A Procedure to Select Meteorological Data for Air Dispersion Modeling of Pesticide Applications in California.

Abstract

This study developed a procedure to select a set of 5-y meteorological data with the potential to estimate the highest concentrations ("worst-case scenario") in air dispersion modeling of pesticide applications with American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD). The study analyzed the relationship between the 95th percentile maximum concentrations estimated by AERMOD and the percentages of low wind speed (LWS, 0.5-2 m/s) in the meteorological data used for the modeling. Statistical analysis showed that they were positively correlated within various distances to different types of emission sources. In addition, the LWS percentages of 1-y data could be used to predict the LWS percentages of 5-y data for the same station. Based on these results, the selection procedure for meteorological data began with the evaluation of 1-y data quality and LWS percentages for all the available stations in California, USA, counties with high use of a pesticide of interest. Five-year meteorological data were then processed for the top 5 stations with the highest LWS percentages to perform AERMOD modeling. Finally, the air concentration estimates of the modeled meteorological data were compared to determine the worst-case scenario data. This procedure provided a strategic plan for selecting meteorological data for AERMOD modeling of pesticide applications in California. The procedure was applied to the modeling of residential structural fumigations and determined that the 5-y (2011-2015) data of the weather station WBAN 93134 (downtown Los Angeles, University of Southern California campus) was the worst-case scenario meteorological data for this modeling case. Integr Environ Assess Manag 2019;15:648-658. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.