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在环境化学研究中绘制机器学习的兴起:一个图书法分析分析

Bojana Stanic¹, Nebojsa Andric¹

¹Department of Biology and Ecology, Faculty of Sciences, University of Novi Sad, Trg Dositeja Obradovica 2, 21000 Novi Sad, Serbia.

|October 28, 2025

概括

机器学习 (ML) 正在彻底改变环境化学物质监测和风险评估. 虽然机器学习对人类健康充满希望,但当前的研究大力支持环境终点,强调需要更好地整合健康数据.

科学领域:

环境科学环境科学
毒理学毒理学毒理学
数据科学数据科学数据科学

背景情况:

机器学习 (ML) 越来越多地被用于环境化学分析.
自2015年以来出版物的大幅增加表明研究兴趣日益增长.
环境科学期刊主导着出版领域,主要来自中国和美国.

研究的目的:

在环境化学监测和危险评估中绘制ML应用的地图.
确定关键的研究主题,算法和新兴趋势.
揭露在ML研究中的化学品覆盖和人类健康整合方面的差距.

主要方法:

在1985年至2025年期间,利用Web of Science核心集合对3150篇同行评审文章进行了图书统计分析.
在VOSviewer和R.进行的同时引用,同时发生和时间趋势分析.
关键词频率分析以确定主题集群和研究偏见.

主要成果:

确定了八个主要的主题集群,重点是ML模型开发,水质,定量结构-活性关系和per-/polyfluoroalkyl物质.
XGBoost 和随机森林是最常被引用的ML算法.
在当前的研究中,对环境终点 (4:1) 存在着明显的偏差,而不是人类健康终点.
新兴主题包括气候变化,微塑料和数字土壤绘图.
,和酸盐被确定为快速生长但研究不足的化学物质.

结论:

ML是环境化学评估的转变性工具,具有风险评估的潜力.
目前的研究显示,在解决人类健康终点和整合健康数据方面存在重大差距.
建议包括扩大化学品范围,加强健康数据链接,利用可解释的人工智能,并促进国际合作,以进行可行的风险评估.

关键词:

在VOSviewer中使用VOSviewer.文献计量分析的分析同时发生的映射映射.环境化学品环境化学品人类健康风险评估评估人类健康风险评估机器学习是机器学习.

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