AIMC Topic: Water Pollutants, Chemical

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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...

Data-Driven Recursive Kinetic Modeling for Fenton Reaction.

Environmental science & technology
The Fenton reaction is a widely used advanced oxidation process for water purification, valued for its simplicity and effectiveness in degrading refractory organic pollutants. However, accurately modeling its degradation kinetics remains challenging ...

Prediction of water quality in the middle area of Yangtze River using efficient machine learning model.

Environmental geochemistry and health
The Yangtze River, as the longest river in China and the third-longest in the world, holds immense significance for the country's ecological security and sustainable development. The water quality in its middle reaches directly impacts millions of pe...

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...

Identification and velocity measurement of microplastics based on machine learning.

Water research
The settling velocity of microplastics (MPs) is a critical parameter for understanding their migration and behavior in aquatic environments. Conventional methods typically focus on tracking individual MPs and often face significant challenges in capt...

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...

Advances and challenges in the ecological risk assessment of engineered nanomaterials in aquatic ecosystems: A review.

The Science of the total environment
Generating appropriate ecological risk assessments to support the rapid growth of nanotechnology requires a comprehensive understanding of the potential effects of engineered nanomaterials (ENMs), both toxic and beneficial, and accurate predictions o...

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...

Comprehensive monitoring of the spatiotemporal variation of water quality and its associated human health risks in Luvuvhu river catchment, Vhembe biosphere reserve, South Africa.

Scientific reports
This study investigates the spatiotemporal variations in water quality and assesses the associated human health risks in the Luvuvhu River Catchment (LRC), South Africa. Water quality parameters such as pH, total dissolved solids (TDS), turbidity, te...

Discovery of Comprehensive Sets of Chemical Constituents as Markers of PFAS Sources through a Nontarget Screening and Machine Learning Approach.

Environmental science & technology
The objective of this study was to identify chemical constituents as markers of six per- and polyfluoroalkyl substance (PFAS) sources including aqueous film-forming foam-impacted groundwater, landfill leachate, biosolids leachate, municipal wastewate...