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Atmospheric Environment 39 (2005) 5947–5958

www.elsevier.com/locate/atmosenv

Elemental composition and reflectance of ambient fineparticles at 21 European locations

Thomas Gotschia,�, Marianne E. Hazenkamp-von Arxb, Joachim Heinrichc,Roberto Bonod, Peter Burneye, Bertil Forsbergf, Deborah Jarvise,Jose Maldonadog, Dan Norbackh, Willem B. Sterni, Jordi Sunyerj,Kjell Torenk, Giuseppe Verlatol, Simona Villanim, Nino Kunzlia,b

aUniversity of Southern California, Los Angeles, USAbInstitute of Social and Preventive Medicine, University of Basel, Switzerland

cGSF National Research Center for Environment and Health, Institute of Epidemiology, Neuherberg, GermanydDepartment of Public Health and Microbiology, University of Turin, Italy

eDepartment of Public Health Sciences, Kings College London, UKfDepartment of Public Health and Clinical Medicine, Umea University, Sweden

gHospital Juan Ramon Jimenez, Huelva, SpainhDepartment of Occupational and Environmental Medicine, University Hospital Uppsala, Sweden

iGeochemical Laboratory, Institute for Mineralogy and Petrography, University of Basel, SwitzerlandjInstitut Municipal d Investigacio Medica (IMIM), Barcelona, SpainkDepartment of Occupational Medicine, Goteborg University, Sweden

lDepartment of Medicine and Public Health, University of Verona, ItalymDepartment of Health Science, University of Pavia, Italy

Received 2 November 2004; accepted 19 June 2005

Abstract

We sampled fine particles (PM2.5) over a 1-year period at 21 central urban monitoring sites in 20 cities of the

European Community Respiratory Health Survey (ECRHS). Particle filters were then analysed for elemental

composition using energy dispersive X-ray fluorescence spectrometry and reflectance (light absorption). Elemental

analyses yielded valid results for 15 elements (Al, As, Br, Ca, Cl, Cu, Fe, K, Mn, Pb, S, Si, Ti, V, Zn).

Annual and seasonal means of PM2.5, reflectance, and elements show a wide range across Europe with the lowest

levels found in Iceland and up to 80 times higher concentrations in Northern Italy. This pattern holds for most

of the air pollution indicators. The mass concentration of S did constitute the largest fraction of the analysed

elements of PM2.5 in all locations. The crustal component varies from less than 10% up to 25% across these

cities. Temporal correlations of daily values vary considerably from city to city, depending on the indicators

compared. Nevertheless, correlations between estimates of long-term exposure, such as annual means, are generally

high among indicators of PM2.5 from anthropogenic sources, such as S, metals, and reflectance. This highlights the

e front matter r 2005 Elsevier Ltd. All rights reserved.

mosenv.2005.06.049

ing author. Tel.: +1323 442 1234; fax: +1 323 442 3272.

ess: gotschi@usc.edu (T. Gotschi).

ARTICLE IN PRESST. Gotschi et al. / Atmospheric Environment 39 (2005) 5947–59585948

difficulty to disentangle effects of specific sources or PM constituents in future health effect analyses using annual

averages.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: Air pollution; PM2.5; Elements; Reflectance; Long-term exposure assessment

1. Introduction

Epidemiological studies have successfully used measures

of particulate matter, i.e. the mass concentration (mgm�3)

of particles with a median aerodynamic diameter of 2.5 or

10mm (PM2.5, PM10), to investigate associations between

air pollution and human health (Brunekreef and Holgate,

2002; US EPA, 2003; WHO, 2003). However, PM2.5 is an

unspecific measure for a mixture of scores of particulate air

pollutants, predominantly but not exclusively originating

from combustion processes. Therefore, a primary goal of

air pollution research is aimed towards identifying culprit

agents of air pollution to understand and prevent adverse

health outcomes. Besides physical aspects such as particle

number, size, or surface, the chemical composition of

particles is likely to play a crucial role (WHO, 2003).

Airborne particulate matter is a mixture of thousands of

different substances, diverse in such critical characteristics

as their solubility, persistence in the atmosphere and in

human tissue, reactivity, toxicity and carcinogenicity, as

well as their chemical structure and elemental composition.

A second incentive to more specifically characterize

ambient particulate pollution is to identify sources of

emissions to be targeted by policies. PM2.5 mass is not

source-specific and its composition can vary significantly

in time and space, primarily due to variations in sources,

emission strength, meteorological conditions, physical

processes, and chemical reactions. Besides combustion

processes, including traffic and industrial emissions,

natural sources such as wind-blown dust or sea spray

can contribute to PM2.5 levels. Further characterization

of PM2.5 samples therefore is needed to attribute health

effects to specific pollution sources.

The European Community Respiratory Health Survey

(ECRHS) is a cohort study addressing, among others, the

long-term effects of air pollution on respiratory illnesses

and lung function in adults of 20 European cities (Fig. 1)

(European Community Respiratory Health Group,

2002). We measured PM2.5 and nitrogen dioxide (NO2)

concentrations over a 1-year period at a central monitor-

ing location in each study centre. In addition we

measured the reflectance and analysed the elemental

composition of the particle samples. PM2.5 and NO2

ambient concentrations have been described previously

(Hazenkamp-von Arx et al., 2003, 2004).

The purpose of this report is to describe the elemental

composition and reflectance of this large set of PM2.5

samples. We first describe the annual and seasonal

means for each location. We then describe the correla-

tions between PM2.5 and elements and reflectance within

each city and discuss different patterns across Europe.

Ultimately, we provide the cross-community correla-

tions of the annual means of all particle metrics. The

correlations across the study centres are of particular

relevance when using community mean levels to

compare health conditions across centres. While highly

correlated constituents can be used as surrogates for

each other, their independent contribution to health

effects cannot be disentangled.

2. Methods

A standardized PM2.5 protocol was implemented and

has been described in detail (Hazenkamp-von Arx et al.,

2003). Briefly, in each of the 21 study centres we used

identical equipment (Basel-Sampler from BGI, Inc.;

Gelman Teflo filters), procedures, and sampling and

storage schemes. In each centre, a central monitoring

site was chosen, either at a pre-existing air monitoring

station, or in collaboration with local air monitoring

authorities. Monitors in Italy and Antwerp City were

located close to roads, which is likely to have affected

some of the metrics. Between June 2000 and December

2001, we sampled 7 days over a 2-week period during

each month, yielding 84 days over a 1-year period.

Weekday samples were exposed 24 h, whereas weekends

were captured on single filters exposed for 48 h (in total

72 filters per centre). The predetermined sampling days

were the same for all centres. Overall, more than 1600

samples were collected. All filters were weighed in the

same laboratory. Reweighing of selected filters demon-

strated a high reliability of the weighing process and

analyses of blank filters did not suggest the need for

adjustment for filter contamination (Hazenkamp-von

Arx et al., 2003). Given the restricted sampling schedule

of 84 days per year, we expect our annual mean

estimates to fall within a 10% margin of error from a

hypothetical true mean based on daily measurements

(unpublished data).

PM2.5 filter samples were analysed for 26 different

chemical elements, using energy dispersive X-ray fluor-

escence spectrometry (ED-XRF), a non-destructive

method we previously applied in the EXPOLIS study

(ED-XRF, Geochemical Laboratory, Institute of Miner-

alogy and Petrography, Basel University/CH-4056

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Fig. 1. Locations of the 21 study centres of ECRHS (note: 2 centres in Antwerp, City and South).

T. Gotschi et al. / Atmospheric Environment 39 (2005) 5947–5958 5949

Basel) (Mathys et al., 2001). ED-XRF is capable of

detecting elements with an atomic number above

Z ¼ 11, but is neither able to analyze low atomic

number elements like H, C, N or O, nor to perform

chemical speciation. Elemental analyses provided accu-

rate results for 14 elements (Al, As, Br, Ca, Cl, Cu, Fe,

K, Mn, Pb, Si, Ti, V, Zn). Measurements of sulphur

were highly correlated with declared sulphur contents of

standard materials (rPearson ¼ 0:98); however, concentra-tions were systematically overestimated by ED-XRF. A

correction factor of 0.42 was derived from parallel

analyses of 12 filters using ion chromatography (ICP)

and applied to ED-XRF sulphur measurements. Cali-

bration for Mg, Na, and P showed low correlations

(rPearsono0:8) between different standards, impeding the

interpretation of the measured values. For 7 elements

(Bi, Cd, Co, Cr, Ga, Ni, Se,) concentrations were too

low on most filters to be detected reliably. Iodine could

not be analysed due to methodological problems. More

details on quality assurance of the method are published

elsewhere (Mathys et al., 2001, 2002).

Since carbon cannot be detected with ED-XRF,

reflectance of the filters was measured and the absorp-

tion coefficient (Abs) calculated. We used a standard

method (Reflectometer EEL model 43; Diffusion Sys-

tems Ltd., London, UK) to measure reflectance, which

has been applied and described earlier (Gotschi et al.,

2002). Repeated reflectance measurements of 78

filters showed an average relative difference of 1.2%.

Elemental carbon (EC) is the dominant light absorbing

substance in airborne particulate matter; therefore,

reflectance can be used as a surrogate measure for EC.

ARTICLE IN PRESST. Gotschi et al. / Atmospheric Environment 39 (2005) 5947–59585950

In urban settings reflectance can be considered a diesel-

specific traffic indicator, since several studies estimated

that in urban settings the major fraction of EC originates

from diesel combustion (66–96%) (Schauer, 2003).

The specific elements measured on PM2.5 will be

briefly described. Sulphur (S) is assumed to represent a

background portion of PM2.5, mainly consisting of

sulphate particles (SO42�), which are oxidation products

formed from sulphur dioxide (SO2) emissions during

long-range transportation in the atmosphere. Lead (Pb)

and bromine (Br) may reflect aspects of traffic emissions.

However, since Pb was banned from gasoline, the origin

of airborne Pb is less clear, possibly stemming from re-

suspension of road dust, brake abrasion, industrial

emissions, and waste incineration (Lee et al., 1994; de

Miguel et al., 1997; Chiaradia and Cupelin, 2000;

Lammel et al., 2002). Bromine is thought to be mainly

emitted by vehicles, though other sources, i.e. fossil fuel

combustion, incineration, sea spray, or crustal material,

exist (Lee et al., 1994; Lammel et al., 2002). Other

metals, particularly iron (Fe), copper (Cu), and zinc

(Zn), are of toxicological interest, since these transition

metals may play a crucial role in the oxidative stress

pathway, hypothesized to be part of the causal explana-

tion of many observed air pollution-related health

effects (Gilliland et al., 1999). Aluminium (Al), calcium

(Ca), and silicon (Si) are the main components of

geogenic matter or crustal material (Andrews et al.,

1996; Press and Siever, 1997). Chlorine (Cl) is a

significant contributor to PM2.5 mass. Sources of

chlorine are sea salt particles, salt particles from street

de-icing, industrial emissions of hydrochloric acid (HCl),

and emissions from waste incineration (US Environ-

mental Protection Agency, 1990). Potassium (K) is

associated with biogenic aerosols from wood combus-

tion, pollen, and spores (Matthias-Maser and Jaenicke,

1994). Vanadium (V) is a trace element emitted during

the combustion of fossil fuels, such as coal and

vanadium-rich fuel oil (WHO, 2000). Titanium (Ti) is

used as a pigment in paints (TiO2) and in metal alloys. It

is abundant in the Earth’s crust. The main sources of Ti

contamination in the general environment are the

combustion of fossil fuels and the incineration of

titanium-containing wastes (WHO, 1984). Major

sources of arsenic (As) are nonferrous metal smelters

and power plants burning arsenic-rich coal (WHO,

2000). Manganese (Mn) emissions can be increased near

foundries and where ferro- and silico-manganese in-

dustries are present (WHO, 2000).

All data in this manuscript are presented as measures

of air concentrations (mgm�3 for PM2.5, ngm�3 for

all elements, and absorption coefficient *105m�1 for

reflectance).

For descriptive purposes, annual and seasonal means

are presented. For calculation of means, single filter data

were weighed by exposure time with each month

assigned equal weight (Hazenkamp-von Arx et al.,

2004). Winter is defined as November to February,

and summer as May to August. Coefficients of variance

(standard deviation/mean) presented for the annual

means reflect the variability of the monthly means.

To compare the composition of PM2.5 across centres,

percent of accounted mass of PM2.5 (for elements only)

and within city Pearson correlations were calculated.

Spearman correlations yielded very similar results and

are therefore not presented. To assess whether indicators

will provide independent information for health ana-

lyses, Pearson correlation coefficients between annual

means were calculated across centres.

Overall, data completeness was high yielding more

than 80% of the scheduled sampling time in all cities,

except for Verona where technical problems occurred

(data completeness 37% of scheduled sampling time).

Therefore, data for Verona need to be interpreted with

caution.

3. Results

The range of annual mean concentrations across the 21

study centres is large for all indicators, showing ratios

between the 90th and the 10th percentile from 2.4 for

sulphur to 11.1 for zinc. As can be seen from Table 1, the

majority of indicators show the highest annual means in

Turin. Exceptions are indicators of crustal material and

some trace metals (Cu, Ti, V, Zn) which show the highest

concentrations in some of the Spanish centres and in

Grenoble. Pavia, located in the same air shed as Turin,

the plain of the river Po (see Fig. 1), showed similarly

high levels. Barcelona and, to a lesser extent, Antwerp

City and Paris are in the range of the Italian centres for

presumably traffic-related indicators (Abs, Pb, Br), but

show significantly lower levels of PM2.5 and sulphur.

Reykjavik in Iceland and the Swedish centres Umea and

Uppsala showed the lowest concentrations for most

indicators, particularly for those associated with anthro-

pogenic activities. In Reykjavik, sulphur concentrations

were more than ten times lower than in Turin, and the

difference for reflectance was more than 40-fold (0.1 vs.

4.3 abs. coeff. m�1).

In general, similar pollution levels have been observed

for centres located in the same regions, such as the three

Swedish (Gothenburg, Umea, Uppsala), the two British

(Ipswich, Norwich), or the three Italian centres

(Pavia, Turin, Verona), respectively. The two centres

in Antwerp, one in the city centre (Antwerp City) and

the other in the southern suburbs (Antwerp South),

11.5 km apart, do suggest pronounced differences for

reflectance within the same city (2.9 vs. 1.7 abs. coeff.

105m�1). Earlier, we reported a comparable difference

in annual mean NO2 for the same two locations (58 vs.

26mgm�3) (Hazenkamp-von.Arx et al., 2004).

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Table 1

Annual means (coefficient of variance) for each centre and indicator

PM2.5 Abs Al As Br Ca Cl Cu Fe K Mn Pb S Si Ti V Zn

Antwerp city 24.1 2.9 177 7.4 5.1 87 1113 9.7 127 182 6.9 28.6 1465 363 5.3 6.8 52.4

(0.60) (0.35) (0.49) (1.23) (0.87) (0.34) (1.29) (0.69) (0.46) (0.79) (0.62) (0.72) (0.34) (0.48) (0.47) (0.59) (0.85)

Antwerp South 20.8 1.7 128 6.3 4.7 46 892 6.3 66 182 5.1 25.8 1453 263 3.6 5.7 45.4

(0.44) (0.44) (0.46) (0.90) (0.71) (0.41) (1.11) (0.97) (0.46) (0.66) (0.54) (0.71) (0.30) (0.58) (0.61) (0.59) (0.86)

Albacete 13.1 1.4 344 1.6 3.9 277 280 4.6 49 350 2.0 11.3 1009 730 5.6 2.7 12.2

(0.25) (0.23) (0.64) (0.53) (0.45) (0.50) (1.22) (0.73) (0.54) (0.50) (0.28) (0.54) (0.36) (0.60) (0.58) (0.40) (0.64)

Barcelona 22.2 3.1 389 12.5 12.1 226 831 21.3 145 430 10.4 52.7 1388 686 19.0 9.0 80.5

(0.34) (0.36) (0.51) (0.51) (0.76) (0.19) (0.97) (0.52) (0.32) (1.41) (0.68) (0.56) (0.34) (0.25) (0.67) (0.29) (0.49)

Basel 17.4 1.7 151 3.6 5.3 60 472 6.5 78 255 3.3 13.5 1039 299 3.1 1.6 32.9

(0.52) (0.27) (0.49) (0.68) (0.51) (0.26) (1.06) (0.34) (0.24) (0.60) (0.36) (0.34) (0.41) (0.34) (0.34) (0.28) (0.43)

Erfurt 16.3 1.7 148 4.5 2.2 52 329 5.0 72 157 3.1 14.8 1144 313 2.8 0.8 38.5

(0.58) (0.43) (0.41) (0.89) (0.53) (0.30) (1.77) (0.44) (0.41) (0.70) (0.42) (0.96) (0.47) (0.39) (0.43) (0.33) (1.02)

Galdakao 16.3 1.9 197 8.6 3.8 199 411 17.9 166 191 23.0 39.0 1585 453 4.0 9.6 149.7

(0.36) (0.23) (0.45) (0.66) (0.37) (0.34) (0.75) (0.57) (0.33) (0.46) (0.42) (0.55) (0.63) (0.42) (0.38) (0.84) (0.61)

Grenoble 19.0 2.6 257 5.8 3.4 139 667 16.7 125 326 10.7 23.2 888 1404 5.5 3.3 185.0

(0.47) (0.36) (0.71) (0.72) (0.59) (1.12) (1.48) (0.56) (0.63) (0.62) (0.71) (0.53) (0.28) (0.65) (0.62) (0.35) (0.81)

Gothenburg 12.6 1.0 97 2.1 2.2 38 540 4.2 52 113 2.7 5.2 903 217 2.4 3.9 15.9

(0.27) (0.29) (0.43) (0.37) (0.34) (0.49) (0.99) (0.60) (0.43) (0.45) (0.42) (0.71) (0.45) (0.57) (0.50) (0.48) (0.43)

Huelva 17.3 1.4 444 12.2 4.9 168 806 26.6 76 297 2.9 26.9 1558 1259 17.1 6.7 40.9

(0.27) (0.26) (0.48) (0.77) (0.30) (0.33) (0.84) (0.61) (0.37) (0.47) (0.34) (0.53) (0.57) (0.43) (1.06) (0.49) (0.63)

Ipswich 16.5 1.3 115 6.4 4.7 37 1147 4.6 41 201 3.3 18.8 999 165 4.5 5.6 22.4

(0.41) (0.39) (0.60) (0.93) (0.72) (0.33) (0.92) (0.78) (0.39) (0.90) (0.56) (1.06) (0.47) (0.31) (1.00) (0.85) (0.60)

Norwich 16.2 1.6 108 4.3 3.9 92 1027 3.2 42 116 2.6 13.6 977 204 2.6 4.5 15.0

(0.32) (0.25) (0.31) (0.57) (0.56) (1.09) (0.76) (0.67) (0.32) (0.36) (0.57) (0.55) (0.40) (0.39) (0.34) (0.77) (0.42)

Oviedo 15.9 2.1 467 6.2 7.1 281 562 9.0 138 232 6.4 22.9 1181 781 7.4 5.5 31.1

(0.23) (0.36) (0.25) (0.51) (0.48) (0.37) (0.79) (0.35) (0.29) (0.28) (0.42) (0.37) (0.56) (0.33) (0.27) (0.32) (0.20)

Pavia 35.3 2.9 228 9.2 11.1 85 963 9.4 124 364 9.9 37.4 1783 539 8.2 4.2 47.0

(0.57) (0.34) (0.34) (0.64) (0.68) (0.39) (1.23) (0.37) (0.36) (0.68) (0.96) (0.58) (0.23) (0.35) (0.55) (0.31) (0.78)

Paris 17.8 2.4 141 3.7 4.1 79 668 10.0 98 180 4.4 15.7 1081 321 3.9 2.2 40.1

(0.39) (0.21) (0.50) (0.45) (0.70) (0.28) (0.94) (0.36) (0.34) (0.49) (0.56) (0.49) (0.39) (0.52) (0.42) (0.42) (0.47)

Reykjavik 3.7 0.1 111 0.9 1.2 41 936 1.7 23 29 0.5 2.6 155 245 3.0 0.4 2.2

(0.45) (0.61) (0.63) (0.17) (0.61) (0.47) (0.88) (0.58) (0.62) (0.44) (0.78) (1.12) (0.56) (0.57) (0.73) (0.31) (0.55)

Tartu 14.8 1.6 156 2.5 2.2 85 289 2.7 32 386 2.8 8.6 892 367 2.7 1.3 32.6

(0.34) (0.31) (0.88) (0.57) (0.63) (0.71) (0.98) (0.60) (0.49) (0.55) (0.52) (0.65) (0.28) (0.95) (0.93) (0.44) (0.48)

Turin 44.9 4.3 380 14.4 21.3 116 1322 23.1 262 471 13.3 63.8 1827 744 8.5 3.6 70.1

(0.50) (0.24) (0.27) (0.48) (0.70) (0.26) (1.08) (0.45) (0.40) (0.69) (0.59) (0.53) (0.24) (0.28) (0.31) (0.40) (0.60)

Umea 5.6 0.6 70 1.1 1.2 22 160 2.4 25 63 1.2 3.0 415 172 1.6 0.9 6.2

(0.21) (0.40) (0.66) (0.50) (0.45) (0.09) (1.26) (0.76) (0.45) (0.33) (0.45) (0.86) (0.38) (0.75) (0.77) (0.56) (0.51)

Uppsala 10.4 1.0 103 1.8 1.7 34 225 4.5 54 116 1.9 5.0 752 247 2.0 1.4 14.9

(0.43) (0.33) (0.44) (0.58) (0.53) (0.46) (1.03) (0.51) (0.56) (0.79) (0.59) (0.86) (0.50) (0.49) (0.43) (0.55) (0.49)

Veronaa 41.5 4.2 336 19.4 22.9 257 1099 22.1 302 411 30.7 80.1 2015 759 8.7 1.9 135.5

(0.42) (0.21) (0.29) (0.42) (0.39) (0.36) (0.87) (0.25) (0.29) (0.56) (0.67) (0.27) (0.45) (0.52) (0.32) (0.44) (0.53)

PM2.5 in mgm�3, reflectance (Abs) in absorption coefficient 105m�1, and elements in ngm�3. Mean values based on less than 50% of

filter values above the limit of detection are printed in italic and should be interpreted with caution. Values from Verona are based on

37% of the scheduled sampling time only and should also be interpreted cautiously.aAnnual mean based on 37% of scheduled sampling time only.

T. Gotschi et al. / Atmospheric Environment 39 (2005) 5947–5958 5951

ARTICLE IN PRESS

Fig. 2. Seasonal patterns of silicon (squares), reflectance (triangles), and sulphur (dots) chosen to represent particles from crustal

material, traffic, and long-range background pollution, respectively. Centres are grouped into geographical regions. Levels of all three

measures have been rescaled so that means equal the mean of PM2.5. Y-scale reflects PM2.5 levels in mgm�3.

T. Gotschi et al. / Atmospheric Environment 39 (2005) 5947–59585952

Temporal variability of monthly means within cities

(coefficients of variance in Table 1) indicates a fairly

consistent pattern across the centres, showing somewhat

lower month-to-month variation for reflectance, and a

tendency towards elevated variation for rarer elements,

as compared to PM2.5, S, Fe, or Ca. Coefficients of

variance are exceptionally high for chlorine. Fig. 2

visualizes regional and local differences in the month-to-

month variation for silicon, reflectance, and sulphur,

chosen to represent crustal, traffic, and secondary

particles, respectively.

For most elements, there is an expected pattern of

higher concentrations during winter months; however,

some deviations occur (Table 2). For example, crustal

indicators, such as Al, Ca, Fe, or Si, are often higher in

summer, especially in Spain, although the pattern is not

entirely consistent. In Galdakao, for all indicators

except bromine and chlorine, levels are higher in

summer than in winter. Chlorine levels are much lower

in summer than in winter in all centres. Similarly to

PM2.5, sulphur levels are increased during winter in

several centres but in many centres sulphur concentra-

tions also increase steadily over the summer months to

reach peak values around August, leading to higher

summer means, when compared to winter means. This

phenomenon seems to be most pronounced in the 5

Spanish centres.

Among the presented elements, sulphur, by far,

accounts for the highest proportion of PM2.5 mass,

ranging from 4.6% in Reykjavik to 8.8% in Galdakao

(average of single filters). Al, Cl, and Si are the only

remaining elements accounting on average for more

than 1% of the PM2.5 mass (range 0.3–3.0%, 1.5–22.9%,

1.3–8.9%, respectively). As, Br, Mn, Ti, and V account

for less than 0.1% of the PM2.5 mass in the majority of

the centres.

Table 3 provides Pearson coefficients for correlations

of daily values of PM2.5 and the other indicators within

each city. Barcelona, Turin, Pavia, Grenoble, followed

by Erfurt and Paris, are the centres with the highest

correlations between PM2.5 and reflectance, Pb, Br, and

Fe, respectively. PM2.5 and sulphur are highly correlated

ARTICLE IN PRESS

Table 2

Winter means (upper row) and summer means (lower row) for each centre and indicator

PM2.5 Abs Al As Br Ca Cl Cu Fe K Mn Pb S Si Ti V Zn

Antwerp citya 37.0 3.9 216 15.0 9.6 113 2725 14.5 187 319 11.5 45.0 1621 393 7.0 9.0 96.2

17.6 2.4 142 3.0 3.5 77 287 8.8 98 108 4.8 24.2 1442 310 4.4 5.9 30.5

Antwerp South 24.4 2.1 133 10.0 7.1 51 1774 9.8 82 260 7.2 37.5 1205 213 3.9 7.3 72.2

17.3 1.4 101 5.3 2.0 40 288 3.4 47 124 3.5 15.5 1581 284 3.1 4.8 27.4

Albacete 15.4 1.7 206 2.5 3.8 205 678 6.7 33 546 1.6 12.0 714 420 3.5 2.4 17.7

11.5 1.2 499 1.0 4.3 405 38 3.3 61 236 2.2 12.1 1195 1064 7.5 2.9 7.6

Barcelona 30.2 4.2 379 18.3 22.5 249 1704 26.8 190 401 17.4 75.0 1298 728 23.5 9.4 105.9

20.0 2.5 512 9.2 4.5 209 237 23.2 124 715 6.5 42.3 1890 767 21.0 10.5 60.6

Basel 23.7 2.1 197 6.7 7.6 66 1044 6.6 85 397 4.2 14.9 1074 262 3.4 1.8 43.2

13.7 1.6 147 2.2 3.6 65 52 7.5 83 174 2.9 13.7 1251 354 3.2 1.5 29.7

Erfurt 19.9 2.1 150 6.5 2.7 63 782 4.9 80 219 3.7 22.3 1019 325 3.2 0.8 61.0

11.0 1.2 164 1.7 1.4 47 21 3.8 59 98 2.4 4.6 1221 346 2.8 1.0 12.0

Galdakaoa 10.8 1.6 119 7.2 4.1 141 507 10.6 127 136 16.1 25.2 673 266 2.6 3.0 72.8

20.9 1.9 238 7.6 3.3 231 179 19.9 169 253 22.6 38.7 2571 526 4.5 11.8 178.9

Grenoble 28.0 3.4 322 9.4 5.3 121 1714 19.9 176 512 16.3 33.5 882 914 6.6 3.5 319.2

12.9 1.8 205 3.6 2.4 209 75 18.0 75 229 5.0 19.3 1005 1365 4.8 2.8 109.3

Gothenburg 12.5 1.2 84 2.2 2.6 40 891 5.1 56 113 3.0 5.0 869 183 2.0 3.4 20.2

11.2 0.9 113 1.6 2.1 45 410 4.2 49 65 2.5 5.0 826 255 3.3 5.3 9.3

Huelva 17.2 1.7 469 16.5 5.5 186 1419 30.7 85 393 3.7 29.1 882 1250 13.0 5.0 54.8

16.9 1.1 491 10.5 4.8 170 206 28.9 80 202 2.6 29.4 2191 1428 24.9 9.2 42.9

Ipswich 21.3 1.8 162 11.0 8.4 41 2418 5.9 50 335 3.6 32.2 896 153 7.3 3.7 34.7

15.0 1.0 97 4.2 2.2 36 218 2.8 37 119 3.4 9.3 1350 189 2.8 10.0 14.4

Norwicha 17.7 2.0 134 6.3 6.4 209 1811 3.5 51 160 4.2 19.5 736 232 3.0 2.8 17.9

14.6 1.3 97 2.2 2.6 49 247 3.7 36 85 1.7 12.0 1255 207 2.3 7.0 12.7

Oviedo 17.5 3.0 483 9.0 10.5 387 996 7.5 117 206 6.3 27.9 766 746 7.5 5.8 35.6

16.7 1.5 485 3.2 4.7 213 142 11.0 166 272 6.4 22.0 1901 843 7.8 6.2 29.2

Pavia 55.3 4.1 226 16.1 19.6 99 2175 11.4 167 655 18.8 61.5 1907 508 8.7 4.9 83.6

19.9 2.1 280 4.7 3.6 87 77 6.7 103 181 5.3 18.6 1928 655 11.0 3.7 29.2

Paris 21.0 2.5 112 4.1 6.3 76 1309 11.5 97 221 5.0 18.4 885 219 3.1 2.3 42.6

15.9 2.3 169 3.8 2.0 95 158 10.5 111 166 4.8 11.8 1363 398 4.9 2.2 39.8

Reykjavik 4.8 0.1 155 0.8 1.5 56 1515 1.5 30 30 0.8 1.1 91 298 4.2 0.4 1.6

3.3 0.2 79 0.9 1.2 34 618 1.7 21 32 0.3 4.7 216 214 3.1 0.4 2.1

Tartu 15.6 1.8 68 3.2 2.3 30 538 2.8 19 433 2.6 9.7 865 120 0.9 1.7 37.0

10.2 1.1 131 1.4 1.0 120 30 1.4 31 160 1.8 3.1 709 341 2.4 0.7 16.3

Turin 69.2 5.4 449 21.7 38.0 137 3015 29.6 379 876 21.9 100.9 2095 850 11.3 5.2 121.9

23.0 3.3 303 7.0 7.4 88 115 15.2 167 205 5.9 34.6 1875 643 6.5 2.5 35.3

Umea 5.8 0.8 50 1.4 1.1 22 332 2.5 21 71 1.4 1.8 397 117 1.0 0.7 7.5

4.9 0.4 66 0.9 0.9 21 56 0.9 20 41 0.7 2.1 334 183 1.4 0.8 3.0

Uppsala 11.5 1.2 78 2.5 2.1 26 346 5.5 50 139 2.4 5.6 826 176 1.5 1.3 18.5

7.2 0.7 95 0.9 0.8 30 189 2.3 35 47 1.0 1.7 529 234 1.8 1.1 6.6

Veronab 51.0 4.7 381 24.7 27.8 319 1859 26.0 358 554 46.8 94.4 2167 1006 10.2 1.3 191.2

16.0 2.6 154 5.1 6.3 92 47 13.0 142 148 8.7 43.7 1402 240 3.3 2.1 92.9

PM2.5 in mgm�3, reflectance (Abs) in absorption coefficient 105m�1, and elements in ngm�3. Mean values based on less than 50% of

filter values above the limit of detection are printed in italic and should be interpreted with caution.aWinter mean based on less than 80% of scheduled sampling time (Antwerp City:60%, Galdakao:66%, Norwich:64%).bWinter and summer mean based on less than 80% of scheduled sampling time (43%, 18%, resp.).

T. Gotschi et al. / Atmospheric Environment 39 (2005) 5947–5958 5953

ARTICLE IN PRESS

Table 3

Within-city Pearson correlation coefficients between PM2.5 and all other indicators

Abs Al As Br Ca Cl Cu Fe K Mn Pb S Si Ti V Zn

Antwerp city 0.63 0.57 0.75 0.71 0.33 0.67 0.43 0.59 0.84 0.74 0.67 0.85 0.32 0.70 0.38 0.75

Antwerp South 0.86 0.49 0.51 0.42 0.41 0.53 0.30 0.66 0.64 0.42 0.41 0.74 0.19 0.54 0.60 0.46

Albacete 0.55 0.25 0.30 0.30 0.21 0.36 0.12 0.23 0.57 0.27 0.11 0.56 0.21 0.26 0.57 0.19

Barcelona 0.79 0.48 0.69 0.81 0.50 0.79 0.59 0.76 0.32 0.59 0.74 0.55 0.49 0.57 0.58 0.67

Basel 0.68 0.24 0.56 0.51 0.19 0.74 0.16 0.56 0.87 0.64 0.32 0.83 0.16 0.46 0.71 0.66

Erfurt 0.81 0.49 0.87 0.54 0.50 0.81 0.52 0.70 0.92 0.77 0.79 0.80 0.39 0.62 0.21 0.84

Galdakao 0.61 0.56 0.45 0.27 0.55 �0.05 0.40 0.52 0.76 0.53 0.42 0.89 0.55 0.57 0.64 0.45

Gothenburg 0.39 0.44 0.42 0.49 0.20 0.10 0.03 0.14 0.65 0.38 0.50 0.81 0.24 0.27 0.35 0.48

Grenoble 0.81 0.23 0.78 0.74 0.09 0.86 0.47 0.72 0.90 0.70 0.75 0.55 0.06 0.26 0.48 0.78

Huelva 0.64 0.20 0.32 0.65 0.39 �0.02 0.24 0.29 0.64 0.47 0.41 0.79 0.40 0.11 0.68 0.31

Ipswich 0.80 0.57 0.58 0.78 0.11 0.67 0.51 0.57 0.56 0.39 0.55 0.73 0.50 0.51 0.40 0.78

Norwich 0.73 0.54 0.33 0.65 0.08 0.30 0.36 0.67 0.64 0.46 0.26 0.80 0.27 0.48 0.51 0.55

Oviedo 0.48 0.61 0.42 0.53 0.35 0.10 0.35 0.53 0.73 0.27 0.43 0.72 0.57 0.64 0.61 0.62

Paris 0.62 0.34 0.69 0.65 0.49 0.45 0.38 0.70 0.84 0.48 0.54 0.75 0.20 0.49 0.50 0.54

Pavia 0.78 0.13 0.81 0.81 0.32 0.90 0.40 0.71 0.83 0.71 0.83 0.56 0.07 0.11 0.36 0.84

Reykjavik �0.05 0.31 0.03 0.29 0.55 0.70 0.00 0.18 0.52 0.26 �0.11 0.42 0.17 0.18 0.01 �0.06

Tartu 0.83 0.19 0.69 0.38 0.02 0.64 0.17 0.28 0.85 0.58 0.47 0.76 0.16 0.16 0.50 0.81

Turin 0.73 0.45 0.70 0.82 0.42 0.91 0.52 0.75 0.86 0.75 0.76 0.56 0.34 0.51 0.84 0.79

Umea 0.50 0.10 0.43 0.27 0.02 �0.06 0.10 0.16 0.74 0.45 0.03 0.87 �0.02 �0.07 0.48 0.64

Uppsala 0.64 0.30 0.66 0.43 0.17 0.08 0.13 0.26 0.69 0.62 0.25 0.85 0.15 0.27 0.65 0.57

Veronaa 0.80 0.43 0.82 0.83 0.61 0.77 0.78 0.82 0.90 0.57 0.84 0.65 0.38 0.61 0.39 0.57

Correlation coefficients above 0.7 are bolded.aBased on 28 filters only.

T. Gotschi et al. / Atmospheric Environment 39 (2005) 5947–59585954

in two thirds of the centres; however Barcelona, Turin,

and Pavia are now among the centres with lower

correlations (r � 0:55). PM2.5 and K show correlations

above 0.7 in 11 centres.

We also investigated pairwise within-city correlations

between all other indicators. Correlations are highest

between Al and Si, and Al and Ti, which show Pearson

coefficients greater than 0.9 in 11 and 9 centres,

respectively. Correlations between Pb and Br are above

0.7 in 16 of the centres. In Barcelona, Turin, and Pavia,

Pb and Br are also correlated with reflectance (r40:7).Also, Pearson correlation coefficients are often higher

than 0.7 for reflectance and Fe (13 centres), Fe and Mn

(13), Si and Ti (13), and Zn and Pb (10). Correlation

tables for each city are available upon request.

Pairwise correlations of annual means across the 21

centres are mostly above 0.8 among markers of

predominantly anthropogenic pollution, i.e. S, Abs, Br,

Pb, As, Fe, and PM2.5, as well as among crustal elements,

i.e. Al, Ca, Si, and Ti (Table 4). In contrast, correlations

are low between anthropogenic and crustal indicators. In

addition, correlations appear somewhat weaker between

Cl, K, Mn, V, Zn and many other indicators. Similar

patterns were observed for seasonal means with correla-

tions slightly lower in summer (data not shown). Results

from the elemental analysis are available in a report

(http://www.ecrhs.org/reports.htm).

4. Discussion

These first trans-European PM2.5 speciation data show

that PM2.5 composition and levels of constituents vary

significantly across Europe, with levels of some toxic

components, i.e. Zn, being up to 80 times higher in

Northern Italy as compared to Iceland. The overall

pattern confirms a north–south pollution gradient across

Europe observed by others (Hamilton and Mansfield,

1991; Pacyna et al., 1991; Hoek et al., 1997; Roemer

et al., 2000). In general, pollution levels were higher in

larger cities and in centres where climate and topography

enhanced accumulation of air pollution, such as in Turin,

Barcelona, and Antwerp. In contrast, areas of low

population density and exposed to small amounts of

pollution from long-range transport, such as Reykjavik

and the northern Swedish centres Umea and Uppsala,

showed the lowest pollution levels for all constituents.

Annual means for crustal indicators deviate consider-

ably from the PM2.5 pattern across centres, most notably

in the Spanish cities. Assuming the likely, although

simplified, chemical structures of crustal compounds in

PM2.5 (i.e. SiO2; Al2O3; 50% CaCO3, 50% CaSO4), this

fraction could make up for 13–25% of the PM2.5 mass in

the Spanish centres, and 24% in the alpine centre

Grenoble, while in the other centres it typically accounts

for less than 10%.

ARTICLE IN PRESS

Table 4

Pearson correlation coefficients of annual means across cities

PM2.5 Abs Al As Br Ca Cl Cu Fe K Mn Pb S Si Ti V

Abs 0.93 1

Al 0.47 0.54 1

As 0.84 0.82 0.64 1

Br 0.91 0.87 0.58 0.88 1

Ca 0.30 0.45 0.84 0.51 0.45 1

Cl 0.64 0.54 0.23 0.62 0.60 0.03 1

Cu 0.63 0.69 0.74 0.87 0.66 0.56 0.40 1

Fe 0.85 0.90 0.58 0.86 0.89 0.54 0.47 0.76 1

K 0.73 0.76 0.71 0.67 0.72 0.58 0.28 0.64 0.63 1

Mn 0.68 0.72 0.38 0.78 0.72 0.54 0.32 0.66 0.88 0.49 1

Pb 0.88 0.89 0.60 0.96 0.93 0.54 0.59 0.80 0.94 0.71 0.86 1

S 0.87 0.81 0.56 0.86 0.75 0.46 0.48 0.72 0.78 0.66 0.70 0.85 1

Si 0.34 0.44 0.80 0.51 0.34 0.64 0.14 0.74 0.45 0.62 0.34 0.43 0.38 1

Ti 0.41 0.46 0.80 0.70 0.51 0.57 0.36 0.79 0.42 0.62 0.29 0.59 0.53 0.64 1

V 0.18 0.25 0.38 0.43 0.14 0.39 0.33 0.50 0.25 0.20 0.31 0.39 0.50 0.24 0.55 1

Zn 0.46 0.60 0.33 0.58 0.41 0.43 0.18 0.67 0.68 0.46 0.80 0.63 0.49 0.60 0.27 0.35

Correlation coefficients above 0.7 are bolded.

T. Gotschi et al. / Atmospheric Environment 39 (2005) 5947–5958 5955

Elevated winter levels of indicators associated with

anthropogenic activities were observed in several cen-

tres, most dramatically in Northern Italy and Antwerp,

where inversion layers are a common phenomenon.

Persistent thermal inversions, combined with low wind

velocities, cause an accumulation of exhaust emissions,

while at the same time the amount of wind-blown

particles from crustal origin is likely to be reduced.

Nevertheless, crustal elements may be increased due to

the accumulation of resuspended road dust.

On the other hand, the warm and dry summer climate

on the Iberian Peninsula, which favours the formation and

lift-up of dust particles, is likely to account for the

increased concentrations of crustal particles in the Spanish

centres, as compared to centres with cooler, more humid

climates. Saharan dust considerably affects this region as

well (Rodriguez et al., 2002, 2004; Viana et al., 2003;

Alastuey et al., 2004). Increased sulphur levels over the

summer months, observed most notably in Spain, are

consistent with atmospheric conversion of sulphur dioxide

(SO2) into sulphate particles (SO42�) taking place more

rapidly in warm air masses, as described in other studies

(Querol et al., 2001; Tanner et al., 2004). As mentioned,

calibration procedures and method comparisons revealed

that ED-XRF sulphur concentrations had to be corrected;

thus the absolute levels need to be interpreted with care.

Relative comparisons between measurements, however,

are not affected.

The extreme seasonal differences in chlorine levels are

a unique finding. These results may not only represent

differences in actual concentrations, or emission levels

but may have methodological reasons. The warmer

summer climate may lead to enhanced evaporation of

hydrochloric acid (HCl) from the filters surface, which

can be formed there by various mechanisms, such as

reactions of acids or ozone with salt particles (NaCl)

(Yao et al., 2001). We also observed strong losses of Cl

on an exposed control filter that was analysed 191 times

for its elemental composition at a temperature of 40 1C

and under a partial vacuum. Cl concentration decreased

from �1600 to �800 ng cm�2 over the first 10 measure-

ments. This, and a similar observation for bromine,

indicates that evaporation of halogenated semi-volatile

organic compounds may have taken place (Mathys et

al., 2002). Although the temperature of 40 1C during the

elemental analysis clearly exceeds outdoor temperatures

even during summer, sampling warmer air may similarly

lead to increased Cl losses due to evaporation from the

filters during summer months, as compared to winter.

Although evaporation of Cl, in some way, is probably

the most important factor, it remains unclear to what

extent increased temperature, ozone levels, differences in

emissions (e.g. street de-icing), or other unknown factors

are responsible for the large differences of Cl observed

between summer and winter levels.

Increased city-to-city variation for reflectance and

other indicators, as compared to regionally fairly homo-

geneous levels for sulphur, probably reflects the more

relevant contribution of locally produced emissions,

including emissions close to the sampling locations. Five

of our monitors are located within 15m of a main street

(Antwerp City, Basel, Pavia, Turin, Verona). Several

studies could show strong concentration gradients for

traffic-related pollutants with increasing distance from

ARTICLE IN PRESST. Gotschi et al. / Atmospheric Environment 39 (2005) 5947–59585956

roadways (Janssen et al., 1997; Roorda-Knape et al.,

1998; Zhu et al., 2002a, b). As a consequence, traffic-

related PM constituents may be oversampled by these

monitors. The reported levels, therefore, reflect exposure

conditions for people living along comparable roads but

not necessarily exposure of the population at large.

Within cities, PM2.5 and sulphur are mostly highly

correlated, since sulphates are a major component of

PM2.5. However, these correlations appear lower where

primary pollution sources become more important, such

as in large cities with high levels of traffic-related

pollutants. The correlations of silicon and aluminium

reflect a relatively constant proportion between SiO2 and

Al2O3 found in crustal material in many areas of Europe

(Putaud et al., 2004). The highly correlated lead and

bromine used to have a major common source in

gasoline. It is unclear though, to what extent this still is

reflected in our data, or whether residues resulting from

road dust or industrial activities are responsible for the

observed correlations. In addition, the correlation

between reflectance and Fe remains unclear. While

reflectance is an indicator for traffic-related soot

emissions, Fe could reflect crustal components of road

dust re-suspended by vehicles, or particles generated

during combustion in engines. Harrison et al. 2003

reported moderate correlations between Fe and NOx,

which they used as an indicator of traffic pollution at a

roadside location.

The diversity of temporal within-city correlation

patterns is a result of the variety of centre specific

characteristics of pollution sources and meteorology and

highlights their relevance for short-term air pollution

levels.

The observed differences in the PM composition

within and across centres may also reflect variability in

PM toxicity, which is not reflected in the PM mass

concentration alone. We hypothesize that the hetero-

geneity in PM composition may partly explain differ-

ences in the PM concentration-response estimates

observed in time-series analyses of mortality and

morbidity in the APHEA study (Le Tertre et al., 2002;

Aga et al., 2003).

A goal within ECRHS is to investigate long-term

effects of air pollution, using the 2000/01 annual means

as the default surrogate for long-term exposure. Despite

the observed within-city diversity of daily and seasonal

patterns for the various indicators, which may reflect

pollution from different origin, our data show that

collinearity among annual means of pollutants com-

pared across cities is large in most cases. Given these

high correlations across cities, it will be difficult to

interpret associations with health effects independently

for the various correlated constituents in cross-commu-

nity comparisons.

Two main factors are likely to be responsible for the

correlations of the long-term estimates across cities

observed for most measured constituents of PM2.5.

First, all study centres are urban areas and thus may

lead to relatively similar mixes of anthropogenic

emission sources across the cities. In contrast to the

absolute amount of emissions, the relative contribution

of the major anthropogenic sources, i.e. traffic, industry,

and domestic emissions, to a city’s total emissions may

not be strongly affected by the size and density of the

city. Second, apart from the total emissions, local

meteorology has a major impact on ambient concentra-

tions. Since it affects many pollutants similarly, it not

only introduces short-term collinearity between pollu-

tants, but also determines their long-term average levels.

Consequently, cities with adverse weather patterns such

as frequent inversion layers will have higher pollution

levels for all pollutants, whereas others with weather

more favourable to pollution dispersion will have lower

levels for all pollutants.

The observation of high correlations among various

constituents does not necessarily imply that population

mean exposures are equally well characterized for each

constituent. Within-city spatial variability may largely

differ among various constituents (Roosli et al., 2001;

Zhu et al., 2002a, b). For indicators which are rather

heterogeneously distributed in space, within-city con-

trasts in exposure, and hence misclassification in

exposure, may be of similar magnitude as the between-

city variability, therefore jeopardizing the investigation

of health effect contrasts across cities. To assess effects

of such heterogeneously distributed constituents, expo-

sure data collected at various locations within cities

would be needed.

A further limitation of our data with regard to their

use as estimates of long-term exposures is the uncer-

tainty to which extent levels measured in 2000/01 may

represent past exposures. Only limited historic air

pollution data are available (Fernandez et al., 2005;

Naef and Xhillari, 2000), indicating more dramatic

changes for S than for example for NO2. We hypothe-

size, though, that for many pollutants the ranking across

the 20 cities did not change dramatically over the past

one or two decades. Erfurt, located in the former

German Democratic Republic, will likely have to be

exempt from this assumption due to the radical changes

in emission sources since the German reunification

(Heinrich et al., 2000).

In summary, we found a substantial diversity of PM

composition patterns across the 21 centres which was

not reflected in the aggregated annual mean estimates of

anthropogenic pollutants. Long-term exposure estimates

for many constituents appear to be driven by common

factors, such as meteorology and population density,

resulting in highly correlated annual means. Conse-

quently, the ability to assess independent contributions

of PM constituents to various long-term health effects in

this study will be limited.

ARTICLE IN PRESST. Gotschi et al. / Atmospheric Environment 39 (2005) 5947–5958 5957

Acknowledgements

We thank Dr. Patrick Mathys (Geochemical Labora-

tory, Institute for Mineralogy and Petrography, Uni-

versity of Basel, Switzerland) for the elemental analyses

of all filter samples.

This work forms part of the ECRHS II project,

funded by the European Commission (Quality of Life

Programme, Environment and Health Key Action;

Project number: QLK4-CT-1999-01237) and by the

Swiss Federal Agency for Education and Science

(BBW-No. 99.0200). T. Gotschi was funded by an

EPA STAR Fellowship. N. Kunzli, head of the air

pollution unit of ECRHS had a Swiss National Science

Foundation Advanced Scientist Fellowship (PROSPER

32-048922.96) and is now supported by the National

Institute of Environmental Health Sciences (Grant

number P30ES07048) and the Hastings Foundation.

The Swedish Environment Protection Agency (SNAP

Project), the Vlaamse Milieu Maatschappij (Dr. E.

Roekens), local authorities and other foundations

supported this study with funds and equipment.

Members of the ECRHS Working Group Air Pollu-

tion and Health: Michael Abramson, Ursula Ackermann-

Liebrich, Lucy Bayer-Oglesby, Roberto Bono, Peter

Burney, Roberto de Marco, Bertil Forsberg, Thorarinn

Gislason, Thomas Gotschi, Marianne E. Hazenkamp,

Joachim Heinrich, Deborah Jarvis, Joost Weyler, Nino

Kunzli, Linnea Lillienberg, Christina Luczynska, Jose

Maldonado, Inga Mills, Dan Norback, Felix Payo Losa,

Albino Poli, Michela Ponzio, Argo Soon, Jordi Sunyer,

Kjell Toren, Giuseppe Verlato, Simona Villani.

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