Source apportionment of fine particles in Tennessee using a source-oriented model

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Abstract

Source apportionment of fine particles (PM2.5, particulate matter < 2 μm in aerodynamic diameter) is important to identify the source categories that are responsible for the concentrations observed at a particular receptor. Although receptor models have been used to do source apportionment, they do not fully take into account the chemical reactions (including photochemical reactions) involved in the formation of secondary fine particles. Secondary fine particles are formed from photochemical and other reactions involving precursor gases, such as sulfur dioxide, oxides of nitrogen, ammonia, and volatile organic compounds. This paper presents the results of modeling work aimed at developing a source apportionment of primary and secondary PM 2.5. On-road mobile source and point source inventories for the state of Tennessee were estimated and compiled. The national emissions inventory for the year 1999 was used for the other states. U.S. Environmental Protection Agency Models3/ Community Multi-Scale Air Quality modeling system was used for the photochemical/secondary particulate matter modeling. The modeling domain consisted of a nested 36-12-4-km domain. The 4-km domain covered the entire state of Tennessee. The episode chosen for the modeling runs was August 29 to September 9, 1999. This paper presents the approach used and the results from the modeling and attempts to quantify the contribution of major source categories, such as the on-road mobile sources (including the fugitive dust component) and coal-fired power plants, to observed PM2.5 concentrations in Tennessee. The results of this work will be helpful in policy issues targeted at designing control strategies to meet the PM2.5 National Ambient Air Quality Standards in Tennessee. Copyright 2007 Air & Waste Management Association.

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Doraiswamy, P., Davis, W. T., Miller, T. L., & Fu, J. S. (2007). Source apportionment of fine particles in Tennessee using a source-oriented model. Journal of the Air and Waste Management Association, 57(4), 407–419. https://doi.org/10.3155/1047-3289.57.4.407

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