Random Forest Model–based Aerosol Optical Depth Inversion and Variation Analysis in China

<p indent="0mm"> Using ground meteorological station monitoring and MAIAC AOD data, a random forest model was constructed to invert the aerosol optical depth (AOD) in China from 2018 to 2019 and to analyze its spatiotemporal variations. The conclusions are as follows: (1) The dataset was divided into four groups corresponding to spring, summer, autumn, and winter. The models were trained with optimized parameters for each group. The final number of decision trees was set to 250, 250, 200, and 200, with maximum depths of 40, 90, 40, and 40, respectively. The model demonstrated high learning accuracy with these parameters, showing strong reliability. The coefficients of determination (<italic>R</italic>²) were 0.925, 0.922, 0.935, and 0.930, and the root mean square errors (<italic>RMSE</italic>) were 0.091, 0.082, 0.055, and 0.065. (2) After completing the AOD inversion based on the determined parameters, the model′s estimation accuracy was verified using AERONET AOD data, yielding an <italic>R</italic>² of 0.891 and an <italic>RMSE</italic> of 0.129, indicating high overall accuracy. Additionally, the annual average AOD values derived from the inversion at various ground meteorological stations showed a trend similar to the original AOD annual averages, albeit slightly higher. (3) Compared to 2018, the overall AOD values in China in 2019 were slightly lower, indicating an improvement in air quality. The AOD values for different seasons followed the order of spring &gt; summer &gt; winter &gt; autumn. (4) The spatial distribution of AOD values in China exhibited a pattern of ″high in the southeast and low in the northwest,″ consistent with the population distribution revealed by the Hu Huanyong Line. High AOD values were observed in the North China Plain, central China, and the Sichuan Basin. The southern Xinjiang region, characterized by arid climate, sparse vegetation, and frequent dust storms, also had high AOD values. Conversely, the Northeast region, the Tibetan Plateau, the Yunnan-Guizhou Plateau, and the Inner Mongolia Plateau, influenced by topography, geographical location, weaker economic strength, and sparse population distribution, exhibited lower AOD values.