贝叶斯网络(BN)模型越来越多地被用作支持概率环境风险评估(ERA)的工具,因为与传统ERA中常用的更简单的方法相比,它们可以更好地解释不确定性。我们使用BN作为元模型来链接概率框架中的各种信息源,预测在给定情景下农药对水生群落的风险。这项研究集中在西班牙自然公园阿尔布费拉周围的稻田上,考虑三种选定的农药:啶虫脒(杀虫剂),MCPA(除草剂),和唑菌酯(杀真菌剂)。开发的BN将两种农药模型的输入和输出联系起来:基于过程的暴露模型(RICEWQ),和概率效应模型(PERPEST)使用基于案例的推理,并使用来自微观和中观实验的数据。该模型将风险分为三个层次:生物终点(例如,软体动物,浮游动物,昆虫,等。),端点组(植物,无脊椎动物,脊椎动物,和社区进程),和社区。农药对生物终点的风险被表征为给定农药浓度间隔的影响概率。终点组的风险计算为对该组中任何终点的联合影响概率。同样,社区级风险计算为任何终点组受到影响的联合概率.这种方法能够比较不同农药类型的终点组的风险。例如,在2050年的情景中,杀虫剂对社区的预测风险(40%的效应概率)主要是对无脊椎动物的风险(36%的风险).相比之下,除草剂对群落的相关风险(63%)是由对植物(35%)和无脊椎动物(38%)的风险造成的;后者可能是通过食物链的毒性的间接影响.这种新颖的方法将暴露空间变异性的量化与水生生态系统不同组成部分的概率风险预测相结合。
Bayesian 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.