?? Author(s) 2013. This work is distributed under the Creative Commons Attribution 3.0 LicenseHierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different ???uorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs). The technique is demonstrated on measurements of various ???uorescent and non-???uorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBSdata sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent the following: non-???uorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the ???rst time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP) measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as ???uorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved

Abstract. 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.

Abstract. The behaviour of primary biological aerosols (PBAs) at an elevated, un-polluted North American forest site was studied using an ultra violet-light induced fluorescence (UV-LIF) measurement technique in conjunction with hierarchical agglomerative cluster analysis (HA-CA). Contemporaneous UV-LIF measurements were made with two wide-band integrated bioaerosol spectrometers, WIBS-3 and WIBS-4, which sampled close to the forest floor and via a continuous vertical profiling system, respectively. Additionally, meteorological parameters were recorded at various heights throughout the forest and used to estimate PBAP (Primary Biological Aerosol Particle) fluxes. HA-CA using data from the two, physically separated WIBS instruments independently yielded very similar cluster solutions.All fluorescent clusters displayed a diurnal minimum at midday at the forest floor with maximum concentration occurring at night. Additionally, the number concentration of each fluorescent cluster was enhanced, to different degrees, during wet periods. A cluster that displayed the greatest enhancement and highest concentration during sustained wet periods appears consistent with behaviour reported for fungal spores. A cluster that appears to be behaviourally consistent with bacteria dominated during dry periods. Fluorescent particle concentrations were found to be greater within the forest canopy than at the forest floor, indicating that the canopy was the main source of these particles rather than the minimal surface vegetation, which appeared to contribute little to overall PBA concentrations at this site.Fluorescent particle concentration was positively correlated with relative humidity (RH), and parameterisations of the aerosol response during dry and wet periods are reported. The aforementioned fungal spore-like cluster displayed a strong positive response to increasing RH. The bacteria-like cluster responded more strongly to direct rain-fall events than other PBA types. Peak concentrations of this cluster are shown to be linearly correlated to the log of peak rainfall rates.Parallel studies by and Prenni et al. (2013) showed that the fluorescent particle concentrations correlated linearly with ice nuclei (IN) concentrations at this site during rain events. We discuss this result in conjunction with our cluster analysis to appraise the candidate IN.

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Sensors that are able to provide reagent-free, continuous monitoring for potential bio-aerosol hazards are required in many environments. In general, increasing the number of optical and spectroscopic properties of individual airborne particles that can be measured increases the level of detection confidence and reduces the risk of false-positive detection. This paper describes the development of relatively low-cost multi-parameter prototype sensors that can monitor and classify the ambient aerosol by simultaneously recording both a 2x2 fluorescence excitation-emission matrix and multiangle spatial elastic scattering data from individual airborne particles. The former can indicate the possible presence of specific biological fluorophores in the particle whilst the latter provides an assessment of particle size and shape.The prototype sensors described continuously sample ambient air through a delivery system, designed so that particles in the sample flow are surrounded by clean, filtered sheath air. The sample particles are then drawn through the focussed light beam from a continuous diode laser. Each individual particle, down to ~0.5µm in size, produces a scattered light pattern that is recorded by a multi-pixel photodetector. The scattered light signal also provides a trigger to initiate the sequential firing of two optically filtered xenon sources that irradiate the particle with pulsed UV radiation centred upon ~280 nm and ~370 nm wavelengths, optimal for excitation of bio-fluorophores tryptophan and NADH respectively. For each excitation wavelength, fluorescence is detected across two bands embracing the peak emissions of the same biofluorophores. Particle classification may be achieved by evaluating these spatial scatter and fluorescence data to appropriately 'position' the particle within multi-parameter space. Particles are measured at rates up to ~125 particles/s (limited by the xenon recharge time), corresponding to all particles for concentrations up to 1.3 x 10 4 particles/l. Analysis of results from aerosols of BG spores and a variety of other materials are described, along with examples of the temporal fluctuations in bio-aerosols in two very different environments.