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 (1D) 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 paper investigates the application of SPMS and 1D-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.
Graphical Abstract
DOI