AIMC Topic: Arsenic

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Machine learning prediction of groundwater arsenic contamination using water quality parameters in the coastal region of Bangladesh.

Environmental geochemistry and health
Groundwater arsenic contamination poses a significant health risk in coastal region of Bangladesh. However, existing studies have rarely applied advanced machine learning (ML) algorithms to predict arsenic concentrations using comprehensive water qua...

Enabling Emergency Response to Arsenic Contamination: Simultaneous and Rapid Identification of Arsenic Speciation by a Machine Learning-Driven Fluorescent Sensor Array.

Environmental science & technology
The rapid identification of arsenic speciation is critical for assessing its toxicity and guiding emergency response during water contamination events, yet it remains a significant challenge for current analytical methods. Herein, a novel machine lea...

Assessment of climate change impacts on arsenic contamination in groundwater through machine learning, remote sensing, and GIS: a review.

Environmental geochemistry and health
More than 50% of the world's largest countries and cities depend on groundwater for their daily needs. In particular, 80% of the largest cities in the Middle East, South Asia, and Central Asia rely on groundwater for drinking, irrigation, and industr...

Driving mechanisms and high-risk area prediction of arsenic pollution in surface water of the Shaanxi Wei River Basin.

Environmental pollution (Barking, Essex : 1987)
The Weihe River Basin, located within the Yellow River Basin, is an ecologically important region increasingly threatened by arsenic (As) contamination in surface water, which poses risks to both environmental security and public health. This study c...

Methods and Uncertainty in Predictions of Arsenic Exposure and Health Outcomes for Private Well Users in Massachusetts.

Environmental science & technology
In the United States, most people get their drinking water from public water systems, whose quality is regulated by the Safe Drinking Water Act; however, an estimated 40 million people rely on unregulated private wells. In Massachusetts ∼500,000 peop...

Predicting arsenic bioaccessibility: A global data-driven machine learning approach and its implication for reducing carbon emissions.

Journal of hazardous materials
Site-specific arsenic (As) bioaccessibility data can improve the accuracy of health risk assessments, but direct measurements are costly and time-consuming. Even when available, measured values such as the mean still yield remediation targets below n...

Groundwater quality assessment and health risk evaluation for schoolchildren in Mujibnagar, Bangladesh: safe consumption guidelines using artificial neural network modeling.

Environmental geochemistry and health
Groundwater is a vital source of drinking water in Bangladesh, with tubewells commonly used, particularly in schools. This study assessed the quality of tubewell water in the southwest region, focusing on iron (Fe), arsenic (As), pH, electrical condu...

A Bayesian Maximum Entropy Fusion model for enhanced prediction and risk assessment of fluoride and arsenic contamination in groundwater.

Journal of contaminant hydrology
In the central and western regions of Jilin Province, excessive groundwater extraction has resulted in elevated levels of fluoride (F) and arsenic (As) in drinking water. Prolonged exposure to these contaminants is linked to endemic health issues, in...

Assessing the transfer of Cd and As from co-contaminated soil to peanut (Arachis hypogaea L.): prediction models and soil thresholds.

Environmental pollution (Barking, Essex : 1987)
In China, the co-contamination of soil with cadmium (Cd) and arsenic (As) is one of the most severe forms of combined pollution. Modeling the transfer of Cd and As from co-contaminated soil to crops has not been thoroughly studied. In this study, fiv...

A novel method for achieving ecological indicator based on vertical soil bacterial communities coupled with machine learning: A case study of a typical tropical site in China.

Journal of hazardous materials
Global industrialization has resulted in severe contamination of soil with heavy metals (HMs). Nevertheless, it is unclear if it affects the depth-resolved bacterial communities. Herein, we collected soil samples at different depths from a typical HM...