Seasonal variations in PM_2.5 composition and their effects on CCN activation properties

PM_2.5 samples were collected in Xi'an during the summer and winter of 2019 to analyze seasonal, diurnal, and pollution-level variations in particle chemical composition. Scanning Mobility CCN Analysis was used to investigate the impact of chemical composition on cloud condensation nuclei (CCN) activation. Results showed increased particle concentrations in winter, with significant rises in SO4²⁻, OM, NO3^-, and Cl⁻. Motor vehicle exhaust was the primary pollution source, while biomass burning air masses further exacerbated winter pollution. Xi'an aerosols exhibited continental characteristics, with κ_ccn = 0.26 ± 0.07 in summer and κ_ccn = 0.33 ± 0.09 in winter. Seasonal κ_ccn differences were mainly attributed to the prevalence of weakly hygroscopic air masses in summer, while higher κ_ccn values were observed in winter and during severe pollution episodes. The κ-closure scheme proposed in this study performed poorly at higher supersaturations (the nucleation mode), with nitrate evaporation losses and the surface tension reduction effect of organics being key factors. A fixed κ-closure scheme could not accurately characterize CCN activity across different seasons, but after appropriate corrections, the closure error was controlled within 6%, achieving a good agreement.