Rao, P. Suresh C.
van Zwieten, Lukas
MetadataShow full item record
StatisticsDisplay Item Statistics
CitationMcGrath, G., Rao, P., Mellander, P., Kennedy, I., Rose, M. and van Zwieten, L. Real-time forecasting of pesticide concentrations in soil. Science of The Total Environment, 2019, 663, 709-717. doi: https://dx.doi.org/10.1016/j.scitotenv.2019.01.401
AbstractForecasting pesticide residues in soils in real time is essential for agronomic purposes, to manage phytotoxic effects, and in catchments to manage surface and ground water quality. This has not been possible in the past due to both modelling and measurement constraints. Here, the analytical transient probability distribution (pdf) of pesticide concentrations is derived. The pdf results from the random ways in which rain events occur after pesticide application. First-order degradation kinetics and linear equilibrium sorption are assumed. The analytical pdfs allow understanding of the relative contributions that climate (mean storm depth and mean rainfall event frequency) and chemical (sorption and degradation) properties have on the variability of soil concentrations into the future. We demonstrated the two uncertain reaction parameters can be constrained using Bayesian methods. An approach to a Bayesian informed forecast is then presented. With the use of new rapid tests capable of providing quantitative measurements of soil concentrations in the field, real-time forecasting of future pesticide concentrations now looks possible for the first time. Such an approach offers new means to manage crops, soils and water quality, and may be extended to other classes of pesticides for ecological risk assessment purposes.
FunderGrains Research and Development Corporation grant; Lee A. Reith Endowment in the Lyles School of Civil Engineering at Purdue University; Environmental Protection Agency
Grant NumberDAN00180; 2016-W-MS-24
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 3.0 United States