For shallow groundwater, hydrogeochemical processes and quality assessment must be addressed because shallow groundwater is freely available in many parts of the globe. Due to recent anthropogenic activities and environmental changes in Sakrand, Sindh, Pakistan, the groundwater is extremely vulnerable. To provide safe drinking and agricultural water, hydrogeochemical analysis is required. Ninety-five groundwater samples were analyzed using agricultural and drinking indices to determine the hydrogeochemical parameters using multivariate analysis such as Pearson correlations, principal component cluster analysis, as well as Piper diagrams and Gibbs plot for drinking and agricultural indices. An abundance of ions was observed through the statistical summary; however, cations and anions were recorded in the orders Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ and HCO₃⁻ > Cl⁻ > SO₄²⁻ > NO₃⁻ > F⁻. The hydrogeochemical process used to quantify the major reactions occurring in the groundwater system showed rock dominance; the Piper diagrams evaluated the water type. A mixed pattern of calcium, magnesium, and chloride ions (Ca²⁺−Mg²⁺−Cl⁻ type) was observed. Additionally, the ion exchange method showed an excess of bicarbonate ions due to carbonic acid weathering. The water quality index (WQI) resulted 32.6% of groundwater being unsuitable for human consumption; however, the United States Salinity Laboratory (USSL) diagram showed 60% of samples were unsuitable for irrigation due to high salinity and the Wilcox diagram depicted 5% of samples lying in the unsuitable region. Most of the water samples were suitable for drinking; only a few samples were unsafe for drinking purposes for children due to the high hazard index.

Groundwater is a critical water supply for safe drinking water, agriculture, and industry worldwide. In the Khanewal district of Punjab, Pakistan, groundwater has severely deteriorated during the last few decades due to environmental changes and anthropogenic activities. Therefore, 68 groundwater samples were collected and analyzed for their main ions and trace elements to investigate the suitability of groundwater sources for drinking and agricultural purposes. Principal component analysis (PCA) and cluster analysis (CA) were employed to determine the major factors influencing groundwater quality. To assess the groundwater’s appropriateness for drinking and irrigation, drinking and agricultural indices were used. The pH of the groundwater samples ranged from 6.9 to 9.2, indicating that the aquifers were slightly acidic to alkaline. The major cations were distributed as follows: Na⁺ > Ca²⁺ > Mg²⁺ > K⁺. Meanwhile, the anions are distributed as follows: HCO₃⁻ > SO₄²⁻ > Cl⁻ > F⁻. The main hydrochemical facies were identified as a mixed type; however, a mixed magnesium, calcium, and chloride pattern was observed. The reverse ion exchange process helps in exchanging Na⁺ with Ca²⁺ and Mg²⁺ ions in the groundwater system. Rock weathering processes, such as the dissolution of calcite, dolomite, and gypsum minerals, dominated the groundwater hydrochemistry. According to the Weight Arithmetic Water Quality Index (WAWQI), 50% of the water samples were unsafe for drinking. The Wilcox diagram, USSL diagram, and some other agricultural indices resulted in around 32% of the groundwater samples being unsuitable for irrigation purposes. The Khanewal’s groundwater quality was vulnerable due to geology and the influence of anthropogenic activities. For groundwater sustainability in Khanewal, management strategies and policies are required.

Water contaminated with arsenic is a worldwide problem. This review presents the arsenic contamination in groundwater, its sources, and possible health risk to humans. Groundwater pollution is the most common route of inorganic arsenic exposure in humans. Arsenic concentrations in different countries were analyzed and projected on a map. Because arsenic is widely spread throughout the Earth’s crust, it is present in trace amounts in practically all waterways. Harmful levels of this toxin have been identified in drinking water in some regions. For drinking purposes, the majority of people use groundwater; excess arsenic levels in groundwater have been linked to a variety of negative health impacts on people. Arsenic exposure is the world’s leading environmental cause of cancer. The main aim of this review is to summarize the effective technologies to remove arsenic from drinking water, such as ion exchange, coagulation/flocculation, and membrane technologies like ultra-filtration and electrodialysis, helping to deal with the adverse effects caused by arsenic exposure. All these technologies present different advantages and disadvantages. Electrocoagulation, adsorption, and phytoremediation are the most efficient and cost-effective technologies. The removal efficiencies of arsenic using these technologies and prospects were also included.

Arsenic (As) contamination in groundwater is a worldwide concern for drinking water safety. Environmental changes and anthropogenic activities are making groundwater vulnerable in Pakistan, especially in Southern Punjab. This study explores the distribution, hydrogeochemical behavior, and pathways of As enrichment in groundwater and discusses the corresponding evolution mechanism, mobilization capability, and health risks. In total, 510 groundwater samples were collected from three tehsils in the Punjab province of Pakistan to analyze As and other physiochemical parameters. Arsenic concentration averaged 14.0 μg/L in Vehari, 11.0 μg/L in Burewala, and 13.0 μg/L in Mailsi. Piper-plots indicated the dominance of Na⁺, SO₄²⁻, Ca²⁺, and Mg²⁺ ions in the groundwater and the geochemical modeling showed negative saturation indices with calcium carbonate and salt minerals, including aragonite (CaCO₃), calcite (CaCO₃), dolomite (CaMg(CO₃)₂), and halite (NaCl). The dissolution process hinted at their potential roles in As mobilization in groundwater. These results were further validated with an inverse model of the dissolution of calcium-bearing mineral, and the exchange of cations between Ca²⁺ and Na⁺ in the studied area. Risk assessment suggested potential carcinogenic risks (CR > 10⁻⁴) for both children and adults, whereas children had a significant non-carcinogenic risk hazard quotient (HQ > 1). Accordingly, children had higher overall health risks than adults. Groundwater in Vehari and Mailsi was at higher risk than in Burewala. Our findings provide important and baseline information for groundwater As assessment at a provincial level, which is essential for initiating As health risk reduction. The current study also recommends efficient management strategies for As-contaminated groundwater.

Groundwater is one of the water resources used to preserve natural water sources for drinking, irrigation, and several other purposes, especially in industrial applications. Human activities related to industry and agriculture result in groundwater contamination. Therefore, investigating water quality is essential for drinking and irrigation purposes. In this work, the water quality index (WQI) was used to identify the suitability of water for drinking and irrigation. However, generating an accurate WQI requires much time, as errors may be made during the sub-index calculations. Hence, an artificial intelligence (AI) prediction model was built to reduce both time and errors. Eighty data samples were collected from Sakrand, a city in the province of Sindh, to investigate the area’s WQI. The classification learners were used with raw data samples and the normalized data to select the best classifier among the following decision trees: support vector machine (SVM), k-nearest neighbors (K-NN), ensemble tree (ET), and discrimination analysis (DA). These were included in the classification learner tool in MATLAB. The results revealed that SVM was the best raw and normalized data classifier. The prediction accuracy levels for the training data were 90.8% and 89.2% for the raw and normalized data, respectively. Meanwhile, the prediction accuracy levels for the testing data were 86.67 and 93.33% for the raw and normalized data, respectively.