Reduction in vehicular emissions attributable to the Covid-19 lockdown in Shanghai: insights from 5 years of monitoring-based machine learning

Wang, Meng; Duan, Yusen; Zhang, Zhuozhi; Yuan, Qi; Li, Xinwei; Han, Shuwen; Huo, Juntao; Chen, Jia; Lin, Yanfen; Fu, Qingyan; Wang, Tao; Cao, Junji; Lee, Shun-cheng

doi:https://doi.org/10.5194/acp-23-10313-2023

Articles | Volume 23, issue 18

https://doi.org/10.5194/acp-23-10313-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-23-10313-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 23, issue 18

Research article

|

15 Sep 2023

Research article |

| 15 Sep 2023

Reduction in vehicular emissions attributable to the Covid-19 lockdown in Shanghai: insights from 5 years of monitoring-based machine learning

Meng Wang, Yusen Duan, Zhuozhi Zhang, Qi Yuan, Xinwei Li, Shuwen Han, Juntao Huo, Jia Chen, Yanfen Lin, Qingyan Fu, Tao Wang, Junji Cao, and Shun-cheng Lee

Supplement

https://doi.org/10.5194/acp-23-10313-2023-supplement

Data sets

Measurement report: characterization and sources of the ambient secondary organic carbon in a Chinese megacity over five years from 2016 to 2020 M. Wang, Y. Duan, W. Xu, Q. Wang, Z. Zhang, Q. Yuan, X. Li, S. Han, H. Tong, J. Huo, J. Chen, S. Gao, Z. Wu, L. Cui, Y. Huang, G. Xiu, J. Cao, Q. Fu, and S. Lee https://doi.org/10.5281/zenodo.6473085

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Short summary

Hourly elemental carbon (EC) and NO_x were continuously measured for 5 years (2016–2020) at a sampling site near a highway in western Shanghai. We use a machine learning model to rebuild the measured EC and NO_x, and a business-as-usual (BAU) scenario was assumed in 2020 and compared with the measured EC and NO_x.