Abstract. One of the challenges of understanding atmospheric organic aerosol (OA)
particles stems from its complex composition. Mass spectrometry is commonly
used to characterize the compositional variability of OA. Clustering of a
mass spectral dataset helps identify components that exhibit similar
behavior or have similar properties, facilitating understanding of sources
and processes that govern compositional variability. Here, we developed an
algorithm for clustering mass spectra, the noise-sorted scanning clustering
(NSSC), appropriate for application to thermal desorption measurements of
collected OA particles from the Filter Inlet for Gases and AEROsols coupled
to a chemical ionization mass spectrometer (FIGAERO-CIMS). NSSC, which
extends the common density-based special clustering of applications with noise (DBSCAN) algorithm, provides a robust, reproducible
analysis of the FIGAERO temperature-dependent mass spectral data. The NSSC
allows for the determination of thermal profiles for compositionally distinct
clusters of mass spectra, increasing the accessibility and enhancing the
interpretation of FIGAERO data. Applications of NSSC to several laboratory
biogenic secondary organic aerosol (BSOA) systems demonstrate the ability of
NSSC to distinguish different types of thermal behaviors for the components
comprising the particles along with the relative mass contributions and
chemical properties (e.g., average molecular formula) of each mass spectral
cluster. For each of the systems examined, more than 80 % of the total
mass is clustered into 9–13 mass spectral clusters. Comparison of the
average thermograms of the mass spectral clusters between systems indicates
some commonality in terms of the thermal properties of different BSOA,
although with some system-specific behavior. Application of NSSC to sets of
experiments in which one experimental parameter, such as the concentration
of NO, is varied demonstrates the potential for mass spectral clustering to
elucidate the chemical factors that drive changes in the thermal properties
of OA particles. Further quantitative interpretation of the thermograms of
the mass spectral clusters will allow for a more comprehensive understanding
of the thermochemical properties of OA particles.
Supplementary material to "A robust clustering algorithm for analysis of composition‐dependent organic aerosol thermal desorption measurements"