Affiliation:
1. Department of Ecotoxicology and Risk Assessment Norwegian Institute for Water Research Oslo Norway
2. Department of Analytical Chemistry, Physical Chemistry and Chemical Engineering University of Alcalá Madrid Spain
3. Water Institute, Madrid Institute for Advanced Studies Parque Científico Tecnológico de la Universidad de Alcalá Alcalá de Henares Spain
4. Cavanilles Institute of Biodiversity and Evolutionary Biology University of Valencia Valencia Spain
5. Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life Sciences Ås Norway
6. Wageningen Environmental Research Wageningen University and Research Wageningen The Netherlands
7. Aquatic Ecology and Water Quality Management Group Wageningen University Wageningen The Netherlands
Abstract
AbstractBayesian network (BN) models are increasingly used as tools to support probabilistic environmental risk assessments (ERAs), because they can better account for uncertainty compared with the simpler approaches commonly used in traditional ERA. We used BNs as metamodels to link various sources of information in a probabilistic framework, to predict the risk of pesticides to aquatic communities under given scenarios. The research focused on rice fields surrounding the Albufera Natural Park (Valencia, Spain), and considered three selected pesticides: acetamiprid (an insecticide), 2‐methyl‐4‐chlorophenoxyacetic acid (MCPA; a herbicide), and azoxystrobin (a fungicide). The developed BN linked the inputs and outputs of two pesticide models: a process‐based exposure model (Rice Water Quality [RICEWQ]), and a probabilistic effects model (Predicts the Ecological Risk of Pesticides [PERPEST]) using case‐based reasoning with data from microcosm and mesocosm experiments. The model characterized risk at three levels in a hierarchy: biological endpoints (e.g., molluscs, zooplankton, insects, etc.), endpoint groups (plants, invertebrates, vertebrates, and community processes), and community. The pesticide risk to a biological endpoint was characterized as the probability of an effect for a given pesticide concentration interval. The risk to an endpoint group was calculated as the joint probability of effect on any of the endpoints in the group. Likewise, community‐level risk was calculated as the joint probability of any of the endpoint groups being affected. This approach enabled comparison of risk to endpoint groups across different pesticide types. For example, in a scenario for the year 2050, the predicted risk of the insecticide to the community (40% probability of effect) was dominated by the risk to invertebrates (36% risk). In contrast, herbicide‐related risk to the community (63%) resulted from risk to both plants (35%) and invertebrates (38%); the latter might represent (in the present study) indirect effects of toxicity through the food chain. This novel approach combines the quantification of spatial variability of exposure with probabilistic risk prediction for different components of aquatic ecosystems. Environ Toxicol Chem 2024;43:182–196. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
Subject
Health, Toxicology and Mutagenesis,Environmental Chemistry