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To what extent the traffic restriction policies can improve its air quality? An inspiration from COVID-19.
Xu, Si-Qing; He, Hong-di; Yang, Ming-Ke; Wu, Cui-Lin; Zhu, Xing-Hang; Peng, Zhong-Ren; Sasaki, Yuya; Doi, Kenji; Shimojo, Shinji.
  • Xu SQ; Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State-Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China.
  • He HD; Data-Driven Management Decision Making Lab, Shanghai Jiao Tong University, Shanghai, 200240 China.
  • Yang MK; Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State-Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China.
  • Wu CL; Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State-Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China.
  • Zhu XH; Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State-Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China.
  • Peng ZR; Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State-Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China.
  • Sasaki Y; International Center for Adaptation Planning and Design, College of Design, Construction and Planning, University of Florida, PO Box 115706, Gainesville, FL 32611-5706 USA.
  • Doi K; Graduate School of Information Science and Technology, Osaka University, Suita, Japan.
  • Shimojo S; Cyber Media Center, Osaka University, Suita, Japan.
Stoch Environ Res Risk Assess ; 37(4): 1479-1495, 2023.
Article in English | MEDLINE | ID: covidwho-2305951
ABSTRACT
In hazy days, several local authorities always implemented the strict traffic-restriction measures to improve the air quality. However, owing to lack of data, the quantitative relationships between them are still not clear. Coincidentally, traffic restriction measures during the COVID-19 pandemic provided an experimental setup for revealing such relationships. Hence, the changes in air quality in response to traffic restrictions during COVID-19 in Spain and United States was explored in this study. In contrast to pre-lockdown, the private traffic volume as well as public traffic during the lockdown period decreased within a range of 60-90%. The NO2 concentration decreased by approximately 50%, while O3 concentration increased by approximately 40%. Additionally, changes in air quality in response to traffic reduction were explored to reveal the contribution of transportation to air pollution. As the traffic volume decreased linearly, NO2 concentration decreased exponentially, whereas O3 concentration increased exponentially. Air pollutants did not change evidently until the traffic volume was reduced by less than 40%. The recovery process of the traffic volume and air pollutants during the post-lockdown period was also explored. The traffic volume was confirmed to return to background levels within four months, but air pollutants were found to recover randomly. This study highlights the exponential impact of traffic volume on air quality changes, which is of great significance to air pollution control in terms of traffic restriction policy. Supplementary Information The online version contains supplementary material available at 10.1007/s00477-022-02351-7.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Randomized controlled trials Language: English Journal: Stoch Environ Res Risk Assess Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Randomized controlled trials Language: English Journal: Stoch Environ Res Risk Assess Year: 2023 Document Type: Article