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Water Pollutants, Chemical

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Machine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data.

Journal of hazardous materials
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...

Integrating machine learning, suspect and nontarget screening reveal the interpretable fates of micropollutants and their transformation products in sludge.

Journal of hazardous materials
Activated sludge enriches vast amounts of micropollutants (MPs) when wastewater is treated, posing potential environmental risks. While standard methods typically focus on target analysis of known compounds, the identity, structure, and concentration...

Interpretable machine learning reveals transport of aged microplastics in porous media: Multiple factors co-effect.

Water research
Microplastics (MPs) easily migrate into deeper soil layers, posing potential risks to subterranean habitats and groundwater. However, the mechanisms governing the vertical migration of MPs in soil, particularly aged MPs, remain unclear. In this study...

CLSSATP: Contrastive learning and self-supervised learning model for aquatic toxicity prediction.

Aquatic toxicology (Amsterdam, Netherlands)
As compound concentrations in aquatic environments increase, the habitat degradation of aquatic organisms underscores the growing importance of studying the impact of chemicals on diverse aquatic populations. Understanding the potential impacts of di...

Trace detection of antibiotics in wastewater using tunable core-shell nanoparticles SERS substrate combined with machine learning algorithms.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Surface-enhanced Raman scattering (SERS) show great potential for rapid and highly sensitive detection of trace amounts of contamination from the environment in the surface aquatic ecosystem. The widespread use of antibiotics has resulted in serious ...

Assessment of groundwater chemistry to predict arsenic contamination from a canal commanded area: applications of different machine learning models.

Environmental geochemistry and health
Groundwater arsenic (As), contamination is a significant issue worldwide including China and Pakistan, particularly in canal command areas. In this study, 131 groundwater samples were collected, and three machine learning models [Random Forest (RF), ...

Polymer Biodegradation in Aquatic Environments: A Machine Learning Model Informed by Meta-Analysis of Structure-Biodegradation Relationships.

Environmental science & technology
Polymers are widely produced and contribute significantly to environmental pollution due to their low recycling rates and persistence in natural environments. Biodegradable polymers, while promising for reducing environmental impact, account for less...

Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators.

Ecotoxicology and environmental safety
The long-term presence of antibiotics in the aquatic environment will affect ecology and human health. Techniques for determining antibiotics are often time-consuming, labor-intensive and costly, and it is desirable to seek new methods to achieve rap...

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
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...

Application of artificial intelligence for nutrient estimation in surface water bodies of basins with intensive agriculture.

Integrated environmental assessment and management
Eutrophication is one of the most relevant concerns due to the risk to water supply and food security. Nitrogen and phosphorus chemical species concentrations determined the risk and magnitude of eutrophication. These analyses are even more relevant ...