High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data

Wang, Yujia; Wang, Hongbin; Zhang, Bo; Liu, Peng; Wang, Xinfeng; Si, Shuchun; Xue, Likun; Zhang, Qingzhu; Wang, Qiao

doi:https://doi.org/10.5194/acp-25-5537-2025

Articles | Volume 25, issue 11

https://doi.org/10.5194/acp-25-5537-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Air quality research at street level – Part II (ACP/GMD inter-journal...

https://doi.org/10.5194/acp-25-5537-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 11

Research article

|

04 Jun 2025

Research article |

| 04 Jun 2025

High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data

Yujia Wang, Hongbin Wang, Bo Zhang, Peng Liu, Xinfeng Wang, Shuchun Si, Likun Xue, Qingzhu Zhang, and Qiao Wang

Supplement

https://doi.org/10.5194/acp-25-5537-2025-supplement

Data sets

Data of High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data Xinfeng Wang and Yujia Wang https://doi.org/10.17632/24t54p6rj2.1

ERA5 hourly data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

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Short summary

This study established a bottom-up approach that employs real-time traffic flows and interpolation to obtain a spatially continuous on-road vehicle emission mapping for the main urban area of Jinan. The diurnal variation, spatial distribution, and emission hotspots were analyzed with clustering and hotspot analysis, showing unique fine-scale variation characteristics of on-road vehicle emissions. Future scenario analysis demonstrates remarkable benefits of electrification on emission reduction.