Environmental and ecological risk assessments are defined as the process for evaluating the likelihood that the environment may be impacted as a result of exposure to stressors. Although this definition implies the calculation of probabilities, risk assessments traditionally rely on nonprobabilistic methods such as calculation of a risk quotient. Bayesian network (BN) models are a tool for probabilistic and causal modeling, increasingly used in many fields of environmental science. Bayesian networks are defined as directed acyclic graphs where the causal relationships and the associated uncertainty are quantified in conditional probability tables. Bayesian networks inherently incorporate uncertainty and can integrate a variety of information types, including expert elicitation. During the last 2 decades, there has been a steady increase in reports on BN applications in environmental risk assessment and management. At recent annual meetings of the Society of Environmental Toxicology and Chemistry (SETAC) North America and SETAC Europe, a number of applications of BN models were presented along with new theoretical developments. Likewise, recent meetings of the European Geosciences Union (EGU) have dedicated sessions to Bayesian modeling in relation to water quality. This special series contains 10 articles based on presentations in these sessions, reflecting a range of BN applications to systems, ranging from cells and populations to watersheds and national scale. The articles report on recent progress in many topics, including climate and management scenarios, ecological and socioeconomic endpoints, machine learning, diagnostic inference, and model evaluation. They demonstrate that BNs can be adapted to established conceptual frameworks used to support environmental risk assessments, such as adverse outcome pathways and the relative risk model. The contributions from EGU demonstrate recent advancements in areas such as spatial (geographic information system [GIS]–based) and temporal (dynamic) BN modeling. In conclusion, this special series supports the prediction that increased use of Bayesian network models will improve environmental risk assessments. Integr Environ Assess Manag 2021;17:53–61. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC)

中文翻译：

---

贝叶斯网络模型的增加使用改善了环境风险评估

环境和生态风险评估被定义为评估环境可能因暴露于压力源而受到影响的可能性的过程。虽然这个定义暗示了概率的计算，但风险评估传统上依赖于非概率方法，例如计算风险商。贝叶斯网络 (BN) 模型是概率和因果建模的工具，越来越多地用于环境科学的许多领域。贝叶斯网络被定义为有向无环图，其中因果关系和相关的不确定性在条件概率表中被量化。贝叶斯网络固有地包含不确定性，并且可以集成各种信息类型，包括专家启发。在过去的 2 年里，BN在环境风险评估和管理中的应用报告稳步增加。在最近的北美环境毒理学和化学学会 (SETAC) 和欧洲 SETAC 年会上，展示了 BN 模型的一些应用以及新的理论发展。同样，欧洲地球科学联盟 (EGU) 最近的会议专门讨论了与水质相关的贝叶斯模型。这个特别系列包含 10 篇基于这些会议演讲的文章，反映了 BN 在系统中的一系列应用，从细胞和种群到流域和国家规模。这些文章报告了许多主题的最新进展，包括气候和管理情景、生态和社会经济终点、机器学习、诊断推理、和模型评估。他们表明，BN 可以适应用于支持环境风险评估的既定概念框架，例如不利结果路径和相对风险模型。EGU 的贡献展示了空间（基于地理信息系统 [GIS]）和时间（动态）BN 建模等领域的最新进展。总之，这个特别系列支持贝叶斯网络模型的更多使用将改善环境风险评估的预测。EGU 的贡献展示了空间（基于地理信息系统 [GIS]）和时间（动态）BN 建模等领域的最新进展。总之，这个特别系列支持贝叶斯网络模型的更多使用将改善环境风险评估的预测。EGU 的贡献展示了空间（基于地理信息系统 [GIS]）和时间（动态）BN 建模等领域的最新进展。总之，这个特别系列支持贝叶斯网络模型的更多使用将改善环境风险评估的预测。2021 年整合环境评估管理；17：53–61。© 2020 作者。由 Wiley Periodicals LLC 代表环境毒理学与化学学会 (SETAC) 出版的综合环境评估和管理