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Updated: May 16, 2026

Experimental Multiscale Methodology for Predicting Material Fouling Resistance

Experimental Multiscale Methodology for Predicting Material Fouling Resistance

通过机器学习有效预测水生环境中的有机分子结构和风险

Shengyu Cao¹, Youliang Tian², Rusong Zhao³

¹State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; School of Environment, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 330106, China.

The Science of the total environment

|January 4, 2025

概括

这项研究开发了新的模型来识别和评估水中的有机寡合物的风险. 这些模型揭示了广泛的污染,石油化工等工业来源是环境污染的主要贡献者.

科学领域:

环境化学环境化学
分析化学分析化学
毒理学毒理学毒理学

背景情况:

质谱数据库缺少许多有机寡合物的分子数据.
现有的风险模型不足以评估这些新出现的污染物.
数以千计的有机单体存在,复杂化了分析和风险评估.

研究的目的:

开发有机结构及其环境风险的预测模型.
在城市水系统中识别和量化有机寡合物.
确定有机污染的来源和分布.

主要方法:

利用气相色谱-轨道质谱仪 (GC-Orbitrap-MS) 进行分子和碎片离子分析.
开发了使用神经网络和支持向量机的分类和回归模型.
应用神经网络来预测持久,生物累积和有毒 (PBT) 潜力.

主要成果:

在中国21个城市的水中鉴定出116种有机寡合物.
在17个城市,危险指数超过1,表明存在重大风险.
染色,织和石化行业被确定为主要排放来源;石化是全国性的来源.

结论:

开发的方法允许对有机化合物进行准确的风险评估.
在城市水域中,有机污染很普遍,造成环境风险.
这种方法对于评估缺乏全面的质谱数据库的化学品是有价值的.

关键词:

新出现的污染物新出现的污染物机器学习是机器学习.不针对目标的查.有机是有机中的一种.这是一个PBT PBT.

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