Atmospheric aerosols play an important role in our environment, affecting air quality and health (Pope and Dockery, 2006) and contributing to the largest uncertainties to the total radiative forcing (IPCC, 2007, 2013). Aerosol affects climate by perturbation of the Earth's radiative budget, directly through absorption and scattering of solar and terrestrial radiation and indirectly by acting as cloud condensation nuclei (Twomey et al., 1984; Albrecht, 1989). Most particles scatter the sunlight, causing a net cooling at the top of the atmosphere (TOA), whereas black carbon (BC) absorbs solar radiation in the whole visible spectrum, thus causing a net warming at the TOA (Jacobson, 2001; Ramanathan and Carmichael, 2008; Bond et al., 2013). Absorbing particles can modify the radiation fluxes directly by absorption of shortwave solar radiation and semi-directly by modifying the temperature distribution of the atmosphere. Absorption in the UV range is important, since it may affect photochemistry, thus reducing tropospheric ozone concentration (Jacobson, 1998; Chen and Bond, 2010). Mineral matter and some organic compounds mainly from biomass burning (BB) emissions, called brown carbon (BrC), can also absorb solar radiation in the UV range of the solar spectrum. BrC contains a large and variable group of organic compounds including humic substances, polyaromatics hydrocarbons and lignin (Andreae and Gelencsér, 2006), and it is formed by inefficient combustion of hydrocarbons (biomass burning) and also by photo-oxidation of biogenic particles (Yang et al., 2009). The light absorption by mineral dust depends on its content of ferric oxides (Sokolik and Toon, 1999; Alfaro et al., 2004).

Thus, the study of the relationship between physicochemical and optical properties of aerosols is strongly required in order to obtain a deeper characterization of atmospheric aerosols and, therefore, a better estimation of their radiative forcing. Some parameters can be derived from multi-wavelength scattering and absorption aerosol measurements in order to describe the optical properties as a function of the wavelength. These parameters, such as single scattering albedo (SSA), asymmetry parameter (g), scattering Ångström exponent (SAE), absorption Ångström exponent (AAE) and single scattering albedo Ångström exponent (SSAAE), are determined by the physicochemical properties of aerosols and are called intensive because they do not depend on the particle mass. These intensive properties present a valuable input for climate models, which require accurate information concerning the variability of atmospheric composition for targeted species via comparison with observations (Laj et al., 2009). Given the huge variety of aerosol emission sources and formation and transformation processes, there is a substantial need for accurate real-time aerosol optical measurements to achieve a low-error estimation of the effects that atmospheric particles have on climate coupling experimental measurements and modelling results (IPCC, 2007, 2013).

In order to get a wide coverage of the spatial variability of aerosols, aerosol optical data are obtained all over the world from both in situ and remote measurements. The in situ optical measurements are usually performed in international networks using automatic instruments which provide real-time data at high temporal and spatial resolution. Some of the most relevant networks are Aerosols, Clouds and Trace Gases Research InfraStructure (ACTRIS; www.actris.net), Global Atmospheric Watch (GAW; www.gaw-wdca.org), Aerosol Robotic Network (AERONET; www.aeronet.gsfc.nasa.gov) and NOAA baseline observatory (www.esrl.noaa.gov).

The WMB is affected by a large variety of emission sources: natural sources such as Saharan dust, marine aerosols and wildfire; industrial and urban emissions from densely populated areas along the coastline and transboundary sources from the European continent (Steinbrecher et al., 2009; Rodríguez et al., 2011; Pey et al., 2013a, b; Garcia-Hurtado et al., 2014). The atmospheric dynamics coupled to local orography gives rise to a complex mixture of pollutants (Millán et al., 1997) where aerosol formation and transformation processes take place and the accumulation of pollutants is very frequent (Rodríguez et al., 2002, 2003; Pérez et al., 2004; Jiménez et al., 2006; Pey et al., 2010; Jorba et al., 2013; Pandolfi et al., 2014b). Furthermore, the high occurrence of Saharan dust events (SDEs), especially during the summer period, also contribute strongly to the increment of PM10 levels in the WMB (Rodríguez et al., 2001, 2015; Querol et al., 2009; Pey et al., 2013a). In fact, more than 70 % of the exceedances of the PM10 daily limit value (2008/50/CE European Directive) at most regional background sites of Spain have been attributed to dust outbreaks (Escudero et al., 2007a). Thus, all these processes lead to a radiative forcing in the WMB that is among the highest in the world (Jacobson, 2001). Nevertheless, there is a large uncertainty in the total radiative forcing by atmospheric aerosols in the Mediterranean area (Mallet et al., 2013). The high occurrence and intensity of SDEs in the WMB give us the opportunity to look deeply into the characterization of the optical properties of mineral dust when mixed with local aerosols. Despite several studies having been published on physical and chemical properties of mineral dust in the WMB region (Rodríguez et al., 2001; Escudero et al., 2007b; Querol et al., 2009; Pey et al., 2013a), very few have studied how SDEs affect the aerosol intensive optical properties (Pandolfi et al., 2011, 2014a; Valenzuela et al., 2015)

Possibly related to the scarce use of biomass burning for domestic heating in the Mediterranean region compared to central and northern Europe, very few studies have been published describing BrC effects on intensive aerosol optical properties in the WMB. However, recent studies have estimated that biomass burning sources in the WMB may contribute more than expected to the measured ambient elemental carbon (EC) and organic carbon (OC) concentrations (Minguillón et al., 2011, 2015; Reche et al., 2012; Mohr et al., 2012; Viana et al., 2013; Pandolfi et al., 2014b). In these studies the biomass burning source was characterized by means of techniques such as positive matrix factorization (PMF) on AMS (aerosol mass spectrometer) or ACSM (Aerosol Chemical Speciation Monitor) data, filter-based analysis of 14C and/or specific chemical tracers such as levoglucosan or K+. Nevertheless, only few studies have used multi-wavelength aethalometer data (Sandradewi et al., 2008) in the WMB (Segura et al., 2014).

The main aim of this work is to provide a deep characterization of the intensive optical properties of atmospheric aerosols in the WMB under specific pollution episodes (SDEs and BB). Thus, here we evaluate the feasibility of using the intensive aerosol optical properties for the near-real-time detection of specific atmospheric events in the WMB. A sensitivity study aimed at calibrating the measured intensive aerosol optical properties is presented and discussed. We show that this calibration is needed to take into account the effects of local pollution on the intensive optical properties during SDEs and BB events. Moreover, we provide the range of variability of the calculated intensive optical properties as a function of the intensity of these events. This information is valuable input for models studying the radiative effects of atmospheric aerosols in this very peculiar area. With this aim, we used high-quality data collected at two stations located in the WMB: Montseny (MSY, regional background station; 720 m a.s.l.) and Montsec (MSA, remote station; 1500 m a.s.l.). A list of acronyms used in this work is provided in Table S1 of the Supplement.

The extensive and intensive aerosol optical properties and the equations used to derive the intensive properties are reported in Table 1 and briefly commented on below.

In order to study some of the aforementioned intensive optical properties over a wider spectral range, the 3λ scattering measurements from the nephelometer were derived at 7 aethalometer wavelengths using the SAE calculated from 3λ measured scattering. Once scattering was obtained at 7λ, we estimated SSA and SSAAE at these 7λ.

a.

The SAE depends on the physical properties of aerosols and mainly on the size of the particles. Generally, SAE lower than 1 or higher than 2 indicates that the scattering is dominated by larger or finer particles respectively (Seinfeld and Pandis, 1998; Schuster et al., 2006). In this study, SAE was estimated from a linear fit of 3λ scattering measured in the 450–635 nm range.

The aethalometer (AE) model allows the detection of fossil fuel combustion (FF) and biomass burning (BB) contributions to the total BC concentrations taking advantage of the different spectral absorption efficiency of the main markers of these two sources: BC for FF combustion and BrC for BB (Sandradewi et al., 2008b). The AE model has also been applied for FF and BB source apportionment to total carbonaceous material (CMtotal= OM + BC) and to organic matter (OM) (Favez et al., 2010). Light absorption measurements at 370–450 and 880–950 nm are used due to the fact that BC from FF combustion has a weak dependence on wavelength whereas BrC from BB shows enhanced absorption at shorter wavelengths. Here we applied the AE model to absorption measurements performed at 370 and 950 nm.

The AE model is usually applied selecting AAE values around 0.8–1.1 for BC from FF combustion (AAEff) and around 1.6–2.2 for BB (AAEbb). It is known that the AE method may lead to high uncertainties in the estimation of biomass burning contribution due to the high variability of AAEbb depending on the wood-burned combustion regime and on the internal mixing with non-absorbing materials (Lewis et al., 2008; Harrison et al., 2013). Thus, AAEff and AAEbb are usually chosen by comparing the AE model outputs with FF and BB contributions to BC and/or OM from other techniques such as chemical mass balance (CMB) model on offline filter measurements, positive matrix factorization (PMF) model on AMS and/or ACSM data or 14C technique (Favez et al., 2010; Herich et al., 2011; Crippa et al., 2013). Here we followed a similar procedure to calibrate the AE model: the optimal AAEff and AAEbb were selected, comparing results from the AE model with those obtained from PMF on simultaneous ACSM hourly data at the MSY station for 1 year (Minguillón et al., 2015). Then, the optimal AAEff and AAEbb for MSY were applied to MSA aethalometer model.

In the present work, CMtotal was calculated as the sum of BC concentration measured by MAAP (637 nm) and OM measured by ACSM. Following Eqs. (1)–(3), CMtotal was expressed as the sum of carbonaceous material from FF combustion (CMff), carbonaceous material from BB emissions (CMbb) and non-combustion organic aerosols (OA). At the MSY station, OA may account for a large contribution mainly in summer and includes principally organic aerosols from biogenic origin as reported in Minguillón et al. (2011) and Pandolfi et al. (2014b). Thus, we included the constant C3 in contrast to previous studies, where it was negligible assuming a low contribution of OA sources. CMff and CMbb were then expressed as the product of the constants (C1 and C2) multiplied by the aerosol absorption due to FF at 950 nm (babs,ff,950) and the aerosol absorption due to BB at 370 nm (babs,bb,370) respectively. The babs,ff,950 and babs,bb,370 were calculated for different values of AAEff and AAEbb following the equations reported in Sandradewi et al. (2008) and then used in Eqs. (1)–(3) for OM source apportionment. Finally, the constants C1, C2 and C3, which related the light absorption to the particulate mass, were calculated using multilinear regression (MLR) analysis. CMtotal=CMff+CMbb+OAOM+BC=C1babs,ff,950+C2babs,bb,370+C3OM+BC=(OMff+BCff)950+(OMbb+BCbb)370+OA Once BCff, BCbb, CMff and CMbb have been estimated, the contributions of FF and BB to OM (OMff and OMbb) can be calculated by subtracting BC from CM (Favez et al., 2010).

The present work shows variations of the intensive aerosol optical properties measured at regional (Montseny) and continental (Montsec) background stations in the WMB. We have studied the feasibility of using the near-real-time optical measurements performed at these stations for the detection of specific atmospheric pollution episodes affecting the WMB: Saharan dust and biomass burning.

The Ångström matrix revealed that Saharan dust events (SDEs) in the WMB were characterized by SAE on average lower than 1 due to the larger size of mineral dust particles and AAE values higher than 1.3 (up to 2.5 depending on the intensity of SDEs), indicating absorption in the UV by iron oxide contained within the mineral dust. Linear relationships were found between AAE and increasing %dust at MSY (0.7) and %PM1-10 at MSA (0.4), confirming the enhanced absorption in the UV due to mineral dust from SDEs. Interestingly, SAE showed higher sensitivity than g for characterizing the size of aerosols, with ranges between 0.55–0.75 and 0.50–0.70 at MSY and MSA respectively during SDEs.

Feasibility of detecting SDEs by means of SSAAE depended on both the location and altitude of the measurement station, which determines the aerosol background concentration and the intensity of the SDE. Better results were shown at higher-altitude locations, at MSA were detected most of the SDE (85 %), whereas at MSY, with a larger exposure to anthropogenic pollutants, the detection of SDEs depended mainly on the intensity of the Saharan dust outbreak. At the MSY site 50 % of SDEs were detected, which were unequivocally identified when the relative contribution of mineral dust to PM10 was higher than 60 %.

The proximity to anthropogenic sources of mainly fine particles can prevent both the Ångström matrix and the SSAAE parameter from detecting SDEs. We have shown that transport of anthropogenic pollutants (mainly finer particles and precursors) from the urbanized/industrialized coastline towards regional areas inland can hinder the effect of mineral dust on the intensive aerosol optical properties during less intense SDEs. We have also shown that regional atmospheric scenarios occurring after SDEs may favour the recirculation of mineral dust at regional level in the WMB. Thus, mineral dust can remain in the atmosphere for a few days after the SDE. This fact is highly relevant for air quality since SDEs frequently promote exceedances in the PM10 daily limit value.

Thus, depending on the background atmospheric conditions, not all SDEs can be clearly detected using SAE, AAE and SSAAE parameters. Additional information provided, e.g. by forecast models, back-trajectory analysis and columnar measurements, is also required to better detect and characterize these events. Nevertheless, aethalometer and nephelometer instruments provide near-real-time measurements and allow a fast detection of the impact of SDEs at ground level. Furthermore, due to the sensitiveness for detecting changes in aerosol size and composition, SSAAE and Angstrom matrix tools are more sensitive compared to other near-real-time measurements.

A sensitivity test using the aethalometer model at MSY showed that the model constants, which are representative of the main emission sources, are actually site-dependent and should be calculated for the area under study. FF sources showed a larger contribution than BB at MSY, leading to C1=1.05 and C2=0.26 (g m-2) for AAEff=1 and AAEbb=2. Moreover C3 was found to be significant mainly due to the large contribution of biogenic sources at MSY, showing values around 0.31 (µg m-3). Linear relations were found for comparisons between OMbb vs. BBOA (R2=0.43) and OMff vs. HOA (R2=0.6) showing fitting slopes of 1.27 and 4.4 respectively, which are consistent with SOA formation from BB and FF (25 and 90 %) emissions. Results from these comparisons were used to calibrate the aethalometer model, pointing to AAEbb=2 and AAEff=1 as the most suitable values for our emplacement.

Annual averages of BCbb contributions at MSY (36 %) and MSA (40 %) were significantly lower compared to other studies in northern Europe, due to less use of biomass burning as a heating system. OMbb contributions accounted for 30 %. BB source contribution to both BC and OM were predominant during winter, with increasing AAE up to 1.5 when %BCbb was higher than 50 %. Nevertheless, BC and OM were led by FF emissions sources during the summer period, due to stronger summer recirculation processes which are strengthened by sea and mountain breezes favouring the transport of pollutants toward regional areas inland. An interesting wildfire episode showed AAE values up to 2, accounting for BB contributions to BC and OM of 73 and 78 % respectively.

The aethalometer model is a powerful tool for reproducing long periods of real-time FF and BB contribution to BC, even in those areas where there is a predominance of carbonaceous material from non-combustion sources and BB emissions does not present very large contributions. BC, as the sum of BCff and BCbb, was well reproduced showing a slight overestimation of 11 and 13 % at MSY and MSA. Results for BCff and BCbb in winter and summer were in agreement with previous studies at MSY deployed by 14C analysis. Furthermore, BCff and NO2, both representative of traffic sources, showed good correlation for the winter period (R2=0.64).

However, the model presents larger uncertainty concerning OM apportionment as reported in other studies (Favez et al., 2010; Herich et al., 2011). Biogenic sources, which present important contributions to our emplacement, are probably slightly underestimated by the model due to the partial apportionment of C3 constant within OMff and OMbb. Furthermore, OMbb might be slightly overestimated due to the account of anthropogenic SOA within it, which can overlap with the absorption in the UV range. Despite the uncertainties associated with the source apportionment technique, OM time variation appears to be well reproduced. Nevertheless, OM formation and transformation processes occurring in the NWM should be taken into account when performing the AE model results, where important photochemical reactions take place led by large anthropogenic emissions and high insolation (mainly in summer).

The differentiation of brown carbon originated from different emission sources by using optical measurements is a challenge, in particular the SOA formation and transformation processes. Due to the uncertainties presented by the aethalometer model for providing absolute concentrations, it is recommended to carry out simultaneous measurements/experiments, applying different techniques not based on optical methods, such as using levoglucosan as a BB tracer or calibrating the model with BBOA obtained from ACSM source apportionment in order to assess the quantification of biomass burning by the model. Nevertheless, the aethalometer model is a very useful tool which provides satisfactory estimations of the temporal variability of the contributions for both biomass burning and fossil fuel emission sources. Further research in characterizing brown carbon by means of optical techniques is needed to exploit the possibilities of the instrument.

The nephelometer and aethalometer instruments are widely used within monitoring networks and present several advantages for near-real-time air-quality monitoring at high temporal resolution. We have demonstrated the potential of the intensive optical parameters obtained from both instruments for detecting specific air pollution scenarios in near real time. This is possible given the high sensitivity of particular intensive aerosol optical parameters to characterize different types of atmospheric aerosols. However, it is necessary to perform a previous sensitivity test in order to evaluate and calibrate the intensive optical properties for detecting specific pollution episodes at different emplacements.

The Montseny and Montsec data sets used for this publication are accessible online on the WDCA (World Data Centre for Aerosols) web page: http://ebas.nilu.no.