June 15, 2014
June 15, 2014
June 18, 2014
24.52.1 - 24.52.15
A GIS-based Atmospheric Dispersion Modeling Project for Introductory Air Pollution Courses Students enrolled in introductory air pollution related courses can have difficultyunderstanding or visualizing dispersion modeling using the Gaussian plume equation. They canalso be challenged by the changing nature of the plume as it travels downwind and combineswith other plumes within a given area. Calculations by hand or in a spreadsheet generally focuson manipulating one or two variables and may only plot one plume in one dimension. Toaddress such limitations, several years ago we developed a customized application integrating aGeospatial Information Science (GIS) program, specifically ESRI’s ArcMap 9.1, with a Matlabscript. When used together with specified atmospheric and source parameters for a Gaussianplume, these programs enabled the graphical display of a grid of downwind concentrations on amap. Recently we conducted a comprehensive redesign of the project using only ESRI’s ArcGIS10.0 for both concentration calculations and plotting. The project scenario asks teams ofapproximately four students, who comprise a “company,” to locate several new cement factoriesand power plants within a given city, calculate the uncontrolled emissions of PM10, and identifymitigation techniques (e.g., increased stack height or incorporation of pollution control devices)to meet the US National Ambient Air Quality Standards (NAAQS). Using a custom interface inArcGIS 10.0, students vary atmospheric stability conditions, stack heights, wind speed, andcalculated controlled emission rates to create an array of downwind plume concentrations fromall existing and new sources, which are plotted on a city map. Since costs increase for higherstacks and more effective control devices, students attempt to locate sources in a manner that willminimize mitigation costs. In ArcGIS 10.0, multiple plume concentrations are then summed andthe resulting impacts on four major urban categories (residential, schools, religious complexes,and hospitals) are quantified and depicted. The student company with the most optimizedsolution (i.e., lowest total cost) that meets the NAAQS for PM10 under given atmosphericconditions is awarded the bid. While the application creates a relatively simple model of thedispersion process, it helps students visualize dispersion on a macro-scale, and the specific effectof the variation of each parameter on downwind concentrations. Post-project assessment dataindicates that all students (n=10) consider themselves knowledgeable on how to use the Gaussiandispersion model to solve for downwind pollutant concentrations. Additionally, 80% of studentssurveyed post-project indicated that the dispersion project increased their knowledge of Gaussiandispersion modeling for air pollutants. Students also report that this project increased theirfamiliarity with ArcGIS and that the project is a useful interdisciplinary coupling ofenvironmental engineering and GIS.
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