Elemental composition and reflectance of ambient fine particles at 21 European locations

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Atmospheric Environment 39 (2005) 5947–5958 www.elsevier.com/locate/atmosenv

Elemental composition and reflectance of ambient fine particles at 21 European locations Thomas Go¨tschia,, Marianne E. Hazenkamp-von Arxb, Joachim Heinrichc, Roberto Bonod, Peter Burneye, Bertil Forsbergf, Deborah Jarvise, Jose Maldonadog, Dan Norba¨ckh, Willem B. Sterni, Jordi Sunyerj, Kjell Tore´nk, Giuseppe Verlatol, Simona Villanim, Nino Ku¨nzlia,b a

University of Southern California, Los Angeles, USA Institute of Social and Preventive Medicine, University of Basel, Switzerland c GSF National Research Center for Environment and Health, Institute of Epidemiology, Neuherberg, Germany d Department of Public Health and Microbiology, University of Turin, Italy e Department of Public Health Sciences, Kings College London, UK f Department of Public Health and Clinical Medicine, Umea University, Sweden g Hospital Juan Ramon Jimenez, Huelva, Spain h Department of Occupational and Environmental Medicine, University Hospital Uppsala, Sweden i Geochemical Laboratory, Institute for Mineralogy and Petrography, University of Basel, Switzerland j Institut Municipal d Investigacio Medica (IMIM), Barcelona, Spain k Department of Occupational Medicine, Go¨teborg University, Sweden l Department of Medicine and Public Health, University of Verona, Italy m Department of Health Science, University of Pavia, Italy b

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

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

E-mail address: [email protected] (T. Go¨tschi). 1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.06.049

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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 (mg m
3) of particles with a median aerodynamic diameter of 2.5 or 10 mm (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 monitoring 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 correlations between PM2.5 and elements and reflectance within each city and discuss different patterns across Europe. Ultimately, we provide the cross-community correlations 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 demonstrated 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 fluorescence spectrometry (ED-XRF), a non-destructive method we previously applied in the EXPOLIS study (ED-XRF, Geochemical Laboratory, Institute of Mineralogy 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).

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 accurate 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, concentrations 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. Calibration for Mg, Na, and P showed low correlations (rPearson o0:8) 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 absorption coefficient (Abs) calculated. We used a standard method (Reflectometer EEL model 43; Diffusion Systems Ltd., London, UK) to measure reflectance, which has been applied and described earlier (Go¨tschi 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.

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In urban settings reflectance can be considered a dieselspecific 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 (SO2
4 ), 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 resuspension 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 explanation 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 Environmental Protection Agency, 1990). Potassium (K) is associated with biogenic aerosols from wood combustion, 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 industries are present (WHO, 2000). All data in this manuscript are presented as measures of air concentrations (mg m
3 for PM2.5, ng m
3 for all elements, and absorption coefficient *105 m
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 analyses, 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 anthropogenic 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. 105 m
1). Earlier, we reported a comparable difference in annual mean NO2 for the same two locations (58 vs. 26 mg m
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 Antwerp city

Al

As

Br

24.1 2.9 177 7.4 5.1 (0.60) (0.35) (0.49) (1.23) (0.87)

Antwerp South 20.8 1.7 128 6.3 4.7 (0.44) (0.44) (0.46) (0.90) (0.71)

Ca

Cl

Cu

Fe

K

Mn

Pb

S

Si

Ti

V

Zn

87 1113 9.7 127 182 6.9 28.6 1465 (0.34) (1.29) (0.69) (0.46) (0.79) (0.62) (0.72) (0.34)

363 5.3 6.8 (0.48) (0.47) (0.59)

52.4 (0.85)

46 892 6.3 (0.41) (1.11) (0.97)

66 182 5.1 25.8 1453 (0.46) (0.66) (0.54) (0.71) (0.30)

263 3.6 5.7 (0.58) (0.61) (0.59)

45.4 (0.86)

Albacete

13.1 1.4 344 1.6 3.9 277 (0.25) (0.23) (0.64) (0.53) (0.45) (0.50)

280 4.6 (1.22) (0.73)

49 350 2.0 11.3 1009 (0.54) (0.50) (0.28) (0.54) (0.36)

730 5.6 2.7 (0.60) (0.58) (0.40)

12.2 (0.64)

Barcelona

22.2 3.1 389 12.5 12.1 226 (0.34) (0.36) (0.51) (0.51) (0.76) (0.19)

831 21.3 145 430 10.4 52.7 1388 (0.97) (0.52) (0.32) (1.41) (0.68) (0.56) (0.34)

686 19.0 9.0 (0.25) (0.67) (0.29)

80.5 (0.49)

Basel

17.4 1.7 151 3.6 5.3 (0.52) (0.27) (0.49) (0.68) (0.51)

60 (0.26)

472 6.5 (1.06) (0.34)

78 255 3.3 13.5 1039 (0.24) (0.60) (0.36) (0.34) (0.41)

299 3.1 1.6 (0.34) (0.34) (0.28)

32.9 (0.43)

Erfurt

16.3 1.7 148 4.5 2.2 (0.58) (0.43) (0.41) (0.89) (0.53)

52 (0.30)

329 5.0 (1.77) (0.44)

72 157 3.1 14.8 1144 (0.41) (0.70) (0.42) (0.96) (0.47)

313 2.8 0.8 38.5 (0.39) (0.43) (0.33) (1.02)

Galdakao

16.3 1.9 197 8.6 3.8 199 (0.36) (0.23) (0.45) (0.66) (0.37) (0.34)

411 17.9 166 191 23.0 39.0 1585 (0.75) (0.57) (0.33) (0.46) (0.42) (0.55) (0.63)

453 4.0 9.6 149.7 (0.42) (0.38) (0.84) (0.61)

Grenoble

19.0 2.6 257 5.8 3.4 139 (0.47) (0.36) (0.71) (0.72) (0.59) (1.12)

667 16.7 125 326 10.7 23.2 (1.48) (0.56) (0.63) (0.62) (0.71) (0.53)

Gothenburg

12.6 1.0 (0.27) (0.29)

Huelva

17.3 1.4 444 12.2 4.9 168 (0.27) (0.26) (0.48) (0.77) (0.30) (0.33)

Ipswich

16.5 1.3 115 6.4 4.7 (0.41) (0.39) (0.60) (0.93) (0.72)

Norwich

16.2 1.6 108 4.3 3.9 (0.32) (0.25) (0.31) (0.57) (0.56)

Oviedo

97 2.1 2.2 (0.43) (0.37) (0.34)

38 540 4.2 (0.49) (0.99) (0.60)

217 2.4 3.9 (0.57) (0.50) (0.48)

15.9 (0.43)

76 297 2.9 26.9 1558 1259 17.1 6.7 (0.37) (0.47) (0.34) (0.53) (0.57) (0.43) (1.06) (0.49)

40.9 (0.63)

37 1147 4.6 (0.33) (0.92) (0.78)

41 201 3.3 18.8 (0.39) (0.90) (0.56) (1.06)

999 (0.47)

165 4.5 5.6 (0.31) (1.00) (0.85)

22.4 (0.60)

92 1027 3.2 (1.09) (0.76) (0.67)

42 116 2.6 13.6 (0.32) (0.36) (0.57) (0.55)

977 (0.40)

204 2.6 4.5 (0.39) (0.34) (0.77)

15.0 (0.42)

15.9 2.1 467 6.2 7.1 281 (0.23) (0.36) (0.25) (0.51) (0.48) (0.37)

562 9.0 138 232 6.4 22.9 1181 (0.79) (0.35) (0.29) (0.28) (0.42) (0.37) (0.56)

781 7.4 5.5 (0.33) (0.27) (0.32)

31.1 (0.20)

Pavia

35.3 2.9 228 9.2 11.1 (0.57) (0.34) (0.34) (0.64) (0.68)

85 (0.39)

963 9.4 124 364 9.9 37.4 1783 (1.23) (0.37) (0.36) (0.68) (0.96) (0.58) (0.23)

539 8.2 4.2 (0.35) (0.55) (0.31)

47.0 (0.78)

Paris

17.8 2.4 141 3.7 4.1 (0.39) (0.21) (0.50) (0.45) (0.70)

79 (0.28)

668 10.0 (0.94) (0.36)

98 180 4.4 15.7 1081 (0.34) (0.49) (0.56) (0.49) (0.39)

321 3.9 2.2 (0.52) (0.42) (0.42)

40.1 (0.47)

Reykjavik

3.7 0.1 111 0.9 1.2 (0.45) (0.61) (0.63) (0.17) (0.61)

41 936 1.7 (0.47) (0.88) (0.58)

23 (0.62)

29 0.5 2.6 155 (0.44) (0.78) (1.12) (0.56)

245 3.0 0.4 (0.57) (0.73) (0.31)

2.2 (0.55)

Tartu

14.8 1.6 156 2.5 2.2 (0.34) (0.31) (0.88) (0.57) (0.63)

85 (0.71)

32 386 2.8 8.6 (0.49) (0.55) (0.52) (0.65)

892 (0.28)

367 2.7 1.3 (0.95) (0.93) (0.44)

32.6 (0.48)

Turin

44.9 4.3 380 14.4 21.3 116 1322 23.1 262 471 13.3 63.8 1827 (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)

744 8.5 3.6 (0.28) (0.31) (0.40)

70.1 (0.60)

Umea

5.6 0.6 (0.21) (0.40)

70 1.1 1.2 (0.66) (0.50) (0.45)

22 160 2.4 (0.09) (1.26) (0.76)

25 (0.45)

63 1.2 3.0 (0.33) (0.45) (0.86)

415 (0.38)

172 1.6 0.9 (0.75) (0.77) (0.56)

6.2 (0.51)

Uppsala

10.4 1.0 103 1.8 1.7 (0.43) (0.33) (0.44) (0.58) (0.53)

34 225 4.5 (0.46) (1.03) (0.51)

54 116 1.9 5.0 (0.56) (0.79) (0.59) (0.86)

752 (0.50)

247 2.0 1.4 (0.49) (0.43) (0.55)

14.9 (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)

806 26.6 (0.84) (0.61)

289 2.7 (0.98) (0.60)

52 113 2.7 5.2 (0.43) (0.45) (0.42) (0.71)

888 1404 5.5 3.3 185.0 (0.28) (0.65) (0.62) (0.35) (0.81) 903 (0.45)

PM2.5 in mg m
3, reflectance (Abs) in absorption coefficient 105 m
1, and elements in ng m
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. a Annual mean based on 37% of scheduled sampling time only.

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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 mg m
3.

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-tomonth 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 concentrations 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

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Table 2 Winter means (upper row) and summer means (lower row) for each centre and indicator PM2.5

Abs

Al

As

Antwerp citya

37.0 17.6

3.9 2.4

216 142

15.0 3.0

Antwerp South

24.4 17.3

2.1 1.4

133 101

Albacete

15.4 11.5

1.7 1.2

Barcelona

30.2 20.0

Basel

Ca

Cl

Cu

Fe

K

Mn

9.6 3.5

113 77

2725 287

14.5 8.8

187 98

319 108

11.5 4.8

45.0 24.2

1621 1442

393 310

7.0 4.4

9.0 5.9

96.2 30.5

10.0 5.3

7.1 2.0

51 40

1774 288

9.8 3.4

82 47

260 124

7.2 3.5

37.5 15.5

1205 1581

213 284

3.9 3.1

7.3 4.8

72.2 27.4

206 499

2.5 1.0

3.8 4.3

205 405

678 38

6.7 3.3

33 61

546 236

1.6 2.2

12.0 12.1

714 1195

420 1064

3.5 7.5

2.4 2.9

17.7 7.6

4.2 2.5

379 512

18.3 9.2

22.5 4.5

249 209

1704 237

26.8 23.2

190 124

401 715

17.4 6.5

75.0 42.3

1298 1890

728 767

23.5 21.0

9.4 10.5

105.9 60.6

23.7 13.7

2.1 1.6

197 147

6.7 2.2

7.6 3.6

66 65

1044 52

6.6 7.5

85 83

397 174

4.2 2.9

14.9 13.7

1074 1251

262 354

3.4 3.2

1.8 1.5

43.2 29.7

Erfurt

19.9 11.0

2.1 1.2

150 164

6.5 1.7

2.7 1.4

63 47

782 21

4.9 3.8

80 59

219 98

3.7 2.4

22.3 4.6

1019 1221

325 346

3.2 2.8

0.8 1.0

61.0 12.0

Galdakaoa

10.8 20.9

1.6 1.9

119 238

7.2 7.6

4.1 3.3

141 231

507 179

10.6 19.9

127 169

136 253

16.1 22.6

25.2 38.7

673 2571

266 526

2.6 4.5

3.0 11.8

72.8 178.9

Grenoble

28.0 12.9

3.4 1.8

322 205

9.4 3.6

5.3 2.4

121 209

1714 75

19.9 18.0

176 75

512 229

16.3 5.0

33.5 19.3

882 1005

914 1365

6.6 4.8

3.5 2.8

319.2 109.3

Gothenburg

12.5 11.2

1.2 0.9

84 113

2.2 1.6

2.6 2.1

40 45

891 410

5.1 4.2

56 49

113 65

3.0 2.5

5.0 5.0

869 826

183 255

2.0 3.3

3.4 5.3

20.2 9.3

Huelva

17.2 16.9

1.7 1.1

469 491

16.5 10.5

5.5 4.8

186 170

1419 206

30.7 28.9

85 80

393 202

3.7 2.6

29.1 29.4

882 2191

1250 1428

13.0 24.9

5.0 9.2

54.8 42.9

Ipswich

21.3 15.0

1.8 1.0

162 97

11.0 4.2

8.4 2.2

41 36

2418 218

5.9 2.8

50 37

335 119

3.6 3.4

32.2 9.3

896 1350

153 189

7.3 2.8

3.7 10.0

34.7 14.4

Norwicha

17.7 14.6

2.0 1.3

134 97

6.3 2.2

6.4 2.6

209 49

1811 247

3.5 3.7

51 36

160 85

4.2 1.7

19.5 12.0

736 1255

232 207

3.0 2.3

2.8 7.0

17.9 12.7

Oviedo

17.5 16.7

3.0 1.5

483 485

9.0 3.2

10.5 4.7

387 213

996 142

7.5 11.0

117 166

206 272

6.3 6.4

27.9 22.0

766 1901

746 843

7.5 7.8

5.8 6.2

35.6 29.2

Pavia

55.3 19.9

4.1 2.1

226 280

16.1 4.7

19.6 3.6

99 87

2175 77

11.4 6.7

167 103

655 181

18.8 5.3

61.5 18.6

1907 1928

508 655

8.7 11.0

4.9 3.7

83.6 29.2

Paris

21.0 15.9

2.5 2.3

112 169

4.1 3.8

6.3 2.0

76 95

1309 158

11.5 10.5

97 111

221 166

5.0 4.8

18.4 11.8

885 1363

219 398

3.1 4.9

2.3 2.2

42.6 39.8

4.8 3.3

0.1 0.2

155 79

0.8 0.9

1.5 1.2

56 34

1515 618

1.5 1.7

30 21

30 32

0.8 0.3

1.1 4.7

91 216

298 214

4.2 3.1

0.4 0.4

1.6 2.1

Tartu

15.6 10.2

1.8 1.1

68 131

3.2 1.4

2.3 1.0

30 120

538 30

2.8 1.4

19 31

433 160

2.6 1.8

9.7 3.1

865 709

120 341

0.9 2.4

1.7 0.7

37.0 16.3

Turin

69.2 23.0

5.4 3.3

449 303

21.7 7.0

38.0 7.4

137 88

3015 115

29.6 15.2

379 167

876 205

21.9 5.9

100.9 34.6

2095 1875

850 643

11.3 6.5

5.2 2.5

121.9 35.3

Umea

5.8 4.9

0.8 0.4

50 66

1.4 0.9

1.1 0.9

22 21

332 56

2.5 0.9

21 20

71 41

1.4 0.7

1.8 2.1

397 334

117 183

1.0 1.4

0.7 0.8

7.5 3.0

Uppsala

11.5 7.2

1.2 0.7

78 95

2.5 0.9

2.1 0.8

26 30

346 189

5.5 2.3

50 35

139 47

2.4 1.0

5.6 1.7

826 529

176 234

1.5 1.8

1.3 1.1

18.5 6.6

Veronab

51.0 16.0

4.7 2.6

381 154

24.7 5.1

27.8 6.3

319 92

1859 47

26.0 13.0

358 142

554 148

46.8 8.7

94.4 43.7

2167 1402

1006 240

10.2 3.3

1.3 2.1

191.2 92.9

Reykjavik

Br

Pb

S

Si

Ti

V

Zn

PM2.5 in mg m
3, reflectance (Abs) in absorption coefficient 105 m
1, and elements in ng m
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. a Winter mean based on less than 80% of scheduled sampling time (Antwerp City:60%, Galdakao:66%, Norwich:64%). b Winter and summer mean based on less than 80% of scheduled sampling time (43%, 18%, resp.).

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Table 3 Within-city Pearson correlation coefficients between PM2.5 and all other indicators

Antwerp city Antwerp South Albacete Barcelona Basel Erfurt Galdakao Gothenburg Grenoble Huelva Ipswich Norwich Oviedo Paris Pavia Reykjavik Tartu Turin Umea Uppsala Veronaa

Abs

Al

As

Br

Ca

Cl

Cu

Fe

K

Mn

Pb

S

Si

Ti

V

Zn

0.63 0.86 0.55 0.79 0.68 0.81 0.61 0.39 0.81 0.64 0.80 0.73 0.48 0.62 0.78
0.05 0.83 0.73 0.50 0.64 0.80

0.57 0.49 0.25 0.48 0.24 0.49 0.56 0.44 0.23 0.20 0.57 0.54 0.61 0.34 0.13 0.31 0.19 0.45 0.10 0.30 0.43

0.75 0.51 0.30 0.69 0.56 0.87 0.45 0.42 0.78 0.32 0.58 0.33 0.42 0.69 0.81 0.03 0.69 0.70 0.43 0.66 0.82

0.71 0.42 0.30 0.81 0.51 0.54 0.27 0.49 0.74 0.65 0.78 0.65 0.53 0.65 0.81 0.29 0.38 0.82 0.27 0.43 0.83

0.33 0.41 0.21 0.50 0.19 0.50 0.55 0.20 0.09 0.39 0.11 0.08 0.35 0.49 0.32 0.55 0.02 0.42 0.02 0.17 0.61

0.67 0.53 0.36 0.79 0.74 0.81
0.05 0.10 0.86
0.02 0.67 0.30 0.10 0.45 0.90 0.70 0.64 0.91
0.06 0.08 0.77

0.43 0.30 0.12 0.59 0.16 0.52 0.40 0.03 0.47 0.24 0.51 0.36 0.35 0.38 0.40 0.00 0.17 0.52 0.10 0.13 0.78

0.59 0.66 0.23 0.76 0.56 0.70 0.52 0.14 0.72 0.29 0.57 0.67 0.53 0.70 0.71 0.18 0.28 0.75 0.16 0.26 0.82

0.84 0.64 0.57 0.32 0.87 0.92 0.76 0.65 0.90 0.64 0.56 0.64 0.73 0.84 0.83 0.52 0.85 0.86 0.74 0.69 0.90

0.74 0.42 0.27 0.59 0.64 0.77 0.53 0.38 0.70 0.47 0.39 0.46 0.27 0.48 0.71 0.26 0.58 0.75 0.45 0.62 0.57

0.67 0.41 0.11 0.74 0.32 0.79 0.42 0.50 0.75 0.41 0.55 0.26 0.43 0.54 0.83
0.11 0.47 0.76 0.03 0.25 0.84

0.85 0.74 0.56 0.55 0.83 0.80 0.89 0.81 0.55 0.79 0.73 0.80 0.72 0.75 0.56 0.42 0.76 0.56 0.87 0.85 0.65

0.32 0.19 0.21 0.49 0.16 0.39 0.55 0.24 0.06 0.40 0.50 0.27 0.57 0.20 0.07 0.17 0.16 0.34
0.02 0.15 0.38

0.70 0.54 0.26 0.57 0.46 0.62 0.57 0.27 0.26 0.11 0.51 0.48 0.64 0.49 0.11 0.18 0.16 0.51
0.07 0.27 0.61

0.38 0.60 0.57 0.58 0.71 0.21 0.64 0.35 0.48 0.68 0.40 0.51 0.61 0.50 0.36 0.01 0.50 0.84 0.48 0.65 0.39

0.75 0.46 0.19 0.67 0.66 0.84 0.45 0.48 0.78 0.31 0.78 0.55 0.62 0.54 0.84
0.06 0.81 0.79 0.64 0.57 0.57

Correlation coefficients above 0.7 are bolded. a Based on 28 filters only.

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 correlations 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 considerably 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%.

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Table 4 Pearson correlation coefficients of annual means across cities

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

PM2.5

Abs

Al

As

Br

Ca

Cl

Cu

Fe

K

Mn

Pb

S

Si

Ti

V

0.93 0.47 0.84 0.91 0.30 0.64 0.63 0.85 0.73 0.68 0.88 0.87 0.34 0.41 0.18 0.46

1 0.54 0.82 0.87 0.45 0.54 0.69 0.90 0.76 0.72 0.89 0.81 0.44 0.46 0.25 0.60

1 0.64 0.58 0.84 0.23 0.74 0.58 0.71 0.38 0.60 0.56 0.80 0.80 0.38 0.33

1 0.88 0.51 0.62 0.87 0.86 0.67 0.78 0.96 0.86 0.51 0.70 0.43 0.58

1 0.45 0.60 0.66 0.89 0.72 0.72 0.93 0.75 0.34 0.51 0.14 0.41

1 0.03 0.56 0.54 0.58 0.54 0.54 0.46 0.64 0.57 0.39 0.43

1 0.40 0.47 0.28 0.32 0.59 0.48 0.14 0.36 0.33 0.18

1 0.76 0.64 0.66 0.80 0.72 0.74 0.79 0.50 0.67

1 0.63 0.88 0.94 0.78 0.45 0.42 0.25 0.68

1 0.49 0.71 0.66 0.62 0.62 0.20 0.46

1 0.86 0.70 0.34 0.29 0.31 0.80

1 0.85 0.43 0.59 0.39 0.63

1 0.38 0.53 0.50 0.49

1 0.64 0.24 0.60

1 0.55 0.27

1 0.35

Correlation coefficients above 0.7 are bolded.

Elevated winter levels of indicators associated with anthropogenic activities were observed in several centres, 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 (SO2
4 ) 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 measurements. 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 homogeneous 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 15 m 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

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roadways (Janssen et al., 1997; Roorda-Knape et al., 1998; Zhu et al., 2002a, b). As a consequence, trafficrelated 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 heterogeneity in PM composition may partly explain differences 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 compared 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-community 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 concentrations. Since it affects many pollutants similarly, it not only introduces short-term collinearity between pollutants, 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 (Ro¨o¨sli et al., 2001; Zhu et al., 2002a, b). For indicators which are rather heterogeneously distributed in space, within-city contrasts in exposure, and hence misclassification in exposure, may be of similar magnitude as the betweencity variability, therefore jeopardizing the investigation of health effect contrasts across cities. To assess effects of such heterogeneously distributed constituents, exposure 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 uncertainty 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 hypothesize, 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. Consequently, the ability to assess independent contributions of PM constituents to various long-term health effects in this study will be limited.

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Acknowledgements We thank Dr. Patrick Mathys (Geochemical Laboratory, Institute for Mineralogy and Petrography, University 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. Go¨tschi was funded by an EPA STAR Fellowship. N. Ku¨nzli, 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 Pollution and Health: Michael Abramson, Ursula AckermannLiebrich, Lucy Bayer-Oglesby, Roberto Bono, Peter Burney, Roberto de Marco, Bertil Forsberg, Thorarinn Gislason, Thomas Go¨tschi, Marianne E. Hazenkamp, Joachim Heinrich, Deborah Jarvis, Joost Weyler, Nino Ku¨nzli, Linnea Lillienberg, Christina Luczynska, Jose Maldonado, Inga Mills, Dan Norba¨ck, Fe´lix Payo Losa, Albino Poli, Michela Ponzio, Argo Soon, Jordi Sunyer, Kjell Tore´n, Giuseppe Verlato, Simona Villani.

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