From emission to immission: the way to pertinent evaluation of transport-related health and environmental impacts
Bibliographic record
Abstract
This contribution focusses on emission/immission contribution assessment of road transport to global pollution source apportionment.The recent evolution in this domain show the impressive reduction of tailpipe emissions when brake and tyre wear PM emissions become the major source of recent powertrains.Inside vehicle immissions have been assessed using a vehicle inserted in the traffic as a 'mobile laboratory'.These immissions may represent the major part of the daily personal space-time exposure budget to air pollution.As an example, each hour spent in a car cabin may represent to a 1 hour exposure above the WHO recommendation limits for both PM and NO2.Time course evolution of car cabin immissions shows an important reduction of PM exposure thanks to the Euro5 regulation which made mandatory the use of particulate filters on light duty diesel-powered vehicles.In the meantime, recent toxicological studies demonstrate the impressive reduction of genotoxity, carcinogenicity, and pro-inflammatory potentials of post Euro5 diesel-vehicle emissions.As a conclusion, the impressive reduction of post Euro5 diesel-vehicle tailpipe emissions leads to a reduction of traffic-related pollutant immissions which will further improve with the expected growing of Euro6 complying vehicles in the forthcoming years.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".