Atmospheric Pollution Research
www.atmospolres.com
SourcesapportionmentoffineandcoarseaerosolinKlangValley,
KualaLumpurusingpositivematrixfactorization
ShamsiahAbdulRahman,MohdSuhaimiHamzah,AbdulKhalikWood,MdSuhaimiElias,
NazaratulAshifaAdullahSalim,EzwizaSanuri
WasteandEnvironmentalTechnologyDivision,MalaysianNuclearAgency,Bangi,43000Kajang,Selangor,MalaysiaABSTRACT
Samplesoffineandcoarsefractionofairborneparticulatematterwerecollectedonweeklybasisduring theperiodfrom 2004to2008atKlangValley,KualaLumpur.The sampleswerecollectedusingaGent stacked filter sampler in two fractions (<2.5μm, fine and 2.5–10μm, coarse sizes). The samples were analyzed for their elemental composition and black carbon content by Particle Induced X–ray Emission (PIXE)andSmokeStainReflectometer,respectively.Thedatasetwasthenanalyzedbythefactoranalysis method, Positive Matrix Factorization technique in order to identify the possible sources of particulate matterandtheircontributiontotheambientparticulatematterconcentrationsintheKlangValley.Five factorsfromPMFsolutionswerefoundforelementalcompositionoffineandcoarseparticulatematter intheKlangValleyarea.Thesourcesidentifiedasmotorvehicles,industry,smoke/biomassburning,soil and road dust. The PMF results showed that motor vehicles were the main source for both fine and coarse particles of Klang Valley. In case of fine particles two stroke and motor vehicles all together contributeabout67.6%ofthefinemassmeasured.Theindustrysourcecontributestoabout16.7%and the rest 26% comprises emissions from smoke/biomass burning and soil source. In case of coarse particles, motor vehicles including two strokes contribute about 55.5% of the coarse mass while the industrysourceapportionedtobeabout17.6%.Soildustincludingroaddustcontributesabout26.8%to thecoarseparticlemass. Keywords: KlangValley Elementalcomposition Positivematrixfactorization Airborneparticulate ArticleHistory: Received:11May2010 Revised:04September2010 Accepted:09September2010 CorrespondingAuthor: ShamsiahAbdulRahman Tel:+603Ͳ89250510 Fax:+603Ͳ89112171 EͲmail:shamsiah@nuclearmalaysia.gov.my ©Author(s)2011.ThisworkisdistributedundertheCreativeCommonsAttribution3.0License. doi:10.5094/APR.2011.0251.Introduction
The Klang Valley region is an area in Malaysia with rapid development and population growth. The area comprise the capital city Kuala Lumpur (latitude 3°8’N; longitude 101°44’E) and itssuburbs,alongwiththeadjoiningcitiesinthestateofSelangor. It is geographically delineated by Titiwangsa Mountains to the northandeastandtheStraitofMalaccatothewest.Thecurrent estimated population is more than 5million. The weather is hot and humid with uniform temperatures throughout the year between 25°C to 35°C. The winds over the region are light and variable,howeverthereareuniformperiodicchangesinthewind flow patterns namely, the southwest monsoon, northeast monͲ soonandinter–monsoonseasons.Thesouthwestmonsoonseason is usually established in the latter half of May or early June and ends in September while the northeast monsoon season usually commences in early November and ends in March. The seasonal wind flow patterns coupled with the local topographic features determine the rainfall distribution patterns over the country. Generally, the primary maximum rainfall occurs in October and November andFebruary is the month with minimum rainfall. The secondary maximum generally occurs in March to May while in June–Julyverylittlerainfalloccursbutitmaybeovercast.
Economicgrowthwithuncontrolleddevelopmentofindustrial premises, non–regulated motor vehicles and human activities related to the high population density in Klang Valley are responͲ siblefortheemissionsofvariouspollutantsinthearea.Duringthe haze episodes in 1997, 1998, 2003, 2004, and 2006 arising from forestfiresinaneighboringcountry,theareaofKlangValleywas themostaffected.Thepollutantsemittedfromforestfiresaswell
asfromvehiclesandindustrialactivitieshavegivenrisetosevere airpollutioninthearea.
Study of aerosol chemical composition is essential to understand the aerosol formation sources of air pollution. The Malaysian Nuclear Agency has been involved in the International Atomic Energy Agency/Regional Cooperative Agreement (IAEA/RCA)projectonairparticulatemattersince1998.Thestudy area is the Klang Valley region. The study of fine particles (PM2.5) forthesamplescollectedduringtheperiodfrom2000to2006at KlangValleyidentifiedfivesourcesofaerosolsintheKlangValley (Rahman et al., 2009a; Rahman et al., 2009b). These sources include sea spray, motor vehicle, smoke, industry, soil and an unknownsource.Thispaperisabouttheelementalconcentrations observedforthesamplescollectedforaperiodof5years(2004– 2008) of the IAEA/RCA project. The study focuses on both fine (PM2.5)andcoarseparticle(between2.5and10μm).Thechemical composition data were determined and receptor modeling using positivematrixfactorizationintheformofEPAPMF3.0(EPA,2010) was used to obtain information on the possible local pollutant sourcesintheKlangValley.Inordertoidentifythemostprobable direction of local point sources, Conditional Probability Functions (CPFs)werecalculatedfromtheresolvedsourcecontributionsand themeasuredwinddirections(Kimetal.,2003).
2.Methodology
2.1.SitedescriptionThe Klang Valley monitoring site is located at Universiti Teknologi Malaysia (UTM), Kuala Lumpur campus (3q10’30’’N,
101q43’24’’E) just north to the city center park and the Petronas Tower,KualaLumpur(Figure1).Thecampusisjustafewhundred meters from the roadside and lies between two highways to the north and south and surrounded by busy roads closely linked to the Kuala Lumpur city center. On average, the wind speed in the areawas7.6m/sduringsouthwestmonsoonwithminimumwind speedof2.8m/sandmaximumwindspeedof13.4m/s,whilethe average wind speed during the northeast monsoon was 6.6m/s with minimum and maximumwind speed of 2.3m/s and 8.3m/s, respectively.Predominantwinddirectionswerefromthewestand southeast as shown in the wind rose plot for the fine and coarse massesinFigure2. 2.2.Sampling AllaerosolsampleswerecollectedusingaGentstacksampler (Hopkeetal.,1997).Thesamplerwasplacedonarooftopofatwo storeybuildingattheUniversitiTeknologiMalaysia,KualaLumpur atapproximately10metersheight.Thesamplerwasprogrammed torunautomaticallyatanairflow–rateof15L/mintocollecttwo fractions(<2.5μmand2.5–10μmaerodynamicdiameterparticles) of 24hour (from 12 noon to 12 noon) samples on 47mm polyͲ carbonatefiltersatafrequencyofaweekorfortnight.
2.3.Massandblackcarbonmeasurement
The total mass of each sample was determined by weighing the filter using a microbalance (METTLER, Model MT5). The balancewasequippedwithaPo–212(alphaemitter)electrostatic
charge eliminator (STATICMASTER) to eliminate the static charge accumulated on the filters before each weighing. Black carbon measurements of both fine and the coarse fraction were performedusinganEELSmokeStainReflectometerandtheblack carbonconcentrationwasdeterminedaccordingtothemethodfor 47mmpolycarbonatefilter(Cohenetal.,2000). 2.4.Elementalanalysis Concentrationoftheelementsforeachsamplewasanalyzed by Particle Induced X–ray Emission (PIXE) (Koltay, 1994; Cohen, 1998) at New Zealand Institute of Geological and Nuclear Science Limited (IGNS). X–ray spectra obtained from the PIXE measureͲ ments were analyzed with GUPIX software developed by Guelph University(Maxwelletal.,1989;Maxwelletal.,1995).Calibration of the PIXE system was performed by irradiating suitable Micromatterthintargetstandards.
2.5.DataanalysisbyPositiveMatrixFactorization(PMF)
Afactoranalysismethod,PositiveMatrixFactorization(PMF), utilizes error estimates of the data to provide optimum data scaling(PaateroandTapper,1993).Themethodisbasedonsolving thefactoranalysisproblembytheleastsquaresapproachusinga data point weighting method which decomposes a matrix of data of dimension n rows and m columns into two matrix, G(nxp) and F(pxm),wherenisthenumberofsampleswhilemisthenumber ofspecies.Themodelcanbewrittenas: Figure1.LocationofsamplingsiteintheKlangvalley.
Figure2.WindRoseplotsshowingthePM2.5andPM2.5–10massforsamplescollectedin2004–2008intheKlangValley. ܺ ൌ ܩܨ ܧ (1) Briefly,adatamatrixXofibyjdimensions,inwhichinumber ofsamplesandjchemicalspeciescanbewrittenas: ݔൌ ݃ ୀଵ ݂ ݁ (2) wherepisthenumberoffactors,fisspeciesprofile,gisamount ofmassand݁istheresidualforeachsample.ThetaskofPMFis tominimizetheobjectfunction(Q),basedupontheuncertainties: ܳ ൌ ሺ݁Ȁݏሻଶ (3)
whereݏistheuncertaintiesinݔ.Theresultsareconstrainedso that all species profiles (matrix F) are non–negative and each sample has a non–negative source contribution (matrix G). Solution of Equation (3) and the model are described in detail elsewhere(PaateroandTapper,1994;Paatero,1997).
TheadvantageofPMFistheabilityofthemodeltohandlethe incomplete data such as missing data, below detection limit data andnegativevaluedata(Chueintaetal.,2001;Songetal.,2001). In this study, any missing data was replaced by the median of all the concentrations measured for each species and its accompanyingerrorwassetatfourtimesthemedianvalue.Data below the detection limit (MDL) were substituted by half of the detection limit values and the uncertainty was set 5/6 times the detection limit value (Polissar et al., 1998). In this study EPA PMF 3.0programwasappliedtothedataset(EPA,2010).Thevariability in the PMF solution in this method can be estimated using a bootstrapping technique, in which new data sets that are consistent with the original data are generated. Each data set is decomposed into profile and contribution matrices, and the resultingprofileandcontributionmatricesarecomparedwiththe baserun(Norrisetal.,2008). 2.6.ConditionalProbabilityFunction(CPF) TheConditionalProbabilityFunction(CPF)wasusedtoanalyze the point source impact from various wind directions. The CPF analysis was applied to the resolved source contribution data estimated from PMF coupled with wind direction values. CPF is definedas: ܥܲܨ ൌmѐɽ ݊οఏ (4) where݉οఏisthenumberofoccurrencesofthewindsector, οߠ, for which the source contribution exceeded a threshold criteria, and݊οఏisthetotalnumberoftimesthewindcamefromthesame wind direction. In this study, the hourly wind direction and the wind speed data used for the analysis were measured by the Meteorological Department located about 15km from the moniͲ toringsite.Thewindsector(ȴT)wassettobe15degreesandall periodswithwindspeedslessthan1m/swereremovedfromthe analysis, and the threshold was set as the highest 25% of the sourcecontribution.
3.ResultandDiscussion
3.1.ElementalconcentrationA total of 164 pair of samples were collected and analyzed covering the period of January 2004 to December 2008. Statistics ofsamplescollectedyearlystartingfrom2004to2008were50,25, 20, 24 and 45 pairs. Concentration of the following twenty elementsweremeasuredandanalyzedforeachsample:Al,As,Br, Ca,Cl,Cr,Cu,Fe,K,Mg,Mn,Na,Ni,P,Pb,S,Si,Ti,VandZn.The average concentrations, standard deviations including percentage ofweightfractionforbothfineandcoarseparticlesarepresented inTable1.Theresultsshowsthatblackcarbon(BC)andSulfur(S) were the major components of the fine aerosol with weight fraction of about 15.8% and 7.21%, respectively. Emissions with highBCandSnormallyrepresentbiomassburning,industryaswell as exhaust from motor vehicles (Watson and Chow, 2001). S also existsintheatmosphereasSO2whichisconvertedtosulfuricacid, H2SO4andneutralizedbyammoniagastoformammoniumsulfate, (NH4)2SO4.
The soil elements (Si, Ca and Al) are always higher in coarse particles. The results show that Si is the major component of the coarse aerosol with weight fraction of 7.58% of the coarse mass followedbyothersoilelementsCaandAlwith weightfraction of 4.38%and3.64%respectively.
3.2.Selectionofsampleandspecies
Inclusion and exclusion of species and samples can significantly influence the PMF results. Time series of species concentrationofthePMFmodelwereusedtoidentifyanyvalues that appeared abnormal as compared to the overall data. In this study, about 5% of the samples for both fine and coarse dataset were excluded from the model in order to avoid unrealistic apportionmentandtominimizetheimpactoftheoccurrenceofa uniqueprofile.SelectionofthespeciesinthePMFmodelisbased on the input data statistics and the concentration/uncertainty scatterplotofthePMFmodel.NiandCuforbothfineandcoarse data,aswellasAsforthecoarsedatawerecategorizedas“bad”
Table1.Meanconcentration(ng/m3 )andStandardDeviationforFineandCoarseParticlesofKlangValley2004–2008 Element Fineparticles Coarseparticles Mean SD Percentage (%) Mean SD Percentage (%) Mass 26851 9 907 100 22 475 8 212 100 BC 4230 2 326 15.8 578 613 2.57 Na 278 183 1.03 361 268 1.61 Mg 124 81 0.46 234 89 1.04 Al 146 78 0.54 817 452 3.64 Si 317 168 1.18 1 703 952 7.58 P 43 25 0.16 98 67 0.44 S 1935 949 7.21 479 288 2.13 Cl 137 70 0.51 282 281 1.25 K 394 240 1.47 410 176 1.82 Ca 131 104 0.49 985 506 4.38 Ti 8.0 10.0 0.03 44 38 0.20 V 4.5 3.4 0.02 5.5 4.0 0.02 Cr 5.2 4.2 0.02 11 11 0.05 Mn 5.9 5.4 0.02 8.8 5.8 0.04 Fe 112 56 0.42 443 230 1.97 Ni 3.4 13 0.01 4.0 2.8 0.02 Cu 17 31 0.06 11 10 0.05 Zn 39 24 0.15 30 20 0.14 As 9.1 6.2 0.03 7.5 5.5 0.03 Br 36 22 0.14 36 28 0.16 Pb 32 31 0.12 22 22 0.10 speciesandexcludedfromthemodelingasmorethan50%ofthe databelowtheminimumdetectionlimit.TheremainingspeciesAl, Br,Ca,Cl,Cr,Fe,K,Mg,Mn,Na,P,Pb,S,Si,Ti,V,Zn,BCandmass were categorized as “weak” and “strong” based on S/N ratio guidelines reported by Paatero and Hopke (2003), as well as the knowledgeofthedataset.
3.3.PMFanalysisresultoffinefraction
The PMF solution for the elemental composition of fine aerosol in the Klang Valley area was found to be a five factor solution. The identified source profiles are presented in Figure 3. Figure4showsthetimeseriesplotofestimateddailycontribution of each source while Figure 5 provides the polar plots of conditionalprobabilityfunction(CPF)foreachsource.
The first factor contributed about 25.7% of the fine mass concentration.Thefactorhasacharacteristicofhighblackcarbon (BC), Fe, Zn as well as P, S, Mn and Br. These components were associatedwithemissionfromtwostrokeengines.Besidesforthis emissionsourceBC,ZnandFewerethekeyindicatorelementsfor the emission source that are attributed to wear of brake linings andtiresaswellasfromlubricatingoils(FergussonandKim,1991; Sternbeck et al., 2002). The lubricating system on a two–strokeis differentfromafour–stroke.Inatwo–strokeengine,oilisinjected into the fuel and is burnt with the fuel. Zinc is an additive in lubricating oil; hence during engine combustion Zn is emitted. MotorcyclesaremajortransportationvehiclesinKlangValleyand someofthemarestillusingtwostrokeengines.Thistypeofengine is also used in grass cutting work. The time series plot (Figure 4) shows that there were a few high peaks in May, October and November during the dry season, while the CPF plot shows that thesourcewasinfluencedbysoutherlywinds.
The second factor represents a source from motor vehicles thatcontributedabout31.9%ofthefinemass.ThesourcefingerͲ printwascharacteristicallyhighinBCtogetherwithAl,Si,S,Ca,Fe and Br. The factor also has high loading of Na and Cl suggesting mixing of emissions from sea spray during transport. Motor vehicleshavealwaysbeenconsideredasoneofthemaincontribuͲ torstoairpollutantsinKualaLumpur(PollutionSourcesInventory,
2004). It was reported that the city of Kuala Lumpur was among the Asian cities that were heavily affected by vehicle emissions (Ebihara et al., 2008). The time series plot shows that the source did not show clear pattern during the study period and it was influenced by southwesterly and southeasterly winds. The samplingsiteliesbetweentwobusyhighways;oneis700minthe northandtheotheris1kmsouthandtherearealsobusyroadsto thewestofthesamplingsitelinkedtoKualaLumpurcitycenter.
ThethirdfactorhashighloadingofBCfollowedbyS,K,Ca,Na and Cl. The source that contributed 17.5% of the fine mass was attributed to smoke or biomass burning (Pollisar et al., 2001). Smokeisknowntobeoneofthemajorsourcesofparticlesinthe KlangValley(Keywoodetal.,2003).ThepresenceofNaandClin the factor suggests the mixing of particles of sea spray during transport. The time series plot shows this factor to be relatively higherduringthedryseason,betweenMaytoOctober,whilethe CPF plot shows that this source is influenced by southerly/ southeasterlywinds.Apartfromsmallindustries,therearealarge numberofresidentialareasinthesouthofthesamplingsite.Open burningmaycontributetothesource.Atotalbanonopenburning hasbeenimposedbythegovernmentofMalaysia,however,ithas not been possible to control this activity as it is an easy way of destroyingthedomesticandgardenwaste.
The fourth factor is attributed to soil dust that contains characteristic elements of Al, Si, Mg, K, Ca and Fe (Watson and Chow,2001).Thissourcecontributedabout8.3%tothefinemass. ThepresenceofPbindicatedtheinfluenceofroaddustandother localsourcestothefactorduringtransport.AlthoughMalaysiahas bannedleadedgasolineinearly1990s,thelegacyofusingleaded gasolineforseveralyearswillremainforalongtimeintheformof dust especially near the roadways or roof–top dust. Daily source contributiondoesnotshowregularseasonalpatternforsoilsource but there were few high peaks in April and May. The CPF plot shows that the source was influenced by northerly and southerly winds.
Thelastfactormaybeattributedtoamixtureofindustriesas characterized by the high amounts of BC, Al, Si, Mg, K, Cl, V, Cr, Mn,ZnandAs.Thissourcecontributedabout16.7%tothefine
Figure3.SourceprofileplotsforfineaerosolsresolvedfivefactorsfortheKlangValley. mass.ThetimeseriesplotshowstherearehighpeaksduringminiͲ mumrainfallinFebruary/MarchandduringthedryseasonMayto August.TheCPFplotshowsthatthissourceisstronglyinfluenced bysoutherlywinds.ThewestcoastoftheMalaysianpeninsulaisan industrial area. There are significant numbers of small industries thatareinvolvedinmetalworksscatteredaroundKlangValleyand the main industrial area located about 15 to 20km to the southern/southwestern side of the sampling site. The industries include iron and steel, paint, aluminum fabrication, pharͲ maceutical,rubbergloveproduction,aswellasfoodindustries.
3.4.PMFanalysisresultofcoarsefraction
The PMF solution for the elemental composition of coarse aerosols(Figure6)inKlangValleywasalsofoundtobeafivefactor solution. The same four sources were identified for the coarse fraction as for the fine fraction results i.e., soil (8.5%), motor vehicle (42.4%), industry (17.6%) and the source from two stroke engines (13.1%). Another factor is road dust which contributes about 18.3% to the coarse mass. Figures 7 and 8 presented the timeseriesplotsofthedailycontributionandCPFplotsforcoarse massfortheidentifiedsources,respectively.
Soil and road dust do not show any regular seasonal pattern but there are few high peaks during the minimum rainfall in
February and during the dry season between April and October. These sources are influenced by southerly and southwesterly winds, respectively. No seasonal variation is apparent for the motorvehiclesourceandtheCPFplotshowsthatthesourceisnot influencedbyanymajorwindsystems.Asdiscussedpreviouslythe sampling site lies between two highways to the north and south and surrounded by roads with heavy traffic. These roads most probably explain the dependence of most sources to wind direction.
TheindustryfactorhashighloadingofSsuggestingmixingof emissions from motor vehicles. The presence of Na and Cl indicatedthatthefactoralsohasinfluencedofemissionsfromsea spray.Thetimeseriesplotsfortwostrokehasdifferentpatternas describedforthefinefractionbutitshouldbenotedthatthereare fewhighpeaksduringthedryseasoninMarch,MayandOctober/ Novembershowingcorrespondencetothesamesourceinthefine fraction.
On the whole, the different sources of fine and coarse particles did not show regular seasonal patterns but have a few high peaks at apparently random times. Moreover wind direction inKlangValleywashighlyimpactedbytopography(generallywind camefromthewestandthesoutheast).Pollutantsaretrappeddue tothevalleytopographyandinadequateflushingsalongthevalley Twostroke Motorvehicle Smoke/biomassburning Soil Industry
Figure4.Timeseriesplotofdailysourcecontributionforfineparticles. 0 10 20 30 40 05.01.2004 05.01.2005 05.01.2006 05.01.2007 05.01.2008 Mass conc. (ng/m3) Date
Two stroke
0 5 10 15 20 25 05.01.2004 05.01.2005 05.01.2006 05.01.2007 05.01.2008 Mass conc. (ng/m3 ) DateMotor vehicle
0 10 20 30 40 05.01.2004 05.01.2005 05.01.2006 05.01.2007 05.01.2008 Mass conc. (ng/m3 ) DateSmoke/biomass burning
0 10 20 30 40 05.01.2004 05.01.2005 05.01.2006 05.01.2007 05.01.2008 Mass conc. (ng/m3) DateSoil
0 5 10 15 20 25 05.01.2004 05.01.2005 05.01.2006 05.01.2007 05.01.2008 Mass conc. (ng/m3) DateIndustry
Figure5.PolarplotsofCPFforthehighest25%offinemasscontributions. Figure6.SourceprofileplotsforcoarseaerosolsresolvedfivefactorsfortheKlangValley. 0.00 0.10 0.20 0.30 0.40 0.500 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315330 345
Two stroke
0.00 0.10 0.20 0.30 0.40 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315330 345Motor vehicle
0.00 0.10 0.20 0.30 0.40 0.50 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315330 345Smoke/biomass burning
0.00 0.10 0.20 0.30 0.40 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330345Soil
0.00 0.10 0.20 0.30 0.40 0.500 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330345Industry
Industry Roaddust Motorvehicle Twostroke SoilFigure7.Timeseriesplotofdailysourcecontributionforcoarseparticles. 0 10 20 30 40 50 60 05.01.2004 05.01.2005 05.01.2006 05.01.2007 05.01.2008 Mass conc. (ng/m3) Date
Soil
0 5 10 15 20 25 05.01.2004 05.01.2005 05.01.2006 05.01.2007 05.01.2008 Mass conc. (ng/m3) DateMotor vehicle
0 20 40 60 05.01.2004 05.01.2005 05.01.2006 05.01.2007 05.01.2008 Mass conc. (ng/m3 ) DateRoad dust
0 20 40 60 80 100 05.01.2004 05.01.2005 05.01.2006 05.01.2007 05.01.2008 Mass conc. (ng/m3) DateIndustry
0 10 20 30 40 50 05.01.2004 05.01.2005 05.01.2006 05.01.2007 05.01.2008 Mass conc. (ng/m3) DateTwo stroke
Figure8.PolarplotsofCPFforthehighest25%ofcoarsemasscontributions.
which runs from the hilly terrains to the north and east, to the Straits of Malacca to the west, thus the CPF plots only provided limitedinsightintothelikelysourcelocations.
4.Conclusions
Chemicalspeciesandconcentrationdatain164pairsoffilter collected from Klang Valley were successfully resolved using PositiveMatrixFactorization(PMF–EPAPMF3)basedfactoranalyͲ sis method, to identify atmospheric pollution sources and source profilesofbothanthropogenicandnaturalsources.Inthisstudy,it was found that fine particles, mainly originated from five anthropogenicsources:twostrokeengines(35.7%),motorvehicle (31.9%), smoke/biomass burning (17.5%), soil dust (8.3%) and industry (16.7%), and coarse particulate matter was mainly contributed by 5 factor source profiles, namely: soil dust (8.5%), motorvehicle(42.4%),roaddust(18.3%),industry(17.6%)andtwo strokeengines(13.1%).
Conditional Probability Function (CPF) was used for underͲ standing point sources seasonal windblown contribution for both fine and coarse particulate matters pollutants. The results of the study may be applied as part of the monitoring regiment to manage, monitor and control atmospheric air pollution in Kuala Lumpur that is worsening as time progresses due to increasing populationandindustrialization.
Acknowledgements
Theauthorswouldliketoacknowledgethefinancialassistant from the International Atomic Energy Agency (IAEA) and the MalaysianNuclearAgency.WewishtothankDrAndreasMarkwitz and Dr Bill Trompetter of Institute of Geological & Nuclear Sciences, New Zealand for analyzing the filters for us under the IAEARRUservices.WewouldalsoliketothankProf.P.K.Hopkeof Clarkson University for his time and effort during the series of IAEA/RCA workshops on PMF and CPF. Finally we would like to thank Prof. D.D. Cohen of the Australia Nuclear Science and Technology Organization (ANSTO) for his useful discussion, suggestionsandcommentsonthedataset.
References
Chueinta, W., Hopke, P.K., Paatero, P., 2000. Investigation of sources of atmospheric aerosol at urban and suburban residential areas in Thailandbypositivematrixfactorization.AtmosphericEnvironment34, 3319–3329.
Cohen,D.D.,Taha,G.,Stelcer,E.,Garton,D.,Box,G.,2000.Measurement and sources of fine particle elemental carbon at several key sites in NSWoverthepasteightyears.Proceedingsof15thInternationalClean airConference,Sydney,Australia,November27Ͳ30,2000,485Ͳ490.
Cohen,D.D.,1998.CharacterisationofatmosphericfineparticlesusingIBA techniques. Nuclear Instrument and Methods in Physics Research
SectionB:BeamInteractionswithMaterialsandAtoms136Ͳ138,14Ͳ22.
Ebihara,M.,Chung,Y.S.,Dung,H.M.,Moon,J.H.,Ni,B.F.,Otoshi,T.,Oura, Y.,Santos,F.L.,Sasajima,F.,Sutisna,Wee,B.S.,Wimolwattanapun,W., Wood, A.K.B.H., 2008. Application of NAA to air particulate matter collected at thirteen sampling sites in eight Asian countries: a collaborative study. Journal of Radioanalytical and Nuclear Chemistry 278,463–467.
EPA, 2010. Positive Matrix Factorization (PMF) 3.0 Model. Available at: http://www.epa.gov/heasd/products/pmf/pmf.html
Fergusson, J.E.,Kim,N.D., 1991.Traceelementsinstreetandhousedust: sourceandspeciation.ScienceoftheTotalEnvironment100,125Ͳ150. Hopke,P.K.,Xie,Y.,Raunemaa,T.,Biegalski,S.,Landsberger,S.,Maenhaut,
W., Artaxo, P., Cohen, D.D., 1997. Characterization of gent stacked filterunitPM10sampler.AerosolScienceandTechnology27,726–735.
Keywood,M.D.,Ayers,G.P.,Gras,J.L.,Boers,R.,Leong,C.P.,2003.Hazein the Klang Valley of Malaysia. Atmospheric Chemistry and Physics 3, 591Ͳ605.
Kim,E.,Larson,T.V.,Hopke,P.K.,Slaughter,C.,Sheppard,L.E.,Claiborn,C., 2003. Source identification of PM2.5 in an arid northwest US city by
positivematrixfactorization.AtmosphericResearch66,291–305. Koltay, E., 1994. The application of PIXE and PIGE techniques in the
analyticsofatmosphericaerosol.NuclearInstrumentsandMethodsin
PhysicsResearchSectionB:85,75–83.
Maxwell,J.A.,Teesdale,W.J.,Cambell,J.L.,1995.TheGuelphPIXEsoftware package II. Nuclear Instruments and Methods in Physics Research B:
BeamInteractionswithMaterialsandAtoms95,407Ͳ421. 0.00 0.10 0.20 0.30 0.400 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330345
Soil
0.00 0.10 0.20 0.30 0.400 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330345Motor vehicles
0.00 0.10 0.20 0.30 0.40 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315330 345Road dust
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330345Industry
0.00 0.10 0.20 0.30 0.40 0.50 0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 315 330345Two stroke
Maxwell,J.A.,Cambell,J.L.,Teesdale,W.J.,1989.TheGuelphPixeSoftware Package. Nuclear Instruments and Methods in Physics Research B:
BeamInteractionswithMaterialsandAtoms43,218Ͳ230.
Norris,G.,Vedantham,R.,Wade,K.,Brown,S.,Prouty,J.,Foley,C.,Martin, L.,2008.EPAPositiveMatrixFactorization(PMF)3.0Fundamentals& User Guide, U.S. Environmental Protection Agency Office of Research andDevelopment,EPA600/RͲ08/108,Washington,pp.12Ͳ32.
Paatero, P., Hopke P.K., 2003. Discarding or downweighting highͲnoise variables in factor analytic models. Analytica Chimica Acta 490, 277Ͳ 289. Paatero,P.,1997.Leastsquaresformulationofrobust,nonͲnegativefactor analysis.ChemometricsandIntelligentLaboratorySystems37,23Ͳ35. Paatero,P.,Tapper,U.,1994.Positivematrixfactorization:anonͲnegative factormodelwithoptimalutilizationoferrorestimatesofdatavalues. Environmetrics5,111–126. Paatero,P.,Tapper,U.,1993.Analysisofdifferentmodesoffactoranalysis asleastsquaresfitproblems.ChemometricsandIntelligentLaboratory Systems18,183–194.
Pollisar, A.V., Hopke, P.K., Poirot R.L., 2001. Atmospheric aerosol over Vermont: chemical composition and sources. Environmental Science
andTechnology35,4604Ͳ4621.
Polissar, A.V., Hopke, P.K., Paatero, P., Malm, W.C., Sisler, J.F., 1998. Atmospheric aerosol over Alaska 2. Elemental composition and sources. Journal of Geophysical ResearchͲAtmospheres 103, 19045Ͳ 19057.
PollutionSourcesInventory,2004.MalaysiaEnvironmentalQualityReport, pp 46Ͳ59. Available from: http://www.doe.gov.my/files/Publication/ General/ENVrep%20fa%205v2.pdf. Rahman,S.A.,Hamzah,M.S.,Wood,A.K.,Elias,M.S.,Salim,N.A.A.,Sanuri, E.,2009a.TheapplicationoffactoranalysistoidentifysourcesinKlang Valleyaerosol.JurnalSainsNuklearMalaysia6,279Ͳ286. Rahman,S.A.,Hamzah,M.S.,Wood,A.K.,Elias,M.S.,Salim,N.A.A.,Sanuri, E.,2009b.Chemicalcompositionandsourceapportionmentofaerosol overtheKlangValley.NuclearScienceJournalofMalaysia21,12Ͳ20. Song, X.H., Polissar, A.V., Hopke, P.K., 2001. Sources of fine particle
composition in the northeastern US. Atmospheric Environment 35, 5277–5286.
Sternbeck,J.,Sjodin,A.A.,Andreasson,K.,2002.Metalemissionsfromroad traffic and the influence of resuspension: results from two tunnel studies.AtmosphericEnvironment36,4735Ͳ4744.
Watson, J.G., Chow, J.C., Houck, J.E., 2001. PM2.5 chemical source profiles
for vehicle exhaust, vegetative burning, geological material and coal burning in Northwestern Colorado during 1995. Chemosphere 43, 1141–1151.
