Machine learning-assisted fluorescence visualization for sequential quantitative detection of aluminum and fluoride ions.

The presence of aluminum (Al3+) and fluoride (F-) ions in the environment can be harmful to ecosystems and human health, highlighting the need for accurate and efficient monitoring. In this paper, an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum (Al3+) and fluoride (F-) ions in aqueous solutions. The proposed method involves the synthesis of sulfur-functionalized carbon dots (C-dots) as fluorescence probes, with fluorescence enhancement upon interaction with Al3+ ions, achieving a detection limit of 4.2 nmol/L. Subsequently, in the presence of F- ions, fluorescence is quenched, with a detection limit of 47.6 nmol/L. The fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python, followed by data preprocessing. Subsequently, the fingerprint data is subjected to cluster analysis using the K-means model from machine learning, and the average Silhouette Coefficient indicates excellent model performance. Finally, a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride ions. The results demonstrate that the developed model excels in terms of accuracy and sensitivity. This groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment, making it a valuable tool for safeguarding our ecosystems and public health.

A multifunctional near-infrared fluorescent probe based on benzothiazole structure for fluoride-ion detection.

Fluoride ions (F-) are one of the essential trace elements for the human body and are widely existed in nature. In this study, we present a novel fluorescent probe (YF-SZ-F) designed and synthesized for the specific detection of F-. The probe exhibits high sensitivity, excellent selectivity, and low cytotoxicity, making it a promising tool for biomedical applications. Imaging experiments conducted on zebrafish and Arabidopsis roots demonstrate the probe's remarkable cellular permeability and biocompatibility, laying a solid foundation for its potential biomedical utility. Furthermore, the probe holds potential for practical applications in environmental monitoring and public health through its capability to detect fluoride ions in water samples and via mobile phone software. This multifaceted functionality underscores the broad applicability and significance of the fluorescent probe, not only in scientific research but also in real-world scenarios, contributing to the development of more convenient and precise methods for fluoride detection.

An inverse relationship between dental fluorosis and Molar Incisor Hypomineralization in Mexican schoolchildren in an area with a high concentration of fluoride in drinking water: A cross-sectional study.

AIM: To evaluate the association between the frequency and severity of dental fluorosis and Molar Incisor Hypomineralization (MIH) in 8-12-year-old schoolchildren living in an area with a high concentration of fluoride in the drinking water. METHODS: The present cross­sectional study was conducted on Mexican children (n = 573) selected from one community presenting a drinking water fluoride concentration of 1.39 ppm/F. The prevalence of dental fluorosis was ascertained using the Thylstrup and Fejerskov Index (TFI). The presence and severity of MIH was evaluated using the European Academy of Pediatric Dentistry (EAPD) criteria. A multinomial regression model was used to estimate the odds ratio (OR) and the 95% confidence intervals (CI), using the severity of MIH as the result. RESULTS: The prevalence of MIH was 37.7% and, by severity, was 16.1% mild, 14.3% moderate, and 7.3% severe. The prevalence of dental fluorosis in permanent dentition was 70.9% (TFI ≥1) and, by severity, was 29.2% (TFI = 0), 45.6% (TFI 1-3) and 25.3% (TFI ≥4), while 54.5% of subjects were found to have poor oral hygiene. Schoolchildren with fluorosis (TFI ≥4) were 49% less likely [OR = 0.51; p = 0.025] to present mild MIH than children with fluorosis (TFI <4). Similarly, children with fluorosis (TFI ≥4) were 53% [OR = 0.47; p = 0.019] and 62% [OR = 0.38; p = 0.036] less likely to present moderate and severe MIH than children with fluorosis (TFI <4). CONCLUSION: An inverse relationship between the presence of fluorosis and MIH was found. The results obtained by the present study may contribute to both the early identification of disorders affecting the enamel and the creation and implementation of long-term oral health prevention, promotion, and intervention programs in the affected population.

Human health risk and water quality assessment due to fluoride and nitrate around Cauvery River basin, southern India.

Good quality water for human consumption, irrigation, and industrial use is very important. Today, around the world, water is contaminated by natural processes and human activities. This study aimed to evaluate the suitability of groundwater for drinking and irrigation, identify the source of fluoride and nitrate contamination, and assess the human health risks around the Cauvery River basin in southern India. A total of 30 groundwater samples were collected and analyzed for hydrochemical parameters, including EC, TDS, pH, Ca, Mg, Na, K, HCO3, Cl, SO4, NO3, and F-. The majority of groundwater samples in the study area are used for drinking and irrigation. The pH of groundwater in the study area was observed to be dominantly alkaline. The levels of TDS, Ca, Na, K, F, and TH exceeded the permissible limits recommended by BIS and WHO. Fluoride and nitrate levels in groundwater exceeded the permissible limits for drinking purposes in 43% and 50% of the samples, respectively. The excessive concentration of fluoride and nitrate in groundwater could pose serious human health problems. Fluoride and nitrate concentrations in groundwater vary between 0.1 and 2 mg/l and 12 and 95 mg/l, respectively. Based on the computation of the drinking water quality index, about 73% of groundwater samples were classified as excellent to good. Health risk was assessed for infants, children, and adults using non-carcinogenic risk indices such as hazard quotients (HQ), hazard indexes (HI), total hazard indices (THI), and carcinogenic risk indices (CR). Infants, children, and adults have different total hazards indexes ranging from 1.508 to 5.733, 1.579 to 6.003, and 0.011 to 0.046, respectively. Health risk assessment results indicated that the hazard index and hazard quotient were above the recommended limit of > 1 in most of the samples for infants and children. Non-carcinogenic risk and carcinogenic risks were more likely to affect infants and children rather than adults through ingestion of contaminated water.

Kinetics of fluoride after brushing with the no-rinse method.

BACKGROUND: Fluoride plays a vital role in preventing dental caries, with its addition to oral care products significantly promoting oral hygiene. A no-rinse brushing method aims to increase fluoride retention in the oral cavity, as rinsing with water decreases fluoride levels in saliva, which could affect remineralization. While the no-rinse brushing method holds promise for improving fluoride retention in the oral cavity, critical inquiries persist regarding its safety. This study investigated the kinetics of oral fluoride and potential risks to fully assess its effectiveness and implications for oral health. METHODS: Ten healthy adults participated in a crossover study comparing the no-rinse with the rinse method. All subjects followed American Dental Association (ADA) brushing guidelines. Levels of fluoride in saliva (supernatant and sediment) and urine were measured over time, and plasma fluoride was measured one hour after brushing. Pharmacokinetic parameters were also calculated from the data. RESULTS: Participants using the no-rinse method had higher fluoride levels in supernatant immediately and up to 30 min post-brushing compared to the rinse method. Fluoride levels in sediment were higher only immediately after brushing. The total fluoride concentration in saliva remained elevated for up to 5 min with the no-rinse method. Systemic fluoride absorption showed no significant difference between the two methods based on blood and urine analysis. CONCLUSION: This research indicates that the no-rinse method can enhance fluoride retention in the oral cavity for up to 30 min after a single brushing. In addition, our findings suggest that this method does not significantly influence systemic fluoride levels or toxicity. REGISTRY: Thai Clinical Trials Registry, TCTR ( http://thaiclinicaltrials.org ). CLINICAL TRIAL REGISTRATION NUMBER: TCTR20231104001 (4/11/2023).

Prediction of the fluoride contents of different crop species via the random forest algorithm.

Fluoride (F) is a trace element that is essential to the human body and occurs naturally in the environment. However, a deficiency or excess of F in the environment can potentially lead to human health issues. The pseudototal amount of F in soil often does not correlate directly with the F content in plants. Instead, the F content within plants tends to have a greater correlation with the bioavailable F in soils. In large-scale soil surveys, only the pseudototal elemental content of soils is typically measured, which may not be highly reliable for developing agricultural zoning plans. There are significant variations in the ability of different plants to accumulate F from soil. Additionally, due to variations in soil elemental absorption mechanisms among different plant species, when multiple crops are grown in an area, it is typically necessary to study the elemental absorption mechanisms of each crop. To address these issues, in this study, we examined the factors influencing F bioaccumulation coefficients in different crops based on 1:50,000 soil geochemical survey data. Using the random forest algorithm, four indicators-bioavailable P, bioavailable Zn, leachable Pb, and Sr-were selected from among 29 parameters to predict the F content within crops to replace bioavailable F in the soil. Compared with the multivariate linear regression (MLR) model, the random forest (RF) model provided more accurate and reliable predictions of the fluoride content in crops, with the RF model's prediction accuracy improving by approximately 95.23%. Additionally, while the partial least squares regression (PLSR) model also offered improved accuracy over MLR, the RF model still outperformed PLSR in terms of prediction accuracy and robustness. Additionally, it maximized the utilization of existing geochemical survey data, enabling cross-species studies for the first time and avoiding redundant evaluations of different types of agricultural products in the same region. In this investigation, we selected the Xining-Ledu region of Qinghai Province, China, as the study area and employed a random forest model to predict the crop F content in soils, providing a new methodological framework for crop production that effectively enhances agricultural quality and efficiency.

Fluoride geochemistry in groundwater at regulated industrial sites.

Few studies apply geochemical concepts governing fluoride fate and transport in natural waters to geochemical conditions at contaminated industrial sites. This has negative implications for designing sampling and compliance monitoring programs and informing remediation decision-making. We compiled geochemical data for 566 groundwater samples from industrial waste streams associated with elevated fluoride and that span a range of geochemical conditions, including alkaline spent potliner, near-neutral pH coal combustion, and acidic gypsum stack impoundments. Like natural systems, elevated fluoride (hundreds to thousands of ppm) exists at the pH extremes and is generally tens of ppm at near-neutral pH conditions. Geochemical models identify pH-dependent fluoride complexation at low pH and carbonate stability at high pH as dominant processes controlling fluoride mobility. Limitations in available thermochemical, kinetic rate, and adsorption/desorption data and lack of complete analyses present uncertainties in quantitative models used to assess fluoride mobility at industrial sites. PRACTITIONER POINTS: Geochemical fundamentals of fluoride fate and transport in groundwater are communicated for environmental practitioners. Fluoride is a reactive constituent in groundwater, and factors that govern attenuation are identified. Geochemical models are useful for identifying fluoride attenuation processes, but quantitative use is limited by thermodynamic data uncertainties.

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.

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 2.8 ppm, with 40% of samples exceeding the World Health Organization's permissible limit (1.5 ppm). 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.

Spatial-temporal distribution characteristics of surface water pollutants and their potential sources in Ngari, China.

The Ngari region has many important rivers and is critical to water resource security and water resource continuity in China and even in adjoining Asian countries. However, the spatial distribution and monthly variation in local water quality have been poorly understood until recently. In this study, the spatial-temporal variations of 12 water quality parameters, including pH, dissolved oxygen (DO), permanganate index (IMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NNH3), total nitrogen (Ntotal), total phosphorus (Ptotal), copper (Cu), fluoride (F), arsenic (As) and cadmium (Cd), were determined from samples collected monthly at 22 water cross-sectional sites in the Ngari region in 2020. The surface water pollution in the southern Ngari region was the most serious, and the water pollution level in winter was higher than that in the other seasons. As (0.0781 ~ 0.6154 mg/L) and F (1.05 ~ 4.64 mg/L) were the main exceedance factors derived from the recharge of high arsenic and fluoride geothermal water and weathering of As and F-bearing minerals. The hazard quotient and carcinogenic risk for As and F at the five contaminated sampling sites indicated potential health risks and even carcinogenicity to local populations. The hydrochemistry types of the lakes and rivers in the Ngari region were mainly chloride water and carbonate water. The results from this study can provide a scientific basis for the prevention and control of surface water pollution in the Ngari region and contribute to subsequent research on the ecology of water bodies.

Salivary proteomic signatures in severe dental fluorosis.

The relationship between dental fluorosis and alterations in the salivary proteome remains inadequately elucidated. This study aimed to investigate the salivary proteome and fluoride concentrations in urine and drinking water among Thai individuals afflicted with severe dental fluorosis. Thirty-seven Thai schoolchildren, aged 6-16, were stratified based on Thylstrup and Fejerskov fluorosis index scores: 10 with scores ranging from 5 to 9 (SF) and 27 with a score of 0 (NF). Urinary and water fluoride levels were determined using an ion-selective fluoride electrode. Salivary proteomic profiling was conducted via LC-MS/MS, followed by comprehensive bioinformatic analysis. Results revealed significantly elevated urinary fluoride levels in the SF group (p = 0.007), whereas water fluoride levels did not significantly differ between the two cohorts. Both groups exhibited 104 detectable salivary proteins. The NF group demonstrated notable upregulation of LENG9, whereas the SF group displayed upregulation of LDHA, UBA1, S100A9, H4C3, and LCP1, all associated with the CFTR ion channel. Moreover, the NF group uniquely expressed 36 proteins, and Gene Ontology and pathway analyses suggested a link with various aspects of immune defense. In summary, the study hypothesized that the CFTR ion channel might play a predominant role in severe fluorosis and highlighted the depletion of immune-related salivary proteins, suggesting compromised immune defense in severe fluorosis. The utility of urinary fluoride might be a reliable indicator for assessing excessive fluoride exposure.

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.

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.

Machine Learning-Assisted Eu(III)-Functionalized HOF-on-HOF Composite-Based Sensor Platform for Precise and Visual Identification of Multiple Pesticides.

Precise and rapid identification of pesticides is crucial to ensure a green environment, food safety, and human health. However, complex sample environments often hinder precise identification, especially for simultaneous differentiation of multiple pesticides. Herein, we first synthesize a Eu(III)-functionalized HOF-on-HOF composite (Eu@PFC-1@MA-TPA) and then utilize principal component analysis (PCA) and a machine learning (ML) algorithm to achieve simultaneous identification of the pesticides 2,6-dichloro-4-nitroaniline (DCN) and thiabendazole (TBZ) and their mixtures. Eu@PFC-1@MA-TPA displays high quantitative identification ability, which can distinguish single DCN and TBZ as low as 1 µM and their mixtures at 5 µM through PCA. In addition, the hydrogel film Eu@PFC-1@MA-TPA/AG is fabricated to monitor DCN and TBZ in drinking water, tap water, river water, and apple juice with high sensitivity. Furthermore, based on the obvious fluorescence color variance of pesticides, Eu@PFC-1@MA-TPA/AG achieves visual and in situ imaging detection of single DCN and TBZ and their mixtures. More importantly, we construct an intelligent artificial vision platform integrating Eu@PFC-1@MA-TPA/AG with a DenseNet algorithm, which can identify the concentrations and types of DCN and TBZ and their mixtures within 1 s with over 98% accuracy. This work develops a precise and rapid analysis method for simultaneous identification of multiple pesticides through combining a visualized fluorescence sensor and an ML algorithm.

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.

Evaluation of fluoride exposure using disability-adjusted life years and health risk assessment in south-western Iran: A novel Monte Carlo simulation.

Consumption of fluoride-contaminated water is a worldwide concern, especially in developing countries, including Iran. However, there are restricted studies of non-single-value health risk assessment and the disease burden regarding fluoride intake nationwide. Prolonged exposure to excessive fluoride has been linked to adverse health effects such as dental and skeletal fluorosis. This can lead to under-mineralization of hard tissues, causing aesthetic concerns for teeth and changes in bone structure, increasing the risk of fractures. As such, we aimed to implement probability-based frameworks using Monte Carlo methods to explore the potential adverse effects of fluoride via the ingestion route. This platform consists of two sectors: 1) health risk assessment of various age categories coupled with a variance decomposition technique to measure the contributions of predictor variables in the outcome of the health risk model, and 2) implementing Monte Carlo methods in dose-response curves to explore the fluoride-induced burden of diseases of dental fluorosis and skeletal fractures in terms of disability-adjusted life years (DALYs). For this purpose, total water samples of 8053 (N=8053) from 57 sites were analyzed in Fars and Bushehr Provinces. The mean fluoride concentrations were 0.75 mg/L and 1.09 mg/L, with maximum fluoride contents of 6.5 mg/L and 3.22 mg/L for the Fars and Bushehr provinces, respectively. The hazard quotient of the 95th percentile (HQ>1) revealed that all infants and children in the study area were potentially vulnerable to over-receiving fluoride. Sobol' sensitivity analysis indices, including first-order, second-order, and total order, disclosed that fluoride concentration (Cw), ingestion rate (IRw), and their mutual interactions were the most influential factors in the health risk model. DALYs rate of dental fluorosis was as high as 981.45 (uncertainty interval: UI 95 % 353.23-1618.40) in Lamerd, and maximum DALYs of skeletal fractures occurred in Mohr 71.61(49.75-92.71), in Fars Province, indicated severe dental fluorosis but mild hazard regarding fractures. Residents of the Tang-e Eram in Bushehr Province with a DALYs rate of 3609.40 (1296.68-5993.73) for dental fluorosis and a DALYs rate of 284.67 (199.11-367.99) for skeletal fractures were the most potentially endangered population. By evaluating the outputs of the DALYs model, the gap in scenarios of central tendency exposure and reasonable maximum exposure highlights the role of food source intake in over-receiving fluoride. This research insists on implementing defluoridation programs in fluoride-endemic zones to combat the undesirable effects of fluoride. The global measures presented in this research aim to address the root causes of contamination and help policymakers and authorities mitigate fluoride's harmful impacts on the environment and public health.

Evaluation of potential human health risks arising from nitrate and fluoride in the groundwater of Aurangabad, Bihar using GIS and chemometric analysis.

This study employed the groundwater pollution index to assess the appropriateness of groundwater for human consumption. Additionally, the hazard index was utilized to evaluate the potential non-carcinogenic risks associated with fluoride and nitrate exposure among children, women, and men in the study region. A total of 103 samples were collected from the Aurangabad district of Bihar. The analyzed samples were assessed using several physicochemical parameters. Major cations in the groundwater are Ca2+ > Mg2+ and major anions are HCO3- > Cl- > SO42- > NO3- > F- > PO43-. Around 17% of the collected groundwater samples surpassed the allowable BIS concentration limits for Nitrate, while approximately 11% surpassed the allowed limits for fluoride concentration. Principal component analysis was utilized for its efficacy and efficiency in the analytical procedure. Four principal components were recovered that explained 69.06% of the total variance. The Hazard Quotient (HQ) of nitrate varies between 0.03-1.74, 0.02-1.47, and 0.03-1.99 for females, males, and children, respectively. The HQ of fluoride varies between 0.04-1.59, 0.04-1.34, and 0.05-1.82 for females, males, and children, respectively. The central part of the district was at high risk according to the spatial distribution maps of the total hazard index (THI). Noncarcinogenic risks due to THI are 47%, 37%, and 28% for children, females, and males, respectively. According to the human health risk assessment, children are more prone to getting affected by polluted water than adults. The groundwater pollution index (GPI) value ranges from 0.46 to 2.27 in the study area. Seventy-five percent of the samples fell under minor pollution and only one fell under high pollution. The spatial distribution of GPI in the research area shows that the central region is highly affected, which means that this water is unsuitable for drinking purposes.

Identifying the genetic mechanism of medium-low temperature fluoride-enriched geothermal groundwater by the self-organizing map and evaluating health risk in the Wugongshan area, southeast China.

Fluoride-enriched groundwater is a serious threat for groundwater supply around the world. The medium-low temperature fluoride-enriched geothermal groundwater resource is widely distributed in the circum-Wugongshan area. And the fluoride concentration of all geothermal samples exceeds the WHO permissible limit of 1.5 mg/L. The Self-Organizing Map method, hydrochemical and isotopic analysis are used to decipher the driving factors and genetic mechanism of fluoride-enriched geothermal groundwater. A total of 19 samples collected from the circum-Wugongshan geothermal belt are divided into four clusters by the self-organizing map. Cluster I, Cluster II, Cluster III, and Cluster IV represent the geothermal groundwater with the different degree of fluoride concentration pollution, the different hydrochemical type, and the physicochemical characteristic. The high F- concentration geothermal groundwater is characterized by HCO3-Na with alkalinity environment. The δD and δ18O values indicate that the geothermal groundwater origins from the atmospheric precipitation with the recharge elevation of 1000-2100 m. The dissolution of fluoride-bearing minerals is the main source of fluoride ions in geothermal water. Moreover, groundwater fluoride enrichment is also facilitated by water-rock interaction, cation exchange and alkaline environment. Additionally, the health risk assessment result reveals that the fluorine-enriched geothermal groundwater in the western part of Wugongshan area poses a more serious threat to human health than that of eastern part. The fluoride health risks of geothermal groundwater for different group show differentiation, 100% for children, 94.74% for adult females, and 68.42% for adult males, respectively. Compared with adult females and adult males, children faced the greatest health risks. The results of this study provide scientific evaluation for the utilization of geothermal groundwater and the protection of human health around the Wugongshan area.

Hydrogeochemical controls on contrasting co-occurrence of geogenic Arsenic (As) and Fluoride (F-) in complex aquifer system of Upper Indus Basin, (UIB) western Himalaya.

Arsenicosis and fluorosis have become severe health hazards associated with the drinking of Arsenic (As) and Fluoride (F-) contaminated groundwater across south-east Asia. Although, significant As and F- concentration is reported from major Himalayan river basins but, the hydrogeochemical processes and mechanisms controlling their contrasting co-occurrence in groundwater is still poorly explored and understood. In the present study, groundwater samples were collected from phreatic and confined aquifers of Upper Indus Basin (UIB), India to understand the hydrogeochemical processes controlling the distribution and co-occurrence of geogenic As and F- in this complex aquifer system. Generally, the groundwater is circum-neutral to alkaline with Na+-HCO3-, Ca2+-Na+-HCO3- and Ca2+-Mg2+-HCO3- water facies signifying the dominance of silicate and carbonate dissolution. The poor correlation of As and F- in groundwater depicted that these geogenic elements have discrete sources of origin with distinct mechanisms controlling their distribution. As enrichment in groundwater is associated with high pH, Fe, Mn and NH4-N suggesting dominance of metal oxide/hydroxide reduction with organic matter degradation. However, F- enrichment in groundwater is associated with high pH, HCO3- and Na+, which is assisted by the incessant dissolution of fluorinated minerals. The study also revealed that high HCO3- facilitates the exchange of hydroxides (OH-) with As and F- on sediment surfaces that contribute to As and F- enrichment in groundwater through desorption. 70% groundwater samples have As and F- concentration above the permissible limit given by WHO. Therefore, continuous exposure to these contaminants may pose severe health hazard of arsenicosis and fluorosis to people living in the region and downstream. The study provides insights into geological sources, hydrogeochemical processes and mechanisms controlling distribution of As and F- in groundwater that will help in developing the appropriate measures to mitigate the impact these contaminants on human health.