Browsing College of Engineering and Mines (CEM) by Subject "Case studies"
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Using the USDA wind erosion equation for comparative modeling of natural and anthropogenic sources of particulates measured at the Fort Greely PM₁₀ monitoring station, Alaska, a case studyIn April of 2010, the Alaska Department of Environmental Conservation (ADEC) opened a compliance case against the U.S. Army Garrison Fort Greely, Alaska (FGA), for then repeated failure to comply with a permit condition requiring the collection of one year of Prevention of Significant Deterioration (PSD)-quality data on ambient levels of particulate matter less than 10 microns in effective aerodynamic diameter (PM₁₀). During the monitoring period of 2012-2013, background levels of PM₁₀ were more than 80% the Alaska Ambient Air Quality Standards (AAAQS) for a total of seven days in the winter of 2012-2013. On March 17, 2014, ADEC requested that FGA provide substantive documentation that PM₁₀ exceedances observed during the monitoring period were of natural provenance and not from anthropogenic sources. In response to this request, the author used Geographic Information System (GIS) technology to analyze basic meteorological data and outputs from the USDA Wind Erosion Equation (WEQ) to generate a simple back-trajectory model for determining the sources and relative contributions to PM₁₀ experienced at a given receptor. Using this model, the author was able to show that the vast majority of PM₁₀ at Fort Greely was natural rather than anthropogenic in nature. The ADEC Division of Air Quality determined that results of this study constituted substantive documentation that PM₁₀ exceedances observed during the monitoring period were of natural provenance and not from anthropogenic sources, and issued a compliance case closure letter on June 20, 2014. In addition to the direct results of the study, the project also serves to demonstrate a low-complexity model that can be used to assess the relative contribution of anthropogenic and natural sources of PM₁₀ at a given receptor. Additionally, it can be used in complex situations as a screening tool to focus data collection efforts on significant sources of PM₁₀ and facilitate the prioritization of PM₁₀ sources for more precise quantitative dispersion or receptor models when precise quantitative data are required.