Appendix F.2. Area and Nonroad Mobile Sources Emissions Inventory DocumentationTABLE OF CONTENTS

I. INTRODUCTION AND SCOPE

**.................................................................. ...................1** 

II. OVERALL METHODOLOGY

**......... ........................**  **................................................... ...............1** 

**II.A. Source Category Indentification....................................**

**................................................... .1** 

**II.B. Area Source Emission Estimation Approach......... ............................................................1** 

**II.C. Nonroad Source Emission Estimation Approach**

**....................................... .......................2** 

III. VISTAS DEVELOPED 2002 AREA SOURCE INVENTORY

**............ ......**  **..............................2**

**III.A. Vehicle Refueling (Stage II) Emissions......**

**............... ..................................................3** 

**III.B. Portable Fuel Containers.................. .........................................................................3** 

**III.C. Forest Fires................................................**

**................................................... .......................3** 

IV. 2009 AREA SOURCE EMISSION INVENTORY DEVELOPMEN T

**............ ......**  **..................5**

**IV.A. Projection of Stationary Area Sources............ .............................................................5** 

**IV.B. Projection of Forest Fires Area Sources............**

**.................................................... ....6** 

V. VISTAS DEVELOPED 2002 NONROAD SOURCE INVENTORY

**...... .......**  **...... ......................7** 

**V.A. Nonroad Model Sources.................................................**

**........ .......................................7** 

**V.B. Aircraft Engines.................................**

**............................................................... ..........7** 

**V.C. Railroad Locomotives........................**

**................................................... .........................................8** 

**V.D. Commercial Marine Vessels (CMV).............**

**................................................... .............................9** 

VI. 2009 NONROAD SOURCE EMISSION INVENTORY DEVELOP MENT

**......................... ..11** 

**IV.A. Projection of Nonroad Model Sources..................**

**.................................... .................11** 

**IV.B. Projection of Non-Nonroad Model Sources............ ..................................................12** LIST OF TABLES

**Table F2-1: 2002 South Carolina Actual and Typical Fire Emissions.............................................5** 

**Table F2-2: 2002 National Rail Transportation Energ y Use by Fuel Type..........................................9** 

**Table F2-3: Estimated Emission Reduction Impacts Ba sed on T-4 Rule............................................14** 

**Table F2-4: EGU Estimated Emission Reduction Impact s Relative to VISTAS 2002 Base Year Values..15** LIST OF ACRONYMS

**Acronym Definition**  **AEO Annual Energy Outlook ATADS Air Traffic Activity Data System**  **BTS Bureau of Transportation Statistics**

**BTU British Thermal Units**

**CAIR Clean Air Interstate Rule CERR Consolidated Emissions Reporting Rule**  **CMU Carnegie Mellon University**

**CMV Commercial Marine Vessel CNG Compressed Natural Gas**  **EDMS Emissions and Dispersion Modeling System**

**EF Emission Factor EGAS 5.0 Economic Growth Analysis System version 5**  **.0 EIA Energy Information Administration**  **EIIP Emissions Inventory Improvement Program**

**FAA Federal Aviation Administration**

**GF Growth Factor HDD Heavy Duty Diesel**  **IAQTR Interstate Air Quality Transport Rule LPG Liquid Petroleum Gas**  **LTO Landing and Takeoff MSW Municipal Solid Waste**  **NAAQS National Ambient Air Quality Standards NAICS North American Industry Classification Syst**  **em NEI National Emissions Inventory** 

**NG Natural Gas NIF National Emissions Inventory Input Format**  **NOx Nitrogen Oxides NWR National Wildlife Refuge**  **OTAQ Office of Transportation and Air Quality**

**PFC Portable Fuel Container**

**PM Particulate Matter QA Quality Assurance**  **RIA Regulatory Impact Analysis**

**SCC Source Classification Code SI Spark-Ignition** 

**SIC Standard Industrial Classification SIWG Special Interest Workgroup** 

**T4 Tier 4 USACE U.S. Army Corps of Engineers**  **USEPA U.S. Environmental Protection Agency**

**USFA U.S. Fire Administration**

**USFWS U.S. Fish and Wildlife Service**

**USFS U.S. Forest Service VISTAS Visibility Improvement State & Tribal Asso**  **ciation of the Southeast**

**VMT Vehicle Miles Traveled VOC Volatile Organic Compounds** **1** AREA AND NONROAD MOBILE SOURCES

EMISSIONS INVENTORY DOCUMENTATION

I. INTRODUCTION AND SCOPE

**The attainment modeling for the Charlotte-Gastonia -Rock Hill, NC-SC 8-hour ozone nonattainment**  **area (referred to as the Metrolina area) was perfor med in conjunction with the regional haze modeling**  **being done by the Southeast Regional Planning Organ ization, Visibility Improvement State and Tribal**  **Association of the Southeast (VISTAS) and the fine particulate matter (PM2.5) and ozone modeling being**  **done by the Association of Southeastern Integrated Planning (ASIP). VISTAS and ASIP are run by the**  **ten Southeast states (Alabama, Florida, Georgia, Ke ntucky, Mississippi, North Carolina, South Carolina** 

**, Tennessee, Virginia and West Virginia). Since the**  **regional haze and PM2.5 modeling uses annual simulations and includes an intermediate year that**  **is the attainment year required for the Metrolina nonattainment area, the South Carolina Department o**  **f Health and Environmental Control (SCDHEC) decided to use this modeling for its attainment dem**  **onstration.**

**The South Carolina emissions inventory was develop ed by the VISTAS/ASIP contractors. A**  **statewide emissions inventory was developed for mod eling purposes and the emission estimates were**  **calculated in tons per year. Section III documents the VISTAS/ASIP-developed 2002 Area Source**  **emissions inventory, and Section IV addresses the d evelopment of the 2009 area source emissions**  **inventory. Sections V and VI document the nonroad emissions inventory for 2002 and 2009,**  **respectively.**

II. OVERALL METHODOLOGY

A. Source Category Identification

**The area source categories were identified from: U. S. Environmental Protection Agency's**  **(USEPA's) guidance document EPA-450/4-91-016,** Procedures for the Preparation of Emission Inventories of Carbon Monoxide and Precursors of Oz one, Vol. 1 **(the Procedures document); the** Emissions Inventory Improvement Program (EIIP) Tech nical Reports, Volume 3, Area Sources, December 2002 version ( **the EIIP Tech Report); and a report entitled,** Documentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for V ISTAS **, which was prepared by VISTAS’ contractor company, MACTEC, Inc.** 

**Nonroad mobile sources were identified from the Pr ocedures document. Nonroad mobile source**  **emissions are estimated by the methodologies sugges ted in: USEPA document, EPA-454/R-05-001,**  Emissions Inventory Guidance for Implementation of Ozone and Particulate Matter National Ambient Air Quality Standards (NAAQS) and Regional Haze Regulat ions **; EPA-450/4-81-026d (Revised)** Procedures for Emission Inventory Preparation, Volume IV; Mobil e Sources **(Mobile Source Procedures); and USEPA's nonroad mobile model, NONROAD2005c, which w**  **as released March 21, 2006.**

B. Area Source Emission Estimation Approach

**Area source emissions are estimated by multiplying an emission factor by some known indicator of**  **collective activity for each source category within the inventory area. An indicator is any parameter**  **associated with the activity level of a source that can be correlated with the air pollutant emissions**  **from that source, such as production, number of employee**  **s, or population.**

**In general, one of the following emissions estimat ion approaches is used to calculate the area source** **2**

**emissions: per capita emission factors, employment -related emission factors, commodity consumption**  **related emission factors, and level of activity-bas ed emission factors. The emission factors used wer**  **e obtained from the EIIP Tech Reports and either the**  **Procedures document or the USEPA's AP 42** Compilation of Air Pollutant Emission Factors, Fifth Edition **(AP 42).**

**There are several methods for estimating the activ ity level for a specific area source category. The**  **se are: treating area sources as point sources, surve**  **ying local activity levels, apportioning national o r**  **statewide activity totals to local inventory areas, using population or employment data.** 

**For certain categories, there can be overlap betwe en the point source emissions and the area source** 

**emissions calculated with emission factors. The 20 02 point source emissions in these categories were**  **identified so that they could be subtracted where a ppropriate.** 

C. Nonroad Source Emission Estimation Approach

**Non-highway mobile sources, sometimes referred to as off-road or nonroad mobile sources, are those**  **sources that can move but do not use the highway sy stem. Off-road mobile sources are further divided**  **into nonroad mobile sources, railroad locomotives, aircraft engines, and commercial marine vessels**  **(CMV). The estimation of emissions from mobile sou rces, like area sources, involves multiplying an**  **activity level by an emission factor.**

**The majority of the off-road mobile emissions were estimated by using the USEPA's off-road mobile**  **model NONROAD2005c. Direct emissions are generated with this model. Aircraft engine emissions**  **were estimated using the Federal Aviation Administr ation (FAA) Emissions and Dispersion Modeling**  **System (EDMS) model. Aircraft operations were inpu t into the model, after which the model predicts th**  **e engine emissions based on average landing and take-**  **off practices for the aircraft type. For railroad locomotive emissions, emission factors were obtaine**  **d from the Mobile Source Procedures document, and the activity level was obtained from the various ra**  **ilroad companies.**

III. VISTAS DEVELOPED 2002 AREA SOURCE INVENTORY

**Area sources represent a collection of many small, unidentified points of air pollution emissions**  **within a specified geographical area, emitting less than the minimum level prescribed for point source**  **s. Because these sources are too small and/or too nume**  **rous to be surveyed and characterized individually, all area source activities are collectively estimat**  **ed. The county is the geographic area for which emissions from area sources are compiled, primarily**  **because counties are the smallest areas for which data used for estimating emissions is readily avail**  **able. Emissions are calculated on an annual basis in** 
**tons per year.**

**South Carolina uses the CERR 2002 inventory as the basis for the VISTAS/ASIP Area Source**  **Inventory. Section III details the portion of the 2002 base year area source inventory, which was**  **developed for VISTAS/ASIP by the VISTAS contractor, MACTEC, Inc. This information was obtained**  **from the report entitled** Documentation of the Base G 2002 Base Year, 2009, an d 2018 Emission Inventories for VISTAS **, which was prepared for VISTAS by MACTEC, Inc. This report is**  **included in Appendix Q.** 

**Several major components of the area source sector of the inventory developed by VISTAS are**  **discussed in Sections III.A. through III.C. Stage II emissions are discussed in Section III.A. and we**  **re removed from the area source inventory and included**  **in the mobile sector of the inventory. Also, emissions from portable fuel containers were added**  **and are discussed in Section III.B. Section III.C describes the development of the fires emissions in**  **ventory and distinguishes the difference between an 3** 

**actual versus typical inventory with regard to fire s.** 

**The following Sections are based on excerpts (with some editing) taken from a document entitled,**  Documentation of the Base G 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS and prepared by MACTEC, Inc.

A. Vehicle Refueling (Stage II) emissions

**For the 2002 inventory, the VISTAS/ASIP States all agreed to remove the Stage II refueling**  **emissions from the area source inventory and includ e them in the nonroad and on-road sectors.** 

B. Portable Fuel Containers

**Portable fuel containers (PFCs), Source Classifica tion Code (SCC) 2501060300, cover emissions**  **from residential and commercial sector portable gas oline containers. Permeation, diurnal, transport,**  **spillage, and vapor displacement emissions are typi cally accounted for in this category. Spillage fro**  **m refueling operations and vapor displacement emissio**  **ns were not included in the inventory to avoid doub le**  **counting refueling in the nonroad sector.**

**MACTEC found that the USEPA had prepared a nationa l inventory of emissions by State for**  **portable fuel containers. Data on emissions from t his source prepared by the USEPA were presented in**  **the report,** Estimating Emissions Associated with Portable Fuel Containers (PFCs), Draft Report, Office of Transportation and Air Quality, USEPA, Report # EP A420-D-06-003, February 2006

**.**

**The 2002 county-level emission estimates were obta ined through an allocation method based on fuel**  **usage. Initially, 2005 emission estimates, excludi ng those from vapor displacement and spillage from**  **refueling operations, were obtained from the USEPA' s report and assumed to be equal to 2002 values.**  **Permeation, diurnal, and transport emission estimat es were summed and allocated to the county-level**  **based on the fuel usage information obtained from t he NONROAD2005 model. The SCCs that use**  **containers for refueling were acquired from the spi llage file of the NONROAD model. Then, the fuel**  **usages by county from the NONROAD 2005 runs prepare d for VISTAS/ASIP were summed for those**  **SCCs by county. The county-level fuel use was then divided by the State total fuel use for the same** 

**SCCs to determine the fraction of total State fuel usage and that fraction was used to allocate the St**  **ate level emissions to the county.** 

C. Forest Fires

**The fires source category includes wildfire, presc ribed burning, and land-clearing fires. These fire**  **s can be intermittent in nature, but many of these ca**  **n produce large quantities of air pollutant emissio ns.**  **Wildfires in certain rural areas can produce large, short-term organic emissions. Prescribed burning**  **is used as a forest management practice to establish f**  **avorable seedbeds, remove competing underbrush, accelerate nutrient cycling, control tree pests and**  **contribute other ecological benefits. Agricultura l**  **burning covers agricultural burning practices used to clear and/or prepare land for planting. In land**  **clearing fires, waste from logging operations is of ten burned under controlled conditions to reduce th**  **e potential fire hazards in forests and to remove bru**  **sh that can serve as a host for destructive insects**

**.**

**The total wildfire acreage burned was obtained fro m the South Carolina Forestry Commission for**  **each county in the State. These numbers however ar e replaced with the 2002 "typical" year for the**  **purpose of modeling. Fire emissions are not easily grown or projected. Thus, the replacement was don**  **e so that the fires represented in the area source in**  **ventory are considered typical and do not reflect a n**  **abnormally low or high year as far as fires. The t ypical year forest fire inventories were developed**  **by 4** 

**MACTEC, Inc., with input from state and federal for est resource staff. The typical year covered wildf**  **ire, prescribed burning, agricultural fires and land cle**  **aring fires. The development of the typical year inventory is described below.** 

**State-level ratios of acres over a longer-term rec ord (three or more years) were developed for each**  **fire type relative to 2002. The 2002 acreage was t hen scaled up or down based on these ratios to deve**  **lop a typical year inventory. VISTAS Fire Special Inte**  **rest Work Group based the ratio on county-level dat a**  **for States that supplied long-term fire-by-fire acr eage data rather than State-level ratios. Where St**  **ates did not supply long-term fire-by-fire acreage data, MAC**  **TEC reverted to using State-level ratios. With one broad exception (wildfires) this method was impleme**  **nted for all fires. MACTEC solicited long term fir e**  **by-fire acreage data by fire type from each VISTAS State. A minimum of three or more years of data**  **were used to develop the ratios. Those data were t hen used to develop a ratio for each county based o**  **n the number of acres burned in each county for each**  **fire type relative to 2002.**

**If VISTAS had long term county-prescribed fire dat a from a State, a county acreage ratio, described**  **below, was developed.**

**Ratio = Long term average county level Rx acres 2002 actual county level Rx acreage** 

**This ratio was then multiplied times the actual 20 02 acreage to get a typical value (basically the**  **long-term average county level acres). Wherever po ssible, this calculation was performed on a fire-by** 

**** **fire basis. The acreage calculated using the ratio was then used with the fuel loading and emission fa**  **ctor**
**values to calculate emissions.**

**There were three exceptions to this method.**

Exception 1: **Use of State Ratios for Wildfires**

**Wildfire estimates were developed using State rati os rather than county ratios because some counties**  **were showing unrealistic ratios, which were created by very short term data records or missing data.**  **In addition, exceptionally large and small fires were**  **removed from the database. VISTAS also removed all**

**fires less than 0.1 acres from the dataset.**

Exception 2: **Correction for Blackened Acres on Forest Service Lands** 

**Acreage values submitted by the U.S. Forest Servic e (USFS) for wildfires and prescribed fires on**  **USFS lands represented perimeter acres rather than "blackened" acres. Therefore, for prescribed fires**  **greater than 100 acres in size, the acreage was adj usted to be 80 percent of the initial reported valu**  **e. For prescribed fires of 100 acres or less, the acreage**  **values were maintained as reported. All reported a creage**  **values for wildfires were adjusted to be 66 percent of their values, as initially reported.** 

Exception 3: **Missing/Non-reported data**

**When VISTAS did not receive data from a VISTAS Sta te for a particular fire type, a composite**  **average for the entire VISTAS region was used to de termine the typical value for that type fire. This**  **technique was applied to all fire types when data w as missing.** 

**For wildfires and prescribed burning, ratios were also developed for "northern" and "southern" tier** **5**

**States within the VISTAS region, and those ratios w ere applied to each State with missing data dependi**  **ng upon whether they were considered a "northern" or "**  **southern" tier State. Development of "southern" an d**  **"northern" tier data was an attempt to account for a change from a predominantly pine/evergreen**  **ecosystem (southern) to a pine/deciduous ecosystem (northern).** 

**Table F2-1 below presents a comparison in tons per year of the 2002 actual fire emissions and the**  **2002 typical fire emissions for NOx and VOC for wil dfires, prescribed burning, agricultural fires and**  **land clearing fires in North Carolina.** 

Table F2-1

2002 South Carolina Actual and Typical Fire Emissio ns

**Actual Fire Emissions Typical Fire Emissions**

**Fire Type NOx (TPY) VOC (TPY) NOx (TPY) VOC (TPY)**

**Wildfires 1098.04 2408.60**  **3752.78 8231.91**  **Prescribed Burning 3574.59 7841.03**  **576.03 1263.55**  **Agricultural Fires - 3351.50** 

**- 2986.33**  **Landclearing Fires 258.94 600.76**  **941.29 2183.84** 

IV. 2009 AREA SOURCE EMISSION INVENTORY DEVELOPMEN T

**This Section describes the methodology used to dev elop the 2009 area source inventory. Separate**  **methods for projecting emissions were used for non- agricultural (stationary area), agricultural area**  **sources and forest fire area sources. The agricult ural area sources method are for ammonia emissions**  **and are not related to ozone formation; therefore, it w**  **ill not be discussed in this documentation. Since ammonia is important for regional haze and fine par**  **ticulate matter, the method for projecting agricult ural**  **area sources will be detailed in the respective Sta te Implementation Plans.** 

**The following Sections are based on excerpts (with some editing) taken from the document entitled,**  Documentation of the Base G 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS and prepared by MACTEC, Inc.

A. Projection of Stationary Area Sources

**VISTAS 2002 base year inventory emissions were use d as a starting point for calculating 2009**  **emissions. MACTEC, Inc. first back-calculated unco ntrolled emissions from the 2002 base year**  **inventory. Growth and control factors were then ap plied based on controls initially identified for th**  **e Clean Air Interstate Rule (CAIR) and growth factors**  **identified for the CAIR projections. In some case s,**  **Economic Growth Analysis System version 5.0 (EGAS 5 .0) growth factors were used if no growth factor**  **was available from the CAIR growth factor files.**

**The 2009 growth factors were obtained from the USE PA growth factors and indirectly from 2010**  **and 2015 CAIR growth factors. Using a 2001 base ye ar, interpolation of 2010 and 2015 CAIR growth**  **factors yielded 2009 growth factors. MACTEC used t he TREND function of Microsoft Excel for**  **interpolation. Interpolated growth factors were ca lculated at the State and SCC level.** 

**In a few cases, additional growth factors had to b e added for sources that had not initially been**  **included in a draft 2002 inventory. These growth f actors were obtained from EGAS 5.0. Finally, updat**  **es 6** 

**to growth factors from EGAS 5.0 were made for fuel- fired emission sources. The updated growth factors**  **reflected the most recent data from the Department of Energy's Annual Energy Outlook (AEO). These**  **data were used to reflect changes in energy efficie ncy resulting from new or updated fuel firing**  **technologies.**

B. Projection of Forest Fires Area Sources

**Several Federal agencies indicated that they had p lans for increased prescribed fire burning in futur**  **e years and that the "typical" fire inventory would l**  **ikely not adequately capture those increases. Thus**

**, MACTEC acquired the data necessary to provide 2009**  **specific projections for the prescribed fire component of the fire inventory.** 

**The U.S. Fish and Wildlife Service (USFWS) submitt ed annual acreage data by National Wildlife**  **Refuge (NWR) and by county, including estimates of acres burned per day for each NWR. USFS**  **provided fire-by-fire acreage estimates based on ma pping projected burning acreage to current 2002**  **modeling days. However, USFWS did not submit data for VISTAS original base year preparation**  **process, thus there was no known USFWS data in the 2002 actual or typical inventories. MACTEC,**  **therefore, developed a method that could use the co unty-level data submitted by USFWS.** 

**Several VISTAS/ASIP States run a prescribed fire-p ermitting program. To avoid double counting,**  **only State data and not USFWS or USFS data was used in those States for the 2002 actual inventory.** 

**The method used by MACTEC to include the USFS data applied a county-level data approach for**  **USFS data where a State had a prescribed fire permi tting program and a fire-by-fire replacement for**  **USFS data in States without permit programs. MACTE C used a county level approach for all of the**  **USFWS data. The approach used for each data set is discussed below.** 

**For USFWS data, 2002 annual county acres burned wa s subtracted from the USFWS projected**  **acreage. A 0.8 factor was applied to the differenc e to account for blackened acres instead of the tot**  **al perimeter acres that were reported. The revised to**  **tal additional USFWS acreage was then added to the total county "typical" acreage to determine future**  **acreage burned for 2009.**

**MACTEC then allocated the increased acreage to cur rent modeling days. The average daily acres**  **burned data provided by USFWS per NWR/county was us ed to allocate the acreage to the correct number**  **of days required to burn all of the acres. Guidanc e supplied by USFWS indicated that up to three time**  **s the average daily acres burned could potentially be**  **allocated to any one day.**

**For the USFS fire-by-fire acreage estimates, MACTE C summed the USFS data at a county-level for**  **States that had permit programs, then added the sum to the typical acreage and allocated the acres to**  **current modeling days. For States that do not have a State-prescribed fire permit program, MACTEC**  **simply replaced the current fire-by-fire records in the database with fire-by-fire records from the US**  **FS**

**and recalculated emissions based on fuel model and fuel loading. VISTAS also applied the same 0.8**  **correction for blackened acres applied to all USFS supplied acreage as the supplied values represented**  **perimeter acres.**

**An additional problem with developing year-specifi c prescribed fire projections was how to**  **adequately capture the temporal profile for those f ires. In the 2002 actual fire inventory, fires occ**  **ur on the same days as state/FLM records. In the 2002 "t**  **ypical" year inventory, fire acreage increased or decreased from acreage on the same fire days as wer**  **e in the 2002 actual inventory, since the acres wer e**  **simply increased for each day based on a multiplier used to convert from actual to typical.** **7**

**When prescribed fires acreage was added to a futur e year, MACTEC added acreage to individual fire**  **days proportional to the annual increase (if acreag e on a day is 10 percent of annual, add 10 percent**  **of projected increase to that same day).** 

V. VISTAS DEVELOPED 2002 NONROAD SOURCE INVENTORY

**Development of emission estimates for nonroad sour ces is documented in the MACTEC, Inc.**  **document entitled,** Documentation of the Base G 2002 Base Year, 2009 and 2018, Emission Inventories for VISTAS

**.**

**Nonroad mobile sources are those sources that can move but do not use the highway system.**  **Examples include lawn mowers, agricultural equipmen t, construction equipment, aircraft engines, railro**  **ad locomotives, powerboats, and commercial marine (shi**  **ps). All but the aircraft engine, railroad locomot ive**  **emissions and commercial marine activity were estim ated using the USEPA's off-road mobile model**  **NONROAD2005c, which was released March 21, 2006. D irect emissions are generated with this model.**  **This version incorporates all the USEPA final nonro ad engine emission standards, including the**  **recreational and large spark-ignition engines rules that were published in the Federal Register in**  **November 2002. Although this model is considered t o be a final model, an updated version is planned**  **that may incorporate revised inputs for the small s park-ignition (SI) (<19 kW) and recreational marine**  **SI categories in conjunction with additional promulgat**  **ed nonroad engine standards.**

**Nonroad mobile sources calculated through the NONR OAD model are discussed in Section V.A.**  **Aircraft, railroad and commercial marine emissions are discussed in Sections V.B. through V.D.** 

A. NONROAD Model Sources

**The nonroad mobile source category includes a dive rse collection of equipment such as lawn**  **mowers, chain saws, tractors, all terrain vehicles, fork lifts and construction equipment. The USEPA**  **NONROAD2005c model generates emissions directly and includes more than 80 different types of**  **equipment. To facilitate analysis and reporting, t he USEPA grouped the equipment types into ten**  **equipment categories. These include:**

**Agricultural equipment Lawn and garden equi pment**  **Airport ground support equipment Logging equip ment**  **Commercial equipment Railroad maintenance e quipment**  **Construction equipment Recreational marine e quipment**  **Industrial equipment Recreational equipment**

**Additionally, the emissions are broken out by five different engine types. These include: 2 stroke**  **and 4-stroke spark engines, diesel engines, liquid petroleum gas (LPG) and compressed natural gas**  **(CNG) fueled engines.**

B. Aircraft Engines

**Aircraft engines, like other engines, emit polluta nts whenever the engines are in operation.**  **However, the only emissions that are of concern for this inventory are the portion of the operation th**  **at occurs below the mixing layer. This is because the**  **emissions tend to disperse whenever the aircraft i s**  **above the mixing layer and, therefore, has little o r no effect on ground level ozone.** 

**The aircraft operations of interest are termed the landing and takeoff (LTO) cycle. The cycle begins**  **when the aircraft approaches the airport, descendin g below the mixing layer, lands, and taxis to the g**  **ate. 8** 

**It continues as the aircraft idles at the gate and then taxis back out to the runway for the subsequen**  **t takeoff and climbout as it heads back to cruising a**  **ltitudes, above the mixing layer.**

**Aircraft can be categorized by use into four class ifications: commercial, air taxis, general aviation** 

**, and military. Commercial aircraft include those us**  **ed for scheduled service transporting passengers an d/or**  **freight. Air taxis, or commuter aircraft, also fly scheduled service carrying passengers and/or freig**  **ht but usually are smaller aircraft and operate on a more**  **limited basis than commercial carriers. General aviation includes all other non-military aircraft u**  **sed for recreational flying, personal transportatio n, and**  **various other activities. Military aircraft covers a wide range of sizes, uses, and operating mission**  **s. The military aircraft are treated as a separate classif**  **ication since the LTO operations reported at the ai rports**  **group all military aircraft together.**

**Emission factors are available for the many aircra ft and engine combinations that exist. Factors for**  **each aircraft exist for four operating modes in the LTO cycle. Emissions are calculated by obtaining**  **data for the number of LTO cycles of the various aircraf**  **t at each airport in question, multiplying by the appropriate factors, and summing the results for th**  **e year under consideration.**

**Development of the 2002 aircraft emissions are des cribed in the MACTEC document entitled,**  Documentation of the Base G 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS **. This document refers back to a document entitled,**  Development of the VISTAS Draft 2002 Mobile Source Emission Inventory (February 2004 Version) **, prepared by E.H. Pechan & Associates, Inc. Both of these**  **documents are included in Appendix Q.**

**The starting point for development of aircraft emi ssions estimates is the 1999 National Emission**  **Inventory (1999 NEI) prepared by the USEPA. These emissions were grown to appropriate values for**  **2002 and 2009 using growth factors developed by the USEPA for the CAIR.** 

C. Railroad Locomotives

**Railroads are categorized by size (Class I, Class 2) and passenger service. Class I railroads are lo**  **ng haul operations, such as Norfolk Southern Corporati**  **on and CSX Corporation. Class II and Class III railroads are short lines, serving localized market**  **s. Passenger service is provided by Amtrak. These entities lease trackage from Class I railroads.** 

**Development of railroad emissions is described in the MACTEC document entitled,**  Documentation of the Base G 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS **. The VISTAS/ASIP**

**railroad emission estimates started with 1999 emiss ion estimates developed for the USEPA's 1999 NEI**  **Version 2 as base year estimates for the VISTAS reg ion. Additional information is provided in**  Development of the VISTAS Draft 2002 Mobile Source Emission Inventory (February 2004 Version), **prepared by E.H. Pechan & Associates, Inc.**

**Projected emissions for 2002 were developed in two steps as described below. For 1999 to 2001,**  **State-level rail fuel consumption was obtained from the Department of Energy, Energy Information**  **Administration's (EIA's) Fuel Oil and Kerosene Sale s. For 2001 to 2002, VISTAS applied national**  **growth factors developed from fuel consumption proj ections in EIA's Annual Energy Outlook. A growth**  **factor of 1.4 was used for locomotives and applied to 1999 emissions to first develop 2001 emissions.**  **Table F2-2 lists the growth factors used to generat e 2002 emissions.** **9** Table F2-2 2002 National Rail Transportation Energy Use by Fue l Type (Trillion BTU)

**2001 2002 Growth Factor**  **(GF)**
**Intercity Rail (Electric) 10.17 10.40 1.0226**  **Intercity Rail (Diesel) 16.60 16.88 1.0169**  **Transit Rail (Electric) 46.36 47.40 1.0224** 

Intercity/Transit Rail Average (SCC 2285002008) 1.0206

**Commuter Rail (Electric) 16.13 16.49 1.0223 Commuter Rail (Diesel)**  **26.31 26.76 1.0171**

Commuter Rail Average (SCC 2285002009) 1.0197

**Freight Rail (Distillate) (SCCs 2285002000, 2285002005, 2285002006,**  **2285002007, 2285002010) 512.81 492.32 0.9600** 

**Source: Department of Energy, Energy Information A dministration, Annual Energy Outlook 2003:**  **Table 34. Transportation Sector Energy Use by Fuel Type Within a Mode** 

D. Commercial Marine Vessels (CMV)

**The following description of development of commer cial marine emission estimates is based on**  **excerpts (with some editing) taken from the MACTEC document entitled**  Documentation of the Base G 2002 Base Year, 2009 and 2018 Emission Inventories f or VISTAS, **and a document entitled,** Development of the VISTAS Draft 2002 Mobile Source Emission Inv entory (February 2004 Version), **prepared by E.H. Pechan & Associates, Inc.** 

**An initial 2002 base year emissions inventory for commercial marine vessels (CMV) was prepared**  **for VISTAS in early 2004. The methods and data use d to develop the inventory are presented in a**  **February 9, 2004 report,** Development of the VISTAS Draft 2002 Mobile Source Emission Inventory (February 2004 Version) **, prepared by E.H. Pechan & Associates, Inc. Revis ions to the initial 2002**  **emissions inventory (prepared by Pechan) were imple mented to ensure that the latest State and local da**  **ta were incorporated. For CMV, South Carolina provide**  **d no revised data.**

**For 2002 commercial marine vessels (CMVs), Pechan used 1999 emission estimates developed for**  **the USEPA's 1999 NEI Version 2 as base year estimat es for the VISTAS region. Pechan then improved**  **the spatial distribution of CMV emission estimates for the VISTAS region.** 

**Ideally, CMV emission estimates would be developed using local activity data that account for**  **vessel type, engine type and mode of operation (cru ise, maneuvering, and hotelling). Creating this ty**  **pe of "bottom-up" emission inventory requires a large amo**  **unt of effort. Therefore, Pechan utilized port specific emission estimates developed for the 1999**  **NEI, distributed using a revised allocation methodology, which incorporates information on the**  **number of port facilities in each county.**

**The 2002 VISTAS commercial marine inventory is bas ed on the USEPA's 1999 NEI Version 2.0,**  **projected to 2002 using appropriate growth factors. The 1999 NEI estimated emissions for these** **10**

**categories according to the following Source Classi fication Codes (SCC):** 

**SCC Descriptor 1 Descriptor 3 Descriptor 6 Descript or 8**  **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** 

**For the 1999 NEI, commercial marine diesel emissio ns were developed by obtaining 2000 emission**  **estimates for all pollutants except SO2 from the US EPA's Office of Transportation and Air Quality**  **(OTAQ) marine diesel regulatory background document ation (Draft Regulatory Impact Analysis -**  **Control of Emissions from Compression-Ignition Mari ne Engines). To estimate emissions for 1999, 2000**  **estimates were backcast using growth factors obtain ed from the draft Regulatory Impact Analysis (RIA)**  **cited above. Steam-powered residual CMV emission e stimates were developed by obtaining fuel usage**  **data from OTAQ and applying fuel-based emission fac tors. A similar method was used for diesel SO**  **2 emissions. National diesel usage was estimated ass**  **uming a sulfur content of 0.25 percent and USEPA**

**emission factors.**

**In apportioning, distillate and residual fuels are considered separately. National diesel emissions**  **were disaggregated into port and underway emissions estimates based on the assumption that 75 percent**  **of distillate fuel is consumed within the port, whi le the remaining fuel is consumed while underway,**  **consistent with USEPA guidance. National residual emissions were disaggregated into port and**  **underway emissions estimates based on the assumptio n that 25 percent of residual fuel is consumed**  **within the port, while the remaining fuel is consum ed while underway.** 

**To allocate to counties, port emissions were assig ned to the 150 largest U.S. ports based on activity**  **obtained from the U.S. Army Corps of Engineers (USA CE). The percentage of total traffic for each port**  **was calculated by dividing the port-level traffic b y the total traffic. Emissions for each port were**  **then assigned to a single county.** 

**Underway emissions are assigned to counties based on a county's shipping lane traffic. The Bureau**  **of Transportation Statistics' (BTS) National Transp ortation Atlas Databases-1999 contains data on the**  **thousand tons per mile traveled for each shipping l ane link in the United States (BTS-CD26). Where**  **navigable rivers form a county or State boundary, t he shipping lane traffic is proportioned to individ**  **ual counties based on the length of shoreline that is s**  **hared. For example, if two counties share a naviga ble**  **river, and both counties have the same length of sh oreline, the shipping traffic is split evenly betwe**  **en the two counties. Shipping lanes that are not within c**  **ounties, for example in the ocean, are associated t o**  **States based on BTS assignments. These waterway we ights are then evenly distributed among the**  **counties within these States that have navigable wa terways. All shipping activity is summed at the**  **county-level and compared with national shipping ac tivity to determine what portion of activity can be**  **attributed to individual counties. These proportio ns were used in disaggregating the national CMV**  **emission estimates to the county level.**

**States that share borders with non-VISTAS States a long the Mississippi and Ohio Rivers have**  **expressed concern about the representativeness of p ort emission estimates at a county-level. Revising**  **the county-level emissions estimates would allow more a**  **ccurate modeling of emissions in the VISTAS**

**states.**

**For underway emissions, Pechan believes that the a llocation procedure results in a reasonable**  **distribution of county-level emissions. However, t he methodology to allocate port emissions results i**  **n all 11** 

**the emissions being assigned to a single county.**

**Port areas encompass multiple States and counties and, in some cases, multiple waterways.**  **Therefore, the emissions allocation process must in corporate all counties in the vicinity of the port**  **where activity is occurring. This is especially true for**  **inland rivers where activity takes place on both r iverbanks**  **and for ten river miles or more outside the port ci ty. The revised methodology allocates port emissio**  **ns based on a surrogate for port-related activity in e**  **ach county, rather than using a single county to de fine**  **the port.**

**The report, Waterborne Commerce of the United Stat es, Calendar Year 1999, hereinafter referred to**  **as Waterborne Commerce, presents the cargo tonnage and number of vessel trips in major waterways of**  **the United States. The report defines port areas, which USACE uses to develop the Top 150 Ports in th**  **e United States by amount of cargo tonnage. As discu**  **ssed previously, the 1999 NEI allocates all the por t**  **emissions to these 150 ports based on the cargo ton nage handled by the port. Pechan uses this allocat**  **ion of emissions to each port area as the starting poin**  **t of its revised allocation process. Port Charlest on**  **(Charleston Harbor, SC) and the Port of Georgetown (Georgetown Harbor, SC - Winyah Bay) are the two**  **main ports in South Carolina.**

**The next step was to develop a list of counties th at make up the port area. Port area definitions we**  **re obtained from Waterborne Commerce. The port area d**  **efinition for Port Charleston was "Ocean to Goose Creek via Cooper River and Town Creek; to the Stand**  **ard Wharf on Ashley River; to the Mount Pleasant Memorial Highway Bridge on Shem Creek; to the Airco**  **Alloys Wharf on Shipyard River; Wando River to Cainhoy." The port area definition for the Port**  **of Georgetown was "Atlantic Ocean Entrance to Winyah Bay, SC, to and including turning basin in S**  **ampit River at the City of Georgetown, SC.". Using the port definitions by river mile, Pechan establis**  **hed which counties are included in each port area. Port** 

**Charleston is included in the counties of Charlesto n and Berkeley. The Port of Georgetown is included**  **in Georgetown County.** 

**The next step in allocating emissions is to develo p a surrogate for the amount of CMV activity in**  **each county of the port area. Pechan assumed that the activity of vessels in each county is related t**  **o the number of port facilities operating in a given coun**  **ty. Port facilities include terminals, piers, whar ves, and**  **docks that are involved in all types of commercial activity and support services. Pechan obtained the**  **number of port facilities in each county from USACE reports, The Port Series Reports. The USACE**  **periodically surveys the commercial marine industry to obtain information on port facilities and publi**  **shes it in The Port Series Reports. The reports give th**  **e name, location, operations, and describe the phys ical** 

**and inter-modal characteristics of the facilities. The data includes the location of the facility by**  **river mile, State, and county.** 

**For each port area, Pechan calculated the ratio be tween the number of port facilities in each county** 

**to the total number of facilities in all counties t hat make up the port area. This ratio was used to**  **allocate emissions for each port area to the county-level.**  **The ratio for Port Charleston was 0.7097 in Charles ton**  **county and 0.2903 in Berkeley county. The ratio fo r the Port of Georgetown was 1.0 in Georgetown**  **County. Pechan was directed to perform the realloc ation for all VISTAS ports.** 

VI. 2009 NONROAD SOURCE EMISSION INVENTORY DEVELOP MENT

**The subsections that follow describe the projectio n process used to develop 2009 nonroad projection** 

**estimates for sources found in the NONROAD model an d those sources estimated outside of the model**  **(locomotives, airplanes, and commercial marine vess els).** 

A. Projection of NONROAD Model Sources**12**

**NONROAD model input files were prepared based on t he 2002 base year inventory input files with**  **appropriate updates for the projection years. Othe r specific updates for the projection years for** 

**NONROAD model sources consist of:**

• **Revision of the emission inventory year in the mode l (as well as various output file naming**  **commands) to be reflective of the projection year.**

• **Revision of the fuel sulfur content for gasoline a nd diesel powered equipment.** 

• **Implementation of a limited number of local control program changes (national control program**  **changes are handled internally within the NONROAD m odel, so explicit input file changes are not**  **required).**

**All equipment population growth and fleet turnover impacts are handled internally within the**  **NONROAD model, so that explicit input file changes are not required.** 

**The final NONROAD2005c that was used for inventory development is capable of handling separate**  **diesel fuel sulfur inputs for land based and marine based nonroad equipment in a single model executio**  **n. The following diesel fuel sulfur values were used:** 

**Diesel Fuel Sulfur (ppm) 2002 2009**

**Land-Based 2500**  **348**

**Marine-Based 2638**  **408**

B. Projection of Non-NONROAD Model Sources

**Using the 2002 base year emissions inventory for a ircraft, locomotives, and CMV prepared as**  **described earlier in this document, corresponding e mission projections for 2009 were developed. The**  **following description is largely taken from the MAC TEC document entitled,**  Documentation of the Base G 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS **. Briefly, the methodology relies on growth and control factors developed from inventori**  **es used in support of recent USEPA rulemakings, and consists of the following steps:** 

**(a) Begin with the 2002 base year emission estima tes for aircraft, locomotive, and CMV.** 

**(b) Detailed inventory data (both before and afte r controls) for these same emission sources for**  **1996, 2010, 2015, and 2020 were obtained from the U SEPA's CAIR Technical Support Document. Using**  **these data, combined growth and control factors for the period 2002 - 2009 were estimated using straig**  **ht line interpolation between 1996 and 2010 (for 2009)**  **. This is done at the State county SCC pollutant le vel** 

**of detail.**

**(c) The USEPA growth and control data are matched against the 2002 VISTAS base year data**  **using State county SCC pollutant as the match key. Ideally, there would be a one-to-one match and the**  **process would end at this point. Unfortunately, ac tual match results were not always ideal, so additi**  **onal matching criteria were required. For subsequent re**  **ference, this initial (highest resolution) matching criterion is denoted as the "CAIR Primary" criterio**  **n. 13** 

**(d) A second matching criterion is applied that u tilizes a similar, but higher-level SCC (lower**  **resolution) matching approach. For example, SCC 22 75020000 (commercial aircraft) in the 2002 base**  **year inventory data would be matched with SCC 22750 00000 (all aircraft) in the CAIR data. This**  **criterion is applied to records in the 2002 base ye ar emissions file that are not matched using the "C**  **AIR Primary" criterion, and is also performed at the St**  **ate county SCC pollutant level of detail. For subsequent reference, this is denoted as the "CAIR**  **Secondary" criterion. A number of unmatched record s**  **continued to remain at the end of this process, so a third level matching criterion was required.** 

**(e) In the third matching step (the most frequent ly used SCC in the USEPA CAIR files for**  **each of the aircraft, locomotive, and commercial ma rine sectors) is averaged at the State level to pro**  **duce a "default" State and pollutant specific growth and**  **control factor for the sector. The resulting fact or is**  **used as a "default" growth factor for all unmatched county SCC pollutant level data in each State. In**  **effect, State-specific growth data are applied to c ounty level data for which an explicit match betwee**  **n the VISTAS 2002 base year data and the USEPA CAIR data**  **could not be developed. The default growth and control SCCs are 2275020000 (commercial aircraft) f**  **or the aircraft sector, 2280002000 (commercial marine diesel total) for the CMV sector, and 228500**  **2000 (railroad equipment diesel total) for the locomotive sector. Matches made using this criteri**  **on are denoted as "CAIR Tertiary" matches.**

**(f) According to USEPA documentation, the CAIR ba seline emissions include the impacts of**  **the (then proposed) Tier 4 (T4) nonroad diesel rule making, which implements a low sulfur fuel**  **requirement that affects both future CMV and locomo tive emissions. However, the impacts of this rule**  **were originally intended to be excluded from the in itial VISTAS 2018 forecast, which was to include on**  **ly "on-the-books" controls. (The T4 rule was finalize**  **d subsequent to the development of the preliminary 2018 inventory in March of 2004.) Given its final**  **status, T4 impacts have now been moved into the "on the books" inventory for nonroad equipment. In add**  **ition, since there are no other proposed rules affecting the nonroad sector between 2002 and 2018,**  **there is no difference between the 2018 "on the books" and 2018 "on the way" inventories for the se**  **ctor; therefore, only a single forecast inventory ( for**  **each evaluation year) was developed. Nevertheless, since the algorithms developed to produce the**  **VISTAS forecasts were developed when there was a di stinction between the "on the books" and "on the**  **way" inventories, the distinct algorithms used to p roduce the two inventories have been maintained eve**  **n though the conceptual distinctions have been lost.**  **This approach was taken for two reasons. First, i t**  **allowed the previously developed algorithms to be u tilized without change. Second, it allowed for**  **separate treatment of the T4 emissions impact which was important as those impacts have changed**  **between the proposed and final T4 rules. Thus, pre vious USEPA inventories that include the proposed**  **T4 impacts would not be accurate, and the procedura l discussion continues to reflect the distinctions**  **between non-T4 and T4 emissions, as these distincti ons continue to be intrinsically important to the**  **forecasting process. A second set of USEPA CAIR fi les that excluded the Tier 4 diesel impacts was**  **therefore obtained, and the same matching exercise described above in steps (b) through (e) was**  **performed using these "No T4" files. It is importa nt to note that the matching exercise described in**  **steps (b) through (e) cannot simply be replaced, because**  **the "No T4" files obtained from the USEPA include only those SCCs specifically affected by the T4 rul**  **e (e.g., diesel CMV and locomotives). In effect, t he**  **matching exercise was augmented (rather than replac ed) with an additional three criteria analogous to**  **those described in steps (c) through (e), and these are denoted as the "No T4 Primary," "No T4**  **Secondary," and "No T4 Tertiary" criteria. Because they exclude the impacts of the proposed T4 rule,**  **matches using the "No T4" criteria supersede matche s made using the basic CAIR criteria (as described**  **in steps (c) through (e) above).** 

**(g) The CAIR matching criteria were overridden fo r any record for which States provided**  **local growth data. Only North Carolina provided th ese forecasts, as North Carolina has provided speci**  **fic growth factors for airport emissions in four counti**  **es. 14** 

**(h) Using this approach, each State county SCC po llutant was assigned a combined growth**  **and control factor using the USEPA CAIR forecast or locally provided data. The 22,838 data records for**  **aircraft, locomotives, and CMV in the 2002 base yea r emissions file were assigned growth factors in**  **accordance with the following breakdown:**

**48 records matched State provided growth factors**

**, 4,179 records matched using the CAIR Primary cri**  **terion, 240 records matched using the CAIR Secondary cri**  **terion, 7,463 records matched using the CAIR Tertiary cr**  **iterion, 720 records matched using the No T4 Primary crit**  **erion, 3,858 records matched using the No T4 Secondary**  **criterion, and 6,330 records matched using the No T4 Tertiary c**  **riterion.**

**(i) Finally, the impacts of the T4 rule as adopte d were applied to the grown "non T4" emission** 

**estimates. The actual T4 emission standards do not affect aircraft, locomotive, or CMV directly, but**  **associated diesel fuel sulfur requirements do affec t locomotives and CMV. Lower fuel sulfur content**  **affects both SO2 and PM emissions. Expected fuel s ulfur content was obtained for 2009 from the**  **USEPA technical support document for the final T4 r ule (**  Final Regulatory Analysis: Control of Emissions from Nonroad Diesel Engines, EPA420 R 04 0 07, May 2004 **). According to that document, the average diesel fuel sulfur content for locomotives**  **and CMV is expected to be 408 parts per million by weight (ppmW) in 2009. This compares to expected n**  **on T4 fuel sulfur levels of 2599 ppmW in 2009.**

**Table F2-3 uses calculated emissions estimates for base and T4 control scenarios to estimate emission**  **reduction impacts.**

Table F2-3

Estimated Emission Reduction Impacts based on T-4 R ule

2009

**CMV SO 2**  **= Non-T4 SO 2**  **× 0.1569**

**Locomotive SO 2**  **= Non-T4 SO 2**  **× 0.1569**

**CMV PM = Non-T4 PM × 0.8962**

**Locomotive PM = Non-T4 PM × 0.8117**

**However, since the diesel fuel sulfur content assu med for the 2002 VISTAS base year inventory,**  **upon which the 2009 inventory was based, is 2500 pp mW, a small adjustment to the emission reduction**  **multiplier calculated from the T4 rule is appropria te since they are measured relative to modestly dif**  **ferent sulfur contents (2599 ppmW for 2009). Correcting f**  **or these modest differences produces the emission reduction impact estimates relative to forecasts ba**  **sed on the VISTAS 2002 inventory shown in Table F2-**

**4. 15**  Table F2-4 Estimated Emission Reduction Impacts Relative to VI STAS 2002 Base

Year Values

2009

**CMV SO 2**  **= Non-T4 SO 2**  **× 0.1632**

**Locomotive SO 2**  **= Non-T4 SO 2**  **× 0.1632**

**CMV PM = Non-T4 PM × 0.9004**

**Locomotive PM = Non-T4 PM × 0.8187**

**These factors were applied directly to the non T4 emission forecasts to produce the final VISTAS**  **2009 emissions inventories for aircraft, locomotive , and CMV.** 

**During the development of the preliminary 2018 VIS TAS inventory in March 2004, this process**  **yielded reasonable results and exhibited no particu lar systematic concerns. However, when the 2009**  **Base F inventory was developed, significant concern s related to SO**  **2 and PM were encountered.**  **Essentially, what was revealed by the Base F 2009 f orecast was a series of apparent inconsistencies in**  **the CAIR 2010 and 2015 emission inventories (as compare**  **d to the 1996 and 2020 CAIR inventories) that were masked during the construction of the "longer-**  **term" 2018 inventory.**

**For the most part, the issue seems to be centered on SO2 and PM records, which are those records**  **primarily affected by the T4 rule. But, as noted ab ove, there does not seem to be any pattern of**  **consistency that would indicate that either inclusi on or exclusion of T4 rule impacts is the underlyin**  **g cause. Moreover, where they occur, the observed gr**  **owth extremes generally affect both SO 2**  **and PM equally, while one would expect PM effects to be bu**  **ffered if the T4 rule was the underlying cause sinc e**  **changes in diesel fuel sulfur content will only aff ect a fraction of PM (e.g., sulfate), while directl**  **y reducing SO**  **2**

**.**

**While forecast inventories for aircraft, locomotiv es, and CMV were developed for 2009 and 2018**  **using both growth methods, it was ultimately decide d to utilize the 1996-2020 growth basis since it**  **provided more reasonable growth rates for 2009.**