%0 Dataset
%T Reconstructing 6-hourly PM2.5 datasets from 1960 to 2020 in China（1960-2020）
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/305c42b1-9f3b-4664-9a16-668aaf12670e
%W NCDC
%R 10.5281/zenodo.6372846
%A None
%K PM2.5;Air Quality;China
%X For decades, PM2.5 has changed the radiation balance on Earth, increasing environmental and health risks, but it was not until 2013 that it was widely monitored in China. Long term historical records of PM2.5 with high temporal resolution are essential, but they are lacking in research and environmental management. In this dataset, we reconstructed a site based dataset of PM2.5 every 6 hours from 1960 to 2020, combining long-term visibility, conventional meteorological observations, emissions, and elevation. The concentration of PM2.5 at each station is estimated based on the advanced machine learning model LightGBM, which utilizes the spatial characteristics of the surrounding 20 meteorological stations. Our model performs comparable or even better in annual cross validation (CV) compared to previous studies (R2=0.7) and spatial CV (R2=0.76), with advantages in long-term recording and high temporal resolution. The model also reconstructed a 0.25 ° × A 0.25 °, 6-hour grid PM2.5 dataset was created by merging spatial features. The results showed that PM2.5 pollution gradually deteriorated or continued from an interdecadal scale before 2010, but eased in the following decade. Although the turning points vary in different regions, PM2.5 has significantly decreased in key areas since 2013 due to clean air actions. Especially in 2020, the annual average of PM2.5 was almost at its lowest historical level since 1960. This PM2.5 dataset provides high-resolution spatiotemporal changes, laying the foundation for research related to air pollution, climate change, and atmospheric chemistry reanalysis