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1 S u m m a r y The impetus for this project is a limitation with modeling capabilities to comprehensively understand and explain airport contributions to local air quality. Although the FAAâs Emis- sions and Dispersion Modeling System (EDMS) represents the state-of-the-art in emissions and dispersion (through USEPAâs AERMOD) modeling capabilities, there are still limita- tions related to the handling of reactive pollutants and the formation of secondary pollutants. Therefore, the goal of this project was to develop guidance for combining measurements with modeling to fully support airport air quality assessments. The scope of the project involved conducting various reviews and demonstrations of mea- surements and modeling through case studies. The resulting assessments involved the use of measured data and various modeling exercises, including sensitivity-type analyses, trend assessments, and modeled-versus-measured comparisons. Although these assessments were extensive in their coverage of various modeling parameters, they did not include formal vali- dation or uncertainty assessments, which were considered outside the scope of this project. The purpose in conducting the assessments was to assess the measurement and modeling techniques for their application to quantifying airport contributions to local air quality. The review work involved examining all applicable emissions and dispersion models, but mainly focusing on EPA- and FAA-recommended models, as well as some alternatives. In addition to EDMS and AERMOD, others, such as CAL3QHC and CALPUFF, were consid- ered for their potential to either replace or augment the current capabilities in those recom- mended models. The model selections involving EDMS and CMAQ were largely based on being able to model chemical transformations and to leverage the FAAâs previous research in this area. For the field measurements, the equipment was selected based on practical applica- tions at airports (i.e., weighing various issues such as cost of equipment, expertise necessary, fidelity of data, and quantity of data). All equipment/techniques used were USEPA reference, equivalent, or other approved methods. Field measurements were conducted as part of the case study at Washington-Dulles Inter- national Airport. This airport was selected based on consideration of such criteria as air- port operations, location of other major sources, and topography. The measurement work involved the collection of both ambient concentration data and source activity information. The equipment included a combination of real-time gaseous analyzers, Summa canisters, draw-tube cartridges, Minivol air samplers with particulate matter (PM) filters, and rotating drum impactors (RDIs). Using the equipment, concentrations of each of the criteria pollut- ants and various hazardous air pollutants (HAPs) were measured. The measured data were assessed to identify various trends such as those due to seasonal effects and relationships to airport operations and atmospheric effects (e.g., wind direc- tions). The measured data allowed direct readings of airport contributions (loadings of Guidance for Quantifying the Contribution of Airport Emissions to Local Air Quality
2 Guidance for Quantifying the Contribution of airport Emissions to Local air Quality each pollutant) to the local environment, thereby providing indications of what pollutants to focus on (i.e., resources used for future measurements). The data also served as a com- parison for the model evaluation work. The model evaluations included a comparison of using higher fidelity data versus default data in EDMS/AERMOD. Various modeled-versus-measured comparisons and sensitivity-type analyses were conducted to better understand the accuracy of the models and their behavior. Further, a novel hybrid modeling approach was developed and applied to combine air quality outputs from CMAQ at a relatively coarser resolution of 4â12 km to provide sec- ondary components of PM2.5 and then use AERMOD to predict concentrations of primary pollutants at a much more spatially resolved network of receptors. The rationale for this was that secondary pollutants tend to be more spatially diffused, while primary pollutants have a much more localized signature, especially when considering airport sources. Airport opera- tors planning to perform air quality impact assessments can adopt this hybrid modeling approach, where a combination of CMAQ and AERMOD are used so as to take advantage of the respective modeling systemâs features to come up with a comprehensive set of outputs for all pollutants at the finest spatial and temporal scales desirable. Based on the analyses of the hybrid modeling outputs under this project, for PM2.5, tem- poral variability dominates. However, for NOx, spatial variability is more important than temporal variability. In addition to these main assessments, a supplemental PM-focused receptor modeling analysis was conducted to help support the source-oriented modeling work conducted through EDMS and CMAQ. This receptor modeling work served as an investigative tool that helped to confirm some of the findings with the source-oriented models, but, more importantly, helped to identify modeling gaps. Another minor supplemental assessment conducted for completeness was based on the potential use of noise data to help corroborate aircraft takeoff and landing incidences. If more temporal fidelity is necessary in modeling aircraft operations, noise monitors can be used to identify the specific incidences in time. However, they are limited by their inability to differentiate incidences occurring on nearby runways and other sources. The outcome from all of the various measurement and modeling reviews, field work, and data and modeling assessments is a set of findings and recommendations that culminated in the guidance materials presented in this report. The overall findings with respect to the goals of this project are that the set of equipment and models demonstrated as part of the case study were adequate (i.e., accomplished their purposes) but there is still significant room for improvement (i.e., to make the models more accurate as well as more robust in their capabilities). As such, a set of suggestions for future research focused on the use of EDMS/ AERMOD, is presented in Chapter 6. These suggestions are ranked based on weighing the resources required to conduct the research and the expected impact on the accuracy and/ or use of the models. The scope of the guidance materials presented in this report is based on leveraging exist- ing guidance and standards available in various USEPA and FAA documents. As such, this reportâs guidance requires a working knowledge of airport and air quality concepts. The guidance materials are, in large part, based on the reviews and findings from the case study assessments conducted under this project. Novices are directed to the USEPA and FAA documents referenced throughout this report.
