Monitoring of fine particulates in Hong Kong and Pearl River Delta region using remote sensing

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Monitoring of fine particulates in Hong Kong and Pearl River Delta region using remote sensing


Author: Bilal, Muhammad
Title: Monitoring of fine particulates in Hong Kong and Pearl River Delta region using remote sensing
Degree: Ph.D.
Year: 2014
Subject: Air -- Pollution -- China -- Hong Kong
Air -- Pollution -- China -- Pearl River Delta
Air -- Pollution -- Remote sensing
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Land Surveying and Geo-Informatics
Pages: xvi, 113 leaves : ill. (some col.) ; 30 cm.
Language: English
InnoPac Record:
Abstract: The main objective of this study is to monitor and understand the behavior of fine particulate matter (PM2.5) over the complex terrain of Hong Kong and the Pearl River Delta (PRD) region. These experience some of the worst air quality conditions in the world due to local emissions from vehicles as well as regional aerosol particles from oceangoing vessels (OGV) and industries throughout the PRD region. Aerosol particulate concentrations have been investigated at regional to global scales using satellite aerosol products. However, to obtain accurate PM2.5 estimates, better aerosol retrieval algorithm which operate at high resolution and over the mixed surface types in the study region, is required. Most satellitebased aerosol optical depth (AOD) algorithms operate at spatial resolutions of several to several tens of kilometers. Also, most use a Radiative Transfer Model (RTM) to construct a look-up table (LUT) to act as a map between measurements and physical quantities. In the current study, a Simplified Aerosol Retrieval Algorithm (SARA) was developed from MODIS images for use over Hong Kong at high (500 m) spatial resolution and without using a LUT. Instead, RTM calculations were applied directly to the MODIS data, with the aerosol properties derived from a local urban Aerosol Robotic Network (AERONET) station at the Hong Kong Polytechnic University, and surface reflectance from the MOD09GA level2 daily surface reflectance product. The 500 m AOD retrieved from the SARA showed a high consistency with ground-based AOD measurements, with average correlation coefficient (R) ~ 0.963, Root Mean Square Error (RMSE) ~ 0.077, and Mean Absolute Error (MAE) ~ 0.065 than MOD04 C005 AOD (R ~ 0.883, RMSE ~ 0.140, and MAE ~ 0.123).
In order to use the satellitederived AOD to develop a PM2.5 model for accurate prediction of PM2.5, the SARAretrieved AOD at 500 m resolution was constrained using surface meteorological variables including temperature (STEMP), relative humidity (SRH), and wind speed (SWS), and Planetary Boundary Layer Height (PBLH) and surface pressure at 500 m resolution. The SARA PM2.5 model of the AODPM2.5 relationship was developed at four urban/suburban air quality stations based on bins of meteorological variables for the years 2007 and 2008 (autumn and winter). An almost perfect correlation (R ~ 0.99) between AOD and PM2.5 was found with the parameters 2.533.40 m/s WS, 4779% RH, and ranges of PBLH from 300449 m. The SARA PM2.5 model was evaluated for derivation of PM2.5 concentrations by comparison with groundlevel PM2.5 at urban/suburban (Central, Tsuen Wan, Tung Chung and Yuen Long) and rural (Tap Mun) air quality stations in Hong Kong. The results demonstrate a better agreement between SARA predicted PM2.5 and groundlevel observed PM2.5 than the existing AOD model, explaining around 80% to 82% of the variability in PM2.5 concentrations. Therefore, it can be concluded that the SARA PM2.5 model is superior to previous method of monitoring of PM2.5 over urban regions. It can be used for detailed monitoring of PM2.5 over regions other than the present study area, by adopting two methodologies: (i) the SARA method of retrieving high resolution (500 m) AOD from satellite images, and (ii) refinement of regression coefficients based on bins of meteorological variables, as proposed in this study.

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