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5 EPA as related to mobile source emissions (5) and of these, formity thresholds. In the regional transportation planning 644 are components of diesel exhaust, including benzene, context, highway emissions are summed and compared to cadmium, formaldehyde, and 1,3-butadiene. In California, the regional emissions budget for Transportation Conformity diesel particulate matter typically is the toxic air contaminant purposes. GHG inventories also report emissions estimates of primary concern; however, there are no specific annual without further processing. limits on its emissions. HAP pollutants broadly fall into two Emissions also can serve as inputs to dispersion models that categories--heavy metals and hydrocarbons--and are often use meteorological information to simulate the atmospheric calculated as a fraction of PM and VOC emissions. dispersion of pollutants and estimate resulting spatial con- Many environmental review documents report air toxics, centrations. Dispersion models are used for project analysis but the methods for estimating and reporting these emissions when there are concerns about air pollution hot spots, partic- are not uniform. The study team relied heavily on a recent re- ularly regarding PM and CO. They are used at the regional port on the preparation and reporting of air toxics in NEPA scale as part of the SIP development process to determine the documents. (6) reductions necessary to achieve the NAAQS. To conduct a health risk assessment, dispersion models feed exposure mod- els, which use data on the demographics, activities, and com- 1.5 Application of Freight muting habits of residents of an area, and calculate the air Emissions pollution concentrations to which they are exposed. Freight transportation emissions estimates influence gov- Given the diversity in application of freight emissions esti- ernment decisions in a number of ways. In some instances, the mates, the required accuracy of the estimates varies widely. estimation of freight emissions directly affects decisions over Some applications require a point estimate to be compared to how public (and private) funds are spent on infrastructure an absolute threshold (e.g., a General Conformity determina- projects and associated mitigation measures. This can occur in tion or SIP emissions budget). Others involve a comparison the preparation of environmental documents to satisfy NEPA of the relative difference in emissions (e.g., NEPA project al- and related state statutes, and in analyses required under the ternatives) or a comparison over time (e.g., climate change General Conformity regulations. In many other instances, plan). The level of accuracy also depends on whether the freight emissions clearly influence government policy and pro- freight emissions are reported or processed in isolation or gram decisions, but the linkage is less direct. For example, stud- combined with emissions from other sources. Freight trans- ies of health impacts of diesel exhaust rely heavily on freight portation dominates the emissions or air quality impacts in emissions estimates. Some of these studies have been very some cases, while in other cases freight is a relatively small influential in shaping air quality policy and diesel emission contributor to the impact. reduction programs, but there may not be a direct connection between a particular study and a government decision. 1.6 Evaluation of Current Methods The attention given to different pollutants depends on the purpose and scale of analysis. GHG emissions from freight are Quantitative estimates of overall accuracy and uncertainty most commonly considered at the state or national scale, as associated with different methods and models could not al- part of GHG inventories and climate change action plans. One ways be provided. There are not enough data to make such a of the most important applications of criteria pollutant emis- quantitative assessment with a good degree of confidence. As sions estimates is at the regional scale, as part of the develop- a result, the examination of accuracy and uncertainty was ment of state implementation plans (SIPs) to satisfy the Clean done mostly on a qualitative basis, identifying strengths and Air Act. Criteria air pollutant estimates are also critical at the weaknesses of methods and models, as well as evaluating the project level to satisfy environmental review under NEPA as parameters that have the largest impact on final emissions well as General Conformity (e.g., for ports and airports). Esti- and highest uncertainty relative to others. The following sub- mation of air toxics emissions is not mandated as it is for cri- sections summarize the evaluation of methods, models, and teria air pollutants. Estimating air toxics emissions is done at parameters for each transportation mode at the national, the project level when there are heightened concerns about state, and local/project level scales. health impacts. National- and regional-scale air toxics analy- sis has been oriented toward research and serves to identify 1.6.1 National priorities for mitigation efforts and further research. Emissions estimates are often reported as is, without fur- At the national level, EPA uses two separate methodologies, ther processing. For example, emissions estimates are used reported in the EPA GHG Inventory (1) and the NEI, (2) to for comparison among project alternatives under NEPA and estimate emissions across all sectors of the economy. These for comparison of project emissions against the General Con- approaches differ from other mode-specific transportation

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6 methodologies in that they span all modes and are best ana- tivity (e.g., vehicle-miles, ton-miles) with industrial and lyzed independently of individual modal methodologies. commercial activity (e.g., fuel expenditures, productivity), The EPA GHG Inventory calculates emissions through a and uncertainties arise from determining the allocation fuel-based analysis. The inventory allocates emissions to each based on data that are not closely related. transportation mode, and to subcategories within each mode · The EPA GHG Inventory then allocates transportation fuel according to fuel consumption and fuel type. Total GHG use to each mode and vehicle type. This step is challenging emissions are calculated as a function of each fuel's carbon because the quality of data varies between modes. Modal content. Although the EPA GHG Inventory does not disaggre- activity is measured through individual data sources such gate freight and nonfreight emissions, it lists modal categories as the Federal Highway Administration (FHWA) for on- in sufficient detail to make such disaggregation possible, albeit road vehicles, Association of American Railroads (AAR) while introducing uncertainties into the calculations. Fuel for rail, and Federal Aviation Administration (FAA) for used in international cargo movements by both marine and aircraft. Although Class I railroads are required to report aircraft is not counted, and the resulting emissions are gener- 100% of fuel consumption nationwide, fuel consumed by ally not allocated to any nation. Class II and III railroads, as well as other modes, is based on Although the EPA GHG Inventory uses a straightforward sampling. It is not clear how the uncertainty in one data set approach to calculating emissions, the NEI methodology is would compare to the uncertainty in other sets. Although comparatively more complex. Because the emissions of crite- these uncertainties do not significantly affect the quantifi- ria air pollutants and air toxics depend on vehicle type, age, cation of emissions from the transportation sector, they and activity, the NEI relies on separate methodologies for have an effect on the modal breakdown of emissions. each transportation mode. In addition, the NEI has much · Further uncertainties arise from the aggregation or disag- more geographic detail than the EPA GHG Inventory. Al- gregation of emissions between geographic scales. The NEI though the EPA GHG Inventory only presents emissions at calculates emissions at several geographic scales, from na- the national level, the NEI allocates emissions to the state and tional to county level. However, for most modes, data are county levels. supplied at only one scale, such as the regional level for air- The two national methodologies have sources of uncertain- craft or the national level for rail. The NEI methodology ties in the calculation of individual modal emissions and in the then either aggregates regional emissions to determine na- evaluation of nationwide inventories. This section focuses on tional emissions, or distributes national emissions among uncertainties that occur in the nationwide analysis, which are individual states and regions. The process of scaling emis- primarily associated with the national collection of fuel data sions adds uncertainty to the results, as more assumptions and its subsequent allocation to individual transportation on emissions at each level are included in the process. modes. National uncertainties include the following: In addition to the process uncertainties described above, · The EPA GHG Inventory allocates national fuel use to the parameters used in national methods also are subject to transportation sectors through different (and unrelated) uncertainties associated with errors or biases in the data sets. data sources. For example, the transportation allocation is The parameters shown in Exhibit 1-1 are used in allocating calculated by comparing an estimate of transportation ac- fuel consumption to the transportation sector and to individ- Exhibit 1-1. National parameters. Impact on Parameter Parameter Methods/Models Emissions Uncertainty Marine Equipment Inventory NEI Low/Moderate High Nonroad Equipment Inventory NEI Low/Moderate Moderate On-road Fleet Mix NEI Low/Moderate Low/Moderate Rail GIS Data NEI Low/Moderate Low/Moderate Economic Sector Activity Data GHG Inventory Moderate/High High Modal Activity Data GHG Inventory, NEI Moderate/High High Modal Emissions Factors NEI Moderate/High Moderate Fuel Carbon Content GHG Inventory High Low Fuel Supply Data GHG Inventory High Low

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7 ual modes, and are used in one or both of the EPA national emissions. This is more of a problem with MOBILE6 than methods. EMFAC2007, given that the latter includes data at the The effect of fuel parameters varies depending on their county level. impact on emissions and uncertainty in their measurement. · The incorporation of congestion effects on emissions is a Parameters such as "fuel supply data" have a high impact but complex issue and topic of much recent debate. MOBILE6 low uncertainty, while parameters such as "marine equipment and EMFAC2007 are not well suited to accurately incorpo- inventory" have low impact but high uncertainty. These rela- rate such effects since they rely on speed correction curves tionships are shown qualitatively in Exhibit 1-1. The deriva- to differentiate emissions by average speed. Previous re- tion of these individual values is presented in the pedigree search has indicated that the use of average speed is not a matrix shown in Section 3.2.4. Criteria to assign scores in the good proxy for congestion levels. To accurately capture the pedigree matrix are included in Appendix A. congestion effects on emissions, a modal emission model (e.g., CMEM) should be used; MOVES2009 also will pro- vide a platform to enable analyses that incorporate the 1.6.2 Heavy-Duty Trucks effects of congestion on emissions through a binning ap- The distinction between on-road passenger and freight proach. A similar discussion applies to truck operations vehicles is usually clear, with passenger vehicles assumed to at intermodal yards or distribution facilities, since their be automobiles, light-duty trucks with a gross vehicle weight operational profiles are very different from long-distance rating (GVWR) of less than 8,500 lbs, and buses, while heavy- over-the-road trucks. duty trucks are those with GVWR of more than 8,500 lbs. · There are several concerns about estimating truck VMT However, there are trucks with a GVWR of more than 8,500 lbs from travel demand models or truck counts. First, the es- that do not move freight. Some examples are utility trucks timation of truck VMT generally does not consider enough used for service and repair of utility infrastructure, tow trucks, truck categories to match the number of truck categories and daily rental trucks. Because it is virtually impossible to in emission models. Second, when used for forecasting separate the activity and emissions of nonfreight heavy-duty truck VMT, travel demand models often do a poor job of trucks from freight trucks, and because nonfreight heavy- representing the complex trip generation and trip distribu- duty trucks are relatively insignificant compared to freight tion patterns of commercial vehicles. Third, the accuracy trucks, generally no attempt is made to distinguish between of average speed at the link level is questioned given that it the two. is not measured directly but is instead estimated from ve- MOBILE6 and EMFAC2007 are the approved models for hicle volume and road capacity. (Link-level speed data may SIPs, conformity analyses, and project-level analysis to fulfill become more precise in coming years with widespread NEPA/CEQA requirements. MOVES2009 is the new EPA rollout of intelligent transportation systems [ITS] to mon- model that will eventually replace MOBILE6 when fully im- itor traffic performance along road segments.) Finally, a plemented, and CMEM is the most established microsimula- high number of time periods is necessary to properly cap- tion emission model. The evaluation also includes a regional ture the speed variations throughout the day, which in- and local method, both of which rely on either MOBILE6 or creases the computation requirements substantially. EMFAC2007. The main drivers of uncertainty associated · Many key parameters for emission analyses are based on with these methods and models are as follow: the Vehicle Inventory and Use Survey (VIUS), which char- acterizes the truck population in the United States. (7) Ex- · Emission models like MOBILE6 and EMFAC are ill-suited amples include truck age distribution and mileage accu- for project-level analyses if key local factors that have a sig- mulation. Because the last version of VIUS was published nificant impact on emissions (e.g., average speed, truck age in 2002 and the 2007 version was canceled, there are con- distribution, vehicle-miles traveled [VMT] share by truck cerns about how outdated such parameters are (e.g., intro- type) are not available. Additionally, these models do not duction of new diesel emission standards). consider road grade, actual vehicle weight, or aerodynamic · In most emission analyses, the distribution of emissions characteristics of vehicles, all of which have a strong effect on throughout a day, week, month, or year typically is not avail- engine power requirements and, consequently, on emissions. able. The temporal distribution of emissions is an important · The representation of local and regional factors (e.g., truck input to air quality analyses because ambient temperature age distribution, mileage accumulation, VMT share by truck and humidity are key factors in air dispersion and in the for- type) by national defaults is a source of substantial uncer- mation of secondary pollutants. tainty. This issue is important because many agencies do · The ability of emission models to incorporate the effects of not have access or resources to collect local data, and rely emission reduction strategies depends on the nature of the on national defaults to represent project-level and regional strategy. For those that affect VMT, such impacts can be

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8 clearly defined. The effects of strategies that affect truck fuel · In the case of modal emission models, driving cycles are a efficiency (e.g., aerodynamic devices) and emission factors direct input to emission calculations and have a high im- (e.g., diesel particulate filters) need to be post-processed pact on final emissions. For those models that do not rely after the model runs. For those strategies that have an effect on driving cycles directly for emission calculations (e.g., on congestion levels (e.g., incident management, conges- MOBILE6, EMFAC2007), driving cycles are important in tion pricing), only modal emission models are able to the calculation of emissions to the extent that a good mix capture such effects. of driving cycles is used to provide a good representation of emission factors. The uncertainty associated with driv- The uncertainty analysis of heavy-duty trucks also included ing cycles can be quite high due to the wide variations in an evaluation of the most important input parameters to emis- vehicle behavior in real-world traffic conditions. sion calculations. The two most important factors to charac- · For those projects that rely on project-derived truck VMT terize the relevance of a parameter in the context of this study data, or those that estimate truck VMT data from com- are the impact on final emissions and the level of uncertainty modity flows, it is necessary to have a good estimate of in the parameter estimates. Exhibit 1-2 provides a qualitative empty miles since they have a direct influence on VMT. Be- representation of the relative importance of different para- cause of a lack of data sources on empty miles, information meters for truck emission calculations. generally is obtained from very aggregated data and, there- The most important considerations regarding the param- fore, uncertainty can be quite high. eters in Exhibit 1-2 are as follow: 1.6.3 Rail · Truck VMT and emission factors are certainly the most im- portant parameters in this study, given their high impact on Because the vast majority of rail activity in the United final emissions. As previously indicated, there are concerns States is handled by freight railroads, most methods to cal- about estimating total truck VMT with travel demand culate rail emissions are specifically tailored to freight. Ad- models, but the level of uncertainty associated with emis- ditionally, identifying freight and passenger traffic is rela- sion factors is higher because of the amount of test data, the tively straightforward because freight rail activity is reported fact that most emission factors rely on a limited number of separately from passenger rail activity. The only exception driving cycles, the fact that some models still rely on engine is the EPA GHG Inventory, where diesel fuel consumption certification data (rather than chassis dynamometer data), needs to be disaggregated between freight and passenger and a lack of test data for all truck categories. railroads. · The share of VMT by truck type is also a key factor since In addition to methods that calculate rail emissions at the emission rates depend substantially on vehicle weight, which national level (EPA GHG Inventory and the NEI), there are is directly correlated with truck class. The main source of other methods at the regional and local/project level scales uncertainty is that rarely is truck activity data provided that estimate fuel consumption by different rail parameters. with enough level of detail to accurately disaggregate it into The only model that calculates rail fuel consumption is the enough truck categories. Train Energy Model, which is not analyzed because it is used Exhibit 1-2. Truck parameters. Geographic Impact on Parameter Parameter Methods/Models Scale Emissions Uncertainty VMT Share by Time of Day All Regional/Local Low/Moderate Moderate/High Fuel Type Distribution All All Moderate Low/Moderate Average Speed MOBILE6, EMFAC2007 Regional/Local Moderate Moderate Classification of Truck Types All All Moderate Moderate Mileage Accumulation All All Moderate Moderate Empty Miles All All Moderate High Truck Age Distribution All All Moderate/High Moderate VMT Share by Truck Type All All Moderate/High Moderate/High Driving Cycle CMEM Local Moderate/High High Truck VMT All All High Moderate/High Emission Factors All All High High

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9 in very isolated cases. The main sources of uncertainty asso- evance of a parameter in the context of this study are the im- ciated with these methods are as follow: pact on final emissions and the level of uncertainty in the parameter estimates. Exhibit 1-3 provides a qualitative repre- · Although Class I railroads are required by the Surface sentation of the relative importance of different parameters Transportation Board (STB) to report 100% of fuel con- for rail emission calculations. sumption nationwide, there are concerns about published The most important considerations regarding the param- rail activity. First, there is a lack of published rail activity eter uncertainty are as follow: for a specific region, so local/project level and regional analyses need to either collect data from local railroads · In addition to emission factors, fuel consumption is the (which is generally challenging) or apportion nationwide most relevant parameter due to its direct impact on emis- or statewide data to regions, which brings many method- sions. The uncertainty associated with fuel consumption ological issues described later in Section 3.4 of this report. estimates can vary dramatically. For example, if fuel con- Second, the accuracy of county-level gross ton-mile (GTM) sumption is measured directly, either at the national scale data reported by railroads is largely questioned. or by a participating railroad at a local project, estimates · Many local/project level and regional emission analyses rely can be quite accurate. However, if fuel consumption is es- on a single measure of fuel consumption index (GTM per timated by means of active mileage, then errors associated gallon) to convert traffic density to fuel consumed. How- with this method will propagate to the estimates of fuel ever, correction factors for grade and commodity group can consumption. be used to minimize the uncertainty associated with the use · Emission factors also have a direct impact on emissions, of a single measure of fuel efficiency. (8) and the associated uncertainty can be quite high due to a · For those analyses that cannot rely on traffic density (because lack of testing data and the wide variation present in the it is not reported by railroads), the use of active track or num- current testing data. Such variation is partly derived from ber of employees to apportion nationwide or statewide the use of different locomotive types for the development fuel consumption can result in emission estimates that are of testing data. highly uncertain. · The share of time in idle mode has a strong effect on emis- · The accurate calculation of switch emissions in railyards re- sion factors, but there is rarely enough information about quires high levels of data because the variation in activity locomotive duty cycles at the project level, or there is a levels per switcher and duty cycles can be substantial. As a measure of uncertainty associated with a "typical" duty result, analyses that rely on default parameters (e.g., aver- cycle. This will likely become less of an issue as railroads im- age number of hours per switcher) can be highly uncertain. plement idle control systems on their fleet (e.g., BNSF has idle control systems in approximately 70% of their fleet). The uncertainty analysis of rail also included an evaluation · EPA emission standards for locomotives are defined as of the most important input parameters to emission calcula- "tiers." The distribution of locomotives across these tiers is tions. The two most important factors to characterize the rel- an important factor when deriving a composite emission Exhibit 1-3. Rail parameters. Geographic Impact on Parameter Parameter Methods/Models Scale Emissions Uncertainty Locomotive Type All (Explicit in Local) All Moderate Moderate/High Empty Miles All Local Moderate Moderate Locomotive Tier Distribution All All Moderate/High Moderate Equipment Type All (Explicit in Local) All Moderate/High Moderate/High Duty Cycles All (Explicit in Regional/Local) Regional/Local Moderate/High High Employees Emissions by Employees Regional/Local High Low Miles of Active Track Emissions by Active Track Regional/Local High Low Number of Switch Locomotives Emissions by Switchers Regional/Local High Low Hours by Switch Locomotive Emissions by Hours Regional/Local High Moderate Traffic Density Emissions by Traffic Density Regional/Local High Moderate/High Emission Factors All All High High Fuel Consumption National National High High

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10 factor, since emission rates vary widely under different Air Resources Board (CARB) to estimate California marine standards. Emission results can be very uncertain if the vessel emissions, a similar approach is taken where emissions locomotive tier distribution is not available from the at the major California ports are estimated and then added participating railroads. together. The difference really relies upon whether a detailed, · Information to describe rail activity data (e.g., traffic den- mid-tier, or streamlined method is used for the individual sity, number of switch locomotives, hours by switch loco- ports and the data collected. motive, miles of active track, number of employees) have a The main sources of uncertainty associated with these direct impact on emissions, but the level of uncertainty methods are as follow: with those estimates varies depending on the parameters. For example, miles of active track, number of employees, · Emissions are linearly related to the number of calls. Accu- and number of switch locomotives are virtually determin- rate assessment of the number of ship calls is critical be- istic estimates, and thus have no uncertainty. However, the cause there can be errors depending upon the source of the issue of whether they provide a good proxy for fuel con- data and the geographic boundaries of the analysis. sumption is still a source of uncertainty for fuel consump- · In the detailed and mid-tier approaches, propulsion power tion estimates. Other estimates such as number of hours is determined directly from Lloyd's Register of Ships data. per switch locomotive or traffic density are subject to On the other hand, auxiliary power is estimated from sur- higher uncertainties since they need to be estimated based veys that produce ratios of auxiliary power to propulsion on limited information from railroads. power by ship type. More accurate determination of aux- · For those projects that rely on project-derived rail activity, iliary power would improve emission calculations. or those that estimate rail activity from commodity flows, · In the detailed approach, propulsion load factors are cal- it is necessary to have a good estimate of empty miles, since culated using the Propeller Law as defined in Section 3.5.2. they have a direct influence on rail activity. Because of a There are inherent errors in applying that law to all ships lack of data sources on empty miles, information is gener- and speed ranges. Currently the Propeller Law is univer- ally obtained from very aggregated data (i.e., railroads re- sally accepted as the method to use to determine propul- port both loaded and empty car-miles by car type nation- sion load factors and it is doubtful that significant errors wide), and thus uncertainty can be quite high. would result from these calculations. In addition, knowl- edge of vessel speed approaching ports may be limited. · Auxiliary load factors have been determined from limited 1.6.4 Waterborne: Ocean-Going Vessels surveys. More precise determination of auxiliary engine Emissions from ocean-going vessels (OGVs) are usually de- load factors, particularly during hotelling, would provide termined at and around ports because these are the entrances more accurate results. and clearances of cargo into the regions of modeling interest. · Emission factors for ships were determined for a small sub- They are estimated using information on number of calls at a set of engines. Although most ships use similar engines, particular port, engine power, load factors, emission factors, this set does not represent a large enough sample to be ac- and time in like modes. curate. This is particularly true of PM emissions. Measure- There are three basic methods for calculating emissions ment techniques of PM emissions vary and there is sensi- from OGVs at ports, namely (1) a detailed methodology where tivity to sampling methodology (e.g., tunnel length). PM considerable information is gathered regarding ships entering emission factors need a more robust data set to determine and leaving a given port, (2) a mid-tier method that uses some them accurately. In addition, current thinking is to esti- detailed information and some information from surrogate mate PM2.5 emission factors as 92% of PM10 emission fac- ports, and (3) a more streamlined method in which detailed in- tors. Various studies have estimated PM2.5 emissions from formation from a surrogate port is used to estimate emissions 80% to 100% of PM10 emissions. Therefore a more accu- at a "like" port. The detailed methodology requires significant rate determination of PM2.5 emission factors is needed. amounts of data and resources and produces the most accurate · Low load adjustment factors to emission factors when the results. The mid-tier and streamlined methods require less data propulsion engine load factor is below 20% also need review- and resources but produce less accurate results. ing. The current methodology as discussed in Section 3.5.2 Since all current methods and models estimate emissions is based upon limited data and rough curve fits. Improve- at ports, the geographic distinctions (i.e., national, regional, ment of the low load adjustment factors can result in more and local/project scale analyses) are less meaningful than in accurate emission calculations when ships are near ports. other sectors. Generally, to estimate national OGV emissions, · Current emission factors were determined for engines built all major ports are modeled and emissions are added together. before year 2000 when the International Maritime Organi- For a regional approach, such as that done by the California zation (IMO) set NOx emission standards on OGV engines.

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11 Exhibit 1-4. OGV parameters. Geographic Impact on Parameter Parameter Methods/Models Scale Emissions Uncertainty Boiler Emission Factors Detailed All Low/Moderate Moderate/High Boiler Loads Detailed and Mid-Tier All Low/Moderate High Fuel Type Detailed All Moderate Moderate Port Selection Mid-Tier and Streamlined All Moderate Moderate Auxiliary Emission Factors Detailed All Moderate Moderate/High Auxiliary Load Factors Detailed and Mid-Tier All Moderate High Auxiliary Power Detailed and Mid-Tier All Moderate High Propulsion Power Detailed and Mid-Tier All Moderate/High Low/Moderate Calls All All Moderate/High Moderate Time in Modes Detailed All Moderate/High Moderate Propulsion Emission Factors Detailed All Moderate/High Moderate/High Propulsion Load Factors Detailed and Mid-Tier All Moderate/High Moderate/High More testing is needed to determine the emission factors Instead, estimates of emissions from tug and towboats and for engines built after 2000 as well as for future IMO Tier II other commercial H/C may be made through other method- and Tier III NOx emission standards. ologies. The differentiation of these methods is due to geo- · In the mid-tier and streamlined methodologies, selecting a graphic scale. typical port that is like the port to be modeled is of utmost The best practice or streamlined approaches discussed in importance. EPA has provided some guidance on how to se- EPA's Current Methodologies (9) comprise the local H/C lect the typical port, and a list has been provided based upon method, and are treated here as the same methodology. detailed inventories prepared at the time. As more ports pre- They rely on various sources for the necessary parameters pare detailed inventories, this list should be expanded. and generally draw on the methodologies of the NONROAD or OFFROAD models. Differences in these methodologies The uncertainty analysis of OGVs also included an evalu- are chiefly dependent on the amount of data directly collected ation of the most important input parameters to emission rather than derived through surrogates. Two additional, spe- calculations. Exhibit 1-4 is based on the relative rankings of cific H/C methodologies are EPA's national-scale Regulatory variability in the input parameters and relative impact on Impact Analysis (RIA), and CARB's analysis of statewide H/C total emission estimates for each parameter. emissions. Total uncertainty in freight-related H/C emissions from these methodologies can be attributed to process uncertainty 1.6.5 Waterborne: Harbor Craft (i.e., degree to which the methods accurately represent actual A wide range of commercial harbor craft (H/C) operate in emissions) and parameter uncertainty (i.e., uncertainty in the the vicinity of ports, including assist tugboats, towboats, and individual elements used for calculations). Three potentially pushboats, ferries and excursion vessels, crew boats, work significant sources of process uncertainty for H/C are as follow: boats, government vessels, dredges and dredging support ves- sels, commercial fishing vessels, and recreational vessels. Many · The appropriateness and representativeness of the charac- of these vessels serve purposes other than just direct goods terizations, movement. To focus the present discussion on freight move- · The groupings used to categorize H/C, and ments only, only those commercial H/C directly involved in · The potential for bias in inputs. goods movement--tug and towboat operations responsible for moving barges--are considered in this analysis. Section 3.6 There are a variety of primary and secondary parameters provides a detailed discussion of H/C emissions calculations that feed into the overall uncertainty and include effects of and uncertainties. characterization of engine deterioration and engine age distri- There are no common models with the capability to esti- bution, both of which are noted to influence total uncertainty mate emissions from these vessels; neither CARB's OFFROAD of estimated emissions. The six principal input parameters nor EPA's NONROAD model consider commercial H/C. used to determine H/C emissions--and therefore the main

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12 Exhibit 1-5. Harbor craft parameters. Geographic Impact on Parameter Parameter Methods/Models Scale Emissions Uncertainty Auxiliary Engine Population EPA RIA Method, CARB H/C National, Regional Moderate High Method Engine Power All All Moderate/High Low/Moderate Activity All All Moderate/High Moderate/High Emission Factors All All Moderate/High Moderate/High Load Factors All All Moderate/High Moderate/High Main Engine Population EPA RIA Method, CARB H/C National, Regional Moderate/High High Method drivers of uncertainty--are listed in Exhibit 1-5. These six · A special case, third methodology is used in CARB's CHE primary parameters have their relative contribution to over- inventory, which essentially employs the best-practice all uncertainty, which is based on the relative rankings of vari- methodology without directly using the OFFROAD model. ability in the input parameters and relative impact on total emission estimates for each parameter. Total uncertainty in the methods used to calculate CHE emissions is due to both process and parameter uncertainty. Three potentially significant sources of process uncertainty 1.6.6 Cargo Handling Equipment are as follow: Cargo handling equipment (CHE) is used to move freight at ports and other intermodal facilities that transfer goods · The appropriateness and representativeness of the model between modes. The diversity of CHE types in use is related characterizations of CHE, to the diversity of freight handled. Similarly, the amount of · The groupings used to categorize CHE, and CHE and its activity are related to the overall amount of · The potential for bias in survey results, inventory counts, freight throughput for a given facility. Depending on the type, or inventory scaling methods. use, and number of CHE, their emissions can be significant contributors to overall goods movement emission invento- Uncertainty in input parameters is another driver of un- ries. Thus, determining emissions from container terminal certainty in total calculated emissions. There are a variety of CHE is important in any land-side emission inventory. Due primary and secondary parameters that feed into overall un- to their use solely to move goods, all CHE emissions are re- certainty, but the five principal input parameters used to de- lated to freight. Section 3.7 discusses CHE emissions calcula- termine CHE emissions--and therefore the main drivers of tions and uncertainties in detail. uncertainty--are listed in Exhibit 1-6. Generally, CHE emissions from freight activities at ports are estimated using either the NONROAD or OFFROAD 1.6.7 Air Transportation emission models--or methods similar to those in the models. Two general categories of methods are used to estimate CHE The representation of freight activity in air transportation emissions. These are referred to as the best practice and is perhaps the most challenging among all modes because un- streamlined methodologies. (10) Generally, these two differ like other modes, goods are transported both in freight and only in the level of direct information collected and employed passenger aircraft. Emissions associated with the transport of in the calculations, as follows: freight by aircraft were analyzed using the following two modeling approaches: · The best practice methodology dictates surveys of all equip- ment to establish correct parameters and then employs the · The primary method for national and regional emission NONROAD or OFFROAD models. analysis in the United States is FAA's System for Assessing · The streamlined methodology allows for a greater degree Aviation's Global Emissions (SAGE). This model may also of freedom in collecting direct information by substituting be extended to global-scale emission inventories. surrogate, or otherwise derived, information. It may then · The Emissions and Dispersion Modeling System (EDMS) either use the models or adjust the methodologies of the was developed by FAA to specifically address the impacts of models themselves for the available information. airport emission sources, including ground-level sources

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13 Exhibit 1-6. CHE parameters. Geographic Impact on Parameter Parameter Methods/Models Scale Emissions Uncertainty Engine Power All All Moderate/High Low/Moderate Activity All All Moderate/High Moderate/High Emission Factors All All Moderate/High Moderate/High Load Factors All All Moderate/High Moderate/High Equipment Population All All Moderate/High High and associated support activity. FAA requires the use of the fuel consumption, trip origination, trip length, type of aircraft, model in performing air quality analyses for aviation sources. destination, flight position, and flight plan, as well as additional The model can separate aircraft by mode (cargo) but does factors such as capacity and delay to estimate emission not distinguish aircraft that carry both cargo and freight. strength. The models do not have the current capability to sep- arate freight-only travel from freight and passenger operations. The main drivers of the uncertainty associated with aircraft Exhibit 1-7 qualitatively shows how the various input param- emissions below 3,000 ft follow in order of importance: eters impact emissions and their relative uncertainty to other model input parameters. The largest uncertainties and greatest · Landing and takeoff procedures mainly consist of engine impacts on emissions are associated with aircraft emission cer- throttle setting, rate of climb/descent, and flight speed. tification because the actual emissions vary widely between air- These parameters have been found to be the most impor- craft engines and are optimized for the four certification points. tant, accounting for 30% to as much as 70% of the total Other important parameters affecting emissions deal with the variance of the emissions. operational characteristics or performance data--particularly · Idle emission rates are uncertain, particularly below the 7% the throttle setting used during take-off and landing. In project- power setting, and these errors may be large and tend to be ing future emissions, moderate uncertainty exists in activity be- an under prediction. cause air cargo is sensitive to economic uncertainties. How · Other important sources of uncertainty in most emissions emissions change with engine age has not been well studied, but data include certification data, the variability of emissions with the very high maintenance standards, these deterioration inherent among engines in the fleet, and the change in changes are anticipated to be minimal. Testing the effects of en- emissions with the age of the engine. gine age on NOx emissions at certification points has shown a 4% bias in engine emissions with age. (11) The best-understood Aircraft emission models operate at the individual flight data parameters are the flight position information because level. They use information on model aircraft performance, most flight location information is captured with FAA radars. Exhibit 1-7. Aircraft parameters. Impact on Parameter Parameter Emissions Uncertainty Emission Certification Low Moderate/High Aircraft Weight Low/Moderate Low/Moderate Engine Age Low/Moderate Moderate/High Flight Position Moderate Low/Moderate Retirement Parameters Moderate Moderate/High On-Time Performance (Capacity and Delay) Moderate/High Low Future Activity Projections Moderate/High Moderate Fuel Flow Rate Moderate/High Moderate/High Aircraft Operations Moderate/High Moderate/High Aircraft Performance (Throttle Setting) Moderate/High High Emission Certification High Moderate/High