Genesis of geogenic contaminated groundwater: As, F and I

Wang, Yanxin; Li, Junxia; Ma, Teng; Deng, Yamin; Gan, Yiqun

doi:10.1080/10643389.2020.1807452

Cited by 111 publications

(74 citation statements)

References 238 publications

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“…Conditions affecting fluoride accumulation. While predictor importance is dominated by climate-related parameters, some of the in-situ groundwater parameters also point to climate-related signals, such as evaporative concentration of Na and EC in arid regions 7,28,29 (Fig. 2a).…”

Section: Resultsmentioning

confidence: 99%

“…High fluoride concentrations are often found naturally in aquifers in acidic igneous basement rocks, volcanic and geothermal rocks as well as derived sedimentary deposits and metamorphic rocks with high pH and alkalinity, low calcium concentrations, higher temperatures, and/or long groundwater residence times 9,27,28 . High pH promotes the desorption of fluoride from clay; hydroxyl anions (OH − ) exchange with F − in F-bearing minerals; and bicarbonate (HCO 3 − ) reacts with fluorite (CaF 2 ) to release fluoride, though dissolved calcium can bind with fluoride and remove it from dissolution to again form fluorite 9,[29][30][31] .…”

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confidence: 99%

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Global analysis and prediction of fluoride in groundwater

2022

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The health of millions of people worldwide is negatively impacted by chronic exposure to elevated concentrations of geogenic fluoride in groundwater. Due to health effects including dental mottling and skeletal fluorosis, the World Health Organization maintains a maximum guideline of 1.5 mg/L in drinking water. As groundwater quality is not regularly tested in many areas, it is often unknown if the water in a given well or spring contains harmful levels of fluoride. Here we present a state-of-the-art global fluoride hazard map based on machine learning and over 400,000 fluoride measurements (10% of which >1.5 mg/L), which is then used to estimate the human population at risk. Hotspots indicated by the groundwater fluoride hazard map include parts of central Australia, western North America, eastern Brazil and many areas of Africa and Asia. Of the approximately 180 million people potentially affected worldwide, most reside in Asia (51–59% of total) and Africa (37–46% of total), with the latter representing 6.5% of the continent’s population. Africa also contains 14 of the top 20 affected countries in terms of population at risk. We also illuminate and discuss the key globally relevant hydrochemical and environmental factors related to fluoride accumulation.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

Global analysis and prediction of fluoride in groundwater

2022

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“…The maximum mean iodine intake difference of 50 μg/d, depending on the living district, is large enough to theoretically affect iodine status in subpopulations within the city and can also provide an additional explanation for the mUIC differences in the previous 2 national surveys: With large differences in WIC between adjacent districts, a limited number of women ( n = 200), and a few villages or neighborhoods ( n = 15–30), a lack of WIC stratification may increase the uncertainty in the point estimate ( 37 , 59 ). Knowledge of fluctuating mineral concentration in underground water from the same Afro-Arabian Rift Valley complex in dry compared with wet and cold compared with hot seasons may indicate a possibility of variation in mUIC obtained during different seasons of the year ( 34 , 60–62 ). These hypotheses call into question the validity of using a single mUIC value to characterize the population iodine status in all of Somaliland.…”

Section: Discussionmentioning

confidence: 99%

Household Water Is the Main Source of Iodine Consumption among Women in Hargeisa, Somaliland: A Cross-Sectional Study

Heen

Romøren

Yassin

et al. 2022

The Journal of Nutrition

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Background Iodine status surveys of women in Somaliland present widely conflicting results. Previous research indicates elevated concentrations of iodine (IQR 18–72 µg/L) in groundwater used for drinking and cooking, but the relationship with iodine intake is not characterized well. Objective We aimed to investigate the contribution of household water iodine concentration (WIC), breastfeeding, total fluid intake, hydration levels, and urine volume on urinary iodine concentration and excretion (UIC, UIE) during 24 hours, and to define iodine status from iodine intake estimates and median UIC, normalized to a mean urine volume of 1.38L/day (hydration-adjusted). Methods The study sample comprised 118 nonpregnant, healthy women aged 15–69. All resided in Hargeisa; 27 breastfeeding. Data collection consisted of a 24-hour (24-h) urine collection, a 24-h fluid intake diary, a beverage frequency questionnaire, and a structured recall interview. We measured UIC and WIC in all urine and in 49 household-water samples using the Sandell–Kolthoff reaction. Results WIC ranged between 3 and 188 μg/L, with significant median differences across the water sources and city districts (P < 0.003). Non-breastfeeding women were borderline iodine sufficient (hydration-adjusted mUIC of 109 [95% CI, 97–121] μg/L), whereas breastfeeding women showed a mild iodine deficiency (73 [95% CI, 54–90] μg/L). There were strong correlations (rho 0.50–0.69, P = 0.001) between WIC and UIC, with iodine from household water contributing more than half of total iodine intake. Multivariate regression showed hydration and breastfeeding status to be the main predictors of UIC. Conclusions Iodine from household water is the main contributor to total iodine intake among women in Hargeisa, Somaliland. Variation in female hydration and spatial and temporal WIC may explain diverging mUIC between studies. Water sources at the extremes of low and high iodine concentration increase the risk of subpopulations with insufficient or more than adequate iodine intake.

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“…Groundwater is the largest reservoir of freshwater and serves as the major source of water for human consumption. , Geogenic contaminated groundwaters (GCGs) decrease the availability of water resources and is a growing public health concern worldwide. , Well-known GCGs include groundwater with high concentrations of arsenic (As > 10 μg/L), fluoride (F > 1.5 mg/L), , and iodine (I > 150 μg/L). ,, These hazardous substances are not typically included in the standard suite of tested water quality parameters and their presence is not evident by taste or smell, so an increasing number of people around the world have experienced endemic diseases due to chronic exposure to GCGs. Machine learning methods such as random forest (RF), artificial neural network, boosted regression trees, and classification and regression trees have been used to estimate the occurrence of GCGs.…”

Section: Introductionmentioning

confidence: 99%

Siamese Network-Based Transfer Learning Model to Predict Geogenic Contaminated Groundwaters

Cao

Xie

Shi

et al. 2022

Environ. Sci. Technol.

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Exposure to geogenic contaminated groundwaters (GCGs) is a significant public health concern. Machine learning models are powerful tools for the discovery of potential GCGs. However, the insufficient groundwater quality data and the fact that GCGs are typically a minority class in data hinder models to produce meaningful GCG predictions. To address this issue, a deep learning method, Siamese network-based transfer learning (SNTL), is used to estimate the probability that hazardous substances are present in groundwater above a threshold based on limited and class-imbalanced data. SNTL greatly reduces the amount of required training data and eliminates negative effects of class-imbalanced data on prediction model performance. The predictions of three typical GCGs (high arsenic/fluoride/iodine groundwater) show that the SNTL models provide higher (about 80%) and more balanced sensitivity and specificity than benchmark Random Forest models, indicating that SNTL models can predict both GCGs and non-GCGs. Therefore, protecting populations from GCG exposure in areas where other prediction methods fail to contribute risk information due to poor groundwater quality data can be enabled by SNTL.

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Genesis of geogenic contaminated groundwater: As, F and I

Cited by 111 publications

References 238 publications

Global analysis and prediction of fluoride in groundwater

Global analysis and prediction of fluoride in groundwater

Household Water Is the Main Source of Iodine Consumption among Women in Hargeisa, Somaliland: A Cross-Sectional Study

Siamese Network-Based Transfer Learning Model to Predict Geogenic Contaminated Groundwaters

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