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Heterogeneous effects of COVID-19 lockdown measures on air quality in Northern China

In response to the spread of COVID-19, China implemented a series of control measures. The causal effect of these control measures on air quality is an important consideration for extreme air pollution control in China. Here, we established a difference-in-differences model to quantitatively estimat...

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Detalles Bibliográficos
Autores principales: Wang, Junfeng, Xu, Xiaoya, Wang, Shimeng, He, Shutong, He, Pan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7657037/
https://www.ncbi.nlm.nih.gov/pubmed/33199939
http://dx.doi.org/10.1016/j.apenergy.2020.116179
Descripción
Sumario:In response to the spread of COVID-19, China implemented a series of control measures. The causal effect of these control measures on air quality is an important consideration for extreme air pollution control in China. Here, we established a difference-in-differences model to quantitatively estimate the lockdown effect on air quality in the Beijing-Tianjin-Hebei (BTH) region. We found that the lockdown measures did have an obvious effect on air quality. The air quality index (AQI) was reduced by 15.2%, the concentration of NO(2), PM10, PM2.5, and CO were reduced by 37.8%, 33.6%, 21.5%, and 20.4% respectively. At the same time, we further explored the heterogeneous effects of travel restrictions and the control measure intensity on air quality. We found that the traffic restrictions, especially the restriction of intra-city travel intensity (TI), exhibited a significant heterogeneous effect on NO(2) with a decrease of approximately 13.6%, and every one-unit increase in control measures intensity reduced the concentration of air pollutants by approximately 2–4%. This study not only provides a natural, experimental basis for control measures on air quality but also indicates an important direction for future control strategies. Importantly, determining the estimated effect helps formulate accurate and effective intervention measures on the differentiated level of air pollution, especially on extreme air pollution.