Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74537
Title: Satellite-based PM2.5 estimation using fine-mode aerosol optical thickness over China
Authors: Yan, X 
Shi, W 
Li, Z
Li, Z
Luo, N
Zhao, W
Wang, H
Yu, X
Issue Date: 2017
Source: Atmospheric environment, 2017, v. 170, p. 290-302
Abstract: Accurate estimation of ground-level PM2.5 from satellite-derived aerosol optical thickness (AOT) presents various difficulties. This is because the association between AOT and surface PM2.5 can be affected by many factors, such as the contribution of fine mode AOT (FM-AOT) and the weather conditions. In this study, we compared the total AOT and FM-AOT for surface PM2.5 estimation using ground-based measurements collected in Xingtai, China from May to June 2016. The correlation between PM2.5 and FM-AOT was higher (r = 0.74) than that between PM2.5 and total AOT (r = 0.49). Based on FM-AOT, we developed a ground-level PM2.5 retrieval method that incorporated a Simplified Aerosol Retrieval Algorithm (SARA) AOT, look-up table–spectral deconvolution algorithm (LUT-SDA) fine mode fraction (FMF), and the PM2.5 remote sensing method. Due to the strong diurnal variations displayed by the particle density of PM2.5, we proposed a pseudo-density for PM2.5 retrieval based on real-time visibility data. We applied the proposed method to determine retrieval surface PM2.5 concentrations over Beijing from December 2013 to June 2015 on cloud-free days. Compared with Aerosol Robotic Network (AERONET) data, the LUT-SDA FMF was more easily available than the Moderate Resolution Imaging Spectroradiometer (MODIS) FMF. The derived PM2.5 results were compared with the ground-based monitoring values (30 stations), yielding an R2 of 0.64 and root mean square error (RMSE) = 18.9 μg/m3 (N = 921). This validation demonstrated that the developed method performed well and produced reliable results.
Keywords: AOT
Fine mode fraction
MODIS
PM2.5
Publisher: Pergamon Press
Journal: Atmospheric environment 
ISSN: 1352-2310
EISSN: 1873-2844
DOI: 10.1016/j.atmosenv.2017.09.023
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