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Water and soil polluted by heavy metals (HMs) are the primary problem due to rapidly increasing urbanization and industrialization. For the treatment of polluted soil, phytoremediation turns into a cost-effective and eco-friendly technique. The current research aimed to examine the load of pollution, specifically HMs, in sediment and wastewater (WW) of the GadoonAmazai Industrial Estate (GAIE), Pakistan and compare the ability of native grass species Cynodon dactylon and Digiteria sanguinalis for the phytoaccumulation of HMs. The industrially polluted soil was analysed for HMs using atomic absorption spectrophotometry (AAS) and compared with healthy soil (irrigated by freshwater), which served as a control. The HM accumulation was considerably higher in the soil irrigated with WW than in control soil samples. The most substantial metal pollutant was manganese (Mn), which accumulated up to 2491.7 mg/kg in the WW irrigated soil. For assessing the bioremoval efficiency of grass species, pot experimentation was performed for 90 days. Soil samples and grasses were collected from the pots to examine the HM removal efficiency. A significant reduction was noted in physicochemical characteristics of the soil, such as electrical conductivity, total organic matter, phosphorus, potassium, and saturation. The grasses removed up to 59.0% of the Cu and 59.9% of Mn from the soil. The highest bioconcentration factor (BCF) and translocation factor (TF) of Cu were observed for D. sanguinalis. While the highest BCF and TF of Mn were obtained for C. dactylon. The research showed that the grass significantly (p ≤ 0.05) reduced HM in soil samples. Moreover, the selected grasses found a higher capability to accumulate HM in the roots than in the shoot. The maximum Cu removal was obtained by D. sanguinalis and Mn by C. dactylon. The research study concluded that phytoremediation using D. sanguinalis and C. dactylon is an eco-friendly and cost-effective method that can be utilized for soil remediation.

The possible zones of submarine groundwater discharge (SGD) and seawater intrusion (SWI) along the west coast of Kanyakumari in the southernmost Indian Peninsula were identified by integrating the surface temperature anomaly, water table fluctuation, and water quality analysis. Temperature map of the sea surface from Landsat 8 satellite data, which helped to demarcate SGD in areas with significant thermal contrast, exists between the seawater and discharging groundwater. Monitoring data of 2019–2021 illustrated patchy, diffuse, and temporally variable groundwater seepage. The spatial distribution map created from groundwater level, and water quality parameters depicted high SGD in areas of high groundwater level and high chances of SWI in regions with low groundwater level. This study identified that the areas with low groundwater level, high EC, and high chloride contents are prone to SWI. Similarly, the regions with high thermal contrast, high groundwater level, and low EC have more SGD. These methods have the potential to be used as preliminary screening tools before implementing detailed studies about the mixing process.

The accurate evaluation of groundwater contamination vulnerability is essential for the management and prevention of groundwater contamination in the watershed. In this study, advanced multiple machine learning (ML) models of Radial Basis Neural Networks (RBNN), Support Vector Regression (SVR), and ensemble Random Forest Regression (RFR) were applied to determine the most accurate performance for the evaluation of groundwater contamination vulnerability. Eight vulnerability factors of DRASTIC-L were rated based on the modified DRASTIC model (MDM) and were used as input data. The adjusted vulnerability index (AVI) with nitrate values was used as output data for the modeling process. The performance of three models was verified using the statistical performance criteria of MAE, RMSE, r2, and ROC/AUC values. The ensemble RFR model showed the highest performance in comparison with standalone SVR and RBNN models. Specifically, ensemble RFR kept all promising solutions during the model performance due to its flexibility and robustness, and the vulnerability map obtained by the RFR model was more accurate for predicting the most vulnerable areas to contamination. It was concluded that ensemble RFR was a robust tool to enhance the evaluation of groundwater contamination vulnerability, and that it could contribute to environmental safety against groundwater contamination.

Recently, groundwater resources have become the main freshwater supply for human activities worldwide, especially in semi-arid regions, and groundwater pollution from anthropological events is one of the chief environmental problems in built-up and industrial coastal areas. Many researchers around the world have conducted studies to evaluate the impact of groundwater management. For this study, GIS based GOD vulnerability models were used to assess the intrinsic impact and risk of pollution of coastal and irrigated areas in Tiruchendur Taluk, Thoothukudi district in Tamil Nadu. Here, GOD stands for G—Groundwater hydraulic confinement, O—Overlying aquifer strata, and D—Depth to groundwater. The parameters of G, O, and D show that 70% of the study area consists of an unconfined aquifer whose central part often consists of sandstone and fine to medium clay with sand along the coast that acts as an aquifer. The recorded value was 1–28 mbgl. The map of vulnerability using the GOD method shows that 32% of the medium vulnerabilities are located in the almost northern part of the study area, where the main source of pollution is from agricultural land and anthropological activities. A total of 39 groundwater samples were collected from different types of aquifers and used to validate the pollution map, using the EC concentration (230 to 15,480 µs/cm with an average of 2758 µs/cm) and NO3− concentration (2 to 120 mg/L with an average of 46 mg/L) in groundwater as indicators of pollution. Finally, we measured how the EC and NO3− parameters represent the medium vulnerability zone of the GOD model based on the pattern of their concentrations in groundwater. Therefore, the GIS with GOD model is the best model among these models for predicting groundwater vulnerability in Tiruchendur Taluk.

The present study was taken with a primary objective to estimate the groundwater quality and its suitability toward sustainable crop productivity in an agriculturally dominant semi-urban area. Elevated levels of nitrate have severe health impacts and affect the human health. Hence, health risks associated with the consumption of nitrate contaminated water were estimated for adults and children in Sonipat district, Haryana. In general, the groundwater was found to be slightly alkaline and moderately hard. The anionic abundance was verified as F– < NO3– < HCO3– < SO42– < Cl–, whereas, the cationic abundance was verified as Ca2+ < Mg2+ < K+ < Na+. Concentration of nitrate varied from 1.34 mg/L to 565 mg/L, with an average value of 47.6 mg/L, and 46% of the groundwater samples had nitrate concentration of more than 45 mg/L. As per the results of Wilcox plot, only 34% of samples were suitable for crop productivity, whereas 23% of the samples fell in category-II of the permeability index. The non-carcinogenic health risk assessment further suggested that hazard quotient values for nitrate reached as high as 18.35 (children), and 13.57 (adult male), suggesting that health risk degree of children has greater health risk than adults in the study region. Overall results suggest an urgent need for intervention to adopt suitable health risk measures to reduce exposure toward nitrate contaminated drinking water. Moreover, agricultural practices must be improved to increase the crop productivity in the affected areas. [ABSTRACT FROM AUTHOR]

Climate models for the 21st century project further reduction in the warm season precipitation and more frequent droughts across Mexico. In the possible scenario of enhanced aridity from global warming, the δ18O (−10.6 to −6.3 ‰) and δ2H (−71.1 to −57.1 ‰) compositions and deuterium-excess (0.2–14.6‰) of shallow groundwater from two different basins (Sandia and El Potosi) with similar geological and geomorphological settings were considered to evaluate the influences of early summer rainfall and later summer tropical storms on aquifers at water-scarce southeast margin of the Chihuahuan Desert. Groundwater of the Sandia Basin was recharged mainly from tropical storms. Higher CO 2 partial pressure (log pCO 2 : −2.70 to −1.61) caused more gypsum dissolution (Ca–Mg–SO 4 facies) and the effect of irrigation return flow (Ca–Mg–Cl facies) was minor. Even though the El Potosi Basin is in proximity, its groundwater was recharged from both the early and late summer precipitations. The multivariate factor analysis helped to separate the process of rock-water interactions from the recharge seasonality. Gypsum dissolution was less as the partial pressure of CO 2 was comparatively lower (log pCO 2 : −3.01 to −2.15), and the ion exchange along with carbonate mineral dissolutions led to Ca–Mg–HCO 3 facies. Over-exploitation under the condition of reduced warm season rainfall would continue to enhance the salinity of groundwater in this region. Hence, the drought mitigation policies should prioritize sustainability of the depleted aquifers and cultivation of salinity resistant crops. • Influence of warm season precipitations on desert margin aquifers. • Evaluation of the contributions from early summer and late summer precipitations. • Rock–water interaction influencing groundwater chemistry. • Different degrees of evaporite mineral dissolutions from distinct pCO2 in watersheds. • Enhancement of groundwater salinity from global warming induced recharge depletion. [ABSTRACT FROM AUTHOR]

The accurate mapping and assessment of groundwater vulnerability index are crucial for the preservation of groundwater resources from the possible contamination. In this research, novel intelligent predictive Machine Learning (ML) regression models of k-Neighborhood (KNN), ensemble Extremely Randomized Trees (ERT), and ensemble Bagging regression (BA) at two levels of modeling were utilized to improve DRASTIC-LU model in the Miryang aquifer located in South Korea. The predicted outputs from level 1 (KNN and ERT models) were used as inputs for ensemble bagging (BA) in level 2. The predictive groundwater pollution vulnerability index (GPVI), derived from DRASTIC-LU model was adjusted by NO 3 –N data and was utilized as the target data of the ML models. Hyperparameters for all models were tuned using a Grid Searching approach to determine the best effective model structures. Various statistical metrics and graphical representations were used to evaluate the superior predictive performance among ML models. Ensemble BA model in level 2 was more precise than standalone KNN and ensemble ERT models in level 1 for predicting GPVI values. Furthermore, the ensemble BA model offered suitable outcomes for the unseen data that could subsequently prevent the overfitting issue in the testing phase. Therefore, ML modeling at two levels could be an excellent approach for the proactive management of groundwater resources against contamination. [Display omitted] • Prediction of accurate GPVI using novel ML models at two levels of modeling. • Selection of hyperparameter values of ML modeling using automated Grid Search cross-validation. • Increase of DRASTIC-LU model performance using KNN, ERT, and BA models. • BA (level 2) is the best ML model by statistical metrics, graphical data, and GPVI spatial maps. [ABSTRACT FROM AUTHOR]