Modeling the weekly cycle of NO<sub>x</sub> and CO emissions and their impacts on O<sub>3</sub> in the Los Angeles‐South Coast Air Basin during the CalNex 2010 field campaign
Bibliographic record
Abstract
Abstract We developed a new nitrogen oxide (NOx) and carbon monoxide (CO) emission inventory for the Los Angeles‐South Coast Air Basin (SoCAB) expanding the Fuel‐based Inventory for motor‐Vehicle Emissions and applied it in regional chemical transport modeling focused on the California Nexus of Air Quality and Climate Change (CalNex) 2010 field campaign. The weekday NOx emission over the SoCAB in 2010 is 620 t d−1, while the weekend emission is 410 t d−1. The NOx emission decrease on weekends is caused by reduced diesel truck activities. Weekday and weekend CO emissions over this region are similar: 2340 and 2180 t d−1, respectively. Previous studies reported large discrepancies between the airborne observations of NOx and CO mixing ratios and the model simulations for CalNex based on the available bottom‐up emission inventories. Utilizing the newly developed emission inventory in this study, the simulated NOx and CO mixing ratios agree with the observations from the airborne and the ground‐based in situ and remote sensing instruments during the field study. The simulations also reproduce the weekly cycles of these chemical species. Both the observations and the model simulations indicate that decreased NOx on weekends leads to enhanced photochemistry and increase of O3 and Ox (=O3 + NO2) in the basin. The emission inventory developed in this study can be extended to different years and other urban regions in the U.S. to study the long‐term trends in O3 and its precursors with regional chemical transport models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".