Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports (2012)

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Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 36 CHAPTER 6: RESULTS This chapter presents and discusses the results of the base case and alternative fuel scenarios PM2.5 Table 7 emissions inventories and air quality impact analyses for the five case study airports. Emissions results for the base case are reported in kilograms rather than tons for consistency and comparison with smaller source specific values (although results are also shown in tons in for information purposes). In terms of ambient air pollution, the results were calculated for averaging periods that reflect the National Ambient Air Quality Standards (NAAQS) for PM2.5 (i.e., the annual average and the 98th percentile of the 24-hour average). The alternative fuel scenarios results are based on the relative change on a number of key indices representing the impact that each alternative fuel and source combination would have on PM2.5 emissions and local PM2.5 BASE CASE pollutant concentrations at each case study airport. Appendix E provides additional details related to the emissions inventories and air quality impact at the five case study airports for all scenarios. The purpose of the base case was to have a foundation against which to determine the benefits of the alternative fuels. However, the base case also provides valuable information that may assist airports with focusing their particulate matter emissions efforts. The overall EDMS-generated results of the PM2.5 Table 7 emissions inventory for the five case study airports (in kilograms per year) are presented in and Figure 7 by emission source category. The PM2.5 emissions inventories developed for the five case study airports indicate that aircraft (taxi, approach, takeoff and climb-out) contribute the greatest percentage of PM2.5 emissions with GSE, APUs and road vehicle sources (on-airport and off-airport roadways, curbsides, and parking facilities) individually contributing to a much smaller extent generally for the case study airports. Stationary sources (e.g., boilers, generators, and fire training) generally contribute only a very small percentage to total airport PM2.5 Aircraft-related emissions are largely a function of the type and size of the aircraft, the airfield taxi and delay times, and meteorological conditions. For example, the larger airports tend to operate larger aircraft and experience greater ground-based taxi times than the smaller airports. GSE emissions are mostly a function of the types of equipment, fuel type used, engine size, age of equipment and operating time. PM emissions. 2.5 APU emissions are a function of the number of aircraft that operate APUs and the duration of APU use. Thus, when comparing airports, it is important to note whether gate power and pre- conditioned air are present and how many gates are equipped, as such infrastructure services can substantially reduce APU operating times. The larger airports tend to have gate power and pre- conditioned air at the terminals, while smaller airports typically have fewer of these gate facilities, use diesel powered ground power units, or have more aircraft that do not use APUs. emissions tend to be higher for diesel-fueled GSE than those for gasoline-fueled equivalents. Road vehicle emissions are a function of the traffic volumes, travel distances, and emission factors, which are dependent on regional emissions controls, vehicle speed and meteorological

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 37 conditions. For this analysis, roadway emissions are split into two geographic distinctions— on-airport and off-airport. Off-airport roadways may, in some cases, include elements of local traffic not related to the airport. Tab le 7 – Annual PM2.5 Source Category Emis s ions Inven tory b y So urce Category (kg un les s s pec ified) ATL LAS PHL SAN MHT Aircraft 32,157 17,604 16,647 7,596 1,853 Ground support equipment 9,829 4,114 14,940 (a) 582 945 Auxiliary power units 12,617 3,800 3,802 2,730 425 Parking facilities 304 86 333 243 11 On-airport roadways 1,798 302 1,212 163 85 Stationary sources 448 5,459 392 588 66 Training fires — — 2,819 — — On-airport total 57,154 31,366 40,145 11,903 3,385 Off-airport roadways 21,766 3,073 12,221 2,026 41 Grand totals 78,920 34,440 52,366 13,928 3,426 Grand totals (tons) 87 38 58 15 4 Aircraft landing/takeoff cycles 489,100 304,386 237,238 109,947 45,836 Passengers (1,000s) 78,125 42,133 24,507 17,759 1,948 Taxi-out time (minutes) 20.7 16.7 21.8 13.6 13.6 Taxi-in time (minutes) 10.8 6.54 6.70 3.78 4.50 Source: ATL (2010), LAS (2010), LAS (2011), PHL (2010), SAN (2009), MHT (2009), Bureau of Transportation Statistics, and FAA Aviation System Performance Metrics Database. A dash (–) indicates that no data were available for these sources (a) The PHL analysis year was 2004 and included a disproportionate amount of diesel GSE compared to other airports, since 2004 PHL has implemented a number of alternative-fueled GSE replacements, and, therefore, the GSE analysis is not a true reflection of PHL in recent years. Using the data shown in Table 7, aircraft represent between 41% and 63% of the total on-airport- related PM2.5 emissions, while GSE represents between 5% and 37%. APUs represent between 9% and 22%, and road vehicles represent between 1% and 5% of the on-airport total PM2.5 Table 7 emissions. also provides information on the number of aircraft landing and takeoff (LTO) cycles, passengers, and average taxi times as a means of giving the emissions totals a perspective. The emission results highlight the differences between unique airports. For instance, the original data collected from LAS for use in the ACRP 02-23 project included re-suspended dust from roadways, which was not the focus of study at any of the other airports. Therefore, the roadway emissions reported in the ACRP 02-23 project for LAS do not include re-suspended roadway dust to enable a fair comparison to be made. Apart from LAS, which has a number of on-airport boilers and cooling towers, and PHL, which includes a fire training facility, stationary sources comprise a small percentage of the total on-airport-related PM2.5 emissions.

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 38 Figure 7 – On-Airport Ann ual PM2.5 (a) The PHL analysis year was 2004 and included a disproportionate amount of diesel GSE compared to other airports, since 2004 PHL has implemented a number of alternative-fueled GSE replacements, and, therefore, the GSE analysis is not a true reflection of PHL in recent years. Emis s ions Inven tory b y Source Category (kg) ALTERNATIVE FUEL SCENARIOS The following section summarizes the alternative fuel scenarios for the EDMS-generated results for each isolated scenario, in terms of percentage reductions for the annual average and 24-hour 98th percentile, for the following key indices: • The total on-airport emissions. • • The maximum distance from the airport to a threshold airport impact concentration level, termed the Radius of Influence (ROI). The ROI is defined as the distance that extends from the source (in this case, the airport reference point) to the farthest receptor distance at which the source has a concentration greater than a specific threshold for a given pollutant. The threshold level for the annual average is 0.3 µg/m The airport impact concentration at the location of the maximum airport impact concentration in the base case. 3 and for the 24-hour 98th percentile it is 1.2µg/m3 • The area in which the air quality impact from the airport is below the threshold level, referred to as the influence area. The threshold level for the annual average is 0.3 µg/m . 3 and for the 24-hour 98th percentile it is 1.2µg/m3 Appendix E includes more detailed tabulated results for the annual average and the 24-hour 98th percentile. . The EDMS-generated results do not typically include results for piston-engine and turboprop aircraft. The emission results for these two generic aircraft types are discussed separately at the end of this chapter.

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 39 The percentage change was only calculated for on-airport roadways and parking lots due to lack of data availability to separate off-airport roadways (as discussed in Chapter 5). In addition, airport operators have some control over on-airport road vehicles but no control over off-airport road vehicle emissions. However, many of the vehicles traveling on on-airport roadways will also be traveling on off-airport roadways. Therefore, the percentage change would actually be larger than summarized below. Summary Results Figure 8 to Figure 14 provide the average and range of percentage reduction (alternative fuel scenarios relative to the base case) based on data from the five case study airports. Results at other airports would be expected to generally fall within these ranges, but may be smaller or larger depending on specific conditions (e.g., aircraft fleet mix, GSE fuel mix, level of operations) at the airport. Emissions In terms of the airport-related emissions, Figure 8 shows that the largest total on-airport emission reductions are provided by the following (listed in descending order): • 100% of aircraft and APU use drop-in fuels (i.e., 50% blends of FT jet fuels from either coal or gas). • Replacing a 100% of GSE with available electric, LPG or CNG equivalents, especially diesel-fueled GSE. • Replacing 100% of diesel with B100 in GSE (though it should be noted that this could have implications for GSE in terms of warranty). • Reducing APU use by providing electrical ground power and pre-conditioned air at 100% of gates. The aircraft emission reductions occur throughout the LTO cycle, but the greatest reductions are during takeoff as that is the operating mode which creates the greatest emissions. The alternative fuel scenario results are dependent on the aircraft fleet mix (i.e., the proportion of jet-fueled aircraft) at each airport. The range of emission reductions for APU reduced use is dependent on the number of operations that have access to gate power and pre-conditioned air, the assumed APU operating times (which in the ACRP 02-23 project were up to 26 minutes), amount of gate delay, and the aircraft fleet mix (i.e., whether or not aircraft have an APU). The emission reductions for the electric, LPG and CNG GSE scenarios are a function of the current fuel mix for the existing GSE and whether the existing types of GSE can be replaced with an alternative-fueled equivalent. Of note, depending on the assumed base case emissions, replacing gasoline GSE with CNG equivalents may result in an increase in emissions. This result is primarily a function of the uncertainty of the emission factors used and does not necessarily mean that the emissions would actually increase. Road vehicle alternative fuel scenarios provide a smaller reduction in overall airport emissions than those for drop-in aircraft and APU fuels, reducing APU use and some GSE alternative fuel scenarios.

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 40 Maximum Concentration In terms of location of the maximum annual average and 24-hour 98th percentile airport impact concentrations, Figure 9 and Figure 10 show that the largest reductions are provided by the following (listed in descending order): • Replacing a 100% of GSE with available electric, LPG or CNG equivalents, especially diesel-fueled GSE. • 100% of aircraft and APU use drop-in fuels (i.e., 50% blend of FT jet fuels from either coal or gas). • Replacing 100% of diesel with B100 in GSE (though it should be noted that this could have implications for GSE in terms of warranty). • Reducing APU use by providing electrical ground power and pre-conditioned air at 100% of gates. Given the nature of the aircraft emissions (i.e., spread out through the airport along taxiways and within the rest of the LTO cycle) and their proximity to the location of maximum concentration receptors (i.e., aircraft tend not to be a large contributor to maximum impact), the benefits of alternative fuel scenarios on reductions of the maximum concentration are not as great as the reductions in overall emissions for aircraft. The maximum concentrations are typically located where there is a large contribution from GSE and APU activities (i.e., near to gates); therefore, it is the GSE and APU scenarios that have the greatest reduction in maximum concentrations. The concentration reductions for other scenarios are relatively small, with the exception of replacing all on-airport road vehicles with an electric equivalent, which is a hypothetical scenario and unlikely to be achieved. It should be noted that the location of the largest maximum concentration is likely to change for a given alternative fuel scenario. Therefore, the Radius of Influence (ROI) and influence area results are better indications of the overall impact the different scenarios will have on local air quality. Radius of Influence The maximum distance from the airport to a threshold airport impact concentration level is referred to as the Radius of Influence (ROI). The threshold level for the annual average is 0.3 µg/m3 and for the 24-hour 98th percentile it is 1.2µg/m3 Figure 11 . shows that the largest reductions in the annual ROI are provided by the following (listed in descending order): • 100% of aircraft and APU use drop-in fuels (i.e., 50% blend of FT jet fuels from either coal or gas). • Replacing a 100% of GSE with available electric equivalents. • Reducing APU use by providing electrical ground power and pre-conditioned air at 100% of gates. • Replacing a 100% of GSE with available LPG or CNG equivalents, especially-diesel- fueled GSE.

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 41 • Replacing 100% of diesel with B100 in GSE (though it should be noted that this could have implications for GSE in terms of warranty). Figure 12 shows that the largest reductions in the 24-hour 98th percentile ROI are provided by the following (listed in descending order): • Replacing a 100% of GSE with available electric equivalents. • 100% of aircraft and APU use drop-in fuels (i.e., 50% blend of FT jet fuels from either coal or gas). • Replacing a 100% of GSE with available LPG or CNG equivalents, especially diesel- fueled GSE. • Replacing 100% of diesel with B100 in GSE (though it should be noted that this could have implications for GSE in terms of warranty). • Reducing APU use by providing electrical ground power and pre-conditioned air at 100% of gates. Similar to the maximum concentration, the reductions in ROI are not as great as the reduction in emissions. However, although GSE emissions contribute a large portion of the overall maximum concentration from all airport operations, they tend to influence a small area. Thus, GSE alternative fuel scenarios have a larger reduction for the maximum concentrations than for the associated ROI or influence area. As with the maximum concentrations, road vehicle alternative fuel scenarios show a smaller reduction in the ROI than those for alternative fuel scenarios for aircraft and APUs and some GSE alternative fuel scenarios. Influence Area The area in which the air quality impact from the airport is below the threshold level is referred to as the influence area. The threshold level for the annual average is 0.3 µg/m3 and for the 24- hour 98th percentile it is 1.2µg/m3 Figure 13 . and Figure 14 show that the largest reductions in both the annual average and the 24- hour 98th percentile influence area are provided by the following (listed in descending order): • Replacing a 100% of GSE with available electric equivalents. • 100% of aircraft and APU use drop-in fuels (i.e., 50% blend of FT jet fuels from either coal or gas). • Replacing a 100% of GSE with available LPG or CNG equivalents, especially diesel- fueled GSE. • Reducing APU use by providing electrical ground power and pre-conditioned air at 100% of gates. • Replacing 100% of diesel with B100 in GSE (though it should be noted that this could have implications for GSE in terms of warranty). The maximum concentration and ROI are statistics that provide a limited focus spatially (e.g., at one receptor point for the maximum) and the influence area measures the overall impact taking into account the spatial variations in emissions. Therefore, results generally show a greater percentage reduction in influence area for aircraft-related scenarios than that for the maximum

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 42 concentration and ROI due to the spatial spread of the aircraft emissions. As with the ROI, road vehicle alternative fuel scenarios show a smaller reduction of influence area than those for alternative fuel scenarios for aircraft and APUs and some GSE alternative fuel scenarios.

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 43 Figure 8 – Alte rna tive Fue l Scenario s vers u s Bas e Cas e – Percen tage Ch an ge of To ta l Airpo rt Emis s ions Note: The implied increase in emissions for the “100% CNG GSE replacing gasoline GSE, where model available” scenario is a theoretical modeling output related to the emission factor source data used, and is not likely to be observed in actual practice.

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 44 Figure 9 – Alte rna tive Fue l Scenario s vers u s Bas e Cas e – Percen tage Ch an ge of Maximum Airport Annual Ave rage Impact Note: The implied increase in emissions for the “100% CNG GSE replacing gasoline GSE, where model available” scenario is a theoretical modeling output related to the emission factor source data used, and is not likely to be observed in actual practice.

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 45 Figure 10 – Alte rna tive Fu el Scena rios vers us Bas e Cas e – Percen tage Chan ge of Maximum Airport 24-hour 98th Percen tile Imp act Note: The implied increase in emissions for the “100% CNG GSE replacing gasoline GSE, where model available” scenario is a theoretical modeling output related to the emission factor source data used, and is not likely to be observed in actual practice.

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 46 Figure 11 – Alte rna tive Fu el Scena rios vers us Bas e Cas e – Percen tage Chan ge of Annu al ROI Note: The implied increase in emissions for the “100% CNG GSE replacing gasoline GSE, where model available” scenario is a theoretical modeling output related to the emission factor source data used, and is not likely to be observed in actual practice.

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 47 Figure 12 – Alte rna tive Fu el Scena rios vers us Bas e Cas e – Percen tage Chan ge of 24-hour 98th Pe rcen tile ROI Note: The implied increase in emissions for the “100% CNG GSE replacing gasoline GSE, where model available” scenario is a theoretical modeling output related to the emission factor source data used, and is not likely to be observed in actual practice.

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 48 Figure 13 – Alte rna tive Fu el Scena rios vers us Bas e Cas e – Percen tage Chan ge of Annu al In fluence Area Note: The implied increase in emissions for the “100% CNG GSE replacing gasoline GSE, where model available” scenario is a theoretical modeling output related to the emission factor source data used, and is not likely to be observed in actual practice.

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 49 Figure 14 – Alte rna tive Fu el Scena rios vers us Bas e Cas e – Percen tage Chan ge of 24-hour 98th Pe rcen tile In fluence Area Note: The implied increase in emissions for the “100% CNG GSE replacing gasoline GSE, where model available” scenario is a theoretical modeling output related to the emission factor source data used, and is not likely to be observed in actual practice.

Airport Cooperative Research Program Project ACRP 02-23: Alternative Fuels as a Means to Reduce PM2.5 Emissions at Airports 50 Turboprop, Turboshaft, and Piston-Engine Aircraft EDMS does not typically include PM2.5 Figure 15 emission results for piston-engine, turboprop, and turboshaft aircraft as there are no FAA accepted emission factors for these aircraft. Therefore, these aircraft were considered separately as part of the sensitivity analysis. This section summarizes the percentage change in annual emissions for turboprop (including turboshaft) and piston-engine aircraft as a result of the alternative fuel scenarios. The alternative fuel scenarios included FT (natural gas) jet fuel for turboprop (including turboshaft) aircraft and 91/96UL AvGas for piston-engine aircraft. provides the results of the alternative fuels scenarios related to turboprop (including turboshaft) and piston-engine aircraft by comparing the base case annual emissions for each aircraft type to the alternative fuel scenario emissions for the same aircraft type, which can be concluded as: • For the five case study airports, the range of emission reductions (compared with the base case) with using of FT (50% blend with natural gas) in turboprop aircraft is between 49% and 50%, with an average of 49%. • For the five case study airports, the range of emission reductions with using 91/96UL AvGas in piston-engine aircraft is between 97% and 98%, with an average of 97%. Figure 15 – Alte rna tive Fu el Scena rios vers us Bas e Cas e – Percen t Change of Annual Emis s ions fo r Airc raft Type