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ARS Home » Plains Area » Las Cruces, New Mexico » Cotton Ginning Research » Research » Publications at this Location » Publication #383831

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: Assessment and modeling of particulate matter concentration and dispersion from low-altitude emission sources

Author
item YANG, ZIJIANG - UNIVERSITY OF MARYLAND
item Whitelock, Derek
item BUSER, MICHAEL
item EVANS, MICHAEL - UNIVERSITY OF MARYLAND
item Hapeman, Cathleen
item TORRENTS, ALBA - UNIVERSITY OF MARYLAND

Submitted to: American Chemical Society National Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 8/24/2021
Publication Date: 8/24/2021
Citation: Yang, Z., Whitelock, D.P., Buser, M.D., Evans, M.N., Hapeman, C.J., Torrents, A. 2021. Assessment and modeling of particulate matter concentration and dispersion from low-altitude emission sources. 262nd ACS (American Chemical Society) National Meeting & Exposition, August 22–26, 2021, Atlanta, Georgia, USA. Presentation only.

Interpretive Summary:

Technical Abstract: Due to the lack of scientifically sound information about particulate matter (PM) emissions and dispersion from low-altitude emission sources, and the tendency toward overestimation by regulatory recommended models, field samples of PM2.5, PM10, and total suspended particle (TSP) were collected during 11 sets of experiments at a typical cotton gin. Concentration profiles were developed, dispersion of the air pollutants was assessed, and the regulatory recommended model (AERMOD) was modified and validated. 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). In addition, pollutant concentrations were overestimated by AERMOD by factors of 64.7, 6.97 and 7.44 on average for PM2.5, PM10, and TSP, and thus dispersion correction factors were developed. Cross-validation results showed that predictive accuracy was greatly improved by applying AERMOD coupled with dispersion correction factors, and the average overprediction factors decreased to 3.75, 1.52 and 1.44 for PM2.5, PM10 and TSP, respectively. Dispersion correction factors are recommended for regulatory and practical use, and similar approaches can be extended for a range of other air pollutants.