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

• Published in: Atmospheric chemistry and physics 2017-06, Vol.17 (11), p.7193-7212
• Main Authors: Zawadowicz, Maria A, Froyd, Karl D, Murphy, Daniel M, Cziczo, Daniel J
• Format: Article
• Language: English
• Subjects: Ablation; Aerodynamics; Aerosol particles; Aerosols; Airborne microorganisms; Aircraft; Atmospheric aerosols; Atmospheric particulates; Atoms & subatomic particles; Aversion learning; Bacteria; Bearing; Bioaerosols; By-products; Chemical composition; Cloud formation; Combustion; Diameters; Dust; Dust storms; Environmental aspects; ENVIRONMENTAL SCIENCES; Fly ash; Fragmentation; Fragments; Fungi; Identification; Ions; Lasers; Learning algorithms; Machine learning; Markers; Mass spectra; Mass spectrometry; Mass spectroscopy; Measurement techniques; Methods; Microscopy; Particle mass; Phosphorus; Scientific imaging; Serial learning; Spectra; Statistical analysis; Statistical methods; Storms
• ISSN: 1680-7324; 1680-7316; 1680-7324
• DOI: 10.5194/acp-17-7193-2017

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.