Impact Of Wind Speed And Direction And Key Meteorological Parameters On Potential Pesticide Drift Mass Loadings From Sequential Aerial Applications

Pesticide spray drift is potentially a significant source of exposure to off-target, adjacent aquatic habitats. To estimate the magnitude of pesticide drift from aerial or ground applications, regulatory agencies in North America, Europe, and elsewhere rely on spray drift models to predict spray drift deposition for risk assessments. Refined assessments should ultimately depend on best-available data for exposure modeling. However, when developing lower tier "screening" assessments designed to indicate whether further refinement is needed, regulators often make conservative assumptions with a resulting increased level of uncertainty in estimating environmental exposure or risk. In the United States, it is generally accepted that, to ensure conservative regulatory assessments, it is reasonable to assume that the wind speed might be 4.47 m/s (10 miles per hour [mph]), the relative humidity and temperature are highly conducive to drift, and the wind is blowing directly toward a receiving water for any given single spray event in a season. However, what is the probability these conditions will all co-occur for each of 4 sequential spray events spaced a week apart (common practice for insecticides)? The refined approach in the present study investigates this question using hourly meteorological data sets for 5 United States Environmental Protection Agency (USEPA) standard crop scenarios to understand how real-world data can reduce unnecessary uncertainty for sequential applications. The impact of wind speeds, temperatures, relative humidity, and wind direction at different times of day on annual drift loadings has been examined using a stepwise process for comparison with corresponding regulatory default loading estimates. The impacts on drift estimates were significant; interestingly, the time of day of the applications impacted variability more than did the selected crop scenario. When all these real-world factors were considered, estimated 30-y total drift loads ranged from 2% to 5% greater than the default estimate (2 of 30 cases due to high afternoon wind speeds) to 51% to 86% reductions (25 of 30 cases) with an overall average reduction of 63%.