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Cost-effective approaches for microplastic pellets characterization using a machine learning tool
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Researchers developed a simplified machine learning method using only a handful of physical characteristics — like color, weight, and size — to classify plastic pellets by polymer type without expensive lab equipment. Tested on samples from Spain, Portugal, and Italy, the approach offers a practical, low-cost tool for monitoring plastic pollution on beaches at scale.
Microplastics, including pellets, are a persistent pollutant on beaches that pose relevant ecological and environmental challenges. Their widespread presence in marine and coastal environments endangers ecosystems, threatens marine life, and risks entering the food chain. Effective microplastic management requires reliable methods for their identification and classification, yet the high cost of required equipment hinders large-scale implementation. Artificial intelligence offers a promising solution for polymer analysis. While machine learning techniques have demonstrated potential in automating microplastic classification, existing approaches often rely on complex models requiring numerous input variables, limiting their practical application. This paper introduces a simplified methodology for pellet polymer classification using a Random Forest model requiring a limited set of variables for training. The approach reduces model complexity while maintaining high classification performance, emphasizing simplicity, speed and efficiency. The method was tested on different pellet samples collected from the coasts of Spain, Portugal and Vulcano Island (Italy). The results highlight the robustness of the proposed model and its suitability to be applied in diverse environmental contexts. By balancing accuracy with computational efficiency, the proposed approach represents a practical tool for pellet classification. This streamlined methodology can offer a significant step forward in microplastic management and pollution mitigation, contributing to the development of cost-effective, scalable solutions for addressing the environmental impacts of microplastics. • Cost-effective AI-based approach for microplastic pellet classification. • Random Forest model classifies polymer types using degradation state, color, weight and size. • The method does not require spectroscopic data, reducing analysis costs and complexity. • Scalable machine learning approach for rapid microplastic identification. • Application to different sites: Galicia, Asturias (Spain), and Volcano Island (Italy).
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