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

Zawadowicz, Maria A.; Froyd, Karl D.; Murphy, Daniel M.; Cziczo, Daniel J.

doi:10.5194/acp-17-7193-2017

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i

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

2017
By Zawadowicz, Maria A. ; Froyd, Karl D. ; Murphy, Daniel M. ; ...
Source: Atmospheric Chemistry and Physics, 17(11), 7193-7212.

[PDF-4.57 MB]

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Journal Title:

Atmospheric Chemistry and Physics
Personal Author:

Zawadowicz, Maria A. ; Froyd, Karl D. ; Murphy, Daniel M. ; Cziczo, Daniel J.

Zawadowicz, Maria A. ; Froyd, Karl D. ; Murphy, Daniel M. ; Cziczo, Daniel J. Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; ESRL (Earth System Research Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences) ; CSL (Chemical Sciences Laboratory)

OAR (Oceanic and Atmospheric Research) ; ESRL (Earth System Research Laboratory) ; CIRES (Cooperative Institute for Research in Environmental Sciences) ; CSL (Chemical Sciences Laboratory) Less -
Description:

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.
Source:

Atmospheric Chemistry and Physics, 17(11), 7193-7212.
DOI:

https://doi.org/10.5194/acp-17-7193-2017
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Journal Article
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CC BY
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urn:sha256:f3475657028364e212cc0ae16b79a7d30d9bf46bf9a45c5d7c4a45828784f80b
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Improved identification of primary biological aerosol particles using single-particle mass spectrometry

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