Emission of Intermediate Volatile Organic Compounds from Animal Dung and Coal Combustion and Its Contribution to Secondary Organic Aerosol Formation in Qinghai-Tibet Plateau, China
Bibliographic record
Abstract
Intermediate volatility organic compounds (IVOCs) are important precursors to secondary organic aerosols (SOAs), but they are often neglected in studies concerning SOA formation. This study addresses the significant issue of IVOCs emissions in the Qinghai-Tibetan plateau (QTP), where solid fuels are extensively used under incomplete combustion conditions for residential heating and cooking. Our field measurement data revealed an emission factor of the total IVOCs (EF IVOCs ) ranging from 1.56 ± 0.03 to 9.97 ± 3.22 g/kg from various combustion scenarios in QTP. The markedly higher EF IVOCs in QTP than in plain regions can be attributed to oxygen-deficient conditions. IVOCs were dominated by gaseous phase emissions, and the primary contributors of gaseous and particulate phase IVOCs are the unresolved complex mixture and alkanes, respectively. Total IVOCs emissions during the heating and nonheating seasons in QTP were estimated to be 31.7 ± 13.8 and 6.87 ± 0.45 Gg, respectively. The estimated SOA production resulting from combined emissions of IVOCs and VOCs is nearly five times higher than that derived from VOCs alone. Results from this study emphasized the pivotal role of IVOCs emissions in air pollution and provided a foundation for compiling emission inventories related to solid fuel combustion and developing pollution prevention strategies.
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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 | 0.001 | 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".