Monitoring and prediction of high fluoride concentrations in groundwater in Pakistan

Geochemical evolution, geostatistical mapping and machine learning predictive modeling of groundwater fluoride: a case study of western Balochistan, Quetta

Around 2.6 billion people are at risk of tooth carries and fluorosis worldwide. Quetta is the worst affected district in Balochistan plateau. Endemic abnormal groundwater fluoride (F - ) lacks spatiotemporal studies. This research integrates geospatial distribution, geochemical signatures, and data driven method for evaluatingF - levels and population at risk. GroundwaterF - ranged from 0 to 3.4 mg/l in (n = 100) with 52% samples found unfit for drinking. Through geospatial IDW tool hotspot areas affected with low and high groundwaterF - levels were identified. Geochemical distribution in geological setups recognized sediment variation leads to highF - (NaHCO3) and lowF - (CaHCO3) water types in low elevation (central plain) and high elevation (mountain foot) respectively. Results of the modified water quality index identified 60% samples to be unsuitable for drinking. Support vector machine (SVM), random forest regression (RFR) and classification and regression tree (CART) machine learning models foundNa + , Salinity andCa + 2 as important contributing variables in groundwaterF - prediction. CART model with R2 value of 0.732 outperformed RFR and SVM in predictingF - . Noncarcinogenic health risk vulnerability fromF - increased from Adults < Teens < Children < Infants. Infants and children with hazard quotient values of 11.3 and 4.2 were the most vulnerable population at risk for consumingF - contaminated groundwater. The research emphasizes on both nutritional need and hazardous effect ofF - , and development of desirable limit forF - .

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.

Advancing groundwater quality predictions: Machine learning challenges and solutions

Machine learning (ML) is revolutionizing groundwater quality research by enhancing predictive accuracy and management strategies for contamination. This comprehensive review explores the evolution of ML technologies and their integration into environmental science, assessing 230 papers to understand the advancements and challenges in groundwater quality research. It reveals that a substantial portion of the research neglects critical preprocessing steps, crucial for model accuracy, with 83 % of the studies overlooking this phase. Furthermore, while model optimization is more commonly addressed, being implemented in 65 % of the papers, there is a noticeable gap in model interpretability, with only 15 % of the research providing explanations for model outcomes. Comparative evaluation of ML algorithms and careful selection of evaluation metrics are deemed essential for determining model fitness and reliability. The review underscores the need for interdisciplinary collaboration, methodological rigor, and continuous innovation to advance ML in groundwater management. By addressing these challenges and implementing solutions, the full potential of ML can be harnessed to tackle complex environmental issues and ensure sustainable groundwater management. This comprehensive and critical review paper can serve as a guiding framework to establish minimum standards for developing ML in groundwater quality studies.

Evaluation of fluoride emissions and pollution from an electrolytic aluminum plant located in Yunnan province

The monitoring and evaluation of fluoride pollution are essentially important to make sure that concentrations do not exceed threshold limit, especially for surrounding atmosphere and soil, which are located close to the emission source. This study aimed to describe the atmospheric HF and edaphic fluoride distribution from an electrolytic aluminum plant located in Yunnan province, on which the effects of meteorological conditions, time, and topography were explored. Meanwhile, six types of solid waste genereted from different electrolytic aluminum process nodes were characterized to analyze the fluoride content and formation characteristics. The results showed that fluoride in solid waste mainly existed in the form of Na3AlF6, AlF3, CaF2, and SiF4. Spent electrolytes, carbon residue, and workshop dust are critical contributors to fluoride emissions in the primary aluminum production process, and the fluorine content is 17.14 %, 33.30 %, and 31.34 %, respectively. Unorganized emissions from electrolytic aluminum plants and solid waste generation are the primary sources of fluoride in the environment, among which the edaphic fluoride content increases most at the sampling sites S1 and S7. In addition, the atmospheric HF concentration showed significant correlations with wind speed, varying wildly from March to September, with daily average and hourly maximum HF concentrations of 4.32 μg/m3 and 9.0 μg/m3, respectively. The results of the study are crucial for mitigating fluorine pollution in the electrolytic aluminum industry.

Gut microbiome and function are altered for individuals living in high fluoride concentration areas in Pakistan

BACKGROUND

Endemic fluorosis refers to the condition when individuals are exposed to excessive amounts of fluoride ion due to living in a region characterized by elevated levels of fluorine in the drinking water, food, and/or air. In Pakistan, a substantial proportion of the population is thereby affected, posing a public health concern.

OBJECTIVES

Assessing how the gut microbiota and its metabolic profiles are impacted by chronic exposure to fluoride in drinking water (that caused Dental Fluorosis) as well as to perceive how this microbiota is connected to adverse health outcomes prevailing with fluoride exposure.

METHODS

Drinking water (n=27) and biological samples (n=100) of blood, urine and feces were collected from 70 high fluoride exposed (with Dental Fluorosis) and 30 healthy control (without Dental Fluorosis) subjects. Water and urinary fluoride concentrations were determined. Serum/plasma biochemical testing was performed. Fecal DNA extraction, 16S rRNA analysis of microbial taxa, their predicted metabolic function and fecal short chain fatty acids (SCFAs) quantification were carried out.

RESULTS

The study revealed that microbiota taxonomic shifts and their metabolic characterization had been linked to certain host clinical parameters under the chronic fluoride exposure. Some sets of genera showed strong specificity to water and urine fluoride concentrations, Relative Fat Mass index and SCFAs. The SCFAs response in fluoride-exposed samples was observed to be correlated with bacterial taxa that could contribute to adverse health effects.

CONCLUSIONS

Microbial dysbiosis as a result of endemic fluorosis exhibits a structure that is associated with risk of metabolic deregulation and is implicated in various diseases. Our results may form the development of novel interventions and may have utility in diagnosis and monitoring.

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.

Geochemical insights of arsenic mobilization into the aquifers of Punjab, Pakistan

It is well known that groundwater arsenic (As) contamination affects million(s) of people throughout the Indus flood plain, Pakistan. In this study, groundwater (n = 96) and drilled borehole samples (n = 87 sediments of 12 boreholes) were collected to investigate geochemical proxy-indicators for As release into groundwater across floodplains of the Indus Basin. The mean dissolved (μg/L) and sedimentary As concentrations (mg/kg) showed significant association in all studied areas viz.; lower reaches of Indus flood plain area (71 and 12.7), upper flood plain areas (33.7 and 7.2), and Thal desert areas (5.3 and 4.7) and are indicative of Basin-scale geogenic As contamination. As contamination in aquifer sediments is dependent on various geochemical factors including particle size (3-4-fold higher As levels in fine clay particles than in fine-coarse sand), sediment types (3-fold higher As in Holocene sediments of floodplain areas vs Pleistocene/Quaternary sediments in the Thal desert) with varying proportion of Al-Fe-Mn oxides/hydroxides. The total organic carbon (TOC) of cored aquifer sediments yielded low TOC content (mean = 0.13 %), which indicates that organic carbon is not a major driver (with a few exceptions) of As mobilization in the Indus Basin. Alkaline pH, high dissolved sulfate and other water quality parameters indicate pH-induced As leaching and the dominance of oxidizing conditions in the aquifers of upper flood plain areas of Punjab, Pakistan while at the lower reaches of the Indus flood plain and alluvial pockets along the rivers with elevated flood-driven dissolved organic carbon (exhibiting high dissolved Mn and Fe and a wide range of redox conditions). Furthermore, we also identified that paired dissolved AsMn values (instead of AsFe) may serve as a geochemical marker of a range of redox conditions throughout Indus flood plains.

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.

Progress in research on the role of fluoride in immune damage

Excessive fluoride intake from residential environments may affect multiple tissues and organs; however, the specific pathogenic mechanisms are unclear. Researchers have recently focused on the damaging effects of fluoride on the immune system. Damage to immune function seriously affects the quality of life of fluoride-exposed populations and increases the incidence of infections and malignant tumors. Probing the mechanism of damage to immune function caused by fluoride helps identify effective drugs and methods to prevent and treat fluorosis and improve people's living standards in fluorosis-affected areas. Here, the recent literature on the effects of fluoride on the immune system is reviewed, and research on fluoride damage to the immune system is summarized in terms of three perspectives: immune organs, immune cells, and immune-active substances. We reviewed that excessive fluoride can damage immune organs, lead to immune cells dysfunction and interfere with the expression of immune-active substances. This review aimed to provide a potential direction for future fluorosis research from the perspective of fluoride-induced immune function impairment. In order to seek the key regulatory indicators of fluoride on immune homeostasis in the future.

Health risk assessment in an area of dental fluorosis disease from high fluoride drinking water: a case study from southeastern Türkiye

This study focuses on identifying fluoride (F‒) concentrations and its health risk assessment (HRA) in drinking water sources in south-eastern Türkiye. Groundwater quality was assessed using some graphical approaches such as Schoeller and Piper diagrams and GIS mapping. Average daily exposure dosages through oral and dermal contact exposure routes were considered to determine the potential health risk of F‒ in groundwater. Groundwater samples were taken from 53 points in spring, summer, autumn, and winter seasons. The results showed that the average annual F‒ concentrations in water resources in the study area were 0.26‒3.62 mg/L. According to the HRA results, the highest F‒ health risk in this region was observed in children, followed by teenagers and adults. This study indicated that there is a strong relationship between the high health risk (4.28 > 3.5) in children and dental fluorosis caused by high F‒ concentration in groundwater.

Hotspot mapping and risk prediction of fluoride in natural waters across the Tibetan Plateau

Intake of high fluoride concentrations through water affects up to 1 billion people worldwide, and the Tibetan Plateau (TP) is one of the most severely affected areas. Knowledge regarding the high fluoride risk areas, the driving factors, and at-risk populations on the TP remains fragmented. We collected 1581 natural water samples from the TP to model surface water and groundwater fluoride hazard maps using machine learning. The geomean concentrations of surface water and groundwater were 0.26 mg/L and 0.92 mg/L, respectively. Surface water fluoride hazard hotspots were concentrated in the north-central region; high fluoride risk areas of groundwater were mainly concentrated in the southern TP. Hazard maps showed a maximum estimate of 15% of the total population in the TP (approximately 1.47 million people) at risk, and 500,000 people considered the most reasonable estimate. Critical environment driving factors were identified, in which climate condition was taken for the vital one. Under the moderate climate change scenario (SSP2.45) for 2089-2099, the high fluoride risk change rate differed inside the TP (surface water -24%-55% and groundwater -56%-50%), and the overall risk increased in natural waters throughout the TP, particularly in the southeastern TP.

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.

A comprehensive review of human health risks of arsenic and fluoride contamination of groundwater in the South Asia region

The present study found that ∼80 million people in India, ∼60 million people in Pakistan, ∼70 million people in Bangladesh, and ∼3 million people in Nepal are exposed to arsenic groundwater contamination above 10 μg/L, while Sri Lanka remains moderately affected. In the case of fluoride contamination, ∼120 million in India, >2 million in Pakistan, and ∼0.5 million in Sri Lanka are exposed to the risk of fluoride above 1.5 mg/L, while Bangladesh and Nepal are mildly affected. The hazard quotient (HQ) for arsenic varied from 0 to 822 in India, 0 to 33 in Pakistan, 0 to 1,051 in Bangladesh, 0 to 582 in Nepal, and 0 to 89 in Sri Lanka. The cancer risk of arsenic varied from 0 to 1.64 × 1-1 in India, 0 to 1.07 × 10-1 in Pakistan, 0 to 2.10 × 10-1 in Bangladesh, 0 to 1.16 × 10-1 in Nepal, and 0 to 1.78 × 10-2 in Sri Lanka. In the case of fluoride, the HQ ranged from 0 to 21 in India, 0 to 33 in Pakistan, 0 to 18 in Bangladesh, 0 to 10 in Nepal, and 0 to 10 in Sri Lanka. Arsenic and fluoride have adverse effects on animals, resulting in chemical poisoning and skeletal fluorosis. Adsorption and membrane filtration have demonstrated outstanding treatment outcomes.

Fluoride exposure confers NRF2 activation in hepatocyte through both canonical and non-canonical signaling pathways

Due to the high abundance in the Earth's crust and industrial application, fluoride is widely present in our living environment. However, excessive fluoride exposure causes toxicity in different organs. As the most important detoxification and excretion organ, liver is more easily involved in fluoride toxicity than other organs, and oxidative stress is considered as the key mechanism related with fluoride hepatotoxicity. In this study, we mainly investigated the role of nuclear factor erythroid-derived 2-like 2 (NRF2, a core transcription factor in oxidative stress) in fluoride exposure-induced hepatotoxicity as well as the related mechanism. Herein, liver cells (BNL CL.2) were treated with fluoride in different concentrations. The hepatotoxicity and NRF2 signaling pathway were analyzed respectively. Our results indicated that excessive fluoride (over 1 mM) resulted in obvious toxicity in hepatocyte and activated NRF2 and NRF2 target genes. The increased ROS generation after fluoride exposure suppressed KEAP1-induced NRF2 ubiquitylation and degradation. Meanwhile, fluoride exposure also led to blockage of autophagic flux and upregulation of p62, which contributed to activation of NRF2 via competitive binding with KEAP1. Both pharmaceutical activation and genetic activation of NRF2 accelerated fluoride exposure-induced hepatotoxicity. Thus, the upregulation of NRF2 in hepatocyte after fluoride exposure can be regarded as a cellular self-defense, and NRF2-KEAP1 system could be a novel molecular target against fluoride exposure-induced hepatotoxicity.

Nitrate prediction in groundwater of data scarce regions: The futuristic fresh-water management outlook

Nitrate contamination in groundwater poses a significant threat to water quality and public health, especially in regions with limited data availability. This study addresses this challenge by employing machine learning (ML) techniques to predict nitrate (NO3--N) concentrations in Mexico's groundwater. Four ML algorithms-Extreme Gradient Boosting (XGB), Boosted Regression Trees (BRT), Random Forest (RF), and Support Vector Machines (SVM)-were executed to model NO3--N concentrations across the country. Despite data limitations, the ML models achieved robust predictive performances. XGB and BRT algorithms demonstrated superior accuracy (0.80 and 0.78, respectively). Notably, this was achieved using ∼10 times less information than previous large-scale assessments. The novelty lies in the first-ever implementation of the 'Support Points-based Split Approach' during data pre-processing. The models considered initially 68 covariates and identified 13-19 significant predictors of NO3--N concentration spanning from climate, geomorphology, soil, hydrogeology, and human factors. Rainfall, elevation, and slope emerged as key predictors. A validation incorporated nationwide waste disposal sites, yielding an encouraging correlation. Spatial risk mapping unveiled significant pollution hotspots across Mexico. Regions with elevated NO3--N concentrations (>10 mg/L) were identified, particularly in the north-central and northeast parts of the country, associated with agricultural and industrial activities. Approximately 21 million people, accounting for 10 % of Mexico's population, are potentially exposed to elevated NO3--N levels in groundwater. Moreover, the NO3--N hotspots align with reported NO3--N health implications such as gastric and colorectal cancer. This study not only demonstrates the potential of ML in data-scarce regions but also offers actionable insights for policy and management strategies. Our research underscores the urgency of implementing sustainable agricultural practices and comprehensive domestic waste management measures to mitigate NO3--N contamination. Moreover, it advocates for the establishment of effective policies based on real-time monitoring and collaboration among stakeholders.

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.

Geochemical control, water quality indexing, source distribution, and potential health risk of fluoride and arsenic in groundwater: Occurrence, sources apportionment, and positive matrix factorization model

Fluoride (F-), and arsenic (As) in the groundwater cause health problems in developing countries, including Pakistan. We evaluated the occurrence, distribution, sources apportionment, and health hazards of F-, and As in the groundwater of Mardan, Pakistan. Therefore, groundwater samples (n = 130) were collected and then analyzed for F-, and As by ion-chromatography (IC) and Inductively-coupled plasma mass-spectrometry (ICP-MS). The F-, and As concentrations in groundwater were 0.7-14.4 mg/L and 0.5-11.2 µg/L. Relatively elevated F-, and As coexists with higher pH, Na+, HCO3-, SO4-2, and depleted Ca+2 due to fluoride, sulfide-bearing minerals, and anthropogenic inputs. Both F-, and/or As are transported in subsurface water through adsorption and desorption processes. Groundwater samples 45%, and 14.2% exceeded the WHO guidelines of 1.5 mg/L and 10 µg/L. Water quality indexing (WQI-model) declared that 35.7% samples are unfit for household purposes. Saturation and undersaturation of minerals showed precipitation and mineral dissolution. Groundwater contamination by PCA-MLR and PMF-model interpreted five factors. The fitting results and R2 values of PMF (0.52-0.99)>PCA-MLR (0.50-0.95) showed high accuracy of PMF-model. Human health risk assessment (HHRA-model) revealed high non-carcinogenic and carcinogenic risk for children than adults. The percentile recovery of F- and As was recorded 98%, and 95% with reproducibility ± 5% error.

Estimation of heavy metal soil contamination distribution, hazard probability, and population at risk by machine learning prediction modeling in Guangxi, China

Due to superposition of diverse pollution sources, soil heavy metal concentrations have been detected to exceed the recommended maximum permissible levels in many areas of Guangxi province, China. However, the heavy metal contamination distribution, hazard probability, and population at risk of heavy metals in the entire Guangxi province remain largely unclear. In this study, machine learning prediction models with different standard risk values determined according to land use types were used to identify high-risk areas and estimate populations at risk of Cr and Ni based on 658 topsoil samples from Guangxi province, China. Our results showed that soil Cr and Ni contamination derived from carbonate rocks was relatively serious in Guangxi province, and that their co-enrichment during soil formation was associated with Fe and Mn oxides and alkaline soil environment. Our established model exhibited excellent performance in predicting contamination distribution (R² > 0.85) and hazard probability (AUC>0.85). Pollution of Cr and Ni exhibited a pattern of decreasing gradually from the central-west areas to the surrounding areas with the polluted area (I_geo>0) of Cr and Ni accounting for approximately 24.46% and 29.24% of total area in Guangxi province, respectively, but only 10.4% and 8.51% of total area was classified as Cr and Ni high-risk regions. We estimated approximately 1.44 and 1.47 million people were potentially exposed to the risk of Cr and Ni contamination, which were mainly concentrated in the Nanning, Laibin, and Guigang. These regions are main heavily-populated agricultural regions in Guangxi, and thus heavy metal contamination localization and risk control in these regions are urgent and essential from the perspective of food safety.

Influence of the chronic groundwater fluoride consumption on cholinergic enzymes, ACHE and BCHE gene SNPs and pro-inflammatory cytokines: A study with Pakistani population groups

Fluoride is one of the abundant elements found in the Earth's crust and is a global environmental issue. The present work aimed to find the impact of chronic consumption of fluoride contained groundwater on human subjects. Five hundred and twelve volunteers from different areas of Pakistan were recruited. Cholinergic status, acetylcholinesterase and butyrylcholinesterase gene SNPs and pro-inflammatory cytokines were examined. Association analysis, regression and other standard statistical analyses were performed. Physical examination of the fluoride endemic areas' participants revealed the symptoms of dental and skeletal fluorosis. Cholinergic enzymes (AChE and BChE) were significantly increased among different exposure groups. ACHE gene 3'-UTR variant and BCHE K-variant showed a significant association with risk of fluorosis. Pro-inflammatory cytokines (TNF-α, IL-1β and IL-6) were found to be increased and have a significant correlation in response to fluoride exposure and cholinergic enzymes. The study concludes that chronic consumption of high fluoride-contained water is a risk factor for developing low-grade systemic inflammation through the cholinergic pathway and the studied cholinergic gene SNPs were identified to be associated with the risk of flurosis.

Distribution of Dental Fluorosis in the Southern Zone of Ecuador: An Epidemiological Study

In recent decades, the increase in fluoride exposure has raised the numbers of dental fluorosis in fluoridated and non-fluoridated communities In Ecuador, but the last national epidemiological study on DF was conducted more than a decade ago. The objective of this cross-sectional descriptive study was to determine the prevalence, distribution and severity of dental fluorosis (DF) using the Dean index in 1606 schoolchildren aged 6 to 12 years from urban and rural environments in provinces that make up the Southern Region of Ecuador. Participants met the inclusion criteria which were age, locality, informed consent document and no legal impediment. The results are presented using percentage frequency measures and chi-square associations. The prevalence of dental fluorosis was 50.1% in the areas of Azuay, Cañar and Morona Santiago, with no significant differences (x2 = 5.83, p = 0.054). The types of DF found most frequently were very mild and mild in all provinces; a moderate degree was more prevalent in Cañar (17%). There was no significant association (p > 0.05) between sex and the presence of dental fluorosis and, with respect to severity, the most frequent degree was moderate at the age of 12 years. The prevalence of dental fluorosis in the area evaluated is high, especially in the light and very light degrees, with a tendency toward moderate levels. It is necessary to carry out studies on the factors that are predisposing to the development of this pathology in the population studied. This research is an update regarding this pathology in Ecuador, so it is concluded that it is necessary to continue developing studies based on the findings obtained, thus contributing to the public health of the country.