Acosta Ramírez, Cammy
(2023)
A framework of urban monitoring sensors to understand air pollution from meso- to local-scale.
PhD thesis, University of Liverpool.
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Abstract
Air pollution in urban areas is a complex phenomenon resulting from interactions between natural features in the urban boundary layer and anthropogenic activities. The dynamic features of air pollution in cities require the deployment of ground-level monitoring networks to capture the impact of meteorological variables and human mobility. This thesis presents a comparative analysis of pollution measurements from meso-scale and local-scale datasets, employing a range of methodologies from traditional time-series analysis to quasi-three-dimensional representations. Dense monitoring networks are emphasised as key to elucidate patterns that are crucial for understanding human exposure to air pollution hotspots. Using meso-scale datasets, the study discusses the effects of the COVID-19 pandemic in the United Kingdom, considering the periods of social restrictions in which business-as-usual temporarily stopped, impacting air pollution concentrations. Substantial declines in nitrogen oxides occured during the first national lockdown, concurrent with heightened ozone and sulphur dioxide concentrations. Reductions in nitrogen dioxide persisted 15 months into the pandemic due to decreased traffic congestion, although both gaseous and particulate pollutants remained heavily influenced by regional meteorology. Beyond immediate pandemic responses, this research examines the interplay between societal adaptation, governmental responses, and air pollution dynamics over a two-year period. Furthermore, it explores the evolving relationship between mobility and air pollution within the context of rising COVID-19 immunity. While meso-scale analyses offer valuable insights into regional air pollution levels, this work underscores the need for high-resolution spatiotemporal data to understand air pollution at a local-scale or city level. The advantages of a dense wireless sensor network are discussed, revealing dominant patterns of particulate matter in the city of Liverpool, United Kingdom. High-resolution datasets enable the application of advanced numerical techniques such as the Proper Orthogonal Decomposition. This technique is successful unveiling elusive pollution patterns and their associations with meteorological and anthropogenic factors. In summary, this document explains air quality dynamics, ranging from immediate pandemic responses at a meso-scale level, to innovative analytical methodologies applied at a local scale. This work builds upon the understanding of the intricacies of pollution patterns, demonstrating the benefits of wireless sensor networks at different scales.
| Item Type: | Thesis (PhD) |
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| Divisions: | Faculty of Science and Engineering > School of Environmental Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 05 Feb 2024 17:00 |
| Last Modified: | 05 Feb 2024 17:00 |
| DOI: | 10.17638/03176861 |
| Supervisors: |
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| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3176861 |
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