Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges

被引：8
作者：
Maurya B.M. [1 ]
Yadav N. [1 ]
T A. [2 ]
J S. [2 ]
A S. [1 ]
V P. [3 ]
Iyer M. [4 ,5 ]
Yadav M.K. [5 ]
Vellingiri B. [1 ]
机构：
[1] Human Cytogenetics and Stem Cell Laboratory, Department of Zoology, School of Basic Sciences, Central University of Punjab, Punjab, Bathinda
[2] Department of Computer Applications, Bharathiar University, Coimbatore
[3] Department of Computer Science and Engineering, Karpagam Academy of Higher Education, Pollachi Main Road, Eachanari Post, Tamil Nadu, Coimbatore
[4] Centre for Neuroscience, Department of Biotechnology, Karpagam Academy of Higher Education, Tamil Nadu, Coimbatore
[5] Department of Microbiology, Central University of Punjab, Punjab, Bathinda
来源：
Chemosphere | 2024年 / 353卷
关键词：
Artificial intelligence; Heavy metals; Human diseases; Machine learning; Wastewater detection;
D O I：
10.1016/j.chemosphere.2024.141474
中图分类号：
学科分类号：
摘要：
Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and in humans upon entering their systems. Recent times have seen an increase in such HM influx incident rates. This requires an instant response in this regard to review the challenges in the available classical methods for HM detection and removal. As well as provide an opportunity to explore the applications of artificial intelligence (AI) and machine learning (ML) for the identification and further redemption of water and wastewater from the HMs. This review of research focuses on such applications in conjunction with the available in-silico models producing worldwide data for HM levels. Furthermore, the effect of HMs on various disease progressions has been provided, along with a brief account of prediction models analysing the health impact of HM intoxication. Also discussing the ethical and other challenges associated with the use of AI and ML in this field is the futuristic approach intended to follow, opening a wide scope of possibilities for improvement in wastewater treatment methodologies. © 2024
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