Automatic pre-screening of outdoor airborne microplastics in micrographs using deep learning

Cabaneros, Sheen Mclean; Chapman, Emma; Hansen, Mark; Williams, Ben; Rotchell, Jeanette

Abstract

Airborne microplastics (AMPs) are prevalent in both indoor and outdoor environments, posing potential health risks to humans. Automating the process of spotting them in micrographs can significantly enhance research and monitoring. Although deep learning has shown substantial promise in microplastic analysis, existing studies have primarily focused on high-resolution images of samples collected from marine and freshwater environments. In contrast, this work introduces a novel approach by employing enhanced U-Net models (Attention U-Net and Dynamic RU-NEXT) along with the Mask Region Convolutional Neural Network (Mask R-CNN) to identify and classify AMPs in lower-resolution micrographs (256 × 256 pixels) obtained from outdoor environments. A key innovation involves integrating classification directly within the U-Net-based segmentation frameworks, thereby streamlining the workflow and improving computational efficiency which is an advancement over previous work where segmentation and classification were performed separately. The enhanced U-Net models attained average classification F1-scores exceeding 85% and segmentation scores above 77%. Additionally, the Mask R-CNN model achieved an average bounding box precision of 73.32% on the test set, a classification F1-score of 84.29%, and a mask precision of 71.31%, demonstrating robust performance. The proposed method provides a faster and more accurate means of identifying AMPs compared to thresholding techniques. It also functions effectively as a pre-screening tool, substantially reducing the number of particles requiring labour-intensive chemical analysis. By integrating advanced deep learning strategies into AMPs research, this study paves the way for more efficient monitoring and characterisation of microplastics.

• Journal Article Type: Article
• Acceptance Date: Mar 4, 2025
• Online Publication Date: Mar 17, 2025
• Publication Date: May 1, 2025
• Deposit Date: Mar 17, 2025
• Publicly Available Date: Mar 18, 2025
• Journal: Environmental Pollution
• Print ISSN: 0269-7491
• Electronic ISSN: 1873-6424
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 372
• Article Number: 125993
• DOI: https://doi.org/10.1016/j.envpol.2025.125993
• Public URL: https://uwe-repository.worktribe.com/output/13947076
• Additional Information: This article is maintained by: Elsevier; Article Title: Automatic pre-screening of outdoor airborne microplastics in micrographs using deep learning; Journal Title: Environmental Pollution; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.envpol.2025.125993; Content Type: article; Copyright: © 2025 The Authors. Published by Elsevier Ltd.