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Spring Festival and COVID‐19 Lockdown: Disentangling PM Sources in Major Chinese Cities

Responding to the 2020 COVID‐19 outbreak, China imposed an unprecedented lockdown producing reductions in air pollutant emissions. However, the lockdown driven air pollution changes have not been fully quantified. We applied machine learning to quantify the effects of meteorology on surface air qual...

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Detalles Bibliográficos
Autores principales: Dai, Qili, Hou, Linlu, Liu, Bowen, Zhang, Yufen, Song, Congbo, Shi, Zongbo, Hopke, Philip K., Feng, Yinchang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206764/
https://www.ncbi.nlm.nih.gov/pubmed/34149113
http://dx.doi.org/10.1029/2021GL093403
Descripción
Sumario:Responding to the 2020 COVID‐19 outbreak, China imposed an unprecedented lockdown producing reductions in air pollutant emissions. However, the lockdown driven air pollution changes have not been fully quantified. We applied machine learning to quantify the effects of meteorology on surface air quality data in 31 major Chinese cities. The meteorologically normalized NO(2), O(3), and PM(2.5) concentrations changed by −29.5%, +31.2%, and −7.0%, respectively, after the lockdown began. However, part of this effect was also associated with emission changes due to the Chinese Spring Festival, which led to ∼14.1% decrease in NO(2), ∼6.6% increase in O(3) and a mixed effect on PM(2.5) in the studied cities that largely resulted from festival associated fireworks. After decoupling the weather and Spring Festival effects, changes in air quality attributable to the lockdown were much smaller: −15.4%, +24.6%, and −9.7% for NO(2), O(3), and PM(2.5), respectively.