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Atmospheric Environment 45 (2011) 5450e5457

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Atmospheric Environment

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Elemental concentration and source identification of PM10 and PM2.5by SR-XRF in Córdoba City, Argentina

María Laura López a,*, Sergio Ceppi b, Gustavo G. Palancar a, Luis E. Olcese a,Germán Tirao b, Beatriz M. Toselli a,*aDepartamento de Físico Química, INFIQC/CLCM/CONICET, Facultad de Ciencias Químicas, Universidad Nacional de Córdoba, Pabellón Argentina, Ciudad Universitaria,5000 Córdoba, Argentinab Facultad de Matemática, Astronomía y Física, IFEG/CONICET, Universidad Nacional de Córdoba, Ciudad Universitaria, 5000 Córdoba, Argentina

a r t i c l e i n f o

Article history:Received 22 February 2011Received in revised form29 June 2011Accepted 3 July 2011

Keywords:Córdoba CityPM2.5PM10X-ray fluorescenceSynchrotron radiation

* Corresponding author. Fax: þ54 351 4334188.E-mail addresses: llopez@fcq.unc.edu.ar (M. L. Ló

(B.M. Toselli).

1352-2310/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.atmosenv.2011.07.003

a b s t r a c t

24-h samplings of PM10 and PM2.5 have been carried out during the period July 2009eApril 2010 at anurban and at a semi-urban site of Córdoba City (Argentina). The samples in the PM2.5 fractionweighted inthe average 71� 21 mg m�3 and 67� 18 mg m�3 respectively, whereas the samples of the same sites in thePM10 fraction weighted 107 � 31 mg m�3 and 101 � 14 mg m�3. The chemical composition of aerosolparticles was determined by synchrotron radiation X-ray fluorescence (SR-XRF). Elemental compositionwas different in the two fractions: in the finer one the presence of elements with crustal origin is reduced,while the anthropogenic elements, with a relevant environmental and health impact, appear to beincreased. An important but unmeasured component is likely constituted by organic and elemental carboncompounds. Multivariate analysis (Positive Matrix Factorization) of the SR-XRF data resolved a number ofcomponents (factors)which, on the basis of their chemical compositions,were assignedphysicalmeanings.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

In urban areas, one of the main pollutants of concern is PM10(particulatematter�10 mmin aerodynamic diameter). Particles thatfall into this diameter range have been implicated as contributing tothe incidence and severity of respiratory diseases. Size and chemicalcomposition are two of the principal parameters that affect the wayinwhich those particles correlatewith population health. PM10 canpenetrate deeply into human lungs. In addition, PM2.5 (particulatematter �2.5 mm in aerodynamic diameter) can contain a highproportion of various toxic metals, organic compounds, etc. Highlevels of PM10 and PM2.5 have been shown to decrease pulmonaryfunction and exacerbate respiratory problems in respiratory-compromised people, i.e., asthmatics. In addition, studies havelinked respiratory-associated hospital admittance with levels ofparticulate matter at concentrations below the current standards. Astrong association between the fine air particulate pollution andmortality rates in six U.S. cities has been also reported (Dockery andPope, 1994). Therefore, the increasing evidence indicating that fineparticulate matter in the atmosphere is responsible for adverse

pez), tosellib@fcq.unc.edu.ar

All rights reserved.

effects on humans led to the imposition of regulative restrictions onPM2.5 and PM10. Thus, The United States adopted the NationalAmbient Air Quality Standard (NAAQS), which sets two standardsfor 24-h average: a limit of 150 mg m�3 for PM10 and 35 mg m�3 forPM2.5. On other hand, the EU legislation for air quality establisheda 24-h limit value of 40 mg m�3 for PM10 and 25 mg m�3 for PM2.5.

The toxicity of the particles is associated not only to higherparticle mass, but also to variations in particle size, shape, andchemical composition. Furthermore,many trace chemical species inparticles occur in the very fine size fractions, which can reachalveolar regions in the lungs. The chemical elements derived fromanthropogenic sources are usually present in the fine fraction(<2.5 mm) while those derived from natural sources are usuallypresent in the coarse fraction. Suspended road dust and soil dust areother potential source of elements. Anthropogenic elements areoriginated fromdifferent sources. Those emitted during the burningof fossil fuels are V, Co, Pb, Ni, and Cr and aremostly associatedwithparticles in the PM2.5 fraction, although some particles are alsopresent in the coarse fraction. Cr, Cu, Mn, and Zn are released intothe atmosphere by metallurgical industries, and traffic pollutioninvolves a wide range of trace element emissions that includes Fe,Ba, Pb, Cu and Zn, whichmay be associated with the fine and coarseparticles (Marcazzan et al., 2001 and Smichowski et al., 2004). Otherhighly specific traffic-related elements include Sb (brake-pads),Pt and Rh (catalytic converters) (Bocca et al., 2006).

M.L. López et al. / Atmospheric Environment 45 (2011) 5450e5457 5451

To characterize bulk aerosol samples in urban air quality studies,it is important to know the elemental composition of the particulatematter. Identification of trace elements in the sampled air can addsubstantial information to pollution source apportionment studies,although they donot contribute significantly to emissions in terms ofmass. Spatial and temporal resolution are also important, since theywill allow for a more profound source assessment of the complexurban air mix (traffic, industry, biomass burning) and to assess theinfluence of the local meteorology. Aerosol concentrations varyaccording to atmospheric conditions. For example, in Córdoba,particulate matter concentration is usually higher in winter monthsthan in the rest of the year, because of the lack of rain and thepersistent temperature inversions. During a campaign carried out bythe City government in the period 1995e2001, this pollutantwas theonly one that has exceeded several times the 24-h standard. All theepisodes were during winter time (Olcese and Toselli, 1997, 1998).Unfortunately these measurements have been stopped in 2001 andnone additional study on particulate composition has been per-formed. This is aggravated by the fact that no additional air qualitymonitoring is currently underway by any governmental agency.

Elements of special interest are S, Si, K, Al, Ba, Ca, Ti, Cr, Mn, Fe,Ni, Cu, Zn, and Pb, since they may serve as fingerprints for differentsources of particulate matter. One of the problems is that aerosolfilter sampling with subsequent analysis is generally reaching itslimit by the minimum quantity of material that has to be collectedfor subsequent analysis. Here, filter collection times were 24 h.

To achieve the abatement of aerosol pollution, it is necessary toidentify sources of particulate matter pollution for which controlmeasures may be possible. The method of source apportionmentusually involves compositional analysis of aerosols. Plasma basedmethods such as inductively coupled plasma optical emission spec-trometry (ICP-OES), and inductively coupled plasma-mass spec-trometry (ICP-MS) are the most used techniques for routine aerosolmulti-elemental characterization (Smichowski et al., 2004; Suzuki,2006). However, due to the complex nature of the aerosol samplesand the low concentrations involved, synchrotron radiation X-rayfluorescence (SR-XRF) analysis was utilized for this study. The tech-nique satisfies the requirements of being sensitive, element-specific,and accurate and has proved to be a powerful tool for the elementalanalysis of ambient air samples (Bukowiecki et al., 2008). SR-XRF isapplicable to major, minor and traces constituents of atmosphericaerosols and is becoming an important tool in atmospheric chem-istry (Cliff et al., 2003). Within the present work, bulk aerosolsamples collected in Córdoba, were quantitatively analyzed bySR-XRFand their originswere identifiedusing themultivariate factoranalysis described inOgulei et al. (2005). Oneof themain advantagesof this technique, compared to conventional XRF, resides on the factthat the count accumulation interval per individual sample spot issubstantially shorter (Bukowiecki et al., 2008). The detection limitsachieved with SR-XRF experiments are in the range of ng m�3 ofambient air, allowing the determination of major and minorcomponents of the filters without additional sample treatment.

Hence, the goals of this work are to quantify the elementspresent in the PM10 and PM2.5 of Córdoba, to correlate them withpossible sources, to study seasonal variations on the particulatematter composition and to compare current levels in Córdoba Citywith other cities of the World.

2. Experimental

2.1. Study area

This study was conducted in Córdoba, Argentina, located at 31�

240 S latitude and 64� 110 W longitude, 470 m.a.s.l. The city islocated in the centre of the country and surrounded by hills. It is the

second largest city with approximately 1.3 million inhabitants andwith an average population density of 2274 inhabitants km�2. Avariety of industrial plants are located in the suburban areassurrounding the city, including major automobile factories, auto-part industries, agro-industries and food processing companies.The central area is densely built up and located in a depression.Most of the massive transport system, constituted almost exclu-sively by buses, runs 24 h (up to 700 units during the day), andcrosses Córdoba downtown. The loessic soil is, due to erosiveagents, lightly waved. For this reason, in the urban area, there aremixed construction levels, soft slopes and low hills. The climate issub-humid with a mean annual precipitation of 790 mm, concen-tratedmainly in summer, a mean annual temperature of 17.4 �C andprevailing winds from NE. Córdoba faces air pollution problems,mainly duringwinter time. Strong radiative inversions occur duringwinter due to long nights, dry air and cloudless sky. Because of that,pollutants and aerosols can be trapped in a layer lower than 200 m,leading to high concentrations of several species and the conse-quent adverse effects on health. This fact is aggravated becauseduring winter time, serious fires, caused accidentally or inten-tionally by the tourists, break out on the hills that border on the cityto the west. The plume caused by the fires usually reaches the cityand causes deterioration of the visibility and the air quality. Theseand other aspects of the dynamics of the region were investigatedusing the HYSPLIT (HYbrid Single-Particle Lagrangian IntegratedTrajectory, NOAA Air Resource Laboratory) model (Draxler andHess, 1997, 1998). The model was used to analyze and correlatethe origin of the air masses with the aerosols collected in Córdoba.HYSPLIT moves backward in time to calculate the possible origin ofan air parcel arriving at a receptor at a particular time. Backtrajectories were calculated to obtain the place where the air masswas located 24 h before arriving to the measurement site. Then,trajectories were grouped depending on its position in a grid, whichis spaced every 30 km in both latitude and longitude (See Fig. 1).From the figure is observed that, 24 h before arriving to the site, theair masses are mostly located towards the northeast. AlthoughFig. 1 shows the average behaviour during the whole year, a similarpattern has been observed for each month.

2.2. Sampling sites

Sampling of aerosol particles has been performed from Mondayto Friday at two sites with different characteristics (amount ofemissions, traffic, density of buildings and/or trees, drivingpatterns, and elevation above the surface). Fig. 2 shows amap of thecity and the location of the sampling sites. In addition, in the figure(filled grey area) it is indicated the major industrial region locatedat the northeast of the city.

Site 1: It is located in a corner of a major transportation avenuethat move people from the central core, is surrounded by highresidential buildings and has a low commercial density. Trafficcongestions occur during most of the day. The daily flow is 48000vehicles and remains constant from 8 A.M. until midnight (close to3000 vehicles per hour). The mobile sources are cars and motor-cycles running on gasoline (around 85%), others minor vehiclesrunning on diesel oil or CNG (14%) and public transportation busesrunning on diesel oil (around 1%). Heavy-duty trucks cannot enterto this area. The sampling instrument was placed in the externalbalcony of the ninth floor of a residential building.

Site 2: Located in an open area, southwest of downtown, in theUniversity Campus, with many trees and an important percentageof bare soil. It is considered as a semi-urban location. The samplinginstrument was placed on a cement yard surrounded by lowbuildings that do not obstruct the air circulation. This site is

Fig. 1. 24-h back-trajectories averaged during the whole period calculated by theHYSPLIT transport model.

M.L. López et al. / Atmospheric Environment 45 (2011) 5450e54575452

separated 150 m from a street that has a moderate flow of vehicles,close to 1000 vehicles per hour during daytime.

2.3. Aerosol sampling, X-ray fluorescence measurements andstatistical analysis

The Deployable Particulate Sampling system with the SKCimpactor was employed for sampling PM2.5 and PM10. The inertialimpactor removed particles larger than the specific cut-point bycapturing them on a disposable 37-mm pre-oiled porous plasticdisk that reduces particle bounce. Particles smaller than the cut-point were collected on 47-mm PTFE filters. A flow rate of10 L min�1 was maintained to ensure the maximum efficiency ofthe instrument and the total sampling volume was registered foreach measurement.

Before the exposure, the filters were stored into a desiccator atroom temperature. These filters were weighted in a microbalancewith an error of �7 mg (Mettler Toledo AB265-S). These measure-ments were performed under controlled temperature conditions.Room humidity was not controlled but was always less than 50%.

Special care was put in handling, using a flat tool for assemblage.After collecting thematerial thefilterswere stored in a desiccator for24 h and thenweighted under the same conditions that the blanks.

The collected samples were later analyzed by synchrotronradiation X-ray fluorescence (SR-XRF) at the Brazilian SynchrotronLight Source Laboratory (LNLS), in Campinas. The electron energywas 1.37 GeV and the maximum ring current was 250 mA. Themeasurements were carried out using the D09B-XRF beamline.Aerosol samples were excited with both white and monochromaticexcitation beams of 10 keV. A multichannel analyzer (MCA) and anintegration time of 500 s were used to record the XRF spectra. Thesample was mounted vertically in front of the side-looking detectorand the beam dimensions were set to 3 mm � 1 mm, by means ofvertical and horizontal slits, respectively. A Si(Li) detector with anenergy resolution of 165 eV (5.9 keV) was placed at the usuallyexcitation geometry, at 90� to the incident-beam direction. Thedistance between the samples and the detector was about 15 mm,and the measurements were obtained in air. Also, for severalsamples at least three XRF spectra were recorded at differentregions in order to verify the homogeneity of the aerosol depositionduring the collection.

XRF spectra were analyzed using a well-accepted internationalXRF code, AXIL (Van Espen et al., 1977). Peak areas, were normal-ized by current intensity and sampling irradiated volume, yieldingthe relative concentrations of elements; values less than 3s of thebackground noise were considered below the detection limit, andwere denoted as zero. However, as light elements (like Na) emitlow-energy X-rays, attenuation and interference may make detec-tion difficult and significantly increase the uncertainty ofmeasurements. Therefore, in this study, light elements with highuncertaintywere not used. Only the elements of interest, such as Al,Si, S, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ba and Pb were analyzed.To perform the elemental quantification the XRF spectrum of a NISTcertified standard (SRM 2783 Air particulate on filter media) wasalso measured and it was used as a reference. For the quantificationof elements not present in the NIST certified standard, the 109498ICP multi-element standard solution for mass spectrometry wasused to prepare several solutions with different concentrations.These solutions were deposited on the same filter support used tocollect aerosols sample. The elemental concentrations in the stan-dard solution were 10 mg L�1. Thus, a micro syringe was used todeposit increasing volumes (ranging from 0.02e2 mL) on a Teflonsupport filter. As Teflon is hydrophobic and the standard solution isaqueous a small drop of methanol was added to allow theadsorption of the solution to the Teflon filter. The deposited solu-tions were dried for 24 h at room temperature and then the areaswere characterized by an optical microscope being its digital imageanalyzed using the Sigma SCAN software. With these solutions, thestandard addition technique (Larson et al., 1973) was used to carryout the elemental quantification of Al, Ba and Pb.

In this study, the positive matrix factorization (PMF) technique(Paatero,1997) was used to assess the possible sources contributingto the PM. PMF is a multivariate factor analysis tool that decom-poses a matrix of speciated sample data into two matrices: factorcontributions and factor profiles. The factors then need to beinterpreted by an analyst in order to determine what source typesare represented using measured source profile information, winddirection analysis, and emission inventories.

3. Results and discussion

3.1. Gravimetric analysis

The sampling was carried out from July 2009 to April 2010. TheSite 1 and Site 2 samples in the PM2.5 fraction weighted in the

a

b

Fig. 3. Concentrations of PM10 and PM2.5 at the two monitoring sites for the studyperiod going from July 2009 to April 2010. (a) Site 1; (b) Site 2.

Fig. 2. Location of the sampling sites, indicated as 1 and 2. The filled dark grey area indicates the dominant industrial zone at the northeast of the city.

M.L. López et al. / Atmospheric Environment 45 (2011) 5450e5457 5453

average 71 � 21 mg m�3 and 67 � 18 mg m�3 respectively, whereasthe samples of the same sites in the PM10 fraction weighted107� 31 mgm�3 and 101�14 mgm�3. The average PM10 and PM2.5values for the two sites are almost 2.5 and 2.8 times higher than thecorresponding EU limit values for air quality. PM10 values reportedin this work are comparable to PM10 values measured at severalsites of the city of Buenos Aires. In this city, average values are in therange between 40 and 100 mg m�3. The high concentrations wereobtained on areas with high traffic incidence. The lowest concen-trations were observed in areas with low traffic incidence, mainlyresidential areas (Bocca et al., 2006).

Fig. 3(a) and (b) panels, shows themonthly variation of themassconcentration for both fractions at the two sites during the studyperiod. During the measurement period the concentration of bothfractions of particulate was very high with no clear trend at oneparticular season. These results seem to be surprising at first sight,but we have attributed it to the extremely dry 2009 year, witha precipitation 200 mm lower than the average. The average ratioPM2.5/PM10 was 0.66 for both sites, indicating that fine particles(PM2.5) dominates in PM10. It is possible to compare these rela-tionships with values reported for other cities of South America,such as Sao Paulo and Santiago de Chile (Castanho and Artaxo,2001; Artaxo et al., 1999). For these cities it was reported that thepercentages of PM2.5 in PM10 are in the range from 40% to 60%.

3.2. Elemental concentrations

Table 1 summarizes the average concentration values in ng m�3

of the detected elements in the two fractions and at the two sites.From the table, it can be observed that at the two sites the absoluteconcentration of the various elements is highly differentiated,ranging from a few ng m�3 for Co and Ni up to some mg m�3 for Si,Ca, Fe and K. The elements have different behaviours not only intheir absolute concentrations in air but also in the relative sharingin the two fractions that constitute PM10. The typical crustalelements Al, Si, K, Ca, Ti, V, Mn, Fe and Ba were detected mostly inthe coarse fraction (PM10-PM2.5). On the other side, S, Cr, Co, Ni

and Zn elements presented similar concentration in both fractions.For the urban site Pb and Al were found in both, PM10 and PM2.5fractions.

In Argentina, studies concerning the chemical composition ofparticulate matter have been mainly focused on the city of BuenosAires (Bogo et al., 2003; Bocca et al., 2006; Fujiwara et al., 2011). Asa comparison, the measured mean concentrations of Cu, Ni, Zn andPb in PM10 at the urban site of Córdoba show values with the sameorder of magnitude as the values measured in urban areas ofBuenos Aires (Reich et al., 2009).

Table 1Average elemental concentrations and standard deviation for PM10 and PM2.5measured at an urban and a semi-urban site during the period July 2009eApril 2010.

Average concentrations values of the detected elements in the two fractionsand at the two sites

Averageconcentration,ng m�3

PM10 PM2.5

Value SD Value SD

Site 1PM 106713 30993 70872 21046Al 100 74 17 17Si 25949 40657 4207 1219S 292 149 134 44K 1928 2754 226 297Ca 3880 4271 234 193Ti 319 470 12 9V 17 9 6 2Cr 8 4 4 1Mn 88 117 14 10Fe 3795 4610 325 215Co 2 1 0.7 0.2Ni 6 2 3 1Cu 27 11 8 5Zn 64 20 34 21Ba 257 210 0 0Pb 13 13 0.9 0.7

Site 2PM 101089 13915 66993 17802Al 78 67 0 0Si 17804 16846 3797 1052S 276 131 140 60K 1698 1262 170 145Ca 3926 1963 289 218Ti 269 192 22 23V 16 4 7 2Cr 8 3 3.5 0.8Mn 73 46 13 9Fe 3048 1840 345 211Co 1.9 0.6 0.6 0.2Ni 4.0 0.6 2.8 0.7Cu 11 4 9 5Zn 32 10 28 22Ba 220 236 0 0Pb 3.0 0.3 0 0

M.L. López et al. / Atmospheric Environment 45 (2011) 5450e54575454

On the other hand, the concentrations of S and Al in BuenosAires (3143 ng m�3 and 958 ng m�3) were one order of magnitudehigher than those measured in Córdoba (300 ng m�3 and100 ng m�3), and much lower values of Ca and Fe were found inBuenos Aires (1287 ng m�3 and 750 ng m�3) than those measuredin this work (3880 ng m�3 and 3800 ng m�3) respectively.

3.3. Multivariate statistics and source apportionment

EPA PMF 3.0 was utilized to determine the major emissionsources that contributed to the ambient PM levels in Córdoba. PMFis a variant of factor analysis that constrains factor loadings andfactor scores to nonnegative values, and has been described indetail elsewhere (Paatero and Tapper, 1994; Paatero, 1997; Paateroand Hopke, 2003). Receptor modelling has been widely used asa tool in air pollution source apportionment studies to assessparticle source contributions (Zhao and Hopke, 2006; Amato et al.,2009). Because PMF is extensively described in the literature, onlyrelevant details of themethod are presented here. The Polissar et al.(1998) procedure was used to assign measured data and associateduncertainties as the PMF input data. The concentration values wereused for the measured data and the sum of the analytical uncer-tainty, as well as one-third of the detection limit value was used asthe overall uncertainty assigned to each measured value. Values

below the detection limit were replaced by half of the detectionlimit values and their overall uncertainties were set at the sum ofhalf of the average detection limits for this element and one-thirdof the detection limit values. We performed the analysisincluding the PM mass as dependent variable. The estimateduncertainties of PM10 and PM2.5 mass concentrations were set atfour times their values so that the large uncertainties decreasedtheir weight in the model fit. Based on EPA’s PMF guidelines(USEPA, 2008), signal-to-noise ratios were used to determinea species categorization. If the signal-to-noise ratio was less than0.2, it was excluded from the analysis. If the signal-to-noise ratiowas greater than 0.2 but less than 2, it was categorized as “weak”and down-weighted and if the signal-to-noise ratio was greaterthan 2, it was categorized as “strong”. The task of PMF is to mini-mize an objective function, Q, defined as the sum of the squares ofresiduals, weighted inversely by the standard variation of the datavalues. For the determination of the number of factors in PMF, theprimary consideration is basically to obtain a good fitting of themodel to the original data. The theoretical Q-value should beapproximately equal to the number of degree of freedom, orapproximately equal to the number of entries of data array. Giventhe number of samples we have available for the analysis, weobtained the calculated Q-values from trials with four factors and20 model runs. The other important feature for this analysis wasthe desired rotation. In PMF a key called FPEAK is used to controlrotations and assist in finding a more reasonable solution. Theoptimal solution is determined by multiple model runs to examinethe effect on the numbers of factors assigned and the differentFPEAK values on the range of results that were both physicallyreasonable and where the objective function Q-value does notchange substantially. The FPEAK value was varied from �2 to þ2and finally set to þ0.001, where the value of robust Q reachesa global minimum. At the end, four factors were identified from thePM10 and PM2.5 fractions of the two data sets. The contributions ofthe different factors are presented in Figs. 4 and 5 for PM2.5 and inFigs. 6 and 7 for PM10 at the two sites.

For PM2.5 at the downtown site (Fig. 4), the most importantfactor in terms of mass contribution (factor 2) has the highest Pb,Ba, Cr, S contribution, as well as high levels of Al, V, Si and Co. Thissource is attributed to resuspended urban road dust because soilparticles, like those containing Al and Si, as well as anthropogenicmetals like Pb, and Cr are included in the chemical profile (Watsonet al., 1994). Vehicles emissions, tire/brake wear debris and roadabrasion contaminate soil with metals especially along roads withheavy traffic loads as the sampling site. Even though the wearproducts are mainly emitted to the air as coarse particles, duringthe braking temperatures at the lining/rotor interface, can becomehigh enough to vaporize many of these materials, which appearafter condensation in the fine size fraction (Salma and Maenhaut,2006 and references therein). In Córdoba the use of Pb additivesin gasoline has been banned in July 1998. As a result, there isa decreasing trend of Pb in the air and consequently Pb concen-tration in road dust should have also decreased. Vehicular Pbemissions are now caused mainly from wear rather than fuelcombustion although fuels contain Pb at trace levels (Smichowskiet al., 2008). The second factor in terms of mass contribution(factor 1) with the highest loadings of Fe and medium loading of Sand Ni appears to represent a traffic-related source (Lough et al.,2005; Fujiwara et al., 2011; Morishita et al., 2011). High levels ofFe in the fine fraction have been associated to abrasion ofmechanical parts of the vehicles, S compounds are added aslubricants and increased levels of Ni are associated to increasedtraffic densities (Amato et al., 2009). This source is mixed withcrustal elements, Al, Mn, Si and Ti, suggesting that these particlesare mixed with urban dust (Begum et al., 2007). The third factor in

Fig. 6. Source profiles of PM10 resolved from PMF and normalized to 100% at Site 1.Fig. 4. Source profiles of PM2.5 resolved from PMF and normalized to 100% at Site 1.

M.L. López et al. / Atmospheric Environment 45 (2011) 5450e5457 5455

terms of mass contribution (factor 4) has high contributions of Znand Cu and was assigned to metallurgical industries and to dieselpowered vehicles. Zn is present in tire wear dust as well as intailpipe emissions due to its use in motor oil, and Cu is present in

Fig. 5. Source profiles of PM2.5 resolved from PMF and normalized to 100% at Site 2.

brake wear dust. The fourth factor in terms of mass contribution(factor 3) presents a high loading for Ca, K, Al, Fe, Mn, Ti, Si, crustalelements, identifying a soil dust source. Construction activities inthe area might contribute to this factor.

Fig. 7. Source profiles of PM10 resolved from PMF and normalized to 100% at Site 2.

a

b

Fig. 8. Source contributions to the PM calculated by the PMF method for Site 1 and Site 2. (a) PM2.5; (b) PM10.

M.L. López et al. / Atmospheric Environment 45 (2011) 5450e54575456

For PM2.5 of the University Campus site (Fig. 5) the mostimportant factor in terms of mass contribution (factor 2) has thehighest Ba, Cr, S, V, Ni, Si and Al contribution, as well as elevatedlevels of Pb. This source is attributed to resuspended urban dust. Niand V are also indicative of oil combustion. The second factor(factor 3) with the highest loadings of Fe, Pb, S andMnwas assignedto a traffic source mixed with soil dust. The third factor in terms ofmass contribution (factor 1) has high contributions of Zn, Ti, and Kand was assigned to metallurgical industries and diesel poweredvehicles. The fourth factor in terms of mass contribution (factor 4)presents a high loading of Cu and medium loading for Ca, Ba, Fe,identifying an industrial source although not clearly identified. Noobvious secondary aerosol factor was resolved by PMF in this study.Even though the number of factors was increased from 4 to 5,a secondary aerosol source could not be distinguished. It is likelythat secondary aerosols were present in the study area even thoughthe model was unable to resolve them as a single source becausethey were mixed with other sources. This result has been previ-ously observed by other authors (Viana et al., 2008 and referencestherein). In this review, the authors point out that secondaryaerosols are subject to mixing with primary or secondary particlesduring long-range transport, and they share the same markerspecies with anthropogenic emissions on the local scale. Therefore,the discrimination between background levels of secondary aero-sols and local anthropogenic contributions is extremely complex.

For PM10 at the downtown site (Fig. 6), the first factor in termsof mass contribution (factor 2) is identified as resuspended urbandust. The metal content of roadway emissions is dominated bycrustal elements and elements associated with tailpipe emissionsas well as brake and tire wear. These metals usually have animportant contribution in coarse aerosol (Lough et al., 2005). Thesecond source in terms of mass contribution (factor 3) representsa source related to traffic because of the relative contributions bythe major elements Ba, Cu, Pb, and Zn. The next factor in terms ofmass contribution (factor 4) has high contributions of Al, Ca and Cu,and could not be assigned. The last factor in terms of masscontribution (factor 1) is characterized by Ni, S and V and has beenidentified as an oil combustion source.

In PMF, the greater the variability of a parameter, the easier it isto extract the signal from the noise. The problem of this kind ofanalysis to resolve aerosol sources in PM10 can be attributed to

a lack of significant variability in the sum of fine and coarseconcentrations of certain key tracers. The result is the appearanceof mixed source profiles not corresponding to the previous analysis.This is why some caution is needed when making interpretationson source profiles based solely on PM10.

For PM10 at the University site (Fig. 7), there are only threefactors, because the fourth factor makes a null contribution to thePM. Here, the first factor in terms of mass contribution (factor 1) isidentified as soil dust. High loadings of Ca, Fe, Mn, K, and Ti char-acterize this profile. This may be the result of local and regional re-suspension by the wind. This source represents wind-blown soilfrom the periphery loose-soil areas, separated to sources of resus-pended road dust. The second source in terms of mass contribution(factor 2) represents a metallurgical industry and diesel poweredvehicles because of the relative contributions by themajor elementsZn, Cu, Ba, Ni, V, Si, and Co. The last factor in terms of mass contri-bution (factor 4) is characterized by the highest loading Pb, Al, Ca,and S has been identified as a resuspended urban dust source.

Fig. 8 summarizes the source contributions to the PM2.5 andPM10 fractions for both sites. The sum of the measured speciesaccounts for 35 and 27% of the total mass for PM10 at sites 1 and 2,while it is 8 and 7% for PM2.5 respectively. However, consideringsulphur mainly as sulphate and the other elements as oxides(Seinfeld, 1986), the 74 and 57% and 16 and 15%, respectively, of themass of PM10 and PM2.5 can be explained. As for the unexplainedmass, it should be remembered that neither carbon nor nitrogencompounds were measured. They represent a considerable part ofPM10 and most of PM2.5.

4. Summary and concluding remarks

The first characterization of aerosol elemental composition forCórdoba, the second most important city of Argentina, in bothPM10 and PM2.5 fraction was achieved. This characterization wasdone using SR-XRF which has proved to be a powerful technique inatmospheric studies. The government air monitoring network inCórdoba stopped working in 2001 and currently no air quality dataare available.

The results of this study suggest that the coarse fraction (thedifference between PM10 and PM2.5) concentration has animportant contribution of ground dust or resuspended material,

M.L. López et al. / Atmospheric Environment 45 (2011) 5450e5457 5457

which is composed mainly by aluminum silicates (Krohling, 1999;Bogo et al., 2003). This is in agreement with the small seasonalvariability of PM10 and PM2.5 levels. A further study should includequantitative separation between exhaust particles and particlesgenerated from re-suspension. The main percentage of PM10corresponds to the smallest particles, PM2.5. The ratios PM2.5/PM10 were consistent with similar findings for other large urbanareas of the World. As it was indicated above, more than 66% of themass concentration of PM10 is PM2.5 at both sites.

PMF was used to analyze the elemental data obtained from theCórdoba aerosol samples with the objective of identifying possiblesources responsible of the aerosol compositions in the city. Foursource types or factors were revealed by the separate treatment ofPM2.5 and PM10 particles by PMF. Traffic and road/constructiondust were the major contributors to fine particle mass along withsmall contributions of oil combustion. For PM10 at the semi-urbansite, three sources were identified. Source apportionment by PMFanalysis based solely on PM10 aerosol composition data, yieldedresults of higher uncertainty with respect to the identification oftypical source profiles, when compared to analyses on fine aerosolcomposition data.

The absolute concentration of the various elements is highlydifferentiated, ranging from a few ng m�3 up to some mg m�3. Theelements have different behaviours not only in their absoluteconcentrations in air but also in the relative sharing in the twofractions that constitute PM10. Al, Si, Ca, and Fe (typical crustalelements) aremore concentrated in the PM (10e2.5) fraction, whiletoxic metals emitted by mobile sources is preferably increased inthe PM2.5 fraction. Thus, PM2.5 was still a mixture of resuspendedurban road dust, toxic metals and a proportion of mineral dust.However, daily-averaged concentrations of PM, as well as meteo-rological parameters, tend to significantly reduce data variabilityand can therefore limit our ability to detect the associationsbetween emission sources/components and heath effect parame-ters. This is particularly apparent in studies performed in largeurban areas, such as Córdoba, where the impacts of emissions fromlocal point and mobile sources are rapidly changing (less thanhourly time scales), and are added to the complex mixture ofregional source emissions transported into the region that vary onlonger time scales (wdays). Based on the results from this study andthe recommendation from international air quality agencies, thereis a need of an urgent government abatement policy to preventfurther exacerbation of the air pollution due to PM in the city.

Acknowledgements

This research was partially supported by the National Synchro-tron Light Laboratory (LNLS), Brazil, by the Agencia Nacional dePromoción Científica y Tecnológica (FONCyT), by the ConsejoNacional de Investigaciones Científicas y Técnicas (CONICET), andSECyT (UNC). M.L. López and S. Ceppi thank CONICET for graduatefellowships.

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