Editorial: Indian scenario on exposure, characterization and health risk appraisal of toxic contaminants in groundwater

Groundwater serves as a major source for domestic, agricultural, and industrial needs in India, serving the demands of both the rural and urban population. However, the increasing urban and industrial growth, improper disposal of domestic and industrial waste, and the extensive use of agrochemicals have contributed to the contamination of groundwater. The consumption of contaminated water may lead to exposure to toxic contaminants, including heavy metals, pesticides, and organic pollutants, posing a grave health threat to millions of people. Frequent monitoring of contaminant concentrations, especially heavy metals, is necessary to assess the nature and extent of the toxic contaminants. Once the contamination is identified, a health risk appraisal is needed to evaluate the probability and severity of adverse health effects based on exposure levels, and it will provide a foundation for targeted interventions. The health risk assessment is required to identify the vulnerable section of the population and design the mitigation measures accordingly. The successful implementation of mitigation measures for toxic contaminants in groundwater requires active involvement of the community, supported by collaborative efforts between government agencies, research institutions, and nongovernmental organisations. This research subject aligns with the objectives of Sustainable Development Goals 3 and 6, which pertain to 'Good Health & Well-Being' and 'Clean Water & Sanitation', respectively.This special issue comprises four peer-reviewed articles covering two reviews and two research articles, primarily focusing on the contamination modelling through application of artificial neural network method, metal contamination in the groundwater and associated health risk assessment, and comparative analysis of conventional vs advanced methods used for bioremediation of heavy metals. The studies incorporated in this research topic indicated the importance of groundwater monitoring for heavy metal concentration and to evaluate the severity of contamination by using various pollution indices, including Heavy Metal Pollution Index (HPI), Nemerow Index (NI), and Ecological Risk Index (ERI). They also put emphasis on the assessment of health risk associated with the metal contamination by calculating Chronic Daily Intake (CDI), Health Quotient (HQ), Hazard Quotient (HI), and Cancer Risk (TCR) values for both adults and children.Kumari et al. evaluated the application of an artificial neural network (ANN) for future fluoride level prediction based on the primary data collected through water sample analysis. The results obtained through correlation analysis can be used to determine the input parameters for the ANN model. Besides ANN, other models such as logistic regression (LR), Random Forest (RF), Monte Carlo Simulation (MCS), Support Vector Machine (SVM) and classification and regression tree (CART) methods can also be used to predict the future fluoride concentration; however, ANN is the best suited as it can address numerous inaccuracies within the data.Bhushan et al. assessed the concentration of major and trace elements in the 85 groundwater samples collected from the Sultanpur district, Uttar Pradesh, India. The hydrogeochemical approaches and multivariate statistical analysis suggest that the weathering of minerals and ion-exchange processes, with limited contributions from anthropogenic activities, controlled the chemical composition of the groundwater. The parameters of concern in the study area include total dissolved solids (TDS), total hardness (TH), fluoride, manganese, and iron.Tripathi et al. investigated the concentration of heavy metals in the 50 groundwater samples collected from the Mirzapur district, Uttar Pradesh, India. Zinc (Zn) and iron (Fe) were found to be the dominant metals in the pre-monsoon and post-monsoon seasons, respectively. The elevated concentrations of arsenic (As) (100 µg/L) and cadmium (Cd) (125 µg/L) were the major concerns in the study area. The Total Carcinogenic Risk (TCR) values for As exceeded the acceptable range, reaching up to 4×10 -3 , indicating potential health risk to vulnerable groups, such as children.compared the conventional and advanced methods for the remediation of heavy metals from wastewater. They also provided insights about methods used for the detection of heavy metal (HM)s and highlighted their suitability, sensitivity, cost, portability, and field applicability. Traditional methods, such as chemical precipitation, ion exchange, membrane filtration, and adsorption, are used globally for the removal of HMs, but these methods have concerns such as cost, efficiency, and environmental impact. Biological processes such as biosorption, bioaccumulation, and phytoremediation eliminate most of the disadvantages associated with conventional methods and offer low cost, less sludge generation, and ecofriendly characteristics. The study indicated the need to combine biological and physicochemical methods and highlighted the need for hybrid methods like the integration of artificial intelligence (AI) and nanotechnology in bioremediation.In conclusion, this research topic highlights the importance of monitoring groundwater contamination to identify the elements of concern and to determine the potential health associated with the parameters to protect both human health and ecosystem integrity. Heavy metal remains a persistent threat to water quality, affecting millions of people worldwide. These studies indicated the need for the use of advanced models, such as ANN, along with the integration of Artificial Intelligence into conventional treatment methods to increase the removal efficiency. The studies included in this research topic indicated urgent and concerted efforts to mitigate the health risk associated with groundwater contamination.Heavy metal contamination; Artificial intelligence; Artificial neural network (ANN); Water quality indices; Carcinogenic risk