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

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.(c) 2022 The Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences.Published by Elsevier B.V.