Fluoride Contamination In Groundwater Research Articles

Fluoride contamination in African groundwater: Predictive modeling using stacking ensemble techniques

Fluoride contamination of groundwater is a severe public health problem in Africa due to natural factors that include geological weathering of fluoride-bearing minerals and climatic conditions characterized by high evaporation rates that highly elevate fluoride levels. Anthropogenic activities further aggravate the problem and have affected millions of people in countries such as; South Africa, Tanzania, Nigeria, Ethiopia, Ghana, Kenya, Mauritania, Botswana, and Egypt. High fluoride levels of up to 10 mg/L have been encountered in parts of the East African Rift Valley, above the WHO's recommended limit of 1.5 mg/L, causing serious dental and skeletal fluorosis among the affected people. In this study, the distributions of F− in groundwater of Africa were forecast using an advanced stacking ensemble learning model based on 11 crucial groundwater physiochemical variables and 6270 accessible statistics of observed concentrations. The enhanced algorithm incorporates randomized trees, Tree-Bag, RF, DT, XGB, and ET Machine as base trainees, with a simple Naïve Bayes as the meta-analyzer. The model's AUC score of 0.86 accurately represented the uneven distributions of groundwater fluoride. The results showed that 20–35 % of the continent's eastern part and 10 % of its western region are at risk of having fluoride levels exceeding WHO limits, with an expected population of around 80 million. Regionally, fluoride contamination ranges from 0.1 to 3 mg/L in West Africa was range from 0.0 to 13.29 mg/L, 0.01–588 mg/L in East Africa, 0.04–65.9 mg/L in South Africa, and 0.1–10.5 mg/L in North and 0.01–1.9 mg/L in Central Africa. Na+ and HCO3− are Africa's leading primary causes of fluoride contamination, with Ca2+ and Cl− contributing to fluoride influence in some parts of the continent. This study helped identify health concerns linked to groundwater fluoride and offered guidance on assessing health risks in areas with sparse sample sizes.

Reversing the tide of trouble: harnessing invasive water hyacinth biochar to mitigate groundwater fluoride contamination

Reversing the tide of trouble: harnessing invasive water hyacinth biochar to mitigate groundwater fluoride contamination

Community level vulnerability of groundwater fluoride contamination and exposure by the application of multi-criteria model

Community level vulnerability of groundwater fluoride contamination and exposure by the application of multi-criteria model

Impact of microbial activity on fluoride release from sediments in areas with high fluoride groundwater: Mechanisms, sources and the lithology diversity

This study explores the interplay between microbial activity and sediment lithology in influencing fluoride release from sediments. Sediment samples, collected from Yuncheng Basin: a region known for significant groundwater fluoride contamination, exhibit fluoride concentrations well above the global average, ranging from 206.2 mg/kg to 780.9 mg/kg. These samples comprising silt, silt loam, and sandy loam, are enriched with minerals such as quartz, calcite, albite, chlorite, and illite.Microbial batch incubation reveals that microbial activity significantly enhances fluoride release, particularly in silt loam sediments. The results from sequential extraction first timely identified that the carbonate-bound and Fe-Al-bound fluoride fractions are the most affected forms of fluoride by microbial activity, highlighting the roles of mineral dissolution and desorption in fluoride mobilization.Further batch incubation experiments demonstrate significant increases in fluoride concentrations, especially in silt loam sediments, indicating the role of microbial processes in accelerating fluoride release. Additionally, the study unveils diverse patterns of dissolved elemental concentrations during incubation, with varying release patterns for calcium, magnesium, iron, aluminum, and manganese. These findings illustrate the complex biogeochemical interactions that govern fluoride mobilization in these sediments.Sequential extraction studies further elucidate the intricate mechanisms of fluoride release, with microbial activity primarily influencing the release of carbonate-bound and Fe-Al-bound fluoride. This study also sheds light on the co-dissolution of fluoride and calcium, offering valuable insights into geochemical processes driven by microbial interactions within the sediment matrix.

Development of modified MgO/biochar composite for chemical adsorption enhancement to cleanup fluoride-contaminated groundwater

Fluoride contamination in groundwater has become a global environmental issue. Magnesium oxide (MgO) has demonstrated effectiveness as an adsorbent in treating fluoride pollution in groundwater. However, its use in powder and fine granular form often results in losses during the adsorption process, posing challenges for post-treatment recovery and potentially causing secondary environmental pollution. In this study, two novel fluoride adsorbents [rice husk (RH) and spent coffee grounds (SCG)-based magnesium oxide (MgO) biochar composites (MgO/RH and MgO/SCG)] were developed to cleanup fluoride-polluted groundwater. During the adsorbent synthesis process, RH and SCG biochar were pyrolyzed at 500 °C and modified by calcination using MgO. Both MgO/RH and MgO/SCG surfaces exhibited abundant pore structures and formed MgO crystal phases. Batch experiments results show that when the MgO/RH and MgO/SCG material dosages were 1 g/L, fluoride removal rates reached 80% and 86% respectively. The isotherms and kinetics of fluoride adsorption with MgO/RH and MgO/SCG followed the Langmuir isotherm equation and pseudo-second-order kinetic model. The maximum fluoride adsorption capacities of MgO/RH and MgO/SCG were 63.47 mg/g and 141.98 mg/g, respectively, indicating these materials used mono-layer adsorption mechanism for fluoride adsorption. The addition of MgO into the pores of porous adsorbent materials effectively increased their reactive sites and enhanced the adsorption performance of carbon materials. Particularly, SCG biochar had a richer pore structure than RH biochar, providing a larger contact surface area, facilitating the effective dispersion and doping of MgO into the pores. Therefore, MgO/SCG composite exhibited excellent fluoride adsorption properties in water, indicating the potential for developing a new type of MgO-modified SCG adsorbent material with green prospects. This composite effectively mitigated fluoride contamination, reducing the fluoride concentration in groundwater. Both RH and SCG are agricultural and food waste by-products, thus offering the opportunity to significantly reduce production, operation, and maintenance costs.

Groundwater fluoride contamination, sources, hotspots, health hazards, and sustainable containment measures: A systematic review of the Ghanaian context

Groundwater quality is globally threatened by geogenic and human activities. These activities release high levels of potentially toxic elements, such as fluoride (F−), which pose significant threats to human health. This has become a global issue, especially in developing countries such as Ghana. Despite efforts to address this issue, knowledge gaps still need to be addressed to ensure safe and healthy drinking water for all Ghanaians. Moreover, Ghana has been reported to be a fluorosis-endemic country but the sources and exact hotspots of F− enrichment in the aquifers on a countrywide scale are lacking in the available literature. Understanding the quality of water used for diverse purposes in Ghana is necessary to achieve the United Nations Sustainable Development Goals like good health and well-being (SDG 3) and clean water and sanitation (SDG 6), among others. Therefore, this study synthesized all previous studies on groundwater F− contamination in Ghana, to identify the sources of F− enrichment in groundwater, delineate the hotspots for fluorosis, assess the associated human health risks, identify the best sustainable defluoridation methods, and recommend policy intervention for high groundwater F− threat to aquifers in Ghana. In the Ghanaian context, F− contamination in groundwater is largely from geogenic sources like the weathering of fluoride-bearing rocks (granitoids and carbonate sedimentary lithologies) from the Birimian and Voltaian Supergroups and the dissolution of fluoride-rich minerals (fluorapatite, amphiboles, fluorite, biotite, and muscovite). Hotspots for high groundwater F− in Ghana are mainly restricted to the Upper East Region (0.10–5.00 mg/L), North East Region (0.01–13.29 mg/L), Northern Region (0.1–11.6 mg/L), and the White Volta River Basin (0.04–3.79 mg/L). The mean and maximum values of F− in these hotspots exceed the maximum permissible level (1.5 mg/L) set by the World Health Organization and Ghana Standards Authority. Most people in these areas suffer from dental fluorosis. Therefore, affordable and sustainable defluoridation technologies as well as community-based initiatives are recommended to deal with this menace.

Evaluating Health Risks Associated With Fluoride and Nitrate Contaminants in Drinking Water to Residents Living in the Chikkaballapur Taluk of Karnataka, India

ABSTRACTAddressing fluoride and nitrate contamination in groundwater is crucial for safeguarding public health. Fluoride contamination in groundwater can cause dental and skeletal fluorosis, along with potential neurotoxic effects. Nitrate contamination can lead to methemoglobinemia in infants and increase cancer risk due to the formation of nitrosamines. The major goal of this study was to examine groundwater quality in the Chickkaballapur urban (CBU) and Chikkaballapur rural (CBR) parts of the Chikkaballapur taluk (CBT), Karnataka, and to investigate the potential health hazards associated with the presence of fluoride and nitrate contaminants. Hazard quotient and total hazard index (THI) calculation methods, as suggested by the United States Environmental Protection Agency, were used in the present study to evaluate non‐carcinogenic risks for individuals of various age categories, including men, women, and children (MWC). A total of 112 samples from rural areas and 41 samples from urban areas of Chikkaballapur taluk were collected during the post‐monsoon season. According to the study's findings, groundwater samples from CBU and CBR surpassed acceptable fluoride concentration limits by 41% and 40%, respectively, while samples from CBR exceeded acceptable nitrate limits by 17%, set by the Bureau of Indian Standards (1 mg/L for fluoride and 45 mg/L for nitrate). All CBU samples remained within the acceptable nitrate limits. The total non‐carcinogenic health risks for MWC ranged from 0.34 to 2.18, 0.40 to 2.58, and 0.46 to 2.95, respectively, for CBU, and from 0.16 to 6.51, 0.19 to 7.69, and 0.21 to 8.80, respectively, for CBR. Furthermore, a significant percentage of the groundwater samples that were collected in CBU (60.98%, 68.29%, and 75.61%) and CBR (48.21%, 62.50%, and 73.21%) exceeded the THI limit for MWC (THI = 1). Hence, based on the health risk assessment, it is evident that children in the study area have greater health risks than men and women.

Evaluation of fluoride contamination in groundwater and its non-carcinogenic health hazards in a drought-prone river basin of South India

The present work emphasizes the extend of groundwater pollution in Nagavathi River basin, south India and to study the potential health risks pertaining to the population there. Sixty-eight groundwater samples were collected and tested for various parameters such as pH, EC, TDS, major anions and major cations. The findings infer that the pH of the water ranges from 6.98 to 8.11, indicating that water is alkaline. TDS maximum value of 2401 mg/l indicates the presence of contaminants in the groundwater, and the electrical conductivity of 39.3% samples are above the range of drinking water standards. Based on Piper's diagram, 70.5% of the water samples are mixed CaMgCl type. The samples are classified as rock dominance in Gibb's diagram, which highlights the relationship between rock chemistry and water composition. The Entropy Water Quality Index (EWQI) was computed to calculate the quality of the water, which exhibits 25% of samples represent excellent to good quality, and 29% of samples represent poor water quality. From the spatial distribution map of fluoride, 16.17% of the samples are in the category of extreme risk with >4 mg/l, which can cause detrimental health issues according to WHO. From the outcomes, it is observed that the Total Hazard Index (THI) values for infants, children, teens, females, males were greater than 1. All the age groups in this region are affected due to fluoride, particularly infants and children are more affected. Proper geochemical investigations and treatment of water before consumption are recommended for the study area. In addition, health hazards due to groundwater contamination must be meticulously studied in future.

Deciphering geochemical fingerprints and health implications of groundwater fluoride contamination in mica mining regions using machine learning tactics.

The contribution of mica mining activities to fluoride (F-) contamination in groundwater has been chased in this study. For the purpose, groundwater samples (n = 40, replicated thrice) were collected during the post-monsoons (September-October) from a mica mining area in the Tisri block of Giridih district, Jharkhand. The study has employed a synergy of classical aquifer chemistry, statistical approaches, different indices, Self-Organising Maps (SOM), and Sobol sensitivity index (SSI) to unveil the underlying aquifer chemistry, identify the impacts of mining activities on groundwater quality and its associated health hazard. Fluoride levels varied from 0.34 to 2.8ppm, with 40% of samples exceeding the World Health Organization's permissible limit (1.5ppm). Physicochemical analysis revealed significant differences in electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH) and major ion concentrations (Na+, HCO3-, Ca2+) between fluoride-contaminated (FC) and fluoride-uncontaminated (FU) groups. Higher Na+ and HCO3- associated with F- contaminated samples, were indicative of silicate weathering and carbonate dissolution as primary geogenic sources for this ion. Health risk assessment (HRA) revealed hazard quotient (HQ) values exceeding unity, indicating non-carcinogenic risks, particularly for children in most samples from group FC. The mean Water Quality Index (WQI) of FC group (156.76 ± 7.30) was significantly higher (p < 0.05) than group FU indicating of its unsuitability. SOM could accurately (80%) predict presence of fluoride in water samples based on other major ions. Sobol sensitivity analysis successfully identified fluoride concentration and body weight as most impactful parameters affecting human health. The integration of advanced modelling techniques and geospatial analysis as Inverse Distance Weightage (IDW) maps has provided a robust framework for ongoing groundwater quality monitoring in mining-affected regions and can help proactive intervention in risk-prone areas. Overall, this comprehensive study takes us a step ahead towards ensuring safe drinking water access for the global community.

Pan India fluoride hazard assessment in groundwater

Fluoride (F¯) contamination in groundwater in India has gained global attention due to human health hazards. India’s hydrogeological heterogeneity, spatio-temporal variability of F¯, and health hazards due to geogenic and geo-environmental control pose unique challenges. Addressing these with only a single region-specific study is not possible. Therefore, this study provides an in-depth, holistic analysis of pan India F¯ contamination, controlling factors, and health hazards using a coupled advanced geostatistical and geospatial approach. Alarming F¯ contaminations are identified in Rajasthan, Telangana, Western Andhra Pradesh, Eastern Karnataka, Parts of Haryana, Gujarat, Madhya Pradesh, Tamil Nadu, Uttar Pradesh, Jharkhand, Bihar, and Chhattisgarh. Probabilistic health-risk evaluation using hot-spot, showed similar spatio-temporal distribution of F¯ contamination. The hazard quotient (HQ) for high F¯ shows more adversity to children than adults. Nationally, 8.65 % and 7.10 % of pre- and post-monsoon sites exceed the recommended safe limit of 1.50 mg/L. The highest average F¯ concentration is in Rajasthan. Very high-risk skeletal fluorosis is possible at around ≤ 2 %, whereas dental caries due to deficiency in F¯ concentration is approximately 40 %. A decisive hierarchy of lithology, geomorphology, soils, and lineaments control are identified on F¯ contamination. Climatic conditions are pivotal in governing all these controlling variables. Thus, in arid/semi-arid dry western regions, F¯ contamination is much higher than in the humid areas. Integration of strengths, weaknesses, opportunities, and threats (SWOT) analysis with the results can aid policymakers and government authorities in achieving sustainable remedial measures for future adaptability.

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.

From drops to drums: Assessing rainwater storage’s quality and quantity for addressing water demands in dry periods – A case study from Arusha Tanzania

Sustainable Development Goal 6 highlight the importance of providing reliable, affordable, and safe source of clean drinking water and sanitation for all by 2030. In Tanzania there is a dire need of a water supply strategy due to high levels of natural fluoride contamination in ground water (upto 74 mg/L) and the challenge of meeting water demand during 5 months long dry period. This study assesses social and technical feasibility of implementing rainwater harvesting (RWH) along with treatment technologies that includes Denutritor® to remove ammonia/pesticides and ultrafiltration to eliminate carbon. This integrated technology known as “Mbinguni Maji” is aimed for the long-term water storage to supply drinking water throughout the dry season. The methodology involves i) Assessing the technical feasibility of RWH using database from QGIS and Water Productivity Open-access portal (WaPOR) from the Food and Agricultural Organization of the United Nations; ii) conducting pilot demonstration in 5 different locations in Tanzania; and iii) conducting socio-economic survey for social acceptance of the technology through detailed questionnaires administered to residential homes, medical facilities, schools, hotels, and water kiosk owner. The result indicates that, from technical perspective, there is ample rainfall (average of 1036 mm/year) to supply water throughout the dry season, primarily for drinking and cooking purpose only (upto a maximum of 10 lpcd). The pilot demonstration confirms that the Mbinguni Maji RWH technology produce water that meets WHO water quality standards. The produced water is free from nutrients like carbon and ammonia, ensuring the possibility of long-term storage without bacterial and algal growth. Furthermore, Lab scale demonstration of Denutritor® show promising result of removing nitrite, ammonia even at high elevated temperature (30 °C), which can be effectively applied in Tanzania. In terms of social acceptance, RWH technology is already widely practised in Tanzania during the rainy season. However, the initial investment costs and operation & maintenance (O&M) concerns hinder the usage of RWH technology. Therefore, to ensure the long-term sustainability of RWH technologies, there is a need of development of comprehensive business plan and community awareness campaign.

Assessing groundwater fluoride contamination scenario in West Bengal, India: A combined approach using meta-analysis, current research, and health risk evaluation

The present study was initiated by the findings of a comprehensive systematic review and meta-analysis, which synthesized all available literature on the patterns and trends of groundwater fluoride (F−) contamination in West Bengal, India. This investigation aimed to provide detailed information on F− contamination at the block level within the state, which is essential for effective monitoring and alleviation efforts addressing the acute and evolving human health concerns in affected areas. This study focuses on the six districts (South 24 Parganas, West Medinipur, Jhargram, East Bardhaman, West Bardhaman and Murshidabad) of West Bengal. Findings from more than 3000 datasets revealed that approximately 10%, 11%, 4%, and 14% of groundwater samples exceeded the safe limit of F− (1.5 mgL−1) from South 24 Parganas, Jhargram, West Bardhaman, and Murshidabad district, respectively. Notably 3% of samples from West Bardhaman displayed class V toxicity where F− concentrations exceeding >10 mgL−1. This research introduces an initial assessment of F− concentrations in groundwater from nine newly identified F− contaminated blocks (Baruipur, Sonarpur, Binpur II, Salanpur, Baraboni, Jamuriya, Pandabeswar, Kandi and Khargram) within the region. Overall, 65 blocks from ten districts have been recognized as F− contaminated zones in West Bengal till 2023. The non-cancerous health risk was found to be disproportionately higher in the western districts (Jhargram, West Bardhaman, and western part of Murshidabad) compared to their southeastern counterparts (East Bardhaman, Paschim Medinipur, and South 24 Parganas). A demographic analysis of health risk indicated infants as the most susceptible group to F− toxicity. The probabilistic health risk at P95 dose for eight blocks further corroborated the heightened vulnerability of infants. These insights offer critical implications for the policy-makers, suggesting an urgent need for tailored health policies to mitigate the risk associated with F− contamination in West Bengal.

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.

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.

Optimizing the fluoride removal from drinking water through adsorption with mesoporous magnetic calcite nanocomposites

Fluoride (F−) contamination in groundwater is a global concern arising from natural processes and human activities. This study investigates the adsorption of F− using surface-activated magnetic calcite nanocomposites (NCs) synthesized via co-precipitation assisted wet-impregnation (CP-WI). Various Fe3O4 loadings and calcination temperatures (300 °C and 500 °C) were explored to optimize synthesis parameters. Batch adsorption experiments were conducted to assess the performance of synthesized nanoparticles (NPs) and nanocomposites (NCs). BET, FTIR spectroscopy, X-ray diffraction, and SEM characterization techniques were employed to analyze the physicochemical properties, including surface functional groups, crystallite size, and morphology. The adsorption process was investigated using RSM statistical design with adsorbent dose (A) and F− concentration (B) as independent variables. 0.5Fe-C-NC-5, calcined at 500 °C, exhibited superior adsorption capacity (7.81 mg/g) compared to Fe3O4 and calcite NPs (6.57 mg/g and 6.81 mg/g, respectively). Maximum F− adsorption occurred within 10 min of contact time. NPs and NCs that were exposed to higher calcination temperatures, reaching up to 500 °C, showed an increase in crystallite development while experiencing a reduction in surface area and an enlargement of pore size compared to those treated at lower temperatures. The BET analysis revealed a narrow, slit-like mix of microporous and mesoporous surface with a type IV isotherm and H3 hysteresis loop, indicating multilayer adsorption followed by capillary condensation. The SEM images displayed columnar shapes of CaCO3 polymorph in the prepared Fe3O4-calcite NP and Fe–C NP, which can be attributed to lower calcination or synthesis temperatures.

A Comparative Study on the Removal of Fluorides from Groundwater: Strategies and Technologies

This study conducts a comprehensive comparative analysis of various strategies and technologies employed for the removal of fluorides from groundwater, addressing the increasing concern over elevated fluoride levels in water sources. High fluoride concentrations pose significant health risks, including dental and skeletal fluorosis, necessitating effective and sustainable removal methods. The research evaluates the efficiency, cost-effectiveness, and environmental impact of different fluoride removal techniques, providing valuable insights for water treatment practitioners, policymakers, and communities grappling with fluoride-contaminated groundwater. The study begins by outlining the prevalence of fluoride contamination in groundwater and the associated health risks, establishing the critical need for reliable removal methods. Subsequently, a review of existing literature presents an overview of conventional and emerging technologies utilized for fluoride removal. Conventional methods such as coagulation, precipitation, and adsorption are examined, considering their efficiency, operational ease, and cost implications. The study then delves into advanced technologies like membrane processes, ion exchange, and electrochemical methods, assessing their applicability and effectiveness in diverse groundwater contexts. Environmental sustainability is a key focus throughout the analysis, with a critical examination of the by-products and waste generated by each removal method. The ecological footprint, including energy consumption and material usage, is considered to provide a holistic understanding of the environmental impact associated with fluoride removal technologies. Furthermore, the economic feasibility of each method is evaluated, considering capital and operational costs. Comparative cost analyses offer insights into the affordability and scalability of different removal technologies, aiding decision-makers in selecting suitable methods based on financial constraints. The study concludes with a synthesized overview of the comparative analysis, emphasizing the strengths and limitations of each fluoride removal method. In summary, this comparative study contributes to the broader understanding of fluoride removal technologies, facilitating informed decision-making in addressing groundwater contamination. The findings are anticipated to inform researchers, water treatment professionals, and policymakers, guiding the selection of appropriate and context-specific fluoride removal methods to ensure safe and potable groundwater sources

Development of innovative and green adsorbents for in situ cleanup of fluoride-polluted groundwater: Mechanisms and field-scale studies

In this study, the magnesium oxide (MgO)-based adsorbents [granulated MgO aggregates (GA-MgO) and surface-modified MgO powder (SM-MgO)] were developed to remediate a fluoride-contaminated groundwater site. Both GA-MgO and SM-MgO had porous, spherical, and crystalline structures. Diameters for GA-MgO and SM-MgO were 1–1.7 mm and 1–10 μm, respectively. The pseudo second-order dynamic adsorption and the Freundlich isotherm could be applied to express the chemical adsorption phenomena. The monolayer adsorption was the dominant mechanism at the initial adsorption period. During the latter part of fluoride adsorption, the multilayer adsorption became the dominant mechanism for fluoride removal from the water phase, which also resulted in the increased adsorption capacity. Higher hydroxide, phosphate, and carbonate concentrations caused a decreased fluoride removal efficiency due to the competition of sorption sites between fluoride and other anions with similar electronic properties. Fluoride removal mechanism using GA-MgO and SM-MgO as the adsorbents was mainly carried out by the chemical adsorption. Reaction paths contained two main processes: (1) formation of magnesium hydroxide after the reaction of MgO with water, and (2) the hydroxyl group of the magnesium hydroxide was replaced by fluoride ions to form magnesium fluoride precipitation. Results from column tests show that up to 61 and 73% of fluoride removal (initial fluoride concentration = 9.3 mg/L) could be obtained after 50 pore volumes of groundwater pumping with GA-MgO and SM-MgO injection, respectively. The GA-MgO system could be applied to contain and remediate fluoride-contaminated groundwater, and SM-MgO could be applied as an immediate fluoride removal alternative to achieve a rapid pollutant removal for emergency responses. Up to 71% of fluoride removal (fluoride concentration = 10.8 mg/L) could be obtained with GA-MgO injection after 30 days of operation. The developed GA-MgO system is a potential and green remediation alternative to contain the fluoride plume significantly.

From fuzzy-TOPSIS to machine learning: A holistic approach to understanding groundwater fluoride contamination

Fluoride (F−) contamination of groundwater is a prevalent environmental issue threatening public health worldwide and in India. This study targets an investigation into spatial distribution and contamination sources of fluoride in Dhanbad, India, to help develop tailored mitigation strategies. A triad of Multi Criteria Decision Making (MCDM) models (Fuzzy-TOPSIS), machine learning algorithms {logistic regression (LR), classification and regression tree (CART), Random Forest (RF)}, and classical methods has been undertaken here. Groundwater samples (n = 283) were collected for the purpose. Based on permissible limit (1.5 ppm) of fluoride in drinking water as set by the World Health Organization, samples were categorized as Unsafe (n = 67) and Safe (n = 216) groups. Mean fluoride concentration in Safe (0.63 ± 0.02 ppm) and Unsafe (3.69 ± 0.3 ppm) groups differed significantly (t-value = −10.04, p < 0.05). Physicochemical parameters (pH, electrical conductivity, total dissolved solids, total hardness, NO3−, HCO3−, SO42−, Cl−, Ca2+, Mg2+, K+, Na+ and F−) were recorded from samples of each group. The samples from ‘Unsafe group’ showed alkaline pH, the abundance of Na+ and HCO3− ions, prolonged rock water interaction in the aquifer, silicate weathering, carbonate dissolution, lack of Ca2+ and calcite precipitation which together facilitated the F− abundance. Aspatial distribution map of F− contamination was created, pinpointing the “contaminated pockets.” Fuzzy- TOPSIS identified that samples from group Safe were closer to the ideal solution. Among these models, the LR proved superior, achieving the highest AUC score of 95.6 % compared to RF (91.3 %) followed by CART (69.4 %). This study successfully identified the primary contributors to F− contamination in groundwater and the developed models can help predicting fluoride contamination in other areas. The combination of different methodologies (Fuzzy-TOPSIS, machine learning algorithms, and classical methods) results in a synergistic effect where the strengths of each approach compensate for the limitations of the other.

Variability of groundwater fluoride and its proportionate risk quantification via Monte Carlo simulation in rural and urban areas of Agra district, India

This study quantifies the groundwater fluoride contamination and assesses associated health risks in fluoride-prone areas of the city of Taj Mahal, Agra, India. The United States Environmental Protection Agency (USEPA) risk model and Monte Carlo Simulations were employed for the assessment. Result revealed that, among various rural and urban areas Pachgain Kheda exhibited the highest average fluoride concentration (5.20 mg/L), while Bagda showed the lowest (0.33 mg/L). Similarly, K.K. Nagar recorded 4.38 mg/L, and Dayalbagh had 1.35 mg/L. Both urban and rural areas exceeded the WHO-recommended limit of 1.5 mg/L, signifying significant public health implications. Health risk assessment indicated a notably elevated probability of non-carcinogenic risk from oral groundwater fluoride exposure in the rural Baroli Ahir block. Risk simulations highlighted that children faced the highest health risks, followed by teenagers and adults. Further, Monte Carlo simulation addressed uncertainties, emphasizing escalated risks for for children and teenagers. The Hazard Quotient (HQ) values for the 5th and 95th percentile in rural areas ranged from was 0.28–5.58 for children, 0.15–2.58 for teenager, and 0.05–0.58 for adults. In urban areas, from the range was 0.53 to 5.26 for children, 0.27 to 2.41 for teenagers, and 0.1 to 0.53 for adults. Physiological and exposure variations rendered children and teenagers more susceptible. According to the mathematical model, calculations for the non-cancerous risk of drinking water (HQ-ing), the most significant parameters in all the targeted groups of rural areas were concentration (CW) and Ingestion rate (IR). These findings hold relevance for policymakers and regulatory boards in understanding the actual impact and setting pre-remediation goals.