Engineering approaches to address erros in measured and predicted particulate matter concentrations
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Some of the air pollution regulations in the United States are based on an application of the National Ambient Air Quality Standards at the property line. Agricultural operations such as cotton gins, feed mills, and cattle feed yards may be inappropriately regulated by such regulations if the current methods of measuring and predicting the concentrations of regulated pollutants are used. The regulated particulate matter pollutants are those with aerodynamic equivalent diameters less than or equal to a nominal 10 and 2.5 micrometers (PM10 and PM2.5) respectively. The current Federal Reference Method PM10 and PM2.5 samplers exhibit oversampling errors when sampling dusts with particle size distributions similar to those of agricultural sources. These errors are due to the interaction of the performance characteristics of the sampler with the particle size distribution of the dust being sampled. The results of this work demonstrate the development of a new sampler that may be used to accurately sample total suspended particulate (TSP) concentrations. The particle size distribution of TSP samples can be obtained and used to more accurately determine PM10 and PM2.5 concentrations. The results of this work indicate that accurate measures of TSP can be taken on a low volume basis. This work also shows that the low volume samplers provide advantages in maintaining more consistent sampling flow rates, and more robust measurements of TSP concentrations in high dust concentrations. The EPA approved dispersion model most commonly used to estimate concentrations downwind from a stationary source is the Industrial Source Complex Short Term version 3 (ISCST3). ISCST3 is known to over-predict downwind concentrations from low level point sources. The results of this research show that the magnitude of these errors could be as much as 250%. A new approach to correcting these errors using the power law with P values as a function of stability class and downwind distance is demonstrated. Correcting the results of ISCST3 using this new approach results in an average estimated concentration reduction factor of 2.3.
Wanjura, John David (2003). Engineering approaches to address erros in measured and predicted particulate matter concentrations. Master's thesis, Texas A&M University. Texas A&M University. Available electronically from