Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

I Crawford,S Ruske,M W Gallagher,D O Topping

doi:10.5194/amt-8-4979-2015

Abstract

Abstract. In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs) by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF) spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4) where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study) ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP) where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misattribution due to poor centroid definition and failure to assign particles to a cluster as a result of the subsampling and comparative attribution method employed by WASP. The methods used here allow for the entire fluorescent population of particles to be analysed, yielding an explicit cluster attribution for each particle and improving cluster centroid definition and our capacity to discriminate and quantify PBAP meta-classes compared to previous approaches.

Highlights

Microorganisms influence climate through their physical and chemical interactions with the atmosphere
The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP) where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5
We suggest that this likely due to errors arising from misattribution due to poor centroid definition and failure to assign particles to a cluster as a result of the subsampling and comparative attribution method employed by WASP

Summary

Introduction

Microorganisms influence climate through their physical and chemical interactions with the atmosphere. There has been renewed interest in how primary biological aerosol particles (PBAPs) interact with and modify clouds. It has been shown that bacterial aerosol such as Pseudomonas syringae can act as ice nuclei (IN) at relatively warm temperatures (Möhler et al, 2007), which even in low concentrations can cause rapid cloud glaciation via the Hallet–Mossop process, leading to premature precipitation (Crawford et al, 2012). It has been hypothesised that a feedback cycle exists where PBAPs associated with plants influence the formation and modification of clouds through ice formation to induce precipitation, creating an environment which is beneficial for plant and microbial growth and stimulating fur-. One of the key drivers for new research into bioprecipitation is a need for more accurate quantification of cloud evolution and precipitation in weather and climate models given its potential impact

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Conclusion