In‐cloud oxidation of SO<sub>2</sub> by O<sub>3</sub> and H<sub>2</sub>O<sub>2</sub>: Cloud chamber measurements and modeling of particle growth
Bibliographic record
Abstract
Controlled cloud chamber experiments were conducted to measure particle growth resulting from the oxidation of SO2 by O3 and H2O2 in cloud droplets formed on sulfuric acid seed aerosol. Clouds were formed in a 590 m3 environmental chamber with total liquid water contents ranging from 0.3–0.6 g m−3 and reactant gas concentrations <10 ppbv for SO2 and H2O2 and <70 ppbv for O3. Aerosol growth was measured by comparison of differential mobility analyzer size distributions before and after each 3–4 min cloud cycle. Predictions of aerosol growth were then made with a full microphysical cloud model used to simulate each individual experimental cloud cycle. Model results of the H2O2 oxidation experiments best fit the experimental data using the third‐order rate constant of Maass et al. [1999] (k = 9.1 × 107 M−2 s−1), with relative aerosol growth agreeing within 3% of measured values, while the rate of Hoffmann and Colvert [1985] produced agreement within 4–9%, and the rate of Martin and Damschen [1981] only within 13–18%. Simulation results of aerosol growth during the O3 oxidation experiments were 60–80% less than the measured values, confirming previous results [Hoppel et al., 1994b]. Experimental results and analyses presented here show that the SO2 ‐ O3 rate constants would have to be more than 5 times larger than currently accepted values to explain the measured growth. However, unmeasured NH3 contamination present in trace amounts (<0.2 ppb) could explain the disagreement, but this is speculative and the source of this discrepancy is still unknown.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".