Constructing a spatiotemporally coherent long-term PM2.5 concentration dataset over China during 1980–2019 using a machine learning approach

Highlights: • Long-term PM_2.5 is essential due to the coverage deficiency of surface observations. • A machine learning model with visibility and many auxiliary data inputs is applied. • A 1-degree gridded daily PM_2.5 dataset over China for 1980–2019 is constructed. • The model performs well with a high coefficient of determination and low bias. • It will be a promising tool for assessing related impacts on environment and climate. The lack of long-term observations and satellite retrievals of health-damaging fine particulate matter in China has demanded the estimates of historical PM_2.5 (particulate matter less than 2.5 μm in diameter) concentrations. This study constructs a gridded near-surface PM_2.5 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 PM_2.5 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 PM_2.5 predictions. From 1980 to 2014, the model-predicted PM_2.5 concentrations increased constantly with the maximum growth rate of 5–10 μg/m³/decade over eastern China. Due to the clean air actions, PM_2.5 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 PM_2.5 concentrations for the past 40 years across China provides a useful means for investigating interannual and decadal environmental and climate impacts related to aerosols.