Emission Factors Analysis for Multiple Vehicles Using an On-Board, In-Use Emissions Measurement System
Why this work is in the frame
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Bibliographic record
Abstract
<div class="htmlview paragraph">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.</div> <div class="htmlview paragraph">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, CO<sub>2</sub>, O<sub>2</sub>) 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.</div> <div class="htmlview paragraph">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.</div>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it