Probabilistic risk assessment of pesticides under future agricultural and climate scenarios using a bayesian network

The use of Bayesian networks (BN) for environmental risk assessment has increased in recent years as they offer a more transparent way to characterize and evaluate uncertainty than the traditional paradigms. In this study, novel probabilistic approach applying BN calculation was further developed explored by linking quotient alternative future scenarios. This extended version model uses predictions from process-based pesticide exposure (World Integrated System Pesticide Exposure - WISPE) characterization toxicity test data effect characterization. probability distributions are combined into (i.e. distribution quotient), common measure exceedance an environmentally safe threshold. used account variabilities predicted agricultural streams, inter-species variability sensitivity among freshwater species. Northern Europe, climate scenarios typically predict temperature precipitation, which can be expected cause increase weed infestations, plant disease insect pests. Such climate-related changes pest pressure turn give rise altered practices, such application rates, adaptation change. WISPE link set consisting two models, three periods (year ranges), case study South-East Norway. up specifying factors soil properties field slope together with chemical pesticides streams adjacent fields. parameterized evaluated five selected pesticides: herbicides clopyralid, fluroxypyr-meptyl, 2-(4-chloro-2-methylphenoxy) acetic acid (MCPA), fungicides prothiocanzole trifloxystrobin. enabled visualization quotients time horizons 2050 2085. posed were general low highest exceeding 1 fluroxypyr-meptyl MCPA. projections here resulted only minor concentrations thereby risk. However, stronger application, represent higher pressures. current specific constrained existing represented one IPCC scenario (A1B) models. Further advancement modelling demonstrated herein, including larger is anticipated result relevant also conditions. will have potential aid targeted management ecological risks support research, industry regulatory needs.