Nocturnal loss of NO<i><sub>x</sub></i> during the 2010 CalNex‐LA study in the Los Angeles Basin
Bibliographic record
Abstract
Abstract The chemical removal of NOx at night in urban areas remains poorly constrained due to uncertainties in the contribution of various loss pathways and the impact of the suppressed nocturnal vertical mixing. Here we present long‐path differential optical absorption spectroscopy observations of nocturnal vertical concentration profiles of O3, NO2, and NO3 in the lower atmosphere (33–556 m above ground level) measured during the CalNex‐LA 2010 study. Positive nocturnal vertical gradients of O3 and NO3 and negative gradients of NO2 were observed during the night. Relatively short lifetime of nocturnal NO3 (less than 1000 s) and high nighttime steady state N2O5 mixing ratios (up to 2 ppb) indicated active nocturnal chemistry during CalNex. Comparison of modeled and observed altitude‐resolved NO3 loss frequencies shows that hydrolysis of N2O5 on aerosols was the dominant loss pathway of NO3 and NOx. Based on this argument, the nocturnal loss rates of NOx, L(NOx), at different altitudes and averaged over the lowest 550 m of the atmosphere were calculated. The nocturnally averaged L(NOx) ranged between 0.8 and 1.3 ppb h−1 for the lower atmosphere with the L(NOx) for the first 8 days at about 1 ppb h−1. This number is close to the one previously determined in Houston in 2009 of ~0.9 ppb h−1. Comparisons between daytime NOx loss due to the OH + NO2 reaction and nighttime L(NOx) show that during CalNex, nocturnal chemistry contributed an average of 60% to the removal of NOx in a 24 h period in the lower atmosphere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".