Publications

Wang, WH; He, QQ; Zhang, M; Zhang, WT; Zhu, HR (2022). Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019. ATMOSPHERIC POLLUTION RESEARCH, 13(11), 101579.

Abstract
Satellite-derived aerosol optical depth (AOD) provides an effective way to investigate global and regional var-iations in atmospheric aerosols. However, due to cloud cover and surface reflectance, AOD datasets derived from satellite instruments generally have non-random missing values, which introduces additional uncertainty into AOD data and limits its downstream. To remedy this problem, this study used a two-stage approach based on spatial interpolation and a random forest model to fill the gaps in data generated by the Multiangle Imple-mentation of Atmospheric Correction (MAIAC) aerosol retrieval algorithm, which provides the best-available AOD product to the global public. The relationship between ground-level fine particulate matter concentra-tions and satellite AOD was considered in the modeling. By gap-filling daily 1-km MAIAC AOD data from 2003 to 2019 over Taiwan, the two-stage model achieved comparable accuracy (coefficient of determination = 0.52, root-mean-square error = 0.22) against ground-level AOD measurements to the accuracy that has been achieved by previous studies. Furthermore, it improved daily high-spatial-resolution AOD estimates to 100% of spatial coverage. Comparisons between the full-coverage estimates and MAIAC retrievals showed that the MAIAC AOD dataset generally underestimated monthly/seasonal/annual mean AOD values in Taiwan. We also used the long-term estimates of the daily 1-km AOD dataset with full coverage to explore the spatiotemporal trends in AOD in Taiwan. The practical approach developed in this study is suitable for application in long-and short-term studies of air pollution and its effects on public health.

DOI:
10.1016/j.apr.2022.101579

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