Despite regulations restricting asbestos use in many developed countries, asbestos-containing materials (ACMs) persist in aging buildings and can release airborne fibers during demolition and renovation. Therefore, continuous monitoring of airborne asbestos fibers is essential for environmental safety and exposure assessment. Fluorescence microscopy (FM) with fluorescently labeled asbestos-binding proteins offers greater sensitivity and selectivity in detection compared with conventional phase contrast microscopy (PCM). However, its practical application is limited by manual sample preparation and microscopic observations. This study introduces the conceptual design and initial development of an automated FM-based sensing system for monitoring airborne asbestos fibers. The system was constructed by modifying a commercial PM2.5 continuous air sampling platform and integrating automated fluorescent staining, FM imaging, and AI-assisted image analysis for fiber recognition and counting. The system automatically reports airborne asbestos concentrations with corresponding fluorescence images and advances the membrane filter to enable continuous measurements. Performance evaluation using pulverized ACMs showed an overall agreement within 14.1% with PCM–scanning electron microscopy measurements at the group level. Although variability was observed at low fiber concentrations owing to stochastic sampling effects, the results validate the feasibility of automated FM-based sensing for continuous environmental monitoring of airborne asbestos fibers.
Kuroda et al. (2026) studied this question.