Publications

Wei, J; Li, ZQ; Sun, L; Xue, WH; Ma, ZW; Liu, L; Fan, TY; Cribb, M (2022). Extending the EOS Long-Term PM2.5 Data Records Since 2013 in China: Application to the VIIRS Deep Blue Aerosol Products. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 60, 4100412.

Abstract
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.

DOI:
10.1109/TGRS.2021.3050999

ISSN:
1558-0644