Process-level simulation of chemical composition, size distribution and cloud condensation nuclei of secondary organic aerosol from α-pinene ozonolysis

Abstract

Secondary organic aerosols (SOA) contribute significantly to cloud condensation nuclei (CCN), which depend on particle size distribution (PSD), chemical composition and the hygroscopicity parameter (κ). Simulating SOA and CCN in chemical transport models relies on parameterizations, which need to be evaluated and improved against process-level models as a benchmark. Here, we simulated SOA concentration, chemical composition, PSD, κ, and CCN in α-pinene ozonolysis, a classical system for SOA studies, using a process-level box model PyCHAM with near-explicit chemical mechanisms. We assessed how CCN, chemical composition, PSD and κ can be modelled against measurements and evaluated the influence of these factors on CCN simulation. The model well simulated SOA mass concentration but overestimated O: C and H: C ratios, suggesting a possible lack of particle-phase chemistry. Highly oxygenated molecules (HOMs) contributed substantially to SOA mass. Simulated κ closely agreed with measurements at moderate supersaturation (0.37 %) but was overestimated at low supersaturation (0.19 %) and underestimated at high supersaturation (0.55 % and 0.73 %). Particle growth and number concentrations were reasonably reproduced, though the simulated PSD was broader and flatter than measurement. Simulated CCN concentrations agreed well with measurements at moderate to high supersaturation (0.37 %–0.73 %) but were overestimated at low supersaturation (0.19 %). Sensitivity analysis highlights the importance of accurately representing both PSD and κ for reliable CCN prediction, especially at supersaturation < 0.4 %. This study also highlights that HOM formation, finer PSD resolution and improved κ parameterizations are warranted in future chemical transport models, and evaluates the ability and limitations of this benchmark model.