Observational Constraints on the Formation of Cl<sub>2</sub> From the Reactive Uptake of ClNO<sub>2</sub> on Aerosols in the Polluted Marine Boundary Layer
Bibliographic record
Abstract
Abstract We use observations from the 2015 Wintertime Investigation of Transport, Emissions, and Reactivity (WINTER) aircraft campaign to constrain the proposed mechanism of Cl2 production from ClNO2 reaction in acidic particles. To reproduce Cl2 concentrations observed during WINTER with a chemical box model that includes ClNO2 reactive uptake to form Cl2, the model required the ClNO2 reaction probability, γ (ClNO2), to range from 6 × 10−6 to 7 × 10−5, with a mean value of 2.3 × 10−5 (±1.8 × 10−5). These field‐determined γ (ClNO2) are more than an order of magnitude lower than those determined in previous laboratory experiments on acidic surfaces, even when calculated particle pH is ≤2. We hypothesize this is because thick salt films in the laboratory enhanced the reactive uptake ClNO2 compared to that which would occur in submicron aerosol particles. Using the reacto‐diffusive length‐scale framework, we show that the field and laboratory observations can be reconciled if the net aqueous‐phase reaction rate constant for ClNO2 (aq) + Cl‐(aq) in acidic particles is on the order of 104 s−1. We show that wet particle diameter and particulate chloride mass together explain 90% of the observed variance in the box model‐derived γ (ClNO2), implying that the availability of chloride and particle volume limit the efficiency of the reaction. Despite a much lower conversion of ClNO2 into Cl2, this mechanism can still be responsible for the nocturnal formation of 10–20 pptv of Cl2 in polluted regions, yielding an atmospherically relevant concentration of Cl atoms the following morning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".