Organic particle types by single-particle measurements using a time-of-flight aerosol mass spectrometer coupled with a light scattering module

Chemical and physical properties of individual ambient aerosol particles can vary greatly, so measuring the chemical composition at the single-particle level is essential for understanding atmospheric sources and transformations. Here we describe 46 days of single-particle measurements of atmospheric particles using a time-of-flight aerosol mass spectrometer coupled with a light scattering module (LS-ToF-AMS). The light scattering module optically detects particles larger than 180 nm vacuum aerodynamic diameter (130 nm geometric diameter) before they arrive at the chemical mass spectrometer and then triggers the saving of single-particle mass spectra. 271 641 particles were detected and sampled during 237 h of sampling in single-particle mode. By comparing timing of the predicted chemical ion signals from the light scattering measurement with the measured chemical ion signals by the mass spectrometer for each particle, particle types were classified and their number fractions determined as follows: prompt vaporization (46%), delayed vaporization (6%), and null (48%), where null was operationally defined as less than 6 ions per particle. Prompt and delayed vaporization particles with sufficient chemical information (i.e., more than 40 ions per particle) were clustered based on similarity of organic mass spectra (using <i>k</i>-means algorithm) to result in three major clusters: highly oxidized particles (dominated by <i>m/z</i> 44), relatively less oxidized particles (dominated by <i>m/z</i> 43), and particles associated with fresh urban emissions. Each of the three organic clusters had limited chemical properties of other clusters, suggesting that all of the sampled organic particle types were internally mixed to some degree; however, the internal mixing was never uniform and distinct particle types existed throughout the study. Furthermore, the single-particle mass spectra and time series of these clusters agreed well with mass-based components identified (using factor analysis) from simultaneous ensemble-averaged measurements, supporting the connection between ensemble-based factors and atmospheric particle sources and processes. Measurements in this study illustrate that LS-ToF-AMS provides unique information about organic particle types by number as well as mass.