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.

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.

Sources, controls, and probabilistic health risk assessment of fluoride contamination in groundwater from a semi-arid region in Gujarat, Western India: An isotope-hydrogeochemical perspective.

Fluoride contamination in groundwaters of a rural region in semi-arid Western India has been studied using combination of geochemical-and-isotopic techniques, in conjunction with Health Quotient assessment approach. The objective of this study is to determine the sources and controls on fluoride content and to evaluate probabilistic non-carcinogenic risk associated with its long-term consumption. F- ranges from 0.3 to 12 mg L-1, shows high spatial variability, and ~ 35% of the samples have F- > 1.5 mg L-1 (WHO maximum limit for drinking). Two sources are identified: high F- results from water-rock interaction of F-bearing minerals in granites and gneisses, while phosphate fertilizers can contribute up to ~ 0.46 mg L-1 of groundwater F- that can be significant for low F- samples. High F- samples are characterized by high pH, Na and alkalinity, and low Ca. Calcite precipitation drives the solubility of F-bearing minerals. Kinetic fractionation of water isotopes (18O and 2H) demonstrates that evaporation plays role in enriching groundwater F-. Non-carcinogenic risk, estimated by Hazard Quotient ([Formula: see text]), ranges from 0.13-5.72 to 0.26-11.86 for adult and children, respectively. Conservative estimate shows that ~ 0.467 million of adults and~0.073 million of children in four sub-districts are under the risk of fluorosis-while the residents of other five sub-districts remain safe from it. Finally, we suggest stakeholders to install F- treatment plants to ensure the health safety of local residents in the high-risk zones, create awareness in farmers for optimum use of fertilizers, and promote rainwater harvesting, for better management of groundwater resources and quality in the region.