Spatiotemporal PM2.5 estimations in China from 2015 to 2020 using an improved gradient boosting decision tree

Fine particulate matter (PM2.5) with spatiotemporal continuity can provide important basis for the assessment of adverse effects on human health. In recent years, researchers have done a lot of work on the surface PM2.5 simulation. However, due to the limitations of data and models, it is difficult to accurately evaluate the spatial and temporal PM2.5 variations on a fine scale. In this study, we adopted the multi-angle implementation of atmospheric correction (MAIAC) aerosol products, and proposed a spatiotemporal model based on the gradient boosting decision tree (GBDT) algorithm to retrieve PM2.5 concentration across China from 2015 to 2020 at 1-km resolution. Our model achieved excellent performance, with overall CV-R-2 of 0.92, and annual CV-R-2 of 0.90-0.93. In addition, the model can also be used for evaluation on different time scales. Compared with previous studies, the model developed in our study performed better and more stable, which showed the highest accuracies in PM2.5 estimation works at 1-km resolution. During the study period, the overall national PM2.5 pollution showed a downward trend, with the annual mean concentration dropping from 42.42 mu g/m(3) to 27.91 mu g/m(3). The largest decrease occurred in Beijing-Tianjin-Hebei (BTH), with a trend of-5.17 mu g/m(3)/yr, while it remains the most polluted region. The area meeting the secondary national air quality standard (<35 mu g/m(3)) increased from similar to 34% to similar to 79%. These results indicate that the atmospheric environment has improved significantly. Moreover, different regions have different time nodes for the start of the continuous standard-met day during the year, and the duration is different as well. Overall, this study can provide reliable large-scale PM2.5 estimations.