We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Identification of Microplastics in Soils Using 2D Geometric Shape Descriptors
AI summary Read the abstract
Researchers applied 2D geometric shape descriptors and Random Forest machine learning to distinguish microplastic particles from soil particles in microscopy images. The results demonstrated promising potential for automated microplastic identification in soil samples, which are more challenging to analyze than water samples.
Abstract. Microplastics (MP), until now mostly studied in aquatic ecosystems, are also largely polluting terrestrial ecosystems, especially soil systems. Overall, there is a lack of robust and fast methods to identify, separate and eliminate MPs from soils. This paper is a first attempt to use 2D shape descriptors and Random Forest Machine Learning method in order to discriminate soil and MP particles. The results of this study demonstrate promising potential of the Machine Learning approach and shape descriptors in this relatively new scientific field of determining MPs in soils.
More Papers Like This
Tall Trees and Small Plastics. Using Random Forest Classification to Identify Microplastic Pollution in Surface Soil Samples
AI summary Read the abstract
Researchers used machine learning (random forest classification) to identify and distinguish twenty types of plastic particles in soil samples from agricultural land. Developing accurate, automated detection methods for microplastics in soil is essential for large-scale environmental monitoring.
Microplastic Analysis in Soil Using Ultra-High-Resolution UV–Vis–NIR Spectroscopy and Chemometric Modeling
AI summary Read the abstract
Researchers tested a new method using UV-visible-near infrared spectroscopy combined with machine learning to identify microplastics in soil samples. They found the technique could rapidly and accurately distinguish between different plastic polymers and natural soil particles. The study offers a promising alternative to current labor-intensive identification methods, potentially making large-scale microplastic soil monitoring more practical.
Detection of Microplastics Using Machine Learning
AI summary Read the abstract
Researchers reviewed and demonstrated machine learning approaches for detecting and classifying microplastics in environmental samples, finding that automated image analysis and spectral classification methods can improve the speed and accuracy of microplastic monitoring compared to manual methods.
Identification of potentially contaminated areas of soil microplastic based on machine learning: A case study in Taihu Lake region, China
AI summary Read the abstract
Researchers applied machine learning models — including random forest and support vector regression — to predict the spatial distribution of soil microplastic pollution in China's Taihu Lake region, finding that soil texture, population density, and proximity to known plastic sources were the dominant drivers, with nearly half of urban soils showing serious contamination.
Machine learning enhanced machine vision system for micro-plastics particles classification
AI summary Read the abstract
Researchers developed a machine learning-based classification system using fluorescence microscopy with Nile Red staining to identify and categorize microplastic types in environmental samples, aiming to provide a faster and more automated alternative to labor-intensive manual identification methods.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.