Quantifying the Loss of Processed Natural Gas Within California's South Coast Air Basin Using Long-term Measurements of Ethane and Methane
Bibliographic record
Abstract
Abstract. California's South Coast Air Basin (SoCAB) is a region in which the top-down methane emissions are underestimated by the bottom-up inventories. To provide insight into the sources of the discrepancy, we analyse a record of atmospheric trace gas total column abundances in the SoCAB starting in the late 1980s. The gases measured include ethane and methane and provide insight into the sources of the excess methane found in the SoCAB. The early few years of the record show a rapid decline in ethane emissions at a much faster rate than decreasing vehicle exhaust or natural gas and crude oil production can explain. Between 2010 and 2015, ethane emissions have grown gradually from 13 ± 4.5 Gg · yr−1 to 25.8 ± 3.9 Gg · yr−1, which is in contrast to the steady production of natural gas liquids over that time. Our methane emissions record begins in 2012 and shows an increase between 2012 and 2015 from 380 ± 78 Gg · yr−1 to 448 ± 91 Gg · yr−1. Since 2012, ethane to methane ratios in the natural gas withdrawn from a storage facility within the SoCAB have been increasing; these ratios are tracked in our atmospheric measurements with about half of the rate of increase. From this, we infer that about half of the excess methane in the SoCAB between 2012–2015 is attributable to losses from the natural gas infrastructure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".