land 805 Offshore Shipping Area

319 NLCD Crop Land 806 Offshore Shipping NEI2014 Activity

320 NLCD Forest Land 807 Navigable Waterway Miles

321 NLCD Recreational Land 820 Ports NEI2014 Activity

340 NLCD Land 850 Golf Courses

350 NLCD Water 860 Mines

500 Commercial Land 890 Commercial Timber

For the onroad sector, the on-network (RPD) emissions were allocated differently from the off-network 

(RPP and RPV). On-network used average annual daily traffic (AADT) data and off network used land 

use surrogates as shown in Table 3-13. Extended (i.e., overnight) idling of trucks were assigned to 

surrogate 205 that is based on locations of overnight truck parking spaces. The underlying data in this 

surrogate was updated for use in the 2014v7.0 platform to include additional data sources and corrections 

based on comments received.

Table 3-13. Off-Network Mobile Source Surrogates

Source type Source Type name Surrogate ID Description

11 Motorcycle 307 NLCD All Development

21 Passenger Car 307 NLCD All Development

31 Passenger Truck 307 NLCD All Development

32 Light Commercial Truck 308

NLCD Low + Med + 

High

41 Intercity Bus 258 Intercity Bus Terminals

42 Transit Bus 259 Transit Bus Terminals

43 School Bus 506 Education

51 Refuse Truck 306 NLCD Med + High

52 Single Unit Short-haul Truck 306 NLCD Med + High

53 Single Unit Long-haul Truck 306 NLCD Med + High

54 Motor Home 304 NLCD Open + Low

61 Combination Short-haul Truck 306 NLCD Med + High

62 Combination Long-haul Truck 306 NLCD Med + High

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

Table 3-14 using 2014 data consistent with what was used to develop the 2014NEI nonpoint oil and gas 

emissions. The primary activity data source used for the development of the oil and gas spatial

surrogates was data from Drilling Info (DI) Desktop’s HPDI database (Drilling Info, 2015). This

database contains well-level location, production, and exploration statistics at the monthly level.

Due to a proprietary agreement with DI Desktop, individual well locations and ancillary

production cannot be made publicly available, but aggregated statistics are allowed. These data were 

supplemented with data from state Oil and Gas Commission (OGC) websites (Illinois, Idaho, Indiana, 
66

Kentucky, Missouri, Nevada, Oregon and Pennsylvania, Tennessee). In many cases, the correct surrogate 

parameter was not available (e.g., feet drilled), but an alternative surrogate parameter was available (e.g., 

number of spudded wells) and downloaded. Under that methodology, both completion date and date of 

first production from HPDI were used to identify wells completed during 2011. In total, over 1.43 million 

unique wells were compiled from the above data sources. The wells cover 34 states and 1,158 counties.

(ERG, 2016b).

Table 3-14. Spatial Surrogates for Oil and Gas Sources

Surrogate Code Surrogate Description

670 Spud Count - CBM Wells

671 Spud Count - Gas Wells

672 Gas Production at Oil Wells

673 Oil Production at CBM Wells

674 Unconventional Well Completion Counts

676 Well Count - All Producing

677 Well Count - All Exploratory

678 Completions at Gas Wells

679 Completions at CBM Wells

681 Spud Count - Oil Wells

683 Produced Water at All Wells

685 Completions at Oil Wells

686 Completions at All Wells

687 Feet Drilled at All Wells

691 Well Counts - CBM Wells

692 Spud Count - All Wells

693 Well Count - All Wells

694 Oil Production at Oil Wells

695 Well Count - Oil Wells

696 Gas Production at Gas Wells

697 Oil Production at Gas Wells

698 Well Count - Gas Wells

699 Gas Production at CBM Wells

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

some surrogates shown in Table 3-12. U.S. Surrogates available for the 2014 modeling platform 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. The U.S. CAP emissions allocated to 

the various spatial surrogates are shown in Table 3-15.
67

Table 315. Selected 2014 CAP emissions by sector for U.S. Surrogates (CONUS domain totals)

Sector ID Description NH3 NOX PM2_5 SO2 VOC

afdust 240 Total Road Miles 253,093

afdust 304 NLCD Open + Low 1,116,883

afdust 306 NLCD Med + High 45,958

afdust 308 NLCD Low + Med + High 139,554

afdust 310 NLCD Total Agriculture 1,169,400

ag 310 NLCD Total Agriculture 2,867,904

cmv_c1c2 801 Port Areas 11 24,413 759 1,492 987

cmv_c1c2 802 Shipping Lanes 281 484,726 13,842 4,129 8,725

nonpt 100 Population 32,222 0 0 0 1,137,409

nonpt 150 Residential Heating - Natural Gas 47,296 219,671 3,593 1,445 13,311

nonpt 170 Residential Heating - Distillate Oil 1,726 34,923 3,680 64,628 1,153

nonpt 180 Residential Heating - Coal 20 101 53 1,086 111

nonpt 190 Residential Heating - LP Gas 121 34,025 175 675 1,321

nonpt 239 Total Road AADT 0 25 552 0 276,354

nonpt 240 Total Road Miles 0 0 0 0 36,941

nonpt 242 All Restricted AADT 0 0 0 0 5,451

nonpt 244 All Unrestricted AADT 0 0 0 0 95,327

nonpt 271 NTAD Class 1 2 3 Railroad Density 0 0 0 0 2,252

nonpt 300 NLCD Low Intensity Development 5,183 24,399 107,748 2,982 76,167

nonpt 304 NLCD Open + Low 0 0 0 0 0

nonpt 306 NLCD Med + High 22,268 239,863 290,187 181,982 864,662

nonpt 307 NLCD All Development 24 53,320 144,940 16,485 611,569

nonpt 308 NLCD Low + Med + High 1,205 187,485 17,977 31,506 72,126

nonpt 310 NLCD Total Agriculture 0 0 37 0 242,713

nonpt 319 NLCD Crop Land 0 0 95 71 293

nonpt 320 NLCD Forest Land 3,984 13 54 0 61

nonpt 505 Industrial Land 0 0 0 0 174

nonpt 535

Residential + Commercial + Industrial + 

Institutional + Government 0 2 130 0 39

nonpt 560 Hospital (COM6) 0 0 0 0 0

nonpt 650 Refineries and Tank Farms 0 22 0 0 101,206

nonpt 711 Airport Areas 0 0 0 0 277

nonpt 801 Port Areas 0 0 0 0 7,862

nonroad 261 NTAD Total Railroad Density 3 2,593 273 4 503

nonroad 304 NLCD Open + Low 4 2,205 191 6 3,245

nonroad 305 NLCD Low + Med 110 23,017 4,557 146 149,863

nonroad 306 NLCD Med + High 345 243,170 15,750 526 126,354

nonroad 307 NLCD All Development 101 36,090 15,361 132 169,762

nonroad 308 NLCD Low + Med + High 673 458,488 38,060 886 69,386

nonroad 309 NLCD Open + Low + Med 111 22,350 1,257 148 44,500

nonroad 310 NLCD Total Agriculture 479 419,553 31,921 667 48,098
68

Sector ID Description NH3 NOX PM2_5 SO2 VOC

nonroad 320 NLCD Forest Land 19 8,900 1,377 25 8,628

nonroad 321 NLCD Recreational Land 157 20,841 15,119 229 553,747

nonroad 350 NLCD Water 215 144,088 8,855 361 448,425

nonroad 693 Well Count - All Wells 10 5,845 229 12 1,566

nonroad 850 Golf Courses 13 2,176 115 17 5,668

nonroad 860 Mines 2 2,760 298 4 549

np_oilgas 670 Spud Count - CBM Wells 0 0 0 0 267

np_oilgas 671 Spud Count - Gas Wells 0 0 0 0 10,989

np_oilgas 672 Gas Production at Oil Wells 0 2,863 0 21,709 127,494

np_oilgas 673 Oil Production at CBM Wells 0 35 0 0 1,795

np_oilgas 674 Unconventional Well Completion Counts 0 47,606 1,823 47 3,150

np_oilgas 678 Completions at Gas Wells 0 3,735 26 6,328 74,408

np_oilgas 679 Completions at CBM Wells 0 16 0 601 2,155

np_oilgas 681 Spud Count - Oil Wells 0 0 0 0 66,565

np_oilgas 683 Produced Water at All Wells 0 10 0 0 67,101

np_oilgas 685 Completions at Oil Wells 0 3,107 130 2,181 50,785

np_oilgas 687 Feet Drilled at All Wells 0 109,487 4,004 628 8,130

np_oilgas 691 Well Counts - CBM Wells 0 38,117 603 15 34,187

np_oilgas 692 Spud Count - All Wells 0 8,628 258 135 366

np_oilgas 693 Well Count - All Wells 0 0 0 0 166

np_oilgas 694 Oil Production at Oil Wells 0 4,375 0 5,468 1,104,120

np_oilgas 695 Well Count - Oil Wells 0 122,856 3,091 63 455,552

np_oilgas 696 Gas Production at Gas Wells 0 59,634 3,131 251 112,335

np_oilgas 697 Oil Production at Gas Wells 0 1,360 0 26 354,406

np_oilgas 698 Well Count - Gas Wells 15 388,677 6,726 310 623,925

np_oilgas 699 Gas Production at CBM Wells 0 3,094 403 32 6,578

onroad 202 Urban Restricted AADT 24,687 790,075 30,439 5,846 149,645

onroad 205 Extended Idle Locations 748 273,106 4,425 104 56,079

onroad 212 Rural Restricted AADT 10,867 684,006 20,322 2,853 77,075

onroad 222 Urban Unrestricted AADT 42,001 1,223,593 54,345 11,950 376,209

onroad 232 Rural Unrestricted AADT 25,027 987,683 33,882 6,434 201,764

onroad 239 Total Road AADT 6,573

onroad 242 All Restricted AADT 315

onroad 258 Intercity Bus Terminals 165 2 0 38

onroad 259 Transit Bus Terminals 58 5 0 171

onroad 304 NLCD Open + Low 821 22 1 2,683

onroad 306 NLCD Med + High 18,500 384 20 22,396

onroad 307 NLCD All Development 560,112 12,560 1,001 1,142,592

onroad 308 NLCD Low + Med + High 83,977 1,583 113 133,883

onroad 506 Education 664 29 1 1,107

rail 261 NTAD Total Railroad Density 2 12,494 297 282 736

rail 271 NTAD Class 1 2 3 Railroad Density 362 767,307 22,868 6,704 39,121
69

Sector ID Description NH3 NOX PM2_5 SO2 VOC

rwc 300 NLCD Low Intensity Development 16,221 32,174 332,700 8,087 351,696

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 

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 Canadian emissions have been updated in the platform 

used for this study based on the 2013 Canadian inventories and associated data. The spatial surrogate data 

came from Environment Canada, along with cross references. The shapefiles they provided were used in 

the Surrogate Tool (previously referenced) to create spatial surrogates. The Canadian surrogates used for 

this platform are listed in Table 3-16. 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. CAPs allocated to the Mexico and Canada surrogates are shown in Table 3-

17. The entries in Table 3-17 are for the othar, othafdust, onroad_can, and onroad_mex sectors.

Table 3-16. Canadian Spatial Surrogates 

Code Canadian Surrogate Description Code Description

100 Population 941 PAVED ROADS

101 total dwelling 942 UNPAVED ROADS

106 ALL_INDUST 945 Commercial Marine Vessels

113 Forestry and logging 950 Combination of Forest and Dwelling

115 Agriculture and forestry activities 955 UNPAVED_ROADS_AND_TRAILS

200 Urban Primary Road Miles 960 TOTBEEF

210 Rural Primary Road Miles 965 TOTBEEF_CD

212 Mining except oil and gas 966 TOTPOUL_CD

220 Urban Secondary Road Miles 967 TOTSWIN_CD

221 Total Mining 968 TOTFERT_CD

222 Utilities 970 TOTPOUL

230 Rural Secondary Road Miles 980 TOTSWIN

240 Total Road Miles 990 TOTFERT

308 Food manufacturing 996 urban_area

321 Wood product manufacturing 1211 Oil and Gas Extraction

323 Printing and related support activities 1212 OilSands

324

Petroleum and coal products 

manufacturing 1251 OFFR_TOTFERT

326 Plastics and rubber products 1252 OFFR_MINES
70

Code Canadian Surrogate Description Code Description

manufacturing

327

Non-metallic mineral product 

manufacturing 1253 OFFR Other Construction not Urban

331 Primary Metal Manufacturing 1254 OFFR Commercial Services

412 Petroleum product wholesaler-distributors 1255 OFFR Oil Sands Mines

416

Building material and supplies wholesalerdistributors 1256 OFFR Wood industries CANVEC

448 clothing and clothing accessories stores 1257 OFFR Unpaved Roads Rural

562

Waste management and remediation 

services 1258 OFFR_Utilities

921 Commercial Fuel Combustion 1259 OFFR total dwelling

923

TOTAL INSTITUTIONAL AND 

GOVERNEMNT 1260 OFFR_water

924 Primary Industry 1261 OFFR_ALL_INDUST

925 Manufacturing and Assembly 1262 OFFR Oil and Gas Extraction

926 Distribtution and Retail (no petroleum) 1263 OFFR_ALLROADS

927 Commercial Services 1264 OFFR_OTHERJET

931 OTHERJET 1265 OFFR_CANRAIL

932 CANRAIL

Table 3-17. CAPs Allocated to Mexican and Canadian Spatial Surrogates in 2014fb

Code

Mexican or Canadian Surrogate 

Description NH3 NOX PM 2_5 SO2 VOC

10 MEX Population 0 216 6 1 434

12 MEX Housing 25,900 112,801 3,949 450 141,616

14 MEX Residential Heating - Wood 0 1,311 16,802 202 115,512

16

MEX Residential Heating - Distillate 

Oil 0 13 0 4 1

20 MEX Residential Heating - LP Gas 0 5,798 176 0 100

22 MEX Total Road Miles 2,660 359,254 10,047 5,692 75,042

24 MEX Total Railroads Miles 0 21,176 473 186 826

26 MEX Total Agriculture 180,582 136,198 28,813 6,529 10,917

32 MEX Commercial Land 0 74 1,615 0 21,898

34 MEX Industrial Land 4 1,080 1,944 0 119,006

36

MEX Commercial plus Industrial 

Land 0 2,027 30 5 95,300

38

MEX Commercial plus Institutional 

Land 3 1,753 81 4 54

40

MEX Residential (RES1-

4)+Comercial+Industrial+Institutiona

l+Government 0 4 11 0 74,853

42 MEX Personal Repair (COM3) 0 0 0 0 5,704

44 MEX Airports Area 0 3,112 88 402 1,062
71

Code

Mexican or Canadian Surrogate 

Description NH3 NOX PM 2_5 SO2 VOC

50

MEX Mobile sources - Border 

Crossing 4 130 1 2 241

100 CAN Population 724 62 733 13 335

101 CAN total dwelling 383 34,234 2,538 4,126 143,234

106 CAN ALL_INDUST 0 0 11,559 0 72

113 CAN Forestry and logging 465 2,521 0 143 7,080

115

CAN Agriculture and forestry 

activities 51 609 2,941 13 1,709

200 CAN Urban Primary Road Miles 1,954 94,668 4,167 328 12,677

210 CAN Rural Primary Road Miles 779 57,206 2,297 134 5,457

212 CAN Mining except oil and gas 0 0 3,442 0 0

220 CAN Urban Secondary Road Miles 3,648 144,371 7,864 696 31,224

221 CAN Total Mining 0 0 56,438 0 0

222 CAN Utilities 79 9,371 54,184 3,299 197

230 CAN Rural Secondary Road Miles 2,024 99,071 4,275 353 14,291

240 CAN Total Road Miles 44 79,579 2,892 84 127,959

308 CAN Food manufacturing 0 0 11,099 0 5,873

321 CAN Wood product manufacturing 261 1,794 0 132 7,673

323

CAN Printing and related support 

activities 0 0 0 0 11,604

324

CAN Petroleum and coal products 

manufacturing 0 1,016 1,220 388 6,050

326

CAN Plastics and rubber products 

manufacturing 0 0 0 0 23,540

327

CAN Non-metallic mineral product 

manufacturing 0 0 6,628 0 0

331 CAN Primary Metal Manufacturing 0 156 5,504 52 73

412

CAN Petroleum product wholesalerdistributors 0 0 0 0 39,650

448

CAN clothing and clothing 

accessories stores 0 0 0 0 112

562

CAN Waste management and 

remediation services 217 1,631 2,268 2,275 16,066

921 CAN Commercial Fuel Combustion 185 23,982 2,251 3,760 1,154

923

CAN TOTAL INSTITUTIONAL 

AND GOVERNEMNT 0 0 0 0 13,707

924 CAN Primary Industry 0 0 0 0 35,196

925 CAN Manufacturing and Assembly 0 0 0 0 69,833

926

CAN Distribtution and Retail (no 

petroleum) 0 0 0 0 6,990

927 CAN Commercial Services 0 0 0 0 30,189

932 CAN CANRAIL 54 119,139 2,773 430 5,936

941 CAN PAVED ROADS 0 0 297,607 0 0

945 CAN Commercial Marine Vessels 187 152,803 5,579 34,596 11,045
72

Code

Mexican or Canadian Surrogate 

Description NH3 NOX PM 2_5 SO2 VOC

950

CAN Combination of Forest and 

Dwelling 1,733 19,252 158,532 2,750 224,453

955

CAN 

UNPAVED_ROADS_AND_TRAIL

S 0 0 440,673 0 0

960 CAN TOTBEEF 0 0 1,236 0 263,913

965 CAN TOTBEEF_CD 280,058 0 0 0 0

966 CAN TOTPOUL_CD 23,809 0 0 0 0

967 CAN TOTSWIN_CD 67,992 0 0 0 0

968 CAN TOTFERT_CD 120,304 0 0 0 0

970 CAN TOTPOUL 0 0 181 0 242

980 CAN TOTSWIN 0 0 756 0 2,585

990 CAN TOTFERT 0 4,227 379,893 9,448 155

996 CAN urban_area 0 0 1,265 0 0

1211 CAN Oil and Gas Extraction 2 29 228,599 152 922

1212 CAN OilSands 126 2,053 0 638 1,754

1251 CAN OFFR_TOTFERT 109 118,124 8,753 79 10,866

1252 CAN OFFR_MINES 42 41,444 3,443 31 4,175

1253

CAN OFFR Other Construction not 

Urban 26 23,606 3,885 20 9,504

1254 CAN OFFR Commercial Services 34 17,807 2,203 29 22,700

1255 CAN OFFR Oil Sands Mines 0 0 0 0 0

1256

CAN OFFR Wood industries 

CANVEC 13 11,553 1,103 10 1,921

1258 CAN OFFR_Utilities 16 8,553 529 14 10,136

1259 CAN OFFR total dwelling 17 5,399 1,409 14 34,499

1260 CAN OFFR_water 8 2,050 302 11 18,222

1261 CAN OFFR_ALL_INDUST 4 4,171 267 3 860

1262 CAN OFFR Oil and Gas Extraction 1 1,036 57 1 148

1263 CAN OFFR_ALLROADS 33 10,427 1,670 28 62,496

1264 CAN OFFR_OTHERJET 1 848 71 1 72

1265 CAN OFFR_CANRAIL 0 85 8 0 14
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. 

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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 system15. 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 CMAQ simulation performed for this 2014 assessment 

used a single domain that covers the entire continental U.S. (CONUS) and large portions of Canada and

 

15 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.
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.

16 With the

temporal flexibility of the model, simulations can be performed to evaluate longer term (annual to multiyear) 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 degradation. 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., 201217.

4.2 CMAQ Model Version, Inputs and Configuration 

This section describes the air quality modeling platform used for the 2014 CMAQ simulation. A modeling

platform is a structured system of connected modeling-related tools and data that provide a consistent and

transparent basis for assessing the air quality response to changes in emissions and/or meteorology. A

platform typically consists of a specific air quality model, emissions estimates, a set of meteorological

inputs, and estimates of “boundary conditions” representing pollutant transport from source areas outside

 

16 U.S. EPA (2014), Draft Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and 

Regional Haze, pp 214. https://www3.epa.gov/ttn/scram/guidance/guide/Draft_O3-PM-RH_Modeling_Guidance-2014.pdf . 

17 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. 
the region modeled. We used the CMAQ modeling system coupled to the Weather Research and 

Forecasting (WRF) meteorological model as part of the 2014 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 2014 CMAQ-WRF simulation 

along with the results of a model performance evaluation in which the 2014 model predictions are 

compared to corresponding measured ambient concentrations.

4.2.1 CMAQ 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 

2014 analysis employed CMAQ version 5.2

18 coupled to the Weather Research and Forecasting (WRF) 

version 3.8.1.

19 Two-way feedback was not utilized in this 2014 simulation, however, CMAQ read WRF 

meteorological data on a five-minute interval. The 2014 CMAQ-WRF run included bi-directional 

ammonia (NH3) air-surface exchange (v2.1) using the Massad formulation20, CB6r3 chemical mechanism, 

AERO6 aerosol module with non-volatile Primary Organic Aerosol (POA), and windblown dust 

algorithms. The CMAQ community model versions 5.0.2 and 5.1 were most recently peer-reviewed in 

September of 2015 for the U.S. EPA.21 

4.2.2 Model Domain and Grid Resolution

The WRF-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 2014 simulation used 

a Lambert Conformal map projection centered at (-97, 40) with true latitudes at 33 and 45 degrees north.

The 12 km WRF-CMAQ domain consisted of 459 by 299 grid cells and 35 vertical layers. Table 4-1 

provides some basic geographic information regarding the 12 km WRF-CMAQ domain. The model

extends vertically from the surface to 50 millibars (approximately 17,600 meters) using a sigma-pressure

coordinate system. Table 4-2 shows the vertical layer structure used in the 2014 simulation. Air quality 

conditions at the outer boundary of the 12-km domain were taken from a global model. 

 

18 CMAQ version 5.2 model code is available from the Community Modeling and Analysis System (CMAS) at: 

http://www.cmascenter.org.

19 Skamarock, W.C. and J.B. Klemp, 2008. A time-split nonhydrostatic atmospheric model for weather research and 

forecasting applications. Journal of Computation Physics, Volume 227, pp. 3465-3485.

20 Massad, R.-S., Nemitz, E., and Sutton, M.A. (2010). Review and parameterization of bi-directional ammonia exchange 

between vegetation and the atmosphere, Atmos. Chem. Phys., 10, 10359-10386, doi:10.5194/acp-10-10359-2010.

21 Moran, M.D., Astitha, M., Barsanti, K.C., Brown, N.J., Kaduwela, A., McKeen, S.A., Pickering, K.E. (September 28, 2015). 

Final Report: Fifth Peer Review of the CMAQ Model, https://www.epa.gov/sites/production/files/2016-

11/documents/cmaq_fifth_review_final_report_2015.pdf. This peer review was focused on CMAQ v5.0.2, which was released 

in May, 2014, as well as CMAQ v5.1, which was released in October 2015. It is available from the Community Modeling and 

Analysis System (CMAS) as well as previous peer-review reports at: http://www.cmascenter.org.
Table 4-1. Geographic Information for 12 km Modeling Domain

National 12 km WRF-CMAQ Modeling Configuration

Map Projection Lambert Conformal Projection

Grid Resolution 12 km

Coordinate Center 97 W, 40 N

True Latitudes 33 and 45 N

Dimensions 459 x 299 x 35

Vertical Extent 35 Layers: Surface to 50 mb level (see Table 4-2)

Table 4-2. Vertical layer structure for 2014 WRF-CMAQ simulation (heights are layer top).

Vertical 

Layers Sigma P Pressure (mb)

Approximate 

Height (m)

35 0.0000 50.00 17,556

34 0.0500 97.50 14,780

33 0.1000 145.00 12,822

32 0.1500 192.50 11,282

31 0.2000 240.00 10,002

30 0.2500 287.50 8,901

29 0.3000 335.00 7,932

28 0.3500 382.50 7,064

27 0.4000 430.00 6,275

26 0.4500 477.50 5,553

25 0.5000 525.00 4,885

24 0.5500 572.50 4,264

23 0.6000 620.00 3,683

22 0.6500 667.50 3,136

21 0.7000 715.00 2,619

20 0.7400 753.00 2,226

19 0.7700 781.50 1,941

18 0.8000 810.00 1,665

17 0.8200 829.00 1,485

16 0.8400 848.00 1,308

15 0.8600 867.00 1,134

14 0.8800 886.00 964

13 0.9000 905.00 797
Vertical 

Layers Sigma P Pressure (mb)

Approximate 

Height (m)

12 0.9100 914.50 714

11 0.9200 924.00 632

10 0.9300 933.50 551

9 0.9400 943.00 470

8 0.9500 952.50 390

7 0.9600 962.00 311

6 0.9700 971.50 232

5 0.9800 981.00 154

4 0.9850 985.75 115

3 0.9900 990.50 77

2 0.9950 995.25 38

1 0.9975 997.63 19

0 1.0000 1000.00 0

Figure 4-1. Map of the 2014 WRF-CMAQ Modeling Domain. The blue box denotes the 12-km

national modeling domain. 
4.2.3 Modeling Period / Ozone Episodes

The 12-km WRF-CMAQ modeling domain was modeled for the entire year of 2014. The annual

simulation included a “ramp-up” period, comprised of 10 days before the beginning of the simulation, to

mitigate the effects of initial concentrations. All 365 model days were used in the annual average levels of 

PM2.5. For the 8-hour ozone, we used modeling results from the period between May 1 and September 

30. This 153-day period generally conforms to the ozone season across most parts of the U.S. and 

contains the majority of days that observed high ozone concentrations.

4.2.4 Model Inputs: Emissions, Meteorology and Boundary Conditions

2014 Emissions: The emissions inventories used in the 2014 air quality modeling are described in Section

3, above.

Meteorological Input Data: The gridded meteorological data for the entire year of 2014 at the 12 km 

continental United States scale domain was derived from version 3.8.122 of the Weather Research and 

Forecasting Model (WRF), Advanced Research WRF (ARW) core.23 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 2014 CMAQ-WRF meteorology 

simulated for 2014 with 2011 National Land Cover Database (NLCD)

24 and using version 2 fourdimensional data assimilation with no nudging in the planetary boundary layer and based on blended 3-

hourly reanalysis fields (combination of 6-hour (Meteorological Assimilation Data Ingest System)

MADIS25 data and intermediate North American Mesoscale Model26 (NAM) 3-hour forecast) organized 

into 12km NAM Data Assimilation System (NDAS) fields up to 50 hPa. The WRF simulation included 

the physics options of the Pleim-Xiu land surface model (LSM) with NLCD woody wetlands lad use 

category recognized, Asymmetric Convective Model version 2 planetary boundary layer (PBL) scheme, 

Morrison double moment microphysics, Kain- Fritsch cumulus parameterization scheme utilizing the 

moisture-advection trigger27 and the RRTMG long-wave and shortwave radiation (LWR/SWR) scheme.28 

In addition, the Group for High Resolution Sea Surface Temperatures (GHRSST)29,30 1km SST data was 

used for SST information to provide more resolved information compared to the more coarse data in the 

NAM analysis. 

 

22 Version 3.6.1 was the current version of WRF at the time the 2013 meteorological model simulation was performed.

23 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.

24 National Land Cover Database 2011, http://www.mrlc.gov/nlcd2011.php

25 Meteorological Assimilation Data Ingest System. http://madis.noaa.gov/.

26 North American Model Analysis-Only, http://nomads.ncdc.noaa.gov/data.php; download from 

ftp://nomads.ncdc.noaa.gov/NAM/analysis_only/. 

27 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

28 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.

29 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.

30Global High Resolution SST (GHRSST) analysis, https://www.ghrsst.org/.
Initial and Boundary Conditions: The lateral boundary and initial species concentrations are provided by a

three-dimensional global atmospheric chemistry model, the GEOS-CHEM31 model version 10-1, using the 

tropchem, NOx_Ox_HC_Aer_Br, mechanism. 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 2014 with a grid resolution of 2.0 

degrees x 2.5 degrees (latitude-longitude). The predictions were processed using the GEOS-2-CMAQ 

tool and used to provide one-way dynamic boundary conditions at one-hour intervals.32,33 More 

information is available about the GEOS-CHEM 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 2014 simulation using state/local monitoring sites data in order to estimate the

ability of the CMAQ modeling system to replicate the 2014 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 = , where P = predicted and O = observed concentrations. 

Mean error (ME) calculates the absolute value of the difference (predicted - observed) divided by the total 

number of replicates (n). Mean error is defined as: 

ME = 

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:

 

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

Cambridge, MA, October 15, 2004.

32Akhtar, F., Henderson, B., Appel, W., Napelenok, S., Hutzell, B., Pye, H., Foley, K., 2012. Multiyear Boundary Conditions 

for CMAQ 5.0 from GEOS-Chem with Secondary Organic Aerosol Extensions, 11th Annual Community Modeling and 

Analysis System conference, Chapel Hill, NC, October 2012.

33 Henderson, B.H., Akhtar, F., Pye, H.O.T., Napelenok, S.L., and Hutzell, W.T. (2014) A database and tool for boundary 

conditions for regional air quality modeling: description and evaluation, Geosci. Model Dev., 7, 339-360.
(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

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 each of the nine National Oceanic and 

Atmospheric Administration (NOAA) climate regions34 of the 12-km U.S. modeling domain (Figure 4-2). 

The regions include the Northeast, Ohio Valley, Upper Midwest, Southeast, South, Southwest, Northern 

Rockies, Northwest and West35,36 as were originally identified in Karl and Koss (1984)37. 

 

34 NOAA, National Centers for Environmental Information scientists have identified nine climatically consistent regions within 

the contiguous U.S., http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php.

35 The nine climate regions are defined by States where: Northeast includes CT, DE, ME, MA, MD, NH, NJ, NY, PA, RI, and 

VT; Ohio Valley includes IL, IN, KY, MO, OH, TN, and WV; Upper Midwest includes IA, MI, MN, and WI; Southeast 

includes AL, FL, GA, NC, SC, and VA; South includes AR, KS, LA, MS, OK, and TX; Southwest includes AZ, CO, NM, and 

UT; Northern Rockies includes MT, NE, ND, SD, WY; Northwest includes ID, OR, and WA; and West includes CA and NV.

36 Note most monitoring sites in the West region are located in California (see Figure 4-2), therefore statistics for the West will 

be mostly representative of California ozone air quality.

37 Karl, T. R. and Koss, W. J., 1984: "Regional and National Monthly, Seasonal, and Annual Temperature Weighted by Area, 

1895-1983." Historical Climatology Series 4-3, National Climatic Data Center, Asheville, NC, 38 pp.
Figure 4-2. NOAA Nine Climate Regions (source: http://www.ncdc.noaa.gov/monitoring-references/maps/usclimate-regions.php#references)

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 

2014 in the continental U.S. were included in the evaluation and were taken from the 2014 State/local 

monitoring site data in the EPA Air Quality System (AQS) and the Clean Air Status and Trends Network 

(CASTNet). 

The 8-hour ozone model performance bias and error statistics for each of the nine NOAA climate regions

and each season are provided in Table 4-4. Seasons were defined as: winter (December-JanuaryFebruary), spring (March-April-May),summer (June, July, August), and fall (September-OctoberNovember). In some instances, observational data were excluded from the analysis and model evaluation 

based on a completeness criterion of 75 percent. Spatial plots of the MB, ME, NMB and NME for 

individual monitors are shown in Figures 4-3 through 4-6, respectively. The statistics shown in these two 

figures were calculated over the ozone season, May through September, using data pairs on days with

observed 8-hour ozone of greater than or equal to 60 ppb.

In general, the model performance statistics indicate that the 8-hour daily maximum ozone concentrations

predicted by the 2014 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 at the AQS sites in the summer and fall is over predicted with the greatest over

prediction in the South and Southeast (NMB ranging between 12 to 34 percent). Likewise, 8-hour ozone 

at the CASTNet sites in the summer is typically over predicted except in the West where the bias shows a 

slight under prediction (NMB of -0.3%). 8-hour ozone is under predicted at AQS and CASTNet sites in 

the Northeast and Upper Midwest in the winter and spring (with NMBs less than approximately 10 

percent in each subregion). 

Model bias at individual sites during the ozone season is similar to that seen on a subregional basis for the

summer. Figure 4-2 shows the mean bias for 8-hour daily maximum ozone greater than 60 ppb is 

generally ±10 ppb across the AQS and CASTNet sites. Likewise, the information in Figure 4-4 indicates

that the bias for days with observed 8-hour daily maximum ozone greater than 60 ppb is within ± 20 

percent at the vast majority of monitoring sites across the U.S. domain. Model error, as seen from Figures

4-4 and 4-6, is generally 2 to 10 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 in the West, Northern 

Rockies, Northeast, Southeast, along portions of the Gulf Coast, and Great Lakes coastline.

Table 4-4. Summary of CMAQ 2014 8-Hour Daily Maximum Ozone Model Performance Statistics 

by NOAA climate region, by Season and Monitoring Network.

Climate 

region

Monitor 

Network Season

No. of 

Obs

MB 

(ppb)

ME 

(ppb)

NMB 

(%)

NME 

(%)

Northeast AQS Winter 12,047 -0.1 5.7 -0.2 18.2
Climate 

region

Monitor 

Network Season

No. of 

Obs

MB 

(ppb)

ME 

(ppb)

NMB 

(%)

NME 

(%)

Spring 16,172 -0.3 5.3 -0.7 12.0

Summer 17,263 6.6 7.9 15.2 18.2

Fall 14,720 8.7 9.3 26.4 28.1

CASTNet Winter 1,280 -0.8 5.2 -2.3 15.5

Spring 1,296 -1.3 5.0 -2.9 11.0

Summer 1,299 4.6 7.2 10.8 17.1

Fall 1,225 8.4 8.9 25.1 26.5

Ohio Valley

AQS Winter 4,065 1.3 5.9 4.2 19.9

Spring 15,708 2.8 5.7 6.0 12.3

Summer 19,913 8.4 9.8 18.9 22.0

Fall 13,392 9.8 10.3 27.6 29.2

CASTNet Winter 1,583 1.2 6.2 3.5 18.8

Spring 1,634 0.2 5.4 0.3 11.0

Summer 1,627 2.2 13.6 4.5 27.5

Fall 1,609 7.8 9.0 21.5 24.9

Upper Midwest

AQS Winter 1,495 -1.8 6.6 -5.8 21.5

Spring 6,916 -0.1 5.4 -0.2 11.8

Summer 9,538 4.4 7.2 10.7 17.4

Fall 5,941 8.1 8.9 24.3 26.6

CASTNet Winter 445 -3.7 7.4 -10.9 21.7

Spring 457 -3.1 6.0 -6.7 12.8

Summer 458 2.0 5.8 5.0 14.5

Fall 444 6.6 7.6 20.2 23.3

Southeast

AQS Winter 6,723 5.9 7.2 17.6 21.3

Spring 14,962 4.2 6.6 9.0 14.1

Summer 16,998 13.5 14.0 33.5 34.7

Fall 13,787 12.6 12.8 34.8 35.4

CASTNet Winter 946 2.6 5.7 7.1 15.6

Spring 998 -0.2 6.3 -0.5 12.5

Summer 998 9.9 10.6 23.9 25.7

Fall 975 9.3 10.1 25.0 27.1

South

AQS Winter 12,064 4.5 6.4 14.6 20.6

Spring 13,851 5.1 7.4 11.5 16.6

Summer 13,756 17.9 18.5 45.8 47.2

Fall 13,265 10.7 11.4 28.6 30.3
Climate 

region

Monitor 

Network Season

No. of 

Obs

MB 

(ppb)

ME 

(ppb)

NMB 

(%)

NME 

(%)

CASTNet Winter 518 3.6 5.8 10.4 16.8

Spring 551 3.1 6.3 6.5 13.4

Summer 537 11.2 15.8 26.4 37.2

Fall 530 10.4 10.8 28.3 29.2

Southwest

AQS Winter 9,341 4.8 7.2 13.2 19.8

Spring 11,234 3.0 5.5 5.7 10.4

Summer 11,517 9.3 11.3 17.8 21.6

Fall 10,311 12.4 12.6 29.3 29.9

CASTNet Winter 774 1.1 6.1 2.5 13.8

Spring 817 0.2 5.8 0.4 10.3

Summer 817 7.1 10.3 13.1 18.9

Fall 801 10.3 11.3 22.4 24.6

Northern 

Rockies

AQS Winter 4,553 -0.4 6.0 -1.2 16.5

Spring 4,898 1.8 5.0 3.8 10.8

Summer 4,872 5.5 7.3 12.1 16.2

Fall 4,799 9.1 9.7 24.2 25.8

CASTNet Winter 607 -1.2 5.2 -3.0 13.3

Spring 637 -0.6 4.8 -1.2 9.6

Summer 628 3.2 7.1 6.8 14.9

Fall 611 8.4 9.3 20.8 23.0

Northwest

AQS Winter 596 5.4 7.4 18.5 25.5

Spring 1,194 0.3 4.7 0.7 11.6

Summer 2,404 5.5 8.3 14.5 21.6

Fall 1,258 8.7 9.8 25.4 28.6

CASTNet Winter -- -- -- -- --

Spring -- -- -- -- --

Summer -- -- -- -- --

Fall -- -- -- -- --

West

AQS Winter 14,984 6.3 8.3 19.0 24.8

Spring 16,829 0.8 5.4 1.6 10.8

Summer 17,881 6.0 10.0 11.9 20.0

Fall 16,611 8.4 10.0 18.7 22.3

CASTNet Winter 530 4.5 7.0 10.8 16.8

Spring 552 -3.0 6.2 -5.4 11.1
Climate 

region

Monitor 

Network Season

No. of 

Obs

MB 

(ppb)

ME 

(ppb)

NMB 

(%)

NME 

(%)

Summer 542 -0.2 10.6 -0.3 17.8

Fall 538 6.4 11.0 12.7 21.8

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

May-September 2014 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 2014 at AQS and CASTNet monitoring sites in the continental U.S. modeling
domain.

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

the period May-September 2014 at AQS and CASTNet monitoring sites in the continental U.S.

modeling domain.

Figure 4-6. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 ppb over 

the period May-September 2014 at AQS and CASTNet monitoring sites in the continental U.S.

modeling domain.

Evaluation for Annual PM2.5 components: The PM evaluation focuses on PM2.5 components including 

sulfate (SO4), nitrate (NO3), total nitrate (TNO3 = NO3 + HNO3), ammonium (NH4), elemental carbon
(EC), and organic carbon (OC). The bias and error performance statistics were calculated on an annual 

basis for each of the nine NOAA climate subregions defined above (provided in Table 4-5). PM2.5

measurements for 2014 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. 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 4-7 through 4-30.

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 12-km modeling domain (with MB values ranging 

from 0.0 to -0.6 μgm-3 and NMB values ranging from near negligible to -32 percent) except at CSN sites 

in the Southeast, Northern Rockies and Northwest as well as IMPROVE sites in the Northwest (over 

predicted NMB ranges between 1 to 38 percent). Sulfate performance shows moderate error, ranging 

from 26 to 66 percent. Figures 4-7 through 4-10, 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 Ohio Valley, 

Upper Midwest, South, Southwest, Northern Rockies, and West (NMBin the range of -17.0 to

-49 percent), except in the Northeast, Northwest, and Southeast where nitrate is over predicted (NMB in 

the range of 7 percent and 49 percent). At IMPROVE rural sites, annual average nitrate is under 

predicted at all subregions, except in the Southeast, Northeast, and Northwest where nitrate is over 

predicted by 23 to 34 percent, respectively. Model performance of total nitrate at sub-urban

CASTNet monitoring sites shows an over prediction in the Northeast, Ohio Valley, Upper Midwest, 

South, Southwest, and Southeast (NMBin the range of 1 to 40 percent), except in the Northern 

Rockies and Western U.S. (NMB in the range of -12 to -27 percent). Model error for nitrate 

and total nitrate is somewhat greater for each of the nine NOAA climate subregions 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 4-13. The exception to this 

is in the Southern Florida, Ohio Valley, Southwest, and Western U.S. of the modeling domain where

there appears to be a greater number of sites with under prediction of nitrate of 10 to 80 percent.

Model error for annual nitrate, as shown in Figures 4-12 and 4-15, is least at sites in portions of the 

Southeast and extending upward 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 -19 to -49 percent. Likewise, ammonium 

performance across the urban CSN sites shows an under prediction in most of the climate subregions 

(ranging from -8 to -45 percent), except in the Northeast, Northwest and Southeast (over prediction of 

NMB 5 and 38 percent, respectively). The spatial variation of ammonium across the majority of 

individual monitoring sites in the Eastern U.S. shows bias within ±50 percent (Figures 4-19 and 4-21). A 

larger bias is seen in the Southeast and in the Northern Rockies, (over prediction bias on average 80 

percent). The urban monitoring sites exhibit larger errors than at rural sites for ammonium. 

Annual average elemental carbon is over predicted in all of the nine subregions at urban and rural sites. 
There is not a large variation in error statistics 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 47 percent), except in the Southwest, Northern Rockies, and Western U.S. where the 

bias ranges between -8 to -31 percent. The model over predicted annual average organic carbon in all 

subregions at urban CSN sites except in the Northern Rockies and Western U.S. (NMB ranges from -8 to 

-19 percent). Similar to 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 2014 Annual PM Species Model Performance Statistics by NOAA 

Climate region, by Monitoring Network.

Pollutant

Monitor 

Network Subregion

No. of 

Obs

MB 

(μgm-3)

ME 

(μgm-3)

NMB 

(%)

NME 

(%)

Sulfate

CSN Northeast 906 -0.3 0.6 -17.0 35.2

Ohio Valley 3,037 -0.4 0.7 -17.6 35.0

Upper Midwest 1,604 -0.1 0.5 -9.4 34.1

Southeast 2,066 0.0 0.6 1.0 34.5

South 1,456 -0.3 0.6 -19.0 34.6

Southwest 783 -0.1 0.3 -9.7 43.1

Northern Rockies 570 0.0 0.4 2.8 51.9

Northwest 620 0.1 0.4 15.2 55.6

West 956 -0.2 0.3 -15.3 39.6

IMPROVE Northeast 1,714 -0.1 0.3 -11.3 33.0

Ohio Valley 797 -0.3 0.5 -14.0 30.1

Upper Midwest 938 -0.1 0.4 -7.6 36.1

Southeast 1,147 -0.1 0.5 -6.6 30.1

South 1,164 -0.3 0.5 -19.1 36.1

Southwest 3,803 -0.1 0.2 -17.0 42.5

Northern Rockies 2,121 0.0 0.2 0.8 45.8

Northwest 1,822 0.1 0.2 38.1 65.8

West 2,400 0.0 0.3 -4.5 50.2

CASTNet Northeast 906 -0.3 0.4 -23.6 27.2

Ohio Valley 893 -0.6 0.6 -25.1 27.2

Upper Midwest 257 -0.3 0.4 -21.9 26.0

Southeast 586 -0.5 0.5 -25.0 27.9

South 377 -0.6 0.6 -30.7 32.2

Southwest 422 -0.1 0.2 -24.3 39.1

Northern Rockies 566 -0.1 0.2 -20.1 36.3

Northwest - - - - -

West 296 -0.2 0.3 -31.8 45.7

Nitrate

CSN Northeast 3,237 0.1 0.7 7.2 56.5

Ohio Valley 2,841 -0.3 0.9 -18.3 55.5

Upper Midwest 1,604 -0.3 0.8 -16.9 46.9

Southeast 2,308 0.3 0.5 49.9 98.1
Pollutant

Monitor 

Network Subregion

No. of 

Obs

MB 

(μgm-3)

ME 

(μgm-3)

NMB 

(%)

NME 

(%)

South 1,238 -0.2 0.6 -20.1 70.4

Southwest 783 -0.3 0.9 -29.1 80.5

Northern Rockies 570 -0.4 0.6 -34.3 59.1

Northwest 523 0.2 0.8 27.4 98.5

West 956 -1.2 1.5 -49.3 65.8

IMPROVE Northeast 1,709 0.1 0.3 33.8 83.7

Ohio Valley 797 -0.2 0.6 -17.7 69.1

Upper Midwest 938 -0.2 0.6 -17.4 59.2

Southeast 1,147 0.1 0.4 27.1 96.8

South 1,164 -0.2 0.5 -25.8 75.6

Southwest 3,801 -0.1 0.2 -28.5 91.1

Northern Rockies 2,120 -0.1 0.2 -36.2 79.3

Northwest 1,807 0.0 0.2 23.3 >100.0

West 2,390 -0.3 0.5 -49.9 79.9

Total Nitrate 

(NO3+HNO3)

CASTNet Northeast 906 0.3 0.5 23.6 37.9

Ohio Valley 893 0.3 0.9 16.1 43.0

Upper Midwest 257 0.0 0.6 1.1 34.9

Southeast 586 0.5 0.8 38.9 64.6

South 377 0.3 0.8 19.7 51.9

Southwest 422 0.1 0.3 11.0 41.8

Northern Rockies 566 -0.1 0.3 -11.8 37.5

Northwest - - - - -

West 296 -0.4 0.7 -26.5 47.1

Ammonium

CSN Northeast 3,237 0.0 0.3 4.9 48.3

Ohio Valley 2,744 -0.2 0.4 -17.0 45.0

Upper Midwest 1,475 -0.1 0.4 -8.4 42.8

Southeast 1,803 0.1 0.3 17.0 56.8

South 1,230 -0.1 0.3 -20.8 49.2

Southwest 765 -0.2 0.3 -45.2 67.7

Northern Rockies 570 -0.1 0.3 -11.0 56.4

Northwest 610 0.1 0.3 37.5 >100.0

West 945 -0.3 0.5 -43.4 64.4

CASTNet Northeast 906 -0.1 0.2 -19.0 28.8

Ohio Valley 893 -0.3 0.3 -27.6 35.0

Upper Midwest 257 -0.2 0.3 -27.5 34.6

Southeast 586 -0.1 0.2 -15.9 28.0

South 377 -0.2 0.2 -26.8 36.6

Southwest 422 -0.1 0.1 -43.1 53.1

Northern Rockies 566 -0.1 0.1 -42.8 51.0
Pollutant

Monitor 

Network Subregion

No. of 

Obs

MB 

(μgm-3)

ME 

(μgm-3)

NMB 

(%)

NME 

(%)

Northwest - - - - -

West 296 -0.1 0.2 -49.3 59.9

Elemental 

Carbon

CSN Northeast 3,186 0.3 0.5 50.4 76.4

Ohio Valley 2,723 0.2 0.3 35.2 62.9

Upper Midwest 1,434 0.2 0.3 54.4 70.8

Southeast 1,766 0.3 0.4 49.9 73.6

South 1,218 0.2 0.3 35.4 55.4

Southwest 769 0.3 0.4 51.7 71.3

Northern Rockies 546 0.1 0.3 24.2 86.8

Northwest 583 0.7 0.9 >100.0 >100.0

West 870 0.0 0.4 5.0 50.2

IMPROVE Northeast 1,730 0.1 0.1 43.9 65.2

Ohio Valley 806 0.1 0.1 25.3 58.2

Upper Midwest 945 0.1 0.1 26.0 57.1

Southeast 1,349 0.1 0.2 41.4 64.2

South 1,160 0.0 0.1 25.7 54.9

Southwest 3,793 0.0 0.1 34.2 72.2

Northern Rockies 2,270 0.0 0.1 10.0 63.7

Northwest 1,815 0.2 0.3 >100.0 >100.0

West 2,391 0.1 0.1 39.5 83.0

Organic 

Carbon

CSN Northeast 3,146 2.3 2.5 >100.0 >100.0

Ohio Valley 2,692 0.5 0.9 32.9 56.6

Upper Midwest 1,426 0.9 1.2 59.7 84.8

Southeast 1,755 1.2 1.5 63.4 81.1

South 1,212 0.4 0.9 23.2 51.9

Southwest 768 0.9 1.2 67.4 89.3

Northern Rockies 510 -0.2 0.9 -18.5 72.2

Northwest 579 1.7 2.3 85.0 >100.0

West 869 -0.2 1.1 -8.7 44.3

IMPROVE Northeast 1,724 0.3 0.6 34.4 68.6

Ohio Valley 806 0.3 0.7 28.2 59.6

Upper Midwest 940 0.2 0.6 20.5 68.9

Southeast 1,349 0.6 1.0 47.2 77.2

South 1,159 0.0 0.5 2.6 50.5

Southwest 3,780 -0.1 0.3 -10.5 56.4

Northern Rockies 2,252 -0.2 0.4 -31.1 57.7

Northwest 1,768 0.3 0.9 42.1 >100.0

West 2,388 -0.1 0.6 -8.1 63.5
94

Figure 4-7. Mean Bias (μgm-3) of annual sulfate at monitoring sites in the continental U.S. 

modeling domain.

Figure 4-8. Mean Error (μgm-3) of annual sulfate at monitoring sites in the continental U.S. 

modeling domain.
95

Figure 4-9. Normalized Mean Bias (%) of annual sulfate at monitoring sites in the continental

U.S. modeling domain.

Figure 4-10. Normalized Mean Error (%) of annual sulfate at monitoring sites in the continental

U.S. modeling domain.
96

Figure 4-11. Mean Bias (μgm-3) of annual nitrate at monitoring sites in the continental U.S. 

modeling domain.

Figure 4-12. Mean Error (μgm-3) of annual nitrate at monitoring sites in the continental U.S. 

modeling domain.
97

Figure 4-13. Normalized Mean Bias (%) of annual nitrate at monitoring sites in the continental U.S. 

modeling domain.

Figure 4-14. Normalized Mean Error (%) of annual nitrate at monitoring sites in the continental

U.S. modeling domain.
98

Figure 4-15. Mean Bias (μgm-3) of annual total nitrate at monitoring sites in the continental U.S. 

modeling domain.

Figure 4-16. Mean Error (μgm-3) of annual total nitrate at monitoring sites in the continental U.S. 

modeling domain.
99

Figure 4-17. Normalized Mean Bias (%) of annual total nitrate at monitoring sites in the continental

U.S. modeling domain.

Figure 4-18. Normalized Mean Error (%) of annual total nitrate at monitoring sites in the 

continental U.S. modeling domain.
100

Figure 4-19. Mean Bias (μgm-3) of annual ammoniumat monitoring sites in the continental U.S. 

modeling domain.

Figure 4-20. Mean Error (μgm-3) of annual ammonium at monitoring sites in the continental U.S. 

modeling domain.
101

Figure 4-21. Normalized Mean Bias (%) of annual ammonium at monitoring sites in the continental

U.S. modeling domain.

Figure 4-22. Normalized Mean Error (%) of annual ammonium at monitoring sites in the 

continental U.S. modeling domain.
102

Figure 4-23. Mean Bias (μgm-3) of annual elemental carbon at monitoring sites in the continental

U.S. modeling domain.

Figure 4-24. Mean Error (μgm-3) of annual elemental carbon at monitoring sites in the continental

U.S. modeling domain.
103

Figure 4-25. Normalized Mean Bias (%) of annual elemental carbon at monitoring sites in the 

continental U.S. modeling domain.

Figure 4-26. Normalized Mean Error (%) of annual elemental carbon at monitoring sites in the 

continental U.S. modeling domain.
104

Figure 4-27. Mean Bias (μgm-3) of annual organic carbon at monitoring sites in the continental U.S. 

modeling domain.

Figure 4-28. Mean Error (μgm-3) of annual organic carbon at monitoring sites in the continental

U.S. modeling domain.
105

Figure 4-29. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the 

continental U.S. modeling domain.

Figure 4-30. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the 

continental U.S. modeling domain.
106

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 2014. 

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: 

(Equation 1)

Where:

Y(s) is the observed concentration at point s. Note that Y(s) could be expressed as , where t indicates 

the model being fit at time t (in this case, t=1,...,365 would represent day of the year.)

is the point-level regressor based on the CMAQ concentration at point s. This value is a weighted 

average of both the gridcell containing the monitor and neighboring gridcells.

is the intercept, where is composed of both a global component and a 

local component that is modeled as a mean-zero Gaussian Process with exponential decay

is the global slope; local components of the slope are contained in the term.

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 
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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 and 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. 

2) The model is "hierarchical" in structure, meaning that the top level parameters in Equation 1 (ie , 

, ) 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 ).

Further information about the development and inner workings of the current version of DS can be found 

in Berrocal, Gelfand and Holland (2012)38 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 

2014 US census tract centroids across the continental U.S. using 2014 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 2014. 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 2014, about 23% of the US Census tracts (16815 out of 72283) 

experienced at least one day with an ozone value above the NAAQS of 75 ppb. 

 

38 Berrocal, V., Gelfand, A., and D. Holland. Space-Time Data Fusion Under Error in Computer Model Output: An Application 

to Modeling Air Quality. Biometrics. 2012. September ; 68(3): 837–848. doi:10.1111/j.1541-0420.2011.01725.x
108

Figure 5-1. 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 2014. Figure 5-2 

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

observations, CMAQ model predictions and DS model results. The DS model estimated that for 2014 

about 34% of the US Census tracts (24328 out of 72283) experienced at least one day with a PM2.5 value 

above the 24-hour NAAQS of 35 g/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 2014 

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

PM2.5 943 0.286 3.42 0.95

O3 1299 -0.0162 4.50 0.96

Table 5-1. Cross-validation statistics associated with the 2014 DS runs.

The statistics indicated by the columns of Table 5-1 are as follows:

- Mean Bias: The bias of each prediction is the DS prediction minus the AQS value. This column 

is the mean of all biases across the CV cases.

- Root Mean Squared Error (RMSE): The bias is squared for each CV prediction, then the square 

root of the mean of all squared biases across all CV predictions is obtained.

- Mean Coverage: A value of 1 is assigned if the measured AQS value lies in the 95% confidence 

interval of the DS prediction (the DS prediction +/- the DS standard error), and 0 otherwise. This 

column is the mean of all those 0’s and 1’s.

5.5 Summary and Conclusions 

The results presented in this report are from an application of the DS fusion model for characterizing 

national air quality for Ozone and PM2.5. DS provided spatial predictions of daily ozone and PM2.5 at 

2010 U.S. census tract centroids by utilizing monitoring data and CMAQ output for 2014. 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 where 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
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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

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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118

United States

Environmental Protection

Agency

Office of Air Quality Planning and Standards

Air Quality Assessment Division

Research Triangle Park, NC

Publication No. EPA-454/R-18-008

October 2018