Atmospheric Environment 35 (2001) 6407–6419

PM10 and PM2.5 source apportionment in the BarcelonaMetropolitan area, Catalonia, Spain

Xavier Querola,*, Andr!es Alastueya, Sergio Rodrigueza, Felici"a Planaa,Carmen R. Ruiza, Nuria Cotsb, Guillem Massagu!eb, Oriol Puigb

a Institute of Earth Sciences Jaume Almera, CSIC, C/Lluis Sol !e Sabar!ıs, s/n, 08028 Barcelona, SpainbDirecci !o General de Qualitat Ambiental, Generalitat de Catalunya. Diagonal 523, 08029 Barcelona, Spain

Received 23 April 2001; received in revised form 19 June 2001; accepted 6 July 2001

Abstract

Levels of total suspended particles, PM10, PM2.5 and PM1 were continuously monitored at an urban kerbside in theMetropolitan area of Barcelona from June 1999 to June 2000. The results show that hourly levels of PM2.5 and PM1

are consistent with the daily cycle of gaseous pollutants emitted by traffic, whereas TSP and PM10 do not follow thesame trend, at least in the diurnal period. The PM2.5/PM10 ratio is dependent on the traffic emissions, whereasadditional contribution sources for the >10mm fraction must be taken into account in the diurnal period. Different

PM10 and PM2.5 source apportionment techniques were compared. A methodology based on the chemicaldetermination of 83% of both PM10 and PM2.5 masses allowed us to quantify the marine (4% in PM10 and o1% inPM2.5), crustal (26% in PM10 and 8% in PM2.5) and anthropogenic (54% in PM10 and 73% in PM2.5) loads. Peaks

of crustal contribution to PM10 (up to 44% of the PM10 mass) were recorded under Saharan air mass intrusions. Adifferent seasonal trend was observed for levels of sulphate and nitrate, probably as a consequence of the differentthermodynamic behaviour of these PM species and the higher summer oxidation rate of SO2. r 2001 Elsevier ScienceLtd. All rights reserved.

Keywords: PM10; PM2.5; Source apportionment; Receptor modelling; Road traffic; Saharan dust; Spain

1. Introduction

A number of epidemiological studies (Schwartzet al., 1996; Dockery and Pope, 1996; Donaldson andMaCnee, 1999) have demonstrated that atmospheric

particulate matter (PM) in urban areas has a clearcorrelation with the number of daily deaths andhospitalisations as a consequence of pulmonary and

cardiac disease responses. These studies show thatmeasurements of thoracic and alveolar particles(PMo10 and o2.5 mm, respectively) correlate betterwith morbidity and mortality than total suspended PM

(TSP). In the light of these studies, the European

Commission has included PM10 limit values for PMmonitoring in the new air quality directive (1999/30/

CE). This directive also considers the possibility ofincluding PM2.5 standards in the 2003 evaluation.The implementation of the new EC directive demands

that all PM monitoring networks replace BS and TSP byPM10 measurements. In Spain, a number of monitoringnetworks had already commenced measurements of

PM10 in 1991. The new PM10 limit values are verystrict when compared with other National standards.Whereas an annual daily limit value of 50mg PM10m�3

has been fixed by US-EPA (1996), the new EC Directive

establishes an annual daily limit of 20mg PM10m�3, anda 24 h limit value of 50mg PM10m�3, which cannot beexceeded more than 7 days/yr.

Given that PM is emitted into the atmosphere by anumber of anthropogenic and natural sources, the

*Corresponding author. Tel.: +34-93-409-5410; fax: +34-

93-411-0012.

E-mail address: xavier.querol@ija.csic.es (X. Querol).

1352-2310/01/$ - see front matter r 2001 Elsevier Science Ltd. All rights reserved.

PII: S 1 3 5 2 - 2 3 1 0 ( 0 1 ) 0 0 3 6 1 - 2

physical and chemical patterns may vary considerably.Both natural and anthropogenic emissions supply

primary (direct emission of PM) and secondary (formedfrom gaseous precursors) PM. On a global scale, PMemissions reach 3400 million tonnes/yr (IPCC, 1996).

Anthropogenic sources account for only 10% of totalPM emissions, whereas the natural primary PM emis-sions reach 85% (2900 million tonnes/yr). Althoughthese figures change drastically on a local scale, natural

emissions may interfere considerably in the PM mon-itoring around large natural PM emission sources(mainly arid and semiarid regions) such as the Medi-

terranean basin.The impact of the long range transport of North

African dust on TSP and PM10 levels recorded in air

quality monitoring stations in Southern Europe hasbeen demonstrated by Bergametti et al. (1989), Chesteret al. (1993), Kubilay and Saydam (1995), Querol et al.

(1998) and Rodr!ıguez et al. (2001). PM from this sourceregion consists mainly of clay minerals, quartz, Ca andMg carbonates, with minor proportions of sulphate,nitrate and carbonaceous particles, with major grain size

modes between 1 and 25 mm (Coude-Gaussen et al.,1987; Molinaroli et al., 1993; Gillies et al., 1996, Afetiand Resch, 2000; Rodr!ıguez et al., 2001), depending on

the source area and on the transport patterns. Thesechemical and physical features clearly contrast with theurban anthropogenic particles that are mainly in the

PM2.5 range and predominantly made up of carbonac-eous particles, sulphate and nitrate.Natural interferences in PM monitoring could be

diminished by measuring PM2.5 instead of PM10 mass

concentration or by measuring PM counts instead ofmass (Querol et al., 2001; Harrison et al., 1999), butcurrent EU legislation is still based on the PM10 mass

measurements. Consequently, source apportionmentanalysis (SAA) may contribute to the evaluation of thenatural/anthropogenic load of PM10 and PM2.5.

The SAA for PM ambient levels have been frequentlybased on dispersion models, in which emission inven-tories for various sources are used as input data to

predict ambient PM concentrations. Alternative proce-dures have been developed based on Lagrangiantrajectory models able to simulate the main processesinvolved in aerosol emission, formation, transport and

deposition (Eldering and Cass, 1996; Kleeman and Cass,1998). Although this procedure is consistent with theexperimental data, it requires a detailed emission

inventory that is not always available. Receptor model-ling techniques are based on the evaluation of dataacquired at receptor sites, and most of them do not

require previously identified emission sources (Henryet al., 1984). These types of models have played a keyrole in the evaluation of PM sources with respect to

national air quality standards in certain countries. In theUnited States, the Chemical Mass Balance Model

(Gertler et al., 1995; Chow et al., 1996) has been widelyused, whereas in Europe receptor modelling techniques

have been mainly based on methodologies that do notrequire chemical profiles from source emissions (Harri-son et al., 1997a, b; Pio et al., 1998).

This paper presents the first results obtained from aresearch project on PM10 and PM2.5 SAA in differentareas of Spain, supported by the Spanish Ministry of theEnvironment and the CICYT (Comisi !on Interminister-

ial de Ciencia y Tecnolog!ıa). The present study focuseson the Barcelona Metropolitan area, an urban andindustrialised area in northeastern Spain.

2. The study area

Barcelona and 32 surrounding towns make up theMetropolitan area (604 km2) with about 3 million

inhabitants (half of the population of Catalonia, NESpain). The urban development of this area forms acontinuum along the Bes "os and Llobregat rivers. One of

the towns in this Metropolitan area is L’Hospitalet(L’H) de Llobregat, where the study site is located. Thistown has an important industrial area, with a total of14,207 economic enterprises, most of them related to

services.The urban dynamism around Barcelona accounts for

a high road traffic density. According to the 1996 data

supplied by the Metropolitan Transport Authority, theweekly mobility distribution in the Metropolitan Regionof Barcelona is: 35.1% private cars, 31.1% public

transport, 33.8% pedestrian. The distribution of thejourneys depending on the day of the week is as follows:81.6% travel on week days, 18.4% at week ends.Furthermore, in addition to a wide range of industrial

activities, two gas power stations (Bes "os and Sant Adri"a)and two city waste incinerators (Sant Adri"a andMontcada) are also based in this area. Nevertheless,

traffic constitutes the major source of air pollution in thearea.Orographic effects play a key role in the atmospheric

dynamics and therefore in the air quality of theBarcelona Metropolitan area (Soriano et al., 1998,2001). The Metropolitan area is located on the

Barcelona coastal flat between the Mediterranean seaand the Collserola range (525m a.s.l.) and between thebasins of the Bes "os (North) and Llobregat (South) rivers(Fig. 1). This geographical context coupled with the

prevalent meteorological scenarios accounts for thedevelopment of atmospheric circulations dominated bythe sea breeze in summer and north winds in winter

(Soriano et al., 1998).In summer, the anthropogenic emissions, mainly from

traffic, reach a maximum from 8 to 11 h LST (local

standard time) simultaneously with the development ofan inland breeze flow (Toll and Baldasano, 2000). The

X. Querol et al. / Atmospheric Environment 35 (2001) 6407–64196408

industrial and urban areas of the Bes "os and Llobregatbasins emit pollution plumes that are transported inlandtowards the Vall!es and Anoia depressions, where they

mix with significant local industrial and urban emis-sions. The Collserola range also reinforces the breezeflow owing to the anabatic winds. These inland fluxes

over the southern Collserola slope transport thecontaminated air masses towards the top of the range.During this upwards transport the pollutants are

injected at different atmospheric heights by seawardflows prevalent at different altitudes in accordance withthe time of day (Toll and Baldasano, 2000; Soriano et al.,2001). Consequently, some of these polluted layers

injected at lower atmospheric levels may reach theoriginal emission source area in the afternoon.In winter, low dispersive conditions due to nocturnal

thermal inversion and a low breeze development accountfor the accumulation of local emissions in the coastalarea during the morning (Soriano et al., 1998). Follow-

ing this scenario, in the mid-morning, the breakdown ofthe thermal inversion induces the dilution of thepollutants, even with emission rates being constant (Tolland Baldasano, 2000). When the thermal inversion

breaks down, convective dynamics results in fumigation

events giving rise to sporadic increases in levels ofpollutants at the surface level. Finally, the thinning ofthe winter urban boundary layer accounts for a smooth

nocturnal increase in levels of gaseous pollutants (withmaximum concentrations recorded about midnight) atall the monitoring stations located in the Barcelona

plain (Fig. 1). The stations located at a certain heightabove sea level do not register this midnight increase inpollutants (St. Gervasi, Fig. 1) probably because they

are outside the nocturnal mixing layer. Typical highpollution events take place when inversion subsidenceepisodes persist in winter.The daily cycle of pollutant levels is characterised by a

peak in the early morning followed by a noon decreaseas a consequence of the dilution processes that are dueto the thickening of the urban boundary layer.

Furthermore, a midnight increase in pollutants is alsoevident in winter (Fig. 1), which is probably due to theconcentration processes caused by the thinning of the

mixing layer (Allegrini et al., 1994).In addition to the local PM emissions, Saharan dust

outbreaks reach the Barcelona area on the order of 7–10events per year, with a major frequency in the summer

and winter–spring periods (Rodr!ıguez et al., 2001).

Fig. 1. Location of the L’H and other (Prat de Llobregat, St. Gervasi and Sagrera) monitoring station in the Barcelona Metropolitan

area (coloured as shadow) in Northeastern Spain and mean semi-hourly NO levels for the study period at these urban monitoring sites.

X. Querol et al. / Atmospheric Environment 35 (2001) 6407–6419 6409

3. Methodology

A traffic/urban air quality monitoring station locatedat L’H (Gornal, Barcelona Metropolitan area, seeFig. 1) was selected for PM10 and PM2.5 measurement

and sampling. The monitoring station belongs to theDepartment of the Environment of the Generalitat deCatalunya. This site was selected because it wasrepresentative most of stations in the Barcelona

Metropolitan area (and in general in the whole Spain),which are located at kerbsides.PM sampling was performed by using MCV high

volume samplers (30m3 h�1) equipped with DIGITELPM10 and PM2.5 inlets. The sampling was carried outat a rate of two and one daily samples per week for

PM10 and PM2.5, respectively, during the study periodfrom 9 June 1999 to 29 June 2000. A total of 115 PM10and 63 PM2.5 daily samples were collected using the

above procedure.A 1

2 fraction of each filter was bulk acid digested (seedetails of the procedure in Querol et al. (2001)) for theanalysis of major cations and trace metals by means of

ICP-AES. Another 1/4 fraction was leached using Milli-Q-grade de-ionised water and the contents of majoranions (SO4

2�, NO3� and Cl�) were determined in the

leachates by means of capillary electrophoresis. More-over, NH4

+ contents in the leachates were also deter-mined by FIA-colorimetry. Total C was determined by

means of a LECO analyser. Additionally, 15 PM10samples homogeneously distributed throughout thestudy period were selected for the analysis of organicand elemental carbon concentrations by thermal–optical

techniques (Pio et al., 1994) at the University of Aveiro(Portugal). Finally, contents of Si and CO3

2� wereindirectly determined from the contents of Al, Ca and

Mg, on the basis of prior experimental equations(2Al2O3=SiO2; 1.5Ca+2.5Mg=CO3

2�).Furthermore, real time TSP, PM10, PM2.5 and PM1

measurements with a laser spectrometer (GRIMM 1108)were also carried out during the whole period. PM10GRIMM levels were compared throughout the study

period with gravimetric measurements to check the dataset quality (Fig. 2).

The SAA was carried out by means of receptormodelling techniques, using principal component ana-lysis to identify major PM sources. The contribution

from these sources to PM10 and PM2.5 was evaluatedby multilinear regression analysis, using the absolutescore factors as independent variables (Thurston andSpengler, 1985; Pio et al., 1998; Harrison et al.,

1997a, b).

4. Results and discussion

4.1. PM levels

Mean PM10 and PM2.5 levels recorded at L’H in thestudy period reached 40.6 mg PM10m�3 and 27.7 mgPM2.5m�3 with 98% of daily data coverage. Eighty sixdaily PM10 values exceeded 50 mgm�3. These values arevery high when compared to the PM10 annual and 24 h

limit values of the new EU Air Quality Directive.However, it should be noted that this is a kerbside andnot an urban background monitoring station for whichthe limit values apply. Mean annual levels of TSP and

PM1 reached 59 and 19 mgm�3, respectively.TSP, PM10, PM2.5 and PM1 were higher in autumn

and winter (Fig. 3), when frequent high PM events were

recorded. This seasonal trend is parallel to that recordedfor NOx levels, and consequently, local emissions areexpected to control the PM levels. However, since PM

levels are still high in August and March when levels ofgaseous pollutants decrease drastically, external PMinputs may be expected in these periods.As at most traffic kerbsides, the daily trend of hourly

TSP and PM10 levels (Fig. 4) reflects the road trafficflow, with maximum levels in the rush hour (7–11 and18–21 h LST periods). However, the hourly PM2.5 and

PM1 levels only reflect the morning traffic emissions.The evening PM10 peak is more evident in winter andautumn than in spring and summer. Furthermore,

PM10, PM2.5 and PM1 levels (for the whole year, butespecially in winter) tend to increase slightly from lateafternoon to midnight (Fig. 4). This trend is similar to

the hourly evolution of gaseous pollutants that alsoshows a secondary midnight increase (Figs. 1, 4 and 5) atall the monitoring stations on the Barcelona coastalplain. This has been attributed to the nocturnal

concentration of pollutants due to the thinning of theurban boundary layer (Allegrini et al., 1994).NO, PM1 and PM2.5 levels are considerably higher in

winter with respect to the other seasons (Fig. 4).However, PM10 and TSP levels are very close for allthe seasons in the period 0–12 h LST (+2 and +1h

GMT in winter and summer, respectively), but muchhigher in the period 13–23 h LST in autumn and winter

Fig. 2. Comparison of the daily PM10 levels obtained with the

laser spectrometer and with the DIGITEL PM10 high volume

sampler.

X. Querol et al. / Atmospheric Environment 35 (2001) 6407–64196410

(Fig. 4). These higher levels of coarse particles recordedin the afternoon and evening of the colder seasons are

probably the consequence of the lower winter dispersiveconditions. In the warmer seasons, when the breezedynamics are more intense, the inland flow transportsthe coarse particles towards the western areas of

Barcelona.

PM2.5/PM10 ratios exhibited minimum values (0.60–0.65) in the traffic hours (8–10 h and 18–21 h LST, see

Fig. 5) as a consequence of the re-suspension of coarseroad dust. Outside these periods, the PM2.5/PM10 ratiowas within the range 0.75–0.85 due to the sedimentation

of the re-suspended coarse fraction. On the other hand,PM10/TSP ratios tended to decrease in the diurnalperiod (8–20 h LST) when values ranging from 0.50 to0.65 were measured as a mean for the whole year

(Fig. 5), whereas in the nocturnal period this ratio waskept close to 0.8. Consequently, it may be assumed thatthe PM2.5/PM10 ratio is directly dependent on the

traffic emissions and turbulence, whereas additionalcontribution sources for the >10mm fraction arepresent in the diurnal period, especially between 10

and 18 h LST. This coarse PM input may be attributedto the inland breeze transport given that the minimumPM10/TSP is recorded at 12 GMT (Fig. 5).

Superimposed on these mean annual trends, sporadicnatural PM inputs, such as Saharan air mass intrusionevents, can significantly reduce the PM2.5/PM10 andPM10/TSP ratios down to 0.5 as a mean daily value, as

recorded in several days in August, September, Octoberand March.

4.2. Chemical characterisation and seasonal patterns

The mean, maximum and minimum contents of major

and trace elements in PM10 and PM2.5 measured atL’H are summarised in Table 1. The mean PM10 andPM2.5 levels calculated by gravimetry for the sampling

period are slightly higher than the mean annual levels(by 24%) due to the exclusion of week end sampling,when PM levels usually decrease.The sum of species for the measured chemical

constituents reached a mean of 83% of the bulk PM10and PM2.5 levels, which allows us to interpret thesources of most of the PM mass. The major components

of PM10 are Cnonmineral (11 mgm�3) and SO2�

4 nonmarine

and NO3� (both around 6 mgm�3). A second group of

components is made up of crustal species (CO32�, SiO2

and Ca) and NH4+ (from 2 to 4 mgm�3). The other

major components are present in concentrations rangingfrom 0.2 to 0.6mgm�3. The levels of trace metals are

relatively low. The averaged annual Pb concentration isclose to 140 ngm�3, much lower than the EU limit value(500 ngm�3).The organic, elemental and mineral carbon fractions

accounted for 70%, 26% and 5% of total carbon inPM10, respectively (Fig. 6). Thus, average annualconcentrations for the carbonaceous species were

estimated at 8 mg OCm�3, 3mg ECm�3 and 0.6 mgMCm�3 for the organic, elemental and mineral carbonin PM10. Although the mineral carbon drastically

decreased from PM10 to PM2.5, as a consequence ofthe reduction of the crustal load, similar total C contents

Fig. 3. NOx, O3, TSP, PM10, PM2.5 and PM1 monthly

averaged hourly levels recorded at L’H ospitalet from June

1999 to May 2000.

X. Querol et al. / Atmospheric Environment 35 (2001) 6407–6419 6411

were obtained. This is probably due to the fact that Ccontents were determined for 115 and 63 PM10 and

PM2.5 samples, respectively.Although the levels of most of the anthropogenic

species are very similar in PM10 and PM2.5, the levels of

the crustal and marine components in PM2.5 onlyaccount for 25–35% of their levels in PM10. The majorexceptions to this trend are:

* Levels of NO3� are reduced only by 30% in PM2.5

with respect to PM10, probably due to the reaction ofnitrate species with sodium chloride and calcium

carbonate, mostly present in the 2.5–10mm fraction.* K and Cl levels are reduced in PM2.5 by only 50%,

which implies the occurrence of fine anthropogenic

Cl and K bearing species, probably emitted fromcombustion emissions (Pacyna, 1998).

* The NH4+ contents are higher in PM2.5 than in

PM10 (by 14% on average) for most of the

simultaneous measurements performed at L’H (Table1). This anomalous feature is due to the well knownreaction between NH4NO3 and NaCl that results in

the loss of gaseous NH4Cl. Given that NaCl aerosolsmainly occur in the PM2.5–10 fraction, this reactiontakes place when PM10 is sampled, resulting in a loss

of NH4+ and Cl� in the PM10. However, when

PM2.5 is sampled, a large proportion of the marineaerosol is retained by the inlet cyclone and, therefore,

most of the fine NH4NO3 remains in the filter.

Levels of NH4+ in PM2.5 are clearly correlated with

the concentrations of SO42�+NO3

� (r2 ¼ 0:91; Fig. 7).The slope of the linear regression (B1.0) strongly

suggests that most of the acid nitrate and sulphateaerosols are neutralised by ammonium. However, in

Fig. 4. Seasonal variation of the hourly TSP, PM10, PM2.5, PM1 and NO levels recorded at L’H from June 1999 to May 2000.

X. Querol et al. / Atmospheric Environment 35 (2001) 6407–64196412

PM10 the correlation is lower (r2 ¼ 0:66), and the slopeof the linear regression (B1.5) reveals an ammoniumdeficit (Fig. 7). These features suggest that a proportion

of sulphate (B15%) and nitrate (B35%) is associatedwith other cations, such as calcium and sodium, in thecoarse mode (PM2.5–10) as it has been described

elsewhere (Harrison et al., 1994; Wakamatsu et al.,1996).The levels of PM10 and PM2.5 components exhibited

different seasonal patterns according to the different

source origin and/or thermodynamic properties. Basedon the seasonal patterns, these components weregrouped as follows:

* The time series of Cnonmineral; NO3�, NH4

+, Pb, Ba,Zn, Cu and Ni levels (Fig. 8) exhibited an autumn–winter maximum. A Cnonmineral mean value of

15mgm�3 was obtained for late autumn and winter,and around 7mgm�3 in summer and spring. NO3

�

exhibited a similar seasonal trend, with winter valuesone order of magnitude higher than those of summer

(12 and 2.5mgm�3, respectively). The mean dailyNH4

+ levels increased in a similar manner in winter,especially in January–February, with respect to the

other seasons. Finally, the levels of the abovementioned heavy metals decreased by a factor of 3in summer with respect to the other seasons. The

seasonal patterns of these components in PM2.5reproduce accurately the variation described for

PM10, pointing, as expected, towards the majoranthropogenic origin of these species.

* The time series of SO2�4 nonmarine levels (Fig. 8) is

characterised by high summer–spring and lowautumn–winter concentrations (mean levels of 8and 5mgm�3, respectively, for both PM10 and

PM2.5). This seasonal pattern contrasts with themaximal Cnonmineral and NO3

� winter concentrations.This differentiation is probably due to the highsummer SO2 oxidative conditions and to the low

thermal stability of NH4NO3 in the warm period.The combination of these two factors might accountfor the higher sulphate production and the volatilisa-

tion of nitrate species in summer. Furthermore, thehigher winter NO levels may also account for thehigher nitrate formation in the cold season.

* The time series of levels of Al, Fe, Mg, Ti, Si and Sr(Fig. 8) did not show a clear seasonal pattern.Sporadic peak concentrations of these elements arestrongly correlated with the Sahara dust intrusion

events detected by Rodr!ıguez et al. (2001) for thesame period (see Fig. 7). The addition of thesecomponents may account for up to 30 mgm�3 of

PM10 when these events occur. The same patternswere observed for PM2.5 demonstrating that afraction of this African mineral dust is still present

in the o2.5mm fraction. However, the addition ofthe levels of these components in PM2.5 recordedunder Saharan events accounted for o6 mgm�3.

0

20

40

60

80

100

02 46 8 10 12 14 16 18 20 22 24

hour

µg

m-3

NO NO2 O3

0

5

10

15

20

25

30

0 2 4 6 8 10 121 4 161 8 202 2 24

hour

mg

m-3 o

r µ

g m

-3

SO2 (µg m-3) 10*CO (mg m-3)

0.40

0.50

0.60

0.70

0.80

0.90

1.00

0 2 4 6 8 10 121 41 6 182 02 2 24

hour

PM2.5/PM10 PM10/TSP

Fig. 5. Mean hourly levels of major gaseous pollutants and PM10/TSP and PM2.5/PM10 ratios measured recorded at L’H from June

1999 to May 2000.

X. Querol et al. / Atmospheric Environment 35 (2001) 6407–6419 6413

* The time series of Ca, CO32�, Cr, K and Mn show an

intermediate seasonal pattern with respect to the

nitrate and Sahara dust elements (see Ca as anexample in Fig. 8). Thus, higher winter backgroundconcentrations are evidenced for these elements, but

the Sahara peaks are superimposed on this seasonaltrend. Consequently, these elements are probablypartially emitted as coarse road dust (calcium

carbonate) and fine combustion particles (K andCr), but are also major components of the Saharadust and local natural mineral dust. From the

background concentrations of this group of elementsand those of the Al group, the inputs of road dustand other possible natural soil re-suspension inPM10 and PM2.5 may be estimated to account for

5–12mgm�3 and 0.5–3mgm�3, respectively, at theBarcelona kerbsides. It should be pointed out thatMg is not present as a Ca–Mg carbonate (dolomite),

Table 1

Maximum (max.), mean and minimum (min.) concentrations of major and trace components of PM10 and PM2.5 measured at L’H

from June 1999 to June 2000. N is the number of daily samples

PM10 PM2.5

max. mean min. max. mean min.

PM (mgm�3) 119.10 49.80 19.20 82.60 35.0 11.60

CO32� (mgm�3) 10.73 4.07 1.10 4.90 0.94 0.25

SiO2 (mgm�3) 8.60 2.96 0.75 4.72 1.05 0.12

Al2O3 (mgm�3) 3.50 1.22 0.30 1.89 0.46 0.05

Ca (mgm�3) 6.37 2.25 0.43 2.80 0.51 >0.10

K (mgm�3) 1.39 0.56 0.09 0.73 0.48 >0.10

Mg (mgm�3) 0.62 0.29 0.08 0.28 0.08 0.02

Ti (mgm�3) 0.15 0.05 0.01 0.07 0.02 >0.01

P (mgm�3) 0.11 0.04 0.01 0.39 0.03 >0.01

Fe (mgm�3) 2.97 0.89 0.20 0.81 0.26 >0.04

Na (mgm�3) 3.23 0.94 0.17 0.47 0.23 >0.01

Cl (mgm�3) 4.21 1.10 0.05 2.50 0.59 >0.01

SO2�4 marine (mgm

�3) 0.81 0.24 0.04 0.11 0.06 >0.01

Cnonmineral(mgm�3) 37.59 11.00 2.42 38.10 11.09 2.42

NH4+ (mgm�3) 13.67 2.71 0.29 13.34 3.18 0.52

NO3� (mgm�3) 26.51 5.72 1.01 18.09 4.03 0.19

SO2�4 nonmarine (mgm

�3) 13.81 6.75 1.43 10.41 5.75 1.13

Zn (ngm�3) 979 250 5 683 178 7

Pb (ngm�3) 467 149 22 392 130 24

Cu (ngm�3) 266 74 14 254 52 14

Ba (ngm�3) 103 38 o2 42 23 o1

Mn (ngm�3) 82 24 4 54 14 o1

V (ngm�3) 46 13 3 37 9 o2

Cr (ngm�3) 17.6 6 o1 63 6 o1

Ni (ngm�3) 38.7 7 o1 34 6 o1

Sr (ngm�3) 20.4 7 o2 20 4 o1

N 115 63

Total (mgm�3) 41.5 29.0

PM (mgm�3) 49.8 34.5

Fig. 6. Organic, black and mineral carbon contents in PM10

versus concentrations of total carbon.

X. Querol et al. / Atmospheric Environment 35 (2001) 6407–64196414

but as a Mg-bearing clay mineral, as deduced fromthe high Mg/Al group element correlation (r > 0:9).Smectite (a Mg-bearing clay) is a major componentof Saharan dust.

* The time series of Cl and Na levels (Fig. 8) exhibitedreverse patterns with maximum Cl and minimum Na

winter concentrations. This demonstrates the anthro-pogenic origin of an important fraction of Cl.However, frequent simultaneous concentration peaks

of Cl and Na are superimposed on this seasonal trenddue to the influence of the marine emissions. Thepartial anthropogenic origin of Cl is clearly evidenced

in the PM2.5 time series, where the sampling ofmarine aerosols is reduced and high winter Cl levelsare still recorded.

4.3. Identification of sources and apportionment ofparticulate mass

Receptor modelling techniques were used in order tosupport the above interpretations. Principal componentanalysis was applied to the PM10 and PM2.5 data set(Table 2) by using only the direct analytical determina-

tions. PM components present in very low concentra-tions (close to the analytical detection limit) were notused (Ni, Cr and Cu). The results indicate that four

PM10 and PM2.5 sources account for 84% and 75% ofPM10 and PM2.5 variance, respectively.For PM10, the first factor was highly associated with

the crustal components (Al, Ca, Fe, Ti, Sr, K and Mg).As stated above, the crustal load may comprise both

natural (Sahara dust outbreaks, and regional naturalresuspension of soil particles) and anthropogenic (road

and demolition dust) mineral dust emission sources. It isnot easy to estimate the contribution of these sources,but an approximation has already been given. The factor

analysis shows that a minor fraction of the Pb, V, Mn,and Ctotal concentrations in PM10 may also beattributed to the crustal component. The second factorshows a high correlation with the secondary species

(SO42�, NO3

� and NH4+). In addition, V (typically

associated with fuel combustion) is also a majorcomponent of this factor. The third factor clearly

represents the marine load as deduced from the highcorrelation with Na, Cl and Mg. Finally, the fourthfactor correlates with road traffic pollutants such as

vehicle exhaust products (Ctotal; Pb, Cl, Mn, NO3� and

NH4+) and in a minor proportion with road dust

components (Ti, Fe, P, Ca). The association of road

dust components with this factor, suggests that theanthropogenic mineral dust emission source is notincluded in the crustal factor.For PM2.5, the vehicular, secondary and crustal

sources contribute considerably to the PM variance, butsome slight differences in the chemical profiles of thesesources are observed. Thus, in contrast to what was

described for PM10, Pb and V are not included in thecrustal factor in PM2.5 (factor 2). This supports the 2.5–10 mm Pb and V crustal load in PM10. The PM2.5

vehicular load does not comprise fractions of road dustcomponents (Ca, Ti, and P), unlike the case of PM10,suggesting that this source is made up of only motorexhaust pollutants in PM2.5. The secondary source is

essentially the same in the PM10 and PM2.5, with thesole difference being that a minor fraction of K isassociated with the secondary component of PM2.5. The

marine load is not present in PM2.5 due to the coarsegrain size distribution of sea-salt particles (mainly in therange 2.5–10mm). The fourth PM2.5 factor (without

equivalent in PM10) is mainly associated with Na and P,but it was not possible to identify their emission source.It should be pointed out that the distinction between

the secondary and nitrate vehicular source obtained bythe principal component analysis may be attributed tothe different thermodynamic properties of sulphate andnitrate or to the presence of two emission sources.

A simple SAA of PM10 and PM2.5 based on thegrouping of the PM components (representing 83% ofthe PM mass) in accordance with the above sources

allowed us to estimate the contributions as follows:

* marine source:4% (2 mg m�3) in PM10 and negligible

in PM2.5.* crustal source: 26% (13 mgm�3) in PM10 and 8%

(2.7 mgm�3) in PM2.5.* vehicular-secondary: 54% (27 mgm�3) in PM10 and

73% (25mgm�3) in PM2.5.

Fig. 7. Correlation between sulphate and nitrate concentra-

tions versus ammonium levels in PM10 and PM2.5.

X. Querol et al. / Atmospheric Environment 35 (2001) 6407–6419 6415

* nondetermined: 17% (8.5 mgm�3) in PM10 and 17%(6 mgm�3) in PM2.5.

The SAA was also obtained by means of a multilinear

regression approach (Table 3). The interception constantusually introduced into the multilinear regression(representing the other nonidentified sources) was not

considered here because it was negative for most of thePM components, and consequently does not have aphysical meaning (Pio et al., 1998). This methodprovides information on the relative contributions

among the sources, but not necessarily on the absoluteconcentrations. If a component anti-correlates with thePMmass (such as the marine Na in this study) the model

interprets the source supplying this component as a PMsink, by yielding a negative PM contribution (see Table3). When comparing the results of the SAA obtained

using this procedure with those achieved from the simplegrouping of PM components (83% of the PM mass) in

accordance with the typical anthropogenic, crustal andmarine sources, the model overestimates the crustal and

anthropogenic contributions, and underestimates themarine source. The underestimated marine contributionand the fact that the load of nonidentified sources (17%of the PM mass) is distributed between the two other

sources, probably account for the crustal and anthro-pogenic overestimation of the model. Although theselimitations are present in the receptor modelling

methodologies, these techniques have been widely usedto produce SAA.

5. Conclusions

* Mean annual levels of PM measured at the kerbside

of L’H were high, but within the range of typicalroad traffic stations (APEG, 1999).

Fig. 8. Time series (June 1999 to June 2000) of levels of selected components in PM10.

X. Querol et al. / Atmospheric Environment 35 (2001) 6407–64196416

* Levels of PM2.5 and PM1 fit very well with thedaily cycle of gaseous traffic pollutants, whereas, in

the diurnal period, TSP and PM10 do not follow

the same trend. The ratio PM2.5/PM10 is directlydependent on the traffic emissions, whereas

additional contribution sources for the >10 mm

Table 3

Multilinear regression analysis applied to major and trace components of PM10 and PM2.5 using the absolutes score factors as

independents variablesa

PM10 PM2.5

Species Crustal Secondary Marine Vehicular Crustal Secondary Vehicular Industrial

PM 36 26 �4 46 5 46 54 �4Al 95 4 4 �2 77 �14 9 25

Ca 74 �1 �6 33 96 4 5 �8K 75 15 �2 16 35 32 55 �20Fe 65 2 �12 50 45 �3 51 10

P 60 15 �2 27 11 �10 10 84

Ti 76 1 3 23 73 �9 11 25

Sr 80 8 8 7 77 �6 25 1

Mn 59 7 �13 53 27 20 73 �16Mg 60 9 40 �9 68 �6 1 37

Na 18 13 82 �17 10 9 9 72

Cl �30 �19 76 79 �1 39 91 �15Ctotal 35 1 �18 86 1 �12 97 15

NO3� 2 48 �7 66 11 48 64 �10

SO2�4 total 24 72 6 �5 �8 69 �5 43

NH4+ �33 92 �13 61 �10 89 45 �12

Pb 37 �1 �22 93 3 �1 109 �7V 19 56 �2 29 �15 72 17 29

aValues are presented as percentage of mass attributed to each source (PM: particles mass).

Table 2

Factor loading matrix for PM10 and PM2.5 at L’H obtained from a principal component analysis and varimax rotationa

PM10 PM2.5

Species Factor 1 Factor 2 Factor 3 Factor 4 Factor 1 Factor 2 Factor 3 Factor 4

PM 0.549 0.472 0.639 0.740 0.519

Al 0.928 0.849 0.314

Ca 0.830 0.353 0.938

K 0.798 0.574 0.548 0.329

Fe 0.753 0.594 0.590 0.751

P 0.763 0.333 0.527

Ti 0.881 0.317 0.941

Sr 0.881 0.428

Mn 0.654 0.583 0.647 0.366

Mg 0.650 0.616 0.944

Na 0.914 �0.260 0.742

Cl 0.769 0.574 0.720

Ctotal 0.388 0.762 0.897

NO3� 0.571 0.633 0.673 0.543 �0.271

SO2�4 total 0.904 0.838 0.285

NH4+ 0.776 0.483 0.481 0.819

Pb 0.458 0.810 0.948

V 0.262 0.733 0.349 0.323 0.675

%Variance 36 15 11 22 26 26 15 7

Source Crustal Secondary Marine Vehicular Vehicular Crustal Secondary Industrial

aOnly factors loadings with moduli larger than 0.25 are shown (PM: particles mass). Factors having eigenvalues larger than 1 are

presented.

X. Querol et al. / Atmospheric Environment 35 (2001) 6407–6419 6417

fraction should be taken into account in the diurnalperiod.

* The SAA of PM10 and PM2.5 was based on ananalytical strategy, enabling us to determine around83% of the PM mass levels. This methodology

allowed us to quantify the marine (4% in PM10and o1% in PM2.5), crustal (26% in PM10 and 8%in PM2.5) and anthropogenic (54% in PM10 and73% in PM2.5) loads.

* Peaks of crustal contribution to PM10 (up to 44%)were recorded under Sahara air mass intrusions. Adifferent seasonal trend was obtained for levels of

sulphate and nitrate, probably as a consequence ofthe different thermodynamic behaviour of these PMspecies and the higher summer oxidation rate of SO2.

* The determination of the PM2.5/PM10 ratios mayassist in distinguishing pollution events from highcrustal PM episodes. Large differences between

simultaneous measurements of PM2.5 and PM10are mainly attributed to crustal and marine contribu-tions.

* In urban areas, the monitoring of PM2.5 permits the

control of the anthropogenic aerosols and diminishesthe interference of natural sources with respect toPM10 measurements. Furthermore, PM2.5 sampling

allows us to reduce the loss of potentially volatilecomponents such as ammonia and chloride.

Acknowledgements

This study has been supported by the Comisi !on

Interministerial de Ciencia y Tecnolog!ıa (AMB98-1044)and the Spanish Ministry of the Environment. Theauthors wish to thank the Department of the Environ-

ment from Catalonia for their collaboration in thisstudy, Dr. C.A. Pio, Dr. T.V. Nunes and Dr. A.Carvalho from the University of Aveiro (Portugal) for

their assistance in the receptor modelling methodologiesand thermo-optics analysis of black and organic carbon,and two anonymous referees for their valuable com-

ments.

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