Characterization and parameterization of aerosol cloud condensation nuclei activation under different pollution conditions

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作者
H. C. Che
X. Y. Zhang
Y. Q. Wang
L. Zhang
X. J. Shen
Y. M. Zhang
Q. L. Ma
J. Y. Sun
Y. W. Zhang
T. T. Wang
机构
[1] Key Laboratory of Atmospheric Chemistry of CMA,
[2] Institute of Atmospheric Composition,undefined
[3] Chinese Academy of Meteorological Sciences,undefined
[4] College of Earth Science,undefined
[5] University of Chinese Academy of Sciences,undefined
[6] LinAn Regional Atmosphere Background Station,undefined
[7] State Key Laboratory of Cryospheric Sciences,undefined
[8] Cold and Arid Region Environmental and Engineering Research Institute,undefined
[9] Chinese Academy of Sciences,undefined
[10] Trinity Consultants,undefined
[11] INC.,undefined
[12] China office,undefined
[13] Heilongjiang Meteorological Bureau,undefined
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摘要
To better understand the cloud condensation nuclei (CCN) activation capacity of aerosol particles in different pollution conditions, a long-term field experiment was carried out at a regional GAW (Global Atmosphere Watch) station in the Yangtze River Delta area of China. The homogeneity of aerosol particles was the highest in clean weather, with the highest active fraction of all the weather types. For pollution with the same visibility, the residual aerosol particles in higher relative humidity weather conditions were more externally mixed and heterogeneous, with a lower hygroscopic capacity. The hygroscopic capacity (κ) of organic aerosols can be classified into 0.1 and 0.2 in different weather types. The particles at ~150 nm were easily activated in haze weather conditions. For CCN predictions, the bulk chemical composition method was closer to observations at low supersaturations (≤0.1%), whereas when the supersaturation was ≥0.2%, the size-resolved chemical composition method was more accurate. As for the mixing state of the aerosol particles, in haze, heavy haze, and severe haze weather conditions CCN predictions based on the internal mixing assumption were robust, whereas for other weather conditions, predictions based on the external mixing assumption were more accurate.
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