Articles | Volume 26, issue 5
https://doi.org/10.5194/hess-26-1261-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-1261-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Mar 2022
Research article |
| 09 Mar 2022
| 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
CORRESPONDING AUTHOR
Information and Computational Sciences, The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH,
Scotland, UK
Zisis Gagkas
Environmental and Biochemical Sciences, The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH,
Scotland, UK
Andy Vinten
Environmental and Biochemical Sciences, The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH,
Scotland, UK
Allan Lilly
Environmental and Biochemical Sciences, The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH,
Scotland, UK
Miriam Glendell
CORRESPONDING AUTHOR
Environmental and Biochemical Sciences, The James Hutton Institute, Craigiebuckler, Aberdeen AB15 8QH,
Scotland, UK
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Cited
18 citations as recorded by crossref.
- Bee bread collected by honey bees (Apis mellifera) as a terrestrial pesticide biomarker to complement water studies S. Stalder et al. https://doi.org/10.1002/ps.8541
- Environmental Site-Specific Risk Assessments—A Review of Methodological Frameworks, Contaminated Land Assessment, and Ecological Risk Characterization Approaches R. Kasparinskis et al. https://doi.org/10.3390/environments13070397
- Spatial prediction of pesticide occurrence in riparian buffer zones using machine learning T. Elsässer et al. https://doi.org/10.1016/j.envpol.2026.128666
- Quantifying the influence of climate change on pesticide risks in drinking water J. Harmon O'Driscoll et al. https://doi.org/10.1016/j.scitotenv.2025.179090
- Enhancing IT audit risk assessment: A comprehensive model using bow-tie and fuzzy Bayesian networks H. Wu et al. https://doi.org/10.1016/j.cose.2026.105036
- Rain-shelter cultivation promotes grapevine health by altering phyllosphere microecology in rainy areas T. He et al. https://doi.org/10.1186/s40793-025-00708-3
- Identifying and testing adaptive management options to increase river catchment system resilience using a Bayesian Network model K. Adams et al. https://doi.org/10.1007/s44288-024-00066-6
- Chemical exposure and risk in river basins: Bayesian integration of environmental model simulations and monitoring data S. Mentzel et al. https://doi.org/10.3389/ffwsc.2026.1789703
- Decision analysis for prioritizing climate change adaptation options: a systematic review E. Amanuma et al. https://doi.org/10.1088/1748-9326/ad61fe
- Natural attenuation of dissolved petroleum fuel constituents in a fractured Chalk aquifer: contaminant mass balance with probabilistic analysis S. Thornton et al. https://doi.org/10.1144/qjegh2023-116
- Occurrence and path pollution of emerging organic contaminants in mineral water of Hranice hypogenic Karst P. Oppeltová et al. https://doi.org/10.3389/fenvs.2024.1339818
- Prediction of pesticide runoff at the global scale and its key influencing factors W. Li et al. https://doi.org/10.1016/j.jhazmat.2025.138783
- Testing of a reimagined pesticide leaching index based on preferential flow theory N. Brindt et al. https://doi.org/10.1016/j.gsd.2026.101645
- Stochastic modelling of pesticide transport to drinking water sources via runoff and resulting human health risk assessment J. Harmon O'Driscoll et al. https://doi.org/10.1016/j.scitotenv.2024.170589
- Achieving agricultural and environmental targets in a changing climate requires a whole-system based approach P. Mellander et al. https://doi.org/10.1007/s44288-025-00321-4
- The role of ponds in pesticide dissipation at the catchment scale: The case of the Save agricultural catchment (Southwestern France) M. Joffre et al. https://doi.org/10.1016/j.scitotenv.2024.173131
- Developing a Bayesian network model for understanding river catchment resilience under future change scenarios K. Adams et al. https://doi.org/10.5194/hess-27-2205-2023
- A systems approach to modelling phosphorus pollution risk in Scottish rivers using a spatial Bayesian Belief Network helps targeting effective mitigation measures M. Glendell et al. https://doi.org/10.3389/fenvs.2022.976933
18 citations as recorded by crossref.
- Bee bread collected by honey bees (Apis mellifera) as a terrestrial pesticide biomarker to complement water studies S. Stalder et al. https://doi.org/10.1002/ps.8541
- Environmental Site-Specific Risk Assessments—A Review of Methodological Frameworks, Contaminated Land Assessment, and Ecological Risk Characterization Approaches R. Kasparinskis et al. https://doi.org/10.3390/environments13070397
- Spatial prediction of pesticide occurrence in riparian buffer zones using machine learning T. Elsässer et al. https://doi.org/10.1016/j.envpol.2026.128666
- Quantifying the influence of climate change on pesticide risks in drinking water J. Harmon O'Driscoll et al. https://doi.org/10.1016/j.scitotenv.2025.179090
- Enhancing IT audit risk assessment: A comprehensive model using bow-tie and fuzzy Bayesian networks H. Wu et al. https://doi.org/10.1016/j.cose.2026.105036
- Rain-shelter cultivation promotes grapevine health by altering phyllosphere microecology in rainy areas T. He et al. https://doi.org/10.1186/s40793-025-00708-3
- Identifying and testing adaptive management options to increase river catchment system resilience using a Bayesian Network model K. Adams et al. https://doi.org/10.1007/s44288-024-00066-6
- Chemical exposure and risk in river basins: Bayesian integration of environmental model simulations and monitoring data S. Mentzel et al. https://doi.org/10.3389/ffwsc.2026.1789703
- Decision analysis for prioritizing climate change adaptation options: a systematic review E. Amanuma et al. https://doi.org/10.1088/1748-9326/ad61fe
- Natural attenuation of dissolved petroleum fuel constituents in a fractured Chalk aquifer: contaminant mass balance with probabilistic analysis S. Thornton et al. https://doi.org/10.1144/qjegh2023-116
- Occurrence and path pollution of emerging organic contaminants in mineral water of Hranice hypogenic Karst P. Oppeltová et al. https://doi.org/10.3389/fenvs.2024.1339818
- Prediction of pesticide runoff at the global scale and its key influencing factors W. Li et al. https://doi.org/10.1016/j.jhazmat.2025.138783
- Testing of a reimagined pesticide leaching index based on preferential flow theory N. Brindt et al. https://doi.org/10.1016/j.gsd.2026.101645
- Stochastic modelling of pesticide transport to drinking water sources via runoff and resulting human health risk assessment J. Harmon O'Driscoll et al. https://doi.org/10.1016/j.scitotenv.2024.170589
- Achieving agricultural and environmental targets in a changing climate requires a whole-system based approach P. Mellander et al. https://doi.org/10.1007/s44288-025-00321-4
- The role of ponds in pesticide dissipation at the catchment scale: The case of the Save agricultural catchment (Southwestern France) M. Joffre et al. https://doi.org/10.1016/j.scitotenv.2024.173131
- Developing a Bayesian network model for understanding river catchment resilience under future change scenarios K. Adams et al. https://doi.org/10.5194/hess-27-2205-2023
- A systems approach to modelling phosphorus pollution risk in Scottish rivers using a spatial Bayesian Belief Network helps targeting effective mitigation measures M. Glendell et al. https://doi.org/10.3389/fenvs.2022.976933
Saved (final revised paper)
Latest update: 31 Jul 2026
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.
Pesticides continue to pose a threat to surface water quality worldwide. Here, we present a...
