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

Viewed

Total article views: 4,729 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,096 1,531 102 4,729 116 155
  • HTML: 3,096
  • PDF: 1,531
  • XML: 102
  • Total: 4,729
  • BibTeX: 116
  • EndNote: 155
Views and downloads (calculated since 29 Sep 2021)
Cumulative views and downloads (calculated since 29 Sep 2021)

Viewed (geographical distribution)

Total article views: 4,729 (including HTML, PDF, and XML) Thereof 4,586 with geography defined and 143 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 21 Mar 2026
Download
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.
Share