Bayesian Space-time Downscaling Fusion
Model (Downscaler) - Derived Estimates of Air
Quality for 2014
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EPA-454/R-18-008
October 2018
Bayesian Space-time Downscaling Fusion Model (Downscaler) -Derived
Estimates of Air Quality for 2014
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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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: Liz Naess (EPA/OAR) and
Elizabeth Mannshardt (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 34
3.4 Emissions References 73
4.0 CMAQ Air Quality Model Estimates 77
4.1 Introduction to the CMAQ Modeling Platform 77
4.2 CMAQ Model Version, Inputs and Configuration 78
5.0 Bayesian space-time downscaling fusion model (downscaler) -Derived Air Quality Estimates.... 106
5.1 Introduction 106
5.2 Downscaler Model 106
5.3 Downscaler Concentration Predictions 107
5.4 Downscaler Uncertainties 112
5.5 Summary and Conclusions 114
Appendix A - Acronyms 115
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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 2014 calendar year generated by
EPA's recently developed data fusion method termed the "downscaler model" (DS). Air quality
monitoring data from the State and Local Air Monitoring Stations (SLAMS) and numerical output from
the Community Multiscale Air Quality (CMAQ) model were both input to DS to predict concentrations
at the 2010 US census tract centroids encompassed by the CMAQ modeling domain. Information on
EPA's air quality monitors, CMAQ model, and downscaler model is included to provide the background
and context for understanding the data output presented in this report. These estimates are intended for
use by statisticians and environmental scientists interested in the daily spatial distribution of ozone and
PM2.5.
DS essentially operates by calibrating CMAQ data to the observational data, and then uses the resulting
relationship to predict "observed" concentrations at new spatial points in the domain. Although similar
in principle to a linear regression, spatial modeling aspects have been incorporated for improving the
model fit, and a 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).
EPA's Office of Air and Radiation's (OAR) Office of Air Quality Planning and Standards (OAQPS)
provides air quality monitoring data and model estimates to the Centers for Disease Control and
Prevention (CDC) for use in their Environmental Public Health Tracking (EPHT) Network.CDC's EPHT
Network supports linkage of air quality data with human health outcome data for use by various public
health agencies throughout the U.S. The EPHT Network Program is a multidisciplinary collaboration
that involves the ongoing collection, integration, analysis, interpretation, and dissemination of data from:
environmental hazard monitoring activities; human exposure assessment information; and surveillance
of noninfectious health conditions. As part of the National EPHT Program efforts, the CDC led the
initiative to build the National EPHT Network (http:// www.cdc.gov/nceh/tracking/default.htm). The
National EPHT Program, with the EPHT Network as its cornerstone, is the CDC's response to requests
calling for improved understanding of how the environment affects human health. The EPHT Network is
designed to provide the means to identify, access, and organize hazard, exposure, and health data from a
1 Bayesian statistical modeling refers to methods that are based on Bayes' theorem, and model the world in terms of
probabilities based on previously acquired knowledge.
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variety of sources and to examine, analyze and interpret those data based on their spatial and temporal
characteristics.
Since 2002, EPA has collaborated with the CDC on the development of the EPHT Network. On
September 30, 2003, the Secretary of Health and Human Services (HHS) and the Administrator of EPA
signed a joint Memorandum of Understanding (MOU) with the objective of advancing efforts to
achieve mutual environmental public health goals2. HHS, acting through the CDC and the Agency for
Toxic Substances and Disease Registry (ATSDR), and EPA agreed to expand their cooperative
activities in support of the CDC EPHT Network and EPA's Central Data Exchange Node on the
Environmental Information Exchange Network in the following areas:
• Collecting, analyzing and interpreting environmental and health data from both agencies (HHS
and EPA).
• Collaborating on emerging information technology practices related to building, supporting, and
operating the CDC EPHT Network and the Environmental Information Exchange Network.
• Developing and validating additional environmental public health indicators.
• Sharing reliable environmental and public health data between their respective networks in an
efficient and effective manner.
• Consulting and informing each other about dissemination of results obtained through work
carried out under the MOU and the associated Interagency Agreement (IAG) between EPA and
CDC.
The best available statistical fusion model, air quality data, and CMAQ numerical model output were
used to develop the estimates. Fusion results can vary with different inputs and fusion modeling
approaches. As new and improved statistical models become available, EPA will provide updates.
Although these data have been processed on a computer system at the Environmental Protection Agency, no
warranty expressed or implied is made regarding the accuracy or utility of the data on any other system or
for general or scientific purposes, nor shall the act of distribution of the data constitute any such warranty. It
is also strongly recommended that careful attention be paid to the contents of the metadata file associated
with these data to evaluate data set limitations, restrictions or intended use. The U.S. Environmental
Protection Agency shall not be held liable for improper or incorrect use of the data described and/or
contained herein.
The four remaining sections and one appendix in the report are as follows:
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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• 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 https://www.epa.gov/clean-air-act-overview.
Under the CAA, the U.S. EPA has established standards, or limits, for six air pollutants known as the
criteria air pollutants: carbon monoxide (CO), lead (Pb), nitrogen dioxide (NO2), sulfur dioxide (SO2),
ozone (O3), and particulate matter (PM). These standards, called the National Ambient Air Quality
Standards (NAAQS), are designed to protect public health and the environment. The CAA established
two types of air quality standards. Primary standards set limits to protect public health, including the
health of "sensitive" populations such as asthmatics, children, and the elderly. Secondary standards set
limits to protect public welfare, including protection against decreased visibility, damage to animals,
crops, vegetation, and buildings. The CAA requires EPA to review these standards at least every five
years. For more specific information on the NAAQS, go to https://www.epa.gov/criteria-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. EPA's Integrated
Science Assessments and Risk and Exposure documents are available at
https://www.epa.gov/naaqs/ozone-o3-air-qualitv-standards. The current NAAQS for ozone (last revised
in 2015) is a daily maximum 8-hour average of 0.070 parts per million [ppm] (for details, see
https://www.epa.gOv/ozone-pollution/setting-and-reviewing-standards-control-ozone-pollution#standards.
The 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 PM10 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 https://www.epa.gov/amtic.
In 2009, approximately 43 percent of the US population was living within 10 kilometers of ozone and
PM2.5 monitoring sites. In terms of US Census Bureau tract locations, 31,341 out of 72,283 census tract
centroids were within 10 kilometers of ozone monitoring sites. Highly populated Eastern US and
California coasts are well covered by both ozone and PM2.5 monitoring network (Figure 2-1).
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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
Distance to the Nearest PM2.5
• 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
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
5 EPA uses exposure models to evaluate the health risks and environmental effects associated with exposure. These models
are limited by the availability of air quality estimates, https://www.epa.gov/technical-air-pollution-resources.
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samples. However, over the past several years, continuous monitors, which can automatically collect,
analyze, and report PM2.5 measurements on an hourly basis, have been introduced. These monitors are
available in most of the major metropolitan areas. Some of these continuous monitors have been
determined to be equivalent to the FRM monitors for regulatory purposes and are called FEM (Federal
Equivalent Methods).
2.2.4 Use of Air Quality Monitoring Data
Air quality monitoring data has been used to provide the information for the following situations:
(1) Assessing effectiveness of SIPs in addressing NAAQS nonattainment areas
(2) Characterizing local, state, and national air quality status and trends
(3) Associating health and environmental damage with air quality levels/concentrations
For the EPHT effort, EPA is providing air quality data to support efforts associated with (2), and (3)
above. Data supporting (3) is generated by EPA through the use of its air quality data and its downscaler
model.
Most studies that associate air quality with health outcomes use air monitoring as a surrogate for exposure
to the air pollutants being investigated. Many studies have used the monitoring networks operated by
state and federal agencies. Some studies perform special monitoring that can better represent exposure to
the air pollutants: community monitoring, near residences, in-house or work place monitoring, and
personal monitoring. For the EPHT program, special monitoring is generally not supported, though it
could be used on a case-by-case basis.
From proximity based exposure estimates to statistical interpolation, many approaches are developed for
estimating exposures to air pollutants using ambient monitoring data (Jerrett et al., 2005). Depending
upon the approach and the spatial and temporal distribution of ambient monitoring data, exposure
estimates to air pollutants may vary greatly in areas further apart from monitors (Bravo et al., 2012).
Factors like limited temporal coverage (i.e., PM2.5 monitors do not operate continuously such as recording
every third day or ozone monitors operate only certain part of the year) and limited spatial coverage (i. e.,
most monitors are located in urban areas and rural coverage is limited) hinder the ability of most of the
interpolation techniques that use monitoring data alone as the input. If we look at the example of Voronoi
Neighbor Averaging (VNA) (referred as the Nearest Neighbor Averaging in most literature), rural
estimates would be biased towards the urban estimates. To further explain this point, assume the scenario
of two cities with monitors and no monitors in the rural areas between, which is very plausible. Since
exposure estimates are guaranteed to be within the range of monitors in VNA, estimates for the rural areas
would be higher according to this scenario.
Air quality models may overcome some of the limitations that monitoring networks possess. Models such
as the Community Multi-Scale Air Quality (CMAQ) modeling systems can estimate concentrations in
reasonable temporal and spatial resolutions. However, these sophisticated air quality models are prone to
systematic biases since they depend upon so many variables (i.e., metrological models and emission
models) and complex chemical and physical process simulations.
Combining monitoring data with air quality models (via fusion or regression) may provide the best results
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in terms of estimating ambient air concentrations in space and time. EPA's eVNA6 is an example of an
earlier approach for merging air quality monitor data with CMAQ model predictions. The downscaler
model attempts to address some of the shortcomings in these earlier attempts to statistically combine
monitor and model predicted data, see published paper referenced in section 1 for more information about
the downscaler model. As discussed in the next section, there are two methods used in EPHT to provide
estimates of ambient concentrations of air pollutants: air quality monitoring data and the downscaler
model estimate, which is a statistical 'combination' of air quality monitor data and photochemical air
quality model predictions (e.g., CMAQ).
2.3 Air Quality Indicators Developed for the EPHT Network
Air quality indicators have been developed for use in the Environmental Public Health Tracking Network
by CDC using the ozone and PM2.5 data from EPA. The approach used divides "indicators" into two
categories. First, basic air quality measures were developed to compare air quality levels over space and
time within a public health context (e.g., using the NAAQS as a benchmark). Next, indicators were
developed that mathematically link air quality data to public health tracking data (e.g., daily PM2.5 levels
and hospitalization data for acute myocardial infarction). Table 2-3 and Table 2-4 describe the issues
impacting calculation of basic air quality indicators.
Table 2-2. Public Health Surveillance Goals and Current Status
Goal
Status
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.
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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Table 2-3. Basic Air Quality Indicators used in EPHT, derived from the EPA data delivered to
CDC
Ozone (daily 8-hr period with maximum concentration—ppm—by Federal Reference Method (FRM))
• Number of days with maximum ozone concentration over the NAAQS (or other relevant benchmarks (by county
and MSA)
• Number of person-days with maximum 8-hr average ozone concentration over the NAAQS & other relevant
benchmarks (by county and MSA)
¦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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3.1 Introduction to Emissions Data Development
The U.S. Environmental Protection Agency (EPA) developed an air quality modeling platform based
primarily on the 2014 National Emissions Inventory (NEI), Version 1 to process year 2014 emission data
for this project. This section provides a summary of the emissions inventory and emissions modeling
techniques applied to Criteria Air Pollutants (CAPs) and the following select Hazardous Air Pollutants
(HAPs) included in the modeling platform: chlorine (CI), hydrogen chloride (HQ), benzene,
acetaldehyde, formaldehyde, napthalene and methanol. This section also describes the approach and data
used to produce emissions inputs to the air quality model. The air quality modeling, meteorological inputs
and boundary conditions are described in a separate section.
The Community Multiscale Air Quality (CMAQ) model 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 version 6 (CB6) with chlorine chemistry used here within
CMAQ allows for explicit treatment of the VOC H APs naphthalene, benzene, acetaldehyde,
formaldehyde and methanol (NBAFM) and includes anthropogenic HAP emissions of HC1 and CI.
The effort to create the 2014 emission inputs for this study included development of emission inventories
for input to a 2014 modeling case, along with application of emissions modeling tools to convert the
inventories into the format and resolution needed by CMAQ. Year-specific fire and continuous emission
monitoring (CEM) data for electric generating units (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 4.5 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 2014 NEI, version 1
with some updates is the primary basis for the inputs to SMOKE. The NEI includes five main data
categories: a) nonpoint (formerly called "stationary area") sources; b) point sources; c) nonroad mobile
sources; d) onroad mobile sources; and e) fires. For CAPs, the NEI data are largely compiled from data
submitted by state, local and tribal (S/L/T) agencies. HAP emissions data are often augmented by EPA
when they are not voluntarily submitted to the NEI by S/L/T agencies. The NEI was compiled using the
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Emissions Inventory System (EIS). EIS includes hundreds of automated QA checks to improve data
quality, and it also supports release point (stack) coordinates separately from facility coordinates. EPA
collaboration with S/L/T agencies helped prevent duplication between point and nonpoint source
categories such as industrial boilers. The 2014 NEI Technical Support Document is available at
https://www.epa.gov/air-emissions-inventories/2014-national-emissions-inventorv-nei-technical-support-
document-tsd (EPA, 2016).
Point source data for the year 2014 as submitted to EIS were used for this study. EPA used the
SMAR.TFIR.E2 system to develop 2014 fire emissions. SMARTFIRE2 categorizes all fires as either
prescribed burning or wildfire categories, and includes improved emission factor estimates for prescribed
burning. Onroad and nonroad mobile source emissions for year 2014 were developed using
MOVES2014a. Canadian emissions reflect year 2013 and Mexican emissions reflect year 2014.
The methods used to process emissions for this study are similar to those documented for EPA's Version
7, 2014 Emissions Modeling Platform that was also used for the preliminary version of the 2014 National
Air Toxics Assessment (NATA), with the exception that many fewer HAPs are included in this platform.
A technical support document (TSD) for the 2014v7 platform is available here https://www.epa.gov/air-
emissions-modeling/2014-version-70-technical-support-document-tsd (EPA, 2017a) and includes
additional details regarding the data preparation and emissions modeling with the exception of the HAP
speciation.
The emissions modeling process, performed using SMOKE v4.5, apportions the emissions inventories
into the grid cells used by CMAQ and temporalizes the emissions into hourly values. In addition, the
pollutants in the inventories (e.g., NOx, PM and VOC) are split into the chemical species needed by
CM AQ. For the purposes of preparing the CM AQ- ready emissions, the NEI emissions inventories by
data category are split into emissions modeling "platform" sectors; and emissions from sources other than
the NEI are added, such as the Canadian, Mexican, and offshore inventories. Emissions sectors within the
emissions modeling platform are separated out from each other when the emissions for that sector are run
through all of the SMOKE programs, except the final merge, independently from emissions in the other
sectors. The final merge program called Mrggrid combines the sector-specific gridded, speciated and
temporalized emissions to create the final CMAQ-ready emissions inputs. Biogenic emissions are
computed and used by CM AQ as it runs.
Table 3-1 presents the sectors in the emissions modeling platform used to develop the year 2014
emissions for this project. The sector abbreviations are provided in italics; these abbreviations are used in
the SMOKE modeling scripts, the inventory file names, and throughout the remainder of this section.
Annual 2014 emission summaries for the U.S. anthropogenic sectors are shown in Table 3-2 (i.e.,
biogenic emissions are excluded). Table 3-3 provides a summary of emissions for the anthropogenic
sectors containing Canadian, Mexican and offshore sources. State total emissions for each sector are
provided in Appendix B, a workbook entitled "Appendix_B_2014_emissions_totals_by_sector.xlsx".
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Table 3-1. Platform Sectors Used in the Emissions Modeling Process
2014 Platform Sector (Abbrev)
NEI Category
Description and resolution of the data input to SMOKE
EGUs (ptegu)
Point
2014 point source EGUs. Replaced with hourly 2014
Continuous Emissions Monitoring System (CEMS) values
for NOX and S02, where the units are matched to the NEI.
Emissions for all sources not matched to CEMS data come
from 2014NEIvl. Annual resolution for sources not
matched to CEMS data, hourly for CEMS sources.
Point source oil and gas
(pt oilgcts)
Point
2014NEIvl point sources that include oil and gas
production emissions processes based on facilities with the
following NAICS: 211* (Oil and Gas Extraction), 213111
(Drilling Oil and Gas Wells) 213112 (Support Activities for
Oil and Gas Operations), 4861* (Pipeline Transportation of
Crude Oil), 4862* (Pipeline Transportation of Natural Gas).
Includes U.S. offshore oil production. Annual resolution.
Remaining non-EGU point
(ptnonipm)
Point
All 2014NEIvl point source records not matched to the
ptegu or pt oilgas sectors. Includes all aircraft and airport
ground support emissions and some rail yard emissions.
Annual resolution.
Point source fire (ptfire)
Fires
Point source day-specific wildfires and prescribed fires for
2014 computed using SMARTFIRE 2. Fires over 20,000
acres on a single day allocated to overlapping grid cells.
Point Source agricultural fires
(ptagfire)
Nonpoint
Agricultural fire sources that were developed by EPA as
point and day-specific emissions; they were put into the
nonpoint data category of the NEI, but in the platform, they
are treated as point sources.
Agricultural (cig)
Nonpoint
Ammonia emissions from 2014NEIv2 nonpoint livestock
and 2014NEIvl fertilizer application; county and annual
resolution.
Area fugitive dust (afdiist adj)
Nonpoint
PMio and PM2.5 fugitive dust sources from the 2014NEIvl
nonpoint inventory; including building construction, road
construction, agricultural dust, and road dust. The
emissions modeling adjustment applies a transport fraction
and a meteorology-based (precipitation and snow/ice cover)
zero-out. County and annual resolution.
Biogenic (beis)
Nonpoint
Biogenic emissions were left out of the CMAQ-ready
merged emissions, in favor of inline biogenics produced
during the CMAQ model run itself.
CI and C2 commercial marine
(cmv clc2)
Nonpoint
2014NEIvl Category 1 (CI) and Category 2 (C2),
commercial marine vessel (CMV) emissions. County and
annual resolution.
C3 commercial marine (cmv c3)
Nonpoint
Within state waters, 2014NEIvl Category 3 commercial
marine vessel (CMV) emissions. Outside of state waters
emissions are based on the Emissions Control Area (ECA)
inventory. Point (to allow for plume rise) and annual
resolution.
Remaining nonpoint (nonpt)
Nonpoint
2014NEIvl nonpoint sources not included in other platform
sectors with adjustments to remove chromium from fugitive
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2014 Platform Sector (Abbrev)
NEI Category
Description and resolution of the data input to SMOKE
dust categories (paved and unpaved roads, construction and
crops and livestock). County and annual resolution.
Nonpoint source oil and gas
(np oilgcts)
Nonpoint
2014NEIvl nonpoint sources from oil and gas-related
processes with specific adjustment in four unitah basin
counties in Utah to correct EPA augmented benzene,
toluene, ethylbenzene and xylenes. County and annual
resolution.
Locomotive (rail)
Nonpoint
Rail locomotives emissions from the 2014NEIvl. County
and annual resolution.
Residential Wood Combustion
(rwc)
Nonpoint
2014NEIvl nonpoint sources with residential wood
combustion (RWC) processes. County and annual
resolution.
Nonroad (nonroctd)
Nonroad
2014NEIvl nonroad equipment emissions developed with
the MOVES2014a using NONROAD2008 version NR08a
and new HAP emission factors than had been used in the
2011NEI. MOVES was used for all states except
California, which submitted their own emissions for the
2014NEIvl. County and monthly resolution.
Onroad (onroctd)
Onroad
2014 onroad mobile source gasoline and diesel vehicles
from parking lots and moving vehicles. Includes the
following modes: exhaust, extended idle, auxiliary power
units, evaporative, permeation, refueling, and brake and tire
wear. For all states except California, developed using
winter and summer MOVES emission factors tables
produced by MOVES2014a.
Onroad California
(onroctd cct ctdj)
Onroad
2014 California-provided CAP and metal HAP onroad
mobile source gasoline and diesel vehicles from parking lots
and moving vehicles based on Emission Factor (EMFAC),
gridded and temporalized using MOVES2014a. Volatile
organic compound (VOC) HAP emissions derived from
California-provided VOC emissions and MOVES-based
speciation.
Onroad Canada (onroctd cctn)
Non-US
Monthly year 2013 Canada (province resolution) onroad
mobile inventory.
Onroad Mexico (onroctd mex)
Non-US
Monthly year 2014 Mexico (municipio resolution) onroad
mobile inventory.
Other area fugitive dust sources
Non-US
Area fugitive dust sources from Canada 2013 inventory
with transport fraction and snow/ice adjustments based on
2014 meteorological data. Annual and province resolution.
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2014 Platform Sector (Abbrev)
NEI Category
Description and resolution of the data input to SMOKE
Other nonpoint and nonroad
(othar)
Non-US
Year 2013 Canada (province resolution) and projected year
2014 Mexico (municipio resolution) nonpoint and nonroad
mobile inventories, annual resolution.
Other point sources not from the
NEI (othpt)
Non-US
Point sources from Canada's 2013 inventory and Mexico
point sources projected to 2014. Annual resolution.
Point fires in Mexico and
Canada (ptfiremxca)
Non-US
Point source day-specific wildfires and prescribed fires for
2014 provided by Environment Canada with data for
missing months and for Mexico filled in using fires from the
Fire INventory (FINN) from National Center for
Atmospheric Research (NCAR) fires (NCAR, 2016 and
Wiedinmyer, C., 2011).
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Table 32. 2014 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.)
Sector
CO
NH3
NOx
PMio
PM2.5
SO2
voc
afdust_adj
6,991,664
975,147
ag
2,867,904
cmv_clc2
44,808
109
227,954
5,978
5,737
3,387
4,488
cmv_c3
11,300
28
112,333
4,221
3,809
38,721
5,248
nonpt
2,894,351
114,049
794,416
712,611
569,249
300,871
3,571,099
np_oilgas
820,021
15
793,601
20,628
20,196
37,794
3,104,473
nonroad
12,425,532
2,244
1,392,082
140,863
133,362
3,163
1,630,321
onroad
21,548,865
103,333
4,622,945
307,113
157,997
28,324
2,170,529
ptagfire
426,253
55,639
12,897
69,528
49,398
3,870
25,816
ptfire
16,949,926
273,912
246,873
1,746,224
1,481,934
130,085
3,921,240
ptegu
735,826
25,933
1,758,567
236,027
183,029
3,241,498
35,523
ptnonipm
2,055,540
64,809
1,193,886
525,313
292,816
880,822
828,083
pt_oilgas
190,142
333
398,535
11,652
11,165
43,576
132,875
rail
119,252
364
779,801
25,094
23,166
6,986
39,857
rwc
2,156,051
16,221
32,174
333,219
332,700
8,087
351,696
Continental
U.S. 60,377,867 3,524,893 12,366,065 11,130,134 4,239,702 4,727,182 15,821,249
Table 33. 2014 Non-US Emissions by Sector within Modeling Domain (tons/yr for Canada, Mexico,
Offshore)
Sector
CO
nh3
NOx
PM10
pm25
SO2
VOC I
Canada othafdust
1,725,731
338,480
Canada othar
2,809,336
496,889
615,888
414,236
228,545
62,379
1,101,710
Canada onroad_can
2,105,867
8,446
474,813
28,204
21,474
1,593
191,505
Canada othpt
1,102,908
17,323
646,981
91,095
47,323
999,891
782,440
Canada ptfire_mxca
6,701,372
4,574
150,552
730,230
618,866
66,034
1,629,916
Canada Subtotal
12,719,483
527,233
1,888,235
2,989,497
1,254,688
1,129,897
3,705,570
Mexico othar
232,017
206,491
212,636
114,414
53,378
7,628
503,968
Mexico onroad_mex
1,823,639
2,660
432,368
14,716
10,649
5,849
158,524
Mexico othpt
188,253
4,669
465,960
72,872
57,479
543,591
66,392
Mexico ptfire_mxca
251,658
4,875
11,048
29,482
24,900
1,813
86,215
Mexico Subtotal
2,495,567
218,695
1,122,012
231,483
146,406
558,882
815,100
Offshore to EEZ
109,269
183
873,194
26,989
25,142
148,445
27,864
Non-US SECAC3
32,807
0
386,133
32,865
30,234
243,088
13,904
2014 Total non-U.S.
15,357,126
746,111
4,269,573
3,280,833
1,456,471
2,080,312
4,562,438
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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
that may be characterized as units such as boilers, reactors, spray booths, kilns, etc. A unit may have
multiple processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas).
With a couple of minor exceptions, this section describes only NEI point sources within the contiguous
U.S. The offshore oil platform (pt oilgas sector) and category 3 CMV emissions (cmv_c3 sector) are
processed by SMOKE as point source inventories and are discussed later in this section. A complete NEI
is developed every three years, with 2014 being the most recently finished complete NEI. A
comprehensive description about the development of the 2014NEIvl is available in the 2014NEIvl TSD
(EPA, 2016a).
In preparation for modeling, the complete set of point sources in the NEI was exported from EIS into the
Flat File 2010 (FF10) format that is compatible with SMOKE (see
https://www.cmascenter.Org/smoke/documentation/4.0/html/ch08s02s08.htmn and was then split into
several sectors for modeling. After moving offshore oil platforms into the othpt sector, and dropping
sources without specific locations (i.e., the FIPS code ends in 777), initial versions of inventories for the
other three point source sectors were created from the remaining 2014 point sources. The point sectors
are: EGUs (ptegu), point source oil and gas extraction-related sources (pt oilgas) and the remaining 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). NBAFM pollutants from the point sectors were not utilized because VOC was
speciated without the use (i.e., integration) of VOC HAP pollutants from the inventory.
The ptnonipm and pt oilgas sector emissions were provided to SMOKE as annual emissions. For sources
in the ptegu sector that could be matched to 2014 CEMS data, hourly CEMS NOx and SO2 emissions for
2014 from EPA's Acid Rain Program were used rather than NEI emissions. For all other pollutants (e.g.,
VOC, PM2.5, 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 sector, annual emissions were
allocated to daily values using IPM region- and pollutant-specific profiles, and similarly, region- and
pollutant-specific diurnal profiles were applied to create hourly emissions.
The non-EGU stationary point source (ptnonipm) emissions were input to SMOKE as annual emissions.
The full description of how the NEI emissions were developed is provided in the NEI documentation, but
a brief summary of their development follows:
a. CAP and HAP data were provided by States, locals and tribes under the Air Emissions Reporting
Rule (AERR)
b. EPA corrected known issues and filled PM data gaps.
c. EPA added HAP data from the Toxic Release Inventory (TRI) where corresponding data was not
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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 the 3-digit tribal code in the NEI. This change was made because SMOKE requires
all sources to have a state/county FIPS code.
• Sources that did not have specific counties assigned (i.e., the county code ends in 777) were not
included in the modeling because it was only possible to know the state in which the sources
resided, but no more specific details related to the location of the sources were available.
• Stack parameters for point sources missing this information were filled in prior to modeling in
SMOKE.
Each of the point sectors is processed separately through SMOKE as described in the following
subsections.
3.2.1.1 EGU sector (ptegu)
The ptegu sector contains emissions from EGUs in the 2014 point source inventory that could be matched
to units found in the National Electric Energy Database System (NEEDS) v5.16 that is used by the
Integrated Planning Model (IPM) to develop future year EGU emissions. It was necessary to put these
EGlJs into a separate sector in the platform because EGlJs use different temporal profiles than other
sources in the point sector and it is useful to segregate these emissions from the rest of the point sources
to facilitate summaries of the data. Sources not matched to units found in NEEDS are placed into the
ptoilgas or ptnonipm sectors. For studies with future year cases, the sources in the ptegu sector are fully
replaced with the emissions output from IPM. It is therefore important that the matching between the NEI
and NEEDS database be as complete as possible because there can be double-counting of emissions in
future year modeling scenarios if emissions for units are projected by IPM are not properly matched to the
units in the point source inventory.
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
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.
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For sources not matched to CEMS data, daily emissions were computed from the NEI annual emissions
using average CEMS data profiles specific to fuel type, pollutant (i.e., NOx, SO2, and other), and IPM
region. Note that pollutants other than NOx, SO2 are allocated based on heat input. To allocate emissions
to each hour of the day, diurnal profiles were created using average CEMS data for heat input specific to
fuel type and IPM region. Sources identified as municipal waste combustors and co-generation units were
temporally allocated using the same emissions for each hour of the year because these sources are
assumed to operate at the same level continuously.
3.2.1.2 Point Oil and Gas Sector (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 2014 oil and gas emissions can be found in Section 4.16 of the 2014NEIvl TSD.
The pt oilgas sector includes emissions from offshore oil platforms.
Table 3-4. Point source oil and gas sector NAICS Codes
NAICS
NAICS description
2111
Oil and Gas Extraction
4862
Pipeline Transportation of Natural Gas
21111
Oil and Gas Extraction
48611
Pipeline Transportation of Crude Oil
48621
Pipeline Transportation of Natural Gas
211111
Crude Petroleum and Natural Gas Extraction
211112
Natural Gas Liquid Extraction
213111
Drilling Oil and Gas Wells
213112
Support Activities for Oil and Gas Operations
486110
Pipeline Transportation of Crude Oil
486210
Pipeline Transportation of Natural Gas
3.2.1.3 Non-IPM Sector (ptnonipm)
Except for some minor exceptions, the non-IPM (ptnonipm) sector contains the point sources that are not
in the ptegu or pt oilgas sectors. For the most part, the ptnonipm sector reflects the non-EGU sources of
the NEI point inventory; however, it is likely that some low-emitting EGUs not matched to the NEEDS
database or to CEMS data are in the ptnonipm sector.
The ptnonipm sector contains a small amount of fugitive dust PM emissions from vehicular traffic on
paved or unpaved roads at industrial facilities, coal handling at coal mines, and grain elevators. Sources
with state/county FIPS code ending with "777" are in the NEI but are not included in any modeling
sectors. These sources typically represent mobile (temporary) asphalt plants that are only reported for
some states, and are generally in a fixed location for only a part of the year and are therefore difficult to
allocate to specific places and days as is needed for modeling. Therefore, these sources are dropped from
the point-based sectors in the modeling platform.
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3.2.2 Day-Specific Point Source Fires (ptfire)
Wildfire and prescribed burning emissions are contained in the ptfire sector. The ptfire sector has emissions
provided at geographic coordinates (point locations) and has daily emissions values. The ptfire sector excludes
agricultural burning and other open burning sources that are included in the ptagfire sector. Emissions are 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 2014 fires were developed using SMARTFIRE 2
(Sullivan, et al., 2008), which uses the National Oceanic and Atmospheric Administration's (NOAA's)
Hazard Mapping System (HMS) fire location information and for 2014 also uses state/local/tribal data as
input. Additional inputs include the CONSUMEv3.0 software application (Joint Fire Science Program,
2009) and the Fuel Characteristic Classification System (FCCS) fuel-loading database to estimate fire
emissions from wildfires and prescribed burns on a daily basis. The method involves the reconciliation of
ICS-209 reports (Incident Status Summary Reports) and GeoMAC Shapefiles with satellite-based fire
detections to determine spatial and temporal information about the fires. A functional diagram of the
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). More information is available in the
2014NEIv 1 TSD.
A difference between the fires for this study and those in the NEI is that the proportion of emissions
allocated to flaming versus smoldering SCCs were adjusted. Flaming fractions were calculated for each
fire based on the flaming and smoldering consumption divided by the total consumption. Smoldering
fractions were calculated by dividing the residual consumption by the total consumption. The fractions
were then applied to the fire emissions in the 2014NEM to obtained revised emissions for the flaming
and smoldering SCCs. The total emissions by state were unchanged, but they were reapportioned to the
flaming and smoldering SCCs to facilitate a more realistic plume rise for fires. Washington state
emissions were adjusted according to emissions supplied by the state. Monthly factors of state data to NEI
data by county, SCC, and pollutant were calculated then applied to the existing daily and annual
emissions data in the FF10 for Washington state only.
Large fires of more than 20,000 acres in a single day were split using GeoM AC
(https://www.geomac.gov/) fire shapes, where available, or otherwise using a circle centered on the detect
1 at/1 on based on 12US2 grid cell overlap. The resulting split fires have emissions and area apportioned
from the original fire into the grid cells based on fraction of area overlap between the fire shape and the
cell. The idea is to prevent all of the emissions from a very large fire from going into a single grid cell,
when in reality the fire emissions were more dispersed than a single point. The area of each of the
"subfires" was computed in proportion to the overlap with that grid cell. These "subfires" were given new
names that were the same as the original, but with "_a", "_b", "_c", and "_d" appended as needed.
The SMOKE-ready inventory files created from the raw daily fires contain both CAPs and HAPs. The
BAFM HAP emissions from the inventory were obtained using VOC speciation profiles (i.e., a "no-
integrate noHAP" use case).
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3.2.3 Nonpoint Sources (afdust, ag, nonpt, np oilgas, rwc)
Several modeling platform sectors were created from the 2014NEIvl nonpoint inventory. This section
describes the stationary nonpoint sources. Locomotives, CI and C2 CMV, and C3 CMV are also
included the 2014NEIvl nonpoint data category, but are mobile sources and are described in a later
section. The 2014NEIvl TSD includes documentation for the nonpoint data.
The nonpoint tribal-submitted emissions are dropped during spatial processing with SMOKE due to the
configuration of the spatial surrogates, which are available by county, but not at the tribal level. In
addition, possible double-counting with county-level emissions is prevented. These omissions are not
expected to have an impact on the results of the air quality modeling at the 12-km scales used for this
platform.
In the rest of this section, each of the platform sectors into which the 2014 nonpoint NEI was divided is
described, along with any data that were updated or replaced with non-NEI data.
3.2.3.1 Area Fugitive Dust Sector (afdust)
The area-source fugitive dust (afdust) sector contains 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.
Where states submitted afdust data, it was assumed that the state-submitted data were not met-adjusted
and therefore the meteorological adjustments were applied. Thus, if states submitted data that were met-
adjusted, these sources would have been adjusted for meteorology twice. Even with that possibility, air
quality modeling shows that, in general, dust is frequently overestimated in the air quality modeling
results.
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3.2.3.2 Agricultural Ammonia Sector (ag)
The agricultural NH3 (ag) sector includes livestock emissions from the 2014NEIv2 nonpoint inventory
and agricultural fertilizer application emissions from the 2014NEIvl nonpoint inventory. The livestock
and fertilizer emissions in this sector are based only on the SCCs starting with 2805 and 2801. The
livestock SCCs are related to beef and dairy cattle, poultry production and waste, swine production, waste
from horses and ponies, and production and waste for sheep, lambs, and goats. The fertilizer SCCs consist
of 15 specific types of ammonia-based fertilizer and one for miscellaneous fertilizers. The "ag" sector
includes all of the NH3 emissions from fertilizer from the NEI. However, the "ag" sector does not include
all of the livestock NH3 emissions, as there is a very small amount of NH3 emissions from livestock in the
ptnonipm inventory (as point sources) in California (883 tons; less than 0.5 percent of state total) and
Wisconsin (356 tons; about 1 percent of state total).
The agricultural NH3 emissions in the NEI are a mix of state-submitted data and EPA estimates. For
2014, the EPA estimates used new methodologies for both livestock and fertilizer emissions. Livestock
emissions were estimated based on daily emission factors by animal and county from a model developed
by Carnegie Mellon University (CMU) (Pinder, 2004, McQuilling, 2015) and 2012 and 2014 U.S.
Department of Agriculture (USD A) agricultural census data. The annual 2014NEIvl estimates for
livestock were updated with revised animal counts for 2014NEIv2 and the resulting 2014NEIv2 numbers
for NH3 were used in this study. Although the 2014NEIv2 also includes VOC for livestock, those
emission were not used in this study because the numbers had not yet been fully evaluated. For
California, state-provided emissions were used for counties and SCCs for which state data were available,
while any county-SCC combinations that used EPA data in 2014NEIvl were updated to use the data from
2014NEIv2. Double-counts between EPA and California data were removed where different SCCs for the
same animal type (e.g., beef and swine) were used, with preference given to the California data.
Annual fertilizer emissions were submitted by three states for all or part of the sector as shown in
parentheses: California (68 percent), Illinois (100 percent) and Georgia (58 percent). The remainder,
estimated by EPA, employed a methodology that uses the bidirectional (bi-di) version of CMAQ and the
Fertilizer Emissions Scenario Tool for CMAQ FEST-C (vl.2). This is described in Section 4.4 of the
2014 NEIvl TSD. These data were used at annual resolution.
3.2.3.3 Agricultural fires (ptagfire)
In the NEI, agricultural fires are stored as county-annual emissions and are part of the nonpoint data
category. For this study agricultural fires are modeled as day specific fires derived from satellite data in a
similar way to the emissions in ptfire, and state-provided agricultural fire data in AZ, CA, FL, GA, HI,
ID, IL, IN, IA, NJ, SC and WA are not used in this study. The first three levels of descriptions for the
agricultural burning SCCs are: 1) Fires - Agricultural Field Burning; Miscellaneous Area Sources;
2) Agriculture Production - Crops - as nonpoint; and 3) Agricultural Field Burning - whole field set on
fire. The SCC 2801500000 does not specify the crop type or burn method, while the more specific SCCs
specify field or orchard crops and, in some cases, the specific crop being grown. New agricultural field
burning SCCs were added to the NEI for 2014 to account for grass/pasture burning (also known as
rangeland burning) which is included the agriculture field burning sector of the NEI. The EPA's
estimation methods were improved from those used in the 2011 NEI and are documented in Section 4.11
of the 2014NEIvl TSD. Improvements include use of multiple satellite detection database and crop level
land use information.
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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. The
EPA estimated emissions for all counties with 2014 oil and gas activity data with the Oil and Gas Tool,
and many S/L/T agencies also submitted nonpoint oil and gas data. Where S/L/T submitted nonpoint
CAPS but no HAPs, the EPA augmented the HAPs using HAP augmentation factors (county and SCC
level) created from the Oil and Gas Tool. The types of sources covered include drill rigs, workover rigs,
artificial lift, hydraulic fracturing engines, pneumatic pumps and other devices, storage tanks, flares, truck
loading, compressor engines, and dehydrators.
An error was discovered in the 2014NEIvl for nonpoint oil and gas emissions of benzene, ethyl benzene,
xylenes and toluene for four counties in the Uinta basin. To compensate for this, emissions from VOC
species were genterated using basin specific speciation profiles instead of using the emissions from the
inventory. The updates affected the following SCCs: 2310010200 (Oil Well Tanks - Flashing &
Standing/Working/Breathing); 2310011201 (Tank Truck/Railcar Loading: Crude Oil); 2310021010
(Storage Tanks: Condensate); and 2310021030 (Tank Truck/Railcar Loading: Condensate), and generally
reduced these HAPs from the 2 condensate-related SCCs and increased benzene by a factor of 3 forthe
two oil tank-related SCCs. Overall, the np oilgas emissions in Utah in the platform are 31 tons lower for
benzene, 29 tons lower for ethylbenzene, 213 tons lower for toluene, and 335 tons lower for xylenes than
the 2014NEIvl.
3.2.3.5 Residential Wood Combustion Sector (rwc)
The residential wood combustion (rwc) sector includes residential wood burning devices such as
fireplaces, fireplaces with inserts (inserts), free standing woodstoves, pellet stoves, outdoor hydronic
heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in firepots and
chimneas. Free standing woodstoves and inserts are further differentiated into three categories:
1) conventional (not EPA certified); 2) EPA certified, catalytic; and 3) EPA certified, noncatalytic.
Generally speaking, the conventional units were constructed prior to 1988. Units constructed after 1988
have to meet EPA emission standards and they are either catalytic or non-catalytic. As with the other
nonpoint categories, a mix of S/L and EPA estimates were used. The EPA's estimates use updated
methodologies for activity data and some changes to emission factors. For more information on the
development of the residential wood combustion emissions, see Section 4.14 of the 2014NEIvl TSD.
3.2.3.6 Other Nonpoint Sources (nonpt)
Stationary nonpoint sources that were not subdivided into the afdust, ag, np oilgas, or rwc sectors were
assigned to the "nonpt" sector. Locomotives and CMV mobile sources from the 2014NEIvl nonpoint
inventory are described with the mobile sources. The types of sources in the nonpt sector include:
• stationary source fuel combustion, including industrial, commercial, and residential and orchard
heaters;
• chemical manufacturing;
• industrial processes such as commercial cooking, metal production, mineral processes, petroleum
refining, wood products, fabricated metals, and refrigeration;
• solvent utilization for surface coatings such as architectural coatings, auto refinishing, traffic
marking, textile production, furniture finishing, and coating of paper, plastic, metal, appliances,
and motor vehicles;
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• solvent utilization for degreasing of furniture, metals, auto repair, electronics, and manufacturing;
• solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal care
products, household products, adhesives and sealants;
• solvent utilization for asphalt application and roofing, and pesticide application;
• storage and transport of petroleum for uses such as portable gas cans, bulk terminals, gasoline
service stations, aviation, and marine vessels;
• storage and transport of chemicals;
• waste disposal, treatment, and recovery via incineration, open burning, landfills, and composting;
• miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive repair
shops.
The nonpt sector includes emission estimates for Portable Fuel Containers (PFCs), also known as "gas
cans." The PFC inventory consists of five distinct sources of PFC emissions, further distinguished by
residential or commercial use. The five sources are: (1) displacement of the vapor within the can; (2)
spillage of gasoline while filling the can; (3) spillage of gasoline during transport; (4) emissions due to
evaporation (i.e., diurnal emissions); and (5) emissions due to permeation. Note that spillage and vapor
displacement associated with using PFCs to refuel nonroad equipment are included in the nonroad
inventory.
3.2.4 Biogenic Sources (beis)
Biogenic emissions were computed based on the same 14j version of the 2014 meteorology data used for
the air quality modeling, and were developed using the Biogenic Emission Inventory System version 3.61
(BEIS3.61) within SMOKE. The BEIS3.61 creates gridded, hourly, model-species emissions from
vegetation and soils. It estimates CO, VOC (most notably isoprene, terpene, and sesquiterpene), and NO
emissions for the contiguous U.S. and for portions of Mexico and Canada. In the BEIS 3.61 two-layer
canopy model, the layer structure varies with light intensity and solar zenith angle (Pouliot and Bash,
2015). Both layers include estimates of sunlit and shaded leaf area based on solar zenith angle and light
intensity, direct and diffuse solar radiation, and leaf temperature (Bash et al., 2015). The new algorithm
requires additional meteorological variables over previous versions of BEIS. The variables output from
the Meteorology-Chemistry Interface Processor (MCIP) that are used to convert WRF outputs to CMAQ
inputs are shown in Table 3-5.
Table 3-5. Meteorological variables required by BEIS 3.61
Variable
Description
LAI
leaf-area index
PRSFC
surface pressure
Q2
mixing ratio at 2 m
RC
convective precipitation per met TSTEP
RGRND
solar rad reaching sfc
RN
nonconvective precipitation per met TSTEP
RSTOMI
inverse of bulk stomatal resistance
SLYTP
soil texture type by USD A category
SOIM1
volumetric soil moisture in top cm
SOIT1
soil temperature in top cm
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Variable
Description
TEMPG
skin temperature at ground
USTAR
cell averaged friction velocity
RADYNI
inverse of aerodynamic resistance
TEMP2
temperature at 2 m
The BEIS3.61 was used in conjunction with Version 4.1 of the Biogenic Emissions Landuse Database
(BELD4.1). The BELD version 4.1 is based on an updated version of the USDA-USFS Forest Inventory
and Analysis (FIA) vegetation speciation based data from 2001 to 2014 from the FIA version 5.1.
Canopy coverage is based on the Landsat satellite National Land Cover Database (NLCD) product from
2011. The FIA includes approximately 250,000 representative plots of species fraction data that are within
approximately 75 km of one another in areas identified as forest by the NLCD canopy coverage. The
2011 NLCD provides land cover information with a native data grid spacing of 30 meters. For land areas
outside the conterminous United States, 500 meter grid spacing land cover data from the Moderate
Resolution Imaging Spectroradiometer (MODIS) is used. BELDv4.1 also incorporates the following:
• 30 meter NASA's Shuttle Radar Topography Mission (SRTM) elevation data
(http://www2.jpl.nasa.gov/srtm/) to more accurately define the elevation ranges of the vegetation
species than in previous versions; and
• 2011 30 meter USD A Cropland Data Layer (CDL) data
(http://www.nass.usda.gov/research/Cropland/Release/).
Biogenic emissions computed with BEIS version 3.61 were left out of the CMAQ-ready merged
emissions, in favor of inline biogenics produced during the CMAQ model run itself.
3.2.5 Mobile Sources (onroad, onroadcaadj, nonroad, cmv_clc2, cmv_c3, rail)
Mobile sources are emissions from vehicles that move and include several sectors. Onroad mobile source
emissions result from motorized vehicles that are normally operated on public roadways. These include
passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks, heavy-duty trucks, and
buses. Nonroad mobile source emissions are from vehicles that do not operate on roads such as tractors,
construction equipment, lawnmowers, and recreational marine vessels. All nonroad emissions are treated
as county-specific low-level emissions (i.e., they are released into model layer 1).
Commercial marine vessel (CMV) emissions are split into two sectors: emissions from Category 1 and
Category 2 vessels are in the cmv c 1 c2 sector, and emissions from the larger Category 3 vessels are in the
cmv_c3 sector where they are treated as point sources with plume rise. Locomotive emissions are in the
rail sector. Having the emissions split into these sectors facilitates separating them in summaries and also
allows for the largest vessels to be modeled with plume rise.
3.2.5.1 Onroad (onroad)
Onroad mobile sources include emissions from motorized vehicles that are normally operate on public
roadways. These include passenger cars, motorcycles, minivans, sport-utility vehicles, light-duty trucks,
heavy-duty trucks, and buses. The sources are further divided between diesel, gasoline, E-85, and
compressed natural gas (CNG) vehicles. The sector characterizes emissions from parked vehicle
processes (e.g., starts, hot soak, and extended idle) as well as from on-network processes (i.e., from
vehicles moving along the roads).
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The onroad SCCs in the modeling platform are more finely resolved than those in the NEI, because the
NEI SCCs distinguish vehicles and fuels, but in the platform they also distinguish between emissions on
roadways, off-network, extended idle, and the various MOVES road-types. For more details on the
approach and for a summary of the inputs submitted by states, see the section 6.4.1 of the 2014NEIvl
TSD.
Except for California, onroad emissions are generated using the SMOKE-MOVES interface that leverages
MOVES generated emission factors (http://www.epa.gov/otaq/models/moves/index.htm). county and
SCC-specific activity data, and hourly meteorological data. SMOKE-MOVES takes into account the
temperature sensitivity of the on-road emissions. Specifically, EPA used MOVES inputs for
representative counties, vehicle miles traveled (VMT), vehicle population (VPOP), and hoteling hours
data for all counties, along with tools that integrated the MOVES model with SMOKE. In this way, it
was possible to take advantage of the gridded hourly temperature data available from meteorological
modeling that are also used for air quality modeling.
SMOKE-MOVES makes use of emission rate "lookup" tables generated by MOVES that differentiate
emissions by process (i.e., running, start, vapor venting, etc.), vehicle type, road type, temperature, speed,
hour of day, etc. To generate the MOVES emission rates that could be applied across the U.S., EPA used
an automated process to run MOVES to produce year 2014-specific emission factors by temperature and
speed for a series of "representative counties," to which every other county was mapped. The
representative counties for which emission factors are generated are selected according to their state,
elevation, fuels, age distribution, ramp fraction, and inspection and maintenance programs. Each county
is then mapped to a representative county based on its similarity to the representative county with respect
to those attributes. For this study, there are 297 representative counties. A detailed discussion of the
representative counties is in the 2014NEIvl TSD, Section 6.6.2.
Once representative counties have been identified, emission factors are generated with MOVES for each
representative county and for two "fuel months" - January to represent winter months, and July to
represent summer months - due to the different types of fuels used. SMOKE selects the appropriate
MOVES emissions rates for each county, hourly temperature, SCC, and speed bin and multiplies the
emission rate by appropriate activity data. For on-roadway emissions, vehicle miles travelled (VMT) is
the activity data, vehicle population (VPOP) is used for many off-network processes, and hoteling hours
are used to develop emissions for extended idling of combination long-haul trucks. These calculations are
done for every county and grid cell in the continental U.S. for each hour of the year.
The SMOKE-MOVES process for creating the model-ready emissions consists of the following steps:
1) Determine which counties will be used to represent other counties in the MOVES runs.
2) Determine which months will be used to represent other month's fuel characteristics.
3) Create inputs needed only by MOVES. MOVES requires county-specific information on
vehicle populations, age distributions, and inspection-maintenance programs for each of the
representative counties.
4) Create inputs needed both by MOVES and by SMOKE, including temperatures and activity
data.
5) Run MOVES to create emission factor tables for the temperatures found in each county.
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6) Run SMOKE to apply the emission factors to activity data (VMT, VPOP, and HOTELING) to
calculate emissions based on the gridded hourly temperatures in the meteorological data.
7) Aggregate the results to the county-SCC level for summaries and quality assurance.
The onroad emissions are processed in four processing streams that are merged together into the onroad
sector emissions after each of the four streams have been processed:
• rate-per-distance (RPD) uses VMT as the activity data plus speed and speed profile information to
compute on-network emissions from exhaust, evaporative, permeation, refueling, and brake and
tire wear processes;
• rate-per-vehicle (RPV) uses VPOP activity data to compute off-network emissions from exhaust,
evaporative, permeation, and refueling processes;
• rate-per-profile (RPP) uses VPOP activity data to compute off-network emissions from
evaporative fuel vapor venting, including hot soak (immediately after a trip) and diurnal (vehicle
parked for a long period) emissions; and
• rate-per-hour (RPH) uses hoteling hours activity data to compute off-network emissions for idling
of long-haul trucks from extended idling and auxiliary power unit process.
The onroad emissions inputs for the platform are the same as for the emissions in the onroad data category
of the 2014NEIvl, described in more detail in Sections 6.4 and 6.5 of the 2014NEIvl TSD. These inputs
are:
• MOVES County databases (CDBs) including Low Emission Vehicle (LEV) table
• Representative counties
• Fuel months
• Meteorology
• Activity data (VMT, VPOP, speed, HOTELING)
An additional step was taken for the refueling emissions. Colorado submitted point emissions for
refueling for some counties7. For these counties, the EPA zeroed out the onroad estimates of refueling
(i.e., SCCs =220xxxxx62) so that the states' point emissions would take precedence. The onroad
refueling emissions were zeroed out using the adjustment factor file (CFPRO) and Movesmrg.
For more detailed information on the methods used to develop the 2014 onroad mobile source emissions
and the input data sets, see Section 6.6 of the 2014NEIvl TSD.
California is the only state agency for which submitted onroad emissions were used in the 2014 NEI vl
and 2014v7.0 platform. California uses their own emission model, EMFAC, which uses emission
inventory codes (EICs) to characterize the emission processes instead of SCCs. The EPA and California
worked together to develop a code mapping to better match EMFAC's EICs to EPA MOVES' detailed set
of SCCs that distinguish between off-network and on-network and brake and tire wear emissions. This
detail is needed for modeling but not for the NEI. This code mapping is provided in
"2014vl_EICtoEPA_SCCmapping.xlsx." California then provided their CAP and HAP emissions by
county using EPA SCCs after applying the mapping. There was one change made after the mapping: the
vehicle/fuel type combination gas intercity buses (first 6 digits of the SCC = 220141), that is not
7 There were 52 counties in Colorado that had point emissions for refueling. Outside Colorado, it was determined that
refueling emissions in the 2014 NEIvl point did not significantly duplicate the refueling emissions in onroad.
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generated using MOVES, was changed to gasoline single unit short-haul trucks (220152) for consistency
with the modeling inventory.
The California onroad mobile source emissions were created through a hybrid approach of combining
state-supplied annual emissions with EPA-developed SMOKE-MOVES runs. Through this approach, the
platform was able to reflect the unique rules in California, while leveraging the more detailed SCCs and
the highly resolved spatial patterns, temporal patterns, and speciation from SMOKE-MOVES. The basic
steps involved in temporally allocating onroad emissions from California based on SMOKE-MOVES
results were:
1) Run CA using EPA inputs through SMOKE-MOVES to produce hourly 2014 emissions hereafter
known as "EPA estimates." These EPA estimates for CA are run in a separate sector called
"onroadca."
2) Calculate ratios between state-supplied emissions and EPA estimates. These were calculated for
each county/SCC/pollutant combination. Unlike in previous platforms, the California data
separated off and on-network emissions and extended idling. However, the on-network did not
provide specific road types, and California's emissions did not include information for vehicles
fueled by E-85, so these differentiations were obtained using MOVES.
3) Create an adjustment factor file (CFPRO) that includes EPA-to-state estimate ratios.
4) Rerun CA through SMOKE-MOVES using EPA inputs and the new adjustment factor file.
Through this process, adjusted model-ready files were created that sum to annual totals from California,
but have the temporal and spatial patterns reflecting the highly resolved meteorology and SMOKE-
MOVES. After adjusting the emissions, this sector is called "onroadcaadj " Note that in emission
summaries, the emissions from the "onroad" and "onroad ca adj" sectors are summed and designated as
the emissions for the onroad sector.
3.2.5.2 MOVES-based Nonroad Mobile Sources (nottroad)
The nonroad equipment emissions in the platform and the NEI result primarily from running the
MOVES2014a model, which incorporates the NONROAD2008 model. MOVES2014a replaces NMIM,
which was used for 2011 and earlier NEIs. MOVES2014a provides a complete set of HAPs and
incorporates updated nonroad emission factors for HAPs. MOVES2014a was used for all states other
than California, which uses their own model. Additional details on the development of the 2014NEIvl
nonroad emissions are available in Section 4.5 the 2014NEIvl TSD.
The magnitude of the annual emissions in the nonroad platform are equivalent to the emissions in the
nonroad data category of the 2014NEIvl. However, the platform has monthly emission totals, which are
provided by MOVES2014a and contain additional pollutants used in the emissions modeling. The
emissions in the modeling platform include NONHAPTOG and ETHANOL, which are not included in
the NEI. NONHAPTOG is the difference between total organic gases (TOG) and explicit species that are
estimated separately such as benzene, toluene, styrene, ethanol, and numerous other compounds and are
integrated into the chemical speciation process. MOVES2014a provides estimates of NONHAPTOG
along with the speciation profile code for the NONHAPTOG emission source. This is accomplished by
using NONHAPTOG#### as the pollutant code in the FF10 inventory file, where #### is a speciation
profile code. Since speciation profiles are applied by SCC and pollutant, no changes to SMOKE were
needed in order to use the FF10 with this profile information. This approach is not used for California,
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because their model provides VOC and traditional speciation is performed in SMOKE instead.
Nonroad emissions for California submitted to NEI were developed using the California Emissions
Projection Analysis Model (CEPAM) that supports various California off-road regulations.
Documentation of the CARB offroad mobile methodology, including CMV sector data, is provided at:
http://www.arb.ca.gOv/msei/categories.htm#offroad motor vehicles. The CARB-supplied nonroad annual
inventory emissions values were temporalized to monthly values using monthly temporal profiles applied
in SMOKE by SCC. Some VOC emissions were added to California to account for situations when VOC
HAP emissions were included in the inventory, but VOC emissions were either less than the sum of the
VOC HAP emissions, or were missing entirely. These additional VOC emissions were computed by
summing benzene, acetaldehyde, formaldehyde, and naphthalene for the specific sources.
3.2.5.3 Locomotive (rail)
The rail sector includes all locomotives in the NEI nonpoint data category. This sector excludes railway
maintenance locomotives and point source yard locomotives. Railway maintenance emissions are
included in the nonroad sector. The point source yard locomotives are included in the ptnonipm sector.
The nonpoint rail data are a mix of S/L and EPA data. For 2014NEIvl, the EPA data were carried
forward from the 2011 NEI. For more information on locomotive sources in the NEI, see Section 4.20 of
the 2014NEIvl TSD.
3.2.5.4 Category 1,2, and3 commercial marine vessels (cmv_clc2 and cmv_3)
The cmv_clc2 sector contains Category 1 and 2 CMV emissions from the 2014 NEIvl. Category 1 and 2
vessels use diesel fuel. All emissions in this sector are annual and at county-SCC resolution; however, in
the NEI they are provided at the sub-county level (port or underway shape ids) and by SCC and emission
type (e.g., hoteling, maneuvering). This sub-county data in the NEI are used to create spatial surrogates.
The emissions for the CMV sector are equivalent to those in the 2014NEIvl nonpoint inventory. For more
information on CMV sources in the NEI, see Section 4.3 of the 2014NEIvl TSD. CI and C2 emissions
that occur outside of state waters are not assigned to states. All CMV emissions in the cmv_clc2 sector
are treated as nonpoint sources, and are placed in layer 1 and allocated to grid cells using spatial
surrogates.
The Category 3 CMV vessels in the cmv_c3 sector use residual oil. The cmv_c3 sector uses 2014NEIvl
emissions in state waters but excludes NEI C3 emissions in Federal Waters (FIPS codes beginning with
85). Instead, more spatially resolved emissions from the Emissions Control Area-International Marine
Organization (ECA-IMO)-based C3 CMV are used. The C3 CMV emissions are treated as point sources
and were developed based on a 4-km resolution ASCII raster format dataset that preserves shipping lanes
and extends within and beyond the federal waters. The treatment of these emissions as point sources
allows for them to have plume rise when modeled by SMOKE and CMAQ. This dataset has been used
since the Emissions Control Area-International Marine Organization (ECA-IMO) project began in 2005,
although it was then known as the Sulfur Emissions Control Area (SECA). The ECA-IMO emissions
consist of large marine diesel engines (at or above 30 liters/cylinder) that until recently were allowed to
meet relatively modest emission requirements and as a result these ships would often burn residual fuel in
that region. The emissions in this sector are comprised of primarily foreign-flagged ocean-going vessels,
referred to as C3 CMV ships. The C3 portion of the CMV inventory includes these ships in several intra-
port modes (i.e., cruising, hoteling, reduced speed zone, maneuvering, and idling) and an underway mode,
and includes near-port auxiliary engine emissions.
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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 emi ssion 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-em i ssi ons-mari ne-di esel. The base year for the ECA inventory is 2002 and consists of these
CAPs: PM10, PM2.5, CO, CO2, M h, NOx, SOx (assumed to be SO2), and hydrocarbons (assumed to be
VOC). EPA developed regional growth (activity-based) factors that were applied to create the 2012
inventory from the 2002 data. The geographic regions listed in the table are shown in Figure 31.
* Technically, these are not really "FIPS" state-county codes, but are treated as such in the inventory and
emissions processing.
Figure 3-1. Illustration of regional modeling domains in ECA-IMO study
The East Coast and Gulf Coast regions were divided along a line roughly through Key Largo (longitude
80° 26' West). Technically, the EEZ FIPS are not really "FIPS" state-county codes, but are treated as
such in the inventory and emissions processing. The Canadian near-shore emissions were assigned to
province-level FIPS codes and paired those to region classifications for British Columbia (North Pacific),
Ontario (Great Lakes) and Nova Scotia (East Coast).
The emissions were converted to SMOKE point source inventory format as described in
https://www3.epa.gov/ttn/chief/conference/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. Note that the year 2011 emissions were used for this 2014 study.
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The assignment of U.S. state/county FIPS codes was restricted to state-federal water boundaries data from
the Mineral Management Service (MMS) that extend approximately 3 to 10 nautical miles (nm) off shore.
Emissions outside the 3 to 10 mile MMS boundary, but within the approximately 200 nm EEZ boundaries
in Figure 3-1, were projected to year 2011 using the same regional adjustment factors as the U.S.
emissions; however, the state/county FIPS codes were assigned as "EEZ" codes and those emissions
processed in the "othpt" sector. Note that state boundaries in the Great Lakes are an exception, extending
through the middle of each lake such that all emissions in the Great Lakes are assigned to a U.S. county or
Ontario. This holds true for Midwest states and other states such as Pennsylvania and New York. The
classification of emissions to U.S. and Canadian FIPS codes was needed to avoid double-counting of C3
CMV U.S. emissions in the Great Lakes, because all CMV emissions in the Midwest RPO are classified
as CI or C2 sources in the CMV inventory.
The SMOKE-ready data have been cropped from the original ECA-IMO entire northwestern quarter of
the globe to cover only the large continental U.S. 36-km "36US1" air quality model domain, the largest
domain used by EPA in recent years.
The original ECA-IMO inventory did not delineate between ports and underway emissions (or other C3
modes such as hoteling, maneuvering, reduced-speed zone, and idling). However, a U.S. ports spatial
surrogate dataset was used to assign the ECA-IMO emissions to ports and underway SCCs 2280003100
and 2280003200, respectively. This had no effect on temporal allocation or speciation because all C3
CMV emissions, unclassified/total, port and underway, share the same temporal and speciation profiles.
For California, the ECA-IMO 2014 emissions were scaled to match those provided by CARB for the
2014NEIvl. Note that CARB has had distinct projection and control approaches for this sector since
2002. These CARB C3 CMV emissions are documented in a staff report available at:
http://www.arb.ca.gov/regact/2010/offroadlsi 10/offroadisor.pdf. The CMV emissions obtained from
CARB include the 2014 regulations to reduce emissions from diesel engines on commercial harbor craft
operated within California waters and 24 nautical miles of the California shoreline.
3.2.6 Emissions from Canada, Mexico (othpt, othar, othafdust, onroadcan, onroadmex,
ptfiremxca)
The emissions from Canada, Mexico, and non-U.S. offshore Category 3 CMV (C3 CMV) and drilling
platforms are included as part of the emissions modeling sectors: othpt, othar, othafdust, onroad can, and
onroad mex. The "oth" refers to the fact that these emissions are usually "other" than those in the U.S.
state-county geographic FIPS, and the remaining characters provide the SMOKE source types: "pt" for
point, "ar" for area and nonroad mobile, "afdust" for area fugitive dust (Canada only). The onroad
emissions for Canada and Mexico are in the onroad can and onroad mex sectors, respectively.
For Canadian point sources, 2013 emissions provided by Environment Canada were used. These included
VOC emissions and CB6 speciation for VOCs although the CB6 VOCs differed slightly from the version
of CB6 in CMAQ. Airport emissions were provided by month. Temporal profiles were provided for all
source categories. Point sources in Mexico were compiled based on a year 2014 inventory projected from
the the Inventario Nacional de Emisiones de Mexico, 2008 (ERG, 2014a; ERG, 2016a). The point source
emissions in the 2014 inventory were converted to English units and into the FF10 format that could be
read by SMOKE, missing stack parameters were gapfilled using SCC-based defaults, and latitude and
longitude coordinates were verified and adjusted if they were not consistent with the reported
municipality. Note that there are no explicit HAP emissions in this inventory.
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For Canadian area and nonroad sources, year-2013 emissions provided by Environment Canada were
used, including CMV emissions. The Canadian inventory included fugitive dust emissions that do not
incorporate either a transportable fraction or meteorological-based adjustments. To properly account for
this, a separate sector called othafdust was created and modeled using the same adjustments as are done
for U.S. sources. Updated Shapefiles used for creating spatial surrogates for Canada were also provided.
For Canada nonroad mobile sources, the provided 2013 monthly emissions were used.
For Canada onroad emissions, month-specific year-2013 emissions provided by Environment Canada
were used. Note that unlike the U.S. and Mexico inventories, there are no explicit HAPs in the onroad
inventories for Canada and, therefore, NBAFM HAPs are created from speciation. For Mexico onroad
emissiosn, a version of the MOVES model for Mexico was run that provided the same VOC HAPs and
speciated VOCs as for the U.S. MOVES model (ERG, 2016a). This includes NBAFM plus several other
VOC HAPs such as toluene, xylene, ethylbenzene and others. Except for VOC HAPs that are part of the
speciation, no other HAPs are included in the Mexico onroad inventory (such as particulate HAPs nor
diesel particulate matter).
Annual 2014 wildland emissions for Mexico and Canada are in the ptfiremxca sector. They were
developed from a combination of Fire Inventory from NCAR (FINN) daily fire emissions and fire data
provided by Environment Canada for the months of June through November and FINN fire emissions
were used to fill in the annual gaps from January through May and December. The FINN fire emissions
come from an updated FINN vl.5 data set that differs from previous 2014 platforms largely in the
inclusion of grassland and cropland coverage. Only CAP emissions are provided in the Canada and
Mexico fire inventories. For FINN fires, listed vegetation type codes of 1 and 9 are defined as agricultural
burning, all other fire detections and assumed to be wildfires. All wildland fires that are not defined as
agricultural are assumed to be wild fires rather than prescribed. FINN fire detects less than 50 square
meters (0.012 acres) are removed from the inventory. The locations of FINN fires are geocoded from
latitude and longitude to FIPS code.
3.2.7 SMOKE-ready non-anthropogenic chlorine inventory
The ocean chlorine gas emission estimates are based on the build-up of molecular chlorine (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
The CMAQ model requires hourly emissions of specific gas and particle species for the horizontal and
vertical grid cells contained within the modeled region (i.e., modeling domain). To provide emissions in
the form and format required by the model, it is necessary to "pre-process" the "raw" emissions (i.e.,
emissions input to SMOKE) for the sectors described above. In brief, the process of emissions modeling
transforms the emissions inventories from their original temporal resolution, pollutant resolution, and
spatial resolution into the hourly, speciated, gridded resolution required by the air quality model.
Emissions modeling includes temporal allocation, spatial allocation, and pollutant speciation. In some
cases, emissions modeling also includes the vertical allocation of point sources, but many air quality
models also perform this task because it greatly reduces the size of the input emissions files if the vertical
layers of the sources are not included.
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As previously discussed, the temporal resolutions of the emissions inventories input to SMOKE vary
across sectors and may be hourly, daily, monthly, or annual total emissions. The spatial resolution, may
be individual point sources, county/province/municipio totals, or gridded emissions and varies by sector.
This section provides some basic information about the tools and data files used for emissions modeling
as part of the modeling platform.
3.3.1 The SMOKE Modeling System
SMOKE version 4.5 was used to pre-process the raw emissions inventories into emissions inputs for
CMAQ. SMOKE executables and source code are available from the Community Multiscale Analysis
System (CMAS) Center at http://www.cniasceiiter.org. Additional information about SMOKE is available
from http://www. smoke-model .org. For sectors that have plume rise, the in-line emissions capability of the
air quality models was used, which allows the creation of source-based and two-dimensional gridded
emissions files that are much smaller than full three-dimensional gridded emissions files. For quality
assurance of the emissions modeling steps, emissions totals by specie for the entire model domain are
output as reports that are then compared to reports generated by SMOKE on the input inventories to
ensure that mass is not lost or gained during the emissions modeling process.
3.3.2 Key Emissions Modeling Settings
When preparing emissions for the air quality model, emissions for each sector are processed separately
through SMOKE, and then the final merge program (Mrggrid) is run to combine the model-ready, sector-
specific emissions across sectors. The SMOKE settings in the run scripts and the data in the SMOKE
ancillary files control the approaches used by the individual SMOKE programs for each sector. Table 3-6
summarizes the major processing steps of each platform sector. The "Spatial" column shows the spatial
approach used: here "point" indicates that SMOKE maps the source from a point location (i.e., latitude
and longitude) to a grid cell; "surrogates" indicates that some or all of the sources use spatial surrogates to
allocate county emissions to grid cells; and "area-to-point" indicates that some of the sources use the
SMOKE area-to-point feature to grid the emissions. The "Speciation" column indicates that all sectors
use the SMOKE speciation step, though biogenics speciation is done within the Tmpbeis3 program and
not as a separate SMOKE step. The "Inventory resolution" column shows the inventory temporal
resolution from which SMOKE needs to calculate hourly emissions. Note that for some sectors (e.g.,
onroad, beis), there is no input inventory; instead, activity data and emission factors are used in
combination with meteorological data to compute hourly emissions.
Table 3-6. Key emissions modeling steps by sector
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
afdust ad]
Surrogates
Yes
annual
ag
Surrogates
Yes
annual
beis
Pre-gridded
land use
in BEIS
computed hourly
cmv clc2
Surrogates
Yes
annual
cmv c3
Point
Yes
annual
in-line
nonpt
Surrogates &
area-to-point
Yes
annual
nonroad
Surrogates &
area-to-point
Yes
monthly
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Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
np oilgas
Surrogates
Yes
annual
onroad
Surrogates
Yes
monthly activity,
computed hourly
onroadcaadj
Surrogates
Yes
monthly activity,
computed hourly
onroad can
Surrogates
Yes
Monthly
onroad mex
Surrogates
Yes
Monthly
othafdust
Surrogates
Yes
Annual
othar
Surrogates
Yes
annual &
monthly
othpt
Point
Yes
annual &
monthly
in-line
ptagfire
Point
Yes
Daily
none
pt oilgas
Point
Yes
Annual
in-line
ptegu
Point
Yes
daily & hourly
in-line
ptfire
Point
Yes
Daily
in-line
ptfire mxca
Point
Yes
Daily
in-line
ptnonipm
Point
Yes
Annual
in-line
rail
Surrogates
Yes
Annual
rwc
Surrogates
Yes
Annual
Biogenic emissions can be modeled two different ways in the CMAQ model. The BEIS model in SMOKE
can produce gridded biogenic emissions that are then included in the gridded CMAQ-ready emissions
inputs, or alternatively, CMAQ can be configured to create "in-line" biogenic emissions within CMAQ
itself. For this study, the in-line biogenic emissions option was used, and so biogenic emissions from
BEIS were not included in the gridded CMAQ-ready emissions.
The "plume rise" column indicates the sectors for which the "in-line" approach is used. These sectors are
the only ones with emissions in aloft layers based on plume rise. The term "in-line" means that the plume
rise calculations are done inside of the air quality model instead of being computed by SMOKE. The air
quality model computes the plume rise using the stack data and the hourly air quality model inputs found
in the SMOKE output files for each model-ready emissions sector. The height of the plume rise
determines the model layer into which the emissions are placed. The cmv_c3, othpt, ptfire, and
ptfiremxca sectors have only "in-line" emissions, meaning that all of the emissions are treated as
elevated sources and there are no emissions for those sectors in the two-dimensional, layer-1 files created
by SMOKE. Day-specific point fires are treated separately for CMAQ modeling in that fire plume rise is
done within CMAQ itself. After plume rise is applied, there will be emissions in every layer from the
ground up to the top of the plume. For the ptagfire sector, all emissions were allocated to layer 1 and
output to gridded 2-D emissions files.
SMOKE has the option of grouping sources so that they are treated as a single stack when computing
plume rise. For the 2014 modeling case, no grouping was performed because grouping combined with
"in-line" processing will not give identical results as "offline" processing (i.e., when SMOKE creates 3-
dimensional files). This occurs when stacks with different stack parameters or lat/lons are grouped,
36
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thereby changing the parameters of one or more sources. The most straightforward way to get the same
results between in-line and offline is to avoid the use of grouping.
3.3.3 Spatial Configuration
For this study, SMOKE was run for the larger 12-km Continental United States "CONUS" modeling
domain (12US1) shown in Figure 3-2 and boundary conditions were obtained from a 2014 run of 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.
12US1C
12US2 Continental US Domain
Figure 3-2. CMAQ Modeling Domain
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3.3.4 Chemical Speciation Configuration
The emissions modeling step for chemical speciation creates the "model species" needed by the air
quality model for a specific chemical mechanism. These model species are either individual chemical
compounds (i.e., "explicit species") or groups of species (i.e., "lumped species"). The chemical
mechanism used for the 2014 platform is the CB6 mechanism (Yarwood, 2010). We used a particular
version of CB6 that we refer to as "CMAQ CB6" that breaks out naphthalene from XYL as an explicit
model species, resulting in model species NAPH and XYLMN instead of XYL and uses SOAALK. This
platform generates the PM2.5 model species associated with the CMAQ Aerosol Module version 6 (AE6).
Table 3-7 lists the model species produced by SMOKE in the platform used for this study.
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Table 3-7. Emission model species produced for CB6 for CMAQ
Inventory Pollutant
Model Species
Model species description
Cl2
CL2
Atomic gas-phase chlorine
HC1
HCL
Hydrogen Chloride (hydrochloric acid) gas
CO
CO
Carbon monoxide
NOx
NO
Nitrogen oxide
N02
Nitrogen dioxide
HONO
Nitrous acid
S02
S02
Sulfur dioxide
SULF
Sulfuric acid vapor
nh3
NH3
Ammonia
NH3 FERT
Ammonia from fertilizer
voc
ACET
Acetone
ALD2
Acetaldehyde
ALDX
Propionaldehyde and higher aldehydes
BENZ
Benzene (not part of CB05)
CH4
Methane
ETH
Ethene
ETHA
Ethane
ETHY
Ethyne
ETOH
Ethanol
FORM
Formaldehyde
IOLE
Internal olefin carbon bond (R-C=C-R)
ISOP
Isoprene
KET
Ketone Groups
MEOH
Methanol
NAPH
Naphthalene
NVOL
Non-volatile compounds
OLE
Terminal olefin carbon bond (R-C=C)
PAR
Paraffin carbon bond
PRPA
Propane
SESQ
Sequiterpenes (from biogenics only)
SOAALK
Secondary Organic Aerosol (SOA) tracer
TERP
Terpenes (from biogenics only)
TOL
Toluene and other monoalkyl aromatics
UNR
Unreactive
XYLMN
Xylene and other polyalkyl aromatics, minus
naphthalene
Naphthalene
NAPH
Naphthalene from inventory
Benzene
BENZ
Benzene from the inventory
Acetaldehyde
ALD2
Acetaldehyde from inventory
Formaldehyde
FORM
Formaldehyde from inventory
Methanol
MEOH
Methanol from inventory
PM10
PMC
Coarse PM >2.5 microns and <10 microns
PM2.5
PEC
Particulate elemental carbon <2.5 microns
PN03
Particulate nitrate <2.5 microns
POC
Particulate organic carbon (carbon only) < 2.5 microns
PS04
Particulate Sulfate <2.5 microns
PAL
Aluminum
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Inventory Pollutant
Model Species
Model species description
PCA
Calcium
PCL
Chloride
PFE
Iron
PK
Potassium
PH20
Water
PMG
Magnesium
PMN
Manganese
PMOTHR
PM2.5 not in other AE6 species
PNA
Sodium
PNCOM
Non-carbon organic matter
PNH4
Ammonium
PSI
Silica
PTI
Titanium
Sea-salt species (non -
PCL
Particulate chloride
anthropogenic)8
PNA
Particulate sodium
The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach were
developed from the SPECIATE 4.5 database (https://www.epa.gov/air-emissions-modeling/speciate-
version-45-through-40). which is the EPA's repository of TOG and PM speciation profiles of air pollution
sources. The SPECIATE database development and maintenance is a collaboration involving the EPA's
Office of Research and Development (ORD), Office of Transportation and Air Quality (OTAQ), and the
Office of Air Quality Planning and Standards (OAQPS), in cooperation with Environment Canada (EPA,
2016). The SPECIATE database contains speciation profiles for TOG, speciated into individual chemical
compounds, VOC-to-TOG conversion factors associated with the TOG profiles, and speciation profiles
for PM2.5.
Some key features and updates to speciation from previous platforms include the following (the
subsections below contain more details on the specific changes):
• VOC speciation profile cross reference assignments for point and nonpoint oil and gas sources
were updated to (1) make corrections to cross references, (2) use new and revised profiles that
were added to SPECIATE4.5 and (3) account for the portion of VOC estimated to come from
flares, based on data from the Oil and Gas estimation tool used to estimate emissions for the NEI.
The new/revised profiles included oil and gas operations in specific regions of the country and a
national profile for natural gas flares;
• Two new PM2.5 profiles from SPECIATE4.5 are used for brake and tirewear;
• speciation profiles developed by the Western Regional Air Partnership (WRAP) are used for the
npoilgas sector were revised;
• VOC speciation for nonroad mobile has been updated to include a different speciation profile
assignment method for VOC (profiles are assigned to SCCs within MOVES2014a which outputs
the emissions with those assignments) and updated profiles;
8 These emissions are created outside of SMOKE
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• VOC and PM speciation for onroad mobile sources occurs within MOVES2014a;
• Speciation for onroad mobile sources in Mexico is done within MOVES and is more consistent
with that used in the United States; and
• The 2013 Canadian point source inventories were provided from Environment Canada with CB6
speciation.
Speciation profiles and cross-references for the 2014 platform are available in the SMOKE input files for
the 2014 platform. Emissions of VOC and PM2.5 emissions by county, sector and profile for all sectors
other than onroad mobile can be found in the sector summaries for the case. Totals of each model species
by state and sector can be found in the Appendix B state-sector totals workbook for this case.
The speciation of VOC includes HAP emissions from the 2014NEIvl in the speciation process. Instead
of speciating VOC to generate all of the species listed in Table 3-7, emissions of five specific HAPs:
naphthalene, benzene, acetaldehyde, formaldehyde and methanol (collectively known as "NBAFM") from
the NEI were "integrated" with the NEI VOC. The integration combines these HAPs with the VOC in a
way that does not double count emissions and uses the HAP inventory directly in the speciation process.
The basic process is to subtract the specified HAPs emissions mass from the VOC emissions mass, and to
then use a special "integrated" profile to speciate the remainder of VOC to the model species excluding
the specific HAPs. The EPA believes that the HAP emissions in the NEI are often more representative of
emissions than HAP emissions generated via VOC speciation, although this varies by sector.
The NBAFM HAPs were chosen for integration because they are the only explicit VOC HAPs in the
CMAQ version 5.2. Explicit means that they are not lumped chemical groups like PAR, IOLE and
several other CB6 model species. These "explicit VOC HAPs" are model species that participate in the
modeled chemistry using the CB6 chemical mechanism. The use of inventory HAP emissions along with
VOC is called "HAP-CAP integration."
The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory formats other
than PTDAY (the format used for the ptfire sector). SMOKE allows the user to specify both the
particular HAPs to integrate via the INVTABLE. This is done by setting the "VOC or TOG component"
field to "V" for all HAP pollutants chosen for integration. SMOKE allows the user to also choose the
particular sources to integrate via the NHAPEXCLUDE file (which actually provides the sources to be
excluded from integration9). For the "integrated" sources, SMOKE subtracts the "integrated" HAPs from
the VOC (at the source level) to compute emissions for the new pollutant "NONHAPVOC." The user
provides NONHAPVOC-to-NONHAPTOG factors and NONHAPTOG speciation profiles10. SMOKE
computes NONHAPTOG and then applies the speciation profiles to allocate the NONHAPTOG to the
other air quality model VOC species not including the integrated HAPs. After determining if a sector is
to be integrated, if all sources have the appropriate HAP emissions, then the sector is considered fully
integrated and does not need a NHAPEXCLUDE file. If, on the other hand, certain sources do not have
9 Since SMOKE version 3.7, the options to specify sources for integration are expanded so that a user can specify the
particular sources to include or exclude from integration, and there are settings to include or exclude all sources within a
sector. In addition, the error checking is significantly stricter for integrated sources. If a source is supposed to be integrated,
but it is missing NBAFM or VOC, SMOKE will now raise an error.
10 These ratios and profiles are typically generated from the Speciation Tool when it is run with integration of a specified list
of pollutants, for example NBAFM.
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the necessary HAPs, then an NHAPEXCLUDE file must be provided based on the evaluation of each
source's pollutant mix. The EPA considered CAP-HAP integration for all sectors in determining whether
sectors would have full, no or partial integration (see Figure 3-3). For sectors with partial integration, all
sources are integrated other than those that have either the sum of NBAFM > VOC or the sum of
NBAFM = 0.
In this platform, we create NBAFM species from the no-integrate source VOC emissions using speciation
profiles. Figure 3-3 illustrates the integrate and no-integrate processes for U.S. Sources. Since Canada
and Mexico inventories do not contain HAPs, we use the 201 lv6.3 approach of generating the HAPs via
speciation, except for Mexico onroad mobile sources where emissions for integrate HAPs were available.
It should be noted that even though NBAFM were removed from the SPECIATE profiles used to create
the GSPRO for both the NONHAPTOG and no-integrate TOG profiles, there still may be small fractions
for "BENZ", "FORM", "ALD2", and "MEOH" present. This is because these model species may have
come from species in SPECIATE that are mixtures. The quantity of these model species is expected to be
very small compared to the BAFM in the NEI. These profiles are listed in Appendix E. There are no
NONHAPTOG profiles that produce "NAPH."
In SMOKE, the INVTABLE allows the user to specify the particular HAPs to integrate. Two different
INVTABLE files are used for different sectors of the platform. For sectors that had no integration across
the entire sector (see Table 3-8), EPA created a "no HAP use" INVTABLE in which the "KEEP" flag is
set to "N" for NBAFM pollutants. Thus, any NBAFM pollutants in the inventory input into SMOKE are
automatically dropped. This approach both avoids double-counting of these species and assumes that the
VOC speciation is the best available approach for these species for sectors using this approach. The
second INVTABLE, used for sectors in which one or more sources are integrated, causes SMOKE to keep
the inventory NBAFM pollutants and indicates that they are to be integrated with VOC. This is done by
setting the "VOC or TOG component" field to "V" for all four HAP pollutants. Note for the onroad
sector, "full integration" includes the integration of benzene, 1,3 butadiene, formaldehyde, acetaldehyde,
naphthalene, acrolein, ethyl benzene, 2,2,4-Trimethylpentane, hexane, propionaldehyde, styrene, toluene,
xylene, and MTBE.
42
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Emissions read y for SMOKE
SMOKE
Compote NONHAPVOC- VOC-(B+ F+ A+M)
emissions for each integrate sou rce
Retain VOC emissions for each no-integrate source
list of "pen nt«gr»:« "
sources i NKAPEXCLUDE)
Specistion Cress s
Re^rence Fil e (GSREFj «
i VCC -to -TDG r»ctc-n
I NONHAFVOC-to-NONHAPTOG
i factors (GSCNV)
Compute moles of each CBC5 mode! species.
Use NONHAPTOG profiles appli ed tc NONHAPTOG
emissionsand B, F, A, M emissions for integrate sources.
Use TOG profilesapplied to TOG for no-integrate sources
TCGind NCN-AFTOG
speciaition "ictcrs
(G5PHO)
Assign speciatfon profile code to each emission source
Compute: NON"A°TOG ernissicns from NON"-A?VOC fc
each integrate source
Compute: TDG emissions from VOC for each no-integrate
source
Spe dated Emissions for VOC species
Figure 3-3. Process of integrating BAFM with VOC for use in VOC Speciation
Table 3-8. Integration status of benzene, acetaldehyde, formaldehyde and methanol (BAFM) for
each platform sector
Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
ptegu
No integration, create NBAFM from VOC speciation
ptnonipm
No integration, create NBAFM from VOC speciation
ptfire
No integration, create NBAFM from VOC speciation
ptfire mxca
No integration, create NBAFM from VOC speciation
ptagfire
No integration, create NBAFM from VOC speciation
ag
N/A - sector contains no VOC
afdust
N/A - sector contains no VOC
beis
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
cmv clc2
Full integration (NBAFM)
cmv c3
Full integration (NBAFM)
rail
Partial integration (NBAFM)
nonpt
Partial integration (NBAFM)
nonroad
Full integration (NBAFM in California, internal to MOVES elsewhere)
np oilgas
Partial integration (NBAFM)
othpt
No integration, create NBAFM from VOC speciation
pt oilgas
No integration, create NBAFM from VOC speciation
rwc
Partial integration (NBAFM)
onroad
Full integration (internal to MOVES)
onroad can
No integration, no NBAFM in inventory, create NBAFM from speciation
onroad mex
Full integration (internal to MOVES-Mexico); however, MOVES-MEXICO speciation
43
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Platform
Sector
Approach for Integrating NEI emissions of Naphthalene (N), Benzene (B),
Acetaldehyde (A), Formaldehyde (F) and Methanol (M)
was CB6-CAMx, not CB6-CMAQ, so post-SMOKE emissions were converted to CB6-
CMAQ
othafdust
N/A - sector contains no VOC
othar
No integration, create NBAFM from VOC speciation
Integration for the mobile sources estimated from MOVES (onroad and nonroad sectors, other than for
California) is done differently. Briefly there are three major differences: 1) for these sources integration
is done using more than just NBAFM, 2) all sources from the MOVES model are integrated and 3)
integration is done fully or partially within MOVES. For onroad mobile, speciation is done fully within
MOVES2014a such that the MOVES model outputs emission factors for individual VOC model species
along with the HAPs. This requires MOVES to be run for a specific chemical mechanism. MOVES was
run for the CB6-CAMx mechanism rather than CB6-CMAQ, so post-SMOKE onroad emissions were
converted to CB6-CMAQ. For nonroad mobile, speciation is partially done within MOVES such that it
does not need to be run for a specific chemical mechanism. For nonroad, MOVES outputs emissions of
HAPs and NONHAPTOG split by speciation profile. Taking into account that integrated species were
subtracted out by MOVES already, the appropriate speciation profiles are then applied in SMOKE to get
the VOC model species. HAP integration for nonroad uses the same additional HAPs and ethanol as for
onroad.
In previous platforms, the GSPROCOMBO feature was used to speciate nonroad mobile and gasoline-
related stationary sources that use fuels with varying ethanol content. In these cases, the speciation
profiles require different combinations of gasoline profiles, e.g. EO and E10 profiles. Since the ethanol
content varies spatially (e.g., by state or county), temporally (e.g., by month), and by modeling year
(future years have more ethanol), the GSPRO COMBO feature allows combinations to be specified at
various levels for different years. For the 2014v7.0 platform, GSPRO COMBO is still used for nonroad
sources in California and for certain gasoline-related stationary sources nationwide. The feature is also
used to combine exhaust and evaporative profiles to use with Mexican nonroad sources, which do not
include the mode in the SCC or pollutant. GSPRO COMBO is no longer needed for nonroad sources in
the US outside of California because nonroad emissions within MOVES have the speciation profiles built
into the results, so there is no need to assign them via the GSREF or GSPRO COMBO feature.
A new method to combine multiple profiles is available in SMOKE4.5. It allows multiple profiles to be
combined by by pollutant, state and county (i.e., state/county FIPS code) and SCC. This was used
specifically for the oil and gas sectors (pt oilgas and np oilgas) because SCCs include both controlled
and uncontrolled oil and gas operations which use different profiles.
Speciation profiles for use with BEIS are not included in SPECIATE. BEIS3.61 includes a species (SESQ)
that was mapped to the CMAQ specie SESQT. The profile code associated with BEIS profiles for use with
CB6 was "B10C6." For additional sector-specific details on VOC speciation for a variety of sectors, see
Section 3.2.1.3 of the 2011v6.2 TSD (EPA, 2015a).
In addition to VOC profiles, the SPECIATE database also contains the PM2.5 speciated into both
individual chemical compounds (e.g., zinc, potassium, manganese, lead), and into the "simplified" PM2.5
components used in the air quality model. For CMAQ 4.7.1 modeling, these "simplified" components
(AE5) are all that is needed. Starting with CMAQ 5.0.1, a new thermodynamic equilibrium aerosol
modeling tool (ISORROPIA) v2 mechanism was added that needs additional PM components (AE6),
44
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which are further subsets of PMFINE (see Table 3-9). The majority of the 2014 platform PM profiles
come from the 911XX series which include updated AE6 speciation11.
Table 3-9. PM model species: AE5 versus AE6
Species name
Species description
AE5
AE6
POC
organic carbon
Y
Y
PEC
elemental carbon
Y
Y
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, cmv_clc2, cmv_c3, rail, onroad can, onroadmex sectors) and for specific SCCs in othar and
ptnonipm, the profile "HONO" splits NOx into NO, NO2, and HONO. Table 3-10 gives the split factor
for these two profiles. The onroad sector does not use the "HONO" profile to speciate NOx.
MOVES2014 produces speciated NO, NO2, and HONO by source, including emission factors for these
species in the emission factor tables used by SMOKE-MOVES. Within MOVES, the HONO fraction is a
constant 0.008 of NOx. The NO fraction varies by heavy duty versus light duty, fuel type, and model
11 The exceptions are 5674 (Marine Vessel - Marine Engine - Heavy Fuel Oil) used for c3marine and 92018 (Draft Cigarette
Smoke - Simplified) used in nonpt.
45
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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
Additional details on speciation for onroad, nonroad, and oil and gas sources, and new PM profiles used
are discussed in the 2014v7.0 TSD (EPA, 2017a).
3.3.5 Temporal Processing Configuration
Temporal allocation (i.e., temporalization) is the process of distributing aggregated emissions to a finer
temporal resolution, thereby converting annual emissions to hourly emissions. While the total emissions
are important, the timing of the occurrence of emissions is also essential for accurately simulating ozone,
PM, and other pollutant concentrations in the atmosphere. Many emissions inventories are annual or
monthly in nature. Temporalization takes these aggregated emissions and, if needed, distributes them to
the month, and then distributes the monthly emissions to the day and the daily emissions to the hours of
each day. This process is typically done by applying temporal profiles to the inventories in this order:
monthly, day of the week, and diurnal.
The temporal factors applied to the inventory are selected using some combination of country, state,
county, SCC, and pollutant. Table 3-11 summarizes the temporal aspects of emissions modeling by
comparing the key approaches used for temporal processing across the sectors. In the table, "Daily
temporal approach" refers to the temporal approach for getting daily emissions from the inventory using
the SMOKE Temporal program. The values given are the values of the SMOKE L TYPE setting. The
"Merge processing approach" refers to the days used to represent other days in the month for the merge
step. If this is not "all," then the SMOKE merge step runs only for representative days, which could
include holidays as indicated by the right-most column. The values given are those used for the SMOKE
M TYPE setting (see below for more information).
Table 3-11. Temporal Settings Used for the Platform Sectors in SMOKE
Platform sector
short name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process Holidays
as separate days
afdust adj
Annual
Yes
week
all
Yes
ag
Annual
Yes
met-based
all
Yes
beis
Hourly
n/a
all
Yes
cmv clc2
Annual
Yes
aveday
aveday
cmv c3
Annual
Yes
aveday
aveday
nonpt
Annual
Yes
week
week
Yes
nonroad
Monthly
mwdss
Mwdss
Yes
46
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Platform sector
short name
Inventory
resolutions
Monthly
profiles
used?
Daily
temporal
approach
Merge
processing
approach
Process Holidays
as separate days
np oilgas
Annual
yes
week
week
Yes
onroad
Annual & monthly1
all
all
Yes
onroad ca adj
Annual & monthly1
all
all
Yes
othafdust adj
Annual
yes
week
week
othar
Annual & monthly
yes
week
week
onroad can
Monthly
week
week
onroad mex
Monthly
week
week
othpt
Annual & monthly
yes
mwdss
mwdss
ptagfire
Daily
all
all
Yes
pt oilgas
Annual
yes
mwdss
mwdss
Yes
ptegu
Daily & hourly
all
all
Yes
ptnonipm
Annual
yes
mwdss
mwdss
Yes
ptfire
Daily
all
all
Yes
ptfire mxca
Daily
all
all
Yes
rail
Annual
yes
aveday
aveday
rwc
Annual
no
met-based
all
Yes
1. Note the annual and monthly "inventory" actually refers to the activity data (VMT and VPOP) for onroad. The
actual emissions are computed on an hourly basis.
The following values are used in Table 3-11: The value "all" means that hourly emissions are computed
for every day of the year and that emissions potentially have day-of-year variation. The value "week"
means that hourly emissions computed for all days in one "representative" week, representing all weeks
for each month. This means emissions have day-of-week variation, but not week-to-week variation
within the month. The value "mwdss" means hourly emissions for one representative Monday,
representative weekday (Tuesday through Friday), representative Saturday, and representative Sunday for
each month. This means emissions have variation between Mondays, other weekdays, Saturdays and
Sundays within the month, but not week-to-week variation within the month. The value "aveday" means
hourly emissions computed for one representative day of each month, meaning emissions for all days
within a month are the same. Special situations with respect to temporalization are described in the
following subsections.
In addition to the resolution, temporal processing includes a ramp-up period for several days prior to
January 1, 2014, which is intended to mitigate the effects of initial condition concentrations. The ramp-up
period was 10 days (December 22-31, 2013). For all anthropogenic sectors, emissions from December
2014 were used to fill in surrogate emissions for the end of December 2013. In particular, December
2014 emissions (representative days) were used for December 2013. For biogenic emissions, December
2013 emissions were processed using 2013 meteorology.
The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated format for
monthly, daily, and hourly emissions inventories than prior formats supported. Previously, processing
monthly inventory data required the use of 12 separate inventory files. With the FF10 format, a single
inventory file can contain emissions for all 12 months and the annual emissions in a single record. This
helps simplify the management of numerous inventories. Similarly, daily and hourly FF10 inventories
47
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contain individual records with data for all days in a month and all hours in a day, respectively.
SMOKE prevents the application of temporal profiles on top of the "native" resolution of the inventory.
For example, a monthly inventory should not have annual-to-month temporalization applied to it; rather,
it should only have month-to-day and diurnal temporalization. This becomes particularly important when
specific sectors have a mix of annual, monthly, daily, and/or hourly inventories. The flags that control
temporalization for a mixed set of inventories are discussed in the SMOKE documentation. The
modeling platform sectors that make use of monthly values in the FF10 files are nonroad, onroad (for
activity data), onroad can, onroadmex, othar, othpt, and ptegu.
3.3.5.1 Standard Temporal Profiles
Some sectors use straightforward temporal profiles not based on meteorology or other factors. For the
ptagfire sector, the inventories are in the daily point fire format, so temporal profiles are only used to go
from day-specific to hourly emissions. For all agricultural burning, the diurnal temporal profile used
reflected the fact that burning occurs during the daylight. This puts most of the emissions during the work
day and suppresses the emissions during the middle of the night. This diurnal profile was used for each
day of the week for all agricultural burning emissions in all states.
For the cmv_clc2 and cmv_c3 sectors, emissions are allocated with flat day of week and flat hourly
profiles. The CI and C2 emissions are allocated with a flat monthly profile, except in the Great Lakes,
where the profiles vary by month. C3 emissions are allocated with monthly profiles developed
specifically for C3, including in the Great Lakes.
For the rail sector, new monthly profiles were developed for the 2014 platform. Monthly temporalization
for rail freight emissions is based on AAR Rail Traffic Data, Total Carloads and Intermodal, for 2014.
For passenger trains, monthly temporalization is based on rail passenger miles data for 2014 from the
Bureau of Transportation Statistics. Rail emissions are allocated with flat day of week profiles, and most
emissions are allocated with flat hourly profiles.
For the ptfire sector, the inventories are in the daily point fire format, so temporal profiles are only used to
go from day-specific to hourly emissions. For the nonroad sector, while the NEI only stores the annual
totals, the modeling platform uses monthly inventories from output from NMIM. For California, the
nonroad inventory is annual only, and monthly temporal profiles are applied in SMOKE.
Diurnal, weekly, and monthly temporal profiles for aviation-related sources were updated in the 2014v7.0
platform based on aviation metrics. Details on these new profiles are available in the 2014v7.0 TSD.
Temporal profiles for small airports (i.e., non-commercial) do not have any emissions between 10pm and
6am due to a lack of tower operations. Industrial processes that are not likely to shut down on Sundays
such as those at cement plants are assigned to other more realistic profiles that included emissions on
Sundays. This also affected emissions on holidays because Sunday emissions are also used on holidays.
For oil and gas sources, monthly profiles were applied based on monthly activity data computed from the
data sources used to develop the 2014 NEI. The profiles were specific to each FIPS and SCC and were
applied as part of the cross reference. For states that used non-standard SCCs not in the EPA data set, flat
profiles were used. Many np_oilgas sources use profiles that represent 24 hours per day, 7 days a week.
For agricultural livestock, annual-to-month profiles were developed based on daily emissions data output
48
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from the CMU model by state and SCC. These profiles were used to temporally allocate 2014NEIv2 ag
livestock emissions to monthly emissions, which are further temporally allocated to hours as described
below in section 3.3.5.3.
3.3.5.2 Temporal Profiles for EGUs
The 2014NEIvl annual EGU emissions not matched to CEMS sources use region/fuel specific profiles
based on average hourly emissions for the region and fuel. Peaking units were removed during the
averaging to minimize the spikes generated by those units. The non-matched units are allocated to hourly
emissions using the following 3-step methodology: annual value to month, month to day, and day to
hour. First, the CEMS data were processed using a tool that reviewed the data quality flags that indicate
the data were not measured. Unmeasured data can cause erroneously high values in the CEMS data. If
the data were not measured at specific hours, and those values were found to be more than three times the
annual mean for that unit, the data for those hours were replaced with annual mean values (Adelman et
al., 2012). These adjusted CEMS data were then used for the remainder of the temporalization process
described below (see Figure 3-4 Figure 3-for an example).
Winter and summer seasons are included in the development of the diurnal profiles as opposed to using
data for the entire year because analysis of the hourly CEMS data revealed that there were different
diurnal patterns in winter versus summer in many areas. Typically, a single mid-day peak is visible in the
summer, while there are morning and evening peaks in the winter as shown in Figure 3-5.
The temporal allocation procedure is differentiated by whether or not the source could be directly
matched to a CEMS unit via ORIS facility code and boiler ID. Note that for units matched to CEMS data,
annual totals of their emissions may be different than the annual values in 2014NEIvl 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.
49
-------
2014CEM 2398 1101 Monthl
400
300
3
o
200
100
P
101 201
JUl
301 401
Hour
h
s
501 601 701
¦Raw CEM Corrected
Figure 3-4. Eliminating unmeasured spikes in CEMS data
Diurnal CEMS Profile for PJM Dom Gas
0.06
0.055
0.05
c
o
<_>
2
0.045
Li-
li
c
0.04
Q
0.035
0.03
0.025
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour
Winter Summer Annual
Figure 3-5. Seasonal diurnal profiles for EGU emissions in a Virginia Region
For sources not matched to CEMS units, temporal profiles are calculated that are used by SMOKE to
allocate the annual emissions to hourly values. For these units, the allocation of the inventory annual
50
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emissions to months is done using average fuel-specific annual-to-month factors generated for each of the
64 IPM regions shown in in Figure 3-6. These factors are based on 2014 CEMS data only. In each
region, separate factors were developed for the fuels: coal, natural gas, and "other," where the types of
fuels included in "other" vary by region. Separate profiles were computed for NOx, SO2, and heat input.
An overall composite profile was also computed and used when there were no CEMS units with the
specified fuel in the region containing the unit. For both CEMS-matched units and units not matched to
CEMS, NOx and SO2 CEMS data are used to allocate NOx and SO2 emissions to monthly emissions,
respectively, while heat input data are used to allocate emissions of all other from monthly to daily
emissions.
Daily temporal allocation of units matched to CEMS was performed using a procedure similar to the
approach to allocate emissions to months in that the CEMS data replaces the inventory data for each
pollutant. For units without CEMS data, emissions were allocated from month to day using IPM-region
and fuel-specific average month-to-day factors based on the 2014 CEMS heat data. Separate month-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 CEMS matched units, NOx and SO2 CEMS data are used to
allocate NOx and SO2 emissions, while CEMS heat input data are used to allocate all other pollutants. An
example of month-to-day profiles for gas, coal, and an overall composite for a region in western Texas is
shown in Figure 3-7.
For units matched to CEMS data, hourly emissions use the hourly CEMS values for NOx and SO2, while
other pollutants are allocated according to heat input values. For units not matched to CEMS data,
temporal profiles from days to hours are computed based on the season-, region- and fuel-specific average
day-to-hour factors derived from the CEMS data for those fuels and regions using the appropriate subset
of data. For the unmatched units, CEMS heat input data are used to allocate all pollutants (including NOx
and SO2) because the heat input data was generally found to be more complete than the pollutant-specific
data. SMOKE then allocates the daily emissions data to hours using the temporal profiles obtained from
the CEMS data for the analysis base year (i.e., 2014 in this case).
Certain sources without CEMS data, such as specific municipal waste combustors (MWCs) and
cogeneration facilities (cogens), were assigned a flat temporal profile by source. The emissions for these
sources have an equal value for each day of the year.
51
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WECC_ID
NENG_CT
PJM_
PJM_ WM AC i
VVECCALN
PJM_
COMD
PJM_EMAC
PJM_SMAC
S_VACA
WECC_HD
S_D_WOTA
Figure 3-6. IPM Regions for EPA Base Case v5.13
Daily temporal fraction: ERC_WEST_NOX_7
o.io
0.08
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
52
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3.3.5.3 Meteorological-based Temporal Profiles
There are many factors that impact the timing of when emissions occur, and for some sectors this includes
meteorology. The benefits of utilizing meteorology as method for temporalization are: (1) a
meteorological dataset consistent with that used by the AQ model is available (e.g., outputs from WRF);
(2) the meteorological model data are highly resolved in terms of spatial resolution; and (3) the
meteorological variables vary at hourly resolution and can therefore be translated into hour-specific
temporalization.
The SMOKE program GenTPRO provides a method for developing meteorology-based temporalization.
Currently, the program can utilize three types of temporal algorithms: annual-to-day temporalization for
residential wood combustion (RWC), month-to-hour temporalization for agricultural livestock ammonia,
and a generic meteorology-based algorithm for other situations. For the 2014 platform, 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
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.
As of the 201 lv6.2 platform and in SMOKE 3.6.5, the temporal profile format was updated to support
more flexibility in profile application. The corresponding version of GenTPRO produces separate files
including the monthly temporal profiles (ATPRO MONTHLY) and day-of-month temporal profiles
(ATPRODAILY), instead of a single ATPRODAILY with day-of-year temporal profiles as it did in
SMOKE 3.5. The new and old temporal allocation results are equivalent when given the same inputs.
For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the temporal
allocation of emissions to days. GenTPRO was used to create an annual-to-day temporal profile for the
RWC sources. These generated profiles distribute annual RWC emissions to the coldest days of the year.
On days where the minimum temperature does not drop below a user-defined threshold, RWC emissions
for most sources in the sector are zero. Conversely, the program temporally allocates the largest
percentage of emissions to the coldest days. Similar to other temporal allocation profiles, the total annual
emissions do not change, only the distribution of the emissions within the year is affected. The
temperature threshold for rwc emissions was 50 °F for most of the country, and 60 °F for the following
states: Alabama, Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and
Texas.
Figure 3-8 illustrates the impact of changing the temperature threshold for a warm climate county. The
plot shows the temporal fraction by day for Duval County, Florida for the first four months of 2007. The
default 50 °F threshold creates large spikes on a few days, while the 60 °F threshold dampens these spikes
and distributes a small amount of emissions to the days that have a minimum temperature between 50 and
60 °F.
53
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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
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.
54
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Heat Load (BTU/hr)
50,000
40.000
30,000
20,000
10,000
0
CO Tj-
-------
GenTPRO was run using the "BASH NH3" profile method to create month-to-hour temporal profiles for
these sources. Because these profiles distribute to the hour based on monthly emissions, the monthly
emissions are obtained from a monthly inventory, or from an annual inventory that has been temporalized
to the month. Figure 3-11 compares the daily emissions for Minnesota from the "old" approach (uniform
monthly profile) with the "new" approach (GenTPRO generated month-to-hour profiles). Although the
GenTPRO profiles show daily (and hourly variability), the monthly total emissions are the same between
the two approaches.
MN ag NH3 livestock temporal profiles
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
-old
-new
Figure 3-11. Example of animal NFb emissions temporalization approaches, summed to daily
emissions
For the afdust sector, meteorology is not used in the development of the temporal profiles, but it is used to
reduce the total emissions based on meteorological conditions. These adjustments are applied through
sector-specific scripts, beginning with the application of land use-based gridded transport fractions and
then subsequent zero-outs for hours during which precipitation occurs or there is snow cover on the
ground. The land use data used to reduce the NEI emissions explains the amount of emissions that are
subject to transport. This methodology is discussed in Pouliot, et al., 2010, and in "Fugitive Dust
Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). The precipitation adjustment is
applied to remove all emissions for days where measureable rain occurs. Therefore, the afdust emissions
vary day-to-day based on the precipitation and/or snow cover for that grid cell and day. Both the
transport fraction and meteorological adjustments are based on the gridded resolution of the platform;
therefore, somewhat different emissions will result from different grid resolutions. Application of the
transport fraction and meteorological adjustments prevents the overestimation of fugitive dust impacts in
the grid modeling as compared to ambient samples.
Biogenic emissions in the beis sector vary by every day of the year because they are developed using
meteorological data including temperature, surface pressure, and radiation/cloud data. The emissions are
computed using appropriate emission factors according to the vegetation in each model grid cell, while
taking the meteorological data into account.
3.3.5.4 Temporal Profiles for Onroad Mobile Sources
For the onroad sector, the temporal distribution of emissions is a combination of more traditional
temporal profiles and the influence of meteorology. This section discusses both the meteorological
influences and the updates to the diurnal temporal profiles for the 2014 platform.
Meteorology is not used in the development of the temporal profiles, but rather it impacts the calculation
of the hourly emissions through the program Movesmrg. The result is that the emissions vary at the
hourly level by grid cell. More specifically, the on-network (RPD) and the off-network parked vehicle
56
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(RPV, RPH, and RPP) processes use the gridded meteorology (MCIP) directly. Movesmrg determines
the temperature for each hour and grid cell and uses that information to select the appropriate emission
factor (EF) for the specified SCC/pollutant/mode combination. In the 2014 platform (and the
2014NEIvl), 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 2014NEIvl
TSD for more details).
Figure 3-12 illustrates the difference between temporalization of the onroad sector and the meteorological
influence via SMOKE-MOVES. Similar temporalization is done for the VMT in SMOKE-MOVES, but
the meteorologically varying emission factors add variation on top of the temporalization.
3
O
0)
I
c
o
2014fa onroad RPD hourly NOX and VMT: Wake County, NC
1.8
13 -g-
o
0.8
0.3
c
o
X
O
7/a/w
0:00 7/9/140:00 7/10/14 0:00 7/11/14 0:00 7/12/14 0:00 7/13/14 0:00 7/14/14 0:00 7/15/M 0E®
Date and time (GMT)
¦ VMT
•NOX
Figure 3-12. Example of SMOKE-MOVES temporal variability of NOx emissions versus activity
For the onroad sector, the "inventories" referred to in Table 3-11 actually consist of activity data, not
emissions. For RPP and RPV processes, the VPOP inventory is annual and does not need
temporalization. For RPD, the VMT inventory is annual for some sources and monthly for other sources,
depending on the source of the data. Sources without monthly VMT were temporalized from annual to
month through temporal profiles. VMT was also temporalized from month to day of the week, and then
to hourly through temporal profiles. The RPD processes require a speed profile (SPDPRO) that consists
of vehicle speed by hour for a typical weekday and weekend day. Unlike other sectors, the temporal
profiles and SPDPRO will impact not only the distribution of emissions through time but also the total
emissions. Because SMOKE-MOVES (for RPD) calculates emissions from VMT, speed and
meteorology, if one shifted the VMT or speed to different hours, it would align with different
temperatures and hence different emission factors. In other words, two SMOKE-MOVES runs with
identical annual VMT, meteorology, and MOVES emission factors, will have different total emissions if
the temporalization of VMT changes. For RPH, the HOTELING inventory is annual and was
temporalized to month, day of the week, and hour of the day through temporal profiles. This is an
analogous process to RPD except that speed is not included in the calculation of RPH.
In previous platforms, the diurnal profile for VMT varied by road type but not by vehicle type and these
profiles were used throughout the nation. Diurnal profiles that could differentiate by vehicle type as well
57
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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 2014NEIvl provided an opportunity to
update these diurnal profiles with information submitted by states, to supplement the data with additional
sources, and to refine the methodology.
States submitted MOVES county databases (CDBs) that included information on the distribution of VMT
by hour of day and by day of week12 (see the 201 1NEIv2 TSD for details on the submittal process for
onroad). The EPA mined the state submitted MOVES CDBs for non-default diurnal profiles13. The list
of potential diurnal profiles was then analyzed to see whether the profiles varied by vehicle type, road
type, weekday versus weekend, and by county within a state. For the MOVES diurnal profiles, the EPA
only considered the state profiles that varied significantly by both vehicle and road types. Only those
profiles that passed these criteria were used in that state or used in developing default temporal profiles.
The Vehicle Travel Information System (VTRIS) is a repository for reported traffic count data to the
Federal Highway Administration (FHWA). The EPA used 2012 VTRIS data to create additional
temporal profiles for states that did not submit temporal information in their CDBs or where those profiles
did not pass the variance criteria. The VTRIS data were used to create state specific diurnal profiles by
HPMS vehicle and road type. The EPA created distinct diurnal profiles for weekdays, Saturday and
Sunday along with day of the week profiles14. In comparison to the temporal profiles from the 2011
emissions modeling platform, the profiles for the 2014 platform include the same 2012 VTRIS data, but
updated data from MOVES CDBs for 2014.
The EPA attempted to maximize the use of state and/or county specific diurnal profiles (either from
MOVES or VTRIS). Where there were no MOVES or VTRIS data, then a new default profile would be
used (see below for description of new profiles). This analysis was done separately for weekdays and for
weekends and, therefore, some areas had submitted profiles for weekdays but defaults for weekends. The
result was a set of profiles that varied geographically depending on the source of the profile and the
characteristics of the profiles (see Figure 3-13).
12 The MOVES tables are the hourvmtfraction and the dayvmtfraction.
13 Further QA was done to remove duplicates and profiles that were missing two or more hours. If they were missing a
single hour, the missing hour could be calculated by subtracting all other hours fractions from 1.
14 Note, the day of the week profiles (i.e., Monday vs Tuesday vs etc) are only from the VTRIS data. The MOVES CDBs only
have weekday versus weekend profiles so they were not included in calculating a new national default day of the week
profile.
58
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Temporal Sources for 2011v2 Mobile Emissions
VTRIS state
MOVES VMT
I 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
59
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0.09
0.08
Hourly Day fraction: national average
passenger cars, urban resticted
0.07
0.06
0.05
\
c
o
s.
rn
i—
0.04
0.03
0.02
0.01
N
L.
Saurday
>¦
ra
N
-Sunday
O
Weekday
0
]
l ;
I 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 IB 19 20 21 22 23 24
hour
0.07
0.06
0.05
0.04
0.03 '
Hourly Day f raction: national average
combo long haul trucks, urban resticted
c
o
¦B
Saturday
>
ra
~
0.02 ,
0.01
0
]
Sunday
Weekday
l ;
I 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 IB 19 20 21 22 23 24
hour
Figure 314. Updated national default profiles for LDGV vs. HHDDV, urban restricted
The gray lines of Figure 3-14 indicate the weekday profile, the blue the Saturday profile, and the orange
the Sunday profile. In comparison, the new default profiles for weekdays places more LDGV VMT
(upper plot) in the rush hours while placing HFCDDV VMT (lower plot) predominately in the middle of
the day with a longer tail into the evening hours and early morning. In addition to creating diurnal
profiles, the EPA developed day of week profiles using the VTRIS data. The creation of the state and
national profiles was similar to the diurnal profiles (described above). Figure 3-15 shows a set of national
default profiles for niral restricted roads (top plot) and urban unrestricted roads (lower plot). Each vehicle
type is a different color on the plots.
60
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Daily Week fraction: national_0_all_2_0
0.18
0.16
0.14
0.12
B 0.10
> 0.08
0.06
0.04
0.02
mon
tue
wed
day
thu
sat
Daily Week fraction: national_0_all_5_0
0.20
0.15
C
o
4—'
I 0.10
>.
ID
Q
0.05
mon
tue
wed
day
thu
sat
Figure 3-15. Updated national default profiles for day of week (top: rural restricted and
bottom: urban restricted)
61
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The EPA also developed a national profile for hoteling by averaging all the combination long-haul truck
profiles on restricted roads (urban and rural) for weekdays to create a single national restricted profile
(orange line in Figure 3-16). This was then inverted to create a profile for hoteling (blue line in Figure 3-
16). This single national profile was used for hoteling irrespective of location.
0.07
0.06
Hourly Day fraction: combo long haul trucks
VMT versus hotelling
c u.lo
o
0.04
m
¦*; 0.03
7*-
n
D 0.02 ,
0.01
0
]
Hotelling
\T.1T
4
L 2 3 4 5 6 7 S 9 10 11 12 13 14 15 16 17 IB 19 20 21 22 23 2
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 week15, and air
basin. These CARB-specific profiles were used in developing EPA estimates for California. Although
the EPA adjusted the total emissions to match California's submittal to the 2014NEIvl, the
temporalization of these emissions took into account both the state-specific VMT profiles and the
SMOKE-MOVES process of incorporating meteorology. For more details on the adjustments to
California's onroad emissions, see the 2014v7.0 TSD.
3.3.6 Vertical Allocation of Emissions
Table 3-5 specifies the sectors for which plume rise is calculated. If there is no plume rise for a sector, the
emissions are placed into layer 1 of the air quality model. Vertical plume rise was performed in-line within
CMAQ for all of the SMOKE point-source sectors (i.e., ptegu, ptnonipm, ptfire, ptagfire, othpt, and
cmv_c3). The in-line plume rise computed within CMAQ is nearly identical to the plume rise that would
be calculated within SMOKE using the Laypoint program. The selection of point sources for plume rise is
pre-determined in SMOKE using the Elevpoint program. The calculation is done in conjunction with the
CMAQ model time steps with interpolated meteorological data and is therefore more temporally resolved
than when it is done in SMOKE. Also, the calculation of the location of the point sources is slightly
different than the one used in SMOKE and this can result in slightly different placement of point sources
near grid cell boundaries.
For point sources, the stack parameters are used as inputs to the Briggs algorithm, but point fires do not
have stack parameters. However, the ptfire inventory does contain data on the acres burned (acres per day)
15 California's diurnal profiles varied within the week. Monday, Friday, Saturday, and Sunday had unique profiles and
Tuesday, Wednesday, Thursday had the same profile.
62
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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 Allocation
The methods used to perform spatial allocation are summarized in this section. For the modeling
platform, spatial factors are typically applied by county and SCC. Spatial allocation was performed for a
national 12-km domain. To accomplish this, SMOKE used national 12-km spatial surrogates and a
SMOKE area-to-point data file. For the U.S., EPA updated surrogates to use circa 2010-2014 data
wherever possible. For Mexico, updated spatial surrogates were used as described below. For Canada,
shapefiles for generating new surrogates were provided by Environment Canada for use with their 2013
inventories. The U.S., Mexican, and Canadian 12-km surrogates cover the entire CONUS domain 12US1
shown in Figure 3-2.
3.3.7.1 Surrogates for U.S. Emissions
There are more than 100 spatial surrogates available for spatially allocating U.S. county-level emissions
to the 12-km grid cells used by the air quality model. Note that an area-to-point approach overrides the
use of surrogates for a limited set of sources. Table 3-12 lists the codes and descriptions of the surrogates.
Surrogate names and codes listed in italics are not directly assigned to any sources for the 2014v7.0
platform, but they are sometimes used to gapfill other surrogates, or as an input for merging two
surrogates to create a new surrogate that is used.
Many surrogates were updated or newly developed for use in the 2014v7.0 platform (Adelman, 2016).
They include the use of the 2011 National Land Cover Database (the previous platform used 2006) and
development of various development density levels such as open, low, medium high and various
combinations of these. These landuse surrogates largely replaced the FEMA category surrogates that
were used in the 2011 platform. Additionally, onroad surrogates were developed using average annual
daily traffic counts from the highway monitoring performance system (HPMS). Previously, the "activity"
for the onroad surrogates was length of road miles. This and other surrogates are described in the
reference Adelman, 2016.
Similar to 2011, the Surrogates for ports (801) and shipping lanes (802) were developed based on the
shapes in the NEI; however they were updated using 2014NEIvl shapefiles and activity data. The
creation of surrogates and shapefiles for the U.S. was generated via the Surrogate Tool. The tool and
documentation for it is available at https://www.cmascenter.org/sa-
tools/documentation/4.2/SurrogateToolUserGuide 4 2.pdf.
63
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Table 3-12. U.S. Surrogates available for the 2014 modeling platform
Code
Surrogate Description
Code
Surrogate Description
N/A
Area-to-point approach (see 3.3.1.2)
505
Industrial Land
100
Population
506
Education
110
Housing
507
Heavy Light Construction Industrial Land
131
urban Housing
510
Commercial plus Industrial
132
Suburban Housing
515
Commercial plus Institutional Land
134
Rural Housing
520
Commercial plus Industrial plus Institutional
137
Housing Change
525
Golf Courses plus Institutional plus
Industrial plus Commercial
140
Housing Change and Population
526
Residential - Non-Institutional
150
Residential Heating - Natural Gas
527
Single Family Residential
160
Residential Heating - Wood '
535
Residential + Commercial + Industrial +
Institutional + Government
170
Residential Heating - Distillate Oil
540
Retail Trade (COM1)
180
Residential Heating - Coal
545
Personal Repair (COM3)
190
Residential Heating - LP Gas |
555
Professional/Technical (COM4) plus General
Government (GOV1)
201
Urban Restricted Road Miles
560
Hospital (COM6)
202
Urban Restricted AADT
575
Light and High Tech Industrial (IND2 +
IND5)
205
Extended Idle Locations
580
Food Drug Chemical Industrial (IND3)
211
Rural Restricted Road Miles
585
Metals and Minerals Industrial (IND4)
212
Rural Restricted AADT
590
Heavy Industrial (IND1)
221
Urban Unrestricted Road Miles
595
Light Industrial (IND2)
222
Urban Unrestricted AADT
596
Industrial plus Institutional plus Hospitals
231
Rural Unrestricted Road Miles ••
650
Refineries and Tank Farms
232
Rural Unrestricted AADT
670
Spud Count - CBM Wells
239
Total Road AADT
671
Spud Count - Gas Wells
240
Total Road Miles
672
Gas Production at Oil Wells
241
Total Restricted Road Miles ¦
673
Oil Production at CBM Wells
242
All Restricted AADT
674
Unconventional Well Completion Counts
243
Total Unrestricted Road Miles :
676
Well Count - All Producing
244
All Unrestricted AADT
677
Well Count - All Exploratory
258
i-
Intercity Bus Terminals
678
Completions at Gas Wells
259
T ransit Bus T erminals
679
Completions at CBM Wells
260
Total Railroad Miles
681
Spud Count - Oil Wells
261
NT AD Total Railroad Density
683
Produced Water at All Wells
271
NT AD Class 12 3 Railroad Density
685
Completions at Oil Wells
272
NTAD Amtrak Railroad Density [
686
Completions at All Wells
273
NTAD Commuter Railroad Density j
687
Feet Drilled at All Wells
275
ERTACRail Yards
691
Well Counts - CBM Wells
280
Class 2 and 3 Railroad Miles ;
692
Spud Count - All Wells
300
NLCD Low Intensity Development
693
Well Count - All Wells
301
NLCD Med Intensity Development ;
694
Oil Production at Oil Wells
302
NLCD High Intensity Development \
695
Well Count - Oil Wells
303
NLCD Open Space |
696
Gas Production at Gas Wells
304
NLCD Open + Low
697
Oil Production at Gas Wells
305
NLCD Low + Med |
| 698
Well Count - Gas Wells
64
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Code
Surrogate Description "
1 Code
Surrogate Description
306
NLCD Med + High \
699
Gas Production at CBM Wells
307
NLCD All Development
710
Airport Points
308
NLCD Low + Med + High
711
Airport Areas
309
NLCD Open + Low + Med j
801
Port Areas
310
NLCD Total Agriculture
802
Shipping Lanes
318
NLCD Pasture Land
805
Offshore Shipping Area
319
NLCD Crop Land
806
Offshore Shipping NE12014 Activity
320
NLCD Forest Land
807
Navigable Waterway Miles
321
NLCD Recreational Land
820
Ports NE12014 Activity
340
NLCD Land
850
Golf Courses
350
NLCD Water
860
Mines
500
Commercial Land ;
890
Commercial Timber
For the onroad sector, the on-network (RPD) emissions were allocated differently from the off-network
(RPP and RPV). On-network used average annual daily traffic (AADT) data and off network used land
use surrogates as shown in Table 3-13. Extended (i.e., overnight) idling of trucks were assigned to
surrogate 205 that is based on locations of overnight truck parking spaces. The underlying data in this
surrogate was updated for use in the 2014v7.0 platform to include additional data sources and corrections
based on comments received.
Table 3-13. Off-Network Mobile Source Surrogates
Source type
Source Type name
Surrogate ID
Description
11
Motorcycle
307
NLCD All Development
21
Passenger Car
307
NLCD All Development
31
Passenger Truck
307
NLCD All Development
NLCD Low + Med +
32
Light Commercial Truck
308
High
41
Intercity Bus
258
Intercity Bus Terminals
42
Transit Bus
259
Transit Bus Terminals
43
School Bus
506
Education
51
Refuse Truck
306
NLCD Med + High
52
Single Unit Short-haul Truck
306
NLCD Med + High
53
Single Unit Long-haul Truck
306
NLCD Med + High
54
Motor Home
304
NLCD Open + Low
61
Combination Short-haul Truck
306
NLCD Med + High
62
Combination Long-haul Truck
306
NLCD Med + High
For the oil and gas sources in the np oilgas sector, the spatial surrogates were updated to those shown in
Table 3-14 using 2014 data consistent with what was used to develop the 2014NEI nonpoint oil and gas
emissions. The primary activity data source used for the development of the oil and gas spatial
surrogates was data from Drilling Info (DI) Desktop's HPDI database (Drilling Info, 2015). This
database contains well-level location, production, and exploration statistics at the monthly level.
Due to a proprietary agreement with DI Desktop, individual well locations and ancillary
production cannot be made publicly available, but aggregated statistics are allowed. These data were
supplemented with data from state Oil and Gas Commission (OGC) websites (Illinois, Idaho, Indiana,
65
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Kentucky, Missouri, Nevada, Oregon and Pennsylvania, Tennessee). In many cases, the correct surrogate
parameter was not available (e.g., feet drilled), but an alternative surrogate parameter was available (e.g.,
number of spudded wells) and downloaded. Under that methodology, both completion date and date of
first production from HPDI were used to identify wells completed during 2011. In total, over 1.43 million
unique wells were compiled from the above data sources. The wells cover 34 states and 1,158 counties.
(ERG, 2016b).
Table 3-14. Spatial Surrogates for Oil and Gas Sources
Surrogate Code
Surrogate Description
670
Spud Count - CBM Wells
671
Spud Count - Gas Wells
672
Gas Production at Oil Wells
673
Oil Production at CBM Wells
674
Unconventional Well Completion Counts
676
Well Count - All Producing
677
Well Count - All Exploratory
678
Completions at Gas Wells
679
Completions at CBM Wells
681
Spud Count - Oil Wells
683
Produced Water at All Wells
685
Completions at Oil Wells
686
Completions at All Wells
687
Feet Drilled at All Wells
691
Well Counts - CBM Wells
692
Spud Count - All Wells
693
Well Count - All Wells
694
Oil Production at Oil Wells
695
Well Count - Oil Wells
696
Gas Production at Gas Wells
697
Oil Production at Gas Wells
698
Well Count - Gas Wells
699
Gas Production at CBM Wells
Not all of the available surrogates are used to spatially allocate sources in the modeling platform; that is,
some surrogates shown in Table 3-12. U.S. Surrogates available for the 2014 modeling platform were not
assigned to any SCCs, although many of the "unused" surrogates are actually used to "gap fill" other
surrogates that are used. When the source data for a surrogate has no values for a particular county, gap
filling is used to provide values for the surrogate in those counties to ensure that no emissions are dropped
when the spatial surrogates are applied to the emission inventories. The U.S. CAP emissions allocated to
the various spatial surrogates are shown in Table 3-15.
66
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Table 315. Selected 2014 CAP emissions by sector for U.S. Surrogates (CONUS domain totals)
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
afdust
240
Total Road Miles
253,093
afdust
304
NLCD Open + Low
1,116,883
afdust
306
NLCD Med + High
45,958
afdust
308
NLCD Low + Med + High
139,554
afdust
310
NLCD Total Agriculture
1,169,400
ag
310
NLCD Total Agriculture
2,867,904
cmv clc2
801
Port Areas
11
24,413
759
1,492
987
cmv clc2
802
Shipping Lanes
281
484,726
13,842
4,129
8,725
nonpt
100
Population
32,222
0
0
0
1,137,409
nonpt
150
Residential Heating - Natural Gas
47,296
219,671
3,593
1,445
13,311
nonpt
170
Residential Heating - Distillate Oil
1,726
34,923
3,680
64,628
1,153
nonpt
180
Residential Heating - Coal
20
101
53
1,086
111
nonpt
190
Residential Heating - LP Gas
121
34,025
175
675
1,321
nonpt
239
Total Road AADT
0
25
552
0
276,354
nonpt
240
Total Road Miles
0
0
0
0
36,941
nonpt
242
All Restricted AADT
0
0
0
0
5,451
nonpt
244
All Unrestricted AADT
0
0
0
0
95,327
nonpt
271
NT AD Class 12 3 Railroad Density
0
0
0
0
2,252
nonpt
300
NLCD Low Intensity Development
5,183
24,399
107,748
2,982
76,167
nonpt
304
NLCD Open + Low
0
0
0
0
0
nonpt
306
NLCD Med + High
22,268
239,863
290,187
181,982
864,662
nonpt
307
NLCD All Development
24
53,320
144,940
16,485
611,569
nonpt
308
NLCD Low + Med + High
1,205
187,485
17,977
31,506
72,126
nonpt
310
NLCD Total Agriculture
0
0
37
0
242,713
nonpt
319
NLCD Crop Land
0
0
95
71
293
nonpt
320
NLCD Forest Land
3,984
13
54
0
61
nonpt
505
Industrial Land
0
0
0
0
174
nonpt
535
Residential + Commercial + Industrial +
Institutional + Government
0
2
130
0
39
nonpt
560
Hospital (COM6)
0
0
0
0
0
nonpt
650
Refineries and Tank Farms
0
22
0
0
101,206
nonpt
711
Airport Areas
0
0
0
0
277
nonpt
801
Port Areas
0
0
0
0
7,862
nonroad
261
NT AD Total Railroad Density
3
2,593
273
4
503
nonroad
304
NLCD Open + Low
4
2,205
191
6
3,245
nonroad
305
NLCD Low + Med
110
23,017
4,557
146
149,863
nonroad
306
NLCD Med + High
345
243,170
15,750
526
126,354
nonroad
307
NLCD All Development
101
36,090
15,361
132
169,762
nonroad
308
NLCD Low + Med + High
673
458,488
38,060
886
69,386
nonroad
309
NLCD Open + Low + Med
111
22,350
1,257
148
44,500
nonroad
310
NLCD Total Agriculture
479
419,553
31,921
667
48,098
67
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Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
nonroad
320
NLCD Forest Land
19
8,900
1,377
25
8,628
nonroad
321
NLCD Recreational Land
157
20,841
15,119
229
553,747
nonroad
350
NLCD Water
215
144,088
8,855
361
448,425
nonroad
693
Well Count - All Wells
10
5,845
229
12
1,566
nonroad
850
Golf Courses
13
2,176
115
17
5,668
nonroad
860
Mines
2
2,760
298
4
549
np oilgas
670
Spud Count - CBM Wells
0
0
0
0
267
np oilgas
671
Spud Count - Gas Wells
0
0
0
0
10,989
np oilgas
672
Gas Production at Oil Wells
0
2,863
0
21,709
127,494
np oilgas
673
Oil Production at CBM Wells
0
35
0
0
1,795
np oilgas
674
Unconventional Well Completion Counts
0
47,606
1,823
47
3,150
np oilgas
678
Completions at Gas Wells
0
3,735
26
6,328
74,408
np oilgas
679
Completions at CBM Wells
0
16
0
601
2,155
np oilgas
681
Spud Count - Oil Wells
0
0
0
0
66,565
np oilgas
683
Produced Water at All Wells
0
10
0
0
67,101
np oilgas
685
Completions at Oil Wells
0
3,107
130
2,181
50,785
np oilgas
687
Feet Drilled at All Wells
0
109,487
4,004
628
8,130
np oilgas
691
Well Counts - CBM Wells
0
38,117
603
15
34,187
np oilgas
692
Spud Count - All Wells
0
8,628
258
135
366
np oilgas
693
Well Count - All Wells
0
0
0
0
166
np oilgas
694
Oil Production at Oil Wells
0
4,375
0
5,468
1,104,120
np oilgas
695
Well Count - Oil Wells
0
122,856
3,091
63
455,552
np oilgas
696
Gas Production at Gas Wells
0
59,634
3,131
251
112,335
np oilgas
697
Oil Production at Gas Wells
0
1,360
0
26
354,406
np oilgas
698
Well Count - Gas Wells
15
388,677
6,726
310
623,925
np oilgas
699
Gas Production at CBM Wells
0
3,094
403
32
6,578
onroad
202
Urban Restricted AADT
24,687
790,075
30,439
5,846
149,645
onroad
205
Extended Idle Locations
748
273,106
4,425
104
56,079
onroad
212
Rural Restricted AADT
10,867
684,006
20,322
2,853
77,075
onroad
222
Urban Unrestricted AADT
42,001
1,223,593
54,345
11,950
376,209
onroad
232
Rural Unrestricted AADT
25,027
987,683
33,882
6,434
201,764
onroad
239
Total Road AADT
6,573
onroad
242
All Restricted AADT
315
onroad
258
Intercity Bus Terminals
165
2
0
38
onroad
259
Transit Bus Terminals
58
5
0
171
onroad
304
NLCD Open + Low
821
22
1
2,683
onroad
306
NLCD Med + High
18,500
384
20
22,396
onroad
307
NLCD All Development
560,112
12,560
1,001
1,142,592
onroad
308
NLCD Low + Med + High
83,977
1,583
113
133,883
onroad
506
Education
664
29
1
1,107
rail
261
NT AD Total Railroad Density
2
12,494
297
282
736
rail
271
NT AD Class 12 3 Railroad Density
362
767,307
22,868
6,704
39,121
68
-------
Sector
ID
Description
NH3
NOX
PM2 5
S02
voc
rwc
300
NLCD Low Intensity Development
16,221
32,174
332,700
8,087
351,696
3.3.7.2 Allocation Methodfor Airport-Related Sources in the U.S.
There are numerous airport-related emission sources in the NEI, such as aircraft, airport ground support
equipment, and jet refueling. The modeling platform includes the aircraft and airport ground support
equipment emissions as point sources. For the modeling platform, EPA used the SMOKE "area-to-point"
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 Canadian emissions have been updated in the platform
used for this study based on the 2013 Canadian inventories and associated data. The spatial surrogate data
came from Environment Canada, along with cross references. The shapefiles they provided were used in
the Surrogate Tool (previously referenced) to create spatial surrogates. The Canadian surrogates used for
this platform are listed in Table 3-16. Surrogates for Mexico are circa 1999 and 2000 and were based on
data obtained from the Sistema Municpal de Bases de Datos (SIMBAD) de INEGI and the Bases de datos
del Censo Economico 1999. CAPs allocated to the Mexico and Canada surrogates are shown in Table 3-
17. The entries in Table 3-17 are for the othar, othafdust, onroad can, and onroadmex sectors.
Table 3-16. Canadian Spatial Surrogates
Code
Canadian Surrogate Description
Code
Description
100
Population
941
PAVED ROADS
101
total dwelling
942
UNPAVED ROADS
106
ALL INDUST
945
Commercial Marine Vessels
113
Forestry and logging
950
Combination of Forest and Dwelling
115
Agriculture and forestry activities
955
UNPAVED ROADS AND TRAILS
200
Urban Primary Road Miles
960
TOTBEEF
210
Rural Primary Road Miles
965
TOTBEEF CD
212
Mining except oil and gas
966
TOTPOUL CD
220
Urban Secondary Road Miles
967
TOTSWIN CD
221
Total Mining
968
TOTFERT CD
222
Utilities
970
TOTPOUL
230
Rural Secondary Road Miles
980
TOTSWIN
240
Total Road Miles
990
TOTFERT
308
Food manufacturing
996
urban area
321
Wood product manufacturing
1211
Oil and Gas Extraction
323
Printing and related support activities
1212
Oil Sands
Petroleum and coal products
324
manufacturing
1251
OFFR TOTFERT
326
Plastics and rubber products
1252
OFFR MINES
69
-------
Code
Canadian Surrogate Description
Code
Description
manufacturing
327
Non-metallic mineral product
manufacturing
1253
OFFR Other Construction not Urban
331
Primary Metal Manufacturing
1254
OFFR Commercial Services
412
Petroleum product wholesaler-distributors
1255
OFFR Oil Sands Mines
416
Building material and supplies wholesaler-
distributors
1256
OFFR Wood industries CANVEC
448
clothing and clothing accessories stores
1257
OFFR Unpaved Roads Rural
562
Waste management and remediation
services
1258
OFFR Utilities
921
Commercial Fuel Combustion
1259
OFFR total dwelling
923
TOTAL INSTITUTIONAL AND
GOVERNEMNT
1260
OFFR water
924
Primary Industry
1261
OFFR ALL INDUST
925
Manufacturing and Assembly
1262
OFFR Oil and Gas Extraction
926
Distribtution and Retail (no petroleum)
1263
OFFR ALLROADS
927
Commercial Services
1264
OFFR OTHERJET
931
OTHERJET
1265
OFFR CANRAIL
932
CANRAIL
Table 3-17. CAPs Allocated to Mexican and Canadian Spatial Surrogates in 2014fb
Code
Mexican or Canadian Surrogate
Description
nh3
NOx
pm25
so2
voc
10
MEX Population
0
216
6
1
434
12
MEX Housing
25,900
112,801
3,949
450
141,616
14
MEX Residential Heating - Wood
0
1,311
16,802
202
115,512
16
MEX Residential Heating - Distillate
Oil
0
13
0
4
1
20
MEX Residential Heating - LP Gas
0
5,798
176
0
100
22
MEX Total Road Miles
2,660
359,254
10,047
5,692
75,042
24
MEX Total Railroads Miles
0
21,176
473
186
826
26
MEX Total Agriculture
180,582
136,198
28,813
6,529
10,917
32
MEX Commercial Land
0
74
1,615
0
21,898
34
MEX Industrial Land
4
1,080
1,944
0
119,006
36
MEX Commercial plus Industrial
Land
0
2,027
30
5
95,300
38
MEX Commercial plus Institutional
Land
3
1,753
81
4
54
40
MEX Residential (RES1-
4)+Comercial+Industrial+Institutiona
1+Government
0
4
11
0
74,853
42
MEX Personal Repair (COM3)
0
0
0
0
5,704
44
MEX Airports Area
0
3,112
88
402
1,062
70
-------
Mexican or Canadian Surrogate
Code
Description
nh3
NOx
pm25
so2
VOC
MEX Mobile sources - Border
50
Crossing
4
130
1
2
241
100
CAN Population
724
62
733
13
335
101
CAN total dwelling
383
34,234
2,538
4,126
143,234
106
CAN ALL INDUST
0
0
11,559
0
72
113
CAN Forestry and logging
465
2,521
0
143
7,080
115
CAN Agriculture and forestry
activities
51
609
2,941
13
1,709
200
CAN Urban Primary Road Miles
1,954
94,668
4,167
328
12,677
210
CAN Rural Primary Road Miles
779
57,206
2,297
134
5,457
212
CAN Mining except oil and gas
0
0
3,442
0
0
220
CAN Urban Secondary Road Miles
3,648
144,371
7,864
696
31,224
221
CAN Total Mining
0
0
56,438
0
0
222
CAN Utilities
79
9,371
54,184
3,299
197
230
CAN Rural Secondary Road Miles
2,024
99,071
4,275
353
14,291
240
CAN Total Road Miles
44
79,579
2,892
84
127,959
308
CAN Food manufacturing
0
0
11,099
0
5,873
321
CAN Wood product manufacturing
261
1,794
0
132
7,673
323
CAN Printing and related support
activities
0
0
0
0
11,604
CAN Petroleum and coal products
324
manufacturing
0
1,016
1,220
388
6,050
326
CAN Plastics and rubber products
manufacturing
0
0
0
0
23,540
CAN Non-metallic mineral product
327
manufacturing
0
0
6,628
0
0
331
CAN Primary Metal Manufacturing
0
156
5,504
52
73
412
CAN Petroleum product wholesaler-
distributors
0
0
0
0
39,650
448
CAN clothing and clothing
accessories stores
0
0
0
0
112
562
CAN Waste management and
remediation services
217
1,631
2,268
2,275
16,066
921
CAN Commercial Fuel Combustion
185
23,982
2,251
3,760
1,154
CAN TOTAL INSTITUTIONAL
923
AND GOVERNEMNT
0
0
0
0
13,707
924
CAN Primary Industry
0
0
0
0
35,196
925
CAN Manufacturing and Assembly
0
0
0
0
69,833
CAN Distribtution and Retail (no
926
petroleum)
0
0
0
0
6,990
927
CAN Commercial Services
0
0
0
0
30,189
932
CAN CANRAIL
54
119,139
2,773
430
5,936
941
CAN PAVED ROADS
0
0
297,607
0
0
945
CAN Commercial Marine Vessels
187
152,803
5,579
34,596
11,045
71
-------
Code
Mexican or Canadian Surrogate
Description
nh3
NOx
pm25
so2
VOC
950
CAN Combination of Forest and
Dwelling
1,733
19,252
158,532
2,750
224,453
955
CAN
UNPAVED ROADS AND TRAIL
S
0
0
440,673
0
0
960
CAN TOTBEEF
0
0
1,236
0
263,913
965
CANTOTBEEF CD
280,058
0
0
0
0
966
CANTOTPOUL CD
23,809
0
0
0
0
967
CANTOTSWIN CD
67,992
0
0
0
0
968
CANTOTFERT CD
120,304
0
0
0
0
970
CAN TOTPOUL
0
0
181
0
242
980
CAN TOTS WIN
0
0
756
0
2,585
990
CAN TOTFERT
0
4,227
379,893
9,448
155
996
CAN urban area
0
0
1,265
0
0
1211
CAN Oil and Gas Extraction
2
29
228,599
152
922
1212
CAN OilSands
126
2,053
0
638
1,754
1251
CAN OFFR TOTFERT
109
118,124
8,753
79
10,866
1252
CAN OFFR MINES
42
41,444
3,443
31
4,175
1253
CAN OFFR Other Construction not
Urban
26
23,606
3,885
20
9,504
1254
CAN OFFR Commercial Services
34
17,807
2,203
29
22,700
1255
CAN OFFR Oil Sands Mines
0
0
0
0
0
1256
CAN OFFR Wood industries
CANVEC
13
11,553
1,103
10
1,921
1258
CAN OFFR Utilities
16
8,553
529
14
10,136
1259
CAN OFFR total dwelling
17
5,399
1,409
14
34,499
1260
CAN OFFR water
8
2,050
302
11
18,222
1261
CAN OFFR ALL INDUST
4
4,171
267
3
860
1262
CAN OFFR Oil and Gas Extraction
1
1,036
57
1
148
1263
CAN OFFR ALLROADS
33
10,427
1,670
28
62,496
1264
CAN OFFR OTHERJET
1
848
71
1
72
1265
CAN OFFR CANRAIL
0
85
8
0
14
72
-------
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Residential Wood Combustion Particles Using the Two-Wavelength Aethalometer", Environ. Sci.
Technol., 45 (17), pp 7387-7393
Wiedinmyer, C., S.K. Akagi, R.J. Yokelson, L.K. Emmons, J.A. Al-Saadi3, J. J. Orlando1, and A. J. Soja.
(2011) "The Fire INventory from NCAR (FINN): a high resolution global model to estimate the
emissions from open burning", Geosci. Model Dev., 4, 625-641. http://www.geosci-model-
dev.net/4/625/2011/ doi: 10.5194/gmd-4-625-2011
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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lality 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 system15 The primary goals for CMAQ are to:
• Improve the environmental management community's ability to evaluate the impact of air quality
management practices for multiple pollutants at multiple scales.
• Improve the scientist's ability to better probe, understand, and simulate chemical and physical
interactions in the atmosphere.
The CMAQ modeling system brings together key physical and chemical functions associated with the
dispersion and transformations of air pollution at various scales. It was designed to approach air quality as
a whole by including state-of-the-science capabilities for modeling multiple air quality issues, including
tropospheric ozone, fine particles, toxics, acid deposition, and visibility degradation CMAQ relies on
emission estimates from various sources, including the U.S. EPA Office of Air Quality Planning and
Standards" current emission inventories, observed emission from major utility stacks, and model estimates
of natural emissions from biogenic and agricultural sources. CMAQ also relies on meteorological
predictions that include assimilation of meteorological observations as constraints. Emissions and
meteorology data are fed into CMAQ and run through various algorithms that simulate the physical and
chemical processes in the atmosphere to provide estimated concentrations of the pollutants. Traditionally,
the model has been used to predict air quality across a regional or national domain and then to simulate the
effects of various changes in emission levels for policymaking purposes. For health studies, the model can
also be used to provide supplemental information about air quality in areas where no monitors exist.
CMAQ was also designed to have multi-scale capabilities so that separate models were not needed for
urban and regional scale air quality modeling. The CMAQ simulation performed for this 2014 assessment
used a single domain that covers the entire continental U.S. (CONUS) and large portions of Canada and
15 Byun, D.W., and K. L. Schere, 2006: Review of the Governing Equations, Computational Algorithms, and Other
Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System. Applied Mechanics Reviews,
Volume 59, Number 2 (March 2006), pp. 51-77.
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Mexico using 12 km by 12 km horizontal grid spacing. Currently, 12 km x 12 km resolution is sufficient
as the highest resolution for most regional-scale air quality model applications and assessments.16 With the
temporal flexibility of the model, simulations can be performed to evaluate longer term (annual to 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 littp://www.cmascenter.ore.
4.1.1 Advantages and Limitations of the CMAQ Air Quality Model
An advantage of using the CMAQ model output for characterizing air quality for use in comparing with
health outcomes is that it provides a complete spatial and temporal coverage across the U.S. CMAQ is a
three-dimensional Eulerian photochemical air quality model that simulates the numerous physical and
chemical processes involved in the formation, transport, and destruction of ozone, particulate matter and
air toxics for given input sets of initial and boundary conditions, meteorological conditions and emissions.
The CMAQ model includes state-of-the-science capabilities for conducting urban to regional scale
simulations of multiple air quality issues, including tropospheric ozone, fine particles, toxics, acid
deposition and visibility degradation. However, CMAQ is resource intensive, requiring significant data
inputs and computing resources.
An uncertainty of using the CMAQ model includes structural uncertainties, representation of physical and
chemical processes in the model. These consist of: choice of chemical mechanism used to characterize
reactions in the atmosphere, choice of land surface model and choice of planetary boundary layer.
Another uncertainty in the CMAQ model is based on parametric uncertainties, which includes
uncertainties in the model inputs: hourly meteorological fields, hourly 3-D gridded emissions, initial
conditions, and boundary conditions. Uncertainties due to initial conditions are minimized by using a 10-
day ramp-up period from which model results are not used in the aggregation and analysis of model
outputs. Evaluations of models against observed pollutant concentrations build confidence that the model
performs with reasonable accuracy despite the uncertainties listed above. A detailed model evaluation for
ozone and PM2.5 species provided in Section 4.3 shows generally acceptable model performance which is
equivalent or better than typical state-of-the-science regional modeling simulations as summarized in
Simon et al., 201217.
4.2 CMAQ Model Version, Inputs and Configuration
This section describes the air quality modeling platform used for the 2014 CMAQ simulation. A modeling
platform is a structured system of connected modeling-related tools and data that provide a consistent and
transparent basis for assessing the air quality response to changes in emissions and/or meteorology. A
platform typically consists of a specific air quality model, emissions estimates, a set of meteorological
inputs, and estimates of "boundary conditions" representing pollutant transport from source areas outside
16U.S. EPA (2014), Draft Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and
Regional Haze, pp 214. https://www3.epa.gov/ttn/scram/guidance/guide/Draft 03-PM-RH Modeling Guidance-2014.pdf.
17 Simon, H., Baker, K.R., and Phillips, S. (2012) Compilation and interpretation of photochemical model performance
statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139.
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the region modeled. We used the CMAQ modeling system coupled to the Weather Research and
Forecasting (WRF) meteorological model as part of the 2014 Platform to provide a national scale air
quality modeling analysis. The CMAQ model simulates the multiple physical and chemical processes
involved in the formation, transport, and destruction of ozone and fine particulate matter (PM2.5).
This section provides a description of each of the main components of the 2014 CMAQ-WRF simulation
along with the results of a model performance evaluation in which the 2014 model predictions are
compared to corresponding measured ambient concentrations.
4.2.1 CMAQ Model Version
CMAQ is a non-proprietary computer model that simulates the formation and fate of photochemical
oxidants, including PM2.5 and ozone, for given input sets of meteorological conditions and emissions. As
mentioned previously, CMAQ includes numerous science modules that simulate the emission, production,
decay, deposition and transport of organic and inorganic gas-phase and pollutants in the atmosphere. This
2014 analysis employed CMAQ version 5.218 coupled to the Weather Research and Forecasting (WRF)
version 3.8.1.19 Two-way feedback was not utilized in this 2014 simulation, however, CMAQ read WRF
meteorological data on a five-minute interval. The 2014 CMAQ-WRF run included bi-directional
ammonia (NH3) air-surface exchange (v2.1) using the Massad formulation20, CB6r3 chemical mechanism,
AER06 aerosol module with non-volatile Primary Organic Aerosol (POA), and windblown dust
algorithms. The CMAQ community model versions 5.0.2 and 5.1 were most recently peer-reviewed in
September of 2015 for the U.S. EPA.21
4.2.2 Model Domain and Grid Resolution
The WRF-CMAQ modeling analyses were performed for a domain covering the continental United States,
as shown in Figure 4-1. This single domain covers the entire continental U.S. (CONUS) and large
portions of Canada and Mexico using 12 km by 12 km horizontal grid spacing. The 2014 simulation used
a Lambert Conform al map projection centered at (-97, 40) with true latitudes at 33 and 45 degrees north.
The 12 km WRF-CMAQ domain consisted of 459 by 299 grid cells and 35 vertical layers. Table 4-1
provides some basic geographic information regarding the 12 km WRF-CMAQ domain. The model
extends vertically from the surface to 50 millibars (approximately 17,600 meters) using a sigma-pressure
coordinate system. Table 4-2 shows the vertical layer structure used in the 2014 simulation. Air quality
conditions at the outer boundary of the 12-km domain were taken from a global model.
18 CMAQ version 5.2 model code is available from the Community Modeling and Analysis System (CMAS) at:
http://www.cmascenter.org.
19 Skamarock, W.C. and J.B. Klemp, 2008. A time-split nonhydrostatic atmospheric model for weather research and
forecasting applications. Journal of Computation Physics, Volume 227, pp. 3465-3485.
20 Massad, R.-S., Nemitz, E., and Sutton, M.A. (2010). Review and parameterization of bi-directional ammonia exchange
between vegetation and the atmosphere, Atmos. Chem. Phys., 10, 10359-10386, doi:10.5194/acp-10-10359-2010.
21 Moran, M.D., Astitha, M„ Barsanti, K.C.. Brown, N.J., Kaduwela, A., McKeen, S.A., Pickering. K.E. (September 28, 2015).
Final Report: Fifth Peer Review of the CMAQ Model, https://www.epa.gov/sites/production/files/2016-
11/documents/cmaa fifth review final report 2015.pdf. This peer review was focused on CMAQ v5.0.2, which was released
in May, 2014, as well as CMAQ v5.1, which was released in October 2015. It is available from the Community Modeling and
Analysis System (CMAS) as well as previous peer-review reports at: http://www.cmascenter.org.
-------
Table 4-1. Geographic Information for 12 km Modeling Domain
National 12 km WRF-CMAQ Modeling Configuration
Map Projection
Lambert Conformal Projection
Grid Resolution
12km
Coordinate Center
97 W, 40 N
True Latitudes
33 and 45 N
Dimensions
459 x 299 x 35
Vertical Extent
35 Layers: Surface to 50 mb level (see Table 4-2)
Table 4-2. Vertical layer structure for 2014 WRF-CMAQ simulation (heights are layer top).
Vertical
Layers
Sigma P
Pressure
(mb)
Approximate
Height (m)
35
0.0000
50.00
17,556
34
0.0500
97.50
14,780
33
0.1000
145.00
12,822
32
0.1500
192.50
11,282
31
0.2000
240.00
10,002
30
0.2500
287.50
8,901
29
0.3000
335.00
7,932
28
0.3500
382.50
7,064
27
0.4000
430.00
6,275
26
0.4500
477.50
5,553
25
0.5000
525.00
4,885
24
0.5500
572.50
4,264
23
0.6000
620.00
3,683
22
0.6500
667.50
3,136
21
0.7000
715.00
2,619
20
0.7400
753.00
2,226
19
0.7700
781.50
1,941
18
0.8000
810.00
1,665
17
0.8200
829.00
1,485
16
0.8400
848.00
1,308
15
0.8600
867.00
1,134
14
0.8800
886.00
964
13
0.9000
905.00
797
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Vertical
Layers
Sigma P
Pressure
(mb)
Approximate
Height (m)
12
0.9100
914.50
714
11
0.9200
924.00
632
10
0.9300
933.50
551
9
0.9400
943.00
470
8
0.9500
952.50
390
7
0.9600
962.00
311
6
0.9700
971.50
232
5
0.9800
981.00
154
4
0.9850
985.75
115
3
0.9900
990,50
77
2
0.9950
995.25
38
1
0.9975
997.63
19
0
1.0000
1000.00
0
12km CONUS nationwide)di
x,y: -2556000,-1728000 1
col: 459 row: 299
Figure 4-1. Map of the 2014 WRF-CMAQ Modeling Domain. The blue box denotes the 12-km
national modeling domain.
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4.2.3 Modeling Period / Ozone Episodes
The 12-km WRF-CMAQ modeling domain was modeled for the entire year of 2014. The annual
simulation included a "ramp-up" period, comprised of 10 days before the beginning of the simulation, to
mitigate the effects of initial concentrations. All 365 model days were used in the annual average levels of
PM2.5. For the 8-hour ozone, we used modeling results from the period between May 1 and September
30. This 153-day period generally conforms to the ozone season across most parts of the U.S. and
contains the majority of days that observed high ozone concentrations.
4.2.4 Model Inputs: Emissions, Meteorology and Boundary Conditions
2014 Emissions: The emissions inventories used in the 2014 air quality modeling are described in Section
3, above.
Meteorological Input Data: The gridded meteorological data for the entire year of 2014 at the 12 km
continental United States scale domain was derived from version 3 .8.1" of the Weather Research and
Forecasting Model (WRF), Advanced Research WRF (ARW) core.23 The WRF Model is a state-of-the-
science mesoscale numerical weather prediction system developed for both operational forecasting and
atmospheric research applications (http://wrf-model.org ). The 2014 CMAQ-WRF meteorology
simulated for 2014 with 2011 National Land Cover Database (NLCD)24 and using version 2 four-
dimensional data assimilation with no nudging in the planetary boundary layer and based on blended 3-
hourly reanalysis fields (combination of 6-hour (Meteorological Assimilation Data Ingest System,)
MADIS25 data and intermediate North American Mesoscale Model26 (NAM) 3-hour forecast) organized
into 12km NAM Data Assimilation System (NDAS) fields up to 50 hPa. The WRF simulation included
the physics options of the Pleim-Xiu land surface model (LSM) with NLCD woody wetlands lad use
category recognized. Asymmetric Convective Model version 2 planetary boundary layer (PBL) scheme,
Morrison double moment microphysics, Kain- Frit sell cumulus parameterization scheme utilizing the
moisture-advection trigger7 and the RRTMG long-wave and shortwave radiation (LWR/SWR) scheme.28
In addition, the Group for High Resolution Sea Surface Temperatures (GHRSST)29'30 1 km SST data was
used for SST information to provide more resolved information compared to the more coarse data in the
NAM analysis.
22 Version 3.6.1 was the current version of WRF at the time the 2013 meteorological model simulation was performed.
23 Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang, W., Powers, J.G., 2008.
A Description of the Advanced Research WRF Version 3.
24 National Land Cover Database 2011, http://www.mrlc.gov/nlcd201 l.php
25 Meteorological Assimilation Data Ingest System, http://madis.noaa.gov/.
26 North American Model Analysis-Only, http://nomads.ncdc.noaa.gov/data.php; download from
ftp ://nomads. ncdc. noaa. gov/NAM/analy sisonly/.
27 Ma, L-M. and Tan, Z-M, 2009. Improving the behavior of the Cumulus Parameterization for Tropical Cyclone Prediction:
Convection Trigger. Atmospheric Research 92 Issue 2, 190-211.
http://www.sciencedirect.com/science/article/pii/S01698095080Q2585
28 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.
29 Stammer, D., F.J. Wentz, and C.L. Gentemann, 2003, Validation of Microwave Sea Surface Temperature Measurements for
Climate Purposes, J. Climate, 16, 73-87.
30 Global High Resolution SST (GHRSST) analysis, https://www.ghrsst.org/.
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Initial and Boundary Conditions: The lateral boundary and initial species concentrations are provided by a
three-dimensional global atmospheric chemistry model, the GEOS-CHEM31 model version 10-1, using the
tropchem, NOx_Ox_HC_Aer_Br, mechanism. The global GEOS-CHEM model simulates atmospheric
chemical and physical processes driven by assimilated meteorological observations from the NASA's
Goddard Earth Observing System (GEOS-5). This model was run for 2014 with a grid resolution of 2.0
degrees x 2.5 degrees (latitude-longitude). The predictions were processed using the GEOS-2-CMAQ
tool and used to provide one-way dynamic boundary conditions at one-hour intervals.32'33 More
information is available about the GEOS-CHEM model and other applications using this tool at:
http://gmao.gsfc.nasa.gov/GEOS/ and http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-5.
4.3 CMAQ Model Performance Evaluation
An operational model performance evaluation for ozone and PM2.5 and its related speciated components
was conducted for the 2014 simulation using state/local monitoring sites data in order to estimate the
ability of the CMAQ modeling system to replicate the 2014 base year concentrations for the 12 km
continental U.S. domain.
There are various statistical metrics available and used by the science community for model performance
evaluation. For a robust evaluation, the principal evaluation statistics used to evaluate CMAQ
performance were two bias metrics, mean bias and normalized mean bias; and two error metrics, mean
error and normalized mean error.
Mean bias (MB) is used as average of the difference (predicted - observed) divided by the total number of
replicates (n). Mean bias is defined as:
MB = - X'/(P — 0) , where P = predicted and O = observed concentrations.
ft
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 = ^£«iP- 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:
31 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric Chemistry Modeling Group, Harvard University,
Cambridge, MA, October 15, 2004.
32Akhtar, F., Henderson, B., Appel, W., Napelenok, S., Hutzell, B., Pye, H., Foley, K., 2012. Multiyear Boundary Conditions
for CMAQ 5.0 from GEOS-Chem with Secondary Organic Aerosol Extensions, 11th Annual Community Modeling and
Analysis System conference, Chapel Hill, NC, October 2012.
33 Henderson, B.H., Akhtar, F., Pye, H.O.T., Napelenok, S.L., and Hutzell, W.T. (2014) A database and tool for boundary
conditions for regional air quality modeling: description and evaluation, Geosci. Model Dev., 7, 339-360.
-------
!(/' O)
NMB = -J- *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:
t\p-a
nme= — *100
n
X(O)
1
The performance statistics were calculated using predicted and observed data that were paired in time and
space on an 8-hour basis. Statistics were generated for each of the nine National Oceanic and
Atmospheric Administration (NOAA) climate regions34 of the 12-km U.S. modeling domain (Figure 4-2).
The regions include the Northeast, Ohio Valley, Upper Midwest, Southeast, South, Southwest, Northern
Rockies, Northwest and West35 36 as were originally identified in Karl and Koss (1984)3'.
U.S. Climate Regions
34 NOAA, National Centers for Environmental Information scientists have identified nine climatically consistent regions within
the contiguous U.S., http://www.ncdc.noaa.gov/monitoring-references/maps/us-climate-regions.php.
35 The nine climate regions are defined by States where: Northeast includes CT, DE, ME, MA, MD. NH, NJ, NY, PA. RI. and
VT; Ohio Valley includes IL, IN, KY. MO. OH. TN, and WV: Upper Midwest includes IA, MI, MN, and VVI, Southeast
includes AL, FL, GA. NC, SC, and VA; South includes AR, KS, LA. MS, OK, and TX; Southwest includes AZ. CO, NM, and
UT; Northern Rockies includes MT. NE, ND, SD, WY; Northwest includes ID, OR, and WA: and West includes CA and NV.
36Note most monitoring sites in the West region are located in California (see Figure 4-2), therefore statistics for the West will
be mostly representative of California ozone air quality.
37 Karl. T. R. and Koss, W. J., 1984: "Regional and National Monthly. Seasonal, and Annual Temperature Weighted by Area,
1895-1983." Historical Climatology Series 4-3, National Climatic Data Center, Asheville, NC, 38 pp.
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Figure 4-2. NOAA Nine Climate Regions (source: http://www.ncdc. noaa.gov/monitoring-references/mai)s/us-
climate-regions.php#references)
In addition to the performance statistics, regional maps which show the MB, ME, NMB, and NME were
prepared for the ozone season, May through September, at individual monitoring sites as well as on an
annual basis for PM2.5 and its component species.
Evaluation for 8-hour Daily Maximum Ozone: The operational model performance evaluation for eight-
hour daily maximum ozone was conducted using the statistics defined above. Ozone measurements for
2014 in the continental U.S. were included in the evaluation and were taken from the 2014 State/local
monitoring site data in the EPA Air Quality System (AQS) and the Clean Air Status and Trends Network
(CASTNet).
The 8-hour ozone model performance bias and error statistics for each of the nine NOAA climate regions
and each season are provided in Table 4-4. Seasons were defined as: winter (December-January-
February), spring (March-April-May), summer (June, July, August), and fall (September-October-
November). In some instances, observational data were excluded from the analysis and model evaluation
based on a completeness criterion of 75 percent. Spatial plots of the MB, ME, NMB and NME for
individual monitors are shown in Figures 4-3 through 4-6, respectively. The statistics shown in these two
figures were calculated over the ozone season, May through September, using data pairs on days with
observed 8-hour ozone of greater than or equal to 60 ppb.
In general, the model performance statistics indicate that the 8-hour daily maximum ozone concentrations
predicted by the 2014 CMAQ simulation closely reflect the corresponding 8-hour observed ozone
concentrations in space and time in each subregion of the 12-km modeling domain. As indicated by the
statistics in Table 4-4, bias and error for 8-hour daily maximum ozone are relatively low in each
subregion, not only in the summer when concentrations are highest, but also during other times of the year.
Generally, 8-hour ozone at the AQS sites in the summer and fall is over predicted with the greatest over
prediction in the South and Southeast (NMB ranging between 12 to 34 percent). Likewise, 8-hour ozone
at the CASTNet sites in the summer is typically over predicted except in the West where the bias shows a
slight under prediction (NMB of -0.3%). 8-hour ozone is under predicted at AQS and CASTNet sites in
the Northeast and Upper Midwest in the winter and spring (with NMBs less than approximately 10
percent in each subregion).
Model bias at individual sites during the ozone season is similar to that seen on a subregional basis for the
summer. Figure 4-2 shows the mean bias for 8-hour daily maximum ozone greater than 60 ppb is
generally ±10 ppb across the AQS and CASTNet sites. Likewise, the information in Figure 4-4 indicates
that the bias for days with observed 8-hour daily maximum ozone greater than 60 ppb is within ± 20
percent at the vast majority of monitoring sites across the U.S. domain. Model error, as seen from Figures
4-4 and 4-6, is generally 2 to 10 ppb and 20 percent or less at most of the sites across the U.S. modeling
domain. Somewhat greater error is evident at sites in several areas most notably in the West, Northern
Rockies, Northeast, Southeast, along portions of the Gulf Coast, and Great Lakes coastline.
Table 4-4. Summary of CMAQ 2014 8-Hour Daily Maximum Ozone Model Performance Statistics
by NOAA climate region, by Season and Monitoring Network.
Climate
Monitor
No. of
MB
ME
NMB
NME
region
Network
Season
Obs
WlSSam
eH*
(%)
(%)
Northeast
AQS
Winter
12,047
-0.1
5.7
-0.2
18.2
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Climate
Monitor
No. of
MB
ME
NMB
NME
region
Network
Season
Obs
(%)
(%)
Spring
16,172
-0.3
5.3
-0.7
12.0
Summer
17,263
6.6
7.9
15.2
18.2
Fall
14,720
8.7
9.3
26.4
28.1
CASTNet
Winter
1,280
-0.8
5.2
-2.3
15.5
Spring
1,296
-1.3
5.0
-2.9
11.0
Summer
1,299
4.6
7.2
10.8
17.1
Fall
1,225
8.4
8.9
25.1
26.5
AQS
Winter
4,065
1.3
5.9
4.2
19.9
Spring
15,708
2.8
5.7
6.0
12.3
Summer
19,913
8.4
9.8
18.9
22.0
Ohio Valley
Fall
13,392
9.8
10.3
27.6
29.2
CASTNet
Winter
1,583
1.2
6.2
3.5
18.8
Spring
1,634
0.2
5.4
0.3
11.0
Summer
1,627
2.2
13.6
4.5
27.5
Fall
1,609
7.8
9.0
21.5
24.9
AQS
Winter
1,495
-1.8
6.6
-5.8
21.5
Spring
6,916
-0.1
5.4
-0.2
11.8
Summer
9,538
4.4
7.2
10.7
17.4
Fall
5,941
8.1
8.9
24.3
26.6
Upper Midwest
CASTNet
Winter
445
-3.7
7.4
-10.9
21.7
Spring
457
-3.1
6.0
-6.7
12.8
Summer
458
2.0
5.8
5.0
14.5
Fall
444
6.6
7.6
20.2
23.3
AQS
Winter
6,723
5.9
7.2
17.6
21.3
Spring
14,962
4.2
6.6
9.0
14.1
Summer
16,998
13.5
14.0
33.5
34.7
Southeast
Fall
13,787
12.6
12.8
34.8
35.4
CASTNet
Winter
946
2.6
5.7
7.1
15.6
Spring
998
-0.2
6.3
-0.5
12.5
Summer
998
9.9
10.6
23.9
25.7
Fall
975
9.3
10.1
25.0
27.1
AQS
Winter
12,064
4.5
6.4
14.6
20.6
South
Spring
13,851
5.1
7.4
11.5
16.6
Summer
13,756
17.9
18.5
45.8
47.2
Fall
13,265
10.7
11.4
28.6
30.3
-------
Climate
Monitor
No. of
MB
ME
NMB
NME
region
Network
Season
Obs
Ef553*
(%)
(%)
CASTNet
Winter
518
3.6
5.8
10.4
16.8
Spring
551
3.1
6.3
6.5
13.4
Summer
537
11.2
15.8
26.4
37.2
Fall
530
10.4
10.8
28.3
29.2
AQS
Winter
9,341
4.8
7.2
13.2
19.8
Spring
11,234
3.0
5.5
5.7
10.4
Summer
11,517
9.3
11.3
17.8
21.6
Fall
10,311
12.4
12.6
29.3
29.9
Southwest
CASTNet
Winter
774
1.1
6.1
2.5
13.8
Spring
817
0.2
5.8
0.4
10.3
Summer
817
7.1
10.3
13.1
18.9
Fall
801
10.3
11.3
22.4
24.6
AQS
Winter
4,553
-0.4
6.0
-1.2
16.5
Spring
4,898
1.8
5.0
3.8
10.8
Summer
4,872
5.5
7.3
12.1
16.2
Northern
Fall
4,799
9.1
9.7
24.2
25.8
Rockies
CASTNet
Winter
607
-1.2
5.2
-3.0
13.3
Spring
637
-0.6
4.8
-1.2
9.6
Summer
628
3.2
7.1
6.8
14.9
Fall
611
8.4
9.3
20.8
23.0
AQS
Winter
596
5.4
7.4
18.5
25.5
Spring
1,194
0.3
4.7
0.7
11.6
Summer
2,404
5.5
8.3
14.5
21.6
Northwest
Fall
1,258
8.7
9.8
25.4
28.6
CASTNet
Winter
—
—
—
-
-
Spring
—
—
—
—
—
Summer
—
—
—
-
-
Fall
—
—
—
—
—
AQS
Winter
14,984
6.3
8.3
19.0
24.8
Spring
16,829
0.8
5.4
1.6
10.8
West
Summer
17,881
6.0
10.0
11.9
20.0
Fall
16,611
8.4
10.0
18.7
22.3
CASTNet
Winter
530
4.5
7.0
10.8
16.8
Spring
552
-3.0
6.2
-5.4
11.1
-------
Climate
reqiori
Monitor
Network
Season
No. of
Obs
MB
(ppb)
ME
(ppb)
NMB
(%)
NME
(%)
Summer
542
-0.2
10.6
-0.3
17.8
Fall
538
6.4
11.0
12.7
21.8
03_8hrmax MB (ppb) for run2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140501 to 20140930
units = ppb
coverage limit = 75%
TRIANGLE=CASTNET_Daily; CIRCLE=AQS_Daily_03;
Figure 4-3. Mean Bias (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period
May-September 2014 at AQS and CASTNet monitoring sites in the continental U.S. modeling
domain.
Q3_8hrmax ME (ppb) for run2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140501 to 20140930
units = ppb
coverage limit = 75%
TRIANGLE=CASTNET_Daily; CIRCLE=AQS_Daily_03;
Figure 4-4. Mean Error (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the period
May-September 2014 at AQS and CASTNet monitoring sites in the continental U.S. modeling
-------
domain.
03_8hrmax NMB (%) for run 2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140501 to 20140930
units = %
coverage limit = 75%
spilnP
- >-' f
FT\>
y
TRIANGLE=CASTNET_Daily; CIRCLE=AQS_Daily_03;
Figure 4-5. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 ppb over
the period May-September 2014 at AQS and CASTNet monitoring sites in the continental U.S.
modeling domain.
03_8hrmax NME (%) for run 2014fb CDC_WRFCMAQ cb6r3_ae6nvPOA12US1 for 20110501 to 20140930
units = %
coverage limit = 75%
TRIANGLE=CASTNET_Daily; CIRCLE=AQS_Daily_03;
Figure 4-6. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 ppb over
the period May-September 2014 at AQS and CASTNet monitoring sites in the continental U.S.
modeling domain.
Evaluation for Annual PM?.s components: The PM evaluation focuses on PM2.5 components including
sulfate (SO4), nitrate (NO3), total nitrate (TNO3 = NO3 + UNO;), ammonium (NH4), elemental carbon
-------
(EC), and organic carbon (OC). The bias and error performance statistics were calculated on an annual
basis for each of the nine NOAA climate subregions defined above (provided in Table 4-5). PM2.5
measurements for 2014 were obtained from the following networks for model evaluation: Chemical
Speciation Network (CSN, 24-hour average), Interagency Monitoring of Protected Visual Environments
(IMPROVE, 24-hour average, and Clean Air Status and Trends Network (CASTNet, weekly average).
For PM2.5 species that are measured by more than one network, we calculated separate sets of statistics for
each network by subregion. In addition to the tabular summaries of bias and error statistics, annual spatial
maps which show the mean bias, mean error, normalized mean bias and normalized mean error by site for
each PM2.5 species are provided in Figures 4-7 through 4-30.
As indicated by the statistics in Table 4-5, annual average sulfate is consistently under predicted at CSN,
IMPROVE, and CASTNet monitoring sites across the 12-km modeling domain (with MB values ranging
from 0.0 to -0.6 |igm~3 and NMB values ranging from near negligible to -32 percent) except at CSN sites
in the Southeast, Northern Rockies and Northwest as well as IMPROVE sites in the Northwest (over
predicted NMB ranges between 1 to 38 percent). Sulfate performance shows moderate error, ranging
from 26 to 66 percent. Figures 4-7 through 4-10, suggest spatial patterns vary by region. The model
bias for most of the Northeast, Southeast, Central and Southwest states are within ±30 percent. The
model bias appears to be slightly greater in the Northwest with over predictions up to 80 percent at
individual monitors. Model error also shows a spatial trend by region, where much of the Eastern states
are 20 to 40 percent, the Western and Central U.S. states are 30 to 80 percent.
Annual average nitrate is under predicted at the urban CSN monitoring sites in the Ohio Valley,
Upper Midwest, South, Southwest, Northern Rockies, and West (NMB in the range of - 1 7.0 to
-49 percent), except in the Northeast, Northwest, and Southeast where nitrate is over predicted (NMB in
the range of 7 percent and 49 percent). At IMPROVE rural sites, annual average nitrate is under
predicted at all subregions, except in the Southeast, Northeast, and Northwest where nitrate is over
predicted by 23 to 34 percent, respectively. M odel performance of total nitrate at sub-urban
CASTNet monitoring sites shows an over prediction in the Northeast, Ohio Valley, Upper Midwest,
South, Southwest, and Southeast (NMB in the range of 1 to 40 percent), except in the Northem
Rockies and Western U.S. (NMB in the range of -12 to -27 percent). Model error for nitrate
and total nitrate is somewhat greater for each of the nine N O A A climate subregions as compared
to sulfate. Model bias at individual sites indicates mainly over prediction of greater than 20 percent at
most monitoring sites in the Eastern half of the U.S. as indicated in Figure 4-13. The exception to this
is in the Southern Florida, Ohio Valley, Southwest, and Western U.S. of the modeling domain where
there appears to be a greater number of sites with under prediction of nitrate of 10 to 80 percent.
Model error for annual nitrate, as shown in Figures 4-12 and 4-15, is least at sites in portions of the
Southeast and extending upward to the Northeast corridor. Nitrate concentrations are typically higher
in these areas than in other portions of the modeling domain.
Annual average ammonium model performance as indicated in Table 4-5 has a tendency for the model
to under predict across the CASTNet sites (ranging from -19 to -49 percent. Likewise, ammonium
performance across the urban CSN sites shows an under prediction in most of the climate subregions
(ranging from -8 to -45 percent), except in the Northeast, Northwest and Southeast (over prediction of
NMB 5 and 38 percent, respectively). The spatial variation of ammonium across the majority of
individual monitoring sites in the Eastern U.S. shows bias within ±50 percent (Figures 4-19 and 4-21). A
larger bias is seen in the Southeast and in the Northern Rockies, (over prediction bias on average 80
percent). The urban monitoring sites exhibit larger errors than at rural sites for ammonium.
Annual average elemental carbon is over predicted in all of the nine subregions at urban and rural sites.
-------
There is not a large variation in error statistics from subregion to subregion or at urban versus rural sites.
Annual average organic carbon is over predicted across most subregions in rural IMPROVE areas (NMB
ranging from 3 to 47 percent), except in the Southwest, Northern Rockies, and Western U.S. where the
bias ranges between -8 to -31 percent. The model over predicted annual average organic carbon in all
subregions at urban CSN sites except in the Northern Rockies and Western U.S. (NMB ranges from -8 to
-19 percent). Similar to elemental carbon, error model performance does not show a large variation from
subregion to subregion or at urban versus rural sites.
Table 4-5. Summary of CMAQ 2014 Annual PM Species Model Performance Statistics by NOAA
Climate region, by Monitoring Network.
Monitor
Pollutant Network
Subregion
No. of
Obs
MB
(|jgm3)
3, m
CO
NMB
(%)
NME
(%)
CSN
Northeast
906
-0.3
0.6
-17.0
35.2
Ohio Valley
3,037
-0.4
0.7
-17.6
35.0
Upper Midwest
1,604
-0.1
0.5
-9.4
34.1
Southeast
2,066
0.0
0.6
1.0
34.5
South
1,456
-0.3
0.6
-19.0
34.6
Southwest
783
-0.1
0.3
-9.7
43.1
Northern Rockies
570
0.0
0.4
2.8
51.9
Northwest
620
0.1
0.4
15.2
55.6
West
956
-0.2
0.3
-15.3
39.6
IMPROVE
Northeast
1,714
-0.1
0.3
-11.3
33.0
Ohio Valley
797
-0.3
0.5
-14.0
30.1
Upper Midwest
938
-0.1
0.4
-7.6
36.1
Southeast
1,147
-0.1
0.5
-6.6
30.1
Sulfate
South
1,164
-0.3
0.5
-19.1
36.1
Southwest
3,803
-0.1
0.2
-17.0
42.5
Northern Rockies
2,121
0.0
0.2
0.8
45.8
Northwest
1,822
0.1
0.2
38.1
65.8
West
2,400
0.0
0.3
-4.5
50.2
CASTNet
Northeast
906
-0.3
0.4
-23.6
27.2
Ohio Valley
893
-0.6
0.6
-25.1
27.2
Upper Midwest
257
-0.3
0.4
-21.9
26.0
Southeast
586
-0.5
0.5
-25.0
27.9
South
377
-0.6
0.6
-30.7
32.2
Southwest
422
-0.1
0.2
-24.3
39.1
Northern Rockies
566
-0.1
0.2
-20.1
36.3
Northwest
West
296
-0.2
0.3
-31.8
45.7
CSN
Northeast
3,237
0.1
0.7
7.2
56.5
Nitrate
Ohio Valley
2,841
-0.3
0.9
-18.3
55.5
Upper Midwest
1,604
-0.3
0.8
-16.9
46.9
Southeast
2,308
0.3
0.5
49.9
98.1
-------
Pollutant
Monitor
Network
Subregion
No. of
Obs
MB
(|jgm3)
a S
3, m
CO
NMB
(%)
NME
(%)
South
1,238
-0.2
0.6
-20.1
70.4
Southwest
783
-0.3
0.9
-29.1
80.5
Northern Rockies
570
-0.4
0.6
-34.3
59.1
Northwest
523
0.2
0.8
27.4
98.5
West
956
-1.2
1.5
-49.3
65.8
IMPROVE
Northeast
1,709
0.1
0.3
33.8
83.7
Ohio Valley
797
-0.2
0.6
-17.7
69.1
Upper Midwest
938
-0.2
0.6
-17.4
59.2
Southeast
1,147
0.1
0.4
27.1
96.8
South
1,164
-0.2
0.5
-25.8
75.6
Southwest
3,801
-0.1
0.2
-28.5
91.1
Northern Rockies
2,120
-0.1
0.2
-36.2
79.3
Northwest
1,807
0.0
0.2
23.3
>100.0
West
2,390
-0.3
0.5
-49.9
79.9
CASTNet
Northeast
906
0.3
0.5
23.6
37.9
Ohio Valley
893
0.3
0.9
16.1
43.0
Upper Midwest
257
0.0
0.6
1.1
34.9
Total Nitrate
(NO3+HNO3)
Southeast
586
0.5
0.8
38.9
64.6
South
377
0.3
0.8
19.7
51.9
Southwest
422
0.1
0.3
11.0
41.8
Northern Rockies
566
-0.1
0.3
-11.8
37.5
Northwest
-
-
-
-
-
West
296
-0.4
0.7
-26.5
47.1
CSN
Northeast
3,237
0.0
0.3
4.9
48.3
Ohio Valley
2,744
-0.2
0.4
-17.0
45.0
Upper Midwest
1,475
-0.1
0.4
-8.4
42.8
Southeast
1,803
0.1
0.3
17.0
56.8
South
1,230
-0.1
0.3
-20.8
49.2
Southwest
765
-0.2
0.3
-45.2
67.7
Northern Rockies
570
-0.1
0.3
-11.0
56.4
Northwest
610
0.1
0.3
37.5
>100.0
Ammonium
West
945
-0.3
0.5
-43.4
64.4
CASTNet
Northeast
906
-0.1
0.2
-19.0
28.8
Ohio Valley
893
-0.3
0.3
-27.6
35.0
Upper Midwest
257
-0.2
0.3
-27.5
34.6
Southeast
586
-0.1
0.2
-15.9
28.0
South
377
-0.2
0.2
-26.8
36.6
Southwest
422
-0.1
0.1
-43.1
53.1
Northern Rockies
566
-0.1
0.1
-42.8
51.0
-------
Monitor
No. of
MB
ME
NMB
NME
Pollutant Network
Subregion
Obs
(|jgm3)
(|jgm3)
(%)
(%)
Northwest - - - - - 1
West
296
-0.1
0.2
-49.3
59.9
CSN
Northeast
3,186
0.3
0.5
50.4
76.4
Ohio Valley
2,723
0.2
0.3
35.2
62.9
Upper Midwest
1,434
0.2
0.3
54.4
70.8
Southeast
1,766
0.3
0.4
49.9
73.6
South
1,218
0.2
0.3
35.4
55.4
Southwest
769
0.3
0.4
51.7
71.3
Northern Rockies
546
0.1
0.3
24.2
86.8
Northwest
583
0.7
0.9
>100.0
>100.0
West
870
0.0
0.4
5.0
50.2
Elemental
Carbon IMPROVE
Northeast
1,730
0.1
0.1
43.9
65.2
Ohio Valley
806
0.1
0.1
25.3
58.2
Upper Midwest
945
0.1
0.1
26.0
57.1
Southeast
1,349
0.1
0.2
41.4
64.2
South
1,160
0.0
0.1
25.7
54.9
Southwest
3,793
0.0
0.1
34.2
72.2
Northern Rockies
2,270
0.0
0.1
10.0
63.7
Northwest
1,815
0.2
0.3
>100.0
>100.0
West
2,391
0.1
0.1
39.5
83.0
CSN
Northeast
3,146
2.3
2.5
>100.0
>100.0
Ohio Valley
2,692
0.5
0.9
32.9
56.6
Upper Midwest
1,426
0.9
1.2
59.7
84.8
Southeast
1,755
1.2
1.5
63.4
81.1
South
1,212
0.4
0.9
23.2
51.9
Southwest
768
0.9
1.2
67.4
89.3
Northern Rockies
510
-0.2
0.9
-18.5
72.2
Northwest
579
1.7
2.3
85.0
>100.0
West
869
-0.2
1.1
-8.7
44.3
Organic
Carbon IMPROVE
Northeast
1,724
0.3
0.6
34.4
68.6
Ohio Valley
806
0.3
0.7
28.2
59.6
Upper Midwest
940
0.2
0.6
20.5
68.9
Southeast
1,349
0.6
1.0
47.2
77.2
South
1,159
0.0
0.5
2.6
50.5
Southwest
3,780
-0.1
0.3
-10.5
56.4
Northern Rockies
2,252
-0.2
0.4
-31.1
57.7
Northwest
1,768
0.3
0.9
42.1
>100.0
West
2,388
-0.1
0.6
-8.1
63.5
-------
S04 MB (ug/m3) for run2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure 4-7. Mean Bias (jigin3) of annual sulfate at monitoring sites in the continental U.S.
modeling domain.
S04 ME (ug/m3) for run2014fb CDC_WRFCMAQ Cb6r3 ae6nvPOA 12US1 for 20140101 to 20141231
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure 4-8. Mean Error (jigm 3) of annual sulfate at monitoring sites in the continental U.S.
modeling domain.
-------
S04 NMB (%) for run 2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure 4-9. Normalized Mean Bias (%) of annual sulfate at monitoring sites in the continental
U.S. modeling domain.
S04 NME (%) tor run 2014fb_CDCWRFCMAQ cb6r3 ae6nvPOA12US1 for 20140101 to 20141231
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure 4-10. Normalized Mean Error (%) of annual sulfate at monitoring sites in the continental
U.S. modeling domain.
95
-------
N03 MB (ug/m3) for run2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = ug/m3
coverage limit = 75%
WRFCMAQ_cb6r3_
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-12. Mean Error (jigm3) of annual nitrate at monitoring sites in the continental U.S.
modeling domain.
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-11. Mean Bias (jignr3) of annual nitrate at monitoring sites in the continental U.S.
modeling domain.
N03 ME (ug/m3) for run2014fb CDC WRFCMAQcb6r3 ae6nvPOA 12US1 for 20140101 to 20141231
96
-------
N03 NMB (%) for run 2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = %
coverage limit = 75%
. ¦ A1-*—~ "
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-13. Normalized Mean Bias (%) of annual nitrate at monitoring sites in the continental U.S.
modeling domain.
N03 NME (%) for run 2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOAJ2US1 for 20140101 to 20141231
units - %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-14. Normalized Mean Error (%) of annual nitrate at monitoring sites in the continental
U.S. modeling domain.
97
-------
TN03 MB (ug/m3) for run2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = ug/m3
coverage limit = 75%
CIRCLE=CASTNET;
Figure 4-15. Mean Bias (jtigm"3) of annual total nitrate at monitoring sites in the continental U.S.
modeling domain.
TN03 ME (ug/m3) for run2014fb CDC WRFCMAQ cb6r3 ae6nvPOA 12US1 for 20140101 to 20141231
units = ug/m3
coverage limit = 75%
CIRCLE=CASTNET;
Figure 4-16. Mean Error (jigrn 3) of annual total nitrate at monitoring sites in the continental U.S.
modeling domain.
98
-------
TN03 NMB (%) for run 2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = %
coverage limit = 75%
¦1--. -V;-' ' y-.-.-- -
CIRCLE=CASTNET;
Figure 4-17. Normalized Mean Bias (%) of annual total nitrate at monitoring sites in the continental
U.S. modeling domain.
TN03 NME (%) for run 2014fb_CDC ,WRFCMAQ_cb6r3 ae6nvPOA 12US1 for 20140101 to 20141231
units = %
coverage limit = 75%
CIRCLE=CASTNET;
Figure 4-18. Normalized Mean Error (%) of annual total nitrate at monitoring sites in the
continental U.S. modeling domain.
99
-------
NH4 MB (ug/m3) for run2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = ug/m3
coverage limit = 75%
CIRCLE=CSN; TRIANGLE=CASTNET;
Figure 4-19. Mean Bias (jignr3) of annual ammonium at monitoring sites in the continental U.S.
modeling domain.
NH4 ME (ug/m3) for run2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 tor 20140101 to 20141231
units = ug/m3
coverage limit = 75%
CIRCLE=CSN; TRIANGLE=CASTNET;
Figure 4-20. Mean Error (jigm3) of annual ammonium at monitoring sites in the continental U.S.
modeling domain.
100
-------
NH4 NMB (%) for run 2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = %
coverage limit = 75%
CIRCLE=CSN; TRIANGLE=CASTNET;
Figure 4-21. Normalized Mean Bias (%) of annual ammonium at monitoring sites in the continental
U.S. modeling domain.
NH4 NME (%) tor run 2014fb_CDC_WRFCMAQ cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = %
coverage limit = 75%
CIRCLE=CSN; TRIANGLE=CASTNET;
Figure 4-22. Normalized Mean Error (%) of annual ammonium at monitoring sites in the
continental U.S. modeling domain.
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EC MB (ug/m3) for run2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-23. Mean Bias (jigm3) of annual elemental carbon at monitoring sites in the continental
U.S. modeling domain.
EC ME (ug/m3) for run2014fb CDC WRFCMAQ cb6r3 ae6nvPOA 12US1 tor 20140101 to 20141231
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-24. Mean Error (figin 3) of annual elemental carbon at monitoring sites in the continental
U.S. modeling domain.
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EC NMB (%) for run 2014fb CDC WRFCMAQ cb6r3 ae6nvP0A 12US1 for 20140101 to 20141231
units = %
coverage limit = 75%
' -
J 1
\/U -
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-25. Normalized Mean Bias (%) of annual elemental carbon at monitoring sites in the
continental U.S. modeling domain.
EC NME (%) tor run 2014fb CDC_WRFCMAQ cb6r3_ ae6nvPOA 12US1 for 20140101 to 20141231
units - %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-26. Normalized Mean Error (%) of annual elemental carbon at monitoring sites in the
continental U.S. modeling domain.
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OC MB (ug/m3) for run2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = ug/m3
coverage limit = 75%
WMJM
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-27. Mean Bias (jigm 3) of annual organic carbon at monitoring sites in the continental U.S.
modeling domain.
OC ME (ug/m3) for run2014fb CDC WRFCMAQ cb6r3_ae6nvPOA12US1 for 20140101 to 20141231
units = ug/m3
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-28. Mean Error (jignr3) of annual organic carbon at monitoring sites in the continental
U.S. modeling domain.
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OC NMB (%) for run 2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = %
coverage limit = 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-29. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the
continental U.S. modeling domain.
OC NME (%) for run 2014fb_CDC_WRFCMAQ_cb6r3_ae6nvPOA_12US1 for 20140101 to 20141231
units = %
coverage limit = 75%
CIRCLE=IM PROVE; TRIANGLE=CSN;
Figure 4-30. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the
continental U.S. modeling domain.
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.!• , , dan space-tin . , •/alliigfii-"' .. :iclel (downsc
/ed Air Quality Estimates
5.1 Introduction
The need for greater spatial coverage of air pollution concentration estimates has grown in recent years as
epidemiology and exposure studies that link air pollution concentrations to health effects have become
more robust and as regulatory needs have increased. Direct measurement of concentrations is the ideal
way of generating such data, but prohibitive logistics and costs limit the possible spatial coverage and
temporal resolution of such a database. Numerical methods that extend the spatial coverage of existing
air pollution networks with a high degree of confidence are thus a topic of current investigation by
researchers. The downscaler model (DS) is the result of the latest research efforts by EPA for performing
such predictions. DS utilizes both monitoring and CMAQ data as inputs, and attempts to take advantage
of the measurement data's accuracy and CMAQ's spatial coverage to produce new spatial predictions.
This chapter describes methods and results of the DS application that accompany this report, which
utilized ozone and PM2.5 data from AQS and CMAQ to produce predictions to continental U.S. 2010
census tract centroids for the year 2014.
5.2 Downscaler Model
DS develops a relationship between observed and modeled concentrations, and then uses that relationship
to spatially predict what measurements would be at new locations in the spatial domain based on the input
data. This process is separately applied for each time step (daily in this work) of data, and for each of the
pollutants under study (ozone and PM2.5). In its most general form, the model can be expressed in an
equation similar to that of linear regression:
Y(s) = jS0( s) + j£»i x(s) + e(s) (Equation 1)
Where:
Y(s) is the observed concentration at point 5. Note that Y(s) could be expressed as Ft(s), where t indicates
the model being fit at time t (in this case, t=l, ...,365 would represent day of the year.)
x(s) is the point-level regressor based on the CMAQ concentration at point 5. This value is a weighted
average of both the gridcell containing the monitor and neighboring gridcells.
pQ(s) is the intercept, where fi0(s) = /?0 +¦ P0(s) is composed of both a global component J?Qand a
local component fi0 (s) that is modeled as a mean-zero Gaussian Process with exponential decay
is the global slope; local components of the slope are contained in the x(s) term.
e(s) is the model error.
DS has additional properties that differentiate it from linear regression:
1) Rather than just finding a single optimal solution to Equation 1, DS uses a Bayesian approach so that
uncertainties can be generated along with each concentration prediction. This involves drawing random
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samples of model parameters from built-in "prior" distributions and assessing their fit on the data on the
order of thousands of times. After each iteration, properties of the prior distributions are adjusted to try to
improve the fit of the next iteration. The resulting collection of /?0 and /^values at each space-time point
are the "posterior" distributions, and the means and standard distributions of these are used to predict
concentrations and associated uncertainties at new spatial points.
2) The model is "hierarchical" in structure, meaning that the top level parameters in Equation 1 (ie jS0(s),
f$v x(s)) are actually defined in terms of further parameters and sub-parameters in the DS code. For
example, the overall slope and intercept is defined to be the sum of a global (one value for the entire
spatial domain) and local (values specific to each spatial point) component. This gives more flexibility in
fitting a model to the data to optimize the fit (i.e. minimize f(s)).
Further information about the development and inner workings of the current version of DS can be found
in Berrocal, Gelfand and Holland (2012)38 and references therein. The DS outputs that accompany this
report are described below, along with some additional analyses that include assessing the accuracy of the
DS predictions. Results are then summarized, and caveats are provided for interpreting them in the
context of air quality management activities.
5.3 Downscaler Concentration Predictions
In this application, DS was used to predict daily concentration and associated uncertainty values at the
2014 US census tract centroids across the continental U.S. using 2014 measurement and CMAQ data as
inputs. For ozone, the concentration unit is the daily maximum 8-hour average in ppb and for PM2.5 the
concentration unit is the 24-hour average in |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 2014. It shows the 4th max
daily maximum 8-hour average ozone for AQS observations, CMAQ model predictions and DS model
results. The DS model estimated that for 2014, about 23% of the US Census tracts (16815 out of 72283)
experienced at least one day with an ozone value above the NAAQS of 75 ppb.
38 Berrocal, V., Gelfand, A., and D. Holland. Space-Time Data Fusion Under Error in Computer Model Output: An Application
to Modeling Air Quality. Biometrics. 2012. September; 68(3): 837-848. doi:10.1111/j.l541-0420.2011.01725.x
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AQS
CMAQ
2014
4'th Max, Daily max
8-hour avg
ozone (ppb)
<-lnf,55]
(55,60]
(60,65]
(65,70]
(70,75]
(75,80]
¦ (80,85]
¦ (85,90]
¦ (90, Inf]
Figure 5-1. Annual 4th max (daily max 8-hour ozone concentrations) derived from AQS, CMAQ
and DS data.
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5.3.2 Summary of PM2.5 Results
Figures 5-2 and 5-3 summarize the AQS, CMAQ and DS PM2.5 data over the year 2014. Figure 5-2
shows annual means and Figure 5-3 shows 98thpercentiles of 24-hour PM2.5 concentrations for AQS
observations, CMAQ model predictions and DS model results. The DS model estimated that for 2014
about 34% of the US Census tracts (24328 out of 72283) experienced at least one day with a PM2.5 value
above the 24-hour NAAQS of 35 |j,g/m3.
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AQS
CMAQ
2014
Annual mean,
24-hour avg
PM2.5 (ug/m3)
(0,3]
(3,5]
(5,8]
(8,10]
(10,12]
(12,15]
(15,18]
¦ (18,lnf]
Figure 5-2. Annual mean PM2.5 concentrations derived from AQS, CMAQ and DS data.
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AQS
CMAQ
2014
98'th percentile,
24-hour avg
PM2.5 (ug/m3)
(0,10]
(10,15]
(15,20]
« (20,25]
(25,30]
(30,35]
(35,40]
¦ (40,45]
¦ (45,50]
¦ (50,Inf]
Figure 5-3. 98th percentile 24-hour average PM2.5 concentrations derived from AQS, CMAQ and
DS data.
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5.4 Downscaler Uncertainties
5.4.1 Standard Errors
As mentioned above, the DS model works by drawing random samples from built-in distributions during
its parameter estimation. The standard errors associated with each of these populations provide a measure
of uncertainty associated with each concentration prediction. Figure 5-4 shows the percent errors
resulting from dividing the DS standard errors by the associated DS prediction. The black dots on the
maps show the location of EPA sampling network monitors whose data was input to DS via the AQS
datasets (Chapter 2). The maps show that, in general, errors are relatively smaller in regions with more
densely situation monitors (ie the eastern US), and larger in regions with more sparse monitoring
networks (ie western states). These standard errors could potentially be used to estimate the probability
of an exceedance for a given point estimate of a pollutant concentration.
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03
PM25
% DS Error
(8,14]
(14,21]
¦ (21,27]
(33,40]
(40,46]
(46,52]
¦ (52,59]
¦ (59,65]
Figure 5-4. Annual mean relative errors (standard errors divided by predictions) from the DS 2014
runs. The black dots show the locations of monitors that generated the AQS data used as input to
the DS model.
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5.4.2 Cross Validation
To check the quality of its spatial predictions, DS can be set to perform "cross-validation" (CV),
which involves leaving a subset of AQS data out of the model run and predicting the concentrations of
those left out points. The predicted values are then compared to the actual left-out values to generate
statistics that provide an indicator of the predictive ability. In the DS runs associated with this report,
10% of the data was chosen randomly by the DS model to be used for the CV process. The resulting CV
statistics are shown below in Table 5-1.
Pollutant
# Monitors
Mean Bias
RMSE
Mean Coverage
PM2.5
943
0.286
3.42
0.95
03
1299
-0.0162
4.50
0.96
Table 5-1. Cross-validation statistics associated with the 2014 DS runs.
The statistics indicated by the columns of Table 5-1 are as follows:
- Mean Bias: The bias of each prediction is the DS prediction minus the AQS value. This column
is the mean of all biases across the CV cases.
- Root Mean Squared Error (RMSE): The bias is squared for each CV prediction, then the square
root of the mean of all squared biases across all CV predictions is obtained.
- Mean Coverage: A value of 1 is assigned if the measured AQS value lies in the 95% confidence
interval of the DS prediction (the DS prediction +/- the DS standard error), and 0 otherwise. This
column is the mean of all those 0's and l's.
5.5 Summary and Conclusions
The results presented in this report are from an application of the DS fusion model for characterizing
national air quality for Ozone and PM2.5. DS provided spatial predictions of daily ozone and PM2.5 at
2010 U.S. census tract centroids by utilizing monitoring data and CMAQ output for 2014. Large-scale
spatial and temporal patterns of concentration predictions are generally consistent with those seen in
ambient monitoring data. Both Ozone and PM2.5 were predicted with lower error in the eastern versus
the western U.S., presumably due to the greater monitoring density in the east.
An additional caution that warrants mentioning is related to the capability of DS to provide predictions at
multiple spatial points within a single CMAQ gridcell. Care needs to be taken not to over-interpret any
within-gridcell gradients that might be produced by a user. Fine-scale emission sources in CMAQ are
diluted into the gridcell averages, but a given source within a gridcell might or might not affect every
spatial point contained therein equally. Therefore DS-generated fine-scale gradients are not expected to
represent actual fine-scale atmospheric concentration gradients, unless possibly where multiple monitors
are present in the gridcell.
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Appendix A - Acronyms
Acronyms
ARW
Advanced Research WRF core model
BEIS
Biogenic Emissions Inventory System
BlueSky
Emissions modeling framework
CAIR
Clean Air Interstate Rule
CAMD
EPA's Clean Air Markets Division
CAP
Criteria Air Pollutant
CAR
Conditional Auto Regressive spatial covariance structure (model)
CARB
California Air Resources Board
CEM
Continuous Emissions Monitoring
CHIEF
Clearinghouse for Inventories and Emissions Factors
CMAQ
Community Multiscale Air Quality model
CMV
Commercial marine vessel
CO
Carbon monoxide
CSN
Chemical Speciation Network
DQO
Data Quality Objectives
EGU
Electric Generating Units
Emission Inventory
Listing of elements contributing to atmospheric release of pollutant
substances
EPA
Environmental Protection Agency
EMFAC
Emission Factor (California's onroad mobile model)
FAA
Federal Aviation Administration
FDDA
Four Dimensional Data Assimilation
FIPS
Federal Information Processing Standards
HAP
Hazardous Air Pollutant
HMS
Hazard Mapping System
ICS-209
Incident Status Summary form
IPM
Integrated Planning Model
ITN
Itinerant
LSM
Land Surface Model
MOBILE
OTAQ's model for estimation of onroad mobile emissions factors
MODIS
Moderate Resolution Imaging Spectroradiometer
MOVES
Motor Vehicle Emission Simulator
NEEDS
National Electric Energy Database System
NEI
National Emission Inventory
NERL
National Exposure Research Laboratory
NESHAP
National Emission Standards for Hazardous Air Pollutants
NH
Ammonia
NMIM
National Mobile Inventory Model
NONROAD
OTAQ's model for estimation of nonroad mobile emissions
NO
Nitrogen oxides
OAQPS
EPA's Office of Air Quality Planning and Standards
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OAR EPA's Office of Air and Radiation
ORD EPA's Office of Research and Development
ORIS Office of Regulatory Information Systems (code) - is a 4 or 5 digit
number assigned by the Department of Energy's (DOE) Energy
Information Agency (EIA) to facilities that generate electricity
ORL One Record per Line
OTAQ EPA's Office of Transportation and Air Quality
PAH Polycyclic Aromatic Hydrocarbon
PFC Portable Fuel Container
PM2.5 Particulate matter less than or equal to 2.5 microns
PM10 Particulate matter less than or equal to 10 microns
PMc Particulate matter greater than 2.5 microns and less than 10 microns
Prescribed Fire Intentionally set fire to clear vegetation
RIA Regulatory Impact Analysis
RPO Regional Planning Organization
RRTM Rapid Radiative Transfer Model
SCC Source Classification Code
SMARTFIRE Satellite Mapping Automatic Reanalysis Tool for Fire Incident
Reconciliation
SMOKE Sparse Matrix Operator Kernel Emissions
TCEQ Texas Commission on Environmental Quality
TSD Technical support document
VOC Volatile organic compounds
VMT Vehicle miles traveled
Wildfire Uncontrolled forest fire
WRAP Western Regional Air Partnership
WRF Weather Research and Forecasting Model
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-18-008
Environmental Protection Air Quality Assessment Division October 2018
Agency Research Triangle Park, NC
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