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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
01 Nov 2017
Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Atmospheric Chemistry and Physics (ACP).
Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on filed measurement in Beijing
Jingye Ren1, Fang Zhang1,2, Yuying Wang1, Xinxin Fan1, Xiaoai Jin1, Weiqi Xu3,4, Yele Sun3,4, Maureen Cribb5, and Zhanqing Li1,5 1State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
2Joint Center for Global Change Studies (JCGCS), Beijing 100875, China
3State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
4University of Chinese Academy of Sciences, Beijing 100049, China
5Earth System Science Interdisciplinary Center and Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland, USA
Abstract. Understanding the impacts of aerosol chemical composition and mixing state on cloud condensation nuclei (CCN) activity in polluted area is crucial for determining CCN number concentrations (NCCN) accurately. In this study, we predict CCN number concentrations (NCCN) by applying κ-Köhler theory under five assumed schemes of aerosol chemical composition and mixing state based on field measurement in Beijing during the winter of 2016. Our results show that the EIS scheme (with an assumption that sulfate, nitrate, and secondary organic aerosols are internally mixed and that primary organic aerosols, POA, and black carbon, BC, are externally mixed; and the chemical composition is size dependent) achieves the best closure to predict NCCN with ratios of predicted-to-measured NCCN (RCCN_p/m) of 0.90–1.12 under both clean and polluted conditions over the campaign. Also, IB scheme (with an assumption of internal mixture and bulk chemical composition for particles) shows good closure with RCCN_p/m of 1.01–1.19 under clean conditions, implying that the IB assumption is sufficient for CCN prediction in continental clean regions. On polluted days, IS scheme (assuming particles with internal mixture and chemical composition is size-resolved) achieve better closure than the IB scheme due to the heterogeneity and variations in particle composition at different sizes. The improved closure achieved using EIS and IS assumptions highlights the importance of measuring size-resolved chemical composition for CCN predictions in polluted regions. NCCN is significantly underestimated (with RCCN_p/m of 0.6–0.8) by using the schemes of external mixture with bulk (EB) or size-resolved composition (ES), implying that the primary particles experience rapid aging and physical mixing processes in urban area. However, our results show that the mixing state of particles plays a minor role on CCN prediction when the κorg exceeds 0.1.
Citation: Ren, J., Zhang, F., Wang, Y., Fan, X., Jin, X., Xu, W., Sun, Y., Cribb, M., and Li, Z.: Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on filed measurement in Beijing, Atmos. Chem. Phys. Discuss.,, in review, 2017.
Jingye Ren et al.
Jingye Ren et al.


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