Enhancement of modeling performance by including organic markers to the PMF modeling for the PM2.5 at Seoul

Sun Min Shin, Jin Young Kim, Ji Yi Lee, Deug Soo Kim, Yong Pyo Kim

Research output: Contribution to journalArticlepeer-review

Abstract

The marginal utility of using organic compounds’ data to the Positive Matrix Factorization (PMF) model in addition to the conventional measurement data of inorganic ions, elements, and organic carbon (OC) and elemental carbon (EC) was evaluated. Three cases of input data were used; (1) case 1: conventional inorganic ions, elements, OC and EC, (2) case 2: case 1 with organic compounds, (3) case 3: same as case 1 except adding levoglucosan. The PM2.5 measurement data in Seoul from October 2012 to September 2013 were used. The performance evaluation parameters determined 9 sources for case 1 and case 3 and 10 sources for case 2. Case 2 with organic compounds not only subdivided biomass burning into local and transported biomass burning but also identified biogenic sources which could not be identified in case 1 and case 3. Furthermore, in case 2, it was possible to apply diagnostic ratios on the polycyclic aromatic hydrocarbons (PAHs) to check the validity of the proposed factor characteristics. The PMF modeling results were compared to the Solver for Mixture Problem (SMP) modeling result which was separately carried out by Kim et al. (2016) using the data set of case 2. The SMP modeling was also able to identify local and transported biomass burning sources. However, it was not possible to classify biogenic source that was identifiable through the PMF modeling. Thus, though it takes extra effort to obtain organic speciation data, applying organic compounds in the PMF modeling can provide more accurate source identification and quantitative contribution estimation.

Original languageEnglish
Pages (from-to)91-104
Number of pages14
JournalAir Quality, Atmosphere and Health
Volume15
Issue number1
DOIs
StatePublished - Jan 2022

Keywords

  • Diagnostic ratios
  • Organic markers
  • PM2.5 source apportionment
  • Positive Matrix Factorization
  • Receptor model

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