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62 Driving Cycles per se, the effects of road grade on emissions are more pro- nounced on heavy-duty trucks than on light-duty vehicles. Emission factors for MOBILE6 and EMFAC2007 are de- The importance of road grade on heavy-duty emissions can veloped based on emissions testing on a standardized driving be evaluated from current modal emissions models. A previ- cycle such as FTP. A great amount of research has been de- ous study that evaluated truck movements over 23 different voted to the development of driving cycles that reflect actual corridors concluded that fuel consumption increased by 10% driving and, as a result, the Heavy-Duty Diesel Test Cycle to 35% as a result of grades. (71) Assuming that fuel con- (HDDTC) was developed by the California Air Resources sumption is a good proxy for emissions, the impacts of road Board. However, the question still remains as to whether a grade are significant on emissions as well. single driving cycle is able to provide enough information for the development of accurate speed correction factors. Modal emissions models such as MOVES and CMEM also Empty Miles rely on prespecified driving cycles, but the development of Empty miles refer to the need for empty equipment to be emission factors does not depend on speed correction factors, relocated to places where it is required. Because additional but on a combination of vehicle-specific power and instan- fuel is consumed (and emissions generated) in the movement taneous speed. The use of different driving cycles also can of empty truck equipment, ideally, empty miles should be reduce the uncertainty associated with the development of considered in emission analyses. emission factors. In national analyses, where truck VMT is estimated based on HPMS data, empty movements are captured as well as Emission Factors loaded movements, since traffic measurements do not differ- entiate trucks based on their cargo. In regional and project- The analysis of emission factors is discussed under each level analyses however, empty movements need to be consid- specific emission model. ered separately. The incorporation of empty miles is very challenging because of a lack of data. Public data sources with Classification of Truck Types aggregate information about empty miles exist. For example, VIUS provides data on empty mileage for different truck The classification of trucks is important because (1) truck types. (Other than a truck's home-base state and the share of trip generation rates depend on how trucks are defined and miles driven within and outside of the home-base state, there (2) emission rates have a strong dependence on equipment is no information in VIUS that could indicate empty mileage type. Depending on the study, trucks might be classified based in specific corridors.) More accurate empty factors for specific on their gross weight, number of axles, or configuration lanes and commodities could be obtained directly from truck- (single-unit, combination). Such variance in classification ing companies, but that is generally unlikely due to confiden- systems prevents the development of trip generation rate aver- tiality issues. It is possible that transportation rates could re- ages across studies. As a result, the number of sample studies flect empty miles, but it is difficult to disaggregate the impacts for a given classification system is small, which increases the of empty miles from other factors such as supply and demand, uncertainties associated with trip generation rates. labor markets, and equipment availability. Due to these chal- Another issue is that the heavy-duty truck categories in lenges, many analyses simply disregard empty movements. A MOBILE6 and EMFAC2007 do not match the categories recent study from FRA (71) estimated the impacts of empty reported under HPMS. As a result, the process of mapping miles on fuel consumed in different truck movements, with a truck categories between these systems is not always straight- fuel penalty between 9% and 21% in fuel efficiency. forward. For example, HPMS currently characterizes heavy- duty trucks in two categories, namely single-unit trucks 3.4 Rail and combination trucks, as opposed to eight categories in MOBILE6, and three categories in EMFAC2007, in both This section includes (1) a brief documentation of the cur- cases according to gross vehicle weight. This issue is being rent practice and methodologies for calculating emissions resolved in MOVES since it categorizes heavy-duty trucks from freight rail, (2) a summary of the strengths and weak- according to the same classification system used by HPMS. nesses of such methods, and (3) an analysis of uncertainty as- sociated with these methods, as well as with the parameters used in the emission calculations. Topics covered include Road Grade streamlined and detailed methods of estimating rail activity, The effects of road grade are not incorporated into MOBILE6 emission factors, and total emissions at the national, regional, or EMFAC2007. Although this is not a freight-related issue and project-level geographic scales. Most rail emission method-

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63 ologies combine fuel-based emission factors with measured or section. Exhibit 3-25 includes the summary of methods to calculated fuel consumption to determine total emissions. calculate rail emissions. However, as data availability varies over different geographic scales, different methodologies are required. 3.4.1 Evaluation of Emission Models Independently of the geographic scale, rail operations are typically categorized in switch and line-haul due to different The calculation of rail emissions does not typically rely on a activity patterns and equipment configurations. Line-haul specific emission model. In some isolated cases, train simula- operations refer to the movement over long distances, gener- tion software also can be used to estimate fuel consumption on ally with newer and more powerful locomotives than switch a given rail line. The best well-known train simulation software operations, and tend to idle less. Switch activities refer to the in the United States is possibly the Train Energy Model (TEM) assembling and disassembling of trains at railyards, sorting of developed by the Transportation Technology Center for the rail cars, and delivery of empty rail cars to terminals. Switch Association of American Railroads. It is a single train simula- operations involve short-distance movements, significant tor for long-haul trains along specific routes, and was designed idling, and older equipment. to calculate journey time and fuel use. Simulation model out- Most rail methodologies rely on fuel consumption data to puts are typically compared against real-world scenarios in determine emissions. Detailed fuel consumption data are typ- order to calibrate the model and adjust the coefficients. Like ically considered sensitive information by railroads. However, most train simulation models, TEM relies on a set of train re- nationwide aggregate fuel consumption data, which are based sistance equations originally developed by W. J. Davis in 1926. on 100% reporting for Class I railroads, are available from in- (72) These equations quantify train resistance based on train dustry or government agencies (i.e., Association of American weight, speed, number of axles, train composition, track cur- Railroads, Energy Information Administration, state agencies, vature, and grade. Fuel consumption can be derived from train private companies via surveys). When fuel consumption data resistance. Since then, the equations have been adapted to more are not available for the region of interest, it must be estimated recent standards, accounting for updated rail equipment and either by apportioning fuel consumption from a larger geo- operational requirements. The use of train simulation software graphic area (top-down) or by aggregating fuel consumption enables the most accurate results, but requires activity data at from individual rail movements (bottom-up). Both methods a level that is not typically available to most agencies. require measurements of rail activity. Because the rail sector has fewer metrics of activity when 3.4.2 Evaluation of Regional Methods compared to other modes, methods for calculating emissions tend to be overly simplified or overly complex, with the atten- Typically, there is little or no published information on dant uncertainties and inaccuracy. Streamlined, or top-down, railroad activity available for a specific region. Thus, state and methods determine emissions based on publicly available data regional air quality agencies must obtain railroad activity data on fuel consumption at the state or national level, and appor- directly from the railroad companies. Railroad companies tion emissions to the state or county level using an available often are reluctant to provide detailed fuel consumption or activity metric, such as traffic density or mileage of active track. activity data due to concerns over distributing sensitive infor- Detailed, or bottom-up, methods calculate fuel consumption mation. Even when these data are provided, they often are not either by measuring freight movements or surveying individ- reported with a high level of detail, due in part to the railroad ual railroad companies. Both approaches are discussed in this company procedures for maintaining such data. Exhibit 3-25. Rail methods. Geographic Method Pollutants Scale EPA GHG Inventory National GHG Locomotive National Emissions Inventory (NEI) National CAP and toxics Line-Haul Emissions by Traffic Density Regional/Local All Line-Haul Emissions by Active Track Regional/Local All Switch Emissions by Number of Switchers or Hours Regional/Local All Line-Haul/Switch Emissions by Employees Regional/Local All Line-Haul/Switch Emissions by Time in Mode Local All Line-Haul Emissions at Marine Terminals Local All

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64 Methods to quantify regional rail emissions can be divided Emission factors (in grams/gallon) are applied to the fuel use in the following types: (1) line-haul emissions by traffic density, figures to estimate annual emissions. (2) line-haul emissions by active track, (3) switch emissions by Using a constant fuel consumption index, which is equiva- number of switchers or hours of operation, and (4) line-haul/ lent to apportioning fuel use by GTM, is an inaccurate method switch emissions by number of employees. for most regional and project-level emission applications be- cause it ignores key local factors such as grade, equipment type (which influences aerodynamic coefficients, and payload Line-Haul Emissions by Traffic Density to tare ratios), and possibly congestion. All of these factors EPA's guidance for regional inventory preparation pro- can have a substantial effect on fuel consumption per ton-mile, vides an approach that estimates line-haul rail fuel consump- as indicated in a recent study from FRA. (71) Correction fac- tion by means of traffic density. (73) In the National Emission tors for grade and commodity group can be used to minimize Inventory (NEI), previously described in Section 3.2.3, EIA's the uncertainty associated with the use of a single measure of estimates of national rail fuel consumption are multiplied by fuel efficiency. There have also been questions about the accu- EPA's national locomotive emission factors. (7475) Na- racy of county-level GTM data reported by railroads. tional rail emissions can be apportioned to individual coun- As indicated by a previous study, a good example of the po- ties based on their share of traffic density (gross ton-miles). tential shortcomings of such an approach is its application in County traffic density is obtained from the National Trans- California. (77) The two Class I railroads that operate in Cali- portation Atlas Database (NTAD), which includes traffic fornia, Union Pacific and Burlington Northern Santa Fe, pri- density data for each track in the United States. (76) To main- marily offer intermodal service over relatively hilly terrain in the tain the confidentiality of railroad data, the NTAD does not Sierra Nevada Mountains. Their national operations however, contain actual traffic density, but six ranges of traffic density, are dominated by coal trains operating at relatively level terrain. of which the medians are used for emission calculations. (77) Because coal trains are much more fuel efficient than inter- A similar method relies on statewide data, which can be modal trains, system fuel consumption index is a very poor used in place of national data. Each freight railroad that op- indicator of regional fuel consumption index in California. erates in a state/region is asked to report gross ton-miles The FRA study and other analyses have estimated meas- (GTM) by county, as well as total fuel consumption in the ures of rail fuel efficiency for different types of trains, lanes, state. If a railroad is able to provide this information, the and commodities, so it is possible to determine a range of statewide line-haul fuel use is apportioned to counties in di- variation in terms of fuel consumption index (Exhibit 3-26). rect proportion to the GTM. Sometimes the railroads per- Correction factors to adjust the systemwide fuel consump- form this fuel use allocation using their own estimate of fuel tion index in EPA's guidance were developed by Sierra Re- use per GTM. search. (78) Such correction factors adjust for the steepness of Another variation of the same method relies on more terrain as well as the proportion of bulk rail traffic. Although project-level data. According to the formula in Equation 4, these factors account for the effects of the most important pa- fuel consumption is determined by dividing traffic density (in rameters on rail fuel efficiency, there are concerns about the GTV) by the systemwide fuel consumption index, measured validity of such factors given that they were estimated based in gross ton-miles per gallon. on outdated data from a single study. Additionally, it is un- certain to what extent such correction factors are used in Rail Traffic Density emissions studies. Fuel Consumption = ( gross ton-miles ) (Equation 4) The use of fuel consumption indexes that are specific to a given lane, train type, and commodity, such as those included ( gallons ) Fuel Consumption Index ( gross ton-miles per gallon ) in the FRA study, provide a more accurate measure of train fuel efficiency. A systemwide fuel consumption index can be determined for each individual railroad by dividing its annual traffic den- Exhibit 3-26. Range of sity by its annual fuel consumption, and these two parame- rail fuel efficiency ters can be obtained from published Surface Transportation (gross ton-miles/gallon). Board (STB) data. This method also is based on the appor- tionment of fuel use by GTM, but it relies on more specific Rail Equipment Min Max data, which can be obtained from each of the participating Double-Stack 523 849 railroads. Mixed 367 691 The fuel use estimates for each railroad are summed, with Auto Rack 542 620 the result being an estimate of total railroad fuel use by county.

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65 Exhibit 3-27. Analysis of strengths and weaknesses--comparison of methods. Emissions by Active Track Emissions by Number of Switch Emissions by Criteria Emissions by Traffic Density Locomotives or Hours Employee Representation of Weakness: Depending on the quality of input Weakness: This method provides a Weakness: This method assumes no variation in terms Weakness: This method is physical processes data, this method can provide an inaccurate very inaccurate estimate of regional of the number of operating hours per switch locomotive (if very inaccurate because it estimate of regional emissions if it assumes emissions because it assumes that that information is not provided by the railroads). It also does not consider that emissions are proportional to gross ton- emissions are proportional to active assumes the same duty cycle across different yards. differences in duty cycles, miles. This assumption ignores the fact that track. This ignores the dependence of operating hours, emissions also depend on type of rail emissions on track utilization, rail commodity carried, equipment, commodity, terrain level, and equipment, commodity, terrain level, and equipment types, terrain, logistics requirements. logistics requirements. and labor productivity. Method sensitivity Weakness: These methods are only dependent on one input. to input parameters Method flexibility Strength: This method can be used with Strength: If local data are not available by the either national or statewide data. participating railroads, surrogate data from average estimates can be used. Representation of Strength: Because this method relies on data future emissions that is published annually and that can be forecasted based on economic projections, emissions can also be forecasted. Data quality Weakness: Because of railroad Strength: If number of switch locomotives is used, the confidentiality, the NTAD only provides process of data collection should be straightforward and ranges of traffic density. There have also accurate. been concerns about the accuracy of county- Weakness: If number of hours is used, data quality can level GTM data reported by railroads. vary widely because there are no standards related to data collection. Spatial variability Weakness: These two methods do not provide a good representation of the differences Weakness: This method does not provide a good across geographies because they ignore the impacts of terrain grade on emissions. representation of the differences across railyards because it assumes the same duty cycle and, sometimes, the same number of hours per switch locomotive. Temporal variability Weakness: Because these methods rely on aggregate data, they do not provide any indication on how emissions are distributed across different months, weeks, or days. The only exception is if the number of hours of operation is collected at different time periods. Review process Weakness: There have not been any studies comparing regional/local emissions from Weakness: Recent emission inventories completed by Weakness: There have not these two methods versus other methods. railroads show large differences in operating hours and been any studies fuel use by switch locomotive. The difference in operating comparing regional/local hours is between 0% to110%, and the difference in fuel emissions from this method use per locomotive is between -32 to +41%. to those of other methods. Endorsements Strength: Based on EPA guidance Strength: Based on EPA and CARB guidance. Summary of Strengths and Weaknesses. The analysis of Summary of Strengths and Weaknesses. The analysis of strengths and weaknesses is provided in Exhibit 3-27. strengths and weaknesses is provided in Exhibit 3-27. Line-Haul Emissions by Active Track Switch Emissions by Number of Switchers or Hours For railroads that are not able to report GTM, mileage of EPA and CARB utilize a simplified approach to estimate active track is used as a proxy. If the railroad is able to report emissions at individual railyards, whose emissions are added statewide line-haul fuel use, fuel use is apportioned to coun- in regional studies. Each freight railroad that operates in a re- ties in direct proportion to the railroad's track mileage by gion is asked to report the number of switch yard locomotives county. If the railroad cannot report statewide fuel use, they operate, by county or by individual yard. Some railroads national-level fuel use (as reported by the Association of also are able to provide hours of switch locomotive use by American Railroads) is apportioned to state and county based county or yard. Railroads are asked to report the average on track mileage. Like the previous method, fuel use estimates annual fuel consumption rate (in gallons per locomotive for each railroad are summed, resulting in an estimate of total per year) of their switch yard locomotives. If railroads can- fuel use by county. Emission factors (in grams/gallon) are ap- not provide this rate, a rate is assumed based on EPA guid- plied to the fuel use to estimate annual emissions. ance or on information from other railroads. Switch yard The main shortfall to this methodology is that active track locomotive fuel use is then calculated by applying a fuel is almost certainly not an accurate proxy for fuel use. In consumption rate to the number of switch yard locomo- most regions, some rail lines are used much more heavily tives, assuming an average locomotive duty cycle. Fuel use than others. Thus, using track length to apportion fuel con- estimates are summed, and emission factors (in grams/gallon) sumption to the county level probably results in significant are applied to the fuel use to estimate annual emissions from inaccuracies. switch locomotives.

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66 This method assumes no variation in terms of the number mation from locomotive event recorders, which record time of operating hours per switch locomotive (if that information spent on each throttle notch, and train performance model- is not provided by the railroads) or the locomotive duty cycle ing software (e.g., Train Energy Model). This is by far the across different yards. As indicated by a recent study, recent most accurate method, but it relies on detailed information emission inventories completed by railroads to support CARB's from railroads, which do not always have the resources to railyard health risk assessment show large differences in collect (or are willing to share) such information. operating hours and fuel use by switch locomotive. As for Studies that rely on this type of methodology are gener- operating hours, the difference between the detailed studies ally performed very sporadically due to the intense resource and those utilizing the standard methodology ranged from requirements--an example is the Booz-Allen study (79) in 0% to almost 110%, while the difference in fuel use per loco- California. Updates to such studies, like those done by CARB, motive ranged from -32% to 41%. (77) typically are based on growth factors that are applied equally to all routes. (80) The use of growth factors is related to sev- Summary of Strengths and Weaknesses. The analysis of eral shortcomings: (1) some growth factors are based on U.S. strengths and weaknesses is included in Exhibit 3-27. economic growth and are not specific to California, (2) growth factors will not reflect changes in train and commodity mix, Line-Haul/Switch Emissions by Employees train length, and locomotive power, all of which have a strong effect on locomotive duty cycles and the time spent on each Class II and III railroads (short line and switch railroads) are throttle notch. (77) In particular, intermodal traffic has in- often unable to provide the information described above (e.g., creased at an annualized rate of 3.9% from 1990 to 2005, well number of switch locomotives, hours of operation). In some above the 3.1% annual increase in rail on average. (81) There- regions (such as Chicago), the number of Class II/III railroads fore, it is highly unlikely that the train or commodity mix will in operation is considered too large to make surveys of individ- remain constant over time. The use of event recorder data ual companies practical. In these cases, fuel consumption can to get time-in-notch and fuel consumption can be done to be estimated by obtaining the number of employees of the rail- extrapolate data from a few trains to the line average. road by county (using a commercial employment database such as Dun & Bradstreet) and a ratio of fuel consumption per Summary of Strengths and Weaknesses. An analysis of employee. strengths and weaknesses is provided in Exhibit 3-28. This method does not take into consideration that different railroads carry different commodities on different types of Line-Haul Emissions at Marine Terminals trains over varying terrain--all of which are factors that have a strong effect on fuel efficiency. Additionally, this method Although EPA guidelines (82) recommend that line-haul also assumes that labor productivity is the same among rail- locomotive activity be measured in terms of fuel consump- roads, which is also a questionable assumption. tion, the estimation of rail-related emissions at port emission inventories typically take an alternative approach to better Summary of Strengths and Weaknesses. The analysis of reflect line-haul operations within marine terminals. Since strengths and weaknesses is provided in Exhibit 3-27. line-haul locomotives move over very short distances within marine terminals, rail activity is measured in hours of opera- 3.4.3 Evaluation of Local/Project-Level tion. Because line-haul emission factors can be expressed in Methods terms of horsepower-hour, rail activity can be calculated in the same unit, as shown in Equation 5. The previous section included methods that could estimate emissions at the regional and local level but that generally do not Line-Haul Number Hours Average Locomotives rely on specific project-level data. This section includes those Rail Activity = of at Load per Train methods that are based on local inputs. Local/project-level ( bhp-hr ) Trains Port Factor analyses that rely on detailed activity data from participating Average railroads result in more accurate rail emissions than regional Locomotive (Equation 5) analyses do. Horsepower Line-Haul/Switch Emissions by Time in Mode In a detailed inventory, all inputs to this equation are ob- tained from the participating railroads, which otherwise need Rail activity can be measured in number of operating hours to be estimated. If local estimates are not available, the num- in each notch for each type of train traveling on each route or ber of containerized trains can be calculated based on the operating at each railyard. Railroads can obtain such infor- number of TEUs, train capacity, an average utilization rate,

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67 Exhibit 3-28. Analysis of strengths and weaknesses--emissions by time in mode. Criteria Strengths Weaknesses Representation of physical Provided that data are available to processes represent the local conditions (grade, equipment type, duty cycles), this is the most accurate method to estimate rail emissions. Method sensitivity to input parameters Method flexibility This method relies on very detailed data requirements. Ability to incorporate effects Because key input parameters are captured of emission reduction in this method, it is generally possible to strategies analyze the effects of emission reduction strategies. Representation of future emissions Consideration of alternative This method can capture the effects of the vehicle/fuel technologies use of hybrid switch locomotives. Data quality Data quality can vary significantly depending on the specific data collection process. Spatial variability If detailed local data are provided, this method gives a good degree of spatial variability. Temporal variability This method does not provide any indication on how emissions are distributed across different months, weeks, or days. Review process Endorsements plus a ratio of empty miles. EPA's best practices guidance for all parameters included in Exhibit 3-30. The criteria to assign port-related emission inventories provides default assump- scores in the pedigree matrix are included in Appendix A. tions for the other inputs based on previous inventories, but relying on average inputs ignores the operational differences Fuel Consumption among different ports. As a result, the difference between using default assumptions and using local assumptions could Class I railroads are required to report fuel use to the Sur- be a factor of up to three times, based on a comparison with face Transportation Board (STB) via Schedule 700 of the R1 emission inventories done by the ports of Los Angeles, (83) Annual Report. As a result, the fuel use data published by the Long Beach, (84) and Seattle and Tacoma. (85) Association of American Railroads (AAR) is based on 100% A more accurate method to quantify line-haul rail activity reporting. Even then, there have been questions about the at marine terminals is to use event recorders to measure fuel accuracy of fuel consumption data reported by railroads. burnt per train mile within a port. For example, the fuel use in Texas reported by railroads for 2001 (220 million gallons) is less than half the locomotive fuel Summary of Strengths and Weaknesses. An analysis of sales for the state as reported by DOE (504 million gallons) for strengths and weaknesses is provided in Exhibit 3-29. that year. Some of this discrepancy can be explained by the fact that railroads often purchase fuel in one state and then 3.4.4 Evaluation of Parameters consume that fuel in another. Unfortunately, there are no Exhibit 3-30 includes a list of parameters used in the meth- mechanisms to verify the fuel consumption data reported ods and models. These parameters are described throughout by railroads. Additionally, there is little correlation between this section. fuel purchases and fuel consumption in a state because locomotives can travel long distances between fuel pur- Pedigree Matrix. Exhibit 3-31 provides a pedigree matrix chases. Note that Class II and III railroads are not required for data quality assessment that assigns quantitative scores to to report fuel use.

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68 Exhibit 3-29. Analysis of strengths and weaknesses--emissions at marine terminals. Criteria Strengths Weaknesses Representation of physical If local estimates of rail activity are not available, processes the use of default assumptions could result in large uncertainties due to operational differences across different marine terminals. Method sensitivity to input parameters Method flexibility This method can be used with either local estimates or national default assumptions. Ability to incorporate effects of emission reduction strategies Representation of future Because this method relies on cargo data to emissions estimate rail activity, economic indicators can be used to forecast emissions. Consideration of alternative vehicle/fuel technologies Data quality Spatial variability If detailed local data are provided, this method provides a good degree of spatial variability. Temporal variability This method does not provide any indication on how emissions are distributed across different months, weeks, or days. Review process Endorsements Exhibit 3-30. Rail parameters. Geographic Pedigree Qualitative Quantitative Parameter Methods/Models Scale Matrix Assessment Assessment Fuel Consumption National National Locomotive Duty Cycles All (explicit in Regional/Local regional/local) Emission Factors All All Locomotive Type All (explicit in local) All Locomotive Tier All All Distribution Empty Miles All Local Traffic Density Emissions by Traffic Regional/Local Density Miles of Active Track Emissions by Active Regional/Local Track Number of Switch Emissions by Regional/Local Locomotives Switchers Hours by Switch Emissions by Hours Regional/Local Locomotive Number of employees Emissions by Regional/Local Employees 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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69 Exhibit 3-31. Pedigree matrix--rail parameters. Technological Correlation Geographic Correlation Temporal Correlation Representativeness Acquisition Method Range of Variation Impact on Result Independence Parameter Fuel Consumption 5 1 3 1 1 1 5 Locomotive Duty Cycles 4 Varies 3-4 Varies 4 5 5 Emission Factors 5 2 3 3-4 4 5 5 Locomotive Type 3 Varies 4 3-4 4 5 4 Locomotive Tier Distribution 4 Varies 4 Varies 1 5 3 Equipment Type 4 1 4 Varies 1 Varies 4 Empty Miles 3 4 3 1 1 2 5 Traffic Density 5 1-2 3-4 1 1 1 Varies Miles of Active Track 5 1 3 1 1 1 1 Number of Switch Locomotives 5 1 4 1 1 1 1 Hours by Switch Locomotive 5 2-3 4 1 1 1 3 Number of employees 5 1 3 1 1 1 1 Although self-reported fuel consumption estimates are con- Switch duty cycle data came from two local railroads with sidered the most accurate data source available, this accuracy over 300 hours of operation by eight trains. The relatively could be improved by reconciling top-down (i.e., fuel con- small number of switch locomotives and railroads brings sumption through fuel sales data) and bottom-up (i.e., fuel concerns about the statistical representation of an average consumption through activity data) approaches. switch duty cycle. Additionally, the variation of the percent- age of time in each throttle notch was very high for both line- haul and switch cycles, as illustrated in Exhibit 3-32. Such Rail Activity high variation is the main reason why the use of an average Rail fuel use needs to be estimated based on rail activity if duty-cycle is a poor substitute for regional or local data. These accurate fuel sales data are not available or are not represen- cycles were developed before the widespread use of idle con- tative of fuel burned in a geographic area. The estimation of trol devices in locomotives, so updated cycles should incor- rail activity in gross ton-miles or number of hours is exam- porate those effects. ined in the previous discussion of methods. Emission Factors Locomotive Duty Cycles Generally, locomotive emission factors are based on EPA's A locomotive duty cycle is a usage pattern expressed as the 1992 emission inventory guidance. (87) Documentation since percentage of time spent in each of the throttle notches. The then has provided updated rail emission factors based on more 1998 rulemaking was based on two duty cycles--one for line- recent emission standards for locomotives, including EPA's haul and one for switch--which were development based on 1998 Regulatory Support Document, and the Sierra Research industry data. (86) Line-haul data were based on 2,475 hours work published in 2004. (86, 88) The most recent emission operated by 63 trains from five Class I railroads across many factors for locomotives are included in EPA's 2008 Regula- regions in the country. Without more information about the tory Impact Assessment, (89) which includes new emission process of sampling and development of an average cycle, it standards for Tier III and Tier IV locomotives. The RIA doc- is reasonable to assume that there were enough data points umentation also provides baseline emission rates for NOx, to provide a good representation of an average duty cycle. PM, HC, and CO in 2008, which are based on average duty

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70 Exhibit 3-32. Duty cycle variation (% time in throttle notch). Line-Haul Duty Cycle Switch Duty Cycle Throttle Notch Average Lowest Highest Average Lowest Highest Idle 38.0 77 1 59.8 82 23 Dynamic Brake 12.5 41 0 N/A N/A N/A 1 6.5 23 0 12.4 18 7 2 6.5 23 0 12.3 18 7 3 5.2 13 2 5.8 20 1 4 4.4 11 1 3.6 17 1 5 3.8 12 0 3.6 15 0 6 3.9 11 0 1.5 10 0 7 3.0 18 0 0.2 1 0 8 16.2 39 0 0.8 4 0 Source: U.S. Environmental Protection Agency (1998): Locomotive Emission Standards, Regulatory Support Document. cycles for switch and line-haul locomotives. (90) However, In most analyses of rail emissions, emission factors are the emission rates for Tier II and older locomotives are still converted from g/bhp-hr to g/gal by applying a factor of based on the previous rulemaking document. 20.8 bhp-hr/gal for line haul, and 18.5 bhp-hr/gallon for Baseline emission rates (NOx, PM10, HC, and CO) by loco- switchers. This assumes a constant brake-specific fuel con- motive type and throttle notch were developed based on data sumption (BSFC) of 0.341 lb/bhp-hr for line-haul and 0.383 lb/ provided by locomotive manufacturers (GM and EMD), and bhp-hr for switchers. These average BSFCs were determined EPA weighted these data by the average duty cycles to esti- through certification test data, but BSFC tends to vary depend- mate average baseline emission rates. Exhibit 3-33 summa- ing on engine size as well as notch setting. Errors in emission rizes the variation in emission rates for NOx and PM. For the factors can result if the locomotives have different duty cycles line-haul cycle, the highest emission rates were roughly twice than those included in the certification tests. However, signif- the lowest rate, while for the switch cycle the highest rates icant changes to emission factors typically occur when there were about four times higher than the lowest rate. This wide are high variations in the share of time spent in notches 5 discrepancy is strictly related to the measurement of emission through 8 versus time in idle. rates and is not influenced by the variation in duty cycles that The emission factor for CO2 tends to be the most accurate was previously examined. Therefore, the errors embedded in because CO2 emissions are proportional to fuel consumption. both parameters will be added and propagated through the PM2.5 emission factors can be calculated by assuming that calculation of rail emissions at the regional or local level. they represent a fixed percentage of PM10 emissions. EPA rec- The emission rates for different locomotive tiers were based ommends the use of 97% based upon an analysis done for on the expected emission reduction compared to the baseline the NONROAD model. This was based upon engines using rates. Tier III will need electronic common rail fuel injection 500 ppm sulfur diesel fuel and may be different for engines systems as well as better oil control. These electronic systems using higher sulfur content. PM10 emission factors reflect the should reduce the amount of uncertainty in emissions factors emission rates expected from locomotives operating on fuel for these engines. Tier IV will most likely need selective cat- with sulfur levels at 3,000 ppm, so it is important that regional alytic reduction (SCR). Additional complexities exist in tam- and local analyses obtain information about the sulfur con- pering and mal maintenance as well as whether the urea tanks tent of diesel fuel used in locomotives. EPA estimates that the are filled. Significant swings in emissions can occur if tamper- PM10 emission rate for locomotives operating on nominally ing or mal maintenance occurs. 500 and 15 ppm sulfur fuel will be 0.05 and 0.06 g/bhp-hr Exhibit 3-33. Baseline emission rates (g/bhp-hr). Line-Haul Duty Cycle Switch Duty Cycle Pollutant Average Lowest Highest Average Lowest Highest PM 0.32 0.22 0.41 0.44 0.22 0.86 NOx 13.0 10.3 18.2 17.4 9.2 33.1 Source: U.S. Environmental Protection Agency (1998): Locomotive Emission Standards, Regulatory Support Document.

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71 lower than the PM10 emission rate for locomotives operating Equipment Type on 3,000 ppm sulfur fuel, respectively. (89) Train type has a strong effect on fuel consumption and, Emissions of SO2 are relatively accurate, and can be cal- consequently, on emissions. Two factors influence this corre- culated through a mass balance approach, since it can be lation, namely the ratio between payload and total car weight reasonably assumed that most of the sulfur in the fuel will (payload plus tare weight), and train aerodynamic resistance. be converted to SO2 (the rest will be emitted as particulate Rail cars with a low ratio between payload and total car matter). weight will have lower fuel efficiency when measured in terms of revenue ton-miles/gallon. A study from FRA evaluated (71) Locomotive Type the differences in fuel efficiency among different types of rail cars. For example, auto haulers, with ratios between payload Most analyses of rail emissions depend on emission rates and total car weight ranging between 25% and 30%, have rel- developed by EPA as part of the rulemaking. As previously in- atively poor fuel efficiency in comparison to other equipment dicated, these emission rates were based on measurements types. In contrast, tank cars and covered hoppers have ratios from 63 locomotives of three types, and a large variation was above 75%, which explains higher fuel efficiencies in compar- observed between the highest and lowest emission measure- ison to other equipment types. ments of the same locomotive type. However, the variation of the minimum measurements across the three locomotive types was not as high, with measurements of NOx for the line- Empty Miles haul cycle ranging from 10.3 to 11.5 g/bhp-hr, and from 0.22 More sophisticated analyses also can account for fuel to 0.25 for PM. Similar variations were observed for maxi- consumed in empty movements by applying an empty fac- mum measurements. The variations across locomotive types tor to Equation 5. If local estimates are not available, data for the switch cycle were higher, especially for the maximum from the R1 report can be used to estimate empty ratios by measurements, which ranged from 15.8 to 33.1 for NOx, and rail car type. (91) from 0.39 to 0.86 for PM. Empty miles refer to the miles spent to get empty equip- These differences are not an issue for analyses where fuel ment to places where it is needed. Because additional fuel consumption data can be obtained directly. However, for those is consumed (and emissions generated) in the movement of analyses where fuel use is estimated based on activity data empty rail cars, ideally, empty miles should be considered in rather than fuel consumption data, variations in locomotive emission analyses. type can increase the difference between actual and modeled In national analyses, where fuel use is estimated based on emission factors. information reported by Class I railroads, empty movements are captured as loaded movements. In regional and local Locomotive Tier Distribution analyses however, empty movements need to be considered separately since fuel use is estimated from rail activity. The Locomotive tier distribution is certainly an important fac- incorporation of empty miles is very challenging due to lack tor when deriving a composite emission factor, since emission of data and the complexity of the logistics of empty move- rates are widely different across locomotive tiers (with the ments. Public data sources with aggregate information about exception of CO), as shown in Exhibit 3-34. Therefore, it is empty miles exist. For example, data from the R1 report can important to obtain the correct locomotive tier distribution be used to estimate empty ratios by rail car type. However, from participating railroads when estimating regional and due to these challenges, many analyses simply disregard local emissions. empty movements. A recent study from FRA estimated the Exhibit 3-34. Emission rates for line-haul and switch locomotives (g/bhp-hr). Line-Haul Locomotives Switch Locomotives Tier PM10 NOx HC PM10 NOx HC Remanufactured Tier 0 0.20 6.70 0.29 0.23 10.62 0.57 Remanufactured Tier I 0.20 6.70 0.29 0.23 9.90 0.57 Remanufactured Tier II 0.08 4.95 0.13 0.11 7.30 0.26 Tier III 0.08 4.95 0.13 0.08 5.40 0.26 Tier IV 0.015 1.00 0.04 0.015 1.00 0.08 Source: EPA (2008).