Yao, Mengxue ORCID: https://orcid.org/0009-0007-6204-397X
(2025)
Effects of urban vegetation on ambient PM2.5 concentrations in real urban environment.
PhD thesis, University of Sheffield.
Abstract
Air pollution is a significant threat to public health, with over 90% of the global population exposed to fine particulate matter (PM2.5) exceeding WHO guidelines. The health and economic burdens of air pollution have drawn increasing attention from researchers, policymakers, and the public. Urban vegetation is often considered a potential strategy for mitigating air pollution. However, air quality is influenced by multiple factors in complex urban environments, including urban form, vegetation, and meteorological conditions, making it challenging to quantify the role of vegetation in real-world settings.
This research investigates the impact of vegetation on PM2.5 concentrations in urban areas, providing insights for urban planning and green infrastructure design. Using Harbin, Changchun, and Shenyang as case studies, this study develops a triple-stage research framework: (1) an ultra-high-resolution downscaling and calibration model for PM2.5 estimation, (2) an urban form cluster analysis categorizing urban areas into 13 types, and (3) an examination of vegetation’s influence on PM2.5 within these clusters. The study employs machine learning regression, clustering, deep learning-based image segmentation, and high-performance computing (HPC).
A novel multi-view urban vegetation index (MvUVI) is introduced to quantify spatial vegetation coverage. Results confirm that vegetation contributes to PM2.5 mitigation but with varying effectiveness across urban form clusters. We propose the Mitigation-Effectiveness Threshold hypothesis, suggesting that vegetation reduces PM2.5 only within certain external emission thresholds. When emissions from traffic or industry exceed these thresholds, the mitigation effect weakens or becomes ineffective.
The findings provide policy guidance not only for China but also for other rapidly urbanising regions worldwide, supporting the adoption of context-specific and evidence-based measures to improve air quality, particularly by highlighting the need to consider emission thresholds alongside urban greening strategies.
Metadata
Supervisors: | Peng, Chengzhi and Smith, Michael |
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Related URLs: | |
Keywords: | Particulate matter (PM2.5), Multi-view vegetation coverage index, Urban form clusters, Satellite remote sensing data downscaling, Ground-level PM2.5 calibration, Vegetation mitigation effectiveness. |
Awarding institution: | University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > School of Architecture (Sheffield) |
Depositing User: | Dr Mengxue Yao |
Date Deposited: | 10 Sep 2025 15:49 |
Last Modified: | 10 Sep 2025 15:49 |
Open Archives Initiative ID (OAI ID): | oai:etheses.whiterose.ac.uk:37425 |
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