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Seasonal variation and source apportionment of Oxygenated Polycyclic Aromatic Hydrocarbons (OPAHs) and Polycyclic Aromatic Hydrocarbons (PAHs) in P

DATE2022-10-31 HIT133

Seasonal variation and source apportionment of Oxygenated Polycyclic Aromatic Hydrocarbons (OPAHs) and Polycyclic Aromatic Hydrocarbons (PAHs) in PM2.5 in Seoul, Korea

Atmospheric Environment


Authors : Sun Min Shin, Ji Yi Lee, Hye Jung Shin, Yong Pyo Kim

ISSN : 1352-2310

  Oxygenated polycyclic aromatic hydrocarbons (OPAHs) are of increasing interest due to their
high toxicity. Thirteen particulate PAHs and ten particulate OPAHs were analyzed by Gas
Chromatography/Mass Spectrometry (GC/MS) in atmospheric particulate matter with aerodynamic
diameters less than or equal to a nominal 2.5 μm (PM2.5) for the period between May 2020 and
January 2021 in Seoul, Republic of Korea. The mean concentration ten OPAHs (5.49 ± 2.40 ng/m3)
was higher than that of thirteen PAHs (3.96 ± 3.36 ng/m3), respectively. The concentration of total
OPAHs was high in winter (9.40 ± 2.50 ng/m3) and low in summer (3.58 ± 0.78 ng/m3), showing
the same trend with PAHs (9.16 ± 3.30 ng/m3 in winter and 0.62 ± 0.15 ng/m3 in summer). When
comparing this study results with the measurement results on 2006 in Seoul, PAHs concentration
dropped by 68% while only 22% was reduced for OPAHs. It suggests either improvement of combustion
technology, i.e., high temperature combustion process in the region or higher ambient oxidation reaction
rates on 2020 than 2006. Major sources and their contributions of PAHs and OPAHs were identified based
on correlation analysis and Positive Matrix Factorization (PMF) modeling. Two cases were carried.
Case 1 is for source apportionment for PM2.5 and case 2 is for OPAHs and PAHs. The number of factors
or sources in the two cases was determined as 7 and 4, respectively. The results of the PMF modeling
using diagnostic ratios (DRs) in case 1 identified 7 factors; secondary nitrate (23%), secondary sulfate (21%),
vehicles (20%), biomass burning (16%), coal combustion (12%), waste incineration (5%), and OPAHs
(5,12-NAQ) emission-related (3%). The PMF modeling results in case 2 revealed 4 factors; vehicles (36%),
coal combustion (30%), biomass burning (25%), and OPAHs (5,12-NAQ) emission related (9%). Significant
correlation between OPAHs and indicators of primary emission sources, and high contribution from sources
with emission characteristics implied that most sources of OPAHs were heavily influenced by primary
emissions rather than secondary formation.

Keyword : OPAHS sources, Positive matrix factorization (PMF), PM2.5 source apportionment, Diagnostic ratios (DRs), Combustion technology change

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