Quantifying Vehicle Emission Factors for Various Ambient Conditions using an On-Road, Real-Time Emissions System
Bibliographic record
Abstract
This paper demonstrates vehicle emission factor measurement using an on-board, on-road system and examines the effects of ambient temperature on those emission factors. Vehicle operating parameters, fuel consumption and emissions were measured on-road using a portable measurement system designed for ease of use with a range of vehicles, drivers and driving situations. The results reported here come from repeated trips over a 17.4 km urban / suburban route with a particular driver and vehicle. As such, the emission factors developed here do not represent the current on-road fleet. However, they show the strong influence of actual operating conditions (particularly ambient temperature) and of the vehicle control system's response to non-standard conditions. This leads to an appreciation for on-road testing as a means to illustrate vehicle emission behavior in real conditions and to highlight conditions which may require more detailed study. A series of trips over a one year period, (with an ambient temperature range of -25 to +20°C), were analyzed to develop emission factor models with one, two or three emission factors. The results emphasize the inadequacy of using a single “grams-per-mile” emission factor to model emissions of vehicles operating over a range of trip lengths and ambient conditions. A two-emission-factor model provides adequate information to accommodate short trip lengths and a three-emission-factor model can fit the detailed behavior but is generally not required.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".