AIMC Topic: Water Pollutants, Chemical
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Deep eutectic solvent-modified polyvinyl alcohol/chitosan thin film membrane for dye adsorption: Machine learning modeling, experimental, and density functional theory calculations.
International journal of biological macromolecules
Jan 3, 2025
The polyvinyl alcohol/chitosan (PVA/CS) thin film membrane was modified using a deep eutectic solvent (DES) to enhance its adsorption capability and mechanical strength for the removal of brilliant green (BG) dye. Batch adsorption experiments, machin...
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
Jan 3, 2025
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)...
Machine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data.
Journal of hazardous materials
Dec 31, 2024
Microplastics are ubiquitous and appear to be harmful, however, the full extent to which these inflict harm has not been fully elucidated. Analysing environmental sample data is challenging, as the complexity in real data makes both automated and man...
Efficient and stable extraction of nano-sized plastic particles enabled by bio-inspired magnetic "robots" in water.
Environmental pollution (Barking, Essex : 1987)
Dec 31, 2024
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...
Assessing the environmental determinants of micropollutant contamination in streams using explainable machine learning and network analysis.
Chemosphere
Dec 31, 2024
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...
Machine learning-based prediction of non-aeration linear alkylbenzene sulfonate mineralization in an oxygenic microalgal-bacteria biofilm.
Bioresource technology
Dec 28, 2024
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...
Combining Group Contribution Method and Semisupervised Learning to Build Machine Learning Models for Predicting Hydroxyl Radical Rate Constants of Water Contaminants.
Environmental science & technology
Dec 26, 2024
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
Dec 24, 2024
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
Dec 20, 2024
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
Dec 20, 2024
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...
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