ALBERT

Description: We demonstrate, for the first time, continuous real-time observations of airborne bio-fluorescent aerosols recorded at the British Antarctic Survey's Halley VI Research Station, located on the Brunt Ice Shelf close to the Weddell Sea coast (lat 75°34′59′′ S, long 26°10′0′′ W) during Antarctic summer, 2015. As part of the NERC MAC (Microphysics of Antarctic Clouds) aircraft aerosol cloud interaction project, observations with a real-time ultraviolet-light-induced fluorescence (UV-LIF) spectrometer were conducted to quantify airborne biological containing particle concentrations along with dust particles as a function of wind speed and direction over a 3-week period. Significant, intermittent enhancements of both non- and bio-fluorescent particles were observed to varying degrees in very specific wind directions and during strong wind events. Analysis of the particle UV-induced emission spectra, particle sizes and shapes recorded during these events suggest the majority of particles were likely a subset of dust with weak fluorescence emission responses. A minor fraction, however, were likely primary biological particles that were very strongly fluorescent, with a subset identified as likely being pollen based on comparison with laboratory data obtained using the same instrument. A strong correlation of bio-fluorescent particles with wind speed was observed in some, but not all, periods. Interestingly, the fraction of fluorescent particles to total particle concentration also increased significantly with wind speed during these events. The enhancement in concentrations of these particles could be interpreted as due to resuspension from the local ice surface but more likely due to emissions from distal sources within Antarctica as well as intercontinental transport. Likely distal sources identified by back trajectory analyses and dispersion modelling were the coastal ice margin zones in Halley Bay consisting of bird colonies with likely associated high bacterial activity together with contributions from exposed ice margin bacterial colonies but also long-range transport from the southern coasts of Argentina and Chile. Dispersion modelling also demonstrated emissions from shipping lanes, and therefore marine anthropogenic sources cannot be ruled out. Average total concentrations of total fluorescent aerosols were found to be 1.9 ± 2.6 L−1 over a 3-week period crossing over from November into December, but peak concentrations during intermittent enhancement events could be up to several tens per litre. While this short pilot study is not intended to be generally representative of Antarctic aerosol, it demonstrates the usefulness of the UV-LIF measurement technique for quantification of airborne bioaerosol concentrations and to understand their dispersion. The potential importance for microbial colonisation of Antarctica is highlighted.

Description: Characterisation of bioaerosols has important implications within environment and public health sectors. Recent developments in ultraviolet light-induced fluorescence (UV-LIF) detectors such as the Wideband Integrated Bioaerosol Spectrometer (WIBS) and the newly introduced Multiparameter Bioaerosol Spectrometer (MBS) have allowed for the real-time collection of fluorescence, size and morphology measurements for the purpose of discriminating between bacteria, fungal spores and pollen.This new generation of instruments has enabled ever larger data sets to be compiled with the aim of studying more complex environments. In real world data sets, particularly those from an urban environment, the population may be dominated by non-biological fluorescent interferents, bringing into question the accuracy of measurements of quantities such as concentrations. It is therefore imperative that we validate the performance of different algorithms which can be used for the task of classification.For unsupervised learning we tested hierarchical agglomerative clustering with various different linkages. For supervised learning, 11 methods were tested, including decision trees, ensemble methods (random forests, gradient boosting and AdaBoost), two implementations for support vector machines (libsvm and liblinear) and Gaussian methods (Gaussian naïve Bayesian, quadratic and linear discriminant analysis, the k-nearest neighbours algorithm and artificial neural networks).The methods were applied to two different data sets produced using the new MBS, which provides multichannel UV-LIF fluorescence signatures for single airborne biological particles. The first data set contained mixed PSLs and the second contained a variety of laboratory-generated aerosol.Clustering in general performs slightly worse than the supervised learning methods, correctly classifying, at best, only 67. 6 and 91. 1 % for the two data sets respectively. For supervised learning the gradient boosting algorithm was found to be the most effective, on average correctly classifying 82. 8 and 98. 27 % of the testing data, respectively, across the two data sets.A possible alternative to gradient boosting is neural networks. We do however note that this method requires much more user input than the other methods, and we suggest that further research should be conducted using this method, especially using parallelised hardware such as the GPU, which would allow for larger networks to be trained, which could possibly yield better results.We also saw that some methods, such as clustering, failed to utilise the additional shape information provided by the instrument, whilst for others, such as the decision trees, ensemble methods and neural networks, improved performance could be attained with the inclusion of such information.

Description: 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 %, 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 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 % and 5.42 %. The largest contribution to the error, in the case of the higher misclassification rate, was the pollen samples where 28.5 % 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 % of the data were 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 for the other data set where 26.8 % of the data were 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 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.

Description: We demonstrate for the first time, continuous real-time observations of airborne bio-fluorescent aerosols recorded at the British Antarctic Survey's Halley VI Research Station, located on the Brunt ice shelf close to the Weddell Sea coast (Lat. 75°34'59"S, Long. 26°10'0"W) during Antarctic Summer, 2015. As part of the NERC MAC (Microphysics of Antarctic Clouds) aircraft aerosol cloud interaction project, observations with a real-time Ultraviolet Light Induced Fluorescence (UV-LIF) spectrometer were conducted to quantify airborne biological containing particle concentrations along with dust particles as a function of wind speed and direction over a three week period. Significant, intermittent enhancements of both non- and bio-fluorescent particles were observed to varying degrees in very specific wind directions and during strong wind events. Analysis of the particle UV induced emission spectra, particle sizes and shapes recorded during these events suggest the majority of particles were likely a subset of dust with weak fluorescence emission responses. A minor fraction, however, were clearly primary biological particles that were very strongly fluorescent, with a subset identified as likely being pollen based on comparison with laboratory data obtained using the same instrument. A strong correlation of biofluorescent particles with wind speed was observed in some, but not all, periods. Interestingly the fraction of fluorescent particles to total particle concentration also increased significantly with wind speed during these events. The enhancement in concentrations of these particles could be interpreted as due to re-suspension from the local ice surface but more likely due to emissions from distal sources within Antarctica as well as intercontinental transport. Likely distal sources identified by back trajectory analyses and dispersion modelling were the coastal ice margin zones in Halley Bay consisting of bird colonies with likely associated high bacterial activity together with contributions from exposed ice margin bacterial colonies but also long range transport from the southern coasts of Argentina and Chile. Average total concentrations of total fluorescent aerosols were found to be 1.9 ± 2.6 L-1 over a 3 week period crossing over from November into December, but peak concentrations during intermittent enhancement events could be up to several 10's L-1. The usefulness of the measurement technique for quantification of airborne bioaerosol concentrations, and to understand their dispersion and potential importance for microbial colonisation of Antarctica is highlighted.

Description: 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.

Description: Characterisation of bio-aerosols has important implications within Environment and Public Health sectors. Recent developments in Ultra-Violet Light Induced Fluorescence (UV-LIF) detectors such as the Wideband Integrated bio-aerosol Spectrometer (WIBS) and the newly introduced Multiparameter bio-aerosol Spectrometer (MBS) has allowed for the real time collection of fluorescence, size and morphology measurements for the purpose of discriminating between bacteria, fungal Spores and pollen. This new generation of instruments has enabled ever larger data sets to be compiled with the aim of studying more complex environments. In real world data sets, particularly those from an urban environment, the population may be dominated by non-biological fluorescent interferents bringing into question the accuracy of measurements of quantities such as concentrations. It is therefore imperative that we validate the performance of different algorithms which can be used for the task of classification. For unsupervised learning we test Hierarchical Agglomerative Clustering with various different linkages. For supervised learning, ten methods were tested; including decision trees, ensemble methods: Random Forests, Gradient Boosting and AdaBoost; two implementations for support vector machines: libsvm and liblinear; Gaussian methods: Gaussian naïve Bayesian, quadratic and linear discriminant analysis and finally the k-nearest neighbours algorithm. The methods were applied to two different data sets measured using a new Multiparameter bio-aerosol Spectrometer which provides multichannel UV-LIF fluorescence signatures for single airborne biological particles. Clustering, in general performs slightly worse than the supervised learning methods correctly classifying, at best, only 72.7 and 91.1 percent for the two data sets respectively. For supervised learning the gradient boosting algorithm was found to be the most effective, on average correctly classifying 88.1 and 97.8 percent of the testing data respectively across the two data sets.