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

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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

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cloud condensation nucleus
research vessel
aerosol
matrix
particle size
wind direction
nucleation
method
sea
analysis
particle
supersaturation
fog
westerly
meteorology

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Cite this

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title = "Characterization of submicron aerosols and CCN over the Yellow Sea measured onboard the Gisang 1 research vessel using the positive matrix factorization analysis method",
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.",
author = "Minsu Park and Yum, {Seong Soo} and Najin Kim and Cha, {Joo Wan} and Beomcheol Shin and Ryoo, {Sang Boom}",
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Characterization of submicron aerosols and CCN over the Yellow Sea measured onboard the Gisang 1 research vessel using the positive matrix factorization analysis method. / Park, Minsu; Yum, Seong Soo; Kim, Najin; Cha, Joo Wan; Shin, Beomcheol; Ryoo, Sang Boom.

In: Atmospheric Research, Vol. 214, 01.12.2018, p. 430-441.

Research output: Contribution to journalArticle

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T1 - Characterization of submicron aerosols and CCN over the Yellow Sea measured onboard the Gisang 1 research vessel using the positive matrix factorization analysis method

AU - Park, Minsu

AU - Yum, Seong Soo

AU - Kim, Najin

AU - Cha, Joo Wan

AU - Shin, Beomcheol

AU - Ryoo, Sang Boom

PY - 2018/12/1

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N2 - 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.

AB - 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.

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