Global aerosol typing classification using a new hybrid algorithm utilizing Aerosol Robotic Network data

Abstract. Aerosols have great uncertainty owing to the complex changes in their composition in different regions. The radiation properties of different aerosol types differ considerably and are vital in studying aerosol regional and/or global climate effects. Traditional aerosol-type identification algorithms, generally based on cluster or empirical analysis methods, are often inaccurate and time-consuming. Hence, we aimed to develop a new aerosol-type classification model using an innovative hybrid algorithm to improve the precision and efficiency of aerosol-type identification. An optical database was built using Mie scattering and a complex refractive index was used as a baseline to identify different aerosol types by applying a random forest algorithm to train the aerosol optical parameters obtained from the Aerosol Robotic Network sites. The consistency rates of the new model with the traditional Gaussian density cluster method were 90 %, 85 %, 84 %, 84 %, and 100 % for dust, mixed-coarse, mixed-fine, urban/industrial, and biomass burning aerosols, respectively. The corresponding precision of the new hybrid algorithm (F-score and accuracy scores) was 95 %, 89 %, 91 %, and 89 %. Lastly, a global map of aerosol types was generated using the new model to characterize aerosol types across the five continents. This study utilizing a novel approach for the classification of aerosol will help improve the accuracy of aerosol inversion and determine the sources of aerosol pollution.