Reactive Uptake of Ozone to Simulated Seawater: Evidence for Iodide Depletion
Bibliographic record
Abstract
The reaction of ozone with iodide in the ocean is a major ozone dry deposition pathway, as well as an important source of reactive iodine to the marine troposphere. Few prior laboratory experiments have been conducted with environmentally relevant ozone mixing ratios and iodide concentrations, leading to uncertainties in the rate of the reaction under marine boundary layer conditions. As well, there remains disagreement in the literature assessment of the relative contributions of an interfacial reaction via ozone adsorbed to the ocean surface versus a bulk reaction with dissolved ozone. In this study, we measure the uptake coefficient of ozone over a buffered, pH 8 salt solution replicating the concentrations of iodide, bromide, and chloride in the ocean over an ozone mixing ratio of 60-500 ppb. Due to iodide depletion in the solution, the measured ozone uptake coefficient is dependent on the exposure time of the solution to ozone and its mixing ratio. A kinetic multilayer model confirms that iodide depletion is occurring not only within ozone's reactodiffusive depth, which is on the order of microns for environmental conditions, but also deeper into the solution as well. Best model-measurement agreement arises when some degree of nondiffusive mixing is occurring in the solution, transporting iodide from deeper in the solution to a thin, diffusively mixed upper layer. If such mixing occurs rapidly in the environment, iodide depletion is unlikely to reduce ozone dry deposition rates. Unrealistically high bulk-to-interface partitioning of iodide is required for the model to predict a substantial interfacial component to the reaction, indicating that the Langmuir-Hinshelwood mechanism is not dominant under environmental conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".