Measurements were performed in the urban agglomerate of Barcelona, a city located in the NE of Spain in the western Mediterranean Basin (WMB). It is geographically constrained by the Llobregat and Besòs valleys (to the SW and NE, respectively) and the coastal range of Collserola to the N. The city has a population of 1.7 million, and the metropolitan area exceeds 4 million. These conditions result in a highly populous urban area and one of the highest car densities in Europe (6100 cars km-2, DGT, 2015). More detailed information about the SAPUSS study area can be found elsewhere (Dall'Osto et al., 2013a). The structure of the planetary boundary layer (PBL) above Barcelona was monitored by simultaneous measurements of ceilometers (Pandolfi et al., 2013). The field campaign took place in Barcelona from 20 September to 20 October 2010, including six sampling sites (Dall'Osto et al., 2013a). From those, the following four monitoring sites are considered for the present study:

Road Site (RS): represents the average conditions of a trafficked road in the city centre (about 17 000 vehicles per day). The monitoring site was located in the Urgell Street, a street canyon with four vehicle lanes (one direction) and cycling lanes in both directions.

High volume samplers DIGITEL DHA-80 and MCV CAV-A/mSb (30 m3 h-1) equipped with PM10 heads collected 12 h samples (from 11:00 to 23:00 and from 23:00 to 11:00, local time) on quartz fibre filters (Pallflex 2500QAT-UP) at the four monitoring sites. A total of 221 filter samples were collected, from which 93 % were collected simultaneously at the four monitoring sites (54 concurrent samples per site).

Meteorological parameters (temperature, relative humidity, wind speed and direction, solar radiation and pressure) were measured at the same sampling sites or at the nearest available meteorological station, as described elsewhere (Dall'Osto et al., 2013a).

As presented in the introduction overview paper of this special issue (Dall'Osto et al., 2013a), the average PM10 concentrations at the four monitoring sites during SAPUSS followed a decreasing trend from the site closest to traffic sources to the one located in the suburban background at 415 m a.s.l. (RS 30.7, UB 25.9, TM 24.8 and 21.8 µg m-3 at TC; Figs. 1 and 2). This suggests that the city surface is enhanced in coarse particles. It is important to note that the high PM1 / PM10 ratios at the towers (TM, TC) are not only due to the low PM10 concentrations, as the absolute values of PM1 detected at the tower sites were also higher than the urban background ground levels. Deng et al. (2015) recently also reported that – overall – the vertical distributions of PM10, PM2.5, and PM1 decreased with height. The lapse rates showed the following sequence: PM10 > PM2.5 > PM1, which indicates that the vertical distribution of fine particles is more uniform than that of coarse particles; and the vertical distribution in summer is more uniform than in other seasons.

Whilst the chemical analysis of the PM mass was only briefly described in an earlier study (Dall'Osto et al., 2013a), in this section the chemical components building up the PM10 mass concentrations are described in more details. The concentrations of each analysed species is shown in Table S1 in the Supplement. Figures 2 and 3 show the absolute and relative contributions to PM10 at each site.

Crustal elements accounted for 15–18 % of the mass, suggesting an unexpectedly homogeneous relative contribution to the total PM10 across the Barcelona urban area (Fig. 2). However, absolute mass concentrations of crustal matter (Fig. 3a) decreased from the city centre sites (5.5 µg m-3 at RS) to those located further inland (3.2 µg m-3 at TC), suggesting higher contribution of dust in the lower level monitoring sites.

Despite the low contribution of trace elements to the bulk PM10 (< 1 %), their concentrations provide valuable information by tracing specific pollution sources. Figure 3e–j show the concentration of a selection of trace elements associated with four known sources affecting the metropolitan area of Barcelona: crustal, road traffic, industry and shipping. The most abundant crustal tracer is Ti followed by far by Rb, Y, Ce, La, Li, Nd, and Ga. Such elements were found in higher concentrations at the ground sites (RS and UB) relative to the tower sites (TM and TC). This might be due to the dust resuspension by road traffic or other anthropogenic sources such as construction works or parks, thus producing a vertical decreasing gradient. The spatial variability of vehicle wear tracers such as (Cu, Sn and Ba; Schauer et al., 2006) is driven by the proximity to traffic sources, with RS being the most polluted site. The industrial emissions tracers for the city of Barcelona (Zn, Mn, Pb, As and Ni; Amato et al., 2009) showed higher concentrations at the UB followed by the RS, TM and TC. This is likely to be due to the pollution plumes originating from the industrial area along the Llobregat Basin (south-western side of Barcelona metropolitan area) that first impacts the UB site (Dall'Osto et al., 2013b). On the other hand, the remaining industrial tracers Cu and Sb (showing a decreasing gradient with the distance to traffic sources) might account for both industrial and traffic-related emissions. Both traffic and industrial tracers showed significant higher values at the ground sites respect the tower sites. In this regard, it is worth remembering that V and Ni (Fig. 3i–j) are usually associated with shipping emissions (Kim and Hopke, 2008; Agrawal et al., 2008). Indeed, higher V levels were found at the sites closer to the coast (TM and RS), in agreement with findings from Minguillón et al. (2014). However, higher Ni concentrations were recorded at the ground sites (UB and RS) respect to the tower sites (TM and TC), probably due to the contribution of other Ni sources reaching the ground sites (e.g. smelters). Viana et al. (2014) reported a V / Ni ratio associated to shipping emissions of 2.3–4.5. In our study the only site within this range was TM (2.4), located closest to the sea side. RS and UB showed lower ratios (1.3 and 1.1, respectively) probably due to the higher impact of an industrial plume containing Ni. TC ratio was 2.0, which might indicate an additional V loading of crustal origin, due to its location on a nearby hill.

The PMF analysis was applied to the PM10 data matrix, which contained 221 samples (56 at RS, 54 at UB, 55 at TM and 56 at TC). The method herein used is the same of the one recently reported by Amato et al. (2016), aiming at characterising similarities and heterogeneities in PM sources and contributions in urban areas from southern Europe. This method is not new, as it was already proven to increase considerably the statistical significance of the analysis (Amato et al., 2009). Different constraints were added into the PMF model (Paatero and Hopke, 2008), in order to reduce rotational ambiguity. The PMF solution chosen was an eight-factor solution, with a Q (constrained) value of 6627, the Q / Qexp ratio 1.17, and an increase dQ of 7.5 % with respect to the base run, due to the implementation of auxiliary equations.

One factor (road dust) was pulled towards the composition of local road dust (average of city centre samples, as reported by Amato et al., 2009). The road dust emission profile was introduced by means of auxiliary equation (pulling equations, Paatero and Hopke, 2008), consisting in pulling a fjk toward the specific target value a: Qaux=fjk-a2σaux2, where σaux is the uncertainty connected to the pulling equation, which expresses the confidence of the user on this equation. In the present study, a pulling equation was used for each species in order to pull concentrations of a given factor toward the target concentration in the road dust emission profile as obtained by Amato et al. (2009). An average profile of four road dust samples collected at four different points of the Avinguda Diagonal was selected as a representative emission profile. The Na / Al in the mineral factor ratio was pulled towards the value reported for earth's crust composition by Mason (1966) in form of auxiliary equation (Paatero and Hopke, 2009).

Overall, a total of eight factors were identified by the application of PMF: (1) “vehicle exhaust and wear”, (2) “road dust”, (3) “mineral”, (4) “aged marine”, (5) “heavy oil”, (6) “industrial”, (7) “sulfate” and (8) “nitrate”. The PMF result has proven to be very robust (the sum of the contributions of the chemical species for each source are close to one, see Table S2), and the PM10 mass has been well reconstructed by the PMF model (see Fig. S1 in the Supplement). The average concentrations of each factor registered at each site are shown in Fig. 5 and Table 1, while Fig. S2 displays the temporal variation of each factor for all sites. To further complete the analysis and interpretation of the results, Polar plots were obtained using the OPENAIR software package of R (Carslaw and Ropkins, 2012; R Development Core Team, 2012). These plots display the different factor concentrations depending on the blowing wind direction and speed, thus allowing deducing the main pollution sources origin (Fig. S3). The eight identified aerosol PMF factors characteristics can be seen in Fig. 4 and are summarised as:

The vehicle exhaust and wear factor profile (Fig. 4a) was dominated by EC and OC originating from vehicle exhaust emissions. Other chemical elements include Cu, Sb, Cr, Fe and Sn (67, 53, 46, 41 and 39 % of the variation, respectively) which are usually present in the brake and tyre wear (Sternbeck et al., 2002; Ntziachristos et al., 2007; Amato et al., 2009). Due to its direct traffic origin, this factor accounts for the highest mass contribution at the RS (27 %, 8.7 µg m-3), followed by UB (18 %, 5.0 µg m-3), TM (11 %, 2.9 µg m-3) and TC (10 %, 1.9 µg m-3; see Table 1 and Fig. 4a). A clear horizontal and vertical gradients can be seen for the PM10 contributions of this source. It originated at the traffic hot spots near RS, UB and TM and was later transported upslope towards TC by the sea breeze, where the maximum concentrations were recorded under SE winds from the city (Fig. S3).

The distance to the emission source strongly influences the concentration levels of certain PMF factors detected at the sampling sites. This is the case of the exhaust and wear and road dust factors, presenting a decreasing concentration gradient with the distance to the traffic hot spots, as the highest concentrations were found at the RS, followed by UB, TM and TC. Regarding the marine factor, the distance to the sea influenced the average concentrations, being highest at TM, followed by RS, UB and TC. In the case of the industrial factor, whose main source is located SW of the city, the UB was the first site impacted by this plume as it showed the highest concentrations, followed by RS, TC and TM. On the other hand, fairly homogeneous concentrations were recorded at the sites for mineral and heavy-oil factors, evidencing that its sources affect the whole urban area. Regarding sulfate, similar concentrations were recorded at the sites, although the higher concentrations displayed at the UB site remain unexplained at this stage. Concerning the nitrate factor, concentrations were found to decrease with the distance to traffic sources from RS to TC. However, at the TM site higher concentrations than at the UB site were recorded due to the elevation of the urban tower site above the ground favouring the formation of particulate nitrate due to colder temperature. Curci et al. (2015) also showed that an important player in determining the upper planetary boundary layer (PBL) aerosol is particulate nitrate, which may reach higher values in the upper PBL (up to 30 %) than in the lower PBL. Overall, the trends are in line with a recent study in a Chinese tower (Han et al., 2015), suggesting that the impact of primary sources from the ground decreased with the increase of height, while the impact of secondary sources mainly influenced by regional sources becomes more prominent.

To further study the vertical variability in the factor concentrations, the ratios between the average concentrations at the “ground” sites (RS, UB) and “tower” sites (TM, TC) were calculated for each factor, and the results are presented in Fig. 5. Three sources were found to be different between ground and tower levels: exhaust and wear, road dust and industry. The highest differences were found for the exhaust and wear and road dust factors, where the concentrations at the ground sites were 2.8 and 1.8 times those at the towers, respectively. The industrial factor concentration at the ground sites were on average 1.6 times higher than at the towers, pointing towards the greater impact of the SW industrial plume on ground levels; although a contribution of the small industrial facilities spread within the city should not be discarded. On the other hand, the remaining factors (mineral factor, aged marine and heavy oil) showed similar contributions at ground and tower sites. A vertical distribution for various chemical species was also previously reported in a number of other studies. For example, Han et al. (2015) recently showed similar percentage levels at the four different heights for Al and Si. However, for the Ca and EC fractions, higher values were observed at lower sampling sites. The percentages of nitrate, sulfate and OC, and the OC / EC ratios were higher at the higher sites. Source apportionment for ambient PM10 showed that the percentage contributions of secondary sources obviously increased with height, while the contribution of cement dust decreased with height. Ho et al. (2015) also reported that vertical variations were observed for mineral and road dust (Si, Ti and Fe) in the PM2.5 region. Similarly, Wu et al. (2014) reported traffic related aerosol (in particular resuspended road dust – traced with Si) and industrial ground activities vertically stratified. In summary – consistent with this SAPUSS study – exhaust traffic, non-exhaust traffic and industrial aerosol sources were the ones mostly affecting the aerosol vertical gradients.

The variability in PM10 concentrations and air mass scenario during SAPUSS (according to the classification presented by Dall'Osto et al., 2013a) shown in Fig. 1 was already briefly discussed in Sect. 3.1. Figure 6 shows the average concentrations at each site (for each factor) under the five air mass scenarios identified during SAPUSS: Atlantic (ATL), Regional (REG), North African west (NAF_W), North African east (NAF_E) and European (EUR). REG scenarios were related to the recirculation of air masses over the study area, thus favouring the accumulation of both primary (vehicle exhaust and wear, industrial) and secondary pollutants (sulfate, nitrate). Overall, concentrations were 32 % higher under these REG air masses due to low pollutants dispersion (Fig. 6). During the study period EUR air masses were related to a rainfall event, thus wet deposition caused a radical decrease in road dust concentrations under this scenario (0.3 µg m-3 vs. 2.7 µg m-3, see Fig. 6b). On the other hand, this factor showed the highest concentrations during NAF_E scenarios due to the increase of its loading and subsequent resuspension. Under North African air masses (NAF_W and NAF_E), average concentration levels of the mineral factor nearly doubled with respect to the average levels for non-African days (9.2 µg m-3 vs. 4.9 µg m-3, Fig. 6c). The NAF_E and EUR air masses crossed the WMB, blowing easterly winds inland and also causing an increase in aged marine aerosols concentration (Fig. 6d). The cruise and commercial port of Barcelona is located south of the city (Fig. S3), and thus under NAF_W air masses and S–SW winds the highest heavy-oil concentrations were recorded at all sites, pointing towards direct port emissions as the main contributor to this factor. ATL air masses were generally related to low concentrations for the different factors (due to pollutants dilution) except for the industrial factor, which might be explained similarly by the accompanying westerly winds under this scenario.

As discussed in Dall'Osto et al. (2013a), a number of possible REG stagnant different scenarios were classified during SAPUSS. As a case study, we consider two different ones: REG_1 (4 days between 29 September and 2 October) and REG_2 (4 days between 14–17 October). Figure S4 shows the meteorological diurnal profiles of the two scenarios. Whilst REG_1 shows warmer temperatures and a daily sea breeze circulation, REG_2 is characterised by colder temperatures, more stagnant air and an absence of a sea breeze circulation. Overall, high PM10 concentrations were recorded under REG air mass due to the accumulation of pollutants. REG_1 presented 19–31 µg m-3 PM10 average mass concentrations across the four sites, whereas REG_2 showed even higher loadings (30–41 µg m-3). The REG_1 episode allowed the transport of heavy oil towards the city with the development of the sea breeze, whereas in the REG_2 episode the poor development of the sea breeze minimized the transport of the shipping emissions towards the city (Fig. S4). It is interesting to note that during the REG_2 recirculation episode (14–17 October), the nitrate PMF factor concentrations were doubled (10.7 vs. 4.9 µg m-3 overall SAPUSS average) at the four monitoring sites, reaching occasionally higher levels at the tower sites (TM, TC) than at ground levels (RS, UB). By contrast, the sulfate PMF factor did not show a larger variation among different REG scenarios. The PMF nitrate/PMF sulfate ratio was found to be 1.2 and 2.3 for REG_1 and REG_2, respectively. As previously observed in a vertical aerosol study in London (Harrison et al., 2012) the cooler temperatures and higher relative humidity on the tower level during the REG_2 scenario can shift the gas/aerosol nitrate equilibrium towards the aerosol phase. In other words, during SAPUSS some aspects of nitrate behaviour were broadly similar to those of sulfate, but other aspects proved very different. During SAPUSS, aerosol time-of-flight mass spectrometer studies (Dall'Osto et al., 2013a) reported two types of nitrate aerosols. Briefly, the first appeared to be associated with local formation processes and occurred at times outside of the long-range transport episode. The second type of nitrate was regionally transported and internally mixed with sulfate, ammonium and both elemental and organic carbon (Dall'Osto et al., 2009). In this regard, it should be noted that the nitrate radical (NO3) is one of the the most important oxidants in the nocturnal boundary layer (NBL; Benton et al., 2010). Little is known about products between the formation of NO3, its reactions with volatile organic compounds (VOCs) and the formation of organic nitrate (Wayne et al., 1991; Brown et al., 2009). The PMF method applied in this aerosol-filter-based PM10 concentrations shows OC being an important component (11 %) for the PMF nitrate factor, although 19 % of the OC component was not described by the PMF and found in the PMF residuals. It is likely that the high concentrations of nitrate found in regional air masses during SAPUSS are a complex mixture of different types of aerosol nitrate, not been distinguished during this PMF analysis and likely due to the poor time resolution (12 h) of the off-line aerosol filter techniques (Dall'Osto et al., 2013a).

The PMF model was applied during this SAPUSS study and the results were presented in Sect. 3.2. However, PMF may not be able to separate similar sources and, due to chemical reactions, apparently “natural” PMF factors like mineral and marine may also include anthropogenic contributions. In order to elucidate the contributions to ambient PM10 concentrations, a combination of additional aerosol source estimation techniques were applied to further elucidate two main natural sources (mineral dust and marine sources) contributing to the PM10 mass during SAPUSS in Barcelona.