Research article

Received: 12 June 2012 Revised: 22 November 2012 Accepted: 25 November 2012 Published online in Wiley Online Library

(wileyonlinelibrary.com) DOI 10.1002/xrs.2484

TXRF characterization and source identificationby positive matrix factorization of airborneparticulate matter sampled in Kenitra City(Morocco)M. Tahri,a* M. Bounakhla,a M. Zghaïd,b A. Benchrif,a F. Zahry,a Y. Noackc andF. Benyaïchb

Samples of fine and coarse fraction of airborne particulate matter were collected on weekly basis during the period fromFebruary 2007 to February 2008 in Kenitra City, Morocco. The samples were collected using a Gent stacked filter sampler intwo fractions (<2.5 m, fine and 2.5–10 m, coarse). The samples were analyzed for their elemental composition by totalreflection X-ray fluorescence. The data set was then analyzed by the factor analysis method, positive matrix factorization toidentify the possible sources of particulate matter and their contribution to the ambient particulate matter concentrations inKenitra City. Four factors from positive matrix factorization solutions were found for elemental composition of fine and coarseparticulate matter at the Kenitra site. In the case of fine particles, metal smelting sources contribute about 52% of the finemass.The road dust contributes to about 22% and the rest 26% comprises emissions frommotor vehicles and soil dust. In the case ofcoarse particles, soil dust contributes about 43% and the two stroke engines about 42% of the coarse mass, whereas road dustand motor vehicles apportioned to be about 10 and 4%, respectively. Copyright © 2013 John Wiley & Sons, Ltd.

* Correspondence to: M. Tahri, National Center of Energy, Sciences and NuclearTechniques (CNESTEN), DASTE Department, Unity of Pollution and Geochemistry,Rabat, Morocco. E-mail: tahri.mounia@gmail.com

a National Center of Energy, Sciences and Nuclear Techniques (CNESTEN), DASTEDepartment, Unity of Pollution and Geochemistry, Rabat, Morocco

b Department of Physics, Faculty of Sciences, University Moulay Ismail, Meknes,Morocco

c CEREGE, Aix-Marseille University, CNRS, IRD, CdF – BP 80 13545 AIX en ProvenceCedex 4 France

Introduction

Total reflection X-ray fluorescence spectrometry (TXRF) is oneof the widely used techniques for multi-elemental analysis ofdifferent type of materials. TXRF is founded on the same princi-ples of the energy dispersive XRF (EDXRF) with, however, onesignificant difference.[1] In contrast to EDXRF, where the primarybeam strikes the sample at an angle of 45�, TXRF uses a glancingangle of a few milliradians. Owing to this grazing incidence, theprimary beam is totally reflected. By illuminating the sample witha beam that is being totally reflected, absorption of the beam inthe supporting substrate is largely avoided, and the associatedscattering is greatly reduced. This also reduced the backgroundnoise substantially. A further contribution to the reduction ofthe background noise is obtained by minimizing the thicknessof the sample.[1] A small drop of the sample (5–100ml of thesubstance dissolved in an appropriate solvent) is placed on asilica carrier. On evaporation of the solvent, a thin film of a fewnanometers thick remains. In practice, the greater part of thescattering normally arising from the sample and its matrix iseliminated leading to low detection limits. Furthermore, thereare no matrix effects because they cannot build up within minuteresidues or thin layers of a sample. Besides its high detectionpower, simplified quantitative analysis is made possible byinternal standard.

The TXRF is a powerful analysis technique, which is verysuitable for small amounts like the ones with aerosol samples.Because of the high detection power and the low sample massesrequired for TXRF analysis, it was used in many studies to charac-terize and quantify air particulate matter pollution.[2–4] Particulatematter (PM) is a complex mixture of extremely small particles and

X-Ray Spectrom. (2013)

liquid droplets. Particle pollution is made up of a number ofcomponents, including acids (such as nitrates and sulfates),organic chemicals, metals and soil or dust particles.[5] The sizeof particles is directly linked to their potential for causing healthproblems; In fact, small particles less than 10 mm in diametercan get into your lungs, and some may even get into yourbloodstream.[4,5] Not all particles cause pollution.[6–8] So, fineparticles are 2.5 mm in diameter and smaller; however, coarseparticles are defined as those having an aerodynamic diameterbetween 2.5 and 10mm.

In Morocco, the study of air pollution is not enough estimated.The elaborated studies have a punctual character. These studieswere initiated in the beginning of the 1990s by the departmentof Environment in the cities that suffer a lot from atmosphericpollution.[9–12] In this context, the present paper aims to estimatethe air pollution by particulate matter in one of the Moroccancities, which is Kenitra City. So, this study is a characterization ofcoarse and fine particulate matter, and an estimation of source

Copyright © 2013 John Wiley & Sons, Ltd.

M. Tahri et al.

2

apportionment of these particles at Kenitra site. The evaluationof source emission was carried out using a receptor model, inparticular positive matrix factorization (PMF).[13]

Materials and methods

Ambient air sampling

Sampling was conducted using a ‘Gent’ stacked filter unitparticle sampler capable of collecting particulate matter inthe range of PM2.5–10 and PM2.5 size fraction on two sequential47-mm diameter filters.[14] The first filter holder is loaded withan 8-mm pore size Nuclepore filter for coarse fraction and thesecond filter is a 0.4 mm pore size Nuclepore filter for finefraction. The sampling was carried out with a flow rate of16 lmin�1.The sampling was carried out in Kenitra city, which is located

about 50 km from Rabat (the kingdom of Morocco) in the borderof Atlantic Ocean. The wind direction is essentially West toNorthwest (coming from the sea). Besides an important motortraffic, the city presents an industrialization composed from smallfirms and port activities.The studied site is located close to bus station (Fig. 1) situated

in the center of the city. This site is characterized by a very highmotor traffic and is a parking place for busses and many ‘bigtaxis’. It is also bordered by railway ‘Fes-Meknes-Rabat’. Thesamplers were installed in a courtyard, separated from vehiclesby a wall about 3m of height.The sampling was carried out during 1 year (from February

2007 to February 2008). Samples were collected on a weeklybasis (one day per week) with 24-h sampling time.

Figure 1. Location of the sampling site (noted as ‘A’ in the map) in Kenitra

wileyonlinelibrary.com/journal/xrs Copyright © 2

Sample preparation and analysis

A major requirement of TXRF technique for filter analysis is theneed to bring into solution the samples to be analyzed. So, theloaded filters were decomposed with 10ml of conc. HNO3 in amicrowave oven (decomposition time 30min). After decompositionand complete cooling, 50 mg of Se was added to each sampleas internal standard, and aliquots of 25 ml were placed ontoquartz carriers for TXRF measurements.

The used TXRF system is that installed in CNESTEN (NationalCentre of Energy, Sciences and Nuclear techniques) in Morocco.This module is equipped with a 2 kW power fine-focus X-ray tubewith a molybdenum anode operating in most case at 30mAand 50 kV. X-ray beam is monochromatized by using a multilayer(W/C) crystal. The fluorescent X-rays of the sample are detectedby a Si(Li) detector with a resolution of 165 at 5.9 keV and nextanalyzed by a Canberra S100 multi-channel analyzer cardcoupled to a computer for data storage and analysis. To completethis study, we have used the atomic absorption spectroscopy,available in the European Centre for Research and TeachingEnvironmental Geosciences (CEREGE) in Aix en Provenance inFrance, to evaluate the contents of Al and Na.

Enrichment factor analysis

Enrichment factor (EF) is widely used to identify the anthropo-genic sources of metallic elements, and it is generally appliedto show the degree of enrichment of a given element comparedwith the relative abundance of that element in crustal mate-rial.[15] In this study, crustal EFs are calculated with Al asreference element. The formula used for EF calculation is asfollows: EFi = (i/j)air/(i/j)crust, where EFi is the EF of species i, j is a

City.

013 John Wiley & Sons, Ltd. X-Ray Spectrom. (2013)

Figure 2. Box–Whisker plot for monthly coarse mass concentration atKenitra site during the study period.

Figure 3. Box–Whisker plot for monthly fine mass concentration atKenitra site during the studied period.

Table 2. Concentration of elements in coarse and fine particulatematter at Kenitra site

Parameter(ng/m3)

PM2.5–10 PM2.5

Mean SD Mean SD

Al 1848 267 140 51

TXRF characterization and source identification in Kenitra City, Morocco

reference element for crustal material, (i/j)air is the ratio of speciesi to species j in the aerosol sample and (i/j)crust is the ration ofspecies i to species j in the crust.[16]

Data analysis by positive matrix factorization

In this study, PMF, EPA–PMF v3.0,[13] was applied to estimate thecontribution of major emission sources to ambient fine andcoarse fractions in the selected site. PMF is one of the receptormodels that have been developed by the United States Environ-mental Protection Agency Office of Research and Development.It is a factor analysis method with individual weighting of matrixelements first described by Paatero and Tapper (1994) andPaatero (1997).[17,18]

The PMF method is based on solving the factor analysisproblem by the least-squares approach using a data pointweighting method, which decomposes a matrix of data ofdimension n rows and m columns into two matrices: G(nxp)and F(pxm), where n is the number of samples and m is thenumbers of species. The model can be written as

X ¼ GF þ E (1)

where E is the matrix of residuals, the unexplained part of X.In environmental pollution problems, one row of X would

consist of the concentrations of all chemical species in onesample, and one column of Y would be the concentration ofone species for each of the samples. One row of the computedF matrix would be the source profile for one source, and thecorresponding column of G would be the amount of this sourcein each individual sample. Required input matrices for PMF areX, the measured values, and the uncertainties of the measuredvalues. PMF requires that all values and uncertainties are positivevalues; therefore, missing data and zero values must be omittedor replaced with appropriate substitute values.

Briefly, a data matrix X of i by j dimensions, in which i is thenumber of samples and j chemical species, can be written as

wij ¼Xp

k¼1

gik fk j þ eij (2)

where p is the number of factors, f is species profile, g is amountof mass and eij is residual of each sample. The task of PMF is tominimize the object function (Q), on the basis of theuncertainties:

One of the most important model parameters to determinewhen using PMF is the number of factors (sources), which PMFwill use to model the data. To determine the number of sourcesthat gave the best solution for the present study, we have usedthe following method. First, the PMF Q function of each solution

Table 1. Summary of coarse and fine particulate matter

Parameter PM2.5–10 ( g/m3) PM2.5 ( g/m

3)

Mean 110 52

Median 115 51

SD 38 12

Maximum 196 86

Minimum 34 30

No. of samples 63 63

SD is standard deviation.

X-Ray Spectrom. (2013) Copyright © 2013 John Wiley &

was checked to see if its value approximately equaled thenumber of elements in the X matrix minus the number ofelements in the Gmatrix.[19] Second, the ‘goodness of fit’ regressionparameters for each resolution were examined to see whichsolution achieved the best ‘fit’.

Ca 11300 2017 1040 169

Cd 2 0.25 2 0.29

Cr 61 10 81 10

Cu 144 27 239 25

Fe 2051 183 1939 227

K 1431 250 397 43

Mn 70 7 20 3

Na 2707 225 588 70

Ni 137 20 161 43

Pb 196 29 303 65

Zn 634 68 1301 176

SD is standard deviation.

Sons, Ltd. wileyonlinelibrary.com/journal/xrs

M. Tahri et al.

4

Results and discussion

Particulate matter concentration

The summary of the coarse and fine particulate matter collectedat Kenitra site during the sampling period is presented in Table 1.The average concentration of PM2.5–10 and PM2.5 were 110 ( g/m

3)and 51 ( g/m3), and the highest concentration was observed to be196 ( g/m3) and 86 ( g/m3), respectively. The monthly variationsof coarse and fine particulate mass have been presentedusing Box–Whisker plots in Figs 2 and 3, respectively. In theBox–Whisker plot, the box represents the distribution of 25–75thpercentile values, and the symbol represents the mean value.The values beyond 75th and 25th percentile are given as whiskers.The plot (Fig. 2) of coarse concentrations shows higher mass

Figure 4. Enrichment factors of elements in PM2.5–10 and PM2.5 atKenitra site during the sampling period.

Figure 5. Source profiles derived from the fine particle data from site.

wileyonlinelibrary.com/journal/xrs Copyright © 2

depositions during summer season (May, June and July) withlow variation in the concentration range. This is clearly seen inthe Box–Whisker plots from the length of the whiskers and fromthe 75th and 25th percentile values. Also, the plot indicates lowerconcentration of coarse particulate matter January, February andMarch. This difference could be attributed to metrologicalconditions. In fact, May, June and July in Kenitra correspond todry period and thus are very favorable to the resuspension of soildust. However, winter season (January, February and March) ismarked by heavy precipitation and so street cleaning and dustcontrol. For fine concentrations, the plot (Fig. 3) shows the lowermass deposition in October. The highest concentrations areobserved in the period from March to July with significantvariation in the concentration range represented by the lengthof the whiskers and the 75th and 25th percentile values.

Elemental concentrations in particulate matter

Table 2 shows, for fine and coarse fraction, the mean concentra-tions and the standard deviation of the 12 elements from 1-yeartime series samples.

The elemental concentrations in the coarse fraction rangedfrom 2 to 11300 ng/m3, and in the fine fraction, it was from 2 to1939 ng/m3. From the results, we can note that the concentrationof elements of soil-related (Al, Ca, Fe, K, Na and Mn) was higher inthe coarse fraction than the fine mass fraction. Subsequently, theelements supposed from anthropogenic sources (Cr, Cu, Pb, Ni,Zn and Cd) were more present in fine than coarse fractionparticulate matter.

Enrichment factor analysis

Enrichment factor analysis is conventionally used for separatingsoil derived elements from the anthropogenic components.[20]

Therefore, to verify the contributing elements of natural sources

013 John Wiley & Sons, Ltd. X-Ray Spectrom. (2013)

TXRF characterization and source identification in Kenitra City, Morocco

associated with PM2.5–10 and PM2.5, EF was calculated for eachelement. In this study, Al is used as a reference element todetermine the EF with respect to crustal abundance.

Conventionally, a cut off for the enrichment of the respectiveelement is considered as 10 to distinguish other elements fromthe crustal based elements. It is believed that the element shouldbe enriched as if the EF of each element is greater than 10,indicating that atmospheric particulate matters come mainlyfrom human activities. The greater value of EF shows the greaterpossibility of air pollution from anthropogenic sources.[21]

Figure 4 shows EF values obtained for the traces elements of coarseand fine particulate matter related to crustal concentrations.

For coarse fraction, the plot shows EF values of (Ca, Fe, Mn, Naand K) smaller than 10 indicating their origin from crustal source.Few elements such as (Cr, Cu and Ni) were found to possess EFvalues in the range of 15–90. Pb and Zn exhibits the maximum

Figure 7. Source profiles derived from the coarse particle data from site.

Figure 6. Distribution of mass for fine fraction among factors.

X-Ray Spectrom. (2013) Copyright © 2013 John Wiley &

enrichment of about 1130 and 368, respectively, indicating thattheir origins are mainly anthropogenic.

For fine fraction, the plot of EF shows values of (Ca, Cr, Cu, Pb,Mn, Ni, Na and K) smaller than 10 suggesting their natural origins.However, EF values of Fe and Zn were in the range of 17–25indicating their origin could be natural or anthropogenic. Wenote that Cr, Cu, Pb and Ni shows enrichment only in the coarsefraction, which indicates their anthropogenic emissions.

Source apportionment using EPA–PMF

PMF analysis result of fine fraction

The PMF solution for the elemental composition of fine aerosol atKenitra site was found to be four-factor solutions. The identifiedsource profiles are presented in Fig. 5. Figure 6 shows thedistribution of mass for total PM2.5 among factors.

The first factor contributed about 13% of the fine mass concentra-tion. This factor is dominated by Cu, PbMn, Ni and Zn characteristic ofmotor vehicle sources (mixed diesel and gasoline engine exhaust).[22]

The second factor represents about 22% of the fine mass. Thisfactor is attributed to road dust because soil particles as well asanthropogenic metals such as Cr, Cu, Pb, Cd and Zn are includedin the chemical profile.[23] Vehicle emissions, tire/brake weardebris and road abrasion contaminate soil with metals especiallyalong road with heavy traffic loads.[24]

The third factor contributed about 52% of the fine massconcentration. This factor characterized by a significant Al andCa and mixed with Cr, Cu, Fe, Mn and Zn could be representedby metal smelting source.[25] There are a significant number ofsmall industrial facilities involved in metal works includingfoundries distributed around the studied site.

The fourth and last factor is attributed to soil dust that containscharacteristic elements of Al, Ca, Fe and K.[26] This source

Sons, Ltd. wileyonlinelibrary.com/journal/xrs

Figure 8. Distribution of mass for coarse fraction among factors.

M. Tahri et al.

6

contributed about 13% of the fine mass. The presence of Pb, Niand Cd indicated the influence of road dust and other localsources to this factor during transport.

PMF analysis result of coarse fraction

The PMF solution for the elemental composition of coarseaerosols (Fig. 7) at Kenitra site was also found to be a four-factor solution. Figure 8 presents the distribution of mass for totalPM2.5–10 among factors.The first factor presents 43% of the coarse mass. This factor has

the characteristic of having high Zn and Fe. This is the character-istic signatures of lubricating oil used in two-stroke engine whereengine oil is mixed with fuel.[27] The combustion process in a two-stroke engine is such that significant quantity of unburnt fuel isreleased long with lube oil. Zn is normally used as additive in thiskind of lube oil. Zn could also be from galvanized materials, tirewear and the use of zinc compounds in rubber production.[28]

This factor includes Cr, Ni and Mn suggesting that part of thisfactor may be attributed to oil combustion fuel.[24]

The second factor represents a source from motor vehicles thatcontributed about 4% of the coarse mass. The source fingerprintwas characteristically high in Cu, Pb, Mn and Ni with the presenceof Al, ca and K.The third factor is attributed to soil dust that contains

characteristic elements of Al, Ca, Fe, Na and K.[26] This sourcecontributed about 43% of the coarse mass. The presence of Pband Ni indicated the influence of road dust and other localsources to the factor during transport.The fourth factor contributed about 10% of the coarse

mass. This source is attributed to road dust because both of soilparticles and anthropogenic metals such as Cr, Cu and Zn areincluded in the chemical profile.

Conclusions

Airborne particulate matter samples were collected using a Gentstacked filter unit in Kenitra City, Morocco, from February 2007 toFebruary 2008 to examine and investigate the possible sources ofair pollution. TXRF technique has been used to determine the con-centration levels of trace elements in the collected filter samples.Time series analysis of particulate matter revealed a seasonal trendwith high concentrations during summer season for coarse particles.

wileyonlinelibrary.com/journal/xrs Copyright © 2

The EF analysis showed high enrichment for elements Cr, Cu, Ni, Pband Zn in the coarse fraction and for Fe and Zn in the fine fraction.

The PMF had been applied, for both fine and coarse fractions, toinfer the possible atmospheric pollution sources and source profilesof both anthropogenic and natural sources. In this study, it wasfound that fine particles mainly originated from four sources: metalsmelting (52%), road dust (22%), soil dust (13%) andmotor vehicles(13%). For coarse particulate matter, it was mainly contributed byfour-factor source profiles, namely, soil dust (43%), two stroke en-gines (42%), road dust (10%) and motor vehicle (4%).

References[1] R. Grieken, A. Markowicz, Handbook of X-Ray Spectrometry, Marcel

Dekker, New York, USA 2002.[2] V. Valkovic, M. Dargie, M. Jaksic, A. Markowicz, A. Tajani, O. Valkovic.

Nucl. Instrum. Methods Phys. Res. 1996, 113(1-4), 363–367.[3] A. C. John, T. A. J. Kuhlbusch, H. Fissan, K. G. Schmidt. Spectrochim.

Acta, Part B 2001, 56(11), 2137–2146.[4] M. Schmeling. Spectrochim. Acta, Part B 2004, 59(8), 1165–1171.[5] M. Z. Jacobson, Atmospheric Pollution: History, Science and Regula-

tion, Cambridge University Press, New York, 2002.[6] G. Thad, Air Quality, 4th edn, Lewis Publishers, CRC Press Company,

Taylor and Francis e-Library, 2005.[7] F. W. Michael, L. C. Daniel, Air Polluants and the Respiratory Tract

(2nd Edn), Taylor and Francis Group NW, USA, 2005.[8] L. S. Ruzer, H. Naomi, Aerosols Handbook: Measurement Dosimetry

and Health Effects, CRC Press, NW, USA, 2005.[9] Casa-Airpol, Study of Air Pollution and Its Impact on Population Health in

Casablanca City, Campaigns of 1998–1999, Ministry of Territorial, Urban,Housing and Environment Planning and Ministry of Health, Rabat,Morocco, 2000.

[10] REEM, Report on the State of the Environment of Morocco, Ministry ofTerritorial, Urban, Housing and Environment Planning and Ministryof Health, Rabat, Morocco, 2001.

[11] Mohammedia-Airpol, Study of Air Pollution and Its Impact on theHealth of Asthmatic Children in Mohammedia City, 2001–2002campaigns, Ministry of Territorial, Urban, Housing and EnvironmentPlanning and Ministry of Health, Rabat, Morocco, 2002.

[12] MATEE, Air pollution in Morocco, situation in 2002, Ministry ofTerritorial, Water and Environment Planning, Department of theAir and National Laboratory of Environment, Department ofSurveillance and Prevention Risks, 2003.

[13] U.S. Environmental Protection Agency (USEPA), EPA Positive MatrixFactorization (PMF) 3.0 Model, 2010.

[14] P. K. Hopke, Y. Xie, T. Raunemaa, S. Biegalski, S. Landsberger,W.Maenhaut, P. Artaxo, D. Cohen.Aerosol Sci. Technol.1997, 27(6), 726–735.

[15] Y. Gao, R. Arimoto, R. A. Duce, D. S. Lee, M. Y. Zhou. J. Geophys. Res.1992, 97(D4), 3367–3777.

[16] S. L. Quiterio, C. R. S. Da Siva, G. Arbilla, V. Escaleira. Atmos. Environ.2004, 38(2), 321–331.

[17] P. Paatero, U. Tapper. Environmetrics 1994, 5(2), 111–126.[18] P. Paatero. Chemom. Intell. Lab. Syst. 1997, 37(1), 23–35.[19] P. Paatero, User’s Guide for Positive Matrix Factorization Programs

PMF2.EXE and PMF3.EXE, University of Helsinki, Helsinki, 1996.[20] Z. Yuanxun, Z. Yuanmao,W. Yingsong, L. Delu, L. Aiguo, L. Yan, Z. Guilin,

Z. Yifei, S. Zuci. J. Radioanal. Nucl. Chem. 2006, 267(2), 497–499.[21] L. Huang, K. Wang, C. S. Yuan, G. Wang. Aerosol Air Qual. Res. 2010,

10(1), 86–93.[22] G. C. Lough, J. J. Schauer, J. S. Park, M. M. Shafer, J. T. DeMinter,

J. P. Weinstein. Environ. Sci. Technol. 2005, 39(3), 829–836.[23] J. C. Watson, J. C. Chow, Z. Lu, F. M. Fujita, D. H. Lowenthal,

D. R. Lawson. Aerosol Sci. Technol. 1994, 21(1), 1–36.[24] A. A. Karanasiou, P. A. Siskos, K. Eleftheriadis. Atmos. Environ. 2009,

43(21), 3385–3395.[25] S. K. Biswas, S. A. Tarafdar, A. Islam, M. Khaliquzzaman, Investigation

of Sources of Atmospheric Particulate Matter (APM) at an Urban Areain Bangladesh, AECD/CH/55, Bangladesh Atomic Energy Commision,Dhaka, Bangladesh, 2001.

[26] J. G. Watson, J. C. Chow, J. E. Houck. Chemosphere 2001, 43(8), 1141–1151.[27] W. Chueinta, P. K. Hopke, P. Paatero. Atmos. Environ. 2000, 34(20),

3319–3329.[28] J. E. Fergusson, N. D. Kim. Sci. Total Environ. 1991, 100, 125–150.

013 John Wiley & Sons, Ltd. X-Ray Spectrom. (2013)