vvEPA
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Agency
Bayesian space-time downscaling fusion model
(downscaler) -Derived Estimates of Air Quality
for 2010
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EPA-454/S-14-001
November 2014
Bayesian space-time downscaling fusion model (downscaler) -Derived
Estimates of Air Quality for 2010
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 2010 Emission Inventories and Approaches 13
3.3 Emissions Modeling Summary 36
4.0 CMAQ Air Quality Model Estimates 60
4.1 Introduction to the CMAQ Modeling Platform 60
4.2 CMAQ Model Version, Inputs and Configuration 62
4.3 CMAQ Model Performance Evaluation 67
5.0 Bayesian space-time downscaling fusion model (downscaler) -Derived Air Quality
Estimates 84
5.1 Introduction 84
5.2 Downscaler Model 84
5.3 Downscaler Concentration Predictions 85
5.4 Downscaler Uncertainties 90
5.5 Summary and Conclusions 92
Appendix A - Acronyms 93
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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 2010 calendar
year generated by EPA's recently developed data fusion method termed the "downscaler
model" (DS). Air quality monitoring data from the National Air Monitoring Stations/State and
Local Air Monitoring Stations (NAMS/SLAMS) and numerical output from the Community
Multiscale Air Quality (CMAQ) model were both input to DS to predict concentrations at the
2010 US census tract centroids encompassed by the CMAQ modeling domain. Information on
EPA's air quality monitors, CMAQ model, and downscaler model is included to provide the
background and context for understanding the data output presented in this report. These
estimates are intended for use by statisticians and environmental scientists interested in the
daily spatial distribution of ozone and PM2.5.
DS essentially operates by calibrating CMAQ data to the observational data, and then uses the
resulting relationship to predict "observed" concentrations at new spatial points in the domain.
Although similar in principle to a linear regression, spatial modeling aspects have been
incorporated for improving the model fit, and a Bayesian1 approaching to fitting is used to
generate an uncertainty value associated with each concentration prediction. The uncertainties
that DS produces are a major distinguishing feature from earlier fusion methods previously
used by EPA such as the "Hierarchical Bayesian" (HB) model (McMillan et al, 2009). The
term "downscaler" refers to the fact that DS takes grid-averaged data (CMAQ) for input and
produces point-based estimates, thus "scaling down" the area of data representation. Although
this allows air pollution concentration estimates to be made at points where no observations
exist, caution is needed when interpreting any within-gridcell spatial gradients generated by
DS since they may not exist in the input datasets. The theory, development, and initial
evaluation of DS can be found in the earlier papers of Berrocal, Gelfand, and Holland (2009,
2010, and 2011).
The data contained in this report are an outgrowth of a collaborative research partnership
between EPA scientists from the Office of Research and Development's (ORD) National
Exposure Research Laboratory (NERL) and personnel from EPA's Office of Air and
Radiation's (OAR) Office of Air Quality Planning and Standards (OAQPS). NERL's Human
Exposure and Atmospheric Sciences Division (HEASD), Atmospheric Modeling Division
(AMD), and Environmental Sciences Division (ESD), in conjunction with OAQPS, work
together to provide air quality monitoring data and model estimates to the Centers for Disease
Control and Prevention (CDC) for use in their Environmental Public Health Tracking (EPHT)
Network.
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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CDC's EPHT Network supports linkage of air quality data with human health outcome data
for use by various public health agencies throughout the U.S. The EPHT Network Program is
a multidisciplinary collaboration that involves the ongoing collection, integration, analysis,
interpretation, and dissemination of data from: environmental hazard monitoring activities;
human exposure assessment information; and surveillance of noninfectious health conditions.
As part of the National EPHT Program efforts, the CDC led the initiative to build the National
EPHT Network (http:// www.cdc.gov/nceh/tracking/default.htm). The National EPHT
Program, with the EPHT Network as its cornerstone, is the CDC's response to requests calling
for improved understanding of how the environment affects human health. The EPHT
Network is designed to provide the means to identify, access, and organize hazard, exposure,
and health data from a 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.
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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The best available statistical fusion model, air quality data, and CMAQ numerical model
output were used to develop the 2010 estimates. Fusion results can vary with different inputs
and fusion modeling approaches. As new and improved statistical models become available,
EPA will provide updates.
Although these data have been processed on a computer system at the Environmental Protection
Agency, no warranty expressed or implied is made regarding the accuracy or utility of the data on
any other system or for general or scientific purposes, nor shall the act of distribution of the data
constitute any such warranty. It is also strongly recommended that careful attention be paid to the
contents of the metadata file associated with these data to evaluate data set limitations, restrictions
or intended use. The U.S. Environmental Protection Agency shall not be held liable for improper
or incorrect use of the data described and/or contained herein.
The four remaining sections and one appendix in the report are as follows.
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 (Os) 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 non- attainment
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 (as of October 2008)
NAAQS for ozone 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 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 PIVb.s
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 NAAQSs for fine particles
(PM2.s) in 1997. The NAAQSs 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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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 users can 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 concentrationppmby 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 and NAMS).
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 2008 National Emissions Inventory (NEI), Version 2 to process year 2010
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 (httg://!AV!LeBa4jg^ 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),
particulate matter less than or equal tolO microns (PMio), and individual component species for
parti culate 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 2010 emission inputs for this study included development of emission
inventories for a 2010 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 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 3.1 was
used to create emissions files for a 12-km national grid. Additional information about SMOKE is
available from to|]3i//www1MM2^^
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 2010 Emission Inventories and Approaches
This section describes the emissions inventories created for input to SMOKE. The 2008 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 provided by S/L/T agencies because they are a voluntarily
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submitted to the NEI. The 2008 NEI was compiled using the Emissions Inventory System (EIS).
EIS includes hundreds of automated QA checks to help improve data quality, and also supports
release point (stack) coordinates separately from facility coordinates. Improved EPA
collaboration with S/L/T agencies prevented duplication between point and nonpoint source
categories such as industrial boilers. Documentation for the 2008 NEI is available at
http://www.epa.gov/ttn/chief/net/2008inventory.htmltfinventorydoc.
2010-specific data submitted by S/L/T agencies was used for point sources that were large
enough to be required to submit emissions to the NEI every year. These are sources with the
potential to emit 2500 tons per year of SO2, NOx, or CO, or 250 tons per year of the other CAPs
besides lead. 2010 continuous emissions monitoring (CEM) data was used where it was
available. For fires, EPA used the SMARTFIRE2 system to develop 2010 emissions.
SMARTFIRE2 was the first system to categorize all fires as either prescribed burning or wildfire
categories, and it also includes improved emission factor estimates for prescribed burning. 2010-
specific data were also developed for onroad, nonroad, and large commercial marine sources.
Some data obtained from regional planning organizations (RPOs) was substituted for NEI data in
cases for which the RPO data was more recently collected. California-provided mobile source
emissions were used. Canadian emissions reflect year 2006, Mexico emissions reflect year 2008
as projected from the 1999 inventory, and offshore emissions reflect year 2008.
The methods used to process emissions for this project are very similar to those documented for
EPA's Version 5, 2007 Emissions Modeling Platform. 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.htmltfpmnaaqs. Electronic copies of inventories similar
to those used for this project are available in the same section of the EMCH.
The emissions modeling process, performed using SMOKE v3.1 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 2010
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 2010 emission summaries for the U.S. anthropogenic sectors are shown in
Table 3-2 (i.e., biogenic emissions are excluded). Table 3-3 provides a summary of emissions for
the anthropogenic sectors containing Canadian, Mexican and offshore sources. State total
emissions for each sector are provided in Appendix B, a workbook entitled
"Appendix_B_2010_emissions_totals_by_sector.xlsx".
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Table 3-1. Platform Sectors Used in the Emissions Modeling Process
2010 Platform Sector 2010 NEI
(Abbrev) Sector
IPM (ptipm) Point
Point non-IPM (ptnonipm) Point
Point source fire (ptfire) Fires
Agricultural (ag)
Nonpoint
Area fugitive dust (afdust) Nonpoint
Remaining nonpoint
(nonpf)
Nonroad (nonroad)
Cl and C2 marine and
locomotive (clc2rail)
C3 commercial marine
(c3marine)
Nonpoint
Nonroad
Nonroad
Nonroad
Description and resolution of the data input
to SMOKE
2010 NEI point source EGUs that can be
mapped to the Integrated Planning Model (IPM)
model. NEI values are replaced with year 2010
hourly continuous emission monitoring (CEM)
NOx and SO2 emissions, where available. Other
pollutants are scaled from 2008 NEI using heat
input.
A mix of 2008 NEI point source emissions with
some 2010 records where data was provided by
states and locals plus 2006 WRAP oil and gas
data; these are emissions not matched to the
ptipm sector, annual resolution, including all
aircraft emissions
Point source day-specific wildfires and
prescribed fires for 2010.
2008 NEI nonpoint NHa emissions from
livestock and fertilizer application; county and
annual resolution with some 2007 monthly
resolution data provided by the Midwest.
2008 NEI nonpoint PMio and PM2.s from
fugitive dust sources (e.g., building construction,
road construction, paved roads, unpaved roads,
agricultural dust), county and annual resolution.
A land use-based transport fraction and 2010-
based precipitation zero-out is applied.
Primarily 2008 NEI nonpoint for sources not
included in other sectors, plus 2006 WRAP oil
and gas data, county and annual resolution.
Year 2010 monthly nonroad emissions from the
National Mobile Inventory Model (NMEVI) plus
California-provided data; county and annual
resolution.
Year 2008 non-rail maintenance locomotives,
and category 1 and category 2 commercial
marine vessel (CMV) emissions sources; county
and annual resolution; year 2010 for California.
Non-NEI, year 2010 category 3 (C3) CMV
emissions projected from year 2002. Developed
for the rule "Control of Emissions from New
Marine Compression-Ignition Engines at or
Above 30 Liters per Cylinder", also known as
the Emissions Control Area-International
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Onroad (onroad)
Onroad
Onroad Refueling
(onroad' rfl)
Onroad
Biogenic (beis)
Biogenic
Other point sources
(othpt)
Other nonpoint and
nonroad (othar)
Other onroad sources
(othon)
N/A
N/A
N/A
Maritime Organization (ECA-IMO) study:
www.epa.gov/otaq/ oceanvessels.htm. (EPA-
420-F-10-041, August 2010). Annual resolution,
treated as point sources.
Year 2010 gridded hourly emissions from
onroad mobile gasoline and diesel vehicles from
parking lots and moving vehicles including
exhaust, evaporative, permeation, and brake and
tire wear. Generated using MOVES 201 Ob
emission factors, 2010 VMT and vehicle
population data, and 2010 gridded met. data. In
California, adjusted to match CA-provided
emissions.
Year 2010 gridded hourly refueling emissions
from onroad mobile gasoline and diesel vehicles
from parking lots and moving vehicles.
Generated using MOVES 201 Ob, emission
actors, 2010 VMT and vehicle population data,
and 2010 gridded met. data. Spatially allocated
to gasoline station locations.
Hour- and grid cell-specific emissions for 2010
generated from the BEIS 3.14 model, including
emissions in Canada and Mexico.
Point sources not from the NEI, including
Canada's 2006 inventory and a 2008 projection
of Mexico's Phase III 1999 inventory; annual
resolution. Also includes 2008 offshore oil point
source emissions for the U.S. from the 2008
NEI.
Nonpoint and nonroad sources not from the NEI,
including annual 2006 Canada sources at
province resolution and a 2008 projection of
annual 1999 Mexico sources at municipio
resolution.
Onroad sources not from the NEI, including
annual 2006 Canada sources at province
resolution and a 2008 projection of 1999 Mexico
sources at municipio resolution.
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Table 3-2. 2010 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.)
Sector
afdust
a,
clc2rail
nonpt
nonroad
onroad (inc. rfl)
ptfire
ptipm
ptnonipm
c3marine
Con.US Total
PMio PMi.5
6,211,274 874,619
216,862
4,336,565
14,497,993
28,044,484
13,170,780
732,585
2,684,750
15,296
63,699,315
3,595,429
559
155,317
2,025
119,366
216,518
24,191
75,288
I
4,188,694
1,321,691
1,230,624
1,720,692
5,665,524
197,824
2,092,202
1,928,047
155,779
14,312,384
43,248
767,225
169,509
276,929
1,356,023
303,455
532,559
5,132
9,665,354
40,467
676,243
160,950
197,366
1,149,172
212,912
378,408
4,674
3,694,812
48,112
402,633
16,426
36,764
104,516
5,279,849
1,372,508
46,151
7,306,960
60,353
6,456,455
2,177,810
2,747,883
3,112,451
34,383
1,116,417
5,934
15,711,685
Table 3-3. 2010 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 othpt
Canada c3 marine
Offshore
c3 marine
2010 TOTAL
CO
3,746,95
4
4,514,63
2
1,148,01
4
9,409,60
0
477,952
659,796
101,309
1,239,05
7
82,133
13,930
83,610
179,673
NH3
537,91
1
21,814
21,138
580,86
3
132,91
O
2,972
0
135,88
5
0
0
NOX
719,026
537,706
861,258
2,117,99
0
199,048
93,849
344,896
637,793
74,277
157,046
960,546
1,191,86
9
PM10
1,422,50
O
15,004
117,254
1,554,76
1
88,354
7,937
122,654
218,944
780
4,708
49,509
54,997
PM25
393,80
4
10,634
68,114
472,55
3
56,824
7,349
90,304
154,47
7
769
4,283
45,403
50,455
SO2
97,714
5,430
1,762,35
0
1,865,49
3
56,418
5,740
740,235
802,393
1,021
38,030
380,001
419,052
VOC
1,267,37
0
277,915
425,792
1,971,07
7
510,965
96,253
78,465
685,684
60,756
5,919
35,509
102,184
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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 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). The point sources used for this study include a
limited set of emissions data for 2010 collected via the NEI process, with 2008 NEI data for any
sources that did not report in 2010. Note that only large sources are required to report annually
as opposed to triennially. This section describes NEI point sources within the contiguous United
States. The offshore oil (othpt sector), fires (ptfire) and category 3 CMV emissions (cSmarine
sector) are point source formatted inventories discussed later in this section. Full documentation
for the development of the 2008 NEI (EPA, 2012), is posted at:
http://www.epa.gov/ttn/chief/net/2008inventory.htmltfinventorydoc.
After removing offshore oil platforms into the othpt sector, two platform sectors were created
from the remaining point sources: the EGU sector - also called the IPM sector (i.e., ptipm) and
the non-EGU sector - also called the non-IPM sector (i.e., ptnonipm). This split facilitates the
use of different SMOKE temporal processing and future-year projection techniques for each of
these sectors. 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. Integration is discussed in detail in Section 3.3.4).
In the 2010 model evaluation case used in this study, for ptipm sector sources with CEM data that
could be matched to the NEI, 2010 hourly SO2 and NOX emissions were used alongside annual
emissions of all other pollutants allocated down to each hour. The hourly electric generating unit
(EGU) emissions were obtained for SO2 and NOX emissions and heat input from EPA's Acid Rain
Program. This data also contained heat input, which was used to allocate the annual emissions for
other pollutants (e.g., VOC, PM2.5, HC1) to hourly values. For unmatched EGU units, annual
emissions were temporalized to days using multi-year averages and to hours using state-specific
averages for that year.
The Non-EGU Stationary Point Sources (ptnonipm) emissions were input to SMOKE as annual
emissions. The full description of how the 2008 NEI emissions were developed is provided in the
NEI documentation, but a summary of their development follows:
a. 2008 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.
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 was added from Mineral Management Services (MMS).
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Note that some sources were large enough to require emissions to be reported to the NEI for
2009. The 2009 emissions were used, where 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 the state/county FIPS code.
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.
Replaced stack parameters with values from the 2008 NEI where 2008 values were
determined to be more realistic.
Replaced facility emissions with 2008 NEI values where the 2010 NEI contained
questionable values.
3.2.1.1 IPM Sector (ptipm)
The ptipm sector contains emissions from EGUs in the 2010 NEI point inventory that could be
matched to the units found in the NEEDS database, version 4.10
(http://www.epa.gov/airmarkets/progsregs/epa-ipm/ index.html). IPM provides future year
emission inventories for the universe of EGUs contained in the NEEDS database. As described
below, matching with NEEDS was done (1) to provide consistency between the 2010 EGU
sources and future year EGU emissions for sources which are forecasted by IPM, and (2) to
avoid double counting when projecting point source emissions.
The 2010 NEI point source inventory contains emissions estimates for both EGU and non-EGU
sources. When future years are modeled, IPM is used to predict the future year emissions for the
EGU sources. The remaining non-EGU point sources are projected by applying projection and
control factors to the base year emissions. It was therefore necessary to identify and separate into
two sectors: (1) sources that are projected via IPM (i.e., the "ptipm" sector) and (2) sources that
are not (i.e., "the "ptnonipm" sector). The two sectors are modeled separately in the base year as
well as the future years.
A primary reason the ptipm sources were separated from the other point sources was due to the
difference in the temporal resolution of the data input to SMOKE. The ptipm sector uses the
available hourly CEM data via a method first implemented in the 2002 platform and still used for
the 2010 platform. Hourly CEM data for 2010 were obtained from the CAMD Data and Maps
website3. For sources and pollutants with CEM data, the actual year 2010 hourly CEM data were
used. The SMOKE modeling system matches the ORIS Facility and Boiler IDs in the NEI
SMOKE-ready file to the same fields in the CEM data, thereby allowing the hourly SO2 and NOX
CEM emissions to be read directly from the CEM data file. The heat input from the hourly CEM
data was used to allocate the NEI annual values to hourly values for all other pollutants from
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CEM sources, because CEMs are not used to measure emissions of these pollutants.
For this project, the point source inventory was reviewed to determine whether additional
matches needed to be made. Newly identified matches for CEM and NEEDS IDs were loaded
into the Emissions Inventory System (EIS) so they could then be written into the modeling files.
Some matches were made outside of EIS when IDs were not mapped one to one between the
systems.
Emissions were scaled from 2008 levels to 2010 levels where possible based on CEM data. For
sources not matching the CEM data ("non-CEM" sources), daily emissions were computed from
the NEI annual emissions using a structured query language (SQL) program and state-average
CEM data. To allocate annual emissions to each month, state-specific, three-year averages of
2008-2010 CEM data were created. These average annual- to-month factors were assigned to
non-CEM sources by state. To allocate the monthly emissions to each day, the 2010 CEM data
were used to compute state-specific month- to-day factors, which were then averaged across all
units in each state. The resulting daily emissions were input into SMOKE. The daily-to-hourly
allocation was performed in SMOKE using diurnal profiles. The development of these diurnal
ptipm-specific profiles, considered ancillary data for SMOKE, is described in a later section.
3.2.1.2 Non-IPMSector (ptnonipm)
The non-IPM (ptnonipm) sector contains all NEI point sources not included in the IPM (ptipm)
sector except for the offshore oil and day-specific fire emissions. For the most part, the ptnonipm
sector reflects the non-EGU component of the NEI point inventory; however, as previously
discussed, it is likely that some small low-emitting EGUs that are not reflected in the CEMs
database are present in the ptnonipm sector. The ptnonipm sector contains a small amount of
fugitive dust PM emissions from vehicular traffic on paved or unpaved roads at industrial
facilities or coal handling at coal mines. In previous versions of the platform, these emissions
were reduced prior to input to SMOKE. However, in this platform the reduction is not made
because of a new methodology used to reduce PM dust based on gridded meteorological data.
For some geographic areas, some of the sources in the ptnonipm sector belong to source
categories that are contained in other sectors. This occurs in the inventory when states, tribes or
local programs report certain inventory emissions as point sources because they have specific
geographic coordinates for these sources. They may use point source SCCs (8-digit) or they may
use non- point, onroad or nonroad (10-digit) SCCs. In the 2008 NEI, examples of these types of
sources include: aircraft and ground support emissions, livestock (i.e., cattle feedlots) in
California, and rail yards.
Some adjustments were made to the point inventory prior to its processing with SMOKE. These
include:
Removing sources with state county codes ending in '777'. These are used for 'portable'
point sources like asphalt plants.
Removing sources with SCCs not typically used for modeling.
Adjusting latitude-longitude coordinates for sources identified to be substantially outside
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the county in which they reside.
Removing offshore oil records as reflected by FIPS=85000 because these sources are
processed in the othpt sector.
Added 2008 ethanol facilities provided by EPA's OTAQ that were not already included
in the 2008 NEI.
Correcting stack parameters for some units with missing or invalid parameter
assignments.
Adding South Dakota emissions because they did not submit to the 2008 NEI.
Adding MeadWestVaco facility in Covington, VA because it was missing in the 2008
NEI.
Adding oil and gas emissions that were not otherwise included in the NEI from the
Western Regional Air Partnership (WRAP) RPO created year 2006 "Phase III" oil and
gas inventory project.
Removing onroad refueling emissions that some states included in the point sector
because these are modeled nationwide using MOVES2010b.
3.2.2 Nonpoint Sources (afdust, ag, nonpt)
The nonpoint emissions sources used in this study are primarily from the 2008 NEI.
Documentation for the 2008 NEI is available at
http://www.epa.gov/ttn/chief/net/2008inventory.htmltfinventorydoc. Prior to processing with
SMOKE, the nonpoint portion of the 2008 NEI was divided into the following sectors for which
the data is processed in consistent ways: area fugitive dust (afdust), agricultural ammonia (ag),
and the other nonpoint sources (nonpt). This section describes stationary nonpoint sources only.
Class 1 & Class 2 (clc2) and Class 3 (c3) commercial marine vessels and locomotives are also in
the 2008 NEI nonpoint data category, but these sources are included in the mobile source portion
of this documentation. Nonpoint tribal-submitted emissions were removed to prevent possible
double counting with county-level emissions. Because the tribal nonpoint emissions are small,
these omissions should not impact results at the 12-km scale used for modeling. This omission
also eliminated the need to develop costly spatial surrogate data to allocate tribal data to grid cells
during the SMOKE processing. Some specific types of nonpoint sources were not included in
the modeling due to one of the following reasons: 1) the sources are only reported by a few
states or agencies, 2) the sources are 'atypical' and small, and/or 3) there are other data available
that appears to be more accurate. Additional details on nonpoint source processing can be found
in the Version 5, 2007 Emissions Modeling Platform documentation discussed earlier.
In the rest of this section, each of the platform sectors into which the 2008 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 PM emission estimates for 2008 NEI
nonpoint SCCs identified by EPA staff as fugitive dust sources. Categories included in this
sector are paved roads, unpaved roads and airstrips, construction (residential, industrial, road and
total), agriculture production and all of the mining 10-digit SCCs beginning with the digits
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"2325." It does not include fugitive dust from grain elevators because these are elevated point
sources.
This sector is separated from other nonpoint sectors to allow for the application of "transport
fraction," and meteorology/precipitation-based reductions. These adjustments are applied via
sector-specific scripts and make use of land use-based gridded transport fractions. 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/eil9/session9/pouliot_pres.pdf, and in Fugitive Dust
Modeling for the 2008 Emissions Modeling Platform (Adelman, 2012). The precipitation
adjustment is then applied to remove all emissions for days on which measureable rain occurs or
there is snow on the ground. Both the transport fraction and meteorological adjustments are
based on the gridded meteorological data; therefore, different emissions could result from
different grid resolutions. Application of the transport fraction and meteorological adjustments
reduces the overestimation of fugitive dust impacts in the grid modeling as compared to ambient
samples.
3.2.2.2 Agricultural Ammonia Sector (ag)
The agricultural NHa "ag" sector is comprised of livestock and agricultural fertilizer application
emissions from the nonpoint sector of the 2008 NEI. The; livestock and fertilizer emissions were
extracted based on SCC. The "ag" sector includes all of the NHs emissions from fertilizer
contained in the NEI. However, the "ag" sector does not include all of the livestock ammonia
emissions, as there are also some NHs emissions from feedlot livestock in the point source
inventory. To prevent double-counting, emissions were not included in the nonpoint ag inventory
for counties in which they were in the point source inventory. A significant error in the 2008 NEI
was corrected in the modeling platform ag sector. A fertilizer application source "N-P-K (multi-
grade nutrient fertilizers)" (SCC=2801700010) in Luna county New Mexico (FIPS=35025), was
6,953 tons of NH3 in the 2008 NEI. This source was corrected by a factor of 1,000 to be 6.953
tons in the modeling platform.
Monthly NH3 emissions provided by the Lake Michigan Air Directors Consortium were used to
replace NEI ag sector emissions in that region due to the improved temporal resolution. 2008
NEI (annual) ag sector emissions were used in all other states. A new temporal allocation
methodology for animal NH3 was implemented for this modeling platform that allocates
monthly emissions down to the hourly level by taking into account temperature and wind speed.
This method is discussed in more detail in the emission modeling portion of this chapter.
3.2.2.3 Other Nonpoint Sources (nonpt)
Stationary nonpoint sources that were not included in the afdust, ag or nonpt sectors were
assigned to the "nonpt" sector. In preparing the nonpt sector, catastrophic releases were excluded
since these emissions were dominated by tire burning, which is an episodic, location-specific
emissions category. Tire burning accounts for significant emissions of particulate matter in some
parts of the country. Because such sources are reported by a very small number of states, and are
inventoried as county/annual totals without the information needed to temporally and spatially
allocate the emissions to the time and location where the event occurred, catastrophic releases
were excluded. All fire emissions, including agricultural, wildfire, and prescribed burning, were
22
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removed and substituted with SMARTFIRE emissions (see the "ptfire" sector). Locomotives and
CMV mobile sources from the 2008 NEI nonpoint inventory are described in the mobile sources
section.
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.
Some adjustments to the 2008 NEI nonpoint data were made using data from regional planning
organizations (RPOs) as follows:
Replaced 2008 NEI oil and gas emissions (SCCs beginning with "23100") with year
2006 Phase III oil and gas emissions for several basins in the WRAP RPO states. These
WRAP Phase III emissions contain point and nonpoint formatted data are discussed in
greater detail at: http://www.wrapair2.org/PhaseIII.aspx. These changes were made only
in counties for which there was WRAP data.
Replaced 2008 NEI nonpoint agriculture burning emissions with year 2008 SMARTFIRE
day-specific county-based emissions aggregated to monthly totals.
Replaced open burning "land clearing" (SCC=2610000500) emissions in Florida and
Georgia with SESARM-provided daily point data, but aggregated to county and monthly
resolution.
Replaced open burning data (SCCs beginning with 261000x) in MARAMA states with
RPO-proved data.
Removed industrial coal combustion emissions (SCC=2102002000) in Tennessee.
Replaced, removed and modified much of the residential wood combustion (RWC)
emissions in the MARAMA, MWRPO and SESARM states with RPO data and non-RPO
corrections, modified the outdoor hydronic heater (OHH) emissions in all states and
indoor furnaces in MWRPO states.
Removed EPA-estimated commercial cooking (SCCs 2302002100 and 2302002200)
duplicate PM emissions in California.
Removed duplicate "Industrial Processes; Food and Kindred Products; Total" source
(SCC=23020000000) in Maricopa county Arizona (FIPS=04013).
The oil and gas changes were already discussed in the ptnonipm section. Other significant
changes are discussed below.
Ag burning
2008 NEI agricultural burning estimates were replaced with more specific data from the Fire
Characteristic Classification System (FCCS) module fuel loadings map in the BlueSky
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Framework (http://blueskyframework.org/modules/fuel-loading/fccs). Year 2008-specific fire
locations from SMARTFIRE version 1 (Sullivan, et al., 2008) were read into the FCCS module
and intersected with the FCCS fuel-loading dataset. The module assigned an FCCS code to each
fire record that reflects the ecosystem geography and potential natural vegetation based on
remote sensing data. Prescribed or unclassified fires having an FCCS code equal to zero (0)
were assumed to be agricultural fires. ArcGIS was used to categorize the fires as occurring on
rangeland, cropland or other land use via USGS 2006 National Land Cover Database (NLCD).
Activity data were analyzed to restrict to cropland fires and assign state and crop-specific
emission factors. Emissions were then appropriately weighted based on known statistics about
each state's crop mix.
These SMARTFIRE-based ag burning emissions were provided in at 1km point source and day-
specific resolution. State-county FIPS codes were assigned using GIS. The emissions were
aggregated to county and monthly resolution and converted to SMOKE nonpoint FF10 format.
This SMARTFIRE-based ag burning dataset includes emissions for all but these 7 of the lower
48 states: CT, DC, MA, ME, NH, RI and VT. These 7 states did not contain any cropland
burning estimates for year 2008 based on this SMARTFIRE approach.
Open burning RPO data
All 2008 NEI open burning emissions (CAPs only) were replaced in the MARAMA states with
the 2007 MARAMA open burning inventory. These MARAMA open burning emissions include
estimates for household waste (SCC=2610030000), land clearing (2610000500) and yard waste
leaf and brush (2610000100 and 2610000400 respectively).
The 2008 NEI land clearing emissions in Georgia and Florida were replaced with SESARM-
based year-2007 data. The SESARM land clearing emissions are based on daily point emissions
from the CONSUME v3.0 model (SESARM, 2012a). These daily point-format emissions were
aggregated to county and monthly resolution as a separate FF10 nonpoint monthly inventory.
TN coal combustion
Tennessee nonpoint industrial coal combustion (SCC=2102002000) emissions are significantly
overestimated in the 2008 NEI because of incorrect reconciliation with the point source
inventory. Nonpoint industrial coal combustion emissions were estimated by subtracting point
source emissions rather than activity. By not accounting for controlled sources, the remaining
activity for nonpoint coal combustion is significantly overestimated. EPA NEI experts
determined that it would be more appropriate to completely remove the nonpoint component of
this sector than to leave the values as they were. The reality for TN industrial coal combustion
nonpoint sector emissions is likely much closer to zero than the value in the 2008 NEI because
these emissions are accounted for in the point source inventory.
Residential Wood Combustion
There were many modifications to the RWC emissions data. First, all RWC outdoor wood
burning devices such as "fire pits and chimneys" (SCC=2104008700) were removed because
they were only reported in a couple of states, RPO inventories did not include them for most
states and emissions were generally insignificant. A market research report (Frost and Sullivan,
2010) developed in support of the potential RWC New Source Performance Standard (NSPS)
indicated slower sales of outdoor hydronic heaters compared to what was assumed for growth
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estimates in the 2008 NEI. Therefore, outdoor hydronic heater appliance counts and emissions
estimates (SCC=2104008610) were recomputed for all states, resulting in a 51% reduction to
outdoor hydronic heater emissions for all states.
In addition, all emissions in the SESARM states (i.e., AL, FL, GA, KY, MS, NC, SC, TN, VA,
WV), including Virginia, were replaced with the SESARM year-2007 inventory (SESARM,
2012b). Urban area RWC were lower than the NEI estimates partially because of the
assumptions about greater penetration of natural gas fireplaces, less access to inexpensive wood
supplies and a lower proportion of housing units with wood burning appliances as primary
heating units than rural areas. Overall, the SESARM RWC estimates are considerably lower than
the 2008 NEI estimates for several states, particularly for "uncertified" and "general" wood
stoves and insert categories: FL, KY, NC, TN, VA and WV. However, emissions in Mississippi
are only slightly reduced and emissions in AL, GA and SC are very similar to those in the
2008NEIv2.
The Midwest RPO (LADCO) states (i.e., IL, IL, MI, OH, WI, MN) year-2007 RWC inventory
was similar to the 2008 NEI for most source types. However, the pellet stoves
(SCC=2104008400), indoor furnaces (2104008510), and outdoor hydronic heater (OHH,
SCC=2104008610) estimates were updated to reallocate the indoor furnaces and OHHs to non-
MSA counties (LADCO, 2012) for several urban areas. Some double counting of appliances
was also fixed in Wisconsin and Michigan. Overall, the MWRPO states totals are very similar to
the 2008 NEI; however, emissions are spatially redistributed from urban to rural areas.
Therefore, for the MWRPO states, the 2008 NEI emissions were used for all RWC sources
except the three aforementioned SCCs that use the 2007 MWRPO data.
Emissions from indoor wood fired furnaces (SCC=2104008510) in several MWRPO states based
were also recomputed based on newer, improved survey data from Minnesota. The 2008 NEI for
these sources started with an assumption of year 2002 Minnesota wood burning survey data of
38 indoor furnaces per 100 woodstoves for Illinois, Indiana, Michigan, Ohio, and Wisconsin.
More recent year 2007 MN survey data resulted in the much lower ratio of 7.3 indoor furnaces
per 100 wood stove units. Thus, for the other five MWRPO states previously listed, the indoor
furnace emissions are normalized by setting the indoor furnace count ratio to wood stoves to
match the 7.6% reported value in Minnesota. The resulting adjustment factors reduce the indoor
furnace emissions in these states by 67% (Wisconsin) to as much as 83% in Ohio.
The MARAMA states (i.e., CT, DE, DC, ME, MD, MA, NH, NJ, NY, PA, RI, VT) year 2007
RWC inventory was either unchanged from the 2008 NEI, or was missing for most states. The
exceptions were New York and Pennsylvania which includes significantly revised RWC
estimates compared to the 2008 NEI. For New York, the MARAMA estimates were not split out
into the refined set of 10 RWC appliance types/SCCs in the NEI. New York only reported
"general" fireplaces (SCC=2104008100) and "EPA certified, non-catalytic" woodstoves
(SCC=2104008320). However, similar to the SESARM and MWRPO improvements, the
MARAMA NY RWC estimates were spatially reallocated from urban to more rural areas and
were also lower state-wide than the NEI. For Pennsylvania, MARAMA RWC estimates were
not much different state-wide on the aggregate, but were refined by SCC and spatially compared
to the 2008 NEI. Therefore, the MARAMA 2007 RWC data is used for New York and
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Pennsylvania and the 2008 NEI emissions are used for all RWC sources in the rest of the
MARAMA states.
The uniform temporalization from month to day was modified to be day-of-year specific as
discussed in more detail in the emissions modeling section. In short, the SMOKE program
(GenTPRO) is used to distribute annual RWC emissions to the coldest days of the year, using
maximum temperature thresholds by-state and/or by-county. On days where the low temperature
does not drop below this threshold, RWC emissions are zero. Conversely, the program
temporally allocates the most relative emissions to the coldest days. This meteorological-based
temporal allocation can have a substantial impact on the amount of RWC emissions in an area on
any given day, particularly in the winter.
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 estimates of the
emissions from each fires value. The ptfire sector for the 2010 Platform excludes agricultural
burning and other open burning sources, which are included in the nonpt sector. The agricultural
burning and other open burning sources are in the nonpt sector because these categories were not
factored into the development of the ptfire sector. Additionally, their year-to-year impacts are not
as variable as wildfires and non-agricultural prescribed/managed burns.
The ptfire sector includes a 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. Note that agricultural burning is not included in the ptfire sector but is
included in the nonpt sector. The point source day-specific emission estimates for 2010 fires rely
on the Satellite Mapping Automated Reanalysis Tool for Fire Incident Reconciliation Version 2
(SMARTFIRE2) system (Raffuse, et al., 2012). Activity data was used from the Monitoring
Trends in Burn Severity (MTBS) project, Incident Command Summary Reports (ICS-209), and
the National Oceanic and Atmospheric Administration's (NOAA's) Hazard Mapping System
(HMS).
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.
The ICS-209 reports for each large wildfire are created daily to enable fire incident commanders
to track the status and resources assigned to each large fire (100 acre timber fire or 300 acre
rangeland fire). The SMARTFIRE system of reconciliation with ICS-209 reports is described in
an Air and Waste Management Association report (Raffuse, et al., 2007). Once the fire
reconciliation process is completed, the 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). The detection of fires with this method is satellite-based.
Additional sources of information used in the fire classification process included MODIS
satellite and fuel moistures derived from fire weather observational data.
The ICS-209 reports for each large wildfire are created daily to enable fire incident commanders
to track the status and resources assigned to each large fire (100 acre timber fire or 300 acre
rangeland fire). Note that the distinction between wildfire and prescribed burn is not as precise as
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with ground-based methods. The fire size was based on the number of satellite pixels and a
nominal fire size of 100 acres/pixel was assumed for a significant number of fire detections when
the first detections were not matched to ICS 209 reports, so the fire size information is not as
precise as ground-based methods.
The activity data and other information were used within the BlueSky Framework to model
vegetation distribution, fuel consumption, and emission rates, respectively. Latitude and
longitude locations were incorporated as a post processing step. The method to classify fires as
WF, WFU, RX (FCCS > 0), and unclassified (FCCS > 0) 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.
Because the FDVIS satellite product from NOAA is based on daily detections, the emission
inventory represents a time-integrated emission estimate. For example, a large smoldering fire
will show up on satellite for many days and would count as acres burned on a daily basis;
whereas a ground-based method would count the area burned only once even it burns over many
days.
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). The BEIS3.14 model creates gridded, hourly,
model-species emissions from vegetation and soils. It estimates CO, VOC, and NOx 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_cmas08.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).
3.2.5 Biogenic Sources (beis)
For CMAQ, biogenic emissions were computed with the BEIS3.14 model within SMOKE using
2010 meteorological data. The BEIS3.14 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_cmas08.ppt.
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)
The 2010 onroad emissions are broken out into two sectors: "onroad" and "onroad_rfl". Aircraft
emissions are in the nonEGU point inventory. The locomotive and commercial marine emissions
are divided into two sectors: "clc2rail" and "c3marine", and the "nonroad" sector contains the
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remaining nonroad emissions. Note that the 2008 NEI includes state-submitted emissions data
for nonroad, but the modeling performed for this platform does not incorporate state-submitted
emissions for the onroad or nonroad sectors, except for California. All tribal data from the
mobile sectors have been dropped because we do not have spatial surrogate data, and the
emissions are small.
The onroad and onroad_rfl sectors are processed separately to allow for different spatial
allocation to be applied to onroad refueling via a gas station surrogate, versus onroad vehicles
that are spatially allocated based on roads and population. Except for California, all onroad and
onroad refueling emissions are generated using the SMOKE-MOVES emissions modeling
framework that leverages MO VES201 Ob-generated outputs
(http://www.epa.gov/otaq/models/moves/index.htm) and gridded hourly meteorological data.
Emissions for onroad (including refueling), nonroad and clc2rail sources in California were
provided by the California Air Resources Board (CARB).
The nonroad sector is based on NMEVI except for California which uses data provided by the
California Air Resources Board (CARB). 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 2010 NMEVI nonroad emissions were generated using activity (e.g., fuels, vehicle
population, etc) data that represent 2010. All nonroad emissions are compiled at the county/SCC
level. Detailed inventory documentation for the 2008 NEI nonroad sectors is available at
Neither NMEVI nor MOVES
generates tribal data.
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 2008 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)
For the Version 5 modeling platform, EPA estimated emissions for every county in the
continental U.S. except for California using similar methods as for the 2008 NEI Versions 2 and
3. The modeling framework took into account the temperature sensitivity of the onroad
emissions. Specifically, county-specific inputs and tools were used that integrated the MOVES
model with the SMOKE emission inventory model to take advantage of the gridded hourly
temperature information available from meteorology modeling used for air quality modeling.
This integrated "SMOKE-MOVES" tool was developed by EPA in 2010 and is in use by states
and regional planning organizations for regional air quality modeling. SMOKE-MOVES
requires emission rate "lookup" tables generated by MOVES that differentiate emissions by
process (running, start, vapor venting, etc.), vehicle type, road type, temperature, speed, hour of
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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 146
"representative counties," to which every other county could be mapped as detailed below.
Using the MOVES emission rates, SMOKE selected appropriate emissions rates for each county,
hourly temperature, SCC, and speed bin and multiplied the emission rate by 2010-specific
activity (i.e., VMT (vehicle miles travelled) or vehicle population) to produce emissions. These
calculations were done for every county, grid cell, and hour in the continental United States.
SMOKE-MOVES can be used with different versions of the MOVES model. For the Version 5
modeling platform, EPA used the latest publically released version: MOVES2010b
(http://www.epa.gov/otaq/models/moves/index.htm). Fuels representative of 2010 were used,
with temperature and humidity values from 2010.
The steps to apply SMOKE-MOVES to create emissions for modeling requires numerous steps,
as follows:
Determine which counties will be used to represent other counties in the MOVES runs.
Determine which months will be used to represent other month's fuel characteristics.
Create MOVES inputs needed only for MOVES runs. MOVES requires county-specific
information on vehicle populations, age distributions, and inspection-maintenance
programs for each of the representative counties.
Create inputs needed both by MOVES and by SMOKE, including a list of year-specific
temperatures and activity data.
Run MOVES to create emission factor tables using year-specific fuel information.
Run SMOKE to apply the emission factors to activities to calculate emissions.
Aggregate the results at the county-SCC level for summaries and quality assurance.
Some data used in the SMOKE-MOVES process is year-specific. When MOVES was run to
generate the emission factors, gasoline and diesel properties for representing counties were based
on 2010 fuel information (i.e., RegionalFuels_2010_20120802. The temperature and humidity
inputs were also based on 2010 values. The VMT used by SMOKE-MOVES was generated by
taking 2010 VMT by state and freeway/non-freeway from FHWA VM-2 tables and allocating to
county and month and roadtype using the 2008 NEI VMT. The VMT was allocated to vehicle
type using FHWA's VM-4 table and to MOVES sourcetype using ratios from MOVES. Vehicle
populations were then generated by applying VMT/vehicle default ratios from MOVES to the
VMT. The same speed data used for the 2008 NEI were also used for this study.
The California emissions were post-processed to incorporate both CARB supplied inventories
and the shape of the meteorologically-based SMOKE-MOVES results by scaling the SMOKE-
MOVES generated totals to match CARB-provided totals. Because CARB provide 2007 and
2011 emissions data, the data for 2010 were linearly interpolated between 2007 and 2011 levels.
For more details on this process, see the Version 5 platform documentation.
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3.2.8 Onroad Refueling (onroad_rfl)
Onroad refueling was modeled very similarly to the other onroad emissions. MOVES2010b was
used produce emission factors (EFs) for refueling. These EFs are at the resolution of the onroad
SCC and were run separately from the other onroad mobile sources to allow for different spatial
allocation. To facilitate this, the EFs were separated into refueling and non-refueling tables.
SMOKE-MOVES was then run using these EF tables as inputs and the results spatially allocated
based on a gas stations spatial surrogate. For California, the SMOKE-MOVES generated
emissions were used for onroad refueling without any adjustments because there were no CARB-
supplied refueling emissions.
3.2.9 Nonroad Mobile Sources NMIM-Based (nonroad)
The nonroad sector includes monthly exhaust, evaporative and refueling emissions from nonroad
engines (not including commercial marine, aircraft, and locomotives) that are derived from
NMEVI for all states except California. NMEVI was run using 2010 meteorological and fuel data
to create county-SCC emissions by month for the 2010 nonroad mobile CAP and HAP sources.
This version of NMEVI ran the NROSa version of NONROAD. The run incorporated Bond rule
revisions to some of the base case inputs and the Bond Rule controls did not take effect until
future years. NMEVI provides nonroad emissions for VOC by three emission modes: exhaust,
evaporative and refueling. Unlike the onroad sector, refueling emissions from nonroad sources
are not separated into a different sector.
EPA default inputs were replaced by state inputs where such data were provided via the 2008 NEI
process. The 2008 NEI documentation describes this and other details of the NMEVI nonroad
emissions development. CAPs and only the necessary HAPs for the nonroad sector (i.e., BAFM,
butadiene, and naphthalene) were included. For this study, NMEVI was run separately for each
county. To aid with the processing by SMOKE, the mode was appended to the pollutant name
and the California NMIM data was replaced with state-supplied data.
For California, year 2010 nonroad emissions values were interpolated between the 2007 and
2011 emissions provided by CARB. The CARB-supplied nonroad annual inventory to monthly
emissions values by using the aforementioned EPA NMEVI monthly inventories to compute
monthly ratios by pollutant and SCC. Some adjustments to the CARB inventory were needed to
convert the provided total organic gas (TOG) to the VOC that was needed by SMOKE.
3.2.10 Nonroad Mobile Sources: Commercial Marine Cl, C2, and Locomotive (clc2rail)
The clc2rail sector contains CAP and HAP emissions from locomotive and commercial marine
sources, except for the category 3/residual-fuel (C3) commercial marine vessels (CMV) found in
the cSmarine sector. The "clc2" portion of this sector name refers to the Class I/II CMV
emissions, not the railway emissions. Railway maintenance emissions are included in the
nonroad sector because these are included in the nonroad NMEVI monthly inventories. The C3
CMV emissions are in the cSmarine sector. Except for California, the emissions in the clc2rail
sector are year 2008 and are composed of the following SCCs: 2280002100 (CMV diesel,
ports), 2280002200 (CMV diesel, underway), 2285002006 (locomotives diesel line haul Class I),
2285002007 (locomotives diesel line haul Class II/III), 2285002008 (locomotives diesel line haul
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passenger trains), 2285002009 (locomotives diesel line haul commuter lines), and 2285002010
(locomotives diesel, yard).
The 2008 NEI Version 2 was the starting point for this sector, but several adjustments were
made. First, the 2008 NEI point inventory contains rail yard emissions for several states and
counties. The NEI point and nonpoint inventories were reviewed for counties with significant
rail yard emissions in both inventories. It was assumed that the point inventory contained more
accurate information when both inventories contained rail yard emissions. Therefore, nonpoint
rail yards were removed from the clc2rail sector for certain counties in California, Maryland,
Oregon and Arizona. For more information, see the Version 5 2007 platform documentation.
Analysis of the total rail emissions in the 2008 NEI showed what appeared to be missing rail line
emissions in Texas. It was determined that line haul emissions from Texas were essentially zero
in the 2008 NEI. Therefore, all line haul emissions from the 2008 NEI were removed and
information from an EPA default dataset of Texas line haul emissions was added. These EPA
line haul emissions are restricted to the Class I and Class II/III operations and add approximately
52,000 tons of NOX to Texas that would otherwise be missing.
For several Texas counties, the C1/C2 CMV emissions in the 2008 NEI included EPA gap filled
values where shape IDs were not populated in the state submittal. The intended Texas submittal
was often much smaller than the EPA-estimated default value for several counties. An example
of this is Harris county (FIPS=48201) where the Texas submittal was approximately 1,200 tons
of NOX for port and underway emissions but not all shape IDs were included. The NEI
methodology used EPA emissions where Texas did not provide estimates and the resulting
double count and overestimate of this top-down method resulted in over 49,000 tons of NOX in
the 2008 NEI in Harris County, Texas. Therefore, the modeling platform used the original Texas
submittal, did not append any EPA emissions, and summed up port and underway for the
modeling files to the county level. Similar corrections to these may have been included in
Version 3 of the 2008 NEI. Other states were impacted by a similar error in the 2008 NEI
Version 2, but for many of these states alternative data were used as discussed below.
For California, the California Air Resources Board (CARB) provided year 2007 and 2011
emissions for all mobile sources, including C1/C2 CMV and rail. These emissions are
documented in a staff report available at:
http://www.arb.ca.gov/regact/2010/offroadlsi 10/offroadisor.pdf. The modeling platform uses
2010 emissions interpolated between the 2007 and 2011 emissions. The C1/C2 CMV emissions
were obtained from the CARB nonroad mobile dataset and include the regulations to reduce
emissions from diesel engines on commercial harbor craft operated within California waters and
24 nautical miles of the California baseline. These emissions were developed using Version 1 of
the CEP AM 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 here: http://www.arb.ca.gov/msei/categories.htmtfoffroad motor vehicles. The CARB
inventory TOG emissions were converted to VOC by dividing the inventory TOG by the
available VOC-to-TOG speciation factor.
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Year-2007 inventories provided by MARAMA, SESARM and the MWRPO were used for the
clc2rail sector emissions in their respective states. Emissions data from MARAMA rather than
SESARM was used for Virginia because the SESARM data included some rather large
emissions for Commuter Lines (SCC=2285002009) that were not reflected in the 2008 NEI nor
the MARAMA dataset. The MWRPO year-2007 clc2rail data were obtained from a subset of
their version 7 emissions modeling file "nrinv.mwrpo_alm.baseCv7.annual.orl.txt", where
MWRPO NEI Inventory Format (NIF)-formatted data were converted to SMOKE ORL format.
The MARAMA dataset was obtained from a subset of their version 3.3 January 27, 2012 vintage
file "ARINV_2007_MAR_Jan2012.txt". The SESARM dataset was obtained from a subset of
the file "nrinv.alm.semap.base07.v093010.orl.txt" developed for the Southeastern Modeling,
Analysis, and Planning (SEMAP) project. All RPO datasets were edited to remove non-clc2rail
sources.
3.2.11 Nonroad mobile sources: C3 commercial marine (c3marine)
The c3marine sector emissions data were 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). These
emissions consist of large marine diesel engines (at or above 30 liters/cylinder) that until very
recently, were allowed to meet relatively modest emission requirements, often burning residual
fuel. The emissions in this sector are comprised of primarily foreign-flagged ocean-going
vessels, referred to as Category 3 (C3) CMV ships.
The c3marine inventory includes these ships in several intra-port modes (cruising, hoteling,
reduced speed zone, maneuvering, and idling) and underway mode and includes near-port
auxiliary engines. An overview of the C3 EGA Proposal to the International Maritime
Organization (EPA-420-F-10-041, August 2010) project and future-year goals for reduction of
NOX, SO2, and PM C3 emissions can be found at:
http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09019.pdf. The resulting ECA-EVIO
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-EVIO emissions data were converted to SMOKE point-source ORL input format as
described in http://www.epa.gov/ttn/chief/conference/ei 17/session6/mason.pdf, thereby allowing
for the emissions to be allocated to modeling layers above the surface layer. As described in the
paper, the ASCII raster dataset was converted to latitude-longitude, mapped to state/county FIPS
codes that extended up to 200 nautical miles (nm) from the coast, assigned stack parameters, and
monthly ASCII raster dataset emissions were used to create monthly temporal profiles. 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-
EVIO controls for these vessels. All non-US emissions (i.e., in waters considered outside of the
200nm EEZ, and hence out of the U.S. territory) are assigned a dummy state/county FIPS
code=98001. The SMOKE-ready data were cropped from the original ECA-EVIO data to cover
only the 36-km CMAQ domain, which is the largest domain used for this effort, and larger than
the 12km domain used in this project.
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The base year EGA inventory is 2002 and consists of these CAPs: PM10, PM2.5, CO, CO2,
NH3, NOX, SOX (assumed to be SO2), and Hydrocarbons (assumed to be VOC). The EPA
developed regional growth (activity-based) factors that we applied to create the 2007v5
inventory from the 2002 data. These growth factors are provided in Table 3-4. The East Coast
and Gulf Coast regions were divided along a line roughly through Key Largo (longitude 80° 26'
West).
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Table 3-4. Growth factors to project the 2002 ECA inventory to 2010
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
1.258
1.096
1.158
1.314
1.061
1.300
PMio
0.478
0.415
0.452
0.499
0.387
1.396
PMl.5
0.475
0.411
0.444
0.495
0.384
1.396
voc
1.436
1.251
1.310
1.489
1.157
1.396
CO
1.436
1.252
1.309
1.486
1.156
1.396
SOi
0.513
0.448
0.508
0.580
0.408
1.396
A modification to the original ECA-IMO c3marine dataset include updating the state of
Delaware county total emissions to reflect comments received during the Cross-State Air
Pollution Rule (CSAPR) emissions modeling platform development:
http://www.epa.gov/ttn/chief/emch/index.htmltffinal. The original ECA-IMO inventory also did
not delineate between ports and underway (or other C3 modes such as hoteling, maneuvering,
reduced-speed zone, and idling) emissions; however, we used a U.S. ports spatial surrogate
dataset to assign the ECA-IMO emissions to ports and underway SCCs - 2280003100 and
2280003200, respectively. This has no effect on temporal allocation or speciation because all C3
emissions, unclassified/total, port and underway, share the same temporal and speciation
profiles.
Canadian near-shore emissions were assigned to province-level FIPS codes and paired those to
region classifications for British Columbia (North Pacific), Ontario (Great Lakes) and Nova
Scotia (East Coast). The assignment of U.S. FIPS was also restricted to state-federal water
boundaries data from the Mineral Management Service (MMS) that extended only
(approximately) 3 to 10 miles offshore. Emissions outside the 3 to 10 mile MMS boundary but
within the approximately 200 nm EEZ boundary in Figure 2 8 were projected to year 2010 using
the same regional adjustment factors as the U.S. emissions; however, the FIPS codes were
assigned as "EEZ" FIPS. 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. The classification of emissions to U.S. and Canadian FIPS codes is
primarily needed only for inventory summaries and is irrelevant for air quality modeling except
potentially for source apportionment of states contributions to transport.
Factors were applied to compute HAP emissions (based on emissions ratios) to VOC to obtain
HAP emissions values. Table 3-5 below shows these factors. Because HAPs were computed
directly from the CAP inventory and the calculations are therefore consistent, the entire c3marine
sector utilizes CAP-HAP VOC integration to use the VOC HAP species directly, rather than
VOC speciation profiles.
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Table 3-5. HAP emission ratios for generation of HAP emissions from criteria emissions for
C3 commercial marine vessels
Pollutant
Acetaldehyde
Benzene
Formaldehyde
Apply to
VOC
VOC
VOC
Pollutant
Code
75070
71432
50000
Factor
0.0002286
9.80E-06
0.0015672
3.2.12 Emissions from Canada, Mexico and Offshore Drilling Platforms (othpt, othar, othon)
The emissions from Canada, Mexico, and offshore 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 code, 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. All "oth" emissions are CAP-only inventories.
For Canada, year-2006 Canadian emissions were used but several modifications were applied to
the inventories:
1. Wildfires or prescribed burning were not included because Canada does not include these
inventory data in their modeling.
2. In-flight aircraft emissions were not included because we do not include these for the
U.S. and we do not have a finalized approach to include in our modeling.
3. A 75% reduction ("transport fraction") was applied to PM for the road dust, agricultural,
and construction emissions in the Canadian "afdust" inventory. This approach is more
simplistic than the county-specific approach used for the U.S., but a comparable approach
was not available for Canada.
4. Speciated VOC emissions from the ADOM chemical mechanism were not included
because we use speciated emissions from the CBS chemical mechanism that Canada also
provided.
5. Residual fuel CMV (C3) SCCs (22800030X0) were removed because these emissions are
included in the c3marine sector, which covers not only emissions close to Canada but
also emissions far at sea. Canada was involved in the inventory development of the
c3marine sector emissions.
6. Wind erosion (SCC=2730100000) and cigarette smoke (SCC=2810060000) emissions
were removed from the nonpoint (nonpt) inventory; these emissions are also absent from
our U.S. inventory.
7. Quebec PM2.5 emissions (2,000 tons/yr) were removed for one SCC (2305070000) for
Industrial Processes, Mineral Processes, Gypsum, and Plaster Products due to corrupt
35
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fields after conversion to SMOKE input format. This error should be corrected in a
future inventory.
8. Excessively high CO emissions were removed from Babine Forest Products Ltd (British
Columbia SMOKE plantid='5188') in the point inventory.
9. The county part of the state/county FIPS code field in the SMOKE inputs were modified
in the point inventory from "000" to "001" to enable matching to existing temporal
profiles.
For Mexico, year 2008 emissions were used that are projections of their 1999 inventory
originally developed by Eastern Research Group Inc., (ERG, 2006) 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 (Institute
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-2008 Mexico
emissions from the 1999 inventory is available at:
http://www.wrapair.org/forums/ef/inventories/MNEI/index.html.
The offshore emissions include point source offshore oil and gas drilling platforms. We used
emissions from the 2008 NEI point source inventory. The offshore sources were provided by the
Mineral Management Services (MMS).
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.ei3a.gov/EmisIiiventorv/2002v3CAPHAP/ documentation for additional details.
3.3 Emissions Modeling Summary
CMAQ requires emissions data to be input as hourly rates of specific gas and particle species for
the horizontal and vertical grid cells contained within the modeled region (i.e., modeling
domain). To provide emissions in the form and format required by the model, it is necessary to
"pre-process" the "raw" emissions (i.e., emissions input to SMOKE) for the sectors described
above. In brief, the process of emissions modeling transforms the emissions inventories from
their original temporal resolution, pollutant resolution, and spatial resolution into the hourly,
speciated, gridded resolution required by the air quality model. The pre-processing steps
involving temporal allocation, spatial allocation, pollutant speciation, and vertical allocation of
point sources are referred to as emissions modeling.
The temporal resolution of the emissions inventories input to SMOKE for the modeling platform
varies across sectors, and may be hourly, monthly, or annual total emissions. The spatial
resolution, which also can be different for different sectors, may be at the level of individual
36
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point sources, county totals, province totals for Canada, or municipio totals for Mexico. This
section provides some basic information about the tools and data files used for emissions
modeling as part of the Version 5 platform. The emissions inventories were discussed in detail
earlier. Therefore, the descriptions of data in this section are limited to the ancillary data used by
SMOKE to perform the emissions modeling steps.
3.3.1 The SMOKE Modeling System
For this study, emission inventories were processed into CMAQ-ready inputs using SMOKE
version 3.1. 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 CMAQ was used, and therefore source-based emissions files were
created rather than the much larger three-dimensional files. For quality assurance purposes,
emissions totals by specie for the entire model domain are output as reports that are then
compared to inventory level reports generated by SMOKE to ensure mass is not lost or gained
during this conversion 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. 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 for the individual
SMOKE programs for each sector. Table 3-6 summarizes the major processing steps of each
platform sector. The "Spatial" column shows the spatial approach: "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 BEIS3 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 which will have 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 and ptfire sectors are the only sectors with only
"in-line" emissions, meaning that all of the emissions are placed in aloft layers and thus there are
no emissions for those sectors in the two-dimensional, layer-1 files created by SMOKE. In
addition to the other settings, no grouping of stacks was performed using the PELVCONFIG file
because grouping done for "in-line" processing will not give identical results as "offline" (i.e.,
processing whereby SMOKE creates 3-dimensional files). The only way to get the same results
37
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between in-line and offline is to choose to have no grouping.
Table 3-6. Key emissions modeling steps by sector
Platform sector
Ptipm
Ptnonipm
Ptfire
Othpt
c3 marine
Ag
Afdust
Beis
clc2rail
Spatial
Point
Point
Point
Point
Point
Surrogates
Surrogates
pre-gridded landuse
Surrogates
Speciation
Yes
Yes
Yes
Yes
Yes
Yes
Yes
in BEIS
Yes
Inventory
resolution
daily & hourly
annual
Daily
annual
annual
annual & monthly
annual
computed hourly
annual
Plume rise
in-line
in-line
in-line
in-line
in-line
Nonpt
Nonroad
Onroad
onroad_rfl
Othar
Othon
surrogates & area-
to-point
surrogates & area-
to-point
Surrogates
Surrogates
Surrogates
Surrogates
Yes
Yes
Yes
Yes
Yes
Yes
annual & monthly for
ag burning and
SESARM open
monthly
computed hourly
computed hourly
annual
annual
3.3.3 Spatial Configuration
For this study, SMOKE and CMAQ were run for a 12-km modeling domain shown in Figure 3-1
(12US1). The grid used a Lambert-Conformal projection, with Alpha =33, Beta = 45 and
Gamma = -97, with a center of X = -97 and Y = 40. Later sections provide details on the spatial
surrogates and area-to-point data used to accomplish spatial allocation with SMOKE.
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12km CONUS nationwide) dotnai
x.y: -2556000.-1728000
col: 459 row: 299
Figure 3-1. CMAQ Modeling Domain
3.3.4 Chemical Speciation Configuration
The emissions modeling step for chemical speciation creates "model species" needed by the air
quality model for a specific chemical mechanism. These model species are either individual
chemical compounds or groups of species, called "model species." The chemical mechanism used
for this study is the Carbon Bond 05 (CB05) mechanism (Yarwood, 2005) with secondary
organic aerosol (SOA) and HONO enhancements as described in
http://www.cmascenter.org/help/model docs/cmaq/4.7/RELEASE NOTES.txt. The mapping of
inventory pollutants to model species is shown in Table 3-7. From the perspective of emissions
preparation, the CB05 with SOA mechanism is the same as was used in the 2005 platform. 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 in CMAQ.
39
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Table 3-7. Model Species Produced by SMOKE for CB05
Inventory Pollutant
CO
NOx
SO2
NH3
VOC
Various additional VOC
species from the biogenics
model which do not map to
the above model species
PMio
PM2.5
Model Species
CO
NO
NO2
SO2
SULF
NH3
ALD2
ALDX
ETH
ETHA
ETOH
FORM
IOLE
ISOP
MEOH
OLE
PAR
TOL
XYL
TERP
PMC
PEC
PNO3
POC
PSO4
PMFINE
Model Species Description
Carbon monoxide
Nitrogen oxide
Nitrogen dioxide
Sulfur dioxide
Sulfuric acid vapor
Ammonia
Acetaldehyde
Propionaldehyde and higher aldehydes
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
Terpenes
Coarse PM > 2.5 microns and < 10 microns
Particulate elemental carbon < 2.5 microns
Parti culate nitrate < 2.5 microns
Particulate organic carbon (carbon only) < 2.5
microns
Particulate sulfate < 2.5 microns
Other particulate matter < 2.5 microns
The approach for speciating PM2.5 emissions supports both CMAQ 4.7.1 with five species (i.e.,
AE5) and CMAQ 5.0 and includes speciation of PM2.5 into 17 PM model species (i.e., AE6).
The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach
were developed from the SPECIATE4.3 database
(http://www.epa.gov/ttn/chief/software/speciate) and is the EPA's repository of TOG and PM
speciation profiles of air pollution sources. A few of the profiles used in the v5 platform will be
published in later versions of the SPECIATE database. The SPECIATE database development
and maintenance is a collaboration involving the EPA's ORD, OTAQ, and the Office of Air
Quality Planning and Standards (OAQPS), and Environment Canada (EPA, 2006a). The
SPECIATE database contains speciation profiles for TOG, speciated into individual chemical
compounds, VOC-to-TOG conversion factors associated with the TOG profiles, and speciation
profiles for PM2.5. The database also contains the PM2.5, speciated into both individual chemical
40
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compounds (e.g., zinc, potassium, manganese, lead) and into the "simplified" PIVh.s components
used in the air quality model. These simplified components for AE5 are:
PSO4 : primary particulate sulfate
PNCb: primary particulate nitrate
PEC: primary particulate elemental carbon
POC: primary particulate organic carbon
PMFINE: other primary particulate, less than 2.5 micrograms in diameter
NOX can be speciated into NO, NO2, and/or HONO. For the non-mobile sources, a single
profile is used "NHONO" to split NOX into NO and NO2 with 10% NO2 and 90% NO. 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 with 90% NO, 9.2% NO2 and 0.8% HONO. The onroad sector does not use the
"HONO" profile to speciate NOX. Instead, 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
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 The SMOKE-MOVES
system is configured to model these species directly without further speciation.
The approach for speciating VOC emissions from non-biogenic sources has the following
characteristics: 1) for some sources, HAP emissions are used in the speciation process to allow
integration of VOC and HAP emissions in the NEI; and, 2) for some mobile sources,
"combination" profiles are specified by county and month and emission mode (e.g., exhaust,
evaporative). SMOKE computes the resultant profile on-the-fly given the fraction of each specific
profile specified for the particular county, month and emission mode. The SMOKE feature called
the GSPRO_COMBO file supports this approach.
The VOC speciation approach for the 2010 Platform includes HAP emissions from the NEI in the
speciation process for some sectors. That is instead of speciating VOC to generate all of the
species needed by the model, emissions of the 4 HAPs, benzene, acetaldehyde, formaldehyde and
methanol (BAFM) from the NEI were integrated with the NEI VOC. The integration process
combines the BAFM HAPs with the VOC in a way that does not double-count emissions and
uses the BAFM directly in the speciation process. Generally, the HAP emissions from the NEI
are believed to be more representative of emissions of these compounds than their generation via
VOC speciation.
The BAFM HAPs were chosen for this special treatment because, with the exception of
BENZENE, they are the only explicit VOC HAPs in the base version of CMAQ 4.7 model. By
"explicit VOC HAPs," we mean 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 was added as a model species
in the base version of CMAQ 4.7, and there was a desire to keep its emissions consistent between
multi- pollutant and base versions of CMAQ.
41
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For specific sources, especially within the onroad and onroad_rfl sectors, we included ethanol in
our integration. To differentiate when a source was integrating BAFM versus EBAFM (ethanol
in addition to BAFM), the speciation profiles which do not include ethanol are referred to as an
"E-profile", for example E10 headspace gasoline evaporative speciation profile 8763 where
ethanol is speciated from VOC, versus 8763E where ethanol is obtained directly from the
inventory. The specific profiles used in 2010 are the same as used for the 2007 platform (see
2007 speciation in Table 3-6 in the 2007v5 TSD). The only differences between 2010 and 2007
are the GSPRO_COMBOs, which represent a different mixture of EO and E10 by county
between the two modeling years.
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 the particular HAPs to integrate and the particular sources to integrate. The HAPs to
integrate are specified in the INVTABLE file, and the sources to integrate are based on the
NHAPEXCLUDE file (which lists the sources that are excluded from integration). For the
"integrate" sources, SMOKE subtracts the "integrate " HAPs from the VOC (at the source level)
to compute emissions for the new pollutant "NONHAPVOC." The user provides
NONHAPVOC-to-NONHAPTOG factors and NONHAPTOG speciation profiles. SMOKE
computes NONHAPTOG and then applies the speciation profiles to allocate the NONHAPTOG
to the other CMAQ VOC species not including the integrated HAPs.
CAP-HAP integration was considered for all sectors and "integration criteria" were developed for
some of those. Table 3-8 summarizes the integration approach for each platform sector. For the
clc2rail sector, the integration criteria were (1) that the source had to have at least one of the 4
HAPs and (2) that the sum of BAFM could not exceed the VOC emissions. For the nonpt sector,
the following integration criteria were used to determine the sources to integrate:
1. Any source for which the sum of B, A, F, or M is greater than the VOC was not
integrated, since this clearly identifies sources for which there is an inconsistency between
VOC and VOC HAPs.
2. For some source categories (those that comprised 80% of the VOC emissions), sources
were selected for integration in the category per specific criteria. For most of these source
categories, sources may be integrated if they had the minimum combination of B, A, F,
and M. For some source categories, all sources were designated as "no-integrate".
3. For source categories that do not comprise the top 80% of VOC emissions, as long as the
source has emissions of one of the B, F, A or M pollutants, then it can be integrated.
42
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Table 3-8. Integration status of benzene, acetaldehyde, formaldehyde and methanol
(BAFM) for each platform sector
Platform Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A), Formaldehyde (F) and
Sector Methanol (M)
Ptipm No integration because emissions of BAFM are relatively small for this sector
Ptnonipm ^° integration because emissions of BAFM are relatively small for this sector and it is not
expected that criteria for integration would be met by a significant number of sources
Ptfire No integration.
Ag N/Asector contains no VOC
Afdust N/Asector contains no VOC
Biog N/Asector contains no inventory pollutant "VOC"; but rather specific VOC species
Clc2rail Partial integration
C3 marine Full integration
Nonpt Partial integration
Partial integrationdid not integrate California emissions, CNG or LPG sources (SCCs
Nonroad beginning with 2268 or 2267) because NMIM computed only VOC and not any HAPs for
these SCCs.
Onroad Full integration
Othar NO integrationnot the NEI
Othon No integrationnot the NEI
Othpt NO integrationnot the NEI
The SMOKE feature to compute speciation profiles from mixtures of other profiles in user-
specified proportions was used in this project. 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 for
onroad and nonroad mobile and gasoline-related related stationary sources. Since the ethanol
content varies spatially (e.g., by state or sources use fuels with varying ethanol content, and
therefore the speciation profiles require different combinations of gasoline and E10 profiles by
county), temporally (e.g., by month) and by modeling year (i.e., future years have more thanol)
the combo feature allows combinations to be specified at various levels for different years.
The INVTABLE and NHAPEXCLUDE SMOKE input files have a critical function in the VOC
speciation process for emissions modeling cases utilizing HAP-CAP integration, as is done for
the 2010 Platform. Two different types of INVTABLE files were developed to use with different
sectors of the platform. For sectors in which we chose no integration across the entire sector a
"no HAP use" INVTABLE was developed in which the "KEEP" flag is set to "N" for BAFM
pollutants. Thus, any BAFM pollutants in the inventory input into SMOKE are dropped. This
both avoids double-counting of these species and assumes that the VOC speciation is the best
available approach for these species for the sectors using the approach. The second INVTABLE
is used for sectors in which one or more sources are integrated and causes SMOKE to keep the
BAFM pollutants and indicates that they are to be integrated with VOC (by setting the "VOC or
TOG component" field to "V" for all four HAP pollutants. This integrate INVTABLE is further
differentiated into sectors that integrate BAFM versus those that integrate EBAFM (e.g., the
onroad and onroad_rfl sectors).
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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
MOVES2010b speciated PM does not map 1-to-l to either the AE5 or AE6 species. Table 3-9
shows the relationship between MOVES2010b exhaust PM2.5 related species and CMAQ AE5
PM species.
Table 3-9. MOVES exhaust PM species versus AE5 species
MOVES2010b Pollutant Name
Primary Exhaust PM2.5 - Total
Primary PM2.5 - Organic Carbon
Primary PM2.5 - Elemental
Carbon
Primary PM2.5 - Sulfate
Paniculate
Variable
name for
Equations
PM25_TOTAL
PM25OM
PM25EC
PM25SO4
Relation to AE5 model
species
Sum of POC, PNO3 and
PMFINE
PEC
PSO4
MOVES species are related as follows:
PM25_TOTAL = PM25EC + PM25OM + PSO4
The five CMAQ AE5 species also sum to total PM2.s:
PM2.5 = POC+PEC+PNO3+PSO4+PMFINE
The basic problem is to differentiate MOVES species "PM25OM" into the component AE5
species (POC, PNO3 and PMFINE). The Moves2smkEF post-processor script takes the
MOVES2010b species (EF tables) and calculates the appropriate AE5 PM2.5 species and
converts them into a format that is appropriate for SMOKE (see http://www.smoke-
model. org/version3.1 /html/chO5s02s04.html for details on the Moves2smkEF script).
For brake wear and tire wear PM, total PM2.5 (not speciated) comes directly from MOVES2010b.
These PM modes are speciated by SMOKE. PMFINE from onroad exhaust is further speciated
by SMOKE into the component AE6 species.
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 species SESQT. The profile code associated
with BEIS3.14 profiles for use with CB05 was "B10C5."
3.3.4 Temporal Processing Configuration
Temporal allocation or temporalization is the process of distributing aggregated emissions to a
finer temporal resolution, such as 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.
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Typically, emissions inventories are annual or monthly in nature. Temporalization takes these
annual emissions and distributes them to the month, the monthly emissions to the day, and the
daily emissions to the hour. This process is typically done by applying temporal profiles
monthly, day of the week, and diurnalto the inventories.
The monthly, weekly, and diurnal temporal profiles and associated cross references used to
create the hourly emissions inputs for the air quality model were similar to those used for the
2005v4.3 platform. Some new methodologies are introduced in this platform and updated
profiles are discussed. Temporal factors are typically applied to the inventory by some
combination of country, state, county, SCC, and pollutant.
Table 3-10 summarizes the temporal aspect of the emissions processing configuration. It
compares the key approaches used for temporal processing across the sectors. The temporal
aspects of SMOKE processing are controlled through (a) the scripts T_TYPE (Temporal type)
and M_TYPE (Mergetype) settings and (b) ancillary data files. In the table, "Daily temporal
approach" refers to the temporal approach for getting daily emissions from the inventory using
the Temporal program. The "Merge processing approach" refers to the days used to represent
other days in the month for the merge step. If not "all", then the SMOKE merge step runs only
for representative days, which could include holidays as indicated by the right-most column. In
addition to the resolution, temporal processing includes a ramp-up period for several days prior to
January 1, 2010, intended to mitigate the effects of initial condition concentrations. The ramp up
period for the national 12km grid was 10 days. For most sectors, the emissions from late
December of 2009 were used to provide emissions for the end of December, 2010.
The Flat File 2010 format (FF10) is a new inventory format for SMOKE that provides a more
consolidated format for monthly, daily, and hourly emissions inventories. Previously, 12
separate inventory files would be required to process monthly inventory data. 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,
individual records contain data for all days in a month and all hours in a day in the daily and
hourly FF10 inventories, respectively.
SMOKE 3.1 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; rather, it should only have month to day and diurnal temporalization. This is
particularly important when 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.
45
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Table 3-10. Temporal Settings Used for the Platform Sectors in SMOKE
Inventory
Platform sector resolution
Monthly
profiles
used?
Daily
temporal
approach
1,2
Merge
processing
approach 1,3
Process
Holidays as
separate
days?
Ptipm daily & hourly All all yes
Ptnonipm annual yes Mwdss all yes
Ptfire Daily All all yes
Ag annual & monthly yes all all yes
Afdust annual yes Week all yes
Beis hourly n/a all yes
c3 marine annual yes Aveday aveday
clc2rail annual yes Mwdss mwdss
Nonpt annual & monthly yes All all yes
Nonroad monthly Mwdss mwdss yes
Onroad annual & monthly51 all all yes
onroad_rfl annual & monthly51 All all yes
Othar annual yes Week week
Othon annual yes Week week
Othpt annual yes Mwdss mwdss
1 Definitions for processing resolution:
all = hourly emissions computed for every day of the year
week = hourly emissions computed for all days in one "representative" week, representing all weeks for each month, which
means emissions have day-of-week variation, but not week-to-week variation within the month
mwdss = hourly emissions for one representative Monday, representative weekday, representative Saturday and representative
Sunday for each month, which means emissions have variation between Mondays, other weekdays, Saturdays and Sundays
within the month, but not week-to-week variation within the month. Also Tuesdays, Wednesdays and Thursdays are treated the
same.
aveday = hourly emissions computed for one representative day of each month, which means emissions for all days of each
month are the same.
2 Daily temporal approach refers to the temporal approach for getting daily emissions from the inventory using the Temporal
program. The values given are the values of the L_TYPE setting.
3 Merge processing approach refers to the days used to represent other days in the month for the merge step. If not "all", then the
SMOKE merge step just run for representative days, which could include holidays as indicated by the rightmost column. The values
given are the values of the M_TYPE setting.
" For onroad and onroad_rfl, the annual and monthly refers to activity data (VMT and VPOP). Emissions are computed on an
hourly basis.
For the EGU emissions in the ptipm sector, hourly CEM NOx and SO2 data were used directly
for sources that match CEMs. For other pollutants, hourly CEM heat input data were used to
allocate the NEI annual values. For sources not matching CEM data ("non-CEM" sources), daily
emissions were computed from the NEI annual emissions using a structured query language
(SQL) program and state-average CEM data. To allocate annual emissions to each month, state-
specific three-year averages of 2008-2010 CEM data were created. These average annual-to-
month factors were assigned to non-CEM sources within each state. To allocate the monthly
emissions to each day, the 2010 CEM data to compute state-specific month-to-day factors,
averaged across all units in each state. These daily emissions were calculated outside of
SMOKE and the resulting daily inventory is used as an input into SMOKE.
46
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The daily-to-hourly allocation was performed in SMOKE using diurnal profiles. The state-
specific and pollutant-specific diurnal profiles for use in allocating the day-specific emissions for
non-CEM sources in the ptipm sector were updated. The 2010 CEM data was used to create
state-specific, day-to-hour factors, averaged over the whole year and all units in each state.
Diurnal factors were calculated using CEM SO2 and NOx emissions and heat input. SO2 and
NOx-specific factors were computed from the CEM data for these pollutants. All other
pollutants used factors created from the hourly heat input data. The resulting profiles were
assigned by state and pollutant.
Two updated diurnal temporal profiles were incorporated into the 2010 modeling platform. For
all agricultural burning, we used a diurnal temporal profile (McCarty et al., 2009) that puts more
of the emissions during the actual work day and suppresses the emissions during the middle of
the night was used. Note that all states used a uniform day of week profile for all agricultural
burning emissions, except for the following states that for which state-specific day of week
profiles were used: Arkansas, Kansas, Louisiana, Minnesota, Missouri, Nebraska, Oklahoma,
and Texas. For residential wood combustion, a profile was used that placed more of the
emissions in the morning and the evening when people are typically using these sources. This
profile is based on an average of 2004 MANE-VU survey based temporal profiles (see
http://www.marama.org/publications folder/ResWoodCombustion/Final report.pdf). When this
profile was compared to a concentration-based analysis of aethalometer measurements in
Rochester, NY (Wang et al. 2011) for various seasons and day of the week it was found that the
updated RWC profile generally tracked the concentration based temporal patterns.
The temporal profile assignments for the Canadian 2006 inventory were provided by
Environment Canada along with the inventory. They provided profile assignments that rely on
the existing set of temporal profiles in the 2002 Platform. For point sources, they provided
profile assignments by PLANTID.
3.3.5 Meteorological-based Temporal Profiles
A significant improvement over previous platforms is the introduction of meteorologically-based
temporalization. We recognize that there are many factors that impact the timing of when
emissions occur. The benefits of utilizing meteorology as method of temporalizing are: (1) a
consistent meteorological dataset as is used by the AQ model (e.g. WRF) is available; (2) the
meteorological model data is highly resolved in terms of spatial resolution; and (3) the
meteorological variables vary at hourly resolution which can translate to hour-specific
temporalization.
The SMOKE program GenTPRO provides a method for developing meteorologically-based
temporalization. Currently, the program can utilize three types of temporal algorithms: RWC,
agricultural livestock ammonia, and a generic meteorology based algorithm. For the 2007
platform, we used the RWC and ag NH3 GenTPRO generated profiles. GenTPRO reads in
gridded meteorology data (MCIP) and 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 algorithm
and the run parameters. For more details on the development of these algorithms and running
GenTPRO, see the GenTPRO documentation http://www. smoke-
47
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model.org/version3. l/GenTPRO_TechnicalSummary_Aug2012_Final.pdf and the SMOKE
manual section http://www.smoke-model.Org/version3.l/html/ch05s03s07.html.
For the RWC algorithm, GenTPRO uses the daily minimum temperature to determine the
temporal allocation of emissions to days. GenTPRO was run to create an annual-to-day temporal
profile for the RWC sources within the nonpt sector. 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 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, just the distribution of the
emissions within the year. Initially, the RWC algorithm used a default temperature threshold of
50 °F. For most of the country, this produced a reasonable distribution of emissions, but for a
few Southern counties all of the emissions were compressed into a few days creating excessively
high daily emissions. GenTPRO was then modified to accept an optional input that defines a
county/state specific alternative temperature threshold. In addition, an alternative RWC
algorithm was created to avoid negative RWC emissions when the daily minimum temperature
was greater than 53.3 °F. For the v5 platform, the alternative RWC algorithm was used for the
whole country, with the default 50 °F threshold for the majority of the states, and a 60 °F
threshold for the following states: Alabama, Arizona, California, Florida, Georgia, Louisiana,
Mississippi, South Carolina, and Texas.
For the agricultural livestock NH3 algorithm, GenTPRO algorithm is based on the Russel and
Cass (1986) equation. This algorithm uses county-average hourly temperature and wind speed to
calculate the temporal profile. GenTPRO was run to create month-to-hour temporal profiles for
these sources. Because these profiles distribute to the hour based on monthly emissions, the
emissions will either come from a monthly inventory or from an annual inventory that has been
temporalized already to the month.
For the onroad and onroad_rfl sectors, meteorology is not used in the development of the
temporal profiles; rather, but meteorology impacts the calculation of the hourly emissions
through the program Movesmrg. The result is that the emissions will vary at the hourly level by
grid cell. More specifically, the on-network (RPD) and the off-network (RPV) exhaust,
evaporative, and evaporative permeation modes use the gridded meteorology (MCIP) directly.
Movesmrg determines the temperature for each hour and grid cell and uses it to select the
appropriate EF for that SCC/pollutant/mode. For the off-network rate per profile (RPP)
emissions, Movesmrg uses the Met4moves output for SMOKE (daily minimum and maximum
temperatures by county) to determine the appropriate EF for that hour and SCC/pollutant. The
result is that the emissions will vary hourly by county. The combination of these three processes
(RPD, RPV, and RPP) is the total onroad emissions, while the combination of the two processes
(RPD, RPV) for the refueling mode only is the total onroad_rfl emissions. Both sectors will
show a strong meteorological influence on their temporal patterns.
3.3.6 Vertical Allocation of Emissions
Table 3-6 specifies the sectors for which plume rise is calculated. If there is no plume rise for a
sector, the emissions are placed into layer 1 of the air quality model. Vertical plume rise was
performed in-line within CMAQ for all of the SMOKE point-source sectors (i.e., ptipm,
48
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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.
See http://www.smoke-model.Org/version2.7/html/ch06s07.html for full documentation of
Laypoint. The selection of point sources for plume rise is pre-determined in SMOKE using the
Elevpoint program (http://www.smoke-model.ore/ version!. 7/html/ch06s03.html). 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 Ancillary Files
The methods used to perform spatial allocation for the 2007 platform are summarized in this
section. For the 2007 platform, spatial factors are typically applied by country and SCC. As
described earlier, spatial allocation was performed for a national 12-km domain. To accomplish
this, SMOKE used national 12-km spatial surrogates and a SMOKE area-to-point data file. For
the U.S., the spatial surrogates used 2010-based data (e.g., population) wherever possible. For
Mexico, the same spatial surrogates were used in the 2005 platform. For Canada we used a set
of Canadian surrogates provided by Environment Canada, also unchanged from the 2005v4.3
platform. The U.S., Mexican, and Canadian 12-km surrogates cover the entire CONUS domain
12US1 shown in Figure 3-1. The remainder of this subsection provides further details on the
origin of the data used for the spatial surrogates and the area-to-point data.
The SMOKE ancillary data files, particularly the cross-reference files, provide the specific
inventory resolution at which spatial, speciation, and temporal factors are applied. For the 2010
Platform, spatial factors were generally applied by country/SCC, speciation factors by
pollutant/SCC or (for combination profiles) state/ county FIPS code and month, and temporal
factors by some combination of country, state, county, SCC, and pollutant.
49
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3.3.7.1 Surrogates for U.S. Emissions
More than sixty spatial surrogates were used to spatially allocate U.S. county-level emissions to
the CMAQ 12-km grid cells. The Surrogate Tool was used to generate all of the surrogates. The
shapefiles input to the Surrogate Tool are provided and documented at
llrt|x//ww^ The tool and updated
documentation for it is available at http://www.ie.unc.edu/cempd/projects/mims/spatial/ and
http://www.cmascenter.org/help/documentati on.cfm?MODEL=spatial_allocator&VERSION=3.
6&temp id=99999. The detailed steps in developing the county boundaries for the surrogates are
documented at ft]3i//ftiM^lJi2}£^
county rev.pdf.
Table 3-11 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, and 2010 National Transportation Atlas Data for ports and navigable waterways. For this
project "Version 3" of the 2010-baed spatial surrogates was used. Not all of the available
surrogates are used to spatially allocate sources in the 2007 platform; that is, some surrogates
shown in Table 3-11 were not assigned to any SCCs. An area-to-point approach overrides the use
of surrogates for some airport-related sources.
Alternative surrogates for ports (801) and shipping lanes (802) were developed from the 2008
NEI shapefiles: Ports_032310_wrf and ShippingLanes_l 11309FINAL_wrf. These surrogates
were used for cl and c2 commercial marine emissions instead of the standard 800 and 810
surrogates, respectively. For the onroad sector, the on-network (RPD) emissions were spatially
allocated to roadways, which the off-network (RPP and RPV) emissions were allocated to
parking areas. For the onroad_rfl sector, the emissions were spatially allocated to gas station
locations.
For the oil and gas sources in the nonpt sector, the WRAP Phase III sources have detailed basin-
specific spatial surrogates shown in Table 3-12. The remaining oil and gas sources used the
2005-based surrogate "Oil & Gas Wells, fflS Energy, Inc. and USGS" (680) developed for oil
and gas SCCs. The surrogates in Table 3-12 were applied for the counties listed in Table 3-13.
3.3.7.3 Allocation Method for Airport-Related Sources in the U.S.
There are numerous airport-related emission sources in the 2005 NEI, such as aircraft, airport
ground support equipment, and jet refueling. In the 2002 platform most of these emissions were
contained in sectors with county-level resolution aim (aircraft), nonroad (airport ground
support) and nonpt (jet refueling), but in the 2005 and 2008 platforms aircraft emissions are
included as point sources as part of the ptnonipm sector.
For the 2010 platform, the SMOKE "area-to-point" approach was used for 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/scramOO 1 /reports/
Emissions%20TSD%20Voll 02-28-08.pdf.
Nearly the same ARTOPNT file was used to implement the area-to-point approach as was done
50
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for the CAP and HAP-2002-based Platform. This was slightly updated from the CAP-only 2002
Platform by further allocating the Detroit-area airports into multiple sets of geographic
coordinates to support finer scale modeling. The updated file was retained for the 2010 Platform.
3.3.7.4 Surrogates for Canada and Mexico Emission Inventories
The Mexican single surrogate (population) was the same as was used in the 2002 and 2005
Platforms. For Canada, surrogates provided by Environment Canada with the 2006 emissions
were used to spatially allocate the 2006 Canadian emissions for the 2005 and 2010 Platforms.
The Canadian surrogate data described in Table 3-14 came from Environment Canada. They
provided both the surrogates and cross references; the surrogates were outputs from the Surrogate
Tool (previously referenced). Per Environment Canada, the surrogates are based on 2001
Canadian census data. The cross-references that Canada originally provided were updated as
follows: all assignments to surrogate '978' (manufacturing industries) were changed to '906'
(manufacturing services), and all assignments to '985' (construction and mining) and '984'
(construction industries) were changed to '907' (construction services) because the surrogate
fractions in 984, 978 and 985 did not sum to 1. Codes for surrogates other than population that
did not begin with the digit "9" were also changed.
Table 3-11. U.S. Surrogates Available for the 2010 Platform
Code Surrogate Description
N/A Area-to-point approach (see 3.3.1.2)
100 Population
110 Housing
120 Urban Population
130 Rural Population
137 Housing Change
140 Housing Change and Population
150 Residential Heating - Natural Gas
160 Residential Heating - Wood
Code Surrogate Description
165 0.5 Residential Heating - Wood plus 0.5 Low
Intensity Residential
170 Residential Heating - Distillate Oil
180 Residential Heating - Coal
190 Residential Heating - LP Gas
200 Urban Primary Road Miles
210 Rural Primary Road Miles
220 Urban Secondary Road Miles
230 Rural Secondary Road Miles
240 Total Road Miles
520
525
527
530
535
540
545
550
555
560
Commercial plus Industrial plus
Golf Courses + Institutional +Industrial +
Commercial
Single Family Residential
Residential - High Density
Residential + Commercial + Industrial +
Institutional
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
51
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250 Urban Primary plus Rural Primary
255 0.75 Total Roadway Miles plus 0.25 Population
260 Total Railroad Miles
270 Class 1 Railroad Miles
280 Class 2 and 3 Railroad Miles
300 Low Intensity Residential
310 Total Agriculture
312 Orchards/Vineyards
320 Forest Land
330 Strip Mines/Quarries
340 Land
350 Water
400 Rural Land Area
500 Commercial Land
505 Industrial Land
510 Commercial plus Industrial
515 Commercial plus Institutional Land
Gas Stations
Refineries and Tank Farms
Refineries and Tank Farms and Gas Stations
Oil and Gas
Airport Areas
Airport Points
Military Airports
Marine Ports
NEI Ports
NEI Shipping Lanes
Navigable Waterway Miles
Navigable Waterway Activity
Golf Courses
Mines
Wastewater Treatment Facilities
Drycleaners
Commercial Timber
Table 3-12. Spatial Surrogates for WRAP Oil and Gas Data
Country
USA
USA
USA
USA
USA
USA
USA
USA
USA
USA
USA
Code
699
698
697
696
695
694
693
692
691
690
689
Surrogate Description
Gas production at CBM wells
Well count - gas wells
Oil production at gas wells
Gas production at gas wells
Well count - oil wells
Oil production at Oil wells
Well count - all wells
Spud count
Well count - CBM wells
Oil production at all wells
Gas production at all wells
Table 3-13. Counties included in the WRAP Dataset
FIPS
8001
8005
8007
8013
8014
8029
8031
State
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
County
Adams
Arapahoe
Archuleta
Boulder
Broomfield
Delta
Denver
FIPS
30075
35031
35039
35043
35045
49007
49009
State
Montana
New Mexico
New Mexico
New Mexico
New Mexico
Utah
Utah
County
Powder River
Me Kinley
Rio Arriba
Sandoval
San Juan
Carbon
Daggett
52
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8039
8043
8045
8051
8063
8067
8069
8073
8075
8077
8081
8087
8095
8103
8107
8115
8121
8123
8125
30003
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Colorado
Montana
Elbert
Fremont
Garfield
Gunnison
Kit Carson
La Plata
Larimer
Lincoln
Logan
Mesa
Moffat
Morgan
Phillips
Rio Blanco
Routt
Sedgwick
Washington
Weld
Yuma
Big Horn
49013
49015
49019
49043
49047
56001
56005
56007
56009
56011
56013
56019
56023
56025
56027
56033
56035
56037
56041
56045
Utah
Utah
Utah
Utah
Utah
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Wyoming
Duchesne
Emery
Grand
Summit
Uintah
Albany
Campbell
Carbon
Converse
Crook
Fremont
Johnson
Lincoln
Natrona
Niobrara
Sheridan
Sublette
Sweetwater
Uinta
Weston
Table 3-14. Canadian Spatial Surrogates for Canadian Emissions
Code
9100
9101
9102
9103
9104
9106
9111
9113
9114
9115
9116
9211
9212
9213
Description
Population
Total dwelling
Urban dwelling
Rural dwelling
Total Employment
ALLJNDUST
Farms
Forestry and logging
Fishing hunting and trapping
Agriculture and forestry activities
Total Resources
Oil and Gas Extraction
Mining except oil and gas
Mining and Oil and Gas Extract
activities
Code
9493
9494
9511
9512
9513
9514
9516
9521
9522
9523
9524
9526
9528
9531
Description
Warehousing and storage
Total Transport and warehouse
Publishing and information services
Motion picture and sound recording
industries
Broadcasting and
tel ecommuni cati ons
Data processing services
Total Info and culture
Monetary authorities - central bank
Credit intermediation activities
Securities commodity contracts and
other financial investment activities
Insurance carriers and related
activities
Funds and other financial vehicles
Total Banks
Real estate
53
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Code
9219
9221
9222
9231
9232
9233
9308
9309
9313
9314
9315
9316
9321
9322
9323
9324
9325
9326
9327
9331
9332
9333
9334
9335
9336
Description
Mining-unspecified
Total Mining
Utilities
Construction except land subdivision
and land development
Land subdivision and land
development
Total Land Development
Food manufacturing
Beverage and tobacco product
manufacturing
Textile mills
Textile product mills
Clothing manufacturing
Leather and allied product
manufacturing
Wood product manufacturing
Paper manufacturing
Printing and related support activities
Petroleum and coal products
manufacturing
Chemical manufacturing
Plastics and rubber products
manufacturing
Non-metallic mineral product
manufacturing
Primary Metal Manufacturing
Fabricated metal product
manufacturing
Machinery manufacturing
Computer and Electronic
manufacturing
Electrical equipment appliance and
component manufacturing
Transportation equipment
manufacturing
Code
9532
9533
9534
9541
9551
9561
9562
9611
9621
9622
9623
9624
9625
9711
9712
9713
9721
9722
9723
9811
9812
9813
9814
9815
9911
Description
Rental and leasing services
Lessors of non-financial intangible
assets (except copyrighted works)
Total Real estate
Professional scientific and technical
services
Management of companies and
enterprises
Administrative and support services
Waste management and remediation
services
Education Services
Ambulatory health care services
Hospitals
Nursing and residential care
facilities
Social assistance
Total Service
Performing arts spectator sports and
related industries
Heritage institutions
Amusement gambling and
recreation industries
Accommodation services
Food services and drinking places
Total Tourism
Repair and maintenance
Personal and laundry services
Religious grant-making civic and
professional and similar
organizations
Private households
Total other services
Federal government public
administration
54
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Code
9337
9338
9339
9411
9412
9413
9414
9415
9416
9417
9418
9419
9420
9441
9442
9443
9444
9445
9446
9447
9448
9451
9452
9453
9454
9455
9481
9482
Description
Furniture and related product
manufacturing
Miscellaneous manufacturing
Total Manufacturing
Farm product wholesaler-distributors
Petroleum product wholesaler-
distributors
Food beverage and tobacco
whol esal er-di stributor s
Personal and household goods
whol esal er-di stributor s
Motor vehicle and parts wholesaler-
distributors
Building material and supplies
whol esal er-di stributor s
Machinery equipment and supplies
whol esal er-di stributor s
Miscellaneous wholesaler-distributors
Wholesale agents and brokers
Total Wholesale
Motor vehicle and parts dealers
Furniture and home furnishings stores
Electronics and appliance stores
Building material and garden
equipment and supplies dealers
Food and beverage stores
Health and personal care stores
Gasoline stations
clothing and clothing accessories
stores
Sporting goods hobby book and
music stores
General Merchandise stores
Miscellaneous store retailers
Non-store retailers
Total Retail
Air transportation
Rail transportation
Code
9912
9913
9914
9919
9920
9921
9922
9923
9924
9925
9926
9927
9928
9929
9930
9931
9932
9933
9941
9942
9943
9944
9945
9946
9947
9950
9960
9970
Description
Provincial and territorial public
administration (9121 to 9129)
Local municipal and regional public
administration (9131 to 9139)
Aboriginal public administration
International and other extra-
territorial public administration
Total Government
Commercial Fuel Combustion
TOTAL DISTRIBUTION AND
RETAIL
TOTAL INSTITUTIONAL AND
GOVERNEMNT
Primary Industry
Manufacturing and Assembly
Distribution and Retail (no
petroleum)
Commercial Services
Commercial Meat cooking
HIGHJET
LOWMEDJET
OTHERJET
CANRAIL
Forest fires
PAVED ROADS
UNPAVED ROADS
HIGHWAY
ROAD
Commercial Marine Vessels
Construction and mining
Agriculture Construction and
mining
Intersection of Forest and Housing
TOTBEEF
TOTPOUL
55
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Code
9483
9484
9485
9486
9487
9488
9491
9492
Description
Water Transportation
Truck transportation
Transit and ground passenger
transportation
Pipeline transportation
Scenic and sightseeing transportation
Support activities for transportation
Postal service
Couriers and messengers
Code
9980
9990
9993
9994
9995
9996
9997
9991
Description
TOTSWIN
TOTFERT
Trail
ALLROADS
30UNPAVED_70trail
Urban area
CHBOISQC
Traffic
56
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Bullock Jr., R, and K. A. Brehme (2002) "Atmospheric mercury simulation using the CMAQ model:
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Ottmar, R.D.; Prichard, S.J.; Vihnanek, R.E.; Sandberg, D.V. 2006. Modification and validation of fuel
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Anthropogenic Fugitive Dust Emission Inventory and Temporal Allocation Using an Updated
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59
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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 system. The primary goals for CMAQ are to:
Improve the environmental management community's ability to evaluate the impact of air
quality management practices for multiple pollutants at multiple scales.
Improve the scientist's ability to better probe, understand, and simulate chemical and
physical interactions in the atmosphere.
The CMAQ modeling system brings together key physical and chemical functions associated
with the dispersion and transformations of air pollution at various scales. It was designed to
approach air quality as a whole by including state-of-the-science capabilities for modeling
multiple air quality issues, including tropospheric ozone, fine particles, toxics, acid deposition,
and visibility degradation. CMAQ relies on emission estimates from various sources, including
the U.S. EPA Office of Air Quality Planning and Standards' current emission inventories,
observed emission from major utility stacks, and model estimates of natural emissions from
biogenic and agricultural sources. CMAQ also relies on meteorological predictions that include
assimilation of meteorological observations as constraints. Emissions and meteorology data are
fed into CMAQ and run through various algorithms that simulate the physical and chemical
processes in the atmosphere to provide estimated concentrations of the pollutants. Traditionally,
the model has been used to predict air quality across a regional or national domain and then to
simulate the effects of various changes in emission levels for policymaking purposes. For health
studies, the model can also be used to provide supplemental information about air quality in areas
where no monitors exist.
CMAQ was also designed to have multi-scale capabilities so that separate models were not
needed for urban and regional scale air quality modeling. The grid spatial resolutions in past
annual CMAQ runs have been 36 km x 36 km per grid for the "parent" domain, and nested within
that domain are 12 km x 12 km grid resolution domains. The parent domain typically covered the
60
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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 2010 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 recommended
for most applications as the highest resolution. 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/CMAQ or
http ://www. cmascenter. org.
4.1.1 Advantages and Limitations of the CMAQ Air Quality Model
An advantage of using the CMAQ model output for characterizing air quality for use in
comparing with health outcomes is that it provides a complete spatial and temporal coverage
across the U.S. CMAQ is a three-dimensional Eulerian photochemical air quality model that
simulates the numerous physical and chemical processes involved in the formation, transport,
and destruction of ozone, particulate matter and air toxics for given input sets of initial and
boundary conditions, meteorological conditions and emissions. The CMAQ model includes
state-of-the-science capabilities for conducting urban to regional scale simulations of multiple air
quality issues, including tropospheric ozone, fine particles, toxics, acid deposition and visibility
degredation. However, CMAQ is resource intensive, requiring significant data inputs and
computing resources.
An uncertainty of using the CMAQ model includes structural uncertainties, representation of
physical and chemical processes in the model. These consist of: choice of chemical mechanism
used to characterize reactions in the atmosphere, choice of land surface model and choice of
planetary boundary layer. Another uncertainty in the CMAQ model is based on parametric
uncertainties, which includes uncertainties in the model inputs: hourly meteorological fields,
hourly 3-D gridded emissions, initial conditions, and boundary conditions. Uncertainties due to
initial conditions are minimized by using a 10 day ramp-up period from which model results are
not used in the aggregation and analysis of model outputs. Evaluations of models against
observed pollutant concentrations build confidence that the model performs with reasonable
accuracy despite the uncertainties listed above. A detailed model evaluation for ozone and
PM2.5 species provided in Section 4.3 shows generally acceptable model performance which is
equivalent or better than typical state-of-the-science regional modeling simulations as
summarized in Simon et al., 20126.
6 Heather Simon, Kirk R. Baker, Sharon Phillips. (2012) Compilation and interpretation of photochemical model
performance statistics published between 2006 and 2012. Atmospheric Environment 61, 124-139.
Online publication date: l-Dec-2012.
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4.2 CMAQ Model Version, Inputs and Configuration
This section describes the air quality modeling platform used for the 2010 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
CMAQ7 model as part of the 2010 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 2010 CMAQ
simulation along with the results of a model performance evaluation in which the 2010 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 2010 analysis employed CMAQ version 4.7.18
which reflects updates to version 4.7 to improve the underlying science which include aqueous
chemistry mass conservation improvements and improved vertical convective mixing. . The
CMAQ model version 4.7 was most recently peer-reviewed in February of 2009 for the U.S.
EPA.9 The model enhancements in version 4.7.1 also include:
1. Aqueous chemistry
Mass conservation improvements
Imposed one second minimum timestep for remainder of the cloud lifetime after 100
'iterations' in the solver
Force mass balance for the last timestep in the cloud by limiting oxidized amount to
mass available
7 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.
8 CMAQ version 4.7.1 model code is available from the Community Modeling and Analysis System (CMAS) at:
http://www.cmascenter.org.
9 Allen, D., Burns, D., Chock, D., Kumar, N., Lamb, B., Moran, M. (February 2009 Draft Version). Report on the
Peer Review of the Atmospheric Modeling and Analysis Division, NERL/ORD/EPA. U.S. EPA, Research Triangle
Park, NC. CMAQ version 4.7 was released on December, 2008. It is available from the Community Modeling and
Analysis System (CMAS) as well as previous peer-review reports at: http://www.cmascenter.org.
62
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Implemented steady state assumption for OH
Only allow sulfur oxidation to control the aqueous chemistry solver timestep (previously,
reactions of OH, GLY, MGLY, and Hg for multipollutant model also controlled the
timestep)
2. Advection
Added additional divergence-based constraint on advection timestep
Vertical advection in the Yamo module is now represented with the PPM scheme to limit
numerical diffusion
3. Model time step determination
Fixed a potential advection time step error
The sum of the advection steps for a given layer time step might not equal the output
time step duration in some extreme cases
- Ensured that the advection steps sum up to the synchronization step
4. Horizontal diffusion
Fixed a potential error
- Concentration data may not be correctly initialized if multiple sub-cycle time steps
are required
Fix to initialize concentrations with values calculated in the previous sub-time step
5. Emissions
Bug fix in EMIS_DEFN.F to include point source layer 1 NH3 emissions
Bug fix to calculate soil NO "pulse" emissions in BEIS
Remove excessive logging of cases where ambient air temperature exceeds 315.0 Kelvin.
When this occurs, the values are just slightly over 315
Bug fix for parallel decomposition errors in plume rise emissions
6. Photolysis
JPROC/phot_table and phot_sat options
Expanded lookup tables to facilitate applications across the globe and vertical
extent to 20km
- Updated temperature adjustments for absorption cross sections and quantum
yields
Revised algorithm that processes TOMS datasets for OMI data format
In-line option
- Asymmetry factor calculation updated using values from Mie theory integrated
over log normal particle distribution; added special treatment for large particles in
asymmetry factor algorithm to avoid numerical instabilities
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
63
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model extends vertically from the surface to 50 millibars (approximately 19 km) 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 Projection
12km
97W,40N
33 and 45 N
459x299x25
25 Layers: Surface to 50mb level (see Table 4-2)
12kmCONUSnat
x.y; 2556000. 1728000
col: 459 row: 299
Figure 4-1. Map of the CMAQ Modeling Domain. The blue box denotes the 12 km national
modeling domain. (Same as Figure 3-3.)
64
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4.2.3 Modeling Period/ Ozone Episodes
The 12 km CMAQ modeling domain was modeled for the entire year of 2010. The 2010 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
2010 Emissions: The emissions inventories used in the 2010 air quality modeling are described
in Section 3, above.
Meteorological Input Data: The gridded meteorological data for the entire year of 2010 at the
12 km continental United States scale domain was derived from version 3.4 of the Weather
Research and Forecasting Model (WRF), Advanced Research WRF (ARW) core.[1] 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
2010 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 and the RRTMG
long-wave and shortwave radiation (LWR/SWR) scheme. ^
The WRF meteorological outputs were processed using the Meteorology-Chemistry Interface
Processor (MCIP) package^, version 4.1.2, to derive the specific inputs to CMAQ: horizontal
wind components (i.e., speed and direction), temperature, moisture, and its related speciated
components was conducted for vertical diffusion rates, and rainfall rates for each grid cell in
each vertical layer. The WRF simulation used the same CMAQ map projection, a Lambert
Conformal projection centered at (-97, 40) with true latitudes at 33 and 45 degrees north. The 12
km WRF domain consisted of 459 by 299 grid cells and 35 vertical layers with a surface layer of
approximately 38 meters. Table 4-2 shows the vertical layer structure used in WRF and the layer
[1] Skamarock, W.C., Klemp, J.B., Dudhia, I, 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.
[2] 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.
[3] 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.
65
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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 2010 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 2010 WRF and CMAQ simulations (heights are layer
top).
CMAQ
Layers
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
WRF
Layers
35
34
33
32
31
30
29
28
27
26
25
24
23
22
21
20
19
18
17
16
15
14
13
12
11
10
Sigma P
0.0000
0.0500
0.1000
0.1500
0.2000
0.2500
0.3000
0.3500
0.4000
0.4500
0.5000
0.5500
0.6000
0.6500
0.7000
0.7400
0.7700
0.8000
0.8200
0.8400
0.8600
0.8800
0.9000
0.9100
0.9200
0.9300
Approximate
Height (m)
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
2,619
2,226
1,941
1,665
1,485
1,308
1,134
964
797
714
632
551
I
66
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7
6
5
4
3
2
1
0
9
8
7
6
5
4
3
2
1
0
0.9400
0.9500
0.9600
0.9700
0.9800
0.9850
0.9900
0.9950
0.9975
1.0000
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-CHEM10
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). This model was run
for 2010 with a grid resolution of 2.0 degrees x 2.5 degrees (latitude-longitude) and 46 vertical
layers up to 0.01 hPa. The predictions were processed using the GEOS-2-CMAQ tool and used
to provide one-way dynamic boundary conditions at one-hour intervals. 11 Ozone was evaluated
from these GEOS-Chem runs by comparing to satellite vertical profiles and ground-based
measurements and found acceptable model performance. More information is available about the
GEOS-CHEM model and other applications using this tool at: http://www-
as. harvard. edu/chemi stry/trop/geos.
4.3 CMAQ Model Performance Evaluation
An operational model performance evaluation for ozone and PM2.5 and its related speciated
components was conducted for the 20010 simulation using state/local monitoring sites data in
order to estimate the ability of the CMAQ modeling system to replicate the 2010 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, normalized mean bias and fractional bias;
and two error metrics, normalized mean error and fractional error. Normalized mean bias
(NMB) is used as a normalization to facilitate a range of concentration magnitudes. This statistic
10 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric Chemistry Modeling Group, Harvard
University, Cambridge, MA, October 15, 2004.
11 Akhtar, F., Henderson, B., Appel, W., Napelenok, S., Hutzell, B., Pye, H., Foley, K., 2012. Multiyear Boundary
Conditions for CMAQ 5.0 from GEOS-Chem with Secondary Organic Aerosol Extensions, 11th Annual Community
Modeling and Analysis System conference, Chapel Hill, NC, October 2012.
67
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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:
*100, where P = predicted concentrations and O = observed
NMB =
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:
*100
NME=
Fractional bias is defined as:
FB=I
n
y((P+0)
v4l 2
*100
FB is a useful model performance indicator because it has the advantage of equally weighting
positive and negative bias estimates. The single largest disadvantage in this estimate of model
performance is that the estimated concentration (i.e., prediction, P) is found in both the numerator
and denominator.
Fractional error (FE) is similar to fractional bias except the absolute value of the difference is
used so that the error is always positive. Fractional error is defined as:
*100
FE- 1
rJi
n
t\p-o\
*((P+0)}
V 1
In addition to the performance statistics, regional maps which show the normalized mean bias
and error were prepared for the ozone season, May through September, at individual monitoring
sites as well as on an annual basis for PM2.5 and its component species.
Evaluation for 8-hour Daily Maximum Ozone: The operational model performance evaluation
for eight-hour daily maximum ozone was conducted using the statistics defined above. Ozone
measurements for 2010 in the continental U.S. were included in the evaluation and were taken
from the 2010 State/local monitoring site data in the Air Quality System (AQS) Aerometric
Information Retrieval System (AIRS). The performance statistics were calculated using
predicted and observed data that were paired in time and space on an 8-hour basis. Statistics
68
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were generated for the following geographic groupings in the 12-km continental U.S. domain!2:
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 normalized mean bias and error for individual monitors are
shown in Figures 4-2 through 4-3. 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 2010 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. Specifically, 8-hour ozone in the summer is
slightly over predicted with the greatest over prediction in Central U.S. (NMB is 20.6 percent).
Ozone performance in spring shows better performance with slight over predictions in most of
the subregions except in the Northeast (slight under prediction of 2.2). In the winter, when
concentrations are generally low, the model slightly under predicts 8-hour ozone with the
exception of the West (NMB is 5.6). In the fall, when concentrations are also relatively low,
ozone is also slightly over predicted; with NMBs less than 7 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. The information in Figure 4-2 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 and in Seattle, WA. 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 4-2
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 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 Figure 4-3, is 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 and Maine, Baton Rouge, LA, Cleveland, OH,
and Seattle, WA.
12 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.
69
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Table 4-4. Summary of CMAQ 20010 8-Hour Daily Maximum Ozone Model Performance
Statistics by Subregion, by Season.
Subregion Season
Northeast Winter
Spring
Summer
Fall
Midwest Winter
Spring
Summer
Fall
Central States Winter
Spring
Summer
Fall
Southeast Winter
Spring
Summer
^^^^ Fall
West Winter
Spring
Summer
Fall
No. of
Obs
6,015
13,825
16,359
11,373
2,720
12,066
16,389
9,666
11,505
16,330
17,956
14,781
7,112
20,567
22,162
16,908
22,696
27,849
30,279
22,984
NMB
(%)
-20.3
-2.2
3.3
4.1
-23.6
3.9
8.3
4.0
-4.6
4.1
20.6
3.4
-4.7
4.9
17.4
5.3
5.6
1.4
7.2
6.9
NME
(%)
22.9
12.2
14.1
16.2
27.7
12.0
16.3
14.2
15.8
13.2
26.7
16.8
12.5
12.8
22.4
15.3
20.9
11.6
16.0
15.6
FB (%)
-23.0
-2.0
3.9
6.1
-27.0
4.6
7.8
5.5
-5.0
4.9
20.6
4.9
-4.6
6.1
18.1
6.7
8.2
2.1
7.6
7.7
FE (%)
26.7
13.1
14.6
17.5
32.7
12.5
16.1
15.2
17.5
13.7
26.2
17.3
13.5
13.5
22.4
15.9
23.0
12.1
16.7
16.2
70
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O3_8hrmax NMB (%) for run 2010ef_v5_10f_12US1 for 20100501 to 20100930
units = %
coverage limit = 75
> 100
90
80
70
60
50
40
30
20
10
0
-10
-20
-30
-40
-50
-60
-70
-80
-90
<-100
CIRCLE=AQS_Daily;
Figure 4-2. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 ppb over the period May-
September 2010 at monitoring sites in the continental U.S. modeling domain.
O3_8hrmax NME (%) for run 2010ef_v5_10f_12US1 for 20100501 to 20100930
units - %
coverage limit = 75
B
>30
28
26
24
22
20
18
16
14
12
10
CIRCLE=AQS_Daily;
Figure 4-3. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60
ppb over the period May-September 2010 at monitoring sites in the continental U.S.
modeling domain.
71
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Evaluation for Annual PMi.s: The PM evaluation focuses on PIVh.s total mass and its components
including sulfate (SCU), nitrate (NOs), total nitrate (TNCb = NCb + HNOs), ammonium (NH4),
elemental carbon (EC), and organic carbon (OC).
The PM2.5 bias and error performance statistics were calculated on an annual basis for each
subregion (Table 4-5). PIVh.s measurements for 2010 were obtained from the following networks
for model evaluation: Chemical Speciation Network (CSN, 24 hour average), Interagency
Monitoring of Protected Visual Environments (IMPROVE, 24 hour average, and Clean Air
Status and Trends Network (CASTNet, weekly average). For PM2.5 species that are measured by
more than one network, we calculated separate sets of statistics for each network by subregion.
For brevity, Table 4-5 provides annual model performance statistics for PM2.5 and its component
species for the five sub-regions in the 12 km continental U.S. domain defined above (Northeast,
Midwest, Southeast, Central, and West). In addition to the tabular summaries of bias and error
statistics, annual spatial maps which show the normalized mean bias and error by site for each
PM2.5 species are provided in Figures 4-4 through 4-17.
As indicated by the statistics in Table 4-5, annual CMAQ PM2.5 for 2009 shows over predictions
at rural IMPROVE monitoring sites and urban CSN monitoring sites in each subregion except in
the Southeast at CSN sites (which shows an under prediction in NMB of 17 percent) and in the
Southeast, Central and West at IMPROVE sites (which shows a slight under prediction in NMB
of 2 to 11 percent). Although not shown here, the mean observed concentrations of PM2.5 are
more than twice as high at the CSN sites (~1 l|ig m"3) as the IMPROVE sites (~5 jig m"3), thus
illustrating the statistical differences between the urban CSN and rural IMPROVE networks.
Annual average sulfate is consistently under predicted at CSN, IMPROVE, and CASTNet
monitoring sites across the modeling domain, with NMB values ranging from near negligible to -
29 percent. Sulfate performance shows moderate error, ranging from 24 to 45 percent. Figures
4-6 and 4-7, suggest spatial patterns vary by region. The model bias for most of the Southeast,
Central and Southwest states are within 0 to -30 percent. The model bias appears to be
slightly greater in the Northwest with over predictions up to 80 percent at individual monitors.
Model error also shows a spatial trend by region, where much of the Eastern states are 20 to 40
percent, the Western and Central U.S. states are 30 to 70 percent.
Annual average nitrate is under predicted at the urban CSN monitoring sites in most of the
subregions (NMB in the range of near negligible to -41 percent), except in the Southeast
where nitrate is over predicted on average by 17.5 percent. At IMPROVE rural sites, annual
average nitrate is over predicted at all subregions (NMBin the range of 3 to 40
percent), except in the West where nitrate is under predicted on average by 26
percent. The bias statistics indicate that the model performance for nitrate is generally best at
the urban CSN monitoring sites. Model performance of total nitrate at sub-urban CASTNet
monitoring sites shows an over prediction across all subregions (NMB in the range of 2 to 27
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 4-8. The
72
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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 Figure 4-9, 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 -7
percent) and over predict across the urban CSN sites (ranging from 6 to 13 percent). 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 shows bias within ±30 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 under predicted across most subregions in rural IMPROVE
areas (NMB ranging from -12 to -17 percent), except in the Northeast where the bias on average
is 30.5 percent. However, the model over predicted annual average organic carbon in most
subregions at urban CSN sites (NMB ranging from 1 to 34 percent), except in the Southeast
where the bias on average is -5.7 percent. 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 (42 to 81 percent).
Table 4-5. Summary of CMAO 2010 Annual PM Species Model Performance Statistic
Pollutant
PM2.5
Sulfate
^°"'lio[ Subregion
Network **
CSN Northeast
Midwest
1 Southeast
Central
| West
IMPROVE Northeast
Midwest
| Southeast
Central
| West
CSN Northeast
Midwest
No. of
Obs
2,882
2,210
2,441
1,671
1,297
2,101
562
1,940
2,429
10,060
2,885
2,226
NMB
(%)
0.7
9.5
-17.0
3.9
06
9.0
0.4
-10.5
-2.4
-8.7
-10.9
-6.6
NME
(%)
39.9
36.6
35.6
39.0
58.7
46.2
38.2
38.4
40.9
49.4
30.3
32.1
FB (%)
1.1
6.2
-23.5
-0.1
_^.
_i-_
-1.4
-17.9
-7.2
-7.7
-0.1
-0.5
FE (%)
39.4
35.6
39.7
38.4
54.0
44.2
40.0
43.3
42.3
48.3
30.7
32.4
73
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n . . Monitor 0 .
Pollutant Kl . . Subregion
Network ^
I Southeast
Central
M West
IMPROVE Northeast
Midwest
B Southeast
Central
B West
CASTNet Northeast
Midwest
B Southeast
Central
B West
CSN Northeast
Midwest
I Southeast
Central
West
Nitrate
IMPROVE Northeast
Midwest
B Southeast
Central
M West
CASTNet Northeast
Midwest
Total Nitrate 0
m/~> i IM^ ^ southeast
(NOs + HNOs)
Central
B West
No. of
Obs
2,453
1,789
1,308
2,035
583
1,919
2,261
9,832
758
618
1,095
378
1017
2,885
2,226
2,453
1,690
1,308
2,031
580
1,919
2,261
9,765
758
618
1,095
378
1,016
NMB
(%)
-20.5
-11.9
1.0
01
-12.2
-17.5
-14.0
-0.1
-15.7
-20.4
-24.5
-28.9
-17.5
_05
-0.3
17.5
-4.1
-40.6
40.3
2.6
22.2
15.4
-26.4
27.2
7.6
18.9
4.7
1.6
NME
(%)
30.9
37.4
43.1
32.4
32.8
30.3
35.2
45.1
22.3
23.7
26.7
31.4
32.9
57.2
49.3
81.1
53.4
71.6
89.2
64.4
95.2
62.4
80.0
37.9
33.3
42.3
31.3
34.8
FB (%)
-22.8
-7.9
_££_
^116
1.9
-17.5
-6.7
15.6
-9.3
-18.8
-28.3
-29.1
_^_.
-37.4
-21.6
-50.0
-45.0
-66.8
-29.8
-40.8
-59.7
-32.0
-82.3
20.3
13.8
13.8
2.0
12.5
FE (%)
35.4
39.3
42.2
36.5
37.3
35.3
37.6
48.7
22.4
24.7
31.5
34.3
36.3
81.8
67.8
101.0
88.7
94.5
97.5
92.6
116.0
92.9
121.0
38.8
30.0
42.4
30.7
39.3
Ammonium CSN
Northeast
2,885
7.9
40.8
34.3
44.2
74
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n . . Monitor 0 .
Pollutant Kl . . Subregion
Network °
Midwest
I Southeast
Central
M West
CASTNet Northeast
Midwest
B Southeast
Central
B West
CSN Northeast
Midwest
I Southeast
Central
B West
Elemental
Carbon
IMPROVE Northeast
Midwest
B Southeast
Central
B West
CSN Northeast
Midwest
Southeast
Central
B West
Organic Carbon
IMPROVE Northeast
Midwest
I Southeast
Central
1 West
No. of
Obs
2,226
2,453
1,789
1,308
758
618
1,095
378
1,017
2,638
2,191
2,403
1,629
1,227
2J17
583
1,924
2,506
10,118
2,112
2,180
2,390
1,624
1,218
2,624
575
1,931
2,502
9,919
NMB
(%)
10.0
9.6
13.0
5.8
0.0
-2.2
-6.7
-0.3
-6.8
32.9
49.6
9.6
74.8
50.9
24.6
13.0
40
17.1
22.3
1.0
16.7
-5.7
23.0
34.0
30.5
-11.6
-16.3
-17.2
-15.1
NME
(%)
38.1
37.2
44.6
77.6
27.7
27.3
25.9
30.7
38.3
59.2
68.4
47.6
91.8
87.3
53.4
46.0
46.9
52.5
72.7
61.1
54.7
42.6
59.2
80.6
65.9
48.6
47.7
51.5
59.9
FB (%)
21.6
12.9
20.7
30.3
^ZO
3.7
_LL.
-0.5
-4.4
29.4
35.1
12.1
46.6
32.8
^115
2.7
_1L.
7.7
8.0
-6.4
12.3
-10.2
13.9
27.9
22.7
-17.5
-33.3
-30.5
-12.9
FE (%)
41.5
38.0
47.1
65.2
28.0
27.0
28.8
32.9
40.9
48.5
52.5
45.6
61.2
65.7
46.8
48.1
45.6
43.4
56.8
56.3
49.1
440
48.7
63.9
55.6
54.8
54.6
55.5
58.1
75
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PM_TOT NMB (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
units = %
coverage limit = 75
>100
90
80
70
60
50
40
30
20
10
0
-10
-20
-30
-40
-50
-60
-70
-80
-90
<-100
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-4. Normalized Mean Bias (%) of annual PMi.s mass at monitoring sites in
the continental U.S. modeling domain.
76
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PM_TOT NME (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
units = %
coverage limit = 7
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-5. Normalized Mean Error (%) of annual PMi.s mass at monitoring sites in the continental U.S.
modeling domain.
SO4 NMB (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
units - %
coverage limit = 75
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure 4-6. Normalized Mean Bias (%) of annual sulfateat monitoring sites in the
continental U.S. modeling domain.
77
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SO4 NME (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
units = %
coverage limit = 75'
>100
90
80
70
60
50
40
30
20
10
'0
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure 4-7. Normalized Mean Error (%) of annual sulfateat monitoring sites in the
continental U.S. modeling domain.
NO3 NMB (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
units = %
coverage limit = 75'
>100
90
80
70
60
50
40
30
20
10
0
-10
-20
-30
-40
-50
-60
-70
-80
-90
<-100
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-8. Normalized Mean Bias (%) of annual nitrate at monitoring sites in the
continental U.S. modeling domain.
78
-------
NO3 NME (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
units = %
coverage limit
>100
90
80
70
60
50
40
30
20
10
'0
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-9. Normalized Mean Error (%) of annual nitrate at monitoring sites in the
continental U.S. modeling domain.
TNO3 NMB (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
units = %
coverage limit = 75
C1RCLE=CASTNET;
Figure 4-10. Normalized Mean Bias (%) of annual total nitrate at monitoring sites in the
continental U.S. modeling domain.
79
-------
TNO3 NME (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
CIRCLE=CASTNET;
Figure 4-11. Normalized Mean Error (%) of annual total nitrate at monitoring sites in the
continental U.S. modeling domain.
NH4 NMB (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
units - %
coverage limit = 75
CIRCLE=CSN; TRIANGLE=CASTNET;
Figure 4-12. Normalized Mean Error (%) of annual ammonium at monitoring sites in the continental U.S.
modeling domain.
80
-------
NH4 NME (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
units = %
coverage limit = 7£
CIRCLE=CSN; TRIANGLE=CASTNET;
Figure 4-13. Normalized Mean Error (%) of annual ammonium at monitoring sites in the
continental U.S. modeling domain.
EC NMB (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
units = %
coverage limit = 7E
>100
190
80
70
60
50
40
30
20
10
o
-10
-20
-30
-40
-50
-60
-70
-80
-90
<-100
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-14. Normalized Mean Bias (%) of annual elemental carbon at monitoring sites in
the continental U.S. modeling domain.
81
-------
EC NME (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
units = %
coverage limit = 75
0. -
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-15. Normalized Mean Error (%) of annual elemental carbon at monitoring sites in
the continental U.S. modeling domain.
PC NMB (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
units = %
coverage limit = 75'
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-16. Normalized Mean Bias (%) of annual organic carbon at monitoring sites in the
continental U.S. modeling domain.
82
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PC NME (%) for run 2010ef_v5_10f_12US1 for 20100101 to 20101231
iJ LJpOT
OT^Jfe
CIRCLE=IMPROVE; TRIANGLE=CSN;
units = %
coverage limit = 7J
>100
90
80
70
| 60
50
40
30
20
10
0
Figure 4-17. Normalized Mean Error (%) of annual organic carbon at monitoring sites in
the continental U.S. modeling domain.
83
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5.0 Bayesian space-time downscaling fusion model (downscaler) -
Derived Air Quality Estimates
5.1 Introduction
The need for greater spatial coverage of air pollution concentration estimates has grown in recent
years as epidemiology and exposure studies that link air pollution concentrations to health effects
have become more robust and as regulatory needs have increased. Direct measurement of
concentrations is the ideal way of generating such data, but prohibitive logistics and costs limit
the possible spatial coverage and temporal resolution of such a database. Numerical methods
that extend the spatial coverage of existing air pollution networks with a high degree of
confidence are thus a topic of current investigation by researchers. The downscaler model (DS)
is the result of the latest research efforts by EPA for performing such predictions. DS utilizes
both monitoring and CMAQ data as inputs, and attempts to take advantage of the measurement
data's accuracy and CMAQ's spatial coverage to produce new spatial predictions. This chapter
describes methods and results of the DS application that accompany this report, which utilized
ozone and PM2.5 data from AQS and CMAQ to produce predictions to continental U.S. 2010
census tract centroids for the year 2010.
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
84
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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 2010 US census tract centroids across the continental U.S. using 2010 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 2010. 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 2010 about half of the US Census tracts
(36206 out of 72283) experienced at least one day with an ozone value above the NAAQS of 75
ppb.
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2010
4'tli Max, Daily max
8-hour avg
ozone (ppb)
<-lnf,55]
(55,60]
(60,65]
(65,70]
(70,75]
(75,80]
(80,85]
(85,90]
(90, Inf]
Figure 5-1. Annual 4th max (daily max 8-hour ozone concentrations) derived from AQS,
CMAQ and DS data.
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5.3.2 Summary of PM2.5 Results
Figures 5-2 and 5-3 summarize the AQS, CMAQ and DS PM2.5 data over the year 2010. Figure
5-2 shows annual means and Figure 5-3 shows 98'th percentiles of 24-hour PM2.5
concentrations for AQS observations, CMAQ model predictions and DS model results. The DS
model estimated that for 2010 about 33% of the US Census tracts (23547 out of 72283)
experienced at least one day with a PM2.5 value above the 24-hour NAAQS of 35 ug/m3.
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AQS
2010
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.
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AQS
2010
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,lrrf]
Figure 5-3. 98th percentile 24-hour average PMi.s concentrations derived from AQS,
CMAQ and DS data.
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5.4 Downscaler Uncertainties
5.4.1 Standard Errors
As mentioned above, the DS model works by drawing random samples from built-in
distributions during its parameter estimation. The standard errors associated with each of these
populations provide a measure of uncertainty associated with each concentration prediction.
Figure 5-4 shows the percent errors resulting from dividing the DS standard errors by the
associated DS prediction. The black dots on the maps show the location of EPA sampling
network monitors whose data was input to DS via the AQS datasets (Chapter 2). The maps show
that, in general, errors are relatively smaller in regions with more densely situation monitors (ie
the eastern US), and larger in regions with more sparse monitoring networks (ie western states).
These standard errors could potentially be used to estimate the probability of an exceedance for a
given point estimate of a pollutant concentration.
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% 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 2010 runs. The black dots show the locations of monitors that generated the AQS data
used as input to the DS model.
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5.4.2 Cross Validation
To check the quality of its spatial predictions, DS can be set to perform "cross-validation" (CV),
which involves leaving a subset of AQS data out of the model run and predicting the
concentrations of those left out points. The predicted values are then compared to the actual left-
out values to generate statistics that provide an indicator of the predictive ability. In the DS runs
associated with this report, 10% of the data was chosen randomly by the DS model to be used for
the CV process. The resulting CV statistics are shown below in Table 5-1.
Pollutant
PM2.5
O3
# Monitors
901
1239
Mean Bias
6.20e-3
1.35e-2
RMSE
2.88
5.12
Mean Coverage
0.96
0.96
Table 5-1. Cross-validation statistics associated with the 2010 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 2010 U.S. census tract centroids by utilizing monitoring data and CMAQ
output for 2010. 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-14-001
Environmental Protection Health and Environmental Impacts Division Nov, 2015
Agency Research Triangle Park, NC
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