Real-World Emission of Particles from Vehicles: Volatility and the Effects of Ambient Temperature
Bibliographic record
Abstract
A majority of the ultrafine particles observed in real-world conditions are systematically excluded from many measurements that help to guide regulation of vehicle emissions. To investigate the impact of this exclusion, coincident near-road particle number (PN) emission factors were quantified up- and downstream of a thermodenuder during two seasonal month-long campaigns with wide-ranging ambient temperatures (−19 to +30 °C) to determine the volatile fraction of particles. During colder temperatures (<0 °C), the volatile fraction of particles was 94%, but decreased to 85% during warmer periods (>20 °C). Additionally, mean PN emission factors were a factor of 3.8 higher during cold compared to warm periods. On the basis of 130 000 vehicle plumes including three additional campaigns, fleet mean emission factors were calculated for PN (8.5 × 10 14 kg-fuel –1 ), black carbon (37 mg kg-fuel –1 ), organic aerosol (51 mg kg-fuel –1 ), and particle-bound polycyclic aromatic hydrocarbons (0.7 mg kg-fuel –1 ). These findings demonstrate that significant differences exist between particles in thermally treated vehicle exhaust as compared to in real-world vehicle plumes to which populations in near-road environments are actually exposed. Furthermore, the magnitude of these differences are dependent upon season and may be more extreme in colder climates.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".