Field Measurements of Gasoline Direct Injection Emission Factors: Spatial and Seasonal Variability
Bibliographic record
Abstract
Four field campaigns were conducted between February 2014 and January 2015 to measure emissions from light-duty gasoline direct injection (GDI) vehicles (2013 Ford Focus) in an urban near-road environment in Toronto, Canada. Measurements of CO2, CO, NOx, black carbon (BC), benzene, toluene, ethylbenzene-xylenes (BTEX), and size-resolved particle number (PN) were recorded 15 m from the roadway and converted to fuel-based emission factors (EFs). Other than for NOx and CO, the GDI engine had elevated emissions compared to the Toronto fleet, with BC EFs in the 73rd percentile, BTEX EFs in the 80-90th percentile, and PN EFs in the 75th percentile during wintertime measurements. Additionally, for three campaigns, a second platform for measuring PN and CO2 was placed 1.5-3 m from the roadway to quantify changes in PN with distance from point of emission. GDI vehicle PN EFs were found to increase by up to 240% with increasing distance from the roadway, predominantly due to an increasing fraction of sub-40 nm particles. PN and BC EFs from the same engine technology were also measured in the laboratory. BC EFs agreed within 20% between the laboratory and real-world measurements; however, laboratory PN EFs were an order of magnitude lower due to exhaust conditioning.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".