Estimation of anthropogenic and volcanic SO <sub>2</sub> emissions from satellite data in the presence of snow/ice on the ground
Notice bibliographique
Résumé
Abstract. Early versions of satellite nadir-viewing UV SO2 data products did not explicitly account for the effects of snow/ice on retrievals. Snow-covered terrain, with its high reflectance in the UV, typically enhances satellite sensitivity to boundary layer pollution. However, a significant fraction of high-quality cloud-free measurements over snow is currently excluded from analyses. This leads to increased uncertainties of satellite emission estimates and potential seasonal biases due to the lack of data in winter months for some high-latitudinal sources. In this study, we investigated how Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) satellite SO2 measurements over snow-covered surfaces can be used to improve the annual emissions reported in our SO2 emissions catalogue (version 2; Fioletov et al., 2023). Only 100 out of 759 sources listed in the catalogue have 10 % or more of the observations over snow. However, for 40 high-latitude sources, more than 30 % of measurements suitable for emission calculations were made over snow-covered surfaces. For example, in the case of Norilsk, the world's largest SO2 point-source, annual emission estimates in the SO2 catalogue were based only on 3–4 summer months, while the addition of data for snow conditions extends that period to 7 months. Emissions in the SO2 catalogue were based on satellite measurements of SO2 slant column densities (SCDs) that were converted to vertical column densities (VCDs) using site-specific clear-sky air mass factors (AMFs), calculated for snow-free conditions. The same approach was applied to measurements with snow on the ground whereby a new set of constant, site-specific, clear sky with snow AMFs was created, and these were applied to the measured SCDs. Annual emissions were then estimated for each source considering (i) only clear-sky and snow-free days, (ii) only clear-sky with snow days, and (iii) a merged dataset (snow and snow-free conditions). For individual sources, the difference between emissions estimated for snow and snow-free conditions is within ±20 % for three-quarters of smelters and oil and gas sources and with practically no systematic bias. This is excellent consistency given that there is typically a factor of 3–5 difference between AMFs for snow and snow-free conditions. For coal-fired power plants, however, emissions estimated for snow conditions are on average 25 % higher than for snow-free conditions; this difference is likely real and due to larger production (consumption of coal) and emissions in wintertime.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 tête enseignante, 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 ».