Abundant Biogenic Oxygenated Organic Aerosol in Atmospheric Coarse Particles: Plausible Sources and Atmospheric Implications
Bibliographic record
Abstract
Secondary organic aerosol (SOA) is a key component in atmospheric aerosols, strongly influencing air quality and climate. Most previous studies focused on SOA formation in the fine aerosol mode, and little is known about SOA formation across a broader size range, especially for the coarse aerosol mode. In this study, we coupled radiocarbon analysis and the offline aerosol mass spectrometric method to characterize water-soluble organic matter in size-segregated samples between 0.056 and 18 μm collected in urban Shenzhen, China. For the first time, detailed size distributions of different types of oxygenated organic aerosols (OOAs) are obtained. Fossil fuel OOA was mostly distributed in fine particles, and biogenic OOA occurred mostly in coarse particles. Organic composition and correlation analyses suggested that the major source of the coarse-mode OOA was more plausible to be heterogeneous reactions of biogenic volatile organic compounds (VOCs) on soil dust rather than primary biological materials. If so, this mechanism would complement the missing sinks of biogenic VOCs, significantly influence the regional and global organic aerosol budgets, and thus should be considered in air quality and climate models. This study highlights the urgent need for laboratory simulations of heterogeneous reactions of various VOCs on soil dust.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".