Otras barreras

Impacts at and around three airports [Atlanta (ATL) (Tier I; 2,650 flights per day), Salt Lake City (SLC) (Tier II; 1,150 flights per day), and Cleveland (CLE) (Tier III; 800 flights per day)] were further analyzed to assess local impacts. These airports were selected due to being located in areas of PM2.5 non-attainment and therefore eligible to participate in the FAA’s Voluntary Airport

Low Emissions (VALE) program (Federal Aviation Administration, 2014b). They are also spatially isolated from other airports, minimizing the influence of emissions from other airports on model results at and around these 3 airports.

ADSC APT model results indicated that aircraft emissions increased PM2.5 in the grid cell con-

taining ATL, SLC, and CLE by 30.0, 10.9, and 8.1 ng m 3 _{in January and 52.9, 5.7, and 10.5 ng}

m 3 _{in July, respectively (Figure 4.4). PM}

2.5 impacts at ATL and SLC were dominated by primary

species directly emitted by aircraft (ASO4, PEC, POA), comprising 71–85% of aircraft-attributable PM2.5 (Figure 4.4). However, at CLE primary species only comprised 53% in January and 25% in

July of aircraft-attributable PM2.5, with higher relative contributions from ammonia nitrate compared

to ATL and SLC. Note that biogenic SOA concentrations were lowered at ATL by aircraft emissions in the AEDT scenario, consistent with previous studies (Arunachalam et al., 2011; Woody et al., 2011; Woody and Arunachalam, 2013) but not in the APT scenarios. Moving 18–90 km downwind of the airports, secondary components such as ammonium nitrate in January and SOA in July became more important (Figure 4.4).

As previously discussed, the PinG results also provide the opportunity to examine subgrid scale impacts, defined as puff plus grid scale aircraft impacts, or concentrations representing exposure at a given receptor location attributable to aircraft (Figure 4.5). AEDT APT concentrations at re- ceptors led to aircraft-attributable PM2.5 estimates approximately an order of magnitude higher than

Figure 4.4: Speciated monthly average aircraft-attributable PM2.5in January and July 2005 for AEDT

(AE), AEDT APT (AEA), and ADSC APT (ADA) in the grid cells containing the Atlanta (ATL), Salt Lake City (SLC), and Cleveland (CLE) airports (top), 19-54 km away from the airports (middle), and 55-90 away from the airports (bottom). Note the change in scale with distance from the airports.

grid-based concentrations alone (Figures C.6 and C.7). Additionally, maximum PM2.5concentrations

were as high as 8.4 µg m 3 _{in January (5-km downwind of the airport) and 25.4 µg m} 3 _{in July}

(1-km downwind of the airport), which is approximately two times the maximum grid-based impact in the area surrounding ATL in January (4.3 µg m 3_{) and approximately 50 times higher the maxi-}

mum aircraft impact in July (0.5 µg m 3_{). However, not all puffs, (including those aloft) pass by the}

receptors and therefore would be detected. It is possible that puffs with higher overall PM2.5 concen-

trations exist but were not included. Furthermore, receptor-based impacts were generally highest 1–5 km downwind of the airport, when puffs were dominated by primary PM species, of relatively small volume, and prior to dilution.

Subgrid scale impacts increased considerably in the ADSC APT case as receptor-based aircraft impacts reached as high as 23.7 µg m 3 _{in January (5-km downwind of the airport) and 59.3 µg}

m 3 _{in July (1-km downwind of the airport) (Figure 4.5). This corresponds to increased aircraft}

impacts of 15.5 µg m 3 _{in January and 33.9 µg m} 3 _{in July over AEDT APT impacts (and 19.4 µg}

m 3 _{in January and 58.8 µg m} 3 _{in July over the maximum grid-based concentrations near ATL).}

The ADSC APT impacts were attributable to an increase in both EC and POA concentrations, which is slightly different than grid-based ADSC APT results where increased aircraft contributions were primarily attributable to EC only. ADSC emission estimates included both higher emissions of EC and POA (Table 4.2). However, much of those emissions occurred at a higher volatility (C* of 103 _µg

m 3_{) relative to AEDT POA emissions (Table 4.3), and the majority of which would be located in the}

gas-phase at ambient conditions. However, in puffs where volumes were smaller relative to the grids and organic aerosol concentrations were higher, higher volatile organics partitioned to the particle phase and increased POA concentrations. Once puff volumes increased or the contents of the puffs were merged into the grid and the organic aerosol concentrations returned to values closer to ambient conditions, these higher volatility POA species partitioned back from the particle phase to gas phase.

4.5 Conclusions

We have successfully used CMAQ-APT to model a large number of aircraft sources (⇠2,000) as

ATL SLC CLE −5 0 5 10 15 20 25 Aircraft − Attributable AEDT ATL SLC CLE −10 0 10 20 30 40 50 60 Airport PM 2.5 ( µ g m − 3) ATL SLC CLE −10 0 10 20 30 40 50 60 Airport Aircraft − Attributable ATL SLC CLE −5 0 5 10 15 20 25 PM 2.5 ( µ g m − 3) ADSC 0 km 1 km 5 km 10 km 25 km 50 km

Figure 4.5: Box-and-whisker plots of aircraft-attributable PM2.5 (grid plus puff concentrations) at

receptors located at the Atlanta (ATL), Salt Lake City (SLC), and Cleveland (CLE) airports and at distances of 1 km, 5 km, 10 km, 25 km and 50 km away in January (top) and July (bottom). Grey dots represent outliers which are defined as values more than 1.5 times the inter-quartile range above the 75th percentile and below the 25th percentile. Figure C.7 in Appendix C replicates this figure but with outliers removed.

and regional scales. Additionally, we have successfully incorporated 1-D plume scale emission es- timates into the modeling framework to provide updated emission estimates that include variations based on ambient conditions and volatility based S/IVOC emissions.

Within the contiguous U.S., monthly average aviation-attributable impacts estimated using ADSC emissions and PinG were 2.7 ng m 3 _{in January and 2.6 ng m} 3 _{in July. This represents an increase}

of 40% in January and 12% in July over AEDT emissions without the use of PinG, primarily due to increased contributions from ammonium nitrate in January and EC in both January and July. Subgrid scale impacts were also much higher in ADSC, with a maximum impact of 23.7 µg m 3 _{in January}

(5-km from the airport) and 59.3 µg m 3_{in July (1-km from the airport) which was 5.5 µg m} 3_higher

in January and 33.9 µg m 3 _{higher in July over AEDT APT impacts (and 19.4 µg m} 3 _{in January}

and 58.8 µg m 3 _{in July over the maximum grid-based concentrations near the airport). The higher}

subgrid impacts in ADSC were largely attributable to the higher primary emissions of EC and POA. The use of PinG with AEDT emissions generally increased aircraft-attributable PM2.5 concentra-

tions in January both locally and regionally due to an increase in ammonium nitrate concentrations compared to scenario with AEDT emissions and without PinG. In July, however, the use of PinG only slightly altered aircraft-attributable PM2.5 (increase of 2%). Subgrid scale impacts at and around the

airport were approximately an order of magnitude higher than grid-based impacts, with concentra- tions reaching as high as 8.4 µg m 3 _{in January and 25.4 µg m} 3 _{in July, or approximately 2 and}

50 times higher than grid-based impacts. Furthermore, the use of PinG prevented the interaction of aircraft NOxemissions and biogenic SOA precursors. Where previous model results indicated aircraft

NOx emissions lowered biogenic SOA concentrations (Chapter 2), this was no longer the case with

PinG.

Future considerations of this work could explore how sensitive model estimates are to the number and location of emitters used to represent aircraft. Specifically, this would include incorporating higher resolution AEDT aircraft locations from single airport AEDT simulations into CMAQ-APT, providing radar based aircraft locations near the airport. Additional considerations include developing ADSC look-up tables for additional aircraft engines to provide a broader base of engines to pull from when developing an ADSC based emission inventory. And finally, considerations should be made

to compare grid-based and subgrid-based aircraft impacts to measurements from field campaigns, though currently a limited number of measurement studies appropriate for comparison to these model results currently exist.