U n ited States E PA/600/R-10/017
Environmental Protection February 2010
Agency www.epa.gov
Hierarchical Bayesian Model
(HBM)-Derived Estimates of
Air Quality for 2002
Annual Report
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Hierarchical Bayesian Model
(HBM)-Derived Estimates of
Air Quality for 2002
Annual Report
Developed by the U.S. Environmental Protection Agency
Office of Research and Development (ORD)
National Exposure Research Laboratory (NERL)
And
Office of Air and Radiation (OAR)
Office of Air Quality Planning and Standards (OAQPS)
Interagency Agreement Number: RW75922615-01-0 (EPA)
Interagency Agreement Number: 07FED63951 (CDC)
Contributors:
Fred Dimmick (EPA/ORD)
Eric Hall (EPA/ORD)
Joe Tikvart (EPA/OAR)
Project Officer
EricS. Hall
National Exposure Research Laboratory (NERL)
109T.W. Alexander Dr.
Durham, NC 27711-0001
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11
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Acknowledgements
The following people served as reviewers of this document and provided valuable comments that
were included: Chris Clark (EPA/OEI), Alice Gilliland (EPA/ORD), David Holland
(EPA/ORD), Steve Howard (EPA/ORD), David Mintz (EPA/OAR), Ed Lillis (EPA/OAR), and
Doug Solomon (EPA/OAR).
in
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IV
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Table of Contents
1.0 Introduction 1
2.0 Air Quality Data 5
2.1 Introduction to Air Quality Impacts in the United States 5
2.1.1 The Clean Air Act 5
2.1.2 Ozone 6
2.1.3 Fine Particulate Matter 6
2.2 Ambient Air Quality Monitoring in the United States 7
2.2.1 Monitoring Networks 7
2.2.2 Air Quality System Database 8
2.2.3 Advantages and Limitations of the Air Quality Monitoring and
Reporting System 9
2.2.4 Use of Air Quality Monitoring Data 9
2.3 Air Quality Indicators Developed for the EPHT Network 10
2.3.1 Rationale for the Air Quality Indicators 11
2.3.2 Air Quality Data Sources 12
2.3.3 Use of Air Quality Indicators for Public Health Practice 12
3.0 Emissions Data 13
3.1 Introduction to the 2002 Platform 13
3.2 2002 Emission Inventories and Approaches 15
3.2.1 2002 Point Sources (ptipm and ptnonipm) 16
3.2.1.1IPM Sector (ptipm) 18
3.2.1.2 Non-IPM Sector (ptnonipm) 19
3.2.2 2002 Nonpoint Sources (afdust, ag, nonpt) 19
3.2.2.1 Area Fugitive Dust Sector (afdust) 19
3.2.2.2 Agricultural Ammonia Sector (ag) 20
3.2.2.3 Other Nonpoint Sources (nonpt) 20
3.2.3 Fires (ptfire, nonptfire and avefire) 21
3.2.4 Day-Specific Point Source Fires (ptfire) 21
3.2.5 County-Level Fires (nonptfire) 21
3.2.6 Development of Wildland Fire Emission Inventories for 2002-2006 22
3.2.7 Biogenic Sources (biog) 26
3.2.8 2002 Mobile sources (onroad, nonroad, aim) 26
3.2.9 2002 Onroad Mobile Sources (onroad) 27
3.2.10 Nonroad Mobile Sources - NMIM-Based Nonroad (nonroad) 27
3.2.11 Nonroad Mobile Sources: Aircraft, Locomotive, and Commercial
Marine (aim) 28
3.2.12 Emissions from Canada, Mexico and Offshore Drilling Platforms
(othpt, othar, othon) 28
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3.3 Emissions Modeling Summary 28
3.3.1 The SMOKE Modeling System 29
3.3.2 Key Emissions Modeling Settings 29
3.3.3 Spatial Configuration 30
3.3.4 Chemical Speciation Configuration 31
3.3.5 Temporal Processing Configuration 33
3.3.6 Vertical Allocation of Day-Specific Fire Emissions 34
3.3.7 Emissions Modeling Ancillary Files 35
3.3.7.1 Spatial Allocation Ancillary Files 35
3.3.7.2 Surrogates for U.S. Emissions 35
3.3.7.3 Allocation Method for Airport-Related Sources in the U.S 36
3.3.7.4 Surrogates for Canada and Mexico Emission Inventories 36
3.3.7.5 Chemical Speciation Ancillary Files 37
3.3.7.6 Temporal Allocation Ancillary Files 38
4.0 CMAQ Air Quality Model Estimates 39
4.1 Introduction to the CMAQ Modeling Platform 39
4.1.1 Advantages and Limitations of the CMAQ Air Quality Model 40
4.2 CMAQ Model Version, Inputs and Configuration 41
4.2.1 Model Version 41
4.2.2 Model Domain and Grid Resolution 41
4.2.3 Modeling Period / Ozone Episodes 43
4.2.4 Model Inputs: Emissions, Meteorology and Boundary Conditions 43
4.3 CMAQ Model Performance Evaluation 46
5.0 Bayesian Model-Derived Air Quality Estimates 55
5.1 Introduction 55
5.2 Hierarchical Bayesian Space-Time Modeling System 55
5.2.1 Introduction to the Hierarchical-Bayesian Approach 55
5.2.2 Advantages and Limitations of the Hierarchical-Bayesian Approach 56
5.3 Results for O3 and PM2.5 57
5.4 Overview of HB Model Predictions 58
5.5 Evaluation of HB Model Estimates 65
5.6 Use of EPA HB Model Predictions 66
VI
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Appendices
Appendix A. Acronyms A-l
Appendix B. Total U.S. Emissions Summary by Sector and by Region for PM2.5 B-l
Appendix C. State-Sector Emissions Summaries for 2002 C-l
Appendix D. State-Sector Emissions Summary for 2002 Base and Future-Year
Base Cases: 2009, 2014, 2020, and 2030 D-l
Appendix E. Metadata E-l
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Vlll
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List of Tables
Table 2-1 Ozone Standard 6
Table 2-2 PM2.5 Standard 7
Table 2-3 Public Health Surveillance Goals and Current Results 10
Table 2-4 Basic Air Quality Indicators 11
Table 3-1 Platform Sectors Used in Emissions Modeling for the CAP 2002 Platform 16
Table 3-2 Summaries by Sector of 2002 Base Year Emissions for the Continental
United States (48 states + District of Columbia) 17
Table 3-3 Summaries by Sector for the Other ("oth") - Canada, Mexico, and
Offshore -2002 Base Year Emissions Within the 36-km Domain 17
Table 3-4 Process/Emissions Modeling Mapping 24
Table 3-5 Key Emissions Modeling Steps by Sector 30
Table 3-6 Model Species Produced by SMOKE for CB05 32
Table 3-7 Temporal Settings Used for the Platform Sectors in SMOKE 34
Table 4-1 Geographic Information for Modeling Domains 42
Table 4-2 Vertical Layer Structure for MM5 and CMAQ (heights are layer top) 44
Table 4-3 Summary of CMAQ 2002 Hourly O3 Model Performance Statistics 49
Table 4-4 Summary of CMAQ 2002 8-Hour Daily Maximum O3 Model
Performance Statistics 50
Table 4-5 Summary of 2002 CMAQ Annual PM2.5 Species Model
Performance Statistics 52
Table 5-1 HB Model Prediction: Example Data File 57
Table 5-2 HB Model Domains for 12-km Applications 58
Table C-l 2002 State Sector Emissions C-3
Table D-la Continental US, VOC, NOX, and CO Emissions by Sector for 2002,
and Projection Years 2009, 2014, 2020, and 2030 D-3
Table D-lb Continental US, SO2, NH3, PMio and PM2.5 Emissions by Sector for 2002,
and Projection Years 2009, 2014, 2020, and 2030 D-21
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List of Figures
Figure 3-1 Wildfire Emissions in the Contiguous 48 States 24
Figure 3-2 Distribution of PM2.5 Emissions 25
Figure 3-3 CMAQ Modeling Domain 31
Figure 3-4 Chemical Speciation Approach Used for the 2002-Based Platform 33
Figure 4-1 Map of the CMAQ Modeling Domain 42
Figure 5-1 HB Prediction (PM2.5) During July 1-4, 2002 59
Figure 5-2 HB Prediction (PM2.5) on July 2, 2002 59
Figure 5-3 Fffl Prediction (PM2.5) Temporarily Matches AQS Data and CMAQ
Estimates 60
Figure 5-4 Fffl Prediction (PM2.5) Compensates When AQS Data is Unavailable 60
Figure 5-5 Fffl Prediction (PM2.5) Mitigates CMAQ Bias when AQS and
CMAQ Values Diverge 61
Figure 5-6 Plot of the Response Surface of PM2 5 Concentrations as Predicted by the Fffl
Model on a Specific Monitoring Day in the Northeast U.S., Along With PM2 5
Measurements on a Specific Monitoring Day from FRM Monitors in the
NAMS/SLAMS Network 62
Figure 5-7 Rotated View of the Response Surface of PM2 5 Concentrations as Predicted
by the FfflM on a Specific Monitoring Day in the Northeast U.S., Along With
PM2 5 Measurements on a Specific Monitoring Day from FRM Monitors in the
NAMS/SLAMS Network 63
Figure 5-8 Rotated View of the Response Surface of PM2 5 Concentrations as Predicted by the
FfflM on a Specific Monitoring Day in the Northeast U.S., Along With
PM2 5 Measurements on a Specific Monitoring Day from FRM Monitors in the
NAMS/SLAMS Network, and the Response Surface as Predicted by the CMAQ
Modeling System 64
Figure 5-9 Fused 36 km O3 Surface for the Continental U.S. (July 26, 2005) 65
Figure B-l PM2.5 in Urban Areas in Western U.S. (2002) B-3
Figure B-2 PM2.5 in Urban Areas in Eastern U.S. (2002) B-3
Figure B-3 PM2.5 in Rural Areas in Western U.S. (2002) B-4
Figure B-4 PM2.5 in Rural Areas in Eastern U.S. (2002) B-4
Figure B-5 PM2.5 in Western U.S. - Rural and Urban (2002) B-5
Figure B-6 PM2.5 in Eastern U.S. - Rural and Urban (2002) B-5
Figure B-7 Total PM2.5 in U.S. (2002) B-6
XI
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Figure E-l PM2.5 Monitoring Data and CMAQ Surface (Separately Displayed -
White Spheres Represent Monitoring Locations and Associated
Concentrations Values) E-5
Figure E-2 Combined PM2.5 Monitoring Data and CMAQ Surface (Via HBM) E-6
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1.0 Introduction
This report describes EPA's Hierarchical Bayesian model-generated (HBM) estimates of Os and
PM2.s concentrations throughout the continental United States during the 2002 calendar year.
HBM estimates provide the spatial and temporal variance of 63 and PM2.5, allowing estimation
of their concentration values across the U.S., independent of where air quality monitors are
physically located. HBM estimates are generated through the statistical 'fusion' of measured air
quality monitor concentration values and air quality model predicted concentration values from
EPA's Community Multiscale Air Quality (CMAQ) computer model. Information on EPA's air
quality monitors, CMAQ model, and HBM model is included to provide the background and
context for understanding the data output presented in this report.
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.
The research which serves as the basis of this report falls under EPA's Long Term Goal 1, Clean
Air and Global Climate Change, Objective 1.6, Enhance Science and Research, Subobjective
1.6.2, Conduct Air Pollution Research of EPA's Strategic Plan. Under Long Term Goal 1,
EPA's objective is to protect and improve the air so it is healthy to breathe and risks to human
health and the environment are reduced. Detailed information on Long Term Goal 1 can be
found at: http://www.epa.gov/ocfo/par/2007par/par07goall_goal.pdf
As noted under Subobjective 1.6.2, through 2010, EPA provides methods, models, data, and
assessment research associated with air pollutants. Under this research effort, EPA provides
modeling support, air quality monitoring data and air quality modeling estimates for CDC to use
in its public health tracking network. It allows EPA and CDC to link air quality data with public
health (health outcome) data. This research provides scientific information and tools for
understanding and characterizing environmental outcomes associated with national, urban, and
residual criteria pollutants. The research contributes to an important EPA research objective,
which is to understand the relationship between exposure to pollution and the resultant health
effects on people.
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 is leading 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
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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. The EPHT Network is a standards-based, secure
information network that was created to be used by many different entities, including
epidemiologists, public health practitioners, academic researchers, schools of public health,
local, state, and federal agencies such as EPA. Levels of access to the data in the EPHT Network
will vary among stakeholders based upon their role and their purpose for using the data. Data
access will be carefully controlled to ensure compliance with federal and state privacy laws
which address the use of health data and other protected personal information. The CDC's
National EPHT Program is establishing the EPHT Network by collaborating with a wide range
of partners with expertise from federal, state, and local health and environmental agencies;
nongovernmental organizations (NGOs); state public health and environmental laboratories; and
Schools of Public Health.
Since 2002, EPA has collaborated with the CDC on the development of the EPHT Network. On
September 30, 2003, the Secretary of Health and Human Services (HHS) and the Administrator
of EPA signed a joint Memorandum of Understanding (MOU) with the objective of advancing
efforts to achieve mutual environmental public health goals.1 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.
Under the auspices of the HHS/EPA MOU, a research project was implemented between 2004
and 2006 to investigate the utility of EPA-generated air quality estimates as an input to the EPHT
Network. The relationship between air pollutants and human health is of interest to both
Agencies. EPA develops and funds ambient air quality monitoring networks to monitor air
pollution and to provide data that may be used to mitigate its impact on our ecosystems and
human health. (Note: AQS and AIRNow are EPA databases containing data collected from
EPA's air quality monitoring networks.) Air quality monitoring data has been used by
researchers to investigate the linkages between human health outcomes and air quality, and by
Available at www.cdc.gov/nceh/tracking/epa_mou.htm
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environmental and public health professionals to develop environmental health indicators which
provide measures of potential human health impacts. However, an analysis of the currently
available methods for generating and characterizing air quality estimates that could be developed
and delivered systematically, and which were also readily available to link with public health
surveillance data, had not been previously attempted. EPA collaborated with the CDC and state
public health agencies in New York, Maine, and Wisconsin on the Public Health Air
Surveillance Evaluation (PHASE) project to address this issue. The project focused on
generating concentration surfaces for ozone and PM2.5, which were subsequently linked with
asthma and cardiovascular disease data. Results of this research project indicated that using a
Hierarchical Bayesian approach to statistically "combine" Community Multiscale Air Quality
(CMAQ) model estimates and air quality monitoring data documented in EPA's AQS provided
better overall estimates of air quality at locations without monitors than those obtained through
other well-known, statistically-based estimating techniques (e.g., kriging).
Ambient air quality monitoring data stored in the Air Quality System (AQS), along with air
quality modeling estimates from CMAQ, can be statistically combined, via a Hierarchical
Bayesian statistical space-time modeling (HBM) system, to provide air quality estimates
(hereafter referred to as Hierarchical Bayesian-derived air quality estimates). These Hierarchical
Bayesian-derived air quality estimates serve as well-characterized inputs to the EPHT Network.
The air quality monitor data, CMAQ modeling estimates, and the Hierarchical Bayesian-derived
air quality estimates can be used to develop meaningful environmental public health indicators
and to link ozone and PM2 5 concentrations with health outcome data. The Hierarchical
Bayesian-derived air quality estimates are based on EPA's current knowledge of predicting
spatial and temporal variations in pollutant concentrations derived from multiple sources of
information. EPA is continuing its research in this critical science area and is implementing this
project to establish procedures for routinely generating the Hierarchical Bayesian-derived air
quality estimates developed in the PHASE project. This effort will assist EPA in making both
ambient air quality monitoring (raw) data and the Hierarchical Bayesian-derived air quality
estimates available to the CDC EPHT Network through EPA's Central Data Exchange (CDX)
Node on the Environmental Information Exchange Network.
Because of EPA's expertise related to generation, analysis, scientific visualization, and reporting
of air quality monitoring data, air quality modeling estimates, and Hierarchical Bayesian-derived
air quality estimates and associated research, the CDC approached EPA to provide technical
support for incorporating air quality data and estimates into its EPHT Network. Because the air
quality data generated could be used by EPA to achieve other research goals related to linking air
quality data and health effects and performing cumulative risk assessments, EPA proposed an
interagency agreement under which each agency would contribute funding and/or in-kind
support to efficiently leverage the resources of both agencies. The major objective of this
research is to provide data and guidance to CDC to assist them in tracking estimated population
exposure to ozone and PM2.5; estimating health impacts to individuals and susceptible
subpopulations; guiding public health actions; and conducting analytical studies linking human
health outcomes and environmental conditions.
This report is divided into six sections and five appendices. The first major section of the report
describes the air quality data obtained from EPA's nationwide monitoring network and the
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importance of the monitoring data in determining health potential health risks. The second major
section of the report details the emissions inventory data, how it is obtained and its role as a key
input into air quality computer models. The third major section of the report describes the
CMAQ computer model and its role in providing estimates of pollutant concentrations across the
U.S. based on either 12-km grid cells (Eastern U.S.) or 36-km grid cells (entire continental U.S.).
The fourth major section of the report explains the 'hierarchical' Bayesian statistical modeling
system which is used to combine air quality monitoring data and air quality estimates from the
CMAQ model into a continuous concentration surface which includes regions without air quality
monitors. The fifth major section provides guidelines and requisite understanding that users
must have when using the 'hierarchical' Bayesian statistical modeling system. The appendices
provide detailed information on air quality data and the hierarchical Bayesian statistical
modeling system.
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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 (O^) and particulate matter (PM). The PHASE project focused on these two pollutants,
since numerous studies have found them to be harmful to public health and the environment, and
there was more 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 periodically review 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
redesignated 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
redesignation. EPA must review and approve the NAAQS compliance data and the maintenance
plan before redesignating 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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2.12 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, in place since 1997, is an 8-hour maximum of 0.075 parts
per million [ppm] (for details, see http://www.epa.gov/ozonedesignations/). An 8-hour
maximum is the maximum of the 24 possible running 8-hour average concentrations for each
calendar day. The Clean Air Act requires EPA to review the NAAQS at least every five years
and revise them as appropriate in accordance with Section 108 and Section 109 of the Act. The
'allowable' ozone values are shown in the table below:
Table 2-1. Ozone Standard
Parts Per Million:
Measurement - (ppm)
1-Hour Standard
8 -Hour Standard
1997
0.12
0.08
2008
0.12
0.075
2.1.3 Fine 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 sizes2 into classes with differing health concerns and
potential sources. Particles less than 10 micrometers in diameter (PMio) pose a health concern
because they can be inhaled into and accumulate in the respiratory system. Particles less than 2.5
micrometers in diameter (PM2.5) are referred to as "fine" particles. Because of their small size,
fine particles can lodge deeply into the lungs. Sources of fine particles include all types of
combustion (motor vehicles, power plants, wood burning, etc.) and some industrial processes.
Particles with diameters between 2.5 and 10 micrometers (PMio-2.s) are referred to as "coarse" or
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
PM10andPM25
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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.
Epidemiological and toxicological studies have demonstrated associations between fine particles
and respiratory and cardiovascular health effects, including irritation of the airways, coughing,
decreased lung function, aggravated asthma, development of chronic bronchitis, irregular
heartbeat, nonfatal heart attacks, and premature death in people with heart or lung disease. These
studies are summarized and integrated in "Air Quality Criteria for Particulate Matter" (EPA
2004). This document and other technical documents related to PM standards are available at
http://www.epa.gov/ttn/naaqs/standards/pm/s_pm_index.html.
The current (as of October 2008) NAAQS for PM2.5 includes both a 24-hour standard to protect
against short-term effects, and an annual standard to protect against long-term effects. The
annual average PM2.5 concentration must not exceed 15 ug/m3, and the 24-hr average
concentration must not exceed 35 micrograms per cubic meter (ug/m3). The current annual
PM2.5 NAAQS was set in 1997 and the current 24-hr PM2.5 NAAQS was set in 2006 (for details,
see http://www.epa.gov/air/criteria.html) and
http://www.epa.gov/oar/particlepollution/naaqsrev2006.html). The EPA quality assurance
standards for PM2 5 monitors specify that the coefficient of variation (CV = standard
deviation/mean) of a monitor measurement must be less than 10%. The relative bias (tendency
for measured values to be higher or lower than 'true' value) for PM2 5 monitor measurements
must be between the range of -10% to +10%. The 'allowable' PM2 5 values are shown in the
table below:
Table 2-2. PM2.5 Standard
Micrograms Per Cubic Meter:
Measurement - (ug/m3)
Annual Average
24-Hour Average
1997
15
65
2006
15
35
2.2 Ambient Air Quality Monitoring in the United States
2.2.1 Monitoring Networks
The Clean Air Act requires every state to establish a network of air monitoring stations for
criteria pollutants, following specific guidelines for their location and operation. 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 whose distribution is
largely determined by the needs of State and local air pollution control agencies. 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.
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The National Air Monitoring Station network (NAMS) is about a 1,000-site subset of the
SLAMS network, with emphasis on areas of maximum concentrations and high population
density in urban and multi-source areas. The NAMS monitoring sites are designed to obtain
more timely and detailed information about air quality in strategic locations and must meet more
stringent monitor siting, equipment type, and quality assurance criteria. NAMS monitors also
must submit detailed quarterly and annual monitoring results to EPA.
The SLAMS and NAMS networks experienced accelerated growth throughout the 1970s. The
networks were further expanded in 1999 following the 1997 revision of the CAA to include
separate standards for fine particles (PM2 5) 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 criteria pollutants other than ozone and PM2 5, the number of monitors
has declined. For more information on SLAMS and NAMS, as well as EPA's other air
monitoring networks go to www.epa.gov/ttn/amtic.
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
The Air Quality System (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). 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 by the end of the quarter following 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 attainment vs. non-
attainment designations, evaluate SIPs, perform modeling for permit review analysis, and
perform other air quality management functions.
AQS was recently converted from a mainframe system to a UNIX-based Oracle system which is
easily accessible to users through the Internet. This new system went into production status in
January 2002. Today, state, local, and tribal agencies submit their data directly to AQS.
Registered users may also retrieve data through the AQS application and through the use of
third-party software such as the Discoverer tool from Oracle Corporation. For more detailed
information about the AQS database, go to http://www.epa.gov/ttn/airs/airsaqs/index.htm.
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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, and require no further
analysis. Furthermore, the data are supported by a comprehensive quality assurance program,
ensuring data of known quality. One disadvantage of using the ambient data is that it is usually
out of spatial and temporal alignment with health outcomes. This spatial and temporal
'misalignment' between air quality monitoring data and health outcomes is influenced by the
following key factors: the living and/or working locations (microenvironments) where a person
spends their time not being co-located with an air quality monitor; time(s)/date(s) when a patient
experiences a health outcome/symptom (e.g., asthma attack) not coinciding with time(s)/date(s)
when an air quality monitor records ambient concentrations of a pollutant high enough to affect
the symptom (e.g., asthma attack either during or shortly after a high PM2.5 day). To
compare/correlate ambient concentrations with acute health effects, daily local air quality data is
needed. 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. Samples from
Federal Reference Method (FRM) PM2 5 monitors are generally collected only 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 Tapered Element Oscillating Microbalance (TEOM)
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 and (as of October 2008) are being assessed for their equivalency to the FRM. Ozone is
monitored daily, but mostly during the ozone season (the warmer months, approximately April
through October). However, year-long data is extremely useful to evaluate whether ozone is a
factor in health outcomes during the non-ozone seasons.
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
hierarchical Bayesian space-time statistical model (HBM).
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 in the implementation of Clean Air Act
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requirements. 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.
Many approaches may be used to assign exposure from monitors or estimate concentrations for a
new time period or location based on existing data. On the simplest level for example, data from
monitoring sites are averaged and applied to the population in an entire county, or the nearest
monitor is assigned to a subject's address. At the next level, variogram analysis may be used to
describe the spatial correlation of the data and interpolate concentrations across space. Such
approaches work well for temporally and spatially robust data, but where data are missing (for
example for PM2.5 data with samples taken every third day), further assumptions and modeling
are needed which add uncertainty into the interpolated concentrations. Finally, air quality
monitoring data can be used with air quality modeling estimates (using emissions inventories)
and incorporated into a Bayesian model to enhance the prediction of ambient air concentrations
in space and time. There are two methods used in EPHT to provide estimates of ambient
concentrations of air pollutants: air quality monitoring data and the Hierarchical Bayesian-
derived air quality estimate, which is a statistical 'combination' of air quality monitor data and
air quality modeling estimates.
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. The approach used divides "indicators" into two categories. First, basic air quality
measures were developed to compare air quality levels over space and time within a public
health context (e.g., using the NAAQS as a benchmark). Next, indicators were developed that
mathematically link air quality data to public health tracking data (e.g., daily PM2.5 levels and
hospitalization data for acute myocardial infarction). Table 2-3 and Table 2-4 describe the issues
impacting calculation of basic air quality indicators.
Table 2-3. Public Health Surveillance Goals and Current Results
Goal
Status
(1) Air data sets and metadata required for
air quality indicators are available to
EPHT state Grantees.
AQS data is available through state
agencies and EPA's AirData and
AirExplorer. EPA and CDC have set up an
TAG for data and air quality data along
with HBM data that was delivered to CDC
in August 2008. Metadata drafts have
been completed.
10
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(2) Estimate the linkage or association of
PM2 5 and ozone on health to:
A. Identify populations that may have
higher risk of adverse health effects due to
PM2.5 and ozone,
B. Generate hypothesis for further
research, and
C. Provide information to support
prevention and pollution control strategies.
Discussions have begun on health-air
linked indicators and the CDC/HEI/EPA
workshop held in January 2008. This goal
will be supported further by the
development of health-air indicators.
(3) 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. Regular, ongoing discussions
are needed between air quality and public
health staffs; dialogue has begun.
Table 2-4. Basic Air Quality Indicators
Ozone (daily 8-hr period with maximum concentration - ppm - by Federal Reference
Method (FRM))
Number of days with maximum ozone concentration over the NAAQS (or other
relevant benchmarks (by county and MSA)
Number of person-days with maximum 8-hr average ozone concentration over the
NAAQS & other relevant benchmarks (by county and MSA)
PM2.s (daily 24-hr integrated samples by FRM)
Average ambient concentrations of particulate matter (< 2.5 microns in diameter)
and compared to annual PM2sNAAQS (by state).
% population exceeding annual PM2.5 NAAQS (by state).
% of days with PM2.5 concentration over the daily NAAQS (or other relevant
benchmarks (by county and MSA)
Number of person-days with PM2 5 concentration over the daily NAAQS & other
relevant benchmarks (by county and MSA)
2.3.1 Rationale for the Air Quality Indicators
The CDC EPHT Network is initially focusing on ozone and PM2 5. These air quality indicators
are based mainly around the NAAQS health findings and program-based measures
(measurement, data and analysis methodologies). The indicators will allow comparisons across
space and time for EPHT actions. They are in the context of health-based benchmarks. By
bringing population into the measures, they roughly distinguish between potential exposures (at
broad scale).
11
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2.3.2 Air Quality Data Sources
The air quality data will be available based on the state/federal air program's data collection and
processing. Air quality data management (EPA's Air Quality System - AQS) and delivery
systems (AirData and AirExplorer) have been used in the PHASE project as the pilot test for air
quality indicators.
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.
12
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3.0 Emissions Data
3.1 Introduction to the 2002 Platform
The 2001 and 2002 emissions platforms are nearly identical in their impact on air quality
modeling estimates. The differences between the 2001 and 2002 emissions platforms are
provided below.
Discussion of Similarities and Differences Between 2001 and 2002 Emissions Modeling
Platforms (Based on Ozone NAAQS Proposal and Final)
Emissions modeling for the Ozone NAAQS Proposal was based on the 2001 emissions modeling
platform. Version 3 of the 2002 emissions modeling platform was used for the Final Ozone
NAAQS. For both platforms, emissions are first projected to a year 2020 Base case. The
following discusses similarities and differences in the 2001 and 2002 emission platforms, as well
as assumptions used to project emissions to the year 2020.
Similarities in the 2001 and 2002 Emissions Modeling Platforms
The 2001 and 2002 emissions platforms share the same Canada, Mexico, and offshore oil
production emissions. Both platforms also share the same wildfire and prescribed burning
emissions. Most input ancillary files used in the emissions processor are also unchanged;
specifically, almost all cross-reference factors used in speciation profile assignments and
temporal and spatial allocations are the same. The land use data for biogenic emissions
(BELD3) is the same. The projection approach for stationary non-EGU emissions is also
unchanged; however, for a couple of source categories, activity growth was slightly modified to
account for the change in starting year 2002, rather than 2001. This effect on year 2020 activity
(growth) factors is very small. Plant closures, consent decrees and settlements, and most
national programs for stationary non-EGUs are applied as consistently as possible in 2002 as in
2001. A cross-reference file was used to match controls for plants in the 2001 inventories to the
2002 inventories.
Key Changes to the Emissions Modeling Platform
The Final Ozone NAAQS utilizes the 2020 inventory, projected from the 2002 Version 3
emissions modeling platform. The proposal utilized the 2001-based, projected to year 2020, "PM
NAAQS" platform. The most significant change in the emissions modeling platform is the
improvements to emissions estimates over multiple inventory sectors. See the 2002, Version 3
documentation for detailed information on these improvements.
The SMOKE input ancillary data was updated to account for new source categories appearing in
different inventory sectors; examples include farms and airports in the point source inventory
and the new inclusion of portable fuel container emissions resulting from the Mobile Source Air
Toxics (MSAT2) Rule. Another significant change in the emissions modeling platforms is the
use of a new chemical mechanism - Carbon Bond (CB05) versus CBIV in the proposal platform.
The total NOX and VOC emissions do not differ significantly by geographic area when
comparing the inventories used in the proposal (2001) and final (2002). Small decreases in NOX
13
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and VOC are evident in the Northeast and Midwest, and small decreases in NOX are also seen in
the rest of the U.S. In contrast, slight overall increases of NOX in Texas and VOC in the rest of
the U.S. can be seen. Year 2020 emissions, projected from the 2001 and 2002 emission
platforms, show slightly less NOX in 2020 in the 2002-based platform in the Northeast, Midwest,
and the rest of the U.S. Perhaps most significant from an air quality modeling aspect is the
relative change in emissions in 2020 when migrating from the 2001 to the 2002 emission
platforms. There are slightly less raw reductions in NOX and VOC for all regions with the
exception of a very slight increase in NOX reductions in 2020 based on 2002 in the Northeast and
California. The net effect of these emission summaries is that large changes in air quality
modeling ozone estimates are unlikely to be explained by significant changes in the overall
emission changes by migrating from the 2001-based emissions platform in the proposal to the
2002-based emissions platform used in the final rulemaking. Emissions inventory summaries
broken down by sectors (e.g., EGU, non-EGU Point, Onroad Mobile, Nonroad Mobile...) also
do not show any significant differences by geographic area for year 2020 between the 2001-
based and 2002-based emission modeling platforms.
The U.S. EPA, hereto referred to as "we," has developed a 2002-based air quality modeling
platform. This is considered to be the 2002 Platform version 3 because the emission inventories
are primarily from Version 3 of the 2002 National Emission Inventory (NEI)
(http://www.epa.gov/ttn/chief/eiinformation.html). This section is a summary of the emissions
inventory and emissions modeling for Criteria Air Pollutants (CAPs), and 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. A complete
description of the 2002 Platform is available as "Technical Support Document: Preparation of
Emissions Inventories for the 2002-based Platform, Version 3, Criteria Air Pollutants, Staff
Report, U.S. EPA, Research Triangle Park, NC 27711, January 2008 (Draft)."
The 2002 Platform for CAPs uses the Community Multiscale Air Quality (CMAQ) model
(http://www.epa.gov/AMD/CMAQ/) for the purposes of modeling ozone (Os) and particulate
matter (PM). The version of CMAQ we used requires hourly and gridded emissions of species
from the following inventory pollutants: carbon monoxide (CO), nitrogen oxides (NOX), volatile
organic compounds (VOC), sulfur dioxide (802), ammonia (NH3), particulate matter less than or
equal to 10 microns (PMio), and individual component species for particulate matter less than or
equal to 2.5 microns (PM2.s). It builds upon the concepts, tools and emissions modeling data
from EPA's 2001 Platform, which was most recently developed for the Regulatory Impact
Analyses for the National Ambient Air Quality Standards for Particle Pollution referred to here
as "PM NAAQS." An earlier version of the 2002 Platform was used for the Clean Air Interstate
Rule Analysis, referred to here as "CAIR."
The effort to create the emission inputs for the 2002 Platform included: (1) development of
emission inventories for a 2002 model evaluation case; (2) updates to the emissions modeling
tools; (3) updates to the emissions modeling ancillary files used with the tools; and (4) execution
of the tools. The 2002 evaluation case uses 2002-specific fire emissions and 2002-specific
continuous emission monitoring (CEM) data for electric generating units (EGUs).
14
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The primary emissions modeling tool used to create the CMAQ model-ready emissions was the
Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system. We used this tool to
create emissions files for a 36-km national grid, and a 12-km Eastern grid (a 12-km Western grid
was also generated by the model). Electronic copies of the data used with SMOKE for the
criteria air pollutants (CAP) 2002 Platform are available at the emissions modeling
clearinghouse, http://www.epa.gov/ttn/chief/emch/, under the section entitled "CAP 2002-Based
Platform, Version 3."
This summary contains two additional sections. Section 3.2 describes the 2002 inventories input
to SMOKE. Section 3.2 also describes the emissions modeling and the ancillary files used with
the emission inventories. Note: Some of the technical methods used are influenced by the need
to project 2002 emissions to future years in other applications of the 2002 modeling platform.
3.2 2002 Emission Inventories and Approaches
The 2002 emission inventory is the base year on which the 2002, 2003, 2004, and 2005 emission
inventories are developed. Numerical results for 2002 are displayed throughout this report, since
they are also essentially identical for 2002, 2003, 2004, and 2005 years.
This section describes the 2002 emissions data created for input to SMOKE. The primary basis
for the 2002 emission inputs for the 2002 Platform is the 2002 National Emission Inventory
(NEI), which includes emissions of CO, NOX, VOC, SO2, NH3, PMio, and PM2.5. Version 3 of
the 2002 NEI was used for the 2002 Platform and is documented at
http://www.epa.gov/ttn/chief/net/2002inventory.htmltfdocumentation. For inventories outside of
the United States, which include Canada, Mexico and offshore emissions, we used the latest
available base year inventories.
The 2002 NEI includes five source sectors: a) nonpoint (formerly called "stationary area")
sources; b) point sources; c) nonroad mobile sources; d) onroad mobile sources; and e) fires.
The fires portion of the inventory includes emissions from wildfires and prescribed burning
computed as hour-specific point sources. For purposes of preparing the CMAQ-ready emissions,
we split the 2002 emissions inventory into several additional "platform" sectors for use in
emissions modeling, and we added biogenic emissions and emissions from sources other than the
NEI such as the Canadian, Mexican and offshore inventories. The significance of an emissions
modeling or a "platform" sector is that it is run through all of the SMOKE programs except the
final merge (Mrggrid) that is independent from the other sectors. The final merge program
combines the sector-specific gridded, speciated and temporalized emissions to create the CMAQ
emission inputs.
Table 3-1 presents the sectors in the 2002 Platform for CAPs. The sector abbreviations are
provided in italics; these abbreviations are used in the SMOKE modeling scripts and inventory
file names and throughout the remainder of this section. Annual emission summaries for 2002
for this platform are shown in Table 3-2, which provides a summary of 2002 Platform emissions
for the U.S. anthropogenic sectors (i.e., excluding biogenic emissions). Table 3-3 provides a
summary of emissions for the anthropogenic sectors containing Canadian, Mexican and offshore
sources.
15
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The emission inventories for input to SMOKE for the 2002 evaluation case are available at the
2002v3CAP site under the link "Data Files" (see "2002emis" directory). The "readme" file
provided indicates the particular zipped files associated with each platform sector. The
remainder of Section 3.2 provides details of the data contained in each of the sectors. Different
levels of detail are provided for different sectors depending upon the availability of reference
information for the data and the degree of changes or manipulation of the data needed for
preparing it for input to SMOKE.
3.2.1 2002 Point Sources (ptipm and ptnonipm)
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).
Table 3-1. Platform Sectors Used in Emissions Modeling for the CAP 2002 Platform
PLATFORM SECTOR
IPM sector: ptipm
Non-IPM sector: ptnonipm
Point source fire sector:
ptftre
Nonpt fire sector:
nonptfire
Agricultural sector: ag
Area fugitive dust sector:
afdust
Remaining nonpoint
sector: nonpt
Nonroad sector: nonroad
Aircraft, locomotive,
marine: aim
Onroad: onroad
2002 NEI
SECTOR
Point
Point
Fires
Fires and
Nonpoint
Nonpoint
Nonpoint
Nonpoint
Mobile:
Nonroad
Mobile:
Nonroad
Mobile:
Description and Resolution of the Data Input to SMOKE
NEI point source EGUs mapped to the Integrated Planning Model
(IPM) model using the National Electric Energy Database System
(NEEDS) database. Hourly files for continuous emission monitoring
(CEM) sources are included only for the 2002 evaluation case. Day-
specific emissions for non-CEM sources created for input into
SMOKE.
All NEI point source records not matched to the ptipm sector, annual
resolution.
Point source day-specific wildfires and prescribed fires for 2002.
Prescribed fires for 2002 for which day-specific data were not
available, county and annual resolution.
NH3 emissions from NEI nonpoint livestock and fertilizer application
sources, county and annual resolution.
PMio and PM2.5 from fugitive dust sources from the NEI nonpoint
inventory (e.g., building construction, road construction, paved roads,
unpaved roads, agricultural dust), county and annual resolution.
All nonpoint sources not otherwise included in other SMOKE sectors,
county and annual resolution.
Monthly nonroad emissions from the National Mobile Inventory Model
(NMIM) using NONROAD2005, other than for California. Monthly
emissions for California created using annual emissions submitted by
the California Air Resources Board (CARB) for the 2002 NEI.
Aircraft, locomotive, commercial marine vessel emissions sources,
county and annual resolution.
Monthly onroad emissions from NMIM using MOBILE6, other than
16
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PLATFORM SECTOR
Biogenic: biog
Other point sources not
from the NEI: othpt
Other nonpoint and
nonroad not from the NEI:
othar
Other onroad sources not
from the NEI: othon
2002 NEI
SECTOR
onroad
NA
NA
NA
NA
Description and Resolution of the Data Input to SMOKE
for California. Monthly emissions for California created using annual
emissions submitted by CARB for the 2002 NEI.
Hour-specific, grid cell-specific emissions generated from the
BEIS3.13 model (includes emissions in Canada and Mexico).
Point sources from Canada's 2000 inventory, Mexico's 1999 inventory,
and offshore point sources from the 2001 Platform, annual resolution.
Canada (province resolution) and Mexico (municipio resolution)
nonpoint and nonroad mobile inventories, annual resolution.
Canada (province resolution) and Mexico (municipio resolution)
onroad mobile inventories, annual resolution.
Table 3-2. Summaries by Sector of 2002 Base Year Emissions for the Continental United States (48 states +
District of Columbia)
Year
2002
Sector
afdust
Ag
Aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
2002 Total
[tons/yr]
voc
0
0
123,676
451,127
7,929,917
2,873,622
4,847,990
42,378
1,425,158
17,693,869
[tons/yr]
NOX
0
0
2,259,844
189,428
1,531,602
2,176,159
7,786,709
4,618,944
2,368,987
20,931,673
[tons/yr]
CO
0
0
806,471
8,554,550
7,526,723
21,386,059
59,810,866
605,148
3,195,469
101,885,285
[tons/yr]
S02
0
0
312,313
49,094
1,250,265
187,284
242,379
10,359,102
2,249,550
14,649,986
[tons/yr]
NH3
0
3,251,990
904
36,777
135,542
1,859
290,708
29,991
154,180
3,901,951
[tons/yr]
PM10
8,901,461
0
97,039
796,229
1,377,055
227,875
205,914
608,718
603,606
12,817,898
[tons/yr]
PM25
1,830,271
0
86,719
684,034
1,100,884
216,658
146,003
501,998
372,330
4,938,898
Table 3-3. Summaries by Sector for the Other ("oth") - Canada, Mexico, and Offshore - 2002 Base Year
Emissions Within the 36-km Domain
Year
2002
Country &
Sector
Canada othar
Canada othon
Canada othpt
Canada
Subtotal
Mexico othar
Mexico othon
Mexico othpt
Mexico
Subtotal
Offshore othpt
2002 Total
[tons/yr]
VOC
1,878,996
410,981
237,957
2,527,933
586,842
183,563
113,044
883,448
70,329
6,893,091
[tons/yr]
NOX
1,060,097
874,564
628,175
2,562,836
249,045
147,519
258,510
655,074
26,628
6,462,448
[tons/yr]
CO
4,282,782
5,810,763
1,149,266
11,242,811
644,733
1,456,285
88,957
2,189,976
6,205
26,871,779
[tons/yr]
S02
227,942
26,376
2,115,572
2,369,890
101,047
8,276
980,359
1,089,682
0
6,919,144
[tons/yr]
NH3
569,738
18,332
23,866
611,937
486,484
2,549
0
489,033
0
2,201,939
[tons/yr]
PM10
1,462,643
19,692
241,081
1,723,417
143,816
6,960
125,385
276,161
0
3,999,156
[tons/yr]
PM25
400,493
18,071
129,342
547,906
92,861
6,377
88,132
187,370
0
1,470,552
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We created two platform sectors from the 2002 point source NEI, v3 for input into SMOKE: the
Integrated Planning Model (IPM) sector (ptipm) and the non-IPM sector (ptnonipm). The
ptnonipm emissions were provided to SMOKE as annual emissions. The ptipm were provided as
hourly emissions data for CEM sources and as day-specific emissions for non-CEM sources.
The point source file was separated into these sectors to facilitate the use of different SMOKE
temporal processing techniques for these sectors; these sectors are described in the following
subsections. We further describe the approach for creating the day-specific non-CEM emissions
in Section 3.2.9. Documentation for the development of the point source NEI is at:
http://www.epa.gov/ttn/chief/net/2002inventory.htmltfdocumentation.
3.2.1.1 IPM Sector (ptipm)
This sector contains emissions from EGUs in the 2002 NEI that we were able match to the 2006
NEEDS database (http://www.epa.gov/airmarkets/progsregs/epa-ipm/index.html), which is used
by the IPM, version 3.0. The IPM model provides future year emission inventories for the
universe of EGUs contained in the NEEDS database. As described below, this matching was
done in order to (1) provide consistency between the 2002 EGU sources and future year EGU
emissions for sources which are forecasted by IPM and (2) avoid double counting in projecting
point source emissions. The 2002 NEI point source inventory contains emissions estimates for
both EGU and non-EGU sources.
Because the IPM v3.0 units are based on the 2006 NEEDS database, we also used this NEEDS
database to identify the set of EGUs in the 2002 NEI point source data to assign to the ptipm
sector. Because of the inconsistencies in identification information for EGU units in the various
available data sets, we performed an extensive analysis to link the NEEDS units to the NEI for
the purpose of splitting the 2002 NEI file into ptipm and ptnonipm sectors. The available data
sets include the 2006 NEEDS, EPA's Clean Air Markets Division (CAMD) hourly CEM
program data and the 2002 NEI. The 2002 NEI point source file includes ORIS Plant IDs and
CAMD Boiler IDs for most of the EGUs to indicate where substitution of hourly CEM emissions
can be reliably performed.
For sources not matching the CEM data ("non-CEM" sources), we computed daily emissions
from the NEI annual emissions using a standard query language (SQL) program and state-
average CEM data. To allocate annual emissions to each month, we created state-specific, three-
year averages of 2001-2003 CEM data. These average annual-to-month factors were assigned to
non-CEM sources by state. To allocate the monthly emissions to each day, we used the 2002
CEM data to compute state-specific month-to-day factors, 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, which are considered ancillary data for SMOKE, is described in Section 3.3.2.
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3.2.1.2 Non-IPM Sector (ptnonipm)
The non-IPM (ptnonipm) sector contains all 2002 NEI point sources that we did not include in
the IPM (ptipm) sector.3 The ptnonipm sector contains fugitive dust PM emissions from
vehicular traffic on paved or unpaved roads at an industrial facility or coal handling at a coal
mine.4 Prior to input to SMOKE, we adjusted the fugitive dust PM emissions by applying
county-specific fugitive dust transportable fraction factors (less than 1). This is discussed further
in Section 3.2.5.
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. We reviewed these sources to determine whether there
were any cases for which the emissions were double counted with those in other sectors; we
found that any double counting is very small.
3.2.2 2002 Non point Sources (afdust, ag, nonpt)
We created several sectors from the 2002 nonpoint NEI. All of these are at county -level and
annual resolution. We removed the nonpoint tribal-submitted emissions as we did not know the
extent to which they may be double counted with the county-level emissions. In addition, the
tribal data would have been dropped during SMOKE processing since there are no spatial
surrogates for tribal data in the 2002 Platform. In the rest of this section, we describe in more
detail each of the platform sectors into which we separated the 2002 nonpoint NEI and the
changes we made to these data. The documentation for the nonpoint sector of the 2002 NEI is
available at: http ://www. epa. gov/ttn/chief/net/2002inventory.html
3.2.2.1 Area Fugitive Dust Sector (afdust)
The area-source fugitive dust (afdust) sector contains PMio and PM2.5 emission estimates for
2002 NEI nonpoint SCCs identified as dust sources by inventory experts. This sector is
separated from other nonpoint sectors to make it easier to apply a "transport fraction" which
reduces emissions based on diminished transport at the scale of our modeling. Application of the
transport fraction prevents the overestimation of fugitive dust impacts in the grid modeling as
compared to ambient samples. Categories included in this sector are paved roads, unpaved roads
and airstrips, construction (residential, industrial, road and total) agriculture production and
mining. It does not include fugitive dust from grain elevators because these are elevated sources.
We created the afdust sector from the 2002 NEI based on SCCs and pollutant codes (i.e.,
and PM2.s) that are considered "fugitive." A complete list of all possible fugitive dust SCCs
(including both 8-digit point source SCCs and 10-digit nonpoint SCCs) is provided at:
http://www.epa.gov/ttn/chief/emch/invent/tf_scc_list2002nei_v2.xls. However, not all of the
SCCs in this file are present in the 2002 NEI. Our approach was to apply the transportable
3Except for the day-specific point source fire emissions data which are included in a separate sector, as discussed in
Section 3.2.1.
4Point source fugitive dust emissions, which represent a very small amount of PM, were treated as a separate sector
in the 2001 Platform.
19
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fractions by county (all afdust SCCs in the same county would receive the same factor). The
approach used to calculate the county fractions and the fractions themselves are available at:
http://www.epa.gov/ttn/chief/emch/invent/transportable_fraction_080305_rev.pdf A limitation
of the transportable fraction approach is the lack of monthly variability which would be expected
due to seasonal changes in vegetative cover. An electronic version of the county-level transport
fractions can be found at:
http://www.epa.gov/ttn/chief/emch/invent/transportfractions052506rev.xls. Note: After the
CMAQ modeling was completed, we discovered that the transportable fraction factors for PM2.5
were inadvertently not applied; therefore, the PM2.5 emissions from this sector are overestimated
in the current version (v3) of the 2002 Platform.
3.2.2.2 Agricultural Ammonia Sector (ag)
The agricultural NH3 "ag" sector comprises livestock and agricultural fertilizer application
emissions from the nonpoint sector of the 2002 NEI. In building this sector, we extracted
livestock and fertilizer emissions based on the SCC. The "ag" sector includes all of the NH3
emissions from fertilizer from the NEI. However, the "ag" sector does include all of the
livestock ammonia emissions, as there are also significant NH3 emissions from livestock in the
point source inventory. Most of the point source livestock NH3 emissions were reported by the
states of Kansas and Minnesota. For these two states, farms with animal operations were
provided as point sources.5
There are also selected livestock NH3 emissions in the point source inventory. We identified
these sources as livestock NH3 point sources based on their facility name. The reason why we
needed to identify livestock NH3 in the ptnonipm sector was to properly implement the emission
projection techniques for livestock sources, which cover all livestock sources, not only those in
the ag sector but also those in the ptnonipm sector.
3.2.2.3 Other Nonpoint Sources (nonpt)
Nonpoint sources that were not subdivided into the afdust, ag or nonpt (Section 3.2.4) sectors
were assigned to the "nonpt" sector. In preparing the nonpt sector, we excluded catastrophic
releases since we found that these emissions were dominated by tire burning, which is an
episodic, location-specific emissions category. Tire burning accounts for significant emissions
of parti culate 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 in the
NEI to temporally and spatially allocate the emissions to the time and location where the event
occurred, we excluded catastrophic releases from the 2002 Platform.
The nonpt sector includes emission estimates for Portable Fuel Containers (PFCs), also known as
"gas cans." Inventories for PFCs were recently developed for EPA's Mobile Source Air Toxics
(MS AT) rule and were incorporated into the 2002 NEI v3. 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.,
5These point source emissions are also identified by the segment ID, which is one of the following: "SWINE,"
"CATTLE," "DAIRY," or "PLTRY."
20
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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.
Statewide total annual VOC inventories were allocated to counties using county-level fuel
consumption ratios from the NONROAD model. Of note from this documentation, the
developers derived the 2002 PFC inventory by linearly interpolating inventories developed for
1999 and 2010.
3.2.3 Fires (ptfire, nonptfire and avefire)
Wildfire and prescribed burning emissions are contained in the ptfire, nonptfire and avefire
sectors. The ptfire sector has emissions provided at geographic coordinates (point locations) and
has daily emissions values, whereas the nonptfire and avefire sectors are county-summed
inventories and have annual total emissions values. For the 2002 model evaluation case, we
modeled 2002 year-specific fires using the emissions from the ptfire and nonptfire sectors. The
universe of sources included with fires sectors for the 2002 Platform exclude agricultural burning
and other open burning sources. These sources are in the nonpt sector of the 2002 Platform
rather than the fire sectors. We chose to keep agricultural burning and other open burning
sources in the nonpt sector. Their year-to-year impacts are not as variable as wildfires and
prescribed/managed burns.
3.2.4 Day-Specific Point Source Fires (ptfire)
The ptfire sector includes wildfire and prescribed6 burning emissions occurring in 2002, which
were used for the 2002 model evaluation case. This sector includes emissions for all 2002
wildfires and most prescribed burns with daily estimates of each fire's emissions. It includes the
latitude/longitude of the fire's origin and other parameters associated with the emissions such as
acres burned and fuel load, which allow for an estimation of plume rise. The inventory
development approach assumed that smoldering occurs in the same grid cell as the flaming
emissions for wildfires only, and on the day after the flaming emissions. In addition to day-
specific pollutant emissions, the ptfire inventories contained data on the acres burned and fuel
consumption for each day. As described in Section 3.2.4, these additional parameters are used in
SMOKE for plume rise calculation.
3.2.5 County-Level Fires (nonptfire)
The nonptfire sector consists of all of the prescribed burning and managed burning emission
sources for which emissions are not available at the spatial or temporal resolution required for
processing in the ptfire sector. Note that there are no wildfires in this sector. The nonptfire
emissions were generated using: (1) point source fire emissions for managed and prescribed
burning in Georgia, as discussed in Section 2.3.1 above, and (2) nonpoint emissions for managed
burning (slash burning) for those states without point source managed burning emissions (i.e.,
Maryland, North Carolina, and Texas).
6For purposes of this document prescribed burning also includes managed burning, i.e., "Other Combustion;
Managed Burning, Slash (Logging Debris)"
21
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3.2.6 Development of Wildland Fire Emission Inventories for 2002-2006
INTRODUCTION
The BlueSky smoke modeling framework and the Satellite Mapping Automatic Reanalysis Tool
for Fire Incident Reconciliation (SMARTFIRE) were applied to facilitate the development of
day-specific wildland fire emission inventories for the continental U.S. for 2003-2005. The
FCCS, Consume 3.0, and FEPS models were used within the BlueSky framework to model
vegetation distribution, fuel consumption, and emission rates, respectively.
Modeling wildland fire emissions requires many pieces of information, including fire location,
ignition time and growth rate, fire intensity, and final size. This information is needed at a daily
or better temporal resolution to be useful for air quality modeling of smoke impacts. Note that
there is significant uncertainty in each of these pieces of information. Emissions from each
wildland fire can be modeled using the formula: Es = A*F*c*EFS where
Es = emissions of species s
A = area burned
F = fuel available for consumption
c = fraction of available fuel consumed
EFS = emission factor (mass of species s emitted per mass of fuel consumed)
DISCUSSION
Fire Detection Data Sets and Tools
Documenting the occurrence of fires, their locations, and their area burned is one of the most
important uncertainties that can be constrained using available observations. Data from the
National Fire Center's ICS-209 ground reporting system provides valuable information on fires
larger than 100 acres that had a federal firefighting response. However, ICS-209 reports have
several limitations as a data source for predicting daily emissions. Daily estimates of actively
burning areas are needed, but ICS-209 reports provide only the ignition point of the fire and an
estimate of the total area burned over the lifetime of the fire. Also, ICS-209 reports are only
created for a subset of fires. Fires that are not tracked with ICS-209 reports include prescribed
burns, agricultural burns, and wildfires for which there is no federal response.
Satellites have been used to detect fires globally for several decades7 and more recently they
have been used to estimate fire size and day-to-day movement to help estimate fire emissions.
However, there are limitations in the use of satellite data for emission inventories. For example,
accurate estimation of the area burned is difficult due to inability of the satellite's sensors to
detect and resolve thermal anomalies. Satellite detection cannot distinguish between a managed
burn and a wildfire. Also, fires that are small, rapidly burning, or obscured by clouds or forest
cover can go undetected.
7Dozier J. 1981. A method for satellite identification of surface temperature fields of subpixel resolution. Remote
Sensing of Environment 11 (3), 221-229.
22
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The National Oceanic and Atmospheric Administration's (NOAA) Hazard Mapping System
(HMS) provides a useful almost real-time database of fire detects.8 The HMS product relies on
data from the MODIS, Advanced Very High Resolution Radiometer (AVHRR), and
Geostationary Earth Observing Satellite (GOES) instruments. Individual detections are
inspected by a trained analyst for false detects and inaccurate geolocation. However, the HMS
data are still ultimately subject to the above limitations.
Ideally, additional information is needed to compensate for the limitations of the satellite-derived
fire detects and the ICS-209 data set. SMARTFIRE uses both satellite-detected and ground-
reported fires to produce daily fire locations and area burned.9 It reconciles ICS-209 ground
reports and hot spots from the HMS. SMARTFIRE was used in this work to prepare four years
(2003-2006) of daily emission estimates for wildland fires for the lower 48 United States,
including wildfire, wildland fire use (WFU), and prescribed burns. Agricultural fires were also
included in the inventory (by assigning a fire as agricultural if agriculture is the underlying land
use of the fire detection area).
The inventory was reproduced three times using different fire information sources: ICS-209
reports alone, MODIS anomalies alone, and the HMS data (which includes both MODIS
anomalies and GOES fire detects) combined with the 209 reports using SMARTFIRE. The
SMARTFIRE application found more fires than either the ICS reports or MODIS detects alone.
Details of the resulting intercomparison and fire detection characteristics spatially and
temporally are presented elsewhere.10
BlueSky Emissions Modeling Pathway
The emissions for all three fire information cases were processed in the same way using the
BlueSky smoke modeling framework.11 The BlueSky framework is designed to facilitate the
operation of predictive models that simulate cumulative smoke impacts, air quality, and
emissions from forest, agricultural, and range fires. The BlueSky framework allows users to
combine state-of-the-science emissions and meteorological and dispersion models to generate
results based on the best available models. In other words, the BlueSky framework connects
8Ruminski M, Kondragunta S., Draxler R.R., and Zeng J. 2006. Recent changes to the Hazard Mapping System.
15th International Emission Inventory Conference, New Orleans, LA. Available on the Internet at
ftp://satepsanone.nesdis.noaa.gov/Publications/EPA msv-conf.pdf.
9SullivanD.C, Raffuse S.M., PrydenD.A., Craig K.I, Reid S.B., Wheeler N.J.M., ChinkinL.R., LarkinN.K.,
Solomon R., and Strand T. 2008. Development and applications of systems for modeling emissions and smoke
from fires: the BlueSky smoke modeling framework and SMARTFIRE: 17th International Emission Inventory
Conference, Portland, OR, June 2-5. Available on the Internet at
http://www.epa.gov/ttn/chief/conference/eil7/sessionl2/raffuse_pres.pdf.
10Raffuse S., Sullivan D., Chinkin L., Gilliland E., Larkin S., Solomon R., and Pace T.G. 2008. Development of
wildland fire emission inventories for 2002-2006 and sensitivity analyses: 17th International Emission Inventory
Conference, Portland, OR, June 2-5. Available on the Internet at
http://www.epa.gov/ttn/chief/conference/eil7/sessionl2/mraffuse_pres.pdf.
"Larkin N.K., O'Neill S.M., Solomon R., Krull C., Raffuse S.M., Rorig M., Peterson J., and Ferguson S.A. 2008.
The BlueSky smoke modeling framework. Int. J. Wildland Fire (in review).
23
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models that provide values for the terms in the above equation. BlueSky allows the user to
choose one of several models at each step in the smoke modeling process. The models used for
this study are shown in the table below:
Table 3-4. Process/Emissions Model Mapping
Process
Fuel Loading
Fuel Consumption
Emissions
Model Used
Fuel Characteristic Classification System (FCCS)
Consume 3.0
Fire Emission Production Simulator (FEPS)
In addition to the standard emission products produced by FEPS (PM2 5, CO, etc.), 29 HAP
species emissions were estimated. Fires were assigned fuel moisture values based on the nearest
weather station from the USDA-FS Wildland Fire Assessment System.
Emissions Estimates using SMARTFIRE
As seen in Figure 3-1 below, wildland fire emissions in the lower 48 states exhibit a bimodal
yearly pattern, with peaks in the spring and late summer/early fall. Over the four years modeled,
emissions in the spring season were fairly consistent year to year. The summer/fall season,
however, showed much more variability. This concentration can be seen in the plot of monthly
average emissions shown below. The springtime emissions are mostly from the southeastern
states, where prescribed burning is a common management practice in spring.
c\i
400
350
300
250
200
150
100
50
0
2003 2004 2005 "2006
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Figure 3-1. Wildfire Emissions in the Contiguous 48 States
24
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Below in Figure 3-2 is the modeled average monthly PM2.5 emitted for the entire modeled time
period (August 2002 through December 2006). The area burned in the spring is similar in
quantity to the area burned in the summer/fall, but the PM2.5 emitted is greater in the
summer/fall. The summer/fall burning is dominated by large wildfires in the West, while the
spring burning is largely prescribed burning in the Southeast, which results in less PM2.5 per area
burned than the western wildfires.
Average Monthly Wildland Fire PM2S Emissions
(2003 - 2006)
29.000 tons
Figure 3-2. Distribution of PM2.s Emissions
CONCLUSIONS
The BlueSky framework was used to produce wildland fire emission inventories for the
conterminous United States from August 2002 to December 2006 using SMARTFIRE as the fire
information source and the most recent models for emission processing (FCCS, Consume 3.0,
and FEPS). The emission inventory processing for 2003-2006 was repeated using ICS-209
reports as the fire information source and repeated again using MODIS fire detection hot spots.
All fire information sources produce similar estimates of area burned in the wildfire-driven
western United States. In the southeastern United States, which has significant prescribed
burning, ICS-209 reports provide little information on area burned. SMARTFIRE reports more
burning than MODIS because it incorporates information from more satellite instruments,
particularly the GOES satellites, which are able to detect many short-lived fires that MODIS may
miss. Previous emission inventory work has treated prescribed burning as an area source, with
25
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county-level spatial resolution and monthly temporal resolution. Satellite data provides better
resolution of the spatial and temporal nature of wildland fire, but more analysis of the detection
rates for different instruments is warranted.
There is significant spatio-temporal variability in wildland fire emissions, especially wildfires.
An annual emission inventory needs to be year-, day-, and location-specific to accurately account
for these emissions. Using one year's emissions for another year may result in poor emission
estimates for modeling purposes.
3.2.7 Biogenic Sources (biog)
For CMAQ, we computed the biogenic emissions based on 2002 meteorology data using the
BEIS3.13 model from SMOKE. The BEIS3.13 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 BEIS3.13 model is described further in:
http ://www. cmascenter. org/conference/2005/ab stracts/2_7. pdf.
The inputs to BEIS include: (1) temperature data at 10 meters which were obtained from the
CMAQ meteorological input files, and (2) land-use data from the Biogenic Emissions Land use
Database, version 3 (BELD3). BELD3 provides data on the 230 vegetation classes at 1-km
resolution over most of North America; the same land-use data were used for the 2001 Platform.
3.2.8 2002 Mobile Sources (onroad, nonroad, aim)
We created three sectors from the mobile source emissions in the 2002 NEI: onroad, nonroad and
a sector containing emissions for aircraft, locomotive and commercial marine vessels (aim). We
created these three separate sectors to handle differences in emissions processing related to the
temporal nature of the inventories and differences in projection methods. All three sectors are at
county and SCC resolution.
The onroad and nonroad sectors utilize emissions generated by the EPA's Office of
Transportation and Air Quality (OTAQ) using the National Mobile Inventory Model (NMIM) for
all of the U.S., except for California.12 NMIM relies on calculations from the MOBILE6 and
NONROAD2005 models as described below, and in NEI documentation. Inputs to NMIM are
posted with the 2002 Emission Inventory. The direct link is:
ftp://ftp.epa.gov/EmisInventory/2002finalnei/mobile sectordata/ncd files/ncd20070727 _2002.zip.
NMEVI creates the onroad and nonroad emissions on a month-specific basis that accounts for
temperature, fuel types, and other variables that vary by month. Inventory documentation for the
2002 NEI v3 onroad and nonroad sectors is also posted with other 2002 NEI documentation; the
direct link is:
ftp://ftp.epa.gov/EmisInventory/2002finalnei/documentation/mobile/2002_mobile_nei_version_3
report 092807.pdf.
While aircraft, locomotive, and commercial marine sources are considered nonroad sources in
the 2002 NEI, they comprise a separate sector for the 2002 platform denoted as "aim." We
12Although OTAQ generated emissions using NMIM for California, these were not used in the 2002 NEI version 3,
but rather were replaced by state-submitted emissions.
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developed the aim sector for the convenience of emission processing and projections. The
NMIM-based nonroad emissions are monthly whereas the aim emissions are annual. In addition,
the NMIM-based nonroad emissions are projected using NMIM, whereas the aim emissions use
national, annual activity-based projection factors. Documentation for "aim" inventory
development is available in several separate documents,
http://www.epa.gov/ttn/chief/net/2002inventory.htmltfdocumentation, and additional revisions to
this documentation are provided in Section 2.5.3.
3.2.9 2002 Onroad Mobile Sources (onroad)
This sector includes exhaust, evaporative, brake wear and tire wear emissions from onroad
sources derived from NMIM (except for California), which contained the version of MOBILE6
used for the final MSAT rule. We did not include the refueling onroad emissions generated by
NMEVI in the onroad sector, because the NEI treats onroad refueling as a stationary source, and it
is in the nonpt sector. We therefore removed refueling emissions from the NMEVI outputs prior
to generating onroad emission files.
The 2002 Platform onroad sector contains VOC emissions separately for exhaust and evaporative
modes, which allowed us to use mode-specific speciation profiles. For the 2002 Platform, the
inventory includes PMio and PM2.5 emissions for three modes13: a) exhaust (EXH); b) brake wear
(BRK) and; c) tire wear (TIR), which similarly facilitated mode-appropriate speciation profiles.
The emission modes are included as part of the pollutant name for the SMOKE emission inputs.
For example, exhaust and evaporative modes for VOC are indicated by EXH__VOC and
EVP_VOC, respectively.
Because the California Air Resources Board (CARB) has their own onroad mobile source
estimation model (EMFAC2002), which is tailored to specific California mobile sources, we
used the CARB-submitted data for the 2002 NEI v3 as well as the platform. CARB provided
EPA with annual-total onroad mobile emissions. We adjusted these emissions using NMIM-
based California emissions to (1) temporalize the emissions to monthly resolution and (2) to
provide them on a consistent basis (i.e., same SCCs and modes) as the NMIM-derived data.
CARB updated their model (EMF AC2007) prior to the completion of our modeling, but they
were not able to provide the results in time for use with version 3 of the 2002 Platform.
3.2.10 Nonroad Mobile Sources - NMIM-Based Nonroad (nonroad)
This sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines
(not including commercial marine, aircraft, and locomotives) derived from NMIM. The NMIM
relied on the version of the NONROAD2005 model used for the marine (spark ignited) SI and
small SI engine proposed rule, published May 18, 2007. We used the NMIM monthly emissions
for all states except California.
Like the onroad emissions, NMIM provides nonroad emissions for VOC by three emission
modes: exhaust, evaporative and refueling. Unlike the onroad sector, refueling emissions for
nonroad sources are not included in the nonpt sector. Rather, we kept these emissions in the
nonroad sector.
13PMio and PM2s in the 2001 Platform were not broken out by mode.
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The NEI nonroad data for California provided by CARB are annual emissions that do not have
the mode-specific data for VOC (exhaust, evaporative, and refueling). We created monthly,
mode-specific emissions for California's nonroad emissions (except for aim sources) using
NMEVI results for California. The process erroneously dropped emissions for certain sources
(FIPS code/SCC combinations) that were not computed via NMEVI; however, the error was
small.
3.2.11 Nonroad Mobile Sources: Aircraft, Locomotive and Commercial Marine (aim)
The aircraft, locomotive and commercial marine (aim) sector contains annual emissions. These
emissions are consistent with the 2002 NEI v3. Note that some aircraft emissions for California,
Illinois, and Minnesota are also contained in the ptnonipm sector, as described above. The
documentation of the 2002 NEI for the aim sector is available at
http://www.epa.gov/ttn/chief/net/2002inventory.htmltfdocumentation. It does not include a
description of the changes to some locomotive and commercial marine sources from v2 of the
2002 NEI, which were made in conjunction with the development of the 2002 Platform. The
updates reflect changes to national total emissions, which were made as part of the proposed
Locomotive/Marine Rule. To preserve the state-submitted data from the 2002 NEI v2, we
adjusted only the EPA-generated emissions. They were adjusted such that the sum of the v2
state-submitted emissions and the revised EPA-generated emissions matched OTAQ's national
totals.
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 parts of
three sectors: othpt, othar, and othon. The "oth" refers to the fact that these emissions are
"other" than those in the 2002 NEI, and the last two digits provide the SMOKE source types: 1)
"pt" for point; 2) "ar" for area, and; 3) "on" for onroad mobile. Except for Mexico, the 2002
Platform used data sets previously used for 2001. For Canada, we used emissions for 2000 since
these were the most recent set of emissions available at the time the 2002 Platform was
developed. For Mexico, we used emissions for 1999. This inventory includes emissions from all
states in Mexico.
The offshore emissions include point source offshore oil and gas drilling platforms. Based on
the CAIR emission inventory documentation, the offshore sources were provided by the Texas
Commission on Environmental Quality (TCEQ). This inventory included emissions for 1992 and
was grown to 2002 based on instructions from TCEQ.
3.3 Emissions Modeling Summary
The CMAQ model requires hourly emissions of specific gas and particle species for the
horizontal and vertical grid cells contained within the modeled region (i.e., modeling domain).
To provide emissions in the form and format required by CMAQ, it is necessary to "preprocess"
the "raw" emissions (i.e., emissions input to SMOKE) for the sectors described in Section 3.2.
In brief, this preprocessing step transforms these emissions from their original temporal
resolution, pollutant resolution, and spatial resolution into the data required by CMAQ. As seen
in Section 3.2, the temporal resolution of the emissions input to SMOKE for the 2002 Platform
28
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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 individual point sources or
county totals (province totals for Canada, municipio totals for Mexico). The pollutants for all
sectors except for biogenics are those inventoried for the NEI. The preprocessing steps
involving temporal allocation, spatial allocation, pollutant speciation, and vertical allocation of
point sources are referred to as emissions modeling. This section provides basic information
about the tools and data files used for emissions modeling as part of the 2002 Platform for CAPs.
We have limited this section's descriptions to the ancillary data SMOKE uses to perform the
emissions modeling steps. All SMOKE inputs and scripts for the 2002 Platform emissions are
available at the Clearinghouse for Inventories and Emissions Factors (CHIEF) Emissions
Modeling Clearinghouse (EMCH) Web site,
http://www.epa.gov/ttn/chief/emch/index.htmltf2002.
3.3.1 The SMOKE Modeling System
We used SMOKE to preprocess the raw emissions to create the emissions inputs for CMAQ.
The SMOKE version 2.4 source code and executables can be used to reproduce our emissions
modeling, and these are available from the Community Multiscale Analysis System (CMAS)
Center at http://www.cmascenter.org. The scripts used for running SMOKE are available on the
CHIEF Web site provided previously in this section.
We made revisions to the SMOKE model for this effort, resulting in SMOKE version 2.4. These
revisions are documented in the SMOKE release notes for SMOKE versions 2.3 and 2.4,
available with the SMOKE documentation at http://www.smoke-model.org. Although the
release of SMOKE version 2.4 happened after we completed our modeling, SMOKE version 2.4
provides essentially the same version of SMOKE used for the 2002-based modeling platform.
Major updates to SMOKE that we developed for the 2002 Platform include:
Support of point-source, day-specific wildfire and prescribed burning fires
Extended one record per line (ORL) format that includes more metadata fields,
particularly fields about the source of the inventory data for each record (e.g., state,
EPA).
New capabilities for temporal allocation using CEM hourly emissions data from EGUs
The ability to use surrogate data files from the Spatial Surrogate Tool
Support for multiple and nonsequential days in the temporal processor
New processing scripts that make it easier to process more sectors than the traditional
sectors of nonpoint, point, onroad, nonroad, and biogenics.
3.3.2 Key Emissions Modeling Settings
Each sector is processed separately through SMOKE, up until the final merge program
(Mrggrid), which combines 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 3-5 summarizes the
major processing steps of each platform sector. The "Spatial" column shows the spatial
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approach: a) "point" indicates that SMOKE maps the source from a point location to a grid cell;
b) "surrogates" indicate that some or all of the sources use spatial surrogates to allocate county
emissions to grid cells; and c) "area-to-point" indicates that some of the sources use the SMOKE
area-to-point feature to grid the emissions (further described in Sections 3.2.7, 3.2.8, 3.2.9, and
3.2.10). 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. Finally, the "Plume rise" column indicates the sectors for
which SMOKE computes vertical plume rise and creates merged emissions that are 3-
dimensional instead of one layer.
Table 3-5. Key Emissions Modeling Steps by Sector
Platform sector
Ptipm
Ptnonipm
Othpt
Nonroad
Other
Aim
Onroad
Othon
Nonpt
Ag
Afdust
Biog
Ptfire
Nonptfire
Avefire
Spatial
point
point
point
surrogates &
area-to-point
surrogates
surrogates &
area-to-point
surrogates
surrogates
surrogates &
area-to-point
surrogates
surrogates
pre-gridded
land use
point
surrogates
surrogates
Speciation
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
in BEIS
Yes
Yes
Yes
Inventory
resolution
daily &
hourly
annual
annual
monthly
annual
annual
monthly
annual
annual
annual
annual
hourly
daily
annual
annual
Plume rise
Yes
Yes
Yes
Yes
3.3.3 Spatial Configuration
For the 2002 Platform, we ran SMOKE and CMAQ for modeling domains with 36-km and 12-
km spatial resolution. Figure 3-3 shows the 36-km Continental United States "CONUS"
modeling domain and the 12-km Eastern US (EUS) domain. All three grids use a Lambert-
Conformal projection, with Alpha = 33, Beta = 45 and Gamma = -97, with a center of X = -97
and Y = 40. Sections 3.2.7, 3.2.8, 3.2.9, and 3.2.10 provide the details on the spatial surrogates
and area-to-point data used to accomplish spatial allocation with SMOKE.
30
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' ' _U>-^.12km Eastern CMAQ Domain
'Xy: -252000,-1284000
*** ^ 0)1:^213 row: 188
latitude of projection's origin: 40
Figure 3-3. 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 the 2002 Platform is the Carbon Bond 05 (CB05) mechanism. Table 3-6 lists the model
species produced by SMOKE for use in CMAQ with the CB05.
For VOC, the speciation approach involves three major steps, as performed by SMOKE: (1)
assignment of speciation profiles to each emission source; (2) conversion of VOC from the
emission source to TOG; and (3) application of speciation profiles that disaggregate TOG into
CB05 model species. The approach for PM2.5 emissions is somewhat simpler, since it does not
require the second step. Figure 3-4 shows the steps involved in chemical speciation for both
VOC and PM2.5, and it identifies the underlying inputs used to develop the CB05-based ancillary
files for the 2002 Platform for CAPs. Section 3.2.29 provides the details about the chemical
speciation ancillary data files used to accomplish these speciation processing steps.
-------
Table 3-6. 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 olefin carbon bond (R-C=C-R)
Isoprene
Methanol
Terminal olefin 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 parti culate matter < 2.5 microns
32
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VOC mass : Speciation i
from emission source | cross reference file
, ,
Assign speciation profile t>IVHJI\t
code to emission source
11
11
Compute moles of each
CB05 model species 4
CB05-specific mapping: SPECIATE4.0 Database
Moles chemical compounds TOG profiles:
to moles of model species (1)Fraction of chemical compound
Provided by Dr. Carter by profile code
(UC Riverside) (2) Conversion factors: VOC-to-TOG
by profile code
VOC to TOG by profile!
TOG split factors:
speciate TOG mass to
moles ot model species
4-
I I
Speciation Tool
VOC Soeciation
PM2.5 mass
from emission source
Speciation
cross reference file
Assign speciation profile
code to emission source
SPECIATE4.0 Database
Simplified PM2.5 profiles:
Fraction of chemical components
by profile code
SMOKE
Compute mass of each
PM2.5 model species
PM2.5 profiles that
speciate PM2.5 mass to
mass of model species
Speciation Tool
PM2.5 Speciation
Figure 3-4. Chemical Speciation Approach Used for the 2002-Based Platform
3.3.5 Temporal Processing Configuration
Table 3-7 summarizes the temporal aspect of the emissions processing configuration. It
compares the key approaches we used for temporal processing across the sectors. We control
temporal aspect of SMOKE processing through (a) the scripts T_TYPE (Temporal type) and
M_TYPE (Merge type) settings and (b) the ancillary data files described in Section 3.3.6.
In addition to the resolution, temporal processing includes a ramp-up period for several days
prior to January 1, 2002, which is intended to mitigate the effects of initial condition
concentrations. The same procedures were used for all grids, but with different ramp-up periods
for each grid:
36km: 10 days (Dec. 22-31)
12 km (East): 3 days (Dec. 29 - 31)
12 km (West): 2 days (Dec 30-31)
For most sectors, our approach used the emissions from December 2002 to fill in surrogate
emissions for the end of December 2001.
33
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Table 3-7. Temporal Settings Used for the Platform Sectors in SMOKE
Platform sector
Ptipm
ptnonipm
Othpt
nonroad
Other
Aim
Onroad
Othon
Nonpt
Ag
Afdust
Biog
Ptfire
nonptfire
Inventory
resolution
daily &
hourly
annual
annual
monthly
annual
annual
monthly
annual
annual
annual
annual
hourly
daily
annual
Monthly
profiles
used?
yes
yes
yes
yes
yes
yes
yes
yes
yes
Daily
temporal
approach1'2
all
mwdss
mwdss
mwdss
mwdss
mwdss
week
mwdss*
mwdss
aveday
aveday
n/a
all
aveday
Merge
processing
approach1'3
all
all
all
mwdss
mwdss
mwdss
week
mwdss*
mwdss
aveday
aveday
n/a
all
aveday
Process Holidays as
separate days?
Yes
Yes
Yes
Yes
Yes
1 Definitions for processing resolution:
all = hourly emissions computed for every day of the year, inventory is already daily.
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 SMOKE's T_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 runs for representative days, which could include holidays as indicated
by the rightmost column. The values given are SMOKE's M_TYPE setting.
* We discovered after the modeling that "week" would have been a more appropriate setting because this sector
includes weekly profiles that vary across days of the week.
3.3.6 Vertical Allocation of Day-Specific Fire Emissions
We used SMOKE to compute vertical plume rise for all of the SMOKE point-source sectors,
which is typically done for emissions modeling for CMAQ. One new feature of the vertical
allocation for the 2002 Platform was the modeling of wildfires and prescribed burning fires as
point sources with plume rise.
The ptfire inventory contains data on the acres burned (acres per day) and fuel consumption (tons
fuel per acre) for each day. SMOKE uses these additional parameters to estimate the plume rise
34
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of emissions into layers above the surface model layer. Specifically, SMOKE uses these data to
calculate heat flux, which is then used to estimate plume rise. In addition to the acres burned and
fuel consumption, SMOKE needs the heat content of the fuel to compute heat flux. We assumed
the heat content to be 8000 Btu/lb of fuel for all fires, because specific data on the fuels were
unavailable in the inventory. Since SMOKE can use a fire-specific heat content value, we
inserted the default 8000 Btu/lb value into the SMOKE-ready fire inventory data for all fires.
The ptfire inventory includes both flaming and smoldering emissions. Smoldering emissions
also have plume rise subject to the meteorological conditions on the day they occur.
The plume rise algorithm applied to the fires is a modification of the Briggs algorithm with a
stack height of zero and a heat release estimated from the fuel loading and fire size. The
SMOKE program Laypoint 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.
Laypoint uses the pressure difference across each layer over the pressure difference across the
entire plume as a weighting factor to assign the emissions to layers. This approach gives plume
fractions by layer and source. See http://www.smoke-model.org/version2.47 for full
documentation of Laypoint and the new day-specific formats for the fire files.
3.3.7 Emissions Modeling Ancillary Files
In this section, we summarize the ancillary data that SMOKE used to perform spatial allocation,
chemical speciation, and temporal allocation for the 2002 Platform. The ancillary data files
provide the specific inventory resolution at which spatial, speciation, and temporal factors are
applied.
3.3.7.1 Spatial Allocation Ancillary Files
As described in Section 3.3.2, we performed spatial allocation for a national 36-km domain and
an Eastern 12-km domain (a Western 12-km domain was also generated). To do this, SMOKE
used national 36-km and 12-km spatial surrogates and a SMOKE area-to-point data file. The
spatial data files we used are available from the 2002v3CAP Web site. The 12-km surrogates
cover the entire CONUS domain, though they are used directly as inputs for the two separate
Eastern and Western domains shown in Figure 3-1. The SMOKE model windowed the Eastern
and Western grids while it created these emissions. The remainder of this subsection provides
further detail on the origin of the data used for the spatial surrogates and area-to-point data.
3.3.7.2 Surrogates for U.S. Emissions
There are 66 spatial surrogates available for spatially allocating U.S. county-level emissions to
the CMAQ 36-km and 12-km grid cells. An area-to-point approach overrides the use of
surrogates for some sources. We used the Surrogate Tool to generate all of the surrogates. The
shapefiles we input to the Surrogate Tool are provided and documented at
http://www.epa.gov/ttn/chief/emch/spatial/spatialsurrogate.html. The document
ftp://ftp.epa.gov/EmisInventory/emiss_shp2006/us/list_of_shapefiles.pdf provides a list and
summary of these shapefiles. The shapefiles used for the surrogate attributes (e.g., population,
agricultural land, marine ports) are the same as those used for the 2001 Platform with two
exceptions: we developed new shapefiles for the "population change" and "oil and gas"
surrogates. We developed these shapefiles to enable the Surrogate Tool to generate these
35
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complex surrogates, which utilize data with different formats (e.g., point locations of refineries
and tank farms versus polygon data for gas stations). Combining the data within a new shapefile
allowed us to generate the surrogates using the Surrogate Tool. The detailed steps in developing
the county boundaries for the 2002 Platform are at
ftp://ftp.epa.gov/EmisInventory/emiss_shp2006/us/metadata_for_2002_county_boundary_shapef
iles rev.pdf.
3.3.7.3 Allocation Method for Airport-Related Sources in the U.S.
There are numerous airport-related emission sources in the 2002 NEI, such as aircraft, airport
ground support equipment, and jet refueling. Most of these emissions are contained in sectors
with county-level resolution - aim (aircraft), nonroad (airport ground support) and nonpt (jet
refueling). We used the SMOKE "area-to-point" approach to allocate the emissions to airport
locations, rather than using airport spatial surrogates, which we found exclude many airports.
Under this approach, SMOKE allocates county emissions to one or more grid cells using an
"ARTOPNT" ancillary file that contains (1) geographic coordinates of airport locations and (2)
allocation factors based on airport-specific aircraft activity. For the 2002 Platform, each airport
was assigned to a single location. Thus, the emissions associated with each airport were
allocated to a single grid cell.
For the 2002 Platform, we created a new 2002-specific ARTOPNT file. The geographic
coordinates and 2002-specific activity information (i.e., landing and takeoffs) used for allocating
emissions to multiple airports in a county were largely taken from the "supplemental"
geographic information system (GIS) data provided with the 2002 NEI, posted under the
"Inventory Data" section ("Mobile Sector Data") at
ftp://ftp.epa.gov/EmisInventory/2002fmalnei/mobile_sector_data/ncd_files/gis_allocati on.
The supplemental data includes geographic coordinates and landing and takeoff (LTO)
information for specific airports, which were used in the development of the aircraft emissions in
the 2002 NEI v3.
3.3.7.4 Surrogates for Canada and Mexico Emission Inventories
Detailed documentation about the Canadian spatial surrogates, their development, and the data
are available at: http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html.
Only the population surrogate was used to grid sources in the Mexico emission inventory,
provided by municipios (analogous to U.S. counties). We updated this surrogate from the 1999-
based population surrogate used in the 2001 Platform to include additional municipios and
updated 2000 population data. We created this updated population surrogate using the Surrogate
Tool. The update to include additional municipios was required because the updated Mexican
inventories (discussed in Section 3.2.16) include more municipios than the inventories
previously used. We obtained the municipio boundaries from the Institute for the Environment,
Center for Environmental Modeling and Policy Development at the University of North Carolina
at Chapel Hill. Municipio population data from the year 2000 were obtained from
www.inegi.gob.mx for only those Mexican states that are within the CONUS 36-km national
domain. The shapefiles used are available at
http://www.epa.gov/ttn/chief/emch/spatial/spatialsurrogate.html and the 12-km and 36-km
36
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surrogate files are on the 2002v3CAP site. Note that the population is "zero" in the Mexico_pop
shapefile for municipios that are part of states located outside the 36-km CONUS domain.
3.3.7.5 Chemical Speciation Ancillary Files
The following data file, provided at the 2002v3CAP site, contains the SMOKE inputs used for
chemical speciation of the inventory species to the CMAQ model species:
ancillary_2002v3mpCAP_smokeformat.zip. This file includes speciation cross-reference
(GSREF), speciation VOC-to-TOG conversion factors (GSCNV) and speciation profiles
(GSPRO). SMOKE environmental variable names, used in the file names, are shown in capital
letters in parentheses.
For VOC speciation, we generated SMOKE-ready TOG-to-model species profiles for the CB05
chemical mechanism using the Speciation Tool. We also used the Speciation Tool to generate a
SMOKE-ready file ("GSCNV") containing profile-specific VOC-to-TOG conversion factors.
One problem identified after using the "GSCNV" file created for 2002 is that it was missing
some entries for mode-specific VOC, "EVP_VOC" and "EXH_VOC" Because most of the
missing entries were not assigned to emissions in 2002 or had a conversion factor of 1.0 (the
default used if the entry is missing), the impact on the speciated VOC was small.
For PM2 5, neither the mass-based PM2 5 files nor the PM2 5 emissions have to be further
converted for use in SMOKE, though the speciation tool was used to convert the profiles from a
database format to SMOKE-ready format. The TOG and PM2 5 speciation factors that are the
basis of the chemical speciation approach were developed from the SPECIATE4.0 database
(http://www.epa.gov/ttn/chief/software/speciate/index.html), which is EPA's repository of TOG
and PM speciation profiles of air pollution sources. EPA developed SPECIATE4.0 through a
collaboration involving EPA's Office of Research and Development (ORD) and EPA's Office of
Air Quality Planning and Standards (OAQPS) at Research Triangle Park, NC, and Environment
Canada. The SPECIATE4.0 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 compounds (e.g., zinc, potassium, manganese, lead), and into the
"simplified" PM2 5 components used in the air quality model. These simplified components are:
PSO4, PNO3, PEC, POC, and PMFINE.
The assignment of profiles in the SPECIATE4.0 database to emissions sources was done in two
steps: (1) an initial profile assignment list was prepared with the SPECIATE4.0 database, and (2)
the list was completed and reviewed by emission inventory development, emission modeling and
emission factor staff in the EPA's OAQPS and the EPA's ORD. For VOC speciation factors,
recommendations for mobile sources and upstream (i.e., petroleum distribution) sources were
obtained from subject experts at OTAQ.
Speciation profiles for use with BEIS are not included in SPECIATE. We added the BEIS3.13
profiles to the SMOKE speciation profiles for CMAQ for CB05. The profile code associated
withBEIS3.13 profiles for use with CB05 is "B10C5."
37
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3.3.7.6 Temporal Allocation Ancillary Files
The emissions modeling step for temporal allocation creates the 2002 hourly emission inputs for
CMAQ by adjusting the emissions from the inventory resolution (annual, monthly, daily or
hourly) that are input into SMOKE. The following data file, provided at the 2002v3CAP site,
contains the files used for temporal allocation of the inventory emissions to hourly emissions:
ancillary_2002v3mpCAP_smokeformat.zip which includes speciation cross-reference
(GSREF), speciation VOC-to-TOG conversion factors (GSCNV) and speciation profiles
(GSPRO). SMOKE environmental variable names, used in the file names, are shown in capital
letters in parentheses.
38
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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 for CMAQ
are typically 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 covers the continental
United States, and the nested 12-km x 12-km domain covers the Eastern or Western United
States. For urban applications, CMAQ has also been applied with a 4-km x 4-km grid resolution
39
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for urban core areas; however, the uncertainties in emissions and meteorology information can
actually increase at this high of a resolution. 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 comparing with health outcomes is that it
has the potential to provide complete spatial and temporal coverage. Additionally,
meteorological predictions, which are also needed when comparing health outcomes, are
available for every grid cell along with the air quality predictions.
A disadvantage of using CMAQ is that, as a deterministic model, it has none of the statistical
qualities of interpolation techniques that fit the observed data to one degree or another.
Furthermore, the emissions and meteorological data used in CMAQ each have large
uncertainties, in particular for unusual emission or meteorological events. There are also
uncertainties associated with the chemical transformation and fate process algorithms used in air
quality models. Thus, emissions and meteorological data plus modeling uncertainties cause
CMAQ to predict best on longer time scale bases (e.g., synoptic, monthly, and annual scales) and
be most error prone at high time and space resolutions compared to direct measures.
One practical disadvantage of using CMAQ output is that the regularly spaced grid cells do not
line up directly with counties or ZIP codes which are the geographical units over which health
outcomes are likely to be aggregated. But it is possible to overlay grid cells with county or ZIP
code boundaries and devise means of assigning an exposure level that nonetheless provides more
complete coverage than that available from ambient data alone. Another practical disadvantage
is that CMAQ requires significant data and computing resources to obtain results for daily
environmental health surveillance.
This section describes the 2002-based Air Quality Modeling Platform (2002 Platform) used by
EPA. 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
CMAQ14 as part of the 2002 Platform to provide a national scale air quality modeling analysis.
14Byun, 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.
40
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The CMAQ model simulates the multiple physical and chemical processes involved in the
formation, transport, and destruction of ozone and fine particulate matter (PM2 5).
This section provides a description of each of the main components of the 2002 Platform along
with the results of a model performance evaluation in which the 2002 model predictions are
compared to corresponding measured concentrations. It is drawn entirely from the following
publication: Air Quality Modeling Platform for the Ozone National Ambient Air Quality
Standard Final Rule Regulatory Impact Analysis, U.S. Environmental Protection Agency, Office
of Air Quality Planning and Standards, Air Quality Assessment Division, Research Triangle
Park, NC, EPA 454/R-08-003, March 2008.
4.2 CMAQ Model Version, Inputs and Configuration
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. This analysis employed a version of CMAQ based on the latest
publicly released version of CMAQ (i.e., version 4.615). CMAQ version 4.6 reflects recent
updates in a number of areas to improve the underlying science. These model enhancements
include:
An updated Carbon Bond chemical mechanism (CB-05) and associated Euler Backward
Iterative (EBI) solver was added.
An updated version of the ISORROPIA aerosol thermodynamics module was added.
The heterogeneous N2Os reaction probability is now temperature- and humidity
dependent.
The gas-phase reactions involving N2O5 and H2O are now included.
An updated version of the vertical diffusion module was added (ACM2).
Additionally, there were a few minor changes made to the release version 4.6.1 .i of CMAQ by
EPA model developers subsequent to its release. The relatively minor changes and new features
of this internal version that was ultimately used in this analysis are described elsewhere.16
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 domain has a parent horizontal grid of 36 km with two
finer-scale 12-km grids over portions of the Eastern U.S. The model extends vertically from the
surface to 100 millibars (approximately 15 km) using a sigma-pressure coordinate system. Air
quality conditions at the outer boundary of the 36-km domain were taken from a global model
15CMAQ version 4.6 was released on September 30, 2006. It is available from the Community Modeling and
Analysis System (CMAS) at: http://www.cmascenter.org.
16 E-mail from Shawn Roselle, Office of Research and Development to Carey Jang, Office of Air Quality Planning
and Standards (dated 04/09/07).
41
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and did not change over the simulations. In turn, the 36-km grid was only used to establish the
incoming air quality concentrations along the boundaries of the 12-km grids. Table 4-1 provides
some basic geographic information regarding the CMAQ domains.
36km Domain
Lower Left Corner; -2736000. j-tue
Number of Cols. Rows: 148x1J2 ',
Lambert Projection: HN'-'
1st std parallel: 33
2nd std parallel; 45
central meridian: -97
latitude of projection's origin 40
Figure 4-1. Map of the CMAQ Modeling Domain. The blue-gray outer box denotes the 36-km national
modeling domain and the light green inner box is the 12-km Eastern U.S. fine grid. (Same as
Figure 3-3.)
Table 4-1. Geographic Information for Modeling Domains
Map Projection
Grid Resolution
Coordinate Center
True Latitudes
Dimensions
Vertical extent
CMAQ Modeling Configuration
National Grid
Western U.S. Fine Grid
Eastern U.S. Fine Grid
Lambert Conformal Projection
36km
12km
12km
97degW,40degN
33degNand45degN
148x112x14
213x192x14
279 x 240 x 14
14 Layers: Surface to 100 millibar level (see Table 4-2)
42
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4.2.3 Modeling Period / Ozone Episodes
The 36-km and both 12-km CMAQ modeling domains were modeled for the entire year of
2002.17 All 365 model days were used in the annual average levels of PM2.5. For the 8-hour
ozone, we used modeling results from the period between May 1 and September 30, 2002. 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 in 2002.
4.2.4 Model Inputs: Emissions, Meteorology and Boundary Conditions
2002 Emissions: A summary of the emissions inventory development is described below. More
detailed documentation on the methods and data summaries of the 2002 Platform emissions was
described in Section 3 of this report and is also available separately.18
We used version 3 of the 2002 Platform which takes emission inventories from the 2002
National Emissions Inventory (NEI) version 3.0. These inventories, with the exception of
California,19 include monthly onroad and nonroad emissions generated from the National Mobile
Inventory Model (NMIM) using versions of MOBILE6.0 and NONROAD2005 consistent with
recent national rule analyses.20'21 The 2002 Platform and its associated chemical mechanism
(CB05) employ updated speciation profiles using data included in the SPECIATE4.0 database.22
In addition, the 2002 Platform incorporates several temporal profile updates for both mobile and
stationary sources.
The 2002 Platform includes emissions for a 2002 base year model evaluation case. The model
evaluation case uses prescribed burning and wildfire emissions specific to 2002, which were
developed and modeled as day-specific, location-specific emissions using an updated version of
Sparse Matrix Operator Kernel Emissions (SMOKE) system, version 2.3, which computes plume
rise and vertically allocates the fire emissions. It also includes continuous emissions monitoring
(CEM) data for 2002 for electric generating units (EGUs) with CEMs.
17We also modeled 10 days at the end of December 2001 as a modeled "ramp up" period. These days are used to
minimize the effects of initial conditions and are not considered as part of the output analyses.
18Technical Support Document: Preparation of Emissions Inventories for the 2002-based Platform, Version 3.0,
Criteria Air Pollutants, and Appendices, January 2008.
19The California Air Resources Board submitted annual emissions for California. These were allocated to monthly
resolution prior to emissions modeling using data from the National Mobile Inventory Model (NMIM).
20MOBILE6 version was used in the Mobile Source Air Toxics Rule: Regulatory Impact Analysis for Final Rule:
Control of Hazardous Air Pollutants from Mobile Sources, U.S. Environmental Protection Agency, Office of
Transportation and Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105, EPA420-R-07-002,
February 2007.
21NONROAD2005 version was used in the proposed rule for small spark ignition (SI) and marine SI rule: Draft
Regulatory Impact Analysis: Control of Emissions from Marine SI and Small SI Engines, Vessels, and Equipment,
U.S. Environmental Protection Agency, Office of Transportation and Air Quality, Office of Transportation and Air
Quality, Assessment and Standards Division, Ann Arbor, MI, EPA420-D-07-004, April 2007.
22See http://www.epa.gov/ttn/chief/software/speciate/index.html for more details.
43
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Meteorological Input Data: The gridded meteorological input data for the entire year of 2002
were derived from simulations of the Pennsylvania State University /National Center for
Atmospheric Research Mesoscale Model. This model, commonly referred to as MM5,23 is a
limited-area, nonhydrostatic, terrain-following system that solves for the full set of physical and
thermodynamic equations which govern atmospheric motions. Meteorological model input
fields were prepared separately for each of the domains shown in Figure 4-1. The MM5
simulations were run on the same map projection as CMAQ. The 36-km national domain was
modeled using MM5 v.3.6.0 using land-surface modifications that were added in v3.6.3. The 12-
km Eastern U.S. grid was modeled with MM5 v3.7.2. These two sets of meteorological inputs
were developed by EPA. For the 12-km Western U.S. domain, we utilized existing MM5
meteorological model data prepared by the Western Regional Air Partnership (WRAP).24
The three meteorological model runs used similar sets of physics options. All three simulations
used the Pleim-Xiu planetary boundary layer and vertical diffusion scheme, the Rapid Radiative
Transfer Model (RRTM) longwave radiation scheme, and the Reisner 1 microphysics scheme.
The EPA cases used the Kain-Fritsch 2 subgrid convection scheme while the WRAP simulation
used the Berts-Miller scheme for subgrid convection. In the EPA simulations, analysis nudging
was utilized above the boundary layer for temperature and water vapor and in all locations for
the wind components, using relatively weak nudging coefficients. The WRAP runs employed
similar four-dimensional data assimilation, but it also included observational nudging of surface
winds. All three sets of model runs were conducted in 5.5-day segments with 12 hours of
overlap for spin-up purposes. Additionally, all three domains contained 34 vertical layers with
an approximately 38-m deep surface layer and a 100-millibar top. The MM5 and CMAQ
vertical structures are shown in Table 4-2 and do not vary by horizontal grid resolution.
Table 4-2. Vertical Layer Structure for MM5 and CMAQ (heights are layer top)
CMAQ Layers
0
1
2
3
4
5
6
MM5 Layers
0
1
2
o
J
4
5
6
7
8
9
10
11
Sigma P
1.000
0.995
0.990
0.985
0.980
0.970
0.960
0.950
0.940
0.930
0.920
0.910
Approximate
Height (m)
0
38
77
115
154
232
310
389
469
550
631
712
Approximate
Pressure (mb)
1000
995
991
987
982
973
964
955
946
937
928
919
23Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth-Generation Perm State/NCAR Mesoscale
Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for Atmospheric Research, Boulder CO.
24Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang and G. Tonnesen. 2004. 2002 Annual MM5 Simulation
to Support WRAP CMAQ Visibility Modeling for the Section 308 SIP/TIP - MM5 Sensitivity Simulations to
Identify a More Optimal MM5 Configuration for Simulating Meteorology in the Western United States. Western)
Regional Air Partnership, Regional Modeling Center, December 10.
(http://pah.cert.ucr.edu/aqm/308/reports/mm5/MM5 SensitivityRevRep_Dec_10_2004.pdf)
44
-------
7
8
9
10
11
12
13
14
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
0.900
0.880
0.860
0.840
0.820
0.800
0.770
0.740
0.700
0.650
0.600
0.550
0.500
0.450
0.400
0.350
0.300
0.250
0.200
0.150
0.100
0.050
0.000
794
961
1,130
1,303
1,478
1,657
1,930
2,212
2,600
3,108
3,644
4,212
4,816
5,461
6,153
6,903
7,720
8,621
9,625
10,764
12,085
13,670
15,674
910
892
874
856
838
820
793
766
730
685
640
595
550
505
460
415
370
325
280
235
190
145
100
The meteorological outputs from all three MM5 sets were processed to create model-ready
inputs for CMAQ using the Meteorology-Chemistry Interface Processor (MCIP),25 version 3.1,
to derive the specific inputs to CMAQ.
Before initiating the air quality simulations, it is important to identify the biases and errors
associated with the meteorological modeling inputs. The EPA 2002 MM5 model performance
evaluations used an approach which included a combination of qualitative and quantitative
analyses to assess the adequacy of the MM5 simulated fields. The qualitative aspects involved
comparisons of the model-estimated synoptic patterns against observed patterns from historical
weather chart archives. Qualitatively, the model fields closely matched the observed synoptic
patterns, which is expected given the use of nudging. The operational evaluation included
statistical comparisons of model/observed pairs (e.g., mean normalized bias, mean normalized
error, index of agreement, root mean square errors, etc.) for multiple meteorological parameters.
For this portion of the evaluation, four meteorological parameters were investigated:
temperature, humidity, wind speed, and wind direction. The operational piece of the analyses
focuses on surface parameters. The Atmospheric Model Evaluation Tool (AMET) was used to
conduct the analyses as described in this report.26 The three individual MM5 evaluations are
25Byun, D.W., and Ching, J.K.S., Eds, 1999. Science algorithms of EPA Models-3 Community Multiscale Air
Quality (CMAQ modeling system, EPA/600/R-99/030, Office of Research and Development).
26Gilliam, R. C., W. Appel, and S. Phillips. The Atmospheric Model Evaluation Tool (AMET): Meteorology
Module. Presented at 4th Annual CMAS Models-3 Users Conference, Chapel Hill, NC, September 26 - 28, 2005.
45
-------
described elsewhere.27'28'29 It was ultimately determined that the bias and error values associated
with all three sets of 2002 meteorological data were generally within the range of past
meteorological modeling results that have been used for air quality applications.30
Initial and Boundary Conditions: The lateral boundary and initial species concentrations are
provided by a three-dimensional global atmospheric chemistry model, the GEOS-CHEM31
model. 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 2002 with a grid resolution of 2.0 degrees x 2.5
degrees (latitude-longitude) and 20 vertical layers. The predictions were used to provide one-way
dynamic boundary conditions at three-hour intervals and an initial concentration field for the
CMAQ simulations. More information is available about the GEOS-CHEM model and other
applications using this tool at: http://www-as.harvard.edu/chemistry/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 using 2002 state/local monitoring sites data in order to estimate the
ability of the CMAQ modeling system to replicate the base year concentrations for the 12-km
Eastern and Western domains. In summary, model performance statistics were calculated for
observed-predicted pairs of daily, monthly, seasonal, and annual concentrations. Statistics were
generated for the following geographic groupings: the entire 12-km Eastern US (EUS) domain,
the entire 12-km Western US (WUS) domain, and five large subregions32: Midwest, Northeast,
Southeast, Central, and West U.S. The "acceptability" of model performance was judged by
comparing our CMAQ 2002 performance results to the range of performance found in recent
regional ozone and PM2.5 model applications (e.g., Clean Air Interstate Rule, Final PM NAAQS
27Brewer I, P. Dolwick, and R. Gilliam. Regional and Local Scale Evaluation of MM5 Meteorological Fields for
Various Air Quality Modeling Applications, Presented at the 87th Annual American Meteorological Society Annual
Meeting, San Antonio, TX, January 15-18, 2007.
28Dolwick, P, R. Gilliam, L. Reynolds, and A. Huffman. Regional and Local-scale Evaluation of 2002 MM5
Meteorological Fields for Various Air Quality Modeling Applications, Presented at 6th Annual CMAS Models-3
Users Conference, Chapel Hill, NC, October 1 - 3, 2007.
29Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang, and G. Tonnesen. Annual 2002 MM5 Meteorological
Modeling to Support Regional Haze Modeling of the Western United States. Prepared for The Western Regional
Air Partnership (WRAP), 1515 Cleveland Place, Suite 200 Denver, CO 80202, March 2005.
30Environ, Enhanced Meteorological Modeling and Performance Evaluation for Two Texas Episodes, August 2001.
31 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric Chemistry Modeling Group, Harvard
University, Cambridge, MA, October 15, 2004.
32The 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 CA, OR, WA, AZ, MM, CO, UT, WY, SD, ND, MT, ID, and NV.
46
-------
Rule).33 These other modeling studies represent a wide range of modeling analyses which cover
various models, model configurations, domains, years and/or episodes, chemical mechanisms,
and aerosol modules.
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
averages the difference (model - observed) over the sum of observed values. NMB is a useful
model performance indicator because it avoids overinflating the observed range of values,
especially at low concentrations. Normalized mean bias is defined as:
t(P-O)
NMB = - - *100, where P = predicted concentrations and O = observed
1(0)
i
Normalized mean error (NME) is also similar to NMB, where the performance statistic is used as
a normalization of the mean error. NME calculates the absolute value of the difference (model -
observed) over the sum of observed values. Normalized mean error is defined as:
NME = -^-n - *100, where P = predicted concentrations and O = observed
Fractional bias is defined as:
FB = -
n
9
i 2
*100, where P = predicted concentrations and O = observed
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.
33See: U.S. Environmental Protection Agency; Technical Support Document for the Final Clean Air Interstate Rule:
Air Quality Modeling; Office of Air Quality Planning and Standards; RTF, NC; March 2005 (CAIR Docket OAR-
2005-0053-2149); and U.S. Environmental Protection Agency, 2006. Technical Support Document for the Final PM
NAAQS Rule: Office of Air Quality Planning and Standards, Research Triangle Park, NC.
47
-------
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:
I
L^ o
' __ ^_ * 100, where P = predicted concentrations and O = observed
Overall, the bias and error statistics shown in Tables 4-3, 4-4, and 4-5 below indicate that the
base case model ozone and PM2.5 concentrations are within the range or close to that found in
recent OAQPS applications. The CMAQ model performance results give us confidence that our
applications of CMAQ using this 2002 Platform provide a scientifically credible approach for
assessing ozone and PM2.5 concentrations. A detailed summary of the CMAQ model
performance evaluation is available separately.34 A summary of the PM2 5 and ozone evaluation
is presented here.
Ozone (Os): The ozone evaluation focuses on the observed and predicted hourly ozone
concentrations and eight-hour daily maximum ozone concentrations using a (observation)
threshold of 40 ppb. This ozone model performance was limited to the period used in the
calculation of projected design values within the analysis, which is May, June, July, August, and
September. Ozone ambient measurements for 2002 were obtained from the Air Quality System
(AQS) Aerometric Information Retrieval System (AIRS). A total of 1,178 ozone measurement
sites were included for evaluation. These ozone data were measured and reported on an hourly
basis.
Table 4-3 and Table 4-4 provide hourly and eight-hour daily maximum ozone model
performance statistics, respectively, calculated for a threshold of 40 ppb of observed and
modeled concentrations, restricted to the ozone season modeled for the 12-km Eastern and
Western U.S. domain and the five subregions. Generally, hourly and eight-hour ozone model
performances are under-predicted in both the 12-km EUS and WUS when applying a threshold
of 40 ppb for the modeled ozone season (May-September). For the 12-km EUS and WUS
domain, the bias and error statistics are comparable for the aggregate of the ozone season and for
each individual ozone month modeled.
34Technical Support Document: 2002 CMAQ Model Performance Evaluation for Ozone and Paniculate Matter,
January 2008. This file is available in the docket for this rulemaking.
48
-------
Table 4-3. Summary of CMAQ 2002 Hourly O3 Model Performance Statistics
CMAQ 2002 Hourly Ozone:
Threshold of 40 ppb
May
June
July
August
September
Seasonal Aggregate
12-kmEUS
12-kmWUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
No. of Obs.
241185
124931
51055
55859
69073
41728
111385
256263
125662
61354
54515
67867
46026
109157
257076
116785
66774
59360
68619
36021
104321
235090
125575
53837
54179
62506
41456
110225
179156
99710
44678
34285
41627
41549
83921
1168770
592663
277698
258198
NMB
(%)
-0.7
-3.7
1.2
3.3
-2.5
-6.4
-3.9
-7.5
-8.37
-8.46
-7.19
-7.2
-10.0
-8.8
-5.3
-12.0
-3.9
-10.5
-3.6
-3.6
-13.6
-8.7
-7.91
-6.4
-10.8
-9.4
-9.3
-8.5
-9.9
-10.7
-8.7
-11.4
-8.2
-12.8
-11.7
-6.4
-8.4
-5.4
-7.3
NME
(%)
15.9
15.9
17.1
16.2
14.1
17.3
16.1
16.8
17.7
17.3
17.9
15.3
17.5
18.2
17.7
21.5
17.0
19.4
16.5
18.7
21.8
17.8
20.1
16.7
19.1
17.3
18.7
20.6
17.2
19.0
16.3
18.5
16.5
18.8
20.0
17.1
18.8
16.9
18.3
FB
(%)
-2.0
-5.0
-0.3
2.4
-3.1
-9.2
-5.2
-9.0
-9.3
-9.9
-8.3
-7.6
-13.5
-9.9
-6.6
-14.9
-4.8
-12.3
-3.9
-6.3
-16.8
-10.2
-10.2
-7.4
-12.4
-9.9
-12.8
-11.1
-11.8
-12.7
-10.6
-12.9
-9.0
-16.6
-13.8
-7.7
-10.3
-6.5
-8.4
FE
(%)
17.1
17.3
18.2
16.9
14.8
20.3
17.6
18.6
19.1
19.1
19.6
16.3
21.2
19.7
19.2
24.3
18.0
21.7
17.2
21.1
24.9
19.7
22.1
18.0
21.4
18.5
22.4
22.8
19.5
21.1
18.4
20.4
17.8
22.8
22.1
18.8
20.7
18.4
20.0
49
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Table 4-4. Summary of CMAQ 2002 8-Hour Daily Maximum O3 Model Performance Statistics
CMAQ 2002 Eight-Hour Maximum
Ozone: Threshold of 40 ppb
May
June
July
August
September
Seasonal Aggregate
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
No. ofObs.
19172
9223
4255
4198
5470
3379
8155
19462
9029
4608
4104
5110
3603
7818
20565
8809
5380
4368
5633
3114
7784
19260
9551
4667
4012
5067
3543
8311
15865
8185
4074
3120
3671
3492
6911
94324
44797
22984
19802
NMB
(%)
3.9
0.2
6.7
7.8
0.6
0.3
-0.1
-3.9
-4.9
-5.3
-3.2
-4.8
-4.5
-5.3
-1.6
-7.4
-0.7
-6.5
-0.9
1.3
-9.0
-5.1
-2.8
-2.9
-8.1
-6.4
-4.0
-3.2
-6.2
-6.7
-6.0
-7.2
-4.5
-8.5
-7.3
-2.6
-4.3
-1.9
-3.6
NME
(%)
12.7
12.6
14.3
13.7
10.9
12.3
12.8
12.3
14.1
12.5
12.7
11.8
12.2
14.5
13.5
17.1
13.0
14.2
13.0
14.4
17.2
13.2
15.8
12.4
13.9
13.4
13.5
16.1
12.6
15.0
11.8
13.3
12.6
13.8
15.9
12.9
14.9
12.8
13.6
FB
(%)
4.3
0.6
6.8
8.2
1.1
0.7
0.3
-3.3
-4.2
-4.7
-2.2
-4.1
-4.4
-4.7
-1.0
-8.1
-0.2
-5.8
-0.1
1.2
-9.9
-4.4
-3.1
-2.2
-7.5
-5.4
-3.9
-3.6
-5.9
-6.9
-6.0
-6.5
-3.8
-8.7
-7.6
-1.9
-4.2
-1.2
-2.5
FE
(%)
12.6
12.8
14.2
13.5
11.0
12.4
12.9
12.4
14.2
12.7
12.8
11.9
12.7
14.7
13.6
18.0
12.9
14.4
13.0
14.7
18.2
13.4
16.1
12.4
14.2
13.4
14.1
16.5
12.9
15.5
12.3
13.3
12.7
14.5
16.4
13.0
15.3
12.9
13.7
50
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CMAQ 2002 Eight-Hour Maximum
Ozone: Threshold of 40 ppb
Southeast
Central
West
No. ofObs.
24951
17131
38979
NMB
(%)
-3.1
-3.3
-4.9
NME
(%)
12.4
13.2
15.3
FB
(%)
-2.3
-3.1
-5.0
FE
(%)
12.4
13.7
15.7
PM2.s: The PM2.5 evaluation focuses on PM2.5 total mass and its components, including sulfate
(SO4), nitrate (NO3), total nitrate (TNO3 = NO3 + HNO3), ammonium (NH4), elemental carbon
(EC), and organic carbon (OC). The PM2.5 performance statistics were calculated for each
month and season individually and for the entire year, as a whole. Seasons were defined as:
winter (December-January-February), spring (March-April-May), summer (June, July, August),
and fall (September-October-November). PM2.5 ambient measurements for 2002 were obtained
from the following networks for model evaluation: Speciation Trends Network (STN - total of
199 sites), Interagency Monitoring of PROtected Visual Environments (IMPROVE - total of
150), and Clean Air Status and Trends Network (CASTNet - total of 83). For PM2.5 species that
are measured by more than one network, we calculated separate sets of statistics for each
network. For brevity, Table 4-5 provides annual model performance statistics for PM2.5 and its
component species for the 12-km Eastern domain, 12-km Western domain, and five subregions
defined above (Midwest, Northeast, Southeast, Central, and West U.S.).
51
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Table 4-5. Summary of 2002 CMAQ Annual PM2.5 Species Model Performance Statistics
CMAQ 2002 Annual
PM25
Total Mass
Sulfate
Nitrate
STN
IMPROVE
STN
IMPROVE
CASTNet
STN
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-km BUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
No. of Obs.
10307
3000
1516
2780
2554
2738
2487
8436
10123
592
2060
1803
1624
9543
10157
2926
1487
2730
2541
2686
2446
8532
10232
597
2070
1805
1671
9645
3173
1158
663
839
1085
229
1118
8770
2726
1488
NMB
(%)
10.8
-5.8
14.9
20.5
-3.9
14.5
-7.4
-2.3
-26.4
8.6
21.0
-13.1
-13.1
-27.8
-3.9
-20.6
3.6
-4.3
-7.6
-3.2
-26.1
-10.8
-7.5
-4.9
-12.3
-9.5
-16.1
-5.5
-11.3
-21.3
-8.3
-12.3
-11.2
-20.7
-20.4
18.3
-45.0
17.4
NME
(%)
42.8
46.9
35.6
48.2
36.0
49.1
46.8
49.0
53.5
41.5
59.4
41.2
49.4
53.1
33.6
41.9
34.9
29.1
33.4
39.2
44.9
33.0
42.4
29.9
30.1
32.9
35.0
43.5
20.5
34.6
19.3
17.9
21.5
27.3
35.3
65.9
63.1
59.1
FB
(%)
5.4
-3.1
13.2
16.6
-10.0
6.0
-4.5
-5.7
-26.3
2.4
17.4
-19.8
-17.6
-27.1
-9.7
-12.2
-2.9
-8.8
-16.3
-7.2
-15.8
-7.2
7.6
-10.0
-9.9
-16.8
-16.0
8.6
-16.3
-11.2
-16.3
-15.6
-17.8
-27.4
-10.7
-29.1
-70.6
-5.0
FE
(%)
42.6
45.0
34.4
42.6
39.7
49.4
44.8
51.4
57.5
41.0
51.6
49.9
57.0
57.2
38.4
43.5
36.2
33.6
38.8
44.3
44.8
40.6
45.7
35.7
36.1
40.5
42.4
45.9
26.1
35.9
24.3
21.6
27.2
33.6
36.1
84.5
95.0
67.3
52
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CMAQ 2002 Annual
Total Nitrate
(NO3+HNO3)
Ammonium
Elemental
Carbon
IMPROVE
CASTNet
STN
CASTNet
STN
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
No. of Obs.
2731
2540
1298
2446
8514
10110
597
2069
1803
1672
9522
3171
1157
662
839
1085
229
1117
10157
2926
1488
2731
2540
2685
2446
3166
1156
661
837
1085
229
1116
10031
2975
1498
2744
2506
2570
2475
NMB
(%)
32.7
8.6
12.7
-47.5
48.4
-34.8
43.0
122.2
33.5
18.1
-39.6
24.4
-19.5
20.5
39.1
22.9
6.2
-20.4
11.9
-23.6
16.0
12.3
7.3
15.0
-30.6
5.3
-16.8
15.3
9.8
-7.7
7.4
-21.1
45.0
43.1
37.1
53.1
16.9
91.7
49.0
NME
(%)
70.4
84.6
52.5
62.8
106.8
80.67
86.0
153.8
112.2
81.0
81.1
37.3
44.2
29.4
46.5
39.5
35.6
45.8
40.6
55.7
39.6
38.4
38.4
46.6
56.7
30.8
42.5
27.6
34.7
30.1
33.1
43.5
78.9
82.6
58.9
76.7
66.0
118.0
86.2
FB
(%)
-10.9
-64.7
-13.4
-73.8
-52.8
-101.0
-37.0
3.5
-78.5
-59.6
-104.0
16.8
-12.0
16.3
29.0
15.8
0.6
-12.1
14.4
7.2
21.8
19.2
6.0
14.3
2.9
2.7
-13.0
13.6
11.9
-9.7
3.0
-14.4
22.1
18.2
24.5
26.3
7.2
41.0
17.1
FE
(%)
78.1
107.5
69.1
95.4
116.4
130.0
102.8
107.5
130.8
114.1
131.1
35.1
46.0
25.3
39.7
37.2
36.2
46.6
45.2
58.1
42.8
42.4
41.8
52.1
59.7
31.6
41.1
25.2
33.9
33.6
35.6
41.4
56.9
61.3
48.3
54.7
51.7
68.1
62.7
53
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CMAQ 2002 Annual
Organic
Carbon
IMPROVE
STN
IMPROVE
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
12-kmEUS
12-km WUS
Northeast
Midwest
Southeast
Central
West
No. of Obs.
8282
10069
599
2056
1795
1532
9493
9726
2903
1447
2641
2474
2504
2408
8287
10082
598
2057
1800
1531
9508
NMB
(%)
-15.0
-14.1
-22.6
11.6
-32.4
-24.3
-15.5
-39.9
-37.6
-45.2
-26.5
-47.4
-43.6
-36.3
-32.4
-34.8
-42.4
-6.4
-46.1
-47.9
-34.5
NME
(%)
49.2
67.2
37.5
57.5
44.6
47.6
67.8
58.0
60.3
60.9
61.7
55.3
54.0
61.4
60.5
60.0
54.8
68.2
58.4
61.6
59.6
FB
(%)
-23.4
-29.5
-27.4
0.5
-42.0
-29.8
-31.3
-41.1
-40.4
-41.6
-19.7
-53.7
-51.3
-37.9
-37.1
-31.2
-40.2
-0.7
-69.7
-61.2
-29.7
FE
(%)
52.8
62.1
46.5
50.8
55.6
55.9
62.7
70.5
69.3
73.1
67.6
70.7
69.7
70.2
67.9
63.0
63.8
60.8
81.3
79.6
61.9
54
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5.0 Bayesian Model-Derived Air Quality Estimates
5.1 Introduction
The need for improved spatial and temporal estimates of air quality has grown rapidly in recent
years, as the development of more thorough air quality related health studies have begun
requiring more thorough characterizations of ground-level air pollution levels. The most direct
way to obtain accurate air quality information is from measurements made at surface monitoring
stations across the country. However, many areas of the U.S. are not monitored and typically, air
monitoring sites are sparsely and irregularly spaced over large areas. One way to address the
limits to ambient air quality data is to combine air quality monitoring data and numerical model
output in a scientifically coherent way for improved spatial and temporal predictions of air
quality. This type of statistical modeling could provide spatial predictions over the temporal
scales used to assess the associations between ambient air quality and public health outcomes
and for assessing progress in air quality under new emission control programs. Hierarchical
Bayesian Modeling (HBM) is used in numerous applications to combine different data sources
with varying levels of uncertainty. This section will briefly introduce the Hierarchical-Bayesian
approach developed by EPA for use in the EPHT program.
The approach discussed in this section combines the strength of both modeled and monitored
pollution concentration values to characterize air quality with estimated accuracy and enhanced
spatial and temporal coverage. The statistical approach is explained in McMillan, N., Holland,
D.M., Morara, M, and Feng, J., "Combining Different Sources of Particulate Data Using
Bayesian Space-Time Modeling," Environmetrics, 2009, DOT: 10.1002/env.984.
5.2 Hierarchical Bayesian Space-Time Modeling System
5.2.1 Introduction to the Hierarchical-Bayesian Approach
EPA's Hierarchical-Bayesian (HB) space-time statistical model combines ambient air quality
data from monitors with modeled CMAQ air quality output to produce daily predictions of
pollution concentrations for defined time and space boundaries. Bayesian analysis decomposes a
complex problem into appropriate linked stages (functions), i.e., a) air quality data; b) CMAQ
model output; c) measurement errors and model bias; and d) the underlying 'true' concentration
surface. A Bayesian approach incorporates 'prior knowledge' (e.g., numerical information
describing known attributes/behaviors, statistical distributions, etc.) of the unknown parameters
in the hierarchical model, which results in an improved estimation of the uncertainty of the 'true'
air pollutant concentration at any location in space and time. A hierarchical model builds a
combined solution, superior to either air quality monitor data or air quality modeling data alone.
The predictions of the ambient concentration 'surface' provided by EPA's HB Model are for a
selected year and with spatial scope spanning across the contiguous U.S. (i.e., the 'lower 48'
states). The HB Model methodology blends the best characteristics of monitored concentration
values and modeled concentration values for prediction of the 'true' concentration values
(surface) over time when both sources of data are available. Air quality monitors are assumed to
measure the true pollutant concentration surface with some measurement error, but no bias. In
contrast, numerical output from source-oriented air quality models is assumed to approximate the
variability of the true surface while exhibiting both measurement error and bias (additive and
55
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multiplicative) across space and time. Given the typical exponentially distributed nature of air
quality data, the HB Model performs its analysis with log-transformed monitoring and modeling
inputs. The HB Model gives more weight to accurate monitoring data in areas where monitoring
data exists, and relies on bias-adjusted model output in non-monitored areas. The HB Model
approach offers the ability to predict important pollution gradients and uncertainties that might
otherwise be unknown using interpolation results based solely on air quality monitoring data.
EPA's HB Model can be used to obtain surrogate measures of air quality for studies addressing
health outcomes.
5.2.2 Advantages and Limitations of the Hierarchical-Bayesian Approach
At a high level, the advantage of HB modeling methodology is its inherent ability to predict air
quality estimates for selected times and spatial scales using air quality monitoring and air quality
modeling data as input, while minimizing the limitations which arise when either of these
methods are applied separately. Another important advantage of the HB modeling approach is
the ability to predict estimates of errors in air quality. The HB modeling approach generates
estimates of air quality for days when monitoring data is missing, in addition to estimating air
quality in areas without monitors. An important disadvantage of HB modeling is the
computational burden imposed on model users. Typically, these models are 'adjusted' by
running numerous simulations, and at times the solutions are difficult to program and require
significant computer resources. Thus, there is the need for EPA to develop an operational
approach to HB modeling. It requires experience and statistical expertise to ensure that proper
(initial) modeling assumptions have been used, that proper convergence criteria have been used
for the HB Model, and that the results are reasonable.
In setting up the procedures for developing the HB Model estimates, EPA selected a set of data
quality objectives, DQOs, to guide the acceptance of the results. Based on an independent data
set (not used in the predictions), EPA calculates (1) the Bias as the absolute difference between
the (log-transformed) measurement generated from the monitor at that location (i.e., the "true"
value) and the log-transformed prediction that is made by the particular model; and (2) the Mean
Square Error (MSE), calculated as the square of the bias. EPA presents three different types of
MSE summaries: (a) day-specific MSE, averaged over all monitoring locations; (b) location-
specific MSE, averaged over all monitoring days; and (c) the overall MSE (i.e., averaged across
locations and time). MSE is a statistical score that represents overall (average) performance in
which large deviation from the "true" value yields larger penalties compared to small errors.
While these performance measures were used in evaluating the results, they have no absolute
acceptance/rejection values and are considered on a case-by-case basis when evaluating the
performance of any years of HB Model application. In general, while the DQO's usefulness is
still being studied and EPA attempts to achieve these DQOs, these measures are helpful at this
time to describe the quality of the HB predictions from one model year to another.
In developing and providing the HB Model results, EPA is attempting to advance the use of
improved air quality estimates. As such, the proper use of the EPA results is important and
discussed further in Section 5.6.
56
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5.3 Results for O3 and PM2.s
The HB Model yields a predicted daily concentration and error estimate for those predictions
within each grid cell for each day within the time period of interest. The concentrations are daily
PM2.5 or 8-hour maximum ozone levels. These predictions fall along a smooth (congruent)
response surface across the entire region. The grid used by the HB Model is the same as that
used in generating the CMAQ estimates. The smoothness of the surface is achieved by: 1) the
choice of prior distributions for air data, CMAQ output, and the true underlying predictive
surface; and 2) the conditional autoregressive model (CAR) spatial covariance structure where a
grid's predicted concentration is assumed to be correlated with neighboring cells (note the HB
Model can handle different size neighborhoods). The resulting HB Model prediction surface
approximates the true underlying response surface while accounting for such factors as
measurement error and potential space-time bias in the CMAQ output.
EPA stores the set of back-transformed predictions (pm25_pred, O3_pred) and standard errors
(pm25_stdd, O3_stdd) from a given execution of the HB Model in tabular (comma-delimited)
format within a file named as in the following example: pm25_surface_36km_2001.csv. Table
5-1 presents an example of the output that can be obtained from this file. One row exists in this
file for each grid cell-date combination within the study area. The relevant variables in this file,
in the order in which they exist (and are portrayed within the column headings of the table), are
as follows:
Date: Represented by the data given in this row, in MM/DD/YYYY format.
Longitude: The x-coordinate value transformed to longitude (degrees).
Latitude: The y-coordinate value transformed to latitude (degrees).
Column: The column associated with model results.
Row: The row associated with model results.
pm25_pred or O3_pred
pm25_stdd or O3_stdd
Table 5-1. HB Model Prediction: Example Data File
Date
01/01/2001
01/01/2001
01/01/2001
01/01/2001
01/01/2001
01/01/2001
01/01/2001
01/01/2001
01/01/2001
01/01/2001
Longitude
-119.315
-119.398
-119.483
-119.567
-119.653
-119.739
-119.826
-119.913
-120.001
-120.09
Latitude
23.43627
23.74126
24.04658
24.35223
24.6582
24.96448
25.27106
25.57793
25.88509
26.19253
Column
12
12
12
12
12
12
12
12
12
12
Row
15
16
17
18
19
20
21
22
23
24
O3_pred (ppb)
23.011
22.979
22.919
22.987
23.19
23.018
23.12
22.997
22.968
22.949
O3_stdd (ppb)
4.6122
4.6784
4.8484
4.7917
4.84
4.8264
4.8651
4.84
4.8308
4.8357
Note: The exact contents of this table may change over time. Please check the accompanying metadata files.
57
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5.4 Overview of HB Model Predictions
Below is a short description of the inputs and outputs for a HB Model application for 2002, 12
km grid, PM2.5. A description of the input metadata and HB Model application can be found in
Appendix E. The air quality data come from EPA AQS, the CMAQ was run by EPA as
documented elsewhere in this report and the HB Model was applied at EPA's NERL. The
domain of the CMAQ model (and therefore the HB Model predictions) is found in the following
table.
Table 5-2. HB Model Domains for 12-km Applications
Study Year
2002
North Bounding
Coordinate
47.78 degNlat
South Bounding
Coordinate
24.33 degNlat
East Bounding
Coordinate
65.26degWlon
West Bounding
Coordinate
122.49 deg W Ion
Figure 5-1 shows the HB Model prediction for PM2.5 during July 1-4, 2002. On July 1, the PM2.5
levels were the highest along the U.S.-Canada border northeast of Lake Erie and into the mid-
Atlantic region. As the days passed, the elevated PM2.5 decreased in intensity and moved
southeast. Examining the figure, it is possible to see the change in PM2.5 level at any point in the
domain. Figure 5-2 shows a close up of the HB Model predictions for July 2. The 12-km grid
can be seen as small squares. Within each grid the predicted PM2 5 concentrations are constant.
As such, the PM2.5 concentrations represent an average over the area where the public is exposed
to ambient PM2.5. Although actual concentrations within grid cells vary over space and time
during a day, the ambient exposure is likely to be somewhat averaged as people move about
within and between grid cells. Given the relationship between ambient concentrations, ambient
exposures and personal exposure is not understood well, one area of study is the degree of
misclassification between exposure and health outcomes based on varying grid sizes.
The HB Model results can track with the AQS data and CMAQ estimates and the predictions can
differ from either the AQS data or the CMAQ estimates. Figure 5-3 shows HB predictions for a
location where the predictions generally follow temporally the CMAQ and AQ data. This figure
shows a series of days where AQS data and CMAQ estimates are fairly consistent. In such
cases, the HB Model predictions track closely to both inputs. Figure 5-4 shows how the HB
Model fills in PM2.5 predictions for days when AQS data are not available (many PM2.5 monitors
are operational and collect samples during 1 day in every 3-day time period). On the
unmonitored days, the HB Model predictions track well with the CMAQ estimates. Figure 5-5
shows a situation where AQS and CMAQ do not agree well and, while the HB Model tends to
mitigate the bias of CMAQ, the HB Model the predictions can be highly affected by CMAQ,
although the day-to-day trends are maintained.
58
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July 1,2002
i
Figure 5-1. HB Prediction (PM2.s) During July 1-4, 2002 (12 km grid cells)
10 20
30
40+
PM2.5 (ug/m3
-,EriE
yeland
kron
PENNSYLVAN
^Pittsburgh
: :-"
'EST VIRGINIA
Q Brid
, PatersonQ m
Ujironkers
.,-'NewYork
@Phi[adelphia
NEW JERSEY
-Richmond
VIRGIN
Not to exact scale
Virginia
-Beach
..Chesapeake
Figure 5-2. HB Prediction (PM2.5) on July 2, 2002 (12 km grid cells)
59
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u
15
3
u
V)
2
o
o
PLOT B-a-B I
Figure 5-3. HB Prediction (PIVb.s) Temporarily Matches AQS Data and CMAQ Estimates
u
Q.
at
re
O)
o
PLOT D D O PN25_p red & -i
Figure 5-4. HB Prediction (PM2.5) Compensates When AQS Data is Unavailable
60
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-------
Figure 5-6. Plot of the Response Surface of PM2.s Concentrations as Predicted by the HB Model on a
Specific Monitoring Day in the Northeast U.S., Along With PM2.5 Measurements on a Specific
Monitoring Day from FRM Monitors in the NAMS/SLAMS Network
62
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Figure 5-7. Rotated View of the Response Surface of PlVfe.s Concentrations as Predicted by the HBM on a
Specific Monitoring Day in the Northeast U.S., Along With PM2.s Measurements on a Specific
Monitoring Day from FRM Monitors in the NAMS/SLAMS Network
Figure 5-8 portrays the same plot as Figure 5-6, but with the CMAQ-estimated PM2.5 surface
added. The CMAQ surface features have more yellow shading within them, implying that the
CMAQ concentration values somewhat underestimate the concentrations relative to the FIB
Model and the monitoring stations. However, in areas in which there are few or no monitoring
stations, the Fffl Model surface corresponds closely with the CMAQ surface. This is to be
expected, as the FIB Model weighs (uses a bias adjustment of) the CMAQ data more heavily in
areas without monitoring data.
63
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Figure 5-8. Rotated View of the Response Surface of PlVfe.s Concentrations as Predicted by the HBM on a
Specific Monitoring Day in the Northeast U.S., Along With PM2.s Measurements on a Specific
Monitoring Day from FRM Monitors in the NAMS/SLAMS Network, and the Response Surface as
Predicted by the CMAQ Modeling System
64
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Fused 36 km Oo Surface, 7/26/05
Figure 5-9. Fused 36 km O3 Surface for the Continental U.S. (July 26, 2005).
Figure 5-9 displays the ozone concentration for the continental U.S. on July 26, 2005. The
spheres represent the concentrations recorded at monitor locations. The green, blue, and yellow
represent the HB concentration surface, which combines the CMAQ model estimates and the
PM2 5 monitor measurements.
5.5 Evaluation of HB Model Estimates
As reported in the McMillan paper (Environmetrics, 2009), model validation analysis was
performed to compare the HB predictive results at 2001 STN/IMPROVE monitoring sites to
predictions at those locations from two other approaches: (1) traditional kriging predictions
based solely on the FRM monitoring data and (2) CMAQ output at these locations. In doing so,
it was assumed the STN/IMPROVE measurements represent the "truth." The IMPROVE
measurements are representative of rural areas (with few monitors) and may help assess the
HBM results for these areas of interest. The potential bias in either the STN or IMPROVE
gravimetric mass measurements compared to FRM data were not considered, although for
65
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gravimetric mass the monitors generally produce the same results. STN data collocated with
FRM monitoring sites used in fitting the HB Model were eliminated from the validation data set,
leaving 44 sites for the validation analysis.
In the validation analysis, mean squared prediction error and bias were calculated to evaluate the
predictive capability of these three different models. To assess the ability of the HB Model to
accurately characterize prediction uncertainty, the percentage of validation data within the
95 percent prediction credible interval was calculated. In the analysis, a similar analysis was
performed for the kriging model by calculating 95 percent confidence intervals at the validation
sites. An exponential variogram model was used for the kriging model. The exponential
parameters were estimated by fitting this model to an empirical variogram based on combining
the daily empirical variograms.
In this analysis, predictions for each day were obtained for the STN/IMPROVE site locations
from the three modeling approaches and the validations statistics were calculated across all days
and sites. The validation only occurs every third day, according to the sampling schedule of
STN/IMPROVE. This corresponds to the full network FRM schedule. Thus, the analysis did
not evaluate sparse monitoring days where data fusion is expected to outperform interpolation
techniques based solely on the monitoring data.
In the analysis, the HBM was run several times using a range of reasonable priors. Then, the
validation analysis assessed the relative predictive performance of the HBM, traditional kriging,
and CMAQ as described above. In terms of mean squared prediction error (MSB), the HBM and
kriging approaches provided similar results across all HBM runs. For bias, the HBM
outperformed kriging by 10 to 15 percent depending on the prior assumptions for Tx andr7 .
CMAQ was nearly unbiased for this analysis.
Kriging uncertainties were reflected in the small percentage (59%) of kriging prediction intervals
capturing the validation data. This compares to HBM predictive interval results of 80 to
90 percent depending on the HBM run. This occurs from the difference between the HBM
results and the 95 percent nominal rate to the difference in the measurement errors in the
validation to those in the FRM data used in fitting the HBM model. Unfortunately, error-free
PM2.5 monitoring data are not available with current PM2.5 monitoring approaches.
5.6 Use of EPA HB Model Predictions
Over the next several years, NERL will be working to improve spatial and temporal estimates of
ambient pollutant concentrations to facilitate improved modeling of human exposure. The goal
is to improve exposure modeling for intracity and intercity exposure comparisons and to develop
better understood exposure surrogates for use in air pollution health studies. Given the uncertain
characterization of air quality, especially at locations at a distance from central monitoring sites,
NERL has been working to develop the HB Model (and other approaches) for estimating
ambient and exposure concentrations for use in health studies, benefits assessments, and other air
program analyses.
66
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The HB Model as developed by NERL is part of an emerging research program. Accordingly, it
should be understood by users of the HB predictions that the underlying statistical model is
continuing to be studied and improved. However, given the uncertain nature of air quality,
especially at locations well-removed from monitoring sites, NERL has been working to develop
the HB Model (and other approaches) for estimating ambient and exposure concentrations for
use in health studies, benefits assessments, and other air program analyses. To encourage
assessments of these predictions from the HB Model, NERL is making the predictions available
based on a general DQO approach of determining whether the predictions from the HB Model
are appropriate for use for these purposes. This approach allows use of uncertain results by
providing the statistical error estimates for the predictions and an assessment of the predictions.
In this manner, users can assess the effects of the uncertainty for the predictions with their
studies.
Based on NERL's current model evaluation results, the HB Model predictions provide credible
predictive surfaces of air quality (ozone and PM2.s), in particular away from monitoring sites.
The HB Model, as initially configured, predicts to the central tendency with the potential
distributions (that is, each estimate represents a mean value from the distribution of possible
values for each space-time point). This means that the HB Model will tend to under-predict very
high values (the implications of this are being investigated). Nevertheless, the HB predictions,
by "filling-in" pollutant concentration values for missing (non-monitored) locations and missing
(unsampled) days of air quality estimates, are likely to be an improvement compared to simply
using the monitoring results. In addition, as the HB Model is a space-time model, it is more
credible than statistical interpolation of the monitoring data where there are missing monitoring
data (this is the predominate issue for 1 in 3 day PM2.s monitoring sites across the U.S.). The HB
Model, and other statistical methods, is more scientifically credible than simple mathematical
techniques, such as inverse distance weighting.
Given the uncertainty and the complexity of using the HB Model predictions, careful use of the
HB predictions is needed. Until a thorough study of several prediction years and scales (grid
sizes) is completed, the results should be used by professionals with an ability to understand
anomalous outcomes when using the predictions in a health study. An exception-based review
of the HB predictions should be undertaken by each researcher, in the context of a study's data
needs, to ensure "outliers" do not influence subsequent analyses. The HB predictions include a
few very high values which cannot be rejected out-of-hand without further study. Studies of the
representativeness of the HB Model predictions and additional experience with the prediction
will provide a better understanding of the limits of using these predictions. The HB Model was
initially designed for use as a source of air quality estimates in case-crossover analyses where
temporal and spatial variability was needed. The predictions could be used within the EPHT
program in health surveillance activities, to generate hypotheses for further studies, and as a
basis for indicators in counties without monitors. They also can be used in Health Impact
Assessments in place of interpolated monitoring data.
EPA continues to research approaches to combining air quality data and model results to predict
statistically air quality estimates for use in health studies and elsewhere in the air program.
There are key scientific questions that the HB Model (and other techniques) may help address.
For example, determining the most representative scale (36 km, 12 km or smaller scale) of
67
-------
ambient air quality measures (as surrogate for ambient exposure or personal exposure) for use in
associating health outcome data with air quality changes needs to be better understood. The
effect of (monitor) measurement variability and CMAQ bias on the usefulness of the HB
predictions is also an important aspect for further improvement of air quality measures used in
health studies.
68
-------
Appendix A
Acronyms
A-l
-------
A-2
-------
Acronyms
BEIS
BlueSky
CAIR
CAMD
CAP
CAR
CEM
CHIEF
CMAQ
CMV
CO
DQO
EGU
Emission
inventory
EPA
EMFAC
FAA
FDDA
FIPS
HMS
ICS-209
IPM
ITN
LSM
MOBILE
MODIS
NEEDS
NEI
NERL
NESHAP
NH3
NMIM
NONROAD
NOX
OAQPS
OAR
OTAQ
ORD
ORL
PFC
PM2.5
PMio
Biogenic Emissions Inventory System
Emissions modeling framework
Clean Air Interstate Rule
EPA's Clean Air Markets Division
Criteria Air Pollutant
Conditional Auto Regressive model
Continuous Emissions Monitoring
Clearinghouse for Inventories and Emissions Factors
Community Multiscale Air Quality model
Commercial marine vessel
Carbon monoxide
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
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
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
EPA's Office of Transportation and Air Quality
EPA's Office of Research and Development
One Record per Line
Portable Fuel Container
Particulate matter less than or equal to 2.5 microns
Parti culate matter less than or equal to 10 microns
A-3
-------
Prescribed
fire
RIA
RPO
RRTM
sec
SMARTFIRE
SMOKE
TCEQ
TSD
VOC
VMT
Wildfire
WRAP
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
A-4
-------
Appendix B
Total U.S. Emissions Summary by Sector and by Region for PM2.s
B-l
-------
B-2
-------
2002 PM25 Urban Areas in the West
350 thousand tons
non-EGU pt
ECU 6%
3%.
fire
18%
onroad
4%
nonroad
6%
area
30%
dust
30%
Figure B-1. PM2.5in Urban Areas in Western U.S. (2002)
2002 PM25 Urban Areas in the East
1.2 million tons
non-EGU pt
10%
nonroad
6%
Figure B-2. PM2.sin Urban Areas in Eastern U.S. (2002)
-------
2002 PM25 Rural Areas in the West
530 thousand tons
non-EGU pt
ECU 5%
3%
\10%
>\ nonroad
onroad^
Figure B-3. PM2.s in Rural Areas in Western U.S. (2002)
2002 PM25 Rural Areas in the East
850 thousand tons
ECU
10% A
non-EGU pt
7%
fire
5%^\
onroad
2% ~^
nonroady
4%
Figure B-4. PM2.s in Rural Areas in Eastern U.S. (2002)
B-4
-------
2002 PM25 West
880 thousand tons
non-EGU pt
5%
onroauj
2%
^area
18%
nonroad
3%
Figure B-5. PM2.5in Western U.S. - Rural and Urban (2002)
2002 PM25 East
2.1 million tons
non-EGU pt
area
24%
Figure B-6. PM2.sin Eastern U.S. - Rural and Urban (2002)
B-5
-------
2002 PM25 Total
3 million tons
fire
ECU
10%
non-EGU pt
8%
onroady
3%
nonroad
4%
Figure B-7. Total PM2.5 in U.S. (2002)
B-6
-------
Appendix C
State-Sector Emissions Summaries for 2002
c-i
-------
C-2
-------
Table C-1. 2002 State Sector Emissions
State-Sector Emissions Summaries for 2002 Base Case
(taken from Appendix D)
State
Alabama
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Alabama Total
Arizona
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Arizona Total
Arkansas
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Arkansas Total
[tons/yr]
2002
VOC
0
0
2,383
8,951
213,956
55,574
104,783
1,394
47,722
434,763
0
0
3,482
21,385
80,463
53,546
85,187
626
4,611
249,300
0
0
2,295
5,821
99,381
35,683
56,465
520
32,044
232,209
[tonsfyr]
2002
NOx
0
0
36,047
3,814
32,024
29,396
153,968
161,767
80,901
497,917
0
0
30,813
10,532
8,637
38,699
159,756
85,967
11,439
345,843
0
0
39,743
2,654
21,453
28,527
83,722
42,218
27,605
245,923
[tons/yr]
2002
CO
0
0
10,328
175,140
188,564
378,753
1,237,459
10,879
174,483
2,175,607
0
0
20,495
440,419
44,127
440,675
836,126
8,185
8,259
1,798,285
0
0
14,371
123,699
174,777
231,619
735,366
4,182
51,502
1,335,515
[tonsfyr]
2002
S02
0
0
4,801
983
52,325
2,734
5,599
448,329
89,762
604,533
0
0
2,297
2,888
2,571
3,858
2,876
70,709
21,702
106,900
0
0
4,648
728
27,260
2,762
3,078
70,754
19,032
128,262
[tons/yr]
2002
NH3
0
57,802
13
752
426
28
5,627
783
2,224
67,655
0
29,493
12
2,020
4,391
35
5,150
566
72
41,740
0
110,954
19
556
7,386
23
3,001
346
1,255
123,540
[tons/yr]
2002
PMio
100,288
0
2,236
16,251
27,785
3,195
4,223
26,138
19,710
199,826
121,322
0
2,617
43,005
12,456
4,174
4,021
9,551
5,723
202,868
92,523
0
1,348
12,027
24,094
3,229
2,202
2,004
14,101
151,529
[tons/yr]
2002
PMZ5
33,476
0
1,878
13,938
23,973
3,044
3,117
22,612
13,647
115,685
19,626
0
2,060
37,151
8,596
3,993
2,951
7,565
3,044
84,987
24,639
0
1,243
10,315
23,062
3,097
1,612
1,750
9,593
75,312
C-3
-------
California
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
California Total
Colorado
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Colorado Total
Connecticut
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Connecticut Total
0
0
19,726
54,619
461,331
148,269
343,693
1,288
54,610
1,083,536
0
0
1,366
13,610
87,037
42,009
84,387
973
90,768
320,150
0
0
845
31
105,580
32,327
47,757
305
4,602
191,447
0
0
175,373
24,563
121,882
240,256
643,919
13,071
91,967
1,311,031
0
0
19,208
6,271
11,464
35,398
127,564
79,167
39,499
318,571
0
0
3,945
14
12,554
17,897
66,813
6,161
6,706
114,091
0
0
108,995
1,157,187
458,977
1,058,968
3,434,055
23,900
97,092
6,339,176
0
0
10,641
288,013
85,393
389,240
1,103,120
7,578
28,063
1,912,049
0
0
12,149
667
69,769
258,776
641,901
1,920
2,133
987,315
0
0
40,887
6,735
77,672
1,015
4,786
1,018
41,761
173,874
0
0
1,224
1,719
6,460
3,545
4,146
92,562
5,331
114,989
0
0
778
4
18,455
1,382
1,667
13,689
2,338
38,313
0
152,308
180
5,117
14,758
161
37,468
1,380
3,367
214,738
0
62,907
5
1,299
71
31
4,408
453
86
69,260
0
4,029
1
3
1,438
17
3,257
182
91
9,017
196,231
0
10,124
113,231
90,509
18,590
23,103
1,905
26,854
480,546
110,878
0
606
28,019
15,059
3,909
3,216
5,446
17,366
184,499
12,528
0
231
65
10,716
1,702
1,610
742
882
28,476
47,562
0
9,534
97,301
73,873
16,334
12,395
1,876
16,655
275,530
25,559
0
553
24,054
13,545
3,746
2,357
4,444
8,922
83,181
2,725
0
210
56
10,446
1,619
1,067
510
691
17,323
C-4
-------
Delaware
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Delaware Total
District of Columbia
afdust
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
District of Columbia Total
Florida
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Florida Total
0
0
483
64
15,468
8,677
11,382
91
4,659
40,823
0
22
0
4,118
1,918
5,423
4
69
11,554
0
0
3,053
56,159
459,700
239,540
362,851
2,236
37,204
1,160,742
0
0
10,429
23
3,259
5,308
21,679
9,533
7,308
57,538
0
571
0
1,740
3,060
8,772
710
418
15,271
0
0
55,127
25,600
29,533
117,138
448,520
272,057
54,078
1,002,054
0
0
2,890
1,332
11,640
65,811
155,366
866
8,853
245,758
0
79
1
1,819
18,061
65,418
50
247
85,676
0
0
43,166
1,193,147
202,108
1,762,587
3,797,717
52,142
86,821
7,137,689
0
0
3,470
6
5,859
471
556
33,104
41,342
84,810
0
45
0
1,559
343
271
1,432
625
4,275
0
0
6,892
7,018
70,489
12,540
21,410
473,636
57,060
649,045
0
12,536
0
5
279
5
903
30
161
13,918
0
0
0
13
2
398
8
4
426
0
37,099
11
5,366
448
125
18,267
5,013
3,030
69,359
6,258
0
452
102
2,007
560
572
1,969
1,041
12,961
2,255
13
0
489
298
219
30
98
3,402
145,566
0
2,391
115,996
41,371
13,637
12,433
32,299
32,193
395,887
863
0
401
87
1,826
534
406
1,693
783
6,594
411
13
0
427
288
150
22
43
1,353
28,017
0
2,175
99,484
38,847
13,001
9,041
28,293
23,604
242,462
C-5
-------
Georgia
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Georgia Total
Idaho
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Idaho Total
Illinois
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Illinois Total
0
0
1,776
21,834
248,214
81,856
185,962
1,182
33,735
574,559
0
0
713
29,989
141,328
23,153
27,934
0
2,113
225,230
0
0
4,205
156
278,553
99,398
164,697
1,536
71,066
619,612
0
0
39,986
7,955
38,919
57,979
307,544
146,351
51,170
649,905
0
0
8,297
14,024
30,317
15,611
44,628
19
11,467
124,363
0
0
120,834
71
47,645
115,426
297,056
179,125
94,009
854,165
0
0
11,058
350,924
194,402
730,260
2,245,133
9,371
131,306
3,672,454
0
0
10,893
630,971
95,417
137,661
389,120
4
23,977
1,288,044
0
0
16,365
3,323
99,568
830,513
2,090,188
14,627
78,820
3,133,402
0
0
3,247
2,010
56,830
5,674
11,238
512,983
56,203
648,183
0
0
645
3,845
2,915
1,616
1,310
0
17,597
27,928
0
0
11,979
20
5,395
10,913
8,514
366,157
138,126
541,103
0
80,733
12
1,299
60
52
10,642
593
4,571
97,962
0
62,376
3
2,856
1,684
14
1,418
0
1,074
69,425
0
106,685
45
15
1,631
88
10,654
174
694
119,986
181,397
0
1,332
28,079
46,751
6,136
8,539
31,663
21,224
325,121
139,528
0
471
61,433
56,403
1,973
1,068
1
4,569
265,445
444,909
0
3,556
323
16,972
11,316
7,772
19,147
30,111
534,106
59,910
0
1,135
24,082
41 ,847
5,867
6,366
25,407
15,692
180,308
28,351
0
447
52,808
27,367
1,889
785
1
2,528
114,175
88,100
0
3,351
277
15,181
10,881
5,700
14,783
15,136
153,409
C-6
-------
Indiana
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Indiana Total
Iowa
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Iowa Total
Kansas
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Kansas Total
0
0
2,224
194
179,635
58,290
140,188
2,015
55,935
438,480
0
0
1,653
197
77,838
52,138
75,852
579
37,943
246,201
0
0
2,133
828
135,449
24,728
52,786
1,062
26,274
243,261
0
0
52,285
88
30,185
64,575
216,188
283,890
80,147
727,359
0
0
33,166
90
15,150
62,066
115,521
81,995
38,861
346,849
0
0
41,147
378
42,286
47,653
85,617
96,943
70,704
384,728
0
0
14,057
4,124
74,953
490,545
1,738,790
15,540
364,487
2,702,495
0
0
7,209
4,185
68,958
309,048
1,055,157
5,444
36,521
1,486,523
0
0
9,118
17,600
850,800
240,503
683,936
6,793
74,809
1,883,560
0
0
5,540
24
59,775
5,981
8,564
785,603
97,442
962,930
0
0
2,787
25
19,832
6,248
2,999
133,047
51,329
216,267
0
0
2,895
103
36,381
4,858
2,893
129,827
10,793
187,750
0
90,815
19
19
4,214
48
7,343
580
3,144
106,183
0
245,778
8
19
7,404
47
3,091
391
4,663
261,401
0
97,384
11
79
12,467
32
2,870
421
60,100
173,364
345,635
0
1,719
401
60,255
6,039
5,518
40,884
25,808
486,257
341 ,542
0
1,021
407
12,833
7,210
2,355
9,907
13,439
388,712
455,984
0
1,237
1,711
108,571
5,360
2,200
7,246
9,430
591,738
65,707
0
1,561
344
32,611
5,803
4,081
33,805
15,085
158,996
57,643
0
997
349
11,476
6,949
1,726
8,904
7,572
95,615
74,515
0
1,207
1,468
83,174
5,179
1,629
5,912
4,941
178,025
C-7
-------
Kentucky
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Kentucky Total
Louisiana
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Louisiana Total
Maine
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Maine Total
0
0
2,487
2,909
105,281
39,806
82,321
1,479
44,884
279,168
0
0
3,960
7,137
135,934
61,307
77,802
1,239
79,781
367,159
0
0
365
1,258
88,028
30,025
26,131
67
5,151
151,026
0
0
70,391
1,326
17,557
31,792
147,749
200,955
38,541
508,311
0
0
216,290
3,254
27,559
28,899
124,192
82,293
211,449
693,935
0
0
1,708
566
7,423
8,271
47,227
1,188
18,895
85,277
0
0
17,830
61,812
108,397
282,098
1,052,158
12,544
110,047
1,644,885
0
0
45,941
151,658
139,222
364,963
943,962
12,682
134,203
1,792,631
0
0
3,650
26,592
104,033
138,111
360,595
1,084
15,861
649,927
0
0
10,096
364
34,229
3,008
5,554
486,499
34,482
0
0
32,796
892
2,378
2,834
4,409
108,106
177,507
328,922
0
0
195
150
9,969
766
1,122
2,137
20,778
35,116
0
50,821
15
278
231
25
4,824
919
1,672
99,481
0
4,285
6,010
23,283
3,376
3,816
22,342
16,375
574,230 58,787
0
35,159
42
682
23,169
29
4,364
1,399
7,878
72,722
0
6,154
1
115
1,616
11
1,467
129
809
10,302
81 ,493
0
7,000
14,746
19,038
3,331
3,379
7,487
28,722
165,196
13,067
0
455
2,480
13,876
1,200
1,178
86
5,963
38,304
23,529
0
3,625
5,155
18,590
3,236
2,842
20,004
9,937
178,967
20,962
0
6,819
12,647
17,862
3,174
2,506
5,990
21 ,082
91,043
4,134
0
405
2,127
13,726
1,131
876
65
4,268
26,732
C-8
-------
Maryland
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Maryland Total
Massachusetts
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Massachusetts Total
Michigan
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Michigan Total
0
0
5,360
353
126,362
51,369
71,591
478
5,758
261,270
0
0
2,443
747
176,731
52,921
71,646
595
7,722
312,806
0
0
2,504
724
248,382
173,241
207,762
1,243
39,832
673,689
0
0
17,106
137
21,715
27,495
121,659
73,527
22,109
283,748
0
0
17,144
341
34,373
30,046
128,362
32,561
15,394
258,220
0
0
43,025
330
43,499
70,912
315,420
141,908
82,202
697,296
0
0
17,581
6,129
141,960
414,390
1,004,611
4,546
94,448
1,683,666
0
0
18,602
15,878
136,753
423,212
960,011
10,922
10,656
1,576,034
0
0
26,763
15,380
94,909
1,013,991
2,744,658
13,367
66,873
3,975,941
0
0
5,707
32
40,864
2,577
3,966
256,761
34,255
344,162
0
0
2,519
93
25,261
2,385
3,172
91,888
14,079
139,397
0
0
14,466
91
42,066
6,367
13,508
348,377
72,631
497,505
0
24,562
22
24
606
28
5,594
271
222
31,330
0
2,208
7
71
4,070
28
5,509
1,103
403
13,401
0
55,273
5
69
429
78
9,813
286
952
66,906
35,393
0
1,635
613
25,058
3,102
3,162
17,996
6,303
93,261
49,646
0
988
1,544
28,552
2,871
3,253
3,730
2,795
93,379
208,843
0
2,637
1,495
30,989
8,199
7,881
13,170
17,151
290,363
7,393
0
496
531
19,764
2,954
2,194
15,722
3,759
52,813
14,810
0
874
1,324
26,536
2,732
2,268
3,224
1,842
53,610
40,894
0
2,389
1,283
24,216
7,782
5,894
10,648
10,346
103,451
C-9
-------
Minnesota
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Minnesota Total
Mississippi
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Mississippi Total
Missouri
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Missouri Total
0
0
1,611
5,047
125,318
97,104
102,566
646
29,541
361,833
0
0
2,386
8,407
156,390
36,056
62,375
629
43,224
309,467
0
0
3,439
1,488
162,795
63,279
124,106
1,496
34,704
391,308
0
0
55,371
2,300
56,700
68,820
163,172
86,917
67,813
501,094
0
0
66,650
3,833
12,212
22,180
105,505
45,850
60,244
316,473
0
0
79,583
678
32,910
52,997
200,379
145,232
38,025
549,803
0
0
8,411
107,237
139,234
452,734
1,314,360
7,468
47,015
2,076,459
0
0
10,656
178,646
129,408
214,179
739,190
5,286
54,587
1,331,952
0
0
18,171
31,611
168,352
479,319
1,598,930
10,827
108,389
2,415,599
0
0
6,592
631
14,747
6,525
2,816
102,152
27,263
160,725
0
0
9,163
1,051
6,796
2,119
3,591
67,593
36,519
126,831
0
0
8,610
186
44,573
5,143
6,148
249,942
111,547
426,149
0
134,830
12
482
1,226
59
5,362
69
27,525
169,566
0
58,575
18
804
196
19
3,606
456
1,414
65,088
0
107,023
19
142
3,830
43
6,918
705
322
119,002
432,054
0
1,665
10,427
26,968
8,097
3,790
7,437
22,425
512,863
139,219
0
3,057
17,370
17,827
2,479
3,058
3,122
19,535
205,667
458,324
0
2,548
3,074
32,399
5,929
5,199
8,868
14,083
530,423
79,303
0
1,643
8,943
24,496
7,759
2,740
234
4,097
129,215
38,120
0
2,668
14,897
16,769
2,370
2,309
2,625
10,019
89,778
96,070
0
2,489
2,636
28,217
5,690
3,819
5,818
7,424
152,163
C-10
-------
Montana
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Montana Total
Nebraska
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Nebraska Total
Nevada
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Nevada Total
0
0
1,309
10,085
23,573
12,968
20,451
355
6,807
75,548
0
0
3,524
837
40,762
18,442
36,940
635
6,527
107,667
0
0
1,057
10,740
22,874
22,720
26,884
483
1,649
86,406
0
0
22,873
5,187
3,797
18,777
36,727
36,577
16,588
140,526
0
0
68,904
381
13,820
39,889
66,226
47,900
11,385
248,506
0
0
12,958
4,910
5,308
18,990
28,320
48,366
7,509
126,362
0
0
5,814
203,759
35,673
85,304
283,678
3,047
29,410
646,686
0
0
10,222
17,780
66,672
155,107
473,870
3,420
5,717
732,788
0
0
11,214
227,965
14,700
208,377
301,082
2,798
6,985
773,121
0
0
1,688
1,422
1,961
2,009
1,062
23,396
13,271
44,809
0
0
4,764
105
29,575
4,181
2,011
67,576
6,018
114,229
0
0
990
1,346
12,476
2,025
360
49,276
1,342
67,815
0
45,890
6
946
50
14
1,032
11
265
48,214
0
166,773
18
80
3,143
27
1,874
190
421
172,525
0
5,598
3
1,026
199
17
1,532
460
164
8,999
188,368
0
711
19,949
5,765
2,344
908
2,404
5,538
225,987
320,650
0
1,958
1,729
12,679
4,637
1,723
1,551
1,623
346,550
61 ,096
0
445
22,169
4,389
2,115
644
3,629
3,240
97,728
40,180
0
690
17,311
5,569
2,261
688
2,077
2,576
71,352
50,787
0
1,942
1,483
8,655
4,484
1,312
1,191
806
70,659
11,371
0
419
19,018
2,735
2,027
399
3,283
1,435
40,687
C-ll
-------
New Hampshire
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
New Hampshire Total
New Jersey
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
New Jersey Total
New Mexico
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
New Mexico Total
0
0
118
301
61 ,483
21,832
21,682
104
1,496
107,015
0
0
2,236
488
151,657
78,629
101,094
1,048
13,282
348,436
0
0
1,982
27,488
36,950
13,499
45,763
563
15,691
141,935
0
0
1,866
137
11,235
8,150
38,799
7,000
2,786
69,973
0
0
35,998
223
26,393
40,876
161,872
34,188
17,206
316,756
0
0
36,714
12,582
7,532
9,681
77,574
78,547
60,358
282,988
0
0
2,305
6,398
74,137
122,530
294,533
643
2,082
502,627
0
0
14,960
10,375
84,145
635,064
1,325,445
3,865
8,375
2,082,228
0
0
8,473
583,216
29,666
119,501
587,028
5,539
32,228
1,365,651
0
0
238
38
7,408
673
880
44,009
2,570
55,815
0
0
14,587
61
10,726
3,378
3,658
51,299
9,930
93,640
0
0
2,550
3,450
2,825
975
2,254
51,016
18,179
81,249
0
1,354
0
29
835
9
1,266
58
56
3,607
0
3,827
11
47
2,648
41
7,635
170
475
14,854
0
36,340
9
2,626
39
9
2,323
10
44
41,401
6,175
0
98
622
13,351
942
969
2,632
459
25,248
16,305
0
1,786
1,009
15,987
4,162
3,805
4,835
3,131
51,020
440,334
0
1,110
56,719
5,984
1,062
1,965
8,024
3,986
519,183
2,194
0
86
534
12,658
891
714
2,305
390
19,772
1,392
0
1,611
865
13,074
3,958
2,537
4,010
2,464
29,910
80,348
0
1,084
48,662
5,346
1,016
1,476
5,557
3,290
146,779
C-12
-------
New York
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
New York Total
North Carolina
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
North Carolina Total
North Dakota
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
North Dakota Total
0
0
2,473
903
608,921
151,345
212,929
857
6,218
983,646
0
0
1,472
58,889
231 ,094
88,972
143,187
920
61,685
586,219
0
0
1,256
527
14,911
13,565
15,356
781
1,249
47,645
0
0
40,659
412
89,986
78,279
290,698
81,201
38,992
620,228
0
0
22,608
11,424
18,869
61,664
242,379
153,226
49,273
559,444
0
0
23,072
240
4,007
38,012
24,832
75,947
9,929
176,039
0
0
22,205
19,195
404,592
1,175,721
2,822,801
12,204
54,133
4,510,852
0
0
9,957
429,388
321,101
746,344
1,786,813
12,112
52,414
3,358,129
0
0
4,832
11,204
20,488
91 869
206,627
5,237
5,778
346,035
0
0
9,353
113
125,559
6,797
8,075
238,034
59,078
447,008
0
0
1,840
696
22,020
5,750
8,683
471,337
56,065
566,392
0
0
1,601
66
5,768
4,106
700
140,535
15,449
168,224
0
49,281
29
86
3,964
79
14,582
2,439
1,241
71,702
0
158,188
7
532
236
54
7,953
124
1,485
168,580
0
71,302
6
50
69
25
733
378
139
72,703
139,896
0
1,780
1,866
83,468
8,303
8,059
13,669
8,565
265,606
91 ,287
0
6,752
11,509
40,945
6,313
6,517
22,259
13,744
199,327
269,751
0
684
1,089
3,751
4,634
608
7,625
1,437
289,580
29,997
0
1,394
1,601
58,823
7,909
5,547
12,081
4,410
121,762
25,474
0
4,789
9,870
38,389
6,035
4,874
16,031
9,828
115,291
50,500
0
670
934
3,241
4,486
455
6,479
1,105
67,870
C-13
-------
Ohio
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Ohio Total
Oklahoma
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Oklahoma Total
Oregon
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Oregon Total
0
0
3,632
178
285,528
103,414
205,348
1,773
29,515
629,389
0
0
1,551
3,749
200,442
38,015
86,133
984
35,176
366,050
0
0
1,843
37,328
242,829
39,821
91,766
142
14,567
428,297
0
0
96,728
81
41,466
90,812
327,388
373,299
65,850
995,625
0
0
26,294
1,709
94,574
31,331
133,152
90,302
72,670
450,033
0
0
43,439
17,857
16,998
26,372
109,066
9,006
15,958
238,696
0
0
29,188
3,787
150,302
910,152
2,600,918
14,817
238,412
3,947,575
0
0
10,093
79,673
385,235
308,218
1,069,135
13,661
50,750
1,916,764
0
0
12,401
778,193
342,444
304,850
1,078,005
1,105
34,389
2,551,388
0
0
11,191
22
19,810
8,254
12,682
1,145,194
111,233
1,308,387
0
0
1,890
469
7,542
3,093
5,344
111,841
38,495
168,673
0
0
4,212
4,896
9,845
2,559
3,488
12,285
5,307
42,592
0
98,711
32
17
8,527
74
10,986
74
6,370
124,789
0
95,061
7
359
11,358
26
4,626
909
3,118
115,463
0
40,655
9
3,542
1,061
24
3,270
162
787
49,509
236,316
0
3,393
368
25,444
8,400
8,049
62,308
14,370
358,650
395,931
0
886
7,747
54,339
3,494
3,501
3,350
9,175
478,422
82,013
0
1,498
75,861
50,681
2,902
2,707
711
9,828
226,200
49,900
0
3,113
316
23,761
8,043
5,933
55,730
10,000
156,798
70,686
0
841
6,644
43,886
3,353
2,592
1,722
5,241
134,966
30,637
0
1,371
65,350
49,407
2,773
2,021
326
6,203
158,088
C-14
-------
Pennsylvania
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Pennsylvania Total
Rhode Island
South Carolina
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
South Carolina Total
0
0
2,425
256
281 ,740
96,797
184,268
1,212
36,871
603,569
0
0
162
8
16,875
8,491
14,366
39
1,894
0
0
961
5,171
185,429
50,041
89,994
506
36,778
368,879
0
0
67,118
117
53,435
62,168
294,414
210,149
89,064
776,465
0
0
876
4
2,964
4,663
16,720
712
2,060
0
0
19,378
2,357
20,281
29,982
134,542
91,296
40,417
338,253
0
0
25,047
5,450
265,035
856,737
2,420,525
17,018
104,570
3,694,382
0
0
2,923
171
5,421
65,923
188,240
453
1,781
0
0
9,393
109,880
145,294
377,166
1,141,561
4,749
56,640
1,844,682
0
0
8,354
32
68,349
5,203
7,885
907,734
88,132
1,085,688
0
0
78
1
3,365
354
425
18
2,649
0
0
1,946
646
30,016
2,816
5,021
212,572
57,307
310,324
0
76,675
14
25
3,689
55
10,618
401
1,334
92,811
0
235
0
1
15
4
854
58
47
0
27,945
4
494
223
27
4,710
306
1,552
35,263
130,508
0
2,376
530
41,841
6,256
7,250
63,198
22,391
274,351
2,501
0
8
17
1,171
427
343
12
288
82,088
0
714
10,684
19,393
3,102
3,588
17,707
12,696
149,971
32,224
0
2,268
454
31 ,263
5,969
5,219
53,067
11,549
142,015
481
0
0
14
1,107
406
209
11
173
25,657
0
668
9,163
18,139
2,960
2,648
13,734
8,159
81,128
C-15
-------
South Dakota
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
South Dakota Total
Tennessee
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Tennessee Total
Texas
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Texas Total
0
0
321
3,985
19,597
12,322
16,177
111
2,431
54,944
0
0
2,152
2,220
148,677
60,023
140,405
843
84,610
438,930
0
0
11,279
13,201
695,600
174,723
308,904
4,745
149,554
1,358,006
0
0
4,164
1,817
5,200
27,219
29,910
15,922
4,776
89,008
0
0
50,692
1,012
18,676
40,970
240,312
155,926
69,070
576,659
0
0
236,223
4,890
274,338
152,771
621,483
259,612
344,073
1,893,390
0
0
2,979
84,689
24,107
79,151
219,053
632
4,068
414,679
0
0
13,001
47,175
119,973
460,143
1,681,568
6,596
115,767
2,444,222
0
0
67,547
256,966
463,577
1,578,739
3,787,848
215,207
283,294
6,653,179
0
0
318
498
10,304
2,901
852
12,545
1,480
28,898
0
0
6,292
277
32,714
3,728
7,674
333,618
84,316
468,619
0
0
27,280
1,178
109,215
14,990
21,522
562,594
245,060
981,840
0
101,949
1
381
51
18
843
50
50
103,343
0
34,210
12
212
164
35
6,671
425
2,394
44,124
0
354,873
57
1,118
1,983
128
21,943
5,941
2,297
388,340
202,326
0
172
8,235
6,683
3,289
746
450
609
222,509
95,767
0
1,853
4,587
26,842
4,225
6,128
16,268
30,328
185,996
1,290,391
0
8,936
25,228
72,265
15,766
16,034
34,257
38,861
1,501,740
38,332
0
156
7,062
4,463
3,181
564
420
291
54,470
22,530
0
1,707
3,934
20,663
4,040
4,667
13,910
22,054
93,505
242,993
0
8,146
21,578
47,394
15,126
11,699
24,920
27,189
399,045
C-16
-------
Tribal Data
aim
ptipm
ptnonipm
Tribal Data Total1
Utah
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Utah Total
Vermont
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Vermont Total
218
241
601
1,060
0
0
2,596
15,469
54,443
25,488
56,206
418
5,826
160,444
0
0
53
393
18,887
10,446
18,139
0
1,097
49,015
858
97
6,623
7,578
0
0
14,640
7,052
6,948
15,026
76,518
73,220
14,998
208,401
0
0
49
179
3,438
4,170
21,783
0
790
30,409
302
828
2,573
3,703
0
0
10,805
328,713
79,323
172,729
764,714
4,506
45,052
1,405,842
0
0
1,220
8,347
43,091
58,906
237,164
0
1,078
349,807
132
6
204
342
0
0
1,065
1,934
3,427
1,437
1,989
33,167
9,305
52,325
0
0
6
49
5,385
368
622
0
911
7,341
1
65
4
69
0
20,448
5
1,479
1,268
14
2,457
269
529
26,469
0
8,821
0
38
214
5
939
11
16
10,043
58
31
1,872
1,961
54,020
0
153
31,961
10,385
1,703
1,658
6,351
6,893
113,124
13,658
0
29
812
5,823
516
645
0
337
21,819
0
31
856
887
7,864
0
140
27,412
9,079
1,625
1,187
4,901
2,955
55,162
4,814
0
21
696
5,415
490
465
0
237
12,137
C-17
-------
Virginia
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Virginia Total
Washington
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Washington Total
West Virginia
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
West Virginia Total
0
0
3,084
3,194
201 ,748
67,441
125,474
726
43,184
444,850
0
0
2,248
2,674
166,658
64,611
159,797
219
12,429
408,636
0
0
1,180
1,721
59,489
16,935
36,949
1,175
14,241
131,691
0
0
39,676
1,456
53,605
45,848
214,393
86,763
61,730
503,471
0
0
66,992
1,484
16,911
42,800
199,767
16,122
24,522
368,598
0
0
32,148
785
14,519
8,407
60,216
227,827
46,627
390,529
0
0
17,758
67,866
208,041
520,042
1,722,600
6,714
63,978
2,606,999
0
0
20,193
52,086
204,125
486,615
1,820,900
1,665
39,106
2,624,689
0
0
5,139
36,578
70,069
117,839
502,130
10,319
89,898
831,973
0
0
5,595
399
32,923
4,289
6,662
239,777
67,691
357,338
0
0
11,488
407
7,254
5,380
5,539
19,108
24,623
73,799
0
0
5,707
215
14,589
780
2,675
509,488
54,107
587,561
0
43,811
13
305
1,621
41
7,889
192
3,500
57,373
0
42,133
151
248
1,711
39
5,168
62
774
50,285
0
9,879
8
165
72
8
1,950
210
688
12,981
60,865
0
1,905
6,599
53,941
4,809
4,939
15,400
13,041
161,498
106,176
0
2,416
5,126
35,624
4,776
4,545
2,456
4,970
166,089
24,640
0
1,478
3,557
12,220
1,005
1,542
31,248
10,625
86,314
19,662
0
1,836
5,659
29,947
4,593
3,486
14,431
9,734
89,350
26,908
0
2,271
4,487
31 ,983
4,567
3,407
2,025
3,224
78,872
11,305
0
1,281
3,050
11,130
956
1,149
28,884
7,450
65,205
C-18
-------
Wisconsin
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Wisconsin Total
Wyoming
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Wyoming Total
Grand Total
0
0
2,060
561
230,068
111,779
96,058
964
31,057
472,549
0
0
1,569
8,852
16,411
9,088
18,072
849
16,771
71,613
17,693,869
0
0
30,307
256
21,994
53,430
172,043
91,128
38,283
407,440
0
0
30,368
4,035
4,309
5,470
32,643
85,207
36,500
198,533
20,931,673
0
0
24,321
11,924
166,779
569,467
1,321,240
10,725
34,197
2,138,654
0
0
4,758
188,099
19,192
53,551
246,059
7,078
23,341
542,078
101,885,285
0
0
4,781
70
6,369
5,015
7,218
192,946
63,651
280,051
0
0
2,088
1,106
6,181
559
905
83,423
33,676
127,938
14,649,986
0
113,949
11
54
266
52
6,006
375
397
121,110
0
18,575
8
846
91
5
893
386
301
21,104
3,901,951
103,735
0
1,353
1,159
26,104
6,090
4,479
5,576
10,466
158,961
272,299
0
866
18,289
3,717
689
799
9,599
19,234
325,494
12,817,898
30,705
0
1,182
994
25,407
5,796
3,317
5,029
5,856
78,287
41,010
0
857
15,686
2,922
659
606
7,936
14,143
83,819
4,938,898
1The small quantity of "aim" tribal emissions that were in the SMOKE inputs were not modeled because the extent to which they
may have already been accounted for in the county estimates was not known. The point estimates were modeled.
C-19
-------
C-20
-------
Appendix D
State-Sector Emissions Summaries for 2002 Base and
Future-Year Base Cases: 2009, 2014, 2020 and 2030
D-l
-------
D-2
-------
The following tables contain the state-sector emission summaries for the 2002 base case and future-year base cases. Table D-la
contains data for VOC, NOX and CO, and Table D-lb contains data for SO2, NH3, PM10, and PM2.5.
Table D-1a: Continental US, VOC, NOx, and CO Emissions by Sectorfor 2002, and Projection years 2009, 2014, 2020, and 2030
State
Alabama
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Alabama Total
Arizona
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Arizona Total
Arkansas
afdust
ag
aim
avefire
nonpt
nonroad
onroad
[tonsfyr]
2002
VOC
0
0
2,383
8,951
213,956
55,574
104,783
1,394
47,722
434,763
0
0
3,482
21,385
80,463
53,546
85,187
626
4,611
249,300
0
0
2,295
5,821
99,381
35.683
56,465
[tonsfyr]
2009
Base
VOC
0
0
2,831
8,951
205,838
46,218
67,451
1,335
38,365
370,990
0
0
3,599
21,385
76,919
41,848
65,051
947
4,164
213,913
0
0
2,357
5,821
96,818
31,954
36,323
[tonslyr]
2014
Base
VOC
0
0
2,916
8,951
197,006
40,250
53,305
1,423
38,365
342,216
0
0
3,818
21,385
71,758
37,833
54,052
950
4,164
193,959
0
0
2,380
5,821
92,455
27,582
29,742
[tonsfyr]
2020
Base
VOC
0
0
3,065
8,951
193,002
36,029
43,750
1,462
38,365
324,624
0
0
4,102
21,385
71,115
35,883
46,416
992
4,164
184,056
0
0
2,418
5,821
91,751
23,439
24,814
[tonsfyr]
2030
Base
VOC
0
0
3,379
8,951
193,002
36,955
39,517
1,462
38,365
321,631
0
0
4.374
21,385
71,115
38,007
46,068
992
4,164
186,105
0
0
2,514
5,821
91,751
23.324
23,579
[tonsfyr]
2002
NOX
0
0
36,047
3,814
32,024
29,396
153,968
161,767
80,901
497,917
0
0
30,813
10,532
8,637
38,699
159,756
85,967
11,439
345,843
0
0
39,743
2,654
21,453
28,527
83,722
[tonsfyr]
2009
Base
NOX
0
0
32,548
3,814
31,978
25,392
91,435
71,365
68,040
324,573
0
0
26,449
10,532
8,618
32,525
104,428
74,862
11,439
268,853
0
0
34,685
2,654
21,436
24,467
50,832
[tonsfyr]
2014
Base
NOX
0
0
32,194
3,814
31,945
20,092
57,113
47,854
68,040
261,053
0
0
26,197
10,532
8,605
25,480
66,634
50,463
11,439
199,350
0
0
33,722
2,654
21,424
19,384
32,840
[tonsfyr]
2020
Base
NOX
0
0
32,815
3,814
31,906
15,494
37,772
39,998
68,040
229,839
0
0
26.459
10,532
8,589
18,219
43,914
50.569
11,439
169,721
0
0
33,539
2.654
21,410
14.231
22,581
[tonsfyr]
2030
Base
NOX
0
0
35,753
3,814
31,906
13,611
28,545
39,998
68,040
221,667
0
0
26,959
10,532
8,589
14,796
37,539
50,569
11,439
160,422
0
0
35,309
2,654
21,410
10,579
18,207
[tonsfyr]
2002
CO
0
0
10,328
175,140
188,564
378,753
1,237,459
10,879
174,483
2,175,607
0
0
20,495
440,419
44,127
440,675
836,126
8,185
8,259
1,798,285
0
0
14,371
123,699
174,777
231,619
735,366
[tonsfyr]
2009
Base
CO
0
0
11,057
175,140
184,082
253,823
733,435
13,708
174,092
1,545,339
0
0
21,458
440,419
42,569
337,232
621,952
19,127
8,259
1,491,014
0
0
14,980
123,699
173,439
162,754
426,247
[tonsfyr]
2014
Base
CO
0
0
11,603
175,140
180,879
237,277
637,881
15,854
174,092
1,432,727
0
0
23,081
440,419
41,456
321,243
567,416
19,204
8,259
1,421,078
0
0
15,847
123,699
172,484
151,555
372,017
[tonsfyr]
2020
Base
CO
0
0
12,303
175,140
177,034
242,916
615,780
15,825
174,092
1,413,091
0
0
24,938
440,419
40,120
336,841
580,651
18,769
8,259
1,449,996
0
0
16,829
123,699
171,336
152,274
360,469
[tonsfyr]
2030
Base
CO
0
0
13,561
175,140
177,034
267,149
640,439
15,825
174,092
1,463,241
0
0
27,011
440,419
40,120
376,783
693,201
18,769
8,259
1,604,562
0
0
18,376
123,699
171,336
163,858
396,136
-------
State
Sector
ptipm
ptnonipm
Arkansas Total
State
California
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
California Total
Colorado
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Colorado Total
Connecticut
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Connecticut Total
[tons/yr]
2002
VOC
520
32,044
232,209
[tons/yr]
2002
VOC
0
0
19,726
54,619
461,331
148,269
343,693
1,288
54,610
1,083,536
0
0
1,366
13,610
87,037
42,009
84,387
973
90,768
320,150
0
0
845
31
105,580
32,327
47,757
305
4,602
191,447
[tons/yr]
2009
Base
VOC
690
27,717
201,682
[tons/yr]
2009
Base
VOC
0
0
20,089
54,619
450,831
127,633
202,321
1,076
46,571
903,139
0
0
1,453
13,610
85,653
33,318
61,463
731
37,097
233,324
0
0
876
31
100,907
23,609
30,899
109
4,182
160,613
[tons/yr]
2014
Base
VOC
790
27,717
186,488
[tons/yr]
2014
Base
VOC
0
0
20,729
54,619
449,537
114,489
149,537
1,372
46,767
837,050
0
0
1,499
13,610
83,973
30,177
53,663
769
35,068
218,759
0
0
956
31
99,011
20,404
24,426
134
4,182
149,143
[tons/yr]
2020
Base
VOC
799
27,717
176,760
[tons/yr]
2020
Base
VOC
0
0
21,681
54,619
451,112
108,594
114,486
1,800
47,121
799,413
0
0
1,557
13,610
84,311
27,864
46,984
838
34,442
209,606
0
0
1,061
31
96,848
19,242
18,176
181
4,182
139,721
[tons/yr]
2030
Base
VOC
799
27,717
175,506
[tons/yr]
2030
Base
VOC
0
0
23,104
54,619
451,112
128,506
87,676
1,800
47,121
793,938
0
0
1,608
13,610
84,311
29,057
46,833
838
34,442
210,698
0
0
1,169
31
96,848
20,383
15,441
181
4,182
138,234
[tons/yr]
2002
NOX
42,218
27,605
245,923
[tons/yr]
2002
NOX
0
0
175,373
24,563
121,882
240,256
643,919
13,071
91,967
1,311,031
0
0
19,208
6,271
11,464
35,398
127,564
79,167
39,499
318,571
0
0
3,945
14
12,554
17,897
66,813
6,161
6,706
114,091
[tons/yr]
2009
Base
NOX
24,262
27,370
185,706
[tons/yr]
2009
Base
NOX
0
0
161,455
24,563
121,674
193,950
492,500
13,111
91,434
1,098,687
0
0
16,132
6,271
11,412
30,263
83,534
64,412
38,342
250,366
0
0
3,834
14
12,498
14,869
38,434
3,391
6,571
79,613
[tonsfyr]
2014
Base
NOX
26,839
27,370
164,233
[tons/yr]
2014
Base
NOX
0
0
155,977
24,563
121,525
153,910
346,901
15,031
91,261
909,168
0
0
15,818
6,271
11,375
24,069
55,858
60,593
38,342
212,326
0
0
3,868
14
12,459
11,646
23,218
4,095
6,571
61,871
[tons/yr]
2020
Base
NOX
26,271
27,370
148,055
[tons/yr]
2020
Base
NOX
0
0
154,530
24,563
121,347
110,789
231,335
16,691
92,143
751,398
0
0
15,869
6,271
11,331
17,609
40,184
61,605
38,342
191,211
0
0
4,003
14
12,411
9,285
13,530
5,447
6,571
51,261
[tonsfyr]
2030
Base
NOX
26.271
27,370
141,799
[tonsfyr]
2030
Base
NOX
0
0
164,113
24,563
121,347
84,400
160,727
16,691
92,143
663,984
0
0
16,103
6,271
11,331
13,876
36,093
61,605
38,342
183,620
0
0
4.140
14
12,411
8,635
8,997
5,447
6,571
46,215
[tonsfyr]
2002
CO
4,182
51,502
1,335,515
[tonsfyr]
2002
CO
0
0
108,995
1,157,187
458,977
1,058,968
3,434,055
23,900
97,092
6,339,176
0
0
10,641
288,013
85,393
389,240
1,103,120
7,578
28,063
1,912,049
0
0
12,149
667
69,769
258,776
641,901
1,920
2,133
987,315
[tonsfyr]
2009
Base
CO
9,082
51,437
961,639
[tonsfyr]
2009
Base
CO
0
0
112,003
1,157,187
447,100
1,028,012
1,942,479
53,864
97,332
4,837,976
0
0
11,608
288,013
82,106
263,050
709,657
11,267
27,533
1,393,233
0
0
12,666
667
65,429
188,681
369,126
8,434
2,131
647,133
[tonsfyr]
2014
Base
CO
12,018
51,437
899,055
[tonsfyr]
2014
Base
CO
0
0
117,785
1,157,187
438,618
1,040,768
1,360,507
65,409
98,997
4,279,271
0
0
12,543
288,013
79,758
250,944
659,782
12,124
27,533
1,330,698
0
0
13,976
667
62,328
175,215
315,324
8,853
2,131
578,494
[tonsfyr]
2020
Base
CO
10,968
51,437
887,013
[tonsfyr]
2020
Base
CO
0
0
124,485
1,157,187
428,438
1,101,374
935,177
82,171
100,789
3,929,619
0
0
13,661
288,013
76,940
261,675
681,244
11,529
27,533
1,360,595
0
0
15,652
667
58,604
181,777
293,072
8,932
2,131
560,836
[tons/yr]
2030
Base
CO
10,968
51,437
935,810
[tons/yr]
2030
Base
CO
0
0
133,684
1,157,187
428,438
1,313,702
654,302
82,171
100,789
3,870,272
0
0
14,931
288,013
76,940
291,702
822,227
11,529
27,533
1,532,875
0
0
17,271
667
58,604
203,437
307,897
8,932
2,131
598,940
-------
State
California
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
California Total
Colorado
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnoniprn
Colorado Total
Connecticut
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Connecticut Total
[tons/yr]
2002
VOC
0
0
19,726
54,619
461,331
148,269
343,693
1,288
54,610
1,083,536
0
0
1,366
13,610
87,037
42,009
84,387
973
90,768
320,150
0
0
845
31
105,580
32,327
47,757
305
4,602
191,447
[tons/yr]
2009
Base
VOC
0
0
20,089
54,619
450,831
127,633
202,321
1,076
46,571
903,139
0
0
1,453
13,610
85,653
33,318
61,463
731
37,097
233,324
0
0
876
31
100,907
23,609
30,899
109
4,182
160,613
[tons/yr]
2014
Base
VOC
0
0
20,729
54,619
449,537
114,489
149,537
1,372
46,767
837,050
0
0
1,499
13,610
83,973
30,177
53,663
769
35,068
218,759
0
0
956
31
99,011
20,404
24,426
134
4,182
149,143
[tons/yr]
2020
Base
VOC
0
0
21,681
54,619
451,112
108,594
114,486
1,800
47,121
799,413
0
0
1,557
13,610
84,311
27,864
46,984
838
34,442
209,606
0
0
1,061
31
96,848
19,242
18,176
181
4,182
139,721
[tons/yr]
2030
Base
VOC
0
0
23,104
54,619
451,112
128,506
87,676
1,800
47,121
793,938
0
0
1,608
13,610
84,311
29,057
46,833
838
34,442
210,698
0
0
1,169
31
96,848
20,383
15,441
181
4,182
138,234
[tons/yr]
2002
NOX
0
0
175,373
24,563
121,882
240,256
643,919
13,071
91,967
1,311,031
0
0
19,208
6,271
11,464
35,398
127,564
79,167
39,499
318,571
0
0
3,945
14
12,554
17,897
66,813
6,161
6,706
114,091
[tonsfyr]
2009
Base
NOX
0
0
161,455
24,563
121,674
193,950
492,500
13,111
91,434
1,098,687
0
0
16,132
6,271
11,412
30,263
83,534
64,412
38,342
250,366
0
0
3,834
14
12,498
14,869
38,434
3,391
6,571
79,613
[tons/yr]
2014
Base
NOX
0
0
155,977
24,563
121,525
153,910
346,901
15,031
91,261
909,168
0
0
15,818
6,271
11,375
24,069
55,858
60,593
38,342
212,326
0
0
3,868
14
12,459
11,646
23,218
4,095
6,571
61,871
[tonsfyr]
2020
Base
NOX
0
0
154,530
24,563
121,347
110,789
231,335
16,691
92,143
751,398
0
0
15,869
6,271
11,331
17,609
40,184
61,605
38,342
191,211
0
0
4,003
14
12,411
9,285
13,530
5,447
6,571
51,261
[tons/yr]
2030
Base
NOX
0
0
164,113
24,563
121,347
84,400
160,727
16,691
92,143
663,984
0
0
16,103
6,271
11,331
13,876
36,093
61,605
38,342
183,620
0
0
4,140
14
12,411
8,635
8,997
5,447
6,571
46,215
[tons/yr]
2002
CO
0
0
108,995
1,157,187
458,977
1,058,968
3,434,055
23,900
97,092
6,339,176
0
0
10,641
288,013
85,393
389,240
1,103,120
7,578
28,063
1,912,049
0
0
12,149
667
69,769
258,776
641,901
1,920
2,133
987,315
[tons/yr]
2009
Base
CO
0
0
112,003
1,157,187
447,100
1,028,012
1,942,479
53,864
97,332
4,837,976
0
0
11,608
288,013
82,106
263,050
709,657
11,267
27,533
1,393,233
0
0
12,666
667
65,429
188,681
369,126
8,434
2,131
647,133
[tons/yr]
2014
Base
CO
0
0
117,785
1,157,187
438,618
1,040,768
1,360,507
65,409
98,997
4,279,271
0
0
12,543
288,013
79,758
250,944
659,782
12,124
27,533
1,330,698
0
0
13,976
667
62,328
175,215
315,324
8,853
2,131
578,494
[tons/yr]
2020
Base
CO
0
0
124,485
1,157,187
428,438
1,101,374
935,177
82,171
100,789
3,929,619
0
0
13,661
288,013
76,940
261,675
681,244
11,529
27,533
1,360,595
0
0
15,652
667
58,604
181,777
293,072
8,932
2,131
560,836
[tons/yr]
2030
Base
CO
0
0
133,684
1,157,187
428,438
1,313,702
654,302
82,171
100,789
3,870,272
0
0
14,931
288,013
76,940
291,702
822,227
11,529
27,533
1,532,875
0
0
17,271
667
58,604
203,437
307,897
8,932
2,131
598,940
-------
State
Delaware
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Delaware Total
District of Columbia
afdust
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
District of Columbia Total
Florida
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Florida Total
[tonsfyr]
2002
VOC
0
0
483
64
15,468
8,677
11,382
91
4,659
40,823
0
22
0
4,118
1,918
5,423
4
69
11,554
0
0
3,053
56,159
459,700
239,540
362,851
2,236
37,204
1,160,742
[tonslyr]
2009
Base
VOC
0
0
531
64
14,558
6,386
7,514
122
4,193
33,368
0
30
0
3,917
1,412
3,621
0
69
9,048
0
0
3,190
56,159
455,329
176,943
225,931
1,869
34,201
953,622
[tonsfyr]
2014
Base
VOC
0
0
557
64
14,210
5,514
5,695
151
4,193
30,383
0
31
0
3,882
1,235
2,719
0
69
7,935
0
0
3,387
56,159
434,205
159,802
179,866
2,143
34,201
869,762
[tonsfyr]
2020
Base
VOC
0
0
599
64
13,919
5,153
4,639
152
4,193
28,719
0
32
0
3,882
1,203
2,215
0
69
7,400
0
0
3,652
56,159
432,980
153,184
150,140
2,522
34,201
832,839
[tonsfyr]
2030
Base
VOC
0
0
695
64
13,919
5,408
4,191
152
4,193
28,621
0
33
0
3,882
1,290
1,984
0
69
7,257
0
0
4,052
56,159
432,980
162,071
147,070
2,522
34,201
839,056
[tonsfyr]
2002
NOX
0
0
10,429
23
3,259
5,308
21,679
9,533
7,308
57,538
0
571
0
1,740
3,060
8,772
710
418
15,271
0
0
55,127
25,600
29,533
117,138
448,520
272,057
54,078
1,002,054
[tonsfyr]
2009
Base
NOX
0
0
10,912
23
3,251
4,559
12,180
9,675
4,682
45,281
0
560
0
1,739
2,536
4,772
3
418
10,027
0
0
52,369
25,600
29,492
104,404
274,295
80,931
54,030
621,122
[tonsfyr]
2014
Base
NOX
0
0
11,292
23
3,246
3,692
7,214
9,380
4,682
39,529
0
535
0
1,738
1,921
2,703
6
418
7,321
0
0
51,374
25,600
29,463
89,130
178,863
56,740
54,030
485,199
[tonsfyr]
2020
Base
NOX
0
0
12,258
23
3,239
2,882
4,404
8,327
4,682
35,813
0
527
0
1,738
1,244
1,536
6
418
5,468
0
0
52,172
25,600
29,428
69,322
125,477
61,118
54,030
417,147
[tonsfyr]
2030
Base
NOX
0
0
15,326
23
3,239
2,500
3,535
8,327
4,682
37,631
0
508
0
1,738
919
1,235
6
418
4,824
0
0
57,895
25,600
29,428
60,662
102,919
61,118
54,030
391,651
[tonsfyr]
2002
CO
0
0
2,890
1,332
11,640
65,811
155,366
866
8,853
246,758
0
79
1
1,819
18,061
65,418
50
247
85,676
0
0
43,166
1,193,147
202,108
1,762,587
3,797,717
52,142
86,821
7,137,689
[tonsfyr]
2009
Base
CO
0
0
3,012
1,332
11,085
48,862
89,775
1,710
8,733
164,509
0
95
1
1,756
15,551
39,318
3
247
56,971
0
0
44,681
1,193,147
198,701
1,222,617
2,105,543
64,310
86,821
4,915,820
[tonsfyr]
2014
Base
CO
0
0
3,160
1,332
10,688
45,738
78,499
2,124
8,733
150,274
0
102
1
1,711
14,178
33,810
6
247
50,055
0
0
47,476
1,193,147
196,267
1,063,733
1,867,949
75,293
86,821
4,530,685
[tonsfyr]
2020
Base
CO
0
0
3,382
1,332
10,211
47,229
76,475
1,938
8,733
149,301
0
111
1
1,658
14,543
33,048
6
247
49,614
0
0
50,664
1,193,147
193,346
1,110,608
1,870,423
75,276
86,821
4,580,285
[tonslyr]
2030
Base
CO
0
0
3,884
1,332
10,211
52,261
81,702
1,938
8,733
160,062
0
128
1
1,658
16,130
35,654
6
247
53,824
0
0
54,692
1,193,147
193,346
1,237,135
2,054,738
75,276
86,821
4,895,156
-------
State
Georgia
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Georgia Total
Idaho
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Idaho Total
Illinois
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Illinois Total
[tonsfyr]
2002
VOC
0
0
1,776
21,834
248,214
81,856
185,962
1,182
33,735
574,559
0
0
713
29,989
141,328
23,153
27,934
0
2,113
225,230
0
0
4,205
156
278,553
99,398
164,697
1,536
71,066
619,612
[tonsfyr]
2009
Base
VOC
0
0
1,976
21,834
242,922
63,348
123,053
1,316
27,728
482,177
0
0
722
29,989
139,434
21,173
19,329
14
1,725
212,386
0
0
4,211
156
274,561
77,488
108,448
2,381
58,404
525,649
[tonsfyr]
2014
Base
VOC
0
0
2,034
21,834
235,296
56,758
101,749
1,363
27,728
446,762
0
0
749
29,989
137,490
18,319
16,912
22
1,725
205,206
0
0
4,185
156
269,109
67,523
87,212
2,560
58,591
489,337
[tonsfyr]
2020
Base
VOC
0
0
2,120
21,834
234,053
52,569
84,255
1,426
27,728
423,986
0
0
782
29,989
136,421
15,135
14,608
26
1,725
198,686
0
0
4,183
156
268,977
61,819
72,527
2,657
58,821
469,141
[tonsfyr]
2030
Base
VOC
0
0
2,239
21,834
234,053
55,247
79,932
1,426
27,728
422,461
0
0
815
29,989
136,421
14,525
13,944
26
1,725
197,444
0
0
4,320
156
268,977
64,816
68,277
2,657
58,821
468,026
[tonsfyr]
2002
NOX
0
0
39,986
7,955
38,919
57,979
307,544
146,351
51,170
649,905
0
0
8,297
14,024
30,317
15,611
44,628
19
11,467
124,363
0
0
120,834
71
47,645
115,426
297,056
179,125
94,009
854,165
[tonsfyr]
2009
Base
NOX
0
0
35,539
7,955
38,853
48,390
185,968
84,937
42,554
444,198
0
0
7,008
14,024
30,305
13,849
28,237
94
11,467
104,983
0
0
104,171
71
47,588
94,695
182,060
83,848
71,002
583,435
[tonsfyr]
2014
Base
NOX
0
0
35,200
7,955
38,806
38,051
116,118
59,755
42,554
338,439
0
0
6,857
14,024
30,296
11,345
18,306
270
11,467
92,565
0
0
99,880
71
47,548
72,953
111,020
89,782
71,514
492,768
[tonsfyr]
2020
Base
NOX
0
0
35,767
7,955
38,750
27,695
75,433
60,722
42,554
288,877
0
0
6,777
14,024
30,285
8,507
12,480
367
11,467
83,906
0
0
98,522
71
47,500
51,566
69,518
71,620
73,036
411,834
[tonsfyr]
2030
Base
NOX
0
0
37,633
7,955
38,750
22,875
57,840
60,722
42,554
268,330
0
0
6,778
14,024
30,285
6,374
10,483
367
11,467
79,777
0
0
102,737
71
47,500
39,673
51,760
71,620
73,036
386,397
[tonsfyr]
2002
CO
0
0
11,058
350,924
194,402
730,260
2,245,133
9,371
131,306
3,672,454
0
0
10,893
630,971
95,417
137,661
389,120
4
23,977
1,288,044
0
0
16,365
3,323
99,568
830,513
2,090,188
14,627
78,820
3,133,402
[tonsfyr]
2009
Base
CO
0
0
11,973
350,924
189,012
519,187
1,362,639
13,152
130,600
2,577,487
0
0
11,307
630,971
94,196
96,798
237,761
549
23,977
1,095,559
0
0
17,898
3,323
93,967
656,876
1,272,670
15,791
78,198
2,138,723
[tonsfyr]
2014
Base
CO
0
0
12,968
350,924
185,162
466,525
1,218,661
15,058
130,600
2,379,898
0
0
12,190
630,971
93,323
91,736
215,805
612
23,977
1,068,615
0
0
18,787
3,323
89,967
599,591
1,121,004
17,649
79,893
1,930,214
[tonsfyr]
2020
Base
CO
0
0
14,244
350,924
180,543
485,869
1,207,141
14,990
130,600
2,384,311
0
0
13,265
630,971
92,276
91,464
213,957
646
23,977
1,066,556
0
0
20,165
3,323
85,166
609,879
1,106,338
18,614
81,767
1,925,253
[tonslyr]
2030
Base
CO
0
0
15,948
350,924
180,543
542,255
1,346,768
14,990
130,600
2,582,027
0
0
14,386
630,971
92,276
97,589
246,310
646
23,977
1,106,156
0
0
23,326
3,323
85,166
674,750
1,226,854
18,614
81,767
2,113,801
-------
o
oo
State
Indiana
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Indiana Total
Iowa
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Iowa Total
Kansas
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Kansas Total
[tonsfyr]
2002
VOC
0
0
2,224
194
179,635
58,290
140,188
2,015
55,935
438,480
0
0
1,653
197
77,838
52,138
75,852
579
37,943
246,201
0
0
2,133
828
135,449
24,728
52,786
1,062
26,274
243,261
[tonsfyr]
2009
Base
VOC
0
0
2,606
194
175,119
45,641
88,653
2,165
51,787
366,164
0
0
1,661
197
75,491
36,504
48,452
850
32,631
195,786
0
0
2,163
828
134,134
18,953
33,638
821
22,766
213,303
[tonsfyr]
2014
Base
VOC
0
0
2,682
194
169,044
39,383
73,730
2,228
51,787
339,048
0
0
1,649
197
71,828
33,409
40,299
908
32,631
180,921
0
0
2,140
828
131,964
16,491
27,861
844
22,766
202,893
[tonsfyr]
2020
Base
VOC
0
0
2,790
194
167,998
35,579
62,110
2,296
51,787
322,754
0
0
1,634
197
70,642
30,975
34,011
980
32,631
171,071
0
0
2,106
828
131,970
15,041
23,429
864
22,766
197,004
[tonsfyr]
2030
Base
VOC
0
0
2,954
194
167,998
37,055
56,619
2,296
51,787
318,904
0
0
1,631
197
70,642
31,468
31,136
980
32,631
168,686
0
0
2,064
828
131,970
15,502
22,563
864
22,766
196,557
[tonsfyr]
2002
NOX
0
0
52,285
88
30,185
64,575
216,188
283,890
80,147
727,359
0
0
33,166
90
15,150
62,066
115,521
81,995
38,861
346,849
0
0
41,147
378
42,286
47,653
85,617
96,943
70,704
384,728
[tonsfyr]
2009
Base
NOX
0
0
46,823
88
30,143
53,155
129,374
133,912
67,032
460,527
0
0
26,959
90
15,090
54,867
73,751
55,090
38,837
264,683
0
0
33,193
378
42,260
40,898
50,252
70,545
70,616
308,143
[tonsfyr]
2014
Base
NOX
0
0
45,667
88
30,113
40,427
82,459
124,167
67,032
389,953
0
0
25,825
90
15,046
44,687
48,103
58,628
38,837
231,217
0
0
31,895
378
42,242
32,678
31,627
51,433
70,616
260,868
[tonsfyr]
2020
Base
NOX
0
0
45,598
88
30,077
28,951
55,049
89,313
67,032
316,109
0
0
25,333
90
14,995
32,571
32,218
59,383
38,837
203,427
0
0
31,222
378
42,219
23,003
21,109
51,547
70,616
240,094
[tonsfyr]
2030
Base
NOX
0
0
47,880
88
30,077
23,097
42,080
89,313
67,032
299,568
0
0
25,870
90
14,995
21,882
26,448
59,383
38,837
187,504
0
0
31,119
378
42,219
14,394
17,589
51,547
70,616
227,862
[tonsfyr]
2002
CO
0
0
14,057
4,124
74,953
490,545
1,738,790
15,540
364,487
2,702,495
0
0
7,209
4,185
68,958
309,048
1,055,157
5,444
36,521
1,486,523
0
0
9,118
17,600
850,800
240,503
683,936
6,793
74,809
1,883,560
[tonsfyr]
2009
Base
CO
0
0
15,202
4,124
71,175
348,138
1,012,624
17,814
364,486
1,833,563
0
0
7,866
4,185
64,017
222,598
621,002
7,942
36,501
964,111
0
0
9,928
17,600
848,391
157,861
391,573
6,229
74,779
1,506,360
[tonsfyr]
2014
Base
CO
0
0
16,157
4,124
68,477
310,959
897,036
18,639
364,486
1,679,878
0
0
8,426
4,185
60,487
205,436
532,276
8,769
36,501
856,081
0
0
10,669
17,600
846,669
143,592
344,300
6,453
74,779
1,444,062
[tonsfyr]
2020
Base
CO
0
0
17,366
4,124
65,240
311,512
878,274
19,602
364,486
1,660,603
0
0
9,118
4,185
56,251
203,942
496,019
9,377
36,501
815,394
0
0
11,524
17,600
844,604
143,812
335,289
7,235
74,779
1,434,843
[tonslyr]
2030
Base
CO
0
0
19,391
4,124
65,240
341,744
949,534
19,602
364,486
1,764,121
0
0
10,227
4,185
56,251
215,849
557,162
9,377
36,501
889,552
0
0
12,849
17,600
844,604
154,768
382,426
7,235
74,779
1,494,262
-------
State
Kentucky
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Kentucky Total
Louisiana
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Louisiana Total
Maine
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Maine Total
[tonsfyr]
2002
VOC
0
0
2,487
2,909
105,281
39,806
82,321
1,479
44,884
279,168
0
0
3,960
7,137
135,934
61,307
77,802
1,239
79,781
367,159
0
0
365
1,258
88,028
30,025
26,131
67
5,151
151,026
[tonsfyr]
2009
Base
VOC
0
0
2,629
2,909
101,927
33,708
51,892
1,561
42,586
237,212
0
0
4,264
7,137
133,648
51,973
49,098
574
61,820
308,515
0
0
370
1,258
82,684
28,193
17,146
236
4,542
134,429
[tonsfyr]
2014
Base
VOC
0
0
2,716
2,909
97,456
29,041
42,149
1,615
42,581
218,465
0
0
4,498
7,137
127,217
44,943
38,299
645
61,820
284,560
0
0
375
1,258
79,463
24,234
14,673
214
4,542
124,759
[tonsfyr]
2020
Base
VOC
0
0
2,868
2,909
96,199
25,361
34,769
1,696
42,589
206,392
0
0
4,966
7,137
126,631
39,985
31,376
714
61,820
272,630
0
0
388
1,258
75,901
19,744
12,325
132
4,542
114,290
[tonsfyr]
2030
Base
VOC
0
0
3,195
2,909
96,199
25,698
31,858
1,696
42,589
204,145
0
0
6,236
7,137
126,631
40,623
31,178
714
61,820
274,339
0
0
423
1,258
75,901
18,497
10,670
132
4,542
111,422
[tonsfyr]
2002
NOX
0
0
70,391
1,326
17,557
31,792
147,749
200,955
38,541
508,311
0
0
216,290
3,254
27,559
28,899
124,192
82,293
211,449
693,935
0
0
1,708
566
7,423
8,271
47,227
1,188
18,895
85,277
[tonsfyr]
2009
Base
NOX
0
0
64,975
1,326
17,480
26,961
85,183
95,712
28,382
320,019
0
0
209,740
3,254
27,535
25,735
70,328
25,960
211,225
573,776
0
0
1,716
566
7,334
7,400
26,670
6,660
18,045
68,390
[tonsfyr]
2014
Base
NOX
0
0
63,275
1,326
17,424
21,171
51,140
68,876
28,382
251,594
0
0
204,037
3,254
27,518
21,286
43,492
27,522
211,225
538,334
0
0
1,711
566
7,270
6,302
16,408
6,208
18,045
56,509
[tonsfyr]
2020
Base
NOX
0
0
63,565
1,326
17,358
15,738
32,067
62,024
28,382
220,459
0
0
205,657
3,254
27,498
16,835
28.662
27,607
211,225
520,739
0
0
1,781
566
7,192
5,464
10,550
3,969
18,045
47,566
[tonsfyr]
2030
Base
NOX
0
0
69,851
1,326
17,358
12,617
24,626
62,024
28,382
216,183
0
0
233,694
3,254
27,498
14,547
23,290
27,607
211,225
541,115
0
0
2,112
566
7,192
5,307
7,687
3,969
18,045
44,878
[tonsfyr]
2002
CO
0
0
17,830
61,812
108,397
282,098
1,052,158
12,544
110,047
1,644,885
0
0
45,941
151,658
139,222
364,963
943,962
12,682
134,203
1,792,631
0
0
3,650
26,592
104,033
138,111
360,595
1,084
15,861
649,927
[tonsfyr]
2009
Base
CO
0
0
19,313
61,812
102,054
203,444
599,767
28,316
110,047
1,124,751
0
0
47,622
151,658
137,300
245,591
539,202
26,764
133,982
1,282,120
0
0
3,769
26,592
97,029
105,822
210,877
5,986
15,107
465,182
[tonsfyr]
2014
Base
CO
0
0
20,293
61,812
97,523
187,975
523,949
28,389
110,047
1,029,988
0
0
48,188
151,658
135,926
232,521
464,965
28,465
133,982
1,195,706
0
0
3,981
26,592
92,024
99,160
187,476
5,357
15,107
429,698
[tonsfyr]
2020
Base
CO
0
0
21,803
61,812
92,085
190,518
508,439
29,085
110,047
1,013,789
0
0
50,103
151,658
134,278
236,980
451,997
28,817
133,982
1,187,815
0
0
4,206
26,592
86,014
97,010
181,502
3,798
15,107
414,230
[tonslyr]
2030
Base
CO
0
0
24,580
61,812
92,085
207,956
543,459
29,085
110,047
1,069,025
0
0
56,877
151,658
134,278
257,335
512,934
28,817
133,982
1,275,881
0
0
4,459
26,592
86,014
103,051
189,853
3,798
15,107
428,874
-------
State
Maryland
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Maryland Total
Massachusetts
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Massachusetts Total
Michigan
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Michigan Total
[tonsfyr]
2002
VOC
0
0
5,360
353
126,362
51,369
71,591
478
5,758
261,270
0
0
2,443
747
176,731
52,921
71,646
595
7,722
312,806
0
0
2,504
724
248,382
173,241
207,762
1,243
39,832
673,689
[tonsfyr]
2009
Base
VOC
0
0
5,482
353
120,308
38,023
48,559
521
4,570
217,815
0
0
2,487
747
168,492
39,377
45,768
384
6,559
263,814
0
0
2,732
724
236,151
147,590
124,457
1,350
32,854
545,857
[tonsfyr]
2014
Base
VOC
0
0
5,733
353
118,030
33,652
38,202
616
4,570
201,154
0
0
2,601
747
165,353
34,017
35,321
370
6,559
244,968
0
0
2,987
724
227,606
124,499
104,607
1,562
32,854
494,838
[tonsfyr]
2020
Base
VOC
0
0
6,069
353
116,078
32,241
31,292
693
4,570
191,296
0
0
2,734
747
161,951
31,809
28,541
427
6,559
232,768
0
0
3,375
724
226,489
104,755
88,580
1,602
32,854
458,378
[tonsfyr]
2030
Base
VOC
0
0
6,432
353
116,078
34,382
28,704
693
4,570
191,211
0
0
2,921
747
161,951
33,486
26,110
427
6,559
232,202
0
0
4,065
724
226,489
103,718
79,226
1,602
32,854
448,677
[tonsfyr]
2002
NOX
0
0
17,106
137
21,715
27,495
121,659
73,527
22,109
283,748
0
0
17,144
341
34,373
30,046
128,362
32,561
15,394
258,220
0
0
43,025
330
43,499
70,912
315,420
141,908
82,202
697,296
[tonsfyr]
2009
Base
NOX
0
0
16,209
137
21,667
23,271
68,358
18,640
17,826
166,108
0
0
15,668
341
34,283
25,055
67,279
11,748
14,849
169,223
0
0
44,993
330
43,424
63,773
189,800
83,271
77,597
503,189
[tonsfyr]
2014
Base
NOX
0
0
15,875
137
21,633
18,960
41,721
20,882
17,826
137,033
0
0
15,702
341
34,219
19,746
38,153
10,341
14,849
133,352
0
0
47,692
330
43,371
52,230
120,465
80,290
77,597
421,975
[tonsfyr]
2020
Base
NOX
0
0
16,480
137
21,592
14,651
25,955
22,653
17,826
119,293
0
0
16,151
341
34,143
15,707
22,168
12,444
14,849
115,802
0
0
53,325
330
43,306
43,538
80,551
79,933
77,597
378,581
[tonsfyr]
2030
Base
NOX
0
0
18,380
137
21,592
12,719
20,660
22,653
17,826
113,967
0
0
17,570
341
34,143
14,545
17,469
12,444
14,849
111,360
0
0
69,133
330
43,306
41,589
60,564
79,933
77,597
372,452
[tonsfyr]
2002
CO
0
0
17,581
6,129
141,960
414,390
1,004,611
4,546
94,448
1,683,666
0
0
18,602
15,878
136,753
423,212
960,011
10,922
10,656
1,576,034
0
0
26,763
15,380
94,909
1,013,991
2,744,658
13,367
66,873
3,975,941
[tonsfyr]
2009
Base
CO
0
0
18,062
6,129
138,168
339,878
599,433
10,599
94,404
1,206,675
0
0
19,525
15,878
129,917
313,857
542,302
9,109
10,621
1,041,209
0
0
28,629
15,380
96,472
688,696
1,461,558
12,926
66,685
2,370,346
[tonsfyr]
2014
Base
CO
0
0
19,265
6,129
135,459
322,626
526,263
11,472
94,404
1,115,618
0
0
20,876
15,878
125,034
292,004
495,891
8,673
10,621
968,976
0
0
31,302
15,380
97,588
620,623
1,273,212
15,503
66,685
2,120,292
[tonsfyr]
2020
Base
CO
0
0
20,716
6,129
132,206
340,408
519,267
12,092
94,404
1,125,222
0
0
22,357
15,878
119,169
302,871
497,918
7,936
10,621
976,750
0
0
34,872
15,380
98,928
605,349
1,221,058
17,748
66,685
2,060,020
[tonslyr]
2030
Base
CO
0
0
22,413
6,129
132,206
382,289
575,218
12,092
94,404
1,224,752
0
0
24,040
15,878
119,169
339,056
557,054
7,936
10,621
1,073,754
0
0
39,287
15,380
98,928
649,546
1,283,487
17,748
66,685
2,171,061
-------
State
Minnesota
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Minnesota Total
Mississippi
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Mississippi Total
Missouri
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Missouri Total
[tonsfyr]
2002
VOC
0
0
1,611
5,047
125,318
97,104
102,566
646
29,541
361,833
0
0
2,386
8,407
156,390
36,056
62,375
629
43,224
309,467
0
0
3,439
1,488
162,795
63,279
124,106
1,496
34,704
391,308
[tonsfyr]
2009
Base
VOC
0
0
1,662
5,047
121,402
86,673
73,518
672
26,591
315,564
0
0
2,516
8,407
154,665
31,622
40,881
363
37,751
276,205
0
0
3,537
1,488
157,282
50,345
79,858
1,692
27,517
321,719
[tonsfyr]
2014
Base
VOC
0
0
1,672
5,047
116,713
76,510
62,866
815
26,652
290,274
0
0
2,548
8,407
149,624
27,255
31,707
429
37,751
257,721
0
0
3,603
1,488
151,104
43,558
65,168
1,771
27,517
294,210
[tonsfyr]
2020
Base
VOC
0
0
1,712
5,047
115,950
67,902
53,880
916
26,726
272,132
0
0
2,651
8,407
148,789
23,620
26,200
472
37,751
247,890
0
0
3,712
1,488
149,030
39,288
53,630
1,764
27,517
276,429
[tonsfyr]
2030
Base
VOC
0
0
1,871
5,047
115,950
60,721
46,670
916
26,726
257,902
0
0
2,997
8,407
148,789
23,727
24,829
472
37,751
246,971
0
0
3,926
1,488
149,030
40,437
51,200
1,764
27,517
275,362
[tonsfyr]
2002
NOX
0
0
55,371
2,300
56,700
68,820
163,172
86,917
67,813
501,094
0
0
66,650
3,833
12,212
22,180
105,505
45,850
60,244
316,473
0
0
79,583
678
32,910
52,997
200,379
145,232
38,025
549,803
[tonsfyr]
2009
Base
NOX
0
0
50,040
2,300
56,616
59,324
106,140
38,630
66,107
379,158
0
0
61,821
3,833
12,169
19,058
62,300
29,058
58,269
246,507
0
0
70,030
678
32,785
46,091
121,016
80,814
33,144
384,556
[tonsfyr]
2014
Base
NOX
0
0
48,393
2,300
56,557
48,614
65,740
41,007
66,325
328,936
0
0
60,034
3,833
12,139
15,120
37,951
23,371
56,826
209,274
0
0
68,145
678
32,695
37,054
75,598
75,127
33,144
322,440
[tonsfyr]
2020
Base
NOX
0
0
48,235
2,300
56,485
36,926
43,094
42,469
66,615
296,125
0
0
60,314
3,833
12,103
11,400
24,286
20,263
56,826
189,025
0
0
67,979
678
32,588
27,534
49,135
73,116
33,144
284,174
[tonsfyr]
2030
Base
NOX
0
0
52,459
2,300
56,485
28,213
35,370
42,469
66,615
283,912
0
0
67,010
3,833
12,103
9,220
18,302
20,263
56,826
187,557
0
0
71,968
678
32,588
20,719
38,513
73,116
33,144
270,726
[tonsfyr]
2002
CO
0
0
8,411
107,237
139,234
452,734
1,314,360
7,468
47,015
2,076,459
0
0
10,656
178,646
129,408
214,179
739,190
5,286
54,587
1,331,952
0
0
18,171
31,611
168,352
479,319
1,598,930
10,827
108,389
2,415,599
[tonsfyr]
2009
Base
CO
0
0
8,982
107,237
132,191
385,326
897,668
5,933
47,365
1,584,702
0
0
11,332
178,646
125,242
149,762
448,208
4,402
53,581
971,174
0
0
19,635
31,611
158,163
333,739
923,851
12,552
108,361
1,587,913
[tonsfyr]
2014
Base
CO
0
0
9,212
107,237
127,159
358,959
786,262
8,643
48,177
1,445,649
0
0
11,801
178,646
122,266
139,143
388,570
6,799
53,581
900,807
0
0
20,840
31,611
150,885
311,545
800,044
13,483
108,361
1,436,771
[tonsfyr]
2020
Base
CO
0
0
9,710
107,237
121,121
356,686
764,896
9,316
49,063
1,418,029
0
0
12,595
178,646
118,696
139,898
379,133
6,440
53,581
888,989
0
0
22,505
31,611
142,152
318,976
768,803
13,461
108,361
1,405,869
[tonslyr]
2030
Base
CO
0
0
11,224
107,237
121,121
369,793
834,696
9,316
49,063
1,502,451
0
0
14,577
178,646
118,696
150,980
410,973
6,440
53,581
933,893
0
0
25,361
31,611
142,152
350,123
863,675
13,461
108,361
1,534,746
-------
State
Montana
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Montana Total
Nebraska
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Nebraska Total
Nevada
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Nevada Total
[tonsfyr]
2002
VOC
0
0
1,309
10,085
23,573
12,968
20,451
355
6,807
75,548
0
0
3,524
837
40,762
18,442
36,940
635
6,527
107,667
0
0
1,057
10,740
22,874
22,720
26,884
483
1,649
86,406
[tonsfyr]
2009
Base
VOC
0
0
1,306
10,085
22,543
11,853
13,325
318
6,431
65,861
0
0
3,523
837
39,632
14,727
24,026
545
5,906
89,196
0
0
1,094
10,740
23,491
16,988
22,191
445
1,493
76,441
[tonsfyr]
2014
Base
VOC
0
0
1,302
10,085
21,452
10,184
11,525
393
6,431
61,373
0
0
3,463
837
37,860
12,636
20,116
549
5,906
81,368
0
0
1,150
10,740
21,718
15,504
19,306
597
1,493
70,506
[tonsfyr]
2020
Base
VOC
0
0
1,297
10,085
21,032
8,322
9,863
421
6,431
57,451
0
0
3,384
837
37,441
11,084
17,017
602
5,906
76,271
0
0
1.223
10,740
21,792
14,803
17,057
674
1,493
67,783
[tonsfyr]
2030
Base
VOC
0
0
1,291
10,085
21,032
7,914
9,039
421
6,431
56,213
0
0
3,294
837
37,441
11,092
16,795
602
5,906
75,966
0
0
1,290
10,740
21,792
15,760
16,380
674
1,493
68,130
[tonsfyr]
2002
NOX
0
0
22,873
5,187
3,797
18,777
36,727
36,577
16,588
140,526
0
0
68,904
381
13,820
39,889
66,226
47,900
11,385
248,506
0
0
12,958
4,910
5,308
18,990
28,320
48,366
7,509
126,362
[tonsfyr]
2009
Base
NOX
0
0
18,260
5,187
3,767
16,576
20,801
36,169
16,122
116,883
0
0
54,285
381
13,798
34,573
40,028
54,034
11,385
208,485
0
0
11,168
4,910
5,306
16,487
20,307
46,403
7,509
112,090
[tonsfyr]
2014
Base
NOX
0
0
17,546
5,187
3,746
13,643
12,900
31,948
15,684
100,654
0
0
51,946
381
13,782
27,908
24,974
38,052
11,385
168,427
0
0
11,212
4,910
5,304
13,033
14,625
32,260
7,509
88,854
[tonsfyr]
2020
Base
NOX
0
0
17,175
5,187
3,720
9,792
8,538
32,457
15,684
92,552
0
0
50,703
381
13,763
19,576
16,298
38,911
11,385
151,019
0
0
11,566
4,910
5,302
9,176
11,256
34,817
7,509
84,537
[tonsfyr]
2030
Base
NOX
0
0
17,180
5,187
3,720
6,048
6,842
32,457
15,684
87,117
0
0
50,664
381
13,763
11,818
13,340
38,911
11,385
140,263
0
0
12,047
4,910
5,302
7,244
9,913
34,817
7,509
81,742
[tonsfyr]
2002
CO
0
0
5,814
203,759
35,673
85,304
283,678
3,047
29,410
646,686
0
0
10,222
17,780
66,672
155,107
473,870
3,420
5,717
732,788
0
0
11,214
227,965
14,700
208,377
301,082
2,798
6,985
773,121
[tonsfyr]
2009
Base
CO
0
0
6,365
203,759
33,199
60,793
164,018
4,273
29,410
501,817
0
0
11,459
17,780
64,882
108,019
276,172
4,404
5,717
488,432
0
0
11,954
227,965
14,229
135,971
231,710
8,072
6,985
636,888
[tonsfyr]
2014
Base
CO
0
0
6,862
203,759
31,432
57,185
146,187
4,906
29,410
479,741
0
0
12,380
17,780
63,603
98,448
241,994
4,601
5,717
444,524
0
0
13,121
227,965
13,893
130,610
223,133
9,139
6,985
624,847
[tonsfyr]
2020
Base
CO
0
0
7,457
203,759
29,312
56,226
142,895
5,141
29,410
474,201
0
0
13,530
17,780
62,069
97,078
234,283
5,028
5,717
435,483
0
0
14,601
227,965
13,490
137,077
236,365
7,369
6,985
643,851
[tonslyr]
2030
Base
CO
0
0
8,299
203,759
29,312
58,516
158,758
5,141
29,410
493,194
0
0
15,482
17,780
62,069
102,149
276,293
5,028
5,717
484,518
0
0
16,127
227,965
13,490
152,899
262,598
7,369
6,985
687,433
-------
State
New Hampshire
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
New Hampshire Total
New Jersey
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
New Jersey Total
New Mexico
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
New Mexico Total
[tonsfyr]
2002
VOC
0
0
118
301
61,483
21,832
21,682
104
1,496
107,015
0
0
2,236
488
151,657
78,629
101,094
1,048
13,282
348,436
0
0
1,982
27,488
36,950
13,499
45,763
563
15,691
141,935
[tonsfyr]
2009
Base
VOC
0
0
124
301
57,548
18,910
14,879
150
721
92,633
0
0
2,326
488
144,567
57,795
64,465
335
10,897
280,873
0
0
1,992
27,488
35,170
11,181
31,131
489
10,786
118,237
[tonsfyr]
2014
Base
VOC
0
0
132
301
55,511
16,065
12,393
165
721
85,289
0
0
2,456
488
142,238
50,544
46,651
398
10,897
253,673
0
0
1,971
27,488
33,450
10,062
26,206
491
10,786
110,454
[tonsfyr]
2020
Base
VOC
0
0
143
301
53,263
13,661
10,267
189
721
78,545
0
0
2,653
488
140,176
48,222
36,413
450
10,897
239,299
0
0
1,941
27,488
33,088
9,237
22,451
519
10,786
105,510
[tonsfyr]
2030
Base
VOC
0
0
159
301
53,263
13,333
9,342
189
721
77,308
0
0
2,999
488
140,176
51,422
32,216
450
10,897
238,649
0
0
1,903
27,488
33,088
9,636
21,317
519
10,786
104,738
[tonsfyr]
2002
NOX
0
0
1,866
137
11,235
8,150
38,799
7,000
2,786
69,973
0
0
35,998
223
26,393
40,876
161,872
34,188
17,206
316,756
0
0
36,714
12,582
7,532
9,681
77,574
78,547
60,358
282,988
[tonsfyr]
2009
Base
NOX
0
0
1,781
137
11,178
6,965
23,734
2,619
2,783
49,197
0
0
35,363
223
26,342
35,077
85,611
7,767
15,578
205,961
0
0
29,265
12,582
7,517
8,353
46,462
63,814
60,297
228,288
[tonsfyr]
2014
Base
NOX
0
0
1,752
137
11,137
5,763
14,685
3,065
2,783
39,322
0
0
34,759
223
26,305
28,499
48,496
9,427
15,578
163,286
0
0
27,994
12,582
7,505
6,706
29,322
58,498
60,240
202,848
[tonsfyr]
2020
Base
NOX
0
0
1,783
137
11,088
4,818
9,164
3,964
2,783
33,737
0
0
35,709
223
26,261
23,040
26,860
11,022
15,578
138,694
0
0
27,361
12,582
7,492
4,981
19,600
58,562
60,240
190,819
[tonsfyr]
2030
Base
NOX
0
0
2,004
137
11,088
4,616
6,716
3,964
2,783
31,309
0
0
40,992
223
26,261
21,269
18,353
11,022
15,578
133,699
0
0
27,304
12,582
7.492
4,047
15,429
58,562
60,240
185,657
[tonsfyr]
2002
CO
0
0
2,305
6,398
74,137
122,530
294,533
643
2,082
502,627
0
0
14,960
10,375
84,145
635,064
1,325,445
3,865
8,375
2,082,228
0
0
8,473
583,216
29,666
119,501
587,028
5,539
32,228
1,365,651
[tonsfyr]
2009
Base
CO
0
0
2,398
6,398
69,710
96,954
180,168
3,375
2,080
361,082
0
0
15,509
10,375
79,593
467,523
751,380
7,240
8,316
1,339,937
0
0
9,222
583,216
28,367
78,164
361,579
6,016
32,228
1,098,792
[tonsfyr]
2014
Base
CO
0
0
2,553
6,398
66,547
85,458
158,722
3,404
2,080
325,161
0
0
16,429
10,375
76,342
442,732
654,205
8,009
8,316
1,216,408
0
0
9,911
583,216
27,439
74,832
326,293
6,052
32,228
1,059,971
[tonsfyr]
2020
Base
CO
0
0
2,721
6,398
62,750
85,704
155,253
3,140
2,080
318,046
0
0
17,644
10,375
72,437
464,876
632,680
7,822
8,316
1,214,149
0
0
10,732
583,216
26,326
78,007
326,676
6,433
32,228
1,063,618
[tonslyr]
2030
Base
CO
0
0
2,916
6,398
62,750
93,178
171,336
3,140
2,080
341,798
0
0
19,580
10,375
72,437
523,535
704,191
7,822
8,316
1,346,255
0
0
11,960
583,216
26,326
86,466
360,766
6,433
32,228
1,107,396
-------
State
New York
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
New York Total
North Carolina
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
North Carolina Total
North Dakota
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
North Dakota Total
[tonsfyr]
2002
VOC
0
0
2,473
903
608,921
151,345
212,929
857
6,218
983,646
0
0
1,472
58,889
231,094
88,972
143,187
920
61,685
586,219
0
0
1,256
527
14,911
13,565
15,356
781
1,249
47,645
[tonsfyr]
2009
Base
VOC
0
0
2,619
903
613,062
121,199
137,568
1,082
5,365
881,799
0
0
1,615
58,889
222,255
68,628
93,913
1,103
53,635
500,039
0
0
1,254
527
14,177
11,430
9,829
846
1,124
39,188
[tonsfyr]
2014
Base
VOC
0
0
2,745
903
621,635
105,522
102,389
1,100
5,365
839,659
0
0
1,690
58,889
212,129
59,717
76,576
1,163
53,633
463,797
0
0
1,240
527
13,461
9,536
8,318
838
1,124
35,045
[tonsfyr]
2020
Base
VOC
0
0
2,936
903
634,915
95,842
80,584
1,107
5,365
821,652
0
0
1,794
58,889
210,082
54,845
63,583
1,256
53,632
444,081
0
0
1.219
527
13,174
7,778
6,933
882
1,124
31,637
[tonsfyr]
2030
Base
VOC
0
0
3,266
903
634,915
98,761
74,771
1,107
5,365
819,089
0
0
1,916
58,889
210,082
57,309
58,393
1,256
53,632
441,477
0
0
1,195
527
13,174
7,197
6,511
882
1,124
30,610
[tonsfyr]
2002
NOX
0
0
40,659
412
89,986
78,279
290,698
81,201
38,992
620,228
0
0
22,608
11,424
18,869
61,664
242,379
153,226
49,273
559,444
0
0
23,072
240
4,007
38,012
24,832
75,947
9,929
176,039
[tonsfyr]
2009
Base
NOX
0
0
38,350
412
90,323
67,078
165,594
39,914
32,096
433,767
0
0
19,886
11,424
18,761
50,178
141,370
67,924
37,071
346,614
0
0
18,121
240
3,987
33,288
14,432
45,049
9,385
124,502
[tonsfyr]
2014
Base
NOX
0
0
38,161
412
90,564
55,658
103,189
34,490
32,096
354,570
0
0
19,482
11,424
18,684
38,433
87,185
67,442
37,071
279,721
0
0
17,336
240
3,972
27,270
8,967
44,009
9,385
111,180
[tonsfyr]
2020
Base
NOX
0
0
39,370
412
90,853
44,756
62,605
33,735
32,096
303,826
0
0
19,554
11,424
18,591
27,759
55,801
59,724
37,071
229,924
0
0
16,916
240
3,955
19,093
5,824
44,560
9,385
99,974
[tonsfyr]
2030
Base
NOX
0
0
44,334
412
90,853
40,396
44,498
33,735
32,096
286,322
0
0
20,381
11,424
18,591
22,701
41,119
59,724
37,071
211,012
0
0
16,912
240
3,955
10,734
4,722
44,560
9,385
90,509
[tonsfyr]
2002
CO
0
0
22,205
19,195
404,592
1,175,721
2,822,801
12,204
54,133
4,510,852
0
0
9,957
429,388
321,101
746,344
1,786,813
12,112
52,414
3,358,129
0
0
4,832
11,204
20,488
91,869
206,627
5,237
5,778
346,035
[tonsfyr]
2009
Base
CO
0
0
23,864
19,195
431,581
895,734
1,596,877
20,230
54,080
3,041,562
0
0
10,817
429,388
312,266
586,105
1,075,088
11,366
52,062
2,477,092
0
0
5,291
11,204
18,845
66,050
116,879
7,659
5,765
231,693
[tonsfyr]
2014
Base
CO
0
0
25,657
19,195
450,860
810,175
1,390,297
17,499
54,080
2,767,764
0
0
11,615
429,388
305,955
540,968
923,026
12,286
52,062
2,275,301
0
0
5,706
11,204
17,671
59,029
101,483
11,260
5,765
212,118
[tonsfyr]
2020
Base
CO
0
0
27,886
19,195
473,993
839,288
1,328,878
17,890
54,080
2,761,211
0
0
12,623
429,388
298,382
560,093
897,310
12,761
52,062
2,262,619
0
0
6,195
11,204
16,262
54,726
96,390
11,383
5,765
201,925
[tonslyr]
2030
Base
CO
0
0
30,840
19,195
473,993
937,245
1,526,678
17,890
54,080
3,059,922
0
0
13,872
429,388
298,382
622,450
965,793
12,761
52,062
2,394,709
0
0
6,944
11,204
16,262
53,229
109,994
11,383
5,765
214,782
-------
State
Ohio
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Ohio Total
Oklahoma
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Oklahoma Total
Oregon
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Oregon Total
[tonsfyr]
2002
VOC
0
0
3,632
178
285,528
103,414
205,348
1,773
29,515
629,389
0
0
1,551
3,749
200,442
38,015
86,133
984
35,176
366,050
0
0
1,843
37,328
242,829
39,821
91,766
142
14,567
428,297
[tonsfyr]
2009
Base
VOC
0
0
4,064
178
275,689
77,465
124,133
1,971
26,436
509,937
0
0
1,566
3,749
196,496
29,720
56,321
958
23,733
312,543
0
0
2,027
37,328
239,638
32,299
58,567
151
10,990
381,001
[tonsfyr]
2014
Base
VOC
0
0
4,207
178
272,521
67,241
100,380
2,106
26,436
473,070
0
0
1,563
3,749
192,447
25,931
46,067
1,019
23,733
294,508
0
0
2,115
37,328
238,352
28,149
47,310
151
10,990
364,395
[tonsfyr]
2020
Base
VOC
0
0
4,423
178
271,610
61,754
81,331
2,149
26,436
447,881
0
0
1,555
3,749
191,677
23,686
39,279
1,088
23,733
284,767
0
0
2,253
37,328
239,218
25,088
39,312
152
10,990
354,342
[tonsfyr]
2030
Base
VOC
0
0
4,798
178
271,610
64,960
74,335
2,149
26,436
444,466
0
0
1,543
3,749
191,677
24,511
37,822
1,088
23,733
284,123
0
0
2,487
37,328
239,218
25,679
32,647
152
10,990
348,502
[tonsfyr]
2002
NOX
0
0
96,728
81
41,466
90,812
327,388
373,299
65,850
995,625
0
0
26,294
1,709
94,574
31,331
133,152
90,302
72,670
450,033
0
0
43,439
17,857
16,998
26,372
109,066
9,006
15,958
238,696
[tonsfyr]
2009
Base
NOX
0
0
88,052
81
41,405
74,081
192,777
94,744
58,970
550,110
0
0
21,261
1,709
94,542
26,945
80,016
83,945
72,517
380,935
0
0
40,199
17,857
17,009
22,638
69,772
9,740
15,767
192,982
[tonsfyr]
2014
Base
NOX
0
0
85,793
81
41,360
55,950
113,811
99,033
58,185
454,213
0
0
20,464
1,709
94,518
21,961
51,065
64,740
71,835
326,293
0
0
39,073
17,857
17,018
18,141
50,073
9,740
15,767
167,669
[tonsfyr]
2020
Base
NOX
0
0
85,843
81
41,307
40,458
72,024
92,780
58,185
390,678
0
0
20,088
1,709
94,490
16,410
34,569
62,434
71,835
301,535
0
0
39,085
17,857
17,028
13,685
33,701
9,768
15,767
146,890
[tonsfyr]
2030
Base
NOX
0
0
92,415
81
41,307
34,425
57,376
92,780
58,185
376,570
0
0
20,149
1,709
94,490
12,410
28,819
62,434
71,835
291,847
0
0
42,312
17,857
17,028
11,363
21,232
9,768
15,767
135,326
[tonsfyr]
2002
CO
0
0
29,188
3,787
150,302
910,152
2,600,918
14,817
238,412
3,947,575
0
0
10,093
79,673
385,235
308,218
1,069,135
13,661
50,750
1,916,764
0
0
12,401
778,193
342,444
304,850
1,078,005
1,105
34,389
2,551,388
[tonsfyr]
2009
Base
CO
0
0
31,189
3,787
145,351
623,175
1,476,811
20,543
238,412
2,539,269
0
0
10,553
79,673
382,569
206,115
636,945
28,415
50,750
1,395,018
0
0
13,077
778,193
333,115
212,615
619,048
3,932
33,794
1,993,775
[tonsfyr]
2014
Base
CO
0
0
32,881
3,787
141,813
564,041
1,265,027
21,862
238,412
2,267,823
0
0
11,225
79,673
380,664
186,014
557,794
29,821
50,750
1,295,939
0
0
13,756
778,193
326,451
193,709
517,087
3,932
33,794
1,866,922
[tonsfyr]
2020
Base
CO
0
0
35,103
3,787
137,566
573,594
1,236,418
22,421
238,412
2,247,301
0
0
11,963
79,673
378,379
190,974
555,227
27,970
50,750
1,294,935
0
0
14,654
778,193
318,454
199,468
478,542
3,942
33,794
1,827,046
[tonslyr]
2030
Base
CO
0
0
38,849
3,787
137,566
633,745
1,362,343
22,421
238,412
2,437,123
0
0
12,989
79,673
378,379
209,343
630,913
27,970
50,750
1,390,016
0
0
16,159
778,193
318,454
220,481
485,899
3,942
33,794
1,856,922
-------
State
Pennsylvania
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Pennsylvania Total
Rhode Island
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Rhode Island Total
South Carolina
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptiprn
ptnonipm
South Carolina Total
[tons/yr]
2002
VOC
0
0
2,425
256
281,740
96,797
184,268
1,212
36,871
603,569
0
0
162
8
16,875
8,491
14,366
39
1,894
41,835
0
0
961
5,171
185,429
50,041
89,994
506
36,778
368,879
[tonsfyr]
2009
Base
VOC
0
0
2,559
256
264,980
79,165
115,276
1,712
30,914
494,863
0
0
166
8
16,553
6,010
10,124
49
1,360
34,271
0
0
997
5,171
185,163
38,078
58,070
609
27,298
315,385
[tonsfyr]
2014
Base
VOC
0
0
2,673
256
261,647
69,633
89,483
1,766
30,914
456,372
0
0
180
8
16,483
5,080
7,869
45
1,360
31,025
0
0
1,019
5,171
181,315
32,999
47,272
655
27,298
295,729
[tonsfyr]
2020
Base
VOC
0
0
2,851
256
260,079
63,072
69,513
1,753
30,914
428,439
0
0
197
8
16,457
4,822
6,810
41
1,360
29,697
0
0
1,050
5,171
182,844
30,535
39,608
746
27,298
287,252
[tonsfyr]
2030
Base
VOC
0
0
3,196
256
260,079
65,413
63,170
1,753
30,914
424,781
0
0
214
8
16,457
5,137
5,805
41
1,360
29,023
0
0
1,107
5,171
182,844
31,883
37,165
746
27,298
286,214
[tonsfyr]
2002
NOX
0
0
67,118
117
53,435
62,168
294,414
210,149
89,064
776,465
0
0
876
4
2,964
4,663
16,720
712
2,060
27,997
0
0
19,378
2,357
20,281
29,982
134,542
91,296
40,417
338,253
[tonsfyr]
2009
Base
NOX
0
0
63,037
117
53,333
51,401
165,444
91,466
76,602
501,400
0
0
824
4
2,960
3,890
9,655
475
1,938
19,747
0
0
17,077
2,357
20,275
24,512
82,299
50,236
29,336
226,092
[tonsfyr]
2014
Base
NOX
0
0
61,438
117
53,260
40,177
99,133
74,225
74,324
402,674
0
0
874
4
2,958
3,053
6,550
396
1,938
15,774
0
0
16,521
2,357
20,271
18,902
52,555
48,449
29,336
188,391
[tonsfyr]
2020
Base
NOX
0
0
61,811
117
53,173
30,881
58,609
69,570
74,324
348,484
0
0
950
4
2,955
2,457
4,125
357
1,938
12,786
0
0
16,424
2,357
20,267
13,850
35,593
34,085
29,336
151,912
[tonsfyr]
2030
Base
NOX
0
0
67,924
117
53,173
27,101
40,758
69,570
74,324
332,967
0
0
1,028
4
2,955
2,309
3,514
357
1,938
12,104
0
0
17,422
2,357
20,267
11,690
27,866
34,085
29,336
143,023
[tonsfyr]
2002
CO
0
0
25,047
5,450
265,035
856,737
2,420,525
17,018
104,570
3,694,382
0
0
2,923
171
5,421
65,923
188,240
453
1,781
264,911
0
0
9,393
109,880
145,294
377,166
1,141,561
4,749
56,640
1,844,682
[tonsfyr]
2009
Base
CO
0
0
26,618
5,450
256,636
612,784
1,323,428
18,116
103,784
2,346,814
0
0
3,009
171
5,142
48,379
113,984
1,926
1,758
174,368
0
0
9,933
109,880
145,455
295,785
691,670
5,535
56,448
1,314,706
[tonsfyr]
2014
Base
CO
0
0
28,094
5,450
250,638
547,716
1,104,821
19,163
103,784
2,059,664
0
0
3,290
171
4,942
44,582
101,917
1,751
1,758
158,410
0
0
10,554
109,880
145,570
271,673
590,040
6,509
56,448
1,190,675
[tonsfyr]
2020
Base
CO
0
0
30,087
5,450
243,439
566,481
1,039,116
18,236
103,784
2,006,592
0
0
3,628
171
4,703
46,080
97,100
1,616
1,758
155,056
0
0
11,272
109,880
145,708
280,073
570,319
6,829
56,448
1,180,530
[tonsfyr]
2030
Base
CO
0
0
33,258
5,450
243,439
631,795
1,137,007
18,236
103,784
2,172,969
0
0
3,959
171
4,703
51,531
105,592
1,616
1,758
169,329
0
0
12,270
109,880
145,708
310,041
618,756
6,829
56,448
1,259,932
-------
State
South Dakota
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
South Dakota Total
Tennessee
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Tennessee Total
Texas
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Texas Total
[tonsfyr]
2002
VOC
0
0
321
3,985
19,597
12,322
16,177
111
2,431
54,944
0
0
2,152
2,220
148,677
60,023
140,405
843
84,610
438,930
0
0
11,279
13,201
695,600
174,723
308,904
4,745
149,554
1,358,006
[tonsfyr]
2009
Base
VOC
0
0
324
3,985
18,840
10,531
10,656
110
1,449
45,897
0
0
2,363
2,220
145,508
49,015
94,247
925
73,801
368,080
0
0
11,889
13,201
675,342
135,696
199,858
4,575
111,767
1,152,328
[tonsfyr]
2014
Base
VOC
0
0
331
3,985
17,980
8,874
9,022
120
1,449
41,762
0
0
2,449
2,220
137,119
42,584
75,248
1,015
73,801
334,436
0
0
12,303
13,201
669,427
120,305
154,959
4,771
111,767
1,086,732
[tonsfyr]
2020
Base
VOC
0
0
339
3,985
17,644
7,352
7,539
142
1,449
38,451
0
0
2,594
2,220
135,241
38,065
60,871
1,202
73,801
313,994
0
0
13,009
13,201
668,063
113,364
128,066
5,010
111,767
1,052,479
[tonsfyr]
2030
Base
VOC
0
0
346
3,985
17,644
6,964
7,203
142
1,449
37,734
0
0
2,866
2,220
135,241
39,112
56,534
1,202
73,801
310,976
0
0
14,522
13,201
668,063
119,864
128,916
5,010
111,767
1,061,342
[tonsfyr]
2002
NOX
0
0
4,164
1,817
5,200
27,219
29,910
15,922
4,776
89,008
0
0
50,692
1,012
18,676
40,970
240,312
155,926
69,070
576,659
0
0
236,223
4,890
274,338
152,771
621,483
259,612
344,073
1,893,390
[tonsfyr]
2009
Base
NOX
0
0
3,349
1,817
5,177
23,755
18,071
2,353
4,776
59,298
0
0
46,834
1,012
18,604
34,909
147,688
53,647
50,451
353,145
0
0
220,853
4,890
274,244
126,111
334,266
136,697
336,557
1,433,617
[tonsfyr]
2014
Base
NOX
0
0
3,252
1,817
5,160
19,402
11,071
2,364
4,776
47,843
0
0
45,791
1,012
18,552
27,163
93,956
54,945
50,451
291,871
0
0
216,266
4,890
274,177
98,419
186,632
135,504
331,121
1,247,009
[tonsfyr]
2020
Base
NOX
0
0
3,202
1,817
5,140
13,665
6,963
2,740
4,776
38,304
0
0
46,115
1,012
18,490
20,352
59.503
39,841
50,451
235,764
0
0
218,785
4,890
274,096
70,456
116,542
135,714
331,121
1,151,604
[tonsfyr]
2030
Base
NOX
0
0
3,216
1,817
5,140
7,961
5,621
2,740
4,776
31,271
0
0
50,251
1,012
18,490
16,955
43,543
39,841
50,451
220,543
0
0
242,786
4,890
274,096
54,433
95,623
135,714
331,121
1,138,663
[tonsfyr]
2002
CO
0
0
2,979
84,689
24,107
79,151
219,053
632
4,068
414,679
0
0
13,001
47,175
119,973
460,143
1,681,568
6,596
115,767
2,444,222
0
0
67,547
256,966
463,577
1,578,739
3,787,848
215,207
283,294
6,653,179
[tonsfyr]
2009
Base
CO
0
0
3,160
84,689
22,176
59,285
129,758
552
4,068
303,689
0
0
14,117
47,175
114,059
305,294
992,698
7,203
115,278
1,595,825
0
0
71,359
256,966
455,678
1,196,045
2,152,885
78,627
281,797
4,493,357
[tonsfyr]
2014
Base
CO
0
0
3,398
84,689
20,796
53,433
113,555
604
4,068
280,543
0
0
14,987
47,175
109,836
281,937
875,126
8,021
115,278
1,452,359
0
0
75,840
256,966
450,036
1,128,017
1,873,424
86,950
281,797
4,153,030
[tonsfyr]
2020
Base
CO
0
0
3,655
84,689
19,141
50,681
108,684
785
4,068
271,703
0
0
16,228
47,175
104,767
287,728
840,777
9,475
115,278
1,421,428
0
0
81,884
256,966
443,265
1,174,843
1,875,580
86,882
281,797
4,201,216
[tonslyr]
2030
Base
CO
0
0
3,947
84,689
19,141
50,964
126,091
785
4,068
289,686
0
0
18,286
47,175
104,767
316,789
901,356
9,475
115,278
1,513,126
0
0
91,779
256,966
443,265
1,309,218
2,180,504
86,882
281,797
4,650,411
-------
oo
State
Tribal Data
Sector
aim
ptipm
ptnonipm
Tribal Data Total
Utah
afdust
ag
aim
avefire
nortpt
nonroad
onroad
ptipm
ptnoniprn
Utah Total
Vermont
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Vermont Total
[tons/yr]
2002
VOC
218
241
601
1,060
0
0
2,596
15,469
54,443
25,488
56,206
418
5,826
160,444
0
0
53
393
18,887
10,446
18,139
0
1,097
49,015
[tons/yr]
2009
Base
VOC
217
30
389
636
0
0
2,737
15,469
53,042
23,357
39,609
501
5,070
139,784
0
0
57
393
18,265
9,755
11,951
0
1,025
41,445
[tons/yr]
2014
Base
VOC
212
30
383
625
0
0
2,969
15,469
50,928
20,391
34,748
600
5,070
130,175
0
0
61
393
17,993
8,420
9,772
0
1,025
37,664
[tons/yr]
2020
Base
VOC
206
24
382
612
0
0
3,276
15,469
50,352
17,151
30,914
644
5,070
122,875
0
0
66
393
17,744
6,901
7,646
0
1,025
33,774
[tons/yr]
2030
Base
VOC
199
24
382
605
0
0
3,565
15,469
50,352
16,833
28,693
644
5,070
120,626
0
0
70
393
17,744
6,524
6,600
0
1,025
32,356
[tons/yr]
2002
NOX
858
97
6,623
7,578
0
0
14,640
7,052
6,948
15,026
76,518
73,220
14,998
208,401
0
0
49
179
3,438
4,170
21,783
0
790
30,409
[tons/yr]
2009
Base
NOX
672
232
6,620
7,524
0
0
12,158
7,052
6,937
13,024
51,752
62,979
14,681
168,583
0
0
51
179
3,416
3,597
13,393
0
790
21,427
[tonsfyr]
2014
Base
NOX
642
232
6,620
7,494
0
0
11,959
7,052
6,929
10,343
35,867
58,224
14,531
144,906
0
0
57
179
3,400
2,898
8,581
0
790
15,905
[tonstyr]
2020
Base
NOX
626
182
6,620
7,428
0
0
12,044
7,052
6,920
7,840
25,070
59,235
14,531
132,692
0
0
64
179
3,382
2,354
5,448
0
790
12,217
[tons/yr]
2030
Base
NOX
626
182
6,620
7,428
0
0
12,307
7,052
6,920
6,675
19,835
59,235
14,531
126,554
0
0
71
179
3,382
2,123
3,908
0
790
10,453
[tons/yr]
2002
CO
302
828
2,573
3,703
0
0
10,805
328,713
79,323
172,729
764,714
4,506
45,052
1,405,842
0
0
1,220
8,347
43,091
58,906
237,164
0
1,078
349,807
[tonsfyr]
2009
Base
CO
347
1,171
2,573
4,091
0
0
11,601
328,713
78,434
119,498
452,333
5,583
45,029
1,041,191
0
0
1,280
8,347
41,058
42,042
131,797
0
1,078
225,602
[tons/yr]
2014
Base
CO
376
1,171
2,581
4,127
0
0
12,705
328,713
77,799
113,186
422,640
6,340
45,029
1,006,412
0
0
1,372
8,347
39,605
38,858
119,269
0
1,078
208,529
[tons/yr]
2020
Base
CO
413
918
2,587
3,918
0
0
14,099
328,713
77,036
114,711
423,561
6,468
45,029
1,009,617
0
0
1,469
8,347
37,862
38,196
119,078
0
1,078
206,029
[tons/yr]
2030
Base
CO
484
918
2,587
3,989
0
0
15,617
328,713
77,036
124,803
477,048
6,468
45,029
1,074,714
0
0
1,560
8,347
37,862
40,914
133,225
0
1,078
222,985
-------
State
Virginia
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Virginia Total
Washington
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Washington Total
West Virginia
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
West Virginia Total
[tonsfyr]
2002
VOC
0
0
3,084
3,194
201,748
67,441
125,474
726
43,184
444,850
0
0
2,248
2,674
166,658
64,611
159,797
219
12,429
408,636
0
0
1,180
1,721
59,489
16,935
36,949
1,175
14,241
131,691
[tonsfyr]
2009
Base
VOC
0
0
3,198
3,194
190,207
50,452
86,161
536
36,335
370,082
0
0
2,433
2,674
159,930
50,334
109,815
342
11,631
337,160
0
0
1,291
1,721
56,958
16,469
21,900
1,289
12,089
111,717
[tonsfyr]
2014
Base
VOC
0
0
3,264
3,194
185,542
44,451
72,948
679
36,335
346,413
0
0
2,548
2,674
154,329
43,632
94,849
350
11,631
310,013
0
0
1,333
1,721
54,651
14,419
17,904
1,328
12,089
103,446
[tonsfyr]
2020
Base
VOC
0
0
3,381
3,194
181,688
41,960
63,234
719
36,335
330,511
0
0
2,738
2,674
150,839
39,373
81,074
349
11,631
288,679
0
0
1.422
1,721
54,047
11,990
14,639
1,332
12,089
97,239
[tonsfyr]
2030
Base
VOC
0
0
3,605
3,194
181,688
44,479
62,669
719
36,335
332,689
0
0
3,152
2,674
150,839
40,459
68,508
349
11,631
277,612
0
0
1,664
1,721
54,047
11,889
12,233
1,332
12,089
94,975
[tonsfyr]
2002
NOX
0
0
39,676
1,456
53,605
45,848
214,393
86,763
61,730
503,471
0
0
66,992
1,484
16,911
42,800
199,767
16,122
24,522
368,598
0
0
32,148
785
14,519
8,407
60,216
227,827
46,627
390,529
[tonsfyr]
2009
Base
NOX
0
0
36,966
1,456
53,529
36,688
123,035
69,736
46,246
367,656
0
0
65,363
1,484
16,812
36,553
123,689
17,357
24,522
285,781
0
0
30,383
785
14,487
6,869
32,252
55,352
37,778
177,906
[tonsfyr]
2014
Base
NOX
0
0
35,646
1,456
53,475
28,740
78,620
44,145
46,246
288,327
0
0
65,801
1,484
16,742
29,715
87,124
17,581
24,522
242,970
0
0
30,081
785
14,464
5,665
19,840
50,926
37,778
159,538
[tonsfyr]
2020
Base
NOX
0
0
35,676
1,456
53,409
21,036
53,731
39,719
46,246
251,274
0
0
69,264
1,484
16,658
22,539
58.303
17,552
24,522
210,321
0
0
31,151
785
14,436
4,437
13,006
45,760
37,778
147,353
[tonsfyr]
2030
Base
NOX
0
0
38,394
1,456
53,409
17,403
49,108
39,719
46,246
245,737
0
0
82,840
1,484
16,658
18,753
38,337
17,552
24,522
200,146
0
0
36,257
785
14,436
3,935
9,265
45,760
37,778
148,216
[tonsfyr]
2002
CO
0
0
17,758
67,866
208,041
520,042
1,722,600
6,714
63,978
2,606,999
0
0
20,193
52,086
204,125
486,615
1,820,900
1,665
39,106
2,624,689
0
0
5,139
36,578
70,069
117,839
502,130
10,319
89,898
831,973
[tonsfyr]
2009
Base
CO
0
0
18,414
67,866
201,223
473,574
1,099,794
9,909
63,557
1,934,337
0
0
21,483
52,086
196,158
359,916
1,174,345
6,954
39,106
1,850,047
0
0
5,692
36,578
67,360
102,117
270,712
10,228
89,898
582,585
[tonsfyr]
2014
Base
CO
0
0
19,296
67,866
196,351
442,744
976,391
11,740
63,557
1,777,945
0
0
22,856
52,086
190,466
319,178
1,016,289
7,264
39,106
1,647,245
0
0
6,067
36,578
65,425
87,138
225,844
10,492
89,898
521,442
[tonsfyr]
2020
Base
CO
0
0
20,457
67,866
190,501
463,608
954,294
11,202
63,557
1,771,484
0
0
24,617
52,086
183,628
328,381
938,502
7,158
39,106
1,573,478
0
0
6,676
36,578
63,103
89,867
210,771
10,572
89,898
507,465
[tonslyr]
2030
Base
CO
0
0
22,189
67,866
190,501
518,461
1,121,935
11,202
63,557
1,995,711
0
0
27,397
52,086
183,628
363,147
954,906
7,158
39,106
1,627,429
0
0
8,049
36,578
63,103
98,181
206,623
10,572
89,898
513,003
-------
to
o
State
Wisconsin
Sector
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Wisconsin Total
Wyoming
afdust
ag
aim
avefire
nonpt
nonroad
onroad
ptipm
ptnonipm
Wyoming Total
Grand Total
[tons/yr]
2002
VOC
0
0
2,060
561
230,068
111,779
96,058
964
31,057
472,549
0
0
1,569
8,852
16,411
9,088
18,072
849
16,771
71,613
17,693,869
[tons/yr]
2009
Base
VOC
0
0
2,263
561
229,658
96,076
61,689
1,085
26,592
417,924
0
0
1,567
8,852
15,646
8,874
11,859
834
13,552
61,185
14,934,802
[tons/yr]
2014
Base
VOC
0
0
2,387
561
228,764
81,098
51,408
1,178
26,592
391,988
0
0
1,540
8,852
15,077
7,619
9,726
892
13,552
57,257
13,867,583
[tons/yr]
2020
Base
VOC
0
0
2,558
561
231,695
68,764
43,691
1,186
26,592
375,047
0
0
1,503
8,852
14,867
6,154
8,177
947
13,552
54,052
13,220,304
[tons/yr]
2030
Base
VOC
0
0
2,799
561
231,695
69,593
39,473
1,186
26,592
371,899
0
0
1,462
8,852
14,867
5,774
7,416
947
13,552
52,869
13,138,328
[tons/yr]
2002
NOX
0
0
30,307
256
21,994
53,430
172,043
91,128
38,283
407,440
0
0
30,368
4,035
4,309
5,470
32,643
85,207
36,500
198,533
20,931,673
[tonslyr]
2009
Base
NOX
0
0
27,468
256
21,984
45,761
103,786
53,488
38,282
291,025
0
0
23,784
4,035
4,295
4,713
17,780
83,587
36,385
174,578
14,898,719
[tonsfyr]
2014
Base
NOX
0
0
27,538
256
21,976
36,575
61,755
57,160
38,282
243,541
0
0
22,747
4,035
4,285
3,958
10,807
59,212
36,385
141,430
12,440,017
[tonsfyr]
2020
Base
NOX
0
0
28,600
256
21,967
28,977
38,899
56,119
38,282
213,100
0
0
22,184
4,035
4,273
3,017
6,983
60,438
36,385
137,316
10,930,663
[tons/yr]
2030
Base
NOX
0
0
32,248
256
21,967
26,142
30,805
56,119
38,282
205,819
0
0
22,173
4,035
4,273
2,312
5,542
60,438
36,385
135,158
10,452,858
[tons/yr]
2002
CO
0
0
24,321
11,924
166,779
569,467
1,321,240
10,725
34,197
2,138,654
0
0
4,758
188,099
19,192
53,551
246,059
7,078
23,341
542,078
101,885,285
[tonsfyr]
2009
Base
CO
0
0
25,819
11,924
162,598
416,240
763,639
10,728
34,197
1,425,145
0
0
5,359
188,099
18,058
40,841
143,301
6,715
23,341
425,714
71,586,336
[tons/yr]
2014
Base
CO
0
0
28,228
11,924
159,612
377,144
678,392
11,908
34,197
1,301,406
0
0
5,787
188,099
17,248
36,120
121,503
7,202
23,341
399,299
65,672,193
[tons/yr]
2020
Base
CO
0
0
31,276
11,924
156,028
372,039
689,891
12,152
34,197
1,307,507
0
0
6,322
188,099
16,276
35,787
116,959
7,654
23,341
394,438
64,912,870
[tons/yr]
2030
Base
CO
0
0
34,712
11,924
156,028
409,058
777,211
12,152
34,197
1,435,283
0
0
7,211
188,099
16,276
37,725
129,138
7,654
23,341
409,445
69,431,177
-------
Table D-1b: Continental US, SO2, NH3, PM-0. and PMS5 Emissions by Sector for 2002, and Projection Years 2009, 2014, 2020, and 2030.
State
Alabama
Sector
sfdust
39
aim
svefirs
lonpt
nonroad
on road
rfpm
3tnofiipm
Alabama Total
Arisona
sfdust
ag
aim
avefira
lonpt
lonroad
on road
ptipm
ptnonipm
Arizona Total
[tons/yr]
2002
S02
0
0
4,801
983
52,325
2,734
5,599
448,328
89,762
604,533
0
0
2,29?
2,888
2,571
3,858
2,876
70,709
21,702
106,900
[tons/yr]
2009
Base
S02
0
0
3,667
983
52,318
464
729
269,794
88,763
M6J19
0
0
713
2,888
2,568
681
862
41,901
21,702
71,315
[tonslyr]
2014
Base
S02
0
0
3,602
983
52,313
42
605
244,393
88,763
390,702
0
0
419
2,888
2,567
41
735
40,434
21,702
68,786
[tonslyr]
2020
Base
S02
0
0
4,241
983
52,308
45
667
187,851
88,763
33i,85B
0
0
475
2,888
2,564
43
888
36,801
21,702
65,361
[tonslyr]
2030
Base
S02
0
D
6,220
983
52.308
50
745
187,851
88,763
336,920
0
0
531
2,888
2,564
48
1,125
36,601
21,702
65,658
[tonslyr]
2002
NH3
0
57,802
13
752
426
28
5,627
783
2,224
67,655
0
29,493
12
2,020
4.391
35
5,150
566
72
41,740
[tonslyr]
2009
Base
NH3
0
64,346
15
752
426
31
6,012
882
2,224
7i,S88
0
29,674
14
2,020
4.391
40
6,417
1,408
72
44,036
[tons/yr]
2014
Base
NH3
0
69.023
16
752
426
33
6.512
1.044
2.224
80,030
0
29.804
15
2.020
4.391
44
7,561
1,414
72
45,321
[tons/yr]
2020
Base
NH3
0
74633
17
752
426
37
7,075
1.034
2.224
86,198
0
29.958
17
2.020
4.391
49
8.994
1.529
72
47,030
[tons/yr]
2030
Base
NH3
0
74.633
20
752
426
41
7.776
1.034
2.224
86,906
0
29.958
20
2.020
4.391
57
11,238
1.529
72
49.28S
[tonslyr]
2002
PM10
100,288
0
2,236
16.251
27.785
3,195
4,223
26,138
19.710
199,826
121,322
0
2,617
43.005
12,456
4,174
4,021
9,551
5.723
202,868
[tonslyr]
2009
Base
PM10
100.332
0
2,248
16.251
27.265
2.625
3,019
16.214
18,644
186,827
121.322
0
2,572
43.005
12,253
3,425
3,441
7.943
5.574
199,535
[tonslyr]
2014
Base
PH10
100.384
0
2.281
16,251
26,944
2,144
2,453
19,576
18,644
188,657
121,322
0
2,602
43,005
12,109
2,855
3.002
7,887
5.574
198,356
[tonslyr]
2020
Base
PM10
100,402
0
2,379
16,251
26,524
1,552
2,298
39,221
18,644
207,271
121,322
0
2.649
43,005
11,935
2,086
3,112
8,403
5.574
198,087
[tons/yr]
2030
Base
PM10
100,402
0
2,663
_J6i251
26,524
1,270
2,447
39,221
18,644
207,422
121.322
0
2,688
43,005
11,935
1,649
3,839
8,403
5,574
198,416
[tons/yr]
2002
PM2 5
33,476
0
1,878
13.938
23,973
3,044
3,117
22,612
13,647
115,685
19,626
0
2,060
37,151
8,596
3.993
2,951
7,565
3.044
84,987
[tons/yr]
2009
Base
PH2 5
33.491
D
1.883
13.938
23483
2.491
1.943
12.147
13.017
102,393
19.626
D
2.017
37.151
8.396
3.264
2.225
5.936
2.963
81,577
[tons/yr]
2014
Base
PM2 5
33.501
D
1.896
13.938
23133
2028
1,357
15,231
13,017
104,151
19,626
D
2,036
37.151
8.253
2.712
1.656
5.897
2.963
80,291
[tons/yr]
2020
Base
PM2 5
33.514
0
1.963
13938
22.712
1.460
1.129
34.364
13.017
122,098
19.626
0
2.071
37.151
8,081
1.967
1.532
6.467
2.963
79,858
[tons/yr]
2030
Base
PM2 5
33.514
0
2.202
13.938
22712
1.184
1,142
34.364
13.017
122,074
19.626
0
2.098
37.151
8.081
1.538
1,812
6.467
2.S63
79,736
o
to
-------
to
to
State
Arkansas
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
jtnoriipm
Arkansas Total
California
rfdust
ag
aim
avelire
lonpt
lonroad
on road
ptipm
ptnonpm
California Total
Colorado
afdust
ag
aim
avefiie
nonp!
lanroad
on road
ptipm
Jtnoriipm
Colorado Total
[lonsfyr]
2002
S02
0
0
4,646
726
27.260
2,762
3,078
70,754
19,032
128,262
0
0
40,887
6,735
77,672
1,015
4,786
1,018
41,761
173,874
0
0
1,224
1,719
6,460
3.545
4,146
92,562
5.331
114,989
[tonslyr]
2009
Base
S02
0
0
3,013
728
27,258
468
405
97,797
18,999
148,667
0
0
27,491
6,735
77,641
455
1,855
6,577
41,477
162,231
0
0
315
1,719
6,459
611
614
73,481
5,322
88,522
[tonsfyr]
20U
Base
S02
0
0
2.706
728
27,256
33
346
39,079
18,999
89,149
0
0
19,337
6,735
77,619
516
2,002
6,259
41,089
153,557
0
0
137
1,719
6,458
43
550
55,605
5,322
69,834
[tonsfyr]
2020
Base
S02
0
0
3.067
728
27,254
35
387
39,582
18,999
90,050
0
0
14,663
6,735
77,592
597
2.189
6,204
41,128
149,308
0
0
156
1,719
6,457
45
645
49.733
5,322
64,078
[tonsfyr]
2030
Base
S02
0
0
4,454
728
27,254
38
463
39.582
18,999
91,517
D
0
21.107
6,735
77,582
780
2.502
6,204
41,128
156,058
0
0
175
1,719
6.457
50
616
49.733
5.322
64,274
[tonsfyr]
2002
NH3
0
110,954
19
556
7,386
23
3,001
346
1,255
123,540
0
152,308
180
5,117
14,756
161
37,468
1,380
3,367
214,738
0
62,907
5
1,299
71
31
4,408
453
86
69,260
[tonsfyr]
2009
Base
NH3
0
118,597
22
556
7,386
26
3.231
822
1,290
131,731
0
156,311
193
5.117
14,665
181
27,409
3,307
3,367
210,550
0
63,846
5
1,299
71
36
5.220
529
86
71,091
[tonsfyr]
2014
Base
NH3
0
124,059
24
556
7.386
28
3,526
856
1,316
137,754
0
159,176
203
5.117
14.600
198
22.337
4.213
3.367
209,211
0
64.517
6
1.299
71
39
6.002
582
86
72,603
[tonsfyr]
2020
Base
NH3
0
130.611
26
556
7.386
31
3.871
843
1,346
144,672
0
162,610
215
5.117
14.520
220
19.206
5.522
3.367
210,777
0
65.320
6
1.299
71
44
6.976
535
86
74,337
[tonsfyr]
2030
Base
NH3
0
130.611
29
556
7.386
35
4.529
843
1.348
145,337
0
162,610
237
5.117
14.520
255
17.590
5.522
3,367
209,217
0
65.320
8
1.299
71
50
8.754
535
86
76,123
[tonsfyr]
2002
PM10
92,523
0
1,346
12,027
24,094
3,229
2,202
2,004
14,101
151,529
196,231
0
10,124
113,231
90.509
18,590
23.103
1.905
28,854
480,546
110,878
0
606
28,019
15.059
3,909
3,216
5,446
17.366
184,499
[tonsfyr]
2009
Base
PM10
92.523
0
1.328
12,027
23,911
2,517
1,613
4,182
13.812
151,915
196.525
0
9.970
113.231
88.498
16,219
23,613
469
25,998
_474i524
110.878
0
595
28.019
14.719
3,163
2,518
5.502
16.676
182,071
[tonsfyr]
2014
Base
PH10
92.523
0
1,320
12,027
23,780
1,951
1,338
5,463
13,812
152,215
196,736
0
9,726
113,231
87,062
14.290
22.170
512
25,998
469,725
110,878
0
590
28,019
14,475
2,602
2,179
5,712
16,677
181,134
[tonsfyr]
2020
Base
PM10
92.524
0
1,340
12,027
23,622
1,314
1,280
5.820
13,812
151,740
196,989
0
9,692
113,231
85,338
12,637
21,262
574
25,998
465,721
110,878
0
590
28,019
14,183
1.901
2,191
7,003
16,879
181,445
[tons/yr]
2030
Base
PM10
92,524
0
1.452
12,027
23.622
890
1.463
5.820
13,812
151,611
196.989
0
10.786
113,231
85,336
15,177
22.604
574
25,998
_J7PJ99
110.878
0
582
28,019
14,183
1,452
2,665
7.003
16.679
181,461
[tonsfyr]
2002
PH2 5
24,639
0
1.243
10,315
23,062
3,097
1,612
1.750
9.593
75,312
47,562
0
9.534
97,301
73.873
16,334
12.395
1.876
16,655
JTSjSJO
25,559
0
553
24,054
13,545
3,746
2.357
4.444
8.922
83,181
[tonsfyr]
2009
Base
PH2 5
24.639
0
1,225
10.315
22.876
2.403
1.034
3.413
9,473
75,381
47.615
0
9381
97.301
71.936
13.989
12,463
348
1 6.284
_25M1?
25.559
0
539
24.054
1 3.204
3.016
1.600
4,713
8.527
81,213
[lonsfyr]
2014
Base
PH2 5
24.639
0
1,217
10.315
22,747
1.858
739
4.656
9,473
75,645
47653
0
9148
97.301
70555
12.025
11.365
374
16,284
264,705
25,559
0
531
24,054
12.961
2.474
1.189
4.932
8.528
80,22 S
[tonsfyr]
2020
Base
PM2 5
24.639
0
1.235
10.315
22.590
1.246
631
4.995
9,473
75,125
47,698
0
9.113
97.301
68896
10,216
10.559
412
16,284
260,479
25,559
0
526
24.054
12,669
1.796
1.069
6.018
8.529
80,219
[tonsfyr]
2030
Base
PM2 5
24.639
0
1,339
10.315
22,590
834
664
4.995
9.473
74,870
47.698
0
10.148
97.301
68896
11,712
11.026
412
16,284
263,477
25,559
0
514
24.054
12,669
1.356
1.241
6.018
8.529
79,940
-------
o
to
State
Connecticut
Sector
afdyst
ag
aim
avefire
nonpt
nonroad
an road
ptipm
ptnonipm
Connecticut Total
Delaware
afdust
V
aim
gvefire
Donpt
lonroad
on road
jtipm
ptnonipm
Delaware Total
District of
Columbia
afdust
aim
svefite
lonpi
lonroad
an road
pSpm
stnoflipm
District of Columbia Total
[tonslyr]
2002
S02
0
0
778
18,455
1.382
1,667
13,689
2,338
38,313
0
0
3,470
6
5.859
471
556
33.104
41.342
84,810
0
45
0
1.559
343
271
1,432
625
4,275
[tonsJyr]
2009
Base
S02
0
0
751
18,447
249
363
6,200
2,330
28,343
0
0
3,243
6
5,858
83
114
23,047
11,538
43,889
0
9
0
1,559
59
46
0
625
2,299
[tonsfyr]
2014
Base
S02
0
0
764
18,441
27
340
5,795
2,330
27,701
0
a
3,234
6
5,857
7
101
21,650
11,538
42,394
0
2
0
1,559
3
42
0
625
2,230
[tonsfyr]
2020
Base
S02
0
0
874
18,434
29
36B
12,473
2,330
34,512
0
0
3,664
6
5,857
B
112
20.757
11,538
41,941
0
2
0
1,559
3
47
0
625
2,236
[ton sly r]
2030
Base
S02
0
0
1,268
18,434
33
413
12,473
2,330
34,955
0
0
5,371
6
5,657
9
124
20,757
11,538
43,662
0
3
0
1,559
3
52
0
625
2,242
[tonsfyr]
2002
NH3
0
4,029
1
3
1,438
17
3,257
182
91
9,017
0
12,536
0
5
279
5
903
30
161
13,918
0
0
0
13
2
398
8
426
[ton sly t]
2009
Base
NH3
0
4,232
1
3
1,407
18
3,515
245
91
9,512
0
14,172
0
5
275
6
994
119
152
15,722
0
0
0
13
3
423
0
143
[tonsfyr]
2014
Base
NH3
0
4.377
1
3
1,385
20
3.779
247
91
9,902
0
15,342
0
5
272
6
1,078
148
152
17,003
0
0
0
13
3
457
0
477
[tonsfyr]
2020
Base
NH3
0
4.551
1
3
1,358
22
4.042
244
91
10,311
0
1S.745
(1
5
268
7
1.178
132
152
18,487
D
0
0
13
3
508
0
529
[tonsfyr]
2030
Base
NH3
0
4.551
1
3
1,358
25
4.510
244
91
10,783
0
16.745
1
5
268
8
1.294
132
152
18,604
0
0
0
13
4
567
0
589
[tonslyr]
2002
PM10
12,528
0
231
65
10,716
1,702
1,610
742
882
28,476
6,258
0
452
102
2,007
560
572
1.969
1,041
12,961
2,255
13
0
489
296
219
30
98
3,402
[tonsfyr]
2009
Base
PM10
12.528
0
237
65
10.152
1.408
1.318
829
880
27,415
6.258
0
511
102
1.933
446
417
6,352
904
16,923
2.255
11
0
481
226
155
0
21
3,143
[tonslyr]
2014
Base
PM10
12,528
0
241
65
9,749
1.195
1.182
3,992
880
29,833
6,258
0
561
102
1,879
367
359
6,466
904
16,896
2,255
11
0
476
176
141
0
21
3,080
[tonsfyr]
2020
Base
PM10
12,528
0
253
65
9,266
933
1,165
11,942
880
37,032
6.258
0
655
102
1.815
275
355
6,610
904
16,974
2,255
11
0
469
109
144
0
21
3,008
[tonsfyr]
2030
Base
PH10
12.528
0
301
65
9,266
823
1,276
11,942
880
37,081
6,258
0
907
102
1,815
224
383
6,610
904
17,203
2.255
10
0
469
64
158
0
21
2,977
[tonsfyr]
2002
PM2 5
2,725
0
210
56
10,448
1,619
1.067
510
691
17,323
863
0
401
87
1.826
534
406
1,693
783
6,594
411
13
0
427
238
150
22
43
1,353
[tonsfyr]
2009
Base
PM2 5
2.725
0
215
56
9,882
1.335
766
791
690
16,460
863
0
454
37
1.751
424
253
3.033
576
7,492
411
11
0
419
218
88
0
12
1,159
[tonsfyr]
2014
Base
PM2 5
2.725
0
218
56
9,479
1.130
611)
3.891
69D
18,798
863
[1
499
37
1.698
348
138
3.217
575
7,478
411
11
0
413
170
72
0
12
1,088
[tonsfyr]
2020
Base
PM2 5
2.725
0
227
56
8,996
876
557
11.696
690
25,823
863
0
583
87
1.634
259
171
3.421
576
7,596
411
10
0
406
104
68
0
12
1,011
[tonsfyr]
2030
Base
PM2 5
2.725
0
271
56
8.S96
766
592
11.696
690
25,792
863
0
810
67
1.634
209
178
3.421
576
7,779
411
10
0
406
60
74
0
12
973
-------
o
to
State
Florida
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
jtnoriipm
Florida Total
Georgia
rfdust
ag
aim
avefire
lonpt
lonroad
on read
ptipm
ptnonpm
Georgia Total
Idaho
afdust
ag
aim
avefiie
nonpt
lanroad
on road
ptipm
Jtnoriipm
Idaho Total
[lonsfyr]
2002
S02
0
0
6,892
7,016
70.489
12,540
21,410
473,836
57,060
649,045
0
0
3,247
2,010
56,830
5,674
11,236
512,883
56,203
648,183
0
0
645
3,845
2,915
1,616
1,310
0
17.59?
27,928
[tonslyr]
2009
Base
S02
0
a
5,893
7,018
70,484
2,110
2,358
155.118
57,024
300,004
0
0
1,969
2,010
56,821
975
1,525
212,600
56,188
332,087
0
0
168
3,845
2,913
275
205
0
17,597
25,003
[tonsfyr]
2014
Base
S02
0
0
5.643
7,018
70.480
174
2.152
154.529
57.024
297,220
0
0
1.607
2,010
56,615
76
1,308
210,100
56,188
328,304
0
0
70
3,645
2.912
19
179
192
17,597
24,815
[tonsfyr]
2020
Base
S02
0
0
6,685
7,018
70.475
184
2.477
92.816
57,024
236,679
0
0
2.101
2,010
56,607
80
1,503
146,083
56,188
264,772
0
0
74
3,645
2.910
20
203
30B
17.597
24,958
[tonsfyr]
2030
Base
S02
0
0
9.612
7,018
70,475
207
2.886
S2.B16
57,024
240,037
0
0
2,660
2,010
56,607
90
1.609
146,083
56,188
265,847
0
0
81
3,645
2.910
22
246
308
17.597
25,009
[tonslyr]
2002
NH3
0
37.099
11
5,366
446
125
16,267
5,013
3,030
69,339
0
80.733
12
1,299
60
52
10,642
593
4,571
97,962
0
62,376
3
2,656
1,634
14
1,418
0
1.074
69,425
[tonsfyr]
2009
Base
NH3
0
36,533
12
5,366
446
139
21,200
3,571
3,030
72,348
0
39,607
13
1,299
60
59
12,213
840
4,531
108,572
0
62.655
4
2,856
1,834
16
1,645
43
1,074
69,977
[tonsfyr]
2014
Base
NH3
0
39,643
12
5.366
446
151
23,969
4,445
3.030
77,064
0
95,949
14
1,299
60
84
13,708
989
4,538
116,673
0
62,855
4
2,856
1.634
18
1853
47
1,074
70,390
[tonsfyr]
2020
Base
NH3
0
40,914
13
5,386
448
166
27.234
4,318
3,030
81,539
0
103.556
16
1.299
60
71
15,548
971
4,597
126,119
0
63,094
4
2,856
1,634
20
2.033
49
1.074
70,864
[tonsfyr]
2030
Base
NH3
0
40,914
15
5.366
448
192
31,607
4,318
3.030
85,889
0
103.556
18
1.299
60
32
18,473
971
4,597
129,057
0
63094
5
2.856
1.634
22
2.514
49
1.074
71,298
[tonsfyr]
2002
PM1Q
145,566
0
2,391
115,996
41,371
13,637
12,433
32,299
32,193
395,887
181.397
0
1.332
23,079
46,751
6,136
8,539
31,663
21,224
325,121
139,528
0
471
61,433
56,403
1,973
1.066
1
4,569
265,445
[tonsfyr]
2009
Base
PM10
145.655
0
2.428
115.996
40935
10.831
9,367
22,325
31,355
379,190
181.397
0
1.320
28.079
46054
5.022
6,232
24.007
20,585
312,696
139,669
0
453
61,433
56.235
1,582
821
2
4,409
264,604
[tonsfyr]
2014
Base
PH10
145,718
0
2,469
115,996
40.623
8.952
8,217
22.166
31,655
375,795
181,397
0
1,327
28,079
45,556
4,150
5,184
23,976
20,585
310,254
139,770
0
449
61,433
56,116
1.253
691
146
4,409
264,267
[tonsfyr]
2020
Base
PM10
145.794
0
2,575
115,996
40,248
6,680
8,251
29,274
31,655
380,473
181,397
0
1,363
28,078
44.958
3,021
5,043
35,951
20,585
320,398
139,891
0
449
61,433
55,972
871
669
242
4,409
263,935
[tonsfyr]
2030
Base
PM10
145,794
0
2.936
115,996
40,248
5,412
9.258
29.274
31,655
380,573
181.397
0
1.457
28,079
44,956
2,412
5.785
35.951
20,585
320,626
139.891
0
445
61,433
55.972
600
772
242
4.409
263,763
[tonsfyr]
2002
PH2 5
28,017
0
2.175
99.434
38.847
13,001
9,041
28.293
23,604
242,462
59,910
0
1.135
24.032
41.847
5,867
6.366
25.407
15,692
J80J08
28,351
0
447
52.806
27.367
1,839
735
1
2.528
114,175
[tonsfyr]
2009
Base
PH2 5
28.035
0
2,199
99.434
38.410
10,302
5,803
14,447
23,430
222,108
59,910
0
1.122
24.032
41.150
4.733
4,025
17.435
15,426
167,984
28,376
0
427
52.806
27,199
1,506
527
1
2,441
11 3,286
[lonsfyr]
2014
Base
PH2 5
28.047
0
2,223
99,434
38.093
8.439
4.407
14,405
23,430
218,584
59910
0
1.120
24032
40652
3,940
2,868
17.435
15,426
165,482
28,393
0
421
52,808
27.030
1,139
330
128
2,441
112,841
[tonsfyr]
2020
Base
PM2 5
28.063
0
2,307
99.484
37,724
6.287
4.006
20.220
23.430
221,520
59910
0
1.142
24.082
40054
2847
2.477
29.266
15.426
175,206
28.414
0
419
52.808
26,936
823
328
214
2.441
112,383
[tonsfyr]
2030
Base
PM2 5
28.063
0
2.636
99,484
37,724
5.040
4,316
20.220
23.430
220,912
59.910
0
1.218
24.062
40054
2,250
2,699
29.266
15,426
174,907
28.414
0
413
52,808
26,936
560
360
214
2.441
112,147
-------
o
to
State
Illinois
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
jtnonipm
Illinois Total
Indiana
rfdust
ag
aim
avefire
lonpt
lonroad
on road
ptipm
ptnonpm
Indiana Total
Iowa
aMust
ag
aim
avefiie
nonp!
lanroad
on road
ptipm
Jtnoriipm
Iowa Total
[lonsfyr]
2002
S02
0
0
11,979
20
5,395
10,913
8,514
366,157
138,126
541,103
0
0
5,540
24
59.775
5,981
8,564
785,603
97,442
962,93d
0
0
2,787
25
19,832
6,248
2,999
133,047
51.329
216,267
[tonslyr]
2009
Base
S02
0
a
7,122
20
5,387
1,933
1,449
294.394
110,325
420,630
0
0
3,648
24
59,770
1,042
974
377,625
97,349
540,433
0
0
1,161
25
19,824
1,063
427
110,609
51,329
184,437
[tonsfyr]
2014
Base
S02
0
0
6,356
20
5,382
126
1,220
270,363
110,401
393,869
0
0
3,419
24
59,765
73
811
375,790
97,349
537,231
0
0
860
25
19,818
59
360
115,301
51,329
187,751
[tonsfyr]
2020
Base
S02
0
0
7,425
20
5,376
132
1,373
272,107
110,500
396,933
0
0
4,024
24
59,760
77
914
336,644
97,349
498,791
0
0
1,007
25
19,811
61
404
116,360
51,329
188,996
[tonsfyr]
2030
Base
S02
0
0
10,872
20
5,376
148
1,607
272,107
110,500
400,630
D
0
5,873
24
59,760
86
1,087
336,644
97,349
500,823
0
0
1,454
25
19,811
66
4S6
116,360
51,329
189,540
[tonslyr]
2002
NH3
0
106,635
45
15
1,631
88
10.654
174
694
119,986
0
90,815
19
19
4,214
48
7,343
530
3,144
106,183
0
245,776
8
19
7,404
47
3,091
391
4,663
261,401
[tonsfyr]
2009
Base
NH3
0
108,164
52
15
1,631
102
11,429
962
633
123,039
0
93,856
22
19
4,214
55
7,896
1,076
3,144
110,283
0
252.909
10
19
7,404
53
3,367
428
4,563
268,352
[tonsfyr]
2014
Base
NH3
0
109,220
56
15
1,631
112
12.465
1,095
633
125,276
0
96,027
24
19
4.214
60
8.618
1.132
3.144
113,238
0
257.995
10
19
7.404
58
3,659
433
4.663
274,292
[tonsfyr]
2020
Base
NH3
0
110.436
62
15
1.631
124
13.748
1.149
633
127,898
0
98.632
26
19
4.214
67
9.482
1.198
3,144
115,784
0
264.101
11
19
7.404
64
4.002
519
4,663
280,784
[tonsfyr]
2030
Base
NH3
0
110.436
72
15
1.631
145
15.809
1.149
633
129,989
0
98.632
31
19
4.214
73
10.975
1.198
3.144
118,291
0
264.101
13
19
7.404
74
4.731
519
4.663
281,574
[tonsfyr]
2002
PM10
444,909
0
3,556
323
16,972
11,316
7,772
19,147
30,111
534,106
345,635
0
1.719
401
60,255
6,039
5,518
40,884
25,808
486,257
341,542
0
1,021
407
12,833
7,210
2.355
9,907
13,439
388,712
[tonsfyr]
2009
Base
PM10
444.909
0
3.431
323
16,262
8,576
5,916
13.373
28,118
520,913
345.635
0
1.710
401
59.765
4.593
4,064
40.455
25,083
481,705
341,542
0
988
407
12.192
5.048
1,771
7.661
11,162
380,789
[tonsfyr]
2014
Base
PH10
444.909
0
3,343
323
15,755
8,671
4,888
14.852
28,118
518,859
345,635
0
1,707
401
59,414
3,562
3.353
40,305
25,083
479,460
341.542
0
961
407
11,734
3,805
1,450
8,074
11,162
379,135
[tonsfyr]
2020
Base
PM10
444,909
0
3,329
323
15,147
4,450
4,615
37.527
28,118
538,417
345,635
0
1,746
401
58,994
2,367
3,195
44,864
25,083
482,284
341,542
0
949
407
11,185
2,539
1,365
8,386
11.162
377,535
[tonsfyr]
2030
Base
PM10
444,909
0
3.561
323
15,147
3,103
5,125
37.527
28,118
537,812
345,635
0
1.909
401
58,994
1,691
3.622
44,864
25,083
482,198
341.542
0
961
407
11,185
1,419
1,585
8.386
11.182
376,647
[tonsfyr]
2002
PH2 5
88,100
0
3,351
277
15,181
10,881
5,700
14,783
15,136
153,409
65,707
0
1,561
344
32.611
5,803
4.081
33,805
15,085
J58J96
57,643
0
997
349
11,476
6,949
1,726
8,904
7,572
95,615
[tonsfyr]
2009
Base
PH2 5
88.100
0
3.231
277
14,471
8.220
3.849
11.017
13,619
142,786
65.707
0
1.552
344
32.120
4.398
2,633
27.477
14,751
J4jyğ83
57,643
0
961
349
10,835
4,859
1.153
5.985
6.395
88,180
[lonsfyr]
2014
Base
PH2 5
88.100
0
3.144
277
13,964
6.375
2.751
12.328
13,619
140,559
65.707
0
1.544
344
31.770
3.401
1.872
27.287
14,751
146,677
57,643
0
934
349
10.378
3.654
815
6.288
6.395
86,455
[tonsfyr]
2020
Base
PM2 5
38.100
0
3,125
277
13,356
4.224
2.291
34.584
13.619
159,578
65.707
0
1.576
344
31350
2,246
1.581
31624
14.751
149,179
57.643
0
919
349
9.828
2.427
677
6,513
6.395
84,751
[tonsfyr]
2030
Base
PM2 5
88.100
0
3,340
277
13.356
2.912
2.410
34.584
13.619
158,599
65.707
0
1.727
344
31.350
1.588
1.693
31.624
14.751
148,783
57,643
0
926
349
9.828
1.340
741
6.513
6.395
83,736
-------
o
to
State
Kansas
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
jtnonipm
Kansas Total
Kentucky
rfdust
ag
aim
avefire
lonpt
lonroad
on road
ptipm
ptnonpm
Kentucky Total
Louisiana
afdust
ag
aim
avefiie
nonp!
lanroad
on road
ptipm
Jtnoriipm
Louisiana Total
[lonsfyr]
2002
S02
0
0
2,895
103
38.381
4,858
2,893
129.827
10,793
187,750
0
0
10.096
364
34,229
3,008
5,554
486,499
34.482
574,231)
0
0
32,796
892
2,378
2,834
4,409
108,106
177.50?
328,922
[tonslyr]
2009
Base
S02
0
0
603
103
36.378
828
379
77,367
10,793
126,150
0
0
8,624
364
34,219
511
657
291,244
28,587
364,205
0
0
30,726
892
2,374
473
566
89,892
177,507
302,432
[tonsfyr]
2014
Base
S02
0
0
131
103
36,375
41
312
48,068
10,793
95,823
0
0
8,713
364
34,211
38
529
244,599
28,587
317,041
0
0
31,131
892
2,372
42
469
86,289
177,507
298,703
[tonsfyr]
2020
Base
S02
0
0
152
103
36,373
42
34B
52,641
10,793
100,152
0
0
10,226
364
34,203
4D
587
223,737
28,587
297,744
0
0
35,876
892
2,370
45
526
87,803
177,507
305,01 E
[tonsfyr]
2030
Base
S02
0
0
194
103
36,373
46
431
52,641
10,783
100,580
D
0
14,957
364
34,203
44
662
223,737
28,587
302,553
0
0
52,721
892
2,370
49
642
87,803
177.507
321,985
[tonslyr]
2002
NH3
0
97,384
11
79
12,467
32
2,870
421
80,100
173,364
0
50,821
15
278
231
25
4,824
919
1,672
58,787
0
35,159
42
682
23,169
29
4,364
1,399
7,678
72,722
[tonsfyr]
2009
Base
NH3
0
97,802
12
79
12,467
37
3.081
377
60.994
171,849
0
53,024
17
278
231
29
5,161
703
1,872
61,115
0
36,915
46
682
23,169
32
4,631
690
7,879
74,044
[tonsfyr]
2014
Base
NH3
0
98.101
13
79
12,467
41
3,343
393
61,633
176,070
0
54,598
18
278
231
32
5,604
741
1,672
63,174
0
36,171
48
682
23.189
34
5.037
821
7.880
75,843
[tonsfyr]
2020
Base
NH3
0
98.458
15
79
12.467
46
3.657
454
62.398
177,574
0
56.486
19
278
231
35
6.121
784
1,672
65,626
0
39.676
51
682
23.169
37
5.547
960
7,881
78,004
[tonsfyr]
2030
Base
NH3
0
98458
17
79
12.467
53
4.421
454
62.398
178,347
0
56,486
22
273
231
40
6,778
784
1.672
66,289
0
39676
56
682
23.169
42
6.620
960
7,881
79,087
[tonsfyr]
2002
PM1Q
455,984
0
1,237
1,711
108,571
5,360
2,200
7,246
9,430
591,738
99,481
0
4,285
5,010
23.283
3,376
3,816
22,342
18,375
178,967
81,493
0
7.000
14,746
19,038
3,331
3,379
7.487
28,722
165,196
[tonsfyr]
2009
Base
PM10
455.984
0
1,185
1,711
108,281
3.794
1,576
7.037
8,890
588,460
99.481
0
4.409
6,010
22.460
2.667
2,731
24.029
15,729
177,516
81,493
0
7.145
14,746
18.790
2.634
2,355
3.828
28,429
159,421
[tonsfyr]
2014
Base
PH10
455.984
0
1,152
1,711
108.075
2,817
1,279
7,178
8,890
587,087
99,481
0
4,62?
6,010
21.872
2,108
2.200
25,780
15,729
177,807
81.493
0
7.072
14,746
18,612
2,091
1,912
3,905
28,429
158,260
[tonsfyr]
2020
Base
PM10
455.984
0
1,127
1,711
107,828
1,838
1,206
7.749
8,890
588,333
99,481
0
5.007
6,010
21.167
1,452
2,048
37,131
15,729
188,025
81,493
0
7,302
14,746
18,399
1.476
1,819
4,612
28.429
158,276
[tons/yr]
2030
Base
PM10
455,984
0
1.087
1,711
107.626
972
1,427
7.749
8,890
585,647
99,481
0
5,730
6,010
21,167
1,052
2,204
37.131
15,729
_J88!503
81.493
0
9,023
14,746
18,399
1,138
2,125
4.612
28.429
159,965
[tonsfyr]
2002
PH2 5
74.515
0
1.207
1.468
83,174
5,179
1,629
5.912
4,941
178,025
23,529
0
3.625
5,155
18.590
3,236
2,842
20,004
9,937
_86J9J9
20,962
0
6,819
12.647
17.862
3,174
2.506
5,990
21.082
91,043
[tonsfyr]
2009
Base
PH2 5
74.515
0
1,155
1.468
82.885
3.660
1.019
5.723
4,718
175,143
23529
0
3.710
5.155
17.767
2.547
1.776
17.733
9.566
_81283
20.962
0
6.932
12.647
17.614
2.500
1.520
3.292
20.899
86,365
[lonsfyr]
2014
Base
PH2 5
74.515
D
1,120
1,468
82,678
2.712
710
5.953
4,718
173,871
23529
0
3850
5.155
17.180
2.00B
1,225
19.050
9,566
81,563
20,962
0
6827
12,647
17.436
1.930
1.058
3.370
20,899
85,179
[tonsfyr]
2020
Base
PM2 5
74.515
0
1,093
1.468
32.430
1.764
594
6.244
4,718
172,825
23529
0
4.123
5.155
16.474
1.377
1.004
30.135
9,566
91,364
20.962
0
7.007
12.647
17.223
1.390
894
4.021
20.899
85,042
[tonsfyr]
2030
Base
PM2 5
74.515
0
1,051
1.468
32,430
922
665
6.244
4,718
172,013
23,529
0
4.723
5.155
16.474
887
1.027
30.135
9,566
91,597
20,962
0
8.606
12,647
17,223
1,061
890
4,021
20.899
86,409
-------
o
to
State
Maine
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
ptnonipm
Maine Total
Maryland
rfdust
ag
aim
avefire
lonpt
lonroad
on road
ptipm
ptnonipm
Maryland Total
Massachusetts
afdust
ag
aim
avefife
nonp!
lanroad
on road
ptipm
Jtnoriipm
Massachusetts Total
[lonsfyr]
2002
S02
0
0
195
150
9,969
766
1,122
2,137
20,778
35,116
0
0
5,707
32
40,364
2,577
3,966
256.761
34,255
344,162
0
0
2,519
93
25,261
2,385
3,172
91,888
14,079
139,397
[tonsfyr]
2009
Base
S02
0
0
155
150
9,956
132
198
34.757
19,390
64,738
0
0
4,007
32
40,857
452
881
50,757
34,061
130,846
0
0
1,819
93
25,248
429
678
12,991
13,814
55,073
[tonsfyr]
2014
Base
S02
0
0
131
150
9,947
18
160
33,238
19,390
63,033
0
0
3,140
32
40,652
41
632
47,642
34,061
126,399
0
0
1,681
93
25,239
46
593
10,825
13,814
52,291
[tonsfyr]
2020
Base
S02
0
0
126
150
9,936
19
176
21,911
19,390
51,708
0
0
2,825
32
40,846
43
709
53,433
34,061
131,949
0
0
1,835
93
25,228
49
658
22,019
13,814
63,696
[tonsfyr]
2030
Base
S02
0
0
176
150
9,936
21
196
21,911
19,390
51,783
0
0
3,921
32
40,646
49
622
53,433
34,061
133,164
0
0
2,503
93
25,226
55
755
22,019
13.614
64,468
[tonsfyr]
2002
NH3
0
6,154
1
115
1,616
11
1,467
129
609
10,302
0
24,562
22
24
606
28
5,594
271
222
31,330
0
2,206
7
71
4,070
28
5,509
1,103
403
13,401
[tonsfyr]
2009
Base
NH3
0
6,540
1
115
1,566
13
1,574
331
343
10,533
0
26.S18
24
24
579
31
6,280
409
222
34,187
0
2.244
6
71
4,021
31
6,023
737
401
13,537
[tonsfyr]
2014
Base
NH3
0
6816
1
115
1,530
14
1,700
326
343
10,847
0
26,036
25
24
559
34
6909
460
222
36,322
0
2.269
9
71
3.936
34
6562
674
401
14,006
[tonsfyr]
2020
Base
NH3
0
7.147
1
115
1.436
15
1.841
130
343
11,129
0
29,851
27
24
535
38
7.634
495
222
33,828
0
2.299
10
71
3.944
37
7.135
601
401
14,548
[tonsfyr]
2030
Base
NH3
0
7.147
1
115
1.436
17
2024
130
343
11,313
0
29.851
30
24
535
43
8,764
495
222
39,965
0
2299
11
71
3.944
43
8.201
601
401
15,571
[tonsfyr]
2002
PM1Q
13,067
0
455
2,430
13,676
1,200
1,176
86
5,963
33,304
35,393
0
1.635
613
25.056
3,102
3,162
17.996
8,303
93,261
49,646
0
986
1,544
23,552
2,871
3,253
3,730
2,795
93,379
[tonsfyr]
2009
Base
PM10
13.067
0
494
2,480
12,996
1,117
834
454
5,243
36,685
35.393
0
1.594
613
24.553
2.537
2,506
6.995
5.477
_?MM
49,648
0
993
1,544
27,661
2,373
2,460
2.349
2.705
89,732
[tonsfyr]
2014
Base
PM10
13,067
0
524
2,480
12.368
960
66?
432
5,243
35,740
35,393
0
1,609
513
24,191
2,150
2.245
16,915
5,477
88,593
49,646
0
1,005
1,544
27,025
2,005
2,187
7,323
2.705
93,440
[tonsfyr]
2020
Base
PM10
13,067
0
585
2,480
11,613
741
622
299
5,243
34,649
35,393
0
1.645
613
23,757
1,677
2,263
23,311
5,477
94,638
49,846
0
1,033
1,544
26,261
1,541
2,205
20,948
2.705
105,884
[tons/yr]
2030
Base
PM10
13,067
0
772
2,480
11.613
632
660
299
5,243
34,766
35.393
0
1.735
613
23,757
1,440
2,571
23.811
5,477
94,798
49.646
0
1,132
1,544
26,261
1,330
2,467
20,948
2,705
106,033
[tonsfyr]
2002
PM2 5
4,134
0
405
2,127
1 3.726
1,131
876
65
4,288
26,732
7,393
0
496
531
19.764
2,954
2,194
15.722
3,759
52,813
14,810
0
874
1,324
26,536
2,732
2.286
3,224
1,842
53,610
[tonsfyr]
2009
Base
PM2 5
4.134
0
441
2.127
12.846
1.047
544
415
3,750
25,304
7.393
0
495
531
19.256
2,406
1.496
5.312
3.332
_40J225
14,810
0
876
1.324
25646
2,252
1,480
1.734
1.794
49,896
[lonsfyr]
2014
Base
PM2 5
4.134
0
467
2,127
12.218
897
372
394
3,750
24,359
7393
0
507
531
18.897
2033
1,175
14.953
3,332
48,821
14,810
0
886
1,324
25.010
1.896
1.136
6.724
1.794
53,581
[tonsfyr]
2020
Base
PM2 5
4.134
0
522
2.127
11,463
690
306
266
3,750
23,259
7,393
0
539
531
18.463
1.575
1.091
21.591
3,332
54,515
14,810
0
912
1.324
24.246
1.448
1.061
20,104
1,794
65,699
[tonsfyr]
2030
Base
PM2 5
4.134
0
694
2.127
11.463
565
307
266
3,750
23,327
7.393
0
621
531
18.463
1.340
1.200
21.591
3.332
54,470
14.810
0
1.003
1.324
24.246
1,240
1.147
20,104
1.794
65,667
-------
to
oo
State
Michigan
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
jtnonipm
Michigan Total
Minnesota
rfdust
ag
aim
avelire
lonpt
lonroad
on road
ptipm
ptnonipm
Minnesota Total
Mississippi
afdust
ag
aim
avefiie
nonp!
lanroad
on road
ptipm
Jtnoriipm
Mississippi Total
[lonsfyr]
2002
S02
0
0
14,466
91
42,066
6,367
13,508
348.377
72,631
497,505
0
0
6,592
631
14,747
6,525
2,816
102,152
27,263
160,725
0
0
9,163
1,051
6,796
2,119
3,591
67,593
36.519
126,831
[tonslyr]
2009
Base
S02
0
a
15,415
91
42,066
1,063
1,362
228,216
71,976
360,190
0
0
5,024
631
14,737
1,138
729
57.217
23,844
103,320
0
0
8,214
1,051
6,790
356
501
51,938
29,914
98,763
[tonsfyr]
20U
Base
S02
0
0
17.203
91
42,066
117
1,106
245,203
71,976
377,761
0
0
4,626
631
14,730
84
616
60,954
23,666
105,707
0
0
8,487
1,051
6,786
29
382
50,434
29.914
97,082
[tonsfyr]
2020
Base
S02
0
0
21.207
91
42,066
125
1,226
243,651
71,976
380,341
0
0
5,562
631
14,721
8B
688
64,060
23,695
109,644
0
0
10,082
1,051
6,781
30
429
56,664
29.914
10i,951
[tonsfyr]
2030
Base
S02
0
0
31,136
91
42,066
140
1,404
243,651
71,976
390,463
D
0
6,164
631
14,721
87
779
64.060
23,695
112,346
0
0
14,622
1,051
6,781
33
505
56,664
29.914
109,771
[tonslyr]
2002
NH3
0
55,273
5
69
429
78
9.613
286
952
66,906
0
134,630
12
482
1.226
59
5,362
69
27,525
169,566
0
56,575
18
604
196
19
3,606
456
1,414
65,088
[tonsfyr]
2009
Base
NH3
0
56,151
6
69
429
88
10,307
771
946
68,767
0
136,892
14
482
1,226
68
5,827
345
26,560
173,114
0
63,939
20
804
196
22
3,866
327
852
70,024
[tons/yr]
2014
Base
NH3
0
56,776
6
69
429
94
11,090
947
946
70,360
0
136,364
15
482
1.226
74
6.356
720
29,298
176,535
0
67.773
21
804
196
23
4.190
513
852
74,371
[tonsfyr]
2020
Base
NH3
0
57.531
6
69
429
102
12.003
1.122
946
72,209
0
140.130
16
482
1.226
82
6.992
770
30,186
179,883
0
72.371
22
804
196
25
4.584
497
852
79,351
[tons/yr]
2030
Base
NH3
0
57.531
7
69
429
117
13.412
1.122
946
73,633
0
140.130
18
482
1.226
93
7.902
770
30.186
180,807
0
72.371
25
804
196
29
5.203
497
852
79,976
[tonsfyr]
2002
PM10
208,643
0
2,637
1.495
30,989
8.199
7,881
13.170
17.151
290,363
432.054
0
1.665
10,427
26,966
8,097
3,790
7,437
22,425
512,863
139,219
0
3.057
17.370
17.827
2,479
3,058
3,122
19,535
205,667
[tonsfyr]
2009
Base
PM10
208.843
0
3.000
1,495
30,209
6,935
5,651
12,070
15.417
283,621
432.054
0
1.655
10,427
26.093
6.277
2.972
11.798
20,345
_5_H162J
139.251
0
3.085
17,370
17.289
1,959
2.281
2.777
18,524
202,537
[tonsfyr]
2014
Base
PM10
208.843
0
3.337
1,495
29.653
5,650
4,538
12,685
15,417
281,619
432,054
0
1,619
10,427
25.468
4,957
2.427
12,544
20,357
509,853
139,274
0
3,091
17,370
16,904
1,537
1,817
3,378
18,524
201,896
[tonsfyr]
2020
Base
PM10
208,843
0
3,929
1,495
26,985
4,058
4,220
12,451
15,417
279,398
432,054
0
1,633
10,427
24.718
3,540
2,282
13,093
20,370
508,117
139,302
0
3,203
17,370
16,443
1.055
1,695
10,168
18.524
207,760
[tons/yr]
2030
Base
PM10
208,643
0
5.386
1,495
28.985
3,298
4,579
12,451
15,417
280,454
432,054
0
1,869
10,427
24,716
2,319
2,451
13.093
20,370
_J2L2?J
139.302
0
3,736
17,370
16,443
777
1,915
10,168
18.524
208,235
[tonsfyr]
2002
PH2 5
40,894
0
2.389
1,283
24,216
7,782
5,894
10,646
10.346
103,451
79,303
0
1,643
8,943
24.496
7,759
2.740
234
4,097
129,215
36,120
0
2,666
14,897
16,769
2,370
2.309
2,625
10,019
89,778
[tons/yr]
2009
Base
PM2 5
40.894
0
2,711
1.283
23,295
S.554
3.714
8,179
9,326
95,954
79.303
0
1.627
8943
23621
5.990
1.920
9.113
3.924
J34J40
38,130
0
2,689
14,897
16.232
1.864
1.527
1,860
9,307
86,507
[lons/yr]
2014
Base
PM2 5
40,894
0
3.009
1.283
22,638
5.326
2.569
8.739
9,326
93,782
79303
0
1.587
8943
22996
4.71S
1.347
9811
3,933
132,636
38,137
0
2.680
14.897
15847
1.459
1.044
2,475
9.307
85,84?
[tons/yr]
2020
Base
PM2 5
40.894
0
3.537
1.283
21,849
3.803
2.108
8.620
9.326
91,419
79303
0
1.594
8943
22.245
3348
1.122
10230
3,942
130,728
38.146
0
2.764
14.897
15.386
996
854
9.133
9.307
91,483
[tons/yr]
2030
Base
PM2 5
40.894
0
4.848
1.263
21.849
3.068
2.154
6.620
9,326
92,042
79.303
0
1.812
8.S43
22.245
2,171
1.143
10.230
3.942
129,790
38.146
0
3.235
14.897
15.366
726
907
9,133
9,307
91,739
-------
to
VO
State
Missouri
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
>tnonipm
Missouri Total
Montana
rfdust
ag
aim
avefire
lonpt
lonroad
on read
ptipm
ptnonipm
Montana Total
Nebraska
afdust
ag
aim
avefiie
nonp!
lanroad
on road
ptipm
Jtnonipm
Nebraska Total
[lons/yr]
2002
S02
0
0
8,610
186
44,513
5,143
6,148
249,942
111,547
426,149
0
0
1.688
1,422
1,961
2,009
1,062
23,396
13,271
44,809
0
0
4,764
105
29,575
4,181
2,011
67,576
6,018
114,229
[tonslyr]
2009
Base
S02
0
a
5,550
186
44,557
885
947
235.578
110,331
398,034
0
0
340
1,422
1,957
340
145
17.149
12,239
33,592
0
0
950
105
29,572
712
286
118,340
6,018
155,982
[tonsfyr]
20U
Base
S02
0
0
5.106
186
44,545
61
797
231,753
110,331
392,779
0
0
62
1,422
1,954
17
115
21,174
9,688
34,433
0
0
163
105
29,570
32
217
41,587
6,018
77,691
[tonsfyr]
2020
Base
S02
0
0
5,930
186
44,531
65
889
241,550
110,331
403,182
0
0
72
1,422
1,951
17
128
22,221
9,688
35,499
0
0
188
105
29,568
33
242
39,642
6,018
75,796
[ton sly t]
2030
Base
S02
0
0
8,593
186
44,531
72
1,059
241,550
110,331
406,322
0
0
83
1,422
1,951
18
153
22,221
9,688
35,536
0
0
225
105
29,568
36
314
39,642
6,018
75,907
[ton sly i]
2002
NH3
0
107.023
19
142
3,830
43
6,918
705
322
119,002
0
45,890
6
946
50
14
1,032
11
265
48,214
0
166,773
18
30
3,143
27
1,874
190
421
172,525
[tonsfyr]
2009
Base
NH3
0
108,403
22
142
3,830
49
7,468
743
322
120,978
0
46,222
7
946
50
16
1,109
195
265
48,310
0
166,836
21
30
3,143
31
1,998
266
422
174,846
[tons/yr]
2014
Base
NH3
0
109,390
23
142
3.830
54
8,133
806
322
122,699
0
46,459
6
946
50
18
1.207
233
265
49,185
0
170.397
22
30
3.143
34
2.159
232
422
176,539
[tons/yr]
2020
Base
NH3
D
110.571
25
142
3,830
59
8.903
814
322
124.666
0
4S.743
9
946
50
20
1.323
247
265
49,601
0
172.205
24
30
3143
38
2.353
307
422
178,571
[tons/yr]
2030
Base
NH3
0
110,571
29
142
3830
68
10.427
814
322
126,203
0
46,743
10
948
50
22
1.549
247
265
49,832
0
172.205
29
30
3.143
44
2968
307
422
179,197
[tonslyr]
2002
PM1Q
453,324
0
2,546
3,074
32,399
5,929
5,199
3.868
14,033
530,423
188,368
0
711
19,949
5,765
2,344
908
2,404
5,538
225,987
320,650
0
1,958
1,729
12,679
4,637
1.723
1,551
1,623
346,550
[tonsfyr]
2009
Base
PM10
458,324
0
2,514
3,074
31.072
4.506
3,836
11,539
13,678
528,542
188.368
0
675
19,949
5.446
1.697
621
5.150
5.388
_2272295
320,650
0
1,862
1,729
12.447
3,257
1.253
3.082
1.614
345,893
[tonslyr]
2014
Base
PM10
458.324
0
2,491
3,074
30.123
3,519
3,144
11,541
13,678
525,895
188,368
0
659
19,949
5.218
1,261
486
8,524
5,323
229,788
320,650
0
1,791
1,729
12,281
2,391
975
3,761
1.614
345,192
[tonslyr]
2020
Base
PM10
458,324
0
2,531
3,074
26,985
2,444
2,975
12,431
13,678
524,442
188,368
0
648
19,949
4.944
821
452
9,203
5,323
229,709
320,650
0
1,732
1.729
12,082
1.530
893
4,012
1,814
344,243
[tons/yr]
2030
Base
PM10
458,324
0
2.769
3,074
28.935
1,671
3,367
12.431
13,678
S24.298
188,368
0
633
19.949
4,944
431
512
9,203
5,323
229,364
320.650
0
1,651
1,729
12.082
747
1,092
4.012
1.614
343,578
[tonsfyr]
2002
PH2 5
96,070
0
2.489
2.636
28,217
5,690
3.819
5.818
7,424
152,163
40,180
0
690
17,311
5.569
2,261
638
2.077
2,576
_ry52
50,787
0
1,942
1,483
8.655
4,434
1,312
1.191
806
70,659
[tonsfyr]
2009
Base
PM2 5
96.070
0
2,448
2.636
26.890
4.311
2.479
9.222
7.034
151,141
40.130
0
653
17.311
5249
1,632
411
3535
2,454
_T\JK
50,737
0
1,844
1.433
8.422
3.144
849
2,434
801
69,815
[lons/yr]
2014
Base
PM2 5
96.070
0
2,419
2.636
25,941
3.357
1.750
9.174
7,034
148,432
40.130
0
636
17.311
5.021
1.210
274
5.604
2,414
72,650
50,737
0
1.773
1.433
8.257
2.304
566
3.163
801
69,134
[tons/yr]
2020
Base
PM2 5
96.070
0
2,448
2636
24,803
2.320
1.470
10.080
7,084
146,911
40180
0
623
17,311
4.747
786
223
6.179
2,414
72,464
50,787
0
1,712
1.483
8.058
1.471
452
3346
801
68,109
[tons/yr]
2030
Base
PM2 5
96.070
0
2.664
2.636
24.803
1.567
1,574
10.080
7.084
146,480
40.160
0
606
17.311
4.747
407
238
6.179
2.414
72,084
50.787
0
1.630
1.483
8.058
711
515
3.346
801
67,330
-------
o
o
State
Nevada
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
ptnoriipm
Nevada Total
New Hampshire
rfdust
ag
aim
avelire
lonpt
lonroad
on road
ptipm
ptnonipm
New Hampshire Total
New Jersey
afdust
ag
aim
avetlie
nonpt
lanroad
on road
ptipm
Jtnoniprn
New Jersey Total
[lonsfyr]
2002
S02
0
0
990
1.346
12,476
2,025
360
49,276
1,342
67,815
0
0
238
38
7,408
673
880
44,009
2,570
55,815
0
0
14,587
61
10,726
3,378
3,658
51,299
9.930
93,640
[tonsfyr]
2009
Base
S02
0
a
454
1,346
12,475
358
177
31,272
1,342
47,424
0
0
218
3B
7,400
119
180
8,279
2,570
18,803
0
0
15,243
61
10,718
607
845
20,935
6,233
54,642
[tonsfyr]
20U
Base
S02
0
0
377
1,346
12.474
22
194
26,457
1,342
42,213
0
0
220
38
7,394
15
148
9.970
2,570
20,354
0
0
16,581
61
10,713
65
600
19,045
6,233
53,498
[tonsfyr]
2020
Base
S02
0
0
429
1,346
12,473
23
235
30,331
1,342
46,179
0
0
252
38
7,387
16
165
14,096
2,570
24,524
0
0
20,019
61
10,707
69
680
20,861
6,233
58,829
[ton sly t]
2030
Base
S02
0
0
479
1,346
12,473
26
278
30,331
1,342
46,276
0
0
363
38
7,387
18
193
14.096
2,570
24,664
0
0
29,344
61
10,707
78
1,016
20,861
6,233
68,300
[ton sly i]
2002
NH3
0
5,598
3
1,026
199
17
1,532
460
164
8,999
0
1,354
0
29
635
9
1,266
58
56
3,607
0
3,627
11
47
2,648
41
7,635
170
475
14,854
[tonsfyr]
2009
Base
NH3
0
5,647
3
1,026
199
20
1,964
585
164
9,608
0
1,377
0
29
803
10
1,417
259
56
3,952
0
3.953
12
47
2,648
45
8,373
537
475
16,091
[tons/yr]
2014
Base
NH3
0
5,682
3
1.026
199
22
2.339
645
164
10,080
0
1.394
0
29
780
11
1.564
258
56
4,092
0
4.044
13
47
2.648
49
9.081
592
475
16,948
[tons/yr]
2020
Base
NH3
0
5.723
4
1.026
199
24
2.795
483
164
10,413
0
1.414
0
29
753
12
1.723
231
56
4,217
0
4.152
13
47
2,648
54
9.860
566
475
17,815
[tons/yr]
2030
Base
NH3
0
5.723
4
1.026
199
28
3.309
483
164
10,937
0
1.414
0
29
753
14
1.999
231
56
4,495
0
4.152
15
47
2,648
63
11.338
566
475
19,303
[tonsfyr]
2002
PM1Q
61,096
0
445
22,169
4,389
2.115
644
3,629
3,240
97,728
6,175
0
96
622
13.351
942
969
2,632
459
25,248
16,305
0
1,786
1,009
15.987
4,162
3.805
4,835
3,131
51,020
[tonsfyr]
2009
Base
PM10
61.096
0
442
22.169
4,331
1.713
638
5.097
3.196
98,682
6.175
0
100
622
12.797
833
760
1.313
459
23,064
16.305
0
1.889
1,009
15,375
3,432
3.107
3.176
2.966
47,259
[tonsfyr]
2014
Base
PH10
61.096
0
450
22.169
4.289
1,418
664
9,268
3,196
102,548
6,175
0
103
522
12.401
707
619
3,470
459
24,556
16,305
0
1,963
1,009
14,937
2,915
2.B30
4,565
2,966
47,490
[tonsfyr]
2020
Base
PM10
61,096
0
464
22,169
4,238
1,018
750
13.192
3,196
106,123
6,175
0
108
622
11,926
541
579
8,306
459
29,216
16,305
0
2,142
1,009
14,411
2,283
2,894
6,656
2.966
48,667
[tons/yr]
2030
Base
PM10
61,096
0
473
22,169
4,238
779
876
13.192
3,196
106,017
6,175
0
123
622
11,926
463
639
8.806
459
29,213
16.305
0
2,749
1,009
14,411
1,992
3,196
6.656
2,966
49,286
[tonsfyr]
2002
PM2 5
11.371
0
419
19,018
2.735
2,027
399
3,283
1,435
40,687
2,194
0
36
534
12.658
891
714
2,305
390
_m?i
1,392
0
1,611
865
1 3,074
3,958
2.537
4,010
2,464
29,910
[tonsfyr]
2009
Base
PH2 5
11.371
0
414
19.018
2.676
1.636
347
4.072
1,420
40,954
2.194
0
87
534
12.104
784
497
1.195
390
_rrJ785
1,392
0
1.703
865
12.462
3.255
1.802
2.594
2.337
26,409
[lons/yr]
2014
Base
PH2 5
11.371
0
420
19.018
2,634
1.349
328
7846
1,420
44,385
2.194
0
89
534
11.706
664
347
3305
390
19,231
1,392
0
1.764
865
12.024
2.756
1.456
3832
2.337
26,425
[tons/yr]
2020
Base
PM2 5
11.371
0
431
19.018
2.584
961
353
11.430
1.420
47,569
2.194
0
S3
534
11.233
506
286
8540
390
23,776
1.392
0
1.922
865
11.498
2.144
1.405
5,731
2,337
27,294
[tons/yr]
2030
Base
PM2 5
11.371
0
438
19.018
2,564
726
406
11.430
1.420
47,393
2.194
0
107
534
11.233
430
298
8.540
390
23,726
1,392
0
2.478
865
11.498
1.855
1.477
5.731
2.337
27,633
-------
State
New Mexico
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
>tnonipm
New Mexico Total
New York
rfdust
ag
aim
avefire
lonpt
lonroad
on read
ptipm
ptnonpm
New York Total
North Carolina
afdust
ag
aim
avefiie
nonp!
lanroad
on road
ptipm
Jtnoriipm
North Carolina Total
[lonsfyr]
2002
S02
0
0
2,550
3,450
2,825
975
2,254
51,016
18,179
81,249
0
0
9,353
113
125,559
6,797
8.075
238,034
59,078
447,008
0
0
1,840
696
22,020
5,750
8,683
471,337
56,065
566,392
[tonslyr]
2009
Base
S02
0
0
522
3,450
2,823
167
337
26,035
16,513
49,846
0
0
7,050
113
125,618
1,209
1,710
140,744
59,043
335,486
0
0
1,044
696
22,006
989
1,147
202,194
54,306
282,382
[tonsfyr]
20U
Base
S02
0
0
106
3,450
2.821
12
280
25,999
16,513
49,182
0
0
6.128
113
125.661
125
1,595
113,236
59,043
305,901
0
0
928
696
21,996
81
963
154,504
54.306
233,475
[tonsfyr]
2020
Base
S02
0
0
122
3,450
2,820
13
319
26,112
16,513
49,349
0
0
6,277
113
125,711
134
1.756
111,224
59,043
304,257
0
0
1,084
696
21,984
86
1,095
144,734
54,306
223,985
[tonsfyr]
2030
Base
S02
0
0
140
3.450
2,820
14
370
26,112
16,513
49,420
D
0
9,061
113
125,711
151
2,170
111,224
59,043
307,473
0
0
1,516
696
21,984
87
1,250
144.734
54,306
224,583
[tonslyr]
2002
NH3
0
36,340
9
2,626
39
9
2,323
10
44
41,401
0
49,281
29
86
3,964
79
14,582
2,439
1,241
71,702
0
158,186
7
532
236
54
7,953
124
1,485
168,580
[tonsfyr]
2009
Base
NH3
0
36,476
11
2,526
39
10
2.638
392
44
42,236
0
49,900
31
86
4,158
89
15,853
1,609
1,239
72,966
0
166,029
8
532
236
60
8,925
574
1,469
179,835
[tonsfyr]
2014
Base
NH3
0
36,574
12
2,626
39
11
2,961
398
44
42,665
0
50,342
33
86
4.297
97
17084
1,279
1,239
7i,457
0
175,054
9
532
236
66
9,900
639
1,470
187,906
[tonsfyr]
2020
Base
NH3
0
36.690
13
2.626
39
12
3.339
464
44
43,226
0
50,873
35
86
4.463
107
18.456
1.279
1.239
76,538
0
183.488
10
532
236
73
11.115
670
1.470
197,594
[tonsfyr]
2030
Base
NH3
0
36.690
15
2.626
39
14
3.862
464
44
43,754
0
50,873
39
86
4.463
123
22.335
1.279
1.239
80,437
0
183.488
11
532
236
84
12.540
670
1,470
199,032
[tonsfyr]
2002
PM10
440,334
0
1,110
56,719
5,984
1,062
1,965
3,024
3,986
519,183
139,896
0
1.780
1,866
83,468
8,303
8,059
13,669
8,565
265,606
91,287
0
6.752
11,509
40.945
6,313
6,517
22,259
13,744
199,327
[tonsfyr]
2009
Base
PM10
440334
0
1,060
56.719
5818
859
1.416
5,334
3.821
515,360
139.896
0
1.826
1,866
87.036
6.8B6
7,022
8019
7,661
260,213
91.287
0
7.029
11,509
39,800
5,104
4,723
21.598
13,326
194,376
[tonsfyr]
2014
Base
PH10
440.334
0
1,029
56.719
5.896
700
1,151
5,347
3,821
514,798
139,896
0
1,865
1,866
89,585
5,734
6,174
28,290
7,661
281,073
91.287
0
7.746
11,509
38,981
4,185
3,865
22,962
13.326
193,860
[tonsfyr]
2020
Base
PM10
44D.334
0
1,004
56,719
5,552
501
1,100
5.544
3,821
514,576
139,896
0
1,965
1,866
92,644
4,304
5,596
31,952
7,661
285,884
91,287
0
8,718
11,509
38,000
3.033
3.701
29,362
13.326
198,935
[tonsfyr]
2030
Base
PM10
440,334
0
966
56,719
5,552
380
1,211
5.544
3,821
514,528
139,896
0
2.267
1,866
92,644
3,512
6.781
31,952
7,661
_286!58J)
91.287
0
9,680
11,509
38,000
2,406
4,026
29,362
13.326
199,596
[tonsfyr]
2002
PH2 5
80,348
0
1.034
48,662
5.346
1,016
1.476
5,557
3,290
146,779
29,997
0
1,394
1.601
58,823
7,909
5.547
1 2.081
4,410
J21J62
25,474
0
4,789
9,870
38,389
6,035
4.874
16,031
9,828
115,291
[tonsfyr]
2009
Base
PH2 5
80.348
0
1.033
48.662
5.178
818
926
4,673
3,179
144,818
29.997
0
1.441
1.601
62022
6.535
4,426
6.441
3,752
Jiyis
25,474
0
4.977
9.870
37.243
4,862
3.077
16.477
9,391
111,371
[lonsfyr]
2014
Base
PH2 5
80348
0
1,000
48,662
5,058
665
641
4.636
3,179
144,239
29997
0
1.494
1.601
64.307
5.427
3.491
26.168
3,752
136,236
25,474
0
5.468
9,870
36.425
3,974
2.157
17.377
9.391
110,135
[tonsfyr]
2020
Base
PM2 5
80.348
0
974
48.662
4.914
472
540
4.874
3,179
143,964
29997
0
1.600
1.601
67049
4.047
2.752
29.757
3,752
140,555
25,474
0
6.137
9870
35.443
2.861
1.827
23.517
9.391
114,520
[tonsfyr]
2030
Base
PM2 5
80.348
0
834
48.662
4.814
355
563
4.874
3,179
143,830
29.897
0
1.861
1.601
67.049
3.274
3,227
29.757
3.752
140,518
25,474
0
6.808
9,870
35,443
2,247
1.884
23,517
9.391
114,634
-------
o
to
State
North Dakota
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
jtnonipm
North Dakota Total
Ohio
rfdust
ag
aim
avelire
lonpt
lonroad
on toad
ptipm
ptnonpm
Ohio Total
Oklahoma
afdust
ag
aim
avefiie
nonp!
lanroad
on road
ptipm
Jtnoriipm
Oklahoma Total
[lonsfyr]
2002
S02
0
0
1,601
66
5,768
4,106
700
140.535
15,449
168,224
0
0
11,191
22
19,810
8,254
12.682
1,145.194
111,233
_ypjj87
0
0
1,890
469
7,542
3,093
5,344
111,841
38,495
168,673
[tonslyr]
2009
Base
S02
0
a
316
66
5,765
696
100
78,026
11,305
96,275
0
0
8,372
22
19,810
1,429
1,414
425,975
109,789
566,810
0
0
469
469
7,538
520
619
165,330
33,153
208,098
[tonsfyr]
20U
Base
S02
0
0
51
66
5.763
26
76
36,622
11,305
53,913
0
0
8.056
22
19,610
112
1,171
364,335
101,330
494,835
0
0
181
469
7,535
36
524
79,570
33.153
121,468
[lonsfyr]
2020
Base
S02
0
0
59
66
5,761
28
85
43.908
11,305
61,212
0
0
9,339
22
19.B10
119
1,305
299,575
101,330
431,i99
0
0
207
469
7,531
38
594
64,002
33,153
105,991
[tonsfyr]
2030
Base
S02
0
0
66
66
5,761
30
107
43,908
11,305
61,244
0
0
13,647
22
19,610
134
1,519
299,575
101,330
436,037
0
0
269
469
7,531
42
728
64,002
33,153
106,194
[tonslyr]
2002
NH3
0
71,302
6
50
69
25
733
378
139
72,703
0
96,711
32
17
8,527
74
10.986
74
6,370
12i,7S9
0
95,061
7
359
11.35B
26
4,626
909
3.118
115,463
[tonsfyr]
2009
Base
NH3
0
71,557
7
50
69
29
758
370
139
72,979
0
101,976
36
17
8,420
83
11,569
1,207
6,370
129,577
0
97,973
6
359
11,356
29
5,089
1,010
3.118
118,943
[tonsfyr]
2014
Base
NH3
0
71,739
7
50
69
32
804
364
139
73,204
0
104,307
39
17
8.344
91
12.466
1,292
6.370
132,925
0
100.054
B
359
11.358
32
5.637
1.186
3.118
121,752
[tonsfyr]
2020
Base
NH3
0
71,957
B
50
69
36
857
376
139
73,491
0
107.105
42
17
8.253
101
13.570
1.330
6.370
136,787
0
102.549
9
359
11.358
35
6.296
1.067
3,118
121,790
[tonsfyr]
2030
Base
NH3
0
71.957
10
50
69
41
1.037
376
139
73,678
0
107,105
49
17
8253
117
15,357
1,330
6,370
138,597
0
102.549
11
359
11.358
40
7.538
1.067
3,113
126,040
[tonsfyr]
2002
PM10
269,751
0
684
1.089
3.751
4,634
608
7,625
1.437
289,580
236,316
0
3.393
366
25.444
8,400
8,049
62,306
14,370
358,650
395,931
0
886
7.747
54,339
3,494
3.501
3,350
9.175
478,422
[tonsfyr]
2009
Base
PM10
269.751
0
652
1,089
3,539
3.177
430
5.960
1.422
286,021
236.316
0
3.424
368
24.784
6.603
5,875
40958
14,039
332,368
395,931
0
853
7.747
53.993
2.636
2.565
5.373
8.903
478,000
[tonsfyr]
2014
Base
PM10
269.751
0
630
1.989
3,387
2,282
336
6,124
1,422
285,022
236,316
0
3,437
368
24,312
5,278
4.807
37,583
13,858
325,960
395,931
0
836
7,747
53,746
2,063
2,123
6,596
8,903
477,948
[tonsfyr]
2020
Base
PM10
269.751
0
612
1,089
3,205
1,433
305
5.960
1,422
283,777
236,316
0
3,561
368
23,746
3,649
4,549
39,452
13,858
325,500
395,931
0
828
7,747
53.449
1.452
2,038
7,609
8.903
477,956
[tonsfyr]
2030
Base
PM10
269,751
0
587
1,089
3.205
603
360
5,960
1,422
282,978
236,316
0
4,095
366
23,746
2,902
5,058
39,452
13,858
325,796
395,931
0
813
7,747
53,449
1,012
2,401
7,609
8.903
477,863
[tonsfyr]
2002
PH2 5
50,500
0
670
934
3,241
4,486
455
6,479
1,105
67,870
49,900
0
3,113
316
23,761
8,043
5,933
55,730
10,000
J56J98
70,686
0
841
6,644
43,886
3,353
2.592
1,722
5.241
134,966
[tonsfyr]
2009
Base
PH2 5
50.500
0
637
934
3.029
3.072
286
5.059
1,103
61,618
49,900
0
3.135
316
23.101
6.299
3.792
30.936
9.705
J27J83
70.686
0
809
6.644
43.540
2.521
1.652
4,311
4,852
135,015
[lonsfyr]
2014
Base
PH2 5
50,500
0
614
934
2,877
2.205
191
5.202
1,103
63,628
49.900
0
3.134
316
22629
5,013
2,673
27.757
9,576
121,001
70,686
0
791
6,644
43.293
1.967
1.173
5534
4,852
134,941
[tonsfyr]
2020
Base
PM2 5
50.500
0
595
934
2.695
1.382
152
5.076
1.103
62,437
49900
0
3235
316
22063
3.443
2.246
29.467
9,576
120,246
70,686
0
779
6.644
42.996
1.377
1.000
6,350
4.852
134,685
[tonsfyr]
2030
Base
PM2 5
50.500
0
569
934
2.695
577
168
5.076
1,103
61,623
49.800
0
3722
316
22.063
2,713
2,364
29.467
9,576
120,120
70.666
0
762
6.644
42.996
948
1.118
6.350
4.852
134,357
-------
State
Oregon
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
ptnonipm
Oregon Total
Pennsylvania
rfdust
ag
aim
avelire
lonpt
lonroad
on road
ptipm
ptnonipm
Pennsylvania Total
Rhode Island
afdust
ag
aim
avefiie
nonp!
lanroad
on road
ptipm
Jtnonipm
Rhode Island Total
[lonsfyr]
2002
S02
0
0
4,212
4,896
9,845
2,559
3,488
12,285
5,30?
42,592
0
0
8,354
32
68,349
5,203
7,885
907.734
83,132
JJ85J88
0
0
78
1
3,365
354
425
18
2,649
6,889
[tonsfyr]
2009
Base
S02
0
a
3,487
4,896
9,846
434
450
12,552
5,307
36,971
0
0
6,729
32
68,335
918
1,369
244,722
81,441
403,546
0
0
64
1
3,364
63
86
0
2,349
5,928
[tonsfyr]
20U
Base
S02
0
0
3.423
4,896
9,846
35
396
12,552
5,307
36,457
0
0
6,553
32
63,326
89
1,169
206,230
75,605
358,004
0
0
68
1
3,364
7
85
0
2,349
5,874
[tonsfyr]
2020
Base
S02
0
0
3,923
4,896
9,847
37
44 B
12,606
5,307
37,064
0
0
7,510
32
68.314
95
1,285
188,589
75,605
341,431
0
0
77
1
3,364
B
94
0
2,349
5,892
[ton sly t]
2030
Base
S02
0
0
5,694
4,886
9,847
41
504
12,606
5,307
38,896
D
0
10,928
32
66,314
107
1,504
186,589
75,605
345,080
0
0
85
1
3,364
9
107
0
2,349
5,915
[ton sly r]
2002
NH3
0
40,655
9
3,542
1,061
24
3,270
162
787
49,509
0
76,675
14
25
3,689
55
10,618
401
1,334
92,811
0
235
0
1
15
4
854
56
47
1,213
[tonsfyr]
2009
Base
NH3
0
41,156
10
3,542
1,061
27
3,758
298
787
50,641
0
79.474
16
25
3,689
62
11,363
1,234
1,298
97,160
0
237
0
1
15
5
940
151
47
1,395
[tons/yr]
2014
Base
NH3
0
41,516
10
3,542
1,061
29
4,181
296
787
51,426
0
81.473
17
25
3.689
68
12.227
1.311
1.298
100,108
0
239
0
1
15
5
1.023
137
47
1,467
[tons/yr]
2020
Base
NH3
0
41.949
11
3,542
1.061
32
4.656
298
787
52,337
0
83,870
18
25
3689
75
13.212
1.237
1.298
103,424
0
241
0
1
15
6
1.122
126
47
1,557
[tonsfyr]
2030
Base
NH3
0
41.949
13
3542
1.061
37
5.213
298
787
52,900
0
83.870
21
25
3689
87
15.234
1.237
1.298
105,460
0
241
0
1
15
6
1.274
126
47
1,710
[ton sly r]
2002
PM1Q
82,013
0
1.496
75,661
50,681
2,902
2,707
711
9,826
226,200
130,506
0
2,376
530
41.641
6,256
7,250
63,196
22,391
274,351
2,501
0
8
17
1,171
427
343
12
288
4,767
[tonsfyr]
2009
Base
PM10
82.013
0
1.509
75.861
49.765
2.358
2.151
392
9.532
223,580
130.508
0
2.396
530
40.757
5.295
5,426
31.689
20,443
_237J44
2.501
0
8
17
1.136
342
290
~i
256
4,556
[tonsfyr]
2014
Base
PH10
82,013
0
1,513
75,861
49.110
1,920
1,697
392
9,532
222,039
130,508
0
2.399
530
39,983
4,442
4,514
31,114
20,393
233,883
2,501
0
B
17
1,110
285
292
7
256
4,475
[tonsfyr]
2020
Base
PM10
82.013
0
1,560
75,861
48,325
1,386
1,537
449
9,532
220,6(2
130,508
0
2,478
530
39.053
3,335
4,293
30,850
20,393
231,440
2,501
0
-1
17
1,080
218
313
6
256
4,398
[tons/yr]
2030
Base
PM10
82,013
0
1.770
75,861
48.325
1,080
1,622
449
9,532
220,651
130,508
0
2,857
530
39,053
2,782
4,821
30.850
20,393
231,794
2,501
0
7
17
1,080
189
356
6
256
4,412
[tonsfyr]
2002
PM2 5
30,637
0
1.371
65,350
49,407
2,773
2,021
326
6,203
158,088
32,224
0
2,268
454
31,263
5,969
5,219
53,067
11,549
J1M15
481
0
0
14
1,107
406
209
11
173
2,401
[tonsfyr]
2009
Base
PH2 5
30.637
0
1.377
65.350
48.479
2.243
1.456
331
6,042
155,917
32224
0
2.276
454
30.179
5.026
3,436
24.131
10,265
107,994
481
0
0
14
1.072
324
150
4
153
2,199
[lonsfyr]
2014
Base
PH2 5
30.637
0
1.373
65.350
47,816
1.822
994
331
6,042
154,365
32224
0
2.267
454
29.404
4.205
2.475
23.255
10,186
_J042£2
481
0
0
14
1.046
270
141
4
153
2,110
[tonsfyr]
2020
Base
PM2 5
30.637
0
1,409
65.350
47.020
1.306
778
385
6.042
152,927
32.224
0
2.329
454
28475
3.138
2.108
23.155
10.186
102,070
481
0
0
14
1.016
205
147
4
153
2,020
[tonsfyr]
2030
Base
PM2 5
30.637
0
1,599
65.350
47.020
1.008
767
365
6.042
152,808
32.224
0
2.677
454
28.475
2,596
2.253
23.155
10.186
102,021
461
0
0
14
1.016
176
165
4
153
2,010
-------
State
South Carolina
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
jtnonipm
South Carolina Total
South Dakota
rfdust
ag
aim
avefire
lonpt
lonroad
on read
ptipm
ptnonpm
South Dakota Total
Tennessee
afdust
ag
aim
aveflie
nonp!
lanroad
on road
ptipm
Jtnoriipm
Tennessee Total
[lonsfyr]
2002
S02
0
0
1,946
646
30,016
2,816
5,021
212.572
57,30?
310,324
0
0
318
498
10,304
2,901
852
12,545
1.480
28,898
0
0
6,292
277
32,714
3,728
7,674
333,618
84,316
468,61 9
[tonslyr]
2009
Base
S02
0
0
1,231
646
30,008
482
651
150,469
56,870
240,357
0
0
69
498
10,301
492
125
12,249
1,480
25,214
0
0
5,318
277
32,705
642
1,039
158,140
83,903
282,025
[tonsfyr]
20U
Base
S02
0
0
1.106
646
30,003
41
551
122.606
56,870
211,824
0
0
16
498
10,299
21
95
4.275
1,480
16,685
0
0
5,393
277
32,698
54
830
137,637
83,903
260,792
[tonsfyr]
2020
Base
S02
0
0
1.267
646
29,996
44
617
97.472
56,870
186,911
0
0
20
49B
10,296
22
103
4,665
1,480
17,284
0
0
6,362
277
32,690
57
946
153,894
83,903
278,130
[tonsfyr]
2030
Base
S02
0
0
1,842
646
29,986
49
720
97.472
56,870
137,585
D
0
23
498
10,296
23
129
4,865
1,480
17,314
0
0
9,273
277
32,690
64
1,104
153,894
83,903
281,205
[tonsfyr]
2002
NH3
0
27,945
4
494
223
27
4,710
306
1,552
35,263
0
101,949
1
381
51
18
843
50
50
103,343
0
34,210
12
212
164
35
6,671
425
2.394
44,124
[tonsfyr]
2009
Base
NH3
0
29.692
5
494
223
30
5,163
343
1,552
37,504
0
102,814
1
381
51
21
901
34
50
104,253
0
35,494
14
212
164
39
7,448
436
2,394
46,202
[tons/yr]
2014
Base
NH3
0
30,941
5
494
223
33
5,643
415
1,552
39,307
0
103,432
1
381
51
23
968
37
50
104,944
0
36,411
15
212
164
43
8,258
487
2.394
47,986
[tonsfyr]
2020
Base
NH3
0
32,440
6
494
223
36
5.206
424
1,552
41,381
0
104,172
2
381
51
26
1.041
48
50
105,770
0
37.512
16
212
164
47
9,235
572
2,394
50,152
[tons/yr]
2030
Base
NH3
0
32440
7
494
223
41
7.137
424
1.552
42,319
0
104.172
2
381
51
30
1,280
48
50
106,013
0
37.512
13
212
164
54
10.488
572
2,394
51,415
[tonsfyr]
2002
PM10
82,086
0
714
10,684
19,393
3,102
3,588
17,707
12,696
149,971
202.326
0
172
8,235
6,683
3,289
746
450
609
222,509
95,767
0
1,853
4,587
26.842
4,225
6,128
16,268
30,328
185,996
[tonsfyr]
2009
Base
PM10
82.099
0
711
10,684
18.768
2,489
2.629
17.282
11.699
146,357
202.326
0
167
8,235
6.434
2.2B6
548
231
515
_220!741
95.767
0
1.869
4,587
26.074
3.416
4,447
10.716
27,121
173,997
[tonsfyr]
2014
Base
PH10
82.108
0
709
10,684
18.318
2,041
2,163
17.579
11,699
145,299
202,326
0
168
8.235
6.256
1,658
419
476
515
220,052
95,767
0
1,858
4,587
25,526
2,759
3.539
14,520
27,121
175,677
[tonsfyr]
2020
Base
PM10
82.117
0
720
10,684
17,780
1,490
2,055
22,836
11,699
149,182
202,326
0
170
8,235
6,042
1,051
372
589
515
219,300
95,767
0
1,906
4,587
24,869
1.954
3.319
38,254
27,121
197,775
[tons/yr]
2030
Base
PM10
82,117
0
779
10,684
17.780
1,208
2,283
22,636
11,699
149,186
202,326
0
172
8,235
6,042
477
436
58B
515
_jmi!
95,767
0
2,193
4,587
24,869
1,496
3,671
38,254
27.121
197,957
[tonsfyr]
2002
PH2 5
25,657
0
668
9,163
18,139
2,960
2,648
1 3,734
8,159
81,128
38,332
0
156
7,062
4,463
3,181
564
420
291
_54J70
22,530
0
1.707
3,934
20,663
4,040
4,667
13,910
22,054
93,505
[tons/yr]
2009
Base
PH2 5
25.661
0
662
9.163
17.512
2.368
1.695
12.638
7.403
77,100
38332
0
151
7.062
4.214
2,207
370
218
249
_52J803
22.530
0
1.720
3,934
19,896
3,253
2.982
8,769
1 9,684
82,769
[lons/yr]
2014
Base
PH2 5
25.664
0
656
9.163
17.064
1.93S
1.200
12.799
7,403
75,884
38332
0
151
7.062
4036
1.599
240
447
249
52,116
22.530
0
1.707
3.934
19.348
2.620
2.026
12,704
19.684
84,553
[tons/yr]
2020
Base
PM2 5
25.667
0
663
9.163
16,526
1.404
1.013
17.647
7.403
79,486
38332
0
152
7.062
3822
1.012
185
531
249
51,345
22.530
0
1,749
3.934
18.690
1.845
1.658
35.970
19.684
106,059
[tons/yr]
2030
Base
PM2 5
25.667
0
715
9.163
16.526
1.127
1,067
17.647
7.403
79,315
38.332
0
152
7.062
3822
455
203
531
249
50,806
22.530
0
2.012
3.934
18.690
1.399
1.719
35.970
19,684
105,938
-------
State
Texas
Texas Total
Tribal Data
Tribal Data Tola
Utah
Utah Total
Sector
afdyst
ag
aim
avefire
lonpt
lonroad
on road
[itipm
ptnonipm
aim
[>tipm
ptnonipm
1
sMust
ng
aim
avefire
lonpt
nonroad
Bnroad
pfipm
3tnoiiipni
[tonsfyr]
2002
S02
0
0
27,280
1,178
109,215
14,990
21,522
562,594
245,060
981,340
132
6
204
342
0
0
1,065
1,934
3,427
1,437
1,989
33,167
9,305
52,325
[tonsJyr]
2009
Base
S02
0
0
23,890
1,178
109,204
2,566
3,084
346,683
172,556
659,160
25
0
203
228
0
0
344
1,934
3,426
251
335
39,360
3,454
54,103
[tonsfyr]
2014
Base
S02
0
0
24,302
1.17B
109.195
180
2,506
339.382
164,923
641,666
3
0
203
206
0
0
213
1,934
3.425
21
310
41,355
7,790
55,047
[tonsfyr]
2020
Base
S02
0
0
28,470
1,178
109.185
189
2.B63
338.519
164.923
645,327
4
0
203
207
0
0
242
1,934
3,423
22
361
44,170
7.790
57,942
[lonsfyr]
2030
Base
S02
0
0
41,368
1,178
109.185
211
3,479
338,519
164.923
658,863
4
0
203
207
0
0
272
1,934
3,423
25
432
44,170
7.7SO
58,045
[tonsfyr]
2002
NH3
0
354,673
57
1,118
1.983
128
21.943
5,941
2.297
388,340
1
65
69
0
20,446
5
1,479
1,266
14
2,457
269
529
26,469
[tonslyr]
2009
Base
NH3
0
360,460
63
1,118
1,983
145
24,825
4,839
2,279
395,512
1
92
96
0
20,960
6
1,479
1,266
16
2,903
372
529
27,534
[tonsfyr]
2014
Base
NH3
0
364.456
67
1.118
1.983
158
27,464
5.537
2.279
403,063
1
92
96
0
21.326
7
1.479
1.266
18
3335
416
529
28,377
[tonslyr]
2020
Base
NH3
0
369242
73
1.118
1.983
175
31.029
6.148
2.279
412,046
1
72
76
0
21.765
a
1.479
1.268
20
3.851
418
529
29,336
[tonslyr]
2030
Base
NH3
0
369242
82
1.118
1.983
202
37.361
6.148
2.279
418,416
1
72
77
0
21.765
9
1.479
1.268
22
4.579
418
529
30,069
[tonsfyr]
2002
PM10
1,290,391
0
8,936
25,226
72,265
15,766
16,034
34,257
38,861
1,501,740
56
31
1.872
1,961
54,020
0
153
31.961
10.385
1,703
1,656
6,351
6.893
113,124
[tonsJyr]
2009
Base
PM10
1,290.973
0
8,940
25.228
71,333
12,270
12,192
35,123
36,535
1,492,592
55
4
1.868
1,92?
54.020
0
152
31,961
10.263
1.463
1.327
5,480
6.577
111,248
[tonslyr]
2014
Base
PH10
1,291,390
0
8,759
25,226
70,866
9,862
10,036
35,202
36,020
1,487,162
52
4
1.868
1,925
54,020
0
157
31,961
10,185
1.199
1,165
7,400
6,551
112,639
[tonsfyr]
2020
Base
PM10
1.291,887
0
8,857
25,228
69,867
6,978
10,043
38,150
38,020
1,487,030
50
3
1.868
1,922
54,020
0
162
31.961
10.085
845
1,176
8,532
6,551
113,333
[tonslyr]
2030
Base
PH10
1,291,887
0
10,280
25,228
69,867
5.127
11,796
38.150
36,020
1,488,357
48
3
1,868
1,919
54,020
0
166
31.961
10,085
647
1,352
8,532
6.551
113,315
[tonsfyr]
2002
PM2 5
242,993
0
8,146
21,576
47,394
15,126
11,699
24.920
27,189
399,045
0
31
856
887
7,864
0
140
27.412
9,079
1,625
1,187
4,901
2,955
55,162
[tonslyr]
2009
Base
PM2 5
243.036
0
8,147
21.576
46,461
11.733
7,710
24,844
25,562
389,120
0
3
852
855
7.864
0
140
27412
8.970
1.389
829
4.265
2.810
53,678
[torsfyr]
2014
Base
PH2 5
243.153
0
7.980
21.578
45.795
9.401
5.342
24,955
25.310
383,512
0
3
852
855
7.864
0
144
27.412
8893
1.135
628
5,940
2.809
54,825
[tonsfyr]
2020
Base
PM2 5
243.232
0
8,070
21.578
44,995
6.607
4.834
27,849
25,310
382,476
0
2
852
854
7.864
0
148
27.412
8800
796
570
6,933
2809
55,332
[lonsfyr]
2030
Base
PM2 5
243.232
0
9,380
21.578
44,995
4.798
5,466
27.849
25.310
382,608
0
2
852
854
7.864
0
152
27.412
8.800
604
628
6,933
2.809
55,203
-------
State
Vermont
Sector
afdyst
ag
aim
avefire
rconpt
nonroad
on road
ptipm
ptnonipm
Vermont Total
Virginia
rfdust
ag
aim
avefire
lonpt
lonroad
on road
ptipm
ptnonipm
Virginia Total
Washington
sfdust
ğg
aim
avefira
fionpt
lonroad
an road
jtipm
ptnoniptn
Washington Total
[lonsfyr]
2002
S02
0
0
6
49
5,385
368
622
0
911
7,341
0
0
5,595
399
32,923
4,289
6.662
239.77?
87,691
357,338
0
0
11,488
407
7,254
5,380
5,539
19.108
24.623
73,799
[tonsfyr]
2009
Base
S02
0
a
6
49
5.382
64
125
0
911
6,538
0
0
3,378
399
32,910
741
1,019
151,541
67,253
257,240
0
0
10,791
407
7,241
707
790
3,954
24,601
43,491
[tonsfyr]
2014
Base
S02
0
0
7
49
5,380
7
103
0
911
6,458
0
0
2,996
399
32,901
60
920
143,291
67,253
252,821
0
0
11,291
407
7.231
57
688
3,946
24,601
48,221
[tonsfyr]
2020
Base
S02
0
0
7
49
5,378
8
11B
0
911
6,471
0
0
3,426
399
32,889
63
1,032
135,834
67,253
240,899
0
0
13,423
407
7.219
60
794
4.064
24.601
50,569
[tonsfyr]
2030
Base
S02
0
0
8
49
5,378
9
148
0
911
6,503
D
0
4.904
399
32,889
71
1,269
135,834
67,253
242,619
0
0
19,682
407
7,219
67
914
4,064
24,601
56,954
[tonsfyr]
2002
NH3
0
8,821
0
38
214
5
939
11
16
10,043
0
43.811
13
305
1,621
41
7,889
192
3,500
57,373
0
42,133
151
248
1.711
39
5.168
62
774
50,285
[tonsfyr]
2009
Base
NH3
0
8,851
0
38
214
5
1.047
0
16
10,172
0
45,905
15
305
1,621
46
8,893
353
3,498
60,638
0
42,712
171
248
1,711
44
6,206
512
771
52,375
[tons/yr]
2014
Base
NH3
0
8,872
0
38
214
6
1,147
0
16
10,293
0
47.402
16
305
1621
51
9.831
471
3,498
63,196
0
43.126
193
248
1,711
48
7.111
537
771
53,744
[tonsfyr]
2020
Base
NH3
D
8.898
0
38
214
6
1.262
0
16
10,434
0
49.197
18
305
1.621
56
10.881
435
3,498
66,011
0
43,622
236
248
1,711
53
8.125
528
771
55,293
[tonsfyr]
2030
Base
NH3
0
8898
0
38
214
7
1.467
0
16
10,639
0
49,197
21
305
1.621
64
13.168
435
3.498
68,310
0
43.622
339
248
1.711
61
9323
528
771
56,602
[tonsfyr]
2002
PM1Q
13.65B
0
29
812
5,823
516
645
0
337
21,819
60,865
0
1.905
6,599
53,941
4,809
4,939
15,400
13,041
161,498
106,176
0
2,416
5.126
35,624
4,776
4,545
2,456
4,970
166,089
[tonsfyr]
2009
Base
PM10
13.658
0
30
812
5,539
463
632
0
337
21,471
60.865
0
1.875
6,599
52.867
3.897
3.736
10317
11,869
152,025
106.176
0
2.601
5.126
34.598
3.742
3,315
3,091
4,895
163,544
[tonsfyr]
2014
Base
PH10
13,658
0
32
812
5.336
390
554
0
337
21,119
60,865
0
1,854
6,599
52.100
3,247
3,278
13,940
11,869
153,752
106.176
0
2,758
5,126
33.864
3,044
2,767
3,090
4,895
161,720
[tonsfyr]
2020
Base
PM10
13,658
0
35
812
5,093
292
557
11
337
2D.783
60,865
0
1.872
6,599
51.179
2,437
3,306
15,385
11,869
154,011
106.176
0
3,074
5.126
32.983
2,207
2,685
3.213
4,895
160,359
[tons/yr]
2030
Base
PM10
13,658
0
37
812
5,093
236
720
0
337
20,893
60,865
0
2.004
6.599
51,179
2,009
3,976
15.835
11,869
154,385
106.176
0
3.959
5,126
32,983
1,704
2.941
3,213
4.895
160,997
[tonsfyr]
2002
PM2 5
4,814
0
21
696
5,415
490
465
0
237
12,137
19,662
0
1,836
5.659
29,947
4,593
3.436
14,431
9,734
89,350
26,908
0
2,271
4.437
31,933
4,567
3,407
2.025
3.224
78,872
[tonsfyr]
2009
Base
PH2 5
4.814
0
22
696
5.151
436
416
0
237
11,774
19.662
0
1.801
5.659
28873
3.709
2.250
8356
8.671
78,981
26.908
0
2,447
4.437
31,023
3563
2,161
2,465
3.139
76,244
[lonsfyr]
2014
Base
PH2 5
4.814
0
24
698
4,962
366
322
0
237
11,422
19662
0
1.774
5.659
28.106
3.079
1.711
11.602
8,671
80,264
26.908
0
2.594
4.437
30,337
2,890
1.550
2.464
3.189
74,419
[tonsfyr]
2020
Base
PM2 5
4.814
0
26
696
4.736
273
291
0
237
11,072
19662
0
1.783
5659
27.185
2,294
1.589
13.372
8.671
80,215
26.908
0
2.895
4.487
29.513
2.081
1.330
2.582
3.189
72,985
[tonsfyr]
2030
Base
PM2 5
4.814
0
28
696
4.736
220
355
0
237
11,085
19.662
0
1.900
5.659
27.185
1.872
1.850
13.372
8.671
80,171
26.808
0
3.749
4.467
29.513
1.590
1.360
2.562
3.189
73,398
-------
State
Wast Viiginia
Sector
sfdust
ğa
aim
avefne
Donpt
lonroad
an road
atipm
stnonipm
West Virginia Total
Wisconsin
afdust
ag
aim
avefire
lonpt
lonroad
an road
ptipm
stnonipm
Wisconsin Total
Wyoming
afdust
ag
aim
avefire
lonpt
lonroad
an road
Mipm
ptnonipm
Wyoming Total
Grand Total
[tonsfyr]
2002
S02
0
0
5,707
215
14,589
780
2,875
509,488
54,10?
587,561
0
0
4,781
70
6,369
5,015
7,218
192,946
63.651
280,051
0
0
2,088
1,106
6,181
559
905
83,423
33,676
127,938
14,649,986
[tonslyr]
2009
Base
S02
0
0
5,433
215
14,585
128
248
200,473
54,106
275,187
0
0
3,992
70
6,370
845
815
141,203
63,431
216,726
0
0
404
1,106
6,179
95
122
62,706
33,653
104.266
9,233,950
[tonsfyr]
2014
Base
S02
0
0
5.830
215
14,582
13
201
173,167
54,106
253,113
0
0
4,195
70
6,370
81
676
125,070
63,431
199,892
0
0
56
1,106
6,178
7
95
53,597
33.653
99,692
8,473,877
[tonslyr]
2020
Base
S02
0
0
7,090
215
14,578
14
217
168,660
54,106
244,879
0
0
5,137
70
6,370
87
766
123,645
63,431
199,507
0
0
65
1,106
6,176
B
103
58,204
33,653
99,31 5
8,171,411
[tonslyr]
2030
Base
S02
0
0
10,433
215
14,578
15
230
168.660
54,106
248,237
0
0
7.496
70
6,370
89
897
123,645
63,431
202,009
0
0
75
1,106
6,176
8
122
58.204
33.653
99,345
8,295,030
[tonslyr]
2002
NH3
0
8,879
8
165
72
8
1,950
210
636
12,981
0
113.949
11
54
266
52
6,006
375
397
121,110
0
16,575
8
846
91
5
693
336
301
21,104
3,901,951
[tonslyr]
2009
Base
NH3
0
10,474
9
165
72
9
1.957
628
638
11,002
0
114,739
13
54
266
60
6,492
533
397
122,653
0
16,801
9
846
91
6
931
414
301
21,398
4,023,868
[tonsfyr]
2014
Base
NH3
0
10,898
10
165
72
10
2,056
644
638
1i,544
0
115,339
14
54
266
65
7,111
662
397
123,957
0
16,963
10
846
91
7
997
443
301
21,656
4,123,379
[tonsfyr]
2020
Base
NH3
0
11,408
11
185
72
11
2,175
649
638
15,178
0
116,109
15
54
266
71
7,894
630
397
125,486
0
19.156
11
846
91
7
1.075
470
301
21,957
4,241,636
[tons/yr]
2030
Base
NH3
0
11.408
12
165
72
13
2.273
649
638
15,280
0
116.109
17
54
266
33
9,065
630
397
126,672
0
19.156
13
846
91
8
1.252
470
301
22,136
4,297,455
[tonslyr]
2002
PM10
24,640
0
1,478
3,557
12,220
1,005
1,542
31,246
10,625
86,314
103.735
0
1,353
1,159
26,104
6,090
4,479
5,576
10,466
158,961
272,299
0
866
13,239
3,717
689
799
9,599
19,234
325,494
12,817,898
[tonslyr]
2009
Base
PM10
24.644
0
1.526
3,557
11,866
906
1.004
22,049
10.097
75,648
103.735
0
1.417
1,159
25.736
4.944
3,403
8.465
9,199
158,058
272,299
0
824
18,289
3,571
565
524
8.653
18,528
323,255
12,554,430
[tons/yr)
2014
Base
PM10
24,647
0
1,573
3,557
11,613
751
792
21,938
10,097
74,967
103,735
0
1,492
1,159
25.474
3,967
2,810
3,831
9,199
156,666
272,299
0
793
18,289
3,467
450
402
9,763
18,528
323,991
12,551,458
[tonslyr]
2020
Base
PM10
24.650
0
1,689
3,557
11,310
541
725
21,957
10,097
71,526
103.735
o
1.632
1,159
25.159
2,840
2,684
8,860
9,199
155,297
272,299
0
766
18,289
3,342
312
365
10,727
18,528
324,629
12,671,074
[tonsfyr]
2030
Base
PM10
24,650
0
2.036
3,557
11,310
455
732
21.957
10,097
74,794
103.735
0
1.954
1,159
25.159
2,296
2,985
8,890
9,199
155,377
272,299
0
730
18,289
3,342
214
407
10,727
18.528
324,536
12,680,651
[tonsfyr]
2002
PH2 5
11,305
0
1,281
3,050
11,130
956
1.149
28,834
7,450
65,205
30,705
0
1.132
994
25.407
5,796
3,317
5,029
5,856
78,287
41,010
0
857
15,636
2,922
659
606
7,936
14.143
83,819
4,938,898
[tonsfyr]
2009
Base
PH2 5
11,309
0
1,322
3.050
10,776
856
646
16,535
7,113
51,609
30.705
0
1.236
994
25040
4.634
2.215
7.151
5,445
77,469
41,010
0
814
15.636
2.776
537
347
7.053
13.941
82,164
4,671,411
[tonsfyr]
2014
Base
PH2 5
11.311
0
1.357
3.050
10.523
707
440
16,219
7,113
50,721
30.705
0
1.300
994
24.777
3747
1.573
7.432
5.445
75,972
41,010
0
732
15.638
2.672
426
228
8032
13.941
82,775
4,661,327
[tonsfyr]
2020
Base
PM2 5
11.314
0
1.453
3.050
10.219
508
358
16.230
7,113
50,246
30705
0
1.421
994
24.462
2,666
1,327
7.441
5.445
74,461
41.010
0
754
15.686
2,547
295
180
8,875
13,941
83,288
4,768,531
[tonsfyr]
2030
Base
PM2 5
11,314
0
1,763
3.050
10.219
424
343
16.230
7.113
50,457
30.705
0
1.709
994
24.462
2,138
1.395
7.441
5.445
74,288
41,010
0
717
15.686
2,547
200
189
8,875
13,941
83,165
4,764,633
-------
D-38
-------
Appendix E
Metadata
E-l
-------
E-2
-------
Metadata
Output Data
The pm25_surface_36km_2002.csv file is the output file from EPA's Hierarchical
Bayesian Model (HBM) that combines PM2.5 or Os monitoring data from National Air
Monitoring Stations/State and Local Air Monitoring Stations (NAMS/SLAMS) and
Models-3/Community Multiscale Air Quality (CMAQ) computer-simulated PM2.5 or Os
data. This file provides a spatial interpolation of air quality that takes advantage of the
strengths of monitoring network observations and modeling estimates to generate daily
surrogate measures for PM25 and relates these measures to available public health data.
The file covers the contiguous lower 48 states of the United States. The time frame
covered is January 1, 2002 through December 31, 2002. The standard errors of the
estimates should be taken in to account when using the results. This file is a comma-
separated values (CSV) file. This is a flat file that is platform-independent. In the
Microsoft Windows computing environment, this file can be read easily by Excel.
The file contains the posterior means and standard errors of the estimated space-time
surface, the posterior means and standard errors of the estimated space-time bias surface,
and the posterior means and standard errors for a surface made up of 12 km x 12 km or
36 km x 36 km contiguous grids. The contiguous 36 km x 36 km grids cover the whole
lower 48 contiguous states of the United States. The contiguous 126 km x 12 km grids
cover either an Eastern segment of the U.S. or Western segment of the U.S. (lower 48
contiguous states of the United States). The file includes the following variables: Date,
Latitude, Longitude, posterior mean estimated PM2.5 or 63 concentration on natural log
scale (PredAvg), row position of grid cell, column position of grid cell, standard error of
the estimated PM2.5 or Os concentration on the natural log scale (PredStd), the natural log
of the estimated CMAQ model data bias (Bias), and the standard error of the estimated
CMAQ model data bias (BiasStd). Values of -999 in the data set represent missing (or
excluded) values. Missing values are generated when grid cells are not included in the
model calculation. These are not actual missing values but intentionally not included in
the grid for calculation of the estimated surface. An example of such a grid cell not
included is grid cells that fall over water.
Input Data
The actual monitoring data from the NAMS/SLAMS network were downloaded from the
Air Quality System (AQS) database. Only Federal Reference Method (FRM) samplers
and only those samplers with sample duration of one day (24-hour integrated sample)
were included in the data set.
The CMAQ data was created from version 4.6 of the model using CBIV mechanism.
The PM2.5 data is a 24-hour integrated PM2.5 concentration calculated on a 12-km x 12-
km grid for the Eastern United States and a 36-km x 36-km grid for the entire United
States. These CMAQ results are based on (1) the emissions data from the EPA's
National Emissions Inventory (NET) 2001 version 3 (developed using mobile emissions
model Mobile 6 but no daily continuous emissions monitoring (CEM) data for the major
E-3
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NOX point sources). In addition, the meteorological data used for these model results is
from Mesoscale Model 5 (MM5) version 3.6.3 simulations (Four Dimensional Data
Assimilation [FDDA], Pleim-Xiu Land Surface Model [LSM]).
The FffiM combines the actual monitoring data (NAMS/SLAMS), the estimated PM2.5 or
Os concentration surface (CMAQ), and the prediction of PM2 5 or Os through space and
time. The model assumes that both the actual monitoring data and the CMAQ data
provide good information about the same underlying pollutant surface, but with different
measurement error structures. It gives more weight to the accurate monitoring data in
areas where monitoring data exists and relies on the CMAQ data and satellite data in
areas where no monitoring data is available. The modeling is divided into hierarchical
components where each level of the hierarchy is modeled conditional on the preceding
levels. To fit the model, a custom-designed Monte Carlo Markov Chain (MCMC)
software algorithm was used. Model-specific input parameters of statistical distributions
for the model and simulation parameters (priors) are specified for each run of the model.
The projections for the grid cell structure are as follows:
Projection: Lambert conformal with spherical earth, radius = 6370.0 km
36-km Resolution 12-km Resolution
NCOLS = 148 NCOLS = 279
NROWS = 112 NROWS = 240
P_ALP = 33.00 P_ALP = 33.00
P_BET = 45.00 P_BET = 45.00
P_GAM = -97.00 P_GAM = -97.00
XCENT = -97.00 XCENT = -97.00
YCENT = 40.00 YCENT = 40.00
XORIG = -2736000.00 XORIG = -1008000.00
YORIG = -2088000.00 YORIG = -1620000.00
XCELL = 36000.00 XCELL = 12000.00
YCELL = 36000.00 YCELL = 12000.00
These values are for the 36- and 12-km grid re solution of CMAQ.
The geographic boundaries of the HB output cover the following region:
111.1 degrees W longitude - West Bounding Coordinate
65.4 degrees W longitude - East Bounding Coordinate
51.25 degrees N latitude - North Bounding Coordinate
23.0 degrees N latitude - South Bounding Coordinate
E-4
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The definitions for the 12-km x 12-km and 36-km x 36-km CMAQ grid cells are
contained in separate text (*.txt) files. These files contain the latitude and longitude
coordinates of the following points for each grid cell: 1) center; 2) southwest corner; 3)
southeast corner; 4) northwest corner; and 5) northeast corner. The AQS data for PM2.s
and 63 are contained in separate text (*.txt) files. These files contain the following data:
parameter occurrence code (for pollutant); state code; city code; site ID; sampling
frequency; data; sample value; monitor protocol (i.e., 1 in 3 days); partition, etc.
Example figures of a) a separate air quality monitor with CMAQ data, and b) combined
air quality monitor data and CMAQ data for PM2.5 are shown below.
Monitor and HBM Concentration (ug/m3)
Figure E-1. PM2.s Monitoring Data and CMAQ Surface (Separately Displayed - White Spheres
Represent Monitor Locations and Associated Concentration Values)
E-5
-------
Monitor and HBM Concentration (ug/m )
Figure E-2. Combined PM2.s Monitoring Data and CMAQ Surface (Via HBM)
Use of HB Data to Generate Health Indicators
The HB output data can be used to generate health (air) indicators which are useful to
researchers when developing health impact assessments (HIA). The HB output is
provided in a gridded (x-y/row-column) format and that format must be translated to
different coordinate systems (e.g., county-based/relevant coordinates) to provide health
indicator data for the area(s) of interest. An important coordinate projection system used
as a standard coordinate representation format to express different location designation
systems in consistent terms is the Lambert Conformal Conic (LCC) projection coordinate
system. The North American Datum (NAD) geodetic system describes the Earth's
ellipsoid based on the latitude and longitude location of an initial point, and serves as the
basis of maps and surveys of the Earth's surface. The NAD-27 datum is based on the
Clarke Ellipsoid (Earth spheroid) of 1866 and is centered at a base station on the Meades
Ranch in Kansas. The NAD-83 grid projection/datum is based on the Geodetic
Reference Spheroid (GRS) of 1980 and is geocentric (e.g., based on the Earth's center
with no directionality or initial point located on the Earth's surface). The NAD
coordinate system is important because health-related data (used to calculate health
indicators) are collected and cataloged based on this coordinate system (e.g., U.S. Census
data is based on NAD-83 coordinates).
The HB output provides ambient concentration data for both ozone and fine particulate
matter in x-y-based grid cells, and to correlate this concentration data with health data,
the x-y locations must be 'mapped' to latitude/longitude locations and then mapped to the
correct datum/projection system linked with the health data. The typical latitude and
E-6
-------
longitude grids are based on the World Geodetic System (WGS) projection for 1984
(WGS-84), while the U.S. Census uses the NAD-83 grid and the SAS statistical analysis
software uses NAD-83 grid projection. When generating the linkage between the
ambient concentration data and the health data, a methodology or protocol must be
developed to relate the appropriate coordinate system/geocoding information between
them.
CDC, EPA, and the state departments of air and/or health of New York, New Jersey,
Massachusetts, and Minnesota have developed an initial set of health indicators using the
HB output data correlated with available health data/information. They have developed a
'relationship file' to map the x-y-based grid cells to latitude/longitude format with the
appropriate datum/projection system(s). Shapefile information also resides in this file
allowing compatibility with GIS map formats/applications. The relationship file has a
grid ID, representing the row and column of the grid cell. This grid ID is a six-digit
identifier from the HB raw data set that concatenates column and row designation. There
are 66,000 grid cells per day times 365 days worth of data (the New York State Health
Department uses SAS to process this data and CDC uses ArcGIS to process the data).
The relationship file recognizes the importance of having consistent geocoding data for
HB grids for Health Impact Analyses (HIA). The U.S. Census files (TIGER2000 files)
are in NAD-83 format, which is what the SAS statistical software processes. The WGS-
84 format is almost exactly like NAD-83 format except there is an offset of a few feet for
grid points (centroids). WGS-84 is used by the CMAQ air quality model. Air Quality
models such as CMAQ, which serve as input to the HB model, uses the meteorological
software MM5 which is based on the Lambert Conformal Conic (LCC) projection. As
long as the HB output data (latitude and longitude grid coordinates) can be mapped to the
NAD-83 or NAD-84 (WGS-84) to match census data, air indicators can be generated for
HIA. The x and y coordinates given in the HB are used to plot the latitude and longitude
with an offset to match non-NAD-83 grid references. When defining Earth points,
coordinate information should be modified into a format compatible with county-based
maps and transformed into an elliptical projection. NetCDF file can be converted in
ArcGIS to make shape files. The New Jersey state air department used the Theissen
Polygon tool on HB data to generate shapefiles.
Note: The value for the Earth's radius used for 2003 - 2005 versions of CMAQ changed
from 6370.997 km (pre-2003) to 6370.000 km.
How CMAQ and HB x-y grid locations are transformed to latitude and longitude values:
There is an IOAPI file providing rows/columns, cell height/width, origin in LCC, offset
by l/2 cell width/height to get center cell (centroid). Conversion uses an LCC routine in
IOAPI library, passing parameters (Earth radius, central meridian [longitude: -97
degrees]), two key latitude values 33 degrees and 45 degrees, central meridian, -97 and
latitude of origin, 40.0. These arguments are required for the LCC routine, which returns
latitude and longitude. The code for transforming an LCC projection (e.g., CMAQ and
HB Model x-y grid coordinates) to latitude and longitude values:
E-7
-------
LCPGEO Fortran Code - LCC Conversion Program
Fortran Code for converting Lambert Conformal Conic to geodetic (lat/lon):
subroutine Icpgeo(iway,phic,xlonc,truelatl,truelat2,xloc,yloc,
& xlon,ylat)
c write(*,*)'INCALL:',phic,xlonc,truelatl,truelat2
c
c LCPGEO performs Lambert Conformal to geodetic (lat/lon) translation
c
c Code based on the TERRAIN preprocessor for MM5 v2.0,
c developed by Yong-Run Guo and Sue Chen, National Center for
c Atmospheric Research, and Pennsylvania State University
c 10/21/1993
c
c Input arguments:
c iway Conversion type
c 0 = geodetic to Lambert Conformal
c 1 = Lambert Conformal to geodetic
c phic Central latitude (deg, neg for southern hem)
c xlonc Central longitude (deg, neg for western hem)
c truelatl First true latitute (deg, neg for southern hem)
c truelat2 Second true latitute (deg, neg for southern hem)
c xloc/yloc Projection coordinates (km)
c xlon/ylat Longitude/Latitude (deg)
c
c Output arguments:
c xloc/yloc Projection coordinates (km)
c xlon/ylat Longitude/Latitude (deg)
c
data conv/57.29578/, a/6370./
c
c Entry Point
c
if (phic.lt.0) then
sign = -1.
else
sign = 1.
endif
pole = 90.
if (abs(truelatl).gt.90.) then
truelatl = 60.
truelat2 = 30.
truelatl = sign*truelatl
Eo
-O
-------
truelat2 = sign*truelat2
endif
xn = aloglO(cos(truelatl/conv)) - aloglO(cos(truelat2/conv))
xn = xn/(aloglO(tan((45. - sign*truelatl/2.)/conv)) -
& aloglO(tan((45. - sign*truelat2/2.)/conv)))
psil = 90. - sign*truelatl
psil = psil/conv
if(phic.lt.0.)then
psil = -psil
pole = -pole
endif
psiO = (pole - phic)/conv
xc = 0.
yc = -a/xn*sin(psil)*(tan(psiO/2.)/tan(psil/2.))**xn
c
c Calculate lat/lon of the point (xloc,yloc)
c
if (iway.eq.l) then
xloc = xloc + xc
yloc = yloc + yc
if (yloc.eq.O.) then
if (xloc.ge.O.) flp = 90./conv
if (xloc.lt.O.) flp = -90./conv
else
if(phic.lt.0.)then
flp = atan2(xloc,yloc)
else
flp = atan2(xloc,-yloc)
endif
endif
flpp = (flp/xn)*conv + xlonc
if (flpp.lt.-180.) flpp = flpp + 360.
if (flpp.gt. 180.) flpp = flpp - 360.
xlon = flpp
c
r = sqrt(xloc*xloc + yloc*yloc)
if(phic.lt.0.)r = -r
cell = (r*xn)/(a*sin(psil))
rxn = 1.0/xn
cell = tan(psil/2.)*cell**rxn
ce!2 = atan(cell)
psx = 2.*cel2*conv
ylat = pole - psx
c
c Calculate x/y from lat/lon
E-9
-------
else
ylon = xlon - xlonc
if (ylon.gt. 180.) ylon = ylon - 360.
if (ylon.lt.-180.) ylon = ylon + 360.
flp = xn*ylon/conv
psx = (pole - ylat)/conv
r = -a/xn*sin(psil)*(tan(psx/2.)/tan(psil/2.))**xn
if(phic.lt.0.)then
xloc = r*sin(flp)
yloc = r*cos(flp)
else
xloc = -r*sin(flp)
yloc = r*cos(flp)
endif
endif
c
c write(*,*)xloc,xc,yloc,yc
xloc = xloc - xc
yloc = yloc - yc
c
return
end
E-10
-------
CMAQ Projection Information - Source:
http://www.baronatns.com/products/ioapi/GRIDDESC.httnl
Coordinate Information
COORD-
NAME
'LAM_40N97W
COORDTYPE
2
P ALP
33.000
P BET
45.000
P GAM
-97.000
XCENT
-97.000
YCENT
40.000
Grid Information
GRID-
NAME
36US1
COORD-
NAME
'LAM_40N97W
XORIG
(m)
-2736000
YORIG
(m)
-2088000
XCELL
(m)
36000
YCELL
(m)
36000
NCOLS
148
NROWS
112
NTHIK
1
P_ALP = "PROJ_ALPHA"
P_BET = "PROJ_BETA"
LAMGRD3 = P_ALP <= P_BET. These are the two latitudes which determine the
projection cone.
P_GAM = the central meridian
XCENT, YCENT = lat/lon coordinates for the center (0, 0) of the Cartesian coordinate
system.
X_ORIG is the X coordinate of the grid origin (lower left corner of the cell at
column=row=l), given in map projection units (meters, except in Lat-Lon coordinate
systems).
Y_ORIG is the Y coordinate of the grid origin (lower left corner of the cell at
column=row=l), given in map projection units (meters, except in Lat-Lon coordinate
systems).
X_CELL is the cell dimension parallel to the X coordinate axis, given in map projection
units (meters, except for Lat-Lon coordinate systems).
E-ll
-------
Y_CELL is the cell dimension parallel to the Y coordinate axis, given in map projection
units (meters, except for Lat-Lon coordinate systems).
NCOLS is the number of columns (dimensionality in the X direction).
NROWS is the number of rows (dimensionality in the Y direction).
NTHIK is the thickness (number) of cells on the boundary domain required to accurately
describe boundary mass flux (e.g., CMAQ uses NTHIK = 1)
ArcMap Projection Information (HB grid example):
Data Type: File Geodatabase Feature Class
Location:
U: \Proj ects\MMccourtney\Gri ds\templ ates\gri d_templ ates. gdb
Feature Class: template_mdhi_12_nb
Feature Type: Simple
Geometry Type: Polygon
Projected Coordinate System: NAD_1983_Lambert_Conformal_Conic
Projection: Lambert_Conformal_Conic
False_Easting: 0.00000000
False_Northing: 0.00000000
Central_Meridian: -97.00000000
Standard_Parallel_l: 33.00000000
Standard_Parallel_2: 45.00000000
Latitude_Of_Origin: 40.00000000
Linear Unit: Meter
Geographic Coordinate System: CustomSpheroidGCS_North_American_1983
Datum:
Prime Meridian: Greenwich
Angular Unit: Degree
Changing a data set's spheroid to a sphere.
1) In ArcCatalog, right click the data set of interest, and choose Properties. Click
the XY Coordinate System tab. Click Modify...
2) From the Geographic Coordinate System of the Projected Coordinate System
Properties window, click Modify...
E-12
-------
Projected Coordinate Syste
General
Name
Projec
Name
False
m Properties
r?ifxi
| N AD_1 983_Lambert_Conf ormaLConic
Parameter
Easting
1 1 False Northing
Central Meridian
Standard Parallel 1
Standard Parallel 2
I, **,
Linea
Name
Meter
**" rt* Ğ-
.
| Value
0.000000000000000000
10.000000000000000000
-97.000000000000000000
33 .000000000000000000
45 .000000000000000000
,-. r.nnnnnnr.nnr.onnnnr.r,
Ml
, Meter _^J
s per unit: F
aphic Coordinate Syster
Name: CustomSpherciidGCS
Angular Unit: Degree (0.01 7'
Prime Meridian: Greenwich {'
Datum:
Spheroid:
SemimajorAxis: G370000.
<
North American 1 **> Sele
=,.. I
).OOOOOOOOCIOOOOO<
New...
:ooooooooooooooc v
Moflijfp... [
OK | Cancel
Apply
3) From the Geographic Coordinate System window, first choose in the
list of datum (it's at the top) and then choose for the spheroid. Enter
6370000 in both the semimajor and semiminor boxes.
Geographic Coordinate System Properties
General
Name: |CustomSpheroidGCS_North_American_1983
Datum
Name: i IH
Spheroid
Name: |
SemimaiorAxis: 16370000
!>" Semiminor Axis:
6370000
Inverse Flattening
Angular Unit
Name: | Degree
Radians per unit: |0.017453232519943239
Prime Meridian
Name: | Greenwich
Longitude:
i
I OK
Cancel
E-13
-------
Projection Information for HB Grid - Example #1
Year
2001
2002
2003
2004
2005
2006
Geographic
Coordinate
System
Lat/Lon
Datum
Spherical
R=6370997
Spherical
R=6370000
Prime
Meridian
NA
Angular
Unit
Degrees
Projected
Coordinate
System
Lambert
Conformal
Conic
False
Easting
0.0
False
Northing
0.0
Central
Meridian
-97.0
Standard
Parallel_l
33.0
Standard
Parallel_2
45.0
Scale
Factor
1.0
Latitude
of Origin
40.0
Linear
Unit
Meters
Grid Descriptive Parameters
Year
2001
2001
2002
2002
2003
2003
2004
2004
2005
2005
2006
2006
Grid
Resolution
(km)
12
36
12
36
12
36
12
36
12
36
12
36
XORIG
(m)
-252000
-2736000
-1008000
-2736000
-1008000
-2736000
-1008000
-2736000
-1008000
-2736000
YORIG
(m)
-1284000
-2088000
-1620000
-2088000
-1620000
-2088000
-1620000
-2088000
-1620000
-2088000
XCELL
(m)
12000
36000
12000
36000
12000
36000
12000
36000
12000
36000
YCELL
(m)
12000
36000
12000
36000
12000
36000
12000
36000
12000
36000
NCOLS
213
148
279
148
279
148
279
148
279
148
NROWS
188
112
240
112
240
112
240
112
240
112
E-14
-------
Projection Information for HB Grid - Example #2
Year
2001
2002
2003
2004
2005
2006
Datum
(i.e.,
NAD83
or
WGS84)
Semimajor
Axis
(m)
(i.e.,
6370000)
Semiminor
Axis
(m)
(i.e.,
637000)
Angular
Unit
(i.e.,
degree
or
radians)
Projected
Coordinate
System
(i.e.,
Lambert
Conformal
Conic)
False
Easting
(i.e.,
0.0)
False
Northing
(i.e., 0.0)
Longitude
of Central
Meridian
(i.e.,
-97.000)
Latitude of
Standard
Parallel 1
(i.e.,
33.000)
Latitude of
Standard
Parallel 2
(i.e.,
45.000)
Latitude
of Origin
(i.e.,
40.0)
Linear
Unit
(i.e.,
meters)
Grid Descriptive Parameters
Year
2001
2001
2002
2002
2003
2003
2004
2004
2005
2005
2006
2006
Grid
Resolution
(km)
12
36
12
36
12
36
12
36
12
36
12
36
XORIG
(m)
(i.e., -2736000)
YORIG
(m)
(i.e., -2088000)
XCELL
(m)
(i.e., 36000)
YCELL
(m)
(i.e., 36000)
NCOLS
(i.e., 148)
NROWS
(i.e., 112)
E-15
-------
-------
-------
vvEPA
United States
Environmental Protection
Agency
Recycled/Recyclable
Printed with vegetable-based ink on
paper that contains a minimum of
50% post-consumer fiber content
processed chlorine free
------- |