An Improved Model for Prediction of PM10 from Surface Mining Operations
Reed, William Randolph
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Air quality permits are required for the construction of all new surface mining operations. An air quality permit requires a surface mining operation to estimate the type and amount of pollutants the facility will produce. During surface mining the most common pollutant is particulate matter having an aerodynamic diameter less than 10 microns (PM10). The Industrial Source Complex (ISC3) model, created by the United States Environmental Protection Agency (U.S. EPA), is a model used for predicting dispersion of pollutants from industrial facilities, including surface mines and quarries. The use of this model is required when applying for a surface mining permit. However, the U.S. EPA and mining companies have repeatedly demonstrated that this model over-predicts the amount of PM10 dispersed by surface mining facilities, resulting in denied air quality permits. Past research has shown that haul trucks create the majority (80-90%) of PM10 emissions from surface mining operations. Therefore, this research concentrated on improving the ISC3 model by focusing on modeling PM10 emissions from mobile sources, specifically haul trucks at surface mining operations. Research into the ISC3 model showed that its original intended use was for facilities that emit pollutants via smoke stacks. The method used to improve the ISC3 model consisted of applying the dispersion equation used by the ISC3 model in a manner more representative of a moving haul truck. A new model called the Dynamic Component Program was developed to allow modeling of dust dispersion from haul trucks. To validate the Dynamic Component Program, field experiments were designed and conducted. These experiments measured PM10 from haul trucks at two different surface mining operations. The resulting analysis of the Dynamic Component Program, ISC3 model, and the actual field study results showed that the Dynamic Component Program was a 77% improvement over the ISC3 model overall.
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