Improved identification of primary biological aerosol particles using single-particle mass spectrometry
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. Here, 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.
- Authors:
- Publication Date:
- Research Org.:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1364084
- Alternate Identifier(s):
- OSTI ID: 1425937
- Grant/Contract Number:
- SC0014487; NNX13AO15G; AGS-1461347; AGS-1339264
- Resource Type:
- Published Article
- Journal Name:
- Atmospheric Chemistry and Physics (Online)
- Additional Journal Information:
- Journal Name: Atmospheric Chemistry and Physics (Online) Journal Volume: 17 Journal Issue: 11; Journal ID: ISSN 1680-7324
- Publisher:
- Copernicus Publications, EGU
- Country of Publication:
- Germany
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Zawadowicz, Maria A., Froyd, Karl D., Murphy, Daniel M., and Cziczo, Daniel J. Improved identification of primary biological aerosol particles using single-particle mass spectrometry. Germany: N. p., 2017.
Web. doi:10.5194/acp-17-7193-2017.
Zawadowicz, Maria A., Froyd, Karl D., Murphy, Daniel M., & Cziczo, Daniel J. Improved identification of primary biological aerosol particles using single-particle mass spectrometry. Germany. https://doi.org/10.5194/acp-17-7193-2017
Zawadowicz, Maria A., Froyd, Karl D., Murphy, Daniel M., and Cziczo, Daniel J. Fri .
"Improved identification of primary biological aerosol particles using single-particle mass spectrometry". Germany. https://doi.org/10.5194/acp-17-7193-2017.
@article{osti_1364084,
title = {Improved identification of primary biological aerosol particles using single-particle mass spectrometry},
author = {Zawadowicz, Maria A. and Froyd, Karl D. and Murphy, Daniel M. and Cziczo, Daniel J.},
abstractNote = {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. Here, 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.},
doi = {10.5194/acp-17-7193-2017},
journal = {Atmospheric Chemistry and Physics (Online)},
number = 11,
volume = 17,
place = {Germany},
year = {Fri Jun 16 00:00:00 EDT 2017},
month = {Fri Jun 16 00:00:00 EDT 2017}
}
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https://doi.org/10.5194/acp-17-7193-2017
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