Box Model Investigation of the Effect of Soot Particles on Ozone Downwind from an Urban Area through Heterogeneous Reactions
Bibliographic record
Abstract
Soot can provide additional surface area where heterogeneous reactions can take place in the atmosphere. These reactions are dependent on the number of reactive sites on the soot surface rather than the soot surface area per se. A box model, MOCCA, is used to investigate the effects of introducing heterogeneous reactions on soot into air parcel passing over an urban area and traveling downwind. The model was run at two soot mass concentrations of 2 microg/m3 and 20 microg/m3 with a surface density of n-hexane and decane. Signifcant change in gasphase concentration was only observed for the higher soot concentration. Due to the noncatalytic nature of the heterogeneous reactions, soot sites are rapidly consumed, and soot site concentrations are greatly reduced shortly after emissions are turned off. Notable changes in gaseous concentrations due to the introduction of heterogeneous reactions are not observed in the urban setting. The impact of heterogeneous reactions is more evident after emissions are turned off (i.e. downwind from the urban center). These changes are minimal for the condition that used n-hexane surface density. For conditions that used decane soot, NOx concentrations showed a slight increase, with NO being higher in the daytime and NO2 at night. The maximum O3 reduction observed when using the higher soot concentration is 7 ppb, downwind of the urban center. Change in O3 concentration was less than 1 ppb when using the lower soot loading. The observed effects of heterogeneous reactions on soot decrease with time.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".