Hydrogeochemical processes controlling the groundwater salinity in the coastal aquifers of Southern Tamil Nadu, India

Groundwater salinization risk assessment using combined artificial intelligence models

Assessing the risk of groundwater contamination is of crucial importance for the management of water resources, particularly in arid regions such as Menzel Habib (south-eastern Tunisia). The aim of this research is to create and validate artificial intelligence models based on the original DRASTIC vulnerability methodology to explain groundwater salinization risk (GSR). To this end, several algorithms, such as artificial neural networks (ANN), support vector regression (SVR), and multiple linear regression (MLR), were applied to the Menzel Habib aquifer system. The results obtained indicate that the DRASTIC Vulnerability Index (VI) ranges from 91 to 141 and is classified into two categories: low and moderate vulnerability. However, the correlation between groundwater total dissolved solids (TDS) and the Vulnerability Index is relatively weak (r < 0.5). Indeed, the original DRASTIC index needs some improvements. To improve it, some adjustments are required, notably by incorporating the TDS-groundwater salinization risk (GSR) indicator. The seven parameters of the original DRASTIC model were used as inputs for the artificial intelligence models, while the GSR values were used as outputs. Performance indicators, such as the correlation coefficient (r) and the Willmott Agreement Index (d), showed that the ANN model outperformed the SVR and MLR models. Indeed, during the training phase, the ANN model obtained r values equal to 0.89 and d values of 0.4, demonstrating the superiority, robustness, and accuracy of ANN-based methodologies over the original DRASTIC model. The findings could provide valuable information to guide management of groundwater contamination risks, especially in arid regions.

Groundwater Salinity Across India: Predicting Occurrences and Controls by Field-Observations and Machine Learning Modeling

Elevated groundwater salinity is unsuitable for drinking and harmful to crop production. Thus, it is crucial to determine groundwater salinity distribution, especially where drinking and agricultural water requirements are largely supported by groundwater. This study used field observation (n = 20,994)-based machine learning models to determine the probabilistic distribution of elevated groundwater salinity (electrical conductivity as a proxy, >2000 μS/cm) at 1 km2 across parts of India for near groundwater-table conditions. The final predictions were made by using the best-performing random forest model. The validation performance also demonstrated the robustness of the model (with 77% accuracy). About 29% of the study area (including 25% of entire cropland areas) was estimated to have elevated salinity, dominantly in northwestern and peninsular India. Also, parts of the northwestern and southeastern coasts, adjoining the Arabian Sea and the Bay of Bengal, were assessed with elevated salinity. The climate was delineated as the dominant factor influencing groundwater salinity occurrence, followed by distance from the coast, geology (lithology), and depth of groundwater. Consequently, ∼330 million people, including ∼109 million coastal populations, were estimated to be potentially exposed to elevated groundwater salinity through groundwater-sourced drinking water, thus substantially limiting clean water access.

Unraveling the paleo-marine signature in saline thermal waters of Cambay rift basin, Western India: Insights from geochemistry and multi isotopic (B, O and H) analysis

Cambay rift basin is the only geothermal province in India that hosts several saline thermal water manifestations having EC values varying from 525 to 10,860 μS/cm. Various ionic ratios (Na/Cl, Br/Cl, Ca/(SO₄ + HCO₃), SO₄/Cl) as well as boron isotopic composition (δ¹¹B = 40.5 to 46 ‰) clearly ascribes the presence of fossil (relics of evaporated seawater) seawater as origin of increased salinity in the majority of thermal waters. Depleted isotopic (δ¹⁸O, δ²H) composition of these thermal waters also substantiates the presence of paleowater in these systems. In rest of the thermal waters, agricultural return flow is found to be source of dissolved solutes as confirmed from different bivariate plots such as B/Cl vs. Br/Cl and δ¹¹B vs. B/Cl as well as from ionic ratios. This study thus provides the diagnostic tools to elucidate the source of variable salinity in the thermal waters circulating in the Cambay rift basin, India.

Understanding the factors contributing to groundwater salinity in the coastal region of Andhra Pradesh, India

The present study focused on understanding the factors responsible for groundwater salinity in the coastal region, Prakasam district, Andhra Pradesh, India. Groundwater samples were collected and analysed for pH, EC, TDS, TA, TH, CH, NCH, EA, Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃^-, Cl^-, SO₄^2-, NO₃^-, and F^-. Groundwater quality was assessed using entropy weighted water quality index (EWQI), Chadha and Gibbs diagrams, ionic ratios, chloro-alkaline indices (CA), saturation indices (SI), principal component analysis (PCA), and hierarchical cluster analysis (HCA). TDS vs TH indicated that 86% of groundwater samples fall under brackish-cum-very hard water-quality type, while TA and TH relationship showed that 70% and 30% of groundwater samples fall under non‑carbonate hardness (NCH) and excess alkalinity (EA), respectively. EWQI classified groundwater samples into medium (22%), poor (40%), and exremely poor (38%) water quality types, indicating that most samples are not suitable for drinking purposes. Hydrogeochemical types in Chadha diagram showed saline water (Na⁺-Cl^-) type in 92% of groundwater samples. Ionic ratios indicated that anthropogenic activities resulting from the leaching of surface water pollutants are the main source of groundwater pollution. Base ion exchange was indicated as the main process in CA indices. SI revealed precipitation of the calcite phase and dissolution of the gypsum and halite phases in groundwater. Evaporation appeared in Gibb's diagram as a primary process rather than a geogenic origin. PC1 (Na⁺, Cl^-, SO₄^2-, Mg²⁺, K⁺, and NO₃^-) and PC2 (HCO₃^- and F^-) were considered salinity-process and alkalinity-process, respectively. The main sources of salinity in groundwater are brackish-water aquaculture and salt-making activities with household waste, septic tank spills, irrigation-return-flows, and chemical fertilizers being secondary sources. HCA classified groundwater samples into Group-I (46%), which represents domestic water, agricultural activities, etc., Group-II (30.24%), which shows the influence of both Group-I and Group-II, and Group-III (23.76%), which specifies brackish-water aquaculture and salt-making activities. Consequently, the present study obviously indicated that the groundwater quality of anthropogenic origin has largely overcome the influence of geogenic sources. The EWQI classification spatially delineated the study region into medium, high, and very high vulnerable zones, covering 28.69%, 32.75%, and 38.56%, respectively. Therefore, it is suggested to control the dumping of domestic waste and septic tank leaks, limit irrigation-return-flows and chemical fertilizers, ban brackish water aquaculture and salt production activities, and strictly implement an aquifer recharge management strategy to ensure human health. This study will assist decision-makers in addressing groundwater salinity issues in coastal regions.

Groundwater quality and human health risk assessment in selected coastal and floodplain areas of Bangladesh

Groundwater aquifers are a common source of drinking water in Bangladesh. However, groundwater contamination is a major public health concern across the country. This research aims to examine the groundwater quality and health concerns using a random sampling process. Multivariate statistical and health risk analyses of elements were performed to determine the source of contaminants and their effects on human health. A total of 24 parameters were analyzed, where Na⁺, NH₄⁺, K⁺, Mg²⁺, F^-, NO₃^-, Mn, Fe, Se, U, and As concentrations were found to be high in different sampling points compared to the Department of Environment of Bangladesh (DoE), and the World Health Organization (WHO) groundwater quality standards. Principal Component Analysis (PCA) and Cluster Analysis (CA) identified the dominant and potential sources of contaminants in the groundwater aquifer, including geogenic, salinity intrusion, industrial, and agricultural. The results of the degree of contamination level (C_d) and the heavy metal pollution index (HPI) showed that 28% and 12% of the sampling points had high levels of heavy metal contamination, indicating a high risk for human health issues. Cr concentrations were found to have a higher carcinogenic (cancer) risk than As and Cd concentrations. Hazard quotient (HQ) and hazard index (HI) scores expressed the hazardous status and possible chronic effects in the context of individual sampling points. For both child and adults, 44% and 36% of the sampling points had a high HI score, indicating the possibility of long-term health risks for local populations.

Delineating the origins and processes of groundwater salinization and quality degradation in a coastal irrigated plain, Korba (Northeastern Tunisia)

From 2006 to 2020, groundwater investigations were conducted in the Korba coastal aquifer in northern Tunisia along two flow paths (transects S1 and S2), perpendicular to the shoreline. Groundwater sampling, hydrodynamic monitoring, and electrical tomography imaging were performed in situ. Geochemical analysis (Ionic ratios, ionic deltas, conventional diagrams, and stable isotopes) and modelling using PHREEQC, and multivariate statistical analysis were applied. The objective was to identify the potential origin of groundwater salinization (i.e., high TDS and NO₃) and to study associated processes. The groundwater flow inversion was corroborated by the piezometric survey in transect S1, where a piezometric depression of 5 m was detected at 4000 m from the seashore. Seawater intrusion and agricultural contamination, mainly through N-fertilizers, both contribute to groundwater mineralization and consequently salinization, according to PCA analysis. The impacted geochemical area of seawater intrusion was estimated to be 4000 and 1500 m, respectively, along transect S1 and transect S2. Inversely, agricultural contamination acts in internal areas beginning at 2000 m and 1500 m from the shoreline for S1 and S2, respectively. Results of different scenarios of inverse geochemical modelling along flow paths indicated that mixing, ion exchange, dissolution of gypsum, and precipitation of dolomite and calcite are the main processes controlling the groundwater composition in the coastal study area.

Combined Modeling of Multivariate Analysis and Geostatistics in Assessing Groundwater Irrigation Sustenance in the Middle Cheliff Plain (North Africa)

The assessment of groundwater irrigation using robust tools is essential for the sustenance of the agro-environment in arid and semi-arid regions. This study presents a reliable method consisting of a combination of multivariate analysis and geostatistical modeling to assess groundwater irrigation resources in the Western Middle Cheliff (Algeria). For this goal, mean data from 87 wells collected during April to July 2017 were used. The hierarchical cluster analysis (HCA) using the Q-mode approach revealed three distinct water types, with mineralization increasing from cluster 1 to cluster 3. The Principal Component Analysis (PCA) utilizing the Varimax method approach allowed the extraction of three main components: the first and second (PC1, PC2), revealing that the geogenic process, have influenced the hydrogeochemical composition of groundwater. The pollution induced by agriculture activities has been related to PC3. Based on the combination of multivariate analysis and geostatistical modeling, the distribution maps were created by interpolating the factor distribution values acquired in the study region using the ordinary kriging (OK) interpolation method. The findings revealed that both natural processes and man-made activities have a substantial impact on the quality of groundwater irrigation. Cluster mapping, another often used combining approach, has shown its effectiveness in assisting groundwater resource management.