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Liquid chromatography coupled to electrospray ionisation high resolution mass spectrometry is an extremely powerful technique for both targeted and non-targeted analysis of organic aerosol.

Our ability to model the chemical and thermodynamic processes that lead to secondary organic aerosol (SOA) formation is thought to be hampered by the complexity of the system. While there are fundamental models now available that can simulate the tens of thousands of reactions thought to take place, validation against experiments is highly challenging. Techniques capable of identifying individual molecules such as chromatography are generally only capable of quantifying a subset of the material present, making it unsuitable for a carbon budget analysis. Integrative analytical methods such as the Aerosol Mass Spectrometer (AMS) are capable of quantifying all mass, but because of their inability to isolate individual molecules, comparisons have been limited to simple data products such as total organic mass and the O : C ratio. More detailed comparisons could be made if more of the mass spectral information could be used, but because a discrete inversion of AMS data is not possible, this activity requires a system of predicting mass spectra based on molecular composition. In this proof-of-concept study, the ability to train supervised methods to predict electron impact ionisation (EI) mass spectra for the AMS is evaluated. Supervised Training Regression for the Arbitrary Prediction of Spectra (STRAPS) is not built from first principles. A methodology is constructed whereby the presence of specific mass-to-charge ratio (m∕z) channels is fitted as a function of molecular structure before the relative peak height for each channel is similarly fitted using a range of regression methods. The widely used AMS mass spectral database is used as a basis for this, using unit mass resolution spectra of laboratory standards. Key to the fitting process is choice of structural information, or molecular fingerprint. Our approach relies on using supervised methods to automatically optimise the relationship between spectral characteristics and these molecular fingerprints. Therefore, any internal mechanisms or instrument features impacting on fragmentation are implicitly accounted for in the fitted model. Whilst one might expect a collection of keys specifically designed according to EI fragmentation principles to offer a robust basis, the suitability of a range of commonly available fingerprints is evaluated. Using available fingerprints in isolation, initial results suggest the generic public MACCS fingerprints provide the most accurate trained model when combined with both decision trees and random forests, with median cosine angles of 0.94–0.97 between modelled and measured spectra. There is some sensitivity to choice of fingerprint, but most sensitivity is in choice of regression technique. Support vector machines perform the worst, with median values of 0.78–0.85 and lower ranges approaching 0.4, depending on the fingerprint used. More detailed analysis of modelled versus mass spectra demonstrates important composition-dependent sensitivities on a compound-by-compound basis. This is further demonstrated when we apply the trained methods to a model α-pinene SOA system, using output from the GECKO-A model. This shows that use of a generic fingerprint referred to as FP4 and one designed for vapour pressure predictions (Nanoolal) gives plausible mass spectra, whilst the use of the MACCS keys in isolation performs poorly in this application, demonstrating the need for evaluating model performance against other SOA systems rather than existing laboratory databases on single compounds. Given the limited number of compounds used within the AMS training dataset, it is difficult to prescribe which combination of approach would lead to a robust generic model across all expected compositions. Nonetheless, the study demonstrates the use of a methodology that would be improved with more training data, fingerprints designed explicitly for fragmentation mechanisms occurring within the AMS, and data from additional mixed systems for further validation. To facilitate further development of the method, including application to other instruments, the model code for re-training is provided via a public Github and Zenodo software repository.

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.

The composition of atmospheric aerosol particles has been found to influence their micro-physical properties and their interaction with water vapour in the atmosphere. Core-shell models have been used to investigate the relationship between composition, viscosity and equilibration time-scales. These models have traditionally relied on the Fickian laws of diffusion with no explicit account of non-ideal interactions. We introduce the Maxwell-Stefan diffusion framework as an alternative method, which explicitly accounts for non-ideal interactions through activity coefficients. E-folding time is the time it takes for the difference in surface and bulk concentration to change by an exponential factor and was used to investigate the interplay between viscosity and solubility and the effect this has on equilibration time-scales within individual aerosol particles. The e-folding time was estimated after instantaneous increases in relative humidity to binary systems of water and an organic component. At low water mole fractions, viscous effects were found to dominate mixing. However, at high water mole fractions, equilibration times were more sensitive to a range in solubility, shown through the greater variation in e-folding times. This is the first time the Maxwell-Stefan framework has been applied to an atmospheric aerosol core-shell model and shows that there is a complex interplay between the viscous and solubility effects on aerosol composition that requires further investigation.

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.