Integrating non-target analysis and machine learning: a framework for contaminant source identification
Machine learning-based non-target analysis (ML-based NTA) faces the critical challenge of linking complex chemical signals to contamination sources. This review proposes a systematic framework of ML-assisted NTA for contaminant source identification, emphasizing the strategies and considerations of key steps in data processing, pattern recognition, and model validation. The framework provides practical guidance for translating raw NTA data to actionable environmental insights that support informed decision-making.
Authors
- Wen-Ze Wu (ORCID: https://orcid.org/0000-0002-9742-3151)
- Xuerong Wang (ORCID: https://orcid.org/0000-0002-3040-9435)
- Guangming Zhu (ORCID: https://orcid.org/0000-0001-5519-3135)
- Pan Ding (ORCID: https://orcid.org/0000-0002-5237-2194)
- Penghui Du (ORCID: https://orcid.org/0000-0002-1422-0501)
- Mengzhu Xue
- Yanchao Jin
- Jining Liu
- Xinhui Liu (ORCID: https://orcid.org/0000-0002-8171-9214)
- Yan-qing Ding (ORCID: https://orcid.org/0000-0002-5265-8459)
- Lily Rui Liang (ORCID: https://orcid.org/0000-0003-1907-6008)
- J. N. Yang (ORCID: https://orcid.org/0000-0003-1692-8738)
- Peng Liu (ORCID: https://orcid.org/0000-0001-9964-7522)
- Leping Li (ORCID: https://orcid.org/0000-0003-4208-0259)
- Pengcheng Li (ORCID: https://orcid.org/0000-0001-9669-5916)
- Xin-Yi Jiao
- Cai-Shan Wang
Institutions
- Fujian Normal University (CN)
- Chinese University of Hong Kong (HK)
- Beijing Normal University (CN)
Publication Details
- Journal
- npj Clean Water
- Published
- 2025-08-18
- DOI
- https://doi.org/10.1038/s41545-025-00504-z
- Citations
- 1
- Primary Topic
- Air Quality Monitoring and Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China