Individual Source Measurements of Methane Emissions from Anthropogenic Sources - Canada and the United States
Notice bibliographique
Résumé
Anthropogenic methane emissions have been identified as a strategic greenhouse gas emission reduction target. The scientific consensus is that the majority of cumulative methane emissions are emitted by a small percentage of high-emitting sites (i.e., super-emitters). However, cumulative methane emissions from lower-emitting sources can be significant if the site counts (i.e. activity data) are high. Accurately quantifying methane emissions from all sources, including low-emitting sources, is a critical component of tracking progress towards reducing emissions. In this thesis, data analysis and field measurements are used to study methane emissions from abandoned oil and gas wells, historic landfills, manholes, and natural gas distribution systems, all sources that have relatively low site level emission rates and require direct on-site measurement methods with to accurately capture the full range of emissions distributions.The static chamber methodology, a direct measurement method, was the primary method used in the field measurements of methane emissions presented in this thesis. We conducted controlled release experiments and explored the role of chamber design parameters. We found that static chambers can quantify methane flowrates ranging from 1 to 500 g/h, which represents the lower range of emission rates when compared to other methane sources such as active oil and gas wells, with an accuracy of ±14%.Within the oil and gas sector, abandoned oil and gas (AOG) wells have the largest activity data, with >4 million wells in the U.S. and >370,000 in Canada. We analyzed methane emissions from 598 published measurements, including previously unpublished measurements of methane emissions we made from 54 AOG wells in Oklahoma and 17 in British Columbia using a static chamber methodology. We developed attribute- and region-specific emission factors of wells which ranged from 1.8x10-3 to 48 g/h of methane for AOG wells in the U.S. and Canada. We estimated that, as of 2020, the annual methane emissions from AOG wells are 20% higher than inventory estimates for the U.S. and 150% higher for Canada.We quantified methane emissions from wastewater utility holes (WUHs) and historic landfills in Montreal (Canada), two sources with high population counts and little direct measurement data. In addition, we quantified emissions from natural gas (NG) distribution systems within the city, which is recognized as a significant methane source for cities. We extrapolated the methane emissions to city-wide estimates and performed a cost-benefit analysis of mitigation strategies. We estimated that historic landfills and WUHs were the second and third highest methane sources in Montreal. We found that historic landfills have high potential for methane reductions at high mitigation costs, methane mitigation from WUHs is low-cost but the methods require further research, and increasing repair rates of NG distribution leaks are a cost-effective mitigation strategy.Recent studies have shown that biogenic sources of methane (e.g., WUHs and urban water bodies) were significant sources in cities. Therefore, we directly measured methane emissions from WUHs and urban water bodies in the Greater Toronto Area (GTA), the largest urban agglomeration in Canada. We found that annual methane emissions from urban water bodies totaled 2,737 t/yr of methane, or 26.4% of emissions from agriculture and wetlands, and that emissions from WUHs totaled 9,122 t/yr of methane, which is more than 10% of the total GTA methane budget.Our findings address several knowledge gaps in methane emissions quantification of sources requiring direct on-site measurements, which are needed to improve greenhouse gas inventories and guide mitigation strategy development. Overall, multi-scale measurements including direct measurements are needed to fill gaps in current inventories and improved data sharing can reduce current limitations and uncertainties
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».