Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 23, issue 18
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

Viewed

Total article views: 2,237 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,695 479 63 2,237 145 51 83
  • HTML: 1,695
  • PDF: 479
  • XML: 63
  • Total: 2,237
  • Supplement: 145
  • BibTeX: 51
  • EndNote: 83
Views and downloads (calculated since 06 Apr 2023)
Cumulative views and downloads (calculated since 06 Apr 2023)

Viewed (geographical distribution)

Total article views: 2,237 (including HTML, PDF, and XML) Thereof 2,175 with geography defined and 62 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Oct 2025
Download
Short summary
Hourly elemental carbon (EC) and NOx 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 NOx, and a business-as-usual (BAU) scenario was assumed in 2020 and compared with the measured EC and NOx.
Read more
Share
Altmetrics
Final-revised paper
Preprint