Groundwater characterization and non-carcinogenic and carcinogenic health risk assessment of nitrate exposure in the Mahanadi River Basin of India.

Agriculture is the mainstay of India's economy and chemical fertilizers have been extensively used to meet increasing demands. Anthropogenic interventions at the soil surface, especially the application of nitrogenous fertilizers in agricultural fields, provide essential nutrients but become major pollutant sources in terrestrial ecosystems and aquatic environments. Groundwater samples from phreatic aquifers of the Mahanadi River Basin, Chhattisgarh, India, showed that the Ca2+-Mg2+-HCO3- freshwater type dominates, followed by the Ca2+-Mg2+-Cl- and Na+-HCO3- types. Increasing trends in the ionic ratios of (NO3-+Cl-)/HCO3- over TDS and of NO3-/Cl- over Cl- indicated the significant impact of anthropogenic pollution on groundwater contamination. Deterministic and probabilistic approaches were used to assess the non-carcinogenic and carcinogenic health risks of nitrate to children and adults. Both approaches produced the same results and indicated children were more prone to non-carcinogenic health risk than adults. An excess gastric cancer risk (ER) exposure model showed that approximately 42% of the groundwater samples had a non-negligible ER (1.00 × 10-4 to 1.00 × 10-5). Sensitivity analysis indicated groundwater nitrate concentration, ingestion rate, and the percentage of nitrite from nitrate were the most significant variables in determining HI and ER. It is suggested to adopt proper management of control policies for reducing the elevated groundwater nitrate concentration in the present study area.

Prediction of groundwater quality using efficient machine learning technique.

To ensure safe drinking water sources in the future, it is imperative to understand the quality and pollution level of existing groundwater. The prediction of water quality with high accuracy is the key to control water pollution and the improvement of water management. In this study, a deep learning (DL) based model is proposed for predicting groundwater quality and compared with three other machine learning (ML) models, namely, random forest (RF), eXtreme gradient boosting (XGBoost), and artificial neural network (ANN). A total of 226 groundwater samples are collected from an agriculturally intensive area Arang of Raipur district, Chhattisgarh, India, and various physicochemical parameters are measured to compute entropy weight-based groundwater quality index (EWQI). Prediction performances of models are determined by introducing five error metrics. Results showed that DL model is the best prediction model with the highest accuracy in terms of R2, i.e., R2 = 0996 against the RF (R2 = 0.886), XGBoost (R2 = 0.0.927), and ANN (R2 = 0.917). The uncertainty of the DL model output is cross-verified by running the proposed algorithm with newly randomized dataset for ten times, where minor deviations in the mean value of performance metrics are observed. Moreover, input variable importance computed by prediction models highlights that DL model is the most realistic and accurate approach in the prediction of groundwater quality.

Medical Geological assessment of fluoride contaminated groundwater in parts of Indo-Gangetic Alluvial plains.

As drinking water is considered as a major pathway of exposure to fluoride in the human body, an endeavor has been made for the assessment of the non-carcinogenic health risk by using hazard quotient (HQ) of fluoride for males, females, and children separately in fluoride affected ground water areas of Indo-Gangetic Alluvial Plains. The study suggests that children groups are more prone to the non-carcinogenic risk of fluoride in the area as HQ for fluoride is more than unitary in 44% (Pre-monsoon) and 38% (Post-monsoon) samples respectively. Field survey conducted in fluoride-affected villagers of the study area portrays cases of mottling of teeth and bone deformities depending on the duration and dosage of fluoride consumption. Petrographic observations of host rocks coupled with molar ratios of chemical species studies exemplify that weathered material developed over the granite-gneiss, mica-schist, amphibolite, granitic intrusive and pegmatite veins due to weathering and extensive water-rock interaction resulting higher concentration of fluoride in groundwater. Likewise, the base exchange index (r1) and meteoric genesis index (r2) advocates that most of the samples belong to Na+-HCO3- type and meteoric origin respectively, and substantiate longer residence time of water along with solute acquisition processes are responsible for elevated fluoride in groundwater. It is, therefore, solar energy-driven electrolytic de-fluoridation technology ought to be provided on a priority basis to the affected inhabitants besides the implementation of rainwater harvesting schemes for mitigation/ dilution of elevated fluoride concentration.

Geochemical signatures and isotopic systematics constraining dynamics of fluoride contamination in groundwater across Jamui district, Indo-Gangetic alluvial plains, India.

A data set of 76 water samples are obtained from surface and sub-surface water bodies to investigate chemical parameters and stable isotopic signatures in order to drive factors leading to fluoride (F-) contamination in groundwater of parts of Jamui district, India. Hydrochemical facies reveals that F- concentration is lower in Ca2+-HCO3- facies representative of recharge area, while discharge area has a tendency towards Na+-HCO3- facies with elevated F- concentration. The ionic ratios Na+/Ca2+>1, Na+/Cl->1, (Ca2++Mg2+)/HCO3-<1, Na++K+ = 0.5TZ+ and Ca2++Mg2+ = TZ+ witness silicate weathering by water-rock interaction coupled with ion exchange and prolonged residence time, are the principle factors for fluoride contamination (3.6 mg/L to 5.8 mg/L) in 67% of deeper bore wells. Geochemical modelling testifies excess of alkalinity due to the dominance of bicarbonate ion leading to calcite precipitation and dissolution of fluoride in solution contributing to fluoride contamination. The chemometric analysis reveals that the water chemistry of the study area is controlled by both anthropogenic and natural sources, and enrichment of fluoride in groundwater is possibly from geogenic source (fractured granite gneiss). The stable isotope plot shows that most of the samples fall along local meteoric water line indicating that the groundwater is originated from local precipitation with a possibility of evaporative enrichment. Groundwater enriched in δ18O is positively correlated with F- suggesting evaporation and longer residence time of water. Spatially elevated F- prevails in the eastern bank of Kiul River and along the groundwater flow direction, which is attributed to control of dynamics of hydrogeological conditions.

Recurrent Ventricular Tachycardia Due to Long QT Syndrome.

Long QT syndrome (LQTS) is a rare inherited heart condition in which delayed repolarization of the heart following a heartbeat, increases the risk of episodes of Torsades de pointes (TdP, a form of irregular heartbeat that originates from the ventricles). These episodes may lead to palpitations, fainting, and sudden death due to ventricular fibrillation. Episodes may be provoked by various stimuli, depending on the subtype of the condition. We are reporting a case of 37 years old male whom we diagnosed to have long QT syndrome on the basis of clinical and ECG findings.