Are Emissions of Black Carbon from Gasoline Vehicles Underestimated? Insights from Near and On-Road Measurements
Bibliographic record
Abstract
Measurements of black carbon (BC) with a high-sensitivity laser-induced incandescence (HS-LII) instrument and a single particle soot photometer (SP2) were conducted upwind, downwind, and while driving on a highway dominated by gasoline vehicles. The results are used with concurrent CO(2) measurements to derive fuel-based BC emission factors for real-world average fleet and heavy-duty diesel vehicles separately. The derived emission factors from both instruments are compared, and a low SP2 bias (relative to the HS-LII) is found to be caused by a BC mass mode diameter less than 75 nm, that is most prominent with the gasoline fleet but is not present in the heavy-duty diesel vehicle exhaust on the highway. Results from both the LII and the SP2 demonstrate that the BC emission factors from gasoline vehicles are at least a factor of 2 higher than previous North American measurements, and a factor of 9 higher than currently used emission inventories in Canada, derived with the MOBILE 6.2C model. Conversely, the measured BC emission factor for heavy-duty diesel vehicles is in reasonable agreement with previous measurements. The results suggest that greater attention must be paid to black carbon from gasoline engines to obtain a full understanding of the impact of black carbon on air quality and climate and to devise appropriate mitigation strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".