Optimization of a thermal desorption-gas chromatography/mass spectrometry method for characterization of semi-volatile organic compounds in high time resolved PM2.5

FLORES RANGEL R. M. , Mertoglu E.

ATMOSPHERIC POLLUTION RESEARCH, cilt.11, sa.3, ss.619-629, 2020 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 11 Konu: 3
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.apr.2019.12.016
  • Sayfa Sayıları: ss.619-629


Organic aerosol (OA) is an important fraction of fine particulate matter. Most primary OA emissions comprise semivolatile organic compounds (SVOCs) that are reactive and may partition into particle and vapor phases. Collection of SVOCs in the particle phase at high time resolution reduces positive and negative artifacts, but requires sensitive analytical techniques for chemical characterization. In this work, we investigated the effect of thermal desorption-gas chromatography/mass spectrometry (TD-GC-MS) analytical variables on 16 polycyclic aromatic hydrocarbons (PAHs) and 29 n-alkanes. Analytical responses of target compounds were mostly influenced by trap desorption time and GC column pressure. The optimal TD-GC-MS trap desorption conditions were 350 degrees C for 10 min at GC constant pressure mode of 17 psi. Good linearity (R-2 > 0.99) was observed for all target analyzes (with the exception of 6 ring PAHs and C-35-C-39 n-alkanes). Method detection limits were 0.038-0.157 ng m(-3 )and 0.028-0.355 m(-)(3) for PAHs and n-alkanes, respectively. The optimized method was tested on PM2.5 samples collected with the high-volume sampling technique at high temporal resolution of 2 h. Analysis of a sample, followed by analysis of a blank, did not show carryover. The optimized TD-GC-MS method showed the capability to resolve diurnal variations of target analyzes in high time resolved PM2.5. Using TD-GC-MS for analysis of high time resolved PM2.5 will contribute to the improvement of source apportionment, emission inventories, and global chemistry-climate models.