Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water’s appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region’s WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that Random Forest and Gradient Boosting lead with 95% and 96% accuracy, followed closely by SVM at 92%. KNN exhibits an accuracy rate of 84%, and Decision Trees achieve 77%. Traditional water quality assessment methods are time-consuming and error-prone; a transformative approach using artificial intelligence and machine learning addresses these limitations. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization.

Potable groundwater contamination through arsenic (As) is a common environmental problem in many developing countries, including Pakistan, with significant human health risk reports. The current research was conducted in District Nankana Sahib, which is a major industrial site in Punjab, Pakistan. According to the Punjab Directorate of Industries in Pakistan, there are more than a thousand industries in this area. These industries produce a lot of waste and effluent, which contaminate the environment with harmful and toxic materials. Continuous irrigation with industrial effluent and sewage sludge may make groundwater sources vulnerable. Therefore, this research was aimed as assessing the hydrochemical profile of groundwater concerning As contamination in the study area using sixty-seven groundwater samples. Multivariate statistical analysis, graphical plots, geochemical modeling, and the water quality index (WQI) were applied to investigate the hydrochemistry of the research area. The outcomes of the WQI revealed that 43% of the samples were of poor quality and not fit for human consumption. About 28% of the groundwater samples showed high arsenic contamination beyond the permitted limit of the World Health Organization (WHO). The piper plot identified three distinct types of water in the research area: calcium, chloride, and calcium-chloride types. The Gibbs diagram illustrated that rock–water interaction influenced the hydrochemistry. Terrigenous sedimentary rocks, such as stream deposits, flood plain deposits, and detrital sedimentary rocks, among other types of sedimentary rocks covered most part the study area. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) indicated that the arsenic in groundwater exhibited a significant positive correlation for pH, Fe and As. Health risk assessments indicated a hazard quotient (HQ) greater than 1, indicating a 28% contribution showing that groundwater ingestion is highly toxic to the local habitats. The results of this study further help in managing future sustainable groundwater management approaches in the Nankana District, Punjab, Pakistan.

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity to appraise water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water's appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region's WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that random forest and gradient boosting eclipsed other algorithms, achieving an accuracy rate of 99%. In close pursuit were SVM and XGBoost, registering accuracy scores of approximately 95% and 93%, respectively, while KNN and Decision Trees garnered accuracy rates of 88% and 87%, respectively. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization

Numerous geothermal resources of medium to low temperature have been reported in southern China. Suichuan County is one of the regions where thermal manifestations are abundant. However, the study regarding the understanding of geothermal water sources, hydrochemical composition and fluid-rock interaction lacks behind. Therefore, this study has characterized the slightly acidic to slightly alkaline bicarbonate geothermal waters of medium-low temperature of the Suichuan area. Geothermal waters of the study area have been evaluated mainly as of HCO₃-Ca-Na hydrochemical type with a maximum temperature of 80 °C. The results indicate the low hydrochemical concentration where HCO₃⁻ acts as a principal anion. Furthermore, the F⁻ content in geothermal and two cold water samples have been found high with a maximum value of 13.4 (mg/L), showing high pH of 9.6 as well. Here, the compilation of deuterium and oxygen-18 isotopic data of geothermal waters showed a local precipitation origin with a recharge elevation ranging from 630–1000 m. The circulation depth and reservoir temperatures are estimated, explaining the deep thermal water behavior. Additionally, the estimation of saturation indices of various minerals shows the geothermal waters’ corrosive or scaling behavior. Subsequently, the geothermal water points in the study area represent a fracture convection formation pattern. Finally, by integrating conventional hydrochemistry along with isotopic data, and considering the geological framework, a conceptual genetic model of the Suichuan thermal ground waters has been discussed. Hydrochemistry and isotopic features along with a conceptual circulation model have been provided by the foundation towards the sustainable management of hydrothermal resources in Suichuan. Proper management policies and practices are required for further development of Suichuan hydrothermal waters.