Suppr超能文献

运用贝叶斯网络评估自然资源损害的新方法。

A novel approach to assessing natural resource injury with Bayesian networks.

机构信息

US Geological Survey, Columbia Environmental Research Center, Columbia, Missouri, USA.

US Forest Service, Pacific Northwest Research Station, Portland, Oregon, USA.

出版信息

Integr Environ Assess Manag. 2024 Mar;20(2):562-573. doi: 10.1002/ieam.4836. Epub 2023 Oct 5.

Abstract

Quantifying the effects of environmental stressors on natural resources is problematic because of complex interactions among environmental factors that influence endpoints of interest. This complexity, coupled with data limitations, propagates uncertainty that can make it difficult to causally associate specific environmental stressors with injury endpoints. The Natural Resource Damage Assessment and Restoration (NRDAR) regulations under the Comprehensive Environmental Response, Compensation, and Liability Act and Oil Pollution Act aim to restore natural resources injured by oil spills and hazardous substances released into the environment; exploration of alternative statistical methods to evaluate effects could help address NRDAR legal claims. Bayesian networks (BNs) are statistical tools that can be used to estimate the influence and interrelatedness of abiotic and biotic environmental variables on environmental endpoints of interest. We investigated the application of a BN for injury assessment using a hypothetical case study by simulating data of acid mine drainage (AMD) affecting a fictional stream-dwelling bird species. We compared the BN-generated probability estimates for injury with a more traditional approach using toxicity thresholds for water and sediment chemistry. Bayesian networks offered several distinct advantages over traditional approaches, including formalizing the use of expert knowledge, probabilistic estimates of injury using intermediate direct and indirect effects, and the incorporation of a more nuanced and ecologically relevant representation of effects. Given the potential that BNs have for natural resource injury assessment, more research and field-based application are needed to determine their efficacy in NRDAR. We expect the resulting methods will be of interest to many US federal, state, and tribal programs devoted to the evaluation, mitigation, remediation, and/or restoration of natural resources injured by releases or spills of contaminants. Integr Environ Assess Manag 2024;20:562-573. Published 2023. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

摘要

量化环境胁迫因子对自然资源的影响具有一定的问题,因为影响关注终点的环境因素之间存在复杂的相互作用。这种复杂性加上数据限制,增加了不确定性,使得很难将特定的环境胁迫因子与损伤终点因果关联起来。《综合环境反应、补偿和责任法案》和《油污法》下的自然资源损害评估和恢复(NRDAR)法规旨在恢复因溢油和有害物质释放到环境中而受损的自然资源;探索替代统计方法来评估影响可能有助于解决 NRDAR 法律索赔。贝叶斯网络(BN)是一种统计工具,可用于估计非生物和生物环境变量对关注的环境终点的影响和相互关系。我们通过模拟影响虚构溪流栖息鸟类物种的酸性矿山排水(AMD)数据,研究了 BN 在损伤评估中的应用。我们将 BN 生成的损伤概率估计与使用水和沉积物化学毒性阈值的更传统方法进行了比较。与传统方法相比,贝叶斯网络具有几个明显的优势,包括正式利用专家知识、使用中间直接和间接效应的概率估计损伤、以及纳入更细致和生态相关的效应表示。鉴于 BN 具有进行自然资源损伤评估的潜力,需要进行更多的研究和现场应用,以确定其在 NRDAR 中的效果。我们期望这些方法将引起许多致力于评估、减轻、修复和/或恢复因污染物释放或溢出而受损的自然资源的美国联邦、州和部落计划的兴趣。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验