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Detection and quantification of microplastic in soils using a 3D Laser Scanning Confocal Microscope
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Researchers applied 3D laser scanning confocal microscopy to detect and quantify microplastics in soil samples, finding it provided higher resolution characterization of particle morphology compared to conventional light microscopy. This study demonstrates the technique as a promising tool for improving the accuracy of microplastic identification and size measurements in complex soil matrices.
There is growing concern regarding the pollution of our environment with plastic materials, whereas especially the dimension of microplastic pollution and its ecological effect is widely discussed. Most studies focus on aquatic environments, while studies in terrestrial systems (mainly soils) are rare. This partly results from the challenges arising when microplastic particles need to be separated from organic and mineral particles. Key analytic techniques for microplastic detection in aquatic and terrestrial systems include Fourier transformation-infrared (FT-IR) and micro-Raman spectroscopy, as well as thermal extraction desorption-gas chromatography-mass spectrometry (TED-GC-MS) and pyrolysis-gas chromatography-mass spectrometry (pyr-GC-MS). While the mass spectrometric methods lack to determine particle sizes, the FT-IR and micro-Raman spectroscopy are very costly and time consuming. Moreover, the latter detection methods are very sensitive to organic matter particles, which are difficult to remove fully during soil sample preparation. Hence, a faster and more robust method to determine microplastic in soils is essential for a wider analysis of this environmental problem. In this study, we combine a density separation scheme with a 3D Laser Scanning Confocal Microscope (Keyence VK-X1000, Japan) analysis to determine the number and size of microplastic particles in soil samples. For the analysis a silty loam (16% sand, 59% silt, 25% clay, 1.3% organic carbon) and a loamy sand (72% sand, 18% silt, 10% clay, 0.9% organic carbon) were spiked with different concentrations of high density Polyethylene (HDPE), low density Polyethylene (LDPE) and Polystyrene (PS) microplastic (HDPE 50 - 100 and 250 - 300 µm, LDPE <50 and 200 - 800 µm, PS <100 µm). 3D Laser Scanning Confocal Microscopy show very promising results while using differences in optical characteristic and especially surface roughness, to distinguish between plastic and mineral as well as organic particles left after density separation. Overall, the 3D Laser Scanning Confocal Microscopy is a promising tool for relatively fast detection and quantification of microplastic in soils, which could perfectly complement the also relative fast mass-spectrometric methods to determine plastic types. However, to result in an operational and automated analyzation process further research based on the 3D Laser Scanning Confocal Microscopy analysis is needed.
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Microplastic detection in arable soil using a 3D Laser Scanning Confocal Microscope coupled with a Machine-Learning Algorithm
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Researchers used 3D laser scanning confocal microscopy paired with machine learning to detect microplastics in agricultural soil. The method successfully identified low-density polyethylene particles from mulching films, providing a faster and more precise tool for tracking plastic contamination in farmland.
Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning
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Scientists developed a new workflow combining 3D laser scanning confocal microscopy with machine learning to detect and quantify microplastics in agricultural soils more efficiently and without hazardous chemicals. The method was validated on soil samples spiked with transparent microplastics, which are particularly difficult to identify. Faster, safer detection tools are essential for understanding how widely microplastics from plastic mulch films and organic fertilizers contaminate farmland soils.
Microplastic detection in arable soil using a 3D Laser Scanning Confocal Microscope coupled with a Machine-Learning Algorithm
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Researchers applied 3D laser scanning confocal microscopy coupled with a machine-learning algorithm for automated detection and quantification of microplastics from LDPE and PP mulch films in arable soil, addressing the lack of accurate quantification methods for agricultural MP contamination from plastic mulching and sewage sludge.
Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning
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Scientists developed a faster, safer way to find tiny plastic particles (microplastics) in farm soil without using dangerous chemicals. This new method could help us better track plastic pollution in the soil where our food grows. Better detection of microplastics in agricultural soil is important because these particles can potentially move from contaminated soil into our food supply.
Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning
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Scientists developed a faster, chemical-free method to detect and measure tiny plastic fragments (as small as 53 microns) in farm soil, using a special 3D microscope paired with computer-assisted image analysis. This matters because microplastics in agricultural soil can end up in our food and water, and better detection tools help researchers track how much plastic pollution is actually building up in the fields that grow our crops. The method worked well in most soils but had more trouble detecting dark-colored plastic bits and fibers in soil with lots of natural organic debris, so there's still room for improvement.
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