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Primary biological aerosol particles (PBAPs), otherwise commonly referred to as 'bioaerosols', are a subset of atmospheric aerosol particles that comprise pollen, fungal spores, bacteria, and viruses, and other biological matter suspended in the atmosphere. Whilst PBAPs are naturally produced, these particles play a fundamental role in the Earth system, influencing natural and agricultural ecosystems, potentially exacerbating animal and human diseases, and contributing to climate processes. However, a considerable lack of understanding remains concerning the pathways that these particles contribute to, owing to the difficulties in detecting and quantifying these particles. This thesis focuses on the detection and characterisation of PBAPs using a real-time online detection technique, known as ultraviolet light-induced fluorescence (UV-LIF) spectrometry. Whilst various UV-LIF spectrometers have been deployed for measurement campaigns in a range of geographic locations worldwide, studies quantifying classification performance and inter-comparisons of different UV-LIF spectrometers are limited. As such, there remain some considerable uncertainties in the detection abilities and consistency of these instruments, with consequent impacts on training algorithms for PBAP classification. Multiple UV-LIF spectrometers were deployed in both ambient measurement campaigns, and laboratory experiments. The initial ambient study, characterising and inter-comparing biofluorescent aerosol emissions from four sites in the UK indicated a similar dominance of wet-discharged fungal spores across most of the study sites, but illustrated the difficulties that currently exist when attempting to characterise these particles following collection by UV-LIF spectrometers. By conducting a laboratory UV-LIF inter-comparison study to assess instrument detection abilities, distinct fluorescent responses could not be identified between the different biological particle groups sampled (bacteria, fungal spores, and pollen). Differences in fluorescent responses were, however, identified between instruments which were nominally the same, and therefore it was suggested that due to these differences training data for supervised techniques is not cross-compatible between instruments. The final comparison study of UV-LIF spectrometers and offline, traditional measurement techniques, illustrated that these instruments are adept at detecting select fungal spores, and bacterial particles in ambient conditions. However, the results from using both unsupervised and supervised analysis techniques illustrated some continuing issues in distinguishing between these two particle groups when using UV-LIF spectrometers. By conducting both ambient and laboratory studies, and demonstrating the use of both unsupervised and supervised particle discrimination and characterisation methods, the findings in this thesis contribute towards an improved understanding of how different UV-LIF spectrometers respond to both known and unknown particle types. The results of which consequently highlight the impacts that this has for the application of different analysis techniques for PBAP characterisation.

Primary biological aerosol particles, including bacteria, fungal spores and pollen are a crucial component of the atmosphere. These particles have a significant impact on human health, especially for those who suffer from hay fever and asthma. They can also influence the weather, acting as ice nuclei (IN) or cloud condensation nuclei (CCN) and can be impactful on agriculture through destruction of crops caused by fungal diseases. It is also vital to monitor these particles in an indoor environment: in hospitals as well as cleanrooms within the pharmaceutical manufacturing process. There are several different techniques that scientists can use to analyse bio-aerosol. Manual techniques, including tape and filter sampling, are often used by meteorological institutions to provide a pollen forecast. Alternatively, in a clean indoor environment, pharmaceutical manufacturers may use culture-based techniques to help prevent contamination. These approaches, however, can suffer from low time resolution and substantial human labour costs. As a consequence, interest in ultraviolet light-induced fluorescence (UV-LIF), which can provide real-time particle measurements, has increased in the last decade. Such technological advancements, especially instruments that produce high resolution data, bring a wealth of complex data that can be difficult to analyse. Consequently, it becomes ever more important to question the algorithmic approach used to analyse the data. One of the potential solutions to overcome this obstacle is machine learning, which has seen increased interest in recent years, with a vast number of applications. As more laboratory data have also become available, it has been possible to test the applicability of a wider variety of algorithms. In this thesis, we evaluate the applicability of these algorithms to data collected using both an earlier developed instrument called the WIBS and an updated version of the WIBS called the MBS. Hierarchical agglomerative clustering is a technique that has been used by researchers in previous studies using the WIBS. In this thesis, we present the first paper, using the MBS as well as extend the current body of research that uses the WIBS, showing some previously unseen disadvantages to this technique. We demonstrate excellent discrimination between laboratory particles for the newer instrument using supervised machine learning techniques but identify critical areas where the application of such algorithms to ambient data are limited and need improvement. Primary biological aerosol particles, including bacteria, fungal spores and pollen are a crucial component of the atmosphere. These particles have a significant impact on human health, especially for those who suffer from hay fever and asthma. They can also influence the weather, acting as ice nuclei (IN) or cloud condensation nuclei (CCN) and can be impactful on agriculture through destruction of crops caused by fungal diseases. It is also vital to monitor these particles in an indoor environment: in hospitals as well as cleanrooms within the pharmaceutical manufacturing process. There are several different techniques that scientists can use to analyse bio-aerosol. Manual techniques, including tape and filter sampling, are often used by meteorological institutions to provide a pollen forecast. Alternatively, in a clean indoor environment, pharmaceutical manufacturers may use culture-based techniques to help prevent contamination. These approaches, however, can suffer from low time resolution and substantial human labour costs. As a consequence, interest in ultraviolet light-induced fluorescence (UV-LIF), which can provide real-time particle measurements, has increased in the last decade. Such technological advancements, especially instruments that produce high resolution data, bring a wealth of complex data that can be difficult to analyse. Consequently, it becomes ever more important to question the algorithmic approach used to analyse the data. One of the potential solutions to overcome this obstacle is machine learning, which has seen increased interest in recent years, with a vast number of applications. As more laboratory data have also become available, it has been possible to test the applicability of a wider variety of algorithms. In this thesis, we evaluate the applicability of these algorithms to data collected using both an earlier developed instrument called the WIBS and an updated version of the WIBS called the MBS. Hierarchical agglomerative clustering is a technique that has been used by researchers in previous studies using the WIBS. In this thesis, we present the first paper, using the MBS as well as extend the current body of research that uses the WIBS, showing some previously unseen disadvantages to this technique. We demonstrate excellent discrimination between laboratory particles for the newer instrument using supervised machine learning techniques but identify critical areas where the application of such algorithms to ambient data are limited and need improvement.