Abstract
Elevated roads heighten traffic emissions and alter airflow patterns, posing unresolved questions regarding their impact on roadside air pollution compared to flat roads. This study utilized drone-based monitoring and machine learning to analyze vertical traffic pollution levels to inform elevated road design. Results revealed that roads elevated about 8 m above ground significantly raised black carbon (BC) levels, strongly associated with diesel vehicle emissions, at heights of 20–40 m. Under-bridge closures caused notable changes in all particle types, including PM2.5 and PM1.0, up to heights of 60 m. Elevated traffic emissions also intensified ozone (O3) vertical gradients. The resilient random forest model further found strong correlations between particle vertical structures and boundary layer height, alongside significant relationships with local temperature, humidity, and solar radiation. Pollution mechanisms varied within 0–20 m compared to higher elevations, particularly extending to 40 m for BC near the closed under-bridge road, suggesting the necessity for tailored control measures.
| Original language | English |
|---|---|
| Article number | 105004 |
| Journal | Transportation Research Part D: Transport and Environment |
| Volume | 148 |
| DOIs | |
| State | Published - Nov 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 11 Sustainable Cities and Communities
Keywords
- Elevated road
- Machine learning
- Traffic pollutants
- Unmanned aerial vehicle monitoring
- Vertical distribution
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