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

Abstract

Measurements of primary biological aerosol particles, 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 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 ambient data collected at Storm Peak Laboratory to show that 0.04–0.3 % of particles in the 200–3000 nm aerodynamic diameter range were identified as bioaerosol.