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通过非目标分析和机器学习模型从新兴的PFAS中识别优先的PBT类化合物

Bao-Cang Han¹, Jin-Song Liu², Aaron Bizimana³

¹Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Jiaxing-Tongji Environmental Research Institute, 1994 Linggongtang Road, Jiaxing, 314051, Zhejiang Province, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.

Environmental pollution (Barking, Essex : 1987)

|October 2, 2023

概括

新兴的per-和多醇基物质 (PFAS) 由于未知的持久性,生物积累和毒性 (PBT) 构成风险. 这项研究确定了282个PBT PFAS,有助于监管评估和污染物查.

科学领域:

环境化学环境化学
毒理学毒理学毒理学
计算化学的计算化学

背景情况:

传统的per-和多基基物质 (PFAS) 正被新兴的PFAS所取代,其环境和健康特性基本上是未知的.
评估这些新化学品的持久性,生物积累和毒性 (PBT) 对环境保护和公共卫生至关重要.

研究的目的:

编制一个全面的新兴PFAS数据库.
用先进的机器学习模型评估新兴PFAS的PBT特性.
确定高优先级的新兴PFAS,以引起监管和行业的注意.

主要方法:

使用非目标分析来识别新兴的PFAS.
创建了一个新兴PFAS的数据库.
机器学习模型,包括图表注意力网络,Insubria PBT指数,EAS-E套件,VEGA和ProTox-II,被用于PBT属性预测.
在化学品供应商和专利数据库中搜索优先级的PFAS.

主要成果:

总共有282个新兴的PFAS同类 (21.8%) 被预测为PBT.
在工业和环境样本中检测到140个PBT PFAS,分为四个主要组.
10.1%的优先级PFAS与化学品供应商有关,而19.6%与专利有关.
水性薄膜形成泡和化工厂被确定为主要来源.

结论:

开发的数据库和PBT查方法为监管机构提供关键数据,如美国毒物控制法和斯德哥尔摩公约.
这种方法为优先考虑新出现的有机污染物提供了有价值的工具.
这些发现强调了对新化学物质进行主动评估的必要性.

关键词:

新兴的PFAS正在出现.机器学习是机器学习.没有目标分析的分析优先考虑的列表列表.查检查查检查查检查

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