Water plays a significant role in sustaining the lives of humans and other living organisms. Groundwater quality analysis has become inevitable, because of increased contamination of water resources and global warming. This study used machine learning (ML) models to predict the water quality index (WQI) and water quality classification (WQC). Forty groundwater samples were collected near the Ranipet industrial corridor, and the hydrogeochemistry and heavy metal contamination were analyzed. WQC prediction employed random forest (RF), gradient boosting (GB), decision tree (DT), and K-nearest neighbor (KNN) models, and WQI prediction used extreme gradient boosting (XGBoost), support vector regressor (SVR), RF, and multi-layer perceptron (MLP) models. The grid search method is used to evaluate the ML model by F1 score, accuracy, recall, precision, and Matthews correlation coefficient (MCC) for WQC and the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and median absolute percentage error (MAPE) for WQI. The WQI results indicate that the groundwater quality of the study area is very poor and unsuitable for drinking or irrigation purposes. The performance metrics of the RF model excelled in predicting both WQC (accuracy = 97%) and WQI (R2 = 91.0%), outperforming other models and emphasizing ML\'s superiority in groundwater quality assessment. The findings suggest that ML models perform well and yield better accuracy than conventional techniques used in groundwater quality assessment studies.

Reservoir temperature estimation is crucial for geothermal studies, but traditional methods are complex and uncertain. To address this, we collected 83 sets of water chemistry and reservoir temperature data and applied four machine learning algorithms. These models considered various input factors and underwent data preprocessing steps like null value imputation, normalization, and Pearson coefficient calculation. Cross-validation addressed data volume issues, and performance metrics were used for model evaluation. The results revealed that our machine learning models outperformed traditional fluid geothermometers. All machine learning models surpassed traditional methods. The XGBoost model, based on the F-3 combination, demonstrated the best prediction accuracy with an R2 of 0.9732, while the Bayesian ridge regression model using the F-4 combination had the lowest performance with an R2 of 0.8302. This study highlights the potential of machine learning for accurate reservoir temperature prediction, offering geothermal professionals a reliable tool for model selection and advancing our understanding of geothermal resources.

This study integrates multivariate statistical analysis and hydrogeochemical modeling to investigate the processes controlling the groundwater composition of a shallow aquifer where increased pumping rates and anthropogenic impacts were prevalent. Eighteen groundwater samples were collected and analyzed for major elements and selected heavy metals. The data were classified on the basis of multivariate statistical analysis into three clusters: C1 (Na-Cl facies), C2 (Ca-SO4 facies), and C3 (Ca-HCO3 facies). The application of factor analysis gave four factors affecting the groundwater chemistry, namely the salinization factor, anthropogenic/secondary enrichment factor, the secondary and the micro-nutrient fertilizers, and the aluminum fertilizer factor. The hydrogeochemical study of the groundwater revealed that the processes controlling the groundwater chemistry in the study area are mainly affected by the groundwater occurrence either to the east or to the west of Bahr Youssef Canal. Generally, the dominant hydrogeochemical processes affecting the groundwater are silicate weathering, ion exchange, irrigation return flow, gypsum applications in soil, and evaporation. The groundwater quality evaluation shows that water quality varies from fair to excellent for drinking purposes, where the best water is located in the northern and central parts of the study area. The suitability of groundwater for irrigation was evaluated using several indices indicating that groundwater is suitable for irrigation in the northwest and western parts of the study area. As some groundwater samples lie in high salinity classes on the US Salinity diagram, it is recommended to use this water for plants with good salt tolerance under good drainage conditions. The integration between the statistical and geochemical tools helps reveal the dominant processes through data reduction and classification.

The presence of pollutants like uranium and arsenic in the groundwater can have a terrible impact on people\'s health (both radiologically and toxicologically) and their economic conditions. Their infiltration into groundwater can occur through geochemical reactions, natural mineral deposits, mining and ore processing. Governments and scientists are working to address these issues, and significant progress has been achieved, but it\'s challenging to deal with and mitigate without adequately understanding the different chemical processes and the mobilization mechanism of these hazardous chemicals. Most of the articles and reviews have focused on the particular form of contaminants and specific sources of pollution, such as fertilizers. However, no literature report exists explaining why particular forms appear and the possible basis of their chemical origins. Hence, in this review, we tried to answer the various questions by devising a hypothetical model and chemical schematic flowcharts for the chemical mobilization of arsenic and uranium in groundwater. An effort has been made to explain how chemical seepage and excessive groundwater use resulted in the change in aquifers\' chemistry, as evidenced by their physicochemical parameters and heavy metal analysis. Many technological advancements have taken place to mitigate these issues. Still, in low-middle-income countries, especially in the Malwa region of Punjab, also known as Punjab\'s cancer belt, paying a high amount for installing and maintaining these technologies is an unviable option. In addition to working to improve people\'s access to sanitary facilities and clean water to drink, the policy-level intervention would focus on increasing community awareness and continued research on developing better and more economical technologies. Our designed model/chemical flowcharts will help policymakers and researchers better understand the problems and alleviate their effects. Moreover, these models can be utilized in other parts of the globe where similar questions exist. This article emphasises the value of understanding the intricate issue of groundwater management through a multidisciplinary and interdepartmental approach.

High background levels of fluorine in groundwater and soil in arid and semi-arid loess regions pose a severe threat to socio-economic development and human health, necessitating the evaluation of fluorine migration in loess. In this study, static leaching and dynamic seepage tests as well as scanning electron microscopy, mercury intrusion porosimetry, and X-ray fluorescence analyses were conducted using loess as the porous medium. Additionally, simulations using PHREEQC software were performed. The results indicated that the studied loess had a high background level of fluorine. Geochemical processes closely related to fluorine include dissolution of gypsum and dolomite, precipitation/dissolution of calcite and fluorite, and ion exchange between CaX and NaX. Under seepage of water with high fluorine levels, soil particles flocculated and formed aggregates; furthermore, the contact area between soil particles reduced, resulting in point-to-point contact between particles. Consequently, pores changed from small (intra- and inter-particle pores) to large (intra- and inter-granular pores) scale pores. Permeability initially decreased rapidly, then remained relatively stable, and subsequently rapidly increased with the passage of time. This was attributed to the dissolution of calcium minerals in loess, yielding Ca2+, which induced the precipitation of fluorite and promoted the dissolution of carbonates. Cation exchange and dissolution of other components were also important factors influencing permeability. The findings of this study can elucidate the coupling between loess microstructure, seepage behavior, and geochemical actions under the influence of high‑fluorine water, and are of great significance for in-situ regional research in loess areas.

Groundwater is a naturally occurring potential source for drinking, irrigation, agricultural and industrial purposes. The population growth and accelerated development of industries and agriculture activity degrade groundwater quality. The groundwater quality of an area was determined by the physical and chemical parameters, influenced by geology, soil, land use, land cover and anthropogenic activities. Perambalur district in Tamil Nadu has been selected as a study area with a total geographical area of around 1757 km2. In the study area, groundwater quality decreases due to the usage of chemical fertilisers and pesticides in agricultural land and mining activities. So, the hydrogeochemical assessment will help to determine the groundwater suitability for drinking. Forty-eight groundwater samples were collected from the study area during the pre-monsoon (July 2021) and post-monsoon season (January 2022). Samples were analysed using the standard methods prescribed by the American Public Health Association for pH, electrical conductivity (EC), total dissolved solids (TDS), calcium, magnesium, sodium, potassium, carbonate, bicarbonate, chloride, sulphate, nitrate and fluoride. The spatial distribution of major physiochemical parameters is mapped using the inverse distance weighted (IDW) interpolation technique. The evaluation of hydrochemical facies from piper plots revealed that the major cation and anion were in the order of Ca2+  > Mg2+  > Na+  > K+ and Cl-  > HCO3-  > SO42-  > NO3- in both seasons, respectively. Further, the plot explains the presence of both permanent and temporary hardness in the groundwater. The evaluation of hydrochemical facies from the piper plot emphasises that the reverse ion exchange controls groundwater chemistry. The assessment of chloro-alkaline indices reveals that the sodium and potassium in groundwater get substituted with magnesium and calcium in the parent rock, which determines the groundwater composition. The values of saturation indices reveal that calcite and dolomite are supersaturated and tend to precipitate. From principal component analysis, the principal components have an eigenvalue of more than 1, containing 79.8% and 79.2% in the total variance in pre-monsoon and post-monsoon, respectively. Most physiochemical parameters like TDS, EC, Na+, Mg2+, Cl- and SO42 - have strong positive loading and are responsible for the changes in groundwater chemistry. Finally, the calculation of the water quality index identified that groundwater quality in post-monsoon tends to decline compared to pre-monsoon.

Exploring the hydrogeochemistry of cave drip water and its response to precipitation events in karst rocky desertification regions is of great significance to the paleoenvironment reconstruction of the karst desertification process using speleothem. We selected three perennial drip sites in the Shijiangjun Cave, located in Guizhou Province, Southwest China, and carried out high-frequency monitoring and sampling during two rainfalls from 22 to 25 May 2016. The major hydrogeochemical parameters of drip water and their relationships with karst desertification were analyzed. The results show that the hydrogeochemistry of the drip water in the Shijiangjun Cave, characterized by HCO3-Ca·Mg, was dominated by the dissolution of calcareous dolomite. The three drip sites were classified into the delayed response type (W1) and the rapid response type (W2 and W3) based on the response speed of the drip water indicators to precipitation, which were highly influenced by the piston effect and precipitation dilution, respectively. Furthermore, the response sensitivity of the drip water indicators to precipitation was constrained by the desertification degree in the rainy season, specifically, the faster response appeared in the higher desertification degree area. It is essential to select appropriate drip sites and establish an applicable indicator system for the evolutional history reconstruction of karst desertification using speleothems.

This study presents the groundwater quality assessment in the north of Isfahan, Iran. In the study area, assessment and measurement of groundwater hydrochemical parameters such as pH, total dissolved solids (TDS), electrical conductivity (EC), sodium absorption ratio (SAR), total hardness, major cations (K+, Na+, Ca2+ and Mg2+) and major anions (Cl-, [Formula: see text] and [Formula: see text]) concentrations were performed. Accordingly, the 66 water samples from different locations were collected during April and May 2015. Water samples collected in the field were analyzed in the laboratory for cations and anions using the standard methods. In this research, the analytical results of physiochemical parameters of groundwater were compared with the standard guideline values as recommended by the world health organization (WHO) for drinking and public health purposes. The pH values of groundwater samples varied from 7.05 to 8.95 with a mean of 7.78, indicating a neutral to slightly alkaline water. TDS values showed that 14% of the samples exceeds the desirable limit given by WHO. EC values varied from 213 to 4320 µS/cm, while 23% of the samples were more than the standard limit. Gibbs diagram had shown that 90% of the samples in the study area fall in the rock weathering zone, and this means that chemical weathering of rock-forming minerals is the main factor controlling the water chemistry in the study area. Irrigation suitability and risk assessment of groundwater are evaluated by measuring EC, %Na, SAR and RSC. According to the dominant cations and anions, five types of water were identified in the water samples: Ca-HCO3, Ca-SO4, Na-Cl, Na-HCO3 and Na-SO4. The results show that the majority of samples (30 samples, 45%) belongs to the mixed Na-SO4 water type. Correlation analysis and principal component analysis was used to identify the relationship between ions and physicochemical parameters. Results indicated that 18 stations of the study area had the best quality and can be used for irrigation and drinking purposes in the future.

Freshwater lenses associated to shell ridges and sand sheets exist on the coastal wetland of Samborombón Bay. As they constitute one of the most vulnerable aquifer systems, it is the aim of this study to determine the hydrogeochemical processes that condition the chemical quality of its groundwater and to assess their present and future capability as sustenance of native woods and local villagers. To achieve this, hydrogeomorphological field surveys were made and groundwater samples were taken. Results show that lenses have a mean thickness of 12m and its chemical quality depends on the dissolution of CO2(g) and carbonates, weathering of silicates and ion exchange. Lenses can be affected by long-term climatic variability and mining. The study of morphology and geochemistry of the freshwater lenses bring lights into important information about the management of water resources and conservation of the environment.

A hydrogeochemical and stable isotope study (2H and 18O) was carried out in the Cuvelai-Etosha Basin in order to characterize available groundwater and to identify possible recharge mechanisms for the perched aquifers. Data were collected during seven field campaigns between 2013 and 2015 from a total of 24 shallow and deep groundwater hand-dug wells. In the investigated groundwaters, hydrogencarbonate is the dominating anion in both well types, whereas cations vary between calcium and magnesium in deep wells, and sodium and potassium in shallow wells. Groundwater chemistry is controlled by dissolution of carbonate minerals, silicate weathering and ion exchange. Stable isotopic composition suggests that deep groundwater is recharged by high-intensity/large rainfall events, whereas the shallow wells can even be recharged by less-intense/small rainfall events. Water in deep wells reflect a mixture of water influenced by evaporation during or before infiltration and water that infiltrated through fast preferential pathways, whereas shallow wells are strongly influenced by evaporation. The findings of this research contribute to improve the understanding of hydrogeochemistry, recharge paths and temporal variations of perched aquifers.