Back to results list
Please use this identifier to cite or link to this item:
|Title:||Long term research of aerosol optical thickness derived from visibility data in China and a case study of the impact of urbanization||Authors:||Zhang, Zhaoyang||Advisors:||Wong, Man-sing (LSGI)
Nichol, Janet Elizabeth (LSGI)
|Keywords:||Atmospheric aerosols -- China
|Issue Date:||2017||Publisher:||The Hong Kong Polytechnic University||Abstract:||Analysis of variations in Aerosol Optical Thickness (AOT) is essential for understanding of climate change and the Earth's radiation budget. However, long-term AOT data exceeding 40 years have not been well modeled and developed. In this study, surface visibility data were used to investigate long-term AOT trends over the last 40 years in China. Although visibility is a widely-used indicator to quantify aerosol loadings, there is still lack of comprehensive studies analyzing the representativeness of visibility in deriving AOT. The Singular Value Decomposition (SVD) analysis of ground-based visibility, MODerate-resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) monthly AOT products between July 2002 and December 2014 indicated that the satellite retrieved AOTs agreed well with ground-based visibility in terms of inter-annual variability. However, large differences in seasonal variability between visibility and AOT were observed due to the seasonal variation of aerosol vertical distribution. These results suggested that visibility observations may only be used to depict inter-annual AOT, and more ancillary information should be used for retrieving seasonal AOT variability. According to these results, the simplified and KM-Elterman algorithms were proposed and developed to derive AOT using surface visibility data which can help address aerosol climatic issues.
This simplified method was developed to derive AOT using surface visibility data and the seasonal aerosol extinction profile. Due to the limitation of the aerosol extinction profile, an improved method, the KM-Elterman algorithm, was also developed to retrieve AOT from surface visibility and other meteorological factors. Results from the two newly developed algorithms showed high correlations with annual MODIS observations (r > 0.82 for simplified method, r > 0.94 for KM-Elterman method). These methods outperformed other previous algorithms (e.g. Qiu, Elterman and M-Elterman methods), and can further be used to analyze the aerosol climatology in China. Five interpolation methods were evaluated for visibility-based AOT interpolation over China, including the Inverse Distance Weighted (IDW) interpolation, Biharmonic Spline (BS) interpolation, Data Interpolating Empirical Orthogonal Functions (DINEOF) interpolation, spatial boundary method, and the Data Interpolating Variational Analysis (DIVA) interpolation. The DINEOF interpolation method outperformed all, and the second and third best interpolation methods were DIVA interpolation and BS interpolation respectively. The aerosol climatology derived in this study can be used to detect long-term effects of climate change, especially to analyze the impact of aerosols on observations of solar radiation fluxes at the Earth's surface. In order to analyze variations in surface aerosols caused by urbanization, the Weather Research and Forecasting-Chemistry (WRF-Chem) model was used. The United States Geological Survey (USGS) and MODIS land-use data were used to represent the spatial distribution of land-use data in 1992 and 2004. It was shown that the overall effects of urbanization on PM2.5 concentrations in urban regions were negative due to increase in the Planetary Boundary Layer Height (PBLH).
|Description:||3, 133 pages : color illustrations
PolyU Library Call No.: [THS] LG51 .H577P LSGI 2017 Zhang
|URI:||http://hdl.handle.net/10397/69902||Rights:||All rights reserved.|
|Appears in Collections:||Thesis|
Show full item record
Files in This Item:
|991021952844203411_link.htm||For PolyU Users||167 B||HTML||View/Open|
|991021952844203411_pira.pdf||For All Users (Non-printable)||9.8 MB||Adobe PDF||View/Open|
Citations as of Dec 10, 2018
Citations as of Dec 10, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.