PNNL

Abstract

ABSTRACT. Secondary organic aerosols (SOA) have highly complex, multi-component composition that governs the gas-particle partitioning of their constituents and the viscosity of the particle phase, both of which play a critical role in their atmospheric transformations and environmental impacts. This study investigates the chemical composition, gas-particle partitioning and viscosity of SOA formed through the ozonolysis of cyclic a-pinene (PSOA) and acyclic ocimene (OSOA) monoterpenes. We employ Temperature-Programmed Desorption combined with Direct Analysis in Real-Time ionization and High-Resolution Mass Spectrometry (TPD-DART-HRMS) to determine the molecular composition and saturation mass concentration of individual SOA components. These data enable calculations of gas-particle partitioning and viscosity estimates under varying atmospheric conditions. Our findings reveal that PSOA particles, characterized by higher molecular mass and lower oxidation state components, are more condensable than OSOA particles, which consist of highly oxidized, lower molecular weight species. Notably, the gas-particle partitioning and viscosity of OSOA, as supported by the poke-flow measurements, show greater sensitivity to total organic mass (tOM) loadings, indicating that atmospheric dilution significantly influences the phase state and partitioning of OSOA. In contrast, PSOA exhibits greater stability under varying tOM conditions. Near emission sources with high tOM loadings, PSOA demonstrates higher viscosity; however, as atmospheric dilution progresses, OSOA viscosity increases substantially, eventually surpassing that of PSOA. These observations indicate distinct dynamic trends in the atmospheric transformations and reactivity of SOA derived from cyclic versus acyclic monoterpenes. Notably, SOA formed from acyclic monoterpenes undergoes more dynamic compositional changes during atmospheric aging, which significantly modulates its viscosity and diffusion properties within aged particles. This study highlights the importance of incorporating these dynamic transformations into atmospheric models to improve predictions of SOA atmospheric loadings, lifetimes, and impacts.