Constructing a spatiotemporally coherent long-term PM concentration dataset over China during 1980-2019 using a machine learning approach.

The lack of long-term observations and satellite retrievals of health-damaging fine particulate matter in China has demanded the estimates of historical PM (particulate matter less than 2.5 μm in diameter) concentrations. This study constructs a gridded near-surface PM 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 PM 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 PM predictions. From 1980 to 2014, the model-predicted PM concentrations increased constantly with the maximum growth rate of 5-10 μg/m/decade over eastern China. Due to the clean air actions, PM concentrations have decreased effectively at a rate over 50 μg/m/decade in the North China Plain and 20-50 μg/m/decade over many regions of China during 2014-2019. The newly generated dataset of 1-degree gridded PM concentrations for the past 40 years across China provides a useful means for investigating interannual and decadal environmental and climate impacts related to aerosols.