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 AtmosphericPollutionResearch5(2014)759Ͳ768

AAtm spheric PPollution RResearch www.atmospolres.com

The 2013 severe haze over the Southern , China: PM2.5 composition and source apportionment

ZheWei1,Li–TaoWang1,Ming–ZhangChen2,YanZheng3

1DepartmentofEnvironmentalEngineering,SchoolofCityConstruction,HebeiUniversityofEngineering,,Hebei056038,China 2SchoolofCivilEngineering,ShijiazhuangRailwayInstitute,No.17North2nd–RingEastRoad,Shijiazhuang,Hebei050043,China 3TheEnvironmentalMonitoringCenterofHandan,Handan,Hebei056002,China

ABSTRACT 

PM2.5sampleswerecollectedandanalyzedforthefirsttimeinHandanCity,whichwaslistedinthetop4pollutedcities inChina,duringDecember2012toJanuary2013whentherecord–breakingseverehazepollutionhappened.Positive MatrixFactorizationmethod(PMF)wasappliedtounderstandmajorsourcestotheseverehazepollutionoverthiscity. –3 The daily average concentration of PM2.5 was 160.1ʅgm , which was 2.1 times of the National Ambient Air Quality –3 2– StandardsofChina(ClassII,AnnualAverageLevel)fordailyaveragePM2.5of75ʅgm .SO4 wasthemostabundantion + – (15.4%),followedbyNH4 andNO3 .Theyaccountedfor39.5%ofPM2.5.Eightfactorswereidentifiedbypositivematrix factorization(PMF)model.Themajorsourceswerecoalcombustionsource(25.9%),secondarysource(21.8%),industry source (16.2%), Ba, Mn and Zn source (12.7%), motor vehicle source (7.7%), road dust source (10.9%), K+, As and V  –1 source(6.3%)andfueloilcombustionsource(2.5%).Themeanvalueofextinctioncoefficient(Bext)was682.1Mm and –1 CorrespondingAuthor: thelargestcontributortoBextwasammoniumsulfatewiththemeanvalueof221.0Mm ,accountedfor32.4%ofthe Li–Tao Wang B . ext :+86Ͳ310Ͳ8578755  :+86Ͳ310Ͳ8578755 Keywords:PM2.5,OC,Bext,PMF,sourceapportionment :[email protected]  ArticleHistory: Received:07February2014 Revised:06June2014 Accepted:08June2014 doi:10.5094/APR.2014.085  1.Introduction 2.ExperimentalandAnalysisMethodology   Extremely severe, widespread haze occurred in Central and 2.1.Sampling Eastern China during January 2013. The haze event attracted a  wide attention locally and worldwide, because the haze in this Handan is located in the intersection area of Hebei, , month was the most serious pollution episode since 1961. ,andShandong.Thesamplingsiteislocatedonthetopofa According to the statistics published by the Ministry of 12–mhighbuildingatSchoolofCityConstruction,HebeiUniversity Environmental Protection for January 2013, the top ten polluted of Engineering (Figure 1). The sampling period was from DecemͲ cities are Xingtai, Shijiazhuang, Baoding, Handan, Langfang, ber1, 2012 to January31, 2013. Daily PM2.5 samples were Hengshui,Jinan,Tangshan,andZhengzhou,sevenofwhich collected continuously from 8:00 am to 7:30 am of the next day arewithinHebei. usingaHighVolumePM2.5airsampler(ThermoScientificCo.)with  20.3×25.4cmquartzfilters(0.2ʅmofporesize).Flowratewas Veryfewstudieswerepursuedtostudythecharacteristicsof 1.13m3min–1andatotalof55sampleswerecollected. particulate matter in Handan, especially for fine particles. In the  recentstudies,Wangetal.(2012),Weietal.(2013)andWanget 2.2.Chemicalcomponentsanalysis al.(2014)appliedtheCMAQmodelandusedtheso–called“Brute  Force” method (BFM) (Dunker et al., 1996) to simulate the air Ionic analyses. A circular portion of the quartz filter (1cm2) was pollution over the southern Hebei cities and address the major extracted into 10mL ultrapure water (18.2Mɏcm) to determine sourcesofPM2.5.Wangetal.(2012)andWangetal.(2014)found the concentration of water–soluble inorganic ions. The extracted that local sources contributed 64.2% to PM2.5 in Handan in solution was filtered through a microporous membrane filter December 2007, 69.2% in January and 63.0% in February, 2013, (0.45ʅmofporesize),andstoredinarefrigeratorat–18°Cuntil respectively. However, to the best of our knowledge, there is no chemical analysis. An ion chromatograph (Dionex, DX–600, USA) researchfocusingonthechemicalcomponentsofPM2.5inHandan was used to measure the water–soluble cations. Another ion City.Inthisstudy,wesampledandanalyzedPM2.5duringthemost chromatoͲgraph (Dionex, ICS–2100, USA) was used to measure polluted period in Handan and applied PMF, a widely implied water–solubleanions. receptor model, to indentify source apportionment of PM2.5, to  supportthepolicy–makinginairpollutioncontrolinthiscity. Carbon analysis. The total carbon, organic carbon and element  carbonwereanalyzedinTsinghuaUniversitythroughmethodology ofTORusedThermal/OpticalCarbonAnalyzer(Model2001A)that

©Author(s)2014.ThisworkisdistributedundertheCreativeCommonsAttribution3.0License. Wei et al. – Atmospheric Pollution Research (APR) 760  was produced by Desert Research Institute (DRI). A piece of 0.01ʅgmL–1,respectively.Theprecisionwas<5%forTCand<10% processed sample filter (0.53cm2) was placed in an environment forOCandEC.TheMDLwas0.82ʅgcm–2forTOCand0.20ʅgcm–2 with pure He gas without O2, and was heated progressively at forTEC,respectively. 140°C, 280°C, 480°C, and 580°C, first to determine organic  carboncontentsOC1,OC2,OC3,andOC4,respectively.Then,inan Elemental analyses. ICP–MS was calibrated by standard injection 2 environment with 2% O2 and 98% He, the sample was further (r >0.99) and the samples were analyzed in triplicate. Relative heatedprogressivelyat580°C,740°C,and840°Ctodeterminethe standarddeviationwaskeptbelow5%.Internalstandardrecovery elementalcarboncontentsEC1,EC2,andEC3. wascontrolledattherangefrom80%to120%.   Elemental analyses. This study used ICP–MS to analyze the 2.4.PositiveMatrixFactorization(PMF) concentrationsoftraceelements(i.e.,Ti,V,Cr,Mn,Ni,Cu,Zn,As,  Se,, Sr  Cd, Ba and Pb). A piece of filter (1cm2) was digested for PMF is a multivariate factor analysis tool that decomposes a 25min at 190 °C with 8mL concentrated nitric acid (BV–III) and matrix of speciated sample data into two matrices,  factor 0.5mL H2O2 (30%) in a Teflon microwave digestion tank (CEM, contributions and factor profiles, which then need to be MARS). Then, the bulk solution was moved into a 100mL flask. interpretedbyananalystastowhatsourcetypesarerepresented High–puritywaterwasusedtomakeupthevolume.Finally,10mL usingmeasuredsourceprofileinformation,winddirectionanalysis, solution was centrifuged for 10min to remove any suspended andemissioninventories(U.S.EPA,2008).Themethodisdescribed solidsbeforeinstrumentalanalyses. ingreaterdetailbyPaateroandTapper(1994)andPaatero(1997).  PMFhasbeensuccessfullyappliedtoidentifythepollutantsources 2.3.QualityAssurance/QualityControl)(QA/QC (Leeetal.,1999;Chueintaetal.,2000;Qinetal.,2006;Brownet  al.,2007;KimandHopke,2008;Yangetal.,2013). Sampling and weighing. There were several low–rise buildings  aroundthesamplingsite.Therewasnotabigemissionsourcein The brief principle is described in the following: A speciated thisarea.Toensuretheflowof1.13m3min–1,flowofthesampling datasetcanbeviewedasadat matrixXofnbymdimensions,in equipmentwascheckedeverymonth.Thesiliconeoilwasreplaced whichnisthenumberofsamplesandmisthenumberofchemical every15daystoensurethesamplingrateofPM2.5.Thesequartz speciesmeasured.SoX=GF+E,G=n×pandF=p×m,wherepisthe filterswerebakedat450°Cfor4hours.Then,filterswereputina numberofpollutantsources,Gisthematrixof sourcecontribution, chamber at 25°C and 50% of relatively humidity for 24h until F is the matrix of species profile, and E is theresidual matrix for sampling.Thesesampledfilterswereputinthesamechamberfor eachsample/species.ThegoalofPMFistoidentifythenumberof 24h until the second weigh. Then, filters were preserved in a pollutantsourcesFandG[seeEquations(1)–(3)].ThePMFsolution refrigeratorthatsetat–20°C.Allprocedureswerestrictlyquality minimizes the object function Q [Equation (3)], based upon the controlledtoavoidanypossiblecontaminationofthesamples. uncertainties (u). Then PMF could calculate the G and F. The  productofGandFcanexplainthesystematicvariationsinX. The sampling method was found to be effective since the  measured concentrations agreed well with those measured using ௣ TEOM 1405D. In this period, three blank filters were  preserved ܺ௜௝ ൌ෍݃௜௞ ݂௞௝ ൅݁௜௝ (1) under the same environment were analyzed to guarantee the ௞ୀଵ accuracyandreliabilityofsampling.Thecalculatedconcentrations  fortheblankfilterswereusedtoadjusttheconcentrationsofthe ௣ measuredcompositions. (2)  ݁௜௝ ൌܺ௜௝ െ෍݃௜௞ ݂௞௝ Ionicanalysesandcarbonanalysis.Glasscontainerswerewashed, ௞ୀଵ  and soaked in ultrapure water for over 24 h. Then, they were ௡ ௠ cleaned with supersonic wavesfor 45min andwere washed two (times with ultrapure water (18.2Mɏcm). The method detection ܳൌ෍෍ൣ݁௜௝Ȁݑ௜௝൧ (3 + + + 2+ 2+ 2– – – – limit(MDL)ofNH4 ,Na ,K ,Mg ,Ca ,SO4 ,Cl ,NO2 andNO3  ௜ୀଵ ௝ୀଵ were 0.01, 0.001, 0.001, 0.004, 0.005, 0.01, 0.005, 0.01 and 

Figure1.LocationofthesamplingsiteinHandancity(Ÿ indicatesHebeiUniversityofEngineering,and*indicatesthesamplingsite). Wei et al. – Atmospheric Pollution Research (APR) 761   Inthisstudy,USEPAPMF3.0wasusedtoanalyzethesource average concentrations of PM2.5 in December and January were apportionment. The 10% of concentrations was used as the 135.1ʅgm–3and190.1ʅgm–3,whichare1.8and2.5timesofthe –3 uncertainty of each species in the data set. If the concentration dailyPM2.5limitvalue(75ʅgm )setbytheNationalAmbientAir was less than or equal to MDL provided, 1/2 MDL was used to QualityStandardinChina(CNAAQS)(MEP,2012),respectively. replace the concentration, the uncertainty was  calculated using  5/6 MDL. Signal to noise (S/N) [see Equation (4), Xij is the InWeietal.(2014)study,therewasatypicalpollutionepisode th th concentrationofthej componentinthei samplingday,Sijisthe during January 2013. The present study was conducted to better standard deviation of Xij] was used to review the dataset. The understand the different characteristics and detailed components dataset was used directly when S/N exceeded 2. The results of particles before and after the episode. Table 1 presents the showedthatthesmallestvalueofS/Nwas6.90ofTi.Therefore,we detailed concentrations measured between January 1–13 and consideredthatthedatasetwasreliable. January14–31.   ଴Ǥହ ௡ ௡ Water–soluble ions in PM2.5. In this period, water–soluble ions ଶ ଶ ܵȀܰ ൌ ͲǤͷ ൥෍൫ܺ௜௝൯ Ȁ෍൫ܵ௜௝൯ ൩  (4) contributed48.3%toPM2.5;almostahalfofPM2.5suggestingthat ௜ୀଵ ௜ୀଵ water–soluble ions are the major fraction in PM2.5. Additionally,  water–soluble ions contributed 54.6% and 42.9% to PM2.5 in Among these measured compositions, 23 species were DecemberandJanuary,respectively.TheproportionofDecember – selected to run the model except that NO2  and Ti. We run the washigherthanJanuary.Itimpliedthattheproportionofwater– modelwith5to12factorstoexplorethebestsolution.Aneight– soluble ions didn’t increase along with increase of absolute factor solution gave the most interpretable and reproducible concentration of PM2.5. The proportions of January 1–13 and results within the iterations tested. Scaled residuals were inͲ January 14–31 were 29.1% and 65.3%, respectively. It indicated spected,and95.6%werebetween–3and3forallofthespecies. thattheproportionofwater–solubleionsincreasedafterthepeak 2– + –  of the episode. The sum of SO4 , NH4  and NO3  accounted for 3.ResultsandDiscussion 39.5%,42.3%and37.2%ofPM2.5duringthisperiod,Decemberand  January,respectively.Theywerethemajorcontributorstowater– 3.1.ChemicalcompositionsofPM2.5 solubleions.TheproportionofJanuary1–13was23.3%whilethe  proportion of January 14–31 was 58.6%. That was to say, in the Table 1 illustrated the concentrations and chemical compoͲ earlystageofepisode,therewerelesswater–solublepollutantsin nents of PM2.5 in different periods. The daily average concenͲ theatmosphere. –3 tration of PM2.5 was 160.1ʅgm  during this period. The daily  

Table1.AverageconcentrationsofchemicalcomponentsinPM2.5duringDecember2012toJanuary2013 MeanValue(ʅgm–3) S.D.a(ʅgm–3)  Whole Whole December January 1–13 14–31 December January 1–13 14–31 Period Period

PM2.5 160.1 135.1 190.1 217.7 139.5 77.9 57.2 89.4 139.5 41.1 OC 26.3 22.7 30.6 38.6 26.1 12.0 8.9 13.9 19.5 6.8 2– SO4  24.6 23.1 26.4 17.0 31.6 14.6 15.0 14.2 12.1 12.8 + NH4  19.9 17.4 22.9 18.9 25.1 9.1 8.5 9.0 10.0 7.8 – NO3  18.8 16.6 21.4 14.9 25.0 9.4 8.1 10.3 9.2 9.3 EC 15.8 16.8 14.7 20.1 11.6 7.3 6.8 7.9 10.5 3.6 Cl– 9.2 4.8 10.9 9.0 6.0 7.1 5.1 3.6 4.2 2.7 K+ 2.4 1.1 2.6 2.4 2.1 2.2 1.2 1.0 1.4 0.8 Na+ 1.3 1.7 0.9 0.9 1.0 0.7 0.6 0.5 0.3 0.6 Ca2+ 0.9 1.3 0.4 0.8 0.2 0.9 1.1 0.4 0.5 0.1 Pb 0.3 0.2 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.1 Zn 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.3 0.1 – –3 –3 –3 –3 –3 –3 –3 –3 –3 –3 NO2  88.0x10  142.3x10  40.4x10  0.6x10  226.5x10  153.1x10  102.6x10  154.8x10  50.9x10  146.9x10  Mg2+ 125.2x10–3 153.5x10–3 91.2x10–3 120.0x10–3 75.1x10–3 82.7x10–3 98.1x10–3 39.6x10–3 35.5x10–3 32.7x10–3 As 21.4x10–3 16.4x10–3 27.5x10–3 26.0x10–3 28.3x10–3 17.7x10–3 13.8x10–3 20.1x10–3 24.4x10–3 18.1x10–3 Ba 10.1x10–3 10.1x10–3 10.1x10–3 16.7x10–3 6.3x10–3 11.3x10–3 12.2x10–3 10.5x10–3 14.5x10–3 4.8x10–3 Cd 4.3x10–3 3.4x10–3 5.3x10–3 4.9x10–3 5.5x10–3 2.7x10–3 2.1x10–3 3.0x10–3 4.1x10–3 2.2x10–3 Cr 8.2x10–3 7.2x10–3 9.4x10–3 8.3x10–3 9.9x10–3 4.8x10–3 4.5x10–3 4.9x10–3 6.7x10–3 3.7x10–3 Cu 18.8x10–3 13.2x10–3 25.4x10–3 18.7x10–3 29.2x10–3 13.6x10–3 10.8x10–3 13.9x10–3 15.8x10–3 11.5x10–3 Mn 67.6x10–3 62.6x10–3 73.4x10–3 96.4x10–3 60.6x10–3 39.0x10–3 1.7x10–3 45.3x10–3 65.7x10–3 22.2x10–3 Ni 3.4x10–3 2.7x10–3 4.2x10–3 3.5x10–3 4.6x10–3 3.5x10–3 1.7x10–3 4.7x10–3 2.4x10–3 5.6x10–3 Se 10.6x10–3 9.0x10–3 12.6x10–3 14.8x10–3 11.4x10–3 6.3x10–3 4.8x10–3 7.4x10–3 11.2x10–3 4.1x10–3 Sr 6.4x10–3 7.4x10–3 5.2x10–3 6.9x10–3 4.2x10–3 6.3x10–3 7.5x10–3 4.1x10–3 5.4x10–3 2.9x10–3 Ti 15.1x10–3 65.7x10–3 11.0x10–3 8.9x10–3 12.2x10–3 20.3x10–3 0.2x10–3 14.6x10–3 19.0x10–3 12.1x10–3 V 5.4x10–3 5.7x10–3 5.0x10–3 5.3x10–3 4.8x10–3 5.9x10–3 4.7x10–3 7.1x10–3 7.1x10–3 7.4x10–3 aStandarddeviation Wei et al. – Atmospheric Pollution Research (APR) 762   2– – + –3 –3 SO4 wasthemostabundantioninthisperiod,itsconcentraͲ concentrationsofNO3 andNH4 were18.8ʅgm and19.9ʅgm , –3 –3 tion ranged from 5.3ʅgm  to 73.3ʅgm  with an average of respectively.Theyaccountedfor11.7%and12.4%ofPM2.5 24.3% –3 24.6ʅgm asshowninFigure2.Itaccountedfor15.4%ofPM2.5 and25.7%oftheallmeasuredions,respectively. and approximately 31.8% of the all measured ions. The average  

 





Figure2.TheaveragedconcentrationsforchemicalcomponentsinPM2.5 duringDec.2012toJan.2013. Thebottomandtopoftheboxrepresentthe25thand75thpercentiledistributionforthecomponent, respectively;theblacklineintheboxrepresentsthe50thpercentilevalue;theendsofthewhiskers representthe10thand90thpercentiledistributionforthecorrespondingcomponent;thesolidround representminimumandthemaximumvalue,respectively;thesquareintheboxrepresentsmeanvalue ofthecomponent. Wei et al. – Atmospheric Pollution Research (APR) 763   OC and EC. The daily average concentrations of OC and EC were elements between January 1–13 and January 14–31, the results 26.3ʅgm–3and15.8ʅgm–3,whichaccountedfor16.4%and9.9% showedthattheconcentrationsofBa,Mn,Pb,Se,Sr,VandZnin of PM2.5 during this period, respectively. In January 1–13, the January 1–13 were higher than in January 14–31 period. The average concentrations of OC and EC were 38.6ʅgm–3 and concentrationsofotherelementswerehigherduringJanuary14– 20.1ʅgm–3,respectively.InJanuary14–31,theaverageconcentraͲ 31. tionsofOCandECwere26.1ʅgm–3and11.6ʅgm–3,respectively.  It was found that water–soluble ions and OC, EC presented 3.2.TheresultsofPMFanalysis differentvariationsbeforeandaftertheepisode.   AsshowninFigure3,thefirstfactorwascharacterizedbyNi, ElementsinPM2.5.Theconcentrationsofmeasuredtraceelements CrandV,whichwereusuallyregardedas themarkercomponents are presented in Table 1. The order of concentrations for the offuel–oilcombustion(KarnaeandJohn,2011;Yangetal.,2013). elements were Zn>Pb>Mn>As>Cu>Ti>Ba>Se>Cr>Sr>V>Cd>Ni durͲ Therefore,thisfactorcouldbeindentifiedasfuel–oilcombustion. –3 ing this period. In comparison with the concentrations of these Thissourcecontributed3.7ʅgm (2.5%)toPM2.5.  (a)  )DFWRU±)XHO2LO&RPEXVWLRQ  )DFWRU)XHO2LO&RPEXVWLRQ





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 9 . 6U &O 6H &U 1L &D &X $V &G %D 3E 1D =Q (& 0J 2& 0Q 62 1+ 12  Figure3. Continued.  Thesecondfactormightbeidentifiedasthecoalcombustion factor could be regarded as industry source, which contributed + 2– + + –3 source.BecausethisfactorhadhighloadingsofNH4 ,SO4 ,Na ,K  24.1ʅgm (16.2%)toPM2.5. – – andNO3 combinedwiththemedianloadingsofCl ,OC,EC,Cr,Se  –3 – + and Cd. This source contributed 38.5ʅgm  (25.9%) to PM2.5. It Thefactor4hadtracerspeciesofCl ,K ,Mn,Zn,Se,Sr,Cd,Ba was noted that the coal combustion was a major contributor to andPb.Theseelementswerequitecommoninnumberofsource PM2.5. categories, including metal industry, biomass burning, waste  incineratorsandsoon(Brownetal.,2007).Therefore,inthisstudy, ThethirdfactorwascharacterizedbyhighloadingsofCr,Mn, this factor was indentified as the Ba, Mn and Zn source, which –3 Ni,Cu,Zn,As,Se,CdandPb.V,CrandMnarecloselyassociated contributed18.9ʅgm (12.7%)toPM2.5. withmetalprocessing(Yuetal.,2013).Moreover,steelmanufacͲ  turinglocatedatwesternofHandanisapillarindustry.ZnandPb Thefifthfactorwassimilartothefourthfactor,anditshould areemittedprimarilyfromthesteelmills(Yangetal.,2013).This also be identified as a mixed source. Zn may be from waste Wei et al. – Atmospheric Pollution Research (APR) 765  incinerator,WhileK+couldbeseenasaveryimportantmarkerof ammoniumsulfaterangedfrom26.5Mm–1to635.2Mm–1withthe biomass burning. oil combustion for industry and utilities was mean value of221.0Mm–1 that accounted for 32.4% to the total –1 characterized by V and Ni, and As could be emitted from coal Bext, followed by 23.2% of EC (ranged from 58.2Mm  to combustion.Eventhoughthesecompositionscomefromdifferent 352.0Mm–1),22.8%ofammoniumnitrate(rangedfrom14.5Mm–1 sources, they are produced by combustion. The fifth factor was to 492.9Mm–1) and 21.6% of OC (ranged from 46.8Mm–1 to named as K+, As and V source. This factor contributed 9.3ʅgm–3 356.5Mm–1). The similar conclusion was that the biggest contriͲ (6.3%)toPM2.5. butorofBextisammoniumsulfatereportedinJinan,inGuangzhou  and in eastern United States (Sisler and Malm, 2000; Yang et al., + 2– – The sixth factor had high loadings of NH4 , SO4  and NO3 . 2007;Taoetal.,2009).Additionally,themeanvaluesofBextwere These ions, in the atmosphere, generated by the photochemical 582.2Mm–1 and 802.0Mm–1 in December and January, reaction of precursor gases (such as SO2 and NOX) emitted from respectively. Comparison with December, all of Bext of OC, coal combustion and motor vehicles. The sixth factor was thus ammonium nitrate and ammonium sulfate increased in January, –3 regarded as the secondary source that contributed 32.5ʅgm  while Bext of EC decreased. A cause was that the absolute (21.8%)toPM2.5onaverage. concentrations of OC, ammonium nitrate and ammonium sulfate  increase, yet the concentration of EC decreased in January. In TheseventhfactorwascharacterizedbyhighloadingofECand addition, for ammonium nitrate and ammonium sulfate, high – 2+ moderate loadings, of Cl , Ca , OC, Mn, Zn, Se, Sr and Pb. EC is relativelyhumiditycouldbeauxiliaryforenhancingBext.Themean emitted from the vehicle engines. Mn, Zn and Pb are typically relatively humidity was 76.2% in January, which was higher than relatedtothebrakeofthemotor(Yangetal.,2013).Inaddition,Zn 60.1%ofDecember. was a component of a common fuel detergent and anti–wear  additiveandcouldpresentthedieselemissions,aswellasintires, OC was the biggest contributor to Bext, followed by EC, brakeliningsandpads(Brownetal.,2007).Ca2+couldbefromthe ammonium sulfate and ammonium nitrate in January 1–13. road dust. So the factor 7 was indentified as the motor source, However, in January 14–31, ammonium sulfate ranked number –3 whichcontributed11.5ʅgm (7.7%)toPM2.5. one, followed by ammonium nitrate, OC and EC. That was also  likely due to the increased concentrations of water–soluble ions The eighth factor was represented by not only Ca2+ but also andthehigherRHinJanuary14–31.TheRHincreasedfrom 54.3% thehighloadingsofSr,Ba,Mg2+andthemedianloadingsofCr,Mn, of January 1–13 to 88.5% of January 14–31, respectively. NiandCu.Metalsulfates(e.g.,CaSO4andMgSO4)canbeformed Meanwhile,thef(RH)increasedfrom1.33to2.46. whenatmosphericH2SO4/SO2reactswithcrustalaerosols.Cr,Mn,  NiandCuwereassociatedwithtailpipeemissions,suchasbrake 4.Conclusion andtirewear(Yuetal.,2013).Sothefactorisregardedastheroad  –3 –3 dust source, which contributed 10.4ʅgm  (6.9%) to PM2.5 on ThedailyaverageconcentrationofPM2.5was160.1ʅgm in average. HandanduringDecember1,2012toJanuary31,2013.Thewater–  soluble ions contributed 48.3% of PM2.5. Almost a half of PM2.5 As shown in Table 2, the average predicted concentration of suggested water–soluble ions were a major fraction in PM2.5 in –3 PM2.5 by PMF was 149.0ʅgm , which was slightly lower than this period. The daily average concentration of January was 160.1ʅgm–3ofthemeasuredconcentrationasshowninTable1. 190.1ʅgm–3, which was higher than 135.1ʅgm–3 of December.  ThehigherproportionofDecemberimpliedthattheproportionof 3.3.ChemicalcompositionforvisibilitydegradationBext water–solubleiondidn’tincreasealongwithincreaseofabsolute  concentrationofPM2.5.ThedailyaverageconcentrationsofOCand –1 –3 –3 Visibility could be calculated by Bext (unit in Mm ) with ECwere26.3ʅgm and15.8ʅgm ,whichaccountedfor16.4% Bext=1.9/visibility(Schichteletal.,2001),whereBextwasextinction and9.9%ofPM2.5duringthisperiod,respectively. coefficient. Bext was calculated by the concentration of chemical  componentsassomeresearchesmentioned,aIMPROVEEquation In the extremely polluted period of January 1–13, the daily –3 (5) was accepted and was used by other researches (Byun and average concentration of PM2.5 was 217.7ʅgm . However, the Ching, 1999; Sister and Malm, 2000). In this Equation, Malm and daily concentration of January 14–31 was only 139.5ʅgm–3. The 2– + – Day (2001) proposed a relative humidity (RH) growth function proportionofthesumofSO4 ,NH4 andNO3 inJanuary1–13was 2– – f(RH), indicated how efficiencies increase for SO4  and NO3  as 23.3%; yet the proportion of January 14–31 was 58.6%. It was 2– + – they absorbed liquid water. (Table 3 gives the detailed value of foundthatSO4 ,NH4 andNO3 presentedlowerconcentrationsin – f(RH)). Where, (NH4)2SO4=4.125 [S]; NH4NO3=1.29 [NO3 ]; POM theearlystageoftheepisode.Thedailyaverageconcentrationsof (particulate organic matter)=1.4 [OC]; fine soil=2.2 [Al]+2.19 [Si] OC and EC were 38.6ʅgm–3 and 20.1ʅgm–3 in January 1–13, +1.63 [Ca]+2.42 [Fe]+1.94 [Ti]; coarse mass=[PM10]–[PM2.5]; 10 respectively. In January 14–31, the average concentrations of OC representsclearairscattering. and EC were 26.1ʅgm–3 and 11.6ʅgm–3, respectively. It was  noted that the concentrations of OC and EC presented higher B |3f(RH)[(NH ) SO +NH NO ]+4[POM]+10[EC] concentrationsintheearlystageoftheepisodethaninthelaterof ext 4 2 4 4 3 (5) +1[finesoil]+0.6[coarsemass]+10 episode.   –3 Considering that the fine soil, coarse mass and clear air The predicted concentration of PM2.5 was 149.0ʅgm  applyingPMFmodel,whichwasslightlylowerthan160.1ʅgm–3of scatteringhadlittleeffectontheBext(Cheungetal.,2005).Sothis studyusedtheequationasfollows: themeasuredconcentration.EightfactorswereidentifiedbyPMF  method. They were coal combustion source, secondary source, industrysource,Ba,MnandZnsource,motorvehiclesource,road Bext|3f(RH)[(NH4)2SO4+NH4NO3]+4[POM]+10[EC] (6) +  dustsource,K ,AsandVsource,fueloilcombustionsource,which contributed 25.9%, 21.8%, 16.2%, 12.7%, 10.9%, 7.7%, 6.3% and Figure 4 illustrated Bext of four major chemical components 2.5%ofPM2.5,respectively. during the different periods. The mean value of Bext was –1  682.1Mm  in this period. The highest contributor to Bext was  Wei et al. – Atmospheric Pollution Research (APR) 766  (a) (b) 800 700 700 600 600 

500

 500   

) )  1 1  Ͳ 400 Ͳ 400 0P Mm Mm

( 300 (   0P  300 ext ext B 200 B 200 %H[W 100%H[W  100 0 0  Ͳ100 Ͳ100  2& (& 1+12 1+ 62 OC2& EC (& NH 1+124NO3 1+ (NH4 62)2SO4 OC EC NH4NO3 (NH4)2SO4  

800 (c) 500 (d) 700 400 600

 

 ) 500 )  1 1

Ͳ Ͳ   300 400 P Mm Mm 0 ( (   0P 200

ext 300 ext B B 200 H[W % %H[W 100 100 0 0   Ͳ100 2& (& 1+12 1+ 62 2& (& 1+12 1+ 62 OC EC NH4NO3 (NH4)2SO4 OC EC NH4NO3 (NH4)2SO4  

800 (e) 700 600  500  ) 1  Ͳ 400 Mm ( 0P  300  ext B 200 %H[W 100 0  Ͳ100 2& (& 1+12 1+ 62 OC EC NH4NO3 (NH4)2SO4 

Figure4.ThemonthlyBextforkeycomponentsofPM2.5.(a),(b),(c),(d)and(e)figurerepresentsthewholeperiod,December,January, 1–13Januaryand14–31January,respectively.  –1 The daily meanvalue of Bext was 682.1Mm  during this peͲ Acknowledgment riod. The daily mean value of Bext ammonium sulfate was  –1 221.0Mm whichaccountedfor32.4%ofBext,followedby24.9% This study was sponsored by the National Natural Science ofEC,22.8%%ofammoniumnitrateand21.6%ofOC.InJanuary FoundationofChina(No.41105105),theNaturalScienceFoundation 1–13, OC was the biggest contributor for Bext, followed by EC, of Hebei Province (No. D2011402019), the State Environmental ammonium sulfate and ammonium nitrate. However, in January Protection Key Laboratory of Sources and Control of Air Pollution 14–31, ammonium sulfate ranked number one, followed by Complex(No.SCAPC201307),theExcellentYoungScientistFoundation ammoniumnitrate,OCandEC.ThatwaslikelyduetohigherRHin ofHebeiEducationDepartment(No.YQ2013031),the Programforthe January14–31. OutstandingYoungScholarsofHebeiProvinceatHEBEU,China. Wei et al. – Atmospheric Pollution Research (APR) 767  

  Wei et al. – Atmospheric Pollution Research (APR) 768   Table 3. Statistical summary of mean f(RH) values in selected relative Malm, W.C., Day, D.E., 2001. Estimates of aerosol species scattering humidityranges characteristics as a function of relative humidity. Atmospheric Environment35,2845–2860. RH(%) f(RH) Paatero,P.,1997.Leastsquaresformulationofrobustnon–negativefactor 30a35 1.16 analysis.ChemometricsandIntelligentLaboratorySystems37,23–35. 35a40 1.21 Paatero, P., Tapper, U. 1994. Positive matrix factorization: a non–negative 40a45 1.22 factormodelwithoptimalutilizationoferrorestimatesofdatavalues. 45a50 1.27 Environments5,111–126. 50a55 1.33 Qin, Y.J., Kim, E., Hopke, P.K., 2006. The concentrations and sources of 55a60 1.38 PM2.5 in metropolitan New York City. Atmospheric Environment 40, S312–S332. 60a65 1.45 Schichtel,B.A.,Husar,R.B.,Falke,S.R.andWilson,W.E.,2001.Hazetrends a 65 70 1.55 over the United States, 1980–1995. Atmospheric Environment 35, 70a75 1.65 5205–5210.

75a80 1.83 Sister, J.F., Malm, W.C., 2000. Interpretation of trends of PM2.5 and 80a85 2.10 reconstructedvisibilityfromtheimprovenetwork.JournaloftheAir& WasteManagementAssociation50,775–789. 85a90 2.46 >90 3.17 Tao, J., Ho, K.F., Chen, L.G., Zhu, L.H., Han, J.L., Xu, Z.C., 2009. Effect of chemicalcompositionofPM2.5 onvisibilityinGuangzhou,China,2007  spring.Particuology7,68–75. References U.S.EPA(U.S.EnvironmentalProtectionAgency),2008.EPAPositiveMatrix  Factorization(PMF)3.0Fundamentals&UserGuide. Brown, S.G., Frankel, A., Raffuse, S.M., Roberts, P.T., Hafner, H.R., Wang,L.T.,Wei,Z.,Yang,J.,Zhang,Y.,Zhang,F.F.,Su,J.,Meng,C.C.,Zhang, Anderson,D.J.,2007.Sourceapportionmentoffineparticulatematter Q., 2014. The 2013 severe haze over southern Hebei, China: Model inPhoenix,AZ,usingpositivematrixfactorization.JournaloftheAir& evaluation, source apportionment, and policy implications. WasteManagementAssociation57,741–752. AtmosphericChemistryandPhysics14,3151–3173. Byun,D.W.,ChingJ.K.S.,1999.ScienceAlgorithmsoftheEPAModels–3 Wang,L.T.,Xu,J.,Yang,J.,Zhao,X.J.,Wei,W.,Cheng,D.D.,Pan,X.M.,Su,J., Community  Multiscale Air Quality (CMAQ) Modeling System, 2012. Understanding haze pollution over the southern Hebei area of EPA/600/R–99/030. chinausingtheCMAQmodel.AtmosphericEnvironment56,69–79. Cheung,H.C.,Wang,T.,Baumann,K.,Guo,H.,2005.Influenceofregional Wei, Z.,  Yang, J., Wang, L T., Wei, W., Zhang, F F. and Su, J., 2014. pollutionoutflowontheconcentrationsoffineparticulatematterand Characteristics of the severe haze episode in Handan city in January, visibility in the coastal area of Southern China. Atmospheric 2013. Environmental Science & Technology (In Chinese) (Article in Environment39,6463–6474. Press). Chueinta, W., Hopke, P.K., Paatero, P., 2000. Investigation of sources of Wei,W.,Wang,L.T.,Pan,X.M.,Cheng,D.D.andSu,J.,2013.Simulation atmospheric aerosol at urban and suburban residential areas in study of haze pollution sources in cities of Hebei Province. Thailandbypositivematrixfactorization.AtmosphericEnvironment34, EnvironmentalScience&Technology36,116–119(InChinese). 3319–3329. Yang, L.X., Cheng, S.H., Wang, X.F., Nie, W., Xu, P.J., Gao, X.M., Yuan, C., Dunker,A.M.,Morris,R.E.,Pollack,A.K.,Schleyer,C.H.,Yarwood,G.,1996. Wang,W.X.,2013.SourceidentificationandhealthimpactofPM ina Photochemicalmodelingoftheimpactoffuelsandvehiclesonurban 2.5 heavilypollutedurbanatmosphereinChina.AtmosphericEnvironment ozone using auto oil program data. Environmental Science & 75,265–269. Technology30,787–801. Yang,L.X.,Wang,D.C.,Cheng,S.H.,Wang,Z.,Zhou,Y.,Zhou,X.H.,Wang, Karnae,S.,John,K.,2011.Sourceapportionmentoffineparticulatematter W.X., 2007. Influence of meteorological conditions and particulate measured in an industrialized coastal urban area of south Texas. matteronvisualrangeimpairmentinJinan,China.ScienceoftheTotal AtmosphericEnvironment45,3769–3776. Environment383,164–173. Kim,E.,Hopke,P.,2008.SourcecharacterizationofambientĮneparticles Yu,L.D.,Wang, G.F.,Zhang,R.J.,Zhang,L.M.,Song,Y.,Wu,B.B.,Li,X.F.,An, at multiple sites in the Seattle area. Atmospheric Environment 42, K.,Chu,J.H.,2013.CharacterizationandsourceapportionmentofPM 6047–6056. 2.5 inanurbanenvironmentinBeijing.AerosolandAirQualityResearch Lee, E., Chan, C.K., Paatero, P., 1999. Application of positive matrix 13,574–583. factorizationinsourceapportionmentofparticulatepollutantsinHong Kong.AtmosphericEnvironment33,3201–3212.  MEP(MinistryofEnvironmentalProtection),2012.ChinaNationalAmbient AirQualityStandards,GB3095–2012.