A core-shell box model for simulating viscosity dependent secondary organic aerosol (CSVA) and its application

Area of Science:

• Atmospheric Chemistry and Physics
• Environmental Science
• Climate Science

Background:

• Secondary organic aerosol (SOA) significantly impacts air quality and climate, but its properties and evolution remain poorly understood and modeled.
• Existing models often lack detailed mechanisms for gas-particle interactions and the influence of particle viscosity on aerosol evolution.

Purpose of the Study:

• To develop and validate a novel kinetic Core-Shell box model for Viscosity dependent SOA simulation (CSVA).
• To investigate the influence of viscosity, relative humidity (RH), and NOx on SOA formation and particle size distribution.
• To elucidate the formation pathways of SOA monomers and dimers.

Main Methods:

• Developed the CSVA model incorporating explicit gas-phase reactions (MCM), H2SO4-NH3-H2O nucleation, viscosity-dependent gas-particle mass transfer, and particle-phase reactions.
• Modeled gas-particle mass transfer using a chain-like reaction analogy to electrical resistance.
• Validated the model against chamber experiments of toluene oxidation, coupling high-resolution Orbitrap mass spectrometry with the MCM mechanism.

Main Results:

• The CSVA model accurately simulates RH-dependent nucleation, particle size-dependent hygroscopic growth of inorganic and organic species, and NOx-dependent SOA formation.
• The model captures viscosity-induced evolution of particle size distribution, including the transition from unimodal to bimodal distributions.
• Identified majority of SOA dimers as peroxyhemiacetals formed from hydroperoxide and aldehyde reactions in the particle phase.

Conclusions:

• The CSVA model provides a robust framework for simulating viscosity-dependent SOA processes, enhancing the understanding of air pollution and climate change.
• Particle viscosity is a critical factor influencing SOA evolution and particle size distribution dynamics.
• The model's ability to reproduce observed phenomena highlights the importance of incorporating detailed kinetic and physical processes in aerosol modeling.