Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005-2017 based on multi-variable random forest model

The aim of our study was to construct random forest models with high-performance, and estimate daily average PM_2.5 concentration and O₃ daily maximum 8h average concentration (O₃-8hmax) of China in 2005-2017 at a spatial resolution of 1km×1km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on ten-fold cross validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly simulations of PM_2.5 gave average model fitting R² values of 0.85, 0.88 and 0.90, respectively; these R² values were 0.77, 0.77, and 0.69 for O₃-8hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM_2.5 and O₃-8hmax simulations. During 2005-2017, PM_2.5 exhibited an overall downward trend, while ambient O₃ experienced an upward trend. Whilst the spatial patterns of PM_2.5 and O₃-8hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristic. Each dataset is the annual mean concentration of PM_2.5 or O₃-8hmax based on the standard grid (Grid.csv) for that year. The coordinate system of the grid is WGS-84.