Articles | Volume 26, issue 5
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-477', Anonymous Referee #1, 28 Oct 2021
    • AC1: 'Reply on RC1', Mads Troldborg, 20 Dec 2021
  • RC2: 'Comment on hess-2021-477', Anonymous Referee #2, 12 Nov 2021
    • AC2: 'Reply on RC2', Mads Troldborg, 20 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (further review by editor) (05 Jan 2022) by Ibrahim Alameddine
AR by Mads Troldborg on behalf of the Authors (15 Jan 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (25 Jan 2022) by Ibrahim Alameddine
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.