Emerging applications of fluorescence excitation-emission matrix with machine learning for water quality monitoring: A systematic review.

Journal: Water research
PMID: 39970782

Abstract

Fluorescence excitation-emission matrix (FEEM) spectroscopy is increasingly utilized in water quality monitoring due to its rapid, sensitive, and non-destructive measurement capabilities. The integration of machine learning (ML) techniques with FEEM offers a powerful approach to enhance data interpretation and improve monitoring efficiency. This review systematically examines the application of ML-FEEM in urban water systems across three primary tasks of ML: classification, regression, and pattern recognition. Contributed by the effectiveness of ML in nonlinear and high dimensional data analysis, ML-FEEM achieved superior accuracy and efficiency in pollutant qualification and quantification. The fluorescence features extracted through ML are more representative and hold potential for generating new FEEM samples. Additionally, the rich visualization capabilities of ML-FEEM facilitate the exploration of the migration and transformation of dissolved organic matter in water. This review underscores the importance of leveraging the latest ML advancements to uncover hidden information within FEEM data, and advocates for the use of pattern recognition methods, represented by self-organizing map, to further elucidate the behavior of pollutants in aquatic environments. Despite notable advancements, several issues require careful consideration, including the portable or online setups for FEEM collection, the standardized pretreatment processes for FEEM analysis, and the smart feedback of long-term FEEM governance.

Authors

  • Wancheng Cai
    State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Yangpu District, Shanghai 200092, China; Ministry of Education Key Laboratory of Yangtze River Water Environment, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
  • Cheng Ye
    Department of Computer Science, Vanderbilt University, 2301 Vanderbilt Place, PMB 351679, Nashville, TN, 37235-1679, USA. cheng.ye.1@vumc.org.
  • Feiyang Ao
    State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Yangpu District, Shanghai 200092, China; Ministry of Education Key Laboratory of Yangtze River Water Environment, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
  • Zuxin Xu
    State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Yangpu District, Shanghai 200092, China; Ministry of Education Key Laboratory of Yangtze River Water Environment, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
  • Wenhai Chu
    State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Yangpu District, Shanghai 200092, China; Ministry of Education Key Laboratory of Yangtze River Water Environment, Tongji University, Shanghai 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China. Electronic address: 1world1water@tongji.edu.cn.

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