Initial application of the noise-sorted scanning clustering algorithm to the analysis of composition-dependent organic aerosol thermal desorption measurements
Cappa, Christopher et al. (2019), Initial application of the noise-sorted scanning clustering algorithm to the analysis of composition-dependent organic aerosol thermal desorption measurements, Dryad, Dataset, https://doi.org/10.25338/B87S43
The FIGAERO-CIMS (Filter Inlet for Gases and AEROsols + chemical ionization mass spectrometer) measures thermal desorption profiles for individual ions evolved from evaporation of organic aerosol components. Often, hundreds of individual thermograms are obtained, reflecting the compositional complexity of organic aerosol. We have developed a novel clustering algorithm, Noise-Sorted Scanning Clustering (NSSC), that 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. The potential of NSSC for analysis of FIGAERO-CIMS data is demonstrated via application to a suite of distinct experiments. A static version of the NSSC algorithm is archived here, and an evolving version at GitHub (doi: 10.5281/zenodo.3361796). The data used to test and develop the NSSC, and reported on in Li et al. (submitted to Atmospheric Measurement Techniques) and in Ziyue Li's dissertation at UC Davis, are archived here. The experiments took place at the Pacific Northwest National Laboratory.
Please see Read Me file for details, and associated publications. Details of data collection and processing can be found in D'Ambro et al. (ACP, 2019) (link), D'Ambro et al. (ACS Earth Space Chem, 2019) (link) and in Li et al. (AMT, submitted).
Please see Read Me file for details. Please contact Chris Cappa or Joel Thornton prior to use of this data or the NSSC algorithm. Any publications that wish to make use of this data or the NSSC algorithm must offer co-authorship.
National Science Foundation, Award: ATM-1151062
U.S. Department of Energy, Award: DE-SC0011791
U.S. Department of Energy, Award: DE-SC0018221