Quantifying anthropogenic methane emissions and their uncertainties using very high spatial and spectral resolution satellite and airborne data
Notice bibliographique
Résumé
Methane is one of the most powerful greenhouse gases that has contributed to about a third of the 2010-2019 global warming relative to the pre-industrial times in 1850-1900. The Upper Silesian Coal Basin in southern Poland is one of the strongest anthropogenic methane (CH4) emitters in Europe, with emissions ranging from 228 to 339 ktCH4yr-1. In that region, ventilation shafts and drainage stations used in coal mines are the main sources of CH4 emissions, of which the mass flows and their sources of uncertainties can be assessed using an adapted version of the Integrated Mass Enhancement (IME) method.This challenge can be tackled using observations from Fabry-Perot imaging Short Wave InfraRed (SWIR) spectrometers onboard of the GHGSat aircraft and GHGSat satellite constellation. GHGSat acquisitions were made in June and July 2022 during a campaign including other measurements and which was partially funded in the framework of UNEP’s International Methane Emissions Observatory. The GHGSat level-2 data provide full-swath CH4 concentration estimations and filtered CH4 plumes with spatial resolutions < 1.1 m on a swath width < 0.75 km for the aircraft, and < 28 m on a swath width < 12 km for the satellites, both featuring a spectral resolution of 0.1 nm. Furthermore, another version of the methane plumes was generated through a Z-test filter.These observations were complemented with local wind profile and plume profile observations to estimate the effective wind speed that accounts for the effects of turbulent diffusion in the plume dissipation. This was achieved using two instruments from the University of Heidelberg: a wind lidar measuring the wind profile up to 200 m height at a sampling rate of ~8 seconds and a hyperspectral SWIR camera featuring a 1 min scanning time, a spatial resolution of 0.8 m and a spectral resolution of 7 nm. Since local wind profile measurements are rarely accessible, this study attempted to find a relationship between the effective wind speed for the methane plumes of that region as a function of the wind speed at 10 m height from the ERA5-Land reanalysis (spatial resolution of 9 km and temporal resolution of 1 h).Finally, a comparison is performed between the methane mass flow estimations derived from GHGSat satellites and aircraft observations with coinciding mass flow estimation from the CH4 safety sensors located inside four of the same ventilation shafts (data collected by AGH University of Kraków) and the hyperspectral camera in June and July 2022. Moreover, another comparison is done with data acquired from a helicopter towed probe (HELiPOD) operated by the DLR and the Technical University of Braunschweig over one of the same shafts in June 2022. While bottom-up inventories may have delays of a few years before being available and require a certain level of trust, satellites can solve these issues through a faster top-down approach but still with relatively high uncertainties and multiple sources. The findings presented in this study can help to quantify the level of contribution from the different sources of uncertainties with high resolution data.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».