Elucidating HONO formation mechanism and its essential contribution to OH during haze events
Bibliographic record
Abstract
Abstract Atmospheric nitrous acid (HONO) chemistry is of critical importance to air quality during polluted haze events. However, current air quality models generally underestimate the concentration of HONO, indicating that a fundamental understanding of haze pollution is lacking. Here, by combining field observations during haze events in Beijing and modeling results, we developed the new parameterization scheme for heterogeneous nitrogen dioxide (NO2) reaction on aerosol surfaces [with the synergistic effects of relative humidity, ammonia, and sulfur dioxide], which has not been considered in existing air quality models. Including NO2 heterogeneous reactions into modeling significantly improves the estimation accuracy of HONO and OH levels, with the contribution reaching up to 91% and 78% during pollution episodes. The OH derived by HONO can partly explain high concentrations of particulate matter. Together, our work provides a new approach to illustrate the formation of HONO, OH, and haze with the consideration of heterogeneous NO2→HONO chemistry.
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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 | 0.001 |
| Open science | 0.000 | 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".