Characterization of nighttime formation of particulate organic nitrates based on high-resolution aerosol mass spectrometry in an urban atmosphere in China
Bibliographic record
Abstract
Abstract. Organic nitrates are important atmospheric species that significantly affect the cycling of NOx and ozone production. However, characterization of particulate organic nitrates and their sources in inorganic nitrate-abundant particles in polluted atmosphere is a big challenge, and has been little performed in the literature. In this study, an Aerodyne high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) was deployed at an urban site in South China from 2015 to 2016 to characterize particulate organic nitrates with high time resolution. Based on two different data processing methods, 13–21 % of the total measured nitrates was identified to be organic nitrates in spring, 41–64 % in summer and 16%–25 % in autumn; however, in winter, most measured nitrates were inorganic. The good correlation between organic nitrates and fresh secondary organic aerosol identified by the positive matrix factorization method at night rather than in the daytime indicated a potentially important role of nighttime secondary formation. Therefore, we theoretically estimated nighttime NO3 radical concentrations and SOA formation using the various VOCs measured simultaneously. Consequently, the calculated products of monoterpene reacting with NO3 agreed well with the organic nitrates in terms of both concentration and variation, suggesting that the biogenic VOC reactions with NO3 at night are the dominant formation pathway for particulate organic nitrates in polluted atmosphere, despite of much higher abundance of anthropogenic VOCs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".