Significance of HO<sub><i>x</i></sub> and peroxides production due to alkene ozonolysis during fall and winter: A modeling study
Bibliographic record
Abstract
In an attempt to identify new mechanisms for the generation of oxidants during fall and winter, we carried out a modeling investigation in which ozonolysis reactions of alkenes that were primarily anthropogenic in origin were considered. Our results indicate that the ozonolysis reactions of these molecules can be the major sources of HOx, H2O2, and organic peroxides during the night and therefore especially during dark seasons. These O3‐initiated oxidation reactions produce more peroxy radicals than those initiated by HO or NO3. This increase in RO2 also results in an increase in HO, HO2, and H2O2. The direct HO formation pathways by ozonolysis of alkenes can form more HO radicals than that from the reaction of O(1D) + H2O during the dark seasons. This additional source of HO can augment significantly atmospheric oxidation. H2O2 formation by ozonolysis also appears to be the most important dark season tropospheric sources of this oxidant. Our modeling results suggest that the existence of pollutant hydrocarbons and trace amount of biogenically produced terpenes can also lead to important production of HOx, H2O2, and organic peroxides. Substantially enhanced gas‐phase production of H2O2 and organic peroxides due to ozonolysis reactions can cause significant liquid‐phase oxidation of S(IV) to S(VI), and hence the role of ozonolysis reactions can be important for the sulfur conversion studies.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".