Machine learning for improved data analysis of biological aerosol using the WIBS

Abstract. Primary biological aerosol including bacteria, fungal spores and pollen have important implications for public health and the environment. Such particles may have different concentrations of chemical fluorophores and will provide different responses in the presence of ultraviolet light which potentially could be used to discriminate between different types of biological aerosol. Development of ultraviolet light induced fluorescence (UV-LIF) instruments such as the Wideband Integrated Bioaerosol Sensor (WIBS) has made is possible to collect size, morphology and fluorescence measurements in real-time. However, it is unclear without studying responses from the instrument in the laboratory, the extent to which we can discriminate between different types of particles. Collection of laboratory data is vital to validate any approach used to analyse the data and to ensure that the data available is utilised as effectively as possible. In this manuscript we test a variety of methodologies on traditional reference particles and a range of laboratory generated aerosols. Hierarchical Agglomerative Clustering (HAC) has been previously applied to UV-LIF data in a number of studies and is tested alongside other algorithms that could be used to solve the classification problem: Density Based Spectral Clustering and Noise (DBSCAN), k-means and gradient boosting. Whilst HAC was able to effectively discriminate between the reference particles, yielding a classification error of only 1.8 %, similar results were not obtained when testing on laboratory generated aerosol where the classification error was found to be between 11.5 % and 24.2 %. Furthermore, there is a worryingly large uncertainty in this approach in terms of the data preparation and the cluster index used, and we were unable attain consistent results across the different sets of laboratory generated aerosol tested. The best results were obtained using gradient boosting, where the misclassification rate was between 4.38 % and 5.42 %. The largest contribution to this error was the pollen samples where 28.5 % of the samples were misclassified as fungal spores. The technique was also robust to changes in data preparation provided a fluorescent threshold was applied to the data. Where laboratory training data is unavailable, DBSCAN was found to be an potential alternative to HAC. In the case of one of the data sets where 22.9 % of the data was left unclassified we were able to produce three distinct clusters obtaining a classification error of only 1.42 % on the classified data. These results could not be replicated however for the other data set where 26.8 % of the data was not classified and a classification error of 13.8 % was obtained. This method, like HAC, also appeared to be heavily dependent on data preparation, requiring different selection of parameters dependent on the preparation used. Further analysis will also be required to confirm our selection of parameters when using this method on ambient data. There is a clear need for the collection of additional laboratory generated aerosol to improve interpretation of current databases and to aid in the analysis of data collected from an ambient environment. New instruments with a greater resolution are likely improve on current discrimination between pollen, bacteria and fungal spores and even between their different types, however the need for extensive laboratory training data sets will grow as a result. </jats:p

Machine learning for improved data analysis of biological aerosol using the WIBS

Primary biological aerosol including bacteria, fungal spores and pollen have important implications for public health and the environment. Such particles may have different concentrations of chemical fluorophores and will respond differently in the presence of ultraviolet light, potentially allowing for different types of biological aerosol to be discriminated. Development of ultraviolet light induced fluorescence (UV-LIF) instruments such as the Wideband Integrated Bioaerosol Sensor (WIBS) has allowed for size, morphology and fluorescence measurements to be collected in real-time. However, it is unclear without studying instrument responses in the laboratory, the extent to which different types of particles can be discriminated. Collection of laboratory data is vital to validate any approach used to analyse data and ensure that the data available is utilized as effectively as possible.In this paper a variety of methodologies are tested on a range of particles collected in the laboratory. Hierarchical agglomerative clustering (HAC) has been previously applied to UV-LIF data in a number of studies and is tested alongside other algorithms that could be used to solve the classification problem: Density Based Spectral Clustering and Noise (DBSCAN), k-means and gradient boosting.Whilst HAC was able to effectively discriminate between reference narrow-size distribution PSL particles, yielding a classification error of only 1.8&thinsp;%, similar results were not obtained when testing on laboratory generated aerosol where the classification error was found to be between 11.5&thinsp;% and 24.2&thinsp;%. Furthermore, there is a large uncertainty in this approach in terms of the data preparation and the cluster index used, and we were unable to attain consistent results across the different sets of laboratory generated aerosol tested.The lowest classification errors were obtained using gradient boosting, where the misclassification rate was between 4.38&thinsp;% and 5.42&thinsp;%. The largest contribution to the error, in the case of the higher misclassification rate, was the pollen samples where 28.5&thinsp;% of the samples were incorrectly classified as fungal spores. The technique was robust to changes in data preparation provided a fluorescent threshold was applied to the data.In the event that laboratory training data are unavailable, DBSCAN was found to be a potential alternative to HAC. In the case of one of the data sets where 22.9&thinsp;% of the data were left unclassified we were able to produce three distinct clusters obtaining a classification error of only 1.42&thinsp;% on the classified data. These results could not be replicated for the other data set where 26.8&thinsp;% of the data were not classified and a classification error of 13.8&thinsp;% was obtained. This method, like HAC, also appeared to be heavily dependent on data preparation, requiring a different selection of parameters depending on the preparation used. Further analysis will also be required to confirm our selection of the parameters when using this method on ambient data.There is a clear need for the collection of additional laboratory generated aerosol to improve interpretation of current databases and to aid in the analysis of data collected from an ambient environment. New instruments with a greater resolution are likely to improve on current discrimination between pollen, bacteria and fungal spores and even between different species, however the need for extensive laboratory data sets will grow as a result.</p

Characterisation and source identification of biofluorescent aerosol emissions over winter and summer periods in the United Kingdom

International audiencePrimary biological aerosol particles (PBAPs) are an abundant subset of atmospheric aerosol particles which comprise viruses, bacteria, fungal spores, pollen, and fragments such as plant and animal debris. The abundance and diversity of these particles remain poorly constrained, causing significant uncertainties for modelling scenarios and for understanding the potential implications of these particles in different environments. PBAP concentrations were studied at four different sites in the United Kingdom (Weybourne, Davidstow, Capel Dewi, and Chilbolton) using an ultraviolet light-induced fluorescence (UV-LIF) instrument, the Wide-band Integrated Bioaerosol Spectrometer (WIBS), versions 3 and 4. Using hierarchical agglomerative cluster (HAC) analysis, particles were statistically discriminated. Fluorescent particles and clusters were then analysed by comparing to laboratory data of known particle types, assessing their diurnal variation and examining their relationship to the meteorological variables temperature, relative humidity, wind speed, and wind direction. Using local land cover types, sources of the suspected fluorescent particles and clusters were then identified. Most sites exhibited a wet discharged fungal spore dominance , with the exception of one site, Davidstow, which had higher concentrations of bacteria, suggested to result from the presence of a local dairy factory and farm. Differences were identified as to the sources of wet discharged fungal spores, with particles originating from arable and horticultural land at Chilbolton, and improved grassland areas at Weybourne. Total fluorescent particles at Capel Dewi were inferred to comprise two sources, with bacteria originating from the broadleaf and coniferous woodland and wet discharged fungal spores from nearby improved grassland areas, similar to Weybourne. The use of the HAC method and a higher fluorescence threshold (9 standard deviations instead of 3) produced clusters which were considered to be biological following the complete analysis. More published data and information on the reaction of different speciated biological particle types to fluctuations in meteorological conditions, such as relative humidity and temperature, would aid particle type character-isation in studies such as this

Exemplarist Environmental Ethics

This article argues that environmental ethics can deemphasize environmental problem‐solving in preference for a more exemplarist mode. This mode will renarrate what we admire in those we have long admired, in order to make them resonate with contemporary ethical needs. First, I outline a method problem that arose for me in ethnographic fieldwork, a problem that I call, far too reductively, “solution thinking.” Second, I relate that method problem to movements against “quandary ethics” in ethical theory more broadly. Third, I discuss some interpretive work I am engaged in about Henry David Thoreau and how it bears on the methodological issues my fieldwork raised. I argue that some of the most important icons of right relation to environment, especially Francis of Assisi and Thoreau, should be envisioned as far more politically invested than they usually are. They demonstrate to scholars of religious ethics that an exemplarist ethic focused on character need not neglect politics