Advancing airborne remote sensing of CO2 and CH4 emissions from point sources
Notice bibliographique
Résumé
The global mean surface temperature (GMST) on Earth in the period 2011 − 2020 has increased ∼ 1.1 °C above preindustrial temperatures. This temperature change results mainly from the increased radiative forcing due to increased levels of anthropogenic greenhouse gases in the atmosphere, especially CO2 and CH4. For an efficient reduction of CO2 and CH4 emissions, their locations and emission strengths have to be known. Additionally, emission reductions must be monitored, and new satellite sensors must be validated. Airborne remote sensing instruments allow observing dedicated regions for source detection, emission monitoring, and satellite validation. However, new instruments and methods to infer gas concentrations from the acquired data are needed to advance the airborne remote sensing of greenhouse gases. In this thesis, three research questions were studied: Is it possible to apply the weighting function modified differential optical absorption spectroscopy (WFM-DOAS) retrieval method to hyperspectral data to infer greenhouse gas emissions? Is the observation and quantification of emissions improved with a new imaging instrument specifically designed for that task? And can the retrieval of greenhouse gases from airborne remote sensing measurements be improved by taking scattering in the atmosphere into account? The first question was studied by applying the WFM-DOAS retrieval to AVIRIS-NG hyperspectral data (spectral resolution ∼ 5.5 nm) acquired during the ABoVE measurement campaign in Canada and a data set containing the observation of a coal mine ventilation shaft plume. In the data set, multiple methane emission plumes could be detected, and the emissions were estimated for five of them. Additionally, the influence of different surface types on the retrieval results was studied. For some surface types, the biases reached ±5 − 10 %, while the retrieval precision was 2 − 5% total column increase. The second question was examined by developing, building, and deploying the MAMAP2DLight instrument successfully. It is an imaging airborne remote sensing spectrometer with ∼ 1.1nm spectral resolution covering the absorption bands of CO2 and CH4 between 1560 and 1690 nm. It observes 28 ground scenes with a spatial resolution of 22 × 6m2, creating an image of the ground while flying over it. The total column precision was 0.28% after binning to 100×100m2 ground scenes, and 0.7% unbinned. During the first measurement flight over the coal-fired power plant Jänschwalde in Eastern Germany in June 2021, the CO2 emission plume was mapped successfully, and emissions of 10.3 Mt CO2 yr−1 were estimated, which were close to the emission estimate based on activity data of 11.6 Mt CO2 yr−1. For the third question, a forward model adapted to the airborne geometry was implemented in the optimal estimation-based Fast atmOspheric traCe gAs retrievaL (FOCAL AIR). It included a parametrized treatment of scattering. Nevertheless, the Jacobian of the forward model could be calculated analytically for all atmospheric parameters, reducing the computational resources needed for the retrieval. In synthetic measurements, treating scattering parametrized reduced errors compared to an absorption-only forward model by up to 50 %. Additionally, applying the FOCAL AIR retrieval and the WFM-DOAS on the MAMAP data set acquired over the power plant Jänschwalde in May 2018, different FOCAL AIR retrieval configurations were tested. With the best retrieval configuration, an emission of 18.6 Mt CO2 yr−1 was estimated for the power plant Jänschwalde, close to the emission estimate from WFM-DOAS retrieval results of 19.4 Mt CO2 yr−1.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».