the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Aug 2026
Kinetic Partitioning with Effective Mass Accommodation for Secondary Organic Aerosol Simulations in Chemical Transport Modeling
Abstract. Secondary organic aerosols (SOA) can adopt liquid, amorphous semi-solid, or glassy solid states in the atmosphere. Previous model simulations have revealed that SOA adopt semisolid or glassy solid phase states over dry regions and in the upper troposphere. A highly viscous phase state in SOA particles can cause kinetic limitations in SOA partitioning, challenging traditional SOA treatments with equilibrium partitioning in chemical transport modeling. In this study, we develop a method to simulate kinetic partitioning of SOA accounting for the impacts of SOA phase state by utilizing effective mass accommodation coefficient (αeff). We initially test this method in box modeling to calculate equilibration timescale of SOA partitioning for a wide parameter space for bulk diffusivity, volatility, and total aerosol mass loading. Comparison with the chemical timestep of 20 min in GEOS-Chem reveals that equilibrium partitioning is still applicable for particles with bulk diffusivity > 10-15 cm2 s-1, whereas kinetic partitioning is required for partitioning of semi-and low-volatile compounds into highly viscous particles. To reduce computational burden, we developed analytical equations with gas-particle mass transfer coefficient by effectively accounting for gas and bulk diffusion limitations. The application of the new αeff kinetic partitioning scheme into GEOS-Chem predicts lowers SOA mass over drylands (e.g., Western US) in the boundary layer due to kinetically limited growth of viscous SOA particles, whereas SOA mass can be described well with equilibrium partitioning in most other areas at the surface and at 850 hPa. In the upper troposphere at 500 hPa, the αeff -kinetic scheme predicts up to 50% higher SOA mass as evaporation is suppressed for glassy particles. The developed method is easily applicable, improving description and treatments of SOA in chemical transport models.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-4764', Anonymous Referee #1, 23 Sep 2026
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RC2: 'Comment on egusphere-2026-4764', Anonymous Referee #2, 20 Oct 2026
Luu et al presents a method to calculate the impacts of viscosity towards production of secondary organic aerosol (SOA). The authors derive different equations (numerical, analytical, and currently assumed accommodation coefficient of 1) to test in box model and demonstrate depending on viscosity of aerosol, the regular assumption vs the more explicit (numerical and analytical) can lead to different results. With this, they recommend a cut-off point where typical equilibrium partitioning could be applied vs a kinetic partitioning scheme using the analytical equation. They then run GEOS-Chem with this new set up to demonstrate how the SOA mass loading could change.
The paper is generally well written and results are of interest for both the GMD community as well as the atmospheric chemistry community at large. The paper can be accepted after the authors address the following comments.Major
1) The authors mention that density is important for the accommodation coefficient; however, the authors never address how density changes with oxidation / production of secondary organic aerosol. However, though other parameter were discussed or set constant for discussion reasons, SOA density was never discussed. What is the impact of this? E.g., the O:C ratio is already being used for Tg so why not use it to calculate density?
2) The results in Fig. S1 and S2 seem important enough that they should be included in the main paper and not placed into the SI. I would recommend showing both contours in Fig. 2.
3) Line 258-259 -- The authors state that tmix would lead to a significant underestimation in SOA mass for C0 = 10 and 100; however, it would seem that would be true for C0=0.1. Also, since tmix is nearly identical to teq for C0=100, is it really a significant underestimation compared to the other two bins? Also, significant implies statistically analysis; however, with the simplifications and assumptions, how different is a value of 0.04 vs 0.06 for C0=10 and 0.06 and 0.1 for C0=0.1?
4) For Fig 3, bulk diffusivities is shown from GEOS-Chem. Is this a normal output or is this something that was specifically done for this study? If done for this study, how was it calculated (online vs offline, assumptions, etc.).
5) For the GEOS-Chem run, the authors discuss differences in regions (e.g., line 367-370); however, these differences are not apparent in the figures shown in the SI.
6) I appreciate the inclusion of limitations. However, there is no discussion in the impact of inorganics with organics and potential for phase separation and/or inorganic impacting the water enough to make the aerosol potentially less viscous than assumed from a purely organic perspective. Does this have any impact?
Minor
1 Lines 41 - 45: Only gas-phase oxidation of precursor gases are mentioned for SOA when other processes, including aqueous chemistry and volatilization and subsequent oxidation of gases from primary OA are important and not mentioned.
2) Line 201, should the first ASOA C0 really be 10^-10 and not 10^-1 (or 0.1)?
3) Check references formatting. For example, Lines 180 and 183
Citation: https://doi.org/10.5194/egusphere-2026-4764-RC2
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This manuscript presents a new modeling framework for secondary organic aerosol (SOA) that incorporates kinetic limitations to partitioning for non-liquid (i.e., amorphous, semi-glassy, and glassy) aerosols. The goal is a framework that can be implemented in CTMs; demonstration of the method is done with GEOS-Chem. A major finding of the work is that SOA treatment in CTMs, which assume instantaneous equilibrium partitioning, does not hold in dry regions of the globe or at higher altitudes (500 hPa). While this finding, itself, is not particularly novel, the implementation in GEOS-Chem is significant. The work is highly relevant for atmospheric chemistry modeling and will be of interest to a broad audience – it certainly fits the scope of GMD. The manuscript is nicely organized and the writing is clear. I highly recommend the manuscript for publication after the following issues are addressed: