Most landfill methane emissions Escape detection in EPA21 surface emission monitoring surveys
Bibliographic record
Abstract
We measured emissions from ten landfills using mobile surveys and Surface Emission Monitoring (SEM) to determine what fraction of emissions can be identified by SEM surveys. SEM is commonly used for regulatory compliance and leak detection at specific locations. However, evolving regulations emphasize the need to manage methane emissions from the entire landfill site, and the suitability of SEM for this objective remains unclear. Using mobile methane measurements and a back-trajectory attribution and rate estimation method, we measured overall site emissions and those of individual landfill components (active face, closed cells, leachate, etc.). We evaluated each component's contribution to the total emissions and compared how much of emissions captured by mobile surveys could be covered by the walking SEM survey. We found that SEM was effective for closed sites, achieving on-average 67% rate coverage. However, SEM missed relevant emission sources at open landfill sites, most notably from the active face, reducing its rate percent coverage to 17%. The limited rate coverage of SEM suggests that using SEM alone is insufficient for measurement-informed management of landfill emissions. We recommend that SEM be augmented by other methods to fill monitoring gaps and provide a more comprehensive assessment of landfill methane emissions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".