Air pollution causes widespread environmental and health problems and severely hinders the quality of life of urban residents. Traffic is critical for human life, but its emissions are a major source of pollution, aggravating urban air pollution. However, the complex interaction between traffic emissions and air pollution in cities and regions has not yet been revealed. In particular, the spread of COVID-19 has led various cities and regions to implement different traffic restriction policies according to the local epidemic situation, which provides the possibility to explore the relationship between urban traffic and air pollution. Here, we explore the influence of traffic on air pollution by reconstructing a multi-layer complex network base on the traffic index and air quality index. We uncover that air quality in the Beijing–Tianjin–Hebei (BTH), Chengdu–Chongqing Economic Circle (CCS), and Central China (CC) regions is significantly influenced by the surrounding traffic conditions after the outbreak. Under different stages of the fight against the epidemic, the influence of traffic in some regions on air pollution reaches the maximum in stage 2 (also called Initial Progress in Containing the Virus). For the BTH and CC regions, the impact of traffic on air quality becomes bigger in the first two stages and then decreases, while for CC, a significant impact occurs in phase 3 among the other regions. For other regions in the country, however, the changes are not evident. Our presented network-based framework provides a new perspective in the field of transportation and environment and may be helpful in guiding the government to formulate air pollution mitigation and traffic restriction policies.
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April 2022
Research Article|
April 26 2022
Network approach reveals the spatiotemporal influence of traffic on air pollution under COVID-19
Weiping Wang;
Weiping Wang
1
School of National Safety and Emergency Management, Beijing Normal University
, Zhuhai 519087, China
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Saini Yang;
Saini Yang
1
School of National Safety and Emergency Management, Beijing Normal University
, Zhuhai 519087, China
2
State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University
, Beijing 100875, China
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Kai Yin;
Kai Yin
3
School of Traffic and Transportation, Beijing Jiaotong University
, Beijing 100044, China
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Zhidan Zhao
;
Zhidan Zhao
4
China Complexity Computation Lab, Department of Computer Science, School of Engineering, Shantou University
, Shantou 515063, China
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Na Ying
;
Na Ying
a)
5
China State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences
, Beijing 100012, China
a)Authors to whom correspondence should be addressed: yingna@mail.bnu.edu.cn and jingfang@bnu.edu.cn
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Jingfang Fan
Jingfang Fan
a)
6
School of Systems Science, Beijing Normal University
, Beijing 100875, China
a)Authors to whom correspondence should be addressed: yingna@mail.bnu.edu.cn and jingfang@bnu.edu.cn
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a)Authors to whom correspondence should be addressed: yingna@mail.bnu.edu.cn and jingfang@bnu.edu.cn
Chaos 32, 041106 (2022)
Article history
Received:
February 10 2022
Accepted:
March 16 2022
Citation
Weiping Wang, Saini Yang, Kai Yin, Zhidan Zhao, Na Ying, Jingfang Fan; Network approach reveals the spatiotemporal influence of traffic on air pollution under COVID-19. Chaos 1 April 2022; 32 (4): 041106. https://doi.org/10.1063/5.0087844
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