Gasoline Direct Injection Engine Emissions of OC and EC: Laboratory Comparisons with Port Fuel Injection Engine
Bibliographic record
Abstract
To better understand carbonaceous aerosol emissions from gasoline vehicles, a gasoline direct injection (GDI) vehicle with and without a gasoline particle filter (GPF) installed and a port fuel injection (PFI) vehicle were tested on a chassis dynamometer using standard emission drive cycles. Carbonaceous particles emitted from the vehicles were collected on quartz filters and analyzed using three different thermal optical protocols to assess the sensitivity of organic carbon (OC) and elemental carbon (EC) emission estimates to the methods, showing OC obtained by the IMPROVE and EC by the NIOSH protocol was the lowest. Compared to the PFI vehicle, the GDI vehicle had higher EC and OC emissions under cold-start cycles by 1415% and 46%, respectively. However, the OC emission from the PFI vehicle was higher than GDI during an aggressive driving cycle by 146%. By considering OC collected on a quartz filter behind a Teflon filter, the emissions from PFI vehicle were found to be more volatile than the GDI vehicle. This is consistent with the OC forming characteristics for GDI and PFI engines, which are pyrolyzed particles from incomplete combustion and incomplete volatilization of fuel droplets, respectively. Generally, the particle phase OC emissions from gasoline engines are more volatile than other sources (e.g., biomass burning), supported by the very low level of pyrolyzed organic carbon (POC) and small differences among protocols in the current study. Once the GDI vehicle was equipped with a GPF, the removal efficiency of EC was > 98%, but OC emissions could increase as a result of regeneration, suggesting that the effect of a GPF on total carbon emitted to the atmosphere needs further evaluation, especially considering the formation of secondary organic aerosol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".