Articles | Volume 26, issue 5
https://doi.org/10.5194/hess-26-1261-2022
https://doi.org/10.5194/hess-26-1261-2022
Research article
 | 
09 Mar 2022
Research article |  | 09 Mar 2022

Probabilistic modelling of the inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian belief networks

Mads Troldborg, Zisis Gagkas, Andy Vinten, Allan Lilly, and Miriam Glendell

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Latest update: 23 Apr 2024
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Short summary
Pesticides continue to pose a threat to surface water quality worldwide. Here, we present a spatial Bayesian belief network (BBN) for assessing inherent pesticide risk to water quality. The BBN was applied in a small catchment with limited data to simulate the risk of five pesticides and evaluate the likely effectiveness of mitigation measures. The probabilistic graphical model combines diverse data and explicitly accounts for uncertainties, which are often ignored in pesticide risk assessments.