### PECHAN Septenber 2007



### 1 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

# SECTION I. BACKGROUND

E.H. Pechan & Associates, Inc. (Pechan) is supporting the Lake Michigan Air Directors Consortium’s (LADCO) efforts to forecast anthro pogenic emissions for the purpose of assessing progress for air quality goals, including goals re lated to regional haze and attainment of the ozone national ambient air quality standards (NAAQS). Under a previous contract with LADCO, Pechan prepared emission activity growth and emission control da ta for all non-electric generating unit (EGU) point, area, and nonroad sour ce categories relative to a base year (2002) inventory supplied by LADCO. In December 2004, Pechan submitted emissions activity growth and control factor files for use by LADCO in emissions modeling. A December 14, 2004 Pechan report documents the contents a nd derivation of these files (Pech an, 2004). Revised files were later provided to LADCO in March 2005.

In September 2005, LADCO contracted with Pech an to conduct the following two tasks to develop updated growth and cont rol factors needed to support future year control strategy analyses for regional haze, particulate matter less than or equal to 2.5 micrometers (PM-2.5), and

ozone:

Task 1: Update control factors to reflect current information pertaining to: (a) Petroleum refinery cases and settlements; (b) Maximum achievable control techno logy (MACT) standard control efficiency assumptions; (c) Residential wood combustion unit lifetime; and (d) Regional Planning Organization (RPO) inventories.

Task 2: Develop non-EGAS default-base d emission activity growth factors for: (a) Priority point source categories; and (b) Priority area source categories.

A December 2005 report describes Pechan efforts to perform these tasks, which resulted in updated emissions activity growth and control factor files (Pechan, 2005). The updates reflect the use of more recent and/or more detailed info rmation than that used in the earlier study. As with the earlier study, this effort involved the pr eparation of emission activity growth and control information relative to a 2002 base year invent ory for future years of interest. Control information was developed for 2007, 2008, 2009, 2012, and 2018 (e.g., 2018 is the first milestone for regional haze reasonable progress de monstrations). Because the incremental level of effort required to develop emission activ ity growth factors for each year over the 2003-2018 period was nominal, Pechan prepared non-E GU point and area and nonroad source growth factors for each year ov er this entire period.

For the current study, LADCO requested that Pechan develop growth and c ontrol factor files to support emission projections from a recently compiled 2005 base year inventory for the following LADCO states: Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin. LADCO requested that Pechan provide files representing changes in emission activity and emission control between the base year and 200 9, 2012, and 2018. As with the previous studies, Pechan provided updated point/area source and ma rine, aircraft, and railroad (MAR) category

### PECHAN Septenber 2007



### 2 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### growth factors for each year over the 2006-2018 pe riod. Control factor development focused on the modeling years of interest, or in the case of point source controls, the specific anticipated implementation date within the forecast period.

This report is organized into this Background section and:

• Section II, which describes the developm ent of the emission activity growth data; • Section III, which discusses how the update d emission control data were compiled; • Section IV, which describes the preparation of the updated growth and control factor files; and • Section V, which presents the referen ces consulted in preparing this report.

### PECHAN Septenber 2007



### 3 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

# SECTION II. EMISSION ACTIVITY GROWTH DATA

A. OVERVIEW

As with the two previous studies, Pechan relied on the data incorporated into Version 5.0 of the Economic Growth Analysis System (EGAS) as the default growth factor data source. 1 

### The EGAS 5.0 projections data are typically derived from two main resource s: (1) version 5.5 of Regional Economic Models Incorporated (REMI )’s state-level economic models; and (2) the Department of Energy (DOE)’s Annual Energy Outlook (AEO) 2004 . While socioeconomic growth indicators from the REMI models prov ide state-level growth rates, the DOE energy forecasts provide regional or national growth rate s (e.g., the same growth rate is applied to each LADCO state because each of these states is includ ed in the DOE’s East North Central division). Instead of relying on REMI’s population forecasts, Pechan de veloped growth factors from county-level population projections avai lable from each LADCO region state.

LADCO requested that Pechan review the grow th indicators applied to particular source classification codes (SCCs) in the 2005 base ye ar inventory. For these “priority” source categories, Pechan evaluated alternative growth methodologies and data sources before selecting a forecasting approach. The balance of this sect ion describes the emission activity growth data developed in this study. Section IV discusses how these data were compiled into the file format required by LADCO.



## B. AREA SOURCE/MAR CATEGORIES

LADCO provided Pechan with a list of priority point/area source and MAR categories for which emission activity projection improvements were to be evaluated. For these source categories, Pechan reviewed U.S. Environmental Prot ection Agency (EPA) SCC documentation and emission estimation guidance to identify the emi ssions activity (throughput) data associated with each SCC. Pechan then investig ated the availability of LADCO state-specific projections for these data.

Table II-1 presents the descrip tions and emissions activity for each priority area source/MAR category. The last column in this table identif ies each category’s grow th indicator assignment under the previous Pechan forecast effort (Pech an, 2005). Table II-2 displays the assigned growth indicator for each priority area/MAR s ource category and any altern ative indicators that were considered. This table also presents the pe rcentage growth rates for the assigned indicators over two forecast periods: 2005-2009 and 2005-2018.

In addition to population data from the L ADCO states and REMI employment data, the following information sources supp lied data used in estimating emission activity growth for the priority area source/MAR categories:

1 Information on these EGAS 5.0 data sources is provided in the report documenting the earliest study Pechan performed for LADCO (Pechan, 2004).PECHANSeptember 20074Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-1. Priority Emission Activity Area Source/MAR CategoriesPOLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISNH3 2805047100 Agriculture Production- LivestockSwine production - deeppit house operations(unspecified animalConfinement Annual average number of swine REMI Farm sector value addedNH3 2805039200 Agriculture Production- LivestockSwine production -operations with lagoons(unspecified animalManure handling andstorageAnnual average number of swine REMI Farm sector value addedNH3 2805047300 Agriculture Production- LivestockSwine production - deeppit house operations(unspecified animalLand application ofmanureAnnual average number of swine REMI Farm sector value addedNH3 2805001100 Agriculture Production- LivestockBeef cattle - finishingoperations on feedlots(drylots)Confinement Annual average number of beefcattleREMI Farm sector value addedNH3 2805039100 Agriculture Production- LivestockSwine production -operations with lagoons(unspecified animalConfinement Annual average number of swine REMI Farm sector value addedNH3 2805003100 Agriculture Production- LivestockBeef cattle - finishingoperations onpasture/rangeConfinement Annual average number of beefcattleREMI Farm sector value addedNH3 2805001300 Agriculture Production- LivestockBeef cattle - finishingoperations on feedlots(drylots)Land application ofmanureAnnual average number of beefcattleREMI Farm sector value addedNH3 2630020000 Wastewater Treatment Public Owned Total Processed Volume of wastewater processed REMI Water and Sanitation sector outputNH3 2805007100 Agriculture Production- LivestockPoultry production - layerswith dry manuremanagement systemConfinement Annual average number of poultry Regression with Food/Kindred Productssector value added as explanatory variableNH3 2805021300 Agriculture Production- LivestockDairy cattle - scrape dairy Land application ofmanureAnnual average number of dairycattleRegression with Farm sector employment asexplanatory variableNH3 2805030000 Agriculture Production- LivestockPoultry Waste Emissions Not ElsewhereClassified (see also 28-05-007 -008-009)Annual average number of poultry REMI Farm sector value addedNH3 2805021200 Agriculture Production- LivestockDairy cattle - scrape dairy Manure handling andstorageAnnual average number of dairycattleRegression with Farm sector employment asexplanatory variableNH3 2104008070 Residential Wood Outdoor Boiler Amount of wood burnedNOX 2285002006 Railroad Equipment Diesel Line Haul Locomotives:Class I OperationsAmount of diesel consumed byClass I line-haul locomotivesAEO Freight rail distillate (nat'l) adjusted forrelative state growth in REMI Total outputNOX 2280002023 Marine VesselsCommercialDiesel Push Boats Amount of diesel consumed bycommercial push boatsAEO Shipping distillate (nat'l) adjusted forrelative state growth in REMI WaterTransportation sector outputNOX 2102006001 Industrial Natural Gas All Boiler Types Volume of natural gas burned byindustrial area source boilersAEO Industrial natural gasPECHANSeptember 20075Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-1 (continued)POLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISNOX 2275020000 Aircraft Commercial Aircraft Total:All Types Number of commercial aircraftlanding-takeoff cyclesFederal Aviation Administration (FAA)itinerant air carrier Landing and Take-Off(LTO) forecasts by stateNOX 2285002010 Railroad Equipment Diesel Yard Locomotives Amount of diesel consumed byyard locomotivesAEO Freight rail distillate (nat'l) adjustedfor relative state growth in REMI TotaloutputNOX 2104006000 Residential Natural Gas Total: All CombustorTypesVolume of residential naturalgas consumedAEO Residential natural gasNOX 2285002009 Railroad Equipment Diesel Line HaulLocomotives:Commuter LinesAmount of diesel consumed bycommuter locomotivesAEO Commuter rail diesel (nat'l)adjusted for relative growth in populationNOX 2280003200 Marine Vessels Commercial Residual Underway emissions Amount of residual oilconsumed by CMV duringunderway operationsAEO Shipping residual oil (nat'l) adjustedfor relative state growth in REMI WaterTransportation sector outputNOX 2102006000 Industrial Natural Gas Total: Boilers and ICEnginesVolume of natural gas burnedby industrial area source boilersand IC enginesAEO Industrial natural gasNOX 2104008070 Residential Wood Outdoor Boiler Amount of wood burnedROG 2461850000 Miscellaneous Non-industrial:CommercialPesticide Application:AgriculturalAll Processes (Not listed for this SCC)Region V employment projections for"Pesticide Handlers, Sprayers"ROG 2461020000 Miscellaneous Non-industrial:CommercialAsphalt Application: AllProcessesTotal: All SolventTypesAmount of solvent used Region Vemployment projections for"Paving, Surfacing, & TampingOperators"ROG 2401200000 Surface Coating Other Special PurposeCoatingsTotal: All SolventTypesAmount of solvent used PopulationROG 2401001000 Surface Coating Architectural Coatings Total: All SolventTypesAmount of solvent used Regression with Population (inc. countylevel) as explanatory variable + projectedsolvent content changeROG 2460100000 Miscellaneous Non-industrial:Consumer and CommercialAll Personal Care Products Total: All SolventTypesAmount of solvent used Population (county-level for LADCOstates)ROG 2501011010 Petroleum and PetroleumProduct StoragePortable Gas Cans Residential Volume ofgasoline stored REMI, Gas and Oil ExpendituresROG 2501060100 Petroleum and PetroleumProduct StorageGasoline Service Stations Stage 2: Total Volume of gasoline pumped bystationsRegression with Gas and OilExpenditures as explanatory variableROG 2460800000 Miscellaneous Non-industrial:Consumer and CommercialAll FIFRA Related Products Total: All SolventTypesAmount of solvent used Regressionwith Population (county-levelfor LADCO states) as explanatoryvariableROG 2501060050 Petroleum and PetroleumProduct StorageGasoline Service Stations Stage 1: Total Volume of gasoline pumpedinto stationsREMI, Gas and Oil ExpendituresROG 2460400000 Miscellaneous Non-industrial:Consumer and CommercialAll Automotive AftermarketProductsTotal: All SolventTypesAmount of solvent used Population (county-level for LADCOstates)ROG 2425000000 Graphic Arts All Processes Total: All SolventTypesAmount of solvent used REMI, Printing and Publishing sectoroutputROG 2401005000 Surface Coating Auto Refinishing: SIC 7532 Total: All SolventTypesAmount of solvent used REMI, Automobile Parking, Repair,Services sector outputROG 2460500000 Miscellaneous Non-industrial:Consumer and CommercialAll Coatings and RelatedProductsTotal: All SolventTypesAmount of solvent used Population (county-level for LADCOstates)PECHANSeptember 20076Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-1 (continued)POLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISROG 2460200000 Miscellaneous Non-industrial:Consumer and CommercialAll Household Products Total: All SolventTypesAmount of solvent used Population (county-level for LADCOstates)ROG 2415020000 Degreasing Fabricated Metal Products(SIC 34): All ProcessesTotal: All SolventTypesAmount of solvent used REMI, Fabricated Metals sectoremploymentROG 2415025000 Degreasing Industrial Machinery andEquipment (SIC 35): AllProcessesTotal: All SolventTypesAmount of solvent used REMI, Machinery and ComputerEquipment sector employmentROG 2460600000 Miscellaneous Non-industrial:Consumer and CommercialAll Adhesives and Sealants Total: All SolventTypesAmount of solvent used Population (county-level for LADCOstates)ROG 2420000370 Dry Cleaning All Processes Special Naphthas Amount of special naphthasusedSCC is not in current growth file; similarSCC (2420010370) is in file w/ REMILaundry sector outputROG 2630010000 Wastewater Treatment Industrial Total Processed Volume of wastewaterprocessedProjected LADCO NEEDS industrial flowdesign forecastROG 2501060101 Petroleum and PetroleumProduct StorageGasoline Service Stations Stage 2:DisplacementLoss/UncontrolledVolume of gasoline pumped viauncontrolledRegression with Gas and OilExpenditures as explanatory variableROG 2415360000 Degreasing AutoRepair Services (SIC75): Cold CleaningTotal: All SolventTypesAmount of solvent used REMI, Automobile Parking, Repair,Services sector outputROG 2401002000 Surface Coating Architectural Coatings -Solvent-basedTotal: All SolventTypesAmount of solvent used REMI, Housing expendituresROG 2401003000 Surface Coating Architectural Coatings -Water-basedTotal: All SolventTypesAmount of solvent used REMI, Housing expendituresROG 2104008070 Residential Wood Outdoor Boiler Amount of wood burnedSO2 2601020000 On-site Incineration Commercial/Institutional TotalAmount of material burned REMI, Commercial sector employmentSO2 2102004000 Industrial DistillateOil Total: Boilers and ICEnginesAmount of distillate oil burnedby area source industrialboilers/IC enginesAEO Industrial distillateSO2 2103004000 Commercial/Institutional Distillate Oil Total: Boilers and ICEnginesAmount of distillate oil burnedby area source commercialboilers/IC enginesNo growth based on historical energydataSO2 2275020000 Aircraft Commercial Aircraft Total: All Types SeeNOX entry FAA itinerant air carrier LTO forecasts bystateSO2 2102005000 Industrial Residual Oil Total: All BoilerTypesAmount of residual oil burnedby area source industrial boilersAEO Industrial residualSO2 2102002000 Industrial Bituminous/SubbituminousCoalTotal: All BoilerTypesAmount of bit/sub coal burnedby area source industrial boilersAEO Industrial steam coalSO2 2285002006 Railroad Equipment Diesel Line HaulLocomotives: Class IOperationsSee NOX entry AEO Freight rail distillate (nat'l) adjustedfor relative state growth in Total outputSO2 2104008070 Residential Wood Outdoor Boiler Amount of wood burnedPECHANSeptember 20077Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-2. Growth Indicators forPriority Area Source/MAR CategoriesTotal % ChangePollutantSCC2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentNH3 2805047100 -1.7 6.2 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805039200 -2.3 4.4 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805047300 -1.6 6.4 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805001100 5.2 16.7 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805039100 -1.6 6.4 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805003100 3.2 3.5 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805001300 5.2 16.7 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2630020000 2.8 9.5 Municipal design flow forecasts from Drinking Water Infrastructure NeedsSurveyNH3 2805007100 -4.5 8.3 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805021300 -10.2 -39.0 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805030000 -5.3 6.8 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805021200 -10.2 -39.0 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2104008070 78.0 84.3 Extrapolation of national 1999-2004 trend in OWB sales (exponentialgrowth) thru 2006; linear growth thru 2008; 2009+ based on ruralpopulation growth rateNOX 2285002006 0.0 0.0 No growth due to contradictory historicand forecast trends DOE 1990-2004 = -1.4% per year; AEOforecast = 1.4% per yearNote that post-2001 trend hasbeen upward and thathistorical data shows severalups and downsNOX 2280002023 4.3 9.9 AEO national Domestic Shippingsector distillate projections adjusted forLADCO region growth in REMI Water Transportation sector outputrelative to nation1998-2004 fuel consumption data forbarge traffic on regional rivers indicatessimilar annual growth rate (1.0%)Forecast data are state-levelNOX 2102006001 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% per year;AEO forecast = 1.4% per yearNOX 2275020000 0.0 0.0 No growth due to contradictory historic and forecast trends (FederalAviation Administration commercialaircraft landing and take-offs data)FAA 1990-2005 = -0.01% per year; FAAforecast = 1.7% per yearNOX 2285002010 0.0 0.0 No growth due to contradictory historic and forecast trend (historic trendbased on 1996-2002 regional Switch and Terminal Services employment)1996-2004 = -1.6% per yearemployment decrease; AEO forecast =1.4% per yearNOX 2104006000 1.7 2.8 AEO residential natural gas consumption forecast DOE 1990-2004 = 0.5% annual; AEOforecast = 0.2%PECHANSeptember 20078Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-2 (continued)Total % ChangePollutantSCC2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentNOX 2285002009 7.4 26.1 Annual growth rate (1.8%) from2005-2009 diesel fuel consumption projectionsreported in Marta 2007 budgetNOX 2280003200 0.4 1.3 1995-2005 Great Lakes region ton-miles trend (0.1% annual growth) AEO forecasts national DomesticShipping residual oil consumption+1.1% per yearUsed historicalgrowth rate sinceregion-specificNOX 2102006000 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% per year;AEO forecast = 1.4% per yearNOX 2104008070 78.0 84.3 Extrapolation of national 1999-2004 trend in OWB sales (exponential growth) thru2006; linear growth thru 2008; 2009+ based on rural population growth rateROG 2461850000 4.8 16.1 Regional employment projections for "Pesticide Handlers, Sprayers" combined withprojected solvent content change from Freedonia's "Solvents to 2010-AgriculturalChemical Market for Solvents" (-0.4% per year)Forecast data arestate-levelROG 2461020000 -1.9 -6.0 No employment growth assumption due to contradictory historic and forecast trends in"Paving, Surfacing, & Tamping Operators" employment, combined with projectedsolvent content change from Freedonia's "Solvents to 2010-Asphalt ProductionMarket for Solvents" (-0.5% per year)Forecast data arestate-levelROG 2401200000 -6.5 -16.1 Population forecast combined withprojected change in paint solvent content fromFreedonia's "Solvents to 2010-Paints/CoatingsMarket for Solvents" (-1.9% per year)Forecast data arecounty-levelROG 2401001000 -9.9 -9.3 Regression with Population forecast as explanatory variable combined with Freedoniaprojected change in proportion of total Architectural coatings that are solvent-based (-2.0% per year)Forecast data arecounty-levelROG 2460100000 -3.9 -11.6 Population forecast combined with projected solvent content change from Freedonia's"Solvents to 2010-Cosmetics & Toiletries Market for Solvents" (-1.5% per year)Forecast data arecounty-levelROG 2501011010 0.2 0.3 Regression equation with Gas and Oil Expenditures as explanatory variable Forecast data arestate-levelROG 2501060100 0.2 0.3 Regression equation with Gas and Oil Expenditures as explanatory variable Forecast data arestate-levelROG 2460800000 -10.5 -15.6 Regression equation with Population as explanatory variable Forecast data arecounty-levelROG 2501060050 0.2 0.3 Regression equation with Gas and Oil Expenditures as explanatory variable Forecast data arestate-levelROG 2460400000 0.1 3.3 Population forecast combined withprojected change in solvent use/vehicle fromFreedonia's "Solvents to 2010-Transportation Markets for Solvents" (-0.4% per year)Forecast data arecounty-levelROG 2425000000 0.0 0.0 No employment growth assumption due to contradictory historic and forecast trendsfor "Printing Machine Operators" employment, and no projected change in solventcontent of ink from Freedonia's "Solventsto 2010-Printing Ink Market for Solvents"ROG 2401005000 -12.9 -38.9 Employment projections for "Automotive Body andRelated Repairers" combined w/change in proportion of automotive coatingsthat are solvent-based from Freedonia's"Automotive Coatings, Adhesives & Sealants-Automotive Coatings Demand byFormulation and Substrate" (-4.3% per year)Forecast data arestate-levelROG 2460500000 -6.5 -16.1 Population forecast combined withprojected change in paint solvent content fromFreedonia's "Solvents to 2010-Paints/CoatingsMarket for Solvents" (-1.9% per year)Forecast data arecounty-levelPECHANSeptember 20079Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-2 (continued)Total % ChangePollutantSCC2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentROG 2460200000 0.6 6.8 Population forecast combined with projected change in cleaning product solventcontent from Freedonia's "Solvents to2010-Cleaning Product Market for Solvents" (-0.1% per year)Forecast data arecounty-levelROG 2415020000 -15.0 -35.9 Fabricated Metals sector employment forecast combined with projected change insolvent use from Freedonia's "Solvents to 2010-Metal Processing Market forSolvents" (-3.7% per year)Forecast data arestate-levelROG 2415025000 0.2 -11.4 Machinery and Computer Equipment sector employment forecast combined withprojected change in solvent use from Freedonia's "Solvents to 2010-Metal ProcessingMarket for Solvents" (-3.7% per year)Forecast data arestate-levelROG 2460600000 -10.0 -24.4 Population forecast combinedwith projected change in solvent content fromFreedonia's "Solvents to 2010-Adhesives andSealants Market for Solvents" (-2.7%per year)Forecast data arecounty-levelROG 2420000370 -1.6 -0.4 Regional employment projections for "Laundry andDry Cleaning Workers" (+0.7% peryear) combined with projected solvent content from Freedonia's "Solvents to 2010-DryCleaning and Other Markets for Solvents" (-0.7%)ROG 2630010000 4.1 13.8 Growth rate from regional industrial wastewater flow design forecast from DrinkingWater Infrastructure Needs SurveyROG 2501060101 0.2 0.3 Regression equation with Gas and Oil Expenditures as explanatory variable Forecast data arestate-levelROG 2415360000 2.4 10.3 Regional employment projections for "Automotive Service Technicians andMechanics" combined with forecast change in solvent use from "Solvents to 2010-Transportation Markets for Solvents" (-0.4% per year)ROG 2401002000 -9.9 -9.3 Regression with Population forecast as explanatory variable combined with Freedoniaprojected change in proportion of total Architectural coatings that are solvent-based (-2.0% per year)Forecast data arestate-levelROG 2401003000 3.6 12.3 Regression equation with Population as explanatory variable Forecast data arestate-levelROG 2104008070 78.0 84.3 Extrapolation of national 1999-2004 trend in OWB sales (exponential growth) thru2006; linear growth thru 2008; 2009+ based on rural population growth rateSO2 2601020000 7.2 15.0 Commercial sector employment forecast Forecast data arestate-levelSO2 2102004000 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = +0.5% per year;AEO forecast = -0.3% per yearSO2 2103004000 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -2.0% per year;AEO forecast = 0.8% per yearSO2 2275020000 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% per year;AEO forecast = +1.7% per yearSO2 2102005000 -49.4 -49.6 AEO forecast for industrial sectorresidual oil consumption DOE 1990-2004 = -6.6% per year;AEO forecast = -5.1% per yearSO2 2102002000 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearPECHANSeptember 200710Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-2 (continued)Total % ChangePollutantSCC2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentSO2 2285002006 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -1.4% per year;AEO forecast = +1.4% per yearNote that post2001 trend isupward & historicaldata has severalups/downsSO2 2104008070 78.0 84.3 Extrapolation of national 1999-2004 trend in OWB sales (exponential growth) thru2006; linear growth thru 2008; 2009+ based on rural population growth rate

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### 11 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### • Animal Husbandry: projected number of animals from EPA’s ammonia emission forecasts for animal husbandry operations (EPA, 2004);

• Marine Vessels Commercial, Diesel–Pus h Boats: historic al (1998-2004) fuel consumption for barge traffic on rive rs in LADCO region (ENVIRON, 2007a);

• Multiple Fuel Combustion categories: DOE East North Central region energy forecasts from AEO 2007 and 1990-2004 LADCO region energy consumption data (DOE, 2007a and 2007b);

• Commercial Aircraft: state-level i tinerant aircraft operations (FAA, 2007);

• Diesel Line Haul Locomotives–Co mmuter Lines: Metra diesel fuel expenditure/price projections (Metra, 2007);

• Pesticide Application–Agric ultural: LADCO region project ed number of “pesticide handlers, sprayers, and applic ators, vegetation” (BLS, 2007);

• Commercial Asphalt Application–All Pro cesses: LADCO region projected number of “paving, surfacing, and ta mping operators” (BLS, 2007);

• Graphic Arts –All Processes: LADC O region projected number of “printing machine operators” (BLS, 2007);

• Surface Coating–Auto Refinishing: SIC 7532: LADCO region projected number of “Automotive Body and Related Repairers” (BLS, 2007);

• Dry Cleaning, All Processes–Special Naphtha s: LADCO region pr ojected number of “Laundry and Dry Cleaning Workers” (BLS, 2007);

• Wastewater Treatment-Industr ial: LADCO region projec ted wastewater treatment industrial design flow (EPA, 2007a); and

• Degreasing–Auto Repair Services (S IC 75): Cold Cleaning: LADCO region projected number of “Automotive Servi ce Technicians and Mechanics” (BLS, 2007).

Many of the above are solvent use categories fo r which Pechan also incorporated projected solvent content changes as forecast by The Freedonia Group, Inc. (Freedonia, 2006).

In cases where energy consumption is the emissions activity, a common growth factor development approach was to compare ava ilable regional histori cal (1990-2004) energy consumption data to AEO 2007 forecast data to determine if the forecast growth rates appear suspect relative to historical trends. Pechan conducted similar historical/forecast activity trend comparisons for the non-fuel combustion priority cat egories whenever historical trend data were readily available (e.g., occupationa l employment data). In sel ecting from alternative data

### PECHAN September 2007



### 12 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### sources/trend data, the ge neral decision-making hierarchy was as follows, listed in order of preference:

1. If the forecast and historical trends were in the same direc tion, Pechan relied on forecast data (an exception was made, however, in ca ses where forecast data were only available on a national-level, but historical data were available for the LADCO region).

2. If the forecast and historical trends were in different dire ctions (e.g., forecast trend is for an increase in activity, but historical trend was a decrease), Pechan applied a no growth assumption.

Outdoor Wood Boilers

Residential Wood Combusti on from Outdoor Wood Boilers (SCC 2104008070) was not originally identified as a source category for gr owth indicator review be cause this category was only recently added to LADCO’ s emissions inventory. Outdoor Wood Boilers (OWBs) have become much more prevalent in the last several years as homeown ers seek ways to avoid recent large increases in natural gas and home heating oil prices. This s ource category does not exist in EPA’s official SCC list, and there is no current EPA emission inventory preparation guidance for this sector. Pechan assumed that this category’ s emissions are based on the estimated number of wood boilers, the average amount of wood burned in each boiler, and emission factors that are related to the amount of wood burned.

Investigations indicate little hist orical and forecast OWB data exist to assist in identifying future trends in LADCO region OWB use: state-specif ic sales from nine manufacturers obtained by EPA from nine manufacturers, and national sa les data obtained by the New York Attorney General’s Office via subpoena of 21 manufact urers. These sales data are for 1999-2004. Because of the much greater manufacturer covera ge for the national data, and because the state estimates indicate that the majority of recent OW Bs sales have occurred in the LADCO states, Pechan focused the historical trend analysis on the national data. Th ese data indicate an extremely high average growth rate of 41 percent per year over the 1999-2004 period. Manufacturers indicate that although OWBs have been available for sale since the 1980s, the very large OWB sales growth rates are new phe nomenon. The growth rates appear to mainly result from homeowner reactions to recent large increases in residential heating prices (e.g., between 1999 and 2004, residential natural gas and di stillate oil prices ro se 61 and 87 percent, respectively). Because DOE data indicate th at natural gas accounts for the majority of residential energy consumption in the LADCO Stat es, and increases in residential natural gas prices continued through 2006 (the average annual price for resi dential natural gas increased 28 percent between 2004 and 2006), Pechan forecas t the national number of OWBs through 2006 via extrapolation of the 1999-2004 national OWB tre nd. In particular, Pechan fit an exponential equation to the 1994-2004 data, and used the equation to estimate 2005 and 2006 OWB counts.

Next, Pechan reviewed AEO 2007 projections of residential natura l gas prices for the East North Central region (which includes 5 of the 6 LADC O region States) to identify whether recent increases are expected to continue. The AEO 2007 projects the average 2 007 price for residential

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### 13 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### natural gas in the East North Central region to be 4 percent lower th an in 2006, and forecasts continued price decreases through th e 2008-2018 period (see table below).

Year Residential Natural Gas Price ($/million Btu) % Change from 2006
2006 12.08
2007 10.92 -4.1
2008 10.80 -5.1
2009 10.28 -9.7
2010 10.02 -11.9
2011 9.61 -15.5
2012 9.48 -16.9
2013 9.28 -18.4
2014 9.32 -18.1
2015 9.27 -18.6
2016 9.37 -17.7
2017 9.60 -15.7
2018 9.56 -20.8



### Given the projected modest price decreases thru 2 008, and the fact that distillate oil prices are forecast to increase 6.1 percent between 2006 a nd 2008, and because one expects a time lag in responding to energy price changes, Pechan assumes that OWB sales will continue to increase at a significant rate through 2008. Pechan specifi cally fit a linear trend line to the 1999-2004 OWB, and projected OWBs in 2007 and 2008 by extending the trend through 2008, and applying each year’s growth rate to the estimated count of OWBs in 2006.

By 2009, Pechan projects that the larger projected declines in natural gas prices, and forecasted decreases in other heating fuel prices, will si gnificantly restrain OW B growth. In addition, because of neighborhood smoke nuisance concerns, and the need for ready access to inexpensive wood, it is expected that the market for OWBs will be generally constrained to heavily-wooded rural areas. Therefore, Pechan forecasts post-200 8 year OWB growth to more closely trend with population growth in these areas. To approxi mate this growth, Pechan compiled 1990 and 2000 total and rural area population data for the LADCO region. These da ta indicate that rural area population grew at approximately 60 percent of the rate of total popula tion over this period. Pechan estimated rural area population growth for the LADCO region by multiplying this adjustment factor by the forecasted growth rate for total population in the LADCO region. The following table displays the projected count of OWBs in the LADCO region for 2005, 2009, 2002, and 2018.

Year Estimated # of OWBs 2005 81,082 2009 144,356 2012 145,911 2018 149,421

### PECHAN September 2007



### 14 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### It is important to note that it is particularly ch allenging to forecast OWB activity given the extremely high OWB sales growth rates that have occurred in recent years. LADCO will want to closely monitor activity and emission trends for this category given its relative importance in the emissions inventory, recent hist orical growth rates, and a dditional unique characteristics.

Finally, Pechan reviewed the complete list of area/MAR source categories in the LADCO base year inventory to identify the priority category growth indicators that could be applied to nonpriority area/MAR categories. This step yielde d priority category growth indicator assignments for an additional 26 area/MAR categories.



## C. NON-EGU POINT SOURCES

Table II-3 displays the priority point source categori es, including the description and emissions activity associated with each cate gory. The last column in this table identifies each category’s growth indicator assignment under th e previous growth and control factor contract. Table II-4 presents the assigned growth indicator for each pr iority point source category and identifies any alternative growth indicators that were considered . Pechan first considered the use of historical throughput data from LADCO stat e point source inventories to identify recent trends that provided sufficient support for extrapolation. As mentioned above, for energy consumption sectors, Pechan compared regional histor ical (1990-2004) energy consumption data to AEO 2007 forecast data to determine if the fo recast growth rates appear suspec t relative to historical trends.

In selecting from alternative growth indicat or data sources, the ge neral decision-making hierarchy was as follows, listed in order of preference:

(1) If throughput data were availa ble for states representing a majority of emissions for a given category, and these data indicated a c onsistent trend, the hist orical throughput trend was extended thru 2009, and held constant th ereafter (two reasons for not extending the trend throughput the entire forecast period ar e that throughput data are only available for a three or a six-year period, and in some cas es the historical throughput decrease was so large that it would eventua lly result in no activity);

(2) If the forecast and historical trends were in the same direction, Pechan relied on the forecast data (an exception was made, however, if the forecast data were only available on a national-level, but the historical data were available for the LADCO region); and

If the forecast and historical tr ends were in different directions (e.g., forecast trend is for an increase in activity, but historic al trend was a decrease), Pechan applied a no growth assumption.PECHANSeptember 200715Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-3. Priority Emission Activity Point Source CategoriesPOLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISNH3 10200601 Industrial Natural Gas > 100 Million Btu/hr Volume of natural gas burned inindustrial pt source boilers of >100MMBtuNo growth based on historical (1990-2001)energy dataNH3 30102599 ChemicalManufacturingCellulosic Fiber Production Other Not Classified Amount of cellulosic fiber produced Avg of REMI employment & output GFs forPlastics, Materials, & Synthetics sectorNH3 10200602 Industrial Natural Gas 10-100 Million Btu/hr Volume of natural gas burned inindustrial pt source boilers of 10-100MMBtuNo growth based on historical energy dataNH3 30199999 ChemicalManufacturingOther Not Classified Specify in Comments Field Amount of (unknown) chemicalproducts producedAvg of REMI employment & output GFs forChemicals sectorNH3 10200204 Industrial Bituminous/SubbituminousCoalSpreader Stoker Amount of bituminous coal burned inspreader stoker industrial pt sourceboilersAEO Industrial steam coalNOX 20200202 Industrial Natural Gas Reciprocating Volume of natural gas burned inindustrial pt source reciprocatingenginesNo growth based on historical energy dataNOX 30500606 Mineral Products Cement Manufacturing (DryProcess)Kilns Amount of cement produced via dryprocessLADCO region historical cement productiongrowth rateNOX 30600201 PetroleumIndustryCatalytic Cracking Units Fluid Catalytic Cracking Unit Amount of fresh feed processed viafluid catalytic cracking units (FCCU)AEO Refined Petroleum Products Supplied(national)NOX 30600104 PetroleumIndustryProcess Heaters Gas-fired Volume of gas burned in petroleumindustry pt source process heatersAEO Refining sector natural gas (national)NOX 10200202 Industrial Bituminous/SubbituminousCoalPulverized Coal: Dry Bottom Amountof bituminous coal burned indry bottom industrial pt source boilersAEO Industrial steam coalNOX 10200601 Industrial Natural Gas > 100 Million Btu/hr Volume of natural gas burned inindustrial pt source boilers of >100MMBtuNo growth based on historical energy dataNOX 30300304 Primary MetalProductionBy-product CokeManufacturingQuenching Amount of coal charged tomanufacture cokeREMI output for Blast Furnaces and BasicSteel products sectorNOX 10200217 Industrial Bituminous/SubbituminousCoalAtmospheric Fluidized BedCombustion: Bubbling Bed(BituminousAmount of bituminous coal burned inbubbling bed industrial pt sourceboilersAEO Industrial steam coalNOX 10200707 Industrial Process Gas Coke Oven Gas Volume of process gas burned in cokeovensAEO Metallurgical coal consumptionprojections (national)NOX 10200602 Industrial Natural Gas 10-100 Million Btu/hr Volume of natural gas burned inindustrial pt source boilers of 10-100MMBtuNo growth based on historical energy dataPECHANSeptember 200716Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-3 (continued)POLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISNOX 20200254 Industrial Natural Gas 4-cycle Lean Burn Volume of natural gas burnedin industrial sector 4-cyclelean burn IC enginesNo growth based on historical energy dataNOX 20100102 Electric Generation Distillate Oil (Diesel) Reciprocating Amount of distillate oil burnedin reciprocating engines forelectricityAEO Electric Generation distillate oilNOX 20200201 Industrial Natural Gas Turbine Volume of natural gas burnedin industrial sector turbinesNo growth based on historical energy dataNOX 39000689 In-process Fuel Use Natural GasGeneral Volume of industrial processpt source natural gas burnedNo growth based on historical energy dataNOX 10200201 Industrial Bituminous/SubbituminousCoalPulverized Coal: WetBottomAmount of bituminous coalburned in wet bottom industrialpt source boilersAEO Industrial steam coalNOX 10200701 Industrial Process Gas Petroleum Refinery Gas Volume of petroleum refinery(still) gas burnedNo growth based on historical energy dataROG 30100399 Chemical ManufacturingAmmonia Production Other Not Classified Amountof ammonia produced REMI output for Agricultural Chemicals sectorROG 30201916 Food and Agriculture Vegetable Oil ProcessingOil Extraction Amount of extractor feed cakeproducedREMI output for Grain Mill Products and Fatsand Oils sectorROG 40500511 Printing/Publishing General Gravure: 2754 Amount of solvent in ink usedby pt sourcesREMI output for Commercial Printing andBusiness Forms sectorROG 30199999 Chemical Manufacturing OtherNot Classified Specify in CommentsFieldAmount of (unknown)chemical products producedAvg of REMI employment & output GFs forChemicals sectorROG 30125099 Chemical Manufacturing Methanol/AlcoholProductionOther Not Classified Amount of methanol/alcoholproducedREMI output for Industrial Chemicals sectorROG 40201301 Surface CoatingOperationsPaper Coating Coating Operation Amount of solvent in coatingused by pt sourcesNo growth based on historical LADCOemissions trendROG 40200101 Surface CoatingOperationsSurface Coating Application- GeneralPaint: Solvent-base Amount of coating mix appliedby pt sourcesNo growth based on historical LADCOemissions trendROG 30102599 Chemical Manufacturing Cellulosic Fiber Production Other Not Classified Amount of cellulosic fiberproducedAvg of REMI employment & output GFs forPlastics, Materials, & Synthetics sectorROG 30500201 Mineral Products Asphalt Concrete Rotary Dryer:Conventional Plant (see3-05-002-50 to -53 forAmount of hot mix asphaltproduced by pt sourcesAvg of REMI employment & output GFs forMisc. Petroleum and Coal Products sectorROG 30201906 Food and Agriculture Vegetable Oil Processing Corn Oil: General Amount of extractor feed cakeproducedREMI output for Grain Mill Products and Fatsand Oils sectorROG 30201919 Food and Agriculture Vegetable Oil ProcessingFugitive Leaks Amount of extractor feed cakeproducedREMI output for Grain Mill Products and Fatsand Oils sectorROG 40200110 Surface CoatingOperationsSurface Coating Application- GeneralPaint: Solvent-base Amount of solvent-basedcoatings applied by pt sourcesHistorical LADCO throughput data trendROG 40201899 Surface CoatingOperationsMetal Coil Coating Other Not Classified Amount of solvent in coatingused by pt sourcesREMI output for Nonferrous Rolling andDrawing sectorROG 40388801 Petroleum ProductStorage at RefineriesFugitive Emissions Specify in CommentsFieldPetroleum product storagecapacity at refineriesAEO Refined Petroleum Products Supplied(national)ROG 40200701 Surface CoatingOperationsSurface Coating Application- GeneralAdhesive Application Amountof adhesive coatingsapplied by pt sourcesREMI output for Total Manufacturing sectorPECHANSeptember 200717Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-3 (continued)POLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISSO2 30600201 Petroleum Industry Catalytic Cracking Units Fluid Catalytic CrackingUnitAmount of fresh feedprocessed via FCCUAEO Refined Petroleum Products Supplied(national)SO2 10200202 Industrial Bituminous/SubbituminousCoalPulverized Coal: DryBottomAmount of bituminous coalburned in dry bottom industrialpt source boilersAEO Industrial steam coalSO2 30600805 Petroleum Industry Fugitive Emissions Miscellaneous:Sampling/Non-AsphaltBlowing/Purging/etc.Barrels of refinery feedprocessedAEO Refined Petroleum Products Supplied(national)SO2 30199999 Chemical Manufacturing OtherNot Classified Specify in CommentsFieldAmount of (unknown)chemical products producedAvg of REMI employment & output GFs forChemicals sectorSO2 10200217 Industrial Bituminous/SubbituminousCoalAtmospheric FluidizedBed Combustion:Bubbling Bed (BituminousAmount of bituminous coalburned in bubbling bedindustrial pt source boilersAEO Industrial steam coalSO2 10200201 Industrial Bituminous/SubbituminousCoalPulverized Coal: WetBottomAmount of bituminous coalburned in wet bottom industrialpt source boilersAEO Industrial steam coalSO2 10200225 Industrial Bituminous/SubbituminousCoalTraveling Grate(Overfeed) Stoker(Subbituminous Coal)Amount of subbituminous coalburned in overfeed stokerindustrial pt source boilersAEO Industrial steam coalSO2 30500606 Mineral Products Cement Manufacturing (DryProcess)Kilns Amount of cement producedvia dry processLADCO region historical cement productiongrowthSO2 30600401 Petroleum Industry Blowdown Systems Blowdown System withVapor Recovery Systemwith FlaringBarrels of refinery feedprocessedAEO Refined Petroleum Products Supplied(national)SO2 10200204 Industrial Bituminous/SubbituminousCoalSpreader Stoker Amount of bituminous coalburned in spreader stokerindustrial pt source boilersAEO Industrial steam coalSO2 10300217 Commercial/Institutional Bituminous/SubbituminousCoalAtmospheric FluidizedBed Combustion:Bubbling Bed (Bitumin.)Amount of bituminous coalburned in bubbling bedcommercial pt source boilersAEO Commercial coalSO2 39000701 In-process Fuel Use Process Gas Coke Oven or BlastFurnaceVolume of coke oven or blastfurnace gas burnedAEO Metallurgical coal consumptionprojections (national)SO2 30103201 Chemical Manufacturing ElementalSulfur Production Mod. Claus: 2 Stage w/oControl (92-95%Removal)Amount of 100% sulfurproducedREMI output for Industrial Chemicals sectorSO2 10300225 Commercial/Institutional Bituminous/SubbituminousCoalTraveling Grate(Overfeed) Stoker(Subbituminous Coal)Amount of subbituminous coalburned in overfeed stokercommercial pt source boilersAEO Commercial coalSO2 10300209 Commercial/Institutional Bituminous/SubbituminousCoalSpreader Stoker(Bituminous Coal)Amount of bituminous coalburned in spreader stokercommercial pt source boilersAEO Commercial coalSO2 10200401 Industrial Residual Oil Grade 6 Oil Amount of residual oil (No. 6)burned in industrial pt sourceboilersNo growth based on historical energy dataPECHANSeptember 200718Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-3 (continued)POLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISEMISSION TREND ANALYSIS CATEGORIES NOT LISTEDABOVE WITH CONSISTENT THROUGHPUT TRENDSVOC 40202201 Petroleum and SolventEvaporationSurface Coating OperationsPlastic Parts: CoatingOperationAmount of solvent used incoating appliedHistorical LADCO throughput data trendNOX 39000699 In Process Fuel Use Natural Gas General Amount of nat gas used inindustrial processesNo growth based on historical energy dataPECHANSeptember 200719Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-4. Growth Indicators forPriority Point Source CategoriesTotal % ChangePollutantSCCEmissionsPriorityCategory2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentNH3 10200601 NOx -11.5 -11.5 Historic throughput trend from 3 states (-3.0% peryear) extended thru 2009; post-2009 held constant.DOE 1990-2004 = -0.01% per year; AEOforecast = +1.4% per yearNH3 30102599 4.9 18.2 Avg of REMI employment & output growth factors forPlastics, Materials, & Synthetics sectorForecast data are state-levelNH3 10200602 NOx -12.2 -12.2 Historic throughput trend from 4 states (-3.2% peryear) extended thru 2009; post-2009 held constant.DOE 1990-2004 = -0.01% per year; AEOforecast = +1.4% per yearNH3 30199999 8.9 25.6 Avg of REMI employment & output growth factors forChemicals sectorForecast data are state-levelNH3 10200204 NOx 2.9 -0.6 AEO forecast for other industrialcoal combustion DOE 1990-2004 = -1.5% per year; AEOforecast = <-0.1% per yearDid not use throughput sinceavailable states represent<50% of regional emissionsNOX 20200202 NOx 0.0 0.0 No growth due to contradictory historic and forecasttrendsDOE 1990-2004 = -0.01% per year; AEOforecast = +1.4% per yearDid not use throughput sinceavailable state represent<50% of regional emissionsNOX 30500606 8.2 29.4 LADCO region historical cement production growthrate (+2.0% per year)NOX 30600201 SO2 0.4 0.4 AEO refinery distillation projections for PetroleumAdministration District (PAD) II, which includes allLADCO states plus additional surrounding statesSimilar 1990-2005 data also includesstates not in LADCO region and showsvery small growth rateDid not use throughput sinceavailable states represent<50% of regional emissionsNOX 30600104 5.9 20.6 1991-2002 MidwestCensus region Refining sectornatural gas consumption growth rate (+1.5% peryear)AEO National Refining sector natural gasconsumption forecast is +2.7% per yearUsed historical growth ratebecause it is regional and ofsimilar direction to AEOnational forecastNOX 10200202 NOx 2.9 -0.6 AEO forecast for other industrialcoal combustion DOE 1990-2004 = -1.5% per year; AEOforecast = <-0.1% per yearDid not use throughput sinceavailable states represent<50% of regional emissionsNOX 10200601 NOx -11.5 -11.5 Historic throughput trend from 3 states (-3.0% peryear) extended thru 2009; post-2009 held constant.DOE 1990-2004 = -0.01% per year; AEOforecast = +1.4% per yearNOX 30300304 -6.2 -19.8 AEO forecast for metallurgicalcoal consumption DOE 1990-2004 = -3.0% per year; AEOforecast = -1.7% per yearNOX 10200217 2.9 -0.6 AEO forecast for other industrialcoal combustion DOE 1990-2004 = -1.5% per year; AEOforecast = <-0.1% per yearNOX 10200707 -6.2 -19.8 AEO forecast for metallurgicalcoal consumption DOE 1990-2004 = -3.0% per year; AEOforecast = -1.7% per yearNOX 10200602 NOx -12.2 -12.2 Historic throughput trend from 4 states (-3.2% peryear) extended thru 2009; post-2009 held constant.DOE 1990-2004 = -0.01% per year; AEOforecast = +1.4% per yearNOX 20200254 0.0 0.0 No growth due to contradictory historic and forecasttrendsDOE 1990-2004 = -0.01% per year; AEOforecast = +1.4% per yearPECHANSeptember 200720Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-4 (continued)Total % ChangePollutantSCCEmissionsPriorityCategory2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentNOX 20100102 1.7 5.5 DOE 1990-2004 historic trend (+0.4%) AEO forecast = +5.0% per year thru2009, but near equivalent decreasefrom 2009 to 2018Used historic trendbecause of largedifference between 2009and 2018 forecast, &historic growth rate is inbetween the 2 forecastvaluesNOX 20200201 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% per year;AEO forecast = +1.4% per yearNOX 39000689 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% per year;AEO forecast = +1.4% per yearNOX 10200201 SO2 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearNo throughput dataavailableNOX 10200701 -1.3 -4.1 DOE 1990-2004 historic trend (-0.3% per year) AEO forecast = 2009 (-1.0% peryear) and 2018 (-0.1% per year)Used historic trendbecause is regionspecific (forecast isnational), and historicchange is in between the2009 & 2018 AEO growthratesROG 30100399 -19.7 -28.6 Freedonia's "Chemical Catalysts to 2009-Ammonia CatalystDemand" - national projections adjusted for relative state growth inREMI output for Agricultural Chemicals sectorForecast data are statelevelROG 30201916 2.2 11.9 Avg of REMI employment & output growth factors for Grain MillProducts and Fats and Oils sectorForecast data are statelevelROG 40500511 0.0 0.0 No growth due to contradictory historic trend versus forecast trendin regional employment for "Printing Machine Operators"Freedonia's "Solvents to2010-Printing Ink Marketfor Solvents" indicates noprojected change insolvent content of inkROG 30199999 8.9 25.6 Avg of REMI employment & output growth factors for ChemicalssectorForecast data are statelevelROG 30125099 1.1 5.3 Freedonia's "Chemical Catalysts to 2009-Alcohols Catalyst Demandby Application" - national projections adjusted for relative stategrowth in REMI output for Industrial Chemicals sectorForecast data are statelevelROG 40201301 0 0 No growth based on consistent historic LADCO emissions trend 2005 emissions dataconfirm previous nogrowth approachROG 40200101 VOC -6.4 -21.4 Regional employmentprojections for "Coating, Painting, andSpraying Machine Operators, and Tenders" adjusted for solventcontent of paints and coatings from Freedonia's "Solvents to 2010-Paints and Coatings Market for Solvents" (-1.9% per year)Forecast data are statelevel; adopted approachbelieved better thanavailable historicthroughput dataPECHANSeptember 200721Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-4 (continued)Total % ChangePollutantSCCEmissionsPriorityCategory2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentROG 30102599 4.9 18.2 Avg of REMI employment& output growth factors for Plastics,Materials, & Synthetics sectorForecast data are statelevelROG 30500201 8.3 21.5 Avg of REMI employment & output growth factors for Misc.Petroleum and Coal Products sectorForecast data are statelevelROG 30201906 2.2 11.9 Avg of REMI employment & output growth factors for Grain MillProducts and Fats and Oils sectorForecast data are statelevelROG 30201919 2.2 11.9 Avg of REMI employment & output growth factors for Grain MillProducts and Fats and Oils sectorForecast data are statelevelROG 40200110 VOC -6.4 -21.4 Regional employmentprojections for "Coating, Painting, andSpraying Machine Operators, and Tenders" adjusted for solventcontent of paints and coatings from Freedonia's "Solvents to 2010-Paints and Coatings Market for Solvents" (-1.9% per year)Forecast data are statelevel; adopted approachbelieved better thanavailable historicthroughput dataROG 40201899 6.9 26.0 Freedonia's "Protective Coatings to 2009-Demand for CoilCoatings" - national projections adjusted for relative state growth inREMI output for Nonferrous Rolling and Drawing sector, adjustedfor projected solvent content information for paints and coatingsfrom "Solvents to 2010-Paints/Coatings Market for Solvents" (-1.9%per year)Forecast data are statelevel; coil coating hasseen significant growthhistorically, and suchgrowth is projected tocontinue in the futureROG 40388801 0.4 0.4 AEO refinery distillationprojections for Petroleum AdministrationDistrict (PAD) II, which includesall LADCO states plus additionalsurrounding states1990-2005 data also includes statesnot in LADCO region and showssimilar very small growth rateROG 40200701 -1.6 -1.0 Freedonia's "Solventsto 2010-Adhesives & Sealants Market forSolvents" national projections, adjusted for relative state growth inREMI output in Total Manufacturing sectorSO2 30600201 SO2 0.4 0.4 AEO refinery distillationprojections for Petroleum AdministrationDistrict (PAD) II, which includesall LADCO states plus additionalsurrounding states1990-2005 data also includes statesnot in LADCO region and showssimilar very small growth rateDid not use throughputsince available staterepresent <50% ofregional emissionsSO2 10200202 NOx 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearNo throughput dataavailableSO2 30600805 SO2 0.4 0.4 AEO refinery distillationprojections for Petroleum AdministrationDistrict (PAD) II, which includesall LADCO states plus additionalsurrounding states1990-2005 data also includes statesnot in LADCO region and showssimilar very small growth rateNo throughput dataavailableSO2 30199999 8.9 25.6 Avg of REMI employment & output GFs for Chemicals sector Forecast data are statelevelSO2 10200217 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearSO2 10200201 SO2 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearNo throughput dataavailableSO2 10200225 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearSO2 30500606 8.2 29.4 LADCO region historicalcement production growth rate (+2.0% peryear)PECHANSeptember 200722Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-4 (continued)Total % ChangePollutantSCCEmissionsPriorityCategory2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentSO2 30600401 0.4 0.4 AEO refinery distillationprojections for Petroleum AdministrationDistrict (PAD) II, which includesall LADCO states plus additionalsurrounding states1990-2005 data also includes statesnot in LADCO region and showssimilar very small growth rateSO2 10200204 NOx 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearDid not use throughputsince available statesrepresent <50% ofregional emissionsSO2 10300217 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -1.2% per year;AEO forecast = +0.0% per yearSO2 39000701 -6.2 -19.8 AEO forecast for metallurgicalcoal consumption DOE 1990-2004 = -3.0% per year;AEO forecast = -1.7% per yearSO2 301032012.5 8.2 1996-2005 recovered elemental sulfur production growth rate(+0.6% per year) for IL + MI + MN + OHSO2 10300225 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -1.2% per year;AEO forecast = +0.0% per yearSO2 10300209 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -1.2% per year;AEO forecast = +0.0% per yearSO2 10200401 SO2 -49.4 -49.6 AEO forecast for industrial residual oil consumption (-5.1% peryear)DOE 1990-2004 = -6.6% per year Used AEO forecast dueto similarity with AEOhistorical trend andthroughput trend (-6.1%)NON-PRIORITY CATEGORIES INCLUDED IN EMISSION TREND ANALYSIS WITH CONSISTENT THROUGHPUT TRENDS:VOC 40202201 VOC -33.5 -33.5 Historic throughput trend from 3 states (-9.7%) extended thru 2009;post-2009 held constant.NOX 39000699 NOx -15.8 -15.8 Historic throughput trend from 3 states (-4.2%) extended thru 2009;post-2009 held constant.

### PECHAN September 2007



### 23 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### Table II-4 also presents the 2005-2009 and 2005-2018 growth rates for the final assigned point source growth indicators. In addition, Pechan re viewed the complete list of point SCCs in the LADCO base year inventor y to identify the priority category growth indicators that could be applied to non-priority point s ource categories. This step yi elded priority category growth indicator assignments for an addi tional 539 point s ource categories.



## D. NONROAD MODEL SOURCES

At LADCO’s request, Pechan analyzed potential improvements to the default growth indicators for the 25 NONROAD model priority source cate gories displayed in Table II-5. With the exception of the all-terrain vehicle, offroad motorcycle, and snowmobile categories, 1989-1996 national equipment population trends form the basi s for the NONROAD growth rates. For these other three categories, NONROA D relies on national equipment population forecasts prepared by a relevant trade association (see Table II-5 for details).

Table II-6 reports this stu dy’s growth indicator assignmen ts for priority NONROAD model source categories. Given the acknowledged sh ortcomings of the NONROAD growth rates (use of 1989-1996 national equipment populations to proj ect future equipment populations in each region of the country), the growth factor improve ments generally reflect th e use of regional/statelevel forecast data that are e xpected to correlate with use of the equipment (i.e., regional occupational employment project ions, state-level economic sector employment forecasts, or state-level landing/take-off projec tions). Table II-6 also disp lays any alternative growth indicators that were considered. For th e three categories for which NONROAD relies on forecasts rather than historical 1989-1996 trends , Pechan compiled available recent historical equipment population estimates. This informati on was used to revise the current national forecast approach to reflect more recent info rmation, and whenever possible, recent LADCO region-specific equipment population trends.

Although the NONROAD model growth rates are fuel -specific, Pechan was unable to develop fuel-specific forecast data. Therefore, Pechan updated a priority category’s growth rates only when the 1989-1996 national equipment populations i ndicated that the cate gory’s fuel-specific growth rate had traditionally be en similar to the overall sect or’s equipment population growth rate. Table II-6 identifies instances where the past fuel-specific growth rate substantially differed from the overall sector’s growth rate. In these cases, Pechan retained the NONROAD model fuel-specific forecast approach. Section IV.A. describes how Pechan incorporated the updated equipment population growth rates into the NONROAD modelPECHANSeptember 200724Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-5. Priority Emission Activity NONROAD Model Source CategoriesSCCDESCRIPTION SUMMARYEMISSIONS ACTIVITYCURRENT GROWTH BASIS2260001030 2-Stroke ATV Population of 2-stroke gasoline ATVs NONROAD (Motorcycle Industry Councilnational 2-stroke gasoline ATV projections)2265001030 4-Stroke ATV Population of 4-stroke gasoline ATVs NONROAD (Motorcycle Industry Councilnational 4-stroke gasoline ATV projections)2260001010 2-Stroke Offroad Motorcycles Population of 2-stroke gasoline offroad motorcycles NONROAD (Motorcycle Industry Council national off-highway motorcycle populationprojections)2265001010 4-Stroke Offroad Motorcycles Population of 4-stroke gasoline offroad motorcycles NONROAD (Motorcycle Industry Council national off-highway motorcycle populationprojections)2267006000 LPG Light Commercial Population of light commercial LPG-fueledequipmentNONROAD (national 1989-1996 LPG light commercial equipment population growth rate)2270004000 Diesel Lawn & GardenEquipmentPopulation of lawn & garden diesel-fueledequipmentNONROAD (national 1989-1996 diesel lawn& garden equipment population growth rate)2270008000 Diesel Airport ServiceEquipmentPopulation of airportservice diesel-fueledequipmentNONROAD (national 1989-1996 diesel airportservice equipment population growth rate)2267008000 LPG Airport Service Equipment Population of airport service LPG-fueled equipment NONROAD (national 1989-1996 total airport service equipment population growth rate)2268008000 CNG Airport Service Equipment Population of airport service CNG-fueled equipment NONROAD (national 1989-1996 total airport service equipment population growth rate)2260001020 2-Stroke Snowmobiles Population of 2-stroke gasoline snowmobiles NONROAD national growth (see below) with state adjustment based on real disposableincome forecasts2265001020 4-Stroke Snowmobiles Population of 4-stroke gasoline snowmobiles NONROAD (International Snowmobile Manufacturers Association national snowmobilepopulation projections)2260007000 2-Stroke Logging Equipment Population of logging 2-stroke gasoline equipmentNONROAD (national 1989-1996 gasoline logging equipment population growth rate)2265007000 4-Stroke Logging Equipment Population of logging 4-stroke gasoline equipmentNONROAD (national 1989-1996 gasoline logging equipment population growth rate)2270006000 Diesel Light Commercial Population of light commercial diesel-fueledequipmentNONROAD (national 1989-1996 diesel light commercial equipment population growth rate)2268006000 CNG Light Commercial Population of light commercial CNG-fueledequipmentNONROAD (national 1989-1996 CNG light commercial equipment population growth rate)2267007000 LPG Logging Equipment Population oflogging LPG-fueled equipment NONROAD(national 1989-1996 logging equipment population growth rate)2268007000 CNG Logging Equipment Populationof logging CNG-fueled equipment NONROAD (national 1989-1996 logging equipment population growth rate)2285002000 Diesel Railway Maintenance Population of railway maintenance diesel-fueledequipmentNONROAD (national 1989-1996 diesel railwaymaintenance equipment population growthrate)2260006000 2-Stroke Light Commercial Populationof light commercial 2-stroke gasolineequipmentNONROAD (national 1989-1996 gasoline light commercial equipment population growth rate)2265006000 4-Stroke Light Commercial Populationof light commercial 4-stroke gasolineequipmentNONROAD (national 1989-1996 gasoline light commercial equipment population growth rate)2270002000 Diesel ConstructionEquipment Population of construction diesel-fueled equipment NONROAD national growth with stateadjustment based on Construction employmentforecasts2270003000 Diesel Industrial Equipment Population of industrial diesel-fueled equipmentNONROAD (national 1989-1996 diesel industrial equipment population growth rate)2267003000 LPG Industrial Equipment Populationof industrial LPG-fueled equipment NONROAD (national 1989-1996 LPG industrial equipment population growth rate)2270001000 Diesel Recreational Vehicles Population of diesel recreational vehicles NONROAD (national 1989-1996 diesel recreational equipment population growth rate)2282020000 Diesel Recreational MarinePopulation of diesel recreational marine vessels NONROAD (national 1989-1996 diesel recreational equipment population growth rate)PECHANSeptember 200725Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-6. Growth Indicators for Priority NONROAD Model Source CategoriesTotal % ChangeSCC2005-20092005-2018Growth Indicator BasisAlternatives ConsideredComment2260001030 10.8 39.7 50% higher growth rate than overall LADCO region"Recreational Vehicle Service Technicians" employmentprojections, which is 1.7%/yr, based on premise thatATV market is less mature than market for otherrecreation vehiclesAvailable ATV/OHV registration data for MI+MN+WI indicateavg. annual growth of 14.6% between 2000 and 2005;however, 2006 data for each state is only +1 or +1.1%;NONROAD shows 2005-2006 = +10.5%. National ATVsales (not population) of +3.8% per year for 2000-2005;2005-2006 sales = -4.2%Did not use long-term historical trendas ATV market appears to be maturingbased on most recent data (this ispredicted by NONROAD model, but notuntil post-2010; NONROAD shows2010-2018 = +2.7%/yr)2265001030 10.8 39.7 50% higher growth rate than overall LADCO region"Recreational Vehicle Service Technicians" employmentprojections, which is 1.7%/yr, based on premise thatATV market is less mature than market for otherrecreation vehiclesAvailable ATV/OHV registration data for MI+MN+WI indicateavg. annual growth of 14.6% between 2000 and 2005;however, 2006 data for each state is only +1 or +1.1%;NONROAD shows 2005-2006 = +10.5%. National ATVsales (not population) of +3.8% per year for 2000-2005;2005-2006 sales = -4.2%Did not use long-term historical trendas ATV market appears to be maturingbased on most recent data (this ispredicted by NONROAD model, but notuntil post-2010; NONROAD shows2010-2018 = +2.7%/yr)2260001010 5.1 17.7 LADCO region employment projections for "MotorcycleMechanics"Unable to compile regional registration trends; national offroad motorcycle sales for 2000-2006 = +2.3%, but 2001-2006 = -1.6%Employment projections are LADCOregion-specific and fall in-betweenrecent national sales trends2265001010 5.1 17.7 LADCO region employment projections for "MotorcycleMechanics"Unable to compile regional registration trends; national offroad motorcycle sales for 2000-2006 = +2.3%, but 2001-2006 = -1.6%Employment projections are LADCOregion-specific and fall in-betweenrecent national sales trends2267006000 19.8 57.5 NONROAD (national 1989-1996 LPG light commercialequipment population growth rate)REMI Commercial sector employment forecast for LADCOregion (2005-2009=+1.8%yr; 2005-2018 = +1.1%/yr)Did not use alternative becausehistorical period indicates substantiallydifferent growth rate for LPG thanoverall sector2270004000 5.8 20.1 LADCO region employment projections for "Landscapingand Groundskeeping Workers"2270008000 -0.7 18.0 State-level FAA itinerant air carrier + air taxi landing andtake-off (LTO) forecast (updated as of December 2006)2267008000 -0.7 18.0 State-level FAA itinerant air carrier + air taxi landing andtake-off (LTO) forecast (updated as of December 2006)2268008000 -0.7 18.0 State-level FAA itinerant air carrier + air taxi landing andtake-off (LTO) forecast (updated as of December 2006)2260001020 3.5 11.9 50% lower growth rate than overall LADCO region"Recreational Vehicle Service Technicians" employmentprojections, which is 1.7%/yr, based on premise thatsnowmobile market is more mature than market for otherrecreation vehiclesAnnual growth in snowmobile registrations for statesrepresenting 92% of 2006 LADCO region registrations:2000-2006 = +0.0%No growth assumption not adoptedbecause lack of snowfall often cited asmajor contributing factor for recentstagnation in snowmobile registrations2265001020 3.5 11.9 50% lower growth rate than overall LADCO region"Recreational Vehicle Service Technicians" employmentprojections, which is 1.7%/yr, based on premise thatsnowmobile market is more mature than market for otherrecreation vehiclesAnnual growth in snowmobile registrations for statesrepresenting 92% of 2006 LADCO region registrations:2000-2006 = +0.0%No growth assumption not adoptedbecause lack of snowfall often cited asmajor contributing factor for recentstagnation in snowmobile registrationsPECHANSeptember 200726Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-6 (continued)Total % ChangeSCC2005-20092005-2018Growth Indicator BasisAlternatives ConsideredComment2260007000 2.2 7.32265007000 2.2 7.3 LADCO region employment projections for "LoggingEquipment Operators"2270006000 7.2 15.0 REMI Commercial sector employment forecast forLADCO region2268006000 7.2 15.0 REMI Commercial sector employment forecast forLADCO region2267007000 2.2 7.3 LADCO region employment projections for "LoggingEquipment Operators"2268007000 2.2 7.3 LADCO region employment projections for "LoggingEquipment Operators"2285002000 12.2 36.7 NONROAD (national 1989-1996 diesel railwaymaintenance equipment population growth rate)LADCO region employment projections for "Rail-Track Laying &Maintenance Equipment Operators" (-1.6%/yr)Did not use alternative becausehistorical period indicatessubstantially different growth rate fordiesel than overall sector2260006000 7.2 15.0 REMI Commercial sector employment forecast forLADCO region2265006000 7.2 15.0 REMI Commercial sector employment forecast forLADCO region2270002000 4.7 16.2 LADCO region employment projections for "OperatingEngineers and Other Construction EquipmentOperators"2270003000 0.1 0.2 LADCO region employment projections for "IndustrialMachinery Mechanics"2267003000 0.1 0.2 LADCO region employment projections for "IndustrialMachinery Mechanics"2270001000 10.4 31.5 NONROAD (national1989-1996 diesel recreationalequipment population growth rate)LADCO region employment projections for "Recreational VehicleService Technicians" (+1.7%/yr)Did not use alternative becausehistorical period indicatessubstantially different growth rate fordiesel than overall sector2282020000 10.4 31.5 NONROAD (national1989-1996 diesel recreationalequipment population growth rate)Two options: 1996-2005 LADCO region recreational boatregistration growth rate (0.8%/yr) and LADCO region employmentprojections (+1.5%/yr) for "Motorboat Mechanics"Did not use alternatives becausehistorical period indicatessubstantially different growth rate fordiesel than overall sector.

### PECHAN September 2007



### 27 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

# SECTION III. UPDATED EMISSION CONTROL DATA

A. NON-EGU POINT SOURCE CONTROLS

1. NO x 

### SIP Call

All states in the LADCO region affected by the NO x 

### (oxides of nitrogen) SIP (State Implementation Plan) Call requirements (OH, IN, IL, MI) indicated that their sources were complying in 2005. The only exception to this is for reciprocating internal combustion engines (RICE) in Illinois. The State of I llinois recommended that an 82 percent NO x 

### control efficiency be applied to large RICE engines that are affected by the SIP Call. The RICE engine requirement in Illinois has a January 1, 2008 complia nce date. Table III-1 lists these engines and the associated NO x 

### control efficiencies applied in the emi ssion projections. This requirement is expected to affect NO x 

### emissions in all projection years.

2. MACT Standards

Table III-2 summarizes the control factors us ed to estimate the post-2005 effects of MACT emission standards on volatile organic compounds (VOC), NO x 

### , and PM emissions in the projection years. The inform ation in this table was developed from EPA guidance on estimating the criteria pollutant emission benefits of MACT standards (Page, 2007). Any post2005 MACT standards that have no expected cr iteria pollutant emission reductions according to the draft EPA guidance were not included in Ta ble III-2. Table III-2 was circulated to the states for review, and Wisconsin provided it s own estimates of the expected VOC and PM emission reductions from these MACT standards in its state. The State of Michigan concurred with the emission reduction estimates made by Wisconsin. Those VOC and PM emission reduction percentages are shown in the two right- most columns of Table III-2. So, the control factor file reflects the EPA estimated values for IL, IN, MN, and OH, and the Wisconsinprovided estimates for MI and WI.

3. Consent Decrees

Previous Pechan-developed control efficienci es by source (Pechan, 2005) and pollutant were merged with the LADCO state 2005 point source file and control factor s assigned accordingly. The 2005 point source control efficienci es (CEs) for sulfur dioxide (SO 2 

### ) and NO x 

### for fluid catalytic cracking units (FCCUs) and heaters and boilers were checked to see whether there is any compliance by 2005. Pechan also added all MA CTEC revisions/additions from their earlier report to the control factor file (MACTEC incor porated cases and settleme nts control factors for refineries that were not evaluated for the 812 st udy) and made any changes/additions that were provided by the state air pollution control agencies. Table III-3 lists all of the LADCO state Non-EGU Point Sources affected by consent decr ees. These sources all have post-2005 control factors applied in the analysis. There are tw o refineries in the st udy area who had either complied with their consent decrees or curtailed applicable operations by 2005, so no future year control factors were applied in this analysis. These two refineries are Premcor Refining in IL and the Flint Hills Refinery in MN.PECHANSeptember200728Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable III-1. RICE Engines in Illinois Affected by NOxSIP CallId Number Device Process Device Description Pollutant%Reduction Comments027807AAC 0003 01 ENGINES 09-ENG AND 10-ENG NOX 59 50/50 for 2 engines =.5+.5(.18) = 59% reduction041804AAC 0009 01 ENGINE 1213 NOX 82041804AAC 0010 01 ENGINE 1214 NOX 82041804AAC 0011 01 ENGINE 1215 NOX 82041804AAC 0012 01 ENGINE 1216 NOX 82041804AAC 0013 01 ENGINE 1217 NOX 82073816AAA 0001 01 WORTHINGTON MLV-10 COMPRESSOR & GAS FIRED ENGINE #12 NOX 82073816AAA 0004 01 CLARK TCV-10 COMPRESSOR& GAS FIRED ENGINE ENGINE #9 NOX 82073816AAA 0012 01 WORTHINGTON MLV-10 COMPRESSOR AND GAS FIRED ENGINE NO. 13 NOX 82073816AAA 0013 01 WORTHINGTON MLV-10 COMPRESSOR AND GAS FIRED ENGINE NO. 14 NOX 82073816AAA 0014 01 WORTHINGTON MLV-10 COMPRESSOR AND GAS FIRED ENGINE NO. 15 NOX 82073816AAA 0015 01 WORTHINGTON MLV-14 ENGINE #10 NOX 82085809AAA 0010 01 3 CLARKCOMPRESSORS NOX 82093802AAF 0003 01 ENGINE E-1008 NOX 82113817AAA 0002 01 ENGINE EC21 NOX 82113817AAA 0003 01 ENGINE IC11 NOX 82113821AAA 0002 01 ENTERPRISE RECIP COMP EC-21 4000 MPEF 3.3.2-1 NOX 82113821AAA 0005 01 COOPER COMPRESSOR CC22 EF 3.3.2-1 4000 HP NOX 82149820AAB 0002 01 2 RECIPROCATING ENGINES (1013 - 1014) NOX 59 50/50 for 2 engines =.5+.5(.18) = 59% reduction149820AAB 0003 01 3 RECIPROCATING ENGINES (1015 - 1017) NOX 82167801AAA 0001 01 ENGINES1116 AND 1117 NOX 82167801AAA 0003 01 1-COOPER RECIPROCATING ENGINE, 4000HP, 1115 NOX 82167801AAA 0008 01 ENGINES1118 AND 1119 NOX 59 50/50 for 2 engines =.5+.5(.18) = 59% reductionPECHANSeptember200729Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable III-2. Post-2005 MACT Standards and Expected VOC, NOx, and PM ReductionsMACT Standard – Source CategoryCode of FederalRegulationsSubpartComplianceDate (existingsources)VOC(% Reduction)NOx(% Reduction)Total PM(% Reduction)Affected SCCsMACTCodeWisconsin andMichigan ValuesVOC PMAsphalt Processing and Asphalt RoofingManufactureLLLLL5/1/20068530505001, 30500101,30500102, 30505010,30601101041810 0Auto and Light Duty TrucksIIII4/26/20074040201601 to 40201632;4020169907020 0Coke Ovens: Pushing, Quenching andBattery StacksCCCCC4/14/2006030300304; 30300303030310 0Fabric Printing, Coating & DyeingOOOO5/29/20066040201101 to 40201199;40201201; 40201210071310 0Integrated Iron and SteelFFFFF5/20/2006(5)2030301501 to 3030159603050 10Iron and Steel FoundriesEEEEE4/22/20075304003XX, 304007XX03085 0Lime ManufacturingAAAAA1/5/200723305016XX04080 10Metal CanKKKK11/13/20067040201702; 40201703 to4020179907070 0Metal FurnitureRRRR5/23/20060402020XX10 0Misc. Coating ManufacturingHHHHH12/11/200664402026XX164210 0Misc. Metal Parts and ProductsMMMM1/2/20070402025XX10 0Misc. Organic Chemical Production andProcesses (MON)FFFF11/10/200666645200XX; 30113001 to30113007; 684300XX;30101005 to 30101099;68445001; 68445010;68445013; 68445020;68445022; 68445101;68445201; 30110002 to30110099; 64820001;64820010; 64821001;64821010; 64822001;64822010; 64823001;64823010; 64823001;64823010; 64880001;64882001; 64882002;64882599; 30105001;30105101 to 30105130;30801001; 31604001;31604002; 31600403;68510001; 68510010;68510011; 68580001;68582001; 68582002;68582599; 30101837;64610301 to 64610350;64610001 to 64610050;64610101 to 64610150;164110 0PECHANSeptember2007Table III-2 (continued)30Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportMACT Standard – Source CategoryCode of FederalRegulationsSubpartComplianceDate (existingsources)VOC(% Reduction)NOx(% Reduction)Total PM(% Reduction)Affected SCCsMACTCodeWisconsin andMichigan ValuesVOC PM64610201 to 64610250;64615001 to 64615030;64620001 to 64620038;64630001 to 64630083;64631001 to 64631083;64632001 to 64632083;64680001; 64682001;64682002; 64682501;64682502; 64682599;64130001 to 64130025;64130101 to 64130125;64130201 to 64130225;64131010 to 64131030;64132001 to 64132030;64133001 to 64133030;64180001; 64182001;64182002; 64182599;64615001; 64620001;65135001Organic Liquids DistributionEEEE2/3/20077040300102, 40300104,40300106, 40300107,40301010-40301021060210 0Plastic PartsPPPP4/19/20070402022XX10 0Plywood and Composite WoodProductsDDDD10/1/200754307007XX; 30700921 to30700971; 30701001 to30701057; 30700602 to307006611624Refractory Products ManufacturingSSSSS4/17/2006810040610 0Reinforced Plastic CompositesProductionWWWW4/21/2006390133710 0Site RemediationGGGGG10/8/2006500504001XX; 50400201,50400202; 504002XX;504100XX; 504101XX;504102XX; 504103XX;504102XX; 504103XX;04104XX; 504105XX;504106XX; 504107XX;50480001; 50482001;50482002; 50482599;50480004080510 0PECHANSeptember 2007Table III-2 (continued)31Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportMACT Standard – Source CategoryCode of FederalRegulationsSubpartComplianceDate (existingsources)VOC(% Reduction)NOx(% Reduction)Total PM(% Reduction)Affected SCCsMACTCodeWisconsin andMichigan ValuesVOC PMStationary Combustion TurbinesYYYY3/5/2007131720100101, 20100201,20200101, 20200103,20200201, 20200203,20200901, 20300102,20300202, 2030020301050 0Taconite Iron Ore ProcessingRRRRR10/30/2006006232302371 to 3230239904110 10Wood Building ProductsQQQQ5/28/200663040202101 to 40202199070310 0**Based on organic hazardous air pollutant (HAP) emission reductionsPECHANSeptember200732Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable III-3. LADCO State Non-EGU Point Sources Affected by Consent Decree Requirementsand Other On-the-Books ControlsIdentification CodesFCCU RequirementsHeater/Boiler RequirementsState County Facility* CompanyLocationStateSO2NOxSO2NOx18 089 00003 BP AmocoWhitingINFCU 500: Install wet gasscrubber; FCU 600: UseSO2adsorbing catalystadditive and/orhydrotreatment.FCU 600: InstallSCR; FCU 500: LowNOxcombustionpromoter and NOxadsorbing catalystadditiveElimination of oil burningand restricting H2S inrefinery fuel gasUse qualifying controls toreduce NOxemissions by9632 tons per year (tpy).39 095 0448010246 BP AmocoToledoOHSO2catalyst additiveInstall SNCR systemElimination of oil burningand restricting H2S inrefinery fuel gasUse qualifying controls toreduce NOxemissions by9632 tpy.17 197 197090AAI CITGO GlobalRefineryLemontILNew wet gas scrubberLow NOxcombustionpromoter (20 ppmvdlimit)Comply with NSPSSubparts A and J for fuelgas combustion devices.Eliminate fuel oil burning.Use qualifying controls toreduce NOxemissions fromlisted units by at least 50% ofthe revised baseline17 119 119090AAA Conoco PhilipsGlobal RefineryRoxanna (WoodRiver)ILInstall new wet gasscrubber (25 ppmvd orlower)FCCU 1: Scrubberbased NOxemissionreduction technologyto achieve 20 ppmvdSubject to NSPS SubpartsAand J for fuel gascombustion devicesUse qualifying controls toreduce NOxemissions fromcombustion units by 4951 tpy17 119 119090AAA Conoco PhilipsGlobal RefineryHartford (WoodRiver)ILInstall new wet gasscrubber (25 ppmvd orlower)FCCU 2: EnhancedSNCRSubject to NSPS SubpartsAand J for fuel gascombustion devicesUse qualifying controls toreduce NOxemissions fromcombustion units by 4951 tpy17 197 197800AAA Exxon-MobilRefineryJolietILInstall new wet gasscrubber (25 ppmvd orlower)Install and operate anSCR systemAccept NSPS Subpart Japplicability for heatersand boilers and reduce oreliminate fuel oil firingUse qualifying controls toreduce NOxemissions fromcombustion units17 033 033808AAB MarathonAshland RefineryRobinsonILExisting wet gasscrubberCatalyst additiveAccept NSPS Subpart Japplicability for heatersand boilers and reduce oreliminate fuel oil firingReduce overall NOxemissions from the controlledheaters and boilers at MAPrefineries by 4,000 tpy.Control methods can include:SCR or SNCR; ULNB;technologies to reach 0.040lbs per MMBtu or lower;alternate SO2single burnertechnology to achieve 0.055lbs per MMBtu or lower; unitshutdowns.PECHANSeptember2007Table III-3 (continued)33Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportIdentification CodesFCCU RequirementsHeater/Boiler RequirementsState County Facility* CompanyLocationStateSO2NOxSO2NOx26 163 A9831 MarathonAshland RefineryDetroit MI SO2catalyst additive Catalyst additiveAccept NSPS Subpart Japplicability for heaters andboilers and reduce oreliminate fuel oil firingReduce overall NOxemissions from thecontrolled heaters andboilers at MAP refineries by4,000 tpy. Control methodscan include: SCR or SNCR;ULNB; technologies toreach 0.040 lbs per MMBtuor lower; alternate SO2single burner technology toachieve 0.055 lbs perMMBtu or lower; unitshutdowns.27 163 2716300003 MarathonAshland RefinerySt Paul Park MN New wet gas scrubberon unit 1; catalystadditive on other unitCatalyst additiveAccept NSPS Subpart Japplicability for heaters andboilers and reduce oreliminate fuel oil firingReduce overall NOxemissions from thecontrolled heaters andboilers at MAP refineries by4,000 tpy. Control methodscan include: SCR or SNCR;ULNB; technologies toreach 0.040 lbs per MMBtuor lower; alternate SO2single burner technology toachieve 0.055 lbs perMMBtu or lower; unitshutdowns.39 151 1576000301 MarathonAshland RefineryCanton OH SO2catalyst additive Catalyst additiveAccept NSPS Subpart Japplicability for heaters andboilers and reduce oreliminate fuel oil firingReduce overall NOxemissions from thecontrolled heaters andboilers at MAP refineries by4,000 tpy. Control methodscan include: SCR or SNCR;ULNB; technologies toreach 0.040 lbs per MMBtuor lower; alternate SO2single burner technology toachieve 0.055 lbs perMMBtu or lower; unitshutdowns.39 095 0448010246 SunocoPetroleumRefineryToledo OH Install new wet gasscrubber to meet 25ppmvd SO2Install SCR systemsor alternatetechnology to meet 20ppmvdAccept NSPS Subpart Japplicability and reduce oreliminate fuel oil burningPECHANSeptember2007Table III-3 (continued)34Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportIdentification CodesState County Facility* CompanyLocationStateNotes17 115 115015AAEADM Decatur IL Settlement agreement17 143 143065AJEADM Peoria IL Settlement agreement17 001 001815AAFADM Quincy IL Settlement agreement18 173 00002Alcoa Warrick Units 1,2,3 IN Settlement agreement*Facility identification codes are thoseused in the 2002 point source files.

### PECHAN September 2007



### 35 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### The Michigan Department of Environmental Quality (DEQ) provided information about the expected emissions reductions associated with settlements affecting Michigan sources. The information from Michigan DEQ was provided fo r Severstal (iron and steel), US Steel, and Marathon refinery. For Seve rstal, the key information provided indicated that NO x 

### emissions after the summer 2007 would be reduced at the blast furnace B and C stoves via a low NO x 

### burner (LNB) installation. A 50 percent NO x 

### control factor was applied with 2007 implementation year based on information in th e Ozone Transport Rulemaking analysis about the expected emission reduction of LNB applied to a blast furnace. For US Steel, the Michigan DEQ-provided information indicated that PM controls would be in stalled during 2005 or 2006 on the basic oxygen furnace and blast furnace B, so it was assumed that these were base year controls and no future year control factor was ap plied. For the Marathon refinery in Michigan, the Michigan DEQ estimated that catalyst additives applied to the FCCU would reduce NO x 

### emissions by 25-50 percent and SO 2 

### emissions by 60-80 percent. The midpoint of each range was used to estimate post-2005 control factors for th is refinery. All other expected controls at Marathon are to reduce PM emissions and were assumed to have occurred by 2005, so no future year PM control factors were applied.

4. On-the-Books (OTB) Control Additions

Table III-3 lists on-the-books controls that were applied to individual f acilities/sources in the future year control factor file. This inform ation was developed from the OTB updated control factor file provided by LADCO from the 2002 ba se year projections. The compliance date information in this file was used to eliminat e controls that had compliance dates of 2005 or earlier. Ohio EPA provided informati on about the expected effects of NO x 

### Reasonably Available Control Technology (RAC T) rules in achieving post- 2005 emission reductions in the Cleveland-Akron, Ohio 8-hour ozone nonattainment area. Table III-4 summarizes the source categories, associated emission control equipmen t to meet the requirements, and the estimated NO x 

### control percentages.

Table III-4. Ohio RACT Rule Summary Cleveland/Akron 8-Hour Ozone Nonattainment Area

Source Category Unit Size (MMBtu/hour) NO x Control Estimated NO x Control Efficiency RICE Engines All Low Emission Combustion 80% ICI Boilers 20-49 Burner Tune-up 10% ICI Boilers 50-99 LNB+FGR 61% ICI Boilers 100-249 LNB+FGR 61% ICI Boilers >250 LNB+FGR 61% Combustion Turbine All Dry LNB 70%

SOURCE: Ohio EPA Division of Air Pollution Control.



### 5. Best Available Retrofit Technology (BART)

Table III-5 lists the BART-eligible sources fo r the states in the LADCO study region. In instances where criteria air pollu tant control percentages (for SO 2 

### and NO x 

### ) are listed in this table, those control percentages were applied in estimating 2018 emissions.PECHANSeptember200736Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable III-5. BART Eligible Non-EGU SourcesEst. Emission ReductionState State ID Source Name CountyCountyID Source ID BART Emission Unit ID Description Stack ID SO2NOxILLINOIS 17 Conoco Phillips Madison 11D 119090AAAILLINOIS 17 Exxon Mobil Will 197 197800AAAILLINOIS 17 CITGO Will 197 197090AAIILLINOIS 17 National Steel –Granite CityMadison 119 119813AAIINDIANA 18 AGC DIVISIONALCOA POWERGENERATINGWarrick 173 2 Boiler #2 Dry Bottom, pulverizedcoal-fired boiler241-242 95 90Boiler #3 Dry Bottom pulverizedcoal-fired boiler242 95 90Boiler #4 Dry Bottom, pulverizedcoal-fired boiler243 95 90INDIANA 18 Alcoa Inc. – Warrick Warrick 173 7 105m.1, 10 POTLINE #3. ROOMS105 AND 106 gtc105M 95 40107M, 108M POTLINE #4. ROOMS107 AND 108 GTC107M 95 40109M,110M POTLINE #5, ROOMS109 AND 110, A-398109M 95 40111M,112M, POTLINE #6 95 40130m.1,104 potline #2, Rooms 103and 104, A-398103m.1 95 40134.63 HDC FURNACECOMLEXES1EH 0 40134.71 OFFLINES #2 134.71 0 40INDIANA 18 ESSROC CEMENTCORP. (Speed)Clark 19 8 EU20 Kiln #1 95 70EU21 Kiln #2 95 70INDIANA 18 GE PLASTICS MT.VERNON INC.Posey 129 2 08-706 CO AND ORGANICSULFIDE STREAMFROM PHOSGENEFED08-70670795 009-001 B&W NATURAL GASAND OIL FIREDBOILER09-001 0 7009-001 Riley Boiler 12-001 95 7012-001 Hot Oil Heater 0 009-002 LASKER BOILER 09-002 95 7509-002 ERIE BOILER 09-002 95 75PECHANSeptember2007Table III-5 (continued)37Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportEst. Emission ReductionState State ID Source Name CountyCountyIDSource ID BART Emission Unit ID Description Stack ID SO2NOxINDIANA 18 ISG-BURNSHARBOR (FormerlyBeth. Steel)Porter 127 1 460-01 #7 Boiler 4 95 7546002 #8 Boiler 5 95 75460-03 #9 Boiler 6 95 75460-04 #10 Boiler 7 95 75460-05 Boiler #11 8 95 75460-06 #12 Boiler 9 95 75512-06 #1 COKE BATTERYPUSHING11 0 0512-08 #1 Coke BatteryUnderfire13 95 75512-14 #2 COKE BATTERYPUSHING12 0 0512-16 #2 COKE BATTERYUNDERFIRE STACK14 95 75520 BLAST FURNACEFUGITIVES0 0520-04 SINTER WINDBOXSTACK25 95 75520-18 BLAST FURNACE DCASTHOUSEEMISSIONS33 0 0520-18 C BLAST FURNACESTOVES31 0 0520-19 BLAST FURNACE DSTOVES34 0 0520-19 BLAST FURNACE CCASTHOUSE33 0 0534 STEELMAKINGFUGITIVES0 0534-01 STEELMAKING HMDSTATION #157 0 0534-02 STEELMAKING HMD#259 0 0534-10 STEELMAKINGVESSELS #1 & #262 0 0534-11 STEELMAKINGVESSELS64 0 0534-23 STEELMAKING FMBOILER65 0 0PECHANSeptember 2007Table III-5 (continued)38Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportEst. Emission ReductionState State ID Source Name CountyCountyID Source ID BART Emission Unit ID Description Stack ID SO2NOx595-24 CASTER #1 80 0 0670-05 HOT STRIP FURNACE#190 95 75670-07 HOT STRIP #3FURNACE92 95 75670-07 HOT STRIP 91 95 75673-14 160" OKATE MILLFURNACE #1112 0 75673-15 160" PLATE MILLFURNACE #2113 0 75673-16.17 160" PLATE MILLFURNACES 4&5110 0 0673-18.19 160" PLATE MILLFURNACES 6&7111 0 0673-20 160" PLATE MILLFURNACE #8114 0 0674.26,27 110" PLATE MILLFURNACES #1122 0 0MICHIGAN 26 Lafarge Midwest Inc. Alpena 7 B1477 Kilns #1-#5MICHIGAN 26 Stone Container Corp. Ontonagon 131 A5754 Riley BoilerPaper Machine #2MICHIGAN 26 Tilden Mining Co Marquette103 B4885 Pelletizing Line #1, includeskiln, furnace, cooler, dryerBoiler #2Primary crusherMICHIGAN 26 Empire Iron Mining Marquette 103B1827 Pelletizing Lines #1 - #3 furnaceBoilers #1 - #3Primary crusherMICHIGAN 26 St. Mary’s Cement(CEMEX)Charlevoix 29 B1559 Kiln and pre-calcinerMICHIGAN 26 New Page Paper(Escanaba)Delta 41 A0884 Boiler #8Boiler #9Recovery furnaceLime kilnMINNESOTA 27 Ipsat Inland St. Louis 137 2713700062MINNESOTA 27 EVTAC-Fairlane St. Louis 137 2713700113MINNESOTA 27 National Steel(Keewatin)St. Louis 137 2713700063MINNESOTA 27 Hibbing Taconite St. Louis 137 2713700061MINNESOTA 27 USS Minntac St. Louis 137 2713700005MINNESOTA 27 Northshore Mining Lake 75 2707500003PECHANSeptember 2007Table III-5 (continued)39Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportEst. Emission ReductionState State ID Source Name CountyCountyID Source ID BART Emission Unit ID Description Stack ID SO2NOxN. DAKOTA 38 Great River Energy –Coal CreekMcLean 55 17N. DAKOTA 38 Basin Electric Power –Leland OldsMercer 57 1N. DAKOTA 38 Great River Energy –StantonMercer 57 4N. DAKOTA 38 Minnkota Power – MRYoungOliver 65 1OHIO 39 Mead Paper Division Ross 67 671010028WISCONSIN 55 Georgia-PacificConsumer Products(Formerly Fort James)Brown 9 405032870 Boiler B26 stoker (coal, tire andother fuels), 350mmBtu/hrS10 85 50Boiler B27 cyclone, 615 mmBtu/hr S10 85 88

### PECHAN September 2007



### 40 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## B. AREA SOURCE/MAR CONTROLS

1. Area Sources

Pechan worked with the LADCO states to dete rmine how to estimate the effect of federal/ state/local rules on area source category emissions. The sub-sections below describe the results of this effort.

a. VOC Solvent Categories

For VOC emissions from consumer products and architectural and maintenance coatings, it was decided to estimate post-2005 VOC emission reducti on credits using EPA guidance to states for estimating the benefits of three Federal ru les being promulgated during calendar year 2007 (Harnett, 2007). These rules will establish or amend VOC content limits for (1) aerosol coatings (new rule), (2) architectural and industrial mainte nance (AIM) coatings (amendments), and (3) household and institutional consumer products (amendments).

EPA estimated that the aerosol coatings rule will achieve the equivalent of a 19 percent reduction in mass VOC emissions from the 1990 baseline. The year 1990 represents the baseline, since there has been no previous Federal rulemaking for aerosol coatings. The creditable reduction that may be claimed is 0.114 pounds per capita. In the LADCO state 2005 emission inventory, this VOC emission reduction is applied to SCC 2460500000, which are Coatings and related products. A 12 percent VOC emission reduction is applied to SCC 2460500000 in each forecast analysis year (i.e., 2009, 2012, and 2018) to estimate the benefit of the federal aerosol coatings rule. This percentage is lower than the equi valent value estimated by EPA because the aerosol coatings rule is a subset of the Coatings and related products categ ory represented by SCC 2460500000.

For AIM coatings, EPA estimates that the amende d Federal AIM rule will achieve a reduction of 31 percent from the post-1998 Federa l rule baseline of 3.6 pounds per capita. This is a creditable reduction of 1.1 pounds per capita. AIM coatin g emission reductions are applied to the following SCCs in the base year LADCO i nventory for each analysis year: 2401001000; 2401002000; 2401003000; 2401008000; 2401008999. 2



### For consumer products, EPA has calculated that the amended Federal rule will achieve a VOC reduction of approximately 29 percent beyond that achieved by the 1998 Federal rule. This is a creditable reduction of 0.9 pounds per capita. Emission reductions from the Federal rule are applied to all Consumer Product source cate gories (SCCs 2460*) in each analysis year.

b. Portable Fuel Containers

For portable fuel containers ( PFCs), while there are state-by-st ate differences in likely rule adoption dates, all state’s control factors are ba sed on the EPA mobile source air toxics (MSAT) rule requirements. EPA adopted emission standa rds for portable fuel containers (such as gas

2 Note that subsequent to delivery of the area source/MAR control file, Wisconsin stated that this last SCC should not be included for their state.

### PECHAN September 2007



### 41 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### cans) under the consumer products authority of th e Clean Air Act. Starting with containers manufactured in 2009, the standard limits eva poration and permeation emissions from these containers to 0.3 grams of hydrocarbons per gallon per day. EPA also adopted test procedures and a certification and compliance program in order to ensure that containers meet the emission standard over a range of in-use conditions.

The VOC emission reduction benefits were estimat ed assuming that the new rule affects PFC sales starting during 2009, and th at each PFC that meets the MSAT standard has 75 percent lower emissions than the PFC being replaced.

To account for the fact that grow th in the portable fuel contai ner population and turnover from old to new containers will be affected by the MSAT rule, 3 

### Pechan calculated projection year emissions using the following equation:

(Eq. 1)

where:

Q N 

### = emissions in projection year Q o 

### = emissions in base year R i 

### = annual retirement rate F e 

### = emission factor ratio for existing sources (1.0) G N 

### = projection year growth factor (projection year activity/base year activity) F n 

### = emission factor ratio for new s ources relative to existing sources t = number of years between ba se year (2002) and projection year

The first term in the equation represents ne w source growth and cont rols, the second term accounts for retirement and controls for exis ting sources, and the third term accounts for replacement source controls. Because retirement was not estimated using a constant annual rate (5 percent were assumed to be retired in the fi rst year, with 10 percent retired in each additional forecast year), Pechan replaced the (1-R i 

### ) t 

### terms in this equation w ith the appropriate proportion of containers retired between the base year a nd the appropriate forecast year. Pechan then computed an overall emission reduction for each future year of interest by comparing the forecast year controlled emissions calculated from this equation to the forecast year uncontrolled emissions. For example, an overall VOC emissi on reduction of 26.4 percent was computed for Illinois. Pechan then back-calculated the appr opriate rule pe netration (RP) value for each forecast year based on the overall emissions reduction, the 75 percent CE value, and an rule effectiveness (RE) of 100 percen t (e.g., the calculated RP for Illinois for 2012 is 35.2 percent).

3 Note that to simplify the analysis P echan assumed that all post-2005 new container growth would be affected by the MSAT rule (due to low growth rates, this assumpti on does not have a significant impact on the overall emission reduction estimates of this rule). 

# ( ) 

# [ ] () () ⎭ ⎬ ⎫ ⎩ ⎨ ⎧ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− − +

− +

− = n e n 

## F t i R F t i R F N G o Q N Q 1 1 1 1

### PECHAN September 2007



### 42 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### c. Residential Wood Heating (Woods toves and Fireplace Inserts)

Pechan developed control fact ors by pollutant and year to account for the effect of the replacement of retired wood stoves/inserts that emit at pre-residential wood heater new source performance standard (NSPS) levels,. These c ontrol factors were developed using an annual 2 percent retirement rate for wood stoves/fire places along with pre- and post-NSPS wood stove and fireplace emission factors. SCCs for "cont rolled" wood stoves and fireplace inserts have no control factors applied. Pechan developed updated residential wood combustion control factors for the LADCO states using the same algorithms applied previously (Pechan, 2004). Table III-6 displays the emission reduction, control efficiency, and rule pe netration percentages modeled.

d. Stage II Vehicle Refueling

Pechan developed updated (2005 base year) Stag e II vehicle refueling control factors via MOBILE runs for the LADCO states. Onroad refue ling control factors were calculated based on the percentage difference between the projection year (2009, 2012, and 2018) MOBILE6 refueling emission factors and the 2005 MO BILE6 refueling emission factors.

MOBILE6 emission factors were calculated at Janua ry and July temperature and fuel conditions. July emission factors were used as the surr ogate for the five-month ozone season (May through September) and the January emission factors were used as the surrogates for the remaining seven months. Temperatures modele d were the January and July average daily monthly maximum and minimum temperatures for each state (i.e., Illi nois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin) based on 30-year average temperatur e data, as used in EPA’s second Section 812 Prospective analysis. MOBILE6 input files were created for each unique combination of: January and July Reid vapor pressure, reformul ated gasoline, oxygenated fuel, gasoline sulfur, and Stage II control programs for each of the st ates mentioned above. Fuel data and Stage II control program information for each state a nd corresponding projection year were based on EPA’s National Mobile Inventory Model (N MIM) County Database (version NDC20060201). Data extracted from NMIM’s County Database fo r these input parameters were based on January and July values.

Stage II control programs for IL, IN, and WI bega n in 1998 with a phase-in year of one year and with a percent efficiency value of 86.0 per cent for LDGVs and LDGTs in the program. Similarly, the HDGVs in the program have 86.0 pe rcent efficiency. For Ohio, these control programs began in 1993 with a two-year phase -in and 77.0 percent efficiency for both the LDGVs + LDGTs and HDGVs in the program.

Modeling these temperature, fuel, and Stage II control inputs (where applicable), Pechan calculated MOBILE6 emission factors for calendar years 2005, 2009, 2012, and 2018.

The resulting MOBILE6 emission factors were fi rst weighted according to the default MOBILE6 VMT mix to determine the weighted average refu eling emission factor fo r all gasoline vehicle types. The resulting January and July emission factors were weighted together according to the number of days in the seven-month season (212 days) and the five-month ozone season (153).PECHANSeptember200743Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable III-6. Residential Wood Combustion NSPS Emission Reductions(percentage values)2009 2012 2018SCC SCC Description PollutantReductionControlEfficiencyRulePenetration ReductionControlEfficiencyRulePenetration ReductionControlEfficiencyRulePenetration2104008001 Total Fireplaces CO 2.9 55.0 5.3 4.9 55.0 8.9 8.5 55.0 15.52104008010 Total Woodstoves CO 3.1 55.0 5.6 5.2 55.0 9.5 9.0 55.0 16.42104008000 Total Fireplaces & Woodstoves CO 3.0 55.0 5.5 5.1 55.0 9.3 8.9 55.0 16.22104008001 Total Fireplaces NOX 1.9 28.6 6.6 3.3 28.6 11.5 5.6 28.6 19.62104008010 Total Woodstoves NOX 2.0 28.6 7.0 3.4 28.6 11.9 5.8 28.6 20.32104008000 Total Fireplaces & Woodstoves NOX 2.0 28.6 7.0 3.3 28.6 11.5 5.8 28.6 20.32104008001 Total Fireplaces PM10-PRI 2.3 35.9 6.4 4.0 35.9 11.1 6.9 35.9 19.22104008010 Total Woodstoves PM10-PRI 2.5 35.9 7.0 4.2 35.9 11.7 7.2 35.9 20.12104008000 Total Fireplaces & Woodstoves PM10-PRI 2.4 35.9 6.7 4.1 35.9 11.4 7.1 35.9 19.82104008001 Total Fireplaces PM25-PRI 2.3 35.9 6.4 4.0 35.9 11.1 6.9 35.9 19.22104008010 Total Woodstoves PM25-PRI 2.5 35.9 7.0 4.2 35.9 11.7 7.2 35.9 20.12104008000 Total Fireplaces & Woodstoves PM25-PRI 2.4 35.9 6.7 4.1 35.9 11.4 7.1 35.9 19.82104008001 Total Fireplaces VOC 5.9 77.4 7.6 9.8 77.4 12.7 17.1 77.4 22.12104008010 Total Woodstoves VOC 5.6 77.4 7.2 9.4 77.4 12.1 16.4 77.4 21.22104008000 Total Fireplaces & Woodstoves VOC 5.4 77.4 7.0 9.2 77.4 11.9 16.0 77.4 20.7Note: Rule effectiveness (RE) of 100 percent for each SCC/year.

### PECHAN September 2007



### 44 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### After this was done for all of the modeled years and state or sub-state ar eas, the overall control efficiency for refueling, due to fleet turnover, was calculated ba sed on the percentage difference between the 2005 and corresponding pr ojection year emission factors. These control efficiencies were then assigned to individual counties, base d on the mapping of fuel and Stage II control parameters to those modeled in the MOBILE6 files.

2. MAR Sources (Locomotives and Marine Vessels)

EPA issued a proposed rule this spring affecting future criteria pollutant emissions from railroad locomotives and commercial marine vessels (C MVs) (EPA, 2007b). These are the two off-road source categories that are addres sed in this report. Base year emissions (2005) information for these two source categories was develope d by ENVIRON under contract to LADCO (ENVIRON, 2007a and b).

Control factors for criteria air pollutants were developed using Chapter 3 (Emissions Inventory) of EPA’s “Draft Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression-Ignition Engines Less than 30 Liters per Cylinder” (EPA, 2007b). This chap ter presents EPA’s analysis of the emissions impact of the proposed rule for three source ca tegories affected: commerci al marine diesel engines, recreational marine diesel engine s, and locomotives. The proposed control requirements include NOx and PM emission standards for Category 1 and Category 2 commercial marine diesel engines (both above and below 37 kilowatts). New NOx and PM emission standards would also apply to all recrea tional diesel engines and locomotives. There are no new standards for HC or CO; however, the PM standards are also expected to decrease HC emissions.

For locomotives, the EPA Regulatory Impact Anal ysis (RIA) chapter was used to develop 2009, 2012 and 2018 estimates of baseline and post-co ntrol emissions by po llutant by locomotive usage. This information is summarized in Ta ble III-7. The RIA examin ed the effect of the proposed rule on emissions for (1) large line haul , (2) large switch, (3) small railroads, and (4) passenger commuter trains. Each of these four usage types was assigned to the base year 2005 emission inventory SCCs. The SCC assignments are shown at the bottom of Table III-7.

### PECHAN September 2007



### 45 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### Table III-7. Locomotive Emissions Reported in EPA Draft RIA

Large Line Haul Large Switch Small Railroads Passenger Commuter
Year Baseline Controlled Baseline Controlled Baseline Controlled Baseline Controlled Volatile Organic Compounds
2006 43,874 5,501 2,891 1,609 2009 43,486 42,008 5,696 5,552 3,032 3,032 1,546 1546 2012 42,891 35,890 5,898 5,364 3,179 3,179 1,476 1301 2018 41,684 23,607 6,325 5,066 3,497 3,497 1,332 771
PM-2.5 2006 27,082 2,202 907 992 2009 24,216 23,661 2,120 2,070 870 870 861 861 2012 23,800 20,672 2,188 2,006 912 912 819 738 2018 22,542 14,516 2,309 1,896 991 991 719 466
PM-10 2006 27,919 2,270 935 1,023 2009 24,965 24,393 2,185 2,134 897 897 888 888 2012 24,536 21,311 2,256 2,068 940 940 845 761 2018 23,240 14,965 2,380 1,954 1,022 1,022 741 480 Oxides of Nitrogen
2006 779,842 86,861 37,690 38,466 2009 755,490 751,364 88,573 87,999 39,528 39,528 32,338 32,338 2012 730,031 692,606 88,909 86,614 41,456 41,456 27,212 25,933 2018 708,525 608,010 90,875 84,612 44,299 44,299 22,559 19,496 SCC(s) 2285002006 2285002010 2285002007 2285002008; 2285002009



### Because the federal locomotive emission standa rds modeled under previous LADCO contracts will continue to achieve emission reductions, it was necessary for Pechan to adjust the information in the new RIA to estimate the total post-base year reductions from the effects of both the existing and proposed locomotive standa rds. Pechan computed a revised set of projected emissions for modeling years 2009, 2012, and 2018 that reflect application of EPA’s assumed locomotive growth rate (1.6 percent per year) to the base year (2006) emissions from their analysis. This step was used to estim ate the emissions for each modeling year excluding the effects of both sets of emission standards. Next, Pechan computed the percentage reduction in emissions between the revised emissions in each modeling year and th e controlled emissions reported in EPA’s draft RIA. Table III-8 show s how the revised baseli ne and percent reduction for each modeling year. For example, for la rge line haul railroads, the baseline 2009 uncontrolled VOC emissions are estimated to be 46,014 tons nationally. Cont rolled emissions of 42,008 tons represent an 8.7 per cent VOC reduction from this uncontrolled emission estimate (46,014 – 42,008 = 4,010; 4,010/46,014 * 100 = 8.7).

### PECHAN September 2007



### 46 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### Table III-8. Percentage Reductions Associated with Federal Locomotive Standards

Large Line Haul Large Switch Small Railroads Passenger Commuter

Year Revised Baseline % Reduction Revised Baseline % Reduction Revised Baseline % Reduction Revised Baseline % Reduction Volatile Organic Compounds

2006 2009 46,014 8.7% 5,769 3.8% 3,032 0.0% 1,687 8.4% 2012 48,258 25.6% 6,051 11.3% 3,180 0.0% 1,770 26.5% 2018 53,080 55.5% 6,655 23.9% 3,498 0.0% 1,947 60.4%
PM-2.5

2006 2009 28,403 16.7% 2,309 10.4% 951 8.5% 1,040 17.2% 2012 29,788 30.6% 2,422 17.2% 998 8.6% 1,091 32.4% 2018 32,765 55.7% 2,664 28.8% 1,097 9.7% 1,200 61.2%
PM-10

2006 2009 29,281 16.7% 2,381 10.4% 981 8.5% 1,073 17.2% 2012 30,709 30.6% 2,497 17.2% 1,028 8.6% 1,125 32.4% 2018 33,777 55.7% 2,746 28.9% 1,131 9.7% 1,238 61.2% Oxides of Nitrogen

2006 2009 817,877 8.1% 91,097 3.4% 39,528 0.0% 40,342 19.8% 2012 857,766 19.3% 95,540 9.3% 41,456 0.0% 42,310 38.7% 2018 943,477 35.6% 105,087 19.5% 45,599 2.8% 46,537 58.1% SCC(s) 2285002006 2285002010 2285002007 2285002008; 2285002009 Note: emissions reported in short tons.



### Analogous control factor calculations to those de scribed above for locomotives were used to compute total CMV emission reduction values representing the effects of both existing and proposed emission standards. Table III-9 presents the EPA baseline emissions, Pechan’s revised baseline emissions (computed using EPA’s 0.9 percent annual growth assumption), EPA’s controlled emissions, and the percentage reducti on estimates applied in this analysis. For example, the CMV standards are expected to reduce VOC emissions by 33.9 percent in 2018.

### PECHAN September 2007



### 47 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### Table III-9. Percentage Reductions A ssociated with Federal CMV Standards

Emissions (short tons)

Year Baseline Revised Baseline Controlled % Reduction Volatile Organic Compounds 2005 17,295 2009 16,870 17,926 16,863 5.9% 2012 16,495 18,414 16,344 11.2% 2018 16,034 19,431 12,851 33.9% PM-2.5 2005 30,042 2009 27,327 31,138 27,324 12.2% 2012 26,657 31,987 26,582 16.9% 2018 22,553 33,753 19,308 42.8% PM-10 2005 30,972 2009 28,172 32,102 28,169 12.3% 2012 27,481 32,977 27,403 16.9% 2018 23,251 34,798 19,905 42.8% Oxides of Nitrogen 2005 825,229 2009 781,105 855,341 781,105 8.7% 2012 743,915 878,643 742,453 15.5% 2018 686,966 927,171 591,991 36.2% Sulfur Dioxide 2005 82,543 2009 46,838 85,555 46,839 45.3% 2012 42,515 87,886 42,515 51.6% 2018 6,054 92,740 5,630 93.9% Carbon Monoxide 2005 153,499 2009 149,966 159,100 149,966 5.7% 2012 146,227 163,434 146,227 10.5% 2018 140,443 172,461 140,443 18.6%



### For commercial marine diesel engines, the RIA examines expected rule emission benefits for four different engine types/sizes . The total CMV emission benefits in each year were used and applied equally to most of the affected SCCs in the 2005 inventory. However, Pechan applied rule penetration (RP) values to two CMV S CCs based on an ENVIRON table indicating RP values of less than 100 percent for these SCCs (see Table III-10).

### PECHAN September 2007



### 48 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### Table III-10. Commercial Marine Vessel Rule Penetration Values

Percentage of Engines Affected by Proposed EPA Standards Source Category Code (SCC) Source Definition Purpose Geographic Area NOx PM-10 HC CO SOx River Traffic 100% 100% 100% 100% 100% 2280002023 Push Boats Barge Freight
Lake Traffic 100% 100% 100% 100% 100% 2280002021 Tugs Vessel assist and support functions Near port 100% 100% 100% 100% 100%

2280003200 Mid-Great Lakes 85% 81% 86% 86% 77%

2280003100 Deep draft Laker and ocean-going large vessels Near port 81% 71% 87% 83% 63%
2280002022 Ferries River or lake ferrying Regular routes 100% 100% 100% 100% 100%

2280002024 Other Commercial Vessels Excursion boats primarily Near dock 100% 100% 100% 100% 100%

2280002025 Dredges Dredging projects Varies 100% 100% 100% 100% 100%

2280002029 Support Vessels General work boats Near port 100% 100% 100% 100% 100%

2280002030, 2280004030 1 Commercial Fishing Market fishing Great Lakes 100% 100% 100% 100% 100%

2280002040, 2280004040 1 Military Coast Guard and Navy Great Lakes 100% 100% 100% 100% 100%

### PECHAN September 2007



### 49 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

# SECTION IV. PREPARATION OF GROWTH AND CONTROL FILES

This section describes the contents of the grow th and control factor files submitted to LADCO earlier this month. The first s ubsection discusses the preparation of the point and area source/ MAR factor file and the revised NONROAD model growth file. The fina l subsection describes the contents of the c ontrol factor files.

Table IV-1 presents the RPO Data Exchange Pr otocol Format for reporting emission growth and control data. Pechan utilized this format to cr eate growth and control f actor files for LADCO. Because the growth factors ( unlike the control factors) do not differ by pollutant, Pechan developed a separate file contai ning only the point and area source/ MAR growth factors. Pechan revised the growth packet portion of the NONROAD model growth file ( NATION.GRW ) to replace the default model equipment population gr owth rates with growth rates based on more recent/more region-specific information. Two sets of control factor files we re prepared: one for area source/MAR categories and one for point source categories. The point and area source/MAR growth and control files were develope d in fixed field ascii format. The format of the default NONROAD model growth file was re tained in the revised version prepared for LADCO. The following subsections describe the contents of the gr owth and control factor files.



## A. GROWTH FACTORS

Pechan compiled the LADCO region growth factor information into the file $LADCO_2005_GF_Final_RPO.txt . Table IV-2 displays the RPO Data Exchange Protocol Format fields and identifies the fields that were p opulated in this file. Th e file contains separate records for each SCC/state for each year between 2006 and 2018 (population-based growth indicator records are reported by SCC/state/county because population projections were available at the county-level).

Pechan revised the input file used by the NONROAD model ( NATION.GRW ) to reflect historical equipment population changes and to estimate future equipment population changes. In particular, Pechan incorporated LADCO st ate-specific records to the GROWTH packet portion of this file. The fixed field format of the data in this packet is as follows:

Characters Description 1-5 FIPS code (00000 = applies to entire na tion; ss000 = applies to all of state ss) 6-10 subregion code (left blank) 11-15 year of estimate (4-digit year) 17-20 indicator code (alphanumeri c code identified within NONROAD) 26-45 value for indicator

### PECHAN September 2007



### 50 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### Table IV-1. RPO Data Exchange Protoc ol Format for Growth/Control Data

Field Name Field Description Field Length RECORD TYPE A code that identifies the type of record (G for growth, C for control) 2

COUNTRY CODE A code that identifies the country (US = United States) 2 STATE PROVINCE TRIBAL CODE The code for the state/province/tribe 4 COUNTY FIPS The FIPS code for the county 3 SIC 4-digit SIC, or 2 digit SIC with remaining digits blank (not zero) 4 SCC EPA source classification code or a fraction of the code 10 SITE ID Unique state/local/tribal ID reported consistently over time 15 EMISSION UNIT ID Unique state/local/tribal ID reported consistently over time 6 EMISSION RELEASE POINT ID State/ local/tribal ID for point /location where emissions are released to ambient air 6

POLLUTANT CODE Pollutant code 9 PROCESS ID Unique state/local/tribal ID reported consistently over time 6 BASE DATE Date that the control st rategy comes into effect 6 FUTURE DATE Future date that the c ontrol strategy affects 6 PRIMARY CONTROL EQUIPMENT CODE Primary control equipment code 10 BASE DATE CONTROL EFFICIENCY Base year % control efficiency(60% reduction = 60) 6 FUTURE DATE CONTROL EFFICIENCY Future year % control efficiency(60% reduction = 60) 6 FUTURE DATE GROWTH FACTOR Growth factor based on changes in throughput, economic growth (unrelated to controls). This is an absolute growth rate not an annual growth rate. 11

CONTROL TYPE MACT, RACT, LAER, SIPCALL, BART, etc 10 FUTURE DATE CHEMICAL SPECIATION PROFILE Code matching speciate chemical speciation profile unless in base year 6

ALLOWABLE EMISSIONS CAP Allowable emissions cap units must be in TONS/day 10 MARKET PENETRATION OF NEW SPECIATION PROFILE Fraction of future year emissions using new speciation profile 6

RESERVED FOR FUTURE USE FIELD 3 (Field used to enter future year control efficiency value where available) 10

RESERVED FOR FUTURE USE FIELD 2 (Field used to enter future year rule effectiveness value where available) 10

RESERVED FOR FUTURE USE FIELD 1 (Field used to enter future year RP value where available) 10 CONTROL DESCRIPTION A text description of the control 80 PRIMARY CONTACT Email address of the primary c ontact/developer of this record 30

### PECHAN September 2007



### 51 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### Table IV-2. Fields Populated in Growth Factor File

Field Name Populated in Growth Factor File?
RECORD TYPE Yes
COUNTRY CODE Yes
STATE PROVINCE TRIBAL CODE Yes COUNTY FIPS Yes (with “000” except for population data)
SIC No
SCC Yes
SITE ID No
EMISSION UNIT ID No
EMISSION RELEASE POINT ID No
POLLUTANT CODE No
PROCESS ID No
BASE DATE Yes
FUTURE DATE Yes PRIMARY CONTROL EQUIPMENT CODE No
BASE DATE CONTROL EFFICIENCY No
FUTURE DATE CONTROL EFFICIENCY No
FUTURE DATE GROWTH FACTOR Yes
CONTROL TYPE No
FUTURE DATE CHEMICAL SPECIATION PROFILE No
ALLOWABLE EMISSIONS CAP No
MARKET PENETRATION OF NE W SPECIATION PROFILE No
RESERVED FOR FUTURE USE FIELD 3 (future year CE) No
RESERVED FOR FUTURE USE FIELD 2 (future year RE) No
RESERVED FOR FUTURE USE FIELD 1 (future year RP) No
CONTROL DESCRIPTION No
PRIMARY CONTACT Yes



## B. CONTROL FACTORS

Pechan compiled control factors for the LADCO states in two sets of ascii f iles: one set for point source controls ( LADCO 2005 Base Year Point Control File.txt ), and the other set for area source/MAR controls ( LADCO 2005 Base Year Area Source and MAR Control File.txt ).

1. Point Source Control Factors

The LADCO 2005 Base Year Point Control File.txt file reports control information at the Process ID-level and for the specif ic date that each control is e xpected to be implemented. Note that the Base Date Control Efficiency field is populated with a zero for every record because Pechan did not have any base year control information other than that reported in the base year inventory supplied by LADCO. LADCO should rely on the control information in the base year inventory to identify the base year level of control. For MA CT standards, the point source control factors are incremental to base year cont rol levels. Because all other point source control

### PECHAN September 2007



### 52 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### factors represent absolute control levels, L ADCO should subtract any existing 2005 inventory level of control from the control factor leve l of control to determ ine net reductions for nonMACT controls. Pechan found very few point source records with control information, so LADCO should expect very little control ov erlap between the 2005 inve ntory and the control file. Table IV-3 identifies the RPO Data Exchange Protocol fields that are populated in the point source control file.

2. Area Source and MAR Control Factors

Pechan compiled the area source and MAR control f actor information into a single ascii file that reports the level of control for each year of in terest (2009, 2012, and 2018). In cases where there is no change in emission reduction after the initia l implementation year, the level of control is repeated for each year. For controls where em ission reductions increase over time (due to increased levels of RP), the level of control in creases for each successive modeling year. Except for the single control for which emission reduc tions are county-specific (Stage II Vehicle Refueling), the area source and MAR c ontrol factor file is expressed at the state-level. In cases where it was feasible to do so, Pechan populated the 5 th 

### , 4 th 

### , and 3 rd 

### fields from the end of each control factor file (“RESERVED FOR FUTURE USE” in the RPO Data Exchange Protocol Format) with future year CE, RE, and RP values, respectively (the field “FUTURE DATE CONTROL EFFICIENCY” was populated with th e overall percentage emission reduction). Table IV-4 identifies the RPO Data Exchange Prot ocol fields that are pop ulated in this file.

### PECHAN September 2007



### 53 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### Table IV-3. Fields Populated in Po int Source Control Factor File

RPO Data Exchange Protocol Format Field Name Populated in Point Source Control Factor File

RECORD TYPE Yes

COUNTRY CODE Yes

STATE PROVINCE TRIBAL CODE Yes

COUNTY FIPS Yes

SIC Yes

SCC Yes

SITE ID Yes

EMISSION UNIT ID Yes

EMISSION RELEASE POINT ID Yes

POLLUTANT CODE Yes

PROCESS ID Yes

BASE DATE Yes

FUTURE DATE 1 Yes
PRIMARY CONTROL EQUIPMENT CODE No

BASE DATE CONTROL EFFICIENCY 2 Yes

FUTURE DATE CONTROL EFFICIENCY 3 Yes

FUTURE DATE GROWTH FACTOR No

CONTROL TYPE Yes

FUTURE DATE CHEMICAL SPECIATION PROFILE No

ALLOWABLE EMISSIONS CAP No

MARKET PENETRATION OF NE W SPECIATION PROFILE No

RESERVED FOR FUTURE USE FIELD 3 (future year CE) No

RESERVED FOR FUTURE USE FIELD 2 (future year RE) No

RESERVED FOR FUTURE USE FIELD 1 (future year RP) No

CONTROL DESCRIPTION Yes

PRIMARY CONTACT Yes

1 Represents date that control is first implemented



### . 2 All records populated with “0" - LADCO should rely on control information reported in base year inventory. 3 Populated with overall percentage emission reduction.

### PECHAN September 2007



### 54 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### Table IV-4. Fields Populated in Ar ea Source/MAR Control Factor File

RPO Data Exchange Protocol Format Field Name Populated in Area Source/MAR Control Factor File
RECORD TYPE Yes
COUNTRY CODE Yes
STATE PROVINCE TRIBAL CODE Yes
COUNTY FIPS Yes
SIC No
SCC Yes
SITE ID No
EMISSION UNIT ID No
EMISSION RELEASE POINT ID No
POLLUTANT CODE Yes
PROCESS ID No
BASE DATE Yes
FUTURE DATE Yes PRIMARY CONTROL EQUIPMENT CODE No
BASE DATE CONTROL EFFICIENCY Yes
FUTURE DATE CONTROL EFFICIENCY 1 Yes
FUTURE DATE GROWTH FACTOR No
CONTROL TYPE No
FUTURE DATE CHEMICAL SPECIATION PROFILE No
ALLOWABLE EMISSIONS CAP No
MARKET PENETRATION OF NE W SPECIATION PROFILE No
RESERVED FOR FUTURE USE FIELD 3 (future year CE) Yes 2

RESERVED FOR FUTURE USE FIELD 2 (future year RE) Yes 2

RESERVED FOR FUTURE USE FIELD 1 (future year RP) Yes 2

CONTROL DESCRIPTION Yes
PRIMARY CONTACT Yes
1 Populated with overall percentage emission reduction (product of CE, RE, and RP). 2 Not populated for Federal locomotive standards or Stage II Vehicle Refueling control program.

### PECHAN September 2007



### 55 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

# SECTION V. REFERENCES

BLS, 2007: U.S. Bureau of Labor Statistics, “State Occupational Em ployment Projections, Long-Term,” accessed from http://www.projectionscentr al.com/projections.asp, Office of Employment Projections, Washi ngton, DC. Accessed May 2007.

DOE, 2007a: U.S. Department of Energy, “A nnual Energy Outlook 2007, with Projections through 2030,” DOE/EIA-0383(2007), Energy In formation Administration, Office of Integrated Analysis and Forecasti ng, Washington, DC. February 2007.

DOE, 2007b: U.S. Department of Energy, “State Energy Consumption, Price, and Expenditure Estimates (SEDS),” accessed from http:/ /www.eia.doe.gov/emeu/states/_seds.html, Energy Information Administration, Office of Integrated Analysis and Forecasting, Washington, D.C. Accessed April 2007.

ENVIRON, 2007a: ENVIRON International Corporation, “Draft LADCO 2005 Commercial Marine Emissions,” prepared for Lake Michig an Air Directors Cons ortium, Des Plaines, IL, March 2, 2007.

ENVIRON, 2007b: ENVIRON International Corporation, “Draft LADCO 2005 Locomotive Emissions,” prepared for Lake Michigan Ai r Directors Consortium, Des Plaines, IL, February 2007.

EPA, 2004: U.S. Environmental Protection Ag ency, “National Emission Inventory—Ammonia Emissions from Animal Husbandry Operati ons, Draft Report,” (no further identifying information available). January 2004.

EPA, 2007a: U.S. Environmental Protection Agency, “Clean Watersheds Needs Survey 2000,” data for LADCO states accessed from http://www.epa.gov/owm/mtb/cwns/index.htm, Office of Wastewater Management, Washington, DC. Accessed May 2007.

EPA, 2007b: U.S. Environmental Protection Ag ency, “Draft Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression-Ignition Engines Less than 30 Lite rs per Cylinder – Chapter 3: Emission Inventory,” Assessment and Standards Di vision, Office of Transportation and Air Quality, EPA420-D-07-001, March 2007.

FAA, 2007: U.S. Federal Aviation Administratio n, “Terminal Area Forecast System,” data for LADCO states accessed from http://www.apo.data.faa.gov/main/taf.asp , Office of Aviation Policy and Plans, Wa shington, DC. Accessed March 2007.

Freedonia, 2006: The Freedonia Group, Inc., “Solvents to 2010, U.S. Industry Study with Forecasts to 2010 & 2015,” Study #2055, Cleveland, OH, April 2006.

### PECHAN September 2007



### 56 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

### Harnett, 2007: William T. Harnett, “Guidance for Estimating VOC and NO x 

### Emission Changes from MACT Standards,” Memorandum to Regional Air Division Directors, U.S. Environmental Protection Agency, Re search Triangle Park, NC, May 11, 2007.

Metra, 2007: Metra, “2007 Metra Program and Budget,” accessed from http://metrarail.com/Budget/2007BudgetBook.pdf , Chicago, IL, May 2007.

Page, 2007: Stephen D. Page, “Emission Re duction Credit for Three Federal Rules for Categories of Consumer and Commercial Pr oducts under Section 183(e) of the Clean Air Act,” Memorandum to Air Division Directors, U.S. Environmental Protection Agency, Research Triangle Park, NC, May 30, 2007.

Pechan, 2004: E.H. Pechan & Associates, Inc ., “Development of Growth and Control Factors for Lake Michigan Air Directors Consor tium (LADCO) – Final Report,” Durham, NC, prepared for Lake Michigan Air Directors Consortium, Des Plaines, IL, December 15, 2004.

Pechan, 2005: E.H. Pechan & Associates, Inc ., “Development of Updated Growth and Control Factors for Lake Michigan Air Direct ors Consortium (LADCO) – Draft Report,” Durham, NC, prepared for Lake Michigan Ai r Directors Consortium, Des Plaines, IL, December 29, 2005.

# Documentation of the Base G2 and Best & Final 2002 Base Year,

2009 and 2018 Emission Inventories for VISTAS

Prepared by:

MACTEC, Inc.

# Documentation of 

# the

Base 

# G2 

# and Best 

# & Final



# 2002 

# Base Year, 2009 

# and 201 8



# Emission 

# Inventories for VISTAS

Prepared for:



# Vislbllity Improvement 

# State and Trlbal Assoclatlon 

# of the Southeast 

# WSTW

March 14,2008

Prepared by:

MACTEC Engineering and Consulting, 

# Inc.

William 

## R Bard



# Sr. Rhipid ScientistDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS

MACTEC, Inc. i 

## Table of Contents

Introduction ................................................................................................................... ..............................................1

1.0 2002 BASE YEAR INVENTORY DEVELOPMENT ....................................................................................... .9

1.1 P OINT S OURCES

............................................................................................................................... ............9
1.1.1 Development of 2002 Po int Source Inventory ..................................................................................... ..9
1.1.1.1 Data Sources ................................................................................................................... ........................... 10
1.1.1.2 Initial Data Evaluation ........................................................................................................ ....................... 11
1.1.1.3 PM Augmen tation................................................................................................................ ...................... 12
1.1.1.4 EGU Analysis ................................................................................................................... ......................... 15
1.1.1.5 QA Review of Base F Inventory.................................................................................................. .............. 16
1.1.1.6 Additional Base G Upda tes and Co rrections...................................................................................... ........ 17
1.1.1.7 Summary of Best & Final 2002 I nventory ......................................................................................... ........ 19 1.1.2 Development of Typical Year EGU in ventory...................................................................................... 23
1.2 A REA S OURCES

............................................................................................................................... ..........25
1.2.1 Development of a “typical ” year fire inventory ................................................................................. .26
1.2.2 Development of n on-fire inve ntory .............................................................................................. ........35
1.2.3 2002 Base G inventory updates .................................................................................................. .........38
1.2.3.1 Changes resulting from St ate review and comment ................................................................................ ... 38 1.2.4 Ammonia and paved road emissions............................................................................................... .....41
1.2.5 Global Changes Ma de for Base G................................................................................................. ......42
1.2.6 Quality Assura nce steps........................................................................................................ ...............43
1.3 M OBILE S OURCES

............................................................................................................................... ......44
1.3.1 Development of on-road mobile so urce input files and VMT estim ates...............................................45
1.3.1.1 Emissions from on-road mobile sources .......................................................................................... .......... 47 1.3.2 Development of non-road emission estimates..................................................................................... .49
1.3.2.1 Emissions from NONRO AD model s ources........................................................................................... ... 49
1.3.2.2 Emissions from Commercial Marine Vesse ls, Locomotives, a nd Airpla nes.............................................. 65 Base G R evision s: .............................................................................................................. ................................81
1.3.2.3 Emissions from NONROAD Model Sources in Illinois, Indian a, and Ohio .............................................. 87 1.3.3 Quality Assura nce steps........................................................................................................ ...............89

2.0 PROJECTION INVENTORY DEVELOPMENT........................................................................................... .90

2.1 P OINT S OURCES

............................................................................................................................... ..........90
2.1.1 EGU Emission Projec tions ....................................................................................................... ...........90
2.1.1.1 Chronology of the Developm ent of EGU Projec tions............................................................................... . 90
2.1.1.2 VISTAS IPM runs for EGU sources................................................................................................ .......... 95
2.1.1.3 Post-Processing of IP M Parsed Files ............................................................................................ ............. 95
2.1.1.4 Eliminating Double C ounting of EGU Units ....................................................................................... ...... 97
2.1.1.5 Quality Assurance Steps ........................................................................................................ .................... 99
2.1.1.6 S/L Adjustments to IPM Modeling Results for Base G Projections........................................................... 99
2.1.1.7 S/L Adjustments to IPM Modeling Re sults for Base G2 2018 Project ions.............................................. 100
2.1.1.8 S/L Adjustments to IPM Modeling Resu lts for Best & Fi nal Projec tions................................................ 109
2.1.1.9 Conversion of MRPO BaseM 2009 EGU Data to SMOKE Input Fo rmat ............................................... 112
2.1.1.10 Summary of 2009/2018 EGU Poin t Source Inve ntories .......................................................................... 112 2.1.2 Non-EGU Emission Projections ................................................................................................... .....116Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories for VISTAS

MACTEC, Inc. ii 2.1.2.1 Growth assumptions for non-EGU sources......................................................................................... ..... 116
2.1.2.2 Source S hutdow ns............................................................................................................... ..................... 118
2.1.2.3 Control Programs applie d to non-E GU sour ces .................................................................................... ... 120
2.1.2.4 Quality Assurance steps........................................................................................................ ................... 123
2.1.2.5 Additional Base G Upda tes and Co rrections...................................................................................... ...... 123
2.1.2.6 Additional Best & Final Updates and Correc tions................................................................................ ... 126
2.1.2.7 Conversion of MRPO BaseM 2009 non-EGU Data to SMOKE Input Fo rmat ........................................ 127
2.1.2.8 Summary of the 2009/2018 non-EGU Po int Source Inve ntories ............................................................. 127 2.2 A REA S OURCES

............................................................................................................................... ........131
2.2.1 Stationary ar ea sources ........................................................................................................ .............131
2.2.1.1 Stationary area source controls ................................................................................................ ................ 132
2.2.1.2 Stationary area source growth.................................................................................................. ................ 134
2.2.1.3 Differences be tween 2009/2018

.................................................................................................. ............. 135 2.2.2 Agricultural ar ea sources ...................................................................................................... ............135
2.2.2.1 Control assumptions for agri cultural area sources .............................................................................. ..... 136
2.2.2.2 Growth assumptions for agri cultural area sources ............................................................................... .... 136 2.2.3 Changes to Prescribed Fire for 2009/2018 Base G...........................................................................141
2.2.4 Quality Assura nce steps........................................................................................................ .............146
2.3 M OBILE S OURCES

............................................................................................................................... ....146
2.3.1 Development of on-road mobi le source inpu t files ............................................................................149
2.3.1.1 Preparation of revised 2018 input data files................................................................................... .......... 149
2.3.1.2 Preparation of initial 2009 input data files ................................................................................... ............ 152 2.3.2 VMT Data ....................................................................................................................... ...................153
2.3.3 Base G R evisions ............................................................................................................... ................158
2.3.4 Development of non-road emission estimat es....................................................................................1 58
2.3.4.1 NONROAD model source s.......................................................................................................... ............ 158
2.3.4.2 Non-NONROAD m odel s ources...................................................................................................... ........ 161
2.3.4.3 Emissions from NONROAD Model Sources in Illinois, Indian a, and Ohio ............................................ 196
2.3.4.4 Differences be tween 2009/2018

.................................................................................................. ............. 196 2.3.5 Quality Assura nce steps........................................................................................................ .............197



### Appendix A: State Emission Totals by Pollutant and Sector
Appendix B: State VMT totals
Appendix C: State Tier 1 Emission Totals
Appendix D : VISTAS Tier 1 Emission Totals
Appendix E: Aircraft PM Excerpt from 2001 Tucson Report
Appendix F : Comparison of Base F and Base G On-Road Mobile Emissions
Appendix G : Conversion of MRPO Ba se M Point Source Data to SMOKE Input Format
Appendix H : EGU Controls for Coal and Oil/gas Units for the Base G/G2 Inventory
Appendix I : EGU Controls for Coal and Oil/gas Units for the B&F InventoryDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

MACTEC, Inc. iii 

## List of Tables

Table I-1 Inventory Version in Use by Year and Source Sector Through B&F - 2002 Table I-2 Inventory Version in Use by Year and Source Sector Through B&F - 2009 Table I-3 Inventory Version in Use by Year and Source Sector Through B&F - 2018 Table 1.1-1 State Data Submittals Used for the Base F 2002 Point Source Inventory. Table 1.1-2 Comparison of Particulate Matter Emi ssions from the S/L Data Submittals and the Base G 2002 VISTAS Point Source Inventory Table 1.1-3 Summary of Updates and Corrections to the Base F 2002 Inventory Incorporated into the 2002 Base G Inventory.
Table 1.1-4 Base G / B&F 2002 VISTAS Point Source Inventory for SO 2 

### (tons/year).
Table 1.1-5 Base G / B&F 2002 VISTAS Point Source Inventory for NO x 

### (tons/year).
Table 1.1-6 Base G / B&F 2002 VISTAS Poin t Source Inventory for VOC (tons/year).
Table 1.1-7 Base G / B&F 2002 VISTAS Poin t Source Inventory for CO (tons/year).
Table 1.1-8 Base G / B&F 2002 VISTAS Point Source Inventory for PM 10 

### -PRI (tons/year).
Table 1.1-9 Base G / B&F 2002 VISTAS Point Source Inventory for PM 2.5 

### -PRI (tons/year). Table 1.1-10 Base G / B&F 2002 VISTAS Point Source Inventory for NH 3 

### (tons/year). Table 1.1-11 Comparison of SO 2 

### and NO x 

### Emissions (tons/year) for EGUs.
Table 1.2-1 Emissions from Fires in the VI STAS Region – Comparison between Original Base Year 2002 (VISTAS 3.1), 2002 Actual and Typical Year Base G Emissions. Table 1.2-2 Summary of St ate Data Submittals for the 2002 VISTAS Area Source Base F Inventory
Table 1.2-3 Data Source Codes and Data Sources for VISTAS 2002 Base F Area Source Emissions Inventory. Table 1.3-1 Representative day mapping for January episode Table 1.3-2 Summary of Base F NONROAD Modeling Revisions
Table 1.3-3 Base F NONRO AD Input File Sequence and Structural Revisions Table 1.3-4 Summary of Base G NONROAD Modeling Revisions Table 1.3-5 Spring 2006 NONROAD Input F ile Sequence and Structural Revisions
Table 1.3-6 Initial 2002 Base Year Aircraft , Locomotive, and Non-Recreational Marine Emissions as Reported in February 2004 Pechan Report (annual tons)
Table 1.3-7 PM-to-NO x 

### Ratios by Aircraft Type In In itial 2002 Base Year Inventory. Table 1.3-8 Tucson, AZ PM-to-NO x 

### Ratios by Aircraft Type. Table 1.3-9 Non-Corresponding Ai rcraft Emissions Records
Table 1.3-10 Initial 2002 Base Year Aircraft , Locomotive, and Non-Recreational Marine Emissions with Modified Aircraft PM Emission Rates (annual tons)Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

MACTEC, Inc. iv 

### List of Tables (continued)

Table 1.3-11 Change in Initial 2002 Base Year Emissions due to Aircraft PM Emission Rate Modifications.
Table 1.3-12 CERR Aircraft NO x 

### Records with No Corresponding PM Record.
Table 1.3-13 Calculated Emission Ratios for VA.
Table 1.3-14 Base F 2002 Base Year Aircraft, Locomotive, and Non-Recreational Marine Emissions (tons/year) Table 1.3-15 Change in 2002 Emissions, Base F Inventory Relative to Initial Inventory Table 1.3-16 Base F Comparis on of Aircraft Emissions (Airports with Aircraft NO x 

### > 200 tons per year)
Table 1.3-17 Base G VA Ai rcraft Records Updates
Table 1.3-18 Calculated Base G Emission Ratios for VA.
Table 1.3-19 Base G-Revised 2002 Base Year Aircraft, Locomotive, and Non-Recreational Marine Emissions (tons/year) Table 1.3-20 Change in 2002 Emissions, Base G Inventory Relative to Base F Inventory Table 1.3-21 Base G Comparis on of Aircraft Emissions (Airports with Aircraft NO x 

### > 200 tons per year) Table 1.3-22 Non-Default Files Used for MRPO Modeling Table 2.1-1 Adjustments to IPM Control Dete rminations Specified by S/L Agencies for the Base G/G2 2009/2018 EGU Inventories. Table 2.1-2 Other Adjustments to IPM Results Specified by S/L Agencies for the Base G/G2 2009/2018 EGU Inventories. Table 2.1-3 Additional Adjustments to IP M Results Specified by S/L Agencies for the B&F 2009/2018 EGU Inventories.
Table 2.1-4 EGU Point Source SO 2 

### Emission Comparison for 2002/2009/2018.
Table 2.1-5 EGU Point Source NO x 

### Emission Comparison for 2002/2009/2018.
Table 2.1-6 EGU Point Source VOC Emission Comparison for 2002/2009/2018.
Table 2.1-7 EGU Point Source CO Emission Comparison for 2002/2009/2018.
Table 2.1-8 EGU Point Source PM 10 

### -PRI Emission Comparison for 2002/2009/2018.
Table 2.1-9 EGU Point Source PM 2.5 

### -PRI Emission Comparison for 2002/2009/2018.
Table 2.1-10 EGU Point Source NH 3 

### Emission Comparison for 2002/2009/2018. Table 2.1-11 Summary of Source Shutdowns Incorporated in Base G Inventory.
Table 2.1-12 Non-EGU Point Source Control Programs Included in 2009/2018 Projection Inventories. Table 2.1-13 Summary of Updates and Co rrections Incorporated into the Base G 2009/2018 Non-EGU Inventories.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

MACTEC, Inc. v 

### List of Tables (continued)

Table 2.1-14 Summary of Updates and Co rrections Incorporated into the B&F 2009/2018 Non-EGU Inventories.
Table 2.1-15 Non-EGU Point Source SO 2 

### Emission Comparison for 2002/2009/2018.
Table 2.1-16 Non-EGU Point Source NO x 

### Emission Comparison for 2002/2009/2018.
Table 2.1-17 Non-EGU Point Source VOC Emission Comparison for 2002/2009/2018.
Table 2.1-18 Non-EGU Point Source CO Emission Comparison for 2002/2009/2018.
Table 2.1-19 Non-EGU Point Source PM 10 

### -PRI Emission Comparison for 2002/2009/2018.
Table 2.1-20 Non-EGU Point Source PM 25-PRI Emission Comparison for 2002/2009/2018.
Table 2.1-21 Non-EGU Point Source NH 3 

### Emission Comparison for 2002/2009/2018.
Table 2.2-1 2002 Base Year Emissions and Perc entage Difference for Base F and Base G (based on actual emissions). Table 2.2-2 2009 Projection Year Emissions and Percentage Difference for Base F and Base G (based on actual emissions). Table 2.2-3 2018 Projection Year Emissions and Percentage Diffe rence for Base F and Base G (based on actual emissions). Table 2.2-4 Percentage Differe nce Between Base F and Base G Fire Emissions by State Table 2.3-1 2002 versus 2018 VMT (million miles per year) Table 2.3-2 VMT and HDD Rule Estimates for North Carolina (million miles per year) Table 2.3-3 VMT and HDD Rule Estimates for South Carolina (million miles per year)
Table 2.3-4 VMT and HDD Rule Estimates for Tennessee (million miles per year)
Table 2.3-5 VMT and HDD Rule Estimates for Virginia (million miles per year)
Table 2.3-6 Pre-Base F 2002 Aircra ft, Locomotive, and Non-Recreational Marine Emissions (annual tons, as of the fall of 2004) Table 2.3-7 Locally Generated Growth Factors for North Carolina
Table 2.3-8 Estimated Emission Reduc tion Impacts based on T-4 Rule
Table 2.3-9 Estimated Emission Reduction Impacts Relative to VISTAS 2002 Base Year Values Table 2.3-10 Diesel CMV Adjustment Ratios for Palm Beach County, FL Table 2.3-11 Overall Adjustment Factors for Palm Beach County, FL
Table 2.3-12 SO 2 

### Emissions for Diesel Rail in Autauga County, AL from the CAIR Projections Table 2.3-13 Growth Options based on CAIR Data Table 2.3-14 Base F 2009 Aircraft, Locomotiv e, and Non-Recreational Marine Emissions (annual tons) -- Based on Growth Using 1996 and 2020 EPA Inventories Table 2.3-15 Base F 2018 Aircraft, Locomotiv e, and Non-Recreational Marine Emissions (annual tons) -- Based on Growth Using 1996 and 2020 EPA Inventories Table 2.3-16 Change in Emissions between 2009 and 2002 Base F Inventories (Based on Growth Using 1996 and 2020 EPA Inventories)Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

MACTEC, Inc. vi 

### List of Tables (continued)

Table 2.3-17 Change in Emissions between 2018 and 2002 Base F Inventories (Based on Growth Using 1996 and 2020 EPA Inventories) Table 2.3-18 Base G 2002 Aircraft, Locomotive, and Non-Recreational Marine Emissions (annual tons) Table 2.3-19 Locally Generated Growth Factors for Kentucky Table 2.3-20 Base G 2009 Aircraft, Locomotiv e, and Non-Recreational Marine Emissions (annual tons) -- Based on Growth Using 1996 and 2020 EPA Inventories Table 2.3-21 Base G 2018 Aircraft, Locomotiv e, and Non-Recreational Marine Emissions (annual tons) -- Based on Growth Using 1996 and 2020 EPA Inventories Table 2.3-22 Change in Emissions between 2009 Base G and 2002 Base F Inventories (Based on Growth Using 1996 and 2020 EPA Inventories) Table 2.3-23 Change in Emissions between 2018 Base G and 2002 Base F Inventories (Based on Growth Using 1996 and 2020 EPA Inventories)Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

MACTEC, Inc. vii 

## List of Figures

Figure 1.2-1 CO Emissions from Agricultural Bu rning for the Original Base Year, 2002 Actual Base G, and 2002 Typical Base G Inventories. Figure 1.2-2 CO Emissions from Land Cleari ng Burning for the Original Base Year, 2002 Actual Base G and 2002 Typi cal Base G Inventories. Figure 1.2-3. CO Emissions from Prescribed Bu rning for the Original Base Year, 2002 Actual Base G and 2002 Typical Base G Inventories. Figure 1.2-4 CO Emissions from Wildfire Burn ing for the Original Base Year, 2002 Actual Base G and 2002 Typical Base G Inventories. Figure 2.2-1 Prescribed Fire Projection for Okeefenokee NWR for 2009 Figure 2.3-1 Impacts of the Appare nt CAIR Inventory Discrepancy Figure 2.3-2 Total Aircraft, Locomotiv e, and CMV CO Emissions (Base F) Figure 2.3-3 Locomotive CO Emissions (Base F) Figure 2.3-4 Total Aircraft, Locomotive, and CMV NO x 

### Emissions (Base F) Figure 2.3-5 Locomotive NO x 

### Emissions (Base F) Figure 2.3-6 Total Aircraft, Locomotive, and CMV PM 10 

### Emissions (Base F) Figure 2.3-7 Locomotive PM 10 

### Emissions (Base F) Figure 2.3-8 Total Aircraft, Locomotive, and CMV PM 2.5 

### Emissions (Base F) Figure 2.3-9 Locomotive PM 2.5 

### Emissions (Base F) Figure 2.3-10 Total Aircraft, Locomotive, and CMV SO 2 

### Emissions (Base F) Figure 2.3-11 Locomotive SO 2 

### Emissions (Base F) Figure 2.3-12 Total Aircraft, Locomotiv e, and CMV VOC Emissions (Base F) Figure 2.3-13 Locomotive VOC Emissions (Base F) Figure 2.3-14 Total Aircraft, Locomotiv e, and CMV CO Emissions (Base G) Figure 2.3-15 Locomotive CO Emissions (Base G) Figure 2.3-16 Total Aircraft, Locomotive, and CMV NO x 

### Emissions (Base G) Figure 2.3-17 Locomotive NO x 

### Emissions (Base G) Figure 2.3-18 Total Aircraft, Locomotive, and CMV PM 10 

### Emissions (Base G) Figure 2.3-19 Locomotive PM 10 

### Emissions (Base G) Figure 2.3-20 Total Aircraft, Locomotive, and CMV PM 2.5 

### Emissions (Base G) Figure 2.3-21 Locomotive PM 2.5 

### Emissions (Base G) Figure 2.3-22 Total Aircraft, Locomotive, and CMV SO 2 

### Emissions (Base G) Figure 2.3-23 Locomotive SO 2 

### Emissions (Base G) Figure 2.3-24 Total Aircraft, Locomotiv e, and CMV VOC Emissions (Base G) Figure 2.3-25 Locomotive VOC Emissions (Base G)Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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## Acronyms and Abbreviations

AEO Annual Energy Outlook AF&PA American Forest and Paper Association
APCD Air Pollution Control District ATP Anti-Tampering Program BLRID Boiler Identification (Boiler ID)
CAA Clean Air Act
CAIR Clean Air Interstate Rule CEM Continuous Emissions Monitoring
CAMD Clean Air Markets Division CERR Consolidated Emissions Reporting Rule CMU Carnegie Mellon University
CMV commercial marine vessels CE Control Efficiency
CO carbon monoxide DENR North Carolina Department of Environment and Natural Resources DHEC South Carolina Department of Health and Environmental Control EDMS Emissions Data Management Systems
ESD Emissions Standards Division EPA Environmental Protection Agency EGU Electric Generating Unit ICF ICF International, Inc. FIP Federal Implementation Plan FLM Federal Land Manager FTP File transfer protocol FR Federal Register
FS Forest Service HDD Heavy Duty Diesel HDD RULE Heavy Duty Diesel Rule ICF ICF International, Inc.
ID Identification I/M Inspection and Maintenance
IPM ® 

### Integrated Planning Model ® 

### IAQTR Interstate Air Quality Transport Rule LTO Landing and take off
MACT Maximum achievab le control technologyDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Acronyms and Abbreviations (continued)

MACTEC MACTEC Engineering and Consulting, Inc.
MOBILE 6 MOBILE emissions es timation model version 6 MRPO Midwest Regional Planning Organization

NH 3 

### Ammonia NEI National Emission Inventory NIF National Emission Inventory Format NLEV National Low Emission Vehicle regulation NMIM National Mobile Inventory Model NONROAD no acronym (model name)
NO x 

### Oxides of nitrogen NWR National Wildlife Refuge

OTB On the books OTW On the way ORIS Office of Regulatory Information Systems OTAQ Office of Transportation and Air Quality OTC Ozone Transport Commission
PFC Portable fuel containers
PM Particulate matter
PM 10 

### -FIL Particulate matter less than or equal to 10 microns in diameter that can be captured on a filter
PM 10 

### -PRI Particulate matter less than or equal to 10 microns in diameter that includes
both the filterable and condensable components of particulate matter
PM 2.5 

### -FIL Particulate matter less than or equal to 2.5 microns in diameter that can be captured on a filter
PM 2.5 

### -PRI Particulate matter less than or equal to 2.5 microns in diameter that includes
both the filterable and condensable components of particulate matter PM-CON Particulate matter created by the co ndensation of hot materials to form particulates, usually less th an 2.5 microns in diameter ppmW parts per million by weight PRI Primary QA/QC Quality Assurance/Quality Control QAPP Quality Assurance Project Plan REMI Regional Economic Models, Inc. RFG Reformulated gasoline RVP Reid Vapor PressureDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Acronyms and Abbreviations (continued)

SCC Source Classification Code SIP State Implementation Plan SIWG Special Interest Workgroup
S/L/T State/Local/Tribal SMOKE Sparse Matrix Operator Ke rnel Emissions Modeling System
S/L State and Local
SO 2 

### Oxides of Sulfur
T4 Tier 4 VISTAS Visibility Improvement State a nd Tribal Association of the Southeast
VMT Vehicle Miles Traveled VOC Volatile organic compounds WRAP Western Regional Air PartnershipDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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## Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018, Em ission Inventories for VISTAS

Introduction

Base G2 document was delivered final in Aug (?) 2007. In fall 2007 states updated specific point source EGU and non-EGU fac ility record in Best and Final (B&F)
inventories for 2009 and 2018 to account for BART controls, consent decrees, corrections to Base G2, and source specific controls. Only EGU and non-EGU point source records were changed. Area, non-road, on-road remained the same as Base G2. In this report all records for area, non-road, and on-road were used in B&F modeling the same as Base G2. This report has been updated from the Base G2 report submitted in July 2007 just for B&F changes to EGU and non-EGU sources. A history of the development of the VISTAS inventory follows. Specific sections of the document detail the modifications made as the inventory progre ssed from Base F through B&F.

The Base G2 inventory included changes in 2018 controls on specifi c electric generating units in GA, FL, NC, and WV. There were no changes in 2009 controls for EGU and no changes between the Base G and Base G2 inventories for non-EGU point, on-road, nonroad, or area sources in 2009 or 2018. The Base G2 modeling run included changes for 2018 EGU controls plus corrections in 2002 typical, 2009, and 2018 for errors in emissions processing in Base G. These corr ections in emissions processing are not seen when comparing the Base G and G2 inventory files.

Base G and Base G2 inventories represent tw o separate model runs, as does the B&F. Since Base G2 supersedes Base G, VISTAS will maintain only the Base G2 and B&F model files since both were used in State Implementation Plan submittals.

History of VISTAS Base and Projection Year Emission Inventory Development

This section is provided to supply the hist ory behind the development of the base and projection year inventories pr ovided to the Visibility Im provement State and Tribal Association of the Southeast (VISTAS) and the Association for Southeast Integrated Planning (ASIP). Through the various itera tions, the inventories that have been developed have typically had version numbers provide d by the contractors who developed the inventories and to a certain extent these were also based on their purpose. Different components of th e 2002 base year inventories have been supplied by E.H. Pechan and Associates, Inc. (Pechan), MACTEC Engineering and Consulting, Inc.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### (MACTEC), and by Alpine Geophysics, Inc. ( AG). The projection year inventories were developed by MACTEC and AG.

The initial 2002 base year inventory was jointly developed by Pechan and MACTEC. Pechan developed the on-road and non-road mobile source components of the inventory while MACTEC developed the point and area source component of the inventory. This version of the inventory included updates to on-road mobile that incorporated information from the 1999 NEI Version 2 final along with updated information on VMT, fuel programs, and other input s to the MOBILE6 model to pr oduce a draft version of the 2002 inventory. For non-road sources, a sim ilar approach was used. Updated State information on temperatures and fuel characte ristics were obtained from VISTAS States
and used with the NONROAD 2002 model to calculate 2002 emissions for NONROAD model sources. These estimates were coupled with data for commercial marine vessels, locomotives and airplanes projected to 2002 us ing appropriate growth surrogates. A draft
version of these inventories wa s prepared in late 2003, with a final version in early 2004. An overview of the development of the on-road component can be found at: http://www.vistas-sesarm.org/documen ts/Pechan_drafton-roadinventory_082803.ppt
while an overview of the non-road component can be found at: http://www.vistas-sesarm.org/docum ents/Pechan_Non-roadInventory_082803.ppt

. Similarly, draft versions of the 2002 point a nd area source base year inventories were prepared by MACTEC in the same timeframe (late 2003 for the draft, final in early 2004). The point source component was based on data submitted by the VISTAS States or on the 1999 NEI. The data submitted by the States ranged from 1999 to 2001 and was all projected to 2002 using appropriate gr owth surrogates from Economic Growth Analysis System (EGAS) version 4. Toxic Rel ease Inventory (TRI) data were used to augment the inventory for NH 3 

### . Continuous Emissions Monitor (CEM) data from the
U.S. EPA’s Clean Air Markets Division wa s used to supply emissions for electric generating utilities (EGUs). Particulate matte r emissions were augmented (when missing) by using emission factor ratios. Details on all these calculations are discussed in
Section 1.1.1.3 of this document.

The area source component of the 2002 draft base year emissions was prepared similarly to the point sources, using State submittals and the 1999 NEI Version 2 final as the basis for projecting emissions to 2002 using EGAS growth factors. For ammonia area sources the Carnegie Mellon University (CMU) ammoni a model was used to calculate emissions. Finally, data on acreage burned on a fire by fire basis was solicited from State forestry agencies in order to calculate fire emissions on a fire by fire basis. Virtually all VISTAS State forestry agencies provided data for these calculations at least for wild andDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### prescribed fires. An overview of the point and area source development methods can be
found at: http://www.vistas-sesarm.org/documen ts/MACTEC_draftpointareainventory_82803.ppt

.
Three interim versions of th e 2002 base year inventory we re developed. The first was delivered in August of 2003, the second in Ap ril of 2004 and the final one in October of 2004. The August 2003 and April 2004 inventor ies were prepared by MACTEC and
Pechan. A draft version of the revised 2002 base year inventory was released in June of 2004, with a final version released in Oct ober 2004. That 2002 base year inventory was solely prepared by MACTEC. The Oct ober 2004 inventory incorporated 2002 Consolidated Emissions Reporting Rule (CERR) data into the inve ntory along with some updated data from the VISTAS States. This inve ntory is typically referred to as version 3.1 of the VISTAS inventory.

Closely following the version 3.1 2002 ba se year inventory, a “preliminary” 2018 projection inventory was devel oped. This “preliminary” 2018 inventory was developed in late 2004 (Oct/Nov) and was designed solely for use in modeling sensitivity runs to provide a quick and dirty assessment of what “on the books” and “ on the way” controls could be expected to provide in terms of improvements to visibility and regional haze impairment. A brief overview of the history of the three versions of the 2002 base year and the 2018 preliminary inventory use can be found at: http://www.vistassesarm.org/documents/STAD1204/2002and2018Emissions14Dec2004.ppt

. Following preparation of the final 3.1 versi on of the 2002 base year inventory, States were asked to review and provide comments on that inventory to MACTEC for update and revision. At the same time MACTEC pr epared a revised draf t version of the 2018 projection inventory (January 2005) and a dr aft version of a 2009 projection inventory (April 2005). All of these were known as vers ion 3.1 and were provided to the VISTAS
States for review and comment. Comments were received and updates to the inventories based on these comments were prepared. The revised inventories we re provided to the
VISTAS States. At that time to be consiste nt with the modeling nomenclature being used by AG in performing their modeling runs, th e inventory became the Base F VISTAS inventory. The Base F inventory was deliver ed for review and comment in August of 2005. In addition, MACTEC deliv ered a report entitled Documentation of the Revised 2002 Base Year, Revised 2018, and Initial 2009 Emission Inventories for VISTAS on August 2, 2005 that described the methods used to develop the Base F inventories. For the Electric Generating Utilities (EGU) different versions of the Integrated Planning Model were used between Base D and Base F, resulting in different projections of future
EGU emissions.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Over the period from August 2005 until J une/July 2006 MACTEC received comments and updates to some categories from VISTAS States, particularly EGU. In addition, a new NONROAD model (NONROAD05) was released. Thus a dditional updates to the inventory were prepared based on the comm ents received along with revised NONROAD
emission estimates from NONROAD05. The resultant inventory became the Base G inventory.

Following release of the Base G inventory in early 2007, four States specified additional changes to reflect their best estimates of EGU emission leve ls and controls in 2018. The resulting 2018 EGU emission inventory is referr ed to as Base G2, which was released in July 2007.

The current version of the VISTAS inventory is referred to as the “Best and Final (B&F)” inventory. States specified additional change s to the point source inventory to reflect improved knowledge of EGU emission levels and controls in 2009 and 2018. States also specified changes to nonEGU sources reflectin g new information on anticipated controls and shutdowns. No changes to any other sour ce sector (e.g., area, fire, nonroad, onroad) were made for the B&F inventory. The 2018 B&F inventory was released in October 2007, and the 2009 B&F inventory was released in December 2007.

This document details the development of the Base G/G2/B&F inventories for 2002,
2009 and 2018. The information that follows de scribes the development of the VISTAS inventory by sector from Base F forward. Unless specific updates were made to an inventory sector, the methods used for Base F were retained. Table I-1 through Table I-3 indicate roughly which version of the inventor y is in use for each sector of the inventory as of the B&F inventory.

Under a separate contract, AG was asked to obtain and convert emission inventory data for the five states that make up the Midwes t Regional Planning Organization (MRPO) for use by VISTAS/ASIP modelers. De tails of this effort are doc umented in an Appendix to this report.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table I-1 Inventory Version in Use by Year and Source Sector Through B&F - 2002

Source AL FL GA KY MS NC SC TN VA WV EGU Base G Base G Base G Base G Base G Base G Base G Base G Base G Base G Non-EGU Point Base F with

some source specific revisions in Base G Base F with

some source specific revisions in Base G Base F with

some source specific revisions in Base G Base F with

some source specific revisions in Base G Base F with

some source specific revisions in Base G Base F with

some source specific revisions in Base G Base F with

some source specific revisions in Base G Base F with

some source specific revisions in Base G Base F with

some source specific revisions in Base G Base F with

some source specific revisions in Base G Area 1 Base F for ammonia

sources (CMU Model) and for some area sources, Base G for selected

sources updated by the State with State supplied data Base F except for some emissions zeroed out (and records removed) for

some southern FL counties for Base G. Base F Base F Base F Base F for ammonia

sources (CMU Model) and for some area sources, Base G for selected

sources updated by the State with State supplied data. Some corrections applied by MACTEC to correct PM values Base F Base F Base F for ammonia Sources (CMU Model) and for some area sources, Base G for selected

sources updated by the State with State supplied data. Base F

On-road Base G Base G Base G Base G Base G Base G Base G Base G Base G Base G Non-road Base G for all

sources included in the NONROAD model.

Base F for

nonNONROAD model
sources, except aircraft and locomotives updated for Base G. Base G for all

sources included in the NONROAD model.

Base F for

nonNONROAD model

sources Base G for all

sources included in the NONROAD model.

Base F for

nonNONROAD model

sources Base G for all

sources included in the NONROAD model.

Base F for

nonNONROAD model

sources except for aircraft in Cincinnati/N. KY Int. Airport, which are Base G. Base G for all

sources included in the NONROAD model.

Base F for

nonNONROAD model

sources Base G for all

sources included in the NONROAD model. NC moved from Southern to Mid-Atlantic State in seasonal adjustment file.

Base F for

nonNONROAD model

sources Base G for all

sources included in the NONROAD model.

Base F for

nonNONROAD model

sources Base G for all

sources included in the NONROAD model.

Base F for

nonNONROAD model

sources Base G for all

sources included in the NONROAD model.

Base F for

nonNONROAD model
sources, except for aircraft emissions which are Base G. Base G for all

sources included in the NONROAD model.

Base F for

nonNONROAD model

sources

Fires Base F Typical Base F Typical Base F Typical Base F Typical Base F Typical Base F Typical Base F Typical Base F Typical Base F Typical Base F Typical Notes:

Base G global Area Source changes that appl y to ALL States: A) removal of Stage II re fueling from area source file to non-road and on-road; B) modification of PM2.5 ratio for several f ugitive dust sources per WRAP methodology; C) addition of portable fuel container (PFC ) emissions to all States based on OTAQ report.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table I-2 Inventory Version in Use by Year and Source Sector Through B&F - 2009

Source AL FL GA KY MS NC SC TN VA WV EGU 1 Best & Final Best & Final Best & Final Best & Final Best & Final Best & Final Best & Final Best & Final Best & Final Best & Fin al Non-EGU Point 2 Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Area Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model.

Some specific

source categories updated using State supplied file to override projected values. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model.

On-road Base G Base G Base G Base G Base G Base G Base G Base G Base G Base G Non-road Base G for all

sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources. Base G for all

sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all

sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all

sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources except for aircraft in Cincinnati/N. KY Int. Airport, which are Base G using State supplied growth factors. Base G for all

sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all

sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all

sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all

sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all

sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all

sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources

Fires Base F typical except for Rx fires Base F typical Base F typical except for Rx fires Base F typical except for Rx fires Base F typical except for Rx fires Base F typical except for Rx fires Base F typical except for Rx fires Base F typical except for Rx fires Base F typical except for Rx fires Base F typical except for Rx fires Notes: 1. All EGU emissions updated with new IPM runs in Base G; additiona l EGU-specific changes specified by States for Best & Final. 2. Revised growth factors from DO E AEO2006 fuel use projectionsDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table I-3 Inventory Version in Use by Year and Source Sector Through B&F - 2018

Source AL FL GA KY MS NC SC TN VA WV EGU 1 Best & Final Best & Final Best & Final Best & Final Best & Final Best & Final Best & Final Best & Final Best & Final Best & Fin al Non-EGU Point 2 Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in Base G2 and B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in Base G2 and B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in Base G2 and B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in B&F Base F methodology but with revised growth factors for fuel fired sources in Base G and

sourcespecific changes in Base G2 and B&F Area Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model.

Some specific

source categories updated using State supplied file to override projected values. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model. Base F with updated AEO growth factors for fuel fired

sources. Agricultural ammonia sources from CMU model.

On-road Base G Base G Base G Base G Base G Base G Base G Base G Base G Base G Non-road Base G for all sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources. Base G for all sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources except for aircraft in Cincinnati/N. KY Int. Airport, which are Base G using State supplied growth factors. Base G for all sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources Base G for all sources included in the NONROAD model.

Base F projection methodologyused for nonNONROAD model

sources

Fires Base F typical except for Rx fires Base F typical Base F typical except for Rx fires Base F typical except for Rx fires Base F typical except for Rx fires Base F typical except for Rx fires Base F typical except for Rx fires Base F typical except for Rx fires Base F typical except for Rx fires Base F typical except for Rx fires Notes : 1. All EGU emissions updated with new IPM runs in Base G; additi onal EGU-specific changes specified by States for Base G2 and B&F. 2. Revised growth factors from DO E AEO2006 fuel use projectionsDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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## 1.0 2002 Base Year Inventory Development

1.1 Point Sources

This section details the development of the 2002 base year inventory for point sources. There were two major components to the development of the point source sector of the inventory. The first component was the incorporation of data submitted by the Visibility Improvement State and
Tribal Association of he Southeas t (VISTAS) States and local (S/L ) agencies to the United States Environmental Protection Agency (EPA) as part of the Consolidated Emissions Reporting Rule (CERR) requirements Work on incorporating the CE RR data into the revise d base year involved: 1) obtaining the data from EPA or the S/L agency, 2) evaluating the emissions and pollutants reported in the CERR submittals, 3) augmenting CERR data with annual emission estimates for
PM 10 

### -PRI and PM 2.5 

### -PRI; 4) evaluating the emissions from electric generating units, 5) completing quality assurance reviews for each component of the point source inventory, and 6) updating the database with corrections or new in formation from S/L agencies based on their review of the 2002 inventory. The processes used to perform those operati ons are described in the first portion of this section.

The second component was the development of a “typical” year inventory for electric generating units (EGUs). VISTAS determined that a typica l year electric generati ng units (EGU) inventory was necessary to smooth out any anomalies in emissions from the EGU sector due to meteorology, economic, and outage factors in 2002. The typical year EGU inventory is intended to represent the five year (2000-2004) period that will be used to determine the regional haze reasonable progress goals. The second part of this section discusses the development of the typical year EGU inventory.

1.1.1 Development of 2002 Point Source Inventory
MACTEC developed a draft 2002 em ission inventory in June 2004 ( Development of the Draft 2002 VISTAS Emission Inventory for Regi onal Haze Modeling – Point Sources , MACTEC, June 18, 2004). The starting point for the dr aft 2002 emission inventory was EPA’s 1999 National Emission Inventory (NEI), Version 2 Final (NEI99V2). Fo r several states, we replaced
the NEI99V2 data with more recent inventorie s for either calendar year 1999, 2000, or 2001 as submitted by the S/L agencies. We also performed several other updates, including updating emission estimates for selected large source of ammonia, incorpor ating 2002 Continuous Emissions Monitoring-(CEM)-based SO 2 

### and NO x 

### emissions for electric utilities, adding PM 10 

### and PM 2.5 

### emissions when they were missing from an S/L submittal, and performing a variety of additional Quality assurance/Quality control (QA/QC) checks.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### The next version of the 2002 inventory (referred to as Base F) was released in August 2005 ( Documentation of the Revised 2002 Base Year, Revised 2018, and Initial 2009 Emission Inventories for VISTAS , MACTEC, August 2, 2005). The primary task in preparing the Base F 2002 base year inventory was the replacement of NEI99V2 data with data submitted by the VISTAS S/L agencies as part of the CERR submittal and included in EPA’s 2002 NEI.

The next version of the 2002 inventory (referred to as Base G) was released in August 2006 and is documented in this report. The primary task in preparing the Base G 2002 base year inventory was the incorporation of corrections and new information as submitted by the S/L agencies based on their review of the Base F inventory. Note that no changes to the Base G 2002 point source inventory were made during the Base G2 and B&F update cycles (in other words, for the 2002 actual and typical inventories, Base G = Base G2 = B&F).

The following subsections document the data sour ces for the Base G/B&F inventory, the checks made on the CERR submittals, the process for augmenting the inventory with PM 10 

### and PM 2.5 

### emissions, the evaluation of EGU emissions, ot her QA/QC checks, and other Base G updates.
The final subsection summarizes the Base G/B&F 2002 inventory by state, pollutant, and sector (EGU and non-EGU).

1.1.1.1 Data Sources
Several data sources were used to compile the Ba se F point source inventor y: 1) the inventories that the S/L submitted to EPA from May through July 2004 as required by the CERR; 2) supplemental data supplied by the S/L agencies that may have been revised or finalized after the CERR submittal to EPA, and 3) the draft VISTAS 2002 inventory in cases where S/L CERR
data were not available. For the Base G inve ntory, we replaced data from Hamilton County, Tennessee, using data from Hamilton County’s CERR submittal as contained in EPA’s 2002 NEI inventory (in Base F, the inventory for Ha milton County was based on the draft VISTAS 2002 inventory, which in turn wa s based on the 1999 NEI).

Table 1.1-1 summarizes the data used as the st arting point for the Base F 2002 inventory. Once all of the files were obtained, MACTEC ra n the files through the EPA National Emission Inventory Format (NIF) Basic Format and Content checking tool to ensure that the files were
submitted in standard NIF format and that there were no referential integrity issues with those files. In a couple of cases small errors were found. For example, in one case non-standard pollutant designations were used for particul ate matter (PM) and ammo nia emissions. MACTEC contacted each VISTAS State poin t source contact person to resolve the issues with the files and
corrections were made. Once all corrections to the native files were made, MACTEC continued with the incorporation of the data into the VIST AS point source files. S/L agencies completed a detailed review of the Base F inventory. A dditional updates and corrections to the Base FDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### inventory were requested by S/L ag encies and incorporated into th e Base G inventory. The Base G changes are documented in more detail in Se ction 1.1.1.6. No additional changes to the Base G inventory were made as part of the Base G2/B&F round of updates.

Table 1.1-1 State Data Submittals Used fo r the Base F 2002 Point Source Inventory.

State / Local Program Point Source Emissions Data Source AL C
FL B GA B KY C MS B NC C SC C TN C VA B
WV B Davidson County, TN B Hamilton County, TN D Memphis/Shelby County, TN B Knox County, TN B Jefferson County, AL B Jefferson County, KY B Buncombe County, NC B

Forsyth County, NC B Mecklenburg County, NC B Key A = Draft VISTAS 2002 B = CERR Submittal from EPA's file transfer protocol (FTP) site C = Other (CERR or other submittal sent directly from S/L agency to MACTEC) D = CERR Submittal from EPA’s NEI 2002 Final Inventory



### 1.1.1.2 Initial Data Evaluation
For the Base F inventory, we conducted an ini tial review of the 2002 poi nt source CERR data in accordance with the QA procedures specified in th e Quality Assurance Proj ect Plan (QAPP) for this project. The following evaluations were co mpleted to identify potential data quality issues
associated with the CERR data:

 

### Compared the number of sites in the CERR su bmittal to the number of sites in the VISTAS draft 2002 inventory; for all States, the number of sites in the CERR submittal
was less than in the VISTAS draft 2002 inve ntory, since the CERR data was limited to major sources, while the VISTAS draft 2002 in ventory contained data for both major and minor sources; verified with S/L contacts th at minor sources not included in the CERR point source inventory were included in the CERR area source inventory.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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###  

### Checked for correct pollutant codes and co rrected to make them NIF-compliant; for example, some S/L agencies reported ammoni a emissions using the CAS Number or as “ammonia”, rather than the NIF-compliant “NH 3 

### ” code.

 

### Checked for types of particulate matter c odes reported (i.e., PM-FIL, PM-CON, PM-PRI,
PM 10 

### -PRI, PM10-FIL, PM 2.5 

### -PRI, PM 2.5 

### -FIL); corrected codes with obvious errors (i.e., changed PMPRI to PM-PRI). (The PM augmentation process for filling in missing PM pollutants is discussed later in Section 1.1.1.3)

 

### Converted all emission values that weren’t in tons to tons to allow for preparation of emission summaries using consistent units.

 

### Checked start and end dates in the PE and EM tables to confirm consistency with the 2002 base year.

 

### Compared annual and daily emissions when daily emissions were reported; in some cases, the daily value was non-zer o (but very small) but th e annual value was zero. This was generally the result of rounding in an S/L agency’s submittal.

 

### Compared ammonia emissions as reported in the CERR submittals and the 2002 Toxics Release Inventory; worked with S/L agencies to resolve any outstan ding discrepancies.

 

### Compared SO 2 

### and NO x 

### emissions for EGUs to EPA’s Clean Air Markets Division CEM database to identify any outstanding discre pancies. (A full discussion of the EGU emissions analysis is discu ssed later in Section 1.1.1.4)

 

### Prepared State-level emission summaries by pollutant for both the EGU and non-EGU sectors to allow S/L agencies to compar e emissions as repo rted in the 1999 NEI Version 2, the VISTAS draft 2002 i nventory, and the CERR submittals.

 

### Prepared facility-level emissi on summaries by pollutant to al low S/L agencies to review facility level emissions for reasonableness and accuracy.

We communicated the results of these analyses through email/telephone exchanges with the S/L point source contacts as well as through Excel summary spreadsheets. S/L agencies submitted corrections and updates as necessary to reso lve any QA/QC issues from these checks.

1.1.1.3 PM Augmentation
Particulate matter emissions can be reporte d in many different forms, as follows:

PM Category Description
PM-PRI Primary PM (include s filterable and condensable)Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### PM-CON Primary PM, condensable por tion only (all less than 1 micron)
PM-FIL Primary PM, filterable portion only

PM 10 

### -PRI Primary PM 10 

### (includes filterable and condensable)

PM 10 

### -FIL Primary PM 10 

### filterable portion only

PM 2.5 

### -PRI Primary PM 2.5 

### (includes filterable and condensable)

PM 2.5 

### -FIL Primary PM 2.5 

### filterable portion only

S/L agencies did not report PM emissions in a consistent manner. The State/local inventories submitted for VISTAS included emissions data for either PM-FIL, PM-PRI, PM 10 

### -FIL,
PM 10 

### -PRI, PM 2.5 

### -FIL, PM 2.5 

### -PRI, and/or PM-CON. From any one of these pollutants, EPA has developed augmentation procedures to estimate PM 10 

### -PRI, PM 10 

### -FIL, PM 2.5 

### -PRI, PM 2.5 

### -FIL, and PM-CON. If not included in a State/local inventory, PM 10 

### -PRI and PM 2.5 

### -PRI were calculated by adding PM 10 

### -FIL and PM-CON or PM 2.5 

### -FIL and PM-CON, respectively.

The procedures for augmenting point source PM emissions are documented in detail in Appendix C of Documentation for the Final 1999 National Emissions Inventory {Version 3} for
Criteria Air Pollutants a nd Ammonia – Point Sources , January 31, 2004). Briefly, the PM data augmentation procedure include s the following five steps:

 

### Step 1: Prepare S/L/T PM and PM 10 

### Emissions for Input to the PM Calculator

 

### Step 2: Develop and Apply Sour ce-Specific Conversion Factors

 

### Step 3: Prepare Factor s from PM Calculator

 

### Step 4: Develop and Apply Algorithms to Esti mate Emissions from S/L/T Inventory Data

 

### Step 5: Review Results and Update the NEI with Emission Estimates and Control Information.

Please refer to the EPA documentation for a co mplete description of the PM augmentation procedures.

Table 1.1-2 compares the original PM emission estimates from the S/L CERR submittals and the
revised 2002 VISTAS emissions estimates calcula ted using the above methodology. This table is intended to show that we took whatever States pr ovided in the way of PM and filled in gaps to
add in PM-CON where emissions were missing in order to calculate PM 10 

### -PRI and PM 2.5 

### -PRI for all processes to get a complete set of pa rticulate data. We did not compare any other pollutants besides PM, since for other pollutant s CERR emissions equal VISTAS emissions. As noted in Table 1.1-2, we made si gnificant revisions to the PM emissions for Kentucky in the Base F inventory and for South Ca rolina in the Base G inventory.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.1-2 Comparison of Particulate Matte r Emissions from the S/L Data Submittals and the Base G 2002 VISTAS Point Source Inventory

State Database PM-PRI PM-FIL PM-CON PM 10 -PRI PM 10 -FIL PM 2.5 -PRI PM 2.5 -FIL
AL CERR 28,803 9,174 0 16,522 6,548 8,895 4,765

VISTAS 43,368 33,336 10,129 32,791 22,661 23,290 13,328
FL CERR 0 33,732 0 0 32,254 0 0

VISTAS 61,728 37,325 24,403 57,243 32,840 46,147 21,744
GA CERR 42,846 0 0 27,489 0 15,750 0

VISTAS 44,835 37,088 7,799 33,202 25,403 22,777 15,085
KY CERR 0 3,809 0 19,748 1,360 0 0

VISTAS 27,719 22,349 5,329 21,326 15,963 14,173 8,749
MS CERR 23,925 0 0 20,968 0 10,937 0

VISTAS 23,928 17,632 6,296 21,089 14,793 11,044 5,739
NC CERR 48,110 0 0 36,222 0 24,159 0

VISTAS 48,114 41,407 6,708 36,992 30,284 27,512 21,113
SC CERR 0 43,837 0 0 32,656 0 21,852

VISTAS 43,844 38,633 5,210 34,799 29,588 26,418 21,207
TN CERR 1,660 25,500 21,482 43,413 22,164 34,167 12,140

VISTAS 56,797 32,085 24,715 50,937 26,269 41,442 16,774
VA CERR 0 0 0 17,065 0 12,000 0

VISTAS 40,856 36,414 4,442 17,065 12,623 12,771 8,607
WV CERR 0 29,277 0 0 14,778 0 8445

VISTAS 36,188 29,392 6,795 22,053 15,258 15,523 8,733

Note 1: CERR refers to data as submitted by S/L agencies; VI STAS refers to data calculated by MACTEC using the PM augmentation methodologies described in this document. Note 2: KY DEP’s initial CERR submittal reported particulate matter emissions using only PM-PRI pollutant code. MACTEC used this pollutant code during the initial PM augmentation routine. In February 2005, KY DEP indicated that data reported usin g the PM-PRI code should actually have been reported using the PM 10 -PRI code. MACTEC performed a subsequent PM augmentation in April 2005 using the PM 10 -PRI code. These changes were reflected in the Base F emission inventory. Note 3: South Carolina Department of Health and Envi ronmental Control (SC DHEC) initial CERR submittal reported particulate matter emissions using the PM-FIL, PM 10 -FIL, and PM 2.5 -FIL pollutant codes. MACTEC used these pollutant codes during the initial PM augmentation routine. In August 2005, SC DHEC indicated that data reported using the PM-FIL, PM 10 -FIL, and PM 2.5 -FIL pollutant codes should actually have been reported using the PM-PRI, PM 10 -PRI, and PM 2.5 _PRI codes. MACTEC performed a subsequent PM augmentation in April 2006 using the revised pollutant codes. These changes were reflected in the Base G emission inventory. Note 4: The emission values in the VISTAS emission rows above differ slightly from the final values in the Base G inventory. This is due to several corrections and updates to the 2002 inventory submitted by S/L agencies after the PM augmentation was performed as discussed in Section 1.1.1.6.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### After the PM augmentation process was performed, we executed a series of checks to identify potential inconsistencies in the PM inventory. These checks included:

 

### PM-PRI less than PM 10 

### -PRI, PM 2.5 

### -PRI, PM 10 

### -FIL, PM 2.5 

### -FIL, or PM-CON;

 

### PM-FIL less than PM 10 

### -FIL, PM 2.5 

### -FIL;

 

### PM 10 

### -PRI less than PM 2.5 

### -PRI, PM 10 

### -FIL, PM 2.5 

### -FIL or PM-CON;

 

### PM 10 

### -FIL less than PM 2.5 

### -FIL;

 

### PM25-PRI less than PM 2.5 

### -FIL or PM-CON;

 

### The sum of PM 10 

### -FIL and PM-CON not equal to PM 10 

### -PRI; and

 

### The sum of PM 2.5 

### -FIL and PM-CON not equal to PM 2.5 

### -PRI. S/L agencies were asked to review this in formation and provide corrections where the inconsistencies were significant. In general, co rrections (or general directions) were provided in the case of the potential inconsistency issues. In other cases, the ag ency provided specific process level pollutant corrections.

Note that for the Base G inventory, only the PM 10 

### -PRI and PM 2.5 

### -PRI emission estimates were retained since they are the only two PM species that are included in the air quality modeling. Other PM species were removed from the Base G inventory to facilita te emissions modeling.

1.1.1.4 EGU Analysis
We made a comparison of the annual SO 2 

### and NO x 

### emissions for EGUs as reported in the S/L agencies CERR submittals and EPA’s Clean Air Markets Division (CAMD) CEM database to identify any outstanding discrepanc ies. Facilities report hourly CE M data to EPA for units that are subject to CEM reporti ng requirements of the NO x 

### State Implementation Plan (SIP) Call rule and Title IV of the Clean Air Act (CAA). EPA sums the hourly CEM emissions to the annual level, and we compared these annual CEM emi ssions to those in the S/L inventories. The 2002 CEM inventory containing NO x 

### and SO 2 

### emissions and heat input data were downloaded from the EPA CAMD web site ( www.epa.gov/airmarkets ).

The first step in the EGU analysis involved prep aring a crosswalk file to match facilities and units in the CAMD inventory to facilities and units in the S/L invent ories. In the CAMD inventory, the Office of Regulat ory Information Systems (ORI S) identification (ID) code identifies unique facilities and the unit ID identifies unique boilers and internal combustion engines (i.e., turbines and reci procating engines). In the S/L inventories, the State and county FIPS and State facility ID togeth er identify unique facilities and the emission unit ID identifies unique boilers or internal co mbustion engines. In most cases, there is a one-to-one correspondence between the CAMD id entifiers and the S/L identifiers. However, in some of the S/L inventories, the emissions for multiple emission units are summed and reported under one emission unit ID. We created an Excel spreadsheet that contained an initial crosswalk with the
ORIS ID and unit ID in the CEM inventor y matched to the Stat e and county FederalDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Implementation Plan (FIPS), State facility ID, and emission unit ID in the S/L inventory. The
initial crosswalk contained both the annual emissi ons summed from the CAMD database as well
as the S/L emission estimate. It should be noted that the initial matc hing of the IDs in both inventories was based on previ ous crosswalks that had been developed for the preliminary VISTAS 2002 inventory and in-house info rmation compiled by MACTEC and Alpine Geophysics. The matching at the facility level was nearly complete. In some cases, however, S/L agency or stakeholder assistance was needed to match some of the CEM units to emission units
in the S/L inventories.

The second step in the EGU analysis was to pr epare an Excel spreadsheet that compared the annual emissions from the hourly CAMD inventor y to the annual emissions reported in the S/L inventory. The facility-level compar ison of CEM to emission inventory NO x 

### and SO 2 

### emissions found that for most facilities, the annual emissi ons from the S/L inventory equaled the CAMD CEM emissions. Minor differences could be explai ned because the facility in the S/L inventory contained additional small or emergency units th at were not included in the CAMD database.

The final step was to compare the SO 2 

### and NO x 

### emissions for select Southern Company units in the VISTAS region. Southern Company is a supe r-regional company that owns EGUs in four VISTAS States – Alabama, Florid a, Georgia, and Mississippi – and participates in VISTAS as an industry stakeholder. Southern Company independently provided emission estimates for 2002 as part of the development of the preliminar y VISTAS 2002 inventory. In most cases, these estimates were reviewed by the States and incorporated into the States CERR submittal. The exception to this was a decision made by Georgi a’s Department of Environmental Protection (GDEP) to utilize CEM-based emissions for th e actual 2002 emissions inventory for sources within the State when Southern Company also provided data. There were no major
inconsistencies between the S outhern Company data, the CAMD data, and the S/L CERR data.

The minor inconsistencies included small diffe rences (<2 percent) in emission estimates, exclusion/inclusion of small gas-fired units in the different databases, and grouping of emission
units in S/L CERR submittals where CAMD listed each unit i ndividually. We compared SO 2 

### and
NO x 

### emissions on a unit by unit basis and di d not find any major inconsistencies.

1.1.1.5 QA Review of Base F Inventory
QA checks were run on the Base F point source invent ory data set to ensure that all corrections provided by the S/L agencies and stakeholders were correctly incorporated into the S/L
inventories and that there were no remaining QA issues. After exporting the inventory to ASCII text files in NIF 3.0, the EPA QA program wa s run on the ASCII files and the QA output was reviewed to verify that all QA issues that could be addressed were resolved.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Throughout the inventory developm ent process, QA steps were pe rformed to ensure that no double counting of emissions occurred, and to en sure that a full and complete inventory was developed for VISTAS. QA was an important com ponent to the inventory development process and MACTEC performed the following QA steps on the point source compone nt of the VISTAS revised 2002 base year inventory:

1. Facility level emission summaries were pr epared and evaluated to ensure that emissions were consistent and th at there were no missing sources.

2. State-level EGU and non-E GU comparisons (by pollutant ) were developed between the Base F 2002 base year inventory, the draft VISTAS 2002 inventory, and the 1999 NEI Version 2 inventory.

3. Data product summaries and raw NIF 3.0 data files were provided to the VISTAS Emission Inventory Technical Advisor and to the Point Source, EGU, and non-EGU Special Interest Work Group representative s for review and comment. Changes based on these comments were reviewed and approve d by the S/L point source contact prior to implementing the changes in the files.

4. Version numbering was used for all i nventory files developed. The version numbering process used a decimal system to track major and minor changes. For example, a major change would result in a version going from Base F1 to Base F2.

1.1.1.6 Additional Base G Updates and Corrections
S/L agencies completed a detailed review of th e Base F inventory. Table 1.1-3 summarizes the updates and corrections to the Base F inventor y that were requested by S/L agencies and incorporated into the Base G inventory.

There was a discrepancy between the ba se year 2002 and 2009/2018 emissions for PM 10 

### -PRI,
PM 2.5 

### -PRI, and NH 3 

### . The 2002 emissions were provided dire ctly by the S/L agencies and were estimated using a variety of techniques (i.e., EP A emission factors, S/L emission factors, sitespecific emission factors, and source test da ta). The 2009/2018 emissions, on the other hand, were estimated by Pechan (see Section 2.1.1.3) usi ng an emission factor file based solely on AP-42 emission factors. An adjustme nt was made for 2002 EGU PM and NH 3 

### emissions to reconcile these differences. The post- processed Integrated Planning Model ® ( 

### IPM ® 

### ) 2009/2018 output uses a set of PM and NH 3 

### emission factors that are “t he most recent EPA approved
uncontrolled emission factors” – these are most likely not the same emission factors used by States and emission inventory pr eparation contractors for estimating these emissions in 2002 for

EGUs in the VISTAS domain. VISTAS performe d a set of modifications to replace 2002 base year PM and NH 3 

### emission estimates with estimates deri ved from the most recent EPA-approved

emission factors. For further details of the me thodology used to make this adjustment, see EGU Emission Factors and Emission Factor Assignment, memorandum from Greg Stella to VISTAS
State Point Source Contacts and VISTAS EG U Special Interest Workgroup, June 13, 2005.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.1-3 Summary of Updates and Corrections to the Base F 2002 Inventory Incorporated into the 2002 Base G Inventory.

Affected State(s) Nature of Update/Correction

TN, WV The latitude and longitude values for TN (except the four local programs) and WV were truncated to two decimal places in the Base F inventory. MACTEC re-e xported the NIF ER tables in a manner that so that the latitude and longitude were not truncated in the Base G inventory.
AL Corrected the latitude and longitude for two facilities: Ergon Terminalling (Site ID: 01-073-010730167) and Southern Power Franklin (Site ID: 01-081-0036).
Corrections to stack parameters at 10 facilities for stack s with parameters that do not appear to fall into the ranges typically termed "acceptable" for AQ modeling.
FL Corrected emission values for the Miami Dade RRF facility (Site ID: 12-086-0250348).
GA Hercules Incorporated (12-051-05100005) had an errone ous process id (#3) within emission unit id SB9 and was deleted. This removes about 6,000 tons of SO 2 from the 2002 inventory.
Provided a revised file of location coordinates at the stack level that was used to replace the location coordinated in the ER file.
NC Made several changes to Base F inventory to correct the following errors: 1. Corrected emissions at Hooker Furniture (S ite ID: 37-081-08100910), release point G-29, 9211.38 tons volatile organic compounds (VOC's) should be 212.2 tons, 529.58 tons PM 10 should be 17.02 tons, 529.58 tons PM2.5 should be 15.79 tons in 2002 inventory. 2. Identified many stack parameters in the ER file that were unrealistic. Several have zero for height, diameter, gas velocity, and flow rate. NC used the procedures outlined in Section 8 of the document ""National Emission Inventory QA and Augmentation Report" to correct unrealistic stack parameters. 3. Identified truncated latitude and longitude values in Base F inventory. NC updated all Title V facility latitude and longitude that was submitted to EPA for those facilities in 2004. Smaller facilities with only two decimal places were not corrected. 4. Corrected emissions for International Paper (3709700045) Emission Unit ID, G-12, should be 1.8844 tons VOCs instead of 2819.19 tons in 2002
SC Corrected PM species emission values. SC DHEC’s initial CERR submittal reported particulate matter emissions using the PM-FIL, PM 10 -FIL, and PM25-FIL pollutant c odes. In August 2005, SC DHEC indicated that data reported using the PM-FIL, PM 10 -FIL, and PM25-FIL pollutant codes should actually have been reported using the PM-PRI, PM 10 -PRI, and PM25_PRI codes. MACTEC performed a subsequent PM augmentation in April 2006 using the revised pollutant codes. These changes were reflected in the Base G emission inventory.
TN Identified six facilities that closed in 2000/2001 but had non-zero emissions in the 2002 Base F inventory. MACTEC changed emissions to zero for all pollutants in the Base G 2002 inventory.
Supplied updated emission inventory for the Bowater facility (47-107-0012) based on the facility’s updated 2002 emission inventory update.
Replaced data from Hamilton County, Tennessee, us ing data from Hamilton County’s CERR submittal as contained in EPA’s 2002 NEI (in Base F, the inventory for Hamilton County was based on the draft VISTAS 2002 inventory, which in turn was based on the 1999 NEI).
Updated emissions for PCS Nitrogen Fertilizer LP (Site ID: 47-157-00146)
WV Updated emissions for Steel of West Virginia (Site ID: 54-011-0009)

Made changes to several Site ID names due to changes in ownership
Made corrections to latitude/longitude and stack para meters at a few facilities for stacks with parameters that do not appear to fall into the ranges typically termed "acceptable" for AQ modeling.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 1.1.1.7 Summary of B&F 2002 Inventory
Tables 1.1-4 through 1.1-10 summarize the B&F 2002 base year inventory. All values are in tons. Note that no changes to the Base G 2002 poi nt source inventory were made during the Base G2 and B&F update cycles (in other words, Base G = Base G2 = B&F)

For the purposes of Tables 1.1-4 through 1.1-10, E GU emissions include the emissions from all processes with a Source Classifi cation Code (SCC) of either 1-01-xxx-xx (External Combustion Boilers – Electric Generation) or 2-01-xxx- xx (Internal Combustion Engines – Electric Generation). Emissions for all other SCCs ar e included in the non-EGU column. Note that aggregating emissions into EGU and non-EGU s ectors based on the above SCCs causes a minor inconsistency with the EGU emissions reported in EPA’s CAMD database. The EGU emissions summarized in these tables may include emissions from some smaller electric generating units in the VISTAS inventory that are not in CAMD ’s 2002 CEM database or the IPM forecasted
emissions. The minor inconsistencies result in a less than 2 percent difference between the summary tables below and the da ta from CAMD’s CEM database.

Table 1.1-4 Base G / B&F 2002 VISTAS Point Source Inventory for SO 2 

### (tons/year).

State All Point Sources EGUs Non-EGUs

AL 544,309 447,828 96,481

FL 518,721 453,631 65,090

GA 568,731 514,952 53,778

KY 518,086 484,057 34,029

MS 103,388 67,429 35,960

NC 522,113 477,990 44,123

SC 259,916 206,399 53,518

TN 413,755 334,151 79,604

VA 305,106 241,204 63,903

WV 570,153 516,084 54,070
Total 4,324,278 3,743,725 580,556

Note : EGU emissions include SCCs 1-01-xxx-xx and 2-01-xxx-xx; non-EGU has all other SCCs.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.1-5 Base G / B&F 2002 VISTAS Point Source Inventory for NO x 

### (tons/year).

State All Point Sources EGUs Non-EGUs

AL 244,348 161,038 83,310

FL 302,834 257,677 45,156

GA 196,767 147,517 49,251

KY 237,209 198,817 38,392

MS 104,661 43,135 61,526

NC 196,782 151,854 44,928

SC 130,394 88,241 42,153

TN 221,652 157,307 64,344

VA 147,300 86,886 60,415

WV 277,589 230,977 46,612
Total 2,059,536 1,523,449 536,087

Note: EGU emissions include SCCs 1-01-xxx-xx and 2-01-xxx-xx; non-EGU has all other SCCs.


### Table 1.1-6 Base G / B&F 2002 VISTAS Point Source Inventory for VOC (tons/year).

State All Point Sources EGUs Non-EGUs

AL 49,332 2,295 47,037

FL 40,995 2,524 38,471

GA 34,952 1,244 33,709

KY 46,321 1,487 44,834

MS 43,852 648 43,204

NC 62,170 988 61,182

SC 38,927 470 38,458

TN 85,254 926 84,328

VA 43,906 754 43,152

WV 15,775 1,180 14,595
Total 461,484 12,516 448,970

Note: EGU emissions include SCCs 1-01-xxx-xx and 2-01-xxx-xx; non-EGU has all other SCCs.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.1-7 Base G / B&F 2002 VISTAS Point Source Inventory for CO (tons/year).

State All Point Sources EGUs Non-EGUs

AL 185,550 11,279 174,271

FL 139,045 57,113 81,933

GA 140,561 9,712 130,850

KY 122,555 12,619 109,936

MS 59,871 5,303 54,568

NC 64,461 13,885 50,576

SC 63,305 6,990 56,315

TN 122,348 7,084 115,264

VA 70,688 6,892 63,796

WV 100,220 10,341 89,879
Total 1,068,604 141,218 927,388

Note: EGU emissions include SCCs 1-01-xxx-xx and 2-01-xxx-xx; non-EGU has all other SCCs.



### Table 1.1-8 Base G / B&F 2002 VISTAS Point Source Inventory for PM 10 

### -PRI (tons/year).

State All Point Sources EGUs Non-EGUs

AL 32,886 7,646 25,240

FL 57,243 21,387 35,857

GA 32,834 11,224 21,610

KY 21,326 4,701 16,626

MS 21,106 1,633 19,472

NC 36,592 22,754 13,838

SC 35,542 21,400 14,142

TN 49,814 14,640 35,174

VA 17,211 3,960 13,252

WV 22,076 4,573 17,503
Total 326,630 113,918 212,714

Note: EGU emissions include SCCs 1-01-xxx-xx and 2-01-xxx-xx; non-EGU has all other SCCs.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.1-9 Base G / B&F 2002 VISTAS Point Source Inventory for PM 2.5 

### -PRI (tons/year).

State All Point Sources EGUs Non-EGUs

AL 23,291 4,113 19,178

FL 46,148 15,643 30,504

GA 22,401 4,939 17,462

KY 14,173 2,802 11,372

MS 11,044 1,138 9,906

NC 26,998 16,498 10,500

SC 27,399 17,154 10,245

TN 39,973 12,166 27,807

VA 12,771 2,606 10,165

WV 15,523 2,210 13,313
Total 239,721 79,269 160,452

Note: EGU emissions include SCCs 1-01-xxx-xx and 2-01-xxx-xx; non-EGU has all other SCCs.


### Table 1.1-10 Base G / B&F 2002 VISTAS Point Source Inventory for NH 3 

### (tons/year).

State All Point Sources EGUs Non-EGUs

AL 2,200 317 1,883

FL 1,657 234 1,423

GA 3,697 83 3,613

KY 1,000 326 674

MS 1,359 190 1,169

NC 1,234 54 1,180

SC 1,553 142 1,411

TN 1,817 204 1,613

VA 3,230 127 3,104

WV 453 121 332
Total 18,200 1,798 16,402

Note: EGU emissions include SCCs 1-01-xxx-xx and 2-01-xxx-xx; non-EGU has all other SCCs.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 1.1.2 Development of Typical Year EGU inventory
VISTAS developed a typical y ear 2002 emission inventory for EGUs to avoid anomalies in emissions due to variability in meteorology, eco nomic, and outage factors in 2002. The typical year inventory represents the five year ( 2000-2004) period and was used to determine the regional haze reasonable progress goals. Actual 2002 emissions were used when comparing the CMAQ modeling results to the 2002 measurements in the model performance evaluation. A
detailed discussion of how the actual and typical year EGU inve ntories were used for modeling
is contained in the Technical Support Document for VISTAS Emissions and Air Quality Modeling to Support Regional Haze State Implementation Plans located on the VISTAS web site ( http://www.vist as-sesarm.org )

Data from EPA’s CAMD were used to de velop normalization fact ors for producing a 2002 typical year inventory for EGUs . We used the ratio of the 2000-2004 average heat input and the 2002 actual heat input to normalize the 2002 actua l emissions. MACTEC obtained data from EPA’s CAMD for utilities regulated by the Acid Rain program. Annual data for the period 2000 to 2004 were obtained from the CAMD web site ( www.epa.gov/airmarkets ). The parameters
available were the SO 2 

### and NO x 

### emission rates, heat input, a nd operating hours. We used the actual 2002 heat input and the av erage heat input for the 5- year period from 2000-2004 as the normalization factor, as follows:

Normalization Factor: 2000-2004 average heat input 2002 actual heat input
If the unit did not operate for all five years, then the 2000-2004 average he at input was calculated for the one or two years in which the unit did operate. For example, if the unit operated only during 2002, then the normalization factor w ould be 1.0. The annual actual emissions were multiplied by the normalization factor to dete rmine the typical emissions for 2002, as follows:

Typical Emissions = 2002 actual emissions x Normalization Factor
After applying the normalization factor, some adjustments were needed for special circumstances. For example, a unit may not have operated in 2002 and thus have zero emissions. If the unit had been permanently retired prio r to 2002, then we used zero emissions for the typical year. If the unit had not been permanently retired and would normally operate in a typical year, then we used the 2001 (or 2000) heat input and emission rate to calculate th e typical year
emissions.

The Southern Company provided ty pical year data for their sour ces. Hourly emissions data for criteria pollutants were provide d. MACTEC aggregated the hourly emissions into annual values. Further documentation of how Southern Company created the typical year inventory for theirDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### units can be found in Developing Southern Company Emissions and Flue Gas Characteristics for VISTAS Regional Haze Mode ling (April 2005, presented at 14 th 

### International Emission Inventory Conference http://www.epa.gov/ttn/chief/conf erence/ei14/session9/kandasamy.pdf ). Since Southern Company only supplied filter able particulate emissions, we ran the PM 10 

### /PM 2.5 

### augmentation routine to calculate annual emission estimates for PM 10 

### -PRI and PM 2.5 

### -PRI. The Southern Company typical year data were used for Southern Company sources in Alabama, Florida, and Mississippi. Georgia EPD elected to use the typical year normalization factor
derived from the CAMD data instead of the Southe rn Company typical year data (as was used in the Base F inventory).

The final step was to replace the 2002 actual emi ssions with the 2002 typical year data described above. MACTEC provided the raw data and resu lts of the typical y ear calculations in a spreadsheet for S/L agency review and comment. Any comments made were incorporated into the Base G inventory.

Table 1.1-11 summarizes emissions by State and pollutant for the actual 2002 EGU inventory and the typical year EGU inventory. Fo r the entire VISTAS region, actual 2002 SO 2 

### emissions were about 1.6 percent higher than the typical year emissions. The differences on a state-be-state basis ranged from actual emissions being 2.3 per cent lower in Kentucky to 10.9 percent higher in Mississippi. For th e entire VISTAS region, actual 2002 NO x 

### emissions were about 1.7 percent lower than the typical year emissions. The diffe rences on a state-be-state basis ranged from actual emissions being 1.6 percen t lower in Kentucky to 6.3 per cent higher in Mississippi.

Table 1.1-11 Comparison of SO 2 

### and NO x 

### Emissions (tons/year) for EGUs.

SO 2 

### Emissions (tons/year) NO x 

### Emissions (tons/year)

State Actual 2002 Typical 2002 Percentage Difference Actual 2002 Typical 2002 Percentage Difference

AL 447,828 423,736 5.4 161,038 154,704 3.9

FL 453,631 444,383 2.0 257,677 255,678 0.8
GA 514,952 517,633 -0.5 147,517 148,126 -0.4

KY 484,057 495,153 -2.3 198,817 201,928 -1.6
MS 67,429 60,086 10.9 43,135 40,433 6.3
NC 477,990 478,489 -0.1 151,854 148,812 2.0
SC 206,399 210,272 -1.9 88,241 88,528 -0.3
TN 334,151 320,146 4.2 157,307 152,137 3.3
VA 241,204 233,691 3.1 86,886 85,081 2.1
WV 516,084 500,381 3.0 230,977 222,437 3.7
Total 3,743,725 3,683,968 1.6 1,523,449 1,497,864 1.7

Note: a negative percentage difference indicates actual emissions are less than the typical year emissions.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 1.2 Area Sources

This section details the development of the Ba se G 2002 base year inventory for area sources. There are three major components of the area source sector of the inventory. The first component is the “typical” year fire inve ntory. Version 3.1 of the VISTAS ba se year fire inventory provided
actual 2002 emissions estimates. Since fire emissions are not easily grown or projected, in order to effectively represent fires in both the base and future year inventories, VISTAS determined that a typical year fire inventor y was necessary. Development of th e “typical” year fire inventory covered wildfire, prescribed burni ng, agricultural fires and land cl earing fires. The first part of this section of the report disc usses the development of the ty pical year fire inventory. The methodology provided in that sectio n is identical to the documentation provided for Base F since the “typical” year inventory was developed as part of the Base F development effort. The major change in Base G for the fire component of the inventory was the developm ent of projection year inventories that represent alternatives to th e “typical” year inventory. These alternative projections incorporated projec ted changes in the acreage burned for prescribed fires on Federal lands. These projections are an augmen tation of the “typical” year inventory.

The second component of the area source inventor y was the incorporation of data submitted by
the VISTAS States to the United States Environm ental Protection Agency (EPA) as part of the CERR. Work on incorporating the CERR data into the revised base year involved: 1) obtaining the data from EPA, 2) evaluating the emissions and pollutants reported in order to avoid double counting and 3) backfilling from the existing VISTAS 2002 base year inventory for missing sources/pollutants. The processes used to perf orm those operations are described in the second portion of this section. That work was performed as part of the Base F inventory effort. In general no changes to that method were made as part of the Base G inventory updates. The methods used for the Base F inventory development effort using the CERR submittals have been maintained in this document. Where necessary, additional documentation has been added to 1) reflect changes that resulted from VISTAS St ates review of the Base F inventory and the incorporation of those changes into Base G, 2) changes made to how certain sources were estimated or 3) addition of new sources not found in Base F.

The final component of the area source invent ory was related to the development of NH 3 

### emission estimates for livestock and fertiliz ers and paved road PM emissions. For the NH 3 

### emission estimates for livestock and fertilizer s we used version 3.6 of the Carnegie Mellon University (CMU) NH 3 

### model. For the paved road PM emissions, we used the most recent estimates developed by EPA as part of the National Emission Inventory (NEI) development effort. EPA had developed an improved method ology for estimating paved road emissions so
those values were substituted dir ectly into the inventory after receiving consensus from all of the VISTAS States to perform the replacement. Deta ils on these methods are provided in the thirdDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### portion of this section of the docum ent. That section is virtually identical to that from the Base F inventory document as there were only a c ouple of changes to the ammonia portion of the inventory and some updates to all fugitive dus t categories including paved roads on a global
basis between Base F and Base G.

Finally, quality assurance steps for each component of the area source inventory are discussed.

1.2.1 Development of a “typical” year fire inventory
Typical year fire emissions were developed st arting from the actual fire acreage data and
emission calculated for each VISTAS State. Th e table below shows the data submitted by each State in the VISTAS region indicating what data was received from each State for the purposes of calculating actua l fire emissions.

Fire Type AL FL GA KY MS NC SC TN VA WV Land Clearing 9 9 9 9 Ag Burning 9 9 9 9

Wildfires 9 9 9 9 9 9 9 9 9 9

Prescribed 9 9 9 9 9 9 9 9 9

In order to effectively characterize fire emissions in the VI STAS region, a typical (as opposed to strictly 2002 year based inventory) was required. Development of a typica l year fire inventory provided the capability of using a comparable data set for both the base year and future years.
Thus fire emissions would remain the same for air quality and visibili ty modeling in both the base and any future years. MACTEC originally proposed five different methods for developing the typical fire year to the VISTAS Fire Sp ecial Interest Work Group (SIWG) and requested their feedback and preference fo r developing the final typical y ear inventory. The method that was selected by SIWG members was to use a met hod similar to that used to develop an early version of a 2018 projection invent ory. For that early 2018 inventor y, State level ratios of acres over a longer term record (three or more years) 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. For Base F and G, the decision of th e VISTAS Fire SIWG was to base the ratio on county level data for States that supplied long term fire-by-fire acreage data rather than Statelevel ratios. Where States did not supply long term fire-by-fire acreage data, MACTEC reverted to using State-level ratios. With one broad ex ception (wildfires) this method was implemented for all fires. MACTEC solicited long term fire- 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. ThoseDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 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.

Thus if we had long term county prescribed fire data from a State, we developed a county acreage ratio of:

acreage Rx level county actual 2002 acres Rx level county average term Long

= Ratio

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 by fire basis. The acreage calculated using th e ratio was then used with the fuel loading and
emission factor values that we already had (and had been revi ewed by the SIWG) to calculate emissions using the same method used for th e 2002 actual values (whi ch were previously documented). The following lists indicate which c ounties used the State ratios by fire type.

Land Clearing Agricultural Fires Prescribed Burning FIPS COUNTY FIPS COUNTY FIPS COUNTY 12086 Miami-Dade County 12037 Franklin County 12043 Glades County 12045 Gulf County 12049 Hardee County 12057 Hillsborough County 12073 Leon County 12077 Liberty County 12081 Manatee County 12095 Orange County 12097 Osceola County 12103 Pinellas County 12115 Sarasota County 13015 Bartow County 13021 Bibb County 13045 Carroll County 13047 Catoosa County 13057 Cherokee County 13059 Clarke County 13063 Clayton County 13073 Columbia County 13077 Coweta County 13083 Dade County 13089 Dekalb County 13097 Douglas County 13117 Forsyth County 13121 Fulton County 13129 Gordon County 13135 Gwinnett County 13137 Habersham County 13143 Haralson County 13147 Hart County 13063 Clayton County 13083 Dade County 13089 Dekalb County 13097 Douglas County 13121 Fulton County 13135 Gwinnett County 13137 Habersham County 13215 Muscogee County 13227 Pickens County 13241 Rabun County 13247 Rockdale County 13311 White County 13059 Clarke County 13083 Dade County 13089 Dekalb County 13097 Douglas County 13121 Fulton County 13123 Gilmer County 13135 Gwinnett County 13139 Hall County 13215 Muscogee County 13241 Rabun County 13247 Rockdale CountyDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Land Clearing Agricultural Fires Prescribed Burning FIPS COUNTY FIPS COUNTY FIPS COUNTY 13151 Henry County 13169 Jones County 13215 Muscogee County 13237 Putnam County 13241 Rabun County 13291 Union County 13311 White County



### There were three exceptions to this method.
Exception 1: Use of State Ratios for Wildfires

The first exception was that wildfires estimates were developed using State ratios rather than county ratios. This change was made after initial quality assurance of the draft estimates revealed that some counties were showing unrealistic values created by very short term data records or missing data that created unrealistic ratios. In addition, exceptionally large and small fires were removed from the database since they were felt to be atypical. For example the Blackjack Complex fire in Georgia was removed from the dataset because the number of acres burned was “atypical” in that fire. We also removed all fires less than 0.1 acres from the dataset.
Exception 2: Correction for Blackened Acres on Forest Service Lands

Following discussions with the United States Fo rest Service (Forest Se rvice) (memo from Cindy Huber and Bill Jackson, dated August 13, 2004), it was determined that the acres submitted by the Forest Service for wildfires and prescribed fires represented perimeter acres rather than “blackened” acres. Thus for wildfires and prescr ibed fires on Forest Service lands, a further correction was implemented to correct the peri meter acre values to blackened acres. The
correction was made based on the size of the fire . For prescribed fires over 100 acres in size the acreage was adjusted to be 80 per cent of the initial re ported value. For pres cribed fires of 100 acres or less the acreage values were maintain ed as reported. For wildfi res, all reported acreage values were adjusted to be 66 percent of their initially reported values. These changes were made to all values reported for Fo rest Service managed lands.

Exception 3: Missi ng/Non-reported data

When we 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. For example, if no agricultural burning long te rm acreage data was reported for a particular State, MACTEC determined an overall VISTAS re gional average ratio that was used to multiplyDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### times the 2002 values to produce th e “typical” values. This techni que was applied to all fire types when data was missing.

In addition, for wildfires and prescribed burning, ratios were developed for “northern” and “southern” tier States within the VISTAS region a nd those ratios were app lied to each State with missing data depending upon whether they were consid ered a “northern” or “southern” tier State. Development of “southern” and “nor thern” tier data was an attempt to account for a change from a predominantly pine/evergreen ecosystem (southe rn) to a pine/deciduous ecosystem (northern).
States classified as “southern” included: AL , FL, GA, MS, and SC. States classified as
“northern” included: KY , NC, TN, VA, and WV.

Finally for land clearing and agri cultural fires, there are no NH 3 

### and SO 2 

### emissions. This is due
to the lack of emission factors for th ese pollutants for these fire types.

Table 1.2-1 shows fire emissions from the orig inal base year emissi on inventory (VISTAS 3.1),
the actual 2002 emissions and th e typical year emissions for the entire VISTAS region. The actual 2002 and typical fire emissions represen t the Base F and Base G 2002 emissions. The typical emissions also represent the 2009 and 2018 emissions for all fire types with the exception of prescribed burning. Revisions made to the typi cal year prescribed fi re emissions for 2009 and 2018 are detailed in the projection section. Also, State level Base G emissions from fires for all years can be found in the tables in Appendix A. Values for fires in thos e tables are “typical” year values.

Figures 1.2-1 through 1.2-4 show the State by Stat e changes in emissions between the original 2002 base year fire inventorie s, the actual 2002 and the typical year inventories for carbon monoxide (CO) by fire type. Due to the relative magnitude of CO emissions compared to other criteria and PM pollutants from fires; this pollutant is normally c hosen to represent the distribution of fires in the example plots.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission InventoriesMACTEC, Inc.30Table 1.2-1 Emissions from Fires in theVISTAS Region – Comparison betweenOriginal Base Year 2002 (VISTAS 3.1), 2002Actual and Typical Year Base G Emissions.CONH3NOXPM10-FILPM10-PRIPM2.5-FILPM2.5-PRISO2VOCTotal LCActual (Base G) 492,409 0 14,568 62,146 62,146 62,146 62,146 0 33,799Typical (Base G) 675,838 0 19,995 80,598 80,598 80,598 80,598 0 46,389VISTAS 3.1 484,240 0 14,327 61,325 61,325 61,325 61,325 0 33,238Total AgActual (Base G) 164,273 0 903 30,958 30,958 30,385 30,385 0 21,946Typical (Base G) 161,667 0 903 30,465 30,465 29,892 29,892 0 21,595VISTAS 3.1 331,073 0 903 41,480 41,480 40,192 40,192 0 41,875Total WFActual (Base G) 298,835 1,333 6,628 28,923 28,923 24,926 24,926 1,611 16,804Typical (Base G) 547,174 2,451 11,955 53,070 53,070 45,635 45,635 3,072 28,491VISTAS 3.1 275,766 1,230 6,133 26,680 26,680 23,002 23,002 1,476 15,718Total RXActual (Base G) 1,678,216 7,616 36,561 168,938 168,938 145,175 145,175 9,839 78,988Typical (Base G) 1,635,776 7,425 35,650 164,811 164,811 141,636 141,636 9,590 76,990VISTAS 3.1 1,724,940 7,822 37,556 173,590 173,590 149,181 149,181 10,101 81,188Key: LC = Land Clearing; Ag = Agricultural burning; WF = wildfires;RX = prescribed burning. Actual and Typical represent Base Fand Base G (e.g., nochange in methodology for Base F and Base G) for 2002.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission InventoriesMACTEC, Inc.31Figure 1.2-1 CO Emissions from Agricultural Burning for the Original Base Year, 2002 Actual Base G, and 2002 TypicalBase G Inventories.CO Emissions0.0020,000.0040,000.0060,000.0080,000.00100,000.00120,000.00140,000.00160,000.00AL FL GA KY MS NC SC TN VA WV

tonsAg Burning - 2002 ActualAg Burning - TypicalAg Burning - VISTAS 3.1Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission InventoriesMACTEC, Inc.32Figure 1.2-2 CO Emissions from Land Clearing Burning for the Original Base Year, 2002 Actual Base G and 2002 TypicalBase G Inventories.CO Emissions0.0020,000.0040,000.0060,000.0080,000.00100,000.00120,000.00140,000.00160,000.00180,000.00AL FL GA KY MS NC SC TN VA WV

tonsLand Clearing - 2002 ActualLand Clearing - TypicalLand Clearing - VISTAS 3.1Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission InventoriesMACTEC, Inc.33Figure 1.2-3 CO Emissions from Prescribed Burning for the Original Base Year, 2002 Actual Base G and 2002 Typical Base GInventories.CO Emissions0.00100,000.00200,000.00300,000.00400,000.00500,000.00600,000.00700,000.00AL FL GA KY MS NC SC TN VA WV

tonsPrescribed - 2002 ActualPrescribed - TypicalPrescribed - VISTAS 3.1Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission InventoriesMACTEC, Inc.34Figure 1.2-4 CO Emissions from Wildfire Burning for the Original Base Year, 2002 Actual Base G and 2002 TypicalBase G Inventories.CO Emissions0.0050,000.00100,000.00150,000.00200,000.00250,000.00AL FL GA KY MS NC SC TN VA WV

tonsWildfires - 2002 ActualWildfires - TypicalWildfires - VISTAS 3.1Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 1.2.2 Development of non-fire inventory
The second task in preparing the area source co mponent of the Base F and Base G 2002 base year inventory was the incorporation of data submitted by the VISTAS States to the EPA as part of the CERR. With few exceptions, Base F and Ba se G inventories for this component of the inventory are identical. Modi fications to the Base F met hodology (described below) only
resulted from modifications from the VISTAS States during review of the Base F inventory. The changes made to the inventory based on these revi ews are described in the last portion of this section of the report. The information presented below describes the method used to incorporate CERR data as part of Base F.

Work on incorporating the CERR data into the 2 002 Base F inventory invo lved: 1) obtaining the data from EPA, 2) evaluating the emissions and pollutants reported in order to avoid double counting and 3) backfilling from the earlier vers ion of the VISTAS 2002 base year inventory for missing sources/pollutants. The processes used to perform those operations are described below. This work did not include any of the fire emi ssion estimates described above. In addition it did not include emission estimates for ammonia from ag ricultural and fertilizer sources. Finally it did not include PM emissions from paved roads. Each of those categories was estimated separately.

Data on the CERR submittals was obtained from EPA’s Draft NEI download file transfer protocol (FTP) site where the data are stored af ter they’ve been processed for review. The data submitted in National Emission Inventory Format (NIF) was downloaded from that site. Once all of the files were obtained, MACTEC ran the files through the EPA NIF Format and Content checking tool to ensure that the files were submi tted in standard NIF format and that there were no issues with those files. In a couple of cases small errors were found. Fo r example, in one case a county FIPs code that was no longer in us e was found. MACTEC contacted each VISTAS State area source contact person to resolve the issu es with the files and corrections were made.
Once all corrections to the native files we re completed, MACTEC continued with the incorporation of the data into the VISTAS area source files.

Our general assumption was that unless we determined otherwise, the CERR submittals represented full and complete inventories. Wh ere a State submitted a complete inventory, our plan was to simply delete the previous 2002 base year data and replace it with the CERR submittal. Prior to this replacement however, we stripped out the following emissions:

1. All wildfire, prescribed burning, land cl earing and agricultural burning emissions submitted to EPA by the States as part of the CERR process were removed since they were to be replaced with emissions estim ated using methods described earlier.
2. All fertilizer and agricult ural ammonia emission records submitted to EPA by the States as part of the CERR process were removed. These were replaced with the estimates developed using the CMU Ammonia model.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 3. All emissions from paved roads submitted to EPA by the States as part of the CERR process were removed. These emissions were replaced with updated emissions developed by U.S. EPA as part of their 2002 NEI development effort.
This approach was used for most State and Local emission submittals to prepare the Base F inventory. There were a few cases where alternat ive data were used to prepare the Base F inventory. In general, these alternatives involve d submittal of alternative files to the CERR data by S/L agencies. Table 1.2-2 below summarizes the data used to prepare the Base F inventory. In general the data were derived fr om one of the following sources:
1. CERR submittal obtained from EPA FTP s ite as directed by VISTAS States;
2. State submitted file (either revised from CERR submittal or separate format);
3. VISTAS original 2002 base year (VISTA S version 3.1 base year file); or
4. EPA’s preliminary 2002 NEI.
Table 1.2-2 Summary of State Data Submi ttals for the 2002 VISTAS Area Source Base F Inventory

State / Local Program Area Source Emissions Data Source
AL B
FL B
GA C
KY A
MS B
NC C
SC B
TN B
VA B
WV A/C Davidson County, TN B Hamilton County, TN C Memphis/Shelby County, TN A Knox County, TN B Jefferson County, AL * so B from State Jefferson County, KY B Buncombe County, NC * so C from State

Forsyth County, NC * so C from State Mecklenburg County, NC * so C from State

A = VISTAS 2002 (version 3.1) B = CERR Submittal from EPA's ftp site C = Other (CERR or other submittal sent directly from State to MACTEC) * = No responseDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### In order to track the sources of data in the final Base F and Base G NIF files, a field was added to the NIF format files developed for VISTAS to track each data source. A field named Data_Source was added to the EM ta ble. A series of codes were a dded to this field to mark the
source of each emissions value in the Base F and Base G inventories. Valu es in this field are
detailed in Table 1.2-3.

Table 1.2-3 Data Source Codes and Data Sources for VISTAS 2002 Base F Area Source Emissions Inventory.

Data Source Codes Data Source

Base F Codes

CMU Model CMU Ammonia model v 3.6

E-02-X or E-99-F or L-02-X or S-02-X EPA CERR submittal (from FTP site)

EPA Paved EPA Paved Road emissions estimates

EPAPRE02NEI EPA Preliminary 2002 NEI

STATEFILE State submitted file

VISTBASYR31 VISTAS 2002 Base Year version 3.1

VISTRATIO Developed from VISTAS Ratios (used only for missing pollutants)

Additional Base G Codes

ALBASEGFILE Base G update file provided by AL

NCBASEGFILE Base G update file provided by NC
OTAQRPT Portable Fuel Container Emissions from OTAQ Report

STELLA Revised data provided by VISTAS EI Advisor Greg Stella

VABASEGFILE Base G update file provided by VA

VAStateFile Revisions/additions to Base G update file provided by VA



### Most States submitted complete inventories for Ba se F. Virginia’s inventory required a two stage update. Virginia’s CERR submittal only containe d ozone precursor pollutants (including CO). For Virginia, MACTEC’s original plan was to maintain the previous 2002 VISTAS base year emissions for non-ozone pollutants and then do a simple replacement for ozone pollutants. However during the QA phase of the work, MACTEC discovered that there were categories that had ozone precursor or CO emissions in the subm ittal that weren’t in the original 2002 VISTAS base year inventory that should have PM or SO 2 

### emissions. For those records, MACTEC used anDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### emissions ratio to build records for emissions of these pollutants. Data for Virginia PM and SO 2 

### emissions were generated by de veloping SCC level ratios to NO x 

### from the VISTAS 2002 base year inventory (version 3.1) or from emission f actors and then calculati ng the emissions based on
that ratio.

1.2.3 2002 Base G inventory updates
After the Base F inventory was submitted and us ed for modeling, VISTAS States were provided an opportunity for further review and comment on the Base F inventory. As a result of this review and comment period, several VISTAS States provided revisions to the Base F inventory.

In addition to and as an outgrowth of some of the comments provided by the States during the review process, some of the changes made to th e inventory were made globally across the entire VISTAS region. This section discusses the specif ic State changes followed by the global changes made to the area source component of the inventory for all VISTAS States.

1.2.3.1 Changes resulting from State review and comment

Alabama

Alabama suggested several changes and had questi ons concerning a few categories in the Base F inventory. The changes/questions were:

1. For Source Classification Code (SCC) 2102005000 (Industrial Boilers: Residual Oil) and SCC 2103007000 (Instit utional/Commercial Heating: Liquefied Petroleum Gas) the Alabam a noted that the Base F VISTAS inventory had values for NO x 

### , VOC and CO for the State, but no values for
SO 2 

### , PM 10 

### or PM 2.5


### .
MACTEC evaluated this information and found th at there were actually emissions for two
counties in AL for that SCC that had either SO 2 

### and/or PM emissions. The data used to develop the 2002 Base F inventory for AL came from th e preliminary 2002 CERR submittals (see above)
which should have included SO 2 

### and PM but did not except fo r two counties. According to MACTEC’s protocol for use of these files, the files received from EPA were to be used “as is” unless the States provided comments during th e Base F comment period to correct the CERR
submittal. No comments were received from AL on the CERR submittal used for Base F. For 2002 Base G, AL provided an updated database file for these SCCs for all counties in the State that provided revised values for emissions and included SO 2 

### and PM. The revised file was used to update the Base F data for Base G.

2. AL noted that the Base F inve ntory included SCC 2401002000 (Solvent Utilization, Surface Coating, Architectural Coatings - Solvent-based, Total: All Solvent Types) and 2401003000 (Solve nt Utilization, Surface Coating,Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Architectural Coatings - Water-based, To tal: All Solvent Types) as well as SCC 2401001000 (Solvent Utilization, Surface Coating, Architectural Coatings, Total: All Solvent Types). This resulted in double counting of the emissions for this category. AL sugge sted removal of the breakdown SCCs
and use of the total SCC.

MACTEC deleted records for the breakdown SC Cs and retained the total all solvents
SCC emissions.

3. AL found the SCCs listed below missing from the Base F VISTAS inventory.

SCC VOC
Emissions SCC Description 2401025000 1139.91 Surface Coatings: Metal Furniture, all coating types 2401030000 425.27 Surface Coatings: Paper, all coating types 2401065000 344.08 Surface Coatings: Electronic a nd Other Electrical, all coating types 2430000000 504.29 Solvent Utilization, Rubber/Plasti cs, All Processes, Total: All Solvent Types 2440020000 3043.78 Solvent Utilization, Miscellaneous Industrial, Adhesive (Industrial) Application, Total: All Solvent Types
Total for AL 5457.32

MACTEC found that the emissions for these SCCs were included in the Base F inventory, but with slightly different total emissions. AL pr ovided an updated county-level emissions file for use in updating the Base G inventory. That file was used to update the NIF records for AL for
those SCCs.

4. AL noted that emissions in the Ba se F inventory were found for SCC 2465000000 and SCCs 2465100000, 2465200000, 2465400000, 2465600000, and 2465800000. These last five SCCs represent a subset of the emissions in the 246500000 SCC resulting in potentia l double counting of emissions.

MACTEC deleted all emissions associated with the Total SCC 2465000000 and retained the subset SCCs for the Base G inventory.

Florida
Florida provided comments indicating that they felt that emissions from the following sources and counties were too high, especially fo r CO and PM and were likely zero:Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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###  motor vehicle fire - Palm Beach County

 woodstoves - Miami Dade, Hillsborough, Orange , Polk, Ft Myers, Pasco and Sarasota
Counties

 fireplaces - Miami Dade and Hillsborough Counties

Emissions from these sources in the counties specified were se t to zero by MACTEC for the Base G inventory.

North Carolina
North Carolina provided corrected emission files for 2002 Base F. A text file with emission values was provided and used to update the Base F emissions to Base G. The updated emissions were applied directly to the Base F NIF file. The file provided was similar to the “EM” NIF table. An update query was used to update the data supplied in the text file to the Access
database NIF file. All ch anges were implemented.

South Carolina
South Carolina had two issues concerning the Ba se F inventory. These issues related to 1)
additional SCCs that were in BASE F 2009 and 2018, but not in 2002 Base F and 2) SCCs that were in the U.S. EPA 2002 NEI inventory, bu t not in the VISTAS 2002, 2009, or 2018 Base F inventory.

MACTEC investigated the additional SCCs f ound in 2009 and 2018 Base F and found that the SCCs actually were not missing in the 2002 Base F inventory but only had emissions for PM.
Thus the emissions were maintained as they were provided in Base F.

With respect to the SCCs that were found in the U.S. EPA 2002 NEI, MACTEC investigated and found that they were not included in the Base F in ventory because they were not included in the 2002 CERR submittal used to produce the Base F updates. The SCCs were apparently added by EPA later in the NEI development process. In addition, MACTEC also evaluated whether or not
the SCCs were found in other VISTAS States Base F inventories. MACTEC found that some States included them and some did not, there was no consistency betwee n the States. MACTEC also found that typically emissions for these SCCs were low in emissions, generally with emissions of only a few tons to tens of tons pe r year. The decision was made with South Carolina
concurrence not to add these SCCs to the Base G inventory. These SCCs were: 210205000, 2102011000, 2103007000, 2103011000, 2104007000, 2104011000, 2302002100, 2302002200, 2302003100, 2302003200, 2610000500, 2810001000, and 281001500.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Virginia
Virginia provided an updated 2002 base year emissi ons file. The data in that file were used to update the Base F inventory emission values to t hose for Base G. In addition, Virginia provided information on several source catego ries that required controls fo r future year projections since
the sources were located in countie s/cities in northern Virginia a nd were subject to future year Ozone Transport Commission (OTC ) regulations. MACTEC added in the base year control levels to the Base G inventory file for these cate gories so that they coul d be estimated correctly in future years. The controls added were fo r mobile equipment repair/refinishing sources, architectural and industrial maintenance coating sources, consumer products sources, and solvent metal cleaning sources. Minor errors were found in some entries for the initial file provided and VA provided a revised file with corrections and minor additions.

1.2.4 Ammonia and paved road emissions
The final component of the Base F inve ntory development was estimation of NH 3 

### emission
estimates for livestock and fertilizers and paved road PM emissions. For the NH 3 

### emission
estimates for livestock and fertilizer s we used version 3.6 of the CMU NH 3 

### model ( http://www.cmu.edu/ammonia/ ). Results from this model were used for all VISTAS States. The
CMU model version 3.6 was used in large part because it had been just recently been updated to include the latest (2002) Census of Agriculture animal population st atistics. Prior to inclusion of the CMU model estimates, MACTEC removed any a mmonia records for agricultural livestock or
fertilizer emissions from the VISTAS 2002 initia l base year inventory. MACTEC also generated emissions from human perspiration and from wildlife using the CMU model and added those
emissions for each State.

For the Base G ammonia inventory, MACTEC removed all wildlife and human perspiration
emissions. VISTAS decided to remove these em issions from the inventory. Human perspiration was dropped due to a discrepancy in the units used for the emission factor that was not resolved prior to preparing the estimate s and wildlife was dropped because VISTAS felt the activity data
was too uncertain. Thus all emi ssions from these two categories were deleted in the Base G 2002 inventory.

For the paved road PM Base F emissions, we us ed the most recent estimates developed by EPA as part of the NEI development effort (Roy Huntley, U.S. EPA, email communication, 8/30/2004). EPA had developed an improved methodology for estima ting paved road emissions
for 2002 and had used that method to calculate emissions for that source category. MACTEC
obtained those emissions from EPA and those values were substituted directly into the inventory after receiving consensus from all of the VISTAS States to perform the replacement. These files
were obtained in March of 2005 in NIF format from the EPA FTP site.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### For the Base G emissions, modifications were ma de to the emissions estimates based on changes suggested by work of the Western Regional Air Partnership and U.S. EPA. Details of these changes are provided below in the section on gl obal changes made as part of the Base G inventory updates.

1.2.5 Global Changes Made for Base G
There were three global changes made between the Base F and the Base G inventory (beyond the
removal of wildlife a nd human perspiration NH 3 

### emissions). These changes were:

1. Removal of Stage II emissions from the area s ource inventory and inclusion in the mobile sector of the inventory,

2. Adjustment of fugitive dust PM 2.5 

### emissions, and

3. Addition of emissions from portable fuel containers.

As part of the Base F review process, several VISTAS States had expressed surprise that the Stage II refueling emission estimates were in th e area source component of the inventory. This decision had been made with SIWG agreement early on in the inventory development process because 1) some States had included it in thei r CERR submittals and 2) because the non-road and on-road mobile estimates had differing activity f actor units and could not be easily combined. However for Base G, the VISTAS States all agree d, especially in light of the different ways in which the emissions were reported in the CERR, to remove the Stage II refueling emissions from the area source inventory and include them in the non-road and on-road sect ors. Thus all records related to Stage II refueling were removed from the area source component of the Base G inventory.

PM 2.5 

### emissions from several fugitive dust sources were also updated for Base G. The Western Regional Air Partnership (WRAP) and U.S. EPA had been investigating overestimation of the
PM 2.5 

### / PM 10 

### ratio in several fugitive dust categories a nd U.S. EPA was in the process of making
revisions to AP-42 for several categories during preparation of the Base G inventory. Based on data received from U.S. EPA, VISTAS decided to revise the PM 2.5 

### emissions from construction, paved roads and unpaved road sources. PM 2.5 

### emissions in Base F were multiplied by 0.67, 0.6, and 0.67 for construction, paved roads and unpave d roads respectively to produce the values found in Base G. No changes were made to PM 10 

### , only to PM 2.5


### . Finally, as part of Virginia’s comments on the Base F inventory, emissions from portable fuel containers were mentioned as being absent from the inventory. MACTEC was tasked with developing a methodology that could be used to a dd these emissions to the Base G area source inventory. In investigating options for a met hod of estimating emissions, MACTEC found that the U.S. EPA had prepared a national invent ory of emissions by St ate for portable fuelDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### containers. Data on emissions from this sour ce prepared by U.S. EPA were presented in, “Estimating Emissions Associated with Portable Fu el Containers (PFCs), Dr aft Report, Office of Transportation and Air Quality, United States Environmenta l Protection Agency, Report # EPA420-D-06-003, February 2006”.

State-level emission estimates for 2005 derive d from Appendix Table B-2 of the PFCs report were used as the starting point for developi ng 2002 county-level emi ssions estimates. State emissions were derived from that table by using al l of the emission estimates in that table with the exception of values for vapor displacement and spillage from refueling operations. Those components of the State emissions were left out of the State-level emissions to avoid double counting refueling emissions in the non-road se ctor. For the purposes of 2002 emission estimates for Base G, the 2005 values were assumed equal to 2002 values.

The 2005 State-level estimates minus the refuelin g component from Appendix Table B-2 of the report were summed for each State and then allo cated to the county-level. The county-level allocation was based on the fuel usage info rmation obtained from th e NONROAD 2005 model runs conducted as part of the Base G inventory development effort (see the 2002 base year Base G non-road section below). MACTEC used th e spillage file from the NONROAD model (normally located in the DATA\EMSFAC directory in a standard installation of NONROAD) to
determine the SCCs that used containers for refu eling. The spillage file contains information by SCC and horsepower indicating whet her or not the refueling occurs using a container or a pump. All SCC and horsepower classes using containers were extracted from the file and crossreferenced with the fuel usage by county for those SCC/horsepower combinations from the appropriate year model runs (2002, 2009 or 2018). Then the fuel usages by county from the NONROAD 2005 runs prepared for VISTAS we re summed for those SCCs by county. The county level fuel use was then divided by the State total fuel use for the sa me SCCs to determine the fraction of total State fuel usage and that fraction was used to allocate the State-level emissions to the county.

1.2.6 Quality Assurance steps
Throughout the inventory developm ent process, quality assuran ce steps were performed to ensure that no double counting of emissions occurred, and to ensu re that a full and complete inventory was developed for VISTAS. Quality assurance was an important component to the inventory development process and MACTEC performed the following QA steps on the area source component of the 2002 Base F inventory:

1. All CERR and NIF format State supplied data submittals were run through EPA’s Format and Content checking software.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 2. SCC level emission summaries were prepared and evaluated to ensure that emissions
were consistent and that th ere were no missing sources.

3. Tier comparisons (by pollutant) were deve loped between the revised 2002 base year inventory and the previous (ver sion 3.1) base year inventory.

4. Fields were either added or used within each NIF data table to track the sources of
data for each emission record.

5. Data product summaries were provided to both the VISTAS Emission Inventory
Technical Advisor and to Area Source and Fi res SIWG representatives for review and comment. Changes based on these comments were implemented in the files.

6. Version numbering was used for all i nventory files developed. The version numbering process used a decimal system to track major and minor changes. For example, a major change would result in a version going from 1.0 to 2.0. A minor change would cause a version number to go from 1.0 to 1.1. Minor changes resulting from largely editorial changes would result in a change from 1.00 to 1.01.

In addition, for the fires invent ory, data related to fuel loading and fuel consumption was reviewed and approved by the VISTAS Fire SIWG to ensure that values used for each type of
fire and each individual fire were appropriate. Members of th e VISTAS Fire SIWG included representatives from most State Divisions of Forestry (or equiva lent) as well as U.S. Forest Service and National Park Service personnel.

For Base G, similar QA steps to those outlined ab ove for Base F were undertaken. In addition, all final NIF files were checked using the EPA Form at and Content checking software and summary information by State and pollutant were prepared comparing the Base F and Base G inventories.

1.3 Mobile Sources

This section describes the re visions made to the initial 2002 VISTAS Base Year emission inventory on-road mobile source input files. For this work actual emission estimates were not made, rather data files consistent with Mobile Emissions Estimation Model Version 6 (MOBILE6) were developed and provided to the VISTAS modeling contract or. These input data files were then run during the VISTAS modeling to generate on-road mobile source emissions using episodic and meteorological specific conditions configured in the sparse matrix operator Kernel Emissions modeling system (SMOKE) emissions processor.

During initial discussions with the VISTAS Mobile Source SIWG, some States indicated a desire
to use CERR mobile source emissions data in place of the VISTAS 2002 inventories generated by E.H. Pechan and Associates, Inc. (the init ial VISTAS 2002 Base Year inventory files).Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### However, the CERR emissions data by itself were not sufficient for an inventory process that includes both base and future year inventories. MACTEC needed to be able to replicate the CERR data rather than simply obtain CERR emissions estimates. The reason for this is that only input files were being prepared to provide revised 2002 estimates during the VISTAS modeling process, rather than the actual emission estimat es and that the 2002 input data files would be used as a starting point for the projected emission estimates. This meant that the appropriate vehicle miles traveled (VMT), MOBILE6, and/ or NONROAD model input data needed to be provided. If these data were pr ovided with the CERR emissions estimates we used it as the starting point for revision of the 2002 Base Y ear inventory. However MACTEC did not have
access to the on-road mobile CERR submissions from EPA, so re-submittal of these data directly to MACTEC was requested in order to begi n compiling the appropria te input file data.

In those cases where States did not provide CE RR on-road mobile source input data files, our default approach was to maintain the data inpu t files and VMT estimates for the initial 2002 Base Year inventory prepared by Pechan.

1.3.1 Development of on-road mobile sour ce input files and VMT estimates
Development of the 2002 on-road input files and VMT was a multi-step process depending upon
what the State mobile source contacts instructed us to use as their data. Information provided below provides incremental revisions made to on -road mobile source inventories or inputs in series from one inventory version to the next. In general the pro cess involved one of three steps from the original 2002 on-road mobile source data.

Base F Revisions

1. The first step was to evaluate the init ial 2002 base year files and make any nonsubstantive changes (i.e., changes only to confirm that the files posted for 2002 by
Pechan were executable and that all the neces sary external files n eeded to run MOBILE6 were present). This approach was taken for AL, FL, GA, MS, SC, and WV. For these
States the determination was made that th e previous files would be okay to use as originally prepared. For SC, the VMT file was updated, but that did not affect the MOBILE6 input files.

2. For other States, modification to the input files was required. The information below indicates what changes were made fo r other States in the VISTAS region.

KY – For Kentucky, the Inspection and Maintenance (I/M) records in the i nput files for Jefferson County were updated in order to better reflect the actual I/M program in the Louisville metropolitan area.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### NC - Substantial revisions were implemented to th ese input files based on input from the State. The modifications necessary to reflect the desires of the State led to comp lete replacement of the previous input files. Among the changes made were:

 The regrouping of counties (including the movement of some counties from one county group to another and the creation of new i nput files for previously grouped counties). There were originally 32 input files; after the ch anges there were 49. The pointer file was corrected to reflect these changes.

 Travel speeds were updated in over 3000 scenarios.

 All I/M records were updated.

 All registration distributions were updated.

 I/M VMT fractions were updated (which only affected the pointer file).

 VMT estimates were updated (which has no di rect effect on the MOBILE6 input files but does ultimately affect emissions).

3. VA and TN – For these States, new input files were provided due to substantive changes
that the State wanted to make relative to the 2002 initial base year i nput files. In addition, revised VMT data were developed for each State.

Base G Revisions
For the production of the VISTAS 2002 Base G i nventory, VISTAS states reviewed the Base F inputs, and provided corrections, updates and supplemental data.

For all states modeled, th e Base G updates include:

Adding Stage II refueling emissions cal culations to the SMOKE processing.

Revised the HDD compliance for all states. (REBUILD EFFECTS = .1)

In addition to the global changes, individual VISTAS states made the following updates:

KY – updated VMT and M6 input values for selected counties.

NC – revised VMT and re gistration distributions.

TN - revised VMT and vehicle registrati on distributions for selected counties.

VA – revised winter RFG calcula tions in Mobile 6 inputs.

WV – revised VMT input data.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### AL, FL, and GA did not provide updates for Base G and therefore the Base F inputs were used
for these States.

1.3.1.1 Emissions from on-road mobile sources
The MOBILE6 module of the Sparse Matrix Op erator Kernel Emissions (SMOKE) model was used to develop the on-road mobile source emissions estimates for CO, NO X 

### , NH 3 

### , SO 2 

### , PM, and VOC emissions. The MOBILE6 parameters, vehicl e fleet descriptions, and VMT estimates are combined with gridded, episode-specific temperatur e data to calculate the gridded, temporalized
emission estimates. The MOBILE6 emissions fact ors are based on episode-specific temperatures predicted by the meteorological model. Further, the MOBILE6 emissions factors model accounts for the following:

 Hourly and daily minimum/maximum temperatures;

 Facility speeds;

 Locale-specific inspection/maintenance (I/M) control programs, if any;

 Adjustments for running losses;

 Splitting of evaporative and exhaust emi ssions into separate source categories;

 VMT, fleet turnover, and changes in fuel composition and Reid vapor pressure (RVP).

The primary input to MOBILE6 is the MOBILE shell file. The MOBILE shell contains the various options (e.g. type of inspection and main tenance program in effect, type of oxygenated fuel program in effect, alternative vehicle mix pr ofiles, RVP of in-use fuel, operating mode) that
direct the calculation of the MOBILE6 emissions factors. The shells used in these runs were based on VISTAS Base F modeling inputs as noted in the previous section.

For this analysis, the on-road mobile source emissions were produced using selected weeks (seven days) of each month and using these days as representative of the entire month. This selection criterion allows for the representation of day-of-the-week variability in the on-road motor vehicles, and models a representation of the meteorological variab ility in each month. The
modeled weeks were selected from mid-m onth, avoiding inclusion of major holidays.

The parameters for the SMOKE runs are as follows:

Episodes: 2002 Initial Base Year, and 2009 and 2018 Future years, using 2009/2018 inventories and modeled using the same meteorology and episode days as 2002.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Episode represented by the following weeks per month: January 15-21 February 12-18
March 12-18 April16-22 May 14-20
June 11-17 July 16-22 August 13-19 September 17-23
October 15-21
November 12-18
December 17-23
Days modeled as holidays for annual run: New Year’s Day - January 1 Good Friday – March 29 Memorial Day – May 27 July 4th Labor Day – September 2 Thanksgiving Day – November 28, 29
Christmas Eve – December 24 Christmas Day – December 25
Output time zone: Greenwich Mean Time (zone 0)
Projection: Lambert Conformal with Alpha=33, Beta=45, Gamma=-97, and center at (-97, 40).

Domain: 36 Kilometer Grid: Origin at (-2736, - 2088) kilometers with 148 rows by 112 columns and 36-km square grid cells. 12 Kilometer Grid: Origin at (108, -1 620) kilometers with 168 rows by 177 columns and 12-km square grid cells.
CMAQ model species: The CMAQ configuration was CB-IV w ith PM. The model species produced were: CO, NO, NO 2 

### , ALD 2 

### , ETH, FORM, ISOP, NR, OLE, PAR, TERPB, TOL, XYL, NH 3 

### , SO 2 

### , SULF, PEC, PMFINE, PNO 3 

### , POA, PSO 4 

### , and PMC.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Meteorology data: Daily (25-hour). SMOKE requires the follo wing five types of MCIP outputs: (1) Grid cross 2-d, (2) Grid cross 3-d, (3) Me t cross 2-d, (4) Met cross 3-d, and (5),
Met dot 3-d.
The reconstructed emissions based on the repres entative week run were calculated by mapping each day of week (Mon, Tue, Wed, etc.) from th e modeled month to the same day of week generated in the representative week run. In the case of holiday s, these days were mapped to representative week Sundays. An example of th is mapping for the January episode is presented in Table 1.3-1 below. Note that although the em issions were generated for individual calendar years (2002, 2009 and 2018) the me teorology is based on 2002.

Table 1.3-1 Representative day mapping for January episode

(Highlighted representative week)

Modeled Representative Modeled Representative Modeled Representative

Date Day Date Day Date Day 1/1/2002* 1/20/2002 1/11/2002 1/18/2002 1/22/2002 1/15/2002 1/2/2002 1/16/2002 1/12/2002 1/19/2002 1/23/2002 1/16/2002 1/3/2002 1/17/2002 1/13/2002 1/20/2002 1/24/2002 1/17/2002 1/4/2002 1/18/2002 1/14/2002 1/21/2002 1/25/2002 1/18/2002 1/5/2002 1/19/2002 1/15/2002 1/15/2002 1/26/2002 1/19/2002 1/6/2002 1/20/2002 1/16/2002 1/16/2002 1/27/2002 1/20/2002 1/7/2002 1/21/2002 1/17/2002 1/17/2002 1/28/2002 1/21/2002 1/8/2002 1/15/2002 1/18/2002 1/18/2002 1/29/2002 1/15/2002 1/9/2002 1/16/2002 1/19/2002 1/19/2002 1/30/2002 1/16/2002 1/10/2002 1/17/2002 1/20/2002 1/20/2002 1/31/2002 1/17/2002 1/21/2002 1/21/2002 * Modeled holiday



### 1.3.2 Development of non-road emission estimates

Emissions from non-road sources were estimated in two steps. First, emissions for non-road
sources that are included in the NONROAD model were deve loped. Second, emissions from
sources not included in the NONR OAD model were estimated. The sections below detail the procedures used for each group of sources.

1.3.2.1 Emissions from NONROAD model sources
An initial 2002 base year emissions inventor y for non-road engines and equipment covered by
the EPA NONROAD model was prepared for VISTAS in early 2004. The methods and assumptions used to develop the inventor y are presented in a February 9, 2004 report

“ Development of the VISTAS Draft 2002 Mobi le Source Emission Inventory (February 2004 Version) ” as prepared by E.H. Pechan & Associates, Inc. Except as otherwise stated below, allDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### aspects of the preparation met hodology documented in that repor t continue to apply to the
revised NONROAD mo deling discussed in this section.

Revisions to the initial 2002 NONROAD emissions inventory we re implemented to ensure that
the latest State and local data were considere d, as well as to more accu rately reflect gasoline
sulfur contents for 2002 and correct other State-specific discrepanc ies. Those revisions comprise the Base F VISTAS non-road inventory. This sec tion details the specific revisions made to the NONROAD model input files for the Base F and Base G VISTAS base year inventories, and provides insight into some key differences between the versions of the NONROAD model employed for the Base F and Ba se G inventories and the prev ious version employed for the initial 2002 base year inve ntory prepared by Pechan.

Revisions to the initial 2002 emissions inventory prepared by Pechan were actually implemented in two stages. An initial set of revisions was implemented in the fall of 2004. Those revisions
resulted in the Base F inventor y. These were followed by a second set of revisions in the spring of 2006. Those estimates produced the Base G base year inventory. To accurately document the combined effects of both sets of revisions, each set is discussed separately below. Unless otherwise indicated, all revisions implemented in Base F were carried directly into the Base G revision process without change. Thus, the invent ories that resulted from the Base F revisions served as the starting poin t for the Base G revisions.

For Base F, three VISTAS States provided de tailed data revisions for consideration in developing revised model i nputs. These States were:

1. North Carolina
2. Tennessee (including a separate su bmission for Davidson County), and
3. Virginia.
The remaining seven VISTAS States indicated th at the initial 2002 VISTAS input files prepared by Pechan continued to reflect the most re cent data available. These States were:

1. Alabama,
2. Florida,
3. Georgia,
4. Kentucky,
5. Mississippi,
6. South Carolina, and
7. West Virginia.
However, it should be recognized that the NONROAD input files for all ten VISTAS States were updated to reflect gasoline sulfur content revisions for the Base F 2002 base year inventory (asDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### discussed below). The original files prepared by Pechan are available on their FTP site in the /pub/VISTAS/MOB_0104/ directory.

Before presenting the specific implemented revisi ons, it is important to note that the Base F 2002 base year inventory utilized a newer release of the NONROAD model than was used for the initial 2002 base year inventory (prepared by Pechan). The Base F 2002 base year inventory, as developed in spring 2004, was based on the Dr aft NONROAD2004 model, which was released by the EPA in May of 2004. This model is no lo nger available on EPA’s website. The initial 2002 base year inventory (prepared by Pech an) was based on the Draft NONROAD2002a version of the model (which is also no longe r available on EPA’s website). Key differences
between the models are as follows:

 Draft NONROAD2004 included the effects of th e Tier 4 non-road engine and equipment standards (this did not impact the Base F 2002 i nventory estimates, but did affect Base F future year forecasts).

 Draft NONROAD2004 included the exhaust emission impacts of the large spark-ignition engine standards; the evaporativ e impacts of these standards are not incorporated (this does not impact 2002 inventory estimates, but does affect future year forecasts).

 Draft NONROAD2004 included revised equipment population estimates.

 The PM 2.5 

### fraction for diesel equipment in Draft NONROAD2004 had been updated from
0.92 to 0.97.

 Draft NONROAD2004 included revisi ons to recreational marine activity, useful life, and
emission rates.

To the extent that these revisions affect 2002 em issions estimates, they will be reflected as
differentials between the initial and Base F 2002 VI STAS base year invent ories. It is perhaps important to identify that, at the time of the Base F inventory revisions; the EPA recognized the
Draft NONROAD2004 model as an appropriate mechanism for SIP development. Although the model was designated as a draft update, it re flected the latest and most accurate NONROAD planning data at that time, as evidenced by th e EPA’s use of that version for the Tier 4 Final Rulemaking.

Prior to the Base G inventory revisions implemented in 2006, th e EPA released another updated version of the NONROAD model, designate d as Final NONROAD2005 (which can be downloaded from: http://www.epa.gov/OMSW WW/nonrdmdl.htm#model). This version ostensibly represents the final version of the model, although certain components of it have been updated since its first release in December 2005. For the Base G inventory developed in the firstDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### half of 2006, all updates of the Final NONROAD2005 model through March 2006 are included. Key differences between Final NONROAD 2005 and Draft NONROAD2004 are as follows:

 Final NONROAD2005 reflects the latest basic emission rate and deterioration data.

 Final NONROAD2005 includes emission estimates for a range of evaporative emissions categories not included in Draft NONROAD 2004 (tank and hose permeation, hot soak, and running loss emissions).

 Final NONROAD2005 includes a revise d diurnal emissions algorithm.

 Final NONROAD2005 includes a revised equipment scrappage algorithm.

 Final NONROAD2005 includes revised state and county equipment allocation data.

 Final NONROAD2005 allows separate sulfur content inputs for marine and land-based
diesel fuel.

 Final NONROAD2005 includes revised convers ion factors for hydrocarbon emissions.

 Final NONROAD2005 includes the evapora tive emission impacts of the large spark-ignition engine standards (this does not impact 2002 inventory estimates, but does affect future year forecasts).

Unfortunately, due to the extensive revisions associated with Final NONROAD2005, input files created for use with Draft NONROAD2004 (e.g., Base F input files) and earlier versions of the model cannot be used directly with Final NONROAD2005 (used for Base G). This created a rather significant impact in that the VI STAS NONROAD modeling process involves the consideration of over 200 unique sets of input data. To avoid crea ting new input files for each of these datasets, a conversion process was unde rtaken wherein each of the Draft NONROAD2004 (Base F) input data files were converted into the proper format required for proper execution in Final NONROAD2005 (Base G). 1 

### This process consisted of the following steps:

 Revise the Draft NONROAD2004 (Base F) input files to include th e following two line EPA-developed comment at the end of the i nput file header (thi s is a nonsubstantive change implemented solely for consistency wi th input files produced directly using Final NONROAD2005):

1 The necessary conversions where deve loped by comparing substantively identical input files created using the

graphical user interfaces for both Draft NONROAD2004 and Final NONROAD2005. The di fferences between the input files indicated the specific revisions necessary to convert existing VISTAS input files into Final NONROAD2005 format.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

MACTEC, Inc. 53 9/2005 epa: Add growth & tech years to OPTIONS packet and Counties & Retrofit files to RUNFILES packet.



###  Revise the Draft NONROAD2004 (Base F) in put files to include the following two command lines after the “Weekday or weekend” command in the PERIOD packet:

Year of growth calc:
Year of tech sel :



###  Revise the Draft NONROAD2004 (Base F) inpu t files to include the following command line after the “Diesel sulfur percent” command in the OPTIONS packet:

Marine Dsl sulfur %: 0.2638



### Note that the value 0.2638 (2638 parts per millio n by weight [ppmW]) is applicable only for 2002 modeling and was accordingly revised (as described below) for both the 2009
and 2018 Base G forecast inventories. The 2638 ppmW sulfur value for 2002 marine
diesel fuel was taken from the 48-State (excludes Alaska a nd Hawaii) tabulation presented in the April 27, 2004 EPA document “ Diesel Fuel Sulfur Inputs for the Draft
NONROAD2004 Model used in the 2004 Non-road Diesel Engine Final Rule .” It should
also be noted that this value differs by about 5 percent from the 2500 ppmW value previously used for the initial 2002 VISTAS modeling (performed by Pechan). Prior to Final NONROAD2005 (used for Base G), the NONROAD model allowed only a single diesel fuel sulfur input that was applied to both land-base d and marine equipment. As documented in the February 9, 2004 report “ Development of the VISTAS Draft 2002 Mobile Source Emission Inventory (February 2004 Version) ” as prepared by E.H. Pechan & Associates, Inc., a value of 2500 ppmWsulfur was used for all 2002 VISTAS NONROAD modeling. Given the ability of Final NONROAD2005 to distinguish a separate sulfur content for marine equi pment and the existing EPA guidance document suggesting an appropriate marine sulfur value of 2638 ppmW for 2002, the existing modeling value of 2500 ppmW was modifi ed (for marine equipment only).

 Replace the Draft NONROAD2004 (Base F) in put files RUNFILES packet command
line:

TECHNOLOGY : c:\non-road\data\tech\tech.dat



### with the command lines:

EXH TECHNOLOGY : c:\non-road\data\tech\tech-exh.dat EVP TECHNOLOGY : c:\non-road\data\tech\tech-evp.datDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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###  Revise the Draft NONROAD2004 (Base F) in put files to include the following two command lines after the “EPS2 AMS” command in the RUNFILES packet:

US COUNTIES FIPS : c:\non-road\data\allocate\fips.dat

RETROFIT :



###  Revise the Draft NONROAD2004 (Base F) inpu t files to include the following command
line after the “Rec marine outbrd” command in the ALLOC FILES packet:

Locomotive NOx : c:\non-road\data\allocate\XX_rail.alo



### Where “XX” varies across input files. Fo r any given file, “XX” is the two digit
abbreviation of the state associated with the scenario being modeled (e.g., for Alabama modeling, XX=AL).

 Replace the Draft NONROAD2004 (Base F) in put files EMFAC FILES packet command
line:

Diurnal : c:\non-road\data\emsfac\diurnal.emf



### with the eight command lines:

Diurnal : c:\non-road\data\emsfac\evdiu.emf TANK PERM : c:\non-road\data\emsfac\evtank.emf NON-RM HOSE PERM : c:\non-road\data\emsfac\evhose.emf RM FILL NECK PERM : c:\non-road\data\emsfac\evneck.emf RM SUPPLY/RETURN : c:\non-road\data\emsfac\evsupret.emf RM VENT PERM : c:\non-road\data\emsfac\evvent.emf HOT SOAKS : c:\non-road\data\emsfac\evhotsk.emf RUNINGLOSS : c:\non-road\data\emsfac\evrunls.emfEVP



###  Revise the Draft NONROAD2004 (Base F) inpu t files to include the following command
line after the “PM exhaust” command in the DETERIORATE FILES packet:

Diurnal : c:\non-road\data\detfac\evdiu.det



### Once revised in this format, the VISTAS non-ro ad input files develope d for use with Draft NONROAD2004 (Base F) were ex ecutable under the Final N ONROAD2005 model (Base G).

The only additional revisions implemented to develop a Final NONRO AD2005-based inventory (Base G) involved elimination of non-default equipment allocation files for North Carolina and West Virginia. Due to concerns about imprope r equipment allocation acr oss counties under the Draft NONROAD2004 model (used for Base F), as well as for earlier versions of the NONROAD model, North Carolina had produced alte rnative allocation data files indicating theDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### number of employees in air tr ansportation by county, the number of wholesale es tablishments by county, and the number of employees in landsca ping services by county. For the same reason, West Virginia had produced alternative equipm ent allocation files indicating the number of employees in air transportation by county, the tonnage of underg round coal production by county, the number of golf courses and count ry clubs by county, the number of wholesale establishments by county, the number of employ ees in logging operations by county, the number of employees in landscaping services by count y, the number of employees in manufacturing operations by county, the number of employees in oil and gas drilling and extraction operations by county, and the number of recreational ve hicle parks and campgrounds by county. These alternative equipment allocation files were us ed for all VISTAS inventory modeling conducted prior to the release of Fi nal NONROAD2005 (i.e., through Base F). However, both North Carolina and West Virginia determined that the de fault allocation file revisions associated with
the release of Final NONROAD2005 we re appropriate to address the concerns that led to the development of the alternative allocation files. As a result, all alternative allocation file
commands were removed from VISTAS N ONROAD2005 (Base G) input files for North Carolina and West Virginia, so that the entire region under the Base G i nventory is now modeled using the default allocation f iles provided with NONROAD2005.

In addition to the alternativ e equipment allocation files, North Carolina had previously developed an alternative seasonal adjustment file that was used for the Base F inventory in place of the default file provided with Draft NONROAD2004 (and ear lier model versions). The alternative data file implemen ted a single change, namely reclassifying North Carolina as a
southeastern state rather than a mid-Atlantic state (as identified in the default data file). Since Final NONROAD2005 continues to identify North Carolina as a mid-A tlantic state, North Carolina requested that the southeastern re classification be con tinued for all NONROAD2005 modeling (Base G). To ensure that any other revi sions associated with the seasonal adjustment
file released with NONROAD2005 we re not overlooked, the previ ously developed alternative seasonal adjustment file for North Carolina was scrapped and a new altern ative file was created from the default seasonal adjustment file provided with Final NONROAD2005 for Base G inventory development. The alternative file, which was used for all North Carolina modeling,
reclassifies North Carolina from a mid-Atlantic to a southeastern state. This represents the only
non-default data file used for VIST AS NONROAD2005-based (Base G) modeling.

The remainder of this section documents all chan ges to the originally established VISTAS input file values as documented in the February 9, 2004 report “ Development of the VISTAS Draft 2002 Mobile Source Emission Inventory (February 2004 Version) ” as prepared by E.H. Pechan & Associates, Inc. Unless specifically stated belo w, all values from that report continue to be used without change in th e latest VISTAS modeling.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Base F Revisions:
For the initial 2002 base year inventory (devel oped by Pechan), all NONROAD modeling runs for VISTAS were performed utilizing a gasoline sulfur content of 339 ppmW and a diesel sulfur content of 2,500 ppmW. Although th e EPA-recommended non-road dies el fuel sulfur content for 2002 is 2,283 ppmW, the 2,500 ppmW sulfur content us ed for the initial 2002 base year VISTAS inventory was designed to remove the effect of lower non-road diesel fuel sulfur limits applicable only in California. (The EPA recommended inputs can be found in “ Diesel Fuel Sulfur Inputs for the Draft NONROAD2004 Mode l used in the 2004 Non-road Diesel Engine
Final Rule ,” EPA, April 27, 2004.) This correction is a ppropriate and was reta ined for the Base F 2002 inventory. Thus, the Base F inventory continued to assume a diesel fuel sulfur content of 2,500 ppmW across the VISTAS region.

However, 339 ppmW is not the EPA recomme nded 2002 gasoline sulfur content for either eastern conventional gasoline areas or Fede ral Reformulated Gasoline (RFG) areas. The
recommended sulfur content for eastern conv entional gasoline is 279 ppmW year-round, while
the recommended sulfur content for RFG areas is 129 ppmW during the summer season and 279 ppmW during the winter season. (Conventional gasoline and RFG sulfur contents for 2002 can
be found in “ User’s Guide to MOBILE6.1 and MOBILE6.2, Mobile Source Emission Factor
Model ,” EPA420-R-03-010, U.S. EPA, August 2003 [pages 149-155] (available at link at http://www.epa.gov/otaq/m6.htm ) and in the source code for MOBILE6.2 at Block Data BD05.)
Given the differences in the EPA-recommended valu es and the value used to generate the initial 2002 base year inventory, the input files for Base F for all VISTAS areas were updated to reflect revised gasoline sulfurcontent assumptions.

Since the VISTAS NONROAD modeling is perfor med on a seasonal basis, and since gasoline
sulfur content in RFG areas varies with th e RFG season, seasonally-spe cific gasoline sulfur
content values were estimated for use in RFG area modeling. In addition, 25 counties in Georgia are subject to a summertime gasoline sulfur lim it of 150 ppmW, so that seasonal sulfur content
estimates were also estimated for these counties. The initial 2002 base year NONROAD inventory (prepared by Pechan) for these Georgia counties was based on a year-round 339 ppmW gasoline sulfur content, but that oversight was corrected in the Base F 2002 base year inventory. Based on the seasonal definitions empl oyed in the NONROAD m odel, monthly sulfur contents were averaged to estimate seasonal gasoline sulfur contents as follows:Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

MACTEC, Inc. 57 Month/Season RFG Areas Conventional Gasoline Areas Georgia Gasoline Control Areas
March 279 ppmW 279 ppmW 279 ppmW April 279 ppmW 279 ppmW 279 ppmW May 129 ppmW 279 ppmW 150 ppmW
Spring 229 ppmW 279 ppmW 236 ppmW
June 129 ppmW 279 ppmW 150 ppmW July 129 ppmW 279 ppmW 150 ppmW August 129 ppmW 279 ppmW 150 ppmW

Summer 129 ppmW 279 ppmW 150 ppmW
September 129 ppmW 279 ppmW 150 ppmW

October 279 ppmW 279 ppmW 279 ppmW November 279 ppmW 279 ppmW 279 ppmW

Fall 229 ppmW 279 ppmW 236 ppmW
December 279 ppmW 279 ppmW 279 ppmW

January 279 ppmW 279 ppmW 279 ppmW February 279 ppmW 279 ppmW 279 ppmW

Winter 279 ppmW 279 ppmW 279 ppmW



### Note that the seasonal data are based on simple arithmetic averages and do not consider any monthly variation in activity (and fuel sales), a nd that the transition betw een summer and winter seasons is also not considered. Additionally, th e summer fuel control s eason is treated as though it applies from May through September, while the summer RFG season actually ends on September 15 and the Georgia fuel control season does not officially begin until June 1. This
treatment is consistent with the treatment of both fuel control programs in the VISTAS on-road vehicle modeling. Each of these influences will result in some error in the estimated sulfur content estimates, but it is expect ed that this error is small rela tive to the overall correction from a year-round sulfur content estimate of 339 ppmW.

All NONROAD modeling revisions made as part of the Base F inve ntory preparation process are presented in Table 1.3-2. Due to more involve d updates in several areas, the number of NONROAD input files as well as sequence numbers used to represent these files was also updated in a few instances (as compared to the files used to create the initial 2002 VISTAS nonroad inventory, as documented in the February 9, 2004 report “ Development of the VISTAS Draft 2002 Mobile Source Emission Inventory (February 2004 Version) ” as prepared by E.H. Pechan & Associates, Inc. These structural revisions are presented in Table 1.3-3, and are providedDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### solely for the benefit of NONROAD modelers as the indicated revisions have no impact on generated emission estimates.

Table 1.3-2 Summary of Base F NONROAD Modeling Revisions

State Revisions Implemented

AL (1) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in all counties and all seasons (all are conventional gasoline areas).

FL (1) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in all counties and all seasons (all are conventional gasoline areas).

GA (1) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in all seasons for conventional gasoline counties. (2) Gasoline sulfur content changed from 339 ppmW to 150 ppmW in the summer for all gasoline control counties. (3) Gasoline sulfur content changed from 339 ppmW to 236 ppmW in the spring and fall for all gasoline control counties. (4) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in the winter for all gasoline control counties. Gasoline control counties: Barrow, Bartow, Butts, Carroll, Cherokee (a), Clayton (a), Cobb (a), Coweta (a), Dawson, De Kalb (a), Douglas (a), Fayette (a), Forsyth (a), Fulton (a), Gwinnett (a), Hall, Haralson, Henry (a), Jackson, Newton, Paulding (a), Pickens, Rockdale (a), Spalding, and Walton

KY (1) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in all seasons for conventional gasoline counties. (2) Gasoline sulfur content changed from 339 ppmW to 129 ppmW in the summer for all gasoline control counties. (3) Gasoline sulfur content changed from 339 ppmW to 229 ppmW in the spring and fall for all gasoline control counties. (4) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in the winter for all gasoline control counties. Gasoline control counties: Boone, Bullitt (b), Campbell, Jefferson, Kenton, and Oldham (b)

MS (1) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in all counties and all seasons (all are conventional gasoline areas).

NC (1) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in all counties and all seasons (all are conventional gasoline areas). (2) Utilize revised (i.e., local) allocati on files for three equipment categories. (3) Utilize revised (i.e., local) seasonal activity data.

SC (1) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in all counties and all seasons (all are conventional gasoline areas).

TN (1) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in all counties and all seasons (all are conventional gasoline areas). (2) Gasoline Reid Vapor Pressure (RVP) values changed in accordance with local recommendations. (3) Temperature data changed in accordance with local recommendations. (4) Counties regrouped in accordance with local recommendations.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.3-2. Summary of Base F NONRO AD Modeling Revisions (continued)

State Revisions Implemented

VA (1) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in all seasons for conventional gasoline counties. (2) Gasoline sulfur content changed from 339 ppmW to 129 ppmW in the summer for all gasoline control counties. (3) Gasoline sulfur content changed from 339 ppmW to 229 ppmW in the spring and fall for all gasoline control counties. (4) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in the winter for all gasoline control counties. (5) Gasoline RVP values changed in accordance with local recommendations. (6) Counties regrouped in accordance with local recommendations. (7) The control effectivene ss for counties subject to St age II controls revised to 77 percent in accordance with local recommendations. Gasoline control counties: Arlington Co., Fairfax Co., Loudoun Co., Prince William Co., Stafford Co., Alexandria City, Fairfax City, Falls Church City, Manassas City, Manassas Park City, Chesterfield Co., Hanover Co., Henrico Co., Colonial Heights City, Hopewell City, Richmond City, James City, York Co., Chesap eake City, Hampton City, Newport News City, Norfolk City, Poquoson City, Portsmouth City, Suffolk City, Virginia Beach City, and Williamsburg City (c)

WV (1) Gasoline sulfur content changed from 339 ppmW to 279 ppmW in all counties and all seasons (all are conventional gasoline areas). (2) Continue to utilize local allocation files for nine equipment categories.

Notes:

(a) County is subject to local control currently, but is scheduled to join the RFG program in January 2005. (b) Control area is a portion of the county, but modeling is performed as though the control applies countywide. (c) The EPA also lists Charles City County as an RFG area, but local planners indicate that Charles City County is a convention al gasoline area and it is modeled as such.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.3-3 Base F NONROAD Input File Sequence and Structural Revisions

State Initial 2002 Base Year Inventory Input File Sequence Numbers Revised 2002 Inventory Input File Sequence Numbers Reason(s) for Change Number of Revised 2002 Inventory NONROAD Input Files

AL 01-08 01-08 No Structural Changes 32 (at 8 per season)
FL 09-10 09-10 No Structural Changes 8 (at 2 per season)
GA 11-13 11-13 No Structural Changes 12 (at 3 per season)
KY 14-22 14-22 No Structural Changes 36 (at 9 per season)
MS 48 48 No Structural Changes 4 (at 1 per season)
NC 23-25 23-25 No Structural Changes 12 (at 3 per season)
SC 26-32 26-32 No Structural Changes 28 (at 7 per season)
TN 33-34 33-34, 49-52 Counties Regrouped 24 (at 6 per season)
VA 35-43 35-38, 40-43 Counties Regrouped 32 (at 8 per season)
WV 44-47 44-47 No Structural Changes 16 (at 4 per season)

All 01-48 01-38, 40-52 204 (at 51 per season)

Note: (1) All files include internal revisions to reflect the data ch anges summarized in Table 1.3-3 above. This table is intended t o present structural revisions that are of interest in assembling the NONROAD model input files into a complete VISTAS region inventory. The indicated revisions do not (in and of them selves) result in emission estimate changes.

(2) The NONROAD model imposes an eight digit input file name li mit, so all input files for the revised 2002 base year inventor y follow a modified naming convention to allow each to be distingui shed from the input files for the initial 2002 base year inven tory. For the initial 2002 base year inventory, the naming convention was:

ss02aaqq , where: ss = the two character State abbreviation, aa = a two character season indicator as follows: AU = autumn, WI = winter, SP = spring, and SU = summer, and qq = the two digit sequence number indicated above.

For the revised 2002 inventory, the naming convention was modified to:

ss02aFqq , where: ss = the two character State abbreviation, a = a one character season indicator as follows: A = autumn, W = winter, S = spring, and X = summer, and qq = the two digit sequence number indicated above.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Base G Revisions:
As described above, the primary modeling revi sion implemented for the Base G 2002 inventory
was the use of the Final NONROAD2005 model (in place of the Base F use of Draft NONROAD2004). However, there we re other minor revisions implemented for 13 Georgia counties and somewhat more significant revisions implemented for Tennessee. In Georgia, Stage II refueling control was assume d for 13 counties that previous ly were modeled as having no refueling control under Base F. In addition, to accommodate th is Stage II change as well as forecast year changes in gasoline vapor pressu re, corresponding changes in the structure and sequence of Georgia NONROAD input files were made. With the exceptio n of the minor Stage II impacts, these structural and sequence cha nges have no impact on 2002 emission estimates, but allow for consistency between 2002 and forecas t year input file structure and sequence. In Tennessee, more significant changes were implem ented to gasoline vapor pressure assumptions, as well as similar minor changes in St age II refueling control assumptions.

In accordance with instructions from Georgia regulators, Stage II refueling control was assumed in the following 13 Georgia counties at a cont rol efficiency value of 81 percent for the Base G inventory:

Cherokee, Clayton, Cobb, Coweta, DeKa lb, Douglas, Fayette, Forsyth, Fulton, Gwinnett, Henry, Paulding, and Rockdale.

No Stage II control was assumed in th ese counties in pr ior inventories.

Tennessee regulators provided revi sed monthly values for gasoline vapor pressure. Based on the seasonal definitions employed in the NONROAD model, monthly va por pressures were averaged to estimate seasonal vapor pressures as follows:Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

MACTEC, Inc. 62 Month/Season Nashville Area Memphis Area Remainder of Tennessee March 13.5 psi 13.5 psi 13.5 psi April 13.5 psi 13.5 psi 13.5 psi May 9.0 psi 9.0 psi 9.0 psi
Spring 12.0 psi 12.0 psi 12.0 psi
June 7.8 psi 7.8 psi 9.0 psi July 7.8 psi 7.8 psi 9.0 psi August 7.8 psi 7.8 psi 9.0 psi

Summer 7.8 psi 7.8 psi 9.0 psi
September 1-15 7.8 psi 7.8 psi 9.0 psi September 16-30 11.5 psi 11.5 psi 11.5 psi October 13.5 psi 13.5 psi 13.5 psi November 13.5 psi 13.5 psi 13.5 psi

Fall 12.2 psi 12.2 psi 12.4 psi
December 15.0 psi 15.0 psi 15.0 psi

January 15.0 psi 15.0 psi 15.0 psi February 13.5 psi 13.5 psi 13.5 psi

Winter 14.5 psi 14.5 psi 14.5 psi

Note: The Nashville area consists of Davidson, Rutherfo rd, Sumner, Williamson and Wilson counties, the Memphis area consists of Shelby County.


### As with the Base F revisions, the seasonal data are based on simple arithmetic averages and do not consider any monthly varia tion in activity (and fuel sales) , nor is the transition between
summer and winter seasons considered. Additiona lly, a monthly average of the September 1-15 and September 16-30 data is cal culated prior to averaging th e September-November data to estimate a fall average vapor pressure, so that the month of September is we ighted identically to
the months of October and November.

Tennessee regulators also indicate d that Stage II vapor recovery was not in effect in Shelby County, so the Base F NONROAD input files for th e county (which assu med Stage II was in place) were revised accordingly.

All Base G NONROAD modeling re visions are presented in Tabl e 1.3-4. As indicated above, the differentiation of inputs across previously grouped counties also required revision to the overall number and sequence of VISTAS N ONROAD input files (as compared to the files used to create both the initial VISTAS non-road inventory, as documented in the February 9, 2004 report

“ Development of the VISTAS Draft 2002 Mobi le Source Emission Inventory (February 2004 Version) ” as prepared by E.H. Pechan & Associates , Inc., and the Base F revised inventory asDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### documented above. These structural revisions ar e presented in Table 1.3-5, and are provided solely for the benefit of NONROAD modelers as the indicated revisions have no impact on generated emission estimates.

Table 1.3-4 Summary of Base G NONROAD Modeling Revisions

State Revisions Implemented

AL (1) Marine diesel sulfur content changed from 2500 ppmW to 2638 ppmW in all counties and seasons.
FL (1) Marine diesel sulfur content changed from 2500 ppmW to 2638 ppmW in all counties and seasons.

GA (1) Marine diesel sulfur content changed from 2500 ppmW to 2638 ppmW in all counties and seasons. (2) Stage II refueling vapor recovery implemented in 13 counties at an efficiency of 81 percent. (3) Counties regrouped to accommodate base and forecast year data differentiations. Stage II control counties: Cherokee, Clayton, Cobb, Coweta, De Kalb, Douglas, Fayette, Forsyth, Fulton, Gwinnett, Henry, Paulding, and Rockdale
KY (1) Marine diesel sulfur content changed from 2500 ppmW to 2638 ppmW in all counties and seasons.
MS (1) Marine diesel sulfur content changed from 2500 ppmW to 2638 ppmW in all counties and seasons.

NC (1) Marine diesel sulfur content changed from 2500 ppmW to 2638 ppmW in all counties and seasons. (2) Revert to default equipment alloca tion files for all equipment categories. (3) Utilize revised (i.e., local) seasonal activity data.
SC (1) Marine diesel sulfur content changed from 2500 ppmW to 2638 ppmW in all counties and seasons.

TN (1) Marine diesel sulfur content changed from 2500 ppmW to 2638 ppmW in all counties and seasons. (2) Gasoline RVP values changed in accordance with local recommendations. (3) Stage II vapor recovery eliminated from Shelby County modeling.
VA (1) Marine diesel sulfur content changed from 2500 ppmW to 2638 ppmW in all counties and seasons.

WV (1) Marine diesel sulfur content changed from 2500 ppmW to 2638 ppmW in all counties and seasons. (2) Revert to default equipment alloca tion files for all equipment categories.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.3-5 Spring 2006 NONROAD Input File Sequence and Structural Revisions

State 2002 Inventory Input File Sequence Numbers (Fall 2004) 2002 Inventory Input File Sequence Numbers (Spring 2006) Reason(s) for Change Number of Final 2002 Inventory NONROAD Input Files

AL 01-08 01-08 No Structural Changes 32 (at 8 per season)
FL 09-10 09-10 No Structural Changes 8 (at 2 per season)
GA 11-13 11-13, 53-54 Counties Regrouped 20 (at 5 per season)
KY 14-22 14-22 No Structural Changes 36 (at 9 per season)
MS 48 48 No Structural Changes 4 (at 1 per season)
NC 23-25 23-25 No Structural Changes 12 (at 3 per season)
SC 26-32 26-32 No Structural Changes 28 (at 7 per season)
TN 33-34, 49-52 33-34, 49-52 No Structural Changes 24 (at 6 per season)
VA 35-38, 40-43 35-38, 40-43 No Structural Changes 32 (at 8 per season)
WV 44-47 44-47 No Structural Changes 16 (at 4 per season)

All 01-38, 40-52 01-38, 40-54 212 (at 53 per season)

Note: (1) All files include internal revisions to reflect the data ch anges summarized in Table 1.3-5 above. This table is intended t o present structural revisions that are of interest in assembling the NONROAD model input files into a complete VISTAS region inventory. The indicated revisions do not (in and of them selves) result in emission estimate changes.

(2) The NONROAD model imposes an eight digit input file name li mit, so all input files for the revised 2002 base year inventor y follow a modified naming convention to allow each to be di stinguished from the input files for the initial 2002 and fall 2004-revised 2002 base year inventory. For the initial 2002 base year inventory, the naming convention was:

ss02aaqq , where: ss = the two character State abbreviation, aa = a two character season indicator as follows: AU = autumn, WI = winter, SP = spring, and SU = summer, and qq = the two digit sequence number indicated above.

For the fall 2004-revised 2002 inventory, th e naming convention was modified to:

ss02aFqq , where: ss = the two character State abbreviation, a = a one character season indicator as follows: A = autumn, W = winter, S = spring, and X = summer, and qq = the two digit sequence number indicated above.

For the spring 2006-revised 2002 inventory, the naming convention was modified to:

ss02aCqq , where: ss = the two character State abbreviation, a = a one character season indicator as follows: A = autumn, W = winter, S = spring, and X = summer, and qq = the two digit sequence number indicated above.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 1.3.2.2 Emissions from Commercial Marine Ve ssels, Locomotives, and Airplanes
An initial 2002 base year emissions inventory for aircraft, locomotives, and commercial marine vessels (CMV) was prepared for VISTAS in ear ly 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) ” as prepared by E.H. Pechan & Associates, Inc. A summary of the initial 2002 ba se year emissions inventory is presented in Table 1.3-6. Except as otherwise stated below, all aspects of the preparation methodology continue to apply to the Base F and Base G emission inventories.

Revisions to the initial 2002 emissions inventory (prepared by Pechan) were implemented to
ensure that the latest State and local data were incorporated as well as to correct an
overestimation of PM emissions from aircraft . Revisions were actually implemented in two stages. An initial set of revisions was implemente d in the fall of 2004. Those revisions constitute the Base F inventory. These were followed by a s econd set of revisions in 2006, which constitute the Base G inventory. To accurately document the combined effects of both sets of revisions, each set is discussed separately below. Unless ot herwise indicated, all re visions implemented for Base F were carried directly into the Base G revision pro cess without change. Thus, the
inventories that resulted from the Base F revisions served as the starting point for the Base G
revisions.

Base F Revisions:

Revisions to the initial 2002 base year emissions inventory we re implemented to ensure that the latest State and local data were incorporated as well as to correct an overestimation of PM
emissions from aircraft. Seven of the ten VISTAS States provided revised inventory data in the form of emissions reported to the EPA under the CERR. States provi ding CERR data were Alabama, Georgia, Mississippi, North Ca rolina, Tennessee (excluding Davidson, Hamilton, Knox, and Shelby Counties), Virginia, and West Virginia.

In many cases, the CERR data were only margina lly different than the initial 2002 base year inventory data, but there were several instances where signific ant updates were evident. The remaining three VISTAS States (Florida, Ke ntucky, and South Carolina), plus Davidson, Hamilton, Knox, and Shelby counties in Tennessee, indicated that the initial 2002 VISTAS inventory continued to reflect the most recent data available. Flor ida did provide updated aircraft emissions data for one county (Miami-Dade) and th ese data were incorpor ated into the Base F 2002 inventory as described below.

Since several States recommended re taining the initial 2002 base year inventory data for Base F, the initial step toward revising the 2002 inventory cons isted of modifying the estimated aircraft
PM emissions of the initial inve ntory. The overestimation of aircraft PM became evident shortlyDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### after the release of th e initial 2002 base year inventory, wh en it was determined that VISTAS region airports would constitute the top seven, and 11 of the top 15, PM sources in the nation. Moreover, PM emissions for one airport (Miami International) were a full order of magnitude larger than all other modeled elemental carbon PM em ission sources. In addition, unexpected relationships across airports we re also observed, with emissi ons for Atlanta’s Hartsfield International being substantially less than thos e of Miami International, even though Atlanta handles over twice as many aircraft operations annually. Given the pervasiveness of this problem, and since the CERR data submitted by St ates was based on the initial 2002 VISTAS inventory data, aircraft PM emissions for th e entire VISTAS regi on were recalculated.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.3-6 Initial 2002 Base Year Aircraft, Locomotive, and Non-Recreational Marine Emissions as Reported in February 2004 Pechan Report (annual tons)

Source State CO NO x PM 10 PM 2.5 SO 2 VOC AL 3,787 175 688 475 17 196 FL 28,518 11,955 46,352 31,983 1,050 3,703 GA 3,175 992 3,919 2,704 94 353 KY 2,666 657 2,597 1,792 63 263 MS 1,593 140 553 381 13 96 NC 6,088 1,548 6,115 4,219 148 613 SC 6,505 515 452 312 88 863 TN 6,854 2,665 7,986 5,510 225 920 VA 17,676 5,607 14,476 9,988 234 3,229 WV 1,178 78 310 214 8 66 Aircraft (2275)

Total 78,040 24,332 83,448 57,578 1,940 10,302 AL 1,195 9,217 917 843 3,337 736 FL 5,888 44,817 1,936 1,781 6,683 1,409 GA 1,038 7,874 334 307 1,173 246 KY 6,607 50,267 2,246 2,066 9,608 1,569 MS 5,687 43,233 1,903 1,750 7,719 1,351 NC 599 4,547 193 178 690 142 SC 1,067 8,100 343 316 1,205 253 TN 4,129 31,397 1,390 1,278 5,753 980 VA 1,198 3,426 929 855 3,258 596 WV 2,094 15,882 668 614 720 497 Commercial Marine (2280)

Total 29,503 218,760 10,858 9,989 40,146 7,779
VA 136 387 28 26 30 59 Military Marine (2283) Total 136 387 28 26 30 59 AL 3,490 26,339 592 533 1,446 1,354 FL 1,006 9,969 247 222 605 404 GA 2,654 26,733 664 598 1,622 1,059 KY 2,166 21,811 542 488 1,321 867 MS 2,302 23,267 578 520 1,429 899 NC 1,638 16,502 410 369 1,001 654 SC 1,160 11,690 291 261 710 462 TN 4,530 44,793 1,110 999 2,689 1,805 VA 1,928 19,334 1,407 1,266 3,443 798 WV 1,105 11,150 277 249 681 436 Locomotives (2285)

Total 21,980 211,588 6,118 5,505 14,947 8,738
Grand Total 129,659 455,067 100,452 73,099 57,062 26,877Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Aircraft do emit PM while operating. However, offi cial EPA inventory proc edures for aircraft generally do not include PM emi ssion factors and, therefore, aircra ft PM is generally erroneously reported as zero. In an effort to overcome this deficiency, the developers of the initial VISTAS 2002 base year aircraft inventory (Pechan) estim ated PM emission rates for aircraft using
estimated NO x 

### emissions and an unreported PM-to-NO x 

### ratio (i.e., PM = NO x 

### times a
PM-to-NO x 

### ratio). According to the initial 2002 ba se year inventory documentation, this approach was applied only to commercial aircraft NO x 

### , but a review of that inventory indicates that the technique was also applied to military, ge neral aviation, and air taxi aircraft in many, but not all, instances. Although there is nothing inhere ntly incorrect with this approach, the accuracy and inconsistent applicati on of the assumed PM-to-NO x 

### ratio results in grossly overestimated
aircraft PM.

Through examination of the initial 2002 base year aircraft inve ntory (prepared by E.H. Pechan and Associates, Inc.), it is apparent that the commercial aircraft PM-to-NO x 

### ratio used to generate PM emission estimates was appr oximately equal to 3.95 (i.e., PM = NO x 

### times 3.95). While the majority of observed commercial aircraft PM-to-NO x 

### ratios in that inventory are equal to 3.95, a few range as low as 3.00. If all aircraft estimates are included (i.e., commercial plus military, general aviation, and air taxi), observed PM-to-NO x 

### ratios range from 0 to 123.0, and average 3.43 as illust rated in Table 1.3-7

Table 1.3-7 PM-to-NO x 

### Ratios by Aircraft Type In Initial 2002 Base Year Inventory.

Aircraft Type Average
PM-to-NO x Range of
PM-to-NO x Average
PM 2.5 / PM 10 Range of
PM 2.5 / PM 10

Undefined (1) 0.046 0-0.062 0.690 0.690-0.690
Military 0.073 0-92.3 0.688 0.333-1.000

Commercial 3.953 3.00-3.953 0.690 0.667-0.696

General Aviation 2.059 0-9.00 0.689 0.500-1.000

Air Taxi 2.734 0-123.0 0.690 0.500-1.000
Aggregate 3.427 0-123.0 0.690 0.333-1.000

Note: (1) Two counties report aircraft emi ssions as SCC 2275000000 “all aircraft.”



### As indicated, the aggregate PM-to-NO x 

### ratio is similar in magnitude to the ratio for commercial
aircraft. This results from the domin ant nature of commercial aircraft NO x 

### emissions relative to
NO x 

### from other aircraft types. It is surmised that ratios that deviate from 3.95 are based on PM emission estimates generated by local planners, wh ich were retained without change in the PM estimation process (although a considerable numbe r of unexplained “zero PM ” records also existDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### in the initial 2002 base year inve ntory dataset). Regardless, based on previous statistical analyses performed in support of aircraft emissions inve ntory development outside the VISTAS region, a
PM-to-NO x 

### ratio of 3.95 is too large by over an order of magnitude.

In analyses performed for the Tucs on, Arizona planning area, PM-to-NO x 

### ratios for aircraft over a standard aircraft landing and takeoff (LTO) cycl e are shown in Table 1.3-8. Data for this table
is taken from “Emissions Inventories for th e Tucson Air Planning Ar ea, Volume I., Study Description and Results,” prepared for the Pi ma Association of Governments, Tucson, AZ, November 2001. Pages 4-40 through 4-42 of that report, which document the statistical derivation of these ratios, are include d in this report as Appendix E.

Table 1.3-8 Tucson, AZ PM-to-NO x 

### Ratios by Aircraft Type.

Aircraft Type PM-to-NO x Commercial Aircraft 0.26

Military Aircraft 0.88

Air Taxi Aircraft 0.50

General Aviation Aircraft 1.90

Note: The PM and NO x emission estimates presented in the Tucson study are for lo cal aircraft operating mode ti mes. For this work, emission estimates for Tucson were recalculated for a standard LTO cycle, so that the ratios presented are applicable to the standard LT O cycle and not a Tucson-specific cycle. Thus, the ratios presented herein vary somewhat from thos e associated with the emission estimates presented in the Tucson study report.



### In reviewing these data, it should be considered that they apply to a standard (i.e., EPA-defined) commercial aircraft LTO cycle. 2 

### Aircraft PM-to-NO x 

### ratios vary with operating mode, so that aircraft at airports with mode times that differ from the standard cycle will exhibit varying ratios. However, conducting an airport-sp ecific analysis for all airports in the VISTAS region was beyond the scope of this work. While local PM-to-NO x 

### ratios could vary somewhat from the indicated standard cycle ratios, any error due to this variation will be significantly less than the order of magnitude error associat ed with the 3.95 commercial aircra ft ratio used for the initial 2002 base year inventory.

It should be recognized that wh ile the Tucson area is far removed from the VISTAS region, the data analyzed to generate the PM-to-NO x 

### ratios is standard aircraft emission factor data routinely employed for inventory purposes th roughout the United States (as encoded in models such as the

2 As defined in AP-42, Compilation of Air Pollutant Emission Factors, Volume II, Mobile Sources, a standard

commercial aircraft LTO cycle consists of 4 minutes of approa ch time, 26 minutes of taxi (7 minutes in plus 19 minutes out), 0.7 minutes of takeoff, and 2.2 minutes of climbout time (approach and climbout times being based on a 3000 foot mixing height).Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Federal Aviation Administration’s Emissions Da ta Management Systems [EDMS]). With the exception of aircraft operating conditions, ther e are no inherent geographic implications
associated with the use of data from the Tucson study. As indicated above, issues associated with local operating conditions have been eliminated by recalculating the Tucson study ratios for a standard LTO cycle.

To implement the revised PM-to-NO x 

### ratios in the Base F inventory, all aircraft PM records were
removed from the initial 2002 base year inventory (prepared by Pe chan). This includes records for which local planners may have estimated PM emissions. This approach was taken for two reasons. First, there is no way to distinguish which records may have been generated by local planners. Second, the data available to local planne rs may be no better than that used to generate the presented PM-to-NO x 

### ratio data, so the consistent app lication of these data to the entire VISTAS region was determined to be the most appropriate approach to generating consistent inventories throughout the region. In undertaking this removal, it became apparent that there was
an imbalance in the aircraft NO x 

### and PM records in the initial 2002 base year inventory. Whereas there were 1,531 NO x 

### records in the NIF emission data sets for this source category, there were only 1,212 PM records. The imbalance was distri buted between three States, South Carolina, Tennessee, and Virginia as follows:

Table 1.3-9 Non-Corresponding Aircraft Emissions Records

Aircraft NO x records with no corresponding PM record:
Aircraft Type South Carolina Virginia Total Military Aircraft 8 100 108

General Aviation Aircraft 14 94 108

Air Taxi Aircraft 5 99 104
Aggregate 27 293 320
Aircraft PM records with no corresponding NO x record:
Aircraft Type Tennessee Total

Air Taxi Aircraft 1 1
Aggregate 1 1



### The unmatched PM record was for Hamilton County (Chattanooga), Tennessee and when removed, was not replaced si nce there was no corresponding NO x 

### record with which to estimate revised PM emissions. It is unclear how this orph aned record originated, but clearly there can be
no air taxi PM emissions without other combusti on-related emissions. Thus, the removal of the
PM 10 

### and PM 2.5 

### records for Hamilton County permanently reduced the overall size of the 2002 initial base year inventory database used as a starting point for Base F by two records.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Of the 320 unmatched NO x 

### records, 269 were records for whic h the reported emission rate was zero. Therefore, even though associated PM reco rds were missing, the overall inventory was not affected. However, the 51 missing records for which NO x 

### emissions were non-zero, did impact PM estimates for the overall inventory.

Replacement PM 10 

### records were calculated for all aircraft NO x 

### records using the PM-to-NO x 

### ratios presented above. Aircraft type-specific ratios were utilized in all cases, except for two counties where aircraft emissions were report ed under the generic aircraft SCC 2275000000. For these counties (Palm Beach County, Florida a nd Davidson County, Tenne ssee), the commercial
aircraft PM-to-NO x 

### ratio was applied since both contain commercial airports (Palm Beach
International and Nashvi lle International).

Replacement aircraft PM 2.5 

### records were also developed. Th e initial 2002 base year inventory
assumed that aircraft PM 2.5 

### was 69 percent of aircraft PM 10 

### . The origin of this fraction is not clear, but it is very low for combustion related PM. The majority of internal combustion engine related PM is typically 1 micron or smaller (PM 1.0 

### ), so that typical inte rnal combustion engine
PM 2.5 

### fractions approach 100 percent. For ex ample, the EPA NONROAD model assumes 92 percent for gasoline engine pa rticulate and 97 percen t for diesel engine particulate. Based on recent correspondence from the EPA, it appears th at the agency is preparing to recommend a
PM 2.5 

### fraction of 98 percent for aircraft. (Augus t 12, 2004 e-mail correspondence from U.S. EPA to Gregory Stella of Alpine Geophys ics.) This is substant ially more consistent with expectations
based on emissions test data for other internal combustion engine sources and was used as the
basis for the recalculated aircraft PM 2.5 

### emission estimates in the Base F inventory.

Although a substantial portion of the initial 2002 base year inventory was ultimately replaced with data prepared by State and local planners under CERR requirements in developing the Base F inventory, it was necessary to first revise the initial 2002 base year aircraft inventory as
described so that records extr acted from the inventory for area s not supplying CERR data for the Base F update would be accurate. Therefore, in no case is the aggregated St ate data reported for the Base F inventory identical to that of the in itial 2002 base year inventory. Even areas relying on the initial 2002 base year inventory will reflect updates in Base F due to changes in emissions
of PM 10 

### and PM 2.5 

### from aircraft.

Table 1.3-10 presents the updated initial 2002 base year inventory estimates. These estimates do not reflect any changes related to modifications made to incorporate the CERR data, but instead indicate the impacts associated solely with the recalculation of aircraft PM emissions alone to apply the more appropriate PM to NO x 

### ratios. Table 1.3-11 presents a summary of the net impacts of these changes, where an over 90 percen t reduction in aircraft PM is observed for all VISTAS areas except South Carolina and Virginia. The reasons for the lesser changes in these
two States is that the overall aircraft NO x 

### inventories for both include a large share of militaryDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### aircraft NO x 

### to which no (or very low) particulate estimates were assigned in the initial 2002 base year inventory. Since these operations are assigned non-zero PM emissions under the revised approach, the increase in military airc raft PM offsets a portion of the reduction in commercial aircraft PM. In Virgin ia, zero (or near zero) PM milita ry operations were responsible for about 35 percent of total aircraft NO x 

### , while the corresponding frac tion in South Carolina was almost 70 percent. As indicated, aggregate aircraft, locomotive, and commercial marine vessel PM is 70-75 percent lower in th e updated 2002 base year inventory.

Table 1.3-10 Initial 2002 Base Year Aircraft, Locomotive, and Non-Recreational Marine
Emissions with Modified Aircraft PM Emission Rates (annual tons)

Source State CO NO x PM 10 PM 2.5 SO 2 VOC AL 3,787 175 64 62 17 196 FL 28,518 11,955 3,193 3,129 1,050 3,703 GA 3,175 992 269 264 94 353 KY 2,666 657 179 175 63 263 MS 1,593 140 44 43 13 96 NC 6,088 1,548 419 411 148 613 SC 6,505 515 409 401 88 863 TN 6,854 2,665 707 692 225 920 VA 17,676 5,607 2,722 2,667 234 3,229 WV 1,178 78 25 24 8 66 Aircraft (2275)

Total 78,040 24,332 8,030 7,870 1,940 10,302 AL 1,195 9,217 917 843 3,337 736 FL 5,888 44,817 1,936 1,781 6,683 1,409 GA 1,038 7,874 334 307 1,173 246 KY 6,607 50,267 2,246 2,066 9,608 1,569 MS 5,687 43,233 1,903 1,750 7,719 1,351 NC 599 4,547 193 178 690 142 SC 1,067 8,100 343 316 1,205 253 TN 4,129 31,397 1,390 1,278 5,753 980 VA 1,198 3,426 929 855 3,258 596 WV 2,094 15,882 668 614 720 497 Commercial Marine (2280)

Total 29,503 218,760 10,858 9,989 40,146 7,779 VA 136 387 28 26 30 59 Military Marine (2283) Total 136 387 28 26 30 59 AL 3,490 26,339 592 533 1,446 1,354 FL 1,006 9,969 247 222 605 404 GA 2,654 26,733 664 598 1,622 1,059 KY 2,166 21,811 542 488 1,321 867 MS 2,302 23,267 578 520 1,429 899 NC 1,638 16,502 410 369 1,001 654 SC 1,160 11,690 291 261 710 462 TN 4,530 44,793 1,110 999 2,689 1,805 VA 1,928 19,334 1,407 1,266 3,443 798 WV 1,105 11,150 277 249 681 436 Locomotives (2285)

Total 21,980 211,588 6,118 5,505 14,947 8,738 Grand Total 129,659 455,067 25,034 23,390 57,062 26,877Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.3-11 Change in Initial 2002 Base Year Emissions due to Aircraft PM Emission Rate
Modifications.

Source State CO NO x PM 10 PM 2.5 SO 2 VOC AL 0% 0% -91% -87% 0% 0% FL 0% 0% -93% -90% 0% 0% GA 0% 0% -93% -90% 0% 0% KY 0% 0% -93% -90% 0% 0% MS 0% 0% -92% -89% 0% 0% NC 0% 0% -93% -90% 0% 0% SC 0% 0% -9% +29% 0% 0% TN 0% 0% -91% -87% 0% 0% VA 0% 0% -81% -73% 0% 0% WV 0% 0% -92% -89% 0% 0% Aircraft (2275)

Total 0% 0% -90% -86% 0% 0% AL 0% 0% 0% 0% 0% 0% FL 0% 0% 0% 0% 0% 0% GA 0% 0% 0% 0% 0% 0% KY 0% 0% 0% 0% 0% 0% MS 0% 0% 0% 0% 0% 0% NC 0% 0% 0% 0% 0% 0% SC 0% 0% 0% 0% 0% 0% TN 0% 0% 0% 0% 0% 0% VA 0% 0% 0% 0% 0% 0% WV 0% 0% 0% 0% 0% 0% Commercial Marine (2280)

Total 0% 0% 0% 0% 0% 0% VA 0% 0% 0% 0% 0% 0% Military Marine (2283) Total 0% 0% 0% 0% 0% 0% AL 0% 0% 0% 0% 0% 0% FL 0% 0% 0% 0% 0% 0% GA 0% 0% 0% 0% 0% 0% KY 0% 0% 0% 0% 0% 0% MS 0% 0% 0% 0% 0% 0% NC 0% 0% 0% 0% 0% 0% SC 0% 0% 0% 0% 0% 0% TN 0% 0% 0% 0% 0% 0% VA 0% 0% 0% 0% 0% 0% WV 0% 0% 0% 0% 0% 0% Locomotives (2285)

Total 0% 0% 0% 0% 0% 0% Grand Total 0% 0% -75% -68% 0% 0%



### As indicated above, for the Base F 2002 base year inventory, data for al l or portions of seven VISTAS States were replaced with corresponding data from recent (as of the fall of 2004) CERR submissions for 2002. Before replacing these data, however, an analysis of the CERR data was performed to ensure consistency with VISTAS inventory methods. It should perhaps also be noted that three of the CERR datasets provi ded for the Base F 2002 base year inventory (specifically those for Tennessee, Virginia, a nd West Virginia) included both annual and daily emissions data. Only the annual data were used. Daily values were removed.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Several important observations resu lted from this analysis. First, it was clear that all of the CERR data continued to rely on the inaccurate ai rcraft PM estimation approach employed for the initial 2002 base year inventory. Therefore, an id entical aircraft PM replacement procedure as described above for updating the initial 2002 ba se year inventory was undertaken for CERR supplied data. As a result, the CERR data for all VISTAS States has been modified for inclusion in the Base F 2002 VISTAS base year inve ntory due to PM replacement procedures.

As was the case with the initial VISTAS 2002 ba se year inventory, ther e were a substantial
number of aircraft NO x 

### records without corresponding PM records, so that the number of
recalculated PM records added to the CERR dataset is greater than the number of PM records removed. The aggregated CERR inve ntory data, reflecting data for all or parts of seven States, consisted of 13,656 records, of which 1,211 were aircraft NO x 

### records. However, the number of corresponding aircraft PM records was 662 (662 PM 10 

### records and 662 PM 2.5 

### records). This
imbalance was distributed as follows:

Table 1.3-12 CERR Aircraft NO x 

### Records with No Corresponding PM Record.

Aircraft Type Georgia Tennessee Virginia Total Military Aircraft 136 136

Commercial Aircraft 4 136 140

General Aviation Aircraft 1 136 137

Air Taxi Aircraft 136 136
Aggregate 1 4 544 549



### From this tabulation, it is clear th at virtually the entire imbalance is associated with the Virginia CERR submission, with minor imbalances in Georgia and Tennessee. Of the 549 unmatched
NO x 

### records, 461 were records for which the repo rted emission rate was zero. Therefore, even though the associated PM records were missi ng, the overall inventory was not affected. However, the 88 missing records for which NO x 

### emissions were non-zero do impact PM emission estimates for the overall inventory.

Replacement aircraft PM records (both PM 10 

### and PM 2.5 

### ) were generated for the CERR dataset using procedures identical to those descri bed above for the upda ted initial 2002 base year inventory.

Further analysis revealed that the CERR da ta for Virginia incl uded only VOC, CO, and NO x 

### emissions for all aircraft, locomotives, a nd non-recreational marine vessels. Since SO 2 

### , PM 10 

### , and PM 2.5 

### records are included in the 2002 VISTAS inventory, an estimation method was developed for these emission species and appl ied to the Virginia CERR data. For PM, theDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### developed methodology was only employed for locomo tive and marine vessel data since aircraft PM was estimated using the PM-to-NO x 

### ratio methodology described above.

Consideration was given to si mply adding the Virginia SO 2 

### and non-aircraft PM records from the initial 2002 VISTAS inventory datase t, but it is very unlikely that either the source distribution
or associated emission rates are identical across the CERR and initial VISTAS inventories. This was confirmed through a comparative analysis of dataset CO records. Th erefore, an estimation methodology was developed using Virginia source-specific SO 2 

### /CO, PM 10 

### /CO, and PM 2.5 

### /PM 10 

### ratios from the initial 2002 base year VISTAS inventory. The calculated ra tios were then applied to the source-specific CERR CO emission estima tes to derive associated source-specific SO 2 

### , PM 10 

### , and PM 2.5 

### emissions for the Base F inventory.

Initially, the development of the emissions ratio s from the initial 2002 base year inventory was performed at the State (i.e., Vi rginia), county, and SCC level of detail. However, it readily
became clear that there were s ubstantial inconsistencies in ra tios for identical SCCs across counties. For example, in one county, the SO 2 

### /CO ratio might be 0.2, while in the next county it
would be 2.0. Since the sources in question are virtually identical (e.g., diesel locomotives) and since the fueling infrastructure for these large non-road equipmen t sources is regional as opposed to local in nature, such variations in emission rates are not realistic. Therefore, a more aggregated approach was employed in which SCC-specific emi ssion ratios were developed for the State as a whole. Through this approach county-to-county vari ation in emission ratios is eliminated, but the underlying variation in CO emissi ons does continue to influence the resulting aggregate emission estimates. The applied emission ratios are as follows:

Table 1.3-13 Calculated Em ission Ratios for VA.

Source SCC SO 2 /CO PM 10 /CO PM 2.5 /CO PM 2.5 /PM 10 Military Aircraft 2275001000 0.0215

Commercial Aircraft 2275020000 0.3292

General Aviation Aircraft 2275050000 0.0002

Air Taxi Aircraft 2275060000 0.0015 Emissions estimated using PM-to-NO x ratios as described previously.

Aircraft Refueling 2275900000 0.0000 0.0000 0.0000

Diesel Commercial Marine 2280002000 0.3697 0.3434 0.3157 0.92

Residual Commercial Marine 2280003000 0.3697 0.3434 0.3157 0.92 Diesel Military Marine 2283002000 0.2422 0.2248 0.2068 0.92

Line Haul Locomotives 2285002005 3.2757 1.2999 1.1696 0.90

Yard Locomotives 2285002010 2.2908 1.2461 1.1205 0.90Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### It is important to recognize that the inconsistenc y of emissions ratios ac ross Virginia counties for sources of virtually identical design, which utilize a regiona l rather than local fueling infrastructure, has potential im plications for other VISTAS States. There is no immediately
obvious reason to believe that such inconsis tencies would be isolated to Virginia.

One final revision to the CERR dataset was undertaken as part of the Base F effort, and that was the removal of two records for unpaved airs trip particulate (SCC 2275085000) in Alabama. Otherwise identical records for these emissions were reported both in terms of filterable and primary particulate. The filterable particulate records were removed as all other particulate
emissions in the VISTAS inventorie s are in terms of prim ary particulate. It is also perhaps worth noting that a series of aircraft refueling r ecords (SCC 2275900000) for Virginia were left in place, even through typically such emissions would be reported under SCC 2501080XXX in the area source inventory. If additiona l VISTAS aircraft refueling emissions are reported under SCC 2501080XXX, then it may be desirabl e to recode these records.

Finally, data for areas of the VISTAS region not represented in the CERR dataset were added to the CERR data by extracting the a ppropriate records from the initial 2002 base year inventory (with revisions for aircraft PM to NO x 

### ratios). Specifically, records applicable to the States of Florida, Kentucky, South Carolina, and the Te nnessee counties of Davidson, Hamilton, Knox, and Shelby were extracted from the revised initial 2002 inventory and added to the CERR dataset to establish the 2002 Base F inventory.

Following this aggregation, one last dataset revision was implemented to complete the development of the 2002 Base F inventory. As indi cated in the introduction of this section, the initial 2002 base year emission estimates for Miami International Airport were determined to be excessive. Although the reason for this inaccuracy was not apparent, revised estimates for
aircraft emissions in Miami-Da de County were obtained from Florida planners and used to overwrite the erroneous estimates. (Aircraft emi ssion estimates were provided in an August 10,
2004 e-mail transmittal from Bruce Coward of Miam i-Dade County to Martin Costello of the Florida Department of Environmental Protection.)

Table 1.3-14 presents a summary of the resul ting Base F VISTAS 2002 base year inventory estimates for aircraft, locomotives, and non-recr eational marine vessels. Table 1.3-15 provides a comparison of the Base F 2002 base year inventor y estimates to those of the initial 2002 base year inventory. As indicated, total emissions for VOC, CO, NO x 

### , and SO 2 

### are generally within 10 percent, but final PM emissions are reduced by 70-80 percent due to the approximate 90 percent reductions in aircraft PM estimates. In addition, the significant changes in Georgia
aircraft emissions are due to the CERR correcti on of Atlanta Hartsfield International Airport emissions, which were significantly underestimated in the initial 2002 base year inventory. TheDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### reduction in Florida aircraft emi ssions due to the correction of Miami International estimates is also apparent.

Lastly, Table 1.3-16 provides a direct comparison of emission estimates from the initial and Base F 2002 base year inventories for all 16 VISTAS region airports with es timated annual aircraft
NO x 

### emissions of 200 tons or greater (as identifie d at the conclusion of the Base F revisions). 3



### The table entries are sorted in order of decreasing NO x 

### and once again, the dramatic reduction in PM emissions is evident. However, in additi on, the appropriate reversal of the relationship
between Atlanta’s Hartsfield and Miami Intern ational Airport is also depicted. As a rough method of quality assurance, Table 1.3-15 also includes a gross estimate of expected airport NO x 

### emissions using detailed NO x 

### estimates developed for Tucs on International Airport in conjunction with the ratio of local to Tucson LTOs. (The Tucson NO x 

### estimates are revised to reflect a standard LTO cycle rather than the Tu cson-specific LTO cycle. This should provide for a more realistic comparison to VISTAS estimates.) This is not meant to serve as anything other than a crude indicator of the propriety of the deve loped VISTAS estimates, and it is clear that the range of estimated-to-expected NO x 

### emissions has been substantia lly narrowed in the Base F 2002 base year inventory. Whereas estimated-to- expected ratios varied from about 0.2 to over 3.5 in the initial 2002 base year i nventory, the range of variation is tightened on both ends, from about 0.5 to 1.75 for the Base F 2002 base year inve ntory. In effect, all es timates are now within a factor of two of the expected estimates, which is quite reasonable given likely variation in local and standard LTO cycles a nd variations in aircraft fleet mix across airports.

It is perhaps important to note that some shif ting in county emissions assignments is evident between the initial and Base F 2002 base year airc raft inventories. For example, for the initial 2002 base year inventory, Atlanta Hartsfield es timates were assigned to Fulton County (FIP 13121), while they are assigned to Clayton Count y (FIP 13063) for the Base F 2002 base year inventory. Similarly, Dulles Intern ational Airport emissions were assigned solely to Fairfax County, Virginia (FIP 51059) in the initial 2002 base year inve ntory, but are split between Fairfax and Loudoun County (FIP 51107) for Base F. Such shifts reflect local planner decision-making and are not an artifac t of the revisions described above.

3 Subsequent revisions performed for Base G result in the addition of the Cincinnati/Northern Kentucky

International Airport to the group of airports with ai rcraft operations generating at least 200 tons of NO x . These revisions are discussed below, including the addition of an appropriately modified version of the aircraft emissions table.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.3-14 Base F 2002 Base Year Aircraft, Locomotive, and Non-Recreational Marine Emissions (tons/year)

Source State CO NO x PM 10 PM 2.5 SO 2 VOC AL 3,787 175 226 87 17 196 FL 25,431 8,891 2,424 2,375 800 3,658 GA 6,622 5,372 1,475 1,446 451 443 KY 2,666 657 179 175 63 263 MS 1,593 140 44 43 13 96 NC 6,088 1,548 419 411 148 613 SC 6,505 515 409 401 88 863 TN 7,251 2,766 734 719 235 943 VA 9,763 2,756 1,137 1,115 786 2,529 WV 1,178 78 25 24 8 66 Aircraft (2275)

Total 70,884 22,899 7,072 6,797 2,607 9,670 AL 1,196 9,218 917 844 3,337 737 FL 5,888 44,817 1,936 1,781 6,683 1,409 GA 1,038 7,875 334 307 1,173 246 KY 6,607 50,267 2,246 2,066 9,608 1,569 MS 5,688 43,233 1,903 1,751 7,719 1,351 NC 599 4,547 193 178 690 142 SC 1,067 8,100 343 316 1,205 253 TN 3,624 27,555 1,217 1,120 4,974 860 VA 972 2,775 334 307 359 483 WV 1,528 11,586 487 448 525 362 Commercial Marine (2280)

Total 28,207 209,972 9,911 9,118 36,275 7,413 VA 110 313 25 23 27 48 Military Marine (2283) Total 110 313 25 23 27 48 AL 3,490 26,339 592 533 1,446 1,354 FL 1,006 9,969 247 222 605 404 GA 2,725 27,453 682 614 1,667 1,086 KY 2,166 21,811 542 488 1,321 867 MS 2,302 23,267 578 520 1,429 899 NC 1,638 16,502 410 369 1,001 654 SC 1,160 11,690 291 261 710 462 TN 2,626 25,627 633 570 1,439 1,041 VA 1,186 11,882 1,529 1,375 3,641 492 WV 1,311 13,224 329 296 808 517 Locomotives (2285)

Total 19,611 187,764 5,833 5,248 14,066 7,777 Grand Total 118,812 420,948 22,841 21,186 52,976 24,908Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.3-15 Change in 2002 Emissions, Base F Inventory Relative to Initial Inventory

Source State CO NO x PM 10 PM 2.5 SO 2 VOC AL 0% 0% -67% -82% 0% 0% FL -11% -26% -95% -93% -24% -1% GA +109% +442% -62% -47% +379% +26% KY 0% 0% -93% -90% 0% 0% MS 0% 0% -92% -89% 0% 0% NC 0% 0% -93% -90% 0% 0% SC 0% 0% -9% +29% 0% 0% TN +6% +4% -91% -87% +4% +2% VA -45% -51% -92% -89% +236% -22% WV 0% 0% -92% -89% 0% 0% Aircraft (2275)

Total -9% -6% -92% -88% +34% -6% AL +0% +0% +0% +0% +0% +0% FL 0% 0% 0% 0% 0% 0% GA +0% +0% +0% +0% +0% +0% KY 0% 0% 0% 0% 0% 0% MS +0% +0% +0% +0% +0% +0% NC +0% +0% +0% +0% +0% +0% SC 0% 0% 0% 0% 0% 0% TN -12% -12% -12% -12% -14% -12% VA -19% -19% -64% -64% -89% -19% WV -27% -27% -27% -27% -27% -27% Commercial Marine (2280)

Total -4% -4% -9% -9% -10% -5% VA -19% -19% -12% -12% -12% -19% Military Marine (2283) Total -19% -19% -12% -12% -12% -19% AL 0% 0% 0% 0% 0% 0% FL 0% 0% 0% 0% 0% 0% GA +3% +3% +3% +3% +3% +3% KY 0% 0% 0% 0% 0% 0% MS 0% 0% 0% 0% 0% 0% NC 0% 0% 0% 0% 0% 0% SC 0% 0% 0% 0% 0% 0% TN -42% -43% -43% -43% -46% -42% VA -38% -39% +9% +9% +6% -38% WV +19% +19% +19% +19% +19% +19% Locomotives (2285)

Total -11% -11% -5% -5% -6% -11% Grand Total -8% -7% -77% -71% -7% -7%Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.3-16 Base F Comparison of Aircraft Emissions (Airports with Aircraft NO x 

### > 200 tons per year)

Airport FIP CO NO x PM 10 PM 2.5 SO 2 VOC Approx. LTOs Predicted NO x VISTAS to Predicted Initial 2002 Base Year Inventory Miami 12086 9,757 5,997 23,706 16,357 525 1,641 150,000 1,680 3.57 Orlando 12095 3,456 2,170 8,578 5,919 204 642 150,000 1,680 1.29 Memphis 47157 3,462 1,934 7,645 5,275 185 603 125,000 1,400 1.38 Reagan 51013 3,892 1,806 7,138 4,925 164 302 100,000 1,120 1.61 Hampton 51650 2,690 1,705 0 0 0 611 Military

Dulles 51059 2,032 1,330 5,246 3,620 0 272 75,000 840 1.58 Orlando-Sanford 12117 3,615 1,225 4,837 3,337 100 351 Atlanta 13121 1,457 913 3,608 2,490 86 274 420,000 4,704 0.19 Fort Lauderdale 12011 1,930 809 3,196 2,206 75 257 75,000 840 0.96 Charlotte 37119 1,643 788 3,113 2,148 75 255 150,000 1,680 0.47

Tampa 12057 1,399 785 3,101 2,140 74 240 75,000 840 0.93 Nashville 47037 1,819 653 40 28 33 239 60,000 672 0.97 Raleigh 37183 1,584 592 2,338 1,613 56 204 75,000 840 0.70 Louisville 21111 1,073 468 1,851 1,277 45 155 60,000 672 0.70 Jacksonville 12031 871 325 1,284 886 31 112 30,000 336 0.97 Palm Beach 12099 1,156 226 0 0 1 13 2 30,000 336 0.67 Aggregate 41,836 21,724 75,682 52,220 1,655 6,290 0.19-3.57 Base F 2002 Base Year Inventory Atlanta 13063 4,121 5,288 1,435 1,406 443 337 420,000 4,704 1.12 Miami 12086 6,670 2,933 805 789 274 1,596 150,000 1,680 1.75 Orlando 12095 3,456 2,170 568 556 204 642 150,000 1,680 1.29 Memphis 47157 3,462 1,934 506 495 185 603 125,000 1,400 1.38 Orlando-Sanford 12117 3,615 1,225 338 332 100 351 Fort Lauderdale 12011 1,930 809 217 212 75 257 75,000 840 0.96 Charlotte 37119 1,643 788 206 202 75 255 150,000 1,680 0.47

Tampa 12057 1,399 785 206 202 74 240 75,000 840 0.93 Nashville 47037 1,819 653 170 166 33 239 60,000 672 0.97

Reagan 51013 1,269 635 171 168 193 97 100,000 1,120 0.57 Dulles 1 51107 1,807 595 164 161 252 153 37,500 420 1.42 Raleigh 37183 1,584 592 156 153 56 204 75,000 840 0.70 Dulles 2 51059 1,095 591 156 153 252 115 37,500 420 1.41 Hampton 51650 858 535 471 461 18 305 Military Louisville 21111 1,073 468 123 121 45 155 60,000 672 0.70 Jacksonville 12031 871 325 87 85 31 112 30,000 336 0.97 Palm Beach 12099 1,156 226 59 58 1 13 2 30,000 336 0.67 Aggregate 37,829 20,550 5,838 5,721 2,312 5,793 0.47-1.75 Net Change -10% -5% -92% -89% +40% -8%

Note: For the Base F inventory, Dulles International Ai rport emissions are split betw een two Virginia counties. Predicted NO x is based on the ratio of airport LTOs to test airport (Tucson International Airport) LTOs and NO x . This is not a rigorous comparison, but rather an approximate indicator of expected magnitude.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Base G Revisions:
Further revisions to the 2002 base year emission s inventory were implemented in response to additional state data submittals in the spring of 2006. The inventories developed through the Base F revision process (as described above) serv ed as the starting point for the 2006 revisions. Thus, unless otherwise indicated below, all documen ted Base F revisions con tinue to apply to the Base G-revised 2002 base year inventory.

As part of the Base G review and update pro cess, Virginia regulators provided 443 updated
emission records for aircraft. These records re flected revisions to ai rcraft VOC, CO, and NO x 

### , and in a few cases SO 2 

### , emissions records that were al ready in the Base F VISTAS 2002 inventory (as opposed to the addition of previ ously unreported data). The specific revisions
broke down as follows:

Table 1.3-17 Base G VA Aircraft Records Updates

Aircraft Type VOC CO NO x SO 2 Total Military Aircraft 9 9 9 1 28

Commercial Aircraft 12 12 12 17 53

General Aviation Aircraft 65 66 66 0 197

Air Taxi Aircraft 56 56 53 0 165
Aggregate 142 143 140 18 443



### Emissions values for each of the 443 record s in the Base F 2002 VISTAS inventory were updated for Base G to reflect the revised data. However, as described above for the Base F revisions, all aircraft SO 2 

### , PM 10 

### , and PM 2.5 

### emissions in Virginia are estimated on the basis of CO (in the case of SO 2 

### ) and NO x 

### emissions (in the cases of PM 10 

### and PM 2.5 

### ). Therefore, since Virginia regulators did not provide updated SO 2 

### emissions for all updated CO emissions records, or updated PM 10 

### or PM 2.5 

### emissions for all updated NO x 

### emissions records, it was necessary to
re-estimate aircraft SO 2 

### , PM 10 

### , and PM 2.5 

### emissions in all cases where updated CO or NO x 

### emissions were provided for Base G (and explicit SO 2 

### and/or PM 10 

### and PM 2.5 

### emissions were not).

The procedure used to estimate the SO 2 

### , PM 10 

### , and PM 2.5 

### emissions revisions was identical to
that described above for the Base F in ventory revisions, except that revised SO 2 

### -to-CO emissions
ratios were calculated for commercial airc raft, where 12 pairs of revised CO and SO 2 

### emissions estimates were available. Although a single pair of revised CO and SO 2 

### emissions records was available for military aircraft, this was deemed an insufficient sample with which to replace the military aircraft SO 2 

### -to-CO emissions ratios previously calc ulated in Base F. However, it is worth noting that the SO 2 

### -to-CO emissions ratio for the revised military aircraft emissions pairDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### was within 16 percent of the previously calculated ratio, so any error associ ated with retention of
the Base F ratio will be minor. Table 1.3-18 presents the emissions ratios.

Table 1.3-18 Calculated Base G Emission Ratios for VA.

Source SCC SO 2 /CO (fall 2004) SO 2 /CO (spring 2006) SO 2 /CO (used in 2006) PM 10 /NO x PM 2.5 /PM 10 Military Aircraft 2275001000 0.0215 0.0180 0.0215 0.88 0.98

Commercial Aircraft 2275020000 0.3292 0.0696 0.0696 0.26 0.98

General Aviation Aircraft 2275050000 0.00016 n/a 0.00016 1.9 0.98

Air Taxi Aircraft 2275060000 0.0015 n/a 0.0015 0.5 0.98



### Application of the SO 2 

### -to-CO emissions ratios to the 130 revi sed aircraft CO records, for which no corresponding SO 2 

### emission revisions were provided, resulted in an additional 130 aircraft
SO 2 

### emission records updates for Virgin ia. Similarly, application of the PM 10 

### -to-NO x 

### emissions
ratios to the 140 revised aircraft NO x 

### records for which no corresponding PM 10 

### emission revisions were provided, resulted in an additional 140 aircraft PM 10 

### emission records updates for Virginia. Application of the PM 2.5 

### -to-PM 10 

### emissions ratios to the 140 revised aircraft PM 10 

### records resulted in an additional 140 aircraft PM 2.5 

### emission records updates for Virginia. Thus, in total, 853 (443+130+140+140) Virginia aircraft emissions records were updated for Base G.

Also as part of the Base G review and update process, Alabama regulators provided 178 updated PM emission records for aircraft (89 records for PM 10 

### and 89 records for PM 2.5 

### ), 42 additional emissions records for locomotives (14 records for VOC, 14 records for CO, and 14 records for
NO x 

### ), and 179 additional emission records for airc raft (30 records for VOC, 30 records for CO,
30 records for NO x 

### , 29 records for SO 2 

### , 30 records for PM 10 

### , and 30 records for PM 2.5 

### ). After review, it was determined that the 178 update d PM emission records for aircraft actually reflected the original (overestimated) aircraft PM data that was repla ced universally throughout the VISTAS region for Base F. Implementing thes e latest revisions would, in effect, “undo” the Base F aircraft PM revisions. Following discussions with Alabama regulators, it was determined
that the 178 aircraft PM records would not be updated for the Base G revisions.

The 42 additional emissions records for locomotiv es were determined to correspond exactly to existing SO 2 

### , PM 10 

### , and PM 2.5 

### emissions records already in the Base F VISTAS 2002 inventory. It is not clear why these existing records contained no corresponding data for VOC, CO, and
NO x 

### , but those data are now reflected through the additional 42 records that have now been added to the Base G 2002 VISTAS inventory for Alabama.

After examining the 179 additional aircraft em issions records in c onjunction with Alabama regulators, it was determined that 17 of the records (commercial aircraft records in Dale,Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Limestone, and Talladega counties) were erroneous and should be excluded from the update. The remaining 162 records reflected additional general aviation, air taxi, and m ilitary aircraft activity in 20 counties and were specifically comp rised of 27 records each for VOC, CO, NO x 

### , SO 2 

### , PM 10 

### , and PM 2.5 

### . There were no further i ssues with the VOC, CO, NO x 

### , and SO 2 

### records and
these were added to the Base G 2002 VISTAS inventory without change. It was, however, apparent that the PM 10 

### and PM 2.5 

### records reflected an overestimation of aircraft PM similar to that which was previously corrected throughout the VISTAS region for Base F (as documented above). To overcome this overestimation, the additional aircraft PM 10 

### and PM 2.5 

### records provided by Alabama regulators were replaced with revised emission estimates developed on the
basis of the PM 10 

### -to-NO x 

### and PM 2.5 

### -to-PM 10 

### ratios documented under the Base F revisions above. So although 27 aircraft PM 10 

### records and 27 aircraft PM 2.5 

### records were added to the 2002 Alabama inventory, they reflected different em issions values than those provided directly by Alabama regulators.

In total, 204 additional emissions records (42 for locomotives and 162 for aircraft) were added to the Base G 2002 Alabama inventory.

Finally, as part of the Base G review and update process, Kentucky regulators provided 12 updated aircraft emission records for Boone Co unty, to correct previously underestimated
aircraft emissions associated with the Cincinna ti/Northern Kentucky Inte rnational Airport. VOC, CO, and NO x 

### emissions data were provided for military, commercial, general aviation, and air taxi aircraft. No associated updates for SO 2 

### , PM 10 

### , or PM 2.5 

### emissions were provided. Corresponding PM 10 

### emission estimates were developed by applying the PM 10 

### -to-NO x 

### ratios presented in Table 1.3-17 above to the updated NO x 

### emission estimates. PM 2.5 

### emission estimates were developed by applying the PM 2.5 

### -to-PM 10 

### ratios from that same table to the
estimated PM 10 

### emissions. SO 2 

### emission estimates were developed by applying the SO 2 

### -to-PM 10 

### ratios developed from the older data (i.e., the da ta being replaced) for Boone County aircraft to the updated PM 10 

### emissions. Thus, a total of 24 invent ory records for Kentucky were updated (VOC, CO, NO x 

### , SO 2 

### , PM 10 

### , and PM 2.5 

### for four aircraft types).

Upon implementation of the universe of updates, 877 existing emission records were revised (853 in Virginia and 24 in Kentucky) and 204 add itional emission records (all in Alabama) were added to the 2002 VISTAS inventory. The total nu mber of aircraft, locomotive, and commercial marine inventory records thus changed from 22,838 records in Base F to 23,042 records in
Base G.

Table 1.3-19 presents a summary of the resul ting Base G VISTAS 2002 base year inventory estimates for aircraft, locomotives, and non-recr eational marine vessels. Table 1.3-20 provides a comparison of the Base G 2002 base year inventor y estimates to those of the Base F 2002 baseDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### year inventory. As indicated, total emissions for VOC, CO, NO x 

### , and SO 2 

### are generally within about 5 percent, with changes restricted to the states of Alabama, Kentucky, and Virginia.

Lastly, Table 1.3-21 provides an updated comparis on of emission estimates from the Base F and Base G 2002 base year inventories for all 17 VI STAS region airports with estimated annual
aircraft NO x 

### emissions of 200 tons or greater. As comp ared to Table 1.3-16, the table reflects the
Base G addition of the Cincinnati/Northern Kent ucky International Airport. Aircraft emission estimates for the other 16 airports ar e unchanged from their Base F values.

Table 1.3-19 Base G-Revised 2002 Base Year Aircraft, Locomotive, and Non-Recreational Marine Emissions (tons/year)

Source State CO NO x PM 10 PM 2.5 SO 2 VOC AL 5,595 185 238 99 18 276 FL 25,431 8,891 2,424 2,375 800 3,658 GA 6,620 5,372 1,475 1,446 451 443 KY 5,577 925 251 246 88 397 MS 1,593 140 44 43 13 96 NC 6,088 1,548 419 411 148 613 SC 6,505 515 409 401 88 863 TN 7,251 2,766 734 719 235 943 VA 11,873 3,885 2,010 1,970 272 2,825 WV 1,178 78 25 24 8 66 Aircraft (2275)

Total 77,712 24,305 8,029 7,734 2,121 10,179 AL 1,196 9,218 917 844 3,337 737 FL 5,888 44,817 1,936 1,781 6,683 1,409 GA 1,038 7,875 334 307 1,173 246 KY 6,607 50,267 2,246 2,066 9,608 1,569 MS 5,688 43,233 1,903 1,751 7,719 1,351 NC 599 4,547 193 178 690 142 SC 1,067 8,100 343 316 1,205 253 TN 3,624 27,555 1,217 1,120 4,974 860 VA 972 2,775 334 307 359 483 WV 1,528 11,586 487 448 525 362 Commercial Marine (2280)

Total 28,207 209,972 9,911 9,118 36,275 7,413 VA 110 313 25 23 27 48 Military Marine (2283) Total 110 313 25 23 27 48 AL 3,518 26,623 592 533 1,446 1,365 FL 1,006 9,969 247 222 605 404 GA 2,654 26,733 664 598 1,622 1,059 KY 2,166 21,811 542 488 1,321 867 MS 2,302 23,267 578 520 1,429 899 NC 1,638 16,502 410 369 1,001 654 SC 1,160 11,690 291 261 710 462 TN 2,626 25,627 633 570 1,439 1,041 VA 1,186 11,882 1,529 1,375 3,641 492 WV 1,311 13,224 329 296 808 517 Locomotives (2285)

Total 19,568 187,328 5,815 5,232 14,022 7,761 Grand Total 125,597 421,918 23,780 22,107 52,444 25,401Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.3-20 Change in 2002 Emissions, Base G Inventory Relative to Base F Inventory

Source State CO NO x PM 10 PM 2.5 SO 2 VOC AL +48% +6% +5% +14% +7% +41% FL 0% 0% 0% 0% 0% 0% GA 0% 0% 0% 0% 0% 0% KY +109% +41% +40% +40% +41% +51% MS 0% 0% 0% 0% 0% 0% NC 0% 0% 0% 0% 0% 0% SC 0% 0% 0% 0% 0% 0% TN 0% 0% 0% 0% 0% 0% VA +22% +41% +77% +77% -65% +12% WV 0% 0% 0% 0% 0% 0% Aircraft (2275)

Total +10% +6% +14% +14% -19% +5% AL 0% 0% 0% 0% 0% 0% FL 0% 0% 0% 0% 0% 0% GA 0% 0% 0% 0% 0% 0% KY 0% 0% 0% 0% 0% 0% MS 0% 0% 0% 0% 0% 0% NC 0% 0% 0% 0% 0% 0% SC 0% 0% 0% 0% 0% 0% TN 0% 0% 0% 0% 0% 0% VA 0% 0% 0% 0% 0% 0% WV 0% 0% 0% 0% 0% 0% Commercial Marine (2280)

Total 0% 0% 0% 0% 0% 0% VA 0% 0% 0% 0% 0% 0% Military Marine (2283) Total 0% 0% 0% 0% 0% 0% AL +1% +1% 0% 0% 0% +1% FL 0% 0% 0% 0% 0% 0% GA 0% 0% 0% 0% 0% 0% KY 0% 0% 0% 0% 0% 0% MS 0% 0% 0% 0% 0% 0% NC 0% 0% 0% 0% 0% 0% SC 0% 0% 0% 0% 0% 0% TN 0% 0% 0% 0% 0% 0% VA 0% 0% 0% 0% 0% 0% WV 0% 0% 0% 0% 0% 0% Locomotives (2285)

Total +0% +0% 0% 0% 0% +0% Grand Total +6% +0% +4% +4% -1% +2%Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 1.3-21 Base G Comparison of Aircraft Emissions (Airports with Aircraft NO x 

### > 200 tons per year)

Airport FIP CO NO x PM 10 PM 2.5 SO 2 VOC Approx. LTOs Predicted NO x VISTAS to Predicted Base F 2002 Base Year Inventory Atlanta 13063 4,121 5,288 1,435 1,406 443 337 420,000 4,704 1.12 Miami 12086 6,670 2,933 805 789 274 1,596 150,000 1,680 1.75 Orlando 12095 3,456 2,170 568 556 204 642 150,000 1,680 1.29 Memphis 47157 3,462 1,934 506 495 185 603 125,000 1,400 1.38 Orlando-Sanford 12117 3,615 1,225 338 332 100 351 Fort Lauderdale 12011 1,930 809 217 212 75 257 75,000 840 0.96 Charlotte 37119 1,643 788 206 202 75 255 150,000 1,680 0.47

Tampa 12057 1,399 785 206 202 74 240 75,000 840 0.93 Nashville 47037 1,819 653 170 166 33 239 60,000 672 0.97

Reagan 51013 1,269 635 171 168 193 97 100,000 1,120 0.57 Dulles 1 51107 1,807 595 164 161 252 153 37,500 420 1.42 Raleigh 37183 1,584 592 156 153 56 204 75,000 840 0.70 Dulles 2 51059 1,095 591 156 153 252 115 37,500 420 1.41 Hampton 51650 858 535 471 461 18 305 Military Louisville 21111 1,073 468 123 121 45 155 60,000 672 0.70 Jacksonville 12031 871 325 87 85 31 112 30,000 336 0.97 Palm Beach 12099 1,156 226 59 58 1 13 2 30,000 336 0.67 Cincinnati 21015 467 144 38 37 14 54 50,000 560 0.26 Aggregate 38,296 20,694 5,876 5,758 2,326 5,847 0.26-1.75 Base G 2002 Base Year Inventory Atlanta 13063 4,121 5,288 1,435 1,406 443 337 420,000 4,704 1.12 Miami 12086 6,670 2,933 805 789 274 1,596 150,000 1,680 1.75 Orlando 12095 3,456 2,170 568 556 204 642 150,000 1,680 1.29 Memphis 47157 3,462 1,934 506 495 185 603 125,000 1,400 1.38 Orlando-Sanford 12117 3,615 1,225 338 332 100 351 Fort Lauderdale 12011 1,930 809 217 212 75 257 75,000 840 0.96 Charlotte 37119 1,643 788 206 202 75 255 150,000 1,680 0.47

Tampa 12057 1,399 785 206 202 74 240 75,000 840 0.93 Nashville 47037 1,819 653 170 166 33 239 60,000 672 0.97

Reagan 51013 1,269 635 171 168 193 97 100,000 1,120 0.57 Dulles 1 51107 1,807 595 164 161 252 153 37,500 420 1.42 Raleigh 37183 1,584 592 156 153 56 204 75,000 840 0.70 Dulles 2 51059 1,095 591 156 153 252 115 37,500 420 1.41 Hampton 51650 858 535 471 461 18 305 Military Louisville 21111 1,073 468 123 121 45 155 60,000 672 0.70 Cincinnati 21015 3,378 411 110 107 39 187 50,000 560 0.73 Jacksonville 12031 871 325 87 85 31 112 30,000 336 0.97 Palm Beach 12099 1,156 226 59 58 1 13 2 30,000 336 0.67 Aggregate 41,207 20,961 5,947 5,828 2,352 5,981 0.47-1.75 Net Change +8% +1% +1% +1% +1% +2%
Note: For the revised inventory, Dulles International Air port emissions are split between two Virginia counties. Predicted NO x is based on the ratio of airport LTOs to test airport (Tucson International Airport) LTOs and NO x . This is not a rigorous comparison, but rather an approximate indicator of expected magnitude.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 1.3.2.3 Emissions from NONROAD Model Sources in Illinois, Indiana, and Ohio
As part of the Base G update process, VISTAS requested that emissions estimates for 2002 be produced for the states of Illinois, Indiana, and Ohio. These estimates were to be produced at the same spatial (i.e., county level by SCC) a nd temporal resolution as estimates for the VISTAS region.

The requested estimates were produced by extracti ng a complete set of c ounty-level input data applicable to each of the three states from th e latest version of th e EPA’s NMIM (National Mobile Inventory Model) model. This included appropriate consideration of all non-default NMIM input files generated by the Midwest Regional Planni ng Organization (MRPO), as described below. These input data were then asse mbled into appropriate i nput files for the Final
NONROAD2005 model and emission estimates we re produced using the same procedure employed for the VISTAS region as part of the Base G updates.

A complete set of monthly input data was deve loped for each county in Illinois, Indiana, and Ohio by extracting data from the following NM IM database files (using the NMIM MySQL query browser):

county, countrynrfile, countyyear, countyyearmonth, countyyearmonthhour, gasoline, diesel, and natural gas

The database files:

countrynrfile, countyyear, c ountyyearmonth, and gasoline

were non-default database files provided to VI STAS by the MRPO, and are intended to reflect the latest planning data bei ng used by MRPO modelers.

From these files, monthly data for gasoline va por pressure, gasoline oxygen content, gasoline sulfur content, diesel sulfur content for la nd-based equipment, diesel sulfur content for marine-based equipment, natural gas sulfur cont ent, minimum daily temperature, maximum daily temperature, and average daily temperature were developed. In addition, th e altitude and Stage II refueling control status of each county, as well as the identity of the associated equipment population, activity, growth, alloca tion, and seasonal distribution f iles, was determined. These
data were then assembled into Final NONRO AD2005 input files on a seasonal basis, with monthly data being arithmetically averaged to produce seasonal equivalents as follows:

Winter = Average of December, January, and February Spring = Average of March, April, and May Summer = Average of June, July, and August, Fall = Average of September, October, and NovemberDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Unlike the VISTAS Base G approach, this appr oach results in the use of the following
non-default data files during the Final NONROAD 2005 modeling process:

Table 1.3-22 Non-Default Files Used for MRPO Modeling

Data File Illinois Indiana Ohio
Activity File 1700002.act 1800002.act 3900002.act
Growth File 17000.grw 18000.grw 39000.grw
Population File 17000.pop 18000.pop 39000.pop

Season File 17000.sea 18000.sea 39000.sea

Inboard Marine Allocation File 17000wib.alo 18000wib.alo 39000wib.alo

Outboard Marine Allocation File 17000wob.alo 18000wob.alo 39000wob.alo

Specific Fuel Consumption MRPO-specific file provided by MRPO modelers (arbitrarily named “mrpoBSFC.emf” for this work)

One compromise was made relative to the level of resolution that is available through the basic approach described above, that being the treatme nt of ambient temperature data. Because NMIM offers a unique temperature profile for ev ery U.S. county -- developed by aggregating temperature data from included and surrounding weat her stations on the basis of their distances from the county population centroid -- it is not possible to explicitly group counties with
otherwise identical i nput streams. Ungrouped however, th ere would be 1,128 distinct input streams to be processed (102 Illinois counties pl us 92 Indiana counties plus 88 Ohio counties at four seasons each), or over five times th e number of files processed for the entire VISTAS region.

To surmount this problem and allow counties with similar temperature profiles to be grouped an approach was employed wherein counties were considered groupable if all temperature inputs 4



### are within ± 2 oF of the corres ponding group average. This criteri on is quite stringent in that it results in less tolerant grouping than that employed for VISTAS modeling, which uses temperature data from the nearest meteorologica l station as opposed to "unique" meteorological

4 Non-road temperature inputs used for county grouping are: winter minimum, spring minimum, summer minimum,

fall minimum, winter maximum, spring maximum, summer maximum, fall maximum, winter average, spring average, summer average, and fall average.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### data for each county. Under this approach, th e actual deviation for grouped counties is much less that ± 2o F for the overwhelming major ity of the 12 grouped temperature inputs.

In addition to the required temperature consis tency, all other input data for counties to be grouped had to be identical for all four seasons . Using this criterion, Illinois emissions were modeled using 12 county groups, Indiana emissions were modeled using 9 county groups, and Ohio emissions were modeled using 10 count y groups. Thus, 31 iterations of NONROAD2002 were required per season, as compared to the 53 iterations per s eason required for the VISTAS region.

It should be noted that a pot ential quality assurance issue was noted in assembling the NONROAD2005 input data for a numb er of Indiana counties. Specifically, the gasoline vapor pressure for most Indiana countie s reflects a value of 9.0 psi in all spring, summer, fall, and winter months. This is likely to indicate a problem with the accu racy of the NMIM databases for these counties, but these data were used as defined for this work.

1.3.3 Quality Assurance steps
Throughout the inventory developm ent process, quality assuran ce steps were performed to ensure that no double counting of emissions occurred, and to ensu re that a full and complete inventory was developed for VISTAS. Quality assurance was an important component to the inventory development process and MACTEC performed the following QA steps on the area source component of the 2002 base year revised:

1. All CERR and NIF format State supplied data submittals were run through EPA’s Format and Content checking software.

2. SCC level emission summaries were prepared and evaluated to ensure that emissions
were consistent and that th ere were no missing sources.

3. Tier comparisons (by pollutant) were deve loped between the revised 2002 base year inventory and the initial base year inventory.

4. Data product summaries were provided to both the VISTAS Emission Inventory
Technical Advisor and to Mobile Source SIWG representatives for review and comment. Changes based on these comments were implemented in the files.

5. Version numbering was used for all i nventory files developed. The version numbering process used a decimal system to track major and minor changes. For example, a major change would result in a version going from 1.0 to 2.0. A minor change would cause a version number to go from 1.0 to 1.1. Minor changes resulting from largely editorial changes would result in a change from 1.00 to 1.01.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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## 2.0 Projection Inventory Development

2.1 Point Sources

We used different approaches for differen t sectors of the point source inventory:

 For the EGUs, VISTAS relied primar ily on the Integrated Planning Model ® 

### (IPM ® 

### ) to project future generation as well as to calcu late the impact of future emission control programs. The IPM results were adjusted based on S/L agency knowledge of planned emission controls at specific EGUs.

 For non-EGUs, we used recently updated growth and control data consistent with the data used in EPA’s CAIR analyses, and supplemented these data with available S/L agency knowledge of planned emission controls or other changes at specific non-EGUs and updated fuel use forecast data for the U.S. Department of Energy.

For both sectors, we generated 2009 and 2018 inve ntories for a combined on-the-books (OTB) and on-the-way (OTW) control scenario. The OTB/OTW control scenario accounts for post2002 emission reductions from promulgated and pr oposed non-EGU federal control programs as of July 1, 2004; the final Clean Ai r Interstate Rule (CAIR); and St ate, local, and site-specific control programs as of Oct ober 1, 2007. Section 2.1.1 discusses the EGU projection inventory development, while Section 2.1.2 discusses the non-EGU projection inventory development.

2.1.1 EGU Emission Projections
The following subsections discuss the following specific aspects of the development of the EGU projections. First, we present a chronology of the EGU development process and discuss key decisions in selecting the fina l methods for performing the em issions projecti ons. Next, we describe the development of the final set of IP M runs that are included in the VISTAS Base G inventory. Next, we describe the process of transforming the IPM parsed files into NIF format. Fourth, we discuss the process for ensuring th at units accounted for in IPM were not doublecounted in the non-EGU inventor y. Fifth, we describe the QA/Q C checks that were made to ensure that the IPM results were properly inco rporated into the VIST AS inventory. Sixth, we document the changes to the IPM results that S/L agencies specified they wanted included in the VISTAS inventory based on new information that were not accounted for in the IPM runs. Finally, we present summaries of the B&F pr ojected EGU emissions by year, state, and pollutant.

2.1.1.1 Chronology of the Development of EGU Projections
At the beginning of the EGU i nventory development process, VI STAS considered three options for developing the VISTAS 2009 and 2018 projection inventories for EGUs:Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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###  Option 1 – Use the results of IPM modeling conducted in support of the proposed Clean Air Interstate Rule (CAIR) base and control case analyses as the starting point and refine the projections with readily available input s from stakeholders; these IPM runs were conducted for 2010 and 2015, which VISTAS would use to represent projected emissions in 2009 and 2018 respectively.

 Option 2 – Use the VISTAS 2002 typical year as the starting point, apply growth factors from the Energy Information Administration, and refine future emission rates with stakeholder input regarding utilization rates, capacity, retirements, and new unit
information.

 Option 3 – Use the results of a new round of IPM modeling sponsored by VISTAS and the Midwest Regional Planning Organization (M RPO). These runs incorporated VISTAS specific unit and regulation modified parame ters, and generate results for 2009 and 2018 explicitly.

An additional consideration for each of the three options was the inclusion of emission projections developed by the S outhern Company specifically for their units. Southern Company is a super-regional company which owns EGUs in Alabama, Florida, Ge orgia, and Mississippi and participates in VISTAS as an industry st akeholder. Southern Company used their energy budget forecast to project net ge neration and heat input for ever y existing and future Southern Company EGU for the years 2009 and 2018. Furthe r documentation of how Southern Company generated the 2009/2018 inventory fo r their units can be found in Developing Southern Company Emissions and Flue Gas Characteristics fo r VISTAS Regional Haze Modeling (April 2005, presented at 14 th 

### International Emission Inventory Conference)

. Each of these three options and the Southern Company projections were discussed in a series of conference calls with the VISTAS EGU Special Interest Work Group (SIWG) during the fall of 2004. During a conference call on December 6, 2004, the VISTAS EGU SIWG approved the use of the latest VISTAS/MRPO sponsored IPM ru ns (Option 3) to re present the 2009 and 2018
EGU forecasts of emissions for the OTB and OT W cases. During the call, Alabama and Georgia specified that they did not wish to use Sout hern Company provided emissions forecasts of 2009 and 2018 to represent the sources in their States. Missi ssippi decided to u tilize the Southern Company projections to represent activity at Southern Company f acilities in Mississippi. After the call, Florida decided agains t using Southern Company provi ded emissions forecasts of 2009 and 2018 to represent the sources in their State. Thus, Southern Company data was used only for Southern Company units in Mississippi fo r both the Base F and Base G projections.

The Option 3 IPM modeling resulted from a jo int agreement by VISTAS and MRPO to work together to develop future year utility emi ssions based on IPM modeling. The decision to useDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### IPM modeling was based in part on a study of utility forecast methods by E.H. Pechan and Associates, Inc. (Pechan) for MRPO, which recommended IPM as a viable methodology (see Electricity Generating Unit {EGU} Grow th Modeling Method Task 2 Evaluation , February 11, 2004). Although IPM results were available from EPA’s modeling to su pport their rulemaking for the Clean Air Interstate Rule (CAIR), VISTAS stakeholders felt that certain model inputs needed to be improved. Thus, VISTAS and MRPO decided to hire contractors to conduct new IPM modeling and to post-process the IPM results. Southern Co mpany projections in 2009 were roughly comparable with IPM. For 2018, Southe rn Company projections were generally less than IPM because of assumptions made by Southern Company on which units would be
economical to control and incorrect data in the NEEDS database which feeds IPM.

In August 2004, VISTAS contracted with ICF Intern ational, Inc., to run IPM to provide utility
forecasts for 2009 and 2018 under two future scenar ios – Base Case and CAIR Case. The Base Case represents the current operation of the power system under currently known laws and regulations (as known at the time the run was made), including those that come into force in the study horizon. The CAIR Case is the Base Case w ith the proposed CAIR rule superimposed. The run results were parsed at th e unit level for the 2009 and 2018 run years. Also in August 2004, MRPO contracted with E.H. Pechan to post-pr ocess the IPM outputs gene rated by ICF to provide model-ready emission files. The IPM output f iles were delivered by ICF to VISTAS in November ( Future Year Electricity Generating Sect or Emission Inventory Development Using the Integrated Planning Model (IPM ® 

### ) in Support of Fine Par ticulate Mass and Visibility Modeling in the VISTAS and Midwest RPO Regions , January 2005), and the post-processed data files were delivered by Pechan to the MRPO in December 2004 ( LADCO IPM Model Parsed File Post-Processing Methodol ogy and File Preparation , February 8, 2005).

On March 10, 2005, EPA issued the final Clean Air Interstate Rule. VISTAS and MRPO, in conjunction with other RPOs, conducted anot her round of IPM modeling which reflected changes to control assumptions based on the final CAIR as well as additional changes to model inputs based on S/L agency and stakeholder comme nts. Several conference calls were conducted in the spring of 2005 to discuss and provide co mments on IPM assumptions related to six main topics: power system operation, generating res ources, emission control technologies, set-up parameters and rule, financial assumptions, a nd fuel assumptions. Based on these discussions, VISTAS sponsored a new set of IPM runs to re flect the final CAIR requirements as well as certain changes to IPM assumptions that were ag reed to by the VISTAS states. This set of IPM
runs is documented in Future Year Electricity Genera ting Sector Emission Inventory Development Using the Integrated Planning Model (IPM ® 

### ) in Support of Fine Particulate Mass and Visibility Modeling in the VISTAS and Midwest RPO Regions , April 2005 (these runs are referred to as the VISTAS Phase I analysis).Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Further refinements to the IPM inputs and a ssumptions were made by the RPOs, and ICF performed the following four runs using IPM dur ing the summer of 2005 (t hese runs are referred to as the VISTAS/CENRAP Phase II analysis):

 Base Case with EPA 2.1.9 coal, gas and oil price assumptions.

 Base Case with EPA 2.1.9 coal and gas suppl y curves adjusted for AEO 2005 reference case price and volume relationships.

 Strategy Case with EPA 2.1.9 coal, gas and oil price assumptions.

 Strategy Case with EPA 2.1.9 coal and gas supply curves adjusted for AEO 2005 reference case price and volume relationships.

The above runs were parsed for 2009 and 2018 run years. The above four runs were based on VISTAS Phase I and the EPA 2.1.9 assumptions. Th e changes that were implemented in the
above four runs are summarized below:

 Unadjusted AEO 2005 electricity demand projec tions were incorpor ated in the above
four runs.

 The gas supply curves were adjusted fo r AEO 2005 reference case price and volume relationships. The EPA 2.1.9 gas supply curves were scaled such th at IPM will solve for AEO 2005 gas prices when the power sector ga s demand in IPM is consistent with AEO 2005 power sector gas demand projections.

 The coal supply curves used in EPA 2.1.9 were scaled in such a manner that the average mine mouth coal prices that the IPM is solv ing in aggregated coal supply regions are comparable to AEO 2005. Due to the fact that the coal grades and supply regions between AEO 2005 and the EPA 2.1.9 are not directly comparable, this was an approximate approach and had to be perf ormed in an iterative fashion. The coal transportation matrix was not updated with EIA assumptions due to significant
differences between the EPA 2.1.9 and EI A AEO 2005 coal supply and coal demand region configurations.

 The cost and performance of new units we re updated to AEO 2005 reference case levels
in all of the above four funs.

 The run years 2008, 2009, 2012, 2015, 2018, 2020 and 2026 were modeled.

 The AEO 2005 life extension costs for fossil a nd nuclear units were incorporated in the
above runs.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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###  The extensive NEEDS comments provide d by VISTAS, MRPO, CENRAP and MANEVU were incorporated into the VISTAS Phase I NEEDS.

 MANE-VU’s comments in regards to the st ate regulations in the northeast were incorporated.

 Renewable Portfolio Standards (RPS) in the northeast was modeled based on the Regional Greenhouse Gas Initiativ e analysis. A single RPS cap was modeled for MA, RI, NY, NJ, MD and CT. These states could buy credits from NY, PJM and New England model regions.

 The investments required under the Illin ois power, Mirant and First Energy NSR settlements were incorpor ated in the above runs.

For the VISTAS/CENRAP Phase II se t of IPM runs, ICF generated two different parsed files. One file includes all fuel burning units (fossil, biomass, landfill gas) as well as non-fuel burning units (hydro, wind, etc.). The s econd file contains just the fo ssil-fuel burning units (e.g., emissions from biomass and landfill gas are omit ted). The RPOs decided to use the fossil-only file for modeling to be consistent with EPA, since EPA used the fossil only results for CAIR analyses. For the 10 VISTAS states, non-fossil fu els accounted for only 0.13 percent of the NOx emissions and 0.04 percent of the SO 2 

### emissions in the 2009 IPM runs.

S/L agencies reviewed the results of the VIST AS/CENRAP Phase II set of IPM runs, which were incorporated into the VISTAS Base F inventory. S/L agen cies primarily reviewed and
commented on the IPM results with respect to IPM decisions on NO x 

### post-combustion controls
and SO 2 

### scrubbers. S/L agencies provided the late st information on when and where new SO 2 

### and NO x 

### controls are planned to come online. S/L agencies also reviewed the IPM results to verify that existing controls and emission rates were properly reflected in the IPM runs. As directed by the S/L agencies, adjustments to the IP M results were made to specific units with any new information they had as part of the permitting process or other contact with the industry that
indicates which units will instal l controls as a result of CAIR and when these new controls will come on-line. Mississippi decided to continue to use the Southern Company projections instead of the IPM projections to repres ent emissions at Southern Comp any facilities in Mississippi. The initial set of state-specified changes to the VI STAS/CENRAP Phase II set of IPM runs were used to create the Base G projec tion inventory (and are documente d later in Section 2.1.1.6). The second set of state specified changes were made only for the 2018 inventory, resulting in the Base G2 2018 inventory (documented later in Se ction 2.1.1.7). The final set of state specified changes applied to both the 2009 and 2018 invent ories and were used to create the B&F 2009 and 2018 inventories (documente d later in Section 2.1.1.8).Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 2.1.1.2 VISTAS IPM runs for EGU sources
The following general summary of the VISTAS IPM ® 

### modeling is based on ICF’s
documentation Future Year Electricity Generating Sector Emission Inventory Development Using the IPM ® 

### in Support of Fine Particulate Mass and Visibility Modeling in the VISTAS and Midwest RPO Regions , April 2005. The ICF documentation is to be used as an extension to EPA's proposed CAIR mode ling runs documented in Documentation Supplement for EPA Modeling Applications (V.2.1.6) Using the IPM , EPA 430/R-03-007, July 2003.

IPM provides “forecasts of least-cost capacity expansion, el ectricity dispatch, and emission control strategies for meeting energy demand and environmental, transmission, dispatch, and reliability constraints.” The underlying database in this modeling is U.S. EPA’s National Electric Energy Data System (NEEDS) released with th e CAIR Notice of Data Availability (NODA). The NEEDS database contains the existing a nd planned/committed unit data in EPA modeling applications of IPM. NEEDS includes basic geog raphic, operating, air em issions, and other data on these generating units. VISTAS States a nd stakeholders provided changes for:

 NO x 

### post-combustion cont rol on existing units

 SO 2 

### scrubbers on existing units

 SO 2 

### emission limitations

 PM controls on existing units

 Summer net dependable capacity

 Heat rate for existing units

 SO 2 

### and NO x 

### control plans based on State ru les or enforcement settlements

The years 2009 and 2018 were explicitly modeled.

2.1.1.3 Post-Processing of IPM Parsed Files
The following summary of the VISTAS/Midwest Regional Planning Organization (MRPO) IPM modeling is based on Pechan’s documentation LADCO IPM Model Parsed File Post-Processing Methodology and File Preparation , February 8, 2005. The essence of the IPM model postprocessing methodology is to take an initial IP M model output file and transform it into air quality model input files. ICF via VISTAS/MRPO provides an initial spre adsheet file containing
unit-level records of both

(1) “existing” units and
(2) committed or new generic aggregates.

All records have unit an d fuel type data; exis ting, retrofit (for SO 2 

### and NO x 

### ), and separate NO x 

### control information; annual SO 2 

### and NO x 

### emissions and heat input; summer season (MaySeptember) NO x 

### and heat input; July day NO x 

### and heat input; coal heat input by coal type;Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### nameplate capacity megawatt (MW), and State FIPS code. Existing units al so have county FIPS code, a unique plant identifier (ORISPL) and unit ID (also called boiler ID) (BLRID); generic
units do not have these data. Th e processing includes estimating va rious types of emissions and adding in control efficiencies, stack paramete rs, latitude-longitude co ordinates, and State identifiers (plant ID, point ID, st ack ID, process ID). Additionally, the generic units are sited in a county and given appropriate IDs. This proce ssing is described in more detail below.

The data are prepared by transforming the generi c aggregates into units similar to the existing units in terms of the available data. The generic aggregates are split into smaller generic units based on their unit types and cap acity, are provided a dummy OR IS unique plant and boiler ID, and are given a county FIPS code based on an algorithm that sites each generic by assigning a sister plant that is in a county based on its atta inment/nonattainment status. Within a State, plants (in county then ORIS plant code order) in attain ment counties are used first as sister sites to generic units, followed by plants in PM nonattain ment counties, followed by plants in 8-hour
ozone nonattainment counties. Note that no LADCO or VISTAS Stat es provided blackout counties that would not be considered when siting generics, so this process is identical to the one used for EPA IPM post-processing.

SCCs were assigned for all units ; unit/fuel/firing/bottom type data were used for existing units’ assignments, while only unit and fuel type were used for generic units’ assignments. Latitudelongitude coordinates were assi gned, first using the EPA-provided data files, secondly using the September 17, 2004 Pechan in-house la titude-longitude file, and la stly using county centroids. These data were only used when the data we re not provided in the 2002 NIF files. Stack parameters were attached, first using the EPA- provided data files, secondly using a March 9, 2004 Pechan in-house stack parameter file based on previous EIA-767 data, and lastly using an EPA June 2003 SCC-based default stack parameter file. These data were only used when the data were not provided in the 2002 NIF files.

Additional data were required for estimating VOC, CO, filterable primary PM 10 

### and PM2.5, PM condensable, and NH 3 

### emissions for all units. Thus, ash a nd sulfur contents were assigned by first using 2002 EIA-767 values for existing units or SCC-based defaults; filterable PM10 and
PM2.5 efficiencies were obtai ned from the 2002 EGU NEI that were based on 2002 EIA-767
control data and the PM Calculat or program (a default of 99.2 percent is used for coal units if necessary); fuel use was back calculated from th e given heat input and a default SCC-based heat content; and emission factors were obtained from an EPA-approved October 7, 2004 Pechan
emission factor file based on AP-42 emission factor s. Note that this update d file is not the one used for estimating emissions for previous EP A post-processed IPM files. Emissions for 28 temporal-pollutant combinations were estimate d since there are seven pollutants (VOC, CO, primary PM 10 

### and PM 2.5 

### , NH 3 

### , SO 2 

### and NO x 

### ) and four temporal periods (annual, summer season, winter season, July day).Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### The next step was to match the IPM unit IDs wi th the identifiers in VISTAS 2002 inventory. A
crosswalk file was used to obtain FIPS State an d county, plant ID (within State and county), and point ID. If the FIPS State and county, plant ID and point ID are in the 2002 VISTAS NIF tables, then the process ID and stack ID are obtained from the NIF; otherwise, defaults, described above, were used.

Pechan provided the post-processe d files in NIF 3.0 format. Two sets of tables were developed :
“NIF files” for IPM units that have a crosswalk match and ar e in the 2002 VISTAS inventory,
and “NoNIF files” for IPM units that are not in the 2002 VISTAS inventory (which includes existing units with or without a crossw alk match as well as generic units).

For Base F and Base G projections , VISTAS reviewed the PM and NH 3 

### emissions from EGUs as provided by Pechan and identified significan tly higher emissions in 2009/2018 than in 2002.
VISTAS determined that Pechan used a set of PM and NH 3 

### emission factors that are “the most recent EPA approved uncontrolled emission fact ors” for estimating 2009/2018 emissions. These factors are most likely not the same emission factors used by States for estimating these
emissions in 2002 for EGUs in the VISTAS doma in. Thus, the emission increase from 2002 to 2009/2018 was simply an artifact of the change in emission factor, not anything to do with changes in activity or control technology application. Also, VIST AS identified an inconsistent use of SCCs for determining emission fact ors between the base and future years.

VISTAS resolution of the PM and NH 3 

### problem is fully documented in EGU Emission Factors and Emission Factor Assignment, memorandum from Greg Stella to VISTAS State Point Source Contacts and VISTAS EGU Special Interest Workgroup, June 13, 2005. The first step was the adjustment of the 2002 base year emissions inventory. Using the latest “EPA-approved” uncontrolled emission factors by SCC, Alpine Geophysics utilized CERR or VISTAS reported annual heat input, fuel throughput, heat, ash and sulfur content to estimate annual uncontrolled emissions for units identified as output by IPM. This step was conducted for non-CEM pollutants (CO, VOC, PM, and NH 3 

### ) only. For PM emissions, the cond ensable component of emissions was calculated and added to the resulting PM pr imary estimations. The resulting emissions were then adjusted by any control efficiency factors reported in the CERR or VISTAS data collection effort. The second adjustment was to the future year inventories. Alpi ne Geophysics updated the SCCs in the future year inventor y to assign the same base year SCC. Using the same methods as described for the 2002 revisions, those non-IPM ge nerated pollutants were estimated using IPM predicted fuel characteristics a nd base year 2002 SCC assignments.

2.1.1.4 Eliminating Double Counting of EGU Units
The following procedures were used to avoi d double counting of EGU emissions in the 2009/2018 point source inventory. The 2002 VISTAS poi nt source emission inventory contains
both EGUs and non-EGUs. Since this file cont ains both EGUs and non-EGU point sources, andDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### EGU emissions are projected using the IPM, it wa s necessary to split the 2002 point source file into two components. The first component cont ains those emission units accounted for in the IPM forecasts. The second component contains all other point sources not accounted for in IPM.

As described in the previous section, Pechan developed 2009/2018 NIF files for EGUs from the IPM parsed files. All IPM matched units were initially removed from the 2009/2018 point source inventory to create the non- EGU inventory (which was projected to 2009/2018 using the nonEGU growth and control factors described in Section 2.1.2). This was done on a unit-by-unit
basis based on a cross-reference table that matc hes IPM emission unit identifiers (ORISPL plant code and BLRID emission unit code) to VISTAS NIF emission unit identifiers (FIPSST state code, FIPSCNTY county code, State Plant ID, Stat e Point ID). When there was a match between
the IPM ORISPL/BLRID and the VISTAS emissi on unit ID, the unit was assigned to the EGU inventory; all other emission units we re assigned to the non-EGU inventory.

If an emission unit was contained in the NIF f iles created by Pechan fr om the IPM output, the corresponding unit was removed from the in itial 2009/2018 point source inventory. The NIF 2009/2018 EGU files from the IPM parsed files were then merged with the non-EGU 2009/2018
files to create the 2009/2018 Ba se F point source files.

Next, we prepared several ad-hoc QA/QC queries to verify that there was no double-counting of
emissions in the EGU a nd non-EGU inventories:

 We reviewed the IPM parsed files {VIS TASII_PC_1f_AllUnits_2009 (To Client).xls and VISTASII_PC_1f_AllUnits_2018 (To Client).xls} to identify EGUs accounted for in IPM. We compared this list of emission units to the non-EGU inventory derived from the VISTAS cross-reference table to verify thatunits accounted for in IPM were not doublecounted in the non-EGU invent ory. As a result of this comparison, we made a few adjustments in the cross-reference table to a dd emission units for four plants to ensure these units accounted for in IPM were moved to the EGU inventory.

 We reviewed the non-EGU inventory to identify remaining emission units with an Standard Industrial Classification (SIC) code of “4911 Electrical Services” or Source
Classification Code of “1-01-xxx-xx External Combustion Boiler, Electric Generation”. We compared the list of sources meeting these selection criteria to the IPM parsed file to
ensure that these units were not double-counted.

S/L agencies also reviewed the 2009/2018 point s ource inventory to verify whether there was any double counting of EGU emissions. In two in stances, S/L agencies provided corrections
where an emission unit was double counted.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 2.1.1.5 Quality Assurance Steps
Quality assurance was an important component to the inventory development process. The following QA steps on the EGU component of th e VISTAS revised 2009/2018 EGU inventory:

1. Provided parsed files (i.e., Excel spreadsheets that provide un it-level results derived from the model plant projections obtained by the IP M) to the VISTAS EGU SIWG for review.

2. Provided facility level emission summar ies for 2009/2018 for both the base case and
CAIR case to the VISTAS EGU SIWG to ensure that emissions were consistent and that there were no missing sources.

3. Compared, at the State-level, emissions from the IPM parsed files and the post-processed NIF files to verify that the post-processed NIF files were consistent with the IPM parsed
file results.
VISTAS requested S/L review of th ese files – the changes specified by states as a result of this review are documented in the following subsection.

2.1.1.6 S/L Adjustments to IPM Modeling Results for Base G Projections
After S/L agency review of the final set of IPM ru ns (as incorporated into the Base F inventory), S/L agencies specified a number of changes to the IPM results to better reflect current
information on when and where future controls would occur. These changes to the IPM results primarily involved S/L agency addition or subtrac tion future emission cont rols based on the best available data from state rules, enforcemen t agreements, compliance plans, permits, and discussions/commitments from individual companies.

For example, Dominion Virginia Power released their company-wide plan to reduce emission to meet the requirements of CAIR and other programs . This plan varies substantially from the IPM
results both in terms current and future controls and timing of these controls. As a result, VA DEQ developed their best estimates of future c ontrols on EGUs in Virginia. Also, Duke Energy and Progress Energy have updated their plans for complying with North Carolina’s Clean Smokestack Act. These plans vary substantially from the IPM results both in terms current and future controls and timing of these controls. As a result, NC DENR replaced the IPM emission projections for 2009 with projec tions from the Duke Energy and Progress Energy compliance plan. NC DENR elected to use the IPM results for 2018.

Some S/L agencies specified changes to the c ontrols assigned by IPM to reflect their best estimates of emission controls. These changes involved either 1) addi ng selective catalytic reduction (SCR) or scrubber controls to units where IPM did not predict SCR or scrubber controls, or 2) removing IPM-assi gned SCR or scrubber controls at units where the S/L agency indicated their were no firm plans for controls at those units. We generally used a controlDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### efficiency of 90 percent when adding or removing SO 2 

### scrubber controls (unless a different control efficiency was provided by the State). We generally used a control efficiency of 90 percent when adding or removing NO x 

### SCR controls at coal-fired plants, 80 percent when adding or removing NO x 

### SCR controls at gas-fired plants, a nd 35 percent when adding or removing NO x 

### SNCR controls (unless a different control efficiency was provid ed by the State). The changes specified by the S/L agencies are summarized in Table 2.1-1. A comparison of the IPM and VISTAS control assumptions fo r all coal-fired EGUs in the Base G/G2 inventories are summarized in Appendix H. In addition to the changes to the IPM-assi gned controls, the S/L agencies also specified other types of changes to the IPM results. These ot her specific changes to
the IPM results are summarized in Table 2.1-2.

S/L agencies provided informati on and/or comment on changes in stack parameters from the 2002 inventory for 2009/2018 inventory. Changes to st ack parameters were also made in cases
where new controls are scheduled to be installed. In cases wher e an emission unit projected to
have a SO 2 

### scrubber in either 2009 or 2018, some states were able to provide revised stack parameters for some units based on design features for the new control system. Other units projected to install scrubbers by 2009 or 2018 ar e not far enough along in the design process to have specific design details. For those units , the VISTAS EGU SIWG made the following assumptions: 1) the scrubber is a wet scrubber; 2) keep the current stack height the same; 3) keep the current flow rate the same, and 4) change th e stack exit temperature to 169 degrees F (this is the virtual temperature derived from a wet temp erature of 130 degrees F). VISTAS determined that exit temperature (wet) of 130 degrees F +/- 5 degrees F is representative of different size units and wet scrubber technology.

2.1.1.7 S/L Adjustments to IPM Modeling Resu lts for Base G2 2018 Projections
Following release of the Base G inventory, four States specified additional changes to reflect
their best estimates of emission controls in 2018. These additional changes are marked with an “*” in Tables 2.1-1 and 2.1-2. The following cha nges were requested and implemented in the
VISTAS 2018 Base G2 EGU emissi ons and modeling inventories:

 

### Florida - Removed scrubbers from Smith units 1 & 2. Added scrubbers to Crist units 4, 5, & 6. Forecast emissions (from 2002 base) using growth factors for Northside units 1A and 2A. These units were estimated to be non operational in the IPM base case run.

 

### Georgia - Added scrubbers to Plant Sc herer (Units 1-4) and Pl ant Yates (Units 6 & 7).

 

### North Carolina - Remove scrubber from F Lee unit 3.

 

### West Virginia - Pleasants Units 1 and 2 had SO2 em issions reduced to account for the facility's inclusion of previously bypassed 15% effluent stream to the scrubber and the control efficiency and emissions will refl ect a change from 79.9% to 95% control.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission InventoriesMACTEC, Inc.101Table 2.1-1 Adjustments to IPM Control Determinations Specified by S/L Agenciesfor the Base G/G2 2009/2018 EGU Inventories.NOxRetrofit Emission ControlsSO2Retrofit Emission ControlsStatePlant Name and IDUnit2009201820092018IPMStateIPMStateIPMStateIPMStateAL James H. MillerORISID=60021 & 2 SCRduringozoneseasonSCRprobableyear rounddue toCAIRSCRduringozoneseasonSCRprobableyear rounddue toCAIRNone None None Scrubber3 & 4 SCRduringozoneseasonSCR yearround fromConsentDecreeSCRduringozoneseasonSCR yearround fromConsentDecreeNone None None ScrubberBarry 1, 2, 3 None SNCR SCR SNCR None None None NoneORISID=3 4 None SNCR SCR SNCR None None Scrubber Scrubber5 None None SCR SCR None None Scrubber ScrubberE C Gaston 1 - 4 SCR None SCR None None None Scrubber ScrubberORISID=26 5 SCR SCR SCR SCR Scrubber None Scrubber ScrubberGorgas 6 & 7 None None NoneNone None None None NoneORISID=8 8 & 9 None None None None None Scrubber None Scrubber10 SCR SCR SCR SCR None Scrubber Scrubber ScrubberCharles R. Lowman 1 None None None None None Scrubber None ScrubberORISID=56 2 & 3 SCR SCR SCR SCR Scrubber Scrubber Scrubber ScrubberFL Lansing Smith 1 None None SCR SCR None None Scrubber None*ORISID=643 2 None None SCR SCR None None Scrubber None*NorthsideORISID=6671A &1BNooperationNooperationNooperationNo control,emissionsforecastedusinggrowthrates*NooperationNooperationNooperationNo control,emissionsforecastedusinggrowthrates*Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission InventoriesMACTEC, Inc.102Table 2.1-1 (continued)NOxRetrofit Emission ControlsSO2Retrofit Emission ControlsStatePlant Name and IDUnit2009201820092018IPMStateIPMStateIPMStateIPMStateFL Crist 4 None None None None None None None Scrubber*ORISID=641 5 None None None None None None None Scrubber*6 None None None None None None None Scrubber*GA Bowen 1BLR SCR SCR SCR SCR None Scrubber ScrubberORISID=703 2BLR SCR SCR SCRSCR None Scrubber Scrubber3BLR SCR SCR SCR SCR Scrubber Scrubber Scrubber4BLR SCR SCR SCR SCRIPM hadretrofitscrubbersbut littleemissionreductionsScrubber Scrubber ScrubberWansley 1 SCR SCR SCR SCR Scrubber Scrubber ScrubberORISID=6052 2 SCR SCR SCR SCRIPM hadretrofitscrubbersbut littleemissionreductionsNone Scrubber ScrubberKraft 1, 2 None None NoneNone None None None NoneORISID=733 3 None None SCRNone None None None NoneMcIntoshORISID=61241 None None SCR None None None None NoneYates 1 None None None None Scrubber Scrubber Scrubber ScrubberORISID=728 2, 3 None None NoneNone None None None None4, 5 None None SCR SCR None None Scrubber None6, 7 None None SCR SCR None None Scrubber Scrubber*Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission InventoriesMACTEC, Inc.103Table 2.1-1 (continued)NOxRetrofit Emission ControlsSO2Retrofit Emission ControlsStatePlant Name and IDUnit2009201820092018IPMStateIPMStateIPMStateIPMStateGA Hammond 1 None None SCR SCR None Scrubber Scrubber ScrubberORISID=708 2 None None SCR SCR None Scrubber Scrubber Scrubber3 None None SCR SCR None Scrubber Scrubber Scrubber4 SCR SCR SCR SCR ScrubberScrubber Scrubber ScrubberScherer 1 None None None None None None None Scrubber*ORISID=6257 2 None None None None None None None Scrubber*3 None None None None None None None Scrubber*4 None None None None None None None Scrubber*KY Ghent 1 None SCR SCR SCR Scrubber Scrubber Scrubber ScrubberORISID=1356 2 None None SCR SCR None Scrubber Scrubber Scrubber3, 4 None SCR SCR SCR NoneScrubber Scrubber ScrubberColeman C1 None None SCR SCR None Scrubber Scrubber ScrubberORISID=1381 C2 None None SCR SCR None Scrubber Scrubber ScrubberC3 None None SCR SCR None Scrubber Scrubber ScrubberHMP&L Station 2 H1 SCR SCR SCR SCR Scrubber Scrubber Scrubber ScrubberH2 None SCR SCR SCR ScrubberScrubber Scrubber ScrubberE W Brown 1 None None None None None Scrubber None ScrubberORISID=1355 2 None None SCR SCR None Scrubber Scrubber Scrubber3 None None SCR SCR None Scrubber Scrubber ScrubberSC Jeffries 3 SCR None SCR None None None None NoneORISID=3319 4 None None NoneNone None None None NoneWateree WAT1 SCR SCR SCR SCRNone Scrubber None ScrubberORISID=3297 WAT2 SCR SCR SCR SCRNone Scrubber Scrubber ScrubberDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission InventoriesMACTEC, Inc.104Table 2.1-1 (continued)NOxRetrofit Emission ControlsSO2Retrofit Emission ControlsStatePlant Name and IDUnit2009201820092018IPMStateIPMStateIPMStateIPMStateSC Canadys CAN1 None None NoneNone None None None NoneORISID=3280 CAN2 None None None None None None None NoneCAN3 None None None None None Scrubber None ScrubberRainey CT1A None SCR None SCR None None None NoneORISID=7834 CT1B None SCR None SCR None None None NoneTN Kingston 1 – 8 SCR SCR SCR SCR None None Scrubber ScrubberORISID=3407 9 None SCR SCR SCR None None Scrubber ScrubberJohnsonville 1 – 10 SCR None SCR SCR None None None NoneORISID=3406WV Willow Island 2 SCR None SCR SCRScrubber None Scrubber ScrubberORISID=3946Kammer 1 -3 SCR None SCR SCR Scrubber None Scrubber ScrubberORISID=3947Note:See Appendix H for a complete list of IPM and VISTAScontrol determinations for all coal and oil/gas units.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-2 Other Adjustments to IPM Re sults Specified by S/L Agencies for the Base G/G2 2009/2018 EGU Inventories.

State Plant Name and ID Unit Nature of Update/Correction

FL Central Power and Lime ORISID= 10333 GEN1 Central Power and Lime (ORI S10333) is a duplicate entry. This is point 18 in Florida Crushed Stone (12-053-0530021). Removed IPM emissions for Central Power and Lime.
Cedar Bay Generating ORISID=10672 GEN1 FLDEP disagrees with IPM projections - no knowledge of expansion of this facility and the cogeneration facility should not grow faster than the underlying industry. Cedar Bay is connected to Stone Container (12-031-0310067). Replaced IPM emissions with 2002 emissions for Cedar Bay (12-031-0310337) times the growth factors for Stone Container.
Indiantown Cogeneration ORISID=50976 GEN1 FLDEP disagrees with IPM projections - no knowledge of expansion of this facility and the cogeneration facility should not grow faster than the underlying industry. Indiantown is connected to Louis Dreyfus Citrus (12-085- 0850002). Replaced IPM emissions with 2002 emissions for Indiantown (12-085-0850102) times the growth factors for Louis Drefus Citrus.

GA Bowen
ORISID=703 1BLR 2BLR 3BLR 4BLR IPM indicated retrofit scrubbers on all 4 units in 2009, but the IPM emissions showed little reductions from 2002 levels. Changed emissions to reflect scrubbers on 3BLR and 4BLR by 2009.
Wansley
ORISID=6052 1, 2 IPM indicated retrofit scrubbers on both units in 2009, but the IPM emissions showed little reductions from 2002 levels. Changed emissions to reflect one scrubber on Unit 1 by 2009.

Riverside
ORISID=734 4 All of plant Riverside was retired from service June 1, 2005; emissions set to zero in 2009 and 2018.

McIntosh
ORISID=727 CT10A CT10B CT11A CT11B The McIntosh Combined Cycl e facility became commercial June 1, 2005. Added 346 tons of NO x and 121 tons of SO 2 per unit to the 2009 and 2018 inventories.

Longleaf Energy Station 1, 2 Longleaf Energy Station is being proposed by LS Power Development, Inc. GA specified that the emissions from this proposed plant be included in the 2018 projections. Boilers 1 and 2 added 1,882 tons of NO x and 3,227 tons of SO 2 per unit to the 2018 inventory.
Duke Murray (55382) 1 Corrected coordinates to 34.7189 and -84.9353

MS R D Morrow
ORISID=6061 1, 2 Revised the 2018 emissions to reflect controls not indicated by IPM. The SO 2 emissions are much lower than IPM, but their expected NO x emissions are actually higher than IPM. The controls will be coming online 2009 or 2010, so the 2009 inventory did not change.
Jack Watson (2049) Victor J Daniel (6073) Chevron Oil (2047) All MS DEQ specified that the em ission projections provided by the Southern Company for their units in Mississippi were to be used instead of the IPM results.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-2 (continued)

State Plant Name and ID Unit Nature of Update/Correction

NC G G Allen (2718) Belews Creek (8042)1 Buck (2720) Cliffside (2721) Dan River (2723) Marshall (2727) Riverbend (2732) All Replaced all IPM 2009 resu lts with emission projections from Duke Power’s NC Clean Air Compliance Plan for 2006. Used IPM results for 2018

Asheville (2706) Cape Fear (2708) Lee (2709) Mayo (6250) Roxboro (2712) Sutton (2713) Weatherspoon (2716) All Replaced all IPM 2009 resu lts with emission projections from Progress Energy’s NC Clean Smokestacks Act Calendar Year 2005 Progress Report. Used IPM results for 2018, except for Lee #3* where IPM projected a retrofit scrubber but NC specified that no scrubber was to be applied.

Dwayne Collier Battle Cogeneration Facility
ORISID=10384 GEN1
GEN2 Dwayne Collier Battle is a duplicate entry. This is Cogentrixof Rocky Mount (37-065-3706500146, stacks G-26 and G27). Duplicate entries were removed both the 2009 and 2018 inventories.
Kannapolis Energy Partners
ORISID=10626 GEN2
GEN3 Kannapolis Energy emissions are being used as credits for another facility. IPM emissions from this facility (37-025- ORIS10626) were removed from the EGU inventory for 2009 and 2018. Emissions from Kannapolis Energy (37-025- 3702500113) were carried forward in the 2009/2018 inventory.

SC Cross
ORISID=130 1, 2 Unit 1: upgrade scrubber from 82 percent to 95 percent removal efficiency by June 30, 2006. Recalculate emissions based on upgrade in control efficiency. Unit 2: upgrade scrubber from 70 percent to 87 percent removal efficiency by June 30, 2006. Recalculate emissions based on upgrade in control efficiency.
Winyah
ORISID=6249 1 – 4 Unit 1: Install scrubber that meets 95 percent removal efficiency by Dec. 31, 2008; Up grade ESP from 0.38 to 0.03 lb/mmBTU by Dec. 31, 2008 Unit 2: Replace scrubber with one that meets 95 percent removal efficiency from 45 percent by Dec. 31, 2008; Upgrade ESP from 0.10 to 0.03 lb/mmBTU by Dec. 31, 2008 Unit 3: Upgrade scrubber from 70 percent to 90 percent removal efficiency by Dec. 31, 2012; Upgrade ESP from 0.10 to 0.03 lb/mmBTU by Dec. 31, 2012 Unit 4: Upgrade scrubber from 70 percent to 90 percent removal efficiency by Dec. 31, 2007; Upgrade ESP from 0.10 to 0.03 lb/mmBTU by Dec. 31, 2007
Recalculated SO 2 and PM emissions based on upgrade in control efficiencies.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-2 (continued)

State Plant Name and ID Unit Nature of Update/Correction

SC Dolphus Grainger
ORISID=3317 1, 2 Unit 1: Upgrade ESP from 0.60 to 0.03 lb/mmBTU by Dec. 31, 2012. Reduced PM 10 and PM25 emissions in 2018 by 95 percent based on change in allowable emission rate
Unit 2: Install low NO x burners that meet 0.46 lb/mmBTU from 0.9 by May 1, 2004. Recalculated NO x emissions using 0.46/lbs/mmBtu and IPM heat input Unit 2: Upgrade ESP from 0.60 to 0.03 lb/mmBTU by Dec. 31, 2012. Reduced PM 10 and PM25 emissions in 2018 by 95 percent based on change in allowable emission rate

Jeffries
ORISID=3319 3, 4 Unit 3: Upgrade ESP from 0.54 to 0.03 lb/mmBTU by Dec. 31, 2012. Reduced PM 10 and PM25 emissions in 2018 by 94.44 percent based on change in allowable emission rate Unit 4: Upgrade ESP from 0.54 to 0.03 lb/mmBTU by Dec. 31, 2012. Reduced PM 10 and PM25 emissions in 2018 by 94.44 percent based on change in allowable emission rate

W S Lee
ORISID=3264 1, 2 IPM does not indicate that these units are installing SOFA NO x control technology by April 30, 2006 to meet 0.27 lb/mmBTU, down from 0.45 lb/mmBtu. Calculated NO x emissions using IPM heat input and 0.27 lbs/mmBtu

Generic Unit
ORISID=900545 All All predictions for generic un its appear reason able with the exception of Plant ID ORIS900545 Point ID GSC45 which was modeled in Georgetown County. It will be very difficult to add new generation this close to the Cape Romain Class I area. Santee Cooper has no plans for future generation in Georgetown County, but does have plans for new future generation in Florence County. This unit was moved to coordinates specified in Florence County.

VA AEP Clinch River
ORISID=3775 1, 2, 3 Used IPM results for 2009; replaced all 2018 IPM results with VADEQ’s growth and control estimates (no SCR or scrubbers).
AEP Glen Lyn
ORISID=3776 51, 52, 6 Used 2009/2018 IPM results for units 51 and 52; used 2009 IPM for unit 6; replaced 2018 IPM for unit 6 with VADEQ’s growth and control estimates (nor SCR or scrubber).

Dominion Clover
ORISID=7213 1, 2 Used 2009/2018 IPM results.

Dominion Bremo
ORISID=3796 3, 4 Used 2009/2018 IPM results.

Dominion Chesterfield
ORISID=3797 3, 4, 5, 6 Replaced all 2009/2018 IPM re sults using VADEQ’s growth and control estimates.

Dominion Yorktown
ORISID=3809 1, 2, 3 Units 1, 2: Used 2009/2018 IPM results for NOx and used VADEQ’s growth and contro l estimates for SO2. Unit 3: IPM predicts zero heat input for this 880 MW #6 oil fired unit. Dominion plans to continue to operate Unit 3. Replaced all 2009/2018 IPM re sults using VADEQ’s growth and control estimates.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-2 (continued)

State Plant Name and ID Unit Nature of Update/Correction

VA Dominion Chesapeake
ORISID=3803 1 – 4 Unit 1: Used 2009/2018 IPM for NOx; used 2009 IPM for SO2; used VADEQ’s growth and control estimates for SO2 (added scrubber that IPM did not have) Unit 2: Used 2009/2018 IPM for NOx; used 2009 IPM for SO2; used VADEQ’s growth and control estimates for SO2 (added scrubber that IPM did not have) Unit 3: Used VA DEQ’s growth and control estimates for 2009 NOx (added SCR that IPM did not have); used IPM result for 2018 NOx; Used 2009/2018 IPM for SO2. Unit 4: Used VA DEQ’s growth and control estimates for 2009 NOx (added SCR that IPM did not have); used IPM result for 2018 NOx; Used 2009/2018 IPM for SO2.

Dominion Possum Point
ORISID=3804 3 & 4
5
6 Unit 3&4: IPM had 137 tons of NO x for these units in 2009 and 111 tons in 2018. VA DEQ specified that the permitted emission rates should be used, which equates to 3,066 tons in 2009 and 2018. Unit 5: IPM had zero heat in put. Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates. Unit 6: Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates.

Potomac River
ORISID=3788 1 - 5 Units 1&2: IPM retired these units. Mirant has no plans at this time to retire any units . Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates. Units 3, 4, 5: Replaced all 2009/2018 IPM results using VADEQ’s growth and control estimates.
WV Albright
ORISID=3942 1, 2 IPM predicted early retirement for these units. AEP indicated there are no plans fo r early retirement. For 2009, used 2002 actual emissions as these units are not likely to retire by 2009. For 2018, used IPM prediction of retirement.

Rivesville
ORISID=3945 7, 8 IPM predicted early retirement for these units. AEP indicated there are no plans fo r early retirement. For 2009, used 2002 actual emissions as these units are not likely to retire by 2009. For 2018, used IPM prediction of retirement.

Willow Island
ORISID=3946 1, 2 Unit 1: IPM predicted early retirement for these units. AEP indicated there are no plans fo r early retirement. For 2009, used 2002 emissions as these units are not likely to retire by 2009. For 2018, used IPM prediction of retirement. Unit 2: IPM predicted SCR and scrubber for 2009. These controls will not be in place by 2009.

North Branch
ORISID=7537 1A, 1B SO 2 Permit Rate was corrected from 2.7 to 0.678 lb/MMBtu. Used SO 2 Permit Rate and IPM predicted total fuel used to calculate SO 2 emissions in 2009 and 2018

Mt. Storm
ORISID=3954 1, 2, 3 SO 2 Permit Rate was corrected from 2.7 to 0.15 lb/MMBtu. Used SO 2 Permit Rate of 0.15 lb/MMBtu and IPM predicted total fuel used to calculate SO 2 emissions in 2009 and 2018

Pleasants Power Station
ORISID=6004 1, 2 IPM applied a scrubber with a 79.9% control efficiency; WV indicated that the control e fficiency should be 95%.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 2.1.1.8 S/L Adjustments to IPM Modeling Results for B&F Projections
For the B&F inventory, the S/L agencies were asked to review the Base G2 inventory with respect to the following items:

 Identify any updates needed to better refl ect current information on when and where
future controls would occur based on the best available data from state rules, enforcement agreements, compliance plans, permits, and discussions/commitments from individual companies;

 Identify any updates needed to change the IPM determination that most oil/gas steam units would either retire early or have no operation in 2009 or 2018; and

 Identify any updates needed to change the IPM assignment and VISTAS post-processing of generic units with specif ic information on new capacity.
The changes specified by the S/L agencies are summarized in Table 2.1- 3. A comparison of the IPM and VISTAS control assumptions for all co al-fired EGUs in the B&F inventories are summarized in Appendix I.

Table 2.1-3 Additional Adjustments to IP M Results Specified by S/L Agencies for the B&F 2009/2018 EGU Inventories.

State Plant Name and ID Unit Nature of Update/Correction

FL Cape Canaveral Indian River Port Everglades Turkey Point Manatee Martin Riviera Anclote CD McIntosh Northside B Suwannee River 1, 2 1, 2, 3 1 – 4 1, 2 1, 2 1, 2 3, 4 1, 2

1 3 3 The IPM 2009/2018 solution has either shut-down these oilfired units or converted them to natural gas only. FLDEP has reason to believe that these units may continue to operate using oil. For some of these units, the owner or operator of the units have provided (and FLDEP approved) an estimate of how the units will be operated in 2009/2018. For others, to be conservative, FLDEP assumed that the oil-fired units will operate in 2009/2018 exactly as they operated in 2002.

Gulf Power Schultz
ORISID=643 1 - 4 Plant is expected to shut down and was taken out of the 2018 projection.

Northside
ORISID=667 1A, 1B These units were estimated to be non operational by IPM in 2009 and 2018. FLDEP believes these units will continue to operate. Emissions were estimated using the 2002 base case emissions and growth factors for Northside units 1A and 2A. The changes for 2009 were made in the B&F inventory; the changes for 2018 were made in the Base G2 inventory.

Crist
ORISID=641 4, 5 6, 7 IPM did not assign scrubbers to these units. Scrubbers are currently being installed and should be operational in 2009. SO2 emissions reduced by 90%.

GA Mitchell
ORISID=727 SG03 GADNR provided new emission projections for 2018.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-3 (continued)

State Plant Name and ID Unit Nature of Update/Correction

GA Kraft
ORISID=733 SG03 GADNR provided new emission projections for 2018.

McIntosh
ORISID=6124 SG01 GADNR provided new emission projections for 2018.

Bowen
ORISID=703 SG03 SG04 GADNR provided new SO2 emission projections for 2009 and 2018 based on a 95% control efficiency instead of 90%.

Hammond
ORISID=708 SG01 to SG04 GADNR provided new SO2 emission projections for 2009 and 2018 based on a 95% control efficiency instead of 90%.

Wansley
ORISID=6052 SG01 GADNR provided new SO2 emission projections for 2009 and 2018 based on a 95% control efficiency instead of 90%.

KY John Sherman Cooper
ORISID=1384 1 IPM did not assign a scrubber to this unit in 2018. KDAQ believes that a scrubber should be assigned for 2018.

John Sherman Cooper
ORISID=1384 2 IPM assigned SCR in 2009. KDAQ does not expect SCR by then; emissions changed to reflect low-NOx burner.

Spurlock Station
ORISID=6041 1, 2 IPM did not assign scrubbers to these units in 2009. Per a consent decree and for BA RT, KDAQ specified a 90% reduction in SO2 emissions from SO2 controls.
Big Sandy
ORISID=1353 BSU1 IPM assigned a scrubber and SCR in 2009. KDAQ does not expect scrubber or SCR controls to be operational in 2009.

MS Entergy Delta Entergy Rex Brown Entergy Baxter Wilson Entergy Gerald Andrus 1, 2 3, 4 1, 2

1 The IPM 2009/2018 solution has either shut-down these oilfired units or converted them to natural gas only. MSDEQ has reason to believe that these units may continue to operate using oil. To be conservative, MSDEQ assumed that the oil-fired units will operate in 2009/2018 exactly as they operated in 2002.

NC Cliffside
ORISID=2721 7 Removed Unit 7 from the 2018 inventory since the NC Utilities Commission disapproved the permit application.

Cape Fear
ORISID=2798 1, 2 IPM assigned scrubbers to both units in 2018; NCDENR indicated that the facility projected Furnace Sorbent Injection. Increased SO2 emis sions to reflect change in control efficiency.

SC 99 Oil-fired Units The IPM 2009/2018 solution has either shut-down 99 oilfired units or converted them to natural gas only. SCDHEC has reason to believe that these units may continue to operate using oil. To be conservative, SCDHEC assumed that the oil-fired units will operate in 2009/2018 exactly as they operated in 2002.
SC Santee Cooper Cross
ORISID=130 4 For both 2009 and 2018, added in a new 660 MW Unit 4 (not in IPM) that is identical to the new Unit 3 (which was in IPM). Used the new Unit 4 to replace the IPM-generated 500 MW coal-fired Generic Unit (O RIS900545) located in the adjacent county.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-3 (continued)

State Plant Name and ID Unit Nature of Update/Correction

SC New Santee Cooper Units Planned for Florence County 1, 2 Santee Cooper is planning two new coal burning units in Florence County, each at 660 MW. These units were not explicitly identified in IPM. Used these new units to replace three IPM-generated 500 MW coal-fired Generic Units (ORIS900145, ORIS900245, ORIS900345) in Darlington and Colleton Counties.

USDOE SRS Area D
ORISID=7652 1 Facility is replacing coal-fired boilers with three biomass boilers. Recalculated emissions for 2018 using emission factors for biomass combustion and IPM heat inputs.
VA Dominion Chesapeake
ORISID=3803 1 - 4 Changed SO2 emissions in 2009 and 2018 to reflect information from the facility on project SO2 controls.

Dominion Southwest Virginia Project 1 For 2018, replace the IPM generated Generic Unit located in Russell county (ORISID=900251) to Wise County to reflect the planned Dominion facility going into Wise County. Used the potential to emit for the Dominion facility.

Clinch River
ORISID=3775 1, 2, 3 Changed emissions in 2018 to reflect requirements of Consent Order. The CO requires SNCR by 12/31/2009; IPM assigned SCR in 2018. The CO caps SO2 emissions at 16,300 tpy starting Jan 1, 2015.

WV Pleasants Power Station
ORISID=6004 1, 2 For both 2009 and 2018, Units 1 and 2 had SO2 emissions reduced to account for the facility's inclusion of previously bypassed 15% effluent stream to the scrubber. The control efficiency and emissions ch anged from 79.9% to 95% control.

Nine Generic Units Generated by IPM IPM placed 746 MW of new fossil fuel-fired generation in West Virginia - 173 MW coal-fired, 24 MW IGCC, and the remainder gas-fired. A 600 MW pulverized coal-fired EGU is under construction, scheduled to be online in 2010 [Longview]; a 98 MW CFB co-generation unit is permitted and expected to be built [Western Greenbrier]; and a 600 MW IGCC plant is currently in the permitting process [Mountaineer IGCC]. WVDEP decided to replace the IPM generic units in WV with the 3 units mentioned above.
Longview
Site ID: 54- 061-0134 1 For 2018 inventory, added Longview which is permitted, under construction, and scheduled to be online in 2010. The unit is a 600 MW pulverized coal-fired unit with baghouse, LNB, SCR, and wet FGD as required controls. Used permitted emission rates for 2018.

WV Western Greenbriar
Site ID: 54-025-0066 1 For 2018 inventory, added Western Greenbrier, which is permitted but not under construction. The unit is a 98 MW coal-fired CFB burning waste co al. Used permitted emission rates for 2018.

Mountaineer IGCC
Site ID: 54-053-00063 1 For 2018 inventory, added Mountaineer IGCC, which has applied for a permit to construct a nominal 600 MW IGCC. Used emission rates from the permit application for 2018.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 2.1.1.9 Conversion of MRPO BaseM 2009 EGU Data to SMOKE Input Format
To support ASIP PM 2.5 

### CAMx modeling of the future ye ar 2009, Alpine Geophysics obtained and processed an emission inventory for the 5 MRPO states (Illinois, Indiana, Michigan, Wisconsin, and Ohio). Appendix x details the technical steps that were made as part of the
conversion of the MRPO BaseM EGU files into IDA format for ASIP PM-2.5 CAMx modeling of the future year 2009.

2.1.1.10 Summary of 2009/2018 EGU Point Source Inventories
Tables 2.1-4 through 2.1-10 compare the Base G 2002 base year inventory to the Base F, Base G/G2 and B&F 2009/2018 projection inventories. Th e Base F projections rely primarily on the results of the IPM, while the Base G and B&F projections include the adjustments to the IPM results specified by the S/L agenci es in the previous section.

Table 2.1-4 EGU Point Source SO 2 

### Emission Comparison for 2002/2009/2018.

2002 2009 2018

State Actual Base G Base F IPM Based Base G IPM with State/local

Updates B&F IPM with Additional State/local

Updates Base F IPM Based Base G2 IPM with State/local

Updates B&F IPM with Additional State/local

Updates AL 447,828 340,194 378,052 378,052 190,099 135,851 135,851
FL 453,631 195,790 186,055 291,831 141,551 138,340 194,028
GA 514,952 534,469 417,449 408,679 180,178 79,430 68,515
KY 484,057 371,944 290,193 271,669 229,603 226,062 222,102
MS 67,429 85,629 76,579 76 ,646 27,230 15,146 15,213
NC 477,990 205,018 242,286 242,286 110,382 114,771 120,165
SC 206,399 171,206 124,608 129,122 121,694 93,274 95,377
TN 334,151 255,400 255,410 255,410 112,662 112,672 112,672
VA 241,204 169,714 193,112 174,777 90,935 114,255 98,988
WV 516,084 226,127 277,489 268,952 124,466 105,935 106,199
3,743,725 2,555,491 2,441,233 2,497,423 1,328,800 1,135,736 1,169,110

Note: Emission summaries above are based on SCCs 1-01-xxx-xx and 2-01-xxx-xx.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-5 EGU Point Source NO x 

### Emission Comparison for 2002/2009/2018.

2002 2009 2018

State Actual Base G Base F IPM Based Base G IPM with State/local

Updates B&F IPM with Additional State/local

Updates Base F IPM Based Base G2 IPM with State/local

Updates B&F IPM with Additional State/local

Updates AL 161,038 70,852 82,305 82,305 42,769 64,358 64,358
FL 257,677 89,610 86,165 132,535 77,080 74,640 87,645
GA 147,517 97,146 98,497 98,497 58,095 75,717 69,856
KY 198,817 107,890 92,021 97,263 64,378 64,378 64,378
MS 43,135 11,475 36,011 47,276 8,945 10,271 21,535
NC 151,853 66,431 66,522 66,521 60,914 62,353 61,110
SC 88,241 43,817 46,915 48 ,668 48,346 51,456 51,751
TN 157,307 41,767 66,405 66,405 31,725 31,715 31,715
VA 86,886 63,220 62,547 64, 358 49,420 66,074 64,344
WV 230,977 63,510 86,328 85,476 51,241 51,241 51,474
1,523,448 655,718 723,717 789,304 492,913 552,203 568,166

Note: Emission summaries above are based on SCCs 1-01-xxx-xx and 2-01-xxx-xx.



### Table 2.1-6 EGU Point Source VOC Em ission Comparison for 2002/2009/2018.

2002 2009 2018

State Actual Base G Base F IPM Based Base G IPM with State/local

Updates B&F IPM with Additional State/local

Updates Base F IPM Based Base G2 IPM with State/local

Updates B&F IPM with Additional State/local

Updates AL 2,295 2,441 2,473 2,473 2,952 2,952 2,952
FL 2,524 1,867 1,910 2,730 2,324 2,422 3,047
GA 1,244 1,571 2,314 2,314 1,903 2,841 2,816
KY 1,487 1,369 1,369 1,369 1,426 1,426 1,426
MS 648 406 404 564 1,124 1,114 1,274
NC 988 974 954 954 1,272 1,345 1,302

SC 470 660 660 723 906 906 931

TN 926 932 932 932 977 976 976
VA 754 685 778 788 903 1,014 980
WV 1,180 1,342 1,361 1,361 1,387 1,387 1,387
12,516 12,247 13,155 14, 208 15,174 16,383 17,091

Note: Emission summaries above are based on SCCs 1-01-xxx-xx and 2-01-xxx-xx.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-7 EGU Point Source CO Em ission Comparison for 2002/2009/2018.

2002 2009 2018

State Actual Base G Base F IPM Based Base G IPM with State/local

Updates B&F IPM with Additional State/local

Updates Base F IPM Based Base G2 IPM with State/local

Updates B&F IPM with Additional State/local

Updates AL 11,279 14,948 14,986 14 ,986 24,342 24,342 24,342
FL 57,113 45,391 35,928 71 ,072 63,673 54,146 85,495
GA 9,712 20,066 23,721 23,721 32,744 44,476 44,269
KY 12,619 15,812 15,812 15, 812 17,144 17,144 17,144
MS 5,303 5,078 5,051 7,116 15,364 15,282 17,348
NC 13,885 15,141 14,942 14 ,942 19,612 20,223 19,870
SC 6,990 11,135 11,135 11,643 14,786 14,786 14,975
TN 7,084 7,221 7,213 7,214 7,733 7,723 7,723
VA 6,892 11,869 12,509 12,535 14,755 15,564 18,850
WV 10,341 11,328 11,493 11 ,493 11,961 11,961 12,397
141,218 157,989 152,790 190,535 222,114 225,647 262,413

Note: Emission summaries above are based on SCCs 1-01-xxx-xx and 2-01-xxx-xx.



### Table 2.1-8 EGU Point Source PM 10 

### -PRI Emission Comparison for 2002/2009/2018.

2002 2009 2018

State Actual Base G Base F IPM Based Base G IPM with State/local

Updates B&F IPM with Additional State/local

Updates Base F IPM Based Base G2 IPM with State/local

Updates B&F IPM with Additional State/local

Updates AL 7,646 6,959 6,969 6,969 7,822 7,822 7,822
FL 21,387 9,384 9,007 20,182 10,310 10,022 12,791
GA 11,224 17,088 17,891 17, 891 18,329 20,909 20,732
KY 4,701 6,463 6,463 6,463 6,694 6,694 6,694
MS 1,633 5,487 4,957 5,182 7,624 7,187 7,412
NC 22,754 22,888 22,152 22 ,152 33,742 37,376 35,275
SC 21,400 28,650 19,395 20 ,041 37,864 28,826 27,640
TN 14,640 15,608 15,608 15 ,608 15,941 15,941 15,941
VA 3,960 4,479 5,508 5,606 12,744 13,832 12,551
WV 4,573 5,471 5,657 5,657 6,349 6,349 5,784
113,918 122,477 113,607 125,750 157,419 154,958 152,642

Note: Emission summaries above are based on SCCs 1-01-xxx-xx and 2-01-xxx-xx.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-9 EGU Point Source PM 2.5 

### -PRI Emission Comparison for 2002/2009/2018.

2002 2009 2018

State Actual Base G Base F IPM Based Base G IPM with State/local

Updates B&F IPM with Additional State/local

Updates Base F IPM Based Base G2 IPM with State/local

Updates B&F IPM with Additional State/local

Updates AL 4,113 3,916 3,921 3,921 4,768 4,768 4,768
FL 15,643 6,250 5,910 14,790 7,171 6,886 9,417
GA 4,939 10,104 10,907 10,907 11,403 13,983 13,881
KY 2,802 4,279 4,279 4,279 4,434 4,434 4,434
MS 1,138 5,310 4,777 4,996 7,469 7,033 7,252
NC 16,498 16,514 15,949 15 ,949 26,966 29,792 28,137
SC 17,154 23,366 16,042 16 ,548 32,180 25,032 23,794
TN 12,166 13,092 13,092 13 ,092 13,387 13,387 13,387
VA 2,606 3,194 4,067 4,165 11,101 11,976 10,773
WV 2,210 2,850 2,940 2,940 3,648 3,648 3,116
79,269 88,875 81,884 91,587 122,527 120,939 118,959

Note: Emission summaries above are based on SCCs 1-01-xxx-xx and 2-01-xxx-xx.



### Table 2.1-10 EGU Point Source NH 3 

### Emission Comparison for 2002/2009/2018.

2002 2009 2018

State Actual Base G Base F IPM Based Base G IPM with State/local

Updates B&F IPM with Additional State/local

Updates Base F IPM Based Base G2 IPM with State/local

Updates B&F IPM with Additional State/local

Updates AL 317 359 359 359 1,072 1,072 1,072
FL 234 1,659 1,631 1,629 3,004 2,976 2,976
GA 83 686 686 686 1,677 1,677 1,677

KY 326 400 400 400 476 476 476

MS 190 333 333 334 827 827 827

NC 54 423 445 445 691 663 663

SC 142 343 343 370 617 617 625

TN 204 227 227 227 241 241 241

VA 127 632 694 694 558 622 606

WV 121 330 330 330 180 180 143
1,798 5,392 5,448 5,474 9,343 9,351 9,306

Note : Emission summaries above are based on SCCs 1-01-xxx-xx and 2-01-xxx-xx.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 2.1.2 Non-EGU Emission Projections
The general approach for assembling future year data was to use growth and control data
consistent with the data used in EPA’s Clean Ai r Interstate Rule analyses, supplement these data with available stakeholder input, and provide the results for stakeholder review to ensure credibility. We used the revised 2002 VISTAS base year inventory, based on the 2002 CERR submittals as the starting point for the non-EGU pr ojection inventories. As described in Section 2.1.1.4, we split the point source inventory in to EGU and non-EGU components. MACTEC performed the following activitie s to apply growth and control factors to the 2002 inventory to generate the 2009 and 2018 pr ojection inventories:

 Obtained, reviewed, and applied the most current growth factors developed by EPA, based on forecasts from an updated Regi onal Economic Models, Inc. (REMI) model (version 5.5) and the latest Annual Energy Outlook published by the Department of Energy (DOE);

 Obtained, reviewed, and applied any State-sp ecific or sector-specific growth factors submitted by stakeholders;

 Obtained and incorporated information regard ing sources that have shut down after 2002 and set the emissions to zero in the projection inventories;

 Obtained, reviewed, and applied control assumptions for programs “on-the-books” and “on-the-way”;

 Provided data files in NIF3.0 format and emission summaries in EXCEL format for review and comment; and

 Updated the database with corrections or ne w information from S/L agencies based on their review of the Base F 2009/2018 inventories.
The following sections discu ss each of these steps.

2.1.2.1 Growth assumptions for non-EGU sources
This section describes the growth factor data used in develo ping the Base F inventory for 2009 and 2018, as well as the changes to the growth factor data made for the Base G inventory.

The growth factor data used in developing the Base F inventory were consistent with EPA’s analyses for the CAIR rulemaking. These growth factors are fully documented in the reports
entitled Development of Growth Factors fo r Future Year Modeling Inventories (dated April 30, 2004) and CAIR Emission Inventory Overview (dated July 23, 2004). Thre e sources of data were used in developing the growth f actors for the Base F inventory:

 State-specific growth rates from the Re gional Economic Model, Inc. (REMI) Policy Insight ® 

### model, Version 5.5 (being used in the development of the EGAS Version 5.0). The REMI socioeconomic data (output by indus try sector, population, farm sector valueDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### added, and gasoline and oil expenditures) ar e available by 4-digit SIC code at the
State level.

 Energy consumption data from the DOE’s Energy Information Administration’s (EIA) Annual Energy Outlook 2004, w ith Projections through 2025 for use in generating growth factors for non-EGU fuel combustion sources. These data include regional or
national fuel-use forecast data that were ma pped to specific SCCs for the non-EGU fuel use sectors (e.g., commercial coal, industria l natural gas). Growth factors for the residential natural gas combustion category, fo r example, are based on residential natural gas consumption forecasts that are reported at the Census division level. These Census divisions represent a group of States (e.g., the South Atla ntic division includes eight
southeastern States and the District of Columbia). Alt hough one would expect different growth rates in each of these States due to unique demographic and socioeconomic trends, EIA’s projects all States within each division using the same growth rate.

 Specific changes for sectors (e.g., plastics, synthetic rubber, carbon black, cement manufacturing, primary metals, fabricated me tals, motor vehicles and equipment) where
the REMI-based rates were unrealistic or hi ghly uncertain. Growth projections for these sectors were based on industry group forecas ts, Bureau of Labor Statistics (BLS) projections and Bureau of Ec onomic Analysis (BEA) histor ical growth from 1987-2002.

In addition to the growth data described above, we received two sets of growth projections from
VISTAS stakeholders.

The American Forest and Paper Association (A F&PA) supplied growth projections for the pulp and paper sector, which were applied to SIC 26xx Paper and Allied Products. The AF&PA projection factors are for the U.S. industry and apply to all States equally. The numbers come from the 15-year forecast for world pulp and recovered paper prepared by Resource Information Systems Inc. (RISI).

AF&PA Growth Factor SIC Code Sector 2002 to 2009 2002 to 2018
2611 Pulp Mills 1.067 1.169
2621 Paper Mills 1.067 1.169
2631 Paperboard Mills 1.067 1.169



### For both the Base F and Base G inventories, we used the above AF&PA growth factors by SIC
instead of the factors obtained from EPA’s CAIR analysis.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### For the Base F inventory, the NCDENR supplie d recent projections for three key sectors in North Carolina where declining production wa s anticipated – SIC 22xx Textile Mill Products, 23xx Apparel and Other Fabrics, and 25xx Furniture and Fixtures. For th e Base G inventory, NCDENR decided to use a gr owth factor of 1.0 for thes e SIC codes for both 2009 and 2018. Although NCDENR has data that shows a steady decline in these industries in NC, NCDENR
wanted to maintain the emission levels at 2002 le vels so the future emission reduction credits were available in the event that they are need ed for nonattainment areas. The specific growth
factors for these industrial se ctors in North Carolina were:

NCDENR Growth Factors for Specific Industrial Sectors

2009 2018 SIC Code Industrial Sector Base F Base G Base F Base G

22xx Textile Mill Products 0.6239 1.00 0.2792 1.00

23xx Apparel and Other Fabrics 0.5867 1.00 0.2247 1.00

25xx Furniture and Fixtures 0.8970 1.00 0.7647 1.00



### For the Base G inventory, we ma de one additional change to th e growth factors. The Base F inventory relied on DOE’s AEO2004 forecasts for projecting emissions fo r fuel-burning SCCs (applies mainly to ICI boilers 1-02-xxx-xx and 1- 03-xxx-xx, as well as in-process fuel use). We replaced the AEO2004 data with the more recent AEO2006 forecas ts (released in February 2006) to reflect changes in the energy market and to improve the emissions growth factors produced. We obtained the corresponding AEO 2006 projection tables from DOE’s web site
located at http://www.eia.doe.gov/oiaf/aeo/supplement/supref.html 

### . We developed tables comparing the growth factors based on AEO2004 and AEO2 006. These comparison tables were reviewed by the S/L agencies. Based on this review, VIST AS decided to use the AEO2006 growth factors for fuel burning SCCs.

We used the EPA’s EGAS model and updated th e corresponding AEO2006 projection tables to create growth factors by SCC. We applied the updated growth factors to 2002 actual emissions and replaced the 2009 and 2018 emissions in NIF EM tables for the affected SCCs.

2.1.2.2 Source Shutdowns

A few states indicated that si gnificant source shut downs have occurred since 2002 and that
emissions from these sources should not be included in the future year inventories. These sources
are identified in Table 2.1-11.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-11 Summary of Source Shutdowns Incorporated in Base G Inventory.

State Description of Source Shutdowns
AL None specified.
FL The following facilities are shutdown and projected emissions were set to zero in 2009/2018. 0570075 CORONET INDUSTRIES, INC.
1050050 U S AGRI-CHEMICALS CORP.
1050051 U.S. AGRI-CHEMICALS CORPORATION These facilities emitted 2,417 tons of SO 2 and 113 tons of NO x in 2002.
GA Georgia indicated that the former Blue Circle (n ow LaFarge) facility in do wntown Atlanta will likely shut down before 2009. The facility has two cement kilns, one of which is already shut down. The second kiln will continue to operate until the new facility in Alabama has enough milling capacity, after which the entire Atlanta facility will be complete ly closed down. This facility emitted 1,617 tons of SO 2 and 587 tons of NO x in 2002.
KY None specified.
MS AF&PA indicated that the International Paper Natchez Mill (28-001-2800100010) has shut down. This facility emitted 1,398 tons of SO 2 and 1,773 tons of NO x in 2002.
The Magnolia Resources - Pachuta Harmony Gas Plant (28-023-00031) is out of business and no longer holds an air permit. This facility emitted 2,257 tons of SO 2 and 134 tons of NO x in 2002.
NC In Base F, two paper mills were identified as being shut down in the 2018 inventory. NCDENR indicated that these mills are not expected to close. The two facilities are Ecusta Business Development (37-175-3717500056) and International Paper (37-083-00007). Their emissions were added back into the Base G 2018 inventory.
BASF Corporation (37-021-724) in Buncombe County is currently operating but has plans to shut down in 2007. This facility emitted 461 tons of SO 2 and 266 tons of NO x in 2002.
SC South Carolina provided a list of facilities that were identified as closing down on or after Jan. 1, 2003. The emissions for these facilities were set to zero in the 2009 and 2018 projection inventories. Emissions from these plants in 2002 were: 6,195 tons of SO 2 , 2,994 tons of NO x , and 2,836 tons of VOC. Most of the emissions were from one facility – Celanese Acetate (45-091-2440-0010) in York County.
TN Davidson County (Nashville) indicated that signi ficant source shutdowns have occurred since data were submitted for the 2002 CERR. Source number 47-037-00002 (Dupont) shut down a portion of their facility, which was permanently taken out of service. Source 47-037- 00050 (Nashville Thermal Transfer Corp.) shut down their municipal waste co mbustors and replaced them with natural gas fired boilers with propane stand by.
Weyerhaeuser (AKA Willamette) Power Boiler 7 (47- 163-0022, EU ID = 017) is being shut down. This emission unit emitted 4,297 tons of SO 2 and 1,443 tons of NO x in 2002.
Liberty Fibers (47-063-0197) in Hamblen County has recently shut down. This facility emitted 5,377 tons of SO 2 ; 2,057 tons of NO x ; and 9,059 tons of VOC in 2002.
VA Rock-Tenn (51-680-00097) received a permit dated 9/13/2003 which required the shutdown of units 1 and 2 by 2/27/2004. This permit was part of a netting exercise that allowed the installation of a new NG/DO boiler. These two units emitted 507 tons of SO 2 and 276 tons of NO x in 2002.
WV None specified.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 2.1.2.3 Control Programs applied to non-EGU sources
We used the same control programs for bot h the 2009 and 2018 non-EGU point inventory. Two control scenarios were develope d: on-the-books (OTB) controls and on-the-way (OTW) controls. The OTB control scenario accounts for post-2002 em ission reductions from promulgated federal, State, local, and site-specific control programs. The OTW contro l scenario accounts for proposed (but not final) control programs that are reas onably anticipated to re sult in post-2002 emission reductions. The methodologies used to account for the emission reductions associated with these emission control programs are discussed in the following sections.

Table 2.1-12 Non-EGU Point Source Control Programs Included in 2009/2018 Projection Inventories.

On-the-Books (Cut-off of July 1, 2004 for Base 1 adoption)

 Atlanta / Northern Kentucky / Birmingham 1-hr SIPs

 Industrial Boiler/Process Heater/RICE MACT (see Section 2.1.2.3.2)

 NO x 

### RACT in 1-hr NAA SIPs

 NO x 

### SIP Call (Phase I- except where States have adopted II already e.g. NC)

 Petroleum Refinery Initiative (October 1, 2003 notice; MS & WV)

 RFP 3 percent Plans where in place for one hour plans

 VOC 2-, 4-, 7-, and 10-year maximum achievable control technology (MACT0 Standards

 Combustion Turbine MACT
On-the-Way

 NO x 

### SIP Call (Phase II – remaining States & IC engines)

2.1.2.3.1 OTB - NO x 

### SIP Call (Phase I)



### Phase I of the NO x 

### SIP call applies to certain large non-E GUs, including large industrial boilers and turbines, and cement kilns. States in the VISTAS region affected by the NO x 

### SIP call have developed rules for the control of NO x 

### emissions that have been approved by EPA. We reviewed the available State rules and guidance documents to determine the affected sources and ozone
season allowances. We also obtained and reviewed information in the EPA’s CAMD NO x 

### Allowance Tracking System – Allowances Held Repor t. Since these controls are to be in effect by the year 2007, we capped the emissions for NO x 

### SIP call affected sources at 2007 levels and carried forward the capped levels for the 2009/ 2018 future year inve ntories. Since the NO x 

### SIP call allowances are given in te rms of tons per ozone season (5 month period from May toDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### September), we calculated annual emissions by mu ltiplying the 5-month allowances by a factor of 12 divided by 5.

2.1.2.3.2 OTB - Industrial Boiler/Process Heater MACT



### EPA anticipates reductions in PM and SO 2 

### as a result of the Industr ial Boiler/Process Heater
MACT standard. The methods used to account for these reductions are the same as those used for the CAIR analysis. Reductions were included for existing units firing solid fuel (coal, wood, waste, biomass) which had a design capacity grea ter than 10 mmBtu/hr. EPA prepared a list of
SCCs for solid fuel industrial and commercial/ institutional boilers and process heaters. We identified boilers greater than 10 mmBtu/hr usin g either the boiler capacity from the VISTAS 2002 inventory, or if the boiler capacity was missing, a default capacity based on a methodology developed by EPA for assigning default capaciti es based on SCC. The applied MACT control efficiencies were 4 percent for SO 2 

### and 40 for percent for PM 10 

### and PM2.5 to account for the cobenefit from installati on of acid gas scrubbers and other co ntrol equipment to reduce HAPs. On June 8, 2007, the U.S. Court of Appeals for the District of Columbia Circuit vacated and remanded the NESHAP for Industrial, Commercial and Institutional Boilers and Process Heaters.
VISTAS States decided to leave the emission re ductions in place since they envision using a 112(j) strategy (e.g., the “MACT hammer”) to obtain similar levels of control)

2.1.2.3.3 OTB - 2, 4, 7, and 10-year MACT Standards



### Maximum achievable control technology (MACT) requirement s were also applied, as documented in the report entitled Control Packet Development and Data Sources, dated July 14, 2004. The point source MACTs and associated em ission reductions were designed from Federal Register (FR) notices and discussions with EP A’s Emission Standards Division (ESD) staff. We did not apply reductions for MACT standards with an initial comp liance date of 2001 or earlier, assuming that the effects of these controls are already accounted for in the 2002 inventories supplied by the States. Emission reductions were applied only for MACT standards with an initial compliance date of 2002 or greater.

2.1.2.3.4 OTB Combustion Turbine MACT



### The projection inventories do not include the NO x 

### co-benefit effects of the MACT regulations for Gas Turbines or stationary Reciprocating In ternal Combustion Engines, which EPA estimates to be small compared to the overall inventory.

2.1.2.3.5 OTB - Petroleum Refinery Initiative (MS and WV)



### Three refineries in the VISTAS region ar e affected by two October 2003 Clean Air Act settlements under the EPA Petroleum Refinery Initi ative. The refineries are: (1) the ChevronDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### refinery in Pascagoula, MS; (2) the Ergon refinery in Vicksburg, MS; and (3) the Ergon refinery in Newell, WV.

The first consent decree pertained to Chevr on refineries in Rich mond and El Segundo, CA; Pascagoula, MS; Salt Lake City, UT; and Kapol ei, HI. Actions required under the Consent
Decree will reduce annual emissions of NO x 

### by 3,300 tons and SO 2 

### by 6,300 tons. The consent decree requires a pr ogram to reduce NO x 

### emissions from refinery heaters and boilers through the
installation of NO x 

### controls that meet at least an SNCR le vel of control. The refineries are to eliminate fuel oil burning in any combustion unit. The consent decree also requires reductions of
NO x 

### and SO 2 

### from the fluid catalytic cracking unit and control of acid gas flaring incidents. The consent decree does not provide sufficient inform ation to calculate emission reductions for the FCCU or flaring at the Pascagoula refinery. Ther efore, we calculated a general percent reduction
for NO x 

### and SO 2 

### by dividing the expected emission reducti ons at the five Chevron refineries by
the total emissions from these five refineries (as reported in the 1999 NE I). This resulted in applying percent reductions of 45 percent for SO 2 

### and 28 percent for NO x 

### to FCCU and flaring emissions at the Chevron Pascagoula refinery.

The second consent decree pertained to the Erg on-West Virginia refinery in Newell, WV; and the Ergon Refining facility in Vi cksburg, MS. The consen t decree requires the two facilities to implement a 6-year program to reduce NO x 

### emission from all heaters and boilers greater than 40 mmBtu/hr, and to eliminate fuel oil burning in any combustion unit (except during periods of natural gas curtailment). Specifically, ultra low NO x 

### burners are required on Boilers A and B at Newell, a low NO x 

### -equivalent level of control for heat er H-101 at Newell and heaters H-1 and H-3 at Vicksburg, and an ultra low NO x 

### burner level of control fo r heater H-451 at Vicksburg.

2.1.2.3.6 OTW - NO x 

### SIP Call (Phase II)



### The final Phase II NO x 

### SIP call rule was finalized on Ap ril 21, 2004. States had until April 21, 2005, to submit SIPs meeting the Phase II NO x 

### budget requirements. The Phase II rule applies to large IC engines, which are primarily used in pipeline transmission service at compressor
stations. We identified affect ed units using the same methodol ogy as was used by EPA in the proposed Phase II rule (i.e., a large IC engine is one that emitted, on average, more than 1 ton per day during 2002). The final rule reflects a contro l level of 82 percent for natural gas-fired IC engines and 90 percent for diesel or dual fuel categories. As show n later in Table 2.1-12, several S/L agencies provided move specific information on the anticipated controls at the compressor
stations. This information was used in the Base G inventory instead of the default approach used by EPA in the proposed Phase II rule.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 2.1.2.3.7 Clean Air Interstate Rule



### CAIR does not require or assume additional emission reductions from non-EGU boilers and
turbines.

2.1.2.4 Quality Assurance steps
Final QA checks were run on the revised projecti on inventory data set to ensure that all corrections provided by the S/L agencies and stake holders were correctly incorporated into the
S/L inventories and that there we re no remaining QA issues that could be addressed during the duration of the project. After e xporting the inventory to ASCII te xt files in NIF 3.0, the EPA QA program was run on the ASCII files and the QA output was reviewed to verify that all QA issues
that could be addressed were resolved

Throughout the inventory developm ent process, quality assuran ce steps were performed to ensure that no double counting of emissions occurred, and to ensu re that a full and complete inventory was developed for VISTAS. Quality assurance was an important component to the inventory development process and MACTEC performed the following QA steps on the point source component of the VISTAS re vised 2002 base year inventory:

Facility level emission summaries were prepar ed and evaluated to ensure that emissions were consistent and reasonable. The su mmaries included base year 2002 emissions, 2009/2018 projected emissions accounting only for growth, 2009/2018 projected emissions accounting for both growth a nd emission reductions from OTB and OTW controls.
State-level non-EGU comparisons (by pollutant ) were developed for the base year 2002 emissions, 2009/2018 projected emissions accounting only for growth, 2009/2018 projected emissions accounting for both gr owth and emission reductions from OTB and OTW controls.
Data product summaries and raw NIF 3.0 data files were provided to the VISTAS Emission Inventory Technical Advisor and to the Point Source, EGU, and non-EGU Special Interest Work Group representati ves for review and comment. Changes based on these comments were reviewed and approved by the S/L point source contact prior to implementing the changes in the files.
Version numbering was used for all invent ory files developed. The version numbering process used a decimal system to track major and minor changes. For example, a major change would result in a versio n going from Base F1 to Base F2.

2.1.2.5 Additional Base G Updates and Corrections
Table 2.1-13 summarizes the updates and corrections to the Base F inventory that were requested by S/L agencies and incorporated in to the Base G 2009/2018 inventories.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-13 Summary of Updates and Corrections Incorporated into the
Base G 2009/2018 Non-EGU Inventories.

State Nature of Update/Correction
AL Corrected the latitude and longitude for two facilities: Ergon Terminalling (Site ID: 01-073- 010730167) and Southern Power Franklin (Site ID: 01-081-0036).
Corrections to stack parameters at 10 facilities for stacks with parameters that do not appear to fall into the ranges typically termed "acceptable" for AQ modeling.

FL Corrected 2009/2018 emission values for the Miami Dade RRF facility (Site ID: 12-086-0250348) based on revised 2002 emissions and application of growth control factors for 2009/2018.
GA Hercules Incorporated (12-051-05100005) had an erroneous process id (#3) within emission unit id SB9 and was deleted. This removes about 6,000 tons of SO 2 from the 2009/2018 inventories.

Provided a revised file of location coordinates at the stack level that was used to replace the location coordinated in the ER file.
There are several sources that have updated their emissions from their BART eligible units. most of these changes were for fairly small (<50 tpy) sources.
NC Made several changes to Base F inve ntory to correct the following errors: 1. Corrected emissions at Hooker Furniture (Site ID: 37-081-3708100910), release point G-29, to use the corrected values in 2002 and carry those same numbers through to 2009 and 2018 since NCDENR assumes zero growth for furniture industry. 2. Identified many stack parameters in the ER file that were unrealistic. Several have zero for height, diameter, gas velocity, and flow rate. NC used the procedures outlined in Section 8 of the document ""National Emission Inventory QA and Augmentation Report" to correct unrealistic stack parameters. 3. Identified truncated latitude and longitude values in Base F inventory. NC updated all Title V facility latitude and longitude that was submitted to EPA for those facilities in 2004. Smaller facilities with only two decimal places were not corrected. 4. Corrected 2018 VOC emissions for International Paper (3709700045) Emission Unit ID, G-12, to reflect changes to the 2002 inventory.

There are three Transcontinental Natural Gas Pipeline facilities in NC that are subject to the NO x SIP call. NCDENR took 2004 emissions and grew them to 2009 & 2018 and capped those units that are subject to the NO x SIP Call Rule. These facility IDs are 37-057-3705700300, 37-097-3709700225, and 37-157-3715700131.
NCDENR applied NO x RACT to a two facilities located in the Charlotte nonattainment area. NCDENR provided 2009 & 2018 emissions for Philip Morris USA (37-025-3702500048) and Norandal USA (37-159-3715900057).
SC Corrected PM species emission values. SC DHEC’s initial CERR submittal reported particulate matter emissions using the PM-FIL, PM 10 -FIL, and PM 2.5 -FIL pollutant codes. In August 2005, SC DHEC indicated that data reported using the PM-FIL, PM 10 -FIL, and PM 2.5 -FIL pollutant codes should actually have been reported using the PM-PRI, PM 10 -PRI, and PM 2.5 _PRI codes. MACTEC performed a subsequent PM augmentation in April 2006 using the revised pollutant codes. These changes were reflected in the Base G 2009/2018 emission inventory.
Specified that the Bowater Inc. facility (45-091-2440-0005) in York County conducted an expansion in 2003/2004 and plans a future expansion. SC provided updated emissions for 2009 and 2018 for this facility.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-13. Continued.

State Nature of Update/Correction
TN Updated 2009/2018 emissions for Eastman Chemical (47-163-0003) based on final (Feb. 2005) BART rule.
Updated 2009/2018 emission inventory for the Bowa ter facility (47-107-0012) based on the facility’s updated 2002 emission inventory update.
Replaced 2009/2018 data from Hamilton County, Tennessee, usin g data from Hamilton County’s CERR submittal as contained in EPA’s 2002 NEI (in Base F, the inventory for Hamilton County was based on the draft VISTAS 2002 inventory, which in turn was based on the 1999 NEI); applied growth and control factors to revised 2002 inventory to generate emission projections for 2009/2018.
Updated 2009/2018 emissions for PCS Nitrogen Fe rtilizer LP (Site ID: 47- 157-00146) based on the facility’s updated 2002 emission inventory update.
The 2002 NEI correctly repor ts the actual emissions for CEMEX (47-093-0008) after the NO x SIP call. There is no reason to suspect that that rate would change in 2008, 2009, or 2018. Emissions for 2009/2018 were set equal to 2002 emissions.
In the Base F 2009/2018 inventories, NO x controls were applied for two units at Columbia Gulf Transmission (47-111-0004). There are no plans for controls at these units, EO3 and EO4. The assumed control efficiency of 82 percent was backed out in the 2009/2018 inventories.
VA VADEQ provided 2009/2018 NO x emission estimates for NO x Phase II gas transmission sources at three Transco facilities (51-011-00 011, 51-137-00027, 51-1 43-00120) which were used to replace the default NO x Phase II control assumptions for these facilities.
VADEQ provided updated 2009/2018 NO x and SO 2 emissions based on new controls required by a November 2005 permit modification and netting exercise. The entire power plant facility is limited to 213 tons of NO x and 107 tons of SO 2 per year. The permit also allowed the installation of 3 new boilers, also under the 213 tons of NO x /year cap.
WV Updated 2009/2018 emissions for Steel of West Virginia (Site ID: 54-011-0009) based on the facility’s updated 2002 emission inventory update.
Made changes to several Site ID names due to changes in ownership
Base F emissions were much too high for Weirton Steel (54-021-0029). WV believes that the source is very unlikely to emit the NO x SIP Call budgeted amounts in 2009 or 2018. WV provided revised emission estimates based on EGAS for 2009/2018.
Made corrections to latitude/longitude and stack parameters at a few facilities for stacks with parameters that do not appear to fall into the ra nges typically termed "accep table" for AQ modeling.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 2.1.2.6 Additional B&F Updates and Corrections
Table 2.1-14 summarizes the updates and correcti ons to the Base G non-EGU inventory that were requested by S/L agencies and incorpor ated into the B&F 2009/2018 non-EGU inventories. The changes were primarily related to better information on anticipated BART controls for specific facilities and emission units.

Table 2.1-14 Summary of Updates and Corrections Incorporated into the
B&F 2009/2018 Non-EGU Inventories.

State Nature of Update/Correction
AL For 2018, incorporated emission changes due to BART controls at Exxon Mobil (Site ID: 01-053- 0007), International Paper (Site ID: 01-079-0001), and Solutia (Site ID: 01-1 03-0010). International Paper (Site ID: 01-079-0001) Unit 004 to be shutdown in the 2018 inventory.
FL For both 2009 and 2018, incorporated emission changes due to BART controls at Georgia Pacific (Site ID: 12-107-1070005) Unit 15.
MS For 2018 only, changed SO2 emission estimate for Pursue Energy (Site ID: 28-121-00036) based on the facility’s estimates of the gas reserve at the site.
For 2018 only, changed emission estimates for all po llutants at several emission units at the Chevron Pascagoula Refinery (Site ID: 28-059-00058) to reflect BART source reductions.
SC For both 2009 and 2018, identified 15 facilities that have permanently closed. Emissions from these facilities set to zero for all pollutants.
TN For both 2009 and 2018, identified seven facilities that have permanently closed. Emissions from these facilities were set to zero for all pollutants.
For both 2009 and 2018, identified three emission units that have permanently closed. Emissions from these units were set to zero for all pollutants. 47-009-0130-002 (APAC – TN, Inc.-Harrison Construction – Asphalt plant), 47-009-0130-003 (APAC – TN, Inc.-Harrison Construction – Asphalt crusher), and 47-139-0004-001 (Intertrade - Number 6 acid plant)
The following individual source will be shut down in 2010: 47-001-0020-002 (DOE, Y-12 – Boilers 1-4). For the 2018 inventory only, emissions from this unit were set to zero for all pollutants.
A portion of 47-163-0003-020101 (Eastman, B-83-1 Stoker Boilers). This source previously consisted of 14 boilers (Boilers 11-24). Boilers 11-17 have been removed from service. Emissions for both 2009 and 2018 were reduced by 26.64%, based on the portion of the heat input capacity that is being removed from service.
SO2 emissions in 2018 from 47-163-0003-021520 (Eastman, B-253-1 Tangential PC Boilers) were reduced by 90% to reflect anticipated BART controls.
Reduced SO2 emissions at 47-157-00475 (Lucite International) in Shelby County as a result of a consent decree w ith U.S. EPA.
VA Changed SO2 emissions in 2009 and 2018 for thir teen facilities to reflect updated information from VADEQ regarding projected SO2 controls.
WV Weirton Steel (54-029-00001) and Wheeling Pittsburgh Steel (54-009-00002) have undergone significant, permanent process changes since 2002. WV DEP staff have consulted with facility staff and determined that calendar year 2004 emissions re present a better basis for future year emissions estimates. Therefore, W VDEP compiled emissions data from the 2004 inventory for these sources and applied the most current VISTAS growth f actors to estimate emissions in 2009 and 2018.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 2.1.2.7 Conversion of MRPO BaseM 2009 non-EGU Data to SMOKE Input Format
To support ASIP PM 2.5 

### CAMx modeling of the future ye ar 2009, Alpine Geophysics obtained and processed an emission inventory for the 5 MRPO states (Illinois, Indiana, Michigan, Wisconsin, and Ohio). Appendix x details the technical steps that were made as part of the
conversion of the MRPO BaseM non-EGU file s into IDA format for ASIP PM-2.5 CAMx modeling of the future year 2009.

2.1.2.8 Summary of the 2009/2018 non-EGU Point Source Inventories
Tables 2.1-15 through 2.1-21 summarize th e revised 2009/2018 non-EGU point source inventories. The “growth only” column does not include the shutdow ns (section 2.1.2.2) or control factors (section 2.1.2.3), only the gr owth factors descri bed in section 2.1.2.1.

Table 2.1-15 Non-EGU Point Source SO 2 

### Emission Comparison for 2002/2009/2018.

2002 2009 2018

State Base G Base F Base G B&F Base F Base G B&F

AL 96,481 100,744 101,246 101,246 112,703 113,224 103,303
FL 65,090 68,549 65,511 62 ,651 79,015 75,047 71,810
GA 53,778 61,535 53,987 53, 987 68,409 59,349 59,349
KY 34,029 35,470 36,418 36, 418 38,806 40,682 40,682
MS 35,960 27,488 25,564 25 ,564 40,195 26,678 25,674
NC 44,123 48,751 42,536 42 ,536 50,415 46,314 46,314
SC 53,518 55,975 48,324 47 ,193 56,968 53,577 52,410
TN 79,604 89,149 70,678 64 ,964 96,606 77,247 56,682
VA 63,903 63,075 62,560 58, 039 69,776 68,909 57,790
WV 54,070 54,698 55,973 55 ,598 60,137 62,193 61,702
580,556 605,434 562,797 548,196 673,030 623,220 575,716

Note: Emission summaries above include all SCCs except 1-01-xxx-xx and 2-01-xxx-xx.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-16 Non-EGU Point Source NO x 

### Emission Comparison for 2002/2009/2018.

2002 2009 2018

State Base G Base F Base G B&F Base F Base G B&F

AL 83,310 69,676 69,409 69 ,409 79,101 78,318 77,960
FL 45,156 44,859 46,020 47 ,125 50,635 51,902 52,959
GA 49,251 51,556 50,353 50, 353 57,323 55,824 55,824
KY 38,392 36,526 37,758 37, 758 40,363 41,034 41,034
MS 61,526 55,877 56,397 56 ,398 62,132 61,533 61,252
NC 44,929 44,877 34,767 34 ,768 47,200 37,801 37,802
SC 42,153 42,501 40,019 39 ,368 44,480 44,021 43,331
TN 64,344 63,431 57,883 57 ,514 70,313 63,453 62,519
VA 60,415 51,335 51,046 51, 001 56,876 55,945 55,734
WV 46,612 40,433 38,031 38 ,023 44,902 43,359 43,280
536,088 501,071 481,683 481,715 553,325 533,190 531,695

Note: Emission summaries above include all SCCs except 1-01-xxx-xx and 2-01-xxx-xx.



### Table 2.1-17 Non-EGU Point Source VOC Emission Comparison for 2002/2009/2018.

2002 2009 2018

State Base G Base F Base G B&F Base F Base G B&F

AL 47,037 46,660 46,644 46 ,644 54,268 54,291 54,290
FL 38,471 36,675 36,880 36 ,882 42,787 42,811 42,813
GA 33,709 34,082 34,116 34, 116 40,267 40,282 40,282
KY 44,834 47,648 47,785 47, 785 55,564 55,861 55,861
MS 43,204 37,921 37,747 37 ,747 45,769 45,338 45,335
NC 61,182 70,464 61,925 61 ,925 76,027 70,875 70,875
SC 38,458 38,273 35,665 34 ,403 44,545 43,656 41,987
TN 84,328 89,380 74,089 73,498 111,608 93,266 92,456
VA 43,152 43,620 43,726 43, 725 53,065 53,186 53,186
WV 14,595 14,012 13,810 13 ,043 16,632 16,565 15,582
448,970 458,735 432,387 429,768 540,532 516,131 512,667

Note: Emission summaries above include all SCCs except 1-01-xxx-xx and 2-01-xxx-xx.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### Table 2.1-18 Non-EGU Point Source CO Emission Comparison for 2002/2009/2018.

2002 2009 2018

State Base G Base F Base G B&F Base F Base G B&F

AL 174,271 176,899 180,369 180,369 194,280 201,794 201,663
FL 81,933 83,937 87,037 87 ,661 96,642 96,819 97,438
GA 130,850 147,362 147,427 147,427 168,570 167,904 167,904
KY 109,936 121,727 122,024 122,024 139,121 139,437 139,437
MS 54,568 58,023 57,748 57 ,749 67,764 66,858 65,884
NC 50,576 53,955 53,744 53 ,744 61,127 62,197 62,197
SC 56,315 62,144 60,473 59 ,934 71,318 68,988 68,415
TN 115,264 123,844 119,665 119,216 146,407 140,942 140,556
VA 63,796 67,046 68,346 68, 326 74,364 76,998 76,846
WV 89,879 100,248 100,045 93,839 119,318 119,332 111,302
927,388 995,185 996,878 990,289 1,138,911 1,141,269 1,131,642

Note: Emission summaries above include all SCCs except 1-01-xxx-xx and 2-01-xxx-xx.



### Table 2.1-19 Non-EGU Point Source PM 10 

### -PRI Emission Comparison for 2002/2009/2018.

2002 2009 2018

State Base G Base F Base G B&F Base F Base G B&F

AL 25,240 25,450 25,421 25 ,421 29,973 29,924 29,889
FL 35,857 39,363 39,872 39 ,947 46,573 46,456 46,492
GA 21,610 23,509 23,103 23, 103 27,781 27,273 27,273
KY 16,626 17,164 17,174 17, 174 20,142 20,153 20,153
MS 19,472 19,200 19,245 19 ,244 22,952 22,859 22,837
NC 13,838 14,738 13,910 13 ,910 15,816 15,737 15,737
SC 14,142 17,631 13,370 12 ,959 20,197 15,139 14,674
TN 35,174 37,040 34,833 34 ,581 45,168 42,280 41,999
VA 13,252 13,043 13,048 13, 046 15,150 15,112 15,111
WV 17,503 17,723 17,090 11 ,882 21,699 21,735 14,202
212,714 224,861 217,066 211,267 265,451 256,668 248,367

Note: Emission summaries above include all SCCs except 1-01-xxx-xx and 2-01-xxx-xx.



### Table 2.1-20 Non-EGU Point Source PM25-PR I Emission Comparison for 2002/2009/2018.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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State Base G Base F Base G B&F Base F Base G B&F

AL 19,178 19,256 19,230 19 ,230 22,628 22,598 22,584
FL 30,504 33,387 33,946 34 ,019 39,436 39,430 39,486
GA 17,462 19,361 18,982 18, 982 22,882 22,416 22,416
KY 11,372 11,680 11,686 11, 686 13,734 13,739 13,739
MS 9,906 9,144 9,199 9,199 10,768 10,739 10,719
NC 10,500 11,192 10,458 10 ,458 11,927 11,825 11,825
SC 10,245 13,101 9,390 9,048 14,947 11,086 10,699
TN 27,807 29,302 27,577 27 ,367 35,750 33,532 33,293
VA 10,165 9,980 9,988 9,988 11,604 11,594 11,605
WV 13,313 13,364 12,769 7,638 16,474 16,516 9,124
160,452 169,767 163,225 157,615 200,150 193,475 185,490

Note: Emission summaries above include all SCCs except 1-01-xxx-xx and 2-01-xxx-xx.



### Table 2.1-21 Non-EGU Point Source NH 3 

### Emission Comparison for 2002/2009/2018.

2002 2009 2018

State Base G Base F Base G B&F Base F Base G B&F

AL 1,883 2,132 2,132 2,132 2,464 2,464 2,464
FL 1,423 1,544 1,544 1,544 1,829 1,829 1,829
GA 3,613 3,963 3,963 3,963 4,799 4,797 4,797

KY 674 733 760 760 839 901 901
MS 1,169 667 668 668 761 764 764
NC 1,180 1,288 1,285 1,285 1,422 1,466 1,466
SC 1,411 1,578 1,578 1,578 1,779 1,779 1,779
TN 1,613 1,861 1,841 1,840 2,240 2,214 2,213
VA 3,104 3,050 3,049 3,045 3,613 3,604 3,604

WV 332 341 341 314 416 413 378
16,402 17,157 17,161 17, 129 20,162 20,231 20,195

Note: Emission summaries above include all SCCs except 1-01-xxx-xx and 2-01-xxx-xx.Documentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### 2.2 Area Sources

This section describes the methodology used to develop the 2009 and 2018 projection Base F and Base G projection inventories. This section describes two a pproaches to these projections. Separate methods for projecting emissions were used for non-agricultural (stationary area) and agricultural area sources (predominantly NH 3 

### emissions). The two methods used for these
sectors are described in the sections that follow.

2.2.1 Stationary area sources
The general approach used to calculate Base F projected emissions for stationary area sources
was as follows:

1. Use the VISTAS Base F 2002 base year invent ory as the starting point for projections.

2. MACTEC then worked with the VISTAS Stat es (via the Stationary Area Source SIWG) to obtain any State specific growth factors and/or future controls from the States to use in developing the projections.

3. MACTEC then back calculated uncontrolled emissions from the Base F 2002 base year inventory based on existing controls reporte d in the 2002 Base F ba se year inventory.

4. Controls (including control efficiency, rule e ffectiveness and rule pe netration) provided by the States or originally developed for us e in estimating projected emissions for U.S. EPA’s Heavy Duty Diesel (HDD) rulemaking em ission projections and used in the Clean Air Interstate Rule (CAIR) projections were th en used to calculate controlled emissions. State submitted controls had precedence ove r the U.S. EPA developed controls.

5. Growth factors supplied from the States or the U.S. EPA’s CAIR emission projections were then applied to project the controlled emissions to th e appropriate year. In some cases EGAS Version 5 growth factors were us ed if no growth factor was available from either the States or the CAIR growth factor files. The use of E GAS Version 5 growth factors was on a case-by-case basis wherever State-supplied or CAIR factors were not
available for SCCs found in the 2002 Base F inventory. Use of the EGAS factors was
necessitated due to the CERR submittals used in constructing the Base F 2002 inventory.
Use of the CERR data resulted in SCCs that were not found in the CAIR inventory and if no State-supplied growth factor was provided required the use of an EGAS growth factor.

6. MACTEC then provided the final draft Base F projection inventory for review and comment by the VISTAS States.

For Base F stationary area sources, no State-s upplied growth or contro l factors were provided.
Thus for all of the sources in this sector of th e inventory, growth and controls for Base F wereDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### applied based on controls initially identified for the CAIR and growth factors identified for the CAIR projections.

For the Base G projections, the Base G 2002 base year inventory (see section 1.2.3) was used as a starting point. States provided some updated fu ture controls but grow th factors used were
identical to those used for Base F. The revised controls for Base G were largely for new sources added as part of the 2002 Base F comments. The calculation of Base G pr ojections was identical to the six steps outlined above with the exception of revisions made to prescribed fire for 2009
and 2018 and for the State of North Carolina. North Carolina provided 2009 and 2018 updated emission files used to update the emissions for each year for several s ource categories. However not all sources in the inventory were included in these NC updates. As a consequence, the final Base G 2009 and 2018 inventory for NC included em issions updated using the NC supplied files and emissions developed using growth and control factors as outlined above.

In a few cases, additional growth factors had to be added for source ca tegories that had not initially been included in the Base F inventory. These growth factors were obtained from EGAS 5.0. Finally updates to growth fact ors 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 us ed to reflect changes in energy efficiency resulting from new or updated fuel firing technologies.

2.2.1.1 Stationary area source controls
The controls obtained by MAC TEC for the HDD rulemaking were controls for the years 2007, 2020, and 2030. Since MACTEC was preparing 2009 and 2018 projections, control values for intermediate years were prepared using a straight lin e interpolation of c ontrol level between 2007 and 2020. The equation used to calculat e the control level was as follows:

CE = (((2020 CE – 2007 CE)/13)*YRS) + 2007 CE

Where:

CE = Control Efficiency for either 2009 or 2018

2020 CE = HDD Control Efficiency value for 2020

2007 CE = HDD Control Efficiency value for 2007

13 = Number of years between 2020 and 2007

YRS = Number of years beyond 2007 to VISTAS Projection yearDocumentation of the Base G2 and Best & Final 2002 Base Year, 2009 and 2018 Emission Inventories

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### For 2009 the value of YRS would be two (2) an d for 2018 the value would be eleven (11). Control efficiency values were determined for VOC, CO and PM. Rule penetration values for each year in the HDD controls tables obtained by MACTEC were always 100 percent so those values were maintained for the VISTAS projections.

Prior to performing the linear interpolation of the controls, MACTEC evaluated controls from the CAIR projections (NOTE: Initially the cont rols came from the IAQTR projections, however the controls used in CAIR were virtually identi cal to those in IAQTR). Those controls appeared
to be identical to those used for the HDD ru lemaking. In addition, MACTEC received some
additional information on some controls for ar ea source solvents (email from Jim Wilson, E.H. Pechan and Associates, Inc. to Gregory Stella , VISTAS Emission Inventory Technical Advisor, 3/5/04) that were used to check against the controls in the HDD rulemaking files. Where those controls proved to be more stringent than th e HDD values, MACTEC updated the control file with those values (which were then used in the interpolation to develop 2009 and 2018 values). Finally, for VOC the HDD controls were initia lly provided at the State-county-SCC level. However, upon direction from the VISTAS Emi ssion Inventory Technical advisor, the VOC
controls were consolidated at the SCC level a nd applied across all counties within the VISTAS region (email from Gregory Stella, Alpine Geophysic s, 3/3/2004) to ensure that no controls were missed due to changes in county FIPS codes and/or SCC designations between the time the HDD controls were developed and 2002.

The equation below indicates how VOC emissions we re projected for stationary area sources.

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

− = 100 _ 100 _ 100 _ 1 2018 2018 2018 2002 2018 

### RP VOC RE VOC CE VOC x VOC VOC



### Where:

VOC 2018 

### = VOC emissions for 2018

VOC 2002 

### = Uncontrolled VOC emissions for 2002

VOC_CE 2018 

### = Control Efficiency for VOC (in this example for 2018)

VOC_RE 2018 

### = Rule Effectiveness for VOC (in this example for 2018)

VOC_RP 2018 

### = Rule Penetration for VOC (in this example for 2018)

A similar equation could be constructed for either PM or CO. It should be noted that the control
efficiencies calculated based on the HDD rulemaking were only applie d if they were greater than any existing 2002 base year controls . No controls were found for SO 2 

### or NO x 

### area sources.