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Water Pollutants, Chemical

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Efficient and stable extraction of nano-sized plastic particles enabled by bio-inspired magnetic "robots" in water.

Environmental pollution (Barking, Essex : 1987)
In this research, a rationally-designed strategy was employed to address the crucial issue of removing nano-plastics (NPs) from aquatic environments, which was based on fabricating sea urchin-like structures of FeO magnetic robots (MagRobots). Throug...

Machine learning-based prediction of non-aeration linear alkylbenzene sulfonate mineralization in an oxygenic microalgal-bacteria biofilm.

Bioresource technology
Microalgal-bacteria biofilm shows great potential in low-cost greywater treatment. Accurately predicting treated greywater quality is of great significance for water reuse. In this work, machine learning models were developed for simulating and predi...

Assessing the environmental determinants of micropollutant contamination in streams using explainable machine learning and network analysis.

Chemosphere
Even at trace concentrations, micropollutants, including pesticides and pharmaceuticals, pose considerable ecological risks, and the increasing presence of synthetic chemical substances in aquatic systems has emerged as a growing concern. Moreover, l...

Accurate prediction of pollution and health risks of iodinated X-ray contrast media in Taihu Lake with machine learning and revealing key environmental factors.

Water research
Iodinated X-ray contrast media (ICM) are commonly detected at considerable concentrations in aquatic environments. The long-term pollution trends in ICM at the whole lake/river scale have not yet been investigated; therefore, the risks associated wit...

Combining Group Contribution Method and Semisupervised Learning to Build Machine Learning Models for Predicting Hydroxyl Radical Rate Constants of Water Contaminants.

Environmental science & technology
Machine learning is an effective tool for predicting reaction rate constants for many organic compounds with the hydroxyl radical (HO). Previously reported models have achieved relatively good performance, but due to scarce data (<1400 records), the ...

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

Environmental geochemistry and health
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 ( ) lacks spatiotemporal studies. This research integrates geospatial...

Recent advances in groundwater pollution research using machine learning from 2000 to 2023: A bibliometric analysis.

Environmental research
Groundwater pollution has become a global challenge, posing significant threats to human health and ecological environments. Machine learning, with its superior ability to capture non-linear relationships in data, has shown significant potential in a...

Alternative assessment of machine learning to polynomial regression in response surface methodology for predicting decolorization efficiency in textile wastewater treatment.

Chemosphere
This study investigated the potential of machine learning (ML) as a substitute for polynomial regression in conventional response surface methodology (RSM) for decolorizing textile wastewater via a UV/HO process. While polynomial regression offers li...

Advancements and challenges in microplastic detection and risk assessment: Integrating AI and standardized methods.

Marine pollution bulletin
Microplastics (MPs) pose significant threats to ecosystems and human health due to their persistence and widespread distribution. This paper provides a comprehensive review of sampling methods for MPs in aquatic environments, soils, and biological sa...

The abiologically and biologically driving effects on organic matter in marginal seas revealed by deep learning-assisted model analysis.

The Science of the total environment
The biogeochemical processes of organic matter exhibit notable variability and unpredictability in marginal seas. In this study, the abiologically and biologically driving effects on particulate organic matter (POM) and dissolved organic matter (DOM)...