Exploring the detailed spatiotemporal characteristics of PM2.5: Generating a full-coverage and hourly PM2.5 dataset in the Sichuan Basin, China

Fine particulate matter (PM2.5) has received worldwide attention due to its threat to public health. In the Sichuan Basin (SCB), PM2.5 is causing heavy health burdens due to its high concentrations and population density. Compared with other heavily polluted areas, less effort has been made to generate a full-coverage PM2.5 dataset of the SCB, in which the detailed PM2.5 spatiotemporal characteristics remain unclear. Considering commonly existing spatio-temporal autocorrelations, the top-of-atmosphere reflectance (TOAR) with a high coverage rate and other auxiliary data were employed to build commonly used random forest (RF) models to generate accurate hourly PM2.5 con-centration predictions with a 0.05 degrees x 0.05 degrees spatial resolution in the SCB in 2016. Specifically, with historical concentrations predicted from a spatial RF (S-RF) and observed at stations, an alternative spatiotemporal RF (AST-RF) and spatiotemporal RF (ST-RF) were built in grids with stations (type 1). The predictions from the AST-RF in grids without stations (type 2) and observations in type 1 formed the PM2.5 dataset. The LOOCV R2, RMSE and MAE were 0.94/0.94, 8.71/8.62 mu g/m3 and 5.58/5.57 mu g/m3 in the AST-RF/ST-RF, respectively. Using the produced dataset, spatiotemporal analysis was conducted for a detailed understanding of the spatiotemporal characteristics of PM2.5 in the SCB. The PM2.5 concentrations gradually increased from the edge to the center of the SCB in spatial distribution. Two high-concentration areas centered on Chengdu and Zigong were observed throughout the year, while another high-concentration area centered on Dazhou was only observed in winter. The diurnal variation had double peaks and double valleys in the SCB. The concentrations were high at night and low in daytime, which suggests that characterizing the relationship between PM2.5 and adverse health outcomes by daily means might be inaccurate with most human activities conducted in daytime.