Estimating Urban CH4 Emissions from Satellite-Derived Enhancement Ratios of CH4, CO2, and CO
Bibliographic record
Abstract
Urban centres are an important source of anthropogenic methane emissions and the focus of recent policy efforts aimed at emissions reductions. But emissions from urban areas are poorly characterized except in the few cities with robust measurement infrastructure. Satellite measurements offer a means for monitoring urban emissions. We use co-located measurements of methane (CH4) and carbon monoxide (CO) from the TROPOspheric Monitoring Instrument (TROPOMI) and carbon dioxide (CO2) from the Orbiting Carbon Observatory 2 and Orbiting Carbon Observatory 3 (OCO-2/3) to calculate CH4:CO2, CH4:CO, and CO:CO2 enhancement ratios over 85 cities. We compare our enhancement ratios to those derived from ground-based instruments in Los Angeles and find good agreement. Then we combine our enhancement ratios with CO and CO2 inventories to calculate CH4 emissions and find good agreement with estimates from past studies. Finally, we compare our satellite-based enhancement ratios to those computed from the bottom-up globally gridded emission inventories, and we find significant differences between them. Combining results from the three enhancement ratios, we find EDGARv8 to best represent CH4 emissions in urban areas with mean errors of 30% compared to CAMS-GLOB-ANTv6.2 (based on EDGARv6.1), and CEDSv2021, with mean errors of 42% and 48%, respectively. These differences are largely driven by EDGARv8 changes in South Central and East Asia. We also find that EDGARv8 and HTAPv3 underestimate CO emissions by a factor of 2-5 for cities in Iran, Turkmenistan, Argentina. Finally, we find that CO emissions are overestimated in some cities in Europe by a factor of ∼2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".