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Bayesian Space-time Downscaling Fusion
Model (Downsealer) - Derived Estimates of Air
Quality for 2019
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EPA-454/R-22-003
October 2022
Bayesian Space-time Downscaling Fusion Model (Downscaler) - Derived Estimates of Air
Quality for 2019
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Authors:
Adam Reff (EPA/OAR)
Sharon Phillips (EPA/OAR)
Alison Eyth (EPA/OAR)
Janice Godfrey (EPA/OAR)
Jeff Vukovich (EPA/OAR)
David Mintz (EPA/OAR)
Acknowledgements
The following people served as reviewers of this document: Liz Naess (EPA/OAR) and David
Mintz (EPA/OAR).
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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 14
3.3 Emissions Modeling Summary 47
3.4 Emissions References 86
4.0 CMAQ Air Quality Model Estimates 90
4.1 Introduction to the CMAQ Modeling Platform 90
4.2 CMAQ Model Version, Inputs and Configuration 91
5.0 Bayesian space-time downscaling fusion model (downscaler) -Derived Air Quality Estimates.... 123
5.1 Introduction 123
5.2 Downscaler Model 123
5.3 Downscaler Concentration Predictions 124
5.4 Downscaler Uncertainties 129
5.5 Summary and Conclusions 131
Appendix A - Acronyms 132
Appendix B - Emissions Totals by Sector 134
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1,0 Introduction
This report describes estimates of daily ozone (maximum 8-hour average) and fine particulate matter
(PM2.5) (24-hour average) concentrations throughout the contiguous United States during the 2019
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 DS is included to provide the
background and context for understanding the data output presented in this report. These estimates are
intended for use by statisticians and environmental scientists interested in the daily spatial distribution of
ozone and PM2.5.
DS essentially operates by calibrating CMAQ data to the observational data, and then uses the resulting
relationship to predict "observed" concentrations at new spatial points in the domain. Although similar
in principle to a linear regression, spatial modeling aspects have been incorporated for improving the
model fit, and a Bayesian1 approach 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 (https://www.cdc.gov/nceh/tracking/). The
National EPHT Program, with the EPHT Network as its cornerstone, is the CDC's response to requests
calling for improved understanding of how the environment affects human health. The EPHT Network is
designed to provide the means to identify, access, and organize hazard, exposure, and health data from a
variety of sources and to examine, analyze and interpret those data based on their spatial and temporal
characteristics.
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.
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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 goals.2 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 EPA, 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 EPA shall not be held liable
for improper or incorrect use of the data described and/or contained herein.
The four remaining sections and appendix in the report are as follows:
Section 2 describes the air quality data obtained from EPA's nationwide monitoring network
and the importance of the monitoring data in determining 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.
2The original HHS and EPA MOU is available at https://www.cdc.gov/nceh/tracking/pdfs/epa mou 2007.pdf.
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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 U.S. census tract centroid locations within the contiguous U.S.
Appendix A provides a description of acronyms used in this report.
Appendix B is a separate spreadsheet that shows emissions totals for the modeling domain and
for each emissions modeling sector (see Section 3 for more details).
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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 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-air-pollutants/naaqs-table.
For general information on the criteria pollutants, go to https://www.epa.gov/criteria-air-pollutants.
When these standards are not met, the area is designated as a nonattainment area. States must develop
state implementation plans (SIPs) that explain the regulations and controls it will use to clean up the
nonattainment areas. States with an EPA-approved SIP can request that the area be designated from
nonattainment to attainment by providing three consecutive years of data showing NAAQS compliance.
The state must also provide a maintenance plan to demonstrate how it will continue to comply with the
NAAQS and demonstrate compliance over a 10-year period, and what corrective actions it will take
should a NAAQS violation occur after designation. EPA must review and approve the NAAQS
compliance data and the maintenance plan before designating the area; thus, a person may live in an area
designated as nonattainment even though no NAAQS violation has been observed for quite some time.
For more information on ozone designations, go to https://www.epa.gov/ozone-designations and for PM
designations, go to 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
problems, aggravate asthma, cause inflammation of lung tissue, and even temporarily decrease the lung
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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-qualitv-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 CAA 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 National Ambient Air Quality Standards
Form of the Standard (parts per million, ppm)
1997
2008
2015
Annual 4th highest daily max 8-hour average, averaged over
three years
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
sources.3 Particles less than 10 micrometers in diameter (PMio) 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.
3 The measure used to classify PM into sizes is the aerodynamic diameter. The measurement instruments used for PM are
designed and operated to separate large particles from the smaller particles. For example, the PM2 5 instrument only captures
and thus measures particles with an aerodynamic diameter less than 2.5 micrometers. The EPA method to measure PMc is
designed around taking the mathematical difference between measurements for PMi0 and PM2 5
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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
"3
concentration must not exceed 12.0 micrograms per cubic meter (ug/m ) based on the annual mean
"3
concentration averaged over three years, and the 24-hr average concentration must not exceed 35 ug/m
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-particulate-
matter-pm-pollution#standards. The standards for PM2.5 are shown in Table 2-2.
Table 2-2. PM2.5 National Ambient Air Quality Standards
Form of the Standard
(micrograms per cubic meter, jig/m3)
1997
2006
2012
Annual mean of 24-hour averages, averaged over 3 years
15.0
15.0
12.0
98th percentile of 24-hour averages, averaged over 3 years
65
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2.2 Ambient Air Quality Monitoring in the United States
2.2.1 Monitoring Networks
The CAA (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 2019, approximately 40 percent of the U.S. population was living within 10 kilometers of ozone and
PM2.5 monitoring sites. Highly populated areas in the eastern U.S. and California are well covered by both
ozone and PM2.5 monitoring network (Figure 2-1).
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25 km - 50 km (11,643 tracts / 53.6 million people)
50 km - 75 km (5,638 tracts / 23.5 million)
75 km -100 km (2,309 tracts / 9.1 million people)
100 km -150 km (1,771 tracts / 6.9 million people)
150 km < (507 tracts /1.8 million people)
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Figure 2-1. Distances from U.S. Census Tract centroids to the nearest monitoring site, 2019.
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In summary, state and local agencies and tribes implement a quality-assured monitoring network to
measure air quality across the U.S. The 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 OAQPS 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 EPHT activities.
The advantage of using ambient data from EPA monitoring networks for comparison 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
needed.4 Spatial gaps exist in the air quality monitoring network, especially in rural areas since the air
quality monitoring network is designed to focus on measurement of pollutant concentrations in high
population density areas. Temporal limits also exist. Hourly ozone measurements are aggregated to daily
values (the daily max 8-hour average is relevant to the ozone standard). Ozone is typically monitored
during the ozone season (the warmer months, approximately April through October). However, year-long
data is available in many areas and is extremely useful to evaluate whether ozone is a factor in health
outcomes during the non-ozone seasons. PM2.5 is generally measured year-round. Most Federal Reference
Method (FRM) PM2.5 monitors collect data one day in every three days, due in part to the time and costs
involved in collecting and analyzing the samples. Additionally, continuous monitors have become
available which can automatically collect, analyze, and report PM2.5 measurements on an hourly basis.
4 EPA uses exposure models to evaluate the health risks and environmental effects associated with exposure. These models
are limited by the availability of air quality estimates, https://www.epa.gov/technical-air-pollution-resources.
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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
Federal Equivalent Methods (FEM).
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 workplace 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 CMAQ 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.
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Combining monitoring data with air quality models (via fusion or regression) may provide the best results
in terms of estimating ambient air concentrations in space and time. EPA's eVNA5 is an example of an
earlier approach for merging air quality monitor data with CMAQ model predictions. DS 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 DS. 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 2-3. Public Health Surveillance Goals and Current Status
Goal
Status
Air data sets and metadata required for air quality
indicators are available to EPHT state Grantees.
Data are available through state agencies and EPA's
AQS. EPA and CDC developed an interagency
agreement, where EPA provides air quality data along
with statistically combined AQS and CMAQ data,
associated metadata, and technical reports that are
delivered to CDC.
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,
Generate hypothesis for further research, and
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.
Produce and disseminate basic indicators and other
findings in electronic and print formats to provide the
public, environmental health professionals, and
policymakers, with current and easy-to-use information
about air pollution and the impact on public health.
Templates and "how to" guides for PM2.5 and ozone
have been developed for routine indicators. Calculation
techniques and presentations for the indicators have been
developed.
5 eVNA is described in the "Regulatory Impact Analysis for the Final Clean Air Interstate Rule", EPA-452/R-05-002, March
2005, Appendix F.
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Table 2-4. 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 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, ufi/m3 by FRM)
Average ambient concentrations of particulate matter (< 2.5 microns in diameter) and compared to
annual PM2.5 NAAQS (by state).
Percent of population exceeding annual PM2.5 NAAQS (by state).
Percent 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 EPA's 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.
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3.0 Emissions Data
3.1 Introduction to Emissions Data Development
The U.S. Environmental Protection Agency (EPA) developed an air quality modeling platform for air
toxics and criteria air pollutants that represents the year 2019. The platform is based on the 2017 National
Emissions Inventory (2017 NEI) published in January 2021 (EPA, 2021) along with other data specific to
the year 2019. The air quality modeling platform consists of all the emissions inventories and ancillary
data files used for emissions modeling, as well as the meteorological, initial condition, and boundary
condition files needed to run the air quality model. This document focuses on the emissions modeling
component of the 2019 modeling platform, including the emission inventories, the ancillary data files, and
the approaches used to transform inventories for use in air quality modeling.
The modeling platform includes all criteria air pollutants and precursors (CAPs), two groups of hazardous
air pollutants (HAPs) and diesel particulate matter. The first group of HAPs are those explicitly used by
the chemical mechanism in the Community Multiscale Air Quality (CMAQ) model (Appel, 2018) for
ozone/particulate matter (PM): chlorine (CI), hydrogen chloride (HC1), benzene, acetaldehyde,
formaldehyde, methanol, naphthalene (the last five are also abbreviated as NBAFM in subsequent
sections of the document). The second group of HAPs consists of 52 HAPs or HAP groups (such as
polycyclic aromatic hydrocarbon groups) added to CMAQ for the purposes of air quality modeling for the
2017 HAP+CAP platform.
Emissions were prepared for the Community Multiscale Air Quality (CMAQ) model
(https://www.epa.gov/cmaq) version 5.3.2'', which was used to model ozone (O3) particulate matter (PM),
and H APs. CMAQ requires hourly and gridded emissions of the following inventory pollutants: carbon
monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOC), sulfur dioxide (SO:),
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
mechanism version 6 (CB6) with chlorine chemistry within CMAQ allows for explicit treatment of the
VOC HAPs naphthalene, benzene, acetaldehyde, formaldehyde and methanol (NBAFM), includes
anthropogenic HAP emissions of HC1 and CI, and can model additional HAPs as described in Section 3.
The short abbreviation for the modeling case name was "2019ge", where 2019 is the year modeled, g
represents that it was based on the 2017 NEI, and e represents that it was the fifth version of a 2017 NEI-
based platform.
The effort to create the emission inputs for this study included development of emission inventories to
represent emissions during the year of 2019, along with application of emissions modeling tools to
convert the inventories into the format and resolution needed by CM AQ.
The emissions modeling platform includes point sources, nonpoint sources, commercial marine vessels
(CMV), onroad and nonroad mobile sources, biogenic emissions and fires for the U.S., Canada, and
Mexico. Some platform categories use more disaggregated data than are made available in the NEI. For
example, in the platform, onroad mobile source emissions are represented as hourly emissions by vehicle
type, fuel type process and road type while the NEI emissions are aggregated to vehicle type/fuel type
6 CMAQ version 5.3.2: https://doi.org/10.5281/zenodo.4081737; https://www.epa.gov/cmaq/cmaq-models-O. CMAQ v5.3.2
is also available from the Community Modeling and Analysis System (CMAS) at: http://www.cmascenter.org.
13
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totals and annual temporal resolution. Emissions from Canada and Mexico are used in the CMAQ
modeling but are not part of the NEI. Year-specific emissions were used for fires, biogenic sources,
fertilizer, point sources, and onroad and n on road mobile sources. Where available, continuous emission
monitoring system (CEMS) data were used for electric generating unit (EGU) emissions. Most of the
remaining emission inventories were adjusted to represent 2019, primarily using 2017-specific emissions
as a starting point.
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 4.8.1 was used to create
CMAQ-ready emissions files for a 12-ktn grid covering the continental U.S. Additional information about
SMOKE is available from http://www.cmascenter.org/smoke.
The gridded meteorological model used to provide input data for the emissions modeling was developed
using the Weather Research and Forecasting Model (WRF,
https://ral.ucar.edu/solutions/products/weather-research-and-forecasting-model-wrf) version 3.8,
Advanced Research WRF core (Skamarock, et al., 2008). The WRF Model is a mesoscale numerical
weather prediction system developed for both operational forecasting and atmospheric research
applications. The WRF was run for 2019 over a domain covering the continental U.S. at a 12km
resolution with 35 vertical layers. The run for this platform included high resolution sea surface
temperature data from the Group for High Resolution Sea Surface Temperature (GHRSST) (see
https://www.ghrsst.org/) and is given the EPA meteorological case abbreviation "19k." The full case
abbreviation includes this suffix following the emissions portion of the case name to fully specify the
abbreviation of the case as "2019ge_cb6_19k."
Following the emissions modeling steps to prepare emissions for CMAQ, it was run for the modeling
domain covering the Continental United States. CMAQ produced outputs for the overall mass, chemistry
and formation for specific hazardous air pollutants (HAPs) formed secondarily in the atmosphere (e.g.,
formaldehyde, acetaldehyde and acrolein. Information about the emissions and associated data files for
this platform are available from this section of the air emissions modeling website
https://www.epa.gov/air-emissions-modeling/2019-emissions-modeling-platform.
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. Additional details
on the development of the emissions inputs to CMAQ are provided in the publication Technical Support
Document (TSD): Preparation of Emissions Inventories for the 2019 North American Emissions
Modeling Platform (EPA, 2022).
3.2 Emission Inventories and Approaches
This section describes the emissions inventories created for input to SMOKE, which are based on the
January 2021 version of the 2017 NEI along with the point source inventory for 2019 and other year
2019-specific data. 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 Emissions Inventory System (EIS). EIS includes
hundreds of automated QA checks to improve data quality, and it also supports release point (stack)
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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 2017 NEI
Technical Support Document describes in detail the development of the 2017 emission inventories and is
available at https://www.epa.gov/air-emissions-inventories/2017-national-emissions-inventory-nei-
technical-support-document-tsd (EPA, 2021).
The full NEI including all emissions source categories is developed every three years, with 2017 being the
most recent year represented wih a full NEI. S/L/T agencies are required to submit large point sources to
the NEI in interim years, including the year 2019. Where available, point source data representing 2019
were used for this study. Point sources in the 2017 NEI that did not have data submitted for the year 2019
and that were not marked as closed were pulled forward from the 2017 NEI into the 2019 point source
inventory. The SMARTFIRE2 system and the BlueSky Pipeline (https://github.com/pnwairfire/bluesky)
emissions modeling system were used to develop year 2019 fire emissions. SMARTFIRE2 categorizes
all fires as either prescribed burning or wildfire categories, and the BlueSky Pipeline system includes fuel
loading, consumption and emission factor estimates for both types of fires. Onroad and nonroad mobile
source emissions were developed for this project for the year 2019 by running MOVES3
(https://www.epa.gov/moves).
With the exception of onroad, nonroad and fire emissions, Canadian emissions were based on the 2019
inventories developed for EPA's Air Quality Time Series (EQUATES) project (Foley, 2020). For
Mexico, year 2016 inventories were projected to 2019. The latest year for which Canada and Mexico
inventories were provided was 2016, although the onroad and nonroad emissions were adjusted to
represent the year 2019 and some additional adjustments to the Canadian emissions were made for
EQUATES.
The emissions modeling process, performed using SMOKE v4.8.1, apportions the emissions inventories
into the grid cells used by CMAQ and temporalizes the emissions into hourly values. In addition, the
pollutants in the inventories (e.g., NOx, 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 within the
emissions modeling platform are separated into sectors for groups of related emissions source categories
that are run through all of the SMOKE programs, except the final merge, independently from emissions
categories in the other sectors. The final merge program called Mrggrid combines low-level sector-
specific gridded, sped at ed and temporalized emissions to create the final CMAQ-ready emissions inputs.
For biogenic and fertilizer emissions, the CMAQ model allows for these emissions to be included in the
CM AQ-ready emissions inputs, or to be computed within CMAQ itself (the "inline" option). This study
uses the inline biogenic emissions option and the CMAQ bidirectional ammonia process for fertilizer
emissions.
Table 3-1 presents the sectors in the emissions modeling platform used to develop the year 2019
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 emission summaries for the U.S. sectors are shown in Table 3-2. 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_2019_emissions_totals_by_sector.xlsx".
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Table 3-1. Platform Sectors Used in the Emissions Modeling Process
Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
EGU units:
ptegu
Point
2019 NEI point source EG Us. replaced with hourly Continuous
Emissions Monitoring System (CEMS) values for NOx and SO;, and
the remaining pollutants temporally allocated according to CEMS heat
input where the units are matched to the NEI. Emissions for all
sources not matched to CEMS data come from 2019 NEI point
inventory. Annual resolution for sources not matched to CEMS data,
hourly for CEMS sources. EG Us closed in 2019 are not part of the
inventorv.
Point source oil and
gas:
ptoilgas
Point
2019 point sources that include oil and gas production emissions
processes for facilities with North American Industry Classification
System (NAICS) codes related to Oil and Gas Extraction, Natural Gas
Distribution, Drilling Oil and Gas Wells, Support Activities for Oil
and Gas Operations, Pipeline Transportation of Crude Oil, and
Pipeline Transportation ofNatural Gas. Includes U.S. offshore oil
production. Production-related sources that did not have 2019 data
were pulled forward from the 2017 NEI and adjusted to 2019. Annual
resolution.
Aircraft and ground
support equipment:
airports
Point
2017 NEI point source emissions from airports, including aircraft and
airport ground support emissions, adjusted to 2019 using Terminal
Area Forecast (TAF) data. Airport-specific factors were used where
available, state average factors were used for regional airports, and no
change was made to military aircraft from 2017. Annual resolution.
Remaining non-
EGU point:
ptnonipm
Point
All 2019 NEI point source records not matched to the airports, ptegu,
or pt_oilgas sectors. Closures were reviewed and implemented based
on the most recent submissions to the Emissions Inventory System
(EIS). Includes 2017 NEI rail yard emissions, adjusted to 2019 using
same projection factors as the rail sector. Annual resolution.
Livestock:
livestock
Nonpoint
2017 NEI nonpoint livestock emissions adjusted to 2019 using USDA
survey data. Livestock includes ammonia and other pollutants (except
PM2.5). County and annual resolution.
Agricultural
Fertilizer
Nonpoint
2019 agricultural fertilizer ammonia emissions computed inline within
CMAQ.
Agricultural fires
with point
resolution: ptagfire
Nonpoint
Agricultural fire sources for year 2019 were developed by EPA as
point and day-specific emissions.7 Only EPA-developed ag. fire data
are used in this study, thus 2017 NEI state submissions are not
included. Agricultural fires are in the nonpoint data category of the
NEI, but in the modeling platform, they are treated as day-specific
point sources. Updated HAP-augmentation factors were applied.
Area fugitive dust:
afdustadj
Nonpoint
PM10 and PM2 5 fugitive dust sources from the 2017 NEI nonpoint
inventory; including building construction, road construction,
agricultural dust, and paved and unpaved road dust; with paved road
dust adjusted to 2019 based on vehicle miles traveled (VMT). The
emissions modeling system applies a transport fraction reduction and a
zero-out based on 2019 gridded hourly meteorology (precipitation and
snow/ice cover). Emissions are county and annual resolution.
7 Only EPA-developed agricultural fire data were included in this study; data submitted by states to the NEI were excluded.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Biogenic:
beis
Nonpoint
Year 2019 emissions from biogenic sources. These were left out of the
CMAQ-ready merged emissions, in favor of inline biogenic emissions
produced during the CMAQ model run itself. Version 3.7 of the
Biogenic Emissions Inventory System (BEIS) was used with Version
5 of the Biogenic Emissions Landuse Database (BELD5).
Category 1, 2 CMV:
cmv_clc2
Nonpoint
2019 Category 1 (CI) and Category 2 (C2), commercial marine vessel
(CMV) emissions based on Automatic Identification System (AIS)
data. Point and hourly resolution.
Category 3 CMV:
cmv c3
Nonpoint
2019 Category 3 (C3) commercial marine vessel (CMV) emissions
based on AIS data. Point and hourly resolution.
Locomotives :
rail
Nonpoint
Line haul rail locomotives emissions for year 2017, projected to 2019
using annual energy outlook (AEO) and additional factors supplied by
ERTAC. County and annual resolution.
Nonpoint source oil
and gas:
npoilgas
Nonpoint
Nonpoint 2017 NEI sources from oil and gas-related processes,
projected to 2019 using based on U.S. Energy Information
Administration (EIA) and Railroad Commission of Texas (TXRRC)
historical production data. County and annual resolution.
Residential Wood
Combustion:
rwc
Nonpoint
2017 NEI nonpoint sources with residential wood combustion (RWC)
processes were used as is, with no projection to represent 2019.
County and annual resolution.
Solvents:
npsolvents
Nonpoint
Emissions of solvents for the year 2019 (Seltzer, 2021). Includes
household cleaners, personal care products, adhesives, architectural
and aerosol coatings, printing inks, and pesticides. Annual and county
resolution.
Remaining
nonpoint:
nonpt
Nonpoint
2017 NEI nonpoint sources not included in other platform sectors. No
adjustments were made to represent 2019. County and annual
resolution.
Nonroad:
nonroad
Nonroad
2019 nonroad equipment emissions developed with MOVES3,
including the updates made to spatial apportionment that were
developed with the 2016vl platform. MOVES3 was used for all states
except California and Texas. California submitted their own emissions
for the 2017 NEI that were adjusted to 2019 based on interpolations
between 2017 and 2023. Texas provided 2017 and 2020 emissions
which were interpolated to 2019. County and monthly resolution.
Onroad:
onroad
Onroad
Onroad mobile source gasoline and diesel vehicles from parking lots
and moving vehicles. Includes the following emission processes:
exhaust, extended idle, auxiliary power units, evaporative, permeation,
refueling, vehicle starts, off network idling, long-haul truck hoteling,
and brake and tire wear. Activity data were projected from 2017 to
2019 using factors developed using data from Federal Highway
Administration and state departments of transportation. MOVES3 was
run for 2019 to generate emission factors.
Onroad California:
onroadcaadj
Onroad
California-provided 2017 CAP and metal HAP onroad mobile source
gasoline and diesel vehicles from parking lots and moving vehicles
based on Emission Factor (EMFAC) 2017, gridded and temporalized
based on outputs from MOVES3. Volatile organic compound (VOC)
HAP emissions derived from California-provided VOC emissions and
MOVES-based speciation. 2019 was interpolated between 2017 and
2023 emissions from EMFAC2017.
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Platform Sector:
abbreviation
NEI Data
Category
Description and resolution of the data input to SMOKE
Point source
prescribed fires:
ptfire-rx
Events
Point source day-specific prescribed fires for 2019 computed using
SMARTFIRE 2 and BlueSky Pipeline. The ptfire emissions were run
as two separate sectors: ptfire-rx (prescribed, including Flint Hills /
grasslands) and ptfire-wild.
Point source
wildfires: ptfire-wild
Events
Point source day-specific wildfires for 2019 computed using
SMARTFIRE 2 and BlueSky Pipeline.
Non-US. Fires:
ptfireothna
N/A
Point source day-specific wildfires and agricultural fires outside of the
U.S. for 2019 from vl.5 of the Fire INventory (FINN) from National
Center for Atmospheric Research (NCAR, 2017 and Wiedinmyer, C.,
2011) for Canada, Mexico, Caribbean, Central American, and other
international fires.
Other Area Fugitive
dust sources not
from the NEI:
othafdust
N/A
Area fugitive dust sources from Canada from EQUATES 2016 with
transport fraction and snow/ice adjustments based on 2019
meteorological data. Annual and province resolution.
Other Point Fugitive
dust sources not
from the NEI:
othptdust
N/A
Point source fugitive dust sources from Canada from EQUATES 2016
with transport fraction and snow/ice adjustments based on 2019
meteorological data. Annual and province resolution.
Other point sources
not from the NEI:
othpt
N/A
Canada and Mexico point source emissions from EQUATES 2016.
Canada point sources were provided by ECCCC and Mexico point
source emissions for 2016 were provided by SEMARNAT. Mexico
sources were projected to 2019 based on national emissions trends
from the Community Emissions Data System (CEDS). Annual and
monthly resolution.
Other non-NEI
nonpoint and
nonroad:
othar
N/A
For Canada except nonroad, EQUATES 2016. Projected Canada
nonroad to 2019 based on US MOVES3 2019/2016 ratios. EQUATES
2016 Mexico (municipio resolution, provided by SEMARNAT)
nonpoint and nonroad mobile inventories were projected to 2019
based on national emissions trends from the Community Emissions
Data System (CEDS). Annual and monthly resolution.
Other non-NEI
onroad sources:
onroadcan
N/A
Monthly onroad mobile inventory for Canada from EQUATES 2016
projected to 2019 using US onroad trends. Separate trends applied
to refueling (gas/diesel) and non-refueling (gas/diesel and
LD/HD). Province resolution.
Other non-NEI
onroad sources:
onroad mex
N/A
Monthly onroad mobile inventory from MOVES-Mexico (municipio
resolution) for 2017, adjusted to 2019 using interpolation between
2017 and 2020.
Other natural emissions are also merged in with the above sectors, including ocean chlorine and sea salt.
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cb)
concentrations in oceanic air masses (Bullock and Brehme, 2002).
Data at 12 km resolution were available and were not modified other than the name "CHLORINE" was
changed to "CL2" because that is the name required by the CMAQ model.
The emission inventories in SMOKE input formats for the platform are available from EPA's Air
Emissions Modeling website: https://www.epa.gov/air-emissions-modeling/2019-emissions-modeling-
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platform. The platform informational text file indicates the particular zipped files associated with each
platform sector. Some emissions data summaries are available with the data files for the 2019 platform.
The types of reports include state summaries of inventory pollutants and model species by modeling
platform sector and county annual totals by modeling platform sector.
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Table 3-2. 2019 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.)
Sector
CO
NH3
NOX
PM10
PM25
S02
voc
afdustadj
5,391,767
753,763
airports
494,906
0
135,054
9,899
8,661
16,907
55,888
cmv clc2
17,699
60
117,678
3,191
3,092
596
4,264
cmv c3
9,509
30
95,659
1,683
1,549
3,864
4,418
fertilizer
1,202,914
livestock
2,602,279
227,985
nonpt
1,927,267
102,898
739,250
572,589
475,154
166,399
818,185
nonroad
10,464,693
1,953
930,665
90,623
85,372
1,168
974,635
np_oilgas
629,304
27
520,213
11,180
11,091
37,872
2,581,373
npsolvcnts
36
58
34
469
448
5
2,516,324
onroad
16,637,063
102,432
2,780,428
215,217
94,256
16,657
1,162,167
ptegu
479,731
20,734
999,436
116,793
98,046
1,017,892
29,201
ptagfire
687,701
146,655
27,373
104,474
66,053
10,683
99,156
ptfire-rx
9,176,460
150,531
163,988
986,337
842,244
80,673
2,200,144
ptfire-wild
2,121,948
34,930
34,299
220,638
186,981
17,581
502,126
ptnonipm
1,359,439
68,482
855,115
380,052
241,176
500,592
760,023
pt_oilgas
174,426
3,759
354,632
13,514
13,190
36,934
143,335
rail
108,825
339
516,008
15,304
14,821
675
21,940
rwc
2,152,689
16,369
33,925
298,738
297,877
7,937
322,528
beis (not
merged)
3,695,221
942,563
25,450,181
TOTAL no beis
46,441,694 ¦
4,454,448
8,303,756
8,432,468
3,193,775
1,916,436
12,423,693
Table 3-3. Non-US Emissions by Sector within the 12US1 Modeling Domain (tons/yr for Canada,
Mexico, Offshore)
Sector
CO
NH3
NOX
PM10
PM25
S02
VOC
Canada ag
492,799
105,147
Canada oil and gas
2D
; 666
7
3,232
185
185
3,933
509,228
Canada othafdust
500,478
77,863
Canada othptdust
124,644
43,736
Canada othar
2,180,838
3,818
298,213
222,283
173,889
16,299
721,690
Canada onroad_can 1,587,108
7,125
327,671
24,783
12,312
1,121
132,251
Canada othpt
1,115,145
19,471
650,682
90,031
43,039
989,862
148,178
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Sector
CO
NH3
NOX
PM10
PM25
S02
VOC
Canada
ptfireothna
1,503,071
30,374
63,142
210,958
178,315
12,143
439,097
Canada cmv_c lc2
2,811
9
18,432
489
474
60
656
Canada cmv_c3
8,198
22
83,037
1,260
1,159
2,936
3,967
Canada subtotal
6,397,837
553,625
1,444,410
1,175,112
530,971
1,026,356
2,060,214
Mexico othar
109,035
115,777
50,564
101,331
32,933
1,576
348,466
Mexico
onroadmex
1,812,455
2,983
447,355
16,112
11,380
6,832
159,395
Mexico othpt
154,181
1,154
172,530
47,798
33,325
345,440
37,488
Mexico
ptfireothna
367,565
7,109
14,666
48,343
41,333
3,026
106,475
Mexico cmv clc2
152
1
1,016
27
26
2
45
Mexico cmv c3
8,091
185
89,440
10,433
9,598
84,305
3,782
Mexico subtotal
2,451,479
127,208
775,571
224,044
128,596
441,181
655,652
Offshore cmv clc2
4,205
13
26,796
700
678
72
1,007
Offshore cmv c3
45,473
538
472,445
30,400
27,968
225,335
21,595
Offshore pt oilgas
51,866
8
49,959
636
635
462
38,803
Can/Mex/offshore
total
8,950,860
681,393
2,769,180
1,430,892
688,848
1,693,406
2,777,271
3.2.1 Point Sources (ptegu, ptoilgas, ptnonipm, and airports)
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 CMV emissions (cmv_clc2 and cmv_c3 sectors)
are processed by SMOKE as point source inventories and are discussed later in this section. A complete
NEI is developed every three years, with 2017 being the most recently finished complete NEI. A
comprehensive description about the development of the 2017 NEI is available in the 2017 NEI TSD
(EPA, 2021). Point inventories are also available in EIS for intermediate years such as 2019. In the
intermediate point inventories, states are required to update larger sources with emissions for the interim
year, while sources not updated by states for the interim year are either carried forward from the most
recent triennial NEI or marked as closed and removed.
In preparation for modeling, the complete set of point sources in the NEI was exported from EIS for the
year 2019 into the Flat File 2010 (FF10) format that is compatible with SMOKE (see
https://www.cmascenter.Org/smoke/documentation/4.8.l/html/ch08s02s08.html) and was then split into
several sectors for modeling. The 20220325 version of the point FF10 file was used for the CMAQ and
AERMOD modeling. In the flat file, sources without specific locations (i.e., the FIPS code ends in 777)
were dropped and inventories for the other point source sectors were created from the remaining point
sources. The point sectors are: EGUs (ptegu), point source oil and gas extraction-related sources
(pt oilgas), airport emissions (airports), and the remaining non-EGUs (ptnonipm). The EGU emissions
were 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) and airport emissions
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(airports) were processed separately for summary tracking purposes and distinct projection techniques
from the remaining non-EGU emissions (ptnonipm).
In some cases, data about facility or unit closures are entered into EIS after the inventory modeling
inventory flat were reviewed and implemented based on the most recent submissions to EIS. Prior to
processing through SMOKE, submitted closures were reviewed and if closed sources were found in the
inventory, those were removed.
For the 2019 platform, an analysis of point source stack parameters (e.g., stack height, diameter,
temperature, and velocity) was performed after some specific examples of unrealistic stack parameters as
default values were noticed. The defaulted values were noticed in data submissions for the states of
Illinois, Louisiana, Michigan, Pennsylvania, Texas, and Wisconsin. Where these defaults were detected
and deemed to be unreasonable for the specific process, the affected stack parameters were replaced by
values from the currently available PSTK file that is input to SMOKE. PSTK contains default stack
parameters by source classification code (SCC). These updates impacted the ptnonipm and ptoilgas
inventories.
The inventory pollutants processed through SMOKE for input to CMAQ for the ptegu, ptoilgas,
ptnonipm, and airports sectors included: CO, NOx, VOC, SO:, NH.S PMio, and PM2.5 and the following
HAPs: HQ (pollutant code = 7647010), CI (code = 7782505), and several dozen other HAPs listed in
Section 3. NBAFM pollutants from the point sectors were utilized. For AERMOD, additional HAPS
were included as described in the 2019 AirTox Screen TSD.
The ptnonipm, pt oilgas, and airports sector emissions were provided to SMOKE as annual emissions.
For sources in the ptegu sector that could be matched to 2019 CEMS data, hourly CEMS NOx and SO2
emissions for 2019 from EPA's Acid Rain Program were used rather than annual inventory emissions.
For all other pollutants (e.g., VOC, PM2.5, HQ), annual emissions were used as-is from the annual
inventory 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 - 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) [the reporting size threshold is larger for inventory years between the triennial inventory
years of 2011,2014, 2017, . . .].
b. EPA corrected known issues and filled PM data gaps.
c. EPA added HAP data from the Toxic Release Inventory (TRI) where corresponding data was not
already provided by states/locals.
d. EPA stored and applied matches of the point source units to units with CEMS data and also for all
EGU units modeled by EPA's Integrated Planning Model (IPM).
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e. Data for airports and rail yards were incorporated.
f. 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 (F1PS)
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.
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 EG Us in the 2019 point source inventory that could be matched
to units found in the National Electric Energy Database System (NEEDS) v6 (see
https://vvvvvv.epa.gov/povver-sector-modeling/national-electric-energy-data-system-needs-v6) that is used
by the Integrated Planning Model (IPM) to develop future year EGU emissions. It was necessary to put
these EGlJs into a separate sector in the platform because EGlJs 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
ptoilgas 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.
The 2019 ptegu emissions inventory is a subset of the point source flat file exported from the Emissions
Inventory System (EIS). In the point source flat file, emission records for sources that have been matched
to the NEEDS database have a value filled into the IPM YN column based on the matches stored within
EIS. Thus, unit-level emissions were split into a separate EGU flat file for units that have a populated
(non-null) ipm_yn field. A populated ipm_yn field indicates that a match was found for the EIS unit in the
NEEDS v6 database. Updates were made to the flat file output from EIS as described in the list below:
ORIS facility and unit identifiers were updated based on additional matches in a cross-platform
spreadsheet, based on state comments, and using the EIS alternate identifiers table as described later in
this section.
Some units in the ptegu sector are matched to Continuous Emissions Monitoring System (CEMS) data via
Office of Regulatory Information System (ORIS) facility codes and boiler IDs. For the matched units, the
annual emissions of NOx and SO2 in the flat file are replaced with the hourly CEMS emissions in base
year modeling. For other pollutants at matched units, the hourly CEMS heat input data are used to
allocate the NEI annual emissions to hourly values. All stack parameters, stack locations, and Source
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Classification Codes (SCC) for these sources come from the flat file. If CEMS data exists for a unit, but
the unit is not matched to the NEI, the CEMS data for that unit are not used in the modeling platform.
However, if the source exists in the NEI and is not matched to a CEMS unit, the emissions from that
source are still modeled using the annual emission value in the NEI temporally allocated to hourly values.
EIS stores many matches from NEI units to the ORIS facility codes and boiler IDs used to reference the
CEMS data. In the flat file, emission records for point sources matched to CEMS data have values filled
into the ORISFACILITYCODE and ORISBOILERID columns. The CEMS data are available at
http://ampd.epa.gov/ampd near the bottom of the "Prepackaged Data" tab. Many smaller emitters in the
CEMS program cannot be matched to the NEI due to inconsistencies in the way a unit is defined between
the NEI and CEMS datasets, or due to uncertainties in source identification such as inconsistent plant
names in the two data systems. In addition, the NEEDS database of units modeled by IPM includes many
smaller emitting EGUs that do not have CEMS. Therefore, there will be more units in the ptegu sector
than have CEMS data.
Matches from the NEI to ORIS codes and the NEEDS database were improved in the platform where
applicable. In some cases, NEI units in EIS match to many CAMD units. In these cases, a new entry was
made in the flat file with a "_M_" in the ipm_yn field of the flat file to indicate that there are "multiple"
ORIS IDs that match that unit. This helps facilitate appropriate temporal allocation of the emissions by
SMOKE. Temporal allocation for EGUs is discussed in more detail in the Ancillary Data section below.
The EGU flat file was split into two flat files: those that have unit-level matches to CEMS data using the
oris facility code and oris boiler id fields and those that do not so that different temporal profiles could
be applied. In addition, the hourly CEMS data were processed through v2.1 of the CEMCorrect tool
(Adelman, 2012) to mitigate the impact of unmeasured values in the data.
3.2.1.2 Point Oil and Gas Sector (ptoilgas)
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.
For sources that otherwise would be pulled forward with 2017 emissions values because 2019-specific
emissions were not available, projection factors by NAICS and state derived from historical production
data from EIA. The factors were applied to those 2017 sources to adjust the emissions to make them
more representative of 2019. Texas historical production data by Texas Railroad district
(http://webapps.rrc.texas.gov/PDO/generalReportAction.do) were used to derive and apply district-
specific factors instead of state-specific. State (plus TX Railroad Commission district) factors were
applied to production-related NAICS. Transportation NAICS were projected using nationally derived
production-related factors for oil and gas. All other NAICS were held constant from 2017 NEI. All Tribal
data and offshore emissions are held constant from 2017 NEI. More information on the development of
the 2017 NEI oil and gas emissions can be found in Section 4.17 of the 2017 NEI TSD.
Table 3-4. Point source oil and gas sector NAICS Codes
NAICS
NAICS description
2111
Oil and Gas Extraction
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NAICS
NAICS description
211111
Crude Petroleum and Natural Gas Extraction
211112
Natural Gas Liquid Extraction
21112
Crude Petroleum Extraction
211120
Crude Petroleum Extraction
21113
Natural Gas Extraction
211130
Natural Gas Extraction
213111
Drilling Oil and Gas Wells
213112
Support Activities for Oil and Gas Operations
2212
Natural Gas Distribution
22121
Natural Gas Distribution
221210
Natural Gas Distribution
237120
Oil and Gas Pipeline and Related Structures Construction
4861
Pipeline Transportation of Crude Oil
48611
Pipeline Transportation of Crude Oil
486110
Pipeline Transportation of Crude Oil
4862
Pipeline Transportation of Natural Gas
48621
Pipeline Transportation of Natural Gas
486210
Pipeline Transportation of Natural Gas
3.2.1.3 A irports Sector (airports)
Emissions at airports were separated from other sources in the point inventory based on sources that have
the facility source type of 100 (airports). The airports sector includes all aircraft types used for public,
private, and military purposes and aircraft ground support equipment. The Federal Aviation
Administration's (FAA) Aviation Environmental Design Tool (AEDT) is used to estimate emissions for
this sector. For 2017, Texas and California submitted aircraft emissions. Additional information about
aircraft emission estimates can be found in section 3.2.2 of the 2017 NEI TSD. Data from the 2020
Terminal Area Forecast (TAF) were used to project 2017 NEI emissions to 2019. EPA used airport-
specific factors where available. Regional airports were projected using state average factors. Military
airports were unchanged from 2017. An update for the 2019 platform was that airport emissions were
spread out into multiple 12km grid cells when the airport runways were determined to overlap multiple
grid cells. Otherwise, airport emissions for a specific airport are confined to one air quality model grid
cell.
3.2.1.4 Non-IPM Sector (ptnonipm)
With some exceptions, the non-IPM (ptnonipm) sector contains the point sources that are not in the ptegu,
pt oilgas, or airports sectors. For the most part, the ptnonipm sector reflects the non-EGU emissions
sources and rail yards. However, it is likely that some low-emitting EGUs not matched to units 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
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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.
The ptnonipm sources (i.e., not EGUs and non -oil and gas sources) were used as-is from the 2019 NEI
point inventory. Unlike in the 2018 platform, instead of removing solvent emissions from the ptnonipm
sector, solvent emissions from point sources are instead removed from the npsolvents sector to prevent
double counting, so that all point sources can be retained in the modeling as point sources rather than as
area sources. The modeling was based on a version of the point flat file which included corrections to how
the selection was implemented in EIS, updates from the state/local review, and updates specific to
ethylene oxide. The np solvents sector is described in more detail in Section 3.2.3.6.
Emissions from rail yards are included in the ptnonipm sector. Railyards were projected to 2019 from the
2017 NEI railyard inventory using factors derived from the Annual Energy Outlook 2018
(http s: //www, ei a. gov/outl ooks/archive/aeo 18/).
3.2.3 Nonpoint Sources (afdust, ag, nonpt, npoilgas, rwc)
This section describes the stationary nonpoint sources in the NEI nonpoint data category. Locomotives,
CI and C2 CMV, and C3 CMV are included in the NEI nonpoint data category but are mobile sources
that are described in Section 2.4. The 2017 NEI TSD includes documentation for the nonpoint data.
Nonpoint tribal emissions submitted to the NEI are dropped during spatial processing with SMOKE due
to the configuration of the spatial surrogates. Part of the reason for this is to prevent possible double-
counting with county-level emissions and also because spatial surrogates for tribal data are not currently
available. These omissions are not expected to have an impact on the results of the air quality modeling at
the 12-km resolution used for this platform.
The following subsections describe how the sources in the NEI nonpoint inventory were separated into
modeling platform sectors, along with any data that were updated (replaced) with non-NEI data.
3.2.3.1 Area Fugitive Dust Sector (afdust)
The area-source fugitive dust (afdust) sector contains PMio 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 using a script that
applies land use-based gridded transport fractions based on landscape roughness, 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, 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 (i.e.,
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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.
Paved road dust emissions were projected from the 2017 NEI (January 2021 version) to 2019 based on
county-level VMT trends. All other afdust SCCs were held constant from the 2017 NEI. For the data
compiled into the 2017 NEI, meteorological adjustments are applied to paved and unpaved road SCCs but
not transport adjustments. This is because the modeling platform applies meteorological adjustments and
transport adjustments based on unadjusted NEI values. For the 2019 platform, the meteorological
adjustments that were applied (to paved and unpaved road SCCs) were backed out in order reapply them
in SMOKE. The FF10 that is run through SMOKE consists of 100% unadjusted emissions, and after
SMOKE all afdust sources have both transport and meteorological adjustments applied according to year
2019 meteorology.
For categories other than paved and unpaved roads, 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 met-adjusted for sources other than paved and unpaved
roads, 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.
3.2.3.2 Agricultural Livestock Sector (livestock)
The livestock emissions in this sector are based only on the SCCs starting with 2805. 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 sector does not include quite 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 addition to NH3, the sector includes livestock emissions from
all pollutants other than PM2.5. PM2.5 from livestock are in the afdust sector.
Agricultural livestock emissions in the 2019 platform were projected from the 2017 NEI (January 2021
version), which is a mix of state-submitted data and EPA estimates. USDA Survey data for 2017 and
2019 was used to create projection factors (https://quickstats.nass.usda.gov/). Livestock emissions utilized
improved animal population data. VOC livestock emissions, new for this sector, were estimated by
multiplying a national VOC/NH3 emissions ratio by the county NH3 emissions. The 2017 NEI approach
for livestock utilizes daily emission factors by animal and county from a model developed by Carnegie
Mellon University (CMU) (Pinder, 2004, McQuilling, 2015) and 2017 U.S. Department of Agriculture
(USDA) National Agricultural Statistics Service (NASS) survey. Details on the approach are provided in
Section 4.5 of the 2017 NEI TSD.
For livestock, meteorological-based temporalization is used for month-to-day and day-to-hour
temporalization. Monthly profiles for livestock are based on the daily data underlying the EPA estimates
from 2014NEIv2.
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3.2.3.3 Agricultural Fertilizer Sector (fertilizer)
As described in the 2017 NEI TSD, fertilizer emissions for this platform are based on the FEST-C model
(https://www.cmascenter.org/fest-c/). The bidirectional version of CMAQ (v5.3) and the Fertilizer
Emissions Scenario Tool for CMAQ FEST-C (vl.3) were used within CMAQ to estimate ammonia (NH3)
emissions from agricultural soils. Fertilizer emissions are output from a run of CMAQ in bi-directional
mode and summarized for inclusion with the rest of the emissions. The bidirectional version of CMAQ
(v5.3) and the Fertilizer Emissions Scenario Tool for CMAQ FEST-C (vl.3) were used to estimate
ammonia (NH3) emissions from agricultural soils. The computed emissions were saved during the CMAQ
run for the purposes of summaries and other model runs that did not use the bidirectional method.
FEST-C is the software program that processes land use and agricultural activity data to develop inputs
for the CMAQ model when run with bidirectional exchange. FEST-C reads land use data from the
Biogenic Emissions Landuse Dataset (BELD), meteorological variables from the Weather Research and
Forecasting (WRF) model, and nitrogen deposition data from a previous or historical average CMAQ
simulation. FEST-C, then uses the Environmental Policy Integrated Climate (EPIC) modeling system
(https://epicapex.tamu.edu/epic/) to simulate the agricultural practices and soil biogeochemistry and
provides information regarding fertilizer timing, composition, application method and amount.
An iterative calculation was applied to estimate fertilizer emissions. First, fertilizer application by crop
type was estimated using FEST-C modeled data. Then CMAQ v5.3 was run with the Surface Tiled
Aerosol and Gaseous Exchange (STAGE) deposition option with bidirectional exchange to estimate
fertilizer and biogenic NH3 emissions.
The approach to estimate year-specific fertilizer emissions consists of these steps:
Run FEST-C and CMAQ model with bidirectional ("bidi") NH3 exchange to produce nitrate
(NO3), Ammonium (NH4+, including Urea), and organic (manure) nitrogen (N) fertilizer usage
estimates, and gaseous ammonia NH3 emission estimates respectively.
Calculate county-level emission factors as the ratio of bidirectional CMAQ NH3 fertilizer
emissions to FEST-C total N fertilizer application.
Assign the NH3 emissions to one SCC: ".. .Miscellaneous Fertilizers" (2801700099).
For livestock and fertilizer, meteorological-based temporalization is used for month-to-day and day-to-
hour temporalization. Monthly profiles for livestock are based on the daily data underlying the EPA
estimates from 2014NEIv2. The fertilizer inventory includes monthly emissions from FEST-C and uses
the same meteorological-based month-to-hour profiles as livestock in the same way as was done for other
recent platforms.
3.2.3.4 Nonpoint Oil-gas Sector (npoilgas)
The nonpoint oil and gas (np oilgas) sector includes onshore and offshore oil and gas emissions. The
EPA estimated emissions for all counties with 2019 oil and gas activity data with 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. Because of the importance of emissions from this sector, special consideration is given to
the speciation, spatial allocation, and monthly temporalization of nonpoint oil and gas emissions, instead
of relying on older, more generalized profiles.
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The 2017NEI version of the Nonpoint Oil and Gas Emission Estimation Tool (i.e., the "tool") was used to
estimate emissions for 2019. Year 2019 oil and gas activity data was supplied to EPA by Enverus'
activity database (www.enverus.com) and from some state air agencies . The tool is an Access database
that utilizes county-level activity data (e.g., oil production and well counts), operational characteristics
(types and sizes of equipment), and emission factors to estimate emissions. The tool created a CSV-
formatted emissions dataset covering all national nonpoint oil and gas emissions. This dataset was then
converted to the FF10 format for use in SMOKE modeling. More details on the inputs for and running of
the tool with 2017 as an example are provided in the 2017 NEI TSD.
3.2.3.5 Residential Wood Combustion Sector (rwc)
The residential wood combustion (rwc) sector includes residential wood burning devices such as
fireplaces, fireplaces with inserts (inserts), free standing woodstoves, pellet stoves, outdoor hydronic
heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in firepots and
chimeneas. 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.
The 2019 platform RWC emissions are unchanged from the data in the 2017 NEI. Some improvements to
RWC emissions estimates were made for the 2017 NEI and were included in this study. The EPA, along
with the Commission on Environmental Cooperation (CEC), the Northeast States for Coordinated Air Use
Management (NESCAUM), and Abt Associates, conducted a national survey of wood-burning activity in
2018. The results of this survey were used to estimate county-level burning activity data. The activity data
for RWC processes is the amount of wood burned in each county, which is based on data from the CEC
survey on the fraction of homes in each county that use each wood-burning appliance and the average
amount of wood burned in each appliance. These assumptions are used with the number of occupied
homes in each county to estimate the total amount of wood burned in each county, in cords for cordwood
appliances and tons for pellet appliances. Cords of wood are converted to tons using county-level density
factors from the U.S. Forest Service. RWC missions were calculated by multiplying the tons of wood
burned by emissions factors. For more information on the development of the residential wood
combustion emissions, see Section 4.15 of the 2017 NEI TSD.
3.2.3.6 Solvents (npsolvents)
The np solvents sector is a diverse collection of emission sources whose emissions are driven by
evaporation. Included in this sector are everyday items, such as cleaners, personal care products,
adhesives, architectural and aerosol coatings, printing inks, and pesticides. These sources exclusively
emit organic gases and feature origins spanning residential, commercial, institutional, and industrial
settings. The organic gases that evaporate from these sources often fulfill other functions than acting as a
traditional solvent (e.g., propellants, fragrances, emollients).
Here, emissions from this sector are derived using the volatile chemical products in python (VCPy)
framework (Seltzer et al., 2021). The VCPy framework is based on the principle that the magnitude and
speciation of organic emissions from this sector are directly related to (1) the mass of chemical products
used, (2) the composition of these products, (3) the physiochemical properties of their constituents that
govern volatilization, and (4) the timescale available for these constituents to evaporate. National product
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usage is preferentially estimated using economic statistics from the U.S. Census Bureau's Annual Survey
of Manufacturers (U.S. Census Bureau, 2019), commodity prices from the U.S. Department of
Transportation's 2012 Commodity Flow Survey (U.S. Department of Transportation, 2015) and the U.S.
Census Bureau's Paint and Allied Products Survey (U.S. Census Bureau, 2011), and producer price
indices, which scale commodity prices to target years, are retrieved from the Federal Reserve Bank of St.
Louis (U.S. Bureau of Labor Statistics, 2020). In circumstances where the aforementioned datasets are
unavailable, default usage estimates are derived using functional solvent usage reported by a business
research company (The Freedonia Group, 2016) or in sales reported in a California Air Resources Board
(CARB) California-specific survey (CARB, 2019). The composition of products is estimated by
generating composites from various CARB surveys from 2007 through 2019, along with profiles reported
in the U.S. EPA's SPECIATE database (EPA, 2019). The physiochemical properties of all organic
components are generated from the quantitative structure-activity relationship model OPERA (Mansouri
et al., 2018) and the characteristic evaporation timescale of each component is estimated using previously
published methods (Khare and Gentner, 2018; Weschler and Nazaroff, 2008).
National-level emissions are then allocated to the county-level using several proxies. Most emissions are
allocated using population as a spatial surrogate. This includes all cleaners, personal care products,
adhesives, architectural coatings, and aerosol coatings. Industrial coatings, allied paint products, printing
inks, and dry cleaning emissions are allocated using county-level employment statistics from the U.S.
Census Bureau's County Business Patterns (U.S. Census Bureau, 2019) and follow the same mapping
scheme used in the U.S. EPA's 2017 National Emissions Inventory (EPA, 2021). Agricultural pesticides
are allocated using county-level agricultural pesticide use, as taken from the 2017 NEI.
The version of VCPy used for this platform includes additional product aggregations, variation in the
VOC-content of products to reflect state-level area source rules relevant to the solvent sector, and the
adoption of an indoor emissions pathway. To compute VCP emissions indoors, each product category is
assigned an indoor usage fraction. All coating and industrial products are assigned a 50% indoor emission
fraction, all pesticides and automotive aftermarket products are assigned a 0% indoor emission fraction,
and all consumer and cleaning products are assigned a 100% indoor emission fraction. The lone exception
are daily use personal care products, which are assumed to have a 50% indoor emission fraction. This
indoor emission assignment enables the mass transfer coefficient to vary between indoor and outdoor
conditions. Typically, the mass transfer coefficent indoors is smaller than the mass transfer coefficient
outdoors due to more stagnant atmospheric conditions, and the newest version of the modeling framework
reflects these dynamics. Indoor product usage utilizes a mass transfer coefficient of 5 m/hr, and the
remaining outdoor portion is assigned a mass transfer coefficient of 30 m/hr (Weschler and Nazaroff,
2008; Khare and Gentner, 2018).
The npsolvents sector also includes emissions from the 2017 NEI not covered by VCPy. This includes
some State, Locality, and Tribal emission submissions for other CAPs, such as CO, NOX, and PM2.5. In
addition, there are some SCCs not covered by VCPy but included in the np solvents sector.
Finally, since emissions from solvents occur from both point and nonpoint SCCs, point source subtraction
is required to ensure emissions from this sector are not double-counted. Point source subtraction for this
sector is performed at the county-level using uncontrolled point source emissions. As such, assumptions
related to the control efficiency of the point sources must be made. In most some cases, metadata
indicates that the point source emission estimates submitted to the NEI feature 80-90% control
efficiencies. Therefore, uncontrolled point source emission calculations are calculated, as necessary, using
the submitted point source emissions, engineering judgement, and an assumed control efficiency. If point
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source subtraction results in negative emissions, emissions will zero out emissions for that source
category in that county. The mapping of nonpoint SCCs to point SCCs follows the same crosswalk
implemented for the 2020 NEI.
3.2.3.7 Other Nonpoint Sources (nonpt)
The 2019 platform nonpt sector inventory is mostly unchanged from the January 2021 version of the 2017
NEI. Stationary nonpoint sources that were not subdivided into the afdust, livestock, fertilizer, np oilgas,
rwc or np solvents sectors were assigned to the "nonpt" sector. Locomotives and CMV mobile sources
from the 2017 NEI 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;
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;
and
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.
Volatile chemical product (aka solvent) SCCs were placed into the solvents sector. The EPA incorporated
new methods to estimate emissions of VOC and associated HAPs from the solvents sector, for this 2019
modeling platform. The new methods result in improved emissions estimates for the nonpoint (county-
wide) solvent emissions. The new emissions method results in improved VOC and HAP estimates for
nonpoint categories of coatings, pesticides, adhesives and sealants, oil & gas exploration solvent use, dry
cleaning, printing inks, cleaning products, personal care products, and other miscellaneous solvent uses.
3.2.4 Biogenic Sources (beis)
Biogenic emissions were computed based on the 19k version of the 2019 meteorology data used for the
air quality modeling and were developed using the Biogenic Emission Inventory System version 3.7
(BEIS3.7) within CMAQ. The BEIS3.7 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 3.7 two-layer canopy model,
the layer structure varies with light intensity and solar zenith angle (Pouliot and Bash, 2015). Both layers
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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 3.7
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 USD A category
SOIM1
volumetric soil moisture in top cm
SOIT1
soil temperature in top cm
TEMPG
skin temperature at ground
USTAR
cell averaged friction velocity
RADYNI
inverse of aerodynamic resistance
TEMP2
temperature at 2 m
BEIS3.7 was used in conjunction with Version 5 of the Biogenic Emissions Landuse Database (BELD5).
The BELD5 is based on an updated version of the USDA-USFS Forest Inventory and Analysis (FIA)
vegetation speciation-based data from 2001 to 2017 from the FIA version 8.0. This same configuration of
BEIS3.7 and BELD5 was used to develop the biogenic emissions in the 2017 NEI. Canopy coverage is
based on the Global Moderate Resolution Imaging Spectroradiometer (MODIS) 20 category data with
enhanced lakes and Fraction of Photosynthetically Active Radiation (FPAR) for vegetation coverage from
National Center for Atmospheric Research (NCAR). 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 MODIS canopy coverage. For land areas outside the conterminous United
States, 500-meter grid spacing land cover data from the MODIS is used. BELD5 also incorporates the
following:
Canadian BELD land use, Updates to Version 4 of the Biogenic Emissions Landuse Database
(BELD4) for Canada and Impacts on Biogenic VOC Emissions
(https://www.epa.gov/sites/production/files/2019-08/documents/8Q0am zhang 2 O.pdf)
2017 30 meter USD A Cropland Data Layer (CDL) data
(http://www.nass.usda.gov/research/Cropland/Release/).
Biogenic emissions computed with BEIS to review and prepare summaries, but they were left out of the
CMAQ-ready merged emissions. Instead, the biogenic emissions are produced inline during the CMAQ
model run which uses the same algorithm described above, but with finer time steps within the air quality
model.
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3.2.5 Mobile Sources (onroad, onroadcaadj, nonroad, cmv_clc2, 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 low-level emissions (i.e., they are released into model layer 1) and most nonroad emission are
represented as county totals. Note that rail yard and airport emissions are part of the NEI point data
category.
Commercial marine vessel (CMV) emissions are split into two sectors: emissions from Category 1 and
Category 2 vessels are in the cmv c 1 c2 sector, and emissions from the larger ocean-going Category 3
vessels are in the cmv_c3 sector. Both CMV sectors 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 CMV to be modeled with plume rise. In addition, CMV
emissions are treated as hourly point source emissions in the modeling platform, although they are part of
the NEI nonpoint data category.
3.2.5.1 Onroad (onroad)
Onroad mobile source include emissions from motorized vehicles operating 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 by the fuel they use, including 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 as they move along the roads). For more details on the approach and for a summary of the
MOVES inputs submitted by states, see section 6.5.1 of the 2017 NEI TSD.
For the 2019 modeling platform, VMT were projected from 2017 to 2019 based mostly on Federal
Highways administration (FHWA) annual VMT changes at the county level. In a few cases, state
Department of Transportation (DOT) data were used instead of FHWA data. Other activity data (i.e.,
starts, on-network idling, VPOP, and hoteling) are projected by applying a ratio of 2017-based
VMT/activity ratios to the 2019 VMT. In addition, a number of states submitted 2017-specific activity
data for incorporation into this platform. Finally, a new MOVES run for 2019 was done using MOVES3.
Except for California, all onroad emissions are generated using the SMOKE-MOVES emissions modeling
framework that leverages MOVES-generated emission factors https://www.epa.gov/moves). county and
SCC-specific activity data, and hourly 2019 meteorological data. Specifically, EPA used MOVES3
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. The onroad source classification codes (SCCs) in the
modeling platform are more finely resolved than those in the National Emissions Inventory (NEI). The
NEI SCCs distinguish vehicles and fuels. The SCCs used in the model platform also distinguish between
emissions processes (i.e., off-network, on-network, and extended idle), and road types.
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M0VES3 includes the following updates from MOVES2014b:
Updated emission rates:
o Updated heavy-duty (HD) diesel running emission rates based on manufacturer in-use
testing data from hundreds of HD trucks
o Updated HD gasoline and compressed natural gas (CNG) trucks
o Updated light-duty (LD) emission rates for hydrocarbons (HC), CO, NOx, and PM
Includes updated fuel information
Incorporates HD Phase 2 Greenhouse Gas (GHG) rule, allowing for finer distinctions among HD
vehicles
Accounts for glider vehicles that incorporate older engines into new vehicle chassis
Accounts for off-network idling - emissions beyond the idling that is already considered in the
MOVES drive cycle
Includes revisions to inputs for hoteling
Adds starts as a separate type of rate and activity data
Except for California, onroad emissions are generated using the SMOKE-MOVES interface that leverages
MOVES generated emission factors (https://www.epa.gov/moves). 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, VMT, VPOP,
starts, 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. Meteorological data were
specific to the year 2019.
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 2019-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, and inspection and maintenance (I&M) 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 294 representative counties in the continental U.S.
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 then 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.
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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.
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 (RPS) uses STARTS activity data to compute off-network emissions from vehicles
starts;
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 (RPHO) uses off network idling hours activity data to compute off-network idling
emissions for all types of vehicles; 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 2019 platform are based on the 2017 NEI, described in more detail in
Section 6 of the 2017 NEI TSD. These inputs include:
MOVES County databases (CDBs) including Low Emission Vehicle (LEV) table
Representative counties
Fuel months
Meteorology
Activity data (VMT, VPOP, speed, HOTELING)
Fuel months and other inputs were consistent with those in the 2017 NEI, although age distributions were
adjusted to represent the year 2019. A list of states that submitted activity data along with a description of
the development of the EPA default activity data sets for VMT, VPOP, and hoteling hours are available in
detail in the 2017 NEI TSD and supporting documents. Hoteling hours activity are used to calculate
emissions from extended idling and auxiliary power units (APUs) by combination long-haul trucks.
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Hoteling hours were capped by county at a theoretical maximum and any excess hours of the maximum
were reduced. For calculating reductions, a dataset of truck stop parking space availability was used,
which includes a total number of parking spaces per county. This same dataset is used to develop the
spatial surrogate for allocating county-total hoteling emissions to model grid cells. The parking space
dataset includes several recent updates based on new truck stops opening and other new information.
There are 8,760 hours in the year 2019; therefore, the maximum number of possible hoteling hours in a
particular county is equal to 8,760 * the number of parking spaces in that county. Hoteling hours were
capped at that theoretical maximum value for 2019 in all counties, with some exceptions.
Because the truck stop parking space dataset may be incomplete in some areas, and trucks may sometimes
idle in areas other than designated spaces, it was assumed that every county has at least 12 parking spaces,
even if fewer parking spaces are found in the parking space dataset. Therefore, hoteling hours are never
reduced below 105,408 hours for the year in any county. If the unreduced hoteling hours were already
below that maximum, the hours were left unchanged; in other words, hoteling activity are never increased
as a result of this analysis. Four states requested that no reductions be applied to the hoteling activity
based on parking space availability: CO, ME, NJ, and NY. For these states, reductions based on parking
space availability were not applied.
The final step related to hoteling activity is to split county totals into separate values for extended idling
(SCC 2202620153) and Auxiliary Power Units (APUs) (SCC 2202620191). New Jersey's submittal of
hoteling activity specified a 30% APU split, and this was used throughout NJ. For the rest of the country,
a 6.9% APU split was used, meaning that during 6.9% of the hoteling hours auxiliary power units are
assumed to be running.
The last pieces of activity data needed for SMOKE-MOVES are related to the average speed of vehicles,
which affects the selection of MOVES emission factors for on-network emissions. One such dataset is the
SPEED inventory read by the SMOKE program Smkinven, which includes a single overall average speed
for each county, SCC, and month. The second dataset is the SPDIST dataset read by the SMOKE program
Movesmrg, which specifies the amount of time spent in each MOVES speed bin for each county, vehicle
(aka source) type, road type, weekday/weekend, and hour of day. SMOKE still requires the SPEED
dataset exist even when hourly speed data is available, even though only the hourly speed data affects the
selection of emission factors. The SPEED and SPDIST datasets are from the 2017 NEI with some of the
data carried over from the Coordinating Research Council A-100 study (CRC, 2017).
MOVES3 was run in emission factor mode to create emission factor tables using CB6 speciation for the
year 2019, for all representative counties and fuel months. The county databases used to run MOVES to
develop the emission factor tables included the state-specific control measures such as the California LEV
program, and fuels represented the year 2019. The range of temperatures run along with the average
humidities used were specific to the year 2019. The remaining settings for the CDBs are documented in
the 2017 NEI TSD. To create the emission factors, MOVES was run separately for each representative
county and fuel month for each temperature bin needed for the calendar year 2019. The MOVES results
were post-processed into CSV-formatted emission factor tables that can be read by SMOKE-MOVES.
California uses their own emission model, EMFAC, which uses emission inventory codes (EICs) to
characterize the emission processes instead of SCCs. EPA had a 2016vl platform-based set of emissions
for 2023. EPA interpolated between 2017 and 2023 to calculate the 2019 onroad emissions for
California. 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-
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network and brake and tire wear emissions. This detail is needed for modeling but not for the NEI. The
California inventory had CAPs only and did not have NH3 or refueling. The EPA added NH3 to the
CARB inventory by using the state total NH3 from MOVES and allocating it at the county level based on
CO. Refueling emissions were projected from the 2017 NEI using county total refueling VOC from
EQUATES 2017 and the 2019 MOVES3 onroad run for CA. CARB VOCs were speciated to VOC HAPs
using MOVES VOC speciation. All other HAPs (e.g., metals and PAHs) are from MOVES.
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 emissions hereafter
known as "EPA estimates." These EPA estimates for CA are run in a separate sector called
"onroadca."
2) Calculate ratios between state-supplied emissions and EPA estimates. The ratios were calculated
for each county/SCC/pollutant combination based on the California onroad emissions inventory.
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 SMOKE-
MOVES. After adjusting the emissions, this sector is called "onroadcaadj " 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 Category 1,2, and3 commercial marine vessels (cmv_clc2 and cmv_3)
The cmv_clc2 sector contains Category 1 and 2 CMV emissions. Category 1 and 2 vessels use diesel
fuel. Some examples of CMV sources included in cmv_clc2 are fishing vessels, tug boats, and oil and gas
platform support vessels. Table 3-6 shows the number of each type of Category 3 vessel identified as part
of the 2020 NEI process. For more information on the CMV sources, see the supplemental
documentation for 2020 NEI CMV8. CI and C2 emissions that occur outside of state waters are not
assigned to states. All CMV emissions in the cmv_clc2 sector are treated as hourly gridded point sources
with stack parameters that should result in them being placed in layer 1. The C1C2 CMV emissions were
computed for 2019 using methods compatible with the 2020 NEI.
8 https://gaftp.epa.gov/Air/nei/2020/doc/supporting_data/nonpoint/CMV/
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Table 3-6. CMV C1C2 Vessels in each Group
Vessel Group
Ship Count
Bulk Carrier
37
Commercial Fishing
1,147
Container Ship
7
Ferry Excursion
441
General Cargo
1,498
Government
1,338
Miscellaneous
1,475
Offshore support
1,149
Reefer
13
Ro Ro
26
Tanker
100
Tug
3,994
Work Boat
77
Total in Inventory:
11,302
The cmv_c3 sector contains large engine CMV emissions. Category 3 (C3) marine diesel engines are
those at or above 30 liters per cylinder, typically these are the largest engines rated at 3,000 to 100,000 hp.
C3 engines are typically used for propulsion on ocean-going vessels including container ships, oil tankers,
bulk carriers, and cruise ships. Emissions control technologies for C3 CMV sources are limited due to the
nature of the residual fuel used by these vessels.9 The cmv_c3 sector contains sources that traverse state
and federal waters; along with sources in waters not covered by the NEI in surrounding areas of Canada,
Mexico, and international waters. For more information on the 2019 CMV sources, see the supplemental
documentation for the 2020 NEI CMV10.
The resulting annual point emissions were converted to an annual point 2010 flat file format (FF10). A set
of standard stack parameters were assigned to each release point in the cmv_c3 inventory. The assigned
stack height was 65.62 ft, the stack diameter was 2.625 ft, the stack temperature was 539.6 °F, and the
velocity was 82.02 ft/s. Hourly emissions were converted to an hourly point 2010 FF10.
The emission factors reflect International Maritime Organization (IMO) Tier 3 NOx regulations that apply
to engines installed on ships constructed (i.e., keel is laid) on or after January 1st, 2016.
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.
Typically, in the NEI, yard locomotive emissions are split between the nonpoint and point categories, but
for this study, all yard locomotive emissions are represented as point sources and included in the
ptnonipm sector.
9 https://www.epa.gov/regulations-emissions-vehicles-and-engines/regulations-emissions-marine-vessels.
10 https://gaftp.epa.gov/Air/nei/2020/doc/supporting data/nonpoint/CMV.
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This study uses the 2017 rail inventory developed for the 2017 NEI by the Lake Michigan Air Directors
Consortium (LADCO) and the State of Illinois with support from various other states. Class I railroad
emissions are based on confidential link-level line-haul activity GIS data layer maintained by the Federal
Railroad Administration (FRA). In addition, the Association of American Railroads (AAR) provided
national emission tier fleet mix information. Class II and III railroad emissions are based on a
comprehensive nationwide GIS database of locations where short line and regional railroads operate.
Passenger rail (Amtrak) emissions follow a similar procedure as Class II and III, except using a database
of Amtrak rail lines. Yard locomotive emissions are based on a combination of yard data provided by
individual rail companies, and by using Google Earth and other tools to identify rail yard locations for rail
companies which did not provide yard data. Information on specific yards were combined with fuel use
data and emission factors to create an emissions inventory for rail yards. More detailed information on
the development of the 2017 rail inventory for this study is available in the 2017 NEI TSD. The 2017
inventory was projected to 2019 using activity-based factors. Pollutant-specific factors were applied on
top of the activity-based changes for the Class I rail.
3.2.5.4 MO VES-based NonroadMobile Sources (nonroad)
The mobile nonroad equipment sector includes all mobile source emissions that do not operate on roads,
excluding commercial marine vehicles, railways, and aircraft. Types of nonroad equipment include
recreational vehicles, pleasure craft, and construction, agricultural, mining, and lawn and garden
equipment. Nonroad equipment emissions were computed by running MOVES3 which incorporates the
NONROAD model. MOVES3 incorporated updated nonroad engine population growth rates, nonroad
Tier 4 engine emission rates, and sulfur levels of nonroad diesel fuels. MOVES provides a complete set of
HAPs and incorporates updated nonroad emission factors for HAPs. MOVES3 was used for all states
other than California, which uses their own model, and the Texas Commission on Environmental Quality
(TCEQ), which provided their own emissions. California nonroad emissions were provided by the
California Air Resources Board (CARB) for the 2017 NEI. The 2019 California nonroad emissions were
interpolated from the 2017 NEI and a 2023 projection from the 2016vl modeling platform, with HAP
augmentation. For Texas, the EPA interpolated to 2019 between data provided for 2017 and 2020 and
applied HAP augmentation.
The spatial allocation updates to agricultural and construction equipment developed as part of the 2016
platform were carried forward into this platform.
MOVES creates a monthly emissions inventory for criteria air pollutants (CAPs) and a full set of HAPs,
plus additional pollutants such as NONHAPTOG and ETHANOL, which are not part of the NEI but are
used for speciation. MOVES provides estimates of NONHAPTOG along with the speciation profile code
for the NONHAPTOG emission source. This was accomplished by using NHTOG#### as the pollutant
code in the Flat File 2010 (FF10) inventory file that can be read into SMOKE, where #### is a speciation
profile code. For California and Texas, NHTOG####-VOC and HAP-VOC ratios from MOVES-based
emissions were applied to VOC emissions so that VOC emissions can be speciated consistently with other
states.
MOVES also provides estimates of PM2.5 by speciation profile code for the PM2.5 emission source,
using PM25_#### as the pollutant code in the FF10 inventory file, where #### is a speciation profile
code. To facilitate calculation of PMC within SMOKE, and to help create emissions summaries, an
additional pollutant representing total PM2.5 called PM25TOTAL was added to the inventory. As with
VOC, PM25_####-PM25TOTAL ratios were calculated and applied to PM2.5 emissions in California
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and Texas so that PM2.5 emissions in California and Texas can be speciated consistently with other
states.
MOVES3 outputs emissions data in county-specific databases, and a post-processing script converts the
data into FF10 format. Additional post-processing steps were performed as follows:
County-specific FFlOs were combined into a single FF10 file.
Emissions were aggregated from the more detailed SCCs modeled in MOVES to the SCCs
modeled in SMOKE. A list of the aggregated SMOKE SCCs is in Appendix A of the 2016vl
platform nonroad specification sheet (NEIC, 2019).
To reduce the size of the inventory, HAPs that are not needed for air quality modeling, such as
dioxins and furans, were removed from the inventory.
To reduce the size of the inventory further, all emissions for sources (identified by county/SCC)
for which total CAP emissions are less than 1*10"10 were removed from the inventory. The
MOVES model attributes a very tiny amount of emissions to sources that are actually zero, for
example, snowmobile emissions in Florida. Removing these sources from the inventory reduces
the total size of the inventory by about 7%.
Gas and particulate components of HAPs that come out of MOVES separately, such as
naphthalene, were combined.
VOC was renamed VOC INV so that SMOKE does not speciate both VOC and NONHAPTOG,
which would result in a double count.
PM25TOTAL, referenced above, was also created at this stage of the process.
Emissions for airport ground support vehicles (SCCs ending in -8005), and oil field equipment
(SCCs ending in -10010), were removed from the inventory at this stage, to prevent a double
count with the airports and np oilgas sectors, respectively.
California emissions from MOVES were deleted and replaced with the CARB-supplied emissions.
3.2.6 Day-Specific Point Source Fires (ptfire)
Wildfire and prescribed burning emissions are contained in the ptfire-rx and ptfire-wild sectors. Both ptfire sectors
have 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 day-specific and include satellite-derived latitude/longitude of the fire's origin and other parameters
associated with the emissions such as acres burned and fuel load, which allow estimation of plume rise.
Figure 3-1 shows the processing stream for wildfire and prescribed burn sources. The emissions estimate
methodology consists of two tools or systems. The first system is called Satellite Mapping Automated
Reanalysis Tool for Fire Incident Reconciliation version 2 (SMARTFIRE2). SMARTFIRE2 is an
algorithm and database system that operate within a geographic information system (GIS) framework.
SMARTFIRE2 combines multiple sources of fire information and reconciles them into a unified GIS
database. It reconciles fire data from space-borne sensors and ground-based reports, thus drawing on the
strengths of both data types while avoiding double-counting. At its core, SMARTFIRE2 is an association
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engine that links reports covering the same fire in any number of multiple databases. In this process, all
input information is preserved, and no attempt is made to reconcile conflicting or potentially contradictory
information (for example, the existence of a fire in one database but not another). In this study, the
national and S/L/T fire information is input into SMARTFIRE2 and then all information is merged and
associated together based on user-defined weights for each fire information dataset. The output from
SMARTFIRE2 is daily acres burned and latitude-longitude coordinates for each fire.
Input Data Sets
(state/local/tribal and national data sets)
Data Preparation
4 4
Data Aggregation and Reconciliation
(SmartFire2)
Oaily fire locations
with fire size and type
BlueSky Pipeline
Daily smoke emissions
for each fire
Emissions Post-Processing
Final Wildland Fire Emissions Inventory
Figure 3-1. Processing flow for fire emission estimates
Inputs to SMARTFIRE2 for 2019 included:
The National Oceanic and Atmospheric Administration's (NOAA's) Hazard Mapping System
(HMS) fire location information
GeoMAC (Geospatial Multi-Agency Coordination), an online wildfire mapping application
designed for fire managers to access maps of current fire locations and perimeters in the
United States
The Incident Status Summary, also known as the "ICS-209", used for reporting specific
information on fire incidents of significance
Hazardous fuel treatment reduction polygons for prescribed burns from the Forest Service
Activity Tracking System (FACTS)
Fire activity on federal lands from the United States Fish and Wildlife Service (USFWS)
Burn scar/fire activity shapefiles for wildfires and some prescribed burns from Monitoring
trends in burn severity (MTBS) website (https://www.mtbs.gov/direct-download)
Prescribed burn activity on federal lands from the Department of Interior (DOI)
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Prescribed burn activity from California Air Resources Board (CARB) specifically from their
Prescribed Fire Incident Reporting System (PFIRS)
Prescribed burn activity from Texas Parks and Wildlife Division (TPWD)
Active fire perimeters from Bureau of Land Management (BLM)
Wildfire and prescribed date, location, and locations from a few S/L/T activity submitters
(includes Georgia, Florida and Kanas(Flint Hills only))
The second system used to estimate emissions is the BlueSky Modeling Pipeline which supports the
calculation of fuel loading and consumption, and emissions using various models depending on the
available inputs as well as the desired results. The contiguous United States and Alaska, where Fuel
Characteristic Classification System (FCCS) fuel loading data are available, were processed using the
modeling chain described in Figure 3-2. The Fire Emissions Production Simulator (FEPS) (Anderson,
2004) in the BlueSky Pipeline generates all the CAP emission factors for wildland fires used in this 2019
study. HAP emission factors were obtained from Urbanski's (2014) work and applied by region and by
fire type.
Figure 3-2. BlueSky Pipeline modeling system
The FCCSv3 cross-reference was implemented along with the LANDFIRE (at 200-meter resolution) to
provide better fuel bed information for the BlueSky Pipeline (BSP). The LANDFIREv2 was aggregated
from the native resolution and projection to 200 meter using a nearest-neighbor methodology.
Aggregation and reprojection was required for the proper function on BSP.
The final products from this process are annual and daily FFlO-formatted emissions inventories. These
SMOKE-ready inventory files contain both CAPs and HAPs. The BAFM HAP emissions from the
inventory were used directly in modeling and were not overwritten with VOC speciation profiles (i.e., an
"integrate HAP" use case).
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3.2.7 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 for
the year 2018 in a similar way to the emissions in ptfire. The state of Florida provided their own
emissions (separate from the other states) for this study.
Daily year-specific agricultural burning emissions are derived from HMS fire activity data, which
contains the date and location of remote-sensed anomalies. The activity is filtered using the 2019 USDA
cropland data layer (CDL). Satellite fire detects over agricultural lands are assumed to be agricultural
burns and assigned a crop type. Detects that are not over agricultural lands are output to a separate file for
use in the ptfire sector. Each detect is assigned an average size of between 40 and 80 acres based on crop
type. Grassland/pasture fires are in the ptfire sector for this modeling platform. Depending on their origin,
grassland fires are in both ptfire-rx and ptfire-wild sectors because both fire types do involve grassy fuels.
Another feature of the database is that the satellite detections for 2019 were filtered out to exclude areas
covered by snow during the winter months. To do this, the daily snow cover fraction per grid cell was
extracted from a 2018 meteorological simulation (WRF). The location of fire detections was then
compared with this daily snow cover file. For any day in which a grid cell has snow cover, that fire
detection was excluded. Due to the inconsistent reporting of fire detections from the Visible Infrared
Imaging Radiometer Suite (VIIRS) platform, any fire detections in the HMS dataset that were flagged as
VIIRS or SUOMI were excluded. In addition, certain crop types (corn and soybeans) have been excluded
from these specific midwestern states: Iowa, Kansas, Indiana, Illinois, Michigan, Missouri, Minnesota,
Wisconsin, and Ohio. Emissions factors were applied to each daily fire to calculate criteria and hazardous
pollutant values. These factors vary by crop type.
Heat flux for plume rise was calculated using the size and assumed fuel loading of each daily agricultural
fire. This information is needed for a plume rise calculation within a chemical transport modeling system.
The daily agricultural and open burning emissions were converted from a tabular format into the
SMOKE-ready daily point flat file format. The daily emissions were also aggregated into annual values
by location and converted into the annual point flat file format.
For this modeling platform, a SMOKE update allows the use of HAP integration for speciation for
PTDAY inventories. The 2018 agricultural fire inventories include emissions for HAPs, so HAP
integration was used for this study.
3.2.8 Emissions from Canada, Mexico (othpt, othar, othafdust, othptdust, onroadcan, onroadmex,
ptfireothna)
The emissions from Canada and Mexico are included as part of the emissions modeling sectors: othpt,
othar, othafdust, othptdust, onroad can, and onroad mex, canada ag, and canada_og2D. 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), and "ptdust" for point fugitive dust (Canada only).
The onroad emissions for Canada and Mexico are in the onroad can and onroad mex sectors,
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respectively. Canadian agricultural and low-level (2-D) oil and gas emissions are split into separate
sectors from other Canada point sources to reduce the size of the othpt sector.
Emissions in these sectors were taken from the EQUATES 2016 inventories. Environment and Climate
Change Canada (ECCC) provided the following inventories for use in EQUATES 2016 and 2017
modeling, which are described in more detail below:
Agricultural livestock and fertilizer, point source format (canadaag sector)
CMV were provided as area sources but converted to point (not currently used)
Agricultural fugitive dust, point source format (othptdust sector)
Other area source dust (othafdust sector)
Onroad (onroad can sector)
- Nonroad and rail (othar sector)
Oil and gas surces (low-level in canada_og2D sector, elevated in othpt sector)
Other area sources (othar sector)
Airports (othpt sector)
Other point sources (othpt sector)
Canadian CMV inventories that had been included in this sector in past modeling platforms are no longer
needed in the cmv_clc2 and cmv_c3 sectors.
Temporal profiles, and shapefiles for creating spatial surrogates, were provided by ECCC in a previous
Canadian emissions dataset and were reused for this study. Other than the CB6 species of NBAFM
present in the speciated point source data, there are no explicit HAP emissions in these Canadian
inventories
Canadian point source inventories provided by ECCC for the EQUATES project for 2016 were used as-is
for 2019. These inventories include emissions for airports and other point sources. The Canadian point
source inventory is pre-speciated for the CB6 chemical mechanism. Point sources in Mexico were
compiled based on inventories projected from the Inventario Nacional de Emisiones de Mexico, 2016
(Secretaria de Medio Ambiente y Recursos Naturales (SEMARNAT)). The point source emissions 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. Only CAPs are covered in the
Mexico point source inventory. For this study, Mexico emissions were projected from 2016 to 2019
using projection factors derived from the Community Emissions Data System (CEDS).
Due to the large number of points in the Canada inventories, the agricultural sources were split into a
separate sector called canada ag so that the sources could be placed into layer 1 as plume rise calculations
were not needed. Similarly, there were a very large number of Canadian oil and gas point sources, many
of which would be appropriate modeled in layer 1. These sources were placed into the canada_og2D
sector for layer 1 modeling. Reducing the size of the othpt sector sped up the air quality model run
Fugitive dust sources of particulate matter emissions excluding land tilling from agricultural activities,
were provided by Environment and Climate Change Canada (ECCC) as part of their 2016 emission
inventory. Different source categories were provided as gridded point sources and area (nonpoint) source
inventories. Gridded point source emissions resulting from land tilling due to agricultural activities were
provided as part of the ECCC 2016 emission inventory. The provided wind erosion emissions were
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removed. The othptdust emissions have a monthly resolution. A transport fraction adjustment that reduces
dust emissions based on land cover types was applied to both point and nonpoint dust emissions, along
with a meteorology-based (precipitation and snow/ice cover) zero-out of emissions when the ground is
snow covered or wet. The EQUATES 2016 inventory was used as-is with 2018 meteorology applied.
ECCC provided year 2016 Canada province, and in some cases sub-province, resolution emissions from
for nonpoint and nonroad sources (othar). The nonroad sources were monthly while the nonpoint and rail
emissions were annual. The 2016 Canada nonroad emissions were projected to 2019 using US MOVES-
based trends. For Mexico, year 2016 Mexico nonpoint and nonroad inventories at the municipio
resolution provided by SEMARNAT were projected to 2019 using projection factors derived from the
Community Emissions Data System (CEDS). All Mexico inventories were annual resolution
The onroad emissions for Canada and Mexico are in the onroad can and onroadmex sectors,
respectively. Emissions for Canada come from the EQUATES 2016 (2016 was the latest year provided
by Environment and Climate Change Canada (ECCC)) and were projected from 2016 to 2019 using US
MOVES-based trends.
For Mexico onroad emissions, 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, 2017). 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). Mexico onroad inventories were generated by MOVES
for the years 2017 and 2020, and then interpolated to 2019 for this study.
Annual 2019 wildland fire emissions for Mexico, Canada, Central America, and Caribbean nations are
included in the ptfire othna sector. Canadian fires, along with fires in Mexico, Central America, and the
Caribbean, were developed from Fire Inventory from NCAR (FINN) 2017 vl.5 daily fire emissions. 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 wildfires rather than prescribed. FINN fire detects of 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.
3.2.9 Ocean Chlorine, Ocean Sea Salt, and Volcanic Mercury
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (Cb)
concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 12 km resolution were
available and were not modified other than the model-species name "CHLORINE" was changed to "CL2"
to support CMAQ modeling.
For mercury, the volcanic mercury emissions that were used in the last several modeling platforms were
not included in this study. The emissions were originally developed for a 2002 multipollutant modeling
platform with coordination and data from Christian Seigneur and Jerry Lin for 2001 (Seigneur et. al, 2004
and Seigneur et. al, 2001). The volcanic emissions from the most recent eruption were not included in the
because they have diminished by the year 2019. Thus, no volcanic emissions were included.
Because of mercury bidirectional flux within the latest version of CMAQ, no other natural mercury
emissions are included in the emissions merge step.
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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.
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 4.8.1 was used to pre-process the raw emissions inventories into emissions inputs for
CMAQ. SMOKE executables and source code are available from the Community Multiscale Analysis
System (CMAS) Center at http://www.cmasceiiter.org. Additional information about SMOKE is available
from http://www. smoke-model .org. For sectors that have plume rise, the in-line emissions capability of the
air quality models was used, which allows the creation of source-based and two-dimensional gridded
emissions files that are much smaller than full three-dimensional gridded emissions files. For quality
assurance of the emissions modeling steps, emissions totals by specie for the entire model domain are
output as reports that are then compared to reports generated by SMOKE on the input inventories to
ensure that mass is not lost or gained during the emissions modeling process.
3.3.2 Key Emissions Modeling Settings
When preparing emissions for the air quality model, emissions for each sector are processed separately
through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready, sector-
specific emissions across sectors. The SMOKE settings in the run scripts and the data in the SMOKE
ancillary files control the approaches used by the individual SMOKE programs for each sector. Table 3-7
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.
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Table 3-7. Key emissions modeling steps by sector
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
afdust ad]
Surrogates
Yes
Annual
airports
Point
Yes
Annual
None
beis
Pre-gridded
land use
in BEIS3.7
computed hourly
fertilizer
Surrogates
No
computed hourly
livestock
Surrogates
Yes
Annual
cmv clc2
Point
Yes
hourly
in-line
cmv c3
Point
Yes
hourly
in-line
nonpt
Surrogates &
area-to-point
Yes
Annual
nonroad
Surrogates
Yes
monthly
np oilgas
Surrogates
Yes
Annual
np solvents
Surrogates
Yes
annual
onroad
Surrogates
Yes
monthly activity,
computed hourly
onroadcaadj
Surrogates
Yes
monthly activity,
computed hourly
onroad can
Surrogates
Yes
monthly
onroad mex
Surrogates
Yes
monthly
othafdust ad]
Surrogates
Yes
annual
othar
Surrogates
Yes
annual &
monthly
othpt
Point
Yes
annual &
monthly
in-line
othptdust adj
Point
Yes
monthly
None
ptagfire
Point
Yes
daily
in-line
pt oilgas
Point
Yes
annual
in-line
Ptegu
Point
Yes
daily & hourly
in-line
ptfire-rx
Point
Yes
daily
in-line
ptfire-wild
Point
Yes
daily
in-line
ptfire othna
Point
Yes
daily
in-line
ptnonipm
Point
Yes
annual
in-line
rail
Surrogates
Yes
annual
rwc
Surrogates
Yes
annual
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 stack parameters and the hourly emissions in the SMOKE
output files for each emissions sector. The height of the plume rise determines the model layer into which
the emissions are placed. All of these particular sectors only have "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 fire emissions are treated differently in
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CMAQ. After plume rise is applied, there are emissions in every layer from the ground up to the top of
the plume. The emissions in the airports and othptdust sectors are all low-level emissions, and so in-line
emissions files are not created for these two sectors. Instead, all airports and othptdust emissions are
output to gridded emissions files, same as if airports and othptdust were area source sectors.
SMOKE has the option of grouping sources so that they are treated as a single stack when computing
plume rise. For this modeling case, no grouping was performed because grouping combined with "in-
line" processing will not give identical results as "offline" processing (i.e., when SMOKE creates 3-
dimensional files). This occurs when stacks with different stack parameters or lat/lons are grouped,
thereby changing the parameters of one or more sources. The most straightforward way to get the same
results between in-line and offline is to avoid the use of grouping.
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.
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 3-3, but the air quality model was run on the smaller 12-km domain
(12US2). The grid used a Lambert-Conforrnal projection, with Alpha = 33, Beta = 45 and Gamma = -97,
with a center of X = -97 and Y = 40. Later sections provide details on the spatial surrogates and area-to-
point data used to accomplish spatial allocation with SMOKE.
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3.3.4 Chemical Speciation Con figuration
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 platform is the CB6 mechanism (Yarwood, 2010). We used an updated version
of CB6 that we refer to as "CB6R3AE7" which includes four new species that were not in the previous
version of CB6: AACD, FACD, APIN, and IVOC. This mapping uses a new systematic methodology for
mapping low volatility compounds. Compounds with very low vapor pressure are mapped to model
species NVOL and intermediate volatility compounds are mapped to a species called IVOC. In previous
mappings, some of these low vapor pressure compounds were mapped to CB6 species. The mechanism
and mapping are described in more detail in a memorandum describing the mechanism files supplied with
the Speciation Tool, the software used to create the CB6 profiles used in SMOKE. It should be noted that
the onroad mobile sector does not use this newer mapping because the speciation is done within MOVES
and the mapping change was made after MOVES had been run.
This platform generates the PM2.5 model species associated with the CMAQ Aerosol Module version 7
(AE7) which has the same PM2.5 model species as version 6 (AE6). The AE7 mechanism is built on the
AE6 and identical in terms of model species and mechanism definition but requires that alpha-pinene
(APIN) be separate from all other m011 oterpenes (TERP) and not included in TERP to avoid double
counting. Table 3-8 lists the model species produced by SMOKE in the platform used for this study.
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Table 3-8. Emission model species produced for CB6R3AE7 for CMAQ
Inventory Pollutant
Model Species
Model species description
Cl2
CL2
Atomic gas-phase chlorine
HC1
HCL
Hydrogen Chloride (hydrochloric acid) gas
CO
CO
Carbon monoxide
NOx
NO
Nitrogen oxide
NOx
N02
Nitrogen dioxide
NOx
HONO
Nitrous acid
S02
S02
Sulfur dioxide
S02
SULF
Sulfuric acid vapor
nh,
NH3
Ammonia
nh,
NH3 FERT
Ammonia from fertilizer
voc
AACD
Acetic acid
voc
ACET
Acetone
voc
ALD2
Acetaldehyde
voc
ALDX
Propionaldehyde and higher aldehydes
voc
APIN
Alpha pinene
voc
BENZ
Benzene (not part of CB05)
voc
CH4
Methane
voc
ETH
Ethene
voc
ETHA
Ethane
voc
ETHY
Ethyne
voc
ETOH
Ethanol
voc
FACD
Formic acid
voc
FORM
Formaldehyde
voc
IOLE
Internal olefin carbon bond (R-C=C-R)
voc
ISOP
Isoprene
voc
IVOC
Intermediate volatility organic compounds
voc
KET
Ketone Groups
voc
MEOH
Methanol
voc
NAPH
Naphthalene
voc
NVOL
Non-volatile compounds
voc
OLE
Terminal olefin carbon bond (R-C=C)
voc
PAR
Paraffin carbon bond
voc
PRPA
Propane
voc
SESQ
Sesquiterpenes (from biogenics only)
voc
SOAALK
Secondary Organic Aerosol (SOA) tracer
voc
TERP
Terpenes (from biogenics only)
voc
TOL
Toluene and other monoalkyl aromatics
voc
UNR
Unreactive
voc
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
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Inventory Pollutant
Model Species
Model species description
Methanol
MEOH
Methanol from inventory
PM10
PMC
Coarse PM >2.5 microns and <10 microns
PM2.5
PEC
Particulate elemental carbon <2.5 microns
PM2.5
PN03
Particulate nitrate <2.5 microns
PM2.5
POC
Particulate organic carbon (carbon only) <2.5 microns
PM2.5
PS04
Particulate Sulfate <2.5 microns
PM2.5
PAL
Aluminum
PM2.5
PCA
Calcium
PM2.5
PCL
Chloride
PM2.5
PFE
Iron
PM2.5
PK
Potassium
PM2.5
PH20
Water
PM2.5
PMG
Magnesium
PM2.5
PMN
Manganese
PM2.5
PMOTHR
PM2.5 not in other AE6 species
PM2.5
PNA
Sodium
PM2.5
PNA
Sodium
PM2.5
PNCOM
Non-carbon organic matter
PM2.5
PNH4
Ammonium
PM2.5
PSI
Silica
PM2.5
PTI
Titanium
The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach were
developed from a draft version of the SPECIATE 5.2 database (https://www.epa.gov/air-emissions-
modeling/speciatel 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). 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 of the speciation approach for this study are listed here and described further in the
subsections below:
Use of the CBR3AE7 mechanism, as described earlier
Non-methane organic gases (NMOG), which are total organic gases with methane subtracted from
it, is included as a pollutant in the emissions output files to assist with the use of these data with
future versions of the CMAQ model.
Several new VOC and PM2.5 profiles slated for the final version of SPECIATE 5.2 were used.
PM2.5 speciation process for nonroad mobile use profiles assigned within M0VES3 (which
outputs the emissions with those assignments).
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As with previous platforms, some Canadian point source inventories are provided from
Environment Canada as pre-speciated emissions, and not all CB6 species were provided; missing
species were supplemented by speciating total VOC.
Speciation profiles and cross-references for this study platform are available in the set SMOKE input files
for the 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 study.
For onroad mobile sources, speciation is done in MOVES, to allow for profiles that vary by model year,
which is not part of the SCC code, to be used. Therefore, cross-references or emissions summaries by
profile for onroad mobile sources are not provided. These profiles are documented in a MOVES technical
report on speciation (EPA, 2020).
A number of speciation profiles for VOC and PM2.5 that had been added in SPECIATE 5.1 (EPA, 2020)
and 5.2 were used. In addition, we profile assignments were updated to incorporate data provided by
states or to correct errors in previous assignments.
For PM2.5 the following profile updates were made for the 2017 platform:
Corrected the wildfire and prescribed fire profile due to error in compositing (the previous profile
included creosote in the average)
Updated the profile for aircraft
Corrected several profile assignments for the petroleum industry
For PM2.5 the following profile and cross-reference updates were made for the 2018 platform:
Corrected the speciation profile assignment for several SCCs which should have been mapped to
the Heat Treating speciation profile for PM2.5 according to comments in the cross-reference file.
Updated the profile for sugar cane burning in the ptagfire sector.
Updated the wildfire and prescribed fire profiles.
Updated SCC 30400740 to use the Natural Gas combustion profile (95475).
For PM2.5, the following speciation profile and cross-reference updates were made for the 2019 platform:
Updated the speciation profile assignments for two pulp and paper SCCs, changing from the
overall default profile to the wood products drying profile (91 128).
Changed SCC 3 1000208 from the surface coating profile (91 129) to the petroleum industry
average profile (91145).
Assignments for new PM2.5 SCCs in the 2019 point inventory were included.
For VOC the following profile updates were made for the 2017 platform:
Volatile consumer products - recent methods to estimate emissions of Volatile Organic
Compounds (VOC) and associated Hazardous Air Pollutants (HAPs) from Volatile Chemical
Products (VCPs) aka solvents were used in this modeling platform. These methods result in
improved emissions estimates for the nonpoint (county-wide) solvent emissions. This emissions
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method results in improved VOC and HAP estimates for nonpoint categories of coatings,
pesticides, adhesives and sealants, oil & gas exploration solvent use, dry cleaning, printing inks,
cleaning products, personal care products, and other miscellaneous solvent uses. See section 3.2.1
for more details.
Oil and Gas - used additional region-specific profiles or updated assignments
o Used county-specific profiles gas for several Wyoming counties developed from data
provided by the Wyoming DEQ
o Used Willi son Basin gas composition data, separate profiles for the Montana and North
Dakota portions of the basin, based on data developed by the Western Regional Air
Partnership WRAP
o Used Central Montana Uplift area gas composition data, based on data developed by the
WRAP
o Updated Uinta basin profile assignments (based on data provided by Utah)
o Used Utah and Wyoming oil and gas produced water pond profiles
o Updated profile assignments (by county and SCC) for nonpoint oil and gas sources that
account for the portion of VOC estimated to come from flares. These were updated using
results from the Oil and Gas estimation tool run that was used for the 2017 NEI
o Updated profile assignment for miscellaneous engines to use internal combustion engine
natural gas profile
Commercial Marine vessel - changed profile assignment to an existing Pre-Tier 1 nonroad diesel
profile because the previous profile was missing key species (aldehydes)
Livestock - updated profile assignments
Agricultural burning - updated profiles for rice straw and wheat straw burning, and used new
sugar cane burning profile
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For VOC the following cross reference updates were made for the 2018 platform:
Changed all 8746 to G8746 (Profile name: Rice Straw and Wheat Straw Burning Composite of
G4420 and G4421)
Changed 2104008230/330 from 1084 to 4642 to match all other RWC SCCs
For solvents, updated all speciation profiles for SCCs in the VCPy inventory
Changed 2680001000 from 0000 to G95241 TOG
Uinta Basin oil/gas profiles:
o Replaced profile 95417 with either UTUBOGC- (2310010300, 231001 1500, 23101 1 1401,
2310010700, 2310010400, 31000107) or UTUBOGD (other SCCs)
o Replaced profile 95418 with UTUBOGF
o Replaced profile 95419 with UTUBOGE
PA gas profiles: Replaced all 8949 with PAG A S01 (F1PS 42059 only), PAGAS02 (F1PS 42019
only), PAGAS03 (F1PS 42125 only). To do this, we first replaced existing county-specific 8949
profiles with the new PAG AS profiles for these three counties in the ERG COMBO GSREF. This
covered 5 SCCs. For all SCCs other than those 5 SCCs, where the national profile assignment is
8949. New county-specific profile assignments were made to the appropriate PAG AS profile for
each of the three counties, added to the Ramboll basin specific GSREF (since that GSREF is also
county-specific and not combo).
Colorado 23 10030300: Set Archuleta/La Plata to SU1ROGWT (counties are in Southern Ute
reservation), rest of Colorado to DJTFLR95
Colorado 2310030220: Set to DJTFLR95 (formerly FLR99)
Colorado 23 10021010: Set Archuleta/La Plata to SU1ROGCT (counties are in Southern Ute
reservation), rest of Colorado to 95398
Changed 23 1000055 1 (CBM produced water) to a new profile, CBMPWWY. Speciation Tool
inputs for this profile tool run by GD1T. Documentation: Profiles are means from WY tests in
SPECIATE, newly composited. Reference: https://doi.org/10.1016/j.scitotenv.2017.1 1.161
Reference: Lyman, Seth N.; Mansfield, Marc L.; Tran, Huy N. Q.; Evans, Jordan D.; Jones,
Colleen; O'Neil, Trevor; Bowers, Ric; Smith, Ann; and Keslar, Cara, "Emissions of organic
compounds from produced water ponds I: Characteristics and speciation" (2018). Chemistry and
Biochemistry Faculty Presentations. Paper 154. Contact: Art Diem and Jeff Vukovich of the
EPA's Office of Air Quality Planning and Standards (OAQPS)
Assignments for new VOC SCCs in the 2018 point inventory were included along with changes to
VOC profiles for 16 point SCCs.
55
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For VOC, the following speciation profile and cross-reference updates were made for the 2019 platform:
Speciation profiles were regenerated using version 5.2 of the SPECIATE database, and with the
latest version of the Speciation T ool which includes greater number precision. SPECIATE 5.2
includes several new speciation profiles for solvents, and the cross-reference was updated to use
those profiles.
The definition of the model species SOAALK was changed. Compared to the 2017 and 2018
platforms, the SOAALK emissions are now generally lower.
Updated the speciation profile assignments for pulp and paper. Two pulp and paper SCCs were
updated from the overall default profile to the pulp and paper industry composite profile (95326),
and four other SCCs were updated from profile 95326 to the pulp and paper pi ay wood veneer
dryer profile (1 189).
For oil and gas, the portion of emissions for SCC 23 10010200 which was speciated using profile
2487 was changed to profile 95247.
All emissions which were previously speciated with profile 101 1 were changed to profile 95404.
This affects SCCs associated with oil production fugitive leaks and venting.
All emissions which were previously speciated with profile 1207 were changed to profile 95782.
This affects produced water from oil and gas production.
Assignments for new VOC SCCs in the 2019 point inventory were included.
The speciation of VOC includes HAP emissions from the emissions inventories 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 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,
including PTDAY (the format used for the ptfire and ptagfire sectors). The ability to use integration with
the PTDAY format was made available in the version of SMOKE used for the v7.1 platform, and this new
feature is used for this particular study because the ptfire and ptagfire inventories for this study include
HAPs. 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
56
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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 integration11). 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 profiles.12 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 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 3-4). 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.
Figure 3-4 illustrates the integrate and no-integrate processes for U.S. Sources. Since Canada and Mexico
inventories do not contain HAPs, we use the 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. 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 3-9), 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 for those sectors. Where applied, 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.
11 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.
12 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.
57
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CMAQ-CB6 species
Figure 3-4. Process of integrating NBAFM with VOC for use in VOC Speciation
Table 3-9. Integration status of naphthalene, benzene, acetaldehyde, formaldehyde and methanol
(NBAFM) for each platform sector
Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
afdust
N/A - sector contains no VOC
airports
No integration, use NBAFM in inventory
beis
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
cmv clc2
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
cmv c3
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
fertilizer
N/A - sector contains no VOC
livestock
Partial integration (NBAFM)
nonpt
Partial integration (NBAFM)
nonroad
Full integration (internal to MOVES)
np oilgas
Partial integration (NBAFM)
np solvents
Partial integration (NBAFM)
onroad
Full integration (internal to MOVES)
onroad can
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
onroadmex
Full integration (internal to MOVES-Mexico); however, MOVES-MEXICO speciation was
older CB6, so post-SMOKE emissions were converted to CB6R3AE6
othafdust
N/A - sector contains no VOC
othar
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
othpt
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
othptdust
N/A - sector contains no VOC
58
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Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
pt oilgas
No integration, use NBAFM in inventory
ptagfire
Full integration (NBAFM)
ptegu
No integration, use NBAFM in inventory
ptfire-rx
Partial integration (NBAFM)
ptfire-wild
Partial integration (NBAFM)
ptfire othna
No integration, no NBAFM in inventory, create NBAFM from VOC speciation
ptnonipm
No integration, use NBAFM in inventory
rail
Full integration (NBAFM)
rwc
Full integration (NBAFM)
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, VOC speciation is done fully
within MOVES 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 CB6R3AE7 mechanism, so no additional species needed to be added after SMOKE-MOVES
processing. For nonroad mobile, speciation is partially done within MOVES such that MOVES does not
need to be run for a specific chemical mechanism. For nonroad, MOVES outputs emissions of HAPs and
NONHAPTOG were split by speciation profile. Taking into account that integrated species were
subtracted out by MOVES already, the appropriate speciation profiles are then applied within 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 GSPROCOMBO feature was used to speciate nonroad mobile and gasoline-
related stationary sources that use fuels with varying ethanol content. In these cases, the speciation
profiles require different combinations of gasoline profiles (e.g., EO 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 2017 platform, GSPRO COMBO is still used for certain
gasoline-related stationary sources nationwide. GSPRO COMBO is no longer needed for nonroad
sources 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.
In Canada, ECCC provided estimates of ethanol mixes by Canadian province. These estimates were used
to develop a GSPRO COMBO for Canadian gasoline onroad emissions. For example, a province where
the average ethanol mix is 6% would have 60% E10 speciation and 40% EO speciation. A 10% ethanol
mix would imply 100% E10 speciation. In Mexico, only EO speciation profiles are used, but the
GSPRO COMBO feature is still used in Mexico for inventories where VOC emissions are not explicitly
defined by mode (e.g., exhaust versus evaporative). Here, the GSPRO COMBO specifies a mix of
exhaust and evaporative speciation profiles. Using the GSPRO COMBO to split total VOC into exhaust
and evaporative components is no longer necessary for Canadian mobile sources, whose inventories
include the mode in the pollutant, or for Mexico onroad sources, where VOC speciation is calculated by
the MOVES model. The GSPRO COMBO is still used for Mexican nonroad sources which do not have
modes in the inventory.
59
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The newer combo method for combining profiles that is available in SMOKE4.5 and later versions is used
in this platform and allows multiple profiles to be combined by pollutant, state and county (i.e.,
state/county FIPS code), and SCC. This is used specifically for the np oilgas sector because SCCs
include both controlled and uncontrolled oil and gas operations which use different profiles. The
underlying data which defines the profile splits for oil and gas was updated for this study.
Speciation profiles for use with BEIS are not included in SPECIATE. BEIS includes a species
Sequiterpenes (SESQ) that was mapped to the CMAQ specie SESQT. The profile code associated with
BEIS profiles for use with CB6 was "BC6E7."
NOx can be speciated into NO, N02, 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_clc2, cmv_c3, rail, onroad can, onroadmex 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. MOVES
produces speciated NO, NO2, and HONO by source, including emission factors for these species in the
emission factor tables used by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant
0.008 of NOx. The NO fraction varies by heavy duty versus light duty, fuel type, and model year, and
equals 1 - NO - HONO. For more details on the NOx fractions within MOVES for onroad, see Exhaust
Emission Rates for Heavv-Dutv Onroad Vehicles in MOVES3 and Exhaust Emission Rates for Light-
Duty Onroad Vehicles in MOVES3.
Table 3-10. NOx speciation profiles
Profile
pollutant
species
split factor
HONO
NOX
N02
0.092
HONO
NOX
NO
0.9
HONO
NOX
HONO
0.008
NHONO
NOX
N02
0.1
NHONO
NOX
NO
0.9
3.3.5 Temporal Processing Configuration
Temporal allocation (i.e., temporalization) is the process of distributing aggregated emissions to a finer
temporal resolution, thereby converting annual emissions to hourly emissions. While the total emissions
are important, the timing of the occurrence of emissions is also essential for accurately simulating ozone,
PM, and other pollutant concentrations in the atmosphere. Many emissions inventories are annual or
monthly in nature. Temporalization takes these aggregated emissions and, if needed, distributes them to
the month, and then distributes the monthly emissions to the day and the daily emissions to the 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
60
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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
Airports
Annual
Yes
week
week
Yes
Beis
Hourly
n/a
all
No
cmv clc2
Annual & hourly
All
all
No
cmv c3
Annual & hourly
All
all
No
Fertilizer
Monthly
met-based
All
Yes
livestock
Annual
Yes
met-based
All
Yes
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
mwdss
mwdss
Yes
np oilgas
Annual
Yes
aveday
aveday
No
onroad
Annual & monthly1
all
all
Yes
onroad ca adi
Annual & monthly1
all
all
Yes
othafdust adj
Annual
Yes
week
all
No
othar
Annual & monthly
Yes
week
week
No
onroad can
Monthly
week
week
No
onroad mex
Monthly
week
week
No
othpt
Annual & monthly
Yes
mwdss
mwdss
No
othptdust adj
Monthly
week
all
No
pt oilgas
Annual
Yes
mwdss
mwdss
Yes
ptegu
Annual & hourly
Yes2
all
All
No
ptnonipm
Annual
Yes
mwdss
mwdss
Yes
ptagfire
Daily
all
all
No
ptfire-rx
Daily
all
all
No
ptfire-wild
Daily
all
all
No
ptfire othna
Daily
all
all
No
rail
Annual
Yes
aveday
aveday
No
rwc
Annual
No3
met-based3
all
No3
np solvents
annual
Yes
aveday
aveday
No
1. Note the annual and monthly "inventory" actually refers to the activity data (VMT, VPOP, starts) for onroad. The
actual emissions are computed on an hourly basis.
2. Only units that do not have matching hourly CEMs data use monthly temporal profiles.
3. Except for 2 SCCs that do not use met-based temporalization.
The following values are used in the above table: The value "all" means that hourly emissions are
computed for every day of the year and that emissions potentially have day-of-year variation. The value
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"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, 2019, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2018). For anthropogenic sectors, emissions from December 2019
were used to fill in surrogate emissions for the end of December 2018. For biogenic emissions, December
2018 emissions were computed using year 2018 meteorology.
The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated format for
monthly, daily, and hourly emissions inventories than prior formats supported. Previously, processing
monthly inventory data required the use of 12 separate inventory files. With the FF10 format, a single
inventory file can contain emissions for all 12 months and the annual emissions in a single record. This
helps simplify the management of numerous inventories. Similarly, daily and hourly FF10 inventories
contain individual records with data for all days in a month and all hours in a day, respectively.
SMOKE prevents the application of temporal profiles on top of the "native" resolution of the inventory.
For example, a monthly inventory should not have annual-to-month temporalization applied to it; rather,
it should only have month-to-day and diurnal temporalization. This becomes particularly important when
specific sectors have a mix of annual, monthly, daily, and/or hourly inventories. The flags that control
temporalization for a mixed set of inventories are discussed in the SMOKE documentation. The
modeling platform sectors that make use of monthly values in the FF10 files are nonroad, onroad (for
activity data), onroad can, onroadmex, othar, othpt, and othptdust. Commercial marine vessels in
cmv_c3 and cmv_clc2 use hourly data in the FF10 files.
3.3.5.1 Standard Temporal Profiles
Some sectors use straightforward temporal profiles not based on meteorology or other factors. For the
ptfire, ptagfire, and ptfire othna sectors, 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 workday 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.
An update in this 2018 platform was an analysis of monthly temporal profiles for non-EGU point sources
in the ptnonipm sector. A number of profiles were found to be not quite flat over the months but were so
close to flat that the difference was not meaningful. These profiles were replaced in the cross reference to
point instead to the flat monthly profile. The codes for the profiles that were replaced were: 202, 214,
220, 221, 222, 223, 227, 257, 263, 264, 265, 266, 267, 269, 271, 272, 279, 280, 295, 302, 303, 304, 305,
306, 309, 310, 327, 329, 332, and 333.
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A monthly temporal profile for freight rail was developed from AAR data for the year 2016
(https://www.aar.org/data-center/rail-traffic-data/) and continues to be used in this study, Monthly Rail
Traffic Data, Total Carloads & Intermodal. Passenger trains use a flat monthly profile. Monthly passenger
miles data are available; however, it is not known if there is a correlation between passenger miles and
actual rail emissions. This is because passenger trains often operate on a fixed schedule, independent of
actual passenger traffic. So, it was decided to not apply a monthly profile to passenger train emissions. All
sources in the rail sector use a flat profile for both day-of-week and hour-of-day temporalization.
For the ptfire and ptagfire sectors, the inventories are in the daily point fire format, so temporal profiles
are only used to go from day-specific to hourly emissions. For ptfire, state-specific hourly profiles were
used, with distinct profiles for prescribed fires and wildfires. For ptagfire, the diurnal temporal profile
used reflected the fact that burning occurs during the daylight hours. Additional details on these profiles
are available in the 2016vl platform TSD (EPA, 2020b).
For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly
inventories from output from MOVES. For California, CARB's annual inventory was temporalized to
monthly using monthly temporal profiles applied in SMOKE by SCC.
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 10 PM and
6 AM 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.
Monthly temporalization of np oilgas emissions is based primarily on monthly factors from the Oil and
Gas Tool (OGT). Factors were specific to each county and SCC. For use in SMOKE, each unique set of
factors was assigned a label (OG17 0001 through OG17 6272), and then a SMOKE-formatted
ATPRO MONTHLY and an ATREF were developed. This dataset of monthly temporal factors included
profiles for all counties and SCCs in the Oil and Gas Tool inventory. Because we are using non-tool
datasets in some states, this monthly temporalization dataset did not cover all counties and SCCs in the
entire inventory used for this study. To fill in the gaps in those states, state average monthly profiles for
oil, natural gas, and combination sources were calculated from Energy Information Administration (EIA)
data and assigned to each county/SCC combination not already covered by the OGT monthly temporal
profile dataset. Coal bed methane (CBM) and natural gas liquid sources in those four states were assigned
flat monthly profiles where there was not already a profile assignment in the ERG dataset.
For agricultural livestock, annual-to-month profiles were developed based on daily emissions data output
from the CMU model by state and SCC. These profiles were used to temporally allocate 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
Electric generating unit (EGU) sources matched to ORIS units were temporally allocated to hourly
emissions needed for modeling using the hourly CEMS data. Those hourly data were processed through
v2.1 of the CEMCorrect tool to mitigate the impact of unmeasured values in the data. An example of
before and after the application of the tool is shown in Figure 3-5.
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2017 January Unit 469_5
2000
1800
1600
&_
3
1400
O
.n
1200
LO
£
1000
X
800
O
Z
600
400
200
0
p>.rocr)Ln^HP>.rocr)Ln^HP>.rocnLn^HP>.rocr)Ln^HP>.rocr)Ln\-ihNrocr)
rMLnr^oroi-ncoorotDco^HrotDcn^H^rtDcnrM^r-vcnrMi-nr^ocN
January 2017 Hour
RawCEM Corrected
Figure 3-5. Eliminating unmeasured spikes in CEMS data
The region, fuel, and type (peaking or non-peaking) must be identified for each input EGU with CEMS
data that are used for generating profiles. The identification of peaking units was done using hourly heat
input data from the 2018 base year and the two previous years (2016 and 2017). The heat input was
summed for each year. Equation 1 shows how the annual heat input value is converted from heat units
(BTU/year) to power units (MW) using the NEEDS v6 derived unit-level heat rate (BTU/kWh). In
equation 2 a capacity factor is calculated by dividing the annual unit MW value by the NEEDS v6 unit
capacity value (MW) multiplied by the hours in the year. A peaking unit was defined as any unit that had
a maximum capacity factor of less than 0.2 for every year (2017, 2018, and 2019) and a 3-year average
capacity factor of less than 0.1.
Equation 1. Annual unit power output
V8760 Hourly HI
s ^1=0 (RTII} *1000 (few)
Annual Unit Output (MW) = 7^77
NEEDS Heat Rate 77^)
\kWhJ
Equation 2. Unit capacity factor
_ Annual Unit Output (MW)
Capacity Factor =
NEEDS
Unit Capacity (^-^)*8760 (h)
Input regions were determined from one of the eight EGU modeling regions based on multi-jurisdictional
planning organization (MJO) and climate regions. Regions were used to group units with similar climate-
based load demands. Region assignment is made on a state level, where all units within a state were
assigned to the appropriate region as shown in Figure 3-6. Unit fuel assignments were made using the
primary NEEDS v6 fuel. Units fueled by bituminous, subbituminous, or lignite are assigned to the coal
fuel type. Natural gas units were assigned to the gas fuel type. Distillate and residual fuel oil were
64
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assigned to the oil fuel type. Units with any other primary fuel were assigned the "other" fuel type.
Currently there is a possible region, fuel, and type group maximum of 64 based on 8 regions, 4 fuels, and
two types (peaking and non-peaking).
The daily and diurnal profiles were calculated for each region, fuel, and peaking type group from the 2019
CEM heat input values. The heat input values were summed for each input group to the annual level at
each level of temporal resolution: monthly, month-of-day, and diurnal. The sum by temporal resolution
value is then divided by the sum of annual heat input in that group to get a set of temporalization factors.
Diurnal factors were created for both the summer and winter seasons to account for the variation in hourly
load demands between the seasons. For example, the sum of all hour 1 heat input values in the group was
divided by the sum of all heat inputs over all hours to get the hour 1 factor. Each grouping contained 12
monthly factors, up to 31 daily factors per month, and two sets of 24 hourly factors. The profiles were
weighted by unit size where the units with more heat input have a greater influence on the shape of the
profile. Composite profiles were created for each region and type across all fuels as a way to provide
profiles for a fuel type that does not have hourly CEM data in that region. Figure 3-7 shows peaking and
non-peaking daily temporal profiles for the gas fuel type in the LADCO region. Figure 3-8 shows the
diurnal profiles for the coal fuel type in the Mid-Atlantic/Northeast Visibility Union (MANE VUj region.
I I SoUhwest
~ West
l l West FtorBi Offiral
Figure 3-6. Small EGU Temporal Profile Regions
65
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2017
Figure 3-7. Example Daily Temporal Profiles for the LADCO region and Gas Fuel Type
Diurnal Small EGU Profile for MANE-VU coal
Figure 3-8. Example Diurnal Profile for MANE-VU Region and Coal Fuel Type
66
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SMOKE uses a cross-reference file to select a monthly, daily, and diurnal profile for each source. For this
platform, the temporal profiles were assigned in the cross-reference at the unit level to EGU sources
without hourly CEM data. An inventory of all EGU sources without CEMS data was used to identify the
region, fuel type, and type (peaking/non-peaking) of each source. As with the input unit the regions are
assigned using the state from the unit FIPS. The fuel is assigned by SCC to one of the four fuel types:
coal, gas, oil, and other. A fuel type unit assignment is made by summing the VOC, NOx, PM2.5, and SO2
for all SCCs in the unit. The SCC that contributed the highest total emissions to the unit for selected
pollutants was used to assign the unit fuel type. Peaking units were identified as any unit with an oil, gas,
or oil fuel type with a NAICS of 22111 or 221112. Some units may be assigned to a fuel type within a
region that does not have an available input unit with a matching fuel type in that region. These units
without an available profile for their group were assigned to use the regional composite profile. Municipal
waste combustors (MWC) and cogeneration units were identified using the NEEDS primary fuel type and
cogeneration flag, respectively, from the NEEDS v6 database.
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 a 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 this platform, meteorological-based
temporalization was used for portions of the rwc sector and for the entirety of 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.0rg/smoke/documentation/3.l/GenTPRO Technical Summary Aug2012 Final.pd
f and https://www.cmascenter.Org/smoke/documentation/4.6/html/ch05s03s05.html respectively.
For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the temporal
allocation of emissions to days. GenTPRO was used to create an annual-to-day temporal profile for the
RWC sources. These generated profiles distribute annual RWC emissions to the coldest days of the year.
On days where the minimum temperature does not drop below a user-defined threshold, RWC emissions
for most sources in the sector are zero. Conversely, the program temporally allocates the largest
percentage of emissions to the coldest days. Similar to other temporal allocation profiles, the total annual
emissions do not change, only the distribution of the emissions within the year is affected. The
temperature threshold for RWC emissions was 50 °F for most of the country, and 60 °F for the following
states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and
Texas.
67
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Figure 3-9 illustrates the impact of changing the temperature threshold for a warm climate county. The
plot shows the temporal fraction by day for Duval County, Florida for the first four months of 2007. The
default 50 °F threshold creates large spikes on a few days, while the 60 °F threshold dampens these spikes
and distributes a small amount of emissions to the days that have a minimum temperature between 50 and
60 °F.
Figure 3-9. Example of RWC temporalization using a 50 °F versus 60°F threshold
The diurnal profile for used for most RWC sources places more of the RWC emissions in the morning
and the evening when people are typically using these sources. This profile is based on a 2004 MANE-
VU survey based temporal profiles (see
http://www.marama.org/publications folder/ResWoodCombustion/Final report.pdf). This profile was
created by averaging three indoor and three RWC outdoor temporal profiles from counties in Delaware
and aggregating them into a single RWC diurnal profile. This new profile 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, chimeneas, etc.)" (i.e., "recreational RWC", SCC=21040087000) do
not use the same meteorological-based temporalization as the rest of the rwc sector, because
meteorological-based temporalization did not agree with observations for how these appliances are used.
For hydronic heaters, 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 3-10 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
68
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were computed from the MN DNR survey (MDNR, 2008) and are illustrated in Figure 3-11. 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.
The 2017 NEI includes two new hydronic heater SCCs, 2104008620 (indoor hydronic heaters) and
2104008630 (pellet-fired hydronic heaters). Both of these SCCs use the same monthly, weekly, and
diurnal temporal profiles as OHHs.
50,000
40,000
30,000
20,000
10,000
0
Heat Load (BTU/hr)
aaaaaaaaaaa
CjCjCjCjCjC3c3c3c3o3o3
aaaaaaaaaaaaa
Oh Oh Oh Oh Oh Oh Oh Oh Oh Oh Oh Oh ^
Figure 3-10. Diurnal profile for OHH, based on heat load (BTU/hr)
Monthly Temporal Activity for OHH & Recreational RWC
Fire Pit/Chimenea
Outdoor Hydronic Heater
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Figure 311. Annual-to-month temporal profiles for OHH and recreational RWC
For the ag sector, agricultural GenTPRO temporalization was applied to both livestock and fertilizer
emissions, and to all pollutants within the ag sector, not just NH3. This is a change from the 2014v7.0
modeling platform, in which agricultural GenTPRO temporalization was only applied to livestock NH3
sources. The GenTPRO algorithm is based on an equation derived by Jesse Bash of EPA ORD based on
69
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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./, = [161500/Ta x e(~1380/T,/,)] x AR,/;
PE;,/; = Ea, / Sum(E, /,)
where
PE;,/; = Percentage of emissions in county i in hour h
Eij, = Emission rate in county i in hour h
Tin = Ambient temperature (Kelvin) in county i in hour h
Vi.h = Wind speed (meter/sec) in county i (minimum wind speed is 0.1 meter/sec)
AR;,/; = Aerodynamic resistance in county i
GenTPRO was run using the "BASH NH3" profile method to create month-to-hour temporal profiles for
these sources. Because these profiles distribute to the hour based on monthly emissions, the monthly
emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized
to the month. Figure 3-12 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.
MN ag NH3 livestock temporal profiles
lli
aAh* .
1/1/2008 2/1/2008 3/1/2008 4/1/2008 5/1/2008 6/1/2008 7/1/2008 8/1/2008 9/1/2008 10/1/2008 11/1/2008 12/1/2008
Figure 312. 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 measurable 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
70
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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 this platform.
Meteorology is not used in the development of the temporal profiles, but rather it impacts the calculation
of the hourly emissions through the program Movesmrg. The result is that the emissions vary at the
hourly level by grid cell. More specifically, the on-network (RPD) and the off-network parked vehicle
(RPV, RPH, and RPP) processes use the gridded meteorology (MCIP) directly. Movesmrg determines
the temperature for each hour and grid cell and uses that information to select the appropriate emission
factor (EF) for the specified SCC/pollutant/mode combination. RPP uses the gridded minimum and
maximum temperature for the day. The combination of these four processes (RPD, RPV, RPH, and RPP)
is the total onroad sector emissions. The onroad sector shows a strong meteorological influence on its
temporal patterns.
Figure 3-13 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.
4 T
2014v2 onroad RPD hourly NOX and VMT: Wake County, NC
3
35 J
2 =t 4
r23
_C > I
1/1
aj 2.5 J
2 i
I 2
- 1.5 £
S. VMT
o
= 15 J
1
""" i 4
1 O NOX
t- 1
§0 J
0 4
7/8/1'
05
n
10:00 7/9/140:00
7/10/140:00
7/11/140:00 7/12/140:00 7/13/140:00 7/14/140:00 7/15/140:00
Date and time (GMT)
Figure 3-13. 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 data were also temporalized from month to day-of-the-week, and
71
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then to hourly through temporal profiles. The RPD processes use an average speed distribution (SPDIST)
that specifies the amount of time spent in each MOVES speed bin for each county, vehicle (aka source)
type, road type, weekday/weekend, and hour of day. Unlike other sectors, the temporal profiles and
SPDIST 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.
For this study, the temporal profiles for the onroad sector come from the 2017 NEI-based platform, which
is a compilation of state/local-provided data and nationally available datasets. VMT day-of-week and
hour-of-day temporal profiles were developed for counties across the continental U.S. as part of the effort
to update the inputs to MOVES and SMOKE-MOVES under CRC A-100 (Coordinating Research
Council, 2017). CRC A-100 data includes profiles by region or county, road type, and broad vehicle
category. There are three vehicle categories: passenger vehicles (source types 11, 21, and 31), commercial
trucks (source types 32, 52, and 53), and combination trucks (source types 61 and 62). CRC A-100 does
not cover buses, refuse trucks, or motor homes, so those vehicle types were mapped to other vehicle types
for which CRC A-100 did provide profiles, as follows: 1) Intercity/transit buses were mapped to
commercial trucks; 2) Motor homes were mapped to passenger vehicles for day-of-week and commercial
trucks for hour-of-day; 3) School buses and refuse trucks were mapped to commercial trucks for hour-of-
day and use a new custom day-of-week profile called LOWSATSUN that has a very low weekend
allocation, since school buses and refuse trucks operate primarily on business days. In addition to
temporal profiles, CRC A-100 data were also used to develop the average speed distribution data
(SPDIST) used by SMOKE-MOVES. In areas where state-provided data and CRC A-100 data does not
exist, hourly speed data is based on MOVES county databases.
The CRC A-100 dataset includes temporal profiles for individual counties, Metropolitan Statistical Areas
(MSAs), and entire regions (e.g., West, South). Counties without temporal profiles specific to itself, or to
its MSA, are assigned to regional temporal profiles. Temporal profiles also vary between MOVES road
types, and there are distinct hour-of-day profiles for each day of the week. Plots of hour-of-day profiles
for passenger vehicles in Fulton County, GA, are shown in Figure 3-14. Separate plots are shown for
Monday, Friday, Saturday, and Sunday, and each line corresponds to a particular MOVES road type (e.g.
road type 2 = rural restricted). Figure 3-15 shows which counties have temporal profiles specific to that
county, and which counties use regional average profiles in the CRC A-100 data.
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Saturday Fulton Co passenger Sunday Fulton Co passenger
Monday Fulton Co passenger
o.i
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
Friday Fulton Co passenger
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 road 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
road 2 road 3 road 4 -road 5
Figure 3-14. Sample onroad diurnal profiles for Fulton County, GA
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Group | | Individual
I I Midwest Region Average of Single County MSA Counties
Midwest Region non-MSA Average
I Northeast Region Average of Single County MSA Counties
] Northeast Region non-MSA Average
South Region Average of Single County MSA Counties
I I South Region non-MSA Average
I I West Region Average of Single County MSA Counties
I I West Region non-MSA Average
¦ Midwest Region Average of Core Counties inside MSAs
I Midwest Region Average of non-Core Counties mside MSAs
~ Northeast Region Average of Core Counties Biside MSAs
~ Northeast Region Average of non-Core Counties inside MSAs
~ South Region Average of Core Counties inside MSAs
~ South Region Average of non-Core Counties inside MSAs
~ West Region Average of Core Counties inside MSAs
~ West Region Average of non-Core Counties inside MSAs
Figure 3-15. Counties for which MOVES Speeds and Temporal Profiles could be Populated
For hoteling, day-of-week profiles are the same as non-hoteling for combination trucks, while hour-of-day
non-hoteling profiles for combination trucks were inverted to create new hoteling profiles that peak
overnight instead of during the day.
State/local-provided data for the 2017 NEI were accepted for use in the 2017 NEI if they were deemed to
be at least as credible as the CRC A-100 data. The 2017 NEI TSD includes more details on which data
were used for which counties. In areas of the contiguous United States where state/local-provided data
were not provided or deemed unacceptable, the CRC A-100 temporal profiles were used, except in
California. All California temporal profiles were carried over from the 2014v7.1 platform, although
California hoteling uses CRC A-100-based profiles just like the rest of the country, since CARB didn't
have a hoteling-specific profile. Monthly profiles in all states (national profiles by broad vehicle type)
were also carried over from 2014vl and applied directly to the VMT. For California, CARB supplied
diurnal profiles that varied by vehicle type, day of the week,1J and air basin. These C ARB-specific
13 California's diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles and
Tuesday, Wednesday, Thursday had the same profile.
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profiles were used in developing EPA estimates for California. Although the EPA adjusted the total
emissions to match interpolated 2016 levels based on California's submitted inventories for 2014 and
2017, 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 section 3.2.5.1.
3.3.6 Vertical Allocation of Emissions
Table 3-7 specifies the sectors for which plume rise is calculated. If there is no plume rise for a sector, the
emissions are placed into layer 1 of the air quality model. Vertical plume rise was performed in-line within
CMAQ for all of the SMOKE point-source sectors (i .e., ptegu, ptnonipm, pt oilgas, ptfire-rx, ptfire-wild,
ptagfire, ptfireothna, 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 Lay point 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 traditional stack parameters. However, the ptfire-rx, ptfire-wild, ptagfire, andptfire_othna
inventories do contain data on the acres burned (acres per day) and fuel consumption (tons fuel per acre)
for each day. CMAQ uses these additional parameters to estimate the plume rise of emissions into layers
above the surface model layer. Specifically, these data are used to calculate heat flux, which is then used to
estimate plume rise. In addition to the acres burned and fuel consumption, heat content of the fuel is
needed to compute heat flux. The heat content was assumed to be 8000 Btu/lb of fuel for all fires because
specific data on the fuels were unavailable in the inventory. The plume rise algorithm applied to the fires is
a modification of the Briggs algorithm with a stack height of zero.
CMAQ uses the Briggs algorithm to determine the plume top and bottom, and then computes the plumes"
distributions into the vertical layers that the plumes intersect. The pressure difference across each layer
divided by the pressure difference across the entire plume is used as a weighting factor to assign the
emissions to layers. This approach gives plume fractions by layer and source. Note that the implementation
of fire plume rise in CMAQ differs from the implementation of plume rise in SMOKE 4.8. This study uses
CMAQ to compute the fire plume rise.
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 2016-2017 data
wherever possible. For Mexico, updated spatial surrogates were used as described below. For Canada,
shapefiles for generating new surrogates were provided by ECCC for use with their 2015 inventories.
The U.S., Mexican, and Canadian 12-km surrogates cover the entire CONUS domain 12US1 shown in
Figure 3-3.
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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 this 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 of the surrogates currently in use were developed for use in the 2014v7.0 platform using recently
available data sets (Adelman, 2016). They include the 2011 National Land Cover Database is used
including 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). Onroad surrogates for this platform do not
distinguish between urban and rural road types, which prevents issues in areas where there are
inconsistent urban and rural definitions between MOVES and the surrogate data.
Recent surrogate updates include:
A public school surrogate (508) was developed for off-network school buses.
Oil and gas surrogates were updated to represent 2019, including a new surrogate for total gas
produced(689)
Corrections were made to the rail surrogates (261/271).
The transit bus terminal surrogate was re-gapfilled with the NLCD medium+high surrogate (306)
Some gridding cross reference corrections / updates were made including the use of NLCD
medium+high surrogate instead of intercity bus terminals for off network emissions from other
buses.
The 500 series surrogates are no longer used and SCCs that used them (e.g., cigarette smoke,
accidental releases) were remapped to NLCD surrogates.
Onroad surrogates were generated to incorporate 2017 Average Annual Daily Traffic (AADT);
Surrogates for the U.S. were generated using the Surrogate Tool to drive the Spatial Allocator with some
surrogates were developed directly within ArcGIS or using the Surrogate Tools DB. The tool and
documentation for the original Surrogate Tool are available at https://www.cmascenter.org/sa-
tools/documentation/4.2/SurrogateToolUserGuide 4 2.pdf and the tool and documentation for the
Surrogate Tools DB is available from https://www.cmascenter.org/surrogate tools db/. The file
Surrogate_specifications_2019_platform_US_Can_Mex.xlsx documents the configuration for generating
the surrogates.
Table 3-12. U.S. Surrogates available for the 2019 modeling platform
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.6.2)
650
Refineries and Tank Farms
100
Population
670
Spud Count - CBM Wells
110
Housing
671
Spud Count - Gas Wells
76
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Code
Surrogate Description
Code
Surrogate Description
150
Residential Heating - Natural Gas
672
Gas Production at Oil Wells
170
Residential Heating - Distillate Oil
673
Oil Production at CBM Wells
180
Residential Heating - Coal
674
Unconventional Well Completion Counts
190
Residential Heating - LP Gas
676
Well Count - All Producing
205
Extended Idle Locations
677
Well Count - All Exploratory
239
Total Road AADT
678
Completions at Gas Wells
240
Total Road Miles
679
Completions at CBM Wells
242
All Restricted AADT
681
Spud Count - Oil Wells
244
All Unrestricted AADT
683
Produced Water at All Wells
258
Intercity Bus Terminals ;
6831
Produced Water at CBM Wells
259
Transit Bus Terminals
6832
Produced Water at Gas Wells
260
Total Railroad Miles ]
6833
Produced Water at Oil Wells
261
NT AD Total Railroad Density
685
Completions at Oil Wells
271
NT AD Class 12 3 Railroad Density
686
Completions at All Wells
300
NLCD Low Intensity Development
687
Feet Drilled at All Wells
304
NLCD Open + Low
689
Gas Produced - Total
305
NLCD Low + Med
691
Well Counts - CBM Wells
306
NLCD Med + High
692
Spud Count -All Wells
307
NLCD All Development
693
Well Count - All Wells
308
NLCD Low + Med + High
694
Oil Production at Oil Wells
309
NLCD Open + Low + Med
695
Well Count - Oil Wells
310
NLCD Total Agriculture
696
Gas Production at Gas Wells
319
NLCD Crop Land
697
Oil Production at Gas Wells
320
NLCD Forest Land
698
Well Count - Gas Wells
321
NLCD Recreational Land
699
Gas Production at CBM Wells
340
NLCD Land
711
Airport Areas
350
NLCD Water
801
Port Areas
500
Commercial Land
805
Offshore Shipping Area
505
Industrial Land '
806
Offshore Shipping NEI2014 Activity
506
Education ?
807
Navigable Waterway Miles
508
Public Schools
808
2013 Shipping Density
510
Commercial plus Industrial \
820
Ports NEI2014 Activity
535
Residential + Commercial + Industrial +
Institutional + Government
850
Golf Courses
560
Hospital (COM6)
860
Mines
For the onroad sector, the on-network (RPD) emissions were allocated differently from the off-network
(RPP, starts, ONI, and RPV). On-network used average annual daily traffic (AADT) data and off network
used land use surrogates as shown in Table 3-13. Emissions from the 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 2016 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
77
-------
Source type
Source Type name
Surrogate ID
Description
31
Passenger Truck
307
NLCD All Development
NLCD Low + Med +
32
Light Commercial Truck
308
High
41
Other Bus
306
NLCD Med + High
42
Transit Bus
259
Transit Bus Terminals
43
School Bus
508
Public Schools
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 npoilgas sector, the spatial surrogates were updated to those shown in
Table 3-14 using 2019 data consistent with what was used to develop the 2017 nonpoint oil and gas
emissions. The exploration and production of oil and gas has increased in terms of quantities and
locations over the last seven years, primarily through the use of new technologies, such as hydraulic
fracturing. Census-tract, 2-km, and 4-km sub-county Shapefiles were developed, from which the 2019
oil and gas surrogates were generated. All spatial surrogates for np oilgas are developed based on known
locations of oil and gas activity for year 2019.
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, 201914). 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 (Alaska, Arizona, Idaho, Illinois, Indiana, Kentucky, Louisiana, Michigan,
Mississippi, Missouri, Nevada, Oregon and Pennsylvania, Tennessee). In cases when the desired
surrogate parameter was not available (e.g., feet drilled), data for an alternative surrogate parameter (e.g.,
number of spudded wells) was downloaded and used. Under that methodology, both completion date and
date of first production from HPDI were used to identify wells completed during 2019.
The spatial surrogates, numbered 670 through 699 and also 6831, 6832, and 6833, were processed at
12km resolution and gapfilled with the Surrogate Tool. The surrogates were first gapfilled using fallback
surrogates. For each surrogate, the last two fallbacks were surrogate 693 (Well Count - All Wells) and
304 (NLCD Open + Low). Where appropriate, other surrogates were also part of the gapfilling procedure.
For example, surrogate 670 (Spud Count - CBM Wells) was first gapfilled with 692 (Spud Count - All
Wells), and then 693 and finally 304.
Table 3-14. Spatial Surrogates for Oil and Gas Sources
Surrogate Code
Surrogate Description
670
Spud Count - CBM Wells
671
Spud Count - Gas Wells
14 https://www.enverus.com/drillinginfo-and-rigdata/
78
-------
Surrogate Code
Surrogate Description
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
689
Gas Produced - Total
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
6831
Produced water at CBM wells
6832
Produced water at gas wells
6833
Produced water at oil 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 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.
Table 3-15. Selected 2019 CAP emissions by sector for U.S. Surrogates (CONUS domain totals,
tons)
Sector
ID
Description
MI;
NOx
PM2S
SO2
voc
afdust
240
Total Road Miles
0
0
315,096
0
0
afdust
304
NLCD Open + Low
0
0
842,116
0
0
afdust
306
NLCD Med + High
0
0
52,278
0
0
79
-------
-1
0
0
,985
208
,055
,039
3_
,181
,798
0
,255
,203
,599
0
,049
,262
,037
0
299
279
,401
596
,730
356
,423
,749
,188
,963
,811
,015
,213
,675
,840
,186
,738
462
89
,284
870
,964
,159
,758
ID
Description
MI;
NOx
PM2S
SO2
308
NLCD Low + Med + High
117,047
310
NLCD Total Agriculture
0
791,881
310
NLCD Total Agriculture
2,602,279
0
100
Population
34,304
0
0
0
150
Residential Heating - Natural Gas
33,550
204,371
4,041
1,365
170
Residential Heating - Distillate Oil
1,531
30,031
3,284
11,510
180
Residential Heating - Coal
1
1
190
Residential Heating - LP Gas
98
31,061
163
712
239
Total Road AADT
22
541
240
Total Road Miles
244
All Unrestricted AADT
271
NTAD Class 12 3 Railroad Density
0
0
0
0
300
NLCD Low Intensity Development
4,823
19,093
94,548
2,882
304
NLCD Open + Low
0
0
0
0
306
NLCD Med + High
23,668
272,514
245,871
131,592
307
NLCD All Development
85
25,798
110,610
,169
308
NLCD Low + Med + High
884
156,033
15,683
10,076
310
NLCD Total Agriculture
0
38
0
319
NLCD Crop Land
0
97
72
320
NLCD Forest Land
3,953
68
273
650
Refineries and Tank Farms
0
16
711
Airport Areas
801
Port Areas
0
261
NTAD Total Railroad Density
1,807
184
304
NLCD Open + Low
1,620
136
305
NLCD Low + Med
96
14,661
3,879
77
306
NLCD Med + High
335
160,244
9,947
265
307
NLCD All Development
101
29,155
15,414
73
308
NLCD Low + Med + High
565
262,271
21,894
231
309
NLCD Open + Low + Med
122
21,080
1,240
99
310
NLCD Total Agriculture
421
305,710
21,805
183
320
NLCD Forest Land
15
3,281
522
321
NLCD Recreational Land
83
13,038
5,523
57
350
NLCD Water
192
113,237
4,467
160
850
Golf Courses
13
2,087
119
10
860
Mines
2,467
240
670
Spud Count - CBM Wells
0
671
Spud Count - Gas Wells
674
Unconventional Well Completion
Counts
27
20,730
496
26
678
Completions at Gas Wells
7,874
193
2,968
679
Completions at CBM Wells
400
681
Spud Count - Oil Wells
80
-------
Sector
ID
Description
MI;
NOx
PM2S
SO2
voc
npoilgas
685
Completions at Oil Wells
0
377
0
1,361
42,017
npoilgas
687
Feet Drilled at All Wells
0
75,545
1,995
107
3,318
npoilgas
689
Gas Produced - Total
0
460
55
4
68,697
npoilgas
691
Well Counts - CBM Wells
0
29,113
521
11
30,841
npoilgas
694
Oil Production at Oil Wells
0
3,695
0
31,403
1,052,276
npoilgas
695
Well Count - Oil Wells
0
129,122
3,032
1,465
676,769
npoilgas
696
Gas Production at Gas Wells
0
211
0
1
50,268
npoilgas
698
Well Count - Gas Wells
0
253,031
4,794
127
498,114
npoilgas
699
Gas Production at CBM Wells
0
47
5
0
5,190
npoilgas
6831
Produced water at CBM wells
0
0
0
0
3,695
npoilgas
6832
Produced water at gas wells
0
0
0
0
38,515
npoilgas
6833
Produced water at oil wells
0
0
0
0
46,549
npsolvents
100
Population
0
0
0
0
1,372,923
npsolvents
240
Total Road Miles
0
0
0
0
48,397
npsolvents
306
NLCD Med + High
33
27
300
1
409,967
npsolvents
307
NLCD All Development
24
6
19
5
527,883
npsolvents
308
NLCD Low + Med + High
0
0
129
0
7,970
npsolvents
310
NLCD Total Agriculture
0
0
0
0
149,185
onroad
205
Extended Idle Locations
342
34,291
780
18
4,084
onroad
242
All Restricted AADT
34,157
925,436
25,956
5,041
134,605
onroad
244
All Unrestricted AADT
63,200
1,496,382
55,842
10,503
374,603
onroad
259
Transit Bus Terminals
15
2,037
50
1
439
onroad
304
NLCD Open + Low
0
687
20
0
4,124
onroad
306
NLCD Med + High
921
95,906
3,529
76
20,258
onroad
307
NLCD All Development
3,576
204,352
7,433
960
594,388
onroad
308
NLCD Low + Med + High
204
19,149
569
56
29,121
onroad
508
Public Schools
16
2,189
81
1
544
rail
261
NT AD Total Railroad Density
14
35,834
1,061
31
1,822
rail
271
NTAD Class 12 3 Railroad Density
324
480,174
13,760
644
20,118
rwc
300
NLCD Low Intensity Development
16,369
33,925
297,877
7,937
322,528
3.3.7.2 Allocation Methodfor 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"
(ARTOPNT) 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://www.epa.gov/sites/production/files/2020-10/documents/emissions tsd voll 02-28-08.pdf.
81
-------
3.3.7.3 Surrogates for Canada and Mexico Emission Inventories
The surrogates for Canada to spatially allocate the Canadian emissions are based on the 2015 Canadian
inventories and associated data. The spatial surrogate data came from ECCC, 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. The population
surrogate was updated for Mexico for the 2014v7.1 platform. Surrogate code 11, which uses 2015
population data at 1 km resolution, replaces the previous population surrogate code 10. The other
surrogates for Mexico are circa 1999 and 2000 and were based on data obtained from the Sistema
Municpal de Bases de Datos (SIMBAD) de INEGI and the Bases de datos del Censo Economico 1999.
Most of the CAPs allocated to the Mexico and Canada surrogates are shown in Table 3-17. The entries in
Table 3-17 are for the othar, othafdust, onroad can, and onroadmex sectors.
Table 3-16. Canadian Spatial Surrogates
Code
Canadian Surrogate Description
Code
Description
100
Population
921
Commercial Fuel Combustion
TOTAL INSTITUTIONAL AND
101
total dwelling
923
GOVERNEMNT
104
capped total dwelling
924
Primary Industry
106
ALL INDUST
925
Manufacturing and Assembly
113
Forestry and logging
926
Distribution and Retail (no petroleum)
200
Urban Primary Road Miles
927
Commercial Services
210
Rural Primary Road Miles
932
CANRAIL
211
Oil and Gas Extraction
940
PAVED ROADS NEW
212
Mining except oil and gas
946
Construction and mining
220
Urban Secondary Road Miles
948
Forest
221
Total Mining
951
Wood Consumption Percentage
222
Utilities
955
UNPAVED ROADS AND TRAILS
230
Rural Secondary Road Miles
960
TOTBEEF
233
Total Land Development
970
TOTPOUL
240
capped population
980
TOTSWIN
308
Food manufacturing
990
TOTFERT
321
Wood product manufacturing
996
urban area
323
Printing and related support activities
1251
OFFR TOTFERT
Petroleum and coal products
324
manufacturing
1252
OFFR MINES
Plastics and rubber products
326
manufacturing
1253
OFFR Other Construction not Urban
Non-metallic mineral product
327
manufacturing
1254
OFFR Commercial Services
331
Primary Metal Manufacturing
1255
OFFR Oil Sands Mines
350
Water
1256
OFFR Wood industries CANVEC
412
Petroleum product wholesaler-
distributors
1257
OFFR UNPAVED ROADS RURAL
448
clothing and clothing accessories
stores
1258
OFFR Utilities
82
-------
Code
Canadian Surrogate Description
Code
Description
482
Rail transportation
1259
OFFR total dwelling
562
Waste management and remediation
services
1260
OFFR water
901
AIRPORT
1261
OFFR ALL INDUST
902
Military LTO
1262
OFFR Oil and Gas Extraction
903
Commercial LTO
1263
OFFR ALLROADS
904
General Aviation LTO
1265
OFFR CANRAIL
945
Commercial Marine Vessels
9450
Commercial Marine Vessel Ports
Table 3-17. 2018 CAPs Allocated to Mexican and Canadian Spatial Surrogates for 12US1 (tons)
Code
Mexican or Canadian Surrogate
Description
MI;
NOx
PM2s
SO2
voc
11
MEX 2015 Population
0
85,151
465
178
223,016
14
MEX Residential Heating - Wood
0
2,333
6,512
190
17,541
16
MEX Residential Heating - Distillate
Oil
1
28
0
0
1
22
MEX Total Road Miles
2,983
359,738
12,086
6,647
71,102
24
MEX Total Railroads Miles
0
19,518
408
185
732
26
MEX Total Agriculture
115,677
18,186
16,239
473
3,847
32
MEX Commercial Land
0
75
1,631
0
27,763
34
MEX Industrial Land
92
2,036
1,257
6
34,866
36
MEX Commercial plus Industrial
Land
5
7,049
305
15
101,386
40
MEX Residential (RES1-
4)+Comercial+Industrial+Institutional
+Government
0
15
61
2
21,041
42
MEX Personal Repair (COM3)
0
0
0
0
5,130
44
MEX Airports Area
0
3,420
48
241
1,294
48
MEX Brick Kilns
0
266
5,297
470
130
50
MEX Mobile sources - Border
Crossing
3
58
2
0
45
100
CAN Population
776
51
604
14
219
101
CAN total dwelling
0
0
0
0
147,322
104
CAN capped total dwelling
347
30,961
2,282
2,473
1,614
106
CAN ALL INDUST
583
113
CAN Forestry and logging
117
1,392
7,471
29
3,938
200
CAN Urban Primary Road Miles
1,572
66,863
2,365
232
7,134
210
CAN Rural Primary Road Miles
628
39,000
1,297
98
3,023
211
CAN Oil and Gas Extraction
1
39
424
41
1,657
212
CAN Mining except oil and gas
0
0
3,051
0
0
220
CAN Urban Secondary Road Miles
2,951
105,604
4,764
482
18,852
221
CAN Total Mining
0
0
13,221
0
0
83
-------
Code
Mexican or Canadian Surrogate
Description
MI;
NOx
PM2s
SO2
voc
222
CAN Utilities
55
3,344
2,859
453
63
230
CAN Rural Secondary Road Miles
1,652
71,811
2,515
257
8,255
240
CAN capped population
327
44,524
1,385
52
95,157
308
CAN Food manufacturing
0
0
18,982
0
17,387
321
CAN Wood product manufacturing
785
4,450
1,543
328
15,455
323
CAN Printing and related support
activities
0
0
0
0
11,693
324
CAN Petroleum and coal products
manufacturing
0
1,179
1,599
462
9,154
326
CAN Plastics and rubber products
manufacturing
0
0
0
0
24,027
327
CAN Non-metallic mineral product
manufacturing
0
0
6,449
0
0
331
CAN Primary Metal Manufacturing
0
156
5,561
29
72
412
CAN Petroleum product wholesaler-
distributors
0
0
0
0
43,724
448
CAN clothing and clothing accessories
stores
0
0
0
0
140
482
CAN Rail transportation
1
4,062
88
1
256
562
CAN Waste management and
remediation services
240
1,924
2,620
2,483
9,199
901
CAN AIRPORT
0
93
9
0
9
921
CAN Commercial Fuel Combustion
187
24,020
2,379
1,414
1,224
923
CAN TOTAL INSTITUTIONAL
AND GOVERNEMNT
0
0
0
0
14,458
924
CAN Primary Industry
0
0
0
0
38,858
925
CAN Manufacturing and Assembly
0
0
0
0
69,488
926
CAN Distribution and Retail (no
petroleum)
0
0
0
0
7,285
927
CAN Commercial Services
0
0
0
0
31,311
932
CAN CANRAIL
48
83,844
1,662
43
3,559
940
CAN PAVED ROADS NEW
27,751
946
CAN Construction and mining
0
0
0
0
9,850
951
CAN Wood Consumption Percentage
957
10,634
107,554
1,519
152,072
955
CAN
UNPAVED ROADS AND TRAILS
383,147
990
CAN TOTFERT
48
4,047
265
6,827
152
996
CAN urban area
0
0
2,994
0
0
1251
CAN OFFR TOTFERT
73
50,505
3,421
48
4,585
1252
CAN OFFR MINES
1
563
38
1
82
1253
CAN OFFR Other Construction not
Urban
68
28,864
3,601
41
10,186
1254
CAN OFFR Commercial Services
43
14,335
2,235
26
35,468
1255
OFFR Oil Sands Mines
0
0
0
0
0
84
-------
Code
Mexican or Canadian Surrogate
Description
MI;
NOx
PM2s
SO2
voc
1256
CAN OFFR Wood industries
CANVEC
7
2,103
214
4
853
1257
CAN OFFR UNPAVED ROADS
RURAL
24
10,272
574
14
24,333
1258
CAN OFFR Utilities
8
3,727
176
5
824
1259
CAN OFFR total dwelling
17
5,844
588
10
12,557
1260
CAN OFFR water
19
5,604
264
22
20,367
1261
CAN OFFR ALL INDUST
4
5,065
145
2
1,068
1262
CAN OFFR Oil and Gas Extraction
1
570
42
0
168
1263
CAN OFFR ALLROADS
3
1,361
129
2
438
1265
CAN OFFR CANRAIL
0
489
15
0
37
85
-------
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. 2016. 2014 Emissions Modeling Platform Spatial Surrogate Documentation. UNC Institute
for the Environment, Chapel Hill, NC. October 1, 2016.
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.
Appel, K.W., Napelenok, S., Hogrefe, C., Pouliot, G., Foley, K.M., Roselle, S.J., Pleim, J.E., Bash, J.,
Pye, H.O.T., Heath, N., Murphy, B., Mathur, R., 2018. Overview and evaluation of the
Community Multiscale Air Quality Model (CMAQ) modeling system version 5.2. In Mensink C.,
Kallos G. (eds), Air Pollution Modeling and its Application XXV. ITM 2016. Springer
Proceedings in Complexity. Springer, Cham. Available at https://doi.org/10.1007/978-3-319-
57645-9_l1.
Bash, J.O., Baker, K.R., Beaver, M.R., Park, J.-H., Goldstein, A.H., 2015. Evaluation of improved land
use and canopy representation in BEIS with biogenic VOC measurements in California (in
preparation).
Bullock Jr., R, and K. A. Brehme (2002) "Atmospheric mercury simulation using the CMAQ model:
formulation description and analysis of wet deposition results." Atmospheric Environment 36, pp
2135-2146.
California Air Resources Board (CARB): Final 2015 Consumer & Commercial Product Survey Data
Summaries, 2019.
CRC, 2017. Improvement of Default Inputs for MOVES and SMOKE-MOVES: Final Report.
Coordinating Research Council Report No. A-100. Available from
http://crcsite.wpengine.com/wp-
content/uploads/2019/05/ERG FinalReport CRCA100 28Feb2017.pdf
U.S. Environmental Protection Agency: 2017 National Emissions Inventory (NEI), Research Triangle
Park, NC, 2020b. https://www.epa.gov/air-emissions-inventories/2017-national-emissions-
inventory-nei-data.
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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 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 Multi scale Air Quality (CMAQ) modeling system.15 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 2019 assessment
used a single domain that covers the entire continental U.S. (CONUS) and large portions of Canada and
Mexico using 12-km by 12-ktn horizontal grid spacing. Currently, 12-km x 12-ktn resolution is sufficient
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.
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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 multi-
year) pollutant climatologies as well as short-term (weeks to months) transport from localized sources. By
making CMAQ a modeling system that addresses multiple pollutants and different temporal and spatial
scales, CMAQ has a "one atmosphere" perspective that combines the efforts of the scientific community.
Improvements will be made to the CMAQ modeling system as the scientific community further develops
the state-of-the-science.
For more information on CMAQ, go to https://www.epa.gov/cmaq or http://www.cmascenter.org.
4.1.1 Advantages and Limitations of the CMAQ Air Quality Model
An advantage of using the CMAQ model output for characterizing air quality for use in comparing with
health outcomes is that it provides a complete spatial and temporal coverage across the U.S. CMAQ is a
three-dimensional Eulerian photochemical air quality model that simulates the numerous physical and
chemical processes involved in the formation, transport, and destruction of ozone, particulate matter, and
air toxics for given input sets of initial and boundary conditions, meteorological conditions, and
emissions. The CMAQ model includes state-of-the-science capabilities for conducting urban to regional
scale simulations of multiple air quality issues, including tropospheric ozone, fine particles, toxics, acid
deposition, and visibility 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 include 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., 2012.17
4.2 CMAQ. Model Version, Inputs and Configuration
This section describes the air quality modeling platform used for the 2019 CMAQ simulation. A modeling
platform is a structured system of connected modeling-related tools and data that provide a consistent and
transparent basis for assessing the air quality response to changes in emissions and/or meteorology. A
platform typically consists of a specific air quality model, emissions estimates, a set of meteorological
inputs, and estimates of boundary conditions representing pollutant transport from source areas outside the
region modeled. We used the CMAQ modeling system as part of the 2019 Platform to provide a national
16U.S. EPA (2018), Modeling Guidance for Demonstrating Air Quality Goals for Ozone, PM2.5, and Regional Haze, pp 205.
https://www3.epa.gov/ttn/scram/guidance/guide/O3-PM-RH-Modeling_Guidance-2018.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.
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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 PM2.5.
This section provides a description of each of the main components of the 2019 CMAQ simulation along
with the results of a model performance evaluation in which the 2019 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 particle pollutants in the
atmosphere. This 2019 analysis employed CMAQ version 5.3.2.18 The 2019 CMAQ run included CB6r3
chemistry19, AER07 aerosol module20 with non-volatile Primary Organic Aerosol (POA), and updated
halogen chemistry21. The CMAQ community model versions 5.0.2 and 5.1 were most recently peer-
reviewed in September of 2016 for the U.S. EPA.22
4.2.2 Model Domain and Grid Resolution
The CMAQ modeling analyses were performed for a domain covering the continental United States, as
shown in Figure 4-1. This single domain covers the entire continental U.S. (CONUS) and large portions
of Canada and Mexico using 12-km by 12-km horizontal grid spacing. The 2019 simulation used a
Lambert Conformal map projection centered at (-97, 40) with true latitudes at 33 and 45 degrees
north. The 12-km 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 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 2019 simulation. Air quality
conditions at the outer boundary of the 12-km domain were taken from the northern hemispheric CMAQ
model (discussed in Section 4.2.4).
18 CMAQ version 5.3.2: https://doi.org/10.5281/zenodo.4081737; https://www.epa.gov/cmaa/cmaa-models-0. CMAQ v5.3.2 is
also available from the Community Modeling and Analysis System (CMAS) at: http://www.cmascenter.org.
19 Luecken, D. J., Yarwood, G., and Hutzell, W. T.: Multipollutant modeling of ozone, reactive nitrogen and HAPs across the
continental US with CMAQ-CB6, Atmos Environ, 201, 62-72, 10.1016/j.atmosenv.2018.11.060, 2019.
20 Xu, L., Pye, H. O. T., He, J., Chen, Y. L., Murphy, B. N., and Ng, N. L.: Experimental and model estimates of the
contributions from biogenic monoterpenes and sesquiterpenes to secondary organic aerosol in the southeastern United States,
Atmos ChemPhys, 18, 12613-12637, 10.5194/acp-18-12613-2018, 2018.
21 Kang, D.; Willison, J.; Sarwar, G.; Madden, M.; Hogrefe, C.; Mathur, R.; Gantt, B.; and Saiz-Lopez, A.: Improving the
Characterization of Natural Emissions in CMAQ, Environmental Manager, A&WMA, October 2021.
22Moran, 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/cmaa 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 \ 5.1, 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.
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Table 4-1. Geographic Information for 2019 12-km Modeling Domain
National 12 km CMAQ Modeling Configuration
Map Projection
Lambert Confbimal Projection
Grid Resolution
12km
Coordinate Center
97W,40N
True Latitudes
33and45N
Dimensions
459 x 299 x 35
Vertical Extent
35Layers: Surfaoeto50mblevel (seeTable4-2)
Table 4-2. Vertical layer structure for 2019 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
12
0.9100
914.50
714
11
0.9200
924.00
632
10
0.9300
933.50
551
9
0.9400
943.00
470
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Vertical
Layers
Sigma P
Pressure
(mb)
Approximate
Height (m)
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 2019 CMAQ Modeling Domain. The blue box denotes the 12-km national
modeling domain.
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4.2.3 Modeling Period / Ozone Episodes
The 12-km CMAQ modeling domain was modeled for the entire year of 2019. The annual simulation
included a spin-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
2019 Emissions: The emissions inventories used in the 2019 air quality modeling are described in Section
3, above.
2019 Meteorological Input Data: The gridded meteorological data for the entire year of 2019 at the 12-
km continental United States scale domain was derived from the publicly available version 4.1.1 of the
Weather Research and Forecasting Model (WRF), Advanced Research WRF (ARW) core.23 The WRF
Model is a state-of-the-science mesoscale numerical weather prediction system developed for both
operational forecasting and atmospheric research applications (http://wrf-model.org). The 12US WRF
model was initialized using the 12-km North American Model (12NAM)24 analysis product provided by
National Climatic Data Center (NCDC). Where 12NAM data was unavailable, the 40-km Eta Data
Assimilation System (EDAS) analysis (ds609.2) from the National Center for Atmospheric Research
(NCAR) was used. Analysis nudging for temperature, wind, and moisture was applied above the
boundary layer only. The model simulations were conducted continuously. The 'ipxwrf program was
used to initialize deep soil moisture at the start of the run using a 10-day spin-up period. The 2019 WRF
meteorology simulated was based on 2011 National Land Cover Database (NLCD).25 The WRF
simulation included the physics options of the Pleim-Xiu land surface model (LSM), Asymmetric
Convective Model version 2 planetary boundary layer (PBL) scheme, Morrison double moment
microphysics, Kain- Fritsch cumulus parameterization scheme utilizing the moisture-advection trigger26
and the RRTMG long-wave and shortwave radiation (LWR/SWR) scheme.27 In addition, the Group for
High Resolution Sea Surface Temperatures (GHRSST)28'29 1 -km SST data was used for SST information
to provide more resolved information compared to the more coarse data in the NAM analysis.
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 North American Model Analysis-Only, http://nomads.ncdc.noaa.gov/data.php; download from
ftp://nomads.ncdc.noaa.gov/NAM/analysis_only/.
25 National Land Cover Database 2011, http://www.mrlc.gov/nlcd201 l.php.
26 Ma, L-M. and Tan, Z-M, 2009. Improving the behavior of the Cumulus Parameterization for Tropical Cyclone Prediction:
Convection Trigger. Atmospheric Research 92 Issue 2, 190-211.
http://www.sciencedirect.com/science/article/pii/S01698095080Q2585.
27 Gilliam. R.C., Plcim. I.E., 2010. Performance Assessment of New Land Surface and Planetary Boundary Layer Physics in the
WRF- ARVV. Journal of Applied Meteorology and Climatology 49, 760-774.
28 Stammer, D., F.J. Wentz, and C.L. Gentemann, 2003, Validation of Microwave Sea Surface Temperature Measurements for
Climate Purposes, J. Climate, 16, 73-87.
29 Global High-Resolution SST (GHRSST) analysis, https://www.ghrsst.org/.
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Additionally, the hybrid-vertical coordinate system was employed, where the model is terrain-following (Eta)
near the surface and isobaric aloft, reducing the influence of surface features on upper-level dynamics.
2019 Initial and Boundary Conditions: The 2019 annual lateral boundary and initial species
concentrations were provided using an Hemispheric CMAQv5.3.2 (H-CMAQ) simulation. The CMAQ
model simulation covered the Northern Hemisphere at 108km horizontal resolution with a 44-layer
vertical structure reaching 50 hPa.30 Simulations use CB6r3 chemistry31, AER07 aerosols32, and updated
halogen chemistry33. Anthropogenic emissions were modeled using representative day emissions, created
by averaging prior emissions on a day-of-week basis by month. Anthropogenic emissions and LNOx
emissions are prior emissions scaled using inverse estimates based on OMIN02 satellite observations.
The inverse modeling system for estimating emissions is described in East et al. (2022, in prep).34 The
2019 prior simulation was initialized with a 1-year spin-up period not considered in the analyses. Biomass
burning emissions were 2019 FINN database35. Soil NOx emissions used were 2018 CAMS v2.1
emissions with canopy reduction factor36. Meteorology used in this H-CMAQ run was 2019 WRF
v4.1.1.37
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 2019 simulation using state/local monitoring sites data in order to estimate the
ability of the CMAQ modeling system to replicate the 2019 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.
30Mathur, R., Xing, J., Gilliam, R., Sarwar, G., Hogrefe, C., Pleim, J., Pouliot, G., Roselle, S., Spero, T. L., Wong, D. C., and
Young, J.: Extending the Community Multiscale Air Quality (CMAQ) modeling system to hemispheric scales: overview of
process considerations and initial applications, Atmos ChemPhys, 17, 12449-12474, 10.5194/acp-17-12449-2017, 2017.
31 Luecken, D. J., Yarwood, G., and Hutzell, W. T.: Multipollutant modeling of ozone, reactive nitrogen and HAPs across the
continental US with CMAQ-CB6, Atmos Environ, 201, 62-72, 10.1016/j.atmosenv.2018.11.060, 2019.
32 Xu, L., Pye, H. O. T., He, J., Chen, Y. L., Murphy, B. N., and Ng, N. L.: Experimental and model estimates of the
contributions from biogenic monoterpenes and sesquiterpenes to secondary organic aerosol in the southeastern United States,
Atmos ChemPhys, 18, 12613-12637, 10.5194/acp-18-12613-2018, 2018.
33Kang, D.; Willison, J.; Sarwar, G.; Madden, M.; Hogrefe, C.; Mathur, R.; Gantt, B.; and Saiz-Lopez, A.: Improving the
Characterization of Natural Emissions in CMAQ, Environmental Manager, A&WMA, October 2021.
34 East, J.D., Henderson, B. H., Napelenok, S. L., Koplitz, S. N., Sarwar, G., Gilliam, R., Lenzen, A., Tong, D., Pierce, R. B.,
Garcia-Menendez, F. Comparing OMI and TROPOMIN02 data assimilation for estimating NOx emissions. In preparation.
2022.
35 Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire
INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model
Dev., 4, 625-641, 10.5194/gmd-4-625-2011, 2011.
36 Simpson, D.: Soil N emissions for 2000-present. (D81.3.6.1.) [dataset], 2018.
37 Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R.,
Peckham, S. E., Grell, G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R., Dimego, G. J., Wang, W.,
Schwartz, C. S., Romine, G. S., Liu, Z. Q., Snyder, C., Chen, F., Barlage, M. J., Yu, W., and Duda, M. G.: THE WEATHER
RESEARCH AND FORECASTING MODEL Overview, System Efforts, and Future Directions, B Am Meteorol Soc, 98,
1717-1737, 10 1175/Bams-D-15-00308 1; 2017.
96
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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 = ~Hi(P ~ 0) , 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 = i£;|P-0|
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:
i(p-o)
NMB = _j *100, where P = predicted concentrations and O = observed
n
I(O)
1
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
i\p-o\
NME = T *100
n
S(O)
1
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 regions38 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 West39'40 as were originally identified in Karl and Koss (1984).41
38 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.
39 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.
40 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.
41 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.
97
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U.S. Climate Regions
Figure 4-2. NOAA Nine Climate Regions (source: htti)://www.ncdc.noaa.gov/monitoring-references/mai)s/us-
climate-regions.i)hi)#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 eight-
hour daily maximum ozone was conducted using the statistics defined above. Ozone measurements in the
continental U.S. were included in the evaluation and were taken from the 2019 state/local monitoring site
data in 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-January-
February), spring (March-April-May), summer (June, July, August), and fall (September-October-
November). 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, April 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 2019 CM AQ 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 and CASTNet sites in the summer is over predicted at all climate regions
(NMB ranging between 0.6 to 8.5 percent) except in the Southwest, Northern Rockies, West, and in the
Northwest at CASTNet sites only where there is a slight under prediction. Likewise, 8-hour ozone at the AQS
98
-------
and CASTNet sites in the fall is typically over predicted across the contiguous U.S. (NMB ranging
between 1.4 to 13.8 percent) except in Southwest and West as well as in the Ohio Valley, Southeast at
CASTNet sites only. However, 8-hour ozone concentrations in the winter and spring are under predicted
at AQS and CASTNet sites in all NOAA climate regions (with NMBs less than approximately 20 percent
in each subregion) except in the winter at AQS sites in the Ohio Valley and Upper Midwest (slight
overprediction of NMB ranging between 3.9 and 4.5 percent).
Model bias at individual sites during the ozone season is similar to that seen on a subregional basis for the
summer. Figure 4-3 shows the mean bias for 8-hour daily maximum ozone greater than 60 ppb is
generally ±15 ppb across the AQS and CASTNet sites. Likewise, the information in Figure 4-5 indicates
that the normalized mean 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 16 ppb and 30 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 central
California, Northern Rockies, Upper Midwest, and Southeast.
Table 4-4. Summary of CMAQ 2019 8-Hour Daily Maximum Ozone Model Performance Statistics
by NOAA climate region, by Season and Monitoring Network.
Climate
Monitor
No. of
MB
ME
NMB
NME
region
AQS
Winter
10,839
-0.5
4.1
-1.6
12.5
Spring
16,565
-5.0
6.6
-11.4
15.0
Summer
16,721
1.6
5.7
3.7
13.1
Northeast
Fall
13,991
1.5
4.6
4.4
13.4
CASTNet
Winter
1,267
-2.0
4.0
-5.8
11.2
Spring
1,287
-6.3
7.2
-13.9
15.7
Summer
1,229
0.8
5.5
1.9
13.2
Fall
1,221
0.9
4.5
2.7
12.9
AQS
Winter
5,901
1.4
4.6
4.5
15.2
Spring
20,951
-3.8
6.1
-8.6
13.8
Summer
20,720
1.0
4.6
2.3
12.3
Ohio Valley
Fall
15,591
1.1
4.4
2.8
11.8
CASTNet
Winter
1,574
-0.1
4.5
-0.2
13.9
Spring
1,602
-5.2
7.1
-11.1
15.3
Summer
1,584
0.2
5.1
0.6
11.5
Fall
1,537
-0.6
4.4
-1.7
11.5
AQS
Winter
2,042
1.3
4.1
3.9
12.5
Upper Midwest
Spring
8,297
-4.1
5.8
-9.6
13.4
Summer
8,858
1.2
5.7
2,8
13.8
Fall
6,225
4.2
5.5
13.8
18.0
99
-------
Climate
region
Monitor
Network
Season
No. of
Obs
MB
(ppb)
ME
(ppb)
NMB
(%)
NME
(%)
CASTNet
Winter
418
-0.9
3.6
-2.5
10.5
Spring
449
-6.6
7.2
-14.8
16.2
Summer
438
0.4
5.6
1.1
14.3
Fall
433
3.4
4.7
11.3
15.6
AQS
Winter
6,698
-2.2
4.7
-6.3
12.9
Spring
15,470
-4.2
6.6
-9.2
14.3
Summer
14,708
3.4
6.3
8.5
15.7
Fall
12,167
0.9
4.5
2.3
11.6
Southeast
CASTNet
Winter
891
-3.4
5.4
-9.2
14.6
Spring
958
-5.9
7.3
-12.5
15.4
Summer
938
1.9
5.7
4.6
14.0
Fall
970
-3.0
6.4
-7.2
15.2
AQS
Winter
10,537
-0.9
5.3
-2.9
16.5
Spring
12,498
-1.7
7.5
-3.8
17.2
Summer
12,313
2.5
6.5
6.1
16.1
Fall
11,844
0.7
5.6
1.9
14.8
South
CASTNet
Winter
502
-1.3
4.6
-3.7
13.4
Spring
523
-3.2
7.4
-7.0
16.5
Summer
507
0.6
6.1
1.4
14.8
Fall
515
0.5
4.7
1.4
12.7
AQS
Winter
9,586
-2.4
5.9
-6.2
15.3
Spring
10,650
-7.3
8.1
-14.3
15.8
Summer
10,695
-3.1
6.1
-5.7
11.2
Fall
10,550
-1.4
4.8
-3.2
11.0
Southwest
CASTNet
Winter
793
-5.1
6.6
-11.5
14.8
Spring
795
-9.0
9.4
-16.8
17.6
Summer
805
-2.0
5.0
-3.8
9.6
Fall
792
-2.5
4.3
-5.4
9.3
AQS
Winter
4,524
-0.7
4.8
-1.9
12.5
Northern
Spring
4,850
-5.8
6.9
-12.4
14.8
Rockies
Summer
4,891
-0.6
4.9
-1.4
10.6
Fall
4,745
2.0
4.4
5.5
12.2
100
-------
Climate
Monitor
No. of
MB
ME
NMB
NME
region
Network
Season
Obs
(%)
(%)
CASTNet
Winter
695
-1.3
5.5
-3.3
13.9
Spring
702
-6.8
7.4
-14.1
15.4
Summer
688
-0.6
4.6
-1.4
10.0
Fall
705
1.3
4.5
3.5
11.9
AQS
Winter
644
-0.8
5.1
-2.6
16.1
Spring
1,337
-4.6
7.9
-11.6
19.8
Summer
2,411
1.3
5.9
3.5
16.2
Northwest
Fall
1,204
3.7
6.0
12.4
20.0
CASTNet
Winter
83
-0.6
3.9
-1.8
11.4
Spring
85
-6.4
6.9
-15.0
16.1
Summer
88
-1.7
4.1
-4.1
9.7
Fall
90
2.8
4.4
8.9
14.2
AQS
Winter
14,058
-2.2
5.2
-6.5
15.4
Spring
16,167
-6.8
7.7
-14.9
17.0
Summer
17,106
-3.7
7.0
-7.3
14.0
West
Fall
15,390
-3.9
6.7
-8.6
14.9
CASTNet
Winter
536
-3.4
5.4
-8.5
13.6
Spring
565
-7.9
8.5
-16.3
17.5
Summer
626
-8.0
8.9
-14.0
15.7
Fall
602
-5.8
7.2
-11.9
14.8
101
-------
03 8hrmax MB (ppb) for run CMAQ_2019ge_MP cb6ae7_19k_12US1 for 20190401 1o 20190930
units = ppb
coverage limit = 75%
* CASTNET Daily AQS Daily
Figure 4-3. Mean Bias (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period
April-September 2019 at AQS and ("AS I Net monitoring sites in the continental U.S. modeling
domain.
units =
ppb
coverage limit = 75%
_
>20
18
16
14
12
10
.
:
2
0
* CASTNET Daily AQS Daily
Figure 4-4. Mean Error (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period
April-September 2019 at AQS and ("AS I Net monitoring sites in the continental U.S. modeling
domain.
03_8hrma> ME (ppb) for run CMAQ 2019ge _MP cb6ae7 19k 12US1 for 20190401 to 20190930
102
-------
03 8hrmax NMB (%) for run CMAQ 2019ge MP cb6ae7 19k 12US1 for 20190401 to 20190930
* CASTNET Daily AQS Daily
Figure 4-5. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 ppb over
the period April-September 2019 at AQS and CAST Net monitoring sites in the continental U.S.
modeling domain.
03 8hrmax NME (%) for run CMAQ 2019ge MP cb6ae7 19k 12US1 for 20190401 to 20190930
* CASTNET Daily AQS Daily
Figure 4-6. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 ppb over
the period April-September 2019 at AQS and CAST Net monitoring sites in the continental U.S.
modeling domain.
103
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Evaluation for Annual PM7.5 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 2019 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
CASTNet, IMPROVE, and CSN monitoring sites across the 12-km modeling domain (with MB values
ranging from -0.0 to -0.4 |igm~3) except at CSN and IMPROVE sites in the Upper Midwest, Northern
Rockies and Northwest and at IMPROVE sites in the Northeast, as well as at CASTNet sites in the
Northwest (MB approximately 0.1 |igm"3). Sulfate performance shows moderate error in the eastern
subregions (average of approximately 30 percent) while Western subregions show slightly larger error
(ranging from 30 to 60 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 and in the Upper Midwest with over
predictions up to approximately 60 percent at individual monitors. Model error also shows a spatial trend
by region, where much of the Eastern states are 10 to 40 percent, the Western and Central U.S. states are
20 to 100 percent.
Annual average nitrate is under predicted at the urban CSN monitoring sites at all N O A A climate
subregions (NMB in the range of -1 to -34 percent), except in the Northwest where nitrate is over
predicted (62.3 percent). At IMPROVE rural sites, annual average nitrate is under predicted at all
subregions, except in the Northeast (18.6 percent) and Northeast (14.0 percent ) where nitrate is over
predicted. Likewise, model performance of total nitrate at sub-urban CASTNet monitoring sites
shows an under prediction at all subregions (NMB in the range of -10.4 to -53.3 percent). Model
error for nitrate and total nitrate is somewhat greater for each of the nine N O A A climate
subregions as compared to sulfate. Model bias at individual sites indicates over prediction of greater
than 10 percent at monitoring sites along the upper Northeast and Northwest coastline as well as in
the Southeast as indicated in Figure 4-13. The exception to this is in the Ohio Valley, South,
Southwest, Northern Rockies 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.
Annual average ammonium model performance as indicated in Table 4-5 has a tendency for the model
to under predict across CASTNet sites (ranging from -22.3 to -59.2 percent). Ammonium performance
across the urban CSN sites shows an under prediction in all NOAA climate subregions (ranging from -4.4
to -66.8 percent), except in the Northwest (over prediction of 28.7 percent). 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 Northeast and in the Northern Rockies, (over
prediction bias on average 80 to 100 percent). The urban monitoring sites exhibit slightly larger errors
than at rural sites for ammonium.
104
-------
Annual average elemental carbon is under predicted in all of the nine climate regions at urban and rural
sites (biases between -9.2 to 50.5 percent) except in the Northwest (over prediction ranging 19.0 to 41.7
percent). 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 4. lto 65.5 percent), except in the Southwest, Northern Rockies and West where the NMB
ranges from -16.0 to -25.4 percent. The model over predicted annual average organic carbon in all
subregions at urban CSN sites except in the Northern Rockies (NMB approximately -31 percent). Similar
to elemental carbon, error model performance does not show a large variation from subregion to
subregion or at urban versus rural sites.
105
-------
Table 4-5. Summary of CMAQ 2019 Annual PM Species Model Performance Statistics by NOAA
Climate region, by Monitoring Network.
Monitor
No. of
MB
ME
NMB
NME
Pollutant Network
Subregion
Obs
(|jgm3)
(|jgm3)
(%)
(%)
CSN
Northeast
3,118
0.0
0.3
-5.8
35.2
Ohio Valley
2,175
-0.1
0.4
-10.8
33.3
Upper Midwest
1,156
0.1
0.3
12.4
38.8
Southeast
2,134
-0.1
0.3
-13.8
33.8
South
1,334
-0.2
0.5
-15.4
38.5
Southwest
1,111
-0.1
0.2
oo
oo
41.4
Northern Rockies
633
0.1
0.3
17.6
46.8
Northwest
776
0.1
0.1
22.3
49.7
West
1,484
-0.3
0.5
-32.1
50.8
IMPROVE
Northeast
1,888
0.0
0.3
3.3
13.8
Ohio Valley
925
-0.2
0.3
-16.2
32.9
Upper Midwest
933
0.0
0.2
6.5
37.2
Southeast
1,500
-0.2
0.4
-22.8
35.4
Sulfate
South
948
-0.2
0.4
-18.9
37.1
Southwest
3,599
-0.0
0.2
-6.9
43.9
Northern Rockies
2,152
0.0
0.2
11.0
42.0
Northwest
1,841
0.1
0.2
35.7
62.7
West
2,432
-0.0
0.2
-6.0
51.7
CASTNet
Northeast
876
-0.1
0.2
-14.2
25.3
Ohio Valley
909
-0.3
0.3
-24.4
26.5
Upper Midwest
248
-0.0
0.2
-2.8
23.2
Southeast
587
-0.4
0.4
-33.5
36.1
South
381
-0.3
0.4
-27.7
30.9
Southwest
426
-0.0
0.1
-8.1
36.0
Northern Rockies
497
-0.0
0.1
-1.1
30.1
Northwest
49
0.1
0.1
20.7
38.5
West
270
-0.2
0.3
-26.9
47.3
CSN
Northeast
3,117
-0.3
0.5
-28.7
47.2
Ohio Valley
2,175
-0.5
0.7
-34.9
46.9
Upper Midwest
1,156
-0.6
0.8
-36.0
47.8
Southeast
2,150
-0.0
0.3
-6.6
70.0
South
1,334
-0.3
0.4
-40.7
62.9
Nitrate
Southwest
1,110
-0.8
0.9
-64.9
70.4
Northern Rockies
632
-0.5
0.6
-49.2
56.6
Northwest
776
0.5
1.1
62.3
>100
West
1,483
-1.2
1.3
-63.1
66.5
IMPROVE
Northeast
1,888
0.1
0.2
18.6
72.1
106
-------
Pollutant
Monitor
Network
Subregion
No. of
Obs
MB
(|jgm3)
3 S
3 m
CO
NMB
(%)
NME
(%)
Ohio Valley
925
-0.3
0.4
-41.8
61.9
Upper Midwest
931
-0.3
0.4
-42.5
52.0
Southeast
1,500
-0.1
0.2
-26.5
73.3
South
948
-0.2
0.3
-41.7
63.8
Southwest
3,597
-0.2
0.2
-78.9
83.8
Northern Rockies
2,151
-0.2
0.2
-58.6
77.7
Northwest
1,833
0.0
0.2
14.0
>100
West
2,431
-0.2
0.2
-47.1
67.7
CASTNet
Northeast
875
-0.1
0.4
-10.4
31.1
Ohio Valley
909
-0.3
0.6
-19.1
32.1
Upper Midwest
248
-0.4
0.5
-26.7
36.0
Total Nitrate
(NO3+HNO3)
Southeast
586
-0.2
0.4
-24.4
43.7
South
381
-0.3
0.5
-27.8
38.1
Southwest
426
-0.3
0.3
-41.4
44.9
Northern Rockies
497
-0.2
0.2
33.0
39.7
Northwest
West
270
-0.5
0.5
-53.3
55.1
CSN
Northeast
3,118
-0.0
0.2
-4.4
47.1
Ohio Valley
2,175
-0.1
0.3
-16.0
42.4
Upper Midwest
1,156
-0.0
0.2
-4.6
44.1
Southeast
2,130
-0.1
0.2
-21.3
59.3
South
1,333
-0.1
0.2
-27.9
53.3
Southwest
1,111
-0.2
0.3
-58.7
68.3
Northern Rockies
632
-0.0
0.2
-0.8
52.1
Northwest
774
0.1
0.3
28.7
>100
West
1,482
-0.4
0.5
-66.8
73.8
Ammonium
CASTNet
Northeast
876
-0.1
0.1
-22.3
33.2
Ohio Valley
909
-0.2
0.2
-35.0
37.4
Upper Midwest
248
-0.2
0.2
-30.4
34.8
Southeast
587
-0.1
0.1
-33.6
42.1
South
381
-0.1
0.2
-28.6
38.7
Southwest
426
-0.1
0.1
-49.3
54.1
Northern Rockies
497
-0.1
0.1
-41.2
45.7
Northwest
49
-0.1
0.1
-53.9
58.2
West
270
-0.1
0.1
-59.2
67.4
Elemental
CSN
Northeast
3,114
-0.2
0.3
-22.6
42.7
Carbon
Ohio Valley
2,179
-0.2
0.3
-33.7
43.0
107
-------
Monitor
Pollutant Network
Subregion
No. of
Obs
MB
(|jgm3)
3 S
3 m
CO
NMB
(%)
NME
(%)
Upper Midwest
1,271
-0.1
0.2
-19.7
41.2
Southeast
1,777
-0.3
0.3
-39.8
48.0
South
1,163
-0.2
0.2
-26.6
38.6
Southwest
1,112
-0.2
0.3
-22.9
44.6
Northern Rockies
584
-0.2
0.2
-50.5
62.9
Northwest
774
0.2
0.7
19.0
77.0
West
1,253
-0.3
0.4
-40.3
44.8
IMPROVE
Northeast
1,804
-0.0
0.1
-9.2
39.5
Ohio Valley
925
-0.1
0.1
-31.8
40.6
Upper Midwest
1,032
-0.1
0.1
-30.2
44.1
Southeast
1,623
-0.1
0.2
-39.1
47.3
South
935
-0.1
0.1
-36.3
44.8
Southwest
3,537
-0.1
0.1
-43.6
58.2
Northern Rockies
2,177
-0.0
0.1
-29.4
57.4
Northwest
1,742
0.1
0.1
41.7
98.9
West
2,242
-0.1
0.1
-35.0
52.4
CSN
Northeast
3,113
0.9
1.2
52.8
71.4
Ohio Valley
2,179
0.4
0.8
23.0
47.6
Upper Midwest
1,272
0.4
0.9
29.5
56.9
Southeast
1,777
0.7
1.0
31.1
47.8
South
1,163
0.6
1.1
32.6
58.8
Southwest
1,112
0.4
0.9
26.4
58.7
Northern Rockies
584
-0.4
0.7
-31.2
54.6
Northwest
774
2.8
3.0
>100
>100
West
1,253
0.2
0.9
8.2
40.5
Organic
Carbon IMPROVE
Northeast
1,816
0.4
0.6
38.3
64.8
Ohio Valley
925
0.3
0.7
28.6
54.6
Upper Midwest
1,053
0.0
0.4
4.1
48.1
Southeast
1,642
0.4
0.7
27.0
52.9
South
939
0.2
0.6
17.6
51.2
Southwest
3,588
-0.1
0.4
-16.0
59.3
Northern Rockies
2,243
-0.1
0.4
-20.2
54.2
Northwest
1,817
0.4
0.7
65.5
>100
West
2,323
-0.2
0.4
-25.4
46.3
108
-------
S04MB
for run CMAQ 2019qe MP cb6ae7 19k 12US1 for 20190101 to 20191231
units = ug/m3
coverage limit = 75%
u
1.0
1.2
1
0.8
0.6
0.4
0
0.2
-0,
¦
::
1.0
IMPROVE
* CSN
CASTNET Weekly
Figure 4-7. Mean Bias (jigm"3) of annual sulfate at monitoring sites in the continental U.S. modeling
domain.
units = ug/m3
coverage limit = 75%
0
>2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
IMPROVE
CSN
CASTNET Weekly
Figure 4-8. Mean Error (jignr3) of annual sulfate at monitoring sites in the continental U.S.
modeling domain.
S04 ME
for run CMAQ 2019ae MP cb6ae7 19k 12US1 for 20190101 to 20191231
109
-------
S04 NMB (%) for run CMAQ_2019ge_MP_cb6ae7_19kJ2US1 for 20190101 1o 20191231
IMPROVE A CSN ¦ CASTNET Weekly
Figure 4-9. Normalized Mean Bias (%) of annual sulfate at monitoring sites in the continental U.S.
modeling domain.
S04 NME (%) for run CMAQ_2019ge_MP cb6ae7_19k 12US1 for 20190101 to 20191231
Figure 4-10. Normalized Mean Error (%) of annual sulfate at monitoring sites in the continental U.S.
modeling domain.
110
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N03 MB (ug/m3) for run CMAQ 2019qe MP cb6ae7 19k 12US1 for 20190101 to 20191231
units = ug/m3
coverage limit = 75%
IMPROVE * CSN
Figure 4-11. Mean Bias (jignr3) of annual nitrate at monitoring sites in the continental U.S. modeling
domain.
units = ug/m3
coverage limit = 75%
¦
>2
1.8
-
1.6
1
0.8
_
¦
0.6
0.4
0.2
n
N03 ME (ug/m3) for run CMAQ_2019ge_MP_cb6ae7_19k_12US1 for 20190101 to 20191231
Figure 4-12. Mean Error (jigm 3) of annual nitrate at monitoring sites in the continental U.S. modeling
domain.
IMPROVE * CSN
111
-------
N03 NMB
19k 12US1 for 20190101 to 20191231
units = %
coverage limit = 75%
IMPROVE * CSN
Figure 4-13. Normalized Mean Bias (%) of annual nitrate at monitoring sites in the continental U.S.
modeling domain.
units = %
coverage limit = 75%
N03NME
for run CMAQ 2019qe MP cb6ae7 19k 12US1 for20190101 to 20191231
> 100
90
80
70
60
50
40
30
20
10
0
Figure 4-14. Normalized Mean Error (%) of annual nitrate at monitoring sites in the continental U.S.
modeling domain.
IMPROVE A CSN
112
-------
TNQ3 MB (ug/m3) for run CMAQ_2019ge_MP_cb6ae7_19k_12US1 tor 20190101 to 20191231
units - ug/m3
coverage limit = 75%
1.6
¦ CASTNET Weekly
Figure 4-15. Mean Bias (jignr3) of annual total nitrate at monitoring sites in the continental U.S.
modeling domain.
1.4
-0.8
-1.4
units = ug/m3
coverage limit = 75%
> 2
1.4
1.2
1
0.8
0.6
0.4
0.2
0
¦ CASTNET Weekly
Figure 4-16. Mean Error (jignr3) of annual total nitrate at monitoring sites in the continental U.S.
modeling domain.
TNQ3 ME (ug/m3) for run CMAQ_2019ge_MP_cb6ae7_19k_12US1 for 20190101 to 20191231
113
-------
TN03 NMB (%) for run CMAQ_2019ge_MP_cb6ae7_19k_12US1 for 20190101 to 20191231
unils =
%
coverage limit = 7£
-
> 100
90
80
70
60
50
40
30
20
10
o
-10
-20
-30
-40
-
-50
-60
-
-70
¦
-80
L
-90
-
<-100
¦ CASTNET Weekly
Figure 4-17. Normalized Mean Bias (%) of annual total nitrate at monitoring sites in the continental U.S.
modeling domain.
114
-------
Figure 4-18. Normalized Mean Error (%) of annual total nitrate at monitoring sites in the continental
U.S. modeling domain.
TNQ3 NME (%) for run CMAQ_2019ge_MP_cb6ae7_19k_12US1 for 20190101 to 20191231
¦ CASTNET Weekly
> 100
units - %
coverage limit = 75%
115
-------
NH4 MB (ug/m3) for run CMAQ_2019ge_MP_cb6ae7_19k_12US1 for 20190101 to 20191231
units = ug/m3
coverage limit = 75%
a CSN ¦ CASTNET Weekly
Figure 4-19. Mean Bias (jugnr3) of annual ammonium at monitoring sites in the continental U.S. modeling
domain.
units - ug/m3
coverage limit = 75%
a CSN ¦ CASTNET Weekly
Figure 4-20. Mean Error (figm 5) of annual ammonium at monitoring sites in the continental U.S.
modeling domain.
NH4 ME (ug/m3) for run CMAQ 2019gs MP cb6ae7 19k 12US1 for 20190101 to 20191231
116
-------
NH4 NMB (%) for run CMAQ_2019ge_MP_cb6ae7_19k_12US1 for 20190101 to 20191231
units = %
coverage limit = 75%
a CSN ¦ CASTNET Weekly
Figure 4-21. Normalized Mean Bias (%) of annual ammonium at monitoring sites in the continental U.S.
modeling domain.
units = %
coverage limit = 75%
NH4 NME
for run CMAQ 2019ae MP cb6ae7 19k 12US1 for 20190101 1o 20191231
Figure 4-22. Normalized Mean Error (%) of annual ammonium at monitoring sites in the continental
U.S. modeling domain.
>100
90
80
70
60
50
40
30
20
* CSN
¦ CASTNET Weekly
117
-------
for run CMAQ 2019ae MP cb6ae7 19k 12US1 for 20190101 to 20191231
EC MB
units = ug/m3
coverage limit = 75%
IMPROVE a CSN
Figure 4-23. Mean Bias (jigin3) of annual elemental carbon at monitoring sites in the continental U.S.
modeling domain.
units = ug.''m3
coverage limit = 75%
Figure 4-24. Mean Error (jigm 5) of annual elemental carbon at monitoring sites in the continental U.S.
modeling domain.
for run CMAQ 2019ae MP cb6ae7 19k 12US1 for 20190101 to 20191231
IMPROVE * CSN
EC ME
118
-------
for run CMAQ 2019ae MP Cb6ae7 19k 12US1 for 20190101 to 20191231
EC NMB
units = %
coverage limit = 75%
IMPROVE a CSN
Figure 4-25. Normalized Mean Bias (%) of annual elemental carbon at monitoring sites in the
continental U.S. modeling domain.
units = %
coverage limit = 75°
1
> 100
J
90
80
_
70
60
50
r
40
¦
30
20
10
¦
0
IMPROVE ± CSN
Figure 4-26. Normalized Mean Error (%) of annual elemental carbon at monitoring sites in the
continental U.S. modeling domain.
EC NME (%) for run CMAQ_2019ge_MP_cb6ae7_19k_12US1 for 20190101 to 20191231
119
-------
OC MB (ug/m3) for run CMAQ_2019ge_MP_cb6ae7_19k_12US1 for 20190101 to 20191231
units = ug/m3
coverage limit = 75%
>2
0.8
0.6
0.4
_
0
-0.8
n
-1.2
-1.4
<-2
IMPROVE * CSN
Figure 4-27. Mean Bias (jignr3) of annual organic carbon at monitoring sites in the continental U.S.
modeling domain.
OC ME (ug/m3) for run CMAQ_2019ge_MP_cb6ae7_19k_12US1 for 20190101 to 20191231
Figure 4-28. Mean Error (jigin"3) of annual organic carbon at monitoring sites in the continental U.S.
modeling domain.
IMPROVE A CSN
120
-------
OC NMB (%)for run CMAQ 2019ge MP cb6ae7_19k 12US1 for 20190101 to 20191231
IMPROVE A CSN
Figure 4-29. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the continental
U.S. modeling domain.
OC NME (%) for run CMAQ 2019ge MP cb6ae7 19k 12US1 for 20190101 to 20191231
IMPROVE * CSN
Figure 4-30. Normalized Mean Error (%) of annual organic carbon at monitoring sites in the
continental U.S. modeling domain.
121
-------
122
-------
\ i ,l lyesian space-time downscalingfusi ¦ i
-------
the fit of the next iteration. The resulting collection of /?0 and /?x 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 (i.e., /?0(s),
x(s)) 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 s(s)).
Further information about the development and inner workings of the current version of DS can be found in
Berrocal, Gelfand and Holland (2012)42 and references therein. The DS outputs that accompany this report are
described below, along with some additional analyses that include assessing the accuracy of the DS
predictions. Results are then summarized, and caveats are provided for interpreting them in the context of air
quality management activities.
5.3 Downscaler Concentration Predictions
In this application, DS was used to predict daily concentration and associated uncertainty values at the 2010
US census tract centroids across the continental U.S. using 2019 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 |ig/m3.
5.3.1 Summary of 8-hour Ozone Results
Figure 5-1 summarizes the AQS, CMAQ and DS ozone data over the year 2019. 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 2019, about 33% of the US Census tracts (23786 out of 72283) experienced at
least one day with an ozone value above the NAAQS of 70 ppb.
42 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.l541-0420.2011.01725.
124
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AQS
45°N-
40°N-
35°N-
30°N-
25°N -
CMAQ
45°N -
40°N -
35°N -
30°N-
25°N -
DS
2019
4'th Max, Daily max
8-hour avg
ozone (ppb)
(-lnf,55]
(55,60]
(60,65]
(65,70]
(70,75]
(75,80]
¦ (80,85]
¦ (85,90]
¦ (90, Inf]
45°N -
40°N-
35°N -
30°N -
25°N-
120°W
110°W
100°W
9o'°W
8o'°W
125
-------
Figure 5-1. Annual 4th max (daily max 8-hour ozone concentrations) derived from AQS, CMAQ and
DS data.
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 2019. Figure 5-2 shows
annual means and Figure 5-3 shows 98th percentiles of 24-hour PM2.5 concentrations for AQS observations,
CMAQ model predictions and DS model results. The DS model estimated that for 2019 about 12% of the US
Census tracts (8449 out of 72283) experienced at least one day with a PM2.5 value above the 24-hour
NAAQS of 35 |ig/m3.
126
-------
AQS
120°W
110°W
100»W
90°W
80DW
Figure 5-2. Annual mean PM2.5 concentrations derived from AQS, CMAQ and DS data.
2019
Annual mean,
24-hour avg
PM2.5 (ug/m3)
(0,3]
(3,5]
(5.8]
(8,10]
(10,12]
(12,15]
(15,18]
¦ (18,lnf]
¦ NA
45° N -
40°N-
35°N-
30°N-
25°N -
45° N
40° N
35°N
30° N
25° N
45°N-
40°N-
35°N-
30°N -
25°N -
127
-------
AQS
2019
98'th percentile,
24-hour avg
PM2.5 (ug/m3)
(0,10]
(10,15]
(15,20]
(20,25]
(25,30]
(30,35]
(35,40]
¦ (40,45]
¦ (45,50]
¦ (50,lnf]
45°N-
40°N -
35°N-
30°N-
25°N -
120°W
110-W
90°W
80° W
100°W
CMAQ
128
-------
Figure 5-3. 98th percentile 24-hour average PM2.5 concentrations derived from AQS, CMAQ and DS
data.
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
(i.e., the eastern US), and larger in regions with more sparse monitoring networks (i.e., 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.
45°N -
25°N -
120°W
100°W
90°W
80°W
35°N -
% DS Error:
ozone
¦ (0,5]
¦ (5,10]
¦ (10,15]
¦ (15,20]
¦ (20,30]
(30,40]
(40,50]
¦ (50,75]
¦ (75,100]
129
-------
% DS Error:
pm25
¦ (0,5]
¦ (5,10]
¦ (10,15]
¦ (15,20]
¦ (20,30]
(30,40]
(40.50]
¦ (50,75]
¦ (75,100]
45°N -
40°N-
35°N -
30°N -
25°N -
120°W
100°W
9o'°W
Figure 5-4. Annual mean relative errors (standard errors divided by predictions) from the DS 2019
runs. The black dots show the locations of monitors that generated the AQS data used as input to the
DS model.
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
964
0.22
2.52
0.95
O3
1231
-0.01
4.25
0.96
Table 5-1. Cross-validation statistics associated with the 2019 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.
130
-------
- 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 O's and l'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 2019. 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 grid cell. Care needs to be taken not to over-interpret any
within-grid cell gradients that might be produced by a user. Fine-scale emission sources in CMAQ are diluted
into the grid cell averages, but a given source within a grid cell 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 grid
cell.
131
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Apper
Acronyms
ARW
Advanced Research WRF core model
BEIS
Biogenic Emissions Inventory System
BlueSky
Emissions modeling framework
BSP
BlueSky Pipeline modeling system
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
HC
Hydrocarbon
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
132
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OAQPS EPA's Office of Air Quality Planning and Standards
OAR EPA's Office of Air and Radiation
ORD EPA's Office of Research and Development
ORIS Office of Regulatory Information Systems (code) - is a 4 or 5 digit
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
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
133
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.I«! «i |i« ,l ' iifn - I'm 1 ii iySi'U'''i
Please see the independent spreadsheet AppendixB_2018_emissions_totals_by_sector.xlsx that provides
inventory and speciation emissions totals for each emissions modeling sector.
134
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-22-003
Environmental Protection Air Quality Assessment Division October 2022
Agency Research Triangle Park, NC
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