Rapid organic aerosol formation downwind of a highway: Measured and model results from the FEVER study
Bibliographic record
Abstract
Abstract The Fast Evolution of Vehicle Emissions from Roadway (FEVER) study was undertaken to strategically measure pollutant gradients perpendicular to a major highway north of Toronto, Canada. A case study period was analyzed when there was an average perpendicular wind direction. Two independent, fast response measurements were used to infer rapid organic aerosol (OA) growth on a spatial scale from 34 m to 285 m at the same time as a decrease was observed in the mixing ratio of primary emitted species, such as CO2 and NOx. An integrated organic gas and particle sampler also showed that near the highway, the aerosol had a larger semivolatile fraction than lower volatile fraction, but over a relatively short distance downwind of the highway, the aerosol transformed to being more low volatile with the change being driven by both evaporation of semivolatile and production of lower volatile organic aerosol. A new 1‐D column Lagrangian atmospheric chemistry model was developed to help interpret the measured increase in the ∆OA/∆CO2 curve from 34 m to 285 m downwind of highway, where the ∆ refers to background‐corrected concentrations. The model was sensitive to the assumptions for semivolatile organic compounds (SVOCs). Different combinations of SVOC emissions and background mixing ratios were able to yield similar model curves and reproduce the observations. Future measurements of total gas‐phase SVOC in equilibrium with aerosol both upwind and downwind of the highway would be helpful to constrain the model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".