Assessment of drinking water quality and identifying pollution sources in a chromite mining region.

Autor: Mohammadpour A; Research Center for Social Determinants of Health, Jahrom University of Medical Sciences, Jahrom, Iran. Electronic address: aminmohammadpour7777@gmail.com., Gharehchahi E; Department of Environmental Health Engineering, School of Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran., Gharaghani MA; Department of Environmental Health Engineering, School of Health, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran., Shahsavani E; Research Center for Social Determinants of Health, Jahrom University of Medical Sciences, Jahrom, Iran., Golaki M; Research Center for Social Determinants of Health, Jahrom University of Medical Sciences, Jahrom, Iran., Berndtsson R; Division of Water Resources Engineering, Department of Building and Environmental Technology, Lund University, Box 118, SE-221 00 Lund, Sweden; Centre for Advanced Middle Eastern Studies, Lund University, Box 201, SE-221 00 Lund, Sweden., Khaneghah AM; Halal Research Center of IRI, Iran Food and Drug Administration, Ministry of Health and Medical Education, Tehran, Iran., Hashemi H; Environmental Health, Department of Environmental Health Engineering, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran. Electronic address: h_hashemi@sums.ac.ir., Abolfathi S; School of Engineering, University of Warwick, Coventry CV47AL, United Kingdom. Electronic address: soroush.Abolfathi@warwick.ac.uk.
Jazyk: angličtina
Zdroj: Journal of hazardous materials [J Hazard Mater] 2024 Oct 04; Vol. 480, pp. 136050. Date of Electronic Publication: 2024 Oct 04.
DOI: 10.1016/j.jhazmat.2024.136050
Abstrakt: Water sources near mining regions are often susceptible to contamination from toxic elements. This study employs machine learning (ML) techniques to evaluate drinking water quality and identify pollution sources near a chromite mine in Iran. Human health risks were assessed using both deterministic and probabilistic approaches. Findings revealed that concentrations of calcium (Ca), chromium (Cr), lithium (Li), magnesium (Mg), and sodium (Na) in the water samples exceeded international safety standards. The Unweighted Root Mean Square water quality index (RMS-WQI) and Weighted Quadratic Mean (WQM-WQI) categorized all water samples as 'Fair', with average scores of 67.95 and 67.19, respectively. Of the ML models tested, the Extra Trees (ET) algorithm emerged as the top predictor of WQI, with Mg and strontium (Sr) as key variables influencing the scores. Principal component analysis (PCA) identified three distinct clusters of water quality parameters, highlighting influences from both local geology and anthropogenic activities. The highest average hazard quotient (HQ) for Cr was 1.71 for children, 1.27 for adolescents, and 1.05 for adults. Monte Carlo simulation for health risk assessment indicated median hazard index (HI) of 4.48 for children, 3.58 for teenagers, and 2.98 for adults, all exceeding the acceptable threshold of 1. Total carcinogenic risk (TCR) exceeded the EPA's acceptable level for 99.38 % of children, 98.24 % of teenagers, and 100 % of adults, with arsenic (As) and Cr identified as the main contributors. The study highlights the need for urgent mitigation measures, recommending a 99 % reduction in concentrations of key contaminants to lower both carcinogenic and non-carcinogenic risks to acceptable levels.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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