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Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning
Summary
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.
Abstract Accurate quantification of microplastics (MPs) in soils remains analytically challenging due to complex mineral and organic soil matrices. Microscopy, spectroscopy and thermoanalytical techniques are widely applied to analyse MPs in soils. However, integrated workflows enabling simultaneous assessment of quantitative surface properties remain limited. Surface roughness and complexity may influence particle-environment interactions and are therefore relevant for understanding the environmental behaviour of MPs. This study developed and evaluated an oxidative- and corrosive-substance-free workflow for the simultaneous assessment of MP abundance, size, shape, and quantitative surface roughness and complexity in agricultural soils. The workflow combines density separation with freezing, 3D Laser Scanning Confocal Microscopy (3D LSCM; Keyence VK-X1000, Japan) and machine-learning-based automated detection. In addition to enabling time-efficient particle classification, machine-learning integrates multi-layer 3D LSCM outputs, including height, laser reflection, and colour (RGB) information. Data acquisition was performed at a pixelxy size of 2.7 μm and a height pitch of 4 μm. The method was evaluated using three agricultural topsoils spiked with transparent and black low-density polyethylene and polypropylene fragments (< 53 μm, 53–100 μm, 100–250 μm) and polypropylene fibres (1000 μm length). MPs ≥ 53 μm were reliably detected with a mean recovery of 80% ± 28% in soils with low to medium particulate organic matter content (POM). As expected, detection performance decreased in soils with high POM content, as POM was not removed during sample preparation, which made it particularly difficult to determine black MPs and fibres. Up to four 25 g samples can be processed and analysed within three days, enabling time-efficient MP (≥ 53 μm) assessment in agricultural soils and complementing established analytical approaches through quantitative surface characterisation. Graphical abstract