Click for next page ( 81


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 80
80 3.6 Waterborne/Harbor Craft Two specific H/C methodologies are EPA's national scale Regulatory Impact Analysis (RIA) done in support of the 2008 A wide range of commercial harbor craft (H/C) is in oper- rulemaking (119) and CARB's analysis of statewide H/C emis- ation at or near ports, including assist tugboats, towboats/ sions analysis in support of its rulemaking. (120) Although pushboats/tugboats, ferries and excursion vessels, crew boats, constrained by the same limitations discussed previously, work boats, government vessels, dredges and dredging sup- these analyses are both sufficiently developed and tailored to port vessels, commercial fishing vessels, and recreational be discussed separately. A third, general methodology is dis- vessels. These vessels serve many purposes other than just cussed for local scale analysis based on available guidance and direct goods movement. From a freight perspective, it is previous project analyses. The following sections discuss these worthwhile to focus only on those commercial H/C (SCC national, regional, and project-scale methodologies. 2280002000) directly involved in goods movement, such as Uncertainty in the resulting H/C emissions from these tug and towboat operations that move freight barges. Emis- methodologies can then be attributed to either process uncer- sions and parameters relative to other commercial H/C are tainty (that is, the degree to which Equation 8, or similar for- not considered here. mulations, represent the actual processes causing emissions) There are no common models with the capability to esti- or parameter uncertainty (that is, the uncertainty in the indi- mate emissions from these vessels; neither CARB's NON- ROAD nor EPA's OFFROAD model considers commercial vidual elements of Equation 8). Evaluation of process uncer- H/C. Instead, estimates of emissions for tug and towboats and tainty is presented in the following three sections by domain; other commercial H/C may be made through other method- discussion of parameter uncertainty also appears for each ologies, such as the best practice or streamlined approaches methodology and is then summarized in Section 3.7.5. discussed in EPA's Current Methodologies document. (9) These In both cases, any known biases should be corrected dur- general approaches rely on various sources for the necessary ing the analysis. The effects of quantifiable residual uncer- parameters and generally draw on the methodologies of the tainty in input parameters on total calculated uncertainty NONROAD or OFFROAD models, or other published stud- may be made using standard error propagation methods, dis- ies. They assemble parameters including a survey or estimate cussed in Section 4.3.4. If no covariance is assumed for the of the vessel and/or engine counts and engine activity and parameters in Equation 8 the net error in total emissions merge this information with emission and load factor data from would be given by Equation 9. the technical literature. For example, H/C emission inventories are commonly calculated using an equipment power method- ( HP i LF i A )2 2 EF + ( EF i LF i A ) HP 2 2 ology, as shown in Equation 8. 2 Emis = (Equation 9) Main + Auxiliary + ( EF i HP i A ) LF 2 2 EMISPollutant = EF ( Tier ) i LF i A i HP i CF + ( EF i HP i LF ) A Pollutant 2 2 Main + Auxiliary (Equation 8) Where Where the sum is over the population of all main and 2 indicates the variance. auxiliary engines active in the fleet and the input parameters are as follow: Note, however, that Equation 9 assumes the number of engines is sufficiently well known to complete the sum. More EF = the emission factor for a given pollutant species and likely, the estimates and uncertainties are made by calcula- engine, tions discussed in Section 3.6.2, which would allow inclusion HP = the engine horsepower, of uncertainty in number as well. LF = the load factor, A = the annual activity, CF = the appropriate emission control factor. 3.6.1 Summary of Methods and Models Any deterioration, low-load, transient, or other adjust- As stated previously, the discussion here will focus on ele- ment effects (if able to be characterized) are considered in the ments of potential methods to estimate H/C emissions gen- age- or tier-distributed EF. Both main and auxiliary engines erally, since few studies focus only on H/C directly involved are included. Differences in the best practices and stream- in goods movement (i.e., ocean and line-haul tug and tow lined methodologies are chiefly dependent on the amount of vessels). Since no models may be used to calculate H/C emis- data directly collected rather than derived through surro- sions directly, Exhibit 3-43 lists only methods. Two specific gates. For the purposes of uncertainty assessment, they will be and one general method are listed, although the structure of treated as the same methodology. each is very similar. The specific methods were developed by

OCR for page 80
81 Exhibit 3-43. Harbor craft inventory methods. Method/Model Type Geographic Scale Pollutants Freight/Passenger EPA RIA Method National NOx, HC, PM, toxics Both Methodology ARB H/C Method Regional NOx, PM, ROG, CO Both Methodology Local H/C Method Method Local/Project All Both regulatory agencies to detail H/C emissions within a set geo- Propulsion and auxiliary engines less than 37 kW (50 hp) graphic range. The general method, which is labeled here as were also considered. Category 2 auxiliary engines were not "the Local H/C Method," is an aggregate of several studies considered, however, as these are only used on Category 3 ves- that have been conducted at the project level. Neither of the sels. These inventories include all commercial harbor craft, specific methodologies, and most of the studies that form the however, not only those directly involved in goods move- basis of the general method, were applied solely to freight- ment. Exhibit 3-44 shows the current definitions of marine moving H/C, although all could be modified to exclude other compression-ignition engine categories. Exhibit 3-45 shows H/C types. the strengths and weaknesses of the EPA RIA Methodology. Calculation Method. Commercial marine diesel en- 3.6.2 Evaluation of National Methods gine inventories for HC, CO, NOx, and PM were estimated and Models using spreadsheet calculations using the formula shown in The most current, national scale inventory of H/C emis- Equation 10. sions is related to EPA's 2008 locomotive and marine engine E = N P L A EF (Equation 10) rulemaking. (119) The Regulatory Impact Analysis (RIA) developed includes a baseline national emission inventory for Where Category 1 and 2 commercial marine vessels, including freight- E is the 50-state emission inventory (tons per year) for related, commercial H/C. (89) commercial marine vessels, N is engine population (units), EPA RIA Methodology P is the average rated power (kW), L is the load factor, In this case, separate inventories were developed for com- A is the engine activity (operating hours/year), and mercial marine diesel engines in the following three principal EF is the emission factor (gram/kW-hr). categories: Average rated power, load factor, and activity parameters Category 1 propulsion engines, are assumed constant across all simulation years but popula- Category 1 auxiliary engines, and tions and emission factors were considered to vary by year Category 2 propulsion engines. and age. Populations and the corresponding age distribution Exhibit 3-44. EPA marine compression ignition engine categories. Approximate Category Specification Use Power Ratings Gross engine power 37 kW* Small harbor craft and recreational 1 < 1,000 kW displacement < 5 liters per cylinder propulsion Displacement 5 and < 30 liters OGV auxiliary engines, harbor craft, 2 1,0003,000 kW per cylinder and smaller OGV propulsion 3 Displacement 30 liters per cylinder OGV propulsion > 3,000 kW * EPA treats all engines with gross power below 37 kW (50 hp) separately.

OCR for page 80
82 Exhibit 3-45. Summary of strengths and weaknesses--EPA RIA methodology. Criteria Strengths Weaknesses Representation of physical Overall average emissions processes included Variety of methods used to account for different processes from all Category 1 and 2 H/C input data Sensitivity to input Method relies on documented inputs and Some inputs show significant differences from parameters discusses necessary choices other studies; resulting overall uncertainty uncharacterized Flexibility Tailored methodology Not directly applicable to H/C subcategories or smaller spatial domains Ability to incorporate effects Designed to model effects of future regulations of emission reduction strategies Representation of future Designed to model effects of future regulations emissions Consideration of alternative vehicle/fuel technologies Data quality Information included and documented from Unknown uncertainty or bias testing and other authorities. Spatial variability No spatial analysis included Temporal variability Produces only annual inventories Review process Unclear from documentation Endorsements EPA are calculated for the baseline year (generally 2002) and then data for uncontrolled engines. Tier I emission factors are esti- projected. Emission factors vary with age to account for the mated for NOx using 2006 certification data by displacement effects of regulations and deterioration. PM emission factors category; other pollutant factors equal the baseline values. also consider the in-use fuel sulfur level. Tier II PM, NOx, and HC emission factors are derived from Generally, the calculation methods are similar to those for 2006 certification data. Certification data relies on sales- CHE, including use of the NONROAD scrappage function, weighted values from the E3 duty cycle. the linear deterioration factor, and sulfur PM adjustments. A parallel method was used for Category 1 auxiliary engines, Inventory results are calculated in bins of power (in kW), en- but certification data from the D2 auxiliary cycle were used to gine displacement (L/cylinder), and power density (kW/L) to derive load factors. Resulting load factor and activity estimates accommodate the form of the regulations, which differ from (from PSR) were 0.56 and 724 h/year for engines less than the standard break points used in the NONROAD model. 750 hp and 0.65 and 2,500 h/year from the 1999 rulemaking for engines greater than 750 hp. A median life of 17 years is Input Parameters. The population parameters were de- used for all Category 1 auxiliary engines. rived by displacement category, power density, and total power Category 2 main engine emissions also were calculated with from historical sales estimates (provided by PSR [the Power a similar methodology, although here separate estimates were Systems Research Database]), combined with scrappage, and made for underway and idling activity. In this parameterization, then disaggregated into power and power density categories an activity-based approach is substituted with a TIM approach. using the 2002 population and engine data. The average power Accordingly, the activity parameter (in hours per year) is sub- values, load, and activity were population-weighted into ap- stituted with the formula shown in Equation 11. propriate bins to compute totals (see discussion under CHE in Section 3.7). ( Likely Annual Transit Days ) ( 24 hours day ) Category 1 main engine load factor and activity estimates for underway emissions were determined from industry analysis and prior rulemak- ( Likely Annual Idling Days ) ( 24 hours day ) (Equation 11) ing as 0.45 and 943 h/year (engines less than 750 hp) and 0.79 for idling emissions and 4,503 h/year (greater than 750 hp). A median life of 13 years is used for all Category 1 main engines from indus- In both cases, a "likely" load factor is used. Minimum, max- try estimates, with an annual growth rate of 1.009 (for domes- imum, likely load factors, and annual transit days are provided, tic shipping from EIA). Baseline emission factors were taken as well as likely idle days. Activity estimates are discussed with a from the 1999 Marine Diesel rulemaking, based on emissions range of methods and resulting estimates, showing the uncer-

OCR for page 80
83 tainty inherent in this parameter via this analysis. In fact, one placement, power density, and age is complex, although no method relies on a Monte Carlo analysis, thus directly incorpo- known bias results from this method. rating uncertainty into the process. Additionally, for ferries Finally, it must be noted that the methodology here gener- (although not considered here as directly associated with goods ally does not distinguish between freight and non-freight movement), emissions are calculated using a total fuel con- movement. Thus, translation of the methods (and, particu- sumption methodology. The median life for all Category 2 main larly, parameters here) to freight-only calculations is likely to engines is taken as 23 years. (121) Emission factors are taken result in bias, due to the different engines used. from the 1999 commercial marine rulemaking (122) except for Tier I NOx, which was updated based on 2006 certification data. Summary of Strengths and Weaknesses. Exhibit 3-45 includes the analysis of strengths and weaknesses for the EPA Uncertainty. Total uncertainty in this method is due to RIA methodology. both process and parameter uncertainty. As discussed for CHE (Section 3.7), three potentially significant sources of 3.6.3 Evaluation of Regional Methods process uncertainty are the and Models 1. Appropriateness and representativeness of the characteri- As for CHE, the only regional analysis of emissions from zations, commercial H/C has been prepared by CARB for its Novem- 2. Groupings used to categorize H/C, and ber 2007 rulemaking. (124) This rule has special provisions 3. Potential for bias in inputs. that apply to tug, tow, and ferry vessels. CARB Harbor Craft Methodology. CARB developed a The process used here is generally appropriate and tailored methodology to estimate emissions from all commercial H/C to its purposes. No spatial disaggregation is provided because in California to support analysis of regulations to reduce this is a national-scale inventory, thus no uncertainty is asso- commercial marine engine emissions. (125) Other goals of ciated with disaggregation or translation of values between the inventory development included updating estimates to regions, which is typical of a top-down inventory. Load and represent the current H/C fleet, showing effects of the various activity factors are based on industry characterization, binned, regulatory programs, and allowing allocation of the statewide and averaged using power and population as weights since emissions to local air pollution control districts (APCDs) and equivalent NONROAD factors are not applicable. Thus, uncer- air basins. Particularly in this last goal, the CARB H/C method- tainty in the final emissions estimates is related to the number ology differs from the EPA RIA methodology. of engines in each bin and the estimates of other parameters by The methodology is based on activity. It uses results from bin. The process used here is generally believed to rely on the CARB's 2004 Commercial Harbor Craft Survey (126) to estimate best information available, minimizing grouping uncertainty average emissions per engine per year for nine types of vessels: and representativeness of the method. commercial fishing vessels, charter fishing vessels, crew and However, some parameters differ significantly from previ- supply boats, ferry/excursion vessels, pilot vessels, tow boats, ously published values, particularly load factors. This could tug boats, work boats, and "others." These regional emissions either represent or correct significant bias. Reference is given are then aggregated to statewide emissions by multiplying num- to the duty cycles from which the load factors are derived, ber of engines in each engine category and in each region by av- however without commonly accepted average harbor craft erage emissions per engine. Among the findings are that tugs duty cycles, assessment of bias is impossible. The same is true and tows (that is, vessels most directly involved in freight move- for emissions and activity factors, which differ from those of ment) account for 4% of the statewide vessel inventory, 7% other studies. (123) of the statewide engine inventory, but about 25% of the Another source of uncertainty in binning is the difference statewide emissions inventory (i.e., between 21% and 25%, in Category 1 and 2 main engines, especially for tug and tow depending on the pollutant). boats. In the rulemaking, EPA cites two different methods to separate values based on power, hull displacement, and other Population. Base year populations are drawn principally categories. The differences in these two methods implied that from the CARB Harbor Craft Survey (126) and aggregated around 6% of tug vessels could not be clearly categorized in with data from the U.S. Coast Guard Vessel Documentation this method. Although this does not affect the total number Program, the California Department of Fish and Game regis- of vessels directly, it does affect the total emissions as emis- tration data, and information from the Port of Los Angeles sion factors, load factors, activity, and other parameters are emissions inventory. Then, spatial distributions to the air dis- dictated by the type of main engines equipped on the vessels. trict and county level were calculated. Future year populations Also, the subdivision of values based on power, engine dis- are based on base year populations aggregated with fleet growth

OCR for page 80
84 rates from local air districts and scrappage rates based on the Hr is the number of annual operating hours of the engine, OFFROAD model. and The CARB survey on which estimates are based collected TF is total, annual, per engine fuel consumption. information for about 900 vessels (i.e., about 1,900 engines), Uncertainty in this approach comes from both parameters or about 20% of the statewide H/C population. Although the and the process. There is uncertainty in the method since it emission methodology assumes the results of the survey are relies on survey results, which may be biased or inappropri- representative of the overall California commercial H/C fleet ately aggregated. There also seems to be no accounting for and scales results up to statewide values, uncertainty is intro- potential deterioration. Parameter uncertainty comes from duced in the parameters resulting from this relatively small the derivation of parameters from the survey, but particularly sample size. Further, although the survey was distributed to from the reliance on BSFC. NONROAD estimates BSFC as approximately 5,000 potential owners and operators, only 0.367 lb/bhp-hr for engines larger than 100 hp, based on 704 surveys were returned. (127) Uncertainty and potential measured fuel consumption values during engine certifica- bias exist in how well these limited responses represent the tion (which translates to 0.052 gal/hp-hr at 7.09 lb/gal for average H/C fleet operating in California. diesel fuel). Although only a 10% discrepancy exists between the two, there is uncertainty as to which, if either, is more ap- Activity and Engine Parameters. Vessel activity parame- ters also were derived from the CARB survey, which included propriate, on the whole, to commercial marine vessels for goods movement. Ultimately, the load factors derived here information on vessel use, age, annual fuel consumption, number of engines per vessel, engine make and model, age, are smaller than those from the EPA RIA method, although more in line with other analyses. horsepower, annual hours of operation, and other informa- In all cases, the uncertainties here are unquantifiable. tion. These data were aggregated into operating profiles by engine type by region. Number of engines per vessel by vessel Emission Factors. Emission factors were taken from the type was also determined from the survey, as was engine life- OFFROAD model, except for the following: time. In this study, total life was defined as the age when 90% of engines retire and useful life (UL) was defined as half of 19961999 model year engines use baseline/Tier 0 (1996) total life. These definitions both differ from the standard emission factors; NONROAD formula used in many studies, although the shape 2000 and later model-year engines use the smaller of EPA of the scrappage curve is very similar to that of the NONROAD emission standards for marine engines or the NOx limits of model. The uncertainty in this method is due to the defini- the IMO MARPOL Annex VI; and tions of the terms as employed. OFFROAD model emission factors were adjusted to reflect Annual activity was derived from the CARB survey. It is un- an E3 test cycle for main engines and D2 test cycle for aux- known if these values are biased, such as toward the activity at iliary engines. the state's largest ports. However, the same uncertainty exists here as with other parameters derived from the survey. Uncertainty in this approach is due primarily to the choices Auxiliary engine load factors were taken as 0.43, which is made in the method, but also to underlying uncertainty in the attributed to the NONROAD model, for all commercial H/C emission factors of the OFFROAD model and baseline EPA except tug boats, where a factor of 0.31 was used, based on the emission factors, as well as in duty cycle characterizations. Port of Los Angeles' study. (83) These values differ from the In particular, the lack of differentiation between 2-stroke and EPA RIA method values, and it is unclear whether the attri- 4-stroke engine emissions may be a significant source of un- bution of the 0.43 factor is appropriate, since NONROAD certainty in the emission factors applied. does not include commercial marine vessels. Thus, some un- Fuel correction and engine deterioration factors employed certainty is associated with use of these parameter values. are derived from the OFFROAD model. Section 3.7.3 discusses Main engine load factors are derived from results of CARB the uncertainty in this model. survey responses to fuel consumption, engine power, and an- Calculation Methodology. Commercial H/C emissions nual operating hours as shown in Equation 12. per engine are estimated as shown in Equation 13. LF = BSFC HP Hr TF ( ) HP (Equation 12) E = EF0 i F i 1 + D i A i LF i Hr (Equation 13) UL Where Where LF is the vessel type specific propulsion engine load factor, BSFC is brake-specific fuel consumption (here taken as E is the amount of emissions inventory, 0.058 gal/hp-hr from manufacturers' marine engine data), EF0 is the model year, horsepower, and engine type (main HP is the rated engine power, or auxiliary) specific zero-hour emission factor,

OCR for page 80
85 F is the fuel correction factor, Until a comprehensive nonroad mobile emissions model D is the (power and pollutant-specific) deterioration factor, is produced and validated, reliance on models such as NON- A is the engine age, ROAD and OFFROAD will be required to estimate emissions UL is the (vessel type and engine-use specific) engine useful parameters. Thus, any process uncertainty in the models and on life, assumptions involving use of these models--which are not HP is the engine-rated horsepower, designed to simulate commercial marine emissions--is propa- LF is the load factor, and gated to total emissions calculation. Process uncertainty from Hr is the annual engine activity (operating hours). groupings is due to the employed methodology, which relies on use of "vessel type specific emission rates . . . scaled up to the Each of the parameters in Equation 13 has already been statewide population" (128) in the database construction. discussed in Section 3.6.3. CARB calculated statewide and Because parameters are specific to engine, fuel, age, vessel type regional emissions using this equation, the aforementioned and/or power, process uncertainty will propagate due to the parameters, a database model to estimate vessel type specific grouping and application of appropriately weighted central emission rates, and scaled up the emissions to statewide values in each bin. These uncertainties are due to choice and as- populations. signment of values to equipment groupings. Additional process Uncertainty in this methodology is due to process and uncertainty--and potential bias--is due to the extrapolation of parameter uncertainty. Uncertainty in each of the parameters small sample set values to statewide H/C populations. Quantifi- has already been discussed in Section 3.6.3. Uncertainty in the cation of these uncertainties, however, generally is infeasible. process is due to any discrepancies between the analysis pre- sented here and the physical processes estimated. Although Summary of Strengths and Weaknesses. The strengths the process used here is believed to rely on the best informa- and weaknesses of the CARB H/C methodology are shown in tion available and capture the dominant processes contribut- Exhibit 3-46. ing to commercial H/C emissions, three potentially significant sources of process uncertainty are as follow: 3.6.4 Evaluation of Local/Project Methods and Models 1. Appropriateness and representativeness of the characteri- zations, including those of the OFFROAD model, Several studies of port-related activity and emissions have 2. Groupings used to categorize H/C, and been conducted that capture commercial H/C emissions at 3. Potential for bias in the raw or extrapolated survey results. the local or project level. These are listed in Exhibit 3-47. Exhibit 3-46. Summary of strengths and weaknesses--CARB H/C methodology. Criteria Strengths Weaknesses Representation of physical Overall average physical processes included processes Sensitivity to input Method relies on best available inputs Method relies on OFFROAD model; parameters uncharacterized overall uncertainty Flexibility Tailored methodology Ability to incorporate effects Not included in base methodology, but could be of emission reduction applied if information provided strategies Representation of future Method projects populations and associated emissions factors Consideration of alternative Fuel effects included No apparent treatment for alternative fuels or vehicle/fuel technologies technologies Data quality Information included from survey of fleet Unknown uncertainties from extrapolation scheme Spatial variability Emissions allocated to county and air basin, but Underlying data applicable only to CA not more finely Temporal variability Only produces annual inventories Review process Available for public review as part of rulemaking Endorsements ARB

OCR for page 80
86 Exhibit 3-47. Recently conducted port inventories containing H/C. Year Data Port Published Year Pollutants Contractor* SO2, NOx, PM10, PM2.5, CO, Selected Alaska Ports (92) 2006 2002 NH3, VOC Pechan Beaumont/Port Arthur (93) 2004 2000 NOx, CO, HC, PM10, SO2 Starcrest NOx, TOG, CO, PM10, PM2.5, Charleston (94) 2008 2005 SO2 Moffatt & Nichol Corpus Christi (95) 2003 1999 NOx, VOC, CO ACES Houston/Galveston (129) 2000 1997 NOx, VOC, CO, PM10 Starcrest NOx, VOC, CO, PM10, PM2.5, Houston (96) 2009 2007 SO2, CO2 Starcrest Great Lakes (Ports of Cleveland, (Tugs Lake Carriers OH, and Duluth, MN) (97) only) 2006 2004 HC, NOx, CO, PM10, PM2.5, SO2 Assoc. (LCA) Lake Michigan Ports (98) 2007 2005 NOx, PM10, PM2.5, HC, CO, SOx Environ NOx, TOG, CO, PM10, PM2.5, Los Angeles (110) 2005 2001 SO2, DPM Starcrest NOx, TOG, CO, PM10, PM2.5, Los Angeles (83) 2007 2005 SO2, DPM Starcrest NOx, TOG, CO, PM10, PM2.5, Los Angeles (99) 2008 2007 SO2, DPM, CO2, CH4, N2O Starcrest NOx, TOG, CO, PM10, PM2.5, Long Beach (130) 2007 2005 SO2, DPM Starcrest NOx, TOG, CO, PM10, PM2.5, Long Beach (100) 2009 2007 SO2, DPM, CO2, CH4, N2O Starcrest NOx, VOC, CO, PM10, PM2.5, New York/New Jersey (131) 2003 2000 SO2 Starcrest (Tugs NOx, VOC, CO, PM10, PM2.5, New York/New Jersey (101) only) 2008 2006 SO2, CO2, N2O, CH4 Starcrest Oakland (102) 2008 2005 NOx, ROG, CO, PM, SOx Environ NOx, HC, CO, SOx, PM10, Bridgewater Portland (103) 2005 2004 PM2.5, CO2, 9 Air Toxics Consulting NOx, TOG, CO, PM10, PM2.5, Puget Sound** (104) 2007 2005 SO2, DPM, CO2, CH4, N2O Starcrest NOx, TOG, CO, PM10, PM2.5, San Diego (105) 2008 2006 SO2, DPM Starcrest Notes: * Starcrest = Starcrest Consulting Group LLC, ACES = Air Consulting and Engineering Solutions; Environ = Environ International Corp. ** Includes the Ports of Anacortes, Everett, Olympia, Port Angeles, Seattle, and Tacoma. A common theme shared by most of these studies is esti- able and the portion of the fleet considered and a method that mating emissions from limited information. In that sense, is similar to that of NONROAD or OFFROAD models. they are typically some variation of the streamlined method- Two of the inventories presented in Exhibit 3-47 discuss ology discussed in EPA's best practices document. (9) How- Great Lakes activity (those by LCA and ENVIRON) and only ever, the level of detailed information on H/C available to the one discusses inland river activity (Bridgewater). However, the studies varies. The similarity of these studies is driven both by nation's inland waterway system is a principal area of opera- the trend to similar methodologies and by the fact that the tions for line-haul tug and tow vessels, as well as an area of majority of studies are made by the same contractor. They are interest in terms of marine emissions. One study that estimates also very similar to the EPA RIA methodology or the CARB emissions at various ports along the inland river system is by H/C methodology, albeit with a more limited spatial scope, ARCADIS. (117) That study collected information on several where variation is made for the amount of information avail- principal ports and performed a detailed emission inventory,

OCR for page 80
87 then used a principal port-like port analysis to scale activity Data may be updated to more current years by scaling, such as and emissions to other harbor areas. based on the calls or tonnage from other databases, although The general method for producing a local/project scale uncertainties would be associated with this scaling. commercial H/C emissions inventory--specifically targeted In many other cases, too, the required level of information is to goods movement--and its associated uncertainties are dis- not available to determine governing parameters and, instead, cussed in the remainder of this section. Here we focus only on must be developed from surrogate data or translated from sim- vessels directly moving goods, as follows: ilar studies. It is likely that this approach will have inferior data quality and greater overall parameter uncertainty, even if the Line-haul and short-haul tug and tow boats that make calls process is identical. along the inland waterway systems, transporting barges For example, vessel counts by vessel type may be drawn from and containerized goods, and the USCG's Merchant Vessels of the United States database as Ocean-service tug and tow boats. done in CARB's harbor craft inventory. (132) However, this database includes no foreign vessels, may not be available for Specifics on the studies listed in Exhibit 3-47 are provided certain periods, suffers from much missing data, and has poor in the individual inventories cited. quality data for location of vessel activity. As discussed above, CARB was able to mitigate some of this uncertainty by focus- ing on larger domains and supplementing with locally specific Local Harbor Craft Methodology information, however, this may not be available in all cases. Input Parameters. To calculate emissions from commer- Similar caveats apply to other databases, such as the U.S. Army cial H/C involved in goods movement, the following informa- Corps of Engineers' comprehensive and current inventory tion needs to be collected from vessel owners and operators for tug and towboats in the United States. (133) Although this for the relevant types of harbor craft operating in the port area: database contains details on approximately 5,000 tow boats, the same caveats on operating domain may apply. In any case, Hours of operation (annual and average daily, plus sched- it is likely that a vessel inventory may need to be estimated from ules if relevant and available); a variety of databases for local inventories, which will exacer- Percentage of time-in-operational modes (e.g., idling, half bate uncertainty in the analysis. Uncertainty in the analysis power, full power); can also arise from external databases if translation between Vessel characteristics; vessel types is necessary. This process uncertainty can directly Number, type, age, and rated power of main engine(s); affect vessel population counts. Additional uncertainty may be Number, type, age, and rated power of auxiliary engine(s); caused by the need to distinguish Category 1 versus Category 2 Other operational parameters such as fuel consumption engines for tug, tow, and push boats, as well as the lack of rates and fuel type; needed data in most databases. Qualitative information regarding how the vessels are used In the case of insufficiently detailed engine age distributions in service, including operating domain; and from direct surveys, a typical approach is to employ continu- Any information on emissions-modifying methods applied ous age distribution profiles such as those commonly used in to the vessels, such as exhaust after-treatment equipment the NONROAD model for both main and auxiliary engines. installed or internal engine modifications. (134) In many cases, reliance on median life, growth, and scrappage will be taken from other studies and age distribu- Ideally, average values of annual operating hours, number tions will be calculated for each vessel and engine type from of main and auxiliary engines, engine power, and engine age the baseline year. Annual, linear growth in the population of should be determined from the information collected from harbor craft is commonly assumed, which may be taken from the vessels operating at the specific port. This approach min- surrogate data, such as regional economic growth. Otherwise, imizes parameter uncertainties because the calculations are default values for annual population growth, such as those made directly on the fleet in question. Process uncertainties used in the 2008 EPA RIA rulemaking, are employed. Process remain on binning and methodology, and should be quanti- uncertainty is associated with the assumed shape of the age fied where possible. distribution. Parameter uncertainty in median age, growth, Inland river activity data often are taken from the ARCADIS and other values assumed or translated from other studies is study. (117) This provides detailed activity information includ- likely to be significantly larger than similar, directly observed ing TIM and number of up- and down-river calls and passes parameters, although quantifying this uncertainty is infeasi- for the 1995 base year segregated by HP bin for two principal ble. Particularly, estimates derived following NONROAD inland river and two Great Lake ports. Although somewhat guidance are known to produce unrealistic values for engine dated, the level of information contained is of high quality. lifetime in marine applications. This can be mitigated by

OCR for page 80
88 forcing consistency between average model year predicted by ered to be adequate. More significant to the total uncertainty the distribution and that drawn from surveys or translated from the resulting emission calculations is the uncertainty in from other studies. each of the input parameters, as discussed in the parameters To minimize uncertainty, load, activity, emission, fuel cor- sections, above. rection, and control factors also should be collected directly from the fleet being studied. This is not common. Rather, val- Summary of Strengths and Weaknesses. An analysis of ues are commonly translated from other studies, such as the local H/C methodology strengths and weaknesses is provided 2008 EPA rulemaking (119), the ports of Los Angeles (83, 99, in Exhibit 3-48. 110) or Puget Sound (104) studies, the EPA best practices (9) document, the ARCADIS (116117) studies, or EPA- or CARB- 3.6.5 Evaluation of Parameters approved technology lists. As previously stated, parameter un- certainties are directly associated with these original values. Exhibit 3-49 summarizes all parameters relevant for calcu- Process uncertainties generally are introduced in the use of lating emissions from harbor craft. Each of these has been these parameters and in the translation of these parameters for detailed under the discussion of the appropriate scale method a particular study. Quantification of these uncertainties is in Sections 3.7.3 and 3.7.4. Only the primary parameters generally not possible. are discussed in detail here; the parameters that are used to derive these parameters may vary and are not listed here. The Emissions Calculation Methodology. Calculation of use of each is detailed in Section 3.6.4. Also as discussed commercial H/C emissions in a local/project-scale inventory above, no quantitative assessments are provided because the typically is done based on the parameters discussed in Section range of parameters is essentially unknown. 3.6.3. As shown in Equation 14, emission estimates are gen- erated as the product of the following: Pedigree Matrix. Exhibit 3-50 shows the pedigree matrix for the five primary parameters determining emissions from Number of harbor craft vessels of a given type operating in harbor craft. Criteria to assign scores in the pedigree matrix the area (NH/C), are included in Appendix A. Note that both main and auxil- Average number of main and auxiliary engines per vessel iary engine populations are ranked as "5" for Range of Vari- (NEng H/C main and NEng H/C aux), ation. This is because the variation in the variation of values Load factor (LFH/C main and LFH/C aux), between methods is wide, which is also considered a "5," as Average annual activity (ActivityH/C main and ActivityH/C aux), documented in Appendix A. Average rated horsepower (HPH/C main and HPH/C aux), and Population. Emissions are linearly related to engine Appropriate (pollutant, age, power, engine type, and, poten- populations. For commercial H/C, both main and auxiliary tially, power density) emission factor (EFpollutant-H/C-main and populations must be characterized, either directly or from EFpollutant-H/C-aux). vessel populations and average engines per vessel. Popula- tions may be characterized either by engine type, horsepower Emissions pollutant , H C = N H C and age bin, or may only be listed by average values, depend- i ( EFPollutant, Main i N Eng, Main i LFMain i Activity Main i HPMain ) ing on the level of detail in the methodology. Thus, while + ( EFPollutant, Aux i N Eng, Aux i LFAux i Activity Aux i HPAux ) accurate assessment of the engine inventory is critical, in (Equation 14) many cases this parameter is uncertain, particularly for more streamlined approaches. For additional discussion, see Sec- In cases based on the ARCADIS methodology for inland tions 3.7.3 and 3.7.4. river operations, emissions are calculated from a time-in- mode-based activity perspective instead of annual activity Load Factors. All methods require use of load factors for and average load factors. Other parameters are as shown in each engine and vessel type. This factor represents the aver- the list of variables for Equation 14. age load experienced by the engine over a period of use, typ- Transient adjustment and deterioration factors also may be ically annually. This factor is ultimately derived from second considered and included in the emission factors parameteriza- order factors, such as the duty cycle. However, estimates for tion for each engine. This approach parallels that for CHE dis- many specific types of equipment are not available and thus cussed in Section 3.7. As there, uncertainty in these emission are aggregated from models, similar types of equipment, or estimates is due to both process and input parameters. Uncer- similar studies. Because emissions are linearly related to the tainty may be included by the limited representation of the load factor, this can have a large impact on the uncertainty of emission processes, especially the use of overall average param- the total emissions. More discussion is presented under Sec- eters. However, this total power approach is generally consid- tions 3.7.3 and 3.7.4.