Laboratory investigation of photochemical oxidation of organic aerosol from wood fires 2: analysis of aerosol mass spectrometer data
Bibliographic record
Abstract
Abstract. Experiments were conducted to investigate the effects of photo-oxidation on organic aerosol (OA) in dilute wood smoke by exposing emissions from soft- and hard-wood fires to UV light in a smog chamber. This paper focuses on changes in OA composition measured using a unit-mass-resolution quadrupole Aerosol Mass Spectrometer (AMS). The results highlight how photochemical processing can lead to considerable evolution of the mass, volatility and level of oxygenation of biomass-burning OA. Photochemical oxidation produced substantial new OA, more than doubling the OA mass after a few hours of aging under typical summertime conditions. Aging also decreased the volatility of the OA and made it progressively more oxygenated. The results also illustrate strengths of, and challenges with, using AMS data for source apportionment analysis. For example, the mass spectra of fresh and aged BBOA are distinct from fresh motor-vehicle emissions. The mass spectra of the secondary OA produced from aging wood smoke are very similar to those of the oxygenated OA (OOA) that dominates ambient AMS datasets, further reinforcing the connection between OOA and OA formed from photo-chemistry. In addition, aged wood smoke spectra are similar to those from OA created by photo-oxidizing dilute diesel exhaust. This demonstrates that the OOA observed in the atmosphere can be produced by photochemical aging of dilute emissions from different types of combustion systems operating on fuels with modern or fossil carbon. Since OOA is frequently the dominant component of ambient OA, the similarity of spectra of aged emissions from different sources represents an important challenge for AMS-based source apportionment studies.
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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 | 0.000 | 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".