Probabilistic modelling of the inherent field-level pesticide pollution risk in a small drinking water catchment using spatial Bayesian belief networks
Mads Troldborg et al.
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Total article views: 1,767 (including HTML, PDF, and XML)
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1,377
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1,767
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HTML: 1,377
PDF: 365
XML: 25
Total: 1,767
BibTeX: 15
EndNote: 20
Views and downloads (calculated since 29 Sep 2021)
Cumulative views and downloads
(calculated since 29 Sep 2021)
Total article views: 868 (including HTML, PDF, and XML)
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666
189
13
868
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HTML: 666
PDF: 189
XML: 13
Total: 868
BibTeX: 10
EndNote: 14
Views and downloads (calculated since 09 Mar 2022)
Cumulative views and downloads
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Total article views: 899 (including HTML, PDF, and XML)
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711
176
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899
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HTML: 711
PDF: 176
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Total: 899
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Views and downloads (calculated since 29 Sep 2021)
Cumulative views and downloads
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Viewed (geographical distribution)
Total article views: 1,767 (including HTML, PDF, and XML)
Thereof 1,711 with geography defined
and 56 with unknown origin.
Total article views: 868 (including HTML, PDF, and XML)
Thereof 832 with geography defined
and 36 with unknown origin.
Total article views: 899 (including HTML, PDF, and XML)
Thereof 879 with geography defined
and 20 with unknown origin.
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...
