Appendix F.2. 

Area and Nonroad Mobile Sources Emissions Inventory Documentation 
TABLE 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 DEVELOPMENT....................................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 DEVELOPMENT...........................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 Energy Use by Fuel Type..........................................9 

Table F2-3: Estimated Emission Reduction Impacts Based on T-4 Rule............................................14 

Table F2-4: EGU Estimated Emission Reduction Impacts 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 System 

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 Association 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 performed in conjunction with the regional haze modeling 

being done by the Southeast Regional Planning Organization, 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, Kentucky, 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 of Health and Environmental Control (SCDHEC) 

decided to use this modeling for its attainment demonstration. 

 The South Carolina emissions inventory was developed by the VISTAS/ASIP contractors. A 

statewide emissions inventory was developed for modeling 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 development 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 Ozone, Vol. 1 (the Procedures document); the 

Emissions Inventory Improvement Program (EIIP) Technical 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 VISTAS, which was prepared by VISTAS’ contractor 

company, MACTEC, Inc. 

 Nonroad mobile sources were identified from the Procedures document. Nonroad mobile source 

emissions are estimated by the methodologies suggested 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 Regulations; EPA-450/4-81-026d (Revised) Procedures 

for Emission Inventory Preparation, Volume IV; Mobile Sources (Mobile Source Procedures); and 

USEPA's nonroad mobile model, NONROAD2005c, which was 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 employees, or population. 

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

emissions: per capita emission factors, employment-related emission factors, commodity consumptionrelated emission factors, and level of activity-based emission factors. The emission factors used were 

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 activity level for a specific area source category. These 

are: treating area sources as point sources, surveying local activity levels, apportioning national or 

statewide activity totals to local inventory areas, using population or employment data. 

 For certain categories, there can be overlap between the point source emissions and the area source 

emissions calculated with emission factors. The 2002 point source emissions in these categories were 

identified so that they could be subtracted where appropriate. 

 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 system. 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 sources, 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 Administration (FAA) Emissions and Dispersion Modeling 

System (EDMS) model. Aircraft operations were input into the model, after which the model predicts the 

engine emissions based on average landing and take-off practices for the aircraft type. For railroad 

locomotive emissions, emission factors were obtained from the Mobile Source Procedures document, and 

the activity level was obtained from the various railroad 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 sources. 

Because these sources are too small and/or too numerous to be surveyed and characterized individually,

all area source activities are collectively estimated. 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 available. 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, and 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 were 

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 inventory and distinguishes the difference between an
3 

actual versus typical inventory with regard to fires. 

 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 include them in the nonroad and on-road sectors. 

 B. Portable Fuel Containers 

 Portable fuel containers (PFCs), Source Classification Code (SCC) 2501060300, cover emissions 

from residential and commercial sector portable gasoline containers. Permeation, diurnal, transport, 

spillage, and vapor displacement emissions are typically accounted for in this category. Spillage from 

refueling operations and vapor displacement emissions were not included in the inventory to avoid double 

counting refueling in the nonroad sector. 

 MACTEC found that the USEPA had prepared a national inventory of emissions by State for 

portable fuel containers. Data on emissions from this 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 # EPA420-D-06-003, February 2006. 

 The 2002 county-level emission estimates were obtained through an allocation method based on fuel 

usage. Initially, 2005 emission estimates, excluding 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 estimates were summed and allocated to the county-level 

based on the fuel usage information obtained from the NONROAD2005 model. The SCCs that use 

containers for refueling were acquired from the spillage file of the NONROAD model. Then, the fuel 

usages by county from the NONROAD 2005 runs prepared 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 Statelevel emissions to the county. 

 C. Forest Fires 

 The fires source category includes wildfire, prescribed burning, and land-clearing fires. These fires 

can be intermittent in nature, but many of these can produce large quantities of air pollutant emissions. 

Wildfires in certain rural areas can produce large, short-term organic emissions. Prescribed burning is 

used as a forest management practice to establish favorable seedbeds, remove competing underbrush, 

accelerate nutrient cycling, control tree pests and contribute other ecological benefits. Agricultural 

burning covers agricultural burning practices used to clear and/or prepare land for planting. In land

clearing fires, waste from logging operations is often burned under controlled conditions to reduce the 

potential fire hazards in forests and to remove brush that can serve as a host for destructive insects. 

 The total wildfire acreage burned was obtained from the South Carolina Forestry Commission for 

each county in the State. These numbers however are replaced with the 2002 "typical" year for the 

purpose of modeling. Fire emissions are not easily grown or projected. Thus, the replacement was done 

so that the fires represented in the area source inventory are considered typical and do not reflect an 

abnormally low or high year as far as fires. The typical year forest fire inventories were developed by 
4 

MACTEC, Inc., with input from state and federal forest resource staff. The typical year covered wildfire, 

prescribed burning, agricultural fires and land clearing fires. The development of the typical year 

inventory is described below. 

 State-level ratios of acres over a longer-term record (three or more years) were developed for each 

fire type relative to 2002. The 2002 acreage was then scaled up or down based on these ratios to develop 

a typical year inventory. VISTAS Fire Special Interest Work Group based the ratio on county-level data 

for States that supplied long-term fire-by-fire acreage data rather than State-level ratios. Where States did 

not supply long-term fire-by-fire acreage data, MACTEC reverted to using State-level ratios. With one

broad exception (wildfires) this method was implemented for all fires. MACTEC solicited long term fireby-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 then used to develop a ratio for each county based on 

the number of acres burned in each county for each fire type relative to 2002. 

 If VISTAS had long term county-prescribed fire data 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 2002 acreage to get a typical value (basically the 

long-term average county level acres). Wherever possible, this calculation was performed on a fire-byfire basis. The acreage calculated using the ratio was then used with the fuel loading and emission factor 

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 ratios 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 Service (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 adjusted to be 80 percent of the initial reported value. For 

prescribed fires of 100 acres or less, the acreage values were maintained as reported. All reported acreage 

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 State for a particular fire type, a composite 

average for the entire VISTAS region was used to determine the typical value for that type fire. This

technique was applied to all fire types when data was missing. 

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

States within the VISTAS region, and those ratios were applied to each State with missing data depending 

upon whether they were considered a "northern" or "southern" tier State. Development of "southern" and 

"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 wildfires, prescribed burning, agricultural fires and land 

clearing fires in North Carolina. 

Table F2-1 

2002 South Carolina Actual and Typical Fire Emissions 

 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 DEVELOPMENT 

 This Section describes the methodology used to develop 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 agricultural area sources method are for ammonia emissions and 

are not related to ozone formation; therefore, it will not be discussed in this documentation. Since 

ammonia is important for regional haze and fine particulate matter, the method for projecting agricultural 

area sources will be detailed in the respective State 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 used as a starting point for calculating 2009 

emissions. MACTEC, Inc. first back-calculated uncontrolled emissions from the 2002 base year 

inventory. Growth and control factors were then applied based on controls initially identified for the 

Clean Air Interstate Rule (CAIR) and growth factors identified for the CAIR projections. In some cases, 

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 USEPA growth factors and indirectly from 2010 

and 2015 CAIR growth factors. Using a 2001 base year, interpolation of 2010 and 2015 CAIR growth 

factors yielded 2009 growth factors. MACTEC used the TREND function of Microsoft Excel for 

interpolation. Interpolated growth factors were calculated at the State and SCC level. 

 In a few cases, additional growth factors had to be added for sources that had not initially been 

included in a draft 2002 inventory. These growth factors were obtained from EGAS 5.0. Finally, updates 
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 efficiency resulting from new or updated fuel firing 

technologies. 

 B. Projection of Forest Fires Area Sources 

 Several Federal agencies indicated that they had plans for increased prescribed fire burning in future 

years and that the "typical" fire inventory would likely 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) submitted 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 mapping 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 county-level data submitted by USFWS. 

 Several VISTAS/ASIP States run a prescribed fire-permitting 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 permitting program and a fire-by-fire replacement for 

USFS data in States without permit programs. MACTEC 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 was subtracted from the USFWS projected 

acreage. A 0.8 factor was applied to the difference to account for blackened acres instead of the total 

perimeter acres that were reported. The revised total 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 current modeling days. The average daily acres 

burned data provided by USFWS per NWR/county was used to allocate the acreage to the correct number 

of days required to burn all of the acres. Guidance supplied by USFWS indicated that up to three times 

the average daily acres burned could potentially be allocated to any one day. 

 For the USFS fire-by-fire acreage estimates, MACTEC 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 USFS 

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-specific prescribed fire projections was how to 

adequately capture the temporal profile for those fires. In the 2002 actual fire inventory, fires occur on 

the same days as state/FLM records. In the 2002 "typical" year inventory, fire acreage increased or 

decreased from acreage on the same fire days as were in the 2002 actual inventory, since the acres were 

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 future year, MACTEC added acreage to individual fire 

days proportional to the annual increase (if acreage 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 sources 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 equipment, construction equipment, aircraft engines, railroad 

locomotives, powerboats, and commercial marine (ships). All but the aircraft engine, railroad locomotive 

emissions and commercial marine activity were estimated using the USEPA's off-road mobile model 

NONROAD2005c, which was released March 21, 2006. Direct emissions are generated with this model. 

This version incorporates all the USEPA final nonroad 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 to be a final model, an updated version is planned 

that may incorporate revised inputs for the small spark-ignition (SI) (<19 kW) and recreational marine SI 

categories in conjunction with additional promulgated nonroad engine standards. 

 Nonroad mobile sources calculated through the NONROAD 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 diverse 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, the USEPA grouped the equipment types into ten 

equipment categories. These include: 

 Agricultural equipment Lawn and garden equipment 

 Airport ground support equipment Logging equipment 

 Commercial equipment Railroad maintenance equipment 

 Construction equipment Recreational marine equipment 

 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 pollutants whenever the engines are in operation. 

However, the only emissions that are of concern for this inventory are the portion of the operation that 

occurs below the mixing layer. This is because the emissions tend to disperse whenever the aircraft is 

above the mixing layer and, therefore, has little or 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, descending below the mixing layer, lands, and taxis to the gate. 
8 

It continues as the aircraft idles at the gate and then taxis back out to the runway for the subsequent 

takeoff and climbout as it heads back to cruising altitudes, above the mixing layer. 

 Aircraft can be categorized by use into four classifications: commercial, air taxis, general aviation, 

and military. Commercial aircraft include those used for scheduled service transporting passengers and/or 

freight. Air taxis, or commuter aircraft, also fly scheduled service carrying passengers and/or freight but 

usually are smaller aircraft and operate on a more limited basis than commercial carriers. General 

aviation includes all other non-military aircraft used for recreational flying, personal transportation, and 

various other activities. Military aircraft covers a wide range of sizes, uses, and operating missions. The 

military aircraft are treated as a separate classification since the LTO operations reported at the airports 

group all military aircraft together. 

 Emission factors are available for the many aircraft 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 aircraft at each airport in question, multiplying by the 

appropriate factors, and summing the results for the year under consideration. 

 Development of the 2002 aircraft emissions are described 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 emissions 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 long 

haul operations, such as Norfolk Southern Corporation and CSX Corporation. Class II and Class III 

railroads are short lines, serving localized markets. 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 emission estimates developed for the USEPA's 1999 NEI 

Version 2 as base year estimates for the VISTAS region. 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 Sales. For 2001 to 2002, VISTAS applied national 

growth factors developed from fuel consumption projections 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 generate 2002 emissions. 
9 

Table F2-2 

2002 National Rail Transportation Energy Use by Fuel 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 Administration, 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 commercial 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 for VISTAS, and a document entitled, Development 

of the VISTAS Draft 2002 Mobile Source Emission Inventory (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 used 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. Revisions to the initial 2002 

emissions inventory (prepared by Pechan) were implemented to ensure that the latest State and local data 

were incorporated. For CMV, South Carolina provided 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 estimates 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 (cruise, maneuvering, and hotelling). Creating this type of 

"bottom-up" emission inventory requires a large amount of effort. Therefore, Pechan utilized portspecific 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 based 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 Classification Codes (SCC): 

SCC Descriptor 1 Descriptor 3 Descriptor 6 Descriptor 8 

2280002100 Mobile Sources Marine Vessels, Commercial Diesel Port emissions 

2280002200 Mobile Sources Marine Vessels, Commercial Diesel Underway emissions 

2280003100 Mobile Sources Marine Vessels, Commercial Residual Port emissions 

2280003200 Mobile Sources Marine Vessels, Commercial Residual Underway emissions 

 For the 1999 NEI, commercial marine diesel emissions were developed by obtaining 2000 emission 

estimates for all pollutants except SO2 from the USEPA's Office of Transportation and Air Quality 

(OTAQ) marine diesel regulatory background documentation (Draft Regulatory Impact Analysis - 

Control of Emissions from Compression-Ignition Marine Engines). To estimate emissions for 1999, 2000 

estimates were backcast using growth factors obtained from the draft Regulatory Impact Analysis (RIA) 

cited above. Steam-powered residual CMV emission estimates were developed by obtaining fuel usage 

data from OTAQ and applying fuel-based emission factors. A similar method was used for diesel SO2

emissions. National diesel usage was estimated assuming 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, while 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 assumption that 25 percent of residual fuel is consumed 

within the port, while the remaining fuel is consumed while underway. 

 To allocate to counties, port emissions were assigned to the 150 largest U.S. ports based on activity

obtained from the U.S. Army Corps of Engineers (USACE). The percentage of total traffic for each port

was calculated by dividing the port-level traffic by 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 Transportation Atlas Databases-1999 contains data on the 

thousand tons per mile traveled for each shipping lane link in the United States (BTS-CD26). Where 

navigable rivers form a county or State boundary, the shipping lane traffic is proportioned to individual 

counties based on the length of shoreline that is shared. For example, if two counties share a navigable 

river, and both counties have the same length of shoreline, the shipping traffic is split evenly between the 

two counties. Shipping lanes that are not within counties, for example in the ocean, are associated to 

States based on BTS assignments. These waterway weights are then evenly distributed among the 

counties within these States that have navigable waterways. All shipping activity is summed at the 

county-level and compared with national shipping activity to determine what portion of activity can be

attributed to individual counties. These proportions were used in disaggregating the national CMV 

emission estimates to the county level. 

 States that share borders with non-VISTAS States along the Mississippi and Ohio Rivers have 

expressed concern about the representativeness of port emission estimates at a county-level. Revising the 

county-level emissions estimates would allow more accurate modeling of emissions in the VISTAS 

states. 

 For underway emissions, Pechan believes that the allocation procedure results in a reasonable 

distribution of county-level emissions. However, the methodology to allocate port emissions results in 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 incorporate 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 riverbanks 

and for ten river miles or more outside the port city. The revised methodology allocates port emissions 

based on a surrogate for port-related activity in each county, rather than using a single county to define 

the port. 

 The report, Waterborne Commerce of the United States, 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 the 

United States by amount of cargo tonnage. As discussed previously, the 1999 NEI allocates all the port 

emissions to these 150 ports based on the cargo tonnage handled by the port. Pechan uses this allocation 

of emissions to each port area as the starting point of its revised allocation process. Port Charleston 

(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 that make up the port area. Port area definitions were 

obtained from Waterborne Commerce. The port area definition for Port Charleston was "Ocean to Goose 

Creek via Cooper River and Town Creek; to the Standard 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 Sampit River at the City of Georgetown, SC.". Using

the port definitions by river mile, Pechan established which counties are included in each port area. Port 

Charleston is included in the counties of Charleston and Berkeley. The Port of Georgetown is included in 

Georgetown County. 

 The next step in allocating emissions is to develop 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 to the 

number of port facilities operating in a given county. Port facilities include terminals, piers, wharves, 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 publishes 

it in The Port Series Reports. The reports give the name, location, operations, and describe the physical 

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 between the number of port facilities in each county 

to the total number of facilities in all counties that 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 Charleston 

county and 0.2903 in Berkeley county. The ratio for the Port of Georgetown was 1.0 in Georgetown 

County. Pechan was directed to perform the reallocation for all VISTAS ports. 

 

VI. 2009 NONROAD SOURCE EMISSION INVENTORY DEVELOPMENT 

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

estimates for sources found in the NONROAD model and those sources estimated outside of the model 

(locomotives, airplanes, and commercial marine vessels). 

 A. Projection of NONROAD Model Sources 
12 

 NONROAD model input files were prepared based on the 2002 base year inventory input files with 

appropriate updates for the projection years. Other specific updates for the projection years for 

NONROAD model sources consist of: 

• Revision of the emission inventory year in the model (as well as various output file naming 

commands) to be reflective of the projection year. 

• Revision of the fuel sulfur content for gasoline and diesel powered equipment. 

• Implementation of a limited number of local control program changes (national control program 

changes are handled internally within the NONROAD model, 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 execution. 

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 aircraft, locomotives, and CMV prepared as 

described earlier in this document, corresponding emission projections for 2009 were developed. The 

following description is largely taken from the MACTEC 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 inventories used in support of recent USEPA rulemakings, and

consists of the following steps: 

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

 (b) Detailed inventory data (both before and after controls) for these same emission sources for 

1996, 2010, 2015, and 2020 were obtained from the USEPA's CAIR Technical Support Document. Using 

these data, combined growth and control factors for the period 2002 - 2009 were estimated using straight 

line interpolation between 1996 and 2010 (for 2009). This is done at the State county SCC pollutant level 

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, actual match results were not always ideal, so additional 

matching criteria were required. For subsequent reference, this initial (highest resolution) matching

criterion is denoted as the "CAIR Primary" criterion. 
13 

 (d) A second matching criterion is applied that utilizes a similar, but higher-level SCC (lower 

resolution) matching approach. For example, SCC 2275020000 (commercial aircraft) in the 2002 base 

year inventory data would be matched with SCC 2275000000 (all aircraft) in the CAIR data. This 

criterion is applied to records in the 2002 base year emissions file that are not matched using the "CAIR 

Primary" criterion, and is also performed at the State county SCC pollutant level of detail. For 

subsequent reference, this is denoted as the "CAIR Secondary" criterion. A number of unmatched records 

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 frequently used SCC in the USEPA CAIR files for 

each of the aircraft, locomotive, and commercial marine sectors) is averaged at the State level to produce 

a "default" State and pollutant specific growth and control factor for the sector. The resulting factor 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 county level data for which an explicit match between 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) for the aircraft sector, 2280002000 (commercial 

marine diesel total) for the CMV sector, and 2285002000 (railroad equipment diesel total) for the 

locomotive sector. Matches made using this criterion are denoted as "CAIR Tertiary" matches. 

 (f) According to USEPA documentation, the CAIR baseline emissions include the impacts of 

the (then proposed) Tier 4 (T4) nonroad diesel rulemaking, which implements a low sulfur fuel 

requirement that affects both future CMV and locomotive emissions. However, the impacts of this rule 

were originally intended to be excluded from the initial VISTAS 2018 forecast, which was to include only 

"on-the-books" controls. (The T4 rule was finalized 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 addition, 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 sector; 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 distinction between the "on the books" and "on the 

way" inventories, the distinct algorithms used to produce the two inventories have been maintained even 

though the conceptual distinctions have been lost. This approach was taken for two reasons. First, it 

allowed the previously developed algorithms to be utilized 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, previous USEPA inventories that include the proposed 

T4 impacts would not be accurate, and the procedural discussion continues to reflect the distinctions 

between non-T4 and T4 emissions, as these distinctions continue to be intrinsically important to the 

forecasting process. A second set of USEPA CAIR files 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 important 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 rule (e.g., diesel CMV and locomotives). In effect, the 

matching exercise was augmented (rather than replaced) 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 matches made using the basic CAIR criteria (as described in 

steps (c) through (e) above). 

 (g) The CAIR matching criteria were overridden for any record for which States provided 

local growth data. Only North Carolina provided these forecasts, as North Carolina has provided specific 

growth factors for airport emissions in four counties. 
14 

 (h) Using this approach, each State county SCC pollutant 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 year 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 criterion, 

 240 records matched using the CAIR Secondary criterion, 

 7,463 records matched using the CAIR Tertiary criterion, 

 720 records matched using the No T4 Primary criterion, 

 3,858 records matched using the No T4 Secondary criterion, and 

 6,330 records matched using the No T4 Tertiary criterion. 

 (i) Finally, the impacts of the T4 rule as adopted 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 affect locomotives and CMV. Lower fuel sulfur content 

affects both SO2 and PM emissions. Expected fuel sulfur content was obtained for 2009 from the 

USEPA technical support document for the final T4 rule (Final Regulatory Analysis: Control of 

Emissions from Nonroad Diesel Engines, EPA420 R 04 007, 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 non 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 Rule 

2009

CMV SO2 = Non-T4 SO2 × 0.1569

Locomotive SO2 = Non-T4 SO2 × 0.1569

CMV PM = Non-T4 PM × 0.8962

Locomotive PM = Non-T4 PM × 0.8117

 However, since the diesel fuel sulfur content assumed for the 2002 VISTAS base year inventory, 

upon which the 2009 inventory was based, is 2500 ppmW, a small adjustment to the emission reduction 

multiplier calculated from the T4 rule is appropriate since they are measured relative to modestly different 

sulfur contents (2599 ppmW for 2009). Correcting for these modest differences produces the emission 

reduction impact estimates relative to forecasts based on the VISTAS 2002 inventory shown in Table F2-

4. 
15 

Table F2-4 

Estimated Emission Reduction Impacts Relative to VISTAS 2002 Base 

Year Values 

2009

CMV SO2 = Non-T4 SO2 × 0.1632

Locomotive SO2 = Non-T4 SO2 × 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 VISTAS inventory in March 2004, this process 

yielded reasonable results and exhibited no particular systematic concerns. However, when the 2009 

Base F inventory was developed, significant concerns related to SO2 and PM were encountered. 

Essentially, what was revealed by the Base F 2009 forecast was a series of apparent inconsistencies in the 

CAIR 2010 and 2015 emission inventories (as compared 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 above, there does not seem to be any pattern of 

consistency that would indicate that either inclusion or exclusion of T4 rule impacts is the underlying 

cause. Moreover, where they occur, the observed growth extremes generally affect both SO2 and PM 

equally, while one would expect PM effects to be buffered if the T4 rule was the underlying cause since 

changes in diesel fuel sulfur content will only affect a fraction of PM (e.g., sulfate), while directly 

reducing SO2. 

 While forecast inventories for aircraft, locomotives, and CMV were developed for 2009 and 2018 

using both growth methods, it was ultimately decided to utilize the 1996-2020 growth basis since it 

provided more reasonable growth rates for 2009.