000908625 001__ 908625
000908625 005__ 20240712100910.0
000908625 0247_ $$2doi$$a10.1038/s41612-022-00276-0
000908625 0247_ $$2Handle$$a2128/32297
000908625 0247_ $$2pmid$$a35789740
000908625 0247_ $$2WOS$$aWOS:000819420000001
000908625 037__ $$aFZJ-2022-02725
000908625 082__ $$a530
000908625 1001_ $$0P:(DE-Juel1)194205$$aShen, Fuzhen$$b0$$eFirst author
000908625 245__ $$aDisentangling drivers of air pollutant and health risk changes during the COVID-19 lockdown in China
000908625 260__ $$aLondon$$bSpringer Nature$$c2022
000908625 3367_ $$2DRIVER$$aarticle
000908625 3367_ $$2DataCite$$aOutput Types/Journal article
000908625 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1676628278_15629
000908625 3367_ $$2BibTeX$$aARTICLE
000908625 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000908625 3367_ $$00$$2EndNote$$aJournal Article
000908625 520__ $$aThe COVID-19 restrictions in 2020 have led to distinct variations in NO2 and O3 concentrations in China. Here, the different drivers of anthropogenic emission changes, including the effects of the Chinese New Year (CNY), China’s 2018–2020 Clean Air Plan (CAP), andthe COVID-19 lockdown and their impact on NO2 and O3 are isolated by using a combined model-measurement approach. In addition, the contribution of prevailing meteorological conditions to the concentration changes was evaluated by applying a machine-learning method. The resulting impact on the multi-pollutant Health-based Air Quality Index (HAQI) is quantified. The results show that the CNY reduces NO2 concentrations on average by 26.7% each year, while the COVID-lockdown measures have led to an additional 11.6% reduction in 2020, and the CAP over 2018–2020 to a reduction in NO2 by 15.7%. On the other hand, meteorological conditions from 23 January to March 7, 2020 led to increase in NO2 of 7.8%. Neglecting the CAP and meteorological drivers thus leads to an overestimate and underestimate of the effect of the COVID-lockdown on NO2 reductions, respectively. For O3 the opposite behavior is found, with changes of +23.3%, +21.0%, +4.9%, and −0.9% for CNY, COVID-lockdown, CAP, and meteorology effects, respectively. The total effects of these drivers show a drastic reduction in multi-air pollutant-related health riskacross China, with meteorology affecting particularly the Northeast of China adversely. Importantly, the CAP’s contribution highlights the effectiveness of the Chinese government’s air-quality regulations on NO2 reduction.
000908625 536__ $$0G:(DE-HGF)POF4-2112$$a2112 - Climate Feedbacks (POF4-211)$$cPOF4-211$$fPOF IV$$x0
000908625 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000908625 65027 $$0V:(DE-MLZ)SciArea-140$$2V:(DE-HGF)$$aGeosciences$$x0
000908625 65017 $$0V:(DE-MLZ)GC-170-2016$$2V:(DE-HGF)$$aEarth, Environment and Cultural Heritage$$x0
000908625 7001_ $$0P:(DE-Juel1)192244$$aHegglin, Michaela Imelda$$b1$$eCorresponding author
000908625 7001_ $$0P:(DE-HGF)0$$aLuo, Yuanfei$$b2
000908625 7001_ $$0P:(DE-HGF)0$$aYuan, Yue$$b3
000908625 7001_ $$0P:(DE-HGF)0$$aWang, Bing$$b4
000908625 7001_ $$0P:(DE-HGF)0$$aFlemming, Johannes$$b5
000908625 7001_ $$0P:(DE-HGF)0$$aWang, Junfeng$$b6
000908625 7001_ $$0P:(DE-HGF)0$$aZhang, Yunjiang$$b7
000908625 7001_ $$0P:(DE-HGF)0$$aChen, Mindong$$b8
000908625 7001_ $$0P:(DE-HGF)0$$aYang, Qiang$$b9
000908625 7001_ $$00000-0001-9531-6478$$aGe, Xinlei$$b10
000908625 773__ $$0PERI:(DE-600)2925628-8$$a10.1038/s41612-022-00276-0$$gVol. 5, no. 1, p. 54$$n1$$p54$$tnpj climate and atmospheric science$$v5$$x2397-3722$$y2022
000908625 8564_ $$uhttps://juser.fz-juelich.de/record/908625/files/Published%20Research%20Paper-Fuzhen%20Shen.pdf$$yOpenAccess
000908625 909CO $$ooai:juser.fz-juelich.de:908625$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire$$qextern4vita
000908625 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)194205$$aForschungszentrum Jülich$$b0$$kFZJ
000908625 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)192244$$aForschungszentrum Jülich$$b1$$kFZJ
000908625 9131_ $$0G:(DE-HGF)POF4-211$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2112$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vDie Atmosphäre im globalen Wandel$$x0
000908625 9141_ $$y2022
000908625 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-02-02
000908625 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000908625 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-02-02
000908625 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2021-02-02
000908625 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000908625 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2021-02-02
000908625 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNPJ CLIM ATMOS SCI : 2021$$d2022-11-10
000908625 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2022-11-10
000908625 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2022-11-10
000908625 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2021-10-13T14:26:52Z
000908625 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2021-10-13T14:26:52Z
000908625 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Blind peer review$$d2021-10-13T14:26:52Z
000908625 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2022-11-10
000908625 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2022-11-10
000908625 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2022-11-10
000908625 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bNPJ CLIM ATMOS SCI : 2021$$d2022-11-10
000908625 920__ $$lyes
000908625 9201_ $$0I:(DE-Juel1)IEK-7-20101013$$kIEK-7$$lStratosphäre$$x0
000908625 9801_ $$aFullTexts
000908625 980__ $$ajournal
000908625 980__ $$aVDB
000908625 980__ $$aI:(DE-Juel1)IEK-7-20101013
000908625 980__ $$aUNRESTRICTED
000908625 981__ $$aI:(DE-Juel1)ICE-4-20101013