Yoni Rodriguez

Project title: Exploring Wind Ramping as a Determinant of Pesticide Drift

Degree: MS (Thesis) | Program: Occupational Hygiene (OH) | Project type: Thesis/Dissertation
Completed in: 2022 | Faculty advisor: Edward Kasner


The Washington State Department of Health investigates hundreds of pesticide illness reports each year, many of which are related to pesticide spray drift. Drift is the movement of pesticide aerosols through the air from an area of application to any unintended site and accounts for up to half of the pesticide-related illnesses among agricultural workers in the United States. Unfavorable wind conditions are a leading contributing factor for illnesses resulting in the off-target movement of pesticides from sprayer sources to human receptors. Meteorological conditions such as wind speed and wind direction directly impact the environmental fate and transport of pesticide aerosols. Washington state requires pesticide applicators to record wind direction and wind speed, usually with a handheld anemometer, at the beginning of a spray. However, the state does not specify a standardized method for measuring these variables, disregarding rapid changes in meteorological conditions throughout a spray period. This project will explore the concept of "wind ramping" as a tool for predicting drift-prone conditions. Wind ramping is defined as large shifts in wind speed and direction at a given location over a period of time of at least 30 minutes. We were primarily interested in sudden positive changes in windspeed. Washington State University's AgWeatherNet system captures weather data at several hundred different meteorological weather stations positioned throughout agricultural regions of eastern Washington. Weather data from five locations that involved human drift cases in Yakima and Benton counties were the primary dataset for this study. Time series-based prediction methods based on autoregressive integrated moving average (ARIMA) models also known as the Box-Jenkins approach, were explored to forecast wind changes 2.5-hours ahead in a local area. The end goal was to develop a tool with that alerts applicators about drift-prone wind conditions to minimize pesticide exposure and improve the practice of pesticide application.

URI: http://hdl.handle.net/1773/48969