1D-CNN Network Based Real-Time Aerosol Particle Classification with Single-Particle Mass Spectrometry

Abstract

Single-particle mass spectrometry (SPMS) is a measurement technique that aims to identify the chemical composition of individual airborne aerosol particles (PM 1 or PM 2.5) in real time. One-dimensional (1-D) spectral data of aerosol particles generated by SPMS carry rich information about the chemical composition associated with the sources of the particles, e.g., traffic and ship emissions, biomass burning, etc. Accurate classification of aerosol particles is essential to understand their sources and effects on human health. This letter investigates the application of SPMS and 1-D-convolutional neural network (1D-CNN) in aerosol particle classification. The proposed 1D-CNN achieved a mean classification accuracy of 90.4% with 13 particle classes. According to the experimental results, the combination of SPMS and 1D-CNN enables real-time collection, analysis, and classification of airborne aerosol particles to be used for highly responsive automated air quality monitoring.