Heming Bai1,2, Zhi Zheng3, Yuanpeng Zhang1,4, He Huang5, Li Wang 4,1,2 1 Research Center for Intelligent Information Technology, Nantong University, Nantong 226019, China
2 Nantong Research Institute for Advanced Communication Technologies, Nantong 226019, China
3 Department of Surveying Engineering, Heilongjiang Institute of Technology, Harbin 150050, China
4 Department of Medical Informatics, Medical School, Nantong University, Nantong 226019, China
5 School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Received:
May 23, 2020
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Revised:
August 28, 2020
Accepted:
October 9, 2020
Download Citation:
||https://doi.org/10.4209/aaqr.2020.05.0257
Bai, H., Zheng, Z., Zhang, Y., Huang, H., Wang, L. (2021). Comparison of Satellite-based PM2.5 Estimation from Aerosol Optical Depth and Top-of-atmosphere Reflectance. Aerosol Air Qual. Res. 21, 200257. https://doi.org/10.4209/aaqr.2020.05.0257
Cite this article:
Aerosol optical depth (AOD) and top-of-atmosphere (TOA) reflectance are two useful sources of satellite data for estimating surface PM2.5 concentrations. Comparison of PM2.5 estimates between these two approaches remains to be explored. In this study, satellite observations of TOA reflectance and AOD from the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite in 2016 over Yangtze River Delta (YRD) and meteorological data are used to estimate hourly PM2.5 based on four different machine learning algorithms (i.e., random forest, extreme gradient boosting, gradient boosting regression, and support vector regression). For both reflectance-based and AOD-based approaches, our cross validated results show that random forest algorithm achieves the best performance, with a coefficient of determination (R2) of 0.75 and root-mean-square error (RMSE) of 18.71 µg m–3 for the former and R2 = 0.65 and RMSE = 15.69 µg m–3 for the later. Additionally, we find a large discrepancy in PM2.5 estimates between reflectance-based and AOD-based approaches in terms of annual mean and their spatial distribution, which is mainly due to the sampling difference, especially over northern YRD in winter. Overall, reflectance-based approach can provide robust PM2.5 estimates for both annual mean values and probability density function of hourly PM2.5. Our results further show that almost all population lives in non-attainment areas in YRD using annual mean PM2.5 from reflectance-based approach. This study suggests that reflectance-based approach is a valuable way for providing robust PM2.5 estimates and further for constraining health impact assessments.HIGHLIGHTS
ABSTRACT
Keywords:
PM2.5, TOA reflectance, Satellite remote sensing, Machine learning
