Pesticide spray applications can cause unintended off-site deposition in non-target water bodies through aerial drift. Setting aside no -spray buffers during pesticide applications can reduce off-site drift deposition and mitigate potential environmental exposure. In the absence of empirical data, quantifying the mass of off-site pesticide drift requires the use of readily available, yet not necessarily representative, distance from edge of the field – mass deposition curves. Furthermore, estimating spray buffer distances that result in pesticide concentrations that meet accepted risk quotients can require tedious iterations of multiple model simulations. This paper will discuss a methodology that integrates field spray drift deposition sampling, statistical analysis, and environmental modeling to efficiently estimate required spray buffer distances. First, spray drift samples are collected from an experimental trial at given distances from the edge of the field. The fraction of applied active ingredient deposited by aerial drift is then calculated for all samples. Using custom computer scripts, the field data is then processed to 1) graph and fit a series of non-linear regressions and generalized linear models, 2) calculate goodness of fit statistics to evaluate the quality of the resulting statistical models, 3) extract model parameters and 4) calculate the pond-integrated drift fraction of applied active ingredient for a given spray buffer.  These results are then combined with programmatic batch simulations of the Pesticide Root Zone (PRZM) and the Variable Volume Water (VVWM) models to calculate expected environmental concentrations of the active ingredient for multiple crops, chemical use patterns, and spray buffer combinations. This process allows efficient determination of appropriate aerial spray buffer distances. A case study with a new active ingredient will be used in this paper to illustrate the aerial spray buffer distance calculation method described.<br/>

Estimating appropriate buffer distances to mitigate environmental risk of spray drift using field data and computer automation

256th National Meeting (2018)

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.