Evaluation of Emissions from Light Duty Trucks with and without the Use of a Gasoline Particulate Filter
Bibliographic record
Abstract
The goal of this study was to investigate the emission impacts of replacing the OEM three-way catalyst on light duty trucks using various, commercially available, gasoline particulate filter (GPF) configurations. Three light duty trucks were tested: a Port Fuel Injection (PFI) PFI_1 and two Gasoline Direct Injection (GDI) vehicles GDI_1 and GDI_2. The testing of these vehicles was conducted on a chassis dynamometer in a controlled test cell environment at two temperatures (25 °C and -7 °C) using the Federal Test Procedure (FTP-75) and the US06 test procedure. All three vehicles were tested in stock OEM configuration followed by testing PFI_1 and GDI_1 with a non-catalyzed GPF and GDI_2 with a catalyzed GPF. GDI_2 was also tested on-road using a portable emissions measurement system (PEMS) with and without the use of the catalyzed GPF. Gaseous emissions, Particulate Matter (PM), Black Carbon (BC) and Particle Number (PN) emissions were measured. Results for the non-catalyzed GPF configuration on PFI_1 and GDI_1 showed a decrease in PM at both standard and cold temperatures, with the biggest decrease being 89% for GDI_1 during the FTP-75 at 25 °C. An increase in NOx was also observed for both vehicles at both standard and cold temperature testing with the non-catalyzed GPF installed. GDI_2 catalyzed GPF results showed a decrease in both PM and NOx at both standard and cold temperature testing with the biggest decrease in PM being 87% during the FTP-75 at -7 °C. On-road results for GDI_2 showed a 67% decrease in PN with no significant difference in NOx while equipped with a catalyzed GPF.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".