ACEA Programme on the Emissions of Fine Particulates from Passenger Cars(2) Part 2: Effect of Sampling Conditions and Fuel Sulphur Content on the Particle Emission
Bibliographic record
Abstract
The results of an investigation of the influence of the sulphur fuel content and different dilution techniques on fine particulate emissions are reported in this paper. Fuels with two different sulphur contents (<10 ppm and approx. 200 ppm) were used for a Diesel and a gasoline vehicle in order to compare four different dilution procedures. These comprised the standard CVS tunnel and two pre-heated and one non-heated direct dilution systems. Various particulate measurement instruments were employed simultaneously, including SMPS, CPC, and ELPI for number and size, the standard gravimetric filter method for mass. In addition, Soxhlet extraction for chemical composition was carried out. A higher fuel sulphur content was found to clearly increase particulate emissions from the Diesel and the gasoline vehicle for higher load. The increase in emissions was due to the contribution of condensed material and most of it could be clearly brought into relation with to sulphur compounds. The comparison between the different dilution systems showed a good agreement for the accumulation mode in number and size. Drastic differences were observed concerning the nucleation mode for the tests with high sulphur fuel. Whereas the pre-heated dilution systems do not show a nucleation at all, the total number concentration was increased up to an order of magnitude for the non-heated systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 0.001 |
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".