Extending the EOS Long-Term PM_2.5 Data Records Since 2013 in China: Application to the VIIRS Deep Blue Aerosol Products

PM _2.5 is hazardous to human health, and high-quality data are thus needed on a routine basis. An attempt is made here to improve the accuracy of near-surface PM _2.5 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 PM _2.5 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 PM _2.5 data records in China. The PM _2.5 estimates are highly consistent with ground-based measurements, with an out-of-sample cross-validation coefficient of determination (CV-R ² ) of 0.88, a root-mean-square error (RMSE) of $16.52~\mu \text{g}/\text{m}^{3}$ , and a mean absolute error of $10~\mu \text{g}/\text{m}^{3}$ at the national scale. Spatiotemporal PM _2.5 variations at monthly scales are also well captured (e.g., $R^{2} =0.91$ –0.94, RMSE = 5.8– $11.6~\mu \text{g}/\text{m}^{3})$ . PM _2.5 varied greatly at regional and seasonal scales across China. Benefiting from emission reduction and air pollution controls, PM _2.5 pollution has reduced dramatically in China with an average of $- 5.6~\mu \text{g}/\text{m}^{3}$ /yr ⁻¹ during 2013–2018. Significant regional reductions are also seen, in particular, in the Beijing–Tianjin–Hebei region ( $- 6.6~\mu \text{g}/\text{m}^{3}$ /yr ⁻¹ , $p < 0.001$ ), and the Deltas of Yangtze River ( $- 6.3~\mu \text{g}/\text{m}^{3}$ /yr ⁻¹ , $p < 0.001$ ) and Pearl River Delta ( $- 4.5~\mu \text{g}/\text{m}^{3}$ /yr ⁻¹ , $p < 0.001$ ). Our study improved the accuracy of near-surface PM _2.5 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.