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33 For nonroad modes, the calculation of emissions of criteria tion possible, albeit while introducing uncertainties into the air pollutants is similar but the measure of freight activity calculations. might be different (e.g., ton-miles in the case of line-haul rail). Although the GHG Inventory uses a straightforward ap- The main difference between criteria air pollutants and air proach to calculating emissions, the NEI methodology is com- toxics is data availability. Although most models include paratively more complex. First, the NEI analyzes a greater emission factors for all criteria air pollutants, the same is not number of pollutants than the GHG Inventory: 6 criteria pol- true for air toxics due to a lack of data. Instead, many models lutants and up to 188 air toxics. In addition, because the emis- estimate emissions from air toxics based on comparative sions of these pollutants depend on vehicle type, age, and ratios from criteria air pollutants. activity, the NEI relies on separate methodologies for each transportation mode. Finally, the NEI has much more geo- graphic detail than the GHG Inventory. Although the latter 3.2 National Methods only presents emissions at the national level, the former allo- At the national level, several inventories measure emissions cates emissions to the state and county level. For these rea- associated with the transportation sector (see Exhibit 3-2). sons, the NEI methodology is presented here in much greater Each methodology discussed here is specific to classes of pol- detail than the GHG Inventory. lutants: greenhouse gas emissions are quantified in the EPA GHG Inventory, (1) and criteria air pollutants and air toxics 3.2.2 EPA GHG Inventory Methodology are quantified in the NEI. (2) Both the EPA GHG Inventory and the NEI capture nationwide emissions across economic In accordance with the 1992 United Nations Framework sectors; in addition to transportation, these inventories in- Convention on Climate Change, EPA produces an annual as- clude industrial, commercial, and residential emission sources. sessment of national greenhouse gas emissions, which spans Because the methodologies of these inventories are consider- several industries and economic sectors including transporta- ably broader than the mode-specific methodologies con- tion. The analysis is based on methodologies, guidelines, and tained in Sections 3.3 through 3.8 of this report, they are best practices established by the Intergovernmental Panel on detailed independently of the modal analyses. This section Climate Change (IPCC), most recently updated in 2006. (39) discusses the strengths, weaknesses, inputs, and results of the Regarding transportation, the EPA GHG analysis calculates EPA GHG Inventory and the NEI. GHG emissions by measuring fossil fuel consumption in each transportation mode. Because emissions are broken down by mode rather than activity, the EPA inventory does not di- 3.2.1 Summary of Methods and Models rectly quantify emissions associated with freight movement. The purpose of EPA's national inventories is to capture na- The GHG Inventory accounts for emissions of three green- tional emissions across all sources and to allocate emissions house gases: CO2, CH4, and N2O. The GHG Inventory does to each sector. Although both the GHG Inventory and NEI not measure HFC emissions. Although both CH4 and N2O report detailed emissions estimates, they differ in the com- have a greater global warming potential than CO2 (the global plexity of analytical methods. The GHG Inventory uses a con- warming effect of CH4 is 21 times greater than that of CO2, sistent methodology to calculate emissions for each category, and the effect of N2O is 310 times greater) their level of emis- but the NEI relies on unique methodologies across modes. sions is so small that their overall effect is negligible in this The GHG Inventory primarily relies on fuel consumption analysis. In the transportation sector, CO2 accounts for 98.4% data to calculate emissions. The inventory allocates emissions of all greenhouse gases. (40) Since transportation emissions to each transportation mode, and to subcategories within of CO2 are caused by the combustion of fossil fuels, such as each mode, according to fuel consumption and fuel type. gasoline, diesel, aviation fuel, and marine bunker oil, the CO2 Total GHG emissions are calculated as a function of the car- emissions inventory is calculated by measuring fuel con- bon content of each fuel. Although the GHG Inventory does sumption from each mode. not disaggregate freight and non-freight emissions, it lists The GHG Inventory measures and reports greenhouse modal categories in sufficient detail to make such disaggrega- gas emissions on an annual basis at the national scale. Since Exhibit 3-2. List of national methods. Method/Model Type Geographic Scale Pollutants Freight/Passenger EPA GHG Inventory Method National GHG Both NEI Method National, State, County CAP, HAP Both

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34 emissions are not allocated to the state and county level, the intensity of each fuel. To perform this calculation, EPA col- inventory is less data intensive, and only requires aggregated lects data on total fuel sales and allocates fuel to each subcat- national fuel consumption data. This makes the methodology egory. The GHG methodology includes the steps shown in less complex and reduces uncertainties from collecting and Exhibit 3-3. aggregating local data, but introduces additional uncertain- Although the process of calculating total fossil fuel emis- ties in allocating national data to the transportation sector, sions is straightforward, the allocation steps introduce uncer- and to each mode individually. Since GHG emissions are re- tainties into the methodology. Each allocation, first to the ported for the analysis year, the GHG Inventory does not fur- transportation sector, then to each mode and vehicle type, re- ther break down the result by season or by month. Although quires additional assumptions and estimates. Although the there is no analysis of future years, the GHG Inventory in- GHG Inventory methodology does not further allocate emis- cludes a comparison of current year emissions to past year sions to freight and non-freight sources, this allocation can be emissions, back to 1990. made using additional assumptions about the freight mix of heavy-duty trucks, rail, and commercial aircraft. Inventory Structure Summary of Strengths and Weaknesses. A summary of The EPA GHG Inventory is structured according to emis- GHG Inventory methodology strengths and weaknesses is sions category, including energy production, industrial provided in Exhibit 3-4. processes, agriculture, and land-use change. Energy produc- tion accounts for the majority of emissions. In 2007, 80% of Analysis of Process Uncertainty. In the EPA GHG Inven- nationwide GHG emissions were due to fossil fuel combus- tory methodology, the greatest elements of uncertainty are tion, and 26% of nationwide emissions were due to fossil fuels present in the allocation of GHG emissions to the transporta- used in transportation. Within the transportation sector, tion sector and subsequently to individual modes. The sector- emissions are divided by fuel type and subdivided by mode level allocation is achieved with a top-down approach that and vehicle type. Gasoline, consumed mainly by passenger measures activity across all economic sectors. In comparison, cars and light-duty trucks, is the largest contributor to trans- the allocation across modes is achieved with a bottom-up ap- portation emissions, followed by diesel fuel, consumed by proach, which applies and compares activity levels for each heavy-duty trucks and rail, and jet fuel. Although the inven- mode. The uncertainty resulting from each allocation is dis- tory does not separate freight and non-freight emissions, the cussed here. A more thorough analysis of the parameter uncer- specificity of the vehicle subcategories allows for freight emis- tainty surrounding each data set can be found in Section 3.2.4. sions to be summed together across modes. Allocations are Although data on total fuel use are considered accurate, the then checked against "bottom-up" fuel use data when avail- allocation of fuel consumption data to end-use sector relies able, such as railroad fuel consumption data from the Surface on a variety of economic and activity measurements, which Transportation Board. (41) may reduce the accuracy of the allocation. Since each metric GHG emissions are calculated using fuel-based (rather has its own sources of error, the allocation of fuel using sev- than activity-based) emission factors derived from the carbon eral metrics creates further uncertainty. Exhibit 3-3. GHG Inventory methodology. Determine total Total fuel sales, available from the Energy Information Administration consumption by fuel type (EIA) are allocated by economic sector (e.g., industrial, commercial, and sector transportation). Data for the overall allocation are supplemented by industry surveys and other end-use consumption metrics. Adjust transportation EPA reconciles the transportation fuel allocation with VMT and other consumption based on activity data from FHWA, AAR, and other sources. This "bottom-up" activity measures analysis serves as a check for fuel consumption and is the basis for the allocation among transportation modes. Allocate GHG emissions to CO2 emissions are calculated based on the carbon content of each fuel; transportation sector CH4 and N2O emissions are calculated based on an activity-based emissions factor. Allocate transportation For on-road vehicles, emissions are assigned based on VMT data from emissions to each mode FHWA, specific to vehicle type. Nonroad data are assigned based on and vehicle subcategory data from AAR, FAA, EIA, and other sources.

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35 Exhibit 3-4. Analysis of strengths and weaknesses--EPA GHG Inventory methodology. Criteria Strengths Weaknesses Representation of physical Methodology does not rely on models of physical EFs for CH4 and N2O are based on vehicle test processes processes for calculation of GHG emissions. data; EFs may become inaccurate if vehicle technology, maintenance, operations change. Model sensitivity to input Since methodology is based on fuel use and parameters activity data, it is not affected by changes in vehicle operations, maintenance, or environment. Ability to incorporate effects of Methodology does not forecast alternative emission reduction strategies scenarios to show benefits of emission reduction strategies. Representation of future Methodology does not predict future trends in GHG emissions emissions, but it does report historical emissions beginning in 1990. Consideration of alternative Methodology captures GHG benefits of alternative vehicle/fuel technologies fuels by including unique GHG EFs for each fuel type. Data quality High data quality. Fuel consumption and activity factors are industry standard; EFs for CH4 and N2O directly measured from vehicle tests. Spatial variability Methodology is only applied at the national level. It does not measure emissions at the regional or local level. Temporal variability Methodology is not subject to temporal fluctuations, since it measures emissions at the national scale. Endorsements Methodology is endorsed by EPA, UN IPCC. Within the transportation sector, fuel use is allocated to 3.2.3 EPA National Emissions Inventory each mode through a comparison of modal activity factors. However, since each activity data set (i.e., VMT for on-road, The NEI documents total emissions of criteria pollutants ton-miles for rail) uses separate sources and methodologies, and air toxics nationwide. This database catalogs emissions the margin of error in each data source is difficult to compare; from point, non-point, and mobile sources, with each trans- it is not clear how the uncertainty in one would compare to portation mode analyzed independently within the mobile the other. Although these uncertainties do not affect the analysis. Depending on the mode, emissions are determined quantification of emissions from the transportation sector, using one of several possible methods: by applying computa- they do affect both the modal breakdown and the estimate of tional models, by combining activity data with emission fac- freight versus non-freight emissions. tors, or by scaling prior emission inventories by a growth Uncertainties in allocation are partially addressed by com- factor. This section discusses how the NEI calculates modal paring the "top-down" allocation to "bottom-up" fuel con- emissions, and analyzes the strengths and weaknesses of sumption data. For example, railroads report fuel consump- each approach. An evaluation of analytical models (e.g., tion data to the Surface Transportation Board. This value is MOBILE6, NONROAD), as applied to each transportation compared against the determined allocation to identify the mode, is discussed in Sections 3.3. to 3.8. magnitude of discrepancy. This approach acts as a partial check Consistent with EPA's mandates in the Clean Air Act as to mitigate uncertainties in allocating fuel consumption. amended in 1990, the NEI measures nationwide emissions of Although the GHG Inventory does not separate freight- 6 criteria pollutants and up to 188 air toxics. The measured related emissions from the total transportation inventory, it criteria pollutants include CO, SOX, NOX, and PM. (There does present vehicle-specific emissions in sufficient detail to are two additional criteria air pollutants: lead and ozone-- allow an estimation of freight emissions. However, this esti- a secondary pollutant formed by the combination of HC and mate requires a different approach for each mode, and relies on NOx.) Measured air toxics include 188 defined compounds. external assumptions about the proportion of freight versus (42) However, not all HAPs are estimated by the mobile passenger travel. For example, on-road categories include both source methodologies. For example, the National Mobile In- gasoline and diesel medium- and heavy-duty trucks, and the ventory Model (NMIM) only produces inventories of 50 HAPs aircraft category specifies emissions from commercial aircraft. for on-road and nonroad sources.

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36 The NEI produces inventory data for a wide range of geo- 2009, EPA is finalizing the 2005 NEI, and collecting data for graphic scales, including the national, state, and county levels. the 2008 analysis year inventory. This range of data presentation allows the inventory to inform air quality analyses by local, state, and federal government NEI Structure and Methodologies agencies as well as private industry. However, depending on the pollutant source, emissions data may be accurate at one The NEI is a comprehensive nationwide inventory from geographic extent but inaccurate in other scopes or regions. all stationary and mobile emission sources (see Exhibit 3-5). For example, when source emissions are calculated in a "top- The breadth of data collection and modeling require unique down" analysis, inventories at the state and regional level are methodological approaches for many emission sources, apportioned from the national inventory. This process may leading to a tiered or bottom-up structure for assembling introduce errors depending on the apportioning methodol- the inventory. NEI calculations are separated into three ogy and available data. Alternatively, inventories collected components: point sources, non-point sources, and mobile using a "bottom-up" approach may be accurate in certain sources. This section focuses on the mobile source compo- regions with thorough data, but inaccurate in regions with nent, because it includes all emissions from freight trans- little available data. These errors propagate to larger scopes as portation. However, a brief discussion of point and non- regional inventories are aggregated to the state and national point sources is included here in order to illuminate the level. A more thorough discussion of uncertainties in inven- scope of the NEI. tory apportionment and aggregation is presented later in this chapter. Mobile Source Emissions The NEI presents emissions data with a limited temporal scope. The inventory is calculated on an annual basis and is All transportation-related emissions are captured within the not broken down using a seasonal or monthly timeframe. In mobile source component of the NEI. This category includes addition, the inventory is only published for the current year, emissions from on-road vehicles, nonroad vehicles (cargo han- and does not forecast emissions for future years. However, dling equipment), locomotives, commercial marine vessels, EPA publishes historical comparisons of the current-year in- and aircraft. Each mode has a different approach to measur- ventory to past NEIs, (43) as well as a long-range analysis of ing emissions and apportioning the national inventory to states emission trends from the year 1900. (44) and counties. Although the inventory for each mode is calcu- EPA publishes the NEI on a three-year cycle; the most re- lated independently, emissions from on-road and nonroad cent NEI was published in 2005 for the 2002 analysis year. In sources are grouped within the National Mobile Inventory addition to the summary reports of emissions statistics, the Model (NMIM), a meta-model that collects input data and NEI data are also distributed in database form. (45) As of processes results for the two modes. Exhibit 3-5. Structure of NEI methodology for mobile source emissions. EPA National Emissions Inventory (NEI) Mobile Sources Point Sources Non-Point Sources (industrial, commercial, etc.) (area sources) National Mobile Locomotive Commercial Marine Aircraft Inventory Model Model: none (OGV, harbor craft) Model: EDMS (NMIM) Apply EFs to fuel Model: none Data from FAA LTO consumption data Carry forward prior database inventory work On-Road Nonroad (CHE) Model: MOBILE6 Model: NONROAD

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37 The methodological approach for each mode varies de- The methodology collects county-level vehicle data, calculates pending on the quality of data and tools available. Depend- emissions using the MOBILE6 model, and allocates the result- ing on the mode, emissions are calculated by applying analyt- ing emissions inventory to the state and county level. ical models, combining activity data with emission factors, or The process is achieved using the National Mobile Inven- applying a growth factor to the results of prior inventories. As tory Model (NMIM), which operates above MOBILE6, pre- a result, the strengths, weaknesses, and uncertainties in the processing input data and postprocessing emission results. NEI mobile source inventory vary by mode. A summary of NMIM contains a database of all county-level information re- the methodologies is as follows: quired to run the emissions model. The NMIM County Data- base, used for both on-road modeling with MOBILE6 and On-road emissions, which include all heavy-duty trucks, are nonroad modeling with NONROAD, contains detailed infor- calculated using EPA's MOBILE6 model, combined with mation on vehicle activity, fleet mix, and infrastructure. This nationwide data on vehicle activity from FHWA. When information, in addition to county-level meteorological and states provided alternate activity or other model inputs, the fuel data, comprises a complete set of data inputs for MOBILE6. state-level data were used in place of EPA inputs. Emissions In the postprocessing phase, NMIM combines emissions re- are allocated to the county level using NMIM. sults from MOBILE6 with nonroad emissions, and reallocates The inventory for nonroad emissions, which include cargo the resulting inventory to the state and county level in a form handling equipment, is calculated using EPA's NMIM, consistent with other components of the NEI. Where states which calculates emissions through the NONROAD model. provide alternate inputs into NONROAD, these values are When states provided alternate model inputs, the state- used in place of the default NMIM inputs. level data were used in place of EPA inputs. Emissions are Although the general approach used in this methodology allocated to the county level using NMIM. has remained consistent since 1990, details of its application The locomotive emissions inventory is developed by com- continue to evolve. In December 2008, EPA issued updated bining locomotive fuel-use data from DOE with published guidance for the on-road methodology used for the 2005 NEI. criteria pollutant emission factors (EFs). HAP emissions The new methodology is more consistent than in prior years, are calculated by applying speciation profiles to VOC and and relies on the MOBILE6 model to compute on-road emis- PM estimates. Emissions are allocated to the county level sions throughout the United States, Puerto Rico, and the Vir- using rail network data from U.S.DOT. gin Islands. In past years, when state emissions inventories The 2002 NEI inventory for commercial marine vessels were available, notably in California, Colorado, and Oregon, (harbor craft, inland vessels, and ocean-going vessels) was the state inventories were used in place of EPA emissions cal- based on emissions estimations produced for "marine diesel culations. However, the 2005 NEI methodology still gives regulations for 2000." Port emissions were disaggregated precedence to state-level VMT and activity data when avail- based on cargo volume, and underway emissions were dis- able. More information about the development and valida- aggregated based on United States Army Corps of Engi- tion of the NEI can be found in the 2002 National Emission neers (U.S. ACE) waterway data. When state data were Inventory (NEI) Preparation Plan--Final. (46) available, they were used in place of EPA inputs. HAP emissions were calculated by applying speciation profiles Summary of Strengths and Weaknesses. A summary of to VOC and PM estimates. NEI on-road methodology strengths and weaknesses is pro- Emissions from commercial aircraft are calculated by ap- vided in Exhibit 3-6. plying airport activity data to FAA's Emissions Disper- sion Modeling System (EDMS) model. HAP emissions Sources of Uncertainty. The NEI on-road methodology are calculated by applying speciation profiles to VOC and introduces uncertainty into several aspects of the approach, PM estimates. The EDMS inventory is measured on the from data collection to inventory assessment. This section county-level scale; state and national emissions are calcu- focuses on uncertainties unique to the NEI method, includ- lated by aggregating project-level emissions. ing uncertainties in allocating emissions across geographic The mobile component of the NEI does not include emis- scales and uncertainties in disaggregating emissions into sions from pipelines. freight and non-freight inventories. Uncertainties associated with MOBILE6 and estimation of truck VMT are discussed in Section 3.3. NEI On-Road Methodology Uncertainty also exists in the way that NMIM aggregates The NEI mobile source component includes a methodol- emissions results from the project-level level. The approach ogy for calculating criteria pollutant and HAP emissions asso- used in NMIM introduces uncertainties about the accuracy ciated with on-road vehicles, including heavy-duty vehicles. of state and national emissions. NMIM uses county-level data

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38 Exhibit 3-6. Summary of strengths and weaknesses--NEI on-road methodology. Criteria Strengths Weaknesses Representation of physical Method represents physical processes through processes MOBILE6 model. Sensitivity to input Method utilizes detailed facility-level and Method has significant data reporting requirements at parameters meteorological data to account for operational the county level; relies on state and local agencies for fluctuations in emissions. accurate input. Flexibility Method is flexible enough to be applicable to all Data reporting requirements are high, in fixed format. counties. Ability to incorporate Method can include county-level emission inspection effects of emission and maintenance programs. reduction strategies Representation of future Method does not predict future emissions. emissions Consideration of Method can incorporate alternative fuels, low emission alternative vehicle/fuel vehicles. technologies Data quality EPA performs data checks and follows up with states and local agencies regarding discrepancies. Spatial variability Method incorporates variations in altitude and meteorology by county. Temporal variability Method only calculates annual emissions. It does not evaluate emissions or fluctuations on seasonal or monthly scales. Review process Draft NEI made available for public and peer review, comment, revisions. Endorsements EPA. sets to calculate local emissions inventories; as such, the ac- sult, there are uncertainties associated with disaggregating curacy of state and national emissions results depends on the county data to a level that is more detailed than was originally accuracy of county data. Although EPA maintains default reported. data sets on each county, local agencies have the opportunity to supplement or replace EPA values with more accurate NEI Rail Methodology county-specific data. Since the accuracy of emissions inven- tories varies by county, any county-level errors will propagate In the NEI, EPA divides rail transportation into the follow- upward when local results are aggregated to the state and ing five categories: national level. Using data outputs from NMIM, the NEI methodology al- Line-haul service (Class I), lows users to disaggregate freight emissions at a high degree Regional and local service (Class II/III), of detail. NMIM reports annual emissions by pollutant and Railyard, by vehicle category. The specified vehicle types are referenced Passenger, and from the MOBILE6 model, and include light-duty vehicles Commuter. (passenger cars), light-duty trucks, medium-duty trucks, and heavy-duty trucks. Data are further allocated to gasoline and Freight transportation is represented in the first three cat- diesel categories. For example, freight emissions can be deter- egories, with the majority of emissions generated by line-haul mined by selecting emissions from certain vehicle classes, transportation. such as Class 8B heavy-heavy-duty trucks. Because county- Unlike the methodologies for on-road, nonroad, and air- level data are typically not reported with the same level of de- craft emissions, the NEI rail methodology does not rely on tail, the NEI relies on MOBILE6's default VMT distribution analytical models to calculate an emissions inventory. In- among truck classes, which is based on national default pa- stead, emissions are calculated directly from industry-wide rameters. National parameters are a poor surrogate for local fuel usage data, and combined with fuel-based emissions fac- parameters since the distribution of VMT by truck classes tors. Data on rail fuel consumption, reported by EIA, are al- should not be consistent across different counties. As a re- located to individual rail categories according to established

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39 category ratios developed for the NEI; this fuel allocation is the challenges of allocating emissions to the county level examined more closely in Section 3.4.4. Since California re- using BTS activity data, are not unique to the NEI and are quires low-sulfur fuel for in-state locomotives, the emis- examined more fully in Section 3.4. sions calculations are performed separately, although the This methodology relies on fuel sales data to estimate rail same methodology is employed. emissions, which--while simplifying the analysis--adds chal- Since the emissions inventory is created using national data, lenges in data collection. Fuel consumption data are most read- it must be distributed to the state and county level using a top- ily available at the national level for the entire rail industry and down approach. Emissions are allocated to counties based on are reported annually by EIA. However, an accurate represen- county-level rail activity data, which is provided by the Bureau tation of rail emissions requires more detailed fuel consump- of Transportation Statistics (BTS). A GIS analysis allocates tion data for each rail company. Although aggregated fuel traffic on rail segments to each county. The inventory of consumption information is available from the Surface Trans- railyard emissions is allocated spatially using a separate ap- portation Board, more detailed data are often unavailable, proach, in which emissions are allocated to urban counties because many companies view fuel consumption as propri- containing Class I railyards. etary information. To distribute fuel consumption among each rail category, EPA devised Source Classification Code (SCC) Summary of Strengths and Weaknesses. A summary of Ratios, or activity correction factors that express the ratio of NEI rail methodology strengths and weaknesses is provided fuel usage attributable to each rail class. For example, EPA in Exhibit 3-7. determined through an analysis outside the NEI that Class I line-haul rail accounts for 85% of rail fuel consumption, and Sources of Uncertainty. The most accurate data for rail allocates fuel use to Class I according to this ratio. However, emission calculations are where fuel is purchased and added EPA's methodology for developing these SCC Ratios is poorly to locomotives, as well as rail activity (in ton-miles) at the documented, and it is difficult to evaluate their accuracy. If the state level. The burn ratio in gallons per ton-mile, and the al- values were developed using a limited data set, then they may location of rail activity to regions are the least known param- introduce considerable uncertainty into the analysis. eters. The NEI rail methodology introduces two principal sources of uncertainty into emissions calculations. One in- NEI Commercial Marine Vessel Methodology stance, discussed in this section, occurs when the NEI distrib- utes rail consumption data to each rail category (i.e., line- The commercial marine vessel (CMV) methodology ac- haul, Class II/III). Additional sources of uncertainty, including counts for emissions from marine transportation. It is broken Exhibit 3-7. Summary of strengths and weaknesses--NEI rail methodology. Criteria Strengths Weaknesses Representation of physical Does not address physical processes; applies average EF processes to fuel consumption. Sensitivity to input parameters Insensitive to all parameters aside from fuel consumption and freight volume. Flexibility Method has little flexibility in data sources and parameters. Ability to incorporate effects of None. emission reduction strategies Representation of future Method does not forecast future emissions. emissions Consideration of alternative Uses California-specific data to account for vehicle/fuel technologies cleaner fuel. Data quality Emission factors based on EPA locomotive standards. (47) Spatial variability Does not account for geographic variations in terrain, speeds. Temporal variability Method only calculates annual emissions. It does not evaluate emissions or fluctuations on seasonal or monthly scales. Review process Draft NEI made available for public and peer review, comment, revisions. (46) Endorsements EPA.

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40 Exhibit 3-8. EPA marine compression-ignition engine categories. Approximate Category Specification Use Power Ratings Gross Engine Power 37 kW* Small harbor craft and recreational 1 < 1,000 kW Displacement < 5 liters per cylinder propulsion OGV auxiliary engines, harbor craft, and 2 Displacement 5 and < 30 liters per cylinder 1,000 3,000 kW smaller OGV propulsion 3 Displacement 30 liters per cylinder OGV propulsion > 3,000 kW * EPA assumes that all engines with a gross power below 37 kW are used for recreational applications and are treated separately from the commercial marine category. down into different categories based upon engine size as way) emissions from these vessels when operating away shown in Exhibit 3-8. from port in U.S. waters. The boundaries for vessels oper- Category 1 and 2 CMVs include "all boats and ships used ating in the oceans generally extend from the U.S. coastline either directly or indirectly in the conduct of commerce or to the 200 nautical mile limit of the Exclusive Economic military activity." (48) CMVs can range from 20-ft charter Zone (EEZ). For ships operating in the Great Lakes, the boats to 1,000-ft tankers and military vessels. Although the boundary extends out to the international boundary with majority of marine vessels are included in this source cate- Canada. gory, recreational marine vessels are classified as nonroad ve- Emissions were developed separately for near-port and un- hicles and included in the nonroad category. derway emissions. For near-port emissions, inventories for Category 1 and 2 CMV emissions inventories for years 2005 2002 were developed for 89 deep water and 28 Great Lakes and 2002 are based on emission estimates EPA performed for ports in the United States. The Waterway Network Ship Traf- the Draft Regulatory Impact Analysis Control of Emissions from fic, Energy, and Environmental Model (STEEM) provides Compression-Ignition Marine Engines. (49) This document emissions from ships traveling in shipping lanes between and uses a bottom-up approach to quantify total marine emis- near individual ports. (51) Near-port inventories were per- sions. First, an engine inventory is built using data on engine formed in a manner similar to the mid-tier methodology dis- sales and scrappage, to which annual load factors are applied cussed in Section 3.5.2. These emissions were married with in order to calculate total marine engine activity. Total CMV the STEEM data, and replaced the less accurate near-port emissions are calculated by combining activity levels with estimates in STEEM. Port call data came from the Army Corps emission factor standards set in the RIA. This emission inven- of Engineers' entrances and clearances data set, which is also tory was carried forward to NEI 2002 and 2005. discussed in Section 3.5.2. Before allocating the inventory to the county level, the NEI Where state agencies had developed a state-wide CMV methodology divides emissions by mode of operation: in/near emissions inventory, these values were given precedence over port and underway operation. The disaggregation follows EPA calculations. As more states perform their own invento- EPA SIP guidance that 75% of distillate fuel and 25% of resid- ries, this inclusion leads to a less consistent overall method- ual fuel is consumed while in/near port. (50) This separation ology. In the 2002 NEI, 26 states submitted statewide inven- into port emissions and underway emissions allows a more tories, and the NEI methodology was applied to emissions in precise geographic allocation. The method for allocating emis- the remaining states and territories. sions geographically is more complex than with on-road or nonroad vehicles, since CMV emissions impact only selected Summary of Strengths and Weaknesses. A summary of counties. The port emissions are allocated among the 150 NEI (46) marine methodology strengths and weaknesses is largest U.S. ports, based on total port traffic. Underway emis- provided in Exhibit 3-9. sions are allocated through a GIS-based approach that over- lays shipping lanes and waterways with county borders. Based Sources of Uncertainty. Category 1 and 2 inventories rely on this analysis, emissions are allocated to counties based on on engine counts determined from Power Systems Research. ton-miles of cargo in adjacent waterways. Port and waterway This database does not determine how many engines are on data were supplied by the Army Corps of Engineers, and GIS each vessel or accurately determine usage or load factors as data were supplied by BTS. discussed in Section 3.5.3. Category 3 data rely on foreign Category 3 NEI inventory includes emissions from both cargo movements and a somewhat streamlined methodology propulsion and auxiliary engines. The inventories include that uses detailed data from typical ports to estimate emissions both near-port emissions as well as the inter-port (under- at other ports. This is discussed in detail in Section 3.5.2.

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41 Exhibit 3-9. Summary of strengths and weaknesses--NEI marine methodology. Criteria Strengths Weaknesses Representation of physical Relies on port call activity for Category 3. None. Methodology does not include physical processes processes in inventory for Category 1 and 2. Sensitivity to input parameters None. Methodology based on estimate of engine inventory for Category 1 and 2. Relies on port call data from U.S. ACE and STEEM for Category 3. Flexibility None. Methodology relies on inventory constructed for year 2000 for Category 1 and 2. Relies on top 117 ports for Category 3. Ability to incorporate effects of None. emission reduction strategies Representation of future None. emissions Consideration of alternative None. Does not consider benefits from new fuels. vehicle/fuel technologies Data quality Emissions calculations rely on assumptions related to equipment inventory. Spatial variability Allocates emissions locally according to county- level marine activity. Temporal variability None. Review process Draft NEI made available for public and peer review, comment, revisions. Endorsements EPA. NEI Nonroad Methodology provide alternate inputs into NONROAD, these values are used in place of the default NMIM inputs. The NEI nonroad category encompasses a wide array of The nonroad methodology has evolved as better tools and vehicles--essentially all motorized vehicles and equipment that data have emerged. The NONROAD model was first applied are not normally operated on public roads. This category also to this category in 2001 for the 1996 inventory. In past years, excludes locomotives, commercial marine vessels, and aircraft, state emissions inventories were used in place of EPA cal- which are analyzed separately. The nonroad category extends to culations, notably in California, Pennsylvania, and Texas. a variety of fuel types, including diesel, gasoline, compressed Inventories for years prior to 1996 were developed retro- natural gas (CNG), and liquefied petroleum gas (LPG). The fol- actively using NONROAD. lowing types of vehicles are included in the nonroad analysis: Summary of Strengths and Weaknesses. A summary of Freight cargo handling equipment (CHE); NEI (46) nonroad methodology strengths and weaknesses is Airport ground support equipment (GSE); provided in Exhibit 3-10. Recreational vehicles and equipment (marine and land based); Sources of Uncertainty. There are sources of uncertainty Farm and construction machinery; and in all nonroad methodologies in terms of data collection, Industrial, commercial, and lawn and garden equipment. equipment emission factors, and other factors. These topics are discussed in more detail in Section 3.6. The approach employed in this methodology is similar to the approach in the on-road methodology: activity, engine NEI Aircraft Methodology mix, and fuel data are collected at the county level, emissions are calculated using EPA's NONROAD model, and the result- The NEI aircraft methodology captures emissions from ing national inventory is apportioned to the state and county all domestic and international aircraft operating within the levels. As in the on-road inventory, data collection and dis- United States. Aircraft are classified by EPA into four cate- aggregation is handled by NMIM, which operates above gories: commercial, air taxi, general aviation, and military. NONROAD. NMIM formats county data into input files for This analysis focuses on emissions from commercial aircraft NONROAD, runs the model, and processes the results to be used to carry freight, passengers, or both. Commercial aircraft consistent with other components of the NEI. Where states tend to be large, powered by jet engines, and operate at large

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42 Exhibit 3-10. Summary of strengths and weaknesses--NEI nonroad methodology. Criteria Strengths Weaknesses Representation of physical Does not address physical processes; applies processes average EF to equipment inventory. Sensitivity to input parameters Flexibility Although the NONROAD model has default equipment distributions, local agencies can submit county-specific data. Ability to incorporate effects of Does not incorporate inspection/maintenance emission reduction strategies profiles or other strategies. Representation of future Does not forecast future emissions. emissions Consideration of alternative Can incorporate alternative fuels such as LPG and vehicle/fuel technologies CNG/LNG. Data quality Can incorporate county-level data submitted by local Quality of data will vary depending on locality. agencies. Default parameters may not capture spatial variations. Spatial variability Does not account for the effect of geography on emissions estimates. Temporal variability Method only calculates annual emissions. It does not evaluate emissions or fluctuations on seasonal or monthly scales. Review process Draft NEI made available for public and peer review, comment, revisions. Endorsements EPA. airports. Emissions from these aircraft are calculated by com- Airport statistics are input into the EDMS model, which bining airport activity data with FAA's EDMS emissions combines LTO data with emissions factors that are specific to model. Since the inventory is estimated independently for each type of aircraft and each phase of the LTO cycle. EDMS each airport, emissions are allocated to the county level by uses default time-in-mode (TIM) values to determine total default. State and national emissions are calculated by aggre- time spent by aircraft in each LTO mode, and calculates emis- gating county-level emissions. sions using measured emissions factors. See Section 3.7 for Data on airport activity is measured in terms of the Landing more information on EDMS. and Takeoff (LTO) cycle, a five-mode approach consisting of the following: Summary of Strengths and Weaknesses. A summary of NEI (46) aircraft methodology strengths and weaknesses is Approach: period beginning when aircraft enters the pol- provided in Exhibit 3-11. lutant "mixing zone," typically at an altitude of 3,000 ft, until landing; Sources of Uncertainty. Sources of uncertainty are dis- Taxi/idle-in: time spent after landing until aircraft is parked cussed in Section 3.8. at the gate and engines turned off; Taxi/idle-out: period from engine startup to takeoff; 3.2.4 Evaluation of Parameters Takeoff: time spent after takeoff that lasts until the aircraft reaches 500 to 1,000 ft; The GHG Inventory and NEI methodologies include Climbout: period following takeoff that concludes when an analysis of all transportation modes; as such, many data aircraft passes out of mixing zone. inputs required for these analyses are the same as inputs re- quired for each modal methodology, as discussed in sub- LTO data is collected in Airport Activity Statistics of Cer- sequent chapters. This section focuses on parameters that are tificated Air Carriers, (52) which captures statistics for all unique to the EPA national methodologies. These parameters domestic carriers. Each LTO cycle is correlated with an air- are all unique to the process of measuring and allocating fuel port location, carrier, and aircraft type. consumption at the national level.

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43 Exhibit 3-11. Summary of strengths and weaknesses--NEI aircraft methodology. Criteria Strengths Weaknesses Representation of physical Accounts for variations in emissions between aircraft processes engines, between LTO modes. Sensitivity to input parameters Sensitive to activity by type of aircraft Flexibility Can include changes in activity at any airport Ability to incorporate effects of None. emission reduction strategies Representation of future None. emissions Consideration of alternative None. vehicle/fuel technologies Data quality FAA maintains detailed activity (LTO) records. Spatial variability Accounts for activity and fleet mix at each airport. Does not incorporate local meteorology. Temporal variability None. Emissions reported annually. Review process Draft NEI made available for public and peer review, comment, revisions. Endorsements EPA. The parameters discussed in this section are used in allo- Parameters Used in Fuel Consumption Calculations cating fuel consumption to the transportation sector and to individual modes, and are used in one or both of the EPA na- Since transportation emissions in the EPA GHG Inventory tional methods. A summary of parameters is presented in are due to the combustion of fossil fuels, the primary input Exhibit 3-12, and more detailed information is provided in into the inventory is data on fuel consumption within each the pedigree matrix (Exhibit 3-13) and the subsequent qual- mode. Although some data sources capture fuel use in indi- itative discussion. vidual modes (e.g., rail), EPA chooses a methodology that measures nationwide fuel consumption and allocates fuel use to economic sectors such as industrial, residential, and trans- Pedigree Matrix portation. This approach has several benefits: it relies on A pedigree matrix, provided in Exhibit 3-13, for data qual- comprehensive fuel data available from EIA, and it accurately ity assessment assigns quantitative scores to all parameters in- measures GHG emissions due to fuel use for the nation as a cluded in Exhibit 3-12. The criteria to assign scores in the whole. However, the process introduces uncertainties when pedigree matrix are included in Appendix A. fuel use and GHG emissions are assigned to the transportation Exhibit 3-12. Parameters for the national inventories. Geographic Pedigree Qualitative Quantitative Parameter Methods/Models Scale Matrix Assessment Assessment Fuel Supply Data GHG Inventory National Economic Sector Activity Data GHG Inventory National Modal Activity Data GHG Inventory, NEI National Fuel Carbon Content GHG Inventory National Modal Emissions Factors NEI National Marine Equipment Inventory NEI National Rail GIS Data NEI National Local Nonroad Equipment NEI National Inventory On-Road Fleet Mix NEI National Key: indicates that a parameter is analyzed in the way denoted by the column: indicates that the parameter is not discussed in the way denoted by the column.

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44 Exhibit 3-13. Pedigree matrix--national parameters. Technological Correlation Geographic Correlation Temporal Correlation Representativeness Acquisition Method Range of Variation Impact on Result Parameter Independence Fuel Supply Data 5 1 1 1 1 1 N/A 1 Economic Sector Activity Data 4 3 Varies 2 Varies 1 N/A 5 Modal Activity Data 4 3 Varies 2 1 1 N/A 5 Fuel Carbon Content 5 1 1 1 1 1 1 1 Modal Emissions Factors 4 2 3 2 3 2 2 Varies Marine Equipment Inventory 2 3 3 4 3 2 N/A 5 Rail GIS Data 2 2 1 1 2 1 1 2 Local Nonroad Equipment Inventory 2 3 3 2 3 5 N/A Varies On-Road Fleet Mix 2 2 2 2 1 2 N/A 2 sector and to individual modes. This section qualitatively fuel consumption data, the EPA methodology distributes fuel analyzes the assumptions made when estimating modal fuel use among economic sectors, to determine the GHG emis- consumption. sions attributable to each sector. As part of this step, EPA rec- onciles the results of a top-down approach, based on EIA Measuring Nationwide Fuel Use--Fuel Supply Data. data, with the results of a bottom-up approach, based on in- The combustion of fossil fuels accounted for 83% of nation- dustry activity measurements. Consistent with IPCC guide- wide GHG emissions in 2007. (53) Since emissions from fuel lines, the bottom-up (or sectorial approach) relies on several combustion constitute the vast majority of the inventory, the data points, including consumption data by EIA and end-use need to accurately measure nationwide fuel use is paramount. energy consumption surveys such as the Manufacturing En- EPA measures total fuel consumption in the United States ergy Consumption Survey, which is conducted every four using data from EIA, primarily the agency's Monthly Energy years. Additional information is used to adjust fuel consump- Review, and additional petroleum product detail. The Monthly tion for the transportation sector: EPA builds an activity- Energy Review reports data on both fuel production (petro- based estimate of fuel consumption from modal data, includ- leum imports, domestic production, and refining) as well as ing FHWA statistics for on-road activity and AAR statistics consumption (by fuel and end-use sector). The fuel produc- for rail activity. tion data conforms to a reporting convention promulgated Several potential sources for error exist in applying this by IPCC and the International Energy Agency (IEA), in which method to allocate fuel consumption to the transportation data are presented in a top-down format. This structure ag- sector. These include gregates data on fuel production and distribution to assess fuel use, referred to as "apparent consumption." These data Consumption data, often collected in the form of fuel ex- are used by the GHG Inventory as the first step in allocating penditures, may distort true fuel usage. For example, collec- fuel consumption. tion methods may focus on large, more efficient consumers, This step in the process contains few uncertainties com- and bypass smaller entities that may use comparatively more pared to subsequent steps. The collection of national fuel data fuel. In addition, data based on fuel prices may be biased as contains few assumptions, since EIA has comprehensive ac- larger consumers can often leverage lower prices due to high cess to primary sources of information. Larger uncertainties purchasing volume. occur in the following steps in which national data are allo- Transportation activity data, collected for each mode inde- cated to the transportation sector and individual modes. pendently by separate agencies, may contain different biases and errors due to differing methodologies. Further, activity Assigning Fuel Use to the Transportation Sector-- sets may be incomplete for modes with limited information Economic Sector Activity Data. After collecting national such as commercial vessels and nonroad equipment.