Groundwater is a vital source of freshwater in both urban and rural regions of the world. However, its injudicious abstraction and rapidly increasing contamination are posing a severe threat for sustainable water supply worldwide. Geographical Information System (GIS)-based groundwater quality evaluation using Groundwater Quality Index (GQI) has been proved to be a cost-effective tool for assessing groundwater quality and its variability at a larger scale. However, the conventional GQI approach is unable to deal with uncertainties involved in the assessment of environmental problems. To overcome this limitation, a novel hybrid framework integrating Fuzzy Logic with the GIS-based GQI is proposed in this study for assessing groundwater quality and its spatial variability. The proposed hybrid framework is demonstrated through a case study in a hard-rock terrain of Southern India using ten prominent groundwater-quality parameters measured during pre-monsoon and post-monsoon seasons. Two conventional GIS-based GQI models GQI-10 (using all the ten groundwater-quality parameters) and GQI-7 (using seven 'concerned/critical' groundwater-quality parameters) as well as hybrid Fuzzy-GIS-based GQI (FGQI) models (using seven critical parameters) were developed for the two seasons and the results were compared. The Trapezoidal membership functions classified the model input parameters into 'desirable', 'acceptable' and 'unacceptable' classes based on the experts' knowledge and water quality standards for drinking purposes. The concentrations of Ca2+, Mg2+, and SO42- in groundwater were found within the WHO desirable limits for drinking water throughout the year, while the concentrations of seven parameters (TDS, NO3--N, Na+, Cl-, K+, F- and Hardness) exceed their permissible limits during pre-monsoon and post-monsoon seasons. A comparative evaluation of GQI models revealed that the FGQI model predicts groundwater quality better than the conventional GQI-10 and GQI-7 models. GQI modeling results suggest that the groundwater of most of eastern and southern parts (∼60% in pre-monsoon season; ∼90% in post-monsoon season) of the study area is unsuitable for drinking. Further, the groundwater quality deteriorates during post-monsoon seasons compared to pre-monsoon seasons, which indicates an increased influx of contaminants from different industries, mining areas, waste disposal sites and agricultural fields during monsoon seasons. This finding calls for the strict enforcement of regulations for proper handling of effluents from various contamination sources in the study area. It is concluded that the fuzzy logic-based decision-making approach (FGQI) is more reliable and pragmatic for groundwater-quality assessment and analysis at a larger scale. It can serve as a useful tool for the water planners and decision makers in efficiently monitoring and managing groundwater quality at watershed or basin scales.

Population growth, increasing urbanization and industrialization, mismanagement, and climate change are accountable for the rising depletion and pollution of groundwater worldwide. Consequently, water security, food security, and environmental security are in jeopardy, leading to a severe concern for the sustainable water supply on the Earth. The contamination of groundwater, a complex and hidden resource, is difficult to detect and treat. Therefore, it is essential to evaluate aquifer’s propensity for contamination to protect this precious resource. In this paper, a novel approach integrating the GWQI (Groundwater Quality Index), AVI (Aquifer Vulnerability Index), and geospatial modeling is proposed to explore aquifer susceptibility to contamination, applied to an unconfined aquifer. The Groundwater Quality Index (GWQI) was developed by the conventional method and the Analytic Hierarchy Process (AHP), whereas the Aquifer Vulnerability Index (AVI) was developed using a modified DRASTIC model. It was found that the spherical semi-variogram along with simple Kriging is suitable for interpolating concentrations of groundwater quality parameters. Geospatial modeling indicated that the AHP-based GWQI map is more accurate than the conventional method. The integration of the best GWQI and AVI resulted in an Aquifer Susceptibility Index (ASI) map, which revealed that >80% of the study area falls under ‘severe’ to ‘very severe’ susceptible zones, while about 20% of the area falls under ‘moderate’ or ‘minimum’ susceptible zones. The validation results confirmed that the developed ASI map is reliable. The ASI map can serve as a useful tool for planners and decision makers to devise sustainable aquifer management programs to protect vital groundwater resources from contamination and ensure a safe and reliable water supply under climate change.

The study on the influence of Natural Organic Matter (NOM) over the individual and combined effects of different nanomaterials on marine species is pertinent. The current study explores the role of Extracellular Polymeric Substances (EPS) in influencing the individual and combined toxic effects of polystyrene nanoplastics (PSNPs) viz. aminated (NH₂-PSNPs), carboxylated (COOH-PSNPs), and plain PSNPs and TiO₂ NPs in the marine crustacean, Artemia salina. A. salina was interacted with pristine PSNPs, pristine TiO₂ NPs, EPS incubated PSNPs, EPS incubated TiO₂ NPs, binary mixture of PSNPs and TiO₂ NPs, and EPS adsorbed binary mixture of PSNPs and TiO₂ NPs for 48 h. The present study proves that, when compared to the pristine toxicity of PSNPs and TiO₂ NPs, the coexposure of TiO₂ NPs with PSNPs resulted in increased toxicity. The adsorption of algal EPS on the NMs (both in their pristine and combined forms) significantly increased the toxic nature of the NMs against A. salina. It was observed that with an increase in the hydrodynamic diameter of the particles, the mortality, oxidative stress, and ingestion of the NMs by A. salina increased. The uptake of Ti by A. salina from 8 mg/L TiO₂ NPs, EPS adsorbed 8 mg/L TiO₂ NPs, 8 mg/L TiO₂ NPs + NH₂-PSNPs and the EPS adsorbed mixture of 8 mg/L TiO₂ NPs, 8 mg/L TiO₂ NPs + NH₂-PSNPs was observed to be 0.043, 0.047, 0.186, and 0.307 mg/g of A. salina. The adsorption of algal EPS on the NMs (both in their pristine and combined forms) significantly increased the toxic nature of the NMs against A. salina. The major outcomes from the current study highlight the role of EPS in exacerbating the toxicity of NMs in marine crustaceans.

India is the largest consumer of groundwater in the world, and it suffers from a groundwater crisis due to the overexploitation of groundwater and the deterioration of its quality at an alarming rate. Rapid urbanization, a growing population, and mismanagement are major driving forces behind these groundwater issues. Thus, increasing problems of water scarcity and water-quality deterioration threaten the sustainability of the water supply. This necessitates the development of novel approaches to assess prevailing groundwater quality scenarios at a large scale, which can help protect this vital freshwater resource from contamination. In this study, for the first time, the effectiveness of three Geographical Information System (GIS)-based Multi-Criteria Decision Analysis (MCDA) models (i.e., ‘Unit Weight’, ‘Rank Sum’, and ‘Analytic Hierarchy Process’) was explored for predicting groundwater quality in a river basin of Southern India. The seasonal concentrations of groundwater quality parameters, viz., Cl⁻, TDS, TH, F⁻, NO₃⁻-N, Na⁺, Mg²⁺, Ca²⁺, K⁺, and SO₄²⁻, were considered for generating their thematic layers. Each thematic layer was classified into suitable feature classes based on the WHO guidelines for drinking water. The thematic layers and the feature classes of individual groundwater quality parameters were assigned relative weights according to the theories of the three MCDA models mentioned above. These thematic layers were then aggregated in GIS to develop Groundwater Quality Index (GQI) maps of the study area for pre-monsoon and post-monsoon seasons. Furthermore, the accuracy of the developed GQI maps was validated using relative operating characteristic curves. The results of the validation indicated that the GIS-based Analytic Hierarchy Process (AHP) model outperformed with prediction accuracies of 71.4% in the pre-monsoon season and about 85% in the post-monsoon season. However, the performances of the Unit Weight and Rank Sum models were found to be average with prediction accuracies varying from 68% to 63% and 64% to 68%, respectively. Thus, the GIS-based AHP model can serve as a reliable scientific tool for predicting seasonal groundwater quality at a river basin scale. It can be very helpful to the policymakers for devising viable management strategies for groundwater protection as well as for ensuring a sustainable water supply.