AUTHOR=Mentzel Sophie , Grung Merete , Holten Roger , Tollefsen Knut Erik , Stenrød Marianne , Moe S. Jannicke 

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

JOURNAL=Frontiers in Environmental Science

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.957926

DOI=10.3389/fenvs.2022.957926

ISSN=2296-665X

ABSTRACT=The use of Bayesian networks (BN) for environmental risk assessment increased in recent years as they offer a more transparent way to characterize risk and evaluate uncertainty than traditional risk assessment paradigms. In this study, a novel probabilistic approach applying a BN for risk calculation was further explored by linking the calculation a risk quotient to alternative future scenarios. This version of the BN model uses predictions from a process-based pesticide exposure model (World Integrated System for Pesticide Exposure - WISPE) in the exposure characterization and toxicity test data in the effect characterization. The probability distributions for exposure and effect are combined into a risk characterization (i.e. the probability distribution of a risk quotient). In Northern Europe, future climate scenarios typically predict increased temperature and precipitation, which can be expected to cause an increase in occurring pest. Such climate-related changes in pest pressure in turn can give rise to altered agricultural practices, such as increased pesticide application rates, as an adaptation to climate change. The WISPE model was used to link a set of scenarios consisting of two climate models, three pesticide application scenarios and three periods (year ranges), for a case study in South-East Norway. The model was parameterized and evaluated for five selected pesticides: clopyralid, fluroxypyr-meptyl, 2-(4-chloro-2-methylphenoxy) acetic acid (MCPA), prothiocanzole and trifloxystrobin. The risk posed by the pesticides were in general low for this case study, with highest probability of the risk quotient exceeding 1 for the two herbicides fluroxypyr-meptyl and MCPA. The future climate projections used here resulted in only minor changes in predicted exposure concentrations and thereby future risk. However, a stronger increase in risk was predicted for the scenarios with increased pesticide application, which can represent an adaptation to a future climate with higher pest pressures. Further advancement of the BN modelling demonstrated herein, including more recent climate scenarios and a larger set of climate models, is anticipated to result in more relevant risk characterization also for future climate conditions. This probabilistic approach will have the potential to aid targeted management of ecological risks in support of future research, industry and regulatory needs.