Cloud condensation nuclei characteristics at the Southern Great Plains site: role of particle size distribution and aerosol hygroscopicity

activation ability of aerosols as cloud condensation nuclei (CCN) is crucial in climate and13 hydrological cycle studies, but their properties are not well known. We investigated the long-term14 measurements of atmospheric aerosol properties, CCN concentrations (NCCN) at supersaturation15 (SS = 0.1-1.0%), and hygroscopicity at the Department of Energy’s Southern Great Plains (SGP)16 site to illustrate the dependence of NCCN on aerosol properties and transport pathways. Cluster17 analysis was applied to the back trajectories of air masses to investigate their respective source18 regions. The results showed that aged biomass burning aerosols from Central America were19 characterized by higher accumulation mode particles (Naccu; median value 805 cm-3) and relatively20 high aerosol hygroscopicity (κ; median value ~0.25) values that result in the higher CCN activation21 and relatively high NCCN (median value 258-1578 cm-3 at a SS of 0.1-1.0%). Aerosols from the22 Gulf of Mexico were characterized by higher Naccu (~35%), and NCCN (230-1721 cm-3 at a SS of23 0.1-1.0%) with the lowest κ (~0.17). In contrast, relatively high nucleation mode particles (Nnucl;24 ~20%) and low NCCN (128-1553 cm-3 at a SS of 0.1-1.0%) with higher κ (~0.30) values were25 observed on the aerosols associated with a westerly wind. The results indicate particle size as the26 most critical factor influencing the ability of aerosols to activate, whereas the effect of chemical27 composition was secondary. Our CCN closure analysis suggests that chemical composition and28 mixing state information are more crucial at lower SS, whereas at higher SS, most particles become29 activated regardless of their chemical composition and size. This study affirms that soluble organic30 fraction information is required at higher SS for better NCCN prediction, but both the soluble31 organics fraction and mixing state are vital to reduce the NCCN prediction uncertainty at lower SS.