Aerosol particle classification are of great importance in many cases. But currently there is almost no universal method to deal with this task. In this study, we develop a one-dimensional convolution neural network taking multi-angle polarization time series signal of single suspended particle as input to identify its category. We train the network and reach quite high accuracy on a large dataset which contains signals of multi-kinds particles such as carbon black, PSL, dust and water-soluble salts. This method gives us a new way of looking deeper into single suspended particles in the air and the knowledge learned in this task may be able to be transferred to deal with tasks of recognizing more kinds or more complex aerosol particles such as bioaerosol or even airborne pathogen.
Characterizing chemical and physical aerosol properties is important to understand their sources, effects, and feedback mechanisms in the atmosphere. In this study, a classification scheme based on multidimensional polarization central spectrum (including P-Hdop, P-Pdop, R-Hdop, R-Pdop at scattering angle 30°, 60°, 85°, 115° respectively) were developing to classify aerosol type. The scheme is obtained thanks to the outstanding set of information on aerosol composition and morphology these polarization properties contain. All these polarization data are recorded in real-time and on each individually flowing aerosol particle in our equipment. Several continuous particles signals are averaged to reduce the measurement uncertainty and size dependence, then high temporal resolution and polarization feature resolution can be obtained simultaneously in our method. Several Standard atmospheric aerosols generated in laboratory are identified and their polarization scattering signatures were evaluated. Experiment results were interpreted based on numerical simulations (Mie theory) to investigate polarization properties resolved by particle complex refractive index and shape. Results of this exploratory study proved useful in drawing a general spectrum database build on multidimensional polarization index to optically identify aerosol types in real time.
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