EPA/60Q/R-12/04S | July 31,2012 | www.epa.gov
Hierarchical Bayesian Model (HBM)-
Derived Estimates of Air Quality for
2008: Annual Report
A
Eric S. Hall (EPA/QRD), Alison M. Eyth
(EPA/OAR), Sharon B. Phillips (EPA/OAR), and
Richard Mason (EPA/OAR)
Office of Research and Development
"xposure Research Labor
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Disclaimer
The information in this document has been funded wholly by the United States
Environmental Protection Agency under a 'funds-in' interagency agreement
RW75922615-01-3 with the Centers for Disease Control and Prevention
(CDC). It has been subjected to the Agency's peer and administrative review
and has been approved for publication as an EPA document.
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EPA/600/R-12/048 | July 31,2012 | www.epa.gov/ord
United States
Environmental Protection
Agency
Hierarchical Bayesian Model (HBM)-
Derived Estimates of Air Quality for
2008: 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)
I Office of Research and Development
I National Exposure Research Laboratory
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Contributors:
Eric S. Hall (EPA/ORD)
Alison M. Eyth (EPA/OAR)
Sharon B. Phillips (EPA/OAR)
Richard Mason (EPA/OAR)
Project Officer
Eric S. Hall
National Exposure Research Laboratory (NERL)
109 T. W. Alexander Dr.
Durham, NC 27711-0001
Acknowledgements
The following people served as reviewers of this document and provided
valuable comments that were included: Alexis Zubrow (EPA/OAR), Richard
Mason (EPA/OAR), Norm Possiel (EPA/OAR), Kirk Baker (EPA/OAR),
Carey Jang (EPA/OAR), Tyler Fox (EPA/OAR), Rachelle Duvall (EPA/ORD),
Melinda Beaver (EPA/ORD), and OAR/OAQPS support contractors from
Computer Sciences Corporation (CSC).
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Table of Contents
1.0 Introduction 1
2.0 Air Quality Data 3
2.1 Introduction to Air Quality Impacts in the United States 3
2.1.1 The Clean Air Act 3
2.1.2 Ozone 3
2.1.3 Paniculate Matter 3
2.2 Ambient Air Quality Monitoring in the United States 4
2.2.1 Monitoring Networks 4
2.2.2 Air Quality System Database 4
2.2.3 Advantages and Limitations of the Air Quality Monitoring and Reporting System 5
2.2.4 Use of Air Quality Monitoring Data 5
2.3 Air Quality Indicators Developed for the EPHT Network 6
2.3.1 Rationale for the Air Quality Indicators 6
2.3.2 Air Quality Data Sources 6
2.3.3 Use of Air Quality Indicators for Public Health Practice 6
3.0 Emissions Data 7
3.1 Introduction to the 2008 Emissions Data Development 7
3.2 2008 Emission Inventories and Approaches 7
3.2.1 Point Sources (ptipm and ptnonipm) 10
3.2.1.1 IPM Sector (ptipm) 10
3.2.1.2 Non-IPM Sector (ptnonipm) 11
3.2.2 Nonpoint Sources (afdust, ag, nonpt) 11
3.2.2.1 Area Fugitive Dust Sector (afdust) 11
3.2.2.2 Agricultural Ammonia Sector (ag) 11
3.2.2.3 Other Nonpoint Sources (nonpt) 12
3.2.4 Day-Specific Point Source Fires (ptfire) 12
3.2.5 Biogenic Sources (beis) 13
3.2.6 Mobile Sources 13
3.2.7 Adjustments to Onroad Mobile Source PM Emissions 14
3.2.8 Onroad Mobile Sources without Adjustments 16
3.2.9 Nonroad Mobile Sources—NMIM-BasedNonroad 16
3.2.10. Nonroad Mobile Sources: Commercial Marine Cl, C2, and Locomotive 16
3.2.11 Nonroad mobile sources: C3 commercial marine 16
3.2.12 Emissions from Canada, Mexico and Offshore Drilling Platforms 17
3.3 Emissions Modeling Summary 18
3.3.1 The SMOKE Modeling System 18
3.3.2 Key Emissions Modeling Settings 18
3.3.3 Spatial Configuration 19
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3.3.4 Chemical Speciation Configuration 19
3.3.4 Temporal Processing Configuration 23
3.3.6 Vertical Allocation of Emissions 23
3.3.7 Emissions Modeling Ancillary Files 23
3.3.7.1 Spatial Allocation Ancillary Files 23
3.3.7.2 Surrogates for U.S. Emissions 23
3.3.7.3 Allocation Method for Airport-Related Sources in the U.S 23
3.3.7.4 Surrogates for Canada and Mexico Emission Inventories 24
3.3.7.5 Chemical Speciation Ancillary Files 24
3.3.7.6 Temporal Allocation Ancillary Files 26
4.0 CMAQ Air Quality Model Estimates 27
4.1 Introduction to the CMAQ Modeling Platform 27
4.1.1 Advantages and Limitations of the CMAQ Air Quality Model 27
4.2 CMAQ Model Version, Inputs and Configuration 29
4.2.1 Model Version 29
4.2.2 Model Domain and Grid Resolution 29
4.2.3 Modeling Period / Ozone Episodes 30
4.2.4 Model Inputs: Emissions, Meteorology and Boundary Conditions 30
4.3 CMAQ Model Performance Evaluation 30
5.0 Bayesian Model-Derived Air Quality Estimates 43
5.1 Introduction 43
5.2 Hierarchical Bayesian Space-Time Modeling System 43
5.2.1 Introduction to the Hierarchical-Bayesian Approach 43
5.2.2 Advantages and Limitations of the Hierarchical-Bayesian Approach 43
5.3 Results for O3 and PM2 5 44
5.4 Overview of HB Model Predictions 44
5.5 Evaluation of HB Model Estimates 46
5.6 Use of EPAHB Model Predictions 50
Appendix A 51
Appendix B 55
Appendix C 171
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List of Figures
Figure 3-1. SMARTFIRE System 13
Figure 3-2. MOVES exhaust temperature adjustment functions 15
Figure 3-3. CMAQ Modeling Domain 19
Figure 3-4. Process of integrating BAFM with VOC for use in VOC Speciation 21
Figure 3-5. Diurnal Profiles for Parking Areas (Pollutants: SO2 andNOx) 25
Figure 4-1. Map of the CMAQ Modeling Domain. The blue box denotes the 12 km national modeling
domain (Same as Figure 3-3.) 28
Figure 4-2. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 ppb over the
period May-September 2008 at monitoring sites in the continental U.S. modeling domain. . 33
Figure 4-3. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 ppb over the
period May-September 2008 at monitoring sites in the continental U.S. modeling domain. . 33
Figure 4-4. Normalized Mean Bias (%) of annual PM25 mass at monitoring sites in the continental U.S.
modeling domain 36
Figure 4-5. Normalized Mean Error (%) of annual PM25 mass at monitoring sites in the continental U.S.
modeling domain 36
Figure 4-6. Normalized Mean Bias (%) of annual Sulfate at monitoring sites in the continental U.S.
modeling domain 37
Figure 4-7. Normalized Mean Error (%) of annual Sulfate at monitoring sites in the continental U.S.
modeling domain 37
Figure 4-8. Normalized Mean Bias (%) of annual Nitrate at monitoring sites in the continental U.S.
modeling domain 38
Figure 4-9. Normalized Mean Error (%) of annual Nitrate at monitoring sites in the continental U.S.
modeling domain 38
Figure 4-10. Normalized Mean Bias (%) of annual Total Nitrate at monitoring sites in the continental U.S.
modeling domain 39
Figure 4-11. Normalized Mean Error (%) of annual Total Nitrate at monitoring sites in the continental
U.S. modeling domain 39
Figure 4-12. Normalized Mean Error (%) of annual Total Nitrate at monitoring sites in the continental
U.S. modeling domain 40
Figure 4-13. Normalized Mean Error (%) of annual Ammonium at monitoring sites in the continental
U.S. modeling domain 40
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Figure 4-14. Normalized Mean Bias (%) of annual Elemental Carbon at monitoring sites in the
continental U.S. modeling domain 41
Figure 4-15. Normalized Mean Error (%) of annual Elemental Carbon at monitoring sites in the
continental U.S. modeling domain 41
Figure 4-16. Normalized Mean Bias (%) of annual Organic Carbon at monitoring sites in the continental
U.S. modeling domain 42
Figure 4-17. Normalized Mean Error (%) of annual Organic Carbon at monitoring sites in the continental
U.S. modeling domain 42
Figure 5-2. HB Prediction (PM25) on July 2, 2002 (12 km grid cells) 46
Figure 5-3. HB Prediction (PM25) Temporally Matches AQS Data and CMAQ Estimates - Note:
Computer_data = CMAQ Output 47
Figure 5-4. FIB Prediction (PM2 5) Compensates When AQS Data is Unavailable on FRM Monitor Non-
Sampling Days - Note: Computer_data = CMAQ Output 47
Figure 5-5. HB Prediction (PM25) Mitigates CMAQ Bias when AQS and CMAQ Values Diverge - Note:
Computer_data = CMAQ Output 48
Figure 5-6. Plot of the Response Surface of PM25 Concentrations as Predicted by the HB Model on a
Specific Monitoring Day in the Northeast U.S., Along With PM25 Measurements on a Specific
Monitoring Day from FRM Monitors in the NAMS/SLAMS Network 48
Figure 5-7. Rotated View of the Response Surface of PM25 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 49
Figure 5-8. Rotated View of the Response Surface of PM25 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 49
Figure 5-9. Fused 36 km O3 Surface for the Continental U.S. (July 26, 2005) 50
Figure C-l. PM25 Monitoring Data and CMAQ Surface (Separately Displayed—White Spheres
Represent Monitor Locations and Associated Concentration Values) 172
Figure C-2. Combined PM2 5 Monitoring Data and CMAQ Surface (Via HBM) 172
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List of Tables
Table 2-1. Ozone Standard 3
Table 2-2. PM25 Standards 4
Table 2-3. Public Health Surveillance Goals and Current Results 6
Table 2-4. Basic Air Quality Indicators 6
Table 3-1. Platform Sectors Used in the Emission 8
Table 3-2. 2008 Continental United States Emissions by Sector (tons/yr in 48 states + D.C.) 9
Table 3-3. 2008 Non-US Emissions by Sector (tons/yr for Canada, Mexico, Offshore) 9
Table 3-5. Regional growth factors used to project 2002 C3 emissions to 2008 17
Table 3-6. HAP emission ratios for generation of HAP emissions from criteria emissions for C3
commercial marine vessels 17
Table 3-8. 2008 Emission Model Species produced by SMOKE using the Carbon Bond 5 (CB05)
Mechanism with Secondary Organic Aerosol (SOA) in CMAQ v4.7 18
Table 3-8. Model Species produced by SMOKE for CB05 with SOAfor CMAQ 4.7 20
Table 3-9. Integration Status of 2008 Benzene, Acetaldehyde, Formaldehyde and Methanol (BAFM)
Species in each Platform Sector 22
Table 4-1. Geographic Information for 12 km Modeling Domain 29
Table 4-2. Vertical layer structure for 2008 WRF and CMAQ simulations (heights are layer top) 31
Table 4-3. Summary of CMAQ 2008 8-Hour Daily Maximum Ozone Model Performance Statistics by
Subregion and by Season 32
Table 4-4. Summary of CMAQ 2008 Annual PM25 Species Model Performance Statistic 35
Table 5-1.HB Model Prediction Example Data File 45
Table 5-2. HB Model Domains for 12-km Applications 45
Table B-la - 2008 Emissions Summary (tons/year), by Species and by Emission Sector/Source 55
Table B-lb - 2008 Emissions Summary (tons/year), by Species, for Canada, Mexico, and Offshore
Emission Sectors 55
Table B-2a 2008 Species Emission by Sector 56
Table B-2b 2008 Species Emission by Sector 56
Table B-2c 2008 Species Emission by Sector 57
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Table B-2d 2008 Species Emission by Sector 57
Table B-3a - 2008 Integrated Planning Model Point Source (ptipm) Emissions by Species
and by US State 59
Table B-3b - 2008 Integrated Planning Model Point Source (ptipm) Emissions by Species
and by US State 60
Table B-3c - 2008 Integrated Planning Model Point Source (ptipm) Emissions by Species
and by US State 61
Table B-3d - 2008 Integrated Planning Model Point Source (ptipm) Emissions by Species
and by US State 62
Table B-2f 2008 Species Emission by Sector 64
Table B-3e - 2008 Integrated Planning Model Point Source (ptipm) Emissions by Species
and by US State 65
Table B-4a - 2008 Integrated Planning Model Non-Point Source (ptnonipm) Emissions by Species
and by US State 67
Table B-4b - 2008 Integrated Planning Model Non-Point Source (ptnonipm) Emissions by Species
and by US State 68
Table B-4c - 2008 Integrated Planning Model Non-Point Source (ptnonipm) Emissions by Species
and by US State 69
Table B-4d - 2008 Integrated Planning Model Non-Point Source (ptnonipm) Emissions by Species
and by US State 71
Table B-4e - 2008 Integrated Planning Model Non-Point Source (ptnonipm) Emissions by Species
and by US State 72
Table B-4f - 2008 Integrated Planning Model Non-Point Source (ptnonipm) Emissions by Species
and by US State 73
Table B-5a - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by
Mexican Federal State 75
Table B-5b - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by
Mexican Federal State 76
Table B-5c - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by
Mexican Federal State 77
Table B-5d - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by
Mexican Federal State 78
Table B-5e - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by
Mexican Federal State 79
Table B-5f - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by
Mexican Federal State 81
Table B-5g - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by
Mexican Federal State 82
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Table B-6a - 2008 Point Source Fire (ptfire) Emissions by Species, by US State 84
Table B-6b - 2008 Point Source Fire (ptfire) Emissions by Species, by US State 85
Table B-6c - 2008 Point Source Fire (ptfire) Emissions by Species, by US State 86
Table B-6d - 2008 Point Source Fire (ptfire) Emissions by Species, by US State 87
Table B-6e - 2008 Point Source Fire (ptfire) Emissions by Species, by US State 88
Table B-6f- 2008 Point Source Fire (ptfire) Emissions by Species, by US State 89
Table B-7a - 2008 Non-Point Source (nonpt) Emissions by Species, by US State 91
Table B-7b - 2008 Non-Point Source (nonpt) Emissions by Species, by US State 92
Table B-7c - 2008 Non-Point Source (nonpt) Emissions by Species, by US State 93
Table B-7d - 2008 Non-Point Source (nonpt) Emissions by Species, by US State 95
Table B-7e - 2008 Non-Point Source (nonpt) Emissions by Species, by US State 96
Table B-7f - 2008 Non-Point Source (nonpt) Emissions by Species, by US State 97
Table B-7g - 2008 Non-Point Source (nonpt) Emissions by Species, by US State 99
Table B-8a - 2008 On Road Non-Temperature Adjusted (on_noadj) Emissions by Species,
by US State 101
Table B-8b - 2008 On Road Non-Temperature Adjusted (on_noadj) Emissions by Species,
by US State 102
Table B-8c - 2008 On Road Non-Temperature Adjusted (on_noadj) Emissions by Species,
by US State 103
Table B-8d - 2008 On Road Non-Temperature Adjusted (on_noadj) Emissions by Species,
by US State 104
Table B-8e - 2008 On Road Non-Temperature Adjusted (on_noadj) Emissions by Species,
by US State 106
Table B-8f - 2008 On Road Non-Temperature Adjusted (on_noadj) Emissions by Species,
by US State 107
Table B-9a - 2008 Non-Road (nonroad) Emissions by Species, by US State 109
Table B-9b - 2008 Non-Road (nonroad) Emissions by Species, by US State 110
Table B-9c - 2008 Non-Road (nonroad) Emissions by Species, by US State Ill
Table B-9d - 2008 Non-Road (nonroad) Emissions by Species, by US State 112
Table B-9e - 2008 Non-Road (nonroad) Emissions by Species, by US State 114
Table B-9f - 2008 Non-Road (nonroad) Emissions by Species, by US State 115
Table B-9g - 2008 Non-Road (nonroad) Emissions by Species, by US State 116
Table B-lOa - 2008 Other Non-Point and Non-Road (othar) Emissions by Species,
by Canadian Province, and by Mexican Federal State 118
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Table B-lOb - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by Canadian Province,
and by Mexican Federal State 119
Table B-lOc - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by Canadian Province,
and by Mexican Federal State 120
Table B-lOd - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by Canadian Province,
and by Mexican Federal State 121
Table B-lOe - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by Canadian Province,
and by Mexican Federal State 122
Table B-lOf - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by
Canadian Province, and by Mexican Federal State 124
Table B-lOg - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by
Canadian Province, and by Mexican Federal State 125
Table B-lOh - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by
Canadian Province, and by Mexican Federal State 126
Table B-lla - 2008 Other On-Road Source (othon) Emissions by Species, by
Canadian Province, and by Mexican Federal State 128
Table B-llb - 2008 Other On-Road Source (othon) Emissions by Species, by
Canadian Province, and by Mexican Federal State 129
Table B-llc - 2008 Other On-Road Source (othon) Emissions by Species, by
Canadian Province, and by Mexican Federal State 130
Table B-lld - 2008 Other On-Road Source (othon) Emissions by Species, by
Canadian Province, and by Mexican Federal State 131
Table B-lle - 2008 Other On-Road Source (othon) Emissions by Species, by
Canadian Province, and by Mexican Federal State 132
Table B-llf - 2008 Other On-Road Source (othon) Emissions by Species, by
Canadian Province, and by Mexican Federal State 133
Table B-llg- 2008 Other On-Road Source (othon) Emissions by Species, by
Canadian Province, and by Mexican Federal State 134
Table B-12a - 2008 Agricultural (ag) Emissions by Species, by US State 136
Table B-12b - 2008 Agricultural (ag) Emissions by Species, by US State 137
Table B-12c - 2008 Agricultural (ag) Emissions by Species, by US State 138
Table B-12d - 2008 Agricultural (ag) Emissions by Species, by US State 139
Table B-12e - 2008 Agricultural (ag) Emissions by Species, by US State 141
Table B-12f-2008 Agricultural (ag) Emissions by Species, by US State 142
Table B-13a - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species,
by US State 144
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Table B-13b - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species,
by US State 145
Table B-13c - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species,
by US State 146
Table B-13c - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species,
by US State 147
Table B-13d - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species,
by US State 149
Table B-13e - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species,
by US State 150
Table B-13f - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species,
by US State 151
Table B-14a - 2008 Area Fugitive Dust (afdust) Emissions by Species, by US State 153
Table B-14b - 2008 Area Fugitive Dust (afdust) Emissions by Species, by US State 154
Table B-14c - 2008 Area Fugitive Dust (afdust) Emissions by Species, by US State 155
Table B-14d - 2008 Area Fugitive Dust (afdust) Emissions by Species, by US State 156
Table B-15a - 2008 C3 Commercial Marine (c3 marine) Emissions by Species, by US State 157
Table B-15b - 2008 C3 Commercial Marine (c3 marine) Emissions by Species, by US State 158
Table B-15d - 2008 C3 Commercial Marine (c3 marine) Emissions by Species, by US State 159
Table B-15e - 2008 C3 Commercial Marine (c3 marine) Emissions by Species, by US State 160
Table B-15f - 2008 C3 Commercial Marine (c3 marine) Emissions by Species, by US State 161
Table B-15g - 2008 C3 Commercial Marine (c3 marine) Emissions by Species, by US State 162
Table B-15h - 2008 C3 Commercial Marine (c3 marine) Emissions by Species, by US State 163
Table B-16a - 2008 Running [vehicle] Exhaust PM (runpm) Emissions by Species, by US State 165
Table B-16b - 2008 Running [vehicle] Exhaust PM (runpm) Emissions by Species, by US State 166
Table B-17a - 2008 Starting [vehicle] Exhaust PM (startpm) Emissions by Species, by US State 168
Table B-17b - 2008 Starting [vehicle] Exhaust PM (startpm) Emissions by Species, by US State 169
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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 2008
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 and Analysis
Division (AMAD), and Environmental Sciences Division
(BSD), in conjunction with OAQPS, work together to
provide air quality monitoring data and model estimates to
the Centers for Disease Control and Prevention (CDC) for
use in their Environmental Public Health Tracking (EPHT)
Network.
CDC's EPHT Network supports linkage of air quality data
with human health outcome data for use by various public
health agencies throughout the U.S. The EPHT Network
Program is 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 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, and academic researchers, schools of
public health, along with 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/EPAMOU, 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
1 Available atwww.cdc.gov/nceh/tracking/epa mou.htm
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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 environmental and public health
professionals to develop environmental health indicators
which provide measures of potential human health impacts.
However, an analysis of the currently available methods for
generating and characterizing air quality estimates that could
be developed and delivered systematically, and which were
also readily available to link with public health surveillance
data, had not been previously attempted. EPA collaborated
with the CDC and state public health agencies in New York,
Maine, and Wisconsin on the Public Health Air Surveillance
Evaluation (PHASE) project to address this issue. The project
focused on generating concentration surfaces for ozone and
PM2 5, which were subsequently linked with asthma and
cardiovascular disease data. Results of this research project
indicated that using a Hierarchical Bayesian approach to
statistically "combine" Community Multiscale Air Quality
(CMAQ) model estimates and air quality monitoring data
documented in EPA's AQS provided better overall estimates
of air quality at locations without monitors than those
obtained through other well-known, statistically-based
estimating techniques (e.g., kriging).
Ambient air quality monitoring data stored in the Air
Quality System (AQS), along with air quality modeling
estimates from CMAQ, can be statistically combined, via a
Hierarchical Bayesian statistical space-time modeling (HBM)
system, to provide air quality estimates (hereafter referred
to as Hierarchical Bayesian-derived air quality estimates).
These Hierarchical Bayesian-derived air quality estimates
serve as well-characterized inputs to the EPHT Network. The
air quality monitor data, CMAQ modeling estimates, and
the Hierarchical Bayesian-derived air quality estimates can
be used to develop meaningful environmental public health
indicators and to link ozone and PM2 5 concentrations with
health outcome data. The Hierarchical Bayesian-derived
air quality estimates are based on EPA's current knowledge
of predicting spatial and temporal variations in pollutant
concentrations derived from multiple sources of information.
EPA is continuing its research in this critical science area
and is implementing this project to establish procedures
for routinely generating the Hierarchical Bayesian-derived
air quality estimates developed in the PHASE project. This
effort will assist EPA in making both ambient air quality
monitoring (raw) data and the Hierarchical Bayesian-derived
air quality estimates available to the CDC EPHT Network
through EPA's Central Data Exchange (CDX) Node on the
Environmental Information Exchange Network.
Because of EPA's expertise related to generation, analysis,
scientific visualization, and reporting of air quality
monitoring data, air quality modeling estimates, and
Hierarchical Bayesian-derived air quality estimates and
associated research, the CDC approached EPA to provide
technical support for incorporating air quality data and
estimates into its EPHT Network. Because the air quality data
generated could be used by EPA to achieve other research
goals related to linking air quality data and health effects
and performing cumulative risk assessments, EPA proposed
an interagency agreement under which each agency would
contribute funding and/or in-kind support to efficiently
leverage the resources of both agencies. The major objective
of this research is to provide data and guidance to CDC to
assist them in tracking estimated population exposure to
ozone and PM2 5; estimating health impacts to individuals and
susceptible subpopulations; guiding public health actions;
and conducting analytical studies linking human health
outcomes and environmental conditions.
This report is divided into five sections and three appendices.
The first major section of the report describes the air quality
data obtained from EPA's nationwide monitoring network
and the importance of the monitoring data in determining
health potential health risks. 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 12-km
grid cells over the 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 (O3)
and paniculate matter (PM2 5). 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
paniculate matter (PM). These standards, called the National
Ambient Air Quality Standards (NAAQS), are designed
to protect public health and the environment. The CAA
established two types of air quality standards. Primary
standards set limits to protect public health, including
the health of "sensitive" populations such as asthmatics,
children, and the elderly. Secondary standards set limits
to protect public welfare, including protection against
decreased visibility, damage to animals, crops, vegetation,
and buildings. The law requires EPA to periodically review
these standards. For more specific information on the
NAAQS, go to www.epa.gov/air/criteria.html. For general
information on the criteria pollutants, go to http://www.epa.
gov/air/urbanair/. When these standards are not met, the area
is designated as a nonattainment area. States must develop
state implementation plans (SIPs) that explain the regulations
and controls it will use to clean up the nonattainment areas.
States with an EPA-approved SIP can request that the
area be redesignated from nonattainment to attainment by
providing three consecutive years of data showing NAAQS
compliance. The state must also provide a maintenance
plan to demonstrate how it will continue to comply with the
NAAQS and demonstrate compliance over a 10-year period,
and what corrective actions it will take should a NAAQS
violation occur after redesignation. EPA must review and
approve the NAAQS compliance data and the maintenance
plan before redesignating the area; thus, a person may live
in an area designated as non-attainment even though no
NAAQS violation has been observed for quite some time. For
more information on designations, go to http://www.epa. gov/
ozonedesignations/ and http://www.epa.gov/pmdesignations.
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.gov/ttn/naaqs/
standards/ozone/s_o3_index.html. The current (as of October
2008) NAAQS for ozone, in place since 1997, is an 8-hour
maximum of 0.075 parts per million [ppm] (for details,
see http://www.epa.gov/ozonedesignationsA. 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:
Parts Per Million:
Measurement—(ppm)
1-Hour Standard
8-Hour Standard
Table 2-1. Ozone Standard
0.12
0.12
0.075
2.1.3 Particulate Matter
PM air pollution is a complex mixture of small and large
particles of varying origin that can contain hundreds of
different chemicals, including cancer-causing agents like
polycyclic aromatic hydrocarbons (PAH), as well as heavy
metals such as arsenic and cadmium. PM air pollution results
from direct emissions of particles as well as particles formed
through chemical transformations of gaseous air pollutants.
The characteristics, sources, and potential health effects
of paniculate matter depend on its source, the season, and
atmospheric conditions.
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As practical convention, PM is divided by size2 into 2 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_25) are referred to as "coarse" or
PMc. Sources of PMc include crushing or grinding operations
and dust from paved or unpaved roads. The distribution of
PM2 5 and PMc, 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
Paniculate 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 March 2012) 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 PM25 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 PM NAAQS was set in 1997 and the current
24-hr PM NAAQS was set in 2006 (for details, see http://
www.epa.gov/air/criteria.html and http://www.epa.gov/oar/
particlepollution/). The EPA quality assurance standards for
PM25 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:
Micrograms Per Cubic Meter:
Measurement - (|ig/m3)
Annual Average
24-Hour Average
Table 2-2. PM25 Standards
1997 2006
15
65
15
35
The measure used to classify PM into sizes is the aerodynamic diameter.
The measurement instruments used for PM are designed and operated to
separate large particles from the smaller particles. For example, the PM2 5
instrument only captures and thus measures particles with an aerodynamic
diameter less than 2.5 micrometers. The EPA method to measure PMc is
designed around taking the mathematical difference between measurements
for PM,,, and PM,,
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.
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 (PM) 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
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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 converted from a mainframe system to a UNIX-
based Oracle system which is easily accessible to users
through the Internet. This system went into production status
in January 2002. Today, state, local, and tribal agencies
submit their data directly to AQS. Registered users may also
retrieve data through the AQS application and through the
use of third-party software such as the Discoverer tool from
Oracle Corporation. For more detailed information about
the AQS database, go to http://www.epa. gov/ttn/airs/airsaqs/
index.htm.
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 PM 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 March
2012) 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 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.
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(1) Air data sets and metadata required for air quality indicators
are available to EPHT state Grantees.
(2) Estimate the linkage or association of PM25 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.
(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.
AQS data is available through state agencies and EPA's AirData and AirExplorer. EPA
and CDC developed an interagency agreement, where EPA provides air quality data
along with HBM modeling data, associated metadata, and technical reports that are
delivered to CDC.
Regular discussions have been held on health-air linked indicators and CDC/HEI/
EPA convened a workshop in January 2008. CDC has collaborated on a health impact
assessment (HIA) with Emory University, EPA and state grantees that can be used to
facilitate greater understanding of these linkages.
Templates and "how to" guides for PM25 and ozone have been developed for routine
indicators. Calculation techniques and presentations for the indicators have been
developed.
Table 2-3. Public Health Surveillance Goals and Current Results
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)
PM9S (daily 24-hr integrated samples by FRM)
Average ambient concentrations of particulate matter (< 2.5 microns in diameter) and compared to annual PM25 NAAQS (by state).
% population exceeding annual PM25 NAAQS (by state).
% of days with PM25 concentration over the daily NAAQS (or other relevant benchmarks (by county and MSA)
Number of person-days with PM25 concentration over the daily NAAQS & other relevant benchmarks (by county and MSA)
Table 2-4. Basic Air Quality Indicators
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.
2.3.1 Rationale for the Air Quality Indicators
The CDC EPHT Network is initially focusing on ozone and
PM25. These air quality indicators are based mainly around
the NAAQS health findings and program-based measures
(measurement, data and analysis methodologies). The
indicators will allow comparisons across space and time
for EPHT actions. They are in the context of health-based
benchmarks. By bringing population into the measures, they
roughly distinguish between potential exposures (at broad
scale).
2.3.2 Air Quality Data Sources
The air quality data will be available 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 2008 Emissions
Data Development
The U.S. EPA developed an air quality modeling platform
based on the 2008 National Emissions Inventory (NEI)
to process year 2008 emission data for this project. This
section provides a summary of the emissions inventory
and emissions modeling techniques applied to Criteria Air
Pollutants (CAPs) and selected Hazardous Air Pollutants
(HAPs). This section also describes the approach and
data used to produce emissions inputs to the air quality
model. The air quality modeling, meteorological inputs and
boundary conditions are described in a separate section.
Some techniques for the 2008 platform have been carried
forward from the 2005v4 platform. . A complete description
of the 2005v4 platform is available in "Technical Support
Document: Preparation of Emissions Inventories for the
Version 4, 2005-based Platform, U.S. EPA, Research Triangle
Park, NC 27711, July 2010" (available from http://www.epa.
gov/ttn/chief/emch/index.html#2005).
The Community Multiscale Air Quality (CMAQ) model
(http://www.epa.gov/AMD/CMAO/) is used to model ozone
(O3) and paniculate matter (PM) for this project. CMAQ
requires hourly and gridded emissions of the following
inventory pollutants: carbon monoxide (CO),nitrogen oxides
(NOX), volatile organic compounds (VOC), sulfur dioxide
(SO2), ammonia (NH3), paniculate matter less than or equal
tolO microns (PM10), and individual component species for
paniculate matter less than or equal to 2.5 microns (PM25).
In addition, the CMAQ CB05 with chlorine chemistry used
here allows for explicit treatment of the VOC HAPs benzene,
acetaldehyde, formaldehyde and methanol (BAFM) and
includes anthropogenic HAP emissions of HC1 and Cl.
The effort to create the 2008 emission inputs for this
study included development of emission inventories for a
2008 model evaluation case, and application of emissions
modeling tools to convert the inventories into the format
and resolution needed by CMAQ. The 2008 evaluation case
uses 2008-specific fire and 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.
SMOKE version 2.7 was used to create emissions files for a
12-km national grid. Additional information about SMOKE
is available from http://www.smoke-model.org.
This summary contains two additional sections. Section 3.2
describes the inventories input to SMOKE and the ancillary
files used along with the emission inventories. Section 3.3
describes the emissions modeling performed to convert the
inventories into the format and resolution needed by CMAQ.
3.2 2008 Emission Inventories and
Approaches
This section describes the emissions inventories created for
input to SMOKE. The primary basis for the emission inputs
for this project is the 2008 NEI, Version 1.7. This version of
the NEI includes emissions of CO, NOx, VOC, SO2, NH3,
PM10, PM25, and selected HAPs. The modeling platform
utilizes select HAPs: chlorine, HC1, benzene, acetaldehyde,
formaldehyde, and methanol.
The 2008 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.
Electronic copies of inventories similar to those used for
this project are available at the 2008 section emissions
modeling clearinghouse: http://www.epa.gov/ttn/chief/
emch/index.html#2008. Documentation for the 2008 NEI is
available at http://www.epa.gov/ttn/chief/net/2008inventory.
html#inventorydoc. For inventories outside of the United
States, including Canada, Mexico and offshore emissions, the
latest available base year inventories were used.
For purposes of preparing the CMAQ- ready emissions,
the NEI is split into several additional emissions modeling
"platform" sectors; and biogenic emissions are added along
with emissions from other sources other than the NEI,
such as the Canadian, Mexican, and offshore inventories.
The significance of an emissions sector for the emissions
modeling platform is that it is run through all of the
SMOKE programs, except the final merge, independently
from the other sectors. The final merge program called
Mrggrid combines the sector- specific gridded, speciated
and temporalized emissions to create the final CMAQ-ready
emission inputs.
Table 3-1 presents the sectors in the emissions modeling
platform used to develop 2008 emissions for this project.
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 2008 emission summaries for the U.S.
anthropogenic sectors are shown in Table 3-2 (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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2005v4 Platform
Sector
IPM sector: ptipm
Non-IPM sector:
Point source fire sector:
ptfire
Agricultural sector: ag
Area fugitive dust sector:
afdust
Remaining nonpoint
sector: nonpt
Nonroad sector:
nonroad
locomotive, and non-C3
commercial marine:
aim no c3
C3 commercial marine:
seca c3
Onroad California, I
based, and MOVES
sources not subject to
temperature adjustments:
on_noadj
Onroad cold-start
gasoline exhaust
mode vehicle from
MOVES subject to
temperature adjustments:
on_moves_startpm
Onroad running gasoline
exhaust mode vehicle
from MOVES subject to
temperature adjustments:
on_moves_runpm
Biogenic: beis
Other point sources not
from the NEI: othpt
Other nonpoint and
nonroad not from the
NEI: othar
Other onroad sources not
from the NEI: othon
2005
NEI
Sector
Point
Point
Fires
Nonpoint
Nonpoint
Nonpoint
Mobile:
Nonroad
Mobile:
Nonroad
Mobile:
Nonroad
Mobile:
onroad
Mobile:
onroad
Mobile:
onroad
N/A
N/A
N/A
N/A
Description and resolution of the data input to SMOKE
2005v2 NEI point source EGUs mapped to the Integrated Planning Model (IPM) model using year 2008
continuous emission monitoring (CEM) NOX and S02emissions from the National Electric Energy Database
System (NEEDS, 2006 version 3.02) database. Hourly files for CEM sources are included for the 2008
evaluation case used for this project. Day-specific emissions for non-CEM sources are year 2005 NEI-based
estimates and were created for input into SMOKE.
Year 2005 emissions for all 2005v2 NEI point source records not matched to the ptipm sector, annual resolution.
Includes all aircraft emissions.
Point source day-specific wildfires and prescribed fires for 2008.
Primarily 2002 NEI nonpoint NH3 emissions from livestock and fertilizer application, county and annual
resolution.
Primarily 2002 NEI nonpoint PM10 and PM25 from fugitive dust sources (e.g., building construction, road
construction, paved roads, unpaved roads, agricultural dust), county/annual resolution.
Primarily 2002 NEI nonpoint sources not otherwise included in other SMOKE sectors, county and annual
resolution. Also includes updated Residential Wood Combustion emissions and year 2005 non-California
Western Regional Air Partnership (WRAP) oil and gas "Phase II" inventory.
Year 2008 monthly nonroad emissions from the National Mobile Inventory Model (NMIM) using NONROAD2005
version nr05c-BondBase for all states except California. Monthly emissions for California created from annual
emissions submitted by the California Air Resources Board (GARB) for the 2005v2 NEI linearly-interpolated with
year 2009 emissions to create year 2008.
Year 2002 non-rail maintenance locomotives, and category 1 and category 2 commercial marine vessel (CMV)
emissions sources, county and annual resolution. Unlike prior platforms, aircraft emissions are now included in
the ptnonipm sector and category 3 CMV emissions are now contained in the seca_c3 sector
Annual point source formatted year 2008 category 3 (C3) CMV emissions, developed for the EPA rule called
"Control of Emissions from New Marine Compression-Ignition Engines at or Above 30 Liters per Cylinder",
usually described as the Area (EGA) study, originally called S02 ("S") EGA.
Year 2008 emissions consisting of two, monthly, county-level components:
MOVES2010-based (December 2009) except for California and gasoline exhaust PM.
California onroad, created using annual EMFAC-based emissions submitted by GARB for the 2005v2 NEI,
linearly-interpolated with year 2009 EMFAC-based submissions.
Year 2008 monthly, county-level MOVES2010-based onroad gasoline emissions subject to temperature
adjustments. Limited to exhaust mode only for PM species. California emissions not included. This sector is
limited to cold start mode emissions that contain different temperature adjustment curves from running exhaust
(see on_moves_runpm sector).
Year 2008 monthly, county-level MOVES2010-based onroad gasoline emissions subject to temperature
adjustments. Limited to exhaust mode only for PM species. California emissions not included. This sector is
limited to running mode emissions that contain different temperature adjustment curves from cold start exhaust
(see on_moves_startpm sector).
Hour-specific, grid cell-specific emissions generated from the BEIS3.14 model -includes emissions in Canada
and Mexico.
Point sources from Canada's 2006 inventory and Mexico's Phase III 1999 inventory, annual resolution. Also
includes annual U.S. offshore oil 2005v2 NEI point source emissions.
Annual year 2006 Canada (province resolution) and year 1999 Mexico Phase I
and nonroad mobile inventories, annual resolution.
(municipio resolution) nonpoint
Year 2006 Canada (province resolution) and year 1999 Mexico Phase III (municipio resolution) onroad mobile
inventories, annual resolution.
Table 3-1. Platform Sectors Used in the Emission
-------
alm_no_c3
nonpt
nonroad
onroad
ptfire
*,
ptnonipm
seca_c3
Con.US Total
270,007
7,376,314
17,902,244
37,903,749
33,600,784
578,111
3,222,221
58,225
100,911,655
3,251,990
773
134,080
2,042
163,735
550,283
20,997
159,003
1,924,925
1,683,490
2,010,786
8,001,667
397,094
3,360,926
2,247,228
688,087
59,366
56,687
4,282,903 20,314,203
Table 3-2. 2008 Continental United States Emissions by Sector (tons/yr in 48 states + B.C.)
67,690
Country &
Sector
Canada othar
Canada othon
Canada othpt
Canada Subtotal
Mexico othar
Mexico othon
Mexico othpt
Mexico Subtotal
Offshore othpt
Offshore seca_c3
2008 TOTAL
3,747,987
4,514,002
1,147,801
9,409,790
350,557
1,066,589
68,422
,485,567
89,800
40,377
11,025,535
537,835
21,810
21,138
580,784
254,600
98
0
256,498
0
0
837,282
718,996
537,665
861,223
2,117,883
171,099
110,203
224,202
505,505
82,571
490,149
3,196,108
1,421,910
15,002
117,254
1,554,167
75,556
5,151
97,146
177,854
839
40,483
1,773,342
97,652
1,332,559
308,318
448,629
2,089,507
429,264
152,265
65,273
646,802
53,399
17,176
2,806,884
* VOC is approximated from a sum of speciated VOC within the modeling domain
Table 3-3. 2008 Non-US Emissions by Sector (tons/yr for Canada, Mexico, Offshore)
-------
3.2.1 Point Sources (ptipm andptnonipm)
Point sources are sources of emissions for which specific
geographic coordinates (e.g., latitude/longitude) are specified,
as in the case of an individual facility. A facility may have
multiple emission points, which may be characterized as
units such as boilers, reactors, spray booths, kilns, etc. A unit
may have multiple processes (e.g., a boiler that sometimes
burns residual oil and sometimes burns natural gas). Note
that this section describes only NEI point sources within the
contiguous U.S.. The offshore oil (othpt sector), fires (ptfire)
and category 3 CMV emissions (cSmarine sector) are point
source formatted inventories discussed later in this section.
Full documentation for the development of the 2008 NEI
(EPA, 2012), is posted at: http://www.epa. gov/ttn/chief/
net/2008inventory.html#inventorydoc
After removing offshore oil platforms into the othpt sector,
two platform sectors for input into SMOKE were created
from the remaining 2008 NEI point sources: the Integrated
Planning Model (IPM) sector (ptipm) and the non-IPM sector
(ptnonipm). This split facilitates the use of different SMOKE
temporal processing and future year projection techniques
for these sectors. The inventory pollutants processed through
SMOKE for both ptipm and ptnonipm sectors were: CO,
NOx, VOC, SO2, NH3, PM10, PM25, HC1 and Cl. Inventory
BAFM emissions from these sectors were not used and
instead the VOC was speciated without any integration of
VOC HAPs (integration is discussed in detail in Sections.3.4.
In the 2008 model evaluation case used in this study, for
ptipm sector sources with CEM data that could be matched
to the NEI, 2008 hourly SO2 and NOx emissions were used
alongside annual emissions of all other pollutants. The hourly
electric generating unit (EGU) emissions were obtained for
SO2 and NOx emissions and heat input from EPA's Acid Rain
Program. This data also contained heat input, which was used
to allocate the annual emissions for other pollutants (e.g.,
VOC, PM2 5, HC1) to hourly values. For unmatched EGU
unit sources, annual emissions were temporalized to days
using multi-year averages and to hours using state-specific
averages.
The Non-EGU Stationary Point Sources (ptnonipm)
emissions were provided to SMOKE as annual emissions.
The emissions were developed as follows:
a. 2008 CAP and HAP were provided by States, locals and tribes
under the Consolidated Emissions Reporting Rule
b. EPA corrected known issues and filled PM data gaps.
c. EPA added HAP data from the Toxic Release Inventory (TRI)
where it was not provided by states/locals.
d. EPA provided data for airports and rail yards.
e. Off-shore platform data was added from Mineral Management
Services (MMS).
The changes made to the 2008 NEI point sources prior to
modeling are as follows:
• The tribal data, which do not use state/county Federal
Information Processing Standards (FIPS) codes in
the NEI, but rather use the tribal code, were assigned
a state/county FIPS code of 88XXX, where XXX is
the3-digit tribal code in the NEI. This change was made
because SMOKE requires the state/county FIPS code.
Stack parameters for some point sources were
defaulted when modeling in SMOKE. SMOKE uses
an ancillary file, called the PSTK file, which provides
default stack parameters by SCC code to either gap fill
stack parameters if they are missing in the NEI or to
correct stack parameters if they are outside the ranges
specified.
3.2.1.1 IPM Sector (ptipm)
The ptipm sector contains emissions from EGUs in the 2008
NEI point inventory that could be matched to the units found
in the NEEDS database, version 4.10 (http://www.epa.gov/
airmarkets/progsregs/epa-ipm/index.html). also used by IPM
version 4.10. IPM provides future year emission inventories
for the universe of EGUs contained in the NEEDS database.
As described below, matching with NEEDS was done (1)
to provide consistency between the 2008 EGU sources and
future year EGU emissions for sources which are forecasted
by IPM, and (2) to avoid double counting in projecting point
source emissions.
The 2008 NEI point source inventory contains emissions
estimates for both EGU and non-EGU sources. When future
years are modeled, IPM is used to predict the future year
emissions for the EGU sources. The remaining non-EGU
point sources are projected by applying projection and
control factors to the base year emissions. It was therefore
necessary to identify and separate into two sectors: (1)
sources that are projected via IPM (i.e., the "ptipm" sector)
and (2) sources that are not (i.e., "the "ptnonipm" sector).
This procedure prevents double-counting or dropping
significant emissions when creating future-year emissions.
The two sectors are modeled separately in the base year as
well as the future years.
A primary reason the ptipm sources were separated from
the other sources was due to the difference in the temporal
resolution of the data input to SMOKE. The ptipm sector
uses the available hourly CEM data via a method first
implemented in the 2002 platform and still used for the
2008 platform. A great deal of detailed work was performed
to match units in the 2005 NEI with units in the NEEDS
database available at that time so the CEM data could be
used. The information on the NEEDS matches was carried
forward into the 2008 platform by loading them into the
Emissions Inventory System (EIS) and writing them into the
modeling files. Hourly CEM data for 2008 were obtained
from the CAMD Data and Maps website3. For sources and
pollutants with CEM data, the actual year 2008 hourly CEM
data were used.
The SMOKE modeling system matches the ORIS Facility
and Boiler IDs in the NEI SMOKE-ready file to the same
fields in the CEM data, thereby allowing the hourly SO2 and
NOx CEM emissions to be read directly from the CEM data
file. The heat input from the hourly CEM data was used to
allocate the NEI annual values to hourly values for all other
pollutants from CEM sources, because CEMs are not used to
measure emissions of these pollutants.
http://camddataandmaps.epa. gov/gdm/index.cfm?fuseaction=emissions.
wizard
-------
For sources not matching the CEM data ("non-CEM"
sources), daily emissions were computed from the NEI
annual emissions using a structured query language (SQL)
program and state-average CEM data. To allocate annual
emissions to each month, state-specific, three-year averages
of 2006-2008 CEM data were created. These average annual-
to-month factors were assigned to non-CEM sources by
state. To allocate the monthly emissions to each day, the
2008 CEM data were used to compute state-specific month-
to-day factors, which were then averaged across all units
in each state. The resulting daily emissions were input into
SMOKE. The daily-to-hourly allocation was performed in
SMOKE using diurnal profiles. The development of these
diurnal ptipm-specific profiles, considered ancillary data for
SMOKE, is described in a later section.
3.2.1.2 Non-IPM Sector (ptnonipm)
The non-IPM (ptnonipm) sector contains all 2008 NEI point
sources not included in the IPM (ptipm) sector4 except for the
offshore oil and day-specific fire emissions. The ptnonipm
sector contains a small amount of fugitive dust PM emissions
from vehicular traffic on paved or unpaved roads at industrial
facilities or coal handling at coal mines.
For some geographic areas, some of the sources in the
ptnonipm sector belong to source categories that are
contained in other sectors. This occurs in the inventory when
states, tribes or local programs report certain inventory
emissions as point sources because they have specific
geographic coordinates for these sources. They may use point
source SCCs (8-digit) or they may use non- point, onroad
or nonroad (10-digit) SCCs. In the 2008 NEI, examples of
these types of sources include: aircraft and ground support
emissions, livestock (i.e., cattle feedlots) in California, and
rail yards.
Some adjustments were made to the 2008 NEI point
inventory prior to its use in modeling. These include:
• Removing sources with state county codes ending in
'777'. These are used for 'portable' point sources like
asphalt plants.
• Removing sources with SCCs not typically used for
modeling.
• Adjusting latitude-longitude coordinates for sources
identified to be substantially outside the county in
which they reside.
3.2.2 Nonpoint Sources (afdust, ag, nonpt)
Documentation for the nonpoint 2008 NEI is available
at http://www.epa.gov/ttn/chief/net/2008inventory.html
inventory doc. Prior to modeling with the nonpoint portion
of the 2008 NEI, it was divided into the following sectors for
which the data is processed in consistent ways: area fugitive
dust (afdust), agricultural ammonia (ag), and the other
nonpoint sources (nonpt). The nonpoint tribal-submitted
4 Except for the offshore oil and day-specific point source fire emissions data
which are included in separate sectors, as discussed in sections 2.6 and
2.3.1, respectively.
emissions were removed to prevent possible double counting
with the county-level emissions. Because the tribal nonpoint
emissions are small, these omissions should not impact
results at the 12-km scale used for modeling. This omission
also eliminated the need to develop costly spatial surrogate
data to allocate tribal data to grid cells during the SMOKE
processing.
In the rest of this section, each of the platform sectors into
which the 2008 nonpoint NEI was divided is described, as are
any changes made to these data.
3.2.2.1 Area Fugitive Dust Sector (afdust)
The area-source fugitive dust (afdust) sector contains PM
emission estimates for 2008 NEI nonpoint SCCs identified
by EPA staff as fugitive dust sources. Categories included
in this sector are paved roads, unpaved roads and airstrips,
construction (residential, industrial, road and total),
agriculture production and all of the mining 10-digit SCCs
beginning with the digits "2325." It does not include fugitive
dust from grain elevators because these are elevated point
sources. 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/dustfractions/tf_scc_list2002nei_v2.xls. However, not
all of the SCCs in this file are present in the 2008 NEI. Note
that for this project, the fugitive dust emissions submitted by
Kansas were replaced by EPA-estimated values due to a error
in Kansas' data identified during data quality assurance.
This sector is separated from other nonpoint sectors to make
it easier to apply a "transport fraction" that reduces emissions
to reflect observed diminished transport from these sources
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. For
this project, the transport fraction was computed dynamically
for each grid cell as a function of the land use fraction and
the hourly soil moisture. Dust transport was prevented when
there was snow cover or when the surface soil layer was wet.
For more information on this approach, see http://www.epa.
gov/ttn/chief/conference/eil9/session9/pouliot.pdf.
3.2.2.2 Agricultural Ammonia Sector (ag)
The agricultural NH3 "ag" sector is comprised of livestock
and agricultural fertilizer application emissions from the
nonpoint sector of the 2008 NEI. The livestock and fertilizer
emissions were extracted based on SCC. The "ag" sector
includes all of the NH3 emissions from fertilizer contained
in the NEI. However, the "ag" sector does not include all of
the livestock ammonia emissions, as there are also a very
small amount of NH3 emissions from feedlotiivestock in the
point source inventory. Emissions were not included in the
nonpoint ag inventory for counties for which they were in
-------
the point source inventory. Therefore, no double counting
occurred. Most of the point source livestock NH3 emissions
were reported by California.
3.2.2.3 Other Nonpoint Sources (nonpt)
Nonpoint sources that were not subdivided into the afdust,
ag or nonpt sectors were assigned to the "nonpt" sector.
In preparing the nonpt sector, catastrophic releases were
excluded since these emissions were dominated by tire
burning, which is an episodic, location-specific emissions
category. Tire burning accounts for significant emissions of
paniculate matter in some parts of the country. Because such
sources are reported by a very small number of states, and are
inventoried as county/annual totals without the information
needed to temporally and spatially allocate the emissions to
the time and location where the event occurred, catastrophic
releases were excluded.
The nonpt sector includes emission estimates for Portable
Fuel Containers (PFCs), also known as "gas cans." The PFC
inventory consists of five distinct sources of PFC emissions,
further distinguished by residential or commercial use. The
five sources are: (1) displacement of the vapor within the can;
(2) spillage of gasoline while filling the can; (3) spillage of
gasoline during transport; (4) emissions due to evaporation
(i.e., diurnal emissions); and (5) emissions due to permeation.
Note that spillage and vapor displacement associated with
using PFCs to refuel nonroad equipment are included in the
nonroad inventory.
3.2.4 Day-Specific Point Source Fires (ptfire)
Wildfire and prescribed burning emissions are contained in
the ptfire sector. The ptfire sector has emissions provided
at geographic coordinates (point locations) and has daily
estimates of the emissions from each fires value. The ptfire
sector for the 2008 Platform excludes agricultural burning
and other open burning sources, which are included in the
nonpt sector. The agricultural burning and other open burning
sources are in the nonpt sector because these categories
were not factored into the development of the ptfire sector.
Additionally, their year-to-year impacts are not as variable as
wildfires and non-agricultural prescribed/managed burns.
The ptfire sector includes a satellite derived latitude/longitude
of the fire's origin and other parameters associated with the
emissions such as acres burned and fuel load, which allow
estimation of plume rise. Note that agricultural burning is
not included in the ptfire sector but is included in the nonpt
sector.
The point source day-specific emission estimates for 2008
fires rely on Sonoma Technology, Inc. 's Satellite Mapping
Automated Reanalysis Tool for Fire Incident Reconciliation
(SMARTFIRE) system (Sullivan, et al., 2008). The BlueSky
Smoke Modeling Framework and SMARTFIRE were applied
to develop day-specific wildland fire emissions for the
continental United States. Using ICS-209 reports and satellite
fire data, SMARTFIRE classifies each fire as either a wildfire
(WF), wildland fire use (WFU), prescribed burn (RX), or
unclassified.
Figure 3-1 shows a functional diagram of the SMARTFIRE
process. SMARTFIRE involves the use of the National
Oceanic and Atmospheric Administration's (NOAA's) Hazard
Mapping System (HMS) fire location information as input
combined with CONSUMEvS.O (Joint Fire Science Program,
2009) and the Fuel Characteristic Classification System
(FCCS) fuel- loading database to estimate fire emissions
from wildfires and prescribed burns on a daily basis.
The SMARTFIRE system of reconciliation with ICS-209
reports is described in an Air and Waste Management
Association report (Raffuse, et al., 2008). Once the fire
reconciliation process is completed, the emissions are
calculated using the U.S. Forest Service's CONSUMERS.0
fuel consumption model and the FCCS fuel-loading
database in the Bluesky Framework (Ottmar, et. al.,
2007),The detection of fires with this method is satellite-
based. Additional sources of information used in the fire
classification process included MODIS satellite and fuel
moistures derived from fire weather observational data.
The activity data and other information were used within the
BlueSky Framework to model vegetation distribution, fuel
consumption, and emission rates, respectively. Latitude and
longitude locations were incorporated as a post processing
step. The method to classify fires as WF, WFU, RX (FCCS
> 0), and unclassified (FCCS > 0) involves the reconciliation
of ICS-209 reports (Incident Status Summary Reports)
with satellite-based fire detections to determine spatial and
temporal information about the fires.
The ICS-209 reports for each large wildfire are created daily
to enable fire incident commanders to track the status and
resources assigned to each large fire (100 acre timber fire or
300 acre rangeland fire).Note that the distinction between
wildfire and prescribed burn is not as precise as with ground-
based methods. The fire size was based on the number of
satellite pixels and a nominal fire size of 100 acres/pixel was
assumed for a significant number of fire detections when
the first detections were not matched to ICS 209 reports, so
the fire size information is not as precise as ground-based
methods.
Because the HMS satellite product from NOAA is based
on daily detections, the emission inventory represents a
time-integrated emission estimate. For example, a large
smoldering fire will show up on satellite for many days and
would count as acres burned on a daily basis; whereas a
ground-based method would count the area burned only once
even it burns over many days. Additional references for this
method are provided in (McKenzie, et al., 2007), (Ottmar, et
al., 2003), (Ottmar, et al., 2006), and (Anderson et al., 2004).
The SMOKE-ready "ORL" inventory files created from the
raw daily fires contain both CAPs and HAPs. The B AFM
HAP emissions from the inventory were obtained using
VOC speciation profiles (i.e., a "no-integrate noHAP" use
case), model-species emissions from vegetation and soils.
It estimates CO, VOC, and NOX emissions for the U.S.,
Mexico, and Canada. The BEIS3.14 model is described
further in: http://www.cmascenter.org/conference/2008/
slides/pouliot tale two cmasOS.ppt
-------
3.2.5 Biogenic Sources (beis)
For CMAQ, biogenic emissions were computed based on
2008 meteorology data using the BEIS3.14 model within
SMOKE. The BEIS3.14 model creates gridded, hourly,
The inputs to BEIS include:
• Temperature data at 2 meters from the CMAQ
meteorological input files,
• Land-use data from the Biogenic Emissions Landuse
Database, version 3 (BELD3) that provides data on the
230 vegetation classes at 1-km resolution over most of
North America.
3.2.6 Mobile Sources (on_noadj, on_moves_runpm, on_
moves_startpm, nonroad, clc2rail, c3marine)
The 2008 onroad emissions are broken out into three sectors:
(1) "on_moves_startpm"; (2) "on_moves_ runpm"; and (3)
"on_noadj". Aircraft emissions are in the nonEGU point
inventory. The locomotive and commercial marine emissions
are divided into two sectors: "clc2rail" and "cSmarine",
and the "nonroad" sector contains the remaining nonroad
emissions. NMDVI was used to compute emissions for the
nonroad sectors. NMIM creates nonroad emissions on a
month-specific basis that accounts for temperature, fuel
types, and other variables that vary by month.
SMARTFIRE System
Functional Diagram
Figure 3-1. SMARTFIRE System
-------
Onroad emissions were computed using MOVES2010 (see
http://www.epa.gov/otaq/models/moves. MOVES2010 was
used to create 2008 emissions by state and month and these
emissions were then allocated to counties using NMIM-based
county-level data. The reason for the state resolution was
due to run-time issues that made a county-specific MOVES
run for the nation infeasible.
The 2008 NMDVI nonroad emissions were generated using
updated activity (fuels, vehicle population, etc) data, but are
otherwise similar in methodology to those generated for the
2005 NEI. Detailed inventory documentation for the 2008
NEI nonroad sectors is available at http://www.epa.gov/
ttn/chief/net/2008inventory.html#inventorydoc. Note that
the 2008 NEI includes state-submitted emissions data for
nonroad, but the modeling performed for this platform does
not incorporate state-submitted emissions for the onroad or
nonroad sectors.
The residual fuel commercial marine vessel (CMV),
also referred to as Category 3 (C3), consists of a set
of approximately 4-km resolution point source format
emissions; these are modeled separately as point sources in
the "cSmarine" sector, and were projected from year 2002 to
year 2008 using OTAQ-supplied pollutant-specific growth
factors.
With the exception of the c3 marine point source-formatted
sector, the mobile sectors are at county and SCC resolution.
Tribal data from the clc2rail sector have been dropped
because spatial surrogate data is not available and the
emissions are small. Also, NMIM and MOVES do not
generate tribal data.
All mobile sectors that have benzene, acetaldehyde,
formaldehyde or methanol present in the inventory data, use
VOC "integration" of BAFM for input into the air quality
model. A few categories of nonroad sources (CNG and
LPG-fueled equipment) do not have BAFM and therefore
utilize the "no-integrate", "no-hap-use" case which means
VOC from these sources is speciated to provide BAFM.
3.2.7 Adjustments to Onroad Mobile Source PM Emissions
(on_moves_runpm, on_moves_startpm)
The on_moves_rupm and on_moves_startpm sectors contain
MOVES2010 emissions for PM for onroad gasoline cold-
start exhaust. These emissions (and the on_moves_runpm
sector discussed in the next section) are processed separately
from the remainder of the onroad mobile emissions because
they are subject to hourly temperature adjustments, and
these temperature adjustments are different for cold-start
and running exhaust modes. Figure 3-2 shows how PM
emissions increase with colder temperatures and how start
exhaust emissions increase more than running exhaust
emissions.
Temperature adjustments were applied to account for the
strong sensitivity of PM exhaust emissions to temperatures
below 72 °F. Because it was not feasible to run MOVES
for all of the gridded, hourly temperatures needed for
modeling, emissions of PM exhaust at 72 °F were created
and temperature adjustments applied after the emissions
were spatially and temporally allocated. The PM adjustments
differed for starting versus running exhaust; and were applied
to gridded, hourly intermediate files using the gridded hourly
temperature data input to the CMAQ model. One result of
this approach is that inventory summaries based on the raw
SMOKE inputs for these sectors are not consistent with the
final modeled emissions because they do not include the
temperature adjustments. As a result, the post-processing for
temperature adjustments included computing the adjusted
emissions totals at state, county, and month resolution to use
for summaries.
The MOVES outputs required additional processing to
develop county-level monthly ORL files for input to
SMOKE. As stated earlier, the spatial resolution of the
MOVES data was at the state level and these data were
allocated to county level prior to input into SMOKE. In
addition, the exhaust PM emissions from MOVES were
partially speciated. To retain the speciated elemental carbon
and sulfate emissions from MOVES, the speciation step that
is usually done in SMOKE was performed prior to SMOKE,
and it was modified to allow the temperature adjustments to
be done only on the species affected by temperature.
Finally, because the start emissions were broken out
separately from running exhaust emissions, they were
assigned to new SCCs (urban and rural parking areas) that
allowed for the appropriate spatial and temporal profiles to be
applied in SMOKE.
A list of the procedures performed to prepare the MOVES
data for input into SMOKE is provided below.
i. State-level emissions were allocated to counties using
state-county emission ratios by SCC, pollutant, and
emissions mode (e.g., evaporative, exhaust) for each
month. The ratios were computed using NMIM 2008
data.
ii. Start and run emissions were assigned to urban
and rural SCCs based on the county-level ratio of
emissions from urban versus rural local roads from
the NMIM onroad gasoline data. For example, the
LDGV start emissions in the state-total MOVES
data (assigned SCC 2201001000) were split into
urban (2201001370) and rural (2201001350) based
on the ratio of LDGV urban (2201001330) and rural
(2201001210) local roads.
-------
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10
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V
•Run Exhaust
•Start Exhaust
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-20 -10
11
i
21
31
41
51
61
71
Temperature (°F)
Figure 3-2. MOVES exhaust temperature adjustment functions.
iii. MOVES-based PM species at 72 °F were converted
to SMOKE-ready PM species. The SMOKE-ready
species are listed below and the speciation technique
used to obtain the SMOKE-ready species is further
discussed in Appendix B of the 2005v4 emissions
modeling platform documentation. Species subject to
temperature adjustment below 72 °F include "_72" in
their names.
• PEC_72: unchanged from PM25EC.
• POC_72: modified PM25OC to remove metals,
PNO3 (computed from MOVES-based PM25EC),
NH4 (computed from MOVES-based PM25SO4
and PNO3), and MOVES-based PM25SO4.
• PSO4: unchanged from PM25SO4.
• PNO3: computed from MOVES-based PM25EC.
• OTHER: sum of computed metals (fraction of
MOVES-based PM25EC) and NH4 (computed from
PNO3andPSO4).
• PMFINE_72: Computed from OTHER and fraction
ofPOC_72.
• PMC_72: Computed as fraction of sum of
PMFINE_72, PEC_72, POC_72, PSO4, and PNO3.
The result of these preprocessing steps is that SMOKE-
ready PM emissions do not exactly match what MOVES
provides. The emissions are conserved during allocation from
the state to county, and from the generic total "start" SCCs
to the two new parking SCCs that end in "350" and"370".
PEC and PSO4 components of PM2 5 emissions are also
conserved as they are simply renamed from the MOVES
specie "PM25EC". However, as seen above, POC, PNO3,
and PMFINE components involve multiplying the MOVES
PM species by components of an onroad gasoline exhaust
speciation profile described in Appendix B of the 2005v4
platform documentation.
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3.2.8 Onroad Mobile Sources without Adjustments
(on_noadj)
The on_noadj sector consists of the remaining onroad
mobile emissions not covered by the on_moves_startpm and
on_ moves_runpm sectors. These monthly MOVES-based
emissions were not temperature adjusted. MOVES outputs
included emissions for the following pollutants and process
combinations:
a. Diesel Exhaust: VOC, NOx, NO, NO2, SO2,
PM25, PM10, NH3, CO, 1,3-butadiene (106990),
acetaldehyde (75070), acrolein (107028), benzene
(71432), and formaldehyde (50000)
b. Gasoline Exhaust: VOC, NOx, NO, NO2, SO2,
NH3, CO, 1,3-butadiene (106990), acetaldehyde
(75070), acrolein (107028), benzene (71432), and
formaldehyde (50000)
c. Evaporative: Non-refueling VOC and benzene
d. Brake and tire wear: Total (not speciated) PM2 5 and
PM10 from gasoline and diesel vehicles
Start and running mode exhaust MOVES emissions for
pollutants other than PM do not require the same intermediate
temperature adjustments and can therefore be processed
with the remaining "no adjust" onroad mobile emissions.
These emissions contain both running and parking sources
and are pre-processed from state-level to county-level much
like the on_moves_startpm and on_moves_runpm sectors
already discussed. The preprocessing for these emissions did
not require species calculations because the raw MOVES
emissions translated directly to SMOKE inventory species.
Note that HONO was computed as a function of MOVES
NOx and NO and NO2 were recomputed so that the total of
the three species was equal to NOx from MOVES. Also,
some pollutants output from MOVES are not included in
this modeling, such as ethanol and CO2. The remainder of
this section discusses the pre-processing required to create
monthly ORL files for the on_noadj sector.
Prior to processing with SMOKE, emissions were converted
from monthly totals to monthly average-day based the on
number of days in each month. Furthermore, this sector
includes exhaust, evaporative, brake wear and tire wear
emissions from onroad sources. This allowed the use of
speciation profiles that are specific to each of these processes.
For this project, the 2008 VMT database was based on
2002 VMT grown to 2008 based on Federal Highway
Administration (FWHA) data, unless state-provided VMT
was available.
Onroad refueling emissions for this 2008 platform are
included in the nonpt and ptnonipm sectors from the 2008
NEI as a combination of state-submitted, where available,
and EPA estimated values.
3.2.9 NonroadMobile Sources—NMIM-Based
Nonroad(nonroad)
The nonroad sector includes monthly exhaust, evaporative
and refueling emissions from nonroad engines (not including
commercial marine, aircraft, and locomotives) derived
from NMIM. The NMIM configuration relied on the version
of the NONROAD2005 model (NR05c-BondBase) used for
the marine spark ignited (SI) and small SI engine proposed
rule, published May 18, 2008 (EPA, 2007c). For 2008, the
NONROAD2005 model (NR05c-BondBase) is equivalent to
NONROAD2008a, since it incorporated Bond rule revisions
to some of the base case inputs and the Bond Rule controls
did not take effect until future years. NMIM provides
nonroad emissions for VOC by three emission modes:
exhaust, evaporative and refueling. Unlike the onroad sector,
refueling emissions are included for the nonroad sector.
NMIM was run with National County Database (NCD)
NCD20100602 to create county-SCC emissions for the
2008 nonroad mobile CAP/HAP sources. Emissions were
converted from monthly totals to monthly average- day
based the on number of days in each month. EPA default
inputs were replaced by state inputs where provided in
NCD20100602. The 2008 NEI documentation describes
this and all other details of the NMIM nonroad emissions
development for the 2008 platform.
3.2.10. Nonroad Mobile Sources: Commercial Marine Cl,
C2, and Locomotive (clc2rail)
The clc2rail sector contains CAP and HAP emissions
from locomotive and commercial marine sources, except
for the category 3/residual-fuel (C3) commercial marine
vessels (CMV) found in the cSmarine sector. The emissions
in the clc2rail sector are year 2008 and are composed of
the following SCCs: 2280002100 (CMV diesel, ports),
2280002200 (CMV diesel, underway), 2285002006
(locomotives diesel line haul Class I), 2285002007
(locomotives diesel line haul Class II/III), 2285002008
(locomotives diesel line haul passenger trains), 2285002009
(locomotives diesel line haul commuter lines), and
2285002010 (locomotives diesel, yard).
For modeling purposes, the only additional change made
to the nonroad data for the 2008 platform was to remove
railway maintenance emissions (SCCs 2285002015,
2285004015, and 2285006015) because these are included
in the nonroad NMIM monthly inventories. For more
information, see the 2008 NEI documentation.
3.2.11 Nonroad mobile sources: C3 commercial marine
(cSmarine)
The raw c3 marine sector emissions data were developed in
an ASCII raster format used since the Emissions Control
Area-International Marine Organization (ECA-IMO) project
began in 2005, then known as the Sulfur Emissions Control
Area (SECA). These emissions consist of large marine
diesel engines (at or above 30 liters/cylinder) that until
recently, were allowed to meet relatively modest emission
requirements, often burning residual fuel. The emissions in
this sector are comprised of primarily foreign-flagged ocean-
going vessels, referred to as Category 3 (C3) ships.
-------
The cSmarine (EGA) inventory includes these ships in ports
and underway mode and includes near-port auxiliary engines.
An overview of the ECA-IMO project and future year goals
for reduction of NOx, SO2, and PMC emissions can be found
at: http://www.epa.gov/nonroad/html.
The base year for the EGA inventory is 2002 and consists
of these CAPs: PM10, PM25, CO, NOx, SOx (assumed to
be SO2), and Hydrocarbons (assumed to be VOC). EPA
developed regional growth (activity-based) factors that
were applied to create a 2008 inventory from the 2002 data.
These growth factors are the same for all pollutants except
NOx, which includes a Tier 1 Standard. The factors are
provided in Table 3-5 and mapped and documented in the
following report: http://www.eoa.giv/ins/regs/bibriad/narube/
cu/420r09007-chap2.pdf.
Region
Alaska
East Coast
Gulf Coast
Hawaii
North Pacific (Washington)
South Pacific (Oregon and California)
Great Lakes
1.114
1.182
1.092
1.212
1.114
1.212
1.082
All_other
pollutants
1.179
1.251
1.156
1.282
1.179
1.282
1.089
Table 3-5. Regional growth factors used to project 2002
C3 emissions to 2008
The raw EGA inventory started as a set of ASCII raster
datasets at approximately 4-km resolution that was converted
to SMOKE point-source ORL input format as described in
this conference paper:
http ://www. epa. gov/ttn/chief/conference/ei 17/session6/
mason_pres.pdf
This paper describes how the ASCII raster dataset was
converted to latitude-longitude, mapped to state/county FIPS
codes that extend up to 200 nautical miles (nm) from the
coast, assigned stack parameters, and how the monthly ASCII
raster dataset emissions were used to create monthly temporal
profiles. Counties were assigned as extending up to 200nm
from the coast because of this was the distance through the
Exclusive Economic Zone (EEZ), a distance that would be
used to define the outer limits of ECA-IMO controls for
these vessels. The 2008 EGA-based C3 inventory delineates
between ports and underway modes using 2008 NEI port
shapefiles to assign point data as ports and all other point data
as underway.
Factors were applied to compute HAP emissions (based
on emissions ratios) to VOC to obtain HAP emissions
values. Table 3-6 below shows these factors. Because HAPs
were computed directly from the CAP inventory and the
calculations are therefore consistent, the entire c3 marine
sector utilizes CAP-HAP VOC integration to use the VOC
HAP species directly, rather than VOC speciation profiles.
The emissions were converted to SMOKE point source
ORL format, allowing for the emissions to be allocated
to modeling layers above the surface layer. All non-US
emissions (i.e., in waters considered outside of the 200nm
EEZ, and hence out of the U.S. territory) are assigned a
dummy state/county FIPS code=98001.
Pollutant Apply to Pollutant Code Factor
Acetaldehyde VOC
Benzene VOC
Formaldehyde VOC
75070
71432
50000
0.0002286
9.795E-06
0.0015672
Table 3-6. HAP emission ratios for generation of HAP
emissions from criteria emissions for C3 commercial
marine vessels
The SMOKE-ready data were cropped from the original
ECA-IMO data to cover only the 36-km CMAQ domain,
which is the largest domain used for this effort, and larger
than the 12km domain used in this project.
3.2.12 Emissions from Canada, Mexico and Offshore
Drilling Platforms (othpt, othar, othon)
The emissions from Canada, Mexico, and Offshore Drilling
Platforms are included as part of three sectors: othpt, othar,
and othon. The "oth" refers to the fact that these emissions
are "other" than those in the 2008 NEI, and the third and
fourth characters provide the SMOKE source types: "pt"
for point, "ar" for "area and nonroad mobile", and "on" for
onroad mobile. Mexico's emissions are unchanged from the
2005 Platform.
For Canada, year 2006 emissions were used, with several
modifications:
i. Wildfires and prescribed burning emissions were
not included because Canada does not include these
inventory data in their modeling.
ii. In-flight aircraft emissions were not included because
these are also not included for the U.S. modeling.
iii. A 75% reduction ("transport fraction") to PM for the
road dust, agricultural, and construction emissions
in the Canadian "afdust" inventory. This approach
is more simplistic than the county-specific approach
used for the U.S., but a comparable approach was not
avail- able for Canada.
iv. Speciated VOC emissions from the ADOM chemical
mechanism were not included.
v. Residual fuel CMV (C3) SCCs (22800030X0) were
removed because these emissions are included in the
c3 marine sector, which covers not only emissions
close to Canada but also emissions far at sea. Canada
was involved in the inventory development of the
c3marine sector emissions.
vi. Wind erosion (SCC=2730100000) and cigarette
smoke (SCC=2810060000) emissions were removed
from the nonpoint (nonpt) inventory; these emissions
are also absent from the U.S. inventory.
vii. Quebec PM2 5 emissions (2,000 tons/yr) were removed
for one SCC (2305070000) for Industrial Processes,
Mineral Processes, Gypsum, Plaster Products due
-------
to corrupt fields after conversion to SMOKE input
format. This error should be corrected in a future
inventory.
viii. Excessively high CO emissions were removed from
Babine Forest Products Ltd (British Columbia plan-
tid='5188') in the point inventory. This change was
made because the value of the emissions was impos-
sibly large.
ix. The county part of the state/count FIPS code field in
the SMOKE inputs were modified in the point inven-
tory from "000" to "001" to enable matching to exist-
ing temporal profiles.
Mexico emissions for 1999 (Eastern Research Group
Inc., 2006) were used as these were developed as part of a
partnership between Mexico's Secretariat of the Environment
and Natural Resources (Secretaria de Medio Ambiente y
Recursos Naturales-SEMARNAT) and National Institute
of Ecology (Institute Nacional de Ecologia-INE), the U.S.
EPA, the Western Governors' Association (WGA), and the
North American Commission for Environmental Cooperation
(CEC). This inventory includes emissions from all states in
Mexico.
The offshore emissions include point source offshore oil and
gas drilling platforms. Offshore emissions from the 2008 NEI
point source inventory were used. The offshore sources were
provided by the Mineral Management Services (MMS).
3.2.13 SMOKE-ready non-anthropogenic chlorine inventory
For the ocean chlorine, the same data as in the CAP and
HAP 2002-based Platform was used. See ftp://ftp.epa.
gov/EmisInventory/2002v3CAPHAP/documentation/
documentation for details.
3.3 Emissions Modeling Summary
SMOKE for the 2008 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 Mexico). The pollutants
for all sectors except for biogenics and ocean chlorine are
those inventoried for the NEI.
The pre-processing steps that comprise the emissions
modeling task include temporal allocation, spatial allocation,
pollutant speciation, and vertical allocation of point. This
section provides some basic information about the tools and
data files other than inventories used for emissions modeling
as part of the 2008 Platform.
3.3.7 The SMOKE Modeling System
SMOKE version 2.7 was used to pre-process the emissions
inventories to create the emissions inputs for CMAQ.
SMOKE executables and source code are available from the
Community Multiscale Analysis System (CMAS) Center at
http://www.cmascenter.org. Additional information about
SMOKE is available from http://www.smoke-model.org.
3.3.2 Key Emissions Modeling Settings
Emissions inventories for each modeling sector are processed
separately through SMOKE to create gridded, hourly,
speciated emissions. The final merge program (Mrggrid)
is then run to combine the model-ready, sector-specific
emissions across sectors. The SMOKE settings in the "run
scripts" and the data in the SMOKE ancillary files control
the approaches used by the individual SMOKE programs for
each sector.
Table 3-7 summarizes the major SMOKE processing steps of
each platform sector. The "Spatial" column shows the spatial
approach: "point" indicates that SMOKE maps the source
from a point (i.e., latitude and longitude) location to a grid
cell, "surrogates" indicates that some or all of the sources
use spatial surrogates to allocate county emissions to grid
cells, and "area-to-point" indicates that some of the sources
use the SMOKE area-to-point feature to grid the emissions.
The "Speciation" column indicates that all sectors use the
SMOKE speciation step, though speciation of biogenic
emissions 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.
ptipm
ptnonipm
othpt
nonroad
othar
seca_c3
alm_no_c3
on_noadj
on_moves_
startpm
on_moves_
runpm
othon
•*—
point
point
point
surrogates &
area-to-point
£3x2 p:r
Yes
daily & hourly
annual
annual
in-line
in-line
in-line
nonpt
surrogates
point
surrogates &
area-to-point
surrogates
surrogates
surrogates
surrogates
surrogates &
area-to-point
surrogates
Yes
Yes
Yes
annual
monthly
monthly
afdust
beis
ptfire
surrogates
pre-gridded
landuse
point
Table 3-8. 2008 Emission Model Species produced by
SMOKE using the Carbon Bond 5 (CB05) Mechanism
with Secondary Organic Aerosol (SOA) in CMAQ v4.7
-------
I2kim CONUS nationwid
x.y: -2SS6000,-172SOOO
col: 459 Itnu: 299
Figure 3-3. CMAQ Modeling Domain
The last column in Table 3-7 is the "plume rise" column. The
sectors with "In-line" in this column are the only ones which
will have emissions in aloft layers, based on their plume rise.
Here the term "in-line" refers to the fact that the plume rise
for each hour is computed inside CMAQ using stack data in
SMOKE output files, rather than within SMOKE. For most
"in-line" sectors, the emissions are divided between ground-
level and aloft emissions based on their characteristics (e.g.,
stack height). The one sector listed with "in-line" only,
cSmarine, was processed so that all of the emissions would
be in aloft layers with no emissions in the 2-dimensional,
layer-1 files created by SMOKE. Rather the speciated and
temporalized source-based CMAQ inputs for cSmarine were
used for the vertical allocation.
One of the issues found was that when using in-line
processing, the PELVCONFIG file cannot allow grouping,
otherwise the "inline" versus "offline" (i.e., processing
whereby SMOKE creates 3-dimensional files) will not give
identical results. Since a PELVCONFIG file with grouping
was used, the in-line approach should be used to exactly
replicate our results.
3.3.3 Spatial Configuration
For this project, we ran SMOKE and CMAQ were run for a
12-km modeling domain as shown in Figure 3-3. The grid
used a Lambert-Conformal projection, with Alpha =33, Beta
= 45 and Gamma = -97, with a center of X = -97 and Y = 40.
Later sections provide details on the spatial surrogates and
area-to-point data used to accomplish spatial allocation with
SMOKE.
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 2005 Platform is the Carbon Bond 05 (CB05) mechanism
(Yarwood, 2005) with secondary organic aerosol (SOA) and
HONO enhancements as described in http://www.cmascenter.
org/help/model_docs/cmaq/4.7/RELEASE_NOTES.txt.
From the perspective of emissions preparation, it is the same
mechanism used in the 2005 and 2002 Platforms. Table
3-8 lists the model species produced by SMOKE for use in
CMAQ.
-------
Inventory
Pollutant
Model
Species
ALDX
IDLE
ISOP
MEOH
Model species description
Carbon monoxide
Nitrogen oxide
Nitrogen dioxide
Nitrous acid
Sulfur dioxide
Sulfuric acid vapor
Ammonia
Acetaldehyde
Propionaldehyde and higher
aldehydes
Benzene (not part of CB05)
Ethene
Ethane
Ethanol
Formaldehyde
Internal olefin carbon bond
(R-C=C-R)
Isoprene
Methanol
Terminal olefin carbon bond
(R-C=C)
Paraffin carbon bond
Various additional
VOC species from
the biogenics
model which do
not map to the
above model
species
XYL
SESQ
TERP
PMC
PM
Toluene and other monoalkyl
aromatics
Xylene and other polyalkyl
aromatics
Sesquiterpenes
Terpenes
Coarse PM > 2.5 microns and
< 10 microns
Particulate elemental carbon
< 2.5 microns
Particulate nitrate < 2.5 microns
Particulate organic carbon (carbon
only) < 2.5 microns
Particulate Sulfate
< 2.5 microns
Other particulate matter < 2.5
microns
Table 3-8. Model Species produced by SMOKE for CB05
with SOA for CMAQ 4.7
It should be noted that the BENZENE model species is not
part of CB05 in that the concentrations of BENZENE do
not provide any feedback into the chemical reactions (i.e.,
it is not "inside" the chemical mechanism). Rather, benzene
is used as a reactive tracer, and as such is impacted by the
CB05 chemistry. BENZENE, along with several reactive
CBO5 species (such as TOL and XYL) plays a role in SOA
formation in CMAQ 4.7.
The approach for speciating PM2 5 emissions is the same
as that described for the 2005 and 2002 platforms except
that two of the onroad sectors were not further speciated
in SMOKE, along with Canadian pre-speciated PM
emissions. The approach for speciating VOC emissions from
non-biogenic sources has the following characteristics: 1) for
some sources, HAP emissions are used in the speciation
process to allow integration of VOC and HAP emissions in
the NEI; and, 2) for some mobile sources, "combination"
profiles are specified by county and month and emission
mode (e.g., exhaust, evaporative). SMOKE computes
the resultant profile on-the-fly given the fraction of each
specific profile specified for the particular county, month and
emission mode. The SMOKE feature called the GSPRO_
COMBO file supports this approach.
The VOC speciation approach for the 2008 Platform includes
HAP emissions from the NEI in the speciation process for
some sectors. That is, instead of speciating VOC to generate
all of the species listed in Table 3-8, emissions of the 4
HAPs, benzene, acetaldehyde, formaldehyde and methanol
(BAFM) from the NEI were integrated with the NEI VOC.
The integration process combines the BAFM HAPs with
the VOC in a way that does not double-count emissions and
uses the BAFM directly in the speciation process. Generally,
the HAP emissions from the NEI are believed to be more
representative of emissions of these compounds than their
generation via VOC speciation.
The BAFM HAPs were chosen for this special treatment
because, with the exception of BENZENE, they are the
only explicit VOC HAPs in the base version of CMAQ 4.7
model. By "explicit VOC HAPs," we mean model species
that participate in the modeled chemistry using the CB05
chemical mechanism. The use of these HAP emission
estimates along with VOC is called "HAP-CAP integration".
BENZENE was chosen because it was added as a model
species in the base version of CMAQ 4.7, and there was
a desire to keep its emissions consistent between multi-
pollutant and base versions of CMAQ.
The integration of HAP VOC with VOC is a feature available
in SMOKE for all inventory formats other than PTDAY (the
format used for the ptfire sector). SMOKE allows the user
to specify the particular HAPs to integrate and the particular
sources to integrate. The HAPs to integrate are specified in
the INVTABLE file, and the sources to integrate are based
on the NHAPEXCLUDE file (which lists the sources that
are excluded from integration5). For the "integrate" sources,
In SMOKE version 2.6 the options to specify sources for integration are
expanded so that a user can specify the particular sources to include or
exclude from integration, and there are settings to include or exclude all
sources within a sector.
-------
Step 1: Analyze Inventory to determine which sources will be "integrate" sources
For each Sector,
Examine Emissions
Sources of VOC and
B,A,F,M
Determine which sources
will not be Integrated (either
whole sector or some
sources within a sector)
based on "Integration
Criteria™
Create list of
"no-integrate"
sources
NHAPEXCLUDE :
Ancillary file
Ready for SMOKE
RcmovcS.FAM from
all sources that will
NOT be integrated
Emissions ready for
SMOKE
Emissions ready for
SMOKE
Step 2: Run SMOKE
j Emissions ready for SMOKE [
SMOKE
Compute NOW HAPVQC= VOC - {B + F + A+M)
emissions for each integrate source
Retain VOC emissions for each no-integrate source
Assign speciation profile code to each emission source F-
Compute: NQNHAPTOG emissions from NONHAPVOCfbr
each integrate source
Compute: TOG emissions from VOC for each no-integrate
source
Compute moles of each CBO5 model species.
Use NONHAPTOG profiles applied to NONHAPTOG
emissions and B, F, A, M emissions for integrate sources.
Use TOG profiles applied to TOG for no-integrate sources
: list of "no-integrate"
H sources (NHAPEXCLUDE)
Cross
Reference File (GSREF)
VOC-to-TOG factors
NON H APVOC-to-N O N H APTOG
factors (GSCNV)
TOG and NONHAPTOG
speciation factors
(GSPRO)
Speciated Ernissionsfor VOC species
Figure 3-4. Process of integrating BAFM with VOC for use in VOC Speciation
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PLATFORM
SECTOR
ptipm
ptnonipm
ptfire
afdust
nonpt
nonroad
alm_no_c3
seca_c3
onroad
biog
othpt
othar
othon
Approach for Integrating NEI emissions of Benzene (B), Acetaldehyde (A), Formaldehyde (F) and
Methanol (M)
No integration because emissions of BAFM are relatively small for this sector
No integration because emissions of BAFM are relatively small for this sector and it is not expected that criteria for integration would be
met by a significant number of sources
Full integration (However, NONHAPVOC computed outside of SMOKE since SMOKE cannot do this calculation for the day-specific fire
formatted files)
N/A—sector contains no VOC
N/A—sector contains no VOC
Partial integration; details provided below table
For other than California: Partial integration—did not integrate CNG or LPG sources (SCC beginning with 2268 or 2267) because
computed only VOC and not any HAPs for these SCCs.
For California: Full integration
Partial integration; details provided below table
Full integration
Full integration
N/A—sector contains no inventory pollutant "VOC"; but rather specific VOC species
No integration—not the NEI
No integration—not the NEI
No integration—not the NEI
Table 3-9. Integration Status of 2008 Benzene, Acetaldehyde, Formaldehyde and Methanol (BAFM) Species in each
Platform Sector
SMOKE subtracts the "integrate" HAPs from the VOC (at
the source level) to compute emissions for the new pollutant
"NONHAPVOC." The user provides NONHAPVOC-
to-NONHAPTOG factors and NONHAPTOG speciation
profiles. SMOKE computes NONHAPTOG and then applies
the speciation profiles to allocate the NONHAPTOG to
the other CMAQ VOC species not including the integrated
HAPs.
CAP-HAP integration was considered for all sectors
and "integration criteria" were developed for some of
those. Table 3-9 summarizes the integration approach for
each platform sector. For the nonpt sector, the following
integration criteria were used to determine the sources to
integrate:
1. Any source for which the sum of B, A, F, or M
is greater than the VOC was not integrated, since
this clearly identifies sources for which there is an
inconsistency between VOC and VOC HAPs. This
includes some cases in which VOC for a source is zero.
2. For some source categories (those that comprised
80% of the VOC emissions), sources were selected
for integration in the category per specific criteria. For
most of these source categories, sources are allowed
to be integrated if they had the minimum combination
of B, A, F, and M specified in the first column. For a
few source categories, all sources were designated as
"no-integrate".
3. For source categories that do not comprise the top 80%
of VOC emissions, as long as the source has emissions
of one of the B, F, A or M pollutants, then it can be
integrated.
4. For the c Ic2rail sector, the integration criteria were (1)
that the source had to have at least one of the 4 HAPs
and (2) that the sum of BAFM could not exceed the
VOC emissions. The criteria for this sector were less
complex than the nonpt sector because it has fewer
source categories.
The SMOKE feature to compute speciation profiles from
mixtures of other profiles in user-specified proportions was
used in this project. The combinations are specified in the
GSPRO_COMBO ancillary file by pollutant (including
pollutant mode, e.g., EXH, VOC), state and county (i.e.,
state/county FIPS code) and time period (i.e., month).
This feature was used for onroad and nonroad mobile and
gasoline-related related stationary sources. Since the ethanol
content varies spatially (e.g., by state or sources use fuels
with varying ethanol content, and therefore the speciation
profiles require different combinations of gasoline and E10
county), temporally (e.g., by month) and by modeling year
(i.e., future years have more thanol) the combo feature allows
combinations to be specified at various levels for different
years.
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3.3.4 Temporal Processing Configuration
Table 3-10 summarizes the temporal aspect of the emissions
processing configuration. It compares the key approaches
used for temporal processing across the sectors. The temporal
aspect of SMOKE processing is controlled through (a) the
scripts T_TYPE (Temporal type) and M_TYPE (Merge
type) settings and (b) ancillary data files. In addition to the
resolution, temporal processing includes a ramp-up period
for several days prior to January 1, 2008, intended to mitigate
the effects of initial condition concentrations. The ramp up
period for the national 12km grid was 10 days. For most
sectors, the emissions from late December of 2008 were used
to provide emissions for the end of December, 2007
3.3.6 Vertical Allocation of Emissions
Table 3-7 specifies the sectors for which plume rise
is calculated. If there is no plume rise for a sector, the
emissions are placed into layer 1 of the air quality model.
Vertical plume rise was performed in-line within CMAQ
for all of the SMOKE point-source sectors (ptipm,
ptnonipm, ptfire, othpt, and cSmarine). The in-line plume
rise computed within CMAQ is nearly identical to the
plume rise that would be calculated within SMOKE using
the Laypoint program. See http ://www. smoke-model, org/
version2.7/SMOKE_v27_manual.pdf (Chapter 6) for full
documentation of Laypoint. The selection of point sources
for plume rise is pre-determined in SMOKE using the
Elevpoint program (http://www.smoke-model.org/version2.II
SMOKE_v27_manual.pdf (Chapter 6). The calculation
is done in conjunction with the CMAQ model time steps
with interpolated meteorological data and is therefore more
temporally resolved than when it is done in SMOKE. Also,
the calculation of the location of the point source is slightly
different than the one used in SMOKE and this can result in
slightly different placement of point sources near grid cell
boundaries.
For point sources, the stack parameters are used as inputs
to the Briggs algorithm, but point fires do not have stack
parameters. However, the ptfire inventory does contain data
on the acres burned (acres per day) and fuel consumption
(tons fuel per acre) for each day. CMAQ uses these additional
parameters to estimate the plume rise of emissions into layers
above the surface model layer. Specifically, these data are
used to calculate heat flux, which is then used to estimate
plume rise.
In addition to the acres burned and fuel consumption, heat
content of the fuel is needed to compute heat flux. The heat
content was assumed to be 8000 Btu/lb of fuel for all fires
because specific data on the fuels were unavailable in the
inventory. The plume rise algorithm applied to the fires is a
modification of the Briggs algorithm with a stack height of
zero.
CMAQ uses the Briggs algorithm to determine the plume
top and bottom, and then computes the plumes' distributions
into the vertical layers that the plumes intersect. The
pressure difference across each layer divided by the pressure
difference across the entire plume is used as a weighting
factor to assign the emissions to layers. This approach gives
plume fractions by layer and source.
3.3.7 Emissions Modeling Ancillary Files
In this section the ancillary data that SMOKE used to
perform spatial allocation, chemical speciation, and temporal
allocation for the 2008 Platform is summarized. The ancillary
data files, particularly the cross-reference files, provide the
specific inventory resolution at which spatial, speciation, and
temporal factors are applied. For the 2008 Platform, spatial
factors were generally applied by country/SCC, speciation
factors by pollutant/SCC or (for combination profiles) state/
county FIPS code and month, and temporal factors by some
combination of country, state, county, SCC, and pollutant.
3.3.7.1 Spatial Allocation Ancillary Files
Spatial allocation was performed for a national 12-km
domain. To do this, SMOKE used national 12-km spatial
surrogates and a SMOKE area-to-point data file. For the U.S.
and Mexico, the same spatial surrogates were used as were
used for the 2005 Platform.
3.3.7.2 Surrogates for U.S. Emissions
More than sixty spatial surrogates were used to spatially
allocate U.S. county-level emissions to the CMAQ 12-km
grid cells. The surrogates are the same as those used for the
2005 Platform. The Surrogate Tool was used to generate
all of the surrogates. The shapefiles input to the Surrogate
Tool are provided and documented at http://www.epa.
gov/ttn/chief/emch/spatial/spatialsurrogate.html. The
document ftp://ftp.epa.gov/EmisInventory/emiss_shp2006/
us/list_of_shapefiles.pdf provides a list and summary of
these shapefiles. The detailed steps in developing the county
boundaries for the surrogates are documented at ftp://ftp.epa.
gov/EmisInventory/emiss_shp2006/us/metadata_for_2002_
county_boundary_shapefiles_rev.pdf. Table 3-11 lists the
codes and descriptions of the surrogates. An area-to-point
approach overrides the use of surrogates for some airport-
related sources. The onroad off-network (parking area)
emissions from the MOVES model were spatially allocated
as shown in Table 3-12.
3.3.7.3 Allocation Method for Airport-Related Sources
in the U.S.
There are numerous airport-related emission sources in
the 2005 NEI, such as aircraft, airport ground support
equipment, and jet refueling. In the 2002 platform most of
these emissions were contained in sectors with county-level
resolution — aim (aircraft), nonroad (airport ground support)
and nonpt (jet refueling), but in the 2005 and 2008 platforms
aircraft emissions are included as point sources as part of the
ptnonipm sector.
For the 2008 platform, the SMOKE "area-to-point" approach
was used for airport ground support equipment (nonroad
sector), and jet refueling (nonpt sector). The approach is
described in detail in the 2002 Platform documentation:
http://www.epa. gov/ttn/scram/reportsindex.htm. Nearly the
same ARTOPNT file was used to implement the area-to-point
approach as was done for the CAP and HAP-2002-based
Platform. This was slightly updated from the CAP-only
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2002 Platform by further allocating the Detroit-area airports
into multiple sets of geographic coordinates to support finer
scale modeling that was done under a different project. The
updated file was retained for the 2008 Platform.
3.3.7.4 Surrogates for Canada and Mexico Emission
Inventories
The Mexican emissions and single surrogate (population)
were the same as those used in the 2002 and 2005 Platforms.
For Canada, surrogates provided by Environment Canada
with the 2006 emissions were used to spatially allocate the
2006 Canadian emissions for the 2005 and 2008 Platforms.
Platform Surrogate-13. Canadian Spatial Surrogates for
2005-based platform Canadian Emission
3.3.7.5 Ch emical Speciation An ciliary Files
Several files are used by SMOKE to convert the inventory
species to the CMAQ model species. The SMOKE
environmental variable names used to specify the files
containing the speciation -related information are shown
below using capital letters in parentheses:
• Inventory table (INVTABLE) to control the pollutants
processed and their key parameters,
• NONHAPVOC emissions calculation exclusions file
(NHAPEXCLUDE),
speciation VOC-to-TOG conversion factors (GSCNV),
• speciation cross references (GSREF) that map SCCs to
speciation profiles,
• speciation profiles (GSPRO) that split the inventory
pollutants into CMAQ species, and
combined, monthly speciation profiles
(GSPRO_COMBO).
For VOC speciation, SMOKE-ready profiles for the CB05
chemical mechanism were generated using the Speciation
Tool (Eyth, 2006), including:
• TOG-to-model species profiles (used only for
no-integrate sources)
• NONHAPTOG-to-model species profiles (used only
for the integrate sources), and
• TOG-to-BENZENE (used only for no-integrate
sources).
Speciation profile entries were added to the Speciation
Tool to convert benzene, acetaldehyde, formaldehyde and
methanol to the model species BENZENE, ALD2, FORM
and METHANOL, respectively. These profiles were used
only for the integrate sources. Note that the 'integrate' and
'no-integrate' sources were processed using the same GSREF
and GSPRO files.
In addition to the speciation profiles, the Speciation Tool
generates the SMOKE-ready speciation conversion files
(GSCNV). Two of these were generated: one containing
profile-specific VOC-to-TOG conversion factors and
the other containing profile-specific NONHAPVOC-to-
NONHAPTOG conversion factors.
The TOG and PM2 5 speciation factors that are the basis of
the chemical speciation approach were developed from the
SPECIATE4.3 database (http://www.epa.gov/ttn/chief/
software/speciate/). EPA's repository of TOG and PM
speciation profiles of air pollution sources. SPECIATE 4.2
development was a collaboration involving EPA's ORE)
and EPA's Office of Air Quality Planning and Standards
(OAQPS) at Research Triangle Park, NC, and Environment
Canada (EPA, 2006c).
The SPECIATE database contains speciation profiles for
TOG, speciated into individual chemical compounds,
VOC-to-TOG conversion factors associated with the TOG
profiles and speciation profiles for PM2 5. The database also
contains the PM2 5 speciated into both individual chemical
compounds (e.g., zinc, potassium, manganese, lead) and into
the "simplified" PM2 5 components used in the air quality
model. These simplified components are:
• PSO4: primary paniculate sulfate
• PNO3: primary paniculate nitrate
• PEC: primary paniculate elemental carbon
• POC: primary paniculate organic carbon
• PMFINE: other primary paniculate, less than 2.5
micrograms in diameter
An issue with SPECIATE 4.3 was that profile 92095 was
inadvertently left out of the database. It was obtained
from EPA ORE) staff and was used for the nonpoint SCC
2101002000. For the other SCCs pertaining to bituminous
coal combustion the sub-bituminous coal combustion profile
(92084) was used. Table 3-14 shows that the
Resulting differences represent only a minor change to
the SMOKE results. Minor changes were made in the PM
profiles in comparison to the 2005 Platform.
These include:
• cSmarine changed from profile Marine Vessel - Main
Engine - Heavy Fuel Oil - Simplified (92200) to
Marine Vessel - Main Engine - Heavy Fuel Oil (5674
in SPECIATE4.3)
• changed Draft Tire Burning - Simplified (92086) to
Draft Solid Waste Combustion - Simplified (92082)
Key changes to the TOG profiles since the 2002 Platform are
as follows:
• Updated the profile for aircraft from 1098 (Aircraft
Landing/Takeoff (LTO)—Commercial) which is from
SPECIATE3.2 and has a profile date of 1989, to 5565B
(Aircraft Exhaust) from SPECIATE4.3.
• Updated the profile for forest fires from 0307
(Miscellaneous Burning - Forest Fires) which was from
SPECIATE3.2 and has a profile date of 1989) to 5560
(Biomass Burning - Extratropical Forest, dated 2/2008
and was based on testing conducted in 2001)
Changed the assignment of residential wood
combustion (including woodstove and fireplace
emissions) and other profiles that formerly used 4641
-------
pollutant
PM2_5
PM2_5
PM2_5
PM2_5
PM2_5
species
PEC
PMFINE
PNO3
POC
PS04
split factors
sub-bituminous 92084
0.0188
0.8266
0.0016
0.0263
0.1267
split factors
bituminous 92095
0.01696
0.827928
0.00208
0.026307
0.126725
Table 3-14. Differences between two profiles used for coal combustion
(Fireplace wood combustion-oak wood) to 4642
(Fireplace wood combustion-pine wood) because
of all three woods tested in the study (oak, pine and
eucalyptus), the most complete testing was done for the
pine wood (for example, benzene was only measured
for pine)
Updated the profiles for mobile onroad and nonroad
sources to use more up-to-date test data. The updated
profiles are:
- 8750: Gasoline Exhaust—Reformulated gasoline
- 8751: Gasoline Exhaust—E10 ethanol gasoline
- 8753: Gasoline Vehicle - Evaporative emission -
Reformulated gasoline
- 8754: Gasoline Vehicle - Evaporative emission - E10
ethanol gasoline
Utilized combination profiles comprised of the
above updated exhaust and evaporative profiles to
match the average ethanol content of fuels used by
different counties and for different months of the year.
Combinations were created based on the fuel properties
data in the NMIM county database.
Speciation profiles for use with BEIS are not included in
SPECIATE. The 2008 Platform uses BEIS3.14 and includes
a species (SESQ) that was not in BEIS3.13 (the version
used for the 2002 Platform). This species was mapped to the
CMAQ species SESQT. The profile code associated with
BEIS3.14 profiles for use with CB05 was "B10C5."
The INVTABLE and NHAPEXCLUDE SMOKE input files
have a critical function in the VOC speciation process for
emissions modeling cases utilizing HAP-CAP integration, as
is done for the 2008 Platform.
Two different INVTABLE files were prepared to use with
different sectors of the platform. For sectors in which we
chose no integration for the entire sector, a "no HAP use",
an INVTABLE that set the "KEEP" flag to "N" for B AFM
was created. Thus, any BAFM in the inventory input into
SMOKE would be dropped. This approach both avoids
double-counting of these species and assumes that the VOC
speciation is the best available approach for these species
for the sectors using the approach. The second INVTABLE,
used for sectors in which one or more sources are integrated,
causes SMOKE to keep the BAFM pollutants and indicates
that they are to be integrated with VOC (by setting the "VOC
or TOG component" field to "V" for all four HAP pollutants.
Weekly Diurnal (SO )
U&H-LOO* too*-
4 6 8 10 12 14 16 IB 20 22 24
Hours of the Day
Weekly Diurnal (NOJ
6 8 10 12 14 16 18 20 22 24
Hours of the Day
Figure 3-5. Diurnal Profiles for Parking Areas (Pollutants: SO and NO )
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Sector-specific NHAPEXCLUDE files were developed
that provide the specific sources that are excluded from
integration.
3.3.7.6 Temporal Allocation Ancillary Files
The emissions modeling step for temporal allocation creates
the hourly emission inputs for CMAQ by adjusting the
emissions from the inventory resolution (annual, monthly,
daily or hourly) input into SMOKE. The temporal resolution
of each of the platform sectors prior to their input into
SMOKE is included in the sector descriptions from Table 3-1
and is repeated in the discussion of temporal settings.
The starting point for the temporal profiles was the 2005
Platform. The monthly, weekly, and diurnal temporal
profiles and associated cross references used to create the
2008 hourly emissions inputs for CMAQ were generally
based on the temporal allocation data used for the 2005
Platform. New profile assignments were added for SCCs
in the 2008 inventory that were not in the 2005 inventory.
Also, the profiles used for ptipm sources without CEM data
were updated to represent the year 2008. Specific temporal
profiles were assigned for the parking area SCCs provided
by MOVES. The remainder of this section discusses
the development of the new temporal profiles or profile
assignments used in the 2008 Platform.
The state- and pollutant-specific diurnal profiles used to
allocate the day-specific emissions for non-CEM sources in
the ptipm sector were updated. The 2008 CEM data was used
to create state-specific, day-to-hour factors averaged over
the whole year and all units in each state. Diurnal factors
were calculated using CEM SO2 and NOx emissions and heat
input. SO2 and NOx-specific factors were computed from
the CEM for these pollutants as shown in Figure 3-5. All
other pollutants used factors created from the hourly heat
input data. The resulting profiles were assigned by state and
pollutant.
The temporal profile assignments for the Canadian 2006
inventory were provided by Environment Canada along with
the inventory. They provided profile assignments that rely
on the existing set of temporal profiles in the 2002 Platform.
For point sources, they provided profile assignments by
PLANTID.
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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 paniculate 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
(paniculate 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 paniculate matter.
To meet the need to address the complex relationships
between pollutants, EPA developed the Community
Multiscale Air Quality (CMAQ) modeling system. The
primary goals for CMAQ are to:
• Improve the environmental management community's
ability to evaluate the impact of air quality management
practices for multiple pollutants at multiple scales.
• Improve the scientist's ability to better probe,
understand, and simulate chemical and physical
interactions in the atmosphere.
The CMAQ modeling system brings together key physical
and chemical functions associated with the dispersion and
transformations of air pollution at various scales. It was
designed to approach air quality as a whole by including
state-of-the-science capabilities for modeling multiple
air quality issues, including tropospheric ozone, fine
particles, toxics, acid deposition, and visibility degradation.
CMAQ relies on emission estimates from various sources,
including the U.S. EPA Office of Air Quality Planning and
Standards' current emission inventories, observed emission
from major utility stacks, and model estimates of natural
emissions from biogenic and agricultural sources. CMAQ
also relies on meteorological predictions that include
assimilation of meteorological observations as constraints.
Emissions and meteorology data are fed into CMAQ and run
through various algorithms that simulate the physical and
chemical processes in the atmosphere to provide estimated
concentrations of the pollutants. Traditionally, the model has
been used to predict air quality across a regional or national
domain and then to simulate the effects of various changes
in emission levels for policymaking purposes. For health
studies, the model can also be used to provide supplemental
information about air quality in areas where no monitors
exist.
CMAQ was also designed to have multi-scale capabilities so
that separate models were not needed for urban and regional
scale air quality modeling. The grid spatial resolutions in past
annual CMAQ runs have been 36 km x 36 km per grid for the
"parent" domain, and nested within that domain are 12 km x
12 km grid resolution domains. The parent domain typically
covered the continental United States, and the nested 12 km x
12 km domain covered the Eastern or Western United States.
The CMAQ simulation performed for this 2008 assessment
used a single domain that covers the entire continental U.S.
(CONUS) and large portions of Canada and Mexico using 12
km by 12 km horizontal grid spacing. For urban applications,
CMAQ has also been applied with a 4-km x 4-km grid
resolution 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/CMAO 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.
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12km CON US nation
x,y: -2556090,-
COl: 459 r0w; 299
Figure 4-1. Map of the CMAQ Modeling Domain. The blue box denotes the 12 km national modeling domain
(Same as Figure 3-3.)
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 air quality modeling platform
used for the 2008 CMAQ simulation. A modeling platform
is a structured system of connected modeling-related
tools and data that provide a consistent and transparent
basis for assessing the air quality response to changes in
emissions and/or meteorology. A platform typically consists
of a specific air quality model, emissions estimates, a
set of meteorological inputs, and estimates of "boundary
conditions" representing pollutant transport from source
areas outside the region modeled. We used the CMAQ6
model as part of the 2008 Platform to provide a national scale
air quality modeling analysis. The CMAQ model simulates
the multiple physical and chemical processes involved in
the formation, transport, and destruction of ozone and fine
paniculate matter (PM25).
This section provides a descnption of each of the main
components of the 2008 CMAQ simulation along with the
results of a model performance evaluation in which the 2008
model predictions are compared to corresponding measured
concentrations.
Byun, D.W., and K. L. Schere, 2006: Review of the Governing Equations,
Computational Algorithms, and Other Components of the Models-3
Community Multiscale Air Quality (CMAQ) Modeling System. Applied
Mechanics Reviews, Volume 59, Number 2 (March 2006), pp. 51-77.
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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. The CMAQ model version 4.7 was
most recently peer-reviewed in February of 2009 for the U.S.
EPA.7 As mentioned previously, CMAQ includes numerous
science modules that simulate the emission, production,
decay, deposition and transport of organic and inorganic
gas-phase and particle-phase pollutants in the atmosphere.
This analysis employed a version of CMAQ based on the
latest publicly released version of CMAQ (i.e., version
4.7.1s) at the time of the 2008 air quality modeling. CMAQ
version 4.7.1 reflects updates to version 4.7 to improve the
underlying science which include aqueous chemistry mass
conservation improvements and improved vertical convective
mixing. The model enhancements in version 4.7.1 also
include:
1. Aqueous chemistry
- Mass conservation improvements
+ Imposed 1 second minimum timestep for remainder
of the cloud lifetime after 100 "iterations" in the
solver
+ Force mass balance for the last timestep in the
cloud by limiting oxidized amount to mass
available
- Implemented steady state assumption for OH
- Only allow sulfur oxidation to control the aqueous
chemistry solver timestep (previously, reactions of
OH, GLY, MGLY, and Hg for multipollutant model
also controlled the timestep)
2. Advection
- Added additional divergence-based constraint on
advection timestep
- Vertical advection in the Yamo module is now
represented with the PPM scheme to limit numerical
diffusion
3. Model time step determination
- Fixed a potential advection time step error
+ The sum of the advection steps for a given layer
time step might not equal the output time step
duration in some extreme cases
+ Ensured that the advection steps sum up to the
synchronization step
Allen, D., Burns, D., Chock, D., Kumar, N., Lamb, B., Moran, M.
(February 2009 Draft Version). Report on the Peer Review of the
Atmospheric Modeling and Analysis Division, NERL/ORD/EPA. U.S.
EPA, Research Triangle Park, NC. CMAQ version 4.7 was released
on December, 2008. It is available from the Community Modeling and
Analysis System (CMAS) as well as previous peer-review reports at:
http://www.cmascenter.org.
CMAQ version 4.7.1 model code is available from the Community
Modeling and Analysis System (CMAS) at: http://www.cmascenter.org.
Map Projection
Grid Resolution
Coordinate Center
True Latitudes
Dimensions
Vertical extent
National 12 km CMAQ Modeling
Configuration
Lambert Conformal Projection
12km
97 W, 40 N
33 and 45 N
459 x 299 x 24
24 Layers: Surface to 50 mb level
(see Table 4-2)
Table 4-1. Geographic Information for 12 km Modeling
Domain
4. Horizontal diffusion
- Fixed a potential error
+ Concentration data may not be correctly initialized
if multiple sub-cycle time steps are required
+ Fix to initialize concentrations with values
calculated in the previous sub-time step
5. Emissions
- Bug fix in EMIS_DEFN.F to include point source
layer 1 NH3 emissions
- Bug fix to calculate soil NO "pulse" emissions in
BEIS
- Remove excessive logging of cases where ambient
air temperature exceeds 315.0 Kelvin. When this
occurs, the values are just slightly over 315
- Bug fix for parallel decomposition errors in plume
rise emissions
6. Photolysis
- JPROC/phot_table and phot_sat options
+ Expanded lookup tables to facilitate applications
across the globe and vertical extent to 20km
+ Updated temperature adjustments for absorption
cross sections and quantum yields
+ Revised algorithm that processes TOMS datasets
for OMI data format
- In-line option
+ Asymmetry factor calculation updated using values
from Mie theory integrated over log normal particle
distribution; added special treatment for large
particles in asymmetry factor algorithm to avoid
numerical instabilities.
4.2.2 Model Domain and Grid Resolution
The CMAQ modeling analyses were performed for a domain
covering the continental United States, as shown in Figure
4-1. This single domain covers the entire continental U.S.
(CONUS) and large portions of Canada and Mexico using!2
km by 12 km horizontal grid spacing. The model extends
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vertically from the surface to 50 millibars (approximately 19
km) using a sigma-pressure coordinate system. Air quality
conditions at the outer boundary of the 12 km domain were
taken from a global model. Table 4-1 provides some basic
geographic information regarding the 12 km CMAQ domain.
4.2.3 Modeling Period /Ozone Episodes
The 12 km CMAQ modeling domain was modeled for
the entire year of 2008. The 2008 annual simulation was
performed in two half-year segments (i.e., January through
June, and July through December) for each emissions
scenario. With this approach to segmenting an annual
simulation we were able to reduce the overall throughput
time for an annual simulation. The annual simulation
included a "ramp-up" period, comprised of 10 days before
the beginning of each half-year segment, to mitigate the
effects of initial concentrations. All 365 model days were
grid cells. The WRF simulation utilized 34 vertical layers
with a surface layer of approximately 38 meters. Table 4-2
shows the vertical layer structure used in WRF and the layer
collapsing approach to generate the CMAQ meteorological
inputs. CMAQ resolved the vertical atmosphere with 24
layers, preserving greater resolution in the PEL.
In terms of the 2008 WRF meteorological model performance
evaluation, an approach which included a combination of
qualitative and quantitative analyses was used to assess the
adequacy of the WRF simulated fields.9 The qualitative used
in the annual average levels of PM. For the 8-hour aspects
involved comparisons of the model-estimated 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
2008 Emissions: The emissions inventories used in the
2008 air quality modeling are described in Section 3, above.
Meteorological Input Data: The gridded meteorological data
for the entire year of 2008 at the 12 km continental United
States scale domain was derived from version 3.1 of the
Weather Research and Forecasting Model (WRF), Advanced
Research WRF (ARW) core.10 Previous CMAQ annual
simulations have typically utilized meteorology provided by
the 5th Generation Mesoscale Model (MM5).11 The WRF
Model is a next-generation mesoscale numerical weather
prediction system developed for both operational forecasting
and atmospheric research applications (http://wrf-model.
org). The 2008 WRF simulation included the physics options
of the Pleim-Xiu land surface model (LSM), Asymmetric
Convective Model version 2 planetary boundary layer (PEL)
9 U.S. Environmental Protection Agency, 2011. Meteorological Model
Performance for Annual 2007 Simulations, Office of Air Quality Planning
and Standards, Research Triangle Park, NC., 27711, EPA-454/R-11-007.
10 Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda,
M.G., Huang, X., Wang, W., Powers, J.G., 2008. A Description of the
Advanced Research WRF Version 3.
11 Grell, G. A., Dudhia, A. J., and Stauffer, D. R., 1994. A description of the
Fifth-Generation PennState/NCAR Mesoscale Model (MM5). NCAR
Technical Note NCAR/TN-398+STR. Available at http://www.mmm.ucar.
edu/mm5/doc 1 .html.
scheme, Morrison double moment microphysics, Kain-
Fritsch cumulus parameterization scheme and the RRTMG
long-wave radiation (LWR) scheme.12
The WRF meteorological outputs were processed to create
model-ready inputs for CMAQ using the Meteorology-
Chemistry Interface Processor (MCIP) package13, version
3.6, to derive the specific inputs to CMAQ: horizontal wind
synoptic patterns against observed patterns from historical
weather chart archives. Additionally, the evaluations
compared spatial patterns of monthly average rainfall and
monthly maximum planetary boundary layer (PEL) heights.
The statistical portion of the evaluation examined the model
bias and error for temperature, water vapor mixing ratio,
solar radiation, and wind fields. These statistical values were
calculated on a monthly basis.
Initial and Boundary Conditions: The lateral boundary
and initial species concentrations are provided by a
three- dimensional global atmospheric chemistry model,
the GEOS-CHEM14 model version 8-02-03. 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 2008 with a grid
resolution of 2.0 degrees x 2.5 degrees (latitude-longitude)
and 47 vertical layers. The predictions were used to provide
one-way dynamic boundary conditions at three-hour intervals
and an initial concentration field for the CMAQ simulations.
A GEOES-Chem evaluation was conducted for the purpose
of validating the 2008 GEOS-Chem simulation for selected
measurements relevant to their use as boundary conditions
for CMAQ and reproducing GEOS-Chem evaluation plots
reported in the literature for previous versions of the model.15
More information is available about the GEOS-CHEM model
and other applications using this tool at:
http ://acmg. seas, harvard, edu/presentations/powerpoints/
geos_chem_mtg/yantosca.ppt.
4.3 CMAQ Model Performance Evaluation
An operational model performance evaluation for ozone and
components (i.e., speed and direction), temperature, moisture,
and its related speciated components was conducted for
vertical diffusion rates, and rainfall rates for each grid cell
in each vertical layer. The WRF simulation used the same
CMAQ map projection, a Lambert Conformal projection
centered at (-97, 40) with true latitudes at 33 and 45 degrees
north. The 12 km WRF domain consisted of 459 by 299 the
2008 simulation using state/local monitoring sites data in
12 Gilliam, R.C., Pleim, J.E., 2010. Performance Assessment of New Land
Surface and Planetary Boundary Layer Physics in the WRF-ARW. Journal
of Applied Meteorology and Climatology 49, 760-774.
13 Otte T.L., Pleim, J.E., 2010. The Meteorology-Chemistry Interface
Processor (MCIP) for the CMAQ modeling system: updates through
v3.4.1. Geoscientific Model Development 3, 243-256.
14 Yantosca, B., 2004. GEOS-CHEMv7-01-02 User's Guide, Atmospheric
Chemistry Modeling Group, Harvard University, Cambridge, MA, October
15, 2004.
15 Lam, Y-E, Fu, J.S., Jacob, D.J., Jang, C., Dolwick, P., 2010. 2006-2008
GEOS-Chem for CMAQ Initial and Boundary Conditions. 9th Annual
CMAS Conference, October 11-13, 2010, Chapel Hill, NC.
-------
Height
(m)
17,145
14,490
12,593
11,094
9,844
8,766
7,815
6,962
6,188
5,477
4,820
4,208
3,635
3,095
2,586
2,198
1,917
1,644
1,466
1 9Q9
\,i:ai
1,121
952
787
705
624
544
465
386
307
230
153
114
76
38
Table 4-2.
Pressure
(mb)
50
95
140
185
230
275
320
365
410
455
500
545
590
635
680
716
743
770
788
Rfifi
ouu
824
842
860
869
878
887
896
905
914
923
932
937
941
946
WRF
34
33
32
31
30
29
28
27
26
25
24
23
22
21
20
19
18
17
16
-IK
1 \j
14
13
12
11
10
9
8
7
6
5
4
3
2
1
Depth
(m)
2,655
1,896
1,499
1,250
1,078
951
853
775
711
657
612
573
539
509
388
281
273
178
174
171
1 1 i
168
165
82
81
80
80
79
78
78
77
38
38
38
38
CMAQ
24
^^B
23
22
^M
21
20
^^M
19
18
17
16
15
14
13
12
11
1 1
10
9
8
7
6
5
4
3
2
1
Vertical layer structure for 2008 WRF
CMAQ simulations
(heights
Depth 1
(m) I
4,552
^M
2,749
2,029
^H
1,627
1,368
^M
1,185
^M
539
509
388
281
273
178
174
171
1 1 i
168
165
163
160
157
78
77
76
38
38
and
are layer top).
indicator because it avoids overinflating the observed range
of values, especially at low concentrations. Normalized mean
bias is denned as:
n
V(P-O)
NMB — — - * 100, where P — predicted concentrations
^ (O) and O = observed
i
Normalized mean error (NME), which 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:
n
X P~O\
NME =1 * 1 00. where P = predicted
•y (0\ concentrations and O = observed
i
Fractional Bias (FB) is denned as:
/ \
V (P-O)
FR = 1 i *100 whprp P = prprlirtprl mnrpntratinns
n n f /r>_j_/-}\' 1^1
^^ v ^ / and O ~~ observed
111 2 J
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:
In \
X \P-O\
FF — ' *1fin wViprp P — nrprlirtprl
\ ' \ ) poTippTilrntioTic; UTiH O — nb^ptvpH
Z^' o
\ i 2 /
order to estimate the ability of the CMAQ modeling system
to replicate the 2008 base year concentrations for the 12 km
continental U.S. domain.
There are various statistical metrics available and used by
the science community for model performance evaluation.
For a robust evaluation, the principal evaluation statistics
used to evaluate CMAQ performance were two bias
metrics, normalized mean bias and fractional bias; and two
error metrics, normalized mean error and fractional error.
Normalized mean bias (NMB) is used as a normalization to
facilitate a range of concentration magnitudes. This statistic
averages the difference (model - observed) over the sum
of observed values. NMB is a useful model performance
In addition to the performance statistics, regional maps which
show the normalized mean bias and error were prepared for
the ozone season, May through September, at individual
monitoring sites as well as on an annual basis for PM2 5 and
its component species.
Evaluation for 8-hour Daily Maximum Ozone: The
operational model performance evaluation for hourly and
eight-hour daily maximum ozone was conducted using
the statistics denned above. Ozone measurements from
1176 sites for 2008 in the continental U.S. were included
in the evaluation and were taken from the 2008 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
-------
Northeast
Season
Winter
Spring
Summer
™ FE
Midwest
Winter
Spring
Summer
Fall
5,132 -10.4 19.6
12,060 -2.5 11.2
15,448 8.8 15.6
10,890 15.9 22
2,706 -7.4 22.8
11,659 -0.3 11.9
16,244 6.7 14.3
9,274 12.1 20.3
-10.2
-2.5
9.2
16.2
-6.4
0.3
6.7
14.4
23.4
12
15.6
22.1
24.6
12.6
14.3
21.6
Central States Winter
Spring
Summer
11,289
15,166
16,641
14,005
-0.4
1.6
19.5
6.4
16
13.2
25.3
18.1
0.5
2.6
20
8
19
14.2
25.3
19.5
Southeast
Winter
Spring
Summer
Fall
6,277 1.5
17,358 1.5
19,419 15.7
14
11.2
21
West
Winter
Spring
Summer
15,080 17.2 21.5
I
23,457 6.1 19.8
27,252 -3 12.2
30,182 8.2 19
2.2
2.8
17.9
18.3
8.6
-2.5
8.1
27,203 11 20.4 11.7
15.1
11.9
22.2
21.9
•
22.1
12.8
18.7
21.2
Table 4-3. Summary of CMAQ 2008 8-Hour Daily
Maximum Ozone Model Performance Statistics by
Subregion and by Season.
following geographic groupings: domain wide and four large
sub-regions16: Midwest, Northeast, Southeast, Central, and
Western U.S.
The 8-hour ozone model performance bias and error statistics
for each subregion and each season are provided in Table
4-4. Seasons were defined as: winter (December-January-
February), spring (March-April-May), summer (June, July,
August), and fall (September-October-November). Spatial
plots of the normalized mean bias and error for individual
monitors are shown in Figures 4-2 through 4-3. The statistics
shown in these two figures were calculated over the ozone
season using data pairs on days with observed 8-hour ozone
of>60ppb.
In general, the model performance statistics indicate that the
8-hour daily maximum ozone concentrations predicted by
"The subregions are defined by States where: Midwest is IL,
IN, MI, OH, and WI; Northeast is CT, DE, MA, MD, ME,
NH, NJ, NY, PA, RI, and VT; Southeast is AL, FL, GA, KY,
MS, NC, SC, TN, VA, and WV; Central is AR, IA, KS, LA,
MN, MO, ME, OK, and TX; West is AK, CA, OR, WA, AZ,
MM, CO, UT, WY, SD, ND, MT, ID, and NV.
the 2008 CMAQ simulation closely reflect the corresponding
8-hour observed ozone concentrations in space and time in
each subregion of the 12 km modeling domain. As indicated
by the statistics in Table 4-4, bias and error for 8-hour daily
maximum ozone are relatively low in each subregion, not
only in the summer when concentrations are highest, but also
during other times of the year. Specifically, 8-hour ozone in
the summer is slightly over predicted with the greatest over
prediction in the Central States (NMB is 19.6 percent). In the
spring, ozone is slightly under predicted in all the subregions
except in the Central states where NMB is near negligible
(NMB is 0.2 percent). In the winter, when concentrations are
generally low, the model under predicts 8-hour ozone with
the exception of the West (NMB is 3.0). In the fall, when
concentrations are also relatively low, ozone is slightly over
predicted; with NMBs less than 12 percent in each subregion.
Model bias at individual sites during the ozone season is
similar to that seen on a subregional basis for the summer.
The information in Figure 4-2 indicates that the bias for days
with observed 8-hour daily maximum ozone greater than 60
ppb is within ± 20 percent at the vast majority of monitoring
sites across the U.S. domain. The exceptions are sites in
and/ or near Minneapolis, Duluth, District of Columbia,
New York City, New Orleans, and San Antonio, as well as a
few areas along the California coast. At these sites observed
concentrations greater than 60 ppb are generally predicted
in the range of ±20 to 40 percent. Looking at the map of
bias, Figure 4-2 indicates that the low bias at these sites is
not evident at other sites in these same areas. This suggests
that the under prediction at these sites is likely due to very
local features (e.g., meteorology and/or emissions) and not
indicative of a systematic problem in the modeling platform.
Model error, as seen from Figure 4-3, is 14 percent or less at
most of the sites across the U.S. modeling domain. Somewhat
greater error is evident at sites in several areas most notably
along portions of the Northeast Corridor and in portions of
Michigan, Minnesota, Louisiana, Texas, and the western most
part of the modeling domain, (e.g., New Mexico, California,
and Washington).
PM2 s: The PM2 5 evaluation focuses on PM2 5 total mass
and its components, including sulfate (SO4), nitrate (NO3),
total nitrate (TNO3 = NO3 + HNO3), ammonium (NH4),
elemental carbon (EC), and organic carbon (OC). The PM25
bias and error performance statistics were calculated on an
ttannual basis for each subregion (Table 4-5). PM2 5 ambient
measurements for 2008 were obtained from the following
networks for model evaluation: Chemical Speciation
Network (CSN—total of 211 sites, 24 hour average),
Interagency Monitoring of PROtected Visual Environments
(IMPROVE—total of 163 sites, 24 hour average), and
Clean Air Status and Trends Network (CASTNet—total of
86, weekly average). For PM2 5 species that are measured
by more than one network, we calculated separate sets of
statistics for each network by subregion. For brevity, Table
4-5 provides annual model performance statistics for PM and
its component species for the 12 km continental U.S. domain
and the five sub-regions defined above (Northeast, Midwest,
Southeast, Central, and West). In addition to the tabular
-------
03_8hrmax NMB (%) for run 2008ab_08c_12US1 for 20080501 to 20080930
units -%
coverage limit • 7S%
>100
180
60
140
120
0
1-20
-40
-60
-80
U-100
C!RCLE=AQS_Daily;
Figure 4-2. Normalized Mean Bias (%) of 8-hour daily maximum ozone greater than 60 ppb over the period May-
September 2008 at monitoring sites in the continental U.S. modeling domain.
Q3_8hrmax NME (%) lor run 2008ab_08c_12US1 for 20080501 to 20080930
ClRCLE=AQS_Daily;
units. %
- 75%
>20
18
16
14
12
10
8
6
4
2
0
Figure 4-3. Normalized Mean Error (%) of 8-hour daily maximum ozone greater than 60 ppb over the period May-
September 2008 at monitoring sites in the continental U.S. modeling domain.
-------
summaries of bias and error statistics, annual spatial maps
which show the normalized mean bias and error by site for
each PM2 5 species are provided in Figures 4-4 through 4-17.
As indicated by the statistics in Table 4-5, annual CMAQ
PM2 5 for 2008 shows a slight under prediction at rural
IMPROVE monitoring sites in each subregion except the
Northeast which shows an over prediction in NMB of 18.1
species are provided in Figures 4-4 through slight over
predictions in the Midwest, Central and West whereas annual
PM2 5 is under predicted in the Southeast (NMB is -2.1
percent) and Northeast (NMB is -42.3).
Although not shown here, the mean observed concentrations
of PM2 5 are more than twice as high at the CSN sites (~16ug
nr3) as the IMPROVE sites (~ 6ug nr3), thus illustrating
the statistical differences between the urban CSN and rural
IMPROVE networks.
Annual average sulfate is consistently under-predicted at
CSN, IMPROVE, and CASTNet monitoring sites across
the modeling domain, with NMB values ranging from -8
percent to -34 percent. Overall, sulfate bias performance is
slightly better at urban CSN sites than at rural IMPROVE
and/or suburban CASTNet sites. Sulfate performance shows
moderate error, ranging from 22 to 45 percent. Annual model
bias and error at individual sites, as displayed in Figures 4-6
and 4-7, suggest spatial patterns vary by region. The model
bias for most of the Southeast, Central and Southwest states
are within -20 to -40 percent. The model bias appears to
be much less (±20 percent) in the Northeast, Midwest, and
Northwest states. A few sites in the Northwest have biases
much greater than 20 percent. Model error also shows a
spatial trend by region, where much of the Eastern states are
20 to 30 percent, the Central U.S. states are 30 to 40 percent,
and the Western states are greater than 40 percent.
Subregion
No. ofObs NMB(%) NME (%) FB (%)
FE (%)
Northeast
Midwest
CSN
PM,
IMPROVE Southeast
CSN
Sulfate
Northeast
Midwest
Southeast
Central
West
Northeast
Midwest
Southeast
Central
West
3,143
2,324
2,851
2,215
2,829
2,301
599
1,887
2,508
9,497
Northeast
idwest
Southeast
Central
West
Northeast
Midwest
Southeast
Central
West
Nitrate
37.9
38,
42.1
44,
34.9
32,
36.7
39,
43.9
31.6
33,
36.9
47,
45.1
6,5
56.0
93,
57.3
42.9
42.3
45,
49.3
48.2
37.4
37.3
43.4
-------
Subregion
No. ofObs NMB(%) NME (%) FB (%)
FE (%)
Northeast
IMPROVE
Total Nitrate
(N03 + HN03)
Southeast
Central
West
Northeast
Midwest
Southeast
Central
West
Northeast
Midwest
Southeast
Central
West
Northeas,
Midwest
Southeast
Central
West
Northeast
Midwest
Southeast
Central
West
Northeast
Midwest
Southeast
Central
West
Elemental
Carbon
Northeast
Midwest
Southeast
Central
West
Northeast
Midwes
Southeast
Central
West
Organic
Carbon
Table 4-4. Summary of CMAQ 2008 Annual PM2 5 Species Model Performance Statistic
-------
Annual average nitrate is over predicted at the urban and
rural monitoring sites in most of the subregions in the 12
km modeling domain (NMB in the range of 24% to 93%),
while nitrate is under predicted in the West (NMB in the
range of -23% to -32%). The bias statistics indicate that
the model performance for nitrate is generally best at the
urban CSN monitoring sites. Model performance of total
nitrate at suburban CASTNet monitoring sites shows an
over prediction across all subregions. Model error for nitrate
is somewhat greater for each subregion as compared to
sulfate. Model bias at individual sites indicates mainly over
prediction of greater than 20 percent at most monitoring sites
in the Eastern half of the U.S. as well and in the extreme
Northwest, as indicated in Figure 4-8. The exception to this
is in the Southwest of the modeling domain where there
appears to be a greater number of sites with under prediction
PM_TOT NMB (%) for run 2008ab_08c 12US1 for 20080101 to 20081231
"
unte-%
coverage lim* - 75%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-4. Normalized Mean Bias (%) of annual PM2 5 mass at monitoring sites in the continental U.S. modeling
domain.
PM TOT NME (%) for run 2008ab_08c_12USl for 20080101 to 2QQ81231
units.
coverage limit - 75%
* -^ -7 \ •_
• C V-1«* • «
•
CIRCLE-IMPROVE; TRIANGLE=CSN;
Figure 4-5. Normalized Mean Error (%) of annual PM2 5 mass at monitoring sites in the continental U.S. modeling
domain.
-------
of nitrate of 20 to 80 percent. Model error for annual nitrate,
as shown in Figure 4-9, is least at sites in portions of the
Midwest and extending eastward to the Northeast corridor.
Nitrate concentrations are typically higher in these areas than
in other portions of the modeling domain.
Annual average ammonium model performance as indicated
in Table 4-5 has a tendency for the model to slightly over
predict in the Northeast, Midwest, and Central U.S. states
across the CSN and CASTNet monitoring sites (NMB
aranging from -0.4 to -10 percent). In contrast, the model
tends to slightly under predict in the Southeast and Western
states at CSN and CASTNet sites (NMB ranging from 5 to
18 percent). There is not a large variation from subregion to
subregion or at urban versus rural sites in the error statistics
for ammonium.
804 NMB (%) for run 200eab_06c_12US1 for 20080101 to 20081231
units-- %
coverage limtt - 75%
>100
60
60
40
20
0
-20
-60
Leo
U-100
CIRCLE=IMPROVE: TRIANGLE=CSN: SOUARE=CASTNET:
Figure 4-6. Normalized Mean Bias (%) of annual Sulfate at monitoring sites in the continental U.S. modeling domain.
SO4 NME (%) for run 2008ab_08c_12US1 for 20080101 to 20081231
coverage limit - 75%
CIRCLE=IMPROVE; TRIANGLE=CSN; SQUARE=CASTNET;
Figure 4-7. Normalized Mean Error (%) of annual Sulfate at monitoring sites in the continental U.S. modeling domain.
-------
Annual average elemental carbon is under predicted in all
subregions at urban sites with the exception of the slight over
prediction in the Northeast. At rural sites, elemental carbon is
over predicted in the Northeast, Central and West, although
elemental carbon is under predicted in the Midwest and
Southeast. Similar to ammonium error model performance,
there is not a large variation from subregion to subregion or
at urban versus rural sites.
Annual average organic carbon is under predicted in the
Midwest, Southeast and Central states at the urban and rural
monitoring sites. In contrast, organic carbon model bias tends
to show a slight over prediction in the Northeast and West.
Similar to ammonium and elemental carbon, error model
performance does not show a large variation from subregion
to subregion or at urban versus rural sites.
N03 NMB (%) for run 2008ab_08c_12US1 for 20080101 to 20081231
- 79%
>100
80
60
40
SO
0
-20
-40
-60
I -80
U-100
CIRCLE=IMPROVE; TRIAMGLE=CSN;
Figure 4-8. Normalized Mean Bias (%) of annual Nitrate at monitoring sites in the continental U.S. modeling domain.
N03 HME (%) for run 2008ab_08c_12USl lor 20080101 to 20081231
units.%
coverage limit - 79%
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-9. Normalized Mean Error (%) of annual Nitrate at monitoring sites in the continental U.S. modeling domain.
-------
TNO3 NMB (%) for run 2Q08ab_08c_12US1 for 20080101 to 20081231
units-
coverage limit- 75%
Figure 4-10. Normalized Mean Bias (%) of annual Total Nitrate at monitoring sites in the continental U.S.
modeling domain.
TNQ3 NME (%) for run 2Q08ab_08c_l2USl for 20080101 to 20081231
unts-%
cctwagt limit. 75%
CIRCLE=CASTNET;
Figure 4-11. Normalized Mean Error (%) of annual Total Nitrate at monitoring sites in the continental U.S.
modeling domain.
-------
NH4 NME (%) for run 20Q8ab_08c_12US1 for 20080101 to 20081231
**>
units -%
coverage limit • 75%
>100
190
180
70
160
150
40
130
120
110
'o
CIRCLE=CSN; TRIANGLE=CASTNET;
Figure 4-12. Normalized Mean Error (%) of annual Total Nitrate at monitoring sites in the continental U.S.
modeling domain.
NH4 NMB (%) for run 2008ab_Q8c_l2USl for 20080101 to 20081231
^f Al
units, %
coverage limit. 75%
>100
180
160
40
120
0
-20
-40
-60
-80
<-100
CIRCLE=CSN; TRIANGLE=CASTNET;
Figure 4-13. Normalized Mean Error (%) of annual Ammonium at monitoring sites in the continental U.S.
modeling domain.
-------
EC NMB (%) lor run 20QBab_08c_12US1 for 20080101 to 20081231
units -*i
coverage limit - 75%
>10Q
80
160
40
20
0
-20
-40
-60
-80
<-100
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-14. Normalized Mean Bias (%) of annual Elemental Carbon at monitoring sites in the continental U.S.
modeling domain.
EC NME (%) for run 2008ab_Q8c_12USl for 20080101 to 20081231
coverage fcnrt - 75%
R
100
90
80
70
60
50
40
30
20
10
0
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-15. Normalized Mean Error (%) of annual Elemental Carbon at monitoring sites in the continental U.S.
modeling domain.
-------
PC NMB (%) lor run 2006ab_06c_12US1 for 20080101 to 20081231
s_
units-%
coverage limit
>100
180
160
140
120
P
-20
-40
I-60
I-80
U-100
CIRCLE=IMPROVE; TRIANGLE=CSN;
Figure 4-16. Normalized Mean Bias (%) of annual Organic Carbon at monitoring sites in the continental U.S.
modeling domain.
PC NME (%) for run 2008ab_08c_12US1 for 20080101 to 20081231
«r i*
units -%
coverage limit • 75%
CIRCLE=IMPRQVE: TRIANGLE=CSN;
Figure 4-17. Normalized Mean Error (%) of annual Organic Carbon at monitoring sites in the continental U.S.
modeling domain.
-------
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, I, "Combining Different Sources of
Paniculate Data Using Bayesian Space-Time Modeling,"
Environmetrics, 2010, 21: pp 48—65, 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) overtime 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 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.
5.3 Results for 03 and PM25
The HB Model yields a predicted daily concentration
and error estimate for those predictions within each grid
cell for each day within the time period of interest. The
concentrations are daily PM2 5 or 8-hour maximum ozone
levels. These predictions fall along a smooth (congruent)
response surface across the entire region. The grid used
by the HB Model is the same as that used in generating
the CMAQ estimates. The smoothness of the surface is
achieved by: 1) the choice of prior distributions for air data,
CMAQ output, and the true underlying predictive surface;
and 2) the conditional autoregressive model (CAR) spatial
covariance structure where a grid's predicted concentration
is assumed to be correlated with neighboring cells (note the
HB Model can handle different size neighborhoods). The
resulting HB Model prediction surface approximates the true
underlying response surface while accounting for such factors
as measurement error and potential space-time bias in the
CMAQ output.
EPA stores the set of back-transformed predictions
(pm25_pred, O3_pred) and standard errors (pm25_stdd,
O3_stdd) from a given execution of the HB Model in tabular
(comma-delimited) format within a file named as in the
following example: pm25_surface_12km_2007.csv. Table 5-1
presents an example of the output that can be obtained from
this file. One row exists in this file for each grid cell-date
combination within the study area. The relevant variables in
this file, in the order in which they exist (and are portrayed
within the column headings of the table), are as follows:
• Date: Represented by the data given in this row, in
MM/DD/YYYY format.
• Longitude: The x-coordinate value transformed to
longitude (degrees).
• Latitude: The y-coordinate value transformed to latitude
(degrees).
Column: The column associated with model results.
• Row: The row associated with model results.
* pm25_pred or O3_pred
* pm25_stdd or O3_stdd
5.4 Overview of HB Model Predictions
Below is a short description of the inputs and outputs for
a HB Model application for 2007, 12 km grid, PM2 5. A
description of the input metadata and HB Model application
can be found in Appendix E. The air quality data was
obtained from EPA's AQS; CMAQ was run by EPA as
documented previously in this report, and; the HB model was
run by EPA/NERL. The domain of the CMAQ model (and
therefore the HB Model predictions) is found in the following
table.
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
PM2 5 decreased in intensity and moved southeast. Examining
the figure, it is possible to see the change in PM2 5 level at
any point in the domain. Figure 5-2 shows a close up of the
HB Model predictions for July 2. The 12-km grid can be
seen as small squares. Within each grid the predicted PM2 5
concentrations are constant. As such, the PM2 5 concentrations
represent an average over the area where the public is
exposed to ambient PM2 5. Although actual concentrations
within grid cells vary over space and time during a day,
the ambient exposure is likely to be somewhat averaged as
people move about within and between grid cells. Given
the relationship between ambient concentrations, ambient
exposures and personal exposure is not understood well,
one area of study is the degree of misclassification between
exposure and health outcomes based on varying grid sizes.
The HB Model results can track with the AQS data and
CMAQ estimates and the predictions can differ from either
the AQS data or the CMAQ estimates. Figure 5-3 shows HB
predictions for a location where the predictions generally
follow temporally the CMAQ and AQ data. This figure shows
a series of days where AQS data and CMAQ estimates are
-------
01/01/2008
01/01/2008
01/01/2008
01/01/2008
01/01/2008
01/01/2008
01/01/2008
01/01/2008
01/01/2008
01/01/2008
Longitude
-119.315
-119.398
-119.483
-119.567
-119.653
-119.739
-119.826
-119.913
-120.001
-120.09
Latitude
23.43627
23.74126
24.04658
24.35223
24.6582
24.96448
25.27106
25.57793
25.88509
26.19253
Column
12
12
12
12
12
12
12
12
12
12
O pred (ppb) O stdd (ppb)
15
16
17
18
19
20
21
22
23
24
23.011
22.979
22.919
22.987
23.19
23.018
23.12
22.997
22.968
22.949
4.6122
4.6784
4.8484
4.7917
4.84
4.8264
4.8651
4.84
4.8308
4.8357
Note: The exact contents of this table may change over time. Please check the accompanying metadata files.
Table 5-l.HB Model Prediction Example Data File
Study Year
2008
Bounding West
Longitude
111.1 degWIon
Bounding East
Longitude
65.4 deg W Ion
Bounding North
Latitude
51.25degNlat
Bounding South
Latitude
23.0 deg N lat
Table 5-2. HB Model Domains for 12-km Applications
10 20
30
40+
I
PM2 s
July 1,2002
*VJ-
t t »A 3 *
July 2, 2002
July 3, 2002
July 4, 2002
V!-
Figure 5-1. HB Prediction (PM ) During July 1-4, 2002 (12 km grid cells)
-------
0 10 20 30 tO*
Figure 5-2. HB Prediction (PM2 5) on July 2,2002 (12 km
grid cells)
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 in a l-in-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 collect samples in a l-in-3 day time period.)
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. 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 HB Model and the monitoring
stations. However, in areas in which there are few or no
monitoring stations, the HB 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.
Figure 5-9 displays the ozone concentration for the
continental U.S. on July 26, 2005. The spheres represent
the concentrations recorded at monitor locations. The green,
blue, and yellow represent the HB concentration surface,
which combines the CMAQ model estimates and the PM2 5
monitor measurements.
5.5 Evaluation of HB Model Estimates
As reported in the McMillan paper (Environmetrics, 2010),
model validation analysis was performed to compare the HB
predictive results at 2001 STN/IMPROVE monitoring sites
to predictions at those locations from two other approaches:
(1) traditional kriging predictions based solely on the FRM
monitoring data and (2) CMAQ output at these locations. In
doing so, it was assumed the STN/IMPROVE measurements
represent the "truth." The IMPROVE measurements are
representative of rural areas (with few monitors) and may
help assess the HBM results for these areas of interest. The
potential bias in either the STN or IMPROVE gravimetric
mass measurements compared to FRM data were not
considered, although for gravimetric mass the monitors
generally produce the same results. STN data collocated with
FRM monitoring sites used in fitting the HB Model were
eliminated from the validation data set, leaving 44 sites for
the validation analysis.
In the validation analysis, mean squared prediction error and
bias were calculated to evaluate the predictive capability of
these three different models. To assess the ability of the HB
Model to accurately characterize prediction uncertainty, the
percentage of validation data within the 95 percent prediction
credible interval was calculated. In the analysis, a similar
analysis was performed for the kriging model by calculating
95 percent confidence intervals at the validation sites. An
exponential variogram model was used for the kriging model.
The exponential parameters were estimated by fitting this
model to an empirical variogram based on combining the
daily empirical variograms.
In this analysis, predictions for each day were obtained
for the STN/IMPROVE site locations from the three
modeling approaches and the validations statistics were
calculated across all days and sites. The validation only
occurs every third day, according to the sampling schedule
of STN/IMPROVE. This corresponds to the full network
FRM schedule. Thus, the analysis did not evaluate sparse
monitoring days where data fusion is expected to outperform
interpolation techniques based solely on the monitoring data.
In the analysis, the HBM was run several times using a range
of reasonable priors. Then, the validation analysis assessed
the relative predictive performance of the HBM, traditional
kriging, and CMAQ as described above. In terms of mean
squared prediction error (MSB), the HBM and kriging
approaches provided similar results across all HBM runs.
For bias, the HBM outperformed kriging by 10 to 15 percent
depending on the prior assumptions for T and T . 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
-------
.(•II
Figure 5-3. HB Prediction (PM25) Temporally Matches AQS Data and CMAQ Estimates -
Note: Computer_data = CMAQ Output
. tu
U
1
O
8.
«
I
I
-
u
•
i
<:
•
-
•
i
i
:
:
•
•
Figure 5-4. HB Prediction (PM2 5) Compensates When AQS Data is Unavailable on FRM Monitor
Non-Sampling Days - Note: Computer_data = CMAQ Output
-------
CO
B)
.
0)
-------
Figure 5-7. Rotated View of the Response Surface of PM 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. 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
-------
Fused 36 km 0, Surface, 7/26/05
Figure 5-9. Fused 36 km O3 Surface for the Continental
U.S. (July 26,2005).
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 PM25 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 a
continuing research program. Accordingly, it should
be understood by users of the HB predictions that the
underlying statistical model is continuing to be studied and
improved. However, given the uncertain nature of air quality,
especially at locations well-removed from monitoring sites,
NERL has been working to develop the HB Model (and
other approaches) for estimating ambient and exposure
concentrations for use in health studies, benefits assessments,
and other air program analyses. To encourage assessments
of these predictions from the HB Model, NERL is making
the predictions available based on a general DQO approach
of determining whether the predictions from the HB Model
are appropriate for use for these purposes. This approach
I 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 predominant issue for l-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 (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 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.
-------
Appendix A
Acronyms
ATSDR
AFM
BEIS
BELD
BlueSky
CSN
ACRONYMS
Database giving users access to air quality data collected at outdoor monitors across U.S.
Website providing public with access to national air quality information
Aerometric Information Retrieval System
Atmospheric Modeling and Analysis Division (of EPA/NERL)
Air Quality System database
Advanced Research WRF core model
Agency for Toxic Substances and Disease Registry (of CDC)
Benzene, Acetaldehyde, Formaldehyde, and Methanol
Biogenic Emissions Inventory System
Biogenic Emission Landuse Database
Emissions modeling framework
Clean Air Act
Clean Air Interstate Rule
EPA's Clean Air Markets Division
Criteria Air Pollutant
Conditional Auto Regressive spatial covariance structure (model)
California Air Resources Board
Clean Air Status and Trends Network
Centers for Disease Control and Prevention
Central Data Exchange
Continuous Emissions Monitoring
Consolidated Emissions Reporting Rule
Clearinghouse for Inventories and Emissions Factors
Community Multiscale Analysis System
Community Multiscale Air Quality model
Commercial marine vessel
Compressed Natural Gas
Carbon monoxide
Chemical Speciation Network
Coefficient of Variation
Data Quality Objectives
Emissions Control Area-International Marine Organization
Exclusive Economic Zone
Electric Generating Units
Listing of elements contributing to atmospheric release of pollutant substances
Emission Factor (California's onroad mobile model)
Environmental Protection Agency
Environmental Public Health Tracking
Environmental Sciences Division (of EPA/NERL)
Federal Aviation Administration
-------
ACRONYMS
Fractional Bias
Fuel Characteristic Classification System
Four Dimensional Data Assimilation
Fractional Error
Federal Equivalent Method
Federal Information Processing Standards
Federal Reference Method
Hazardous Air Pollutant
Hierarchical Bayesian Modeling
Human Exposure and Atmospheric Sciences Division (of EPA/NERL)
Health and Human Services Department
Hazard Mapping System
Nitrous Acid
Interagency Agreement
Incident Status Summary form
Interagency Monitoring of Protected Visual Environments
Integrated Planning Model
Itinerant
Light Duty Gas Vehicle
Liquified Petroleum Gas
Land Surface Model
Long Wave Radiation
Maximum Achievable Control Technology
Mesoscale Model 5
Minerals Management Service
OTAQ's model for estimation of onroad mobile emissions factors
Moderate Resolution Imaging Spectroradiometer
Memorandum of Understanding
Mobile Source Air Toxics
Mean Square Error
Motor Vehicle Emission Simulator
National Ambient Air Quality Standards
National Ambient Air Monitoring Station
National Electric Energy Database System
National Emission Inventory
National Exposure Research Laboratory
National Emission Standards for Hazardous Air Pollutants
Ammonia
Normalized Mean Bias
Normalized Mean Error
National Mobile Inventory Model
Nitrogen Dioxide
National Oceanic and Atmospheric Administration
OTAQ's model for estimation of nonroad mobile emissions
Nitrogen oxides
EPA's Office of Air Quality Planning and Standards
EPA's Office of Air and Radiation
EPA's Office of Research and Development
-------
ORIS
Wildfire
WRAP
WRF
ACRONYMS
Office of Regulatory Information Systems (code) - is a 4 or 5 digit number assigned by the Department of
Energy's (DOE) Energy Information Agency (EIA) to facilities that generate electricity
One Record per Line
EPA's Office of Transportation and Air Quality
Polycyclic Aromatic Hydrocarbon
Particulate Elemental Carbon
Planetary Boundary Layer
Public Health Air Surveillance Evaluation Project
Portable Fuel Container
Particulate Matter
Particulate matter less than or equal to 2.5 microns
Particulate matter less than or equal to 10 microns
Particulate matter with aerodynamic diameter between 2.5 and 10 microns
Coarse particulate matter (see definition for PM1025) (Coarse PM)
Primary particulate matter less than 2.5 micrometers in diameter
Primary Particulate nitrate
Particulate Organic Carbon
Intentionally set fire to clear vegetation
Primary Particulate sulfate
Regulatory Impact Analysis
Regional Planning Organization
Rapid Radiative Transfer Model
Source Classification Code
Sulfur Emissions Control Area
State Implementation Plan
State and Local Air Monitoring Stations
Satellite Mapping Automatic Reanalysis Tool for Fire Incident Reconciliation
Sparse Matrix Operator Kernel Emissions
Sulfur Dioxide
Secondary Organic Aerosol
Texas Commission on Environmental Quality
Tapered Element Oscillating Microbalance
Total Organic Gases
Toxic Release Inventory
Technical support document
Volatile organic compounds
Vehicle miles traveled
Uncontrolled forest fire
Western Regional Air Partnership
Weather Research and Forecasting Model
-------
-------
Appendix B
U.S. 2008 Emissions Inventory Totals by:
Sector, Pollutant Species, and Region
(e.g., US State, Canadian Province, or
Mexican Federal State)
c1c2rail
nonp,
nonroad
onroad*
ptfire
*.
ptnonipm
cSmarine
Con.US Total
237,860
5,225,880
17,895,250
35,987,232
30,326,302
679,542
2,932,043
13,195
93,297,303
3,569,837
636
169,918
2,074
137,064
496,460
24,294
69,114
•
4,469,397
1,438,940
1,266,655
1,874,385
7,452,168
348,254
3,066,741
2,045,916
142,243
17,635,302
49,163
1,660,293
183,854
318,388
3,026,530
421,782
581,026
12,932
11,948,027
46,364
936,028
175,326
243,744
2,564,856
349,378
380,814
11,871
5,500,523
50,358
592,585
31,766
66,370
207,860
7,843,262
1,577,530
108,779
10,478,508
" onroad emissions are the sum of on_noadj, startpm, and runpm sectors
54,524
6,603,445
2,445,581
3,147,282
7,136,612
36,677
1,055,754
5,082
20,484,957
Table B-la - 2008 Emissions Summary (tons/year), by Species and by Emission Sector/Source
Country &
Sector
Canada othar
Canada othon
Canada othpt
Canada Subtotal
Mexico othar
Mexico othon
Mexico othpt
Mexico Subtotal
Offshore othpt
Canada
cSmarine
Offshore
cSmarine
2008 TOTAL
[tons/yr]
CO
3,756,550
4,528,720
1,151,363
9,436,633
351,324
1,069,878
68,615
1,489,817
90,046
12,909
[tons/yr]
NHL
539,370
21,883
21,194
582,447
255,125
1,904
11,093,696
839,476
[tons/yr]
NCX
720,293
539,473
863,597
2,123,362
171,396
110,580
224,853
506,829
82,797
155,913
930,958
3,643,946
[tons/yr]
1,424,994
15,053
117,583
1,557,630
75,679
5,168
97,422
178,269
842
12,944
77,552
1,814,293
[tons/yr]
PWU
394,648
10,669
68,306
473,623
49,111
4,735
72,471
126,317
839
11,861
71,307
672,085
[tons/yr]
SCL
98,047
5,448
1,767,089
1,870,584
82,908
6,143
651,756
740,807
1,966
96,028
577,849
3,191,206
[tons/yr]
VOC*
1,277,485
277,959
431,876
1,987,320
398,409
137,234
58,664
594,307
51,240
5,470
32,730
2,665,597
"* - VOC emissions are inventory (""actual"" VOC), gridded (within 12km US1 grid).
Canada othpt includes a sum of all pre-speciated inventory gridded VOC."
Table B-lb - 2008 Emissions Summary (tons/year), by Species, for Canada, Mexico, and Offshore Emission Sectors
-------
ALDX BENZENE CL,
HCL MONO
ptipm
24,231
69,251
21,194
496,516
169,911
136,992
2,073
1,748
37,898
33,970
169,630
51,497
90,484
55,955
62,151
19,002
1,446
319
677,603
2,939,606
1,151,363
30,329,749
5,210,185
35,995,095
17,987,121
4,107,874
5,598,598
103,339
8,852,668
1,106,523
2,013,686
2,761,256
1,845,924
777,237
313,459
1,136,008
6,759,289
1,675,826
802,514
585,048
c1c2rail
afdust
cSmarine
beis
ocean_c!2
on_moves_runpm
on_moves_startpm
SMOKE TOTAL
306,806
204,049
86,360
34,829
126,223
585,477
171,307
83,265
59,805
689,496
524,101
84,032.2 113,191,712 289,153 107,671 5,313,942 21,075,364 1,904,011
Table B-2a 2008 Species Emission by Sector
Sector
ptipm
ptnonipm
othpt
ptfire
PCA PEC PI-L
nonpt
on_noadj
nonroad
othar
othon
ag
c1c2rail
afdust
cSmarine
beis
ocean_c!2
on_moves_runpm
on_moves_stortpm
SMOKE TOTAL 80,380
11,623
8,709
1
5,998
2,792
•
102
11,896
21
158
27
38,807
246
20,699
36,289
3,619
260,962
61,990
119,769
96,525
55,636
8,498
378
35,845
1,166
475
5,354 3,783
6,075 1,533
713,283 43,383 7,608,637
PMC PMFINE PNCX POC PSO, PTI
273,462 568 17,124
237,954 3,677 51,142
51,894 282 5,995
1,033,548 15,048 1,234,826
466,331 2,606 383,328
22,351 215 35,685
29,092 228 48,358
261,610 979 115,959
1,918 20 4,890
3,677 679 2,334
2,274 53 8,182
750,047 663 36,373
47,725 0 10,704
6,667 14 31,749 206
2,149 11 9,585 7
3,190,701 25,043 1,996,235 185,245 8,289.1
7,818,020
1,581,458
1,767,089
207,881
586,868
50,468
31,491
180,954
11,591
782,954
172,370
12,515
0
»
9,293
0
0
1,950
0
13,069,271 196,129
Table B-2b 2008 Species Emission by Sector
-------
Sector
MODEL READY
US ONLY
Low level totals
ptipm elevated
ptnonipm elevated
cSmarine elevated
othpt elevated
ptfire elevated
Model-ready
domain totals
ALDX
464,684
3
2,609
307
163
222,482
690,248.5
BENZENE
338,321
1,743
15,604
0
510
169,630
525,808.9
CL,
82,635
171
1,226
0
• °
0
84,031.9
CO
93,481,209
79,274,228
674,667
2,044,690
103,339
971,263
30,329,749
113,397,936
HCL
10,182
235,230
43,742
0
0
0
289,154
MONO
97,886
0
0
9,835
0
0
107,721
NH,
4,727,053
24,124
46,209
0
21,194
496,516
5,315,096
NO
16,896,879
15,085,733
2,750,244
1,393,715
1,106,523
710,032
313,459
21,359,705
NO, 1
1,674,582
1,248,238
305,583
154,857
113,146
78,892
34,829
1,935,545
Table B-2c 2008 Species Emission by Sector
Sector
MODEL READY
US ONLY
Low level totals
ptipm elevated
ptnonipm
elevated
cSmarine
elevated
othpt elevated
ptfire elevated
Model-ready
PCA
57,484
11,572
5,236
246
1,232
5,998
R-l 7RR
PEC
409,542
20,511
21,959
475
2,728
260,962
7-lfi -177
PH2O
4,145
78
2,558
35,196
1,601
0
/n t;7Q
PMC
6,902,011
103,338
87,721
8,392
72,744
461,726
7 RV; cm
PMFINE
1,645,313
272,175
148,772
47,725
96,453
1,033,548
T o/n QRR
PNO,
6,820
561
2,478
0
355
15,048
ot;oR-i
POC
696,187
17,000
35,194
10,704
9,405
1,234,826
o nrm-17
PSO,
47,824
36,182
40,076
36,167
16,472
20,759
-IQ7 /1«n
PTI
6,117
1,387
411
5
132
901
RQZ1
SO,
1,111,155
7,758,359
1,479,486
782,954
2,383,677
207,881
-i/?70/? t;-io
SULF
12,322
171,542
12,265
0
10,971
0
on? -inn
domain totals ' ' ' '
Table B-2d 2008 Species Emission by Sector
-------
113,954
41,900
37,699
5,130
61,855
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
• District of
Columbia
Florida
Georgia
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal
Utah
Vermont
33,338
11,702
16,905
16,144
26,908
8,305
15,601
35,024
565
3,748
3,633
13,110
6,877
8,257
21,002
2,899
3,466
1,295
990
3,105
16,082
12,656
19,198
7,150
15,663
12,534
962
20,823
169
11,326
572
6,918
221,564
5,115
3,882
1,194
159,684
107,550
125,119
199,533
48,901
52,788
158,934
45,609
689
36,067
9,525
108,497
60,460
43,164
93,055
28,231
42,981
14,192
4,654
13,543
28,752
35,698
60,066
67,340
242,302
79,329
9,417
186,334
200
45,740
13,851
86,496
155,616
82,221
60,543
296
15,655
12,750
9,126
106,924
8,074
3,193
9,441
7,476
96
7,648
836
4,442
8,455
2,057
9,566
722
2,305
915
524
4,648
696
4,063
23,407
1,546
49,732
5,651
1,112
16,476
5
18,299
242
17,602
6,003
7,302
100,438
5,568
1,756
34,287
4,760
85
5,949
677
3,054
3,508
1,136
6,103
734
1,879
361
281
4,613
678
2,518
19,368
305
44,770
3,528
706
7,893
5
14,579
229
7,167
21,425
10,626
1,968
44
5,320
11,619
7,025
906
43
3,635
1,058
197
352
22
359
795
1,477
57
202
196
207
202
481
143
6
192
225
154
174
274
1,705
150
369
66
718
252
423
4
283
34
207
4,406
306
23
15
267,793
516,020
281,824
601,085
109,284
212,619
482,400
15,205
20,191
458
-------
Inventory
State
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Con. US
Table B-3a - 2008
CO
5,617
2,641
9,925
12,736
13,061
679,542
NOX
51 ,787
10,920
100,349
50,263
72,398
3,066,741
Integrated
Modeled
State ALD, ALDX
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New
Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Planning
PM10
3,027
489
6,303
3,367
14,794
PM25
1,552
459
4,616
337
5,733
421,782 349,378
Model Point
BENZENE CH4
39
17
21
32
19
4
3
0
74
68
74
89
30
38
73
55
0
15
9
58
47
23
70
19
21
5
7
5
15
45
53
34
60
559
332
48
1,026
217
151
36
0
2,012
220
75
103
15
85
41
825
75
9
729
203
90
703
214
46
3
78
37
421
155
703
84
0
12
Source
CL
0
0
0
0
22
0
0
0
1
0
0
0
45
4
0
0
0
0
0
4
8
0
0
0
0
0
2
0
0
2
2
0
0
NH3
269
92
33
452
440
24,294
S02
132,093
2,322
312,268
134,639
80,758
7,843,262
(ptipm) Emissions by
CO
11,448
8,582
4,240
7,515
5,163
8,874
763
3
33,260
11,677
16,859
16,070
26,823
8,278
15,558
34,923
563
3,737
3,623
13,082
6,860
8,245
20,961
2,891
3,456
1,291
987
3,100
16,041
12,638
19,168
7,128
15,619
ETH
2
0
4
22
0
2
0
0
58
0
1
0
1
6
0
0
0
0
7
8
23
0
1
8
3
0
5
1
0
10
11
0
0
voc
548
15
1,191
1,044
828
36,677
Species and
ETHA
178
70
83
8
91
15
13
0
182
317
347
415
137
149
341
100
0
69
134
238
131
43
327
88
95
22
15
22
40
57
203
161
284
CHLORINE
3
^m
0
48
'
by US
ETOH
0
0
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
17
0
0
0
0
0
4
0
0
0
9
0
0
"
State
FORM
234
134
17
274
89
72
14
0
786
92
24
48
2
12
16
274
35
4
98
68
14
245
87
0
0
43
14
176
41
222
27
0
5
HCL
5,194
<
3,301
1,567
10,062
236,949
HCL
3,876
1,934
8,098
2
148
1,125
2,280
16,785
20,077
2,206
18,614
1,198
343
8,490
7,102
0
1,796
21,920
2,810
1,734
2,746
487
7,651
1,395
932
1,540
23
2,871
14,029
180
17,213
-------
Modeled
State
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Domain total
ALD,
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
ALDX
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
BENZENE
54
3
33
0
47
6
41
176
28
13
11
22
1
55
99
38
1,748
CH4
690
359
394
18
114
0
18
2,506
0
67
23
133
0
6
222
12
13,872
CL,
0
0
7
0
2
0
0
17
0
2
2
3
0
0
48
0
171
CO
12,510
958
20,774
169
11,303
571
6,897
220,982
5,099
3,871
1,190
5,602
2,631
9,898
12,701
13,019
677,603
ETH
0
0
7
0
27
0
0
18
0
1
11
1
0
0
68
0
310
ETHA
137
15
127
0
104
27
191
470
132
62
3
98
3
256
203
178
6,385
ETOH
0
0
2
0
23
0
0
0
0
0
10
0
0
0
44
0
116
FORM
195
154
145
8
27
0
9
760
0
14
0
76
0
2
4
5
4,567
HCL
679
30
22,910
0
5,625
1,371
5,091
3,987
114
456
40
5,178
«
3,292
1,563
10,028
236,309
Table B-3b - 2008 Integrated Planning Model Point Source (ptipm) Emissions by Species and by US State
Modeled
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
MONO
ISOP MEOH
NHL PERT
Florida
Georgia
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
102,224
38,501
33,757
16,183
10,781
12,480
19,937
4,890
161,835
107,809
124,800
199,368
48,897
53,017
157,896
49,385
697
36,655
9,540
106,904
60,515
43,233
-------
Modeled
State
Missouri
Montana
Nebraska
Nevada
New
Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Domain total
Table B-3c -
MONO
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
IOLE ISOP MEOH
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
2008 Integrated
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
Planning
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
Model Point
NH, NH,_
143
6
191
224
153
173
273
1,700
149
368
66
716
251
422
4
282
34
206
4,395
305
23
15
268
92
33
451
439
24,231
Source (ptipm)
_FERT
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
«
0
»
0
NO
83,830
26,741
38,855
14,404
4,188
12,284
25,741
31,876
53,519
60,166
215,851
73,378
8,517
167,223
180
39,763
12,467
77,473
142,888
74,017
55,559
266
38,529
9,827
89,458
44,920
67,488
2,761,256
Emissions by Species
NO,
9,314
2,971
4,317
1,600
465
1,365
2,860
3,542
5,947
6,685
23,983
8,153
946
18,580
20
4,418
1,385
8,608
15,876
8,224
6,173
30
4,281
1,092
9,940
4,991
7,499
306,806
and by
NO..
93,144
29,713
43,173
16,004
4,654
13,649
28,601
35,418
59,466
66,851
239,835
81,531
9,463
185,804
200
44,181
13,852
86,081
158,764
82,241
61,732
296
42,810
10,919
99,398
49,911
74,986
3,068,063
US State
NVOL
0
0
0
»
0
«
0
o
0
»
0
0
o
0
»
0
»
0
o
0
»
0
»
0
0
0
1
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Illinois
3
0
57
73
82
701
340
429
-------
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New
Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Domain total
6
9
18
48
37
66
32
4
29
0
25
6
44
119
30
14
1
23
1
59
49
41
1,513
14
40
19
240
32
56
59
153
477
139
18
32
1,148
12
2,454
162
27
448
0
850
14
313
343
418
51
0
65
25
274
514
407
721
558
39
360
0
255
69
485
1,810
335
153
•
280
8
653
663
44
1,418
94
16
259
0
493
8
181
762
242
30
»
38
15
158
19
341
18,904
493
450
18,540
11
197
11,623
523
Table B-3d - 2008 Integrated Planning Model Point Source (ptipm) Emissions by Species and by US State
-------
Modeled
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
New
Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
2,812
2,821
1,262
1,271
525
486
1,879
281
4,602
676
2,512
19,326
304
44,642
3,520
703
7,874
5
14,543
228
5,304
11,588
7,004
903
42
PMC PMFINE PMG
2,419
1,048
805
26
1,098
65
272
PMN PMOTHR PNA PNCOM PNH,
District of
Columbia
Florida
Georgia
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
3
17,559
5,991
7,283
99,938
5,550
1,751
34,191
4,749
85
5,932
674
3,048
3,498
1,135
6,090
732
1,874
359
1
2,664
6,732
1,819
6,407
2,499
1,432
1,244
2,708
19
1,695
159
2,103
4,932
919
3,453
473
424
553
242
213
151
-------
Modeled
State
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Domain total
Table B-2f 2008
PM,fi
1,548
457
4,603
336
5,714
348,214
Species
Modeled
State PNO,
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New
Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
7
5
2
26
3
3
2
0
79
9
6
60
4
3
20
49
1
4
5
7
2
15
5
1
1
2
1
14
3
19
13
1
31
18
PMC
1,480
30
1,681
3,022
9,032
104,345
PMFINE
1,105
356
3,784
270
4,703
273,462
PMG
0
0
0
0
0
57
PMN PMOTHR
0
»
1
»
2
90
824
252
2,682
190
3,334
193,366
PNA
0
0
0
0
0
15
PNCOM
30
9
59
6
73
6,860
PNH.
4
1
16
1
20
1,132
Emission by Sector
POC PSI
141 224
151 136
56 104
310 1
48 34
25 34
69 164
1 0
1,251 1,058
251 514
249 648
3,429 8,898
180 498
73 150
1,089 3,077
895 151
18 0
192 531
65 18
383 281
241 332
191 37
213 540
76 60
59 168
40 20
15 22
266 362
53 48
261 84
633 1,733
86 19
1,627 3,704
285 245
PSO4
311
283
137
110
51
61
194
1
2,527
607
741
10,286
566
179
3,487
511
12
610
149
287
343
104
620
69
194
38
33
464
67
464
1,971
23
4,392
346
PTI
11
7
5
0
2
2
8
0
52
25
31
426
24
7
148
9
0
25
1
9
13
2
26
4
8
1
1
17
2
4
83
2
178
12
SO,
365,866
44,268
73,307
665
56,718
7,691
33,666
212
270,195
516,096
281 ,777
604,918
109,300
95,938
344,936
82,364
1,042
228,360
46,519
329,729
75,238
65,866
276,747
22,249
75,699
9,347
36,897
24,800
11,843
70,200
232,633
132,593
740,076
107,851
SULF
6,375
1,017
1,792
3
1,384
105
763
3
5,413
11,662
6,089
13,746
2,671
2,340
7,783
1,832
16
5,152
1,006
7,461
1,830
846
6,660
508
1,846
212
829
214
260
1,424
5,187
3,792
14,443
2,353
TERP
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
TOL
141
62
72
14
71
12
10
0
152
251
270
330
109
120
270
98
0
55
24
188
98
41
258
69
75
18
11
17
35
61
159
127
225
122
UNK
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
UNR
263
109
142
79
133
23
20
0
349
467
508
615
203
235
503
236
0
103
71
361
219
98
481
130
141
33
28
33
74
167
313
236
418
266
XYL
147
60
73
1
74
12
10
0
143
262
282
345
113
123
282
83
0
57
24
197
103
35
269
72
78
18
11
17
33
50
166
133
235
114
-------
Modeled
State
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Domain total
PNO,
5
9
0
10
0
4
101
4
1
0
4
1
3
0
3
568
POC
75
295
1
542
7
178
2,390
221
38
15
75
22
148
14
181
17,124
PSI
41
677
0
1,311
21
474
549
631
77
6
99
38
414
30
515
28,780
PSO4
68
847
0
1,476
23
541
971
714
91
3
257
46
470
34
583
36,361
PTI
2
33
0
62
1
23
44
30
4
0
5
2
20
1
25
1,394
SO,
11,344
867,323
5
160,948
13,535
212,666
485,838
15,205
20,188
2
124,240
2,320
312,270
134,606
81,923
7,818,020
SULF
277
18,071
0
3,639
332
4,854
12,422
363
327
0
2,779
57
7,057
3,166
2,007
172,370
TERP
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
TOL
12
96
0
76
22
151
408
105
45
0
78
3
203
147
141
5,051
UNK
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
UNR
23
193
0
188
40
281
885
194
87
19
146
5
378
376
261
10,135
XYL
13
98
0
80
23
158
385
109
47
»
81
3
212
154
147
5,133
Table B-3e - 2008 Integrated Planning Model Point Source (ptipm) Emissions by Species and by US State
-------
Inventory
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
VOC CHLORINE
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
108,422
31,564
26,495
40,091
117,341
45,822
4,295
6,794
435
115,538
83,096
14,400
25,499
94,067
336,232
29,363
24,281
67,103
101,867
16,679
75,041
16,066
77,519
29,566
35,131
78,955
27,764
7,516
12,098
4,501
15,627
20,858
72,072
57,424
10,338
247,583
41,709
35,420
96,241
1,635
3,835
90,406
2,285
53,182
261,906
1,568
178
51
927
11,873
1
126
0
1,739
5,551
572
1,100
1,289
836
3,397
1,576
177
6,233
555
0
326
713
1,854
1,482
1,513
49
1,016
77
46
950
0
1,328
1,372
6,002
3,011
2,340
3
1,618
115
1,840
986
2,240
67,339
69,291
18,107
37,346
89,779
51,380
4,666
4,695
567
54,823
56,690
23,100
12,681
85,187
64,696
40,834
52,973
40,371
143,692
16,072
21,781
13,773
75,324
58,689
51,990
40,767
14,348
11,981
14,156
2,315
15,195
28,709
43,694
37,813
11,403
61,996
63,722
14,085
70,078
563
1,430
28,851
165
47,779
238,062
36,882
3,065
8,286
8,986
38,516
19,077
290
1,219
47
27,678
2,312
3,542
5,539
22,216
7,242
147
1,060
45
65,886
5,096
34,910
14,025
26,993
8,356
518
7,441
286
31,534
5,351
2,998
27,469
42,221
71,685
1,095
3,053
69
76
1
204
30
3
1,375
•
47
802
724
186
0
221
44,576
46,429
26,124
7,500
95,802
71,386
51,632
7,310
30,908
137,957
12,550
4,368
923
-------
Inventory
State CO NH, NCX PM,n PM_ SO0 VOC CHLORINE
Tribal 6,808 30 13,603 2,780 908 46 3,132
Utah 17,999 546 25,430 7,373 2,470 8,128 6,928
413 96 18 6 10 44
Virginia 71,086 1,285 52,931 9,206 6,245 50,131 27,793
Washington 65,856 350 27,613 4,762 3,806 13,450 12,692
West Virginia 55,817 273 34,629 7,041 4,218 31,699 10,896
Wisconsin 63,491 471 39,185 9,315 1,146 58,688 30,503
Wyoming 33,359 281 42,310 29,897 14,441 26,213 17,478
Con. US 2,932,043 69,114 2,045,916 581,026 380,814 1,577,530 1,055,754
Table B-4a - 2008 Integrated Planning Model Non-Point Source (ptnonipm) Emissions by Species and by US
3,220
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
ALDX BENZENE
ETHA ETOH
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
283
0
85
321
450
355
11
12
0
378
338
0
37
679
366
262
133
130
290
40
54
50
270
196
220
164
28
50
63
316
0
81
390
472
193
13
2,
1
448
434
«
33
790
374
272
135
179
464
52
52
54
293
237
288
210
37
50
67
1,201
«
53
1,064
1,423
901
15,609
10,674
7,348
106,270
64,348
76
0
1
205
30
3
108,715
0
26,563
40,197
117,570
45,920
1,099
0
185
893
1,860
583
41
142
1,606
1,257
«
73
1,778
1,373
712
543
670
2,750
286
84
167
785
618
1,425
354
277
175
93
280
6,488
26
66,253
10,724
«
452
46,624
7,941
10,943
24,871
5,168
53,126
714
1,491
4,78
13,778
16,448
13,785
15,215
4,637
2,419
992
0
28
1
»
0
136
1
22
5
32
97
17
0
'
18
22
100
6
1
24
0
4,306
6,812
436
115,885
83,309
«
25,567
94,308
337,139
29,441
24,345
67,280
102,142
16,712
75,240
16,106
77,714
29,635
35,226
79,164
27,838
7,534
12,127
4,509
50
48
0
1,860
1,178
0
77
2,373
1,177
1,172
224
800
2,684
279
169
260
870
838
812
360
181
58
135
49
2,502
0
218
1,273
3,482
6,865
13
136
0
1,584
1,320
«
91
2,563
530
1,126
574
913
6,223
235
90
234
1,356
340
2,876
310
519
63
107
54
416
0
38
395
1,395
243
6
16
0
914
406
•
46
1,417
1,376
2,256
176
18,173
572
169
30
83
471
736
564
398
28
93
19
22
-------
Modeled
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Con. US
ALDX BENZENE CH,
ETHA ETOH
80
19
129
398
41
473
76
109
255
0
13
232
5
345
1,000
2
68
3
0
216
169
65
506
70
9,545
76
28
133
466
52
480
136
118
275
•
22
267
6
357
1,249
2
79
4
0
239
182
70
562
79
10,848
349
,70
218
1,303
150
1,235
801
354
1,778
•
38
758
2
925
5,056
23
360
3
0
1,733
515
360
1,052
831
37,898
9,423
11,397
44,877
26,379
13,609
43,798
14,161
4,174
15,927
•
18,288
23,355
0
17,808
77,702
8,362
4,904
»
0
14,343
36,818
13,855
21,676
32,577
966,514
5
1
27
105
0
12
11
67
37
»
0
23
0
120
91
0
1,806
»
0
25
8
11
24
21
3,228
15,662
20,913
72,250
57,576
10,365
248,254
41,821
35,514
96,403
»
3,842
90,647
2,291
53,322
262,599
6,826
18,045
1,170
0
71,271
66,030
55,966
63,659
33,440
2,939,606
267
66
525
1,981
140
1,094
417
322
1,103
•
52
886
17
1,504
8,153
11
334
10
0
1,489
444
820
1,409
243
41,559
247
677
711
1,278
408
1,325
1,147
709
1,914
»
216
985
0
978
6,068
0
1,284
676
1,944
709
2,430
60,487
50
16
107
905
90
531
112
142
380
o
19
297
0
7,375
766
743
181
147
494
35
42,895
Table B-4b - 2008 Integrated Planning Model Non-Point Source (ptnonipm) Emissions by Species and by US State
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
57
568
NVOL OLE
PCA PCL
18,155
37,439
89,949
51,503
4,679
4,706
41
13
-------
Modeled
State
Indiana
^^m
Kansas
Kentucky
Louisiana
H.
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Con. US
NO,
6,475
4«
5,310
4,030
14,400
,.«
2,172
1,365
7,529
5,866
5,211
4,071
1,437
1,198
1,392
230
1,498
2,874
4,317
3,762
1,142
6,198
6,384
1,404
7,002
0
141
2,889
16
4,759
23,696
1,364
2,539
20
0
5,273
2,748
3,471
3,921
4,241
204,049
NO..
64,856
40,936
53,114
40,477
144,064
16,109
21,833
13,807
75,506
58,838
52,131
40,861
14,384
12,012
14,187
2,320
15,232
28,786
43,798
37,907
11,432
62,151
63,885
14,122
70,254
0
1,433
28,924
166
47,906
238,685
13,640
25,494
206
0
53,049
27,685
34,714
39,290
42,417
2,051,026
NVOL
33
36
9
24
40
10
3
14
30
20
47
18
2
2
4
1
10
1
6
140
5
50
11
13
22
o
6
140
0
45
72
0
5
0
0
50
22
8
23
10
1,335
OLE
2,458
1,282
387
986
3,061
200
179
282
1,948
953
1,905
864
258
109
272
73
435
127
425
1,745
155
1,962
1,240
389
2,036
o
105
1,733
9
1,650
6,597
10
578
5
0
1,393
734
538
1,409
453
54,779
PAL
531
75
59
459
454
5
24
7
166
259
26
142
48
*
36
0
39
9
74
57
16
420
62
51
209
o
0
74
0
228
406
30
36
0
0
81
191
85
20
612
6,094
PAR
18,489
10,437
10,948
13,271
43,230
2,183
1,391
2,230
13,887
13,779
18,338
8,736
2,282
1,902
1,495
337
6,124
4,894
3,693
18,875
1,886
15,804
15,556
3,691
15,089
o
595
12,826
44
15,249
70,361
2,619
4,430
281
0
15,065
7,054
6,329
17,270
10,800
592,923
PCA
488
198
50
301
463
26
89
6
859
575
61
198
47
5
65
344
15
28
118
104
21
409
177
138
390
o
0
179
0
181
442
20
68
0
0
136
68
166
21
210
8,709
PCL
1,422
131
102
483
457
41
166
12
571
276
131
85
14
7
20
16
48
7
50
133
34
1,097
149
220
398
o
0
111
0
367
299
2
47
0
0
99
44
54
35
37
8,785
PEC
2,067
342
322
911
3,973
148
210
280
758
1,252
491
278
188
303
301
35
336
184
592
629
389
1,334
464
338
1,232
o
46
343
8
733
4,293
57
630
7
0
571
353
271
242
551
36,289
PFE
1,041
150
53
223
301
*
83
6
256
211
50
47
23
5
15
15
24
*
31
53
24
914
43
96
419
o
0
69
0
320
263
,9
39
o
0
81
18
73
22
232
6,384
Table B-4c - 2008 Integrated Planning Model Non-Point Source (ptnonipm) Emissions by Species and by US State
-------
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
PMC PMFINE PMG
PMN PMOTHR
218
0
53
26
247
87
0
11
0
784
0
35
193
320
65
1
3
0
36,970
0
8,307
9,007
38,541
18,962
291
1,222
47
27,732
»
3,551
5,553
22,218
7,213
147
1,062
45
9,238
«
4,756
3,454
16,324
11,749
144
159
17,634
0
2,796
3,244
12,174
4,999
55
639
19
80
48
0
19
161
185
86
71
139
405
2
17
; 4
248
178
38
55
23
16
19
3 264
46
13
38
20
6
133
104
16
150
0
0
i 40
0
63
677
128
0
13
122
987
70
24
384
1,126
111
212
4
476
261
187
84
32
7
16
22
12
12
63
201
11
377
60
247
205
0
0
140
0
228
18,668
8,452
0
2,788
20,525
35,724
9,020
5,010
21,535
46,941
3,674
4,130
1,778
20,294
22,540
8,779
9,142
5,146
2,451
4,501
3,197
3,193
2,242
5,801
10,682
2,927
23,772
8,457
9,922
19,891
•
173
7,718
50
13,266
15,057
6,025
0
2,082
11,295
26,052
5,625
3,287
13,115
40,523
2,669
2,739
1,209
11,916
14,225
6,635
3,997
1,715
1,613
1,826
3,086
2,519
1,025
4,074
7,489
2,271
19,592
4,865
7,330
13,387
»
133
4,959
10
9,295
3,611
2,427
•
706
9,230
9,671
3,394
1,724
8,420
6,418
1,005
1,391
569
8,377
8,315
2,144
5,145
3,431
838
2,675
111
675
1,217
1,727
3,193
656
4,180
3,592
2,592
6,504
•
40
2,759
40
3,971
8,840
3,333
0
1,496
7,292
17,155
4,089
2,068
9,358
22,886
1,282
1,797
558
7,362
9,507
3,779
2,856
1,063
935
1,123
1,828
1,454
548
2,277
4,291
1,355
12,932
2,973
3,920
8,207
»
41
2,845
1
6,328
25
3
0
1
15
61
10
3
75
67
2
4
0
14
21
6
18
4
1
6
0
2
0
3
0
13
56
12
3
25
0
41
0
28
13
4,467
1,919
0
1,158
5,075
9,411
2,813
1,295
5,736
12,955
458
834
408
3,069
5,858
2,057
1,757
580
763
721
1,131
972
342
1,449
2,483
990
7,128
1,791
1,524
4,904
»
29
1,353
0
3,606
-------
Modeled
PMC PMFINE PMG
PMN PMOTHR
11,241
Texas
Tribal Data
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Con. US
Table B-4d - 2008 Integrated Planning Model Non-Point Source (ptnonipm) Emissions by Species and by US State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
i.
Kansas
Kentucky
Louisiana
yiaine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
-------
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin 22 56 2 4 158 55 112
Wyoming 41 238 44 95 603 1,413 757
Con. US 11,131 20,108 1,938 3,677 51,142 16,672 52,461 1,485
Table B-4e - 2008 Integrated Planning Model Non-Point Source (ptnonipm) Emissions by Species and by US State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
286
11
11
1,971
2,373
0
74
3,283
-------
Indiana
i.
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Con. US
Table B-4f-2008
71,567
51,746
7,293
30,991
138,340
12,581
27,325
6,430
57,287
24,078
18,310
91,007
Integrated Planning Model Non-Point Source (ptnonipm) Emissions by Species and by US State
-------
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Gulf of Mexico
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
48
478,929
873
956
321,702
15,285
2,120
15,236
19,900
2,345
1,360
755
2,419
1,746
89,813
7,996
4,702
6,976
5,200
1,600
1,312
1,307
30,657
13,769
5,927
24,380
1,974
173,086
64
1,582
396,891
721
792
3,536
44,419
2,204
3,469
25,331
12,940
119
11,580
244
4
51
6,882
9
649
24,566
19,178
37,436
22,647
M
6,651
129,435
398
2,226
4,403
6,842
3,367
11,741
6,891
31,136
177
728
2,646
3,607
1,751
10,386
3,747
19,303
75
7,158
13,299
1,750
520
6,267
4,302
5,087
16,245
11,087
4,701
169
1,700
464,052
29,327
20,884
85,916
166,332
100,185
71,857
182,347
211,110
2,916
27,382
122,802
206,388
1,961
393,488
20,105
428,647
13,578
30,551
13,596
_
1,430
56,831
26,033
129,756
90,401
45
130
65,388
475,691
1,131
15,397
183,804
3,358
1,129
91,941
132,647
113,387
173,368
160,336
167,404
3,199
942
0
31,678
345
24,624
7,042
1,719
0
12
20
13
11
122
0
-------
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
1,472,421
184,800
1,747,033
23,827
0
23,827
51,580
3,665
708
1,044,281
494,745
1,621,607
86,149
2,063
555
130,545
327,678
459,062
55,034
1,673
312
78,237
219,269
298,343
371,159
30,801
21
1,984,507
2,903,265
4,889,733
Table B-5a - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by Mexican
Federal State
Inventory
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Gulf of Mexico
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Duebla
Quebec
Queretaro
ETOH FORM IOLE ISOP MEOH NVOL OLE
PAR TERP TOL
28
13
3
80
0
43
23
27
1
7
50
0
14
33
18
145 2,395 15
11
412
140
86
189
36
0
95
0
1,796
7
16
0
56 610
66
0
41
56
60
34
50
626
1
29
0
-------
Inventory
ETOH FORM IOLE ISOP MEOH NVOL OLE
PAR TERP TOL
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
Table B-5b - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by Mexican
Federal State
Modeled
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Gulf of Mexico
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
ALD0 ALDX BENZENE CH,
ETH ETHA ETOH FORM MONO
-------
Modeled
ALD0 ALDX BENZENE CH,
ETH ETHA ETOH FORM MONO
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Duebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
Table B-5c - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by Mexican
Federal State
Modeled
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
IOLE ISOP MEOH NH, NH3 PERT NO
NOU NVOL OLE
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Gulf of Mexico
Hidalgo
0
25
126
7
16
0
0
34
38
0
0
36
0
0
0
0
0
1
62
0
0
0
0
8
15
0
0
0
0
0
0
0
0
0
5
218
0
12
0
0
28
51
0
0
1
0
0
0
0
9,002
0
0
1,727
0
0
0
0
0
0
0
0
0
0
0
0
0
458,522
5,666
4,706
21,127
«
0
18,040
128,828
0
0
3,953
0
0
74,517
0
50,947
630
523
2,347
«
0
2,004
14,314
•
0
439
0
•
8,280
0
509,469
6,296
5,228
23,474
°
0
20,045
143,143
•
0
4,393
0
•
82,797
0
0
3
130
0
1
14
•
0
0
0
•
0
0
0
6,454
956
60
37
»
0
137
786
»
0
15
120
0
-------
Modeled
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
IOLE ISOP MEOH NHL NH3 PERT NO
NCX NVOL OLE
0
0
0
359
40
398
4,070
4,522
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
Table B-5d - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by Mexican
Federal State
96
0
0
777,237 86,360 863,597
202,367 22,485 224,853
1,054,121 117,125 1,171,246
Modeled
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
PAR PCA
0 0
0 159,026
^^^H
138 8,379 96
8 516 5
0 765 0
PCL PEC
0
2,431
86
157
94
0
PFE PH2O
0
0
91
4
0
0
0
-------
Modeled
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Gulf of Mexico
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
\lunavut
Oaxaca
Ontario
Prince Edward
Island
PFE PH2O
0
a 181
555
0
sral 0
15
o 0
0
co 4
0
0
0
0
i 0
0
es 0
0
(ick 0
nd 0
a 0
>n 195
0
0
0
0
2,068
3,188
0
0
1,688
0
0
51,431
0
0
8,822
0
0
0
0
0
141
14
465
18,040
0
3,564
0
122
435
0
0
10
0
0
11
0
0
0
0
0
0
0
0
0
0
0
146
0
0
0
0
12
878
0
0
11
0
0
0
0
0
0
0
0
0
0
0
0
0
0
60
0
0
0
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
Table B-5e - 2008
Federal State
0
0
0
0
0
1
998
0
72
0
0
0
0
0
0
2,164
2,168
835
0
0
0
207,242
705
1,376
0
448
0
0
0
0
0
380,880
36,407
468,717
0
0
0
0
0
12
483
0
43
0
0
0
0
0
1
1,351
1,364
0
0
0
0
0
9
41
0
1
0
0
0
0
0
0
1,018
1,018
0
739
421
0
0
16
0
0
92
1,979
0
20
11
0
27
1,523
0
8,038
29,418
117,583
97,422
215,847
0
6,928
28,394
0
2,657
1,849
1,756
10,415
68,306
72,471
141,616
Other Point Source (othpt) Emissions by Species, by Canadian Province, and by Mexican
-------
Modeled
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Dampeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Gulf of Mexico
Hidalgo
Jalisco
Manitoba
Mexico
PMC PMFINE PMG PMN PMOTHR PNA PNCOM PNH4 PNCX POC
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
-------
Modeled
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
PMC PMFINE PMG PMN PMOTHR PNA PNCOM PNH4 PNCX POC
0
0
0
0
49,277
24,952
74,231
0
0
0
0
51,894
51,837
103,966
0
0
0
0
0
42
42
0
0
0
0
0
110
110
0
0
48,072 0
34,254 910
82,467 910
0
0
2,351
2,467
4,874
0
108
108
282
204
496
Table B-5f - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by Mexican
Federal State
5,995
6,177
12,332
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Gulf of Mexico
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
lunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
-------
Modeled
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
PSI PSO4
SULF TERP TOL
UNK UNR
0
0
0
535
2,952
•
427
0
0
0
0
0
0
0
833
1
6
•
4
0
0
0
0
0
1,767,089 0 1,583
651,756 10,971 187
2,420,811 10,971 1,770
0
0
0
Table B-5g - 2008 Other Point Source (othpt) Emissions by Species, by Canadian Province, and by Mexican
Federal State
-------
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
815,060
153,499
763,056
11,761,623
166,925
7,419
811
951,881
956,092
1,317,703
20,114
16,090
7,724
231,626
112,241
2,053,970
5,683
13,398
1,227
15,756
225,310
565,401
249,573
780,030
6,919
271,990
419
33,860
137,208
28,934
712,907
21,445
15,798
276,901
3,468,077
33,257
331,083
14,030
215,389
1,167,486
125,243
1,201
323,217
715,910
13,448
2,536
12,497
192,271
2,729
121
13
15,619
15,799
21,510
332
267
128
3,851
1,851
33,471
93
221
20
260
3,683
9,300
4,109
12,722
115
4,457
7
554
2,259
475
11,790
350
260
4,579
56,584
548
5,464
231
3,542
19,151
2,051
20
5,330
11,696
14,769
,966
9,032
120,963
1,716
275,298
29,490
285
76,618
168,127
-------
Inventory
State
West Virginia
Wisconsin
Wyoming
Con. US
Table B-6a -2008
Modeled
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
I South Carolina
CO
112,246
31,420
1,089,154
30,326,302
Point Source
ALD,
14,438
2,723
13,422
206,411
2,930
130
14
16,769
16,962
23,091
356
286
137
4,134
1,987
35,936
100
238
22
279
3,959
9,985
4,411
13,657
124
4,785
7
595
2,426
510
12,657
376
279
4,915
60,774
588
5,866
NH3
1,845
517
17,782
496,460
Fire (ptfire)
NOX
1,692
512
10,022
348,254
Emissions by
ALDX BENZENE
6,026
1,136
5,602
86,155
1,223
54
6
6,999
7,080
9,638
149
119
57
1,725
830
15,000
42
99
9
116
1,652
4,168
1,841
5,700
52
1,997
3
248
1,012
213
5,283
157
116
2,052
25,367
246
2,448
4,595
866
4,271
65,688
932
41
5
5,337
5,398
7,349
113
91
44
1,316
632
11,436
32
76
7
89
1,260
3,178
1,404
4,346
39
1,523
2
189
772
162
4,028
120
89
1,564
19,341
187
1,867
PM10
11,562
3,271
106,477
PM25
9,798
2,772
90,235
3,026,530 2,564,856
S02
893
262
6,705
207,860
voc
26,527
7,436
255,614
7,136,612
Species, by US State
CH4
44,067
8,310
40,966
630,001
8,942
397
44
51,182
51,771
70,478
1,087
873
418
12,617
6,066
109,684
306
725
66
850
12,082
30,477
13,463
41,684
378
14,604
23
1,815
7,403
1,556
38,631
1,148
852
15,003
185,494
1,796
17,904
CO
815,117
153,499
763,418
11,761,813
166,946
7,419
811
951,963
956,155
1,317,707
20,114
16,090
7,724
231,625
112,256
2,054,224
5,683
13,398
1,227
15,756
225,591
565,472
249,572
780,028
6,919
271,990
419
33,860
137,211
28,934
712,931
21,446
15,799
276,897
3,469,785
33,258
331,088
ETH
12,425
2,343
11,551
177,635
2,521
112
12
14,431
14,597
19,872
306
246
118
3,558
1,710
30,926
86
205
19
240
3,407
8,593
3,796
11,753
107
4,118
6
512
2,087
439
10,892
324
240
4,230
52,302
506
5,048
ETHA
5,644
1,064
5,247
80,685
1,145
51
6
6,555
6,630
9,026
139
112
54
1,616
111
14,047
39
93
8
109
1,547
3,903
1,724
5,338
48
1,870
3
232
948
199
4,948
147
109
1,921
23,756
230
2,293
ETOH
168
32
156
2,395
34
2
0
195
197
268
4
3
2
48
23
417
1
3
0
3
46
116
51
158
1
56
0
7
28
6
147
4
3
57
705
7
68
-------
Modeled
South Dakota 248
Tennessee 3,802
Texas 20,558
Utah 2,202
Vermont 21
Virginia 5,723
Washington 12,561
West Virginia 1,981
Wisconsin 555
Wyoming 19,089
533,022
ALDX BENZENE
104
1,587
8,581
919
9
2,389
5,243
827
232
7,968
222,482
1,210
6,542
701
'
1,821
3,997
631
,77
6,075
169,630
758
11,605
62,746
6,722
65
17,469
38,337
6,048
1.695
58,264
1,626,874
14,030
215,381
1,167,410
125,243
1,201
323,302
716,189
112,272
31,418
1,089,155
30,329,749
214
3,272
17,692
1,895
97
1,486
8,036
861
3
44
239
26
Table B-6b - 2008 Point Source Fire (ptfire) Emissions by Species, by US State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevac
New Hampshire
New Jersey
New Mexico
21,133
3,985
19,646
302,132
4,288
191
21
24,546
24,828
33,800
521
419
200
6,051
2,909
52,602
147
348
32
408
5,794
14,616
6,457
19,991
181
w
11
870
3,550
20,574,857
7,101,615
11,944,171
238,402,285
2,828,609
174,273
20,536
20,254,369
26,147,024
21,892,268
412,803
509,115
168,185
8,311,207
2,572,079
15,898,558
87,562
318,614
22,620
340,567
2,919,085
11,148,075
4,771,773
11,053,699
225,542
8,697,819
7,014
553,957
5,613,881
18,777
3,541
17,455
268,438
3,810
„,
19
21,808
22,059
30,030
463
372
178
5,376
2,585
46,735
130
309
28
362
5,148
12,986
5,737
17,761
161
6,223
10
773
3,155
13,449
2,536
12,503
192,274
2,729
121
13
15,621
15,800
21,510
332
267
128
3,851
1,851
33,475
93
221
20
260
3,687
9,301
4,109
12,722
115
4,457
7
554
2,260
13,293
2,669
8,131
108,868
1,545
-------
New York
746
906,660
40
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
18,527
551
409
7,195
88,958
861
8,586
364
5,565
30,091
3,224
31
8,378
18,385
2,900
813
27,942
780,207
33,287,971
328,656
315,106
9,314,804
47,620,189
1,081,903
8,509,440
353,228
5,151,986
25,210,399
2,637,367
24,292
10,294,958
12,519,367
2,015,285
705,045
22,289,503
605,538,324
Table B-6c - 2008 Point Source Fire (ptfire) Emissions by Species, by US State
9,020
313,459
Modeled
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
NO,
1,477
297
903
12,096
172
8
2
1,280
1,858
1,194
35
35
15
569
199
1,568
8
26
2
26
230
NO..
14,770
2,966
9,034
120,965
1,717
77
19
12,802
18,576
11,942
354
345
147
5,691
1,991
15,675
85
259
21
264
2,302
NVOL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
OLE
17,456
3,292
16,228
249,560
3,542
157
17
20,275
20,508
27,918
430
346
166
4,998
2,403
43,449
121
287
26
337
4,786
PAL
34
7
30
565
8
0
0
39
40
64
1
1
0
10
5
78
0
1
0
1
9
PAR
38,644
7,287
35,925
552,467
7,842
349
39
44,883
45,400
61,805
953
766
367
11,064
5,320
96,185
268
636
58
746
10,595
PCA
53
33
48
3,091
42
2
0
89
64
362
1
1
1
19
11
138
0
1
0
2
16
PCL
181
315
169
32,031
434
25
0
568
230
3,790
4
4
2
95
75
615
2
3
2
12
76
PEC
7,958
1,413
7,054
96,566
1,378
59
8
8,796
9,433
10,612
196
162
76
2,370
1,074
18,207
54
132
11
149
2,045
PFE
32
6
29
430
6
0
0
36
39
48
1
1
0
10
4
75
0
1
0
1
8
PH2O
0
o
0
0
0
»
0
0
0
»
0
0
0
0
0
»
0
0
0
0
0
-------
Modeled
State
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
NO,
877
406
650
18
335
1
37
226
40
1,431
22
25
554
3,004
56
608
25
329
1,540
150
2
571
698
169
51
1,002
34,829
Table B-6d - 2008 Point
Modeled
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
PK
103
221
98
22,614
306
18
0
377
133
2,678
2
2
1
60
NO..
8,775
4,062
6,500
183
3,354
7
370
2,259
398
14,315
215
251
5,537
30,044
564
6,076
249
3,290
15,399
1,497
22
5,707
6,983
1,693
512
10,022
348,288
Source Fire
PM10
86,179
16,394
76,427
1,161,221
16,482
733
90
96,675
102,206
128,657
2,117
1,749
823
25,823
NVOL
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
(ptfire)
PM,fi
73,033
13,893
64,769
984,086
13,968
621
76
81,928
86,616
109,031
1,794
1,482
697
21,884
OLE
12,073
5,333
16,512
150
5,785
9
719
2,933
616
15,303
455
337
5,943
73,479
711
7,092
300
4,597
24,855
2,663
26
6,920
15,186
2,396
671
23,080
644,448
Emissions
PMC
13,146
2,501
11,658
177,135
2,514
112
14
14,747
15,591
19,626
323
267
126
3,939
PAL
23
10
37
0
14
0
1
7
1
34
1
1
12
157
1
14
1
9
48
6
0
15
32
5
1
54
1,376 1
PAR
26,726
11,806
36,554
331
12,807
20
1,592
6,492
1,365
33,877
1,007
747
13,156
162,665
1,575
15,701
665
10,177
55,024
5,895
57
15,319
33,619
5,304
1,486
51,093
,426,656
PCA
38
16
197
0
85
0
6
35
7
142
6
1
34
744
5
22
2
22
115
27
50
141
7
2
321
5,998
PCL
142
60
2,031
2
901
0
52
354
71
1,343
58
4
259
7,382
38
78
12
148
768
263
A
436
1,361
24
7
3,406
PEC
5,392
2,397
6,311
72
2,222
4
294
1,204
249
6,650
176
151
2,676
28,764
311
3,237
133
2,016
10,736
1,074
1 9
3,012
6,061
1,068
302
8,685
57,830 260,962
PFE
22
10
28
0
10
0
1
5
1
29
1
1
11
125
1
13
1
8
44
5
A
13
26
4
1
39
1,127
PH2O
0
0
o
0
»
0
0
0
0
0
»
0
o
0
o
0
»
0
o
0
Q
0
»
0
o
0
o
by Species, by US State
PMFINE PMG
27,397
5,600
24,309
0
2
0
410,147 238
5,793
266
29
31,238
32,512
45,776
673
556
262
8,266
3
0
0
3
0
28
0
0
0
0
PMN
8
1
7
37
1
0
0
8
9
4
0
0
0
2
PMOTHR
949
185
842
13,290
188
'
1
1,071
1,126
1,477
23
,9
9
285
PNA
99
50
89
4,652
64
3
0
152
119
542
2
«
1
34
PNCOM
25,630
4,675
22,723
324,166
4,616
201
27
28,491
30,386
35,743
630
520
245
7,650
-------
1 Modeled
State
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
PK
50
380
2
2
1
8
48
84
35
1,433
1
638
0
36
250
50
940
41
2
176
5,190
26
45
8
99
515
185
0
302
954
13
4
2,410
40,542
Table B-6e - 2008 Point
I Modeled
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
PNH.
250
86
223
7,438
103
5
0
PWL
11,830
197,941
584
1,431
129
1,646
22,256
58,470
25,975
75,652
783
27,350
44
3,362
14,304
2,946
76,622
2,113
1,638
29,740
337,527
3,482
35,073
1,479
22,224
118,286
12,552
127
34,046
70,366
11,565
3,271
106,477
PM,fi
10,026
167,747
495
1,213
109
1,395
18,861
49,551
22,013
64,112
664
23,178
37
2,849
12,122
2,497
64,934
1,790
1,389
25,203
286,040
2,951
29,723
1,253
18,834
100,243
10,637
108
28,852
59,632
9,801
2,772
90,235
3,026,869 2,565,143
PMC
1,805
30,194
89
218
20
251
3,395
8,919
3,962
11,540
119
4,172
7
513
2,182
449
11,688
322
250
4,537
51,487
531
5,350
226
3,390
18,044
1,915
19
5,193
10,734
1,764
499
16,242
461,726
Source Fire (ptfire) Emissions
PNO,
780
81
690
3,412
53
1
1
POC PSI
36,656 8
6,686 14
32,499 7
463,657 1,404
6,602 19
288 1
38 0
PSCX
243
113
217
10,305
141
8
0
PMFINE
3,830
63,202
188
455
43
535
7,116
18,615
8,265
26,643
249
9,863
14
1,131
4,996
1,026
25,996
745
521
9,726
116,544
1,149
11,155
483
7,205
38,324
4,318
40
11,328
24,049
3,675
1,039
38,258
1,033,548
by Species, by
PTI SO,
49 7,241
5 1,420
43 5,314
190 76,304
3 1,083
0 48
0 9
PMG
0
2
0
0
0
0
0
0
0
15
0
7
0
0
3
1
10
0
0
2
54
0
0
0
1
4
2
•
3
10
0
0
26
415
US State
SULF
0
0
0
0
0
0
0
PMN
1
18
0
0
0
0
2
5
2
3
0
1
0
0
1
0
4
0
0
2
15
0
3
0
2
10
1
»
2
4
1
0
2
158
TERP
2,040
385
1,897
29,167
414
18
2
PMOTHR
131
2,183
6
16
1
18
246
644
286
865
9
316
0
38
163
34
864
24
18
331
3,830
39
386
16
246
1,312
142
«
381
796
127
36
1,227
34,208
TOL
6,626
1,250
6,160
94,727
1,345
60
7
PNA PNCOM I
19
250
1
2
0
3
29
70
31
297
1
126
0
9
53
11
221
8
2
56
1,136
?
41
3
37
195
41
0
80
217
13
4
479
9,253
UNK UNR
0 46,213
0 8,715
0 42,962
0 660,688
0 9,378
0 417
0 46
3,482
58,726
173
426
37
484
6,598
17,375
7,721
21,159
233
7,530
13
968
4,022
830
21,941
590
487
8,704
95,605
1,014
10,428
433
6,537
34,807
3,564
38
9,864
20,059
3,440
973
29,388
863,352
XYL
1,448
273
1,346
20,694
294
13
1
-------
Modeled
State
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
PNH,
331
298
857
6
5
2
81
41
602
2
4
1
6
69
172
76
477
2
195
0
16
86
17
385
13
5
113
1,897
14
102
6
78
415
69
0
149
371
33
9
747
15,859
Table B-6f- 2008 Point
PNO,
787
921
320
19
16
7
224
95
1,743
5
13
1
13
194
524
234
236
7
45
0
20
52
11
410
6
15
222
1,454
24
316
11
177
946
57
1
221
346
105
30
200
15,048
Source
POC
40,748
43,458
51,124
901
744
350
10,942
4,981
83,990
247
609
53
692
9,436
24,850
11,043
30,264
333
10,771
19
1,384
5,753
1,187
31,382
844
697
12,449
136,742
1,450
14,914
620
9,349
49,781
5,097
54
14,108
28,690
4,920
1,391
42,035
1,234,826
PSI PSO.
25 359
10 291
166 1,199
0 6
0 5
0 2
4 82
3 45
26 605
0 2
0 4
0 1
1 7
3 70
6 169
3 74
89 658
0 2
40 278
0 0
2 20
16 117
3 24
59 497
3 19
0 5
11 130
323 2,537
2 17
3 100
1 6
6 87
33 457
11 92
0 0
19 183
60 486
1 32
0 9
149 1,056
2,530 20,759
Fire (ptfire) Emissions by
PTI
49
58
17
1
1
0
14
6
109
0
1
0
1
12
33
15
13
0
2
0
1
3
1
25
0
1
14
86
1
20
1
11
59
3
0
14
21
7
2
10
901
SO,
7,097
8,876
8,056
176
159
71
2,514
984
11,660
45
124
11
133
1,458
4,573
2,076
4,595
79
1,935
4
226
1,150
218
6,760
137
129
2,619
20,785
284
2,965
123
1,726
8,611
877
11
2,826
4,529
893
262
6,705
207,881
Species, by US
SULF
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
State
TERP TOL
2,370 7,696
2,397 7,784
3,263 10,597
50 163
40 131
19 63
584 1,897
281 912
5,078 16,492
14 46
34 109
3 10
39 128
559 1,817
1,411 4,583
623 2,024
1,930 6,268
17 57
676 2,196
1 3
84 273
343 1,113
72 234
1,789 5,809
53 173
39 128
695 2,256
8,588 27,891
83 270
829 2,692
35 114
537 1,745
2,905 9,435
311 1,011
3 10
809 2,627
1,775 5,764
280 909
78 255
2,697 8,761
75,320 244,617
UNK
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
UNR
53,675
54,293
73,911
1,140
916
438
13,232
6,362
115,027
321
761
70
892
12,671
31,961
14,119
43,714
396
15,315
24
1,903
7,764
1,632
40,513
1,204
893
15,733
194,529
1,883
18,776
795
12,170
65,803
7,049
68
18,320
40,204
6,342
1,777
61,102
1,706,119
XYL
1,681
1,701
2,315
36
29
14
414
199
3,603
10
24
2
28
397
1,001
442
1,369
12
480
1
60
243
51
1,269
38
28
493
6,093
59
588
25
381
2,061
221
2
574
1,259
199
56
1,914
53,440
-------
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
P.R.
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
62,051
15,144
56,976
102,821
484,547
97,394
50,762
6,987
5,311
180
1,296
816
802
799
35,810
59,833
4,177
37,860
12,153
17,370
31,772
70,670
9,710
97,768
79,131
268,202
63,065
33,511
9,398
5,758
253,306
189,319
20,384
52,970
208,973
167,257
69,349
74,639
61,290
159,883
26,738
62,018
84,470
188,531
112,973
59,465
120,482
16,669
41,004
39,408
-------
Tribal
Utah
Vermont
V.I.
Virginia
Washington
West Virginia
Wisconsin
Wyoming
U.S. TOTAL
7,741
40,429
50,376
18
117,042
131,717
50,161
221,507
18,429
5,225,880
3,250
170
169,918
27,026
17,246
1,266,655
211
30,808
9,605
32
36,164
26,660
27,875
42,451
124,493
1,660,293
Table B-7a - 2008 Non-Point Source (nonpt) Emissions by Species, by US State
Inventory
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
«s
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
ACETALD
BENZENE
CHLORINE
FORMALD
METHANO
2,714
-------
Inventory
New York
North Carolina
North Dakota
Ohio
Oklahoma
Dregon
Pennsylvania
P.R.
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal
Utah
Vermont
V.I.
Virginia
Washington
West Virginia
Wisconsin
Wyoming
U.S. TOTAL
ACETALD
BENZENE
CHLORINE
FORMALD
METHANO
13,443
34,901
1,601
19,204
Table B-7b - 2008 Non-Point Source (nonpt) Emissions by Species, by US State
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
ALDX BENZENE
611
0
830
1,295
5,157
853
404
76
52
2,853
2,026
0
1,023
1,380
1,145
715
1,074
1,766
2,710
464
»
1,275
938
5,744
2,246
923
,66
121
1,973
1,359
»
1,035
2,232
1,885
1,128
864
843
1,955
224
»
467
479
2,823
812
388
113
40
855
0
18,203
1,223
394,044
6,278
2,585
5,992
359
0
0
0
0
1,595
0
0
0
0
1,115
1,400
»
605
1,949
1,660
534
806
635
1,347
2,613
5,018
0
1,357
11,059
7,681
1,779
1,900
3,014
62,328
«
57,170
102,944
486,980
97,704
51,025
7,020
5,341
111,199
208,820
«
122,701
159,192
118,758
51,689
81,963
74,924
1,043
5,137
175,649
103
1,885
3,065
0
1,191
2,591
1,966
822
1,690
1,374
4,347
145,923
ETHA ETOH
78
3,841
1,825
1,276
6,227
2,101
1,007
131
443
747
7,632
670
268
84
5,093
2,629
9,258
3,864
2,635
424
33
247
272
0
128
694
496
213
168
226
407
454
13,608
11,842
0
2,428
9,571
6,566
2,266
1,906
3,570
7,180
-------
Modeled
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
ALDX BENZENE
ETHA ETOH
Missouri
Montana
Nebraska
Nevada
New
Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
4,963
22,790
216,646
104,607
7,483
630
60,031
32,663
190,933
239,919
24,861
9,620
2,736
3,346
11,109
14,844
75,070
12,381
94,986
262,982
613
5,427
19,757
40,557
50,629
0
117,527
132,184
50,365
222,508
18,482
5,210,185
7,555
364
2,378,258
Table B-7c -2008Non
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
70,938
-Point Source (nonpt) Emissions by Species, by US State
753
7,391
543,755
5,172
407
247,855
-------
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
NVOL OLE
543
0
1,593
1,209
7,272
793
823
223
130
580
4,087
0
1,502
4,827
2,292
566
1,932
524
3,199
606
1,017
1,944
3,617
1,922
500
1,881
207
290
435
555
4,928
297
8,894
2,365
182
3,747
8,463
477
6,159
0
224
1,033
183
2,664
32,431
0
5,433
»
15,930
12,086
72,719
7,925
8,225
2,233
1,301
5,796
40,868
0
15,017
48,267
22,919
5,658
19,318
5,238
31,986
6,058
10,174
19,440
36,173
19,219
4,998
18,805
2,069
2,904
4,346
5,551
49,283
2,967
88,940
23,653
1,821
37,468
84,632
4,767
61,587
n
2,245
10,326
1,831
26,638
324,315
94
»
50
147
329
63
4
8
7
235
319
0
153
647
430
195
116
35
65
39
32
75
235
261
56
193
25
113
33
20
35
10
377
379
67
326
53
60
304
n
1
128
67
140
416
747
0
2,052
1,322
7,220
1,544
590
122
69
2,983
2,468
0
1,384
2,395
2,322
1,735
1,907
1,012
2,724
659
856
865
2,692
2,746
1,169
1,560
352
984
420
423
860
426
2,992
2,937
590
2,197
1,447
1,574
2,281
n
181
897
572
5,084
7,952
0
236
0
666
54
285
106
22
4
0
246
621
0
90
154
162
60
33
183
343
52
52
30
125
221
32
96
168
21
589
18
54
115
156
133
91
125
78
49
152
n
5
39
28
214
388
0
52,525
0
71,771
52,668
178,133
35,740
21,172
6,420
3,650
175,579
130,379
0
29,374
138,600
109,542
39,491
52,932
40,698
113,619
15,783
44,820
61,507
121,303
67,368
41,785
81,482
10,832
23,511
26,039
14,638
77,419
25,142
228,864
119,292
12,006
112,674
176,392
34,686
132,331
0
6,162
67,354
16,249
68,287
1,158,957
0
57
»
225
16
143
27
6
1
0
63
400
°
35
73
59
16
10
45
87
13
14
14
35
56
11
26
40
6
144
6
16
29
87
65
22
36
45
14
47
0
1
12
7
141
99
441
»
461
1,061
1,758
8,
145
11
3
895
1,983
»
164
979
514
360
688
530
1,726
254
313
96
597
550
977
760
130
205
73
252
86
194
743
4,622
200
1,016
455
307
967
0
30
622
104
741
1,033
625
»
864
1,371
6,140
830
543
68
44
1,391
3,556
»
437
2,308
1,457
672
1,202
905
2,295
658
730
681
2,233
1,641
1,252
1,290
238
359
218
549
528
355
2,381
5,912
285
2,557
773
1,094
2,261
0
143
840
176
1,520
1,976
87
»
490
20
150
40
9
2
0
93
246
°
38
89
72
23
13
68
126
20
20
13
49
83
12
36
62
8
218
'
23
42
71
59
34
48
30
19
60
0
2
15
10
97
147
-------
Modeled
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
851
382
0
2,248
701
489
2,710
1,729
126,223
8,508
3,819
0
22,478
7,007
4,891
27,101
17,295
1,262,231
NVOL OLE
112
3
0
253
162
3
293
5
7,170
628
460
0
2,081
2,468
458
2,193
577
84,177
261
13
0
169
68
202
80
1,229
8,312
43,761
5,288
0
93,455
54,795
17,529
80,635
104,562
4,397,202
70
Table B-7d - 2008 Non-Point Source (nonpt) Emissions by Species, by US State
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
,„.
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New
Hampshire
PMC PMFINE PMG
PMN PMOTHR
380
22,770
11,892
40,231
34,821
14,523
20,294
29,859
44,834
11,572
15,545
14,618
48,286
48,296
17,013
25,542
19,751
6,591
61,034
7,019
14,849
3,986
-------
Modeled
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
PMC PMFINE PMG
PMN PMOTHR
10
TOTAL 570
Table B-7e - 2008 Non-Point
9,864 6,101
6,060 10,257
35,939 12,226
60,009 15,240
4,169 7,846
37,996 11,066
12,162 9,936
17,428 4,484
31,880 14,403
•
2,337 425
9,305 4,464
2,509 2,472
21,699 18,544
33,937 38,401
0
9,021 21,533
8,210 1,419
0 0
20,978 15,134
20,444 6,209
9,306 18,362
35,628 6,888
16,644 106,372
930,996 715,393
259 123,015
30,210 1,646,388
Source (nonpt) Emissions by Species, by US State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
1
94
219
0
18
277
5,229
11,142
°
1,450
77
210
333
25
32
2
1
170
382
°
35
48
48
348
34
20
68
96
•
16
Illinois
Indiana
Iowa
Kansas
69
35
55
8,109
5,989
2,674
3,157
300
144
74
137
127
89
26
44
5,843
5,755
43,417
7,936
4,049
592
442
8,816
21,738
0
2,266
12,791
9,262
4,068
4,801
1,805
120
659
249
54
10
0
573
5,945
0
181
397
393
139
74
400
202
1,549
69
158
13
47
2
228
3
1
-------
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
18,404
12,52
5,222
Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL 3,303 242,179 6,130 2,606 383,328
Table B-7f - 2008 Non-Point Source (nonpt) Emissions by Species, by US State
-------
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
802
12
46
322
831
262
70,438
13,108
675
13,378
4,534
939
124,025
-------
0
19,832
14,354
,504
22,554
22,229
1,239,962
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
Table B-7g - 2008 Non-Point Source (nonpt) Emissions by Species, by US State
-------
5,298
1,997
14,884
2,262
1,979
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
yiissouri
Montana
Nebraska
Nevada
New
Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
39,844
173
7,588
310
200
2,197,368
1,330,232
23,944
213,608
1,476,899
913,953
409,573
348,360
553,591
516,371
212,681
600,162
608,242
1,392,478
60,095
418,114
890,436
151,901
239,143
208,052
466,426
262,676
18,977
30,173
295,947
203,152
88,018
74,844
135,128
116,128
39,001
128,727
124,247
270,195
152,126
110,817
187,018
34,085
58,679
41,130
852,691
299,214
1,713,545
1,479,833
114,127
1,464,295
589,173
444,184
1,325,520
92,638
663,972
116,200
983,913
2,567,424
-------
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Cont. US
366,771
83,469
1,074,205
761,089
277,027
783,934
126,203
35,987,232
Table B-8a - 2008 On Road Non-Temperature Adjusted (on_noadj) Emissions by Species, by US State
Inventory
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
ACETALD
FORMALD
1,069
-------
Inventory
ACETALD
BENZENE
FORMALD
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermon
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Cont. US
Table B-8b - 2008 On Road Non-Temperature Adjusted (on_noadj) Emissions by Species, by US State
111,525
47,263
33,449
110,284
7,992
48,292
9,960
73,032
231,231
28,606
6,499
82,822
55,563
20,769
65,063
10,174
2,982,099
3,397
1,389
1,460
3,196
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
891
«
991
469
4,630
757
531
140
50
2,887
1,492
0
244
2,228
1,430
641
468
783
702
239
871
881
1,797
1,289
864
«
1,053
554
3,303
513
425
124
36
2,468
1,412
0
183
1,762
1,359
481
393
853
740
197
634
666
1,498
816
BENZENE
1,557
«
1,852
1,064
7,884
1,705
883
252
89
5,620
3,450
«
640
3,586
2,100
m
966
1,380
1,241
524
1,637
1,445
3,926
ETHA ETOH FORM
6,128
»
6,628
3,897
34,812
6,031
3,836
1,052
374
20,049
12,403
»
2,108
15,869
9,005
4,289
3,512
5,298
4,663
2,080
6,549
6,246
14,475
9,103
646,800
»
688,102
433,349
3,258,226
583,679
341,228
108,135
39,671
2,192,300
1,331,467
»
213,484
1,476,831
914,870
410,878
348,836
554,965
517,828
213,824
599,860
607,183
1,391,111
861,849
3,063
«
3,352
1,951
16,879
2,906
1,883
518
181
9,889
6,090
«
1,017
7,788
4,541
2,107
1,723
2,686
2,357
1,009
3,178
3,052
7,063
4,404
944
»
1,021
600
5,361
929
591
162
58
3,089
1,911
»
325
2,444
1,387
660
541
816
718
320
1,009
962
2,230
1,402
310
»
478
0
7,582
872
1,242
0
0
0
0
»
0
3,067
2,524
1,190
319
409
0
0
0
143
857
2,358
1,327
o
1,549
833
6,602
1,052
753
212
70
4,082
2,400
o
368
3,121
2,009
829
686
1,231
1,068
367
1,238
1,197
2,679
1,604
-------
Modeled
State
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
«*
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
ALD,
602
1,228
157
387
284
196
1,149
431
2,049
1,328
184
1,885
745
521
1,622
129
632
188
1,164
3,872
409
98
1,160
862
321
1,150
165
47,329
ALD..
693
1,141
207
323
183
,53
629
489
1,123
1,339
131
1,580
715
452
1,387
60
720
145
1,117
5,121
339
66
981
833
270
647
191
41,369
BENZENE
1,005
2,342
456
620
607
377
2,012
855
4,044
3,554
294
3,396
1,388
1,461
3,200
216
1,567
301
2,150
6,357
1,075
197
2,641
2,357
679
1,965
378
90,484
CH.
3,817
8,946
1,441
2,551
2,198
1,610
8,908
2,841
17,559
12,770
1,249
13,617
5,300
4,100
12,494
939
5,558
1,278
8,407
23,575
3,610
828
10,247
6,777
2,559
8,715
1,241
351 ,541
CO
419,585
890,742
152,576
239,773
207,930
159,793
852,421
300,170
1,712,494
1,480,399
114,751
1,465,833
589,708
444,543
1,327,568
92,504
664,669
116,785
983,974
2,567,805
366,326
83,852
1,074,598
760,521
278,269
786,366
126,664
35,995,095
ETH
1,959
4,437
722
1,266
1,056
781
4,248
1,450
8,336
6,231
611
6,701
2,638
2,009
6,118
445
2,757
629
4,180
12,382
1,749
397
4,970
3,350
1,250
4,170
627
173,104
ETHA
588
1,378
222
393
339
248
1,372
438
2,704
1,967
192
2,097
817
632
1,925
145
856
197
1,295
3,632
556
128
1,579
1,044
394
1,342
191
54,147
ETOH
0
743
18
747
324
0
1,477
148
2,332
0
302
1,318
0
319
111
14
0
338
0
154
75
0
0
266
0
1,731
0
31,770
FORM
940
1,850
306
526
382
291
1,478
679
2,769
2,350
233
2,653
1,116
767
2,383
151
1,125
249
1,741
6,504
646
138
1,875
1,332
479
1,461
277
69,980
Table B-8c - 2008 On Road Non-Temperature Adjusted (on_noadj) Emissions by Species, by US State
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
MONO
1,175
0
1,551
681
5,388
874
627
166
60
3,702
2,088
0
240
856
0
956
505
4,467
709
424
136
51
3,071
1,603
0
248
MEOH
172
NH3 PERT
134,108
11,632
6,856
602
146,915
177,026
77,710
614,290
99,656
71,567
19,004
15,278
6,759
53,855
8,768
6,168
1,637
193,855
85,150
673,533
109,298
78,362
20,808
7,517
462,702
261,012
686
-------
Modeled
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
MONO IOLE
ISOP MEOH
NH3 PERT
2,350
1,614
701
596
1,075
925
311
1,023
986
2,145
1,210
883
1,487
271
467
327
251
1,181
638
2,115
2,135
188
2,228
1,016
675
1,994
109
1,019
219
1,669
5,691
554
114
1,545
1,202
436
1,083
248
59,232
1,750
1,027
463
429
673
683
234
830
779
1,673
947
568
1,082
164
282
303
191
1,060
385
1,994
1,628
129
1,633
732
487
1,739
129
743
136
1,119
3,477
429
101
1,273
809
317
926
147
44,498
0
0
1
1
0
1
0
0
1
0
0
0
1
2
0
0
1
0
0
0
0
18
23,192
15,919
6,786
5,844
10,504
9,168
3,020
10,186
9,729
21,002
11,851
8,592
14,647
2,630
4,480
3,403
2,429
12,029
6,244
21,503
21,011
1,820
22,039
10,010
6,685
19,535
1,118
10,059
2,105
16,155
55,737
5,538
1,111
15,444
11,651
4,252
10,686
2,404
585,477
293,700
201,791
87,567
74,442
134,409
115,596
38,884
127,897
123,244
268,126
151,224
110,319
185,826
33,930
58,320
40,936
31,327
147,643
79,694
264,358
266,857
23,525
278,548
127,015
84,427
249,266
13,591
127,390
27,364
208,585
711,354
69,204
14,207
193,063
150,284
54,455
135,385
31,025
7,403,997
Table B-8d - 2008 On Road Non-Temperature Adjusted (on_noadj) Emissions by Species, by US State
-------
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
PMC PMFINE PNCX
32,369
0
36,496
18,997
172,482
27,011
16,968
5,058
Columbia
lorida
Georgia
Hawaii
Idaho
Ilinois
Indiana
9,186
67,932
41,294
18,336
16,292
25,940
25,705
8,685
30,674
29,005
63,057
37,025
21,525
41,291
6,256
11,275
11,463
7,057
40,157
14,779
74,913
60,436
5,045
62,175
27,369
18,519
64,571
4,702
27,816
5,372
41,884
133,601
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
-------
Modeled
State
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
NVOL
11
2
32
30
9
20
7
1,455
OLE
2,103
479
5,989
4,008
1,504
4,977
747
207,526
PAR
15,960
3,707
47,084
30,671
11,785
35,442
5,592
1,688,560
PEC
1,023
203
2,691
2,695
854
1,910
545
119,769
PM10
2,137
406
5,940
5,367
1,541
4,015
922
248,390
PM,fi
1,520
293
4,111
3,993
1,174
2,822
742
179,463
PMC
617
112
1,829
1,375
367
1,194
180
68,927
PMFINE
195
34
569
464
115
365
62
22,351
PNO,
2
0
5
5
1
3
1
215
Table B-8e - 2008 On Road Non-Temperature Adjusted (on_noadj) Emissions by Species, by US State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
42
66
485
20
0
25
13
70
10
10
3
5,050
0
5,853
3,308
25,959
4,956
2,916
815
284
8,267
0
9,090
5,080
43,457
7,210
4,248
1,365
28,096
15,608
0
2,495
18,464
11,055
4,990
4,327
6,777
6,404
2,404
8,110
7,696
17,141
10,172
5,322
10,998
1,697
3,048
2,960
1,916
10,831
3,764
20,558
15,904
54
31
0
4
40
33
11
9
21
18
4
13
14
33
17
17
26
5
8
4
3
12
12
20
29
17,189
10,356
o
1,792
11,839
7,170
3,244
2,913
4,481
4,004
1,598
5,065
4,687
11,748
6,880
3,333
7,325
1,317
2,021
1,802
1,194
6,394
2,626
12,560
10,563
500
28,683
16,096
»
2,596
17,943
10,300
4,695
4,335
6,760
6,524
2,504
8,393
7,899
17,438
9,705
5,403
11,028
1,748
2,839
2,893
1,990
10,623
3,774
20,489
16,435
-------
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
Table B-8f - 2008 On Road Non-Temperature Adjusted (on_noadj) Emissions by Species, by US State
1,382
16,615
6,978
4,941
16,469
-------
Inventory
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
^^^
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
^m
CO
295,017
67,354
364,951
202,649
2,004,320
294,194
187,766
b4,ob1
14,842
1,326,819
569,066
57,611
113,521
664,101
364,343
230,793
178,779
225,712
289,631
124,591
298,069
330,176
708,166
389,825
175,475
364,418
76,299
112,444
160,130
104,912
487,327
90,199
919,179
621,937
68,535
707,850
234,919
234,888
642,954
196,287
51,048
314,089
61,431
359,895
1,249,819
144,530
49,465
6,833
NH3
30
'
40
26
191
35
18
*
3
136
58
6
16
100
54
52
37
29
32
13
31
31
85
65
21
48
16
31
20
10
45
10
88
59
29
78
29
26
61
14
5
30
21
39
142
16
5
1
NOX
25,922
3,380
33,771
24,840
164,716
31,315
15,887
4,675
2,686
108,015
50,533
4,504
14,122
100,145
56,103
57,133
42,489
27,925
25,833
7,344
25,216
26,270
65,661
61,028
19,458
47,628
16,921
36,061
17,045
7,145
36,609
8,547
70,432
52,772
34,607
74,559
27,706
23,432
54,162
10,219
4,077
25,712
24,721
35,813
132,059
13,205
3,651
501
PM10
2,676
711
3,465
2,588
16,049
3,212
1,427
454
230
11,025
5,095
445
1,619
8,755
4,690
5,220
3,930
2,732
2,706
1,125
2,573
2,404
6,886
6,308
2,012
4,631
1,759
3,380
1,779
842
3,481
874
6,993
5,189
3,311
6,542
2,711
2,401
5,363
1,179
348
2,532
2,378
3,484
12,519
1,485
467
55
PM25
2,540
665
3,303
2,472
15,260
3,066
1,353
432
221
10,486
4,854
423
1,542
8,394
4,492
5,026
3,792
2,609
2,569
1,055
2,443
2,282
6,515
6,024
1,915
4,432
1,692
3,263
1,697
793
3,302
834
6,638
4,944
3,201
6,239
2,593
2,285
5,095
1,116
330
2,409
2,296
3,318
11,974
1,410
441
52
S02
464
394
671
464
211
608
247
83
58
2,108
960
76
276
1,905
1,030
1,052
816
506
473
136
454
423
1,154
1,106
355
880
337
701
321
120
602
166
1,208
987
684
1,337
518
430
917
194
63
481
484
641
2,538
252
65
11
evp VOC
8,584
1,009
7,976
4,724
34,663
3,869
3,127
923
261
38,202
10,578
1,665
2,021
9,831
6,477
3,467
2,563
5,086
10,345
2,030
5,693
5,035
14,290
8,432
5,816
7,140
1,100
1,902
3,004
1,668
8,678
1,887
15,433
11,563
972
10,282
5,090
3,899
10,400
4,575
810
6,497
1,044
8,121
26,648
2,731
776
194
exh VOC
37,044
17,599
31,989
26,906
169,713
28,472
19,993
5,445
1,121
138,053
52,117
4,470
19,283
66,077
38,431
32,725
16,082
28,262
41,138
26,450
30,745
34,159
119,590
75,634
25,550
42,267
11,006
12,640
14,694
17,233
48,449
8,945
105,064
57,836
10,635
65,896
24,505
28,035
66,644
15,358
5,246
31,358
9,497
40,533
106,954
20,582
9,048
681
-------
Inventory
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Don. US
468,871
384,786
100,286
433,801
44,072
17,895,250
46
43
9
57
6
2,074
38,939
38,046
7,325
47,279
4,844
1,874,385
3,955
3,813
922
4,938
577
183,854
3,765
3,632
871
4,680
549
175,326
evp VOC exh VOC
42,625
44,437
13,891
86,050
8,058
1,997,110
Table B-9a - 2008 Non-Road (nonroad) Emissions by Species, by US State
Inventory
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
rflVOC
78
ACETALD
evp BENZ
exh BENZ
rfl BENZ
FORMALD
16
33
36
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
1,277
-------
rflVOC
ACETALD
evp BENZ
Inventory
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Con. US
Table B-9b - 2008 Non-Road (nonroad) Emissions by Species, by US State
exh BENZ
rfl BENZ
FORMALD
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
BENZENE
ETH ETHA ETOH
27
2,105
787
0
231
1,099
617
546
362
404
545
248
452
459
1,438
964
35
1,692
692
40
4,536
1,790
155
23,429
8,603
4,730
5,208
5,850
20,942
13,275
14,973
1,336,522
570,821
«
114,501
664,768
365,080
232,845
178,792
227,690
293,716
126,313
299,512
332,024
714,334
393,792
3,076
0
2,543
2,335
13,804
2,344
1,662
449
82
11,233
4,208
«
1,666
5,726
3,337
3,151
1,615
2,387
3,462
2,214
2,510
2,777
9,985
6,727
986
0
802
700
4,374
715
540
142
24
3,609
1,325
220
»
182
0
4,019
296
772
0
0
0
0
509
1,648
976
819
356
730
1,105
729
802
901
3,225
2,042
0
1,737
1,302
938
154
170
0
0
0
94
372
2,379
FORM
771
•
871
621
4,333
794
433
125
49
3,154
1,314
»
429
1,992
1,083
1,046
692
687
811
46
710
748
2,383
1,765
-------
Modeled
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
«*
Oklahoma
ETH ETHA ETOH FORM
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
343
628
168
289
242
178
654
136
1,295
826
260
977
383
368
954
0
70
434
202
573
1,845
Vermont 92
Virgin Islands 0
Virginia 609
Washington 586
West Virginia 163
Wisconsin 941
Wyoming 90
TOTAL 28,551
Table B-9c - 2008 Non-Road
177,684
366,748
76,767
112,522
160,135
106,040
489,786
90,678
925,858
624,984
68,557
708,953
236,305
236,158
645,649
0
51,475
316,402
61,591
362,341
1,253,646
145,639
49,979
470,845
387,033
101,225
437,701
44,520
17,987,121
US State
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
MONO
207
»
268
197
1,310
248
127
37
21
862
401
MEOH NHL
NH3_FERT
768
0
677
513
3,361
518
369
107
22
3,060
1,054
0
31
1,043
1,032
270
1,685
170
48,524
25,842
0
33,499
24,637
163,695
31,031
15,858
4,660
2,663
107,754
50,182
-------
Modeled
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
MONO
ISOP MEOH NH,
NHL PERT
22,618
23,567
58,733
54,518
17,431
42,545
15,056
32,071
15,208
32,885
7,642
63,152
47,191
30,766
66,589
24,754
20,959
48,451
3,666
23,030
21,989
32,043
117,962
11,809
3,269
34,832
34,045
6,563
42,190
4,328
1,675,826
Table B-9d - 2008 Non-Road
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
25,131
26,186
65,258
60,576
19,368
47,272
16,729
35,634
16,897
7,136
36,538
8,491
70,168
52,434
34,184
73,988
27,505
23,288
53,835
0
4,073
25,589
24,432
35,604
131,069
13,121
3,632
0
38,702
37,827
7,293
46,878
4,809
1,862,029
[ (nonroad) Emissions by Species, by US State
-------
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
NVOL OLE
3,664
PFE PH2O
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
0
15
6
63
12
5
0
3,042
2,619
16,336
2,694
1,981
530
97
28,246
»
25,274
18,978
127,518
19,587
14,020
3,927
857
1,244
0
1,815
1,400
7,829
1,732
642
151
0
0
0
o
1
o
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
48,374
19,248
29,875
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
12
-------
Modeled
State
Texas
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
NVOL
48
5
1
0
15
13
3
10
1
585
OLE
10,146
2,014
900
0
4,088
4,313
1,368
8,614
793
193,855
PAL
0
0
0
0
0
0
0
0
0
4
PAR
84,236
13,739
5,657
0
30,545
29,975
9,727
54,465
5,069
1,425,569
PCA
7
1
0
0
2
2
0
3
0
102
PCL
6
1
0
0
2
2
0
2
0
87
PEC
7,021
703
185
0
1,995
1,957
385
2,221
285
96,525
PFE
3
•
0
0
1
1
0
1
0
38
PH2O
0
»
0
0
0
0
0
0
0
0
PK
1
»
0
o
0
o
0
0
0
7
Table B-9e - 2008 Non-Road (nonroad) Emissions by Species, by US State
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
2,682
0
3,448
2,581
16,000
3,194
1,428
454
228
11,026
5,079
0
1,616
8,705
4,661
5,180
3,889
2,726
2,718
1,135
2,570
2,406
6,898
6,292
2,014
4,611
1,745
3,344
1,769
847
3,484
872
PM2 5
2,544
•
3,287
2,464
15,209
3,048
1,354
431
220
10,485
4,838
•
1,539
8,345
4,463
4,986
3,751
2,602
2,579
1,064
2,439
2,282
6,525
6,006
1,916
4,411
1,678
3,228
1,687
798
3,303
831
PMC PMFINE PMG
138
0
162
117
790
146
74
22
542
242
0
77
360
PMN PMOTHR PNA PNCOM
197
194
137
123
139
71
131
123
374
286
98
200
67
116
82
49
180
41
21
12
-------
Modeled
New York
North Carolina
North Dakota
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
PMC PMFINE PMG
PMN PMOTHR PNA
PNCOM
7,003
5,178
3,274
6,511
2,699
2,397
5,357
0
349
2,531
2,354
3,480
12,458
1,486
469
0
3,944
3,806
925
4,936
578
183,337
Table B-9f - 2008 Non-Road (nonroad) Emissions by Species, by US State
I Modeled
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
lowa
Kansas
Kentucky
Louisiana
Maine
PNH,
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
PNO,
3
0
4
3
21
4
2
1
0
12
6
0
2
11
7
6
5
4
3
1
POC
778
0
909
664
4,471
821
423
125
47
3,030
1,366
0
430
2,055
1,131
1,100
785
701
782
394
PSI
1
0
1
1
7
1
1
0
0
5
2
0
1
2
1
1
1
1
1
1
PSO,
8
0
10
8
53
9
6
1
1
28
16
0
4
30
18
16
13
9
7
2
PTI
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
SO,
461
0
665
459
212
602
246
82
58
2,096
952
0
273
1,888
1,020
1,040
806
502
471
135
SULF
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
TERP
17
0
25
17
111
23
9
3
2
78
36
0
10
64
33
39
31
19
16
4
TOL
7,413
0
6,290
5,088
32,193
5,096
3,638
1,037
203
28,444
10,013
0
3,497
11,513
6,828
5,340
2,652
5,339
8,420
4,846
UNK
6
0
10
4
40
7
3
1
1
30
13
0
2
16
8
6
5
6
5
2
UNR
3,933
0
3,617
2,903
18,799
2,972
2,019
588
140
15,573
5,963
0
1,756
6,856
3,989
3,130
1,861
2,984
4,359
2,155
XYL
7,630
»
6,401
5,319
31,831
5,189
3,551
1,080
204
29,395
10,370
0
3,684
11,381
6,650
5,328
2,672
5,500
8,768
•
5,140
-------
Modeled
State
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
PNH4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
7
PN03
3
3
9
7
3
6
2
4
2
1
5
1
9
7
4
9
3
3
7
0
1
3
3
5
16
2
1
0
5
5
1
6
1
228
POC
734
702
2,104
1,608
553
1,132
376
660
457
278
1,019
230
2,018
1,398
616
1,730
665
656
1,533
0
102
699
460
945
3,092
425
150
0
1,075
1,023
287
1,460
160
48,358
PSI
1
1
4
2
1
1
0
0
1
1
2
0
4
2
0
3
1
1
3
0
0
1
0
2
4
1
0
0
2
2
1
3
0
73
PS04
7
9
21
18
6
14
5
10
4
2
12
2
23
18
9
24
8
7
20
0
1
9
7
12
41
4
1
0
12
11
3
15
1
578
PTI
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
S02
451
420
1,143
1,095
352
872
333
692
318
119
599
165
1,200
979
675
1,325
513
427
909
0
62
478
479
636
2,515
250
64
0
732
696
129
801
94
31,491
SULF
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
TERP
17
16
36
41
12
32
13
26
13
4
23
6
45
36
26
43
19
16
34
0
2
17
18
23
94
9
2
0
27
26
5
26
3
1,248
TOL
5,945
6,419
22,231
13,054
5,097
7,827
1,870
1,996
2,770
3,186
9,217
1,723
19,546
11,142
1,515
11,998
4,702
5,147
12,534
0
999
6,124
1,452
7,866
20,938
3,823
1,654
0
7,974
8,129
2,656
15,182
1,462
374,027
UNK
6
6
11
8
3
8
1
4
5
2
9
2
16
13
2
13
4
5
12
0
1
6
2
7
31
3
1
0
10
8
2
7
1
374
UNR
3,165
3,472
10,332
6,528
2,676
4,500
1,097
1,388
1,638
1,539
5,104
983
10,590
6,770
1,068
7,021
2,707
2,848
6,691
0
541
3,658
940
4,383
12,506
2,016
763
0
4,842
4,528
1,407
7,297
703
207,301
XYL |
6,173
6,678
23,287
13,118
5,314
8,032
1,960
1,914
2,791
3,366
9,319
1,767
19,952
11,563
1,499
12,184
4,880
5,314
13,057
0
1,041
6,363
1,433
8,183
21,541
4,005
1,751
0
8,287
8,462
2,782
15,556
1,544
383,208
Table B-9g - 2008 Non-Road (nonroad) Emissions by Species, by US State
-------
Canada 2006; Mexico 1999
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
9,255
464,690
42,320
5,196
543,613
43,598
307,151
69,406
27,292
11,400
34,005
45,164
88,824
177,489
98,521
117,633
157,420
201,999
150,837
30,391
24,138
141,855
8,692
5,703
25,700
3,157
644,389
6,007
1,097
112,037
7,659
50,737
17,112
4,952
2,102
3,598
11,605
19,939
28,541
18,233
1,541
121,113
4,487
5,775
10,661
18,782
1,742
13,061
3,121
7,037
13,324
23,601
14,374
17,269
19,652
21,453
20,117
3,824
28,097
102,434
28,972
26,276
4,789
16,224
37,271
21,323
34,638
74,343
29,462
106,990
83,220
118,701
33,291
5,998
33,576
19,284
33,332
7,012
267,289
1,302,135
188,398
1,018,512
29,063
34,432
98,867
175,797
61,425
85,957
15,275
4,367
35,078
115,370
65,473
51,474
42,693
38,039
7,946
128,466
38,077
16,908
106,106
6,829
7,749
14,476
80,855
21,724
22,496
16,207
32,073
6,603
47,316
11,942
31,595
238,635
5,676
5,629
17,716
229,110
16,844
11,620
11,859
14,110
5,397
55,880
14,300
62,337
78,763
73,276
53,066
29,464
360,169
92,356
35,186
4,002,230
40,875
556,747
14,646
776,196
15,736
1,768,338
7,115
463,061
3,620
104,205
26,308
1,339,919
-------
Canada 2006; Mexico 1999
Mexico Total 2,925,901 1,321,883 595,212 525,127 392,852 218,319 1,960,517
TOTAL 6,928,131 1,878,630 1,371,408 2,293,465 855,914 322,523 3,300,437
Table B-lOa - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by Canadian Province, and by Mexican
Federal State
Modeled
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
ALDU BENZENE
ETH ETHA ETOH
0
390,703
42,406
3,828
480,530
-------
Modeled
Tamaulipas
Tlaxcala
Veracruz
Yucatan
460
0
0
0
ALDU BENZENE
356
0
457
0
0
0
213
0
0
0
20,761
0
•
0
ETH ETHA ETOH
235
0
0
0
42
0
0
0
1,342
0
0
0
305
0
Zacatecas
Canada Total
Mexico Total
33
35,815
7,918
43,733
23
28,198
5,603
33,802
9
55,296
6,855
62,151
50
2,041,938
8,074
2,050,012
1,041
3,756,550
351,324
4,107,874
19
62,930
4,771
67,701
5
32,775
1,128
33,903
14
41,953
14,720
56,673
30
35,252
7,000
42,252
Table B-lOb - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by Canadian Province, and by Mexican
Federal State
Modeled
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
ALDU BENZENE
ETH ETHA ETOH
FORM
0
390,703
42,406
3,828
480,530
-------
Modeled
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
ALDU BENZENE
ETH ETHA ETOH
FORM
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
33 23 9 50 1,041 19 5 14 30
35,815 28,198 55,296 2,041,938 3,756,550 62,930 32,775 41,953 35,252
7,918 5,603 6,855 8,074 351,324 4,771 1,128 14,720 7,000
43,733 33,802 62,151 2,050,012 4,107,874 67,701 33,903 56,673 42,252
Table B-lOc - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by Canadian Province, and by Mexican
Federal State
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
0
153,608
24,611
6,783
83,334
-------
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
20
0
672
0
5,911
53
0
2,020
0
10
0
315
0
0
5,641
0
0
85,974
0
0
93,955
0
2
115,568
51,648
51,580
0
9,861
0
0
0
0
0
0
0
0
1
71,749
15,324
20,288
0
13,615
0
12,037
1,969
1,948
539,370
255,125
794,495
288
0
802,514
220,313
3,086
0
9,770
0
0
0
7,378
1,580
2,098
0
1,410
0
0
104,397
0
0
1
79,723
17,027
22,542
0
15,128
0
39 419
67,254 720,293
16,011 171,396
83,265 891,689
Table B-lOd - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by Canadian Province, and by Mexican
Federal State
Modeled
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
NVOL OLE
0
PCA PCL
PFE PH2O
-------
Modeled
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
NVOL OLE
PCL PEC
PFE PH2O
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
0
0
0
16
1
19
86
0
0
400
0
211
0
0
0
330
34
47
0
51
0
0
0
0
1
1,858
463
2,321
0
0
»
1,912
365
1,597
796
0
0
16,231
402
0
13,837
0
»
0
6,018
836
892
0
367
0
16
60,270
5,825
66,095
0
0
•
225
67
224
17
0
0
2,329
42
0
1,323
0
•
0
1,130
141
47
0
77
0
0
0
»
18,660
2,909
17,699
54,708
2
0
233,229
3,347
0
157,775
0
»
0
53,663
21,969
34,102
0
24,606
0
0
0
•
271
90
272
16
0
0
3,051
46
0
1,702
0
•
0
1,392
45
21
0
26
0
0
0
»
56
10
41
44
0
0
261
6
0
303
0
»
0
110
97
409
0
7
0
0
0
•
895
211
1,085
1,395
0
0
11,564
226
0
8,081
0
•
0
5,452
1,623
1,867
0
1,292
0
0
0
»
188
57
183
12
0
0
2,304
34
0
1,201
0
»
0
903
92
31
0
50
0
6
8,777
522
9,299
392
705,800
294,204
1,000,005
2
11,671
225
11,896
0
2,014
790
2,804
36
43,491
12,146
55,636
4
7,765
347
8,112
0
0
•
17
4
16
1
0
0
174
3
0
129
0
•
0
57
1
1
0
1
0
0
828
7
836
0
0
»
321
87
315
48
0
0
1,479
49
0
1,880
0
»
0
451
137
345
0
26
0
2
6,908
845
7,753
Table B-lOe - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by Canadian Province, and by Mexican
Federal State
-------
Modeled
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
PMC PMFINE PMG PMN PMOTHR PNA PNCOM PNH,
Guerrero
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
/ukon
-------
Modeled
State
Zacatecas
Canada Total
Mexico Total
TOTAL
PWL
458
1,424,994
75,679
1,500,672
PM,fi
211
394,648
49,111
443,758
PMC
247
1,030,346
26,568
1,056,914
PMFINE
110
242,176
19,435
261,610
PMG
0
831
13
844
PMN
0
241
9
250
PMOTHR
38
118,151
5,118
123,268
PNA
0
1,249
169
1,419
PNCOM
40
53,035
9,766
62,801
PNH.
0
290
168
458
Table B-lOf - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by Canadian Province, and by Mexican
Federal State
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
-------
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Zacatecas
Canada Total
Mexico Total
TOTAL
26
0
6
0
0
0
0
869
110
979
3,359
0
718
0
126
0
63
99,942
16,017
115,959
Table B-lOg - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by Canadian Province, and by Mexican
Federal State
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
-------
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
Table B-lOh - 2008 Other Non-Point and Non-Road (othar) Emissions by Species, by Canadian Province, and by Mexican
Federal State
-------
Inventory Totals (Canada - 2006; Mexico -1999)
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British
Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
**
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
51,156
787,448
135,646
13,074
587,324
23,169
110,136
161,063
115,759
16,569
813,226
66,416
206,136
97,332
64,276
547,239
262,215
894,875
128,606
66,134
29,792
147,496
79,015
136,611
391,398
96,671
1,212,175
27,533
208,013
978,672
57,535
37,062
96,895
399,253
91,371
81,056
53,000
125,260
42,848
221,679
64,848
1,805
89
1,262
67
70,139
625
3,293
26
2,643
8,388
105
5,915
50,394
17,696
1,713
42,481
2,453
11,340
15,784
11,396
1,746
68,638
6,133
18,617
10,156
5,980
47,275
29,128
77,043
11,922
6,288
3,116
14,224
19,447
13,901
2,257
97,578
7,482
24,259
12,738
7,612
65,657
16,827
106,628
15,363
8,026
11,800
64,827
1,669
-------
Inventory Totals (Canada -2006; Mexico- 1999)
State
Zacatecas
Canada Total
Mexico Total
TOTAL
CO
41,502
4,617,742
5,149,744
9,767,487
PM10
175
15,310
22,622
37,932
PM25
160
10,853
20,724
31,577
NOX
3,568
549,231
480,164
1,029,395
S02
208
5,535
26,902
32,437
NH3
64
22,252
8,346
30,598
voc
4,450
283,879
631,594
915,473
Table B-lla - 2008 Other On-Road Source (othon) Emissions by Species, by Canadian Province, and by Mexican Federal State
Modeled
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
ALDU BENZENE CH,
ETH ETHA ETOH
FORM
0
761,875
136,058
9,936
576,782
-------
Modeled
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Zacatecas
Canada Total
Mexico Total
TOTAL
ALDU BENZENE CH,
ETH ETHA ETOH
742
4,528,720
1,069,878
5,598,598
Table B-llb - 2008 Other On-Road Source (othon) Emissions by Species, by Canadian Province, and by Mexican Federal State
Modeled
MONO IOLE ISOP MEOH
NHL PERT
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward Island
13,517 146,931
304 3,304
-------
Modeled
Puebla
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Tabasco
Tamaulipas
Tlaxcala
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
MONO IOLE ISOP MEOH
0
0
0
0
0
0
0
0
NH, PERT
1
4,316
885
5,200
1
4,286
2,117
6,403
0
0
0
0
0
0
0
0
0
0
64
539,473
110,580
59,805 650,053
Table B-llc - 2008 Other On-Road Source (othon) Emissions by Species, by Canadian Province, and by Mexican Federal State
Modeled
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
NVOL OLE
PFE PH2O
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
-------
Modeled
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
NVOL OLE
PFE PH2O
2
13
0
0
17
562
3,582
0
0
4,432
4,673
29,772
0
0
36,863
171
m
0
0
1,465
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
0
16
0
0
0
6
2
0
0
70
34
115
0
4,009
0
0
0
1,892
599
723
0
639
0
0
0
0
5
19,029
9,397
28,426
952
0
33,350
0
•
0
15,723
4,971
6,009
0
5,304
0
0
0
•
43
158,204
78,124
236,327
0
1
1
1
0
1
0
0
0
•
0
13
8
21
43
0
1,553
0
0
0
555
150
179
0
158
0
0
0
0
1
6,304
2,194
8,498
Table B-lld - 2008 Other On-Road Source (othon) Emissions by Species, by Canadian Province, and by Mexican Federal State
Modeled
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
Manitoba
Mexico
PMC PMFINE PMG
PMOTHR
PNCOM
-------
Modeled
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
PMC PMFINE PMG
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
0
0
0
0
529
48
414
1,802
0
0
4,176
89
0
3,545
0
0
0
1,139
353
421
0
371
0
0
0
0
3
15,053
5,168
20,222
0
390
34
290
1,651
0
139
2,720
14
124
151
0
»
1,457
0
»
0
0
36
4
32
258
0
0
351
PMOTHR
0
PNCOM
0
67
0
2,542
0
»
0
882
324
386
0
340
0
22
0
1,003
0
»
0
257
30
35
0
31
0
3
10,669
4,735
0
4,384
433
0
263
0
0
0
85
50
60
0
53
0
0
0
»
0
1,180
739
15,405 4,817 1,918 3
Table B-lle - 2008 Other On-Road Source (othon) Emissions by Species, by Canadian Province, and by Mexican Federal State
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
-------
Guanajuato
Guerrero
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
/ukon
Zacatecas
Canada Total
Mexico Total
TOTAL
Table B-llf - 2008 Other On-Road Source (othon) Emissions by Species, by Canadian Province, and by Mexican Federal State
Aguascalientes
Alberta
Baja Calif Norte
Baja Calif Sur
-------
British Columbia
Campeche
Chiapas
Chihuahua
Coahuila
Colima
Distrito Federal
Durango
Guanajuato
Guerrero
Hidalgo
Jalisco
Manitoba
Mexico
Michoacan
Morelos
NW Territories
Nayarit
New Brunswick
Newfoundland
Nova Scotia
Nuevo Leon
Nunavut
Oaxaca
Ontario
Prince Edward
Island
Puebla
Quebec
Queretaro
Quintana Roo
San Luis Potosi
Saskatchewan
Sinaloa
Sonora
Tabasco
Tamaulipas
Tlaxcala
Veracruz
Yucatan
Yukon
Zacatecas
Canada Total
Mexico Total
TOTAL
Table B-llg- 2008 Other On-Road Source (othon) Emissions by Species, by Canadian Province, and by Mexican Federal State
-------
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Illinois
Indiana
Iowa
ansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
-------
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Con. US
Table B-12a - 2008 Agricultural (ag) Emissions by Species, by US State
10,119
Modeled
Alabama
ALDU BENZENE
0
ETHA ETOH FORM IOLE
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
• Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Neva
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
-------
Modeled
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Con. US
Table B-12b - 2008 Agricultural (ag) Emissions by Species, by US State
ETHA ETOH FORM IOLE
Modeled
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
ISOP MEOH
0
0
62,077
39,498
120,404
284,940
68,571
2,491
13,087
0
33,397
84,817
0
103,069
151,471
101,754
295,933
167,089
51,731
37,022
4,743
27,063
2,155
60,845
183,807
58,549
124,543
NH3 PERT
0
NVOL OLE
0
0
-------
Modeled
Montana
Nebraska
L Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
ISOP MEOH
NH3 PERT
NVOL OLE
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
83,619
97,365
42,695
70,797
263
29,861
131,548
33,938
288,808
0
44,699
7,56
41,663
42,766
12,447
115,427
19,287
3,574,855
Con. US
Table B-12c - 2008 Agricultural (ag) Emissions by Species, by US State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
-------
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Con. US
Table B-12d - 2008 Agricultural (ag) Emissions by Species, by US State
000
152 24,085 7,396
16,689
-------
Modeled
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
• Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevai
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
PMFINE PMG PMN PMOTHR PNA PNCOM PNH,
0
-------
Modeled
PMFINE PMG PMN PMOTHR PNA PNCOM PNH,
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Con. US
60
0
0
32
0
Table B-12e - 2008 Agricultural (ag) Emissions by Species, by US State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
2,334
-------
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Con. US
Table B-12f - 2008 Agricultural (ag) Emissions by Species, by US State
-------
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
5,654
18,660
-------
33,704
43,484
26,515
7,048
36,686
1,438,940
Tribal
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Cont US
Table B-13a - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species, by US State
Inventory
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
ACETALD
BENZENE
CHLORIN
FORMALD METHANOL
New Hampshire
New Jersey
New Mexico
New York
North Carolina
-------
Inventory
North Dakota
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Cont US
745
1,394
1,239
883
ACETALD
14
BENZENE
CHLORIN
FORMALD METHANOL
28
,6
34
1,473
5
315
0
77
59
36
79
3,187
Table B-13b - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species, by US State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
11
1,126
-------
Modeled
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
ALDX BENZENE CH,
ETH ETHA ETOH
North Carolina
North Dakota
0*
Oklahoma
Oregon
Pennsylvania
• Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
Table B-13c - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species, by US State
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
FORM
92
0
74
66
236
47
16
10
91
89
MONO
267
•
189
,9,
709
,2,
54
MEOH
14
•
11
,2
34
'
3
NHL PERT
0
241
13
30,009
0
21,249
21,492
79,724
13,586
6,062
2,256
395
32,521
27,081
3,068
0
2,172
2,197
8,150
1,389
620
231
40
3,324
2,768
-------
Modeled
Hawaii
Idaho
Illinois
Indiana
FORM MONO
ISOP MEOH NHL NHL PERT
0
7,662
48,885
27,860
23,715
32,343
29,693
134,674
7,549
16,556
0,000
28,965
31,514
22,743
42,641
21,450
64,641
6,095
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
4,056
2,289
2,659
1,205
1,428
4,969
1,820
1,796
2,480
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
Table B-13c - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species, by US State
-------
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New
Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
439
13
44,091
24,882
28,901
13,093
15,517
54,007
19,780
-------
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
9,206
738
33,796
43,603
26,587
17,095
36,786
1,442,883
Table B-13d - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species, by US State
Modeled
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
PMC PMFINE
PMN PMOTHR PNA
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New
Hampshire
New Jersey
New Mexico
New York
North Carolina
0 1,161 1,099 62
0000
0 0 786 723 63
0 0 800 748 52
0 0 2,983 2,833 150
0 0 503 463 40
220
91
12
210
12
-------
Modeled
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
PMC PMFINE PMG
PMN PMOTHR PNA
Table B-13e - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species, by US State
Modeled
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
PNCOM
48
»
32
33
124
20
9
4
1
53
42
0
11
76
44
36
48
47
224
12
27
16
PNH4
0
»
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
PN03
1
0
1
1
3
1
0
0
0
1
1
0
0
2
1
1
1
1
6
0
1
0
POC
193
0
127
132
499
81
37
15
2
211
170
0
46
303
175
145
191
189
899
48
107
63
PSI
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
PS04
3
0
2
2
8
1
1
0
0
4
3
0
1
5
3
2
3
3
15
1
2
1
PTI
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
S02
1,194
0
244
571
3,266
155
306
258
20
1,703
733
0
86
1,245
807
374
375
1,189
8,317
419
849
637
SULF
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
TERP
12
0
12
10
31
7
2
1
0
10
13
0
4
23
13
13
17
11
35
2
5
3
TOL
100
0
93
81
265
60
16
7
2
89
104
0
33
189
105
101
140
98
329
19
47
28
UNK
3
0
5
4
9
3
0
0
0
2
5
0
2
8
4
5
7
3
2
0
1
1
UNR
143
0
141
118
384
90
21
10
3
121
153
0
51
278
153
152
212
138
423
25
65
41
XYL |
80
0
63
59
213
40
14
6
1
79
76
0
22
137
78
69
94
79
320
17
41
24
-------
Modeled
State
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal Data
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
PNCOM
47
49
36
66
31
98
9
1
66
33
40
18
20
77
26
27
37
0
4
13
5
39
152
0
12
1
50
63
39
23
50
2,041
PNH.
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
PNO,
1
1
1
2
1
3
0
0
2
1
1
0
1
2
1
1
1
0
0
0
0
1
4
0
0
0
1
2
1
1
1
53
POC
188
196
145
266
126
391
37
4
266
133
162
72
81
310
105
107
147
0
16
53
21
158
611
0
47
3
199
252
155
94
202
8,182
PSI
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
PSO,
3
3
2
4
2
7
1
0
4
2
3
1
1
5
2
2
2
0
0
1
0
3
10
0
1
0
3
4
3
2
3
137
PTI
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
SO,
1,574
1,163
1,082
1,170
244
745
69
76
2,506
258
988
273
158
1,580
229
609
332
0
156
699
40
805
7,431
0
701
5
1,197
1,793
1,045
436
384
50,496
SULF
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
TERP
8
12
8
19
11
36
3
0
10
12
10
6
7
21
9
7
14
0
1
4
2
11
42
0
5
0
12
14
9
7
18
542
TOL
77
104
67
161
92
286
27
3
94
98
85
46
59
177
75
62
119
0
6
35
15
90
364
0
38
2
105
119
76
56
148
4,593
UNK
1
4
2
7
5
15
1
0
0
5
3
2
3
7
4
2
4
0
0
2
1
4
10
0
2
0
4
3
2
2
8
170
UNR
103
150
91
237
139
433
41
4
121
148
121
69
90
258
114
90
169
0
7
52
23
131
502
0
58
3
150
166
107
82
224
6,604
XYL
70
82
57
119
62
192
18
*
93
66
67
33
40
134
51
48
95
»
6
24
10
68
302
»
26
1
83
100
63
42
99
3,567
Table B-13f - 2008 Cl and C2 Marine and Locomotive (clc2rail) Emissions by Species, by US State
-------
Arizona
Arkansas
California
Colorado
onnecticut
Delaware
DC
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
36,479
74,002
30,431
115,655
71,377
88,023
56,121
26,29
25,174
5,873
9,978
16,271
50,743
95,566
40,473
102,285
45,642
74,501
24,514
2,957
4,857
84,202
35,764
32,518
71,206
61,837
90,927
26,874
33,025
922
26,772
43,572
25,256
290,62
25,889
4,310
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermon'
-------
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Con. US Total
136,362
183,646
87,291
239,548
477,186
16,236,331
Table B-14a - 2008 Area Fugitive Dust (afdust) Emissions by Species, by US State
Modeled (after transport fraction and met-adjustment)
Alabama
Arizona
Arkansas
California
Colorado
Connecticu
Delaware
District o
Florida
Georgia
Idaho
Illinois
Indiana
_
Kansas
Kentucky
Louisiana
aine
Maryland
Massachuse
Michigan
Minnesota
Mississipp
Missouri
Montana
Nebraska
Nevada
New Hampsh
New Jersey
New Mexico
New York
North Caro
North Dako
0*
Oklahoma
354
1,369
1,230
1,634
1,321
23
30
5
619
834
526
2,750
1,489
2,238
1,600
371
438
35
147
114
697
1,942
658
1,572
1,265
2,435
917
9
53
2,344
285
435
2,198
U42
2,711
358
1,352
598
2,461
845
26
25
'
687
662
562
1,459
828
940
1,377
226
287
32
128
171
481
1,046
389
1,187
681
1,311
1,240
•
44
3,253
273
278
833
638
2,071
11
37
22
38
25
1
1
«
20
21
12
48
28
38
35
8
11
1
3
3
16
36
13
33
24
45
23
«
2
65
8
10
37
22
57
34
44
16
64
21
3
3
'
57
40
8
35
27
23
36
13
15
3
8
6
24
24
15
33
14
29
12
'
9
60
20
24
15
28
45
39
2,046
220
307
1,457
791
2,022
PK PMFINE PMG
6,030
22,919
15,389
32,436
17,985
409
465
94
11,018
12,670
8,882
35,046
19,461
26,826
24,663
5,052
6,218
578
2,270
2,323
9,909
25,114
8,767
22,990
16,448
31,447
17,464
148
858
45,864
4,670
6,024
25,678
14,918
39,795
32
95
39
97
47
66
55
39
65
54
53
108
"
25
«
6
14
39
72
26
93
50
91
39
'
6
292
23
20
45
37
163
-------
Modeled (after transport fraction and met-adjustment)
Oregon
Pennsylvan
Rhode Isla
South Caro
South Dako
Tennessee
PMFINE PMG
Texas
Utah
Vermont
4,998
15,439
5,307
133,966
10,388
3,135
11,203
708
10,009
22,729
750,047
Virginia
Washington
West Virgi
Wisconsin
Wyoming
Con.USTotel
Table B-14b - 2008 Area Fugitive Dust (afdust) Emissions by Species, by US State
Modeled (after transport fraction and met-adjustment)
PMOTHR
PNCOM
Alabama
Arizona
Arkansas
California
Colorado
Connecticu
Delaware
District o
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachuse
Michigan
Minnesota
Mississipp
Missouri
-------
Modeled (after transport fraction and met-adjustment)
PMOTHR
PNCOM
Montana
Nebraska
Nevada
New Hampsh
New Jersey
New Mexico
New York
North Caro
North Dako
Rhode Isla
South Caro
South Dako
Tennessee
Vermont
Virginia
Washington
West Virgi
Wisconsin
Wyoming
Con. US Total
Table B-14c - 2008 Area Fugitive Dust (afdust) Emissions by Species, by US State
Modeled (after transport fraction and met-adjustment)
Alabama
Arizona
Arkansas
California
Colorado
Connecticu
Delaware
District o
Florida
Georgia
Idaho
Kansas
Kentucky
42,789
129,882
82,796
268,222
94,383
2,435
2,333
584
73,212
84,542
63,769
190,175
109,904
134,260
175,268
27,708
11,966
13,557
9,405
36,685
20,446
27,992
26,130
5,354
-------
Modeled (after transport fraction and met-adjustment)
Louisiana
Maine
Maryland
Massachuse
Michigan
Minnesota
Mississipp
Missouri
Montana
Nebraska
Nevada
New Hampsh
New Jersey
New Mexico
New York
Morth Caro
North Dako
Ohio
Oklahoma
Oregon
Pennsylvan
Rhode Isla
South Caro
South Dako
Tennessee
Texas
Utah
Vermont
Virginia
Washington
WestVirgi
Wisconsin
Wyoming
Con.USTotel
45,541
4,056
15,926
22,182
70,332
171,748
59,073
178,150
114,832
214,382
166,203
1,084
4,288
463,354
34,289
36,524
152,021
95,444
315,956
47,104
34,156
1,466
41,230
90,757
32,101
1,082,541
88,483
2,304
23,364
70,286
6,034
62,647
210,891
3,937
147,699
Table B-14d - 2008 Area Fugitive Dust (afdust) Emissions by Species, by US State
-------
Inventory
Alabama
Alaska
California
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Hawaii
Illinois
Indiana
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
New Jersey
New York
355
826
1,428
118
183
0
1,850
166
227
9
4
1,837
91
389
301
905
26
98
609
448
910
10,310
12,755
1,419
2,862
1
22,323
1,869
2,689
107
47
18,979
1,087
4,637
3,578
11,316
320
1,163
6,719
5,166
81
839
923
121
281
0
2,122
164
239
9
4
1,676
96
404
319
906
27
103
618
432
75
111
848
111
259
0
1,953
151
220
8
4
1,538
88
372
294
834
25
94
569
398
623
6,277
8,285
965
2,152
1
17,655
1,574
1,740
67
32
12,815
818
3,077
3,054
6,728
206
827
8,611
3,165
Non-US SECA
C3
North Carolina
Offshore to EEZ
C*
Oregon
Pennsylvania
Rhode Island
South Carolina
Texas
Virginia
Washington
West Virginia
Wisconsin
British Columbia
Nova Scotia
Ontario
Total
Con. US total
Canada total
165,937
124
56,209
2,0
252
278
18
278
1,134
250
1,768
2
65
2,002,109
1,452
166,068
125
680,381
2,613
2,475
3,352
213
3,142
9,671
2,974
20,259
28
807
56,508
212
230
288
19
282
1,357
255
1,811
»
65
115
51,946
195
211
266
17
260
1,246
235
1,643
*
60
1,772
421,992
1,577
1,453
3,059
482
4,102
10,368
2,373
12,433
"
489
5,108
10,020
398
251,919
13,195
15,526
61,901
120,624
4,979
3,025,236
142,243
187,504
5,137
10,028
399
252,152
12,932
15,564
4,661
9,237
367
231,857
11,871
14,265
38,074
74,401
2,961
1,886,277
108,779
115,436
VOC ACETALD BENZENE FORMALD
33
349
393
48
76
0
726
73
97
4
2
668
36
163
124
384
11
40
261
197
152,783 1,232,054 70,447
51
23,879
89
132
111
7
119
377
106
823
'
27
2,171
4,255
169
106,448
5,082
6,595
16
110
Table B-15a - 2008 C3 Commercial Marine (c3 marine) Emissions by Species, by US State
-------
Modeled
Alabama
Alaska
California
Connecticut
Delaware
ALDU BENZENE CH,
ETHA ETOH
District of
Columbia
Florida
Georgia
Hawaii
Illinois
Indiana
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
New Jersey
New York
Non-US SECA
C3
North Carolina
Offshore to
EEZ
Ohio
Oregon
Pennsylvania
Rhode Island
South Carolina
Texas
Virginia
Washington
West Virginia
Wisconsin
British
Columbia
90
143
29,769
47,431
229
365
418
668
Nova Scotia
Ontario
Total
Con. US total
Canada total
Table B-15b - 2008 C3 Commercial Marine (c3 marine) Emissions by Species, by US State
20
32
-------
Modeled
Alabama
Alaska
California
Connecticut
Delaware
District of
Columbia
MONO IOLE ISOP MEOH
NVOL OLE
Florida
Georgia
Hawaii
Illinois
Indiana
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
New Jersey
New York
Non-US SECAC3
North Carolina
Offshore to EEZ
Ohio
Oregon
Pennsylvania
Rhode Island
South Carolina
Texas
Virginia
Washington
West Virginia
Wisconsin
British Columbia
Nova Scotia
Ontario
Total
Con. US total
Canada total
Table B-15d - 2008
C3 Commercial Marine (c3 marine) Emissions by Species, by
-------
Alabama
Alaska
California
Connecticut
Delaware
District of Columbia
Florida
Hawaii
Indiana
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
New Jersey
New York
Non-US SECAC3
North Carolina
Offshore to EEZ
«*
Oregon
Pennsylvania
Rhode Island
South Carolina
Texas
Virginia
Washington
West Virginia
Wisconsin
British Columbia
Nova Scotia
Ontario
Total
Con. US total
Canada total
Table B-15e - 2008 C3 Commercial Marine (c3 marine) Emissions by Species, by US State
-------
Modeled
Alabama
Alaska
California
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Illinois
Indiana
Louisiana
Maine
Marylan'
Massachusetts
Michigan
Minnesota
Mississippi
New Jersey
New York
PMC PMFINE PMG PMN PMOTHR PNA PNCOM PNH,
Non-US SECAC3
North Carolina
Offshore to EEZ
Ohio
Oregon
Pennsylvania
Rhode Island
South Carolina
Texas
Virginia
Washington
West Virginia
Wisconsin
British Columbia
Nova Scotia
Total
Con. US total
Canada total
Table B-15f - 2008 C3
Commercial Marine (c3 marine) Emissions by Species, by US State
-------
Alabama
Alaska
California
Connecticut
Delaware
District of Columbia
Florida
Hawaii
Indiana
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
New Jersey
New York
Non-US SECAC3
North Carolina
Offshore to EEZ
«*
Oregon
Pennsylvania
Rhode Island
South Carolina
Texas
Virginia
Washington
West Virginia
Wisconsin
British Columbia
Nova Scotia
Ontario
Total
Con. US total
Canada total
0
Table B-15g - 2008 C3 Commercial Marine (c3 marine) Emissions by Species, by US State
-------
Modeled
Alabama
Alaska
California
Connecticut
Delaware
District of Columbia
Florida
Georgia
Hawaii
Illinois
Indiana
Louisian;
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
New Jersey
New York
TERP TOL
UNK UNR
ALD2 and FORM don't match up exactly due to
molecular weights
Non-US SECAC3
North Carolina
Offshore to EEZ
Ohio
Oregon
Pennsylvania
Rhode Island
South Carolina
Texas
Virginia
Washington
West Virginia
Wisconsin
British Columbia
Nova Scotia
Ontario
Total
Con. US total 61 592 0
Canada total 65 637 0
Table B-15h - 2008 C3 Commercial Marine (c3 marine) Emissions by Species, by US State
-------
72 degrees
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
6.2
58.0
PMC 72
38.8
PMFINE 72
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
exas
Utah
Vermont
Virginia
94.1
0.3
0.3
0.1
1.7
0.3
0.2
0.0
0.0
1.0
0.5
0.1
0.5
0.3
0.1
0.1
0.2
0.2
0.1
0.3
0.3
0.5
0.3
0.2
0.3
0.0
0.1
0.1
0.1
0.4
0.1
0.7
0.5
0.0
0.5
0.2
0.2
0.6
0.1
0.2
0.0
0.3
1.3
0.1
0.0
0.4
-------
72 degrees
Washington
West Virginia
Wisconsin
Wyoming
Cont. US
PMC 72
PMFINE 72
5.9
2.0
a
1.0
316.7
55.4
19.0
53.4
8.9
2,960.3
Table B-16a - 2008 Running [vehicle] Exhaust PM (runpm) Emissions by Species, by US State
Temperature adjusted
OTHER
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
-------
Temperature adjusted
OTHER
PMFINE
88.7
141.5
390.3
76.5
1,5
185.5
South Carolina 5.0 67.3 49.7
Tennessee 6.6 102.5 79.5
Utah 2.7 56.6 43.4
Vermont 0.8 19.6 11.4
Virginia 8.6 136.9 104.4
West Virginia 2.0 37.7 27.9
Wyoming 1.0 24.5 16.8
TOTAL 316.7 5,353.9 3,783.2
Table B-16b - 2008 Running [vehicle] Exhaust PM (runpm) Emissions by Species, by US State
0.2
0.0
0.3
1.3
0.1
0.0
0.4
0.3
0.1
0.3
0.0
14.4
-------
72 DEGREES
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
PMC 72 PMFINE 72
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
-------
72 DEGREES
Wisconsin
Wyoming
Cont. US
PMC 72 PMFINE 72
4.6
0.7
232.1
43.3
6.7
2,169.6
11.2
1.7
18.4
2.8
916.3
0.2
0.0
10.6
68.7
10.5
3,421.0
Table B-17a - 2008 Starting [vehicle] Exhaust PM (startpm) Emissions by Species, by US State
Temperature adjusted
State Name
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of
Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
OTHER
4.4
PMFINE
0.2
0.0
-------
Temperature adjusted
State Name
OTHER
PMFINE
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
Table B-17b - 2008 Starting [vehicle] Exhaust PM (startpm) Emissions by Species, by US State
-------
-------
Appendix C
Metadata
Output Data
The pm25_surface_12km_2007.csv (or o3_
surface_12km_2007.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, 2007 through
December 31, 2007. 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 contiguous grids. The contiguous 12 km
x 12 km grids cover the whole 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 intentionally excluded)
values. Excluded 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 4.7 (4.7.1) of
the model which includes improved aqueous chemistry
and photolysis mechanisms. The PM2 5 data is a 24-hour
integrated PM2 5 concentration calculated on a 12-km x
12- km grid for the entire United States. These CMAQ
results are based on emission inputs for the 2005v4
Platform from the 2005 National Emission Inventory (NEI),
Version 2, which includes emissions of CO, NOx, VOC,
SO2, NH3, PM10, and PM2 5 and hazardous air pollutants
(HAPs), including chlorine, HC1, benzene, acetaldehyde,
formaldehyde, and methanol. In addition, the meteorological
data used for these model results is from the Weather
Research and Forecasting Model (WRF) version 3.1
simulations (Advanced Research WRF [ARW] core).
The HBM combines the actual monitoring data (NAMS/
SLAMS), the estimated PM2 5 or O3 concentration surface
(CMAQ), and the prediction of PM2 5 or O3 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
12-km Resolution
NCOLS = 459
NROWS = 299
P_ALP= 33.00
P_BET = 45.00
P_GAM = -97.00
XCENT = -97.00
YCENT = 40.00
XORIG = -2556000.00
YORIG=-1728000.00
XCELL = 12000.00
YCELL = 12000.00
These values are for the 12-km grid resolution of CMAQ.
-------
Monitor and HBM Concentration (pg'm
Figure C-1. PM25 Monitoring Data and CMAQ Surface (Separately Displayed—White Spheres Represent Monitor
Locations and Associated Concentration Values)
Monitor and HBM CoMMtHtkM (Mfttn
Figure C-2. Combined PM Monitoring Data and CMAQ Surface (Via HBM)
-------
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
The definitions for the 12-km x 12-km CMAQ grid cells are
contained in a text (*.txt) file. The file contains 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 PM25 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.
Use of HB Data to Generate Health Indicators
The HB output data can be used to generate health (air)
indicators which are useful to researchers when developing
health impact assessments (HIA). The HB output is provided
in a gridded (x-y/row-column) format and that format must
be translated to different coordinate systems (e.g., county-
based/relevant coordinates) to provide health indicator data
for the area(s) of interest. An important coordinate projection
system used as a standard coordinate representation format to
express different location designation systems in consistent
terms is the Lambert Conformal Conic (LCC) projection
coordinate system. The North American Datum (NAD)
geodetic system describes the Earth's ellipsoid based on the
latitude and longitude location of an initial point, and serves
as the basis of maps and surveys of the Earth's surface.
The NAD-27 datum is based on the Clarke Ellipsoid (Earth
spheroid) of 1866 and is centered at a base station on the
Meades Ranch in Kansas. The NAD-83 grid projection/
datum is based on the Geodetic Reference Spheroid (GRS)
of 1980 and is geocentric (e.g., based on the Earth's center
with no directionality or initial point located on the Earth's
surface). The NAD coordinate system is important because
health-related data (used to calculate health indicators) are
collected and cataloged based on this coordinate system (e.g.,
U.S. Census data is based on NAD-83 coordinates).
The HB output provides ambient concentration data for both
ozone and fine paniculate 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 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.
How CMAO and HB x-y grid locations are transformed to
latitude and longitude values:
There is an IOAPI file providing rows/columns, cell height/
width, origin in LCC, offset by 1A 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:
-------
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)
write(*, *) 'INC ALL:' ,phic,xlonc,truelatl ,truelat2
LCPGEO performs Lambert Conformal to geodetic
(lat/lon) translation
Code based on the TERRAIN preprocessor for MM5
v2.0, developed by Yong-Run Guo and Sue Chen,
National Center for Atmospheric Research, and
Pennsylvania State University
10/21/1993
Input arguments:
iway Conversion type
0 = geodetic to Lambert Conformal
1 = Lambert Conformal to geodetic
phic Central latitude (deg, neg for southern hem)
xlonc Central longitude (deg, neg for western hem)
truelatl First true latitude (deg, neg for southern hem)
truelat2 Second true latitude (deg, neg for southern
hem)
xloc/yloc Projection coordinates (km)
xlon/ylat Longitude/Latitude (deg)
Output arguments:
xloc/yloc Projection coordinates (km)
xlon/ylat Longitude/Latitude (deg)
data conv/57.29578/, a/6370./
c—Entry Point
c
if (phic.lt.O) then
sign= -1.
else
sign= 1.
endif
pole = 90.
if (abs(truelatl).gt.90.) then
truelatl = 60.
truelat2 = 30.
truelatl = sign*truelatl
truelat2 = sign*truelat2
endif
xn = aloglO(cos(truelatl/conv)) - aloglO(cos(truelat2/
conv))
xn= xn/(aloglO(tan((45. - sign*truelatl/2.)/conv)) -
& aloglO(tan((45. - sign*truelat2/2.)/conv)))
psil= 90. - sign*truelatl
psil = psil/conv
if (phic.lt.O.) then
psil = -psil
pole = -pole
endif
psiO = (pole - phic)/conv
xc = 0.
yc = -a/xn*sin(psil)*(tan(psiO/2.)/tan(psil/2.))**xn
c—Calculate lat/lon of the point (xloc,yloc)
c
if (iway.eq.l) then
xloc = xloc + xc
yloc = yloc + yc
if (yloc.eq.O.) then
if (xloc.ge.O.) flp = 90./conv
if (xloc.lt.0.) 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.-180.) flpp = flpp + 360.
if (flpp.gt. 180.) flpp = flpp - 360.
xlon = flpp
c
r = sqrt(xloc*xloc + yloc*yloc)
if(phic.lt.0.)r=-r
cell = (r*xn)/(a*sin(psil))
rxn= 1.0/xn
cell = tan(psil/2.)*cell**rxn
ce!2 = atan(cell)
psx = 2.*cel2*conv
ylat = pole - psx
c
c—Calculate x/y from lat/lon
c
else
ylon = xlon - xlonc
if (yloagt. 180.) ylon = ylon - 360.
if (ylon.lt.-180.) ylon = ylon + 360.
flp = xn*ylon/conv
psx = (pole - ylat)/conv
r = -a/xn*sin(psil)*(tan(psx/2.)/tan(psil/2.))**xn
if(phic.lt.0.)then
xloc = r*sin(flp)
yloc = r*cos(flp)
else
xloc = -r*sin(flp)
yloc = r*cos(flp)
endif
endif
write(*,*)xloc,xc,yloc,yc
xloc = xloc - xc
yloc = yloc - yc
return
end
*****************************************
CMAQ Projection Information—Source:
http: //www.b aronam s. com/products/i oapi/GRID-
DESC.html
-------
Coordinate Information
COORD-NAME COORDTYPE P ALP
P BET
P GAM
XCENT
YCENT
'LAM 40N97W
33.000
45.000
-97.000
-97.000
40.000
Grid Information
GRID-
NAME
COORD-NAME
XORIG
YORIG
XCELL YCELL
NCOLS
NROWS
NTH IK
12US1
'LAM 40N97W -1008000
-1620000
12000 12000
279
240
P_ALP = "PROJ_ALPHA"
P_BET = "PROJ_BETA"
LAMGRD3 = P_ALP <= P_BET. These are the
two latitudes which determine the projection
cone.
P_GAM = the central meridian
XCENT, YCENT = lat/lon coordinates for the
center (0, 0) of the Cartesian coordinate sys-
tem.
X_ORIG is the X coordinate of the grid
origin (lower left corner of the cell at
column=row=l), given in map projection units
(meters, except in Lat-Lon coordinate systems).
Y_ORIG is the Y coordinate of the grid
origin (lower left corner of the cell at
column=row=l), given in map projection units
(meters, except in Lat-Lpn 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 sys-
tems).
Y_CELL is the cell dimension parallel to the Y
coordinate axis, given in map projection units
(meters, except for Lat-Lon coordinate sys-
tems).
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 de-
scribe boundary mass flux (e.g., CMAQ uses
NTHIK = 1)
ArcMap Projection Information (HB grid ex-
ample):
Data Type: File Geodatabase
Feature Class
Location: U:\Projects\MMc-
courtney\Gri ds\templ ate s\grid_templ ate s. gdb
Feature Class: template_mdhi_12_
nb
Feature Type: Simple
Geometry Type: Polygon
Projected Coordinate System: NAD 1983 Lam-
bert_Conformal_Conic
Projection: Lambert_Conformal_Conic
False_Easting: 0.00000000
False_Northing: 0.00000000
Central Meridian: -97.00000000
Standard_Parallel_l: 33.00000000
Standard_Parallel_2: 45.00000000
Latitude_Of_Origin: 40.00000000
Linear Unit: Meter
Geographic Coordinate System: Custom -
SpheroidGC S_North_American_l 983
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...
"rojcclird Coordfrwilc Syslem Proijcrliei
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.
graphir Coordinate System Properties
Nome
Vdu»
0 QQQQOQQQOOOOOOOOOQ
-97.CNXOOQOQOGOCOOOOQC
M.OODOQQOOOOQQMOOOQ
4S GDCKKHOOOOOOOGOOO
UrmaUr*
Hems
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Projection Information for HB Grid—Example #1
2001
2002
2003
2004
2005
2006
2007
2008
Lat/Lon
Spherical
R=6370997
Spherical
R=6370000
Lambert
NA Degrees Conformal
Conic
0.0 0.0 -97.0 33.0 45.0 1.0 40.0 Meters
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Grid Descriptive Parameters
Grid
Resolution
(km)
XORIG
(m)
-252000
-2736000
-1008000
-2736000
-1008000
-2736000
-1008000
-2736000
-1008000
-2736000
-1008000
-2736000
-1008000
YORIG
(m)
-1284000
-2088000
-1620000
-2088000
-1620000
-2088000
-1620000
-2088000
-1620000
-2088000
-1620000
-2088000
-1620000
XCELL
12000
36000
12000
36000
12000
36000
12000
36000
12000
36000
12000
36000
12000
YCELL
(m)
12000
36000
12000
36000
12000
36000
12000
36000
12000
36000
12000
36000
12000
NCOLS
213
148
279
148
279
148
279
148
279
148
279
148
279
NROWS
188
112
240
m
240
112
240
112
240
112
240
112
240
Projection Information for HB Grid—Example #2
2001
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&EPA
United States
Environmental Protection
Agency
PRESORTED STANDARD
POSTAGE & FEES PAID
EPA
PERMIT NO G-35
Office of Research arvd Development (8101R)
Washington. DC 20460
Official Business
Penally for Private Use
S3&0
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