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Microplastics detection in agricultural soil combining 3D Laser Scanning Confocal Microscopy with machine learning
Summary
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.
Microplastic (MP) contamination in agricultural soils is increasingly linked to low-density plastics originating from plasticulture (e.g., mulching) and from MP-contaminated organic fertilisers such as compost and sewage sludge. Although these entry pathways are well documented, robust quantification of MP in soil remains challenging and time-consuming. Established microscopic–spectroscopic approaches (µ-Raman, µ-FTIR) are highly effective in aquatic matrices but require intensive soil sample preparation because soil organic matter (SOM) interferes with polymer identification. Many soil protocols rely on density separation with high-density salt solutions (e.g., ZnCl₂) and chemical oxidation to remove SOM with hazardous and corrosive reagents that can modify MP in relevant ecotoxicological parameters like size, shape, and surface properties. Additionally, MP surface quantification is still rarely integrated into routine analysis, despite its relevance to toxicity. To address these limitations, this study developed and rigorously evaluated a hazard-free workflow for MP detection and surface property quantification in contaminated agricultural soils without the need for SOM removal. The key advance is automated MP detection in the presence of SOM, while enabling 3D surface property quantification by surface roughness-related descriptors. The workflow combines (i) sample preparation for 25 g soil (
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