The knowledge of properties of ice crystals such as size, shape, concavity and roughness is critical in the context of radiative properties of ice and mixed-phase clouds. Limitations of current cloud probes to measure these properties can be circumvented by acquiring two-dimensional light-scattering patterns instead of particle images. Such patterns were obtained in situ for the first time using the Small Ice Detector 3 (SID-3) probe during several flights in a variety of mid-latitude mixed-phase and cirrus clouds. The patterns are analysed using several measures of pattern texture, selected to reveal the magnitude of particle roughness or complexity. The retrieved roughness is compared to values obtained from a range of well-characterized test particles in the laboratory. It is found that typical in situ roughness corresponds to that found in the rougher subset of the test particles, and sometimes even extends beyond the most extreme values found in the laboratory. In this study we do not differentiate between small-scale, fine surface roughness and large-scale crystal complexity. Instead, we argue that both can have similar manifestations in terms of light-scattering properties and also similar causes. Overall, the in situ data are consistent, with ice particles with highly irregular or rough surfaces being dominant. Similar magnitudes of roughness were found in growth and sublimation zones of cirrus. The roughness was found to be negatively correlated with the halo ratio, but not with other thermodynamic or microphysical properties found in situ. Slightly higher roughness was observed in cirrus forming in clean oceanic air masses than in a continental, polluted one. Overall, the roughness and complexity are expected to lead to increased shortwave cloud reflectivity, in comparison with cirrus composed of more regular, smooth ice crystal shapes. These findings put into question suggestions that climate could be modified through aerosol seeding to reduce cirrus cover and optical depth, as the seeding may result in decreased shortwave reflectivity.

We describe a prototype laboratory light-scattering instrument that integrates two approaches to airborne particle characterization: spatial light-scattering analysis and intrinsic fluorescence measurement, with the aim of providing an effective means of classifying biological particles within an ambient aerosol. The system uses a single continuous-wave 266-nm ultraviolet laser to generate both the spatial elastic scatter data (from which an assessment of particle size and shape is made) and the particle intrinsic fluorescence data from particles in the approximate size range of 1–10-µm diameter carried in a sample airflow through the laser beam. Preliminary results suggest that this multiparameter measurement approach can provide an effective means of classifying different particle types and can reduce occurrences of false-positive detection of biological aerosols.

Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS data 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-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first 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 fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.

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.

Total bacteria, fungal spore and yeast counts were compared with ultraviolet-light-induced fluorescence (UV-LIF) measurements of ambient aerosol at the summit of the Puy de Dôme (PdD) mountain in central France (1465 m a.s.l), which represents a background elevated site. Bacteria, fungal spores and yeast were enumerated by epifluorescence microscopy (EFM) and found to number 2.2 to 23 L⁻¹ and 0.8 to 2 L⁻¹, respectively. Bacteria counts on two successive nights were an order of magnitude larger than in the intervening day. A wide issue bioaerosol spectrometer, version 3 (WIBS-3) was used to perform UV-LIF measurements on ambient aerosol sized 0.8 to 20 μm. Mean total number concentration was 270 L⁻¹ (σ = 66 L⁻¹), found predominantly in a size mode at 2 μm for most of the campaign. Total concentration (fluorescent + non-fluorescent aerosol) peaked at 500 L⁻¹ with a size mode at 1 μm because of a change in air mass origin lasting around 48 h. The WIBS-3 features two excitation and fluorescence detection wavelengths corresponding to different biological molecules, although non-biological interferents also contribute. The mean fluorescent particle concentration after short-wave (280 nm; associated with tryptophan) excitation was 12 L⁻¹ (σ = 6 L⁻¹), and did not vary much throughout the campaign. In contrast, the mean concentration of particles fluorescent after long-wave (370 nm; associated with NADH) excitation was 95 L⁻¹ (σ = 25 L⁻¹), and a nightly rise and subsequent fall of up to 100 L⁻¹ formed a strong diurnal cycle in the latter. The two fluorescent populations exhibited size modes at 3 μm and 2 to 3 μm, respectively. A hierarchical agglomerative cluster analysis algorithm was applied to the data and used to extract different particle factors. A cluster concentration time series representative of bacteria was identified. This was found to exhibit a diurnal cycle with a maximum peak appearing during the day. Analysis of organic mass spectra recorded using an aerosol mass spectrometer (AMS; Aerodyne Inc.) suggests that aerosol reaching the site at night was more aged than that during the day, indicative of sampling the residual layer at night. Supplementary meteorological data and previous work also show that PdD lies in the residual layer/free troposphere at night, and this is thought to cause the observed diurnal cycles in organic-type and fluorescent aerosol particles. Based on the observed disparity between bacteria and fluorescent particle concentrations, fluorescent non-PBA is likely to be important in the WIBS-3 data and the surprisingly high fluorescent concentration in the residual layer/free troposphere raises questions about a ubiquitous background in continental air during the summer.