Simulation of Mexico City plumes during the MIRAGE-Mex field campaign using the WRF-Chem model
Bibliographic record
Abstract
Abstract. The quantification of tropospheric O3 production in the downwind of the Mexico City plume is a major objective of the MIRAGE-Mex field campaign. We used a regional chemistry-transport model (WRF-Chem) to predict the distribution of O3 and its precursors in Mexico City and the surrounding region during March 2006, and compared the model with in-situ aircraft measurements of O3, CO, VOCs, NOx, and NOy concentrations. The comparison shows that the model is capable of capturing the timing and location of the measured city plumes, and the calculated variability along the flights is generally consistent with the measured results, showing a rapid increase in O3 and its precursors when city plumes are detected. However, there are some notable differences between the calculated and measured values, suggesting that, during transport from the surface of the city to the outflow plume, ozone mixing ratios are underestimated by about 0–25% during different flights. The calculated O3-NOx, O3-CO, and O3-NOz correlations generally agree with the measured values, and the analyses of these correlations suggest that photochemical O3 production continues in the plume downwind of the city (aged plume), adding to the O3 already produced in the city and exported with the plume. The model is also used to quantify the contributions to OH reactivity from various compounds in the aged plume. This analysis suggests that oxygenated organics (OVOCs) have the highest OH reactivity and play important roles for the O3 production in the aging plume. Furthermore, O3 production per NOx molecule consumed (O3 production efficiency) is more efficient in the aged plume than in the young plume near the city. The major contributor to the high O3 production efficiency in the aged plume is the reaction RO2+NO. By contrast, the reaction of HO2+NO is rather uniformly distributed in the plume.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".