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, 2018). Here
we show for the first time a systematic application of HAC to a comprehensive
set of laboratory data collected for many individual particle types using the
wideband integrated bioaerosol sensor (WIBS-4A)
(Savage et al., 2017). The impact of the ratio of
particle concentrations on HAC results was investigated, showing that
clustering quality can vary dramatically as a function of ratio. Six
strategies for particle preprocessing were also compared, concluding that
using raw fluorescence intensity (without normalizing to particle size) and
logarithmically transforming data values (scenario B) consistently produced
the highest-quality results for the particle types analyzed. A total of 23
one-to-one matchups of individual particles types was investigated. Results
showed a cluster misclassification of < 15 % for 12 of 17 numerical
experiments using one biological and one nonbiological particle type each.
Inputting fluorescence data using a baseline +3σ threshold
produced a lower degree of misclassification than when inputting either all particles
(without a fluorescence threshold) or a baseline +9σ threshold.
Lastly, six numerical simulations of mixtures of four to seven components
were analyzed using HAC. These results show that a range of 12 %–24 % of
fungal clusters was consistently misclassified by inclusion of a mixture of
nonbiological materials, whereas bacteria and diesel soot were each able to
be separated with nearly 100 % efficiency. The study gives significant
support to clustering analysis commonly being applied to data from commercial
ultraviolet laser/light-induced fluorescence (UV-LIF) instruments 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.
Evaluation of a hierarchical agglomerative clustering method applied to WIBS laboratory data for improved discrimination of biological particles by comparing data preparation techniques
