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AMT | Articles | Volume 11, issue 8
Atmos. Meas. Tech., 11, 4929–4942, 2018
https://doi.org/10.5194/amt-11-4929-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 11, 4929–4942, 2018
https://doi.org/10.5194/amt-11-4929-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 30 Aug 2018

Research article | 30 Aug 2018

Evaluation of a hierarchical agglomerative clustering method applied to WIBS laboratory data for improved discrimination of biological particles by comparing data preparation techniques

Nicole J. Savage and J. Alex Huffman
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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, https://doi.org/10.5194/acp-14-8559-2014, 2014. 
Crawford, I., Ruske, S., Topping, D. O., and Gallagher, M. W.: Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol, Atmos. Meas. Tech., 8, 4979–4991, https://doi.org/10.5194/amt-8-4979-2015, 2015. 
Crawford, I., Lloyd, G., Herrmann, E., Hoyle, C. R., Bower, K. N., Connolly, P. J., Flynn, M. J., Kaye, P. H., Choularton, T. W., and Gallagher, M. W.: Observations of fluorescent aerosol-cloud interactions in the free troposphere at the High-Altitude Research Station Jungfraujoch, Atmos. Chem. Phys., 16, 2273–2284, https://doi.org/10.5194/acp-16-2273-2016, 2016. 
Després, V. R., Huffman, J. A., Burrows, S. M., Hoose, C., Safatov, A. S., Buryak, G. A., Fröhlich-Nowoisky, J., Elbert, W., Andreae, M. O., Pöschl, U., and Jaenicke, R.: Primary Biological Aerosol Particles in the Atmosphere: A Review, Tellus B, 64, 15598, https://doi.org/10.3402/tellusb.v64i0.15598, 2012. 
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We show the systematic application of hierarchical agglomerative clustering (HAC) to comprehensive bioaerosol and non-bioaerosol laboratory data collected with the wideband integrated bioaerosol sensor (WIBS-4A). This study investigated various input conditions and used individual matchups and computational mixtures of particles; it will help improve clustering results applied to data from the ultraviolet laser and light-induced fluorescence instruments commonly used for bioaerosol research.
We show the systematic application of hierarchical agglomerative clustering (HAC) to...
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