Sources of organic aerosols in east China: A modeling study with high-resolution intermediate-volatility and semi-volatile organic compound emissions
Bibliographic record
Abstract
Organic aerosol (OA) contributes a large fraction of atmospheric submicron aerosol and has negative impacts on air quality, climate, and human health. Sources of OA still remains unclear due to the inadequacy of emission inventory and modeling system. Herein, we established a high-resolution emission inventory of intermediate-volatility and semi-volatile organic compounds (I/SVOCs) and applied it into CMAQ to simulate POA and SOA from different sources in eastern China. Comprehensive observation data of organic carbon (OC), primary and secondary organic aerosol (POA, SOA), and precursors were used for the verification of model performance. With the addition of I/SVOC emissions, OA simulations in each season were substantially improved by increasing the modeled SOA by 1.2 times. I/SVOC emissions contributed 53.6% of SOA and 23.5% of total OA on average. Cooking emissions dominated the POA concentrations in most of the cities. I/SVOC emissions from industrial sources have become the predominant source of regional SOA, followed by those from mobile sources. The differences in OA source contributions between the cities implies that differentiated control measures shall be considered to OA pollution mitigation. Meanwhile, more localized I/SVOCs emission measurements and more sophisticated SOA simulation mechanisms are urgently needed to further improve the identification of OA sources in eastern China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".