Articles | Volume 6, issue 2
Atmos. Meas. Tech., 6, 337–347, 2013
Atmos. Meas. Tech., 6, 337–347, 2013

Research article 13 Feb 2013

Research article | 13 Feb 2013

Cluster analysis of WIBS single-particle bioaerosol data

N. H. Robinson1, J. D. Allan1,2, J. A. Huffman3, P. H. Kaye4, V. E. Foot5, and M. Gallagher1 N. H. Robinson et al.
  • 1The Centre for Atmospheric Science, School of Earth Atmospheric and Environmental Science, The University of Manchester, Manchester, UK
  • 2The National Centre for Atmospheric Science, The University of Manchester, Manchester, UK
  • 3Department of Chemistry and Biochemistry, University of Denver, CO, USA
  • 4Centre for Atmospheric & Instrumentation Research, STRI, University of Hertfordshire, Hatfield, AL10 9AB, UK
  • 5DSTL, Porton Down, Salisbury, Wiltshire, SP4 0JQ, UK

Abstract. Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP) measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.