Measurements of VOCs by proton transfer reaction mass spectrometry at a rural Ontario site: Sources and correlation to aerosol composition
Bibliographic record
Abstract
Proton transfer reaction mass spectrometry (PTR‐MS) volatile organic compound (VOC) measurements were made at the Centre for Atmospheric Research Experiments at Egbert, Ontario, over a five week period in May to June 2007. VOCs were also analyzed by gas chromatography and with a fluorescence detector sensitive to formaldehyde. This is a well positioned site for contrasting biogenic versus anthropogenic sources. In particular, with southerly flow from Southern Ontario, the VOC signal was observed to have strong anthropogenic influence, as seen through high levels of aromatics, acetylene and NO x . With flow from the northwest, the VOCs had substantial biogenic character, with elevated mixing ratios of monoterpenes, isoprene, and possibly 2‐methyl‐3‐buten‐2‐ol (MBO) with increasing temperatures. Positive matrix factorization (PMF) was applied to the PTR‐MS data set yielding two noninstrumental factors, one related to primary anthropogenic emissions and one arising from secondary atmospheric oxidation. There is a tight correspondence between the composition of the submicron organic aerosol and the VOCs. In particular, (1) organic aerosol loadings highly correlate with long‐lived VOC tracers such as acetone, (2) the degree of oxygenation of the organic aerosol scaled with the photochemical age of the air, as determined through the toluene‐to‐benzene ratio, and (3) there is a tight correlation between PMF factors from the AMS and PTR‐MS data sets, with the HOA AMS factor scaling with the anthropogenic emissions PTR‐MS factor and the OOA AMS factor matched to the photochemical oxidation PTR‐MS factor. Finally, the PTR‐MS PMF factors were used for VOC source apportionment.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".