Introduction
Atmospheric aerosol particles affect the Earth's climate through the direct
scattering and absorption of solar radiation but also through indirect
processes acting as cloud condensation nuclei (IPCC, 2007). Precise
measurements of aerosol properties are required to reduce the current
uncertainties on radiative forcing (IPCC, 2007, 2013), and further research
that aims to study the existing
relationship between aerosol optical and chemical properties is needed to
better understand the air quality–climate link. However, a thorough
quantification of the direct and indirect aerosol effects on the Earth's
radiative budget is difficult to achieve (Zieger et al., 2012). The high
spatial and temporal variability of atmospheric aerosols along with the large
differences in particle composition and size (Andrews et al., 2011; Bond et
al., 2013; Haywood et al., 1999), results in a changing radiative forcing
from local to global scales (Collaud Coen et al., 2013). On the global scale,
atmospheric aerosols are estimated to cool the Earth system (Chen et al.,
2011; IPCC, 2013). Most aerosol components (mainly sulfate, nitrate, organics
and mineral matter) scatter the sunlight causing a net cooling at the top of
the atmosphere (TOA); conversely other particles, such as black carbon (BC),
absorb solar radiation in the whole visible spectrum, thus leading to a net
warming at TOA (Jacobson, 2001a; Ramanathan and Carmichael, 2008). Assessing
the role of aerosols on climate forcing often requires reducing their
physicochemical properties to a set of parameters that describe their optical
properties (Hand and Malm, 2007). The mass scattering and absorption
efficiencies (MSEs and MAEs) are key intensive optical parameters that relate
the mass concentration of specific chemical species to the particle light
scattering (σsp) and absorption (σap)
coefficients. These intensive optical parameters depend on intrinsic aerosol
properties, such as particle effective radius, particle mass density or
refractive index, and they are very useful to better parameterize the
aerosols direct radiative effect in atmospheric climate models (Seinfeld and
Pandis, 1998). In fact, Obiso et al. (2017) has recently assessed the
T-matrix optical code to simulate MSEs of different aerosol sources,
considering the MSEs reported in the present study as reference parameters
representative of the NW Mediterranean area.
Several studies have been published on the absorption efficiency of black
carbon (BC) calculated as the ratio between σap and elemental
carbon (EC) concentrations. Given that BC is the most important
light-absorbing particle in the atmosphere, its MAE has been extensively
studied in the literature (i.e. Bond
et al., 2013; Pandolfi et al., 2014a; Reche et al., 2011, among others). In some
cases, the MAE of BC has been observed to change depending on the degree of
the internal mixing of BC with non-absorbing material, such as sulfate and
organic compounds (Jacobson, 2001b; Moffet and Prather, 2009; Ramana et al., 2010; Zanatta et
al., 2016). Recently, the potential for organic carbon as an absorber of UV
and visible light through their brown carbon (BrC) content, has been also
reported in the literature (i.e. Lu et
al., 2015; Updyke et al.,
2012).
The MSEs of different chemical aerosol components have been extensively
reported for many locations (Vrekoussis et al., 2005; Titos et al., 2012;
Cheng et al., 2015 and references therein). An example is the study performed
by the IMPROVE (Interagency Monitoring of Protected Visual Environments)
programme, which has been considered as a reference for reporting mass
extinction efficiencies depending on particle composition (Hand and Malm,
2006 and 2007). Global MSEs for dry ammonium sulfate
[(NH4)2SO4], ammonium nitrate [NH4NO3], organic matter
(OM), soil dust and sea salt were obtained by means of a multilinear
regression (MLR) model. In the IMPROVE model, σsp
measurements (from 1990 to 2007) were used as independent variable whereas
the aforementioned externally mixed chemical species were used as dependent
variables. In addition, the IMPROVE study demonstrated that the
reconstruction of σsp can be inversely computed by means of
the calculated MSE and the mass concentration of chemical species. Revised
versions of the IMPROVE algorithm have been published that aim to reduce the
bias on the predicted values, which accounted for a 25 % overestimate of
the measured σsp coefficient (Ryan et al., 2005; Pitchford
et al., 2007). However, none of the published studies dealing with the
estimation of MSEs have considered the internal mixing state of atmospheric
aerosols, given that each chemical specie was treated separately from the
other.
In the present study a different approach of the MLR method is presented,
where the aerosol source contributions obtained by means of the PMF (positive
matrix factorization) model, instead of the single chemical species, were
used as dependent variables in the MLR model. An important characteristic of
the PMF factors is that these take into account the internal mixing of
atmospheric particles. In fact, as evidenced by the PMF sources chemical
profiles, these are constituted by some main tracers (which define the
source) but are also enriched in other chemical compounds. Receptor models
such as PMF are powerful and widely used techniques to design air quality
mitigation strategies (i.e: Belis et al., 2013; Viana et al., 2008), thanks
to the capability of these models to identify key pollutant emission sources
and calculate their contributions to the measured PM mass concentration.
Thus, the MLR model applied using the PMF source contributions and the
measured σsp and σap allows quantifying the
potential of different aerosol particle sources to scatter or absorb visible
light and therefore directly linking the air quality and climate effects of airborne PM.
With this approach we estimated the MSEs and MAEs of aerosol particle sources
identified at urban, regional and remote environments in the NE of Spain.
Furthermore, the computed MSEs and MAEs were used to reconstruct the particle
σsp and σap over an 11-year period at the
MSY regional site, thus allowing trend analyses. Trend analyses of particle
optical properties are extremely relevant for the detection of changes in
atmospheric composition depending on changes in natural or anthropogenic
emissions, atmospheric processes and sinks (Collaud Coen
et al., 2013). Several studies have shown that the air quality abatement
strategies adopted in the recent years have resulted in a decrease in
anthropogenic pollutants in Europe (EEA, 2013; Barmpadimos et al., 2012; Querol et al., 2014; Pandolfi et al.,
2016). However, the control of pollutant emissions is currently conflicted,
involving a trade-off between the impacts on environmental health and the
Earth's climate, and therefore current mitigation strategies could increase
climate warming while improving air quality (Shindell et
al., 2012). A relevant outcome of this new approach is the chance to study
the effects that air quality mitigation strategies are having on light
extinction in the area under study.
Map location and topographic profiles of Barcelona (BCN; urban
background), Montseny (MSY; regional background) and Montsec (MSA; remote
mountain-top background) measurement sites.
Methodology
Sampling sites and meteorology
The western Mediterranean Basin (WMB) is characterized by warm summers and
temperate winters with irregular precipitation rates throughout the year. In
winter the location of the Azores high-pressure system favours the entry of
Atlantic advections that clear the atmosphere of pollutants. In summer,
atmospheric dynamics coupled to local orography result in local/regional
circulations with a consequent accumulation of pollutants (Millán et al., 1997). Recirculation and aging of pollutants is
favoured by weak gradient atmospheric conditions, scarce precipitation and
continuous exposure to solar radiation driving photochemical reactions (Rodríguez et al.,
2002; Pérez et al.,
2004). Additionally, large mineral dust contributions from Saharan dust
events may exceed air quality standards
(Escudero et al., 2007; Querol et al.,
2009). The conjunction of all these processes surrounding the WMB lead to a
radiative forcing among the highest in the word (Jacobson,
2001a).
PM chemical and optical measurements were performed at three sampling sites
located in NE Spain (Fig. 1). The large coastal Barcelona urban area (BCN;
41∘23′ N, 02∘6′ E, 80 m a.s.l.) is one of
the most populated areas in the NW Mediterranean, resulting in a very high
road traffic density. Additionally, the metropolitan area is surrounded by a
broad industrial area and is home to one of the major
harbours in the Mediterranean Basin, with a large number of cruise ships (Pey
et al., 2013). The conjunction of these emission sources contribute greatly
to the air quality degradation in the area (Querol et al., 2001; Pey et al.,
2008; Amato et al., 2009; Reche et al., 2011; Dall'Osto et al., 2013).
The Montseny regional background station (MSY; 41∘19′ N,
02∘21′ E, 720 m a.s.l.) is located in the Montseny natural
park in a densely forested area, 50 km to the N–NE of the Barcelona urban
area and 25 km from the Mediterranean coast. Despite the site being far
enough from the industrialized and populated Barcelona metropolitan region,
it can be affected by anthropogenic emissions transported to regional inland
areas (Pérez et al., 2008).
The Montsec continental background site is a remote high altitude
location (MSA; 42∘3′ N, 0∘44′ E, 1570 m a.s.l.) on the southern side of the Pre-Pyrenees at the Montsec
d'Ares mountain range, located 140 km to the NW of Barcelona and 140 km to
the WNW of MSY. Despite the high-altitude location and the frequent free-troposphere conditions during the cold season, the station can be slightly
influenced by anthropogenic emissions during the warmer period, when it is
positioned within the planetary boundary layer (PBL) (Ripoll et al., 2014).
The three sites are members of the Catalonian air quality monitoring
network. Additionally, MSY and MSA are part of the ACTRIS (Aerosol, Clouds
and Trace gases Research InfraStructure) and GAW (Global Atmosphere Watch)
networks. Aerosol optical properties at the sites are measured following
standard network protocols (WMO/GAW, 2016). Further information
characterizing physical, chemical and optical properties of atmospheric
aerosols detailing the prevailing atmospheric dynamics at the three stations
can be found in Querol et al. (2001), Pey et al. (2009,
2010), Reche et al. (2011), Pandolfi et al. (2011,
2014a), Cusack
et al. (2012) and Ealo et al. (2016).
Measurements and instrumentation
Aerosol light scattering coefficients were measured every 5 min at three
wavelengths (450, 525 and 635 nm) with a LED-based integrating nephelometer
(model Aurora 3000, ECOTECH Pty, Ltd, Knoxfield, Australia). σsp measurements were collected at MSY for the period 2010–2014 (Table S1 in the Supplement) using a PM10 cut-off inlet. Measurements at MSA were carried out
using a PM2.5 cut-off inlet from 2011 to March 2014 and then
replaced with a PM10 cut-off inlet. σsp measurements at
BCN are not available. Calibration of the two nephelometers was performed
three times per year using CO2 as span gas, while zero adjusts were
performed once per day using internally filtered particle-free air. The
relative humidity (RH) threshold was set by using a processor-controlled
automatic heater inside the Aurora 3000 nephelometer to ensure a sampling RH
of less than 40 % (GAW report 226). σsp coefficients were
corrected for non-ideal illumination of the light source and for truncation
of the sensing volumes following the procedure described in
Müller et al. (2011a).
Aerosol light absorption coefficient at 637 nm (Müller
et al., 2011b) was measured at 1 min resolution with a Multi-Angle
Absorption Photometer (MAAP, model 5012, Thermo) operated in the heated
sampling mode and connected to a PM10 cut-off inlet. σap measurements were collected at BCN, MSY and MSA for the periods 2009–2014,
2010–2014 and 2011–2014 respectively.
Gravimetric PM10 mass concentrations were determined by standard
gravimetric procedures, according to UNE-EN 12341, 1999 protocol (Alastuey
et al., 2011). Samples were collected every 3 to 4 days on 150 mm quartz
micro-fibre filters (Pallflex 2500 QAT-UP and Whatman QMH) using high-volume
samplers (DIGITEL DH80 and/or MCV CAV-A/MSb at 30 m3 h-1) for the
periods 2004–2014 at BCN and MSY, and for the period 2010–2014 at MSA.
Chemical offline filter analyses were carried out at the three sites
following the procedure proposed by Querol et al. (2001). A quarter of the
filter was acid digested (HNO3 : HF : HClO4). The resulting solution
was analysed by inductively coupled plasma atomic emission spectroscopy
(ICP-AES; IRIS Advantage TJA Solutions THERMO) for the determination of
major elements (Al, Ca, Fe, K, Na, Mg, S, Ti, P) and by inductively coupled plasma mass spectrometry (ICP-MS; X Series II, THERMO) for the trace
elements (Li, Ti, V, Cr, Mn, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Cd, Sn, Sb, Ba,
rare earths, Pb, Bi, Th, U). In order to examine the accuracy of the acid
digestion, a few milligrams of the reference material NIST 1633b were added
to a quarter of the blank filter. Another quarter of each filter was water
extracted to determine soluble anions. The nitrate, sulfate and chloride
concentrations were resolved by ion high-performance liquid chromatography
(HPLC) using a WATERS ICpakTM anion column with a WATERS 432 conductivity
detector and the ammonium concentrations with an ion selective electrode
(MODEL 710 A+, THERMO Orion). Organic carbon (OC) and EC concentrations
were determined by a thermal-optical carbon analyser (SUNSET) following the
EUSAAR2 thermal protocol (Cavalli et al., 2010).
Blank filters were analysed together with the samples, and concentrations
were subtracted from those found in the samples in order to calculate the
ambient concentrations.
Positive matrix factorization model (PMF)
The positive matrix factorization (PMF) model (PMFv5.0, EPA) was
individually applied to the daily chemical speciated data collected at BCN,
MSY and MSA for source identification and apportionment to PM10. Source
contributions obtained for BCN and MSY can be found in Pandolfi et al. (2016),
whereas sources identified at MSA are presented in this study. Detailed
information describing the PMF model can be found in the literature (Paatero and
Tapper, 1994; Paatero, 1997; Paatero and Hopke, 2003; Paatero et al., 2005).
The PMF model is a factor analytical tool based on the weighted
least-squares method, which reduces the dimension of the input matrix (i.e.
the daily chemical speciated data) to a limited number of factors (or
sources). Calculation of individual uncertainties and detection limits were
based on the approach by Escrig et al. (2009) and Amato
et al. (2009), wherein both the analytical uncertainties and the standard
deviations of species concentrations in the blank filters were considered in
the uncertainty calculations. This procedure provides a criterion with which to
separate the species which retain a significant signal from the ones
dominated by noise, based on the signal-to-noise S/N ratio defined by
Paatero and Hopke (2003). Species with S/N greater than 1 may often indicate
a good signal, though this depends on how uncertainties are determined. In
order to avoid any bias in the PMF results, the data matrix was uncensored;
i.e. negative, zero and below detection limit values were included in the
analyses.
The PMF was run in robust mode (Paatero, 1997) and rotational ambiguity was
handled by means of the FPEAK parameter (Paatero et al., 2005). The final
number of sources was selected based on several criteria: (i) investigating the
variation of the objective function Q (defined as the ratio between
residuals and errors in each data value) depending on the number of sources
(i.e. Paatero et al., 2002), (ii) studying the physical meaningfulness of factor
profiles and contributions and (iii) analysing the scaled residuals and the G
space plots.
Multilinear regression model (MLR)
Previous studies based on the IMPROVE algorithm have applied the multilinear
regression (MLR) method to estimate the mass scattering and extinction
efficiencies (MSEs and MAEs) of chemical species (White, 1986; Vasconcelos et
al., 2001; Hand and Malm, 2007). This kind of regression model between
chemical species mass concentration and aerosol particle scattering or
extinction coefficients assumes an externally mixed aerosol. However, the
apportionment of scattering by more than one species to the total scattering
depends on the assumption of the internal or external mixing state of
atmospheric aerosols, as already noted previous studies (White, 1986). The
assumption of internal mixing among chemical species that form a single
variable in the regression equation will reduce the possible collinearity
among the dependent variables of the MLR model, making the regression
coefficients less sensitive to data uncertainties at the same time (Hand and
Malm, 2007). As shown in the matrix correlation in Fig. S1, a very low
correlation was observed between pairs of aerosol sources identified with the
PMF model at the three sites considered here.
In this study, we used the PM10 source contributions
(µg m-3) as dependent variables in the MLR and the measured
σsp and σap coefficients (Mm-1) as
independent ones. Thus, the resulting regression coefficients of the model
represent the MSEs and MAEs (m2 g-1) of mixed aerosol modes, given
that the sources from PMF take into account the possible internal mixing
among chemical species. Moreover, the MLR method assumes that all the species
contributing to σsp and σsp are included in
the equation. Thus, a better model performance is achieved here given that we
used the full PM10 chemical speciation in the PMF model for source
identification and apportionment. Following Eqs. (1) and (2) (as example for
MSY), the partial σsp and σap contribution
of each source can be computed as the product between the PM10 source
contributions and the corresponding MSEs/MAEs. Then, total aerosol light
σsp and σap can be modelled as the sum of
the scattering or absorption source contributions.
σsp,PM10λ=MSESecondarysulfateλ⋅Secondarysulfate+MSESecondarynitrateλ⋅Secondarynitrate+MSEV-Niλ⋅V-Ni+MSEAgedorganicsλ⋅Agedorganics+MSEMineralλ⋅Mineral+MSEAgedmarineλ⋅Agedmarine+MSEIndustrial/trafficλ⋅Industrial/trafficσap,PM10λ=MAESecondarysulfateλ⋅Secondarysulfate+MAESecondarynitrateλ⋅Secondarynitrate+MAEV-Niλ⋅V-Ni+MAEAgedorganicsλ⋅Agedorganics+MAEMineralλ⋅Mineral+MAEAgedmarineλ⋅Agedmarine+MAEIndustrial/trafficλ⋅Industrial/traffic
It should be considered that changes in the sampling conditions (i.e. RH or
size cut-off) or differences in the chemical analysis methods used on
sampled filters can affect the intensive particle optical properties (Delene and Ogren, 2002) and
consequently the comparison among the computed MSEs and MAEs. In fact, the
resulting efficiencies can be biased by the cut-off inlet, given that
absorbing aerosols tend to be predominately in the submicron fraction (Andrews
et al., 2011). In this study both σsp and σap were
collected using a PM10 cut-off inlet, thus guaranteeing uniformity
among the performed optical measurements. An exception occurs at MSA, where
a PM2.5 cut-off inlet was used until March 2014 and then replaced by a
PM10 inlet. Therefore, a slight overestimation of the MSEs obtained for
aged marine and mineral sources at MSA might be expected when sampling was performed through
the PM2.5 inlet, given that particles contained in these sources are mainly
present in the coarse fraction and significantly contribute to PM1–10 mass concentration (Ripoll et al., 2015a). However, an estimation of the
influence of the inlet change on the resulting MSEs and MAEs at MSA is
difficult to achieve, given the relatively short σsp and σap time series available, thus preventing performing two
different MLR analyses for the two fractions. Moreover, scattering RH was
controlled below 40 % at MSY and MSA in order to minimize the hygroscopic
growth of the particles and then prevent a significant enhancement in the
scattering efficiencies. An overestimation of the scattering or absorption
efficiencies can also be due to the fact that the MLR method tends to give
more weight to those variables that are more accurately measured (such as
sulfate), and conversely, underestimates the regression coefficients for
species with larger uncertainty (i.e. organic matter) (White and Macias,
1987). In the present study, a comparison between modelled and measured
coefficients was performed using quantitative statistics. With this aim, the
root mean square error (RMSE) and fractional bias (FB) were computed for
modelling evaluation. FB is described in Eq. (3)
(Ryan et al., 2005), where σspsim is the modelled scattering coefficient and σsp
is the measured value for each daily data point.
FB=σspsim-σspσsp
A total of 303, 379 and 503 daily data points were used in the MLR analysis
for source apportionment analysis of absorption at MSA, MSY and BCN respectively, whereas 222 and 307 daily data points were considered for MSE
calculation at MSA and MSY.
(a) Source chemical profiles and (b) source
contributions to PM10 mass concentration obtained at MSA by means of the
PMF model. PM10 average concentration, and absolute
(µg m-3) and relative (%) source contributions are reported
for the study period (2010–2014).
Absolute (µg m-3) and relative (%) average source
contribution to PM10 at BCN and MSY during the period 2004–2014
(Pandolfi et al., 2016) and for MSA during the period 2010–2014.
(µg m-3;
PM10
Aged
Mineral
Aged
Secondary
Secondary
Industrial/
Industrial/
V-Ni
Traffic
Road dust
%)
marine
organics
nitrate
sulfate
traffic
metallurgy
resuspension
BCN
34.0 ± 17.1;
5.73 ± 5.2;
4.61 ± 5.3;
4.45 ± 4.9;
4.67 ± 4.8;
0.96 ± 0.9;
3.32 ± 2.8;
5.14 ± 4.6;
4.25 ± 4.5;
100
16.9
13.6
13.1
13.7
2.8
9.8
15.1
12.5
MSY
16.7 ± 9.3;
1.76 ± 1.8;
2.70 ± 4.9;
3.78 ± 2.7;
1.31 ± 2.1;
3.95 ± 3.7;
1.43 ± 1.1;
0.71 ± 0.7;
100
10.6
16.2
22.7
7.9
23.7
8.6
4.3
MSA
9.7 ± 8.2;
1.08 ± 1.3;
2.27 ± 5.2;
2.84 ± 2.0;
0.72 ± 1.0;
0.87 ± 1.0;
1.09 ± 1.0;
0.79 ± 1.0;
100
11.1
23.6
29.4
7.5
9.0
11.3
8.2
Statistical tests for trends study
The Theil-Sen slope estimate (TS) (Theil, 1950; Sen, 1968) is a
non-parametric test which was investigated for the monthly averages of light
scattering and absorption in order to test for the occurrence of a non-null
slope in the data series during the period 2004–2014 at MSY. The total and
annual reduction of these optical parameters was investigated using bootstrap
resampling for the monthly deseasonalized time series, reducing the possible
influence of outliers on trend estimates and obtaining robust slope p
values.
A multi-exponential fit aiming to study temporal trends in the
multi-exponential form (Shatalov et al., 2015) was used to represent
the decomposed modelled monthly temporal series in the main component, seasonal
component and residual component. Additionally, this technique allowed us to
estimate the non-linearity (NL) parameter for the trends. An NL of 10 %
was used as threshold to define a linear trend (NL < 10 %).
Results
Source profiles and contributions to PM10
Seven aerosol particle sources were identified at MSA in the PM10
fraction by performing a PMF analysis for the period 2010–2014. The chemical
profiles and source contributions to the measured PM10 mass are shown in
Fig. 2 and Table 1. These results will be studied together with the chemical
profiles (Fig. S2) and source contributions (Table 1) previously quantified
by Pandolfi et al. (2016) for BCN and MSY for the period 2004–2014. The
highest PM10 average concentration was found at the BCN urban station,
followed by the regional (MSY) and remote (MSA) background sites
(34.0 ± 17.1, 16.7 ± 9.3 and
9.6 ± 8.2 µg m-3 respectively), consistent with the
progressive distance between the three stations and important emission
sources. On average, the most abundant sources contributing to PM10 mass
concentration at MSA were aged organics, followed, in this order, by mineral,
industrial/traffic, aged marine, secondary sulfate, V-Ni bearing and
secondary nitrate. Aged organics sources was mainly traced by OC and EC with maxima in summer, pointing to
a large contribution from biogenic emission sources, and accounted for
2.8 ± 2.0 µg m-3 (29 %) of the PM10 mass
concentration. The internal mixing with EC suggests a contribution from
combustion sources to this source. However, the aged organics source at MSA
can be considered to be dominated by secondary organic aerosols (SOAs)
arising from biogenic volatile organic compounds (VOCs) due to the
predominance of OC in the chemical profile. Furthermore, the higher summer
VOC oxidative potential occurring in the
Mediterranean should be considered, which enhances SOA concentrations due to
both higher insolation and tropospheric ozone concentration (Fuzzi et al.,
2006). This assertion is in agreement with previous studies deployed at MSA
where SOA was found to be the foremost constituent of PM1 organic
aerosols (OAs), especially in summer (90 %) (Ripoll et al., 2015a). The
mineral source, traced by typical crustal elements such as Al, Ca, Mg, Fe,
Ti, Rb and Sr, was related to both Saharan dust events and regional/local
mineral contribution and accounted for an average PM10 contribution of
2.3 ± 5.2 µg m-3 (24 %). The industrial/traffic
source, primarily traced by Pb, Zn, As, Sb, Cu and Ni, contributed
1.1 ± 1.0 µg m-3 (11 %). Aged marine sources,
mainly traced by Na and Cl, and in a minor proportion by Mg, SO42-
and NO3-, contributed 1.1 ± 1.3 µg m-3
(11 %). Secondary sulfate, mainly traced by SO42- and
NH4+, and secondary nitrate, traced by NO3- and NH4+
but also enriched in EC, contributed 0.9 ± 1.0 µg m-3
(9 %) and 0.7 ± 1.0 µg m-3 (8 %) respectively.
The V-Ni bearing source, traced by V,
Ni and SO42-, represented the direct emissions from heavy oil
combustion, mainly shipping in the study area, and contributed
0.8 ± 1.0 µg m-3 (8 %). In contrast to BCN and MSY,
the V-Ni bearing source at MSA was not enriched in EC, possibly because of
the high altitude of this station and its position far from the NW
Mediterranean coastline and shipping emissions.
Relative (%) monthly average source contribution to PM10
concentration (µg m-3) at (a) BCN, (b) MSY and
(c) MSA, absorption (Mm-1) at 637 nm
(d, e and f) and scattering (Mm-1) at
525 nm (g, h). PM10 source contributions were obtained from
the PMF model, whereas scattering and absorption contributions were modelled
by means of the PMF–MLR technique. The study period ranges between 2004–2014
at BCN and MSY and between 2010–2014 at MSA.
Common sources identified at the three stations were mineral, aged marine, secondary nitrate, secondary sulfate and V-Ni. The sources
identified in BCN showed similar contributions, ranging from 10 to
17 % of the total PM10 mass concentration, except for the
industrial source (3 %), given that most of the secondary industrial aerosols are
apportioned to other secondary sources presented in this study. At BCN,
sources traced by pollutants from anthropogenic activities were mostly
related to fresh emissions from the Barcelona metropolitan area (i.e.
traffic and road dust resuspension), from the surrounding industrial zone (industrial) and from vessel traffic
(V-Ni). However at MSY and MSA, which are representative of regional and remote
backgrounds, pollutants were transported together from the urban and
industrial areas of Barcelona, thus resulting in an aged aerosol mixed with local
pollutants. Larger relative contributions of mineral and aged organics sources were observed at
the MSA high-altitude site due to a less direct exposure to anthropogenic
emissions (Fig. 3c). In agreement with previous studies (Ripoll et al.,
2015b; Ealo et al., 2016), a higher relative mineral contribution was found at MSA
(23 %) compared to MSY (16 %) and BCN (14 %). However, a higher
absolute mineral contribution mainly originating from local sources was observed at
BCN (4.6 ± 5.3 µg m-3). The aged organics source also presented a higher
relative contribution at MSA (29 %) compared to MSY (23 %). However,
this source was not identified at BCN, where the traffic source explained the
majority of the measured OC. The aged marine source in Barcelona showed higher absolute
and relative contributions (5.7 ± 5.2 µg m-3; 17 %) due
to its proximity to the coast compared to MSY (1.8 ± 1.8 µg m-3; 11 %) and MSA (1.1 ± 1.3 µg m-3; 11 %).
Higher relative contributions of secondary sulfate and secondary nitrate were found at MSY (24 and 8 %)
compared to MSA (9 and 7 %), likely because of the longer distance from
MSA to the Barcelona metropolitan area. Moreover, the free-troposphere
conditions typically occurring in MSA during the colder months prevented the
direct transport of aerosol particles from anthropogenic sources to the
station. The V-Ni bearing source showed similar absolute contributions at MSY
(0.7 ± 0.7 µg m-3; 4 %) and MSA (0.8 ± 1.0 µg m-3; 8 %) despite the longer distance from MSA to the Mediterranean
coast, pointing to a possible influence of long-range transport affecting
the mountain-top site. It should be noted that the current increasing
shipping emissions contribute greatly to air quality degradation in
coastal areas (Viana et al., 2014), but also
in regional and remote environments as consequence of atmospheric transport
processes.
Overall, the impact of the identified aerosol sources at the different
background sites depended on the distance to important emission sources and
on the aging and transport of aerosol particles to regional and remote
inland areas driven by orography and meteorology, thus mostly explaining the
differences in the chemical profiles of the sources identified at the three
sites.
Seasonal variation of source contributions to PM10
Monthly average source contributions to PM10 obtained at the three
stations are shown in Fig. 3. MSY and MSA were characterized by a marked
PM10 seasonal variation with higher concentrations in summer (June, July
and August) and lower in winter (December, January and February), in
agreement with previous studies (Pérez et al., 2008; Ripoll et al.,
2014). The summer increase is related to the higher frequency of Saharan dust
events, the recirculation of air masses that prevent air renovation, the
resuspension processes due to the dryness of soils, the low precipitation and
the formation of secondary aerosols (Rodriguez et al., 2002). The lower
winter concentrations can be explained by the high frequency of Atlantic
advections leading to a higher dispersion of pollutants and to higher
precipitation rates compared to summer. Moreover, the reduced contribution
from the PBL in winter due to frequent thermal inversions also contributed to
the relatively low PM10 mass concentration observed at MSY, and
especially at MSA (Pandolfi et al., 2014a). The PM10 concentration peak
observed in February and March at MSY is remarkable and might be attributed
to the winter regional pollution episodes typical of the WMB (Pandolfi et
al., 2014b). Such scenarios are characterized by anticyclonic conditions
which favour the accumulation of pollutants close to the emission sources,
and the subsequent transport of pollutants towards the station with the daily
increase in the PBL. Pandolfi et al. (2014b) and Pey et al. (2010) reported
high nitrate concentrations during these atmospheric conditions at MSY, in
agreement with the increased contributions of secondary nitrate shown in
Fig. 3b during this time of the year. The relatively low PM10
concentration observed in August at BCN and MSY could be partially explained
by reduced anthropogenic activities in the Barcelona metropolitan and
industrial areas as a result of the holiday period in Spain. This result is
supported by the minima of absolute contributions observed in August for
industrial and traffic sources at BCN (0.6 ± 0.6 and
2.7 ± 1.6 µg m-3 respectively) and for the
industrial/traffic source at MSY
(0.9 ± 0.7 µg m-3). The higher precipitation rates
observed in August compared to June–July (Perez et al., 2008) might also
contribute to reducing PM10 concentrations at MSY. Conversely at MSA,
the highest PM10 concentration was observed in August, probably due to
the frequent Saharan dust events affecting the mountain top site, in
accordance with the highest absolute contribution found for the mineral
source in August (4.8 ± 4.8 µg m-3).
Higher relative contributions of aged marine (23 %), mineral (18 %), secondary sulfate (16 %) and V-Ni bearing
(13 %) sources were observed on average in summer at BCN. By contrast,
traffic (23 %), secondary nitrate (21 %) and industrial (4 %) sources maximized in winter (Fig. 3a). The
seasonal variation of secondary sulfate and secondary nitrate can be attributed to a higher SOA contribution,
the favoured formation of sulfate and the nitrate gas–aerosol partitioning,
leading to the thermal instability of secondary nitrate during the warmer period, as was
already observed in the area under study using offline filter sampling
(Pey et al., 2009;
Ripoll et al., 2015b) and online measurements
(Ripoll et al., 2015a). In contrast to BCN, a
higher relative contribution of secondary sources, some of them related to
natural processes, was observed at MSY and MSA (3b and 3c). Increased
contributions of secondary sulfate were observed in summer (29 and 8 % at MSY and MSA), whereas secondary nitrate maximized in winter (17 and 11 %). Aged organics showed the
highest contribution in relative terms in winter (30 and 45 % at MSY
and MSA); however the highest absolute contributions were
observed in summer (4.8 ± 2.8 and 4.1 ± 1.9 µg m-3).
This result is in agreement with the higher SOA formation found at MSA
(Ripoll et al., 2015a) and MSY (Minguillón et al., 2015) during the warm
period. The mineral source (19 and 27 % at MSY and MSA)
maximized in summer, although high contributions were also observed in
spring. Similarly to BCN, aged marine (14 and 13 % for MSY and MSA)
and V-Ni bearing (5 and 11 %) sources showed the highest contributions in summer,
whereas the industrial/traffic source maximized in winter (11 and 17 %).
Scattering and absorption efficiencies (MSEs and MAEs;
m2 g-1) calculated for the different aerosol sources identified by
PMF at BCN, MSY and MSA in the PM10 fraction. Scattering
Ångström exponent (SAE) and single scattering albedo (SSA)
coefficients were obtained for each source at MSY and MSA. Note that SAE was
not considered for the aged marine source at Montsec due to the
PM2.5 cut-off inlet. The study period ranges between 2010–2014 at BCN
and MSY and 2011–2014 at MSA.
Aged marine
Mineral
Aged
Secondary
Secondary
Industrial/
Industrial/
V-Ni
Traffic
Road dust
organics
nitrate
sulfate
traffic
metallurgy
resuspension
BCN
MAE 637
0.108 ± 0.021
0.087 ± 0.050
0.284 ± 0.040
0.359 ± 0.035
0.138 ± 0.185
0.928 ± 0.058
1.672 ± 0.050
0.062 ± 0.084
MSY
MSE 450
1.205 ± 0.385
1.046 ± 0.130
1.990 ± 0.258
10.456 ± 0.494
5.860 ± 0.256
2.241 ± 0.982
10.844 ± 1.850
MSE 525
1.211 ± 0.316
1.262 ± 0.106
1.414 ± 0.212
8.783 ± 0.405
4.508 ± 0.210
2.057 ± 0.805
8.029 ± 1.516
MSE 635
1.201 ± 0.284
1.429 ± 0.096
0.916 ± 0.190
6.980 ± 0.364
3.092 ± 0.188
2.425 ± 0.723
4.687 ± 1.362
MAE 637
0.027 ± 0.018
0.005 ± 0.007
0.169 ± 0.011
0.234 ± 0.028
0.122 ± 0.010
0.867 ± 0.047
0.526 ± 0.065
SAE
0.010
-0.896
2.254
1.175
1.861
0.556∗
2.451
SSA
0.978
0.997
0.844
0.968
0.962
0.736
0.899
MSA
MSE 450
0.036 ± 0.407
0.931 ± 0.115
2.114 ± 0.338
9.839 ± 0.978
13.825 ± 0.792
2.714 ± 0.644
4.823 ± 0.659
MSE 525
(-)0.054 ± 0.332
1.077 ± 0.093
1.335 ± 0.275
7.839 ± 0.797
10.699 ± 0.537
2.354 ± 0.525
3.538 ± 0.537
MSE 635
(-)0.036 ± 0.268
1.276 ± 0.076
0.617 ± 0.223
6.006 ± 0.645
7.439 ± 0.522
2.044 ± 0.425
2.274 ± 0.435
MAE 637
0.015 ± 0.010
0.029 ± 0.003
0.14 ± 0.009
0.364 ± 0.023
0.173 ± 0.021
0.206 ± 0.016
0.165 ± 0.017
SAE
–
-0.914
3.594
1.432
1.804
0.819
2.189
∗ SAE for the industrial/traffic source at MSY
was calculated in the range 450–525 nm.
Mass scattering and absorption efficiencies of aerosol sources
Source-dependent mass scattering (at 450, 525 and 635 nm) and absorption (at
637 nm) efficiencies obtained at the different sites are shown in Table 2.
The MSEs and MAEs for some of the sources reported in this study cannot be
directly compared to MSEs and MAEs published in the literature for specific
chemical species, given that the sources identified from PMF take into
account the possible particle internal mixing. Similar MSEs were observed for
secondary nitrate at MSY and MSA (8.8 ± 0.4 and
7.8 ± 0.8 m2 g-1 respectively at 525 nm). These values are
in the upper range when compared to MSEs reported in the literature for the
ammonium nitrate specie calculated through stoichiometry. Hand and
Malm (2007) determined MSEs of 3.2 ± 1.2 m2 g-1 for dry
PM2.5 ammonium nitrate, Cheng et al. (2015) obtained values of
4.3 ± 0.6 m2 g-1 under high mass loading in Shanghai, Tao
et al. (2014) found MSEs ranging from 1.7 ± 0.8 in summer to
6.7 ± 1.8 m2 g-1 in winter in Chengdu (China) and Titos et
al. (2012) observed a coefficient of 5 ± 2 m2 g-1 for
nitrate ion in an urban area in southern Spain. MSEs for secondary sulfate
were quite different between MSY and MSA (4.5 ± 0.2 and
10.7 ± 0.5 m2 g-1), probably due to differences in the
source origin and the related particle size. Hand and Malm (2007) published
lower values for the total mode of dry ammonium sulfate ranging between 0.8
and 2.4 m2 g-1, whereas a MSE of
3.5 ± 0.5 m2 g-1 was found by Cheng et al. (2015) in a
polluted environment. Tao et al. (2014) showed MSEs of 4.4 ± 0.7 and
5.7 ± 0.2 m2 g-1 in winter and summer respectively for the
PM2.5 fraction in Chengdu. MSEs for non-sea-salt (nss) sulfate ion were
calculated at Finokalia and Erdemli from the slope between total scattering
and nss sulfate concentration, showing values of 5.9 ± 1.8 and
5.7 ± 1.4 m2 g-1 (Vrekoussis et al., 2005). Higher MSEs
were found in an urban background in the south of Spain (Titos et al., 2012)
and in the Negev desert (Formenti et al., 2001) at 7 ± 1 and
7 ± 2 m2 g-1. Given that in our study sulfate
concentrations were mainly explained by secondary sulfate and V-Ni sources,
significant differences were also observed for the MSE of the V-Ni bearing
source at MSY and MSA (8.0 ± 1.5 and
3.5 ± 0.5 m2 g-1). The V-Ni bearing source at MSY
originated mainly from shipping emissions at regional (vessel traffic in the
Mediterranean) and local (Barcelona harbour) scales. Conversely at MSA,
located at a higher altitude, this source might also be influenced by
continental transboundary transport and then internally mixed with different
chemical species. In fact, as shown in Fig. 2a for MSA and in Fig. S2 for
MSY, the V-Ni bearing source profile at MSA is enriched in OC, which is not
observed at MSY. The aged marine source at MSA showed negative MSEs at 525
and 635 nm. This was likely due to the larger distance from the coast of
MSA, thus preventing a strong signal from the aged marine source at this
site, and/or due to the PM2.5 cut-off inlet used at the beginning of the
measurement period which prevented the sampling of coarse particles. However,
MSEs for the aged marine source at MSY (1.2 ± 0.3 m2 g-1)
exhibited values within the same range as those reported by Hand and Malm
(2007) for coarse-mode sea salt (1.0 m2 g-1). The MSE for the
mineral source (1.3 ± 0.1 and 1.1 ± 0.1 m2 g-1) was
similar at MSY and MSA. This similarity could be explained by the low
reactivity of mineral dust particles, which were mostly externally mixed with
other chemical species. Thus, less chemical transformation can be expected
for mineral particles during the transport towards the stations. Lower MSEs
were found for mineral matter by Hand and Malm (2007)
(0.7 ± 0.2 m2 g-1), by Titos et al. (2012) in the urban
background of Granada (0.2 ± 0.3 m2 g-1) and by Vrekoussis
et al. (2005) in Erdemli (0.2 m2 g-1). Similar coefficients were
obtained by Vrekuossis et al. (2005) in Finokalia (1 m2 g-1), and
by Pereira et al. (2008) and Wagner et al. (2009) for mineral dust in
Portugal, 1 ± 0.1 and 0.9 m2 g-1 respectively. The MSE for
the aged organics source (1.4 ± 0.2 and
1.3 ± 0.3 m2 g-1) was also quite similar at MSY and MSA,
probably due to similarities in the processes that govern the OA formation at
both sites, which originated mainly from local/regional biogenic emissions
and SOA formation (Minguillón et al., 2015; Ripoll et al., 2015a). A
similar MSE (1.4 m2 g-1) was reported by Hand and Malm (2007) for
the total mode of primary organic matter (POM). Higher MSEs were found by
Cheng et al. (2015) during a pollution episode
(4.5 ± 0.7 m2 g-1) and by Tao et al. (2014) in China
(4.8 ± 0.8 and 6.5 ± 0.5 m2 g-1 in summer and
winter). The industrial/traffic source showed similar MSEs at MSY
(2.1 ± 0.8 m2 g-1) and MSA
(2.3 ± 0.5 m2 g-1). This similarity was related to the
common origin of this source at both sites (i.e. emission from the traffic
and industrial activities). It is remarkable that MSEs for some of the
sources identified in this work which contribute greatly to air quality
degradation, such as industrial/traffic or V-Ni, are not available in the
literature.
Prior studies dealing with the absorption efficiency of aerosol particles
referred mainly to BC particles and to the possible effect of coating with
non-absorbing material (Bond et al., 2013; Ramana et al., 2010). Other
studies have reported the MAE of mineral matter (Linke et al., 2006) and OA
(Lu et al., 2015; Updyke et al., 2012) due to the significant contribution of
BrC to UV light absorption. However, to the author's knowledge, this is the
first time that absorption efficiencies, as well as scattering efficiencies,
are computed for aerosol particle sources. MAE values at 637 nm for the
three sites are summarized in Table 2. The highest absorption efficiencies
were observed for the traffic source identified at BCN
(1.672 ± 0.050 m2 g-1) and for the industrial/traffic
source at MSY (0.867 ± 0.047 m2 g-1) and MSA
(0.206 ± 0.02 m2 g-1) due to their content in BC particles
from fossil fuel combustion. The V-Ni bearing source, which contributed
greatly to light scattering, also exhibited high MAE at BCN (0.928 ± 0.058 m2 g-1) with
decreasing coefficients at MSY (0.526 ± 0.065 m2 g-1) and
MSA (0.165 ± 0.017 m2 g-1).We have shown here that the V-Ni
bearing source, which is progressively becoming more relevant for air quality
degradation due to the increased shipping emissions in recent years (Viana et
al., 2014), also has an important effect on light absorption as consequence
of the internal mixing with combustion aerosols. The large MAE observed for
secondary nitrate at MSA (0.364 ± 0.023 m2 g-1) was due to
the fact that this source explained around 20 % of the measured EC
concentration (Fig. 2a). Recently, Ripoll et al. (2015b) have shown the
increased concentration of nitrate, ammonium, EC and traffic/industrial
tracers at MSA under European scenarios. Such scenarios are characterized by
the transport of polluted air masses at high altitude from central and
eastern Europe to the MSA site. This fact may explain the internal mixing of
BC particles in the chemical profile of secondary nitrate, and consequently
the high MAE values found for this source at MSA. Lower MAEs for the
secondary nitrate source were observed at BCN and MSY (0.28 ± 0.040 and
0.234 ± 0.028 m2 g-1) compared to MSA. Lower MAEs were
observed for secondary sulfate (0.359 ± 0.035, 0.122 ± 0.010 and
0.173 ± 0.021 m2g-1) at BCN, MSY and MSA. Overall, higher
absorption efficiencies were observed for the main anthropogenic sources at
BCN, where fresh primary pollutants, mostly composed of darker particles, are
emitted within the metropolitan, industrial and harbour areas. However, lower
MAEs were found for the same pollutant sources at MSY and MSA. This result
points to a decrease in the absorption efficiency towards inland areas, as a
consequence of the different mixing and aging of pollutants during the
transport towards the stations. Aerosol sources dominated by natural
contributions, such as aged marine and mineral sources, showed the lowest MAE
at MSY and MSA. The road dust resuspension source, which was partially
composed of mineral matter, exhibited the lowest MAE at BCN
(0.062 ± 0.084 m2 g-1). The mineral source presented MAE
values of 0.09 ± 0.05, 0.005 ± 0.007 and
0.03 ± 0.003 m2 g-1 at BCN, MSY and MSA. Coefficients in
the same order of magnitude at 660 nm were found for the Sahara–Sahel and
Gobi deserts, ranging between 0.01 and 0.02 m2 g-1 (Alfaro et
al., 2004), and for El Cairo and Morocco, 0.02 ± 0.004 and
0.06 ± 0.014 m2 g-1 (Linke et al., 2006). Aged marine also
exhibited low absorption efficiencies at BCN, MSY and MSA
(0.108 ± 0.021, 0.027 ± 0.018 and
0.015 ± 0.010 m2 g-1), but was higher at BCN due to a
possible mixing with darker particles in the urban area. Similarly to the
results observed for MSEs, aged organics showed similar MAEs at MSY and MSA
(0.169 ± 0.011 and 0.140 ± 0.009 m2 g-1) due to the
local/regional origin of this source with a similar composition at both
sites. The absorption efficiency of this latter source was mainly explained
by the EC contained within the source chemical profile, but also might be
partially due to the presence of light-absorbing material detected as OC,
such as BrC (Putaud et al., 2014).
SAE and SSA of aerosol sources
The source-specific scattering Ångström exponents (SAEs) were
calculated as a linear fit of 3λ MSEs in the 450–635 nm range
(Table 2). The MSE values used for computing SAE are shown in Table 2. The
SAE parameter provides information on the size of the particles; generally a
SAE lower than 1 or higher than 2 indicates that the scattering is dominated
by large or fine particles respectively (Schuster et al., 2006). Aged
organics and V-Ni bearing sources showed the highest SAE at MSY (2.2 and 2.4
respectively) and MSA (3.6 and 2.2), pointing to a predominance of fine
particles within these sources. Previous studies have demonstrated the strong
contribution from shipping emissions to fine aerosols (Viana et al., 2009),
and especially to ultrafine particles (Saxe and Larsen, 2004). As reported in
Table 2 for both MSY and MSA, the SAE of secondary sulfate (1.9 and 1.8) and
secondary nitrate (1.2 and 1.4) sources was lower compared to the SAE of the
aged organics source. This was probably due to the contribution of very fine
primary organic aerosols (POAs) to the aged organic source, whereas both
SOAs and secondary inorganic aerosols are expected to strongly contribute to
the accumulation mode (Sun et al., 2016). The lowest SAE was observed for
mineral (-0.9 at MSY and MSA) and aged marine (0.01 at MSY) sources, which
primarily consist of coarse-mode particles. A relatively low SAE was found
for the industrial/traffic source (0.6 and 0.8 at MSY and MSA respectively),
which could be related to specific industrial processes in the area under
study that include handling of dusty materials.
The single scattering albedo (SSA) coefficients obtained for each source at
MSY are summarized in Table 2 and provide information on the relative
importance of scattering or absorption in the light extinction process. The
corresponding SSA to each source was computed as the ratio between the
source-specific MSE and the sum of
MSE and
MAE (Table 2). As expected, the
sources that internally mixed with combustion particles, such as
industrial/traffic, aged organics and V-Ni, exhibited lower SSAs of 0.74,
0.84 and 0.9.
Conversely, aged marine and mineral sources showed the highest coefficients
of 1 and 0.98, leading to a scattering dominance in the light extinction
process. Accordingly to studies in the literature, the mineral source showed
a SSA close to 1. Linke et al. (2006) observed values around 0.98–0.99 at
532 nm, and lower coefficients were found by Müller et al. (2011c) for
mineral dust (0.96) and marine (0.95) aerosols at 530 nm. Note that
equivalent wavelengths should be considered when comparing SSA with
coefficients in the literature due to the strong wavelength dependence of
mineral dust particles.
Seasonal variation of source contributions to scattering and
absorption
Monthly source contributions to the total scattering and absorption
coefficients are shown in Fig. 3. The partial σsp and σap apportioned to each source was calculated as the product between
the aerosol source contribution and the corresponding MSEs or MAEs (Eqs. 1 and
2). According to the scattering efficiencies previously reported in Table 2,
average scattering for the whole period was mainly dominated by secondary sulfate (35 and
33 % at MSY and MSA) and secondary nitrate (24 and 21 %) (Fig. 3g and
h). The annual cycle of secondary sulfate and secondary nitrate scattering coefficients followed those of the
PM10 mass concentration, with maxima in summer (46 and 35 % at
MSY and MSA) and winter (42 and 29 %) respectively. The
scattering contribution from aged organics accounted for 11 and 16 % of the total
σsp at MSY and MSA. The V-Ni bearing source exhibited
substantial contribution to σsp in summer (16 %), despite the
relatively low contribution to PM10 mass concentration (5 % and
10 % at MSY and MSA). Less relevant were the scattering
contributions from industrial/traffic (6 and 11 % at MSY and MSA) and
mineral (7 %) sources, peaking in winter and summer respectively.
Relationship between modelled and measured optical parameters:
absorption at 637 nm for (a) MSA, (b) MSY and (c)
BCN and scattering at 525 nm (d and e
for MSA and MSY).
The traffic source at BCN and the industrial/traffic source at MSY clearly
exerted a major influence on light absorption contributing 54 and 41 % to
σap respectively, despite the relatively low PM10
contributions (16 and 10 %). Maxima contributions were observed in winter
at BCN for the traffic source (65 %) and in October–January at MSY for
the industrial/traffic source (46 %), whereas a lower influence of
industrial/traffic was observed on average at MSA (18 %) (Fig. 3 d, e,
f). Interestingly, the V-Ni bearing source also played an important role in
light absorption, especially in summer as a consequence of the increased
vessel traffic in the Mediterranean but also because of the more intense sea
breeze circulations, transporting pollutants to inland regions. Average
contributions to σap in summer were 31 % at BCN,
17 % at MSY and 16 % at MSA. Therefore, traffic, industrial/traffic
and V-Ni bearing sources, which highly influenced air quality, also
significantly contributed to σap, especially in those sites
closer to the emission sources. Aged organics became a relevant source in the
absorption process at the regional and remote background sites, contributing
on average 20 and 32 % due to both its large contribution to PM10
and its relatively large MAE
compared to other sources. Secondary sulfate contributed on average 10, 16
and 12 % to the total σap at BCN, MSY and MSA, whereas
secondary nitrate showed increasing contributions to σap
towards inland areas (8, 10 and 21 %), markedly maximizing during the
colder months.
Reconstruction of scattering and absorption coefficients
Scattering (σsp) and absorption (σap) time series
were reconstructed by means of the sum of the partial scattering and
absorption contributions determined for each source (Eqs. 1 and 2). Strong
correlations were found between the measured and modelled extensive optical
parameters at the three sites (Fig. 4). Results showed good agreement for
σsp at 525 nm at MSY (R2 = 0.88) and MSA (R2 = 0.92).
σap at 637 nm also exhibited a good correlation when comparing
measured and predicted coefficients at BCN (R2 = 0.81), MSY
(R2 = 0.80) and MSA (R2 = 0.93). Slopes were close to one in all
cases and ranged between 0.96 and 0.98. These results are consistent
with the good agreement obtained in the MLR model for MSE and MAE
calculation. A R2 of 0.96 was obtained for all the cases ensuring the
accuracy of the regression coefficients computed for each site. The root
mean square error (RMSE) was calculated for the observed–modelled data sets,
showing low dispersion and high accuracy in the modelled values. Scattering
and absorption coefficients were well reproduced by the model, showing RMSE
values of 8.76 and 6.06 Mm-1 for σsp at MSY and MSA, and
values of 2.61, 0.55 and 0.23 Mm-1 for σap at BCN, MSY
and MSA. The fractional bias (FB) between measured and
predicted coefficients was calculated for each sampling site following
Eq. (3). Results are shown in Fig. 5, where the FB is broken down by
quintile from lowest to highest σsp and σap
values. According to published results (Ryan et al., 2005 and references
therein), a consistent overestimation was observed for all the modelled
coefficients in the lower ranges of σsp and σap,
showing the highest bias in the 1st quintile. Biases were
substantially reduced in the median range values, whereas a minor
underestimation was observed for the highest σsp and σap values, 4th and 5th quintiles, with negative FB. On
average, a 3.8 and 5.6 % overprediction was obtained for the modelled
σap coefficients at MSY and BCN, using 503 and 375 daily data
points in the analysis. A σsp overprediction at MSY pointed to
4 % using 307 daily points. However at MSA, σsp and σap coefficients biased the observed values by 30.9 and 19.9 %,
considering 220 and 303 data points in the analysis. A larger overestimation
of the measured coefficients at MSA might be mainly explained by the lower
number of daily chemical data used in the PMF model for the quantification
of source contributions, but also because of the lower number of scattering
and absorption data points available for the MLR analysis.
Average fractional bias (FB) calculated for the observed-modelled
data pairs of scattering (Sc) and absorption (Abs) coefficients at BCN, MSY
and MSA broken down by quintile from the lowest to highest scattering and
absorption coefficient values. n accounts for the number of daily data
points used in the FB calculation.
Relationship between modelled and measured (a) scattering at
525 nm and (b) absorption at 637 nm at MSY for the period January
2015–December 2015.
Time series of the daily average modelled and measured extensive
optical coefficients (scattering at 525 nm and absorption at 637 nm) for
(a) BCN and (b) MSY during the period 2004–2014, and for
(c) MSA during the period 2010–2014.
An independent subset of the study period was considered in order to
further evaluate the PMF–MLR technique and the accuracy of the method to
simulate optical properties when chemical source contributions were
available. Therefore, a new PMF was performed in order to obtain the source
contributions for the period 2004–2015 at MSY. With this aim, the simulation
of σsp and σap coefficients for the period
January–December 2015 was carried out by means of the source-specific MSEs
and MAEs previously obtained in the MLR analysis for the period 2010–2014.
Good agreement was found between modelled and measured σsp
(R2 = 0.85) and σap (R2 = 0.76) coefficients, at 525
and 637 nm respectively, showing slopes close to one for the year 2015 at
MSY (Fig. 6). This analysis confirms the confidence of the PMF–MLR technique
to accurately estimate σsp and σap coefficients
when chemical data are available.
As a result of the aforementioned sensitivity test, long-term time series of
σsp and σap were satisfactory reconstructed for
the period 2004–2014 at BCN and MSY and for the period 2011–2014 at MSA
(Fig. 7), when PM10 chemical speciated data were available.
Temporal trends for the monthly average absorption at 637 nm and
scattering at 525 nm obtained by means of the multi-exponential test
at MSY during the period 2004–2014. The time series were decomposed into
simulated coefficient (green), trend (red), main component (black), seasonal
component (blue) and residue (grey).
Long-term trends in scattering and absorption coefficients at
MSY
Long-term trends of σsp and σap and their
relationship with the trends of PM10 source contributions were
investigated for an 11-year period at MSY (2004–2014). The trend of σap at BCN was not studied due to the change in the location of the BCN
sampling station in 2009 (Pandolfi et al., 2016), which affected mainly the
contribution from the traffic source. The short time series available for chemical
species concentration at MSA made the analysis of σsp and σap trends unfeasible at this station.
Temporal trends of the deseasonalized monthly averages for the modelled
σsp and σap at MSY for the period 2004–2014 are
shown in Table 3. The multi-exponential (ME) approach allowed
σsp and σap time series to decompose in main, seasonal and
residual components (Fig. 8). Linear trends were identified for σsp and σap, given that the non-linearity (NL) parameter
was less than 10 % (Shatalov et al., 2015). Statistically significant
decreasing trends were found for both σsp and σap
at MSY (Table 3). σap decreased by -4.1 % yr-1 (-0.16 Mm-1 yr-1), whereas a reduction of -4.6 % yr-1
(-2.14 Mm-1 yr-1) was obtained for σsp at 635 nm. Very
similar trends were observed for σsp at 450 (-4.4 % yr-1) and 525 nm (-4.5 % yr-1).
Theil-Sen (TS) trends at a 95 % confidence level for
deseasonalized monthly averages of scattering absorption time series at MSY
during the period 2004–2014. AR (Mm-1; %) is average reduction; TR
(%) is total reduction; the significance of the trends (p value trend)
was obtained by means of TS method using monthly averages:
∗∗∗ (p value < 0.001), ∗∗ (p
value < 0.01), ∗ (p value < 0.05). The
non-linearity parameter (%) was calculated by means of the
multi-exponential (ME) test.
TS
ME
p value
AR
AR
TR
NL
slope
(Mm-1 yr-1)
(% yr-1)
(%)
(%)
MSY
Sc 525
∗∗∗
-2.14
-4.57
-50
5.56
Abs 637
∗∗∗
-0.16
-4.13
-45
4.17
According to these results, decreasing trends were also observed for the
majority of the PM10 source contributions identified at MSY for the
period 2004–2014, except for aged organics and aged marine sources (Pandolfi et al., 2016). A
reduction in the absorption coefficient was directly related to the
significant decreasing trends found by Pandolfi et al. (2016) for strong
light-absorbing sources, such as industrial/traffic (-5.09 % yr-1) and V-Ni bearing source (-5.82 % yr-1). The observed scattering decreasing trend could be mainly
associated with a reduction in the contributions from those sources which
scattered light more efficiently, i.e. secondary nitrate and secondary sulfate. In Pandolfi et al. (2016) these
sources showed reduction rates of -6.27 and -4.82 % yr-1 respectively. A marked decline has also been observed for nitrate and sulfate
particles in other European monitoring sites since 1990, as outlined in the
EMEP report 1/2016 (Colette et al., 2016). Other studies have been published
in recent years, clearly showing that the concentrations of PM and other
air pollutants, such as SO2 and NO2, have markedly decreased
during the last 15 years in many European countries (EEA, 2013; Barmpadimos
et al., 2012; Cusack et al., 2012; Querol et al., 2014; among others).
Querol et al. (2014) and Pandolfi et al. (2016) investigated trends of PM
chemical components and aerosol sources at MSY, providing further
explanation on the causes leading to the reduction of the atmospheric
pollutants in the area. The financial crisis affecting Spain from 2008
contributed a reduction in the ambient PM concentrations. A decrease in
secondary nitrate can be explained by the reduction of ambient NOX and NH3 concentrations (Querol et al., 2014). The decreasing trend of the
secondary sulfate source may be supported by the reduction of sulfate particles, mainly
attributed to the gas desulfurization at several facilities (Pandolfi et al.,
2016). A decrease in secondary sulfate may be also explained by the 75 %
reduction of SO2 concentration in the Barcelona harbour, supported by
the regulation of sulfur content in shipping emissions in EU harbours from
2010 (Schembari et al., 2012). This regulation together with the 2007 ban
around Barcelona on the use of heavy oils and petroleum coke for power
generation, which contributed to a drastic decrease in V and Ni
concentrations (Querol et al., 2014), were the main causes of the
observed reduction of the contribution of the V-Ni bearing source. This source was
enriched in sulfate and combustion particles as shown in the source chemical
profile, and, as was already observed, contributed simultaneously to both
σsp and σap. Thus, the abatement strategies
adopted in the recent years might have caused changes in the internal
mixture of particles emitted from the V-Ni bearing source, and consequently in the
contribution of this source to light extinction.
Only a few studies have been published in Europe aiming to study trends of
particle optical properties. Statistically significant downward trends of PM
mass concentration, σsp, σap and SSA were found in
the Po valley (Italy) for the period 2004–2010 (Putaud et al., 2014). A
higher decreasing rate was observed for σsp (-2.8 % yr-1) compared to σap (-1.1 % yr-1), likely due to
the increasing contribution of light-absorbing organic matter to light
absorption during cold months in the Po Valley (Putaud et al., 2014). In the
present study, smaller differences between σsp and σap were observed at MSY, accounting for the total reduction trends
of -50 and -45 % respectively. This fact might be explained by the
different background sites considered, whereas the Po Valley is a highly
polluted area, MSY is representative of a cleaner environment where biomass
burning emissions, which contribute greatly to light absorption, are
considerably lower (Minguillón et al., 2015; Ealo et al., 2016).
Further research on light scattering and absorption long-term trends and
their relation to changes in atmospheric composition is needed to better
understand the role of aerosols on optical properties and on the climate
system. Based on the published studies and the present results, further
efforts focusing on the reduction of atmospheric pollutants containing BC
particles (mainly emitted from fossil fuel combustion and biomass burning
sources) need to be addressed. Given the toxicity of their chemical tracers,
as well as their large contribution to light absorption,
industrial/traffic and V-Ni bearing sources must be reduced through the implementation of win–win
policies which aim to improve air quality and public health and mitigate
climate warming.
Summary and conclusions
Mass scattering and absorption efficiencies (MSEs and MAEs) of different
aerosol particle sources were investigated at urban, regional and remote
backgrounds in the NW Mediterranean, using unique large data sets of
PM10 chemical speciation and particle optical properties. For this
purpose, a new approach was presented that aimed to apportion the PM10
source contributions arising from a PMF model to the measured particle
σsp and σap coefficients.
Seven aerosol sources were identified at the Montsec (MSA) mountain-top site,
where aged organics (29 %) was the foremost constituent of PM10,
followed by mineral (24 %), industrial/traffic (11 %), aged marine
(11 %), secondary sulfate (9 %), V-Ni bearing (8 %) and secondary
nitrate (7 %). The same sources were found at the regional background of
Montseny (MSY), showing that relative
PM10 contributions of secondary aerosol sources were higher at the
background sites than at the urban station in Barcelona (BCN). Aged organics
was not identified at BCN; however specific pollutant sources related to the
direct anthropogenic emissions were isolated (traffic, industrial/metallurgy
and road dust resuspension). The impact of aerosol sources and the
different chemical profiles obtained at the three sites depended on the
distance and transport of pollutants to inland areas, driven by orography and
meteorology.
The highest absorption efficiencies were attributed to aerosol sources
internally mixed with BC particles. The traffic source at BCN (MAE = 1.7 m2 g-1) and the mixed industrial/traffic source at MSY (MAE = 0.87 m2 g-1)
exerted a major influence on light absorption and reached the highest
contributions during the colder period (65 and 46 %).
The V-Ni bearing source was the second most efficient light-absorbing source in BCN
(MAE = 0.93 m2 g-1), showing also a notable absorption
efficiency at MSY and MSA (0.53 and 0.16 m2 g-1).
This source contributed greatly in summer to both σsp (16 % at
MSY and MSA) and σap (31, 17 and 16 %, at BCN, MSY
and MSA) due to the internal mixing of sulfate and combustion
aerosols. These combustion sources were relevant but not dominant at MSY and
MSA, where secondary aerosol sources (secondary sulfate, secondary nitrate and aged organics) gained relative importance in
the light extinction process. A high spatial variability of MAE was observed
for most of the anthropogenic sources, from high values at the BCN site to
decreasing coefficients at the background stations, pointing to the aging
and mixing state of aerosols as key factors influencing light absorption.
The highest scattering efficiencies were observed for secondary sulfate (4.5 and 10.7 m2g-1
at MSY and MSA), secondary nitrate (8.8 and 7.8 m2 g-1) and V-Ni bearing (8 and 3.5 m2 g-1) sources, dominating the
scattering throughout the year with marked seasonal cycles. Secondary nitrate contributed greatly in winter (42 and 29 % at MSY and
MSA), whereas in summer secondary sulfate (46 and 35 %) was the main contributor to
scattering.
Sources internally mixed with relatively dark and fine particles and greatly contributing to light absorption, such as industrial/traffic,
aged organics and V-Ni, were simultaneously characterized
with low single scattering albedo (SSA) and a high scattering Ångström
exponent (SAE). Conversely, mineral and aged marine showed
the lowest SAE and the highest SSA, scattering being the dominant process in
the light extinction. These findings for the intensive parameters were
consistent at MSY and MSA. The observed variability in the intensive optical
properties of aerosol sources provides valuable constraints for future
simulations of aerosol parameters.
Significant decreasing trends were observed for the modelled scattering (-4.6 % yr-1) and absorption (-4.1 % yr-1) series at MSY for the
period 2004–2014. The scattering reduction was mainly attributed to the
decrease in the contributions from secondary nitrate, secondary sulfate and V-Ni bearing sources, whereas the absorption
decreasing trend was mainly related to the decrease in industrial/traffic and V-Ni bearing sources. Given
the toxicity of their chemical tracers, as well as the large contribution to
light absorption, further efforts need to be addressed to reduce aerosol
sources containing combustion particles, such as industrial/traffic and V-Ni bearing sources. However,
further studies focusing on the study of long-term trends of optical
parameters and their relationship with changes in atmospheric composition
are needed to assess future win–win mitigation strategies.
Findings from the PMF–MLR technique are summarized as follows:
The apportionment of PM source contributions to scattering and absorption
allows the determination of MSEs and MAEs of atmospheric aerosol sources,
taking into account the particle-internal mixing.
Knowledge of both MSEs and MAEs makes it possible to study the
existing relationship between the sources contributing to air quality
degradation and their potential to absorb and scatter visible light.
Anthropogenic sources such as secondary sulfate, secondary nitrate,
traffic, industrial/traffic and V-Ni bearing source, which contribute greatly to
air quality degradation, also revealed a substantial contribution to light
extinction in the NW Mediterranean.
For the first time, to the author's knowledge, this work quantifies the
absorption efficiency exerted by the different aerosol sources constituting
the PM10 mass concentration, in contrast to previous studies where
light absorption was entirely attributed to BC particles. Interestingly,
secondary sulfate, secondary nitrate and organic aerosols, for which
light-absorbing properties are poorly represented in current climate models,
significantly contributed to light absorption due to internal mixing
with BC or BrC particles, and especially in regional and remote areas.
The proposed approach allowed a satisfactory reconstruction of σsp and σap compared to previous studies, given that the
sum of the source contributions used in the MLR model reached around 100 %
of the measured PM10 mass concentration. Correlation coefficients are
higher than 0.8 with slopes close to 1.0 between modelled and measured
σsp and σap.
Statistically significant decreasing trends were observed for the modelled
σsp and σap series, mirroring the effectiveness
of the mitigation strategies adopted to improve air quality. The
simultaneous analysis of the trends of climate relevant aerosols parameters
(σsp and σap) together with the trends of PM
source contributions allowed us to study the effects that the abatement
strategies implemented in the last years are having on atmospheric
composition and light extinction.