001037651 001__ 1037651
001037651 005__ 20250203215420.0
001037651 0247_ $$2doi$$a10.5194/acp-24-4177-2024
001037651 0247_ $$2ISSN$$a1680-7316
001037651 0247_ $$2ISSN$$a1680-7324
001037651 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-00816
001037651 0247_ $$2WOS$$aWOS:001198245900001
001037651 037__ $$aFZJ-2025-00816
001037651 082__ $$a550
001037651 1001_ $$0P:(DE-HGF)0$$aTao, Chenliang$$b0
001037651 245__ $$aDiagnosing ozone–NO x –VOC–aerosol sensitivity and uncovering causes of urban–nonurban discrepancies in Shandong, China, using transformer-based estimations
001037651 260__ $$aKatlenburg-Lindau$$bEGU$$c2024
001037651 3367_ $$2DRIVER$$aarticle
001037651 3367_ $$2DataCite$$aOutput Types/Journal article
001037651 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1738573031_11959
001037651 3367_ $$2BibTeX$$aARTICLE
001037651 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001037651 3367_ $$00$$2EndNote$$aJournal Article
001037651 520__ $$aNarrowing surface ozone disparities between urban and nonurban areas escalate health risks in densely populated urban zones. A comprehensive understanding of the impact of ozone photochemistry on this transition remains constrained by current knowledge of aerosol effects and the availability of surface monitoring. Here we reconstructed spatiotemporal gapless air quality concentrations using a novel transformer deep learning (DL) framework capable of perceiving spatiotemporal dynamics to analyze ozone urban–nonurban differences. Subsequently, the photochemical effect on these discrepancies was analyzed by elucidating shifts in ozone regimes inferred from an interpretable machine learning method. The evaluations of the model exhibited an average out-of-sample cross-validation coefficient of determination of 0.96, 0.92, and 0.95 for ozone, nitrogen dioxide, and fine particulate matter (PM2.5), respectively. The ozone sensitivity in nonurban areas, dominated by a nitrogen-oxide-limited (NOx-limited) regime, was observed to shift towards increased sensitivity to volatile organic compounds (VOCs) when extended to urban areas. A third “aerosol-inhibited” regime was identified in the Jiaodong Peninsula, where the uptake of hydroperoxyl radicals onto aerosols suppressed ozone production under low NOx levels during summertime. The reduction of PM2.5 could increase the sensitivity of ozone to VOCs, necessitating more stringent VOC emission abatement for urban ozone mitigation. In 2020, urban ozone levels in Shandong surpassed those in nonurban areas, primarily due to a more pronounced decrease in the latter resulting from stronger aerosol suppression effects and less reduction in PM2.5. This case study demonstrates the critical need for advanced spatially resolved models and interpretable analysis in tackling ozone pollution challenges.
001037651 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001037651 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001037651 7001_ $$0P:(DE-HGF)0$$aPeng, Yanbo$$b1$$eCorresponding author
001037651 7001_ $$0P:(DE-HGF)0$$aZhang, Qingzhu$$b2$$eCorresponding author
001037651 7001_ $$00000-0002-9161-7086$$aZhang, Yuqiang$$b3
001037651 7001_ $$0P:(DE-Juel1)177767$$aGong, Bing$$b4
001037651 7001_ $$0P:(DE-HGF)0$$aWang, Qiao$$b5
001037651 7001_ $$0P:(DE-HGF)0$$aWang, Wenxing$$b6
001037651 773__ $$0PERI:(DE-600)2069847-1$$a10.5194/acp-24-4177-2024$$gVol. 24, no. 7, p. 4177 - 4192$$n7$$p4177 - 4192$$tAtmospheric chemistry and physics$$v24$$x1680-7316$$y2024
001037651 8564_ $$uhttps://juser.fz-juelich.de/record/1037651/files/acp-24-4177-2024.pdf$$yOpenAccess
001037651 909CO $$ooai:juser.fz-juelich.de:1037651$$popen_access$$popenaire$$pVDB$$pdriver$$pdnbdelivery
001037651 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)177767$$aForschungszentrum Jülich$$b4$$kFZJ
001037651 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5111$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001037651 9141_ $$y2024
001037651 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-21
001037651 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2024-12-21
001037651 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001037651 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2024-12-21
001037651 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2022-12-20T09:38:07Z
001037651 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2022-12-20T09:38:07Z
001037651 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2024-12-21
001037651 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2024-12-21
001037651 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-21
001037651 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001037651 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Open peer review$$d2022-12-20T09:38:07Z
001037651 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2024-12-21
001037651 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-21
001037651 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-21
001037651 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001037651 980__ $$ajournal
001037651 980__ $$aVDB
001037651 980__ $$aI:(DE-Juel1)JSC-20090406
001037651 980__ $$aUNRESTRICTED
001037651 9801_ $$aFullTexts