Yuying Wang

Uncertainty in Predicting CCN Activity of Aged and Primary Aerosols

Understanding particle CCN activity in diverse atmospheres is crucial when evaluating aerosol indirect effects. Here, aerosols measured at three sites in China were categorized as different types for attributing uncertainties in CCN prediction in terms of a comprehensive dataset including size-resolved CCN activity, size-resolved hygroscopic growth factor, and chemical composition. We show that CCN activity for aged aerosols is unexpectedly underestimated ~22% at a supersaturation (S) of 0.2% when using κ-Kohler theory with an assumption of an internal mixture with measured bulk composition that has typically resulted in an overestimate of the CCN activity in previous studies. We conclude that the underestimation stems from neglect of the effect of aging/coating on particle hygroscopicity, which is not considered properly in most current models. This effect enhanced the hygroscopicity parameter (κ) by between ~11% (polluted conditions) and 30% (clean days), as indicated in diurnal cycles of κ based on measurements by different instruments. In the urban Beijing atmosphere heavily influenced by fresh emissions, the CCN activity was overestimated by 45% at S=0.2%, likely because of inaccurate assumptions of particle mixing state and because of variability of chemical composition over the particle size range. For both fresh and aged aerosols, CCN prediction exhibits very limited sensitivity to κSOA, implying a critical role of other factors like mixing of aerosol components within and between particles in regulating CCN activity. Our findings could help improving CCN parameterization in climate models.

Aerosol chemistry and particle growth events at an urban downwind site in the North China Plain

The North China Plain (NCP) has experienced frequent severe haze pollution events in recent years. While extensive measurements have been made in megacities, aerosol sources, processes, and particle growth at urban downwind sites remain less understood. Here, an aerosol chemical speciation monitor and a scanning mobility particle sizer, along with a suite of collocated instruments, were deployed at the downwind site of Xingtai, a highly polluted city in the NCP, for real-time measurements of submicron aerosol (PM1) species and particle number size distributions during May and June 2016. The average mass concentration of PM1 was 30.5 (±19.4) μg m−3, which is significantly lower than that during wintertime. Organic aerosols (OAs) constituted the major fraction of PM1 (38 %), followed by sulfate (25 %) and nitrate (14 %). Positive matrix factorization with the multilinear engine version 2 showed that oxygenated OA (OOA) was the dominant species in OA throughout the study, on average accounting for 78 % of OA, while traffic and cooking emissions both accounted for 11 % of OA. Our results highlight that aerosol particles at the urban downwind site were highly aged and mainly from secondary formation. However, the diurnal cycle also illustrated the substantial influence of urban emissions on downwind sites, which are characterized by similar pronounced early morning peaks for most aerosol species. New particle formation and growth events were also frequently observed (58 % of the time) on both clean and polluted days. Particle growth rates varied from 1.2 to 4.9 nm h−1 and our results showed that sulfate and OOA played important roles in particle growth during clean periods, while OOA was more important than sulfate during polluted events. Further analyses showed that particle growth rates have no clear dependence on air mass trajectories. Published by Copernicus Publications on behalf of the European Geosciences Union. 14638 Y. Zhang et al.: Aerosol chemistry and particle growth events

Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on filed measurement in Beijing

Understanding the impacts of aerosol chemical composition and mixing state on cloud condensation nuclei (CCN) activity in polluted areas is crucial for accurately predicting CCN number concentrations (NCCN). In this study, we predict NCCN under five assumed schemes of aerosol chemical composition and mixing state based on field measurements in Beijing during the winter of 2016. Our results show that the best closure is achieved with the assumption of size dependent chemical composition for which sulfate, nitrate, secondary organic aerosols, and aged black carbon are internally mixed with each other but externally mixed with primary organic aerosol and fresh black carbon (external–internal size-resolved, abbreviated as EI– SR scheme). The resulting ratios of predicted-to-measured NCCN (RCCN_p/m) were 0.90 – 0.98 under both clean and polluted conditions. Assumption of an internal mixture and bulk chemical composition (INT–BK scheme) shows good closure with RCCN_p/m of 1.0 –1.16 under clean conditions, implying that it is adequate for CCN prediction in continental clean regions. On polluted days, assuming the aerosol is internally mixed and has a chemical composition that is size dependent (INT–SR scheme) achieves better closure than the INT–BK scheme due to the heterogeneity and variation in particle composition at different sizes. The improved closure achieved using the EI–SR and INT–SR assumptions highlight the importance of measuring size-resolved chemical composition for CCN predictions in polluted regions. NCCN is significantly underestimated (withRCCN_p/m of 0.66 – 0.75) when using the schemes of external mixtures with bulk (EXT–BK scheme) or size-resolved composition (EXT– SR scheme), implying that primary particles experience rapid aging and physical mixing processes in urban Beijing. However, our results show that the aerosol mixing state plays a minor role in CCN prediction when the κorg exceeds 0.1.

Retrieval of Cloud Condensation Nuclei Number Concentration Profiles From Lidar Extinction and Backscatter Data

6 7 1State Key Laboratory of Earth Surface Processes and Resource Ecology and College of Global 8 Change and Earth System Science, Beijing Normal University, Beijing, China, 9 2 Department of Atmospheric Science, University of Wyoming, Laramie, Wyoming, USA, 10 3University of Maryland, College Park, Maryland, USA, 11 4Key Laboratory of Atmospheric Composition and Optical Radiation, Anhui Institute of Optics 12 and Fine Mechanics, Chinese Academy of Sciences, Anhui, China, 13 5 NASA Langley Research Center, Hampton, Virginia, USA 14 6School of Physics, Peking University, Beijing, China, 15 7State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, 16 Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China.