Center for Space and Remote Sensing Research

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姓名

阮光越(Nguyen Quang Viet) 查詢紙本館藏

畢業系所

環境科技博士學位學程

論文名稱

Center for Space and Remote Sensing Research
(Center for Space and Remote Sensing Research)

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至系統瀏覽論文 (2026-11-1以後開放)

摘要(中)

綠地和建設土地的空間配置對空氣品質產生多方面的影響。對它們的適當規劃、設計和管理可以顯著促進更清潔、更健康的空氣，使我們的居住空間更宜居且更可持續。空氣污染對全球實現可持續發展目標構成重大障礙。綠色城市的概念是一項新興政策，旨在通過智能城市規劃的實施，為市民創建一個宜居的環境。綠地提供了一種基於自然的解決方案，可以改善空氣質量和人類健康。鑒於建築環境內部組件之間的複雜和相互依賴的相互作用，城市規劃師和政策制定者將綠地和建成土地的空間布局納入城市規劃是至關重要的。這種整合最大程度地優化了生態系統服務對當地社區的效益。台灣擁有眾多現地觀測站，為研究景觀模式如何影響73個空氣質量監測站（AQMSs）周圍的環境空氣提供了理想的機會。該研究採用了偏最小二乘-結構方程建模（PLS-SEM），這是一種分析感興趣組件之間因果效應關係的強大方法。綠地格局（GSP）和建成區土地格局（BUP）的模式使用不同緩衝距離（250、500、1000和1500米）從AQMMs測量的景觀指標來衡量。這些景觀指標用作構建GSP和BUP維度的觀察變量。此外，還計算了2015年至2020年整個時期的乾季（11月至4月）和雨季（5月至10月）的月度空氣質量數據。這些觀察變量用於構建室外空氣維度（OADs），包括氣象維度（空氣溫度 - TEMP、相對濕度 - RH 和風速 - WS）和空氣污染物維度（氣體污染物 - GP、顆粒污染物 - PP 和臭氧 - OZONE）。
由於涉及複雜的相互作用，本研究旨在根據不同的緩衝區大小和季節組合，利用PLS-SEM開發多個假設，探索GSP和BUP與空氣環境維度（OADs）之間的不同方面關係。對於測量模型和結構模型，評估PLS-SEM的有效性。
首先，本研究探討了GSP、人為成分（AC）和空氣環境維度（OADs）之間的複雜關係，其中綠地充當中介因素（第五章 – 假說1）。研究得出了一些重要結論：（1）AC對每個OAD的影響比GSP更普遍且更強烈，尤其是對GP的影響；（2）AC通過GSP對GP、PP和RH在兩個季節中產生了中介影響，並在濕季對TEMP產生中介影響。同時，由於AC的影響導致綠地減少，可能會增加GP、PP和TEMP；（3）維度之間的交互作用主要在距離AQMSs 1000米的緩衝區內更為顯著，而不是其他緩衝區大小（500和1500米）；（4）包括MPA、MESH、LPA、PLAND、TCA、TE和COHE在內的七個景觀指標被確定用於構建GSP，可以有效降低PP和TEMP。
其次，本研究探討了綠地（GSP）與空氣環境維度（OADs）之間的複雜關係，其中氣象維度充當中介因素（第六章 – 假說2）。研究得出了一些重要結論：（1）GSP對GP的影響較強，而在乾季與濕季相比，其對PP的影響較弱。雖然對TEMP的影響較小，但在乾季對RH的影響比濕季更大；（2）GSP在兩個季節中介調節了空氣污染物維度，其中RH作為主要的中介因素；（3）維度之間的交互作用主要在距離AQMSs 1000米的緩衝區內更為顯著，而不是其他緩衝區大小（500和1500米）；（4）包括ED、TE、MESH、LPA、PLAND、TCA和COHE在內的七個景觀指標被確定用於構建GSP。
第三，本研究探討了城市建成土地（BUP）與空氣環境維度（OADs）之間的複雜關係，其中TEMP維度充當中介因素（第七章 – 假說3）。研究得出了一些重要結論：（1）在濕季期間，BUP對OADs的影響更強烈。特別是，城市活動區（BAM）對OADs的影響比城市邊緣區（BEM）更為顯著；（2）BAM在多個OADs上持續展示出最大的影響，而BEM的影響則在不同尺度上有所變化；（3）BAM在任何尺度上都對GP產生強烈的影響，而BEM對其影響微乎其微。它通過TEMP在濕季期間對PP產生中介影響；（4）建議選擇分散形式是減少城市發展不良影響的更好策略，而不是採用緊湊形式，同時要考慮尺度因素。
基於研究結果，本研究建議優先減少氣體污染物和空氣溫度，強調GSP的有益效應，同時最小化人為因素（AC）的負面影響。此外，採用分離型的BUP形式相較於緊湊型的BUP，可以更好地緩解城市發展的負面影響。這些研究結果可以提供有意義的證據，以支持旨在實現城市可持續發展的城市規劃實踐和立法政策。此外，本研究提出了一個綜合模型，結合偏最小二乘結構方程模型（PLS-SEM）和景觀指標，用於探索景觀格局和空氣環境之間的複雜關係。這種方法在評估其他複雜關係時也具有實用性，例如綠色空間、空氣和水環境以及人類健康之間的關係。

摘要(英)

The spatial configuration of greenspaces and built-up land plays multifaceted impacts on air quality. Proper planning, design, and management of them can significantly contribute to cleaner and healthier air, making our living spaces more livable and sustainable. However, air pollution poses a significant obstacle to achieving sustainable development objectives worldwide. The concept of greener city is an emerging policy aimed at creating a livable environment for citizens through the implementation of smart urban planning. Greenspaces offer a natural-based solution for improving air quality and human health. Given the complex and interdependent interactions among the components within the build environment, it is crucial for urban planners and policymakers to integrate the spatial arrangement of greenspace and built-up land into urban planning. This integration optimizes the effectiveness of ecosystem service for the local community. Taiwan, with its abundance of in-situ observation sites, provides an ideal opportunity to investigate how the landscape patterns influence the ambient air in the vicinity of 73 air quality monitoring stations (AQMSs). The study employed the Partial Least Squares - Structural Equation Modeling (PLS-SEM), a powerful approach for analyzing causal-effect relationships among components of interest. The greenspace pattern (GSP) and built-up land pattern (BUP) were measured using landscape metrics at different buffer distances (250, 500, 1000, and 1500 m) from the AQMMs. These landscape metrics serve as observed variables for constructing the GSP and BUP dimensions. Additionally, monthly air quality data was calculated for the dry season (November to April) and the wet season (May to October) throughout the entire period of 2015-2020. These observed variables are used to construct the outdoor air dimensions (OADs), which encompass the dimensions of meteorology (air temperature – TEMP, relative humidity – RH, and wind speed – WS), and the dimensions of air pollutant (gaseous pollutant – GP, particle pollutant – PP, and OZONE).
Due to the intricate interactions involved, the study aims to develop multiple hypotheses to explore different aspects of the relationships between the GSP and BUP with the OADs for each buffer size and season combination, utilizing the PLS-SEM. The validity of the PLS-SEM is evaluated for both the measurement and the structural models.
Firstly, the study explores the complex relationships between the GSP, anthropogenic component (AC), and the OADs, with GSP acting as a mediator (Chapter V – Hypothesis 1). Several key findings emerged: (1) The impact of the AC on each OAD is more prevalent and stronger than the GSP, particularly on the GP; (2) The AC obtains a mediation impact on the GP, PP, and RH through the GSP during the two seasons, and on the TEMP during the wet season. Meanwhile, the reduction of greenspace due to the impact of the AC can lead to increase the GP, PP, and TEMP; (3) The interactions among the dimensions are primarily more significant within the 1000-m buffer surroundings the AQMSs than other buffer sizes (500 and 1500 m); (4) The seven landscape metrics, including the MPA, MESH, LPA, PLAND, TCA, TE, and COHE, are confirmed to construct the GSP, which can effectively reduce the PP and TEMP.
Secondly, the study explores the complex relationships between the GSP and the OADs, with the dimensions of meteorology serving as mediators (Chapter VI – Hypothesis 2). Several key findings emerged: (1) The GSP has a stronger effect on the GP, whereas its effect on the PP is weaker during the dry season compared to the wet season. While its effect on the TEMP is smaller, it has a greater impact on the RH during the dry season than the wet season; (2) The GSP mediates the air pollutant dimensions during the two seasons, with the RH acting as a primary mediator; (3) The interactions among the dimensions are primarily more significant within the 1000-m buffer surroundings the AQMSs than other buffer sizes (500 and 1500 m); (4) The seven landscape metrics, including the ED, TE, MESH, LPA, PLAND, TCA, and COHE, are confirmed to construct the GSP.
Thirdly, the study explores the complex relationships between the BUP and the OADs, with the dimension of TEMP playing as a mediator (Chapter VII – Hypothesis 3). Several key findings emerged: (1) The BUP has a stronger impact on the OADs during the wet season. Especially, the BAM has a more significant impact on the OADs than the BEM; (2) The BAM consistently exhibits the most substantial impact on several OADs, whereas the BEM’s impact varies across scales; (3) The BAM strongly impacts the GP at any scale, whereas the BEM insignificantly affects it. It mediates the PP through the TEMP during the wet season; (4) It is suggested that opting for a separate form is a better strategy for minimizing the adverse impacts of urban development than adopting a compact form, along with scale consideration.
Based on the findings, the study recommends giving priority to the reduction of gaseous pollutants and air temperature by highlighting the beneficial effects of the GSP while minimizing the negative impacts of the AC. Additionally, employing a separate form of the BUP can be a better strategy for mitigating the negative impacts of urban growth compared to a compact form of the BUP. These findings can offer meaningful evidence to support urban planning practices and legislative policies aimed at achieving urban sustainable development. Moreover, the study proposes a comprehensive model which integrates the PLS-SEM and landscape metrics surroundings the AQMSs to explore the complex relationships among the landscape patterns and air environment. This approach would be practical to assess other complex relationships of interest, such as those among greenspace, air and water quality, and human health.

關鍵字(中)

★ 空氣環境
★ 建設用地
★ 複雜關係
★ 綠地
★ 室外空氣
★ 偏最小二乘-結構方程建模

關鍵字(英)

★ built-up land
★ complex relationship
★ greenspace
★ landscape patterns
★ outdoor air
★ Partial Least Squares - Structural Equation Modeling

論文目次

CHINESE ABSTRACT i
ABSTRACT iv
List of Tables x
List of Figures xi
Explanation of Symbols xiii
CHAPTER I. Introduction - 1 -
1.1. Motivation and research questions - 1 -
1.1.1. Research motivation - 1 -
1.1.2. Research questions - 3 -
1.2. Aims and objectives - 3 -
1.3. Significance and scope - 4 -
1.3.1. Significance - 4 -
1.3.2. Scope - 5 -
1.4. Thesis outline - 5 -
CHAPTER II. Description of the study area - 7 -
2.1. Geographical location of Taiwan - 7 -
2.2. Seasonal characteristics of air pollutants and meteorology - 9 -
2.2.1. Air pollutant concentrations - 9 -
2.2.2. Meteorological parameters - 12 -
CHAPTER III. Literature Review - 11 -
3.1. Greenspaces as a supportive means for sustainable development - 11 -
3.2. Overview on landscape patterns - 12 -
3.2.1. Landscape patterns - 12 -
3.2.2. Landscape metrics as indicators of landscape patterns - 13 -
3.3. Urban growth versus greenspaces - 12 -
3.3.1. Existing issues in urban development - 15 -
3.3.2. Role of greenspaces - 16 -
3.4. Landscape patterns associated with air quality - 17 -
3.4.1. Greenspace pattern - 17 -
3.4.2. Built-up land pattern - 18 -
3.5. Need for investigating landscape patterns in a complex relationship - 19 -
CHAPTER IV. Materials and Methodologies - 22 -
4.1. Conceptual framework - 22 -
4.2. Data acquisition - 24 -
4.3. Land use/land cover classification from Sentinel-2 images - 26 -
4.4. Landscape metric computation for greenspace and built-up land - 28 -
4.5. Analysis of complex relationships - 30 -
4.5.1. Introduction to Structural Equation Modeling - 30 -
4.5.2. Application of the PLS-SEM model - 31 -
CHAPTER V. Complex Relationships of Anthropogenic Component and Greenspace Pattern with Outdoor Air in Taiwan - 33 -
5.1. Introduction - 33 -
5.2. Methodologies - 33 -
5.2.1. Landscape metric measurement for greenspace - 33 -
5.2.2. Quantifying human-greenspace interactions with outdoor air dimensions - 34 -
5.3. Results - 36 -
5.3.1. Anthropogenic characteristics around the AQMSs - 38 -
5.3.2. Landscape metrics of greenspace - 39 -
5.3.3. Evaluation of the PLS-SEM performance - 40 -
5.3.4. Direct effects of the GSP and AC on outdoor air dimensions - 41 -
5.3.5. Greenspace pattern as a mediator - 44 -
5.3.6. Total effect of the AC on air pollutant dimensions - 44 -
5.4. Discussions - 45 -
5.4.1. Influences of the GSP and AC on outdoor air dimensions - 45 -
5.4.2. Landscape metrics associated with outdoor air dimensions - 48 -
5.4.3. Consideration on the GSP’s landscape metrics and anthropogenic factors in urban planning - 48 -
CHAPTER VI. Greenspace Pattern, Meteorology and Air Pollutant in Taiwan: A Multifaceted Connection - 50 -
6.1. Introduction - 50 -
6.2. Methodology - 50 -
6.2.1. Overall framework - 50 -
6.2.2. Landscape metric measurement for greenspace - 52 -
6.2.3. Quantifying the complex relationship of interest - 52 -
6.3. Results - 53 -
6.3.1. Landscape metric characteristics of greenspace - 54 -
6.3.2. Evaluation of the PLS-SEM performance - 55 -
6.3.3. Complex effects among the GSP, meteorology and air pollutants - 56 -
6.3.4. Summarized seasonal difference in the complex effect among dimensions - 59 -
6.4. Discussions - 61 -
CHAPTER VII. Built-up Land Pattern and Outdoor Air in Taiwan: Insights across Multiple Scales - 64 -
7.1. Introduction - 64 -
7.2. Methodologies - 65 -
7.2.1. BUP measurement through landscape metrics - 65 -
7.2.2. Quantifying the complex relationships of the BUP with outdoor air - 65 -
7.3. Results - 68 -
7.3.1. BUP surrounding the AQMSs at different scales - 68 -
7.3.2. Evaluation of the PLS-SEM performance at different buffer sizes - 69 -
7.3.3. Direct effect of the BUP on outdoor air dimensions - 70 -
7.3.4. Air temperature as a mediator - 72 -
7.3.5. Total effect of the BUP on air pollutant dimensions - 74 -
7.4. Discussions - 75 -
7.4.1. Seasonal effect of the BUP on outdoor air dimensions - 75 -
7.4.2. Built-up land pattern associated with outdoor air dimensions - 75 -
7.4.3. Consideration of the BUP in modifying outdoor air dimensions - 77 -
CHAPTER VIII. Conclusions and Recommendations - 79 -
8.1. Conclusions - 79 -
8.2. Limitations and further considerations - 80 -
References - 83 -

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指導教授

劉說安(Yuei-An Liou)

審核日期

2023-10-25

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