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Enregistrement W4412120935 · doi:10.5194/epsc-dps2025-370

Diffusion of Martian Methane in Concert with Destruction Via Adsorption Onto UV-Activated Perchlorate in Martian Dust

2025· preprint· en· W4412120935 sur OpenAlexaff
Elisa Dong, John E. Moores, Madeline Walters, Grace Bischof, Germán Martínez, Mark Gordon

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomainePhysics and Astronomy
ThématiquePlanetary Science and Exploration
Établissements canadiensYork University
Organismes subventionnairesnon disponible
Mots-clésMartianAdsorptionPerchlorateMethaneDiffusionAtmosphere of MarsAstrobiologyMartian surfaceChemistryMaterials scienceChemical engineeringMars Exploration ProgramPhotochemistryPhysicsPhysical chemistryThermodynamicsOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

The observation of temporal and spatial variability in methane concentration on Mars is puzzling, as the current 300-year photochemical lifetime of methane should ensure that it is well mixed throughout the atmosphere (Atreya et al., 2007; Wong et al., 2003). For instance, the spectrometer onboard the Mars Science Laboratory (MSL) rover has observed a significantly higher methane concentration near the surface of Mars than has been observed above 5 km by the instruments aboard the Trace Gas Orbiter (TGO). Near the surface, MSL sees approximately a 0.4 ppbv concentration of background methane that varies on diurnal and seasonal timescales with periodic spikes of over 20 ppbv (Webster et al., 2018, 2021). By contrast, in the middle and upper atmosphere, no methane is observed by TGO to below the detection threshold of ~0.02 ppbv (Korablev et al., 2019; Montmessin et al., 2021).Previously, these differences were explained by considering the different timing and airmasses being sampled by these measurements – at night and near the surface for MSL and at sunset or sunrise and at altitude for TGO (Moores, et al., 2019). This framework required that the methane decrease from its relatively high surface concentration to a much lower concentration at altitude. While this is possible purely through atmospheric mixing and dilution, if the amount of methane being injected into the atmosphere near the surface is sufficiently large this methane will eventually build up in the upper atmosphere.In this work, we consider the effect of a recently quantified destruction mechanism described by Zhang et al., (2022). Their derived destruction rates using a simplified model of the atmosphere suggest that methane adsorbed onto a surface with UV-activated perchlorate can contribute to the destruction of methane on the order of hours to days. This model is appealing for several reasons: (1) it can rapidly destroy methane, (2) it operates only during the day when both MSL and TGO observe methane to be low and not at night when MSL observes methane concentration to be high, and (3) destruction occurs on the surfaces of dust grains which are in abundance in the lower atmosphere. To evaluate the effects of this destruction mechanism with a more realistic representation of the Martian atmosphere, we couple its effects into a martian vertical diffusion model (VDM) that was previously developed by Walters et al (2024). Walters et al. (2024) previously investigated methane flux from the surface of Mars in a 1-D VDM that incorporated eddy diffusivity coefficients derived from GEM-Mars, a global climate model, to model the effects of transport and diffusion of methane in the VDM (Neary & Daerden, 2018; Stroud et al., 2005). The methane is injected at the surface, and the methane mixing ratio is tracked throughout the vertical column using 1 m layers in half-hour time steps for select sols. While Walters et al. (2024) were able to closely replicate the required flux to replicate the SAM-TLS measurements, the diffusion and removal at the top layer was insufficient to decrease methane below 0.02 ppbv at the top of their model at 5 km above the surface.As part of the inclusion of a destruction mechanism in the VDM simulation procedure (Fig. 1), we made the following changes to the model of Walters et al. (2024): (1) we incorporated a Conrath profile to describe the dust distribution in the atmosphere (Conrath, 1975), (2) we added in a radiative transfer model to estimate the available UVC for the destruction mechanism (Smith & Moores, 2020) at each step in the VDM, and (3) we also added a simple box model on top of the VDM to track the methane concentration in the portion of the atmosphere that is visible to TGO. Diffusion into this upper box is dependent on the relative concentration of methane in the top layer in the VDM and the box.Figure 1. Plots showing the concentration of methane in the atmosphere repeated over Ls=180o in MY 35 between 0 and 6000 m above the surface. The y-axis shows the height in the simulation, and the x-axis shows the time during the sol after a 1-day spinup. On the left, we show the simulation using the method produced by Walters et al. (2024) where methane can build up in the atmosphere if methane is continuously provided into the system. This model demonstrates the effect of diffusion alone, where there is a build up at night, and diffusion throughout the atmosphere. On the right, we show the same simulation with the inclusion of the destruction mechanism from Zhang et al. (2022).We report our preliminary results incorporating the effects of oxidation of activated perchlorate surfaces as our methane destruction mechanism for select sols and compare them to the available methane measurements.ReferencesAtreya, S. K., Mahaffy, P. R., & Wong, A. S. (2007). PSS, 55(3), 358–369. Conrath, B. J. (1975). Icarus, 24(1), 36–46. Korablev, O., Vandaele, A. C., et al. (2019). Nature, 568(7753), 517–520. Montmessin, F., Korablev, O. I., et al. (2021). A&A, 650, A140. Moores, J. E., Gough, R. V., et al. (2019). Nature Geoscience, 12(5), 321–325. Moores, J. E., King, P. L., et al. (2019). GRL, 46(16), 9430–9438. Neary, L., & Daerden, F. (2018). Icarus, 300, 458–476. Smith, C. L., & Moores, J. E. (2020). Icarus, 338, 113497. Stroud, C., Makar, P., et al. (2005). JGR D: Atmospheres, 110(17), 149–162. Walters, M. E., Dong, E. et al. (2024). 10th Mars.Webster, C. R., Mahaffy, P. R., et al (2018). Science, 360(6393), 1093–1096. Webster, C. R., Mahaffy, P. R., et al. (2021). A&A, 650, A166. Wong, A. S., Atreya, S. K., & Encrenaz, T. (2003). JGR: Planets, 108(E4), 5026. Zhang, X., Berkinsky, D., et al. (2022). Icarus, 376, 114832.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,016

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,018
Tête enseignante GPT0,243
Écart entre enseignants0,226 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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