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As the concentrations of primary components of fine particulate matter (PM2.5) have substantially decreased, the contribution of secondary inorganic aerosols to PM2.5 pollution has become more prominent. Therefore, understanding the variations in and characteristics of secondary inorganic aerosols is vital to further reducing PM2.5 concentrations in the future. In this study, an ensemble back-propagation neural network model was built by combining 3D numerical models, observation data, and machine learning methods, to estimate the conce