《PM2.5源貢獻(xiàn)研究的國(guó)內(nèi)外文獻(xiàn)綜述》5100字

PM2.5源貢獻(xiàn)研究的國(guó)內(nèi)外文獻(xiàn)綜述目前常用的PM2.5源貢獻(xiàn)分析方法主要分為兩類：基于監(jiān)測(cè)的受體模型法和基于空氣質(zhì)量模式的方法ADDINEN.CITEADDINEN.CITE.DATA[\o"Xie,2016#450"20,\o"Wang,2016#449"21]，每種方法在實(shí)際應(yīng)用時(shí)均有其各自的優(yōu)勢(shì)和局限性。基于監(jiān)測(cè)的受體模型法受體模型法是通過(guò)使用統(tǒng)計(jì)分析方法來(lái)匹配污染源與采集的空氣污染樣品之間共同的化學(xué)和物理特征，并據(jù)此將監(jiān)測(cè)到的空間中給定受體點(diǎn)的污染物濃度分配給對(duì)應(yīng)的污染源，以得到不同污染源的貢獻(xiàn)。受體模型法因?yàn)槠洳僮骱?jiǎn)單而廣泛應(yīng)用于PM2.5源解析ADDINEN.CITEADDINEN.CITE.DATA[\o"Wang,2009#552"22,\o"Bi,2011#553"23]，主要的方法包括化學(xué)質(zhì)量平衡法（ChemicalMassBalance，CMB）和因子分析法（FactorAnalysis，F(xiàn)A）；其中，F(xiàn)A主要包括正交矩陣因子分解法（PositiveMatrixFactorization，PMF）、主成分分析法（PrincipalComponentAnalysis，PCA）ADDINEN.CITE<EndNote><Cite><Author>Belis</Author><Year>2013</Year><RecNum>465</RecNum><DisplayText><styleface="superscript">[24]</style></DisplayText><record><rec-number>465</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1617796159">465</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Belis,C.A.</author><author>Karagulian,F.</author><author>Larsen,B.R.</author><author>Hopke,P.K.</author></authors></contributors><titles><title>Criticalreviewandmeta-analysisofambientparticulatemattersourceapportionmentusingreceptormodelsinEurope</title><secondary-title>AtmosphericEnvironment</secondary-title></titles><periodical><full-title>AtmosphericEnvironment</full-title><abbr-1>Atmos.Environ.</abbr-1><abbr-2>AtmosEnviron</abbr-2></periodical><pages>94-108</pages><volume>69</volume><keywords><keyword>PM</keyword><keyword>Particulatematter</keyword><keyword>Sourceapportionment</keyword><keyword>Receptormodels</keyword><keyword>Europe</keyword><keyword>Exceedances</keyword><keyword>Carbonaceousfraction</keyword><keyword>Particulateorganiccarbon</keyword></keywords><dates><year>2013</year><pub-dates><date>2013/04/01/</date></pub-dates></dates><isbn>1352-2310</isbn><urls><related-urls><url>/science/article/pii/S1352231012010540</url></related-urls></urls><electronic-resource-num>10.1016/j.atmosenv.2012.11.009</electronic-resource-num></record></Cite></EndNote>[\o"Belis,2013#465"24]等方法。目前最為常用的是利用質(zhì)量平衡原理進(jìn)行計(jì)算的CMB和PMF。CMB的前身為化學(xué)元素平衡法（ChemicalElementBalance，CEB），是早在1972年由Miller等人ADDINEN.CITE<EndNote><Cite><Author>Miller</Author><Year>1972</Year><RecNum>479</RecNum><DisplayText><styleface="superscript">[25]</style></DisplayText><record><rec-number>479</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1621256160">479</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Miller,M.S.</author><author>Friedlander,S.K.</author><author>Hidy,G.M.</author></authors></contributors><titles><title>AchemicalelementbalanceforthePasadenaaerosol</title><secondary-title>JournalofColloidandInterfaceScience</secondary-title></titles><periodical><full-title>JournalofColloidandInterfaceScience</full-title><abbr-1>J.ColloidInterfaceSci.</abbr-1><abbr-2>JColloidInterfaceSci</abbr-2><abbr-3>JournalofColloid&InterfaceScience</abbr-3></periodical><pages>165-176</pages><volume>39</volume><number>1</number><dates><year>1972</year><pub-dates><date>1972/04/01/</date></pub-dates></dates><isbn>0021-9797</isbn><urls><related-urls><url>/science/article/pii/002197977290152X</url></related-urls></urls><electronic-resource-num>/10.1016/0021-9797(72)90152-X</electronic-resource-num></record></Cite></EndNote>[\o"Miller,1972#479"25]提出的，后經(jīng)Waston等人ADDINEN.CITE<EndNote><Cite><Author>Watson</Author><Year>1984</Year><RecNum>480</RecNum><DisplayText><styleface="superscript">[26]</style></DisplayText><record><rec-number>480</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1621256228">480</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Watson,JohnG.</author><author>Cooper,JohnA.</author><author>Huntzicker,JamesJ.</author></authors></contributors><titles><title>Theeffectivevarianceweightingforleastsquarescalculationsappliedtothemassbalancereceptormodel</title><secondary-title>AtmosEnviron(1967)</secondary-title></titles><periodical><full-title>AtmosEnviron(1967)</full-title></periodical><pages>1347-1355</pages><volume>18</volume><number>7</number><dates><year>1984</year><pub-dates><date>1984/01/01/</date></pub-dates></dates><isbn>0004-6981</isbn><urls><related-urls><url>/science/article/pii/000469818490043X</url></related-urls></urls><electronic-resource-num>/10.1016/0004-6981(84)90043-X</electronic-resource-num></record></Cite></EndNote>[\o"Watson,1984#480"26]重命名得來(lái)。CMB主要是利用輸入的污染源譜數(shù)據(jù)以及受體點(diǎn)采集測(cè)量獲得的化學(xué)成分譜數(shù)據(jù)，通過(guò)內(nèi)在的數(shù)學(xué)方法計(jì)算得到不同污染源對(duì)大氣顆粒物濃度的貢獻(xiàn)大小。由CMB計(jì)算得到的源解析結(jié)果在污染源類別判別及物理意義層面的解釋較為明確，并且相對(duì)于其他受體模型方法，特別是PMF，CMB對(duì)樣品量無(wú)要求。目前，CMB已在國(guó)內(nèi)外大量的PM2.5源解析研究中得到了應(yīng)用。例如，Villalobos等ADDINEN.CITE<EndNote><Cite><Author>Villalobos</Author><Year>2015</Year><RecNum>481</RecNum><DisplayText><styleface="superscript">[27]</style></DisplayText><record><rec-number>481</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1621258661">481</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Villalobos,AnaM.</author><author>Barraza,Francisco</author><author>Jorquera,Héctor</author><author>Schauer,JamesJ.</author></authors></contributors><titles><title>ChemicalspeciationandsourceapportionmentoffineparticulatematterinSantiago,Chile,2013</title><secondary-title>ScienceofTheTotalEnvironment</secondary-title></titles><periodical><full-title>ScienceoftheTotalEnvironment</full-title><abbr-1>Sci.TotalEnviron.</abbr-1><abbr-2>SciTotalEnviron</abbr-2></periodical><pages>133-142</pages><volume>512-513</volume><keywords><keyword>Sourceapportionment</keyword><keyword>PM</keyword><keyword>Santiago</keyword><keyword>Chile</keyword><keyword>CMB</keyword><keyword>Organicmolecularmarkers</keyword></keywords><dates><year>2015</year><pub-dates><date>2015/04/15/</date></pub-dates></dates><isbn>0048-9697</isbn><urls><related-urls><url>/science/article/pii/S0048969715000091</url></related-urls></urls><electronic-resource-num>/10.1016/j.scitotenv.2015.01.006</electronic-resource-num></record></Cite></EndNote>[\o"Villalobos,2015#481"27]利用CMB對(duì)圣地亞哥秋冬季進(jìn)行PM2.5源解析發(fā)現(xiàn)，木煙以及硝酸鹽是當(dāng)?shù)囟綪M2.5最重要的來(lái)源，并且60%的有機(jī)碳和20%的PM2.5主要由木煙貢獻(xiàn)；Liu等ADDINEN.CITE<EndNote><Cite><Author>Liu</Author><Year>2017</Year><RecNum>482</RecNum><DisplayText><styleface="superscript">[28]</style></DisplayText><record><rec-number>482</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1621259717">482</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Liu,Baoshuang</author><author>Li,Tingkun</author><author>Yang,Jiamei</author><author>Wu,Jianhui</author><author>Wang,Jiao</author><author>Gao,Jixin</author><author>Bi,Xiaohui</author><author>Feng,Yinchang</author><author>Zhang,Yufen</author><author>Yang,Haihang</author></authors></contributors><titles><title>SourceapportionmentandanovelapproachofestimatingregionalcontributionstoambientPM2.5inHaikou,China</title><secondary-title>EnvironmentalPollution</secondary-title></titles><periodical><full-title>EnvironmentalPollution</full-title><abbr-1>Environ.Pollut.</abbr-1><abbr-2>EnvironPollut</abbr-2></periodical><pages>334-345</pages><volume>223</volume><keywords><keyword>CMBmodel</keyword><keyword>Sourceapportionment</keyword><keyword>PM</keyword><keyword>Regionalcontribution</keyword></keywords><dates><year>2017</year><pub-dates><date>2017/04/01/</date></pub-dates></dates><isbn>0269-7491</isbn><urls><related-urls><url>/science/article/pii/S0269749117301756</url></related-urls></urls><electronic-resource-num>/10.1016/j.envpol.2017.01.030</electronic-resource-num></record></Cite></EndNote>[\o"Liu,2017#482"28]以中國(guó)?？谑蠵M2.5為研究對(duì)象，應(yīng)用CMB對(duì)其進(jìn)行源解析研究發(fā)現(xiàn)，對(duì)?？谑蠵M2.5貢獻(xiàn)最主要的污染源分別是汽車尾氣、再懸浮粉塵以及二次硫酸鹽；張玉梅等ADDINEN.CITE<EndNote><Cite><Author>張玉梅</Author><Year>2015</Year><RecNum>483</RecNum><DisplayText><styleface="superscript">[29]</style></DisplayText><record><rec-number>483</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1621260295">483</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>張玉梅,</author><author>張衛(wèi)東,</author><author>王軍玲,北京化工大學(xué)化學(xué)工程學(xué)院</author><author>北京市發(fā)展和改革委員會(huì),</author><author>北京市環(huán)境保護(hù)科學(xué)研究院,</author></authors></contributors><titles><title>大氣PM_(2.5)源解析“源清單化學(xué)質(zhì)量平衡法(I-CMB)”模型的建立與應(yīng)用</title><secondary-title>大氣科學(xué)學(xué)報(bào)</secondary-title></titles><periodical><full-title>大氣科學(xué)學(xué)報(bào)</full-title></periodical><pages>279-284%@1674-7097%L32-1803/P%WCNKI</pages><volume>38</volume><number>02</number><keywords><keyword>細(xì)顆粒物PM2.5</keyword><keyword>源清單</keyword><keyword>源解析</keyword><keyword>數(shù)學(xué)模型</keyword></keywords><dates><year>2015</year></dates><urls></urls></record></Cite></EndNote>[\o"張玉梅,2015#483"29]以北京市PM2.5為研究對(duì)象，應(yīng)用CMB對(duì)其源解析結(jié)果進(jìn)行分析發(fā)現(xiàn)，燃煤是PM2.5的最大貢獻(xiàn)源，同時(shí)來(lái)自機(jī)動(dòng)車、工業(yè)排放、揚(yáng)塵等的排放貢獻(xiàn)也不容小覷。雖然CMB的計(jì)算相對(duì)簡(jiǎn)單，但是為了保證CMB得到的源解析結(jié)果具有代表性、科學(xué)性和合理性，就必須確保輸入的污染源譜能夠反映本地污染源排放的貢獻(xiàn)特征，但事實(shí)上，我國(guó)本地化的污染源譜還十分缺乏ADDINEN.CITE<EndNote><Cite><Author>張延君</Author><Year>2015</Year><RecNum>484</RecNum><DisplayText><styleface="superscript">[30]</style></DisplayText><record><rec-number>484</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1621261280">484</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>張延君,</author><author>鄭玫,</author><author>蔡靖,</author><author>閆才青,</author><author>胡泳濤,</author><author>RussellArmisteadG</author><author>王雪松,</author><author>王書肖,</author><author>張遠(yuǎn)航%+北京大學(xué)環(huán)境科學(xué)與工程學(xué)院,環(huán)境模擬與污染控制國(guó)家重點(diǎn)聯(lián)合實(shí)驗(yàn)室</author><author>Schoolof,Civil</author><author>EnvironmentalEngineering,GeorgiaInstituteofTechnology</author><author>清華大學(xué)環(huán)境學(xué)院,環(huán)境模擬與污染控制國(guó)家重點(diǎn)聯(lián)合實(shí)驗(yàn)室</author></authors></contributors><titles><title>PM_(2.5)源解析方法的比較與評(píng)述</title><secondary-title>科學(xué)通報(bào)</secondary-title></titles><periodical><full-title>科學(xué)通報(bào)</full-title></periodical><pages>109-121+1-2%@0023-074X%L11-1784/N%WCNKI</pages><volume>60</volume><number>02</number><keywords><keyword>PM2.5</keyword><keyword>源解析</keyword><keyword>受體模型</keyword><keyword>方法對(duì)比</keyword></keywords><dates><year>2015</year></dates><urls></urls></record></Cite></EndNote>[\o"張延君,2015#484"30]。因此，只需要輸入受體點(diǎn)化學(xué)成分譜信息，而不需要輸入污染源譜信息的因子分析法PMF得到了普遍使用。PMF是最早由Paatero等ADDINEN.CITE<EndNote><Cite><Author>Paatero</Author><Year>1994</Year><RecNum>434</RecNum><DisplayText><styleface="superscript">[31]</style></DisplayText><record><rec-number>434</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1615728753">434</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Paatero,Pentti</author><author>Tapper,Unto</author></authors></contributors><titles><title>Positivematrixfactorization:Anon-negativefactormodelwithoptimalutilizationoferrorestimatesofdatavalues</title><secondary-title>Environmetrics</secondary-title></titles><periodical><full-title>Environmetrics</full-title><abbr-1>Environmetrics</abbr-1><abbr-2>Environmetrics</abbr-2></periodical><pages>111-126</pages><volume>5</volume><number>2</number><dates><year>1994</year></dates><isbn>1180-4009</isbn><urls><related-urls><url>/doi/abs/10.1002/env.3170050203</url></related-urls></urls><electronic-resource-num>10.1002/env.3170050203</electronic-resource-num></record></Cite></EndNote>[\o"Paatero,1994#434"31]提出的基于輸入的受體點(diǎn)化學(xué)成分譜信息，利用最小二乘法反算得到各污染源貢獻(xiàn)量以及污染源譜信息的方法。Jain等ADDINEN.CITE<EndNote><Cite><Author>Jain</Author><Year>2020</Year><RecNum>485</RecNum><DisplayText><styleface="superscript">[32]</style></DisplayText><record><rec-number>485</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1621263866">485</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Jain,Srishti</author><author>Sharma,S.K.</author><author>Vijayan,N.</author><author>Mandal,T.K.</author></authors></contributors><titles><title>Seasonalcharacteristicsofaerosols(PM2.5andPM10)andtheirsourceapportionmentusingPMF:AfouryearstudyoverDelhi,India</title><secondary-title>EnvironmentalPollution</secondary-title></titles><periodical><full-title>EnvironmentalPollution</full-title><abbr-1>Environ.Pollut.</abbr-1><abbr-2>EnvironPollut</abbr-2></periodical><pages>114337</pages><volume>262</volume><keywords><keyword>PM</keyword><keyword>Chemicalcomponents</keyword><keyword>Seasonalvariability</keyword><keyword>Sourceapportionment</keyword></keywords><dates><year>2020</year><pub-dates><date>2020/07/01/</date></pub-dates></dates><isbn>0269-7491</isbn><urls><related-urls><url>/science/article/pii/S0269749119342009</url></related-urls></urls><electronic-resource-num>/10.1016/j.envpol.2020.114337</electronic-resource-num></record></Cite></EndNote>[\o"Jain,2020#485"32]對(duì)意大利德里市2013年至2016年P(guān)M2.5和PM10進(jìn)行采樣，利用PMF對(duì)采集到的樣品數(shù)據(jù)進(jìn)行源解析分析發(fā)現(xiàn)，交通源排放對(duì)PM10的貢獻(xiàn)最大，而對(duì)PM2.5貢獻(xiàn)最大是生物質(zhì)燃燒源；溫維等ADDINEN.CITE<EndNote><Cite><Author>溫維</Author><Year>2014</Year><RecNum>486</RecNum><DisplayText><styleface="superscript">[33]</style></DisplayText><record><rec-number>486</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1621301247">486</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>溫維</author><author>韓力慧</author><author>代進(jìn)</author><author>王剛</author><author>劉超</author><author>程水源</author></authors></contributors><titles><title>唐山夏季PM2.5污染特征及來(lái)源解析</title><secondary-title>北京工業(yè)大學(xué)學(xué)報(bào)</secondary-title></titles><periodical><full-title>北京工業(yè)大學(xué)學(xué)報(bào)</full-title></periodical><pages>751-758</pages><number>5</number><dates><year>2014</year></dates><urls></urls></record></Cite></EndNote>[\o"溫維,2014#486"33]以唐山市2012年7月收集的PM2.5樣品的化學(xué)成分?jǐn)?shù)據(jù)為基礎(chǔ)，通過(guò)PMF對(duì)其進(jìn)行源解析可知，對(duì)唐山市夏季PM2.5貢獻(xiàn)較大的污染源主要包括金屬冶金工業(yè)、揚(yáng)塵源、交通源等。與CMB相比，盡管PMF不需要輸入污染源譜信息，但在進(jìn)行相關(guān)結(jié)果解釋時(shí)要求有一定的先驗(yàn)知識(shí)，這就導(dǎo)致了解析出來(lái)的污染源類別以及污染源個(gè)數(shù)可能帶有較大不確定的主觀性。此外PMF容易受到氣象因素變化的影響，因此為了減少氣象因素帶來(lái)的影響，在使用PMF時(shí)往往需要輸入大量的樣品（通常大于100個(gè)ADDINEN.CITEADDINEN.CITE.DATA[\o"Song,2006#487"34,\o"Ke,2008#488"35]）來(lái)進(jìn)行統(tǒng)計(jì)計(jì)算。總的來(lái)說(shuō)，基于監(jiān)測(cè)的受體模型法雖然操作簡(jiǎn)單，并且其解析得到的污染源結(jié)果也較為可靠，但是它自身也存在某些局限性。首先，因?yàn)槭荏w模型法是基于實(shí)際監(jiān)測(cè)數(shù)據(jù)進(jìn)行解析的，因此它的結(jié)果僅局限于特定的監(jiān)測(cè)點(diǎn)并且只能用來(lái)解釋監(jiān)測(cè)點(diǎn)附近的污染情況；其次，受體模型法的結(jié)果會(huì)繼承大氣PM2.5采樣過(guò)程中或者在對(duì)樣品進(jìn)行化學(xué)成分測(cè)量時(shí)產(chǎn)生的各種不確定性；另外，受體模型法不能用于進(jìn)行二次組分的污染來(lái)源識(shí)別，也不能用于區(qū)域傳輸貢獻(xiàn)解析ADDINEN.CITE<EndNote><Cite><Author>Li</Author><Year>2018</Year><RecNum>436</RecNum><DisplayText><styleface="superscript">[36]</style></DisplayText><record><rec-number>436</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1615728972">436</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Li,L.</author><author>An,J.Y.</author><author>Zhou,M.</author><author>Qiao,L.P.</author><author>Zhu,S.H.</author><author>Yan,R.S.</author><author>Ooi,C.G.</author><author>Wang,H.L.</author><author>Huang,C.</author><author>Huang,L.</author><author>Tao,S.K.</author><author>Yu,J.Z.</author><author>Chan,A.</author><author>Wang,Y.J.</author><author>Feng,J.L.</author><author>Chen,C.H.</author></authors></contributors><titles><title>AnintegratedsourceapportionmentmethodologyanditsapplicationovertheYangtzeRiverDeltaregion,China</title><secondary-title>EnvironmentalScience&Technology</secondary-title></titles><periodical><full-title>EnvironmentalScience&Technology</full-title><abbr-1>Environ.Sci.Technol.</abbr-1><abbr-2>EnvironSciTechnol</abbr-2></periodical><pages>14216-14227</pages><volume>52</volume><number>24</number><dates><year>2018</year><pub-dates><date>2018/12/18</date></pub-dates></dates><publisher>AmericanChemicalSociety</publisher><isbn>0013-936X</isbn><urls><related-urls><url>/10.1021/acs.est.8b01211</url></related-urls></urls><electronic-resource-num>10.1021/acs.est.8b01211</electronic-resource-num></record></Cite></EndNote>[\o"Li,2018#436"36]?；诳諝赓|(zhì)量模式的方法空氣質(zhì)量模式是通過(guò)利用提供的研究區(qū)域的排放、地形以及氣象數(shù)據(jù)，基于科學(xué)的理論、實(shí)驗(yàn)和假設(shè)，使用數(shù)值方法對(duì)污染物在大氣經(jīng)歷的物理過(guò)程和化學(xué)過(guò)程進(jìn)行描述的一種數(shù)學(xué)工具。為了實(shí)現(xiàn)在大氣中模擬污染物的形成過(guò)程，一系列空氣質(zhì)量模式被開發(fā)并加以應(yīng)用。這些模式主要包括第一代拉格朗日軌跡模式，第二代歐拉網(wǎng)格模式和第三代空氣質(zhì)量模式系統(tǒng)（Models-3），直至目前正在開發(fā)的氣象-化學(xué)在線耦合空氣質(zhì)量模式。第一代空氣質(zhì)量模式主要包括高斯擴(kuò)散模式和拉格朗日箱模式OZIPM/EKMA（OzoneIsoplethPlottingMethod/EmpiricalKineticModelingApproach），此模式可以初步估算城市尺度的一次污染物和臭氧形成狀況。第二代空氣質(zhì)量模式以歐拉網(wǎng)格模式為主，主要包括城市UAM（UrbanAirshedModel）模式，區(qū)域酸沉降RADM（RegionalAcidDepositionModel）模式以及區(qū)域氧化ROM（RegionalOxidantModel）模式。為了使模式模擬的大氣狀態(tài)更接近于實(shí)際狀況，第二代空氣質(zhì)量模式實(shí)現(xiàn)了將氣象變量和反應(yīng)機(jī)制的復(fù)雜性考慮在內(nèi)，但此類模式的研究對(duì)象主要為參與光化學(xué)反應(yīng)所涉及到的氣態(tài)或固態(tài)污染物，因而因而其模擬結(jié)果通常僅為單一介質(zhì)（氣相或固相）的輸出濃度。在Models-3模式出現(xiàn)之前，幾乎所有空氣污染模式或是為了解決某些特定條件下的大氣污染或傳輸問(wèn)題而設(shè)計(jì)，如氣溶膠污染、光化學(xué)污染、惰性氣體污染等；或是針對(duì)某種特定傳輸反應(yīng)機(jī)制而設(shè)計(jì)，如擴(kuò)散、光化學(xué)反應(yīng)、干沉降、濕沉降及酸沉降等。事實(shí)上，實(shí)際大氣環(huán)境中的許多污染問(wèn)題都存在密切聯(lián)系，因此將環(huán)境污染問(wèn)題分割來(lái)看并不符合大氣科學(xué)規(guī)律。因此，基于“一個(gè)大氣”（One-Atmosphere）觀念的Models-3模式應(yīng)運(yùn)而生，在一次工作中可以同時(shí)完成各種污染現(xiàn)象的模擬，從而對(duì)空氣質(zhì)量的全面評(píng)價(jià)起到了科學(xué)支撐的作用。常用的Models-3模式包括CMAQ（CommunityMulti-scaleAirQuality）、CAMx（ComprehensiveAirQualityModelwithExtensions）等。隨著對(duì)大氣科學(xué)不斷深入的探索，人們逐漸了解到氣象與環(huán)境之間存在互相影響的過(guò)程，因而WRF-Chem（WeatherResearchandForecasting-Chemistrymodel）等氣象-化學(xué)雙向在線耦合的數(shù)值模式逐漸被開發(fā)。當(dāng)前，敏感性分析技術(shù)、源示蹤技術(shù)以及響應(yīng)曲面建模技術(shù)是常用的三種基于空氣質(zhì)量模式的PM2.5源貢獻(xiàn)分析方法。敏感性分析技術(shù)可以通過(guò)每次單獨(dú)改變一個(gè)模式變量（例如排放數(shù)據(jù)、初始場(chǎng)、邊界場(chǎng)等）來(lái)量化該變量對(duì)模擬目標(biāo)污染物濃度的影響，主要包括強(qiáng)力法（BruteForceMethod，BFM）、去耦合直接法（DecoupledDirectMethod，DDM）和高階DDM（HDDM）。其中，BFM是通過(guò)每次單獨(dú)改變某個(gè)輸入?yún)?shù)，比較改變參數(shù)前后的PM2.5濃度結(jié)果，以兩次PM2.5模擬結(jié)果的差值作為該參數(shù)最終產(chǎn)生的貢獻(xiàn)量，是最簡(jiǎn)單且最常用的敏感性分析方法ADDINEN.CITE<EndNote><Cite><Author>Yamaji</Author><Year>2012</Year><RecNum>432</RecNum><DisplayText><styleface="superscript">[37]</style></DisplayText><record><rec-number>432</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1614695867">432</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Yamaji,Kazuyo</author><author>Uno,Itsushi</author><author>Irie,Hitoshi</author></authors></contributors><titles><title>InvestigatingtheresponseofEastAsianozonetoChineseemissionchangesusingalinearapproach</title><secondary-title>AtmosphericEnvironment</secondary-title></titles><periodical><full-title>AtmosphericEnvironment</full-title><abbr-1>Atmos.Environ.</abbr-1><abbr-2>AtmosEnviron</abbr-2></periodical><pages>475-482</pages><volume>55</volume><keywords><keyword>TroposphericO</keyword><keyword>NOtroposphericverticalcolumndensity</keyword><keyword>Ochemistry</keyword><keyword>EastAsian-scaletransport</keyword></keywords><dates><year>2012</year><pub-dates><date>2012/08/01/</date></pub-dates></dates><isbn>1352-2310</isbn><urls><related-urls><url>/science/article/pii/S1352231012002415</url></related-urls></urls><electronic-resource-num>10.1016/j.atmosenv.2012.03.009</electronic-resource-num></record></Cite></EndNote>[\o"Yamaji,2012#432"37]，如Huang等ADDINEN.CITEADDINEN.CITE.DATA[\o"Huang,2018#489"38]以珠三角地區(qū)PM2.5污染為研究對(duì)象，應(yīng)用基于CMAQ的強(qiáng)力法分析來(lái)自各區(qū)域排放對(duì)該區(qū)域PM2.5的影響。然而，BFM的計(jì)算成本會(huì)隨著輸入?yún)?shù)擾動(dòng)次數(shù)的增加呈線性增加，并且模擬較小的濃度變化可能會(huì)受到數(shù)值誤差的強(qiáng)烈影響ADDINEN.CITE<EndNote><Cite><Author>Koo</Author><Year>2009</Year><RecNum>404</RecNum><DisplayText><styleface="superscript">[39]</style></DisplayText><record><rec-number>404</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1605768684">404</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Koo,Bonyoung</author><author>Wilson,GaryM.</author><author>Morris,RalphE.</author><author>Dunker,AlanM.</author><author>Yarwood,Greg</author></authors></contributors><titles><title>Comparisonofsourceapportionmentandsensitivityanalysisinaparticulatematterairqualitymodel</title><secondary-title>EnvironmentalScience&Technology</secondary-title></titles><periodical><full-title>EnvironmentalScience&Technology</full-title><abbr-1>Environ.Sci.Technol.</abbr-1><abbr-2>EnvironSciTechnol</abbr-2></periodical><pages>6669-6675</pages><volume>43</volume><number>17</number><dates><year>2009</year><pub-dates><date>2009/09/01</date></pub-dates></dates><publisher>AmericanChemicalSociety</publisher><isbn>0013-936X</isbn><urls><related-urls><url>/10.1021/es9008129</url></related-urls></urls><electronic-resource-num>10.1021/es9008129</electronic-resource-num></record></Cite></EndNote>[\o"Koo,2009#404"39]。DDM相比于BFM較為復(fù)雜，該方法是通過(guò)求解與模式方程解耦的靈敏度方程來(lái)提供有關(guān)污染源對(duì)PM2.5濃度的貢獻(xiàn)信息，但是僅適用于較低比例的排放控制情景，在大比例的排放控制情景中，DDM計(jì)算得到的PM2.5濃度受到來(lái)自污染源排放變化的非線性貢獻(xiàn)誤差較大ADDINEN.CITEADDINEN.CITE.DATA[\o"Dunker,1984#427"40,\o"Ivey,2015#571"41]。為了改進(jìn)DDM在量化非線性影響方面存在的局限性，研究者們后續(xù)研發(fā)出了通過(guò)直接計(jì)算高階導(dǎo)數(shù)來(lái)獲得非線性貢獻(xiàn)的HDDMADDINEN.CITEADDINEN.CITE.DATA[\o"Hakami,2003#462"42,\o"Hakami,2004#567"43]，但是它并不適用于多個(gè)排放變量（大于3個(gè)）同時(shí)變化的情景ADDINEN.CITE<EndNote><Cite><Author>Dunker</Author><Year>2002</Year><RecNum>389</RecNum><DisplayText><styleface="superscript">[44,45]</style></DisplayText><record><rec-number>389</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1605681872">389</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Dunker,AlanM.</author><author>Yarwood,Greg</author><author>Ortmann,JeromeP.</author><author>Wilson,GaryM.</author></authors></contributors><titles><title>Comparisonofsourceapportionmentandsourcesensitivityofozoneinathree-dimensionalairqualitymodel</title><secondary-title>EnvironmentalScience&Technology</secondary-title></titles><periodical><full-title>EnvironmentalScience&Technology</full-title><abbr-1>Environ.Sci.Technol.</abbr-1><abbr-2>EnvironSciTechnol</abbr-2></periodical><pages>2953-2964</pages><volume>36</volume><number>13</number><dates><year>2002</year><pub-dates><date>2002/07/01</date></pub-dates></dates><publisher>AmericanChemicalSociety</publisher><isbn>0013-936X</isbn><urls><related-urls><url>/10.1021/es011418f</url></related-urls></urls><electronic-resource-num>10.1021/es011418f</electronic-resource-num></record></Cite><Cite><Author>Hakami</Author><Year>2004</Year><RecNum>381</RecNum><record><rec-number>381</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1605680914">381</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Hakami</author><author>Amir</author></authors></contributors><titles><title>Nonlinearityinatmosphericresponse:Adirectsensitivityanalysisapproach</title><secondary-title>J.Geophys.Res.-Atmos</secondary-title></titles><periodical><full-title>J.Geophys.Res.-Atmos</full-title></periodical><pages>-</pages><volume>109</volume><number>D15</number><dates><year>2004</year></dates><urls></urls></record></Cite></EndNote>[\o"Dunker,2002#527"44,\o"Hakami,2004#381"45]。源示蹤技術(shù)可以在一次模式模擬中通過(guò)跟蹤多種反應(yīng)性示蹤物從污染源排放到最終形成目標(biāo)污染物的整個(gè)過(guò)程來(lái)分配各來(lái)源貢獻(xiàn)，可用于量化目標(biāo)污染物濃度受到來(lái)自不同污染源組（不同的排放區(qū)域、不同的源類型）的貢獻(xiàn)量，進(jìn)而有利于政策制定ADDINEN.CITE<EndNote><Cite><Author>Dunker</Author><Year>2002</Year><RecNum>527</RecNum><DisplayText><styleface="superscript">[44]</style></DisplayText><record><rec-number>527</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1622099127">527</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Dunker,AlanM.</author><author>Yarwood,Greg</author><author>Ortmann,JeromeP.</author><author>Wilson,GaryM.</author></authors></contributors><titles><title>Comparisonofsourceapportionmentandsourcesensitivityofozoneinathree-dimensionalairqualitymodel</title><secondary-title>EnvironmentalScience&Technology</secondary-title></titles><periodical><full-title>EnvironmentalScience&Technology</full-title><abbr-1>Environ.Sci.Technol.</abbr-1><abbr-2>EnvironSciTechnol</abbr-2></periodical><pages>2953-2964</pages><volume>36</volume><number>13</number><dates><year>2002</year><pub-dates><date>2002/07/01</date></pub-dates></dates><publisher>AmericanChemicalSociety</publisher><isbn>0013-936X</isbn><urls><related-urls><url>/10.1021/es011418f</url></related-urls></urls><electronic-resource-num>10.1021/es011418f</electronic-resource-num></record></Cite></EndNote>[\o"Dunker,2002#527"44]。其中，耦合在CAMx里面的PSAT（ParticulateSourceApportionmentTechnology）和耦合在CMAQ里面的ISAM（IntegratedSourceApportionmentModel）是最常用的兩種方法。例如，Wagstrom和PandisADDINEN.CITE<EndNote><Cite><Author>Wagstrom</Author><Year>2011</Year><RecNum>492</RecNum><DisplayText><styleface="superscript">[46]</style></DisplayText><record><rec-number>492</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1621325570">492</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Wagstrom,K.M.</author><author>Pandis,S.N.</author></authors></contributors><titles><title>Contributionoflongrangetransporttolocalfineparticulatematterconcerns</title><secondary-title>AtmosphericEnvironment</secondary-title></titles><periodical><full-title>AtmosphericEnvironment</full-title><abbr-1>Atmos.Environ.</abbr-1><abbr-2>AtmosEnviron</abbr-2></periodical><pages>2730-2735</pages><volume>45</volume><number>16</number><keywords><keyword>Pollutanttransport</keyword><keyword>Airqualitymodeling</keyword><keyword>Sourceapportionment</keyword></keywords><dates><year>2011</year><pub-dates><date>2011/05/01/</date></pub-dates></dates><isbn>1352-2310</isbn><urls><related-urls><url>/science/article/pii/S1352231011001841</url></related-urls></urls><electronic-resource-num>/10.1016/j.atmosenv.2011.02.040</electronic-resource-num></record></Cite></EndNote>[\o"Wagstrom,2011#492"46]利用CAMx-PSAT模式對(duì)美國(guó)東部的一次和二次顆粒物進(jìn)行了本地和區(qū)域傳輸?shù)呢暙I(xiàn)評(píng)估；Kim等ADDINEN.CITE<EndNote><Cite><Author>Kim</Author><Year>2017</Year><RecNum>382</RecNum><DisplayText><styleface="superscript">[47]</style></DisplayText><record><rec-number>382</rec-number><foreign-keys><keyapp="EN"db-id="9pz0da5tve9r5devvrg5rv9qws2zz0tavrx0"timestamp="1605681031">382</key></foreign-keys><ref-typename="JournalArticle">17</ref-type><contributors><authors><author>Kim,ByeongUk</author><author>Bae,Changhan</author><author>Kim,HyunCheol</author><author>Kim,Eunhye</author><author>Kim,Soontae</author></authors></contributors><titles><title>Spatiallyandchemicallyresolvedsourceapportionmentanalysis:Casestudyofhighparticulatematterevent</title><secondary-title>AtmosphericEnvironment</secondary-title></titles><periodical><full-title>AtmosphericEnvironment</full-title><abbr-1>Atmos.Environ.</abbr-1><abbr-2>AtmosEnviron</abbr-2></periodical><pages>55-70</pages><volume>162</volume><number>aug.</number><dates><year>2017</year></dates><urls></urls><electronic-resource-num>10.1021/es9008129</electronic-resource-num></record></Cite></EndNote>[\o"Kim,2017#382"47]針對(duì)韓國(guó)首爾市在2014年2月發(fā)生的高濃度顆粒物污染事件，利用CAMx-PSAT模式分別從空間和化學(xué)成分方面分析了顆粒物的主要來(lái)源；Liu等ADDINEN.CITE<EndNote><Cite><Author>Liu</Author><Year>2020</Year><RecNum>491</RecNum><DisplayText><styleface="superscript">[48]</