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45 Assigning Fuel Use to Vehicle Types--Modal Activity sions to the nonroad mobile category. The GHG Inventory Data. This stage of distribution allocates total transporta- does not distinguish between emissions from construction tion fuel consumption to individual modes and sub-allocates equipment and agricultural machinery, and emissions from to vehicle types. The modal distribution is completed using a nonroad trucks. combination of data from the activity analysis conducted in the prior step and EIA data on individual fuel types. These 3.3 Heavy-Duty Trucks two data sources serve to confirm or reconcile differences in reporting. For example, rail fuel statistics are reported by AAR This section includes (1) a brief documentation of the cur- based on company surveys, while the same data are reported rent practice and methodologies for calculating emissions by EIA based on responses from fuel distributors. Similar from heavy-duty trucks, (2) a summary of the strengths and comparisons can be conducted for aviation fuel and marine weaknesses of such methods, and (3) an analysis of uncer- bunker fuel. The distribution of gasoline and diesel fuel is tainty associated with these methods, as well as with the pa- more complex, as the fuels are used in several modes, but is rameters used in the emission calculations. Although the conducted using activity data. However, the distribution of estimation of truck emissions is conceptually simple (i.e., fuel within vehicle types requires additional data and as- emissions are the product of freight activity and emission sumptions. For on-road vehicles, the distribution among pas- factors), the analytical procedures for emission estimation senger cars, light-duty trucks, and medium- and heavy-duty can be quite complex depending on the goals of the analysis trucks is completed using detailed VMT data from FHWA. and the level of data and resource availability. Exhibit 3-14 Similarly, aircraft consumption can be separated into com- summarizes the main methods and models to estimate truck mercial and other sources using FAA flight records. However, emissions. there is no comparable detailed source of information for dis- tributing fuel use among categories of marine vessels. 3.3.1 Evaluation of Emission Models To the extent that GHG inventories by vehicle subcategory can be used to inform an analysis of an individual vehicle Despite the high number of existing emission models, type, this step in the methodology can introduce additional this section focuses on the four most widely used models. uncertainties into future analyses. Sources of error include EPA's MOBILE6 and CARB's EMFAC2007 are the two approved models for State Implementation Plan (SIPs), For on-road vehicles, this step requires data on vehicle fuel conformity analyses, and project-level analyses under NEPA types (gasoline versus diesel) as well as activity by vehicle and CEQA, respectively. EPA's MOVES2009, which brings type. Since these two data sources are maintained by sepa- many methodological improvements over MOBILE6, is cur- rate agencies, EIA and FHWA, respectively, their category rently in draft form, but will eventually replace MOBILE6. definitions and relationships may not align. This issue is The Comprehensive Modal Emissions Model (CMEM), de- magnified when considering alternative fuels with a small veloped by UC Riverside, is the most established micro-scale vehicle share, such as CNG and LPG. The GHG Inventory emissions model. does not disaggregate alternative fuel usage to vehicle categories. MOBILE6 Uncertainty exists in activity data in the nonroad category, including the comparative activity of mobile nonroad ve- MOBILE6 is an emission factor model designed by EPA hicles versus stationary nonroad equipment. This creates to produce motor vehicle emission factors for use in trans- added challenges in correctly allocating fuel use and emis- portation analyses, including SIP development, transportation Exhibit 3-14. List of truck methods and models. Geographic Method/Model Type Pollutants Freight/Passenger Scale MOBILE6 Model All All Both MOVES2009 Model All All Both EMFAC2007 Model All All Both CMEM Model Local All Both Regional Method Method Regional All Both Local Method Method Local All Both

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46 conformity, and project-level analysis required under NEPA. Summary of Strengths and Weaknesses. A summary It can be used at any geographic level within the United of MOBILE6 strengths and weaknesses is provided in States. Exhibit 3-15. With the release of MOBILE6 in 2001 came several im- provements regarding heavy-duty vehicles (HDVs) over its Analysis of Process Uncertainty. The emissions rates previous version, MOBILE5: (1) increase in the number of generated by MOBILE6 require a multitude of input assump- HDV categories, (2) addition of off-cycle NOx impacts as a re- tions that can either be MOBILE6's national defaults or user- sult of control strategies that optimize fuel economy over specified parameters. MOBILE6 is particularly sensitive to emissions (i.e., defeat device issue), and (3) incorporation of assumptions regarding vehicle age, VMT by vehicle class, aver- 2004 and 2007 HDV emission standards, including the use of age speeds, and temperature. (55) This discussion focuses on the low-sulfur fuel starting in 2006. (54) following key issues: (1) emission factors, (2) truck age distri- Exhibit 3-15. Strengths and weaknesses--MOBILE6. Criteria Strengths Weaknesses Representation of EFs incorporate effects of vehicle EFs are based on engine testing (rather than chassis dynamometer physical processes average speeds. testing). EFs are based on a single driving cycle. Model assumes that brake and tire EFs, which are based on passenger cars, are the same for HDVs. PM EFs are based solely on heavier truck classes; therefore PM emissions from the lighter classes of HDVs might be overestimated. There are concerns about how speed correction factors capture speed/congestion effects on emissions. Data on age distribution, mileage accumulation rates, and fuel ratios are not available for all truck categories. Model does not consider high emitters or mal maintenance and tampering for HDVs. Model does not consider start emissions for diesel vehicles. Model sensitivity to Number of engine starts and soak time cannot be modified by user. input parameters Other than HC, CO, and NOx, emissions of other pollutants are not sensitive to vehicle average speed or facility type. EFs do not take air conditioning effects into account. Model does not consider effects of road grade or pavement quality. Model flexibility Few inputs are required. National default parameters (VMT mix by vehicle class, vehicle age distribution) can be overridden by local estimates. Ability to incorporate Model is able to capture the effects of Model is not able to capture the effects of strategies that affect effects of emission strategies that change truck VMT, vehicle pavement quality or congestion level. reduction strategies average speed (for HC, CO, NOx), fleet average age. Representation of EFs can be estimated up to 2050. There are concerns as to whether the assumptions used to estimate future emissions future EFs are still in line with latest vehicle technology trends. Data quality Data based upon engine testing and conversion factors are applied to calculate grams per mile. These conversion factors are fixed by weight class and may not be representative of heavy-duty freight trucks. Spatial variability There are concerns as to whether national defaults are representative of regional and local parameters. Temporal variability Review process There have been many independent analyses and reviews of MOBILE6. Endorsements MOBILE6 is the required model for SIPs and conformity analyses.

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47 bution and mileage accumulation, (3) how truck speeds affect PM idling emission rates in MOBILE6 (reported in grams/ truck emissions, (4) high emitters, (5) diesel fraction, (6) start hour) are based on the heavier classes of HDVs, so MOBILE6 emissions and soak time, and (7) classification of trucks. likely overestimates idle PM rates from lighter classes of HDVs. Additionally, these rates are not corrected for diesel sulfur Emission Factors--General. The emission factors in content, nor do they account for more stringent PM standards MOBILE6 were based on engine test data submitted by man- in the 2007 rule. ufacturers as part of the certification process (in g/bhp-hr). (54) As a result, emission factors needed to be converted (to Emission Factors--Air Toxics. Data on air toxics emis- grams/mile) based on fuel density, brake-specific fuel con- sions from HDVs are very sparse, and emission factors used in sumption (BSFC), and fuel economy. Because of the wide MOBILE6 are based on very few data points. (58) The imple- variation in gross vehicle weight, fuel economy, horsepower mentation of the 2007 standards, which are likely to require ratings, and transmission types, the gram/mile emission fac- particulate filters, will certainly increase the margin of error of tors derived from engine test data had much higher uncertain- current air toxic emission factors for HDVs in MOBILE6. ties than those calculated by vehicle dynamometer testing. This report (54) also compared MOBILE6 emission factors Truck Age Distribution and Mileage Accumulation. The for HDVs with chassis dynamometer data. Results indicated default truck age distribution and mileage accumulation in that HC and CO emissions in MOBILE6 matched well with MOBILE6 were developed based on a report that estimated available test data, while NOx emissions seem to be overesti- truck age distribution in 1996 from vehicle registration data. mated for older models (before 1979) and underestimated for (59) Mileage accumulation was estimated from the 1992 TIUS. newer models (1994 and later). EPA developed exponential fit curves to convert the 1996 truck MOBILE6 is not designed to measure second-by-second age distribution to other years, but the mileage accumulation emission rates, but it relies on specific driving cycles to gen- rates in 1996 were used for the remaining years. This adds a erate emission rates. For light-duty vehicles, there are differ- degree of uncertainty in the analysis of emissions, because ent driving cycles assumed for each of the four facility types. mileage accumulation rates are likely to evolve over time. For heavy-duty trucks however, all vehicle categories are The default parameters for truck age distribution and based on the same driving cycle--the Federal Test Procedure mileage accumulation are important to the extent that emission (FTP) transient cycle--independently of the facility type. Ad- factors vary by truck model year. Exhibit 3-16 illustrates the ditionally, MOBILE6 does not incorporate the effects of road relative difference in emission factors of CO2, NOx, CO, HC, grade, actual vehicle weight, or vehicle aerodynamics, all of and PM10 relative to a 1981 HDV8b truck. In comparison to which have a strong effect on emission factors. other pollutants, CO2 emission factors are not very sensitive to truck model year and remain constant after 1996. Other Emission Factors--PM. Previous research indicated sev- pollutants' emission factors are very sensitive to truck model eral deficiencies in the estimation of PM emission factors in year. As a result, assumptions regarding truck age distribu- MOBILE6, mainly from carrying over the algorithms from tion and mileage age distribution have a large impact on fleet- PART5, the previous model for PM emission factors. (56) average emission factors and on total emissions. PART5 is believed to underestimate emissions from real ve- hicles, primarily because it is based on low-mileage, proper Average Speed. Although MOBILE6 does not enable user- functioning vehicles, and does not consider high emitters to customized driving cycles, speed correction factors are used the same degree. (57) to differentiate emissions of HC, CO, and NOx by vehicle av- MOBILE6 accounts for the implementation of the 2007 erage speed. For heavy-duty trucks, MOBILE6 inherited the PM emission standards for HDVs, which require the imple- same speed correction factors from MOBILE5, as opposed to mentation of low-sulfur diesel fuel (15 ppm limit) and a 90% light-duty vehicles, for which adjusted speed correction fac- reduction in exhaust PM emission standards for HDVs. (58) tors were developed. The uncertainties associated with the The assumptions associated with brake and tire PM emissions use of speed correction factors to adjust emission factors by were not affected. A significant shortcoming of MOBILE6 vehicle average speed are discussed in Section 3.3.4. is that it assumes the same brake and tire PM emission factors (in grams/mile) for all vehicle classes. Because these factors High Emitters. Having been identified as one of the main were developed from passenger car testing, brake and tire PM issues in MOBILE6, correctly representing the share of high emissions from HDVs are likely underestimated. This is the emitters is challenging for many reasons, including (1) the case because brake and tire wear should be proportional to number of high emitters is relatively small, (2) the range in the energy required to stop a vehicle, which, in turn, is a func- emissions is quite large, and (3) owners of high emitters are tion of vehicle weight and speed. typically reluctant to submit their vehicles to testing. (55)

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48 Exhibit 3-16. MOBILE6's sensitivity to truck model year (HDV8b). 1.0 Emission Factors Relative to 1981 0.8 CO2 NOx 0.6 CO HC 0.4 PM 0.2 - 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 Truck Model Year MOBILE6 incorporates correction factors to account for high start emissions for any diesel vehicles, thus adding another set emitters, and despite many criticisms about the underlying of uncertainties to the emission calculations. methodology, it is a step in the right direction. However, such correction factors are applied to light-duty vehicles only, so Classification of Trucks. Although MOBILE6 includes high emitter heavy-duty trucks are not considered. The effects 16 categories of heavy-duty trucks, data on age distribution, of tampering and mal maintenance in heavy-duty vehicles mileage accumulation rates, and fuel ratios are not available also are disregarded. for all truck categories. (59) There are only 7 categories for registration distributions by age and only 18 categories for av- Diesel Fraction. Diesel fraction, defined as the share of erage annual mileage accumulation rates by age. As a result, diesel vehicles in a particular vehicle category, is important some weight classes were combined (Classes 4 and 5 as well since emission rates are different for diesel and gasoline- as Classes 6 and 7), and it was assumed that such classes had powered vehicles. Although users can input specific diesel the same age distribution. fractions for each model year within each vehicle category, this is rarely done due to a lack of project-specific informa- EMFAC2007 tion. As a result, most analyses rely on default values provided in MOBILE6. The main source of uncertainty relates to the Developed by the California Air Resources Board (ARB), fact that MOBILE6 assumes that diesel fractions for vehicles EMFAC is the approved emissions model in California, and of model years later than 1996 have the same diesel fraction it is used for SIP development, conformity analysis, and other as a 1996 model year. (60) Although this is not an issue for analyses that are typically conducted using MOBILE6 in other Class 8 trucks, which are virtually all diesel powered, diesel states. The model produces emission rates and inventories for fraction for Classes 2b through 7 have varied quite substan- criteria air pollutants, CO2, and CH4. Air toxics can be speci- tially from 1972 to 1996. ated using CARB factors. EMFAC2007 produces emission calculations at the county, regional, and state levels, for past, Start Emissions and Soak Time. Start emissions are those current, and future years. that occur immediately after a cold engine start; soak time rep- The overall approach for emission calculations in EMFAC resents the time between when the engine is turned off and the and MOBILE6 is very similar, where regression analyses of next time it is restarted. Emission rates for heavy-duty gaso- primary data sets are used to calculate emission rates and cor- line vehicles include engine starts, and the number of engine rection factors. Because the approaches are very similar, the starts and the soak time distribution cannot be adjusted by the main potential limitations in accuracy are the same as those user (such adjustment is possible for light-duty vehicles). (60) described in the discussion of MOBILE6. One main differ- Because the trip length can be quite different for different ence is the way that EMFAC2007 handles off-cycle emissions truck trips, the inability to customize start emissions can add due to the defeat device. MOBILE6 allows the user to specify uncertainty to emission rates. MOBILE6 does not consider how fast the device will be removed, while EMFAC2007 makes

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49 assumptions that the device will not be removed in 1994 to emission inventory development and to quantify the influ- 1998 trucks. ence of tampering and mal maintenance (T&M) on heavy- At its core, the development of EMFAC2007 was based on duty emissions. (61) the inclusion of area-specific activity data for various regions The update of heavy-duty emission factors was a signifi- within California, including vehicle registration, mileage ac- cant improvement. In EMFAC's previous version, heavy-duty cumulation, vehicle age distributions, and VMT, as well as truck emission factors were developed from testing of various temperature and humidity profiles. engines on an engine dynamometer rather than of the entire vehicle on a chassis dynamometer. (55) As a result, emission Summary of Strengths and Weaknesses. A summary factors needed to be converted (to grams/mile) based on fuel of EMFAC2007 strengths and weaknesses is provided in density, BSFC, and fuel economy. Because of the wide varia- Exhibit 3-17. tion in gross vehicle weight, fuel economy, horsepower rat- Analysis of Process Uncertainty. ings, and transmission types, the gram/mile emission factors derived from engine test data had much higher uncertainties Development of Heavy-Duty Truck Emission Factors. than those calculated by vehicle dynamometer testing. EMFAC2007 updated heavy-duty truck emission factors and Although the resulting database is the largest available, the speed correction factors based on new data obtained through fleet is still too small to accurately characterize changes be- the CRC E55/E59 Project, whose objective was to reduce the tween model years. Another source of uncertainty in this uncertainty of heavy-duty truck emission factors by quanti- method is that all emissions were measured on a very limited fying PM emissions in the South Coast Air Basin to support number of driving cycles. Exhibit 3-17. Analysis of strengths and weaknesses--EMFAC2007. Criteria Strengths Weaknesses Representation of physical Updated EFs based on chassis dynamometer Emission factors are based on a very limited number processes testing from CRC E55/E59 project. of driving cycles. EFs incorporate effects of average trip speeds. Not enough data on EF to accurately differentiate among different truck model years. There are concerns about how speed correction factors capture speed/congestion effects on emissions. Relies on "average trip" drive cycle, rather than facility-specific information. Model sensitivity to input Model does not consider effects of road grade or parameters pavement quality. Model flexibility Few inputs are required. County-based default parameters (VMT mix by vehicle class, vehicle age distribution) are included in the model, but can be overridden by local estimates. Ability to incorporate effects of Model is able to capture the effects of strategies Model is not able to capture the effects of strategies emission reduction strategies that change truck VMT, vehicle average speed that affect pavement quality. (for HC, CO, NOx), fleet average age. Representation of future EFs can be estimated up to 2050. There are concerns as to whether the assumptions emissions used to estimate future EFs are still in line with latest vehicle technology trends. Consideration of alternative Only indirectly through input of different EFs. vehicle/fuel technologies Data quality California-specific default data. Other regions can tailor EMFAC using other values. Spatial variability County-based input parameters differentiate Cannot be applied to facility level. Valid only at county results. and state level. Temporal variability Truck VMT distribution based on outdated data. Review process There have not been many independent analyses and reviews of EMFAC. Endorsements EMFAC is the required model for SIPs and conformity analyses within California.

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50 Characterization of Congestion and Modal Emissions. along an uncongested arterial, or along a congested freeway. EMFAC2007 uses trip-based speed correction factors (rather Although the emission factors under these two scenarios are than facility-based correction factors in MOBILE6). Trip- very different, EMFAC2007 cannot differentiate between based speed correction factors can be appropriate for the them. The modal approach however, has the ability to dif- development of regional emission inventories, but they fall ferentiate these two scenarios, thus creating the differences short when the objective is to estimate local or project-level between the two models. emissions. This is the case since the outputs from travel de- mand models include speed at the link level and not at the trip Truck VMT Distribution. Because EMFAC2007 is also level. Therefore, adjusting emissions at the link level with speed intended to estimate emissions inventories at the county level, correction factors at the trip level is not consistent, and is an it relies on a methodology to allocate VMT information (re- important source of uncertainty. Additionally, EMFAC2007 ported by Council of Governments [COGs] and MPOs) to does not incorporate the effects of road grade, actual equip- specific vehicle categories. VMT estimates are provided by ment weight, or equipment aerodynamics, all of which have travel demand models and validated by traffic count data. a strong effect on emission factors. VMT estimates are generated at different levels of resolution, In order to assess the degree of uncertainty associated with and heavy-duty truck VMT is typically not provided separately. the use of speed correction factors in the development of emis- In contrast to its previous version, which allocated VMT to sion factors in EMFAC2007, a comparison was done with each vehicle category based on registration data, EMFAC2007 emission factors generated by a modal approach (i.e., second- allocates VMT based on (estimated) travel data. (63) The pri- by-second approach) where the driving cycles developed by mary data source was a 1999 Caltrans heavy-duty truck sur- Sierra Research were adapted for heavy-duty trucks. (62) In vey, which was used to estimate the fraction of heavy-duty Exhibit 3-18, the black line represents EMFAC2007 emission truck VMT traveled in each county in California, as well as factors, while the other lines represent modal emission factors mileage accumulation rates and truck age distribution. on freeways and arterials, respectively. Both approaches pro- The survey included origin and destination information vide comparable results for uncongested freeways at high but not route, so the latter had to be estimated based on speeds, but very different results for congested freeways and shortest-path algorithms. To reduce the uncertainty in this arterials. Unlike MOVES, EMFAC2007 differentiates GHG method, these routes were validated with actual truck routes emissions based on average trip speed but does not consider collected by GPS data. A second source of validation included congestion explicitly. Additionally, EMFAC2007 does not other annual publications by Caltrans. differentiate among different roadway types. For instance, a A statistical comparison of these two data sources indi- vehicle with an average speed of 30 mph could be traveling cated that results were consistent for one year. In order to es- Exhibit 3-18. Emission factor comparison for heavy-duty trucks: EMFAC2007 vs. modal approach. 6,000 5,000 Moves CO2 EF (grams/mile) 4,000 EMFAC Moves Art C- Moves Art 3,000 Moves Moves 2,000 Moves Moves 1,000 Moves Fwy - 0 10 20 30 40 50 60 70 80 Speed (mph)

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51 timate truck VMT distribution for earlier and later years, only Another source of uncertainty is that the gross vehicle one of the data sources was used, which accounted for differ- weight (GVW) assignment to each truck is not based on DMV ent growth rates in different counties. MVSTAFF, which is registration information (because such information is often maintained by Caltrans, predicts statewide VMT based on a not available), but through cross-checking vehicle identifica- variety of model inputs including socioeconomic parameters tion number with and vehicle reference books, which only in- (e.g., population, income, economic growth rates), as well as dicates the manufacturer-specified GVW, and not the actual the past 25 years of vehicle registration information. Natu- average GVW. rally, there are uncertainties associated with such estimates, given that vehicle registration and socioeconomic patterns MOVES2009 might not be the best indicators of heavy-duty truck patterns in the state. MOVES is EPA's most recent emission model, which will The truck VMT in each area was calculated by taking the eventually replace MOBILE6 and NONROAD when fully im- product of the registered truck population (by model year), plemented. Its most current version--MOVES2009--has out of state fraction, and accumulation mileage rates. Truck recently been released. It calculates emissions of GHGs, crite- VMT was then redistributed to specific counties based on the ria air pollutants, and some air toxics from highway vehicles, methodology previously described. There are a few sources of and it allows multiple scale analysis--from modal emission uncertainty associated with the process of estimating truck analyses to NEI estimation. VMT, as follow: An uncertainty analysis of MOVES is challenging since it is still in draft version, and it is not yet approved for official use. As a result, there have not been many studies or analyses re- Based on a 1998 study (64), it is assumed that 25% of lated to MOVES. Because MOVES is still under development, trucks are out-of-state trucks, and a factor of 1.33 is applied it is important to define whether one should analyze MOVES to total state VMT. There are two main issues with this in its current form or the version of MOVES when fully im- method: plemented. Such distinction will be present throughout the It assumes that accumulation mileage rates for in-state analysis. trucks are the same for out-of-state trucks. This issue The main improvements MOVES offers in comparison to might be resolved with the Interstate Registration Pro- MOBILE6 can be summarized as follows: gram, which will reevaluate accumulation mileage rates for in-state and out-of-state trucks. Employs a "modal" emission rate approach "as a prelude It assumes a percentage of out-of-state trucks based on to finer-scale modeling"; (65) a single study at a given point in time. Relies primarily on second-by-second data to develop emis- Accrual rates by truck age are based on the 1992 Truck In- sions rates, which better represents the physical processes ventory and Use Survey, again another snapshot in time. from heavy-duty vehicles, including the ability to model cold starts and extended idling; Other sources of uncertainty relate to the way VMT infor- Is designed to work with transparent databases, which can mation is provided by COGs and MPOs. Although some juris- be modified and updated depending on the user's needs; dictions provide explicit VMT estimates for heavy-duty trucks Includes energy consumption, N2O, and CH4 explicitly; (e.g., SCAG), the majority of organizations provide total VMT Uses a graphical user interface. unclassified by vehicle type. So the allocation of VMT to the Summary of Strengths and Weaknesses. MOBILE6 has specific categories of heavy-duty trucks is uncertain. been highly scrutinized, and many of its pitfalls are tentatively Truck Age Distribution. EMFAC2007 relies on a statewide addressed in the development of MOVES. At the same time, model year distribution for heavy-duty trucks, which is gen- MOVES is still under development, and the lack of available erated based on registration data that CARB receives annu- studies prevents a more comprehensive uncertainty analysis. ally from the DMV. Although this method is likely to be rea- As a result, the analysis of strengths and weaknesses of MOVES sonable for statewide analyses, the model year distribution will be based on the main differences over MOBILE6 that could diverge from the state average in isolated counties. For relate to the representation of heavy-duty truck emissions example, there is a significant amount of drayage traffic, (see Exhibit 3-19). which is typically moved by an older fleet, in proximity to the Analysis of Process Uncertainty. Ports of Los Angeles and Long Beach. Therefore, the use of a statewide model year distribution would not be representa- Binning Approach. MOVES uses a binning approach tive of the actual fleet. to calculate modal emissions, and unique source bins are

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52 Exhibit 3-19. Comparison of MOVES and MOBILE6. Criteria MOBILE6 MOVES Comment Geographic Scale Micro-scale analysis When fully implemented Both models enable the estimation of regional and Macro-scale analysis national emission inventories. Air Pollutants Criteria air pollutants Both models include all criteria air pollutants. MOVES adds energy consumption, N2O, and CH4 Greenhouse gases Incomplete explicitly. Air toxics When fully implemented. When fully implemented, MOVES will integrate with Life-cycle emissions GREET to provide well-to-wheels emissions. Representation of MOVES employs a "modal" emission rate approach that Ability to consider user- physical processes will allow users to model emissions on a second-by- specified driving cycles second basis based on user-specified driving cycles. Emission factors based on MOBILE6 uses engine certification data while MOVES actual in-use emissions uses second-by second vehicle emission rates. Extended idling Cold starts Vehicle Weight Because MOVES classifies heavy-duty vehicles based on Ability to consider different how VMT/fuel data are reported, it provides fewer HDT HDT categories categories than MOBILE does. Ability to consider different MOVES expands the number of available facility types. facility types Representation of Estimation of Future future emissions Calendar Years Consideration of alternative vehicle/fuel Incomplete technologies Model flexibility MOVES is designed to work with transparent databases, Relationship Database which can be modified and updated depending on the user's needs. Because MOVES is based on a modal approach, it is Ability to incorporate more capable of capturing the effects of many emission effects of emission reduction strategies, such as improvements in pavement reduction strategies quality, reduction in congestion, etc. Graphical user interface When fully implemented, MOVES will enable the Uncertainty assessment assessment of uncertainty based on the uncertainty of some inputs. Review process Although MOBILE6 has been highly scrutinized, the final version of MOVES has not been released yet. 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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53 differentiated by characteristics that significantly influence The strengths of this approach included the use on real fuel/energy consumption and emissions. (66) At its most dis- trucks (as opposed to engine testing), driving cycles based on aggregated level, emissions can be calculated by real-world conditions over a wide range of operating condi- tions, and the inclusion of actual deterioration and mainte- Geography: the entire United States, at the county level; nance. The sample, which was of relatively small size (100 trucks Facility Types: including off-network roads, rural and of 30 model years), was biased to older (and potentially dirt- urban restricted access roadways (i.e., freeways and inter- ier) trucks with unknown maintenance history (or degree of states), and rural and urban roads with unrestricted access; tampering). Additionally, driving cycles were not randomly Time Spans: energy/emission output by hour of the day for sampled. calendar years 1990 and 1999 through 2050, with options to Because there were some bins without data, supplemental run at more aggregate month or year levels; methods were used to "fill the holes." After an evaluation of Vehicle Types: all highway vehicle sources, including six different methods for hole filling, two methods were selected: heavy-duty truck categories (i.e., refuse, single-unit short- (1) the use of PERE (Physical Emission Rate Estimator), which haul, single-unit long-haul, combination short-haul, com- models fuel consumption on a second-by-second basis ac- bination long-haul, mobile home). All vehicle types are cording to a power demand equation, and (2) interpolation further subdivided according to fuel type, engine technol- of neighboring cells populated with data. ogy, loaded weight, and engine size. Although light-duty vehicles were well covered (i.e., over Energy/Emission Outputs: energy consumption (e.g., total 90% of bins were filled with primary data), there were rela- energy, petroleum-based energy, and fossil fuel-based en- tively more holes in bins associated with heavy-duty trucks. ergy), N2O, CH4, atmospheric CO2, CO2 equivalent, total Single-unit trucks had 65% of bins filled, and combination gaseous hydrocarbons, CO, NOx, and PM; trucks had less than 36% of bins filled. In particular, the heavi- Emissions Processes: running, start, extended idle (e.g., est truck classes (over 60,000 lbs) were very poorly repre- heavy-duty truck "hotelling"), well-to-pump, brake wear, sented, so there are concerns related to the validity of such tire wear, evaporative permeation, evaporative fuel vapor factors. venting, and evaporative fuel leaks. Running emissions are further subdivided in vehicle-specific power and instanta- Relational Database. MOVES relies on a relational data- neous speed bins. This method produces 15 bins defined base that contains default information for the entire United by combinations of speed and vehicle-specific power. Idle States. The data for this database come from many sources in- and decelerations are considered separately, resulting in 17 cluding EPA, Census Bureau vehicle surveys, FHWA travel total bins. data, as well as other federal, state, local, industry, and aca- demic sources. The database is transparent, so users can mod- Development of Emission Factors. MOVES provides ify the data with updated local inputs, which might be more methodological improvements over MOBILE6 as it relates to appropriate for analyses at the project or regional level. the development of emission factors for heavy-duty trucks. The emission factors in MOVES rely upon second-by-second CMEM emission data, which allows a much broader range of data to be used in the development of emission rates. Emissions data UC Riverside's Comprehensive Modal Emissions Model were compiled from previous EPA test programs and from (CMEM) estimates vehicle emissions at the micro-scale level. several external sources, including the Coordinating Research It uses a parameterized physical approach that breaks down Council (CRC), UC Riverside, Texas Department of Trans- the entire combustion process into different components that portation, University of Texas, and West Virginia University. correspond to physical phenomena associated with vehicle EPA contracted with Eastern Research Group (ERG) to assist operation. Particular emphasis was taken to model the effects in the acquisition, quality checks, and compilation of data of road grade, variable ignition timing, and truck platoon sce- collected by outside parties. narios, where aerodynamic effects can provide a significant The information included in the second-by-second emis- benefit in terms of fuel savings. The UC Riverside team also sion data was used to develop energy rates for each vehicle simulated instantaneous fuel consumption in a number of type. Each data point was allocated to a bin, which was char- actual heavy-duty trucks to calibrate their model. acterized by vehicle type, instantaneous speed, and vehicle- CMEM relies on second-by-second input data including specific power. All measurements falling into each bin were instantaneous speed and road grade, as well as on detailed ve- then averaged. The end result of this process was a table con- hicle configuration (e.g., engine power rating, aerodynamic taining energy rates (in kJ per hour) and coefficients of vari- coefficient, rolling resistance coefficient, transmission, weight). ation by bin. As a result, CMEM is generally used for project-level analyses

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54 where a high degree of confidence is needed for a particular driving cycles is typically impacted by other vehicles on the scenario. road, road grade, wind conditions, and safety concerns; Vehicle Sampling Variability: although there is little data Summary of Strengths and Weaknesses. A summary of to estimate vehicle-to-vehicle variability, data from CARB CMEM strengths and weaknesses is provided in Exhibit 3-20. indicate that there is considerable variability in emissions across different model years and equipment manufacturers. Analysis of Process Uncertainty. CMEM's analysis of model uncertainty developed by UC Riverside was divided into the following three areas: 3.3.2 Evaluation of Regional Methods On-road vehicle emission inventories developed by MPOs Emissions Measurement Variability: although measure- and state air quality agencies are the most detailed when com- ment instruments were calibrated prior to each HDDV test, pared to other transportation modes. Trucking emissions are a certain degree of inherent emission measurement vari- typically calculated as part of the total on-road vehicle emis- ability always exists. The instrument precision varied from sions estimation process. Because on-road vehicles are one of less than 0.5% for CO2 to just under 11% for NOx; the largest sources of pollutant emissions, and because of Vehicle Operation Variability: it was found that small dif- Transportation Conformity determination requirements, the ferences in driving the specified driving cycles accounted process used for estimating on-road vehicle activity and emis- for 5% to 10% variability in emissions. Following specified sions is often more complex than for other transportation Exhibit 3-20. Analysis of strengths and weaknesses--CMEM. Criteria Strengths Weaknesses Representation of physical CMEM measures fuel and emissions rates on a second-by- processes second basis according to a set of input parameters that describe the vehicle, driving cycle, and road facility. CMEM's main advantage over MOVES/PERE is that it considers vehicle operational history effects (i.e., how the last seconds of operations affect fuel consumption/emissions). Model development was not dependent on pre-specified driving cycles. Model sensitivity to input The model outputs are sensitive to all parameters that have a parameters strong effect on fuel consumption and emissions (e.g., vehicle characteristics, fuel characteristics, engine specifications, road grade, second-by-second driving cycle). Model flexibility A driving cycle might not be representative of average traffic mix. If the goal of the analysis is to represent a mix of vehicles and traffic conditions, computation requirements can be heavy. Ability to incorporate effects of Model can measure individual project-level impacts, such as emission reduction strategies changes in congestion levels, use of HOV lanes, incident management programs, traffic signal coordination. Representation of future emissions Modeler needs to know exactly the effects of future scenarios on all input parameters to the model. Consideration of alternative vehicle/fuel technologies Spatial variability Because CMEM is a micro-scale emissions model, it is well set up to capture the variability of emissions based on local conditions (e.g., road grade, pavement quality, ambient temperature). Review process Uncertainty analysis is performed specifically for heavy-duty truck module. Endorsements "Research grade" model--not established for industry use.

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55 sources. All large metropolitan areas develop detailed esti- 9. MOBILE6 input scripts are developed for information mates of VMT and on-road emissions by vehicle class and such as fuel Reid vapor pressure (RVP), engine tamper- roadway functional class. For emission inventory purposes, ing levels, inspection and maintenance programs, and ve- some regions rely on the MPO travel demand forecasting hicle emission standards. If emissions are being calculated model to determine VMT and vehicle speeds, calibrating the for a specific day or month, MOBILE also requires input model to observed traffic counts. Other regions estimate VMT information for factors such as maximum and minimum directly from traffic counts. temperature and sunrise and sunset times. Emission factors are developed using EPA's MOBILE6 10. MOBILE6 produces emission factors and VMT weight- model or, in California, CARB's EMFAC model. Development ing factors, typically for each county, urban/rural area, of emission factors requires regionally specific information and roadway functional type. VMT is multiplied by the on inspection and maintenance (I/M) programs, fuel charac- appropriate emission factors to determine emissions. In teristics, temperature information, vehicle age distribution, California, emissions are estimated using the EMFAC and vehicle mileage accumulation by model year. model developed by CARB. A previous report has summarized the methods to estimate freight emissions at six metropolitan areas, namely Baltimore, It is typically assumed that any heavy-duty truck (i.e., any Chicago, Dallas-Fort Worth, Detroit, Houston, and Los truck over 8,500 lbs GVW) is a "freight truck." In reality, there Angeles. (67) All six study regions use a similar methodology are heavy-duty trucks that do not move freight. Some exam- to estimate on-road vehicle emissions, which can be summa- ples of non-freight heavy-duty trucks are utility trucks used for rized in the following steps: service and repair of utility infrastructure, construction trucks (e.g., winches, concrete mixers and equipment transport vehi- 1. The region's MPO uses a four-step travel demand model cles), urban garbage haulers, tow trucks, service industry trucks to estimate base year and future year traffic volumes by used primarily to transport equipment, and daily rental trucks. link. In some cases, the model estimates truck trips inde- Because it is virtually impossible to separate the activity and pendent of passenger vehicle trips (i.e., independent truck emissions of non-freight heavy-duty trucks from freight trucks, trip generation and trip distribution modules). In other and because non-freight heavy-duty trucks are relatively in- cases, the models estimate only passenger vehicle trips, significant compared to freight trucks, generally no attempt and truck volumes are calculated as a percentage of pas- is made to distinguish between the two. senger vehicle volumes. 2. As required by EPA, the MPO adjusts the travel model Summary of Strengths and Weaknesses. A summary of traffic volumes based on observed traffic counts. In this strengths and weaknesses of regional MPO methods is pro- way, the model is calibrated to reflect base year condi- vided in Exhibit 3-21. tions as accurately as possible. 3. The MPO estimates traffic volumes on local roads that Analysis of Process Uncertainty. The analysis of process are not represented in a travel model. Some MPOs do uncertainty of this regional method is captured within the this estimation themselves (e.g., the Baltimore MPO); discussion of parameter uncertainty, including the following: others rely on local roadway VMT provided by the state DOT (e.g., the Detroit MPO). Estimation of truck VMT by travel demand models; 4. Daily traffic volumes by link are disaggregated to hourly Use of average speed information; volumes, using observed traffic counts. Use of emission factors; because the emission factors are 5. Model traffic volumes at the link level are allocated to estimated with either EMFAC (for California) or MOBILE major vehicle types, based on traffic count information. (for the remaining states), the analysis of uncertainty asso- 6. VMT is summed by vehicle type and facility type. ciated with the estimation of emission factors is included 7. The MOBILE6 model requires VMT by 16 different ve- in the discussion of these two models. hicle types. Most regions do not have VMT or traffic count information at this level of detail, so they rely on 3.3.3 Evaluation of Local/Project Methods the MOBILE6 defaults to apportion VMT into these 16 vehicle types. Typically, the calculation of freight emissions at the local 8. Hourly speeds are estimated for each link. Because emis- or project level can rely on more accurate estimates of freight sion factors vary with vehicle speed, the distribution of activity, which is generally estimated in VMT. The emission VMT by speed can have an important effect on emissions. factors are generally extracted from the same models used in MPOs use equations that compare link-level volume and national or regional approaches, but they are commensurate capacity to estimate speed. with the level of detail included in activity data. For example,

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56 Exhibit 3-21. Analysis of strengths and weaknesses--regional MPO method. Criteria Strengths Weaknesses Representation of physical Travel demand models are calibrated by Overall, there are many concerns related to the accuracy of processes current traffic counts. travel demand models in estimating truck VMT. Truck VMT data are not disaggregated into all truck categories in MOBILE6. There are concerns about whether MOBILE6 and EMFAC can accurately capture congestion effects through average speed. Travel demand models do not calculate average speed directly, but rather estimate it through traffic volume and road capacity. Model sensitivity to input The level of detail associated with truck travel activity from parameters travel demand models is not commensurate with the level of detail required by emissions models. Ability to incorporate effects Model is able to capture the effects of of emission reduction strategies that change truck VMT, vehicle strategies average speed (for HC, CO, NOx), fleet average age. Representation of future Future emissions can be represented to the emissions extent that travel demand models can forecast truck VMT. Consideration of alternative Typically, there are none. vehicle/fuel technologies Data quality Depending on the region, truck VMT are estimated as a share of passenger vehicle VMT, otherwise they are estimated through land-use categories as a function of employment. Spatial variability Travel demand models are specific to a given region of interest. Temporal variability Some regions have travel demand models that Most regions have travel demand models that are based have the ability to model traffic in different on a 24-h period. periods throughout the day. This method does not typically capture speed variations within the hour. Endorsements This is the method that most MPOs rely on to calculate regional emissions. if activity data includes traffic volumes at different speed bins, culation of emissions. If VMT for each vehicle type is not then emission factors can be estimated based on these same available, the state or county average VMT distribution speed bins. (i.e., travel fractions) can be used as a surrogate method. The approach used to calculate freight emissions at the More sophisticated analyses include speed and conges- local level can be summarized in five steps: tion effects on emissions. In those cases, VMT by vehicle type and average speed are determined for each roadway 1. Configuration of vehicle types link in the study area. Because congestion levels can vary Because emission models have their own vehicle classifi- quite rapidly, the definition of time periods is important. cation system, agencies need to understand which specific To properly evaluate the effects of congestion on GHG vehicle types should be considered in an estimation of emissions, VMT should be determined at different time emissions from heavy-duty trucks. Exhibit 3-22 includes periods during the day. There is no standard method to the vehicle types that are considered in the three main determine truck idling hours and, ideally, project-level data emission models. are collected. 2. Determination of vehicle activity 3. Determination of road level of service and driving cycles Truck activity is characterized in terms of VMT and idling For those analyses that include the effects of congestion on hours. For analyses that do not include the effects of speed emissions, congestion levels are characterized for each and congestion on emissions, aggregate measures of VMT roadway segment in all project scenarios. Level of service by vehicle type in the study area are sufficient for the cal- (LOS) characterizes congestion levels and is the primary

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57 Exhibit 3-22. Heavy-duty truck types. MOBILE6 EMFAC2007 MOVES2009 Class 2b HDV (8,501-10,000 lbs GVWR) LHDT Light heavy-duty trucks Single-Unit Short-Haul Trucks Class 3 HDV (10,001-14,000 lbs GVWR) (8,501-14,000 lbs GVWR) Class 4 HDV (14,001-16,000 lbs GVWR) MHDT Medium heavy-duty trucks Single-Unit Long-Haul Trucks Class 5 HDV (16,001-19,500 lbs GVWR) (14,001-33,000 lbs GVWR) Class 6 HDV (19,501-26,000 lbs GVWR) Class 7 HDV (26,001-33,000 lbs GVWR) Combination Short-Haul Trucks Class 8a HDV (33,001-60,000 lbs GVWR) HHDT Heavy heavy-duty trucks ( > Class 8b HDV (>60,000 lbs GVWR) 33,000 lbs GVWR) Combination Long-Haul Trucks measurement used to determine the operating quality of a Idling emission factors are generally calculated for the roadway segment or intersection. Methods applied to cal- lowest possible speed in grams of pollutant per mile, and culate LOS are provided in the Highway Capacity Manual multiplied by that speed to estimate an emission factor in (68), which is the industry standard that guides roadway grams per hour. operational analyses. 5. Calculation of emissions The derivation of emission factors that take road LOS into Emissions are calculated by multiplying freight activity by account depends on the development of customized driving the appropriate emission factors. cycles, which consist of a series of data points representing the speed of a vehicle versus time, usually on a second-by- Summary of Strengths and Weaknesses. The analysis of second basis. Because the development of project-specific strengths and weaknesses for local methods will vary signifi- driving cycles is time and resource intensive, standard cantly depending on the method utilized for truck activity es- driving cycles can be used as a surrogate method. An EPA timation. If a travel demand model is used, then the strengths research project developed a set of driving cycles under a and weaknesses will be similar to those described in the re- variety of congestion levels for different road types. (62) gional model. The following section describes the strengths The representation of congestion and LOS by a driving and weaknesses when other methods are applied. cycle is often criticized, since traffic patterns and delay can Analysis of Process Uncertainty. vary substantially within the same LOS. Additional research could indicate alternative methods to consider congestion Estimation of Truck VMT. Some project-level analyses in the evaluation of emissions from on-road sources. There estimate truck VMT based on regional travel demand mod- also has been criticism against the methodology used by els, whose uncertainties are discussed in the regional method. Sierra Research in the development of their driving cycles. However, many local/project level analyses rely on project- 4. Calculation of emission factors specific data, which is more accurate than data estimated by One of two models is generally used to calculate emission models. Even though there will be variation between esti- factors, namely EMFAC in California, and MOBILE6 else- mated and actual truck traffic, this is a source of uncertainty where. Depending on the analysis, the emission factors ex- inherit to project-level analysis, and it is beyond the scope of tracted by these models can represent specific truck types, this analysis to provide methods to more accurately estimate model years, fuel types, and engine technologies. For more truck VMT at the project level. However, other sources of sophisticated analyses that include the effects of conges- uncertainty that could be improved are tion, emission factors also can depend on average speed. In order to consider customized driving cycles in the esti- Estimation of truck weight: this is key since emissions are mation of emission factors, a modal emission model needs highly dependent on truck weight. to be used. CMEM, which was developed by UC Riverside Determination of truck specifications: if project-level analy- under an EPA contract, is arguably the most established ses rely on more specific truck configurations, the emission modal emission model. However, MOVES was designed factors need to be consistent with the modeled truck. The to enable micro-scale emissions analyses, and also can be most important elements to characterize trucks involve used in analyses that consider customized driving cycles. truck class, engine power, gross weight, and fuel type. MOBILE6 and EMFAC do not consider different driving cycles explicitly (GHG emissions in MOBILE6 are insen- Truck Age Distribution. Many project-level analyses still sitive to speed). rely on the national average vehicle age distribution, which

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58 sometimes is not a good proxy for local vehicle age distribu- Determination of Emission Factors. Emission models tions. For example, if a project is associated with a specific such as MOBILE6 and EMFAC are not able to generate emis- type of traffic, it will more likely focus on a group of carriers sion factors that rely on customized driving cycles. For project- that will tend to use a fleet of trucks whose age range is nar- level analyses that characterize congestion by developing a rower. For example, long-distance trucks that transport time- customized driving cycle, a modal emission model is necessary. sensitive cargo tend to be newer, while drayage fleets that Two examples are CMEM and MOVES, whose uncertainties transport international cargo between port terminals and are discussed in Section 3.3.2. local facilities tend to be older than the national average fleet. The development of composite emission factors, which de- pend on the distributions of truck model year, engine tech- Determination of Driving Patterns. Project-level analy- nology, fuel type, and vehicle weight to characterize the proj- ses that still rely on average emission factors are implicitly as- ect truck fleet average, is generally impacted by the fact that suming average driving patterns that might be representative the development of such distributions often relies on very lim- of national patterns, but not necessarily of local driving con- ited information. The use of default distributions included in ditions. For example, if project-related traffic occurs solely at emissions models brings the issue of whether such distribu- night, when traffic flows are generally smooth, or solely dur- tions are representative of local scenarios. ing peak times, when traffic flows are usually interrupted, average traffic patterns will probably not provide a good rep- resentation of actual driving patterns. 3.3.4 Evaluation of Parameters For those projects that do estimate project-specific driving Exhibit 3-23 includes a list of parameters used in the meth- patterns, the following issues might arise: ods and models previously described. There is usually a high degree of variation in traffic pat- Pedigree Matrix terns, so considerable resources need to be spent in order to develop a mix of driving patterns that provide a good A pedigree matrix (see Exhibit 3-24) for data quality assess- representation of actual driving conditions. ment assigns quantitative scores to most of the parameters in- Modal emission models rely on entire driving cycles to es- cluded in Exhibit 3-23. The criteria to assign scores in the timate emission factors, and it is very rare that project-level pedigree matrix are included in Appendix A. analyses have the resources to develop a number of driving cycles that will provide a good representation of project- related driving patterns. More typically, project-level analy- Truck VMT ses rely on traffic volumes and road capacity information In addition to emission factors, a total measure of truck to determine average speed and road LOS. VMT is the parameter with the biggest impact on emissions. The representation of road LOS and average speeds by spe- It is recognized that current methodologies do not provide cific driving cycles is often criticized because there can be a estimates of truck VMT with a reasonable degree of accuracy high degree of variation in driving patterns even within the for emission calculation purposes. The main issues relate to same LOS, especially for the more congested levels of service; (1) how truck movements are represented in travel demand The emission analyses that rely on the standard definitions models, (2) how truck trip generation data are developed, of road LOS require the use of driving cycles that represent and (3) the level of detail included with truck VMT. such levels of service. To date, the driving cycles from Sierra Research (62) are the only ones that were developed Estimation of Truck VMT in Travel Demand Models. with the aim of representing the standard levels of service All large metropolitan areas develop detailed estimates of VMT defined by the Highway Capacity Manual. There are criti- by vehicle class and roadway functional class. For emission cisms of the validity of the statistical methods used by Sierra inventory purposes, some regions rely on the MPO travel Research in the development of those cycles. Further, these demand forecasting model to determine VMT and vehicle cycles were developed for light-duty vehicles, not heavy- speeds, calibrating the model to observed traffic counts. duty trucks. To date, there are no driving cycles developed Other regions estimate VMT based directly on traffic counts. for heavy-duty trucks that aim to characterize different Many MPOs use a four-step travel demand model to esti- road levels of service. mate base year and future year traffic volumes by link. In Another issue is the time resolution of the analysis. For some cases, the model estimates truck trips independent of congestion patterns to be properly characterized, time res- passenger vehicle trips (i.e., independent truck trip genera- olution needs to be evaluated in shorter time periods, usu- tion and trip distribution modules). In other cases, the mod- ally less than one hour, and ideally less than 30 minutes. els estimate only passenger vehicle trips, and truck volumes

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59 Exhibit 3-23. Parameters. Geographic Pedigree Qualitative Quantitative Parameter Methods/Models Scale Matrix Assessment Assessment Truck VMT All All VMT Share by Truck Type All All VMT Share by Time of Day All Regional/Local Truck Age Distribution All All Mileage Accumulation All All Distribution of Emission All All Control Technology Truck Fuel Type Distribution All All Average Speed MOBILE6, EMFAC2007 Regional/Local Driving Cycles CMEM Local Emission Factors All All Classification of Truck All All Types Road Grade CMEM Local Empty Miles All All 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. Exhibit 3-24. Pedigree matrix--truck parameters. Technological Correlation Geographic Correlation Temporal Correlation Representativeness Acquisition Method Range of Variation Impact on Result Independence Parameter Truck VMT 5 2 1 Varies 1 1 1 4 VMT Share by Truck Type 4 3 3 5 3 2 1 4 VMT Share by Time of Day 2 3 1 5 Varies 2 1 4 Truck Age Distribution 4 2 3 Varies 3 1-2 1 3 Mileage Accumulation 3 2 3 5 3 2 1 3 Truck Fuel Type Distribution 3 3 3 N/A N/A N/A 1 2 Average Speed 3 3 1 1 1 1 1 3 Driving Cycles 3 2 1 4 3 2 1 5 Emission Factors 5 2 1 Varies 3 2 1 5 Classification of Truck Types 3 1 1 N/A N/A N/A 1 3 Empty Miles 3 3 3 5 3 2 1 5

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60 are calculated as a percentage of passenger vehicle volumes. lenging to apply truck trip generation rates outside of the In both cases, many MPOs recognize that the methods to es- local area where the data collection took place. timate truck VMT are less sophisticated than those used for passenger VMT. Thus, the uncertainties associated with truck When truck trip generation data are obtained from traffic VMT are higher when compared to passenger VMT. counts, the accuracy of the equipment and the selection of Travel demand models use a computerized representation count locations are the most important parameters to deter- of the regional roadway system that includes all freeways and mine data uncertainty. Most studies in the literature estimate arterials but typically few or no local streets. This is probably rates based on small samples (fewer than 10 observations), not a big concern for heavy-duty trucks, since just a small with high variability from site to site. share of truck miles are traveled on local roads. For projects where truck VMT is estimated from commodity- based models, the number of truck trips is usually calculated Truck Trip Generation Data. Truck trip generation data by converting total tonnage transported by truck into truck are used to estimate truck traffic patterns and, consequently, trips by a payload conversion factor. These methods tend to truck VMT. A previous NCHRP report summarized the cur- underestimate urban trips, since they do not account for trip rent state of practice on the development of truck trip genera- chaining nor local and delivery activity. They also exclude tion data. (69) Conclusions point out that the state of the prac- construction, service, and utility-related truck trips, which are tice in truck trip generation data are primitive when compared not captured in commodity flows. Using commodity-based to passenger trip generation data. Therefore, new truck trip models in regional applications generates challenges because data collection methods, capable of better characterizing truck flows are generally allocated to traffic analysis zones (TAZ) flows at the metropolitan level, need to be developed. (70) using employment shares by industry, and employment data Most states and MPOs have not developed truck travel de- by industry at the TAZ level is difficult to obtain. mand models, and most often truck traffic is estimated as a fixed percentage of total vehicle flows. There currently are no Level of Activity Detail. The level of detail associated well-accepted methods of estimating truck trip generation with truck travel activity from current travel demand models rates, and those models that do utilize some type of method- is not commensurate with the level of detail required by emis- ology typically estimate truck trip generation rates through sions models, which ideally require detailed activity informa- land-use categories as a function of employment. Land uses tion disaggregated by truck type, truck weight, model year, are generally collected by surveys. Sources of errors include fuel type, engine technology, ambient temperature, road type, the following: average speed, and fuel type, among others. If only aggregate estimates of truck VMT are available, average distributions, Land-use categories are very broad, and there is a high which might not be representative of regional or local condi- degree of variability of trip rates within these categories, as tions, must be used to estimate an average emission factor. well as from region to region; Truck VMT data generally used in emission analyses at the Land-use categories were originally developed to correlate national level rely on information from FHWA's Highway with the movement of people not freight; Statistics, which in turn is based on data obtained by the High- Inaccuracy of self-administered travel diary surveys (re- way Performance Monitoring System (HPMS). The HPMS spondents can be concerned about revealing confidential in- provides data that characterize the extent, condition, perfor- formation and distrusting of government) and small travel mance, use, and operating characteristics of the nation's high- survey samples due to low response rates; ways. States are required to report annually to FHWA aggregate Inappropriateness of employment as an explanatory estimates of VMT in collector and local roads, which account variable--many experts indicate that industrial output is a for over 15% of total highway VMT in the United States. Cur- better indicator of truck trip generation rates than employ- rent practices used by the states to report local VMT estimates ment, since labor productivity varies widely within indus- vary significantly and are not typically documented properly. try category (within the same land-use category), from firm However, because the vast majority of heavy-duty truck traf- to firm, and over time; fic occurs along arterials and larger facilities, the uncertainty Lack of a consistent truck classification system--typical associated with freight VMT should be smaller than for pas- approaches include GVW, configuration (e.g., single-unit, senger VMT. combination), and number of axles, but there is no ac- cepted methodology, which makes it difficult to compare Truck VMT Share by Truck Type trip generation rates; and A high degree of variability in the underlying economic ac- Another source of uncertainty is that truck VMT needs to tivities that generate truck activity, which makes it chal- be disaggregated into the different truck categories in emis-

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61 sion models. For example, with MOBILE6, truck VMT data Moderate: those trucks with engines meeting standards need to be disaggregated into eight classes. If only total VMT starting in 1990 and continuing on through 2003. This is is estimated, then the data need to be disaggregated into 16 because emission control in these engines was mostly due vehicle classes. Because most regions do not have VMT or to engine modifications such as better fuel injection, tur- traffic count information at this level of detail, they rely on bocharger improvements, combustion cylinder geometry the MOBILE6 defaults to apportion VMT into these vehicle improvements, and use of after coolers. classes. Because there is a wide variation in VMT distribution Advanced: in 2004 most engines required exhaust gas re- across vehicle categories, the use of national average travel circulation to control NOx emissions. fractions to apportion VMT to specific vehicle categories is After treatment: for trucks with engines built in 2007 and certainly a weak method that could add significant uncer- later, these will require catalyzed diesel particulate filters tainty to emissions estimates. and other catalytic devices to reduce NOx emissions. The distribution of emission control technology is usually Truck VMT Share by Time of Day built into the emission factors and takes into account engines The estimation of truck VMT by time of day is important in a given model year that meet future or prior emission stan- for emission analysis because average speed and congestion dards due to averaging, banking, and trading. Areas with sig- levels, which can be important inputs, can be very different in nificant amounts of engines that meet future emission stan- peak versus off-peak periods. Additionally, ambient temper- dards can provide errors in the emission factors. ature is also an important input for some pollutants, espe- cially for NOx, which has a strong impact on ground ozone Average Speed levels. In most current truck travel demand models, 24-h trip generation rates are disaggregated into time periods based on The most common method used to represent congestion traffic counts from different time periods. Because of the lim- or driving patterns in emission models is by assuming an av- ited number of traffic counts, there is uncertainty in the algo- erage speed at each roadway link. The implicit assumption rithms used to apply the share determined by each count is that average speed is a good proxy for congestion. Both location to those links where traffic counts are not available. MOBILE6 and EMFAC develop base emission rates for vari- ous truck classes using standard driving cycles. These base rates are then adjusted to a particular average speed and road Truck Age Distribution type by using speed correction factors. There are four impor- tant sources of uncertainty with this method: Because emission factors vary by model year, a composite emission factor needs to be developed based on truck age dis- The use of average speed is not the best method to repre- tribution. As previously mentioned, project-level and regional sent driving patterns. The development of MOVES, which analyses typically rely on national age distributions, which relies on a modal approach, is an indication of the short- bring uncertainty into emissions analyses since the accuracy comings associated with the characterization of driving of these analyses depends on how well national age distribu- patterns by a single estimate of average speed. tions reflect local and regional fleets. The variability in truck Travel demand models do not calculate average speed di- age distribution nationwide is important to the extent that rectly, but use estimates of traffic volume and road capacity emission factors vary by truck model year. The discussion of to estimate average speed. Speed/volume relationships are MOBILE6 includes how emission factors vary by model year. not always very accurate, and are sometimes adjusted so Although emissions of CO2 are not very sensitive to truck that modeled traffic volumes match observed volumes. (55) model year, the emissions of criteria air pollutants are gener- Average speeds are estimated at an hourly basis, so this ally very sensitive to truck model year. method does not capture speed variations within the hour, which can be quite significant especially during peak times. Distribution of Emission Control Technology In the case of MOBILE6, emission factors only vary by speed for HC, CO and NOx emissions. Other pollutants are Diesel emission control technology is broken down into insensitive to speed variations and do not represent real the following four categories: world conditions. Uncontrolled: generally trucks built prior to 1990 would More accurate methods of characterizing driving patterns be considered uncontrolled because no federal heavy-duty are the use of emission factors that are based on specific road emission standard existed before 1990. Emission standards levels of service, or on a combination of vehicle-specific power started in California for heavy-duty engines in 1987. and instantaneous speed.