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85 The focus of this project has been the quantification of HAP emissions from aircraft as a function of ambient conditions and engine type. The most immediate use of emission indices is for the generation of emission inventories, as required for environmental impact statements. Such emission inventories are also a crucial input for air quality models. Most air quality models, however, do not have the spatial resolution to predict differences in air quality between the airport ground and the airport fence line. Data collected as part of this project can partially elucidate the nature of air quality at these locations, as well as in downwind neighborhoods. Overall the emerg- ing conclusion is that the air on airport grounds and near airports is usually not well mixed, and characterized by âbackground urbanâ air most of the time punctuated by occasional plumes of high concentrations that, depending on pollutant, can increase the average concentration considerably. Below we discuss examples at or near three airports: MDW (on the runway), ORD (on the runway and at the fence line), and OAK (2 km downwind). Mixed Airport Emissions at ORD After sampling the advected plumes at ORD and before the dedicated tests of the two united aircraft, the mobile lab was stationed for 25 minutes at the downwind perimeter of the airport grounds, approximately 100 m from the closest idling aircraft and over 200 m from the terminals where there was constant activity by GSE (ground support equipment) vehicles. Earlier on the airport grounds there was a clear dis- tinction between the âbackgroundâ concentrations of the vari- ous pollutants measured and the exhaust plumes from nearby aircraft and GSE vehicles, which appear as short âspikesâ in the time series data (Figure D-1 between 5:45 and 6:00 pm.) At the downwind location (6:25 pm onward in Figure D-1), the numerous exhaust plumes were more spread out in time (and therefore space) and the plumes had combined enough that the background concentrations observed earlier appeared elevated. For example, NOx concentrations were usually between 12â14 ppb at the 1st location (5:45 pm to 6:07 in Figure D-1) and reached concentrations as high as 200 ppb for short durations, with an average concentration of 37 ppbv. At the 2nd location (after 6:20 pm in Figure D-1), [NOx] was rarely below 22 ppbv and most spikes were less than 70 ppbv, but the average concentration was a very similar 36 ppbv. Similar results were observed for HCHO: the back- ground (in-between-plume) concentration in the 1st position was 0.4 ppbv, with an average value of 2.6 ppb caused by a small number of short spikes that exceeded 50 ppbv. At the 2nd sampling location, [HCHO] was usually between 0.9 and 3.7 ppbv, with an average value of 1.8 ppbv. Do the concentrations and ratios of concentrations of differ- ent species provide information that can be used to apportion the different emission sources (aircraft, GSE, etc)? Analysis of the data will surely contribute to an answer to this question. For example, a quick analysis of a one-minute section of data from 6:30 pm to 6:31 pm indicates a combined NOx emission index of 6 g/kg fuel and an HCHO emission index of 0.3 g/kg. Comparison of these numbers to tabulated values for differ- ent emission sources may provide clues to the predominant sources. The NOx emission index is within the (wide) range known for gasoline, diesel, and aircraft. The HCHO emis- sion index, however, far exceeds that for gasoline and diesel vehicles, which are typically between 0.01 and 0.13 g/kg, but is lower than the values reported at these temperatures for CFM56-3x and CFM56-7x engines. This indicates a significant aircraft contribution. Further analysis of all species measured can provide more information. Midway On-Runway Measurements Similar measurements were recorded on February 17, 2010 on the MDW runway. Figure D-2 shows particle number density, formaldehyde, CO2, and CO during a 30- minute time period. The air observed consisted of relatively A p p e n d i x d Links Between Emissions and Air Quality in the Terminal and Fence Line
86 Figure D-1. Time series of four pollutants measured at ORD. Between 5:45 and 6:07, the mobile lab was positioned near the runways and observed short âspikesâ of exhaust plumes. Between 6:20 and 6:50, the mobile lab was positioned several 100 meters downwind of aircraft and GSE activity, and observed an overall increase in the concentrations of all species, with âsmootherâ plumes that cannot always be individually identified. Figure D-2. Time series of data recorded on the MDW runway.
87 clean air most of the time punctuated by occasional short transients of very high concentrations of most pollutants measured. The average formaldehyde and particle number concentrations in between the occasional plumes were 0.2 ppbv and 103 particles/cm3, respectively. As a result of the ~12 aircraft whose exhaust was sampled during this 35 minute time period, the average concentrations were increased to 1.4 ppbv and 187 Ã 103 particles/cm3âresults very similar to the ORD data shown above. San Leandro Measurements At the conclusion of the JETS-APEX2 study, the Aerodyne Mobile Laboratory spent two days at the San Leandro Marina (Figure D-3), which is ~2 km downwind of the OAK runway. Winds were consistently from the northwest. Although lim- ited in duration, these measurements are unique in that they enabled observations of diluted airport emissions with no interferences from non-airport sources, since there is no land in between the emissions and the measurement site. A 6-hour time-series of HCHO, CO, NOx, CO2, and PM number concentration is shown in Figure D-4. Individual air- craft exhaust plumes resulting from idle, take-off, and landing activity could be resolved. The average HCHO concentration in the time series shown is 1.3 ppb, while the interpolated background value is approximately 0.8 (similar to the back- ground value observed on the airport grounds). More impor- tantly, a dilution factor of ~5000 can be inferred for most of the observed plumes based on comparison of the observed CO2 to known CO2 concentrations at the exit of a high bypass turbine engine. Such a simplified source-receptor scheme would be an ideal scenario for testing dispersion models. Figure D-3. San Leandro Marina and Oakland International Airport. Figure D-4. Time series of measurements recorded 2 km downwind of OAK.
