Integrating air quality modelling, low-cost sensing and greenspace quantification for enhanced urban air quality and net-zero cities

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dc.check.date2027-12-31
dc.contributor.advisorNyhan, Marguerite
dc.contributor.advisorHellebust, Stig
dc.contributor.advisorexternalO'Dowd, Colin
dc.contributor.authorO'Regan, Anna Claireen
dc.contributor.funderSFI Research Centre for Energy, Climate and Marine
dc.contributor.funderUniversity College Cork
dc.date.accessioned2024-09-26T10:54:23Z
dc.date.available2024-09-26T10:54:23Z
dc.date.issued2024en
dc.date.submitted2024
dc.description.abstractUrbanisation is rapidly increasing worldwide. Currently, 55% of the global population live in urban areas and this is projected to increase to 70% by 2050. While urban areas are sites for innovation and economic growth, they are also key hotspots for poor air quality and climate-related impacts. Air pollution poses a significant risk to public health, with 96% of urban populations exposed to unhealthy levels of air pollution. As such, data on urban air quality is essential to identify pollution sources as well as spatial and temporal trends. This can support the formation of policies, ensuring compliance with regulatory limits while striving to meet the stringent World Health Organization (WHO) guidelines to protect public health. Air pollution and greenhouse gas (GHG) emissions often stem from common sources. Consequently, there is significant potential to develop policies that enhance air quality while also maximising reductions in GHG emissions. Rapid urban expansion is significantly impacting greenspace, with a notable decline observed due to increased demand for grey infrastructure. Greenspace offers many environmental benefits, including reducing air pollution and mitigating against the impacts of climate change, while also positively influencing residents’ health. As such, prioritising strategic greenspace developments is crucial. This research is driven by a need to improve our understanding of air pollution, greenspace and their associations, with an overarching aim of decarbonising cities. Firstly, a comprehensive review of global literature was conducted, identifying the current state-of-the-art in air pollution and GHG emissions modelling and monitoring efforts. Furthermore, innovative methods for quantifying urban greenspace were explored. Air pollution, specifically nitrogen dioxide (NO2), was modelled in high spatial and temporal resolution for Cork City, Ireland. The output of the dispersion model enables the identification of pollution sources while also capturing fluctuations in pollution levels over time and space. Moreover, a data fusion technique, regression kriging, was employed which integrated the urban dispersion model output with large-scale citizen science data. The citizen science data was measured using diffusion tubes at 642 locations across the study domain. The data-fusion model provided improved accuracy of air pollution levels and population exposure. Urban greenspace was quantified using 751,644 Google Street View (GSV) images, capturing a street-level view of greenspace at 125,274 locations across three major cities in Ireland. The associations between street-level greenspace, health and socioeconomics were explored. Higher levels of greenspace were associated with improved self-reported health and areas in the upper quartiles of greenspace had higher levels of income and lower levels of unemployment. Furthermore, with the advancements in air pollution sensing technologies such as ‘low-cost’ sensors, this research aimed to explore the relationship between greenspace and air pollution. This analysis demonstrated associations between higher levels of greenspace and lower levels of air pollution in urban areas. This research provides novel contributions across science and policy. It advances scientific knowledge and methodologies in air quality science and urban greenspace. Moreover, the research findings and high-resolution datasets can inform data-driven policies such as the National Clean Air Strategy (CAS) and Climate Action Plan, while also advancing UN Sustainable Development Goals including ‘Goal 11: Sustainable Cities and Communities’, ‘Goal 3: Good Health and Wellbeing’ and ‘Goal 13: Climate Action’. There is great potential to design effective strategies that strive to improve air quality and ensure optimal planning and provision of greenspace, thereby accelerating the transition to net-zero. Adopting an integrated approach in urban planning will ensure the development of cities that have good air quality, ample exposure to greenspace and net-zero emissions.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationO'Regan, A. C. 2024. Integrating air quality modelling, low-cost sensing and greenspace quantification for enhanced urban air quality and net-zero cities. PhD Thesis, University College Cork.
dc.identifier.endpage293
dc.identifier.urihttps://hdl.handle.net/10468/16454
dc.language.isoenen
dc.publisherUniversity College Corken
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 2/12/RC/2302_P2/IE/MAREI_Phase 2/
dc.relation.projectUniversity College Cork (Dr. Elmer Morrissey Memorial Scholarship), (The John Murphy Postgraduate Research Fellowship in Civil Engineering)
dc.rights© 2024, Anna Claire O'Regan.
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAir pollution
dc.subjectAir quality modelling
dc.subjectUrbanisation
dc.subjectUrban greenspace
dc.subjectGoogle Street View
dc.subjectLow-cost sensors
dc.titleIntegrating air quality modelling, low-cost sensing and greenspace quantification for enhanced urban air quality and net-zero cities
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD - Doctor of Philosophyen
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