A Bayesian network approach to environmental risk assessment of pesticides: direct and indirect effects of climate change

Weather patterns of Northern Europe are projected to change with increased temperature and precipitation by 2050. These climatic changes can potentially affect the transport and degradation of pesticides in the environment. Moreover, pesticide application patterns are expected to be altered as plant disease and insect pests potentially increase. Other agricultural practices are also expected to change such as crop types and application rate. We have used a Bayesian network model to better integrate these potential direct and indirect climate change effects on pesticide exposure, in a probabilistic approach to pesticide risk assessment. The Bayesian network serves as a meta-model to incorporate the predictions from a pesticide fate and transport model (i.e. WISPE). In this study, we ran the exposure prediction model for specific environmental factors linked to a representative Norwegian study area such as soil and site parameters together with chemical properties, under different scenarios of climate model projections and pesticide application patterns. The Bayesian network links the pesticide exposure predictions derived for this study area to effect distributions derived from toxicity tests to predict the probability distribution of the risk quotient to surrounding aquatic ecosystemsThus, this approach takes into account both direct climate change impacts (on pesticides fate and transport) and indirect effects (on pesticide application). Compared to traditional (deterministic) risk assessment methods, this probabilistic approach can better account for uncertainty associated with climate projections,