ABSTRACT

Cotton gins are required to obtain operating permits from state air pollution regulatory agencies (SAPRA) which regulate the amount of particulate matter that can be emitted. Industrial Source Complex Short Term version 3 (ISCST3) is the Gaussian dispersion model currently used by some SAPRAs to predict downwind concentrations used in the regulatory process in the absence of field sampling data. The maximum ambient concentrations for PM₁₀ and PM_2.5 are set by the National Ambient Air Quality Standard (NAAQS) at 150 µg/m³ and 65 µg/m³ respectively. Some SAPRAs use the NAAQS concentrations as property line concentrations for regulatory purposes. This paper reports the results of a unique approach to estimating downwind PM₁₀ and PM_2.5 concentrations using Monte Carlo simulation, the Gaussian dispersion equation, the Hino Power Law, and a particle size distribution that characterizes the dust typically emitted from cotton gin exhausts. These results were then compared to a ten minute concentration (C₁₀) and the concentrations that would be theoretically measured by a FRM PM₁₀ and PM_2.5 sampler.The total suspended particulate (TSP) emission rate, particle size distributions, and sampler performance characteristics were assigned to triangular distributions to simulate the real world operation of the gin and sampling systems. The TSP emission factor given in AP-42 for cotton gins was used to derive the PM mass emission rate from a 40 bale per hour plant. The Gaussian equation was used to model the ambient TSP concentration downwind from the gin. The performance characteristics for the PM₁₀ and PM_2.5 samplers were then used to predict what the measured concentration would be for two PSD conditions. The first PSD assumption was that the mass median diameter (MMD) and geometric standard deviation (GSD) were constant at 12µm and 2 and the second scenario assigned a triangular distribution to the MMD and GSD of {15, 20, 25}µm and {1.8, 2.0, 2.2} respectively. The results show that the PM_2.5 fraction of the dust emitted under either PSD condition was negligible when compared to the NAAQS for PM_2.5 of 65µg/m³. The results also demonstrate that correcting for wind direction changes within the hour using the power law reduces the ambient concentration by a factor of 2.45. The measured downwind concentrations from the samplers reported higher 24-hour averages for each of the ten days modeled than the concentrations predicted by the new model.