Gas–particle partitioning of semivolatile organic compounds when wildfire smoke comes to town
Bibliographic record
Abstract
Wildfires have become an increasingly important source of organic gases and particulate matter in the western USA. A large fraction of organic particulate matter emitted in wildfires is semivolatile, and the oxidation of organic gases in smoke can form lower-volatility products that then condense on smoke particulates. In this research, we measured the gas- and particle-phase concentrations of semivolatile organic compounds (SVOCs) during the 2017 northern California wildfires in a downwind urban area, using semivolatile thermal desorption aerosol gas chromatography (SV-TAG), and measured SVOCs in a rural site affected by biomass burning using cTAG (comprehensive thermal desorption aerosol gas chromatography mass spectrometry) in Idaho in 2018. Commonly used biomass burning markers such as levoglucosan, mannosan, and nitrocatechols were found to stay predominantly in the particle phase, even when the ambient organic aerosol (OA) was relatively low. The phase partitioning of SVOCs is observed to be dependent on their saturation vapor pressure, while the equilibrium absorption model underpredicts the particle-phase fraction of most of the compounds measured. Wildfire organic aerosol enhanced the condensation of polar compounds into the particle phase but not some nonpolar compounds, such as polycyclic aromatic hydrocarbons.
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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 | 0.001 | 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".