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ARS Home » Plains Area » Lubbock, Texas » Cropping Systems Research Laboratory » Cotton Production and Processing Research » Research » Publications at this Location » Publication #168770


item Buser, Michael

Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/29/2005
Publication Date: 10/15/2005
Citation: Wanjura, J.D., Buser, M.D., Parnell, Jr., C.B., Shaw, B.W., Lacey, R.E. 2005. A simulated approach to estimating PM10 and PM2.5 concentrations downwind from cotton gins. Transactions of the ASAE. 48(5):1919-1925.

Interpretive Summary: Cotton gins are required by the state air pollution regulatory agencies to comply with air quality regulations. Some states base their regulations upon compliance with the National Ambient Air Quality Standards (NAAQS) for particulate matter less than a nominal 10 microns in particle diameter. States using the NAAQS have two methods to determine if a ginning facility is in compliance, namely field sampling and dispersion modeling. Field sampling requires the use of EPA approved samplers to determine the concentrations of PM at the property line. Field sampling is a costly process. The EPA approved samplers sampling dusts typically emitted from cotton gins will measure concentrations that are inaccurate due to inherent errors. Dispersion modeling uses industry specific emission factors to predict what the downwind concentrations would be from a gin. The emission factor currently used for the ginning industry is inaccurate, and as such introduces error into the predicted concentrations. The current dispersion model used to model the emissions from cotton gins is Industrial Source Complex Short Term version 3 (ISCST3). ISCST3 uses Gaussian dispersion along with the Pasquill-Gifford atmospheric classification system to calculate what it calls one hour average concentrations. The concentrations calculated by ISCST3 are actually ten minute concentrations. The assumption that the concentrations calculated are one hour concentrations introduces significant error in the predicted concentrations. This manuscript describes the methodology of a new model that corrects for the errors encountered in the current model. The errors in field sampling and dispersion modeling result in the inappropriate regulation of agricultural industries. The adoption of the model presented in this manuscript will allow for all low level point sources to be more appropriately regulated.

Technical 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 fraction of the dust emitted under either PSD condition was negligible when compared to the NAAQS for PM2.5 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.