Full-coverage 1 km daily ambient PM2.5 and O-3 concentrations of China in 2005-2017 based on a multi-variable random forest model

The health risks of fine particulate matter (PM2.5) and ambient ozone (O-3) have been widely recognized in recent years. An accurate estimate of PM2.5 and O-3 exposures is important for supporting health risk analysis and environmental policy-making. The aim of our study was to construct random forest models with high-performance and estimate daily average PM2.5 concentration and O-3 daily maximum of 8 h average concentration (O-3-8 hmax) of China in 2005-2017 at a spatial resolution of 1 km x 1 km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on 10-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 estimations of PM2.5 from test datasets gave average model-fitting R-2 values of 0.85, 0.88 and 0.90, respectively; these R-2 values were 0.77, 0.77 and 0.69 for O-3-8 hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM2.5 and O-3-8 hmax estimations. During 2005-2017, PM2.5 concentration exhibited an overall downward trend, while ambient O-3 concentration experienced an upward trend. Whilst the spatial patterns of PM2.5 and O-3-8 hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristics.