Methane Emissions Across a Diverse Set of Large Landfills in the United States and Canada
Bibliographic record
Abstract
Methane emissions from the waste sector may represent a significant fraction of the global anthropogenic methane budget. However, few comprehensive studies across the broad landscape of waste operations exist to validate existing bottom-up models that underpin reporting programs and national inventories. In this study, we flew two airborne imaging spectrometers to map emissions at large landfills across 18 states in the U.S. and three provinces in Canada between 2016-2022. This technology is particularly sensitive to point source methane emissions and can geolocate source locations to within several meters. We observed point sources at a high fraction (52%) of sites and observed high emission persistence (60%), or point source detection frequency, at sites we surveyed multiple times. Airborne derived emissions correlate poorly with EPA reported emission and are on average higher, which could point to some issues with models that underpin reporting protocols. We validated imaging spectrometer aerial emission rates against the Scientific Aviation mass balance technique at 15 landfills, and find good agreement between these two independent measurement systems. Sustained measurements across many landfills and waste sites are needed to validate inventories and provide actionable data for industry operators and enforcement agencies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".