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Phytorisque: an Integrated Assessment Tool for Evaluating the Environmental Risk of Pesticides

Monseur, L.; de Maere, J.-B.; Guillitte, C.; Nihorimbere, G.; Janssens, L.; Bragard, C.

2026-05-04 ecology
10.64898/2026.04.30.721842 bioRxiv
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IntroductionThe environmental impacts of pesticides have raised increasing concern, prompting the development of indicators to assess associated risks across ecosystems. Two main categories are generally distinguished: score-based indicators, which aggregate variables into scores, and risk-based indicators, grounded in the definition of risk as the product of hazard and exposure. Although more data-intensive and more complex to implement, risk-based indicators are recognized to better preserve proportionality with actual risk levels. ObjectivesThis study presents Phytorisque, a model based on the exposure-toxicity ratio to monitor risks associated with pesticide use in Walloon agriculture, from farm to regional scales, and to identify the most contributing active substances in support of risk-reduction policies MethodPhytorisque is a hybrid model that combines mechanistic, empirical, and statistical approaches, integrating quantities of active substances, their ecotoxicological characteristics, and their mobility, persistence, and bioaccumulation properties to generate indices specific to different environmental compartments. ResultsThe indices obtained enable comparison across substances, agricultural sectors, years, and management scenarios. The Phytorisque model provides an integrated assessment of risk across environmental compartments. It can monitor risk evolution over the years for policy impacts evaluation, diagnose the most problematic substances and prospect environmental risks associated with the use of chemical phytoproducts. ConclusionsPhytorisque provides an integrated risk assessment approach adapted to temporal monitoring, diagnosis, and forecasting. It is a relevant operational tool for supporting regional strategies aimed at reducing pesticide-related risks. The model is also transferable to other regions through the adaptation of parameters to local conditions and context.

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