Oxidative properties of ambient PM2.5 and elemental composition: Heterogeneous associations in 19 European cities

Atmospheric Environment 43 (2009) 4595–4602

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Oxidative properties of ambient PM2.5 and elemental composition: Heterogeneous associations in 19 European cities Tim S. Nawrot a, b, Nino Kuenzli c, d, *, Jordi Sunyer c, e, Tingming Shi f, Teresa Moreno g, Mar Viana g, Joachim Heinrich h, Bertil Forsberg i, Frank J. Kelly j, Muhammad Sughis b, Benoit Nemery b, Paul Borm f, k a

Centre for Environmental Sciences, Hasselt University, Diepenbeek, Belgium Occupational & Environmental Medicine, KULeuven, Leuven, Belgium Centre for Research in Environmental Epidemiology (CREAL), Municipal Institute of Medical Research (IMIM-Hospital del Mar), CIBER Epidemiologia y Salud Publica (CIBERESP), Barcelona, Spain d Catalan Institute for Research and Advanced studies (ICREA), Barcelona, Spain e University Pompeu Fabra, Barcelona, Spain f ¨ r Umweltmedizinische Forschung at the Heinrich Heine University, Du ¨ sseldorf, Germany Particle Research, Institut fu g Institute of Environmental Assessment and Water Research (IDÆA-CSIC), Barcelona, Spain h Helmholtz Zentrum Muenchen, National Research Center for Environmental Health, Institute of Epidemiology, Munich, Germany i Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden j MRC/HPA Centre for Environment and Health, King’s College London, London, United Kingdom k Centre of Expertise in Life Sciences, Zuyd University, Heerlen, The Netherlands b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 October 2008 Received in revised form 3 June 2009 Accepted 9 June 2009

We assessed the extent to which constituents of PM2.5 (transition metals, sodium, chloride) contribute to the ability to generate hydroxyl radicals (OH) in vitro in PM2.5 sampled at 20 locations in 19 European centres participating in the European Community Respiratory Health Survey. PM2.5 samples (n ¼ 716) were collected on filters over one year and the oxidative activity of particle suspensions obtained from these filters was then assessed by measuring their ability to generate OH in the presence of hydrogen peroxide. Associations between OH formation and the studied PM constituents were heterogeneous. The total explained variance ranged from 85% in Norwich to only 6% in Albacete. Among the 20 centres, 15 showed positive correlations between one or more of the measured transition metals (copper, iron, manganese, lead, vanadium and titanium) and OH formation. In 9 of 20 centres OH formation was negatively associated with chloride, and in 3 centres with sodium. Across 19 European cities, elements which explained the largest variations in OH formation were chloride, iron and sodium. Ó 2009 Elsevier Ltd. All rights reserved.

Keywords: Air pollution Elemental analysis Fine particle Hydroxy radical formation Oxidative stress Reactive oxidant species

1. Introduction Epidemiologic studies demonstrate that both acute and chronic exposure to air pollution are associated with detrimental health effects (Brunekreef and Holgate, 2002; Gotschi et al., 2008; Nawrot et al., 2007). Furthermore, experimental studies indicate that inflammation and oxidative stress are involved in mediating the adverse health effects of exposure to air pollution (Kelly, 2003; Alfaro-Moreno et al., 2008; Nel, 2005; Donaldson et al., 1997). The formation of OH by PM has been associated in vitro with premutagenic DNA adducts and oxidative DNA damage (Knaapen et al.,

* Corresponding author at: Public Health University of Basel, Institute of Social and Preventive Medicine at Swiss Tropical Institute Basel, Steinengraben 49, 4051 Basel, Switzerland. Tel.: þ34 93 221 6448. E-mail address: [email protected] (N. Kuenzli). 1352-2310/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2009.06.010

2002; Shi et al., 2003) as well as in vivo human inflammatory responses after bronchial instillation (Schaumann et al., 2004). Thus, the formation of OH by PM may be a relevant marker for the initiation of various health effects (Borm et al., 2007). Various chemical compounds in ambient particulate matter, including transition metals and aromatic organic compounds, may contribute to adverse effects through intrinsic generation of reactive oxygen species (de Kok et al., 2006). In the context of both toxicological and epidemiological research, it is well accepted that the PM10 mass is not ideal but represents a surrogate for the biologically effective dose (BED). This is self-evident from the fact that much of the PM10 mass consists of low-toxicity components such as ammonium sulphates and nitrates, sea salt (sodium chloride), crustal dust and road resuspended dust. By contrast, relatively tiny masses of transition metals and organic species may redox cycle and make a major contribution to the BED. So, although our current and future particulate matter standards are set on mass, we know that most of

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the mass is actually biologically inactive. In fact, studies have shown that the particle number, which is not necessarily related to mass, can be a better descriptor of some health effects. This can be explained by the fact that combustion derived nanoparticles, the dominant particle type by number in urban air, represents a key component of the particulate matter mix because they contain a large surface area, transition metals and organics. Experimental studies have shown that these three components have a role in the pro-inflammatory effects of particulate matter and model particles in animal and in vitro models. A common mechanism linking these parameters is their ability to generate oxidative stress in the lung both by direct generation of reactive oxygen species (ROS) and indirectly through the induction of inflammatory responses in the lung. This has been suggested to be the primary mechanism of lung injury caused by PM10 and its components, and a number of research groups have set out to measure ROS production by particulate matter sampled from ambient air and link this to biological effects in vivo and in vitro (Ayres et al., 2008; Borm et al., 2007). While redox activity of PM would be an appealing measure to study the oxidative stress responses in humans, routine measurements are not yet available, and no common standard has yet been established to describe this toxicologically relevant feature. However, constituents of PM are nowadays often available as many studies speciate ambient PM, thus, major determinants of PM redox activity may be available. We studied the independent determinants of OH radical formation on PM using an array of PM constituents such as transition metals, as well as its light reflectance and ambient temperature. We assessed these characteristics for a large sample of ambient PM2.5 collected over 1 year at 20 monitoring sites in 19 cities participating in the European Community Respiratory Health Survey II (ECRHS II) and studied the ability of the particulates to generate hydroxyl-radicals in vitro (Janson et al., 2001; Hazenkamp-von Arx et al., 2003; Kunzli et al., 2006) Previously, we demonstrated that across European cities, average PM2.5 redox activity was correlated neither with PM2.5 concentration nor absorbance (Kunzli et al., 2006). In the current study, we investigate the determinants of redox activity for each city independently. 2. Methods 2.1. Sampling and characterization of PM2.5 ECRHS II (European Community Respiratory Health Survey II) follows up on the populations assessed cross-sectionally in 1990– 1993 (Janson et al., 2001; Burney et al., 1994). In total, 21 centres in 20 cities participated in the air pollution module implemented in ECRHS II. The lack of standardized air pollution monitoring networks across Europe required an assessment of the long-term average air quality in each center. The measurements started between June and December 2000 and lasted at least 12 months. The primary focus was the gravimetric sampling of PM2.5. The methods and the main results have been published elsewhere (Hazenkamp-von Arx et al., 2003; Kunzli et al., 2006). In brief, a standardized protocol was implemented, using identical equipment [Basel-Sampler (BGI Inc., Waltham, MA, USA); Teflon filters] and a single weighing laboratory. The sampling schedule was designed to sample 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/center). Annual means were derived from the average of these filters. Samples were stored at 80  C and measurement of OHformation was done within 24 months from original sampling. As described by Gotschi et al. (2005), PM2.5 filter samples were analyzed for 26 different chemical elements, using energy-

dispersive X-ray fluorescence spectrometry (ED-XRF). Fifteen elements were detected reliably and in most centres. As carbon could not be detected with XRF, light absorbance was measured as a surrogate for elemental carbon, using previously described standard methods and devices (Reflectometer EEL model 43; Diffusion Systems Ltd., London, U.K) (Gotschi et al., 2002). PM2.5 was reported as mass concentrations per volume of air (mg m3). In this analysis, we report the intrinsic measures of the elemental composition and absorbance of the filter. This reflects inherent properties of the sampled dust, measured on a ‘standard’ quantity of dust (PM2.5) rather than as a mass concentration. The intrinsic measures do not directly reflect ‘exposure’ but, in line with the redox activity (see below) a specific quality of the dust (PM2.5) that people (living around the monitor) were ‘exposed’ to. The units are normalized to mass. For the analyses of oxidative properties, every second filter (n ¼ 716) underwent a standardized extraction in 1 mL metal-free water, with the resultant suspension then diluted to a concentration of 200 mg mL1. In brief, to prepare PM suspensions from Teflon filters, the support ring was removed, and the filter was placed into double-distilled water and agitated (5 min) before sonication in a water bath (5 min). After sonication, a further 5 min agitation was performed. Blank filters were treated in the same way and used as controls in all experiments. Difference between the weighed laboratory blank mass during the study and the beginning of the study was on average 1.4 mg m3 (std:2.2). Generation of OH by particle suspensions was studied in the presence of H2O2 and the spin trap 5,5-dimethyl-1-pyrroline-N-oxide (DMPO). For OH measurement, 50 mL of the particle suspension was mixed with 50 mL H2O2 [0.5 M in phosphate-buffered saline (PBS)] and 100 mL DMPO (0.05 M in PBS). The mixture was incubated in the dark and shaken continuously at 37  C before being filtered through a 0.1-mm-pore filter (Acrodisc 25-mm syringe filter; Pall Gelman Laboratory, Ann Arbor, MI, USA). The clear filtrate was transferred immediately to a 100-mL glass capillary and measured with a Miniscope MS100 EPR spectrometer (Magnettech, Berlin, Germany) under standard conditions. Quantification was carried out as the sum of total amplitudes of DMPO– OH quartet signal, and the outcome is expressed as the total amplitude in arbitrary units, related to the same instrumental settings. The DMPO/peroxide assay is mainly sensitive to transition metals (Shi et al., 2003). Experiments with nitro-PAHs and pure quinones did not result in detectable DMPO-OH formation. We used a positive control (EVA-91) a fly-ash obtained from pulverized coal combustion which is less rich in metals then ROFAs and closer to environmental PM. This positive control is used in every single set of experiments to correct for potential drift in the signal, the variation between experiments was less than 5%. In previous studies we have investigated and published (Shi et al., 2003) the ability of different metals and valencies to generate hydroxyl radicals in the DMPO/Peroxide method. The data showed that kinetics of OH formation differed between different metals, and that also the maximal capacity of OH-generation was different. The highest activity is noted with Cu2þ, V2þ and Fe2þ. In addition work with metal coated particles and residual oil fly-ashes (that are very rich in transition metals) showed that some particle associated metals can also be Fenton-reactive. The final results is therefore an addition of Fenton bio-available metals induced OH-generation, and a closer measure of the measure to induce oxidative stress than a sum of metal components. The chemical reproducibility of the method (applied to the same sample) is within 5%. This allows application of the methods to compare PM samples between different days or sites on the same day (e.g. Shi et al., 2003; Schaumann et al., 2004). A signal to noise ratio of 3 was used to set the detection limit. Typically loaded filters has S/N ratio’s well over 20–100.

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11.9 19.4 17.7 27.6 28.0 25.7 60.7 21.0 23.5 19.8 14.1 13.6 20.3 35.1 17.6 6.58 11.0 24.7 24.5 30.3

To quantify the weight of the particulates recovered from the filters, we used two methods to estimate the concentration per milliliter: comparative turbidometry at 405 nm against a carbon black standard (Huber 990, 260 nm; H. Haeffner & Co., Ltd., Chepstow, UK), and weighing of a subset of 32 Teflon filters before and after removal of particles and weighing the filter after drying. The first method is based on the comparison of the ‘‘blackness’’ of the particle suspension to standard carbon black (260 nm) by spectroscopic absorption at 405 nm. Although part of the PM is water soluble and may be seen as an extract, our comparative experiments using gravimetric analysis of PM before and after filter extraction confirmed the correlation between turbidometry and gravimetric analysis. It also demonstrated that average recovery for 30 PM2.5 samples using this procedure was about 80% (Shi et al., 2003). We further studied the variation by measuring 47 samples four times at the same area. The coefficient of variance was for S 0.4%, Si 0.7%, Al 1.6%, Fe 3.6%, Zn 5.6%, Pb 14.9%, for Cu 17.6%, Ti 13.3%, As 10%, Mn 7.1%, V 30.6%, Na 3.3% and Cl 2.0%. Mean daily outdoor temperature was obtained from the country national meteorological institute (although data for Norwich and Ipswich were not made available by the British Meteorological Office).

Albacete, Spain Antwerp City, Belgium Antwerp South, Belgium Barcelona, Spain Basel, Switzerland Erfurt, Germany Galdakao, Spain Gothenburg, Sweden Grenoble, France Huelva, Spain Ipswich, UK Norwich, UK Oviedo, Spain Paris, France Pavia, Italy Reykjavik, Iceland Tartu, Estonia Turin, Italy Umea, Sweden Uppsala, Sweden

2.2. Statistical analyses

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Data represent geometric means or in case of PM2.5 and absorbance the arithmetic mean was reported. Between brackets the ratio of the 75th–25th percentile interval is given.

Cl Na V Mn As Ti Cu

(506) 0.32 (2.5) 0.10 (2.2) 0.15 (3.1) 0.20 (1.7) 94.8 (2.0) 6.38 (8.2) (7.6) 0.24 (1.8) 0.19 (4.1) 0.27 (2.8) 0.26 (3.2) 132.8 (3.9) 21.08 (7.8) (28) 0.16 (3.1) 0.22 (3.7) 0.22 (3.2) 0.26 (2.0) 106.3 (3.1) 18.5 (7.3) (2.1) 0.65 (1.8) 0.15 (60.4) 0.11 (84.9) 0.36 (1.6) 129.5 (1.6) 18.0 (5.4) (5.0) 0.21 (2.2) 0.18 (2.6) 0.20 (2.7) 0.08 (2.0) 90.6 (2.5) 13.0 (9.3) (5.3) 0.17 (2.1) 0.20 (2.8) 0.19 (1.7) 0.05 (3.5) 79.5 (2.9) 5.79 (8.1) (3.5) 0.23 (2.2) 0.32 (4.5) 1.12 (2.1) 0.40 (2.4) 154.8 (1.7) 14.36 (5.5) (5.0) 0.13 (2.4) 0.14 (3.4) 0.18 (2.2) 0.24 (4.5) 121.3 (3.8) 16.51 (11.4) (3.4) 0.26 (1.7) 0.19 (4.4) 0.45 (2.2) 0.13 (3.1) 106.1 (1.5) 12.50 (7.2) (3.5) 0.58 (2.1) 0.31 (5.6) 0.16 (1.8) 0.35 (2.6) 206.7 (2.2) 16.00 (8.3) (9.0) 0.18 (4.9) 0.24 (3.4) 0.15 (4.0) 0.21 (3.3) 164.3 (3.7) 26.36 (18.7) (106.2) 0.147 (2.9) 0.186 (2.9) 0.101 (3.0) 0.247 (2.3) 155.3 (4.4) 28.54 (10.0) (2.9) 0.45 (1.8) 0.26 (5.7) 0.34 (2.5) 0.32 (1.9) 120.1 (2.3) 18.6 (5.1) (2.9) 0.22 (2.0) 0.18 (2.6) 0.23 (2.1) 0.10 (2.5) 121.7 (2.4) 14.7 (4.3) (2.4) 0.21 (4.2) 0.27 (2.0) 0.21 (1.8) 0.12 (2.0) 41.06 (1.9) 8.67 (1.1) (1198.7) 0.34 (11.1) 0.27 (2.5) 0.07 (24.3) 0.12 (2.4) 403.3 (2.7) 128.5 (7.8) (606) 0.16 (2.6) 0.13 (2.5) 0.16 (2.6) 0.07 (2.5) 66.85 (2.0) 8.32 (6.2) (1.9) 0.20 (1.9) 0.30 (1.8) 0.29 (2.0) 0.08 (1.8) 45.63 (1.7) 11.90 (8.0) (1149) 0.23 (6.8) 0.18 (1.8) 0.15 (2.4) 0.12 (2.6) 111.1 (2.7) 5.98 (4.0) (4.6) 0.16 (2.5) 0.13 (2.4) 0.14 (1.9) 0.12 (2.1) 87.69 (2.0) 9.60 (6.7) 0.02 0.10 0.07 0.67 0.20 0.15 0.59 0.11 0.36 0.83 0.06 0.04 0.36 0.45 0.18 0.07 0.03 0.47 0.02 0.16 (6.6) (3.5) (2.2) (1.7) (3.5) (4.8) (2.1) (666.3) (2.8) (4.0) (4.3) (3.7) (2.3) (4.6) (1.6) (419.5) (3.8) (1.6) (1233) (14.3) 0.27 0.65 0.60 1.98 0.28 0.24 1.85 0.06 0.67 0.72 0.23 0.22 1.26 0.45 0.97 0.00 0.18 1.31 0.06 0.09 (3.0) (2.9) (2.5) (1.8) (2.0) (2.1) (2.0) (2.1) (3.4) (3.1) (3.2) (1.7) (2.2) (1.9) (1.9) (7.7) (1.7) (1.8) (2.8) (1.8) 0.46 1.76 1.95 3.08 1.80 1.62 6.15 1.04 5.45 1.06 0.85 0.75 1.74 2.09 1.18 0.28 2.05 1.50 0.77 1.27 (3.6) (2.1) (2.3) (1.5) (2.0) (1.9) (1.6) (2.3) (2.0) (2.3) (2.1) (1.7) (2.0) (1.6) (1.9) (25.8) (4.1) (1.9) (3.6) (3.1)

Pb Zn Fe Al

2.00 5.54 2.95 6.36 5.26 3.89 8.99 3.25 6.18 4.42 2.33 1.80 7.62 4.41 3.95 2.12 1.18 6.19 3.40 3.45 (3.6) (1.6) (1.9) (26.3) (2.9) (2.5) (1.7) (1.9) (2.7) (2.4) (2.1) (1.7) (2.0) (1.8) (2.6) (5.7) (2.9) (2.0) (2.7) (2.5) 19.4 7.66 6.08 6.38 7.79 8.41 10.70 6.26 9.29 23.9 6.04 6.55 28.09 7.25 6.91 14.56 6.55 8.68 10.20 7.37 (3.0) (2.1) (2.9) (2.1) (2.8) (2.5) (1.7) (3.0) (4.2) (1.9) (2.7) (2.0) (2.0) (2.3) (3.4) (9.9) (3.6) (2.0) (3.6) (3.8) 41.9 15.72 11.7 28.96 19.95 19.06 26.12 12.3 51.38 65.8 10.78 12.02 47.86 16.68 15.09 36.71 14.66 17.88 23.86 18.25 (2.2) (1.5) (1.9) (2.0) (1.6) (1.9) (1.7) (1.9) (2.6) (2.5) (2.5) (1.8) (1.7) (1.8) (2.5) (3.0) (1.7) (2.3) (1.6) (2.0) 72.85 62.56 74.21 63.29 58.06 71.59 83.96 60.4 46.25 76.60 57.69 56.37 68.00 58.40 55.36 28.58 58.40 41.64 59.67 59.09 1.4 2.8 1.6 3.2 1.7 1.7 1.9 1.1 2.6 1.4 1.3 1.7 2.1 2.4 3.0 0.1 1.6 4.3 0.8 1.1 14.0 22.6 19.8 25.0 16.8 15.4 16.6 14.0 19.1 18.2 16.9 18.3 16.7 18.6 39.1 3.8 13.8 48.2 6.2 11.3 

(1.5) (2.1) (1.5) (1.6) (2.2) (1.6) (3.0) (2.2) (1.7) (1.5) (2.6) (2.1) (2.8) (1.6) (1.5) (2.5) (1.9) (1.6) (1.9) (1.8)

Si PM2.5 Abs S OH City

For database management and statistical analysis, we used SAS software (version 9.1; SAS Institute Inc., Cary, NC, USA). We logarithmically transformed variables with a non-Gaussian distribution. We represented the central tendency and spread of transformed variables by the geometric mean and the ratio of its 75th to 25th percentile interval. Within each center, we investigated the associations between OH formation and PM constituents across the available filters using single and multiple linear regressions. Covariates were identified by a stepwise regression procedure with the p-values for variables to enter and to stay in the model set at 0.10. Covariates considered for entry in the model were sulphur, iron, silicon, aluminum, zinc, lead, copper, titanium, arsenic, manganese, vanadium, sodium and chloride. We additionally ran sensitivity analysis by forcing either PM2.5 mass concentration or absorbance into the models, and constructed a multiple regression analysis including the major determinants identified by the initial stepwise procedure and variables which do not intercorrelate well (this set of variables included sulphur, silicon, iron, and sodium). Finally, based on a priori assumptions (Ghio et al., 1999) we studied interactions within each location between sulphur and iron and between temperature and iron on OH formation. Locations with significant interactions and in the same direction were pooled and studied in a mixed model. In exploratory analysis and, in expansion of previous cross-city comparisons (Kunzli et al., 2006), we aggregated the data to mean annual concentrations by location and explored associations of the OH formation and PM across locations to distance from the sea, mean annual temperature and population density. 3. Results Table 1 presents geometric mean annual concentrations of the PM2.5 characteristics and the ratio of their 75th to 25th percentile distribution. The median (IQR) population density was 2661 (1031– 5209) persons km2 with the lowest population density in Albacete (125 persons km3) and the highest in Paris (24 948 persons km2). The mean annual PM2.5 ranged from 3.8 mg m3 in Reykjavik to 48.2 mg m3 in Turin (Table 1). The OH formation was the lowest in Reykjavik and the highest in Galdakao (Table 1).

Table 1 PM2.5 characteristics measured at 20 locations in 19 European cities, expressed as intrinsic activity (units are per standard mass).

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3.1. Independent correlates within locations We noted heterogeneous associations between OH formation and the available PM constituents. The total explained variance ranged from 85% in Norwich to only 6% in Albacete, suggesting that significant sources of OH formation are unaccounted for with the variables considered in this analysis. Among centres, no correlation was observed between the explained variance of the OH formation and its annual mean (r ¼ 0.15; p ¼ 0.80) nor with it’s temporal variation expressed as the location-specific difference between the 75th and the 25th percentile (r ¼ 0.20; p ¼ 0.34). Among the 20 centres, 15 showed positive correlations between one or more of the measured transition metals (copper, iron, zinc, manganese, lead, vanadium and titanium) and OH formation (Tables 2 and 3). The positive associations between transition metals and OH formation show certain geographic trends: whereas positive associations for iron were found mostly in central and Northern Europe (Basel, Tartu, Umea), for zinc in central and Southern Europe (Grenoble, Huelva, Norwich), manganese (Norwich, Oviedo, Paris) and vanadium (Antwerp. Barcelona, Norwich). In other words, iron did not show positive associations with OH formation in Southern Europe (French or Spanish locations). Among the Spanish sites, we observed that OH formation increased with increasing PM 2.5 and absorbance levels (from Albacete to Barcelona), with the unique exception of Galdakao. However, Galdakao is a heavily industrialized area characterized by metallurgy and steelworks, with high levels of metals impacting on OH formation. With a population 50 times smaller than that of Barcelona, the levels of metalliferous PM components such as iron, zinc, manganese or vanadium, and that of sulphur, are nonetheless similar to or even higher than those in Barcelona (Table 1). Thus, the levels of iron, zinc, lead and vanadium showed a marked correlation with OH formation at all the Spanish sites. With regard to the components of sea spray, at 9 of 20 centres OH formation was negatively associated with chloride, and in 3 centres with sodium. The elements which explained the largest variations in  OH formation were chloride, iron and sodium (Table 2). The temporal correlation of OH formation across the two Antwerp locations – 11 km apart – was relatively high (r ¼ 0.52; p ¼ 0.002). In contrast, the studied PM2.5 constituents were not temporally correlated between the two locations except iron (r ¼ 0.39; p ¼ 0.04), zinc

(r ¼ 0.42; p ¼ 0.03) and sodium (r ¼ 0.49; p ¼ 0.009). In multivariate models this resulted in a heterogeneous set of factors explaining the variation of OH formation in the two locations in Antwerp (Table 2). This heterogeneity remained also in models restricted to days with parallel measurements taken at both stations. In further analysis, we studied the determinants of OH formation by forcing PM2.5 or absorbance (measure of black smoke) into the regression models. In general, PM mass concentrations or the absorbance did not explain much of the variance in OH formation (less than 8%), except for Barcelona and Turin, with 26% (r ¼ 0.51) and 22% (r ¼ 0.47) of the OH variance explained by PM2.5 mass concentration, respectively. It is noticeable that these two sites showed high PM2.5 annual means (25.0 and 48.2 mg m3, respectively). Of the 20 centres, 8 showed significant (p < 0.08) positive associations with mass PM2.5 concentration, while 2 centres (Norwich and Ipswich) showed inverse associations with the PM2.5 mass concentration. In these 2 centres the OH formation was also significantly and inversely correlated with sodium. We ran sensitivity analysis to explore further the role of iron. In five out of the 13 centres in which iron did not enter the stepwise regression model, it was significantly and positively associated with  OH formation if forced into the initial model (Galdakao, Erfurt, Gothenburg, Grenoble and Oviedo). Additionally, to study the robustness of the stepwise selection procedures we ran a multiple regression model in which the following PM constituents were forced: sulphur, silicon, iron and sodium (Table 2, forced model). The restricted model showed a drop in the explained variance of the  OH formation, especially in Antwerp city, Antwerp South, Gothenburg, Grenoble, Huelva, Ipswich, Reykjavik, Tartu, and Uppsala (Table 2). In an additional sensitivity analysis we forced vanadium, manganese and cupper and observed positive significant observations in Antwerp Sourth (copper), Basel (copper and manganese), Galdakao (Vanadium and copper), Huelva (copper), Paris (manganese), Reykjavik (manganese), and Turin (manganese). Finally, we studied possible effect-modification by outdoor temperature and sulphur load. In 7 centres there appeared to be a positive association between OH formation and outdoor temperature (r ¼ 0.29 to 0.50; p > 0.08), while PM2.5 constituents with exception of sulphur were not systematically influenced by outdoor temperature. The association between sulphur and

Table 2a Independent correlates of OH formation among four Northern European cities. Model A: Stepwise selection modela Independent variables

Model B: Forced modelc b

Estimate

2

Partial R

2

p

Total R

Gothenburg Silica

22.23 (5.30–41.88)

0.17

0.0127

Reykjavik Iron Sulphur Chloride

16.40 (10.35–22.78) 21.40 (10.80–33.0) 10.34(16.14 to 4.14)

0.40 0.21 0.10

0.0001 0.0006 0.0040

Tartu Chloride Titanium Iron

16.14(20.00 to 12.09) 9.97(17.91 to 1.26) 4.55 (0.14 to 9.47)

0.57 0.05 0.04

<0.0001 0.0435 0.0679

Uppsala Sodium Titanium Aluminum

12.48(23.73 to 0.42) 92.96 (55.01 to 140.18) 45.44(57.51 to 29.94)

0.11 0.18 0.31

0.0584 0.0096 <0.0001

Independent variables

0.17

Total R2 0.00

None 0.71

0.34 Sodium Sulphur

0.66

0.32 Sulphur

0.60

0.16 Sodium

a Model A: Stepwise model: significant and independent correlates of PM oxidative activity (OH$ formation) were identified by stepwise regression within each center. Covariates shown within one centre are, thus, mutually adjusted. Variables considered for entry into the model were those reported in Table 1 with exception of PM2.5 and absorbance. b Effect sizes (95% CI) were calculated for a two-fold increase in the independent variables (log transformed) with exception for outdoor temperature and intrinsic blackness of the filter which was expressed as percentage change (95% CI) in OH formation. c Model B: Forced model: multiple regression model including: sulphur, silica, iron and sodium. Driven factors were those which reached the level significance (p < 0.10).

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Table 2b Independent correlates of OH formation among 8 locations in 7 central European cities. Model A: Stepwise selection modela Independent variables

Estimateb

Model B: Forced modelc Partial R2

Total R2

p

Antwerp city Iron Lead Vanadium

50.75 (22.90–84.91) 8.88 (3.69–14.33) 14.75 (0.81–30.61)

0.26 0.15 0.07

0.0019 0.0081 0.0459

Antwerp South Zinc Arsenic Chloride Sulphur Titanium Aluminum

23.43 (9.75–38.82) 9.80 (1.67–18.57) 15.93 (8.15–24.26) 44.0 (13.0–84.74) 15.04 (3.66–27.67) 12.53(25.02 to 2.04)

0.27 0.10 0.08 0.07 0.05 0.04

0.0014 0.0308 0.0408 0.0332 0.0534 0.0996

Basel Iron Sodium Zinc

30.07 (12.22–50.77) 17.37 (7.16–28.56) 20.41 (7.45–34.92)

0.60 0.07 0.07

<0.0001 0.0117 0.0031

Erfurt Sodium Copper Chloride

14.03(21.06 to 6.39) 3.64 (0.83 to 6.52) 4.63(7.96 to 1.18)

0.32 0.06 0.05

0.0004 0.0595 0.0723

Grenoble Zinc Chloride

22.01 (12.67 to 32.12) 12.68(18.95 to 5.93)

0.32 0.18

0.0003 0.0011

Ipswich Chloride Lead

17.26(23.87 to 10.09) 3.64 (0.41 to 7.87)

0.36 0.06

0.0003 0.09

Norwich Chloride Manganese Sodium Vanadium Zinc Aluminum

10.26(14.50 to 5.82) 9.71 (3.01 to 16.85) 14.98(21.79 to 7.58) 13.11 (5.37 to 21.42) 7.94 (1.94 to 14.29) 12.11(21.55 to 1.55)

0.53 0.17 0.08 0.04 0.01 0.02

<0.0001 0.0003 0.0023 0.0099 0.0811 0.0346

Paris Sodium Chloride Manganese

16.50(22.55 to 9.98) 7.12(10.90 to 3.19) 7.60 (0.89 to 16.82)

0.37 0.19 0.03

<0.0001 0.0005 0.0904

Independent variables

0.48

Total R2 0.19

Iron

0.61

0.1 None

0.74

0.66 Sodium

0.43

0.39 Sodium

0.50

0.00 None

0.42

0.27 Sulphur

0.85

0.68 Sulphur Sodium Iron

0.59

0.52 Sulphur Sodium

a Model A: Stepwise model: significant and independent correlates of PM oxidative activity (OH$ formation) were identified by stepwise regression within each center. Covariates shown within one centre are, thus, mutually adjusted. Variables considered for entry into the model were those reported in Table 1 with exception of PM2.5 and absorbance. b Effect sizes (95% CI) were calculated for a two-fold increase in the independent variables (log transformed) with exception for outdoor temperature and intrinsic blackness of the filter which was expressed as percentage change (95% CI) in OH formation. c Model B: Forced model: multiple regression model including: sulphur, silica, iron and sodium. Driven factors were those which reached the level significance (p < 0.10).

outdoor temperature was strong in all centres (r  0.42; p  0.0028) except in Gothenburg, Tartu, Umea and Uppsala. Independent of sulphur, the interaction terms between iron and outdoor temperature reached statistical significance in four out of 18 locations (Erfurt, Grenoble, Pavia, Uppsala; no temperature data for Norwich and Ipswich). The association between OH formation and iron were stronger on days with lower outdoor temperature. In addition, negative interactions between sulphur and iron load on the OH radical formation were observed in Erfurt (p < 0.0001), Antwerp city (p ¼ 0.11), and Norwich (p ¼ 0.06) meaning that on days with lower sulphur, OH was significantly higher for a given iron content than on days with a higher intrinsic sulphur load. Combining the centres in which significant interactions were observed into a mixed regression model confirmed the previously mentioned significant interactions in the city-by-city approach. 3.2. Aggregated correlates between locations In analyses of the aggregate mean across cities, we noted a strong correlation between the population density and both the

annual mean PM2.5 (r ¼ 0.50; p ¼ 0.02) and OH formation (r ¼ 0.46; p ¼ 0.048), the annual mean OH formation was not associated with distance from the sea (r ¼ 0.19; p ¼ 0.42). As expected, there was an inverse correlation between the distance from the sea and the annual mean sodium (r ¼ 0.48; p ¼ 0.036) and chloride (r ¼ 0.54; p ¼ 0.01) concentration. Mean annual outdoor temperature correlated strongly with mean annual iron load (r ¼ 0.71; p ¼ 0.0007) and the mean annual PM2.5 mass concentration (r ¼ 0.57; p ¼ 0.0095). Mean annual OH formation did not correlate significantly with mean annual outdoor temperature (r ¼ 0.25; p ¼ 0.31). 4. Discussion and conclusions Transition metals contribute to the oxidative capacity of particulate matter, and may represent a major determinant of their toxicity (Aust et al., 2002; Campen et al., 2001; McNeilly et al., 2004; Costa and Dreher, 1997). We observed among 20 European locations that the oxidative capacity of PM, measured by OH formation, varied widely, and was explained by variations in PM2.5 mass

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T.S. Nawrot et al. / Atmospheric Environment 43 (2009) 4595–4602

Table 2c Independent correlates of OH formation among 7 Cities in Southern Europe. Model A: Stepwise selection modela Independent variables

Model B: Forced modelc

Estimateb

Partial R2

p

Albacete Chloride

3.30(7.40 to 0.97)

0.06

0.13

Barcelona Vanadium Sodium

16.29 (0.80 to 36.32) 17.54(34.09 to 3.15)

0.12 0.11

0.11 0.10

Galdakao Vanadium Chloride Copper

50.90 (23.18 to 84.87) 13.73 (2.95 to 25.63) 7.01 (0.43 to 15.02)

0.42 0.15 0.04

<0.0001 0.0017 0.0750

Huelva Chloride Zinc

10.20(14.29 to 5.92) 11.28 (4.76 to 18.21)

0.24 0.20

0.0024 0.0015

Oviedo Sulphur Manganese Sodium

108.35 (58.60 to 173.72) 26.69 (11.66 to 43.74) 19.0(34.12 to 0.39)

0.32 0.19 0.05

0.0005 0.0015 0.0549

Pavia Iron Zinc Sulphur

41.30 (25.04 to 59.69) 22.80 (7.30 to 40.53) 8.32(16.80 to 1.00)

0.53 0.12 0.03

<0.0001 0.0019 0.0884

Turin Iron Copper Zinc Arsenic Lead

29.94 (1.71 to 66.01) 32.59 (12.56 to 56.18) 15.65(26.31 to 3.45) 9.72 (1.54 to 18.55) 15.41 (2.05 to 36.0)

0.67 0.07 0.04 0.02 0.01

<0.0001 0.0026 0.0167 0.0641 0.0972

Total R2

Independent variables

Total R2

0.06

None

0.0

0.23

None

0.0

0.61

0.40 Iron

0.44

0.23 Sodium

0.56

0.59 Iron Sulphur Sodium

0.68

0.56 Iron Sulphur

0.81

0.64 Iron

a Model A: Stepwise model: significant and independent correlates of PM oxidative activity (OH$ formation) were identified by stepwise regression within each center. Covariates shown within one centre are, thus, mutually adjusted. Variables considered for entry into the model were those reported in Table 1 with exception of PM2.5 and absorbance. b Effect sizes (95% CI) were calculated for a two-fold increase in the independent variables (log transformed) with exception for outdoor temperature and intrinsic blackness of the filter which was expressed as percentage change (95% CI) in OH formation. c Model B: Forced model: multiple regression model including: sulphur, silica, iron and sodium. Driven factors were those which reached the level significance (p < 0.10).

concentration. Furthermore, the contribution of transition metals to the redox activity was rather heterogeneous across these locations, precluding the development of a single common ‘European’ model. As a general finding holds that iron, chloride and sodium were important associates of OH variance, but even in these cases, the predictive power varied largely across Europe. Also between the two

Antwerp stations the associations were different, one with a positive and one with a negative association with chloride and sodium. The temporal changes in temperature did not systematically influence the ambient aerosol concentration or composition, although a strong correlation between outdoor temperature and sulphur load on the filter was seen in most centres. The increased

Table 3 Summary of independent and significantly correlated covariates of OH formation. City Albacete, Spain Antwerp City, Belgium Antwerp South, Belgium Barcelona, Spain Basel, Switzerland Erfurt, Germany Galdakao, Spain Gothenburg, Sweden Grenoble, France Huelva, Spain Ipswich, UK Norwich, UK Oviedo, Spain Paris, France Pavia, Italy Reykjavik, Iceland Tartu, Estonia Turin, Italy Umea, Sweden Uppsala, Sweden

S

Si

Al

Fe

Zn

Pb

Cu

Ti

As

Mn

V

Na

Cl –

þ –

þ

þ

þ

þ

þ

þ

þ þ

þ

þ þ þ

– þ –

þ

– þ

þ – – –

þ þ –

þ

þ –

þ þ þ

þ – þ

þ þ þ þ þ –

þ

– – –

–

þ – –

– –

þ

þ

þ – þ

– –

Stepwise model: significant and independent correlates of PM oxidative activity (OH formation) were identified by stepwise regression within each center. Each correlate in the table was therefore adjusted for the others. Variables considered for entry into the model were those reported. Estimates are given in Table 2.

T.S. Nawrot et al. / Atmospheric Environment 43 (2009) 4595–4602

sulphur concentration during days with a higher temperature may be attributed to the enhanced formation of secondary aerosols due to higher temperature and humidity. The redox activity of PM-associated metals depends not only on the concentration in the sample but also on bioavailability, chemical speciation, and oxidation state, which are not properly reflected in the elemental mass concentration derived by ED-XRF. Sulfate may mobilize iron from the surface of particles (Ghio et al., 1999) and in addition may modify transition-metal-catalyzed oxidative reaction in vivo by scavenging OH to yield less reactive inorganic radicals such as SO4 (De Laat et al., 2004). Thus, we explored how sulphur content may modify the association between iron and OH formation. We did in fact observe a relatively higher OH formation for a given intrinsic iron load on days with low sulphur. In some of the centres temperature was independently and positively associated with OH formation. Among studies of specific particulate matter components and mortality in Phoenix, AZ (Mar et al., 2006) and Washington, DC, (Ito et al., 2006) sulfate-related particulate matter showed the strongest associations among the source-apportioned particulate matter components. One possible explanation is that sulfate particles are more harmful; another potential explanation is that a higher warm weather rate of photochemical conversion from sulphur dioxide to sulfuric acid may make transition metals more water soluble and therefore more bio-available to cells (Gavett et al., 1997). Furthermore, production of secondary organic aerosols has been frequently observed during laboratory investigations in smog chamber experiments, arising from light irradiation of gaseous mixtures containing anthropogenic (aromatics) and also biogenic (terpenes) organic compounds (Plaza et al., 2006; Lee et al., 2004). Also, higher formation of carbon into organic aerosols has been observed in summer campaigns compared to winter (Plaza et al., 2006). These might also explain why some of the time-series studies find stronger associations between induced mortality by particulate air pollution in summer compared with winter (Nawrot et al., 2007). The inverse associations between OH formation and sodium and chloride may reflect the bioavailability, by the pH of the particulates, or might represent an epiphenomenon or surrogate for other metals. On the other hand if the total PM mass consists of low toxic compounds, such as sea salt (sodium, chloride), an inverse association can be explained by competition with the more toxic compounds. Although the temporal OH formation correlated reasonably well between the two Antwerp locations, heterogeneity of the determinants of the OH formation between the two locations only 11 km apart suggests the strong influence of locally-derived compounds. Differences in specific anthropogenic sources likely contribute to this heterogeneity: in the south of Antwerp a large non-ferrous smelter and a waste incinerator are present, whereas PM compositions in the centre of Antwerp are more influenced by traffic roads and the influence of and the Antwerp chemical industry in the seaport of Antwerp (Staessen et al., 2001). We did not measure polycyclic aromatic hydrocarbons concentrations (PAHs) or levels of endotoxins. PAHs may react in solo or in concert with transition metals to form OH. Previously, de Kok et al. (2005) and Ntziachristos et al. (2007) showed a positive association between PAH concentrations and radical-generating capacity of PM. In the latter study, however, no correlation was reported between radical formation and metal or transition metal concentrations or the interaction between polycyclic aromatic hydrocarbons and metal concentrations. A filter-based assessment of redox activity suffers from the inability to capture redox active volatile compounds, and old filters may differ in their redox characteristics from freshly collected PM samples. All our filters underwent the same procedures and storage, so that it is unlikely that effects related to filter handling may introduce systematic location-

4601

specific errors. However, redox activity in filter-based PM may also depend on the content and distribution of PM species. In this respect it is likely that the highly variable fraction of labile organic species in different locations may have affected the preservation of these species, and thus the overall PM activity. However, this can be counteracted by the fact that the assay here used is more sensitive to the activity of non-labile PM species, such as transition metals. Also part of the cross-centre heterogeneity may be explained by the inherent limitations of filter-based PM sampling, though there were no other affordable techniques available to collect PM when ECHRS was being conducted. We measured the total element concentrations by use of XRF. A disadvantage of this method is that no information is given on the bioavailability of the measured metals, which might give an explanation for the observed heterogeneity in e.g. the selection of iron as determinant. The main biologic argument for measuring OH formation from particles stems from the well supported notion that PM-induced oxidative stress is central to the observed toxicity in vivo and underlies many of the health effects observed in the population at large (Nel, 2005; Ayres et al., 2008). The importance of these radicalgenerating processes has been shown in studies demonstrating DNA oxidation in response to PM-induced formation of OH (Donaldson et al., 1997; Knaapen et al., 2002). In the absence of suitable models for redox activity the development of affordable measurement tools to characterize redox activity of ambient PM seems to be a useful strategy (Borm et al., 2007). OH formation in an oxidant environment (in the presence of H2O2) might differ from biological responses such as antioxidant depletion in the respiratory tract lining fluid, e.g. ascorbic acid as a reducing agent allows the assessment of radical reactions with the oxidative compounds on PM. Therefore, purely chemical assays are not the only option for routine assessment of potential oxidative stress activity of PM. Previous analysis (Kunzli et al., 2006) has shown that OH in the presence of hydrogen peroxide, using electron spin resonance and 5,5-dimethyl-1-pyrroline-N-oxide as spin trap, or by establishing their capacity to deplete antioxidants were highly correlated, demonstrating that particle suspensions recovered from filters contain components able to cause oxidative stress under very different conditions. Further OH formation of radicals was related to induction of oxidative DNA damage in lung epithelial cells in vitro (Shi et al., 2003). We conclude that although methodological challenges in our determination of OH radical formation of PM may have resulted in some loss in predictive power, the substantial differences and patterns observed between our measuring sites cannot be explained by methodological issues alone. Instead, we consider that a range of local conditions all affect the redox activity of ambient particles in a complex way, so that while we observed some common determinants of PM2.5 radical activity, there are marked differences both across Europe and even within the same city. The main methodological appeal of predicting OH radical formation from other PM characteristics instead of measuring redox activity is that the latter is far less readily available than data on PM constituents and blackness. However, given the complexity of mixed anthropogenic PM sources at any given urban location, it is likely that competing influences on oxidative stress in different PM samples currently denies the identification of a simple surrogate of radical formation as no set of elemental measures has been able to explain the oxidant variation in 19 European cities. Acknowledgements Funding was provided by ECRHS II: European Commission (QLK4CT-1999-01237), Swiss Federal Agency for Education and Science (BBW 99.0200), Swedish Environment Protection Agency, local authorities and other foundations, a U.S. Environmental Protection

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Agency fellowship FP-91637101-0 (T.G.), National Institute for Environmental Health Sciences P30ES07048 and 5P01ES11627 (N.K.), the Hastings Foundation and FWO (Fund for Scientific Research – Flanders, krediet voor navorsers [TN]). The authors declare they have no competing financial interests. References Alfaro-Moreno, E., Nawrot, T.S., Vanaudenaerde, B.M., Hoylaerts, M.F., Vanoirbeek, J.A., Nemery, B., Hoet, P.H., 2008. Co-cultures of multiple cell types mimic pulmonary cell communication in response to urban PM10. Eur. Respir. J. 32, 84–94. Aust, A.E., Ball, J.C., Hu, A.A., Lighty, J.S., Smith, K.R., Straccia, A.M., Veranth, J.M., Young, W.C., 2002. Particle characteristics responsible for effects on human lung epithelial cells. Res. Rep. Health Eff. Inst. 110, 1–76. Ayres, J.G., Borm, P., Cassee, F.R., Castranova, V., Donaldson, K., Ghio, A., Harrison, R.M., Hider, R., Kelly, F., Kooter, I.M., Marano, F., Maynard, R.L., Mudway, I., Nel, A., Sioutas, C., Smith, S., Baeza-Squiban, A., Cho, A., Duggan, S., Froines, J., 2008. Evaluating the toxicity of airborne particulate matter and nanoparticles by measuring oxidative stress potential–a workshop report and consensus statement. Inhal. Toxicol. 20, 75–99. Borm, P.J., Kelly, F., Kunzli, N., Schins, R.P., Donaldson, K., 2007. Oxidant generation by particulate matter: from biologically effective dose to a promising, novel metric. Occup. Environ. Med. 64, 73–74. Brunekreef, B., Holgate, S.T., 2002. Air pollution and health. Lancet 360, 1233–1242. Burney, P.G., Luczynska, C., Chinn, S., Jarvis, D., 1994. The European community respiratory health survey. Eur. Respir. J. 7, 954–960. Campen, M.J., Nolan, J.P., Schladweiler, M.C., Kodavanti, U.P., Evansky, P.A., Costa, D.L., Watkinson, W.P., 2001. Cardiovascular and thermoregulatory effects of inhaled PM-associated transition metals: a potential interaction between nickel and vanadium sulfate. Toxicol. Sci. 64, 243–252. Costa, D.L., Dreher, K.L., 1997. Bioavailable transition metals in particulate matter mediate cardiopulmonary injury in healthy and compromised animal models. Environ. Health Perspect. 105, 1053–1060. de Kok, T.M., Driece, H.A., Hogervorst, J.G., Briede, J.J., 2006. Toxicological assessment of ambient and traffic-related particulate matter: a review of recent studies. Mutat. Res. 613, 103–122. de Kok, T.M., Hogervorst, J.G., Briede, J.J., van Herwijnen, M.H., Maas, L.M., Moonen, E.J., Driece, H.A., Kleinjans, J.C., 2005. Genotoxicity and physicochemical characteristics of traffic-related ambient particulate matter. Environ. Mol. Mutagen 46, 71–80. De Laat, J., Truong, L.G., Legube, B., 2004. A comparative study of the effects of chloride, sulfate and nitrate ions on the rates of decomposition of H2O2 and organic compounds by Fe(II)/H2O2 and Fe(III)/H2O2. Chemosphere 55, 715–723. Donaldson, K., Brown, D.M., Mitchell, C., Dineva, M., Beswick, P.H., Gilmour, P., MacNee, W., 1997. Free radical activity of PM10: iron-mediated generation of hydroxyl radicals. Environ. Health Perspect. 105, 1285–1289. Gavett, S.H., Madison, S.L., Dreher, K.L., Winsett, D.W., McGee, J.K., Costa, D.L., 1997. Metal and sulfate composition of residual oil fly ash determines airway hyperreactivity and lung injury in rats. Environ. Res. 72, 162–172. Ghio, A.J., Stoneheurner, J., McGee, J.K., Kinsey, J.S., 1999. Sulfate content correlates with iron concentrations in ambient air pollution particles. Inhal. Toxicol. 11, 293–307. Gotschi, T., Oglesby, L., Mathys, P., Monn, C., Manalis, N., Koistinen, K., Jantunen, M., Hanninen, O., Polanska, L., Kunzli, N., 2002. Comparison of black smoke and PM2.5 levels in indoor and outdoor environments of four European cities. Environ. Sci. Technol. 36, 1191–1197. Gotschi, T., Hazenkamp-Von Arxb, M.E., Heinrich, J., Bono, R., Burney, P., Forsberg, B., Jarvis, D., Maldonado, J., Norback, D., Stern, W.B., Sunyer, J., Toren, K., Verlato, G., Villani, S., Kunzli, N., 2005. Elemental composition and reflectance of ambient fine particles at 21 European locations. Atmos. Environ. 39, 5947–5958.

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