High fluoride in groundwater and associated non-carcinogenic risks at Tiruvannamalai region in Tamil Nadu, India

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.

Assessing variability and hydrochemical characteristics of groundwater fluoride contamination and its associated health risks in East Singhbhum district of Jharkhand, India

Groundwater pollution caused by fluoride is a significant concern for the global population owing to its toxicity, which has negative health consequences. Industrial discharges, agricultural practices, and improper waste disposal are primary concerns in evaluating the degree of fluoride contamination in the selected districts of Eastern India. In a targeted area sampling approach, exactly 196 samples were collected during pre- and post-monsoon, and precise fluoride detection was performed using Ion-Selective Electrodes. Fluoride levels in pre-monsoon water were observed within a range of 0.02 to 2.7 mg/L, with an average abundance of 0.4 ± 0.50. In post-monsoon, the concentration ranged from 0.02 to 4.7 mg/L (mean 0.53 ± 0.60). The study found that 97 % of groundwater samples had acceptable fluoride levels within the 1.5 mg/L limit during pre and post-monsoon. Moreover, approximately 87 % of the samples exhibit fluoride content below the 1 mg/L limit. The hazard quotient was observed to be 0.17 to 0.58 in adults, 0.23 to 0.79 in children and 0.36 to 1.26 in infants during pre-monsoon, whereas 0.05 to 0.55 in adults, 0.12 to 0.74 in children and 0.11to 1.19 in infants during post monsoon. The above data indicates that infants had the highest risk of fluoride exposure, with a significant negative correlation between fluoride and calcium ions. Fluoride had minimal to no link with other ions, a modest positive correlation with sulfate, and a weak negative relationship with overall hardness and alkalinity across both seasons. The present study contributes towards the identification of fluoride levels in various areas, making society aware of water contamination and its health impacts.

Arsenic and fluoride occurrence in groundwater of an alluvial fan-delta junction zone in an arid climate: Implication for potential health risk and irrigation water quality

Alluvial fans and deltas are two environments with different hydrochemical conditions. Their junction zones, as mixing environments, are variably influenced by different processes, leading to variable environmental conditions. The purpose of this study is to investigate groundwater quality in the junction zone of these environments in the northern part of the Jazmourian depression (known as the Rudbar plain) in southeastern Iran to determine the dominant processes, assess arsenic and fluoride health risks, and evaluate irrigation water quality. A total of 33 samples from deep drilled wells were taken, and the concentrations of major ions and elements were determined. Additionally, statistical and hydrochemical analyses were undertaken. The dominant processes in the delta are evaporation and ion exchange, while the dominant process in the fan environment is silicate hydrolysis. Among the samples, 26.7% were mainly affected by the delta, and 73.3% were mainly affected by fan conditions. Although the majority of groundwater samples were suitable for irrigation based on quality standards, a significant portion exceeded the acceptable level for Na%. Non-carcinogenic health risk assessments indicated that arsenic hazard risks exceeded thresholds in 63.3% of cases for children and 36% for adults. Carcinogenic health risks associated with arsenic and fluoride exceeded acceptable levels in 4 and 2 stations, respectively. Elevated As concentrations contribute to a greater average health risk in parts of fans environment.

Prediction of geogenic source of groundwater fluoride contamination in Indian states: A comparative study of different supervised machine learning algorithms

India has been dealing with fluoride contamination of groundwater for the past few decades. Long-term exposure of fluoride can cause skeletal and dental fluorosis. Therefore, an in-depth exploration of fluoride concentrations in different parts of India is desirable. This work employs machine learning algorithms to analyze the fluoride concentrations in five major affected Indian states (Andhra Pradesh, Rajasthan, Tamil Nadu, Telangana and West Bengal). A correlation matrix was used to identify appropriate predictor variables for fluoride prediction. The various algorithms used for predictions included K-nearest neighbor (KNN), logistic regression (LR), random forest (RF), support vector classifier (SVC), Gaussian NB, MLP classifier, decision tree classifier, gradient boosting classifier, voting classifier soft and voting classifier hard. The performance of these models is assessed over accuracy, precision, recall and error rate and receiver operating curve. As the dataset was skewed, the performance of models was evaluated before and after resampling. Analysis of results indicates that the RF model is the best model for predicting fluoride contamination in groundwater in Indian states.

Assessment of groundwater fluoride and human health effects in a hard rock province of south India: Implications from Pollution Index Model (PIM) and Geographical Information System (GIS) techniques

This research examines whether the groundwater in the Sivakasi Region of South India is suitable for consumption, and assesses the possible health hazards for various age demographics including infants, children, teenagers, and adults. A total of 77 groundwater samples were gathered, covering a total area of 580 km2 and analyzed for major and minor ions. The hydrogen ion concentration (pH) of the samples indicates neutral to marginally alkaline. The total dissolved solids (TDS) fluctuate from 255 to 2701 mg/l and electrical conductivity varies from 364 to 3540 µS/cm. A wide range of fluoride concentration was detected (0.1 to 3.2 mg/l) with nearly 38% groundwater samples surpassing the proposed limit (1.5 mg/l) suggested by the World Health Organization in 2017. Gibbs plot analysis suggested that most of the samples were influenced by geogenic factors, primarily rock weathering in this region. Correlation analysis showed that most of the samples were impacted by both natural and human sources. The pollution index of groundwater (PIG) fluctuated from 0.67 to 2.60 with approximately 30% and 53% of samples falling into insignificant and low pollution categories, respectively. Furthermore, 10% and 5% of total samples were characterized as moderate and high pollution levels, and 2% as very high pollution category. Spatial analysis using GIS revealed that 440.63 km2 were within safe fluoride levels according to the WHO standards, while 139.32 km2 were identified as risk zone. The principal component analysis (PCA1) showed strong positive loadings on EC (0.994), TDS (0.905), Mg2+ (0.910), Cl- (0.903) and HCO3- (0.923) indicating rock water interaction. PCA2 accounts the high positive factor loading on HCO3- (0.864) indicating ion exchange and mineral leaching. The PCA1 and PCA2 indicated that variables such as mineral leaching and rock water interaction are the major mechanisms contributing to the chemical signatures in groundwater, which may support for the elevated fluoride levels in certain areas. Risk assessments, including Hazard Quotient results showed that 71%, 61% 38%, and 34% of groundwater samples exceeded the permissible THI limit (THI > 1) for infants, children, teenagers, and adults, respectively. The study recommends implementing measures such as denitrification, defluorination, rainwater harvesting, and improved sanitation infrastructure to enhance the health conditions in the study region. Additionally, it suggests introducing educational programs in rural areas to create awareness about the health dangers due to consumption of water with high fluoride levels.

Integrating deep learning and regression models for accurate prediction of groundwater fluoride contamination in old city in Bitlis province, Eastern Anatolia Region, Türkiye

Groundwater resources in Bitlis province and its surroundings in Türkiye's Eastern Anatolia Region are pivotal for drinking water, yet they face a significant threat from fluoride contamination, compounded by the region's volcanic rock structure. To address this concern, fluoride levels were meticulously measured at 30 points in June 2019 dry period and September 2019 rainy period. Despite the accuracy of present measurement techniques, their time-consuming nature renders them economically unviable. Therefore, this study aims to assess the distribution of probable geogenic contamination of groundwater and develop a robust prediction model by analyzing the relationship between predictive variables and target contaminants. In this pursuit, various machine learning techniques and regression models, including Linear Regression, Random Forest, Decision Tree, K-Neighbors, and XGBoost, as well as deep learning models such as ANN, DNN, CNN, and LSTM, were employed. Elements such as aluminum (Al), boron (B), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), phosphorus (Pb), lead (Pb), and zinc (Zn) were utilized as features to predict fluoride levels. The SelectKbest feature selection method was used to improve the accuracy of the prediction model. This method identifies important features in the dataset for different values of k and increases model efficiency. The models were able to produce more accurate predictions by selecting the most important variables. The findings highlight the superior performance of the XGBoost regressor and CNN in predicting groundwater quality, with XGBoost consistently outperforming other models, exhibiting the lowest values for evaluation metrics like mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE) across different k values. For instance, when considering all features, XGBoost attained an MSE of 0.07, an MAE of 0.22, an RMSE of 0.27, a MAPE of 9.25%, and an NSE of 0.75. Conversely, the Decision Tree regressor consistently displayed inferior performance, with its maximum MSE reaching 0.11 (k = 5) and maximum RMSE of 0.33 (k = 5). Furthermore, feature selection analysis revealed the consistent significance of boron (B) and cadmium (Cd) across all datasets, underscoring their pivotal roles in groundwater contamination. Notably, in the machine learning framework evaluation, the XGBoost regressor excelled in modeling both the "all" and "rainy season" datasets, while the convolutional neural network (CNN) outperformed in the "dry season" dataset. This study emphasizes the potential of XGBoost regressor and CNN for accurate groundwater quality prediction and recommends their utilization, while acknowledging the limitations of the Decision Tree Regressor.

Spatial distribution of groundwater fluoride and arsenic and its related disease in typical drinking endemic regions

c exposure to geogenic fluoride and arsenic iChronic exposure to geogenic fluoride and arsenic in groundwater has a deleterious influence on the health of billions of people globally. The health status of residents impacted by connected diseases is urgently needed. A twelve-year study was carried out to identify the spatial distribution pattern of high fluoride/arsenic groundwater in an arid/semi-arid area and to estimate the population exposed to related disease. A geostatistical interpolation method and a disease inversion model were used. The results indicated that fluoride/arsenic-rich groundwater primarily accumulated in basins of Shanxi Province. Groundwater fluoride exposure provided a health concern to 3.16 million persons (9.08 % of the population), including 2.50 million children at risk of dental caries. Exposure to groundwater arsenic caused a health risk to 4.38 million inhabitants (12.58 % of total), with 1.92 million at risk of lung cancer, 1.87 million at risk of bladder cancer, and 0.29 million at risk of skin cancer, respectively. The pollution and impact of groundwater fluoride and arsenic vary greatly among residents in different environments, and accurate assessment of the affected population is of great significance for residents' health and water quality management. Our research study complements the critical data on the disease risks associated with geogenic-contaminated groundwater and provides scientific basis of water quality management for policy makers.

Natural and anthropogenic factors regulating fluoride enrichment in groundwater of the Nansi Lake Basin, Northern China

Excess fluoride (F-) in groundwater can be hazardous to human health of local residents who rely upon it. Beside natural sources, anthropogenic input may be an additional source to be considered. Twenty surface water and 396 groundwater samples were collected from the Nansi Lake Basin, with hydrogeochemical and isotope techniques employed to clarify the spatial variability, source, and the natural and anthropogenic factors regulating the occurrence of high F- groundwater. The factors responsible for elevated F- levels in surface water and deep confined aquifers are discussed based on their hydraulic relationship. Also a conceptual model of F- enrichment with different aquifer systems is put forward based on the geomorphic units of the basin. The results show that F- concentration is between 0.1 and 6.9 mg/L in the west of Lake, while ranged from 0.03 to 1.74 mg/L in the east of Lake. The hydrogeological setting and lithology are the primary factor determining the provenance of high-fluoride groundwater in the basin. Fluoride mainly originated from the dissolution of fluorine-bearing minerals, and is affected by the alkaline groundwater environment, cation exchange, adsorption, and evaporation. The landforms on the east side of Nansi Lake are low hills and piedmont sedimentary plains, where the aquifers consist of karst fissure water and overlying porewater. High F- groundwater is not observed in this area due to its rapid flow and Ca2+-enriched hydrochemical characteristics. The anthropogenic input (such as fertilizer application on farms and illegal industrial pollutant discharge), contribute F- to groundwater in varying degrees, especially in the shallow aquifers east of the lake and in some parts west of the lake. This work is a clear example of how natural processes together with human activities can affect the chemical quality of groundwater, which is essential to safeguard the sustainable management of water resources in semi-arid areas.

Fluoride presence in drinking water along the southeastern part of El Bajío Guanajuatense, Guanajuato, Mexico: sources and health effects

Drinking water with a high natural concentration of fluoride (F^-) has serious consequences for the health of the rural population in the state of Guanajuato, Mexico, where the water contains levels of F^- that are not allowed by national and international regulations (1.5 mg/L). This health problem is very common in multiple states throughout Mexico where drinking water is generally extracted from aquifers that are hosted in fractured volcanic rocks of the Tertiary. These aquifers show similar geological characteristics: deep basins that formed as a result of felsic eruptive events and the extensional deformation of the Basin and Range and are now filled with unconsolidated sediments. In this study, we assessed the occurrence of F in volcanic rocks collected at 11 sampling sites along the Sierra de Codornices in Guanajuato (ranging between 0.01299 and 0.146 wt%, average 0.039 wt%, and SD = 0.039 wt%; n = 10), a region where both rural and urban communities consume drinking water with a high F^- content (up to 7.1 (mg/L). The F content is dispersed in volcanic rocks, and the highest levels are present in felsic rocks. The statistical and hydrogeochemical results of a sampling campaign of 32 wells in the Juventino Rosas (JR) and Villagran (Vill) municipalities in 2019 suggest that F^- mobilization in groundwater is the product of silicate weathering and the dissolution of volcanic glass, alkaline desorption in the surfaces of F-containing minerals, and possibly ion exchange of minerals and clays or deep fluids enriched with F^-, in addition to the precipitation of carbonates that decrease the Ca²⁺ concentration in groundwater. All of these processes can be accelerated by groundwater geothermal characteristics within the study area. The hydrogeochemical, fluoride exposure risk, and fluoride pollution index (FPI) results, as well as the epidemiological survey, indicate that teenagers and older adults from Praderas de la Venta are at risk of exposure to F^- due to the high concentrations ingested over a long period, the toxicity of the element, and its ability to accumulate in the bones. Extended exposure to elevated levels increases the risk. This work allows us to observe how the populations of JR and Vill can be exposed to high F^- contents in drinking water due to the geological characteristics of the region.

Distribution, enrichment mechanisms, and health risk assessment of high-fluorine groundwater in the Yudong Plain, Henan Province, China

The Yudong Plain is in the eastern part of Henan Province, China, where there is little rain and high evaporation. Compared to other areas in Henan Province, the groundwater fluorine content is generally high, which affects the health of residents. Based on the systematic analysis of water chemistry data of shallow and mid-depth groundwater samples in the Yudong Plain, the causes of shallow and mid-depth high-fluorine groundwater in the Yudong Plain were explored using mathematical statistics, spatial interpolation, and ion ratios. The results show that the fluorine contents of both shallow and mid-depth groundwater in the study area are high. The shallow samples had fluorine contents ranging from 0.1 to 4.89 mg/L, with an exceedance rate of 48% and an average content of 1.15 mg/L. The fluorine content of mid-depth samples ranged from 0.14 to 3.32 mg/L, with an exceedance rate of 68% and an average content of 1.33 mg/L. The shallow high-fluorine groundwater is mainly distributed in the central low-lying area, and its main hydrochemical type is HCO₃-Na·Mg; the mid-depth high-fluorine groundwater is mainly distributed in strips in the north and east of the study area, and its main water chemistry type is HCO₃-Na. Fluorine enrichment in shallow groundwater in the study area is controlled by rock weathering, evaporation concentration, and competitive adsorption, while leaching and dissolution of fluorine-containing minerals in sedimentary strata are the main factors influencing fluorine enrichment in mid-depth groundwater. The results of the human health risk assessment (HRA) showed that the mean non-carcinogenic hazard quotients (HQs) in shallow groundwater were 0.95, 0.64, 0.57, and 0.55 for infants, children, teenagers, and adults, respectively, while the mean non-carcinogenic HQs in mid-depth groundwater were 1.11, 0.74, 0.66, and 0.63, respectively. The study provides a scientific basis for the rational development and use of groundwater in the area and offers theoretical support for the prevention and control of groundwater pollution.

Spatial distribution characteristics and prediction of fluorine concentration in groundwater based on driving factors analysis

Excess fluoride (F^-) in groundwater can be hazardous to human health. A total of 360 ground water samples was collected from northern Anhui, China, to study the levels, distribution, and source of F^-. And on this basis, predicting the spatial distribution of F^- in a wider scale space. The range of F^- was 0.1-5.8 mg/L, with a mean value of 1.2 mg/L, and 26.4 % of the samples exceeded the acceptable level of 1.5 mg/L. Moreover, the water-rock interaction (fluorite dissolution) and cation alternate adsorption were considered to be two main driving factors of high F^- in groundwater. To further illustrate the spatial effects, the BME-RF model was established by combining the main environmental factors. The spatial distribution of F^- was quantitatively predicted, and the response to environmental variables was analyzed. The R² of BME-RF model reached 0.93, the prediction results showed that the region with 1.0-1.5 mg/L of F^- accounts for 47.2 % of the total area. The predicted F^- content of nearly 70 % of groundwater in this area has exceeded 1.0 mg/L, which was dominated by Na⁺ and HCO₃^- type. The spatial variability of F^- in the study area was mainly affected by hydrogeological conditions, and the vertical distribution characteristics were related to the spatial variation of slope, distance from runoff, and hydrochemical types. The results of the study provide new insights into the F^- concentration prediction in underground environment, especially in the borehole gap area.

Occurrence, controlling factors and noncarcinogenic risk assessment based on Monte Carlo simulation of fluoride in mid-layer groundwater of Huaibei mining area, North China

Fluoride groundwater pollution is a major challenge to ensuring a safe groundwater supply for the global community. This study emphasized mid-layer groundwater (MG) as the main water supply source in the Huaibei mining area, North China. A total of 74 groundwater samples were taken to determine the hydrochemistry, source provenance, driving forces of high-fluoride groundwater, and associated probabilistic health risk using Monte Carlo simulation. The fluoride concentration in 55.56 % of the MG samples exceeded the Chinese drinking water permissible limit of 1 mg/L. In addition, MG is characterized by the hydrochemical faces of HCO₃^- type and Na⁺ type, lower Ca²⁺ and higher TDS concentration. Fluoride enrichment was predominantly controlled by the geogenic sources of fluorite dissolution, silicate weathering and lateral supply from the Carboniferous Taiyuan Formation limestone aquifer (CLA). In addition, the driving forces of high-fluoride groundwater were an alkaline environment, low Ca²⁺ concentration, high Na⁺ and HCO₃^- concentration, cation exchange between Ca²⁺ and Na⁺ on the surface of clay minerals, and competitive adsorption of HCO₃^-. The health risk assessment of F^- for noncarcinogenic risk showed that the HQ values of 28.16 % of groundwater samples exceeded the safety limit of 1 for infants, followed by 2.1 % for children and 0 % for both adult females and males. Infants and children are more prone to the impact of excessive F^-. The findings of this study will provide new insights into the geochemical behavior of F^- and the safety of drinking water.