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 PM2.5 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 PM2.5 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 PM2.5 and O3-8hmax simulations. During 2005-2017, PM2.5 exhibited an overall downward trend, while ambient O3 experienced an upward trend. Whilst the spatial patterns of PM2.5 and O3-8hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristic.

Each dataset is the annual mean concentration of PM2.5 or O3-8hmax based on the standard grid (Grid.csv) for that year. The coordinate system of the grid is WGS-84.