Source Profiles of Particle‐Bound Phenolic Compounds and Aromatic Acids From Fresh and Aged Solid Fuel Combustion: Implication for the Aging Mechanism and Newly Proposed Source Tracers
Bibliographic record
Abstract
Abstract Phenolic compounds and aromatic acids, as oxygenated aromatic compounds, can participate in photochemical reactions to form secondary organic aerosols (SOAs), and thus strongly impact climate and human health. In the present study, on‐site combustion experiments were conducted to determine primary emissions and secondary formation of phenolic compounds and aromatic acids released from burning of a variety of solid fuels using a potential aerosol mass‐oxidation flow reactor (PAM‐OFR). Emission factors (EFs) of phenolic compounds and aromatic acids from aged samples were 1.04 to 4.04 and 0.90 to 2.80 times those in the fresh PM2.5, respectively, implying significant amounts of these compounds produced from atmospheric aging processes. Substantially different emission profiles of phenolic compounds were observed between coal and biomass burning, with coal combustion mainly released single‐ring species (82%–86% in primary and 86%–89% in secondary emissions), while biomass burning released more two‐, three‐, and four‐ring species (59%–69% in primary and 50%–58% in secondary emissions). Aromatic acids emission profiles from coal and biomass burning also differed considerably, with biomass burning producing significantly higher (>2 times) abundance of dibasic acids than coal combustion, suggesting higher potential of producing additional ‐COOH group from biomass burning. Benzenediol, cresol, dimethylphenol, 1‐pyrenol, phenanthrenol, and hydroxylbenzonic acid were identified as SOA as they were mainly formed during simulated aging processes. Benzenediol acid/phenanthrenol was much lower from biomass (3.70 ± 1.29) than coal (62.7 ± 9.61), and these values remained stable after aging, suggesting this ratio being suitable as tracer for distinguishing different fuels combustion in source apportionment analysis.
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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 | 0.000 |
| 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".