Influence of oil and gas emissions on summertime ozone in the Colorado Northern Front Range
Bibliographic record
Abstract
Abstract Tropospheric O 3 has been decreasing across much of the eastern U.S. but has remained steady or even increased in some western regions. Recent increases in VOC and NO x emissions associated with the production of oil and natural gas (O&NG) may contribute to this trend in some areas. The Northern Front Range of Colorado has regularly exceeded O 3 air quality standards during summertime in recent years. This region has VOC emissions from a rapidly developing O&NG basin and low concentrations of biogenic VOC in close proximity to urban‐Denver NO x emissions. Here VOC OH reactivity (OHR), O 3 production efficiency (OPE), and an observationally constrained box model are used to quantify the influence of O&NG emissions on regional summertime O 3 production. Analyses are based on measurements acquired over two summers at a central location within the Northern Front Range that lies between major regional O&NG and urban emission sectors. Observational analyses suggest that mixing obscures any OPE differences in air primarily influenced by O&NG or urban emission sector. The box model confirms relatively modest OPE differences that are within the uncertainties of the field observations. Box model results also indicate that maximum O 3 at the measurement location is sensitive to changes in NO x mixing ratio but also responsive to O&NG VOC reductions. Combined, these analyses show that O&NG alkanes contribute over 80% to the observed carbon mixing ratio, roughly 50% to the regional VOC OHR, and approximately 20% to regional photochemical O 3 production.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".