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Magnetism of anthropogenic airborne particulate matter



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Sheikh, Hassan Aftab 


The global burden of disease associated with ambient particulate matter (PM2.5) pollution is the leading threat to life expectancy according to the Air Quality Life Index (AQLI) 2023 report.

The study of airborne PM is key to understanding both its source, and its impact on human health. The focus of this thesis is the study of Fe-bearing PM which is abundant in urban microenvironments. I start by exploring ways of monitoring and constraining the source of magnetic PM signatures in Lahore, Pakistan. I employ use of First Order Reversal Curves (FORCs) to unmix signals from ’passive biomonitors’— leaves. FORC signatures of leaf samples combine aspects of both exhaust residue and brake-pad end-members, suggesting that FORC fingerprints have the potential to identify and quantify the relative contributions from exhaust and non-exhaust (brake-wear) emissions. This thesis then examines into the indoor micro-environment of the London Underground (LU). I find that the LU is dominated by ultrafine (<100 nm) maghemite particles. The oxidised nature of the magnetic PM suggests that PM exposure in the LU is dominated by resuspension of aged dust particles relative to freshly abraded, metallic particles from the wheel-track-brake system. Therefore, I suggest that periodic removal of accumulated dust from underground tunnels might provide a cost-effective strategy for reducing exposure. I then apply magnetic modelling tools to real-world LU particles for a comparison to the experimental data. The thesis then looks at determining the efficacy of roadside green infrastructure (GI) in improving local air quality through the deposition and/or dispersion of airborne PM. I use a combination of magnetic measurements, electron microscopy, and fluid flow modelling to show that air quality downwind of a carefully selected and designed GI significantly improves through the deposition of vehicle-derived PM on leaves. I then demonstrate the application of a machine learning technique on PM hyperspectral imaging data sets. The automated method improves accuracy and reliability of chemical phase identification that is often limited by subjective human interpretation. Using magnetic and microscopy methods, I conclude that ultrafine magnetic particles are abundant and ubiquitous in urban microenvironments; and that their presence may be masked by larger particles in mass-specific traditional air quality monitoring methods.





Harrison, Richard


Air pollution, Environmental Magnetism, Magnetism, Particulate matter, Urban air pollution


Doctor of Philosophy (PhD)

Awarding Institution

University of Cambridge
Cambridge Trust PhD funding