The atmospheric aerosols have significant influence on human health, the environment and the climate system. The atmospheric boundary layer (ABL) reflects processes of the near-surface atmosphere and concentration of pollutants. Ground-based laser radar can monitor the vertical distribution of atmospheric aerosols stably and continuously. It provides dynamic information for timing observations of the ABL and environmental forecasting, if aerosols can be monitored and evaluated using lidar technology. There is a gap in the study of ABL observations during the presence of a residual layer and aerosol intrusion, as well as deficiencies in the accuracy and poor computational efficiency of the gradient method. This paper combines the physical meaning of the latter method with characteristics of a lidar timing chart and local optimum model, which based on space-time proximity. Then a polarization-Mie scattering lidar system is used to observe the vertical distribution of aerosols over time at Taihu observation site, which is in a newly developed area of the city of Wuxi, Jiangsu Province, China. Observation and analysis is carried out for two cases in terms of pollution at the end of 2012. Then corresponding estimation model was built with gradient method and local optimum model based on range-corrected signals. In the case of steady weather and mixed pollution, results of the gradient method and local optimum model were very similar. However, the gradient method has more error in the case of pollution intrusion with the residual layer. The local optimum model based on the space-time proximity theory considers vertical eigenvalues and horizontal correlations, thereby greatly reducing the effects of low clouds, signal interference, weak signals, bi-layered aerosols, and residual layer condition. Compared with the gradient method, the local optimum model had a smaller O(n) and greater stability in computer automatic identification. ABL identification in the case with the residual layer and aerosol intrusion was solved with use of lidar technology and the local optimum model. The accuracy and computational efficiency problems of the gradient method were resolved using automatic operation.