Geographic variability in freshwater methane hydrogen isotope ratios and its implications for global isotopic source signatures
Notice bibliographique
Résumé
Abstract. There is growing interest in developing spatially resolved methane (CH4) isotopic source signatures to aid in geographic source attribution of CH4 emissions. CH4 hydrogen isotope measurements (δ2H–CH4) have the potential to be a powerful tool for geographic differentiation of CH4 emissions from freshwater environments, as well as other microbial sources. This is because microbial δ2H–CH4 values are partially dependent on the δ2H of environmental water (δ2H–H2O), which exhibits large and well-characterized spatial variability globally. We have refined the existing global relationship between δ2H–CH4 and δ2H–H2O by compiling a more extensive global dataset of δ2H–CH4 from freshwater environments, including wetlands, inland waters, and rice paddies, comprising a total of 129 different sites, and compared these with measurements and estimates of δ2H–H2O, as well as δ13C-CH4 and δ13C–CO2 measurements. We found that estimates of δ2H–H2O explain approximately 42 % of the observed variation in δ2H–CH4, with a flatter slope than observed in previous studies. The inferred global δ2H–CH4 vs. δ2H–H2O regression relationship is not sensitive to using either modelled precipitation δ2H or measured δ2H–H2O as the predictor variable. The slope of the global freshwater relationship between δ2H–CH4 and δ2H–H2O is similar to observations from incubation experiments but is different from pure culture experiments. This result is consistent with previous suggestions that variation in the δ2H of acetate, controlled by environmental δ2H–H2O, is important in determining variation in δ2H–CH4. The relationship between δ2H–CH4 and δ2H–H2O leads to significant differences in the distribution of freshwater δ2H–CH4 between the northern high latitudes (60–90∘ N), relative to other global regions. We estimate a flux-weighted global freshwater δ2H–CH4 of −310 ± 15 ‰, which is higher than most previous estimates. Comparison with δ13C measurements of both CH4 and CO2 implies that residual δ2H–CH4 variation is the result of complex interactions between CH4 oxidation, variation in the dominant pathway of methanogenesis, and potentially other biogeochemical variables. We observe a significantly greater distribution of δ2H–CH4 values, corrected for δ2H–H2O, in inland waters relative to wetlands, and suggest this difference is caused by more prevalent CH4 oxidation in inland waters. We used the expanded freshwater CH4 isotopic dataset to calculate a bottom-up estimate of global source δ2H–CH4 and δ13C-CH4 that includes spatially resolved isotopic signatures for freshwater CH4 sources. Our bottom-up global source δ2H–CH4 estimate (−278 ± 15 ‰) is higher than a previous estimate using a similar approach, as a result of the more enriched global freshwater δ2H–CH4 signature derived from our dataset. However, it is in agreement with top-down estimates of global source δ2H–CH4 based on atmospheric measurements and estimated atmospheric sink fractionations. In contrast our bottom-up global source δ13C-CH4 estimate is lower than top-down estimates, partly as a result of a lack of δ13C-CH4 data from C4-plant-dominated ecosystems. In general, we find there is a particular need for more data to constrain isotopic signatures for low-latitude microbial CH4 sources.
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 | 0,000 | 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 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 ».