AIMC Topic: Microplastics

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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...
Neural Networks, Computer Humans Environmental Monitoring Machine Learning Water Pollutants, Chemical Spectroscopy, Fourier Transform Infrared Microplastics
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Unraveling Microplastic Effects on Gut Microbiota across Various Animals Using Machine Learning.

ACS nano Dec 26, 2024
Microplastics, rapidly expanding and durable pollutant, have been shown to significantly impact gut microbiota across a spectrum of animal species. However, comprehensive analyses comparing microplastic effects on gut microbiota among these species a...
Mice Gastrointestinal Microbiome Machine Learning Animals Microplastics
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Rapid detection of microplastics in chicken feed based on near infrared spectroscopy and machine learning algorithm.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Dec 20, 2024
The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classificatio...
Animal Feed Microplastics Least-Squares Analysis Discriminant Analysis Chickens Support Vector Machine Food Contamination Principal Component Analysis Animals Spectroscopy, Near-Infrared Machine Learning Algorithms
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Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics.

The Science of the total environment Dec 14, 2024
Microplastics (MPs), the plastic debris smaller than 5 mm, are ubiquitous in waterbodies and have been shown to be toxic to aquatic organisms, especially to microalgae. The aim of this study is to use machine learning models to predict the effects of...
Microplastics Microalgae Machine Learning Environmental Monitoring Water Pollutants, Chemical
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Towards A universal settling model for microplastics with diverse shapes: Machine learning breaking morphological barriers.

Water research Dec 12, 2024
Accurately predicting the settling velocity of microplastics in aquatic environments is a prerequisite for reliably modeling their transport processes. An increasing number of settling models have been proposed for microplastics with fragmented, film...
Microplastics Models, Theoretical Water Pollutants, Chemical Machine Learning
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Machine learning based workflow for (micro)plastic spectral reconstruction and classification.

Chemosphere Dec 3, 2024
With the advancement of artificial intelligence, it is foreseeable that computer-assisted identification of microplastics (MPs) will become increasingly widespread. Therefore, exploring a machine learning-based workflow to facilitate the identificati...
Neural Networks, Computer Machine Learning Support Vector Machine Algorithms Workflow Environmental Monitoring Microplastics
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Microplastics and Trash Cleaning and Harmonization (MaTCH): Semantic Data Ingestion and Harmonization Using Artificial Intelligence.

Environmental science & technology Nov 11, 2024
With the rapid expansion of microplastic research and reliance on semantic descriptors, there is an increasing need for plastic pollution data harmonization. Data standards have been developed but are seldom implemented across research sectors, geogr...
Plastics Algorithms Microplastics Environmental Monitoring Artificial Intelligence Software
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Enhancing the content of phycoerythrin through the application of microplastics from Porphyridium cruentum produced in wastewater using machine learning methods.

Journal of environmental management Nov 6, 2024
Microalgae can produce secondary metabolites like phycoerythrin (Phy). The effects of some microplastics (MPs), wastewater (WW), and light intensity (LI) parameters, including complex data sets, on Phy concentration from Porphyridium cruentum were in...
Machine Learning Porphyridium Wastewater Phycoerythrin Microplastics Microalgae
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From microplastics to pixels: testing the robustness of two machine learning approaches for automated, Nile red-based marine microplastic identification.

Environmental science and pollution research international Oct 24, 2024
Despite the urgent need for accurate and robust observations of microplastics in the marine environment to assess current and future environmental risks, existing procedures remain labour-intensive, especially for smaller-sized microplastics. In addi...
Environmental Monitoring Water Pollutants, Chemical Plastics Oxazines Microplastics Machine Learning
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Effects of the co-exposure of microplastic/nanoplastic and heavy metal on plants: Using CiteSpace, meta-analysis, and machine learning.

Ecotoxicology and environmental safety Oct 23, 2024
Micro/nanoplastics (MNPs) and heavy metals (HMs) coexist worldwide. Existing studies have reported different or even contradictory toxic effects of co-exposure to MNPs and HMs on plants, which may be related to various influencing factors. In this st...
Metals, Heavy Microplastics Cadmium Soil Pollutants Machine Learning Plants Nanoparticles
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