High Contribution of Secondary Formation to Brown Carbon in China Humid Haze: Enhancing Role of Ammonia and Amines
Bibliographic record
Abstract
To better understand the sources and formation mechanisms of atmospheric brown carbon (BrC) in China haze, an intensive field observation was conducted in the North China Plain (NCP) during the 2023 winter. Our results showed that compared to that (28%) in dry haze, the contribution of secondary formation to BrC was significantly enhanced during humid haze, accounting for 46% of BrC production with the aqueous-phase reaction as the dominant formation pathway. The strong correlations between light absorption at λ 365nm and water-soluble organic nitrogen compounds, particularly imidazoles (IMs), indicated a key role of nitrogen-containing organic compounds in the aqueous-phase BrC formation process. In the humid haze, IMs are largely produced by liquid-phase reactions of carbonyls with amines and free ammonia (NH 3 (aq)), which accounted for 57% of the total IMs in the humid haze events. Amines produced IMs more efficiently and less pH dependent than NH 3 (aq), with alkyl IMs and oxidized IMs being their products, respectively. Both types of BrC increased with increasing levels of amines and NH 3 (aq) during humid haze, suggesting their enhancing roles in BrC formation in China haze, which should be accounted for by models for better simulating the physicochemical characteristics and climate effects of atmospheric BrC.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".