Hydrogeochemistry study and groundwater quality assessment in the north of Isfahan, Iran

Predicting groundwater phosphate levels in coastal multi-aquifers: A geostatistical and data-driven approach

The groundwater (GW) resource plays a central role in securing water supply in the coastal region of Bangladesh and therefore the future sustainability of this valuable resource is crucial for the area. However, there is limited research on the driving factors and prediction of phosphate concentration in groundwater. In this work, geostatistical modeling, self-organizing maps (SOM) and data-driven algorithms were combined to determine the driving factors and predict GW phosphate content in coastal multi-aquifers in southern Bangladesh. The SOM analysis identified three distinct spatial patterns: K+Na+pH, Ca2+Mg2+NO₃-, and HCO₃-SO₄2-PO43-F-. Four data-driven algorithms, including CatBoost, Gradient Boosting Machine (GBM), Long Short-Term Memory (LSTM), and Support Vector Regression (SVR) were used to predict phosphate concentration in GW using 380 samples and 15 prediction parameters. Forecasting accuracy was evaluated using RMSE, R2, RAE, CC, and MAE. Phosphate dissolution and saltwater intrusion, along with phosphorus fertilizers, increase PO43- content in GW. Using input parameters selected by multicollinearity and SOM, the CatBoost model showed exceptional performance in both training (RMSE = 0.002, MAE = 0.001, R2 = 0.999, RAE = 0.057, CC = 1.00) and testing (RMSE = 0.001, MAE = 0.002, R2 = 0.989, RAE = 0.057, CC = 0.998). Na+, K+, and Mg2+ significantly influenced prediction accuracy. The uncertainty study revealed a low standard error for the CatBoost model, indicating robustness and consistency. Semi-variogram models confirmed that the most influential attributes showed weak dependence, suggesting that agricultural runoff increases the heterogeneity of PO43- distribution in GW. These findings are crucial for developing conservation and strategic plans for sustainable utilization of coastal GW resources.

Impact of road transport system on groundwater quality inferred from explainable artificial intelligence (XAI)

Road networks constitute a vital component of modern society, facilitating rapid transportation and driving economic activities by enabling the smooth movement of goods and people. However, the expansion of road systems carries significant environmental considerations, particularly regarding its impact on groundwater quality. Thus, it is crucial to understand the complex relationship between groundwater quality and the road traffic system. This paper aims to identify the impact of road transport systems on groundwater quality using a data-driven approach. Specifically, road network and groundwater chemistry data in Texas were obtained from an open data portal. This study was carried out in two phases: the explainable artificial intelligence (XAI) modeling phase and the multivariate analysis phase. In the XAI modeling phase, a prediction model was developed using eXtreme Gradient Boosting (XGB), with groundwater chemistry parameters as output features and road transport attributes as input features, i.e., elevation, annual average daily traffic, distance, lane-miles, speed limit and well depth. Furthermore, the relationships between groundwater chemistry parameters and road transport attributes were examined using feature importance and accumulated local effect (ALE). In the multivariate phase, Piper diagrams and principal component analysis (PCA) were utilized to identify the source of the selected groundwater chemistry parameters from the XAI models. The results of the prediction model showed that five groundwater chemistry parameters were significantly impacted by road transport systems with below a mean absolute percentage error of 0.20, including, pH, temperature, aluminum (Al), bicarbonate (HCO3-), and alkalinity. Additionally, XAI models were developed to understand the relationship between the road transport attributes on five selected parameters. The findings collectively indicated that the Texas groundwater qualities are greatly impacted by road transport systems within a distance of 50-meters and a well depth of 100-meters. This study provides a novel contribution to monitoring point sources of groundwater pollution using XAI techniques.

Using unsupervised machine learning models to drive groundwater chemistry and associated health risks in Indo-Bangla Sundarban region

Groundwater is an essential resource in the Sundarban regions of India and Bangladesh, but its quality is deteriorating due to anthropogenic impacts. However, the integrated factors affecting groundwater chemistry, source distribution, and health risk are poorly understood along the Indo-Bangla coastal border. The goal of this study is to assess groundwater chemistry, associated driving factors, source contributions, and potential non-carcinogenic health risks (PN-CHR) using unsupervised machine learning models such as a self-organizing map (SOM), positive matrix factorization (PMF), ion ratios, and Monte Carlo simulation. For the Sundarban part of Bangladesh, the SOM clustering approach yielded six clusters, while it yielded five for the Indian Sundarbans. The SOM results showed high correlations among Ca2+, Mg2+, and K+, indicating a common origin. In the Bangladesh Sundarbans, mixed water predominated in all clusters except for cluster 3, whereas in the Indian Sundarbans, Cl--Na+ and mixed water dominated in clusters 1 and 2, and both water types dominated the remaining clusters. Coupling of SOM, PMF, and ionic ratios identified rock weathering as a driving factor for groundwater chemistry. Clusters 1 and 3 were found to be influenced by mineral dissolution and geogenic inputs (overall contribution of 47.7%), while agricultural and industrial effluents dominated clusters 4 and 5 (contribution of 52.7%) in the Bangladesh Sundarbans. Industrial effluents and agricultural activities were associated with clusters 3, 4, and 5 (contributions of 29.5% and 25.4%, respectively) and geogenic sources (contributions of 23 and 22.1% in clusters 1 and 2) in Indian Sundarbans. The probabilistic health risk assessment showed that NO3- poses a higher PN-CHR risk to human health than F- and As, and that potential risk to children is more evident in the Bangladesh Sundarban area than in the Indian Sundarbans. Local authorities must take urgent action to control NO3- emissions in the Indo-Bangla Sundarbans region.

Rapid groundwater decline and some cases of recovery in aquifers globally

Groundwater resources are vital to ecosystems and livelihoods. Excessive groundwater withdrawals can cause groundwater levels to decline1-10, resulting in seawater intrusion11, land subsidence12,13, streamflow depletion14-16 and wells running dry17. However, the global pace and prevalence of local groundwater declines are poorly constrained, because in situ groundwater levels have not been synthesized at the global scale. Here we analyse in situ groundwater-level trends for 170,000 monitoring wells and 1,693 aquifer systems in countries that encompass approximately 75% of global groundwater withdrawals18. We show that rapid groundwater-level declines (>0.5 m year-1) are widespread in the twenty-first century, especially in dry regions with extensive croplands. Critically, we also show that groundwater-level declines have accelerated over the past four decades in 30% of the world's regional aquifers. This widespread acceleration in groundwater-level deepening highlights an urgent need for more effective measures to address groundwater depletion. Our analysis also reveals specific cases in which depletion trends have reversed following policy changes, managed aquifer recharge and surface-water diversions, demonstrating the potential for depleted aquifer systems to recover.

Analysis of self-organizing maps and explainable artificial intelligence to identify hydrochemical factors that drive drinking water quality in Haor region

Water contamination undermines human survival and economic growth. Water resource protection and management require knowledge of water hydrochemistry and drinking water quality characteristics, mechanisms, and factors. Self-organizing maps (SOM) have been developed using quantization and topographic error approaches to cluster hydrochemistry datasets. The Piper diagram, saturation index (SI), and cation exchange method were used to determine the driving mechanism of hydrochemistry in both surface and groundwater, while the Gibbs diagram was used for surface water. In addition, redundancy analysis (RDA) and a generalized linear model (GLM) were used to determine the key drinking water quality parameters in the study area. Additionally, the study aimed to utilize Explainable Artificial Intelligence (XAI) techniques to gain insights into the relative importance and impact of different parameters on the entropy water quality index (EWQI). The SOM results showed that thirty neurons generated the hydrochemical properties of water and were organized into four clusters. The Piper diagram showed that the primary hydrochemical facies were HCO3--Ca2+ (cluster 4), Cl---Na+ (all clusters), and mixed (clusters 1 and 4). Results from SI and cation exchange show that demineralization and ion exchange are the driving mechanisms of water hydrochemistry. About 45 % of the studied samples are classified as "medium quality"," that could be suitable as drinking water with further refinement. Cl- may pose increased non-carcinogenic risk to adults, with children at double risk. Cluster 4 water is low-risk, supporting EWQI findings. The RDA and GLM observations agree in that Ca2+, Mg2+, Na+, Cl- and HCO3- all have a positive and significant effect on EWQI, with the exception of K+. TDS, EC, Na+, and Ca2+ have been identified as influencing factors based on bagging-based XAI analysis at global and local levels. The analysis also addressed the importance of SO4, HCO3, Cl, Mg2+, K+, and pH at specific locations.

Groundwater hydrogeochemistry and non-carcinogenic health risk assessment in major river basins of Punjab, India

The Indian Punjab state is drained by the four rivers, along with a well-connected network of canals, and is now dealing with a slew of water quality issues and problems. In this study, basin-wise hydrogeochemical modelling of 323 groundwater samples and identification of NO3- and F- enrichment pathways in aquifer systems of Punjab were studied using different plots and multivariate statistics. To evaluate the groundwater quality and human health risks, an entropy-based water quality index and Monte Carlo simulation were used, respectively. Spatial distribution of NO3- indicated that its very high values were prominent in parts of southwestern Punjab falling under LSRB, along with few pockets in eastern and northeastern Punjab falling under MSRB and GRB. High NO3- values (> 45.0 mg/L) were found in 15.0% of Ravi River Basin (RRB) groundwater samples, 22.86% of Beas River Basin (BRB), 23.52% of Middle Sutlej River Basin (MSRB), 36.9% of Lower Sutlej River Basin (LSRB), and 21.31% of Ghaggar River Basin (GRB). The spatial distribution of NO3- revealed elevated concentrations (> 100 mg/L) in the southwestern part of Punjab, particularly in LSRB and localized pockets in the eastern and northeastern areas of Punjab within MSRB and GRB. High F- concentration (> 1.5 mg/L) was observed in 15.12% and 21.31% groundwater samples of LSRB and GRB, respectively. Spatially southern parts falling under LSRB and GRB reflected high F- content (> 1.5 mg/L) in groundwater. In LSRB, evaporative and anthropogenic processes influence the groundwater quality. The results of interionic relationships and statistical analysis revealed that NO3- has anthropogenic origin and that is being aggravated by leaching, the evaporation processes, animal excreta, septic tanks and irrigation return flows in LSRB and GRB, while F- is geogenic in nature. Hazard index (HI) values in 14.63%, 22.2%, 24.6%, 49.58%, and 34.42% samples for adults and 21.95%, 27.7%, 42.0%, 72.3%, and 52.46% samples for children were higher than unity in RRB, BRB, MSRB, LSRB, and GRB, respectively. The basin-wise demarcation of various groundwater quality parameter and assessment of human health risk would be of significance for the management of water resources.

Hydrogeochemical characterization and quality assessment of groundwater resource in Savar - an industrialized zone of Bangladesh

Savar Upazila in the Dhaka District is a rapidly expanding city with a diverse range of industries and agricultural activities. This expansion poses environmental challenges including the threat to groundwater contamination. Based on these considerations, the objective of this research is to carry out a shallow groundwater hydrogeochemical characterization and an assessment of the suitablity of the groundwater for drinking and irrigational purposes using a geochemical approach, multivariate statistical techniques, and some indices of groundwater quality. The hydrogeochemical analyses of 42 groundwater samples from shallow depths (18 - 76 m) showed that the order of concentrations of cations, anions, and metals was Ca²⁺ > Na⁺ > Mg²⁺ > K⁺, HCO₃^- > Cl^- > SO₄^2- > NO₃^-, and Cr > As > Pb > Mn > Fe, respectively. Weathering of silicates was found to be the most significant hydrogeochemical process governing the chemistry of groundwater. Cation exchange also plays a significant role in the evolution of the groundwater chemistry. Principal component analysis and hierarchical cluster analysis suggested that anthropogenic activities are influencing groundwater quality. A drinking water quality index map showed that about 91% of the groundwater samples were in the excellent category and suitable for human consumption, with only a few samples exceeding the standards of the WHO and Bangladesh for concentrations of Ca²⁺, Mg²⁺, HCO₃^-, Fe, Mn, and As. An analysis of irrigation quality parameters found that most of the groundwater samples were either excellent or good for agricultural uses, except for one sample in the Tetuljhora Union that was unsuitable based on residual sodium carbonate. This finding may be useful to local governments in understanding the current status of groundwater quality, tracking potential threats of contamination, and initiating appropriate measures for long-term groundwater resource management.

Conceptual Evaluation of Factors Controlling Groundwater Chemistry in Ad-Dawadmi, Saudi Arabia, Using Visualization and Multiple Lines of Evidence

Understanding the hydrogeochemical and physicochemical processes and factors controlling the chemical characteristics of groundwater (GW) is essential in water resources studies. In this work, the authors explored, applied, and evaluated the practicality of a series of analysis methods, exploring their ability to improve the representation of the generic GW chemical datasets. The demonstration resulted in a detailed explanation of findings and interpretations, which benefits newcomers who may not be experts in managing such data. Visualization-based, facile, readily readable, and interpretable graphs were tuned and applied to identify the interconnected controlling factors. The examined varieties were bubble diagrams, 3D surface plots, and scatter box plot matrices. Box plot matrices yielded intensive information about the significant interacting parameters in one graph. Employing bubble diagrams produced vast detail, allowing the identification of the significant processes and enabling the recognition of internally acting mechanisms that were otherwise hidden. The identified GW evolution processes include aerosol dissolution, evaporation, direct and reverse ion exchange, precipitation of calcium salts, flushing out of soil-bounded salts, and rock weathering. pH and HCO3− fluctuations coupled with evaporation were recognized as prominent factors giving rise to the vicious salinization cycle, which is thought to be the process causing the worst deterioration in the GW quality and the salinity within the study area.

Investigating sources, driving forces and potential health risks of nitrate and fluoride in groundwater of a typical alluvial fan plain

Groundwater of alluvial fan plains is the foremost water source, especially in arid/semiarid regions. Its contaminants are big issues for water supply and public health concern. To reveal the groundwater chemistry, contaminants sources and health threats in alluvial aquifers, 81 groundwaters were collected from a typical alluvial fan plain of northern China for nitrogen, fluoride and major ions analysis. Statistical analysis and hydrochemical diagrams as well as human health risk assessment were performed. Nitrate is widely distributed and 53% of groundwaters exceed the permissible limit with the maximum concentration up to 326 mg/L. The distributions of nitrite, ammonia and fluoride contaminants are sporadic in spatial, and the concentrations of fluoride in groundwaters are slightly beyond the permissible limit of 1 mg/L. The hydrochemical facies shift from HCO₃-Ca or Mixed HCO₃-Na·Ca type to Mixed Cl-Mg·Ca and ClCa type with the increase of nitrate content. Two factors (Factor-1 and Factor-2) are extracted by factor analysis and account 63% of the total variances. The positive loading of F^- and negative loading of NO₃^- on Factor-2 reveal geogenic and anthropogenic origins, respectively. The significant positive loadings of TDS, TH, SO₄^2-, Cl^-, Ca²⁺, Mg²⁺ on Factor-1 reveal the governing mechanisms on groundwater chemistry by intermixed sources of geogenic origins and anthropogenic inputs. Hydrogeochemical evolution in the study area is driven by both water-rock interaction and anthropogenic forces. Anthropogenic inputs/influences are the dominated forces increasing groundwater nitrate content and salinity in the piedmont zone and the residential and industrial zone of the southeastern lower parts, and would pose potential non-carcinogenic risks to various populations via oral intake pathway. Rational measures should be taken to protect groundwater quality out of the threats of anthropogenic pollution. The geogenic fluoride in groundwater would threat the health of children through oral pathway and should be also concerned. CAPSULE: The driving forces of groundwater chemistry in alluvial fan plains were revealed using integrated approach of factor analysis and geostatistical modelling.

Hydrochemical characterization and quality assessment of groundwater in the hilly area of the Taihang Mountains in Henan Province, China

This study evaluated the quality of groundwater and its suitability for drinking and irrigation in the hilly area of the Taihang Mountains in Henan Province, China. Groundwater samples were collected from 43 unconfined and 20 confined wells and analyzed. The pollution index of groundwater (PIG) was estimated based on the physicochemical parameters, and seven indices, including the sodium adsorption ratio (SAR), sodium percentage (%Na), residual sodium carbonate (RSC), permeability index (PI), magnesium ratio (MR), Kelley's ratio (KR), and corrosivity ratio (CR), were calculated to qualify the groundwater within the research area for irrigation activities. Multivariate statistical techniques were performed to better understand the hydrochemical processes. Chemical analysis showed that the dominant cation and anion were Ca²⁺ and HCO₃^-, respectively, and the principal hydrochemical facies was Ca-Mg-HCO₃. In terms of pH, total dissolved solids, Na⁺, Cl^-, F^-, and SO₄^2-, most samples were well within the limits prescribed by Chinese standards for drinking water quality, but more than half of the unconfined samples exceeded the specified limits for total hardness and nitrate. The PIG values suggested the pollution level was insignificant for all confined water samples and 72.09% of unconfined water samples, but the PIG distribution map showed that the water in the south central part of the study area had low to moderate pollution. According to the computed values of SAR, %Na, RSC, PI, KR, and MR and the results of a salinity diagram, the results further indicated that most of the studied samples were appropriate for irrigation usage. Only the CR values rendered 41.86% of the unconfined samples and 20% of the confined samples unfit for irrigation. Hence, proper measures are needed to resolve the corrosivity problem. Factor analysis resulted in the extraction of 3 factors that explained 81% of the data variability, and the extracted factors pointed towards geogenic factors governing the groundwater quality.

Demarcation of groundwater quality domains using GIS for best agricultural practices in the drought-prone Shanmuganadhi River basin of South India

A study was conducted to evaluate the suitability of groundwater in the drought-prone Shanmuganadhi River basin of south India for best agricultural practices since the surface water that exists in this basin is not sufficient to meet out the demand. As the quality of groundwater is not uniform in the hard rock aquifers of this basin, the work was carried out to demarcate the suitable groundwater quality zones for the agricultural activities. Sixty-one groundwater samples were collected and analyzed for various parameters such as electrical conductivity (EC), pH, TDS, major cations (Ca²⁺, Mg²⁺, Na⁺, and K⁺) and anions (Cl^-, SO₄^2-, HCO₃^-, PO₄^3-, NO₃^-, and F^-). To demarcate the feasible zones for agricultural practices, irrigation water quality parameters like EC, sodium adsorption ratio (SAR), percent sodium (Na %), residual sodium carbonate (RSC), magnesium hazard ratio (MHR), Kelly's ratio (KR), and permeability index (PI) were computed. Furthermore, the irrigation water quality representation diagrams like USSL, Wilcox, and Doneen were prepared, and their outputs were spatially plotted using the Geographical Information System (GIS) to identify the suitability domains of groundwater for irrigational practices. Interpretation of irrigation water quality parameters and diagrams indicate that 2% of groundwater samples represented "low" salinity, 26% of samples represented "medium" salinity, 66% of samples represented "high" salinity, and 6% of samples represented "very high" salinity. Similarly, about 59% of samples represented the low alkaline/sodium category and 41% of them represented the medium alkaline category. The USSL output shows that about 2% of samples of the basin signified "low salinity with low alkalinity" category (C1S1), 28% of samples signified the "medium salinity with low alkalinity" category (C2S1), 33% of samples signified "high salinity with low alkalinity" category (C3S1), 28% of samples signified the "high salinity with medium alkalinity" category (C3S2), and 10% of samples signified the "very high salinity with medium alkalinity" category (C4S2). Groundwater is suitable for irrigation in 277.52 km² area of the basin. It is moderately suitable in an area of 318.46 km² and poorly suitable over 38.64 km². This study recommends that groundwater with moderate suitability could only be used for irrigating permeable soils and for cultivating salt-tolerant crops. The addition of gypsum to soil might be helpful to increase the infiltration capacity and osmotic activity. However, poorly suitable area should be avoided for agricultural practices.

Investigation of Groundwater Contamination and Health Implications in a Typical Semiarid Basin of North China

Groundwater chemistry and its potential health risks are as important as water availability in arid and semiarid regions. This study was conducted to determine the contamination and associated health threats to various populations in a semiarid basin of north China. A total of 78 groundwater samples were collected from the shallow unconfined aquifers. The results showed that the phreatic water was slightly alkaline, hard fresh water with ions in the order of Ca2+ > Na++K+ > Mg2+ and HCO3− > SO42− > Cl−. Four hydrochemical elements, NO3−, F−, Mn and Zn, exceeded the permissible limits. NO3− and F− contaminants may pose health risks to local residents, while the risks of Mn and Zn are negligible. Dermal exposure is safe for all populations, while the oral pathway is not. Minors (i.e., infants and children) are susceptible to both NO3− and F− contaminants, and adults only to NO3−. The susceptibility of various populations is in the order of infants > children > adult males > adult females. Anthropogenic activities are responsible for the elevated levels of NO3−, Zn, Total dissolved solids (TDS), while F− and Mn are from geogenic sources. Thus, differential water supplies, strict control of waste, and rational irrigation practices are encouraged in the basin.

Quality criteria for groundwater use from a rural part of Wanaparthy District, Telangana State, India, through ionic spatial distribution (ISD), entropy water quality index (EWQI) and principal component analysis (PCA)

The present study region comprises granite and granite gneisses aquifer system constituted by Precambrian rocks. Groundwater is the primary source for drinking and other domestic purposes. Many developing regions in the world suffer from lack of safe drinking water. A rural part of Wanaparthy District in Telangana State, India, is one of them. For this reason, the groundwater samples collected from the study region were analyzed for pH, TDS, Ca²⁺, Mg²⁺, Na⁺, K⁺, HCO₃^-, Cl^-, SO₄^2-, NO₃^- and F^- and evaluated groundwater quality criteria, using ionic spatial distribution (ISD), entropy water quality index (EWQI) and principal component analysis (PCA). The ISD maps show that some locations are not suitable for drinking purpose due to exceeding concentrations of TDS, Mg²⁺, Na⁺, K⁺, HCO₃^-, Cl^-, NO₃^-and F^-, compared to those with national drinking water quality standards. According to the EWQI, about 3%, 47%, 43% and 7% of the total area come under the excellent, good, medium and extremely poor water quality types for drinking purpose, respectively. Chadha's diagram classified the area as carbonate hardness (63%), non-carbonate alkali (17%), carbonates alkali (13%) and non-carbonate hardness (7%) zones. The binary diagrams (Na⁺ + K⁺ vs TC, Na⁺ vs Ca²⁺ and HCO₃^- vs TC) indicate that the quality of groundwater is controlled by influences of water-rock interactions, mineral weathering and dissolution, ion exchange and evaporation as well as the impact of anthropogenic sources. The PCA transferred the chemical variables into three principal components accounts for about 81% of the total variance. The high positive loadings of PC1 (Cl^-, TDS, SO₄^2-, Na⁺, NO₃^-, Mg²⁺ and HCO₃^-) stand for processes of silicate weathering and dissolution, ion exchange and evaporation, and the influence of domestic waste waters, irrigation return flows and chemical fertilizers on the groundwater system, the PC2 (F^- and pH) signifies the alkaline nature of groundwater, which causes fluorosis, and the PC3 (K⁺) is a result of potassium fertilizers. The study helps to take remediate measures at a specific site and hence suggests the treatment of water before its drinking and also the recharge of the aquifer artificially to improve the groundwater quality.

Anthropogenic Effects on Hydrogeochemical Characterization of the Shallow Groundwater in an Arid Irrigated Plain in Northwestern China

Many irrigated plains in arid and semi-arid regions have groundwater quality issues due to both intensive human activity and natural processes. Comprehensive studies are urgently needed to explore hydrogeochemical evolutions, investigate possible pollution sources, and understand the controls on groundwater compositions in such regions. Here, we combine geostatistical techniques and hydrogeochemical assessments to characterize groundwater quality over time in the Yinchuan Plain (a typical irrigated plain in China), using 12 physicochemical variables derived from sampling in 600 and 602 wells in 2004 and 2014, respectively. Our results show that groundwater-rock interactions and evaporation are the key natural factors controlling groundwater compositions. Hydrogeochemical water types in both 2004 and 2014 were Ca-HCO3, Na-Cl, and mixed Ca·Mg-Cl. Along with the hydrogeochemical compositions, we used ionic ratios and the saturation index to delineate mineral solution reactions and weathering processes. Dissolution of gypsum, halite, fluorite, and mirabilite, along with silicate weathering and cation exchange, were identified in the study area. Our results indicated rising ion concentrations in groundwater, which could be the result of anthropogenic influences. Increasing total hardness and nitrates over the study period were most likely caused by agricultural activity and the discharge of waste water from human residential areas.

Groundwater quality assessment in an industrial hotspot through interdisciplinary techniques

Dependency on groundwater has increased due to unprecedented growth of industries as well as settlements. Therefore, assessment of groundwater quality to determine its impact on human and environment has become essential. The major objective of this study was to frame a methodology for complete assessment of groundwater quality in a highly industrialized area comprising of iron, steel, fertilizer, cement, chemical, heavy machinery manufacturing, thermal power, coal mining, and allied industries. Physico-chemical parameters of water samples were analyzed from strategic locations during pre- and post-monsoon seasons. The primary analysis through the water quality index showed 50% of the sampling locations in pre-monsoon and 65% in post-monsoon seasons have very poor quality. Hence, the health risk calculated through hazard index indicates that the water is unsafe for drinking. Chemical indices such as sodium percentage, sodium adsorption ratio, residual sodium carbonate, permeability index, and magnesium hazard suggest that the water can be used for irrigation. High corrosivity ratio at 90% sampling locations specifies its unsuitability for use in industrial production. Factor analysis and other statistical methods justified that the pollution of groundwater was attributed to geogenic, as well as anthropogenic, activities. This research demonstrates the usefulness of interdisciplinary techniques for complete assessment of groundwater quality and representation of complex data set into a presentable and understandable form for proper communication with public, regulatory authorities, as well as policy makers, responsible for water management.