vvEPA
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Bayesian space-time downscaling fusion model
(downscaler) -Derived Estimates of Air Quality
for 2011
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EPA-454/S-15-001
April 2015
Bayesian space-time downscaling fusion model (downscaler) -Derived
Estimates of Air Quality for 2011
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Authors:
Adam Reff (EPA/OAR)
Sharon Phillips (EPA/OAR)
Alison Eyth (EPA/OAR)
David Mintz (EPA/OAR)
Acknowledgements
The following people served as reviewers of this document and provided valuable comments that
were included: Liz Naess (EPA/OAR), Tyler Fox (EPA/OAR), and Dennis Doll (EPA/OAR).
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Contents
Contents 1
1.0 Introduction 2
2.0 Air Quality Data 5
2.1 Introduction to Air Quality Impacts in the United States 5
2.2 Ambient Air Quality Monitoring in the United States 7
2.3 Air Quality Indicators Developed for the EPHT Network 11
3.0 Emissions Data 13
3.1 Introduction to Emissions Data Development 13
3.2 2011 Emission Inventories and Approaches 13
3.3 Emissions Modeling Summary 35
3.4 Emissions References 62
4.0 CMAQ Air Quality Model Estimates 66
4.1 Introduction to the CMAQ Modeling Platform 66
4.2 CMAQ Model Version, Inputs and Configuration 68
4.3 CMAQ Model Performance Evaluation 73
5.0 Bayesian space-time downscaling fusion model (downscaler) -Derived Air Quality
Estimates 94
5.1 Introduction 94
5.2 Downscaler Model 94
5.3 Downscaler Concentration Predictions 95
5.4 Downscaler Uncertainties 100
5.5 Summary and Conclusions 102
Appendix A - Acronyms 103
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1.0 Introduction
This report describes estimates of daily ozone (maximum 8-hour average) and PM2.5 (24-hour
average) concentrations throughout the contiguous United States during the 2011 calendar
year generated by EPA's recently developed data fusion method termed the "downscaler
model" (DS). Air quality monitoring data from the State and Local Air Monitoring Stations
(SLAMS) and numerical output from the Community Multiscale Air Quality (CMAQ) model
were both input to DS to predict concentrations at the 2010 US census tract centroids
encompassed by the CMAQ modeling domain. Information on EPA's air quality monitors,
CMAQ model, and downscaler model is included to provide the background and context for
understanding the data output presented in this report. These estimates are intended for use by
statisticians and environmental scientists interested in the daily spatial distribution of ozone
andPM2.5.
DS essentially operates by calibrating CMAQ data to the observational data, and then uses the
resulting relationship to predict "observed" concentrations at new spatial points in the domain.
Although similar in principle to a linear regression, spatial modeling aspects have been
incorporated for improving the model fit, and a Bayesian1 approaching to fitting is used to
generate an uncertainty value associated with each concentration prediction. The uncertainties
that DS produces are a major distinguishing feature from earlier fusion methods previously
used by EPA such as the "Hierarchical Bayesian" (HB) model (McMillan et al, 2009). The
term "downscaler" refers to the fact that DS takes grid-averaged data (CMAQ) for input and
produces point-based estimates, thus "scaling down" the area of data representation. Although
this allows air pollution concentration estimates to be made at points where no observations
exist, caution is needed when interpreting any within-gridcell spatial gradients generated by
DS since they may not exist in the input datasets. The theory, development, and initial
evaluation of DS can be found in the earlier papers of Berrocal, Gelfand, and Holland (2009,
2010, and 2011).
The data contained in this report are an outgrowth of a collaborative research partnership
between EPA scientists from the Office of Research and Development's (ORD) National
Exposure Research Laboratory (NERL) and personnel from EPA's Office of Air and
Radiation's (OAR) Office of Air Quality Planning and Standards (OAQPS). NERL's Human
Exposure and Atmospheric Sciences Division (HEASD), Atmospheric Modeling Division
(AMD), and Environmental Sciences Division (ESD), in conjunction with OAQPS, work
together to provide air quality monitoring data and model estimates to the Centers for Disease
Control and Prevention (CDC) for use in their Environmental Public Health Tracking (EPHT)
Network.
CDC's EPHT Network supports linkage of air quality data with human health outcome data
for use by various public health agencies throughout the U.S. The EPHT Network Program is
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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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 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.
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 isavailableatwww.cdc.gov/nceh/tracking/partners/epa mou 2007.htm.
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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.
• 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 (Cb) and particulate matter (PM). The EPHT program has focused on these two
pollutants, since numerous studies have found them to be most pervasive and harmful to public
health and the environment, and there are extensive monitoring and modeling data available.
2.1 Introduction to Air Quality Impacts in the United States
2.1.1 The Clean Air Act
In 1970, the Clean Air Act (CAA) was signed into law. Under this law, EPA sets limits on how
much of a pollutant can be in the air anywhere in the United States. This ensures that all
Americans have the same basic health and environmental protections. The CAA has been
amended several times to keep pace with new information. For more information on the CAA,
go to http ://www. epa. gov/oar/caa/.
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 (802), ozone (Os), and particulate matter (PM). These standards, called the National
Ambient Air Quality Standards (NAAQS), are designed to protect public health and the
environment. The CAA established two types of air quality standards. Primary standards set
limits to protect public health, including the health of "sensitive" populations such as asthmatics,
children, and the elderly. Secondary standards set limits to protect public welfare, including
protection against decreased visibility, damage to animals, crops, vegetation, and buildings. The
law requires EPA to review periodically these standards. For more specific information on the
NAAQS, go to www.epa.gov/air/criteria.html. For general information on the criteria pollutants,
go to http://www.epa.gov/air/urbanair/6poll.html.
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 http://www.epa.gov/ozonedesignations/ and
http://www.epa.gov/pmdesignations.
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2.1.2 Ozone
Ozone is a colorless gas composed of three oxygen atoms. Ground level ozone is formed when
pollutants released from cars, power plants, and other sources react in the presence of heat and
sunlight. It is the prime ingredient of what is commonly called "smog." When inhaled, ozone can
cause acute respiratory problems, aggravate asthma, cause inflammation of lung tissue, and even
temporarily decrease the lung capacity of healthy adults. Repeated exposure may permanently scar
lung tissue. lexicological, human exposure, and epidemiological studies were integrated by EPA
in "Air Quality Criteria for Ozone and Related Photochemical Oxidants." It is available at
http://www.epa.gOv/ttn/naaqs/standards/ozone/s o3 index.html. The current NAAQS for ozone
(last revised in 2008) is a daily maximum 8-hour average of 0.075 parts per million [ppm] (for
details, see http://www.epa.gov/air/criteria.html. 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.
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 sourcesS. 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.s) 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 (PMio-2.s) 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 PMio, 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
http://www.epa.gOv/ttn/naaqs/standards/pm/s pm index.html.
3 The measure used to classify PM into sizes is the aerodynamic diameter. The measurement instruments used for PM
are designed and operated to separate large particles from the smaller particles. For example, the PM2 5 instrument only
captures and thus measures particles with an aerodynamic diameter less than 2.5 micrometers. The EPA method to
measure PMc is designed around taking the mathematical difference between measurements for PM10 and PM2 5
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The current NAAQS for PIVb.s (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/m3)
based on the annual mean concentration averaged over three years, and the 24-hr average
concentration must not exceed 35 ug/m3 based on the 98th percentile 24-hour average
concentration averaged over three years. More information is available at
http://www.epa.gov/air/criteria.html and http://www.epa.gov/oar/particlepollution/. The
standards for PM2.5 values are shown in Table 2-1.
Table 2-1. PMi.s Standards
Micrograms Per Cubic Meter:
Measurement - (ug/m3)
Annual Average
24-Hour Average
1997
15.0
65
2006
15.0
35
2012
12.0
35
2.2 Ambient Air Quality Monitoring in the United States
2.2.1 Monitoring Networks
The Clean Air Act (Section 319) requires establishment of an air quality monitoring system
throughout the U.S. The monitoring stations in this network have been called the State and Local
Air Monitoring Stations (SLAMS). The SLAMS network consists of approximately 4,000
monitoring sites set up and operated by state and local air pollution agencies according to
specifications prescribed by EPA for monitoring methods and network design. All ambient
monitoring networks selected for use in SLAMS are tested periodically to assess the quality of
the SLAMS data being produced. Measurement accuracy and precision are estimated for both
automated and manual methods. The individual results of these tests for each method or
analyzer are reported to EPA. Then, EPA calculates quarterly integrated estimates of precision
and accuracy for the SLAMS data.
The SLAMS network experienced accelerated growth throughout the 1970s. The networks were
further expanded in 1999 based on the establishment of separate NAAQS for fine particles
(PM2.s) 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 www. epa. gov/ttn/amtic.
In 2009, approximately 43 percent of the US population was living within 10 kilometers of
ozone and PM2.5 monitoring sites. In terms of US Census Bureau tract locations, 31,341 out of
72,283 census tract centroids were within 10 kilometers of ozone monitoring sites. Highly
populated Eastern US and California coasts are well covered by both ozone and PM2.5
monitoring network (Figure 2-1).
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Distance to the Nearest Ozone Monitor
• 41 - 10.000 meters
10.001-25.000 meters
25.001 - 50.000 meters
50,001 - 75.000 meters
75,001 -100,000 meters
• 100.001-150.000 meters
• 150.001 - 333.252 meters
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• 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 www. epa. gov/airtrends).
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
http://www.epa.gov/ttn/airs/airsaqs/index.htm.
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 needed4. 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
4 EPA uses exposure models to evaluate the health risks and environmental effects associated with exposure.
These models are limited by the availability of air quality estimates, http://www.epa.gov/ttn/fera/index.html.
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data is available in many areas and is extremely useful to evaluate whether ozone is a factor in
health outcomes during the non-ozone seasons. PM2.5 is generally measured year-round. Most
Federal Reference Method (FRM) PM2.5 monitors collect data one day in every three days, due in
part to the time and costs involved in collecting and analyzing the samples. However, over the
past several years, continuous monitors, which can automatically collect, 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
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sophisticated air quality models are prune to systematic biases since they depend upon so many
variables (i.e., metrological models and emission models) and complex chemical and physical
process simulations.
Combining monitoring data with air quality models (via fusion or regression) may provide the
best results in terms of estimating ambient air concentrations in space and time. EPA's eVNA5
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 PIVb.s 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.
5 eVNA is described in the "Regulatory Impact Analysis for the Final Clean Air Interstate Rule", EPA-452/R-05-002,
March 2005, http://www.epa.gov/cair/pdfs/finaltech08.pdf. 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)
PM2 5 (daily 24-hr integrated samples -ug/m3-by FRM)
• Average ambient concentrations of paniculate 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 PIVh.s. 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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3.0 Emissions Data
3.1 Introduction to Emissions Data Development
The U.S. Environmental Protection Agency (EPA) developed an air quality modeling platform
based primarily on the 201 1 National Emissions Inventory (NEI), Version 1 to process year 201 1
emission data for this project. This section provides a summary of the emissions inventory and
emissions modeling techniques applied to Criteria Air Pollutants (CAPs) and the following select
Hazardous Air Pollutants (HAPs): chlorine (Cl), hydrogen chloride (HC1), benzene,
acetaldehyde, formaldehyde and methanol. This section also describes the approach and data
used to produce emissions inputs to the air quality model. The air quality modeling,
meteorological inputs and boundary conditions are described in a separate section.
The Community Multiscale Air Quality (CMAQ) model (httgy/iQviLeEa^gv/M^IM^ is
used to model ozone (63) and parti culate matter (PM) for this project. CMAQ requires hourly
and gridded emissions of the following inventory pollutants: carbon monoxide (CO),nitrogen
oxides (NOx), volatile organic compounds (VOC), sulfur dioxide (SCh), ammonia (NHa),
parti culate matter less than or equal to 10 microns (PMio), and individual component species for
particulate matter less than or equal to 2.5 microns (PIVh.s). In addition, the CMAQ CB05 with
chlorine chemistry used here allows for explicit treatment of the VOC HAPs benzene,
acetaldehyde, formaldehyde and methanol (BAFM) and includes anthropogenic HAP emissions
of HC1 and Cl.
The effort to create the 201 1 emission inputs for this study included development of emission
inventories for a 201 1 model evaluation case, and application of emissions modeling tools to
convert the inventories into the format and resolution needed by CMAQ. An evaluation case
uses year-specific fire and continuous emission monitoring (CEM) data for electric generating
units (EGUs), whereas other types of modeling cases can use averages for these sources. The
primary emissions modeling tool used to create the CMAQ model-ready emissions was the
Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system. SMOKE version 3.5.1
was used to create emissions files for a 12-km national grid. Additional information about
SMOKE is available from teg^Twww^OTiote^
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 2011 Emission Inventories and Approaches
This section describes the emissions inventories created for input to SMOKE. The 201 1 NEI is
the primary basis for the inputs to SMOKE and includes five main categories of source sectors:
a) nonpoint (formerly called "stationary area") sources; b) point sources; c) nonroad mobile
sources; d) onroad mobile sources; and e) fires. For CAPs, the NEI data are largely compiled
from data submitted by state, local and tribal (S/L/T) agencies. HAP emissions data are often
augmented by EPA when they are not voluntarily submitted to the NEI by S/L/T agencies. The
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2011 NEI was compiled using the Emissions Inventory System (EIS). EIS includes hundreds of
automated QA checks to improve data quality, and it also supports release point (stack)
coordinates separately from facility coordinates. EPA collaboration with S/L/T agencies helped
preveny duplication between point and nonpoint source categories such as industrial boilers.
Documentation for the 2011 NEI is available at
http://www.epa.gov/ttn/chief/net/2011inventory.htmltfinventorydoc.
EPA used the SMARTFIRE2 system to develop 2011 fire emissions. SMARTFIRE2 categorizes
all fires as either prescribed burning or wildfire categories, and includes improved emission
factor estimates for prescribed burning. Onroad mobile source emissions in the 201 INEIvl were
developed using MOVES2010b; however, the 2011 emissions modeling platform used a
different version of MOVES, hence forth referred to as "MOVESTierSFRM" that facilitated the
representation of the final Tier 3 standards in future years. When given the same inputs, these
two versions of MOVES produce similar emissions estimates for the year 2011. Canadian
emissions reflect year 2006, as those were the latest available at the time of the modeling.
Mexican emissions reflect year 2012 as projected from their 1999 inventory, and offshore
emissions reflect year 2008 because 2011 data were not yet available at the time of the modeling.
The methods used to process emissions for this project are very similar to those documented for
EPA's Version 6.1, 2011 Emissions Modeling Platform that was also used for the proposed
Ozone National Ambient Air Quality Standards (NAAQS). A technical support document
(TSD) for this platform is available at EPA's emissions modeling clearinghouse (EMCH):
http://www.epa.gov/ttn/chief/emch/index.htmltf2011 (EPA, 2014a) and includes additional
details regarding some aspects of the data preparation and emissions modeling. Electronic
copies of the main inventories and ancillary data used for this project are available from the
version 6.1 section of the EMCH.
The emissions modeling process, performed using SMOKE v3.5.1 apportions the emissions
inventories into the grid cells used by CMAQ and temporalizes the emissions into hourly values.
In addition, the pollutants in the inventories (e.g., NOx and VOC) are split into the chemical
species needed by CMAQ. For the purposes of preparing the CMAQ- ready emissions, the
broader NEI emissions inventories are split into emissions modeling "platform" sectors; and
biogenic emissions are added along with emissions from other sources other than the NEI, such
as the Canadian, Mexican, and offshore inventories. The significance of an emissions sector for
the emissions modeling platform is that emissions for that sector are run through all of the
SMOKE programs, except the final merge, independently from emissions in the other sectors.
The final merge program called Mrggrid combines the sector-specific gridded, speciated and
temporalized emissions to create the final CMAQ-ready emissions inputs.
Table 3-1 presents the sectors in the emissions modeling platform used to develop the 2011
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 2011 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
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"Appendix_B_2011_emissions_totals_by_sector.xlsx".
Table 3-1. Platform Sectors Used in the Emissions Modeling Process
2011 Platform Sector
(Abbrev)
EGU non-peaking units
(ptegu)
EGU peaking units
(ptegu_pk)
Point source oil and gas
(pt_oilgas)
2011 NEI
Sector
Point
Point
Point
Remaining non-EGU point „ . ^
, f . ° F Point
(ptnonipm)
Point source fire (ptfire) Fires
Agricultural (ag)
Nonpoint
Area fugitive dust (afdusf) Nonpoint
Nonpoint source oil and gas ,. T
, ., , Nonpoint
(np_oiigas)
Residential Wood N
Combustion (rwc)
Remaining nonpoint (nonpf) Nonpoint
C3 commercial marine
(cBmarine)
Nonpoint
Description and resolution of the data input to
SMOKE
2011 NEI point source EGUs determined to operate
as non-peaking units. The 201 INEIvl emissions are
replaced with hourly 2011 CEMS values for NOX
and SO2, where the units are matched to the NEI.
Annual resolution for non-CEMS sources, hourly for
sources matched to CEMS.
Same as ptegu sector, but limited to EGUs
determined to operate as peaking units. All sources
in this sector have CEMS data for 2011 and are
therefore hourly.
201 INEIvl point sources with oil and gas production
emissions processes.
All 201 INEIvl point source records not matched to
the ptegu, ptegu_pk, and pt_oilgas sectors, except for
offshore point sources that are in the othpt sector.
Includes all aircraft emissions and some rail yard
emissions. Annual resolution.
Point source day-specific wildfires and prescribed
fires for 2011 computed using SMARTFIRE 2,
except for Georgia-submitted emissions. Consistent
with 201 INEIvl.
NH3 emissions from 201 INEIvl nonpoint livestock
and fertilizer application, county and annual
resolution.
PM10 and PM2.5 from fugitive dust sources in the
201 INEIvl nonpoint inventory, including building
construction, road construction, agricultural dust, and
road dust; however, unpaved and paved road dust
emissions differ from the NEI in that they do not
have a precipitation adjustment. Instead, the
emissions modeling adjustment applies a transport
fraction and a meteorology-based (precipitation and
snow/ice cover) zero-out. County and annual
resolution.
201 INEIvl nonpoint sources from oil and gas-
related processes. County and annual resolution.
201 INEIvl NEI nonpoint sources with Residential
Wood Combustion (RWC) processes. County and
annual resolution.
201 INEIvl nonpoint sources not included in other
platform sectors; county and annual resolution.
Category 3 (C3) CMV emissions projected to 2011
from year 2002 values. These emissions are not from
the NEI, but rather were developed for the rule called
"Control of Emissions from New Marine
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Cl and C2 marine and
locomotive (clc2rail)
Nonpoint
Nonroad (nonroad)
Nonroad
Onroad non-refueling
(onroad)
Onroad
Onroad Refueling
(onroad_rfl)
Biogenic (beis)
Onroad
Biogenic
Other point sources not from ..
the NEI (othpt)
Compression-Ignition Engines at or Above 30 Liters
per Cylinder", usually described as the Emissions
Control Area- International Maritime Organization
(ECA-IMO) study:
http://www.epa.gov/otaq/oceanvessels.htm. (EPA-
420-F-10-041, August 2010). U.S. states-only
emissions (zero in Midwest); see othpt sector for all
non-U.S. emissions. Treated as point sources to
reflect shipping lanes, annual resolution.
Locomotives and primarily category 1 (Cl) and
category 2 (C2) commercial marine vessel (CMV)
emissions sources from the 201 INEIvl nonpoint
inventory. Midwestern states' CMV emissions,
including Class 3 sources, are from a separate year
2010 emissions inventory. County and annual
resolution.
201 INEIvl nonroad equipment emissions developed
with the National Mobile Inventory Model (NMIM)
using NONROAD2008 version NROSa. NMIM was
used for all states except California and Texas, which
submitted their own emissions to the 201 INEIvl.
County and monthly resolution.
2011 onroad mobile source gasoline and diesel
vehicles from parking lots and moving vehicles.
Includes the following modes: exhaust, extended
idle, evaporative, permeation, and brake and tire
wear. For all states except California and Texas,
based on monthly MOVES emissions tables from
MOVESTier3FRM. Texas emissions are from the
201 INEIvl and are based on MOVES 2010b, and
California emissions are based on Emission Factor
(EMFAC). MOVES-based emissions computed for
each hour and model grid cell using monthly and
annual activity data (e.g., VMT, vehicle population).
201 INEIvl onroad mobile gasoline and diesel
vehicle refueling emissions for all states. Based on
MOVESTier3FRM emissions tables. Computed
hourly based on temperature and for each model grid
cell.
Hour- and grid cell-specific emissions for 2011
generated from the BEIS 3.14 model, including
emissions in Canada and Mexico.
Point sources from Canada's 2006 inventory and
Mexico's 2012 inventory grown from year 1999
(ERG, 2009; Wolf, 2009). Also includes all non-
U.S. C3 CMV and U.S. offshore oil production,
which are unchanged from the 2008 NEI point source
annual emissions.
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Other nonpoint and nonroad
(othar)
Other onroad sources
(othori)
N/A
N/A
Annual year 2006 Canada (province resolution) and
year 2012 (grown from 1999) Mexico Phase III
(municipio resolution) nonpoint and nonroad mobile
inventories.
Year 2006 Canada (province resolution) and year
2012 (grown from 1999) Mexico Phase III
(municipio resolution) onroad mobile inventories,
annual resolution.
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Table 3-2. 2011 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.)
Sector
afdust
ag
clc2rail
nonpt
np_oilgas
nonroad
onroad_adj
onroad_rfl
c3marine
ptfire
ptegu
ptegu_pk
ptnonipm
pt_oilgas
rwc
Con.US Total
PMio PMi.5 SOi
18,502,317 2,487,403
173,437
3,046,375
642,182
13,952,389
25,230,444
n
12,425
22,580,113
719,414
8,662
2,565,936
20,579
2578,229
3,517,371
481
142,323
0
2,627
118,130
362,910
21,644
425
74,841
112
20,343
71,530,185 4,261,207
1,046,095
832,166
653,219
1,630,409
5,591,695
124,725
347,103
1,925,742
21,941
1,767,748
17,026
35,672
13,993,540
34,670
715,709
21,756
162,420
287,540
n
4,279
2,362,132
259,011
2,159
491,837
1,833
389,019
23,234,681
32,367
533,248
17,200
154,660
207,517
3,909
2,005,142
188,811
1,886
338,447
1,810
388,288
6,360,688
17,651
392,638
17,195
4,031
28,475
1
38,645
177,107
4,596,656
28,476
1,071,950
55,142
8,986
6,436,952
47,714
3,792,612
2,273,214
2,024,633
2,576,504
161,415
4,954
5,174,593
32,288
783
872,433
87,842
446,972
17,495,956
Table 3-3. 2011 Non-US Emissions by Sector within Modeling Domain (tons/yr for Canada,
Mexico, Offshore)
Sector
Canada othar
Canada othon
Canada othpt
Canada Subtotal
Mexico othar
Mexico othon
Mexico othpt
Mexico Subtotal
Offshore to EEZ
Non-US c3marine
Offshore Subtotal
2011 TOTAL
CO
2,810,350
3,303,239
560,661
6,674,250
439,901
423,978
116,609
980,488
130,419
17,168
147,587
7,802,325
NH3
386,147
17,572
15,543
419,263
109,861
3,247
0
113,108
0
0
0
532,371
NOX
462,996
392,209
369,993
1,225,198
189,592
76,880
414,399
680,871
610,644
202,516
813,159
2,719,229
PM10
810,747
11,075
65,782
887,604
69,523
7,593
137,512
214,628
16,961
17,199
34,160
1,136,392
PM25
248,907
7,712
39,828
296,447
45,923
6,970
101,884
154,778
15,525
15,823
31,348
482,573
SO2
61,179
4,046
825,675
890,900
26,559
1,413
828,418
856,390
133,606
127,563
261,168
2,008,459
VOC
932,322
199,939
157,170
1,289,431
499,145
73,888
83,838
656,872
81,286
7,297
88,583
2,034,886
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3.2.1 Point Sources (ptipm andptnonipm)
Point sources are sources of emissions for which specific geographic coordinates (e.g.,
latitude/longitude) are specified, as in the case of an individual facility. A facility may have
multiple emission release points, which may be characterized as units such as boilers, reactors,
spray booths, kilns, etc. A unit may have multiple processes (e.g., a boiler that sometimes burns
residual oil and sometimes burns natural gas). With a couple of minor exceptions, this section
describes only NEI point sources within the contiguous United States. The offshore oil platform
(othpt sector) and category 3 CMV emissions (cSmarine and othpt sectors) are processed by
SMOKE as point source inventories and are discussed later in this section. Full documentation
for the development of the 2011 NEI (EPA, 2014b), is posted at:
http://www.epa.gov/ttn/chief/net/2011inventory.htmltfinventorydoc.
After moving offshore oil platforms into the othpt sector, and dropping sources without specific
locations (i.e., the FIPS code ends in 777), initial versions of the other four platform point source
sectors were created from the remaining 201 INEIvl point sources. The point sectors are: the
EGU sector for non-peaking units (ptegu), the EGU sector for peaking units (ptegu_pk), point
source oil and gas extraction -related emissions (pt_oilgas) and the remaining non-EGU sector
also called the non-IPM (ptnonipm) sector. 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 EGU sectors are further split into "peaking" (ptegu_pk) and non-peaking units
to allow for better analysis of the impact of peaking units. The oil and gas sector emissions
(pt_oilgas) were processed separately for summary tracking purposes and distinct future-year
projection techniques from the remaining non-EGU emissions (ptnonipm).
The inventory pollutants processed through SMOKE for both the ptipm and ptnonipm sectors
were: CO, NOX, VOC, SO2, NH3, PM10, and PM2.5 and the following HAPs: HC1 (pollutant
code = 7647010), and Cl (code = 7782505). BAFM from these sectors was not utilized because
VOC was speciated without the use (i.e., integration) of VOC HAP pollutants from the
inventory.
The ptnonipm and pt_oilgas sector emissions were provided to SMOKE as annual emissions.
For 201 INEIvl sources in the ptegu and ptegu_pk sectors that could be matched to 2011 CEMS
data, hourly CEMS NOx and SO2 emissions for 2011 from EPA's Acid Rain Program were used
rather than NEI emissions. For all other pollutants (e.g., VOC, PM2.5, HC1), annual emissions
were used as-is from the NEI, but were allocated to hourly values using heat input from the
CEMS data. For the unmatched units in the ptegu and ptegu_pk sectors, annual emissions were
allocated to daily values using IPM region- and pollutant-specific profiles, and similarly, region-
and pollutant-specific diurnal profiles were applied to create hourly emissions.
The Non-EGU Stationary Point Sources (ptnonipm) emissions were input to SMOKE as annual
emissions. The full description of how the 2011 NEI emissions were developed is provided in the
NEI documentation, but a brief summary of their development follows:
a. 2011 CAP and HAP data were provided by States, locals and tribes under the
Consolidated Emissions Reporting Rule
b. EPA corrected known issues and filled PM data gaps.
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c. EPA added HAP data from the Toxic Release Inventory (TRI) where corresponding data
was not already provided by states/locals.
d. EPA provided data for airports and rail yards.
e. Off-shore platform data were added from Mineral Management Services (MMS),
although in 201 INEIvl, these data were still from 2008 because the 2011 data were not
yet available.
The changes made to the NEI point sources prior to modeling with SMOKE are as follows:
• The tribal data, which do not use state/county Federal Information Processing Standards
(FIPS) codes in the NEI, but rather use the tribal code, were assigned a state/county FIPS
code of 88XXX, where XXX is the3-digit tribal code in the NEI. This change was made
because SMOKE requires all sources to have a state/county FIPS code.
• Sources that did not have specific counties assigned (i.e., the county code ends in 777)
were not included in the modeling because it was only possible to know the state in
which the sources resided, but no more specific details related to the location of the
sources were available.
• Stack parameters for some point sources were defaulted when modeling in SMOKE.
SMOKE uses an ancillary file, called the PSTK file, which provides default stack
parameters by SCC code to either gap fill stack parameters if they are missing in the NEI
or to correct stack parameters if they are outside the ranges specified.
3.2.1.1 ECU sector (ptegu)
The ptegu and ptegu_pk sectors contain emissions from EGUs in the 201 INEIvl point inventory
that could be matched to units found in the NEEDS v5.13 database
(http://www.epa.gov/powersectormodeling/BaseCasev513.html). It was necessary to put these
EGUs into separate sectors in the platform because IPM projects future emissions for the EGUs
defined in the NEEDS database, and emissions for sources in the ptegu and ptegu_pk sectors are
replaced with IPM outputs in the future year modeling case. Sources not matched to units found
in NEEDS are placed into the pt_oilgas or ptnonipm sectors and are projected to the future year
using projection and control factors appropriate for their source categories. It is important that
the matching between the NEI and NEEDS database be as complete as possible because there
can be double-counting of emissions in the future year if emissions for units are projected by
IPM are not properly matched to the units in the NEI.
Some units in the ptegu and ptegu_pk sectors are matched to CEMS data via ORIS facility codes
and boiler ID. For these units, SMOKE replaces the 2011 emissions of NOX and SO2 with the
CEMS emissions, thereby ignoring the annual values specified in the NEI. For other pollutants,
the hourly CEMS heat input data are used to allocate the NEI annual emissions to hourly values.
All stack parameters, stack locations, and SCC codes for these sources come from the NEI.
Because these attributes are obtained from the NEI, the chemical speciation of VOC and PM2.5
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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 NEI, the CEMS data for that unit
is not used in the modeling platform. However, if the source exists in the NEI and is just not
matched to a CEMS unit, the emissions from that source would be modeled using the annual
emission value in the NEI 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 modeling platform.
For sources not matched to CEMS data (i.e., "non-CEMS" sources), daily emissions were
computed from the NEI annual emissions using average CEMS data profiles specific to fuel
type, pollutant, and IPM region. 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. For
future-year scenarios, there are no CEMS data available for specific units, but the shape of the
CEMS profiles is preserved for sources that are carried into the future year. This method keeps
the temporal behavior of the base and future year cases as consistent as possible.
3.2.1.2 ECU Peaking Unit Sector (ptegu_pk)
The ptegu_pk sector includes sources identified by EPA as peaking units. The units were
separated into this sector to facilitate analyses of the impact of peaking units. Aside from their
inclusion in this sector, in all other ways they are treated in the same way as CEMS sources in
the ptegu sector because all of them are matched to CEMS data. To identify units for inclusion
in this sector, EPA required them to satisfy two tests: (1) the capacity factor was less than 10%
over a 3 year average (2010-2012), and (2) the capacity factor was less than 20% in each of the 3
years. Here, "capacity factor" means either: (1) The ratio of a unit's actual annual electric output
(expressed in MWe/hr) to the unit's nameplate capacity (or maximum observed hourly gross load
(in MWe/hr) if greater than the nameplate capacity) times 8760 hours; or (2) The ratio of a unit's
annual heat input (in million BTUs or equivalent units of measure) to the unit's maximum rated
hourly heat input rate (in million BTUs per hour or equivalent units of measure) times 8,760
hours. The list of units in the ptegu_pk sector is provided in the file
ftp://ftp.epa.gov/EmisInventory/20 llv6/vlplatform/reports/2011_emissions/Peakers_CAMD_20
11.080213 NEI IPM match.xls).
3.2.1.3 Non-IPM Sector (pt_oilgas)
The pt_oilgas sector includes sources with the SCCs identified as oil and gas sources. 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. The nonpoint emissions can be found in the
np_oilgas sector. More information on the development of the 2011 oil and gas emissions can be
found in Section 3.21 of the 2011NEIvl TSD.
3.2.1.4 Non-IPM Sector (ptnonipm)
Except for some minor exceptions, the non-IPM (ptnonipm) sector contains the 201 INEIvl point
sources that are not in the ptegu, ptegu_pk, or pt_oilgas sectors. For the most part, the ptnonipm
21
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sector reflects the non-EGU sources of the NEI point inventory; however, it is likely that some
small low-emitting EGUs not matched to the NEEDS database or to CEMS data are present in
the ptnonipm sector. The sector also includes some ethanol plants that have been identified by
EPA but are not in 201 INEIvl. 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. Some point sources in the 201 INEIvl that are not
included in any modeling sectors are sources with state/county FIPS code ending with "777".
These sources 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 for modeling. Therefore, these sources are dropped from the
point-based sectors in the modeling platform.
Another difference between the 201 INEIvl data and the modeling platform is the addition of
some ethanol production facilities identified by EPA but were not found in the NEI. For some
rule development work, EPA developed a list of corn ethanol facilities for 2011. Many of these
ethanol facilities were included in the 201 INEIvl, but those that were not matched were added
to the ptnonipm sector in a separate inventory data file. Locations and FIPS codes for these
ethanol plants were verified using web searches and Google Earth. EPA believes that some of
these sources are not included in the NEI as point sources because they do not meet the 100
ton/year potential-to-emit threshold for NEI point sources. In other cases, EPA is following up
with states to evaluate whether the state data should include these point sources. Emission rates
for the ethanol plants were obtained from EPA's updated spreadsheet model for upstream
impacts developed for the Renewable Fuel Standard (RFS2) rule.
3.2.2 Nonpoint Sources (ofdust, ag, nonpt, np_oilgas, rwc)
Several modeling platform sectors were created from the 201 INEIvl nonpoint inventory. This
section describes the stationary nonpoint sources. Note that locomotives, Cl and C2 CMV, and
C3 CMV are also included the 201 INEIvl nonpoint data category, but are mobile sources and
are placed into the clc2rail and cSmarine sectors, respectively. The 201 INEIvl TSD available
from http://www.epa.gov/ttn/chief/net/2011inventory.html includes documentation for the
nonpoint sector of the 201 INEIvl.
Nonpoint tribal-submitted emissions are dropped during spatial processing with SMOKE
because the spatial surrogates are available at the county, but not the tribal level. In addition,
possible double-counting with county-level emissions is prevented. These omissions are not
expected to have an impact on the results of the air quality modeling at the 12-km scales used for
this platform.
The emissions modeling sector inventories start with the NEI data. Several source categories
were not included in the modeling platform inventories for the following reasons: 1) these
sources are only reported by a small number of states or agencies, 2) these sources are 'atypical'
and have small emissions, and/or 3) EPA has have other data the Agency believes to be more
accurate. The following subsections describe how the remaining sources in the 201 INEIvl
nonpoint inventory were separated into 2011 modeling platform sectors, along with any data that
were updated replaced with non-NEI data.
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In the rest of this section, each of the platform sectors into which the 2011 nonpoint NEI was
divided is described, along with any changes made to these data.
3.2.2.1 Area Fugitive Dust Sector (afdust)
The area-source fugitive dust (afdust) sector contains PM10 and PM2.5 emission estimates for
nonpoint SCCs identified by EPA staff as dust sources. Categories included in the afdust sector
are paved roads, unpaved roads and airstrips, construction (residential, industrial, road and total),
agriculture production, and mining and quarrying. It does not include fugitive dust from grain
elevators, coal handling at coal mines, or vehicular traffic on paved or unpaved roads at
industrial facilities because these are treated as point sources so they are properly located.
The afdust sector is separated from other nonpoint sectors to allow for the application of a
"transport fraction," and meteorological/precipitation-based 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),
http://www.epa.gov/ttn/chief/conference/eil9/session9/pouliot_pres.pdf, and in "Fugitive Dust
Modeling for the 2008 Emissions Modeling Platform" (Adelman, 2012). The purpose of
applying the transport fraction and meteorological adjustments is to reduce the overestimation of
fugitive dust in the grid modeling as compared to ambient observations. 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.
The dust emissions in the modeling platform are not the same as the 201 INEIvl emissions
because the NEI paved and unpaved road dust emissions include a built-in precipitation
reduction that is based on average meteorological data, which is at a coarser temporal and spatial
resolution than the modeling platform meteorological adjustment. Due to this, in the platform
paved and unpaved road emissions data was used that did not include any precipitation-based
reduction. This allows the entire sector to be processed consistently so that the same grid-specific
transport fractions and meteorological adjustments can be applied. Where states submitted afdust
data, it was assumed that the state-submitted data were not met-adjusted and therefore the
meteorological adjustments were still applied. Thus, it is possible that these sources may have
been adjusted twice. Even with that possibility, air quality modeling shows that in general, dust
is frequently overestimated in the air quality modeling results.
3.2.2.2 Agricultural Ammonia Sector (ag)
The agricultural NHs "ag" sector is comprised of livestock and agricultural fertilizer application
emissions from the nonpoint sector of the 2011 NEI. The livestock and fertilizer emissions were
extracted based on SCC. The "ag" sector includes all of the NHa emissions from fertilizer
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contained in the NEI. However, the "ag" sector does not include all of the livestock ammonia
emissions, as there are also some NFb emissions from feedlot livestock in the point source
inventory in California (175 tons) and Wisconsin (125 tons). To prevent double-counting,
emissions were not included in the nonpoint ag inventory for counties in which they were in the
point source inventory.
3.2.2.3 Nonpoint Oil-gas Sector (np_oilgas)
The nonpoint oil and gas (np_oilgas) sector contains onshore and offshore oil and gas emissions.
EPA estimated emissions for all counties with 2011 oil and gas activity data with the Oil and Gas
Tool, and many S/L/T agencies also submitted nonpoint oil and gas data. The types of sources
covered include drill rigs, workover rigs, artificial lift, hydraulic fracturing engines, pneumatic
pumps and other devices, storage tanks, flares, truck loading, compressor engines, and
dehydrators. For more information on the development of the oil and gas emissions in the
201 INEIvl, see Section 3.21 of the 201 INEIvl TSD. See the pt_oilgas sector of this document
for more information on point source oil and gas sources.
3.2.2.4 Residential Wood Combustion Sector (rwc)
The residential wood combustion (rwc) sector includes residential wood burning devices such as
fireplaces, fireplaces with inserts (inserts), free standing woodstoves, pellet stoves, outdoor
hydronic heaters (also known as outdoor wood boilers), indoor furnaces, and outdoor burning in
firepots and chimeneas. Free standing woodstoves and inserts are further differentiated into three
categories: conventional (not EPA certified); EPA certified, catalytic; and EPA certified,
noncatalytic. Generally speaking, the conventional units were constructed prior to 1988. Units
constructed after 1988 had to meet EPA emission standards and they are either catalytic or non-
catalytic. For more information on the development of the residential wood combustion
emissions, see Section 3.14 of the 201 INEIvl TSD.
3.2.2.5 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
201 INEIvl nonpoint inventory were assigned to the clc2rail sector. The types of sources in the
nonpt sector include:
• stationary source fuel combustion, including industrial, commercial, and residential;
• chemical manufacturing;
• industrial processes such as commercial cooking, metal production, mineral processes,
petroleum refining, wood products, fabricated metals, and refrigeration;
• solvent utilization for surface coatings such as architectural coatings, auto refinishing,
traffic marking, textile production, furniture finishing, and coating of paper, plastic,
metal, appliances, and motor vehicles;
• solvent utilization for degreasing of furniture, metals, auto repair, electronics, and
manufacturing;
• solvent utilization for dry cleaning, graphic arts, plastics, industrial processes, personal
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care products, household products, adhesives and sealants;
• solvent utilization for asphalt application and roofing, and pesticide application;
• storage and transport of petroleum for uses such as portable gas cans, bulk terminals,
gasoline service stations, aviation, and marine vessels;
• storage and transport of chemicals;
• waste disposal, treatment, and recovery via incineration, open burning, landfills, and
composting;
• agricultural burning and orchard heating;
• miscellaneous area sources such as cremation, hospitals, lamp breakage, and automotive
repair shops.
Most sources in this sector have annual emissions that are temporally allocated to hourly values
using temporal profiles, but the annual agricultural burning estimates are treated as monthly
values. The annual values in the 201 INEIvl were split into monthly emissions by aggregating
the data up to monthly values from daily estimates of emissions.
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 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 nonpt 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 fire emissions are consistent with the fires stored in the Events data category
of the 201 INEIvl. For more information on the development of the 201 INEIvl fire inventory, see
Section 5.1 of the 201 INEIvl TSD.
The point source day-specific emission estimates for 2011 fires rely on SMARTFIRE 2
(Sullivan, et al., 2008), which uses the National Oceanic and Atmospheric Administration's
(NOAA's) Hazard Mapping System (HMS) fire location information as input. Additional inputs
include the CONSUMEvS.O 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) with satellite-based fire
detections to determine spatial and temporal information about the fires. A functional diagram
of the SMARTFIRE 2 process of reconciling fires with ICS-209 reports is available in the
documentation (Raffuse, et al., 2007). Once the fire reconciliation process is completed, the
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emissions are calculated using the U.S. Forest Service's CONSUMEvS.O fuel consumption
model and the FCCS fuel-loading database in the BlueSky Framework (Ottmar, et. al., 2007).
SMARTFIRE 2 estimates were used directly for all states except Georgia and Florida. For
Georgia, the satellite-derived emissions were removed from the ptfire inventory and replaced
with a separate state-supplied ptfire inventory. Adjustments were also made to Florida as
described in Section 5.1.4 of the 201 INEIvl TSD. These changes made the data in the ptfire
inventory consistent with the data in the 201 INEIvl.The SMOKE-ready "ORL" inventory files
created from the raw daily fires contain both CAPs and HAPs. The BAFM HAP emissions from
the inventory were obtained using VOC speciation profiles (i.e., a "no-integrate noHAP" use
case).
3.2.5 Biogenic Sources (beis)
For CMAQ, biogenic emissions were computed with the BEIS3.14 model within SMOKE using
2011 meteorological data. TheBEIS3.14 model creates gridded, hourly, model-species emissions
from vegetation and soils). It estimates CO, VOC (most notably isoprene, terpine, and
sesquiterpene), and NO emissions for the U.S., Mexico, and Canada. The BEIS3.14 model is
described further in http://www.cmascenter.org/conference/2008/slides/pouliot tale two
cmasOS.ppt. Additional references for this method are provided in (McKenzie, et al., 2007),
(Ottmar, et al., 2003), (Ottmar, et al., 2006), and (Anderson et al., 2004). The inputs to BEIS
include:
• Temperature data at 2 meters from the CMAQ meteorological input files,
• Land-use data from the Biogenic Emissions Landuse Database, version 3 (BELD3) that
provides data on the 230 vegetation classes at 1-km resolution over most of North
America.
3.2.6 Mobile Sources (onroad, onroad_rfl, nonroad, clc2rail, c3marine)
Onroad mobile sources include emissions from motorized vehicles that are normally operated on
public roadways. These include passenger cars, motorcycles, minivans, sport-utility vehicles,
light-duty trucks, heavy-duty trucks, and buses. The sources are further divided between diesel
and gasoline vehicles. The sector characterizes emissions from off-network processes (e.g.
starts, hot soak, and extended idle) and on-network processes (i.e., from vehicles moving along
the roads).
For the 2011 platform, the 2011 onroad emissions are separated into two sectors: (1) "onroad"
and (2) "onroad_rfl". The onroad and onroad_rfl sectors are processed separately to allow for
different spatial allocation to be applied to onroad refueling, which is allocated using a gas
station surrogate, versus onroad vehicles, which are allocated using surrogates based on roads
and population. Except for California and Texas, all onroad and onroad refueling emissions are
generated using the SMOKE-MOVES emissions modeling framework that leverages MOVES
generated outputs (http://www.epa.gov/otaq/models/moves/index.htm) and hourly meteorology.
All tribal data from the mobile sectors have been dropped because the (1) emissions are small,
(2) the emissions could be double-counted with state-provided onroad emissions, (3) all tribal
data was developed using the older model MOBILE6, and (4) because spatial surrogate data at
the tribal level is not currently available. Emissions for onroad (including refueling), nonroad
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and clc2rail sources in California were provided by the California Air Resources Board
(CARS).
The locomotive and commercial marine vessel (CMV) emissions are divided into two nonroad
sectors: "clc2rail" and "cSmarine". The clc2rail sector includes all railway and most rail yard
emissions as well as the gasoline and diesel-fueled Class 1 and Class 2 CMV emissions. The
cSmarine sector emissions contain the larger residual fueled ocean-going vessel Class 3 CMV
emissions and are treated as point emissions with an elevated release component; all other
nonroad emissions are treated as county-specific low-level emissions (i.e., are in model layer 1).
The NEI cSmarine emissions were replaced with a set of approximately 4-km resolution point
source format emissions. These data are used for all states, including California, as well as
offshore and international emissions within our air quality modeling domain, and are modeled
separately as point sources in the "cSmarine" sector.
3.2.7 Onroad non-refueling (onroad)
Onroad mobile sources include emissions from motorized vehicles that are normally operate on
public roadways. These include passenger cars, motorcycles, minivans, sport-utility vehicles,
light-duty trucks, heavy-duty trucks, and buses. The sources are further divided between diesel
and gasoline vehicles. The sector characterizes emissions from off-network processes (e.g.
starts, hot soak, and extended idle) as well as from on-network processes (i.e., from vehicles
moving along the roads). For the 201 Iv6.1 platform, the 2011 onroad emissions are separated
into two sectors: (1) "onroad" and (2) "onroad_rfl". The onroad and onroad_rfl sectors are
processed separately to allow for different spatial allocation to be applied to onroad refueling,
which is allocated using a gas station surrogate, versus onroad vehicles, which are allocated
using surrogates based on roads and population. Except for California and Texas, all onroad and
onroad refueling emissions are generated using the SMOKE-MOVES emissions modeling
framework that leverages MOVES generated outputs
(http://www.epa.gov/otaq/models/moves/index.htm) and hourly meteorology. All tribal data
from the mobile sectors have been dropped because the emissions are small, the emissions could
be double-counted with state-provided onroad emissions, all tribal data was developed using the
older model MOBILE6, and because spatial surrogate data is not currently available.
For the continental U.S., EPA used a modeling framework that took into account the temperature
sensitivity of the on-road emissions. Specifically, EPA used MOVES inputs for representative
counties, vehicle miles traveled (VMT) and vehicle population (VPOP) data for all counties,
along with tools that integrated the MOVES model with SMOKE. In this way, it was possible to
take advantage of the gridded hourly temperature information available from meteorology
modeling used for air quality modeling. The "SMOKE-MOVES" integration tool was developed
by EPA in 2010 and is in use by states and regional planning organizations for regional air
quality modeling of onroad mobile sources. SMOKE-MOVES requires that emission rate
"lookup" tables be generated by MOVES which differentiate emissions by process (i.e., running,
start, vapor venting, etc.), vehicle type, road type, temperature, speed, hour of day, etc.
To generate the MOVES emission rates that could be applied across the U.S., EPA used an
automated process to run MOVES to produce emission factors by temperature and speed for a
series of "representative counties," to which every other county was mapped. Using the MOVES
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emission rates, SMOKE selects appropriate emissions rates for each county, hourly temperature,
SCC, and speed bin and multiplied the emission rate by activity (VMT (vehicle miles travelled)
or VPOP (vehicle population)) to produce emissions. These calculations were done for every
county and grid cell, in the continental U.S. for each hour of the year.
Using SMOKE-MOVES for creating the model-ready emissions requires numerous steps:
1) Determine which counties will be used to represent other counties in the MOVES runs.
2) Determine which months will be used to represent other month's fuel characteristics.
3) Create MOVES inputs needed only by MOVES. MOVES requires county-specific
information on vehicle populations, age distributions, and inspection-maintenance
programs for each of the representative counties.
4) Create inputs needed both by MOVES and by SMOKE, including a list of temperatures
and activity data.
5) Run MOVES to create emission factor tables using the MOVESTierSFRM model
(specifically, model "Moves 20121002f' with default database
"movesdb201210021_truncatedgfre")
6) Run SMOKE to apply the emission factors to activity data (VMT and VPOP) to calculate
emissions
7) Aggregate the results to the county-SCC level for summaries and quality assurance
The California and Texas onroad emissions were created through a hybrid approach of
combining state-supplied annual emissions (from the 201 INEIvl) with EPA developed
SMOKE-MOVES runs. Through this approach, the platform was able to reflect California's
unique rules and Texas' detailed modeling, 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 California's and Texas' onroad emissions based on
SMOKE-MOVES results were:
1) Run CA/TX using EPA inputs through SMOKE-MOVES to produce hourly 2011
emissions hereafter known as "EPA estimates". These EPA estimates for CA/TX are run
in a separate sector called "onroad_catx".
2) Calculate ratios between state-supplied emissions and EPA estimates6. For Texas, these
ratios were calculated for each county/SCC7 (fuel and vehicle type)/pollutant
combination. For California, these were calculated for each county/SCC3 (fuel
type)/pollutant combination. These were not calculated at a greater resolution because
California's emissions did not provide data for all vehicle types.
3) Create an adjustment factor file (CFPRO) that includes EPA-to-state estimate ratios. For
extended idle adjustments, each specific state ratio (county/SCC Group (7 or 3)/pollutant)
6 These ratios were created for all matching pollutants. These ratios were duplicated for all appropriate modeling
species. For example, EPA used the NOx ratio for NO, NO2, MONO and used the PIVh.s ratio for PEC, PNO3, POC,
PSO4, and PMFINE (For more details on NOx and PM speciation, see Sections 3.2.3 and Error! Reference source not
ound.). For VOC model-species, if there was an exact match (e.g., BENZENE), EPA used that HAP pollutant ratio.
For other VOC-based model-species that didn't exist in the NEI inventory, EPA used VOC ratios.
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was multiplied by the extended idle adjustment factor (see the 201 INEIvl TSD for
details).
4) Rerun CA/TX through SMOKE-MOVES using EPA inputs and the new adjustment
factor file.
Through this process, adjusted model-ready files were created that sum to California's and
Texas' annual totals, but have the temporal and spatial patterns reflecting the highly resolved
meteorology and SMOKE-MOVES. After adjusting the emissions, this sector is called
"onroad_catx_adj". Note that in emission summaries, the emissions from the "onroad" and
"onroad_catx_adj" sectors are summed and designated as the emissions for the onroad sector.
3.2.8 Onroad Refueling (onroad_rfl)
Onroad refueling is modeled very similarly to other onroad emissions, and were generated using
MOVESTierSFRM. The onroad_rfl emissions are spatially allocated to gas station locations.
Because the refueling emission factors use the same SCCs as the other onroad models, refueling
was run in a separate sector from the other onroad mobile sources to allow for the different
spatial allocation. To facilitate this, the refueling EFs were separated from the other emission
factors into rate-per-distance (RPD) refueling and rate-per-vehicle (RPV) refueling tables7.
SMOKE-MOVES was run using these EF tables as inputs, and spatially allocated using a gas
stations spatial surrogate. Lastly, the SMOKE program Mrggrid combined RPD refueling and
RPV refueling into a single onroad_rfl model-ready output for final processing with the other
sectors prior to use in CMAQ.
EPA SMOKE-MOVES generated emissions for onroad refueling were used without any
adjustments for all states, including California and Texas. These emissions were used instead of
state submissions to provide a consistent approach nationwide and also because most states did
not submit refueling emissions for diesel fuel. Since the 201 INEIvl includes the state-submitted
emissions, the platform and the NEI refueling emissions in the nonpoint category are inconsistent
for states that submitted refueling emissions. For states that didn't submit emissions, the
approaches are similar but not identical because of differences in the MOVES version,
specifically 201 Ob for the NEI and TierSFRM for the modeling platform.
3.2.9 Nonroad Mobile Sources — NMIM-Based (nonroad)
The nonroad equipment emissions are equivalent to the emissions in the nonroad data category
of the 201 INEIvl, with the exception that the modeling platform emissions also include monthly
totals. All nonroad emissions are compiled at the county/SCC level. NMEVI (EPA, 2005)
creates the nonroad emissions on a month-specific basis that accounts for temperature, fuel
types, and other variables that vary by month. The nonroad sector includes monthly exhaust,
evaporative and refueling emissions from nonroad engines (not including commercial marine,
aircraft, and locomotives) that EPA derived from NMEVI for all states except California and
Texas. Additional details on the development of the 201 INEIvl nonroad emissions are available
in Section 4.5 the 201 INEIvl TSD.
7The Moves2smk post-processing script has command line arguments that will either consolidate or split out the
refueling EF.
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California year 2011 nonroad emissions were submitted to the 201 INEIvl and are also
documented in a staff report (ARB, 2010a). The nonroad sector emissions in California were
developed using a modular approach and include all rulemakings and updates in place by
December 2010. These emissions were developed using Version 1 of the CEP AM which
supports various California off-road regulations such as in-use diesel retrofits (ARB, 2007),
Diesel Risk-Reduction Plan (ARB, 2000) and 2007 State Implementation Plans (SIPS) for the
South Coast and San Joaquin Valley air basins (ARB, 201 Ob).
The CARB-supplied 201 INEIvl nonroad annual inventory emissions values were converted to
monthly values by using the aforementioned EPA NMEVI monthly inventories to compute
monthly ratios by county, SCC7 (fuel, engine type, and equipment type group), mode, and
pollutant. SCC7 ratios were used because the SCCs in the CARB inventory did not align with
many of the SCCs in EPA NMEVI inventory. By aggregating up to SCC7, the two inventories
had a more consistent coverage of sources. Some VOC emissions were added to California to
account for situations when VOC HAP emissions were included in the inventory, but there were
no VOC emissions. These additional VOC emissions were computed by summing benzene,
acetaldehyde, and formaldehyde for the specific sources.
Texas year 2011 nonroad emissions were also submitted to the NEI. The 201 INEIvl nonroad
annual inventory emissions values were converted to monthly values by using EPA's NMIM
monthly inventories to compute monthly ratios by county, SCC7, mode, and poll.
3.2.10 Nonroad Mobile Sources: Commercial Marine Cl, C2, and Locomotive (clc2rail)
The clc2rail sector contains locomotive and smaller CMV sources, except for railway
maintenance locomotives and C3 CMV sources outside of the Midwest states. The "clc2"
portion of this sector name refers to the Class 1 and 2 CMV emissions, not the railway
emissions. Railway maintenance emissions are included in the nonroad sector. The C3 CMV
emissions are in the cSmarine sector. All emissions in this sector are annual and at the county-
SCC resolution.
The starting point for the clc2rail sector is the 201 INEIvl nonpoint inventory for all but specific
Midwest states, which are instead derived from the Great Lakes 2010 CMV inventory. The
modeling platform emissions for the clc2rail SCCs were extracted from the NEI nonpoint
inventory. For more information on CMV sources in the NEI, see Section 4.3 of the 201 INEIvl
TSD. For more information on locomotives, see Section 4.4 of the 201 INEIvl TSD.
The difference between the 201 INEIvl and the modeling platform for this sector is due to the
availability of alternative data from the Midwest RPO. Year-2010 emissions were received from
the Lake Michigan Air Directors Consortium for tug boats, Great Lakes vessels ("Lakers") and
inland waterways for states within the Midwest RPO and Minnesota, hereafter simply referred to
as "MWRPO" (http://www.ladco.org/). The states in the MWRPO are: Illinois, Indiana,
Michigan, Minnesota, Ohio and Wisconsin. These MWRPO CMV emissions include coverage
for bordering states/counties along the inland waterways such as the Mississippi and Ohio rivers
in Iowa, Missouri, Kentucky, West Virginia, Pennsylvania and New York. The LADCO 2010
inventory was used to replace EPA-estimated CMV emissions in the MWRPO states, but was
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not used to replace the 201 INEIvl emissions in the bordering non-MWRPO states.
Some modifications to the MWRPO CMV data were made prior to SMOKE processing:
• Emissions provided at the level of NEI Shape IDs were aggregated to county-level.
• The 201 INEIvl was used to determine which counties had ports; for those counties that
had ports, 90% of emissions in the MWPRO inventory were assigned as underway
(SCC=2280002200) and 10% were assigned as port emissions (SCC=2280002100).
• Emissions were converted to short tons and PM2.5 was added by assuming it is equal to
92% of PM10 at the suggestion of the MWRPO.
• Tugs were assigned a unique SCC (2280002021) to allow for unique spatial allocation
(see Section 3.4.1).
• Tugs were assigned from MWRPO total to counties based on 201 INEIvl county-level
activity information for tug vessels.
Because the Great Lakes vessels include all CMV activity on the Great Lakes, EPA-estimated
C3 CMV (cSmarine) sector emissions (discussed in the following section) in the MWRPO states
were removed to avoid potential double-counting of C3 CMV with the LADCO inventory in the
MWRPO states.
3.2.11 Nonroad mobile sources: C3 commercial marine (c3marine)
The U.S. C3 CMV inventory was developed based on a 4-km resolution ASCII raster format
dataset used since the Emissions Control Area-International Marine Organization (ECA-EVIO)
project began in 2005, then known as the Sulfur Emissions Control Area (SECA). The ECA-
EVIO data are used instead of the 201 INEIvl data for the modeling platform because
accompanying estimates of emission projections for future years are available. In addition, the
inventory preserves shipping lanes in federal waters while these are not stored within the NEI
data. Keeping the sources in this sector separate from smaller CMV sources allows for the
emissions to be elevated above the surface layer within the AQ model. The ECA-EVIO data are
used for all states with C3 CMV emissions. For the MWPRO states, the ECA-EVIO C3 CMV
emissions in the Great Lakes are assumed to be misclassified as C3 vessels for which emissions
are included in the clc2rail sector as part of the LADCO inventory, therefore the ECA-EVIO
emissions are not included in the c3marine sector.
The development of this ECA-EVIO-based C3 CMV inventory is discussed below; however, all
non-U.S. emissions (Canadian emissions and emissions farther offshore than U.S. waters) are
processed in the "othpt" sector. This splitting of the C3 CMV emissions from the farther
offshore emissions allows for easier summaries of U.S.-only and state or county total emissions.
The ECA-EVIO 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 often burn
residual fuel. The emissions in this sector are comprised of primarily foreign-flagged ocean-
going vessels, referred to as C3 CMV ships. The c3marine inventory includes these ships in
several intra-port modes (i.e., cruising, hoteling, reduced speed zone, maneuvering, and idling)
and an underway mode, and includes near-port auxiliary engine emissions. An overview of the
C3 EC A Proposal to the International Maritime Organization (EPA-420-F-10-041, August 2010)
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project and future-year goals for reduction of NOx, SO2, and PM C3 emissions can be found at:
http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09019.pdf. The resulting ECA-IMO
coordinated strategy, including emission standards under the Clean Air Act for new marine
diesel engines with per-cylinder displacement at or above 30 liters, and the establishment of
Emission Control Areas is at: http://www.epa.gov/oms/oceanvessels.htm.
The ECA-IMO emissions data were converted to SMOKE point-source ORL input format as
described in http://www.epa.gov/ttn/chief/conference/eil7/session6/mason.pdf. 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. Counties were assigned as extending up to 200nm from the coast because this was the
distance to the edge of the U.S. Exclusive Economic Zone (EEZ), a distance that defines the
outer limits of ECA-IMO controls for these vessels.
The base year ECA inventory is 2002 and consists of these CAPs: PMio, PM2.s, CO, CO2, NH3,
NOx, SOx (assumed to be 802), and hydrocarbons (assumed to be VOC). EPA developed
regional growth (activity-based) factors that were applied to create the 2011 inventory from the
2002 data. These growth factors are provided in Table 3-4. The emissions were converted to
SMOKE point source inventory format, allowing for the emissions to be allocated to modeling
layers above the surface layer. 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 in Table 3-4. 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.
For California, the ECA-IMO 2011 emissions were scaled by county to match those provided by
CARB for year 2011 because CARB has had distinct projection and control approaches for this
sector since 2002. These CARB C3 CMV emissions are documented in a staff report available
at: http://www.arb.ca.gov/regact/2010/offroadlsi 10/offroadisor.pdf. The CMV emissions
obtained from the CARB nonroad mobile dataset include the 2011 regulations to reduce
emissions from diesel engines on commercial harbor craft operated within California waters and
24 nautical miles of the California shoreline. These emissions were developed using Version 1
of the California Emissions Projection Analysis Model (CEPAM) that supports various
California off-road regulations. The locomotive emissions were obtained from the CARB trains
dataset "ARMJ_RF#2002_ANNUAL_TRAINS.txt". Documentation of the CARB offroad
mobile methodology, including clc2rail sector data, is provided at:
http://www.arb.ca.gov/msei/categories.htmtfoffroad motor vehicles.
The geographic regions listed in the table are shown in Figure 3-1. The East Coast and Gulf
Coast regions were divided along a line roughly through Key Largo (longitude 80° 26' West).
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).
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Table 3-4. Growth factors to project the 2002 ECA inventory to 2011
Region
East Coast (EC)
Gulf Coast (GC)
North Pacific (NP)
South Pacific (SP)
Great Lakes (GL)
Outside ECA
EEZ
FIPS
85004
85003
85001
85002
n/a
98001
NOx
1.301
1.114
1.183
1.367
1.072
1.341
PMio
0.500
0.428
0.467
0.525
0.394
1.457
PMl.5
0.496
0.423
0.458
0.521
0.390
1.457
voc
(HC)
1.501
1.288
1.353
1.565
1.177
1.457
CO
1.501
1.288
1.353
1.562
1.176
1.457
SOi
0.536
0.461
0.524
0.611
0.415
1.457
* 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 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, 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 these 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
MWRPO states and other states such as Pennsylvania and New York. The classification of
emissions to U.S. and Canadian FIPS codes is needed to avoid double-counting of C3 CMV U.S.
emissions in the Great Lakes because, as discussed in the previous section, all CMV emissions in
the Midwest RPO are processed in the "clc2rail" sector.
The emissions were converted to SMOKE point source inventory format, allowing for the
emissions to be allocated to modeling layers above the surface layer. All non-US, non-EEZ
33
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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 EGA" factors in Table 3-4.
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 years8.
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.
3.2.12 Emissions from Canada, Mexico and Offshore Drilling Platforms (othpt, othar, othon)
The emissions from Canada, Mexico, and non-U.S. offshore Class 3 Commercial Marine Vessels
(C3 CMV) and drilling platforms are included as part of three emissions modeling sectors: othpt,
othar, and othon. The "oth" refers to the fact that these emissions are usually "other" than those
in the U.S. state-county geographic FIPS, and the third and fourth characters provide the
SMOKE source types: "pt" for point, "ar" for "area and nonroad mobile", and "on" for onroad
mobile.
The ECA-EVIO-based C3 CMV emissions for non-U.S. states are processed in the othpt sector.
These C3 CMV emissions include those assigned to Canada, those assigned to the Exclusive
Economic Zone (EEZ, defined as those emissions just beyond U.S. waters approximately 3-10
miles offshore, extending to about 200 nautical miles from the U.S. coastline), and all other
offshore emissions -far offshore and non-U.S. These emissions are included in the othpt sector
for simplicity of creating U.S.-only emissions summaries. Otherwise, these emissions are
developed in the same way as the U.S. C3 CMV emissions in the c3marine sector.
The othpt sector also includes point source offshore oil and gas drilling platforms that are beyond
U.S. state-county boundaries in the Gulf of Mexico. For these offshore emissions, the 2008 NEI
version 3 point source inventory data were used because the 2011 data were not yet available.
This is consistent with the 201 INEIvl. Updated offshore oil and gas drilling emissions are
expected to be incorporated into version 2 of the 2011 NEI. The 2008-based offshore emission
sources were provided by the Mineral Management Services (MMS).
For Canada, year-2006 Canadian emissions were the latest available at the time the modeling
was performed. These were the starting point with the addition of several modifications to these
inventories. The SCCs in these inventories were changed to the generic 39999999 and the
industrial code information was removed to preserve confidentiality. The Canadian point sources
were split into three inventory files:
8 The extent of the "36US1" domain is similar to the full geographic region shown in Figure 3-1. Note that this
domain is not specifically used in this 2011 platform, although spatial surrogates that can be used with it are
provided.
34
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• ptinv_canada_point_2006_orl_13aug2013_v3_orl.txt: contains point sources for all
pollutants except VOC;
• ptinv_canada_point_cb5_2006_orl_13aug2013_vl_orl.txt: contains VOC emissions split
into CB05 species;
• ptinv_canada_point_uog_2006_orl_02mar2009_vO_orl.txt: contains oil and gas-related
sources.
The year-2006 nonpoint emissions provided by Canada were unchanged from EPA 2007
platform. Inventory files were provided for area fugitive dust, agricultural, commercial marine,
railroad, nonroad, aircraft, and other area sources. Canadian onroad emissions are also
unchanged from the EPA 2007 platform.
For Mexico, point, nonpoint, and onroad emissions for year 2012 are projections of their 1999
inventory originally developed by Eastern Research Group Inc., (ERG, 2006; ERG, 2009; Wolf,
2009) as part of a partnership between Mexico's Secretariat of the Environment and Natural
Resources (Secretaria de Medio Ambiente y Recursos Naturales-SEMARNAT) and National
Institute of Ecology (Instituto Nacional de Ecologia-INE), the U.S. EPA, the Western Governors'
Association (WGA), and the North American Commission for Environmental Cooperation
(CEC). This inventory includes emissions from all states in Mexico. A background on the
development of year-2012 Mexico emissions from the 1999 inventory is available at:
http://www.wrapair.org/forums/ef/inventories/MNEI/index.html.
3.2.13 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. The same data was
used as in the CAP and HAP 2002-based Platform was used. See
ftp://ftp.epa.gov/EiTiisInventorv/2002v3CAPHAP/ documentation for additional details.
3.3 Emissions Modeling Summary
CMAQ requires hourly emissions of specific gas and particle species for the horizontal and
vertical grid cells contained within the modeled region (i.e., modeling domain). To provide
emissions in the form and format required by the model, it is necessary to "pre-process" the
emission inventories (i.e., emissions input to SMOKE) for the sectors as 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 distribution of
the sources does not need to be provided as an input.
The temporal resolutions of the emissions inventories input to SMOKE vary across sectors, and
35
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may be hourly, daily, monthly, or annual total emissions, or even emission factors and activity
data. The spatial resolution also varies: it may be individual point sources,
county/province/municipio totals, or gridded emissions. This section provides some basic
information about the tools and data files used for emissions modeling as part of the modeling
platform.
3.3.1 The SMOKE Modeling System
SMOKE version 3.5.1 was used to pre-process the raw emissions inventories into emissions
inputs for CMAQ. SMOKE executables and source code are available from the Community
Multiscale Analysis System (CMAS) Center at http://www.cmascenter.org. Additional
information about SMOKE is available from 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 3-5 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 TmpbeisS
program and not as a separate SMOKE step. The "Inventory resolution" column shows the
inventory temporal resolution from which SMOKE needs to calculate hourly emissions. Note
that for some sectors (e.g., onroad, beis), there is no input inventory; instead, activity data and
emission factors are used in combination with meteorological data to compute hourly emissions.
Finally, the "plume rise" column indicates the sectors for which the "in-line" approach is used.
These sectors are the only ones with emissions in aloft layers based on plume rise. The term "in-
line" means that the plume rise calculations are done inside of the air quality model instead of
being computed by SMOKE. The air quality model computes the plume rise using the stack data
and the hourly air quality model inputs found in the SMOKE output files for each model-ready
emissions sector. The height of the plume rise determines the model layer into which the
emissions are placed. The c3marine, othpt, and ptfire sectors are the only sectors with only "in-
line" emissions, meaning that all of the emissions are placed in aloft layers and there are no
emissions for those sectors in the two-dimensional, layer-1 files created by SMOKE.
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Table 3-5. Key emissions modeling steps by sector
Platform sector
Afdust
Ag
Beis
clc2rail
c3 marine
Nonpt
Nonroad
np oilgas
Onroad
onroad rfl
Othar
Othon
Othpt
pt oilgas
ptegu
ptegu_pk
ptfire
ptnonipm
rwc
Spatial
Surrogates
Surrogates
Pre-gridded
land use
Surrogates
Point
Surrogates &
area-to-point
Surrogates &
area-to-point
Surrogates
Surrogates
Surrogates
Surrogates
Surrogates
Point
Point
Point
Point
Point
Point
Surrogates
Speciation
Yes
Yes
inBEIS3.14
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Inventory
resolution
annual
annual
(some monthly)
computed hourly
annual
annual
annual
(some monthly)
monthly
annual
monthly activity,
computed hourly
monthly activity,
computed hourly
annual
annual
annual
annual
daily & hourly
daily & hourly
daily
annual
annual
Plume rise
in-line
in-line
in-line
in-line
in-line
in-line
in-line
SMOKE has the option of grouping sources so that they are treated as a single stack when
computing plume rise. For the 2011 platform, no grouping was performed because grouping
combined with "in-line" processing will not give identical results as "offline" processing (i.e.,
when SMOKE creates 3-dimensional files). This occurs when stacks with different stack
parameters or lat/lons are grouped, thereby changing the parameters of one or more sources. The
most straightforward way to get the same results between in-line and offline is to avoid the use
of grouping.
3.3.3 Spatial Configuration
For this study, SMOKE was run for the smaller 12-km CONtinental United States "CONUS"
modeling domain (12US2) shown in Figure 3-2 and boundary conditions were obtained from a
2011 run of 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
37
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spatial surrogates and area-to-point data used to accomplish spatial allocation with SMOKE.
12US1 Continental US Domain
12US2 Continental US Domain
Figure 3-2. CMAQ Modeling Domain
3.3.4 Chemical Speciation Configuration
The emissions modeling step for chemical speciation creates the "model species" needed by the
air quality model for a specific chemical mechanism. These model species are either individual
chemical compounds or groups of species, called "model species." The chemical mechanism
used for the 2011 platform is the CB05 mechanism (Yarwood, 2005). The same base chemical
mechanism is used within both CMAQ and CAMx, but the implementation differs slightly
between the two models. The specific versions of CMAQ and CAMx used in applications of this
platform include secondary organic aerosol (SOA) and HONO enhancements.
From the perspective of emissions preparation, the CB05 with SOA mechanism is the same as
was used in the 2007 platform. Table 3-6 lists the model species produced by SMOKE for use in
CMAQ and CAMx. It should be noted that the BENZENE model species is not part of CB05 in
that the concentrations of BENZENE do not provide any feedback into the chemical reactions
(i.e., it is not "inside" the chemical mechanism). Rather, benzene is used as a reactive tracer and
as such is impacted by the CB05 chemistry. BENZENE, along with several reactive CB05
species (such as TOL and XYL) plays a role in SOA formation.
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The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach
were developed from the SPECIATE 4.3 database
(http ://www. epa. gov/ttn/chief/software/speci ate), which is EPA's repository of TOG and PM
speciation profiles of air pollution sources. However, a few of the profiles used in the v6
platform will be published in later versions of the SPECIATE database after the release of this
documentation. The SPECIATE database development and maintenance is a collaboration
involving EPA's ORD, OTAQ, and the Office of Air Quality Planning and Standards (OAQPS),
in cooperation with Environment Canada (EPA, 2006a). The SPECIATE database contains
speciation profiles for TOG, speciated into individual chemical compounds, VOC-to-TOG
conversion factors associated with the TOG profiles, and speciation profiles for
Speciation profiles and cross-references for 201 Iv6 platform are available in spreadsheet form
from ftp://ftp.epa.gov/EmisInventory/20 Ilv6/vlplatform/reports/speciation_profiles/. The
profiles are in the Excel files "gspro_201 l.xlsx" and "gspro_combo_201 l.xlsx,
gsref_201 1 .xlsx". The cross reference information is in "gsref_201 1 .xlsx. A spreadsheet
showing emission totals for each speciation profile for the 201 led case by modeling sector is
available in the file "201 led_speciation_profile_CAPs_febl 12014.xlsx". Note that the
emissions totals differ slightly from the 201 lef case, as do some of the VOC to TOG conversion
factors. However, the reports still convey the relative importance of each speciation profile in
terms of emissions affected.
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Table 3-6. Emission model species produced for CB05 with SOA for CMAQ 5.0.1
Inventory Pollutant
C12
HC1
CO
NOX
SO2
NH3
VOC
VOC species from the biogenics
model that do not map to model
species above
PMio
PM2.510
Sea-salt species (non -
anthropogenic) n
Model Species
CL2
HCL
CO
NO
NO2
HONO
SO2
SULF
NH3
ALD2
ALDX
BENZENE
CH4
ETH
ETHA
ETOH
FORM
IOLE
ISOP
MEOH
OLE
PAR
TOL
XYL
SESQ
TERP
PMC
PEC
PNO3
POC
PSO4
PMFINE
PCL
PNA
Model species description
Atomic gas-phase chlorine
Hydrogen Chloride (hydrochloric acid) gas
Carbon monoxide
Nitrogen oxide
Nitrogen dioxide
Nitrous acid
Sulfur dioxide
Sulfuric acid vapor
Ammonia
Acetaldehyde
Propionaldehyde and higher aldehydes
Benzene (not part of CB05)
Methane9
Ethene
Ethane
Ethanol
Formaldehyde
Internal olefm carbon bond (R-C=C-R)
Isoprene
Methanol
Terminal olefm carbon bond (R-C=C)
Paraffin carbon bond
Toluene and other monoalkyl aromatics
Xylene and other polyalkyl aromatics
Sesquiterpenes
Terpenes
Coarse PM > 2.5 microns and < 10 microns
Particulate elemental carbon < 2.5 microns
Particulate nitrate < 2.5 microns
Particulate organic carbon (carbon only) < 2.5 microns
Particulate Sulfate < 2.5 microns
Other particulate matter < 2.5 microns
Particulate chloride
Particulate sodium
*The same species names are used for the CAMX model with exceptions as follows:
1. CL2 is not used in CAMx
2. CAMX paniculate sodium is NA (in CMAQ it is PNA)
3. CAMX uses different names for species that are both in CBO5 and SOA for the following: TOLA=TOL, XYLA=XYL,
ISP=ISOP, TRP=TERP. They are duplicate species in CAMX that are used in the SOA chemistry. CMAQ uses the same
names in CB05 and SOA for these species.
4. CAMx uses a different name for sesquiterpenes: CMAQ SESQ = CAMX SQT
5. CAMx paniculate species have different names for organic carbon, coarse paniculate matter and other paniculate mass:
CMAQ uses POC, PMC, PMFINE, and PMOTHR, while CAMX uses POA, CPRM, FCRS, and FPRM, respectively.
^Technically, CH4 is not a VOC but part of TOG. Although emissions of CH4 are derived, the AQ models do not use
40
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The speciation of VOC includes HAP emissions from the 201 INEIvl in the speciation process.
Instead of speciating VOC to generate all of the species listed in Table 3-6, emissions of four
specific HAPs: benzene, acetaldehyde, formaldehyde and methanol (collectively known as
"BAFM") from the NEI were "integrated" with the NEI VOC. The integration process
(described in more detail below) combines these HAPs with the VOC in a way that does not
double count emissions and uses the HAP inventory directly in the speciation process. The basic
process is to subtract the specified HAPs emissions mass from VOC emissions mass and to then
use a special "integrated" profile to speciate the remainder of VOC to the model species
excluding the specific HAPs. EPA believes that generally, the HAP emissions from the NEI are
more representative of emissions of these compounds than their generation via VOC speciation.
The BAFM HAPs (benzene, acetaldehyde, formaldehyde and methanol) were chosen because,
with the exception of BENZENE, they are the only explicit VOC HAPs in the base version of
CMAQ 5.0.1 (CAPs only with chlorine chemistry) model. Explicit means that they are not
lumped chemical groups like the other CB05 species. These "explicit VOC HAPs" are model
species that participate in the modeled chemistry using the CB05 chemical mechanism. The use
of these HAP emission estimates along with VOC is called "HAP-CAP integration". BENZENE
was chosen because it is a model species in the base version of CMAQ 5.0.1, and there was a
desire to keep its emissions consistent between multi-pollutant and base versions of CMAQ.
For specific sources, especially within the onroad and onroad_rfl sectors, the integration included
ethanol. To differentiate when a source was integrating BAFM versus EBAFM (ethanol in
addition to BAFM), the speciation profiles that do not include ethanol are referred to as an "E-
profile" (to be used when the ethanol comes from the inventory pollutant). For example, use E10
headspace gasoline evaporative speciation profile 8763 when ethanol is speciated from VOC, but
use 8763E when ethanol is obtained directly from the inventory.
The integration of HAP VOC with VOC is a feature available in SMOKE for all inventory
formats other than PTDAY (the format used for the ptfire sector). SMOKE allows the user to
specify both the particular HAPs to integrate via the INVTABLE and the particular sources to
integrate via the NHAPEXCLUDE file (which actually provides the sources to be excluded from
integration12). 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
profiles13. 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
these emissions because the anthropogenic emissions are dwarfed by the CH4 already in the atmosphere.
10 For CMAQ 5.0, PIVh.s is speciated into a finer set of PM components. Listed in this table are the AE5 species
11 These emissions are created outside of SMOKE
12 In SMOKE version 3.5.1, the options to specify sources for integration are expanded so that a user can specify the
particular sources to include or exclude from integration, and there are settings to include or exclude all sources
within a sector. In addition, the error checking is significantly stricter for integrated sources. If a source is
supposed to be integrated, but it is missing BAFM or VOC, SMOKE will now raise an error.
13 These ratios and profiles are typically generated from the Speciation Tool when it is run with integration of a
specified list of pollutants, for example BAFM.
41
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emissions, then the sector is considered fully integrated and does not need a NHAPEXCLUDE
file. If on the other hand, certain sources do not have the necessary HAPs, then an
NHAPEXCLUDE file must be provided based on the evaluation of each source's pollutant mix.
EPA considered CAP-HAP integration for all sectors and developed "integration criteria" for
some of them.
The process of partial integration for B AFM means that the B AFM records in the input
inventories do not need to be removed from any sources in a partially integrated sector because
SMOKE does this automatically using the INVTABLE configuration. For EBAFM integration,
this process is identical to that shown in the figure except for the addition of ethanol (E) to the
list of subtracted HAP pollutants. For full integration, the process would be very similar except
that the NHAPEXCLUDE file would not be used and all sources in the sector would be
integrated.
In SMOKE, the INVTABLE allows the user to specify both the particular HAPs to integrate.
Two different types of INVTABLE files are included for use with different sectors of the
platform. For sectors that had no integration across the entire sector (see Error! Reference
ource not found.7), EPA created a "no HAP use" INVTABLE in which the "KEEP" flag is set
to "N" for BAFM pollutants. Thus, any BAFM pollutants in the inventory input into SMOKE
are automatically dropped. This approach both avoids double-counting of these species and
assumes that the VOC speciation is the best available approach for these species for sectors using
this approach. The second INVTABLE, used for sectors in which one or more sources are
integrated, causes SMOKE to keep the inventory BAFM pollutants and indicates that they are to
be integrated with VOC. This is done by setting the "VOC or TOG component" field to "V" for
all four HAP pollutants. This type of INVTABLE is further differentiated into a version for
those sectors that integrate BAFM and another for those that integrate EBAFM, such as the
onroad and onroad_rfl sectors.
Table 3-7. Integration status of benzene, acetaldehyde, formaldehyde and methanol
(BAFM) for each platform sector
Platform
Sector
ptegu
ptegujk
ptnonipm
ptfire
othar
othon
ag
afdust
biog
nonpt
np oilgas
pt oilgas
rwc
nonroad
Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A),
Formaldehyde (F), Methanol (M), and Ethanol (E)
No integration
No integration
No integration
No integration
No integration
No integration
N/A - sector contains no VOC
N/A - sector contains no VOC
N/A - sector contains no inventory pollutant "VOC"; but rather specific VOC species
Partial integration (BAFM and EBAFM)
Partial integration (BAFM)
Partial integration (BAFM)
Partial integration (BAFM)
Partial integration (BAFM)
42
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Platform
Sector
clc2rail
othpt
cSmarine
onroad
onroad rfl
Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A),
Formaldehyde (F), Methanol (M), and Ethanol (E)
Partial integration (BAFM)
Partial integration (BAFM)
Full integration (BAFM)
Full integration (EBAFM and BAFM)
Full integration (EBAFM and BAFM)
SMOKE can compute speciation profiles from mixtures of other profiles in user-specified
proportions. The combinations are specified in the GSPRO_COMBO ancillary file by pollutant
(including pollutant mode, e.g., EXH VOC), state and county (i.e., state/county FIPS code) and
time period (i.e., month).This feature was used to speciate onroad and nonroad mobile and
gasoline-related related stationary sources that use fuels with varying ethanol content. In these
cases, the speciation profiles require different combinations of gasoline profiles, e.g. EO and E10
profiles. Since the ethanol content varies spatially (e.g., by state or county), temporally (e.g., by
month) and by modeling year (future years have more ethanol) the GSPRO_COMBO feature
allows combinations to be specified at various levels for different years. SMOKE computes the
resultant profile using the fraction of each specific profile assigned by county, month and
emission mode.
The GSREF file indicates that a specific source uses a combination file with the profile code
"COMBO". Because the GSPRO_COMBO file does not differentiate by SCC and there are
various levels of integration across sectors, sector specific GSPRO_COMBO files are used. For
the onroad and onroad_rfl sectors, the GSPRO_COMBO uses E-profiles (i.e. there is EBAFM
integration). Different profile combinations are specified by the mode (e.g. exhaust, evaporative,
refueling, etc.) by changing the pollutant name (e.g. EXH NONHAPTOG,
EVP_NONHAPTOG, RFL_NONHAPTOG). For the nonpt sector, a combination of BAFM
and EBAFM integration is used. Due to the lack of SCC-specificity in the GSPRO_COMBO,
the only way to differentiate the sources that should use BAFM integrated profiles versus E-
profiles is by changing the pollutant name. For example, EPA changed the pollutant name for
the PFC future year inventory so the integration would use EVP NONHAPVOC to correctly
select the E-profile combinations, while other sources used NONHAPVOC to select the typical
BAFM profiles.
Speciation profiles for use with BEIS are not included in SPECIATE. The 2010 Platform uses
BEIS3.14 and includes a species (SESQ) that was not in BEIS3.13 (the version used for the 2002
Platform). This species was mapped to the CMAQ specie SESQT. The profile code associated
with BEIS3.14 profiles for use with CB05 was "B10C5." For additional sector-specific details on
VOC speciation for a variety of sectors, see Section 3.2.1.3 of the TSD Preparation of Emission
Inventories for the Version 6.1 Emissions Modeling Platform (EPA, 2014a).
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. For CMAQ 5.0.1, there is a new
thermodynamic equilibrium aerosol modeling tool (ISORROPIA) v2 mechanism that needs
additional PM components (AE6), which are further subsets of PMFINE (see Table 3-8). EPA
43
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speciated PIVh.s so that it included both AE5 and AE6 PM model species without causing any
double counting. Therefore, emissions from this platform can be used with either CMAQ 4.7.1
or CMAQ 5.0.1. The majority of the 2011 platform PM profiles come from the 911XX series
which include updated AE6 speciation14.
Table 3-8. PM model species: AE5 versus AE6
species name
POC
PEC
PSO4
PNO3
PMFINE
PNH4
PNCOM
PFE
PAL
PSI
PTI
PCA
PMG
PK
PMN
PNA
PCL
PH2O
PMOTHR
species description
organic carbon
elemental carbon
Sulfate
Nitrate
unspeciated PM2.5
Ammonium
non-carbon organic matter
Iron
Aluminum
Silica
Titanium
Calcium
Magnesium
Potassium
Manganese
Sodium
Chloride
Water
PM2.5 not in other AE6 species
AE5
Y
Y
Y
Y
Y
N
N
N
N
N
N
N
N
N
N
N
N
N
N
AE6
Y
Y
Y
Y
N
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
Y
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 1-to-l 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
Version 6.1 platform TSD (EPA, 2014a).
NOx can be speciated into NO, NO2, and/or HONO. For the non-mobile sources, EPA used a
single profile "NHONO" to split NOx into NO and NO2. For the mobile sources except for
onroad (including nonroad, clc2rail, cSmarine, othon sectors) and for specific SCCs in othar and
ptnonipm, the profile "HONO" splits NOx into NO, NO2, and HONO. The onroad sector does
not use the "HONO" profile to speciate NOx. MOVES2010b 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
14 The exceptions are 5674 (Marine Vessel - Marine Engine - Heavy Fuel Oil) used for cSmarine and 92018 (Draft
Cigarette Smoke-Simplified) used in nonpt.
44
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NO fraction varies by heavy duty versus light duty, fuel type, and model year. The NO2 fraction
= 1 - NO - HONO. For more details on the NOx fractions within MOVES, see
http://www.epa.gov/otaq/models/moves/documents/420rl2022.pdf. HONO is not calculated
directly by the Tier 3 proposal version of MOVES. For these EF tables, the calculation of
HONO and the NO2 fraction are calculated externally by the moves2smk script15. The SMOKE-
MOVES system then models these species directly without further speciation.
3.3.4 Temporal Processing Configuration
Temporal allocation (i.e., temporalization) is the process of distributing aggregated emissions to
a finer temporal resolution, thereby converting annual emissions to hourly emissions. While the
total emissions are important, the timing of the occurrence of emissions is also essential for
accurately simulating ozone, PM, and other pollutant concentrations in the atmosphere. Many
emissions inventories are annual or monthly in nature. Temporalization takes these aggregated
emissions and if needed distributes them to the month, and then distributes the monthly
emissions to the day and the daily emissions to the hour. This process is typically done by
applying temporal profiles to the inventories in this order: monthly, day of the week, and diurnal.
The temporal profiles and associated cross references used to create the hourly emissions inputs
for the 2011 air quality modeling platform were similar to those used for the 2007 platform. The
temporal factors applied to the inventory are selected using some combination of country, state,
county, SCC, and pollutant. Table 3-9 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 3-9. Temporal Settings Used for the Platform Sectors in SMOKE
Platform
Sector
ptegu
ptegu_pk
ptnonipm
pt_oilgas
ptfire
othpt
nonroad
Inventory
resolutions
Daily & hourly
Daily & hourly
Annual
Annual
Daily
Annual
Monthly
Monthly
profiles
used?
yes
yes
yes
Daily
temporal
approach
all
all
mwdss
mwdss
all
mwdss
mwdss
Merge
processing
approach
all
all
mwdss
mwdss
all
mwdss
mwdss
Process
Holidays as
separate days
Yes
Yes
Yes
Yes
Yes
Yes
15 A specific version of the moves2smk script was developed to do this calculation of HONO. The typical version
assumes that HONO was calculated directly by MOVES2010b.
45
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Platform
Sector
othar
clc2rail
c3 marine
onroad
onroad_rfl
othon
nonpt
np oilgas
rwc
ag
afdust_adj
beis
Inventory
resolutions
Annual
Annual
Annual
Annual & monthly1
Annual & monthly2
Annual
Annual & monthly
Annual
Annual
Annual
Annual
Hourly
Monthly
profiles
used?
yes
yes
yes
yes
yes
yes
no
yes
yes
Daily
temporal
approach
week
mwdss
aveday
all
all
week
all
mwdss
met-based
all
week
n/a
Merge
processing
approach
week
mwdss
aveday
all
all
week
all
mwdss
All
all
all
all
Process
Holidays as
separate days
Yes
Yes
Yes
Yes
Yes
Yes
Yes
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.
2. Note the annual and monthly "inventory" actually refers to the activity data (VMT and VPOP) for
onroad_rfl. The actual emissions are computed on an hourly basis.
The following values are used in the table: The value "all" means that hourly emissions
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, 2011, which is intended to mitigate the effects of initial condition
concentrations. The ramp-up period was 10 days (December 22-31, 2010). For most sectors,
emissions from December 2011 were used to fill in surrogate emissions for the end of December
2010. In particular, December 2011 emissions (representative days) were used for December
2010. For biogenic emissions, December 2010 emissions were processed using 2010
meteorology.
The Flat File 2010 format (FF10) inventory format for SMOKE provides a more consolidated
format for monthly, daily, and hourly emissions inventories than previous formats supported.
Previously, to process monthly inventory data required the use of 12 separate inventory files.
46
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With the FF10 format, a single inventory file can contain emissions for all 12 months and the
annual emissions in a single record. This helps simplify the management of numerous
inventories. Similarly, daily and hourly FF10 inventories contain individual records with data
for all days in a month and all hours in a day, respectively.
SMOKE 3.5.1 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 (e.g. the nonpt sector). 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, and the agricultural burning inventory within the nonpt sector.
The 201 INEIvl annual EGU emissions are allocated to hourly emissions using the following 3-
step methodology: annual value to month, month to day, and day to hour. The temporal
allocation procedure is differentiated by whether or not the source could be directly matched to a
CEMS unit via ORIS facility code and boiler ID. Prior to temporal allocation, as many sources
as possible were matched to CEMS data via ORIS facility code and boiler ID. EIS stores a base
set of previously matched units via alternate facility and unit IDs. For any units not yet matched,
reports were generated by unit to identify potential matches with the NEI. The reports included
FIPS state/county code, facility name, and NOx and SO2 emissions. Units were considered
matches if the FIPS state/county code matched, the facility name was similar, and the NOx and
SO2 emissions were similar.
For sources not matched to CEMS measurements, the first two steps of the allocation are done
outside of SMOKE. For sources in the ptegu and ptegu_pk sectors that are matched to CEMS
data, annual totals of the emissions may be different than the annual values in 201 INEIvl
because the CEMS data actually replaces the inventory data. All units in the ptegu_pk sector
with non-zero emissions for 2011 were matched to CEMS data.
For units not matched to CEMS data, the allocation of the inventory annual emissions to months
is done using average fuel-specific season-to-month factors generated for each of the 64 IPM
regions shown in Figure 3-3. These factors are based 2011 CEMS data only. In each region,
separate factors were developed for the fuels coal, natural gas, and "other", where the types of
fuels included in "other" vary by region. Separate profiles were computed for NOX and SO2, and
heat input. An overall composite profile was also computed and was used in a few cases in which
the fuel-specific profile was too irregular, or there were no CEMS units with the specified fuel in
the region containing the unit. For both CEMS and non-CEMS matched units, NOX and SO2
CEMS data are used to allocate NOX and SO2 emissions, while CEMS heat input data is used to
allocate all other pollutants.
For the clc2rail and c3marine sectors, emissions are allocated with flat monthly and day of week
profiles, and most emissions are also allocated with flat hourly profiles.
For the nonpt sector, most the inventories are annual except for the agricultural burning (SCC
47
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2801500000) inventory which was allocated to months by adding up the available values for
each day of the month. For all agricultural burning, the diurnal temporal profile used reflected
the fact that burning occurs during the daylight hours - see Figure 3-4 (McCarty et al., 2009).
This puts most of the emissions during the work day and suppresses the emissions during the
middle of the night. A uniform profile was used for each day of the week for all agricultural
burning emissions in all states except for the following, for which state-specific day of week
profiles were used: Arkansas, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma,
and Texas.
PJM_EMAC
PJM SMAC
ICF201Z032esvK002
Figure 3-3. IPM Regions for EPA Base Case v5.13
48
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Comparison of Agricultural Burning Temporal Profiles
New McCarty Profile
OLD EPA
123456789 101112131415161718192021222324
Figure 3-4. Agricultural burning diurnal temporal profile
For the ptfire sector, the inventories are in the daily point fire format ORL PTDAY. The ptfire
sector is used in model evaluation cases. The 2007 and earlier platforms had additional
regulatory cases that used averaged fires and temporally averaged EGU emissions, but the 2011
platform uses base year-specific (i.e., 2011) data for all cases.
For the nonroad sector, while the NEI only stores the annual totals, the modeling platform uses
monthly inventories from output from NMEVI. For California, a monthly inventory was created
from CARB's annual inventory using EPA-estimated NMEVI monthly results to compute
monthly ratios by pollutant and SCC7 and these ratios were applied to the CARB inventory to
create a monthly inventory.
3.3.5 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 is highly resolved in terms of spatial resolution;
and (3) the meteorological variables vary at hourly resolution and can therefore be translated into
hour-specific temporalization.
The SMOKE program GenTPRO provides a method for developing meteorology-based
temporalization. Currently, the program can utilize three types of temporal algorithms: annual-
to-day temporalization for residential wood combustion (RWC), month-to-hour temporalization
for agricultural livestock ammonia, and a generic meteorology-based algorithm for other
situations. For the 2011 platform, meteorological-based temporalization was used for portions of
the rwc sector and for livestock within the ag sector.
49
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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/GenTPRO Technical Summary Aug2012
_Final.pdf and http://www.cmascenter.org/smoke/documentation/3.5. l/html/ch05s03s07.html,
respectively.
For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the
temporal allocation of emissions to days. GenTPRO was used to create an annual-to-day
temporal profile for the RWC sources. These generated profiles distribute annual RWC
emissions to the coldest days of the year. On days where the minimum temperature does not
drop below a user-defined threshold, RWC emissions for most sources in the sector are zero.
Conversely, the program temporally allocates the largest percentage of emissions to the coldest
days. Similar to other temporal allocation profiles, the total annual emissions do not change,
only the distribution of the emissions within the year is affected. The temperature threshold for
rwc emissions was 50 °F for most of the country, and 60 °F for the following states: Alabama,
Arizona, California, Florida, Georgia, Louisiana, Mississippi, South Carolina, and Texas.
Figure 3-5 illustrates the impact of changing the temperature threshold for a warm climate
county. The plot shows the temporal fraction by day for Duval County, Florida for the first four
months of 2007. The default 50 °F threshold creates large spikes on a few days, while the 60 °F
threshold dampens these spikes and distributes a small amount of emissions to the days that have
a minimum temperature between 50 and 60 °F.
RWC temporal profile, Duval County, FL, Jan -Apr
D.M
a.:i'f,
oo?
> 0.025
|
i 0.02
I
i 0.015
0 01
oo;i^
,«.
|T
t
i in
i ii i
' ' 1 '
; i :" i
60F, alternate formula
- -50F, default formula
Figure 3-5. 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
50
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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. Additional details regarding
temporalization of RWC sources can be found in Section 3.3.3 of the 201 Iv6.1 Platform TSD.
For the agricultural livestock NFb algorithm, the GenTPRO algorithm is based on an equation
derived by Jesse Bash of EPA ORD based on the Zhu, Henze, et al. (2013) empirical equation.
This equation is based on observations from the TES satellite instrument with the GEOS-Chem
model and its adjoint to estimate diurnal NH3 emission variations from livestock as a function of
ambient temperature, aerodynamic resistance, and wind speed. The equations are:
Et.h = [161500/Ta x e(-1380/V] x AR,/,
PEa = Ei,h I Sum(Ea)
where
• PE;,/2 = Percentage of emissions in county /' on hour h
• Ei,t, = Emission rate in county /' on hour h
• TZ,/, = Ambient temperature (Kelvin) in county /' on hour h
• Vi,h = Wind speed (meter/sec) in county /' (minimum wind speed is 0.1 meter/sec)
• ARz,/, = Aerodynamic resistance in county /'
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 3-6 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
Figure 3-6. Example of new animal NHs emissions temporalization approach, summed to
daily emissions
For the onroad and onroad_rfl sectors, 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 2011
51
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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 (RPV 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
previous platform, RPP used county level minimum and maximum temperature ranges for the
day to determine the appropriate EF. This potentially overestimated the temperature range for
any particular grid cell, which would result in increased emissions for vapor-venting. In the
2011 platform (and the 201 INEIvl), 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 three processes (RPD,
RPV, and RPP) is the total onroad sector emissions, while the combination of the two processes
(RPD, RPV) for the refueling mode only is the total onroad_rfl sector emissions. Both sectors
show a strong meteorological influence on their temporal patterns (see the 201 INEIvl TSD for
more details).
Figure 3-7 illustrates the difference between temporalization of the onroad sector used in the
2005 and earlier platforms and the meteorological influence via SMOKE-MOVES. In the plot,
the "MOVES" inventory is a monthly inventory that is temporalized by SCC to day-of-week and
hour. Similar temporalization is done for the VMT in SMOKE-MOVES, but the
meteorologically varying EFs add an additional variation on top of the temporalization. Note,
the SMOKE-MOVES run is based on the 2005 platform and previous temporalization of VMT to
facilitate the comparison of the results. In the figure, the MOVES emissions have a repeating
pattern within the month, while the SMOKE-MOVES shows day-to-day (and hour-to-hour)
variability. In addition, the MOVES emissions have an artificial jump between months which is
due to the inventory providing new emissions for each month that are then temporalized within
the month but not between months. The SMOKE-MOVES emissions have a smoother transition
between the months.
52
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80
75
70
~ 65
M
c
o
- 60
yl
c
o
£55
i
m 50
45
40
35
BHM (Jefferson Co., AL) daily NOX
I
J J
— MOVES
— SMOKE-MOVES
rH rH (N
Julian date
Figure 3-7. Example of SMOKE-MOVES temporal variability of NOx emissions
For the onroad and onroad_rfl sectors, the "inventories" actually consist of activity data. For
RPP and RPV processes, the VPOP inventory is annual and does not need temporalization. For
RPD, the VMT inventory is monthly and was temporalized to days of the week and then to
hourly VMT through temporal profiles. The RPD processes require a speed profile (SPDPRO)
that consists of vehicle speed by hour for a typical weekday and weekend day. Unlike other
sectors, the temporal profiles and SPDPRO will impact not only the distribution of emissions
through time but also the total emissions. Because SMOKE-MOVES' process RPD calculates
emissions from VMT, speed and meteorology, if one shifted the VMT or speed to different
hours, it would align with different temperatures and hence different EF. In other words, two
SMOKE-MOVES runs with identical annual VMT, meteorology, and MOVES EF, will have
different total emissions if the temporalization of VMT changes.
In previous platforms, the diurnal profile for VMT16 varied by road type but not by vehicle type.
These profiles were used throughout the nation. EPA wanted to create new diurnal profiles that
could differentiate by vehicle type as well as by road type and would potentially vary over
geography. The 201 INEIvl process provided an opportunity to update the diurnal profile with
information submitted by states. States submitted MOVES county databases (CDBs) that
included information on the distribution of VMT by hour of day and by day of week17 (see the
201 INEIvl TSD for details on the submittal process for onroad). EPA decided not to update the
day of week profile because MOVES only differentiated weekday versus weekend while the
default SMOKE profiles differentiated each of the 7 days. EPA mined the state submitted
MOVES CDBs for non-default diurnal profiles18. The list of potential diurnal profiles was then
16 These same profiles were used for onroad emissions in the 2005 platform.
17 The MOVES tables are the hourvmtfraction and the dayvmtfraction.
18 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.
53
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analyzed to see whether the profiles varied by vehicle type, road type, weekday vs. weekend, and
by county within a state.
EPA attempted to maximize the use of state and/or county specific diurnal profiles. If a specific
state or county's profiles varied by vehicle type or/and road type, then the submitted profile was
used. If the profile had less variability than the old SMOKE defaults (i.e. neither varied by
vehicle type nor road type), then a new default profile would be used (see below for description
of new profiles). This analysis was done separately for weekdays and for weekends, therefore
some areas had submitted profiles for weekdays but defaults for weekends. The result was a set
of profiles that varied geographically depending on whether or not the profile was submitted and
the characteristics of the profiles.
A new set of diurnal profiles was developed from the submitted profiles that varied by both
vehicle type and road type. Before developing the national profiles, there needs to be a mapping
between MOVES road types and SMOKE road types (i.e., the last three digits of the SCC) and
between MOVES source types and SMOKE vehicle types. The mapping between road types is
relatively straight forward. Basically the road types are consolidated into 4 types in MOVES,
therefore the new profiles will not differentiate at the level of the SMOKE road type. For
example, the SMOKE "urban interstate" (SCCLAST3=230) will have the same profile as the
SMOKE "urban other freeways and expressways" (SCCLAST3=250). The mapping between
MOVES source type and SMOKE vehicle type is more complicated; it is a many-to-many
mapping. Figure 3-8 Illustrates the difference between the profiles for the light-duty gas vehicles
versus the heavy-duty diesel vehicles. For additional details on the updated onroad mobile
temporal profiles, see Section 3.5.5 of the 2011v6.1 Platform TSD.
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, http://www.epa.gov/ttn/chief/conference/ei 19/session9/pouliot_pres.pdf, 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.
54
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Hourly VMT fraction: multi_comp_2201001_230-250_weekday
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
0.00
Hourly VMT fraction: multi_comp_2230074_230-250_weekday
0.08
o.oe
> 0.04
0.02
0.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
hour
Figure 3-8. Updated national default profiles for LDGV (top) vs. HHDDV (bottom), urban
restricted weekday
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
55
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sector, the emissions are placed into layer 1 of the air quality model. Vertical plume rise was
performed in-line within CMAQ for all of the SMOKE point-source sectors (i.e., ptipm,
ptnonipm, ptfire, othpt, and c3marine). The in-line plume rise computed within CMAQ is nearly
identical to the plume rise that would be calculated within SMOKE using the Laypoint program.
The selection of point sources for plume rise is pre-determined in SMOKE using the Elevpoint.
The calculation is done in conjunction with the CMAQ model time steps with interpolated
meteorological data and is therefore more temporally resolved than when it is done in SMOKE.
Also, the calculation of the location of the point source is slightly different than the one used in
SMOKE and this can result in slightly different placement of point sources near grid cell
boundaries.
For point sources, the stack parameters are used as inputs to the Briggs algorithm, but point fires
do not have stack parameters. However, the ptfire inventory does contain data on the acres burned
(acres per day) 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 the national 12-km domain "12US2" (see Figure 3-2). To accomplish this,
SMOKE used national 12-km spatial surrogates and a SMOKE area-to-point data file. For the
U.S., EPA updated surrogates to use circa 2010-2011 data wherever possible. For Mexico,
updated spatial surrogates were used as described below. For Canada, surrogates provided by
Environment Canada were used and are unchanged from the 2007 platform. The U.S., Mexican,
and Canadian 12-km surrogates cover the entire CONUS domain 12US1 shown in Figure 3-2.
The remainder of this subsection provides further detail on the origin of the data used for the
spatial surrogates and the area-to-point data.
Additional documentation on the 2011 spatial surrogates is available at
ftp://ftp.epa.gov/EmisInventorv/2011v6/vlplatform/reports/spatial surrogates/ in the files
US_SpatialSurrogate_Documentation_v091113.pdf and
US_SpatialSurrogate_Workbook_v093013.xlsx. The spatial cross reference file is in
gsref_2011.xlsx. Plots of the spatial surrogates are available in
all_surrogate_maps_201 lplatform_12USl_v2.pdf. Note that these are plots of the surrogate
fractions summed by grid cell, so grid cells that overlap multiple counties can show values
56
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greater than one. These maps are only to give an idea of the spatial distribution of the surrogates.
Allocations of CAP emissions to each of the surrogate codes is given in
201 led_spatial_surrogate_CAPs_febl 12014.xlsx. One noteworthy change between the
201 Iv6.0 and 201 Iv6.1 platforms is the update of the oil and gas surrogates.
3.3.7.1 Surrogates for U.S. Emissions
There are more than 70 spatial surrogates available for spatially allocating U.S. county-level
emissions to the 12-km grid cells used by the air quality model. An area-to-point approach
overrides the use of surrogates for some sources. Table 3-10 lists the codes and descriptions of
the surrogates. The surrogates in bold have been updated with 2010-based data, including 2010
census data at the block group level, 2010 American Community Survey Data for heating fuels,
2010 TIGER/Line data for railroads and roads, the 2006 National Land Cover Database, 2011
gas station and dry cleaner data, and the 2012 National Transportation Atlas Data for rail-lines,
ports and navigable waterways. Surrogates for ports (801) and shipping lanes (802) were
developed based on the 2011NEIvl shapefiles: Ports_032310_wrf and
ShippingLanes_l 11309FINAL_wrf, but also included shipping lane data in the Great Lakes and
support vessel activity data in the Gulf of Mexico.
The creation of surrogates and shapefiles for the U. S. was generated via the Surrogate Tool. The
tool and documentation for it is available at http://www.ie.unc.edu/cempd/projects/mims/spatial/
and http://www.cmascenter.org/help/documentation.cfm?MODEL=spatial_allocator&
VERSION=3.6&temp id=99999.
Table 3-10. U.S. Surrogates available for the 2011 modeling platform
Code
N/A
100
110
120
130
137
140
150
160
165
170
180
190
200
210
220
230
240
Surrogate Description
Area-to-point approach (see 3.3. 1.2)
Population
Housing
Urban Population
Rural Population
Housing Change
Housing Change and Population
Residential Heating - Natural Gas
Residential Heating - Wood
0.5 Residential Heating - Wood plus 0.5 Low
Intensity Residential
Residential Heating - Distillate Oil
Residential Heating - Coal
Residential Heating - LP Gas
Urban Primary Road Miles
Rural Primary Road Miles
Urban Secondary Road Miles
Rural Secondary Road Miles
Total Road Miles |
Code
520
525
527
530
535
540
545
550
555
560
565
570
575
580
585
590
595
596
Surrogate Description
Commercial plus Industrial plus Institutional
Golf Courses + Institutional +Industrial +
Commercial
Single Family Residential
Residential - High Density
Residential + Commercial + Industrial +
Institutional + Government
Retail Trade
Personal Repair
Retail Trade plus Personal Repair
Professional/Technical plus General
Government
Hospital
Medical Office/Clinic
Heavy and High Tech Industrial
Light and High Tech Industrial
Food, Drug, Chemical Industrial
Metals and Minerals Industrial
Heavy Industrial
Light Industrial
Industrial plus Institutional plus Hospitals
57
-------
Code
250
255
260
270
261
271
280
300
310
312
320
330
340
350
400
500
505
510
515
Surrogate Description
Urban Primary plus Rural Primary
0.75 Total Roadway Miles plus 0.25 Population
Total Railroad Miles
Class 1 Railroad Miles
NT AD Total Railroad Density
NT AD Class 1, 2, 3 Railroad Density
Class 2 and 3 Railroad Miles
Low Intensity Residential
Total Agriculture
Orchards/Vineyards
Forest Land
Strip Mines/Quarries
Land
Water
Rural Land Area
Commercial Land
Industrial Land
Commercial plus Industrial
Commercial plus Institutional Land
Code
600
650
675
680
700
710
720
800
801
802
807
808
810
812
850
860
870
880
890
Surrogate Description
Gas Stations
Refineries and Tank Farms
Refineries and Tank Farms and Gas Stations
Oil & Gas Wells, IHS Energy, Inc. and
USGS
Airport Areas
Airport Points
Military Airports
Marine Ports
NEI Ports
NEI Shipping Lanes
Navigable Waterway Miles
Gulf Tug Zone Area
Navigable Waterway Activity
Midwest Shipping Lanes
Golf Courses
Mines
Wastewater Treatment Facilities
Drycleaners
Commercial Timber
For the onroad sector, the on-network (RPD) emissions were spatially allocated to roadways, and
the off-network (RPP and RPV) emissions were allocated to population. For the onroad_rfl
sector, the emissions were spatially allocated to gas station locations. For the oil and gas sources
in the np_oilgas sector, the spatial surrogates were updated to those shown in Table 3-11 using
2011 data consistent with what was used to develop the 2011NEI nonpoint oil and gas emissions.
Note that the "Oil & Gas Wells, fflS Energy, Inc. and USGS" (680) is older and based on circa-
2005 data. These surrogates were based on the same GIS data of well locations and related
attributes as was used to develop the 201 INEIvl data for the oil and gas sector. The data
sources included Drilling Info (DI) Desktop's HPDI database (Drilling Info, 2012) aggregated to
grid cell levels, along with data from Oil and Gas Commission (OGC) websites. Well completion
data from HPDI was supplemented by implementing the methodology for counting oil and gas
well completions developed for the U.S. National Greenhouse Gas Inventory. Under that
methodology, both completion date and date of first production from HPDI were used to identify
wells completed during 2011. In total, over 1.08 million unique well locations were compiled
from the various data sources. The well locations cover 33 states and 1,193 counties (ERG,
2014).
Table 3-11. Spatial Surrogates for Oil and Gas Sources
Surrogate
Code
681
682
683
684
Surrogate Description
Spud count - Oil Wells
Spud count - Horizontally-drilled wells
Produced Water at all wells
Completions at Gas and CBM Wells
58
-------
685
686
687
688
689
692
693
694
695
697
698
Completions at Oil Wells
Completions at all wells
Feet drilled at all wells
Spud count - Gas and CBM Wells
Gas production at all wells
Spud count - All Wells
Well count - all wells
Oil production at oil wells
Well count - oil wells
Oil production at Gas and CBM Wells
Well counts - Gas and 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 3-10 were not assigned to any SCCs, although many of
the "unused" surrogates are actually used to "gap fill" other surrogates that are assigned. 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. Additional information on U.S. Spatial
Surrogates, including total CAP emissions for each spatial surrogate, is available in Section 3.4.1
of the 2011v6.1 Platform TSD (EPA, 2014a).
3.3.7.3 Allocation Method for Airport-Related Sources in the U.S.
There are numerous airport-related emission sources in the NEI, such as aircraft, airport ground
support equipment, and jet refueling. The modeling platform includes the aircraft emissions as
point sources. For the modeling platform, EPA used the SMOKE "area-to-point" approach for
only airport ground support equipment (nonroad sector), and jet refueling (nonpt sector). The
approach is described in detail in the 2002 platform documentation:
http://www.epa.gov/scram001/reports/Emissions%20TSD%20Voll 02-28-08.pdf.The
ARTOPNT file that lists the nonpoint sources to locate using point data was unchanged from the
2005-based platform.
3.3.7.4 Surrogates for Canada and Mexico Emission Inventories
The surrogates for Canada to spatially allocate the 2006 Canadian emissions are unchanged from
the 2007 platform. The spatial surrogate data came from Environment Canada, along with cross
references. Over 100 surrogates were provided and were outputs from the Surrogate Tool
(previously referenced), although only about 40 were used in the modeling platform. Per
Environment Canada, the surrogates are based on 2001 Canadian census data.
The 2011 platform uses about 20 surrogates for Mexico became. The surrogates are circa 1999
and 2000 and were based on data obtained from the Sistema Municpal de Bases de Datos
(SEVIBAD) de INEGI and the Bases de datos del Censo Economico 1999. Most of the CAPs
allocated to the Mexico and Canada surrogates are shown in Table 3-12. The entries in this table
are for the othar sector except for the MEX Total Road Miles and The CAN traffic rows, which
59
-------
are for the othon sector.
Table 3-12. CAPs Allocated to Mexican and Canadian Spatial Surrogates
Srg
code
22
10
12
14
16
20
22
24
26
28
32
34
36
38
40
42
44
46
48
50
9100
9101
9106
9113
9115
9116
Description
MEX Total Road Miles
MEX Population
MEX Housing
MEX Residential Heating - Wood
MEX Residential Heating - Distillate Oil
MEX Residential Heating - LP Gas
MEX Total Road Miles
MEX Total Railroads Miles
MEX Total Agriculture
MEX Forest Land
MEX Commercial Land
MEX Industrial Land
MEX Commercial plus Industrial Land
MEX Commercial plus Institutional
Land
Residential (RES 1-
4)+Commercial+Industrial+Institutional
+ Government
MEX Personal Repair (COM3)
MEX Airports Area
MEX Marine Ports
Brick Kilns - Mexico
Mobile sources - Border Crossing -
Mexico
CAN Population
CAN total dwelling
CAN ALL INDUST
CAN Forestry and logging
CAN Agriculture and forestry activities
CAN Total Resources
NH3
15,965
0
0
0
0
0
0
0
679,21
2
0
0
0
0
0
0
0
0
0
0
0
603
643
133
1,582
160
0
NOX
370,86
7
0
161,01
3
20,093
38
25,303
0
74,969
164,14
4
16,224
125,21
1
45,831
0
6,400
8
0
14,639
124,95
1
776
454
0
46,256
21,526
8,561
239,55
3
17
PM25
34,396
0
17,483
211,52
5
0
787
0
1,669
72,372
67,683
7,726
5,684
0
216
20
0
0
2,991
6,691
0
276
12,783
381
28,622
25,318
0
SO2
13,71
3
0
2,123
2,859
11
63
0
663
2,127
660
0
59,20
1
0
84
0
0
1,149
1,482
0
0
0
14,69
8
3,921
1,809
9,092
0
voc
375,27
6
431,23
1
452,68
5
380,57
2
2
614
3,513
2,824
43,958
79,018
286,98
2
133,44
0
332,49
5
28,293
241,71
0
33,616
6,857
1,099
10,244
2,668
304
32,944
2
36,114
26,526
5
60
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Srg
code
9212
9221
9222
9233
9308
9323
9324
9327
9331
9412
9416
9448
9562
9921
9924
9925
9931
9932
9942
9945
9946
9947
9950
9960
9970
9980
Description
CAN Mining except oil and gas
CAN Total Mining
CAN Utilities
CAN Total Land Development
CAN Food manufacturing
CAN Printing and related support
activities
CAN Petroleum and coal products
manufacturing
CAN Non-metallic mineral product
manufacturing
CAN Primary Metal Manufacturing
CAN Petroleum product wholesaler-
distributors
CAN Building material and supplies
wholesaler-distributors
CAN clothing and clothing accessories
stores
CAN Waste management and
remediation services
CAN Commercial Fuel Combustion
CAN Primary Industry
CAN Manufacturing and Assembly
CAN OTHERJET
CAN CANRAIL
CAN UNPAVED ROADS
CAN Commercial Marine Vessels
CAN Construction and mining
CAN Agriculture Construction and
mining
CAN Intersection of Forest and Housing
CAN TOTBEEF
CAN TOTPOUL
CAN TOTSWIN
NH3
0
42
189
17
0
0
0
0
0
0
2
0
165
494
0
0
9
109
40
28
247
19
1,053
176,15
6
74,204
122,09
4
NOX
0
2,292
14,882
20,789
0
0
0
238
98
0
0
0
893
33,816
0
0
14,388
122,69
4
3,462
45,454
156,77
0
37,452
11,700
0
0
0
PM25
5,391
45,374
369
1,928
0
0
2,402
7,708
5,062
0
1,461
0
1,596
2,750
0
0
548
4,093
3,499
6,404
10,070
536
120,04
5
7,420
2
996
SO2
0
728
1,124
981
0
0
0
2,941
12
0
3,259
0
1,998
35,47
1
0
0
1,139
5,737
48
14,32
5
5,667
26
1,671
0
0
0
voc
0
26
255
2,551
4,535
25,203
0
1,218
6
70,125
560
328
16,551
850
219,28
2
139,22
7
7,629
3,304
152,67
4
61,139
17,180
32,683
173,13
0
317,39
4
264
3,186
61
-------
Srg
code
9990
9991
9994
9995
9996
Description
CAN TOTFERT
CAN traffic
CAN ALLROADS
CAN30UNPAVED 70trail
CAN urban area
NH3
178,79
1
22,294
0
0
0
NOX
0
550,89
6
0
0
0
PM25
9,279
10,888
55,468
106,70
7
284
SO2
0
5,548
0
0
0
voc
0
285,10
4
0
0
0
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"Characterization of Residential Wood Combustion Particles Using the Two-Wavelength
Aethalometer", Environ. Sci. Technol., 45 (17), pp 7387-7393
Wolf, et al, 2009. Developing Mexico National Emissions Inventory Projections for the Future
Years of 2008, 2012, and 2030. Available at:
http://www.epa.gov/ttnchiel/conference/ei 18/session2/wolf.pdf
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.
Zue, Henze, et al, 2013. "Constraining U.S. Ammonia Emissions using TES Remote Sensing
Observations and the GEOS-Chem adjoint model", Journal of Geophysical Research:
Atmospheres, 118: 1-14.
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4.0 CMAQ Air Quality Model Estimates
4.1 Introduction to the CMAQ Modeling Platform
The Clean Air Act (CAA) provides a mandate to assess and manage air pollution levels to protect
human health and the environment. EPA has established National Ambient Air Quality Standards
(NAAQS), requiring the development of effective emissions control strategies for such pollutants
as ozone and particulate matter. Air quality models are used to develop these emission control
strategies to achieve the objectives of the CAA.
Historically, air quality models have addressed individual pollutant issues separately. However,
many of the same precursor chemicals are involved in both ozone and aerosol (particulate matter)
chemistry; therefore, the chemical transformation pathways are dependent. Thus, modeled
abatement strategies of pollutant precursors, such as volatile organic compounds (VOC) and NOx
to reduce ozone levels, may exacerbate other air pollutants such as particulate matter. To meet
the need to address the complex relationships between pollutants, EPA developed the Community
Multiscale Air Quality (CMAQ) modeling system19- The primary goals for CMAQ are to:
• Improve the environmental management community's ability to evaluate the impact of air
quality management practices for multiple pollutants at multiple scales.
• Improve the scientist's ability to better probe, understand, and simulate chemical and
physical interactions in the atmosphere.
The CMAQ modeling system brings together key physical and chemical functions associated
with the dispersion and transformations of air pollution at various scales. It was designed to
approach air quality as a whole by including state-of-the-science capabilities for modeling
multiple air quality issues, including tropospheric ozone, fine particles, toxics, acid deposition,
and visibility degradation CMAQ relies on emission estimates from various sources, including
the U.S. EPA Office of Air Quality Planning and Standards' current emission inventories,
observed emission from major utility stacks, and model estimates of natural emissions from
biogenic and agricultural sources. CMAQ also relies on meteorological predictions that include
assimilation of meteorological observations as constraints. Emissions and meteorology data are
fed into CMAQ and run through various algorithms that simulate the physical and chemical
processes in the atmosphere to provide estimated concentrations of the pollutants. Traditionally,
the model has been used to predict air quality across a regional or national domain and then to
simulate the effects of various changes in emission levels for policymaking purposes. For health
studies, the model can also be used to provide supplemental information about air quality in areas
where no monitors exist.
CMAQ was also designed to have multi-scale capabilities so that separate models were not
needed for urban and regional scale air quality modeling. The grid spatial resolutions in past
19 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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annual CMAQ runs have been 36 km x 36 km per grid for the "parent" domain, and nested within
that domain are 12 km x 12 km grid resolution domains. The parent domain typically covered the
continental United States, and the nested 12 km x 12 km domain covered the Eastern or Western
United States. The CMAQ simulation performed for this 2011 assessment used a single domain
that covers the entire continental U.S. (CONUS) and large portions of Canada and Mexico using
12 km by 12 km horizontal grid spacing. Currently, 12 km x 12 km resolution is sufficient as the
highest resolution for most regional-scale air quality model applications and assessments.20 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 http://www.epa.gov/asmdnerl/Research/RIA/cmaq.html
or http://www.cmascenter.org.
4.1.1 Advantages and Limitations of the CMAQ Air Quality Model
An advantage of using the CMAQ model output for characterizing air quality for use in
comparing with health outcomes is that it provides a complete spatial and temporal coverage
across the U.S. CMAQ is a three-dimensional Eulerian photochemical air quality model that
simulates the numerous physical and chemical processes involved in the formation, transport,
and destruction of ozone, particulate matter and air toxics for given input sets of initial and
boundary conditions, meteorological conditions and emissions. The CMAQ model includes
state-of-the-science capabilities for conducting urban to regional scale simulations of multiple air
quality issues, including tropospheric ozone, fine particles, toxics, acid deposition and visibility
degredation. However, CMAQ is resource intensive, requiring significant data inputs and
computing resources.
An uncertainty of using the CMAQ model includes structural uncertainties, representation of
physical and chemical processes in the model. These consist of: choice of chemical mechanism
used to characterize reactions in the atmosphere, choice of land surface model and choice of
planetary boundary layer. Another uncertainty in the CMAQ model is based on parametric
uncertainties, which includes uncertainties in the model inputs: hourly meteorological fields,
hourly 3-D gridded emissions, initial conditions, and boundary conditions. Uncertainties due to
initial conditions are minimized by using a 10 day ramp-up period from which model results are
not used in the aggregation and analysis of model outputs. Evaluations of models against
observed pollutant concentrations build confidence that the model performs with reasonable
accuracy despite the uncertainties listed above. A detailed model evaluation for ozone and
PM2.5 species provided in Section 4.3 shows generally acceptable model performance which is
equivalent or better than typical state-of-the-science regional modeling simulations as
20 U. S. EPA (2014), Draft Modeling Guidance for Demonstrating Attainment of Air Quality Goals for Ozone,
PM2.5, and Regional Haze, pp 214. http://www.epa.gov/ttn/scram/guidance/guide/Draft O3-PM-
RH Modeling Guidance-2014.pdf.
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summarized in Simon et al., 201221.
4.2 CMAQ Model Version, Inputs and Configuration
This section describes the air quality modeling platform used for the 2011 CMAQ simulation. A
modeling platform is a structured system of connected modeling-related tools and data that
provide a consistent and transparent basis for assessing the air quality response to changes in
emissions and/or meteorology. A platform typically consists of a specific air quality model,
emissions estimates, a set of meteorological inputs, and estimates of "boundary conditions"
representing pollutant transport from source areas outside the region modeled. We used the
CMAQ model as part of the 2011 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.s).
This section provides a description of each of the main components of the 2011 CMAQ
simulation along with the results of a model performance evaluation in which the 2011 model
predictions are compared to corresponding measured concentrations.
4.2.1 Model Version
CMAQ is a non-proprietary computer model that simulates the formation and fate of
photochemical oxidants, including PM2.5 and ozone, for given input sets of meteorological
conditions and emissions. As mentioned previously, CMAQ includes numerous science modules
that simulate the emission, production, decay, deposition and transport of organic and inorganic
gas-phase and pollutants in the atmosphere. This 2011 analysis employed CMAQ version 5.0.222
which reflects updates to version 5.0.1 which include several changes to the science algorithms
to improve the underlying science. The CMAQ simulation included parameterizations to
estimate the vertical distribution nitrogen oxide emissions generated due to lightning as well as
to estimate bi-directional ammonia flux. The CMAQ model version 5.0 was most recently peer-
reviewed in June of 2011 for the U.S. EPA.23 The model enhancements in version 5.0.2 include:
1. SOA yield update
2. Gas-phase chemistry
3. Sulfate inhibition effect in aqueous chemistry
4. CSQY_DATA files
5. WRF land use options
21 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.
22 CMAQ version 5.0.2 model code is available from the Community Modeling and Analysis System (CMAS) at:
http://www.cmascenter.org.
23Brown, N.J., Allen, D.T., Amar, P., Kallos, G., McNider, R., Russell, A.G., Stockwell, W.R. (September 2011).
Final Report: Fourth Peer Review of the CMAQ Model,
http://www.epa.gov/AMD/Reviews/2011 CMAQ Review FinalReport.pdf. CMAQ version 5.0 was released on
February, 2012. It is available from the Community Modeling and Analysis System (CMAS) as well as previous
peer-review reports at: http://www.cmascenter.org.
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6. Ammonia bidirectional exchange and dry deposition change
7. M3DRY backward compatibility with MCIP for wind staggering
8. Vertical advection time step
9. Aerosol updates
10. ACONCbugfix
4.2.2 Model Domain and Grid Resolution
The CMAQ modeling analyses were performed for a domain covering the continental United
States, as shown in Figure 4-1. This single domain covers the entire continental U.S. (CONUS)
and large portions of Canada and Mexico using 12 km by 12 km horizontal grid spacing. The
model extends vertically from the surface to 50 millibars (approximately 17,600 meters) using a
sigma-pressure coordinate system. Air quality conditions at the outer boundary of the 12 km
domain were taken from a global model. Table 4-1 provides some basic geographic information
regarding the 12 km CMAQ domain.
Table 4-1. Geographic Information for 12 km Modeling Domain
National 12 km CMAQ Modeling Configuration
Map Projection
Grid Resolution
Coordinate Center
True Latitudes
Dimensions
Vertical Extent
Lambert Conformal Proj ection
12km
97W,40N
33 and 45 N
396x246x25
25 Layers: Surface to 50mb level (see Table 4-2)
69
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Figure 4-1. Map of the CMAQ Modeling Domain. The purple box denotes the 12 km
national modeling domain. (Same as Figure 3-3.)
4.2.3 Modeling Period/ Ozone Episodes
The 12 km CMAQ modeling domain was modeled for the entire year of 2011. The 2011 annual
simulation was performed in two half-year segments (i.e., January through June, and July through
December) for each emissions scenario. With this approach to segmenting an annual simulation
we were able to reduce the overall throughput time for an annual simulation. The annual
simulation included a "ramp-up" period, comprised of 10 days before the beginning of each half-
year segment, to mitigate the effects of initial concentrations. All 365 model days were used in
the annual average levels of PIVh.s. 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
2011 Emissions: The emissions inventories used in the 2011 air quality modeling are described
in Section 3, above.
Meteorological Input Data: The gridded meteorological data for the entire year of 2011 at the
12 km continental United States scale domain was derived from version 3.424 of the Weather
Research and Forecasting Model (WRF), Advanced Research WRF (ARW) core.25 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
2011 WRF simulation included the physics options of the Pleim-Xiu land surface model (LSM),
Asymmetric Convective Model version 2 planetary boundary layer (PEL) scheme, Morrison
double moment microphysics, Kain- Fritsch cumulus parameterization scheme utilizing the
moisture-advection trigger26 and the RRTMG long-wave and shortwave radiation (LWR/SWR)
scheme.27 In addition, the Group for Fligh Resolution Sea Surface Temperatures (GFIRSST)28
1km SST data was used for SST information to provide more resolved information compared to
the more coarse data in the NAM analysis. Landuse and land cover data are based on the
National Land Cover Database 2006.29
The WRF meteorological outputs were processed using the Meteorology-Chemistry Interface
24 Version 3.4 was the current version of WRF at the time the 2011 meteorological model simulation was performed.
25 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.
26 Ma, L-M. and Tan, Z-M, 2009. Improving the behavior of the Cumulus Parameterization for Tropical Cyclone
Prediction: Convection Trigger. Atmospheric Research 92 Issue 2, 190-211.
http://www.sciencedirect.com/science/article/pii/S0169809508002585
27 Gilliam, R.C., Pleim, J.E., 2010. Performance Assessment of New Land Surface and Planetary Boundary Layer
Physics in the WRF-ARW. Journal of Applied Meteorology and Climatology 49, 760-774.
28 Stammer, D., FJ. Wentz, and C.L. Gentemann, 2003, Validation of Microwave Sea Surface Temperature
Measurements for Climate Purposes, J. Climate, 16, 73-87.
29Fry, J., Xian, G., Jin, S., Dewitz, J., Homer, C., Yang, L., Barnes, C., Herold, N., and Wickham, J., 2011.
Completion of the 2006 National Land Cover Database for the Conterminous United States, PE&RS, Vol.
77(9):858-864.
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Processor (MCIP) package30, version 4.1.3, to derive the specific inputs to CMAQ: horizontal
wind components (i.e., speed and direction), temperature, moisture, and its related speciated
components was conducted for vertical diffusion rates, and rainfall rates for each grid cell in
each vertical layer. The WRF simulation used the same CMAQ map projection, a Lambert
Conformal projection centered at (-97, 40) with true latitudes at 33 and 45 degrees north. The 12
km WRF domain consisted of 396 by 246 grid cells and 35 vertical layers up to 50 mb. Table 4-2
shows the vertical layer structure used in WRF and the layer collapsing approach to generate the
CMAQ meteorological inputs. CMAQ resolved the vertical atmosphere with 25 layers,
preserving greater resolution in the PEL.
In terms of the 2011 WRF meteorological model performance evaluation, a combination of
qualitative and quantitative analyses was used to assess the adequacy of the WRF simulated
fields. The qualitative aspects involved comparisons of the model-estimated synoptic patterns
against observed patterns from historical weather chart archives. Additionally, the evaluations
compared spatial patterns of monthly average rainfall and monthly maximum planetary boundary
layer (PEL) heights. The statistical portion of the evaluation examined the model bias and error
for temperature, water vapor mixing ratio, solar radiation, and wind fields. These statistical
values were calculated on a monthly basis.
Table 4-2. Vertical layer structure for 2011 WRF and CMAQ simulations (heights are layer
top).
CMAQ WRF . Pressure Approximate
Layers Layers (mrj) Height (m)
25
24
23
22
21
20
19
18
35
34
33
32
31
30
29
28
27
26
25
24
23
22
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
5000
9750
14500
19250
24000
28750
33500
38250
43000
47750
52500
57250
62000
66750
17,556
14,780
12,822
11,282
10,002
8,901
7,932
7,064
6,275
5,553
4,885
4,264
3,683
3,136
30 Otte T.L., Pleim, J.E., 2010. The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling
system: updates through v3.4.1. Geoscientific Model Development 3, 243-256.
71
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17
16
15
14
13
12
11
10
9
7
6
5
4
3
2
1
0
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
0
0.7
0.74
0.77
0.8
0.82
0.84
0.86
0.88
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
0.98
0.985
0.99
0.995
0.9975
1
71500
75300
78150
81000
82900
84800
86700
88600
90500
91450
92400
93350
94300
95250
96200
97150
98100
98575
99050
99525
99763
100000
2,619
2,226
1,941
1,665
1,485
1,308
1,134
964
797
714
632
551
470
390
311
232
154
115
77
38
19
0
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 (standard version 8-03-02 with 8-02-03 chemistry). The global GEOS-CHEM model
simulates atmospheric chemical and physical processes driven by assimilated meteorological
observations from the NASA's Goddard Earth Observing System (GEOS-5). This model was run
for 2011 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 A GEOS-Chem evaluation was conducted for the purpose of
validating the 2011 GEOS-Chem simulation for predicting selected measurements relevant to
their use as boundary conditions for CMAQ. This evaluation included using satellite retrievals
paired with GEOS-Chem grid cells.33 More information is available about the GEOS-CHEM
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.
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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 PIVh.s and its related speciated
components was conducted for the 201 1 simulation using state/local monitoring sites data in
order to estimate the ability of the CMAQ modeling system to replicate the 201 1 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 (ri). Mean bias is defined as:
MB = -£™(P — 0) , where P = predicted and O = observed concentrations.
Mean error (ME) calculates the absolute value of the difference (predicted - observed) divided by
the total number of replicates (n). Mean error is defined as:
Normalized mean bias (NMB) is used as a normalization to facilitate a range of concentration
magnitudes. This statistic averages the difference (model - observed) over the sum of observed
values. NMB is a useful model performance indicator because it avoids overinflating the
observed range of values, especially at low concentrations. Normalized mean bias is defined as:
NMB = — - *100, where P = predicted concentrations and O = observed
i
Normalized mean error (NME) is also similar to NMB, where the performance statistic is used as
a normalization of the mean error. NME calculates the absolute value of the difference (model -
observed) over the sum of observed values. Normalized mean error is defined as:
NME= -J - *100
In addition to the performance statistics, regional maps which show the MB, ME, NMB, and
NME were prepared for the ozone season, May through September, at individual monitoring sites
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as well as on an annual basis for PIVh.s 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 2011 in the continental U.S. were included in the evaluation and were taken
from the 2011 State/local monitoring site data in the EPA Air Quality System (AQS) and the
Clean Air Status and Trends Network (CASTNet). The performance statistics were calculated
using predicted and observed data that were paired in time and space on an 8-hour basis.
Statistics were generated for the following geographic groupings in the 12-km continental U.S.
domain34: five large subregions: Midwest, Northeast, Southeast, Central and Western U.S.
The 8-hour ozone model performance bias and error statistics for each subregion and each season
are provided in Table 4-4. Seasons were defined as: winter (December-January- February),
spring (March-April-May), summer (June, July, August), and fall (September-October-
November). Spatial plots of the mean bias, mean error, normalized mean bias and error for
individual monitors are shown in Figures 4-2 through 4-5. The statistics shown in these two
figures were calculated over the ozone season, May through September, using data pairs on days
with observed 8-hour ozone of greater than or equal to 60 ppb.
In general, the model performance statistics indicate that the 8-hour daily maximum ozone
concentrations predicted by the 2011 CMAQ simulation closely reflect the corresponding 8-hour
observed ozone concentrations in space and time in each subregion of the 12 km modeling
domain. As indicated by the statistics in Table 4-4, bias and error for 8-hour daily maximum
ozone are relatively low in each subregion, not only in the summer when concentrations are
highest, but also during other times of the year. Generally, 8-hour ozone in the summer is slightly
over predicted with the greatest over prediction in Central U.S. (NMB is 15.7 percent) at AQS
and CASTNet sites. However, 8-hour ozone is slightly underpredicted in the West at CASTNet
sites (NMB is -4.5 percent). Ozone performance in spring shows better performance with slight
under predictions in most of the subregions except in the Southeast (slight over prediction of 5.4 at
AQS and 0.8 at CASTNet sites). In the winter, when concentrations are generally low, the model
slightly under predicts 8-hour ozone with the exception of the West at AQS sites (NMB is 6.3). In
the fall, when concentrations are also relatively low, ozone is slightly over predicted; with NMBs
less than 11 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 ±4 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. The exceptions are sites along the California coast, St. Paul, WI, and Cleveland, OH. At
these sites observed concentrations greater than 60 ppb are generally predicted in the range of
±20 to 60 percent. Looking at the map of bias, Figure 4-4 indicates that the higher or lower bias at
these sites is not evident at other sites in these same areas. This suggests that the under prediction
34 The subregions are defined by States where: Midwest is IL, IN, MI, OH, and WI; Northeast is CT, DE, MA, MD,
ME, NH, NJ, NY, PA, RI, and VT; Southeast is AL, FL, GA, KY, MS, NC, SC, TN, VA, and WV; Central is AR, IA,
KS, LA, MN, MO, ME, OK, and TX; West is AK, CA, OR, WA, AZ, MM, CO, UT, WY, SD, ND, MT, ID, and NV.
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at these sites is likely due to very local features (e.g., meteorology and/or emissions) and not
indicative of a systematic problem in the modeling platform. Model error, as seen from Figures
4-3 and 4-5, is 14 ppb and 20 percent or less at most of the sites across the U.S. modeling
domain. Somewhat greater error is evident at sites in several areas most notably along portions of
the California coastline, Northeast coastline, Great Lakes coastline, Seattle, WA, Salem, OR, and
North Dakota.
Table 4-4. Summary of CMAQ 2011 8-Hour Daily Maximum Ozone Model Performance
Statistics by Subregion, by Season and Monitoring Network.
Monitor
Subregion Network Season
AQS Winter
Spring
I Summer
Northeast Fall
CASTNet Winter
I Spring
Summer
•^^^ Fall
AQS Winter
Spring
B Summer
Fall
Midwest
CASTNet Winter
B Spring
Summer
B Fall
AQS Winter
Spring
B Summer
Central States Fall
CASTNet Winter
B Spring
Summer
B Fall
Southeast AQS Winter
Spring
No. of
Obs
7,445
14,741
15,835
12,697
1,188
1,160
1,217
1,295
2,894
11,329
15,887
9,482
1,015
962
905
979
11,632
16,667
17,459
15,496
660
634
645
612
5,741
19,791
MB
(PPb)
-2.8
-0.5
1.7
6.7
-3.1
-1.3
1.5
3.2
-1.6
0.2
2.3
2.9
-1.6
-1.0
0.0
2.2
-0.2
1.8
7.6
1.7
-2.8
-1.8
1.7
-0.2
-0.1
2.5
ME
(PPb)
5.4
5.5
7.4
3.4
4.8
5.2
6.4
5.7
5.0
5.8
7.8
6.6
4.6
4.8
6.0
5.2
5.4
6.2
11.3
6.4
5.7
6.2
7.5
4.82
5.2
6.4
NMB
(%)
-9.1
-1.2
3.5
10.9
-9.2
-2.9
3.2
9.5
-5.7
0.5
4.7
6.1
-4.8
-2.2
0.1
6.1
-0.7
-3.7
15.7
3.9
-7.6
-3.7
3.3
-0.6
-0.4
5.4
NME
(%)
17.6
12.9
15.4
20.9
14.1
11.6
14.0
17.1
17.6
13.5
15.6
14.2
14.2
10.6
11.9
14.2
16.7
12.9
23.5
14.6
15.6
12.9
14.7
11.0
14.1
13.8
75
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Monitor
Subregion Network Season
Summer
Fall
CASTNet Winter
B^^^^^^ Spring
Summer
•^^^ Fall
AQS Winter
Spring
Summer
Fall
West
CASTNet Winter
B Spring
Summer
B Fall
No. of
Obs
22,093
17,050
1,756
1,824
1,777
1,827
25,735
29,951
32,485
28,901
1,614
1,649
1,654
1,609
MB
(PPb)
6.7
3.6
-0.9
0.4
4.4
1.9
2.2
-1.8
1.0
3.4
-1.6
-4.5
-2.6
0.6
ME
(PPb)
10.2
6.7
4.6
5.6
8.0
5.5
6.7
5.9
7.6
7.0
5.6
6.5
7.2
5.6
NMB
(%)
13.5
9.0
-2.5
0.8
9.0
4.8
6.3
-3.6
1.9
8.0
-3.8
-8.4
-4.5
1.3
NME
(%)
20.7
16.9
12.7
11.9
16.1
13.7
19.4
12.0
14.8
16.5
12.9
12.2
12.7
11.9
03 ehrmi> MB Ippb) kx run20l lel_v6 1 lg ItngNO t>K)l 25L
-------
O3_$htm» ME {ppbi tor njn»n«l_v«_11oJlngNO_bldi_:»<. lo« MlIOMI to »1 IOS30
$~T
F
Jy^\
rf->E:
• ~afr \ A
•* » ^ : jb
^^>%'
-ppb
if>3« liTrf • 75%
j 13
| 16
14
12
TRIANGLE.CASTNET^Daily:CIRCLE-AOS_Da>ly;
Figure 4-4. Mean Error (ppb) of 8-hour daily maximum ozone greater than 60 ppb over the
period May-September 2011 at AQS and CASTNet monitoring sites in the continental U.S.
modeling domain.
O3 tOmaic NMB (%> to* tun »11H vt 1 lg nngHO D40I TO. tor 20110501 10 »t 10930
TRIANGLE.CASTNET,Oaily;CIRCLEWkQS_Da>ly;
Figure 4-3. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60
ppb over the period May-September 2011 at AQS and CASTNet monitoring sites in the
continental U.S. modeling domain.
77
-------
O3 8tmn«» NME (N) kx run 201 l
-------
average nitrate is over predicted at all subregions, except in the Central and Western
U.S. where nitrate is under predicted. Model performance of total nitrate at sub-urban
CASTNet monitoring sites also shows an over prediction across all subregions (NMB in the
range of near negligible to 27 percent), except in the Central and Western U.S. (NMB
on average is underpredicted by 23 percent). Model error for nitrate is somewhat greater
for each subregion as compared to sulfate. Model bias at individual sites indicates mainly
over prediction of greater than 20 percent at most monitoring sites in the Eastern half of the
U.S. as indicated in Figure 4-12. The exception to this is in the Southern Florida and the
Southwest of the modeling domain where there appears to be a greater number of sites with
under prediction of nitrate of 20 to 80 percent. Model error for annual nitrate, as shown in
Figures 4-11 and 4-13, is least at sites in portions of the Midwest and extending eastward to
the Northeast corridor. Nitrate concentrations are typically higher in these areas than in other
portions of the modeling domain.
Annual average ammonium model performance as indicated in Table 4-5 has a tendency for
the model to under predict across the CASTNet sites (ranging from near negligible to -34
percent). Ammonium performance across the urban CSN sites shows an over prediction
(ranging from 5 to 24 percent) in the Northeast, Midwest, and Southeast, and an under prediction
(ranging from 4 to 29 percent in the Central and Western U.S. There is not a large variation from
subregion to subregion or at urban versus rural sites in the error statistics for ammonium. The
spatial variation of ammonium across the majority of individual monitoring sites in the Eastern
U.S. shows bias within ±30 percent. A larger bias is seen in the Southwest, bias on average 60-
70 percent.
Annual average elemental carbon is over predicted in all subregions at urban and rural sites.
Similar to ammonium error there is not a large variation from subregion to subregion or at urban
versus rural sites.
Annual average organic carbon is over predicted across most subregions in rural IMPROVE areas
(NMB ranging from 4 to 39 percent), except in the Central and Southeast U.S. where the bias is
2 to 19 percent respectively. The model over predicted annual average organic carbon in all
subregions at urban CSN sites. Similar to ammonium and elemental carbon, error model
performance does not show a large variation from subregion to subregion or at urban versus rural
sites.
Table 4-5. Summary of CMAQ 2011 Annual PM Species Model Performance Statistics by
>ubregion, by Monitoring JNetwork.
Pollutant
Sulfate
Monitor
Network Subregion
CSN Northeast
Midwest
| Southeast
Central
West
No. of
Obs
2,898
2,448
^2^84
1,643
2,544
MB
(ugm3)
-0.1
-0.3
-0.4
-0.2
-0.1
ME
(ugm3)
_2L.
0.8
_2^L
0.7
0.4
NMB
(%)
-4.5
-13.1
^_141
-10.8
-8.4
NME
(%)
^30.8
33.1
^305
34.3
45.5
79
-------
Pollutant
Nitrate
Total Nitrate
(N03 + HNOs)
Ammonium
Monitor
Network Subregion
IMPROVE Northeast
Midwest
| Southeast
Central
| West
CASTNet Northeast
Midwest
Southeast
Central
| West
CSN Northeast
Midwest
| Southeast
Central
| West
IMPROVE Northeast
Midwest
Southeast
Central
West
CASTNet Northeast
Midwest
| Southeast
Central
| West
CSN Northeast
Midwest
Southeast
Central
No. of
Obs
1,840
464
^1675
2,203
^9£18
746
566
1,094
369
1,028
2,898
2,448
^2^84
1,643
^2^44
1,839
464
1,675
1,643
9,484
^746
566
^1094
369
^1028
2,898
2,448
2,584
1,643
MB
(ugm3)
0.1
-0.2
^_04
-0.3
_2^L
_^_
-0.6
-0.7
-0.6
-0.1
0.4
0.3
_^_
0.0
_-L
^OA
0.1
0.2
0.0
0.0
^OA
0.0
^^00
-0.4
^^_02
0.2
0.1
0.1
0.0
ME
(ugm3)
0.5
0.6
_2L.
0.5
^^03
^^05
0.7
.08
0.6
0.2
0.8
0.8
_--.
.5
^^12
^^05
0.5
0.4
0.7
0.2
^^06
0.6
^^06
0.6
_-L
0.4
.4
0.3
0.4
NMB
(%)
4.4
-12.0
^_16_4
-16.5
_^1.
-15.3
-25.5
-26.6
-31.0
-20.3
40.4
47.4
^56^5
-3.6
^^2^0
113.0
11.7
42.6
-0.2
-13.2
^26^5
1.6
_2L.
-20.6
^_26H2
24.3
5.3
14.5
-4.0
NME
(%)
35.9
32.9
^315
34.6
^467
22.7
27.8
29.0
34.0
36.5
73.4
6.8
^99^8
53.1
^6^0
155.0
64.1
112.0
47.1
97.5
^397
23.5
^39_1
34.3
^47^4
46.8
38.7
44.6
42.1
80
-------
Pollutant
Elemental
Carbon
Organic Carbon
Monitor
Network Subregion
West
CASTNet Northeast
Midwest
Southeast
Central
West
CSN Northeast
Midwest
Southeast
Central
West
IMPROVE Northeast
Midwest
Southeast
Central
West
CSN Northeast
Midwest
Southeast
Central
West
IMPROVE Northeast
Midwest
Southeast
Central
West
No. of
Obs
2,544
746
566
1,094
369
^1028
2,750
2,426
2,513
1,625
2,510
1,916
474
^1784
2,331
10,058
2,735
2,412
2,500
1,622
2,493
1,911
473
1,785
2,332
9,921
MB
(ugm3)
-0.2
0.0
-0.2
-0.1
0.1
-0.1
0.5
0.5
0.3
0.6
0.7
0.2
0.1
0.1
0.1
0.1
0.4
0.8
0.5
1.2
1.9
0.4
0.1
0.0
0.0
ME
(ugm3)
0.4
0.2
0.3
0.2
0.2
0.1
0.6
.6
0.5
0.7
0.8
0.2
0.1
_-_
0.2
0.2
0.9
1.1
1.1
1.6
2.4
0.9
0.7
_--.
0.7
0.5
NMB
(%)
-29.2
0.9
-15.9
-14.9
-20.3
-33.6
^73^2
80.3
54.5
121.0
92.8
70.9
40.6
^26^6
44.2
^802
38.9
53.9
25.2
71.5
93.7
38.9
12.3
-2.2
4.3
NME
(%)
67.5
27.5
27.1
27.0
34.5
^545
90.6
91.4
74.2
129.0
111.0
87.0
57.8
^557
68.5
117.0
^841
70.9
54.1
95.8
118.0
84.1
61.2
^49^4
55.4
73.5
81
-------
SO4 MB |ug m3> lot iun201l«l »S llg KngMO Mdl_2SL lor 20110101 10 20111231
s
18
-
V*
•JOB
Jo 6
-m.4
Jo?
1°
1-0.2
1-0.4
I (16
1-0.8
CIRCLE-IMPROVE. CIRCLE.CSN; TRIANGLE.CASTNET;
Figure 4-6. Mean Bias (ugnr3) of annual sulfateat monitoring sites in the continental
U.S. modeling domain.
SO4 ME |UB m3) to ru«g)1 m_v6_) Ifl HnqMO tiifli 251. tot 20110101 IB 201112S1
:
1 8
08
1 0.6
1 04
1 0.2
'o
CIRCLE-IMPROVE; CIRCLE-CSN; TRIANGLE-CASTNET;
Figure 4-7. Mean Error (jigm"3) of annual sulfateat monitoring sites in the continental
U.S. modeling domain.
82
-------
SCM HUB (V tor tun 2011«I v»Jlg UngMO t»m 251 tot 20110101 to 20111231
CIRCLE-IMPROVE; CIRCLE=CSN; TRIANGLE=CASTNET;
Figure 4-8. Normalized Mean Bias (%) of annual sulfateat monitoring sites in the
continental U.S. modeling domain.
SO4 NME (•>)«" run 201 lefve 11g ItngNO bide 251 Id 20110101 lo 20111231
CIRCLE-IMPROVE: CIRCLE-CSN; TRIANGLE=CASTNET;
Figure 4-9. Normalized Mean Error (%) of annual sulfateat monitoring sites in the
continental U.S. modeling domain.
83
-------
NO3 MB (ug.mj) lot iun?011»l v« llg Hr>gMO frdl 231 tei 20110101 to 20111231
-2
1 8
1.S
F
10.8
•(0.6
J0.4
Jo?
lo
1-0.2
1-0.4
1-06
CIRCLE-tMPROVE; CIRCLE.CSN; TRIANGLE.CASTNET;
Figure 4-10. Mean Bias (jignr3) of annual nitrate at monitoring sites in the continental U.S.
modeling domain.
N03 ME (ugima) far mnaoii»i.v«_ii
i 10101102011 mi
1 e
F
1 0.8
1 0.6
I
1 0.2
CIRCLE.IMPROVE; CIRCLE=CSN; TRIANGLE.CASTNET;
Figure 4-11. Mean Error (ugnr3) of annual nitrate at monitoring sites in the continental
U.S. modeling domain.
84
-------
NO3 NUB (*«| lo» tun 20I1«I v« llg BngMO t»di 2SL lot 20110101 lo 30111231
.100
190
\ 80
170
4 60
1 h°
] 40
430
I20
Jio
lo
1-10
I-so
1-30
1-40
1-50
1-60
1-70
1-80
1-90
l<-100
CIRCLE-IMPROVE; CIRCLE-CSN; TRIANGLE-CASTNET;
Figure 4-12. Normalized Mean Bias (%) of annual nitrate at monitoring sites in the
continental U.S. modeling domain.
HOI HUi IM tor run 201 lei y« J iaJlngNO b«*_»t. tot 20110101 lo 20111231
l>100
eo
I 5°
r
1 30
I 70
I 10
':
CIRCLE-IMPROVE: CIRCLE-CSN: TRIANGLE-CASTNET;
Figure 4-13. Normalized Mean Error (%) of annual nitrate at monitoring sites in the
continental U.S. modeling domain.
85
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T«O3 MB lug m3> KM ruo201 lei v6 I Ig MnqNO brfi 25L lor 20110101 10 20111231
CIRCLE.CASTNET.
Figure 4-14. Mean Bias (ugnr3) of annual total nitrate at monitoring sites in the continental
U.S. modeling domain.
TNO3HE(u9m3)K»runa>ll«l v6 llg llngNO bkli 25)-loi 20110101 to 20111231
CIRCLE.CASTNET;
Figure 4-15. Mean Error (jignr3) of annual total nitrate at monitoring sites in the
continental U.S. modeling domain.
86
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TN03NMB;'-.llornill2011tr vft 11g llngNO bidi 251-tot 20110101 1020111231
unt»-%
c*M*f*g« km . 75%
CIRCLE.CASTNET.
Figure 4-16. Normalized Mean Bias (%) of annual total nitrate at monitoring sites in the
continental U.S. modeling domain.
TN03 NHE IN) tor tun 201161 »6 11g llnpNO bid. 25L tot 20110101 lo 20111231
CIRCLE.CASTNET:
Figure 4-17. Normalized Mean Error (%) of annual total nitrate at monitoring sites in the
continental U.S. modeling domain.
87
-------
MH4MB(ugm3)k>riun2011H v« llfl nnytlO BMI ML lot 20110101 IP20111231
CIRCLE.CSN; CIRCLE-CASTNET.
Figure 4-18. Mean Bias (jignr3) of annual ammonium at monitoring sites in the continental
U.S. modeling domain.
NM4 ME I up m3) KK niraOl 1«l_v« 11g nngNO bidl 251. toe 20110101 lo 20111231
CIRCLE^CSN; CIRCLE=CASTNET:
Figure 4-19. Mean Error (ugnr3) of annual ammonium at monitoring sites in the
continental U.S. modeling domain.
88
-------
HHJ NMB |S) tor run 201HI V6 llg UnflMO b*t 2SL tor 2OIIOI01 to 20111231
CIRCLE.CSN; CIRCLE=CASTNET;
Figure 4-20. Normalized Mean Bias (%) of annual ammonium at monitoring sites in the
continental U.S. modeling domain.
Mm NMg (X) lor run 201l«l v8 1 lg llngNO bK» 2SL to* 20110101 to 20111231
CIRCLE^CSN; CIRCLE.CASTNET:
Figure 4-21. Normalized Mean Error (%) of annual ammonium at monitoring sites in the
continental U.S. modeling domain.
89
-------
EC Me (ug m3l KM run2Oll«l vg llg llngNO bid« 251 Kx 20110101 to 20111231
-2
1.8
HO 8
J c ?
lo
1-02
1-04
1-0.6
1-0.8
CIRCLE=IMPROVE; CIRCLE=CSN.
Figure 4-22. Mean Bias (jigm"3) of annual elemental carbon at monitoring sites in the
continental U.S. modeling domain.
EC ME (ugm3)tof njn201!ef v« llg ItngMObldi 25L lor 20110101 to20111231
1 8
r
JOB
1 0.6
I 04
1 0.2
'c
CIRCLE^IMPflOVE. CIRCLE=CSN;
Figure 4-23. Mean Error (ugnr3) of annual elemental carbon at monitoring sites in the
continental U.S. modeling domain.
90
-------
EC HUB |%) tot run 2011»lv« llg Hr>gr«OI>tai 231 toi 20110101 10 20111231
CIRCLE-IMPROVE: CIRCLE-CSN;
Figure 4-24. Normalized Mean Bias (%) of annual elemental carbon at monitoring sites in
the continental U.S. modeling domain.
tCNME CM to> run 20111-1 v6 I1q UnqHO 6k)i 25L tor 20110101 lo 20111231
CIRCLE=IMPROVE; ClRCLE=CSN,
Figure 4-25. Normalized Mean Error (%) of annual elemental carbon at monitoring sites in
the continental U.S. modeling domain.
91
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PC MB lug m31 tot ru«i20l 1H v« 11Q llngMO bidl 25L tor 20110101 lo 20111231
CIRCLE-IMPROVE: CIRCLE-CSN;
Figure 4-26. Mean Bias (jignr3) of annual organic carbon at monitoring sites in the
continental U.S. modeling domain.
PC ME |ua'm3) ta« rmiJOl l«l v» 1 lp JlngNO b»i 251 lot 20110101 lo SO111231
^a
0.8
Io6
I
CIRCLE=IMPROVE; CIRCLE=CSN;
Figure 4-27. Mean Error (ugnr3) of annual organic carbon at monitoring sites in the
continental U.S. modeling domain.
92
-------
PC NMB (*.! to fun 201l«f v» 11Q llngNQ bull 2SL to 20110101 to 20111231
>100
190
1 80
•{ S'j
J 4-1
1 3'-'
lsu
JlO
lo
1-10
1-20
1-30
1-40
1-50
1-60
1-70
1-80
1-90
CIRCLE.IMPROVE; CIRCLE.CSN;
Figure 4-28. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the
continental U.S. modeling domain.
PC HUE (%) tot inn a>n«l_»«_iigJintMO_biai_23». tei201101011020111231
• 100
•J9J
1 80
1 70
J6
I5"'
Jj.
1 30
I :
1 1C
CIRCLE=!MPROVE. CIRCLE=CSN;
Figure 4-29. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the
continental U.S. modeling domain.
93
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5.0 Bayesian space-time downscaling fusion model (downscaler) -
Derived Air Quality Estimates
5.1 Introduction
The need for greater spatial coverage of air pollution concentration estimates has grown in recent
years as epidemiology and exposure studies that link air pollution concentrations to health effects
have become more robust and as regulatory needs have increased. Direct measurement of
concentrations is the ideal way of generating such data, but prohibitive logistics and costs limit
the possible spatial coverage and temporal resolution of such a database. Numerical methods
that extend the spatial coverage of existing air pollution networks with a high degree of
confidence are thus a topic of current investigation by researchers. The downscaler model (DS)
is the result of the latest research efforts by EPA for performing such predictions. DS utilizes
both monitoring and CMAQ data as inputs, and attempts to take advantage of the measurement
data's accuracy and CMAQ's spatial coverage to produce new spatial predictions. This chapter
describes methods and results of the DS application that accompany this report, which utilized
ozone and PM2.5 data from AQS and CMAQ to produce predictions to continental U.S. 2011
census tract centroids for the year 2011.
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 PIVh.s). In its most
general form, the model can be expressed in an equation similar to that of linear regression:
Y(s, t) = ~/J0(s, t) + ^(s, t) * ~x(s, t) + e(s, t) (Equation 1)
Where:
Y(s,t) is the observed concentration at point s and time t.
~x(s,t) is the CMAQ concentration at time t. This value is a weighted average of both the
gridcell containing the monitor and neighboring gridcells.
~fio(s,t) is the intercept, and is composed of both a global and a local component.
fti(t) is the global slope; local components of the slope are contained in the ~x(s,t) term.
e(s,t) is the model error.
DS has additional properties that differentiate it from linear regression:
1) Rather than just finding a single optimal solution to Equation 1, DS uses a Bayesian approach
so that uncertainties can be generated along with each concentration prediction. This involves
drawing random samples of model parameters from built-in "prior" distributions and assessing
their fit on the data on the order of thousands of times. After each iteration, properties of the
prior distributions are adjusted to try to improve the fit of the next iteration. The resulting
collection of~/?o and/?; values at each space-time point are the "posterior" distributions, and the
94
-------
means and standard distributions of these are used to predict concentrations and associated
uncertainties at new spatial points.
2) The model is "heirarchical" in structure, meaning that the top level parameters in Equation 1
(ie ~fio(s,t), fiift), ~x(s,t)) are actually defined in terms of further parameters and sub-parameters
in the DS code. For example, the overall slope and intercept is defined to be the sum of a global
(one value for the entire spatial domain) and local (values specific to each spatial point)
component. This gives more flexibility in fitting a model to the data to optimize the fit (i.e.
minimize e(s,t)).
Further information about the development and inner workings of the current version of DS can
be found in Berrocal, Gelfand and Holland (2011) and references therein. The DS outputs that
accompany this report are described below, along with some additional analyses that include
assessing the accuracy of the DS predictions. Results are then summarized, and caveats are
provided for interpreting them in the context of air quality management activities.
5.3 Downscaler Concentration Predictions
In this application, DS was used to predict daily concentration and associated uncertainty values
at the 2011 US census tract centroids across the continental U.S. using 2011 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 2011. 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 2011 over half of the US Census tracts
(42455 out of 72283) experienced at least one day with an ozone value above the NAAQS of 75
ppb.
95
-------
2011
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.
96
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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 2011. Figure
5-2 shows annual means and Figure 5-3 shows 98th percentiles of 24-hour PM2.5 concentrations
for AQS observations, CMAQ model predictions and DS model results. The DS model
estimated that for 2011 about 30% of the US Census tracts (21336 out of 72283) experienced at
least one day with a PM2.5 value above the 24-hour NAAQS of 35 ug/m3.
97
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AQS
2011
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 PMi.s concentrations derived from AQS, CMAQ and DS data.
98
-------
2011
98'th percentile,
24-hour avg
PM2.5 (ug/m3)
(0,10]
(10,15]
(15,20]
• (20,25]
• (25,30]
(30,35]
(35,40]
• (40,45]
• (45,50]
• (50,lnf]
Figure 5-3. 98th percentile 24-hour average PMi.s concentrations derived from AQS,
CMAQ and DS data.
99
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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.
100
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^•v^J-j '/ •' *.'-\'* .-\;'
% DS Error
(10,15]
• (15,20]
• (20,25]
• (25,30]
(30,36]
(36,41]
(41,46]
• (46,51]
• (51,56]
Figure 5-4. Annual mean relative errors (standard errors divided by predictions) from the
DS 2011 runs. The black dots show the locations of monitors that generated the AQS data
used as input to the DS model.
101
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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
PM2.5
O3
# Monitors
830
1290
Mean Bias
3.933e-2
1.096e-2
RMSE
2.82
4.87
Mean Coverage
0.96
0.95
Table 5-1. Cross-validation statistics associated with the 2011 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 O's and 1's.
5.5 Summary and Conclusions
The results presented in this report are from an application of the DS fusion model for
characterizing national air quality for Ozone and PIVh.s. DS provided spatial predictions of daily
ozone and PM2.5 at 2011 U.S. census tract centroids by utilizing monitoring data and CMAQ
output for 2011. Large-scale spatial and temporal patterns of concentration predictions are
generally consistent with those seen in ambient monitoring data. Both ozone and PIVh.s were
predicted with lower error in the eastern versus the western U.S., presumably due to the greater
monitoring density in the east.
An additional caution that warrants mentioning is related to the capability of DS to provide
predictions at multiple spatial points within a single CMAQ gridcell. Care needs to be taken not
to over-interpret any within-gridcell gradients that might be produced by a user. Fine-scale
emission sources in CMAQ are diluted into the gridcell averages, but a given source within a
gridcell might or might not affect every spatial point contained therein equally. Therefore DS-
generated fine-scale gradients are not expected to represent actual fine-scale atmospheric
concentration gradients, unless possibly multiple monitors are present in the gridcell.
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A -
Acronyms
ARW
BEIS
BlueSky
CAIR
CAMD
CAP
CAR
CARS
CEM
CHIEF
CMAQ
CMV
CO
CSN
DQO
EGU
Emission Inventory
EPA
EMFAC
FAA
FDDA
FIPS
HAP
HMS
ICS-209
IPM
ITN
LSM
MOBILE
MODIS
MOVES
NEEDS
NEI
NERL
NESHAP
NH
NMIM
NONROAD
NO
OAQPS
OAR
Advanced Research WRF core model
Biogenic Emissions Inventory System
Emissions modeling framework
Clean Air Interstate Rule
EPA's Clean Air Markets Division
Criteria Air Pollutant
Conditional Auto Regressive spatial covariance structure (model)
California Air Resources Board
Continuous Emissions Monitoring
Clearinghouse for Inventories and Emissions Factors
Community Multiscale Air Quality model
Commercial marine vessel
Carbon monoxide
Chemical Speciation Network
Data Quality Objectives
Electric Generating Units
Listing of elements contributing to atmospheric release of pollutant
substances
Environmental Protection Agency
Emission Factor (California's onroad mobile model)
Federal Aviation Administration
Four Dimensional Data Assimilation
Federal Information Processing Standards
Hazardous Air Pollutant
Hazard Mapping System
Incident Status Summary form
Integrated Planning Model
Itinerant
Land Surface Model
OTAQ's model for estimation of onroad mobile emissions factors
Moderate Resolution Imaging Spectroradiometer
Motor Vehicle Emission Simulator
National Electric Energy Database System
National Emission Inventory
National Exposure Research Laboratory
National Emission Standards for Hazardous Air Pollutants
Ammonia
National Mobile Inventory Model
OTAQ's model for estimation of nonroad mobile emissions
Nitrogen oxides
EPA's Office of Air Quality Planning and Standards
EPA's Office of Air and Radiation
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ORD
ORIS
ORL
OTAQ
PAH
PFC
PM2.5
PMio
PMc
microns
Prescribed Fire
RIA
RPO
RRTM
SCC
SMARTFIRE
SMOKE
TCEQ
TSD
VOC
VMT
Wildfire
WRAP
WRF
EPA's Office of Research and Development
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
One Record per Line
EPA's Office of Transportation and Air Quality
Polycyclic Aromatic Hydrocarbon
Portable Fuel Container
Particulate matter less than or equal to 2.5 microns
Particulate matter less than or equal to 10 microns
Particulate matter greater than 2.5 microns and less than 10
Intentionally set fire to clear vegetation
Regulatory Impact Analysis
Regional Planning Organization
Rapid Radiative Transfer Model
Source Classification Code
Satellite Mapping Automatic Reanalysis Tool for Fire Incident
Reconciliation
Sparse Matrix Operator Kernel Emissions
Texas Commission on Environmental Quality
Technical support document
Volatile organic compounds
Vehicle miles traveled
Uncontrolled forest fire
Western Regional Air Partnership
Weather Research and Forecasting Model
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/S-15-001
Environmental Protection Air Quality Assessment Division April, 2015
Agency Research Triangle Park, NC
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