Extending the EOS Long-Term PM2.5 Data Records Since 2013 in China: Application to the VIIRS Deep Blue Aerosol Products

PM2.5 is hazardous to human health, and highquality data are thus needed on a routine basis. An attempt is made here to improve the accuracy of near-surface PM2.5 estimates using the newly released aerosol product derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite with the Deep Blue retrieval algorithm. A high-quality PM2.5 data set is generated at a spatial resolution of 6 km from 2013 to 2018 by applying the space-time extremely randomized trees (STET) model, which also aims to extend the Earth Observing System (EOS) long-term PM2.5 data records in China. The PM2.5 estimates are highly consistent with ground-based measurements, with an out-of-sample cross-validation coefficient of determination (CV-R2) of 0.88, a root-mean-square error (RMSE) of 16.52 mu g/m(3), and a mean absolute error of 10 mu g/m3 at the national scale. Spatiotemporal PM2.5 variations at monthly scales are also well captured (e.g., R-2 = 0.91-0.94, RMSE = 5.8-11.6 mu g/m(3)). PM2.5 varied greatly at regional and seasonal scales across China. Benefiting from emission reduction and air pollution controls, PM2.5 pollution has reduced dramatically in China with an average of -5.6 mu g/m(3)/yr(-1) during 2013-2018. Significant regional reductions are also seen, in particular, in the Beijing-Tianjin-Hebei region (-6.6 mu g/m(3)/yr(-1), p < 0.001), and the Deltas of Yangtze River (-6.3 mu g/m(3)/yr(-1), p < 0.001) and Pearl River Delta (-4.5 mu g/m(3)/yr(-1), p < 0.001). Our study improved the accuracy of near-surface PM2.5 estimates in terms of their spatiotemporal variations at a relatively long-term record, which is important for future air pollution and health studies in China.

Automated summary

This study aimed to improve the accuracy of near-surface PM2.5 estimates using the newly released aerosol product derived from the VIIRS satellite with the Deep Blue retrieval algorithm. A high-quality PM2.5 data set was generated using the STET model, showing consistent results with ground-based measurements and capturing spatiotemporal variations of PM2.5 in China. The study also revealed a significant reduction in PM2.5 pollution in China during 2013-2018.