Demystifying the methane clouds over Dhaka, Bangladesh
Notice bibliographique
Résumé
Urban areas are at the forefront of climate change impacts, with cities being responsible for ~75% of global GHG emissions. Methane, a GHG 80 times more potent than CO2, is significantly emitted from a range of urban sources including biogenic (e.g., landfills, drainage channels, and wetlands) and abiogenic (e.g., transportation, compressor stations, and oil and gas leaks). The real-time monitoring and precise identification of methane sources are crucial for targeted mitigation and the development of climate-resilient urban planning.While mobile methane analyzer systems for monitoring methane have been deployed in European and North American cities, their use in densely populated tropical megacities with inferior infrastructure, like Dhaka, Bangladesh, is limited. This limitation obstructs a comprehensive understanding and mitigation of methane emissions on a global scale. Dhaka stands as the world's seventh most populous city, is acutely vulnerable to the impacts of climate change, and contends with extreme air pollution, ranking within the most polluted 1% of cities globally. Satellite imagery has persistently revealed a dense methane cloud above Dhaka, but the precise sources and extent of these emissions remain largely uncharted. Moreover, the potential methane sources in Dhaka may vary from those in other cities. Identifying and measuring these specific sources is imperative for formulating effective mitigation strategies.In pursuit of this goal, we conducted a comprehensive ground-based mobile survey aimed to identify and quantify methane emissions in Dhaka, offering an intricate spatial and temporal emission profile of various urban sources. Using a human-propelled tri-wheeler equipped with a mobile gas analyzer system, we measured real-time CH4 concentrations across ~1300 km during 38 surveys conducted in the winter and summer of 2023. The vehicle also featured a mobile weather station and GPS logger, recording plume locations alongside meteorological data. From the methane plumes identified, we directly measured methane flux from urban soils, drainages, wastewater channels, landfills, and wetlands. We created methane emission maps using spatial interpolation, determined plume characteristics with the Gaussian dispersion model, and computed emission rates from diverse urban sources using a flux calculation algorithm.Preliminary findings show that average near-ground methane levels in Dhaka were 5.75 ppm (range: 2.04–309 ppm) in winter and 4.29 ppm (range: 2.05–230 ppm) in summer 2023, markedly surpassing the global background level of ~2.0 ppm, with frequent local spikes above 100 ppm. Our research reveals that in contrast to other global cities, biogenic sources are the dominant methane contributors in Dhaka, succeeded by gas leaks from pipelines and CNG stations. Urban wastewater channels and landfills emerge as the principal biogenic emitters, with substantial contributions from urban canals, wetlands, and soils in developed wetlands. Measurements at a major landfill indicated a methane emission rate of ~500 nmol.m-2s-1, and even the capped landfill a decade post-closure emit methane at notable rates (~9.4 nmol.m-2s-1), indicating they are the significant contributor of the methane cloud observed over Dhaka. These results emphasize the urgent need for targeted mitigation strategies that focus on the primary sources identified, to effectively tackle methane emissions in tropical megacities like Dhaka.
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 | 0,001 | 0,002 |
| 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) | 0,002 | 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 ».