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89 Exhibit 3-48. Summary of strengths and weaknesses--local H/C methodology. Criteria Strengths Weaknesses Representation of physical Dominant physical processes typically Structure of methodology is fluid; it must be processes included ensured that adequate representation is included Sensitivity to input parameters High overall sensitivity to parameters, which are generally uncertain; some sensitivity mitigated by ensuring consistency between interim results (e.g., average age) Flexibility Extremely flexible--fluid method structure allows variation for available inputs and surrogates Ability to incorporate effects of Straightforward to include effects in emission reduction strategies calculations if use and effectiveness is known Representation of future Future-year populations calculated may be emissions projected if growth factors are known Consideration of alternative Alternative technologies may be included by vehicle/fuel technologies adjusting emission factors and populations Data quality No specific model on which to rely; information often comes from sources and surrogates of varying quality Spatial variability Tailored methodology allows application to range of domains, down to small/project scale Temporal variability Study may be designed for annual, daily, or seasonal inventories, depending on input data Review process Varies by application Endorsements Varies by application Engine Power. Engine power represents the total rated Emission Factors. All methods require the use of emission power of each of the engine types installed on commercial H/C. factors, although their source and quality level may vary. They Calculation of H/C emissions may require either disaggrega- may be defined for a given combination of engine power, tion into bins of specific type, age, and horsepower range or density, size, and age, or vary only by equipment type and/ may just sum individual engines or even use overall averages, or age. As for other factors used to calculate emissions, the depending on the level of detail of the study. Because emissions result is linearly proportional to this value, thus the impact are linearly related to total power, this can have a large impact of uncertainty in this parameter on that for the final calcu- on the uncertainty of total emissions. For additional discus- lations can be significant. For additional discussion, see Sec- sion, see Sections 3.7.3 and 3.7.4. tions 3.7.3 and 3.7.4. Activity. Commercial H/C engine activity determines the average operating hours of a given engine and vessel type 3.7 Cargo Handling Equipment in an annual period, and is typically described in hours per year. It may be broken down into bins of total power, power Cargo handling equipment (CHE) is used to move or density, engine size, or left aggregated only at the H/C type support movement of freight between modes at intermodal level, depending on the methodology. Because emissions are facilities, such as ports. Particularly at ports, a wide range of linearly related to activity, uncertainty in this parameter can CHE is in use due to the diversity of cargo. Examples of have a large impact on the uncertainty of total emissions. types of CHE include However, because activity also figures into the age distribu- tion of the NONROAD model, impact of its uncertainty may · Cranes, be somewhat mitigated if parameters are adjusted to ensure · Forklifts, consistency in the age distribution. For additional discussion, · Manlifts, see Sections 3.7.3 and 3.7.4. · Sweepers,
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90 Exhibit 3-49. Parameters. Geographic Pedigree Qualitative Quantitative Parameter Methods/Models Scale Matrix Assessment Assessment Main Engine EPA RIA method, CARB H/C method National, Population Regional Auxiliary Engine EPA RIA method, CARB H/C method National, Population Regional Harbor Craft Secondary: used to derive engine Local Population populations in local H/C method Number of Secondary: used to derive engine Local Engines per populations in local H/C method Vessel Load Factors All All Emission Factor All All Engine Power All All Activity All All Deterioration Optional and secondary: used to derive in- All Factor use emission factors. Growth Factor Optional and secondary: needed for future- All year projections Engine Age Optional and secondary: needed to All determine average emission factors Median Life Optional and secondary: needed to determine All age distribution Scrappage Intermediary: derived from equipment age All and median life Duty Cycle Secondary: used to derive load and transient All adjustment factors Use of Retrofit Optional, secondary. Used to calculate All Devices control factors on resulting emissions and/or correct modeled emission factors. Fuel Type Secondary: used to determine emission All factors 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. · Container handlers, Container terminals use CHE most extensively, while truck- · Generators, to-rail equipment and dry bulk terminals also have high use of · Specialized bulk handlers, CHE. As examples, in 2007, the Port of Long Beach found that · Nonroad vehicles, 81% of the CHE portwide was employed by its container ter- · Rail pushers, minals and that 8% of total NOx emissions were due to CHE · Stackers, (100); the Port of Houston found that 15% of its 2007 total · Skid steer loaders, NOx emissions came from CHE (96); New York/New Jersey · Top handlers, found that 25% of their 2006 NOx emissions were due to CHE. · Tractors, (101) Thus, determining emissions from container terminal · Excavators, CHE is important in any landside emission inventory. · Welders, and Generally, CHE emissions from freight activities at ports · Yard tractors. are estimated using either the NONROAD or OFFROAD
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91 Exhibit 3-50. Pedigree matrix--harbor craft equipment parameters. Technological Correlation Geographic Correlation Temporal Correlation Representativeness Acquisition Method Range of Variation Impact on Result Independence Parameter Main 4 Varies Varies Varies N/A Varies Varies 5 Engine Population Auxiliary 3 Varies Varies Varies N/A Varies Varies 5 Engine Population Load Factor 4 2-3 1 2 N/A Varies 2 4 Emission 4 2-3 1 2 N/A Varies 3 4 Factor Engine 4 1 Varies Varies N/A Varies 1 1 Power Activity 4 Varies 1-2 3 N/A Varies 3 4 emission models--or methods similar to those employed in of Equation 15). Evaluation of process uncertainty is presented these models--combined with parameters representing the in Sections 3.7.1 to 3.7.4. Evaluation of parameter uncertainty CHE present, such as rated power, model year, type of fuel is presented in Section 3.7.5. In both cases, any known biases used, annual hours of operation, load data, use of retrofit de- should be corrected. The effects of quantifiable uncertainty in vices or other emission mitigation measures, and fuel type. input parameters on total calculated uncertainty may be made Uncertainty in each of these input parameters can lead to sig- using standard error propagation methods, discussed in Sec- nificant uncertainty in the final emissions estimated. Mod- tion 4.3.4. If no covariance is assumed for the parameters in els/methods and parameters are discussed separately in the Equation 15, the net error in total emissions would be given by following sections, however, the relationship between the two Equation 16, where 2 indicates the variance. must be kept in mind. 2 emissions = ( NPLA ) 2 f + ( f PLA) 2 N + ( f NLA) 2 P 2 2 2 For example, the OFFROAD model generates emission + ( f NPA) 2 L ( f NPL ) 2 A 2 2 inventories for a given type of equipment using an equipment- (Equation 16) total power methodology as shown in Equation 15. Emissions = f N P L A (Equation 15) 3.7.1 Summary of Methods and Models Where Two general categories of methods are used to estimate CHE emissions. These are referred to as the best practice and f is the emission factor, streamlined methodologies. (10) Generally, these two differ P is the maximum rated equipment horsepower, only in the level of direct information collected and employed L is the load factor, in the calculations. The best practice methodology dictates A is the annual activity, surveys of all equipment to establish correct parameters and N is the equipment population, and then employs the NONROAD or OFFROAD models; the In which f incorporates adjustments due to deterioration, streamlined methodology allows for a greater degree of free- transient use, and age-related effects. dom in collecting direct information by substituting surro- Uncertainty in the resulting CHE emissions can then be gate or otherwise derived information. It may then either use attributed to either the process uncertainty (that is, the degree the models, or adjust the methodologies of the models them- to which Equation 15--or other OFFROAD algorithms-- selves for the available information. A special case, third represents the actual emissions process) and parameter un- methodology is used in CARB's CHE inventory, which certainty (that is, the uncertainty in the individual elements is essentially the best practice methodology without directly
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92 Exhibit 3-51. List of cargo handling equipment methods and models. Method/Model Type Geographic Scale Pollutants Freight/Passenger NONROAD Emissions Model County or Larger* HC, NOX, CO, CO2, Freight SOX, and PM; for exhaust and non- exhaust emissions OFFROAD Emissions Model County, Air Basin, or CO2 and CH4,** HC, Freight Statewide (CA only) CO, NOX, and PM; for exhaust, evaporative, and start. Best Practices Method All All Freight Methodology*** Streamlined Method All All Freight Methodology ARB Methodology Method County, Air Basin, or All Freight Statewide (CA only) Notes: * Model use is restricted to countywide definitions, but emission factors and methods may be extracted at scales down to equipment level. ** CO2 and CH4 emissions are produced by OFFROAD, however, these estimates are not currently used as the basis for CARB's official GHG inventory which is based on fuel usage information. (135 ) *** As documented in EPA's draft best practice document. (10 ) This method includes locally specific information on fleets and use of models. As documented in EPA's draft best practice document. (10 ) This method includes use of surrogates for missing locally specific information. As documented by CARB. (136 ) The method used to derive the statewide CHE inventory is a slightly modified version of the best practices methodology, but without directly relying on the OFFROAD model, allowing a modified calculation of deterioration. using the OFFROAD model. Exhibit 3-51 lists these three port of CARB's Mobile Cargo Handling Equipment Regulation methods and two models. (adopted December 2005, effective December 2006), (138) CARB developed a statewide emission estimation methodol- ogy and corresponding emission inventory for CHE. (139) 3.7.2 Evaluation of National Methods The regulation is in support of a statewide emission control and Models strategy for CHE at ports and intermodal railyards. There are currently no national scale inventories of CHE emissions exclusively. The EPA prepares the NEI every three CARB CHE Methodology years, which includes emissions from nonroad sources, gener- ally broken out by SCC. Similarly, for the 2004 (Tier IV) Non- The CARB methodology, based on a survey conducted by CARB in early 2004 and the ports of Los Angeles (110) and road Diesel Rulemaking, EPA prepared a baseline national Long Beach (84) emission inventories available at that time, es- emission inventory for nonroad engines with populations timated population and activity data for CHE statewide by based on commercial inventories of equipment sales and equipment type. The study developed emissions estimates at calculations made via national-scale runs of the NONROAD 16 ports and 14 intermodal railyards in the state for 8 equip- model. (137) However, no details are given specifically to CHE, ment types. (CHE emissions also were estimated for the health as results are reported only for "land-based nonroad engines." risk studies for major rail yards in California.) Exhibit 3-52 Given the lack of a national-scale CHE emissions inventory, no shows these eight equipment types, the corresponding SCCs, uncertainties in such modeling are addressed here. and the SCC type. (Note that for most equipment types, multi- ple fuels are possible. The SCCs shown here are for off-highway 3.7.3 Evaluation of Regional Methods diesel.) and Models CARB (139) summarizes the methodology as follows: California has conducted the only regional analysis of CHE Briefly, the approach used to develop the cargo handling emissions. To evaluate statewide emissions from CHE in sup- equipment emissions inventory estimates entailed determining
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93 Exhibit 3-52. NONROAD cargo handling equipment certainty are (1) the appropriateness and representativeness types. of the OFFROAD model characterizations of CHE, (2) the groupings used to categorize CHE, and (3) the potential for Aggregated CHE Type Estimated SCC SCC Type bias in survey results. Cranes 2270002045 Construction The OFFROAD model itself is discussed in the following Excavators 2270002036 Construction subsection. The parameters used in this method are shown in Forklifts 2270003020 Industrial Container handling equipment 2270003050 Industrial Exhibit 3-53 and discussed in Section 3.7.5. Other general industrial equipment 2270003040 Industrial Sweepers/scrubbers 2270003030 Industrial Equipment Groupings Tractors/loaders/backhoes 2270002063 Construction Yard trucks 2270003070 Industrial The CARB CHE methodology states its choice to group equipment into eight categories (listed above) to make the analysis compatible with the OFFROAD model. (Note that this is different from the discussion in the Summary in that the average annual emissions per engine for each equipment type and then multiplying that value by the total number of engines aerial lifts are grouped into general industrial equipment.) in that grouping. The majority of the inputs that went into There is no particular bias or additional process uncertainty developing the average annual emissions came from individual associated with groupings as long as the parameters within engine profiles developed using the information from a cargo each group are appropriately weighted and applied, and re- handling equipment survey conducted by the [C]ARB in 2004 sults are provided at the same resolution. That is, the result of and cargo handling equipment population information pro- vided by the ports of Los Angeles and Long Beach. These inputs total emissions calculations from more highly resolved cate- were then processed using a template based on the [C]ARB's gories than those here should be consistent with the results of OFFROAD model to estimate annual emissions per engine for this study if values within each group are appropriately con- each equipment type. This data was then expanded to include sidered. As in all similar cases, resolution must be balanced the estimated statewide population of cargo handling equip- with accuracy; here the level of resolution was dictated by the ment fitting a specific age and horsepower range. To estimate use in the OFFROAD model. port specific emissions, the populations of cargo handling equipment were allocated based on the [C]ARB Survey and the Specific discussion of uncertainty with parameters is given port-specific data. Emission estimates were developed for the below. However, process uncertainty is associated with the as- eight types of equipment described. . . . Estimates for NOx, HC, signment of average parameters to bins. For example, in and PM were made. preparing emissions for cranes, the load factor used should be a number-weighted average of the load factors from each This methodology only differs from the best practice crane in the sample set. However, this value is not well known. methodology by not relying directly on the OFFROAD (140) The error in this parameter is the difference between the value model and, instead, slightly modifying the calculation of used and the true average from all equipment in the bin. This deterioration. (141) uncertainty can be due to choice and assignment of values to Total uncertainty in this method is due to both process and equipment groupings. parameter uncertainty. Although the process used here is generally believed to rely on the best information available at Potential Survey Bias. There is potential for bias in survey the time, three potentially significant sources of process un- methods due to misreporting of equipment. This could be due Exhibit 3-53. Parameters from the CARB CHE inventory. Input Factor Source of Data (Gas and Diesel) 2004 CARB Survey of Statewide Ports & Rail Yards; POLA & POLB data Population (base year 2004) (2002) Useful life 2004 CARB Survey of Statewide Ports & Rail Yards Activity (h/yr) 2004 CARB Survey of Statewide Ports & Rail Yards 2004 CARB Survey of Statewide Ports & Rail Yards; POLA & POLB data Average horsepower (2002); Power Systems Research (1996) Load factor Power Systems Research (1996) 2004 CARB Survey of Statewide Ports & Rail Yards; POLA & POLB data Allocation factor (2002) Growth factor 2002 POLA Container TEUs data Survival rate Power Systems Research (1996) Source: CARB, Cargo Handling Equipment One Pager.
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94 to a desire to underreport equipment or overstate control tech- produce model-year specific population distributions for nologies to underestimate emissions resulting from activities at years 1970 to 2040, allocated to geographic regions. Baseline a facility, omission of specific facilities due to a size cutoff, for emission factors are corrected for in-use and ambient condi- example, or many other reasons. As noted earlier, any known tions. Emission inventories are resolved to the county, air bias should be removed from a sample set prior to analysis. basin, or air district by fuel type, engine type, equipment cat- Sampling was made by CARB for over 120 owner/operators egory, and horsepower group. (140) statewide and results incorporated with detailed inventories Uncertainty in emission estimates by the OFFROAD from the 2001/2002 Port of Los Angeles (POLA) and Port of model is driven by several aspects of the model, both in its Long Beach (POLB) inventories. CARB corrected Los Ange- structure and its input parameters. les and Long Beach inventories to a common year assuming a 3% annual growth factor. To adjust for limited information, Calculation Method. The basic emissions calculations in CARB applied corrections to survey results for equipment the model are summarized by Equation 17. populations where data were under-reported or not reported. Thus, no residual bias is likely for this study. Emissions = f N P L A (Equation 17) Summary of Strengths and Weaknesses. CARB CHE As noted above, this is essentially a total power approach methodology strengths and weaknesses are described in to emissions calculations, rather than a TIM calculation or Exhibit 3-54. a fuel consumption approach. On average, a total power approach and a TIM approach should agree, if the more detailed activity profile and load in a given power setting OFFROAD Model agree with the average load factor employed by the power The OFFROAD2007 model is CARB's current emissions approach. (As noted by the lack of use of OFFROAD in creat- and emission factor model designed to incorporate effects of ing the California GHG inventory (10), a fuel consumption proposed regulations, technology types, and seasonal condi- approach is not generally expected to agree.) However, uncer- tions on emissions of nonroad equipment except ocean-going tainty is inherent in this parameterization due to the physical vessels, commercial harbor craft, locomotives, agricultural representation of annual activity. irrigation engines, and gas cans. The model consists of three Additional uncertainty due to best estimate parameters for main modules: population, activity, and emissions factor. average use conditions also exists in the model. This is dis- Population is determined from a calendar year 2000 baseline cussed in Section 3.7.5. However, OFFROAD model-specific equipment population, adjusted for growth and scrappage to discussion follows here. Exhibit 3-54. Summary of strengths and weaknesses--CARB CHE methodology. Criteria Strengths Weaknesses Representation of physical Dominant physical processes included processes Sensitivity to input parameters Method relies on well studied model inputs, Uncharacterized overall uncertainty and modifies when necessary Flexibility Tailored methodology Ability to incorporate effects of Information included from local authorities on emission reduction strategies reduction strategies implemented Representation of future Method relies on well studied model inputs emissions Consideration of alternative Information included from local authorities on vehicle/fuel technologies reduction strategies implemented Data quality Information included from local authorities; known biases corrected Spatial variability Applicable only to CA; emissions resolved only to county level Temporal variability Produces only annual inventories Review process Unclear from documentation Endorsements ARB
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95 Population Parameters. Population in OFFROAD2007 Use of on-road deterioration rates, application of esti- is determined as the calendar year 2000 baseline equipment mated duty cycles, and assumed zero-hour emission factors population adjusted for growth and scrappage. Growth factors all may add uncertainty into model results. Nonroad CHE are based on socioeconomic indicators such as housing units active at ports is likely to have a different duty cycle than sim- and manufacturing employment by category, by county, and ilar nonroad equipment used in other industrial applications. with respect to year 1990 sales. Scrappage is fixed by equipment Further, the use of on-road deterioration factors from a 1990 age and/or use and depends on engine type and horsepower study (142) seems unlikely to represent a current fleet of non- group. For all CHE types, useful life is represented in years and road engines. Sources of zero-hour emission factors also are is driven by the engine's expected life (note that useful life for unclear. Each of these leads to an unquantified uncertainty in lawn and garden equipment and recreational vehicles is deter- the model results. Note that the CARB CHE inventory did mined by the equipment life). As for the NONROAD model, not rely on deterioration rates in the OFFROAD model. the equipment useful life is defined by the sample median; total Summary of Strengths and Weaknesses. Strengths lifetime is twice the useful life. and weaknesses of the OFFROAD model are described in Since emissions estimates are linearly proportional to pop- Exhibit 3-55. ulation, significant uncertainties may result from uncertainties in population, as discussed in Section 3.7.5. For OFFROAD, particularly, many of these uncertainties are driven by the pop- 3.7.4 Evaluation of Local/Project-Level ulation projections to specific calendar years. These uncertain- Methods and Models ties may be mitigated by using observed counts of CHE instead, Several studies of CHE emissions have been conducted at as in the CARB CHE methodology. Uncertainties also exist in the local/project level. Principally, these include studies at ports the methods used to allocate populations to smaller domains, throughout the United States, as detailed by Exhibit 3-56. such as counties or air basins. Similarly, the shape of the age-to- Other studies of note include CHE active at intermodal rail- median age curve could be inappropriate for a given equipment yards throughout California (143) and NEPA and CEQA type. Neither of these uncertainties is generally quantifiable, but studies that have characterized impacts from CHE. (144) could lead to uncertainty in resulting inventories. Typically, these studies either rely on the best practices methodology directly or a variation of it, where calculations are Activity Parameters. Activity estimates in OFFROAD made externally, but in a similar method to that of NONROAD 2007 include annual average usage, load factors, brake-specific or OFFROAD models. In some cases, particularly for the less fuel consumption (BSFC), and number of starts per year. detailed studies, a streamlined approach is used. These methods Values are included for each equipment category by fuel and and models are discussed in the following subsections. engine types and horsepower group. Activity profiles also include seasonal and temporal variations by industrial category. Best Practice Methodology Uncertainty exists in these parameters on the appropriate cate- gory binning and application across categories. Particularly, this Best practices in developing an emissions inventory from is true for equipment that could have uses in multiple indus- CHE activity dictate that one should gather detailed informa- tries or placed in a more general category. There also are issues tion on all CHE present at the port in question (within the with attributing usage fractions to freight activity only. For study boundaries) and make simulations using the NON- example, an average (no peak) usage pattern is exhibited by ROAD (outside of California) or OFFROAD (in California) airport ground service and TRUs while construction and in- model. This methodology is rooted in observations of all active dustrial equipment is assigned primarily a weekday profile. CHE, including information on the following: However, much CHE is likely to be considered industrial · Equipment type, equipment, although having a profile more similar to air · Rated horsepower, GSE. Similarly, a skid steer loader used in a mining applica- · Model year, tion is not likely to represent the activity profile of one used · Type of fuel used, including fuel sulfur level for diesel, at a bulk cargo terminal. · Annual hours of operation, · Equipment load data, and Emission Factors. Exhaust emission factors are engine- · Retrofit devices or other emission mitigation measures specific and vary by fuel type, horsepower group, and model employed. year. Equipment-specific emission rates are based on the combination of engine emission factors and equipment duty Using the data collected on equipment numbers, types, cycles. Deterioration rates are generally based on on-road horsepower, model year, hours of operation and load data, in- emissions data. puts can be generated for the various NONROAD (OFFROAD)
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96 Exhibit 3-55. Summary of strengths and weaknesses--OFFROAD model. Criteria Strengths Weaknesses Representation of physical Dominant physical processes included processes Sensitivity to input Model relies on user-customizable inputs; Uncharacterized overall uncertainty parameters sensitivity to these inputs varies Flexibility Moderately flexible; customization requires familiarity with model, or replication of calculations Ability to incorporate effects May be included after model runs, using CARB- of emission reduction certified reductions; unclear how to include in strategies simulations Representation of future Projections available in the model emissions Consideration of alternative Unclear vehicle/fuel technologies Data quality Generally structured from best available information Spatial variability Applicable only to CA; emissions resolved only to county level Temporal variability Produces only annual inventories Review process Unclear from documentation Endorsements ARB Exhibit 3-56. Recently conducted port inventories containing CHE. Year Data Port Pollutants* Contractor* Published Year Charleston (94) 2008 2005 NOx, TOG, CO, PM10, PM2.5, SO2 Moffatt & Nichol Houston/Galveston 2003 2001 NOx, VOC, CO Starcrest (145) Houston (96) 2009 2007 NOx, VOC, CO, PM10, PM2.5, SO2, CO2 Starcrest Los Angeles (110) 2005 2001 NOx, TOG, CO, PM10, PM2.5, SO2, DPM Starcrest Los Angeles (83) 2007 2005 NOx, TOG, CO, PM10, PM2.5, SO2, DPM Starcrest NOx, TOG, CO, PM10, PM2.5, SO2, DPM, CO2, Los Angeles (99) 2008 2007 Starcrest CH4, N2O Long Beach (146) 2004 2002 NOx, TOG, CO, PM10, PM2.5, SO2, DPM Starcrest Long Beach (130) 2007 2005 NOx, TOG, CO, PM10, PM2.5, SO2, DPM Starcrest NOx, TOG, CO, PM10, PM2.5, SO2, DPM, CO2, Long Beach (100) 2009 2007 Starcrest CH4, N2O New York/New Jersey 2003 2002 NOx, VOC, CO, PM10, PM2.5, SO2 Starcrest (147) New York/New Jersey 2005 2004 NOx, VOC, CO, PM10, PM2.5, SO2 Starcrest (148) New York/New Jersey NOx, VOC, CO, PM10, PM2.5, SO2, CO2, N2O, 2008 2006 Starcrest (101) CH4 Oakland** (102) 2008 2005 NOx, ROG, CO, PM, SOx Environ NOx, HC, CO, SOx , PM 10, PM2.5, CO2, 9 Air Bridgewater Portland (103) 2005 2004 Toxics Consulting NOx, TOG, CO, PM10, PM2.5, SO2, DPM, CO2, Puget Sound*** (104) 2007 2005 Starcrest CH4, N2O San Diego (105) 2008 2006 NOx, TOG, CO, PM10, PM2.5, SO2, DPM Starcrest Notes: * Starcrest = Starcrest Consulting Group LLC, Environ = Environ International Corp. ** Definitive results are not included for cargo handling equipment in this inventory. *** Includes the ports of Anacortes, Everett, Olympia, Port Angeles, Seattle, and Tacoma.
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97 equipment types to determine emissions for CHE at the port. ditions, accidental omission of facilities or equipment, a desire Use of retrofit or emission control devices must be treated to misrepresent activity, or incorrect survey methodology or outside the model. In these cases, emission factors may be de- results processing, for example. As noted earlier, any known termined using the NONROAD (OFFROAD) models for diesel bias should be removed from a sample set prior to analysis. If equipment and then appropriate emission reduction per- an appropriate survey is conducted following the best practice centages applied. For retrofit devices such as diesel oxidation guidelines, these uncertainties should be small. catalysts, diesel particulate filters, or other technologies, re- ductions specified in the following sources should be applied: Summary of Strengths and Weaknesses. Best practice EPA's Verified Retrofit Technology website; (149) EPA's Diesel methodology strengths and weaknesses are provided in Emission Quantifier; (150) or CARB's list of currently veri- Exhibit 3-57. fied technologies. (151) Other sources may be relied upon, but may be considered more uncertain. Streamlined Methodology Specific discussion of these models and their associated un- certainties is given in Section 3.7.3. Total uncertainty in this In cases where all necessary information is not available, method is due to both process and parameter uncertainty. The resulting emissions from CHE activity may be approximated process described here is generally structured to rely on the best using a more streamlined approach than that of the best prac- information available for a given project. However, at the time, tice approach, allowing emission estimations without directly three potentially significant sources of process uncertainty observed equipment inventories and other parameters. are (1) the appropriateness and representativeness of the Recently, a variety of detailed, local/project-scale CHE NONROAD (OFFROAD) model characterizations, (2) the emission inventories have become available (see Exhibit 3-56). groupings used to categorize CHE in analysis, and (3) the po- Unlike vessel emissions, there is no standardized methodol- tential for bias or error in equipment inventory counts. ogy for developing estimates of port CHE emissions. Devel- The appropriateness and uncertainty of the models is dis- oping a detailed CHE inventory may require extensive time cussed in their respective sections. and resources to survey tenants within the study boundaries re- garding their equipment. As an alternative to this level of effort, Equipment Groupings. The best practice methodology CHE emissions are sometimes estimated based on inputs should minimize uncertainty associated with grouping CHE developed for CHE inventories prepared by other sources. The into categories by following the categories already provided essence of a streamlined CHE evaluation is to estimate any by each model to make the analysis compatible with the model missing values in a local survey of equipment types, counts, being employed. As in all similar cases, resolution must be bal- and/or parameters from other published studies--commonly anced with accuracy; here the level of resolution will be dic- by applying ratios of known parameters, such as cargo tonnage tated by the emissions model. throughput--to other detailed ports, followed by calculations There is no particular bias or additional process uncertainty using the NONROAD (OFFROAD) model or methodology. associated with groupings as long as the parameters within Uncertainty in this method can be significant, although each group are appropriately weighted and applied, and results general quantification of this uncertainty is difficult. Uncer- are provided at the same level of resolution. That is, the result of total emissions calculations from more highly resolved cat- tainty is propagated into the analysis via the parameters input egories should be consistent with the total emissions from a to the model, such as in the number inventory and properties coarser study if values within each group are appropriately of CHE. For example, one might use tonnage throughput ra- considered. Specific discussion of uncertainty with input pa- tios between two projects to determine the number of cranes rameters for the OFFROAD model is given above; discussion at a second project from that at the first, but translate all other of the NONROAD model is below. However, process uncer- parameters for those cranes (e.g., power, load, activity) di- tainty is associated with the assignment of average parameters rectly from the values at the known port. The net uncertainty to bins. This uncertainty can be due to choice and assignment on resulting emissions could be tracked from the uncertainty of values to equipment groupings. Because the bins are deter- resulting from the scaled input parameters, but the source of mined by model designations and the CHE sample size is this uncertainty is the process used to translate the parameters. expected to be moderate to small for local/project-scale analy- Specifically, it is due to the assumptions used and choices ses, fleet characterization should not cause much uncertainty made. Bias can be minimized by selecting projects that are sim- in the emissions analysis. ilar, both in scope (by using methodologies such as the princi- pal port-like analysis of the ARCADIS guidance, (116117) for Survey Error. There is potential for bias and error in sur- example) and in equipment age, activity, and other parame- vey methods due to miscounting of equipment at the facility ters. Regardless, uncertainty in this methodology is likely to for a variety of reasons such as inappropriate boundary con- be significant.
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98 Exhibit 3-57. Summary of strengths and weaknesses--best practice methodology. Criteria Strengths Weaknesses Representation of physical Dominant physical processes included processes Sensitivity to input parameters Method relies on detailed user inputs that may General, overall uncertainty unknown not be readily available, but should produce best results Flexibility Low Flexibility; requires detailed data collection Ability to incorporate effects of Best information available; effects may be emission reduction strategies included after model runs Representation of future Projections available in the model and emissions customizable to local information Consideration of alternative May be achieved in methodology with suitable vehicle/fuel technologies model runs Data quality Structured from best available information Spatial variability Applicable to any location, but data requirements likely limit to smaller spatial scales Temporal variability Most likely limited to annual inventories Review process Documented in EPA Methodology Guidance Endorsements EPA Summary of Strengths and Weaknesses. Strengths and NONROAD2008. The NONROAD model (152) predicts weaknesses of the streamlined methodology are shown in emissions for recreational land and marine vehicles as well Exhibit 3-58. as logging, agricultural, construction, industrial, and lawn and garden equipment. It includes over 80 basic and 260 spe- cific types of nonroad equipment stratified by horsepower NONROAD Model rating, and considers equipment fueled by gasoline, diesel, In April 2009, EPA released the current version of its compressed natural gas (CNG), and liquefied petroleum gas nonroad, mobile emissions and emission factor model, (LPG). NONROAD2008 also includes emission reductions Exhibit 3-58. Summary of strengths and weaknesses--streamlined methodology. Criteria Strengths Weaknesses Representation of physical Dominant physical processes included processes Sensitivity to input Method relies on surrogates for missing inputs; parameters results highly sensitive to quality of inputs Flexibility Highly flexible; customizable to data limitations Ability to incorporate effects Highly customizable. of emission reduction strategies Representation of future Projections available in the model and customizable emissions to local information Consideration of alternative May be achieved in methodology with suitable vehicle/fuel technologies model runs Data quality Structured from available information Spatial variability Applicable to any location. Data flexibility allows multiple spatial scales Temporal variability Designed for annual inventories, but scalable with appropriate information Review process Documented in EPA Methodology Guidance Endorsements
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99 associated with the 2008 diesel recreational marine standards sions for a given calendar year, growth can be set to zero so from the locomotive/marine and small spark-ignition (SI) that the emissions will not increase over time and the results and SI recreational marine final rules. The model is capable will be accurate for the analysis year. of estimating subcounty emissions with specific inputs. How- For future forecasts, updated inputs for population and ever, the practical geographic domains vary between county activity are required. This is due to the NONROAD method- and national extents. NONROAD is intended to eventually ology, which calculates both population and age distribution be replaced by a version of the MOVES model that will incor- in which the model uses a population growth rate to project porate nonroad modeling capability. EPA has indicated that equipment populations from a base year to an evaluation it intends to include this capability in the release of the final year. (155) For all base years (projected or current) the model version of MOVES2010 (focused on on-road vehicles and fits the population numbers to a predetermined form as a func- scheduled to be released by the end of 2009), however, that tion of growth and scrappage. The number of units of each version would not be expected to yield substantially different model year (or, equivalently, age) is determined for each age results compared to NONROAD2008. (153) for 50 years back. Populations with ages greater than twice the Exhibit 3-59 provides an example of equipment types and the median life are assumed scrapped. (156) corresponding SCC used in the NONROAD model to estimate Significant uncertainty in emissions may arise from this for- emissions from CHE. The majority of CHE can be classified mulation of age distribution, due to the assignment of engine into one of these equipment types. (Note that for most equip- tiers to specific ages (and power bins). The driving parameters ment types, multiple fuels are possible. The SCCs shown here here are the growth rates, shape of the population distribution are for off-highway diesel.) curve, and median lifetime of equipment. Any event that Uncertainty in emission estimates by the NONROAD model leads to a difference in real world age distribution from that is driven by several aspects of the model, both in its structure assumed by the model will lead to different average emission and its input parameters. factors, and thus different emissions. This bias could result Population Parameters. NONROAD maintains 1996, from a mischaracterization of equipment median life or 1998, and 1999 baseline populations and determines future growth rates, both of which shift the overall curve of popula- year populations by assigning an average growth rate to esti- tion versus age. The resulting uncertainty could bias the results mate emissions in subsequent years. (154) To produce emis- in either direction, as an under- (over-) estimated median life Exhibit 3-59. NONROAD cargo handling equipment types. Aggregated CHE Type Estimated SCC SCC Type Compressor 2270006015 Commercial Crane 2270002045 Construction Forklift 2270003020 Industrial Manlift 2270003010 Industrial Sweeper 2270003030 Industrial Car loader 2270003050 Industrial Chassis rotator 2270003040 Industrial Empty container handler 2270003050 Industrial Generator 2270006005 Commercial Light tower 2270002027 Construction Specialized bulk handler 2270003050 Industrial Nonroad vehicle 2270002051 Construction Gantry Crane 2270002060 Industrial Rail pusher 2270003040 Industrial Reach stacker 2270003050 Industrial Roller 2270002015 Construction Side handler 2270003050 Industrial Skid steer loader 2270002072 Construction Top handler 2270003050 Industrial Tractor 2270002063 Construction Excavator 2270002036 Construction Welder 2270006025 Commercial Yard Tractor 2270003070 Industrial
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100 would lead to relatively more (fewer) newer engines and lower wear out would lead to overestimation of the fleet, thus the net (higher) overall emissions. Similarly, a difference in the general bias due to in-use lifetime is expected to be small. (157) Uncer- shape of the real world age distribution from that parameter- tainty due to the representativeness of on-road engine lifetimes ized in the model could lead to bias in either direction. This for off-road applications is unknown, although EPA did con- bias is more difficult to quantify without explicitly knowing the sider the data underlying these estimates in NONROAD devel- full age distribution of the sample population, but one exam- opment. (158) ple could be in cases where a type of equipment, engine, or Load factors in NONROAD are based on seven operational technology is newly introduced and the older tail of the age duty cycles for agricultural tractors, backhoe-loaders, crawler distribution is not yet populated. In that case, the bias would tractors, skid-steer loaders, arc welders, wheel loaders, and be to higher emissions estimates by over-predicting the num- excavators. Extrapolation of the seven duty cycles to every ber of older, more polluting engines. type of equipment was done by grouping the seven cycles into Uncertainty in the results may also be attributed to correct three categories--transient cycles with high loads (average of estimation of growth factors by equipment type. This param- 0.59 with range 0.480.78), transient cycles with low loads eter may be set in model inputs, however, and is directly con- (average of 0.21 with range 0.190.23), and steady-state cycles trollable by the user. Mischaracterization, however, could (average of 0.43)--and assigning all equipment to one of these lead to significant bias in resulting populations. A similar sce- three categories. (157) Uncertainty in the measured range of nario exists with equipment population. Default NONROAD high- and low-cycle values is about 10% to 30%, although that equipment populations by geographic areas are determined for the steady cycles is uncharacterized, but could be as high from national-level estimates using economic factors, such as about 80%. Uncertainty due to assignment of measured as construction expenditures, farm acreage, and building emission factors to equipment groups is unknown. However, square footage. (157) Reliance on these, rather than directly the assumed load factor is likely to be a significant source of observed current year population counts, may lead to bias in uncertainty in NONROAD modeling, both in directly calcu- resulting emissions. lating equipment emissions and in determining population As emissions estimates are linearly proportional to popu- age distribution. EPA claims that the effects of load and life- lation, significant uncertainties may result from uncertainties time in determining emissions and population are offsetting in population, as discussed in Section 3.7.5. In all cases, these when computing total emissions, such that uncertainties in uncertainties may be mitigated by using observed counts of these parameters should have little effect on total emission CHE of each age for the given project. uncertainty. (157) Activity values in NONROAD are based on surveys of Usage Parameters. Engine median lifetime shapes the equipment users by a private company using proprietary population distribution, as discussed previously. Annual methods that estimate annual activity by equipment type but activity and load determine the engine usage. All are dis- not by engine size, age, or model year. The uncertainty in cussed together by EPA. (158) The parameters are related average values and the actual sensitivity of activity to equip- because NONROAD uses annual activity and load factor ment size and age are all unknown. (157) Thus, the effects of values to calculate emissions by engine type and uses activ- these on overall emissions estimates is unknown. ity, load factor, and median life together to calculate fleet NONROAD estimates brake-specific fuel consumption age distributions. See Equation 18. (BSFC) as 0.408 lb/bhp-hr for engines smaller than (or equal to) 100 hp and 0.367 lb/bhp-hr for engines larger than 100 hp, Median Life At Full Load ( hours ) Median Life ( years ) = based on measured fuel consumption values during engine Activity ( hours year ) Load Factor certification. (157) Uncertainty in these estimates is unknown. (Equation 18) Emission Factors. Emission factors in NONROAD (159) NONROAD assumes equipment lifetime equals engine life; consist of zero-hour, steady-state emission factors, transient engine life is determined based on the expected lifetime of adjustment factors, and deterioration factors; fuel sulfur im- highway diesel engines operated continuously at full load and pacts on emission rates are included. Zero-hour, steady-state adjusted to in-use values by dividing by the average load factor emission factors (EFs) are a function of model year and power, and annual activity. The NONROAD methodology assumes which defines the technology type. Transient adjustment fac- that nonroad engines are not rebuilt and that equipment never tors (TAFs) vary by equipment type. Deterioration factors fails before the engine is worn out. These underlying assump- (DFs) are functions of the technology type and engine age. See tions in the model may lead to significant resulting uncertainty Equation 19. in calculated emissions. However, engine rebuilds would lead the model to underestimate the equipment fleet, while engine EFInUse = EFSteadyState TAF DF (Equation 19)
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101 In addition to exhaust emissions, crankcase HC emissions As noted, this is essentially a total power approach to are computed as a simple 2% fraction of exhaust HC emis- emissions calculations, rather than a TIM calculation or a sions for Tier 0 to III engines and are zero for Tier IV engines. fuel consumption approach. On average, a total power ap- Zero-hour, steady-state emission factors are drawn from a proach and a TIM approach should agree, if the more de- variety of sources, including NEVES (baseline engines > 50 hp), tailed activity profile and load in a given power setting CARB's OFFROAD values (Tier 0 engines less than 50 hp), agree with the average load factor employed by the power emission rate tests (Tier 0 engines greater than 50 hp), EPA en- approach. However, uncertainty is inherent in the model gine certification data (all Tier I engines and Tier II engines due to the physical representation of annual activity. Addi- 300600 hp), methods for the remaining Tier II and all Tier III tional uncertainty due to best estimate parameters for aver- engines (including compliance margins on emission standards, age use conditions also exists in the model. This is discussed certification results, CARB engine test data, and engineering in Section 3.7.5. judgment). All Tier IV emission factors are based on compli- ance margins from emission standards. Since each element is Summary of Strengths and Weaknesses. NONROAD chosen based on the best available information, all bias is as- model strengths and weaknesses are shown in Exhibit 3-60. sumed to be minimized. However, significant but unquantified uncertainty persists in most factors. Factors based on standards OFFROAD Model are likely to be less uncertain, since engines must be designed to meet specific thresholds, but a range of values is still likely. The OFFROAD model was discussed in Section 3.7.3. Since TAFs (159) are applied to the emission factors of all engines the model is appropriate at the project/local scale as well as except Tier IV, where transient control is expected to be part the regional scale, the discussion is not repeated here. of all engine design. TAFs in NONROAD were calculated by averaging tests for each engine, pollutant, and test cycle, and 3.7.5 Evaluation of Parameters comparing these measured emission factors for off-road equip- ment duty cycles to the zero-hour steady state emission factors. Exhibit 3-61 summarizes all parameters relevant for calcu- Thus, in-use emission factors should have reduced uncer- lating emissions from CHE. Each of these has been detailed tainty relative to using zero-hour steady state emission rates as under the discussion of the appropriate model or method emission factors. in Section 3.7.3 and 3.7.4. Only the primary parameters are Deterioration factors (159) in NONROAD increase with en- discussed in detail here. That is, many of the parameters are gine age up to its median life, at which point it is held constant, used to derive the parameters in Equations 15 and 20, but under the assumption that increased deterioration is offset by not discussed here. The use of each is detailed above. Also maintenance. For compression ignition engines, deterioration as discussed previously, no quantitative assessments are is linear. In all cases, due to a lack of data for nonroad engines, provided, because the range of parameters is essentially the factors are based on data derived from highway engines. unknown. Uncertainty in these factors is unknown, particularly any addi- tional effects due to deterioration, mal maintenance, tamper- Pedigree Matrix. Exhibit 3-62 shows the pedigree matrix ing, or the effects from use of fuel with various sulfur levels. for the five primary parameters determining emissions from CHE. Criteria to assign scores in the pedigree matrix are Calculation Method. The basic emissions calculations in included in Appendix A. Note that population is ranked as a the model are summarized by Equation 20. "5" for the range of values. This is actually because the varia- tion in the variation of values between methods is wide, which Emissions = i j Popi , j Powerj LFi Ai EFi , j is also considered a "5" in Appendix A. (Equ uation 20) Population. Emissions are linearly related to the equip- ment population, as shown by the previously provided equa- Where tions. Populations should be determined for each type of Popi,j is the population of engines of equipment type i equipment, for each horsepower and age bin employed. Thus, within power bin j, although accurate assessment of the equipment inventory is Powerj is the average power (hp) of bin j, critical, in many cases this parameter is uncertain, particularly LFi is the load factor (fraction of available power) of equip- for projected years or streamlined methods. More discussion ment type i, has been presented under Sections 3.7.3 and 3.7.4. Note that Ai is the annual activity (hours/year) of equipment type i, population is shown as "varies" in Exhibit 3-62 because the and range of values varies too widely to be ranked, depending on EF is the emission factor (g/hp-hr). the methodology employed.
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102 Exhibit 3-60. Summary of strengths and weaknesses--NONROAD model. Criteria Strengths Weaknesses Representation of physical Dominant physical processes included processes Sensitivity to input Sensitivity to some parameters mitigated by parameters model structure (load, activity); overall sensitivity depends on the parameters Flexibility Moderately flexible; most inputs adjustable in input files Ability to incorporate effects Unclear of emission reduction strategies Representation of future Future year populations calculated in the emissions model Consideration of alternative Unclear vehicle/fuel technologies Data quality Model relies on best available information at time of development, with public review Spatial variability Applicable to domains from countywide to national Temporal variability Designed for annual inventories Review process Publicly reviewed Endorsements EPA Exhibit 3-61. Parameters. Geographic Pedigree Qualitative Quantitative Parameter Methods/Models Scale Matrix Assessment Assessment Population All All Load Factor All All Emission All All Factor Engine All All Power Activity All All Deterioration Optional and secondary: used to derive All Factor in-use emission factors Growth Optional and secondary: needed for All Factor future-year projections Engine Age Optional and secondary: needed to All determine average emission factors Median Life Optional and secondary: needed to All determine age distribution Scrappage Intermediary: derived from equipment All age and median life Duty Cycle Secondary: used to derive load and All transient adjustment factors. Use of Optional, secondary: used to calculate All Retrofit control factors on resulting emissions Devices and/or correct modeled emission factors Fuel Type Secondary: used to determine emission All factors 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.