Sources of Particulate Matter Emissions Variability From a Gasoline Direct Injection Engine
Bibliographic record
Abstract
Particulate matter (PM) emissions from gasoline direct injection (GDI) engines are a concern due to the health effects associated with ultrafine PM. This experimental study investigated sources of PM emissions measurement variability observed in previous tests and also examined the effect of ethanol content in gasoline on PM emissions. Some engine operating parameters (fuel and oil temperature, PCV filtration) and test conditions (dilution air conditions) were studied and controlled but could not account for the level of measurement variability observed. Fourier Transform Infrared Spectrometry (FTIR) measurements of gas phase hydrocarbon emissions provided evidence that changes in fuel composition were responsible for the variability. Exhaust emissions of toluene and ethanol were correlated positively with PM emissions, while emissions of isobutylene correlated negatively. Exhaust emissions of toluene and isobutylene were interpreted as markers of gasoline aromatic content and gasoline volatility respectively. Tests conducted with gasoline containing added toluene (10% v/v) supported this hypothesis and led to the overall conclusion that the PM emissions variability observed can be attributed to changes in the composition of the pump gasoline being used. Tests conducted with gasoline containing added ethanol (10% and 30% v/v) found that increasing ethanol fuel content increased PM emissions at the steady-state operating condition utilized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".