Preprints
https://doi.org/10.5194/egusphere-2026-4908
https://doi.org/10.5194/egusphere-2026-4908
21 Aug 2026
 | 21 Aug 2026
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

Comparison of the GECKO-A and SAPRC MechGen Atmospheric Chemical Mechanism Generators

Julia Lee-Taylor, William P. L. Carter, Bernard Aumont, John J. Orlando, Zhizhao Wang, Marie Camredon, Richard Valorso, and Kelley C. Barsanti

Abstract. Atmospheric chemical mechanisms describe the photolysis and oxidation reactions of volatile organic compounds (VOCs), including bimolecular and unimolecular reactions of product radicals. Fully explicit representation of a single VOC can require thousands to millions of chemical reactions and species. Consequently, explicit chemical mechanism generation has proceeded through the use of automated tools that rely on observational data and structure activity relationships (SARs) to estimate rate coefficients and branching ratios. Here we compare, for the first time, two automated mechanism generators, GECKO-A (Generator of Explicit Chemical Kinetics of Organics in the Atmosphere and SAPRC MechGen (SAPRC Mechanism Generator). We specifically compare mechanisms for ten representative VOCs and the resultant properties of generated product mixtures as relevant for atmospheric chemistry. The observed differences in product mixtures can be explained by systematic differences between the mechanism generators after the initial reaction steps. GECKO-A has a more detailed representation of photolysis, including Norrish type-2 photolysis, which results in the formation of reactive alkenes from aldehydes. MechGen includes more alkoxy radical isomerization processes and autoxidation via H-shift reactions, the latter of which are not currently included in GECKO-A. Also MechGen assumes fast cyclization of alkyl radicals to form lactones or carbonates, which is not assumed by GECKO-A, and for which available theoretical predictions are inconsistent. These differences highlight needs for additional experimental data, particularly for uncertain products and yields, as well as for near continuous updating of these mechanism generation tools as sufficient data and new SARs become available.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics.

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Julia Lee-Taylor, William P. L. Carter, Bernard Aumont, John J. Orlando, Zhizhao Wang, Marie Camredon, Richard Valorso, and Kelley C. Barsanti

Status: open (until 02 Oct 2026)

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Julia Lee-Taylor, William P. L. Carter, Bernard Aumont, John J. Orlando, Zhizhao Wang, Marie Camredon, Richard Valorso, and Kelley C. Barsanti
Julia Lee-Taylor, William P. L. Carter, Bernard Aumont, John J. Orlando, Zhizhao Wang, Marie Camredon, Richard Valorso, and Kelley C. Barsanti
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Latest update: 21 Aug 2026
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Short summary
Air quality models require representation of gas-phase compounds and their products. While these model mechanisms are typically small, one compound can form millions of products. Small mechanisms can be derived by hand, but generation of complete mechanisms must be automated. The two most common automated generators for atmospheric chemical mechanisms are compared here for the first time. The comparison highlights underlying differences and implications for predictions of atmospheric chemistry.
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