Characterization of submicron aerosols and CCN over the Yellow Sea measured onboard the Gisang 1 research vessel using the positive matrix factorization analysis method

Minsu Park, Seong Soo Yum, Najin Kim, Joo Wan Cha, Beomcheol Shin, Sang Boom Ryoo

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Aerosol size distributions and cloud condensation nuclei (CCN) number concentrations were measured in spring 2017 over the Yellow Sea on board the research vessel Gisang 1. The average number concentration of particles larger than 10 nm and CCN at 0.65% supersaturation (S) were 7622 ± 4038 cm−3 and 4821 ± 1763 cm−3, respectively. Characteristics of aerosol size distribution data were analyzed using a positive matrix factorization (PMF) method. It was found that only 6 Factors could explain the aerosol size distribution reasonably well. Factors 1 and 2 indicated nucleation mode particles, Factor 3 indicated Aitken mode particles, and Factors 4, 5, and 6 indicated accumulation mode particles. The concentrations of nucleation and Aitken mode particles showed a clear wind direction dependence; high under westerly winds due to the high concentrations of particles and precursor gases in eastern China. Meanwhile, the concentration of larger particles and CCN showed no significant wind direction dependence. Aerosol size distribution was also significantly influenced by meteorology. Small particles were predominant during clear days. In contrast during mist or fog days, the aerosol size distribution shifted to larger sizes. A CCN closure experiment was conducted using results of the PMF analysis. The assumption of internally mixed particles led to overestimation of predicted CCN concentrations but agreement was significantly better when externally mixed particles were considered. The logarithmic curve fit of NCCN(S) = 4825 ∗ log S + 4933 was found to very well explain measured CCN concentrations at a few S over the Yellow Sea, and therefore is recommended as input CCN spectral data for numerical models that explicitly treat CCN activation.

Original languageEnglish
Pages (from-to)430-441
Number of pages12
JournalAtmospheric Research
Volume214
DOIs
Publication statusPublished - 2018 Dec 1

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

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