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Groundwater Arsenic Contamination Throughout China

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TLDR
A statistical risk model is developed that classifies safe and unsafe areas with respect to geogenic arsenic contamination in China, using the threshold of 10 micrograms per liter, the World Health Organization guideline and current Chinese standard for drinking water.
Abstract
Arsenic-contaminated groundwater used for drinking in China is a health threat that was first recognized in the 1960s. However, because of the sheer size of the country, millions of groundwater wells remain to be tested in order to determine the magnitude of the problem. We developed a statistical risk model that classifies safe and unsafe areas with respect to geogenic arsenic contamination in China, using the threshold of 10 micrograms per liter, the World Health Organization guideline and current Chinese standard for drinking water. We estimate that 19.6 million people are at risk of being affected by the consumption of arsenic-contaminated groundwater. Although the results must be confirmed with additional field measurements, our risk model identifies numerous arsenic-affected areas and highlights the potential magnitude of this health threat in China.

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Journal ArticleDOI

Global threat of arsenic in groundwater

TL;DR: A global model for predicting groundwater arsenic levels suggests that 94 million to 220 million people are potentially exposed to high arsenic concentrations in groundwater, the vast majority of which are in Asia.
Journal ArticleDOI

Arsenic contamination of groundwater: A global synopsis with focus on the Indian Peninsula

TL;DR: In this paper, an overview of the current scenario of arsenic contamination of groundwater in various countries across the globe with an emphasis on the Indian Peninsula is presented and the corrective measures available include removing arsenic from groundwater using filters, exploring deeper or alternative aquifers, treatment of the aquifer itself, dilution method by artificial recharge to groundwater, conjunctive use and installation of nano-filter, among other procedures.
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Arsenic and selenium toxicity and their interactive effects in humans

TL;DR: It is hypothesized that at low concentration, Se can decrease As toxicity via excretion of As-Se compound [(GS3)2AsSe](-), but at high concentration, excessive Se can enhance As toxicity by reacting with S-adenosylmethionine and glutathione, and modifying the structure and activity of arsenite methyltransferase.
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A review of high arsenic groundwater in Mainland and Taiwan, China: Distribution, characteristics and geochemical processes

TL;DR: The contribution of competitive desorption to high As concentrations is still unknown and remains to be discovered, relative to reductive dissolution of Fe oxides, especially in the inland basins as mentioned in this paper.
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Applications of water-stable metal-organic frameworks in the removal of water pollutants: A review.

TL;DR: In this article, the authors comprehensively review state-of-the-art research progress on the promising potential of metal-organic frameworks (MOFs) as excellent nanomaterials to remove contaminants from the water environment.
References
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Journal ArticleDOI

Applied Logistic Regression.

TL;DR: Applied Logistic Regression, Third Edition provides an easily accessible introduction to the logistic regression model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction

TL;DR: The Elements of Statistical Learning: Data Mining, Inference, and Prediction as discussed by the authors is a popular book for data mining and machine learning, focusing on data mining, inference, and prediction.
Journal ArticleDOI

A review of the source, behaviour and distribution of arsenic in natural waters

TL;DR: The scale of the problem in terms of population exposed to high As concentrations is greatest in the Bengal Basin with more than 40 million people drinking water containing ‘excessive’ As as mentioned in this paper.
Journal ArticleDOI

An Introduction to the Bootstrap

Scott D. Grimshaw
- 01 Aug 1995 - 
TL;DR: Statistical theory attacks the problem from both ends as discussed by the authors, and provides optimal methods for finding a real signal in a noisy background, and also provides strict checks against the overinterpretation of random patterns.
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