Technical Support Document: 

Preparation of Emissions Inventories 

For the 2002-based Platform, Version 3, 

Criteria Air Pollutants 

January 2008 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Air Quality Assessment Division 

Contacts: 

Madeleine Strum, Air Quality Modeling Group 

Marc Houyoux and Rich Mason, Emissions Inventory and Analysis Group 
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TABLE OF CONTENTS

ACRONYMS............................................................................................................................................................. III

LIST OF TABLES.................................................................................................................................................... IV

LIST OF FIGURES....................................................................................................................................................V

LIST OF APPENDICES ............................................................................................................................................V

1 INTRODUCTION..............................................................................................................................................1

2 2002 EMISSION INVENTORIES AND APPROACHES..............................................................................2

2.1 2002 POINT SOURCES (PTIPM AND PTNONIPM).............................................................................................4

2.1.1 IPM sector (ptipm).................................................................................................................................5

2.1.2 Non-IPM sector (ptnonipm)...................................................................................................................8

2.2 2002 NONPOINT SOURCES (AFDUST, AG, NONPT).........................................................................................8

2.2.1 Area Fugitive dust sector (afdust)..........................................................................................................9

2.2.2 Agricultural Ammonia sector (ag) .......................................................................................................11

2.2.3 Other nonpoint sources (nonpt) ...........................................................................................................14

2.3 FIRES (PTFIRE, NONPTFIRE AND AVEFIRE)..................................................................................................15

2.3.1 Day-specific point source fires (ptfire) ................................................................................................16

2.3.2 County-level fires (nonptfire)...............................................................................................................19

2.3.3 Average fires (avefire) .........................................................................................................................19

2.4 BIOGENIC SOURCES (BIOG)........................................................................................................................22

2.5 2002 MOBILE SOURCES (ONROAD, NONROAD, ALM)..................................................................................22

2.5.1 Onroad mobile sources (onroad).........................................................................................................23

2.5.2 Nonroad mobile sources –NMIM-based nonroad (nonroad) ..............................................................24

2.5.3 Nonroad mobile sources: aircraft, locomotive and commercial marine (alm)...................................25

2.6 EMISSIONS FROM CANADA, MEXICO AND OFFSHORE DRILLING PLATFORMS (OTHPT, OTHAR, OTHON) ....26

3 EMISSIONS MODELING SUMMARY........................................................................................................27

3.1 THE SMOKE MODELING SYSTEM..............................................................................................................28

3.2 KEY EMISSIONS MODELING SETTINGS........................................................................................................28

3.2.1 Spatial configuration ...........................................................................................................................29

3.2.2 Chemical speciation configuration ......................................................................................................31

3.2.3 Temporal processing configuration.....................................................................................................32

3.2.4 Vertical allocation of day-specific fire emissions ................................................................................34

3.3 EMISSIONS MODELING ANCILLARY FILES...................................................................................................34

3.3.1 Spatial allocation ancillary files..........................................................................................................35

3.3.2 Chemical speciation ancillary files......................................................................................................41

3.3.3 Temporal allocation ancillary files......................................................................................................43

4 DEVELOPMENT OF FUTURE YEAR EMISSION INVENTORIES.......................................................48

4.1 STATIONARY SOURCE PROJECTIONS: IPM SECTOR (PTIPM)......................................................................52

4.2 STATIONARY SOURCE PROJECTIONS: NON-IPM SECTORS (PTNONIPM, NONPT, AG, AFDUST) .....................52

4.2.1 Livestock emissions growth (ag, ptnonipm).........................................................................................53

4.2.2 Residential wood combustion growth (nonpt)......................................................................................53

4.2.3 Gasoline Stage II growth and control (nonpt, ptnonipm) ....................................................................55

4.2.4 Portable fuel container growth and control (nonpt)............................................................................59

4.2.5 Stationary Source control programs/plant closures (ptnonipm, nonpt)...............................................59

4.3 MOBILE SOURCE PROJECTIONS ..................................................................................................................63

4.3.1 Onroad.................................................................................................................................................63

4.3.2 Nonroad ...............................................................................................................................................64

4.3.3 Aircraft (alm, ptnonipm) ......................................................................................................................66

4.3.4 Locomotives (alm) ...............................................................................................................................68

4.3.5 Commercial marine vessels (alm)........................................................................................................68

4.4 CANADA, MEXICO, AND OFFSHORE SOURCES (OTHAR, OTHON, OTHPT) ....................................................69
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5 REFERENCES.................................................................................................................................................70

Acronyms 

BEIS Biogenic Emissions Inventory System 

CAIR Clean Air Interstate Rule 

CAMD EPA’s Clean Air Markets Division 

CAP Criteria Air Pollutant 

CEM Continuous Emissions Monitoring 

CHIEF Clearinghouse for Inventories and Emissions Factors

CMAQ Community Multiscale Air Quality model 

CMV Commercial marine vessel 

CO Carbon monoxide 

EGU Electric generating units 

EPA Environmental Protection Agency 

EMFAC Emission Factor (California’s onroad mobile model) 

FAA Federal Aviation Administration 

FIPS Federal Information Processing Standards 

GF Growth factor 

HAP Hazardous Air Pollutant 

IPM Integrated Planning Model 

ITN Itinerant 

MOBILE OTAQ’s model for estimation of onroad mobile emissions factors 

NEEDS National Electric Energy Database System 

NEI National Emission Inventory 

NESHAP National Emission Standards for Hazardous Air Pollutants 

NH3 Ammonia 

NMIM National Mobile Inventory Model 

NONROAD OTAQ’s model for estimation of nonroad mobile emissions 

NOX Nitrogen oxides 

OAQPS EPA’s Office of Air Quality Planning and Standards 

OTAQ EPA’s Office of Transportation and Air Quality 

ORD EPA’s Office of Research and Development 

PF Projection Factor, can account for growth and/or controls 

PFC Portable Fuel Container 

PM2.5 Particulate matter less than or equal to 2.5 microns 

PM10 Particulate matter less than or equal to 10 microns

RIA Regulatory Impact Analysis 

RPO Regional Planning Organization 

SCC Source Classification Code 

SMOKE Sparse Matrix Operator Kernel Emissions 

SO2 Sulfur dioxide 

TAF Terminal Area Forecast 

TCEQ Texas Commission on Environmental Quality 

TSD Technical support document 

VOC Volatile organic compounds 

VMT Vehicle miles traveled 

WRAP Western Regional Air Partnership 
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List of Tables 

Table 2-1. Platform sectors used in emissions modeling for the CAP 2002 Platform .................. 3 

Table 2-2. Airport Ground Support Equipment Emissions in ptnonipm (double counted)........... 8 

Table 2-3. SCCs in the afdust platform sector............................................................................ 10 

Table 2-4. Summary of speciated PM2.5 emissions (tons) without and with the application of the 

transport factor for all 50 states. ........................................................................................... 11 

Table 2-5. Livestock SCCs extracted from the 2002 NEI nonpoint inventory to create the ag 

platform sector ...................................................................................................................... 11 

Table 2-6. Fertilizer SCCs extracted from the 2002 NEI for inclusion in the “ag” sector .......... 13 

Table 2-7. Portable Fuel Container SCCs.................................................................................... 15 

Table 2-8. Universe of 2002 NEI SCCs representing emissions in the ptfire, nonptfire and 

avefire modeling sectors ....................................................................................................... 16 

Table 2-9. SCCs in the ptfire sector............................................................................................. 17 

Table 2-10. SCCs in the nonptfire sector..................................................................................... 19 

Table 2-11. Average fire SCCs.................................................................................................... 19 

Table 2-12. Comparison of 2002 Platform avefire emissions with 2002-specific fire (sum of 

ptfire and nonptfire) emissions ............................................................................................. 20 

Table 2-13. Magnitude of error in California monthly nonroad emission files........................... 25 

Table 2-14. SCCs extracted for the aircraft, locomotive and commercial marine (alm) sector .. 26 

Table 2-15. Summary of the othpt, othar and othon sectors in the 2002 Platform...................... 27 

Table 3-1. Key emissions modeling steps by sector.................................................................... 29 

Table 3-2. Descriptions of the 2002-based Platform Grids ......................................................... 30 

Table 3-3. Model Species produced by SMOKE for CB05......................................................... 31 

Table 3-4. Temporal Settings Used for the Platform Sectors in SMOKE................................... 33 

Table 3-5. U.S. Surrogates Available for the 2002 Platform....................................................... 36 

Table 3-6. Surrogates assigned to Portable Fuel Container Emission Categories....................... 38 

Table 3-7. Gasoline-related speciation profiles used for 2002 .................................................... 42 

Table 3-8. Gasoline-related speciation profiles used for future years ......................................... 43 

Table 3-9. Onroad Mobile Weekly Profiles for Rural Roadways: 2002 Platform vs 2001 

Platform................................................................................................................................. 45 

Table 3-10. Onroad Mobile Weekly Profiles for Urban Roadways: 2002 Platform vs 2001 

Platform................................................................................................................................. 45 

Table 3-11. Updated Nonroad Mobile Weekly Profiles.............................................................. 46 

Table 3-12. Temporal Profile Assignments for Portable Fuel Containers................................... 47 

Table 4-1. Summaries by Sector of 2002, 2009, 2014, 2020 and 2030 base year emissions for 

the Continental United States (48 states + District of Columbia)......................................... 50 

Table 4-2. Summaries by Sector for the Other (“oth”) -Canada, Mexico, and Offshore- 2002, 

2009, 2014, and 2020 base year emissions within the 36 km domain.................................. 51 

Table 4-3. Growth factors for Animal Operations....................................................................... 53 

Table 4-4. Projection Factors for Residential Wood Combustion Sources ................................. 54 

Table 4-5. Emissions for which Stage II projection factors were misapplied to 2002 emissions 56 

Table 4-6. Summary of Gasoline Stage II emissions in base and future years............................ 57 

Table 4-7. States with post-2009 and post-2014 refinery controls .............................................. 60 

Table 4-8. Control programs applied to the stationary sources in ptnonipm and nonpt inventory 

sectors ................................................................................................................................... 61 
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Table 4-9. SCC Aggregation approach for computing projection factors for California nonroad 

emissions............................................................................................................................... 65 

Table 4-10. Factors used to grow aircraft emissions ................................................................... 67 

Table 4-11. Point Source SCCs representing aircraft emissions ................................................. 67 

List of Figures 

Figure 3-1. CMAQ modeling domain........................................................................................... 30 

Figure 3-2. Chemical Speciation Approach Used for the 2002-based Platform........................... 32

Figure 3-3. Mexican States included in the 36 km Modeling Domain......................................... 40 

List of Appendices 

APPENDIX A: Development of a 2002 National Prescribed Fire Inventory 

APPENDIX B: Summary of Surrogate Assignments to Emission Sources 

APPENDIX C: Summary of Speciation Profile Assignments to U.S. Emission Sources 

APPENDIX D: State-Sector Emissions Summaries for 2002 Base and Future-year 

Base Cases: 2009, 2014, 2020 and 2030 

APPENDIX E: Description of Animal Population Data and Projections 

APPENDIX F: Development of Animal Population Growth factors 

APPENDIX G: Description of VMT growth approach 

APPENDIX H: Summary of Portable Fuel Container (PFC) Inventories and Resulting 

Projection Factors 

APPENDIX I: Summary of VMT for 2002 and Projected Years 

APPENDIX J: National Inventories of Commercial Marine Vessel and Locomotive Emissions 

for 2002 and Projection Years used to Develop Projection Factors 
1

1 Introduction 

The U.S. Environmental Protection Agency (EPA), hereto referred to as “we,” has developed a 

2002-based air quality modeling platform. This document describes the emissions inventory and 

emissions modeling for this platform for Criteria Air Pollutants (CAPs). This is the 2002 

Platform version 3 (v3), because the emission inventories we used are primarily from v3 of the 

2002 National Emission Inventory (NEI) (http://www.epa.gov/ttn/chief/eiinformation.html). From 

this point on, we refer to it simply as the “2002 Platform.” This document describes the 

approach and data used to produce the emission inputs to the air quality model. The air quality 

modeling, meteorological inputs and boundary conditions will be described in separate volumes 

of a 2002 Platform report, which, when completed, will be posted on www.epa.gov/ttn/scram. A 

multi-pollutant version of the 2002 Platform, which includes hazardous air pollutants (HAPs) in 

addition to CAPs, will also be available as a separate volume at the above address. 

The 2002 Platform for CAPs uses the Community Multiscale Air Quality (CMAQ) model 

(http://www.epa.gov/AMD/CMAQ/) for the purposes of modeling ozone (O3) and particulate 

matter (PM). The version of CMAQ we used requires hourly and gridded emissions of species 

from the following inventory pollutants: carbon monoxide (CO), nitrogen oxides (NOX), volatile 

organic compounds (VOC), sulfur dioxide (SO2), ammonia (NH3), particulate matter less than or 

equal to 10 microns (PM10), and individual component species for particulate matter less than or 

equal to 2.5 microns (PM2.5). 

The effort to create the emission inputs for the 2002 Platform included 

(1) development of emission inventories for a 2002 model evaluation case, 

(2) development of emission inventories for a 2002 base case and projected years consistent 

with that base, 

(3) updates to the emissions modeling tools, 

(4) updates to the emissions modeling ancillary files used with the tools, and 

(5) execution of the tools. 

The primary emissions modeling tool used to create the CMAQ model-ready emissions was the 

Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system. We used this tool to 

create emissions files for a 36 km national grid, a 12 km Eastern grid and a 12 km Western grid 

for the following cases: 

• 2002 evaluation 

• 2002 base case 

• 2009 base case 

• 2014 base case 

• 2020 base case 

• 2030 base case 

The differences between the 2002 evaluation and the 2002 base case are that the evaluation case 

uses 2002-specific fire emissions and 2002-specific continuous emission monitoring (CEM) data 

for electric generating units (EGUs), whereas the 2002 base case includes an average 2002 

scenario for fires and EGUs for which future years can be compared. 
2

The 2002 Platform builds upon the concepts, tools and emissions modeling data from EPA’s 

2001 Platform, which was most recently developed for the Regulatory Impact Analyses for the 

National Ambient Air Quality Standards for Particle Pollution (EPA, 2006a), referred to here as 

“PM NAAQS.” An earlier version of the 2001 Platform was used for the Clean Air Interstate 

Rule Analysis (EPA, 2005a), referred to here as “CAIR.” This document references emissions 

documentation for the PM NAAQS and CAIR and highlights the similarities and differences 

between the latest version of the 2001 Platform and the 2002 Platform.

This volume contains five sections and several appendices. Section 2 describes the 2002 

inventories input to SMOKE. Section 3 describes the emissions modeling and the ancillary files 

used with the emission inventories. Section 4 describes the development of the projection year 

inventories. Section 5 provides references. Appendices A through J provide additional details 

about specific technical methods and emissions data summaries. 

Electronic copies of the data used with SMOKE for the CAP 2002 Platform are available at the 

emissions modeling clearinghouse, http://www.epa.gov/ttn/chief/emch/, under the section 

entitled “CAP 2002-Based Platform, Version 3.” This is referred to as the “2002v3CAP site” 

throughout this document. 

2 2002 emission inventories and approaches 

Section 2 describes the 2002 emissions data created for input to SMOKE. The primary basis for 

the 2002 emission inputs for the 2002 Platform is the 2002 National Emission Inventory (NEI), 

which includes emissions of CO, NOX, VOC, SO2, NH3, PM10, and PM2.5 as well as hazardous 

air pollutants (HAPs). The HAP emissions are part of the 2002 multi-pollutant (CAP+HAP) 

component of the 2002 Platform; as described earlier, this multi-pollutant component will be 

documented in a separate volume of the 2002 Platform report. 

Documentation for the 2002 NEI can be found at: 

http://www.epa.gov/ttn/chief/net/2002inventory.html#documentation. Version 3 of the 2002 

NEI was used for the 2002 Platform. For inventories outside of the United States, which include 

Canada, Mexico and offshore emissions, we used the latest available base-year inventories that 

also had consistent future-year inventories. 

The 2002 NEI includes five sectors: nonpoint (formerly called “stationary area”) sources, point 

sources, nonroad mobile sources, onroad mobile sources, and fires. The fires portion of the 

inventory includes emissions from wildfires and prescribed burning computed as hour-specific 

point sources. For purposes of preparing the CMAQ-ready emissions, we split the 2002 

emissions inventory into several additional “platform” sectors for use in emissions modeling, and 

we added biogenic emissions and emissions from sources other than the NEI such as the 

Canadian, Mexican and offshore inventories. The significance of an emissions modeling or 

“platform” sector is that it is run through all of the SMOKE programs except the final merge 

(Mrggrid) independently from the other sectors. The final merge program combines the sectorspecific gridded, speciated and temporalized emissions together to create the CMAQ emission 

inputs. 
3

Table 2-1 presents the sectors in the 2002 Platform for CAPs. The sector abbreviations are 

provided in italics; these abbreviations are used in the SMOKE modeling scripts and inventory 

file names, and throughout the remainder of this document. We did not use all sectors for all 

modeling cases; in particular, we used the ptfire and nonptfire platform sectors only for the 2002 

model performance evaluation. We used the avefire platform sector for all modeling cases 

except for the model performance evaluation. The rationale for using average fires in the 2002 

base case and future-year cases rather than the 2002 year-specific fires (ptfire and nonptfire) is 

described in Section 2.3.3. 

 Table 2-1. Platform sectors used in emissions modeling for the CAP 2002 Platform 

PLATFORM 

SECTOR 

2002 NEI 

SECTOR 

Description and resolution of the data input to SMOKE 

IPM sector: 

ptipm 

Point NEI point source EGUs mapped to the Integrated Planning 

Model (IPM) model using the National Electric Energy 

Database System (NEEDS) database. Hourly files for

continuous emission monitoring (CEM) sources are included 

only for the 2002 evaluation case. Day-specific emissions for 

non-CEM sources created for input into SMOKE. 

Non-IPM sector: 

ptnonipm

Point All NEI point source records not matched to the ptipm sector, 

annual resolution. 

Point source fire 

sector: ptfire 

Fires Point source day-specific wildfires and prescribed fires for 

2002. This sector used only for the 2002 evaluation case. 

Nonpt fire 

sector: 

nonptfire 

Fires and 

Nonpoint 

Prescribed fires for 2002 for which day-specific data were not 

available, county and annual resolution. This sector used only 

for the 2002 evaluation case. 

Average-fire 

sector: avefire 

NA Average-year wildfire and prescribed fire emissions derived 

from the 2001 Platform avefire sector, county and annual 

resolution. Used for the 2002 base year and the future base 

model runs, but not for the model evaluation case. 

Agricultural 

sector: ag

Nonpoint NH3 emissions from NEI nonpoint livestock and fertilizer 

application sources, county and annual resolution. 

Area fugitive 

dust sector: 

afdust

Nonpoint PM10 and PM2.5 from fugitive dust sources from the NEI 

nonpoint inventory (e.g., building construction, road 

construction, paved roads, unpaved roads, agricultural dust), 

county and annual resolution. 

Remaining 

nonpoint sector: 

nonpt

Nonpoint All nonpoint sources not otherwise included in other SMOKE 

sectors, county and annual resolution. 

Nonroad sector: 

nonroad

Mobile: 

Nonroad 

Monthly nonroad emissions from the National Mobile 

Inventory Model (NMIM) using NONROAD2005, other than 

for California. Monthly emissions for California created using 

annual emissions submitted by the California Air Resources 

Board (CARB) for the 2002 NEI. 

Aircraft, 

locomotive, 

marine: alm

Mobile: 

Nonroad 

Aircraft, locomotive, commercial marine vessel emissions 

sources, county and annual resolution. 
4

PLATFORM 

SECTOR 

2002 NEI 

SECTOR 

Description and resolution of the data input to SMOKE 

Onroad: onroad Mobile: 

onroad 

Monthly onroad emissions from NMIM using MOBILE6, 

other than for California. Monthly emissions for California 

created using annual emissions submitted by CARB for the 

2002 NEI. 

Biogenic: biog NA Hour-specific, grid cell-specific emissions generated from the 

BEIS3.13 model (includes emissions in Canada and Mexico). 

Other point 

sources not from 

the NEI: othpt

NA Point sources from Canada’s 2000 inventory, Mexico’s 1999 

inventory, and offshore point sources from the 2001 platform, 

annual resolution. 

Other nonpoint 

and nonroad not 

from the NEI: 

othar

NA Canada (province resolution) and Mexico (municipio 

resolution) nonpoint and nonroad mobile inventories, annual 

resolution. 

Other onroad 

sources not from 

the NEI: othon

NA Canada (province resolution) and Mexico (municipio 

resolution) onroad mobile inventories, annual resolution. 

Annual emission summaries for 2002 and the future years covered by this platform are provided 

in Section 4. Table 4-1 provides a summary of 2002 Platform emissions for the U.S. 

anthropogenic sectors (i.e., excluding biogenic emissions). Table 4-2 provides a summary of 

emissions for the anthropogenic sectors containing Canadian, Mexican and offshore sources. 

The emission inventories for input to SMOKE for the 2002 base and evaluation case are 

available at the 2002v3CAP site under the link “Data Files” (see “2002emis”directory). The 

“readme” file provided indicates the particular zipped files associated with each platform sector. 

The remainder of Section 2 provides details of the data contained in each of the sectors. 

Different levels of detail are provided for different sectors depending upon the availability of 

reference information for the data, the degree of changes or manipulation of the data needed for 

preparing it for input to SMOKE, and errors discovered after emissions modeling was 

completed. 

2.1 2002 Point sources (ptipm and ptnonipm) 

Point sources are sources of emissions for which specific geographic coordinates (e.g., 

latitude/longitude) are specified, as in the case of an individual facility. A facility may have 

multiple emission points, which may be characterized as units such as boilers, reactors, spray 

booths, kilns, etc. A unit may have multiple processes (e.g., a boiler that sometimes burns 

residual oil and sometimes burns natural gas). 

We created two platform sectors from the 2002 point source NEI, v3 for input into SMOKE: the 

Integrated Planning Model (IPM) sector (ptipm) and the non-IPM sector (ptnonipm). The 

ptnonipm emissions were provided to SMOKE as annual emissions. The ptipm were provided as 

hourly emissions data for CEM sources and as day-specific emissions for non-CEM sources. We 

further describe the approach for creating the day-specific non-CEM emissions in Section 2.1.1. 
5

Documentation for the development of the point source NEI is at: 

http://www.epa.gov/ttn/chief/net/2002inventory.html#documentation 

The changes made to the 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 FIPS code of 

88XXX, where XXX is the 3-digit tribal code in the NEI. We made this change because 

SMOKE requires the state/county FIPS code. 

• We defaulted stack parameters for some point sources 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 hard-coded in SMOKE for 

acceptable values. The SMOKE PSTK file is contained in the ancillary file directory of 

the 2002v3CAP site (http://www.epa.gov/ttn/chief/emch/). 

• We applied a transport fraction to all SCCs that we identified as PM fugitive dust, to 

prevent the overestimation of fugitive dust impacts in the grid modeling as described in 

Section 2.2.1. 

The point source file was separated into ptipm and ptnonipm sectors to facilitate the use of 

different SMOKE temporal processing techniques and future year projection techniques for these 

sectors. These sectors are described in the following subsections. 

2.1.1 IPM sector (ptipm) 

This sector contains emissions from EGUs in the 2002 NEI that we were able match to the 2006 

NEEDS database (http://www.epa.gov/airmarkets/progsregs/epa-ipm/index.html), which is used 

by the IPM, version 3.0. The IPM model provides future year emission inventories for the 

universe of EGUs contained in the NEEDS database. As described below, this matching was 

done in order to (1) provide consistency between the 2002 EGU sources and future year EGU 

emissions for sources which are forecasted by IPM and (2) avoid double counting in projecting 

point source emissions. 

The 2002 NEI point source inventory contains emissions estimates for both EGU and non-EGU 

sources. The 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 separate sectors all 

sources that are projected via the IPM from those that are not. This procedure prevents doublecounting or dropping significant emissions in creating the future-year emissions. The matching 

process relies on imperfect data; consequently, we experienced a small degree of dropped and/or 

double-counted emissions for sources that we could not match. We believe that the unmatched 

units are small because we have reviewed both the NEI and the NEEDS database to ensure that 

all significant EGUs have been captured in the matching process. 

Methodology to split the EGU from the non-EGU sources

Because the IPM v3.0 units are based on the 2006 NEEDS database, we also used this NEEDS 

database to identify the set of EGUs in the 2002 NEI point source data to assign to the ptipm 
6

sector. Because of the inconsistencies in identification information for EGU units in the various 

available data sets, we performed an extensive analysis to link the NEEDS units to the NEI for 

the purpose of splitting the 2002 NEI file into ptipm and ptnonipm sectors. The available data 

sets include the 2006 NEEDS, the EPA’s Clean Air Markets Division (CAMD) hourly CEM 

program data and the 2002 NEI. The 2002 NEI point source file includes ORIS Plant IDs and 

CAMD Boiler IDs for most of the EGUs, to indicate where substitution of hourly CEM 

emissions can be reliably performed. However, many of the smaller emitters in CAMD's hourly 

CEM programs are not identified by ORIS Plant IDs in the 2002 NEI, due to uncertainties in 

source identification and inconsistencies in the way a unit is defined between the NEI and 

CAMD datasets. In addition, the NEEDS database includes a larger universe of many smaller 

emitting EGUs, which are not included in the CAMD hourly CEM programs. 

The first step in the process to link the NEEDS units to the NEI was to identify all sources in the 

NEI that might potentially be IPM/NEEDS units. This included any sources in the NEI with an 

ORIS Plant ID or Boiler ID or any facilities or units with an SIC or NAICS code that indicated 

electrical generation. We also performed a manual search of the NEI (using plant names, and 

any other helpful information in NEEDS and NEI, such as county) for any NEEDS units with 

significant generating capacities that were not already matched to the NEI. We performed the 

manual search task to account for any large NEEDS generators that may be a cogeneration unit 

located within an industrial (not a primarily electric generating station) facility. 

We then built a table of CAP emissions totals for all emission units identified in the previous 

steps to perform the actual tagging of units as “ptipm” sources. In addition to unit-level 

emissions sums, this table also included the number of different SCCs at the unit and the first and 

last SCC codes (as sorted in ascending order) for each unit. The following procedures were used 

to flag the units for inclusion in the ptipm sector: 

(1) We compared the NEI ORIS Plant IDs and CAMD Unit IDs to the IDs in the NEEDS 

database (note that we only included NEEDS units with a start year of 2002 or earlier 

since post-2002 NEEDS units would not be reflected in the 2002 NEI). Units that 

matched were included in the ptipm sector. 

(2) We manually matched NEI units that had large 2002 NOX and SO2 emissions with units 

in the NEEDS database using facility names, county locations, fuel and size as indicators. 

Those units that matched the NEEDS data using these fields were included in the ptipm 

sector. For example, we matched one large NEI emitter with an SCC indicating it was 

burning petroleum coke to a NEEDS unit described as a "Coal Steam" unit. 

(3) We assigned NEI units which had SCC codes that are not associated with combustion 

processes covered by IPM to the ptnonipm sector. We further checked that the NEI NOX

and SO2 emissions at these units were low emissions, to give further confidence to our 

ptnonipm assignment. For example, any cooling towers, coal storage pile fugitives, or 

mining operations at a facility with EGU boilers were not put into the ptipm sector, 

because IPM does not provide emissions projections for such units. Similarly, we also 

assigned any NEI units where the SCCs and emissions indicated they were generating 

electricity from the combustion of landfill gas to the ptnonipm sector, because IPM does 

not provide emissions projections for such units. We assigned units burning municipal 

waste or biomass to the ptnonipm sector for the same reason. 
7

(4) We further reviewed the NEI units that remained unassigned after the above steps to 

identify those units with the largest NOX emissions or with any combustion-related 

SCCs. After manually reviewing these individually, we found that the largest emitting 

such units were IPM-related, and flagged them to be included in the ptipm sector. 

The above review identified a few significant ptipm units that would not have been noted 

otherwise. In particular, some NEI units with large emissions and clear indications that they 

were EGUs did not appear in the NEEDS database, presumably because they had shut down after 

2002 and before the 2006 date-of-record used for NEEDS. These units were included in the 

ptipm sector, so that their 2002 emissions would not be projected for future years as part of the 

ptnonipm sector. In addition to the industrial cogeneration units already matched to NEEDS, we 

found a few large NEI emitters with Industrial Process-related SCCs, i.e., their SCC codes began 

with 399 or 301900; these were confirmed to be EGU boilers with matching NEEDS entries, and 

thus were included in the ptipm sector. We determined two other such large emitting units with 

similar SCCs to be blast furnace flares which we assigned to the ptnonipm sector. 

During the review of the industrial cogeneration units in NEEDS, we could sometimes identify 

the associated NEI facility, but not a specific emissions unit at that facility matching a NEEDS 

unit. In such cases, the NEEDS unit was excluded from the future-year ptipm sector emissions, 

and the entire NEI facility was included in the ptnonipm sector. 

Creation of temporally resolved emissions for the ptipm sector

Another reason we separated out the ptipm sources was due to the difference in the temporal 

resolution of the data input to SMOKE. The ptipm sector used the available hourly CEM data in 

a manner that is an improvement over past platforms. For sources with CEMs, we used the 

actual hourly CEM data for the 2002 evaluation case. The hourly CEM data were obtained from 

the CAMD Data and Maps website2. The SMOKE modeling system matches the ORIS facility 

and Boiler IDs in the NEI ORL file to the same fields in the CEM data. This allowed us to use 

the hourly SO2 and NOX CEM emissions directly from the CEM data file. We used the heat 

input from the hourly CEM data to allocate the NEI annual values for all other pollutants from 

CEM sources, because hourly data for these other pollutants are not available with the hourly 

CEM. 

For sources not matching the CEM data (“non-CEM” sources), we computed daily emissions 

from the NEI annual emissions using a standard query language (SQL) program and stateaverage CEM data. To allocate annual emissions to each month, we created state-specific, threeyear averages of 2001-2003 CEM data. These average annual-to-month factors were assigned to 

non-CEM sources by state. To allocate the monthly emissions to each day, we used the 2002 

CEM data to compute state-specific month-to-day factors, averaged across all units in each state. 

The resulting daily emissions were input into SMOKE. The daily-to-hourly allocation was 

performed in SMOKE using diurnal profiles. The development of these diurnal ptipm-specific 

profiles, which are considered ancillary data for SMOKE, is described in Section 3.3.3. 

 

2

 http://camddataandmaps.epa.gov/gdm/index.cfm?fuseaction=prepackaged.progressresults_smoke 
8

For the 2002 base case we do not use year-specific CEM data, and for future-year scenarios, 

there are no CEM data available for specific units. Thus, for the base and future-year cases, we 

used the above procedures (i.e., same procedures as “non-CEM” sources) for computing daily 

emissions for input to SMOKE for all ptipm sources. 

2.1.2 Non-IPM sector (ptnonipm)

The non-IPM (ptnonipm) sector contains all 2002 NEI point sources that we did not include in 

the IPM (ptipm) sector3. The ptnonipm sector contains fugitive dust PM emissions from 

vehicular traffic on paved or unpaved roads at an industrial facility or coal handling at a coal 

mine4

. Prior to input to SMOKE, we adjusted the fugitive dust PM emissions by applying 

county-specific fugitive dust transportable fraction factors (less than 1). This is discussed further

in Section 2.2.1. 

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 nonpoint, onroad or nonroad (10-digit) SCCs. In the 2002 NEI, examples of these types of sources 

include: onroad vehicular travel at a ski resort in California, equipment emissions at ski resort in 

California, airport ground support emissions in Minnesota, and aircraft emissions, gas stations 

and livestock (i.e., animal husbandry) in a number of locations. 

We reviewed these sources to determine whether there were any cases for which the emissions 

were double counted with those in other sectors. Except for the airport ground support emissions 

in Minnesota (shown in Table 2-2), we did not double count any other emissions. The doublecounted airport ground support emissions are very small, representing less than 0.05 % of the 

ptnonipm sector nationally (less than 1% in Minnesota) for all pollutants other than CO. The 

percent double counted for CO for the ptnonipm sector is 0.24% nationally (17% in Minnesota). 

Considering all sectors, the double counted CO is only 0.38% of CO emissions in Minnesota. 

Table 2-2. Airport Ground Support Equipment Emissions in ptnonipm (double counted) 

VOC 

[tons/yr] 

NOX

[tons/yr] 

CO 

[tons/yr] 

NH3 

[tons/yr] 

SO2

[tons/yr] 

PM2.5 

[tons/yr] 

PM10 

[tons/yr] 

ptnonipm airport ground 

support emissions 

(occurring only in the 

State of Minnesota) 300 395 8047.3 0 34 13 14 

2.2 2002 Nonpoint sources (afdust, ag, nonpt) 

We created several sectors from the 2002 nonpoint NEI. All of these are at county-level and 

annual resolution. We removed the nonpoint tribal-submitted emissions as we did not know the 

 

3

 Except for the day-specific point source fire emissions data which are included in a separate sector, as discussed in 

section 2.3.1. 

4

Point source fugitive dust emissions, which represent a very small amount of PM, were treated as a separate sector 

in the 2001 Platform. 
9

extent to which they may be double counted with the county-level emissions. In addition, the 

tribal data would have been dropped during SMOKE processing since there are no spatial 

surrogates for tribal data in the 2002 Platform. 

The documentation for the nonpoint sector of the 2002 NEI is available at: 

http://www.epa.gov/ttn/chief/net/2002inventory.html 

In the rest of this section, we describe in more detail each of the platform sectors into which we 

separated the 2002 nonpoint NEI, and the changes we made to these data. 

2.2.1 Area Fugitive dust sector (afdust) 

The area-source fugitive dust (afdust) sector contains PM10 and PM2.5 emission estimates for 

2002 NEI nonpoint SCCs identified as dust sources by inventory experts. This sector is 

separated from other nonpoint sectors to make it easier to apply a “transport fraction” which 

reduces emissions based on diminished transport at the scale of our modeling. Application of the 

transport fraction prevents the overestimation of fugitive dust impacts in the grid modeling as 

compared to ambient samples. Categories included in this sector are paved roads, unpaved roads 

and airstrips, construction (residential, industrial, road and total) agriculture production and 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 sources. 

We created the afdust sector from the 2002 NEI based on SCCs and pollutant codes (i.e., PM10

and PM2.5) that are considered “fugitive”. A complete list of all possible fugitive dust SCCs 

(including both 8-digit point source SCCs and 10-digit nonpoint SCCs) is provided at: 

http://www.epa.gov/ttn/chief/emch/invent/tf_scc_list2002nei_v2.xls. However, not all of the 

SCCs in this file are present in the 2002 NEI. The SCCs included in the 2002 NEI that comprise 

the 2002 platform afdust sector (which are a subset of the SCCs in the web link) are provided in 

Table 2-3. 
10

Table 2-3. SCCs in the afdust platform sector 

2002 SCC 2002 SCC Description 

2275085000 Mobile Sources;Aircraft;Unpaved Airstrips;Total 

2294000000 Mobile Sources;Paved Roads;All Paved Roads;Total: Fugitives 

2296000000 Mobile Sources;Unpaved Roads;All Unpaved Roads;Total: Fugitives 

2296005000 Mobile Sources;Unpaved Roads;Public Unpaved Roads;Total: Fugitives 

2296010000 Mobile Sources;Unpaved Roads;Industrial Unpaved Roads;Total: Fugitives 

2311000000 Industrial Processes;Construction: SIC 15 - 17;All Processes;Total 

2311010000 Industrial Processes;Construction: SIC 15 - 17;Residential;Total 

2311010040 Industrial Processes;Construction: SIC 15 - 17;Residential;Ground Excavations 

2311010070 Industrial Processes;Construction: SIC 15 - 17;Residential;Vehicle Traffic 

2311020000 Industrial Processes;Construction: SIC 15 - 17;Industrial/Commercial/Institutional;Total 

2311020040 Industrial Processes;Construction: SIC 15 - 17;Industrial/Commercial/Institutional;Ground 

Excavations 

2311030000 Industrial Processes;Construction: SIC 15 - 17;Road Construction;Total 

2325000000 Industrial Processes;Mining and Quarrying: SIC 14;All Processes;Total 

2801000000 Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Total 

2801000002 Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Planting 

2801000003 Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Tilling 

2801000005 Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Harvesting 

2801000007 Miscellaneous Area Sources;Agriculture Production - Crops;Agriculture - Crops;Loading 

2805000000 Miscellaneous Area Sources;Agriculture Production - Livestock;Agriculture - Livestock;Total

2805001000 Miscellaneous Area Sources;Agriculture Production - Livestock;Beef cattle - finishing 

operations on feedlots (drylots);Dust Kicked-up by Hooves (use 28-05-020, -001, -002, or -003 

for Waste 

Our approach was to apply the transportable fractions by county (all afdust SCCs in the same 

county would receive the same factor). The approach used to calculate the county fractions and 

the fractions themselves are available at: 

http://www.epa.gov/ttn/chief/emch/invent/transportable_fraction_080305_rev.pdf

As the approach paper mentions, a limitation of the transportable fraction approach is the lack of 

monthly variability which would be expected due to seasonal changes in vegetative cover. 

An electronic version of the county-level transport fractions can be found at: 

http://www.epa.gov/ttn/chief/emch/invent/transportfractions052506rev.xls

After the CMAQ modeling was completed, we discovered that the transportable fraction factors 

for PM2.5 were inadvertently not applied; therefore, the PM2.5 emissions from this sector are 

overestimated in the current version (v3) of the 2002 Platform. 

Table 2-4 shows the differences in PM2.5 and its component species (defined in 3.3.2) across all 

sectors with and without application of the transportable factor. Note that the below summary is 

across all 50 states and the District of Columbia (i.e., including Alaska and Hawaii), whereas 

most other summaries in this document are for just the conterminous U.S. To create this table, 

we correctly applied the transportable fraction to the afdust sector in order to compare the 

resultant emissions (labeled as “with transport factor”) with those in the 2002 Platform (labeled 

“without transport factor”). 
11

Table 2-4. Summary of speciated PM2.5 emissions (tons) without and with the application 

of the transport factor for all 50 states.

Sector PM2.5 POC PEC PSO4 PNO3 PMFINE 

Afdust – without 

transport factor 1,830,271 88,766 3,112 4,589 3,212 1,730,664 

alm 86,954 13,173 57,419 5,787 82 10,486 

avefire 684,034 392,910 66,731 7,045 2,247 215,101 

nonpt 1,101,688 476,170 85,586 28,446 3,153 508,332 

nonroad 218,025 67,227 130,774 775 414 18,818 

onroad 146,973 38,160 79,928 1,097 174 27,605 

ptipm 501,998 23,348 15,005 67,438 1,096 395,110 

ptnonipm 372,330 53,054 22,051 56,033 3,258 237,927 

Grand Total- without 

transport factor 

4,942,273 1,152,809 460,606 171,211 13,637 3,144,043 

 PM2.5 POC PEC PSO4 PNO3 PMFINE 

Afdust – with transport 

factor 1,041,837 46,307 1,539 2,758 1,743 989,490 

alm 86,954 13,173 57,419 5,787 82 10,486 

avefire 684,034 392,910 66,731 7,045 2,247 215,101 

nonpt 1,101,688 476,170 85,586 28,446 3,153 508,332 

nonroad 218,025 67,227 130,774 775 414 18,818 

onroad 146,973 38,160 79,928 1,097 174 27,605 

ptipm 501,998 23,348 15,005 67,438 1,096 395,110 

ptnonipm 372,330 53,054 22,051 56,033 3,258 237,927 

Grand Total- with 

transport factor 

4,153,839 1,110,350 459,033 169,379 12,167 2,402,870 

Difference in Grand 

Total (tons) 

788,434 42,459 1,573 1,832 1,470 741,173 

% Diff 19.0% 3.8% 0.3% 1.1% 12.1% 30.8% 

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 2002 NEI. In building this sector we extracted 

livestock and fertilizer emissions based on the SCC. The livestock SCCs are listed in Table 2-5, 

and the fertilizer SCCs are listed in Table 2-6. 

Table 2-5. Livestock SCCs extracted from the 2002 NEI nonpoint inventory to create the 

ag platform sector 

SCC SCC Description*

2805000000 Agriculture - Livestock;Total 

2805001100 Beef cattle - finishing operations on feedlots (drylots);Confinement 

2805001200 Beef cattle - finishing operations on feedlots (drylots);Manure handling and storage 

2805001300 Beef cattle - finishing operations on feedlots (drylots);Land application of manure 

2805002000 Beef cattle production composite;Not Elsewhere Classified 

2805003100 Beef cattle - finishing operations on pasture/range;Confinement 

2805007100 Poultry production - layers with dry manure management systems;Confinement 

2805007300 Poultry production - layers with dry manure management systems;Land application of manure 

2805008100 Poultry production - layers with wet manure management systems;Confinement 
12

SCC SCC Description*

2805008200 Poultry production - layers with wet manure management systems;Manure handling and storage 

2805008300 Poultry production - layers with wet manure management systems;Land application of manure 

2805009100 Poultry production - broilers;Confinement 

2805009200 Poultry production - broilers;Manure handling and storage 

2805009300 Poultry production - broilers;Land application of manure 

2805010100 Poultry production - turkeys;Confinement 

2805010200 Poultry production - turkeys;Manure handling and storage 

2805010300 Poultry production - turkeys;Land application of manure 

2805018000 Dairy cattle composite;Not Elsewhere Classified 

2805019100 Dairy cattle - flush dairy;Confinement 

2805019200 Dairy cattle - flush dairy;Manure handling and storage 

2805019300 Dairy cattle - flush dairy;Land application of manure 

2805020001 Cattle and Calves Waste Emissions;Milk Cows 

2805020002 Cattle and Calves Waste Emissions;Beef Cows 

2805020003 Cattle and Calves Waste Emissions;Heifers and Heifer Calves 

2805020004 Cattle and Calves Waste Emissions;Steers, Steer Calves, Bulls, and Bull Calves 

2805021100 Dairy cattle - scrape dairy;Confinement 

2805021200 Dairy cattle - scrape dairy;Manure handling and storage 

2805021300 Dairy cattle - scrape dairy;Land application of manure 

2805022100 Dairy cattle - deep pit dairy;Confinement 

2805022200 Dairy cattle - deep pit dairy;Manure handling and storage 

2805022300 Dairy cattle - deep pit dairy;Land application of manure 

2805023100 Dairy cattle - drylot/pasture dairy;Confinement 

2805023200 Dairy cattle - drylot/pasture dairy;Manure handling and storage 

2805023300 Dairy cattle - drylot/pasture dairy;Land application of manure 

2805025000 Swine production composite;Not Elsewhere Classified (see also 28-05-039, -047, -053) 

2805030000 Poultry Waste Emissions;Not Elsewhere Classified (see also 28-05-007, -008, -009) 

2805030001 Poultry Waste Emissions;Pullet Chicks and Pullets less than 13 weeks old 

2805030002 Poultry Waste Emissions;Pullets 13 weeks old and older but less than 20 weeks old 

2805030003 Poultry Waste Emissions;Layers 

2805030004 Poultry Waste Emissions;Broilers 

2805030007 Poultry Waste Emissions;Ducks 

2805030008 Poultry Waste Emissions;Geese 

2805030009 Poultry Waste Emissions;Turkeys 

2805035000 Horses and Ponies Waste Emissions;Not Elsewhere Classified 

2805039100 Swine production - operations with lagoons (unspecified animal age);Confinement 

2805039200 Swine production - operations with lagoons (unspecified animal age);Manure handling and storage 

2805039300 Swine production - operations with lagoons (unspecified animal age);Land application of manure 

2805040000 Sheep and Lambs Waste Emissions;Total 

2805045000 Goats Waste Emissions;Not Elsewhere Classified 

2805045002 Goats Waste Emissions;Angora Goats 

2805045003 Goats Waste Emissions;Milk Goats 

2805047100 Swine production - deep-pit house operations (unspecified animal age);Confinement 

2805047300 Swine production - deep-pit house operations (unspecified animal age);Land application of manure 

2805053100 Swine production - outdoor operations (unspecified animal age);Confinement 

* All SCC Descriptions begin “Miscellaneous Area Sources;Agriculture Production – Livestock” 

The “ag” sector includes all of the NH3 emissions from fertilizer from the NEI. However, the 

“ag” sector does include all of the livestock ammonia emissions, as there are also significant NH3

emissions from livestock in the point source inventory. Most of the point source livestock NH3
13

emissions were reported by the states of Kansas and Minnesota. For these two states, farms with 

animal operations were provided as point sources using the following SCCs5: 

• 30202000: Industrial Processes; Food and Agriculture; Beef Cattle Feedlots; Hogs and 

Swines 

• 30202001: Industrial Processes; Food and Agriculture; Beef Cattle Feedlots; Feedlots 

General 

• 30202101: Industrial Processes; Food and Agriculture; Eggs and Poultry Production; 

Manure Handling: Dry 

• 30203099: Industrial Processes; Food and Agriculture; Dairy Products; Other Not 

Classified 

There are also livestock NH3 emissions in the point source inventory with SCCs of 39999999 

(Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial 

Processes; Other Not Classified) and 30288801 (Industrial Processes; Food and Agriculture; 

Fugitive Emissions; Specify in Comments Field). We identified these sources as livestock NH3

point sources based on their facility name. The reason why we needed to identify livestock NH3

in the ptnonipm sector was to properly implement the emission projection techniques for 

livestock sources, which cover all livestock sources, not only those in the ag sector, but also 

those in the ptnonipm sector. This is discussed further in Section 4.2.1. 

Table 2-6. Fertilizer SCCs extracted from the 2002 NEI for inclusion in the “ag” sector

2002 SCC 2002 SCC Description* 

2801700001 Anhydrous Ammonia 

2801700002 Aqueous Ammonia 

2801700003 Nitrogen Solutions 

2801700004 Urea 

2801700005 Ammonium Nitrate 

2801700006 Ammonium Sulfate 

2801700007 Ammonium Thiosulfate 

2801700010 N-P-K (multi-grade nutrient fertilizers)

2801700011 Calcium Ammonium Nitrate 

2801700012 Potassium Nitrate 

2801700013 Diammonium Phosphate 

2801700014 Monoammonium Phosphate 

2801700015 Liquid Ammonium Polyphosphate 

2801700099 Miscellaneous Fertilizers 

* All descriptions include “Miscellaneous Area Sources; Agriculture 

Production – Crops; Fertilizer Application” as the beginning of the 

description. 

 

5

 These point source emissions are also identified by the segment ID, which is one of the following: “SWINE”, 

“CATTLE”, “DAIRY”, or “PLTRY”. 
14

2.2.3 Other nonpoint sources (nonpt) 

Nonpoint sources that were not subdivided into the afdust, ag or nonptfire (Section 2.3.2) sectors 

were assigned to the “nonpt” sector. This sector is similar to the “oarea” sector in the 2001 

Platform. The differences are that: 

• the nonpt sector contains agricultural burning and open burning, whereas in the 2001 

platform, these source categories were not in “oarea” but rather were in the fires sector; 

• the nonpt sector does not include Canada and Mexico emissions (which are in the 2002 

Platform “othar” sector); whereas in the 2001 Platform they are in the “oarea” sector; 

• the nonpt sector includes estimates for portable fuel container (PFC) emissions, which 

were not included in the 2001 Platform. The development of these emission estimates 

are explained in this section. 

In preparing the nonpt sector we excluded catastrophic releases (SCC 28300XX000) since we 

found that these emissions were dominated by tire burning (SCC 28300000000), which is an 

episodic, location-specific emissions category. Tire burning accounts for significant emissions 

of particulate matter in some parts of the country. An example of such an event is the Starlight 

Lane Tire Fire, which occurred in Roanoke, Virginia, and burned for 24 days in March 2002, 

emitting approximately 4000 tons of PM2.5. Because such sources are reported by a very small 

number of states, and are inventoried as county annual totals without the information in the NEI 

to temporally and spatially allocate the emissions to the time and location where the event 

occurred, we excluded catastrophic releases from the 2002 Platform. 

The nonpt sector includes emission estimates for PFCs, also known as “gas cans.” Inventories 

for PFCs were recently developed for EPA’s Mobile Source Air Toxics (MSAT) rule (EPA, 

2007a) and were incorporated into the 2002 NEI v3. These inventories were not part of the 2001 

Platform or part of earlier versions of the 2002 NEI. They are also not included in the nonpoint 

NEI documentation (as of the date of this document) since they were added between version 2 

(v2) and v3 of the 2002 NEI, so we provide more detail on these emissions below. 

 

The PFC inventory consists of ten SCCs (listed in Table 2-7) that represent five distinct sources 

of PFC emissions, further distinguished by residential or commercial use. The five sources are: 

● Emissions associated with filling the gas cans at the gas pump 

(1) Displacement of the vapor within the can 

(2) Spillage of gasoline while filling the can 

● Emissions associated with transporting the gas can to the piece of nonroad equipment 

(3) Spillage of gasoline during transport 

● Emissions (adjusted for changes in ambient temperature) associated with storage of the 

gasoline in the PFCs 

 (4) Emissions due to evaporation (i.e., diurnal emissions) 

(5) Emissions due to permeation 
15

Table 2-7. Portable Fuel Container SCCs. 

PFC SCC SCC Description* 

2501011011 Residential Portable Fuel Containers: Permeation 

2501011012 Residential Portable Fuel Containers: Evaporation 

2501011013 Residential Portable Fuel Containers: Spillage During Transport 

2501011014 Residential Portable Fuel Containers: Refilling at the Pump: Vapor 

Displacement 

2501011015 Residential Portable Fuel Containers: Refilling at the Pump: Spillage 

2501012011 Commercial Portable Fuel Containers: Permeation 

2501012012 Commercial Portable Fuel Containers: Evaporation 

2501012013 Commercial Portable Fuel Containers: Spillage During Transport 

2501012014 Commercial Portable Fuel Containers: Refilling at the Pump: Vapor 

Displacement 

2501012015 Commercial Portable Fuel Containers: Refilling at the Pump: Spillage 

* All descriptions include “Storage and Transport;Petroleum and Petroleum Product Storage” as the beginning 

of the description.

Note that spillage and vapor displacement associated with using PFCs to refuel nonroad 

equipment are included in the nonroad inventory. To prevent double counting, these two 

processes are not included in the above SCCs. 

Detailed documentation of the methods used to estimate national and statewide VOC inventories 

for PFCs are described in a technical support document (Landman, 2007). Statewide total annual 

VOC inventories were allocated to counties using county-level fuel consumption ratios from the 

NONROAD model. This methodology is described in a technical support document for 

national-scale modeling in the MSAT rule (EPA, 2007b). Of note from this documentation, the 

developers derived the 2002 PFC inventory by linearly interpolating inventories developed for 

1999 and 2010. 

2.3 Fires (ptfire, nonptfire and avefire) 

Wildfire and prescribed burning emissions are contained in the ptfire, nonptfire and avefire 

sectors. The ptfire sector has emissions provided at geographic coordinates (point locations) and 

has daily emissions values, whereas the nonptfire and avefire sectors are county-summed 

inventories and have annual total emissions values. For the 2002 evaluation case, we modeled 

2002 year-specific fires using the emissions from the ptfire and nonptfire sectors. For the 2002 

base case, these sectors were replaced by the avefire sector. 

For the 2002 Platform, the following SCCs from the 2002 NEI are considered “fires” (note that 

the actual SCC description includes “Miscellaneous Area Sources” as the first tier level). 
16

Table 2-8. Universe of 2002 NEI SCCs representing emissions in the ptfire, nonptfire and 

avefire modeling sectors 

SCC 2002 SCC Description * 

2810001000 Other Combustion;Forest Wildfires;Total 

28100010F0 Other Combustion;Forest Wildfires;Flaming 

28100010S0 Other Combustion;Forest Wildfires;Smoldering 

2810005000 Other Combustion;Managed Burning, Slash (Logging Debris);Total 

28100050F0 Other Combustion;Managed Burning, Slash (Logging Debris);Flaming 

2810015000 Other Combustion;Prescribed Burning for Forest Management;Total 

28100150F0 Other Combustion;Prescribed Burning for Forest Management;Flaming 

28100150F1 Other Combustion;Prescribed Burning for Forest Management;Flaming Natural 

2810020000 Other Combustion;Prescribed Burning of Rangeland;Total 

28100200F0 Other Combustion;Prescribed Burning of Rangeland;Flaming 

* all SCC descriptions begin with “Miscellaneous Area Sources;” 

The universe of sources included with fires sectors for the 2002 Platform differs from the 2001 

Platform in that the 2002 Platform fire sectors exclude agricultural burning and other open 

burning sources. These sources are in the nonpt sector of the 2002 Platform rather than the fire 

sectors. We chose to keep agricultural burning and other open burning sources in the nonpt 

sector because these categories were not factored into the development of the average fire sector 

(as described in 2.3.3). Additionally, their year-to-year impacts are not as variable as wildfires 

and prescribed/managed burns. 

2.3.1 Day-specific point source fires (ptfire) 

The ptfire sector includes wildfire and prescribed6 burning emissions occurring in 2002, which 

were used for the 2002 model evaluation case. We did not include emissions from this sector in 

the 2002 base case or any of the future year cases. This sector includes emissions for all 2002 

wildfires and most prescribed burns with daily estimates of each fire’s emissions. It includes the 

latitude/longitude of the fire’s origin and other parameters associated with the emissions such as 

acres burned and fuel load, which allow estimation of plume rise. 

The SCCs in this sector are listed in Table 2-9. 

 

6

 For purposes of this document prescribed burning also includes managed burning, i.e., “Other Combustion; 

Managed Burning, Slash (Logging Debris)”
17

Table 2-9. SCCs in the ptfire sector. 

SCC 2002 SCC Description * 

2810001000 Other Combustion;Forest Wildfires;Total 

28100010F0 Other Combustion;Forest Wildfires;Flaming 

28100010S0 Other Combustion;Forest Wildfires;Smoldering 

2810005000 Other Combustion;Managed Burning, Slash (Logging Debris);Total 

28100050F0 Other Combustion;Managed Burning, Slash (Logging Debris);Flaming 

2810015000 Other Combustion;Prescribed Burning for Forest Management;Total 

28100150F0 Other Combustion;Prescribed Burning for Forest Management;Flaming 

28100150F1 Other Combustion;Prescribed Burning for Forest Management;Flaming Natural 

2810020000 Other Combustion;Prescribed Burning of Rangeland;Total 

28100200F0 Other Combustion;Prescribed Burning of Rangeland;Flaming 

* all SCC descriptions begin with “Miscellaneous Area Sources;” 

The “F” and the “S” indicate whether the process is flaming (F) or smoldering (S). The 

inventory development approach assumed that smoldering occurs in the same grid cell as the 

flaming emissions for wildfires only, and on the day after the flaming emissions. 

The use of point source and day-specific data for fires is a new feature to EPA’s modeling 

platform. In the 2001 Platform, all fire emissions were treated and modeled exclusively in the 

first (surface) model layer at the county resolution, and spatially allocated to grid cells using 

surrogates related to forest land. In the 2001 Platform, emissions were temporally allocated with 

monthly and diurnal profiles (meaning each day of the month burns uniformly) using statespecific and regional profiles. 

The new point source day-specific emission estimates for 2002 fires were developed as a joint 

effort of the Western Regional Air Partnership (WRAP) and the U.S. EPA. Specifically, the 

development of the wildfire emissions inventory for the CAPs was conducted by Air Sciences, 

Inc., managed by the WRAP and funded by the Regional Planning Organizations (RPOs). The 

U.S. EPA added emission estimates for 29 HAPs, using emission factors. The prescribed fire 

inventory was developed for these same pollutants by EC/R, Inc. using methods consistent with 

the wildfire inventory. 

The development of wildfire inventory started with the ICS-209 reports compiled at 

http://www.nifc.gov/fire_info/fire_stats.htm by the National Fire Information Center in Boise, 

Idaho. Air Sciences, Inc. reviewed the data and made corrections and improvements to the fire 

size, location, start and end dates, and fuel type and loading. The default source of information 

on fuel type and loading was the National Fire Danger Rating System (NFDRS), 

http://www.wfas.net/content/view/29/44/. Fuel moisture information from the Wildland Fire

Assessment System, http://www.wfas.net/content/view/23/38/ was input to the Fire Emissions 

Production System model to produce a “look-up” matrix of fuel consumption and emissions for 

the RPO regions, NFDRS Fuel Type and a range of moistures. This “look-up” matrix was the 

basis for estimating emissions for each fire on each day. Documentation for the Wildfire 

Inventory prepared by the WRAP for all the RPOs is at 

http://www.wrapair.org/forums/fejf/tasks/FEJFtask7InterRPO.html. (Air Sciences, et. al., 2007) 
18

The prescribed fire emissions estimates were computed using this same matrix, but EC/R 

obtained the acres burned and date-location information directly from the RPOs. Only 38 States 

provided information on prescribed burning, but these are believed to be the States that conduct 

most of the burning. Of these, only Virginia’s data had insufficient spatial detail to develop a 

point source inventory, and the fires in some state’s data were only spatially resolved to the 

county centroid. The data for Georgia were only temporally resolved to the month of the burn. 

For Georgia, we summed the monthly emissions to create an annual total by county which we 

included in the nonptfire sector. We do not have Virginia’s prescribed burning data in our 

platform in either ptfire or nonptfire sectors. Additional documentation for the prescribed fire 

emission inventory development is provided in Appendix A. 

The ptfire data files input to SMOKE utilize a new format developed to facilitate SMOKE’s 

computation of plume rise (discussed in Section 3.2.4). The data that change each day such as 

pollutant emissions were included in the “FIREEMIS” day-specific (PTDAY) ORL file; the 

parameters that are constant for the year, such as a list of all fires, were included in a separate 

fire inventory (PTINV) file. For ease of processing, we chose to include both HAPs and CAPs 

in the FIREEMIS file. For the 2002 Platform, there is one fire inventory file and 24 

“FIREEMIS” files. Twelve of these files contain day-specific emissions for individual months, 

and twelve contain the emissions for the last day of each month. We used separate files 

containing emissions for the last day of each month, because we processed each month through 

SMOKE separately. Since emissions are in local time, we needed to include the last few hours 

from the previous month in the SMOKE intermediate files. 

In addition to the day-specific pollutant emissions, the ptfire inventories contained data on the 

acres burned and fuel consumption for each day. As described in Section 3.2.4, these additional 

parameters are used in SMOKE for the plume rise algorithm. 

Subsequent to modeling, we identified three errors in the ptfire sector emissions. These errors 

only affect the 2002 model evaluation case because the ptfire sector emissions were not used for 

the 2002 base case or future-year cases. These errors are: 

1. The calculation of SO2 emissions for prescribed burning in the NEI v3 used the emission 

factor for NH3. 

2. The fuel loading values (tons fuel consumed per acre burned) used in the plume rise 

calculation were a factor of 24 too high because the data originally used to construct the 

SMOKE ORL files came from hourly data which were summed across 24 hours to 

generate day-specific data. The fuel loading values were mistakenly summed along with 

the emissions data. 

3. Several states for which the prescribed burning data were not sufficient to develop pointlevel day specific emission estimates, but were sufficient to develop county-level annual 

emission estimates were inadvertently dropped and excluded from the 2002 NEI v3. 

These states are: Connecticut, Delaware, Hawaii, Maine, Massachusetts, New 

Hampshire, New York, Pennsylvania, Rhode Island, Vermont, and Virginia. While we 

are unsure of the impact of the prescribed burning emissions for Virginia, we believe the 

prescribed burning for the other states is small. 
19

2.3.2 County-level fires (nonptfire) 

The nonptfire sector consists of all of the prescribed burning and managed burning emission 

sources for which emissions are not available at the spatial or temporal resolution required for 

processing in the ptfire sector. The SCCs in this sector are listed in Table 2-10. 

Table 2-10. SCCs in the nonptfire sector 

SCC 2002 SCC Description * 

2810005000 Other Combustion;Managed Burning, Slash (Logging Debris);Total 

28100050F0 Other Combustion;Managed Burning, Slash (Logging Debris);Flaming 

28100150F0 Other Combustion;Prescribed Burning for Forest Management;Flaming 

* all SCC descriptions begin with “Miscellaneous Area Sources;” 

Note that there are no wildfires in this sector. The nonptfire emissions were generated using: 

(1) point source fire emissions for managed and prescribed burning in Georgia, as discussed in 

Section 2.3.1 above, and (2) nonpoint emissions for managed burning (slash burning) for those 

states without point source managed burning emissions (i.e., Maryland, North Carolina, and 

Texas). 

In order to retain the monthly resolution provided by the ptfire file for Georgia, we developed 

SCC-specific, Georgia-specific profiles for use with the nonptfire sector (only used for the 

evaluation case). Note that the SO2 emissions for Georgia in the nonptfire sector suffer from the 

same error as the Georgia fire emissions in the ptfires sector (see Section 2.3.1). 

2.3.3 Average fires (avefire) 

The average fire sector includes emissions from wildfires, prescribed burning and managed 

burning. We used this sector for the 2002 base case, and all future year cases. As noted above, 

avefire emissions are annual, county-level emissions. 

The purpose of the avefire sector is to represent emissions for a typical or average year’s fires for 

use in projection year inventories since future year fires are not known. Using an average of 

multiple years of data reduces the possibility that a single-year's high or low fire activity would 

unduly affect future year model-predicted concentrations. 

The specific SCCs in the avefire sector are listed in Table 2-11. 

Table 2-11. Average fire SCCs 

SCC 2002 SCC Description * 

2810001000 Other Combustion;Forest Wildfires;Total 

2810005000 Other Combustion;Managed Burning, Slash (Logging Debris);Total 

2810015000 Other Combustion;Prescribed Burning for Forest Management;Total 

* all SCC descriptions begin with “Miscellaneous Area Sources;” 

We created the avefire sector for the 2002 Platform by removing agricultural and open burning 

from the 2001 Platform avefire sector (EPA, 2005a), since these categories are in the 2002 nonpt 

sector. Table 2-12 compares the avefire emissions to the 2002-specific fire emissions (sum of 

ptfire and nonptfire). 
20

Table 2-12. Comparison of 2002 Platform avefire emissions with 2002-specific fire (sum of ptfire and nonp

State 

Tons CO avefire 

Tons CO 

2002 fires 

CO % diff 

Tons NH3 avefire

Tons NH3 2002 fires 

NH3 % diff 

Tons NOX avefire 

Tons NOX 2002 fires 

NOX % diff 

Tons PM2.5 avefire 

Tons PM2.5 2002 fires 

Tons PM2.5 % diff

Tons SO2 avefire

Tons SO2 2002 fires 

SO% 

Alabama 175,141 294,496 -41% 752 4,708 -84% 3,814 3,060 25% 13,938 24,150 -42% 983 4,645 -Arizona 440,419 1,071,505 -59% 2,020 17,142 -88% 10,532 6,953 51% 37,151 85,126 -56% 2,888 5,743 -Arkansas 123,698 67,776 83% 556 1,083 -49% 2,654 406 >100% 10,315 5,359 92% 728 1,071 -California 1,157,187 654,793 77% 5,117 10,473 -51% 24,563 8,100 >100% 97,302 54,565 78% 6,735 4,842 Colorado 288,013 966,816 -70% 1,299 15,467 -92% 6,271 7,688 -18% 24,054 77,744 -69% 1,719 5,600 -Connecticut 667 4 >100% 3 0 NA 14 0 >100% 56 0 NA 4 0 NA Delaware 1,332 93 >100% 5 1 >100% 23 1 >100% 87 7 >100% 6 0 NA District of Columbia 1 0 NA 0 0 NA 0 0 NA 0 0 NA 0 0 NA Florida 1,193,146 879,032 36% 5,366 14,058 -62% 25,600 8,924 >100% 99,484 71,956 38% 7,018 13,959 -Georgia 350,925 380,011 -8% 1,299 6,076 -79% 7,955 4,184 90% 24,082 31,316 -23% 2,010 5,865 -Idaho 630,971 62,678 >100% 2,856 1,002 >100% 14,024 696 >100% 52,808 5,170 >100% 3,845 604 >1Illinois 3,323 2,589 28% 15 41 -63% 71 20 >100% 277 207 34% 20 32 -Indiana 4,124 163 >100% 19 3 >100% 88 2 >100% 344 13 >100% 24 2 >1Iowa 4,185 1,087 >100% 19 17 8% 90 7 >100% 349 86 >100% 25 17 Kansas 17,600 1,251 >100% 79 20 >100% 378 10 >100% 1,468 100 >100% 103 15 >1Kentucky 61,812 2,238 >100% 278 34 >100% 1,326 21 >100% 5,155 180 >100% 364 17 >1Louisiana 151,659 43,452 >100% 682 693 -2% 3,254 310 >100% 12,647 3,466 >100% 892 675 Maine 26,592 77 >100% 115 1 >100% 566 1 >100% 2,127 6 >100% 150 0 NA Maryland 6,129 45 >100% 24 0 >100% 137 1 >100% 531 5 >100% 32 0 NA Massachusetts 15,878 289 >100% 71 5 >100% 341 3 >100% 1,324 23 >100% 93 2 >1Michigan 15,380 408 >100% 69 6 >100% 330 3 >100% 1,283 32 >100% 91 5 >1Minnesota 107,237 43,058 >100% 482 686 -30% 2,300 294 >100% 8,943 3,427 >100% 631 430 Mississippi 178,646 11,794 >100% 804 184 >100% 3,833 121 >100% 14,897 960 >100% 1,051 116 >1Missouri 31,611 2,911 >100% 142 45 >100% 678 31 >100% 2,636 238 >100% 186 32 >1Montana 203,759 180,536 13% 946 2,888 -67% 5,187 1,369 >100% 17,311 14,473 20% 1,422 1,322 

Nebraska 17,780 500 >100% 80 8 >100% 381 3 >100% 1,483 39 >100% 105 8 >1Nevada 227,965 23,560 >100% 1,026 376 >100% 4,910 262 >100% 19,018 1,944 >100% 1,346 172 >1New Hampshire 6,398 30 >100% 29 0 NA 137 0 NA 534 2 >100% 38 0 NA New Jersey 10,375 284 >100% 47 4 >100% 223 3 >100% 865 23 >100% 61 2 >1New Mexico 583,216 137,289 >100% 2,626 2,195 20% 12,582 1,232 >100% 48,662 11,132 >100% 3,450 917 >1New York 19,195 313 >100% 86 5 >100% 412 3 >100% 1,601 25 >100% 113 2 >1North Carolina 429,388 330,303 30% 532 306 74% 11,424 9,095 26% 9,870 1,582 >100% 696 257 >1North Dakota 11,204 8,573 31% 50 136 -63% 240 61 >100% 934 684 37% 66 58 
21

State 

Tons CO avefire 

Tons CO 

2002 fires 

CO % diff 

Tons NH3 avefire

Tons NH3 2002 fires 

NH3 % diff 

Tons NOX avefire 

Tons NOX 2002 fires 

NOX % diff 

Tons PM2.5 avefire 

Tons PM2.5 2002 fires 

Tons PM2.5 % diff

Tons SO2 avefire

Tons SO2 2002 fires 

SO% 

Ohio 3,787 8,802 -57% 17 141 -88% 81 95 -14% 316 724 -56% 22 132 -Oklahoma 79,672 4,790 >100% 359 75 >100% 1,709 37 >100% 6,644 381 >100% 469 58 >1Oregon 778,194 2,280,250 -66% 3,542 36,480 -90% 17,857 23,587 -24% 65,350 186,965 -65% 4,896 15,355 -Pennsylvania 5,450 65 >100% 25 1 >100% 117 1 >100% 454 5 >100% 32 0 NA Rhode Island 171 0 >100% 1 0 >100% 4 0 >100% 14 0 NA 1 0 NA South Carolina 109,880 97,437 13% 494 1,551 -68% 2,357 1,032 >100% 9,163 7,996 15% 646 1,490 -South Dakota 84,689 4,841 >100% 381 77 >100% 1,817 31 >100% 7,062 384 >100% 498 27 >1Tennessee 47,175 914 >100% 212 14 >100% 1,012 9 >100% 3,934 72 >100% 277 6 >1Texas 256,966 27,640 >100% 1,118 273 >100% 4,890 226 >100% 21,578 2,379 >100% 1,178 174 >1Utah 328,713 234,566 40% 1,479 3,751 -61% 7,052 2,439 >100% 27,412 19,241 42% 1,934 1,581 Vermont 8,347 69 >100% 38 1 >100% 179 1 >100% 696 5 >100% 49 0 NA Virginia 67,866 1,249 >100% 305 19 >100% 1,456 12 >100% 5,659 100 >100% 399 8 >1Washington 52,086 109,827 -53% 248 1,756 -86% 1,484 1,295 15% 4,487 9,110 -51% 407 1,264 -West Virginia 36,578 646 >100% 165 10 >100% 785 6 >100% 3,050 51 >100% 215 4 >1Wisconsin 11,924 2,563 >100% 54 41 32% 256 20 >100% 994 206 >100% 70 37 Wyoming 188,099 97,672 93% 846 1,561 -46% 4,035 961 >100% 15,686 7,975 97% 1,106 682 sum 8,554,551 8,039,286 6% 36,777 123,414 -70% 189,428 82,613 >100% 684,035 629,635 9% 49,094 67,229 -
22

2.4 Biogenic sources (biog) 

For CMAQ, we computed the biogenic emissions based on 2002 meteorology data using the 

BEIS3.13 model from SMOKE. 

The BEIS3.13 model creates gridded, hourly, model-species emissions from vegetation and soils. 

It estimates CO, VOC, and NOX emissions for the U.S., Mexico, and Canada. The BEIS3.13 

model is described further in: http://www.cmascenter.org/conference/2005/abstracts/2_7.pdf

(Schwede, et. al, 2005). 

The inputs to BEIS include: 

• temperature data at 10 meters which were obtained from the CMAQ meteorological input 

files, 

• land-use data from the Biogenic Emissions Landuse Database, version 3 (BELD3). 

BELD3 data provides data on the 230 vegetation classes at 1 km resolution over most of 

North America; the same land-use data were used for the 2001 platform. 

2.5 2002 Mobile sources (onroad, nonroad, alm) 

We created three sectors from the mobile source emissions in the 2002 NEI: onroad, nonroad 

and a sector containing emissions for aircraft, locomotive and commercial marine vessels (alm). 

We created these three separate sectors to handle differences in emissions processing related to 

the temporal nature of the inventories and differences in projection methods. 

All three sectors are at county and SCC resolution. The alm emissions input to SMOKE contain 

tribal data (state FIPS code was set to “88” similarly to point sources as discussed in Section 

2.1), but these data are not in the CMAQ files since they were dropped during the SMOKE 

spatial allocation process. 

The onroad and nonroad sectors utilize emissions generated by the EPA’s Office of 

Transportation and Air Quality (OTAQ) using NMIM (EPA, 2005b) for all of the U.S. except for 

California.7 The NMIM relies on calculations from the MOBILE6 and NONROAD2005 models 

as described below, and in the NEI documentation. Inputs to NMIM are posted with the 2002 

Emission Inventory. The direct link is: 

ftp://ftp.epa.gov/EmisInventory/2002finalnei/mobile_sector_data/ncd_files/ncd20070727_2002.zip. The 

NMIM creates the onroad and nonroad emissions on a month-specific basis that accounts for 

temperature, fuel types, and other variables that vary by month. Inventory documentation for the 

2002 NEI v3 onroad and nonroad sectors is also posted with other 2002 NEI documentation; the 

direct link is: 

ftp://ftp.epa.gov/EmisInventory/2002finalnei/documentation/mobile/2002_mobile_nei_version_3

_report_092807.pdf. 

 

7

 Although OTAQ generated emissions using NMIM for California, these were not used in the 2002 NEI version 3, 

but rather were replaced by state-submitted emissions. 
23

While aircraft, locomotive, and commercial marine sources are considered nonroad sources in 

the 2002 NEI, they comprise a separate sector for the 2002 platform denoted as “alm.” We 

developed the alm sector for the convenience of emission processing and projections. The 

NMIM-based nonroad emissions are monthly whereas the alm emissions are annual. In addition, 

the NMIM-based nonroad emissions are projected using NMIM, whereas the alm emissions use 

national, annual activity-based projection factors. Documentation for “alm” inventory 

development is available in several separate documents posted at 

http://www.epa.gov/ttn/chief/net/2002inventory.html#documentation, and additional revisions to 

this documentation are provided in Section 2.5.3. 

2.5.1 Onroad mobile sources (onroad) 

This sector includes exhaust, evaporative, brakewear and tirewear emissions from onroad 

sources derived from NMIM (except for California), which contained the version of MOBILE6 

used for the final MSAT rule (EPA, 2007a). A summary of the 2002 vehicle miles traveled 

(VMT) inputs (along with future year VMT used in the projections and discussed in Section 

4.3.1) is contained in Appendix I. 

We did not include the refueling onroad emissions generated by NMIM in the onroad sector, 

because the NEI treats onroad refueling as a stationary source, and it is in the nonpt sector 

(gasoline distribution, Stage II, SCC=2501060100). We therefore removed the refueling 

emissions from the NMIM outputs prior to generating the onroad emission files for SMOKE. 

Similar to the 2001 Platform, the 2002 Platform onroad sector contains VOC emissions 

separately for exhaust and evaporative modes, which allowed us to use mode-specific speciation 

profiles. For the 2002 Platform, the inventory includes PM10 and PM2.5 emissions for three 

modes8

: exhaust (EXH), brakewear (BRK) and tirewear (TIR), which similarly facilitated modeappropriate speciation profiles. The emission modes are included as part of the pollutant name 

for the SMOKE emission inputs. For example, exhaust and evaporative modes for VOC are 

indicated by EXH__VOC and EVP__VOC, respectively. Since the mode is part of the pollutant 

name, the SCCs provided to SMOKE use a 10th digit of zero, instead of the letter that would 

otherwise denote the emission mode. 

The onroad emission estimation from NMIM is improved over the version used for the 2001 

Platform as follows 

• We made MOBILE6 Model adjustments for VOC to account for vehicle “cold starts” 

which increase VOC emissions at cold temperatures. Newer vehicles meeting Tier 2 

have higher emissions than previously estimated when the engine is first started at 

temperatures below 50 F. A detailed discussion of these adjustments can be found in the 

document "Cold Temperature Effects on Vehicle HC Emissions," (EPA, 2006b). 

• We made MOBILE6 Model adjustments to correct the handling of oxygenates, which 

affects VOC and HAP estimates. 

• We updated the external MOBILE6 data input files which account for the introduction of 

new California highway vehicle emission standards, beginning with the 2004 model year. 

These standards have been formally adopted by 11 states (California, Connecticut, 

 

8

 PM10 and PM2.5 in the 2001 Platform were not broken out by mode. 
24

Maine, Massachusetts, New Jersey, New York, Oregon, Pennsylvania, Rhode Island, 

Vermont and Washington). These standards supersede the federal certification standards 

for highway vehicles sold in those states. 

• We improved the county-level gasoline Reid Vapor Pressure (RVP) estimates based on 

an updated analysis of fuels. Previously, the fuel survey results from a few counties in a 

state were used for all counties in the state. However, often the surveyed counties would 

have RVP control programs, so that the other counties in the state, without controls, 

would have inappropriately low RVP. We changed the RVP for counties without fuel 

surveys and without RVP control to use federally regulated RVP levels instead. 

• Emissions in the 2002 Platform include the modes for VOC, PM10 and PM2.5, whereas 

emissions in the 2001 Platform had mode-specific emissions for VOC only. 

Because the California Air Resources Board (CARB) has their own onroad mobile source 

estimation model (EMFAC2002), which is tailored to specific California mobile sources, we 

used the CARB-submitted data for the 2002 NEI v3 as well as the platform. CARB provided 

EPA with annual-total onroad mobile emissions. We adjusted these emissions using NMIMbased California emissions to (1) temporalize the emissions to monthly resolution and (2) to 

provide them on a consistent basis (i.e., same SCCs and modes) as the NMIM-derived data. 

CARB updated their model (EMFAC2007) prior to the completion of our modeling, but they 

were not able to provide the results in time for use with version 3 of the 2002 Platform. The 

updated emissions, however, were used in the development of the projection year emissions as 

discussed in Section 4.3.1. 

2.5.2 Nonroad mobile sources –NMIM-based nonroad (nonroad) 

This sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines 

(not including commercial marine, aircraft, and locomotives) derived from NMIM. The NMIM 

relied on the version of the NONROAD2005 model used for the marine (spark ignited) SI and 

small SI engine proposed rule, published May 18, 2007 (EPA, 2007c). As with the onroad 

sector, we used the NMIM monthly emissions for all states except California. 

Like the onroad emissions, NMIM provides nonroad emissions for VOC by three emission 

modes: exhaust, evaporative and refueling. Unlike the onroad sector, refueling emissions for 

nonroad sources are not included in the nonpt sector. Rather, we kept these emissions in the 

nonroad sector. 

The version of NONROAD used for the 2002 Platform has the following improvements over 

previous versions of this model: 

General

• Added the ability to estimate effects of ethanol blends on fuel hose and tank permeation. 

• Improved RVP data as discussed in Section 2.5.1. 

Small SI inputs

• Revised tank permeation and hose permeation based on EPA and industry test data (and 

small revisions to tank sizes). 

• Added fuel tank diffusion losses to diurnal emissions based on test data. 
25

• Updated emission factors and deterioration rates for Phase 2 engines based on new test 

data. 

• Corrected technology mix for snow blowers to account for 4-stoke engines (previously 

assumed all 2-stroke). 

• Added hot soak and running loss emission estimates for handheld equipment. 

Recreational Marine inputs

• Revised all brake specific fuel consumption (BSFC) inputs based on re-analysis of data 

(no significant effect on emissions). 

• Revised PM emission factor for all 2-stroke engines based on oil consumption and 

scavenging estimates. 

• Revised evaporative-related temperature profiles and personal watercraft hose permeation 

(minor changes). 

• Revised fuel tank and hose permeation based on new EPA and industry test data. 

• Added hot soak and running loss emission estimates for all recreational marine. 

• Updated model inputs for high performance sterndrive/inboard engines >600 hp. 

The NEI nonroad data for California provided by CARB are annual emissions that do not have 

the mode-specific data for VOC (exhaust, evaporative, and refueling). We created monthly, 

mode-specific emissions for California’s nonroad emissions (except for alm sources) using 

NMIM results for California. Details on this process are documented separately (Strum, 2007). 

The process erroneously dropped emissions for certain sources (FIPS code/SCC combinations) 

that were not computed via NMIM; however, as shown in Table 2-13 below, the error is small. 

Table 2-13. Magnitude of error in California monthly nonroad emission files 

Pollutant 

Calif. 

Annual 

Emis (tons) 

Calif. Annual Emis 

after monthly 

dissaggregation (tons) Diff (tons) % difference 

CO 1,058,968 1,061,607 -2,639 -0.2% 

NH3 161 161 0 0.0% 

NOX 240,256 241,190 -934 -0.4% 

PM10 18,590 18,634 -44 -0.2% 

PM25 16,334 16,374 -40 -0.2% 

SO2 1,015 1,017 -2 -0.2% 

VOC 148,269 148,692 -423 -0.3% 

The largest errors in NOX emissions occur in San Francisco (06075) and Colusa (06011) 

counties, and the largest errors in VOC occur in Glenn (06021) and Modoc (06049) counties. 

2.5.3 Nonroad mobile sources: aircraft, locomotive and commercial 

marine (alm) 

The aircraft, locomotive and commercial marine (alm) sector contains annual emissions for the 

SCCs listed in Table 2-14. These emissions are consistent with the 2002 NEI v3. Note that 

some aircraft emissions for California, Illinois, and Minnesota are also contained in the ptnonipm 

sector, as described above. 
26

Table 2-14. SCCs extracted for the aircraft, locomotive and commercial marine (alm) 

sector 

SCC SCC Description 

2275000000 Mobile Sources;Aircraft;All Aircraft Types and Operations;Total 

2275001000 Mobile Sources;Aircraft;Military Aircraft;Total 

2275020000 Mobile Sources;Aircraft;Commercial Aircraft;Total: All Types 

2275050000 Mobile Sources;Aircraft;General Aviation;Total 

2275060000 Mobile Sources;Aircraft;Air Taxi;Total 

2280002100 Mobile Sources;Marine Vessels, Commercial;Diesel;Port emissions 

2280002200 Mobile Sources;Marine Vessels, Commercial;Diesel;Underway emissions 

2280003100 Mobile Sources;Marine Vessels, Commercial;Residual;Port emissions 

2280003200 Mobile Sources;Marine Vessels, Commercial;Residual;Underway emissions 

2280004000 Mobile Sources;Marine Vessels, Commercial;Gasoline;Total, All Vessel Types 

2285002006 Mobile Sources;Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations 

2285002007 Mobile Sources;Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations 

2285002008 

Mobile Sources;Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains 

(Amtrak) 

2285002009 Mobile Sources;Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines 

2285002010 Mobile Sources;Railroad Equipment;Diesel;Yard Locomotives 

The documentation of the 2002 NEI for the alm sector is available at 

http://www.epa.gov/ttn/chief/net/2002inventory.html#documentation. It does not include a 

description of the changes to some locomotive and commercial marine sources from v2 of the 

2002 NEI, which were made in conjunction with the development of the 2002 Platform. The 

updates reflect changes to national total emissions, which were made as part of the proposed 

Locomotive/Marine Rule (EPA, 2007d). The updates affect the following SCCs: 

2285002006 Mobile Sources;Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations 

2285002007 Mobile Sources;Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations 

2285002008 

Mobile Sources;Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains 

(Amtrak) 

2285002009 Mobile Sources;Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines 

2285002010 Mobile Sources;Railroad Equipment;Diesel;Yard Locomotives 

2280002000 Mobile Sources;Marine Vessels, Commercial;Diesel;Port and Underway emissions combined 

To preserve the state-submitted data from the 2002 NEI v2, we adjusted only the EPA-generated 

emissions for the above SCCs. They were adjusted such that the sum of the v2 state-submitted 

emissions and the revised EPA-generated emissions matched OTAQ’s national totals. 

In addition, since SCCs for diesel port and underway are combined in OTAQ's estimates (but are 

estimated separately in the 2002 NEI), the 2002 NEI v3 emissions were allocated to port and 

underway SCCs based using the same proportions as used in the 2002 NEI v2. 

2.6 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 
27

“other” than those in the 2002 NEI, and the last two digits provide the SMOKE source types: 

“pt” for point, “ar” for “area”, and “on” for onroad mobile. Except for Mexico’s emissions, the 

2002 Platform used datasets previously used for the 2001 Platform. 

For Canada we used emissions for 2000 since these were the most recent set of emissions which 

were available at the time the 2002 Platform was developed. 

For Mexico we used emissions for 1999 (Eastern Research Group Inc., 2006) which were 

developed as part of a partnership between Mexico's Secretariat of the Environment and Natural 

Resources (Secretaría de Medio Ambiente y Recursos Naturales-SEMARNAT) and National 

Institute of Ecology (Instituto Nacional de Ecología-INE), the U.S. EPA, the Western Governors' 

Association (WGA), and the North American Commission for Environmental Cooperation 

(CEC). Unlike the Mexico inventory used for the 2001 Platform that covered only the northern 

border states, this inventory includes emissions from all states in Mexico. The emissions values 

in the northern border states have also been updated since we used the 2001 Platform emissions. 

The offshore emissions include point source offshore oil and gas drilling platforms. We used the 

same data as used in the 2001 Platform. Based on the CAIR emission inventory documentation 

(EPA, 2005a), the offshore sources were provided by the Texas Commission on Environmental 

Quality (TCEQ). This inventory included emissions for 1992, and was grown to 2002 based on 

instructions from TCEQ. 

Table 2-15 summarizes the data in the “oth” sectors. 

Table 2-15. Summary of the othpt, othar and othon sectors in the 2002 Platform. 

Sector Components Changes from 2001 Platform 

Mexico, 1999, point Used updated version of Mexico’s 1999 inventory 

Canada, 2000, point 

othpt 

Offshore, 2002, point 

Mexico, 1999, nonpoint Used updated version of Mexico’s 1999 inventory 

Mexico, 1999, nonroad Used updated version of Mexico’s 1999 inventory 

Canada, 2000, nonpoint Dropped emissions for SCC 2806015000 (Domestic 

Animal Waste) and SCC 2810003000 (Cigarette 

Smoke). 

othar 

Canada, 2000, nonroad 

othon Mexico, 1999, onroad Used updated version of Mexico’s 1999 inventory 

Canada, 1995, onroad 

3 Emissions modeling summary 

The CMAQ model requires hourly emissions of specific gas and particle species for the 

horizontal and vertical grid cells contained within the modeled region (i.e., modeling domain). 

To provide emissions in the form and format required by CMAQ, it is necessary to “pre-process” 

the “raw” emissions (i.e., emissions input to SMOKE) for the sectors described above in 

Section 2. In brief, this pre-processing step transforms these emissions from their original 

temporal resolution, pollutant resolution, and spatial resolution into the data required by CMAQ. 
28

As seen in Section 2, the temporal resolution of the emissions input to SMOKE for the 2002 

Platform varies across sectors, and may be hourly, monthly, or annual total emissions. The 

spatial resolution, which also can be different for different sectors, may be individual point 

sources or county totals (province totals for Canada, municipio totals for Mexico). The 

pollutants for all sectors except for biogenics are those inventoried for the NEI. The preprocessing steps involving temporal allocation, spatial allocation, pollutant speciation, and 

vertical allocation of point sources are referred to as emissions modeling. This section provides 

some basic information about the tools and data files used for emissions modeling as part of the 

2002 Platform for CAPs. Since we devoted Section 2 to describing the emissions inventories, 

we have limited this section’s descriptions of data to the ancillary data SMOKE uses to perform 

the emissions modeling steps 

All SMOKE inputs and scripts for the 2002 Platform emissions are available at the 

Clearinghouse for Inventories and Emissions Factors (CHIEF) Emissions Modeling 

Clearinghouse (EMCH) website, http://www.epa.gov/ttn/chief/emch/index.html#2002. 

3.1 The SMOKE modeling system 

We used SMOKE to pre-process the raw emissions to create the emissions inputs for CMAQ. 

The SMOKE version 2.4 source code and executables can be used to reproduce our emissions 

modeling, and these are available from the Community Multiscale Analysis System (CMAS) 

Center at http://www.cmascenter.org. The scripts used for running SMOKE are available on the 

CHIEF website provided previously in this section. 

We made revisions to the SMOKE model for this effort, resulting in SMOKE version 2.4. These 

revisions are documented in the SMOKE release notes for SMOKE versions 2.3 and 2.4, 

available with the SMOKE documentation at http://www.smoke-model.org. Although the 

release of SMOKE version 2.4 happened after we completed our modeling, SMOKE version 2.4 

provides essentially the same version of SMOKE used for the 2002-based modeling platform. 

Major updates to SMOKE that we developed for the 2002 Platform include: 

• Support of point-source, day-specific wildfire and prescribed burning fires 

• Extended ORL format that includes more metadata fields, particularly fields about the 

source of the inventory data for each record (e.g., state, EPA). 

• New capabilities for temporal allocation using CEM hourly emissions data from EGUs 

• The ability to use surrogate data files from the Spatial Surrogate Tool 

• Support for multiple and nonsequential days in the temporal processor 

• New processing scripts that make it easier to process more sectors than the traditional 

sectors of nonpoint, point, onroad, nonroad, and biogenics. 

3.2 Key emissions modeling settings 

Each sector is processed separately through SMOKE, up until the final merge program 

(Mrggrid), which combines the model-ready, sector-specific emissions across sectors. The 

SMOKE settings in the run scripts and the data in the SMOKE ancillary files control the 

approaches used for the individual SMOKE programs for each sector. Table 3-1 summarizes the 

major processing steps of each platform sector. The “Spatial” column shows the spatial 
29

approach: “point” indicates that SMOKE maps the source from a point 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 (further described in Section 3.3.1). The “Speciation” 

column indicates that all sectors use the SMOKE speciation step, though biogenics speciation is 

done within BEIS3 and not as a separate SMOKE step. The “Inventory resolution” column 

shows the inventory temporal resolution from which SMOKE needs to calculate hourly 

emissions. Finally, the “plume rise” column indicates the sectors for which SMOKE computes 

vertical plume rise and creates merged emissions that are 3-dimensional instead of one layer. 

Table 3-1. Key emissions modeling steps by sector 

Platform 

sector Spatial Speciation 

Inventory

resolution 

Plume 

rise 

ptipm point Yes daily & 

hourly 

Yes 

ptnonipm point Yes annual Yes 

othpt point Yes annual Yes 

nonroad surrogates &

area-to-point 

Yes monthly 

othar surrogates Yes annual 

alm surrogates &

area-to-point 

Yes annual 

onroad surrogates Yes monthly 

othon surrogates Yes annual 

nonpt surrogates &

area-to-point 

Yes annual 

ag surrogates Yes annual 

afdust surrogates Yes annual 

biog pre-gridded 

landuse in BEIS hourly 

ptfire point Yes daily Yes 

nonptfire surrogates Yes annual 

avefire surrogates Yes annual 

3.2.1 Spatial configuration 

For the 2002 Platform, we ran SMOKE and CMAQ for modeling domains with 36 km and 

12 km spatial resolution. Figure 3-1 shows the 36 km CONtinental United States “CONUS” 

modeling domain, the 12 km eastern domain (EUS), and the 12 km western domain (WUS). 
30

Figure 3-1. CMAQ modeling domain 

36km Domain Boundary

12km East Domain Boundary

12km West Domain Boundary

All three grids use a Lambert-Conformal projection, with Alpha = 33, Beta = 45 and 

Gamma = -97, with a center of X = -97 and Y = 40. Table 3-2 describes the grids for the three 

domains. 

Table 3-2. Descriptions of the 2002-based Platform Grids 

Common 

Name 

Grid Cell 

Size 

Description 

(see Figure 3-1) Grid name 

Parameters listed in SMOKE grid 

description (GRIDDESC) file: 

 projection name, xorig, yorig, 

 xcell, ycell, ncols, nrows, nthik 

US 36 km or 

CONUS-36 36 km 

Entire conterminous 

US plus some of 

Mexico/Canada 

US36KM_148X112 ‘LAM_40N97W', -2736.D3, -2088.D3, 

36.D3, 36.D3, 148, 112, 1 

Big East 12 km 12 km 

Goes west to Colorado, 

covers some 

Mexico/Canada 

EUS12_279X240 ‘LAM_40N97W', -1008.D3 , -1620.D3, 

12.D3, 12.D3, 279, 240, 1 

West 12 km 12 km 

Goes east to Oklahoma, 

covers some of 

Mexico/Canada 

US12_213X192 'LAM_40N97W', -2412.D3 , -972.D3, 

12.D3, 12.D3, 213, 192, 1 

Section 3.3.1 provides the details on the spatial surrogates and area-to-point data used to 

accomplish spatial allocation with SMOKE. 
31

3.2.2 Chemical speciation configuration 

The emissions modeling step for chemical speciation creates “model species” needed by the air 

quality model for a specific chemical mechanism. These model species are either individual 

chemical compounds or groups of species, called “model species.” The chemical mechanism 

used for the 2002 Platform is the Carbon Bond 05 (CB05) mechanism (Yarwood, 2005). This is 

an updated mechanism from CB4, which had been used in the 2001 Platform. Table 3-3 lists the 

model species produced by SMOKE for use in CMAQ with the CB05. 

Table 3-3. Model Species produced by SMOKE for CB05 

Inventory Pollutant Model Species Model species description 

CO CO Carbon monoxide 

NOX NO Nitrogen oxide 

NO2 Nitrogen dioxide 

SO2 SO2 Sulfur dioxide 

SULF Sulfuric acid vapor 

NH3 NH3 Ammonia 

ALD2 Acetaldehyde 

ALDX Propionaldehyde and higher aldehydes 

ETH Ethene 

ETHA Ethane 

ETOH Ethanol 

FORM Formaldehyde 

IOLE Internal olefin carbon bond (R-C=C-R) 

ISOP Isoprene 

MEOH Methanol 

OLE Terminal olefin carbon bond (R-C=C) 

PAR Paraffin carbon bond 

TOL Toluene and other monoalkyl aromatics 

VOC 

XYL Xylene and other polyalkyl aromatics 

Various additional 

VOC species from 

the biogenics model 

which do not map to 

the above model 

species 

TERP Terpenes 

PM10 PMC Coarse PM > 2.5 microns and ≤ 10 microns 

PEC Particulate elemental carbon ≤ 2.5 microns 

PNO3 Particulate nitrate ≤ 2.5 microns 

POC Particulate organic carbon (carbon only) ≤ 2.5 

microns 

PSO4 Particulate Sulfate ≤ 2.5 microns 

PM2.5 

PMFINE Other particulate matter ≤ 2.5 microns 
32

For VOC, the speciation approach involves three major steps, as performed by SMOKE: 

• Assignment of speciation profiles to each emission source 

• Conversion of VOC from the emission source to TOG 

• Application of speciation profiles that disaggregate TOG into CB05 model species. 

The approach for PM2.5 emissions is somewhat simpler, since it does not requite the second 

step. Figure 3-2 shows the steps involved in chemical speciation for both VOC and PM2.5, and it 

identifies the underlying inputs used to develop the CB05-based ancillary files for the 2002 

Platform for CAPs. 

Figure 3-2. Chemical Speciation Approach Used for the 2002-based Platform 

VOC mass 

from emission source 

Convert VOC to TOG

TOG split factors:

speciate TOG mass to 

moles of model species

Compute moles of each 

CB05 model species

Assign speciation profile SMOKE

code to emission source

conversion factors: 

VOC to TOG by profile

Speciation

cross reference file

SPECIATE4.0 Database 

TOG profiles: 

(1)Fraction of chemical compound 

by profile code

(2) Conversion factors: VOC-to-TOG

by profile code

CB05-specific mapping:

Moles chemical compounds

to moles of model species

Provided by Dr. Carter

(UC Riverside)

Speciation Tool

VOC Speciation

PM2.5 mass 

from emission source 

PM2.5 profiles that 

speciate PM2.5 mass to 

mass of model species

Compute mass of each 

PM2.5 model species

Assign speciation profile SMOKE

code to emission source

Speciation

cross reference file

SPECIATE4.0 Database 

Simplified PM2.5 profiles: 

Fraction of chemical components 

by profile code

Speciation Tool

PM2.5 Speciation

Section 3.3.2 provides the details about the data files used to accomplish these speciation 

processing steps. 

3.2.3 Temporal processing configuration 

Table 3-4 summarizes the temporal aspect of the emissions processing configuration. It 

compares the key approaches we used for temporal processing across the sectors. We control 

temporal aspect of SMOKE processing through (a) the scripts T_TYPE (Temporal type) and 

M_TYPE (Merge type) settings and (b) the ancillary data files described in Section 3.3.3. 
33

Table 3-4. Temporal Settings Used for the Platform Sectors in SMOKE 

Platform 

sector 

Inventory

resolution 

Monthly

profiles

used? 

Daily 

temporal 

approach 1,2

Merge 

processing 

approach 1,3

Process 

Holidays as 

separate days? 

ptipm daily & 

hourly all all yes 

ptnonipm annual yes mwdss all yes 

othpt annual yes mwdss all 

nonroad monthly mwdss mwdss yes 

othar annual yes mwdss mwdss 

alm annual yes mwdss mwdss 

onroad monthly week week yes 

othon annual yes mwdss* mwdss* 

nonpt annual yes mwdss mwdss yes 

ag annual yes aveday aveday 

afdust annual yes aveday aveday 

biog hourly n/a n/a 

ptfire daily all all 

nonptfire annual yes aveday aveday 

1 Definitions for processing resolution: 

all = hourly emissions computed for every day of the year, inventory is already daily 

week = hourly emissions computed for all days in one “representative” week, representing all weeks for

each month, which means emissions have day-of-week variation, but not week-to-week variation 

within the month 

mwdss= hourly emissions for one representative Monday, representative weekday, representative 

Saturday and representative Sunday for each month, which means emissions have variation between 

Mondays, other weekdays, Saturdays and Sundays within the month, but not week-to-week variation 

within the month. Also Tuesdays, Wednesdays and Thursdays are treated the same. 

aveday = hourly emissions computed for one representative day of each month, which means emissions 

for all days of each month are the same. 

2 Daily temporal approach refers to the temporal approach for getting daily emissions from the 

inventory using the Temporal program. The values given are SMOKE’s T_TYPE setting. 

3 Merge processing approach refers to the days used to represent other days in the month for the merge 

step. If not “all”, then the SMOKE merge step just run for representative days, which could include 

holidays as indicated by the rightmost column. The values given are SMOKE’s M_TYPE setting. 

* We discovered after the modeling that “week” would have been a more appropriate setting because this

sector includes weekly profiles that vary across days of the week. 

In addition to the resolution, temporal processing includes a ramp-up period for several days 

prior to January 1, 2002, which is intended to mitigate the effects of initial condition 

concentrations. The same procedures were used for all grids, but with different ramp-up periods 

for each grid: 

• 36 km: 10 days (Dec 22 - Dec 31) 

• 12 km (East): 3 days (Dec 29 - Dec 31) 

• 12 km (West): 2 days (Dec 30 - Dec 31) 
34

For most sectors, our approach used the emissions from December 2002 to fill in surrogate 

emissions for the end of December 2001. In particular, for sectors not requiring vertical plume 

rise calculations (i.e., SMOKE-area and mobile sectors, including nonptfire and avefire), we 

used December 2002 emissions (representative days) for December 2001. For othpt and 

ptnonipm, we used December 2002 emissions (representative days) with 2001 meteorology for 

plume rise. For ptipm, we applied temporal profiles to the annual inventory (rather than using 

day-specific December emissions), with 2001 meteorology for plume rise. For ptfire, we used 

the day-specific December 2002 emissions directly (i.e., no representative days), using 2002 

meteorology for plume rise. For biogenics, we processed December 2001 emissions using 2001 

meteorology. 

3.2.4 Vertical allocation of day-specific fire emissions 

We used SMOKE to compute vertical plume rise for all of the SMOKE point-source sectors, 

which is typically done for emissions modeling for CMAQ. One new feature of the vertical 

allocation for the 2002 Platform was the modeling of wildfires and prescribed burning fires as 

point sources with plume rise. 

The ptfire inventory contains data on the acres burned (acres per day) and fuel consumption (tons 

fuel per acre) for each day. SMOKE uses these additional parameters to estimate plume rise of 

emissions into layers above the surface model layer. Specifically, SMOKE uses these data to 

calculate heat flux, which is then used to estimate plume rise. In addition to the acres burned and 

fuel consumption, SMOKE needs the heat content of the fuel to compute heat flux. We assumed 

the heat content to be 8000 Btu/lb of fuel for all fires, because specific data on the fuels were 

unavailable in the inventory. Since SMOKE can use a fire-specific heat content value, we 

inserted the default 8000 Btu/lb value into the SMOKE-ready fire inventory data for all fires. 

The plume rise algorithm applied to the fires is a modification of the Briggs algorithm [Briggs, 

1971 and 1972] with a stack height of zero and a heat release estimated from the fuel loading and 

fire size. The SMOKE program Laypoint uses the Briggs algorithm to determine the plume top 

and bottom, and then computes the plumes’ distributions into the vertical layers that the plumes 

intersect. Laypoint uses the pressure difference across each layer over the pressure difference 

across the entire plume as a weighting factor to assign the emissions to layers. This approach 

gives plume fractions by layer and source. See http://www.smoke-model.org/version2.4/ for full 

documentation of Laypoint and the new day-specific formats for the fire files. 

The ptfire inventory includes both flaming and smoldering emissions. Smoldering emissions 

also have plume rise subject to the meteorological conditions on the day they occur. No 

distinction in the SMOKE processing is made with respect to flaming or smoldering other than 

the different SCCs. 

3.3 Emissions modeling ancillary files 

In this section we summarize the ancillary data that SMOKE used to perform spatial allocation, 

chemical speciation, and temporal allocation for the 2002 Platform. The ancillary data files 

provide the specific inventory resolution at which spatial, speciation, and temporal factors are 

applied. For the 2002 Platform, we generally applied spatial factors by country/SCC, speciation 
35

factors by pollutant/SCC, and temporal factors by some combination of country, state, county, 

SCC, and pollutant. 

3.3.1 Spatial allocation ancillary files 

As described in Section 3.2.1, we performed spatial allocation for a national 36 km domain, an 

Eastern 12 km domain, and a Western 12 km domain. To do this, SMOKE used national 36 km 

and 12 km spatial surrogates and a SMOKE area-to-point data file. The spatial data files we used 

can be obtained from the files listed below, available from the 2002v3CAP website. 

• 36km_surg_2002v3mpCAP_smokeformat.zip: surrogate files for 36 km spatial 

resolution 

• 12km_surg_2002v3mpCAP_smokeformat.zip: surrogate files for 12 km spatial 

resolution 

• ancillary_2002v3mpCAP_smokeformat.zip: spatial related data included are the grid 

description (GRIDDESC), surrogate description (SRGDESC), surrogate cross reference 

file (AGREF), and area-to-point (ARTOPNT) file 

The 12 km surrogates cover the entire CONUS domain, though they are used directly as inputs 

for the two separate Eastern and Western Domains shown in Figure 3-1. The SMOKE model 

windowed the Eastern and Western grids while it created these emissions. The remainder of this 

subsection provides further detail on the origin of the data used for the spatial surrogates and 

area-to-point data. 

3.3.1.1 Surrogates for U.S. Emissions 

There are 66 spatial surrogates available for spatially allocating U.S. county-level emissions to 

the CMAQ 36 km and 12 km grid cells. As described in Section 3.3.1.2, an area-to-point 

approach overrides the use of surrogates for some sources. Table 3-5 lists the codes and 

descriptions of the surrogates. 
36

Table 3-5. U.S. Surrogates Available for the 2002 Platform 

Code Surrogate Description Code Surrogate Description 

N/A Area-to-point approach (see 3.3.1.2) 515 Commercial plus Institutional Land 

100 Population 520 Commercial plus Industrial plus Institutional 

110 Housing 525

Golf Courses + Institutional +Industrial + 

Commercial 

120 Urban Population 527 Single Family Residential 

130 Rural Population 530 Residential - High Density 

137 Housing Change 535

Residential + Commercial + Industrial + 

Institutional + Government 

140 Housing Change and Population 540 Retail Trade 

150 Residential Heating - Natural Gas 545 Personal Repair 

160 Residential Heating - Wood 550 Retail Trade plus Personal Repair 

165

0.5 Residential Heating - Wood plus 0.5 Low 

Intensity Residential 555

Professional/Technical plus General 

Government 

170 Residential Heating - Distillate Oil 560 Hospital 

180 Residential Heating - Coal 565 Medical Office/Clinic 

190 Residential Heating - LP Gas 570 Heavy and High Tech Industrial 

200 Urban Primary Road Miles 575 Light and High Tech Industrial 

210 Rural Primary Road Miles 580 Food, Drug, Chemical Industrial 

220 Urban Secondary Road Miles 585 Metals and Minerals Industrial 

230 Rural Secondary Road Miles 590 Heavy Industrial 

240 Total Road Miles 595 Light Industrial 

250 Urban Primary plus Rural Primary 596 Industrial plus Institutional plus Hospitals 

255 0.75 Total Roadway Miles plus 0.25 Population 600 Gas Stations 

260 Total Railroad Miles 650 Refineries and Tank Farms 

270 Class 1 Railroad Miles 675 Refineries and Tank Farms and Gas Stations 

280 Class 2 and 3 Railroad Miles 700 Airport Areas 

300 Low Intensity Residential 710 Airport Points 

310 Total Agriculture 720 Military Airports 

312 Orchards/Vineyards 800 Marine Ports 

320 Forest Land 807 Navigable Waterway Miles 

330 Strip Mines/Quarries 810 Navigable Waterway Activity 

340 Land 850 Golf Courses 

350 Water 860 Mines 

400 Rural Land Area 870 Wastewater Treatment Facilities 

500 Commercial Land 880 Drycleaners 

505 Industrial Land 890 Commercial Timber 

510 Commercial plus Industrial 

We did not use all of the available surrogates to spatially allocate sources in the 2002 Platform; 

that is, some surrogates in Table 3-5 were not assigned to any SCCs. Appendix B provides the 

U.S. emissions assigned by the available surrogates for the CONUS domain region. 
37

Creation of surrogates and shapefiles

We used the Surrogate Tool (Eyth, 2006) to generate all of the surrogates. The shapefiles we 

input to the Surrogate Tool are provided and documented at 

http://www.epa.gov/ttn/chief/emch/spatial/spatialsurrogate.html. The document 

ftp://ftp.epa.gov/EmisInventory/emiss_shp2006/us/list_of_shapefiles.pdf provides a list and 

summary of these shapefiles. The shapefiles used for the surrogate attributes (e.g., population, 

agricultural land, marine ports) are the same as those used for the 2001 Platform with two 

exceptions: we developed new shapefiles for the “population change” and “oil and gas” 

surrogates. We developed these shapefiles to enable the Surrogate Tool to generate these 

complex surrogates, which utilize data with different formats (e.g., point locations of refineries 

and tank farms versus polygon data for gas stations). Combining the data within a new shapefile 

allowed us generate the surrogates using the Surrogate Tool. 

County boundaries

For the 2001 Platform, we had used the same surrogates in Table 3-5; however, we regenerated 

these surrogates prior to use in the 2002 Platform due to changes in the political boundaries. 

Specifically, we included changes to boundaries for Broomfield County, CO and the 

incorporation of the city of Clifton Forge, VA into a county, since these changes occurred 

between 2001 and 2002. 

We also improved the county boundaries used for computing the water surrogate, which 

SMOKE used to spatially allocate pleasure craft emissions. For the water surrogate, we created 

county boundaries that extend a half-mile (804.7 meters) from the coastline into the oceans, 

bays, and Great Lakes. This was done because pleasure craft, and in particular personal 

watercraft, are not expected, on average, to go more than one half of a mile from the coast. For 

all other surrogates, county boundaries extend much farther over water; boundaries include all of 

the Great Lakes, bays, sounds, and extend outward into the ocean by approximately 3 miles. 

The detailed steps in developing the county boundaries for the 2002 Platform are provided in 

ftp://ftp.epa.gov/EmisInventory/emiss_shp2006/us/metadata_for_2002_county_boundary_shapefiles_rev.pdf. 

Other spatial surrogate improvements since the 2001 Platform

Other improvements made to the U.S. surrogates and surrogate assignments from the 2001 

Platform are: 

1. The commercial cooking nonpoint emissions (SCCs 2302002000, 2302002100, 

2302002200, 2302003000, 2302003100 and 2302003200) were assigned to surrogate 500 

(Commercial Land). Commercial cooking emissions in the 2001 Platform had been 

assigned to surrogate 580 (Food, Drug, Chemical Industrial land), which relates to the 

location of industrial processes rather than commercial establishments. 

2. The residential wood combustion emissions for wood stoves and fireplace inserts were 

assigned to surrogate 165 “50% low intensity residential land and 50% residential heating 

–wood”. This represents a change from the 2001 Platform which utilized surrogate 160, 

“residential heating--wood,” for these sources. Surrogate 160 reflects the use of wood as 

the primary heating source. We changed to surrogate 165 because we believe that the 

new surrogate does a better job of including areas where residential wood burning may 

occur that is not solely used as a primary heat source. 
38

3. The residential wood combustion emissions for fireplaces were assigned to the “low 

intensity residential land” surrogate, rather than “residential heating—wood,” which was 

used in the 2001 Platform. We made this change because fireplaces are not generally 

used as a primary source of home heating. In addition, “low intensity residential land” 

gives a spatial pattern for fireplace wood combustion that better fits a common sense 

view of where more fireplace usage is likely to occur. 

4. We assigned surrogates to SCCs in the 2002 NEI that were not in the 2001 Platform, and 

we updated the cross reference file linking SCCs to surrogates to include these new 

SCCs. 

5. We assigned surrogates to the portable fuel container (PFC) emissions (described in 

Section 2.2.3). Emissions from these sources were not in the 2001 Platform. Table 3-6 

provides these assignments. 

Table 3-6. Surrogates assigned to Portable Fuel Container Emission Categories 

SCC Description Surrogate Code 

2501011011 Residential Portable Fuel Containers: Permeation Single family dwelling* 527 

2501011012 Residential Portable Fuel Containers: 

Evaporation Single family dwelling 527 

2501011013 Residential Portable Fuel Containers: Spillage 

During Transport Total road miles 240 

2501011014 Residential Portable Fuel Containers: Refilling at 

the Pump: Vapor Displacement 

Gasoline stations 600 

2501011015 Residential Portable Fuel Containers: Refilling at 

the Pump: Spillage 

Gasoline stations 600 

2501012011

Commercial Portable Fuel Containers: 

Permeation 

golf courses + commercial 

+ industrial + institutional 

area 

525 

2501012012

Commercial Portable Fuel Containers: 

Evaporation 

golf courses + commercial 

+ industrial + institutional 

area 

525 

2501012013 Commercial Portable Fuel Containers: Spillage 

During Transport 

Total road miles 240 

2501012014 Commercial Portable Fuel Containers: Refilling 

at the Pump: Vapor Displacement 

Gasoline stations 600 

2501012015 Commercial Portable Fuel Containers: Refilling 

at the Pump: Spillage 

Gasoline stations 600 

* this surrogate had not previously been used in the 2001-based platform, other than as a secondary surrogate for 

counties that had no data for surrogate 300, Low Intensity Residential Land 

Errors resulting from surrogates

We discovered an error in surrogate 525 for Lincoln County, WY after we had already used 

these data. The error caused emissions allocated with this surrogate to be approximately double 

what they should have been in one grid cell (46, 66) and 50% too high overall in Lincoln 

County. This error occurred in processing emissions for the 2002 evaluation case (36 km 

resolution only), but not the 2002 base case or future-year cases. The correct data are provided in 

the 2002v3CAP site. 
39

3.3.1.2 Allocation Method for Airport-Related Sources in the U.S. 

There are numerous airport-related emission sources in the 2002 NEI, such as aircraft, airport 

ground support equipment, and jet refueling. Most of these emissions are contained in sectors 

with county-level resolution – alm (aircraft), nonroad (airport ground support) and nonpt (jet 

refueling). 

Similar to the 2001 Platform, we used the SMOKE “area-to-point” approach to allocate the 

emissions to airport locations, rather than using airport spatial surrogates, which we found 

exclude many airports. Under this approach, SMOKE allocates county emissions to one or more 

grid cells using an “ARTOPNT” ancillary file that contains (1) geographic coordinates of airport 

locations and (2) allocation factors based on airport-specific aircraft activity. For the 2002 

Platform, each airport was assigned to a single location, and therefore the emissions associated 

with each airport were allocated to a single grid cell. 

For the 2002 Platform, we created a new 2002-specific ARTOPNT file. The geographic 

coordinates and 2002-specific activity information (i.e., landing and takeoffs) used for allocating 

emissions to multiple airports in a county were largely taken from the “supplemental” 

geographic information system (GIS) data provided with the 2002 NEI, posted under the 

“Inventory Data” section” (“Mobile Sector Data”) at 

ftp://ftp.epa.gov/EmisInventory/2002finalnei/mobile_sector_data/ncd_files/gis_allocation. The 

supplemental data includes geographic coordinates and landing and takeoff (LTO) information 

for specific airports, which were used in the development of the aircraft emissions in the 2002 

NEI v3. 

We made a few changes to the geographic information from the NEI supplemental GIS data as 

follows: 

1. We added geographic coordinates when the locations were missing from the NEI 

supplemental data. We obtains these geographic coordinates from a 2003 Bureau of 

Transportation Statistics (BTS) “Master Coordinate file”, which we downloaded from the 

Aviation Support Tables at http://www.transtats.bts.gov/ (the actual data set we used is no 

longer available). 

2. We changed state/county FIPS codes in the NEI supplemental GIS data when the 

state/county FIPS codes were inconsistent with the geographic coordinates. We used Google 

Earth to determine that the coordinates were the correct locations for airports in these cases. 

3. We added airport locations to the ARTOPNT file when counties that contained emissions in 

the 2002 NEI did not appear in the NEI supplemental GIS data. To do this, we used the 2003 

BTS data, referenced in item 1. For the few cases where we were unable to find airports in 

BTS for counties with airport-related emissions in the NEI, we used airports from other 

counties. In this situation, we used the state/county FIPS code from the NEI, and the 

geographic coordinates from the BTS. This mainly occurred in the small Virginia citycounties, where airport-related emissions in the 2002 NEI may have been inadvertently 

assigned to counties without airports. These cases are documented in the ARTOPNT file, 

since they create an inconsistency between the county codes and the geographic coordinates. 
40

3.3.1.3 Surrogates for Canada and Mexico Emission Inventories 

We used the 2001 Platform surrogates for Canada to spatially allocate the Canadian emissions 

for the 2002 Platform. Detailed documentation about the Canadian spatial surrogates, their 

development, and the data are available at: 

http://www.epa.gov/ttn/chief/emch/spatial/newsurrogate.html. 

As in the 2001 Platform, only the population surrogate was used to grid sources in the Mexico 

emission inventory, provided by municipios (analogous to U.S. counties). We updated this 

surrogate from the 1999-based population surrogate used in the 2001 Platform to include 

additional municipios and updated 2000 population data. We created this updated population 

surrogate using the Surrogate Tool. 

The update to include additional municipios was required because the updated Mexican 

inventories (discussed in Section 2.6) include more municipios than the inventories used for the 

2001 Platform. We obtained the municipio boundaries from the Institute for the Environment, 

Center for Environmental Modeling and Policy Development at the University of North Carolina 

at Chapel Hill. Municipio population data from the year 2000 were obtained from 

www.inegi.gob.mx for only those Mexican states that are within the CONUS 36 km national 

domain. These states are shown in Figure 3-3. 

The shapefiles used in the Surrogate Tool are available at 

http://www.epa.gov/ttn/chief/emch/spatial/spatialsurrogate.html and the 12 km and 36 km 

surrogate files are on the 2002v3CAP site. Note that the population is “zero” in the Mexico_pop 

shapefile for municipios that are part of states located outside the 36 km CONUS domain. 

Figure 3-3. Mexican States included in the 36 km Modeling Domain 
41

3.3.2 Chemical speciation ancillary files 

The following data file, provided at the 2002v3CAP site, contains the SMOKE inputs used for 

chemical speciation of the inventory species to the CMAQ model species. SMOKE 

environmental variable names, used in the file names, are shown in capital letters in parentheses: 

• ancillary_2002v3mpCAP_smokeformat.zip: includes speciation cross reference 

(GSREF), speciation VOC-to-TOG conversion factors (GSCNV) and speciation profiles 

(GSPRO) 

For VOC speciation, we generated SMOKE-ready TOG-to-model species profiles for the CB05 

chemical mechanism using the Speciation Tool (Eyth, 2006). We also used the Speciation Tool 

to generate a SMOKE-ready file (“GSCNV”) containing profile-specific VOC-to-TOG 

conversion factors. The use of profile-specific factors is a procedural improvement from the 

SCC-specific approach that was used in the 2001 Platform, since it allows changes to profile 

assignments by SCC without requiring changes to the VOC-to-TOG conversion factors. 

One problem identified after using the “GSCNV” file created for 2002 is that it was missing 

some entries for mode-specific VOC, “EVP__VOC” and “EXH__VOC.” Because most of the 

missing entries were not assigned to emissions in 2002 or had a conversion factor of 1.0 (the 

default used if the entry is missing), the impact on the speciated VOC was small. Note that this 

issue was corrected for future-year cases, creating a trivial inconsistency between the 2002 base 

and future year-base cases. 

For PM2.5, neither the mass-based PM2.5 profiles nor the PM2.5 emissions have to be further 

converted for use in SMOKE, though the Speciation Tool was used to convert the profiles from a 

database format to SMOKE-ready format. 

The TOG and PM2.5 speciation factors that are the basis of the chemical speciation approach 

were developed from the SPECIATE4.0 database 

(http://www.epa.gov/ttn/chief/software/speciate/index.html) which is EPA's repository of TOG 

and PM speciation profiles of air pollution sources. EPA developed SPECIATE 4.0 through a 

collaboration involving EPA’s Office of Research and Development (ORD) and EPA’s Office of 

Air Quality Planning and Standards (OAQPS) at Research Triangle Park, NC, and Environment 

Canada (EPA, 2006c). The SPECIATE4.0 database contains speciation profiles for TOG, 

speciated into individual chemical compounds, VOC-to-TOG conversion factors associated with 

the TOG profiles, and speciation profiles for PM2.5. The database also contains the PM2.5

speciated into both individual chemical compounds (e.g., zinc, potassium, manganese, lead), and 

into the “simplified” PM2.5 components used in the air quality model. These simplified 

components are: 

• PSO4 : primary particulate sulfate 

• PNO3: primary particulate nitrate 

• PEC: primary particulate elemental carbon 

• POC: primary particulate organic carbon 

• PMFINE: other primary particulate, less than 2.5 micrograms in diameter 
42

Note that in the 2001 Platform, “POA” was used instead of POC. While POC represents only 

the carbon mass of the organic carbon PM2.5 components, POA has a 20% increase in the PM2.5

fraction compared to POC. This additional amount of mass is intended to account for other 

atoms in the organic carbon particles, such as hydrogen and oxygen. For the 2002 Platform, we 

decided to use emissions of POC and to make any organic fraction adjustments part of the postprocessing of CMAQ outputs. 

The assignment of profiles in the SPECIATE4.0 database to emissions sources was done in two 

steps: 

 (1) an initial profile assignment list was prepared with the SPECIATE4.0 database 

 (2) the list was completed and reviewed by emission inventory development, emission 

modeling and emission factor staff in the EPA’s OAQPS and the EPA’s ORD. 

For VOC speciation factors, recommendations for mobile sources and upstream (i.e., petroleum 

distribution) sources were obtained from subject experts at OTAQ. 

Speciation profiles for use with BEIS are not included in SPECIATE. We added the BEIS3.13 

profiles to the SMOKE speciation profiles for CMAQ for CB05. The profile code associated 

with BEIS3.13 profiles for use with CB05 is “B10C5.” 

Appendix C provides tables with the VOC and PM2.5 inventory mass assigned to each of the 

profiles for the U.S. states included in the domain for 2002. 

For certain gasoline-related sources, we assigned different profiles for the future-year cases 

(2009 and beyond) than for the 2002 base and evaluation cases in order to account for the influx 

of ethanol fuel in the 2007 timeframe and beyond. Table 3-7 and Table 3-8 describe these 

profiles for 2002 and the future years, respectively. 

Table 3-7. Gasoline-related speciation profiles used for 2002 

Profile Code 

(SPECIATE4.0) Profile Description Sources Using this Profile, 2002 

1305 Industry Average (circa 1990) Gasoline 

Composite (Hot Soak + Diurnal) 

Evaporative 

Evaporative emissions from onroad and 

nonroad gasoline-fueled mobile sources 

1313 Industry Average (circa 1990) Gasoline 

Exhaust 

Exhaust emissions from onroad and 

nonroad gasoline-fueled mobile sources 

8737 Composite Profile - Non-oxygenated 

Gasoline Headspace Vapor 

Gasoline evaporative processes that 

occur at ambient temperature including 

vehicle refueling, PFC, storage tanks and 

transport of gasoline 

8734 Composite Profile – Non-oxygenated 

Gasoline 

Gasoline Service Stations, Stage I Splash 

Filling 
43

Table 3-8. Gasoline-related speciation profiles used for future years 

Profile Code 

(SPECIATE4.0)

Profile Description Sources Using this Profile for years 

2009, 2014, 2020 and 2030 

1301 20% Ethanol Composite (Hot Soak + 

Diurnal) Evaporative 

Evaporative emissions from onroad and 

nonroad gasoline-fueled mobile sources 

1314 10% Ethanol Exhaust Exhaust emissions from onroad and 

nonroad gasoline-fueled mobile sources 

8736 Composite Profile - Ethanol Blended 

Gasoline Headspace Vapor 

Gasoline evaporative processes that 

occur at ambient temperature including 

vehicle refueling, PFC, storage tanks and 

transport of gasoline 

8733 Composite Profile – Ethanol Blended 

Gasoline 

Gasoline Service Stations, Stage I Splash 

Filling 

We discovered a few problems in the assignments after we completed the air quality model runs. 

The data provided at the 2002v3CAP site correct these problems, and the GSCNV problem 

discussed previously. The two assignment problems and corrections are: 

• For aircraft VOC, we initially used profile 2752 (Aircraft - Atlanta - August 27, 1990), 

but this was changed to profile 1098 (Aircraft Landing/Takeoff (LTO) – Commercial) 

based on recommendations from the Federal Aviation Administration. 

• For SCCs 30400398 and 30400399 (both described as “Industrial Processes;Secondary 

Metal Production;Grey Iron Foundries;Other Not Classified”) we inadvertently used 

profile 1098 (Aircraft Landing/Takeoff (LTO) – Commercial). This was changed to 

profile 1089 (Secondary Metal Production - Gray Iron Foundries - Pouring/Casting). 

3.3.3 Temporal allocation ancillary files 

The emissions modeling step for temporal allocation creates the 2002 hourly emission inputs for 

CMAQ by adjusting the emissions from the inventory resolution (annual, monthly, daily or 

hourly) that are input into SMOKE. The temporal resolution of each of the platform sectors 

prior to their input into SMOKE is included in the sector descriptions from Table 2-1 and 

repeated in the discussion of temporal settings in Table 3-4. 

The monthly, weekly, and diurnal temporal profiles and associated cross references used to 

create the 2002 hourly emissions inputs for CMAQ were generally based on the temporal 

allocation data used for the 2001 Platform. We modified or corrected profiles for particular 

categories where updated data were available, and added new profile assignments for new SCCs 

that were not in the 2001 inventory. 

The following data file, provided at the 2002v3CAP site, contains the files used for temporal 

allocation of the inventory emissions to hourly emissions. SMOKE environmental variable 

names, used in the file names, are shown in capital letters in parentheses: 

• ancillary_2002v3mpCAP_smokeformat.zip: includes temporal cross reference files 

used across all inventory sectors (ATREF, MTREF, and PTREF) and for ptipm sector 

(used for electric generating units) for the evaluation case (PTREF) and, temporal 

profiles (ATPRO, MTPRO, and PTPRO) 
44

The remainder of this section discusses the development of the new temporal profiles or profile 

assignments listed below: 

• ptipm: updated diurnal profiles for non-CEM sources 

• onroad and nonroad: updated weekly profiles 

• nonpt (PFC): assignment of monthly, weekly and diurnal profiles 

• ag (fertilizer): correction of monthly profiles 

Diurnal Profiles for Electric Generating Units (ptipm)

We updated the state-specific and pollutant-specific diurnal profiles for use in allocating the dayspecific emissions for non-CEM sources in the ptipm sector. We used the 2002 CEM data to 

create state-specific, day-to-hour factors, averaged over the whole year and all units in each state. 

We calculated the diurnal factors using CEM SO2 and NOX emissions and heat input. We 

computed SO2 and NOX-specific factors from the CEM data for these pollutants. All other 

pollutants used the hourly heat input data. We assigned the resulting profiles by state and 

pollutant. 

Onroad Mobile: Weekly Profiles 

We use weekly profiles to allocate the monthly total mobile source emissions to each day of the 

week. We changed the weekly profiles for onroad mobile sources to account for differences in 

the weekly pattern of heavy duty and light duty vehicles in urban versus rural areas. These 

changes were made based on guidance and references (Chinkin, et. al, 2002 and the Highway 

Capacity Manual, 2000) provided by subject experts at OTAQ. In the 2001 Platform, we used 

two profiles for weekly variation that did not vary by vehicle type; they varied only by urban 

versus rural roadway. 

Field study data (Chinkin, et. al, 2002) indicate that the fraction of weekend emissions for light 

duty vehicles (LDVs) on rural roadways is larger than what was used in the 2001 Platform. The 

opposite effect is shown for heavy duty vehicles (HDVs) on rural roads on weekends. For urban 

roadways, the data for LDVs indicates somewhat greater activity during weekdays versus 

weekends, whereas the data for HDVs indicates much greater activity on the weekdays versus 

weekends. These weekday versus weekend relationships for LDVs and HDVs are consistent 

with data collected by the Minnesota Department of Transportation and published in the 

Highway Capacity Manual (2000). 

Tables 3-9 and 3-10 show the 2002 Platform weekly profiles for onroad mobile sources as 

compared to those used in the 2001 Platform for rural and urban roadways, respectively. 
45

Table 3-9. Onroad Mobile Weekly Profiles for Rural Roadways: 2002 Platform vs 2001 

Platform 

 Profile Weights and Percent (%) of emissions* 

Profile 

code 

Vehicle 

Type 

Mon Tues Wed Thur Fri Sat Sun Sum 

2002 Platform

20022 HDV 1678 

(16.8%) 

1678 

(16.8%) 

1678 

(16.8%) 

1678 

(16.8%) 

1594 

(15.9%) 

881 

(8.8%) 

814 

(8.8%) 

10001 

20021 LDV 1205 

(12.1%) 

1205 

(12.1%) 

1205 

(12.1%) 

1205 

(12.1%) 

1825 

(18.3%) 

1530 

(15.3%) 

1825 

(18.3%) 

10000 

2001 Platform

2002 ALL 

TYPES 

101 

(14.3%) 

100 

(14.2%) 

100 

(14.2%) 

109 

(15.5%) 

111 

(15.8%) 

94 

(13.4%) 

89 

(12.6%) 

704 

* Percent of emissions = 100*WEIGHT/SUM

Table 3-10. Onroad Mobile Weekly Profiles for Urban Roadways: 2002 Platform vs 2001 

Platform 

 Profile Weights and Percent (%) of emissions* 

Profile 

code 

Vehicle 

Type 

Mon Tues Wed Thur Fri Sat Sun Sum 

2002 Platform

20032 HDV 1770

(17.7%) 

1770

(17.7%) 

1770

(17.7%) 

1770

(17.7%) 

1717

(17.2%) 

696

(7.0%) 

505

(5.0%) 

9998 

20031 LDV 1475

(14.8%) 

1475

(14.8%) 

1475

(14.8%) 

1475

(14.8%) 

1595

(16.0%) 

1342

(13.4%) 

1163

(11.6%) 

10000 

2001 Platform

2003 ALL 

TYPES 

109 

(15.6%)

108 

(15.4%)

106 

(15.1%)

114 

(16.3%)

109 

(15.6%)

86 

(12.3%)

68 

(9.7%)

700 

* Percent of emissions = 100*WEIGHT/SUM

Nonroad mobile: Weekly Profiles

We updated the nonroad weekly profiles for allocating monthly nonroad sector emissions to 

weekdays versus weekends using data provided in the NONROAD model’s technical 

memorandum “Weekday and Weekend Day Temporal Allocation of Activity In The Draft 

NONROAD2004 Model” (EPA, 2004). This reference provides default activity allocation 

fractions for weekend days versus weekdays by nonroad equipment types. Table 3-11 

summarizes the updates. 
46

Table 3-11. Updated Nonroad Mobile Weekly Profiles 

Equipment Category

Previous Weekly Profile 

Code and description 

Week-day 

fraction from 

OTAQ 

memorandum

*

Weekend day 

fraction from 

OTAQ 

memorandum

*

2002 

Platform 

Weekly 

Profile 

Code 

Recreational 7: all days are the same 0.1111 0.2222 9 

Construction 7: all days are the same 0.1667 0.0833 18 

Industrial 7: all days are the same 0.1667 0.0833 18 

Residential Lawn and Garden 7: all days are the same for 

lawn mowers 

17: 0.16 wkday; 0.10 wknd for 

other residential eqmt. 

0.1111 0.2222 9 

Commercial Lawn and Garden 17: 0.16 wkday; 0.10 wknd 0.1600 0.1000 17 

Agricultural 17: 0.16 wkday; 0.10 wknd 0.1667 0.0833 18 

Light Commercial 17: 0.16 wkday; 0.10 wknd 0.1667 0.0833 18 

Logging 17: 0.16 wkday; 0.10 wknd 0.1667 0.0833 18 

Airport Service 7: all days are the same 0.1429 0.1429 7 

Railway Maintenance 7: all days are the same 0.1800 0.0500 19 

Recreational Marine 7: all days are the same 0.0600 0.3500 16 

Transportation A/C 

Refrigeration 

7: all days are the same 0.1429 0.1429 7 

Underground Mining 7: all days are the same 0.1667 0.0833 18 

Oil Field Equipment 7: all days are the same 0.1429 0.1429 7 

* The values are the fractions of weekly activity allocated to each weekday and each weekend day. To get the fraction 

for all weekdays, multiply the weekday fraction by 5. Similarly, to get the weekend fraction, multiply the weekend 

day fraction by 2. All equipment types within a category (e.g., excavators within the construction equipment 

category) are assigned the weekday and weekend day fractions for that category. 

Monthly, Weekly and Diurnal Profiles for Portable Fuel Containers (PFC)

We assigned temporal profiles to allocate the annual PFC emissions in the nonpt sector using 

several existing temporal profiles, since we did not have data to create new profiles specific to 

these new SCCs for the 2002 Platform. Table 3-12 lists the profile assignments. 
47

Table 3-12. Temporal Profile Assignments for Portable Fuel Containers 

SCC 

Temporal Profile: 

Monthly Variation* 

Temporal Profile: 

Day of Week Variation 

Temporal Profile: 

Diurnal variation* 

2501011011: 

Residential PFC: 

Permeation 

Code=4, from Jan-Dec: 

13 13 91 91 91 137 137 137 

92 92 92 13 Rationale: less 

during colder months, more 

during hotter months 

Code=7 no day of week 

variation since the 

variation is caused by 

temperature which is not 

related to day of week 

Code=36, 12am to 11pm 11 8 

7 6 7 7 9 10 16 25 43 60 76 

88 94 103 97 89 75 60 51 32 18 

11, Variation reflects higher 

temperatures at middle of day 

2501011012: 

Residential PFC: 

Evaporation 

Code=4, Same rationale as 

SCC 2501011011 

Code=7, Same rationale as 

SCC 2501011011 

Code=36, Same rationale as 

SCC 2501011011 

2501011013: 

Residential PFC: 

Spillage During 

Transport 

Code=33, *from Jan-Dec: 

63 63 87 87 87 100 100 100 

83 83 83 63 

Rationale: slightly more 

activity in hotter months 

(more use of gas cans for 

lawnmowers, boats) 

Code=9, use same as 

residential lawn and garden 

temporal profile (0.1111 

wkdy, 0.22222 wknd) 

Code=14 12am to 11pm: 0 0 

0 0 0 0 0 0 714 714 714 714 

714 714 714 714 714 714 714 

714 714 714 0 0, Equal 

activity from 8am to 10pm, no 

activity from 10pm to 8am 

Rationale: Most of activity to 

be done during daytime. 

2501011014: 

Residential PFC: 

Refilling at the Pump: 

Vapor Displacement 

Code=33, Same rationale as 

SCC 2501011013 

Code=9, use same as 

residential lawn and garden 

temporal profile 

Same rationale as SCC 

2501011013 

CODE=14 , Same rationale as 

SCC 2501011013 

2501011015: 

Residential PFC: 

Refilling at the Pump: 

Spillage 

Code=33, Same rationale as 

for SCC 2501011013 

Code=9 Same rationale as 

for SCC 2501011013 

CODE=14, Same rationale as 

2501011013 

2501012011: 

Commercial PFC: 

Permeation 

Code=4, 

Same rationale as SCC 

2501011011 

Code=7 

Same rationale as SCC 

2501011011 

Code=36 

Same rationale as SCC 

2501011011 

2501012012: 

Commercial PFC: 

Evaporation 

Code=4, 

Same rationale as SCC 

2501011011 

Code=7 

Same rationale as SCC 

2501011011 

Code=36 

Same rationale as SCC 

2501011011 

2501012013: 

Commercial PFC: 

Spillage During 

Transport 

Code=33, *from Jan-Dec: 

63 63 87 87 87 100 100 100 

83 83 83 63. Rationale: 

slightly more commercial 

activity in hotter months 

(more use of gas cans for 

lawnmowers, boats) 

Code = 17, same as 

commercial lawn and 

garden (weekday=0.16, 

weekend=0.100) 

CODE=13, 12am to 11pm, 0 0 

0 0 0 0 0 0 769 769 769 769 

769 769 769 769 769 769 769 

769 769 0 0 0, Equal activity 

from 8am to 9pm, no activity 

from 9pm to 8am. Rationale: 

Most of activity to be done 

during daytime. 

2501012014: 

Commercial PFC: 

Refilling at the Pump: 

Vapor Displacement 

Code = 33, Same rationale 

as SCC 2501012013 

Code = 17, Same rationale 

as SCC 2501012013 

Code=13, Same rationale as 

SCC 2501012013 

2501012015: 

Commercial PFC: 

Refilling at the Pump: 

Spillage 

Code = 33, Same rationale 

as SCC 2501012013 

Code = 17, Same rationale 

as SCC 2501012013 

Code=13, Same rationale as 

SCC 2501012013 

* arrays of values in table represent profile weights 
48

Monthly Fertilizer Profiles

We modified the monthly profiles for fertilizer SCCs for eight northern states (Illinois, Indiana, 

Iowa, Minnesota, Nebraska, New York, Ohio and Wisconsin) to shift emissions from December 

to earlier in the fall (i.e., September through November). We made this change based on the 

understanding that fall fertilizer applications are made before snow covers the ground. 

4 Development of Future Year Emission Inventories 

This section describes the methods we used for developing future-year emissions for 2009, 2014, 

2020, and 2030. The projection methodology varied by sector. For some sectors, we relied on 

the output of models that incorporate activity changes and control programs to estimate futureyear emissions. For other sectors we applied projection factors and/or percent reductions to 

account for emissions growth based on activity growth and/or emission reductions due to control 

programs controls or closures. The following bullets summarize the projection methods used for 

sources in the various sectors: 

• IPM sector (ptipm): Unit-specific estimates from IPM, version 3.1. 

• Non-IPM sector (ptnonipm): Projection factors and percent reductions that reflect 

emission reductions due to control programs, plant closures, and settlements; used 

consistent projections approaches for ptnonipm livestock, aircraft and gasoline stage II 

emissions as are used for the sectors that contain the bulk of these emissions. 

• Average fires sector (avefire): No growth or control. 

• Agricultural sector (ag): Projection factors for livestock estimates based on expected 

changes in animal population; no growth or control for fertilizer NH3. 

• Area fugitive dust sector (afdust): Projection factors for dust categories related to 

livestock estimates based on expected changes in animal population; no growth or control 

for other categories in this sector. 

• Remaining Nonpoint sector (nonpt): Projection factors and percent reductions that 

reflect emission reductions due to control programs. Residential wood combustion 

projections based on growth in lower-emitting stoves and a reduction in higher emitting 

stoves. PFC projection factors reflecting impact of MSAT rule. Gasoline stage II 

projection factors based on NMIM-estimated VOC refueling estimates for future years. 

• Nonroad mobile sector (nonroad): Output from the NONROAD2005 model, which 

was run using NMIM; 

• Aircraft, locomotive, commercial marine sector (alm): Terminal area forecast 

information for aircraft, as aggregated to the national level, accounting for projected 

changes in landing/takeoff activity. Projection factors for commercial marine and 

locomotives which reflect activity growth and controls, as generated by national 

estimates provided by subject experts at EPA’s OTAQ.

• Onroad mobile sector (onroad): Output of onroad mobile sources emission from the 

MOBILE6 model (not including refueling emissions), which was run using NMIM. 

• Other onroad sector (othon): Year-specific, future-year Canadian emissions (obtained 

from Environment Canada) grown and controlled from the data used in the 2002 base 

case. 1999 emissions for Mexico for all future-year scenarios. 

• Other nonroad/nonpoint (othar): Same description applies as “other onroad.”
49

• Other Point (othpt): Same description applies as “other onroad,” plus the 2002 

inventory of offshore emissions for all future-year scenarios. 

• Biogenic: 2002 emissions used for all future-year scenarios. 

Table 4-1 provides a summary of anthropogenic national total emissions by sector and pollutant. 

A more detailed summary by sector, state, and pollutant for the U.S. is provided in Appendix D. 

This information is also available electronically at the 2002v3CAP site, which also contains 

annual total emissions by state and sector after application of chemical speciation factors. 

The remainder of this section is organized by either entire source sector or by specific emissions 

category within a source sector for which a distinct set of data were used or developed for 

purposes of projections. This organization allows consolidation of the discussion of the 

emissions categories that are contained in multiple sectors, since the data and approaches used 

across the sectors are consistent, and need not be repeated. Sector names associated with the 

emissions categories are provided in parentheses. 
50

Table 4-1. Summaries by Sector of 2002, 2009, 2014, 2020 and 2030 base year emissions for 

the Continental United States (48 states + District of Columbia). 

Year Sector 

[tons/yr] 

VOC 

[tons/yr] 

NOX

[tons/yr] 

CO 

[tons/yr] 

SO2

[tons/yr]

NH3 

[tons/yr] 

PM10 

[tons/yr]

PM2.5 

2002 afdust 0 0 0 0 0 8,901,461 1,830,271 

 ag 0 0 0 0 3,251,990 0 0 

 alm 123,676 2,259,844 806,471 312,313 904 97,039 86,719 

 avefire 451,127 189,428 8,554,550 49,094 36,777 796,229 684,034 

 nonpt 7,929,917 1,531,602 7,526,723 1,250,265 135,542 1,377,055 1,100,884 

 nonroad 2,873,622 2,176,159 21,386,059 187,284 1,859 227,875 216,658 

 onroad 4,847,990 7,786,709 59,810,866 242,379 290,708 205,914 146,003 

 ptipm 42,378 4,618,944 605,148 10,359,102 29,991 608,718 501,998 

 ptnonipm 1,425,158 2,368,987 3,195,469 2,249,550 154,180 603,606 372,330 

2002 Total 17,693,869 20,931,673 101,885,285 14,649,986 3,901,951 12,817,898 4,938,898 

2009 afdust 0 0 0 0 0 8,902,659 1,830,492 

 ag 0 0 0 0 3,349,298 0 0 

 alm 129,545 2,052,643 852,496 239,443 1,010 97,768 87,150 

 avefire 451,127 189,428 8,554,550 49,094 36,777 796,229 684,034 

 nonpt 7,723,351 1,529,620 7,361,301 1,250,024 135,342 1,354,159 1,077,637 

 nonroad 2,305,942 1,839,505 15,897,170 32,307 2,104 181,111 171,230 

 onroad 3,134,889 4,723,144 35,085,683 35,839 307,508 161,499 100,549 

 ptipm 42,768 2,390,659 648,345 5,557,430 36,806 490,783 368,016 

 ptnonipm 1,147,179 2,173,721 3,186,791 2,069,813 155,022 570,222 352,304 

2009 Total 14,934,802 14,898,719 71,586,336 9,233,950 4,023,868 12,554,430 4,671,411 

2014 afdust 0 0 0 0 0 8,903,518 1,830,650 

 ag 0 0 0 0 3,418,828 0 0 

 alm 133,744 2,005,296 905,473 227,419 1,087 98,752 87,586 

 avefire 451,127 189,428 8,554,550 49,094 36,777 796,229 684,034 

 nonpt 7,547,362 1,528,204 7,243,136 1,249,852 135,199 1,337,804 1,061,032 

 nonroad 2,017,473 1,467,923 14,667,974 2,976 2,296 146,716 137,921 

 onroad 2,526,203 2,981,427 30,393,359 31,058 331,002 137,481 74,819 

 ptipm 46,093 2,104,728 716,731 4,869,100 41,755 561,556 433,471 

 ptnonipm 1,145,581 2,163,011 3,190,969 2,044,379 156,433 569,401 351,814 

2014 Total 13,867,583 12,440,017 65,672,193 8,473,877 4,123,379 12,551,458 4,661,327 

2020 afdust 0 0 0 0 0 8,904,542 1,830,838 

 ag 0 0 0 0 3,502,212 0 0 

 alm 140,269 2,020,926 973,765 257,552 1,200 102,735 90,712 

 avefire 451,127 189,428 8,554,550 49,094 36,777 796,229 684,034 

 nonpt 7,515,029 1,526,504 7,101,297 1,249,645 135,028 1,318,174 1,041,101 

 nonroad 1,830,241 1,094,566 15,069,996 3,196 2,536 107,009 99,368 

 onroad 2,089,022 1,942,023 29,281,916 34,962 361,913 132,567 65,185 

 ptipm 48,994 1,991,510 735,819 4,532,415 43,843 740,401 605,467 

 ptnonipm 1,145,622 2,165,705 3,195,527 2,044,547 158,127 569,416 351,825 

2020 Total 13,220,304 10,930,663 64,912,870 8,171,411 4,241,636 12,671,074 4,768,531 
51

Year Sector 

[tons/yr] 

VOC 

[tons/yr] 

NOX

[tons/yr] 

CO 

[tons/yr] 

SO2

[tons/yr]

NH3 

[tons/yr] 

PM10 

[tons/yr]

PM2.5 

2030 afdust 0 0 0 0 0 8,904,542 1,830,838 

 ag 0 0 0 0 3,502,212 0 0 

 alm 151,923 2,204,535 1,077,221 374,356 1,434 116,926 103,378 

 avefire 451,127 189,428 8,554,550 49,094 36,777 796,229 684,034 

 nonpt 7,515,029 1,526,504 7,101,297 1,249,645 135,028 1,318,174 1,041,101 

 nonroad 1,896,401 882,616 16,715,105 3,688 2,922 85,964 77,869 

 onroad 1,929,231 1,492,560 32,051,657 41,286 417,113 148,999 70,121 

 ptipm 48,994 1,991,510 735,819 4,532,415 43,843 740,401 605,467 

 ptnonipm 1,145,622 2,165,705 3,195,527 2,044,547 158,127 569,416 351,825 

2030 Total 13,138,328 10,452,858 69,431,177 8,295,030 4,297,455 12,680,651 4,764,633 

Table 4-2. Summaries by Sector for the Other (“oth”) -Canada, Mexico, and Offshore2002, 2009, 2014, and 2020 base year emissions within the 36 km domain 

Year 

Country & 

Sector 

[tons/yr]

VOC 

[tons/yr]

NOX

[tons/yr] 

CO 

[tons/yr]

SO2

[tons/yr]

NH3 

[tons/yr]

PM10 

[tons/yr]

PM2.5 

2002 Canada othar 1,878,996 1,060,097 4,282,782 227,942 569,738 1,462,643 400,493 

 Canada othon 410,981 874,564 5,810,763 26,376 18,332 19,692 18,071 

 Canada othpt 237,957 628,175 1,149,266 2,115,572 23,866 241,081 129,342 

 

Canada 

Subtotal 2,527,933 2,562,836 11,242,811 2,369,890 611,937 1,723,417 547,906 

 Mexico othar 586,842 249,045 644,733 101,047 486,484 143,816 92,861 

 Mexico othon 183,563 147,519 1,456,285 8,276 2,549 6,960 6,377 

 Mexico othpt 113,044 258,510 88,957 980,359 0 125,385 88,132 

 

Mexico 

Subtotal 883,448 655,074 2,189,976 1,089,682 489,033 276,161 187,370 

 Off-shore othpt 70,329 26,628 6,205 0 0 0 0

2002 Total 6,893,091 6,462,448 26,871,779 6,919,144 2,201,939 3,999,156 1,470,552 

2009 Canada othar 1,991,247 1,108,211 5,358,685 221,081 281,785 1,491,052 459,915 

 Canada othon 201,050 465,181 3,738,918 2,258 24,034 7,339 6,729 

 Canada othpt 319,478 654,572 1,326,302 1,964,497 21,384 264,692 159,141 

 

Canada 

Subtotal 2,511,774 2,227,963 10,423,905 2,187,836 327,204 1,763,084 625,785 

 Mexico othar 586,842 249,045 644,733 101,047 486,484 143,816 92,861 

 Mexico othon 183,429 147,419 1,455,121 8,270 2,547 6,955 6,372 

 Mexico othpt 113,044 258,510 88,957 980,359 0 125,385 88,132 

 

Mexico 

Subtotal 883,314 654,974 2,188,811 1,089,676 489,031 276,156 187,366 

 Off-shore othpt 70,329 26,628 6,205 0 0 0 0

2009 Total 6,860,506 5,792,503 25,231,637 6,555,024 1,632,469 4,078,481 1,626,301 
52

Year 

Country & 

Sector 

[tons/yr]

VOC 

[tons/yr]

NOX

[tons/yr] 

CO 

[tons/yr]

SO2

[tons/yr]

NH3 

[tons/yr]

PM10 

[tons/yr]

PM2.5 

2014 Canada othar 2,070,071 1,131,722 5,421,618 223,415 319,077 1,866,812 488,006 

 Canada othon 149,687 281,663 3,593,196 2,606 27,876 4,294 3,972 

 Canada othpt 332,121 662,111 1,378,551 1,965,484 23,315 276,689 165,517 

 

Canada 

Subtotal 2,551,879 2,075,496 10,393,365 2,191,504 370,267 2,147,795 657,495 

 Mexico othar 586,842 249,045 644,733 101,047 486,484 143,816 92,861 

 Mexico othon 183,429 147,419 1,455,121 8,270 2,547 6,955 6,372 

 Mexico othpt 113,044 258,510 88,957 980,359 0 125,385 88,132 

 

Mexico 

Subtotal 883,314 654,974 2,188,811 1,089,676 489,031 276,156 187,366 

 Off-shore othpt 70,329 26,628 6,205 0 0 0 0

2014 Total 6,940,715 5,487,569 25,170,557 6,562,360 1,718,597 4,847,903 1,689,721 

2020 Canada othar 2,063,413 1,126,276 5,021,305 222,949 346,364 1,723,387 476,603 

 Canada othon 132,289 185,106 3,535,542 2,910 31,167 3,384 3,140 

 Canada othpt 342,612 667,650 1,428,595 1,946,907 25,228 286,923 171,448 

 

Canada 

Subtotal 2,538,314 1,979,032 9,985,441 2,172,766 402,759 2,013,695 651,191 

 Mexico othar 586,842 249,045 644,733 101,047 486,484 143,816 92,861 

 Mexico othon 183,429 147,419 1,455,121 8,270 2,547 6,955 6,372 

 Mexico othpt 113,044 258,510 88,957 980,359 0 125,385 88,132 

 

Mexico 

Subtotal 883,314 654,974 2,188,811 1,089,676 489,031 276,156 187,366 

 Off-shore othpt 70,329 26,628 6,205 0 0 0 0

2020 Total 6,913,585 5,294,641 24,354,709 6,524,883 1,783,580 4,579,702 1,677,114 

4.1 Stationary Source Projections: IPM sector (ptipm) 

The future-year data for the ptipm sector were created by the IPM model version 3.0. The EPA 

Clean Air Markets Division (CAMD) manages the development of this model and maintains a 

website dedicated to documenting the latest IPM version: 

http://www.epa.gov/airmarkets/progsregs/epa-ipm/index.html. At the time we developed this 

report, the IPM 3.0 documentation is provided as a summary table 

(http://www.epa.gov/airmarkets/progsregs/epa-ipm/docs/summary2006.pdf) with references to 

the more textual documentation available for IPM version 2.1.9 

(http://www.epa.gov/airmarkets/progsregs/epa-ipm/past-modeling.html#version2004). 

We used IPM results for 2010 to represent 2009, 2015 to represent 2014, and we used the 2020 

IPM results for 2020. We also used the 2020 results for 2030, since we chose only to project 

mobile sources to 2030, and the IPM results were only available up until 2025. 

4.2 Stationary Source Projections: non-IPM sectors (ptnonipm, 

nonpt, ag, afdust) 

Projections of U.S. stationary sources other than ptipm involved applying growth factors and/or 

controls to certain categories within the ptnonipm, nonpt, ag and afdust platform sectors. This 

subsection provides details on the data and projection methods used for these sectors. In 

estimating future-year emissions, we assumed no emissions growth for many stationary non-IPM 
53

sources. This “no-growth” assumption is based on an examination of historical emissions and 

economic data. While we are working toward improving this approach in future emissions 

platforms, we are still using this assumption for this platform. More details on the rationale for 

this approach can be found in Appendix D of the Regulatory Impact Assessment for the PM 

NAAQS rule (EPA, 2006a). 

4.2.1 Livestock emissions growth (ag, ptnonipm) 

Growth in emissions from livestock in the ag, afdust and ptnonipm sectors was based on 

projections of growth in animal population. Table 4-3 provides the growth factors for animal 

categories. Except for dairy cows and turkey production, the animal projection factors are 

derived from national-level animal population projections from the U.S. Department of 

Agriculture (USDA) and the Food and Agriculture Policy and Research Institute (FAPRI). For 

dairy cows and turkeys we assumed that there would be no growth in emissions. This 

assumption was based on an analysis of historical trends in the number of such animals 

compared to production rates. Although productions rates have increased, the number of animals 

has declined. Thus, we do not believe that production forecasts provide representative estimates 

of the future number of cows and turkeys; therefore, we did not use these forecasts for estimating 

future-year emissions from these animals. In particular, the dairy cow population is projected to 

decrease in the future as it has for the past few decades; however, milk production will be 

increasing over the same period. Note that the ammonia emissions from dairies are not directly 

related to animal population but also nitrogen excretion. With the cow numbers going down and 

the production going up we suspect the excretion value will be changing, but we assumed no 

change because we did not have a quantitative estimate. 

Table 4-3. Growth factors for Animal Operations 

Projection factors used in the 2002 platform 

Animal Category 2009 2014 2020 

Dairy Cow 1.000 1.000 1.000 

Beef 1.010 1.016 1.025 

Pork 1.042 1.071 1.107 

Broilers 1.161 1.275 1.413 

Turkeys 1.000 1.000 1.000 

Layers 1.112 1.192 1.289 

Poultry Average 1.125 1.214 1.321 

Overall Average 1.044 1.075 1.112 

Appendix E provides the animal population data and regression curves used to derive the growth 

factors. Appendix F provides the cross references of livestock sources in the ag, afdust and 

ptnonipm sectors to the animal categories in Table 4-3. 

4.2.2 Residential wood combustion growth (nonpt) 

We projected residential wood combustion emissions based on the expected increase in the 

number of low-emitting wood stoves and the corresponding decrease in other types of wood 

stoves. As newer, cleaner woodstoves replace older, higher-polluting wood stoves, there will be 

an overall reduction of the emissions from these sources. The approach cited here was 

developed as part of a modeling exercise to estimate the expected benefits of the woodstoves 
54

change-out program (http://www.epa.gov/woodstoves/index.html). Details of this approach can 

be found in Section 2.3.3 of the PM NAAQS Regulatory Impact Analysis (EPA, 2006a). There 

are two differences between the 2001 and 2002 Platforms for emissions from woodstoves. First, 

we did not include the Libby Montana woodstove change-out program in our future-year 

scenarios, because we did not include local control programs in the future emissions from 

sources in other stationary source sectors. Second, the 2002 Platform contains an additional SCC 

for outdoor wood burning equipment (SCC 2104008070), which was not present in the 2001 

Platform. We have assumed the same 1% per year growth rate for this new SCC as for 

fireplaces. 

The specific assumptions we made were: 

• Fireplaces, SCC=2104008001: increase 1%/yr 

• Old woodstoves, SCC=2104008002, 2104008010, or 2104008051: decrease 2%/yr 

• New woodstoves, SCC=2104008003, 2104008004, 2104008030, 2104008050, 

2104008052 or 2104008053: increase 2%/yr 

For the general woodstoves and fireplaces category (SCC 2104008000) we computed a weighted 

average distribution based on 19.4% fireplaces, 71.6% old woodstoves, 9.1% new woodstoves 

using 2002 Platform emissions for PM2.5. These fractions are based on the fraction of emissions 

from these processes in the states that did not have the “general woodstoves and fireplaces” SCC 

in the 2002 NEI. This approach results in an overall decrease of 1.056% per year for this source 

category. 

Table 4-4 presents the projection factors used to project 2002 emissions for residential wood 

combustion. 

Table 4-4. Projection Factors for Residential Wood Combustion Sources 

SCC and Description* 2009 

Factor 

2014 

Factor 

2020 

Factor 

2104008000: Total: Woodstoves and Fireplaces 0.9261 0.8733 0.8099 

2104008001: Fireplaces: General 

2104008070: Outdoor Wood Burning Equipment 1.0700 1.1200 1.1800 

2104008002: Fireplaces: Insert; non-EPA certified 

2104008010: Woodstoves: General 

2104008051: Non-catalytic Woodstoves: Non-EPA certified 

0.8600 0.7600 0.6400 

2104008003: Fireplaces: Insert; EPA certified; non-catalytic 

2104008004: Fireplaces: Insert; EPA certified; catalytic 

2104008030: Catalytic Woodstoves: General 

2104008050: Non-catalytic Woodstoves: EPA certified 

2104008052: Non-catalytic Woodstoves: Low Emitting

2104008053: Non-catalytic Woodstoves: Pellet Fired

1.1400 1.2400 1.3600 

*All descriptions begin with: “Stationary Source Fuel Combustion;Residential;Wood” 
55

4.2.3 Gasoline Stage II growth and control (nonpt, ptnonipm) 

Emissions from Stage II gasoline operations in the 2002 NEI are contained in both nonpt and 

ptnonipm sectors. The only SCC in the nonpt inventory used for gasoline Stage II emissions is 

2501060100 (Storage and Transport; Petroleum and Petroleum Product Storage; Gasoline 

Service Stations; Stage II: Total). The following SIC and SCC codes are associated with 

gasoline Stage II emissions in the ptnonipm sector:

• SIC 5541 (Automotive Dealers & Service Stations, Gasoline Service Stations, Gasoline 

service stations) 

• SCC 40600401 (Petroleum and Solvent Evaporation;Transportation and Marketing of 

Petroleum Products;Filling Vehicle Gas Tanks - Stage II;Vapor Loss w/o Controls) 

• SCC 40600402 (Petroleum and Solvent Evaporation;Transportation and Marketing of 

Petroleum Products;Filling Vehicle Gas Tanks - Stage II;Liquid Spill Loss w/o Controls) 

• SCC 40600403 (Petroleum and Solvent Evaporation;Transportation and Marketing of 

Petroleum Products;Filling Vehicle Gas Tanks - Stage II;Vapor Loss w/o Controls) 

• SCC 40600499 (Petroleum and Solvent Evaporation;Transportation and Marketing of 

Petroleum Products;Filling Vehicle Gas Tanks - Stage II;Not Classified 

We used a consistent approach across nonpt and ptnonipm to projection these gasoline stage II 

emissions. The approach involved computing VOC-specific projection factors from the NMIM 

results for onroad refueling, using ratios of future–year emissions to 2002 emissions. The 

approach accounts for three elements of refueling growth and control: (1) activity growth (due to 

VMT growth as input into NMIM), (2) emissions reductions from Stage II control programs at 

gasoline stations, and (3) emissions reductions resulting from the phase in over time of newer 

vehicles with onboard Stage II vehicle controls. We assumed that all areas with Stage II controls 

in 2002 continue to have Stage II controls in all future calendar years. 

We computed the VOC projection factors at a county-specific, annual resolution as shown 

below: 

PF[county, future year] = VOC_RFL[county, future year]/VOC_RFL[county, 2002] 

where VOC_RFL is the VOC refueling emissions for onroad sources from NMIM. 

We applied these projection factors to both nonpt and ptnonipm sector gasoline stage II sources. 

For Stage II sources in the ptnonipm sector, we attempted to apply the county-specific projection 

factors to sources which had SIC=5541 and any of the four SCC codes identified above. We did 

not intend to apply these projection factors to sources unless the SIC was 5541 in order to avoid 

inappropriately adjusting refueling at industrial plants. However, the SIC information was 

inadvertently dropped when the projection factors were applied in SMOKE. As a result, all point 

sources with the above SCCs were projected, except for situations in which no sources in the 

county had SIC=5541. If no sources in a county had SIC=5541, then the sources in that county 

did not get projected using a county-level VOC refueling ratio, because we only used the county 

ratios for counties meeting both the SCC and SIC criteria. 
56

The impact of the projection of refueling emissions for which the SIC was not 5541 is small, as 

shown Table 4-5. 

Table 4-5. Emissions for which Stage II projection factors were misapplied to 2002 

emissions 

State 

2002 ptnonipm 

VOC emissions: 

 All gas stage II 

processes (tons) 

2002 ptnonipm VOC 

emissions: 

Gas stage II processes 

with SIC= 5541 (tons) 

Percent of 2002 

ptnonipm stage II 

emissions with 

SIC=5541 

2002 emissions 

with SIC not 

equal to 5541 

(tons) * 

California 1,605 1,465 91.2% 141 

Colorado 10,620 10,343 97.4% 278 

Kentucky 190 186 97.9% 4 

North 

Carolina 10 5 53.2% 5 

Tribal Data 28 28 100.0% 0 

OVERALL 12,453 12,026 96.6% 427 

* These are the emissions for which the projection factor was misapplied 

Table 4-6 shows the 2002 and future year base emissions from nonpt (SCC 2501060100) and 

ptnonipm (SCCs 40600401, 40600402, 40600403 or 40600499) gasoline Stage II emissions. 
57

Table 4-6. Summary of Gasoline Stage II emissions in base and future years 

State 

Gas 

Stage II 

VOC 2002 

nonpt 

(tons) 

Gas 

Stage II 

VOC 2002 

ptnonipm 

(tons) 

Gas 

Stage II 

VOC 2009 

nonpt 

(tons) 

Gas 

Stage II 

VOC 2009 

ptnonipm 

(tons) 

Gas 

Stage II 

VOC 2014 

nonpt 

(tons) 

Gas 

Stage II 

VOC 2014 

ptnonipm 

(tons) 

Gas 

Stage II 

VOC 2020 

nonpt 

(tons) 

Gas Stage 

II 

VOC 2020 

ptnonipm 

(tons) 

Alabama 8454.14 0.35 4424.07 0.35 2672.87 0.35 2058.77 0.35 Alaska 869.85 440.56 284.85 232.69 Arizona 7274.86 4147.52 2716.34 2302.96 Arkansas 4874.82 2651.05 1637.63 1266.12 California 6305.46 1654.57 4958.29 1406.41 4749.46 1412.50 4964.58 1526.07 Colorado 389.43 10620.49 224.77 5856.45 145.09 3827.40 119.31 3201.51 Connecticut 620.74 488.66 451.98 455.72 Delaware 515.23 279.86 201.41 179.55 District of Columbia 104.17 71.16 61.45 61.46 Florida 20296.20 27.48 11144.60 27.48 7569.32 27.48 6321.63 27.48 Georgia 11009.57 9.00 5969.52 9.00 4158.67 9.00 3525.35 9.00 Hawaii 1446.07 749.93 451.93 350.30 Idaho 1962.14 1155.07 738.32 589.50 Illinois 8988.01 11.99 4858.77 11.99 3278.41 11.99 2794.47 11.99 Indiana 10085.06 0.04 5235.92 0.04 3292.99 0.04 2658.95 0.04 Iowa 4455.61 2719.04 1661.18 1185.14 Kansas 3709.81 0.13 1966.17 0.13 1216.80 0.13 965.28 0.13 Kentucky 5234.34 189.68 2811.89 154.20 1782.57 148.50 1438.65 157.31 Louisiana 5989.40 32.00 3106.67 28.15 1920.49 28.15 1532.46 28.15 Maine 1182.79 668.96 438.10 362.24 Maryland 3211.88 16.25 1745.82 16.10 1293.54 16.10 1163.80 16.10 Massachussetts 1521.74 7.54 982.13 7.54 855.69 7.54 860.85 7.54 Michigan 14611.55 31.28 7641.00 31.15 4716.80 31.15 3678.22 31.15 Minnesota 7953.52 4215.70 2420.42 1795.29 Mississippi 4843.45 2348.00 1362.94 1090.95 Missouri 7212.07 63.62 4087.16 62.88 2650.45 62.88 2159.25 62.88 Montana 1316.50 707.96 439.91 350.43 
58

State 

Gas 

Stage II 

VOC 2002 

nonpt 

(tons) 

Gas 

Stage II 

VOC 2002 

ptnonipm 

(tons) 

Gas 

Stage II 

VOC 2009 

nonpt 

(tons) 

Gas 

Stage II 

VOC 2009 

ptnonipm 

(tons) 

Gas 

Stage II 

VOC 2014 

nonpt 

(tons) 

Gas 

Stage II 

VOC 2014 

ptnonipm 

(tons) 

Gas 

Stage II 

VOC 2020 

nonpt 

(tons) 

Gas Stage 

II 

VOC 2020 

ptnonipm 

(tons) 

Nebraska 2496.80 1329.64 830.41 655.97 Nevada 575.71 431.08 369.47 377.03 New Hampshire 804.47 0.98 466.12 0.98 340.31 0.98 298.89 0.98 New Jersey 2414.61 0.20 1275.65 0.19 918.02 0.19 814.69 0.19 New Mexico 3139.37 1752.34 1109.15 892.66 New York 14137.71 1.35 7558.69 1.35 4938.55 1.35 3896.01 1.35 North Carolina 13233.72 9.85 6716.51 5.10 4253.41 3.41 3384.61 2.75 North Dakota 910.39 480.03 294.59 231.08 

Ohio 3863.81 16.81 2137.92 16.75 1350.95 16.75 1151.85 16.75 Oklahoma 6228.32 3288.87 2106.15 1684.52 Oregon 3674.14 2527.28 1846.14 1358.46 Pennsyvlania 9529.87 1.79 5199.12 1.79 3567.25 1.79 2950.73 1.79 Rhode Island 253.80 181.14 148.82 135.06 South Carolina 7538.41 3752.20 2340.16 1824.07 South Dakota 1071.45 560.17 346.79 270.72 Tennessee 9529.66 5956.74 3891.25 2850.29 Texas 17925.90 17.42 8840.39 17.42 6255.26 17.42 5624.26 17.42 Utah 3382.00 0.14 2217.93 0.14 1489.03 0.14 1154.85 0.14 Vermont 262.32 188.78 159.23 155.70 Virginia 9188.74 46.85 4480.18 44.86 2987.58 44.86 2398.67 44.86 Washington 4179.39 0.97 2851.10 0.97 2264.70 0.97 1899.43 0.97 West Virginia 3231.73 0.43 1483.11 0.43 933.98 0.43 703.70 0.43 Wisconsin 5687.27 0.12 2992.99 0.12 1773.68 0.12 1573.23 0.12 Wyoming 1099.70 557.27 351.06 273.73 Tribal Data 27.58 18.00 11.58 10.29 
59

4.2.4 Portable fuel container growth and control (nonpt) 

We obtained future-year VOC emissions from Portable Fuel Containers (PFCs) from inventories 

developed and modeled for EPA’s MSAT rule (EPA, 2007a and EPA, 2007b). Additional 

information on the PFC inventories can be found in Section 2.2.3, above. The future-year 

emissions reflect projected increases in fuel consumption, state programs to reduce PFC 

emissions, standards promulgated in the MSAT rule, and impacts of the Renewable Fuel 

Standard (RFS) on gasoline volatility. Future-year emissions for PFCs were available for 2010, 

2015, 2020, and 2030. In creating the inventories for the 2002 Platform, we used the 2010 and 

2015 emissions to represent 2009 and 2014, respectively. The PFCs emissions for 2020 were 

used in our platform inventories for 2020 as well as for 2030 in order to be consistent with the 

approach of using 2020 nonpt emissions for the 2030 scenario. 

Although we could have used the projected PFC inventories directly, we chose to compute 

projection factors based on future and 2002 PFC emissions by SCC, since we used this approach 

for other sources in the nonpt sector. We developed and applied the factors by State and SCC 

because differences in VOC projection factors did not vary across counties within the state. This 

is because the impacts of differences in average gasoline properties on VOC within a state were 

not accounted for in the PFC inventories. Appendix H provides the 2002 and future-year PFC 

inventories, along with the resulting projection factors for PFC VOC emissions. 

4.2.5 Stationary Source control programs/plant closures (ptnonipm, nonpt) 

We applied emissions reduction factors to the 2002 emissions for particular sources in the 

ptnonipm and nonpt sectors to reflect the impact of stationary-source control programs and plant 

closures. Our approach differed from what we did for similar sources in the 2001 Platform, 

because we did not apply controls from ozone and PM2.5 State Implementation Plans. Since the 

available SIP control data were limited to incomplete sets of controls from only four SIPs, we 

decided that it was inconsistent to apply these data when other SIPs were not represented. 

Other aspects of our future-year base controls included the following: 

• We did not include MACT rules where compliance dates were prior to 2002. 

• We included plant closures (i.e., emissions were zeroed out for future years) where 

information indicated that the plant was actually closed. However, plants projected to close 

in the future (post-2007) were not removed in the future years. 

• In addition to plant closures, we included the effects on ptnonipm sector emissions of the 

NOX SIP Call and Department of Justice Settlements and Consent Decrees. We also 

included estimated impacts of HAP standards per Section 112, 129 of the Clean Air Act on 

ptnonipm and nonpt sector emissions, based on expected CAP co-benefits to sources in these 

sectors. 

• The same reductions were applied across all years with the exception of refinery facility/SCC 

reductions in the states and pollutants listed in Table 4-7. The refinery Consent Decrees are 

the only set of controls in which some of the compliance dates are beyond 2008. Refinery 

reductions associated with compliance dates after December 31, 2008 were not applied for 

the 2009 projection. Similarly, reductions with compliance dates after December 31, 2013 
60

were not applied for the 2014 projection. The latest compliance date is in 2015; therefore all 

controls were applied for the 2020 projection. 

• We applied all of the control programs as replacement controls, which means that any 

existing percent reductions (“baseline control efficiency”) reported in the NEI were removed 

prior to the addition of the percent reductions due to these control programs. 

Table 4-7. States with post-2009 and post-2014 refinery controls 

Post-2009 refinery controls NOx SO2 PM 

California X X 

Colorado X X X 

Illinois X X X 

Louisiana X 

Mississippi X 

Montana X X X 

New Mexico X 

Ohio X X X 

Oklahoma X 

Pennsylvania X X X 

Texas X X X 

Utah X X X 

Post-2014 refinery controls 

Washington X 

After we modeled the future-year emissions, we discovered that an issue in the 2002 NEI v3 

point source baseline control efficiency caused an overestimation of PM2.5 emission reductions. 

The PM2.5 estimates developed by augmenting based on a PM10 estimate (to gap fill presumably 

missing emissions) for a source did not carry forward the baseline control efficiency associated 

with PM10. For these sources, the PM2.5 baseline control efficiency was missing. Since we

would expect similar control efficiencies for PM2.5 and PM10, the application of control programs 

for PM2.5 for these presumably already-controlled sources likely overestimated the actual 

reduction, resulting in underestimated PM2.5 in the future year inventory. We estimated the 

impact of this underestimation to be less than 1500 tons of PM2.5. To put this into perspective, 

the entire ptnonipm PM2.5 inventory contains about 370,000 and 350,000 total tons in 2002 and 

2020, respectively. Therefore, the potential over-control is less than 0.5% of ptnonipm PM2.5 in 

2020. 

Table 4-8 lists the ptnonipm and nonpt control programs and the affected pollutants. 
61

Table 4-8. Control programs applied to the stationary sources in ptnonipm and nonpt 

inventory sectors 

Control Strategies 

(Grouped by Affected Pollutants or Standard and Approach Used to 

Apply to the Inventory) 

Pollutants 

Affected 

Approach or 

Reference: 

PTNONIPM Controls 

NOX SIP Call (Phase II): 

Cement Manufacturing 

Large Boiler/Turbine Units 

Large IC Engines

NOx 1 

DOJ Settlements: plant SCC controls 

Alcoa, TX 

MOTIVA, DE

NOx, SO2 2 

Refinery Consent Decrees: plant/SCC controls NOx, PM, SO2 3 

Closures, pre-2007: plant control of 100%

Auto plants 

Pulp and Paper 

Plants closed in preparation for 2005 inventory

all 4 

Industrial Boiler/Process Heater plant/SCC controls for PM PM 5 

MACT rules, national, VOC: national applied by SCC, MACT 

Boat Manufacturing 

Polymers and Resins III (Phenolic Resins) 

Polymers and Resins IV (Phenolic Resins) 

Wood Building Products Surface Coating 

Generic MACT II: Spandex Production, Ethylene manufacture 

Large Appliances 

Miscellaneous Organic NESHAP (MON): Alkyd Resins, Chelating Agents, 

Explosives, Phthalate Plasicizers, Polyester Resins, Polymerized Vinylidene 

Chloride 

Manufacturing Nutritional Yeast 

Oil and Natural Gas 

Petroleum Refineries -Catalytic Cracking, Catalytic Reforming, & Sulfur 

Plant Units 

Pesticide Active Ingredient Production 

Publicly Owned Treatment Works 

Reinforced Plastics 

Rubber Tire Manufacturing 

Asphalt Processing & Roofing 

Combustion Sources at Kraft, Soda, and Sulfite Paper Mills 

Fabric Printing, Coating and Dyeing 

Iron & Steel Foundries 

Metal: Can, Coil 

Metal Furniture 

Miscellaneous Metal Parts & Products 

Municipal Solid Waste Landfills 

Paper and Other Web 

Plastic Parts 

Plywood and Composite Wood Products 

Wet Formed Fiberglass Production 

Wood Building Products Surface Coating 

Carbon Black Production 

Cellulose Products Manufacturing 

Cyanide Chemical Manufacturing 

VOC EPA, 2007e 
62

Control Strategies 

(Grouped by Affected Pollutants or Standard and Approach Used to 

Apply to the Inventory) 

Pollutants 

Affected 

Approach or 

Reference: 

Friction Products Manufacturing 

Leather Finishing Operations 

Miscellaneous Coating Manufacturing 

Organic Liquids Distribution (Non-Gasoline) 

Refractory Products Manufacturing 

Sites Remediation

Solid Waste Rules (Section 129d/111d) 

Hospital/Medical/Infectious Waste Incinerator Regulations NOx, PM, SO2 EPA, 2005a 

MACT rules, national, PM: 

Portland Cement Manufacturing 

Secondary Aluminum 

PM 6 

MACT rules, plant-level, VOC: 

Auto Plants VOC 7 

MACT rules, plant-level, PM & SO2: 

Lime Manufacturing PM, SO2 8 

MACT rules, plant-level, PM: 

Taconite Ore PM 9 

 

NONPT Controls 

 

Municipal Waste Landfills: project factor of 0.25 applied, VOC EPA, 2007e 

APPROACHES: 

1. Used Emission Budget Inventories report (EPA, 1999) for list of SCCs for application of controls, and for 

percent reductions (except IC Engines). Used Federal Register on Response to Court decisions (Federal 

Register, 2004) for IC Engine percent reductions and geographic applicability 

2. For ALCOA consent decree, used http:// cfpub.epa.gov/compliance/cases/index.cfm; for MOTIVA: used 

information sent by State of Delaware 

3. Used data provided by Brenda Shine, EPA, OAQPS 

4. Closures obtained from EPA sector leads; most verified using the world wide web. 

5. Used data list of plants provided by project lead from 2001-based platform; required mapping the 2001 plants 

to 2002 NEI plants due to plant id changes across inventory years 

6. Same as used in CAIR, except added SCCs appeared to be covered by the rule: both reductions based on 

preamble to final rule. (Portland Cement used a weighted average across two processes ) 

7. Percent reductions recommended and plants to apply to reduction to were based on recommendations by rule 

lead engineer, and are consistent with the reference: EPA, 2007e 

8. Percent reductions recommended are determined from the existing plant estimated baselines and estimated 

reductions as shown in the Federal Register Notice for the rule. SO2 % reduction will therefore be 

6147/30,783 = 20% and PM10 and PM2.5 reductions will both be 3786/13588 = 28% 

9. Same approach used in CAIR: FR notice estimates reductions of "PM emissions by 10,538 tpy, a reduction of 

about 62%." Used same list of plants as were identified based on tonnage and SCC from CAIR. 
63

4.3 Mobile source projections 

Onroad and nonroad mobile source emissions for each future year were generated using NMIM 

in a manner consistent with the 2002 base year NEI emissions. A complete set of the inputs for 

future year NMIM calculations are available as part of the documentation for the upcoming rule, 

"Control of Emissions of Air Pollution from Locomotive Engines and Marine CompressionIgnition Engines Less Than 30 Liters per Cylinder (Regulatory Impact Analysis)." When this 

rule is final, a database with all of the NMIM inputs will be available in the rule docket (see 

http://www.epa.gov/otaq/marine.htm#regs).

For non-NMIM generated mobile sources (i.e., aircraft, locomotives, and commercial marine 

vessels), projection factors were computed and applied to the corresponding 2002 inventories. 

These projection factors reflect the combined effects of growth and reductions from national 

control programs. 

A description of the projection approach used for each of the mobile source sectors is provided in 

the sections below. 

4.3.1 Onroad 

Onroad emissions for all of the U.S. other than California were projected for 2009, 2014, 2020 

and 2030 using NMIM. These future year emissions account for increased activity (vehicle 

miles traveled) and changes in fuels, fleet turnover, and inspection and maintenance programs 

that account for implementation of national and local regulations. 

We generated future-year VMT (input to NMIM) using Annual Energy Outlook (AEO) growth 

estimates (using 2006 AEO estimates) by vehicle type. Appendix G provides the VMT 

projection methodology. 

The NMIM future year inputs also accounted for national and some local control programs. For 

national control programs, they incorporated the expected impacts of national regulations 

promulgated prior to July 2007; these include the “Tier 2 Rule,” the “2007 Onroad Heavy-Duty 

Rule,” the Final “Mobile Source Air Toxics Rule” (MSAT Final), and the “Renewable Fuel 

Standard” (RFS). 

For the state and voluntary programs, we included the National Low Emission Vehicle Program 

(NLEV) and Ozone Transport Commission (OTC) LEV program (http://www.epa.gov/otaq/levnlev.htm) in the future year inventories. These were included based on state submission of the 

relevant external files. These programs affect northeastern states. We also modeled reformulated 

gasoline opt-in programs using state-submitted external files and EPA fuel tables. In addition, 

we assumed that all state programs existing in 2002 continued in all future calendar years. 

We included programs that might affect future VMT (e.g., public transportation, car-pooling, 

congestion pricing) only if states submitted VMT that modeled these programs. We do not have 

documentation from the states describing whether or not such programs were incorporated in 

states’ VMT estimates. 
64

We did not include state regulations or voluntary programs that encourage no refueling or 

evening refueling on Ozone Action Days. We also did not include diesel retrofit and anti-idling 

programs affecting school buses and diesel trucks. 

We did not use NMIM to generate future year onroad emissions for California, because the 2002 

base year estimates were based on EMFAC2002, as submitted as part of the NEI by CARB. For 

California, we chose an approach that would maintain consistency between the base year and 

future year emissions. This approach involved computing projection factors from a consistent 

set of future and 2002-year data based on the EMFAC2007 model provided by CARB. We 

generated projection factors by dividing the EMFAC2007-based emissions for the future years 

by the EMFAC2007-based emissions for 2002. We then applied the projection factors to the 

2002 emission estimates from on EMFAC2002. Due to time considerations, we were unable to 

incorporate the 2002 emissions from EMFAC2007 directly into the onroad sector of the 2002 

Platform. 

Where possible, we applied the projection factors at the county, SCC, mode and pollutant level. 

This could not be done where the 2002 emissions were zero, and the future emissions were 

greater than zero. In this case we applied more aggregated ratios at the state, SCC and pollutant 

level. We also applied onroad-total, state-level ratios to project exhaust PM10 and exhaust PM2.5. 

We applied these projection factors to the 2002 inventory uniformly, such that all onroad SCCs 

and all counties had the same projection factors for each of these pollutants. The reason we 

computed an aggregated factor was to generate a consistent statewide future-to-base trend for 

these pollutants as had been estimated by CARB using EMFAC2007. When we applied 

projection factors for these pollutants at the SCC level (i.e., by vehicle type), the onroad 

statewide trend we computed was different from the CARB trend. We determined this to be due 

to the difference in the distribution of emissions by vehicle class between the EMFAC2007 and 

EMFAC2002 inventories for 2002. 

4.3.2 Nonroad 

With the exception of California, U.S. emissions for the nonroad sector (defined as the 

equipment types covered by NMIM) were projected for 2009, 2014, 2020 and 2030 using 

NMIM. These future-year emissions account for increases in activity (based on NONROAD 

model default growth estimates) and changes in fuels and engines that reflect implementation of 

national regulations and local control programs. 

The national regulations incorporated in the modeling are those promulgated prior to July 2007, 

and beginning about 1990. Recent rules include: 

• “Clean Air Nonroad Diesel Final Rule - Tier 4” (http://www.epa.gov/nonroaddiesel/2004fr.htm ), published June 29, 2004, and, 

• Control of Emissions From Nonroad Large Spark-Ignition Engines, and Recreational 

Engines (Marine and Land-Based), November 8, 2002 (“Pentathalon Rule”). 

Not included is the “Proposed Emission Standards for New Nonroad Spark-ignition Engines, 

Equipment, and Vessels” (“Bond Rule”), proposed April 17, 2007, or the “Proposal for More 

Stringent Emissions Standards for Locomotives and Marine Compression-Ignition Engines”, 

published April 3, 2007. We have not included voluntary programs such as programs 
65

encouraging either no refueling or evening refueling on Ozone Action Days and diesel retrofit 

programs. 

The NMIM was not used to generate future-year nonroad emissions for California, other than for 

NH3. We used the NMIM for California future nonroad NH3 emissions because CARB did not 

provide these data for any nonroad vehicle types. As we did for onroad emissions, we chose a 

projection approach that would maintain consistency between the base year and future-year 

emissions for nonroad emissions in California. We divided the OFFROAD2007 future-year 

emissions by the OFFROAD2007 estimates for 2002. We then applied these projection factors 

to the 2002 estimates in the 2002 Platform which were based on an earlier version of California’s 

nonroad model. As in the case of the California onroad emissions, we were unable to 

incorporate the 2002 emissions from OFFROAD2007 directly into nonroad sector of the 2002 

Platform. 

Where possible, we applied the projection factors using at the county, SCC, mode, and pollutant 

level. In many cases, the SCCs used in the OFFROAD2007 model were more detailed than 

those in the earlier California data, and we therefore developed and applied projection factors at 

a more aggregate level. Table 4-9 shows the aggregation of California SCCs that we needed to 

make to apply to the more general SCCs in the 2002 NEI. 

Table 4-9. SCC Aggregation approach for computing projection factors for California 

nonroad emissions. 

SCC in NEI Description Aggregated SCCs used for projection factor 

2260002000 

Off-highway Vehicle Gasoline, 2-Stroke;Construction and 

Mining Equipment;Total 

use 2265002***(sum across all construction and 

mining equip.) 

2260003000 

Off-highway Vehicle Gasoline, 2-Stroke;Industrial 

Equipment;Total use 2265003***(sum across all indust equip.) 

2260004000 

Off-highway Vehicle Gasoline, 2-Stroke;Lawn and Garden 

Equipment;All 

use 2265004*** + 2260004*** (sum across all gas 

lawn and garden.) 

2260006000 

Off-highway Vehicle Gasoline, 2-Stroke;Commercial 

Equipment;Total 

use 2265006*** + (sum across all gas commercial 

equip) 

2260007000 

Off-highway Vehicle Gasoline, 2-Stroke;Logging 

Equipment;Total use 2265007*** + (sum across all gas logging equip)

2265001020 

Off-highway Vehicle Gasoline, 4-Stroke;Recreational

Equipment;Snowmobiles use 2 stroke snowmobiles (2260001020) 

2265002000 

Off-highway Vehicle Gasoline, 4-Stroke;Construction and 

Mining Equipment;Total 

use 2265002***(sum across all construction and 

mining equip.) 

2265003000 

Off-highway Vehicle Gasoline, 4-Stroke;Industrial 

Equipment;Total use 2265003***(sum across all indust equip.) 

2265004000 

Off-highway Vehicle Gasoline, 4-Stroke;Lawn and Garden 

Equipment;All 

use 2265004*** + 2260004*** (sum across all gas 

lawn and garden.) 

2265005000 

Off-highway Vehicle Gasoline, 4-Stroke;Agricultural

Equipment;Total use 2265005***(sum across all agriculture equip.) 

2265006000 

Off-highway Vehicle Gasoline, 4-Stroke;Commercial 

Equipment;Total 

use 2265006*** + (sum across all gas commercial 

equip) 

2265007000 

Off-highway Vehicle Gasoline, 4-Stroke;Logging 

Equipment;Total use 2265007*** + (sum across all gas logging equip)

2265008000 

Off-highway Vehicle Gasoline, 4-Stroke;Airport Ground 

Support Equipment;Total use 2265008005 

2268003000 CNG;Industrial Equipment;All use 2268003***(sum across all indust equip.) 
66

SCC in NEI Description Aggregated SCCs used for projection factor 

2268006000 CNG;Commercial Equipment;All use 2268003***(sum across all commercia equip.) 

2268008000 CNG;Airport Ground Support Equipment;All use 2268008005 airport ground support equip CNG 

2270002000 

Off-highway Vehicle Diesel;Construction and Mining 

Equipment;Total 

use 2270002***(sum across all construction and 

mining equip.) 

2270003000 Off-highway Vehicle Diesel;Industrial Equipment;Total use 2270003***(sum across all indust equip.) 

2270003060 

Off-highway Vehicle Diesel;Industrial 

Equipment;ACRefrigeration use 2270003***(sum across all indust equip.) 

2270004000 

Off-highway Vehicle Diesel;Lawn and Garden 

Equipment;All 

use 2270004*** (sum across all gas lawn and garden 

diesel.) 

2270005000 Off-highway Vehicle Diesel;Agricultural Equipment;Total 

use 2270005***(sum across all agriculture equip. 

diesel) 

2270006000 Off-highway Vehicle Diesel;Commercial Equipment;Total 

use 2270006*** + (sum across all gas commercial 

equip diesel) 

2270007000 Off-highway Vehicle Diesel;Logging Equipment;Total use 2270007015 

2270008000 

Off-highway Vehicle Diesel;Airport Ground Support 

Equipment;Total use 2270008005 

2282005000 Pleasure Craft;Gasoline 2-Stroke;Total 

use 2282005*** (sum across 2stroke gas 

pleasurecraft) 

2282010000 Pleasure Craft;Gasoline 4-Stroke;Total use 2282010005 

2282020000 Pleasure Craft;Diesel;Total use 228202**** (sum of diesel pleasure craft) 

There were no corresponding OFFROAD2007 emissions for SO2 for compressed natural gas 

fueled equipment; therefore, the future-year emissions remained at the same levels as in the 2002 

NEI. 

4.3.3 Aircraft (alm, ptnonipm) 

We projected aircraft emissions based solely on activity growth using on data on itinerant (ITN) 

operations at airports. The ITN operations are defined as aircraft take-offs whereby the aircraft 

leaves the airport vicinity and lands at another airport, or aircraft landings whereby the aircraft 

has arrived from outside the airport vicinity. Projected ITN information is available from the 

Federal Aviation Administration’s (FAA) Terminal Area Forecast (TAF) System 

(http://www.apo.data.faa.gov/main/taf.asp (publication date February 2006). This information 

is available for approximately 3300 individual airports, for all years up to 2025. We aggregated 

and applied this information at the national level by summing the airport-specific (U.S. airports 

only) ITN operations to national totals by year and by aircraft operation, for each of the four 

operations: commercial, general, air taxi, military. We computed growth factors for each 

operation type by dividing future-year ITN by 2002-year ITN. We assigned factors to inventory 

SCCs based on the operation. We used the commercial aircraft growth factor for the broad SCC 

representing all aircraft types and operations (SCC 2275000000). 

The methods that the FAA used for developing the ITN data in the TAF are documented in 

http://www.faa.gov/data_statistics/aviation/taf_reports/media/TAF_Summary_Report_FY2005-

2025.pdf. 

Because the TAF estimates go out to the year 2025, we used the emissions estimated for 2025 in 

our 2030 scenario. Table 4-10 provides the national level growth factors for aircraft. 
67

Table 4-10. Factors used to grow aircraft emissions 

Year 

Commercial aircraft 

(SCC 2275020000) 

Air Taxi 

(SCC 2275060000) 

General aviation 

(SCC 2275050000) 

Military 

(SCC 2275010000) 

2030 1.482 1.646 1.185 0.965 

2020 1.329 1.502 1.130 0.964 

2014 1.168 1.351 1.070 0.963 

2009 1.047 1.240 1.000 0.963 

Because the above SCCs are also found in the point source inventory for a few states (as 

discussed in Section 2.5.3), we also applied the growth factors above to aircraft emissions for 

these SCCs in the ptnonipm sector. We also applied the above growth factors to four additional 

point source SCCs, listed in Table 4-119. 

Table 4-11. Point Source SCCs representing aircraft emissions 

SCC SCC description 

Projection Factor 

based on... Comment 

27501015 

Internal Combustion Engines;Fixed Wing 

Aircraft L & TO Exhaust;Military;Jet 

Engine: JP-5 

Military aircraft 

2275010000 

SCC is in 2002 NEI for FIPS = 

49045, 06059, 06065 06037, 

12033 

27502011 

Internal Combustion Engines;Fixed Wing 

Aircraft L & TO Exhaust;Commercial;Jet 

Engine: Jet A 

Commercial aircraft 

2275020000 

SCC is in 2002 NEI for FIPS = 

06037, 06059, 06065, 06071 

27505001 

Internal Combustion Engines;Fixed Wing 

Aircraft L & TO Exhaust;Civil;Piston 

Engine: Aviation Gas 

General aviation 

2275050000 

SCC is in 2002 NEI for FIPS = 

06037, 06059, 06065, 06071, 

88206 (tribal) 

27505011 

Internal Combustion Engines;Fixed Wing 

Aircraft L & TO Exhaust;Civil;Jet Engine: 

Jet A 

General aviation 

2275050000 

SCC is in 2002 NEI for FIPS = 

06037, 06059, 06065, 06071, 

88206 (tribal) 

We did not apply growth factors to any point sources with SCC 27602011 (Internal Combustion 

Engines; Rotary Wing Aircraft L & TO Exhaust; Commercial; Jet Engine: Jet A) because the 

plant names associated with these point sources appeared to represent industrial facilities rather 

than airports. This SCC is only in one county, Santa Barbara, California (State/County FIPS 

06083). 

None of our aircraft emission projections account for any control programs. We considered the 

NOX standard adopted by the International Civil Aviation Organization’s (ICAO) Committee on 

Aviation Environmental Protection (CAEP) in February 2004, which is expected to reduce NOX

by approximately 2% in 2015 and 3% in 2020. However, this rule has not yet been adopted as 

an EPA (or U.S.) rule; therefore, the effects of this rule were not included in the future-year 

emissions projections. 

 

9

 Aircraft emissions were submitted as point sources by California, Illinois, and Minnesota, as described in section 

2.5.3. 
68

4.3.4 Locomotives (alm) 

Future locomotive emissions were calculated using projection factors which were computed 

based on national annual summaries of locomotive emissions in 2002 and future years, as 

provided by OTAQ. Appendix J provides these national annual summaries. 

The future-year locomotive emissions account for increased fuel consumption based on Energy 

Information Administration (EIA) fuel consumption projections for freight rail (EPA, 2007d), 

and emissions reductions resulting from emissions standards prior to the Locomotive-Marine 

rule, which was proposed April 7, 2007. These standards are: 

• "Clean Air Nonroad Diesel Final Rule - Tier 4" (http://www.epa.gov/nonroaddiesel/2004fr.htm), published June 29, 2004. This rule lowered diesel sulfur content. 

• Locomotive Emissions Final Rulemaking, signed December 17, 1997. 

Voluntary retrofits under the National Clean Diesel Campaign 

(http://www.epa.gov/otaq/diesel/index.htm) are not included in our projections. 

4.3.5 Commercial marine vessels (alm) 

The 2002 commercial marine vessel (CMV) emissions include separate estimates (via separate 

SCCs) for ports versus underway emissions (underway emissions occur while the ship is 

traveling along shipping lanes). 

Future emissions diesel and residual-fueled CMVs were calculated using national level 

projection factors computed from national, annual U.S. emission summaries for CMV by fuel 

type (summed across ports and underway) for 2002 and each of the future years. These data 

account for both growth and the effects of controls. Appendix J provides these national annual 

summaries. 

Emissions growth was based on activity data, as follows: 

• For diesel-fueled vessels, growth is based on EIA fuel consumption projections for 

domestic shipping. 

• For residual-fueled vessels, growth is based freight tonnage as well as future changes in 

fleet makeup. 

Emissions reductions from the following control programs were included in the development of 

the projection factors: 

• Clean Air Nonroad Diesel Final Rule - Tier 4" (http://www.epa.gov/nonroaddiesel/2004fr.htm), published June 29, 2004. This rule lowered diesel sulfur content. 

• Emission Standards for Commercial Marine Diesel Engines (published December 29, 

1999). 

• Tier 1 Marine Diesel Engines—Final Emission Standards (published February 28, 2003). 

Note that we used the same projection factor for both port and underway CMV emissions. 

The development of the future-year CMV national, annual inventories is described in more detail 

in the regulatory impact analysis for the Proposed Locomotive and Marine Rule (EPA, 2007e). 
69

The future year data do not include emissions for gasoline-powered commercial marine vessels. 

Since the 2002 emissions for these vessels were relatively low, we used the 2002 emissions for 

each of the future year scenarios (i.e., projection factor of 1). For residual-fueled CMV, the 

future year data include emissions for all CAPs except NH3. Since NH3 emissions from residualfueled CMV are thought to be based on the amount of fuel consumed, similar to SO2 from 

residual-fueled CMV, the projection factor for SO2 was used to project NH3. 

4.4 Canada, Mexico, and Offshore sources (othar, othon, othpt) 

Future-year inventories were not available for Mexico or offshore sources. As a result, all 

future-year scenarios contain the same inventories as were used for 2002 for Mexico and 

offshore sources. 

For Canada, we used future year inventories supplied by Environment Canada for 2010, 2015, 

2020, and 2030. These are the same inventories as were used in the 2001 Platform. We used the 

2010 and 2015 emissions for our 2009 and 2014 scenarios; the 2020 emissions were used in both 

the 2020 and 2030 scenarios. The future-year Canadian emissions are consistent with the base 

year emissions, as described in Section 2.6. 
70

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