Advancing non-target analysis of emerging environmental contaminants with machine learning: Current status and future implications

IF 10.3 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Alexa Canchola , Lillian N. Tran , Wonsik Woo , Linhui Tian , Ying-Hsuan Lin , Wei-Chun Chou
{"title":"Advancing non-target analysis of emerging environmental contaminants with machine learning: Current status and future implications","authors":"Alexa Canchola ,&nbsp;Lillian N. Tran ,&nbsp;Wonsik Woo ,&nbsp;Linhui Tian ,&nbsp;Ying-Hsuan Lin ,&nbsp;Wei-Chun Chou","doi":"10.1016/j.envint.2025.109404","DOIUrl":null,"url":null,"abstract":"<div><div>Emerging environmental contaminants (EECs) such as pharmaceuticals, pesticides, and industrial chemicals pose significant challenges for detection and identification due to their structural diversity and lack of analytical standards. Traditional targeted screening methods often fail to detect these compounds, making non-target analysis (NTA) using high-resolution mass spectrometry (HRMS) essential for identifying unknown or suspected contaminants. However, interpreting the vast datasets generated by HRMS is complex and requires advanced data processing techniques. Recent advancements in machine learning (ML) models offer great potential for enhancing NTA applications. As such, we reviewed key developments, including optimizing workflows using computational tools, improved chemical structure identification, advanced quantification methods, and enhanced toxicity prediction capabilities. It also discusses challenges and future perspectives in the field, such as refining ML tools for complex mixtures, improving inter-laboratory validation, and further integrating computational models into environmental risk assessment frameworks. By addressing these challenges, ML-assisted NTA can significantly enhance the detection, quantification, and evaluation of EECs, ultimately contributing to more effective environmental monitoring and public health protection.</div></div>","PeriodicalId":308,"journal":{"name":"Environment International","volume":"198 ","pages":"Article 109404"},"PeriodicalIF":10.3000,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environment International","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0160412025001552","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Emerging environmental contaminants (EECs) such as pharmaceuticals, pesticides, and industrial chemicals pose significant challenges for detection and identification due to their structural diversity and lack of analytical standards. Traditional targeted screening methods often fail to detect these compounds, making non-target analysis (NTA) using high-resolution mass spectrometry (HRMS) essential for identifying unknown or suspected contaminants. However, interpreting the vast datasets generated by HRMS is complex and requires advanced data processing techniques. Recent advancements in machine learning (ML) models offer great potential for enhancing NTA applications. As such, we reviewed key developments, including optimizing workflows using computational tools, improved chemical structure identification, advanced quantification methods, and enhanced toxicity prediction capabilities. It also discusses challenges and future perspectives in the field, such as refining ML tools for complex mixtures, improving inter-laboratory validation, and further integrating computational models into environmental risk assessment frameworks. By addressing these challenges, ML-assisted NTA can significantly enhance the detection, quantification, and evaluation of EECs, ultimately contributing to more effective environmental monitoring and public health protection.
用机器学习推进新兴环境污染物的非目标分析:现状和未来影响
新兴环境污染物(EECs),如药品、农药和工业化学品,由于其结构多样性和缺乏分析标准,对检测和鉴定构成了重大挑战。传统的靶向筛选方法往往无法检测到这些化合物,因此使用高分辨率质谱(HRMS)进行非目标分析(NTA)对于识别未知或可疑的污染物至关重要。然而,解释由HRMS生成的大量数据集是复杂的,需要先进的数据处理技术。机器学习(ML)模型的最新进展为增强NTA应用提供了巨大的潜力。因此,我们回顾了关键的发展,包括使用计算工具优化工作流程,改进化学结构识别,先进的量化方法和增强的毒性预测能力。它还讨论了该领域的挑战和未来前景,例如改进用于复杂混合物的ML工具,改进实验室间验证,以及进一步将计算模型集成到环境风险评估框架中。通过解决这些挑战,ml辅助的NTA可以显著增强对eec的检测、量化和评估,最终有助于更有效的环境监测和公共健康保护。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Environment International
Environment International 环境科学-环境科学
CiteScore
21.90
自引率
3.40%
发文量
734
审稿时长
2.8 months
期刊介绍: Environmental Health publishes manuscripts focusing on critical aspects of environmental and occupational medicine, including studies in toxicology and epidemiology, to illuminate the human health implications of exposure to environmental hazards. The journal adopts an open-access model and practices open peer review. It caters to scientists and practitioners across all environmental science domains, directly or indirectly impacting human health and well-being. With a commitment to enhancing the prevention of environmentally-related health risks, Environmental Health serves as a public health journal for the community and scientists engaged in matters of public health significance concerning the environment.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信