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An Easily Adopted Workflow for the Preparation, Filtration, and Quantification of Microplastic Standards
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This paper describes a faster, cheaper laboratory workflow for preparing and counting microplastic particles, replacing slow manual filtration with a vacuum-based system that runs 11 times faster and recovers more particles. A machine learning image analysis tool was also validated, achieving strong agreement with manual counting while reducing human error. Making microplastic measurement more accessible and reproducible is critical for generating the consistent data needed to understand contamination levels globally.
Microplastic (MP) pollution poses an emerging environmental concern, yet current methods for isolation and quantification are often time-consuming, costly, and poorly adapted to real-world variability. In this study, a workflow for the preparation, filtration, and quantification of MP standards, emphasizing environmental relevance and methodological efficiency, was developed and evaluated. To address the scarcity of irregularly shaped MP standards, low-cost, environmentally representative standards were lab-prepared by grinding and sieving plastic sheets. These MPs were successfully categorized according to sizes up to ~250 μm and dyed for enhanced visibility. The filtration efficiency for two systems, a long-circuit pump (LC-pump) and a short-circuit vacuum (SC-vacuum), was compared. The SC-vacuum method demonstrated a more than 11-fold increase in filtration speed and higher MP recovery rates for both polystyrene and polypropylene standards. Ethanol-based solvents significantly improved MP dispersion and recovery for irregular shapes of the MPs, including polystyrene and polypropylene. Finally, a user-guided machine learning tool (Ilastik) was implemented for automated MP quantification. Ilastik showed a strong correlation with manual counting (r = 0.824) and reduced variability, offering a reproducible and time-efficient alternative. By cutting down cost, time, and technical complexity relative to existing MP analysis techniques, this workflow provides a more accessible path toward consistent and scalable environmental MP assessments.
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