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ARS Home » Plains Area » Las Cruces, New Mexico » Cotton Ginning Research » Research » Publications at this Location » Publication #378037

Research Project: Improving the Production and Processing of Western and Long-Staple Cotton and Companion Crops to Enhance Quality, Value, and Sustainability

Location: Cotton Ginning Research

Title: Improving modeling of low-altitude particulate matter emission and dispersion: A cotton gin case study

item YANG, ZIJIANG - University Of Maryland
item EVANS, MICHAEL - University Of Maryland
item Buser, Michael
item Hapeman, Cathleen
item TORRENTS, ALBA - University Of Maryland
item Whitelock, Derek

Submitted to: Journal of Environmental Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/30/2022
Publication Date: 4/13/2022
Citation: Yang, Z., Evans, M.D., Buser, M.D., Hapeman, C.J., Torrents, A., Whitelock, D.P. 2022. Improving modeling of low-altitude particulate matter emission and dispersion: A cotton gin case study. Journal of Environmental Science.

Interpretive Summary: Cotton gins in some states find it difficult to meet the requirements necessary to obtain air quality permits through modeling. The Environment Protection Agency (EPA)-recommended dispersion models used by the states were not developed for low-level point sources such as cotton gins. Past research studies suggest that these models could be over-predicting cotton gin boundary line particulate matter concentrations by as much as a factor of 10. These modeling errors could make it extremely difficult for cotton gins to meet modeled concentration limits set by the individual states. Current EPA dispersion models were evaluated utilizing measured cotton gin stack emissions with simultaneously measured ambient concentrations and onsite meteorological information from a previous cotton gin particulate matter sampling campaign. The particulate matter emissions dispersions were characterized, influential factors were investigated, and the EPA-recommended model (AERMOD) was modified and validated based on the field datasets. AERMOD was found to overestimate concentrations of particulate matter with diameter less than or equal to 2.5 µm by more than 60 times and concentrations of particulate matter with diameter less than or equal to 10 µm by more than 6 times. Correction factors for AERMOD were developed that greatly improved predictive accuracy These data and observations will be useful in developing and evaluating dispersion models for low-level sources like cotton gins. Also, dispersion correction factors were recommended for regulatory and practical use to assist U.S. cotton gins in meeting air quality permit requirements.

Technical Abstract: Monitoring and modeling of airborne particulate matter (PM) from low-altitude sources is becoming an important regulatory target as the adverse health consequences of PM become better understood. However, application of models not specifically designed for simulation of PM from low-altitude emissions may bias predictions. To address this problem, we describe the modification and validation of an air dispersion model for the simulation of low-altitude PM dispersion from a typical cotton ginning facility. We found that the regulatory recommended model (AERMOD) overestimated pollutant concentrations by factors of 64.7, 6.97 and 7.44 on average for PM2.5, PM10, and TSP, respectively. Pollutant concentrations were negatively correlated with height (p < 0.05), distance from source (p < 0.05) and standard deviation of wind direction (p < 0.001), and positively correlated with average wind speed (p < 0.001). Based on these results, we developed dispersion correction factors for AERMOD and cross-validated the revised model against independent observations, reducing overestimation factors to 3.75, 1.52 and 1.44 for PM2.5, PM10 and TSP, respectively. Further reductions in model error may be obtained from use of additional observations and refinement of dispersive correction factors. More generally, the correction permits the validated adjustment and application of pre-existing models for risk assessment and development of remediation techniques. The same approach may also be applied to improve simulations of other air pollutants and environmental conditions of concern.