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Verification Of FAA's Emissions And Dispersion Modeling System (EDMS)Martin, Anjoli 01 January 2006 (has links)
Air quality has been a major environmental concern for many years. Recently the issue of airport emissions has presented growing concerns and is being studied in much more depth. Airport emissions come from a variety of point, line and area sources, making emissions modeling for airports very complex and more involved. Accurate air quality models, specific to airport needs, are required to properly analyze this complex array of air pollution sources created by airports. Accurate air quality models are needed to plan for increased growth of current airports and address concerns over proposed new ones. The Federal Aviation Administration's (FAA) Emissions and Dispersion Modeling System (EDMS) is a program that is the required model for assessing emissions from airport sources. This research used EDMS Version 4.21, which incorporates the EPA dispersion model AERMOD, to model detailed airport data and compare the model's predicted values to the actual measured carbon monoxide concentrations at 25 locations at a major U.S. airport. Statistics relating the model characteristics as well as trends are presented. In this way, a thorough investigation of the accuracy of the EDMS modeled values of carbon monoxide was possible. EDMS modeling included two scenarios, the first scenario referred to as practice detail included general airport information that a modeler could find from the airport being studied and the second scenario referred to as research detail utilized very detailed information from observer logs during a three day observation period. Each of the modeling scenarios was compared to the field measured data and to each other. These comparisons are important to insure the model is adequately describing emissions sources at airports. Data analysis of this study was disappointing since measured levels of CO were generally higher than modeled values. Since EDMS is continually changing and improving perhaps these results can help enhance future models.
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