Impact of low-emission driving behavior on brake wear PM emissions: insights from a real-world evaluation
Bibliographic record
Abstract
As vehicle exhaust regulations become more stringent, non-exhaust particulate matter (PM) emissions, particularly from brake wear, which accounts for up to 55% mass of these emissions, have become major contributors to traffic-related PM. However, how low-emission driving behavior influences brake wear PM emissions in real-world conditions remains unclear. In this study, we developed a low-emission driving assistance application and, for the first time, evaluated the real-world impact of low-emission driving behavior (LEDB) on brake wear PM 2.5 and PM 10 emissions. LEDB training was implemented for volunteer drivers in Leeds and Helsinki, resulting in average reductions in brake wear PM 2.5 emissions by 22.8% and PM 10 emissions by 26.1%. Additionally, the promotion strategies for LEDB training are discussed, and the expected emission reduction effects across different implementation scenarios are analyzed. These findings demonstrate that LEDB represents a promising and cost-effective approach that could contribute to reductions in brake wear emissions and improved air quality.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".