Capturing the Variability in Instantaneous Vehicle Emissions Based on Field Test Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Emission models are important tools for traffic emission and air quality estimates. Existing instantaneous emission models employ the steady-state “engine emissions map” to estimate emissions for individual vehicles. However, vehicle emissions vary significantly, even under the same driving conditions. Variability in the emissions at a specific driving condition depends on various influencing factors. It is important to gain insight into the effects of these factors, to enable detailed modeling of individual vehicle emissions. This study employs a portable emissions measurement system (PEMS), to collect vehicle emissions including the corresponding parameters of engine condition, vehicle activity, catalyst temperature, geography, and meteorology, to analyze the variability in emission rates as a function of those factors, across different vehicle specific power (VSP) categories. We observe that carbon dioxide, carbon monoxide, nitrogen oxides, and particle number emissions are strongly correlated with engine parameters (engine speed, torque, load, and air-fuel ratio) and vehicle activity parameters (vehicle speed and acceleration). In the same VSP bin, emissions per second on highways and ramps are higher than those on arterial roads, and the emissions when the vehicle is traveling downhill tend to be higher than the emissions during uphill traveling, because of higher observed speeds and accelerations. Morning emissions are higher than afternoon emissions, due to lower temperatures.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 it