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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.01164 |
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| _version_ | 1866915317911912448 |
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| author | Xu, Nan |
| author_facet | Xu, Nan |
| contents | Air pollution can be studied in the urban structure regulated by transport networks. Transport networks can be studied as geometric and topological graph characteristics through designed models. Current studies do not offer a comprehensive view as limited models with insufficient features are examined. Our study finds geometric patterns of pollution-indicated transport networks through 0.3 million image interpretations of global cities. These are then described as part of 12 indices to investigate the network-pollution correlation. Strategies such as improved connectivity, more balanced road types and the avoidance of extreme clustering coefficient are identified as beneficial for alleviated pollution. As a graph-only study, it informs superior urban planning by separating the impact of permanent infrastructure from that of derived development for a more focused and efficient effort toward pollution reduction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01164 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transport Network, Graph, and Air Pollution Xu, Nan Physics and Society Computer Vision and Pattern Recognition Air pollution can be studied in the urban structure regulated by transport networks. Transport networks can be studied as geometric and topological graph characteristics through designed models. Current studies do not offer a comprehensive view as limited models with insufficient features are examined. Our study finds geometric patterns of pollution-indicated transport networks through 0.3 million image interpretations of global cities. These are then described as part of 12 indices to investigate the network-pollution correlation. Strategies such as improved connectivity, more balanced road types and the avoidance of extreme clustering coefficient are identified as beneficial for alleviated pollution. As a graph-only study, it informs superior urban planning by separating the impact of permanent infrastructure from that of derived development for a more focused and efficient effort toward pollution reduction. |
| title | Transport Network, Graph, and Air Pollution |
| topic | Physics and Society Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.01164 |