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In The Science of the total environment

The lack of long-term observations and satellite retrievals of health-damaging fine particulate matter in China has demanded the estimates of historical PM2.5 (particulate matter less than 2.5 μm in diameter) concentrations. This study constructs a gridded near-surface PM2.5 concentration dataset across China covering 1980-2019 using the space-time random forest model with atmospheric visibility observations and other auxiliary data. The modeled daily PM2.5 concentrations are in excellent agreement with ground measurements, with a coefficient of determination of 0.95 and mean relative error of 12%. Besides the atmospheric visibility which explains 30% of total importance of variables in the model, emissions and meteorological conditions are also key factors affecting PM2.5 predictions. From 1980 to 2014, the model-predicted PM2.5 concentrations increased constantly with the maximum growth rate of 5-10 μg/m3/decade over eastern China. Due to the clean air actions, PM2.5 concentrations have decreased effectively at a rate over 50 μg/m3/decade in the North China Plain and 20-50 μg/m3/decade over many regions of China during 2014-2019. The newly generated dataset of 1-degree gridded PM2.5 concentrations for the past 40 years across China provides a useful means for investigating interannual and decadal environmental and climate impacts related to aerosols.

Li Huimin, Yang Yang, Wang Hailong, Li Baojie, Wang Pinya, Li Jiandong, Liao Hong


Atmospheric visibility, Clean air actions, Fine particulate matter, Space-time random forest model, Spatial and temporal variation