Improvements on Gaussian mixture model and its application in identifying aerosol types in two major cities in the Yangtze River Delta, China.

Accurately identifying the authentic local aerosol types is one of the fundamental tasks in studying aerosol radiative effects and model assessment. In this paper, improvements were made to the traditional Gaussian Mixture Model, leading to the following results: 1) This study introduces several improvements to the traditional Gaussian Mixture Model (GMM), referred to as M-GMMs. These improvements include the incorporation of multivariate kurtosis coefficients, Mahalanobis distance instead of Euclidean distance, and weights of variables. The M-GMMs overcome the issues related to dimensional units and correlations among multiple parameters, thereby enhancing the estimation of the covariance matrix. 2) The proposed M-GMMs model was evaluated for its clustering performance using machine-generated data with known classifications and real iris flower data. The results demonstrated that the classification performance of M-GMMs was superior to other models. Furthermore, compared to the slightly less effective K-means algorithm (which requires manual definition of the number of aerosol types), the M-GMMs model was able to automatically iterate and produce consistent classification results based on similar characteristics. 3) There is still a significant disparity between the characteristics of real stations and typical aerosols. Directly evaluating local aerosols using the characteristics of typical aerosols results in substantial errors. However, the M-GMMs model can effectively reflect the authentic aerosol characteristics at the local level. 4) The M-GMMs model was utilized to perform cluster analysis on the Xuzhou and Nanjing stations of AERONET. This analysis yielded quantitative proportions, temporal distribution characteristics, and spectral distribution features of aerosol types in the two regions. The improved M-GMMs model presented in this paper enables more accurate and continuous characterization of aerosol type variations. Its findings hold significant theoretical and practical value in reassessing aerosol radiative effects.