2001. Chemical Characterization and Source Identification Apportionment of Fine and Coarse Air...

8/3/2019 2001. Chemical Characterization and Source Identification Apportionment of Fine and Coarse Air Particles

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Atmospheric Environment 36 (2002) 949961

Chemical characterization and source identification/

apportionment of fine and coarse air particles

in Thessaloniki, Greece

E. Manoli, D. Voutsa, C. Samara*

Environmental Pollution Control Laboratory, Department of Chemistry, Aristotle University of Thessaloniki, 540 06 Thessaloniki, Greece

Received 23 April 2001; received in revised form 10 September 2001; accepted 17 September 2001

Abstract

The distribution of air particulate mass and selected particle components (trace elements and polycyclic aromatic

hydrocarbons (PAHs)) in the fine and the coarse size fractions was investigated at a traffic-impacted urban site in

Thessaloniki, Greece. 7676% on average of the total ambient aerosol mass was distributed in the fine size fraction.

Fine-sized trace elemental fractions ranged between 51% for Fe and 95% for Zn, while those of PAHs were between

95% and 99%. A significant seasonal effect was observed for the size distribution of aerosol mass, with a shift to larger

fine fractions in winter. Similar seasonal trend was exhibited by PAHs, whereas larger fine fractions in summer were

shown by trace elements. The compositional signatures of fine and coarse particle fractions were compared to that of

local paved-road dust. A strong correlation was found between coarse particles and road dust suggesting strong

contribution of resuspended road dust to the coarse particles. A multivariate receptor model (multiple regression on

absolute principal component scores) was applied on separate fine and coarse aerosol data for source identification andapportionment. Results demonstrated that the largest contribution to fine-sized aerosol is traffic (38%) followed by

road dust (28%), while road dust clearly dominated the coarse size fraction (57%). r 2002 Elsevier Science Ltd. All

rights reserved.

Keywords: Air particles; Trace elements; Polycyclic aromatic hydrocarbons; Receptor models; Principal component analysis

1. Introduction

Recent concern about the health effects of air

pollution has focused on particulate matter (PM) andseveral epidemiological studies have indicated a strong

link between elevated particle concentrations and

increased mortality and morbidity (Dockery and Pope,

1994). Despite the drastic reduction of urban particulate

pollution in cities resulted from the improvement of coal

usage and the shift toward other fossil fuels (oil or

natural gas) for domestic heating, the densification of

the urban net combined with population growth and

increasing importance of traffic have contributed to

reinforce urban particulate pollution. Furthermore,

particles produced by cars are much smaller than coal

particles and found in the breathable size fraction.

A bimodal distribution of ambient aerosol has beenreported for many urban sites (Aceves and Grimalt,

1993; Lin et al., 1999; Lioy and Daisey, 1987). The

coarse fraction is mainly due to crustal material, paved-

road dust, non-catalyst equipped gasoline engines and

background sea salts. The fine fraction is a mixture of

primary and secondary aerosol emitted from anthro-

pogenic rather than natural sources or formed by

vapour nucleation/condensation mechanisms (Kleeman

and Cass, 1998; Hildemann et al., 1991). Current

research is focused on fine particles (PM2.5) because

they may be transported over long distances, penetrate

deep into the lungs and are also enriched with toxicants.

*Corresponding author. Fax: +30-31-997747.

E-mail address: [email protected] (C. Samara).

1352-2310/02/$ - see front matter r 2002 Elsevier Science Ltd. All rights reserved.

PII: S 1 3 5 2 - 2 3 1 0 ( 0 1 ) 0 0 4 8 6 - 1


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Since strategies to improve ambient air quality in

large urban centres typically involve the reduction of

emissions from primary sources, it is useful to be able to

observe the separate contributions that different sources

make to the ambient particle size distribution and

chemical composition. In recent years, receptor models

have been proved as being important tools for separatelyvisualizing source contributions to particulate air quality

(Lioy and Daisey, 1987; Ehrman et al., 1992; Thurston

and Spengler, 1985; Samara et al., 1994a, b). However,

relatively limited research has been done on the

identification and the apportionment of size-related

aerosol sources (Ehrman et al., 1992; Thurston and

Spengler, 1985; Kleeman and Cass, 1998; Miranda et al.,

1996; Swietlicki et al., 1996).

The objectives of this paper were: (a) to investigate the

distribution of particle mass and particle components,

such as trace elements and polycyclic aromatic hydro-

carbons (PAHs), in the fine and the coarse size range inan urban location, and (b) to use a suite of aerosol data

for source identification and apportionment. The

research was conducted at a traffic-affected site of the

city of Thessaloniki in northern Greece, in the frame-

work of a long-term research aiming at the investigation

of local air particles with respect to size distribution,

chemical composition and sources of emission (Viras

et al., 1991; Samara et al., 1990, 1994a,b, 1995;

Misaelides et al., 1993; Tsitouridou and Samara, 1993;

Kouimtzis et al., 1995; Samara and Tsitouridou, 2000).

2. Experimental

2.1. Site description

Thessaloniki (401620E, 221950N) is one of the most

densely populated cities in Greece and in Europe (16,000

inhabitants km2). It is a coastal city surrounded by

several stable residential communities while an extended

industrial zone is located north-westerly. Oil refining,

petrochemical, fertilizer and cement production, non-

ferrous metal smelting, iron and steel manufacturing,

truck and auto painting, metal recovery facilities,

electrolytic MnO2

production, anodized Al, scrap metalincineration, tire production and lubricating oil recovery

are the main industrial activities in the area.

The climate of Thessaloniki is temperate strongly

influenced by the sea breeze. Mean monthly values of

relative humidity range between 47% and 80%, while

those of temperature between 5.51C (in January) and

281C (in August). Prevailing wind directions are N/NW

(B25%), S/SW (B30%) and calms (B20%).

2.2. Sampling and analysis

Aerosol sampling took place in a residential/commer-

cial area, at a site located aside one of the citys busiest

roadways comprising six lanes (three each way). The

average traffic working-day rate at this site is 2800

vehicles/day. Although there is a continuing change

in the size and composition of motor fleet, at the time

of the study buses accounted for about 10% of the

overall fleet and taxis (diesel engined) for another 10%.

The rest were gasoline engined passenger cars, about40% fitted with catalytic converters. The sampler was

situated on the roof of an atmospheric pollution

monitoring station (B3 m a.g.l.) located 5 m aside the

closest lane.

Fine (o3mm) and coarse (310 mm) aerosol samples

were collected on 45 working days within the period

June 1994May 1995. A high-volume cascade impactor

(Sierra Instruments) providing a 50% cut-off point of

3.0 mm at 40 CFM was used for this purpose. Fine and

coarse particles were collected on glass fibre filters

(1000 1500 sheets and 500 700 slotted sheets, respec-

tively). All samplings had a 24 h duration. Loaded andunloaded filters were dried in a darkened desiccator for

24 h before weighing. Loaded filters were stored in the

dark, in aluminium foils, at 201C until extraction and

analysis could be completed.

Half of each loaded filter was ultrasonically treated

with a mixture of concentrated HNO3 and HCl for trace

element extraction (Samara et al., 1990). Extracts were

subsequently submitted to flameless (for Cr, Cd, Cu,

Mn, Pb, Zn, Fe, Ni, V) or hydride generation AAS (for

As), according to standard analytical procedures. The

analytical precision of all elemental species was better

than 10%.

The other half of the filter was used for PAH analysis.

The extraction procedure and the method of determina-

tion are described in detail elsewhere (Samara et al.,

1995; Papageorgopoulou et al., 1999). Briefly, filter

samples were ultrasonically extracted with acetonitrile

under recovery rates in the range 87102%. No further

clean-up was performed. Concentrated extracts were

analyzed by means of reversed-phase HPLC using

fluorescence detection. Separation was performed on a

5 mm Hypersil Green PAH column (100 4.5 mm2) with

corresponding guard cartridge. The mobile phase was a

CH3CNH2O gradient comprising 50% CH3CN over

5 min, 50100% CH3CN between 5 and 20min and100% CH3CN for 10 min. Five pairs of excitation and

emission wavelengths were used for detection. The

system was calibrated with 16 PAHs, 15 compounds

(acenaphthylene was omitted since it is only weakly

fluorescent) specified in the EPA Method 610 (US EPA,

1977), plus benzo[e] pyrene which is frequently used as a

reference PAH. The precision of all identified species

was better than 10% for peak height.

Road dust was collected from the road side near the

aerosol sampler. The fractiono2 mm was used for trace

element and PAH analysis employing the same methods

as for filter samples.

E. Manoli et al. / Atmospheric Environment 36 (2002) 949961950


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2.3. Data analysis techniques

Statistical analysis of data (analysis of variance,

correlation analysis, absolute principal component

analysis (PCA)) was performed using the SPSS statis-

tical software (SPSS Inc., 1998).

3. Results and discussion

3.1. Concentrations of fine and coarse-sized species

Tables 1 and 2 show a breakdown of the concentra-

tions of PM and individual particle components found

in each size fraction, while the seasonal variability of

concentrations is given in Table 3.

As shown, the total (fine+coarse) particle concentra-

tions measured were towards the highest values

reported for urban PM10 (Turnbull and Harrison,

2000; Ruellan and Cachier, 2001; Harrison and Jones,

1995; Balachandran et al., 2000; Kao and Friedlander,1995). Historically, Thessaloniki has been encountered

serious air-quality problems with air particles, with TSP

concentrations exceeding by far the annual limit of

150 mg m3. Although a 30% reduction has been

obtained during the last decade, current TSP levels are

still high. According to the Directive 1999/30/EC, from

1 January 2001 Member States are obliged to measure

Table 1

Particle mass (mg m3) and elemental concentrations (ng m3) in the fine and coarse fraction

Species Fine (N 45) Coarse (N 45) ra

Mean Median Min Max Mean Median Min Max

PM 97 102 15 174 30 32 6 62 0.699**

As 1.5 1.5 0.4 2.8 0.61 0.52 0.33 1.12 0.366*

Cr 4.8 3.6 1.2 18.8 2.9 1.9 0.4 21 0.184

Cd 0.87 0.70 0.15 3.90 0.11 0.10 0.05 0.40 0.429*

Cu 168 142 45 542 90 92 39 160 0.256

Mn 23 21 8 42 24 15 1 110 0.260

Pb 127 108 32 386 29 30 7 58 0.104

Zn 521 441 227 1631 25 24 4 56 0.167

Fe 297 300 80 529 291 306 52 568 0.431*

Ni 17 15 3.2 42 6.1 5.0 1.9 13 0.497*

V 41 27 3.0 116 6.9 5.2 1.3 27 0.024

aSpearman correlation coefficient between fine and coarse concentrations.

*Correlation significant at the 0.01 level.

**Correlation significant at the 0.05 level).

Table 2

PAH concentrations (ng m3) in the fine and coarse fraction

Species Fine (N 45) Coarse (N 45) ra

Mean Median Min Max Mean Median Min Max

Ph 1.81 1.49 0.46 7.54 0.08 0.06 0.01 0.24 0.376*

An 0.30 0.24 0.04 1.17 0.01 0.01 0.05 0.06 0.561**

Fl 6.15 5.02 1.00 17.24 0.12 0.10 0.02 0.27 0.574**Py 10.87 7.98 0.99 48.04 0.18 0.17 0.02 0.51 0.634**

B[a]An 1.85 1.41 0.25 5.97 0.02 0.01 0.05 0.08 0.748**

Chry 3.12 2.43 0.45 15.31 0.05 0.04 0.01 0.27 0.722**

B[e]Py 9.67 5.56 1.79 74.00 0.17 0.13 0.02 1.14 0.664**

B[b]Fl 2.77 2.40 0.53 8.96 0.04 0.03 0.01 0.14 0.696**

B[k]Fl 1.28 1.02 0.18 5.00 0.02 0.01 0.01 0.08 0.692**

B[a]Py 2.91 1.88 0.35 20.61 0.03 0.02 0.01 0.27 0.720**

dB[a;h] !An 0.67 0.47 0.10 3.82 0.01 0.01 0.05 0.04 0.675**B[ghi]Pe 6.58 5.11 1.23 26.00 0.10 0.08 0.01 0.36 0.734**

I[1,2,3-cd]Py 2.53 2.10 0.50 9.66 0.04 0.03 0.01 0.18 0.768**

aSpearman correlation coefficient between fine and coarse concentrations.

*Correlation significant at the 0.05 level.

**Correlation significant at the 0.01 level).

E. Manoli et al. / Atmospheric Environment 36 (2002) 949961 951


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ambient PM10 and PM2.5 concentrations and prepare

action plans for their reduction. A strict PM10 standard

(40 mg m3 annual average) has to be met by the year

2005 (EC, 1999), while a lower annual PM2.5 limit has

been recommended by the European Committee for

Standardization (20 mg m3, to be met by the year 2005,

CEN, 1997).

Trace elements and PAHs were found at concentra-

tions broadly consistent with those reported for various

urban European locations (Lee et al., 1994; Halsall et al.,

1994; Menichini et al., 1999). Mean Pb was below the

annual EC limit (500 ng m3), however, B[a]Py exceeded

the current standard set in Italy (1 ng m3, Valerio et al.,

1996). The profile of PAHs in fine and coarse aerosol has

been previously investigated by means of diagnostic

ratios and an overwhelming contribution of traffic

emissions was suggested (Samara et al., 1995).

The significant correlation found between fine and

coarse particle mass concentrations (Tables 1 and 2)suggests similar emission and dispersion processes for

the two modes. Significant, however, low correlation has

been reported for other sites (Burton et al., 1996). Fine

and coarse PAH concentrations were also strongly

correlated, particularly those of the heavier species,

underlying similar PAH sources in the two modes.

Moderate correlation was found between fine and coarse

Ni, Fe, Cd and As.

The mass concentrations of fine and coarse particle

fractions did not show significant seasonal variation

(Table 3). On the contrary, trace elements exhibited

variable seasonal trend. Concentrations of fine Cd, Cu,

Mn, Zn, Fe and V were significantly higher in summer

than in winter, whereas from the coarse elements only

Zn and Fe showed significant seasonal dependence.

When the mass proportion (ng mg1 aerosol) is con-

sidered, significant enrichment of fine particles with Cd,

Cu, Mn, Pb, Zn, Fe, Ni and V during summer is derived,

while coarse particles are also enriched in summer with

Zn and Fe. It could therefore be suggested that these

trace elements are preferably emitted from sources of the

warm period of the year. Concentrations of PAHs

bound to both fine and coarse particles showed a

significant seasonal trend with higher values during the

cold period. The reasons for that may be seasonal (as

their decay in the atmosphere will be slowest in the

winter months and also semi-volatile compounds will be

relatively enriched in particles due to lower temperatures

and high particle concentrations) and/or source-related

(due to the greater contribution of space heating and

road traffic in winter). On the contrary, the enrichmentof particles with PAHs was similar throughout the year.

Enrichment factors (EF) of trace elements in the fine

and coarse particle fraction relative to the earths crust

were calculated to indicate the extent of contribution of

sources other than natural crust to the ambient

elemental levels. The EF of an element E in an aerosol

sample is defined as

EF E atm=R atm

E crust=R crust;

where R is a reference element. There is no widely

accepted rule for the choice of the reference element; Si,

Table 3

Seasonal variability of particle mass and component concentrations

Species Fine Coarse

Colda

(N 34)

Warmb

(N 11)

Seasonal

variationcColda

(N 34)

Warmb

(N 11)

Seasonal

variationc

As (ngm3) 1.5 1.4 F 0.59 0.47 F

Cr (ngm3) 4.5 5.9 F 3.2 1.9 F

Cd (ngm3) 0.71 1.3 ** 0.12 0.10 F

Cu (ngm3) 135 270 ** 85 105 F

Mn (ngm3) 21 29 ** 27 14 F

Pb (ngm3) 122 141 F 29 29 F

Zn (ngm3) 408 872 ** 21 38 **

Fe (ng m3) 262 409 ** 263 377 *

Ni (ngm3) 15 21 F 6.4 5.2 F

V (ng m3) 35 60 * 7.0 6.7 F

SPAH (ng m3) 60 20 ** 0.97 0.51 *

PM (mg m3) 101 82 F 30 31 F

a

15 October 15 April.b15 April 15 October.c Insignificant at the 0.05 level.

*Significant at the 0.05 level.

**Significant at the 0.01 level.



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Al and Fe are usually used for this purpose. In this

study, Fe was used as reference, while the composition

of the earths crust was taken from Mason and Moore

(1982).

Results given in Fig. 1 show that Pb and Zn are the

most enriched elements in the fine size range followed by

Cd, Cu and As. For these elements non-crustal sourcesmay be suggested. Nevertheless, enrichment of crustal

components in fine particles can also occur as a result of

their transport to some distance before being removed

from the atmosphere by deposition processes (Kao and

Friedlander, 1995). Pb and Cu were the most enriched

elements in the coarse size fraction. On the other hand,

Mn and Cr showed low EF in both fractions thus

suggesting that crustal sources predominate in both size

ranges. Emissions from crustal sources are mostly in the

coarse particle size range, but 1130% of the PM10crustal mass has been reported as being in the fine

fraction (Kao and Friedlander, 1995). All elements, withimportant non-crustal sources (As, Cd, Cu, Pb, Zn, Ni,

V), exhibited lower EF values in the coarse mode. Lower

EF values with increasing particle size have been

reported for Pb, Zn, Ni and Cd (Bayens and Dedeur-

waerder, 1991; Santamaria et al., 1990; Chan et al.,

1997). On the other hand, Eleftheriadis and Colbeck,

1993, found increasing EFs with size for coarse V, Cu

and Cr with highest enrichment at around 10 mm and at

the very large sizes.

3.2. Distribution in the fine and coarse modes

The distribution of total particle mass and aerosol

components in the fine size range is presented in Fig. 2

for the cold and the warm time period. Fine particles

accounted for 7676% of the total PM, consistently to

reported values (5090%, Chan et al., 1997; Burton

et al., 1996; Ruellan and Cachier, 2001; Kao and

Friedlander, 1995). The fine particle fraction during

the cold period was slightly, yet significantly at the 0.05

level, higher than the corresponding summer fraction.

Similar seasonal trend was also observed in other works

(Chow et al., 1994; Harrison et al., 1997). Severalreasons could be responsible for that: higher strength of

fine particle emission sources (e.g. oil combustion for

heating and/or limitation of coarse particle resuspension

by wet precipitations during winter), aerosol dynamics

0

25

50

75

100

PM As Cr Cd C

uM

nPb Zn Fe Ni V Ph An Fl Py

B[]A

nCh

ry

B[e]Py

B[b]

Fl

B[k]F

l

B[]P

y

dB[

,h]A

n

B[gh

i]Pe

I[1,2,

3-cd

]Py

FIN

EFR

AC T

IO N

(% )

COLD WARM

Fig. 2. Distribution of particle mass and particle components in the fine size fraction.

0.1

1

10

100

1000

10000

Asfine

Asco

arse

Crfine

Crco

arse

Cufine

Cuco

arse

Cdfine

Cdco

arse

Pbfine

Pbco

arse

Mn

fine

Mn

coar

se

Znfine

Znco

arse

Vfin

e

Vco

arse

Nifine

Nico

arse

ENRICHMENTFACTOR

Fig. 1. EF (range and geometric mean) of fine and coarse-sized elements.



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(e.g. particle growth, dry and wet deposition) that

favour particle growth during summer, transfer of soil

dust with winds, etc. Lin et al. (1999) observed that the

distribution of ambient particles in Chicago between the

fine and the coarse mode was strongly dependent upon

the wind speed: low-speed winds raised the fine particle

fraction, whereas high-speed winds raised the coarseparticle fraction. Harrison et al. (1997) also reported

reduction of PM2.5 by increasing wind speed as a

dilution effect.

All elements which may be considered originating from

anthropogenic sources such as automotive traffic, oil

combustion or industrial processes (Pb, As, V, Ni, Zn,

Cd) exhibited a predominant occurrence in the fine

particle fraction (7095%). On the other hand, elements

such as Fe and Mn, which are also originating from

natural sources (soil dust) were almost equally distributed

in the two fractions. These findings are in general terms

consistent with the majority of the reported data (Chanet al., 1997; Santamaria et al., 1990; Lin et al., 1999; Rizio

et al., 1999; Harrison and Jones, 1995; Balachandran

et al., 2000). Nevertheless, different distributional data

have also been reported, as for example for Arnhem

aerosol, where the PM2.5 fractions of Zn and Cu

accounted only 25% and 10%, respectively, of the

corresponding PM10 fractions (Janssen et al., 1997),

and for aerosol in Long Beach, where the fine fraction of

Zn was found to be only 13% (Chow et al., 1994). A

seasonal effect significant at the 0.05 level was observed

on the distribution of Cd, Mn, and Ni, however, with

larger fine fractions in summer, contrarily to that

observed for particle mass. This is inconsistent with the

drift of the mass median diameters of trace elements (Ni,

Cu, Mn, Fe) to higher values in summer, which was

reported by Lyons et al. (1993) for Los Angeles.

Particle-bound PAHs were predominantly (9699%)

found in the fine size range despite the bimodal

distribution of particles. The proportion of PAHs in

fine particles was about one order of magnitude higher

than in coarse particles. The association of PAHs with

submicron ambient particles has been well documented

and attributed to their emission from combustion

processes (Cecinato et al., 1999; Poster et al., 1995;

Venkataraman and Friedlander, 1994; Aceves andGrimalt, 1993; Baek et al., 1991). Although no major

differences between low- and high-molecular weight

homologues were observed, a relative increase of the

lighter PAHs in the coarse size fraction is apparent and

might be attributed to differences in their sources, e.g. to

contributions from street dust which is dominated by

Ph, Fl and Py (Aceves and Grimalt, 1993) or to their

gas-particle partitioning behaviour (Venkataraman and

Friedlander, 1994).

The distribution of PAHs between the fine and the

coarse size fractions showed similar seasonal trend with

PM. In the cold period, the fine fraction of all PAH

species ranged between 96.1% and 98.4%, whereas in

the warm period it decreased slightly in the range 92.2

97.8%. This decrease was greater for the lower

molecular weight PAHs probably due to repartition

from the vapour phase onto larger particles under

certain conditions. Similar seasonal effect on the size

distribution of ambient PAHs has also been reported byother investigators (Aceves and Grimalt, 1993; Venka-

taraman and Friedlander, 1994; Baek et al., 1991).

Venkataraman and Friedlander (1994) observed that,

among the 5-ring and larger PAH species, B[a]Py

exhibited the strongest seasonal variability. The authors

attributed this variability to its greater reactivity in

comparison to the other non-volatile PAHs, that may

lead to a faster loss from the fine particle fraction due to

reactive decay in summer. In our study, the differences

between the winter and summer distributions for all

non-volatile PAHs were very low (0.21.1%), however,

the highest values were observed for the reactive B[a]Pyand dB[a;h]An.

3.3. Particle vs. road dust composition

Road dust has been reported as being important

contributor to airborne PM (Rogge et al., 1993; Ruellan

and Cachier, 2001). Resuspended by wind and vehicle-

induced turbulences, road dust particles from multiple

sources (automobile exhausts, lubricating oil residues,

tire and brake lining wear, street surface weathering, leaf

detritus, garden soil, etc.) are injected into the atmo-

sphere and redeposited. The chemical composition of

road dust, including trace metals and PAHs, has been

reported by several investigators (Hildemann et al.,

1991; Rogge et al., 1993).

The compositional signature of local road dust is

presented in Fig. 3 in comparison to the average

compositional signatures of fine and coarse particles.

All signatures were significantly correlated between each

other, however, the signature of road dust appeared to

be more strongly correlated to coarse particles

(r 0:568) than to fine particles (r 0:486). Thissuggests stronger contribution of road dust resuspension

to the coarse particle fraction.

3.4. Source identification and apportionment

Most source identification/apportionment applica-

tions have been based on inorganic aerosol components,

primarily trace elements often combined with ionic

components and/or gaseous pollutants (Kao and Fried-

lander, 1995; Lioy and Daisey, 1987; Ehrman et al.,

1992; Miranda et al., 1996; Swietlicki et al., 1996). The

potential use of PAHs as tracers of different combustion

sources has been explored by several investigators (Li

and Kamens, 1993; Lioy and Daisey, 1987). Three major

disadvantages make this use questionable: (a) different



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source categories have been found to provide similar or

overlapping fingerprints; (b) certain PAHs (e.g. B[a]An

and B[a]Py) have a relatively short atmospheric half-life

under photochemical smog conditions (Lioy and Daisey,

1987; Halsall et al., 1994; Schauer et al., 1996); (c) the

temperature-controlled vapour-particle partitioning of

the lower molecular weight PAHs (Ph, Fl, Py) can

probably affect PCA. (Harrison et al., 1996; Simcik et al.,

1999).

In the present study, source identification and

apportionment was performed on two separate data

sets combining trace elements and particle-bound PAHs.The model used consisted of first determining the

number and identity of sources using PCA with Varimax

rotation. Source contributions were calculated next by

using backward stepwise multiple regression of particle

mass concentration on the absolute principal component

scores (APCS) according to the equation:

Y r XP

j1

kiAPCSi;

where Y is the particle mass concentration, Ki is the ith

regression coefficient, p is the number of sources and r is

a constant representing the contribution from non-

specified sources. A detailed description of the modelling

approach can be found elsewhere (Thurston and

Spengler, 1985).

Tables 4 and 5 display the rotated PC loadings for the

fine and coarse aerosol fractions. Four PCs were

obtained with eigenvalues >1 summing almost 85% of

the total variance in the fine particle data set. The first

PC presented high loadings for the heavier PAHs (Chry

up to I[1,2,3-cd]Py) and for Pb, thus it was interpreted as

representing vehicle emissions (Harrison et al., 1996;

Simcik et al., 1999). The second PC was highly loadedon Ph, Fl, Py and B[a] An while showing moderate

loadings for benzo[b; k] fluoranthenes and I[1,2,3-cd]Py.Most of these PAHs have been reported as predominat-

ing in diesel particles (Harrison et al., 1996; Li and

Kamens, 1993). Therefore, PC2 was selected to represent

the diesel emission signal. The third PC was highly

loaded on Zn, Cu, Cd, Mn, Ni, Fe, Mn and Cr and was

interpreted as road dust. High loadings on Fe, Mn and

Cr reflect the bulk matrix of road dust which is soil,

while the correlation of the other metals indicate some

other sources as road dust, such as tire wear (source of

Zn), brakedrum abrasion (source of Fe), vehicular

Fig. 3. Compositional signatures of fine and coarse atmospheric particles and paved-road dust.



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Table 4

Rotated principal component matrix for fine aerosol (Factor loadings smaller than 70.225 are not given)

PC1 PC2 PC3 PC4

As F F F 0.778

Cr F F 0.553 0.690

Cu F F 0.792 FCd F F 0.802 F

Pb 0.593 F 0.601 F

Mn F F 0.747 0.476

Zn 0.396 F 0.801 F

V F F 0.295 0.758

Ni 0.279 0.320 0.712 0.229

Fe F 0.541 0.525 0.513

Ph 0.232 0.876 F F

An 0.640 0.510 F F

Fl 0.445 0.790 0.294 F

Py 0.376 0.843 F F

B[a] !An 0.551 0.718 F F

Chry 0.921 0.296 F F

B[e]Py 0.920 0.290 F F

B[b]Fl 0.893 0.395 F F

B[k]Fl 0.886 0.357 F F

B[a]Py 0.960 F F F

dB[a;h]An 0.948 F F FB[ghi]Pe 0.804 0.401 F F

I[1,2,3cd]Py 0.937 0.301 F F

Variance % 37.0 17.8 17.3 12.7

Source type Vehicle Diesel Road dust Fuel oil

Table 5

Rotated principal component matrix for coarse aerosol (factor loadings smaller than 70.225 are not given)

PC1 PC2 PC3 PC4 PC5

As 0.439 F 0.448 0.543 0.394

Cr F F 0.730 F 0.630

Cu F F 0.396 0.872 F

Cd F F F 0.644 F

Pb 0.297 F 0.617 F F

Mn F F 0.554 0.696 F

Zn F F 0.836 F F

V F F 0.725 0.252 F

Ni 0.338 0.387 F F 0.602

Fe F F 0.878 F F

Ph 0.340 0.818 0.291 F F

An 0.834 0.405 F F FFl 0.477 0.806 F F F

Py 0.354 0.812 0.251 F F

B[a] !An 0.423 0.630 0.424 F F

Chry 0.951 F F F F

B[e]Py 0.847 F F F F

B[b]Fl 0.857 F 0.225 F F

B[k]Fl 0.902 0.353 F F F

B[a]Py 0.971 0.266 F F F

dB[a;h]An 0.954 F F F FB[ghi]Pe 0.842 0.368 F F F

I[1,2,3-cd]Py 0.926 0.233 F F F

Variance % 37.4 14.0 13.3 13.3 7.2

Source type Vehicle Diesel Road dust Industrial Fuel oil



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Fig. 4. Mean source contribution to fine aerosol.



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Fig. 5. Mean source contribution to coarse aerosol.



11/13

emissions (source of Pb), diesel engines (source of Cu),

etc. (Samara et al., 1994a; Swietlicki et al., 1996;

Thurston and Spengler, 1985; Hildemann et al., 1991).

The fourth PC was loaded on V, As, Fe, Mn, Cr and

moderately on Ni. Arsenic is usually considered as coal

burning tracer, while oil combustion has been reported

as been characterized primarily by Ni and V andsecondarily by Fe and Mn (Harrison et al., 1996;

Swietlicki et al., 1996; Miranda et al., 1996; Samara

et al., 1994a). Given that coal burning in our area is very

limited occurring only in few small-sized industrial

activities (e.g. non-ferrous metal smelters), whereas oil

burning is used for both space heating and industrial

purposes, PC4 was selected to represent oil combustion.

In the coarse particle data set, five PCs were obtained

with eigenvalues >1 explaining 85% of the total

variance. Four of them, PC1, PC2, PC3 and PC5

showed similar loading patterns with PC1, PC2, PC3

and PC4 of the fine particle data set, thus they wereinterpreted as vehicle, diesel, road dust and oil combus-

tion source types, respectively. The fourth PC was

primarily loaded on Cu, Cd, Mn and As and secondarily

on V. This PC was interpreted as representing emissions

from several metallurgical activities occurring in the

industrial area (Samara et al., 1994a).

The multiple regression of aerosol and individual

constituent masses on APCS exhibited good correlation

between observed and predicted values with correlation

coefficients in the range 0.680.99. In cases that a

regression constant significant at the 0.05 level was

derived, it was interpreted as representing unidentified

sources. Mean source contributions to fine and coarse

aerosol are shown in Figs. 4 and 5, respectively. Results

showed that the largest contributor to aerosol mass in

the fine size fraction is traffic with a total contribution

38%, whereas road dust clearly dominated the coarse

size fraction. Vehicular emissions appeared to be the

unique source of fine-sized PAHs, whereas coarse-sized

PAHs were additionally originated from road dust, coal

fired non-ferrous metal smelters and oil combustion.

Road dust was found to be stronger contributor to fine

and coarse Pb than vehicular emissions. These findings

are different from those reported by Harrison et al.

(1996) for Birmingham, where road dust was unexpect-edly found to be the major contributor to PAHs, while

direct traffic emissions to Pb. As mentioned, sometimes

these two sources are incompletely separated by PCA as

road dust particles are resuspended mainly due to

vehicular movements (Harrison et al., 1996; Okamoto

et al., 1990). Lin et al. (1999), using B[e] Py as an

indicator, estimated the contribution of traffic to the

concentration of 47 ring PAHs in Birmingham city

centre at 8082%, while 6084% of the total PAH traffic

emissions was attributed to diesel vehicles. On the

contrary, non-catalytic gasoline vehicles were found to

be by far the largest contributor of PAHs in Los Angeles

basin, despite the overwhelming contribution of diesel

exhausts to the fine mass (33% vs. 6% for gasoline

vehicles) (Schauer et al., 1996).

The source apportionment findings of this study are in

general agreement with the majority of reported data,

which point out traffic emissions and traffic-induced

road dust resuspension as the major sources of fineurban aerosol with sum contributions up to 80%

(Okamoto et al., 1990; Miranda et al., 1996, 2000;

Harrison et al., 1996; Alonso et al., 1997; Schauer et al.,

1996).

The source types identified in this study are similar to

those found in a previous receptor modelling exercise

carried out in Thessaloniki, the relative contribution of

sources are, however, different since previous modelling

was not oriented to specific aerosol fractions but to total

suspended particles (Samara et al., 1994a, b). It is

believed that current apportionment results will be

useful to the local authorities to regulate air PM.

4. Conclusions

The distribution of particle mass, trace element and

particle-bound PAH concentrations in the fine

(o3.0 mm) and coarse (>3.0mm) size fractions was

investigated at a trafficked-site in Thessaloniki, Greece.

7676% on average of the total ambient aerosol mass

was distributed in the fine size fraction. Fine-sized trace

elemental fractions ranged from about 50% for Fe and

Mn up to 95% for Zn. The fine-sized PAH fractions

were between 95% and 99% for all species.

The size distribution of aerosol mass exhibited

significant seasonal dependence with a shift to larger

fine fractions in winter. Similar seasonal trend was

exhibited by PAHs, whereas larger fine fractions in

summer were shown by trace elements.

The compositional signature of local paved-road dust

was found to be strongly correlated to that of coarse

particles thus suggesting significant contribution of

resuspended road dust to this particle fraction.

A multivariate receptor model (multiple regression on

absolute principal component scores, MR/APCS) ap-

plied on separate fine and coarse aerosol data enabledmajor source types to be identified and apportioned:

gasoline and diesel emissions, road dust, metallurgical

processes and oil combustion. Traffic was found to be

the largest contributor to fine-sized aerosol (total

contribution 38%) followed by road dust (28%). Road

dust clearly dominated the coarse size fraction (57%).

Acknowledgements

The authors wish to thank the Secretariat for Science

and Technology, Ministry of Development, and the



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Organization for the Master Plan Implementation and

Environmental Protection of Thessaloniki for research

funding. They are also grateful to Prof. V. Simeonov,

Kliment Ohridski University, Sofia, Bulgaria for

useful comments on receptor modelling.

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