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dc.contributor.authorZhao, Bin
dc.contributor.authorWu, Wenjing
dc.contributor.authorWang, Shuxiao
dc.contributor.authorXing, Jia
dc.contributor.authorChang, Xing
dc.contributor.authorLiou, Kuo-Nan
dc.contributor.authorJiang, Jonathan H.
dc.contributor.authorGu, Yu
dc.contributor.authorJang, Carey
dc.contributor.authorFu, Joshua
dc.contributor.authorZhu, Yun
dc.contributor.authorWang, Jiandong
dc.contributor.authorLin, Yan
dc.contributor.authorHao, Jiming
dc.date.accessioned2018-07-31T13:17:29Z
dc.date.available2018-07-31T13:17:29Z
dc.date.created2018-01-26T12:31:32Z
dc.date.issued2017
dc.identifier.citationAtmospheric Chemistry and Physics. 2017, 17 (19), 12031-12050.nb_NO
dc.identifier.issn1680-7316
dc.identifier.urihttp://hdl.handle.net/11250/2507024
dc.description.abstractThe Beijing–Tianjin–Hebei (BTH) region has been suffering from the most severe fine-particle (PM2:5) pollution in China, which causes serious health damage and economic loss. Quantifying the source contributions to PM2:5 concentrations has been a challenging task because of the complicated nonlinear relationships between PM2:5 concentrations and emissions of multiple pollutants from multiple spatial regions and economic sectors. In this study, we use the extended response surface modeling (ERSM) technique to investigate the nonlinear response of PM2:5 concentrations to emissions of multiple pollutants from different regions and sectors over the BTH region, based on over 1000 simulations by a chemical transport model (CTM). The ERSM-predicted PM2:5 concentrations agree well with independent CTM simulations, with correlation coefficients larger than 0.99 and mean normalized errors less than 1 %. Using the ERSM technique, we find that, among all air pollutants, primary inorganic PM2:5 makes the largest contribution (24–36 %) to PM2:5 concentrations. The contribution of primary inorganic PM2:5 emissions is especially high in heavily polluted winter and is dominated by the industry as well as residential and commercial sectors, which should be prioritized in PM2:5 control strategies. The total contributions of all precursors (nitrogen oxides, NOx ; sulfur dioxides, SO2; ammonia, NH3; non-methane volatile organic compounds, NMVOCs; intermediate-volatility organic compounds, IVOCs; primary organic aerosol, POA) to PM2:5 concentrations range between 31 and 48 %. Among these precursors, PM2:5 concentrations are primarily sensitive to the emissions of NH3, NMVOCCIVOC, and POA. The sensitivities increase substantially for NH3 and NOx and decrease slightly for POA and NMVOCCIVOC with the increase in the emission reduction ratio, which illustrates the nonlinear relationships between precursor emissions and PM2:5 concentrations. The contributions of primary inorganic PM2:5 emissions to PM2:5 concentrations are dominated by local emission sources, which account for over 75% of the total primary inorganic PM2:5 contributions. For precursors, however, emissions from other regions could play similar roles to local emission sources in the summer and over the northern part of BTH. The source contribution features for various types of heavy-pollution episodes are distinctly different from each other and from the monthly mean results, illustrating that control strategies should be differentiated based on the major contributing sources during different types of episodes.nb_NO
dc.language.isoengnb_NO
dc.publisherEuropean Geosciences Unionnb_NO
dc.rightsAttribution 3.0 Unported (CC BY 3.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/*
dc.titleA modeling study of the nonlinear response of fine particles to air pollutant emissions in the Beijing-Tianjin-Hebei regionnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.rights.holder© Author(s) 2017nb_NO
dc.source.pagenumber12031-12050nb_NO
dc.source.volume17nb_NO
dc.source.journalAtmospheric Chemistry and Physicsnb_NO
dc.source.issue19nb_NO
dc.identifier.doi10.5194/acp-17-12031-2017
dc.identifier.cristin1552700
dc.relation.projectNational Science Foundation of China: 21625701nb_NO
dc.relation.projectNational Science Foundation of China: 21521064nb_NO
dc.relation.projectMOST National Key R & D program: 2016YFC0207601nb_NO
dc.relation.projectStrategic Pilot Project of Chinese Academy of Sciences: XDB05030401nb_NO
dc.relation.projectUCLA Sustainable Los Angeles Grand Challenge 2016: YZ-50958nb_NO
dc.relation.projectJet Propulsion Laboratory, California Institute of Technologynb_NO
cristin.unitcode7464,30,23,0
cristin.unitnameNedbørfeltprosesser
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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Attribution 3.0 Unported (CC BY 3.0)
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