An ensemble estimation model of PM2.5 concentration was proposed on the basis of extreme gradient boosting, gradient boosting, random forest model, and stacking model fusion technology. Measured PM2.5 data, MERRA-2 AOD and PM2.5 reanalysis data, meteorological parameters, and night light data sets were used. On this basis, the spatiotemporal evolution features of PM2.5 concentration in China during 2000-2019 were analyzed at monthly, seasonal, and annual temporal scales. The results showed that:① Monthly PM2.5 concentration in China from 2000-2019 can be estimated reliably by the ensemble model. ② PM2.5 annual concentration changed from rapid increase to remaining stable and then changed to significant decline from 2000-2019, with turning points in 2007 and 2014. The monthly variation of PM2.5 concentration showed a U shape that first decreased then increased, with the minimum value in July and the maximum value in December. ③ Natural geographic conditions and human activities laid the foundation for the annual spatial pattern change of PM2.5 concentration in China, and the main trend of monthly spatial pattern change of PM2.5 concentration was determined by meteorological conditions. ④ At an annual scale, the national PM2.5 concentration average center of standard deviation ellipse moved eastward from 2000-2014 and westward from 2014-2018. At a monthly scale, the average center shifted to the west from January to March, moved northward then southward from April to September, and shifted to the east from September to December.
Keywords: China; PM2.5; ensemble model estimating PM2.5 concentration; multiple time scales; spatio-temporal evolution.