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Volume 17, issue 11
Atmos. Chem. Phys., 17, 7193–7212, 2017
https://doi.org/10.5194/acp-17-7193-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 17, 7193–7212, 2017
https://doi.org/10.5194/acp-17-7193-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 16 Jun 2017

Research article | 16 Jun 2017

Improved identification of primary biological aerosol particles using single-particle mass spectrometry

Maria A. Zawadowicz1, Karl D. Froyd2,3, Daniel M. Murphy2, and Daniel J. Cziczo1,4 Maria A. Zawadowicz et al.
  • 1Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, UK
  • 2NOAA Chemical Sciences Division, Boulder, Colorado, USA
  • 3Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA
  • 4Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Abstract. Measurements of primary biological aerosol particles (PBAP), especially at altitudes relevant to cloud formation, are scarce. Single-particle mass spectrometry (SPMS) has been used to probe aerosol chemical composition from ground and aircraft for over 20 years. Here we develop a method for identifying bioaerosols (PBAP and particles containing fragments of PBAP as part of an internal mixture) using SPMS. We show that identification of bioaerosol using SPMS is complicated because phosphorus-bearing mineral dust and phosphorus-rich combustion by-products such as fly ash produce mass spectra with peaks similar to those typically used as markers for bioaerosol. We have developed a methodology to differentiate and identify bioaerosol using machine learning statistical techniques applied to mass spectra of known particle types. This improved method provides far fewer false positives compared to approaches reported in the literature. The new method was then applied to two sets of ambient data collected at Storm Peak Laboratory and a forested site in Central Valley, California to show that 0.04–2 % of particles in the 200–3000 nm aerodynamic diameter range were identified as bioaerosol. In addition, 36–56 % of particles identified as biological also contained spectral features consistent with mineral dust, suggesting internal dust–biological mixtures.

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This paper reports the results of laboratory and field measurements of primary biological aerosol particles using single-particle mass spectrometry (SPMS). Identification of biological particles using SPMS can be challenging, as their mass spectra can present features similar to phosphorus-containing minerals and combustion by-products. Using a large database of laboratory measurements, a criterion for the identification of biological particles has been developed.
This paper reports the results of laboratory and field measurements of primary biological...
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