06 Apr 2023
 | 06 Apr 2023

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

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

Abstract. Exposure to element carbon (EC) and NOx is a public health issue that has been gaining increasing interest, with high exposure levels generally observed in traffic environments e.g., roadsides. Shanghai, home to approximately 25 million in the Yangtze River Delta (YRD) region in east China, has one of the most intensive traffic activities in the world. However, our understanding of the trend in vehicular emissions and, in particular, in response to the strict Covid-19 lockdown is limited partly due to a lack of long-term observation dataset and application of advanced mathematical models. In this study, NOx and EC were continuously monitored at a near highway sampling site in west Shanghai for 5 years (2016–2020). The long-term dataset was used to train the machine learning model, rebuilding the NOx and EC in a business-as-usual (BAU) scenario in 2020. The reduction in NOx and EC attributable to lockdown was found to be smaller than it appeared because the first week of lockdown overlapped with the lunar new year holiday, whereas, at a later stage of lockdown, the reduction (50–70 %) attributable to the lockdown was more significant, confirmed by satellite monitoring of NO2. In contrast, the impact of the lockdown on vehicular emissions cannot be well represented by simply comparing the concentration before and during the lockdown for conventional campaigns. This study demonstrates the value of continuous air pollutant monitoring at a roadside on a long-term basis. Combined with the advanced mathematical model, air quality changes upon future emission control and/or event-driven scenarios are expected to be better predicted.

Meng Wang et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-204', Anonymous Referee #1, 26 Apr 2023
  • RC2: 'Comment on egusphere-2023-204', Anonymous Referee #2, 06 May 2023

Meng Wang et al.


Total article views: 316 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
224 81 11 316 29 0 5
  • HTML: 224
  • PDF: 81
  • XML: 11
  • Total: 316
  • Supplement: 29
  • BibTeX: 0
  • EndNote: 5
Views and downloads (calculated since 06 Apr 2023)
Cumulative views and downloads (calculated since 06 Apr 2023)

Viewed (geographical distribution)

Total article views: 276 (including HTML, PDF, and XML) Thereof 276 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 29 May 2023
Short summary
Hourly EC and NOx were continuously measured for five years (2016–2020) at a near highway sampling site in west 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.