Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

5 Scopus Citations
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Author(s)

  • Paritosh Pande
  • Manish Shrivastava
  • John E. Shilling
  • Alla Zelenyuk
  • Qi Zhang
  • Qi Chen
  • Nga Lee Ng
  • Yue Zhang
  • Masayuki Takeuchi
  • Quazi Z. Rasool
  • Yuwei Zhang
  • Bin Zhao
  • Ying Liu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)932–942
Number of pages11
Journal / PublicationACS Earth and Space Chemistry
Volume6
Issue number4
Online published4 Apr 2022
Publication statusPublished - 21 Apr 2022

Abstract

We apply machine learning approaches sparse multinomial logistic regression to classify aerosol mass spectrometer (AMS) unit mass resolution (UMR) data followed by an ensemble regression technique for source apportionment of organic aerosols (OA). The classifier was trained on 60 well characterized laboratory and positive matrix factorization (PMF) deconvolved reference spectra to identify eight OA types. These include four laboratory-derived secondary organic aerosol (SOA) spectra, which include isoprene photooxidation SOA, isoprene epoxydiols (IEPOX) SOA, a monoterpene SOA type that includes α-pinene and β-pinene SOA, and aromatic SOA from oxidation of naphthalene and m-xylene precursors, as well as PMF deconvolved spectra for three primary organic aerosol (POA) types, namely, hydrocarbon-like organic aerosol (HOA), biomass burning organic aerosol (BBOA), and cooking OA (COA), and a more oxidized oxygenated OA type (MO-OOA). A 5-fold cross-validation strategy, repeated 10 times, was used to assess the classifier’s performance. The classifier had high classification accuracy for COA, aromatic SOA, and isoprene SOA spectra but incorrectly classified ∼9% by number of MO-OOA spectra as BBOA, 12% of BBOA spectra as HOA (and vice versa), and 18% of IEPOX-SOA spectra as aromatic SOA. Next, an ensemble regression model was trained on an artificially generated dataset consisting of mixtures of different OA types to assess its ability to predict fractional mass abundances from classification probabilities of various OA species obtained from the multinomial logistic regression classifier trained on the reference spectra. Ultimately, the proposed approach was applied for source apportionment of aircraft-based AMS measurements of OA UMR spectra during the HI-SCALE field campaign. On two representative days (May 6th and 18th, 2016), the algorithm determined that ∼50−60% of OA by mass was MO-OOA, which represented a highly aged organic aerosol mixture from different sources. On both days, BBOA was determined to contribute less than 10% to OA by mass. However, on May 18th, the aromatic SOA fraction was higher compared to that on May 6th. The proposed approach is capable of rapidly analyzing AMS data in real time, making it suitable for applications where rapid source apportionment of AMS OA spectra is desirable.

Research Area(s)

  • aerosol mass spectrometer, SOA, machine learning, logistic regression, classification, ensemble regression, source apportionment

Citation Format(s)

Novel Application of Machine Learning Techniques for Rapid Source Apportionment of Aerosol Mass Spectrometer Datasets. / Pande, Paritosh; Shrivastava, Manish; Shilling, John E. et al.
In: ACS Earth and Space Chemistry, Vol. 6, No. 4, 21.04.2022, p. 932–942.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review