Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment
Document Type
Article
Publication Date
11-1-2024
Abstract
Metal pollutants pose significant and enduring threats to human health and the environment, mainly due to their non-biodegradable nature. Traditional monitoring of these pollutants involves costly and time-consuming analytical methods. However, this conventional approach struggles to grasp the intricate and nonlinear connections between metals and the factors affecting pollution dynamics. Machine learning (ML) has emerged as a promising solution, capable of handling large datasets, diverse inputs, and complex patterns. This review highlights insights from developing innovative ML models to monitor metal pollutants. Beginning with an overview of commonly used ML algorithms for this purpose, the review comprehensively explores recent advancements in ML applications across various environmental matrices. It covers source apportionment, detection, quantification, and ecological risk assessment in solids, water, and air. The review also identifies research gaps and proposes future directions for maximizing ML's potential in preventing and managing environmental metal pollution.
Identifier
85204713906 (Scopus)
Publication Title
TrAC - Trends in Analytical Chemistry
External Full Text Location
https://doi.org/10.1016/j.trac.2024.117980
e-ISSN
18793142
ISSN
01659936
Volume
180
Grant
109,139,140
Fund Ref
China Agricultural Research System
Recommended Citation
Nkinahamira, François; Feng, Anqi; Zhang, Lijie; Rong, Hongwei; Ndagijimana, Pamphile; Guo, Dabin; Cui, Baihui; and Zhang, Huichun, "Machine learning approaches for monitoring environmental metal pollutants: Recent advances in source apportionment, detection, quantification, and risk assessment" (2024). Faculty Publications. 114.
https://digitalcommons.njit.edu/fac_pubs/114
