Articles | Volume 10, issue 2
Research article
03 Mar 2017
Research article |  | 03 Mar 2017

Evaluation of machine learning algorithms for classification of primary biological aerosol using a new UV-LIF spectrometer

Simon Ruske, David O. Topping, Virginia E. Foot, Paul H. Kaye, Warren R. Stanley, Ian Crawford, Andrew P. Morse, and Martin W. Gallagher

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Cited articles

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Crawford, I., Bower, K. N., Choularton, T. W., Dearden, C., Crosier, J., Westbrook, C., Capes, G., Coe, H., Connolly, P. J., Dorsey, J. R., Gallagher, M. W., Williams, P., Trembath, J., Cui, Z., and Blyth, A.: Ice formation and development in aged, wintertime cumulus over the UK: observations and modelling, Atmos. Chem. Phys., 12, 4963–4985,, 2012.
Crawford, I., Robinson, N. H., Flynn, M. J., Foot, V. E., Gallagher, M. W., Huffman, J. A., Stanley, W. R., and Kaye, P. H.: Characterisation of bioaerosol emissions from a Colorado pine forest: results from the BEACHON-RoMBAS experiment, Atmos. Chem. Phys., 14, 8559–8578,, 2014.
Short summary
Particles such as bacteria, pollen and fungal spores have important implications within the environment and public health sectors. Here we evaluate the performance of various different methods for distinguishing between these different types of particles using a new instrument. We demonstrate that there may be better alternatives to the currently used methods which can be further investigated in future research.