A Bayesian technique for quantifying methane emissions using vehicle-mounted sensors with a Gaussian plume model
Bibliographic record
Abstract
Understanding the uncertainties associated with methane emission estimates is crucial for prioritizing leak repair interventions, enforcing environmental regulations, and modeling climate change. This paper presents a model-based Bayesian approach for describing the uncertainties associated with methane emissions estimates derived from vehicle-based concentration measurements, combined with the Gaussian plume dispersion model (GPM) and anemometry data. The approach begins by deriving a probability density function (pdf) that defines the likelihood of measuring a given release rate conditional on the true release rate. The width of the likelihood pdf is dominated by the GPM model error, which is explored using computational fluid dynamics simulations. The likelihood pdf is combined with a prior pdf that encodes what is known about the emission before the measurement to yield the posterior pdf, which comprehensively defines what is known about the release rate based on measurements and prior information. The technique is assessed by comparing releases inferred from single plume transects with ground truth emission rates, and it is found that the 90% creditability interval contains the true release rate approximately 90% of the time. The Bayesian approach can also be used to optimize measurement paths and/or consider the limitations of these technologies with respect to atmospheric conditions. • Methane emission estimates must be considered in the context of uncertainty. • Uncertainty estimates may be derived empirically or through a model-based approach. • Bayesian inference can be used to incorporate prior information and model error. • Model error for the Gaussian plume model is derived from CFD. • Technique demonstrated on controlled releases measured with a truck-mounted TDLAS sensor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".