oEPA
United States EPA/600/R-10/018
Environmental Protection February 2010
Agency www.epa.gov
Hierarchical Bayesian Model
(HBM)-Derived Estimates of
Air Quality for 2003
Annual Report
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Hierarchical Bayesian Model
(HBM)-Derived Estimates of
Air Quality for 2003
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
Eric S. Hall
National Exposure Research Laboratory (NERL)
109 T.W. Alexander Dr.
Durham, NC 27711-0001
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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).
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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 2003 Platform 13
3.2 2002 Emission Inventories and Approaches 14
3.2.1 2002 Point Sources (ptipm and ptnonipm) 15
3.2.1.1 IPM Sector (ptipm) 17
3.2.1.2 Non-IPM Sector (ptnonipm) 18
3.2.2 2002 Nonpoint Sources (afdust, ag, nonpt) 18
3.2.2.1 Area Fugitive Dust Sector (afdust) 18
3.2.2.2 Agricultural Ammonia Sector (ag) 19
3.2.2.3 Other Nonpoint Sources (nonpt) 19
3.2.3 Fires (ptfire, nonptfire and avefire) 20
3.2.4 Day-Specific Point Source Fires (ptfire) 20
3.2.5 County-Level Fires (nonptfire) 20
3.2.6 Development of Wildland Fire Emission Inventories for 2002-2006 21
3.2.7 Biogenic Sources (biog) 25
3.2.8 2002 Mobile sources (onroad, nonroad, aim) 25
3.2.9 2002 Onroad Mobile Sources (onroad) 26
3.2.10 Nonroad Mobile Sources - NMIM-Based Nonroad (nonroad) 26
3.2.11 Nonroad Mobile Sources: Aircraft, Locomotive, and Commercial
Marine (aim) 27
3.2.12 Adjustments to 2002 NEI for 2003-2005 27
3.3 Emissions Modeling Summary 30
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3.3.1 The SMOKE Modeling System 30
3.3.2 Key Emissions Modeling Settings 31
3.3.3 Spatial Configuration 32
3.3.4 Chemical Speciation Configuration 32
3.3.5 Temporal Processing Configuration 34
3.3.6 Vertical Allocation of Day-Specific Fire Emissions 35
3.3.7 Emissions Modeling Ancillary Files 36
3.3.7.1 Spatial Allocation Ancillary Files 36
3.3.7.2 Surrogates for U.S. Emissions 36
3.3.7.3 Allocation Method for Airport-Related Sources in the U.S 37
3.3.7.4 Surrogates for Canada and Mexico Emission Inventories 37
3.3.7.5 Chemical Speciation Ancillary Files 38
3.3.7.6 Temporal Allocation Ancillary Files 39
4.0 CMAQ Air Quality Model Estimates 41
4.1 Introduction to the CMAQ Modeling Platform 41
4.1.1 Advantages and Limitations of the CMAQ Air Quality Model 42
4.2 CMAQ Model Version, Inputs and Configuration 43
4.2.1 Model Version 43
4.2.2 Model Domain and Grid Resolution 44
4.2.3 Modeling Period / Ozone Episodes 46
4.2.4 Model Inputs: Emissions, Meteorology and Boundary Conditions 46
4.3 CMAQ Model Performance Evaluation 48
5.0 Bayesian Model-Derived Air Quality Estimates 55
5.1 Introducti on 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
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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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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 15
Table 3-2 Summaries by Sector of 2002 Base Year Emissions for the Continental
United States (48 states + District of Columbia) 16
Table 3-3 Summaries by Sector for the Other ("oth") - Canada, Mexico, and
Offshore - 2002 Base Year Emissions Within the 36-km Domain 16
Table 3-4 Process/Emissions Model Mapping 23
Table 3-5 MOBILE6 Onroad and Nonroad Model Versions 29
Table 3-6 Key Emissions Modeling Steps by Sector 31
Table 3-7 Model Species Produced by SMOKE for CB05 33
Table 3-8 Temporal Settings Used for the Platform Sectors in SMOKE 35
Table 4-1 Geographic Information for Modeling Domains 45
Table 4-2 Vertical Layer Structure for MM5 and CMAQ (heights are layer top) 46
Table 4-3 Summary of CMAQ 2003 Hourly O3 Model Performance Statistics 49
Table 4-4 Summary of CMAQ 2003 8-Hour Daily Maximum O3 Model
Performance Statistics 51
Table 4-5 Summary of CMAQ 2003 Annual PM2.5 Species Model Performance
Statistics 53
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, PM10 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 23
Figure 3-2 Distribution of PM2.5 Emissions 24
Figure 3-3 CMAQ Modeling Domain 32
Figure 3-4 Chemical Speciation Approach Used for the 2002-Based Platform 34
Figure 4-1 Map of the CMAQ Modeling Domain 45
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 HB Prediction (PM2.5) Temporarily Matches AQS Data and CMAQ
Estimates 60
Figure 5-4 HB Prediction (PM2.5) Compensates When AQS Data is Unavailable 60
Figure 5-5 HB 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 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
Figure 5-7 Rotated View of the Response Surface of PM2.5 Concentrations as Predicted
by the HBM 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 HBM 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
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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 O3 and
PM2.5 concentrations throughout the continental United States during the 2003 calendar year.
HBM estimates provide the spatial and temporal variance of O3 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 PM25; 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 (03) 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 (SO2), ozone (O3), and particulate matter (PM). These standards, called the National
Ambient Air Quality Standards (NAAQS), are designed to protect public health and the
environment. The CAA established two types of air quality standards. Primary standards set
limits to protect public health, including the health of "sensitive" populations such as asthmatics,
children, and the elderly. Secondary standards set limits to protect public welfare, including
protection against decreased visibility, damage to animals, crops, vegetation, and buildings. The
law requires EPA to 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://vvvvvv.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://vvvvvv.epa.gov/ozonedesi gnations/ and
http://www.epa.gov/pmdesignations.
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2.1.2 Ozone
Ozone is a colorless gas composed of three oxygen atoms. Ground level ozone is formed when
pollutants released from cars, power plants, and other sources react in the presence of heat and
sunlight. It is the prime ingredient of what is commonly called "smog." When inhaled, ozone
can cause acute respiratory problems, aggravate asthma, cause inflammation of lung tissue, and
even temporarily decrease the lung capacity of healthy adults. Repeated exposure may
permanently scar lung tissue. Toxicological, 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.g0v/ttn/naaqs/standards/0z0ne/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)
1997
2008
1-Hour Standard
0.12
0.12
8-Hour Standard
0.08
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 (PM10) pose a health concern
because they can be inhaled into and accumulate in the respiratory system. Particles less than 2.5
micrometers in diameter (PM2.5) are referred to as "fine" particles. Because of their small size,
fine particles can lodge deeply into the lungs. Sources of fine particles include all types of
combustion (motor vehicles, power plants, wood burning, etc.) and some industrial processes.
Particles with diameters between 2.5 and 10 micrometers (PM10-2.5) are referred to as "coarse" or
2The 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
PMi0andPM25
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PMc. Sources of PMc include crushing or grinding operations and dust from paved or unpaved
roads. The distribution of PMi0, 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/naaqsrev20Q6.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)
1997
2006
Annual Average
15
15
24-Hour Average
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2.2 Ambient Air Quality Monitoring in the United States
2.2.1 Monitoring Networks
The Clean Air Act 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://vvvvvv.epa.gov/ttn/airs/airsaqs/index.htm.
8
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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
9
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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
IAG 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)
PM2.5 (daily 24-hr integrated samples by FRM)
Average ambient concentrations of particulate matter (< 2.5 microns in diameter)
and compared to annual PM2 5 NAAQS (by state).
% population exceeding annual PM2.5 NAAQS (by state).
% of days with PM2.5 concentration over the daily NAAQS (or other relevant
benchmarks (by county and MSA)
Number of person-days with PM2.5 concentration over the daily NAAQS & other
relevant benchmarks (by county and MSA)
2.3.1 Rationale for the Air Quality Indicators
The CDC EPHT Network is initially focusing on ozone and PM2.5. These air quality indicators
are based mainly around the NAAQS health findings and program-based measures
(measurement, data and analysis methodologies). The indicators will allow comparisons across
space and time for EPHT actions. They are in the context of health-based benchmarks. By
bringing population into the measures, they roughly distinguish between potential exposures (at
broad scale).
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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.
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3.0 Emissions Data
3.1 Introduction to the 2003 Platform
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.htmn. 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 (O3) 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 (SO2), ammonia (NH3), particulate matter less than or
equal to 10 microns (PM10), and individual component species for particulate matter less than or
equal to 2.5 microns (PM2.5). 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).
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/emchA under the section entitled "CAP 2002-Based
Platform, Version 3."
This summary contains two additional sections. Section 3.2 describes the 2002-2005 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.
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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 2002-2005 emissions data created for input to SMOKE. The primary
basis for the 2002-2005 emission inputs for the 2002 Platform is the 2002 National Emission
Inventory (NEI), which includes emissions of CO, NOx, VOC, SO2, NH3, PM10, 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.html#documentation. 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.
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.
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.
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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
2002 NEI
SECTOR
Description and Resolution of the Data Input to SMOKE
IPM sector: ptipm
Point
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.
Non-IPM sector: ptnonipm
Point
All NEI point source records not matched to the ptipm sector, annual
resolution.
Point source fire sector:
ptfire
Fires
Point source day-specific wildfires and prescribed fires for 2002.
Nonpt fire sector:
nonptfire
Fires and
Nonpoint
Prescribed fires for 2002 for which day-specific data were not
available, county and annual resolution.
Agricultural sector: ag
Nonpoint
NH3 emissions from NEI nonpoint livestock and fertilizer application
sources, county and annual resolution.
Area fugitive dust sector:
afdust
Nonpoint
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.
Remaining nonpoint
sector: nonpt
Nonpoint
All nonpoint sources not otherwise included in other SMOKE sectors,
county and annual resolution.
Nonroad sector: nonroad
Mobile:
Nonroad
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,
marine: aim
Mobile:
Nonroad
Aircraft, locomotive, commercial marine vessel emissions sources,
county and annual resolution.
Onroad: onroad
Mobile:
onroad
Monthly onroad emissions from NMIM using MOBILE6, other than
for California. Monthly emissions for California created using annual
emissions submitted by CARB for the 2002 NEI.
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PLATFORM SECTOR
2002 NEI
SECTOR
Description and Resolution of the Data Input to SMOKE
Biogenic: biog
NA
Hour-specific, grid cell-specific emissions generated from the
BEIS3.13 model (includes emissions in Canada and Mexico).
Other point sources not
from the NEI: othpt
NA
Point sources from Canada's 2000 inventory, Mexico's 1999 inventory,
and offshore point sources from the 2001 Platform, annual resolution.
Other nonpoint and
nonroad not from the NEI:
other
NA
Canada (province resolution) and Mexico (municipio resolution)
nonpoint and nonroad mobile inventories, annual resolution.
Other onroad sources not
from the NEI: othon
NA
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
Sector
[tons/yr]
voc
[tons/yr]
NOx
[tons/yr]
CO
[tons/yr]
S02"
[tons/yr]
nh3
[tons/yr]
PM10
[tons/yr]
PM25
2002
afdust
0
0
0
0
0
8,901,461
1,830,271
Ag
0
0
0
0
3,251,990
0
0
Aim
123,676
2,259,844
806,471
312,313
904
97,039
86,719
avefire
451,127
189,428
8,554,550
49,094
36,777
796,229
684,034
nonpt
7,929,917
1,531,602
7,526,723
1,250,265
135,542
1,377,055
1,100,884
nonroad
2,873,622
2,176,159
21,386,059
187,284
1,859
227,875
216,658
onroad
4,847,990
7,786,709
59,810,866
242,379
290,708
205,914
146,003
ptipm
42,378
4,618,944
605,148
10,359,102
29,991
608,718
501,998
ptnonipm
1,425,158
2,368,987
3,195,469
2,249,550
154,180
603,606
372,330
2002 Total
17,693,869
20,931,673
101,885,285
14,649,986
3,901,951
12,817,898
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
Country &
Sector
[tons/yr]
VOC
[tons/yr]
NOi
[tons/yr]
CO
[tons/yr]
S02"
[tons/yr]
NH3
[tons/yr]
PM10
[tons/yr]
PM25
2002
Canada othar
1,878,996
1,060,097
4,282,782
227,942
569,738
1,462,643
400,493
Canada othon
410,981
874,564
5,810,763
26,376
18,332
19,692
18,071
Canada othpt
237,957
628,175
1,149,266
2,115,572
23,866
241,081
129,342
Canada
Subtotal
2,527,933
2,562,836
11,242,811
2,369,890
611,937
1,723,417
547,906
Mexico othar
586,842
249,045
644,733
101,047
486,484
143,816
92,861
Mexico othon
183,563
147,519
1,456,285
8,276
2,549
6,960
6,377
Mexico othpt
113,044
258,510
88,957
980,359
0
125,385
88,132
Mexico
Subtotal
883,448
655,074
2,189,976
1,089,682
489,033
276,161
187,370
Offshore othpt
70,329
26,628
6,205
0
0
0
0
2002 Total
6,893,091
6,462,448
26,871,779
6,919,144
2,201,939
3,999,156
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.html#documentation.
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 Nonpoint 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., PMio
and PM2.5) 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 see 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.
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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 particulate matter in some parts of the country. Because such sources are reported by a very
small number of states, and are inventoried as county annual totals without the information 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
(MSAT) 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."
19
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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)"
20
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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 5
A = area burned
F = fuel available for consumption
c = fraction of available fuel consumed
EFS = emission factor (mass of species 5 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.
7
Dozier J. 1981. A method for satellite identification of surface temperature fields of subpixel resolution. Remote
Sensing of Environment 11 (3), 221-229.
21
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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
models that provide values for the terms in the above equation. BlueSky allows the user to
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.
9Sullivan D.C., Raffuse S.M., PrydenD.A., Craig K.J., 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/rafruse 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.
nLarkin 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).
22
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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
Model Used
Fuel Loading
Fuel Characteristic Classification System (FCCS)
Fuel Consumption
Consume 3.0
Emissions
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 below in Figure 3-1, 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/)
c
o
-I—<
LO
c\j
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
23
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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 PM25 Emissions
(2003 - 2006)
29,000 tons
W
Figure 3-2. Distribution of PM2.5 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
24
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miss. Previous emission inventory work has treated prescribed burning as an area source, with
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/abstracts/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/EmisInventorv/2002finalnei/mobile sector data/ncd files/ncd20070727 2002.zip.
NMIM 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.uov/Emislnventorv/2002finalnei/documentation/mobile/2002 mobile nei version 3
report 092807.pdf.
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.
25
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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
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.html#documentation. 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
NMIM 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 NMIM 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 (EMFAC2007) 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
13PMiq and PM2 5 in the 2001 Platform were not broken out by mode.
26
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nonroad sources are not included in the nonpt sector. Rather, we kept these emissions in the
nonroad sector.
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
NMIM results for California. The process erroneously dropped emissions for certain sources
(FIPS code/SCC combinations) that were not computed via NMIM; 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.html#documentation. 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 Adjustments to 2002 NEI for 2003-2005
EGUs
Annual emissions estimates for EGUs for all NEI air pollutants (both Criteria and Hazardous air
pollutants) for three years (2003, 2004, 2005) were developed using data reported to the
USEPA's Clean Air Marketing Division's (CAMD) Acid Rain database. The Acid Rain
database contains hourly emissions for SO2 and NOx emissions plus hourly heat input amounts.
These three values are reported to the database by the largest electric generating facilities,
usually based upon continuous emissions monitors (CEMs). The general approach to develop
emission estimates for all pollutants for these sources that would be compatible in both structure
and individual process identification and release point parameters with the NEI requirements was
to ratio the existing 2002 NEI emissions values up or down to the other three years, using
information from the Acid Rain database to determine the appropriate ratios.
For all pollutants except the directly monitored SO2 and NOx, the ratio of the Acid Rain heat
input for one of the three years to the Acid Rain heat input for 2002 was used as the adjusting
ratio to estimate the 2003, 2004, or 2005 emissions. For SO2 and NOx, the ratio of the actual
Acid Rain emissions values to the 2002 NEI emissions values were used as the adjusting ratio to
estimate the 2003, 2004, or 2005 emissions. The SO2 and NOx emissions in the NEI for 2003,
2004, and 2005 will thus be equal to the actual monitored emissions seen in the Acid Rain
database. For all other pollutants, the NEI emissions for the three years essentially assume that
each unit was emitting at the same rate (per BTU of heat input) as it did in 2002.
27
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The ratios were developed for each emissions unit that could be found and reliably matched
between the 2002 NEI and the 2002 Acid Rain database. If a unit was found in both of these
data sets, then the Acid Rain values for the additional three years were either found or it was
verified that the unit had ceased operating (in which case a ratio of zero was used to zero out
2003, 2004, or 2005 emissions). The ratios were developed using annual total sums of the
reported hourly S02, NOx, or heat input. Ratios were developed for a total of 2,144 emission
units that could be matched between the 2002 NEI and the 2002 Acid Rain database. The 2,144
units are uniquely identified by the combination of fields "ORISPLCODE" and "UNITID" in
the Acid Rain database. These 2144 Acid Rain units matched up to 2,168 units as defined in the
2002 NEI, due to differences in the way some state and local air agencies identify or define
individual units in their NEI submissions. For the instances where multiple NEI "units" had
been matched to a single Acid Rain unit, the sum of all S02 and NOx emissions in the 2002 NEI
was used as the denominator of the ratio. Lastly, the ratios that were thus developed at the
emission unit level were applied to all individual process-level emissions at those units. All NEI
emissions are reported at the process-level, which is a sub-division of an emission unit. For
EGU and other combustion sources, the processes within an emission unit typically represent the
different fuels that were burned in the unit.
The Acid Rain data used for this procedure was downloaded March 26, 2007 from CAMD's
"Data and Maps" Web page (http://camddataandmaps.epa.gov/gdm/).
1. Select "Emissions":
(http://camddataandmaps.epa. uov/udm/index.cfm?fuseaction=emissions. wizard)
2.
Select
"Unit Level Emissions" on left side of Web page
3.
Select
"Time Frame" on left side of Web page
4.
Select
"Annual" in menu box in center of Web page
5.
Select
"2002" in second menu box that appears in center of Web page
6.
Select
"Quick Reports" on left side of Web page
7.
Select
"Unit Level Emissions Quick Report" in menu box in top-center of Web page
8.
Select
"Annual" in menu box in mid-center of Web page
9.
Select
"2002" in second menu box that appears in mid-center of Web page
10
. Select "Acid Rain Program" in menu box in bottom-center of Web page
11. Select "Get Report" button at bottom of Web page
The resulting query will provide the number of facilities and number of units for the selected
year(s). There are buttons to allow the user to: a) obtain report definitions; b) print the report
28
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page; c) download the data from the query (in either *.csv or *.txt format); d) download the
caveats for the data (in either *.csv or *.txt format); or e) start a new query. This procedure can
be repeated for multiple years. The *.csv formats can be imported to an MS Excel spreadsheet
or an MS Access database.
Other Stationary Sources (Point and Nonpoint)
Emission estimates for other stationary sources, including both point and nonpoint stationary
sources, were held constant at the level in Version 3 of the 2002 NEI. The only exception to this
was that some information on plants that closed after 2002 was incorporated into the emissions
modeled. Emissions for plants that closed were set to zero.
Onroad and Nonroad Mobile Sources
Emission estimates for all pollutants were developed using EPA's National Mobile Inventory
Model (NMIM), which uses MOBILE6 to calculate onroad emission factors. State and local
agencies had an opportunity to provide model inputs (vehicle populations, fuel characteristics,
VMT, etc) for base years 2002 and 2005v2. Where applicable, these inputs were used in the
other years. For example, for each of these three years, a full VMT database at the county,
roadway type, and vehicle type level of detail was developed from Federal Highway
Administration (FHWA) information. For states and local areas that submitted VMT data that
were incorporated in the 2002 NEI, the 2002 NEI VMT data were grown to 2003, 2004, and
2005vl using growth factors developed from the FHWA data. These grown VMT data replaced
the baseline FHWA-based VMT data. For 2005v2, where state and local agencies provided new
2005 VMT estimates, they replaced the 2005vl VMT.
Emission estimates for NONROAD model engines were developed using EPA's National
Mobile Inventory Model (NMIM), which incorporates NONROAD2005. Where states provided
alternate nonroad inputs, these data replaced EPA default inputs, as described above.
Details on the model versions used for each base year's run are documented in the table below.
For more information on how NMIM is run, refer to the 2005 NEI documentation posted at
ftp://ftp.epa.gov/EmisInventory/2005_nei/mobile/2005_mobile_nei_version_2_report.pdf
Table 3-5. MOBILE6 Onroad and Nonroad Model Versions
ln\onion
Year
MOIill.l. Version
NONKOAI) Version
NMIM Version
NCI) Version
2003
M6203 CHC\M6203 ChcOxFixNMIM.exe
nr05c-
BondBase\NR05c.exe
NMIM20070410
NCD2007072"
2004
M6203 CHC\M6203 ChcOxFixNMIM.exe
nr05c-
BondBase\NR05c.exe
NMIM20070410
NCD20070912
2005 VI
M6203 ChcOxFixNMIM
NR05c-BondBase
NMIM20070410
NCD20070912
Fires
This data will be supplied upon completion of the processing/analysis for the fires data.
29
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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
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.html#2002.
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.
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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 3-6 summarizes the
major processing steps of each platform sector. The "Spatial" column shows the spatial
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 3-6. Key Emissions Modeling Steps by Sector
Platform sector
Spatial
Speciation
Inventory
resolution
Plume rise
Ptipm
point
Yes
daily &
hourly
Yes
Ptnonipm
point
Yes
annual
Yes
Othpt
point
Yes
annual
Yes
Nonroad
surrogates &
area-to-point
Yes
monthly
Other
surrogates
Yes
annual
Aim
surrogates &
area-to-point
Yes
annual
Onroad
surrogates
Yes
monthly
Othon
surrogates
Yes
annual
Nonpt
surrogates &
area-to-point
Yes
annual
Ag
surrogates
Yes
annual
Afdust
surrogates
Yes
annual
Biog
pre-gridded
land use
in BEIS
hourly
Ptfire
point
Yes
daily
Yes
Nonptfire
surrogates
Yes
annual
Avefire
surrogates
Yes
annual
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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.
V
's -v_ 1 2km Eastern CMAQ Domain
Y1 v*y: -252000,-1284000
^ ' Ł\ 213 row: 188
36km Domain \ "v ;
Lower Left Corner: -2736000, ;-2088000
Number of Cols, Rows: 148x112 X
Lambert Projection: *\ '-V
1st std parallel: 33 •, )
2nd std parallel: 45
central meridian -97
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 3-7 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
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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 3-7. Model Species Produced by SMOKE for CB05
Inventory Pollutant
Model Species
Model Species Description
CO
CO
Carbon monoxide
NOx
NO
Nitrogen oxide
N02
Nitrogen dioxide
so2
so2
Sulfur dioxide
SULF
Sulfuric acid vapor
nh3
nh3
Ammonia
VOC
ALD2
Acetaldehyde
ALDX
Propionaldehyde and higher aldehydes
ETH
Ethene
ETHA
Ethane
ETOH
Ethanol
FORM
Formaldehyde
IOLE
Internal olefin carbon bond (R-C=C-R)
ISOP
Isoprene
MEOH
Methanol
OLE
Terminal olefin carbon bond (R-C=C)
PAR
Paraffin carbon bond
TOL
Toluene and other monoalkyl aromatics
XYL
Xylene and other polyalkyl aromatics
Various additional VOC
species from the biogenics
model which do not map to
TERP
Terpenes
the above model species
PM10
PMC
Coarse PM >2.5 microns and <10 microns
PM25
PEC
Particulate elemental carbon < 2.5 microns
PNO3
Particulate nitrate <2.5 microns
POC
Particulate organic carbon (carbon only) < 2.5
microns
PSO4
Particulate sulfate < 2.5 microns
PMFINE
Other particulate matter <2.5 microns
33
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VOC mass
from emission source
Speciation
cross reference file
Assign speciation profile
code to emission source
Convert VOC to TOG
Compute moles of each
CB05 model species
SMOKE
CB05-specific mapping:
Moles chemical compounds
to moles of model species
Provided by Dr. Carter
(UC Riverside)
conversion factors: 3*
VOC to TOG by profile:
TOG split factors:
speciate TOG mass to
moles of model species
SPECIATE4.0 Database
TOG profiles:
(1)Fraction of chemical compound
by profile code
(2) Conversion factors: VOC-to-TOG
by profile code
Speciation Tool
VOC Speciation
SPECIATE4.0 Database
Simplified PM2.5 profiles:
Fraction of chemical components
by profile code
Speciation
cross reference file
PM2.5 mass
from emission source
Assign speciation profile
code to emission source
PM2.5 profiles that
speciate PM2.5 mass to
mass of model species
Compute mass of each
PM2.5 model species
Speciation Tool
SMOKE
PM2.5 Speciation
Figure 3-4. Chemical Speciation Approach Used for the 2002-Based Platform
3.3.5 Temporal Processing Configuration
Table 3-8 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:
• 36 km: 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.
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Table 3-8. Temporal Settings Used for the Platform Sectors in SMOKE
Platform sector
Inventory
resolution
Monthly
profiles
used?
Daily
temporal
approach1'2
Merge
processing
approach1'3
Process Holidays as
separate days?
Ptipm
daily &
hourly
all
all
yes
ptnonipm
annual
yes
mwdss
all
yes
Othpt
annual
yes
mwdss
all
nonroad
monthly
mwdss
mwdss
yes
Other
annual
yes
mwdss
mwdss
Aim
annual
yes
mwdss
mwdss
Onroad
monthly
week
week
yes
Othon
annual
yes
mwdss*
mwdss*
Nonpt
annual
yes
mwdss
mwdss
yes
Ag
annual
yes
aveday
aveday
Afdust
annual
yes
aveday
aveday
Biog
hourly
n/a
n/a
Ptfire
daily
all
all
nonptfire
annual
yes
aveday
aveday
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.
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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
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/version2.4/ for full
documentation of Laypoint and the new day-specific formats for the fire files.
3.3.1 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://vvvvvv.epa.gov/ttn/chief/emch/spatial/spatialsurrogate.html. The document
ftp://ftp.epa.gov/EmisInventory/emiss shp2006/us/1 ist 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
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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
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/EmisInventorv/2002fmalnei/mobile sector data/ncd files/gis allocation.
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://vvvvvv.epa.gov/ttn/chief/emch/spatial/nevvsurrogate.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 CON US 36-km national
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domain. The shapefiles used are available at
http://www.epa.gov/ttn/chief/emch/spatial/spatialsurrogate.html and the 12-km and 36-km
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://vvvvvv.epa.gov/ttn/chief/softvvare/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 PM25. The database also contains the PM25 speciated into
both individual chemical compounds (e.g., zinc, potassium, manganese, lead), and into the
"simplified" PM25 components used in the air quality model. These simplified components are:
PS04, 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."
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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.
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40
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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
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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 2003-2006 Air Quality Modeling Platform. 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
2003-2006 Platform to provide a national scale air quality modeling analysis. The CMAQ model
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.
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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 2003-2006 Platform
along with the results of a model performance evaluation in which the 2003-2006 model
predictions are compared to corresponding measured concentrations. It is drawn entirely from
the following publication: Technical Support Document for the Proposed Locomotive/Marine
Rule: Air Quality Modeling," U.S. Environmental Protection Agency, Office of Air Quality
Planning and Standards, Air Quality Assessment Division, Research Triangle Park, NC, EPA
454/R-07-004, March 2007.
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 4.715). CMAQ version 4.7 reflects updates to
previous versions of the model to improve the underlying science. These model enhancements
in version 4.7 include:
1) Aerosols
- Secondary Organic Aerosol (SOA) Model Enhancements
+ Updates: isoprene SOA, sesquiterpene SOA, polymerization, acid-catalyzed
SOA, NOx-dependent SOA yields, and enthalpy of vaporization
+ In-cloud SOA formation pathways (glyoxal, methylglyoxal)
+ Changes in gas-phase chemistry mechanism, emissions speciation, and biogenic
emissions model, to represent SOA precursors
- Coarse PM
+ Semi-volatile inorganic components (NO3", CI", and NH4+) can condense and
evaporate from the coarse mode, via dynamic mass transfer
+ Nonvolatile sulfate can condense on the coarse mode
+ Variable standard deviation of coarse mode size distribution
+ Emissions of sea salt from the surf zone
- Heterogeneous reaction probability
+ Re-derived parameterization based on Davis et al. (2008)
2) Chemistry
- HONO enhancements
+ Heterogeneous reaction on aerosol and ground surfaces
15CMAQ version 4.7 was released on December I, 2008. It is available from the Community Modeling and
Analysis System (CMAS) at: http://www.cmascenter.org.
43
-------
+ Emissions from mobile sources
- Photolysis Options (beta versions)
+ In-line photolysis rate module, with aerosol feedback
+ Photolysis rates adjusted using satellite-derived cloud information
(currently table-approach only)
- Aqueous Chemistry
+ Added two organic oxidation reactions (glyoxal, methylglyoxal)
+ Updates to Henry's Law constants based on literature review
- Base CB05 mechanism with Q2 chemistry
- Multi-pollutant Capability
+ Include HAPs and Hg in single modeling platform
3) In-line options
- Dry Deposition
+ Moved calculation into CCTM
- Emissions
+ Integrated BEIS into CCTM
+ Incorporated Plume-rise into CCTM
- Bi-directional NH3 and Hg surface flux
+ For NH3, fertilizer emissions will be applied through the flux model
(under development)
4) Emissions
- Biogenic emissions: added sesquiterpene emissions
- Sea-salt emissions
+ Updated flux parameterizations and surf zone emissions
+ Used spatial allocator to produce ocean file
- Speciation changes for HONO and benzene
5) Clouds
- Convective cloud model
+ Revised to reduce layer configuration differences
+ Changed the integration timestep
- Resolved cloud model
+ Correction in precipitation flux calculation
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
44
-------
finer-scale 12-km grids over a portion 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
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.
i w'/f
¦
Jj2km Eastern CMAQ Domain
V J
Lower Lett Corner; -2736000, ;-20880kf V /^s\
Number of Cols, Rows: 148x132* s
Lambert Projection: \ \ \
1st std parallel: 33 \ ) \ \ /
2nd std parallel: 45 f
cenlral meridian: -97 f
^ v> I-
t,y -252000,-1284000
'0^ 213 low: 188
36km Domain
lalilude 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
CMAQ Modeling Configuration
National Grid
Eastern U.S. Fine Grid
Map Projection
Lambert Conformal Projection
Grid Resolution
36 km
12 km
Coordinate Center
97 W, 40 N
True Latitudes
33 and 45 N
Dimensions
148x112x24
279x1240x24
Vertical extent
24 Layers: Surface to 100 nib level (see Table 4-2)
45
-------
4.2.3 Modeling Period / Ozone Episodes
The 36-km and both 12-km CMAQ modeling domains were modeled for the entire years of
2003-2006. All 365 (366 in 2004) model days were used in the annual average levels of PM2.5.
For the 8-hour ozone, we used modeling results from the period between May 1 and September
30. This 153-day period generally conforms to the ozone season across most parts of the U.S.
and contains the majority of days that observed high ozone concentrations.
4.2.4 Model Inputs: Emissions, Meteorology and Boundary Conditions
2003-2006Emissions: The emissions inventories used in the 2003-2006 air quality modeling are
described in Section 3, above.
Meteorological Input Data: The gridded meteorological data for the entire years of 2003-2006 at
36 km were derived from simulations of the Pennsylvania State University / National Center for
Atmospheric Research Mesoscale Model. This model, commonly referred to as MM5,16 is a
limited-area, nonhydrostatic, terrain-following system that solves for the full set of physical and
thermodynamic equations which govern atmospheric motions. For this analysis, version 3.7.4 of
MM5 was used for both the 36- and 12-km domains. The 36-km horizontal domain consisted of
165 by 129 cell grids. The 12-km MM5 domain consisted of a 290 x 251 grid cell domain that
extends well beyond the 12-km CMAQ grid.
The meteorological outputs from both MM5 sets were processed to create model-ready inputs for
CMAQ using the Meteorology-Chemistry Interface Processor (MCIP),17 version 3.4, to derive
the specific inputs to CMAQ: horizontal wind components (i.e., speed and direction),
temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each vertical
layer. The MM5 was run on the same map projection as CMAQ. Both the 36- and 12-km MM5
simulations utilized 34 vertical layers with a surface layer of approximately 38 meters. 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
MM5 Layers
Sigma P
Approximate
Height (m)
Approximate
Pressure (mb)
0
0
1.000
0
1000
1
1
0.995
38
995
2
2
0.990
77
991
3
3
0.985
115
987
4
0.980
154
982
4
5
0.970
232
973
16Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth-Generation Penn State/NCAR Mesoscale
Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for Atmospheric Research, Boulder, CO.
17Byun, 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). Please also see:
http://www.cmascenter.org.
46
-------
6
0.960
310
964
5
7
0.950
389
955
8
0.940
469
946
6
9
0.930
550
937
10
0.920
631
928
11
0.910
712
919
7
12
0.900
794
910
13
0.880
961
892
14
0.860
1,130
874
8
15
0.840
1,303
856
16
0.820
1,478
838
17
0.800
1,657
820
9
18
0.770
1,930
793
19
0.740
2,212
766
10
20
0.700
2,600
730
21
0.650
3,108
685
11
22
0.600
3,644
640
23
0.550
4,212
595
12
24
0.500
4,816
550
25
0.450
5,461
505
26
0.400
6,153
460
13
27
0.350
6,903
415
28
0.300
7,720
370
29
0.250
8,621
325
30
0.200
9,625
280
14
31
0.150
10,764
235
32
0.100
12,085
190
33
0.050
13,670
145
34
0.000
15,674
100
The key MM5 model physics options that were utilized are as follows:
• Cumulus Parameterization: Kain-Fritsch 2
• Planetary Boundary Layer Scheme: Asymmetric Convective Model version 2
• Explicit Moisture Scheme: Reisner 2
• Radiation Scheme: RRTM
• Land Surface Model: Pleim-Xiu
Similar to the 2001 MM5 model performance evaluations, we 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
47
-------
model fields closely matched the observed synoptic patterns, which is expected given the use of
nudging.
Initial and Boundary Conditions: The lateral boundary and initial species concentrations are
provided by a three-dimensional global atmospheric chemistry model, the GEOS-CHEM18
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 24 vertical layers. The 2003-2006 CMAQ 36-km simulations
used non-year specific GEOS-CHEM data, which was created by taking the median value of the
2002 GEOS-CHEM data described above. 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
The statistical portion of the evaluation examined the model bias and error for temperature, water
vapor mixing ratio, and the index of agreement for the wind fields. These statistical values were
calculated on a regional basis. Table 4-3 shows the results of the statistical evaluation of ozone
data calculated for a threshold of 40 ppb of observed and modeled concentrations, for the 12-km
Eastern U.S. domain and the four subregions (Midwest, Northeast, Southeast, and Central U.S.)
An operational model performance evaluation for ozone and PM2.5 and its related speciated
components was conducted for 2003-2006 using 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 domain.
There are various statistical metrics available and used by the science community for model
performance evaluation. For a robust evaluation, the principal evaluation statistics used to
evaluate CMAQ performance were two bias metrics, normalized mean bias (NMB) and
fractional bias (FB); and two error metrics, normalized mean error (NME) and fractional error
(FE). 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. It 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:
pp-o)
NMB = - *100, where P = predicted concentrations and O = observed
Z(o)
18Yantosca, B., 2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric Chemistry Modeling Group, Harvard
University, Cambridge, MA, October 15, 2004.
48
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Table 4-3. Summary of CMAQ 2003 Hourly 03 Model Performance Statistics
CMAQ 2003 Hourly Ozone:
Threshold of 40 ppb
No. of
Obs.
NMB
(%)
NME
(%)
FB
(%)
FE
(%)
May
12-km EUS
200319
-4.2
14.2
-5.2
15.3
Northeast
34932
-0.2
14.4
-1.5
15.0
Midwest
44010
-0.5
13.9
-1.8
14.6
Southeast
54456
-6.6
13.7
-7.2
14.5
Central
44444
-7.9
15.6
-9.7
17.6
West
NA
NA
NA
NA
NA
June
12-km EUS
221751
-6.9
15.9
-8.2
17.4
Northeast
50572
-9.2
17.4
-11.0
19.4
Midwest
55634
-7.8
16.2
-9.2
17.9
Southeast
50390
-4.2
14.4
-4.7
15.0
Central
41444
-8.1
16.1
-9.8
18.0
West
NA
NA
NA
NA
NA
July
12-km EUS
214051
-3.5
15.6
-4.7
16.5
Northeast
57684
-4.4
15.8
-5.7
17.0
Midwest
53147
-4.1
15.5
-5.6
16.5
Southeast
44368
-0.8
15.0
-1.4
15.3
Central
34907
-4.0
15.7
-5.1
16.6
West
NA
NA
NA
NA
NA
August
12-km EUS
192737
-1.7
16.5
-3.1
17.4
Northeast
37570
0.2
17.6
-1.7
18.5
Midwest
45755
-0.8
15.6
-2.2
16.5
Southeast
43209
2.6
15.9
2.0
15.8
Central
41655
-6.1
16.2
-7.6
17.8
West
NA
NA
NA
NA
NA
September
12-km EUS
141133
-6.8
14.8
-8.2
16.4
Northeast
18653
-4.8
13.9
-6.4
15.2
Midwest
29565
-5.9
14.4
-7.4
15.8
Southeast
43868
-3.9
13.6
-4.5
14.3
Central
33746
-12.5
17.4
-15.4
20.4
West
NA
NA
NA
NA
NA
Summer Aggregate
12-km EUS
969991
-4.6
15.4
-5.9
16.6
Northeast
199411
-3.7
15.8
-5.3
17.0
Midwest
228111
-3.8
15.1
-5.2
16.3
Southeast
236291
-2.6
14.5
-3.2
15.0
Central
196196
-7.7
16.2
-9.5
18.1
West
NA
NA
NA
NA
NA
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:
pp-4
NME = -^—n *100, where P = predicted concentrations and O = observed
L(o)
49
-------
Fractional bias is defined as:
/
I n
, l(P-O)
A i
FB
*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.
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:
Ozone (12 km Eastern U.S.): The operational model performance evaluation for hourly and 8-
hour daily maximum ozone was conducted using the statistics defined above. Ozone
measurements from 836 sites for 2003, 750 sites for 2004, 817 sites for 2005, and 874 sites for
2006 in the Eastern U.S. were included in the evaluation and were taken from the 2003-2006
state/local monitoring site data in the Air Quality System (AQS) Aerometric Information
Retrieval System (AIRS). The performance statistics were calculated using predicted and
observed data that were paired in time and space on an hourly and/or 8-hour basis. Statistics
were generated for the following geographic groupings: domainwide and four large subregions19:
Midwest, Northeast, Southeast, and Central U.S.
Hourly Ozone Evaluation
Ozone (Oj): Table 4-4 provides hourly ozone model performance statistics calculated for a
threshold of 40 ppb of observed and modeled concentrations, for the 12-km Eastern U.S. domain
and the four subregions (Midwest, Northeast, Southeast, and Central U.S.). Hourly ozone is
under-predicted domainwide when applying a threshold of 40 ppb for these modeled time
periods.
19The 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, NE, OK, and TX.
f
I n
A i
FE
*100, where P = predicted concentrations and O = observed
50
-------
Table 4-4. Summary of CMAQ 2003 8-Hour Daily Maximum 03 Model Performance Statistics
CMAQ 2003 Maximum 8-hr Average
No. of
NMB
NME
FB
FE
Ozone: Threshold of 40 ppb
Obs.
(%)
(%)
(%)
(%)
12-km
EUS
16344
-0.2
11.1
0.1
11.1
Northeast
2861
4.5
12.0
4.2
11.7
May
Midwest
3788
3.5
11.0
3.2
10.9
Southeast
4396
-3.1
10.6
-2.6
10.7
Central
3574
-4.0
11.5
-3.6
11.6
West
NA
NA
NA
NA
NA
12-km
EUS
17544
-3.5
12.2
-3.2
12.4
Northeast
3760
-4.5
12.9
-3.9
13.0
June
Midwest
4469
-4.4
12.3
-4.1
12.7
Southeast
4132
-2.2
11.2
-1.7
11.3
Central
3351
-5.0
12.7
-5.0
13.2
West
NA
NA
NA
NA
NA
12-km
EUS
17576
0.3
12.0
0.4
12.0
Northeast
4363
-0.4
11.3
-0.2
11.4
July
Midwest
4271
-0.2
11.5
-0.3
11.5
Southeast
4137
2.8
12.2
2.9
12.0
Central
2958
-0.9
13.1
-1.0
13.3
West
NA
NA
NA
NA
NA
12-km
EUS
17154
2.7
13.3
2.6
13.1
Northeast
3398
5.1
14.6
4.7
14.3
August
Midwest
4042
3.1
12.0
3.1
11.9
Southeast
4221
6.9
13.9
6.7
13.3
Central
3535
-3.0
12.6
-2.9
12.8
West
NA
NA
NA
NA
NA
12-km
EUS
13294
-3.4
11.6
-3.4
11.8
Northeast
1890
-0.6
10.9
-0.8
11.0
September
Midwest
2753
-3.1
11.1
-3.2
11.3
Southeast
4155
-0.9
10.7
-0.7
10.7
Central
3041
-9.3
13.7
-9.6
14.3
West
NA
NA
NA
NA
NA
12-km
EUS
81912
-0.82
12.0
-0.70
12.1
Northeast
16272
0.82
12.3
0.80
12.3
Summer Aggregate
Midwest
19323
-0.22
11.6
-0.26
11.7
Southeast
21041
0.7
11.7
0.92
11.6
Central
17240
-5.7
12.7
-4.4
13.0
West
NA
NA
NA
NA
NA
51
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PM2.5: The PM2.5 evaluation focuses on PM2.5 total mass and its components, including sulfate
(S04), nitrate (N03), total nitrate (TNO3 = N03 + 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 2003-2006 were
obtained from the following networks for model evaluation: Speciation Trends Network (STN -
total of 199 sites for 2003, 205 sites for 2004, 203 sites for 2005, and 178 sites for 2006),
Interagency Monitoring of PROtected Visual Environments (IMPROVE - total of 89 sites for
2003, 98 sites for 2004 and 2005, and 92 sites for 2006), and Clean Air Status and Trends
Network (CASTNet - total of 66 sites for 2003, 67 sites for 2004 and 2005, and 68 sites for
2006). 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 and the four sub-
regions defined above (Northeast, Midwest, Southeast and Central U.S.).
52
-------
Table 4-5. Summary of CMAQ 2003 Annual PM2.5 Species Model Performance Statistics
CMAQ 2003 Annual
No. of
Obs.
NMB
(%)
NME
(%)
FB
(%)
FE
(%)
12-km EUS
11212
1.1
38.8
-3.7
40.8
Northeast
2634
9.6
39.8
7.2
37.0
STN
Midwest
2140
4.7
31.1
4.9
31.9
Southeast
2622
1
OO
00
35.6
-13.2
39.0
pm25
Total Mass
Central
2989
2.4
48.0
-7.8
50.9
West
NA
NA
NA
NA
NA
12-km EUS
8858
-9.8
43.6
-14.8
47.9
Northeast
2634
9.6
39.8
7.2
37.0
IMPROVE
Midwest
596
2.0
37.0
-4.8
40.7
Southeast
1645
-21.4
40.7
-25.4
49.5
Central
2429
-9..4
42.9
-14.0
48.6
12-km EUS
13400
-11.0
33.4
-9.2
37.6
Northeast
3167
-8.1
33.8
-4.3
35.9
STN
Midwest
2340
-2.4
31.7
0.7
34.7
Southeast
3304
-14.8
32.5
-15.1
35.9
Central
3651
-17.2
35.6
-14.7
42.4
West
NA
NA
NA
NA
NA
12-km EUS
7949
-15.5
35.4
-5.5
41.1
Northeast
1848
-14.6
36.7
-5.5
40.3
Sulfate
IMPROVE
Midwest
492
-7.5
31.1
-1.1
34.7
Southeast
1257
-18.6
34.2
-15.1
40.4
Central
2161
-20.3
35.5
-15.1
41.4
West
NA
NA
NA
NA
NA
12-km EUS
3172
-8.9
20.8
-10.8
24.6
Northeast
823
-6.4
19.4
-7.6
22.1
CASTNet
Midwest
655
-5.5
19.6
-7.8
21.3
Southeast
1102
-10.9
21.1
-14.9
24.5
Central
244
-25.6
29.2
-28.2
34.3
West
NA
NA
NA
NA
NA
12-km EUS
11735
19.3
66.2
-11.4
76.0
Northeast
3167
25.1
63.4
0.0
67.2
STN
Midwest
2339
19.8
61.5
10.6
64.2
Southeast
3304
21.6
85.9
-33.4
91.3
Central
1987
16.7
59.9
-3.5
72.1
Nitrate
West
NA
NA
NA
NA
NA
12-km EUS
7944
27.6
80.8
-30.3
96.1
Northeast
1847
71.6
110.8
-2.6
91.2
IMPROVE
Midwest
492
31.4
74.3
-19.8
89.9
Southeast
1257
34.6
103
-40.9
104
Central
2158
16.3
65.7
-16.1
85.9
West
NA
NA
NA
NA
NA
53
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Total
Nitrate
(N03 +
HNO3)
CASTNet
12-km EUS
3172
27.4
40.0
17.8
37.3
Northeast
822
35.7
43.4
26.1
38.1
Midwest
655
27.2
35.2
22.9
30.3
Southeast
1102
25.5
43.0
15.5
41.3
Central
244
10.7
35.6
2.1
34.0
West
NA
NA
NA
NA
NA
Ammonium
STN
12-km EUS
13399
11.6
44.7
15.8
48.1
Northeast
3167
13.2
45.3
23.6
47.6
Midwest
2339
17.4
42.6
25.5
44.4
Southeast
3304
6.5
42.3
8.0
43.9
Central
3651
10.9
48.1
11.1
54.2
West
NA
NA
NA
NA
NA
CASTNet
12-km EUS
3172
11.1
34.5
8.1
33.8
Northeast
823
13.5
34.5
14.9
33.0
Midwest
655
23.2
37.0
22.7
33.6
Southeast
1102
-2.1
30.6
-4.4
33.3
Central
244
8.5
36.1
2.8
37.7
West
NA
NA
NA
NA
NA
Elemental
Carbon
STN
12-km EUS
13398
30.8
68.9
13.3
54.3
Northeast
3163
33.3
65.7
15.1
50.6
Midwest
2328
18.7
47.4
12.0
44.5
Southeast
3302
11.3
58.4
1.2
49.2
Central
3670
77.0
107.4
31.4
65.9
West
NA
NA
NA
NA
NA
IMPROVE
12-km EUS
9835
-19.0
53.0
-30.8
58.2
Northeast
2129
-22.8
48.3
-24.6
54.5
Midwest
628
-20.2
44.9
-37.8
54.2
Southeast
1807
-38.1
49.4
-55.3
67.6
Central
2564
-14.6
49.2
-21.9
54.5
West
NA
NA
NA
NA
NA
Organic
Carbon
STN
12-km EUS
12598
-41.9
55.5
-48.0
70.1
Northeast
3013
-29.4
51.6
-30.1
61.9
Midwest
2222
-48.5
56.9
-48.0
70.6
Southeast
3240
-49.7
55.5
-62.9
75.6
Central
3264
-38.7
58.1
-49.8
71.7
West
NA
NA
NA
NA
NA
IMPROVE
12-km EUS
9835
-42.7
61.4
-67.2
77.9
Northeast
2129
-38.4
54.9
-48.5
66.3
Midwest
628
-52.6
53.9
-74.0
75.9
Southeast
1807
-56.4
61.1
-83.5
88.5
Central
2564
-41.8
58.1
-64.6
76.0
West
NA
NA
NA
NA
NA
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, DOI: 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 03 and PM2.5
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, 03_pred) and standard errors
(pm25_stdd, 03_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 03_pred
• pm25_stdd or 03_stdd
Table 5-1. HB Model Prediction: Example Data File
Date
Longitude
Latitude
Column
Row
03_pred (ppb)
03_stdd (ppb)
01/01/2001
-119.315
23.43627
12
15
23.011
4.6122
01/01/2001
-119.398
23.74126
12
16
22.979
4.6784
01/01/2001
-119.483
24.04658
12
17
22.919
4.8484
01/01/2001
-119.567
24.35223
12
18
22.987
4.7917
01/01/2001
-119.653
24.6582
12
19
23.19
4.84
01/01/2001
-119.739
24.96448
12
20
23.018
4.8264
01/01/2001
-119.826
25.27106
12
21
23.12
4.8651
01/01/2001
-119.913
25.57793
12
22
22.997
4.84
01/01/2001
-120.001
25.88509
12
23
22.968
4.8308
01/01/2001
-120.09
26.19253
12
24
22.949
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 2003, 12
km grid, PM25. 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
Bounding West
Longitude
Bounding East
Longitude
Bounding North
Latitude
Bounding South
Latitude
2003
122.49 deg W Ion
65.26 deg W Ion
47.78 deg N lat
24.33 deg N lat
Figure 5-1 shows the HB Model prediction for PM25 during July 1-4, 2002. On July 1, the PM25
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 PM25 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
July 2, 2002
July 4, 2002
July 3, 2002
Figure 5-1. HB Prediction (PM2 5) During July 1-4, 2002 (12 km grid cells)
10
20
30
40+
PM2.5 (pg/m
©Eri?
veland
kron
PENN SYLVAN
-Pittsburgh
/EST VIRGINIA
RtLAND
CONNE
Stamford,T| JBrk
Paterson- q • •
" _jp,onkers
New York
Philadelphia
j f '' HEW JERSEY
Iti mor6 '•
BffiShington, D.C.
Delaware
-.Richmond
VIRGINIA
Harnpton@ Virgini
Beach
Not to exact scale
©
.Chesapeake
Figure 5-2. HB Prediction (PM2.5) on July 2, 2002 (12 km grid cells)
59
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ID=29136
07/01/5 002
07/16/2002
08/01/2002
Lumputer_ddt~ + + + Man!tor.dat
Figure 5-3. HB Prediction (PM2.5) Temporarily Matches AQS Data and CMAQ Estimates
0 7/01/2002
07/16/2002
09/01/2002
PLOT ~ ~ ~ PH25_p red
*A PM2S_stdd
Computer_dota
Figure 5-4. HB Prediction (PM2.5) Compensates When AQS Data is Unavailable
60
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E
¦ffi
E
o
!5
3
O
V
a.
V)
E
re
o>
o
o
Figure 5-5. HB Prediction (PM2.5) Mitigates CMAQ Bias when AQS and CMAQ Values Diverge
Another way to view the ability of the HB Model to fill in estimates of air quality where no
monitor exists can be seen in the following figures. The HB Model response surface is plotted
with the grid demarcations in Figure 5-6 along with the measurements taken at the monitoring
stations. Figure 5-7 rotates this plot to portray its 3-dimensionality, so that differences between
the HB Model predictions and the monitoring data points can be better seen. The view portrayed
in Figure 5-7 is as seen from the position of the red arrow in Figure 5-6. As in the previous
figures, different colors represent different concentration gradients (as noted within the legend
included in the plot). These figures show how the HB Model prediction surface aligns closely
with the monitoring station data in most instances, except for a cluster of data points in the upper
center of the plot.
I D=135 124
ai
Computer_di
PLOT
~ ~ ~ PN25_p red
A PM25.
; t dd
+
+
+
Md
61
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Figure 5-6. Plot of the Response Surface of PM2.5 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 PM2.5 Concentrations as Predicted by the HBM 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
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 FLB Model surface corresponds closely with the CMAQ surface. This is to be
expected, as the HB 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 PM2.5 Concentrations as Predicted by the HBM 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
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Fused 36 km 03 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 TIB 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 (Environmetries, 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
I IBM 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 (MSE), 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 and r! .
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.
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
66
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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 PM2.5), 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 PM2.5 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
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
67
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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
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Appendix A
Acronyms
A-l
-------
A-2
-------
Acronyms
BEIS
Biogenic Emissions Inventory System
BlueSky
Emissions modeling framework
CAIR
Clean Air Interstate Rule
CAMD
EPA's Clean Air Markets Division
CAP
Criteria Air Pollutant
CAR
Conditional Auto Regressive model
CEM
Continuous Emissions Monitoring
CHIEF
Clearinghouse for Inventories and Emissions Factors
CMAQ
Community Multiscale Air Quality model
CMV
Commercial marine vessel
CO
Carbon monoxide
DQO
Data Quality Objectives
EGU
Electric Generating Units
Emission
inventory
Listing of elements contributing to atmospheric release of pollutant
substances
EPA
Environmental Protection Agency
EMFAC
Emission Factor (California's onroad mobile model)
FAA
Federal Aviation Administration
FDDA
Four Dimensional Data Assimilation
FIPS
Federal Information Processing Standards
HMS
Hazard Mapping System
ICS-209
Incident Status Summary form
IPM
Integrated Planning Model
ITN
Itinerant
LSM
Land Surface Model
MOBILE
OTAQ's model for estimation of onroad mobile emissions factors
MODIS
Moderate Resolution Imaging Spectroradiometer
NEEDS
National Electric Energy Database System
NEI
National Emission Inventory
NERL
National Exposure Research Laboratory
NESHAP
National Emission Standards for Hazardous Air Pollutants
nh3
Ammonia
NMIM
National Mobile Inventory Model
NONROAD
OTAQ's model for estimation of nonroad mobile emissions
NOx
Nitrogen oxides
OAQPS
EPA's Office of Air Quality Planning and Standards
OAR
EPA's Office of Air and Radiation
OTAQ
EPA's Office of Transportation and Air Quality
ORD
EPA's Office of Research and Development
ORL
One Record per Line
PFC
Portable Fuel Container
pm2,
Particulate matter less than or equal to 2.5 microns
PMio
Particulate matter less than or equal to 10 microns
A-3
-------
Prescribed
fire
Intentionally set fire to clear vegetation
RIA
Regulatory Impact Analysis
RPO
Regional Planning Organization
RRTM
Rapid Radiative Transfer Model
see
Source Classification Code
SMARTFIRE
Satellite Mapping Automatic Reanalysis Tool for Fire Incident
Reconciliation
SMOKE
Sparse Matrix Operator Kernel Emissions
TCEQ
Texas Commission on Environmental Quality
TSD
Technical support document
VOC
Volatile organic compounds
VMT
Vehicle miles traveled
Wildfire
Uncontrolled forest fire
WRAP
Western Regional Air Partnership
A-4
-------
Appendix B
Total U.S. Emissions Summary by Sector and by Region for PM2.5
B-l
-------
B-2
-------
2002 PM25 Urban Areas in the West
350 thousand tons
non-EGU pt
EGU 6%
onroadu.
4% ^
nonroad
Figure B-1. PlVfe.sin Urban Areas in Western U.S. (2002)
2002 PM25 Urban Areas in the East
1.2 million tons
non-EGU pt
10%
EGU
1
dust
( \
35%
4%
onroad
4%
aim
nonroad
6%
area
24%
2%
Figure B-2. PM2 sin Urban Areas in Eastern U.S. (2002)
B-3
-------
2002 PM25 Rural Areas in the West
530 thousand tons
non-EGU pt
EGU 5%
nonroad
1%
onroad
Figure B-3. PM2.5 in Rural Areas in Western U.S. (2002)
2002 PM25 Rural Areas in the East
850 thousand tons
non-EGU pt
EGUa 7%
10%
nie
5% / \ i
onroad §
2% fcSjjig
nonroad
111 dust
4% \ 1
!!i 47%
area^-—»
"^atr
n
24%
^1%
Figure B-4. PM2.5 in Rural Areas in Eastern U.S. (2002)
B-4
-------
2002 PM25 West
880 thousand tons
non-EGU pt
5%
onroa
2%
area
18%
nonroad
3%
Figure B-5. PlVfe.sin Western U.S. - Rural and Urban (2002)
2002 PM25 East
2.1 million tons
non-EGU pt
ECU 8%
13%
fire A
4% m
onroad F5
3% W
nonroad J \
5%
area
24%
Figure B-6. PM2.sin Eastern U.S. - Rural and Urban (2002)
B-5
-------
2002 PM25 Total
3 million tons
,non-EGU pt
8%
onroad
3%
nonroad
aim
2%
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)
[ton sty r]
[tonsfyr]
[tonsfyr]
[ton sty r]
[tonsfyr]
[tons/yr]
[tons/yr]
2002
2002
2002
2002
2002
2002
2002
State
Sector
VOC
NOx
CO
S02
NH3
PM10
PMzs
Alabama
afdust
0
0
0
0
0
100,288
33,476
ag
0
0
0
0
57,802
0
0
aim
2,383
36,047
10,328
4,801
13
2,236
1,878
avefire
8,951
3,814
175,140
983
752
16,251
13,938
nonpt
213,956
32,024
188,564
52,325
426
27,785
23,973
nonroad
55,574
29,396
378,753
2,734
28
3,195
3,044
onroad
104,783
153,968
1,237,459
5,599
5,627
4,223
3,117
ptipm
1,394
161,767
10,879
448,329
783
26,138
22,612
ptnonipm
47,722
80,901
174,483
89,762
2,224
19,710
13,647
Alabama Total
434,763
497,917
2,175,607
604,533
67,655
199,826
115,685
Arizona
afdust
0
0
0
0
0
121,322
19,626
ag
0
0
0
0
29,493
0
0
aim
3,482
30,813
20,495
2,297
12
2,617
2,060
avefire
21,385
10,532
440,419
2,888
2,020
43,005
37,151
nonpt
80,463
8,637
44,127
2,571
4,391
12,456
8,596
nonroad
53,546
38,699
440,675
3,858
35
4,174
3,993
onroad
85,187
159,756
836,126
2,876
5,150
4,021
2,951
ptipm
626
85,967
8,185
70,709
566
9,551
7,565
ptnonipm
4,611
11,439
8,259
21,702
72
5,723
3,044
Arizona Total
249,300
345,843
1,798,285
106,900
41,740
202,868
84,987
Arkansas
afdust
0
0
0
0
0
92,523
24,639
ag
0
0
0
0
110,954
0
0
aim
2,295
39,743
14,371
4,648
19
1,348
1,243
avefire
5,821
2,654
123,699
728
556
12,027
10,315
nonpt
99,381
21,453
174,777
27,260
7,386
24,094
23,062
nonroad
35,683
28,527
231,619
2,762
23
3,229
3,097
onroad
56,465
83,722
735,366
3,078
3,001
2,202
1,612
ptipm
520
42,218
4,182
70,754
346
2,004
1,750
ptnonipm
32,044
27,605
51,502
19,032
1,255
14,101
9,593
Arkansas Total
232,209
245,923
1,335,515
128,262
123,540
151,529
75,312
C-3
-------
California
afdust
0
0
0
0
0
196,231
47,562
ag
0
0
0
0
152,308
0
0
aim
19,726
175,373
108,995
40,887
180
10,124
9,534
avefire
54,619
24,563
1,157,187
6,735
5,117
113,231
97,301
nonpt
461,331
121,882
458,977
77,672
14,758
90,509
73,873
nonroad
148,269
240,256
1,058,968
1,015
161
18,590
16,334
onroad
343,693
643,919
3,434,055
4,786
37,468
23,103
12,395
ptipm
1,288
13,071
23,900
1,018
1,380
1,905
1,876
ptnonipm
54,610
91,967
97,092
41,761
3,367
26,854
16,655
California Total
1,083,536
1,311,031
6,339,176
173,874
214,738
480,546
275,530
Colorado
afdust
0
0
0
0
0
110,878
25,559
ag
0
0
0
0
62,907
0
0
aim
1,366
19,208
10,641
1,224
5
606
553
avefire
13,610
6,271
288,013
1,719
1,299
28,019
24,054
nonpt
87,037
11,464
85,393
6,460
71
15,059
13,545
nonroad
42,009
35,398
389,240
3,545
31
3,909
3,746
onroad
84,387
127,564
1,103,120
4,146
4,408
3,216
2,357
ptipm
973
79,167
7,578
92,562
453
5,446
4,444
ptnonipm
90,768
39,499
28,063
5,331
86
17,366
8,922
Colorado Total
320,150
318,571
1,912,049
114,989
69,260
184,499
83,181
Connecticut
afdust
0
0
0
0
0
12,528
2,725
ag
0
0
0
0
4,029
0
0
aim
845
3,945
12,149
778
1
231
210
avefire
31
14
667
4
3
65
56
nonpt
105,580
12,554
69,769
18,455
1,438
10,716
10,446
nonroad
32,327
17,897
258,776
1,382
17
1,702
1,619
onroad
47,757
66,813
641,901
1,667
3,257
1,610
1,067
ptipm
305
6,161
1,920
13,689
182
742
510
ptnonipm
4,602
6,706
2,133
2,338
91
882
691
Connecticut Total
191,447
114,091
987,315
38,313
9,017
28,476
17,323
C-4
-------
Delaware
afdust
0
0
0
0
0
6,258
863
ag
0
0
0
0
12,536
0
0
aim
483
10,429
2,890
3,470
0
452
401
avefire
64
23
1,332
6
5
102
87
nonpt
15,468
3,259
11,640
5,859
279
2,007
1,826
nonroad
8,677
5,308
65,811
471
5
560
534
onroad
11,382
21,679
155,366
556
903
572
406
ptipm
91
9,533
866
33,104
30
1,969
1,693
ptnonipm
4,659
7,308
8,853
41,342
161
1,041
783
Delaware Total
40,823
57,538
246,758
84,810
13,918
12,961
6,594
District of Columbia
afdust
0
0
0
0
0
2,255
411
aim
22
571
79
45
0
13
13
avefire
0
0
1
0
0
0
0
nonpt
4,118
1,740
1,819
1,559
13
489
427
nonroad
1,918
3,060
18,061
343
2
298
288
onroad
5,423
8,772
65,418
271
398
219
150
ptipm
4
710
50
1,432
8
30
22
ptnonipm
69
418
247
625
4
98
43
District of Columbia Total
11,554
15,271
85,676
4,275
426
3,402
1,353
Florida
afdust
0
0
0
0
0
145,566
28,017
ag
0
0
0
0
37,099
0
0
aim
3,053
55,127
43,166
6,892
11
2,391
2,175
avefire
56,159
25,600
1,193,147
7,018
5,366
115,996
99,484
nonpt
459,700
29,533
202,108
70,489
448
41,371
38,847
nonroad
239,540
117,138
1,762,587
12,540
125
13,637
13,001
onroad
362,851
448,520
3,797,717
21,410
18,267
12,433
9,041
ptipm
2,236
272,057
52,142
473,636
5,013
32,299
28,293
ptnonipm
37,204
54,078
86,821
57,060
3,030
32,193
23,604
Florida Total
1,160,742
1,002,054
7,137,689
649,045
69,359
395,887
242,462
C-5
-------
,910
0
,135
,082
,847
,867
,366
,407
,692
,308
,351
0
447
,808
,367
,889
785
1
,528
,175
,100
0
,351
277
,181
,881
,700
,783
,136
,409
afdust
ag
aim
1,776
avefire
21,834
nonpt
248,214
nonroad
81,856
onroad
185,962
ptipm
1,182
ptnonipm
33,735
0
39,986
7,955
38,919
57,979
307,544
146,351
51,170
0
11,058
350,924
194,402
730,260
2,245,133
9,371
131,306
3,247
2,010
56,830
5,674
11,238
512,983
56,203
),733
12
1,299
60
52
10,642
593
4,571
574,559
649,905
3,672,454
648,183
97,962
afdust
0
0
0
0
0
aa_
0
62,376
aim
713
8,297
10,893
645
avefire
29,989
14,024
630,971
3,845
2,856
nonpt
141,328
30,317
95,417
2,915
nonroad
23,153
15,611
137,661
1,616
14
onroad
27,934
44,628
389,120
1,310
1,418
ptipm
0
19
0
ptnonipm
2,113
11,467
23,977
17,597
1,074
225,230
124,363
1,288,044
27,928
69,425
afdust
0
0
0
0
aa_
0
0
0
106,685
aim
4,205
120,834
16,365
11,979
45
avefire
156
71
3,323
20
15
nonpt
278,553
47,645
99,568
5,395
1,631
nonroad
99,398
115,426
830,513
10,913
onroad
164,697
297,056
2,090,188
8,514
10,654
ptipm
1,536
179,125
14,627
366,157
174
ptnonipm
71,066
94,009
78,820
138,126
694
619,612
854,165
3,133,402
541,103
119,J
C-6
-------
,707
0
,561
344
,611
,803
,081
,805
,085
,996
,643
0
997
349
,476
,949
,726
,904
,572
,615
,515
0
,207
,468
,174
,179
,629
,912
,941
,025
afdust
ag
aim
avefire
nonpt
2,224
194
179,635
nonroad
58,290
onroad
140,188
ptipm
2,015
ptnonipm
55,935
0
52,285
30,185
64,575
216,188
283,890
>,147
0
14,057
4,124
74,953
490,545
1,738,790
15,540
364,487
5,540
24
59,775
5,981
5,564
785,603
97,442
0_
90,815
19 _
19 _
4,214
7,343
580
3,144
438,480
727,359
2,702,495
962,930
106,183
afdust
0
0
0
0
0
aa_
0
245,778
aim
1,653
33,166
7,209
2,787
avefire
197
90
4,185
25
19
nonpt
77,E
15,150
68,958
19,832
7,404
nonroad
52,138
62,066
309,048
6,248
47
onroad
75,852
115,521
1,055,157
3,091
ptipm
579
81,995
5,444
133,047
391
ptnonipm
37,943
38,861
36,521
51,329
4,663
246,201
346,849
1,486,523
216,267
261,401
afdust
0
0
0
0
0
aa_
0
97,384
aim
2,133
41,147
9,118
2,895
11
avefire
378
17,600
103
79
nonpt
135,449
42,286
850,800
36,381
12,467
nonroad
24,728
47,653
240,503
32
onroad
52,786
85,617
683,936
2,893
2,870
ptipm
1,062
96,943
6,793
129,827
421
ptnonipm
26,274
70,704
74,809
10,793
60,100
243,261
384,728
1,883,560
187,750
173,364
C-7
-------
,529
0
,625
,155
,590
,236
,842
,004
,937
,967
,962
0
,819
,647
,862
,174
,506
,990
,082
,043
,134
0
405
,127
,726
,131
876
65
,268
,732
afdust
ag
aim
2,487
avefire
2,909
nonpt
105,281
nonroad
39,806
onroad
82,321
ptipm
1,479
ptnonipm
44,884
0
70,391
1,326
17,557
31,792
147,749
200,955
38,541
0
17,830
61,812
108,397
282,098
1,052,158
12,544
110,047
0
10,096
364
34,229
5,554
486,499
34,482
50,821
15
278
231
25
4,824
919
1,672
279,168
508,311
1,644,885
574,230
afdust
0
0
0
0
aa_
0
0
0
35,159
aim
3,960
216,290
45,941
32,796
42
avefire
7,137
3,254
151,658
892
682
nonpt
135,934
27,559
139,222
2,378
23,169
nonroad
61,307
28,899
364,963
2,834
29
onroad
77,802
124,192
943,962
4,409
4,364
ptipm
1,239
82,293
12,682
108,106
1,399
ptnonipm
79,781
211,449
134,203
177,507
7,f
367,159
693,935
1,792,631
328,922
72,722
afdust
0
0
0
0
0
aa_
6,154
aim
365
1,708
3,650
195
avefire
1,258
566
26,592
150
115
nonpt
88,028
7,423
104,033
1,616
nonroad
30,025
8,271
138,111
766
onroad
26,131
47,227
360,595
1,122
1,467
ptipm
67
1,
1,1
2,137
129
ptnonipm
5,151
18,895
15,861
20,778
151,026
85,277
649,927
35,116
10,302
C-8
-------
Maryland
afdust
0
0
0
0
0
35,393
7,393
ag
0
0
0
0
24,562
0
0
aim
5,360
17,106
17,581
5,707
22
1,635
496
avefire
353
137
6,129
32
24
613
531
nonpt
126,362
21,715
141,960
40,864
606
25,058
19,764
nonroad
51,369
27,495
414,390
2,577
28
3,102
2,954
onroad
71,591
121,659
1,004,611
3,966
5,594
3,162
2,194
ptipm
478
73,527
4,546
256,761
271
17,996
15,722
ptnonipm
5,758
22,109
94,448
34,255
222
6,303
3,759
Maryland Total
261,270
283,748
1,683,666
344,162
31,330
93,261
52,813
Massachusetts
afdust
0
0
0
0
0
49,646
14,810
ag
0
0
0
0
2,208
0
0
aim
2,443
17,144
18,602
2,519
7
988
874
avefire
747
341
15,878
93
71
1,544
1,324
nonpt
176,731
34,373
136,753
25,261
4,070
28,552
26,536
nonroad
52,921
30,046
423,212
2,385
28
2,871
2,732
onroad
71,646
128,362
960,011
3,172
5,509
3,253
2,268
ptipm
595
32,561
10,922
91,888
1,103
3,730
3,224
ptnonipm
7,722
15,394
10,656
14,079
403
2,795
1,842
Massachusetts Total
312,806
258,220
1,576,034
139,397
13,401
93,379
53,610
Michigan
afdust
0
0
0
0
0
208,843
40,894
ag
0
0
0
0
55,273
0
0
aim
2,504
43,025
26,763
14,466
5
2,637
2,389
avefire
724
330
15,380
91
69
1,495
1,283
nonpt
248,382
43,499
94,909
42,066
429
30,989
24,216
nonroad
173,241
70,912
1,013,991
6,367
78
8,199
7,782
onroad
207,762
315,420
2,744,658
13,508
9,813
7,881
5,894
ptipm
1,243
141,908
13,367
348,377
286
13,170
10,648
ptnonipm
39,832
82,202
66,873
72,631
952
17,151
10,346
Michigan Total
673,689
697,296
3,975,941
497,505
66,906
290,363
103,451
C-9
-------
,303
0
,643
,943
,496
,759
,740
234
,097
,215
,120
0
,668
,897
,769
,370
,309
,625
,019
,778
,070
0
,489
,636
,217
,690
,819
,818
,424
,163
afdust
ag
aim
avefire
nonpt
1,611
5,047
125,318
nonroad
97,104
onroad
102,566
ptipm
646
ptnonipm
29,541
0
55,371
2,300
56,700
68,820
163,172
86,917
67,813
8,411
107,237
139,234
452,734
1,314,360
7/
47,015
6,592
631
14,747
6,525
2,816
102,152
27,263
0_
134,830
12 _
482 _
1,226
59
5,362
69
27,525
361,833
501,094
2,076,459
160,725
169,566
afdust
0
0
0
0
0
aa_
0
0
58,575
aim
2,386
66,650
10,656
9,163
18
avefire
8,407
3,833
178,646
1,051
nonpt
156,390
12,212
129,408
6,796
196
nonroad
36,056
22,180
214,179
2,119
19
onroad
62,375
105,505
739,190
3,591
3,606
ptipm
629
45,850
5,286
67,593
456
ptnonipm
43,224
60,244
54,587
36,519
1,414
309,467
316,473
1,331,952
126,831
65,088
afdust
0
0
0
0
0
aa_
0
0
107,023
aim
3,439
79,583
18,171
8,610
19
avefire
1,.
678
31,611
186
142
nonpt
162,795
32,910
168,352
44,573
3,830
nonroad
63,279
52,997
479,319
5,143
43
onroad
124,106
200,379
1,598,930
6,148
6,918
ptipm
1,496
145,232
10,827
249,942
705
ptnonipm
34,704
38,025
108,389
111,547
322
391,308
549,803
2,415,599
426,149
119,002
C-10
-------
,180
0
690
,311
,569
,261
688
,077
,576
,352
,787
0
,942
,483
,655
,484
,312
,191
806
,659
,371
0
419
,018
,735
,027
399
,283
,435
,687
afdust
ag
aim
avefire
1,309
10,085
nonpt
23,573
nonroad
12,968
onroad
20,451
ptipm
355
ptnonipm
6,807
0
22,873
5,187
3,797
18,777
36,727
36,577
16,588
5,814
203,759
35,673
85,304
283,678
3,047
29,410
1,422
0_
45,890
946
1,961
50
2,009
14
1,062
1,032
23,396
13,271
265
75,548
140,526
646,686
44,809
48,214
afdust
0
0
0
0
aa_
0
0
166,773
aim
3,524
68,904
10,222
4,764
18
avefire
837
381
17,780
105
nonpt
40,762
13,820
66,672
29,575
3,143
nonroad
18,442
39,889
155,107
4,181
27
onroad
36,940
66,226
473,870
2,011
1,874
ptipm
635
47,900
3,420
67,576
190
ptnonipm
6,527
11,385
5,717
6,018
421
107,667
248,506
732,788
114,229
172,525
afdust
0
0
0
0
0
aa_
0
0
5,598
aim
1,057
12,958
11,214
990
avefire
10,740
4,910
227,965
1,346
1,026
nonpt
22,874
5,308
14,700
12,476
199
nonroad
22,720
18,990
208,377
2,025
17
onroad
26,884
28,320
301,082
360
1,532
ptipm
483
48,366
2,798
49,276
460
ptnonipm
1,649
7,509
6,985
1,342
164
86,406
126,362
773,121
67,815
i,999
C-ll
-------
New Hampshire
afdust
0
0
0
0
0
6,175
2,194
ag
0
0
0
0
1,354
0
0
aim
118
1,866
2,305
238
0
98
86
avefire
301
137
6,398
38
29
622
534
nonpt
61,483
11,235
74,137
7,408
835
13,351
12,658
nonroad
21,832
8,150
122,530
673
9
942
891
onroad
21,682
38,799
294,533
880
1,266
969
714
ptipm
104
7,000
643
44,009
58
2,632
2,305
ptnonipm
1,496
2,786
2,082
2,570
56
459
390
New Hampshire Total
107,015
69,973
502,627
55,815
3,607
25,248
19,772
New Jersey
afdust
0
0
0
0
0
16,305
1,392
ag
0
0
0
0
3,827
0
0
aim
2,236
35,998
14,960
14,587
11
1,786
1,611
avefire
488
223
10,375
61
47
1,009
865
nonpt
151,657
26,393
84,145
10,726
2,648
15,987
13,074
nonroad
78,629
40,876
635,064
3,378
41
4,162
3,958
onroad
101,094
161,872
1,325,445
3,658
7,635
3,805
2,537
ptipm
1,048
34,188
3,865
51,299
170
4,835
4,010
ptnonipm
13,282
17,206
8,375
9,930
475
3,131
2,464
New Jersey Total
348,436
316,756
2,082,228
93,640
14,854
51,020
29,910
New Mexico
afdust
0
0
0
0
0
440,334
80,348
ag
0
0
0
0
36,340
0
0
aim
1,982
36,714
8,473
2,550
9
1,110
1,084
avefire
27,488
12,582
583,216
3,450
2,626
56,719
48,662
nonpt
36,950
7,532
29,666
2,825
39
5,984
5,346
nonroad
13,499
9,681
119,501
975
9
1,062
1,016
onroad
45,763
77,574
587,028
2,254
2,323
1,965
1,476
ptipm
563
78,547
5,539
51,016
10
8,024
5,557
ptnonipm
15,691
60,358
32,228
18,179
44
3,986
3,290
New Mexico Total
141,935
282,988
1,365,651
81,249
41,401
519,183
146,779
C-12
-------
New York
afdust
0
0
0
0
0
139,896
29,997
ag
0
0
0
0
49,281
0
0
aim
2,473
40,659
22,205
9,353
29
1,780
1,394
avefire
903
412
19,195
113
86
1,866
1,601
nonpt
608,921
89,986
404,592
125,559
3,964
83,468
58,823
nonroad
151,345
78,279
1,175,721
6,797
79
8,303
7,909
onroad
212,929
290,698
2,822,801
8,075
14,582
8,059
5,547
ptipm
857
81,201
12,204
238,034
2,439
13,669
12,081
ptnonipm
6,218
38,992
54,133
59,078
1,241
8,565
4,410
New York Total
983,646
620,228
4,510,852
447,008
71,702
265,606
121,762
North Carolina
afdust
0
0
0
0
0
91,287
25,474
ag
0
0
0
0
158,188
0
0
aim
1,472
22,608
9,957
1,840
7
6,752
4,789
avefire
58,889
11,424
429,388
696
532
11,509
9,870
nonpt
231,094
18,869
321,101
22,020
236
40,945
38,389
nonroad
88,972
61,664
746,344
5,750
54
6,313
6,035
onroad
143,187
242,379
1,786,813
8,683
7,953
6,517
4,874
ptipm
920
153,226
12,112
471,337
124
22,259
16,031
ptnonipm
61,685
49,273
52,414
56,065
1,485
13,744
9,828
North Carolina Total
586,219
559,444
3,358,129
566,392
168,580
199,327
115,291
North Dakota
afdust
0
0
0
0
0
269,751
50,500
ag
0
0
0
0
71,302
0
0
aim
1,256
23,072
4,832
1,601
6
684
670
avefire
527
240
11,204
66
50
1,089
934
nonpt
14,911
4,007
20,488
5,768
69
3,751
3,241
nonroad
13,565
38,012
91,869
4,106
25
4,634
4,486
onroad
15,356
24,832
206,627
700
733
608
455
ptipm
781
75,947
5,237
140,535
378
7,625
6,479
ptnonipm
1,249
9,929
5,778
15,449
139
1,437
1,105
North Dakota Total
47,645
176,039
346,035
168,224
72,703
289,580
67,870
C-13
-------
,900
0
,113
316
,761
,043
,933
,730
,000
,798
,686
0
841
,644
,886
,353
,592
,722
,241
,966
,637
0
,371
,350
,407
,773
,021
326
,203
,088
afdust
ag
aim
avefire
nonpt
3,632
178
285,528
nonroad
103,414
onroad
205,348
ptipm
1,773
ptnonipm
29,515
0
96,728
81
41,466
90,812
327,388
373,299
65,850
0
29,188
3,787
150,302
910,152
2,600,918
14,817
238,412
0
11,191
22
19,810
8,254
12,682
1,145,194
111,233
98,711
32 _
17 _
8,527
74
10,986
74
6,370
629,389
995,625
3,947,575
1,308,387
124,789
afdust
0
0
0
0
aa_
0
0
95,061
aim
1,551
26,294
10,093
avefire
3,749
1,709
79,673
469
359
nonpt
200,442
94,574
385,235
7,542
11,358
nonroad
38,015
31,331
308,218
3,093
26
onroad
86,133
133,152
1,069,135
5,344
4,626
ptipm
984
90,302
13,661
111,841
909
ptnonipm
35,176
72,670
50,750
38,495
3,118
366,050
450,033
1,916,764
168,673
115,463
afdust
0
0
0
0
aa_
0
0
40,655
aim
1,843
43,439
12,401
4,212
avefire
37,328
17,857
778,193
3,542
nonpt
242,829
16,998
342,444
9,845
1,061
nonroad
39,821
26,372
304,850
2,559
24
onroad
91,766
109,066
1,078,005
3,270
ptipm
142
9,006
1,105
12,285
162
ptnonipm
14,567
15,958
34,389
5,307
787
428,297
238,696
2,551,388
42,592
49,509
C-14
-------
Pennsylvania
afdust
0
0
0
0
0
130,508
32,224
ag
0
0
0
0
76,675
0
0
aim
2,425
67,118
25,047
8,354
14
2,376
2,268
avefire
256
117
5,450
32
25
530
454
nonpt
281,740
53,435
265,035
68,349
3,689
41,841
31,263
nonroad
96,797
62,168
856,737
5,203
55
6,256
5,969
onroad
184,268
294,414
2,420,525
7,885
10,618
7,250
5,219
ptipm
1,212
210,149
17,018
907,734
401
63,198
53,067
ptnonipm
36,871
89,064
104,570
88,132
1,334
22,391
11,549
Pennsylvania Total
603,569
776,465
3,694,382
1,085,688
92,811
274,351
142,015
Rhode Island
afdust
0
0
0
0
0
2,501
481
ag
0
0
0
0
235
0
0
aim
162
876
2,923
78
0
8
0
avefire
8
4
171
1
1
17
14
nonpt
16,875
2,964
5,421
3,365
15
1,171
1,107
nonroad
8,491
4,663
65,923
354
4
427
406
onroad
14,366
16,720
188,240
425
854
343
209
ptipm
39
712
453
18
58
12
11
ptnonipm
1,894
2,060
1,781
2,649
47
288
173
South Carolina
afdust
0
0
0
0
0
82,088
25,657
ag
0
0
0
0
27,945
0
0
aim
961
19,378
9,393
1,946
4
714
668
avefire
5,171
2,357
109,880
646
494
10,684
9,163
nonpt
185,429
20,281
145,294
30,016
223
19,393
18,139
nonroad
50,041
29,982
377,166
2,816
27
3,102
2,960
onroad
89,994
134,542
1,141,561
5,021
4,710
3,588
2,648
ptipm
506
91,296
4,749
212,572
306
17,707
13,734
ptnonipm
36,778
40,417
56,640
57,307
1,552
12,696
8,159
South Carolina Total
368,879
338,253
1,844,682
310,324
35,263
149,971
81,128
C-15
-------
South Dakota
afdust
0
0
0
0
0
202,326
38,332
ag
0
0
0
0
101,949
0
0
aim
321
4,164
2,979
318
1
172
156
avefire
3,985
1,817
84,689
498
381
8,235
7,062
nonpt
19,597
5,200
24,107
10,304
51
6,683
4,463
nonroad
12,322
27,219
79,151
2,901
18
3,289
3,181
onroad
16,177
29,910
219,053
852
843
746
564
ptipm
111
15,922
632
12,545
50
450
420
ptnonipm
2,431
4,776
4,068
1,480
50
609
291
South Dakota Total
54,944
89,008
414,679
28,898
103,343
222,509
54,470
Tennessee
afdust
0
0
0
0
0
95,767
22,530
ag
0
0
0
0
34,210
0
0
aim
2,152
50,692
13,001
6,292
12
1,853
1,707
avefire
2,220
1,012
47,175
277
212
4,587
3,934
nonpt
148,677
18,676
119,973
32,714
164
26,842
20,663
nonroad
60,023
40,970
460,143
3,728
35
4,225
4,040
onroad
140,405
240,312
1,681,568
7,674
6,671
6,128
4,667
ptipm
843
155,926
6,596
333,618
425
16,268
13,910
ptnonipm
84,610
69,070
115,767
84,316
2,394
30,328
22,054
Tennessee Total
438,930
576,659
2,444,222
468,619 44,124
185,996
93,505
Texas
afdust
0
0
0
0
0
1,290,391
242,993
ag
0
0
0
0
354,873
0
0
aim
11,279
236,223
67,547
27,280
57
8,936
8,146
avefire
13,201
4,890
256,966
1,178
1,118
25,228
21,578
nonpt
695,600
274,338
463,577
109,215
1,983
72,265
47,394
nonroad
174,723
152,771
1,578,739
14,990
128
15,766
15,126
onroad
308,904
621,483
3,787,848
21,522
21,943
16,034
11,699
ptipm
4,745
259,612
215,207
562,594
5,941
34,257
24,920
ptnonipm
149,554
344,073
283,294
245,060
2,297
38,861
27,189
Texas Total
1,358,006
1,893,390
6,653,179
981,840
388,340
1,501,740
399,045
C-16
-------
Tribal Data
aim
218
858
302
132
1
58
0
ptipm
241
97
828
6
65
31
31
ptnonipm
601
6,623
2,573
204
4
1,872
856
Tribal Data Total1
1,060
7,578
3,703
342
69
1,961
887
Utah
afdust
0
0
0
0
0
54,020
7,864
ag
0
0
0
0
20,448
0
0
aim
2,596
14,640
10,805
1,065
5
153
140
avefire
15,469
7,052
328,713
1,934
1,479
31,961
27,412
nonpt
54,443
6,948
79,323
3,427
1,268
10,385
9,079
nonroad
25,488
15,026
172,729
1,437
14
1,703
1,625
onroad
56,206
76,518
764,714
1,989
2,457
1,658
1,187
ptipm
418
73,220
4,506
33,167
269
6,351
4,901
ptnonipm
5,826
14,998
45,052
9,305
529
6,893
2,955
Utah Total
160,444
208,401
1,405,842
52,325
26,469
113,124
55,162
Vermont
afdust
0
0
0
0
0
13,658
4,814
ag
0
0
0
0
8,821
0
0
aim
53
49
1,220
6
0
29
21
avefire
393
179
8,347
49
38
812
696
nonpt
18,887
3,438
43,091
5,385
214
5,823
5,415
nonroad
10,446
4,170
58,906
368
5
516
490
onroad
18,139
21,783
237,164
622
939
645
465
ptipm
0
0
0
0
11
0
0
ptnonipm
1,097
790
1,078
911
16
337
237
Vermont Total
49,015
30,409
349,807
7,341
10,043
21,819
12,137
C-17
-------
Virginia
afdust
0
0
0
0
0
60,865
19,662
ag
0
0
0
0
43,811
0
0
aim
3,084
39,676
17,758
5,595
13
1,905
1,836
avefire
3,194
1,456
67,866
399
305
6,599
5,659
nonpt
201,748
53,605
208,041
32,923
1,621
53,941
29,947
nonroad
67,441
45,848
520,042
4,289
41
4,809
4,593
onroad
125,474
214,393
1,722,600
6,662
7,889
4,939
3,486
ptipm
726
86,763
6,714
239,777
192
15,400
14,431
ptnonipm
43,184
61,730
63,978
67,691
3,500
13,041
9,734
Virginia Total
444,850
503,471
2,606,999
357,338
57,373
161,498
89,350
Washington
afdust
0
0
0
0
0
106,176
26,908
ag
0
0
0
0
42,133
0
0
aim
2,248
66,992
20,193
11,488
151
2,416
2,271
avefire
2,674
1,484
52,086
407
248
5,126
4,487
nonpt
166,658
16,911
204,125
7,254
1,711
35,624
31,983
nonroad
64,611
42,800
486,615
5,380
39
4,776
4,567
onroad
159,797
199,767
1,820,900
5,539
5,168
4,545
3,407
ptipm
219
16,122
1,665
19,108
62
2,456
2,025
ptnonipm
12,429
24,522
39,106
24,623
774
4,970
3,224
Washington Total
408,636
368,598
2,624,689
73,799
50,285
166,089
78,872
West Virginia
afdust
0
0
0
0
0
24,640
11,305
ag
0
0
0
0
9,879
0
0
aim
1,180
32,148
5,139
5,707
8
1,478
1,281
avefire
1,721
785
36,578
215
165
3,557
3,050
nonpt
59,489
14,519
70,069
14,589
72
12,220
11,130
nonroad
16,935
8,407
117,839
780
8
1,005
956
onroad
36,949
60,216
502,130
2,675
1,950
1,542
1,149
ptipm
1,175
227,827
10,319
509,488
210
31,248
28,884
ptnonipm
14,241
46,627
89,898
54,107
688
10,625
7,450
West Virginia Total
131,691
390,529
831,973
587,561
12,981
86,314
65,205
C-18
-------
Wisconsin
afdust
0
0
0
0
0
103,735
30,705
ag
0
0
0
0
113,949
0
0
aim
2,060
30,307
24,321
4,781
11
1,353
1,182
avefire
561
256
11,924
70
54
1,159
994
nonpt
230,068
21,994
166,779
6,369
266
26,104
25,407
nonroad
111,779
53,430
569,467
5,015
52
6,090
5,796
onroad
96,058
172,043
1,321,240
7,218
6,006
4,479
3,317
ptipm
964
91,128
10,725
192,946
375
5,576
5,029
ptnonipm
31,057
38,283
34,197
63,651
397
10,466
5,856
Wisconsin Total
472,549
407,440
2,138,654
280,051
121,110
158,961
78,287
Wyoming
afdust
0
0
0
0
0
272,299
41,010
ag
0
0
0
0
18,575
0
0
aim
1,569
30,368
4,758
2,088
8
866
857
avefire
8,852
4,035
188,099
1,106
846
18,289
15,686
nonpt
16,411
4,309
19,192
6,181
91
3,717
2,922
nonroad
9,088
5,470
53,551
559
5
689
659
onroad
18,072
32,643
246,059
905
893
799
606
ptipm
849
85,207
7,078
83,423
386
9,599
7,936
ptnonipm
16,771
36,500
23,341
33,676
301
19,234
14,143
Wyoming Total
71,613
198,533
542,078
127,938
21,104
325,494
83,819
Grand Total
17,693,869
20,931,673
101,885,285
14,649,986
3,901,951
12,817,898
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 S02, NH3, PMio, 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
Sector
[tonsfyr]
2002
VOC
[tonsfyr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tonsfyr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
Alabama
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,383
2,831
2,916
3,065
3,379
36,047
32,548
32,194
32,815
35.753
10,328
11,057
11,603
12,303
13,561
avefire
8,951
8,951
8,951
8,951
8,951
3,814
3,814
3,814
3,814
3,814
175,140
175,140
175,140
175,140
175,140
nonpt
213,956
205,838
197,006
193,002
193,002
32,024
31,978
31,945
31,906
31,906
188,564
184,082
180,879
177,034
177,034
nonroad
55,574
46,218
40,250
36,029
36,955
29,396
25,392
20,092
15,494
13,611
378,753
253,823
237,277
242,916
267,149
onroad
104,783
67,451
53,305
43,750
39,517
153,968
91,435
57,113
37,772
28,545
1,237,459
733,435
637,881
615,780
640,439
ptipm
1.394
1,335
1,423
1,462
1,462
161,767
71,365
47,854
39,998
39,998
10,879
13,708
15,854
15,825
15,825
ptnonipm
47,722
38,365
38,365
38,365
38,365
80,901
68,040
68,040
68,040
68,040
174,483
174,092
174,092
174,092
174,092
Alabama Total
434,763
370,990
342,216
324,624
321,631
497,917
324,573
261,053
229,839
221,667
2,175,607
1,545,339
1,432,727
1,413,091
1,463,241
Arizona
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
3,482
3,599
3,818
4,102
4,374
30,813
26,449
26,197
26,459
26,959
20,495
21,458
23,081
24,938
27,011
avefire
21,385
21,385
21,385
21,385
21,385
10,532
10,532
10,532
10,532
10,532
440,419
440,419
440,419
440,419
440,419
nonpt
80,463
76,919
71,758
71,115
71,115
8,637
8,618
8,605
8,589
8,589
44,127
42,569
41,456
40,120
40,120
nonroad
53,546
41,848
37,833
35,883
38,007
38,699
32,525
25,480
18,219
14,796
440,675
337,232
321,243
336,841
376,783
onroad
85,187
65,051
54,052
46,416
46,068
159,756
104,428
66,634
43,914
37,539
836,126
621,952
567,416
580,651
693,201
ptipm
626
947
950
992
992
85,967
74,862
50,463
50,569
50,569
8,185
19,127
19,204
18,769
18,769
ptnonipm
4,611
4,164
4,164
4,164
4,164
11,439
11,439
11,439
11,439
11,439
8,259
8,259
8,259
8,259
8,259
Arizona Total
249,300
213,913
193,959
184,056
186,105
345,843
268,853
199,350
169,721
160,422
1,798,285
1,491,014
1,421,078
1,449,996
1,604,562
Arkansas
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,295
2,357
2,380
2,418
2,514
39,743
34,685
33,722
33,539
35,309
14,371
14,980
15,847
16,829
18,376
avefire
5,821
5,821
5,821
5,821
5,821
2,654
2,654
2,654
2,654
2,654
123,699
123,699
123,699
123,699
123,699
nonpt
99,381
96,818
92,455
91,751
91,751
21,453
21,436
21,424
21,410
21,410
174,777
173,439
172,484
171,336
171,336
nonroad
35,683
31,954
27,582
23,439
23,324
28,527
24,467
19,384
14,231
10,579
231,619
162,754
151,555
152,274
163,858
onroad
56,465
36,323
29,742
24,814
23,579
83,722
50,832
32,840
22,581
18,207
735,366
426,247
372,017
360,469
396,136
-------
State
Sector
[tons/yr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tons/yr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tons/yr]
2030
Base
VOC
[tons/yr]
2002
NOX
[tons/yr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
ptipm
520
690
790
799
799
42,218
24,262
26,839
26,271
26,271
4,182
9,082
12,018
10,968
10,968
ptnonipm
32,044
27,717
27,717
27,717
27,717
27,605
27,370
27,370
27,370
27,370
51,502
51,437
51,437
51,437
51,437
Arkansas Total
232,209
201,682
186,488
176,760
175,506
245,923
185,706
164,233
148,055
141,799
1,335,515
961,639
899,055
887,013
935,810
State
Sector
[tons/yr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tons/yr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tons/yr]
2030
Base
VOC
[tons/yr]
2002
NOX
[tons/yr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tons/yr]
2030
Base
CO
California
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
19,726
20,089
20,729
21,681
23,104
175,373
161,455
155,977
154,530
164,113
108,995
112,003
117,785
124,485
133,684
avefire
54,619
54,619
54,619
54,619
54,619
24,563
24,563
24,563
24,563
24,563
1,157,187
1,157,187
1,157,187
1,157,187
1,157,187
nonpt
461,331
450,831
449,537
451,112
451,112
121,882
121,674
121,525
121,347
121,347
458,977
447,100
438,618
428,438
428,438
nonroad
148,269
127,633
114,489
108,594
128,506
240,256
193,950
153,910
110,789
84,400
1,058,968
1,028,012
1,040,768
1,101,374
1,313,702
onroad
343,693
202,321
149,537
114,486
87,676
643,919
492,500
346,901
231,335
160,727
3,434,055
1,942,479
1,360,507
935,177
654,302
ptipm
1,288
1,076
1,372
1,800
1,800
13,071
13,111
15,031
16,691
16,691
23,900
53,864
65,409
82,171
82,171
ptnonipm
54,610
46,571
46,767
47,121
47,121
91,967
91,434
91,261
92,143
92,143
97,092
97,332
98,997
100,789
100,789
California Total
1,083,536
903,139
837,050
799,413
793,938
1,311,031
1,098,687
909,168
751,398
663,984
6,339,176
4,837,976
4,279,271
3,929,619
3,870,272
Colorado
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,366
1,453
1,499
1,557
1,608
19,208
16,132
15,818
15,869
16,103
10,641
11,608
12,543
13,661
14,931
avefire
13,610
13,610
13,610
13,610
13,610
6,271
6,271
6,271
6,271
6,271
288,013
288,013
288,013
288,013
288,013
nonpt
87,037
85,653
83,973
84,311
84,311
11,464
11,412
11,375
11,331
11,331
85,393
82,106
79,758
76,940
76,940
nonroad
42,009
33,318
30,177
27,864
29,057
35,398
30,263
24,069
17,609
13,876
389,240
263,050
250,944
261,675
291,702
onroad
84,387
61,463
53,663
46,984
46,833
127,564
83,534
55,858
40,184
36,093
1,103,120
709,657
659,782
681,244
822,227
ptipm
973
731
769
838
838
79,167
64,412
60,593
61,605
61,605
7,578
11,267
12,124
11,529
11,529
ptnonipm
90,768
37,097
35,068
34,442
34,442
39,499
38,342
38,342
38,342
38,342
28,063
27,533
27,533
27,533
27,533
Colorado Total
320,150
233,324
218,759
209,606
210,698
318,571
250,366
212,326
191,211
183,620
1,912,049
1,393,233
1,330,698
1,360,595
1,532,875
Connecticut
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
845
876
956
1,061
1,169
3,945
3,834
3,868
4,003
4,140
12,149
12,666
13,976
15,652
17,271
avefire
31
31
31
31
31
14
14
14
14
14
667
667
667
667
667
nonpt
105,580
100,907
99,011
96,848
96,848
12,554
12,498
12,459
12,411
12,411
69,769
65,429
62,328
58,604
58,604
nonroad
32,327
23,609
20,404
19,242
20,383
17,897
14,869
11,646
9,285
8,635
258,776
188,681
175,215
181,777
203,437
onroad
47,757
30,899
24,426
18,176
15,441
66,813
38,434
23,218
13,530
8,997
641,901
369,126
315,324
293,072
307,897
ptipm
305
109
134
181
181
6,161
3,391
4,095
5,447
5,447
1,920
8,434
8,853
8,932
8,932
ptnonipm
4,602
4,182
4,182
4,182
4,182
6,706
6,571
6,571
6,571
6,571
2,133
2,131
2,131
2,131
2,131
Connecticut Total
191,447
160,613
149,143
139,721
138,234
114,091
79,613
61,871
51,261
46,215
987,315
647,133
578,494
560,836
598,940
-------
State
Sector
[tons/yr]
2002
VOC
[tonsfyr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tonsfyr]
2020
Base
VOC
[tons/yr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
California
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
19,726
20,089
20,729
21,681
23,104
175,373
161,455
155,977
154,530
164,113
108,995
112,003
117,785
124,485
133,684
avefire
54,619
54,619
54,619
54,619
54,619
24,563
24,563
24,563
24,563
24,563
1,157,187
1,157,187
1,157,187
1,157,187
1,157,187
nonpt
461,331
450,831
449,537
451,112
451,112
121,882
121,674
121,525
121,347
121,347
458,977
447,100
438,618
428,438
428,438
nonroad
148,269
127,633
114,489
108,594
128,506
240,256
193,950
153,910
110,789
84,400
1,058,968
1,028,012
1,040,768
1,101,374
1,313,702
onroad
343,693
202,321
149,537
114,486
87,676
643,919
492,500
346,901
231,335
160,727
3,434,055
1,942,479
1,360,507
935,177
654,302
ptipm
1,288
1,076
1,372
1,800
1,800
13,071
13,111
15,031
16,691
16,691
23,900
53,864
65,409
82,171
82,171
ptnonipm
54,610
46,571
46,767
47,121
47,121
91,967
91,434
91,261
92,143
92,143
97,092
97,332
98,997
100,789
100,789
California Total
1,083,536
903,139
837,050
799,413
793,938
1,311,031
1,098,687
909,168
751,398
663,984
6,339,176
4,837,976
4,279,271
3,929,619
3,870,272
Colorado
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,366
1,453
1,499
1,557
1,608
19,208
16,132
15,818
15,869
16,103
10,641
11,608
12,543
13,661
14,931
avefire
13,610
13,610
13,610
13,610
13,610
6,271
6,271
6,271
6,271
6,271
288,013
288,013
288,013
288,013
288,013
nonpt
87,037
85,653
83,973
84,311
84,311
11,464
11,412
11,375
11,331
11,331
85,393
82,106
79,758
76,940
76,940
nonroad
42,009
33,318
30,177
27,864
29,057
35,398
30,263
24,069
17,609
13,876
389,240
263,050
250,944
261,675
291,702
onroad
84,387
61,463
53,663
46,984
46,833
127,564
83,534
55,858
40,184
36,093
1,103,120
709,657
659,782
681,244
822,227
ptipm
973
731
769
838
838
79,167
64,412
60,593
61,605
61,605
7,578
11,267
12,124
11,529
11,529
ptnonipm
90,768
37,097
35,068
34,442
34,442
39,499
38,342
38,342
38,342
38,342
28,063
27,533
27,533
27,533
27,533
Colorado Total
320,150
233,324
218,759
209,606
210,698
318,571
250,366
212,326
191,211
183,620
1,912,049
1,393,233
1,330,698
1,360,595
1,532,875
Connecticut
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
845
876
956
1,061
1,169
3,945
3,834
3,868
4,003
4,140
12,149
12,666
13,976
15,652
17,271
avefire
31
31
31
31
31
14
14
14
14
14
667
667
667
667
667
nonpt
105,580
100,907
99,011
96,848
96,848
12,554
12,498
12,459
12,411
12,411
69,769
65,429
62,328
58,604
58,604
nonroad
32,327
23,609
20,404
19,242
20,383
17,897
14,869
11,646
9,285
8,635
258,776
188,681
175,215
181,777
203,437
onroad
47,757
30,899
24,426
18,176
15,441
66,813
38,434
23,218
13,530
8,997
641,901
369,126
315,324
293,072
307,897
ptipm
305
109
134
181
181
6,161
3,391
4,095
5,447
5,447
1,920
8,434
8,853
8,932
8,932
ptnonipm
4,602
4,182
4,182
4,182
4,182
6,706
6,571
6,571
6,571
6,571
2,133
2,131
2,131
2,131
2,131
Connecticut Total
191,447
160,613
149,143
139,721
138,234
114,091
79,613
61,871
51,261
46,215
987,315
647,133
578,494
560,836
598,940
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
Delaware
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
483
531
557
599
695
10,429
10,912
11,292
12,258
15,326
2,890
3,012
3,160
3,382
3,884
avefire
64
64
64
64
64
23
23
23
23
23
1,332
1,332
1,332
1,332
1,332
nonpt
15,468
14,558
14,210
13,919
13,919
3,259
3,251
3,246
3,239
3,239
11,640
11,085
10,688
10,211
10,211
noriroad
8,677
6,386
5,514
5,153
5,408
5,308
4,559
3,692
2,882
2,500
65,811
48,862
45,738
47,229
52,261
onroad
11,382
7,514
5,695
4,639
4,191
21,679
12,180
7,214
4,404
3,535
155,366
89,775
78,499
76,475
81,702
ptipm
91
122
151
152
152
9,533
9,675
9,380
8,327
8,327
866
1,710
2,124
1,938
1,938
ptnonipm
4,659
4,193
4,193
4,193
4,193
7,308
4,682
4,682
4,682
4,682
8,853
8,733
8,733
8,733
8,733
Delaware Total
40,823
33,368
30,383
28,719
28,621
57,538
45,281
39,529
35,813
37,631
246,758
164,509
150,274
149,301
160,062
District of Columbia
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
22
30
31
32
33
571
560
535
527
508
79
95
102
111
128
avefire
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
nonpt
4,118
3,917
3,882
3,882
3,882
1,740
1,739
1,738
1,738
1,738
1,819
1,756
1,711
1,658
1,658
nonroad
1,918
1,412
1,235
1,203
1,290
3,060
2,536
1,921
1,244
919
18,061
15,551
14,178
14,543
16,130
onroad
5,423
3,621
2,719
2,215
1,984
8,772
4,772
2,703
1,536
1,235
65,418
39,318
33,810
33,048
35,654
ptipm
4
0
0
0
0
710
3
6
6
6
50
3
6
6
6
ptnonipm
69
69
69
69
69
418
418
418
418
418
247
247
247
247
247
District of Columbia Total
11,554
9,048
7,935
7,400
7,257
15,271
10,027
7,321
5,468
4,824
85,676
56,971
50,055
49,614
53,824
Florida
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
3,053
3,190
3,387
3,652
4,052
55,127
52,369
51,374
52,172
57,895
43,166
44,681
47,476
50,664
54,692
avefire
56,159
56,159
56,159
56,159
56,159
25,600
25,600
25,600
25,600
25,600
1,193,147
1,193,147
1,193,147
1,193,147
1,193,147
nonpt
459,700
455,329
434,205
432,980
432,980
29,533
29,492
29,463
29,428
29,428
202,108
198,701
196,267
193,346
193,346
nonroad
239,540
176,943
159,802
153,184
162,071
117,138
104,404
89,130
69,322
60,662
1,762,587
1,222,617
1,063,733
1,110,608
1,237,135
onroad
362,851
225,931
179,866
150,140
147,070
448,520
274,295
178,863
125,477
102,919
3,797,717
2,105,543
1,867,949
1,870,423
2,054,738
ptipm
2,236
1,869
2,143
2,522
2,522
272,057
80,931
56,740
61,118
61,118
52,142
64,310
75,293
75,276
75,276
ptnonipm
37,204
34,201
34,201
34,201
34,201
54,078
54,030
54,030
54,030
54,030
86,821
86,821
86,821
86,821
86,821
Florida Total
1,160,742
953,622
869,762
832,839
839,056
1,002,054
621,122
485,199
417,147
391,651
7,137,689
4,915,820
4,530,685
4,580,285
4,895,156
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
Georgia
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,776
1,976
2,034
2,120
2,239
39,986
35,539
35,200
35,767
37,633
11,058
11,973
12,968
14,244
15,948
avefire
21,834
21,834
21,834
21,834
21,834
7,955
7,955
7,955
7,955
7,955
350,924
350,924
350,924
350,924
350,924
nonpt
248,214
242,922
235,296
234,053
234,053
38,919
38,853
38,806
38,750
38,750
194,402
189,012
185,162
180,543
180,543
noriroad
81,856
63,348
56,758
52,569
55,247
57,979
48,390
38,051
27,695
22,875
730,260
519,187
466,525
485,869
542,255
onroad
185,962
123,053
101,749
84,255
79,932
307,544
185,968
116,118
75,433
57,840
2,245,133
1,362,639
1,218,661
1,207,141
1,346,768
ptipm
1,182
1,316
1,363
1,426
1,426
146,351
84,937
59,755
60,722
60,722
9,371
13,152
15,058
14,990
14,990
ptnonipm
33,735
27,728
27,728
27,728
27,728
51,170
42,554
42,554
42,554
42,554
131,306
130,600
130,600
130,600
130,600
Georgia Total
574,559
482,177
446,762
423,986
422,461
649,905
444,198
338,439
288,877
268,330
3,672,454
2,577,487
2,379,898
2,384,311
2,582,027
Idaho
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
713
722
749
782
815
8,297
7,008
6,857
6,777
6,778
10,893
11,307
12,190
13,265
14,386
avefire
29,989
29,989
29,989
29,989
29,989
14,024
14,024
14,024
14,024
14,024
630,971
630,971
630,971
630,971
630,971
nonpt
141,328
139,434
137,490
136,421
136,421
30,317
30,305
30,296
30,285
30,285
95,417
94,196
93,323
92,276
92,276
nonroad
23,153
21,173
18,319
15,135
14,525
15,611
13,849
11,345
8,507
6,374
137,661
96,798
91,736
91,464
97,589
onroad
27,934
19,329
16,912
14,608
13,944
44,628
28,237
18,306
12,480
10,483
389,120
237,761
215,805
213,957
246,310
ptipm
0
14
22
26
26
19
94
270
367
367
4
549
612
646
646
ptnonipm
2,113
1,725
1,725
1,725
1,725
11,467
11,467
11,467
11,467
11,467
23,977
23,977
23,977
23,977
23,977
Idaho Total
225,230
212,386
205,206
198,686
197,444
124,363
104,983
92,565
83,906
79,777
1,288,044
1,095,559
1,068,615
1,066,556
1,106,156
Illinois
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
4,205
4,211
4,185
4,183
4,320
120,834
104,171
99,880
98,522
102,737
16,365
17,898
18,787
20,165
23,326
avefire
156
156
156
156
156
71
71
71
71
71
3,323
3,323
3,323
3,323
3,323
nonpt
278,553
274,561
269,109
268,977
268,977
47,645
47,588
47,548
47,500
47,500
99,568
93,967
89,967
85,166
85,166
nonroad
99,398
77,488
67,523
61,819
64,816
115,426
94,695
72,953
51,566
39,673
830,513
656,876
599,591
609,879
674,750
onroad
164,697
108,448
87,212
72,527
68,277
297,056
182,060
111,020
69,518
51,760
2,090,188
1,272,670
1,121,004
1,106,338
1,226,854
ptipm
1,536
2,381
2,560
2,657
2,657
179,125
83,848
89,782
71,620
71,620
14,627
15,791
17,649
18,614
18,614
ptnonipm
71,066
58,404
58,591
58,821
58,821
94,009
71,002
71,514
73,036
73,036
78,820
78,198
79,893
81,767
81,767
Illinois Total
619,612
525,649
489,337
469,141
468,026
854,165
583,435
492,768
411,834
386,397
3,133,402
2,138,723
1,930,214
1,925,253
2,113,801
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
Indiana
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,224
2,606
2,682
2,790
2,954
52,285
46,823
45,667
45,598
47,880
14,057
15,202
16,157
17,366
19,391
avefire
194
194
194
194
194
88
88
88
88
88
4,124
4,124
4,124
4,124
4,124
nonpt
179,635
175,119
169,044
167,998
167,998
30,185
30,143
30,113
30,077
30,077
74,953
71,175
68,477
65,240
65,240
nonroad
58,290
45,641
39,383
35,579
37,055
64,575
53,155
40,427
28,951
23,097
490,545
348,138
310,959
311,512
341,744
onroad
140,188
88,653
73,730
62,110
56,619
216,188
129,374
82,459
55,049
42,080
1,738,790
1,012,624
897,036
878,274
949,534
ptipm
2,015
2,165
2,228
2,296
2,296
283,890
133,912
124,167
89,313
89,313
15,540
17,814
18,639
19,602
19,602
ptnonipm
55,935
51,787
51,787
51,787
51,787
80,147
67,032
67,032
67,032
67,032
364,487
364,486
364,486
364,486
364,486
Indiana Total
438,480
366,164
339,048
322,754
318,904
727,359
460,527
389,953
316,109
299,568
2,702,495
1,833,563
1,679,878
1,660,603
1,764,121
Iowa
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,653
1,661
1,649
1,634
1,631
33,166
26,959
25,825
25,333
25,870
7,209
7,866
8,426
9,118
10,227
avefire
197
197
197
197
197
90
90
90
90
90
4,185
4,185
4,185
4,185
4,185
nonpt
77,838
75,491
71,828
70,642
70,642
15,150
15,090
15,046
14,995
14,995
68,958
64,017
60,487
56,251
56,251
nonroad
52,138
36,504
33,409
30,975
31,468
62,066
54,867
44,687
32,571
21,882
309,048
222,598
205,436
203,942
215,849
onroad
75,852
48,452
40,299
34,011
31,136
115,521
73,751
48,103
32,218
26,448
1,055,157
621,002
532,276
496,019
557,162
ptipm
579
850
908
980
980
81,995
55,090
58,628
59,383
59,383
5,444
7,942
8,769
9,377
9,377
ptnonipm
37,943
32,631
32,631
32,631
32,631
38,861
38,837
38,837
38,837
38,837
36,521
36,501
36,501
36,501
36,501
Iowa Total
246,201
195,786
180,921
171,071
168,686
346,849
264,683
231,217
203,427
187,504
1,486,523
964,111
856,081
815,394
889,552
Kansas
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,133
2,163
2,140
2,106
2,064
41,147
33,193
31,895
31,222
31,119
9,118
9,928
10,669
11,524
12,849
avefire
828
828
828
828
828
378
378
378
378
378
17,600
17,600
17,600
17,600
17,600
nonpt
135,449
134,134
131,964
131,970
131,970
42,286
42,260
42,242
42,219
42,219
850,800
848,391
846,669
844,604
844,604
nonroad
24,728
18,953
16,491
15,041
15,502
47,653
40,898
32,678
23,003
14,394
240,503
157,861
143,592
143,812
154,768
onroad
52,786
33,638
27,861
23,429
22,563
85,617
50,252
31,627
21,109
17,589
683,936
391,573
344,300
335,289
382,426
ptipm
1,062
821
844
864
864
96,943
70,545
51,433
51,547
51,547
6,793
6,229
6,453
7,235
7,235
ptnonipm
26,274
22,766
22,766
22,766
22,766
70,704
70,616
70,616
70,616
70,616
74,809
74,779
74,779
74,779
74,779
Kansas Total
243,261
213,303
202,893
197,004
196,557
384,728
308,143
260,868
240,094
227,862
1,883,560
1,506,360
1,444,062
1,434,843
1,494,262
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
Kentucky
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,487
2,629
2,716
2,868
3,195
70,391
64,975
63,275
63,565
69,851
17,830
19,313
20,293
21,803
24,580
avefire
2,909
2,909
2,909
2,909
2,909
1,326
1,326
1,326
1,326
1,326
61,812
61,812
61,812
61,812
61,812
nonpt
105,281
101,927
97,456
96,199
96,199
17,557
17,480
17,424
17,358
17,358
108,397
102,054
97,523
92,085
92,085
nonroad
39,806
33,708
29,041
25,361
25,698
31,792
26,961
21,171
15,738
12,617
282,098
203,444
187,975
190,518
207,956
onroad
82,321
51,892
42,149
34,769
31,858
147,749
85,183
51,140
32,067
24,626
1,052,158
599,767
523,949
508,439
543,459
ptipm
1,479
1,561
1,615
1,696
1,696
200,955
95,712
68,876
62,024
62,024
12,544
28,316
28,389
29,085
29,085
ptnonipm
44,884
42,586
42,581
42,589
42,589
38,541
28,382
28,382
28,382
28,382
110,047
110,047
110,047
110,047
110,047
Kentucky Total
279,168
237,212
218,465
206,392
204,145
508,311
320,019
251,594
220,459
216,183
1,644,885
1,124,751
1,029,988
1,013,789
1,069,025
Louisiana
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
3,960
4,264
4,498
4,966
6,236
216,290
209,740
204,037
205,657
233,694
45,941
47,622
48,188
50,103
56,877
avefire
7,137
7,137
7,137
7,137
7,137
3,254
3,254
3,254
3,254
3,254
151,658
151,658
151,658
151,658
151,658
nonpt
135,934
133,648
127,217
126,631
126,631
27,559
27,535
27,518
27,498
27,498
139,222
137,300
135,926
134,278
134,278
nonroad
61,307
51,973
44,943
39,985
40,623
28,899
25,735
21,286
16,835
14,547
364,963
245,591
232,521
236,980
257,335
onroad
77,802
49,098
38,299
31,376
31,178
124,192
70,328
43,492
28,662
23,290
943,962
539,202
464,965
451,997
512,934
ptipm
1,239
574
645
714
714
82,293
25,960
27,522
27,607
27,607
12,682
26,764
28,465
28,817
28,817
ptnonipm
79,781
61,820
61,820
61,820
61,820
211,449
211,225
211,225
211,225
211,225
134,203
133,982
133,982
133,982
133,982
Louisiana Total
367,159
308,515
284,560
272,630
274,339
693,935
573,776
538,334
520,739
541,115
1,792,631
1,282,120
1,195,706
1,187,815
1,275,881
Maine
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
365
370
375
388
423
1,708
1,716
1,711
1,781
2,112
3,650
3,769
3,981
4,206
4,459
avefire
1,258
1,258
1,258
1,258
1,258
566
566
566
566
566
26,592
26,592
26,592
26,592
26,592
nonpt
88,028
82,684
79,463
75,901
75,901
7,423
7,334
7,270
7,192
7,192
104,033
97,029
92,024
86,014
86,014
nonroad
30,025
28,193
24,234
19,744
18,497
8,271
7,400
6,302
5,464
5,307
138,111
105,822
99,160
97,010
103,051
onroad
26,131
17,146
14,673
12,325
10,670
47,227
26,670
16,408
10,550
7,687
360,595
210,877
187,476
181,502
189,853
ptipm
67
236
214
132
132
1,188
6,660
6,208
3,969
3,969
1,084
5,986
5,357
3,798
3,798
ptnonipm
5,151
4,542
4,542
4,542
4,542
18,895
18,045
18,045
18,045
18,045
15,861
15,107
15,107
15,107
15,107
Maine Total
151,026
134,429
124,759
114,290
111,422
85,277
68,390
56,509
47,566
44,878
649,927
465,182
429,698
414,230
428,874
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tonsfyr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
Maryland
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
5,360
5,482
5,733
6,069
6,432
17,106
16,209
15,875
16,480
18,380
17,581
18,062
19,265
20,716
22,413
avefire
353
353
353
353
353
137
137
137
137
137
6,129
6,129
6,129
6,129
6,129
nonpt
126,362
120,308
118,030
116,078
116,078
21,715
21,667
21,633
21,592
21,592
141,960
138,168
135,459
132,206
132,206
nonroad
51,369
38,023
33,652
32,241
34,382
27,495
23,271
18,960
14,651
12,719
414,390
339,878
322,626
340,408
382,289
onroad
71,591
48,559
38,202
31,292
28,704
121,659
68,358
41,721
25,955
20,660
1,004,611
599,433
526,263
519,267
575,218
ptipm
478
521
616
693
693
73,527
18,640
20,882
22,653
22,653
4,546
10,599
11,472
12,092
12,092
ptnonipm
5,758
4,570
4,570
4,570
4,570
22,109
17,826
17,826
17,826
17,826
94,448
94,404
94,404
94,404
94,404
Maryland Total
261,270
217,815
201,154
191,296
191,211
283,748
166,108
137,033
119,293
113,967
1,683,666
1,206,675
1,115,618
1,125,222
1,224,752
Massachusetts
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,443
2,487
2,601
2,734
2,921
17,144
15,668
15,702
16,151
17,570
18,602
19,525
20,876
22,357
24,040
avefire
747
747
747
747
747
341
341
341
341
341
15,878
15,878
15,878
15,878
15,878
nonpt
176,731
168,492
165,353
161,951
161,951
34,373
34,283
34,219
34,143
34,143
136,753
129,917
125,034
119,169
119,169
nonroad
52,921
39,377
34,017
31,809
33,486
30,046
25,055
19,746
15,707
14,545
423,212
313,857
292,004
302,871
339,056
onroad
71,646
45,768
35,321
28,541
26,110
128,362
67,279
38,153
22,168
17,469
960,011
542,302
495,891
497,918
557,054
ptipm
595
384
370
427
427
32,561
11,748
10,341
12,444
12,444
10,922
9,109
8,673
7,936
7,936
ptnonipm
7,722
6,559
6,559
6,559
6,559
15,394
14,849
14,849
14,849
14,849
10,656
10,621
10,621
10,621
10,621
Massachusetts Total
312,806
263,814
244,968
232,768
232,202
258,220
169,223
133,352
115,802
111,360
1,576,034
1,041,209
968,976
976,750
1,073,754
Michigan
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,504
2,732
2,987
3,375
4,065
43,025
44,993
47,692
53,325
69,133
26,763
28,629
31,302
34,872
39,287
avefire
724
724
724
724
724
330
330
330
330
330
15,380
15,380
15,380
15,380
15,380
nonpt
248,382
236,151
227,606
226,489
226,489
43,499
43,424
43,371
43,306
43,306
94,909
96,472
97,588
98,928
98,928
nonroad
173,241
147,590
124,499
104,755
103,718
70,912
63,773
52,230
43,538
41,589
1,013,991
688,696
620,623
605,349
649,546
onroad
207,762
124,457
104,607
88,580
79,226
315,420
189,800
120,465
80,551
60,564
2,744,658
1,461,558
1,273,212
1,221,058
1,283,487
ptipm
1,243
1,350
1,562
1,602
1,602
141,908
83,271
80,290
79,933
79,933
13,367
12,926
15,503
17,748
17,748
ptnonipm
39,832
32,854
32,854
32,854
32,854
82,202
77,597
77,597
77,597
77,597
66,873
66,685
66,685
66,685
66,685
Michigan Total
673,689
545,857
494,838
458,378
448,677
697,296
503,189
421,975
378,581
372,452
3,975,941
2,370,346
2,120,292
2,060,020
2,171,061
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
Minnesota
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,611
1,662
1,672
1,712
1,871
55,371
50,040
48,393
48,235
52,459
8,411
8,982
9,212
9,710
11,224
avefire
5,047
5,047
5,047
5,047
5,047
2,300
2,300
2,300
2,300
2,300
107,237
107,237
107,237
107,237
107,237
nonpt
125,318
121,402
116,713
115,950
115,950
56,700
56,616
56,557
56,485
56,485
139,234
132,191
127,159
121,121
121,121
nonroad
97,104
86,673
76,510
67,902
60,721
68,820
59,324
48,614
36,926
28,213
452,734
385,326
358,959
356,686
369,793
onroad
102,566
73,518
62,866
53,880
46,670
163,172
106,140
65,740
43,094
35,370
1,314,360
897,668
786,262
764,896
834,696
ptipm
646
672
815
916
916
86,917
38,630
41,007
42,469
42,469
7,468
5,933
8,643
9,316
9,316
ptnonipm
29,541
26,591
26,652
26,726
26,726
67,813
66,107
66,325
66,615
66,615
47,015
47,365
48,177
49,063
49,063
Minnesota Total
361,833
315,564
290,274
272,132
257,902
501,094
379,158
328,936
296,125
283,912
2,076,459
1,584,702
1,445,649
1,418,029
1,502,451
Mississippi
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,386
2,516
2,548
2,651
2,997
66,650
61,821
60,034
60,314
67,010
10,656
11,332
11,801
12,595
14,577
avefire
8,407
8,407
8,407
8,407
8,407
3,833
3,833
3,833
3,833
3,833
178,646
178,646
178,646
178,646
178,646
nonpt
156,390
154,665
149,624
148,789
148,789
12,212
12,169
12,139
12,103
12,103
129,408
125,242
122,266
118,696
118,696
nonroad
36,056
31,622
27,255
23,620
23,727
22,180
19,058
15,120
11,400
9,220
214,179
149,762
139,143
139,898
150,980
onroad
62,375
40,881
31,707
26,200
24,829
105,505
62,300
37,951
24,286
18,302
739,190
448,208
388,570
379,133
410,973
ptipm
629
363
429
472
472
45,850
29,058
23,371
20,263
20,263
5,286
4,402
6,799
6,440
6,440
ptnonipm
43,224
37,751
37,751
37,751
37,751
60,244
58,269
56,826
56,826
56,826
54,587
53,581
53,581
53,581
53,581
Mississippi Total
309,467
276,205
257,721
247,890
246,971
316,473
246,507
209,274
189,025
187,557
1,331,952
971,174
900,807
888,989
933,893
Missouri
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
3,439
3,537
3,603
3,712
3,926
79,583
70,030
68,145
67,979
71,968
18,171
19,635
20,840
22,505
25,361
avefire
1,488
1,488
1,488
1,488
1,488
678
678
678
678
678
31,611
31,611
31,611
31,611
31,611
nonpt
162,795
157,282
151,104
149,030
149,030
32,910
32,785
32,695
32,588
32,588
168,352
158,163
150,885
142,152
142,152
nonroad
63,279
50,345
43,558
39,288
40,437
52,997
46,091
37,054
27,534
20,719
479,319
333,739
311,545
318,976
350,123
onroad
124,106
79,858
65,168
53,630
51,200
200,379
121,016
75,598
49,135
38,513
1,598,930
923,851
800,044
768,803
863,675
ptipm
1,496
1,692
1,771
1,764
1,764
145,232
80,814
75,127
73,116
73,116
10,827
12,552
13,483
13,461
13,461
ptnonipm
34,704
27,517
27,517
27,517
27,517
38,025
33,144
33,144
33,144
33,144
108,389
108,361
108,361
108,361
108,361
Missouri Total
391,308
321,719
294,210
276,429
275,362
549,803
384,556
322,440
284,174
270,726
2,415,599
1,587,913
1,436,771
1,405,869
1,534,746
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
Montana
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,309
1,306
1,302
1,297
1,291
22,873
18,260
17,546
17,175
17,180
5,814
6,365
6,862
7,457
8,299
avefire
10,085
10,085
10,085
10,085
10,085
5,187
5,187
5,187
5,187
5,187
203,759
203,759
203,759
203,759
203,759
nonpt
23,573
22,543
21,452
21,032
21,032
3,797
3,767
3,746
3,720
3,720
35,673
33,199
31,432
29,312
29,312
nonroad
12,968
11,853
10,184
8,322
7,914
18,777
16,576
13,643
9,792
6,048
85,304
60,793
57,185
56,226
58,516
onroad
20,451
13,325
11,525
9,863
9,039
36,727
20,801
12,900
8,538
6,842
283,678
164,018
146,187
142,895
158,758
ptipm
355
318
393
421
421
36,577
36,169
31,948
32,457
32,457
3,047
4,273
4,906
5,141
5,141
ptnonipm
6,807
6,431
6,431
6,431
6,431
16,588
16,122
15,684
15,684
15,684
29,410
29,410
29,410
29,410
29,410
Montana Total
75,548
65,861
61,373
57,451
56,213
140,526
116,883
100,654
92,552
87,117
646,686
501,817
479,741
474,201
493,194
Nebraska
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
3,524
3,523
3,463
3,384
3,294
68,904
54,285
51,946
50,703
50,664
10,222
11,459
12,380
13,530
15,482
avefire
837
837
837
837
837
381
381
381
381
381
17,780
17,780
17,780
17,780
17,780
nonpt
40,762
39,632
37,860
37,441
37,441
13,820
13,798
13,782
13,763
13,763
66,672
64,882
63,603
62,069
62,069
nonroad
18,442
14,727
12,636
11,084
11,092
39,889
34,573
27,908
19,576
11,818
155,107
108,019
98,448
97,078
102,149
onroad
36,940
24,026
20,116
17,017
16,795
66,226
40,028
24,974
16,298
13,340
473,870
276,172
241,994
234,283
276,293
ptipm
635
545
549
602
602
47,900
54,034
38,052
38,911
38,911
3,420
4,404
4,601
5,028
5,028
ptnonipm
6,527
5,906
5,906
5,906
5,906
11,385
11,385
11,385
11,385
11,385
5,717
5,717
5,717
5,717
5,717
Nebraska Total
107,667
89,196
81,368
76,271
75,966
248,506
208,485
168,427
151,019
140,263
732,788
488,432
444,524
435,483
484,518
Nevada
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,057
1,094
1,150
1,223
1,290
12,958
11,168
11,212
11,566
12,047
11,214
11,954
13,121
14,601
16,127
avefire
10,740
10,740
10,740
10,740
10,740
4,910
4,910
4,910
4,910
4,910
227,965
227,965
227,965
227,965
227,965
nonpt
22,874
23,491
21,718
21,792
21,792
5,308
5,306
5,304
5,302
5,302
14,700
14,229
13,893
13,490
13,490
nonroad
22,720
16,988
15,504
14,803
15,760
18,990
16,487
13,033
9,176
7,244
208,377
135,971
130,610
137,077
152,899
onroad
26,884
22,191
19,306
17,057
16,380
28,320
20,307
14,625
11,256
9,913
301,082
231,710
223,133
236,365
262,598
ptipm
483
445
597
674
674
48,366
46,403
32,260
34,817
34,817
2,798
8,072
9,139
7,369
7,369
ptnonipm
1,649
1,493
1,493
1,493
1,493
7,509
7,509
7,509
7,509
7,509
6,985
6,985
6,985
6,985
6,985
Nevada Total
86,406
76,441
70,506
67,783
68,130
126,362
112,090
88,854
84,537
81,742
773,121
636,888
624,847
643,851
687,433
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
New Hampshire
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
118
124
132
143
159
1,866
1,781
1,752
1,783
2,004
2,305
2,398
2,553
2,721
2,916
avefire
301
301
301
301
301
137
137
137
137
137
6,398
6,398
6,398
6,398
6,398
nonpt
61,483
57,548
55,511
53,263
53,263
11,235
11,178
11,137
11,088
11,088
74,137
69,710
66,547
62,750
62,750
noriroad
21,832
18,910
16,065
13,661
13,333
8,150
6,965
5,763
4,818
4,616
122,530
96,954
85,458
85,704
93,178
onroad
21,682
14,879
12,393
10,267
9,342
38,799
23,734
14,685
9,164
6,716
294,533
180,168
158,722
155,253
171,336
ptipm
104
150
165
189
189
7,000
2,619
3,065
3,964
3,964
643
3,375
3,404
3,140
3,140
ptnonipm
1,496
721
721
721
721
2,786
2,783
2,783
2,783
2,783
2,082
2,080
2,080
2,080
2,080
New Hampshire Total
107,015
92,633
85,289
78,545
77,308
69,973
49,197
39,322
33,737
31,309
502,627
361,082
325,161
318,046
341,798
New Jersey
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,236
2,326
2,456
2,653
2,999
35,998
35,363
34,759
35,709
40,992
14,960
15,509
16,429
17,644
19,580
avefire
488
488
488
488
488
223
223
223
223
223
10,375
10,375
10,375
10,375
10,375
nonpt
151,657
144,567
142,238
140,176
140,176
26,393
26,342
26,305
26,261
26,261
84,145
79,593
76,342
72,437
72,437
nonroad
78,629
57,795
50,544
48,222
51,422
40,876
35,077
28,499
23,040
21,269
635,064
467,523
442,732
464,876
523,535
onroad
101,094
64,465
46,651
36,413
32,216
161,872
85,611
48,496
26,860
18,353
1,325,445
751,380
654,205
632,680
704,191
ptipm
1,048
335
398
450
450
34,188
7,767
9,427
11,022
11,022
3,865
7,240
8,009
7,822
7,822
ptnonipm
13,282
10,897
10,897
10,897
10,897
17,206
15,578
15,578
15,578
15,578
8,375
8,316
8,316
8,316
8,316
New Jersey Total
348,436
280,873
253,673
239,299
238,649
316,756
205,961
163,286
138,694
133,699
2,082,228
1,339,937
1,216,408
1,214,149
1,346,255
New Mexico
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,982
1,992
1,971
1,941
1,903
36,714
29,265
27,994
27,361
27,304
8,473
9,222
9,911
10,732
11,960
avefire
27,488
27,488
27,488
27,488
27,488
12,582
12,582
12,582
12,582
12,582
583,216
583,216
583,216
583,216
583,216
nonpt
36,950
35,170
33,450
33,088
33,088
7,532
7,517
7,505
7,492
7,492
29,666
28,367
27,439
26,326
26,326
nonroad
13,499
11,181
10,062
9,237
9,636
9,681
8,353
6,706
4,981
4,047
119,501
78,164
74,832
78,007
86,466
onroad
45,763
31,131
26,206
22,451
21,317
77,574
46,462
29,322
19,600
15,429
587,028
361,579
326,293
326,676
360,766
ptipm
563
489
491
519
519
78,547
63,814
58,498
58,562
58,562
5,539
6,016
6,052
6,433
6,433
ptnonipm
15,691
10,786
10,786
10,786
10,786
60,358
60,297
60,240
60,240
60,240
32,228
32,228
32,228
32,228
32,228
New Mexico Total
141,935
118,237
110,454
105,510
104,738
282,988
228,288
202,848
190,819
185,657
1,365,651
1,098,792
1,059,971
1,063,618
1,107,396
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
New York
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,473
2,619
2,745
2,936
3,266
40,659
38,350
38,161
39,370
44,334
22,205
23,864
25,657
27,886
30,840
avefire
903
903
903
903
903
412
412
412
412
412
19,195
19,195
19,195
19,195
19,195
nonpt
608,921
613,062
621,635
634,915
634,915
89,986
90,323
90,564
90,853
90,853
404,592
431,581
450,860
473,993
473,993
noriroad
151,345
121,199
105,522
95,842
98,761
78,279
67,078
55,658
44,756
40,396
1,175,721
895,734
810,175
839,288
937,245
onroad
212,929
137,568
102,389
80,584
74,771
290,698
165,594
103,189
62,605
44,498
2,822,801
1,596,877
1,390,297
1,328,878
1,526,678
ptipm
857
1,082
1,100
1,107
1,107
81,201
39,914
34,490
33,735
33,735
12,204
20,230
17,499
17,890
17,890
ptnonipm
6,218
5,365
5,365
5,365
5,365
38,992
32,096
32,096
32,096
32,096
54,133
54,080
54,080
54,080
54,080
New York Total
983,646
881,799
839,659
821,652
819,089
620,228
433,767
354,570
303,826
286,322
4,510,852
3,041,562
2,767,764
2,761,211
3,059,922
North Carolina
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,472
1,615
1,690
1,794
1,916
22,608
19,886
19,482
19,554
20,381
9,957
10,817
11,615
12,623
13,872
avefire
58,889
58,889
58,889
58,889
58,889
11,424
11,424
11,424
11,424
11,424
429,388
429,388
429,388
429,388
429,388
nonpt
231,094
222,255
212,129
210,082
210,082
18,869
18,761
18,684
18,591
18,591
321,101
312,266
305,955
298,382
298,382
nonroad
88,972
68,628
59,717
54,845
57,309
61,664
50,178
38,433
27,759
22,701
746,344
586,105
540,968
560,093
622,450
onroad
143,187
93,913
76,576
63,583
58,393
242,379
141,370
87,185
55,801
41,119
1,786,813
1,075,088
923,026
897,310
965,793
ptipm
920
1,103
1,163
1,256
1,256
153,226
67,924
67,442
59,724
59,724
12,112
11,366
12,286
12,761
12,761
ptnonipm
61,685
53,635
53,633
53,632
53,632
49,273
37,071
37,071
37,071
37,071
52,414
52,062
52,062
52,062
52,062
North Carolina Total
586,219
500,039
463,797
444,081
441,477
559,444
346,614
279,721
229,924
211,012
3,358,129
2,477,092
2,275,301
2,262,619
2,394,709
North Dakota
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,256
1,254
1,240
1,219
1,195
23,072
18,121
17,336
16,916
16,912
4,832
5,291
5,706
6,195
6,944
avefire
527
527
527
527
527
240
240
240
240
240
11,204
11,204
11,204
11,204
11,204
nonpt
14,911
14,177
13,461
13,174
13,174
4,007
3,987
3,972
3,955
3,955
20,488
18,845
17,671
16,262
16,262
nonroad
13,565
11,430
9,536
7,778
7,197
38,012
33,288
27,270
19,093
10,734
91,869
66,050
59,029
54,726
53,229
onroad
15,356
9,829
8,318
6,933
6,511
24,832
14,432
8,967
5,824
4,722
206,627
116,879
101,483
96,390
109,994
ptipm
781
846
838
882
882
75,947
45,049
44,009
44,560
44,560
5,237
7,659
11,260
11,383
11,383
ptnonipm
1,249
1,124
1,124
1,124
1,124
9,929
9,385
9,385
9,385
9,385
5,778
5,765
5,765
5,765
5,765
North Dakota Total
47,645
39,188
35,045
31,637
30,610
176,039
124,502
111,180
99,974
90,509
346,035
231,693
212,118
201,925
214,782
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
Ohio
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
3,632
4,064
4,207
4,423
4,798
96,728
88,052
85,793
85,843
92,415
29,188
31,189
32,881
35,103
38,849
avefire
178
178
178
178
178
81
81
81
81
81
3,787
3,787
3,787
3,787
3,787
nonpt
285,528
275,689
272,521
271,610
271,610
41,466
41,405
41,360
41,307
41,307
150,302
145,351
141,813
137,566
137,566
noriroad
103,414
77,465
67,241
61,754
64,960
90,812
74,081
55,950
40,458
34,425
910,152
623,175
564,041
573,594
633,745
onroad
205,348
124,133
100,380
81,331
74,335
327,388
192,777
113,811
72,024
57,376
2,600,918
1,476,811
1,265,027
1,236,418
1,362,343
ptipm
1,773
1,971
2,106
2,149
2,149
373,299
94,744
99,033
92,780
92,780
14,817
20,543
21,862
22,421
22,421
ptnonipm
29,515
26,436
26,436
26,436
26,436
65,850
58,970
58,185
58,185
58,185
238,412
238,412
238,412
238,412
238,412
Ohio Total
629,389
509,937
473,070
447,881
444,466
995,625
550,110
454,213
390,678
376,570
3,947,575
2,539,269
2,267,823
2,247,301
2,437,123
Oklahoma
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,551
1,566
1,563
1,555
1,543
26,294
21,261
20,464
20,088
20,149
10,093
10,553
11,225
11,963
12,989
avefire
3,749
3,749
3,749
3,749
3,749
1,709
1,709
1,709
1,709
1,709
79,673
79,673
79,673
79,673
79,673
nonpt
200,442
196,496
192,447
191,677
191,677
94,574
94,542
94,518
94,490
94,490
385,235
382,569
380,664
378,379
378,379
nonroad
38,015
29,720
25,931
23,686
24,511
31,331
26,945
21,961
16,410
12,410
308,218
206,115
186,014
190,974
209,343
onroad
86,133
56,321
46,067
39,279
37,822
133,152
80,016
51,065
34,569
28,819
1,069,135
636,945
557,794
555,227
630,913
ptipm
984
958
1,019
1,088
1,088
90,302
83,945
64,740
62,434
62,434
13,661
28,415
29,821
27,970
27,970
ptnonipm
35,176
23,733
23,733
23,733
23,733
72,670
72,517
71,835
71,835
71,835
50,750
50,750
50,750
50,750
50,750
Oklahoma Total
366,050
312,543
294,508
284,767
284,123
450,033
380,935
326,293
301,535
291,847
1,916,764
1,395,018
1,295,939
1,294,935
1,390,016
Oregon
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,843
2,027
2,115
2,253
2,487
43,439
40,199
39,073
39,085
42,312
12,401
13,077
13,756
14,654
16,159
avefire
37,328
37,328
37,328
37,328
37,328
17,857
17,857
17,857
17,857
17,857
778,193
778,193
778,193
778,193
778,193
nonpt
242,829
239,638
238,352
239,218
239,218
16,998
17,009
17,018
17,028
17,028
342,444
333,115
326,451
318,454
318,454
nonroad
39,821
32,299
28,149
25,088
25,679
26,372
22,638
18,141
13,685
11,363
304,850
212,615
193,709
199,468
220,481
onroad
91,766
58,567
47,310
39,312
32,647
109,066
69,772
50,073
33,701
21,232
1,078,005
619,048
517,087
478,542
485,899
ptipm
142
151
151
152
152
9,006
9,740
9,740
9,768
9,768
1,105
3,932
3,932
3,942
3,942
ptnonipm
14,567
10,990
10,990
10,990
10,990
15,958
15,767
15,767
15,767
15,767
34,389
33,794
33,794
33,794
33,794
Oregon Total
428,297
381,001
364,395
354,342
348,502
238,696
192,982
167,669
146,890
135,326
2,551,388
1,993,775
1,866,922
1,827,046
1,856,922
-------
State
Sector
[tons/yr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tons/yr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tons/yr]
2030
Base
VOC
[tons/yr]
2002
NOX
[tons/yr]
2009
Base
NOX
[tons/yr]
2014
Base
NOX
[tons/yr]
2020
Base
NOX
[tons/yr]
2030
Base
NOX
[tons/yr]
2002
CO
[tons/yr]
2009
Base
CO
[tons/yr]
2014
Base
CO
[tons/yr]
2020
Base
CO
[tons/yr]
2030
Base
CO
Pennsylvania
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,425
2,559
2,673
2,851
3,196
67,118
63,037
61,438
61,811
67,924
25,047
26,618
28,094
30,087
33,258
avefire
256
256
256
256
256
117
117
117
117
117
5,450
5,450
5,450
5,450
5,450
nonpt
281,740
264,980
261,647
260,079
260,079
53,435
53,333
53,260
53,173
53,173
265,035
256,636
250,638
243,439
243,439
nonroad
96,797
79,165
69,633
63,072
65,413
62,168
51,401
40,177
30,881
27,101
856,737
612,784
547,716
566,481
631,795
onroad
184,268
115,276
89,483
69,513
63,170
294,414
165,444
99,133
58,609
40,758
2,420,525
1,323,428
1,104,821
1,039,116
1,137,007
ptipm
1,212
1,712
1,766
1,753
1,753
210,149
91,466
74,225
69,570
69,570
17,018
18,116
19,163
18,236
18,236
ptnonipm
36,871
30,914
30,914
30,914
30,914
89,064
76,602
74,324
74,324
74,324
104,570
103,784
103,784
103,784
103,784
Pennsylvania Total
603,569
494,863
456,372
428,439
424,781
776,465
501,400
402,674
348,484
332,967
3,694,382
2,346,814
2,059,664
2,006,592
2,172,969
Rhode Island
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
162
166
180
197
214
876
824
874
950
1,028
2,923
3,009
3,290
3,628
3,959
avefire
8
8
8
8
8
4
4
4
4
4
171
171
171
171
171
nonpt
16,875
16,553
16,483
16,457
16,457
2,964
2,960
2,958
2,955
2,955
5,421
5,142
4,942
4,703
4,703
nonroad
8,491
6,010
5,080
4,822
5,137
4,663
3,890
3,053
2,457
2,309
65,923
48,379
44,582
46,080
51,531
onroad
14,366
10,124
7,869
6,810
5,805
16,720
9,655
6,550
4,125
3,514
188,240
113,984
101,917
97,100
105,592
ptipm
39
49
45
41
41
712
475
396
357
357
453
1,926
1,751
1,616
1,616
ptnonipm
1,894
1,360
1,360
1,360
1,360
2,060
1,938
1,938
1,938
1,938
1,781
1,758
1,758
1,758
1,758
Rhode Island Total
41,835
34,271
31,025
29,697
29,023
27,997
19,747
15,774
12,786
12,104
264,911
174,368
158,410
155,056
169,329
South Carolina
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
961
997
1,019
1,050
1,107
19,378
17,077
16,521
16,424
17,422
9,393
9,933
10,554
11,272
12,270
avefire
5,171
5,171
5,171
5,171
5,171
2,357
2,357
2,357
2,357
2,357
109,880
109,880
109,880
109,880
109,880
nonpt
185,429
185,163
181,315
182,844
182,844
20,281
20,275
20,271
20,267
20,267
145,294
145,455
145,570
145,708
145,708
nonroad
50,041
38,078
32,999
30,535
31,883
29,982
24,512
18,902
13,850
11,690
377,166
295,785
271,673
280,073
310,041
onroad
89,994
58,070
47,272
39,608
37,165
134,542
82,299
52,555
35,593
27,866
1,141,561
691,670
590,040
570,319
618,756
ptipm
506
609
655
746
746
91,296
50,236
48,449
34,085
34,085
4,749
5,535
6,509
6,829
6,829
ptnonipm
36,778
27,298
27,298
27,298
27,298
40,417
29,336
29,336
29,336
29,336
56,640
56,448
56,448
56,448
56,448
South Carolina Total
368,879
315,385
295,729
287,252
286,214
338,253
226,092
188,391
151,912
143,023
1,844,682
1,314,706
1,190,675
1,180,530
1,259,932
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonslyr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonslyr]
2030
Base
VOC
[tons/yr]
2002
NOX
[tons/yr]
2009
Base
NOX
[tons/yr]
2014
Base
NOX
[tons/yr]
2020
Base
NOX
[tons/yr]
2030
Base
NOX
[tons/yr]
2002
CO
[tonslyr]
2009
Base
CO
[tons/yr]
2014
Base
CO
[tons/yr]
2020
Base
CO
[tons/yr]
2030
Base
CO
South Dakota
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
321
324
331
339
346
4,164
3,349
3,252
3,202
3,216
2,979
3,160
3,398
3,655
3,947
avefire
3,985
3,985
3,985
3,985
3,985
1,817
1,817
1,817
1,817
1,817
84,689
84,689
84,689
84,689
84,689
nonpt
19,597
18,840
17,980
17,644
17,644
5,200
5,177
5,160
5,140
5,140
24,107
22,176
20,796
19,141
19,141
nonroad
12,322
10,531
8,874
7,352
6,964
27,219
23,755
19,402
13,665
7,961
79,151
59,285
53,433
50,681
50,964
onroad
16,177
10,656
9,022
7,539
7,203
29,910
18,071
11,071
6,963
5,621
219,053
129,758
113,555
108,684
126,091
ptipm
111
110
120
142
142
15,922
2,353
2,364
2,740
2,740
632
552
604
785
785
ptnonipm
2,431
1,449
1,449
1,449
1,449
4,776
4,776
4,776
4,776
4,776
4,068
4,068
4,068
4,068
4,068
South Dakota Total
54,944
45,897
41,762
38,451
37,734
89,008
59,298
47,843
38,304
31,271
414,679
303,689
280,543
271,703
289,686
Tennessee
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,152
2,363
2,449
2,594
2,866
50,692
46,834
45,791
46,115
50,251
13,001
14,117
14,987
16,228
18,286
avefire
2,220
2,220
2,220
2,220
2,220
1,012
1,012
1,012
1,012
1,012
47,175
47,175
47,175
47,175
47,175
nonpt
148,677
145,508
137,119
135,241
135,241
18,676
18,604
18,552
18,490
18,490
119,973
114,059
109,836
104,767
104,767
nonroad
60,023
49,015
42,584
38,065
39,112
40,970
34,909
27,163
20,352
16,955
460,143
305,294
281,937
287,728
316,789
onroad
140,405
94,247
75,248
60,871
56,534
240,312
147,688
93,956
59,503
43,543
1,681,568
992,698
875,126
840,777
901,356
ptipm
843
925
1,015
1,202
1,202
155,926
53,647
54,945
39,841
39,841
6,596
7,203
8,021
9,475
9,475
ptnonipm
84,610
73,801
73,801
73,801
73,801
69,070
50,451
50,451
50,451
50,451
115,767
115,278
115,278
115,278
115,278
Tennessee Total
438,930
368,080
334,436
313,994
310,976
576,659
353,145
291,871
235,764
220,543
2,444,222
1,595,825
1,452,359
1,421,428
1,513,126
Texas
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
11,279
11,889
12,303
13,009
14,522
236,223
220,853
216,266
218,785
242,786
67,547
71,359
75,840
81,884
91,779
avefire
13,201
13,201
13,201
13,201
13,201
4,890
4,890
4,890
4,890
4,890
256,966
256,966
256,966
256,966
256,966
nonpt
695,600
675,342
669,427
668,063
668,063
274,338
274,244
274,177
274,096
274,096
463,577
455,678
450,036
443,265
443,265
nonroad
174,723
135,696
120,305
113,364
119,864
152,771
126,111
98,419
70,456
54,433
1,578,739
1,196,045
1,128,017
1,174,843
1,309,218
onroad
308,904
199,858
154,959
128,066
128,916
621,483
334,266
186,632
116,542
95,623
3,787,848
2,152,885
1,873,424
1,875,580
2,180,504
ptipm
4,745
4,575
4,771
5,010
5,010
259,612
136,697
135,504
135,714
135,714
215,207
78,627
86,950
86,882
86,882
ptnonipm
149,554
111,767
111,767
111,767
111,767
344,073
336,557
331,121
331,121
331,121
283,294
281,797
281,797
281,797
281,797
Texas Total
1,358,006
1,152,328
1,086,732
1,052,479
1,061,342
1,893,390
1,433,617
1,247,009
1,151,604
1,138,663
6,653,179
4,493,357
4,153,030
4,201,216
4,650,411
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tons/yr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
Tribal Data
aim
218
217
212
206
199
858
672
642
626
626
302
347
376
413
484
ptipm
241
30
30
24
24
97
232
232
182
182
828
1,171
1,171
918
918
ptnonipm
601
389
383
382
382
6,623
6,620
6,620
6,620
6,620
2,573
2,573
2,581
2,587
2,587
Tribal Data Total
1,060
636
625
612
605
7,578
7,524
7,494
7,428
7,428
3,703
4,091
4,127
3,918
3,989
Utah
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,596
2,737
2,969
3,276
3,565
14,640
12,158
11,959
12,044
12,307
10,805
11,601
12,705
14,099
15,617
avefire
15,469
15,469
15,469
15,469
15,469
7,052
7,052
7,052
7,052
7,052
328,713
328,713
328,713
328,713
328,713
nonpt
54,443
53,042
50,928
50,352
50,352
6,948
6,937
6,929
6,920
6,920
79,323
78,434
77,799
77,036
77,036
nonroad
25,488
23,357
20,391
17,151
16,833
15,026
13,024
10,343
7,840
6,675
172,729
119,498
113,186
114,711
124,803
onroad
56,206
39,609
34,748
30,914
28,693
76,518
51,752
35,867
25,070
19,835
764,714
452,333
422,640
423,561
477,048
ptipm
418
501
600
644
644
73,220
62,979
58,224
59,235
59,235
4,506
5,583
6,340
6,468
6,468
ptnonipm
5,826
5,070
5,070
5,070
5,070
14,998
14,681
14,531
14,531
14,531
45,052
45,029
45,029
45,029
45,029
Utah Total
160,444
139,784
130,175
122,875
120,626
208,401
168,583
144,906
132,692
126,554
1,405,842
1,041,191
1,006,412
1,009,617
1,074,714
Vermont
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
53
57
61
66
70
49
51
57
64
71
1,220
1,280
1,372
1,469
1,560
avefire
393
393
393
393
393
179
179
179
179
179
8,347
8,347
8,347
8,347
8,347
nonpt
18,887
18,265
17,993
17,744
17,744
3,438
3,416
3,400
3,382
3,382
43,091
41,058
39,605
37,862
37,862
nonroad
10,446
9,755
8,420
6,901
6,524
4,170
3,597
2,898
2,354
2,123
58,906
42,042
38,858
38,196
40,914
onroad
18,139
11,951
9,772
7,646
6,600
21,783
13,393
8,581
5,448
3,908
237,164
131,797
119,269
119,078
133,225
ptipm
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ptnonipm
1,097
1,025
1,025
1,025
1,025
790
790
790
790
790
1,078
1,078
1,078
1,078
1,078
Vermont Total
49,015
41,445
37,664
33,774
32,356
30,409
21,427
15,905
12,217
10,453
349,807
225,602
208,529
206,029
222,985
-------
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tonsfyr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tonsfyr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
Virginia
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
3,084
3,198
3,264
3,381
3,605
39,676
36,966
35,646
35,676
38,394
17,758
18,414
19,296
20,457
22,189
avefire
3,194
3,194
3,194
3,194
3,194
1,456
1,456
1,456
1,456
1,456
67,866
67,866
67,866
67,866
67,866
nonpt
201,748
190,207
185,542
181,688
181,688
53,605
53,529
53,475
53,409
53,409
208,041
201,223
196,351
190,501
190,501
nonroad
67,441
50,452
44,451
41,960
44,479
45,848
36,688
28,740
21,036
17,403
520,042
473,574
442,744
463,608
518,461
onroad
125,474
86,161
72,948
63,234
62,669
214,393
123,035
78,620
53,731
49,108
1,722,600
1,099,794
976,391
954,294
1,121,935
ptipm
726
536
679
719
719
86,763
69,736
44,145
39,719
39,719
6,714
9,909
11,740
11,202
11,202
ptnonipm
43,184
36,335
36,335
36,335
36,335
61,730
46,246
46,246
46,246
46,246
63,978
63,557
63,557
63,557
63,557
Virginia Total
444,850
370,082
346,413
330,511
332,689
503,471
367,656
288,327
251,274
245,737
2,606,999
1,934,337
1,777,945
1,771,484
1,995,711
Washington
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,248
2,433
2,548
2,738
3,152
66,992
65,363
65,801
69,264
82,840
20,193
21,483
22,856
24,617
27,397
avefire
2,674
2,674
2,674
2,674
2,674
1,484
1,484
1,484
1,484
1,484
52,086
52,086
52,086
52,086
52,086
nonpt
166,658
159,930
154,329
150,839
150,839
16,911
16,812
16,742
16,658
16,658
204,125
196,158
190,466
183,628
183,628
nonroad
64,611
50,334
43,632
39,373
40,459
42,800
36,553
29,715
22,539
18,753
486,615
359,916
319,178
328,381
363,147
onroad
159,797
109,815
94,849
81,074
68,508
199,767
123,689
87,124
58,303
38,337
1,820,900
1,174,345
1,016,289
938,502
954,906
ptipm
219
342
350
349
349
16,122
17,357
17,581
17,552
17,552
1,665
6,954
7,264
7,158
7,158
ptnonipm
12,429
11,631
11,631
11,631
11,631
24,522
24,522
24,522
24,522
24,522
39,106
39,106
39,106
39,106
39,106
Washington Total
408,636
337,160
310,013
288,679
277,612
368,598
285,781
242,970
210,321
200,146
2,624,689
1,850,047
1,647,245
1,573,478
1,627,429
West Virginia
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,180
1,291
1,333
1,422
1,664
32,148
30,383
30,081
31,151
36,257
5,139
5,692
6,067
6,676
8,049
avefire
1,721
1,721
1,721
1,721
1,721
785
785
785
785
785
36,578
36,578
36,578
36,578
36,578
nonpt
59,489
56,958
54,651
54,047
54,047
14,519
14,487
14,464
14,436
14,436
70,069
67,360
65,425
63,103
63,103
nonroad
16,935
16,469
14,419
11,990
11,889
8,407
6,869
5,665
4,437
3,935
117,839
102,117
87,138
89,867
98,181
onroad
36,949
21,900
17,904
14,639
12,233
60,216
32,252
19,840
13,006
9,265
502,130
270,712
225,844
210,771
206,623
ptipm
1,175
1,289
1,328
1,332
1,332
227,827
55,352
50,926
45,760
45,760
10,319
10,228
10,492
10,572
10,572
ptnonipm
14,241
12,089
12,089
12,089
12,089
46,627
37,778
37,778
37,778
37,778
89,898
89,898
89,898
89,898
89,898
West Virginia Total
131,691
111,717
103,446
97,239
94,975
390,529
177,906
159,538
147,353
148,216
831,973
582,585
521,442
507,465
513,003
-------
o
to
o
State
Sector
[tonsfyr]
2002
VOC
[tons/yr]
2009
Base
VOC
[tons/yr]
2014
Base
VOC
[tons/yr]
2020
Base
VOC
[tonsfyr]
2030
Base
VOC
[tons/yr]
2002
NOX
[tonsfyr]
2009
Base
NOX
[tonsfyr]
2014
Base
NOX
[tonsfyr]
2020
Base
NOX
[tonsfyr]
2030
Base
NOX
[tonsfyr]
2002
CO
[tonsfyr]
2009
Base
CO
[tonsfyr]
2014
Base
CO
[tonsfyr]
2020
Base
CO
[tonsfyr]
2030
Base
CO
Wisconsin
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
2,060
2,263
2,387
2,558
2,799
30,307
27,468
27,538
28,600
32,248
24,321
25,819
28,228
31,276
34,712
avefire
561
561
561
561
561
256
256
256
256
256
11,924
11,924
11,924
11,924
11,924
nonpt
230,068
229,658
228,764
231,695
231,695
21,994
21,984
21,976
21,967
21,967
166,779
162,598
159,612
156,028
156,028
nonroad
111,779
96,076
81,098
68,764
69,593
53,430
45,761
36,575
28,977
26,142
569,467
416,240
377,144
372,039
409,058
onroad
96,058
61,689
51,408
43,691
39,473
172,043
103,786
61,755
38,899
30,805
1,321,240
763,639
678,392
689,891
777,211
ptipm
964
1,085
1,178
1,186
1,186
91,128
53,488
57,160
56,119
56,119
10,725
10,728
11,908
12,152
12,152
ptnonipm
31,057
26,592
26,592
26,592
26,592
38,283
38,282
38,282
38,282
38,282
34,197
34,197
34,197
34,197
34,197
Wisconsin Total
472,549
417,924
391,988
375,047
371,899
407,440
291,025
243,541
213,100
205,819
2,138,654
1,425,145
1,301,406
1,307,507
1,435,283
Wyoming
afdust
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ag
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
aim
1,569
1,567
1,540
1,503
1,462
30,368
23,784
22,747
22,184
22,173
4,758
5,359
5,787
6,322
7,211
avefire
8,852
8,852
8,852
8,852
8,852
4,035
4,035
4,035
4,035
4,035
188,099
188,099
188,099
188,099
188,099
nonpt
16,411
15,646
15,077
14,867
14,867
4,309
4,295
4,285
4,273
4,273
19,192
18,058
17,248
16,276
16,276
nonroad
9,088
8,874
7,619
6,154
5,774
5,470
4,713
3,958
3,017
2,312
53,551
40,841
36,120
35,787
37,725
onroad
18,072
11,859
9,726
8,177
7,416
32,643
17,780
10,807
6,983
5,542
246,059
143,301
121,503
116,959
129,138
ptipm
849
834
892
947
947
85,207
83,587
59,212
60,438
60,438
7,078
6,715
7,202
7,654
7,654
ptnonipm
16,771
13,552
13,552
13,552
13,552
36,500
36,385
36,385
36,385
36,385
23,341
23,341
23,341
23,341
23,341
Wyoming Total
71,613
61,185
57,257
54,052
52,869
198,533
174,578
141,430
137,316
135,158
542,078
425,714
399,299
394,438
409,445
Grand Total
17,693,869
14,934,802
13,867,583
13,220,304
13,138,328
20,931,673
14,898,719
12,440,017
10,930,663
10,452,858
101,885,285
71,586,336
65,672,193
64,912,870
69,431,177
-------
Table D-1b: Continental US, S02, NH3, PM-o, and PM25 Emissions by Sector for 2002, and Projection Years 2009, 2014, 2020, and 2030.
State
Sector
[ton sfyrj
2002
S02
[tonsfyr]
2009
Base
S02
[tonsfyr]
2014
Base
S02
[tonsfyr]
2020
Base
S02
[tonsfyr]
2030
Base
S02
[tonsfyr]
2002
NH3
[tonsfyr]
2009
Base
NH3
[tonsfyr]
2014
Base
NH3
[tonsfyr]
2020
Base
NHS
[tonsfyr]
2030
Base
NH3
[tonsfyr]
2002
PM10
[tonsfyr]
2009
Base
PM10
[tonsfyr]
2014
Base
PM10
[tonsfyr]
2020
Base
PM10
[tonsfyr]
2030
Base
PM10
[tonsfyr]
2002
PM2 5
[tonsfyr]
2009
Base
PM2 5
[tonsfyr]
2014
Base
PM2 5
[tonsfyr]
2020
Base
PM2 5
[tonsfyr]
2030
Base
PM2 5
Alabama
afdust
0
0
0
0
0
0
0
0
0
0
100,286
100.332
100,364
100,402
100,402
33,476
33.491
33.501
33.514
33.514
ag
0
0
0
0
0
57.B02
64,346
69.023
74633
74.633
0
0
0
0
0
0
0
D
0
0
aim
4,801
3,667
3,602
4,241
6,220
13
15
16
17
20
2.236
2,248
2,281
2,379
2,663
1,878
1.883
1.896
1.963
2.202
avefirs
983
983
983
983
983
752
752
752
752
752
16,251
16,251
16,251
16,251
16,251
13,936
13.938
13.936
13 938
13.938
lonpi
52,325
52,318
52,313
52,308
52,3(1B
426
426
426
426
426
27,785
27.295
26,944
26,524
26,524
23,973
23 483
23133
22.712
22712
fionroad
2,734
464
42
45
50
28
31
33
37
41
3,195
2.625
2,144
1,552
1,270
3,044
2.491
2028
1.460
1.164
snroad
5,599
729
605
667
745
5,627
6,012
6,512
7,075
7.776
4,223
3,019
2,453
2,298
2,447
3,117
1,943
1.357
1,129
1,142
ptipm
448,329
269,794
244,393
187,851
187,851
783
882
1.044
1.034
1.034
26,136
16.214
19,576
39,221
39,221
22,612
12.147
15.231
34.364
34.364
ptnonipm
89,762
88,763
88,763
88,763
86,763
2,224
2,224
2.224
2.224
2224
19,710
18,644
18,544
18,644
18,644
13,647
13.017
13.017
13,017
13.017
Alabama Total
804,533
416.719
390,702
334,858
336,920
67,655
74,688
80,030
86,198
86,906
199,826
186,627
188,657
207,271
207,422
115,685
102,393
104,151
122,098
122,074
Arizona
afdust
0
0
0
0
0
0
0
0
0
0
121,322
121.322
121,322
121,322
121,322
19,626
19.626
19.626
19 626
19.626
sg
0
0
0
0
0
29,493
29,674
29.804
29.958
29,958
0
0
0
0
0
0
0
0
0
0
aim
2,297
713
419
475
531
12
14
15
17
20
2,617
2,572
2,602
2,649
2,688
2,060
2.017
2036
2.071
2,098
avefire
2,888
2,888
2,888
2,888
2,888
2,020
2,020
2.020
2.020
2.020
43,005
43.005
43,005
43,005
43,005
37,151
37.151
37.151
37.151
37.151
ronpt
2,571
2,568
2,567
2,564
2,564
4,391
4,391
4,391
4.391
4.391
12,456
12.253
12,109
11,935
11,935
8,596
8.396
8.253
8.081
8,081
nonroad
3,856
681
41
43
48
35
40
44
49
57
4,174
3,425
2.855
2,086
1,649
3,993
3.264
2.712
1.967
1.538
onroad
2,876
862
735
888
1,125
5,150
6,417
7,561
8,994
11,238
4,021
3,441
3,002
3,112
3,839
2,951
2.225
1.656
1.532
1,812
ptipm
70,709
41.901
40,434
36,801
36,801
566
1,408
1.414
1.529
1.529
9,551
7,943
7,887
8,403
8,403
7,565
5.936
5.897
6.467
6.467
ptnonipm
21,702
21,702
21,702
21,702
21,702
72
72
72
72
72
5,723
5.574
5,574
5,574
5,574
3,044
2.963
2.963
2.963
2.963
Arizona Total
106,900
71,315
68,786
65,361
65,658
11,740
44,036
45,321
47,030
49,285
202,868
199,535
198,356
198,087
198,416
84,987
81,577
80,291
79,858
79,736
-------
o
to
to
State
Sector
[tonsfyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonsfyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonslVr]
2014
Base
NH3
[tonsfyr]
2020
Base
NHS
[tonsfyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonsfyr]
2009
Base
PM10
[tonslyr]
2014
Base
PM10
[tonsfyr]
2020
Base
PM10
Itons/yr]
2030
Base
PM10
[tons/yr]
2002
PM2 5
[tonsfyr]
2009
Base
PM2 5
[tons/yr]
2014
Base
PM2 5
[tons/yr]
2020
Base
PM2 5
[tonsfyr]
2030
Base
PM2 5
Arkansas
afdust
0
0
0
0
0
0
0
0
0
0
92,523
92.523
92,523
92,524
92,524
24,639
24.639
24.639
24.639
24.639
m
0
0
0
0
0
110,954
118,597
124,059
130.611
130.611
0
0
0
0
0
0
0
0
0
0
aim
4,646
3,013
2,708
3.067
4,454
19
22
24
26
29
1,346
1,328
1,320
1,340
1,452
1,243
1,225
1.217
1.235
1,339
av elite
728
728
728
72B
728
556
556
556
556
556
12,027
12,027
12,027
12,027
12,027
10,315
10.315
10.315
10.315
10.315
nonpt
27,260
27,258
27,256
27,254
27,254
7,386
7,336
7,336
7.336
7,336
24,094
23,911
23,780
23,622
23,622
23,062
22.878
22.747
22,590
22,590
nonroad
2,762
468
33
35
38
23
26
28
31
35
3,229
2,517
1,951
1.314
890
3,097
2.403
1.858
1.246
834
onroad
3,078
405
346
387
463
3,001
3,231
3,526
3,871
4,529
2,202
1,613
1,336
1,280
1,463
1,612
1,034
739
631
664
dtipm
70,754
97,797
39,079
39,582
39,582
346
622
85 B
843
843
2,004
4,182
5,463
5,820
5,820
1,750
3,413
4.656
4.995
4.995
ptnonipm
19,032
18,999
18,999
18,999
18,999
1,255
1,290
1,316
1,346
1,346
14,101
13,812
13,812
13,812
13,812
9,593
9,473
9,473
9,473
9,473
Arkansas Total
128,262
148,667
89,149
90,050
91,517
123,540
131,731
137,754
144,672
145,337
151,529
151,915
152,215
151,740
151,611
75,312
75,381
75,645
75,125
74,870
California
afdust
0
0
0
0
0
0
0
0
0
0
196,231
196.525
196,736
196,989
196,989
47,562
47.615
47.653
47.698
47.698
m
0
0
0
0
0
152,308
156,311
159,176
162,610
162,610
0
0
0
0
0
0
0
0
0
0
aim
40,887
27,491
19,337
14,863
21,107
180
193
203
215
237
10,124
9.970
9,726
9,692
10,788
9,534
9.381
9.148
9.113
10.148
avefire
6,735
6,735
6,735
6,735
6,735
5,117
5,117
5,117
5,117
5,117
113,231
113.231
113,231
113,231
113,231
97,301
97.301
97.301
97.301
97.301
nonpt
77,672
77,641
77,619
77,592
77,582
14,758
14,665
14.600
14.520
14.520
90,509
88.498
87,062
85,338
85,338
73,873
71.938
70.555
68896
68.896
nonroad
1,015
455
516
597
790
161
131
198
220
255
13,590
16,219
14,290
12,637
15,177
16,334
13.939
12.025
10,216
11,712
anroad
4,786
1,855
2,002
2,189
2,502
37,468
27,409
22.337
19.206
17,590
23,103
23,813
22,170
21,262
22,604
12.395
12,463
11.365
10.559
11.026
Ptipm
1,018
6,577
6,259
6,204
6,204
1,330
3,307
4.213
5.522
5.522
1,905
469
512
574
574
1,876
348
374
412
412
ptnonipm
41,761
41,477
41,089
41,128
41,128
3,367
3,367
3,367
3,367
3,367
26,854
25,998
25,998
25,998
25,998
16,655
16.234
16,234
16,284
16.2B4
California Total
173,874
162,231
153,557
149,308
156,058
214,738
210,550
209,211
210,777
209,217
430,546
474,524
469,725
465,721
470,699
275,530
269,318
264,705
260,479
263,477
Colorado
afdust
0
0
0
0
0
0
0
0
0
0
110,876
110,873
110,876
110,878
110,878
25,559
25.559
25,559
25,559
25,559
ag
0
0
0
0
0
62,907
63,846
64,517
65,320
65 320
0
0
0
0
0
0
0
0
0
0
aim
1,224
315
137
156
175
5
5
6
6
8
606
595
590
590
582
553
539
531
526
514
avefire
1,719
1,719
1,719
1,719
1,719
1,299
1,299
1,299
1,299
1.299
23,019
28.019
28,019
28,019
28,019
24,054
24.054
24.054
24.054
24.054
nonpt
6,460
6,459
6,453
6,457
6,457
71
71
71
71
71
15,059
14,719
14,475
14,183
14,183
13,545
13.204
12.961
12.669
12.669
nonroad
3,545
611
43
45
50
31
36
39
44
50
3,909
3.163
2,602
1,901
1,452
3,746
3.018
2.474
1.796
1.356
onroad
4,146
614
550
645
816
4,408
5,220
6,002
S.976
8754
3,216
2,513
2,179
2,191
2,665
2,357
1.600
1.139
1.069
1,241
ptipm
92,562
73,481
55,605
49,733
49,733
453
529
532
535
535
5,446
5.502
5,712
7,003
7,003
4,444
4,713
4.932
6.018
6,018
ptnonipm
5.331
5,322
5,322
5,322
5,322
86
36
36
36
36
17.366
16.676
16,677
16.679
16,679
8,922
8,527
8,528
8529
8.529
Colorado Total
114,989
88,522
69,834
64,078
64,274
69,260
71,091
72,603
71,337
76,123
184,499
182,071
181,134
181,445
181,461
83,181
81,213
80,226
80,219
79,940
-------
o
to
U>
State
Sector
[tonsfyr]
2002
S02
[tonsfyr]
2009
Base
S02
[tonslyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NHS
[tonslyr]
2014
Base
NH3
[tonslyr]
2020
Base
NH3
[tonslyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonslyr]
2009
Base
PM10
[tonslyr]
2014
Base
PH10
[tonsfyr]
2020
Base
PM10
[tonslyr]
2030
Base
PM10
[tonslyr]
2002
PM2 5
[tonslyr]
2009
Base
PM2 5
[tonslyr]
2014
Base
PM2 5
[tonslyr]
2020
Base
PM2 5
[tonslyr]
2030
Base
PM2 5
Connecticut
afdust
0
0
0
0
0
0
0
0
0
0
12,526
12.528
12,528
12,528
12,528
2,725
2.725
2.725
2.725
2.725
ag
0
0
0
0
0
4,029
4,232
4.377
4.551
4.551
0
0
0
0
0
0
0
0
0
0
aim
778
751
764
874
1.26B
1
1
1
1
1
231
237
241
253
301
210
215
218
227
271
avefire
4
4
4
4
4
¦>
3
3
3
3
65
65
65
65
65
56
56
56
56
56
rconpt
18,455
18,447
18,441
18,434
18,434
1,438
1,407
1,385
1,358
1,358
10,716
10.152
9,749
9,266
9,266
10,446
9,882
9,479
8.996
8,996
nonroad
1.382
249
27
29
33
17
18
20
22
25
1,702
1.408
1,195
933
823
1,619
1.335
1.130
876
766
an road
1,867
363
340
368
413
3,257
3,515
3.779
4042
4.510
1,610
1.316
1,182
1,165
1,276
1,067
766
610
557
592
ptipm
13,689
6,200
5,795
12,473
12,473
182
245
247
244
244
742
829
3,992
11,942
11,942
510
791
3,891
11.696
11,696
ptnonipm
2,338
2,330
2,330
2,330
2,330
91
91
91
91
91
882
880
880
880
880
691
690
690
690
690
Connecticut Total
38,313
28,343
27,701
34,512
34,955
9,017
9,512
9,902
10,311
10,783
28,476
27,415
29,833
37,032
37,081
17,323
16,460
18,798
25,823
25,792
Delaware
afdust
0
0
0
0
0
0
0
0
0
0
6,256
6.258
6,258
6,258
6,258
863
863
863
863
863
n
0
0
0
0
0
12,536
14,172
15,342
16,745
16.745
0
0
0
0
0
0
0
0
0
0
aim
3,470
3,243
3,234
3,664
5,371
0
0
0
0
1
452
511
561
655
907
401
454
499
583
810
avefire
6
6
6
6
6
5
5
5
5
5
102
102
102
102
102
87
87
87
87
87
tonpt
5,859
5,858
5,857
5,857
5,857
279
275
272
268
268
2,007
1.933
1,879
1,815
1,815
1,826
1.751
1.698
1.634
1.634
nonroad
471
83
7
8
9
5
6
6
7
8
560
446
367
275
224
534
424
348
259
209
3-road
556
114
101
112
124
903
994
1,078
1.178
1.294
572
417
359
355
383
406
253
188
171
178
ottpm
33,104
23,047
21,650
20,757
20,757
30
119
146
132
132
1.969
6,352
6,466
6.610
6,610
1,693
3.083
3.217
3.421
3.421
ptnonipm
41,342
11,538
11,538
11,538
11,538
161
152
152
152
152
1,041
904
904
904
904
783
576
576
576
576
Delaware Total
84,810
43,889
42,394
41,941
43,662
13,918
15,722
17,003
18,487
18,604
12,961
16,923
16,896
16,974
17,203
6,594
7,492
7,478
7,596
7,779
District of
Columbia
afdust
0
0
0
0
0
0
0
0
0
0
2,255
2,255
2,255
2,255
2,255
411
411
411
411
411
aim
45
9
2
2
3
0
0
0
0
0
13
11
11
11
10
13
11
11
10
10
avefire
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
fianpt
1,559
1,559
1,559
1,559
1,559
13
13
13
13
13
489
481
476
469
469
427
419
413
406
406
nonroad
343
59
3
3
3
2
3
3
3
4
296
226
176
109
64
286
218
170
104
60
anroad
271
46
42
47
62
398
423
457
508
567
219
155
141
144
158
150
88
72
68
74
ptipm
1.432
0
0
0
0
8
0
0
0
0
30
0
0
0
0
22
0
0
0
0
EJtnonipiri
625
625
625
625
625
4
4
4
4
4
96
21
21
21
21
43
12
12
12
12
District of Columbia Total
4,275
2,299
2,230
2,236
2,242
426
143
477
529
589
3.402
3,143
3.080
3.003
2,977
1,353
1,159
1,088
1,011
973
-------
o
to
State
Sector
[tonsfyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonslyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonslVr]
2014
Base
NH3
[tonslyr]
2020
Base
NHS
[tons/yr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonsfyr]
2009
Base
PM10
[tonslyr]
2014
Base
PM10
[tonslyr]
2020
Base
PM10
Itons/yr]
2030
Base
PM10
[tonslyr]
2002
PM2 5
[tons/yr]
2009
Base
PM2 5
[tons/yr]
2014
Base
PM2 5
[tons/yr]
2020
Base
PM2 5
[tonsfyr]
2030
Base
PM2 5
Florida
afdust
0
0
0
0
0
0
0
0
0
0
145,566
145.655
145,718
145,794
145,794
28,017
28035
28047
28.063
28.063
m
D
0
0
0
0
37,099
38,533
39,643
40.914
40,914
0
0
0
0
0
0
0
0
0
0
aim
6,892
5,893
5.843
6,685
9,612
11
12
12
13
15
2,391
2.426
2,469
2,575
2,936
2,175
2,199
2,223
2,307
2.636
avefire
7,016
7,018
7,018
7,01 B
7,018
5,366
5,366
5,366
5.366
5,366
115,996
115.996
115,996
115,996
115,996
99.434
99.484
99.484
99.484
99.484
nonpt
70,489
70,484
70,480
70,475
70,475
448
446
446
448
448
41,371
40935
40,623
40,248
40,246
38,847
38,410
38,098
37,724
37.724
nonroad
12,540
2,110
174
184
207
125
139
151
166
192
13,637
10,831
8,952
6,680
5,412
13,001
10.302
8.489
6.287
5.040
onroad
21,410
2,358
2,152
2,477
2,886
1B.267
21,200
23,969
27.234
31,607
12,433
9,367
8,217
8,251
9,258
9,041
5803
4.407
4.006
4.316
Dtipm
473,636
155,118
154,529
92,816
92,816
5,013
3,571
4,445
4,318
4.318
32,299
22,325
22.166
29,274
29,274
28,293
14,447
14.405
20.220
20.220
ptnonipm
57,060
57,024
57,024
57,024
57,024
3,030
3,030
3,030
3,030
3.030
32,193
31,655
31,655
31,655
31,655
23,604
23.430
23,430
23.430
23,430
Florida Total
$49,045
300,004
297,220
236,679
240,037
69,359
72,348
77,064
81,539
85,889
395,887
379,190
375,795
380,473
380,573
242,462
222,108
218,581
221,520
220,912
Georgia
afdust
0
0
0
0
0
0
0
0
0
0
181,397
181.397
181,397
181,397
181,397
59,910
59.910
59.910
59.910
59.910
m
0
0
0
0
0
30,733
89,607
95,949
103,556
103.556
0
0
0
0
0
0
0
0
0
0
aim
3,247
1,968
1,807
2,101
2,860
12
13
14
16
18
1,332
1.320
1,327
1,363
1.457
1.135
1.122
1.120
1.142
1.218
avefire
2,010
2,010
2,010
2,010
2,010
1,299
1,299
1,299
1.299
1.299
28,079
28.079
28,079
28,079
28,079
24,032
24.032
24.032
24.082
24.082
nonpt
56,830
56,821
56,615
56,807
56,807
60
60
60
60
60
46,751
46 054
45,556
44,958
44,956
41,847
41.150
40 652
40 054
40.054
nonroad
5,674
975
76
80
90
52
59
64
71
32
6,136
5.022
4,150
3,021
2,412
5,867
4.783
3.940
2,847
2,250
anroad
11,236
1,525
1,308
1,503
1,809
10,642
12,213
13,708
15.548
18.473
8,539
6.232
5,184
5,043
5,785
6,366
4.025
2.868
2,477
2,699
ptipm
512,983
212,600
210,100
146,083
146,083
593
B40
989
971
971
31,663
24.007
23,976
35,951
35.951
25.407
17.485
17.485
29.266
29.266
ptnonipm
56,203
56,188
56,188
56,188
56,188
4,571
4,531
4,538
4,597
4,597
21,224
20,585
20,585
20,585
20,585
15,692
15,426
15,426
15.426
15,426
Georgia Total
648,183
332,087
328,304
264,772
265,847
97,962
108,672
116,673
126,119
129,057
325,121
312,696
310,254
320,398
320,626
180,308
167,981
165,482
175,206
174,807
Idaho
afdust
0
0
0
0
0
0
0
0
0
0
139,528
139,669
139,770
139,891
139,891
28,351
28,376
28,393
28,414
28.414
ag
0
0
0
0
0
62,376
62,655
62,855
63,094
63.094
0
0
0
0
0
0
0
0
0
0
aim
645
168
70
74
81
3
4
4
4
5
471
453
449
449
445
447
427
421
419
413
avefire
3,845
3,845
3,845
3,845
3,645
2,856
2,856
2,856
2.856
2,856
61,433
61,433
61,433
61,433
61,433
52,808
52.808
52.808
52.808
52.808
nonpt
2,915
2,913
2,912
2,910
2,910
1,684
1,634
1,634
1,634
1.634
56,403
56.235
56,116
55,972
55,972
27,367
27.199
27.030
26.936
26.936
nonroad
1,616
275
19
20
22
14
16
18
20
22
1,973
1,582
1,253
871
600
1,889
1,506
1.139
823
560
onroad
1,310
205
179
203
246
1,418
1,645
1853
2.083
2.514
1.068
821
691
669
772
785
527
330
328
360
ptipm
0
0
192
308
308
0
43
47
49
49
1
2
146
242
242
1
1
12B
214
214
ptnonipm
17.597
17,597
17,597
17,597
17.597
1,074
1,074
1.074
1.074
1.074
4,569
4,409
4,409
4,409
4,409
2.528
2.441
2.441
2.441
2.441
Idaho Total
27,928
25,003
24,815
24,958
25,009
69,425
69,977
70,390
70,864
71,298
265,445
264,604
264,267
263,935
263,763
114,175
113,286
112,841
112,383
112,117
-------
o
to
Ltl
State
Sector
[tonslyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonslyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonslVr]
2014
Base
NH3
[tonsJVr]
2020
Base
NHS
[tonslyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonslyr]
2009
Base
PM10
[tonslyr]
2014
Base
PM10
[tonslyr]
2020
Base
PM10
Itonslyr]
2030
Base
PM10
[tonslyr]
2002
PM2 5
[tonslyr]
2009
Base
PM2 5
[tonslyr]
2014
Base
PM2 5
[tonslyr]
2020
Base
PM2 5
[tonslyr]
2030
Base
PM2 5
Illinois
afdust
0
0
0
0
0
0
0
0
0
0
444,909
444,909
444,909
444.909
444,909
88.100
88.100
88.100
88.100
88.100
m
D
0
0
0
0
106,685
10B,164
109,220
110.488
110.486
0
0
0
0
0
0
0
0
0
0
aim
11,979
7,122
6.356
7.425
10,672
45
52
56
62
72
3,556
3,431
3,343
3,329
3.561
3,351
3,231
3.144
3.125
3,340
avefire
20
20
20
20
20
15
15
15
15
15
323
323
323
323
323
277
277
277
277
277
nonpt
5,395
5,387
5,382
5,376
5,376
1,631
1,631
1.631
1.631
1.631
16,972
16,262
15,755
15,147
15,147
15,181
14,471
13,964
13.356
13,356
nanroad
10,913
1,933
126
132
148
88
102
112
124
145
11,316
8,576
6,671
4,450
3,103
10,881
8.220
6.375
4.224
2.912
on road
8,514
1,449
1,220
1,373
1,607
10,654
11,429
12,465
13.748
15,809
7,772
5,916
4,888
4,615
5,125
5,700
3,849
2.751
2,291
2,410
dtipm
366,15?
294,394
270,363
272.107
272,107
174
962
1.095
1.149
1,149
19,147
13,378
14.852
37,527
37,527
14,783
11.017
12.328
34.584
34.564
ptnonipm
138,126
110,325
110,401
110,500
110,500
694
683
683
683
683
30,111
28,118
28,116
28,118
26,116
15,136
13,619
13,619
13,619
13,619
Illinois Total
541,103
420,630
393,869
396,933
400,630
119,986
123,039
125,276
127,898
129,989
534,106
520,913
518,859
538,417
537,812
153,409
142,786
140,559
159,578
158,599
Indiana
afdust
0
0
0
0
0
0
0
0
0
0
345,635
345635
345,635
345,635
345,635
65,707
65.707
65.707
65.707
65.707
ag
0
0
0
0
0
90,815
93,856
96.027
98.632
98.632
0
0
0
0
0
0
0
0
0
0
aim
5,540
3,649
3,419
4,024
5,673
19
22
24
26
31
1,719
1.710
1,707
1,746
1,909
1.561
1.552
1.544
1.576
1.727
avefire
24
24
24
24
24
19
19
19
19
19
401
401
401
401
401
344
344
344
344
344
nonpt
59,775
59,770
59,765
59,760
59,760
4,214
4,214
4.214
4.214
4.214
60,255
59.765
59,414
58,994
58,994
32.611
32.120
31.770
31 350
31.350
nanroad
5,981
1,042
73
77
86
48
55
60
67
78
6,039
4.593
3,562
2,367
1,691
5,803
4.398
3.401
2,246
1.5B8
anroad
8,564
974
611
914
1,087
7,343
7,696
6.618
9.432
10.975
5,516
4.064
3,353
3,195
3,622
4.081
2.633
1.872
1.581
1.693
Ptipm
785,603
377,625
375,790
336,644
336,644
580
1,076
1.132
1.198
1.198
40,884
40.455
40,305
44,864
44,864
33,805
27 477
27.287
31.624
31.624
ptnonipm
97,442
97,349
97,349
97,349
97,349
3,144
3,144
3,144
3,144
3,144
25,808
25,083
25,083
25,083
25,083
15,085
14,751
14,751
14.751
14,751
Indiana Total
962,930
540,433
537,231
438,791
500,823
106,183
110,283
113,238
116,784
118,291
486,257
481,705
479,460
482,284
482,198
158,996
148,983
146,677
149,179
148,783
Iowa
afdust
0
0
0
0
0
0
0
0
0
0
341,542
341,542
341,542
341,542
341,542
57,643
57,643
57,643
57,643
57.643
ag
0
0
0
0
0
245,778
252,909
257.995
264.101
264.101
0
0
0
0
0
0
0
0
0
0
aim
2,787
1,161
860
1,007
1,454
8
10
10
11
13
1,021
986
961
949
961
997
961
934
919
926
avefire
25
25
25
25
25
19
19
19
19
19
407
407
407
407
407
349
349
349
340
349
nonpt
19,832
19,824
19,818
19,811
19,811
7,404
7,404
7,404
7.404
7.404
12,833
12,192
11,734
11,185
11,185
11.476
10.835
10.378
9.828
9.828
nanroad
8,246
1,063
59
61
66
47
53
58
64
74
7,210
5.048
3,805
2,539
1,419
6,949
4.859
3654
2.427
1.340
onroad
2,999
427
360
404
496
3,091
3,367
3.659
4.002
4,781
2,355
1,771
1,450
1,365
1,585
1,726
1.153
815
677
741
ptipm
133,047
110,609
115,301
116,360
116,360
391
428
483
519
519
9,907
7.681
8,074
8,366
6.386
8,904
5.985
6,288
6,513
6,513
ptnonipm
51.329
51,329
51,329
51.329
51,329
4,663
4,663
4.663
4,663
4.663
13,439
11.162
11,182
11.162
11,162
7,572
6,395
6.395
6.395
6,395
Iowa Total
216,267
184,437
187,751
188,996
189,540
261,401
268,352
274,292
280,784
281,574
388,712
380,789
379,135
377,535
376,647
95,615
88,180
86,455
84,751
83,736
-------
o
to
On
State
Sector
[tonslyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonslyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonslVr]
2014
Base
NH3
[tonsJVr]
2020
Base
NHS
[tonslyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonslyr]
2009
Base
PM10
[tonslyr]
2014
Base
PM10
[tonslyr]
2020
Base
PM10
Itonslyr]
2030
Base
PM10
[tonslyr]
2002
PM2 5
[tonslyr]
2009
Base
PM2 5
[tonslyr]
2014
Base
PM2 5
[tonslyr]
2020
Base
PM2 5
[tonslyr]
2030
Base
PM2 5
Kansas
afdust
0
0
0
0
0
0
0
0
0
0
455,984
455,984
455,984
455,984
455,984
74,515
74.515
74.515
74.515
74.515
m
D
0
0
0
0
97,384
97,802
98.101
9B.458
98.458
0
0
0
0
0
0
0
0
0
0
aim
2,895
603
131
152
194
11
12
13
15
17
1,237
1,185
1,152
1,127
1,087
1,207
1,155
1,120
1,093
1,051
avefire
103
103
103
103
103
79
79
79
79
79
1,711
1,711
1,711
1,711
1.711
1,466
1.468
1.468
1,468
1.468
nonpt
36.381
36.378
36,375
36,373
36,373
12,467
12,467
12,467
12,467
12.467
108,571
108,281
108,075
107,826
107,826
83,174
82,885
82.678
82.430
82.430
nonroad
4.858
828
41
42
46
32
37
41
46
53
5,360
3.794
2,817
1.838
972
5,179
3.660
2.712
1.764
922
on road
2,893
379
312
348
431
2.B70
3,081
3,343
3657
4.421
2,200
1,576
1,279
1,206
1,427
1,629
1.019
710
594
665
dtipm
129,82?
77,367
48.068
52,641
52,641
421
377
393
454
454
7,246
7.037
7,178
7,749
7,749
5,912
5,723
5.953
6.244
6.244
ptnonipm
10,793
10,793
10,793
10,793
10,793
60,100
60,994
61,633
62,398
62,398
9,430
8,890
8,890
8,890
8,890
4,941
4,718
4,718
4,718
4,718
Kansas Total
187,750
126,150
95,823
100,452
100,580
173,364
174,849
176,070
177,574
178,347
591,738
588,460
587,087
586,333
585,647
178,025
175,143
173,871
172,825
172,013
Kentucky
afdust
0
0
0
0
0
0
0
0
0
0
99,481
99.461
99,481
99,461
99,481
23,529
23.529
23.529
23.529
23.529
m
0
0
0
0
0
50,821
53,024
54,598
56,486
56,486
0
0
0
0
0
0
0
0
0
0
aim
10,096
8,624
3.713
10.226
14,957
15
17
18
19
22
4,285
4.409
4,627
5,007
5.730
3,625
3.710
3.850
4.123
4.723
avefire
364
364
364
364
364
278
278
278
278
278
6,010
6,010
6,010
6,010
6,010
5,155
5.155
5.155
5.155
5.155
nonpt
34,229
34,219
34,211
34,203
34,203
231
231
231
231
231
23,283
22.460
21,872
21,167
21,167
18,590
17.767
17.180
16.474
16.474
nonroad
3,008
511
38
40
44
25
29
32
35
40
3,376
2.667
2,108
1,452
1,052
3,236
2,547
2,008
1.377
9B7
anroad
5,554
657
529
587
662
4,824
5,161
5,604
6,121
6,776
3,816
2,731
2,200
2,048
2,204
2,842
1,776
1,225
1.004
1.027
Ptipm
486,499
291,244
244,599
223,737
223,737
919
703
741
784
784
22,342
24.029
25,780
37,131
37.131
20,004
17.733
19.050
30.135
30.135
ptnonipm
34,482
28,587
28,587
28,587
28,587
1,672
1,672
1,672
1,672
1.672
16,375
15,729
15,729
15,729
15,729
9,937
9.566
9,566
9,566
9,566
Kentucky Total
574,230
364,205
317,041
297,744
302,553
58,787
61,115
63,174
65,626
66,289
178,967
177,516
177,807
188,025
188,503
86,919
81,783
81,563
91,364
91,597
Louisiana
afdust
0
0
0
0
0
0
0
0
0
0
81,493
81.493
81,493
81,493
81,493
20,962
20.962
20,962
20,962
20,962
ag
0
0
0
0
0
35,159
36,915
38.171
39.676
39.676
0
0
0
0
0
0
0
0
0
0
aim
32,796
30,726
31,131
35,876
52,721
42
46
48
51
56
7,000
7.145
7,072
7,302
9,023
6,819
6.932
6.827
7,007
8,606
avefire
892
892
892
892
892
682
682
682
632
682
14,746
14,746
14,746
14,746
14,746
12,647
12.647
12,647
12.647
12,647
nonpt
2,378
2,374
2,372
2,370
2,370
23,169
23,169
23,169
23.169
23.169
19,036
18.790
18,612
18,399
16,399
17,862
17.614
17,436
17,223
17,223
nonroad
2,834
473
42
45
49
29
32
34
37
42
3,331
2.634
2,091
1,476
1,138
3,174
2.500
1.980
1.390
1,061
anroad
4,409
566
469
526
642
4,364
4,631
5.037
5.547
6.620
3,379
2,355
1,912
1,819
2,125
2,506
1,520
1.058
894
990
ptipm
108,106
89,892
86,289
87,803
87,803
1.399
690
821
960
960
7,487
3.828
3,905
4,612
4,612
5,990
3.292
3.370
4,021
4,021
ptnonipm
177.507
177,507
177,507
177,507
177,507
7,878
7,879
7,880
7,881
7,881
28,722
28,429
28,429
28.429
28,429
21,082
20,899
20.899
20,899
20,899
Louisiana Total
328,922
302,432
298,703
305,018
321,985
72,722
74,044
75,843
78,004
79,087
165,196
159,421
158,260
158,276
159,965
91,043
86,365
85,179
85,042
86,409
-------
o
to
State
Sector
[tonsfyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonsfyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonslVr]
2014
Base
NH3
[tonsfyr]
2020
Base
NHS
[tonsfyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonsfyr]
2009
Base
PM10
[tonsfyr]
2014
Base
PM10
[tonsfyr]
2020
Base
PM10
Itonsfyr]
2030
Base
PM10
[tonsfyr]
2002
PM2 5
[tonsfyr]
2009
Base
PM2 5
[tonsfyr]
2014
Base
PM2 5
[tonsfyr]
2020
Base
PM2 5
[tonsfyr]
2030
Base
PM2 5
Maine
afdust
0
0
0
0
0
0
0
0
0
0
13,067
13.067
13,067
13,067
13,067
4,134
4.134
4.134
4,134
4.134
m
0
0
0
0
0
6,154
6,540
6,816
7,147
7.147
0
0
0
0
0
0
0
0
0
0
aim
195
155
131
126
178
1
1
1
1
1
455
494
524
585
772
405
441
467
522
694
iv el if a
150
150
150
150
150
115
115
115
115
115
2,480
2,480
2,480
2,460
2,480
2,127
2.127
2.127
2.127
2.127
nonpt
9,969
9,956
9,947
9,936
9,936
1,616
1,506
1,530
1,486
1.486
13,876
12,996
12,368
11,613
11.613
13,726
12,846
12.218
11.463
11.463
nonroad
766
132
18
19
21
11
13
14
15
17
1,200
1,117
960
741
632
1,131
1.047
897
690
585
onroad
1,122
198
160
176
196
1,467
1,574
1,700
1,841
2024
1,178
834
667
622
660
876
544
372
306
307
dtipm
2,13?
34,757
33,238
21,911
21,911
129
331
328
180
180
86
454
432
299
299
65
415
394
266
266
ptnoiiipm
20,778
19,390
19,390
19,390
19,390
BOB
343
343
343
343
5,963
5,243
5,243
5,243
5,243
4,268
3,750
3,750
3,750
3,750
Maine Total
35,116
64,738
63,033
51,708
51,783
10,302
10,533
10,847
11,129
11,313
38,304
36,685
35,740
34,649
34,766
26,732
25,304
24,359
23,259
23,327
Maryland
afdust
0
0
0
0
0
0
0
0
0
0
35,393
35.393
35,393
35,393
35,393
7,393
7.393
7.393
7.393
7.393
m
0
0
0
0
0
24,562
26,618
28,088
29,851
29,851
0
0
0
0
0
0
0
0
0
0
aim
5,707
4,007
3,140
2,825
3,921
22
24
25
27
30
1,635
1.594
1,609
1,645
1.735
496
495
507
539
621
avefire
32
32
32
32
32
24
24
24
24
24
613
613
613
613
613
531
531
531
531
531
nonpt
40,864
40,857
40,852
40,846
40,846
606
579
559
535
535
25,058
24.553
24,191
23,757
23,757
19,764
19.258
18 897
18.463
18.463
nonroad
2,577
452
41
43
49
28
31
34
38
43
3,102
2.537
2,150
1,677
1,440
2,954
2,408
2,033
1.575
1,340
anroad
3,966
681
632
709
822
5,594
6,280
6909
7.634
8,764
3,162
2,506
2,245
2,263
2,571
2,194
1.496
1.175
1,091
1.200
ptipm
256,761
50,757
47,642
53,433
53,433
271
409
460
495
495
17,996
6.995
16,915
23,811
23,811
15,722
5.312
14.953
21.591
21.591
ptnonipm
34,255
34,061
34,061
34,061
34,061
222
222
222
222
222
6,303
5,477
5,477
5,477
5,477
3,759
3,332
3,332
3,332
3,332
Maryland Total
344,162
130,846
126,399
131,949
133,164
31,330
34,187
36,322
33,826
39,965
93,261
79,668
88,593
94,638
94,798
52,813
40,225
48,821
54,515
54,470
Massachusetts
afdust
0
0
0
0
0
0
0
0
0
0
49,646
49,646
49,646
49,646
49,646
14,810
14,810
14,810
14,810
14.810
ag
0
0
0
0
0
2,208
2,244
2.269
2.299
2.299
0
0
0
0
0
0
0
0
0
0
aim
2,519
1,819
1,681
1,835
2,503
7
B
9
10
11
988
993
1,005
1,033
1,132
874
876
88 S
912
1,003
avefire
93
93
93
93
93
71
71
71
71
71
1,544
1,544
1,544
1,544
1,544
1,324
1.324
1.324
1,324
1,324
nonpt
25,261
25,248
25,239
25,228
25,228
4,070
4,021
3,986
3,944
3,944
28,552
27,661
27,025
26,261
26,261
26,536
25.646
25.010
24,246
24,246
nonroad
2,385
429
46
49
55
28
31
34
37
43
2,871
2,373
2,005
1,541
1,330
2,732
2.252
1.896
1,448
1,240
onroad
3,172
678
593
658
755
5,509
6,023
6562
7,185
8,201
3,253
2,460
2,187
2,205
2,467
2,268
1,460
1.136
1.061
1.147
ptipm
91,888
12,991
10,825
22,019
22.019
1.103
737
674
601
601
3,730
2,349
7,323
20,948
20,948
3,224
1,734
6,724
20,104
20,104
jtnonipm
14,079
13,814
13,814
13,814
13,814
403
401
401
401
401
2,795
2.705
2,705
2,705
2,705
1,842
1,794
1,794
1,794
1,794
Massachusetts Total
139,397
55,073
52,291
63,696
64,468
13,401
13,537
14,006
11,548
15,571
93,379
89,732
93,440
105,884
106,033
53,610
49,896
53,581
65,699
65,667
-------
o
to
00
State
Sector
[tonslyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonsfyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonsJVr]
2014
Base
NH3
[tonsJVr]
2020
Base
NHS
[tons/yr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonsfyr]
2009
Base
PM10
[tonslyr]
2014
Base
PM10
[tonsfyr]
2020
Base
PM10
Itons/yr]
2030
Base
PM10
[tons/yr]
2002
PM2 5
[tonsfyr]
2009
Base
PM2 5
[tons/yr]
2014
Base
PM2 5
[tons/yr]
2020
Base
PM2 5
[tonsfyr]
2030
Base
PM2 5
Michigan
afdust
0
0
0
0
0
0
0
0
0
0
208,643
208.843
208,843
208,843
208,843
40,894
40.894
40.894
40.894
40.894
m
0
0
0
0
0
55,273
56,151
56.77B
57.531
57.531
0
0
0
0
0
0
0
0
0
0
aim
14,466
15,415
17.203
21,207
31,136
5
6
6
6
7
2,637
3.000
3,33?
3,929
5,386
2,389
2,711
3.009
3,537
4.848
avefire
91
91
91
91
91
69
69
69
69
69
1,495
1,495
1,495
1,495
1,495
1,283
1.283
1.283
1,283
1.283
nonpt
42,066
42,066
42,066
42,066
42,066
429
429
429
429
429
30,989
30,209
29,653
28,985
28,985
24,216
23.295
22.638
21.849
21.849
nonroad
8,367
1,063
117
125
140
78
38
94
102
117
8.199
6,935
5,650
4.058
3,298
7,782
6.554
5.326
3.803
3.068
on road
13,508
1,362
1,106
1,226
1,404
9.B13
10,307
11,090
12.003
13.412
7,881
5,651
4,538
4,220
4,579
5,894
3.714
2.569
2,108
2.154
dtipm
348,37?
228,218
245,203
243,651
243.651
286
771
947
1.122
1.122
13,170
12.070
12,685
12,451
12,451
10,648
8.179
8.739
8.620
8.620
ptnonipm
72,631
71,976
71,976
71,976
71,976
952
946
946
946
946
17,151
15,417
15,417
15,417
15,417
10,346
9,326
9,326
9,326
9,326
Michigan Total
497,505
360,190
377,761
380,341
390,463
66,906
68,767
70,360
72,209
73,633
290,363
283,621
281,619
279,398
280,454
103,451
95,954
93,782
91,419
92,042
Minnesota
afdust
0
0
0
0
0
0
0
0
0
0
432,054
432,054
432,054
432,054
432,054
79,303
79.303
79.303
79.303
79.303
m
0
0
0
0
0
134,830
136,892
138,364
140,130
140.130
0
0
0
0
0
0
0
0
0
0
aim
6,592
5,024
4,826
5,562
8,164
12
14
15
16
18
1,665
1.655
1,619
1,633
1,869
1,643
1.627
1.587
1.594
1.812
avefire
631
631
631
631
631
482
482
482
482
482
10,427
10,427
10,427
10,427
10,42?
8,943
B.943
8 943
8 943
8 943
nonpt
14,747
14,737
14,730
14,721
14,721
1,226
1,226
1.226
1.226
1.226
26,966
26.093
25,466
24,718
24,718
24,496
23.621
22996
22.245
22.245
nonroad
6,525
1,138
84
88
97
59
68
74
82
93
8,097
6.277
4,957
3,540
2,319
7,759
5.990
4.716
3,348
2,171
anroad
2,816
729
618
688
779
5,362
5,827
6.356
6,992
7,902
3,790
2.972
2,427
2,282
2,451
2,740
1,920
1,347
1,122
1.143
Ptipm
102,152
57,217
60,854
64,060
64,060
69
345
720
770
770
7,437
11.798
12,544
13,083
13,033
234
9.113
9.811
10.230
10.230
ptnonipm
27,263
23,844
23,866
23,895
23,895
27,525
28,560
29298
30,186
30,186
22,425
20,345
20,357
20,370
20,370
4,097
3,924
3,933
3,942
3,942
Minnesota Total
160,725
103,320
105,707
109,644
112,346
169,566
173,114
176,535
179,883
180,807
512,863
511,620
509,853
508,117
507,301
129,215
134,440
132,636
130,728
129,790
Mississippi
afdust
0
0
0
0
0
0
0
0
0
0
139,219
139,251
139,274
139,302
139,302
38,120
38,130
38,137
38.146
38,146
ag
0
0
0
0
0
58,575
63,938
67.773
72.371
72,371
0
0
0
0
0
0
0
0
0
0
aim
9,163
8,214
8,487
10,082
14.B22
18
20
21
22
25
3,057
3,085
3,091
3,203
3,736
2,668
2.689
2,680
2,764
3,235
avefire
1,051
1,051
1,051
1,051
1,051
804
804
804
804
804
17,370
17,370
17,370
17,370
17,370
14,897
14.897
14,897
14.897
14.897
nonpt
6,796
6,790
6,786
6,781
6,781
196
196
196
196
196
17,827
17,289
16,904
16,443
16,443
16,769
16.232
15.847
15.386
15.386
nonroad
2,119
356
29
30
33
19
22
23
25
29
2,479
1.959
1,537
1,055
777
2,370
1.864
1.459
996
726
onroad
3,591
501
382
429
505
3,606
3,866
4,190
4.584
5,203
3,058
2,281
1,817
1,695
1,915
2,309
1.52?
1.044
854
907
ptipm
67,593
51,938
50,434
56,664
56,664
456
327
513
497
497
3,122
2.777
3,378
10,168
10,168
2,625
1.860
2,475
9,133
9,133
ptnonipm
36.519
29,914
29,914
29,914
29,914
1,414
852
852
852
852
19,535
18,524
18,524
18.524
18,524
10,019
9,307
9,307
9,307
9.307
Mississippi Total
126,831
98,763
97,082
104,951
109,771
65,088
70,024
74,371
79,351
79,976
205,667
202,537
201,896
207,760
208,235
89,778
86,507
85,847
91,483
91,739
-------
o
to
VO
State
Sector
[tonslyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonslyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonslVr]
2014
Base
NH3
[tonsfyr]
2020
Base
NHS
[tonslyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonslyr]
2009
Base
PM10
[tonslyr]
2014
Base
PM10
[tonslyr]
2020
Base
PM10
Itonslyr]
2030
Base
PM10
[tonslyr]
2002
PM2 5
[tonslyr]
2009
Base
PM2 5
[tonslyr]
2014
Base
PM2 5
[tonslyr]
2020
Base
PM2 5
[tonslyr]
2030
Base
PM2 5
Missouri
afdust
0
0
0
0
0
0
0
0
0
0
453,324
458.324
458,324
458.324
458,324
96,070
96.070
96.070
96.070
96.070
m
D
0
0
0
0
107,023
108,403
109,390
110,571
110.571
0
0
0
0
0
0
0
0
0
0
aim
8,610
5,550
5,106
5,930
8.593
19
22
23
25
29
2,546
2,514
2,491
2,531
2,769
2,489
2,448
2.419
2,448
2.664
avefire
186
186
186
186
186
142
142
142
142
142
3,074
3,074
3,074
3,074
3,074
2,636
2.636
2.636
2.636
2.636
nonpt
44,573
44,557
44,545
44,531
44,531
3,830
3,830
3,830
3 830
3,830
32,399
31,072
30,123
28,985
28,985
28,217
26,890
25.941
24,803
24.803
nonroad
5,143
885
61
65
72
43
49
54
59
68
5.929
4.506
3,519
2,444
1,671
5,690
4.311
3.357
2.320
1.567
onroad
6,148
947
797
889
1,059
6,918
7,468
8,133
8,903
10,427
5,199
3,336
3,144
2,975
3,367
3,819
2.479
1.750
1.470
1.574
ptipm
249,942
235,578
231,753
241,550
241,550
705
743
806
814
814
3,868
11,539
11,541
12,431
12,431
5,818
9.222
9.174
10.080
10.080
ptnoriipm
111,547
110,331
110,331
110,331
110,331
322
322
322
322
322
14,033
13,673
13,678
13,678
13,678
7,424
7,084
7,034
7,084
7,084
Missouri Total
426,149
398,034
392,779
403,482
406,322
119,002
120,978
122,699
124,666
126,203
530,423
528,542
525,895
521,442
524,298
152,163
151,141
148,432
146,911
146,480
Montana
afdust
0
0
0
0
0
0
0
0
0
0
188,368
188.363
188,368
188,368
188,368
40,180
40.180
40.180
40.180
40.180
m
a
0
0
0
0
45.B90
46,222
46,459
48,743
46,743
0
0
0
0
0
0
0
0
0
0
aim
1,686
340
62
72
83
6
7
6
9
10
711
675
659
648
633
690
653
636
623
606
avefire
1,422
1,422
1,422
1,422
1,422
946
946
946
946
946
19,949
19,949
19,949
19,949
19,949
17,311
17.311
17.311
17.311
17.311
nonpt
1,961
1,957
1,954
1,951
1,951
50
50
50
50
50
5,765
5.446
5,218
4,944
4,944
5,569
5249
5.021
4.747
4.747
nonroad
2,009
340
17
17
18
14
16
18
20
22
2,344
1.697
1,261
821
431
2,261
1.632
1,210
786
4D7
anroad
1,062
145
115
128
153
1,032
1,109
1,207
1.323
1,549
906
621
486
452
512
688
411
274
223
238
Ptipm
23,396
17,149
21,174
22,221
22,221
11
195
233
247
247
2,404
5.150
8,524
9,203
9,203
2,077
3585
5.604
6.179
6.179
ptnonipm
13,271
12,239
9,688
9,688
9,688
265
265
265
265
265
5,538
5,383
5,323
5,323
5,323
2,576
2.454
2.414
2.414
2.414
Montana Total
44,809
33,592
34,433
35,439
35,536
48,214
48,310
49,185
49,601
49,832
225,987
227,295
229,788
229,709
229,364
71,352
71,475
72,650
72,464
72,084
Nebraska
afdust
0
0
0
0
0
0
0
0
0
0
320,650
320,650
320,650
320,650
320,650
50,787
50,787
50,787
50.787
50.787
ag
0
0
0
0
0
166,773
168,886
170,397
172.205
172.205
0
0
0
0
0
0
0
0
0
0
aim
4,764
950
163
18B
225
18
21
22
24
29
1.958
1,862
1,791
1,732
1,651
1,942
1,844
1.773
1,712
1,630
avefire
105
105
105
105
105
80
30
80
80
80
1,729
1,729
1,729
1,729
1,729
1,483
1,483
1,483
1,483
1,483
nonpt
29,575
29,572
29,570
29,568
29,568
3,143
3,143
3,143
3.143
3.143
12.679
12.447
12,281
12,082
12,082
8,655
8.422
8.257
8.058
8.058
nonroad
4,181
712
32
33
36
27
31
34
38
44
4,637
3.257
2,391
1,530
747
4,484
3.144
2.304
1.471
711
onroad
2,011
286
217
242
314
1,874
1,998
2,159
2.353
2.968
1,723
1.253
975
893
1,092
1,312
849
566
452
515
ptipm
67,576
118,340
41,587
39,642
39,642
190
266
282
307
307
1,551
3.082
3,761
4,012
4,012
1,191
2.484
3,163
3,346
3,346
ptnonipm
6.018
6,018
6,018
6,018
6,018
421
422
422
422
422
1,623
1.614
1.614
1.614
1,614
806
801
801
801
801
Nebraska Total
114,229
155,982
77,691
75,796
75,907
172,525
174,846
176,539
173,571
179,197
346,550
345,893
345,192
344,243
343,578
70,659
69,815
69,134
68,109
67,330
-------
o
u>
o
State
Sector
[tonsfyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonsfyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonsfVr]
2014
Base
NH3
[tonsfyr]
2020
Base
NHS
[tonsfyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonsfyr]
2009
Base
PM10
[tonsfyr]
2014
Base
PM10
[tonsfyr]
2020
Base
PM10
Itonsfyr]
2030
Base
PM10
[tonsfyr]
2002
PM2 5
[tonsfyr]
2009
Base
PM2 5
[tonsfyr]
2014
Base
PM2 5
[tonsfyr]
2020
Base
PM2 5
[tonsfyr]
2030
Base
PM2 5
Nevada
afdust
0
0
0
0
0
0
0
0
0
0
61,096
61.096
61,096
61,096
61,096
11,371
11.371
11.371
11.371
11.371
m
0
0
0
0
0
5.59B
5.647
5,682
5.723
5.723
0
0
0
0
0
0
0
0
0
0
aim
990
454
377
429
479
3
3
3
4
4
445
442
450
464
473
419
414
420
431
438
iv el if a
1,346
1,346
1,346
1,346
1,346
1,026
1,026
1.026
1.026
1.026
22,169
22.169
22,169
22,169
22,169
19,018
19.018
19.018
19.018
19 018
nonpt
12,476
12,475
12,474
12,473
12,473
199
199
199
199
199
4,389
4,331
4,289
4,238
4,238
2,735
2.676
2.634
2,584
2.584
nonroad
2,025
358
22
23
26
17
20
22
24
28
2,115
1,713
1,418
1.018
779
2,027
1.636
1.349
961
726
onroad
360
177
194
235
278
1,532
1,964
2,339
2,795
3.309
644
638
664
750
876
399
347
32 B
353
406
dtipm
49,276
31,272
26,457
30,331
30.331
460
535
645
483
483
3,629
5.097
9,268
13,192
13,192
3,283
4.072
7.846
11.430
11.430
ptnonipm
1,342
1,342
1,342
1,342
1,342
164
164
164
164
164
3,240
3.196
3,196
3,196
3,196
1,435
1,420
1,420
1,420
1,420
Nevada Total
67,815
47,124
42,213
46,179
46,276
8,999
9,608
10,080
10,413
10,937
97,728
98,682
102,548
106,123
106,017
40,687
40,954
44,385
47,569
47,393
New Hampshire
afdust
0
0
0
0
0
0
0
0
0
0
6,175
6.175
6,175
6,175
6,175
2,194
2.194
2.194
2.194
2.194
m
0
0
0
0
0
1,354
1,377
1,394
1,414
1,414
0
0
0
0
0
0
0
0
0
0
aim
238
219
220
252
363
0
0
0
0
0
96
100
103
108
123
86
87
89
93
107
avefire
3B
38
38
3B
38
29
29
29
29
29
622
622
622
622
622
534
534
534
534
534
nonpt
7,408
7,400
7,394
7,387
7,387
835
803
780
753
753
13,351
12.797
12,401
11,926
11,926
12,658
12.104
11.708
11.233
11.233
nonroad
673
119
15
16
18
9
10
11
12
14
942
833
707
541
463
891
784
664
506
430
anroad
880
180
148
165
1S3
1,266
1,417
1.504
1.723
1,999
969
760
619
579
639
714
497
347
286
298
ptipm
44,009
8,279
9,970
14,096
14,096
58
259
258
231
231
2,632
1.318
3,470
8,806
8,806
2,305
1.195
3.305
8 540
8.540
ptnonipm
2,570
2,570
2,570
2,570
2,570
56
56
56
56
56
459
459
459
459
459
390
390
390
390
390
New Hampshire Total
55,815
18,803
20,354
24,524
24,664
3,607
3,952
4,092
4,217
4,495
25,248
23,064
24,556
29,216
29,213
19,772
17,785
19,231
23,776
23,726
New Jersey
afdust
0
0
0
0
0
0
0
0
0
0
18,305
16.305
16,305
16,305
16,305
1,392
1.392
1.392
1,392
1.392
ag
0
0
0
0
0
3,827
3,953
4.044
4.152
4.152
0
0
0
0
0
0
0
0
0
0
aim
14,587
15,243
16,581
20,019
29,344
11
12
13
13
15
1,786
1.889
1,963
2,142
2,749
1,611
1.703
1.764
1,922
2,478
avefire
61
61
61
61
61
47
47
47
47
47
1,009
1,009
1,009
1,009
1,009
865
865
865
865
865
nonpt
10,726
10,718
10,713
10,707
10,707
2.64B
2.648
2.648
2,648
2 648
15,987
15,375
14,937
14,411
14,411
13,074
12.462
12.024
11.498
11.498
nonroad
3,378
607
65
69
78
41
45
49
54
63
4,162
3,432
2,915
2,283
1,992
3,958
3.255
2.756
2.144
1.855
onroad
3,658
845
800
880
1,016
7,635
6,373
9.081
9.860
11.338
3,805
3,107
2,830
2,894
3,198
2,537
1.802
1.456
1.405
1.477
ptipm
51,299
20,935
19,045
20,861
20,861
170
537
592
566
566
4,835
3,176
4,565
6,656
6,656
4,010
2.594
3832
5.731
5.731
jtnonipm
9.930
6,233
6,233
6,233
6,233
475
475
475
475
475
3,131
2,966
2,966
2,966
2,966
2,464
2.337
2.337
2.337
2.337
New Jersey Total
93,640
54,642
53,498
58,829
68,300
14,854
16,091
16,948
17,815
19,303
51,020
47,259
17,490
48,667
49,286
29,910
26,409
26,425
27,294
27,633
-------
State
Sector
[tonsfyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonsfyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonslVr]
2014
Base
NH3
[tonsfyr]
2020
Base
NHS
[tonsfyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonsfyr]
2009
Base
PM10
[tonsfyr]
2014
Base
PM10
[tonsfyr]
2020
Base
PM10
Itonsfyr]
2030
Base
PM10
[tonsfyr]
2002
PM2 5
[tonsfyr]
2009
Base
PM2 5
[tonsfyr]
2014
Base
PM2 5
[tonsfyr]
2020
Base
PM2 5
[tonsfyr]
2030
Base
PM2 5
New Mexico
afdust
0
0
0
0
0
0
0
0
0
0
440,334
440,334
440,334
440,334
440,334
80,348
80.348
80 348
80.348
80.348
m
0
0
0
0
0
36,340
36,476
36.574
36.690
36.690
0
0
0
0
0
0
0
0
0
0
aim
2,550
522
106
122
140
9
11
12
13
15
1,110
1,060
1,029
1,004
986
1,084
1,033
1,000
974
934
av elite
3,450
3,450
3,450
3,450
3,450
2,626
2,026
2,626
2.626
2.626
56,719
56.719
56,719
56,719
56,719
48,662
48.662
48.662
48.662
48.662
nonpt
2,825
2,823
2,821
2,620
2,820
39
39
39
39
39
5,984
5.816
5,696
5,552
5,552
5.346
5,178
5,058
4.914
4.914
nonroad
975
167
12
13
14
9
10
11
12
14
1,062
859
700
501
380
1,016
818
665
472
355
onroad
2,254
337
280
319
370
2,323
2,638
2,961
3.339
3,862
1,965
1.416
1,151
1,100
1,211
1,476
926
641
540
563
dtipm
51,016
26,035
25.999
26,112
26,112
10
392
398
464
464
8,024
5.334
5,347
5,544
5,544
5,557
4.673
4.686
4.874
4.874
ptnonipm
18,179
16,513
16,513
16,513
16,513
44
44
44
44
44
3,986
3,821
3,821
3,821
3,821
3,290
3,179
3,179
3,179
3,179
New Mexico Total
81,249
49,84$
49,182
49,349
49,420
41,401
42,236
42,665
43,226
43,754
519,183
515,360
514,798
514,576
514,528
146,779
144,818
144,239
143,964
143,830
New York
afdust
0
0
0
0
0
0
0
0
0
0
139,896
139.896
139,896
139,896
139,896
29,997
29.997
29.997
29.997
29.997
m
0
0
0
0
0
49,281
49,900
50.342
50.873
50,873
0
0
0
0
0
0
0
0
0
0
aim
9,353
7,050
8,128
6,277
9.061
29
31
33
35
39
1,780
1.826
1,865
1,965
2.267
1.394
1.441
1.494
1.600
1.861
avefire
113
113
113
113
113
86
86
86
86
86
1,866
1,866
1,866
1,866
1,866
1,601
1.601
1.601
1.601
1.601
nonpt
125,559
125,618
125,661
125,711
125,711
3,964
4,158
4.297
4.463
4.463
83,468
87.036
89,585
92,644
92,644
58,823
62022
64.307
6 7 049
67.049
nonroad
6,797
1,209
125
134
151
79
89
97
107
123
8,303
6.886
5,734
4,304
3,512
7,909
6.535
5.427
4.047
3,274
anroad
8,075
1,710
1.595
1,756
2,170
14,582
15,853
17.084
18.456
22.335
8,059
7.022
6.174
5,596
6,781
5,547
4.426
3.491
2.752
3.227
ptipm
238,034
140,744
113,238
111,224
111,224
2,439
1,609
1.279
1.279
1.279
13,669
8019
28,290
31,952
31,952
12,081
6.441
26.168
29.757
29.757
ptnonipm
59,078
59,043
59,043
59,043
59,043
1,241
1,239
1.239
1.239
1,239
8,565
7,661
7,661
7,661
7,661
4,410
3.752
3,752
3,752
3.752
New York Total
447,008
335,456
305,901
304,257
307,473
71,702
72,966
74,457
76,538
80,437
263,606
260,213
281,073
285,884
286,580
121,762
116,215
136,236
140,555
140,518
North Carolina
afdust
0
0
0
0
0
0
0
0
0
0
91,287
91,287
91,287
91,287
91,287
25,474
25,474
25,474
25,474
25.474
ag
0
0
0
0
0
158,188
168,029
175.054
183.488
183.488
0
0
0
0
0
0
0
0
0
0
aim
1,840
1,044
928
1,084
1,516
7
6
5
10
11
6,752
7 029
7,746
8,718
9,680
4,789
4.977
5.468
6,137
6,808
avefire
696
696
696
696
696
532
532
532
532
532
11,509
11,509
11,509
11,509
11,509
9,870
9.870
9,870
9870
9.870
nonpt
22,020
22,006
21,996
21,984
21,984
236
236
236
236
236
40,945
39.800
38,981
38,000
38,000
38,389
37.243
36.425
35.443
35.443
nonroad
5,750
989
81
86
97
54
60
66
73
84
6,313
5.104
4,185
3,033
2,406
6,035
4.862
3.974
2.861
2.247
onroad
8,683
1,147
963
1,095
1,250
7,953
8,925
9.900
11.115
12.540
6,517
4.723
3,865
3,701
4,026
4,874
3.077
2.157
1.827
1.864
ptipm
471,337
202,194
154,504
144,734
144,734
124
574
639
670
670
22,259
21.598
22,962
29,362
29,362
16,031
16.477
17.377
23.517
23,517
ptnonipm
56.065
54,306
54,306
54,306
54,306
1,485
1,469
1.470
1.470
1.470
13,744
13,326
13,326
13.326
13,326
9,828
9.391
9.391
9.391
9.391
North Carolina Total
566,392
282,382
233,475
223,985
224,583
160,580
179,835
187,906
197,594
199,032
199,327
194,376
193,860
198,935
199,596
115,291
111,371
110,135
114,520
114,634
-------
o
u>
to
State
Sector
[tonslyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonslyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonslVr]
2014
Base
NH3
[tonslyr]
2020
Base
NHS
[tons/yr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonslyr]
2009
Base
PM10
[tonslyr]
2014
Base
PM10
[tonslyr]
2020
Base
PM10
Itons/yr]
2030
Base
PM10
[tonslyr]
2002
PM2 5
[tons/yr]
2009
Base
PM2 5
[tons/yr]
2014
Base
PM2 5
[tons/yr]
2020
Base
PM2 5
[tons/yr]
2030
Base
PM2 5
North Dakota
afdust
0
0
0
0
0
0
0
0
0
0
269,751
269.751
269,751
269,751
269,751
50,500
50.500
50.500
50.500
50.500
m
D
0
0
0
0
71,302
71,557
71,739
71,957
71,957
0
0
0
0
0
0
0
0
0
0
aim
1,601
316
51
59
68
6
7
7
8
10
684
652
630
612
587
670
637
614
595
569
avefire
66
66
66
66
66
50
50
50
50
50
1,089
1,089
1,089
1,089
1,089
934
934
934
934
934
nonpt
5.768
5,765
5,763
5,761
5,761
69
09
69
69
69
3,751
3,539
3,387
3,205
3,205
3,241
3.029
2.877
2.695
2.695
nonroad
4,106
696
26
28
30
25
29
32
36
41
4,634
3.177
2,282
1.433
603
4,486
3.072
2.205
1.382
577
on road
700
100
7B
85
107
733
758
804
857
1,037
608
430
336
305
360
455
286
191
152
168
dtipm
140,535
78,026
36.622
43,908
43,908
378
370
364
376
376
7,625
5.960
6,124
5,960
5,960
6,479
5.059
5.202
5.076
5.076
ptnonipm
15,449
11,305
11.305
11,305
11,305
139
139
139
139
139
1,437
1.422
1,422
1,422
1,422
1,105
1,103
1,103
1,103
1,103
North Dakota Total
168,224
96,275
53,913
61,212
61,244
72,703
72,979
73,204
73,491
73,678
289,580
286,021
285,022
283,777
282,978
67,870
64,618
63,626
62,437
61,623
Ohio
afdust
0
0
0
0
0
0
0
0
0
0
236,316
236.316
236,316
236,316
236,316
49,900
49.900
49.900
49.900
49.900
m
0
0
0
0
0
98,711
101,976
104,307
107,105
107.105
0
0
0
0
0
0
0
0
0
0
aim
11.191
8,372
3,056
9.339
13,647
32
36
39
42
49
3,393
3.424
3,437
3,561
4,095
3.113
3.135
3.134
3.235
3.722
avefire
22
22
22
22
22
17
17
17
17
17
366
368
368
368
368
316
316
316
316
316
nonpt
19,810
19,810
19,810
19.810
19,810
8,527
8,420
8.344
8253
8253
25,444
24.784
24,312
23,746
23,746
23.761
23.101
22.629
22063
22.063
nonroad
8,254
1,429
112
119
134
74
83
91
101
117
8,400
6.603
5,278
3,649
2,902
8,043
6.299
5.01 B
3,443
2.713
anroad
12,682
1,414
1,171
1.305
1,519
10,986
11,569
12,466
13.570
15,357
8,049
5.875
4,807
4,549
5,058
5,933
3.792
2.673
2,246
2.364
Ptipm
1,145,194
425,975
364.335
299,575
299,575
74
1,207
1.292
1,330
1,330
62,308
40.958
37,583
39,452
39.452
55,730
30.936
27.757
29.467
29.467
ptnonipm
111,233
109,789
101,330
101,330
101,330
6,370
6,370
6,370
6,370
6,370
14,370
14,039
13,858
13,858
13,858
10,000
9,705
9,576
9,576
9,576
Ohio Total
1.308,387
566,810
494,835
431,499
436,037
124,789
129,677
132,925
136,787
138,597
358,650
332,368
325,960
325,500
325,796
156,798
127,183
121,004
120,246
120,120
Oklahoma
afdust
0
0
0
0
0
0
0
0
0
0
395,931
395.931
395,931
395,931
395,931
70,686
70.686
70,686
70,686
70,686
ag
0
0
0
0
0
95,061
97,973
100,054
102,549
102.549
0
0
0
0
0
0
0
0
0
0
aim
1,890
469
181
207
269
7
6
8
9
11
886
853
83B
828
813
841
809
791
779
762
avefire
469
469
469
469
469
359
359
359
359
359
7,747
7,747
7,747
7,747
7,747
6,644
6.644
6,644
6.644
6,644
nonpt
7,542
7,538
7.535
7,531
7,531
11,358
11.35B
11.358
11,358
11,358
54,339
53.993
53,746
53,449
53,449
43,886
43.540
43.293
42.996
42,996
nonroad
3,093
520
36
38
42
26
29
32
35
40
3,494
2.636
2,063
1,452
1,012
3,353
2.521
1.967
1.377
948
onroad
5,344
619
524
594
728
4,626
5,089
5,637
6,296
7,538
3,501
2,565
2,123
2,038
2,401
2,592
1.652
1.173
1.000
1.118
ptipm
111,841
165,330
79.570
64,002
64,002
909
1,010
1.186
1,067
1,067
3,350
5.373
6,598
7,609
7,609
1,722
4,311
5.534
6,350
6.350
ptnonipm
38.495
33,153
33,153
33,153
33,153
3.118
3,118
3,118
3,118
3,118
9,175
8,903
8,903
8.903
8,903
5,241
4,852
4.852
4.852
4.852
Oklahoma Total
168,673
208,098
121,468
105,994
106,194
115,463
118,943
121,752
124,790
126,040
478,422
478,000
477,948
477,956
477,863
134,966
135,015
134,941
134,685
134,357
-------
o
u>
u>
State
Sector
[tonsfyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonsfyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonsfVr]
2014
Base
NH3
[tonsfyr]
2020
Base
NHS
[tonsfyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonsfyr]
2009
Base
PM10
[tonsfyr]
2014
Base
PM10
[tonsfyr]
2020
Base
PM10
Itonsfyr]
2030
Base
PM10
[tonsfyr]
2002
PM2 5
[tonsfyr]
2009
Base
PM2 5
[tonsfyr]
2014
Base
PM2 5
[tonsfyr]
2020
Base
PM2 5
[tonsfyr]
2030
Base
PM2 5
Oregon
afdust
0
0
0
0
0
0
0
0
0
0
82.013
82.013
82,013
82,013
82,013
30,637
30.637
30.637
30.637
30.637
m
0
fl
0
0
0
40,655
41,1 SB
41.51 B
41,949
41.949
0
0
0
0
0
0
0
0
0
0
aim
4,212
3,487
3,423
3,923
5,694
9
10
10
11
13
1,496
1,509
1,513
1,560
1,770
1,371
1,377
1.373
1.409
1,599
iv el if a
4,896
4,896
4,896
4,896
4,896
3.542
3,542
3,542
3,542
3,542
75,661
75.861
75,861
75,861
75,861
65,350
65.350
65.350
65.350
65.350
nonpt
9,845
9,846
9,846
9,647
9,847
1,061
1,901
1.061
1.061
1,061
50,681
49.765
49,110
48,325
48,325
49,407
48.479
47,816
47.020
47,020
nonroad
2,559
434
35
37
41
24
27
29
32
37
2,902
2.358
1,920
1.386
1,080
2,773
2.243
1.822
1.306
1.008
onroad
3,488
450
398
448
504
3,270
3,758
4,181
4,656
5.213
2,707
2,151
1,697
1,537
1,622
2,021
1.458
994
778
767
dtipm
12,285
12,552
12,552
12,606
12,606
162
298
298
298
298
711
392
392
449
449
326
331
331
385
385
ptnoiiipm
5,307
5,307
5,307
5,307
5,307
787
787
787
787
787
9,828
9.532
9,532
9,532
9,532
6,203
6,042
6.042
6,042
6,042
Oregon Total
42,592
36,971
36,457
37,064
38,896
49,509
50,641
51,426
52,337
52,900
226,200
223,580
222,039
220,662
220,651
158,088
155,917
154,365
152,927
152,808
Pennsylvania
afdust
0
0
0
0
0
0
0
0
0
0
130,506
130.508
130,506
130,508
130,508
32,224
32.224
32.224
32,224
32.224
m
a
a
0
0
0
76,675
79,474
81,473
33.870
83.870
0
0
0
0
0
0
0
0
0
0
aim
8,354
6,729
6.553
7.510
10.928
14
16
17
18
21
2,376
2.396
2,399
2,478
2,857
2.268
2.276
2.267
2.329
2.677
avefire
32
32
32
32
32
25
25
25
25
25
530
530
530
530
530
454
454
454
454
454
nonpt
68,349
68,335
68,326
68,314
68,314
3,689
3,689
3.689
3689
3689
41,641
40.757
39,983
39,053
39.053
31.263
30.179
29.404
28.475
28.475
nonroad
5,203
918
89
95
107
55
62
68
75
87
6,256
5.295
4,442
3,335
2,782
5,969
5.028
4,205
3,138
2.596
anroad
7,885
1,369
1,169
1.285
1,504
10,618
11,363
12,227
13.212
15.234
7,250
5,426
4,514
4,293
4,821
5,219
3.436
2,475
2,108
2,253
Ptipm
907,734
244,722
206,230
188,589
186,589
401
1,234
1.311
1.237
1.237
63,198
31.689
31,114
30,850
30,850
53,067
24.131
23.255
23.155
23.155
ptnonipm
88,132
81,441
75,605
75,605
75,605
1,334
1,298
1.298
1,298
1,298
22,391
20.443
20,393
20,393
20,393
11,549
10.265
10,186
10,186
10,186
Pennsylvania Total
1,085,688
403,546
358,004
341,431
345,080
92,811
97,160
100,108
103,424
105,460
274,351
237,044
233,883
231,440
231,794
142,015
107,994
104,472
102,070
102,021
Rhode Island
afdust
a
0
0
0
0
0
0
0
0
0
2,501
2,501
2,501
2,501
2,501
481
481
481
481
481
ag
0
0
0
0
0
235
237
239
241
241
0
0
0
0
0
0
0
0
0
0
aim
78
64
68
77
85
0
0
0
0
0
8
8
B
"7
7
0
0
0
0
0
avefire
1
1
1
1
1
1
1
1
1
1
17
17
17
17
17
14
14
14
14
14
aonpt
3,365
3,364
3,364
3,364
3,364
15
15
15
15
15
1,171
1,136
1,110
1,080
1,080
1,107
1.072
1,046
1,016
1,016
aanroad
354
63
7
8
9
4
5
5
6
6
427
342
285
218
189
406
324
270
205
176
onroad
425
86
85
94
107
854
940
1.023
1.122
1.274
343
290
292
313
356
209
150
141
147
165
ptiprn
18
0
0
0
0
58
151
137
126
126
12
1
I
7
6
6
11
4
4
4
4
jtnonipm
2,849
2,349
2,349
2,349
2,349
47
47
47
47
47
288
256
256
256
256
173
153
153
153
153
Rhode Island Total
6,889
5,928
5,874
5,892
5,915
1,213
1,395
1,467
1,557
1,710
4,767
4,556
4,175
4,398
4,412
2,401
2,199
2,110
2,020
2,010
-------
o
u>
4^
State
Sector
[tonslyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonslyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonslVr]
2014
Base
NH3
[tonslyr]
2020
Base
NHS
[tonslyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonslyr]
2009
Base
PM10
[tonslyr]
2014
Base
PM10
[tonslyr]
2020
Base
PM10
Itonslyr]
2030
Base
PM10
[tons/yr]
2002
PM2 5
[tonslyr]
2009
Base
PM2 5
[tonslyr]
2014
Base
PM2 5
[tonslyr]
2020
Base
PM2 5
[tonslyr]
2030
Base
PM2 5
South Carolina
afdust
0
0
0
0
0
0
0
0
0
0
82.088
82,099
82,108
32.117
82,117
25,657
25.661
25.664
25.667
25.667
m
D
0
0
0
0
27,945
29,692
30,941
32,440
32.440
0
0
0
0
0
0
0
0
0
0
aim
1,946
1,231
1.108
1,267
1.842
4
5
5
6
7
714
711
709
720
779
668
662
656
663
715
m elite
646
646
646
646
646
494
494
494
494
494
10,684
10,684
10,684
10,684
10,684
9,163
9.163
9.163
9.163
9.163
nonpt
30,016
30,008
30,003
29,996
29,996
223
223
223
223
223
19,393
18,766
18,318
17,780
17,780
18,139
17,512
17.064
16.526
16.526
nanroad
2,816
482
41
44
49
27
30
33
36
41
3,102
2,489
2,041
1.490
1,208
2,960
2.368
1.936
1.404
1.127
onroad
5,021
651
551
617
720
4,710
5,163
5,643
6,206
7.137
3,588
2,629
2,163
2,055
2,283
2,648
1,695
1.200
1.013
1.067
dtipm
212,572
150,469
122.606
97,472
97,472
306
343
415
424
424
17,707
17,282
17,579
22,636
22,636
13,734
12.638
12.799
17.647
17.647
ptnonipm
57,307
56,870
56,870
56,870
56,870
1,552
1,552
1,552
1,552
1,552
12,696
11,699
11,699
11,699
11,699
8,159
7.403
7,403
7,403
7,403
South Carolina Total
310,324
240,357
211,824
186,911
187,5S5
35,263
37,504
39,307
41,381
42,319
149,971
146,357
145,299
149,182
149,186
81,128
77,100
75,884
79,486
79,315
South Dakota
afdust
0
0
0
0
0
0
0
0
0
0
202,326
202,326
202,326
202,326
202,326
38,332
38.332
38 332
38332
38.332
m
0
0
0
0
0
101,949
102,814
103,432
104,172
104,172
0
0
0
0
0
0
0
0
0
0
aim
318
68
18
20
23
1
1
2
2
172
167
166
170
172
156
151
151
152
152
avefire
498
498
498
49B
498
381
381
381
381
381
8,235
8,235
8,235
8,235
8,235
7,062
7.062
7.062
7.062
7.062
nonpt
10,304
10,301
10,299
10,296
10,296
51
51
51
51
51
6,683
6434
6,256
8,042
6,042
4,463
4.214
4036
3822
3.822
nanroad
2,901
492
21
22
23
18
21
23
26
30
3,289
2.286
1,658
1,051
477
3,181
2,207
1.599
1.012
455
anroad
852
125
95
103
129
843
901
968
1,041
1,230
746
548
419
372
436
564
370
240
185
203
Ptipm
12,545
12,249
4.275
4,865
4,865
50
34
37
48
48
450
231
476
589
589
420
218
447
531
531
ptnonipm
1,480
1,480
1,480
1,480
1,480
50
50
50
50
50
609
515
515
515
515
291
249
249
249
249
South Dakota Total
23,898
25,214
16,685
17,284
17,314
103,343
104,253
104,944
105,770
106,013
222,509
220,741
220,052
219,300
218,791
54,470
52,803
52,116
51,345
50,806
Tennessee
afdust
0
0
0
0
0
0
0
0
0
0
95,767
95,767
95,767
95,767
95,767
22,530
22,530
22.530
22,530
22.530
ag
0
0
0
0
0
34,210
35,494
36,411
37,512
37.512
0
0
0
0
0
0
0
0
0
0
aim
6,292
5,318
5,393
6,362
9,273
12
14
15
16
18
1,853
1.869
1,858
1,906
2,193
1,707
1.720
1.707
1,749
2,012
avefire
277
277
277
277
277
212
212
212
212
212
4,587
4,587
4,587
4,587
4,587
3,934
3.934
3,934
3.934
3,934
nonpt
32,714
32,705
32,698
32,690
32.6SD
164
164
164
164
164
26,842
26,074
25,526
24,869
24,869
20,663
19.896
19,348
18.690
18,690
nanroad
3,728
642
54
57
64
35
39
43
47
54
4,225
3,416
2,759
1,954
1,496
4,040
3.253
2,620
1.845
1,399
»nroad
7,674
1,039
830
946
1,104
6,671
7,448
B,258
9,235
10.488
6.12B
4,447
3,539
3,319
3,671
4,667
2.982
2.026
1.658
1.719
ptipm
333,618
158,140
137,637
153,894
153,894
425
436
487
572
572
16,268
10,716
14,520
38,254
38,254
13,910
8.769
12,704
35.970
35.970
ptnonipm
84.316
83,903
83.903
83.903
83,903
2,394
2,394
2.394
2.394
2.394
30,328
27,121
27,121
27.121
27,121
22,054
19.684
19.684
19.684
19.684
Tennessee Total
468,619
282,025
260,792
278,130
281,205
44,124
46,202
47,986
50,152
51,415
185,996
173,997
175,677
197,775
197,957
93,505
82,769
84,553
106,059
105,938
-------
o
u>
State
Sector
[tonsfyr]
2002
S02
[tonsJyr]
2009
Base
S02
[tonsfyr]
2014
Base
S02
[tonsfyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonsfyr]
2014
Base
NH3
[tonsfyr]
2020
Base
NH3
[tonsfyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonsJyr]
2009
Base
PM10
[tonsfyr]
2014
Base
PM10
[tonsfyr]
2020
Base
PM10
[tonsfyr]
2030
Base
PM10
[tonsfyr]
2002
PM2 5
[tonsfyr]
2009
Base
PM2 5
[tonsfyr]
2014
Base
PM2 5
[tonsfyr]
2020
Base
PM2 5
[tonsfyr]
2030
Base
PM2 5
Texas
aMust
0
0
0
0
0
0
0
0
0
0
1,290,391
1,290,973
1.291,390
1,291,887
1,291,687
242.993
243.036
243.153
243.232
243.232
sg
0
0
0
0
0
354,873
360,460
364.456
369242
369 242
0
0
0
0
0
0
0
0
0
0
aim
27,280
23,890
24,302
28,470
41,368
57
63
67
73
82
8,936
8,940
8,759
8,857
10,280
8,146
8.147
7.980
8,070
9,380
avefire
1,178
1,178
1,178
1,178
1,178
1,118
1,118
1.118
1.118
1.118
25,226
25.223
25,228
25,228
25,228
21,578
21.578
21.578
21.578
21,578
lonpt
109,215
109,204
109,195
109,185
109,185
1,983
1.983
1,983
1,983
1.983
72,265
71,333
70,666
69,867
69,867
47,394
46.461
45.795
44.995
44,995
nanroad
14,990
2,566
180
189
211
128
145
158
175
202
15,766
12,270
9,862
6,978
5,127
15,126
11.733
9.401
6.607
4.798
[inroad
21,522
3,084
2,506
2,863
3,479
21,943
24,625
27,464
31.029
37.361
16,034
12,192
10,036
10,043
11,798
11,699
7.710
5.342
4.834
5.466
ptipm
562,594
346,683
339,382
338.519
338,519
5,941
4.839
5,537
6,146
6.148
34,257
35,123
35,202
38,150
38,150
24,920
24,844
24,955
27.849
27.849
ptnonipm
245,060
172,556
164,923
164,923
164,923
2,297
2,279
2,279
2,279
2.279
38,861
36,535
36,020
36,020
36,020
27,189
25.562
25.310
25.310
25,310
Texas Total
981,840
659,160
641,666
645,327
650,063
388,340
395,512
403,063
412,046
418,416
1,501,740
1,492,592
1,487,162
1,487,030
1,488,357
399,045
389,120
383,512
382,476
382,608
Tribal Data
aim
132
25
3
4
4
1
1
1
1
1
58
55
52
50
48
0
0
0
0
0
ptipm
6
0
0
0
0
65
92
92
72
72
31
4
4
3
3
31
3
3
2
2
ptnonipm
204
203
203
203
203
4
4
4
4
4
1,872
1,868
1,866
1.868
1,868
856
852
852
852
852
Tribal Data Total
342
228
206
207
207
69
96
96
76
77
1,961
1,927
1,925
1.922
1,919
887
855
855
854
854
Utah
afdust
0
0
0
0
0
0
0
0
0
0
54,020
54,020
54,020
54.020
54,020
7,864
7.864
7.864
7.864
7.864
sg
0
0
0
0
0
20,448
20,960
21.326
21.765
21.765
0
0
0
0
0
0
0
0
0
0
aim
1,065
344
213
242
272
5
6
7
8
9
153
152
157
162
166
140
140
144
148
152
svefire
1,934
1,934
1,934
1,934
1,934
1,479
1,479
1.479
1.479
1.479
31,961
31,961
31,961
31,961
31,961
27,412
27.412
27.412
27.412
27.412
lanpt
3,427
3,426
3,425
3,423
3,423
1,268
1.26B
1.268
1.268
1.268
10,385
10.268
10,185
10,085
10,085
9,079
8.970
8893
8.800
8.800
nanroad
1,437
251
21
22
25
14
16
18
20
22
1,703
1.463
1,199
845
647
1,625
1.389
1.135
796
604
anroad
1,989
335
310
361
432
2,457
2,903
3335
3.851
4.579
1,658
1.327
1,165
1,176
1,352
1,187
829
628
570
628
ptipm
33.167
39,360
41,355
44,170
44,170
269
372
416
418
418
6,351
5,480
7,400
8,532
6,532
4,901
4.265
5.940
6.933
6,933
ptnonipm
9,305
8,454
7,790
7,790
7,790
529
529
529
529
529
6,693
6.577
6,551
6,551
6,551
2.955
2.810
2.809
2.809
2.809
Utah Total
52,325
54,103
55,047
57,942
58,045
26,469
27,534
28,377
29,336
30,069
113,124
111,243
112,639
113,333
113,315
55,162
53,678
54,825
55,332
55,203
-------
o
u>
G\
State
Sector
[tonsfyr]
2002
S02
[tonslyr]
2009
Base
S02
[tonsfyr]
2014
Base
S02
[tonslyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonslVr]
2014
Base
NH3
[tonsfyr]
2020
Base
NH3
[tonsfyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonsfyr]
2009
Base
PM10
[tonsfyr]
2014
Base
PM10
[tonsfyr]
2020
Base
PM10
Itonsfyr]
2030
Base
PM10
[tonsfyr]
2002
PM2 5
[tonsfyr]
2009
Base
PM2 5
[tons/yr]
2014
Base
PM2 5
[tonsfyr]
2020
Base
PM2 5
[tonsfyr]
2030
Base
PM2 5
Vermont
afdust
0
0
0
0
0
0
0
0
0
0
13.65B
13.658
13,658
13,658
13,658
4,814
4.814
4.814
4.814
4.814
m
0
fl
0
0
0
B.B21
B.B51
8,872
8.898
8,898
0
0
0
0
0
0
0
0
0
0
aim
6
6
7
j
8
0
0
0
0
0
29
30
32
35
37
21
22
24
26
28
iv el if a
49
49
49
49
49
38
38
38
38
38
812
812
812
812
812
696
696
696
696
696
nonpt
5,385
5,382
5,380
5,378
5,378
214
214
214
214
214
5,823
5,539
5,336
5,093
5,093
5,415
5,151
4.962
4.736
4.736
nonroad
368
64
7
8
9
5
5
6
6
7
516
463
390
292
236
490
436
366
273
220
onroad
822
125
103
118
148
939
1,047
1,147
1.262
1,467
645
632
554
557
720
465
418
322
291
355
Utipm
0
0
0
0
0
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
ptnonipm
911
911
911
911
911
16
16
16
16
16
337
337
337
33?
337
237
237
237
237
237
Vermont Total
7,341
6,538
6,458
6,471
6,503
10,043
10,172
10,293
10,434
10,639
21,819
21,471
21,119
20,783
20,893
12,137
11,774
11,422
11,072
11,085
Vinginia
afdust
0
0
0
0
0
0
0
0
0
0
60,865
60.865
60,865
60,865
60,865
19,662
19.662
19.662
19.662
19.662
m
a
0
0
0
0
43,811
45,905
47,402
49,197
49.197
0
0
0
0
0
0
0
0
0
0
aim
5,595
3,378
2.698
3,428
4,904
13
15
16
18
21
1,905
1.875
1,854
1.872
2,004
1,836
1.801
1.774
1.783
1.900
avefire
399
399
399
399
399
305
305
305
305
305
6,599
6,599
6,599
6,599
6,599
5,659
5.659
5.659
5659
5.659
nonpt
32,923
32,910
32.901
32,889
32,889
1,621
1,621
1621
1.621
1.621
53,941
52.867
52,100
51,179
51,179
29,947
28.873
28.106
27.185
27.185
nonroad
4,289
741
60
63
71
41
46
51
56
64
4,809
3.897
3,247
2,437
2,009
4,593
3.709
3.079
2,294
1,872
anroad
6.662
1,019
920
1,032
1,269
7,889
8,893
9,831
10.881
13.168
4,939
3.738
3,278
3,306
3,976
3,486
2.250
1.711
1,589
1.850
ptipm
239,7??
151,541
148,291
135,834
135,834
192
353
471
435
435
15,400
10317
13,940
15,885
15,885
14,431
8356
11.602
13.372
13.372
ptnonipm
67,691
67,253
67,253
67,253
67,253
3,500
3,498
3,498
3,498
3,498
13,041
11,869
11,869
11,869
11,869
9,734
8.671
8,671
8,671
8.671
Virginia Total
357,338
257,240
252,821
240,899
242,619
57,373
60,638
63,196
66,011
68,310
161,498
152,025
153,752
154,011
154,385
89,350
78,981
80,264
80,215
80,171
Washington
afdust
0
0
0
0
0
0
0
0
0
0
106,176
106.176
106,176
106,176
106,176
26,908
26.908
26.908
26.908
26.908
sg
0
a
0
0
0
42,133
42,712
43,126
43.622
43.622
0
0
0
0
0
0
0
0
0
0
aim
11,488
10,791
11,291
13,423
19,682
151
171
193
236
339
2,416
2.601
2,758
3,074
3,959
2,271
2.447
2.594
2,895
3.749
avefirs
40?
407
407
407
407
248
248
248
248
248
5,126
5.126
5,126
5,126
5,126
4,487
4.487
4.487
4.487
4.487
nonpt
7,254
7,241
7,231
7,219
7,219
1.711
1.711
1,711
1.711
1.711
35,624
34.598
33,864
32,983
32,983
31,983
31.023
30.337
29.513
29.513
nonroad
5,380
707
57
60
67
39
44
48
53
61
4,776
3.742
3,044
2,20?
1,704
4,567
3.563
2.890
2.081
1.590
onroad
5,539
790
688
794
914
5,168
6,206
7.111
8.125
9,323
4,545
3,315
2,767
2,685
2,941
3,40?
2.161
1.550
1.330
1.3B0
ptipm
19,108
3,954
3,946
4,064
4,064
62
512
537
528
528
2,456
3.091
3.090
3.213
3,213
2,025
2.465
2.464
2.582
2.582
ptnonipm
24,623
24,601
24,601
24,601
24,601
774
771
771
771
771
4,970
4,895
4,895
4,895
4,895
3,224
3.189
3.189
3.189
3.189
Washington Total
73,799
48,491
48,221
50,569
56,954
50,285
52,375
53,744
55,293
56,602
166,089
163,544
161,720
160,359
160,997
78,872
76,244
74,419
72,985
73,398
-------
o
u>
-J
State
Sector
[tonslyr]
2002
S02
[tonsJyr]
2009
Base
S02
[tonslyr]
2014
Base
S02
Ctonsfyr]
2020
Base
S02
[tonslyr]
2030
Base
S02
[tonslyr]
2002
NH3
[tonslyr]
2009
Base
NH3
[tonslyr]
2014
Base
NH3
[tonslyr]
2020
Base
NH3
[tonslyr]
2030
Base
NH3
[tonslyr]
2002
PM10
[tonslyr]
2009
Base
PM10
[tonslyr]
2014
Base
PM10
[tonslyr]
2020
Base
PM10
[tonslyr]
2030
Base
PM10
[tonslyr]
2002
PM2 5
[tonslyr]
2009
Base
PM2 5
[tonslyr]
2014
Base
PM2 5
[tonslyr]
2020
Base
PFi12 5
[tonslyr]
2030
Base
PM2 5
West Virginia
sfdust
0
0
0
0
0
0
0
0
0
0
24,640
24.644
24,647
24,650
24,650
11,305
11.309
11.311
11.314
11.314
ag
0
0
0
0
0
9,679
10,474
10,896
11.408
11.408
0
0
0
0
0
0
0
0
0
0
aim
5,707
5,433
5,830
7,090
10,433
8
9
10
11
12
1,476
1.526
1,573
1,689
2,036
1,281
1.322
1,357
1.453
1.763
avefire
215
215
215
215
215
165
165
165
165
165
3,557
3,557
3,557
3,557
3,557
3,050
3.050
3.050
3.050
3.050
io np!
14,589
14,585
14,582
14,578
14,578
72
72
72
72
72
12,220
11,866
11,613
11,310
11,310
11,130
10.776
10,523
10.219
10.219
nonroad
780
128
13
14
15
8
9
10
11
13
1,005
906
751
541
455
956
856
707
508
424
onroad
2,675
248
201
217
230
1,950
1,957
2.056
2.175
2,273
1,542
1,004
792
725
732
1,149
648
440
358
343
ptipm
509,488
200,473
178.167
168,660
168,660
210
626
644
649
649
31,248
22,049
21.938
21.957
21,957
28,884
16.535
16.219
16.230
16.230
ptnonipm
54,107
54,106
54,106
54,106
54,106
636
sae
636
638
638
10,625
10.097
10,097
10,097
10,097
7,450
7,113
7,113
7,113
7,113
West Virginia Total
587,561
275,187
253,113
244,879
248,237
12,981
14,002
14,544
15,173
15,280
86,314
75,648
74,967
74,526
74,794
65,205
51,609
50,721
50,246
50,457
Wisconsin
sfdust
a
0
0
0
0
0
0
0
0
0
103,735
103.735
103,735
103,735
103,735
30,705
30.705
30.705
30.705
30,705
m
0
0
0
0
0
113,949
114,739
115,339
116,109
116,109
0
0
0
0
0
0
0
0
0
0
aim
4,781
3,992
4,195
5,137
7,496
11
13
14
15
17
1,353
1.417
1,492
1,632
1,954
1.182
1.236
1.300
1.421
1.709
avefire
70
70
70
70
70
54
54
54
54
54
1,159
1,159
1,159
1,159
1,159
994
994
994
994
994
nonpt
6.369
6,370
6,370
6,370
6,370
266
266
266
266
266
26,104
25.736
25,474
25,159
25,159
25,407
25040
24.777
24.462
24.462
nonroad
5,015
845
81
87
99
52
60
65
71
33
6,090
4,944
3,967
2,840
2,296
5,796
4.634
3.747
2.666
2.138
on road
7,216
815
676
766
697
6,006
6,492
7.111
7,894
9,065
4,479
3,403
2,810
2,684
2,985
3,317
2,215
1,573
1.327
1.395
ptipm
192,946
141,203
125,070
123,645
123,645
375
533
662
630
630
5,576
8.465
8,831
8,890
8,890
5,029
7.151
7.432
7.441
7.441
otnoiiipm
63,651
83,431
63,431
63,431
63,431
397
397
397
397
397
10,466
9,199
9,199
9,199
9,199
5,856
5,445
5,445
5,445
5,445
Wisconsin Total
280,051
216,726
199,892
199,507
202,009
121,110
122,653
123,957
125,486
126,672
158,961
158,058
156,666
155,297
155,377
78,287
77,469
75,972
74,461
74,288
Wyoming
afdust
0
0
0
0
0
0
0
0
0
0
272,299
272,299
272,299
272,299
272,299
41,010
41.010
41,010
41,010
41,010
ag
0
0
0
0
0
16,575
16,801
16.963
19,156
19.156
0
0
0
0
0
0
0
0
0
0
aim
2,088
404
56
65
75
8
9
10
11
13
866
324
793
766
730
857
814
732
754
717
avefiie
1,106
1,106
1,106
1,106
1,106
646
846
846
846
846
18,289
18,289
18,289
18,289
18,289
15,686
15.636
15.636
15.686
15,666
io opt
6,181
6,179
6,178
6,176
6,176
91
91
91
91
91
3,717
3,571
3,467
3,342
3,342
2,922
2.776
2.672
2,547
2,547
nonroad
559
95
7
8
6
5
6
7
7
8
689
565
450
312
214
659
537
426
295
200
on road
905
122
95
103
122
693
931
997
1.075
1.252
799
524
402
365
407
606
347
226
130
169
pfem
83,423
62,706
58,597
58,204
56,204
386
414
443
470
470
9,599
8,653
9,763
10,727
10,727
7,936
7,053
8.032
8,875
8,875
ptnonipm
33.676
33,653
33.653
33,653
33,653
301
301
301
301
301
19,234
18,528
18,528
18,528
18,528
14,143
13,941
13.941
13.941
13.941
Wyoming Total
127,938
104,266
99,692
99,315
99,345
21,104
21,398
21,656
21,957
22,136
325,494
323,255
323,991
324,629
324,536
83,819
82,164
82,775
83,288
83,165
Grand T otal
14,649,986
9,233,950
8,473,877
8,171,411
8,295,030
3,901,951
4,023,868
4,123,379
4,241,636
4,297,455
12,817,898
12,554,430
12,551,458
12,671,074
12,680,651
4,938,898
4,671,411
4,661,327
4,768,531
4,764,633
-------
D-38
-------
Appendix E
Metadata
E-l
-------
E-2
-------
Metadata
Output Data
The pm25_surface_36km_2003.csv file is the output file from EPA's Hierarchical
Bayesian Model (HBM) that combines PM2.5 or O3 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 O3
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 PM2.5 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, 2003 through December 31, 2003. 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 O3 concentration on natural log
scale (PredAvg), row position of grid cell, column position of grid cell, standard error of
the estimated PM2.5 or O3 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 (NEI) 2001 version 3 (developed using mobile emissions
model Mobile 6 but no daily continuous emissions monitoring (CEM) data for the major
E-3
-------
N0X 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 HBM combines the actual monitoring data (NAMS/SLAMS), the estimated PM2.5 or
03 concentration surface (CMAQ), and the prediction of PM2.5 or 03 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 resolution 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
-------
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 PM2.5
and O3 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 (|jg/m3)
Figure E-1. PM2.5 Monitoring Data and CMAQ Surface (Separately Displayed - White Spheres
Represent Monitor Locations and Associated Concentration Values)
E-5
-------
MBMSSffi
Monitor and HBM Concentration (pg/m )
Figure E-2. Combined PM2.5 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 I IB 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-v 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 '/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 lcpgeo(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)
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
E-8
-------
truelat2 = sign*truelat2
endif
xn = aloglO(cos(truelatl/conv)) - alogl0(cos(truelat2/conv))
xn = xn/(alogl0(tan((45. - sign*truelatl/2.)/conv)) -
& alogl0(tan((45. - sign*truelat2/2.)/conv)))
psil = 90. - sign*truelatl
psil = psil/conv
if (phic.lt.O.) then
psil = -psil
pole = -pole
endif
psiO = (pole - phic)/conv
xc = 0.
yc = -a/xn*sin(psil)*(tan(psi0/2.)/tan(psil/2.))**xn
—Calculate lat/lon of the point (xloc,yloc)
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.O.) then
flp = atan2(xloc,yloc)
else
flp = atan2(xloc,-yloc)
endif
endif
flpp = (flp/xn)*conv + xlonc
if (flpp.lt.-l80.) flpp = flpp + 360.
if (flpp.gt. 180.) flpp = flpp - 360.
xlon = flpp
r = sqrt(xloc*xloc + yloc*yloc)
if (phic.lt.O.) r = -r
cell = (r*xn)/(a*sin(psil))
rxn = 1.0/xn
cell = tan(psil/2.)*cell**rxn
cel2 = atan(cell)
psx = 2.*cel2*conv
ylat = pole - psx
—Calculate x/y from lat/lon
-------
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.O.) 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.baronams.com/products/ioapi/GRIDDESC.html
Coordinate Information
COORD-
NAME
COORDTYPE
P ALP
P BET
P GAM
XCENT
YCENT
'LAM_40N97W
2
33.000
45.000
-97.000
-97.000
40.000
Grid Information
GRID-
NAME
COORD-
NAME
XORIG
(m)
YORIG
(m)
XCELL
(m)
YCELL
(m)
NCOLS
NROWS
NTHIK
36US1
'LAM_40N97W'
-2736000
-2088000
36000
36000
148
112
1
PALP = "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).
YORIG 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\Grids\templates\grid_templates.gdb
Feature Class: template_mdhi_12_nb
Feature Type: Simple
Geometry Type: Polygon
Projected Coordinate System: NAD_1983_Lambert_Conformal_Conic
Projection: LambertConformalConic
False_Easting: 0.00000000
False_Northing: 0.00000000
Central_Meridian: -97.00000000
Standard_Parallel_l: 33.00000000
Standard_Parallel_2: 45.00000000
LatitudeOfOrigin: 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 System Properties
General |
Name:
NAD_1983_Larnbert_Conformal_Conic
Projection
Name:
"3
Parameter
Value
False_Easting
0.000000000000000000
F alse_Northing
0.000000000000000000
Central Meridian
-97.000000000000000000
Standard Parallel 1
33.000000000000000000
Standard Parallel 2
45.000000000000000000
1 n**" Ovinln
it ri nnnnnnnnnnnrinririnnn
Linear Unit
Name:
Meters per unit:
"3]
Geographic Coordinate System
Name: CustomSpheroidGCS_North_American_1 ¦
Angular Unit: Degree (0.017453292519943299)
Prime Meridian: Greenwich (0.00000000000000(
Datum:
Spheroid:
Semimajor Axis: G370000.000000000000000C '
HI, — nii _ 31 ~
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
0®
General
Name: | CustomS pheroidG CS_N orth_Arnerican_1983
Datum
Name:
Spheroid
Name: |
Semimajor Axis: 16370000
(* Semiminor Axis:
F
C Inverse Flattening [~~
3
"3
Angular Unit
Name: | Degree
Radians pei unit: 10.017453292519943299
"31
Prime Meridian
Name: | Greenwich -
Longitude: | o ° | 0 | 0
]
J Apply
E-13
-------
Projection Information for HB Grid - Example #1
Year
Geographic
Coordinate
System
Datum
Prime
Meridian
An gular
Unit
Projected
Coordinate
System
False
Easting
False
Northing
Central
Meridian
Standard
Parallell
Standard
Parallel_2
Scale
Factor
Latitude
of Origin
Linear
Unit
2001
Lat/Lon
Spherical
R=6370997
NA
Degrees
Lambert
Conformal
Conic
0.0
0.0
-97.0
33.0
45.0
1.0
40.0
Meters
2002
Spherical
R=6370000
2003
2004
2005
2006
Grid Descriptive Parameters
Year
Grid
Resolution
(km)
XORIG
(m)
YORIG
(m)
XCELL
(m)
YCELL
(m)
NCOLS
NROWS
2001
12
-252000
-1284000
12000
12000
213
188
2001
36
-2736000
-2088000
36000
36000
148
112
2002
12
-1008000
-1620000
12000
12000
279
240
2002
36
-2736000
-2088000
36000
36000
148
112
2003
12
-1008000
-1620000
12000
12000
279
240
2003
36
-2736000
-2088000
36000
36000
148
112
2004
12
-1008000
-1620000
12000
12000
279
240
2004
36
-2736000
-2088000
36000
36000
148
112
2005
12
-1008000
-1620000
12000
12000
279
240
2005
36
-2736000
-2088000
36000
36000
148
112
2006
12
2006
36
E-14
-------
Projection Information for HB Grid - Example #2
Year
Datum
Semimajor
Axis
(m)
Semiminor
Axis
(m)
Angular
Unit
Projected
Coordinate
System
False
Easting
False
Northing
Longitude
of Central
Meridian
Latitude of
Standard
Parallel 1
Latitude of
Standard
Parallel 2
Latitude
of Origin
Linear
Unit
2001
(i.e.,
NAD83
or
WGS84)
(i.e.,
6370000)
(i.e.,
637000)
(i.e.,
degree
or
radians)
(i.e.,
Lambert
Conformal
Conic)
(i.e.,
0.0)
(i.e., 0.0)
(i.e.,
-97.000)
(i.e.,
33.000)
(i.e.,
45.000)
(i.e.,
40.0)
(i.e.,
meters)
2002
2003
2004
2005
2006
Grid Descriptive Parameters
Year
Grid
Resolution
(km)
XORIG
(m)
YORIG
(m)
XCELL
(m)
YCELL
(m)
NCOLS
NROWS
2001
12
2001
36
(i.e., -2736000)
(i.e., -2088000)
(i.e., 36000)
(i.e., 36000)
(i.e., 148)
(i.e., 112)
2002
12
2002
36
2003
12
2003
36
2004
12
2004
36
2005
12
2005
36
2006
12
2006
36
E-15
-------
-------
-------
SEPA
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
------- |