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

Abstract

The use of Bayesian networks (BN) for environmental risk assessment has increased in recent years as they offer a more transparent way to characterize risk and evaluate uncertainty than the traditional risk assessment paradigms. In this study, a novel probabilistic approach applying a BN for risk calculation was further developed and explored by linking the calculation a risk quotient to alternative future scenarios. This extended 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), a common measure of the exceedance of an environmentally safe exposure threshold. The BN model was used to account for variabilities of the predicted pesticide exposure in agricultural streams, and inter-species variability in sensitivity to the pesticide among freshwater species. In Northern Europe, future climate scenarios typically predict increased temperature and precipitation, which can be expected to cause an increase in weed infestations, plant disease and insect pests. 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 set up for the case study by specifying environmental factors such as soil properties and field slope together with chemical properties of pesticides to predict the pesticide exposure in streams adjacent to the agricultural fields. The model was parameterized and evaluated for five selected pesticides: the three herbicides clopyralid, fluroxypyr-meptyl, and 2-(4-chloro-2-methylphenoxy) acetic acid (MCPA), and the two fungicides prothiocanzole and trifloxystrobin. This approach enabled the calculation and visualization of probability distribution of the risk quotients for the future time horizons 2050 and

A Bayesian network approach to assess the influence of climate change and pesticide use practices on the ecological risks of pesticides in a protected Mediterranean wetland

“…Risk Quotients (RQs) were predicted using Bayesian networks built with the Netica software (Norsys Software Corp., www.norsys.com) using the guidelines provided by Marcot et al ., (2006) and Pollino & Henderson (2010). The Bayesian network followed a simplified structure of the one carried out by Mentzel et al (2022), and was composed of eight nodes ( Fig. 3).…”

“…Furthermore, they can function as meta-models, integrating information and knowledge from several sources and sub-models into a single predictive tool. Differently to traditional regulatory approaches, these probabilistic approaches enable better consideration of uncertainty, and can incorporate spatial and temporal variability into pesticide exposure distributions, as well as distributions that represent the sensitivity of the non-target species potentially affected by the pesticides (Mentzel et al, 2022;Piffady et al, 2021).…”

“…Furthermore, they can function as meta-models, integrating information and knowledge from several sources and sub-models into a single predictive tool. Differently to traditional regulatory approaches, these probabilistic approaches enable better consideration of uncertainty, and can incorporate spatial and temporal variability into pesticide exposure distributions, as well as distributions that represent the sensitivity of the non-target species potentially affected by the pesticides (Mentzel et al ., 2022; Piffady et al ., 2021). Despite their potential to assess ecological risks given the influence of different pesticide management scenarios, their application to assess the influence of climate change or the implementation of new environmental policies is yet very limited (Kaikkonen et al ., 2021; Moe et al ., 2021).…”

Using a Bayesian Network Model to Predict Risk of Pesticides on Aquatic Community Endpoints in a Rice Field—A Southern European Case Study

“…While being used in situations where data are limited, BNs are able to incorporate various sources of information, for example, expert elicitation, other model outputs, or other information literature (Carriger et al, 2016; Carriger & Newman, 2012; Gibert et al, 2018; Hamilton & Pollino, 2012). Moreover, BNs can act as a metamodel (see Martínez‐Megías et al, 2023; Mentzel et al, 2022) allowing the incorporation of input and output data and assumptions from different models, for example, process and case‐based prediction models into a single network model. This approach can allow the display of uncertainty of all model compartments in a transparent way.…”

Using a Bayesian network model to predict effects of pesticides on aquatic community endpoints in a rice field – A southern European case study

“…Besides, BNs have the ability to act as a meta-model (e.g. Mentzel et al (2022)), allowing for the incorporation of inputs and outputs from various different models (in a single model). Summarized, they are probabilistic graphical models that contain nodes (variables) linked through arcs representing conditional probability tables (CPT) (Aguilera et al, 2011; Kaikkonen et al, 2021).…”