Impact of Ambient Temperature on Gaseous and Particle Emissions from a Direct Injection Gasoline Vehicle and its Implications on Particle Filtration
Bibliographic record
Abstract
Gaseous and particle emissions from a gasoline direct injection (GDI) and a port fuel injection (PFI) vehicle were measured at various ambient temperatures (22°C, -7°C, -18°C). These vehicles were driven over the U.S. Federal Test Procedure 75 (FTP-75) and US06 Supplemental Federal Test Procedure (US06) on Tier 2 certification gasoline (E0) and 10% by volume ethanol (E10). Emissions were analyzed to determine the impact of ambient temperature on exhaust emissions over different driving conditions. Measurements on the GDI vehicle with a gasoline particulate filter (GPF) installed were also made to evaluate the GPF particle filtration efficiency at cold ambient temperatures. The GDI vehicle was found to have better fuel economy than the PFI vehicle at all test conditions. Reduction in ambient temperature increased the fuel consumption for both vehicles, with a much larger impact on the cold-start FTP-75 drive cycle observed than for the hot-start US06 drive cycle. Colder ambient temperatures were also found to increase CO, THC, and particle emissions over the FTP-75 drive cycle, with little impact on the emissions over the US06 drive cycle. E10 was found to decrease particle number emissions from the PFI vehicle over both test cycles and all ambient temperatures. E10 almost always led to higher particle emissions from the GDI vehicle, except over the FTP-75 drive cycle at standard temperature. Limited soot regeneration in the GPF was observed at cold ambient temperatures over the FTP-75 drive cycle. However, the particle filtration efficiency of the GPF did not significantly change during cold ambient testing. On average, the mass-based GPF filtration efficiency over the FTP-75 drive cycle was observed to vary from 62% at standard temperature to 92% at -18°C. When based on particle number, the GPF filtration efficiency varied from 85% at standard temperature to 80% at -18°C. Over the US06 drive cycle, multiple spontaneous soot regenerations were observed and led to lower particle filtration efficiency. Mass-based filtration efficiency of the GPF was found to vary from 36% at standard temperature to 52% at -18°C. Number-based filtration efficiency varied from 83% at standard temperature to 60% at -18°C.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".