## 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 an d Analysis Groupii 

## 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 P OINT SOURCES ( PTIPM AND PTNONIPM ).................................................. ...........................................4

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

................................................... .............................................5

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

................................................... ......................................8

2.2 2002 N ONPOINT 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 F IRES ( 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 B IOGENIC SOURCES ( BIOG )..................................................

................................................... ...................22

2.5 2002 M OBILE 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 c ommercial marine (alm)............................. ......25

2.6 E MISSIONS FROM C ANADA

, M EXICO AND O FFSHORE D RILLING P LATFORMS ( OTHPT

, OTHAR

, OTHON ) ....26

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

................................................... ............................27

3.1 T HE SMOKE MODELING SYSTEM

................................................... ................................................... ........28

3.2 K EY 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 E MISSIONS 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 S TATIONARY S OURCE P ROJECTIONS : IPM SECTOR ( PTIPM ).................................................. ....................52

4.2 S TATIONARY S OURCE P ROJECTIONS : 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, ptnoni pm) ................................................ ....................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 M OBILE 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 C ANADA

, M EXICO

, AND O FFSHORE SOURCES ( OTHAR

, OTHON

, OTHPT ) .................................................. ..69iii

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 emissi ons factors

NEEDS National Electric Energy Database System

NEI National Emission Inventory

NESHAP National Emission Standards for Hazardous Air Pollu tants

NH 3 Ammonia

NMIM National Mobile Inventory Model

NONROAD OTAQ’s model for estimation of nonroad mobile emiss ions

NO X 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 co ntrols

PFC Portable Fuel Container

PM 2.5 Particulate matter less than or equal to 2.5 micron s
PM 10 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

SO 2 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 Partnershipiv 

## List of Tables

Table 2-1. Platform sectors used in emissions mode ling for the CAP 2002 Platform .................. 3 Table 2-2. Airport Ground Support Equipment Emissi ons in ptnonipm (double counted)........... 8

Table 2-3

. SCCs in the afdust platform sector..............

................................................... ........... 10 Table 2-4. Summary of speciated PM 2.5 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 em issions with 2002-specific fire (sum of ptfire and nonptfire) emissions ....................

................................................... ...................... 20 Table 2-13. Magnitude of error in California month ly nonroad emission files.......................... . 25 Table 2-14. SCCs extracted for the aircraft, locom otive and commercial marine (alm) sector .. 26 Table 2-15. Summary of the othpt, othar and othon sectors in the 2002 Platform...................... 2 7 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 CB0 5.................................................. ....... 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 Co ntainer Emission Categories....................... 3 8 Table 3-7. Gasoline-related speciation profiles us ed for 2002 ........................................ ............ 42 Table 3-8. Gasoline-related speciation profiles us ed 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 Urba n Roadways: 2002 Platform vs 2001

Platform...........................................

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

................................................... ........... 46 Table 3-12. Temporal Profile Assignments for Porta ble 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 ref inery controls ..................................... ......... 60 Table 4-8. Control programs applied to the station ary sources in ptnonipm and nonpt inventory

sectors ............................................

................................................... .................................... 61v

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

emissions..........................................

................................................... .................................. 65 Table 4-10. Factors used to grow aircraft emission s .................................................. ................. 67 Table 4-11. Point Source SCCs representing aircraf t emissions ........................................ ......... 67



## List of Figures

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

................................................... ..................... 30 Figure 3-2. Chemical Speciation Approach Used for t he 2002-based Platform........................... 32 Figure 3-3. Mexican States included in the 36 km Mo deling Domain...................................... ... 40



## List of Appendices

APPENDIX A: Development of a 2002 National Prescri bed Fire Inventory APPENDIX B: Summary of Surrogate Assignments to Em ission Sources APPENDIX C: Summary of Speciation Profile Assignme nts to U.S. Emission Sources

APPENDIX D: State-Sector Emissions Summaries for 2 002 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 Growt h factors APPENDIX G: Description of VMT growth approach APPENDIX H: Summary of Portable Fuel Container (PF C) Inventories and Resulting Projection Factors APPENDIX I: Summary of VMT for 2002 and Projected Years

APPENDIX J: National Inventories of Commercial Mar ine Vessel and Locomotive Emissions for 2002 and Projection Years used to Develop Proje ction Factors1



## 1 Introduction

The U.S. Environmental Protection Agency (EPA), her eto referred to as “we,” has developed a 2002-based air quality modeling platform. This doc ument describes the emissions inventory and emissions modeling for this platform for Criteria A ir Pollutants (CAPs). This is the 2002 Platform version 3 (v3), because the emission inven tories 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 P latform.” This document describes the approach and data used to produce the emission inpu ts to the air quality model. The air quality modeling, meteorological inputs and boundary condit ions will be described in separate volumes of a 2002 Platform report, which, when completed, w ill 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 separ ate volume at the above address.

The 2002 Platform for CAPs uses the Community Multi scale Air Quality (CMAQ) model ( http://www.epa.gov/AMD/CMAQ/ ) for the purposes of modeling ozone (O3) and parti culate matter (PM). The version of CMAQ we used requires hourly and gridded emissions of species from the following inventory pollutants: carbon mo noxide (CO), nitrogen oxides (NO X ), volatile organic compounds (VOC), sulfur dioxide (SO 2 ), ammonia (NH 3 ), particulate matter less than or equal to 10 microns (PM 10 ), and individual component species for particulate matter less than or equal to 2.5 microns (PM 2.5 ).

The effort to create the emission inputs for the 20 02 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 fil es 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) mod eling 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 2 002 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 a nd emissions modeling data from EPA’s 2001 Platform, which was most recently developed fo r 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 Platfor m was used for the Clean Air Interstate Rule Analysis (EPA, 2005a), referred to here as “CA IR.” This document references emissions documentation for the PM NAAQS and CAIR and highlig hts the similarities and differences

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

This volume contains five sections and several appe ndices. Section 2 describes the 2002 inventories input to SMOKE. Section 3 describes th e emissions modeling and the ancillary files

used with the emission inventories. Section 4 desc ribes the development of the projection year inventories. Section 5 provides references. Appen dices A through J provide additional details about specific technical methods and emissions data summaries.

Electronic copies of the data used with SMOKE for t he 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.” Thi s 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 t he 2002 National Emission Inventory (NEI), which includes emissions of CO, NO X , VOC, SO 2 , NH 3 , PM 10 , and PM 2.5 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 earlie r, this multi-pollutant component will be documented in a separate volume of the 2002 Platfor m 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 inventorie s 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 (form erly called “stationary area”) sources, point sources, nonroad mobile sources, onroad mobile sour ces, and fires. The fires portion of the inventory includes emissions from wildfires and pre scribed burning computed as hour-specific point sources. For purposes of preparing the CMAQ- ready emissions, we split the 2002 emissions inventory into several additional “platfo rm” sectors for use in emissions modeling, and we added biogenic emissions and emissions from sour ces other than the NEI such as the Canadian, Mexican and offshore inventories. The si gnificance 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. Th e final merge program combines the sectorspecific gridded, speciated and temporalized emissi ons 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 i n the SMOKE modeling scripts and inventory file names, and throughout the remainder of this do cument. We did not use all sectors for all modeling cases; in particular, we used the ptfire a nd 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 r ationale for using average fires in the 2002 base case and future-year cases rather than the 200 2 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 SMO KE

IPM sector: ptipm Point NEI point source EGUs mapped to the Integrate d Planning Model (IPM) model using the National Electric Energ y Database System (NEEDS) database. Hourly files for continuous emission monitoring (CEM) sources are in cluded only for the 2002 evaluation case. Day-specific em issions for non-CEM sources created for input into SMOKE.

Non-IPM sector: ptnonipm Point All NEI point source records not matched to t he ptipm sector,

annual resolution.

Point source fire

sector: ptfire Fires Point source day-specific wildfires and presc ribed fires for 2002. This sector used only for the 2002 evaluatio n case. Nonpt fire

sector: nonptfire Fires and

Nonpoint Prescribed fires for 2002 for which day-specific da ta were not

available, county and annual resolution. This sect or used only

for the 2002 evaluation case. Average-fire

sector: avefire NA Average-year wildfire and prescribed fire emissi ons derived from the 2001 Platform avefire sector, county and a nnual resolution. Used for the 2002 base year and the fu ture base model runs, but not for the model evaluation case. Agricultural

sector: ag Nonpoint NH 3 emissions from NEI nonpoint livestock and fertiliz er application sources, county and annual resolution. Area fugitive

dust sector: afdust Nonpoint PM 10 and PM 2.5 from fugitive dust sources from the NEI nonpoint inventory (e.g., building construction, ro ad construction, paved roads, unpaved roads, agricultu ral dust), county and annual resolution. Remaining nonpoint sector:

nonpt Nonpoint All nonpoint sources not otherwise include d 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 tha n for California. Monthly emissions for California c reated using annual emissions submitted by the California Air Re sources Board (CARB) for the 2002 NEI.
Aircraft, locomotive,

marine: alm Mobile:

Nonroad Aircraft, locomotive, commercial marine vessel emis sions

sources, county and annual resolution.4

PLATFORM

SECTOR 2002 NEI

SECTOR Description and resolution of the data input to SMO KE

Onroad: onroad Mobile:

onroad Monthly onroad emissions from NMIM using MOBILE6,

other than for California. Monthly emissions for C alifornia created using annual emissions submitted by CARB fo r the

2002 NEI. Biogenic: biog NA Hour-specific, grid cell-specific emissions gene rated from the BEIS3.13 model (includes emissions in Canada and Me xico). Other point

sources not from

the NEI: othpt NA Point sources from Canada’s 2000 inventory, Mexi co’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 (municip io resolution) nonpoint and nonroad mobile inventories , annual

resolution.

Other onroad

sources not from

the NEI: othon NA Canada (province resolution) and Mexico (municip io resolution) onroad mobile inventories, annual resol ution.

Annual emission summaries for 2002 and the future y ears 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 emi ssions). 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 “Dat a Files” (see “2002emis”directory). The “readme” file provided indicates the particular zip ped 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 differe nt sectors depending upon the availability of reference information for the data, the degree of c hanges or manipulation of the data needed for preparing it for input to SMOKE, and errors discove red after emissions modeling was completed.



### 2.1 2002 Point sources (ptipm and ptnonipm)

Point sources are sources of emissions for which sp ecific geographic coordinates (e.g., latitude/longitude) are specified, as in the case o f an individual facility. A facility may have multiple emission points, which may be characterize d as units such as boilers, reactors, spray booths, kilns, etc. A unit may have multiple proce sses (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-sp ecific 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 sour ce 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 Fede ral 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 source s 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 par ameters 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 containe d 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 follow ing subsections.



### 2.1.1 IPM sector (ptipm)

This sector contains emissions from EGUs in the 200 2 NEI that we were able match to the 2006 NEEDS database ( http://www.epa.gov/airmarkets/progsregs/epa-ipm/ind ex.html ), which is used by the IPM, version 3.0. The IPM model provides fu ture 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 th e 2002 EGU sources and future year EGU emissions for sources which are forecasted by IPM a nd (2) avoid double counting in projecting point source emissions.

The 2002 NEI point source inventory contains emissi ons 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 ap plying 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 t hat are not. This procedure prevents doublecounting or dropping significant emissions in creat ing 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 N EI and the NEEDS database to ensure that all significant EGUs have been captured in the matc hing process.

Methodology to split the EGU from the non-EGU sourc es

Because the IPM v3.0 units are based on the 2006 NE EDS database, we also used this NEEDS database to identify the set of EGUs in the 2002 NE I point source data to assign to the ptipm6

sector. Because of the inconsistencies in identifi cation information for EGU units in the various available data sets, we performed an extensive anal ysis to link the NEEDS units to the NEI for the purpose of splitting the 2002 NEI file into pti pm and ptnonipm sectors. The available data sets include the 2006 NEEDS, the EPA’s Clean Air Ma rkets 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 w here 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 i n the 2002 NEI, due to uncertainties in

source identification and inconsistencies in the wa y a unit is defined between the NEI and CAMD datasets. In addition, the NEEDS database inc ludes a larger universe of many smaller emitting EGUs, which are not included in the CAMD h ourly CEM programs.

The first step in the process to link the NEEDS uni ts to the NEI was to identify all sources in the NEI that might potentially be IPM/NEEDS units. Thi s included any sources in the NEI with an ORIS Plant ID or Boiler ID or any facilities or uni ts 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, suc h as county) for any NEEDS units with significant generating capacities that were not alr eady matched to the NEI. We performed the manual search task to account for any large NEEDS g enerators that may be a cogeneration unit located within an industrial (not a primarily elect ric generating station) facility.

We then built a table of CAP emissions totals for a ll emission units identified in the previous steps to perform the actual tagging of units as “pt ipm” 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 ID s to the IDs in the NEEDS database (note that we only included NEEDS units wi th 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 N O X and SO 2 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 emit ter 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 NO X and SO 2 emissions at these units were low emissions, to gi ve further confidence to our ptnonipm assignment. For example, any cooling towe rs, coal storage pile fugitives, or mining operations at a facility with EGU boilers we re 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 sam e reason.7

(4) We further reviewed the NEI units that remained una ssigned after the above steps to identify those units with the largest NO X 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 larg e emissions and clear indications that they were EGUs did not appear in the NEEDS database, pre sumably because they had shut down after

2002 and before the 2006 date-of-record used for NE EDS. These units were included in the ptipm sector, so that their 2002 emissions would no t be projected for future years as part of the ptnonipm sector. In addition to the industrial cog eneration units already matched to NEEDS, we found a few large NEI emitters with Industrial Proc ess-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 determi ned two other such large emitting units with

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

During the review of the industrial cogeneration un its in NEEDS, we could sometimes identify the associated NEI facility, but not a specific emi ssions unit at that facility matching a NEEDS unit. In such cases, the NEEDS unit was excluded f rom the future-year ptipm sector emissions, and the entire NEI facility was included in the ptn onipm sector.

Creation of temporally resolved emissions for the p tipm sector

Another reason we separated out the ptipm sources w as due to the difference in the temporal resolution of the data input to SMOKE. The ptipm s ector 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 website 2 . The SMOKE modeling system matches the ORIS facil ity

and Boiler IDs in the NEI ORL file to the same fiel ds in the CEM data. This allowed us to use the hourly SO 2 and NO X 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 po llutants are not available with the hourly

CEM.

For sources not matching the CEM data (“non-CEM” so urces), we computed daily emissions from the NEI annual emissions using a standard quer y 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 fac tors, 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 dev elopment of these diurnal ptipm-specific profiles, which are considered ancillary data for S MOKE, is described in Section 3.3.3.

2 http://camddataandmaps.epa.gov/gdm/index.cfm?fusea ction=prepackaged.progressresults_smoke8

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) sector 3 . The ptnonipm sector contains fugitive dust PM em issions from vehicular traffic on paved or unpaved roads at an i ndustrial facility or coal handling at a coal

mine 4 . Prior to input to SMOKE, we adjusted the fugitiv e dust PM emissions by applying county-specific fugitive dust transportable fractio n factors (less than 1). This is discussed further

in Section 2.2.1.

For some geographic areas, some of the sources in t he ptnonipm sector belong to source categories that are contained in other sectors. Th is occurs in the inventory when states, tribes or local programs report certain inventory emissions a s point sources because they have specific geographic coordinates for these sources. They mayuse point source SCCs (8-digit) or nonpoint, onroad or nonroad (10-digit) SCCs. In the 2 002 NEI, examples of these types of sources

include: onroad vehicular travel at a ski resort i n California, equipment emissions at ski resort in California, airport ground support emissions in Min nesota, and aircraft emissions, gas stations and livestock (i.e., animal husbandry) in a number of locations.

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

were double counted with those in other sectors. E xcept for the airport ground support emissions in Minnesota (shown in Table 2-2), we did not doubl e count any other emissions. The doublecounted airport ground support emissions are very s mall, representing less than 0.05 % of the ptnonipm sector nationally (less than 1% in Minneso ta) for all pollutants other than CO. The percent double counted for CO for the ptnonipm sect or is 0.24% nationally (17% in Minnesota). Considering all sectors, the double counted CO is o nly 0.38% of CO emissions in Minnesota.

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

VOC [tons/yr] NO X

[tons/yr] CO

[tons/yr] NH3

[tons/yr] SO 2

[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 N EI. 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 emis sions data which are included in a separate sector, as discussed in

section 2.3.1.

4 Point source fugitive dust emissions, which represe nt a very small amount of PM, were treated as a sep arate 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 pr ocessing since there are no spatial surrogates for tribal data in the 2002 Platform.

The documentation for the nonpoint sector of the 20 02 NEI is available at: http://www.epa.gov/ttn/chief/net/2002inventory.html

In the rest of this section, we describe in more de tail 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 conta ins PM 10 and PM 2.5 emission estimates for 2002 NEI nonpoint SCCs identified as dust sources b y inventory experts. This sector is separated from other nonpoint sectors to make it ea sier 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 f ugitive dust impacts in the grid modeling as compared to ambient samples. Categories included i n this sector are paved roads, unpaved roads and airstrips, construction (residential, industria l, road and total) agriculture production and all o f the mining 10-digit SCCs beginning with the digits “2325.” It does not include fugitive dust from grain elevators because these are elevated sou rces.

We created the afdust sector from the 2002 NEI base d on SCCs and pollutant codes (i.e., PM 10 and PM 2.5 ) that are considered “fugitive”. A complete list of all possible fugitive dust SCCs (including both 8-digit point source SCCs and 10-di git nonpoint SCCs) is provided at: http://www.epa.gov/ttn/chief/emch/invent/tf_scc_lis t2002nei_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 Airstrip s;Total
2294000000 Mobile Sources;Paved Roads;All Paved Roa ds;Total: Fugitives
2296000000 Mobile Sources;Unpaved Roads;All Unpaved Roads;Total: Fugitives
2296005000 Mobile Sources;Unpaved Roads;Public Unpa ved Roads;Total: Fugitives
2296010000 Mobile Sources;Unpaved Roads;Industrial Unpaved Roads;Total: Fugitives
2311000000 Industrial Processes;Construction: SIC 1 5 - 17;All Processes;Total
2311010000 Industrial Processes;Construction: SIC 1 5 - 17;Residential;Total
2311010040 Industrial Processes;Construction: SIC 1 5 - 17;Residential;Ground Excavations
2311010070 Industrial Processes;Construction: SIC 1 5 - 17;Residential;Vehicle Traffic
2311020000 Industrial Processes;Construction: SIC 1 5 - 17;Industrial/Commercial/Institutional;Total 2311020040 Industrial Processes;Construction: SIC 1 5 - 17;Industrial/Commercial/Institutional;Ground

Excavations
2311030000 Industrial Processes;Construction: SIC 1 5 - 17;Road Construction;Total
2325000000 Industrial Processes;Mining and Quarryin g: SIC 14;All Processes;Total
2801000000 Miscellaneous Area Sources;Agriculture P roduction - Crops;Agriculture - Crops;Total
2801000002 Miscellaneous Area Sources;Agriculture P roduction - Crops;Agriculture - Crops;Planting
2801000003 Miscellaneous Area Sources;Agriculture P roduction - Crops;Agriculture - Crops;Tilling
2801000005 Miscellaneous Area Sources;Agriculture P roduction - Crops;Agriculture - Crops;Harvesting
2801000007 Miscellaneous Area Sources;Agriculture P roduction - Crops;Agriculture - Crops;Loading
2805000000 Miscellaneous Area Sources;Agriculture P roduction - Livestock;Agriculture - Livestock;Total 2805001000 Miscellaneous Area Sources;Agriculture P roduction - 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 fractio ns by county (all afdust SCCs in the same county would receive the same factor). The approac h used to calculate the county fractions and

the fractions themselves are available at: http://www.epa.gov/ttn/chief/emch/invent/transporta ble_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/transportf ractions052506rev.xls

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

for PM 2.5 were inadvertently not applied; therefore, the PM 2.5 emissions from this sector are overestimated in the current version (v3) of the 20 02 Platform.

Table 2-4 shows the differences in PM 2.5 and its component species (defined in 3.3.2) acros s all sectors with and without application of the transpo rtable 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 PM 2.5 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 NH 3 “ag” sector is comprised of livestock and agricult ural 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 (dry lots);Confinement

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

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

2805002000 Beef cattle production composite;Not Elsewhere Clas sified

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

2805007100 Poultry production - layers with dry manure managem ent systems;Confinement

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

2805008100 Poultry production - layers with wet manure managem ent systems;Confinement12

SCC SCC Description*

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

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

2805009100 Poultry production - broilers;Confinement

2805009200 Poultry production - broilers;Manure handling and s torage

2805009300 Poultry production - broilers;Land application of m anure
2805010100 Poultry production - turkeys;Confinement

2805010200 Poultry production - turkeys;Manure handling and st orage
2805010300 Poultry production - turkeys;Land application of ma nure
2805018000 Dairy cattle composite;Not Elsewhere Classified

2805019100 Dairy cattle - flush dairy;Confinement

2805019200 Dairy cattle - flush dairy;Manure handling and stor age
2805019300 Dairy cattle - flush dairy;Land application of manu re
2805020001 Cattle and Calves Waste Emissions;Milk Cows

2805020002 Cattle and Calves Waste Emissions;Beef Cows

2805020003 Cattle and Calves Waste Emissions;Heifers and Heife r Calves

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

2805021100 Dairy cattle - scrape dairy;Confinement

2805021200 Dairy cattle - scrape dairy;Manure handling and sto rage
2805021300 Dairy cattle - scrape dairy;Land application of man ure
2805022100 Dairy cattle - deep pit dairy;Confinement

2805022200 Dairy cattle - deep pit dairy;Manure handling and s torage

2805022300 Dairy cattle - deep pit dairy;Land application of m anure
2805023100 Dairy cattle - drylot/pasture dairy;Confinement

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

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

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

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

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

2805030002 Poultry Waste Emissions;Pullets 13 weeks old and ol der 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 Cla ssified

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

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

2805039300 Swine production - operations with lagoons (unspeci fied 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 (unspe cified animal age);Confinement

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

2805053100 Swine production - outdoor operations (unspecified animal age);Confinement * All SCC Descriptions begin “Miscellaneous Area So urces;Agriculture Production – Livestock”

The “ag” sector includes all of the NH 3 emissions from fertilizer from the NEI. However, the “ag” sector does include all of the livestock ammon ia emissions, as there are also significant NH 3 emissions from livestock in the point source invent ory. Most of the point source livestock NH 313

emissions were reported by the states of Kansas and Minnesota. For these two states, farms with animal operations were provided as point sources us ing the following SCCs 5 :

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

Swines

• 30202001: Industrial Processes; Food and Agricultu re; Beef Cattle Feedlots; Feedlots

General

• 30202101: Industrial Processes; Food and Agricultu re; Eggs and Poultry Production;

Manure Handling: Dry

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

Classified

There are also livestock NH 3 emissions in the point source inventory with SCCs of 39999999 (Industrial Processes; Miscellaneous Manufacturing Industries; Miscellaneous Industrial Processes; Other Not Classified) and 30288801 (Indu strial Processes; Food and Agriculture; Fugitive Emissions; Specify in Comments Field). We identified these sources as livestock NH 3 point sources based on their facility name. The re ason why we needed to identify livestock NH 3 in the ptnonipm sector was to properly implement th e emission projection techniques for livestock sources, which cover all livestock source s, not only those in the ag sector, but also those in the ptnonipm sector. This is discussed fu rther 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 Source s; Agriculture Production – Crops; Fertilizer Application” as the beginning of the description.

5 These point source emissions are also identified b y the segment ID, which is one of the following: “S WINE”, “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 i s 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 “oare a” 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 Platf orm they are in the “oarea” sector;

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

were not included in the 2001 Platform. The develo pment of these emission estimates are explained in this section.

In preparing the nonpt sector we excluded catastrop hic releases (SCC 28300XX000) since we found that these emissions were dominated by tire b urning (SCC 28300000000), which is an episodic, location-specific emissions category. Ti re 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 PM 2.5 . Because such sources are reported by a very smal l number of states, and are inventoried as county ann ual 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 th e 2002 Platform.

The nonpt sector includes emission estimates for PF Cs, also known as “gas cans.” Inventories for PFCs were recently developed for EPA’s Mobile S ource 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 NE I. 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 det ail on these emissions below.

The PFC inventory consists of ten SCCs (listed in T able 2-7) that represent five distinct sources of PFC emissions, further distinguished by resident ial 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 ca n to the piece of nonroad equipment (3) Spillage of gasoline during transport ● Emissions (adjusted for changes in ambient temper ature) associated with storage of the gasoline in the PFCs (4) Emissions due to evaporation (i.e., diurnal e missions) (5) Emissions due to permeation15

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 Duri ng 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 Durin g Transport

2501012014 Commercial Portable Fuel Containers: Refilling at t he Pump: Vapor

Displacement

2501012015 Commercial Portable Fuel Containers: Refilling at t he Pump: Spillage

* All descriptions include “Storage and Transport;Pet roleum and Petroleum Product Storage” as the beginn ing of the description.

Note that spillage and vapor displacement associate d with using PFCs to refuel nonroad equipment are included in the nonroad inventory. T o prevent double counting, these two processes are not included in the above SCCs.

Detailed documentation of the methods used to estim ate national and statewide VOC inventories for PFCs are described in a technical support docum ent (Landman, 2007). Statewide total annual VOC inventories were allocated to counties using co unty-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, 2007 b). Of note from this documentation, the developers derived the 2002 PFC inventory by linear ly interpolating inventories developed for

1999 and 2010.



### 2.3 Fires (ptfire, nonptfire and avefire)

Wildfire and prescribed burning emissions are conta ined 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 a nd 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 t he ptfire and nonptfire sectors. For the 2002 base case, these sectors were replaced by the avefi re 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;Flamin g 28100010S0 Other Combustion;Forest Wildfires;Smolde ring 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 R angeland;Total 28100200F0 Other Combustion;Prescribed Burning of R angeland;Flaming * all SCC descriptions begin with “Miscellaneous Ar ea 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 exc lude agricultural burning and other open burning sources. These sources are in the nonpt se ctor 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 i nto 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 prescribed 6 burning emissions occurring in 2002, which

were used for the 2002 model evaluation case. We d id 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 esti mates of each fire’s emissions. It includes the latitude/longitude of the fire’s origin and other p arameters 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 a lso 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;Flamin g 28100010S0 Other Combustion;Forest Wildfires;Smolde ring 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 R angeland;Total 28100200F0 Other Combustion;Prescribed Burning of R angeland;Flaming * all SCC descriptions begin with “Miscellaneous Ar ea Sources;”

The “F” and the “S” indicate whether the process is flaming (F) or smoldering (S). The inventory development approach assumed that smolder ing occurs in the same grid cell as the flaming emissions for wildfires only, and on the da y after the flaming emissions.

The use of point source and day-specific data for f ires 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 resolutio n, and spatially allocated to grid cells using surrogates related to forest land. In the 2001 Pla tform, emissions were temporally allocated with monthly and diurnal profiles (meaning each day of t he month burns uniformly) using statespecific and regional profiles.

The new point source day-specific emission estimate s for 2002 fires were developed as a joint effort of the Western Regional Air Partnership (WRA P) 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 Regiona l Planning Organizations (RPOs). The U.S. EPA added emission estimates for 29 HAPs, usin g emission factors. The prescribed fire inventory was developed for these same pollutants b y 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 ma de 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 Dang er 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” matr ix of fuel consumption and emissions for the RPO regions, NFDRS Fuel Type and a range of moi stures. This “look-up” matrix was the basis for estimating emissions for each fire on eac h day. Documentation for the Wildfire Inventory prepared by the WRAP for all the RPOs is at http://www.wrapair.org/forums/fejf/tasks/FEJFtask7I nterRPO.html . (Air Sciences, et. al., 2007)18

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

obtained the acres burned and date-location informa tion directly from the RPOs. Only 38 States provided information on prescribed burning, but the se are believed to be the States that conduct most of the burning. Of these, only Virginia’s dat a 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 te mporally resolved to the month of the burn. For Georgia, we summed the monthly emissions to cre ate an annual total by county which we included in the nonptfire sector. We do not have V irginia’s prescribed burning data in our platform in either ptfire or nonptfire sectors. Ad ditional documentation for the prescribed fire emission inventory development is provided in Appen dix 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 processin g, 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 da y-specific emissions for individual months, and twelve contain the emissions for the last day o f 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 tim e, we needed to include the last few hours from the previous month in the SMOKE intermediate f iles.

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 alg orithm.

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 err ors are:

1. The calculation of SO 2 emissions for prescribed burning in the NEI v3 use d the emission

factor for NH 3

. 2. The fuel loading values (tons fuel consumed per acr e burned) used in the plume rise calculation were a factor of 24 too high because th e data originally used to construct the SMOKE ORL files came from hourly data which were su mmed across 24 hours to generate day-specific data. The fuel loading value s were mistakenly summed along with

the emissions data.

3. Several states for which the prescribed burning dat a were not sufficient to develop pointlevel day specific emission estimates, but were suf ficient to develop county-level annual emission estimates were inadvertently dropped and e xcluded from the 2002 NEI v3. These states are: Connecticut, Delaware, Hawaii, M aine, Massachusetts, New Hampshire, New York, Pennsylvania, Rhode Island, Ve rmont, 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 prescri bed burning and managed burning emission

sources for which emissions are not available at th e 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 Ar ea Sources;”

Note that there are no wildfires in this sector. T he nonptfire emissions were generated using: (1) point source fire emissions for managed and pre scribed 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 emissio ns (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 wit h the nonptfire sector (only used for the evaluation case). Note that the SO 2 emissions for Georgia in the nonptfire sector suffe r from the same error as the Georgia fire emissions in the ptf ires sector (see Section 2.3.1).



### 2.3.3 Average fires (avefire)

The average fire sector includes emissions from wil dfires, prescribed burning and managed burning. We used this sector for the 2002 base cas e, and all future year cases. As noted above, avefire emissions are annual, county-level emission s.

The purpose of the avefire sector is to represent e missions for a typical or average year’s fires for use in projection year inventories since future yea r 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 concentra tions.

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 Ar ea 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).20Table 2-12. Comparison of 2002 Platform avefire emissions with 2002-specific fire (sum of ptfire andnonptfire) emissionsStateTons COavefireTonsCO2002firesCO %diffTonsNH3avefireTonsNH32002firesNH3% diffTonsNOXavefireTonsNOX2002firesNOX% diffTonsPM2.5avefireTonsPM2.52002firesTonsPM2.5% diffTonsSO2avefireTonsSO22002firesSO2% diffTonsVOCavefireTonsVOC2002firesVOC% diffAlabama 175,141 294,496 -41%7524,708 -84%3,8143,060 25%13,93824,150 -42%9834,645 -79%8,95167,718 -87%Arizona 440,419 1,071,505 -59%2,02017,142 -88%10,5326,953 51%37,15185,126 -56%2,8885,743 -50%21,385246,406 -91%Arkansas 123,698 67,776 83%5561,083 -49%2,654406 >100%10,3155,359 92%7281,071 -32%5,82115,582 -63%California 1,157,187 654,793 77%5,11710,473 -51%24,5638,100 >100%97,30254,565 78%6,7354,842 39%54,619150,565 -64%Colorado 288,013 966,816 -70%1,29915,467 -92%6,2717,688 -18%24,05477,744 -69%1,7195,600 -69%13,610222,363 -94%Connecticut 667 4 >100%30 NA 140 >100%560 NA 40 NA 311 >100%Delaware 1,332 93 >100%51 >100%231 >100%877 >100%60 NA 6415 >100%District of Columbia1 0 NA 00 NA 00 NA 00 NA 00 NA 00 NAFlorida 1,193,146 879,032 36%5,36614,058 -62%25,6008,924 >100%99,48471,956 38%7,01813,959 -50%56,159201,396 -72%Georgia 350,925 380,011 -8%1,2996,076 -79%7,9554,184 90%24,08231,316 -23%2,0105,865 -66%21,83487,388 -75%Idaho 630,971 62,678 >100%2,8561,002 >100%14,024696 >100%52,8085,170 >100%3,845604 >100%29,98914,405 >100%Illinois 3,323 2,589 28%1541 -63%7120 >100%277207 34%2032 -39%156418 -63%Indiana 4,124 163 >100%193 >100%882 >100%34413 >100%242 >100%19435 >100%Iowa 4,185 1,087 >100%1917 8%907 >100%34986 >100%2517 42%197250 -21%Kansas 17,600 1,251 >100%7920 >100%37810 >100%1,468100 >100%10315 >100%828268 >100%Kentucky 61,812 2,238 >100%27834 >100%1,32621 >100%5,155180 >100%36417 >100%2,909511 >100%Louisiana 151,659 43,452 >100%682693 -2%3,254310 >100%12,6473,466 >100%892675 32%7,1379,987 -29%Maine 26,592 77 >100%1151 >100%5661 >100%2,1276 >100%1500 NA 1,25817 >100%Maryland 6,129 45 >100%240 >100%1371 >100%5315 >100%320 NA 3536 >100%Massachusetts 15,878 289 >100%715 >100%3413 >100%1,32423 >100%932 >100%74766 >100%Michigan 15,380 408 >100%696 >100%3303 >100%1,28332 >100%915 >100%72493 >100%Minnesota 107,237 43,058 >100%482686 -30%2,300294 >100%8,9433,427 >100%631430 46%5,0478,321 -39%Mississippi 178,646 11,794 >100%804184 >100%3,833121 >100%14,897960 >100%1,051116 >100%8,4072,705 >100%Missouri 31,611 2,911 >100%14245 >100%67831 >100%2,636238 >100%18632 >100%1,488663 >100%Montana 203,759 180,536 13%9462,888 -67%5,1871,369 >100%17,31114,473 20%1,4221,322 8%10,08541,519 -76%Nebraska 17,780 500 >100%808 >100%3813 >100%1,48339 >100%1058 >100%837112 >100%Nevada 227,965 23,560 >100%1,026376 >100%4,910262 >100%19,0181,944 >100%1,346172 >100%10,7405,403 99%New Hampshire6,398 30 >100%290 NA 1370 NA 5342 >100%380 NA 3017 >100%New Jersey 10,375 284 >100%474 >100%2233 >100%86523 >100%612 >100%48864 >100%New Mexico 583,216 137,289 >100%2,6262,195 20%12,5821,232 >100%48,66211,132 >100%3,450917 >100%27,48831,566 -13%New York 19,195 313 >100%865 >100%4123 >100%1,60125 >100%1132 >100%90371 >100%North Carolina 429,388 330,303 30%532306 74%11,4249,095 26%9,8701,582 >100%696257 >100%58,88957,744 2%North Dakota 11,204 8,573 31%50136 -63%24061 >100%934684 37%6658 14%5271,967 -73%21StateTons COavefireTonsCO2002firesCO %diffTonsNH3avefireTonsNH32002firesNH3% diffTonsNOXavefireTonsNOX2002firesNOX% diffTonsPM2.5avefireTonsPM2.52002firesTonsPM2.5% diffTonsSO2avefireTonsSO22002firesSO2% diffTonsVOCavefireTonsVOC2002firesVOC% diffOhio 3,787 8,802 -57%17141 -88%8195 -14%316724 -56%22132 -83%1781,955 -91%Oklahoma 79,672 4,790 >100%35975 >100%1,70937 >100%6,644381 >100%46958 >100%3,7491,095 >100%Oregon 778,194 2,280,250 -66%3,54236,480 -90%17,85723,587 -24%65,350186,965 -65%4,89615,355 -68%37,328524,433 -93%Pennsylvania 5,450 65 >100%251 >100%1171 >100%4545 >100%320 NA 25614 >100%Rhode Island 171 0 >100%10 >100%40 >100%140 NA 10 NA 80 NASouth Carolina 109,880 97,437 13%4941,551 -68%2,3571,032 >100%9,1637,996 15%6461,490 -57%5,17122,252 -77%South Dakota 84,689 4,841 >100%38177 >100%1,81731 >100%7,062384 >100%49827 >100%3,9851,104 >100%Tennessee 47,175 914 >100%21214 >100%1,0129 >100%3,93472 >100%2776 >100%2,220207 >100%Texas 256,966 27,640 >100%1,118273 >100%4,890226 >100%21,5782,379 >100%1,178174 >100%13,2014,080 >100%Utah 328,713 234,566 40%1,4793,751 -61%7,0522,439 >100%27,41219,241 42%1,9341,581 22%15,46953,948 -71%Vermont 8,347 69 >100%381 >100%1791 >100%6965 >100%490 NA 39316 >100%Virginia 67,866 1,249 >100%30519 >100%1,45612 >100%5,659100 >100%3998 >100%3,194285 >100%Washington 52,086 109,827 -53%2481,756 -86%1,4841,295 15%4,4879,110 -51%4071,264 -68%2,67425,258 -89%West Virginia 36,578 646 >100%16510 >100%7856 >100%3,05051 >100%2154 >100%1,721147 >100%Wisconsin 11,924 2,563 >100%5441 32%25620 >100%994206 >100%7037 88%561398 41%Wyoming 188,099 97,672 93%8461,561 -46%4,035961 >100%15,6867,975 97%1,106682 62%8,85222,462 -61%sum 8,554,551 8,039,286 6%36,777123,414 -70%189,42882,613 >100%684,035629,635 9%49,09467,229 -27%451,1271,825,284 -75%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-s pecies emissions from vegetation and soils. It estimates CO, VOC, and NO X emissions for the U.S., Mexico, and Canada. The B EIS3.13

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 f rom the CMAQ meteorological input files,

• land-use data from the Biogenic Emissions Landuse D atabase, version 3 (BELD3). BELD3 data provides data on the 230 vegetation clas ses 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 emi ssions in the 2002 NEI: onroad, nonroad and a sector containing emissions for aircraft, loc omotive and commercial marine vessels (alm). We created these three separate sectors to handle d ifferences in emissions processing related to the temporal nature of the inventories and differen ces 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” simila rly to point sources as discussed in Section 2.1), but these data are not in the CMAQ files sinc e they were dropped during the SMOKE spatial allocation process.

The onroad and nonroad sectors utilize emissions ge nerated by the EPA’s Office of Transportation and Air Quality (OTAQ) using NMIM (E PA, 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 v ary by month. Inventory documentation for the 2002 NEI v3 onroad and nonroad sectors is also post ed with other 2002 NEI documentation; the

direct link is: ftp://ftp.epa.gov/EmisInventory/2002finalnei/docume ntation/mobile/2002_mobile_nei_version_3 _report_092807.pdf

.

7 Although OTAQ generated emissions using NMIM for C alifornia, these were not used in the 2002 NEI vers ion 3, but rather were replaced by state-submitted emissio ns.23

While aircraft, locomotive, and commercial marine s ources are considered nonroad sources in the 2002 NEI, they comprise a separate sector for t he 2002 platform denoted as “alm.” We developed the alm sector for the convenience of emi ssion processing and projections. The NMIM-based nonroad emissions are monthly whereas th e alm emissions are annual. In addition, the NMIM-based nonroad emissions are projected usin g NMIM, whereas the alm emissions use national, annual activity-based projection factors. Documentation for “alm” inventory development is available in several separate docume nts 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, brakewea r 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 summa ry of the 2002 vehicle miles traveled (VMT) inputs (along with future year VMT used in th e projections and discussed in Section 4.3.1) is contained in Appendix I.

We did not include the refueling onroad emissions g enerated by NMIM in the onroad sector, because the NEI treats onroad refueling as a statio nary 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 onr oad 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 inc ludes PM 10 and PM 2.5 emissions for three

modes 8 : exhaust (EXH), brakewear (BRK) and tirewear (TIR ), which similarly facilitated modeappropriate speciation profiles. The emission mode s are included as part of the pollutant name for the SMOKE emission inputs. For example, exhaus t 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 10 th digit of zero, instead of the letter that would

otherwise denote the emission mode.

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

Platform as follows

• We made MOBILE6 Model adjustments for VOC to accoun t for vehicle “cold starts” which increase VOC emissions at cold temperatures. Newer vehicles meeting Tier 2 have higher emissions than previously estimated whe n 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 ha ndling of oxygenates, which

affects VOC and HAP estimates.

• We updated the external MOBILE6 data input files wh ich 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 st ates (California, Connecticut,

8 PM 10 and PM 2.5 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 Pr essure (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. How ever, often the surveyed counties would have RVP control programs, so that the other counti es 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 re gulated RVP levels instead.

• Emissions in the 2002 Platform include the modes fo r VOC, PM 10 and PM 2.5 , whereas emissions in the 2001 Platform had mode-specific em issions for VOC only.

Because the California Air Resources Board (CARB) h as 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 e missions 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 c ompletion of our modeling, but they were not able to provide the results in time for us e with version 3 of the 2002 Platform. The updated emissions, however, were used in the develo pment of the projection year emissions as

discussed in Section 4.3.1.



### 2.5.2 Nonroad mobile sources –NMIM-based nonroad (n onroad)

This sector includes monthly exhaust, evaporative a nd refueling emissions from nonroad engines (not including commercial marine, aircraft, and loc omotives) 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, 20 07 (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 em issions 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 secto r. Rather, we kept these emissions in the

nonroad sector.

The version of NONROAD used for the 2002 Platform h as the following improvements over previous versions of this model:

General

• Added the ability to estimate effects of ethanol bl ends 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 o n EPA and industry test data (and small revisions to tank sizes).

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

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

data.

• Corrected technology mix for snow blowers to accoun t 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 an d 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 sterndriv e/inboard engines >600 hp.

The NEI nonroad data for California provided by CAR B are annual emissions that do not have the mode-specific data for VOC (exhaust, evaporativ e, and refueling). We created monthly, mode-specific emissions for California’s nonroad em issions (except for alm sources) using NMIM results for California. Details on this proce ss are documented separately (Strum, 2007). The process erroneously dropped emissions for certa in 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 month ly 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 NO X emissions occur in San Francisco (06075) and Colus a (06011) counties, and the largest errors in VOC occur in Gl enn (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 con sistent with the 2002 NEI v3. Note that some aircraft emissions for California, Illinois, a nd Minnesota are also contained in the ptnonipm sector, as described above.26

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

sector

SCC SCC Description 2275000000 Mobile Sources;Aircraft;All Aircraft Typ es and Operations;Total
2275001000 Mobile Sources;Aircraft;Military Aircraf t;Total
2275020000 Mobile Sources;Aircraft;Commercial Aircr aft;Total: All Types
2275050000 Mobile Sources;Aircraft;General Aviation ;Total
2275060000 Mobile Sources;Aircraft;Air Taxi;Total 2280002100 Mobile Sources;Marine Vessels, Commercia l;Diesel;Port emissions 2280002200 Mobile Sources;Marine Vessels, Commercia l;Diesel;Underway emissions 2280003100 Mobile Sources;Marine Vessels, Commercia l;Residual;Port emissions
2280003200 Mobile Sources;Marine Vessels, Commercia l;Residual;Underway emissions 2280004000 Mobile Sources;Marine Vessels, Commercia l;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 secto r 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 c ommercial marine sources from v2 of the 2002 NEI, which were made in conjunction with the d evelopment 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 a ffect 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, Commercia l;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 s uch that the sum of the v2 state-submitted emissions and the revised EPA-generated emissions m atched OTAQ’s national totals.

In addition, since SCCs for diesel port and underwa y 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 u sed in the 2002 NEI v2.



### 2.6 Emissions from Canada, Mexico and Offshore Dril ling Platforms (othpt, othar, othon)

The emissions from Canada, Mexico, and Offshore Dri lling Platforms are included as part of three sectors: othpt, othar, and othon. The “oth” refers to the fact that these emissions are27

“other” than those in the 2002 NEI, and the last tw o digits provide the SMOKE source types: “pt” for point, “ar” for “area”, and “on” for onroa d 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 w ere the most recent set of emissions which

were available at the time the 2002 Platform was de veloped.

For Mexico we used emissions for 1999 (Eastern Rese arch 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 Ecologí a-INE), the U.S. EPA, the Western Governors' Association (WGA), and the North American Commissio n for Environmental Cooperation (CEC). Unlike the Mexico inventory used for the 20 01 Platform that covered only the northern border states, this inventory includes emissions fr om all states in Mexico. The emissions values in the northern border states have also been update d since we used the 2001 Platform emissions.

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

same data as used in the 2001 Platform. Based on t he CAIR emission inventory documentation (EPA, 2005a), the offshore sources were provided by the Texas Commission on Environmental Quality (TCEQ). This inventory included emissions f or 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 Mexi co’s 1999 inventory Mexico, 1999, nonroad Used updated version of Mexic o’s 1999 inventory Canada, 2000, nonpoint Dropped emissions for SCC 2 806015000 (Domestic Animal Waste) and SCC 2810003000 (Cigarette Smoke). othar

Canada, 2000, nonroad

Mexico, 1999, onroad Used updated version of Mexico ’s 1999 inventory othon Canada, 1995, onroad



## 3 Emissions modeling summary

The CMAQ model requires hourly emissions of specifi c 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 require d 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 tran sforms these emissions from their original temporal resolution, pollutant resolution, and spat ial resolution into the data required by CMAQ.28

As seen in Section 2, the temporal resolution of th e 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 Canad a, municipio totals for Mexico). The pollutants for all sectors except for biogenics are those inventoried for the NEI. The preprocessing steps involving temporal allocation, spa tial allocation, pollutant speciation, and vertical allocation of point sources are referred t o as emissions modeling. This section provides

some basic information about the tools and data fil es 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 c reate the emissions inputs for CMAQ.

The SMOKE version 2.4 source code and executables c an be used to reproduce our emissions modeling, and these are available from the Communit y 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 effor t, 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 comp leted 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 20 02 Platform include:

• Support of point-source, day-specific wildfire and prescribed burning fires

• Extended ORL format that includes more metadata fie lds, 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 Sp atial Surrogate Tool

• Support for multiple and nonsequential days in the temporal processor

• New processing scripts that make it easier to proce ss more sectors than the traditional sectors of nonpoint, point, onroad, nonroad, and bi ogenics.



### 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-s pecific emissions across sectors. The SMOKE settings in the run scripts and the data in t he SMOKE ancillary files control the approaches used for the individual SMOKE programs f or each sector. Table 3-1 summarizes the major processing steps of each platform sector. Th e “Spatial” column shows the spatial29

approach: “point” indicates that SMOKE maps the so urce from a point location to a grid cell, “surrogates” indicates that some or all of the sour ces use spatial surrogates to allocate county emissions to grid cells, and “area-to-point” indica tes that some of the sources use the SMOKE area-to-point feature to grid the emissions (furthe r described in Section 3.3.1). The “Speciation” column indicates that all sectors use the SMOKE spe ciation 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 indica tes the sectors for which SMOKE computes vertical plume rise and creates merged emissions th at 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 mo deling domains with 36 km and 12 km spatial resolution. Figure 3-1 shows the 36 km CON tinental U nited S tates “CONUS” modeling domain, the 12 km eastern domain (EUS), an d 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 s urrogates 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. T hese model species are either individual chemical compounds or groups of species, called “mo del species.” The chemical mechanism used for the 2002 Platform is the Carbon Bond 05 (C B05) 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 wit h the CB05.

Table 3-3. Model Species produced by SMOKE for CB0 5

Inventory Pollutant Model Species Model species des cription

CO CO Carbon monoxide

NO Nitrogen oxide NOX NO2 Nitrogen dioxide

SO2 Sulfur dioxide SO2 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 microns32

For VOC, the speciation approach involves three maj or 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 disaggregat e TOG into CB05 model species.

The approach for PM2.5 emissions is somewhat simple r, since it does not requite the second step. Figure 3-2 shows the steps involved in chemi cal speciation for both VOC and PM 2.5 , and it identifies the underlying inputs used to develop th e CB05-based ancillary files for the 2002

Platform for CAPs.

Figure 3-2. Chemical Speciation Approach Used for t he 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 SMOKE Assign speciation profile

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 SMOKE Assign speciation profile

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 f iles used to accomplish these speciation processing steps.



### 3.2.3 Temporal processing configuration

Table 3-4 summarizes the temporal aspect of the emi ssions processing configuration. It compares the key approaches we used for temporal pr ocessing 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 th e year, inventory is already daily week = hourly emissions computed for all days in on e “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 Mond ay, representative weekday, representative Saturday and representative Sunday for each month, which means emissions have variation between Mondays, other weekdays, Saturdays and Sundays with in the month, but not week-to-week variation within the month. Also Tuesdays, Wednesdays and Th ursdays are treated the same. aveday = hourly emissions computed for one represen tative 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 e missions from the inventory using the Temporal program. The values gi ven 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” woul d have been a more appropriate setting because this sector includes weekly profiles that vary across da ys 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 miti gate 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 f rom December 2002 to fill in surrogate emissions for the end of December 2001. In particu lar, for sectors not requiring vertical plume rise calculations (i.e., SMOKE-area and mobile sect ors, including nonptfire and avefire), we used December 2002 emissions (representative days) for December 2001. For othpt and ptnonipm, we used December 2002 emissions (represen tative days) with 2001 meteorology for plume rise. For ptipm, we applied temporal profile s to the annual inventory (rather than using day-specific December emissions), with 2001 meteoro logy 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 proc essed December 2001 emissions using 2001 meteorology.



### 3.2.4 Vertical allocation of day-specific fire emis sions

We used SMOKE to compute vertical plume rise for al l 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 o f wildfires and prescribed burning fires as point sources with plume rise.

The ptfire inventory contains data on the acres bur ned (acres per day) and fuel consumption (tons fuel per acre) for each day. SMOKE uses these addi tional 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 t he 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 SMO KE-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 he at release estimated from the fuel loading and fire size. The SMOKE program Laypoint uses the Bri ggs algorithm to determine the plume top and bottom, and then computes the plumes’ distribut ions into the vertical layers that the plumes intersect. Laypoint uses the pressure difference a cross each layer over the pressure difference across the entire plume as a weighting factor to as sign 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 smol dering 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 re spect to flaming or smoldering other than

the different SCCs.



### 3.3 Emissions modeling ancillary files

In this section we summarize the ancillary data tha t SMOKE used to perform spatial allocation, chemical speciation, and temporal allocation for th e 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 appli ed spatial factors by country/SCC, speciation35

factors by pollutant/SCC, and temporal factors by s ome 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. T o do this, SMOKE used national 36 km and 12 km spatial surrogates and a SMOKE area-to-po int data file. The spatial data files we used can be obtained from the files listed below, availa ble 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 (SRGD ESC), 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 sh own in Figure 3-1. The SMOKE model windowed the Eastern and Western grids while it cre ated 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 spati ally 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 s ources. 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 s patially allocate sources in the 2002 Platform; that is, some surrogates in Table 3-5 were not assi gned 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 docume nted at http://www.epa.gov/ttn/chief/emch/spatial/spatialsu rrogate.html . The document ftp://ftp.epa.gov/EmisInventory/emiss_shp2006/us/li st_of_shapefiles.pdf provides a list and summary of these shapefiles. The shapefiles used f or the surrogate attributes (e.g., population, agricultural land, marine ports) are the same as th ose used for the 2001 Platform with two exceptions: we developed new shapefiles for the “p opulation change” and “oil and gas” surrogates. We developed these shapefiles to enabl e the Surrogate Tool to generate these complex surrogates, which utilize data with differe nt 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 Surrog ate Tool.

County boundaries For the 2001 Platform, we had used the same surroga tes 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 com puting the water surrogate, which SMOKE used to spatially allocate pleasure craft emi ssions. For the water surrogate, we created county boundaries that extend a half-mile (804.7 me ters) from the coastline into the oceans, bays, and Great Lakes. This was done because pleas ure craft, and in particular personal watercraft, are not expected, on average, to go mor e 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 i nto the ocean by approximately 3 miles.

The detailed steps in developing the county boundar ies for the 2002 Platform are provided in
ftp://ftp.epa.gov/EmisInventory/emiss_shp2006/us/me tadata_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 230 2002000, 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 Ind ustrial land), which relates to the location of industrial processes rather than commer cial establishments.

2. The residential wood combustion emissions for wood stoves and fireplace inserts were assigned to surrogate 165 “50% low intensity reside ntial land and 50% residential heating –wood”. This represents a change from the 2001 Pla tform which utilized surrogate 160, “residential heating--wood,” for these sources. Su rrogate 160 reflects the use of wood as the primary heating source. We changed to surrogat e 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 sou rce.38

3. The residential wood combustion emissions for firep laces 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 bec ause fireplaces are not generally used as a primary source of home heating. In addit ion, “low intensity residential land” gives a spatial pattern for fireplace wood combusti on that better fits a common sense view of where more fireplace usage is likely to occ ur.

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 contain er (PFC) emissions (described in Section 2.2.3). Emissions from these sources were n ot in the 2001 Platform. Table 3-6 provides these assignments.

Table 3-6. Surrogates assigned to Portable Fuel Co ntainer 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 th e 2001-based platform, other than as a secondary su rrogate for counties that had no data for surrogate 300, Low In tensity 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 w ith 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 emission s for the 2002 evaluation case (36 km resolution only), but not the 2002 base case or fut ure-year cases. The correct data are provided in

the 2002v3CAP site.39

3.3.1.2 Allocation Method for Airport-Related Sources in th e 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), nonr oad (airport ground support) and nonpt (jet refueling).

Similar to the 2001 Platform, we used the SMOKE “ar ea-to-point” approach to allocate the emissions to airport locations, rather than using a irport 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 c ontains (1) geographic coordinates of airport locations and (2) allocation factors based on airpo rt-specific aircraft activity. For the 2002 Platform, each airport was assigned to a single loc ation, and therefore the emissions associated with each airport were allocated to a single grid c ell.

For the 2002 Platform, we created a new 2002-specif ic 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 lar gely taken from the “supplemental” geographic information system (GIS) data provided w ith the 2002 NEI, posted under the “Inventory Data” section” (“Mobile Sector Data”) a t ftp://ftp.epa.gov/EmisInventory/2002finalnei/mobile _sector_data/ncd_files/gis_allocation . The supplemental data includes geographic coordinates a nd landing and takeoff (LTO) information for specific airports, which were used in the devel opment 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 coo rdinates 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 suppl emental 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 co rrect 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/cou nty FIPS code from the NEI, and the geographic coordinates from the BTS. This mainly o ccurred in the small Virginia citycounties, where airport-related emissions in the 20 02 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 coun ty codes and the geographic coordinates.40

3.3.1.3 Surrogates for Canada and Mexico Emission Inventori es We used the 2001 Platform surrogates for Canada to spatially allocate the Canadian emissions

for the 2002 Platform. Detailed documentation abou t the Canadian spatial surrogates, their development, and the data are available at: http://www.epa.gov/ttn/chief/emch/spatial/newsurrog ate.html

.

As in the 2001 Platform, only the population surrog ate was used to grid sources in the Mexico emission inventory, provided by municipios (analogo us 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 d ata. We created this updated population surrogate using the Surrogate Tool.

The update to include additional municipios was req uired 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 boundarie s from the Institute for the Environment, Center for Environmental Modeling and Policy Develo pment 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 avail able at http://www.epa.gov/ttn/chief/emch/spatial/spatialsu rrogate.html and the 12 km and 36 km surrogate files are on the 2002v3CAP site. Note th at the population is “zero” in the Mexico_pop shapefile for municipios that are part of states lo cated outside the 36 km CONUS domain.

Figure 3-3. Mexican States included in the 36 km Mo deling Domain41



### 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 name s, 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 fa ctors is a procedural improvement from the SCC-specific approach that was used in the 2001 Pla tform, 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 2 002 or had a conversion factor of 1.0 (the default used if the entry is missing), the impact o n 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 PM 2.5 , neither the mass-based PM 2.5 profiles nor the PM 2.5 emissions have to be further converted for use in SMOKE, though the Speciation T ool was used to convert the profiles from a database format to SMOKE-ready format.

The TOG and PM 2.5 speciation factors that are the basis of the chemi cal speciation approach were developed from the SPECIATE4.0 database ( http://www.epa.gov/ttn/chief/software/speciate/inde x.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 an d Development (ORD) and EPA’s Office of Air Quality Planning and Standards (OAQPS) at Resea rch Triangle Park, NC, and Environment Canada (EPA, 2006c). The SPECIATE4.0 database contains speciation profil es for TOG, speciated into individual chemical compounds, VOC-t o-TOG conversion factors associated with the TOG profiles, and speciation profiles for PM 2.5 . The database also contains the PM 2.5 speciated into both individual chemical compounds ( e.g., zinc, potassium, manganese, lead), and into the “simplified” PM 2.5 components used in the air quality model. These s implified
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 m icrograms in diameter42

Note that in the 2001 Platform, “POA” was used inst ead of POC. While POC represents only the carbon mass of the organic carbon PM 2.5 components, POA has a 20% increase in the PM 2.5 fraction compared to POC. This additional amount o f mass is intended to account for other atoms in the organic carbon particles, such as hydr ogen and oxygen. For the 2002 Platform, we decided to use emissions of POC and to make any org anic fraction adjustments part of the postprocessing of CMAQ outputs.

The assignment of profiles in the SPECIATE4.0 datab ase to emissions sources was done in two
steps: (1) an initial profile assignment list was prepar ed with the SPECIATE4.0 database (2) the list was completed and reviewed by emissi on inventory development, emission modeling and emission factor staff in the EPA’s OAQ PS and the EPA’s ORD. For VOC speciation factors, recommendations for mob ile sources and upstream (i.e., petroleum distribution) sources were obtained from subject ex perts at OTAQ.

Speciation profiles for use with BEIS are not inclu ded 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 PM 2.5 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 d ifferent profiles for the future-year cases (2009 and beyond) than for the 2002 base and evalua tion 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, respectivel y.

Table 3-7. Gasoline-related speciation profiles us ed for 2002

Profile Code (SPECIATE4.0) Profile Description Sources Using this Profile, 200 2 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 Filling43

Table 3-8. Gasoline-related speciation profiles us ed 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 onr oad 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 aft er we completed the air quality model runs. The data provided at the 2002v3CAP site correct the se problems, and the GSCNV problem discussed previously. The two assignment problems and corrections are:

• For aircraft VOC, we initially used profile 2752 (A ircraft - Atlanta - August 27, 1990), but this was changed to profile 1098 (Aircraft Land ing/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 Clas sified”) we inadvertently used profile 1098 (Aircraft Landing/Takeoff (LTO) – Comm ercial). This was changed to profile 1089 (Secondary Metal Production - Gray Iro n 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 re solution 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 we re generally based on the temporal

allocation data used for the 2001 Platform. We mod ified or corrected profiles for particular categories where updated data were available, and a dded 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 emi ssions. 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, an d PTREF) and for ptipm sector (used for electric generating units) for the evalua tion case (PTREF) and, temporal profiles (ATPRO, MTPRO, and PTPRO)44

The remainder of this section discusses the develop ment of the new temporal profiles or profile assignments listed below:

• ptipm: updated diurnal profiles for non-CEM source s

• onroad and nonroad: updated weekly profiles

• nonpt (PFC): assignment of monthly, weekly and diu rnal profiles

• ag (fertilizer): correction of monthly profiles

Diurnal Profiles for Electric Generating Units (pti pm)

We updated the state-specific and pollutant-specifi c 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, average d over the whole year and all units in each state. We calculated the diurnal factors using CEM SO 2 and NO X emissions and heat input. We computed SO 2 and NO X -specific factors from the CEM data for these pollu tants. All other pollutants used the hourly heat input data. We ass igned the resulting profiles by state and pollutant.

Onroad Mobile: Weekly Profiles

We use weekly profiles to allocate the monthly tota l mobile source emissions to each day of the week. We changed the weekly profiles for onroad mob ile sources to account for differences in the weekly pattern of heavy duty and light duty veh icles 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 t hat the fraction of weekend emissions for light duty vehicles (LDVs) on rural roadways is larger th an what was used in the 2001 Platform. The opposite effect is shown for heavy duty vehicles (H DVs) on rural roads on weekends. For urban roadways, the data for LDVs indicates somewhat grea ter activity during weekdays versus weekends, whereas the data for HDVs indicates much greater activity on the weekdays versus weekends. These weekday versus weekend relationshi ps 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 p rofiles for onroad mobile sources as compared to those used in the 2001 Platform for rur al 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 Urba n 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 allocati ng monthly nonroad sector emissions to weekdays versus weekends using data provided in the NONROAD model’s technical memorandum “Weekday and Weekend Day Temporal Alloca tion of Activity In The Draft NONROAD2004 Model” (EPA, 2004). This reference pro vides default activity allocation fractions for weekend days versus weekdays by nonro ad 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 1 8

Residential Lawn and Garden 7: all days are the sam e 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 wkn d 0.1600 0.1000 17

Agricultural 17: 0.16 wkday; 0.10 wknd 0.1667 0.083 3 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 0.1 429 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 a llocated 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, multipl y the weekend day fraction by 2. All equipment types within a ca tegory (e.g., excavators within the construction eq uipment category) are assigned the weekday and weekend day fractions for that category.

Monthly, Weekly and Diurnal Profiles for Portable F uel Containers (PFC)

We assigned temporal profiles to allocate the annua l PFC emissions in the nonpt sector using several existing temporal profiles, since we did no t have data to create new profiles specific to

these new SCCs for the 2002 Platform. Table 3-12 l ists the profile assignments.47

Table 3-12. Temporal Profile Assignments for Porta ble 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 weigh ts48

Monthly Fertilizer Profiles

We modified the monthly profiles for fertilizer SCC s for eight northern states (Illinois, Indiana, Iowa, Minnesota, Nebraska, New York, Ohio and Wisco nsin) to shift emissions from December to earlier in the fall (i.e., September through Nov ember). 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 deve loping 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 chan ges and control programs to estimate futureyear emissions. For other sectors we applied proje ction factors and/or percent reductions to account for emissions growth based on activity grow th and/or emission reductions due to control programs controls or closures. The following bulle ts 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 refl ect emission reductions due to control programs, plant closures, and settlements; used consistent projections approaches for ptnonipm live stock, 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 NH 3

.

• Area fugitive dust sector (afdust): Projection factors for dust categories related to livestock estimates based on expected changes in an imal 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 stove s 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 refu eling 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 nati onal level, accounting for projected changes in landing/takeoff activity. Projection fa ctors for commercial marine and locomotives which reflect activity growth and contr ols, 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 (obta ined from Environment Canada) grown and controlled from the data used in the 2002 base case. 1999 emissions for Mexico for all future-yea r 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 natio nal total emissions by sector and pollutant. A more detailed summary by sector, state, and pollu tant for the U.S. is provided in Appendix D. This information is also available electronically a t the 2002v3CAP site, which also contains annual total emissions by state and sector after ap plication of chemical speciation factors.

The remainder of this section is organized by eithe r entire source sector or by specific emissions category within a source sector for which a distinc t 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]

NO X [tons/yr]

CO [tons/yr]

SO 2 [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 7 96,229 684,034
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 97,76 8 87,150
avefire 451,127 189,428 8,554,550 49,094 36,777 7 96,229 684,034
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 2,1 04 181,111 171,230
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 4 90,783 368,016
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 98,75 2 87,586
avefire 451,127 189,428 8,554,550 49,094 36,777 7 96,229 684,034
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 2,29 6 146,716 137,921
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 5 61,556 433,471
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 1,200 102,7 35 90,712
avefire 451,127 189,428 8,554,550 49,094 36,777 7 96,229 684,034
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 2,53 6 107,009 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 7 40,401 605,467
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 4,768,53151

Year Sector [tons/yr]

VOC [tons/yr]

NO X [tons/yr]

CO [tons/yr]

SO 2 [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 7 96,229 684,034
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 7 40,401 605,467
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 with in the 36 km domain

Year Country &

Sector [tons/yr]

VOC [tons/yr]

NO X [tons/yr]

CO [tons/yr]

SO 2 [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 6 11,937 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 2,54 9 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 1,089,682 489,03 3 276,161 187,370
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 24,0 34 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 3 27,204 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 2,54 7 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 1,089,676 489,03 1 276,156 187,366
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 1,626,30152

Year Country &

Sector [tons/yr]

VOC [tons/yr]

NO X [tons/yr]

CO [tons/yr]

SO 2 [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 27,8 76 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 3 70,267 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 2,54 7 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 1,089,676 489,03 1 276,156 187,366
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 31,1 67 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 40 2,759 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 2,54 7 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 1,089,676 489,03 1 276,156 187,366
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 (pti pm)

The future-year data for the ptipm sector were crea ted by the IPM model version 3.0. The EPA Clean Air Markets Division (CAMD) manages the devel opment of this model and maintains a website dedicated to documenting the latest IPM ver sion: http://www.epa.gov/airmarkets/progsregs/epa-ipm/ind ex.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/doc s/summary2006.pdf ) with references to

the more textual documentation available for IPM ve rsion 2.1.9 ( http://www.epa.gov/airmarkets/progsregs/epa-ipm/pas t-modeling.html#version2004 ).

We used IPM results for 2010 to represent 2009, 201 5 to represent 2014, and we used the 2020

IPM results for 2020. We also used the 2020 result s for 2030, since we chose only to project mobile sources to 2030, and the IPM results were on ly available up until 2025.



### 4.2 Stationary Source Projections: non-IPM sectors (ptnonipm, nonpt, ag, afdust)

Projections of U.S. stationary sources other than p tipm 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 project ion methods used for these sectors. In estimating future-year emissions, we assumed no emi ssions growth for many stationary non-IPM53

sources. This “no-growth” assumption is based on a n examination of historical emissions and economic data. While we are working toward improvi ng this approach in future emissions platforms, we are still using this assumption for t his platform. More details on the rationale for this approach can be found in Appendix D of the Reg ulatory 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, afdus t 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 produ ction, the animal projection factors are derived from national-level animal population proje ctions from the U.S. Department of Agriculture (USDA) and the Food and Agriculture Pol icy 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 t rends 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 product ion 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 parti cular, the dairy cow population is projected to decrease in the future as it has for the past few d ecades; however, milk production will be increasing over the same period. Note that the amm onia emissions from dairies are not directly related to animal population but also nitrogen excr etion. With the cow numbers going down and the production going up we suspect the excretion va lue will be changing, but we assumed no change because we did not have a quantitative estim ate.

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 correspo nding 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 so urces. The approach cited here was developed as part of a modeling exercise to estimat e the expected benefits of the woodstoves54

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 Regulator y Impact Analysis (EPA, 2006a). There

are two differences between the 2001 and 2002 Platf orms for emissions from woodstoves. First, we did not include the Libby Montana woodstove chan ge-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 gro wth 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 2104 008051: decrease 2%/yr

• New woodstoves, SCC=2104008003, 2104008004, 2104008 030, 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 PM 2.5 . These fractions are based on the fraction of emi ssions from these processes in the states that did not hav e the “general woodstoves and fireplaces” SCC in the 2002 NEI. This approach results in an overa ll decrease of 1.056% per year for this source
category.

Table 4-4 presents the projection factors used to p roject 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 cert ified 0.8600 0.7600 0.6400

2104008003: Fireplaces: Insert; EPA certified; non -catalytic 2104008004: Fireplaces: Insert; EPA certified; cat alytic 2104008030: Catalytic Woodstoves: General 2104008050: Non-catalytic Woodstoves: EPA certifie d 2104008052: Non-catalytic Woodstoves: Low Emitting 2104008053: Non-catalytic Woodstoves: Pellet Fired 1.1400 1.2400 1.3600

*All descriptions begin with: “Stationary Source F uel 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 invent ory used for gasoline Stage II emissions is 2501060100 (Storage and Transport; Petroleum and Pe troleum 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, Ga soline Service Stations, Gasoline service stations)

• SCC 40600401 (Petroleum and Solvent Evaporation;Tra nsportation and Marketing of Petroleum Products;Filling Vehicle Gas Tanks - Stag e II;Vapor Loss w/o Controls)

• SCC 40600402 (Petroleum and Solvent Evaporation;Tra nsportation and Marketing of Petroleum Products;Filling Vehicle Gas Tanks - Stag e II;Liquid Spill Loss w/o Controls)

• SCC 40600403 (Petroleum and Solvent Evaporation;Tra nsportation and Marketing of Petroleum Products;Filling Vehicle Gas Tanks - Stag e II;Vapor Loss w/o Controls)

• SCC 40600499 (Petroleum and Solvent Evaporation;Tra nsportation and Marketing of Petroleum Products;Filling Vehicle Gas Tanks - Stag e II;Not Classified

We used a consistent approach across nonpt and ptno nipm to projection these gasoline stage II emissions. The approach involved computing VOC-spe cific projection factors from the NMIM results for onroad refueling, using ratios of futur e–year emissions to 2002 emissions. The approach accounts for three elements of refueling g rowth and control: (1) activity growth (due to VMT growth as input into NMIM), (2) emissions reduc tions from Stage II control programs at gasoline stations, and (3) emissions reductions res ulting from the phase in over time of newer vehicles with onboard Stage II vehicle controls. W e assumed that all areas with Stage II controls in 2002 continue to have Stage II controls in all f uture 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 on road sources from NMIM.

We applied these projection factors to both nonpt a nd ptnonipm sector gasoline stage II sources.

For Stage II sources in the ptnonipm sector, we att empted to apply the county-specific projection factors to sources which had SIC=5541 and any of th e four SCC codes identified above. We did not intend to apply these projection factors to sou rces unless the SIC was 5541 in order to avoid inappropriately adjusting refueling at industrial p lants. However, the SIC information was inadvertently dropped when the projection factors w ere 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 refu eling ratio, because we only used the county ratios for counties meeting both the SCC and SIC cr iteria.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 emiss ions from nonpt (SCC 2501060100) and ptnonipm (SCCs 40600401, 40600402, 40600403 or 4060 0499) gasoline Stage II emissions.57Table 4-6. Summary of Gasoline Stage II emissionsin base and future yearsStateGasStage IIVOC 2002nonpt(tons)GasStage IIVOC 2002ptnonipm(tons)GasStage IIVOC 2009nonpt(tons)GasStage IIVOC 2009ptnonipm(tons)GasStage IIVOC 2014nonpt(tons)GasStage IIVOC 2014ptnonipm(tons)GasStage IIVOC 2020nonpt(tons)Gas StageIIVOC 2020ptnonipm(tons)Alabama 8454.14 0.35 4424.07 0.35 2672.87 0.35 2058.77 0.35Alaska 869.85 440.56 284.85 232.69Arizona 7274.86 4147.52 2716.34 2302.96Arkansas 4874.82 2651.05 1637.63 1266.12California 6305.46 1654.57 4958.29 1406.41 4749.461412.50 4964.58 1526.07Colorado 389.43 10620.49 224.77 5856.45 145.09 3827.40 119.31 3201.51Connecticut 620.74 488.66 451.98 455.72Delaware 515.23 279.86 201.41 179.55District ofColumbia 104.17 71.16 61.45 61.46Florida 20296.20 27.48 11144.60 27.48 7569.32 27.486321.63 27.48Georgia 11009.57 9.00 5969.52 9.00 4158.67 9.00 3525.35 9.00Hawaii 1446.07 749.93 451.93 350.30Idaho 1962.14 1155.07 738.32 589.50Illinois 8988.01 11.99 4858.77 11.99 3278.41 11.992794.47 11.99Indiana 10085.06 0.04 5235.92 0.04 3292.99 0.04 2658.95 0.04Iowa 4455.61 2719.04 1661.18 1185.14Kansas 3709.81 0.13 1966.17 0.13 1216.80 0.13 965.28 0.13Kentucky 5234.34 189.68 2811.89 154.20 1782.57 148.50 1438.65 157.31Louisiana 5989.40 32.00 3106.67 28.15 1920.49 28.151532.46 28.15Maine 1182.79 668.96 438.10 362.24Maryland 3211.88 16.25 1745.82 16.10 1293.54 16.101163.80 16.10Massachussetts 1521.74 7.54 982.13 7.54 855.69 7.54860.85 7.54Michigan 14611.55 31.28 7641.00 31.15 4716.80 31.153678.22 31.15Minnesota 7953.52 4215.70 2420.42 1795.29Mississippi 4843.45 2348.00 1362.94 1090.95Missouri 7212.07 63.62 4087.16 62.88 2650.45 62.882159.25 62.88Montana 1316.50 707.96 439.91 350.4358StateGasStage IIVOC 2002nonpt(tons)GasStage IIVOC 2002ptnonipm(tons)GasStage IIVOC 2009nonpt(tons)GasStage IIVOC 2009ptnonipm(tons)GasStage IIVOC 2014nonpt(tons)GasStage IIVOC 2014ptnonipm(tons)GasStage IIVOC 2020nonpt(tons)Gas StageIIVOC 2020ptnonipm(tons)Nebraska 2496.80 1329.64 830.41 655.97Nevada 575.71 431.08 369.47 377.03New Hampshire 804.47 0.98 466.12 0.98 340.31 0.98 298.89 0.98New Jersey 2414.61 0.20 1275.65 0.19 918.02 0.19 814.69 0.19New Mexico 3139.37 1752.34 1109.15 892.66New York 14137.71 1.35 7558.69 1.35 4938.55 1.35 3896.01 1.35North Carolina 13233.72 9.85 6716.51 5.10 4253.41 3.41 3384.61 2.75North Dakota 910.39 480.03 294.59 231.08Ohio 3863.81 16.81 2137.92 16.75 1350.95 16.75 1151.85 16.75Oklahoma 6228.32 3288.87 2106.15 1684.52Oregon 3674.14 2527.28 1846.14 1358.46Pennsyvlania 9529.87 1.79 5199.12 1.79 3567.25 1.792950.73 1.79Rhode Island 253.80 181.14 148.82 135.06South Carolina 7538.41 3752.20 2340.16 1824.07South Dakota 1071.45 560.17 346.79 270.72Tennessee 9529.66 5956.74 3891.25 2850.29Texas 17925.90 17.42 8840.39 17.42 6255.26 17.42 5624.26 17.42Utah 3382.00 0.14 2217.93 0.14 1489.03 0.14 1154.850.14Vermont 262.32 188.78 159.23 155.70Virginia 9188.74 46.85 4480.18 44.86 2987.58 44.862398.67 44.86Washington 4179.39 0.97 2851.10 0.97 2264.70 0.97 1899.43 0.97West Virginia 3231.73 0.43 1483.11 0.43 933.98 0.43703.70 0.43Wisconsin 5687.27 0.12 2992.99 0.12 1773.68 0.12 1573.23 0.12Wyoming 1099.70 557.27 351.06 273.73Tribal Data 27.58 18.00 11.58 10.2959



### 4.2.4 Portable fuel container growth and control (n onpt)

We obtained future-year VOC emissions from Portable Fuel Containers (PFCs) from inventories developed and modeled for EPA’s MSAT rule (EPA, 200 7a 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 consu mption, 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, respecti vely. The PFCs emissions for 2020 were used in our platform inventories for 2020 as well a s 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 inven tories directly, we chose to compute projection factors based on future and 2002 PFC emi ssions by SCC, since we used this approach for other sources in the nonpt sector. We develope d and applied the factors by State and SCC because differences in VOC projection factors did n ot vary across counties within the state. This is because the impacts of differences in average ga soline 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 fa ctors for PFC VOC emissions.



### 4.2.5 Stationary Source control programs/plant clos ures (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 f or similar sources in the 2001 Platform, because we did not apply controls from ozone and PM 2.5 State Implementation Plans. Since the available SIP control data were limited to incomple te sets of controls from only four SIPs, we decided that it was inconsistent to apply these dat a when other SIPs were not represented.

Other aspects of our future-year base controls incl uded the following:

• We did not include MACT rules where compliance date s were prior to 2002.

• We included plant closures (i.e., emissions were ze roed out for future years) where information indicated that the plant was actually c losed. However, plants projected to close in the future (post-2007) were not removed in the f uture years.

• In addition to plant closures, we included the effe cts on ptnonipm sector emissions of the

NO X SIP Call and Department of Justice Settlements and Consent Decrees. We also included estimated impacts of HAP standards per Sec tion 112, 129 of the Clean Air Act on ptnonipm and nonpt sector emissions, based on expec ted CAP co-benefits to sources in these

sectors.

• The same reductions were applied across all years w ith the exception of refinery facility/SCC reductions in the states and pollutants listed in T able 4-7. The refinery Consent Decrees are the only set of controls in which some of the compl iance dates are beyond 2008. Refinery reductions associated with compliance dates after D ecember 31, 2008 were not applied for the 2009 projection. Similarly, reductions with co mpliance dates after December 31, 201360

were not applied for the 2014 projection. The late st compliance date is in 2015; therefore all controls were applied for the 2020 projection.

• We applied all of the control programs as replaceme nt controls, which means that any existing percent reductions (“baseline control effi ciency”) 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 ref inery 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 disc overed that an issue in the 2002 NEI v3 point source baseline control efficiency caused an overestimation of PM 2.5 emission reductions.

The PM 2.5 estimates developed by augmenting based on a PM 10 estimate (to gap fill presumably missing emissions) for a source did not carry forwa rd the baseline control efficiency associated

with PM 10 . For these sources, the PM 2.5 baseline control efficiency was missing. Since we would expect similar control efficiencies for PM 2.5 and PM 10 , the application of control programs

for PM 2.5 for these presumably already-controlled sources li kely overestimated the actual reduction, resulting in underestimated PM 2.5 in the future year inventory. We estimated the impact of this underestimation to be less than 1500 tons of PM 2.5 . To put this into perspective, the entire ptnonipm PM 2.5 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 PM 2.5 in

2020.

Table 4-8 lists the ptnonipm and nonpt control prog rams and the affected pollutants.61

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

Control Strategies (Grouped by Affected Pollutants or Standard and App roach Used to Apply to the Inventory) Pollutants

Affected Approach or

Reference:

PTNONIPM Controls

NO X 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 manuf acture Large Appliances Miscellaneous Organic NESHAP (MON): Alkyd Resins, C helating 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 Pape r 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, 2007e62

Control Strategies (Grouped by Affected Pollutants or Standard and App roach 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 Regul ations 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 & SO 2 : Lime Manufacturing PM, SO2 8

MACT rules, plant-level, PM:

Taconite Ore PM 9

NONPT Controls

Municipal Waste Landfills: project factor of 0.25 a pplied, VOC EPA, 2007e

APPROACHES:

1. Used Emission Budget Inventories report (EPA, 1999) for list of SCCs for application of controls, and f or percent reductions (except IC Engines). Used Federa l Register on Response to Court decisions (Federal Register, 2004) for IC Engine percent reductions an d geographic applicability

2. For ALCOA consent decree, used http:// cfpub.epa.go v/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 verif ied using the world wide web.

5. Used data list of plants provided by project lead f rom 2001-based platform; required mapping the 2001 plants to 2002 NEI plants due to plant id changes across i nventory 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 we ighted average across two processes )

7. Percent reductions recommended and plants to apply to reduction to were based on recommendations by ru le lead engineer, and are consistent with the referenc e: EPA, 2007e

8. Percent reductions recommended are determined from the existing plant estimated baselines and estimate d 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 wil l both be 3786/13588 = 28%

9. Same approach used in CAIR: FR notice estimates red uctions of "PM emissions by 10,538 tpy, a reduction of about 62%." Used same list of plants as were ident ified 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 Locomot ive 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 inpu ts will be available in the rule docket (see http://www.epa.gov/otaq/marine.htm#regs ).

For non-NMIM generated mobile sources (i.e., aircra ft, locomotives, and commercial marine vessels), projection factors were computed and appl ied to the corresponding 2002 inventories. These projection factors reflect the combined effec ts of growth and reductions from national control programs.

A description of the projection approach used for e ach 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 Cal ifornia were projected for 2009, 2014, 2020 and 2030 using NMIM. These future year emissions a ccount for increased activity (vehicle miles traveled) and changes in fuels, fleet turnove r, and inspection and maintenance programs that account for implementation of national and loc al regulations.

We generated future-year VMT (input to NMIM) using Annual Energy Outlook (AEO) growth estimates (using 2006 AEO estimates) by vehicle typ e. Appendix G provides the VMT projection methodology.

The NMIM future year inputs also accounted for nati onal and some local control programs. For national control programs, they incorporated the ex pected 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” (M SAT Final), and the “Renewable Fuel Standard” (RFS).

For the state and voluntary programs, we included t he National Low Emission Vehicle Program (NLEV) and Ozone Transport Commission (OTC) LEV pro gram ( http://www.epa.gov/otaq/levnlev.htm ) in the future year inventories. These were includ ed based on state submission of the relevant external files. These programs affect nort heastern states. We also modeled reformulated gasoline opt-in programs using state-submitted exte rnal 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 th at 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 p rograms 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 sub mitted as part of the NEI by CARB. For California, we chose an approach that would maintai n consistency between the base year and future year emissions. This approach involved comp uting projection factors from a consistent set of future and 2002-year data based on the EMFAC 2007 model provided by CARB. We generated projection factors by dividing the EMFAC2 007-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 direc tly into the onroad sector of the 2002

Platform.

Where possible, we applied the projection factors a t the county, SCC, mode and pollutant level.

This could not be done where the 2002 emissions wer e zero, and the future emissions were greater than zero. In this case we applied more ag gregated ratios at the state, SCC and pollutant level. We also applied onroad-total, state-level r atios to project exhaust PM 10 and exhaust PM 2.5. We applied these projection factors to the 2002 inv entory uniformly, such that all onroad SCCs and all counties had the same projection factors fo r each of these pollutants. The reason we computed an aggregated factor was to generate a con sistent statewide future-to-base trend for these pollutants as had been estimated by CARB usin g 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 fo r 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 incr eases in activity (based on NONROAD model default growth estimates) and changes in fuel s and engines that reflect implementation of national regulations and local control programs.

The national regulations incorporated in the modeli ng 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-Ignit ion Engines, and Recreational Engines (Marine and Land-Based), November 8, 2002 ( “Pentathalon Rule”).

Not included is the “ Proposed Emission Standards for New Nonroad Spark-i gnition Engines, Equipment, and Vessels ” (“Bond Rule”), proposed April 17, 2007, or the “ Proposal for More Stringent Emissions Standards for Locomotives and M arine Compression-Ignition Engines ”, published April 3, 2007. We have not included volu ntary programs such as programs65

encouraging either no refueling or evening refuelin g on Ozone Action Days and diesel retrofit

programs.

The NMIM was not used to generate future-year nonro ad emissions for California, other than for

NH 3 . We used the NMIM for California future nonroad N H 3 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. W e then applied these projection factors

to the 2002 estimates in the 2002 Platform which we re based on an earlier version of California’s

nonroad model. As in the case of the California on road emissions, we were unable to incorporate the 2002 emissions from OFFROAD2007 dir ectly into nonroad sector of the 2002

Platform.

Where possible, we applied the projection factors u sing at the county, SCC, mode, and pollutant level. In many cases, the SCCs used in the OFFROAD 2007 model were more detailed than those in the earlier California data, and we theref ore developed and applied projection factors at a more aggregate level. Table 4-9 shows the aggreg ation 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 Gar den

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 Gar den

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 Grou nd

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 Eq uipment;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 Eq uipment;Total use 2270006*** + (sum across all gas commercial equip diesel) 2270007000 Off-highway Vehicle Diesel;Logging Equip ment;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 u se 2282010005 2282020000 Pleasure Craft;Diesel;Total use 228202* *** (sum of diesel pleasure craft)

There were no corresponding OFFROAD2007 emissions f or SO 2 for compressed natural gas fueled equipment; therefore, the future-year emissi ons remained at the same levels as in the 2002

NEI.



### 4.3.3 Aircraft (alm, ptnonipm)

We projected aircraft emissions based solely on act ivity growth using on data on itinerant (ITN) operations at airports. The ITN operations are def ined as aircraft take-offs whereby the aircraft leaves the airport vicinity and lands at another ai rport, or aircraft landings whereby the aircraft has arrived from outside the airport vicinity. Pro jected ITN information is available from the Federal Aviation Administration’s (FAA) Terminal Ar ea Forecast (TAF) System ( http://www.apo.data.faa.gov/main/taf.asp (publication date February 2006). This informati on is available for approximately 3300 individual airp orts, 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 commercia l aircraft growth factor for the broad SCC representing all aircraft types and operations (SCC 2275000000).

The methods that the FAA used for developing the IT N data in the TAF are documented in http://www.faa.gov/data_statistics/aviation/taf_rep orts/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 nationa l level growth factors for aircraft.67

Table 4-10. Factors used to grow aircraft emission s

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 gr owth 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 4-11 9

.

Table 4-11. Point Source SCCs representing aircraf t 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 source s with SCC 27602011 (Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust; Comme rcial; Jet Engine: Jet A) because the plant names associated with these point sources app eared to represent industrial facilities rather than airports. This SCC is only in one county, San ta Barbara, California (State/County FIPS 06083).

None of our aircraft emission projections account f or any control programs. We considered the

NO X standard adopted by the International Civil Aviati on Organization’s (ICAO) Committee on Aviation Environmental Protection (CAEP) in Februar y 2004, which is expected to reduce NO X by approximately 2% in 2015 and 3% in 2020. Howeve r, this rule has not yet been adopted as an EPA (or U.S.) rule; therefore, the effects of th is rule were not included in the future-year emissions projections.

9 Aircraft emissions were submitted as point sources by California, Illinois, and Minnesota, as describ ed in section

2.5.3.68



### 4.3.4 Locomotives (alm)

Future locomotive emissions were calculated using p rojection factors which were computed

based on national annual summaries of locomotive em issions in 2002 and future years, as provided by OTAQ. Appendix J provides these nation al annual summaries.

The future-year locomotive emissions account for in creased fuel consumption based on Energy Information Administration (EIA) fuel consumption p rojections for freight rail (EPA, 2007d), and emissions reductions resulting from emissions s tandards prior to the Locomotive-Marine rule, which was proposed April 7, 2007. These stan dards are:

• " Clean Air Nonroad Diesel Final Rule - Tier 4 " ( http://www.epa.gov/nonroaddiesel/2004fr.htm ), published June 29, 2004. This rule lowered diese l sulfur content.

• Locomotive Emissions Final Rulemaking, signed Decem ber 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 i nclude 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 we re 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 an d 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 fol lows:

• For diesel-fueled vessels, growth is based on EIA f uel consumption projections for domestic shipping.

• For residual-fueled vessels, growth is based freigh t tonnage as well as future changes in fleet makeup.

Emissions reductions from the following control pro grams 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 dies el sulfur content.

• Emission Standards for Commercial Marine Diesel Eng ines (published December 29, 1999).

• Tier 1 Marine Diesel Engines—Final Emission Standar ds (published February 28, 2003). Note that we used the same projection factor for bo th port and underway CMV emissions.

The development of the future-year CMV national, an nual 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 g asoline-powered commercial marine vessels.

Since the 2002 emissions for these vessels were rel atively 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 exc ept NH 3 . Since NH 3 emissions from residualfueled CMV are thought to be based on the amount of fuel consumed, similar to SO 2 from residual-fueled CMV, the projection factor for SO 2 was used to project NH 3

.


### 4.4 Canada, Mexico, and Offshore sources (othar, ot hon, othpt)

Future-year inventories were not available for Mexi co 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 supplie d 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 scena rios; the 2020 emissions were used in both the 2020 and 2030 scenarios. The future-year Canad ian emissions are consistent with the base year emissions, as described in Section 2.6.70



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