A Human-Portable Mass Flux Method for Methane Emissions Quantification: Controlled Release Testing Performance Evaluation
Bibliographic record
Abstract
Abstract. Quantifying methane (CH4) emissions from anthropogenic sources is essential for compliance, inventory, and verification efforts. One established mass balance approach is the mobile flux plane method, in which emissions can be estimated from measurements of CH4 at various heights. Most traditional applications require either a drone or aircraft, both of which can be expensive, or limiting to deploy in all situations. To broaden applicability and improve practicality, we adapted the flux plane method using a long telescoping pole and backpack-mounted CH4 sensor. We explored accuracy through 44 controlled release experiments (0.2–5.6 kg CH4/h) from a 2.4 m stack. Measurements were taken at 6 heights (0.8–5.6 m) by walking transects 10–30 m downwind. The data yielded a mean relative error of -10.1 %, with 68 % of estimates within ±38.3 % of true values. Results are comparable to drone-based methods. We also tested an optimized Gaussian plume model using partial vertical profiles to address issues where the pole could not measure the top of the plume. This approach was slightly less accurate than the flux plane method but had less bias. Overall, results show that multi-level, telescoping pole-based CH4 measurements combined with flux plane or Gaussian models can quantify emissions from elevated sources in a logistically practical manner with results comparable to other widely used mobile quantification methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".