Pesticides are priority concerns in aquatic risk assessment due to their widespread use, ongoing development of new molecules, and potential effects from short‐ and long‐term exposures to aquatic life. Water quality assessments are also challenged by contrasting pesticide behaviors (e.g., mobility, half‐life time, solubility) in different environmental contexts. Furthermore, monitoring networks are not well adapted to the pesticide media transfer dynamics and therefore fail at providing a reliable assessment of pesticides. We present here a Bayesian belief network that was developed in a cooperative process between researchers specializing in Bayesian modeling, soil sciences, agronomy, and diffuse pollutants to provide a tool for stakeholders to assess surface water contamination by pesticides. It integrates knowledge on dominant transfer pathways according to basin physical context and climate for different pesticides properties, such as half‐life duration and affinity to organic C, to develop an assessment of risks of contamination for every watershed in France. The resulting model, ARPEGES (Analyse de Risque PEsticide pour la Gestion des Eaux de Surface; trans. Risk analysis of contamination by pesticides for surface water management), was developed in R. A user‐friendly R interface was built to enable stakeholders to not only obtain ARPEGES' results, but also freely use it to test management scenarios. Though it is applicable to any chemical, its results are illustrated for S‐Metolachlor, a pesticide that was widely used on cereals crops worldwide. In addition to providing contamination potential, ARPEGES also provides a way to diagnose its main explaining factors, enabling stakeholders to focus efforts in the most potentially affected basins, but also on the most probable cause of contamination. In this context, the Bayesian belief network allowed us to use information at different scales (i.e., regional contexts for climate, pedology at the basin scale, pesticide use at the municipality scale) to provide an expert assessment of the processes driving pesticide contamination of streams and the associated uncertainties. Integr Environ Assess Manag 2021;17:188–201. © 2020 SETAC

中文翻译：

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ARPEGES：贝叶斯信念网络，用于评估法国河网的农药污染风险。

由于农药的广泛使用，新分子的不断开发以及短期和长期暴露于水生生物的潜在影响，因此农药是水生风险评估中的优先考虑事项。在不同环境条件下，对比农药行为（例如，流动性，半衰期，溶解度），也对水质评估提出了挑战。此外，监测网络不能很好地适应农药介质的传播动态，因此无法提供可靠的农药评估。我们在此介绍一个贝叶斯信念网络，该贝叶斯信念网络是在贝叶斯建模，土壤科学，农学和弥散性污染物研究人员之间的合作过程中开发的，旨在为利益相关者提供一种评估农药对地表水污染的工具。它根据流域的自然环境和气候，结合不同农药特性（例如半衰期和对有机碳的亲和力）的优势转移途径的知识，以评估法国每个流域的污染风险。在R中开发了最终模型ARPEGES（表面活性剂的Risque农药分析；用于地表水管理的跨农药污染风险分析）。建立了用户友好的R界面，使利益相关者不必不仅可以获得ARPEGES的结果，而且可以自由地使用它来测试管理方案。尽管它适用于任何化学药品，但其结果已证明适用于全球范围内广泛用于谷物作物的杀虫剂异丙甲草胺。除了提供潜在的污染外，ARPEGES还提供了一种诊断其主要解释因素的方法，使利益相关者能够将工作重点放在受影响最大的盆地，同时也要关注最可能的污染原因。在这种情况下，贝叶斯信仰网络使我们能够使用不同规模的信息（即气候的区域背景，流域规模的土壤学，市政规模的农药使用），以对驱动溪流农药污染的过程进行专家评估以及相关的不确定性。Integr环境评估管理2021; 17：188–201。©2020 SETAC