Spatial Variation of Aerosol Chemical Composition and Organic

Article pubs.acs.org/est

Spatial Variation of Aerosol Chemical Composition and Organic Components Identified by Positive Matrix Factorization in the Barcelona Region Claudia Mohr,*,†,§ Peter F. DeCarlo,†,∥ Maarten F. Heringa,† Roberto Chirico,†,⊥ René Richter,† Monica Crippa,†,# Xavier Querol,‡ Urs Baltensperger,† and André S. H. Prévôt† †

Laboratory of Atmospheric Chemistry, Paul Scherrer Institute (PSI), Villigen 5232, Switzerland Institute for Environmental Assessment and Water Research (IDAEA-CSIC), Barcelona 08034, Spain

‡

S Supporting Information *

ABSTRACT: The spatial distribution of PM1 components in the Barcelona metropolitan area was investigated using on-road mobile measurements of atmospheric particle- and gas-phase compounds during the DAURE campaign in March 2009. Positive matrix factorization (PMF) applied to organic aerosol (OA) data yielded 5 factors: hydrocarbon-like OA (HOA), cooking OA (COA), biomass burning OA (BBOA), and low volatility and semivolatile oxygenated OA (LV-OOA and SV-OOA). The area under investigation (∼500 km2) was divided into six zones (city center, harbor, industrial area, precoastal depression, 2 mountain ranges) for measurements and data analysis. Mean zonal OA concentrations are 4.9−9.5 μg m−3. The area is heavily impacted by local primary emissions (HOA 14−38%, COA 10−18%, BBOA 10−12% of OA); concentrations of traffic-related components, especially black carbon, are biased high due to the on-road nature of the measurements. The formation of secondary OA adds more than half of the OA burden outside the city center (SV-OOA 14−40%, LV-OOA 17−42% of OA). A case study of one measurement drive from the shore to the precoastal mountain range furthest downwind of the city center indicates the importance of nonfossil over anthropogenic secondary OA based on OA/CO.

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INTRODUCTION Adverse health effects of atmospheric aerosols are of special concern in highly populated areas.1 The identification and quantification of their sources, the basis for the design of mitigation strategies, remains challenging due to their complex nature. The organic fraction of atmospheric aerosols (organic aerosol, OA), often the major component in submicron particulate matter (PM1),2 is composed of 1000s of compounds3 subject to chemical reactions within the particles4,5 and the surrounding gas phase via condensation, evaporation, gas-phase oxidation, and recondensation processes.6−8 Recent advances in the apportionment of OA sources have been achieved through the combination of aerosol mass spectrometer (AMS) measurements9 and the factor analytical model positive matrix factorization (PMF).10 The application of this method has led to the identification of diverse OA components: Hydrocarbonlike OA (HOA, related to fresh traffic emissions), biomass burning OA (BBOA, from domestic wood burning or wildfire emissions), cooking OA, and different types of oxygenated OA (OOA, related to secondary OA), i.e., low volatility OOA (LVOOA), characterized as a regional, heavily aged OA, and the less aged semivolatile OOA (SV-OOA)11,.11,−16 Most of these studies were based on stationary measurements during a certain period of time. However, the evolution of pollutants in the plume © 2015 American Chemical Society

of a major source region such as an urban center or the characterization of the sources and processes of atmospheric pollutants of a whole region are best assessed by mobile measurements, ground based or using aircraft.17−25 The densely populated metropolitan area of Barcelona, Spain (5.4 million inhabitants) is subject to very high air pollution levels due to the combination of its Mediterranean climate, abundant biogenic and anthropogenic emissions, orography, and atmospheric dynamics. 26,27 Limited to the SE by the Mediterranean Sea and to the NW-SW by the Catalan coastal ranges, Barcelona experiences a daily cycle of nocturnal offshore flows, transporting air masses from the surrounding valleys (e.g., the densely populated Vallés) to the city center, and diurnal sea breeze, advecting polluted coastal air masses inland.28 The Llobregat river valley SW of the city center contains both extensive industrial and agricultural areas. This paper investigates the sources, processes, and evolution of PM1 in the Barcelona metropolitan area during late winter (when frequent cyclonic episodes further enhance pre-existing high Received: Revised: Accepted: Published: 10421

April 29, 2015 July 30, 2015 August 3, 2015 August 3, 2015 DOI: 10.1021/acs.est.5b02149 Environ. Sci. Technol. 2015, 49, 10421−10430

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Environmental Science & Technology

Figure 1. Route driven for the mobile measurements, and zones defined for the analysis. The part highlighted in red shows the traverse driven on 20 March. The two stars with the same color bracket the subparts of the route used for the calculation of the OA/CO slopes in the two different regions, respectively. The red dot depicts the location of the urban background station during the DAURE campaign.21

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PM1 concentrations29), with a special focus on the organic fraction. Mobile measurements were performed in March 2009 within the international DAURE campaign (Determination of the sources of atmospheric Aerosols in Urban and Rural Environments in the western Mediterranean).26 Concurrent stationary measurements of various particle- and gas-phase parameters at an urban background site investigated and quantified PM sources such as road traffic, constructiondemolition works, shipping emissions, and photochemical processes.30 Contributions of biomass burning31 or cooking21 emissions to OA, the fossil and nonfossil fractions of organic carbon,32 and the particulate trace metal content33 were assessed, among others. The results from the mobile measurements complement these findings by adding information on the evolution of the Barcelona city plume and the emission sources in the regions surrounding the city. Since measurements were carried out on-road, levels and composition of PM1 are influenced by close traffic emissions from circulating vehicles.

EXPERIMENTAL SECTION Mobile Measurements. Sixteen days of mobile measurements (61 sampling hours) were performed in the Barcelona metropolitan area with the Paul Scherrer Institute mobile laboratory, an IVECO Daily van equipped with several continuous online gas- and particle-phase instruments,34 between March 1 and 26, 2009. Figure 1 shows a map of the Barcelona metropolitan area and the driving route. It was divided into 6 different zones for both the measurements and the data analysis: City center (zone 1), harbor (zone 2), the industrialized Llobregat valley (zone 3), the coastal mountain range (zone 4), the precoastal mountain range (zone 6) and the heavily populated precoastal depression Vallés (zone 5). Table 1 summarizes the zones, driving dates, and times. For a discussion of zone identification, see SI. Instrumentation. Table S1 lists the instruments deployed in the mobile laboratory and gives information on the parameters measured, the detection limits, and the time resolution of the data acquisition. Details are provided elsewhere;34 only 10422

DOI: 10.1021/acs.est.5b02149 Environ. Sci. Technol. 2015, 49, 10421−10430

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Environmental Science & Technology Table 1. Zones of Barcelona Measurement Route driving times zone

name

date (2009)

drive 1

1

City center

01.03 04.03 06.03 10.03 25.03 26.03 01.03 18.03 19.03 11.03 18.03 23.03 04.03 09.03 10.03 12.03 20.03 23.03 25.03 26.03. 12.03 20.03 23.03 26.03 12.03 20.03 23.03 26.03

08:45−11:15 10:15−12:30 09:45−12:00

2

3

4

Harbor

Industrial valley Llobregat Coastal mountain range

5

Precostal depression Vallés

6

Precoastal mountain range Montserrat

drive 2

15:45−20:15 15:00−18:30 07:15−11:45 11:15−13:00 10:30−12:45 08:45−12:00 09:30−12:15 11:45−12:30 11:15−11:45 17:15−17:45 14:45−15:00 14:30−15:00 14:00−14:15 16:15−16:30 08:30−09:00 15:00−15:30 15:00−15:30 14:15−14:45 15:00−15:30 15:30−16:30 15:30−16:30 14:45−15:45 15:30−16:15

13:00−16:00 14:45−17:00 14:15−17:00

19:45−20:00 17:15−17:45 17:15−18:00 16:30−16:45 18:00−18:15 11:15−11:30 16:30−17:00 16:30−17:00 15:45−16:15 16:15−16:45

atmospheric scenarioa

time intervals for data analysisb

T A C A T T T A A A A T A A A A T T T T A T T T A T T T

t1: 07:00−12:45 t2: 14:45−20:30

t1: 10:15−13:15 t2: 12:45−17:15 t1: 08:30−12:30 t2: 14:00−17:15 t1: 08:15−09:15 t2:11:00−12:45 t3: 13:45−15:15 t4: 16:00−18:30 t5:19:30−20:15

t1: 14:00−15:45 t2: 15:30−17:15

t1: 14:45−16:30

a The prevailing atmospheric scenarios as defined by Pandolfi et al.26 For a short description of the atmospheric scenarios see main text. bTime intervals the data were binned to for the investigation of the influence of the time of day on the spatial variation. A single drive corresponds to a single time interval in a zone on a particular day, e.g., 10:30−12:45 in zone 1 on 19.03. The times for AM drives are printed in bold for clarity.

(1107, Grimm Labortechnik GmbH & Co. KG) and corrected with PM1 sample loadings collected on filters.21 This resulted in a CE = 0.7 for f NO3 ≤ 0.25, CE = 1 for f NO3 ≥ 0.78, and a linear increase between those values for 0.25 ≤ f NO3 ≤ 0.78 (Figure S1). The CO2+ ion signal was corrected for elevated gas-phase contributions by a time-dependent factor based on gaseous CO2 measurements. PMF was applied to organic high resolution (HR) mass spectral data (time resolution 6 s). The input matrices, comprising the complete mobile data set, were prepared as previously described.34 All data are in local standard time (UTC + 1) and at local pressure and temperature conditions (10−25 °C and 943−1048 hPa). Mobile measurements are usually carried out during short periods of time only (few hours per day during few weeks per year). Analyses of the spatial distribution of parameters based on mobile measurements thus always bear the risk of being overly influenced by meteorological conditions and diurnal patterns.23 This is especially true for the present study, where the mean concentration values for the different locations are based on aggregate data from several measurement drives spread out across several weeks. To reduce the influence of the temporal component on the spatial distribution, the time series of each parameter X measured in the mobile laboratory during a single drive (see Table 1) was divided by a normalization factor N based on the concentration of X measured simultaneously at the urban

deviations from this setup are provided here. The focus of the present analysis is on size-resolved chemical composition data of nonrefractory PM1 measured by the Aerodyne high-resolution time-of-flight aerosol mass spectrometer35 (AMS). AMS data were acquired in the higher sensitivity V-Mode35 at a time resolution of 6 s. The PM1 equivalent black carbon fraction (EBC) was derived from aerosol light absorption measurements at 670 nm by a multiangle absorption photometer (MAAP 5012, Thermo), time resolution 1 s. An ozone monitor (Dual Beam, Model 205, 2B Technologies, Inc.) added for this campaign measured O3 concentrations based on the absorption of UV light at 254 nm. NO2 concentrations are available from March 10, 2009 onward (when the chromium trioxide converter used to measure NOx34 ceased to function; earlier data, where a separation of NO and NO2 is not possible, are excluded from the analysis). All gases were measured with a time resolution of 1 s. Data Analysis. AMS data were processed using the software SQUIRREL v.1.51B and PIKA v1.1.10B within Igor Pro 6.22A (Wavemetrics). The collection efficiency (CE) was determined based on (a) the nitrate fraction f NO3 of the species measured by AMS36 and (b) comparison of the AMS when the mobile laboratory was measuring at the urban background site (location shown in Figure 1) with a scanning mobility particle sizer (SMPS, TSI 3071 type differential mobility analyzer37 custom built by PSI, condensation particle counter CPC 1022, TSI), a second AMS, and PM1 measured by an optical particle counter 10423


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Figure 2. Factor profiles of the 5-factor high resolution PMF solution found for the organic data matrix measured by AMS. Peaks are colored according to their elemental composition.

Figure 3. Average normalized concentrations for PM1 components per zone. Horizontal lines give the average concentrations for the different time intervals. The error bars represent the standard error of the mean.

background station. The normalization factor NDy, of species X for an individual drive, Dy, is given as follows: ⎧ X _BGD y ⎫ ⎬ ⎨ND y = X _BG ⎭ ⎩ ⎪

⎪

⎪

⎪

with X _BGDy denoting the background station mean value of X for the time interval of Dy and X _BG the background station campaign mean value of X. Individual factors for particle components instead of a single factor for all components based on total PM were used since different meteorological conditions

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Figure 4. Spatial variation of the normalized OA (Org) concentration and average relative composition of OA per zone and time interval. The standard errors of the mean of the reported percentage values are below 1% except for HOA, where they are up to 3%. The higher uncertainty for HOA is due to the influence of surrounding traffic on-road.

as the factors’ atomic oxygen to carbon (O:C) and organic matter (OM) to organic carbon (OC) ratios for both solutions. The noisy characteristics of the 6-s mobile data and different magnitudes of the peaks resulted in high scatter in the correlations, thus 5 min averages were used for most of the time series correlations (numbers in italics). PMF was run in the robust mode and the solution was analyzed as outlined in the SI from Mohr et al.,21 and the 5-factor solution was chosen. The most central rotation (f peak = 0) and a pseudorandom start value SEED = 23 were selected based on the correlations displayed in Table S3. Figures S4 and S5 show the mass fractions of the 5-factorial solution as a function of fpeak and SEED values tested, respectively. The ratio of Q (parameter to be minimized by the PMF algorithm10) over Qexp (expected Q, degree of freedom of the fitted data is) 1.18 for five factors, indicating a good error estimation.12,38 The 4-factor solution was discarded due to one factor being a combination of COA and BBOA (Figure S6); the 6-factor solution yielded a “residual” factor (Figure S7). Mobile and stationary HOA, COA, and LV-OOA factor mass spectra are correlated with R2 values of 0.99, 0.92, and 0.96, respectively (Table S3). The O:C ratios of the mobile and stationary HOA factors are similarly low (0.01 and 0.03, respectively). For the COA, the stationary factor exhibits a higher O:C ratio (0.21) than the mobile factor (0.1), indicating that measurements on-road were closer to the cooking sources with lower atmospheric residence time and lower exposure to oxidants. The mobile LV-OOA factor has a higher O:C ratio (0.98) than the stationary LV-OOA factor (0.75), likely due to mobile measurements including more aged and processed air masses downwind of the city center. The BBOA spectra (R2 = 0.77) exhibit differences in the signals at m/z 44 (and 28), which are higher in the mobile spectrum. This is also reflected in the O:C ratios (0.31 and 0.24 for the mobile and stationary factor profile, respectively). The SV-OOA spectra show the least similarities (R2 = 0.69), mostly due to the signal of the CHO ion

(e.g., relative humidity or radiation) can influence individual particle components differently. For PMF factors (Figure 2), the Org normalization factor was used (normalization was done after PMF analysis). Normalization factors per drives and species are presented in the SI, as well as a detailed discussion on the influence of the normalization on zonal mean values, and limitations of the method employed. We ask the reader to keep in mind that the mass concentrations reported in Figures 3 and 4 are normalized data. In addition, the data were binned into different time of day intervals to give an indication of the influence of the diurnal pattern of parameters on the spatial variation. Table 1 specifies the time of day bins and the prevailing atmospheric scenarios as defined by Pandolfi et al.26 which influenced meteorological conditions, strength of the sea breeze cycle, mixing/residual layer height, and thus pollutant concentrations in the region. Briefly, scenario A is characterized by the most severe pollution episodes, recirculation of air masses, the precoastal mountain range lying within the mixing and residual layers, and a weakened sea breeze cycle; scenario C by North Atlantic advection flushing the planetary boundary layer; T denotes the transition periods with strong sea breeze cycles and a decoupling of pollutant concentrations between the city center and the precoastal mountain range.

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RESULTS AND DISCUSSION PMF Results. Five OA factors were identified in the mobile data set, similar to the parallel stationary AMS data from the urban background site.21 These factors are shown in Figure 2, and are identified as the following: Hydrocarbon-like OA (HOA) from traffic (mostly diesel) emissions; biomass burning OA (BBOA) mostly from open agricultural fires;31 cooking OA (COA); and two different oxygenated OA (OOA), related to secondary OA: Low-volatility OOA (LV-OOA), and semivolatile OOA (SV-OOA). The R2 values of the correlations of the stationary and the mobile factor profiles, and of the time series of the PMF factors and ancillary data are shown in Table S3, as well 10425


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Environmental Science & Technology (R2 = 0.86 for the linear regression fit excluding CHO). Again, the O:C ratio of the mobile factor is higher than that of the stationary factor (0.54 and 0.32, respectively). Correlation of EBC with HOA (R2 = 0.63) is better than with BBOA (R2 = 0.24) and is likely due in part to sample bias from on-road sampling and variability in emission of BC from biomass combustion sources39 (Table S3). This indicates that the majority of EBC sampled originated from traffic related fossil fuel combustions (supported by the R2 = 0.71 of the correlation of HOA and CO, and R2 = 0.52 for HOA and NO2). These values are comparable to correlation factors found for measurements of traffic-related pollutants in Queens, New York with a soot particle AMS (R2 = 0.5 and 0.6 for HOA (including refractory BC) and BC or NOx, respectively).40 Usually in ambient data sets the time series of LV-OOA and SO4 are well correlated due to their regional characteristics.11 This is not the case for the present data (R2 = 0.30). We speculate that shipping emissions which occur spatially and temporally separated from urban emissions locally influence the SO4 concentrations.41,42 LV-OOA exhibits a temporal evolution (R2 = 0.40) similar to, e.g., the organic fraction of m/z 44 (f44), a surrogate for the degree of oxidation and thus photochemical age of the air mass.43 SV-OOA correlates well with NO3 (R2 = 0.74) due to its volatile nature, as observed in other data sets, for example, refs 11 and 21. Regional Overview. Figure 3 gives an overview of the normalized mean concentrations of PM1 components measured on-road per zone and time interval. For a comparison of normalized to measured mean values, see Table S2. Corrected particle number and gas phase concentrations are given in Table S4. Apparent from Figure 3 is the relatively uniform distribution of PM1 component concentrations across the Barcelona metropolitan region. EBC is the major component in all zones, with the highest zonal mean value measured in the heavily populated Vallés region (zone 5) followed by the city center (zone 1). These EBC values are biased high due to the influence of surrounding vehicles when measuring on-road. This influence is also visible in the large differences of EBC mean zonal values between different times of day (horizontal bars). In addition, urban EBC background concentrations might be more reduced compared to on-road concentrations (due to tailpipe emissions being a major source) than for AMS species. This effect is even more distinct during daytime when traffic activity is high. Mobile measurements were only performed during the day. The normalization to relatively lower background data will thus bias EBC values high relative to AMS species and make the direct comparison difficult. Consequently, zonal mean EBC values should only be interpreted qualitatively for concentration levels in different zones, but at the same time are indicative of high emissions in the entire region. The inorganic PM1 components NO3, NH4, SO4, and Chloride (Chl) show little variability in concentration and fractional contribution across the Barcelona metropolitan area. An exception are the industrial zones of the Llobregat valley, where Chl shows significantly higher concentrations, most likely due to HCl emissions from industries44 (not discernible in Figure 3, compare Figure S8). The ammonium nitrate/ammonium sulfate ratio is 1 day) air masses in other studies (pink slope in Figure 5, for example, refs 47−49). Since the aging time of this case study is much less than a day (∼2.5 h), the secondary OA mass formed downwind of the Barcelona city center might not be purely of anthropogenic origin. In fact, Minguillón et al.32 found that at Montseny, a measurement station during DAURE on the same precoastal mountain range, ∼ 72% of secondary OA carbon was nonfossil and from biogenic, biomass burning, and urban nonfossil sources (e.g., cooking). Ignoring that urban nonfossil sources also count as anthropogenic precursors, and taking only the fossil 28% of secondary OA into account, yields a slope of OA/CO of ∼24 μg m−3 ppmv−1 (black slope in Figure 5). Kleinman et al.32 calculated OA/CO ratios for different photochemical lifetimesa rough comparison shows that a slope of 24 μg m−3 ppmv−1 corresponds to ∼0.1 unit of photochemical age which in turn corresponds to ∼2.5 h, in good agreement with the time difference of 2 h between the start and end point of this measurement drive. The calculated secondary OA mass yield of 75 μg m−3 ppmv−1 (difference between ∼85 μg m−3 ppmv−1 at Montseny and ∼10 μg m−3 ppmv−1 in the city center) was compared to the mass increase in SV- and LV-OOA (normalized to the fit CO plume mean values scaled to 1, Figure S9) between start and end point of the measurement drive and showed good agreement as well (Figure S9b). Overall, the spatial distribution of on-road measured PM1 components shows that during March 2009, the entire Barcelona metropolitan area was heavily impacted by local emissions from traffic, cooking activities, and, to a lesser extent, biomass burning. Industrial areas, the Barcelona airport, harbor, an extensive highway network and agricultural areas surround the city center. During the measurements, several open fires were observed by the drivers (zone 3). The municipality of Barcelona has one of the highest car densities per km2 in Europe,50 and (primary) road traffic emission effects also include high concentrations of NO2 (48 ppbv), CO (556 ppbv), and particle number concentrations (115 000 cm−3). Note that since measurements were done on road, the concentrations of compounds related to traffic emissions will be enhanced compared to background concentrations, but may be indicative of near-road concentrations. The formation of secondary aerosol, mostly OA, favored by high precursor emissions of both anthropogenic and biogenic origin, and high photochemical activity in the Mediterranean climate adds more than half of the OA burden outside the city center. We conclude that the Barcelona metropolitan area is an important source of regional PM1, compared to, e.g., Paris, which has a very

photochemical activity from gaseous precursors emitted in the city center, which can be upwind of the harbor at this time of day.28 The high LV-OOA contribution to OA (32%, 2.2 μg m−3) may be partly due to aging of SV-OOA and advection of processed air masses by the sea breeze. In addition, the two afternoon drives in zone 2 were both during meteorological episode A, characterized by the most severe pollution episodes and high LV-OOA levels due to recirculation of air masses. The LV-OOA concentrations might thus be biased high despite normalization to urban background values. Zone 6 (Montserrat), a forested, mountainous, and sparsely populated region, exhibits the second-highest contribution of OOA. In contrast to zone 2, t1, LV-OOA (42%, 2.9 μg m−3) is higher than SV-OOA (21%, 1.5 μg m−3). From the city center to zone 6, there is a general trend in increasing contributions from (LV-)OOA, indicative of formation and aging of OA in the plume, as it evolves in the atmosphere downwind of the city. This will be investigated in the next section. OOA Formation in the Urban Plume. The formation of secondary OA from anthropogenic precursors in the plume downwind of a city can be investigated by the ratio of OA to CO.22,47−49 Generally, ΔCO (the CO concentrations minus a background value) is used as an inert tracer of urban emissions including aerosol precursors to account for dilution effects (evaporation upon dilution is assumed not to affect this relationship24,49). We use the portion of the measurement drive on March 20 from zone 1 (city center) to zone 6 (Montserrat, precoastal mountain range) to estimate anthropogenic secondary OOA formation downwind of the Barcelona city center. The meteorological conditions were governed by a transition period with low mixing layer height and a strong sea breeze effect.26 The measurement started at ∼14:00 local time, well after the sea breeze was established, at the shore upwind of the city center and reached Montserrat, with 711 m. a. s. l. the highest point in the measurement route, at 16:05 local time (see portion of the route highlighted in red, Figure 1). Figure 5 shows the measured OA plotted vs CO concentrations. Note that no CO background was subtracted here (issues with CO back-

Figure 5. OA (Org, μg m−3) vs CO (ppbv) for the portion of the drive highlighted in Figure 1 on March 20, colored by local time. The subsets of the data used to calculate the slopes using orthogonal distance regression are indicated by the stars in Figure 1 and color coded accordingly. The black curve gives the slope for purely fossil secondary OA based on the findings by Minguillón et al.32 Parameters a and b denote slopes and intercepts (only where data were fit, see text). 10427


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Environmental Science & Technology low impact on the air quality of its surroundings.51 In addition, the diurnal cycle of mountain wind and sea breeze can lead to recirculation of processed air masses mixing with new emissions, possibly causing slight changes in the relative contributions of the various components as a function of time of day. However, since this affects both the city center and the surrounding areas, and since the whole region is heavily populated and has abundant primary emissions sources, the overall concentrations and composition of PM1 are relatively similar across the whole area.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.5b02149. Experimental section zone identification; instruments; normalization of mobile data; results PMF results; regional overview; and additional references (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Phone: +49−721/608−2 2696; fax: +49-721/608-2 4332; email: [email protected] (C.M.). Present Addresses §

Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany. ∥ Department of Civil, Architectural, and Environmental Engineering and Department of Chemistry, Drexel University, Philadelphia, PA, U.S.A. ⊥ Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), UTAPRAD-DIM, Frascati, Italy. # European Commission Joint Research Centre (JRC), Institute for Environment and Sustainability, Ispra, Italy. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank the organizers of the DAURE project and the staff at IDAEA for their support. We acknowledge the IMBALANCE project of the Competence Center Environment and Sustainability (CCES) and the EU-FP7 project EUCAARI for financial support and “Accion Complementaria DAURE” from the Spanish Ministry of Science and Innovation (CGL2007-30502E/CLI) for infrastructure support. P.F.D. is grateful for the postdoctoral support from the US-NSF (IRFP# 0701013).

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REFERENCES

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