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COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts

This paper proposes a novel grey spatiotemporal model and quantitatively analyzes the spillover and momentum effects of the COVID-19 lockdown policy on the concentration of PM2.5 (particulate matter of diameter less than 2.5 μm) in Wuhan during the COVID-19 pandemic lockdown from 23 January to 8 Apr...

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Detalles Bibliográficos
Autores principales: Gao, Mingyun, Yang, Honglin, Xiao, Qinzi, Goh, Mark
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8750743/
https://www.ncbi.nlm.nih.gov/pubmed/35034989
http://dx.doi.org/10.1016/j.seps.2022.101228
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author Gao, Mingyun
Yang, Honglin
Xiao, Qinzi
Goh, Mark
author_facet Gao, Mingyun
Yang, Honglin
Xiao, Qinzi
Goh, Mark
author_sort Gao, Mingyun
collection PubMed
description This paper proposes a novel grey spatiotemporal model and quantitatively analyzes the spillover and momentum effects of the COVID-19 lockdown policy on the concentration of PM2.5 (particulate matter of diameter less than 2.5 μm) in Wuhan during the COVID-19 pandemic lockdown from 23 January to 8 April 2020 inclusive, and the post-pandemic period from 9 April 2020 to 17 October 2020 inclusive. The results suggest that the stringent lockdowns lead to a reduction in PM2.5 emissions arising from a momentum effect (9.57–18.67%) and a spillover effect (7.07–27.60%).
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spelling pubmed-87507432022-01-11 COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts Gao, Mingyun Yang, Honglin Xiao, Qinzi Goh, Mark Socioecon Plann Sci Article This paper proposes a novel grey spatiotemporal model and quantitatively analyzes the spillover and momentum effects of the COVID-19 lockdown policy on the concentration of PM2.5 (particulate matter of diameter less than 2.5 μm) in Wuhan during the COVID-19 pandemic lockdown from 23 January to 8 April 2020 inclusive, and the post-pandemic period from 9 April 2020 to 17 October 2020 inclusive. The results suggest that the stringent lockdowns lead to a reduction in PM2.5 emissions arising from a momentum effect (9.57–18.67%) and a spillover effect (7.07–27.60%). Elsevier Ltd. 2022-10 2022-01-11 /pmc/articles/PMC8750743/ /pubmed/35034989 http://dx.doi.org/10.1016/j.seps.2022.101228 Text en © 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Gao, Mingyun
Yang, Honglin
Xiao, Qinzi
Goh, Mark
COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts
title COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts
title_full COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts
title_fullStr COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts
title_full_unstemmed COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts
title_short COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts
title_sort covid-19 lockdowns and air quality: evidence from grey spatiotemporal forecasts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8750743/
https://www.ncbi.nlm.nih.gov/pubmed/35034989
http://dx.doi.org/10.1016/j.seps.2022.101228
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AT gohmark covid19lockdownsandairqualityevidencefromgreyspatiotemporalforecasts