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A GIS-based Atmospheric Dispersion Modeling Project for Introductory Air Pollution Courses

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2014 ASEE Annual Conference & Exposition


Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014



Conference Session

Sustainability and Hands-on Engineering Education

Tagged Division

Environmental Engineering

Page Count


Page Numbers

24.52.1 - 24.52.15



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Paper Authors


Andrew Ross Pfluger P.E. U.S. Military Academy Orcid 16x16

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Major Andrew Pfluger, U.S. Army, was an Assistant Professor in the Department of Geography and Environmental Engineering at the United States Military Academy. He earned a B.S. in Civil Engineering from USMA and a M.S. and Engineer Degree in Environmental Engineering and Science from Stanford University. He is a licensed PE in the state of Delaware.

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Philip J. Dacunto Department of Geography and Environmental Engineering, United States Military Academy

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LTC Dacunto is an Assistant Professor in the Department of Geography and Environmental Engineering at the United States Military Academy (West Point, NY). He received an M.S. and Ph.D. in Environmental Engineering and Science from Stanford University.

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Michael Hendricks United States Military Academy

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Colonel Mike Hendricks is an Associate Professor of Geospatial Information Science in the Geography and Environmental Engineering Department at the United States Military Academy. His research interests include; geospatial support to land navigation training with GPS, GIS education, mobile education, mapping, and cartography. In addition, he is involved in producing large-scale topographic maps for the sport of Orienteering and is the officer representative to West Point’s Orienteering team.

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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.

Pfluger, A. R., & Dacunto, P. J., & Hendricks, M. (2014, June), A GIS-based Atmospheric Dispersion Modeling Project for Introductory Air Pollution Courses Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--19944

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