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Process-Level Modeling Can Simultaneously Explain Secondary Organic Aerosol Evolution in Chambers and Flow Reactors.

Yicong He1, Andrew T Lambe2, John H Seinfeld3

  • 1Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States.

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|May 3, 2022
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Summary
This summary is machine-generated.

This study shows a kinetic model can explain secondary organic aerosol (SOA) evolution in both environmental chambers (ECs) and oxidation flow reactors (OFRs). Combining EC and OFR data refines SOA parameters for atmospheric models.

Keywords:
OFRaerosol processesenvironmental chamberheterogeneous oxidationkinetic modelingnucleationphase statewall loss

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Area of Science:

  • Atmospheric Chemistry
  • Aerosol Science
  • Environmental Modeling

Background:

  • Secondary organic aerosol (SOA) parameters are often derived from short-term environmental chamber (EC) experiments.
  • These parameters are extrapolated to longer timescales in atmospheric models, but rarely validated with data from oxidation flow reactors (OFRs).
  • Simultaneously using EC and OFR data is challenging due to differing timescales and experimental artifacts.

Purpose of the Study:

  • To develop a kinetic model capable of simultaneously explaining SOA evolution in both EC and OFR experiments.
  • To investigate the consistency of SOA parameters across different aging timescales and reactor types.
  • To improve SOA parameterizations for atmospheric models by integrating data from multiple experimental platforms.

Main Methods:

  • Developed a kinetic SOA chemistry and microphysics model incorporating processes like wall losses, aerosol phase state, and heterogeneous oxidation.
  • Used α-pinene SOA data from an EC experiment (<1 day aging) to fit model parameters.
  • Validated the model and parameters against SOA mass, O:C ratio, and size distribution data from OFR experiments (0.4–13 days aging).

Main Results:

  • The model, accounting for various processes, successfully explained SOA evolution in both EC and OFR experiments with a single set of parameters.
  • Without accounting for all processes, the model initially overestimated SOA formation in OFRs by a factor of 3-16.
  • Comprehensive modeling reconciled observed SOA mass, composition (O:C), and size distribution changes across aging timescales.

Conclusions:

  • Environmental chamber and oxidation flow reactor data for SOA can be modeled consistently.
  • A synergistic approach using both EC and OFR data leads to more refined SOA parameters.
  • Improved SOA parameters are crucial for enhancing the accuracy of atmospheric models.