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A Physically Based PM$_{\text{2.5}}$ Estimation Method Using AERONET Data in Beijing Area.

Authors :
Chen, Guili
Guang, Jie
Xue, Yong
Li, Ying
Che, Yahui
Gong, Shaoqi
Source :
IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing; Jun2018, Vol. 11 Issue 6, p1957-1965, 9p
Publication Year :
2018

Abstract

Over the past few years, regional air pollution has frequently occurred in Mid-Eastern China, especially in Beijing. As the primary pollutant in urban air, atmospheric particulate matter (PM) not only leads to the decrease of atmospheric visibility, but also increases the mortality and morbidity of respiratory system diseases. By analyzing aerosol volume size distribution data downloaded from the AERONET official website, we find that the size distribution of aerosol in Beijing appears a bimodal log-normal structures and parameters of fine mode in AERONET data are mainly contributed by PM $_{\text{2.5}}$. In this paper, a physically based model is developed to estimate the concentration of PM $_{\text{2.5}}$ , in which, fine mode aerosol optical depth (AOD) at 440, 550, and 675 nm, Effective Radius of the Fine particles, ground-based fine particulate matter (PM $_{\text{2.5}}$ ) data, relative humidity, and boundary layer height data from 2015 to 2016 are used. Those from 2015 are used for calculating integrated extinction efficiencies (〈Qext〉) based on the model, and those from 2016 are used for PM $_{\text{2.5}}$ validation. Result shows that R 2 of retrieved PM $_{\text{2.5}}$ against ground-based PM $_{\text{2.5}}$ can reach to 0.70 and RMSE is 33.67 μg/m3 at Beijing site at 440 nm. This study concludes that this method has the potential to retrieve PM $_{\text{2.5}}$ by using AERONET AOD in Beijing, which is independent of ground-based PM $_{\text{2.5}}$ measurement. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19391404
Volume :
11
Issue :
6
Database :
Complementary Index
Journal :
IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing
Publication Type :
Academic Journal
Accession number :
130518083
Full Text :
https://doi.org/10.1109/JSTARS.2018.2817243