Author: Yu, Xinyu
Title: Satellite-based full-coverage aerosol optical depth and fine particulate matter estimation
Advisors: Wong, Man Sing (LSGI)
Degree: Ph.D.
Year: 2023
Subject: Atmospheric aerosols
Air -- Pollution
Hong Kong Polytechnic University -- Dissertations
Department: Department of Land Surveying and Geo-Informatics
Pages: xx, 148 pages : color illustrations, maps
Language: English
Abstract: Atmospheric aerosols have a significant impact on climate change and urban air quality, like fine-mode aerosol particulate (PM2.5), which is a primary air pollutant with adverse effects on the environment and public health. Aerosol optical depth (AOD) is a pivotal aerosol optical parameter to quantify the amount of aerosol loading in an atmospheric column and is generally used as an intermediate for air pollutant concentration estimation. Satellite-based AOD products can provide long-term continuous observations with large spatial coverage. However, the prevalence of ubiquitous missing values in satellite-based AOD products limits their further application for regional air pollution estimations, especially in urban areas. Therefore, this PhD study aims to develop novel approaches for generating accurate gap-free AOD and PM2.5 datasets at a high spatio-temporal resolution, and the major innovations of this thesis are described as follows.
(1) To overcome the data availability issue of satellite-based AOD data, a two-step data-driven model was proposed to derive the long-term full-coverage AOD dataset with high precision and high resolution in the Guangdong-Hong Kong-Macao Greater Bay Area (the GBA). First, after cloud screening, the missing values of MODIS top-of-atmosphere (TOA) and surface reflectance were reconstructed using the Data Interpolating Empirical Orthogonal Functions (DINEOF). Subsequently, the geospatial and temporal Light Gradient Boosting Machine (GT-LGB) model was developed for AOD estimation, aided by the reconstructed TOA/surface reflectance and other influencing variables. The comparison results show that the derived AOD dataset has a good agreement with the ground-based measurements (R=0.82) and outperforms the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product with higher spatial coverage and accuracy.
(2) Most satellite-based approaches for air pollution estimation are difficult to extract the spatio-temporal dependencies effectively, leading to lower accuracy in long-term prediction and assessment of episodic changes. Thus, a hierarchical geospatial long short-term memory method (HG-LSTM) by considering the geospatial autocorrelation was proposed for hourly PM2.5 estimation with 2-km spatial resolution in Yangtze River Delta (YRD) urban agglomeration. Results illustrate the superior accuracy of HG-LSTM via the site-based and year-based cross-validation (CV) tests, indicating geospatial autocorrelation exerts non-negligible impacts on the PM2.5 estimation. The estimations are highly consistent with the in-situ observations with an R2 of around 0.88. As well, most deviations (over 80%) between the estimations and ground-based measurements are less than 10 μg/m3.
(3) The long-term variations of aerosol optical parameters in Hong Kong from 2006 to 2021 were analyzed by using the AERONET measurements and satellite-based observations. Results show that the year 2012 can be deemed as a turning point, with an upward trend in AOD and Angstrom exponent (AE) before 2012 and then downwards after 2012. For single scattering albedo (SSA), a rising trend was exhibited in both pre- and post-2012 periods. It indicates that AOD and AE levels, as well as the proportion of absorbing aerosols in Hong Kong were reduced during the study period. The interactions between AOD and meteorological factors were also explored based on the extreme gradient boosting (XGBoost) model. Specifically, lower relative humidity, higher wind speed in southwest directions and lower temperature are beneficial to the abatement of aerosol loads in Hong Kong.
(4) Additionally, the multi-spatiotemporal trends of AOD in the GBA during 2001–2020 were also investigated using MODIS MAIAC AOD data based on the Theil-Sen Median trend analysis and Hurst exponent methods. Similarly, there is a slight upward tendency (0.009 yr-1) in the pre-2012 period but a pronounced downward trend (-0.03 yr-1) in the post-2012 period, suggesting an overall declining trend in the study area. The variation trends of individual pixels exhibit spatial heterogeneity, with a higher decreasing rate in the western parts. Moreover, AOD values are highly associated with land use types, with elevated AOD in unused land, water bodies and construction land, while grassland, cropland and woodland have lower AOD.
This thesis provides several promising approaches for full-coverage AOD and PM2.5 estimations with high resolution, as well as the analysis of long-term variation trends and the association with driven factors. The results and implications are beneficial for the follow-up applications of climate change, air quality and micro-physical studies.
Rights: All rights reserved
Access: open access

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