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Discussion papers | Copyright
https://doi.org/10.5194/amt-2018-109
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 07 May 2018

Research article | 07 May 2018

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Atmospheric Measurement Techniques (AMT).

Evaluation of a Hierarchical Agglomerative Clustering Method Applied to WIBS Laboratory Data for Improved Discrimination of Biological Particles by Comparing Data Preparation Techniques

Nicole Savage1,a and J. Alex Huffman1 Nicole Savage and J. Alex Huffman
  • 1University of Denver, Department of Chemistry and Biochemistry, Denver, USA
  • anow at: Aerosol Devices, Inc.

Abstract. Hierarchical agglomerative clustering (HAC) analysis has been successfully applied to several sets of ambient data (e.g. Crawford et al., 2015; Robinson et al., 2013) and with respect to standardized particles in the laboratory environment (Ruske et al., 2017). Here we show for the first time a systematic application of HAC to a comprehensive set of laboratory data collected using the wideband integrated bioaerosol sensor (WIBS-4A) (Savage et al., 2017). The impact of particle ratio on HAC results was investigated, showing that clustering quality can vary dramatically as a function of ratio. Six strategies for particle pre-processing were also compared, concluding that using raw fluorescence intensity (without normalizing to particle size) and inputting all data in logarithmic bins consistently produced the highest quality results. A total of 23 one-on-one matchups of individual particles types were investigated. Results showed cluster misclassification of <15% for 12 of 17 analytical experiments using one biological and one non-biological particle type each. Inputting fluorescence data using a baseline +3σ threshold produced lower misclassification than when inputting either all particles (without fluorescence threshold) or a baseline +9σ threshold. Lastly, six synthetic mixtures of four to seven components were analyzed. These results show that a range of 12–24% of fungal clusters were consistently misclassified by inclusion of a mixture of non-biological materials, whereas bacteria and diesel soot were each able to be separated with nearly 100% efficiency. The study gives significant support to the application of clustering analysis to data from commercial UV-LIF instruments being commonly used for bioaerosol research across the globe and provides practical tools that will improve clustering results within scientific studies as a part of diverse research disciplines.

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Nicole Savage and J. Alex Huffman
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Nicole Savage and J. Alex Huffman
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Latest update: 17 Aug 2018
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The manuscript shows the systematic application of hierarchical agglomerative clustering (HAC) to a comprehensive set of laboratory data collected using the wideband integrated bioaerosol sensor (WIBS-4A). Analysis was performed by investigating a variety of technical conditions and using both individual match-ups and synthetic mixtures of particles. The study will help improve clustering results applied to data from UV-LIF instruments used broadly for bioaerosol research.
The manuscript shows the systematic application of hierarchical agglomerative clustering (HAC)...
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