Abstract
River Mahi drains through semi-arid regions (Western India) and is a major Arabian Sea draining river. As the principal surface water source, its water quality is important to the regional population. Therefore, the river water was sampled extensively (n = 64, 16 locations, 4 seasons and 2 years) and analyzed for 11 trace elements (TEs; Sr, V, Cu, Ni, Zn, Cd, Ba, Cr, Mn, Fe, and Co). Machine learning (ML) and multivariate statistical analysis (MVSA) were applied to investigate their possible sources, spatial–temporal-annual variations, evaluate multiple water quality parameters [heavy metal pollution index (HPI), heavy metal evaluation index (HEI)], and health indices [hazard quotient (HQ), and hazard index (THI)] associated with TEs. TE levels were higher than their corresponding world average values in 100% (Sr, V and Zn), 78%(Cu), 41%(Ni), 27%(Cr), 9%(Cd), 8%(Ba), 8%(Co), 6%(Fe), and 0%(Mn), of the samples. Three principal components (PCs) accounted for 74.5% of the TE variance: PC-1 (Fe, Co, Mn and Cu) and PC-2 (Sr and Ba) are contributed from geogenic sources, while PC-3 (Cr, Ni and Zn) are derived from geogenic and anthropogenic sources. HPI, HEI, HQ and THI all indicate that water quality is good for domestic purposes and poses little hazard. ML identified Random forest as the most suitable model for predicting HEI class (accuracy: 92%, recall: 92% and precision: 94%). Even with a limited dataset, the study underscores the potential application of ML to predictive classification modeling.
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Acknowledgements
We acknowledge the support received from the Physical Research Laboratory during our field campaigns and chemical analyses. We thank Pandit Deendayal Energy University for providing financial support in conducting field trips. We thank all the anonymous reviewers and the Editor for their detailed comments, which improved the quality of the manuscript.
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Study conception and design by AD and SS. Research work performed by SS. Manuscript written by AD and SS. Analytical measurements (at Physical Research Laboratory Ahmedabad) performed by SS. with AK. and MG. Machine learning algorithms were performed by SS, and PS. Mapping was done by RR.
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Singh, S., Das, A., Sharma, P. et al. Spatiotemporal variations, sources, pollution status and health risk assessment of dissolved trace elements in a major Arabian Sea draining river: insights from multivariate statistical and machine learning approaches. Environ Geochem Health 46, 130 (2024). https://doi.org/10.1007/s10653-024-01885-9
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DOI: https://doi.org/10.1007/s10653-024-01885-9