Modeling the impact of ClNO<sub>2</sub> on ozone formation in the Houston area
Bibliographic record
Abstract
During the summer of 2006, nitryl chloride (ClNO 2 ) mixing ratios of over 1 ppb were measured in the Houston urban area. Nitryl chloride is potentially important to atmospheric chemistry in urban environments because its photolysis products include both NO 2 and chlorine atoms. Chlorine atoms have previously been shown to significantly increase ozone formation in urban Houston. Photochemical modeling was performed using the Comprehensive Air quality Model with extensions (CAMx) to estimate the effects of observed nitryl chloride concentrations on local chemistry in southeast Texas. CAMx was modified to include a formation mechanism for nitryl chloride as well as its photolysis reaction. Comparisons between model predictions and ambient measurements showed that the model‐predicted ClNO 2 concentrations were within the range of observed data. Model simulations predict that ClNO 2 increases total reactive chlorine mass by 20–40% in the atmosphere of southeast Texas. Despite the high reactivity of chlorine, nitryl chloride caused only modest increases in ozone concentrations (up to 1.0–1.5 ppb when baseline 1‐h average ozone concentrations were between 60 and 85 ppb). The chemistry and physical processes which affect ozone formation were further investigated using box model simulations and a Lagrangian process analysis tool (LPA) within the gridded photochemical modeling simulations. These analyses showed that vertical dispersion and local atmospheric composition moderated the effect of nitryl chloride on ozone mixing ratios.
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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 | 0.000 |
| 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".