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The fine-mode aerosol optical depth (AOD_f) is an important parameter for the environment and climate change study, which mainly represents the anthropogenic aerosols component. The Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL) instrument can detect polarized signal from multi-angle observation and the polarized signal mainly comes from the radiation contribution of the fine-mode aerosols, which provides an opportunity to obtain AOD_f directly. However, the currently operational algorithm of Laboratoire d’Optique Atmosphérique (LOA) has a poor AOD_f retrieval accuracy over East China on high aerosol loading days. This study focused on solving this issue and proposed a grouped residual error sorting (GRES) method to determine the optimal aerosol model in AOD_f retrieval using the traditional look-up table (LUT) approach and then the AOD_f retrieval accuracy over East China was improved. The comparisons between the GRES retrieved and the Aerosol Robotic Network (AERONET) ground-based AOD_f at Beijing, Xianghe, Taihu and Hong_Kong_PolyU sites produced high correlation coefficients (r) of 0.900, 0.933, 0.957 and 0.968, respectively. The comparisons of the GRES retrieved AOD_f and PARASOL AOD_f product with those of the AERONET observations produced a mean absolute error (MAE) of 0.054 versus 0.104 on high aerosol loading days (AERONET mean AOD_f at 865 nm = 0.283). An application using the GRES method for total AOD (AOD_t) retrieval also showed a good expandability for multi-angle aerosol retrieval of this method.