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Water Pollutants, Chemical

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Data driven AI (artificial intelligence) detection furnish economic pathways for microplastics.

Journal of contaminant hydrology
Microplastics pollution is killing human life, contaminating our oceans, and lasting for longer in the environment than it is used. Microplastics have contaminated the geochemistry and turned the water system into trash barrel. Its detection in water...

Adsorption simulation of 2,4-D pesticide on novel zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complexes using machine learning approach.

Environmental science and pollution research international
The capacity of zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complex (Zn(NH-TBA)) and modified Zn(NH-TBA)COMe complex for removal of 2,4-dichlorophenoxyacetic acid (2,4-D) from aqueous solutions was investigated through adso...

Loss of micropollutants on syringe filters during sample filtration: Machine learning approach for selecting appropriate filters.

Chemosphere
Prefiltration before chromatographic analysis is critical in the monitoring of environmental micropollutants (MPs). However, in an aqueous matrix, such monitoring often leads to out-of-specification results owing to the loss of MPs on syringe filters...

A hybrid deep learning approach to predict hourly riverine nitrate concentrations using routine monitored data.

Journal of environmental management
With high-frequency data of nitrate (NO-N) concentrations in waters becoming increasingly important for understanding of watershed system behaviors and ecosystem managements, the accurate and economic acquisition of high-frequency NO-N concentration ...

Integrating advanced techniques and machine learning for landfill leachate treatment: Addressing limitations and environmental concerns.

Environmental pollution (Barking, Essex : 1987)
This review article explores the challenges associated with landfill leachate resulting from the increasing disposal of municipal solid waste in landfills and open areas. The composition of landfill leachate includes antibiotics (0.001-100 μg), heavy...

Transferability of Machine Learning Models for Geogenic Contaminated Groundwaters.

Environmental science & technology
Machine learning models show promise in identifying geogenic contaminated groundwaters. Modeling in regions with no or limited samples is challenging due to the need for large training sets. One potential solution is transferring existing models to s...

Enhanced biodegradation of phenol under Cr(VI) stress by microbial collaboration and potential application of machine learning for phenol biodegradation.

Water science and technology : a journal of the International Association on Water Pollution Research
Cr(VI) and phenol commonly coexist in wastewater, posing a great threat to the environment and human health. However, it is still a challenge for microorganisms to degrade phenol under high Cr(VI) stress. In this study, the phenol-degrading strain Z...

Nanoplastics in Water: Artificial Intelligence-Assisted 4D Physicochemical Characterization and Rapid In Situ Detection.

Environmental science & technology
For the first time, we present a much-needed technology for the in situ and real-time detection of nanoplastics in aquatic systems. We show an artificial intelligence-assisted nanodigital in-line holographic microscopy (AI-assisted nano-DIHM) that au...

Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study.

Chemosphere
In this study, neural networks and support vector regression (SVR) were employed to predict the degradation over three pharmaceutically active compounds (PhACs): Ibuprofen (IBP), diclofenac (DCF), and caffeine (CAF) within a stirred reactor featuring...

Online sequential analysis of volatile and semivolatile organic compounds in water matrices by double robotic sample preparations and dual-channel mono and comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry system.

Journal of chromatography. A
The monitoring of organic compounds in aquatic matrices poses challenges due to its complexity and time-intensive nature. To address these challenges, we introduce a novel approach utilizing a dual-channel mono (D) and comprehensive two-dimensional (...