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Bayesian space-time downscaling fusion model
(downscaler) - Derived Estimates of Air Quality
for 2012
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EPA-450/R-16-001
March 2016
Bayesian space-time downscaling fusion model (downscaler) -Derived
Estimates of Air Quality for 2012
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Authors:
Adam Reff (EPA/OAR)
Sharon Phillips (EPA/OAR)
Alison Eyth (EPA/OAR)
David Mintz (EPA/OAR)
Acknowledgements
The following people served as reviewers of this document and provided valuable comments that
were included: Liz Naess (EPA/OAR), Tyler Fox (EPA/OAR), and Dennis Doll (EPA/OAR).
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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 13
3.3 Emissions Modeling Summary 31
3.4 Emissions References 65
4.0 CMAQ Air Quality Model Estimates 68
4.1 Introduction to the CMAQ Modeling Platform 68
4.2 CMAQ Model Version, Inputs and Configuration 69
4.3 CMAQ Model Performance Evaluation 74
5.0 Bayesian space-time downscaling fusion model (downscaler) -Derived Air Quality Estimates 96
5.1 Introduction 96
5.2 Downscaler Model 96
5.3 Downscaler Concentration Predictions 97
5.4 Downscaler Uncertainties 102
5.5 Summary and Conclusions 104
Appendix A - Acronyms 105
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1.0 Introduction
This report describes estimates of daily ozone (maximum 8-hour average) and PM2.5 (24-hour
average) concentrations throughout the contiguous United States during the 2011 calendar year
generated by EPA's recently developed data fusion method termed the "downscaler model" (DS). Air
quality monitoring data from the State and Local Air Monitoring Stations (SLAMS) and numerical
output from the Community Multiscale Air Quality (CMAQ) model were both input to DS to predict
concentrations at the 2010 US census tract centroids encompassed by the CMAQ modeling domain.
Information on EPA's air quality monitors, CMAQ model, and downscaler model is included to provide
the background and context for understanding the data output presented in this report. These estimates
are intended for use by statisticians and environmental scientists interested in the daily spatial
distribution of ozone and PM2.5.
DS essentially operates by calibrating CMAQ data to the observational data, and then uses the resulting
relationship to predict "observed" concentrations at new spatial points in the domain. Although similar
in principle to a linear regression, spatial modeling aspects have been incorporated for improving the
model fit, and a Bayesian1 approaching to fitting is used to generate an uncertainty value associated
with each concentration prediction. The uncertainties that DS produces are a major distinguishing
feature from earlier fusion methods previously used by EPA such as the "Hierarchical Bayesian" (HB)
model (McMillan et al, 2009). The term "downscaler" refers to the fact that DS takes grid-averaged
data (CMAQ) for input and produces point-based estimates, thus "scaling down" the area of data
representation. Although this allows air pollution concentration estimates to be made at points where
no observations exist, caution is needed when interpreting any within-gridcell spatial gradients
generated by DS since they may not exist in the input datasets. The theory, development, and initial
evaluation of DS can be found in the earlier papers of Berrocal, Gelfand, and Holland (2009, 2010, and
2011).
The data contained in this report are an outgrowth of a collaborative research partnership between EPA
scientists from the Office of Research and Development's (ORD) National Exposure Research
Laboratory (NERL) and personnel from EPA's Office of Air and Radiation's (OAR) Office of Air
Quality Planning and Standards (OAQPS). NERL's Human Exposure and Atmospheric Sciences
Division (HEASD), Atmospheric Modeling Division (AMD), and Environmental Sciences Division
(ESD), in conjunction with OAQPS, work together to provide air quality monitoring data and model
estimates to the Centers for Disease Control and Prevention (CDC) for use in their Environmental
Public Health Tracking (EPHT) Network.
CDC's EPHT Network supports linkage of air quality data with human health outcome data for use by
various public health agencies throughout the U.S. The EPHT Network Program is a multidisciplinary
collaboration that involves the ongoing collection, integration, analysis, interpretation, and
dissemination of data from: environmental hazard monitoring activities; human exposure assessment
information; and surveillance of noninfectious health conditions. As part of the National EPHT
1 Bayesian statistical modeling refers to methods that are based on Bayes' theorem, and model the world in terms of
probabilities based on previously acquired knowledge.
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Program efforts, the CDC led the initiative to build the National EPHT Network (http://
www.cdc.gov/nceh/tracking/default.htm). The National EPHT Program, with the EPHT Network as its
cornerstone, is the CDC's response to requests calling for improved understanding of how the
environment affects human health. The EPHT Network is designed to provide the means to identify,
access, and organize hazard, exposure, and health data from a variety of sources and to examine,
analyze and interpret those data based on their spatial and temporal characteristics.
Since 2002, EPA has collaborated with the CDC on the development of the EPHT Network. On
September 30, 2003, the Secretary of Health and Human Services (HHS) and the Administrator of
EPA signed a joint Memorandum of Understanding (MOU) with the objective of advancing efforts to
achieve mutual environmental public health goals2. HHS, acting through the CDC and the Agency for
Toxic Substances and Disease Registry (ATSDR), and EPA agreed to expand their cooperative
activities in support of the CDC EPHT Network and EPA's Central Data Exchange Node on the
Environmental Information Exchange Network in the following areas:
• Collecting, analyzing and interpreting environmental and health data from both agencies (HHS
and EPA).
• Collaborating on emerging information technology practices related to building, supporting,
and operating the CDC EPHT Network and the Environmental Information Exchange
Network.
• Developing and validating additional environmental public health indicators.
• Sharing reliable environmental and public health data between their respective networks in an
efficient and effective manner.
• Consulting and informing each other about dissemination of results obtained through work
carried out under the MOU and the associated Interagency Agreement (IAG) between EPA and
CDC.
The best available statistical fusion model, air quality data, and CMAQ numerical model output were
used to develop the estimates. Fusion results can vary with different inputs and fusion modeling
approaches. As new and improved statistical models become available, EPA will provide updates.
Although these data have been processed on a computer system at the Environmental Protection Agency, no
warranty expressed or implied is made regarding the accuracy or utility of the data on any other system or
for general or scientific purposes, nor shall the act of distribution of the data constitute any such warranty. It
is also strongly recommended that careful attention be paid to the contents of the metadata file associated
with these data to evaluate data set limitations, restrictions or intended use. The U.S. Environmental
Protection Agency shall not be held liable for improper or incorrect use of the data described and/or
2 HHS and EPA agreed to extend the duration of the MOU, effective since 2002 and renewed in 2007, until June 29, 2017. The
MOU is available at www.cdc.gov/nceh/tracking/partners/epa mou 2007.htm.
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contained herein.
The four remaining sections and one appendix in the report are as follows:
• Section 2 describes the air quality data obtained from EPA's nationwide monitoring network
and the importance of the monitoring data in determining health potential health risks.
• Section 3 details the emissions inventory data, how it is obtained and its role as a key input into
the CMAQ air quality computer model.
• Section 4 describes the CMAQ computer model and its role in providing estimates of pollutant
concentrations across the U.S. based on 12-km grid cells over the contiguous U.S.
• Section 5 explains the downscaler model used to statistically combine air quality monitoring
data and air quality estimates from the CMAQ model to provide daily air quality estimates for
the 2010 US census tract centroid locations within the contiguous U.S.
• The appendix provides a description of acronyms used in this report.
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2.0 Air Quality Data
To compare health outcomes with air quality measures, it is important to understand the origins of those
measures and the methods for obtaining them. This section provides a brief overview of the origins and
process of air quality regulation in this country. It provides a detailed discussion of ozone (O3) and
particulate matter (PM). The EPHT program has focused on these two pollutants, since numerous studies
have found them to be most pervasive and harmful to public health and the environment, and there are
extensive monitoring and modeling data available.
2.1 Introduction to Air Quality Impacts in the United States
2.1.1 The Clean Air Act
In 1970, the Clean Air Act (CAA) was signed into law. Under this law, EPA sets limits on how much of
a pollutant can be in the air anywhere in the United States. This ensures that all Americans have the same
basic health and environmental protections. The CAA has been amended several times to keep pace with
new information. For more information on the CAA, go to http://www.epa. gov/clean-air-act-overview.
Under the CAA, the U.S. EPA has established standards or limits for six air pollutants, known as the
criteria air pollutants: carbon monoxide (CO), lead (Pb), nitrogen dioxide (NO2), sulfur dioxide (SO2),
ozone (O3), and particulate matter (PM). These standards, called the National Ambient Air Quality
Standards (NAAQS), are designed to protect public health and the environment. The CAA established
two types of air quality standards. Primary standards set limits to protect public health, including the
health of "sensitive" populations such as asthmatics, children, and the elderly. Secondary standards set
limits to protect public welfare, including protection against decreased visibility, damage to animals,
crops, vegetation, and buildings. The law requires EPA to review periodically these standards. For more
specific information on the NAAQS, go to https://www.epa.gov/criteria-air-pollutants/naaqs-table. For
general information on the criteria pollutants, go to https://www.epa.gov/criteria-air-pollutants. .
When these standards are not met, the area is designated as a nonattainment area. States must develop
state implementation plans (SIPs) that explain the regulations and controls it will use to clean up the
nonattainment areas. States with an EPA-approved SIP can request that the area be designated from
nonattainment to attainment by providing three consecutive years of data showing NAAQS compliance.
The state must also provide a maintenance plan to demonstrate how it will continue to comply with the
NAAQS and demonstrate compliance over a 10-year period, and what corrective actions it will take
should a NAAQS violation occur after designation. EPA must review and approve the NAAQS
compliance data and the maintenance plan before designating the area; thus, a person may live in an area
designated as nonattainment even though no NAAQS violation has been observed for quite some time.
For more information on designations, go to https://www.epa.gov/ozone-designations and
https://www.epa.gov/particle-pollution-designations.
2.1.2 Ozone
Ozone is a colorless gas composed of three oxygen atoms. Ground level ozone is formed when pollutants
released from cars, power plants, and other sources react in the presence of heat and sunlight. It is the
prime ingredient of what is commonly called "smog." When inhaled, ozone can cause acute respiratory
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problems, aggravate asthma, cause inflammation of lung tissue, and even temporarily decrease the lung
capacity of healthy adults. Repeated exposure may permanently scar lung tissue. Toxicological, human
exposure, and epidemiological studies were integrated by EPA in "Air Quality Criteria for Ozone and
Related Photochemical Oxidants." It is available at https://www.epa.gov/naaqs/ozone-o3-air-quality-
standards. The current NAAQS for ozone (last revised in 2015) is a daily maximum 8-hour average of
0.070 parts per million [ppm] (for details, see https://www.epa.gov/ozone-pollution/setting-and-
reviewing-standards-control-ozone-pollution#standards. The Clean Air Act requires EPA to review the
NAAQS at least every five years and revise them as appropriate in accordance with Section 108 and
Section 109 of the Act. The standards for ozone are shown in Table 2-1.
Table 2-1. Ozone Standards
Parts Per Million: Measurement - (ppm)
1997
2008
2015
4th Highest Daily Max 8-hour average
0.08
0.075
0.070
2.1.3 Particulate Matter
PM air pollution is a complex mixture of small and large particles of varying origin that can contain
hundreds of different chemicals, including cancer-causing agents like polycyclic aromatic hydrocarbons
(PAH), as well as heavy metals such as arsenic and cadmium. PM air pollution results from direct
emissions of particles as well as particles formed through chemical transformations of gaseous air
pollutants. The characteristics, sources, and potential health effects of particulate matter depend on its
source, the season, and atmospheric conditions.
As practical convention, PM is divided by sizes into classes with differing health concerns and potential
sources4. Particles less than 10 micrometers in diameter (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.
4 The measure used to classify PM into sizes is the aerodynamic diameter. The measurement instruments used for PM are
designed and operated to separate large particles from the smaller particles. For example, the PM2 5 instrument only captures
and thus measures particles with an aerodynamic diameter less than 2.5 micrometers. The EPA method to measure PMc is
designed around taking the mathematical difference between measurements for 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
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 Standards
Micrograms Per Cubic Meter:
Measurement - (ug/m3)
1997
2006
2012
Annual Average
15.0
15.0
12.0
24-Hour Average
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2.2 Ambient Air Quality Monitoring in the United States
2.2.1 Monitoring Networks
The Clean Air Act (Section 319) requires establishment of an air quality monitoring system throughout
the U.S. The monitoring stations in this network have been called the State and Local Air Monitoring
Stations (SLAMS). The SLAMS network consists of approximately 4,000 monitoring sites set up and
operated by state and local air pollution agencies according to specifications prescribed by EPA for
monitoring methods and network design. All ambient monitoring networks selected for use in SLAMS are
tested periodically to assess the quality of the SLAMS data being produced. Measurement accuracy and
precision are estimated for both automated and manual methods. The individual results of these tests for
each method or analyzer are reported to EPA. Then, EPA calculates quarterly integrated estimates of
precision and accuracy for the SLAMS data.
The SLAMS network experienced accelerated growth throughout the 1970s. The networks were further
expanded in 1999 based on the establishment of separate NAAQS for fine particles (PM2.5) in 1997. The
NAAQS for PM2.5 were established based on their link to serious health problems ranging from increased
symptoms, hospital admissions, and emergency room visits, to premature death in people with heart or
lung disease. While most of the monitors in these networks are located in populated areas of the country,
"background" and rural monitors are an important part of these networks. For more information on
SLAMS, as well as EPA's other air monitoring networks go to http://www3.epa. gov/ttn/amtic/.
In 2009, approximately 43 percent of the US population was living within 10 kilometers of ozone and
PM2.5 monitoring sites. In terms of US Census Bureau tract locations, 31,341 out of 72,283 census tract
centroids were within 10 kilometers of ozone monitoring sites. Highly populated Eastern US and
California coasts are well covered by both ozone and PM2.5 monitoring network (Figure 2-1).
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Distance to the Nearest
• 41 -10.000 meters
• 10,001 - 25,000 meters
25,001 - 50.000 meters
50,001 - 75.000 meters
75,001 -100,000 meters
• 100,001 - 150,000 meters
• 150.001 - 333,252 meters
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Ws i: *»' /: i 7W+ P
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rest PM2.5 Monitor • ^ ^ • -'-{v « * « -
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Distance to the Nearest PM2.5 Monitor
• 41 -10,000 meters
• 10,001 - 25,000 meters
25,001 - 50,000 meters
50,001 -75,000 meters
75,001 - 100.000 meters
• 100,001 - 150,000 meters
• 150,001 -333,252 meters
-
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r
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Figure 2-1. Distances from US Census Tract centroids to the nearest monitoring site, 2009.
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In summary, state and local agencies and tribes implement a quality-assured monitoring network to
measure air quality across the United States. EPA provides guidance to ensure a thorough understanding
of the quality of the data produced by these networks. These monitoring data have been used to
characterize the status of the nation's air quality and the trends across the U.S. (see
https://www.epa.gov/air-trends).
2.2.2 Air Quality System Database
EPA's Air Quality System (AQS) database contains ambient air monitoring data collected by EPA, state,
local, and tribal air pollution control agencies from thousands of monitoring stations. AQS also contains
meteorological data, descriptive information about each monitoring station (including its geographic
location and its operator), and data quality assurance and quality control information. State and local
agencies are required to submit their air quality monitoring data into AQS within 90 days following the
end of the quarter in which the data were collected. This ensures timely submission of these data for use
by state, local, and tribal agencies, EPA, and the public. EPA's Office of Air Quality Planning and
Standards and other AQS users rely upon the data in AQS to assess air quality, assist in compliance with
the NAAQS, evaluate SIPs, perform modeling for permit review analysis, and perform other air quality
management functions. For more details, including how to retrieve data, go to https://www.epa.gov/aqs.
2.2.3 Advantages and Limitations of the Air Quality Monitoring and Reporting System
Air quality data is required to assess public health outcomes that are affected by poor air quality. The
challenge is to get surrogates for air quality on time and spatial scales that are useful for Environmental
Public Health Tracking activities.
The advantage of using ambient data from EPA monitoring networks for comparing with health outcomes
is that these measurements of pollution concentrations are the best characterization of the concentration of
a given pollutant at a given time and location. Furthermore, the data are supported by a comprehensive
quality assurance program, ensuring data of known quality. One disadvantage of using the ambient data
is that it is usually out of spatial and temporal alignment with health outcomes. This spatial and temporal
'misalignment' between air quality monitoring data and health outcomes is influenced by the following
key factors: the living and/or working locations (microenvironments) where a person spends their time not
being co-located with an air quality monitor; time(s)/date(s) when a patient experiences a health
outcome/symptom (e.g., asthma attack) not coinciding with time(s)/date(s) when an air quality monitor
records ambient concentrations of a pollutant high enough to affect the symptom (e.g., asthma attack
either during or shortly after a high PM2.5 day). To compare/correlate ambient concentrations with acute
health effects, daily local air quality data is needed5. Spatial gaps exist in the air quality monitoring
network, especially in rural areas, since the air quality monitoring network is designed to focus on
measurement of pollutant concentrations in high population density areas. Temporal limits also exist.
Hourly ozone measurements are aggregated to daily values (the daily max 8-hour average is relevant to
the ozone standard). Ozone is typically monitored during the ozone season (the warmer months,
approximately April through October). However, year-long data is available in many areas and is
extremely useful to evaluate whether ozone is a factor in health outcomes during the non-ozone seasons.
PM2.5 is generally measured year-round. Most Federal Reference Method (FRM) PM2.5 monitors collect
data one day in every three days, due in part to the time and costs involved in collecting and analyzing the
samples. However, over the past several years, continuous monitors, which can automatically collect,
5 EPA uses exposure models to evaluate the health risks and environmental effects associated with exposure. These models
are limited by the availability of air quality estimates, http://www.epa.gov/ttn/fera/index.html.
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analyze, and report PM2.5 measurements on an hourly basis, have been introduced. These monitors are
available in most of the major metropolitan areas. Some of these continuous monitors have been
determined to be equivalent to the FRM monitors for regulatory purposes and are called FEM (Federal
Equivalent Methods).
2.2.4 Use of Air Quality Monitoring Data
Air quality monitoring data has been used to provide the information for the following situations:
(1) Assessing effectiveness of SIPs in addressing NAAQS nonattainment areas
(2) Characterizing local, state, and national air quality status and trends
(3) Associating health and environmental damage with air quality levels/concentrations
For the EPHT effort, EPA is providing air quality data to support efforts associated with (2), and (3) above.
Data supporting (3) is generated by EPA through the use of its air quality data and its downscaler model.
Most studies that associate air quality with health outcomes use air monitoring as a surrogate for exposure
to the air pollutants being investigated. Many studies have used the monitoring networks operated by
state and federal agencies. Some studies perform special monitoring that can better represent exposure to
the air pollutants: community monitoring, near residences, in-house or work place monitoring, and
personal monitoring. For the EPHT program, special monitoring is generally not supported, though it
could be used on a case-by-case basis.
From proximity based exposure estimates to statistical interpolation, many approaches are developed for
estimating exposures to air pollutants using ambient monitoring data (Jerrett et al., 2005). Depending
upon the approach and the spatial and temporal distribution of ambient monitoring data, exposure
estimates to air pollutants may vary greatly in areas further apart from monitors (Bravo et al., 2012).
Factors like limited temporal coverage (i.e., PM2.5 monitors do not operate continuously such as recording
every third day or ozone monitors operate only certain part of the year) and limited spatial coverage (i. e.,
most monitors are located in urban areas and rural coverage is limited) hinder the ability of most of the
interpolation techniques that use monitoring data alone as the input. If we look at the example of Voronoi
Neighbor Averaging (VNA) (referred as the Nearest Neighbor Averaging in most literature), rural
estimates would be biased towards the urban estimates. To further explain this point, assume the scenario
of two cities with monitors and no monitors in the rural areas between, which is very plausible. Since
exposure estimates are guaranteed to be within the range of monitors in VNA, estimates for the rural areas
would be higher according to this scenario.
Air quality models may overcome some of the limitations that monitoring networks possess. Models such
as the Community Multi-Scale Air Quality (CMAQ) modeling systems can estimate concentrations in
reasonable temporal and spatial resolutions. However these sophisticated air quality models are prune to
systematic biases since they depend upon so many variables (i.e., metrological models and emission
models) and complex chemical and physical process simulations.
Combining monitoring data with air quality models (via fusion or regression) may provide the best results
in terms of estimating ambient air concentrations in space and time. EPA's eVNA6 is an example of an
6 eVNA is described in the "Regulatory Impact Analysis for the Final Clean Air Interstate Rule", EPA-452/R-05-002, March
2005, Appendix F.
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earlier approach for merging air quality monitor data with CMAQ model predictions. The downscaler
model attempts to address some of the shortcomings in these earlier attempts to statistically combine
monitor and model predicted data, see published paper referenced in section 1 for more information about
the downscaler model. As discussed in the next section, there are two methods used in EPHT to provide
estimates of ambient concentrations of air pollutants: air quality monitoring data and the downscaler
model estimate, which is a statistical 'combination' of air quality monitor data and photochemical air
quality model predictions (e.g., CMAQ).
2.3 Air Quality Indicators Developed for the EPHT Network
Air quality indicators have been developed for use in the Environmental Public Health Tracking Network
by CDC using the ozone and PM2.5 data from EPA. The approach used divides "indicators" into two
categories. First, basic air quality measures were developed to compare air quality levels over space and
time within a public health context (e.g., using the NAAQS as a benchmark). Next, indicators were
developed that mathematically link air quality data to public health tracking data (e.g., daily PM2.5 levels
and hospitalization data for acute myocardial infarction). Table 2-3 and Table 2-4 describe the issues
impacting calculation of basic air quality indicators.
Table 2-2. Public Health Surveillance Goals and Current Status
Goal
Status
Air data sets and metadata required for air quality
indicators are available to EPHT state Grantees.
AQS data are available through state agencies and EPA's
Air Quality System (AQS). EPA and CDC developed an
interagency agreement, where EPA provides air quality
data along with statistically combined AQS and
Community Multiscale Air Quality (CMAQ) Model
data, associated metadata, and technical reports that are
delivered to CDC.
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.
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Table 2-3. Basic Air Quality Indicators used in EPHT, derived from the EPA data delivered to
CDC
Ozone (daily 8-hr period with maximum concentration—ppm—bv Federal Reference Method (FRM))
• Number of days with maximum ozone concentration over the NAAQS (or other relevant benchmarks (by county
and MSA)
• Number of person-days with maximum 8-hr average ozone concentration over the NAAQS & other relevant
benchmarks (by county and MSA)
PM.2.5 (daily 24-hr integrated samples -u#/m:,-by FRM)
• Average ambient concentrations of particulate matter (< 2.5 microns in diameter) and compared to annual
PM2.5 NAAQS (by state).
• % population exceeding annual PM2.5 NAAQS (by state).
• % of days with PM2.5 concentration over the daily NAAQS (or other relevant benchmarks (by county and MSA)
• Number of person-days with PM2.5 concentration over the daily NAAQS & other relevant benchmarks (by
county and MSA)
2.3.1 Rationale for the Air Quality Indicators
The CDC EPHT Network is initially focusing on ozone and PM2.5. These air quality indicators are based
mainly around the NAAQS health findings and program-based measures (measurement, data and analysis
methodologies). The indicators will allow comparisons across space and time for EPHT actions. They
are in the context of health-based benchmarks. By bringing population into the measures, they roughly
distinguish between potential exposures (at broad scale).
2.3.2 Air Quality Data Sources
The air quality data will be available in the US EPA Air Quality System (AQS) database based on the
state/federal air program's data collection and processing. The AQS database contains ambient air
pollution data collected by EPA, state, local, and tribal air pollution control agencies from thousands of
monitoring stations (SLAMS).
2.3.3 Use of Air Quality Indicators for Public Health Practice
The basic indicators will be used to inform policymakers and the public regarding the degree of hazard
within a state and across states (national). For example, the number of days per year that ozone is above
the NAAQS can be used to communicate to sensitive populations (such as asthmatics) the number of days
that they may be exposed to unhealthy levels of ozone. This is the same level used in the Air Quality
Alerts that inform these sensitive populations when and how to reduce their exposure. These indicators,
however, are not a surrogate measure of exposure and therefore will not be linked with health data.
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,... 7. ,' <\
3.1 Introduction to Emissions Data Development
The U.S. Environmental Protection Agency (EPA) developed an air quality modeling platform based
primarily on the 201 1 National Emissions Inventory (NEI), Version 2 to process year 2012 emission data
for this project. This section provides a summary of the emissions inventory and emissions modeling
techniques applied to Criteria Air Pollutants (CAPs) and the following select Hazardous Air Pollutants
(HAPs): chlorine (CI), hydrogen chloride (HC1), benzene, acetaldehyde, formaldehyde and methanol. This
section also describes the approach and data used to produce emissions inputs to the air quality model.
The air quality modeling, meteorological inputs and boundary conditions are described in a separate
section.
The Community Multi scale Air Quality (CMAQ) model ffattp://www.epa.gov/'AMD/CMAO/1 was used to
model ozone (O3) and particulate matter (PM) for this project. CMAQ requires hourly and gridded
emissions of the following inventory pollutants: carbon monoxide (CO),nitrogen oxides (NO\), volatile
organic compounds (VOC), sulfur dioxide (SO:), ammonia (NH3), particulate matter less than or equal to
10 microns (PMin), and individual component species for particulate matter less than or equal to 2.5
microns (PM2.5). In addition, the Carbon bond 2005 (CB05) with chlorine chemistry used here within
CMAQ allows for explicit treatment of the VOC H APs benzene, acetaldehyde, formaldehyde and
methanol (BAFM) and includes anthropogenic HAP emissions of HC1 and CI.
The effort to create the 2012 emission inputs for this study included development of emission inventories
for input to a 2012 modeling case, along with application of emissions modeling tools to convert the
inventories into the format and resolution needed by CMAQ. Year-specific fire and continuous emission
monitoring (CEM) data for electric generating units (EGlJs) were used. The primary emissions modeling
tool used to create the CMAQ model-ready emissions was the Sparse Matrix Operator Kernel Emissions
(SMOKE) modeling system. SMOKE version 3.6.5 was used to create CMAQ-ready emissions files for a
12-ktn national grid. Additional information about SMOKE is available from
http ://cmascenter. org / smoke.
This chapter contains two additional sections. Section 3.2 describes the inventories input to SMOKE and
the ancillary files used along with the emission inventories. Section 3.3 describes the emissions modeling
performed to convert the inventories into the format and resolution needed by CMAQ.
3.2 Emission Inventories and Approaches
This section describes the emissions inventories created for input to SMOKE. The 201 1 NEI, version 2
with updates for the year 2012 is the primary basis for the inputs to SMOKE. The NEI includes five main
categories of source sectors: a) nonpoint (formerly called "stationary area") sources; b) point sources; c)
nonroad mobile sources; d) on road mobile sources; and e) fires. For CAPs, the NEI data are largely
compiled from data submitted by state, local and tribal (S/L/T) agencies. HAP emissions data are often
augmented by EPA when they are not voluntarily submitted to the NEI by S/L/T agencies. The NEI was
compiled using the Emissions Inventory System (EIS). EIS includes hundreds of automated QA checks
to improve data quality, and it also supports release point (stack) coordinates separately from facility
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coordinates. EPA collaboration with S/L/T agencies helped prevent duplication between point and
nonpoint source categories such as industrial boilers. Documentation for the 201 1 NEI is available at
http://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventorv-nei-documentation.
Point source data for the year 2012 as submitted to EIS were used for this study. EPA used the
S MA RTF IRE 2 system to develop 2012 fire emissions. SMARTFIRE2 categorizes all fires as either
prescribed burning or wildfire categories, and includes improved emission factor estimates for prescribed
burning. Onroad mobile source emissions for year 2012 were developed using MOVES2014. Nonroad
mobile source emissions for year 2012 were developed using the National Mobile Inventory Model
(NMIM). Canadian emissions reflect year 2010 and Mexican emissions reflect year 2008, as those were
the latest data available at the time of the modeling.
The methods used to process emissions for this study are very similar to those documented for EPA's
Version 6.2, 201 1 Emissions Modeling Platform that was also used for the final Ozone National Ambient
Air Quality Standards (N AAQS) and for the proposed Cross-State Air Pollution Rule Update. A
technical support document (TSD) for this platform is available at EPA's emissions modeling
clearinghouse (EMCH): https://www.epa.gov/air-emissions-modeling/2011-version-62-platform (EPA,
2015a) and includes additional details regarding the data preparation and emissions modeling.
The emissions modeling process, performed using SMOKE v3.6.5, apportions the emissions inventories
into the grid cells used by CMAQ and temporalizes the emissions into hourly values. In addition, the
pollutants in the inventories (e.g., NOx and VOC) are split into the chemical species needed by CM AQ.
For the purposes of preparing the CMAQ- ready emissions, the broader NEI emissions inventories are
split into emissions modeling "platform" sectors; and biogenic emissions are added along with emissions
from other sources other than the NEI, such as the Canadian, Mexican, and offshore inventories. The
significance of an emissions sector for the emissions modeling platform is that emissions for that sector
are run through all of the SMOKE programs, except the final merge, independently from emissions in the
other sectors. The final merge program called Mrggrid combines the sector-specific gridded, speciated
and temporalized emissions to create the final CMAQ-ready emissions inputs.
Table 3-1 presents the sectors in the emissions modeling platform used to develop the year 2012
emissions for this project. The sector abbreviations are provided in italics; these abbreviations are used in
the SMOKE modeling scripts, the inventory file names, and throughout the remainder of this section.
Annual 2012 emission summaries for the U.S. anthropogenic sectors are shown in Table 3-2 (i.e.,
biogenic emissions are excluded). Table 3-3 provides a summary of emissions for the anthropogenic
sectors containing Canadian, Mexican and offshore sources. State total emissions for each sector are
provided in Appendix B, a workbook entitled "Appendix_B_2012_emissions_totals_by_sector.xlsx".
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Table 3-1. Platform Sectors Used in the Emissions Modeling Process
2012 Platform Sector (Abbrev)
NEI Categry
Description and resolution of the data input to SMOKE
EGUs (ptegu)
Point
2012 point source EGUs submitted to EIS and gapfilled
with 201 1NEIv2 emissions. Replaced with hourly 2012
CEMS values forNOX and S02, where the units are
matched to the inventory. Annual resolution for non-
CEMS sources, hourly for sources matched to CEMS.
Point source oil and gas
(pt oilgcts)
Point
2012 point sources with oil and gas production NAICS
codes, gapfilled with 201 1NEIv2 oil and gas sources.
Remaining non-EGU point
(ptnonipm)
Point
2012 point source records not matched to the ptegu or
pt_oilgas sectors, except for offshore point sources that are
in the othpt sector. Includes all aircraft emissions and
some rail yard emissions. Annual resolution.
Point source fire (ptfire)
Fires
Point source day-specific wildfires and prescribed fires for
2012 computed using SMARTFIRE 2. Fires over 20,000
acres on a single day allocated to overlapping grid cells.
Agricultural Fires (cigfire)
Nonpoint
Agricultural fires from 201 1NEIv2
Agricultural (cig)
Nonpoint
NH3 emissions from 201 1NEIv2 nonpoint livestock and
fertilizer application, county and annual resolution but
temporalized using 2012 meteorological data.
Area fugitive dust (afdiist adj)
Nonpoint
PM10 and PM2.5 from fugitive dust sources in the
201 1NEIv2 nonpoint inventory, including building
construction, road construction, agricultural dust, and road
dust. Unpaved and paved road dust emissions differ from
the NEI in that the NEI uses an average meteorological
adjustment while the modeling uses gridded, hourly 2012
meteorological data to reduce emissions via a transport
fraction based on land use, plus emissions are zeroed out
during periods of precipitation and snow/ice cover. County
and annual resolution.
Nonpoint source oil and gas
(up oilgcts)
Nonpoint
201 1NEIv2 nonpoint sources from oil and gas-related
processes with updates for five Utah counties. County and
annual resolution.
Residential Wood Combustion
(,rwc)
Nonpoint
201 1NEIv2 NEI nonpoint sources for Residential Wood
Combustion (RWC) processes but temporalized using
2012 meteorological data. County and annual resolution.
Remaining nonpoint (nonpt)
Nonpoint
201 1NEIv2 nonpoint sources not included in other
platform sectors; county and annual resolution.
C3 commercial marine
(c3mctrine)
Nonpoint
201 1NEIv2 Category 3 (C3) commercial marine vessel
(CMV) emissions. County and annual resolution.
CI and C2 marine and
locomotive (clc2rctil)
Nonpoint
201 1NEIv2 locomotives and primarily category 1 (CI) and
category 2 (C2) commercial marine vessel (CMV)
emissions sources. County and annual resolution.
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2012 Platform Sector (Abbrev)
NEI Categry
Description and resolution of the data input to SMOKE
Nonroad (nonroad)
Nonroad
2012 nonroad equipment emissions developed with the
National Mobile Inventory Model (NMIM) using
NONROAD2008 version NR08a. NMIM was used for all
states except California-submitted 2011 data is used as is
and Texas emissions for 2011 are projected to 2012 using
data from EPA trends. County and monthly resolution.
Onroad (onroad)
Onroad
2012 onroad mobile source gasoline and diesel vehicles
from parking lots and moving vehicles. Includes the
following modes: exhaust, extended idle, auxiliary power
units, evaporative, permeation, refueling and brake and tire
wear. For all states except California, based on monthly
MOVES emissions tables from MOVES2014. California
emissions are based on Emission Factor (EMFAC) and
were projected to 2012 using factors specific to each air
basin. MOVES-based emissions computed for each hour
and model grid cell using monthly and annual activity data
(e.g., VMT, vehicle population).
Biogenic (beis)
Biogenic
Hour- and grid cell-specific emissions for 2012 generated
from the BEIS 3.61 model, including emissions in Canada
and Mexico.
Other area fugitive dust sources
N/A
Area fugitive dust sources from Canada 2010 inventory
with transport fraction and snow/ice adjustments based on
2012 meteorological data. Annual and province
resolution.
Other point sources not from
the NEI (othpt)
N/A
Point sources from Canada's 2010 inventory and Mexico's
2008 inventory. Also includes all non-U.S. and non-
Canada C3 CMV and U.S. offshore oil production from
2011NEIv2. Annual resolution.
Other nonpoint and nonroad
(othctr)
N/A
Year 2010 Canada (province resolution) and year 2008
(municipio resolution) nonpoint and nonroad mobile
inventories, annual resolution.
Year 2010 Canada (province resolution) and year 2008
Other onroad sources (othon) N/A Mexico (municipio resolution) onroad mobile inventories,
annual resolution.
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Table 3-2. 2012 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.)
Sector
CO
NH3
NOx
PMio
PM2.5
SO2
voc
Sector
CO
NH3
NOX
PM10
PM2_5
S02
voc
afdust_adj
7,156,383
986,032
ag
3,515,198
agfire
956,243
3,321
42,767
140,728
93,959
16,224
74,783
clc2rail
180,579
511
1,075,217
35,359
33,019
12,609
48,281
nonpt
1,645,989
94,242
720,454
491,825
404,258
275,655
3,672,249
np_oilgas
627,764
0
641,611
16,850
15,395
18,338
2,548,410
nonroad
13,266,383
2,668
1,546,449
154,415
146,950
2,779
1,869,401
onroad
23,707,819
112,443
5,180,893
346,279
175,360
29,309
2,448,957
c3marine
12,532
68
131,382
10,168
9,043
86,373
5,149
ptfire
30,696,364
502,685
361,109
3,071,146
2,602,666
213,027
7,226,100
ptegu
803,584
26,954
1,829,235
226,421
171,851
3,416,894
36,968
ptnonipm
2,226,713
58,339
1,210,541
475,089
312,253
976,937
824,521
pt_oilgas
220,304
5,373
469,889
13,850
13,295
66,731
157,224
rwc
2,517,844
19,693
34,436
381,476
381,252
8,954
442,541
Continental U.S.
76,861,119
4,341,496
13,243,984
12,519,989
5,345,333
5,123,830
19,354,584
Table 3-3. 2012 Non-US Emissions by Sector within Modeling Domain (tons/yr for Canada, Mexico,
Offshore)
Sector
CO
nh3
NOx
PM10
pm25
SO2
VOC I
Canada othafdust
821,220
120,724
Canada othar
3,023,643
327,439
362,798
159,741
131,777
70,475
889,624
Canada othon
3,039,490
18,700
346,500
17,670
12,245
1,706
178,868
Canada othpt
497,442
13,110
267,633
70,196
29,243
545,999
129,473
Canada Subtotal
6,560,576
359,249
976,931
1,068,827
293,989
618,179
1,197,964
Mexico othar
278,932
163,416
183,435
99,120
50,343
10,707
412,293
Mexico othon
3,369,179
7,997
244,293
2,431
1,628
4,931
320,121
Mexico othpt
153,467
3,715
287,064
55,320
42,222
454,837
53,958
Mexico Subtotal
3,801,578
175,128
714,791
156,871
94,192
470,476
786,373
Offshore to EEZ
175,833
185
902,448
26,320
24,611
139,550
81,825
Non-US SECA C3
17,231
0
202,987
17,253
15,871
127,928
7,314
Total non-U.S.
10,555,218
534,562
2,797,158
1,269,271
428,664
1,356,132
2,073,476
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3.2.1 Point Sources (ptegu, ptoilgas andptnonipm)
Point sources are sources of emissions for which specific geographic coordinates (e.g., latitude/longitude)
are specified, as in the case of an individual facility. A facility may have multiple emission release points,
which may be characterized as units such as boilers, reactors, spray booths, kilns, etc. A unit may have
multiple processes (e.g., a boiler that sometimes burns residual oil and sometimes bums natural gas).
With a couple of minor exceptions, this section describes only NEI point sources within the contiguous
United States. The offshore oil platform (othpt sector) and category 3 CMV emissions outside of state
waters (othpt sector) are processed by SMOKE as point source inventories and are discussed later in this
section. Full documentation for the development of the 201 1NEIv2 (EPA, 2015b), is posted at:
http://www.epa.gov/air-emissions-inventories/2011-national-emissions-inventorv-nei-documentation.
A complete NEI is developed every three years, with 2011 being the most recently finished complete NEI.
In years other than the triennial NEI years, including 2012, states are only required to submit emissions
for Type A point sources. Type A point sources have the potential to emit more than 2500 tons of CO,
NOx, or SO2, or more than 250 tons of VOC, PM10, PM2.5 or NH3. Due to this potential incompleteness in
the submitted sources for 2012, non-submitted but still operating sources were pulled forward from the
201 1NEIv2 inventory.
In preparation for modeling, the complete set of point sources was exported from EIS and split into
several sectors for modeling. After moving offshore oil platforms into the othpt sector, and dropping
sources without specific locations (i.e., the FIPS code ends in 777), initial versions of the other three point
source sectors were created from the remaining 2012 point sources. The point sectors are: EGUs (ptegu),
point source oil and gas extraction-related sources (pt oilgas) and the remaining non-EGUs (ptnonipm).
The EGU emissions are split out from the other sources to facilitate the use of distinct SMOKE temporal
processing and future-year projection techniques. The oil and gas sector emissions (pt oilgas) were
processed separately for summary tracking purposes and distinct future-year projection techniques from
the remaining non-EGU emissions (ptnonipm).
The inventory pollutants processed through SMOKE for both the ptipm and ptnonipm sectors were: CO,
NOX, VOC, S02, NH3, PM10, and PM2.5 and the following HAPs: HQ (pollutant code = 7647010),
and CI (code = 7782505). BAFM from these sectors was not utilized because VOC was speciated without
the use (i.e., integration) of VOC HAP pollutants from the inventory.
The ptnonipm and pt oilgas sector emissions were provided to SMOKE as annual emissions. For sources
in the ptegu sector that could be matched to 2012 CEMS data, hourly CEMS NOx and SO2 emissions for
2012 from EPA's Acid Rain Program were used rather than NEI emissions. For all other pollutants (e.g.,
VOC, PM2.5, HC1), annual emissions were used as-is from the NEI, but were allocated to hourly values
using heat input from the CEMS data. For the unmatched units in the ptegu and ptegu_pk sectors, annual
emissions were allocated to daily values using IPM region- and pollutant-specific profiles, and similarly,
region- and pollutant-specific diurnal profiles were applied to create hourly emissions.
The non-EGU stationary point source (ptnonipm) emissions were input to SMOKE as annual emissions.
The full description of how the NEI emissions were developed is provided in the NEI documentation, but
a brief summary of their development follows:
a. CAP and HAP data were provided by States, locals and tribes under the Air Emissions Reporting
Rule (AERR)
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b. EPA corrected known issues and filled PM data gaps.
c. EPA added HAP data from the Toxic Release Inventory (TRI) where corresponding data was not
already provided by states/locals.
d. EPA provided data for airports and rail yards.
e. Off-shore platform data were added from the Bureau of Ocean Energy Management (BOEM).
The changes made to the NEI point sources prior to modeling with SMOKE are as follows:
• The tribal data, which do not use state/county Federal Information Processing Standards (F1PS)
codes in the NEI, but rather use the tribal code, were assigned a state/county FIPS code of 88XXX,
where XXX is the3-digit tribal code in the NEI. This change was made because SMOKE requires
all sources to have a state/county FIPS code.
• Sources that did not have specific counties assigned (i.e., the county code ends in 777) were not
included in the modeling because it was only possible to know the state in which the sources
resided, but no more specific details related to the location of the sources were available.
• Stack parameters for some point sources were defaulted when modeling in SMOKE. SMOKE uses
default stack parameters by SCC code to gap fill stack parameters if they are missing in the input
inventories. Also, SMOKE adjusts any stack parameter values that are outside of the SMOKE-
defined range of acceptable values.
3.2.1.1 EGU sector (ptegu)
The ptegu sector contains emissions from EGUs in the 2012 point source inventory that could be matched
to units found in the National Electric Energy Database System (NEEDS) v5.14 that is used by the
Integrated Planning Model (IPM) to develop future year EGU emissions. It was necessary to put these
EGlJs into a separate sector in the platform because IPM projects future emissions for the EGlJs defined
in the NEEDS database. In future year modeling cases, emissions for sources in the ptegu sector are fully
replaced with IPM outputs. Sources not matched to units found in NEEDS are placed into the ptoilgas
or ptnonipm sectors and are projected to the future year using projection and control factors appropriate
for their source categories. It is important that the matching between the NEI and NEEDS database be as
complete as possible because there can be double-counting of emissions in future year modeling scenarios
if emissions for units are projected by IPM are not properly matched to the units in the point source
inventory.
Some units in the ptegu sector are matched to CEMS data via ORIS facility codes and boiler ID. For these
units, SMOKE replaces the emissions of NOX and S02 with the CEMS emissions, thereby ignoring the
annual values specified in the point source inventory. For other pollutants, the hourly CEMS heat input
data are used to allocate the ptegu inventory annual emissions to hourly values. All stack parameters,
stack locations, and SCC codes for these sources come from the point source inventory. Because these
attributes are obtained from the inventory, the chemical speciation of VOC and PM2.5 for the sources is
selected based on the SCC or in some cases, based on unit-specific data. If CEMS data exists for a unit,
but the unit is not matched to the inventory, the CEMS data for that unit is not used in the modeling
platform. However, if the source exists in the inventory and is not matched to a CEMS unit, the emissions
from that source would be modeled using the annual emission value in the inventory and would be
allocated to daily values using region-, fuel- and pollutant-specific average profiles. EIS stores many
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matches from EIS units to the ORIS facility codes and boiler IDs used to reference the CEMS data. Some
additional matches were made at the release point level in the emissions modeling platform.
For sources not matched to CEMS data (i.e., "non-CEMS" sources), daily emissions were computed from
the annual emissions using average CEMS data profiles specific to fuel type and IPM region and based on
input. To allocate emissions to each hour of the day, diurnal profiles were created using average CEMS
data for heat input specific to fuel type and IPM region.
3.2.1.2 Non-IPM Sector (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. More information on
the development of the 2011 oil and gas emissions can be found in Section 3.20 of the 201 !NEIv2 TSD.
Table 3-4. Point source oil and gas sector NAICS Codes
NAICS
NAICS description
2111
Oil and Gas Extraction
2212
Natural Gas Distribution
4862
Pipeline Transportation of Natural Gas
21111
Oil and Gas Extraction
22121
Natural Gas Distribution
48611
Pipeline Transportation of Crude Oil
48621
Pipeline Transportation of Natural Gas
211111
Crude Petroleum and Natural Gas Extraction
211112
Natural Gas Liquid Extraction
213111
Drilling Oil and Gas Wells
213112
Support Activities for Oil and Gas Operations
221210
Natural Gas Distribution
486110
Pipeline Transportation of Crude Oil
486210
Pipeline Transportation of Natural Gas
3.2.1.3 Non-IPM Sector (ptnonipm)
Except for some minor exceptions, the non-IPM (ptnonipm) sector contains the point sources that are not
in the ptegu or pt oilgas sectors. For the most part, the ptnonipm sector reflects the non-EGU sources of
the NEI point inventory; however, it is likely that some small low-emitting EGUs not matched to the
NEEDS database or to CEMS data are in the ptnonipm sector. The sector also includes some ethanol
plants that have been identified by EPA and require special treatment in the future cases as they are
impacted by mobile source rules.
The ptnonipm sector contains a small amount of fugitive dust PM emissions from vehicular traffic on
paved or unpaved roads at industrial facilities, coal handling at coal mines, and grain elevators. Sources
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with state/county FIPS code ending with "777" are in the NEI but are not included in any modeling
sectors. These sources typically represent mobile (temporary) asphalt plants that are only reported for
some states, and are generally in a fixed location for only a part of the year and are therefore difficult to
allocate to specific places and days as is needed for modeling. Therefore, these sources are dropped from
the point-based sectors in the modeling platform.
3.2.2 Day-Specific Point Source Fires (ptfire)
Wildfire and prescribed burning emissions are contained in the ptfire sector. The ptfire sector has emissions
provided at geographic coordinates (point locations) and has daily emissions values. The ptfire sector excludes
agricultural burning and other open burning sources that are included in the agfire sector. Emissions are day-
specific and include satellite-derived latitude/longitude of the fire's origin and other parameters associated with the
emissions such as acres burned and fuel load, which allow estimation of plume rise.
The point source day-specific emission estimates for 2012 fires rely on SMARTFIRE 2 (Sullivan, et al.,
2008), which uses the National Oceanic and Atmospheric Administration's (NOAA's) Hazard Mapping
System (HMS) fire location information as input. Additional inputs include the CONSUMEv3.0 software
application (Joint Fire Science Program, 2009) and the Fuel Characteristic Classification System (FCCS)
fuel-loading database to estimate fire emissions from wildfires and prescribed burns on a daily basis. The
method involves the reconciliation of 1CS-209 reports (Incident Status Summary Reports) and GeoMAC-
Shapefiles with satellite-based fire detections to determine spatial and temporal information about the
fires. A functional diagram of the SM ARTFIRE 2 process of reconciling fires with ICS-209 reports is
available in the documentation (Raffuse, et al., 2007). Once the fire reconciliation process is completed,
the emissions are calculated using the U.S. Forest Service's CONSUMEv3.0 fuel consumption model and
the FCCS fuel-loading database in the BlueSky Framework (Ottmar, et. al., 2007).
The SMOKE-ready "ORL" inventory files created from the raw daily fires contain both CAPs and HAPs.
The BAFM HAP emissions from the inventory were obtained using VOC speciation profiles (i.e., a "no-
integrate noHAP" use case). For the 2012 modeling, fires over 20,000 acres in a single day were split into
the respective grid cells that they overlapped. The idea of this was to prevent all emissions from a very
large fire going into a single grid cell, when in reality the fire was more dispersed than a single point. The
large fires were each projected as a circle over the area centered on the specified latitude and longitude,
and then apportioned into the grid cells they overlapped. The area of each of the "subfires" was computed
in proportion to the overlap with that grid cell. These "subfires" were given new names that were the
same as the original, but with "_a", "_b", "_c", and "_d" appended as needed.
3.2.3 Nonpoint Sources (afdust, ag, agfire, nonpt, np oilgas, rwc)
Many modeling platform sectors were created from the 201 1NEIv2 nonpoint category. This section
describes the stationary nonpoint sources. Note that locomotives, C1 and C2 CMV, and C3 CMV are also
included the 201 1NEIv2 nonpoint data category, but are mobile sources and are placed into the clc2rail
and c3marine sectors, respectively. The 201 1NEIv2 TSD includes documentation for the nonpoint data
category of the 201 1NEIv2.
Nonpoint tribal-submitted emissions are dropped during spatial processing with SMOKE because the
spatial surrogates are available at the county, but not the tribal level. In addition, possible double-counting
with county-level emissions is prevented. These omissions are not expected to have an impact on the
results of the air quality modeling at the 12-km scales used for this platform.
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In the rest of this section, each of the platform sectors into which the 201 1 nonpoint NEI was divided is
described, along with any changes made to these data for this study.
3.2.3.1 Area Fugitive Dust Sector (afdust)
The area-source fugitive dust (afdust) sector contains 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 with a script that
applies land use-based gridded transport fractions followed by another script that zeroes out emissions for
days on which at least 0.01 inches of precipitation occurs or there is snow cover on the ground. The land
use data used to reduce the NEI emissions determines the amount of emissions that are subject to
transport. This methodology is discussed in (Pouliot, et al., 2010),
https://www3.epa.gov/ttn/chief/conference/eil9/session9/pouliot pres.pdf. and in "Fugitive Dust
Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). Both the transport fraction and
meteorological adjustments are based on the gridded resolution of the platform (e.g., 12km grid cells);
therefore, different emissions will result if the process were applied to different grid resolutions. A
limitation of the transport fraction approach is the lack of monthly variability that would be expected with
seasonal changes in vegetative cover. While wind speed and direction are not accounted for in the
emissions processing, the hourly variability due to soil moisture, snow cover and precipitation is
accounted for in the subsequent meteorological adjustment.
3.2.3.2 Agricultural Ammonia Sector (ag)
The agricultural NH3 (ag) sector includes livestock and agricultural fertilizer application emissions from
the 201 1NEIv2 nonpoint inventory. The livestock and fertilizer emissions in this sector are based only on
the SCCs starting with 2805 and 2801. The livestock SCCs are related to beef and dairy cattle, poultry
production and waste, swine production, waste from horses and ponies, and production and waste for
sheep, lambs, and goats. The fertilizer SCCs consist of 15 specific types of ammonia-based fertilizer and
one for miscellaneous fertilizers. The "ag" sector includes all of the NH3 emissions from fertilizer from
the NEI. However, the "ag" sector does not include all of the livestock ammonia emissions, as there are
also a small amount of NH3 emissions from livestock feedlots in the ptnonipm inventory (as point
sources) in California (175 tons) and Wisconsin (125 tons). The annual agricultural burning estimates are
treated as monthly values for modeling. The annual values in the 201 1NEIv2 were split into monthly
emissions by aggregating the data up to monthly values from daily estimates of emissions.
3.2.3.3 Agriculturalfires (agfire)
The agricultural fire (agfire) sector contains emissions from agricultural fires. These emissions were
placed into the sector based on their SCC code. All SCCs starting with 28015 were included. The first
three levels of descriptions for these SCCs are: Fires - Agricultural Field Burning; Miscellaneous Area
Sources; Agriculture Production - Crops - as nonpoint; Agricultural Field Burning - whole field set on
fire. The SCC 2801500000 does not specify the crop type or burn method, while the more specific SCCs
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specify field or orchard crops, and in some cases the specific crop being grown. For more information on
how emissions for agricultural fires were developed in the 201 1NEIv2, see Section 5.2 of the 201 1NEIv2
TSD.
3.2.3.4 Nonpoint Oil-gas Sector (npoilgas)
The nonpoint oil and gas (np oilgas) sector contains onshore and offshore oil and gas emissions. EPA
estimated emissions for all counties with 2011 oil and gas activity data with the Oil and Gas Tool, and
many S/L/T agencies also submitted nonpoint oil and gas data. The types of sources covered include drill
rigs, workover rigs, artificial lift, hydraulic fracturing engines, pneumatic pumps and other devices,
storage tanks, flares, truck loading, compressor engines, and dehydrators. For more information on the
development of the oil and gas emissions in the 201 1NEIv2, see Section 3.20 of the 201 1NEIv2 TSD.
3.2.3.5 Residential Wood Combustion Sector (rwc)
The residential wood combustion (rwc) sector includes residential wood burning devices such as
fireplaces, fireplaces with inserts (inserts), free standing woodstoves, pellet stoves, outdoor hydronic
heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in firepots and
chimneas. Free standing woodstoves and inserts are further differentiated into three categories:
conventional (not EPA certified); EPA certified, catalytic; and EPA certified, noncatalytic. Generally
speaking, the conventional units were constructed prior to 1988. Units constructed after 1988 had to meet
EPA emission standards and they are either catalytic or non-catalytic. For more information on the
development of the residential wood combustion emissions, see Section 3.14 of the 201 1NEIv2 TSD.
3.2.3.6 Other Nonpoint Sources (nonpt)
Stationary nonpoint sources that were not subdivided into the afdust, ag, np oilgas, or rwc sectors were
assigned to the "nonpt" sector. The types of sources in the nonpt sector include:
• stationary source fuel combustion, including industrial, commercial, and residential;
• chemical manufacturing;
• industrial processes such as commercial cooking, metal production, mineral processes, petroleum
refining, wood products, fabricated metals, and refrigeration;
• solvent utilization for surface coatings such as architectural coatings, auto refinishing, traffic
marking, textile production, furniture finishing, and coating of paper, plastic, metal, appliances,
and motor vehicles;
• solvent utilization for degreasing of furniture, metals, auto repair, electronics, and manufacturing;
• solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal care
products, household products, adhesives and sealants;
• solvent utilization for asphalt application and roofing, and pesticide application;
• storage and transport of petroleum for uses such as portable gas cans, bulk terminals, gasoline
service stations, aviation, and marine vessels;
• storage and transport of chemicals;
• waste disposal, treatment, and recovery via incineration, open burning, landfills, and composting;
• agricultural burning and orchard heating;
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• miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair
shops.
The nonpt sector includes emission estimates for Portable Fuel Containers (PFCs), also known as "gas
cans." The PFC inventory consists of five distinct sources of PFC emissions, further distinguished by
residential or commercial use. The five sources are: (1) displacement of the vapor within the can; (2)
spillage of gasoline while filling the can; (3) spillage of gasoline during transport; (4) emissions due to
evaporation (i.e., diurnal emissions); and (5) emissions due to permeation. Note that spillage and vapor
displacement associated with using PFCs to refuel nonroad equipment are included in the nonroad
inventory.
3.2.4 Biogenic Sources (beis)
Biogenic emissions were developed using the Biogenic Emission Inventory System, version 3.61
(BEIS3.61) within SMOKE. This was an update from the emissions in the 2011 modeling platform that
used BEIS 3.14, and from the 201 1NEIv2 that used BEIS 3.60. Like BEIS 3.14, BEIS3.61 creates
gridded, hourly, model-species emissions from vegetation and soils. It estimates CO, VOC (most notably
isoprene, terpene, and sesquiterpene), and NO emissions for the contiguous U.S. and for portions of
Mexico and Canada.
Updates in BEIS3.61 include the incorporation of Version 4 of the Biogenic Emissions Land use
Database (BELD4), and the incorporation of a canopy model to estimate leaf-level temperatures (Pouliot
and Bash, 2015). BELD4 is based on an updated version of the USDA-USFS Forest Inventory and
Analysis (FIA) vegetation speciation based data from 2002 to 2013 from the Forest Inventory and
Analysis version 5.1. Canopy coverage is based on the Landsat satellite NLCD product from 2001, since
no canopy product was developed for the 2006 NLCD. The FIA includes approximately 250,000
representative plots of species fraction data that are within approximately 75 km of one another in areas
identified as forest by the NLCD canopy coverage. The 2006 NLCD provides land cover information with
a native data grid spacing of 30 meters. For land areas outside the conterminous United States, 500 meter
grid spacing land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) is used.
BEIS version 3.61 includes a two-layer canopy model. Layer structure varies with light intensity and solar
zenith angle. Both layers of the canopy model include estimates of sunlit and shaded leaf area based on
solar zenith angle and light intensity, direct and diffuse solar radiation, and leaf temperature (Bash et al.,
2015). The new algorithm requires additional meteorological variables over previous versions of BEIS.
The variables output from the Meteorology-Chemistry Interface Processor (MCIP) that are used to
convert WRF outputs to CMAQ inputs are shown in Table 3-5.
Table 3-5. Meteorological variables required by BEIS 3.61
Variable
Description
LAI
leaf-area index
PRSFC
surface pressure
Q2
mixing ratio at 2 m
RC
convective precipitation per met TSTEP
RGRND
solar rad reaching sfc
RN
nonconvective precipitation per met TSTEP
RSTOMI
inverse of bulk stomatal resistance
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Variable
Description
SLYTP
soil texture type by USD A category
S0IM1
volumetric soil moisture in top cm
S0IT1
soil temperature in top cm
TEMPG
skin temperature at ground
USTAR
cell averaged friction velocity
RADYNI
inverse of aerodynamic resistance
TEMP2
temperature at 2 m
3.2.5 Mobile Sources (onroad, onroadcaadj, nonroad, clc2rail, c3marine)
Onroad mobile sources include emissions from motorized vehicles that are normally operated on public
roadways. These include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks,
heavy-duty trucks, and buses. The sources are further divided between diesel and gasoline vehicles. The
sector characterizes emissions from off-network processes (e.g. starts, hot soak, and extended idle) and
on-network processes (i.e., from vehicles moving along the roads).
The 2012 onroad emissions are in the onroad sector. Except for California, all onroad emissions are
generated using the SMOKE-MOVES emissions modeling framework that leverages MOVES generated
outputs (http://www.epa.gov/otaq/models/moves/) and hourly meteorology. All tribal data from the
mobile sectors have been dropped because the (1) emissions are small, (2) the emissions could be double-
counted with state-provided onroad emissions, (3) all tribal data was developed using the older model
M0BILE6, and (4) because spatial surrogate data at the tribal level is not currently available. Emissions
for onroad (excluding refueling), nonroad and clc2rail sources in California are based on data provided
by the California Air Resources Board (CARB).
The locomotive and commercial marine vessel (CMV) emissions are divided into two nonroad sectors:
"clc2rail" and "c3marine". The clc2rail sector includes all railway and most rail yard emissions as well
as the gasoline and diesel-fueled Class 1 and Class 2 CMV emissions. The c3marine sector emissions
contain the larger residual fueled ocean-going vessel Class 3 CMV emissions that are within state waters,
while those outside of state waters are in the othpt sector. All nonroad emissions are treated as county-
specific low-level emissions (i.e., they are released into model layer 1).
3.2.5.1 Onroad (onroad)
Onroad mobile sources include emissions from motorized vehicles that are normally operate on public
roadways. These include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks,
heavy-duty trucks, and buses. The sources are further divided between diesel and gasoline vehicles. The
sector characterizes emissions from off-network processes (e.g. starts, hot soak, and extended idle) as well
as from on-network processes (i.e., from vehicles moving along the roads).
To develop the onroad mobile source emissions for the continental U.S., EPA used a modeling framework
that took into account the temperature sensitivity of the on-road emissions. Specifically, EPA used
MOVES inputs for representative counties, vehicle miles traveled (VMT) and vehicle population (VPOP)
data for all counties, along with tools that integrated the MOVES model with SMOKE. In this way, it
was possible to take advantage of the gridded hourly temperature information available from meteorology
modeling used for air quality modeling.
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SMOKE-MOVES requires that emission rate "lookup" tables be generated by MOVES which
differentiate emissions by process (i.e., running, start, vapor venting, etc.), vehicle type, road type,
temperature, speed, hour of day, etc. To generate the MOVES emission rates that could be applied across
the U.S., EPA used an automated process to run MOVES to produce year 2012-specific emission factors
by temperature and speed for a series of "representative counties," to which every other county was
mapped. Using the MOVES emission rates, SMOKE selects appropriate emissions rates for each county,
hourly temperature, SCC, and speed bin and multiplied the emission rate by activity (VMT (vehicle miles
travelled), VPOP (vehicle population)), or HOTELING (hours of extended idle) to produce emissions.
These calculations were done for every county and grid cell, in the continental U.S. for each hour of the
year.
The SMOKE-MOVES process for creating the model-ready emissions consists of the following steps:
1) Determine which counties will be used to represent other counties in the MOVES runs.
2) Determine which months will be used to represent other month's fuel characteristics.
3) Create MOVES inputs needed only by MOVES. MOVES requires county-specific information on
vehicle populations, age distributions, and inspection-maintenance programs for each of the
representative counties.
4) Create inputs needed both by MOVES and by SMOKE, including temperatures and activity data.
5) Run MOVES to create emission factor tables for the temperatures found in each county.
6) Run SMOKE to apply the emission factors to activity data (VMT, VPOP, and HOTELING) to
calculate emissions based on the gridded hourly temperatures in the meteorological data.
7) Aggregate the results to the county-SCC level for summaries and quality assurance.
To generate the 2012 onroad mobile source emissions, SMOKE-MOVES was run using 2011 activity
data and 2012 emission factors. This decision was made after an analysis of VMT trends from 2011 to
2012 showed that there was very little change between the two years. Onroad emissions for California
were created through a hybrid approach of combining state-supplied annual emissions (from the
201 1NEIv2) with EPA developed SMOKE-MOVES data. Through this approach, the platform was able
to reflect California's unique rules, while leveraging the more detailed SCCs and the highly resolved
spatial patterns, temporal patterns, and speciation from SMOKE-MOVES. The California-provided year
2011 emissions were adjusted to a 2012 inventory using the ratio of 2012 to 2011 factors retrieved from
the EMFAC 2011 web data base (http://www.arb.ca.gov/emfac/). Factors were applied by air basin, fuel,
and pollutant. NH3 used CO ratios (CARB did not provide NH3); VOC and all VOC HAPs used TOG
ratios. Some counties are in multiple air basins, but for simplicity, each county was assigned to a single
air basin for the projections. There are 15 air basins in California.
3.2.5.2 NMIM-Based Nonroad Mobile Sources (nottroad)
The nonroad equipment emissions were developed by obtaining monthly totals based on a run of NMIM
(EPA, 2005) for year 2012. All nonroad emissions are compiled at the county/SCC level. NMIM creates
the nonroad emissions on a month-specific basis that accounts for temperature, fuel types, and other
variables that vary by month. The nonroad sector includes monthly exhaust, evaporative and refueling
emissions from nonroad engines (not including commercial marine, aircraft, and locomotives) that EPA
derived from NMIM for all states except California and Texas. Additional details on the development of
the 201 1NEIv2 nonroad emissions, which provided the starting databases for the 2012 run, are available
in Section 4.5 the 201 1NEIv2 TSD.
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Texas year 2011 nonroad emissions were also submitted to the NEI. The 201 INEIvl nonroad annual
inventory emissions values were converted to monthly values by using EPA's NMIM monthly inventories
to compute monthly ratios by county, SCC7, mode, and poll. They were then projected to 2012 using
trend lines based on the 2012 NMIM run as compared to EPA 2011 NMIM run.
California year 2011 nonroad emissions were submitted to the 201 1NEIv2 and are also documented in a
staff report (ARB, 2010a) and are used to represent year 2012 emissions. The nonroad sector emissions
in California were developed using a modular approach and include all rulemakings and updates in place
by December 2010. These emissions were developed using Version 1 of the CEP AM which supports
various California off-road regulations such as in-use diesel retrofits (ARB, 2007), Diesel Risk-Reduction
Plan (ARB, 2000) and 2007 State Implementation Plans (SIPS) for the South Coast and San Joaquin
Valley air basins (ARB, 2010b).
The CARB-supplied 201 1NEIv2 nonroad annual inventory emissions values were converted to monthly
values by using the aforementioned EPA NMIM monthly inventories to compute monthly ratios by
county, SCC7 (fuel, engine type, and equipment type group), mode, and pollutant. SCC7 ratios were used
because the SCCs in the CARB inventory did not align with many of the SCCs in EPA NMIM inventory.
By aggregating up to SCC7, the two inventories had a more consistent coverage of sources. Some VOC
emissions were added to California to account for situations when VOC HAP emissions were included in
the inventory, but there were no VOC emissions. These additional VOC emissions were computed by
summing benzene, acetaldehyde, and formaldehyde for the specific sources.
3.2.5.3 Category 1 and 2 Commercial Marine and Locomotive (cl c2rail)
The clc2rail sector contains locomotive and smaller CMV sources, except for railway maintenance
locomotives and C3 CMV sources outside of the Midwest states. The "clc2" portion of this sector name
refers to the Class 1 and 2 CMV emissions, not the railway emissions. Railway maintenance emissions
are included in the nonroad sector. The C3 CMV emissions are in the c3marine sector. All emissions in
this sector are annual and at the county-SCC resolution. The emissions include the offshore portion of the
Cl and C2 commercial marine sources, including fishing vessels and oil rig support vessels in the Gulf of
Mexico. Emissions that occur outside of state waters are not assigned to states. The emissions for clc2rail
sector are equivalent to those in the 201 1NEIv2 nonpoint inventory. For more information on CMV
sources in the NEI, see Section 4.3 of the 201 1NEIv2 TSD. For more information on locomotives, see
Section 4.4 of the 201 1NEIv2 TSD.
3.2.5.4 Category 3 commercial marine (c3marine)
The Category 3 (C3) CMV sources in the c3marine sector of the 201 lv6.2 platform run on residual oil
and use the SCCs 2280003100 and 2280003200 for port and underway emissions, respectively, and are
consistent with the 201 1NEIv2. Emissions for this sector use state-submitted values and EPA-developed
emissions in areas where states did not submit. This sector only includes emissions in state-waters and
the emissions are treated as nonpoint sources. Thus, the c3marine emissions are placed in layer 1 and
allocated to grid cells using spatial surrogates. C3 CMV emissions outside of state waters, and non-U.S.
emissions farther offshore than U.S. waters are processed in the "othpt" sector. C3 emissions for Canada
were provided by Environment Canada for year 2010 and are found in the othar inventory.
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The EPA-estimated C3 CMV emissions for year 2011 were developed based on a 4-km resolution ASCII
raster format dataset that preserves shipping lanes. This dataset has been used since the Emissions
Control Area-International Marine Organization (ECA-IMO) project began in 2005, although it was then
known as the Sulfur Emissions Control Area (SEC A). The ECA-IMO emissions consist of large marine
diesel engines (at or above 30 liters/cylinder) that until recently were allowed to meet relatively modest
emission requirements and as a result these ships would often burn residual fuel in that region. The
emissions in this sector are comprised of primarily foreign-flagged ocean-going vessels, referred to as C3
CMV ships. The c3marine inventory includes these ships in several intra-port modes (i.e., cruising,
hoteling, reduced speed zone, maneuvering, and idling) and an underway mode, and includes near-port
auxiliary engine emissions.
An overview of the C3 ECA Proposal to the International Maritime Organization (EPA-420-F-10-041,
August 2010) project and future-year goals for reduction of NOx, SO2, and PM C3 emissions can be
found at: http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09019.pdf The resulting ECA-IMO
coordinated strategy, including emission standards under the Clean Air Act for new marine diesel engines
with per-cylinder displacement at or above 30 liters, and the establishment of Emission Control Areas is
available from https://www.epa.gov/regulations-emissions-vehicles-and-engines/international-standards-
reduce-emissions-marine-diesel. The base year for the ECA inventory is 2002 and consists of these
CAPs: PM10, PM2.5, CO, CO2, NH3, NOx, SOx (assumed to be SO2), and hydrocarbons (assumed to be
VOC). EPA developed regional growth (activity-based) factors that were applied to create the 2012
inventory from the 2002 data. The geographic regions listed in the table are shown in Figure 3-1. * The
East Coast and Gulf Coast regions were divided along a line roughly through Key Largo (longitude 80
26' West). Technically, the EEZ FIPS are not really "FIPS" state-county codes, but are treated as such in
the inventory and emissions processing. The Canadian near-shore emissions were assigned to province-
level FIPS codes and paired those to region classifications for British Columbia (North Pacific), Ontario
(Great Lakes) and Nova Scotia (East Coast).
* Technically, these are not really "FIPS" state-county codes, but are treated as such in the inventory and
emissions processing.
¥/J f/Jk
Qk
\SP\
\U'Gt
\\ f
A U7
Figure 3-1. Illustration of regional modeling domains in ECA-IMO study
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The emissions were converted to SMOKE point source inventory format as described in
https://www3.epa.gov/ttn/chief/conference/eil7/session6/mason.pdf, which allows for the emissions to be
allocated to modeling layers above the surface layer. As described in the paper, the ASCII raster dataset
was converted to latitude-longitude, mapped to state/county FIPS codes that extended up to 200 nautical
miles (nm) from the coast, assigned stack parameters, and monthly ASCII raster dataset emissions were
used to create monthly temporal profiles. All non-US, non-EEZ emissions (i.e., in waters considered
outside of the 200 nm EEZ, and hence out of the U.S. and Canadian ECA-IMO controllable domain) were
simply assigned a dummy state/county FIPS code=98001, and were projected to year 2011 using the
"Outside ECA" factors.
The assignment of U.S. state/county FIPS codes was restricted to state-federal water boundaries data from
the Mineral Management Service (MMS) that extend approximately 3 to 10 nautical miles (nm) off shore.
Emissions outside the 3 to 10 mile MMS boundary, but within the approximately 200 nm EEZ boundaries
in Figure 3-1, were projected to year 2012 using the same regional adjustment factors as the U.S.
emissions; however, the state/county FIPS codes were assigned as "EEZ" codes and those emissions
processed in the "othpt" sector. Note that state boundaries in the Great Lakes are an exception, extending
through the middle of each lake such that all emissions in the Great Lakes are assigned to a U.S. county or
Ontario. This holds true for Midwest states and other states such as Pennsylvania and New York. The
classification of emissions to U.S. and Canadian FIPS codes was needed to avoid double-counting of C3
CMV U.S. emissions in the Great Lakes because, as discussed in the previous section, all CMV emissions
in the Midwest RPO are processed in the "clc2rail" sector.
The SMOKE-ready data have been cropped from the original ECA-IMO entire northwestern quarter of
the globe to cover only the large continental U.S. 36-km "36US1" air quality model domain, the largest
domain used by EPA in recent years.
The original ECA-IMO inventory did not delineate between ports and underway emissions (or other C3
modes such as hoteling, maneuvering, reduced-speed zone, and idling). However, a U.S. ports spatial
surrogate dataset was used to assign the ECA-IMO emissions to ports and underway SCCs 2280003100
and 2280003200, respectively. This had no effect on temporal allocation or speciation because all C3
CMV emissions, unclassified/total, port and underway, share the same temporal and speciation profiles.
For California, the ECA-IMO 2011 emissions were scaled to match those provided by CARB for year
2011 because CARB has had distinct projection and control approaches for this sector since 2002. These
CARB C3 CMV emissions are documented in a staff report available at:
http://www.arb.ca.gov/regact/2010/offroadlsi 10/offroadisor.pdf. The CMV emissions obtained from the
CARB nonroad mobile dataset include the 2011 regulations to reduce emissions from diesel engines on
commercial harbor craft operated within California waters and 24 nautical miles of the California
shoreline. These emissions were developed using Version 1 of the California Emissions Projection
Analysis Model (CEPAM) that supports various California off-road regulations. The locomotive
emissions were obtained from the CARB trains dataset "ARMJ_RF#2002_ANNUAL_TRAINS.txt".
Documentation of the CARB offroad mobile methodology, including clc2rail sector data, is provided at:
http://www.arb.ca.gOv/msei/categories.htm#offroad motor vehicles.
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3.2.6 Emissions from Canada, Mexico and Offshore Drilling Platforms (othpt, othar, othon,
othafdust)
The emissions from Canada, Mexico, and non-U.S. offshore Class 3 Commercial Marine Vessels (C3
CMV) and drilling platforms are included as part of four emissions modeling sectors: othpt, othar, othon,
and othafdust. The "oth" refers to the fact that these emissions are usually "other" than those in the U.S.
state-county geographic FIPS, and the remaining characters provide the SMOKE source types: "pt" for
point, "ar" for "area and nonroad mobile", and "on" for onroad mobile.
The ECA-IMO-based C3 CMV were processed in the othpt sector. These C3 CMV emissions include
those assigned to U.S. federal waters, those assigned to the Exclusive Economic Zone (EEZ; defined as
those emissions beyond the U.S. Federal waters approximately 3-10 miles offshore, and extending to
about 200 nautical miles from the U.S. coastline), along with any other offshore emissions. These
emissions are developed in the same way as the EPA-dataset described in the c3marine sector described
above. Emissions in U.S. waters are aggregated into large regions and included in the 201 1NEIv2 using
special FIPS codes. Because these emissions are treat as point sources, shipping lane routes can be
preserved and they may be allocated to air quality model layers higher than layer 1.
For Canadian point sources, 2010 emissions provided by Environment Canada were used. Note that VOC
was not provided for Canadian point sources, but any VOC emissions were speciated into CB05 species.
Temporal profiles and speciated emissions were also provided. Point sources in Mexico were compiled
based on the Inventario Nacional de Emisiones de Mexico, 2008 (ERG, 2014a). The point source
emissions in the 2008 inventory were converted to English units and into the FF10 format that could be
read by SMOKE, missing stack parameters were gapfilled using SCC-based defaults, and latitude and
longitude coordinates were verified and adjusted if they were not consistent with the reported
municipality. Note that there are no explicit HAP emissions in this inventory.
The othpt sector also includes point source offshore oil and gas drilling platforms that are beyond U.S.
state-county boundaries in the Gulf of Mexico. For these offshore emissions, data from the 201 1NEIv2
were used.
For Canada area, nonroad mobile, and onroad mobile sources, year-2010 emissions provided by
Environment Canada were used, including C3 CMV emissions. The Canadian inventory included fugitive
dust emissions that do not incorporate either a transportable fraction or meteorological-based adjustments.
To properly account for these issues, a separate sector called othafdust was created and modeled using the
same adjustments as are done for U.S. sources. Updated Shapefiles for creating spatial surrogates for
Canada were also provided.
Area and nonroad mobile sources in Mexico were compiled from the Inventario Nacional de Emisiones
de Mexico, 2008 (ERG, 2014a). The 2008 emissions were quality assured for completeness, SCC
assignments were made when needed, the pollutants expected for the various processes were reviewed,
and adjustments were made to ensure that PMio was greater than or equal to PM2.5. The resulting
inventory was written using English units to the nonpoint FF10 format that could be read by SMOKE.
Note that unlike the U.S. inventories, there are no explicit HAPs in the nonpoint or nonroad inventories
for Canada and Mexico, and therefore all HAPs are created from speciation.
Onroad mobile sources in Mexico were compiled from the Inventario Nacional de Emisiones de Mexico,
2008 (ERG, 2014a). SCCs compatible with the 201 1NEIv2 were assigned to the 2008 onroad mobile
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source emissions in Mexico, and it was enforced that PMio be greater than or equal to PM2.5. Quality
assurance of the onroad mobile source emissions data revealed that Baja California, Michoacan, and
Nuevo Leon had significantly high per capita emissions for all pollutants and should be considered to be
outliers. The emissions for these states were replaced with values computed based on the average per
capita emissions for the remaining states. The data were written using English units to the nonpoint FF10
format that could be read by SMOKE. Note that unlike the U.S. inventories, there are no explicit HAPs in
the onroad mobile inventories for Canada and Mexico, and therefore all HAPs are created from
speciation.
3.2.7 SMOKE-ready non-anthropogenic chlorine inventory
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (C12)
concentrations in oceanic air masses (Bullock and Brehme, 2002). Data at 36 km and 12 km resolution
were available and were not modified other than the name "CHLORINE" was changed to "CL2" because
that is the name required by the CMAQ model.
3.3 Emissions Modeling Summary
CMAQ requires hourly emissions of specific gas and particle species for the horizontal and vertical grid
cells contained within the modeled region (i.e., modeling domain). To provide emissions in the form and
format required by CMAQ, it is necessary to "pre-process" the emission inventories for each of the
sectors described above. In brief, the process of emissions modeling transforms the emissions inventories
from their original temporal resolution, pollutant resolution, and spatial resolution into the hourly,
speciated, gridded resolution required by the air quality model. Thus, emissions modeling includes
temporal allocation, spatial allocation, and pollutant speciation. In some cases, emissions modeling also
includes the vertical allocation of point sources, but many air quality models also perform this task
because it greatly reduces the size of the input emissions files if the vertical distribution of the sources
does not need to be provided as an input.
The temporal resolutions of the emissions inventories input to SMOKE vary across sectors, and may be
hourly, daily, monthly, or annual total emissions, or it could instead be emission factors and activity data.
The spatial resolution also varies: it may be individual point sources, county/province/municipio totals, or
gridded emissions. This section provides some basic information about the tools and data files used for
emissions modeling as part of the modeling platform.
3.3.1 The SMOKE Modeling System
SMOKE version 3.6.5 was used to pre-process the raw emissions inventories into emissions inputs for
CMAQ. SMOKE executables and source code are available from the Community Multiscale Analysis
System (CMAS) Center at http://www.cmascen.ter.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.
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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 3-6
summarizes the major processing steps of each platform sector. The "Spatial" column shows the spatial
approach used: here "point" indicates that SMOKE maps the source from a point location (i.e., latitude
and longitude) to a grid cell; "surrogates" indicates that some or all of the sources use spatial surrogates to
allocate county emissions to grid cells; and "area-to-point" indicates that some of the sources use the
SMOKE area-to-point feature to grid the emissions. The "Speciation" column indicates that all sectors
use the SMOKE speciation step, though biogenics speciation is done within the Tmpbeis3 program and
not as a separate SMOKE step. The "Inventory resolution" column shows the inventory temporal
resolution from which SMOKE needs to calculate hourly emissions. Note that for some sectors (e.g.,
onroad, beis), there is no input inventory; instead, activity data and emission factors are used in
combination with meteorological data to compute hourly emissions.
Finally, the "plume rise" column indicates the sectors for which the "in-line" approach is used. These
sectors are the only ones with emissions in aloft layers based on plume rise. The term "in-line" means
that the plume rise calculations are done inside of the air quality model instead of being computed by
SMOKE. The air quality model computes the plume rise using the stack data and the hourly air quality
model inputs found in the SMOKE output files for each model-ready emissions sector. The height of the
plume rise determines the model layer into which the emissions are placed. The othpt sector has only "in-
line" emissions, meaning that all of the emissions are treated as elevated sources and there are no
emissions for those sectors in the two-dimensional, layer-1 files created by SMOKE. Day-specific point
fires are treated separately for CMAQ modeling in that fire plume rise is done within CMAQ itself. After
plume rise is applied, there will be emissions in every layer from the ground up to the top of the plume.
Table 3-6. Key emissions modeling steps by sector
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
afdust
Surrogates
Yes
annual
ag
Surrogates
Yes
annual
agfire
Surrogates
Yes
monthly
beis
Pre-gridded
land use
in BEIS
computed hourly
clc2rail
Surrogates
Yes
annual
c3 marine
Surrogates
Yes
annual
nonpt
Surrogates &
area-to-point
Yes
annual
nonroad
Surrogates &
area-to-point
Yes
monthly
np oilgas
Surrogates
Yes
annual
onroad
Surrogates
Yes
monthly activity,
computed hourly
othafdust
Surrogates
Yes
annual
othar
Surrogates
Yes
annual
32
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Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
othon
Surrogates
Yes
annual
othpt
Point
Yes
annual
in-line
pt oilgas
Point
Yes
annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptfire
Point
Yes
daily
in-line
ptnonipm
Point
Yes
annual
in-line
rwc
Surrogates
Yes
annual
SMOKE has the option of grouping sources so that they are treated as a single stack when computing
plume rise. For the 2012 modeling case, no grouping was performed because grouping combined with
"in-line" processing will not give identical results as "offline" processing (i.e., when SMOKE creates 3-
dimensional files). This occurs when stacks with different stack parameters or lat/lons are grouped,
thereby changing the parameters of one or more sources. The most straightforward way to get the same
results between in-line and offline is to avoid the use of grouping.
33
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3.3.3 Spatial Configuration
For this study, SMOKE was run for the smaller 12-kni CONtinental United States "CONUS" modeling
domain (12US2) shown in Figure 3-2 and boundary conditions were obtained from a 2011 run of GEOS-
Chem. The grid used a Lambert-Conformal projection, with Alpha = 33, Beta = 45 and Gamma = -97,
with a center of X = -97 and Y = 40. Later sections provide details on the spatial surrogates and area-to-
point data used to accomplish spatial allocation with SMOKE.
12US1 Continental US Domain
12ITS2 Continental US Domain
Figure 3-2. CMAQ Modeling Domain
3.3.4 Chemical Speciation Configuration
The emissions modeling step for chemical speciation creates the "model species" needed by the air
quality model for a specific chemical mechanism. These model species are either individual chemical
compounds or groups of species, called "model species." The chemical mechanism used for this study is
the CB05 mechanism (Yarwood, 2005). The versions of CMAQ used for this study include secondary
organic aerosol (SOA) and HONO enhancements. The PM2.5 model species are those associated with the
CMAQ Aerosol Module, version 6 (AE6). This modeling case uses CB05 with speciation profile
mappings that were updated in February, 2015. Table 3-7 lists the model species produced by SMOKE
for use in CMAQ. Speciation profiles and cross-references are available in the SMOKE input files for the
201 lv6.2 emissions modeling platform.
34
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Table 3-7. Emission model species produced for CB05 with SOA for CMAQ 5.0.2
Inventory Pollutant
Model Species
Model species description
Ch
CL2
Atomic gas-phase chlorine
HC1
HCL
Hydrogen Chloride (hydrochloric acid) gas
CO
CO
Carbon monoxide
NOx
NO
Nitrogen oxide
N02
Nitrogen dioxide
HONO
Nitrous acid
so2
S02
Sulfur dioxide
SULF
Sulfuric acid vapor
nh3
NH3
Ammonia
NH3 FERT
Ammonia from fertilizer
voc
ALD2
Acetaldehyde
ALDX
Propionaldehyde and higher aldehydes
BENZENE
Benzene (not part of CB05)
CH4
Methane
ETH
Ethene
ETHA
Ethane
ETOH
Ethanol
FORM
Formaldehyde
IOLE
Internal olefin carbon bond (R-C=C-R)
ISOP
Isoprene
MEOH
Methanol
NVOL
Non-volatile compounds
OLE
Terminal olefin carbon bond (R-C=C)
PAR
Paraffin carbon bond
SESQ
Sequiterpenes (from biogenics only)
TERP
Terpenes
TOL
Toluene and other monoalkyl aromatics
UNR
Unreactive
XYL
Xylene and other polyalkyl aromatics
PMio
PMC
Coarse PM >2.5 microns and <10 microns
PM2.5
PEC
Particulate elemental carbon <2.5 microns
PN03
Particulate nitrate <2.5 microns
POC
Particulate organic carbon (carbon only) <2.5 microns
PS04
Particulate Sulfate <2.5 microns
PMFINE7
Other particulate matter <2.5 microns
Sea-salt species (non -
PCL
Particulate chloride
anthropogenic)8
PNA
Particulate sodium
It should be noted that the BENZENE model species is not part of CB05 in that the concentrations of
BENZENE do not provide any feedback into the chemical reactions (i.e., it is not "inside" the chemical
mechanism). Rather, benzene is used as a reactive tracer and as such is impacted by the CB05 chemistry.
BENZENE, along with several reactive CB05 species (such as TOL and XYL) plays a role in SOA
formation.
7 For CMAQ 5.0, PMFINE is speciated into a finer set of PM components. Listed in this table are the AE5 species
8 These emissions are created outside of SMOKE
35
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The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach were
developed from the SPECIATE 4.4 database (https://www.epa.gov/air-emissions-modeling/speciate-
version-45-through-40). which is EPA's repository of TOG and PM speciation profiles of air pollution
sources. However, a few of the profiles used in the v6.2 platform will be published in later versions of the
SPECIATE database after the release of this documentation. The SPECIATE database development and
maintenance is a collaboration involving EPA's ORD, OTAQ, and the Office of Air Quality Planning and
Standards (OAQPS), in cooperation with Environment Canada (EPA, 2006a). The SPECIATE database
contains speciation profiles for TOG, speciated into individual chemical compounds, VOC-to-TOG
conversion factors associated with the TOG profiles, and speciation profiles for PM2.5.
The speciation of VOC includes HAP emissions from the 201 1NEIv2 in the speciation process. Instead
of speciating VOC to generate all of the species listed in Table 3-7, emissions of four specific HAPs:
benzene, acetaldehyde, formaldehyde and methanol (collectively known as "BAFM") from the NEI were
"integrated" with the NEI VOC. The integration process (described in more detail below) combines these
HAPs with the VOC in a way that does not double count emissions and uses the HAP inventory directly
in the speciation process. The basic process is to subtract the specified HAPs emissions mass from VOC
emissions mass and to then use a special "integrated" profile to speciate the remainder of VOC to the
model species excluding the specific HAPs. EPA believes that generally, the HAP emissions from the
inventory are more representative of emissions of these compounds than their generation via VOC
speciation.
The BAFM HAPs (benzene, acetaldehyde, formaldehyde and methanol) were chosen because, with the
exception of BENZENE, they are the only explicit VOC HAPs in the base version of CMAQ 5.0.2 (CAPs
only with chlorine chemistry) model. Explicit means that they are not lumped chemical groups like the
other CB05 species. These "explicit VOC HAPs" are model species that participate in the modeled
chemistry using the CB05 chemical mechanism. The use of these HAP emission estimates along with
VOC is called "HAP-CAP integration". BENZENE was chosen because it is a model species in the base
version of CMAQ 5.0.2, and there was a desire to keep its emissions consistent between multi-pollutant
and base versions of CMAQ.
The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats other
than PTDAY (the format used for the ptfire sector). SMOKE allows the user to specify both the
particular HAPs to integrate via the INVTABLE and the particular sources to integrate via the
NHAPEXCLUDE file (which actually provides the sources to be excluded from integration9). For the
"integrated" sources, SMOKE subtracts the "integrated" HAPs from the VOC (at the source level) to
compute emissions for the new pollutant "NONHAPVOC " The user provides NONHAPVOC-to-
NONHAPTOG factors and NONHAPTOG speciation profiles10. SMOKE computes NONHAPTOG and
then applies the speciation profiles to allocate the NONHAPTOG to the other air quality model VOC
species not including the integrated HAPs. After determining if a sector is to be integrated, if all sources
have the appropriate HAP emissions, then the sector is considered fully integrated and does not need a
9 In SMOKE version 3.6.5, the options to specify sources for integration are expanded so that a user can specify the particular
sources to include or exclude from integration, and there are settings to include or exclude all sources within a sector. In
addition, the error checking is significantly stricter for integrated sources. If a source is supposed to be integrated, but it is
missing BAFM or VOC, SMOKE will now raise an error.
10 These ratios and profiles are typically generated from the Speciation Tool when it is run with integration of a specified list
of pollutants, for example BAFM.
36
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NHAPEXCLUDE file. If on the other hand, certain sources do not have the necessary HAPs, then an
NHAPEXCLUDE file must be provided based on the evaluation of each source's pollutant mix. EPA
considered CAP-HAP integration for all sectors and developed "integration criteria" for some of them.
The process of partial integration for BAFM is illustrated in Figure 3-3 and means that the BAFM records
in the input inventories do not need to be removed from any sources in a partially integrated sector
because SMOKE does this automatically using the INVTABLE configuration. For EBAFM integration,
this process is identical to that shown in the figure except for the addition of ethanol (E) to the list of
subtracted HAP pollutants. For full integration, the process would be very similar except that the
NHAPEXCLUDE file would not be used and all sources in the sector would be integrated.
Emissions ready for SMOKE :
SMOKE
Compute NONHAPVOC- VOC- (B +¦ F * A+M)
emissionsfor each integrate source
Retain VOC emissions for each no-integratesc j'ce
Compute moles of each CBQ5 model species.
Use NONHAPTOG profiles applied to NONHAPTOG
emissions and B, F, A, Mi emissions for integrate sources.
Use TOG prof'es applied to TOG for no-integrate sources
Assign speciation profile code to each emission source
________
each integrate sou rce
Compute: TOG emissions from VOC for each no-integrate
source
: list of "no-i
r sources (NHAPEXCLUDE}
Specie ti on Lross J
«,e*T5rence ci I e tGSREF) l
VOC-to-TOG teeners
NON HAPVOC-to-N C N M 4FTOG
factors, (G5CNV)
TOG and NONHAPTOG :
specistion factors ;
iGSPRO) •
Speciated r missions for VOC species
Figure 3-3. Process of integrating BAFM with VOC for use in VOC Speciation
In SMOKE, the INVTABLE allows the user to specify both the particular HAPs to integrate. Two
different types of INVTABLE files are included for use with different sectors of the platform. For sectors
that had no integration across the entire sector (see Table 3-8), EPA created a "no HAP use" INVTABLE
in which the "KEEP" flag is set to "N" for BAFM pollutants. Thus, any BAFM pollutants in the
inventory input into SMOKE are automatically dropped. This approach both avoids double-counting of
these species and assumes that the VOC speciation is the best available approach for these species for
sectors using this approach. The second INVTABLE, used for sectors in which one or more sources are
integrated, causes SMOKE to keep the inventory BAFM pollutants and indicates that they are to be
integrated with VOC. This is done by setting the "VOC or TOG component" field to "V" for all four HAP
pollutants. This type of INVTABLE is further differentiated into a version for those sectors that integrate
BAFM and another for those that integrate EBAFM.
37
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Table 3-8. Integration status of benzene, acetaldehyde, formaldehyde and methanol (BAFM) for
each platform sector
Platform
Sector
Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A),
Formaldehyde (F), Methanol (M), and Ethanol (E)
ptegu
No integration
ptnonipm
No integration
ptfire
No integration
othar
No integration
othon
No integration
ag
N/A - sector contains no VOC
agfire
Partial integration (BAFM)
afdust
N/A - sector contains no VOC
biog
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
clc2rail
Partial integration (BAFM)
c3marine
Partial integration (BAFM)
nonpt
Partial integration (BAFM)
nonroad
Partial integration (BAFM)
np_oilgas
Partial integration (BAFM)
othpt
Partial integration (BAFM)
pt_oilgas
Partial integration (BAFM)
rwc
Partial integration (BAFM)
onroad
Full integration (benzene, 1,3 butadiene, formaldehyde, acetaldehyde, naphthalene,
acrolein, ethyl benzene, 2,2,4-Trimethylpentane, hexane, propionaldehyde,
styrene, toluene, xylene, MTBE)
SMOKE can compute speciation profiles from mixtures of other profiles in user-specified proportions.
The combinations are specified in the GSPROCOMBO ancillary file by pollutant (including pollutant
mode, e.g., EXH VOC), state and county (i.e., state/county FIPS code) and time period (i.e., month).
This feature was used to speciate nonroad mobile and gasoline-related related stationary sources that use
fuels with varying ethanol content. In these cases, the speciation profiles require different combinations of
gasoline profiles, e.g. 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.
SMOKE computes the resultant profile using the fraction of each specific profile assigned by county,
month and emission mode.
The GSREF file indicates that a specific source uses a combination file with the profile code "COMBO".
Because the GSPRO COMBO file does not differentiate by SCC and there are various levels of
integration across sectors, sector specific GSPRO COMBO files are used. Different profile combinations
are specified by the mode (e.g. exhaust, evaporative, refueling, etc.) by changing the pollutant name (e.g.
EXH NONHAPTOG, EVP NONHAPTOG, RFL NONHAPTOG). For the nonpt sector, BAFM
integration is used.
Speciation profiles for use with BEIS are not included in SPECIATE. BEIS3.61 includes a species (SESQ)
that was mapped to the CMAQ specie SESQT. The profile code associated with BEIS profiles for use with
CB05 was "B10C5." For additional sector-specific details on VOC speciation for a variety of sectors, see
Section 3.2.1.3 of the 2011v6.2 TSD (EPA, 2015a).
38
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In addition to VOC profiles, the SPECIATE database also contains the PM2.5 speciated into both
individual chemical compounds (e.g., zinc, potassium, manganese, lead), and into the "simplified" PM2.5
components used in the air quality model. For CMAQ 4.7.1 modeling, these "simplified" components
(AE5) are all that is needed. Starting with CMAQ 5.0.1, a new thermodynamic equilibrium aerosol
modeling tool (ISORROPIA) v2 mechanism was added that needs additional PM components (AE6),
which are further subsets of PMFINE (see Table 3-9). The majority of the 2011 platform PM profiles
come from the 911XX series which include updated AE6 speciation11.
Table 3-9. PM model species: AE5 versus AE6
species name
species description
AE5
AE6
POC
organic carbon
Y
Y
PEC
elemental carbon
Y
Y
PS04
Sulfate
Y
Y
PN03
Nitrate
Y
Y
PMFINE
unspeciated PM2.5
Y
N
PNH4
Ammonium
N
Y
PNCOM
non-carbon organic matter
N
Y
PFE
Iron
N
Y
PAL
Aluminum
N
Y
PSI
Silica
N
Y
PTI
Titanium
N
Y
PCA
Calcium
N
Y
PMG
Magnesium
N
Y
PK
Potassium
N
Y
PMN
Manganese
N
Y
PNA
Sodium
N
Y
PCL
Chloride
N
Y
PH20
Water
N
Y
PMOTHR
PM2.5 not in other AE6 species
N
Y
Unlike other sectors, the onroad sector has pre-speciated PM. This speciated PM comes from the
MOVES model and is processed through the SMOKE-MOVES system. Unfortunately, the MOVES
speciated PM does not map one-to-one to the AE5 speciation (nor the AE6 speciation) needed for CMAQ
modeling. For additional details on PM speciation, see Section 3.2.2 of the 201 lv6.2 platform TSD
(EPA, 2015a).
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, clc2rail, c3marine, othon sectors) and for specific SCCs in othar and ptnonipm, the profile
"HONO" splits NOx into NO, NO2, and HONO. Table 3-10 gives the split factor for these two profiles.
The onroad sector does not use the "HONO" profile to speciate NOx. MOVES2014 produces speciated
11 The exceptions are 5674 (Marine Vessel - Marine Engine - Heavy Fuel Oil) used for c3marine and 92018 (Draft Cigarette
Smoke - Simplified) used in nonpt.
39
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NO, NO2, and HONO by source, including emission factors for these species in the emission factor tables
used by SMOKE-MOVES. Within MOVES, the HONO fraction is a constant 0.008 of NOx. The NO
fraction varies by heavy duty versus light duty, fuel type, and model year and equals 1 - NO - HONO.
For more details on the NOx fractions within MOVES, see
http://www.epa.gov/otaq/models/moves/documents/420rl2022.pdf.
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 hour. This
process is typically done by applying temporal profiles to the inventories in this order: monthly, day of
the week, and diurnal.
In SMOKE 3.6.5 and in the 201 lv6.2 platform, more readable and flexible file formats are used for
temporal profiles and cross references. The profiles and cross references were initially created by
converting the 201 lv6.1 platform temporal profiles into the new formats, and then any specific
adjustments for the 201 lv6.2 platform were made. The temporal factors applied to the inventory are
selected using some combination of country, state, county, SCC, and pollutant. Table 3-11 summarizes
the temporal aspects of emissions modeling by comparing the key approaches used for temporal
processing across the sectors. In the table, "Daily temporal approach" refers to the temporal approach for
getting daily emissions from the inventory using the SMOKE Temporal program. The values given are
the values of the SMOKE L TYPE setting. The "Merge processing approach" refers to the days used to
represent other days in the month for the merge step. If this is not "all", then the SMOKE merge step runs
only for representative days, which could include holidays as indicated by the right-most column. The
values given are those used for the SMOKE M TYPE setting (see below for more information).
40
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Table 3-11. Temporal Settings Used for the Platform Sectors in SMOKE
Platform sector
short name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process Holidays
as separate days
afdust adj
Annual
Yes
week
all
Yes
ag
Annual
Yes
met-based
all
Yes
agfire
Monthly
week
week
Yes
beis
Hourly
n/a
all
Yes
clc2rail
Annual
Yes
mwdss
mwdss
c3marine
Annual
Yes
aveday
aveday
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
mwdss
Mwdss
Yes
np oilgas
Annual
yes
week
week
Yes
onroad
Annual & monthly1
all
all
Yes
onroad ca adj
Annual & monthly1
all
all
Yes
othafdust adj
Annual
yes
week
week
othar
Annual
yes
week
week
othon
Annual
yes
week
week
othpt
Annual
yes
mwdss
mwdss
pt oilgas
Annual
yes
mwdss
mwdss
Yes
ptegu
Daily & hourly
all
all
Yes
ptnonipm
Annual
yes
mwdss
mwdss
Yes
ptprescfire
Daily
all
all
Yes
ptwildfire
Daily
all
all
Yes
rwc
Annual
no
met-based
all
Yes
1. Note the annual and monthly "inventory" actually refers to the activity data (VMT and VPOP) for onroad. The
actual emissions are computed on an hourly basis.
The following values are used in the table: The value "all" means that hourly emissions are computed for
every day of the year and that emissions potentially have day-of-year variation. The value "week" means
that hourly emissions computed for all days in one "representative" week, representing all weeks for each
month. This means emissions have day-of-week variation, but not week-to-week variation within the
month. The value "mwdss" means hourly emissions for one representative Monday, representative
weekday (Tuesday through Friday), representative Saturday, and representative Sunday for each month.
This means emissions have variation between Mondays, other weekdays, Saturdays and Sundays within
the month, but not week-to-week variation within the month. The value "aveday" means hourly
emissions computed for one representative day of each month, meaning emissions for all days within a
month are the same. Special situations with respect to temporalization are described in the following
subsections.
In addition to the resolution, temporal processing includes a ramp-up period for several days prior to
January 1, 2012, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2011). For most sectors, emissions from December 2012 were
used to fill in surrogate emissions for the end of December 2011. In particular, December 2012 emissions
41
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(representative days) were used for December 2011. For biogenic emissions, December 2011 emissions
were processed using 2011 meteorology.
The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated format for
monthly, daily, and hourly emissions inventories than prior formats supported. Previously, processing
monthly inventory data required the use of 12 separate inventory files. With the FF10 format, a single
inventory file can contain emissions for all 12 months and the annual emissions in a single record. This
helps simplify the management of numerous inventories. Similarly, daily and hourly FF10 inventories
contain individual records with data for all days in a month and all hours in a day, respectively.
SMOKE prevents the application of temporal profiles on top of the "native" resolution of the inventory.
For example, a monthly inventory should not have annual-to-month temporalization applied to it; rather,
it should only have month-to-day and diurnal temporalization. This becomes particularly important when
specific sectors have a mix of annual, monthly, daily, and/or hourly inventories. The flags that control
temporalization for a mixed set of inventories are discussed in the SMOKE documentation. The
modeling platform sectors that make use of monthly values in the FF10 files are agfire, nonroad, onroad
(for activity data), and ptegu.
3.3.5.1 Standard Temporal Profiles
Some sectors use straightforward temporal profiles not based on meteorology or other factors. For the
agfire sector, the emissions were allocated to months by adding up the available values for each day of the
month. For all agricultural burning, the diurnal temporal profile used reflected the fact that burning
occurs during the daylight. This puts most of the emissions during the work day and suppresses the
emissions during the middle of the night. A uniform profile for each day of the week was used for all
agricultural burning emissions in all states, except for the following states that for which EPA used state-
specific day of week profiles: Arkansas, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma,
and Texas.
For the clc2rail and c3marine sectors, emissions are allocated with flat monthly and day of week profiles,
and most emissions are also allocated with flat hourly profiles. For the ptwildfire and ptprescfire sectors,
the inventories are in the daily point fire format ORL PTDAY, so temporal profiles are only used to go
from day-specific to hourly emissions.
For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly
inventories from output from NMIM. For California, a monthly inventory was created from CARB's
annual inventory using EPA-estimated NMIM monthly results to compute monthly ratios by pollutant and
SCC7 and these ratios were applied to the CARB inventory to create a monthly inventory.
Updates were made to temporal profiles for the ptnonipm sector in the 201 lv6.2 platform based on
comments and data review by EPA staff. Temporal profiles for small airports (i.e., non-commercial) were
updated to eliminate emissions between 10pm and 6am due to a lack of tower operations. Industrial
process that are not likely to shut down on Sundays such as those at cement plants were assigned to other
more realistic profiles that included emissions on Sundays. This also affected emissions on holidays
because Sunday emissions are also used on holidays. Some cross reference updates for temporalization of
the npoilgas sector were made in the 201 lv6.2 platform based on comments received - npoilgas sources
were assigned to profiles that were 24 hours per day, 7 days a week.
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3.3.5.2 Temporal Profiles for EGUs
The 201 1NEIv2 annual EGU emissions are allocated to hourly emissions using the following 3-step
methodology: annual value to month, month to day, and day to hour. Several updates were made to EGU
temporalization in the 201 lv6.2 platform. First, the CEMS data were processed using a tool that
reviewed the data quality flags that indicate the data were not measured. Unmeasured data can cause
erroneously high values to appear in the CEMS data. If the data were not measured at specific hours, and
those values were found to be more the 3 times the annual mean for that unit, the data for those hours
were replaced with annual mean values (Adelman, et al., 2012). These adjusted CEMS data were then
used for the remainder of the temporalization process described below (see Figure 3-4 for an example).
Another update in the 201 lv6.2 platform was the incorporation of winter and summer seasons into the
development of the diurnal profiles as opposed to using data for the entire year. Analysis of the hourly
CEMS data revealed that there were different diurnal patterns in winter versus summer in many areas.
Typically a single mid-day peak is visible in the summer, while there are morning and evening peaks in
the winter as shown in Figure 3-5. Finally, we identified specific CEMS sources as partial year reporters
(e.g., those that only run in the summer) and allocated the difference between the CEMS and the annual
emissions to months that did not have any CEMS data available.
50000
2011 CEM of 6019 1 Month 11
Raw CEM
v2.1 Corrected
40000
30000
20000
10000
0
07
14
21
28
Nov
2011
Figure 3-4. Eliminating unmeasured spikes in CEMS data
43
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Diurnal CEMS Profile for PJM_Dom Gas
o.io
Annual Average
Summer Average
Winter Average
0.08
0.06
o 0.04
0.02
0.00
Hour
Figure 3-5. Seasonal diurnal profiles for EGU emissions in a Virginia Region
The temporal allocation procedure is differentiated by whether or not the source could be directly
matched to a CEMS unit via ORIS facility code and boiler ID. Prior to temporal allocation, as many
sources as possible were matched to CEMS data via ORIS facility code and boiler ID. Units were
considered matches if the FIPS state/county code matched, the facility name was similar, and the NOx
and SO2 emissions were similar. EIS stores a base set of previously matched units via alternate facility
and unit IDs. Additions to these matches were made for this study. For any units that are matched, the
ORIS facility and boiler ID columns of the point FF10 inventory files are filled with the information on
the rows for the corresponding NEI unit. Note that for units matched to CEMS data, annual totals of their
emissions may be different than the annual values in the inventory because the CEMS data actually
replaces the inventory data for the seasons in which the CEMS are operating. If a CEMS-matched unit is
determined to be a partial year reporter, as can happen for sources that run CEMS only in the summer,
emissions totaling the difference between the annual emissions and the total CEMS emissions are
allocated to the non-summer months.
For sources not matched to CEMS units, the allocation of annual emissions to months and then days are
done outside of SMOKE and then daily emissions are output to day-specific inventory files. For these
units, the allocation of the inventory annual emissions to months is done using average fuel-specific
season-to-month factors generated for each of the 64 IPM regions shown in Figure 3-6. These factors are
based 2011 CEMS data only. In each region, separate factors were developed for the fuels: coal, natural
gas, and "other", where the types of fuels included in "other" vary by region. Separate profiles were
computed for NOx, SO2, and heat input. An overall composite profile was also computed and was used
when there were no CEMS units with the specified fuel in the region containing the unit. For both CEMS-
matched units and units not matched to CEMS, NOx and SO2 CEMS data are used to allocate NOx and
44
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S02 emissions to daily emissions, respectively, while heat input data are used to allocate emissions of all
other pollutants.
Daily temporal allocation of units matched to CEMS was performed using a procedure similar to the
approach to allocate emissions to months in that the CEMS data replaces the inventory data for each
pollutant. For units without CEMS data, emissions were allocated from month to day using IPM-region
and fuel-specific average month-to-day factors based on the 2011 CEMS data. Separate month-to-day
allocation factors were computed for each month of the year using heat input for the fuels coal, natural
gas, and "other" in each region. For both CEMS and non-CEMS matched units, NOx and SO2 CEMS data
are used to allocate NOx and SO2 emissions, while CEMS heat input data are used to allocate all other
pollutants. An example of month-to-day profiles for gas, coal, and an overall composite for a region in
western Texas is shown in Figure 3-7.
For units matched to CEMS data, hourly emissions use the hourly CEMS values for NOx and S02, while
other pollutants are allocated according to heat input values. For units not matched to CEMS data,
temporal profiles from days to hours are computed based 011 the season-, region- and fuel-specific average
day-to-hour factors derived from the CEMS data for those fuels and regions using the appropriate subset
of data. For the unmatched units, CEMS heat input data are used to allocate all pollutants (including NOx
and SO2) because the heat input data was generally found to be more complete than the pollutant-specific
data. SMOKE then allocates the daily emissions data to hours using the temporal profiles obtained from
the CEMS data for the analysis base year (i.e., 2011 in this case).
MAP
WAUE
WECCJD
PJM_
COMO
WECC_NNV
SPPNEBR
S_VACA
WEC_SDGE
ERC_WEST
FRCC
S_0_VtfOTA
Figure 3-6. IPM Regions for EPA Base Case v5.13
45
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Daily temporal fraction: ERC_WEST_NOX_7
o.io
0.08
E 0.06
E 0.04
0.02
0.00
day
Figure 3-7. Month-to-day profiles for different fuels in a West Texas Region
For the ptfire sector, the inventories are in the daily point fire format ORL PTDAY. The ptfire sector is
used in model evaluation cases. The 2007 and earlier platforms had additional regulatory cases that used
averaged fires and temporally averaged EGU emissions, but the 2011 platform uses base year-specific
(i.e., 2011) data for all cases.
For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses monthly
inventories from output from NMIM. For California, a monthly inventory was created from CARB's
annual inventory using EPA-estimated NMIM monthly results to compute monthly ratios by pollutant and
SCC7 and these ratios were applied to the CARB inventory to create a monthly inventory.
3.3.5.3 Meteorological-based Temporal Profiles
There are many factors that impact the timing of when emissions occur, and for some sectors this includes
meteorology. The benefits of utilizing meteorology as method for temporalization are: (1) a
meteorological dataset consistent with that used by the AQ model is available (e.g., outputs from WRF);
(2) the meteorological model data are highly resolved in terms of spatial resolution; and (3) the
meteorological variables vary at hourly resolution and can therefore be translated into hour-specific
temporalization.
The SMOKE program GenTPRO provides a method for developing meteorology-based temporalization.
Currently, the program can utilize three types of temporal algorithms: annual-to-day temporalization for
residential wood combustion (RWC), month-to-hour temporalization for agricultural livestock ammonia,
and a generic meteorology-based algorithm for other situations. For the 2011 platform, meteorological-
based temporalization was used for portions of the rwc sector and for livestock within the ag sector.
GenTPRO reads in gridded meteorological data (output from MCIP) along with spatial surrogates, and
46
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uses the specified algorithm to produce a new temporal profile that can be input into SMOKE. The
meteorological variables and the resolution of the generated temporal profile (hourly, daily, etc.) depend
on the selected algorithm and the run parameters. For more details on the development of these
algorithms and running GenTPRO, see the GenTPRO documentation and the SMOKE documentation at
http://www.cmascenter.Org/smoke/documentation/3.l/GenTPRQ Technical Summary Aug2012 Final, pd
f and http://www.cmascenter.Org/smoke/documentation/3.5.l/html/ch05s03s07.html respectively.
In the 201 lv6.2 platform and in SMOKE 3.6.5, the temporal profile format has been updated. GenTPRO
now produces separate files including the monthly temporal profiles (ATPRO MONTHLY) and day-of-
month temporal profiles (ATPRODAILY), instead of a single ATPRODAILY with day-of-year
temporal profiles as it did in SMOKE 3.5. The results are the same either way, so the temporal profiles
themselves are effectively the same in 201 lv6.2 as they were in 201 lv6.0 since the meteorology is the
same, but they are formatted differently.
For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the temporal
allocation of emissions to days. GenTPRO was used to create an annual-to-day temporal profile for the
RWC sources. These generated profiles distribute annual RWC emissions to the coldest days of the year.
On days where the minimum temperature does not drop below a user-defined threshold, RWC emissions
for most sources in the sector are zero. Conversely, the program temporally allocates the largest
percentage of emissions to the coldest days. Similar to other temporal allocation profiles, the total annual
emissions do not change, only the distribution of the emissions within the year is affected. The
temperature threshold for rwc emissions was 50 °F for most of the country, and 60 °F for the following
states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and
Texas.
Figure 3-8 illustrates the impact of changing the temperature threshold for a warm climate county. The
plot shows the temporal fraction by day for Duval County, Florida for the first four months of 2007. The
default 50 °F threshold creates large spikes on a few days, while the 60 °F threshold dampens these spikes
and distributes a small amount of emissions to the days that have a minimum temperature between 50 and
60 °F.
RWC temporal profile, Duval County, FL, Jan - Apr
60F, alternate formula
50F, default formula
Figure 3-8. Example of RWC temporalization in 2007 using a 50 versus 60 °F threshold
The diurnal profile for used for most RWC sources places more of the RWC emissions in the morning
and the evening when people are typically using these sources. This profile is based on a 2004 MANE-
VU survey based temporal profiles (see
47
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http://www.marama.org/publications folder/ResWoodCombustion/Final report.pdf). This profile was
created by averaging three indoor and three RWC outdoor temporal profiles from counties in Delaware
and aggregating them into a single RWC diurnal profile. This new profile was compared to a
concentration based analysis of aethalometer measurements in Rochester, NY (Wang et al. 2011) for
various seasons and day of the week and found that the new RWC profile generally tracked the
concentration based temporal patterns.
The temporalization for "Outdoor Hydronic Heaters" (i.e.,"OHH", SCC=2104008610) and "Outdoor
wood burning device, NEC (fire-pits, chimneas, etc.)" (i.e., "recreational RWC", SCC=21040087000)
were updated because the meteorological-based temporalization used for the rest of the rwc sector did not
agree with observations for how these appliances are used. For OHH, the annual-to-month, day-of-week
and diurnal profiles were modified based on information in the New York State Energy Research and
Development Authority (NYSERDA) "Environmental, Energy Market, and Health Characterization of
Wood-Fired Hydronic Heater Technologies, Final Report" (NYSERDA, 2012) as well as a Northeast
States for Coordinated Air Use Management (NESCAUM) report "Assessment of Outdoor Wood-fired
Boilers" (NESCAUM, 2006). A Minnesota 2008 Residential Fuelwood Assessment Survey of individual
household responses (MDNR, 2008) provided additional annual-to-month, day-of-week and diurnal
activity information for OHH as well as recreational RWC usage.
The diurnal profile for OHH, shown in Figure 3-9 is based on a conventional single-stage heat load unit
burning red oak in Syracuse, New York. The NESCAUM report describes how for individual units, OHH
are highly variable day-to-day but that in the aggregate, these emissions have no day-of-week variation.
In contrast, the day-of-week profile for recreational RWC follows a typical "recreational" profile with
emissions peaked on weekends. Annual-to-month temporalization for OHH as well as recreational RWC
were computed from the MN DNR survey (MDNR, 2008) and are illustrated in Figure 3-10. OHH
emissions still exhibit strong seasonal variability, but do not drop to zero because many units operate year
round for water and pool heating. In contrast to all other RWC appliances, recreational RWC emissions
are used far more frequently during the warm season.
Heat Load (BTU/hr)
50,000
40,000
30,000
20,000
10,000
-------
Monthly Temporal Activity for OHH & Recreational RWC
100
V)
I 90
1 80
70
60
50
40
30
20
10
0
Figure 3-10. Annual-to-month temporal profiles for OHH and recreational RWC
For the agricultural livestock NH3 algorithm, the GenTPRO algorithm is based on an equation derived by
Jesse Bash of EPA ORD based on the Zhu, Henze, et al. (2013) empirical equation. This equation is based
on observations from the TES satellite instrument with the GEOS-Chem model and its adjoint to estimate
diurnal NH3 emission variations from livestock as a function of ambient temperature, aerodynamic
resistance, and wind speed. The equations are:
Ea = [161500/T,/; x e("1380/V] x AR,/;
PE;,/; = Ea, / Sum(E, /,)
where
• PE;,/; = Percentage of emissions in county i on hour h
• Eij, = Emission rate in county i on hour h
• Tij, = Ambient temperature (Kelvin) in county i on hour h
• Vi,/; = 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 "BASHNH3" profile method to create month-to-hour temporal profiles for
these sources. Because these profiles distribute to the hour based on monthly emissions, the monthly
emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized
to the month. Figure 3-11 compares the daily emissions for Minnesota from the "old" approach (uniform
monthly profile) with the "new" approach (GenTPRO generated month-to-hour profiles). Although the
GenTPRO profiles show daily (and hourly variability), the monthly total emissions are the same between
the two approaches.
Fire Pit/Chimenea
Outdoor Hydronic Heater
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
49
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MN ag NH3 livestock temporal profiles
-old
-new
0.0
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 3-11. Example of new animal NH3 emissions temporalization approach, summed to daily
emissions
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to
reduce the total emissions based on meteorological conditions. These adjustments are applied through
sector-specific scripts, beginning with the application of land use-based gridded transport fractions and
then subsequent zero-outs for hours during which precipitation occurs or there is snow cover on the
ground. The land use data used to reduce the NEI emissions explains the amount of emissions that are
subject to transport. This methodology is discussed in Pouliot, et al., 2010, and in "Fugitive Dust
Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). The precipitation adjustment is
applied to remove all emissions for days where measureable rain occurs. Therefore, the afdust emissions
vary day-to-day based on the precipitation and/or snow cover for that grid cell and day. Both the
transport fraction and meteorological adjustments are based on the gridded resolution of the platform;
therefore, somewhat different emissions will result from different grid resolutions. Application of the
transport fraction and meteorological adjustments prevents the overestimation of fugitive dust impacts in
the grid modeling as compared to ambient samples.
Biogenic emissions in the beis sector vary by every day of the year because they are developed using
meteorological data including temperature, surface pressure, and radiation/cloud data. The emissions are
computed using appropriate emission factors according to the vegetation in each model grid cell, while
taking the meteorological data into account.
3.3.5.4 Temporal Profiles for Onroad Mobile Sources
For the onroad sector, the temporal distribution of emissions is a combination of more traditional
temporal profiles and the influence of meteorology. This section discusses both the meteorological
influences and the updates to the diurnal temporal profiles for the 201 lv6.2 platform.
Meteorology is not used in the development of the temporal profiles, but rather it impacts the calculation
of the hourly emissions through the program Movesmrg. The result is that the emissions vary at the
hourly level by grid cell. More specifically, the on-network (RPD) and the off-network parked vehicle
(RPV, RPH, and RPP) processes use the gridded meteorology (MCIP) directly. Movesmrg determines
the temperature for each hour and grid cell and uses that information to select the appropriate emission
factor (EF) for the specified SCC/pollutant/mode combination. In the 2011 platform (and the
201 1NEIv2), RPP was updated to use the gridded minimum and maximum temperature for the day. This
more spatially resolved temperature range produces more accurate emissions for each grid cell. The
combination of these four processes (RPD, RPV, RPH, and RPP) is the total onroad sector emissions.
The onroad sector show a strong meteorological influence on their temporal patterns (see the 201 1NEIv2
TSD for more details).
50
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Figure 3-12 illustrates the difference between temporalization of the onroad sector used in the 2005 and
earlier platforms and the meteorological influence via SMOKE-MOVES. In the plot, the "MOVES"
inventory is a monthly inventory that is temporalized by SCC to day-of-week and hour. Similar
temporalization is done for the VMT in SMOKE-MOVES, but the meteorologically varying EFs add an
additional variation on top of the temporalization. Note, the SMOKE-MOVES run is based on the 2005
platform and previous temporalization of VMT to facilitate the comparison of the results. In the figure,
the MOVES emissions have a repeating pattern within the month, while the SMOKE-MOVES shows
day-to-day (and hour-to-hour) variability. In addition, the MOVES emissions have an artificial jump
between months which is due to the inventory providing new emissions for each month that are then
temporalized within the month but not between months. The SMOKE-MOVES emissions have a
smoother transition between the months.
BHM (Jefferson Co., AL) daily NOX
MOVES
SMOKE-MOVES
«Hu">o>mis*r-«mcnmrs*-!Hir>o>ror>.rHincnmr-.r-iLno>ror^Hin
o«HiN^tmr^ooo>iHfN*tinvocoo^rH(NminiDco®OfNfoin^o
oooooooOiHiHHrHrHiHiHrMrMfNfNfNjfNrMmmmmm
ininininintnininininininininininmininininintninifiinin
ooooooooooooooooooooooooooo
ooooooooooooooooooooooooooo
rsl(N(NfM(NfNrMrsl(NrMfNrMfMrM(NrM(NfNrM(NfNfNfNrMfSfNfN
Julian date
Figure 3-12. Example of SMOKE-MOVES temporal variability of NOx emissions
For the onroad sector, the "inventories" referred to in Table 3-11 actually consist of activity data, not
emissions. For RPP and RPV processes, the VPOP inventory is annual and does not need temporalization.
For RPD, the VMT inventory is monthly and was temporalized to days of the week and then to hourly
VMT through temporal profiles. The RPD processes require a speed profile (SPDPRO) that consists of
vehicle speed by hour for a typical weekday and weekend day. Unlike other sectors, the temporal profiles
and SPDPRO will impact not only the distribution of emissions through time but also the total emissions.
Because SMOKE-MOVES (for RPD) calculates emissions from VMT, speed and meteorology, if one
shifted the VMT or speed to different hours, it would align with different temperatures and hence
different EF. In other words, two SMOKE-MOVES runs with identical annual VMT, meteorology, and
MOVES EF, will have different total emissions if the temporalization of VMT changes. For RPH, the
HOTELING inventory is monthly and was temporalized to days of the week and to hour of the day
through temporal profiles. This is an analogous process to RPD except that speed is not included in the
calculation of RPH.
51
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In previous platforms, the diurnal profile for VMT varied by road type but not by vehicle type and these
profiles were used throughout the nation. Diurnal profiles that could differentiate by vehicle type as well
as by road type and would potentially vary over geography were desired. In the development of the
201 lv6.0 platform, the EPA updated these profiles to include information submitted by states in their
MOVES county databases (CDBs). The development of the 201 1NEIv2 and the 201 lv6.2 platform
provided an opportunity to update these diurnal profile with new information submitted by states, to
supplement the data with additional sources, and to refine the methodology.
States submitted MOVES county databases (CDBs) that included information on the distribution of VMT
by hour of day and by day of week (see the 201 1NEIv2 TSD for details on the submittal process for
onroad). EPA mined the state submitted MOVES CDBs for non-default diurnal profiles. Further QA was
done to remove duplicates and profiles that were missing two or more hours. If they were missing a
single hour, the missing hour could be calculated by subtracting all other hours' fractions from 1. The list
of potential diurnal profiles was then analyzed to see whether the profiles varied by vehicle type, road
type, weekday vs. weekend, and by county within a state.
For the MOVES diurnal profiles, EPA only considered the state profiles that varied significantly by both
vehicle and road types. Only those profiles that passed these criteria were used in that state or used in
developing default temporal profiles. The Vehicle Travel Information System (VTRIS) is a repository for
reported traffic count data to the Federal Highway Administration (FHWA). EPA used 2012 VTRIS data
to create additional temporal profiles for states that did not submit temporal information in their CDBs or
where those profiles did not pass the variance criteria. The VTRIS data were used to create state specific
diurnal profiles by HPMS vehicle and road type. EPA created distinct diurnal profiles for weekdays,
Saturday and Sunday along with day of the week profiles. Note that the day of the week profiles (ie.
Monday vs Tuesday vs etc) are only from the VTRIS data. The MOVES CDBs only have weekday vs
weekend profiles so they were not included in calculating a new national default day of the week profile
EPA attempted to maximize the use of state and/or county specific diurnal profiles (either from MOVES
or VTRIS). Where there was no MOVES or VTRIS data, then a new default profile would be used (see
below for description of new profiles). This analysis was done separately for weekdays and for
weekends, therefore some areas had submitted profiles for weekdays but defaults for weekends. The
result was a set of profiles that varied geographically depending on the source of the profile and the
characteristics of the profiles (see Figure 3-13).
52
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Temporal Sources for 2011v2 Mobile Emissions
VTRIS state
MOVES VMT
CARB
VTRIS/MOVES national
average
Figure 3-13. Use of submitted versus new national default profiles
A new set of diurnal profiles was developed from the submitted profiles that varied by both vehicle type
and road type. For the purposes of constructing the national default diurnal profiles, EPA created
individual profiles for each state (averaging over the counties within) to create a single profile by state,
vehicle type, road type, and the day (i.e. weekday vs Saturday vs Sunday). The source of the underlying
profiles was either MOVES or VTRIS data. The states individual profiles were averaged together to
create a new default profile. Figure 3-14 shows two new national default profiles for light duty gas
vehicles (LDGV, SCC6 220121) and combination long-haul diesel trucks (HHDDV, SCC6 220262) on
restricted urban roadways (interstates and freeways). The blue lines indicate the weekday profile, the
green the Saturday profile, and the red the Sunday profile. In comparison, the new default profiles for
weekdays places more LDGV VMT (upper plot) in the rush hours while placing HHDDV VMT (lower
plot) predominately in the middle of the day with a longer tail into the evening hours and early morning.
In addition to creating diurnal profiles,
EPA also developed day of week profiles using the VTRIS data. The creation of the state and national
profiles was similar to that of the diurnal profiles (described above). Figure 3-15 shows a set of national
default profiles for rural restricted roads (top plot) and urban unrestricted roads (lower plot). Each vehicle
type is a different color on the plots.
Some counties may use national defaults for certain days
53
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Hourly Day fraction: national_0_21_4_all
0.08
0.07
0.06
0.05
C
O
nab onal_0_2 l_4_weekday
¦M
£ 0.04
4—
—. retional_0_21_4_saturday
nabonal_0_2 l_4_sunday
0.03
0.02
0.01
0.00
hour
Hourly Day fraction: national_0_62_4_all
0.07
0.06
0.05
c
o
_ rebonal_0_62_4_weekday
0.04
ro
Q
national_0_62_4_sunday
0.03
0.02
0.01
hour
Figure 3-14. Updated national default profiles for LDGV vs. HHDDV, urban restricted
54
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Daily Week fraction: national_0_all_2_0
0.18
0.16
0.14
0.12
B 0.10
sr o.o8
0.06
0.04
0.02
mon
wed
day
thu
tue
sat
Daily Week fraction: national_0_all_5_0
0.20
0.15
2 0.10
>.
0.05
mon
wed
day
thu
tue
sat
Figure 3-15. Updated national default profiles for day of week (rural restricted top and
urban restricted bottom)
In addition to creating diurnal profiles for VMT, EPA developed a national profile for hoteling. EPA
averaged all the combination long-haul truck profiles on restricted roads (urban and rural) for weekdays to
create a single national restricted profile (blue line in Figure 3-16). This was then inverted to create a
55
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profile for hoteling (green line in Figure 3-16). This single national profile was used for hoteling
irrespective of location.
Hourly Day fraction: national_0_all_0_0
0.06
0.05
0.04
0.03
0.02
0.01
0.00
1
hour
Figure 3-16. Combination long-haul truck restricted and hoteling profile
For California, CARB supplied diurnal profiles that varied by vehicle type, day of the week (Monday,
Tuesday-Thursday, Saturday, and Sunday), and air basin. These CARB specific profiles were used in
developing EPA estimates for California. Although EPA adjusted the total emissions to match
California's submittal to the 201 1NEIv2, the temporalization of these emissions took into account both
the state-specific VMT profiles and the SMOKE-MOVES process of incorporating meteorology. For
more details on the adjustments to California's onroad emissions, see the 201 1NEIv2 TSD.
3.3.6 Vertical Allocation of Emissions
Table 3-5 specifies the sectors for which plume rise is calculated. If there is no plume rise for a sector, the
emissions are placed into layer 1 of the air quality model. Vertical plume rise was performed in-line within
CMAQ for all of the SMOKE point-source sectors (i.e., ptipm, ptnonipm, ptfire, othpt, and c3marine). The
in-line plume rise computed within CMAQ is nearly identical to the plume rise that would be calculated
within SMOKE using the Laypoint program. The selection of point sources for plume rise is pre-
determined in SMOKE using the Elevpoint. The calculation is done in conjunction with the CMAQ model
time steps with interpolated meteorological data and is therefore more temporally resolved than when it is
done in SMOKE. Also, the calculation of the location of the point source is slightly different than the one
used in SMOKE and this can result in slightly different placement of point sources near grid cell
boundaries.
For point sources, the stack parameters are used as inputs to the Briggs algorithm, but point fires do not
have stack parameters. However, the ptfire inventory does contain data on the acres burned (acres per day)
56
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and fuel consumption (tons fuel per acre) for each day. CMAQ uses these additional parameters to
estimate the plume rise of emissions into layers above the surface model layer. Specifically, these data are
used to calculate heat flux, which is then used to estimate plume rise. In addition to the acres burned and
fuel consumption, heat content of the fuel is needed to compute heat flux. The heat content was assumed to
be 8000 Btu/lb of fuel for all fires because specific data on the fuels were unavailable in the inventory. The
plume rise algorithm applied to the fires is a modification of the Briggs algorithm with a stack height of
zero.
CMAQ uses the Briggs algorithm to determine the plume top and bottom, and then computes the plumes"
distributions into the vertical layers that the plumes intersect. The pressure difference across each layer
divided by the pressure difference across the entire plume is used as a weighting factor to assign the
emissions to layers. This approach gives plume fractions by layer and source.
3.3.7 Emissions Modeling Spatial A llocation
The methods used to perform spatial allocation are summarized in this section. For the modeling
platform, spatial factors are typically applied by county and SCC. Spatial allocation was performed for a
national 12-km domain. To accomplish this, SMOKE used national 12-km spatial surrogates and a
SMOKE area-to-point data file. For the U.S., EPA updated surrogates to use circa 2010-2011 data
wherever possible. For Mexico, updated spatial surrogates were used as described below. For Canada
surrogates provided by Environment Canada were used and are unchanged from the 2007 platform. The
U.S., Mexican, and Canadian 12-km surrogates cover the entire CONUS domain 12US1 shown in
Figure 3-2.
The details regarding how the 201 lv6.2 platform surrogates were created are available from
ftp://newftp.epa.gov/Air/emismod/2011/v2platform/spatial surrogates/ in the files
US SpatialSurrogate Workbook v072115.xlsx and US SpatialSurrogate Documentation v()70JI5.pdf
and Surrogate!ools Scripts 2014.zip available. The remainder of this subsection provides further detail
on the origin of the data used for the spatial surrogates and the area-to-point data.
3.3.7.1 Surrogates for U.S. Emissions
There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions
to the 12-km grid cells used by the air quality model. As described in Section 3.4.2, an area-to-point
approach overrides the use of surrogates for a limited set of sources. Table 3-12 lists the codes and
descriptions of the surrogates. Surrogate names and codes listed in italics are not directly assigned to any
sources for the 201 lv6.2 platform, but they are sometimes used to gapfill other surrogates, or as an input
for merging two surrogates to create a new surrogate that is used.
Many surrogates use circa 2010-based data, including 2010 census data at the block group level, 2010
American Community Survey Data for heating fuels, 2010 TIGER/Line data for railroads and roads, the
2006 National Land Cover Database, 2011 gas station and dry cleaner data, and the 2012 National
Transportation Atlas Data for rail-lines, ports and navigable waterways. Surrogates for ports (801) and
shipping lanes (802) were developed based on the 201 1NEIv2 shapefiles: Ports_032310_wrf and
ShippingLanes l 11309FINAL_wrf, but also included shipping lane data in the Great Lakes and support
vessel activity data in the Gulf of Mexico. The creation of surrogates and shapefiles for the U.S. was
generated via the Surrogate Tool. The tool is available from http://cmascenter.org and documentation for
it is available at https://www.cmascenter.Org/sa-tools/documentation/4.2/html/srgtool/
57
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SurrogateToolUserGuide 4 2.htm.
Table 3-12. U.S. Surrogates available for the 2011 modeling platform
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.3.1.2)
507
Heavy Light Construction Industrial Land
100
Population
510
Commercial plus Industrial
110
Housing ;
515
Commercial plus Institutional Land
120
Urban Population ]
520
Commercial plus Industrial plus Institutional
130
Rural Population
525
Golf Courses + Institutional +Industrial +
Commercial
137
Housing Change
526
Residential Non-Institutional
140
Housing Change and Population
527
Single Family Residential
150
Residential Heating - Natural Gas
530
Residential - High Density
160
Residential Heating - Wood \
535
Residential + Commercial + Industrial +
Institutional + Government
165
0.5 Residential Heating - Wood plus 0.5 Low
Intensity Residential
540
Retail Trade
170
Residential Heating - Distillate Oil
545
Personal Repair
180
Residential Heating - Coal
550
Retail Trade plus Personal Repair
190
Residential Heating - LP Gas
555
Professional/Technical plus General
Government
200
Urban Primary Road Miles
560
Hospitals
205
Extended Idle Locations
565
Medical Offices/Clinics
210
Rural Primary Road Miles
570
Heavy and High Tech Industrial
220
'
Urban Secondary Road Miles :
575
Light and High Tech Industrial
221
Urban Unrestricted Roads
580
Food, Drug, Chemical Industrial
230
Rural Secondary Road Miles
585
Metals and Minerals Industrial
231
Rural Unrestricted Roads
590
Heavy Industrial
240
Total Road Miles
595
Light Industrial
250
Urban Primary plus Rural Primary
596
Industrial plus Institutional plus Hospitals
255
0.75 Total Roadway Miles plus 0.25 Population
600
Gas Stations
256
Off-Network Short-Haul Trucks
650
Refineries and Tank Farms
257
Off-Network Long-Haul Trucks
675
Refineries and Tank Farms and Gas Stations
258
Intercity Bus Terminals
680
Oil & Gas Wells, IHS Energy, Inc. and
USGS (see updated surrogates in Table 3-19)
259
T ransit Bus T erminals
700
Airport Areas
260
Total Railroad Miles
710
Airport Points
261
NT AD Total Railroad Density
720
Military Airports
270
Class 1 Railroad Miles 1
800
Marine Ports
271
NTAD Class 1, 2, 3 Railroad Density
801
NEI Ports
280
Class 2 and 3 Railroad Miles
802
NE1 Shipping Lanes
300
Low Intensity Residential
806
Offshore Shipping NEI NOx
310
Total Agriculture
807
Navigable Waterway Miles
312
Orchards/Vineyards
808
Gulf Tug Zone Area
320
Forest Land
810
Navigable Waterway Activity
330
Strip Mines/Quarries
812
Midwest Shipping Lanes
340
Land ]
820
Ports NEI NOx
350
Water ij
I 850
Golf Courses
58
-------
Code
Surrogate Description
1 ^0('e
Surrogate Description
400
Rural Land Area
1 860
Mines
500
Commercial Land
1 870
Wastewater Treatment Facilities
505
Industrial Land
1 880
Drycleaners
506
Education
1 890
Commercial Timber
For the onroad sector, the on-network (RPD) emissions were spatially allocated to roadways, and the off-
network (RPP and RPV) emissions were allocated according to the mapping in Table 3-13. The refueling
emissions were spatially allocated to gas station locations (surrogate 600). On-network (i.e., on-roadway)
mobile source emissions were assigned to the following surrogates: rural restricted access to rural
primary road miles (210), rural unrestricted access to 231, urban restricted access to urban primary road
miles (200), and urban unrestricted access to 221. In the 201 lv6.2 platform, emissions from the extended
(i.e., overnight) idling of trucks were assigned to a new surrogate 205 that is based on locations of
overnight truck parking spaces.
Table 3-13
>. Off-Network Mobile Source Surrogates
Source type
Source Type name
Surrogate ID
11
Motorcycle
535
21
Passenger Car
535
31
Passenger Truck
535
32
Light Commercial Truck
510
41
Intercity Bus
258
42
Transit Bus
259
43
School Bus
506
51
Refuse Truck
507
52
Single Unit Short-haul Truck
256
53
Single Unit Long-haul Truck
257
54
Motor Home
526
61
Combination Short-haul Truck
256
62
Combination Long-haul Truck
257
For the oil and gas sources in the np oilgas sector, the spatial surrogates were updated to those shown in
Table 3-14 using 2011 data consistent with what was used to develop the 2011NEI nonpoint oil and gas
emissions. Note that the "Oil & Gas Wells, IHS Energy, Inc. and USGS" (680) is older and based on
circa-2005 data. These surrogates were based on the same GIS data of well locations and related
attributes as was used to develop the 201 1NEIv2 data for the oil and gas sector. The data sources include
Drilling Info (DI) Desktop's HPDI database (Drilling Info, 2012) aggregated to grid cell levels, along
with data from Oil and Gas Commission (OGC) websites. Well completion data from HPDI was
supplemented by implementing the methodology for counting oil and gas well completions developed for
the U.S. National Greenhouse Gas Inventory. Under that methodology, both completion date and date of
first production from HPDI were used to identify wells completed during 2011. In total, over 1.08 million
unique well locations were compiled from the various data sources. The well locations cover 33 states and
1,193 counties (ERG, 2014b). Although basically the same surrogates were used, some minor updates to
the oil and gas surrogates were made in the 201 lv6.2 platform to correct some mis-located emissions.
59
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Table 3-14. Spatial Surrogates for Oil and Gas Sources
Surrogate
Code
Surrogate Description
681
Spud count - Oil Wells
682
Spud count - Horizontally-drilled wells
683
Produced Water at all wells
684
Completions at Gas and CBM Wells
685
Completions at Oil Wells
686
Completions at all wells
687
Feet drilled at all wells
688
Spud count - Gas and CBM Wells
689
Gas production at all wells
692
Spud count - All Wells
693
Well count - all wells
694
Oil production at oil wells
695
Well count - oil wells
697
Oil production at Gas and CBM Wells
698
Well counts - Gas and CBM Wells
Some spatial surrogate cross reference updates were made between the 201 lv6.1 platform and the
201 lv6.2 platform aside from the reworking of the onroad mobile source surrogates described above.
These updates included the following:
• Nonroad SCCs using spatial surrogate 525 (50% commercial + industrial + institutional, 50% golf
courses) were changed to 520 (100% commercial + industrial + institutional). The golf course
surrogate 850, upon which 525 is partially based, is incomplete and subject to hot spots;
• Some nonroad SCCs for commercial equipment in New York County had assignments updated to
surrogate 340;
• Commercial lawn and garden equipment was updated to use surrogate 520; and
• Some county-specific assignments for RWC were updated to use surrogate 300.
Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is,
some surrogates shown in Table were not assigned to any SCCs, although many of the "unused"
surrogates are actually used to "gap fill" other surrogates that are used. When the source data for a
surrogate has no values for a particular county, gap filling is used to provide values for the surrogate in
those counties to ensure that no emissions are dropped when the spatial surrogates are applied to the
emission inventories.
3.3.7.2 Allocation Method for Airport-Related Sources in the U.S.
There are numerous airport-related emission sources in the NEI, such as aircraft, airport ground support
equipment, and jet refueling. The modeling platform includes the aircraft and airport ground support
60
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equipment emissions as point sources. For the modeling platform, EPA used the SMOKE "area-to-point"
approach for only jet refueling in the nonpt sector. The following SCCs use this approach: 2501080050
and 2501080100 (petroleum storage at airports), and 2810040000 (aircraft/rocket engine firing and
testing). The ARTOPNT approach is described in detail in the 2002 platform documentation:
https://www3.epa.gov/scram001/reports/Emissions%20TSD%20Voll 02-28-08.pdf. The ARTOPNT file
that lists the nonpoint sources to locate using point data were unchanged from the 2005-based platform.
3.3.7.3 Surrogates for Canada and Mexico Emission Inventories
The surrogates for Canada to spatially allocate the 2010 Canadian emissions have been updated in the
201 lv6.2 platform. The spatial surrogate data came from Environment Canada, along with cross
references. The surrogates they provided were outputs from the Surrogate Tool (previously referenced).
The Canadian surrogates used for this platform are listed in Table 3-15. The leading "9" was added to the
surrogate codes to avoid duplicate surrogate numbers with U.S. surrogates. Surrogates for Mexico are
circa 1999 and 2000 and were based on data obtained from the Sistema Municpal de Bases de Datos
(SIMBAD) de INEGI and the Bases de datos del Censo Economico 1999. Most of the CAPs allocated to
the Mexico and Canada surrogates are shown in Table 3-16. The entries in this table are for the othar
sector except for the "MEX Total Road Miles" and the "CAN traffic" rows, which are for the othon
sector.
Table 3-15. Canadian Spatial Surrogates
Code
Canadian Surrogate Description
Code
Description
9100
Population
92424
BARLEY
9101
total dwelling
92425
BUCWHT
9103
rural dwelling
92426
CANARY
9106
ALL INDUST
92427
CANOLA
9111
Farms
92428
CHICPEA
9113
Forestry and logging
92429
CORNGR
9211
Oil and Gas Extraction
92425
BUCWHT
9212
Mining except oil and gas
92430
CORNSI
9221
Total Mining
92431
DFPEAS
9222
Utilities
92432
FLAXSD
9233
Total Land Development
92433
FORAGE
9308
Food manufacturing
92434
LENTIL
9321
Wood product manufacturing
92435
MUSTSD
9323
Printing and related support activities
92436
MXDGRN
9324
Petroleum and coal products manufacturing
92437
OATS
9327
Non-metallic mineral product manufacturing
92438
ODFBNS
9331
Primary Metal Manufacturing
92439
OTTAME
9412
Petroleum product wholesaler-distributors
92440
POTATS
9416
Building material and supplies wholesaler-
distributors
92441
RYEFAL
9447
Gasoline stations
92442
RYESPG
9448
clothing and clothing accessories stores
92443
SOYBNS
9481
Air transportation
92444
SUGARB
9482
Rail transportation
92445
SUNFLS
61
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Code
9562
9921
9924
9925
9932
9941
9942
9945
9946
9948
9950
9955
9960
9970
9980
9990
9996
9997
91201
92401
92402
92403
92404
92405
92406
92407
92408
92409
92410
92412
92413
92414
92416
92417
92418
92419
92421
92422
92423
Canadian Surrogate Description
Code
Description
Waste management and remediation services
92446
TOBACO
Commercial Fuel Combustion
92447
TRITCL
Primary Industry
92448
WHITBN
Manufacturing and Assembly
92449
WHTDUR
CANRAIL
92450
WHTSPG
PAVED ROADS
92451
WHTWIN
UNPAVED ROADS
92452
BEANS
Commercial Marine Vessels
92453
CARROT
Construction and mining
92454
GRPEAS
Forest
92455
OTHVEG
Combination of Forest and Dwelling
92456
SWCORN
UNPAVED ROADS AND TRAILS
92457
TOMATO
TOTBEEF
92430
CORNSI
TOTPOUL
92431
DFPEAS
TOTSWIN
92432
FLAXSD
TOTFERT
92433
FORAGE
urban area
92434
LENTIL
CHBOISQC
92435
MUSTSD
traffic bcw
92436
MXDGRN
BULLS
92437
OATS
BFCOWS
92438
ODFBNS
BFHEIF
92439
OTTAME
CALFU1
92440
POTATS
FDHEIF
92441
RYEFAL
STEERS
92442
RYESPG
MLKCOW
92443
SOYBNS
MLKHEIF
92444
SUGARB
MBULLS
92445
SUNFLS
MCALFU1
92446
TOBACO
BROILER
92447
TRITCL
LAYHEN
92448
WHITBN
TURKEY
92449
WHTDUR
BOARS
92450
WHTSPG
GRWPIG
92451
WHTWIN
NURPIG
92452
BEANS
SOWS
92453
CARROT
IMPAST
92454
GRPEAS
UNIMPAST
92455
OTHVEG
ALFALFA
92456
92457
SWCORN
TOMATO
62
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Table 3-16. CAPs Allocated to Mexican and Canadian Spatial Surrogates
Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
VOC
22
MEX Total Road Miles
15,965
370,867
34,396
13,713
375,276
10
MEX Population
0
0
0
0
431,231
12
MEX Housing
0
161,013
17,483
2,123
452,685
14
MEX Residential Heating - Wood
0
20,093
211,525
2,859
380,572
16
MEX Residential Heating - Distillate Oil
0
38
0
11
2
20
MEX Residential Heating - LP Gas
0
25,303
787
63
614
22
MEX Total Road Miles
0
0
0
0
3,513
24
MEX Total Railroads Miles
0
74,969
1,669
663
2,824
26
MEX Total Agriculture
679,212
164,144
72,372
2,127
43,958
28
MEX Forest Land
0
16,224
67,683
660
79,018
32
MEX Commercial Land
0
125,211
7,726
0
286,982
34
MEX Industrial Land
0
45,831
5,684
59,201
133,440
36
MEX Commercial plus Industrial Land
0
0
0
0
332,495
38
MEX Commercial plus Institutional Land
0
6,400
216
84
28,293
40
Residential (RES 1 -4)+Commercial+Industrial+
Institutional+ Government
0
8
20
0
241,710
42
MEX Personal Repair (COM3)
0
0
0
0
33,616
44
MEX Airports Area
0
14,639
0
1,149
6,857
46
MEX Marine Ports
0
124,951
2,991
1,482
1,099
48
Brick Kilns - Mexico
0
776
6,691
0
10,244
50
Mobile sources - Border Crossing - Mexico
0
454
0
0
2,668
9100
CAN Population
603
0
276
0
304
9101
CAN total dwelling
643
46,256
12,783
14,698
32,944
9106
CAN ALL INDUST
133
21,526
381
3,921
2
9113
CAN Forestry and logging
1,582
8,561
28,622
1,809
36,114
9115
CAN Agriculture and forestry activities
160
239,553
25,318
9,092
26,526
9116
CAN Total Resources
0
17
0
0
5
9212
CAN Mining except oil and gas
0
0
5,391
0
0
9221
CAN Total Mining
42
2,292
45,374
728
26
9222
CAN Utilities
189
14,882
369
1,124
255
9233
CAN Total Land Development
17
20,789
1,928
981
2,551
9308
CAN Food manufacturing
0
0
0
0
4,535
9323
CAN Printing and related support activities
0
0
0
0
25,203
CAN Petroleum and coal products
9324
manufacturing
0
0
2,402
0
0
CAN Non-metallic mineral product
9327
manufacturing
0
238
7,708
2,941
1,218
9331
CAN Primary Metal Manufacturing
0
98
5,062
12
6
9412
CAN Petroleum product wholesaler-distributors
0
0
0
0
70,125
63
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Code
Mexican or Canadian Surrogate Description
nh3
NOx
pm25
so2
VOC
9416
CAN Building material and supplies
wholesaler-distributors
2
0
1,461
3,259
560
9448
CAN clothing and clothing accessories stores
0
0
0
0
328
9562
CAN Waste management and remediation
services
165
893
1,596
1,998
16,551
9921
CAN Commercial Fuel Combustion
494
33,816
2,750
35,471
850
9924
CAN Primary Industry
0
0
0
0
219,282
9925
CAN Manufacturing and Assembly
0
0
0
0
139,227
9931
CAN OTHERJET
9
14,388
548
1,139
7,629
9932
CAN CANRAIL
109
122,694
4,093
5,737
3,304
9942
CAN UNPAVED ROADS
40
3,462
3,499
48
152,674
9945
CAN Commercial Marine Vessels
28
45,454
6,404
14,325
61,139
9946
CAN Construction and mining
247
156,770
10,070
5,667
17,180
9947
CAN Agriculture Construction and mining
19
37,452
536
26
32,683
9950
CAN Intersection of Forest and Housing
1,053
11,700
120,045
1,671
173,130
9960
CAN TOTBEEF
176,156
0
7,420
0
317,394
9970
CAN TOTPOUL
74,204
0
2
0
264
9980
CAN TOTS WIN
122,094
0
996
0
3,186
9990
CAN TOTFERT
178,791
0
9,279
0
0
9991
CAN traffic
22,294
550,896
10,888
5,548
285,104
9994
CAN ALLROADS
0
0
55,468
0
0
9995
CAN 30UNPAVED 70trail
0
0
106,707
0
0
9996
CAN urban area
0
0
284
0
0
64
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3.4
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K.E.; Elman. E. 2007. Mapping fuels at multiple scales: landscape application of the Fuel
Characteristic Classification System. Canadian Journal of Forest Research. 37:2421-2437.
NYSERDA, 2012; "Environmental, Energy Market, and Health Characterization of Wood-Fired
Hydronic Heater Technologies, Final Report". New York State Energy Research and
Development Authority (NYSERDA). Available from:
http://www.nvserda.ny.gov/Publications/Case-Studies/-
/media/Files/Publications/Research/Environmental/Wood-Fired-Hvdronic-Heater-Tech.ashx.
Ottmar, R.D.; Sandberg, D.V.; Riccardi, C.L.; Prichard, S.J. 2007. An Overview of the Fuel Characteristic
Classification System - Quantifying, Classifying, and Creating Fuelbeds for Resource Planning.
Canadian Journal of Forest Research. 37(12): 2383-2393. FCCS is available at:
http://www.fs.fed.us/pnw/fera/fccs/index.shtml
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Pouliot, G. and J. Bash, 2015. Updates to Version 3.61 of the Biogenic Emission Inventory System
(BEIS). Presented at Air and Waste Management Association conference, Raleigh, NC, 2015.
Pouliot, G., H. Simon, P. Bhave, D. Tong, D. Mobley, T. Pace, and T. Pierce . (2010) "Assessing the
Anthropogenic Fugitive Dust Emission Inventory and Temporal Allocation Using an Updated
Speciation of Particulate Matter." International Emission Inventory Conference, San Antonio, TX.
Available at http://www.epa.gov/ttn/chief/conference/eil9/session9/pouliot.pdf
Raffuse, S., D. Sullivan, L. Chinkin, S. Larkin, R. Solomon, A. Soja, 2007. Integration of Satellite-
Detected and Incident Command Reported Wildfire Information into BlueSky, June 27, 2007.
Available at: https://www.airfire.org/smartfire.
Skamarock, W., J. Klemp, J. Dudhia, D. Gill, D. Barker, M. Duda, X. Huang, W. Wang, J. Powers, 2008.
A Description of the Advanced Research WRF Version 3. NCAR Technical Note. National
Center for Atmospheric Research, Mesoscale and Microscale Meteorology Division, Boulder, CO.
June 2008. Available at: http://www.mmm.ucar.edu/wrf/users/docs/arw v3.pdf
Sullivan D.C., Raffuse S.M., Pryden D.A., Craig K.J., Reid S.B., Wheeler N.J.M., Chinkin L.R., Larkin
N.K., Solomon R., and Strand T. (2008) Development and applications of systems for modeling
emissions and smoke from fires: the BlueSky smoke modeling framework and SMARTFIRE: 17th
International Emissions Inventory Conference, Portland, OR, June 2-5.
Wang, Y., P. Hopke, O. V. Rattigan, X. Xia, D. C. Chalupa, M. J. Utell. (2011) "Characterization of
Residential Wood Combustion Particles Using the Two-Wavelength Aethalometer", Environ. Sci.
Technol., 45 (17), pp 7387-7393
Yarwood, G., S. Rao, M. Yocke, and G. Whitten, 2005: Updates to the Carbon Bond Chemical
Mechanism: CB05. Final Report to the US EPA, RT-0400675. Available at
http://www.camx.com/publ/pdfs/CB05 Final Report 120805.pdf.
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4.0 CMAQ Air Quality Model Estimates
4.1 Introduction to the CMAQ Modeling Platform
The Clean Air Act (CAA) provides a mandate to assess and manage air pollution levels to protect human
health and the environment. EPA has established National Ambient Air Quality Standards (NAAQS),
requiring the development of effective emissions control strategies for such pollutants as ozone and
particulate matter. Air quality models are used to develop these emission control strategies to achieve the
objectives of the CAA.
Historically, air quality models have addressed individual pollutant issues separately. However, many of
the same precursor chemicals are involved in both ozone and aerosol (particulate matter) chemistry;
therefore, the chemical transformation pathways are dependent. Thus, modeled abatement strategies of
pollutant precursors, such as volatile organic compounds (VOC) and NOx to reduce ozone levels, may
exacerbate other air pollutants such as particulate matter. To meet the need to address the complex
relationships between pollutants, EPA developed the Community Multi scale Air Quality (CMAQ)
modeling system" The primary goals for CMAQ are to:
• Improve the environmental management community's ability to evaluate the impact of air quality
management practices for multiple pollutants at multiple scales.
• Improve the scientist's ability to better probe, understand, and simulate chemical and physical
interactions in the atmosphere.
The CMAQ modeling system brings together key physical and chemical functions associated with the
dispersion and transformations of air pollution at various scales. It was designed to approach air quality as
a whole by including state-of-the-science capabilities for modeling multiple air quality issues, including
tropospheric ozone, fine particles, toxics, acid deposition, and visibility degradation CMAQ relies on
emission estimates from various sources, including the U.S. EPA Office of Air Quality Planning and
Standards" current emission inventories, observed emission from major utility stacks, and model estimates
of natural emissions from biogenic and agricultural sources. CMAQ also relies on meteorological
predictions that include assimilation of meteorological observations as constraints. Emissions and
meteorology data are fed into CMAQ and run through various algorithms that simulate the physical and
chemical processes in the atmosphere to provide estimated concentrations of the pollutants. Traditionally,
the model has been used to predict air quality across a regional or national domain and then to simulate the
effects of various changes in emission levels for policymaking purposes. For health studies, the model can
also be used to provide supplemental information about air quality in areas where no monitors exist.
CMAQ was also designed to have multi-scale capabilities so that separate models were not needed for
urban and regional scale air quality modeling. The grid spatial resolutions in past annual CMAQ runs
have been 36 km x 36 km per grid for the "parent" domain, and nested within that domain are 12 km x 12
km grid resolution domains. The parent domain typically covered the continental United States, and the
11 Byun, D.W., and K. L. Schere, 2006: Review of the Governing Equations, Computational Algorithms, and Other
Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Applied Mechanics Reviews,
Volume 59, Number 2 (March 2006), pp. 51-77.
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nested 12 km x 12 km domain covered the Eastern or Western United States. The CMAQ simulation
performed for this 2012 assessment used a single domain that covers the entire continental U.S. (CONUS)
and large portions of Canada and Mexico using 12 km by 12 km horizontal grid spacing. Currently, 12 km
x 12 km resolution is sufficient as the highest resolution for most regional-scale air quality model
applications and assessments.12 With the temporal flexibility of the model, simulations can be performed to
evaluate longer term (annual to multi-year) pollutant climatologies as well as short-term (weeks to
months) transport from localized sources. By making CMAQ a modeling system that addresses multiple
pollutants and different temporal and spatial scales, CMAQ has a "one atmosphere" perspective that
combines the efforts of the scientific community. Improvements will be made to the CMAQ modeling
system as the scientific community further develops the state-of-the-science.
For more information on CMAQ, go to https://www.epa.gov/cmaq or http://www.cmascenter.org.
4.1.1 Advantages and Limitations of the CMAQ Air Quality Model
An advantage of using the CMAQ model output for characterizing air quality for use in comparing with
health outcomes is that it provides a complete spatial and temporal coverage across the U.S. CMAQ is a
three-dimensional Eulerian photochemical air quality model that simulates the numerous physical and
chemical processes involved in the formation, transport, and destruction of ozone, particulate matter and
air toxics for given input sets of initial and boundary conditions, meteorological conditions and emissions.
The CMAQ model includes state-of-the-science capabilities for conducting urban to regional scale
simulations of multiple air quality issues, including tropospheric ozone, fine particles, toxics, acid
deposition and visibility degredation. However, CMAQ is resource intensive, requiring significant data
inputs and computing resources.
An uncertainty of using the CMAQ model includes structural uncertainties, representation of physical and
chemical processes in the model. These consist of: choice of chemical mechanism used to characterize
reactions in the atmosphere, choice of land surface model and choice of planetary boundary layer.
Another uncertainty in the CMAQ model is based on parametric uncertainties, which includes
uncertainties in the model inputs: hourly meteorological fields, hourly 3-D gridded emissions, initial
conditions, and boundary conditions. Uncertainties due to initial conditions are minimized by using a 10
day ramp-up period from which model results are not used in the aggregation and analysis of model
outputs. Evaluations of models against observed pollutant concentrations build confidence that the model
performs with reasonable accuracy despite the uncertainties listed above. A detailed model evaluation for
ozone and PM2.5 species provided in Section 4.3 shows generally acceptable model performance which is
equivalent or better than typical state-of-the-science regional modeling simulations as summarized in
Simon et al., 201213.
4.2 CMAQ Model Version, Inputs and Configuration
This section describes the air quality modeling platform used for the 2012 CMAQ simulation. A modeling
platform is a structured system of connected modeling-related tools and data that provide a consistent and
transparent basis for assessing the air quality response to changes in emissions and/or meteorology. A
12U.S. EPA (2014), Draft Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and
Regional Haze, pp 214. http://www.epa.gov/ttn/scram/guidance/guide/Draft 03-PM-RH Modeling Guidance-2014.pdf.
13 Simon, H., Baker, K.R., and Phillips, S. (2012) Compilation and interpretation of photochemical model performance
statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139.
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platform typically consists of a specific air quality model, emissions estimates, a set of meteorological
inputs, and estimates of "boundary conditions" representing pollutant transport from source areas outside
the region modeled. We used the CMAQ model as part of the 2012 Platform to provide a national scale
air quality modeling analysis. The CMAQ model simulates the multiple physical and chemical processes
involved in the formation, transport, and destruction of ozone and fine particulate matter (PM2.5).
This section provides a description of each of the main components of the 2012 CMAQ simulation along
with the results of a model performance evaluation in which the 2012 model predictions are compared to
corresponding measured concentrations.
4.2.1 Model Version
CMAQ is a non-proprietary computer model that simulates the formation and fate of photochemical
oxidants, including PM2.5 and ozone, for given input sets of meteorological conditions and emissions. As
mentioned previously, CMAQ includes numerous science modules that simulate the emission, production,
decay, deposition and transport of organic and inorganic gas-phase and pollutants in the atmosphere. This
2012 analysis employed CMAQ version 5.0.214 which reflects updates to version 5.0.1 which include
several changes to the science algorithms to improve the underlying science. The CMAQ model version
5.0 was most recently peer-reviewed in June of 2011 for the U.S. EPA.15 The model enhancements in
version 5.0.2 include:
1. SOA yield update
2. Gas-phase chemistry
3. Sulfate inhibition effect in aqueous chemistry
4. CSQY DATA files
5. WRF land use options
6. Ammonia bidirectional exchange and dry deposition change
7. M3DRY backward compatibility with MCIP for wind staggering
8. Vertical advection time step
9. Aerosol updates
10. ACONC bug fix
4.2.2 Model Domain and Grid Resolution
The CMAQ modeling analyses were performed for a domain covering the continental United States, as
shown in Figure 4-1. This single domain covers the entire continental U.S. (CONUS) and large portions
of Canada and Mexico using 12 km by 12 km horizontal grid spacing. The model extends vertically from
the surface to 50 millibars (approximately 17,600 meters) using a sigma-pressure coordinate system. Air
quality conditions at the outer boundary of the 12 km domain were taken from a global model. Table 4-1
14 CMAQ version 5.0.2 model code is available from the Community Modeling and Analysis System (CMAS) at:
http://www.cmascenter.org.
15 Brown. N.J., Allen. D.T., A mar. P., Kallos. G.. McNider, R.. Russell, AG.. Stockwcll. W.R. (September 2011). Final
Report: Fourth Peer Review of the CMAQ Model. https://www.epa.gov/sites/production/files/2Q16-
11/documents/cmaq fifth review final report 2015.pdf. CMAQ version 5.0 was released on February, 2012. It is
available from the Community Modeling and Analysis System (CMAS) as well as previous peer-review reports at:
http://www.cmascenter.org.
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provides some basic geographic information regarding the 12 km CMAQ domain.
12US2 domain
x,y origin: -2412C
col: 396 row:246
Figure 4-1. Map of the CMAQ Modeling Domain. The purple box denotes the 12 km national
modeling domain. (Same as Figure 3-3.)
Table 4-1. Geographic Information for 12 km Modeling Domain
National 12 km CMAQ Modeling Configuration
Map Projection
Grid Resolution
Lambert Conforrnal Projection
12 km
Coordinate Center
97W,40N
True Latitudes
33 and 45 N
Dimensions
396 x 246 x 25
Vertical Extent
25 Layers: Surface to 50 mb level (see Table 4-2)
4.2.3 Modeling Period / Ozone Episodes
The 12 km CMAQ modeling domain was modeled for the entire year of 2012. The 2012 annual
simulation was performed in two half-year segments (i.e., January through June, and July through
December) for each emissions scenario. With this approach to segmenting an annual simulation we were
able to reduce the overall throughput time for an annual simulation. The annual simulation included a
"ramp-up" period, comprised of 10 days before the beginning of each half-year segment, to mitigate the
effects of initial concentrations. All 365 model days were used in the annual average levels of PM2.5. For
the 8-hour ozone, we used modeling results from the period between May 1 and September 30. This 153-
day period generally conforms to the ozone season across most parts of the U.S. and contains the majority
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of days that observed high ozone concentrations.
4.2.4 Model Inputs: Emissions, Meteorology and Boundary Conditions
2012 Emissions: The emissions inventories used in the 2012 air quality modeling are described in Section
3, above.
Meteorological Input Data: The gridded meteorological data for the entire year of 2012 at the 12 km
continental United States scale domain was derived from version 3.6.116 of the Weather Research and
Forecasting Model (WRF), Advanced Research WRF (ARW) core.17 The WRF Model is a state-of-the-
science mesoscale numerical weather prediction system developed for both operational forecasting and
atmospheric research applications fhttp://wrf-model.org). The 2012 WRF simulation included the physics
options of the Pleim-Xiu land surface model (LSM), Asymmetric Convective Model version 2 planetary
boundary layer (PBL) scheme, Morrison double moment microphysics, Kain- Frit sell cumulus
parameterization scheme utilizing the nioisture-advection trigger18 and the RRTMG long-wave and
shortwave radiation (LWR/SWR) scheme.19 In addition, the Group for High Resolution Sea Surface
Temperatures (GHRSST)20 1 km SST data was used for SST information to provide more resolved
information compared to the more coarse data in the NAM analysis. Landuse and land cover data are
based on the National Land Cover Database 2006.21
The WRF meteorological outputs were processed using the Meteorology-Chemistry Interface Processor
(MCIP) package", version 4.2, to derive the specific inputs to CMAQ: horizontal wind components (i.e.,
speed and direction), temperature, moisture, and its related speciated components was conducted for
vertical diffusion rates, and rainfall rates for each grid cell in each vertical layer. The WRF simulation
used the same CMAQ map projection, a Lambert Conformal projection centered at (-97, 40) with true
latitudes at 33 and 45 degrees north. The 12 km WRF domain consisted of 396 by 246 grid cells and 35
vertical layers up to 50 nib. Table 4-2 shows the vertical layer structure used in WRF and the layer
collapsing approach to generate the CMAQ meteorological inputs. CMAQ resolved the vertical
atmosphere with 25 layers, preserving greater resolution in the PBL.
In terms of the 2012 WRF meteorological model performance evaluation, a combination of qualitative
and quantitative analyses was used to assess the adequacy of the WRF simulated fields. The qualitative
aspects involved comparisons of the model-estimated synoptic patterns against observed patterns from
16 Version 3.6.1 was the current version of WRF at the time the 2012 meteorological model simulation was performed.
17 Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang, W., Powers, J.G., 2008.
A Description of the Advanced Research WRF Version 3.
18 Ma, L-M. and Tan, Z-M, 2009. Improving the behavior of the Cumulus Parameterization for Tropical Cyclone Prediction:
Convection Trigger. Atmospheric Research 92 Issue 2, 190-211.
http://www.sciencedirect.com/science/article/pii/S01698095080Q2585
19 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.
20 Stammer, D., F.J. Wentz, and C.L. Gentemann, 2003, Validation of Microwave Sea Surface Temperature Measurements for
Climate Purposes, J. Climate, 16, 73-87.
21 Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J., 2011. Completion of the
2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol. 77(9):858-864.
22 Otte T.L., Plcim, J.E., 2010. The Meteorology-Clicmistiy Interface Processor (MCIP) for the CMAQ modeling system:
updates through v3.4.1. Gcoscicntific Model Development 3, 243-256.
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historical weather chart archives. Additionally, the evaluations compared spatial patterns of monthly
average rainfall and monthly maximum planetary boundary layer (PBL) heights. The statistical portion of
the evaluation examined the model bias and error for temperature, water vapor mixing ratio, solar
radiation, and wind fields. These statistical values were calculated on a monthly basis.
Table 4-2. Vertical layer structure for 2012 WRF and CMAQ simulations (heights are layer top).
CMAQ
Layers
WRF
Layers
Sigma P
Pressure
(mb)
Approximate
Height (m)
25
35
0
5000
17,556
34
0.05
9750
14,780
24
33
0.1
14500
12,822
32
0.15
19250
11,282
23
31
0.2
24000
10,002
30
0.25
28750
8,901
22
29
0.3
33500
7,932
28
0.35
38250
7,064
21
27
0.4
43000
6,275
26
0.45
47750
5,553
20
25
0.5
52500
4,885
24
0.55
57250
4,264
19
23
0.6
62000
3,683
18
22
0.65
66750
3,136
17
21
0.7
71500
2,619
16
20
0.74
75300
2,226
15
19
0.77
78150
1,941
14
18
0.8
81000
1,665
13
17
0.82
82900
1,485
12
16
0.84
84800
1,308
11
15
0.86
86700
1,134
10
14
0.88
88600
964
Q
13
0.9
90500
797
y
12
0.91
91450
714
8
11
0.92
92400
632
10
0.93
93350
551
9
0.94
94300
470
f
8
0.95
95250
390
6
7
0.96
96200
311
5
6
0.97
97150
232
4
5
0.98
98100
154
3 4 0.985 98575 115
73
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CMAQ WRF
Layers Layers
Pressure Approximate
(mb) Height (m)
Sigma P
2
3
2
0.99 99050
0.995 99525
0.9975 99763
1 100000
77
38
19
0
0
0
Initial and Boundary Conditions: The lateral boundary and initial species concentrations are provided by a
three-dimensional global atmospheric chemistry model, the GEOS-CHEM23 model (standard version 8-03-
02 with 8-02-03 chemistry). The global GEOS-CHEM model simulates atmospheric chemical and
physical processes driven by assimilated meteorological observations from the NASA's Goddard Earth
Observing System (GEOS-5). This model was run for 2012 with a grid resolution of 2.0 degrees x 2.5
degrees (latitude-longitude). The predictions were processed using the GEOS-2-CMAQ tool and used to
provide one-way dynamic boundary conditions at one-hour intervals.24 A GEOS-Chem evaluation was
conducted for the purpose of validating the 2012 GEOS-Chem simulation for predicting selected
measurements relevant to their use as boundary conditions for CMAQ. This evaluation included using
satellite retrievals paired with GEOS-Chem grid cells 25 More information is available about the GEOS-
CHEM model and other applications using this tool at: http://gmao.gsfc.nasa.gov/GEOS/ and
http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-5.
4.3 CMAQ Model Performance Evaluation
An operational model performance evaluation for ozone and PM2.5 and its related speciated components
was conducted for the 2012 simulation using state/local monitoring sites data in order to estimate the
ability of the CMAQ modeling system to replicate the 2012 base year concentrations for the 12 km
continental U.S. domain.
There are various statistical metrics available and used by the science community for model performance
evaluation. For a robust evaluation, the principal evaluation statistics used to evaluate CMAQ
performance were two bias metrics, mean bias and normalized mean bias; and two error metrics, mean
error and normalized mean error.
Mean bias (MB) is used as average of the difference (predicted - observed) divided by the total number of
replicates (n). Mean bias is defined as:
MB = ~Hi(P ~ O) , where P = predicted and O = observed concentrations.
23 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric Chemistry Modeling Group, Harvard University,
Cambridge, MA, October 15, 2004.
24 Akhtar, F., Henderson, B„ Appel, W., Napelenok, S., Hutzell, B„ Pye, H„ Foley, K., 2012. Multiyear Boundary Conditions
for CMAQ 5.0 from GEOS-Chem with Secondary Organic Aerosol Extensions, 11th Annual Community Modeling and
Analysis System conference. Chapel Hill, NC, October 2012.
25 Henderson B.H., Akhtar, F„ Pye, H.O.T., Napelenok, S.L., and Hutzell, W.T. (2014) A database and tool for boundary
conditions for regional air quality modeling: description and evaluation, Geosci. Model Dev., 7, 339-360.
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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£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:
t(P-o)
NMB = *100, where P = predicted concentrations and O = observed
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 = -J *100
n
I(o)
1
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 for
2012 in the continental U.S. were included in the evaluation and were taken from the 2012 State/local
monitoring site data in the EPA Air Quality System (AQS) and the Clean Air Status and Trends Network
(CASTNet). The performance statistics were calculated using predicted and observed data that were
paired in time and space on an 8-hour basis. Statistics were generated for the following geographic
groupings in the 12-km continental U.S. domain26: five large sub regions: Midwest, Northeast, Southeast,
Central and Western U.S.
The 8-hour ozone model performance bias and error statistics for each subregion and each season are
provided in Table 4-4. Seasons were defined as: winter (December-January- February), spring (March-
April-May), summer (June, July, August), and fall (September-October-November). Spatial plots of the
mean bias, mean error, normalized mean bias and error for individual monitors are shown in Figures 4-2
through 4-5. The statistics shown in these two figures were calculated over the ozone season. May
through September, using data pairs on days with observed 8-hour ozone of greater than or equal to 60
ppb.
In general, the model performance statistics indicate that the 8-hour daily maximum ozone concentrations
26 The subrcgions arc defined by States where: Midwest is IL, IN, MI, OH. and WI; Northeast is CT, DE, MA, MD, ME, NH,
NJ, NY, PA. Rl. and VT; Southeast is AL. FL. GA. KY. MS. NC. SC. TN, VA. and WV; Central is AR. I A. KS. LA. MN. MO.
NE, OK, and TX; West is AK, CA. OR. WA. AZ. NM, CO. UT, WY, SD. ND, MT, ID. and NV.
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predicted by the 2012 CMAQ simulation closely reflect the corresponding 8-hour observed ozone
concentrations in space and time in each subregion of the 12 km modeling domain. As indicated by the
statistics in Table 4-4, bias and error for 8-hour daily maximum ozone are relatively low in each
subregion, not only in the summer when concentrations are highest, but also during other times of the year.
Generally, 8-hour ozone in the summer is slightly under predicted with the greatest under prediction in the
Midwest U.S. at AQS and CASTNet sites (NMB is -6.7 percent and -10.3 percent, respectively).
However, 8-hour ozone is slightly over predicted in Central U.S. at AQS sites (NMB is 4.0 percent) and
in the Southeast at AQS and CASTNet sites (NMB is 7.2 percent and 2.0 percent, respectively). Ozone
performance in spring shows better performance with a mix of slight under and over predictions in the
U.S. at AQS and CASTNet sites. In the winter, when concentrations are generally low, the model slightly
over predicts 8-hour maximum ozone with the exception of the Northeast at CASNet sites (NMB is -1.2
percent). In the fall, when concentrations are also relatively low, ozone is slightly over predicted (with
NMBs less than 11 percent in each subregion); except for under prediction at AQS sites in the Central and
the Western U.S (NMB is -2.7 percent and -1.0 percent, respectively).
Model bias at individual sites during the ozone season is similar to that seen on a subregional basis for the
summer. Figure 4-2 shows the mean bias for 8-hour daily maximum ozone greater than 60 ppb is
generally ±6 ppb across the AQS and CASTNet sites. For some areas in the Northeast, Midwest, Central
and California mean bias is under predicted by 8 to 12 ppb. Likewise, the information in Figure 4-4
indicates that the bias for days with observed 8-hour daily maximum ozone greater than 60 ppb is within ±
20 percent at the vast majority of monitoring sites across the U.S. domain. Model error, as seen from
Figures 4-3 and 4-5, is generally 14 ppb and 20 percent or less at most of the sites across the U.S.
modeling domain. Somewhat greater error is evident at sites in several areas most notably along portions
of the California coastline, Northeast coastline, Great Lakes coastline, Seattle, WA, and North Dakota.
Table 4-4. Summary of CMAQ 2012 8-Hour Daily Maximum Ozone Model Performance Statistics
by Subregion, by Season and Monitoring Network.
Monitor
No. of
MB
ME
NMB
NME
Subregion
Network
Season
Obs
(PPb)
(PPb)
(%)
(%)
AQS
Winter
11,007
0.7
4.3
2.4
14.9
Spring
16,122
1.2
5.7
2.6
12.6
Summer
17,696
-0.3
7.4
-0.6
14.9
Northeast
Fall
14,473
2.5
6.2
7.5
18.7
CASTNet
Winter
1,248
-0.4
3.7
-1.2
11.5
Spring
1,309
0.1
5.0
0.2
10.8
Summer
1,238
-1.4
6.6
-2.9
13.9
Fall
1,106
2.1
5.6
6.2
16.3
76
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Subregion
Monitor
Network
Season
No. of
Obs
MB
(PPb)
ME
(PPb)
NMB
(%)
NME
(%)
AQS
Winter
4,192
2.5
4.6
9.6
17.5
Spring
12,777
07
5.6
1.5
11.6
Summer
16,772
-37
7.5
-6.7
13.6
Midwest
Fall
10,055
0.3
6.0
0.8
16.9
CASTNet
Winter
1,037
1.8
4.0
6.2
13.5
Spring
992
-0.8
5.0
-1.5
10.2
Summer
952
-5.8
7.4
-10.3
13.1
Fall
980
0.0
5.1
14.2
0.9
AQS
Winter
12,455
2.0
5.1
6.1
16.0
Spring
16,394
2.7
6.8
5.7
14.5
Summer
17,814
2.0
9.7
4.0
19.7
Central States
Fall
16,391
0.7
6.5
1.6
15.9
CASTNet
Winter
655
0.2
3.9
0.5
11.2
Spring
688
-0.8
5.7
-1.6
11.6
Summer
645
-3.3
7.7
-6.3
14.5
Fall
652
-1.1
5.8
-2.7
13.9
AQS
Winter
7,623
2.5
5.4
7.0
15.3
Spring
21,864
3.4
6.5
7.2
13.9
Southeast
Summer
24,106
4.2
8.6
9.0
18.8
Fall
18,706
3.7
6.7
9.6
17.3
CASTNet
Winter
1,904
1.7
4.4
4.9
12.5
Spring
1,901
0.8
5.3
1.6
10.9
Summer
1,864
1.0
7.2
2.0
14.5
Fall
1,832
1.5
5.4
3.9
13.6
AQS
Winter
29,291
2.7
5.5
7.8
16.0
Spring
32,626
-0.7
5.5
-1.5
10.8
Summer
35,804
-0.9
7.4
-1.7
14.0
Fall
32,898
1.3
6.3
3.1
14.8
West
CASTNet
Winter
1,634
0.0
4.3
0.0
10.3
Spring
1,675
-3.5
5.7
-6.4
10.3
Summer
1,639
-3.4
7.4
-6.0
13.0
Fall
1,599
-0.4
4.9
-1.0
10.8
77
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03 fttwma* MB (ppb) lor run2012eheb05v2 v6 12g 12US2 lor 20120501 lo 20120930
>20
• •
-2
A •
-10
-12
-14
-16
-18
<-20
TRIANGLE»CASTNET_Daily;CIRCLE»AQS_Daily:
Figure 4-2. Mean Bias (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period
May-September 2012 at AQS and CAST Net monitoring sites in the continental U.S. modeling
domain.
03_8hrmax ME (ppb) lor run2012»h Cb05v2 v6 12g 12US2 k>f 20120501 to 20120930
TRIANGLE*CASTNET_Daily:CIRCLE=AQS_Daily:
Figure 4-4. Mean Error (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period
May-September 2012 at AQS and CASTNet monitoring sites in the continental U.S. modeling
domain.
78
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03 8hrmai NMB (V) lor run 2012eh cb05v2 v6_12g_12US2 tor 20120501 to 20120930
TRIANGLE-CASTNETJDaily; Cl RCLE=AQS_Da»ly:
Figure 4-3. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 ppb over
the period May-September 2012 at AQS and CASTNet monitoring sites in the continental U.S.
modeling domain.
03 fthrma* NME (%) tor tun 2012eh cb05v2 *6 12g 12US2 tor 20120601 10 20120930
TRIANGLE-CASTNET_Daily;CIRCLE=AQS_Da>ly:
Figure 4-5. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 ppb over
the period May-September 2012 at AQS and CASTNet monitoring sites in the continental U.S.
modeling domain.
79
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Evaluation for Annual I'M- >- 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 sub region (Table 4-5). PM2.5 measurements for 2012 were obtained from the following
networks for model evaluation: Chemical Speciation Network (CSN, 24 hour average). Interagency
Monitoring of Protected Visual Environments (IMPROVE, 24 hour average, and Clean Air Status and
Trends Network (CASTNet, weekly average). For PM2.5 species that are measured by more than one
network, we calculated separate sets of statistics for each network by sub region. For brevity. Table 4-5
provides annual model performance statistics for the PM2.5 component species for the five sub-regions in
the 12 km continental U.S. domain defined above (Northeast, Midwest, Southeast, Central, and West). In
addition to the tabular summaries of bias and error statistics, annual spatial maps which show the mean
bias, mean error, normalized mean bias and normalized mean error by site for each PM: 5 species are
provided in Figures 4-6 through 4-29.
As indicated by the statistics in Table 4-5, annual average sulfate is consistently under predicted at CSN,
IMPROVE, and C ASTNet monitoring sites across the modeling domain, with MB values ranging from
0.0 to -0.6 |igm-3 and NMB values ranging from near negligible to -30.3 percent. Sulfate performance
shows moderate error, ranging from 24 to 5 1.4 percent. Figures 4-6 through 4-9, suggest spatial patterns
vary by region. The model bias for most of the Northeast, Southeast, Central and Southwest states are
within ±30 percent. The model bias appears to be slightly greater in the Northwest with over predictions
up to 80 percent at individual monitors. Model error also shows a spatial trend by region, where much of
the Eastern states are 20 to 40 percent, the Western and Central U.S. states are 30 to 80 percent.
Annual average nitrate is under predicted at the urban CSN monitoring sites in the Northeast,
Midwest, and Southeast (NMB in the range of 7.7 to 19.9 percent), except in the Central and
Western U.S. where nitrate is under predicted (NMB is -7.4 percent and -36.5 percent, respectively ).
At IMPROVE rural sites, annual average nitrate is over predicted at all sub regions, except in
the Western U.S. where nitrate is under predicted by -25.8 percent. Model performance of total
nitrate at sub-urban CASTNet monitoring sites also shows an over prediction across all subregions
(NMB in the range of 3.7 to 19 percent), except in the Central and Western U.S. (NMB on
average is underpredicted by 23 percent). Model error for nitrate is somewhat greater for each
sub region as compared to sulfate. Model bias at individual sites indicates mainly over prediction of
greater than 20 percent at most monitoring sites in the Eastern half of the U.S. as indicated in Figure 4-
12. The exception to this is in the Southern Florida and the Southwest of the modeling domain where
there appears to be a greater number of sites with under prediction of nitrate of 20 to 80 percent.
Model error for annual nitrate, as shown in Figures 4-1 1 and 4-13, is least at sites in portions of the
Midwest and extending eastward to the Northeast corridor. Nitrate concentrations are typically higher
in these areas than in other portions of the modeling domain.
Annual average ammonium model performance as indicated in Table 4-5 has a tendency for the model
to under predict across the CASTNet sites (ranging from 7 to -40 percent). Ammonium performance
across the urban CSN sites shows an over prediction (ranging from 7 to 28 percent) in the Northeast,
Midwest, Southeast, and Central U.S., and an under prediction of approximately 25 percent in the
Western U.S. There is not a large variation from subregion to subregion or at urban versus rural sites in
the error statistics for ammonium. The spatial variation of ammonium across the majority of individual
80
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monitoring sites in the Eastern U.S. shows bias within ±30 percent. A larger bias is seen in the Southwest,
bias on average 60-70 percent.
Annual average elemental carbon is over predicted in all subregions at urban and rural sites. Similar to
ammonium error there is not a large variation from subregion to subregion or at urban versus rural sites.
Annual average organic carbon is over predicted across most subregions in rural IMPROVE areas (NMB
ranging from 3 to 56 percent), except in the Western U.S. where the bias is approximately -2 percent. The
model over predicted annual average organic carbon in all subregions at urban CSN sites. Similar to
ammonium and elemental carbon, error model performance does not show a large variation from
subregion to subregion or at urban versus rural sites.
Table 4-5. Summary of CMAQ 2012 Annual PM Species Model Performance Statistics by
Subregion, by Monitoring Network.
Monitor
No. of
MB
ME
NMB
NME
Pollutant Network
Subregion
Obs
(|jgm3)
(|jgm3)
(%)
(%)
CSN
Northeast
2,054
-0.1
0.6
-5.3
29.7
Midwest
1,747
-0.3
0.7
-12.5
30.9
Southeast
1,813
-0.1
0.6
-7.2
29.5
Central
1,150
-0.2
0.6
-10.8
32.2
West
1,624
0.0
0.4
-5.3
44.6
IMPROVE
Northeast
1,824
0.2
0.4
14.7
39.5
Midwest
442
-0.1
0.5
-6.1
34.4
Sulfate
Southeast
1,750
-0.2
0.6
-11.4
31.9
Central
2,253
-0.2
0.5
-13.7
34.4
West
9,636
0.0
0.3
-3.7
51.4
CASTNet
Northeast
788
-0.3
0.4
-15.6
24.0
Midwest
592
-0.5
0.6
-24.2
26.3
Southeast
1,134
-0.6
0.6
-26.3
28.7
Central
386
-0.5
0.6
-30.3
33.6
West
1,039
-0.1
0.3
-21.7
40.4
CSN
Northeast
2,054
0.1
0.7
7.7
62.4
Midwest
1,747
0.2
0.7
11.0
51.2
Southeast
1,813
0.1
0.4
19.9
85.0
Nitrate
Central
1,150
-0.1
0.6
-7.4
60.0
West
1,624
-0.5
0.9
-36.5
68.0
81
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Monitor
No. of
MB
ME
NMB
NME
Pollutant
Network
Subregion
Obs
(|jgm3)
(|jgm3)
(%)
(%)
IMPROVE
Northeast
1,824
0.2
0.4
67.6
118.0
Midwest
442
0.2
0.5
38.5
74.2
Southeast
1,749
0.1
0.4
20.2
106.0
Central
2,252
0.1
0.5
14.9
73.7
West
9,603
-0.1
0.2
-25.8
89.6
CASTNet
Northeast
788
0.3
0.5
19.0
36.6
Midwest
592
0.7
0.1
7.7
27.4
Total Nitrate
(NOs + HNOs)
Southeast
1,134
0.1
0.6
3.7
41.8
Central
386
-0.3
0.6
-17.4
38.2
West
1,038
-0.2
0.4
-27.6
45.7
CSN
Northeast
2,054
0.1
0.3
18.3
43.7
Midwest
1,747
0.1
0.3
7.2
37.8
Southeast
1,813
0.1
0.3
27.9
52.0
Central
1,150
0.0
0.3
6.7
50.1
West
1,624
-0.2
0.3
-25.4
75.1
Ammonium
CASTNet
Northeast
788
0.0
0.2
-6.9
26.6
Midwest
592
-0.1
0.3
-7.1
27.0
Southeast
1,134
-0.1
0.2
-17.3
32.1
Central
386
-0.1
0.2
-14.0
36.5
West
1,039
-0.1
0.1
-39.9
56.5
CSN
Northeast
1,979
0.3
0.5
67.8
84.6
Midwest
1,668
0.3
0.4
56.2
71.0
Southeast
1,775
0.3
0.4
46.4
71.1
Central
1,144
0.5
0.5
94.1
106.0
West
1,582
0.6
0.7
99.0
117.0
Elemental
Carbon
IMPROVE
Northeast
1,929
0.2
0.2
67.8
84.6
Midwest
473
0.1
0.1
40.5
63.7
Southeast
1,806
0.1
0.2
40.1
64.1
Central
2,304
0.1
0.2
38.9
61.7
West
10,366
0.1
0.2
70.9
108.0
82
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Monitor
No. of
MB
ME
NMB
NME
Pollutant
Network
Subregion
Obs
(%)
(%)
CSN
Northeast
1,966
1.2
1.6
75.3
95.1
Midwest
1,629
0.6
1.0
38.3
60.0
Southeast
1,775
0.6
1.2
32.6
61.6
Central
1,139
0.8
1.2
52.5
78.4
West
1,574
1.8
2.2
101.0
123.0
Organic Carbon
IMPROVE
Northeast
1,927
0.5
0.8
55.6
86.8
Midwest
472
0.4
0.7
39.4
76.8
Southeast
1,817
0.1
0.8
9.3
60.8
Central
2,307
0.0
0.5
3.0
53.2
West
10,124
0.0
0.6
-1.8
66.2
-------
S04 MB (UQ m3) lor run2012eh cbOSv2 v6 12g 12US210( 20120101 to 20121231
unfix . ug m3
eov»r»g« UM • 75%
CIRCLE-IMPROVE: TRIANGLE-CSN; SQUARE-CASTNET:
Figure 4-6. Mean Bias (jugm"3) of annual sulfate at monitoring sites in the continental U.S.
modeling domain.
S04 ME (u« m3) (Of run2012eh cb05v2 v« 12g 12US2 lor 20120101 lo 20121231
CIRCLE-IMPROVE; TRIANGLE-CSN; SQUARE-CASTNET;
Figure 4-7. Mean Error (jigin 3) of annual sulfate at monitoring sites in the continental U.S.
modeling domain.
84
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S04 NMB (V) tor run 2012*h cb05v2 v« 12g 12US2 tor 20120101 10 20121231
CIRCLE-IMPROVE; TRIANGLE*CSN; SQUARE=CASTNET:
Figure 4-8. Normalized Mean Bias (%) of annual sulfate at monitoring sites in the continental
U.S. modeling domain.
SCWNME fi) torrun2012»h cb05v2 vfi 12g 12US2tor 20120101 to 20121231
CIRCLE.IMPROVE; TRIANGLE=CSN, SQUARE-CASTNET.
Figure 4-9. Normalized Mean Error (%) of annual sulfate at monitoring sites in the continental
U.S. modeling domain.
85
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N03 MB (ug m3) (or run2012«h_cb06v2_v6_12g_12US2 fo» 20120101 to 20121231
CIRCLE-IMPROVE. TRIANGLE-CSN:
Figure 4-10. Mean Bias (jignr3) of annual nitrate at monitoring sites in the continental U.S.
modeling domain.
N03 ME lug m3) tor ruo2012eh cb05*2 v6 12g 12US2 lor 20120101 to 20121231
CIRCLE.IMPROVE: TRIANGLE-CSN;
Figure 4-11. Mean Error (jugrn3) of annual nitrate at monitoring sites in the continental U.S.
modeling domain.
86
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N03 NMB <%) lor run 2012ehcb05v2v6 12g12US2 lor 20120101 to 20121231
uncs>%
00v«r*g« I«r* . 75%
CIRCLE-IMPROVE: TRIANGLE-CSN:
Figure 4-12. Normalized Mean Bias (%) of annual nitrate at monitoring sites in the continental U.S.
modeling domain.
N03 NME (V) lor run 2012»h cb05v2_v6^12gJ2US2 lor 20120101 10 20121231
CIRCLE-IMPROVE; TRIANGLE-CSN:
Figure 4-13. Normalized Mean Error (%) of annual nitrate at monitoring sites in the continental
U.S. modeling domain.
87
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TflQ3MB2012eh cb05v2 v6 12g 12US2 lot 20120101 to 20121231
una - ug/m3
ccvt*}* Itrtf
CIRCLE-CASTNET;
Figure 4-14. Mean Bias (jignr3) of annual total nitrate at monitoring sites in the continental U.S.
modeling domain.
. 75%
CIRCLE-CASTNET;
Figure 4-15. Mean Error (jigin 3) of annual total nitrate at monitoring sites in the continental U.S.
modeling domain.
88
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TN03 NMB {%) lor run 2012eh cb05v2 v6 12g 12US2 tor 20120101 to 20121231
CIRCLE-CASTNET:
Figure 4-16. Normalized Mean Bias (%) of annual total nitrate at monitoring sites in the continental
U.S. modeling domain.
TN03 NME (^») tof run 2012frh Cb05v2 w6 12q 12US2 (Of 20120101 to 20121231
CIRCLE-CASTNET;
Figure 4-17. Normalized Mean Error (%) of annual total nitrate at monitoring sites in the
continental U.S. modeling domain.
89
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NH4 MB (u»m3> kx run2012eh d)05v2 v$ 12g 12US21or 30130101 to 20121231
CIRCLE.CSN; TRIANGLE-CASTNET;
Figure 4-18. Mean Bias (jignr3) of annual ammonium at monitoring sites in the continental U.S.
modeling domain.
NH4 ME 4uQ tn3> (Of run2012eh Cb05v2 v6 12g 12US2 lor 20120101 to 20121231
CIRCLE=CSN; TRIANGLE=CASTNET;
Figure 4-19. Mean Error (jignr3) of annual ammonium at monitoring sites in the continental U.S.
modeling domain.
90
-------
2012eh cb05v2 v6 12g 12US2
NH4 NMB (%) lor tun
lor 20120101 10 20121231
unCs •%
30
20
10
0
-10
-20
-30
-40
-50
-60
-70
-80
-90
<-100
CIRCLE =CSN: TRIANGLE*CASTNET;
Figure 4-20. Normalized Mean Bias (%) of annual ammonium at monitoring sites in the continental
U.S. modeling domain.
NH4 NME C%) lot run 2012eh Cb05v2 12g 12US2 tor 20120101 to 20121231
CIRCLE=CSN; TRIANGLE=CASTNET;
Figure 4-21. Normalized Mean Error (%) of annual ammonium at monitoring sites in the
continental U.S. modeling domain.
91
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EC MB (ugm3) tor mn2012ch_cb05v2_v€
12a 12US2 lor 20120101 to 20121231
unCs •
OW^e lit* . 75%
ClRCLE=IMPROVE. TRIANGLE=CSN:
Figure 4-22. Mean Bias (jigm3) of annual elemental carbon at monitoring sites in the continental
U.S. modeling domain.
EC ME (ug m3) lor run2012*h Cb06v2 v6_12g 12US2 tor 20120101 to 20121231
units * o® m3
Urn* . 75%
ClRCLE=IMPROVE; TRIANGLE=CSN:
Figure 4-23. Mean Error (jigm3) of annual elemental carbon at monitoring sites in the continental
U.S. modeling domain.
92
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EC NMB <%) lot run 2012el> d>05v2_vC_12g_12t)S2 lor 20120101 to 20121231
79%
ClRCLE-l MPROVE: TRIANGLE*CSN:
Figure 4-24. Normalized Mean Bias (%) of annual elemental carbon at monitoring sites in the
continental U.S. modeling domain.
EC NME <%) tot run 2012rti cb05»2 v6 12g 12US2 lor 20120101 lo 20121231
ClRCLE=IMPROVE: TRIANGLE=CSN;
Figure 4-25. Normalized Mean Error (%) of annual elemental carbon at monitoring sites in the
continental U.S. modeling domain.
93
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fun 2012chCb05v2v6_12g 12US2
OC MB lug m3) lor
(Of 20120101 to 20121231
ClRCLE=IMPROV£: TRIANGLE-CSN:
Figure 4-26. Mean Bias (jigiir3) of annual organic carbon at monitoring sites in the continental U.S.
modeling domain.
(ug m3) lot run2012eh cb05v2 v6 12g
ClRCLE=IMPROVE: TRIANGLE=CSN:
Figure 4-27. Mean Error (jignr3) of annual organic carbon at monitoring sites in the continental
U.S. modeling domain.
OC ME
12US2 tor 20120101 to 20121231
ug/rm3
kr*- 75N
94
-------
OC NMB (%) tor tun 2012eh eb05v2 v6_12g 12US2 lor 20120101 10 20121231
units • %
etNtimjt Imt • 79%
ClRCLE=IMPROVE: TRIANGLE=CSN;
Figure 4-28. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the
continental U.S. modeling domain.
OC NME (%) lor run 2012eh cb05v2_v«_12fl_12US2 tor 20120101 to 20121231
CIRCLE=IMPROVE: TRIANGLE-CSN:
Figure 4-29. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the
continental U.S. modeling domain.
95
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5.0 Bayesian space-time downscaling fusion model (downscaler) -
Derived Air Quality Estimates
5.1 Introduction
The need for greater spatial coverage of air pollution concentration estimates has grown in recent years as
epidemiology and exposure studies that link air pollution concentrations to health effects have become
more robust and as regulatory needs have increased. Direct measurement of concentrations is the ideal
way of generating such data, but prohibitive logistics and costs limit the possible spatial coverage and
temporal resolution of such a database. Numerical methods that extend the spatial coverage of existing
air pollution networks with a high degree of confidence are thus a topic of current investigation by
researchers. The downscaler model (DS) is the result of the latest research efforts by EPA for performing
such predictions. DS utilizes both monitoring and CMAQ data as inputs, and attempts to take advantage
of the measurement data's accuracy and CMAQ's spatial coverage to produce new spatial predictions.
This chapter describes methods and results of the DS application that accompany this report, which
utilized ozone and PM2.5 data from AQS and CMAQ to produce predictions to continental U.S. 2010
census tract centroids for the year 2012.
5.2 Downscaler Model
DS develops a relationship between observed and modeled concentrations, and then uses that relationship
to spatially predict what measurements would be at new locations in the spatial domain based on the input
data. This process is separately applied for each time step (daily in this work) of data, and for each of the
pollutants under study (ozone and PM2.5). In its most general form, the model can be expressed in an
equation similar to that of linear regression:
Y(s, t) = ~/?0 (s, t) + /?i (s, t) * ~x(s, t) + e(s, t) (Equation 1)
Where:
Y(s,t) is the observed concentration at point 5 and time I.
~x(s,t) is the CMAQ concentration at time t. This value is a weighted average of both the gridcell
containing the monitor and neighboring gridcells.
~Po(s,t) is the intercept, and is composed of both a global and a local component.
Pi(t) is the global slope; local components of the slope are contained in the ~x(s,t) term.
s(s,t) is the model error.
DS has additional properties that differentiate it from linear regression:
1) Rather than just finding a single optimal solution to Equation 1, DS uses a Bayesian approach so that
uncertainties can be generated along with each concentration prediction. This involves drawing random
samples of model parameters from built-in "prior" distributions and assessing their fit on the data on the
order of thousands of times. After each iteration, properties of the prior distributions are adjusted to try to
improve the fit of the next iteration. The resulting collection of ~Po and Pi 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.
96
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2) The model is "heirarchical" in structure, meaning that the top level parameters in Equation 1 (ie
~fio(s,t), ~x(s,t)) are actually defined in terms of further parameters and sub-parameters in the DS
code. For example, the overall slope and intercept is defined to be the sum of a global (one value for the
entire spatial domain) and local (values specific to each spatial point) component. This gives more
flexibility in fitting a model to the data to optimize the fit (i.e. minimize s(s,t)).
Further information about the development and inner workings of the current version of DS can be found
in Berrocal, Gelfand and Holland (2011) and references therein. The DS outputs that accompany this
report are described below, along with some additional analyses that include assessing the accuracy of the
DS predictions. Results are then summarized, and caveats are provided for interpreting them in the
context of air quality management activities.
5.3 Downscaler Concentration Predictions
In this application, DS was used to predict daily concentration and associated uncertainty values at the
2012 US census tract centroids across the continental U.S. using 2012 measurement and CMAQ data as
inputs. For ozone, the concentration unit is the daily maximum 8-hour average in ppb and for PM2.5 the
concentration unit is the 24-hour average in |j,g/m3.
5.3.1 Summary of 8-hour Ozone Results
Figure 5-1 summarizes the AQS, CMAQ and DS ozone data over the year 2012. It shows the 4th max
daily maximum 8-hour average ozone for AQS observations, CMAQ model predictions and DS model
results. The DS model estimated that for 2012, about 75% of the US Census tracts (54633 out of 72283)
experienced at least one day with an ozone value above the NAAQS of 75 ppb.
97
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AQS
2012
4'th Max, Daily max
8-hour avg
ozone (ppb)
(-Inf,55]
(55,601
(60,65]
(65,70]
(70,75)
(75,80]
¦ (80,85]
¦ (85,90]
¦ (90, Inf]
Figure 5-2. Annual 4th max (daily max 8-hour ozone concentrations) derived from AQS, CMAQ
and DS data.
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5.3.2 Summary of PM2.5 Results
Figures 5-2 and 5-3 summarize the AQS, CMAQ and DS PM2.5 data over the year 2012. Figure 5-2
shows annual means and Figure 5-3 shows 98'th percentiles of 24-hour PM2.5 concentrations for AQS
observations, CMAQ model predictions and DS model results. The DS model estimated that for 2012
about 39% of the US Census tracts (28402 out of 72283) experienced at least one day with a PM2.5 value
above the 24-hour NAAQS of 35 ug/m3.
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AQS
2012
Annual mean,
24-hour avg
PM2.5 (ug/m3)
(0,3]
(3,5]
(5,8]
(8,10]
(10,12]
(12,15]
(15,18]
¦ (18,Inf]
Figure 5-2. Annual mean P.M2.5 concentrations derived from AQS, CMAQ and DS data.
CMAQ
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CMAQ
2012
98'th percentile,
24-hour avg
PM2.5 (ug/m3)
(0,10]
(10,151
(15,201
(20,25]
(25,30]
(30,351
(35,40]
¦ (40,451
¦ (45,50]
¦ (50,Inf|
Figure 5-3. 98th percentile 24-hour average PM2.5 concentrations derived front AQS, CMAQ and
DS data.
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5.4
Downscaler Uncertainties
5.4.1 Standard Errors
As mentioned above, the DS model works by drawing random samples from built-in distributions during
its parameter estimation. The standard errors associated with each of these populations provide a measure
of uncertainty associated with each concentration prediction. Figure 5-4 shows the percent errors
resulting from dividing the DS standard errors by the associated DS prediction. The black dots on the
maps show the location of EPA sampling network monitors whose data was input to DS via the AQS
datasets (Chapter 2). The maps show that, in general, errors are relatively smaller in regions with more
densely situation monitors (ie the eastern US), and larger in regions with more sparse monitoring
networks (ie western states). These standard errors could potentially be used to estimate the probability
of an exceedance for a given point estimate of a pollutant concentration.
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% DS Error
(10,15]
(15,20]
¦ (25,30]
(30,36]
(36,41]
(41,46]
¦ (46,51]
PM25
Figure 5-4. Annual mean relative errors (standard errors divided by predictions) from the DS 2012
runs. The black dots show the locations of monitors that generated the AQS data used as input to
the DS model.
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5.4.2 Cross Validation
To check the quality of its spatial predictions, DS can be set to perform "cross-validation" (CV),
which involves leaving a subset of AQS data out of the model run and predicting the concentrations of
those left out points. The predicted values are then compared to the actual left-out values to generate
statistics that provide an indicator of the predictive ability. In the DS runs associated with this report,
10% of the data was chosen randomly by the DS model to be used for the CV process. The resulting CV
statistics are shown below in Table 5-1.
Pollutant
# Monitors
Mean Bias
RMSE
Mean Coverage
PM2.5
815
0.17
2.93
0.96
03
1311
-0.01
4.58
0.95
Table 5-1. Cross-validation statistics associated with the 2012 DS runs.
The statistics indicated by the columns of Table 5-1 are as follows:
Mean Bias: The bias of each prediction is the DS prediction minus the AQS value. This column is the
mean of all biases across the CV cases.
Root Mean Squared Error (RMSE): The bias is squared for each CV prediction, then the square root of the
mean of all squared biases across all CV predictions is obtained.
Mean Coverage: A value of 1 is assigned if the measured AQS value lies in the 95% confidence interval of
the DS prediction (the DS prediction +/- the DS standard error), and 0 otherwise. This column is the mean
of all those 0's and 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
2012 U.S. census tract centroids by utilizing monitoring data and CMAQ output for 2012. Large-scale
spatial and temporal patterns of concentration predictions are generally consistent with those seen in
ambient monitoring data. Both ozone and PM2.5 were predicted with lower error in the eastern versus the
western U.S., presumably due to the greater monitoring density in the east.
An additional caution that warrants mentioning is related to the capability of DS to provide predictions at
multiple spatial points within a single CMAQ gridcell. Care needs to be taken not to over-interpret any
within-gridcell gradients that might be produced by a user. Fine-scale emission sources in CMAQ are
diluted into the gridcell averages, but a given source within a gridcell might or might not affect every
spatial point contained therein equally. Therefore, DS-generated fine-scale gradients are not expected to
represent actual fine-scale atmospheric concentration gradients, unless possibly multiple monitors are
present in the gridcell.
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Appendix A - Acronyms
Acronyms
ARW
Advanced Research WRF core model
BEIS
Biogenic Emissions Inventory System
BlueSky
Emissions modeling framework
CAIR
Clean Air Interstate Rule
CAMD
EPA's Clean Air Markets Division
CAP
Criteria Air Pollutant
CAR
Conditional Auto Regressive spatial covariance structure (model)
CARB
California Air Resources Board
CEM
Continuous Emissions Monitoring
CHIEF
Clearinghouse for Inventories and Emissions Factors
CMAQ
Community Multiscale Air Quality model
CMV
Commercial marine vessel
CO
Carbon monoxide
CSN
Chemical Speciation Network
DQO
Data Quality Objectives
EGU
Electric Generating Units
Emission Inventory
Listing of elements contributing to atmospheric release of pollutant
substances
EPA
Environmental Protection Agency
EMFAC
Emission Factor (California's onroad mobile model)
FAA
Federal Aviation Administration
FDDA
Four Dimensional Data Assimilation
FIPS
Federal Information Processing Standards
HAP
Hazardous Air Pollutant
HMS
Hazard Mapping System
ICS-209
Incident Status Summary form
IPM
Integrated Planning Model
ITN
Itinerant
LSM
Land Surface Model
MOBILE
OTAQ's model for estimation of onroad mobile emissions factors
MODIS
Moderate Resolution Imaging Spectroradiometer
MOVES
Motor Vehicle Emission Simulator
NEEDS
National Electric Energy Database System
NEI
National Emission Inventory
NERL
National Exposure Research Laboratory
NESHAP
National Emission Standards for Hazardous Air Pollutants
NH
Ammonia
NMIM
National Mobile Inventory Model
NONROAD
OTAQ's model for estimation of nonroad mobile emissions
NO
Nitrogen oxides
OAQPS
EPA's Office of Air Quality Planning and Standards
OAR
EPA's Office of Air and Radiation
ORD
EPA's Office of Research and Development
ORIS
Office of Regulatory Information Systems (code) - is a 4 or 5 digit
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number assigned by the Department of Energy's (DOE) Energy
Information Agency (EIA) to facilities that generate electricity
ORL One Record per Line
OTAQ EPA's Office of Transportation and Air Quality
PAH Polycyclic Aromatic Hydrocarbon
PFC Portable Fuel Container
PM2.5 Particulate matter less than or equal to 2.5 microns
PM10 Particulate matter less than or equal to 10 microns
PMc Particulate matter greater than 2.5 microns and less than 10 microns
Prescribed Fire Intentionally set fire to clear vegetation
RIA Regulatory Impact Analysis
RPO Regional Planning Organization
RRTM Rapid Radiative Transfer Model
SCC Source Classification Code
SMARTFIRE Satellite Mapping Automatic Reanalysis Tool for Fire Incident
Reconciliation
SMOKE Sparse Matrix Operator Kernel Emissions
TCEQ Texas Commission on Environmental Quality
TSD Technical support document
VOC Volatile organic compounds
VMT Vehicle miles traveled
Wildfire Uncontrolled forest fire
WRAP Western Regional Air Partnership
WRF Weather Research and Forecasting Model
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United States Office of Air Quality Planning and Publication No. EPA-
Environmental Protection Standards 450R16001
Agency Air Quality Assessment Division March 2016
Research Triangle Park, NC
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