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We focus on the causes of fluctuations in wintertime PM 10  in nine regional core cities of China using two machine learning models, Random Forest (RF) and Recurrent Neural Network (RNN). RF and RNN both show high performance in predicting hourly PM 10  using only gaseous air pollutants (SO 2 , NO 2  and CO) as inputs, showing the predominance of the secondary inorganic aerosol and implying the existence of thermodynamic equilibrium between gaseous air pollutants and PM 10 . Also, we find the following results. The correlation of gaseous air pollutants and PM 10  were more relevant than that of meteorological conditions and PM 10 . CO was the predominant factor for PM 10  in the Beijing-Tianjin-Hebei Plain and the Yangtze River Delta while SO 2  and NO 2  were also important features for PM 10  in the Pearl River Delta and Sichuan Basin. The spatial heterogeneity and temporal homogeneity of PM 10  in China are revealed. The long-range transported PM 10  was substantiated to be insignificant, except in the sandstorms. The severity of PM 10  was attributable to the lopsided shift of thermodynamic equilibrium and the phenology of indigenous flora.

Exploring the spatial heterogeneity and temporal homogeneity of ambient PM10 in nine core cities of China