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Atmospheric Environment 54 (2012) 250e259

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Atmospheric Environment

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Elemental characterization and source identification of size resolved atmosphericparticles in French classrooms

Dinh Trinh Tran a,b,*, Laurent Y. Alleman a,b, Patrice Coddeville a,b, Jean-Claude Galloo a,b

aUniversité Lille Nord de France, F-59000 Lille, Franceb Ecole des Mines de Douai, Département Chimie et Environnement, 941 rue Charles Bourseul, BP 10838, 59508 Douai Cedex, France

a r t i c l e i n f o

Article history:Received 4 July 2011Received in revised form25 January 2012Accepted 3 February 2012

Keywords:IndoorOutdoorParticlesTrace metalsElemental distributionSource origin

* Corresponding author. Ecole des Mines de DoEnvironnement, 941 rue Charles Bourseul, BP 10838,

E-mail address: trinhtd@vnu.edu.vn (D.T. Tran).

1352-2310/$ e see front matter � 2012 Elsevier Ltd.doi:10.1016/j.atmosenv.2012.02.021

a b s t r a c t

Indoor airborne particles were chemically characterized to identify their major sources in 3 elementaryschools presenting different site typologies (rural, urban, and industrial) in the North of France. Thesampling campaigns were conducted simultaneously indoors and outdoors during 2 weekly periodssuccessively in each school, in presence and absence of pupils.

The indoor weekly mean PM10 mass concentrations in presence of pupils varied from 72.7 to85.3 mgm�3, generally exceeding the WHO guidelines. The presence/absence PM10 ratios confirm thePM10 concentrations raise during children’s activities. Their presence leads to an increase of elementalconcentration (ngm�3) but does not influence the elemental distribution in the different particulatefractions.

Crustal elements represent an important portion (7e10%) of the indoor PM10 mass, mostly driven bythe Ca content (4.4e7.2%) due to the use of chalk. The trace elements (As, Cd, Cu, Pb, Sb, .) are enrichedin the fine fractions (70e100%) compared to the coarse ones. Crustal elements (Al, Ca, Ti, Sr, .) presenthigher concentrations in the coarse fractions (40e60%).

Elemental ratios and Cluster Analysis confirmed different particulate metal sources from the schoolsurroundings. Among these sources, re-suspension dust, traffic, and marine aerosols were observed in allschools. Mixed anthropogenic sources were identified in urban and industrial sites, and petro-chemistrywas only evidenced in the school near the industrial zone. Indoors, these outdoor anthropogenicparticles represent the only sources of trace elements evidenced during this study.

� 2012 Elsevier Ltd. All rights reserved.

1. Introduction

Hours spent indoors may vary with occupation, age, gender andstatus. In urbanized regions, people spend almost 90% of their timeexposed to the indoor atmosphere (Schweizer et al., 2007).However, Indoor Air Quality (IAQ) is poorly monitored in compar-ison towhat is generally conducted outdoors (Harrison et al., 2005),due notably to the impracticality for developing a survey on everyindoor environment (homes, offices, schools, transports, recrea-tional areas .). In addition, indoor air often presents a morecomplex and polluted environment due to a poor air exchange rate(AER) and specific emission sources in these secluded and some-times crowded areas (He et al., 2005).

Exposure to indoor airborne particulate pollutants can consti-tute a potential health threat (Pope et al., 2002), especially to

uai, Département Chimie et59508 Douai Cedex, France.

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children who represent a sensitive group, with a non fully devel-oped respiratory system, spending most of their time at home, intransport facilities or at school. The health effects of particlesdepend strongly on their size, specific surface area, number andchemical composition, e.g. their heavy metal contents (Framptonet al., 1999). These potentially toxic elements may originate fromvarious industrial or urban sources (Swietlicki and Krejci, 1996).Inhaled PM10 (particulate matter <10 mm) are deposited in variousplaces of the respiratory tract, the fine fraction (<2.5 mm) reachingthe pulmonary alveoli where a small part may settle and evenpenetrate into the bloodstream (Schwarze et al., 2006).

Indoors, the particle (PM) concentration variability is mostlydriven by the activities taking place inside the building. Outdoors,such variability is usually associated to economic activities andtransport or to meteorological conditions. We thus need to takeinto account the indoor/outdoor dichotomy in terms of particle sizedistribution, chemical composition and source origin.

Up to now, only a few studies have characterised the indoorparticles chemical composition and little is known regarding theschool environment (Fromme et al., 2008). A better knowledge of

D.T. Tran et al. / Atmospheric Environment 54 (2012) 250e259 251

the PM concentrations and their composition in schools is neces-sary to accurately evaluate the time-weighed exposure of childrento airborne particles. Classrooms normally lack of typical indoor PMsources such as heating combustion, smoking and cooking. Yet,they present a complex environment governed by several factors:number of occupants, type of activities, building air tightness,outdoor PM concentration and composition, ventilation system,and sources from educational supplies, school furniture’s, paintingand building materiel (Yu and Crump, 1998; Wolkoff et al., 2006). Anumber of studies on school IAQ have focused on respirable sus-pended particles in classroom and their influencing factors(Blondeau et al., 2005; Guo et al., 2008). Other investigators haveexamined various indoor air pollutants including volatile organiccompounds (VOCs), formaldehyde, nitrogen dioxide, carbonmonoxide and ozone (Morawska et al., 2009). However, the avail-able data about the chemical composition especially in terms ofheavy metal of indoor PM is very limited (Wang et al., 2006;Gemenetzis et al., 2006), particularly in schools (Fromme et al.,2008). Very few of these studies included trace elements analysesin PM10 (Janssen et al., 1999) or PM2.5 (Na et al., 2004; Molnár et al.,2007), and lacked of information regarding their elemental sizedistribution.

The purpose of the present paper is to identify the sources ofparticles in classrooms and to determine the influence of the chil-dren’s activities to the elemental distribution for different particlesize fractions.

To do so, the indoor and outdoor major and trace elementconcentrations weremeasured according to their particle size (PM1,PM1e2.5 and PM2.5e10), taking into account the site typology and thechildren’s activities.

2. Materials and methods

2.1. Sampling sites and schools characteristics

Three elementary schools (Fig. 1) were selected in the Nord-Pas-de-Calais region (NPdC, France) near the North Sea coast and theBelgian border. The temperate oceanic climate of the region isrelatively humid. Predominant wind directions are SW, N and NE(Rimetz-Planchon et al., 2008). It is one of the most densely

Fig. 1. Location of the three schools in th

populated regions in France, largely industrialized and connectedby high traffic roadways.

Our sampling sites were selected to match three types of envi-ronments: industrial and coastal (school A), urban/traffic (school B)and rural (school C). Schools were also chosen upon edifice char-acteristics such as building age, ventilation system, internalcovering including flooring, wall and ceiling, and windows struc-ture summarized in Table 1.

School A (1950s) made of brick is located close to an industrialzone in the conurbation of Dunkirk (about 1.5 km from the mainindustrial stacks) with several industrial activities such as steelindustries, fuel and lubricant refineries, power and cement plants(Alleman et al., 2010). High traffic density roads and highways (A25w 40 000 vehicles day�1 and A16 w 30 000 vehicles day�1) arelocated nearby as well as one of the largest French commercialharbours (Dunkirk harbour), with 45 million tons of freight trans-port in 2009. The school is surrounded by scattered storey buildingshaving little impact on wind flow patterns.

School B (1960s) is a single brick building situated in the citycentre of Arras (43 000 inhabitants in 2007), and surrounded byhigh density traffic streets especially during rush hours. There isa parking lot in the close proximity and several gas stations ata distance of about 400e500 meters. The school is surrounded byseveral building blocks that could influence the wind direction.

School C (1950s)made of brick is located in a rural community ofa few dozen of houses (280 inhabitants in 2006). This village isaway from industrial and urban pollutant emission sources, withthe exception of the highway A26 at about 800 m north.

2.2. Measurement strategy

The 2 weekly sampling campaigns for each school were dividedinto 2 periods: in presence and in absence of pupils in the class-rooms. The occupied periods consisted of teaching hours duringschool days including Monday, Tuesday, Thursday, and Friday,accounting for 28 hours of sampling. The unoccupied periodsincluded all nights of weekdays and weekends, representing about125 hours. The teaching hours started at 08 h45 and finished at16 h45. Two 15-minute breaks were taken each school day in themorning and in the afternoon, and the lunch time lasted from

e Nord-Pas de Calais region (NPdC).

Table 1Main characteristics of the three schools.

School A School B School C

Level/number Downstairs/1 Downstairs/1 Downstairs/0Occupation 25 childrenþ 1 teacher 27 childrenþ 1 teacher 24 childrenþ 1 teacherSurface (m2) 59.4 58.5 60.4Volume (m3) 201.5 234 181.3Internal coating Ground: slab, tile;

Wall: painting;Ceiling: painting

Ground: slab, tile;Wall: painting;Ceiling: painting

Ground: plastic coating;Wall: painting;Ceiling: false ceiling

Chalkboard Yes Yes YesVentilation Window opening Window opening Window openingWindow Wood, single glazing Wood, double glazing Wood, single glazingInternal connections Corridor, door with 1 adjacent class Corridor, door with 1 adjacent class Outdoor, door with 1 adjacent classWindow number and school orientation 2, between SE and SW 2, between SW and NW 3, between NE and SE

D.T. Tran et al. / Atmospheric Environment 54 (2012) 250e259252

11 h45 to 13 h15. These relatively short breaks were accounted foroccupied periods.

The sampling campaigns were conducted successively in eachschool from May 13th 2009 to July 1st 2009 under relatively cooland stable meteorological conditions during each campaign.Outdoor average temperatures were 15� 2 �C, 16� 3 �C and21�3 �C in school A, B and C respectively during occupied periodsand 14� 3 �C, 13� 3 �C and 17�4 �C during unoccupied periods.

For each school campaign, two series of particles sampling andmeasurements (from Wednesday to the next Wednesday) werecarried out during two consecutive weeks. The same type ofequipments was employed indoors and outdoors. To collect parti-cles during unoccupied and occupied periods, two identicalsampling equipments were used. In order to reduce contaminationrisks, the two sets of impactors and the Microvol inlet assemblywere simply switched before the arrival of the pupils and just aftertheir departure. All indoor air sampling equipments were placed ona solid table at 1.2 m above the floor and 1 m from the wall in theback of the class for children’s safety and teaching constraints.Outdoor sampling equipments were placed in the courtyard besidethe façade, at 1.5 m above the ground, in a wooden frame shelterwith wire mesh and a plastic roof as a protection for bad weatherand children’s games, while still maintaining an adequate aeratedenvironment.

Other parameters such as temperature, relative humidity andCO2 were monitored indoors and outdoors during the campaigns.In addition, information about daily school activities in the studiedclassrooms (number of pupils present, closing/opening state ofdoors and windows, cleaning method and detergents used) werecollected by questionnaires.

2.3. Aerosol sampling and chemical analysis

Aerosol samples were collected simultaneously indoors andoutdoors on 47 mm diameter Zefluor filters (Pall corporation,0.45 mm porosity) by filtration using the MicroVol 1100 (Ecotech,Pty Ltd) at a constant flow-rate of 3 lmin�1 through a PM10 samplerinlet. Four-stage cascade impactors Dekati (Dekati Ltd., Tampere,Finland), running at a flow-rate of 30 lmin�1 were also deployed tocollect different particle size fractions. These impactors collected,on Teflon filters (25 mm, 2 mmporosity, Zefluor), the particles largerthan 10 mm, between 10 and 2.5 mm (PM2.5e10 in the text), andbetween 2.5 and 1 mm (PM1e2.5). The backup filter samplingparticles below 1 mm (PM1) is a 47 mm diameter Teflon filter(Mitex, Millipore). Teflon filters were selected as they are non-hygroscopic and chemically inert (Jantunen et al., 2002) andtherefore, improve the analytical quality of the analyses.

To assess the PM10, PM2.5 and PM1, successive fractions collectedon the cascade impactor stages (filter substrates) were summed.

For example, the PM2.5 is the addition of the filter collectingparticles between 2.5 and 1 mm and the backup filter. The samplingflow rate was calibrated by a TSI mass flow-meter before and aftereach sampling campaign. Volumes of filtered air varied from 5 to230 m3, depending on samplers and sampling periods.

Filters were handled with great care (gloves and Teflon forceps)to avoid damage, loss or contamination. They were kept in cleanPetri dishes and stored in a temperature and humidity controlledroom for 48 h before weighing (DIN EN 12341). Each filter wasweighed four times before and after each exposure with a micro-balance (Mettler-Toledo UMT2, Switzerland) with a 0.1 mg readingaccuracy. The difference of weighed mass and the sampled airvolume were used to calculate the PM mass concentrations.

The samples were then digested at 200 �C in Teflon reactorswith a microwave oven (Ethos TC, Milestone) in a clean room. Theacid digestion mixture (4 mL HNO3 Merck Supra-pure (65%), 1 mLH2O2 Fluka TraceSelectUltra) was optimized to quantitativelymineralise 36 major and trace elements.

The concentrations of Al, Ba, Ca, Fe, K, Mg, Na, and Ti weremeasured by ICP-AES (Iris Intrepid, ThermoElemental); that of Ag,As, Bi, Cd, Ce, Co, Cr, Cs, Cu, Eu, Hf, La, Mn, Mo, Ni, Pb, Pd, Pt, Rb, Rh,Sb, Se, Sr, Th, W, V, Zn and Zr by ICP-MS (Elan 6100 DRC, PerkinElmer), following optimised procedures (Lamaison et al., 2009).

2.4. Data quality assurance and validation

For particle mass concentrations, 10 field and 10 laboratory filterblanks (5 filters 25 mm and 5 filters 47 mm for each blank) wereweighted before and after each sampling campaign to estimate thedrift and repeatability of the microbalance. The particle mean massconcentration uncertainties for the different particle fractions werecalculated at about 8% and always lower than 18%.

For elemental concentrations, different blank solutions wererepeatedly measured during each analytical session to quantify thebackground signal and/or potential sample contamination. Thereagent blanks, the method blanks, the laboratory and the fieldfilter blanks reflecting the contamination risks allowed us to assessthe Instrumental Detection Limits (IDL) and the Method DetectionLimits (MDL). In general, the contaminant contents of the labora-tory and field filter blanks represented respectively less than 5% and13% of their concentrations in the different particle fractions.

The accuracy was regularly checked by analysing a small mass(0.5 to 4 mg) of Standard Reference Materials (Urban particulatematter-NIST 1648 and Indoor dust-NIST 2584 (NIST: NationalInstitute of Standard and Technology, Gaithersburg, MD, USA). Theywere used to estimate the analytical reproducibility and to validatethe whole procedure. Most analysed elements presented very goodrecovery rates (from 85 to 110%) with the exception of the Sireaching only 28%e30% of its mass in these SRMs.

D.T. Tran et al. / Atmospheric Environment 54 (2012) 250e259 253

Quality control (QC) solutions based on serial dilutions of arti-ficial multi-elemental standards (100 ppm), traceable to the NIST,containing precise amounts of trace elements (400 ppt) in a similaracid matrix as the samples were prepared daily. QC solutions,analysed every five samples to check within-run stability(Lamaison et al., 2009), were validated if the difference was lessthan 5%.

Finally, 25 elements (Al, Ag, As, Ba, Bi, Ca, Cd, Co, Cr, Cu, Fe, K, La,Mg, Mn, Mo, Na, Ni, Pb, Sb, Se, Sr, Ti, V and Zn) were selected due totheir adequate results in term of IDL and MDL, recovery rate andtheir exploitable concentrations in atmospheric particles.

2.5. Factor analysis

Outdoor particles may originate from various sources such assoil, resuspended dust, sea salt, industrial processes including fuelcombustion, metal industry, refinery, incinerator, or vehicularemissions (Lewtas, 2007).

In this work, we used Cluster Analysis (CA) combined withcorrelation and elemental ratio methods to outdoor PM10 of eachschool along with information about clearly identified or likelysources reported in previous studies to identify their main emissionsources.

Due to the limited number of outdoor samples for each schoolcompared to the number of variables (elements), we applied the CAon outdoor PM10 samples collected with the Microvol and Dekatiduring occupied and unoccupied periods (25 analysed elements for10 PM10 samples), assuming that the outdoor PM10 were notstrongly influenced by the children activities. Moreover, the PM10element concentrations sampled by Dekati and Microvol showedrelatively close and proportional results, allowing us to combinethese two sets of samples.

To reduce the number of variables, we employed a two stepmethodology. First, an inter-elemental Pearson correlation on theinitial matrix containing 25 elements and 10 samples was per-formed by associating highly correlated elements in a commongroup (P� 0.05). In each group, only one or two representativeelements were selected for the next step. The choice of theseelements was based on the following criteria (1) robust elementspresenting concentrations significantly higher than the MDL, (2)elements strongly correlated with others within the same groupand known as tracer of a specific source in the studied site. Thesecond step consisted of a CA based on the standardized matrix ofthe new reducedmatrix (with a reduced number of variables) using

Fig. 2. Indoor and outdoor PM10 concentrations in 3 elementary schools of NPdC

Ward’s method and Euclidean distances. The interpretation wasmade with regard to probable outdoor particulate sources of eachschool. We will discuss results of outdoor particles sources inschool A (industrial) separately from schools B and C displayinga more general characteristic profile of the region NPdC.

3. Results and discussion

3.1. Indoor and outdoor PM10 mass concentrations

In this study, we used the average values of the two weeklymeasurements for mass and element concentrations since therewere no significant differences between these two measurements.The indoor and outdoor PM10 mass concentrations obtained byMicrovol are summarized in Fig. 2. Weekly indoor PM10 concen-trations obtained in presence of children ranged from 72.7 (schoolC) to 85.3 mgm�3 (school B), while these values in absence ofchildren were much lower, between 13.2 (school C) and22.3 mgm�3 (school A). These PM10 values largely exceeded thedaily limit values (50 mgm�3) for indoor air (WHO and Frenchrecommendations) in presence of pupils. These higher indoorPM10 concentrations in schools A and B than school C wereassociated to the doors more frequently opened in the last one(confirmed by questionnaire). Compared to indoor air, outdoorPM10 concentrations were much lower during occupied periodsranging from 35.6 mgm�3 (school C) to 55.7 mgm�3 (school A) andslightly higher than during unoccupied periods, from 23.0 mgm�3

(school C) to 29.1 mgm�3 (school A). Consequently, the presence/absence PM10 ratios varied from 3.4 to 5.5 indoors and from 1.3 to1.8 outdoors.

Considering the specific typology of the schools, no significantdifferences in PM10 concentrations were clearly evidenced indoorand outdoor, with the exception of higher outdoor PM10 concen-tration collected at school A during occupied period compared toschool B and C (Fig. 2). This difference may be attributed to morefrequent dust-off of clouts near the outdoor samplers in thecourtyard of school A as discussed later.

The physical activities in classroomswere undoubtedly themainsources of indoor PM10 as also observed in previous works (Janssenet al., 1997; Fromme et al., 2008). On the contrary, during unoc-cupied periods, the building envelope of these schools preventedthe penetration of the coarse fraction of outdoor PM10 owing totheir filtration capacity (Thatcher and Layton, 1995; Nazaroff,2004).

(n¼ 2 weekly measurements). The error bars represent the 2 extreme values.

D.T. Tran et al. / Atmospheric Environment 54 (2012) 250e259254

3.2. Major and trace element concentrations according to particlesize distribution

The range of elemental concentrations (ngm�3) for differentindoor and outdoor particle fractions, in presence and absence ofchildren, of the three schools are reported in Table 2. As expected,indoor PM10 elemental concentrations were clearly higher inpresence of pupils than during their absence mainly due to moreimportant PM10 mass concentrations (Table 2). On the contrary,outdoor PM10 displayed elemental contents relatively comparablebetween these two scenarios.

Table 2Indoor and outdoor concentrations of PM1, PM2.5, PM10 (mgm�3) and associated elementsthe Dekati and PM1, PM2.5, PM10 were determined by adding successive fractions from t

Occupied period

Schools A B C

PM1 PM2.5 PM10 PM1 PM2.5 PM10 PM1 PM2.5 PM

Indoor airPM 37.7 63.5 84.8 29.6 44.0 85.2 33.3 49.5 72.7Ag 0.39 0.64 0.78 0.09 0.16 0.33 0.04 0.10 0.18Al 90.2 310.0 498.0 75.0 193.0 457.0 53.5 138.0 231As 1.30 1.60 1.80 0.54 0.58 0.69 0.22 0.29 0.35Ba 4.20 17.40 28.90 0.97 7.50 18.10 1.80 6.10 13.1Bi 0.12 0.18 0.23 0.08 0.17 0.44 0.05 0.10 0.14Ca 666 2688 4381 336 997 2627 472 1310 222Cd 0.25 0.38 0.47 0.07 0.10 0.14 0.09 0.16 0.20Co 0.33 0.65 0.97 0.17 0.39 0.84 0.23 0.55 0.89Cr 2.34 4.40 8.70 3.00 4.40 9.00 1.90 2.90 4.00Cu 3.40 7.15 10.30 2.30 4.20 9.20 1.40 2.90 4.90Fe 272 571 836 56 1112 253 61 137 219K 78 171 263 38 75 195 56 105 179La 0.19 0.92 1.30 0.05 0.19 0.36 0.07 0.19 0.26Mg 28.2 95.4 153.0 10.3 35.3 96.3 13.7 57.9 96.9Mn 9.5 22.8 33.2 1.8 3.1 6.5 2.5 5.0 7.9Mo 0.47 0.62 0.84 0.75 0.93 1.50 0.14 0.20 0.23Na 35 108 154 18 54 159 8 104 191Ni 4.3 5.9 8.5 2.4 2.9 4.8 2.0 2.6 3.5Pb 5.7 8.8 11.2 3.7 5.3 9.5 3.4 5.2 6.4Sb 0.46 0.72 0.88 0.42 0.59 0.93 0.24 0.39 0.51Se 0.53 0.58 0.58 0.48 0.52 0.58 0.32 0.43 0.56Sr 1.9 7.7 12.2 1.1 2.7 6.9 0.9 2.7 4.6Ti 24.9 71.4 109.0 11.2 24.3 59.0 10.2 22.0 39.5V 7.9 9.4 10.3 4.4 6.6 16.9 2.6 3.0 3.3Zn 19.0 51.1 80.1 11.5 21.2 39.5 10.6 22.0 32.2

Outdoor airPM 31.3 38.1 51.0 14.3 18.7 35.1 13.3 16.3 29.6Ag 0.13 0.15 0.16 0.03 0.04 0.05 0.04 0.04 0.05Al 52.4 129.5 267.7 20.4 82.9 153.8 54.4 86.1 135As 1.22 1.61 1.83 0.20 0.25 0.28 0.34 0.38 0.41Ba 1.60 4.91 9.47 0.90 5.93 10.68 2.56 5.78 7.64Bi 0.11 0.15 0.17 0.03 0.07 0.10 0.05 0.07 0.08Ca 333 1149 2391 127 505 1098 437 633 979Cd 0.34 0.43 0.49 0.08 0.10 0.12 0.16 0.20 0.22Co 0.26 0.37 0.53 0.08 0.25 0.44 0.23 0.31 0.45Cr 2.30 2.58 4.62 0.90 2.92 4.67 0.98 1.25 2.15Cu 2.88 4.85 7.11 1.93 4.34 6.69 1.19 1.92 2.70Fe 354 952 1765 36 119 206 56 100 161K 59 111 199 19 39 67 52 69 93La 0.34 1.04 1.36 0.03 0.23 0.32 0.07 0.16 0.22Mg 28.9 126.6 266.5 7.6 52.4 99.3 17.9 54.0 93.6Mn 15.1 30.2 54.2 1.6 3.6 5.7 2.3 3.7 5.7Mo 0.44 0.59 0.81 0.29 0.65 0.91 0.08 0.09 0.12Na 25 176 372 12 124 228 6 96 185Ni 4.9 6.0 7.6 1.3 2.5 3.2 1.8 1.8 2.2Pb 6.7 8.5 10.3 3.4 4.9 5.8 4.4 5.4 6.0Sb 0.44 0.68 0.91 0.39 0.72 0.99 0.28 0.39 0.47Se 0.80 0.86 0.88 0.40 0.49 0.54 0.37 0.46 0.53Sr 0.7 1.9 3.5 0.4 1.3 2.6 0.9 1.5 2.2Ti 7.3 13.7 25.6 4.5 10.9 19.5 7.9 11.2 16.5V 11.7 13.5 15.0 6.0 10.8 14.2 3.1 3.3 3.5Zn 12.3 19.7 29.3 8.4 19.0 27.4 11.6 19.0 23.1

Major and crustal elements such as Al, Ca, Fe, K, Mg, Na, and Tiwere the most abundant in indoor and outdoor PM10 (Fig. 3).Cumulative percentages of these elements for indoor PM10 obtainedin presence of pupils varied from 7.2% (school B) to 10.3% (school A)against a range of 1.8% (school C) to 3.8% (school A) in absence ofchildren. Outdoors, these differences were less remarkable withvalues ranging from 6.0% (school C) to 10.6% (school A) duringoccupied periods against 3.6% (school C) to 6.7% (school A) duringunoccupied periods. By contrast, the total mass percentages of theother trace elements analysed in thisworkweremuch lower, varyingfrom0.1 to 0.2% of indoor PM10 and from0.2 to 0.5% of outdoor PM10.

(ngm�3) in the three schools (n¼ 2). The different size fractions were collected withhe impactor stages.

Unoccupied period

A B C

10 PM1 PM2.5 PM10 PM1 PM2.5 PM10 PM1 PM2.5 PM10

20.2 24.0 24.8 11.0 19.6 22.2 9.2 11.9 13.20.05 0.06 0.06 0.01 0.02 0.03 0.01 0.01 0.01

.0 5.2 13.4 14.5 10.1 41.6 50.9 4.3 13.0 17.10.33 0.36 0.36 0.37 0.40 0.41 0.23 0.25 0.26

0 0.50 2.02 2.54 0.12 1.30 2.10 0.14 0.88 1.800.05 0.06 0.06 0.02 0.04 0.04 0.01 0.02 0.02

0 48 199 248 21 139 194 12 44 750.15 0.17 0.17 0.04 0.04 0.04 0.05 0.06 0.060.12 0.16 0.17 0.03 0.07 0.09 0.05 0.07 0.091.19 1.97 2.30 0.86 1.60 2.10 0.30 0.62 1.401.57 2.30 2.56 0.77 1.60 2.10 0.36 0.68 0.8261 92 103 41 79 88 12 28 3832 35 35 12 19 30 26 35 390.08 0.21 0.24 0.01 0.03 0.03 0.03 0.10 0.124.9 13.8 16.2 1.1 10.1 12.2 2.1 22.2 31.12.8 4.1 4.5 0.8 1.7 1.9 0.5 1.0 1.20.33 0.43 0.45 0.79 1.40 1.60 0.06 0.10 0.1412 31 35 6 27 31 7 706 1004.1 4.8 5.0 0.9 1.2 1.6 1.7 1.9 2.43.4 3.8 3.9 1.5 2.0 2.1 2.3 2.5 2.60.20 0.24 0.25 0.24 0.32 0.35 0.17 0.22 0.230.19 0.19 0.19 0.23 0.26 0.26 0.22 0.28 0.300.3 0.8 1.0 0.1 0.4 0.5 0.1 0.3 0.43.1 6.4 7.6 1.0 3.1 4.1 0.4 1.5 2.08.6 9.1 9.2 2.0 3.6 4.1 3.8 4.0 4.16.5 10.0 11.0 2.3 4.9 5.8 2.5 3.7 4.3

12.3 19.7 29.3 8.4 19.0 27.4 11.6 19.0 23.10.07 0.08 0.08 0.04 0.04 0.05 0.01 0.01 0.01

.6 7.1 24.4 48.8 9.1 39.9 77.9 7.8 23.4 32.11.53 1.61 1.66 0.24 0.29 0.32 0.26 0.31 0.331.74 2.84 3.78 0.18 1.72 3.43 0.19 3.42 4.790.16 0.18 0.19 0.03 0.04 0.06 0.02 0.02 0.0331 88 193 18 87 197 15 56 1100.27 0.29 0.30 0.07 0.08 0.09 0.06 0.07 0.080.45 0.50 0.53 0.04 0.08 0.13 0.06 0.10 0.125.15 5.68 5.95 0.61 1.80 2.39 0.33 1.25 1.353.59 4.71 5.75 1.27 3.29 5.06 0.37 1.12 1.51111 220 355 19 79 141 14 43 6529 45 64 24 46 70 28 47 660.25 0.58 0.80 0.02 0.08 0.13 0.03 0.14 0.175.7 38.3 78.5 3.9 36.6 71.3 2.2 26.7 48.616.1 23.9 32.3 0.9 2.4 3.8 0.9 2.2 3.30.79 0.87 0.92 0.24 0.44 0.53 0.04 0.11 0.1211 88 181 11 101 193 6 101 18914.1 15.0 15.4 1.3 1.9 2.1 1.9 2.5 2.617.2 17.9 18.3 2.8 3.6 4.0 2.7 3.0 3.10.89 1.05 1.17 0.53 0.77 0.96 0.25 0.33 0.381.89 1.93 1.94 0.51 0.61 0.61 0.37 0.50 0.530.9 1.4 2.0 0.1 0.5 0.9 0.1 0.4 0.719.1 21.6 25.2 1.6 4.8 8.7 0.9 2.6 4.139.6 40.7 41.4 3.4 5.5 6.7 4.3 4.6 4.831.3 36.1 38.4 4.3 8.7 11.6 3.3 6.3 7.4

Fig. 3. Percentage of major and trace elements in indoor PM10 of three schools (n¼ 2 weekly measurements).

D.T. Tran et al. / Atmospheric Environment 54 (2012) 250e259 255

Among the 25 elements analysed in indoor and outdoor PM10, Cawas the most abundant. It accounted for 7.2, 4.4, and 6.8% of totalindoor PM10mass sampledduringoccupiedperiods, respectively forschool A, B, and C (Fig. 3). During unoccupied periods, these valueswere only 2.2, 1.1, and 0.5%, respectively. This was attributed to theuse of chalk during school-time (confirmed by questionnaires) andindoor activities (walking, sitting and playing). Similar observationsobtained for outdoor PM10 suggested that children’s activities in thecourtyard and the dust-off of clouts close to outdoor samplers werethe main reasons of these differences between sampling sites andbetween occupied and unoccupied periods. Previous studies havealso reported that crustal elements were the most abundant inindoor PM10 (Gemenetzis et al., 2006), particularly Ca resulting fromchalk dust in classrooms (Janssen et al., 1999; Fromme et al., 2008).

Generally, the fine fractions PM1 and PM2.5 collected indoorswere significantly enriched with trace elements such as As, Cd, Pb,Bi, Mo, Ni, Sb, V, especially in absence of children. On the contrary,crustal elements such as Al, Ca, Ba, K, Sr, and Ti were primarilyfound in the coarse fraction PM2.5e10 (Table 3). For outdoor parti-cles, no notable differences between occupied and unoccupiedperiods were observed.

Elemental concentrations of indoor and outdoor PM1, PM2.5 andPM10 (Table 2) tend to reflect the typology of the studied sites.Indeed, most elemental concentrations on indoor and outdoorPM10, both occupied and unoccupied scenarios, were higher in

school A (industrial) than in the others, apart from some elementssuch as Al, Ca, Sr, and Ti (Fig. 3). Concentrations of minor and traceelements such as As, Cu, Cr, Mn and Sb in indoor and outdoor PM10were also lower in school C than in school A and B where roadtraffic and industries may present an important source of theseelements. Similarly, Na and Mg associated to marine aerosols weresignificantly higher in PM10 both indoors and outdoors for school A(closer to the seashore) than for school B and C.

The school A situated in close proximity to industries displayedhigher concentrations of petrochemical tracers Ni and V (Allemanet al., 2010) than the others in different particle fractions. Thepresence of steel industry and ferromanganese might also lead tohigher concentrations of Fe and Mn in indoor and outdoor PM.Similarly, the tracers of road traffic Cu and Sb (Weckwerth, 2001;Sternbeck et al., 2002) in indoor and outdoor PM1, PM2.5 and PM10were, as expected, higher at school B (urbanwith a relatively densecar traffic) than at school A (industrial) and C (rural) (Table 2). Theseobservations clearly evidenced the relationship between theoutdoor particle emissions and the IAQ.

3.3. Indoor and outdoor elemental distribution in PM1, PM1e2.5, andPM2.5e10

This section aims at evidencing the influence of children’sactivities in classrooms on the behaviour of each particle size

Table 3Elemental distribution (major elements in bold) in different indoor particulate fractions (%), n¼ 2. The percentage of the PM1e2.5 in PM10 (PM1e2.5/10) is the difference betweenthe percentages of PM2.5 in PM10 (PM2.5/10) and PM1 in PM10 (PM1/10).

Periods Occupied period Unoccupied period

Schools A B C A B C

Fractions PM1/10 PM2.5/10 PM1/10 PM2.5/10 PM1/10 PM2.5/10 PM1/10 PM2.5/10 PM1/10 PM2.5/10 PM1/10 PM2.5/10

Al 18.1 62.2 16.4 42.8 28.3 64.1 36.1 89.4 20.3 82.4 58.5 86.8Ba 14.7 60.9 12.3 41.7 13.5 46.2 19.9 79.6 6.1 62.9 8.3 49.6Ca 15.3 61.7 12.7 37.6 22.9 60.4 19.0 79.2 11.7 73.0 49.0 76.5Fe 30.4 67.1 22.0 44.5 31.7 65.1 57.8 89.9 47.3 90.2 63.9 85.5K 29.5 65 19.2 48.1 30.8 58.7 88.8 99.9 48.7 89.7 84.4 94.6Mg 18.5 62.4 10.7 35.9 13.4 58.6 28.5 82.6 9.1 82.8 39.7 80.4Na 22.5 70.3 11.5 36.2 12.7 51.3 33.9 87.3 17.8 86.4 39.6 81.1Sr 15.4 62.9 15.6 39.9 20.9 59.1 25.7 81.2 22.6 84.6 21.8 70.7Ti 20.0 58.4 13.5 36.6 26.4 56.2 29.8 79.5 59.4 84.3 19.2 74.2Ag 43.9 77.5 27.8 49.0 24.9 55.8 79.7 94.8 52.3 85.2 85.9 100.0As 69.7 90.3 78.5 84.5 62.4 80.3 90.4 98.7 90.1 99.0 87.4 97.9Bi 49.0 78.2 28.6 39.6 39.1 72.4 83.9 97.1 59.2 87.6 72.2 94.3Cd 51.4 79.3 55.0 70.4 44.2 73.3 90.7 99.3 81.9 96.7 89.2 96.7Co 34.3 67.0 19.3 42.4 26.1 62.1 72.9 93.9 33.8 77.8 57.2 77.6Cr 25.9 45.8 31.3 46.0 54.6 75.2 51.8 85.6 39.4 72.0 20.1 47.1Cu 32.5 68.9 24.9 54.1 28.9 59.8 59.9 88.6 42.2 81.9 42.8 82.6La 16.9 70.3 12.9 55.5 25.8 72.0 41.5 90.8 21.4 85.5 23.8 89.0Mn 28.2 68.4 26.8 47.3 33.8 63.7 61.6 92.0 44.1 86.9 38.4 77.3Mo 52.6 67.0 49.1 61.5 64.2 85.8 72.6 95.2 48.9 89.1 41.5 69.9Ni 49.0 68.0 47.3 56.7 62.2 78.1 81.5 95.1 56.9 78.4 71.8 82.7Pb 49.7 78.7 39.0 56.4 52.5 78.4 88.3 97.4 71.8 93.4 91.3 98.2Sb 53.0 82.7 45.5 62.4 49.6 77.0 80.4 96.0 68.0 92.4 76.0 93.5Se 91.2 100.0 77.6 86.4 58.8 79.6 100.0 100.0 86.6 99.9 70.2 95.1V 76.8 91.2 47.8 61.9 78.7 89.5 93.7 99.1 60.8 90.6 94.3 98.9Zn 24.6 65.1 27.4 50.1 33.9 66.6 59.2 90.4 39.7 83.7 57.3 84.0

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fraction (PM1, PM1e2.5, and PM2.5e10) directly collected on impac-tor’s stages.

Trace elements such as As, Bi, Cd, Pb and V represented 50e91%in PM1, 9e20% in PM1e2.5 and less than 20% in PM2.5e10 (Table 3),confirming their unimodal distribution in the accumulation mode(PM1) previously observed in the region (Lamaison, 2006). Crustalelements Al, Ba, Ca, Mg, Sr and Ti commonly presenting a unimodaldistribution in the supermicronic fraction, accounted for less than20% in the PM1 and 50 to 65% in the PM1e2.5 fraction.

As previously shown, elemental concentrations of outdoorparticles in the three schools were relatively comparable betweenoccupied and unoccupied periods.

On the contrary, indoor particles showed a different behaviourbetween these two periods. Indeed, during unoccupied periods, therelative percentages of all elements (particularly trace elements) inthe PM1 and PM1e2.5 fractions increased strongly compared to theoccupied periods (Table 3). This was attributed to the proportionalrapid decrease of the coarse fraction PM2.5e10 after teaching hoursdue to their higher deposition velocity (Thatcher and Layton, 1995;Lai, 2002) and lower penetration capacity (Lai, 2002; He et al.,2005; Nazaroff, 2004). These phenomena led to a faster decreaseof the coarse fraction compared to the fine fractions in indoor PM10

sampled during unoccupied periods. The elemental percentages inthe fine fractions of PM10 during these periods were thereforeamplified compared to the coarse one.

Fig. 4. Dendrogram of the cluster analysis of the outdoor samples of school A (n¼ 10).

3.4. Outdoor PM sources

3.4.1. Outdoor PM sources of school ASeven tracers (Sr, Fe, Mn, Mg, Na, V, and Sb) were finally selected

for CA and sourcing interpretation. As the use of other tracers foreach specific source than the selected elements did not change theappearance of the dendrogam in Fig. 4, the choice of these elementswas considered robust. The separation of different groups in thisdendrogam was based on the largest sudden jump (gap) in thedistance coefficient (gap from proximity 2.4e3.6).

The CA results showed that major elements Al, Ba, La, Ca, Co, andTi were highly correlatedwith Sr (selected as a natural tracer) whileFe, Mn were associated with natural and industrial-relatedelements (As, Bi, Cd, Cr, Mo, Ni, Pb, Se, Zn .) suggesting bothorigins. Possible industrial sources of Fe andMn in the studied zoneare ferromanganese production plants, steel and cement industries(Alleman et al., 2010). Elements associated tomarine aerosols K, Mgand Na were highly correlated. In addition, the traffic markers Cuand Sb were correlated together and classed differently fromprevious groups (Fig. 4). Finally, V generally linked to petrochem-istry and fuel combustion was slightly correlated with Ni (and ina less extent with La), another marker of petrochemistry, itselfassociated to industry-related elements implying the presence ofmore than one source of Ni in the studied zone.

To confirm these results, we calculated elemental ratiosbetween representative elements of each source, and comparedwith previous studies conducted close to school A.

Average ratio of Na/Mg of ten outdoor samples was 2.2� 0.7against 8.4 reported by Alleman et al. (2010) for samples obtained ina period where winds were frequently coming from the sea shore.Our lower Na/Mg ratio may be associated to the influence of otheremission sources of Mg such as the use of dolomite CaMg(CO3)2 andcoal in steel industry in the studied zone (Lamaison, 2006). Continiet al. (2010) also reported influences of different sources other thanmarine aerosols to account for low Na/Mg ratio (2.9).

D.T. Tran et al. / Atmospheric Environment 54 (2012) 250e259 257

The Cu/Sb ratio, frequently employed as a signal of traffic-related aerosols (Iijima et al., 2007) displayed average value of6.6�1.0, very close to 6.5 (Alleman et al., 2010) and slightly higherthan 4.6� 2.3 found by Sternbeck et al. (2002). Possible othersource of Cu closed to school A is a bronze foundry situated in the160e210� sector, a wind direction regularly observed during thisschool’s sampling campaign.

The mean ratio V/Ni (marker of petro-chemistry) obtained inthis study was 2.37� 0.91 against 2.34 reported by Alleman et al.(2010). Our results were also close to results observed by Yuanet al. (2006) for residual fuel combustion (V/Ni¼ 2.1) and bySwietlicki and Krejci (1996) during their study in central Europe(range from 1.4 to 1.9).

3.4.2. Outdoor PM sources of school B and CApplying the same procedure, 4 sources of outdoor PM10 were

revealed in school B and 3 in school C. The sources in school B wererespectively natural (Al, Ba, Ca, La, Sr, and Ti), road traffic (Sb, Cu,and Mn); others anthropogenic (Ag, As, Bi, Cd, Cr, Fe, Mo, Ni, Pb, Se,V, Zn...), and marine aerosols (Na, Mg). As expected, school C (ruralsite) showed a typical background concentration of the regionrepresented by the following sources: natural (Al, Ca, La, Sr, Ba, Ti),traffic-related particles (Cu, Sb, Mn), and marine aerosols (Mg, Na).

Mean Cu/Sb ratio of school B and C were respectively 6.9, and5.8, rather comparable to results from school A and previouslypublished data (Sternbeck et al., 2002).

Globally, the results about marine aerosols implied that thewhole region is under oceanic influence (Lamaison, 2006). Themean Na/Mg ratio in school B and Cwere respectively 3.0� 0.4, and2.2�1.2.

Fig. 5. Indoor/outdoor elemental ratios of PM1, PM1e2.5 and PM

3.5. Indoor PM sources

The PM10 of outdoor origins after depositing on indoor surfacesare potentially mixed with PM10 of indoor origin by children activi-ties. This may induce collinearity between explanatory variables inthe CA, preventing us to distinguish between indoor and outdoorsources. We thus applied different statistical analysis to determinethe indoor particles sources. The information from questionnairesabout occupant’s activities in classrooms and about building char-acteristics helped us to interpret indoor particle sources whileoutdoor sources were previously identified for each school.

As shown in section 3.1, mass concentration (in mgm�3) ofindoor PM10 sampled during teaching hours were about 2 timeshigher than outdoor ones in any schools due to the presence of thechildren. Consequently, elemental concentrations (in ngm�3) ofmost elements in these indoor PM10 were from slightly to signifi-cantly higher than outdoors. A similar tendency was observed forthe different particulate size fractions (Table 2). The use of indoor/outdoor elemental concentration ratios (I/O) to identify sources ofthese elements as well as their penetration capacity as employed inseveral previous studies (Na et al., 2004; Wang et al., 2006) maytherefore introduce some uncertainties.

To avoid this particle mass influence, we expressed elementalconcentration in ng mg�1 of particles to calculate indoor/outdoor (I/O) elemental ratios for each particulate size fraction. The results forPM1, PM1e2.5 and PM2.5e10 fractions sampled during teaching hoursare reported in Fig. 5.

Most elements, in particular trace elements, showed I/O ratios inPM1, PM1e2.5 and PM2.5e10 fractions below or very close to 1, exceptfor somemajor elements such as Al (in school B only), Ba, Ca, Sr and

2.5e10 in the three schools in presence of children (n¼ 2).

D.T. Tran et al. / Atmospheric Environment 54 (2012) 250e259258

Ti in school A and B displaying ratios significantly above 1. Theseresults implied that these trace elements have no indoor sourcesand that the presence of children did not change significantly theelemental distribution in different indoor particle fractions. On thecontrary, the major elements Al, Ba, Ca, Sr and Ti were enriched inindoor particles compared to outdoor ones, an indication of indoorsources for these elements. The probable indoor sources could bechalk dusts and re-suspension of coarse particles containing highercontents of major elements as previously discussed. The I/O ratiosfor Bi slightly higher than 1 in the different fractions in school A andB and for As in PM1 for school B could be related to a largeruncertainty due to very low concentration of this element or tounidentified indoor sources of these elements. The enrichments ofZn in the indoor PM1e10 fraction compared to outdoor ones inschools A might either be associated to sample contaminations(Lamaison, 2006) or from unknown origins.

Inversely, in absence of pupils, I/O elemental ratios of nearly allelements for each size fraction were below 1 due to the lack ofimportant indoor sources during that period (Table 2). Only Ca,presenting ratios greater than 1 for most particulate fractionsconfirmed its indoor source from chalk dusts, still remaining insuspension after the occupants’ departure from the classrooms.

4. Conclusions

This work is one of very few studies on indoor and outdoorelemental composition of airborne particles in school environment.It provides detailed information about the exposure of children toparticles according to their size distribution (PM1, PM1e2.5 andPM2.5e10) and elemental composition, especially heavy metals thatmay present a serious threat to children health. The presence ofchildren increases the PM10 mass concentration (about 2 timeshigher than the WHO guideline value for 24 h), especially for thecoarse fraction PM2.5e10, impacting their elemental concentration(ngm�3) but not significantly their elemental distribution betweensize fractions.

Elemental concentrations of indoor particles are considerablyaffected by the site typology for most trace elements, particularlyclose to industrial (school A) and traffic sources (school A and B).

Major and crustal elements (Al, Ca, Fe, K, Mg, Na, and Ti) presentthe highest percentages in indoor and outdoor PM10 in each school.Their cumulative percentages in indoor PM10 sampled in presenceof children (from 7.2% to 10.3%) are much higher than in absence ofchildren (1.4% to 3.8%) in classrooms. Ca, the most abundantelement in indoor and outdoor PM10 is associated to chalk dusts.Trace elements represent very low percentages from 0.2 to 0.5% ofoutdoor PM10 and from 0.1 to 0.2% of indoor PM10. The concen-trations of potentially toxic metals such as As, Cd, Ni, and Pb arealways lower than the European limit values for atmospheric PM10(Directives 2008/50/CE and 2004/107/CE). Chalk dusts, resus-pension by children activities and transport of outdoor soil dust arethe most ubiquitous sources of Ca, Al, Fe, Sr, and Ti. The otherelements are mainly from outdoor sources such as natural, roadtraffic, marine aerosols, petro-chemistry and industrial mixedsources.

Acknowledgement

This research project was part of the “Institut de Recherche enEnvironnement Industriel” (IRENI) and was financially supportedby the Nord-Pas-de-Calais Regional Council, by the French Ministryof Higher Education and Research, by the European RegionalDevelopment Funds (through the Regional Delegation for Researchand Technology) and Ecole des Mines de Douai-Armines research

centre. We thank Bruno Malet for its valuable and continuoustechnical support.

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