Representing Freight in Air Quality and Greenhouse Gas Models (2010) / Chapter Skim
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From page 30... ...
. Depending on data availability and the complexity of analytical methods, emissions might be calculated separately by vehicle type or other factors that affect emission factors (e.g., average speed, road level of service)
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In a limited number of cases, fuel data are available and can be used directly in calculating CO2. For instance, for GHG inventory development, state fuel tax records are often used to estimate motor vehicle fuel consumption and CO2.
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3.1.2 Criteria Air Pollutants and Air Toxics Emissions of criteria air pollutants and air toxics are not directly proportional to fuel consumption, with emissions rates per mile being affected by vehicle emissions control technologies. Therefore, emission factors for on-road vehicles are usually presented in per mile units, and analyses of these pollutants require information on VMT and the distribution of miles by vehicle type (e.g., automobile, light-duty truck, heavyduty truck)
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Although most models include emission factors for all criteria air pollutants, the same is not true for air toxics due to a lack of data. Instead, many models estimate emissions from air toxics based on comparative ratios from criteria air pollutants.
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Analysis of Process Uncertainty. In the EPA GHG Inventory methodology, the greatest elements of uncertainty are present in the allocation of GHG emissions to the transportation sector and subsequently to individual modes.
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This database catalogs emissions from point, non-point, and mobile sources, with each transportation mode analyzed independently within the mobile analysis. Depending on the mode, emissions are determined using one of several possible methods: by applying computational models, by combining activity data with emission factors, or by scaling prior emission inventories by a growth factor.
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Structure of NEI methodology for mobile source emissions. EPA National Emissions Inventory (NEI)
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Uncertainties associated with MOBILE6 and estimation of truck VMT are discussed in Section 3.3. Uncertainty also exists in the way that NMIM aggregates emissions results from the project-level level.
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Unlike the methodologies for on-road, nonroad, and aircraft emissions, the NEI rail methodology does not rely on analytical models to calculate an emissions inventory. Instead, emissions are calculated directly from industry-wide fuel usage data, and combined with fuel-based emissions factors.
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From page 39... ...
Since the emissions inventory is created using national data, it must be distributed to the state and county level using a topdown approach. Emissions are allocated to counties based on county-level rail activity data, which is provided by the Bureau of Transportation Statistics (BTS)
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From page 40... ...
This database does not determine how many engines are on each vessel or accurately determine usage or load factors as discussed in Section 3.5.3. Category 3 data rely on foreign cargo movements and a somewhat streamlined methodology that uses detailed data from typical ports to estimate emissions at other ports.
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From page 41... ...
Sources of Uncertainty. There are sources of uncertainty in all nonroad methodologies in terms of data collection, equipment emission factors, and other factors.
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Sensitivity to input parameters Flexibility Although the NONROAD model has default equipment distributions, local agencies can submit county-specific data. Ability to incorporate effects of emission reduction strategies Does not incorporate inspection/maintenance profiles or other strategies.
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From page 43... ...
Parameters for the national inventories. Parameter Methods/Models GeographicScale Pedigree Matrix Qualitative Assessment Quantitative Assessment Fuel Supply Data GHG Inventory National Economic Sector Activity Data GHG Inventory National Modal Activity Data GHG Inventory, NEI National Fuel Carbon Content GHG Inventory National Modal Emissions Factors NEI National Marine Equipment Inventory NEI National Rail GIS Data NEI National Local Nonroad Equipment Inventory NEI National On-Road Fleet Mix NEI National Key: indicates that a parameter is analyzed in the way denoted by the column: indicates that the parameter is not discussed in the way denoted by the column.
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From page 44... ...
After collecting national fuel consumption data, the EPA methodology distributes fuel use among economic sectors, to determine the GHG emissions attributable to each sector. As part of this step, EPA reconciles the results of a top-down approach, based on EIA data, with the results of a bottom-up approach, based on industry activity measurements.
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From page 45... ...
Although the estimation of truck emissions is conceptually simple (i.e., emissions are the product of freight activity and emission factors) , the analytical procedures for emission estimation can be quite complex depending on the goals of the analysis and the level of data and resource availability.
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From page 46... ...
can be overridden by local estimates. Ability to incorporate effects of emission reduction strategies Model is able to capture the effects of strategies that change truck VMT, vehicle average speed (for HC, CO, NOx)
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From page 47... ...
The default parameters for truck age distribution and mileage accumulation are important to the extent that emission factors vary by truck model year. Exhibit 3-16 illustrates the relative difference in emission factors of CO2, NOx, CO, HC, and PM10 relative to a 1981 HDV8b truck.
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(60) Because the trip length can be quite different for different truck trips, the inability to customize start emissions can add uncertainty to emission rates.
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Ability to incorporate effects of emission reduction strategies Model is able to capture the effects of strategies that change truck VMT, vehicle average speed (for HC, CO, NOx) , fleet average age.
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(63) The primary data source was a 1999 Caltrans heavy-duty truck survey, which was used to estimate the fraction of heavy-duty truck VMT traveled in each county in California, as well as mileage accumulation rates and truck age distribution.
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The truck VMT in each area was calculated by taking the product of the registered truck population (by model year) , out of state fraction, and accumulation mileage rates.
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Emission factors based on actual in-use emissions MOBILE6 uses engine certification data while MOVES uses second-by second vehicle emission rates. Extended idling Cold starts Vehicle Weight Ability to consider different HDT categories Because MOVES classifies heavy-duty vehicles based on how VMT/fuel data are reported, it provides fewer HDT categories than MOBILE does.
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From page 53... ...
The emission factors in MOVES rely upon second-by-second emission data, which allows a much broader range of data to be used in the development of emission rates. Emissions data were compiled from previous EPA test programs and from several external sources, including the Coordinating Research Council (CRC)
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From page 54... ...
Representation of future emissions Modeler needs to know exactly the effects of future scenarios on all input parameters to the model. Consideration of alternative vehicle/fuel technologies Spatial variability Because CMEM is a micro-scale emissions model, it is well set up to capture the variability of emissions based on local conditions (e.g., road grade, pavement quality, ambient temperature)
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From page 55... ...
The analysis of process uncertainty of this regional method is captured within the discussion of parameter uncertainty, including the following: • Estimation of truck VMT by travel demand models; • Use of average speed information; • Use of emission factors; because the emission factors are estimated with either EMFAC (for California) or MOBILE (for the remaining states)
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From page 56... ...
Model sensitivity to input parameters The level of detail associated with truck travel activity from travel demand models is not commensurate with the level of detail required by emissions models. Ability to incorporate effects of emission reduction strategies Model is able to capture the effects of strategies that change truck VMT, vehicle average speed (for HC, CO, NOx)
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Some project-level analyses estimate truck VMT based on regional travel demand models, whose uncertainties are discussed in the regional method. However, many local/project level analyses rely on projectspecific data, which is more accurate than data estimated by models.
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In some cases, the model estimates truck trips independent of passenger vehicle trips (i.e., independent truck trip generation and trip distribution modules)
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From page 59... ...
Parameter Methods/Models GeographicScale Pedigree Matrix Qualitative Assessment Quantitative Assessment Truck VMT All All VMT Share by Truck Type All All VMT Share by Time of Day All Regional/Local Truck Age Distribution All All Mileage Accumulation All All Distribution of Emission Control Technology All All Truck Fuel Type Distribution All All Average Speed MOBILE6, EMFAC2007 Regional/Local Driving Cycles CMEM Local Emission Factors All All Classification of Truck Types All All Road Grade CMEM Local Empty Miles All All Key: indicates that a parameter is analyzed in the way denoted by the column: indicates that the parameter is not discussed in the way denoted by the column. Exhibit 3-24.
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Truck trip generation data are used to estimate truck traffic patterns and, consequently, truck VMT. A previous NCHRP report summarized the current state of practice on the development of truck trip generation data.
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Because there is a wide variation in VMT distribution across vehicle categories, the use of national average travel fractions to apportion VMT to specific vehicle categories is certainly a weak method that could add significant uncertainty to emissions estimates. Truck VMT Share by Time of Day The estimation of truck VMT by time of day is important for emission analysis because average speed and congestion levels, which can be important inputs, can be very different in peak versus off-peak periods.
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From page 62... ...
Modal emissions models such as MOVES and CMEM also rely on prespecified driving cycles, but the development of emission factors does not depend on speed correction factors, but on a combination of vehicle-specific power and instantaneous speed. The use of different driving cycles also can reduce the uncertainty associated with the development of emission factors.
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From page 63... ...
National CAP and toxics Line-Haul Emissions by Traffic Density Regional/Local All Line-Haul Emissions by Active Track Regional/Local All Switch Emissions by Number of Switchers or Hours Regional/Local All Line-Haul/Switch Emissions by Employees Regional/Local All Line-Haul/Switch Emissions by Time in Mode Local All Line-Haul Emissions at Marine Terminals Local All
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, previously described in Section 3.2.3, EIA's estimates of national rail fuel consumption are multiplied by EPA's national locomotive emission factors.
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Switch yard locomotive fuel use is then calculated by applying a fuel consumption rate to the number of switch yard locomotives, assuming an average locomotive duty cycle. Fuel use estimates are summed, and emission factors (in grams/gallon)
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Because line-haul emission factors can be expressed in terms of horsepower-hour, rail activity can be calculated in the same unit, as shown in Equation 5. In a detailed inventory, all inputs to this equation are obtained from the participating railroads, which otherwise need to be estimated.
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From page 67... ...
Ability to incorporate effects of emission reduction strategies Because key input parameters are captured in this method, it is generally possible to analyze the effects of emission reduction strategies. Representation of future emissions Consideration of alternative vehicle/fuel technologies This method can capture the effects of the use of hybrid switch locomotives.
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From page 68... ...
Regional/Local Emission Factors All All Locomotive Type All (explicit in local) All Locomotive Tier Distribution All All Empty Miles All Local Traffic Density Emissions by Traffic Density Regional/Local Miles of Active Track Emissions by Active Track Regional/Local Number of Switch Locomotives Emissions by Switchers Regional/Local Hours by Switch Locomotive Emissions by Hours Regional/Local Number of employees Emissions by Employees Regional/Local Key: indicates that a parameter is analyzed in the way denoted by the column: indicates that the parameter is not discussed in the way denoted by the column.
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From page 69... ...
which includes new emission standards for Tier III and Tier IV locomotives. The RIA documentation also provides baseline emission rates for NOx, PM, HC, and CO in 2008, which are based on average duty 69 Parameter Im pa ct on R es ult Ac qu isi tio n M eth od In de pe nd en ce Re pr es en tat ive ne ss Te m po ra l C or re lat ion Ge og ra ph ic Co rre lat ion Te ch no lo gic al Co rre lat ion Ra ng e o f V ar iat ion Fuel Consumption 5 1 3 1 1 1 5 Locomotive Duty Cycles 4 Varies 3-4 Varies 4 5 5 Emission Factors 5 2 3 3-4 4 5 5 Locomotive Type 3 Varies 4 3-4 4 5 4 Locomotive Tier Distribution 4 Varies 4 Varies 1 5 3 Equipment Type 4 1 4 Varies 1 Varies 4 Empty Miles 3 4 3 1 1 2 5 Traffic Density 5 1-2 3-4 1 1 1 Varies Miles of Active Track 5 1 3 1 1 1 1 Number of Switch Locomotives 5 1 4 1 1 1 1 Hours by Switch Locomotive 5 2-3 4 1 1 1 3 Number of employees 5 1 3 1 1 1 1 Exhibit 3-31.
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From page 70... ...
This was based upon engines using 500 ppm sulfur diesel fuel and may be different for engines using higher sulfur content. PM10 emission factors reflect the emission rates expected from locomotives operating on fuel with sulfur levels at 3,000 ppm, so it is important that regional and local analyses obtain information about the sulfur content of diesel fuel used in locomotives.
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From page 71... ...
These differences are not an issue for analyses where fuel consumption data can be obtained directly. However, for those analyses where fuel use is estimated based on activity data rather than fuel consumption data, variations in locomotive type can increase the difference between actual and modeled emission factors.
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From page 72... ...
Detailed Methodology In the detailed methodology, emissions from OGVs are estimated from detailed information on ship calls at a given port together with detailed ship characteristics, time and speed in each mode, load factors, and emission factors. The more detailed the information collected, the more accurate the results.
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2008 2006 Detailed NOx, TOG, CO, PM10, PM2.5,SO2, DPM Starcrest Notes: * Starcrest = Starcrest Consulting Group LLC, ACES = Air Consulting and Engineering Solutions; Environ = Environ International Corp.
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(83) Auxiliary load factors are specified by ship type and time in mode.
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During hotelling, the main propulsion engines are off, and only the auxiliary engines are operating, unless the ship is cold ironing. Hotelling times also can be determined from pilot records of vessel arrival and departure times when other data are not available.
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From page 76... ...
. Load factors and emission factors for each ship type and DWT range can be calculated using a method similar to that in the detailed methodology.
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Sensitivity to input parameters Strength: Method relies on detailed user inputs that may not be readily available, but should produce best results Weakness: General, overall uncertainty unknown Weakness: Method relies on surrogates for missing inputs; results highly sensitive to quality of inputs Flexibility Weakness: Requires detailed data collection Strength: Customizable to data limitations Ability to incorporate effects of emission reduction strategies Strength: Best information available; effects may be included in use of different EFs Strength: Highly customizable Representation of future emissions Strength: Projections available in the model and customizable to local information Consideration of alternative vehicle/fuel technologies Strength: May be achieved in methodology by using appropriate EFs Weakness: Does not consider alternative vehicle/fuel technologies Data quality Strength: Structured from best available information Weakness: Structured from available information Spatial variability Strength: Applicable to any location, but data requirements likely limit to smaller spatial scales Strength: Applicable to any location; data flexibility allows multiple spatial scales Temporal variability Weakness: Most likely limited to annual inventories Strength: Designed for annual inventories, but scalable with appropriate information Review process Strength: Documented in EPA Methodology Guidance Endorsements Strength: EPA endorsed Exhibit 3-40. Summary of strengths and weaknesses -- comparison among methodologies.
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From page 78... ...
78 Parameter Methods/Models GeographicScale Pedigree Matrix Qualitative Assessment Quantitative Assessment Calls All All Engine Power Detailed and mid-tier All Load Factor Detailed and mid-tier All Activity Detailed All Emission Factors Detailed All Port Selection Mid-tier and streamlined All Fuel Type Secondary; used to determine emission factors All Growth Factor Optional and secondary; needed for future year projections All Engine Age Distribution Optional and secondary; needed to determine average emission factors All Key: indicates that a parameter is analyzed in the way denoted by the column: indicates that the parameter is not discussed in the way denoted by the column. Exhibit 3-41.
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As more ports prepare detailed inventories, this list should be expanded. 79 Parameter Im pa ct on R es ult Ac qu isi tio n M eth od In de pe nd en ce Re pr es en tat ive ne ss Te m po ra l C or re lat ion Ge og ra ph ic Co rre lat ion Te ch no lo gic al Co rre lat ion Ra ng e o f V ar iat ion Calls 4 1-2 1-2 1-2 1 1 Varies 3 Engine Power 4 2 1 1 1 Varies 2 2 Load Factor 4 3-4 3 2 1 Varies 3 4 Activity 4 2-4 3 3 1 Varies 1 3 Emission Factors 4 2-3 1-2 4-5 3 Varies 3 4 Port Selection 4 4 3 N/A N/A Varies N/A N/A Exhibit 3-42.
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From page 80... ...
Where the sum is over the population of all main and auxiliary engines active in the fleet and the input parameters are as follow: EF = the emission factor for a given pollutant species and engine, HP = the engine horsepower, LF = the load factor, A = the annual activity, CF = the appropriate emission control factor. Any deterioration, low-load, transient, or other adjustment effects (if able to be characterized)
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From page 81... ...
. Average rated power, load factor, and activity parameters are assumed constant across all simulation years but populations and emission factors were considered to vary by year and age.
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From page 82... ...
82 Criteria Strengths Weaknesses Representation of physical processes Overall average emissions processes included from all Category 1 and 2 H/C Variety of methods used to account for different input data Sensitivity to input parameters Method relies on documented inputs and discusses necessary choices Some inputs show significant differences from other studies; resulting overall uncertainty uncharacterized Flexibility Tailored methodology Not directly applicable to H/C subcategories or smaller spatial domains Ability to incorporate effects of emission reduction strategies Designed to model effects of future regulations Representation of future emissions Designed to model effects of future regulations Consideration of alternative vehicle/fuel technologies Data quality Information included and documented from testing and other authorities. Unknown uncertainty or bias Spatial variability No spatial analysis included Temporal variability Produces only annual inventories Review process Unclear from documentation Endorsements EPA Exhibit 3-45.
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From page 83... ...
Although this does not affect the total number of vessels directly, it does affect the total emissions as emission factors, load factors, activity, and other parameters are dictated by the type of main engines equipped on the vessels. Also, the subdivision of values based on power, engine displacement, power density, and age is complex, although no known bias results from this method.
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From page 84... ...
emission factors; • 2000 and later model-year engines use the smaller of EPA emission standards for marine engines or the NOx limits of the IMO MARPOL Annex VI; and • OFFROAD model emission factors were adjusted to reflect an E3 test cycle for main engines and D2 test cycle for auxiliary engines. Uncertainty in this approach is due primarily to the choices made in the method, but also to underlying uncertainty in the emission factors of the OFFROAD model and baseline EPA emission factors, as well as in duty cycle characterizations.
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From page 85... ...
These are listed in Exhibit 3-47. 85 Criteria Strengths Weaknesses Representation of physical processes Overall average physical processes included Sensitivity to input parameters Method relies on best available inputs Method relies on OFFROAD model; uncharacterized overall uncertainty Flexibility Tailored methodology Ability to incorporate effects of emission reduction strategies Not included in base methodology, but could be applied if information provided Representation of future emissions Method projects populations and associated factors Consideration of alternative vehicle/fuel technologies Fuel effects included No apparent treatment for alternative fuels or technologies Data quality Information included from survey of fleet Unknown uncertainties from extrapolation scheme Spatial variability Emissions allocated to county and air basin, but not more finely Underlying data applicable only to CA Temporal variability Only produces annual inventories Review process Available for public review as part of rulemaking Endorsements ARB Exhibit 3-46.
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From page 86... ...
The similarity of these studies is driven both by the trend to similar methodologies and by the fact that the majority of studies are made by the same contractor. They are also very similar to the EPA RIA methodology or the CARB H/C methodology, albeit with a more limited spatial scope, where variation is made for the amount of information available and the portion of the fleet considered and a method that is similar to that of NONROAD or OFFROAD models.
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From page 87... ...
(134) In many cases, reliance on median life, growth, and scrappage will be taken from other studies and age distributions will be calculated for each vessel and engine type from the baseline year.
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From page 88... ...
Other parameters are as shown in the list of variables for Equation 14. Transient adjustment and deterioration factors also may be considered and included in the emission factors parameterization for each engine.
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From page 89... ...
Flexibility Extremely flexible -- fluid method structure allows variation for available inputs and surrogates Ability to incorporate effects of emission reduction strategies Straightforward to include effects in calculations if use and effectiveness is known Representation of future emissions Future-year populations calculated may be projected if growth factors are known Consideration of alternative vehicle/fuel technologies Alternative technologies may be included by 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 Exhibit 3-48. Summary of strengths and weaknesses -- local H/C methodology.
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From page 90... ...
Generally, CHE emissions from freight activities at ports are estimated using either the NONROAD or OFFROAD 90 Parameter Methods/Models Geographi c Scale Pedigree Matrix Qualitative Assessment Quantitative Assessment Main Engine Population EPA RIA method, CARB H/C method National, Regional Auxiliary Engine Population EPA RIA method, CARB H/C method National, Regional Harbor Craft Population Secondary: used to derive engine populations in local H/C method Local Number of Engines per Vessel Secondary: used to derive engine populations in local H/C method Local Load Factors All All Emission Factor All All Engine Power All All Activity All All Deterioration Factor Optional and secondary: used to derive in- use emission factors. All Growth Factor Optional and secondary: needed for future- year projections All Engine Age Optional and secondary: needed to determine average emission factors All Median Life Optional and secondary: needed to determine age distribution All Scrappage Intermediary: deriv ed from equipment age and median life All Duty Cycle Secondary: used to derive load and transient adjustment factors All Use of Retrofit Devices Optional, secondary.
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From page 91... ...
Where f is the emission factor, P is the maximum rated equipment horsepower, L is the load factor, A is the annual activity, N is the equipment population, and In which f incorporates adjustments due to deterioration, transient use, and age-related effects. Uncertainty in the resulting CHE emissions can then be attributed to either the process uncertainty (that is, the degree to which Equation 15 -- or other OFFROAD algorithms -- represents the actual emissions process)
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From page 92... ...
All Freight Notes: * Model use is restricted to countywide definitions, but emission factors and methods may be extracted at scales down to equipment level.
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From page 93... ...
Equipment Groupings The CARB CHE methodology states its choice to group equipment into eight categories (listed above) to make the analysis compatible with the OFFROAD model.
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Emissions f N P L A (Equation 17) = 94 Criteria Strengths Weaknesses Representation of physical processes Dominant physical processes included Sensitivity to input parameters Method relies on well studied model inputs, and modifies when necessary Uncharacterized overall uncertainty Flexibility Tailored methodology Ability to incorporate effects of emission reduction strategies Information included from local authorities on reduction strategies implemented Representation of future emissions Method relies on well studied model inputs Consideration of alternative vehicle/fuel technologies Information included from local authorities on 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 Exhibit 3-54.
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From page 95... ...
Equipment-specific emission rates are based on the combination of engine emission factors and equipment duty cycles. Deterioration rates are generally based on on-road emissions data.
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From page 96... ...
2008 2006 NO x , TOG, CO, PM 10 , PM 2.5 , SO 2 , DPM Starcrest Notes: * Starcrest = Starcrest Consulting Group LLC, Environ = Environ International Corp.
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From page 97... ...
Specific discussion of uncertainty with input parameters for the OFFROAD model is given above; discussion of the NONROAD model is below. However, process uncertainty is associated with the assignment of average parameters to bins.
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From page 98... ...
. NONROAD2008 also includes emission reductions 98 Criteria Strengths Weaknesses Representation of physical processes Dominant physical processes included Sensitivity to input parameters Method relies on detailed user inputs that may not be readily available, but should produce best results General, overall uncertainty unknown Flexibility Low Flexibility; requires detailed data collection Ability to incorporate effects of emission reduction strategies Best information available; effects may be included after model runs Representation of future emissions Projections available in the model and customizable to local information Consideration of alternative vehicle/fuel technologies May be achieved in methodology with suitable 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 Exhibit 3-57.
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From page 99... ...
Any event that leads to a difference in real world age distribution from that assumed by the model will lead to different average emission factors, and thus different emissions. This bias could result from a mischaracterization of equipment median life or growth rates, both of which shift the overall curve of population versus age.
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From page 100... ...
Uncertainty due to assignment of measured emission factors to equipment groups is unknown. However, the assumed load factor is likely to be a significant source of uncertainty in NONROAD modeling, both in directly calculating equipment emissions and in determining population age distribution.
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From page 101... ...
TAFs in NONROAD were calculated by averaging tests for each engine, pollutant, and test cycle, and comparing these measured emission factors for off-road equipment duty cycles to the zero-hour steady state emission factors. Thus, in-use emission factors should have reduced uncertainty relative to using zero-hour steady state emission rates as emission factors.
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From page 102... ...
Summary of strengths and weaknesses -- NONROAD model. Parameter Methods/Models GeographicScale Pedigree Matrix Qualitative Assessment Quantitative Assessment Population All All Load Factor All All Emission Factor All All Engine Power All All Activity All All Deterioration Factor Optional and secondary: used to derive in-use emission factors All Growth Factor Optional and secondary: needed for future-year projections All Engine Age Optional and secondary: needed to determine average emission factors All Median Life Optional and secondary: needed to determine age distribution All Scrappage Intermediary: derived from equipment age and median life All Duty Cycle Secondary: used to derive load and transient adjustment factors.
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From page 103... ...
As for other factors used to calculate emissions, the result is linearly proportional to this value, thus the impact of uncertainty in this parameter on that for the final calculations can be significant. Emission factors are determined from a range of activities, including measurements, certification databases, and engineering judgment.
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From page 104... ...
This version of SAGE dynamically models aircraft performance, fuel consumption, and emissions, and includes such factors as capacity and delay at airports. The model does not have the current capability to separate freight-only travel from freight and passenger operations nor does the model include military air cargo activity.
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From page 105... ...
Analysis of Process Uncertainty. To estimate emissions, SAGE uses Boeing Fuel Flow Method 2 (BFFM2)
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From page 106... ...
Model sensitivity to input parameters No formal sensitivity analysis has been conducted with the model but the model is highly dependent upon the emission indices, which are a function of the fuel burn which, in turn, are sensitive to aerodynamic and engine performance, aircraft take-off weight, and flight speed. Individual flights not using winds aloft information also use standard day ambient temperature.
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From page 107... ...
• Motor vehicle activity can be incorporated into the model using information on the number of vehicle trips and average speed while traveling on roadways with emission factors based on EPA's MOBILE 6.2. National default age distribution can use a base year assignment up to 2025.
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From page 108... ...
Model sensitivity to input parameters Aircraft emissions in EDMS are dependent upon two main parameters: the emission factors obtained from the aircraft/engine combination and the vertical flight profile. Within the flight profile the least well-established parameter is the TIM.
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From page 109... ...
Most commonly applied air quality model formulations are deterministic and include Gaussian plume, puff, and box models. These models approximate the physical (e.g., transport, dispersion, and removal)
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From page 110... ...
in each grid cell during a time step. However, this approach is physically limited for small spatial scale applications due to artificial dilution of emissions, unrealistic near-source concentrations, and spatially unresolved receptors for sizes smaller than an individual grid cell.
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From page 111... ...
Dispersion Modeling Methodology Dispersion models simulate the effects of atmospheric turbulence, mixing depth, and wind flow that drives the advection and diffusion of pollutants following their release into the atmosphere. Dispersion models simulate these processes as either a straight line Gaussian plume or as an advecting puff.
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From page 112... ...
resolution Sensitivity to input parameters A number of parameters may affect model results Highly susceptible to uncertain, complex inputs Flexibility Ability to incorporate effects of emission reduction strategies Yields most realistic air quality impacts since model explicitly treats nearly all of the important chemical and dispersion processes Indirect: incorporated via emission characterization Representation of future emissions Incorporated via emission characterization. Consideration of alternative vehicle/fuel technologies Indirect: incorporated via emission characterization Data quality Models are typically verified against observed data for some air pollutants, lending confidence to other air concentration predictions Relies on numerous inputs of varying quality and uncertainty Spatial variability Generally limited to grid cell resolution (typically 2 km or more)
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From page 113... ...
They could be used to represent exhaust stacks of hotelling vessels, for example. Input parameters required include location, instantaneous or average emission rate, release height, exit temperature, exit velocity, and stack inside diameter (or flow rate)
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From page 114... ...
time for chemical transformation reactions, settling, 114 Criteria Strengths Weaknesses Representation of physical processes Dominant processes generally parameterized, as long as operated within model limitations (e.g., spatial scale) Model formulations are generally simplistic Sensitivity to input parameters Generally rely on readily available inputs Susceptible to uncertain inputs Flexibility Generally adaptable to a variety of scenarios and available information Gaussian plume models operate on an underlying assumption of a steady-state Ability to incorporate effects of emission reduction strategies Indirect: incorporated via emission characterization Representation of future emissions Indirect: incorporated via emission characterization Consideration of alternative vehicle/fuel technologies Indirect: incorporated via emission characterization Data quality Varies: relies on input data quality and model formulations; is particularly susceptible to inappropriate model choice or input variables Spatial variability Can model concentrations in close proximity to source and with arbitrarily high spatial resolution Gaussian plume model formulations may not represent variability well in complex terrain or wind flow regimes Temporal variability Limited only by input data resolution Review process Models and methods continuously updated and expanded.
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From page 115... ...
115 Parameter Im pa ct on R es ult Ac qu isi tio n M eth od In de pe nd en ce Re pr es en tat ive ne ss Te m po ra l C or re lat ion Ge og ra ph ic Co rre lat ion Te ch no lo gic al Co rre lat ion Ra ng e o f V ar iat ion Emission rate 4 Varies Varies Varies N/A N/A Varies 5 Source location 3 Varies Varies Varies N/A N/A Varies 5 Emission temporal profile 3 Varies Varies Varies N/A N/A Varies 5 Release height 3 Varies Varies Varies N/A N/A Varies 5 Initial plume size and shape 3 Varies Varies Varies N/A N/A Varies 5 Source orientation, size, and shape 2 Varies Varies Varies N/A N/A Varies 5 Exhaust temperature and other buoyancy parameters 3 Varies Varies Varies N/A N/A Varies 5 Exhibit 3-71. Pedigree matrix -- harbor craft equipment parameters.
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