Emission Factors Analysis for Multiple Vehicles Using an On-Board, In-Use Emissions Measurement System
Bibliographic record
Abstract
Despite progressive implementation of stringent emission regulations, vehicle tailpipe emissions remain the major source of air pollution problems in most urban areas. To control and reduce tailpipe pollutants, it is critical to understand in-use emissions as a basis for any future emission controls. At present, emission factors are mainly studied by chassis dynamometer methods. However, concerns have been raised about the extent to which emissions produced by on-road vehicles can be predicted using emission factors developed based on standardized dynamometer test procedures. This paper describes an on-board, in-use vehicle emissions measurement system which measures tailpipe emission rates while the vehicle is in real service experiencing complex traffic conditions, driver behavior and weather. The instantaneous mass flow rate (g/s) of fuel and five typical emission gases (NOx, HC, CO, CO2, O2) are recorded along with operating parameters such as mass air flow, vehicle speed, engine speed, ambient temperature, coolant temperature, etc. The equipment consists of an ECM OBD-II scanner, a mass air flow meter and two emissions analyzers, coordinated and recorded by a laptop computer. The measurement package is adapted for easy transition from vehicle to vehicle and, at only 17 kg (38 lbs), has minimal impact on vehicle operation. The paper presents a set of vehicle emission factors based on sixty on-road tests with five typical mid-life vehicles in urban, highway and aggressive driving situations. Tailpipe emission factors for HC, CO and NOx are developed in terms of g/kW.h, g/km and g/kgFuel. Based on these emission functions and the idle emission rate measurements, an emission model is developed for estimating the variation of tailpipe cumulative emissions for vehicles experiencing real-world driving conditions which are significantly different from the standard test sequences.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".