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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Cited articles

Aguilera, P. A., Fernandez, A., Fernandez, R., Rumi, R., and Salmeron, A.: Bayesian networks in environmental modelling, Environ. Modell. Softw., 26, 1376–1388, https://doi.org/10.1016/j.envsoft.2011.06.004, 2011. 
Aller, L., Bennet, T., Leher, J. H., Petty, R. J., and Hackett, G.: DRASTIC: a standardized system for evaluating ground water pollution potential using hydrogeological settings, EPA, 641 pp., 1987. 
Babaei, H., Nazari-Sharabian, M., Karakouzian, M., and Ahmad, S.: Identification of critical source areas (CSAs) and evaluation of best management practices (BMPs) in controlling eutrophication in the Dez River basin, Environments, 6, 1–15, https://doi.org/10.3390/environments6020020, 2019. 
Bereswill, R., Streloke, M., and Schulz, R.: Risk mitigation measures for diffuse pesticide entry into aquatic ecosystems: proposal of a guide to identify appropriate measures on a catchment scale, Integr. Environ. Assess., 10, 286–298, https://doi.org/10.1002/ieam.1517, 2014. 
Beven, K., Asadullah, A., Bates, P., Blyth, E., Chappell, N., Child, S., Cloke, H., Dadson, S., Everard, N., Fowler, H. J., Freer, J., Hannah, D. M., Heppell, K., Holden, J., Lamb, R., Lewis, H., Morgan, G., Parry, L., and Wagener, T.: Developing observational methods to drive future hydrological science: Can we make a start as a community?, Hydrol. Process., 34, 868–873, https://doi.org/10.1002/hyp.13622, 2019. 
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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.
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