Institute of Environmental Toxicology and Chemistry, Huxley College of the Environment, Western Washington University, Bellingham, Washington, USA.
Integr Environ Assess Manag. 2021 Jan;17(1):79-94. doi: 10.1002/ieam.4351. Epub 2020 Nov 16.
In 2012, a regional risk assessment was published that applied Bayesian networks (BN) to the structure of the relative risk model. The original structure of the relative risk model (RRM) was published in the late 1990s and developed during the next decade. The RRM coupled with a Monte Carlo analysis was applied to calculating risk to a number of sites and a variety of questions. The sites included watersheds, terrestrial systems, and marine environments and included stressors such as nonindigenous species, effluents, pesticides, nutrients, and management options. However, it became apparent that there were limits to the original approach. In 2009, the relative risk model was transitioned into the structure of a BN. Bayesian networks had several clear advantages. First, BNs innately incorporated categories and, as in the case of the relative risk model, ranks to describe systems. Second, interactions between multiple stressors can be combined using several pathways and the conditional probability tables (CPT) to calculate outcomes. Entropy analysis was the method used to document model sensitivity. As with the RRM, the method has now been applied to a wide series of sites and questions, from forestry management, to invasive species, to disease, the interaction of ecological and human health endpoints, the flows of large rivers, and now the efficacy and risks of synthetic biology. The application of both methods have pointed to the incompleteness of the fields of environmental chemistry, toxicology, and risk assessment. The low frequency of exposure-response experiments and proper analysis have limited the available outputs for building appropriate CPTs. Interactions between multiple chemicals, landscape characteristics, population dynamics and community structure have been poorly characterized even for critical environments. A better strategy might have been to first look at the requirements of modern risk assessment approaches and then set research priorities. Integr Environ Assess Manag 2021;17:79-94. © 2020 SETAC.
2012 年，发表了一项区域风险评估，该评估将贝叶斯网络（BN）应用于相对风险模型的结构中。相对风险模型（RRM）的原始结构于 20 世纪 90 年代末发表，并在接下来的十年中得到了发展。RRM 与蒙特卡罗分析相结合，用于计算多个地点和各种问题的风险。这些地点包括流域、陆地系统和海洋环境，包括非本地物种、废水、农药、营养物质和管理选择等胁迫因素。然而，显然原始方法存在局限性。2009 年，相对风险模型过渡到 BN 的结构。贝叶斯网络具有几个明显的优势。首先，BN 天生就包含类别，并且像相对风险模型一样，使用等级来描述系统。其次，可以使用多种途径和条件概率表（CPT）来组合多个胁迫因素之间的相互作用，以计算结果。熵分析是用于记录模型灵敏度的方法。与 RRM 一样，该方法现已应用于广泛的一系列地点和问题，从林业管理到入侵物种，再到疾病，生态和人类健康终点的相互作用，大河的流动，以及现在的合成生物学的功效和风险。这两种方法的应用都表明环境化学、毒理学和风险评估领域存在不完整性。暴露-反应实验和适当分析的频率较低，限制了构建适当 CPT 的可用输出。即使对于关键环境，多种化学物质、景观特征、人口动态和群落结构之间的相互作用也描述不足。更好的策略可能是首先考虑现代风险评估方法的要求，然后确定研究重点。综合环境评估与管理 2021;17:79-94. © 2020 SETAC.
