Role of Traffic Emission on Temporal and Spatial Characteristics of Pollutant Concentration on Urban Road Network: A Case of Beijing
Bibliographic record
Abstract
This research aims to calculate PM2.5 concentration on the road network by considering the network-wide traffic status, which can be used to support research about the impact of urban road network pollution concentration on health. The increase in the use and number of vehicles has brought about a large amount of vehicle exhaust emissions and increased urban air pollutants. This is also one of the important reasons why this issue is worth studying. In this research, traffic emission was an estimate based on network-wide traffic status which was calculated from vehicle trajectories and spatial variance-covariance matrix. An identification method of external input pollutants is proposed to determine the occurrence of external pollutants imported into the urban area. To calculate the impact of multiple influencing factors on the pollution concentration of the entire road network, a multivariate linear model was adopted to calculate a variety of influencing factors and calibrate the model parameters by collecting real data. The results show that traffic emissions, external input pollution, and wind impact are the main factors affecting the PM2.5 concentration on urban road networks. Combined with real-time traffic data, we can obtain the temporal and spatial characteristics of the pollutant concentration of the road network. For policymakers, our research can provide a method for calculating the PM2.5 concentration on the road network, which is useful for establishing a health assessment framework in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".