Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on field measurements in urban 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 (N-CCN). In this study, we predict N-CCN 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 EISR scheme). The resulting ratios of predicted-to-measured N-CCN (R-CCN_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 R-CCN_ 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. N CCN is significantly underestimated (with R-CCN_p/m of 0.66 -0.75) when using the schemes of external mixtures with bulk (EXT-BK scheme) or size-resolved composition (EXTSR 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 kappa(org) exceeds 0.1.