Atmospheric Implications of Large C<sub>2</sub>‐C<sub>5</sub> Alkane Emissions From the U.S. Oil and Gas Industry
Bibliographic record
Abstract
Abstract Emissions of C 2 ‐C 5 alkanes from the U.S. oil and gas sector have changed rapidly over the last decade. We use a nested GEOS‐Chem simulation driven by updated 2011NEI emissions with aircraft, surface, and column observations to (1) examine spatial patterns in the emissions and observed atmospheric abundances of C 2 ‐C 5 alkanes over the United States and (2) estimate the contribution of emissions from the U.S. oil and gas industry to these patterns. The oil and gas sector in the updated 2011NEI contributes over 80% of the total U.S. emissions of ethane (C 2 H 6 ) and propane (C 3 H 8 ), and emissions of these species are largest in the central United States. Observed mixing ratios of C 2 ‐C 5 alkanes show enhancements over the central United States below 2 km. A nested GEOS‐Chem simulation underpredicts observed C 3 H 8 mixing ratios in the boundary layer over several U.S. regions, and the relative underprediction is not consistent, suggesting C 3 H 8 emissions should receive more attention moving forward. Our decision to consider only C 4 ‐C 5 alkane emissions as a single lumped species produces a geographic distribution similar to observations. Due to the increasing importance of oil and gas emissions in the United States, we recommend continued support of existing long‐term measurements of C 2 ‐C 5 alkanes. We suggest additional monitoring of C 2 ‐C 5 alkanes downwind of northeastern Colorado, Wyoming, and western North Dakota to capture changes in these regions. The atmospheric chemistry modeling community should also evaluate whether chemical mechanisms that lump larger alkanes are sufficient to understand air quality issues in regions with large emissions of these species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".