Airborne Particulate Matter Sensing via Laser Filament-Interaction and Deep Learning

Airborne particulate matter, including mineral dust and biological aerosols, such as pollen, presents growing challenges to public health, agriculture, and environmental monitoring. Current technologies lack the capability to identify single particles remotely in real-time. Here, we demonstrate the feasibility of femtosecond laser filamentation combined with optical imaging and lightweight deep learning for the remote identification of airborne particulates. Chalk dust, pollen grains, and salt crystals were delivered into a laser-generated filament, where their optical emission events were captured and analyzed. A convolutional neural network trained on these events achieved high classification accuracy across all categories, with a mean accuracy of 87.5%. Grad-CAM visualizations confirmed that the network focused on discriminative spatial and spectral features for chalk, salt, and pollen. This study demonstrates the feasibility of remote, species-level airborne particulate detection and lays the foundation for intelligent, real-time atmospheric sensing platforms.