Development of a Bayesian network for probabilistic risk assessment of pesticides
Sophie Mentzel, Merete Grung, Knut Erik Tollefsen, Marianne Stenrød, Karina Petersen, S. Jannicke Moe
Abstract
Abstract Conventional environmental risk assessment of chemicals is based on a calculated risk quotient, representing the ratio of exposure to effects of the chemical, in combination with assessment factors to account for uncertainty. Probabilistic risk assessment approaches can offer more transparency by using probability distributions for exposure and/or effects to account for variability and uncertainty. In this study, a probabilistic approach using Bayesian network modeling is explored as an alternative to traditional risk calculation. Bayesian networks can serve as meta-models that link information from several sources and offer a transparent way of incorporating the required characterization of uncertainty for environmental risk assessment. To this end, a Bayesian network has been developed and parameterized for the pesticides azoxystrobin, metribuzin, and imidacloprid. We illustrate the development from deterministic (traditional) risk calculation, via intermediate versions, to fully probabilistic risk characterization using azoxystrobin as an example. We also demonstrate the seasonal risk calculation for the three pesticides. Integr Environ Assess Manag 2022;18:1072–1087. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). KEY POINTS A Bayesian network (BN) was developed to carry out probabilistic risk calculation. The BN model is used to calculate risk of pesticides to freshwater ecosystems. The BN predicts probabilities of exceeding alternative levels of the risk quotient. The BN can incorporate uncertainties more transparently than traditional methods.