| dc.description.abstract | 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. | en_US |