Contaminant transport from solid waste dumps: Implications for environmental degradation with a focus on capital territory regions.

Open dumping of Municipal solid waste is the main method of solid waste management in Pakistan. To investigate the impacts of leachate transportation from these waste dumps on groundwater quality, two sites were selected (I-12 in Islamabad and Lohsar in Rawalpindi), water samples were collected from existing wells during summer, winter, and rainy seasons and were analyzed for physiochemical parameters using standard methods. Most groundwater samples showed contamination and values of various parameters exceeded the desired limits set by the World Health Organization (WHO) and the National Standard for Drinking Water Quality (NSDWQ), especially during the rainy season, whereas the least contamination in groundwater samples was observed during the winter season. The results obtained were, pH: 5.75-7.87, Electrical Conductivity (EC): 103-3460 µS cm-1, Total Dissolved Solids (TDS): 436-4425 mg L-1, Total Alkalinity (TA): 190-1330 mg L-1, Total Hardness (TH): 128-676 mg L-1, Chlorides (Cl⁻): 56.7-893.3 mg L-1, Nitrates (NO3⁻): 7.8-19.9 mg L-1, Dissolved Oxygen (DO): 6.1-20.8 mg L-1, Biological Oxygen Demand (BOD): 1.0-44.0 mg L-1 and Chemical Oxygen Demand (COD): 56-272 mg L-1. The findings suggest that the magnitude of groundwater contamination from leachate transportation is intricately influenced by factors such as leachate composition, seasonal variations and distance from the dumpsite. The contamination level reduced along the distance from the dumps. Except Copper (Cu) and Zinc (Zn), the concentration of all other heavy metals including, Iron (Fe), Chromium (Cr), Nickel (Ni), Lead (Pb), Cadmium (Cd) was found above standard appreciable limits. The t-test showed a significant difference in parameter concentrations for all seasons except for Cd and Zn. The calculation of water quality index through CCME (Canadian Council of Ministers of Environment) model revealed that all the groundwater samples around both the dump sites were of poor and marginal quality. In general, the groundwater quality of both study areas is not suitable for the drinking purpose. The study suggests regular testing and treating groundwater before use, use of engineered landfills, covering landfills with clay and vegetation and use of alternative strategies like composting and recycling for waste management.

Integrating machine learning models with cross-validation and bootstrapping for evaluating groundwater quality in Kanchanaburi province, Thailand.

Exploring the potential of new models for mapping groundwater quality presents a major challenge in water resource management, particularly in Kanchanaburi Province, Thailand, where groundwater faces contamination risks. This study aimed to explore the applicability of random forest (RF) and artificial neural networks (ANN) models to predict groundwater quality. Particularly, these two models were integrated into cross-validation (CV) and bootstrapping (B) techniques to build predictive models, including RF-CV, RF-B, ANN-CV, and ANN-B. Entropy groundwater quality index (EWQI) was converted to normalized EWQI which was then classified into five levels from very poor to very good. A total of twelve physicochemical parameters from 180 groundwater wells, including potassium, sodium, calcium, magnesium, chloride, sulfate, bicarbonate, nitrate, pH, electrical conductivity, total dissolved solids, and total hardness, were investigated to decipher groundwater quality in the eastern part of Kanchanaburi Province, Thailand. Our results indicated that groundwater quality in the study area was primarily polluted by calcium, magnesium, and bicarbonate and that the RF-CV model (RMSE = 0.06, R2 = 0.87, MAE = 0.04) outperformed the RF-B (RMSE = 0.07, R2 = 0.80, MAE = 0.04), ANN-CV (RMSE = 0.09, R2 = 0.70, MAE = 0.06), and ANN-B (RMSE = 0.10, R2 = 0.67, MAE = 0.06). Our findings highlight the superiority of the RF models over the ANN models based on the CV and B techniques. In addition, the role of groundwater parameters to the normalized EWQI in various machine learning models was found. The groundwater quality map created by the RF-CV model can be applied to orient groundwater use.

Delineation of groundwater potential zones and its extent of contamination from the hard rock aquifers in west-Bengal, India.

This study evaluates the groundwater potential and quality in the parts of Chhotanagpur Gneissic Complex situated in the East Indian Shield. The region has faced groundwater development challenges for several decades. Therefore, in the study area, it is crucial to address the depletion of both groundwater quality and quantity, as this facilitates the identification of potential uncontaminated groundwater zones. The present study interprets the groundwater potential zones (GWPZ) utilizing an analytical hierarchical process (AHP) integrated with hydrogeochemical analysis. Several thematic maps were prepared to delineate the GPWZ. It has been found that ∼0.6% of the study area has a very good potential zone, 14.4% has good, 52% has moderate, and approximately 32% and 0.9% have low to very low prospective groundwater resources, respectively. The authentication of results was found to be excellent (91.4%) with the Area Under Curve (AUC). Analysis of hydrogeochemical data suggests that Mixed Ca-Na-HCO3, Mixed Ca-Mg-Cl, Ca-HCO3, and Na-Cl are the dominant water types in the study area. The principal component analysis suggests that Na+, Mg2+, Cl-, NO3-, and SO42- significantly contribute to groundwater chemistry. The K-means clustering and hierarchical cluster analysis classified groundwater samples into three clusters based on the hydrogeochemical characteristics. It is inferred that silicate weathering and reverse ion reactions through rock-water interaction control geogenic processes for groundwater chemistry. It is also inferred that regions with poor to unsuitable water quality indexes also have low GWPZ. Further, groundwater for irrigation is also accessed and found unsuitable at some locations. This research contributes to comprehending groundwater characteristics in analogous geological regions globally. Additionally, it assists in implementing preventive actions to mitigate groundwater contamination, consequently lowering health risks and formulating sustainable plans for the future.

Analysing agricultural plant protection product concentrations in groundwater in Germany: Nationwide database with site and compound insights.

Pesticides from agricultural practices are among the most pressing reasons why groundwater sources do not reach the good chemical status standards as required by the European Water Framework directive. Complementary to previous federal pesticide reports, we analysed groundwater-monitoring data from 13 German Länder assembled in a database consisting of 26.192 groundwater measuring sites sampled between 1973 and 2021 of in total 521 parent compounds and metabolites. This study focuses on agricultural plant protection products. The monitored substance spectrum and site density developed over time and differs between Länder. More than 95 % of all samples lie below the respective (multiple) limits of quantification (LOQ). We thus report the frequency of exceedance above concentration thresholds, which allows to compare measurements temporally and spatially. Pesticide detections were found in all aquifer types, land uses and well screen depths. Most detections of higher concentrations were found in agricultural areas, at shallow screen depth in porous aquifers. Karst aquifers showed also a higher percentage of samples in higher concentration classes. Metabolites with high mobility and persistence were found in higher concentration ranges. Herbicides and metabolites thereof dominate the top 20 of pesticides that most frequently exceed 0.1 µg L-1. The ranking for 2010-2019 includes both authorised and banned compounds and their occurrence is discussed in the context of their mobility, persistence and underlying monitoring density. Yearly exceedance frequencies above 0.05, 0.1 µg L-1 and higher thresholds of metazachlor and its esa-metabolite, and national sales data of the parent compound did not show a temporal correlation in subsequent years. This study stresses the need for the harmonisation of heterogeneous pesticide data. Further, a characterisation of the groundwater data used to analyse pesticide occurrence in selected concentration ranges for relevant site factors and compound properties and provides a pesticide ranking based on exceedance frequencies is provided.

Assessing groundwater quality dynamics in Madhya Pradesh: Chemical contaminants and their temporal patterns.

Groundwater is essential for maintaining ecosystem health and overall well-being as a pivotal resource for plants and animals. The increasing public consciousness of the deterioration of groundwater quality has emphasized the significance of undertaking extended evaluations of groundwater water quality, particularly in regions undergoing substantial hydrological alterations. This study primarily aims to investigate the spatio-temporal variations in groundwater quality and evaluate its suitability for potable purposes in the region of Madhya Pradesh. The study combines the Mann-Kendall (MK) test and Sen's Slope (SS) to analyze the changes in groundwater quality of all 51 districts of Madhya Pradesh, India, utilizing 12 water quality indices using MATLAB. Data was sourced from the Central Ground Water Board (CGWB) in India from the year 2001-2021. The data was then tested for homogeneity at all 1154 sampling stations using the software XLSTAT. Piper plot clustering characterized the state's groundwater as bicarbonate-calcium-magnesium (HCO3--Ca2+-Mg2+) type. The study found that the groundwater in the area is heavily impacted by high levels of nitrate and hardness, which is caused by an increase in multivalent cations. The water was classified as ranging from hard to extremely hard, and approximately 25.49% of the state's groundwater has nitrate levels that exceed the acceptable limits. The MK test showed a significant increasing correlation in trends for parameters such as nitrate, sulfate, fluoride, chloride, bicarbonate, total hardness, and electrical conductivity. It also showed a significant decreasing correlation for calcium, magnesium, potassium, and sodium. These results were observed at a confidence level of 95%. The analysis of trends has shown that human-related factors have a considerable effect on the characteristics of groundwater quality. It is therefore recommended that such human-related factors be taken into consideration when developing policies for managing groundwater resources. Consequently, these policies should emphasize the strict enforcement of rules and standards that limit the overuse of fertilizers, ensure the appropriate disposal of municipal solid and liquid wastes, and regulate industrial pollutants.

Driving mechanism of groundwater quality and probabilistic health risk quantification in the central Yinchuan Plain.

The environmental changes from climatic, terrestrial and anthropogenic drivers can significantly influence the groundwater quality that may pose a threat to human health. However, the driving mechanism of groundwater quality and potential health risk still remains to be studied. In this paper, 165 groundwater samples were analyzed to evaluate the groundwater quality, driving mechanism, and probabilistic health risk in the central Yinchuan Plain by applying fuzzy comprehensive evaluation method (FCEM), redundance analysis (RDA) and Monte Carlo simulation. The results showed that hydrochemical evolution of groundwater were strongly influenced by water-rock interaction, evaporation and human activities. While 55.2% of groundwater samples reached the drinking water quality standard (Class I, II and III), 44.8% of samples exceeded the standard limits of Class III water quality (Class IV and V), indicating a high pollution level of groundwater. Mn, TDS, NH4+, NO3-, Fe, F-, NO2-, As were among major indicators that influence the groundwater quality due to the natural and anthropogenic processes. The RDA analysis revealed that climatic factors (PE: 10.9%, PRE: 1.1%), GE chemical properties (ORP: 20.7%, DO: 2.4%), hydrogeological factors (BD: 16.5%, K: 4.1%), and terrestrial factors (elevation: 1.2%; distanced: 5.6%, distancerl: 1.5%, NDVI: 1.2%) were identified as major driving factors influencing the groundwater quality in the study area. The HHRA suggested that TCR values of arsenic in infants, children and teens greatly exceeded the acceptable risk threshold of 1E-4, indicating a high cancer risk with a basic trend: infants > children > teens, while TCR values of adults were within the acceptable risk level. THI values of four age groups in the RME scenario were nearly ten times higher than those in the CTE scenario, displaying a great health effect on all age groups (HQ > 1). The present study provides novel insights into the driving mechanism of groundwater quality and potential health hazard in arid and semi-arid regions.

Public data from three US states provide new insights into well integrity.

Oil and gas wells with compromised integrity are a concern because they can potentially leak hydrocarbons or other fluids into groundwater and/or the atmosphere. Most states in the United States require some form of integrity testing, but few jurisdictions mandate widespread testing and open reporting on a scale informative for leakage risk assessment. In this study, we searched 33 US state oil and gas regulatory agency databases and identified records useful for evaluating well integrity in Colorado, New Mexico, and Pennsylvania. In total, we compiled 474,621 testing records from 105,031 wells across these states into a uniform dataset. We found that 14.1% of wells tested prior to 2018 in Pennsylvania exhibited sustained casing pressure (SCP) or casing vent flow (CVF)-two indicators of compromised well integrity. Data from different hydrocarbon-producing regions within Colorado and New Mexico revealed a wider range (0.3 to 26.5%) of SCP and/or CVF occurrence than previously reported, highlighting the need to better understand regional trends in well integrity. Directional wells were more likely to exhibit SCP and/or CVF than vertical wells in Colorado and Pennsylvania, and their installation corresponded with statewide increases in SCP and/or CVF occurrence in Colorado (2005 to 2009) and Pennsylvania (2007 to 2011). Testing the ground around wells for indicators of gas leakage is not a widespread practice in the states considered. However, 3.0% of Colorado wells tested and 0.1% of New Mexico wells tested exhibited a degree of SCP sufficient to potentially induce leakage outside the well.

Using numerical methods for map the spatiotemporal geogenic and anthropogenic influences on the groundwater in a detrital aquifer in south Spain.

The presence of trace elements in water for domestic supply or irrigation could pose a significant toxic risk for health, due to direct consumption or bioaccumulation through the ingestion of vegetables irrigated with this water. This paper studies the presence of 41 trace elements plus nitrate and bromate in groundwater, using a multivariate statistical tool based on Principal Component Analysis and a geostatistical Kriging method to map the results. Principal Component Analysis revealed 11 significant principal components, which account for 82% and 81% of the total variance (information) respectively for the two dates analysed. Ordinary Kriging was applied to draw maps of the trace elements and PC scores. This research breaks new ground in terms of the large number of parameters used and in terms of the analysis of spatiotemporal variations in these parameters. The results obtained indicate that PC1 represents the natural quality of the aquifer (geogenic) and that there is little change in the average PC1 value between the two dates studied (June near the peak recharge point and November at the end of summer). Agriculture is the human activity that causes the greatest variations in the quality of the groundwater due to the use of fertilizers and due to watering crops with wastewater (PC7_J and PC5_N, June and November, respectively). Other elements of industrial origin, which are dangerous for human health, such as Pb, Cu and Cd, are grouped together in other principal components. The results show that the decline, or even complete absence, of natural recharge during the summer months leads to an increase in the TEs produced by human activity. This indicates that a temporary reduction in the natural recharge could worsen the quality of water resources. Based on the interpretation of the estimated maps, a synthetic map was created to show the spatial distribution of the areas affected by geogenic and anthropogenic factors. Studies with a global approach like this one are necessary in that the possible sources of pollution that could alter the quality of the groundwater and the amount of trace elements and other potentially harmful substances could increase as time goes by. The main advantage of the methodology proposed here is that it reduces the number of parameters, so simplifying the results. This makes it easier to interpret the results and manage the quality of the water.

Improving multidimensional normal cloud model to evaluate groundwater quality with grey wolf optimization algorithm and projection pursuit method.

Groundwater quality is related to several uncertain factors. Using multidimensional normal cloud model to reduce the randomness and ambiguity of the integrated groundwater quality evaluation is important in environmental research. Previous optimizations of multidimensional normal cloud models have focused on improving the affiliation criteria of the evaluation results, neglecting the weighting scheme of multiple indicators. In this study, a new multidimensional normal cloud model was constructed for the existing one-dimensional normal cloud model (ONCM) by combining the projection-pursuit (PP) method and the Grey Wolf Optimization (GWO) algorithm. The effectiveness and robustness of the model were analyzed. The results showed that compared with ONCM, the new multidimensional normal cloud model (GWOPPC model) integrated multiple evaluation parameters, simplified the modeling process, and reduced the number of calculations for the affiliation degree. Compared with other metaheuristic optimization algorithms, the GWO algorithms converged within 20 iterations during 20 simulations showing faster convergence speed, and the convergence results of all objective functions satisfy the iteration accuracy of 0.001, which indicates that the algorithm is more stable. Compared to the traditional entropy weights (0.27, 0.23, 0.47, 0.44, 0.29, 0.59, 0.12) or principal component weights (0.38, 0.33, 0.42, 0.34, 0.47, 0.29, 0.38), the weight allocation scheme provided by the GWOPP method (0.50, 0.48, 0.05, 0.38, 0.02. 0.51 and 0.32) considers the density of the distribution of all samples in the data set space. Among all 55 groundwater samples, the GWOPPC model has 21 samples with lower evaluation ratings than the fuzzy evaluation method, and 28 samples lower than the Random Forest method or the WQI method, indicating that the GWOPPC model is more conservative under the conditions of considering fuzziness and randomness. This method can be used to evaluate groundwater quality in other areas to provide a basis for the planning and management of groundwater resources.

Investigating the effects of surface water recharge on groundwater quality using hydrochemistry and ANFIS model: A case study Minia Governorate, Egypt.

Monitoring and assessing groundwater quality and quantity lays the basis for sustainable management. Therefore, this research aims to investigate various factors that affect groundwater quality, emphasizing its distance to the primary source of recharge, the Nile River. To this end, two separate study areas have been considered, including the West and West-West of Minia, Egypt, located around 30 and 80 km from the Nile River. The chosen areas rely on the same aquifer as groundwater source (Eocene aquifer). Groundwater quality has been assessed in the two studied regions to investigate the difference in quality parameters due to the river's distance. The power of machine learning to associate different variables and generate beneficial relationships has been utilized to mitigate the cost consumed in chemical analysis and alleviate the calculation complexity. Two adaptive neuro-fuzzy inference system (ANFIS) models were developed to predict the water quality index (WQI) and the irrigation water quality index (IWQI) using EC and the distance to the river. The findings of the assessment of groundwater quality revealed that the groundwater in the west of Minia exhibits suitability for agricultural utilization and partially meets the criteria for potable drinking water. Conversely, the findings strongly recommend the implementation of treatment processes for groundwater sourced from the West-West of Minia before its usage for various purposes. These outcomes underscore the significant influence of surface water recharge on the overall quality of groundwater. Also, the results revealed the uncertainty of using sodium adsorption ratio (SAR), Sodium Percentage (Na%), and Permeability Index (PI) techniques in assessing groundwater for irrigation and recommended using IWQI. The developed ANFIS models depicted perfect accuracy during the training and validation stages, reporting a coefficient of correlation (R) equal to 0.97 and 0.99 in the case of WQI and 0.96 and 0.98 in the case of IWQI. The research findings could incentivize decision-makers to monitor, manage, and sustain groundwater.

Setting goals for agricultural nitrogen emission reduction to ensure safe air and groundwater quality: A case study of Quzhou, the North China Plain.

Setting nitrogen (N) emission targets for agricultural systems is crucial to prevent to air and groundwater pollution, yet such targets are rarely defined at the county level. In this study, we employed a forecasting-and-back casting approach to establish human health-based nitrogen targets for air and groundwater quality in Quzhou county, located in the North China Plain. By adopting the World Health Organization (WHO) phase I standard for PM2.5 concentration (35 µg m-3) and a standard of 11.3 mg NO3--N L-1 for nitrate in drinking water, we found that ammonia (NH3) emissions from the entire county must be reduced by at least 3.2 kilotons year-1 in 2050 to meet the WHO's PM2.5 phase I standard. Additionally, controlling other pollutants such as sulfur dioxide (SO2) and nitrogen oxides (NOx) is necessary, with required reductions ranging from 16% to 64% during 2017-2050. Furthermore, to meet the groundwater quality standard, nitrate nitrogen (NO3--N) leaching to groundwater should not exceed 0.8 kilotons year-1 by 2050. Achieving this target would require a 50% reduction in NH3 emissions and a 21% reduction in NO3--N leaching from agriculture in Quzhou in 2050 compared to their respective levels in 2017 (5.0 and 2.1 kilotons, respectively). Our developed method and the resulting N emission targets can support the development of environmentally-friendly agriculture by facilitating the design of control strategies to minimize agricultural N losses.

Hydrogeochemical characteristics of groundwater contamination in Guwahati city, Assam, India: Tracing the elemental Threads.

Groundwater serves as an important resource for drinking and agriculture in many countries, including India. Assessing the quality of groundwater is essential for understanding its chemical characteristics and suitability for consumption. This study aims to explore the factors affecting the hydrogeochemical changes in groundwater within Guwahati City, Assam, India. Groundwater samples were collected and analyzed for major and trace elements, as well as anion concentrations. Concentrations of As, Al, Ba, Cu, F-, Fe, Mn, and Pb exceeded the permissible limits set by both World Health Organization (WHO) and Bureau of Indian Standards (BIS), indicating serious health concerns for the local inhabitants. The distribution pattern of trace elements exceeding the guideline values is intricate, suggesting widespread contamination of groundwater throughout the study area. The Heavy Metal Pollution Index (HPI) and Water Quality Index (WQI) revealed that, except for the central zone, groundwater across the entire study area requires intervention. Piper plot illustrated that the groundwater is predominantly of Ca-HCO3 type, indicating the dominance of alkaline earth and weak acids. Groundwater hydrogeochemistry is mainly controlled by rock-water interaction and evolves through silicate weathering, carbonate weathering, and cation exchange processes. Multivariate statistical analysis identified distinct groups of groundwater based on chemical characteristics, emphasizing the role of both natural processes and anthropogenic activities in influencing groundwater quality. Regular monitoring, management, and intervention of groundwater sources throughout the study area are crucial for long-term use. The findings of this study will assist stakeholders, regulators, and policymakers in formulating strategies for the sustainable use of groundwater.

Groundwater for drinking and industrial purposes: A study of water stability and human health risk assessment from black sand mineral rich coastal region of Kerala, India.

Tempero-spatial analysis of groundwater to disseminate the level of drinking water quality and industrial suitability to meet the developmental requirement of a region is a significant area of research. Accordingly, groundwater quality and geochemical interactions prevailed in a black sand mineral rich coastal village is systematically presented in appraisal of drinking and industrial uses for economic engineering purposes. The study area focused is Alappad village, Kollam, Kerala, India has numerous ecological features in a sustainable perspective. The region is unique with placer deposits where an alluvial soil aquifer-saline water-freshwater interaction occurs. This dynamics decides the pertinent hydro geochemistry, potable and designated uses of ground water in season wise. Coastal area is hereby presented based on water quality parameters predicted with the health risk assessment model with a view on human health and cancer risk due to ions (Pb, Ni, Cu, Ba, Fe, Al, Mn, Zn) in groundwater.. To ascertain industrial usage, ground water is evaluated by Langelier saturation index (LSI), Ryznar stability index (RSI), Aggressive index (AI), Larson-Skold index (LS) and Puckorius scaling index (PSI) and inferences are complemented. Chemical weathering and evaporation processes are the natural factors controlling hydrochemistry of this aquifer. This complex coastal system has Nemerow pollution index (NPI) of moderate pollution for total dissolved ions of Fe and lesser for Cu, and Cr present in groundwater. LSI indicates, water is scale forming but non corrosive (46% in PRM, 20% in MON and 47% in POM). Water quality index (WQI) in POM (ranged 28.7-79.9) was excellent for drinking, followed by PRM (23.6-218.2) and MON (33.4-202.7) seasons. This groundwater bears temporary hardness with the dominance of Ca-Mg-HCO3 water type. Health risk assessment of non-carcinogenic risk index of trace metals (Fe, Zn, Mn, and Pb) revealed, children are at 'low risk' and 'medium' risk with Ni and Cu. The carcinogenic risk index indicated 93% of samples were high Ni induced cancer risk for children, and 87% for adults due to long term ingestion (drinking water intake) pathway. Studies specific on placer mineral deposited coastal region of India are not sufficiently reported with a focus on the above perspectives. Growing need of rare earths for material, device and energy applications, placer mineral explorations can destabilise the coastal hydrosphere. Interrelations of mineral soil - water chemistry prevailed and health hazard predicted would kindle a set of sustainable deliberations. This study summarises the drinking and industrial use of coastal groundwater for future development and human well-being on the basis of quality criteria, corrosion proneness, water stability and health risk factors.

Groundwater quality prediction and risk assessment in Kerala, India: A machine-learning approach.

Despite its critical importance for health, agriculture, and the economy, and its key role in supporting climate change adaptation, groundwater quality remains vulnerable to contamination and is often neglected until significant deterioration. The groundwater resources of Kerala, one of the southernmost states of India, are under escalating stress and scarcity, despite a high well density with 62% of the population relying on groundwater from approximately 6.5 million open wells. This study investigates the detailed hydrogeochemistry and predicts groundwater quality zones of the state using machine-learning techniques viz, extreme gradient boosting (XGBoost), support vector regression (SVR), artificial neural network (ANN) and random forest (RF) regression. The hydrogeochemical analysis reveals varying groundwater quality across the state. Among the different machine learning models, RF shows higher goodness of fit (R2: 0.922) with minimal prediction error (root mean square error: 6.29 and mean absolute error: 3.12). The predicted groundwater quality was validated using the spatially distributed stiff diagrams, visually representing water composition trends of each well. The very good, good, moderate and poor groundwater quality zones occupy 31.7%, 40.4%, 20.4%, and 7.4% of the state aligning accurately with the groundwater quality scenario of the state. Additionally, groundwater drinking risk assessment was conducted, considering that 7.4% of the state experiences poor-quality groundwater. Integrating groundwater quality maps with population data, the study assessed potential health risks due to consuming untreated water. Nearly 0.59 million people across 252 local self-government bodies (LSGs) are susceptible to consuming poor quality groundwater, which may pose potential health risks. This observation provides valuable insights for sustainable groundwater management and public health safeguarding and the findings of the present study are useful for achieving sustainable development goal (SGD) 6 (clean water and sanitation) and long-term groundwater management in Kerala.

Interplay of natural and anthropogenic inputs on the groundwater contamination of Beenaganj-Chachura block.

The present research work investigates the impact of natural and anthropogenic inputs on the chemistry and quality of the groundwater in the Beenaganj-Chachura block of Madhya Pradesh, India. A total of 50 groundwater samples were examined for nitrates, fluoride, chlorides, total dissolved solids, calcium, magnesium, pH, total hardness, and conductivity, and their impact on entropy-weighted water quality index and pollution index of groundwater (PIG) was investigated via the response surface methodology (RSM) using the central composite design. According to analytical findings, Ca, Mg, Cl-, SO42-, and NO3- exceed the desired limit and permitted limit set by the Bureau of Indian Standards (BIS) and the World Health Organization (WHO). According to PIG findings, 76, 16, and 8% of groundwater samples, respectively, fell into the insignificant, low, and moderate pollution categories. The regression coefficients of the quadratic RSM models for the experimental data provided excellent results. Thus, RSM provides an excellent means to obtain the optimized values of input parameters to minimize the PIG values.

Appraisal of contamination, hydrogeochemistry, and Monte Carlo simulation of health risks of groundwater in a lithium-rich ore area.

This study incorporated hydrogeochemical facies, the entropy-weighted water quality index (EWQI), multivariate statistics, and probabilistic human exposure assessment to investigate hydrogeochemistry, analyze groundwater quality, and estimate potential risks to human health in a lithium-rich ore area (Jadar River basin, Serbia). The findings designated the Ca·Mg-HCO3 hydrogeochemical type as the predominant type of groundwater, in which rock weathering and evaporation control the major ion chemistry. Due to the weathering of a lithium-rich mineral (Jadarite), the lithium content in the groundwater was very high, up to 567 mg/L, with a median value of 4.3 mg/L. According to the calculated EWQI, 86.4% of the samples belong to poor and extremely poor quality water for drinking. Geospatial mapping of the studied area uncovered several hotspots of severely contaminated groundwater. The risk assessment results show that groundwater contaminants pose significant non-carcinogenic and carcinogenic human health risks to residents, with most samples exceeding the allowable limits for the hazard index (HI) and the incremental lifetime cancer risk (ILCR). The ingestion exposure pathway has been identified as a critical contaminant route. Monte Carlo risk simulation made apparent that the likelihood of developing cancerous diseases is very high for both age groups. Sensitivity analysis highlighted ingestion rate and human body weight as the two most influential exposure factors on the variability of health risk assessment outcomes.

Groundwater quality evaluation of the weathering mantle in crystalline basement aquifer system, southern Brazil.

Groundwater is the main source of water for more than 2 billion people worldwide. In southern Brazil, the Crystalline Basement Aquifer System is composed of strategic groundwater reservoirs. Groundwater is mostly taken from shallow wells, and it is often used without any treatment, which poses a risk to public health. The present study aims to evaluate shallow groundwater quality and the geochemistry of shallow and deep groundwater located in the municipality of Canguçu, southern Brazil. The physicochemical and microbiological parameters of groundwater samples collected from shallow wells were monitored and analyzed using ANOVA variance analysis and water quality index (CCME WQI) approaches. Also, the results were compared with secondary data from deep wells. The monitored shallow wells had thermotolerant coliforms, Escherichia coli, pH, potassium, manganese, iron, and nitrate in disagreement with the guidelines of the World Health Organization. Moreover, variance analysis showed that the parameters temperature, dissolved oxygen, pH, chloride, and magnesium were the most influenced by seasonal variations. According to the CCME WQI, most samples had good quality (60%), 28% had fair quality, and 12% had poor quality. In addition, the field campaigns with higher precipitation rates also presented fair quality. Therefore, most of the shallow groundwater quality is affected by surface pollutants from the urban area, aggravated in rainy periods. Whereas deep groundwater is influenced by geochemistry mechanisms. The results revealed the risk of water consumption for public health and the urgent need for better maintenance of these wells and water treatment implementation.

A GIS approach for groundwater quality evaluation with entropy method and fluoride exposure with health risk assessment.

This study used the entropy water quality index to analyse the suitability of groundwater for human consumption as well as the hazard index to identify the probable non-carcinogenic dangers among children, women, and men in Nawada, Bihar (India). A total of 75 groundwater samples were taken from hand pumps and tube/bore wells in the pre-monsoon of 2017, and they were evaluated for various physicochemical characteristics. The region's groundwater major cations and anions are dominated by Ca2+ > Mg2+ and [Formula: see text] > Cl- > [Formula: see text] > NO3- > F > [Formula: see text]. Fluoride, chloride, and hardness exceeded WHO and BIS safe standards. Calcium, sodium, magnesium, sulphate, and chloride showed positive correlations, indicating water-rock interactions and mineral leaching and dissolution. Ionic cross-plots reveal that the dissolution of carbonate minerals was the primary source of calcium and magnesium in the groundwater. Also, silicate weathering contributed to these ions in the groundwater. The entropy water quality index (EWQI) found that 79% of groundwater samples were drinkable, whereas 21% were not consumable. The eastern, western, and some southern study areas have the worst drinking water quality. The main source of fluoride toxicity in people is groundwater. For all sampling locations, the HQ fluoride was calculated to be in the ranges of 0.04-3.69 (male), 0.04-3.27 (female), and 0.05-4 (children), indicating a considerably greater risk than the permissible levels (> 1). The fluoride-based non-carcinogenic risks are 27%, 20%, and 21% for children, women, and men, respectively. Children have higher risks from polluted water than adults, according to the non-carcinogenic health risk assessment. This study establishes a standard for regional and global scientific studies that help decision-makers and planners determine the quality of groundwater and fluoride risk and management.

Assessing drinking water quality in Eloued, south-East Algeria, using the groundwater pollution index (GPI) and the synthetic pollution index (SPI) model.

Groundwater quality degradation is a significant environmental issue worldwide, with potentially severe economic consequences and harm to ecosystems and biodiversity. This can directly affect human health, particularly in developing countries where rapid and uncontrolled urbanization is on the rise. Groundwater is the primary resource for meeting the water needs of the Eloued region, located in southeastern Algeria. Water is considered unfit for human consumption if its physico-chemical elements exceed national or international standards or guidelines. We used the GPI and SPI indices to evaluate the quality of groundwater suited for drinking. Groundwater samples were obtained from 22 wells at depths of more than 250 m. Standard analytical procedures were used to determine the physicochemical characteristics of the collected samples, which included pH, EC, TDS, Na+, Ca+2, Mg+2, K+, Cl-, HCO3-, SO4-2, NO3-, NO2-, NH4+ and PO4-3. Multivariate statistical analysis and GIS techniques were used to process the results. The results of the selected physicochemical parameters were compared with World Health Organization (WHO) guidelines to determine the quality of drinking water. The findings indicate that the waters of the terminal complex aquifer are salty and contain medium to high quantities of main ions that surpass the established drinking water limits. The primary ions' relative abundance is Cl- > SO4-2 > HCO3- > NO3 for anions and Na+ > Ca+2 > Mg+2 > K+ for cations. Groundwater chemical types were dominated by Na+, Ca+2, Cl-, and SO4-2. Principal Component Analysis (PCA) showed that alteration and dissolution of carbonates, evaporates, salts, partly silicates, and evaporation, are the main reasons affecting the chemical composition of water in Eloued. The GPI results show that 18.18%, 54.54%, and 27.27% of the water samples were classed as lightly polluted, moderately polluted, or substantially polluted for drinking purposes, respectively. According to the SPI study, 9.09%, 36.36%, 36.36%, and 18.18% were considered drinkable, mildly contaminated, moderately polluted, and seriously polluted for drinking purposes, respectively. According to the GPI and SPI models' geographical distribution maps, potable water is generally scarce and concentrated in the northeastern section of the research area, near the town of Ourmes.

Assessment of groundwater quality for irrigation purpose in aquifers of the Upper Brahmaputra floodplains of Assam, India.

Groundwater is an essential natural resource for mankind. Due to various geogenic and anthropogenic causes, groundwater quality has raised serious concern over the years. In this study, groundwater quality was evaluated for its suitability for irrigation in the Jorhat and Golaghat Districts of Assam, India. A total of 100 groundwater samples were collected from shallow aquifers (< 35 m) from different locations during the pre-monsoon season (March-April 2022). Groundwater in the study area is slightly alkaline in nature (mean pH value of 7.44). The average cations and anions chemistry are in the order of Na+ > Ca2+ > Mg2+ > K+ and HCO3- > Cl- > SO42- > CO32-, respectively. Ca-Mg-HCO3 followed by Na-Ca-HCO3-Cl are the primary water types in the study area. Pearson's correlation matrix showed a positive correlation between TDS and EC (r = 0.78) and sodium showed a positive correlation with TDS and bicarbonate (r = 0.62 and r = 0.65), respectively. Gibbs plot indicated that rock-water interaction is the dominant factor that controls the chemistry of the groundwater of the area. Irrigation parameters like Sodium Absorption Ratio (SAR), Permeability Index (PI), Magnesium Absorption Ratio (MAR), Kelly's Ratio (KR), and Irrigation Water Quality Index (IWQI) indicated that groundwater is overall suitable for irrigation. USSL diagram illustrated that most of the samples fell into the C2 (medium salinity) and S1 (low sodium hazard) categories. Wilcox plot showed the samples fell in excellent to good categories indicating fitness of groundwater for irrigation in the area.