Investigations of Bioaerosol Emission and Dispersion Patterns

  • Douglas Morrison

Student thesis: Phd

Abstract

Bioaerosols are airborne particles of biological origin. They include viruses, bacteria, fungal spores, pollen, plant fibres, metabolites, toxins, and more. Despite often accounting for less than 1% of the particles within an aerosol mixture, bioaerosols are increasingly recognised for the effects they can have on air quality, human health, ecosystems, climate and atmosphere–ocean biogeochemical cycles. This broad range of effects has attracted newfound interest from a number of disciplines, ranging from climate modelling to epidemiology. Until recently, observations of bioaerosols were limited to offline techniques. Whether it was using filter samples, cascade impactors or a volumetric spore trap, subsequent microscopical analysis was often labour intensive and slow. However, technological developments have given rise to emerging online methods such as ultra-violet light induced fluorescence (UV-LIF) spectrometry. By utilising the intrinsic fluorescent properties of specific biological compounds, bioaerosols can be rapidly observed. Since the advent of the first UV-LIF spectrometers, a number of advancements have been made to the instruments. This has increased their sensitivity to specific wavelengths and improved the user’s capacity to discriminate between distinct particle types. Although early versions of UV-LIF spectrometers have already been employed in a number of environments, including rainforests, city centres and even Antarctica, new opportunities have arisen to conduct experiments with greater levels of data output. As such, it is useful to re-visit old sampling sites with more modern instruments, as well as sample in novel environments. The research discussed throughout this document involves some of the longest sampling campaigns to use UV-LIF spectrometry to date, and has taken place in a number of locations. These include Cape Verde, Hong Kong, Barbados and the UK. A number of instrument models have been used, with subsequent analysis aided by machine learning technqiues such as hierarchical agglomerative cluster analysis.
Date of Award31 Dec 2021
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorMartin Gallagher (Supervisor) & David Topping (Supervisor)

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