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Satellite-Based Estimation of High-Resolution Ground-Level PM2.5 Concentrations Across China Using Space-Time Regression Modeling.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Satellite-Based Estimation of High-Resolution Ground-Level PM2.5 Concentrations Across China Using Space-Time Regression Modeling./
作者:	He, Qingqing.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	182 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-08, Section: B.
Contained By:	Dissertations Abstracts International80-08B.
標題:	Geographic information science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13837824
ISBN:	9780438851221

Satellite-Based Estimation of High-Resolution Ground-Level PM2.5 Concentrations Across China Using Space-Time Regression Modeling.
He, Qingqing.

Satellite-Based Estimation of High-Resolution Ground-Level PM2.5 Concentrations Across China Using Space-Time Regression Modeling. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 182 p.

Source: Dissertations Abstracts International, Volume: 80-08, Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2018.

This item must not be sold to any third party vendors.

Fine particulate matter, or less (PM2.5), has attracted the public's concern as posing adverse impacts to human health and atmospheric environment. Over the past three decades, the rapid urbanization and industrialization of China has led to PM2.5 becoming a dominant factor in air pollution, especially over urban areas, and thus an unprecedented issue of public concern. Traditional monitoring method relied on sparsely located sites or satellite-derived PM2.5 concentrations at coarse spatial resolution, however, cannot fully capture the large spatial heterogeneity of PM2.5 pollution. The lack of spatial fine-scale ground PM2.5 data seriously hinders comprehensively understanding the epidemiological and health effect of PM 2.5 in China and cannot provide sufficient information to support environmental management and decision. Given the advent of severe PM2.5 levels in China, this study developed a statistical PM2.5-AOD model to spatiotemporally estimate daily surface PM2.5 concentrations in China for a long-term period using high-resolution satellite remote sensing data, as so to provide fine-scale exposure information and fundamental basis for environmental policy-making and health effect assessment. To enhance model performance, a fused 3-km AOD dataset was produced by blending the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) 3- km resolution Dark Target (DT) AOD data with the 10-km resolution MODIS Deep Blue (DB) AOD data. The reconstructed 3-km AOD data not only show the comparable accuracy with MODIS operational 3-km DT AOD and remain the fine-scale spatial gradients of aerosol loading to the utmost extent but also significantly improve the data availability. Moreover, we used the fused 3-km AOD as an alternative perspective on quantitatively analyzing the spatiotemporal variations of particle concentrations across China at a fine scale from 2003 to 2016, because there was no long-term ground-level monitoring data for modeling and satellite-derived AOD is directly related to surface PM2.5. Using this newly reconstructed 3-km AOD dataset, surface PM2.5 measurements, and ancillary information, a space-time regression model that is an improved geographically and temporally weighted regression (GTWR) with an efficient weighting mechanism for the determination of optimal parameter values, was developed to estimate a large set of daily PM2.5 concentrations for China. Comparison results indicated that the proposed GTWR model, with an R2 of 0.85 in model fitting and 0.80 in cross-validation (CV), notably outperforms the popular spatiotemporal model (the two-stage model), which have R2 of 0.75 and 0.72 for the two processes, respectively. Finally, aided by high-resolution PM2.5 predictions derived from satellite AOD images at a 3-km grid, the spatiotemporal pattern of PM 2.5 concentrations in China from 2013 to 2016 was characterized in detail. The exposure of Chinese population to fine particle pollution throughout the study period was also quantitatively examined at national, regional, and city levels. In general, there is an increasing gradient from east to west for the concentration of PM2.5 in China, corresponding well with the general variation of the Chinese population and inversely with the distribution of terrain in China. The spatial pattern of population-weighted mean PM 2.5 concentrations demonstrated that population exposure to fine particulate matter for most cities tended to be higher than the spatial average of PM 2.5 concentrations without weighting by population. Cities in Xinjiang, Hebei, and Shandong were exposed to high levels of population exposure above 95 μg/m3, and Tibet, Yunnan, and Hainan was the cleanest province with a majority of cities had population-weighted PM2.5 less than the national secondary standard of 35 μg/m3. Corresponding to the overall reduction in PM2.5 concentration from 2013 to 2016, the proportion of the exposed population to high PM2.5 concentrations declined over time during the study period, which was also in line with the temporal trend of aerosol loading in China. However, the overwhelming majority of Chinese population remained exposed to risky levels of PM2.5 pollution above 35 μg/m3 until 2016. The results of this study indicate that satellite-based PM2.5 reconstruction provides a promising way to fill the gaps left by surface PM 2.5 monitoring network in China. The spatial pattern and temporal change of fine-scale PM2.5 concentrations can provide fundamental data and additional information for air quality, facilitating environmental management and policy-making. Also, PM2.5 data generated in this study, with better spatial representation, can provide fine-scale exposure data and allow exposure assessed at the urban city and district levels, directly benefiting epidemiologic studies and health impacts of PM2.5 in China.

ISBN: 9780438851221Subjects--Topical Terms:

3432445
Geographic information science.
Subjects--Index Terms:

Aod

Satellite-Based Estimation of High-Resolution Ground-Level PM2.5 Concentrations Across China Using Space-Time Regression Modeling.
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520 $a Fine particulate matter, or less (PM2.5), has attracted the public's concern as posing adverse impacts to human health and atmospheric environment. Over the past three decades, the rapid urbanization and industrialization of China has led to PM2.5 becoming a dominant factor in air pollution, especially over urban areas, and thus an unprecedented issue of public concern. Traditional monitoring method relied on sparsely located sites or satellite-derived PM2.5 concentrations at coarse spatial resolution, however, cannot fully capture the large spatial heterogeneity of PM2.5 pollution. The lack of spatial fine-scale ground PM2.5 data seriously hinders comprehensively understanding the epidemiological and health effect of PM 2.5 in China and cannot provide sufficient information to support environmental management and decision. Given the advent of severe PM2.5 levels in China, this study developed a statistical PM2.5-AOD model to spatiotemporally estimate daily surface PM2.5 concentrations in China for a long-term period using high-resolution satellite remote sensing data, as so to provide fine-scale exposure information and fundamental basis for environmental policy-making and health effect assessment. To enhance model performance, a fused 3-km AOD dataset was produced by blending the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) 3- km resolution Dark Target (DT) AOD data with the 10-km resolution MODIS Deep Blue (DB) AOD data. The reconstructed 3-km AOD data not only show the comparable accuracy with MODIS operational 3-km DT AOD and remain the fine-scale spatial gradients of aerosol loading to the utmost extent but also significantly improve the data availability. Moreover, we used the fused 3-km AOD as an alternative perspective on quantitatively analyzing the spatiotemporal variations of particle concentrations across China at a fine scale from 2003 to 2016, because there was no long-term ground-level monitoring data for modeling and satellite-derived AOD is directly related to surface PM2.5. Using this newly reconstructed 3-km AOD dataset, surface PM2.5 measurements, and ancillary information, a space-time regression model that is an improved geographically and temporally weighted regression (GTWR) with an efficient weighting mechanism for the determination of optimal parameter values, was developed to estimate a large set of daily PM2.5 concentrations for China. Comparison results indicated that the proposed GTWR model, with an R2 of 0.85 in model fitting and 0.80 in cross-validation (CV), notably outperforms the popular spatiotemporal model (the two-stage model), which have R2 of 0.75 and 0.72 for the two processes, respectively. Finally, aided by high-resolution PM2.5 predictions derived from satellite AOD images at a 3-km grid, the spatiotemporal pattern of PM 2.5 concentrations in China from 2013 to 2016 was characterized in detail. The exposure of Chinese population to fine particle pollution throughout the study period was also quantitatively examined at national, regional, and city levels. In general, there is an increasing gradient from east to west for the concentration of PM2.5 in China, corresponding well with the general variation of the Chinese population and inversely with the distribution of terrain in China. The spatial pattern of population-weighted mean PM 2.5 concentrations demonstrated that population exposure to fine particulate matter for most cities tended to be higher than the spatial average of PM 2.5 concentrations without weighting by population. Cities in Xinjiang, Hebei, and Shandong were exposed to high levels of population exposure above 95 μg/m3, and Tibet, Yunnan, and Hainan was the cleanest province with a majority of cities had population-weighted PM2.5 less than the national secondary standard of 35 μg/m3. Corresponding to the overall reduction in PM2.5 concentration from 2013 to 2016, the proportion of the exposed population to high PM2.5 concentrations declined over time during the study period, which was also in line with the temporal trend of aerosol loading in China. However, the overwhelming majority of Chinese population remained exposed to risky levels of PM2.5 pollution above 35 μg/m3 until 2016. The results of this study indicate that satellite-based PM2.5 reconstruction provides a promising way to fill the gaps left by surface PM 2.5 monitoring network in China. The spatial pattern and temporal change of fine-scale PM2.5 concentrations can provide fundamental data and additional information for air quality, facilitating environmental management and policy-making. Also, PM2.5 data generated in this study, with better spatial representation, can provide fine-scale exposure data and allow exposure assessed at the urban city and district levels, directly benefiting epidemiologic studies and health impacts of PM2.5 in China.
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