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Discussion papers
https://doi.org/10.5194/acp-2019-733
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-733
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 04 Sep 2019

Submitted as: research article | 04 Sep 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

A robust clustering algorithm for analysis of composition‐dependent organic aerosol thermal desorption measurements

Ziyue Li1, Emma L. D'Ambro2,3,7, Siegfried Schobesberger2,4, Cassandra J. Gaston2,8, Felipe D. Lopez-Hilfiker2,9, Jiumeng Liu5,a, John E. Shilling5, Joel A. Thornton2,3, and Christopher D. Cappa1,6 Ziyue Li et al.
  • 1Atmospheric Science Graduate Group, University of California, Davis, CA, USA
  • 2Department of Atmospheric Sciences, University of Washington, Seattle WA, USA
  • 3Department of Chemistry, University of Washington, Seattle WA, USA
  • 4Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
  • 5Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland WA, USA
  • 6Department of Civil and Environmental Engineering, University of California, Davis, CA, USA
  • 7Oak Ridge Institute for Science and Education, US Environmental Protection Agency, Research Triangle Park, NC, USA
  • 8Rosenstiel School of Marine & Atmospheric Science, University of Miami FL, USA
  • 9TofWerk AG, Thun, Switzerland
  • anow at: School of Environment, Harbin Institute of Technology, Harbin, Heilongjiang, China

Abstract. One of the challenges of understanding atmospheric organic aerosol (OA) stems from its complex composition. Mass spectrometry is commonly used to characterize the compositional variability of OA. Clustering of a mass spectral data set helps identify components that exhibit similar behavior or have similar properties, facilitating understanding of sources and processes that govern compositional variability. Here, we developed a novel clustering algorithm, Noise-Sorted Scanning Clustering (NSSC), and apply it to thermal desorption measurements from the Filter Inlet for Gases and AEROsols coupled to a chemical ionization mass spectrometer (FIGAERO CIMS). NSSC provides a robust, reproducible analysis of the FIGAERO temperature-dependent mass spectral data. The NSSC allows for determination of thermal profiles for compositionally distinct clusters, 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 cluster. For each of the systems examined, more than 80 % of the total mass is clustered into 9–13 clusters. Comparison of the average thermograms of the clusters between systems indicate some commonalty 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 clustering to elucidate the chemical factors that drive changes in the thermal properties of OA. Further quantitative interpretation of the clustered thermograms followed by clustering will allow for more comprehensive understanding of the thermochemical properties of OA.

Ziyue Li et al.
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Ziyue Li et al.
Data sets

Initial application of the noise-sorted scanning clustering algorithm to the analysis of composition-dependent organic aerosol thermal desorption measurements C. D. Cappa, Z. Li, E. L. D’Ambro, S. Schobesberger, J. E. Shilling, F. Lopez-Hilfiker, J. Liu, C. J. Gaston, and J. A. Thornton https://doi.org/10.25338/B87S43

Model code and software

Noise Sorted Scanning Clustering Algorithm Z. Li and C. D. Cappa https://doi.org/10.5281/zenodo.3361797

Ziyue Li et al.
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Short summary
We discuss the development and application of a new, robust clustering method for the interpretation of compound-specific organic aerosol thermal desorption profiles. We demonstrate the utility of clustering for analysis and interpretation of the composition and volatility of secondary organic aerosol. We show that the thermal desorption profiles are represented by only 9–13 distinct clusters, with the number of clusters obtained dependent on the precursor and formation conditions.
We discuss the development and application of a new, robust clustering method for the...
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