Using atmospheric in situ mobile measurements to monitor urban methane emissions
Bibliographic record
Abstract
Despite the Paris Agreement, greenhouse gas (GHG) concentrations in the atmosphere continue to increase because of the anthropogenic activities, and this is inducing catastrophic effects. Past studies revealed that urban areas are responsible of a large part of these emissions and many cities already started to implement climate actions to reduce their GHG emissions and address climate change. The effectiveness of these actions depends on accurate knowledge of the many sources of GHG in each city, so that efforts are focused on the sources whose emission reduction would be the most effective. Atmospheric measurements are useful to locate and characterize these sources and to monitor the evolution of their emissions. Different approaches have been developed during the past decades including stationary and mobile surface-based in situ measurements, remote sensing of solar absorption spectra from space and from the ground, or aircraft-based observations. All these techniques are complementary and provide information about urban GHG emissions at different scales. In situ mobile measurements of methane mixing ratios have been performed in the two largest cities of Canada using 1) a high-precision gas analyzer providing continuous measurements, 2) a weather station measuring wind speed and direction, and 3) a GPS recording coordinates during the campaigns. These mobile surveys allow rapid screening of large areas, the revisit of specific sites to monitor the evolution of their emissions over time, and can therefore improve our understanding of the emissions at local scale. Methane emissions of the Greater Toronto Area (GTA) have been intensively investigated since 2018 with a total of 84 days of measurements corresponding to a distance of about 8,000 km. A one-week campaign has also been realized in November 2019 in Montreal corresponding to a distance of about 1,100 km. Methane enhancements observed during these surveys have been identified, classified into three categories depending on their magnitudes and areas, and attributed to potential sources, several of which are not catalogued in FLAME-GTA, the point source level inventory developed for the Toronto metropolitan area. Important methane sources in the GTA have been surveyed regularly since 2018 and their emissions have been estimated using an inverse modeling framework with a Gaussian model and compared to the inventory-based estimates of FLAME-GTA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".