Carbonaceous aerosols are related to adverse human health effects. Therefore,
identification of their sources and analysis of their chemical composition is
important. The offline AMS (aerosol mass spectrometer) technique offers quantitative separation of organic
aerosol (OA) factors which can be related to major OA sources, either primary
or secondary. While primary OA can be more clearly separated into sources,
secondary (SOA) source apportionment is more challenging because different
sources – anthropogenic or natural, fossil or non-fossil – can yield
similar highly oxygenated mass spectra. Radiocarbon measurements provide
unequivocal separation between fossil and non-fossil sources of carbon. Here
we coupled these two offline methods and analysed the OA and organic carbon
(OC) of different size fractions (particulate matter below 10 and
2.5 µm – PM10 and PM2.5, respectively) from the Alpine
valley of Magadino (Switzerland) during the years 2013 and 2014
(219 samples). The combination of the techniques gave further insight into
the characteristics of secondary OC (SOC) which was rather based on the type
of SOC precursor and not on the volatility or the oxidation state of OC, as
typically considered. Out of the primary sources separated in this study,
biomass burning OC was the dominant one in winter, with average
concentrations of 5.36 ± 2.64 µg m-3 for PM10 and
3.83 ± 1.81 µg m-3 for PM2.5, indicating that wood
combustion particles were predominantly generated in the fine mode. The
additional information from the size-segregated measurements revealed a
primary sulfur-containing factor, mainly fossil, detected in the coarse size
fraction and related to non-exhaust traffic emissions with a yearly average
PM10 (PM2.5) concentration of
0.20 ± 0.24 µg m-3
(0.05 ± 0.04 µg m-3). A primary biological OC (PBOC) was also
detected in the coarse mode peaking in spring and summer with a yearly average
PM10 (PM2.5) concentration of
0.79 ± 0.31 µg m-3
(0.24 ± 0.20 µg m-3). The secondary OC was separated
into two oxygenated, non-fossil OC factors which were identified based on
their seasonal variability (i.e. summer and winter oxygenated organic carbon, OOC) and a third anthropogenic OOC factor which correlated
with fossil OC mainly peaking in winter and spring, contributing on average 13 % ± 7 %
(10 % ± 9 %) to the total OC in PM10 (PM2.5). The winter OOC was also connected
to anthropogenic sources, contributing on average
13 % ± 13 % (6 % ± 6 %) to the total OC in PM10 (PM2.5). The
summer OOC (SOOC), stemming from
oxidation of biogenic emissions, was more pronounced in the fine mode, contributing on average 43 % ± 12 %
(75 % ± 44 %) to the total OC in PM10 (PM2.5). In total the non-fossil OC
significantly dominated the fossil OC throughout all seasons, by contributing
on average 75 % ± 24 % to the total OC. The results also
suggested that during the cold period the prevailing source was residential
biomass burning while during the warm period primary biological sources and
secondary organic aerosol from the oxidation of biogenic emissions became
important. However, SOC was also formed by aged fossil fuel combustion
emissions not only in summer but also during the rest of the year.
Introduction
The field deployment of the time-of-flight aerosol mass spectrometer
(HR-ToF-AMS, Canagaratna et al., 2007) has advanced our understanding of
aerosol chemistry and dynamics. The HR-ToF-AMS provides quantitative mass
spectra of the non-refractory particle component, including, but not limited
to, organic aerosol (OA), ammonium sulfate and nitrate, by combining the
flash vaporization of particle species and the electron ionization of the
resulting gases. The application of positive matrix factorization (PMF,
Paatero, 1997) techniques has demonstrated that the collected OA mass spectra
contain sufficient information to quantitatively distinguish aerosol sources.
However, the cost and intensive maintenance requirements of this instrument
significantly hinder its systematic, long-term deployment as part of a dense
network and most applications are limited to few weeks of measurements
(Jimenez et al., 2009; El Haddad et al., 2013; Crippa et al., 2013). This
information is critical for model validation and policy directives. The
Aerodyne aerosol chemical speciation monitors (ACSM, Ng et al., 2011;
Fröhlich et al., 2013) were developed as a low-cost, low-maintenance
alternative to the AMS; however, their reduced chemical resolution can limit
the factor separation achievable by source apportionment.
The recent utilization of the AMS for the offline analysis of ambient filter
samples (Daellenbach et al., 2016) has significantly broadened the spatial
and temporal scales accessible to high-resolution AMS measurements
(Daellenbach et al., 2017; Bozzetti et al., 2017a, b). In addition, the
technique enables measurement of aerosol composition outside the normal size
transmission window of the AMS; the standard AMS can measure up to only
1 µm, or ∼ 2.5 µm with a newly developed
aerodynamic lens (Williams et al., 2013; Elser et al., 2016). This capability
has been used to quantify the contributions of primary biological organic
aerosol to OA in PM10 filters (Bozzetti et al., 2016). Finally, the
offline AMS technique allows a retrospective reaction to critical air quality
events. For example, one of the applications of this approach had been to
examine a severe haze event in China which affected a total area of
∼ 1.3 million km2 and ∼ 800 million people (Huang et al.,
2014).
A major limitation of the technique is the resolution of low water
solubility fractions, as the recoveries of some of them are not accessible.
Despite this, source apportionment results obtained using this technique are
in good agreement with online AMS or ACSM measurements. PMF analysis of
offline AMS data has yielded factors related with primary emissions from
traffic, biomass burning and coal burning, and secondary organic aerosols
(SOA) differentiated according to their different seasonal contributions.
Nevertheless, the identification of SOA precursors using the AMS has proven
challenging, due to the evolution of different precursors towards chemically
similar species and the extensive fragmentation by the electron ionization
used in the AMS.
The radiocarbon (14C) analysis of particulate matter has proven to be a
powerful technique providing an unequivocal distinction between non-fossil
(e.g. biomass burning and biogenic emissions) and fossil (e.g. traffic
exhaust emissions and coal burning) sources (Lemire et al., 2002; Szidat et
al., 2004, 2009). The measurement of the 14C content of total carbon
(TC), which comprises the elemental carbon (EC) originating from combustion
sources and the organic carbon (OC), had been the subject of many studies
(Schichtel et al., 2008; Glasius et al., 2011; Genberg et al., 2011; Zhang et
al., 2012, , 2016; Zotter et al., 2014b; Bonvalot et al., 2016). Results have shown that in
European sites, especially in Alpine valleys, the non-fossil sources play an
important role during winter due to biomass burning and in summer due to
biogenic sources (Gelencsér et al., 2007; Zotter et al., 2014b).
Moreover, at regional background sites close to urbanized areas in Europe
(Dusek et al., 2017) as well as in megacities such as Los Angeles and
Beijing, fossil OA may also exhibit significant contributions to the total OA (Zotter
et al., 2014a; Zhang et al., 2017). However, the determination of the
14C content in EC and OC separately is challenging and therefore not
often attempted for extended datasets.
The coupling of the offline AMS/PMF with radiocarbon analysis provides
further insight into the sources of organic aerosols and in particular those
related to SOA precursors. Such combination has been already attempted
(Minguillón et al., 2011; Zotter et al., 2014a; Huang et al., 2014;
Beekmann et al., 2015; Ulevicius et al., 2016); however, the focus has rather
been on high OA concentration episodes, while little is known about the
yearly cycle of the most important SOA precursors and the size resolution of
the different fossil and non-fossil OA fractions.
Here, we present offline AMS measurements of a total of 219 samples, 154 of
which are PM10 samples representative of the years 2013 and 2014 and 65
PM2.5 concurrent with PM10 samples for the year 2014 (January to
September). 14C analysis was also performed on a subset of 33 PM10
samples, covering the year 2014. The size-segregated samples offered better
insights into the mechanism by which the different fractions enter the
atmosphere, while the coupling of offline AMS/PMF and 14C analysis
provided a more profound understanding of the SOA fossil and non-fossil
precursors on a yearly basis.
MethodsSite and sampling collection
Magadino is located in an Alpine valley in the Southern part of Switzerland,
south of the Alps (Fig. S1 in the Supplement). The station
(46∘9′37′′ N, 8∘56′2′′ E, 204 m a.s.l.) belongs
to the Swiss National Air Pollution Monitoring Network (NABEL) and is
classified as a rural background site. It is located relatively far from busy
roads or residential areas and surrounded by agricultural fields and forests.
It is ca. 1.4 km away from Cadenazzo train station, ca. 8 km from Lake
Maggiore (Lago Maggiore) and ca. 7 km from the small Locarno Airport.
The filter samples under examination are 24 h integrated PM10 (from
4 January 2013 to 28 September 2014, with a 4-day interval) and PM2.5
(from 3 January to 28 September 2014, with a 4-day interval). PM was
sampled and collected on 14 cm (exposed diameter) quartz fibre filters,
using a high volume sampler (500 L min-1). After the sampling, filter
samples and field blanks were wrapped in lint-free paper and stored at
-20 ∘C.
Offline AMS method
The offline AMS method is thoroughly described by Daellenbach et al. (2016).
Briefly, four punches of 16 mm diameter from each filter sample are extracted
in 15 mL of ultrapure water (18.2 MΩ cm at 25 ∘C with
total organic carbon, TOC, < 3 ppb), followed by insertion in an
ultra-sonic bath for 20 min at 30 ∘C. The water-extracted
samples are then filtered through a 0.45 µm nylon membrane syringe
and inserted to an Apex Q nebulizer (Elemental Scientific Inc., Omaha, NE,
USA) operating at 60 ∘C. The resulting aerosols generated in Ar
(≥ 99.998 % vol., Carbagas, 3073, Gümligen, Switzerland) were
dried by a Nafion dryer and subsequently injected and analysed by the
HR-ToF-AMS.
To correct for the interference of NH4NO3 on the CO2+
signal as described in Pieber et al. (2016), several dilutions of
NH4NO3 in ultrapure water were measured regularly as well. The
CO2+ signal was then calculated as
CO2,real=CO2,meas-CO2,measNO3,measNH4NO3,pure⋅NO3,meas,
where CO2,real represents the corrected CO2+
signal, CO2,meas and
NO3,meas are signals from the samples measured,
and the correction factor CO2,measNO3,measNH4NO3,pure was
determined during the campaign by measuring aqueous NH4NO3.
14C analysis
Based on the instrumentation setup described in Agrios et al. (2015) and on
the method described in Zotter et al. (2014b), radiocarbon analysis of TC and
EC was conducted on a set of 33 filters. The 14C content of blank
filters was measured for TC only, as there was no EC found on these filters.
All the 14C results are given in fractions of modern carbon
(fM) representing the 14C /12C ratios of each
sample relative to the respective 14C /12C ratio of the
reference year 1950 (Stuiver and Polach, 1977).
14C measurements of TC
For the determination of the 14C content of TC, a Sunset OC / EC
analyser (Model 4L, Sunset Laboratory, USA) equipped with a non-dispersive
infrared (NDIR) detector was first used in order to combust each filter punch
(1.5 cm2) under pure O2 (99.9995 %) at 760 ∘C for
400 s. The generated CO2 was then captured online by a zeolite trap
within a gas inlet system (GIS) and then injected in the accelerator mass
spectrometer (AMS*) mini carbon dating system (MICADAS) at the
Laboratory for the Analysis of Radiocarbon with AMS* (LARA), University of
Bern, Switzerland (Szidat et al., 2014) for 14C measurement. (Note that we used AMS* and AMS as
abbreviations for the accelerator mass spectrometer and the aerosol mass spectrometer, respectively, to avoid confusion.)
The fM of TC underwent a blank correction following an isotopic
mass balance approach:
fMb,cor=mCsample⋅fM,sample-mCb⋅fM,bmCsample-mCb,
where fMb,cor is the blank corrected fM;
mCsample and mCb are the carbon
mass in sample and blank, respectively; and fM,sample and
fM,b are the fM measured for sample and blank,
respectively. Error propagation was applied for the determination of the
fMb,cor uncertainty. The fM,b was
0.61 ± 0.10 and the concentration of the blank
1.1 ± 0.2 µg C m-3.
14C measurements of EC
For the EC isolation of the samples, each filter punch (1.5 cm2) was
analysed by the Sunset EC / OC analyser with the use of the Swiss_4S
protocol developed by Zhang et al. (2012). According to the protocol, the
heating is conducted in four different steps under different gas conditions:
step one under pure O2 at 375 ∘C for 150 s, step two under
pure O2 at 475 ∘C for 180 s, step three under He
(> 99.999 %) at 450 ∘C for 180 s followed by an
increase in the temperature up to 650 ∘C for another 180 s, and step
four under pure O2 at 760 ∘C for 150 s. Each filter sample was
previously water extracted and dried, in order to minimize the positive
artefact induced by the OC by removing the water-soluble OC (WSOC), which is
known to produce charring (Piazzalunga et al., 2011; Zhang et al., 2012). By
this method, the water-insoluble OC (WINSOC) was removed during the first
three steps of the Swiss_4S protocol. In the fourth step, EC was combusted
and then trapped in the GIS and measured by the AMS* MICADAS, as
described above.
This protocol was preferred over the protocols commonly used in
thermo-optical methods (EUSAAR 2 or NIOSH) because it optimises the separation
of the two fractions OC and EC by minimizing (i) the positive artefact of
charring produced by WSOC during the first three steps and (ii) the premature
losses, during the removal of the WINSOC in the third step, of the less
refractory part of EC which may preferentially originate from non-fossil
sources such as biomass burning.
Following a similar principle to Zotter et al. (2014b), both charring and EC
yield, which is the part of EC that remained on the filter after step three
and before step four in the Swiss_4S protocol, were quantified and corrected
for with the help of the laser mounted on the Sunset analyser. The laser
transmittance is monitored continuously during the heating process. Charring
in step three was quantified as
CharringS3=maxATNS3-initialATNS2initialATNS1,
where ATN refers to the laser attenuation, maxATNS3
is the maximum attenuation in step three, and initialATNS2 and initialATNS1 are the initial
attenuations in step two and one, respectively.
The EC yield in step three was quantified as
ECyieldS3=initialATNS3maxATNS3⋅initialATNS2maxATNS1,
The average charred OC was found to be 4 ± 2 % and the recovered EC
for all samples was on average 71 ± 7 %.
As there is a linear relationship between the fraction of modern carbon for
EC (fMEC) and the EC yield (Zhang et al., 2012), the slope can
be used to extrapolate fMEC to 100 % EC yield. According
to Zotter et al. (2014), a slope of 0.35 ± 0.11 was considered to
correct all fMEC to 100 % of EC yield, such that
fMEC,total=slope⋅1-ECyieldS3+fMEC.
Calculation of 14C content of OC
The fraction of modern carbon of OC (fMOC) was calculated
following a mass balance approach:
fMOC=TC⋅fMTC-EC⋅fMECTC-EC,
where TC and EC are the concentrations of total and elemental carbon,
respectively, and fMTC and fMEC are the fractions
of modern carbon of TC and EC, respectively. The uncertainty of
fMOC was calculated by propagating the error of each component
of Eq. (6).
Nuclear bomb peak correction
The expected fM coming from fossil samples should be equal to
zero due to the complete decay of 14C until now, whereas the
fM from non-fossil samples is expected to be unity. However, due
to the extensive nuclear bomb testing during the late 1950s and early 1960s,
the radiocarbon amount in the atmosphere increased dramatically because of
the high neutron flux during the explosions. Therefore the measured
fM of non-fossil samples may exhibit values greater than one
(Levin et al., 2010a). To correct for this effect, the fM is
normalized to a reference non-fossil fraction (fNF,ref) which
represents the amount of 14C currently in the atmosphere compared to
1950, before the nuclear bomb tests. As EC comes from either biomass burning
or fossil sources, the non-fossil fraction of EC (fNF,EC) equals
the fM coming from biomass burning (fM,bb). The
latter was estimated by a tree growth model (Mohn et al., 2008) and was equal
to 1.101. The non-fossil fraction of OC (fNF,OC) is calculated
as
fNF,OC=pbio⋅fM,bio+pbb⋅fM,bb,
where fM,bio (= 1.023) is the fraction of modern carbon of
biogenic sources and was estimated from 14CO2 measurements in
Schauinsland (Levin et al., 2010a). The fractions of biogenic sources
(pbio) and biomass burning (pbb) to the total
non-fossil sources were set to 0.5 since both sources are important in
Magadino during the year (biomass burning in winter, biogenic sources in
summer).
Additional measurements
Organic and elemental carbon fractions were determined by a Sunset
EC / OC analyser with the use of the EUSAAR-2 thermal-optical
transmittance protocol (Cavalli et al., 2010). Water-soluble organic carbon
was measured by a total organic carbon analyser (Jaffrezo et
al., 2005) with the use of catalytic oxidation of water-extracted filter
samples and detection of the resulting CO2 with an NDIR. The
concentrations of major ionic species (K+, Na+, Mg2+,
Ca2+, NH4+, Cl-, NO3- and SO42-) as well as
methane sulfonic acid (MSA) were determined by ion chromatography (Jaffrezo
et al., 1998). Anhydrous sugars (levoglucosan, mannosan, galactosan) were
analysed by an ion chromatograph (Dionex ICS3000) using high-performance
anion exchange chromatography (HPAEC) with pulsed amperometric detection.
Cellulose was analysed by performing enzymatic conversion of cellulose to
D-glucose (Kunit and Puxbaum, 1996) and D-glucose was determined by HPAEC.
Source apportionmentMethod
The obtained organic mass spectra from the offline AMS measurements were
analysed by positive matrix factorization (Paatero and Tapper, 1994;
Ulbrich et al., 2009). PMF attempts to solve the bilinear matrix equation,
Xij=∑kGi,kFk,j+Ei,j,
by following the weighted least-squares approach. In the case of aerosol mass
spectrometry, i represents the time index, j the
fragment and k the factor number. If Xij is the
matrix of the organic mass spectral data and si,j the
corresponding error matrix, Gi,k the matrix of the factor
time series, Fk,j the matrix of the factor profiles and
Ei,j the model residual matrix, then PMF determines
Gi,k and Fk,j such that the ratio of the
Frobenius norm of Ei,j over si,j is
minimized. The allowed Gi,k and Fk,j are
always non-negative. The input error matrix si,j includes
the measurement uncertainty (ion-counting statistics and ion-to-ion signal
variability at the detector) (Allan et al., 2003) as well as the blank
variability. Fragments with a signal-to-noise ratio (SNR) below 0.2 were
removed and the ones with SNR lower than 2 were down-weighted by a factor of
3, as recommended by Paatero and Hopke (2003). Both input data and error
matrices were scaled to the calculated water-soluble organic matter
(WSOMi) concentration:
WSOMi=OMOC⋅WSOCi,
where OMOC is determined from the AMS
measurements and WSOCi is the water-soluble OC measured by the TOC
analyser.
The Source Finder toolkit (SoFi v.4.9, Canonaco et al., 2013) for IGOR Pro
software package (Wavemetrics, Inc., Portland, OR, USA) was used to run the
PMF algorithm. The PMF was solved by the multilinear engine 2 (ME-2, Paatero,
1999), which allows the constraining of the Fk,j elements to
vary within a certain range defined by the scalar α (0≤α≤1), such that the modelled Fk,j′ equals
Fk,j′=Fk,j±α⋅Fk,j.
Here we constrained only the hydrocarbon-like factor (HOA) from
high-resolution mass spectra analysed by Crippa et al. (2013).
Sensitivity analysis
To understand the variability of our dataset we explored 4–10 factor
solutions and retained the 7-factor solution as the best representation of
the data. The exploration of the PMF solutions is thoroughly described in
Sect. S.1 in the Supplement.
We assessed the accuracy of PMF results by bootstrapping the input data
(Davison and Hinkley, 1997). New input data and error matrices were created
by randomly resampling the time series from the original input matrix
(223 samples in total: 219 + 4 remeasurements from the PM10
samples), with replacement; i.e. any sample from the whole population can be
resampled more than once. Each sample measurement included on average blocks
of 12 mass spectral repetitions; therefore, resampling was performed on the
blocks. Out of the 223 original samples, some of them were represented several times,
while some others not at all. Overall, the resampled data made
up on average 64 ± 2 % of the total original data per bootstrap
run. We performed 180 bootstrap runs, with each of the generated matrices
being perturbed by varying the Xij element within twice the
corresponding error matrix si,j. Within the resampling
operation, the α value used to set the HOA constraining strength was
varied between 0 and 1 with an increment of 0.1 to assess the sensitivity of
the results on the α value.
To select the physically plausible solutions we applied two criteria:
We accepted solutions where the average absolute concentrations of all
factors in PM2.5 did not statistically significantly exceed their
concentrations in PM10. For this we performed a paired t test with a
significance level of 0.01 (Fig. S2 and Table S1 in the Supplement).
We excluded outlier solutions identified by examining the correlation of
factor time series from bootstrap runs with their respective factor time
series from the average of all bootstrap runs. The rejected solutions
included factors that did not correlate with the corresponding average factor
time series, meaning that one of the factors was not separated (Fig. S3 in the
case of water-soluble primary biological organic carbon, PBOC).
In total 24 bootstrap runs were retained after the application of the
aforementioned criteria.
Recoveries
In order to rescale the WSOC concentration of a factor k to its total
concentration OCk, we used factor recoveries (Rk) as proposed by
Daellenbach et al. (2016). First, the WSOMk was calculated as
WSOMk=fk,WSOM⋅WSOCmeasured⋅OMOCbulk,
where
fk,WSOM=WSOMk,measured∑kWSOMk,measured
and
OMOCbulk
is estimated from the input data matrix for the PMF.
The WSOMk was converted to WSOCk to fit the measured OC
concentrations (determined by the Sunset EC / OC analyser). The
WSOCk was determined as
WSOCk=fk,WSOM⋅WSOCmeasured⋅OMOCbulk(OMOC)k,
where (OMOC)k is calculated from each
factor profile.
Finally, the recoveries were applied following Eq. (15):
OCi,k=WSOCi,kRk.
To assess the recoveries and their uncertainties, we evaluated the sum of
OCi,k against the measured OC (OCi,measured) by
fitting Eq. (16). The starting values for the Rk fitting were based on
Bozzetti et al. (2016) (for RPBOA) and Daellenbach et al. (2016)
except RSCOA, which was randomly varied between 0 and 1
(increment: 10-4). While RHOA and RSCOA were
constrained, RPBOA, RBBOA, RWOOA,
RAOOA and RSOOA were determined by a non-negative
multilinear fit (see below in Sect. 4.3 for a description of these PMF
factors from offline AMS results). The multilinear fit was chosen to be
non-negative because a negative Rk would mean a negative concentration
of WSOCk or OCk. The fit was performed 100 times for each of the
retained bootstrap solutions.
OCi,measured=∑kWSOCi,kRk
Each fit was initiated by perturbing the OCi,k and the
WSOCi,k concentrations within their uncertainties, assuming a
normal distribution of errors, to assess the influence of measurement
precision on Rk. Additionally, we introduced a constant 5 % accuracy
bias corresponding to the OC and WSOC measurement accuracy.
To select the environmentally meaningful solutions we applied the following
criteria:
To retain the recoveries that achieved the OC mass closure, we estimated
the OC residuals and discarded solutions where OC residuals were
statistically different from 0 within 1 standard deviation for each size
fraction individually and for winter and summer individually.
We also examined the dependence between the WSOC residuals and each factor
WSOCi,k (t test, α=0.001). Overall, 55 % of the
solutions were retained.
The physically plausible range of the recoveries is [0, 1]. However, the
mathematically possible range can exceed the upper limit. Rk larger
than 1 would mean that WSOCk is larger than OCk and is, therefore,
non-physical. For this reason, out of the accepted solutions that survived
the previous two criteria, the retained Rk combinations were weighted
according to their physical interpretability. More specifically, fitting
results with Rk larger than 1 were down-weighted according to the
measurement uncertainties of WSOC and OC (see Sect. S.2, Fig. S4).
Concentrations of OM, EC and major ionic species for the years 2013
and 2014 (a), their seasonal concentrations (b) and relative
contributions to the total measured mass within the particulate matter (PM10)(c). The
sum of the ions Na+, K+, Mg2+, Ca2+ and Cl- are
included in the indication “Ions*”.
Time series of OC and EC (a) concentrations in PM10.
14C analysis results with the relative contributions of EC fossil, OC
fossil, OC non-fossil and EC non-fossil to the TC (b).
Results and discussionPM10 composition
PM10 in Magadino has been characterized by high carbonaceous
concentrations during winter (Gianini et al., 2012a; Zotter et al., 2014b).
This is clearly illustrated in Fig. 1 where an overview of the PM10
composition is presented in Fig. 1a with Fig. 1b and c summarizing the
concentrations and relative contributions of each component to the total
PM10 averaged per season. The peaks of OM and EC during winter (daily
averages up to 26 and 5.9 µg m-3, respectively) are
indications of the increased wood-burning activity. Other Alpine sites close
to Magadino, such as Roveredo and San Vittore in Switzerland, have also
exhibited high OM concentrations due to residential wood burning (Szidat et
al., 2007, for PM10 in Roveredo, Lanz et al., 2010, for PM1 in
Roveredo and Zotter et al., 2014b, for PM10 in San Vittore and Roveredo).
The organic contribution dominated the inorganic fraction not only in winter,
but also throughout both years (Fig. 1c). Note that the EC concentrations are
much lower in spring compared to winter (Fig. 1b). The main inorganic
aerosols contributing to the total PM are NO3-, SO42- and
NH4+. NO3- represented the second major component of
PM10, exhibiting a seasonal cycle with higher concentrations during
winter (2.9 µg m-3). The notable discrepancy of NO3-
concentrations between the first (2013) and second (2014) winter could be
explained by the lower temperatures in January–February 2013 compared to
2014. Conversely, SO42- showed a rather stable yearly cycle with
slightly higher concentrations in summer (1.9 µg m-3)
compared to winter (1.3 µg m-3), despite a shallower boundary
layer height in winter.
Concentrations in PM10 of OCf(a),
OCnf(b), ECf(c) and
ECnf(d) colour-coded by seasons. The ratios
OCf/ ECf,
OCnf/ ECnf, and ECnf/ EC are
also displayed in (a), (b) and (d), respectively.
Median OC and EC non-fossil fractions per season in PM10 with
interquartile range.
Autumn Winter Spring Summer Q25Q50Q75Q25Q50Q75Q25Q50Q75Q25Q50Q75fNF,OC0.710.770.830.870.880.930.700.750.790.730.760.79fNF,EC0.360.410.440.440.520.560.420.490.510.380.390.4214C analysis results
So far radiocarbon results have been reported mostly for relatively short
periods of time (Bonvalot et al., 2016), mainly describing high
concentration events, and only a few studies report measurements on a yearly
basis (Genberg et al., 2011; Gilardoni et al., 2011; Zotter et al., 2014b;
Zhang et al., 2016, 2017; Dusek et al., 2017). Here, for a
subset of 33 PM10 filters from the year 2014, we present yearly
contributions of OCnf, OCf, ECnf and
ECf.
Overall the total carbon concentrations followed a yearly pattern mainly
caused by the shallow planetary boundary layer and the enhanced biomass
burning activity during winter, with OC reaching on average (± 1
standard deviation) 9.4 ± 4.5 and EC
2.6 ± 1.5 µg m-3 (Fig. 2a). During the rest of the
year, TC remained rather stable with much lower concentrations
(OCavg=3.7± 1.9 and ECavg=0.8± 0.7 µg m-3). 14C results indicate that
non-fossil sources prevail over the fossil ones in Magadino. More
specifically, we found that in winter on average fNF,OC=0.9± 0.1 and fNF,EC=0.5± 0.1, which is in
agreement with the reported fractions by Zotter et al., 2014b
(fNF,OC= 0.8 ± 0.1 and fNF,EC=0.5± 0.2). Table 1 summarizes the fNF per fraction season wise.
OCnf was the dominant part of TC throughout the year with
contributions of up to 80 % in winter and 71 % in summer (Fig. 2b)
and average concentrations of 8.5 ± 4.2 and
2.4 ± 0.6 µg m-3 in winter and summer, respectively
(Fig. 3b). Such high contributions in winter strongly indicate that biomass
burning (BB) from residential heating is the main source of carbonaceous
aerosols in this region, similar to previous reports (Jaffrezo et al., 2005;
Puxbaum et al., 2007; Sandradewi et al., 2008; Favez et al., 2010; Zotter et
al., 2014b). The coefficient of determination R2 between
OCnf and levoglucosan, a characteristic marker for BB, was 0.92
(Fig. S7a), and the slope
(OCnf/ levoglucosan = 4.8 ± 0.3) lies within the
reported range by Zotter et al. (2014b) for Magadino (which was
6.9 ± 2.6).
The concentration of ECnf was significantly higher in winter
(average 1.3 ± 0.7 µg m-3) compared to the rest of the
year (spring average: 0.4 ± 0.2 µg m-3, summer average:
0.21 ± 0.06 µg m-3, autumn average:
0.43 ± 0.41 µg m-3) (Fig. 3d). ECnf is
considered to originate solely from BB, for instance from residential wood
burning in winter. This assumption is supported by the very high correlation
(R2=0.95) with levoglucosan (Fig. S7b) and the slope
(ECnf/ levoglucosan = 0.82 ± 0.03) which is also
in agreement with the literature (Zotter et al., 2014b; Herich et al., 2014).
The strong correlation between OCnf and ECnf, driven
mainly by the winter data points, supports the fact that OCnf is
mostly from biomass burning in winter (Fig. S6a). In late spring, summer and
early autumn, the contribution of ECnf decreased significantly
(on average to 0.23 ± 0.07 µg m-3). The low correlation
of OCnf and ECnf during this period (Fig. S6a), in
combination with the increase in the OCnf/ ECnf
ratio in summer (Fig. 3b), suggests that a part of the secondary
OCnf originates from non-combustion sources, e.g.
biogenic/natural sources.
In total, the relative contribution of the fossil fraction to the TC was
27 %. Excluding winter, ECf exhibited slightly higher
concentrations than ECnf (Fig. 3c and d). The average
concentrations of ECf were 1.26 ± 0.93, 0.41 ± 0.35,
0.31 ± 0.07 and 0.63 ± 0.56 µg m-3 for winter,
spring, summer and autumn, respectively (Fig. 3c). The increase in
ECf witnessed in winter could be mainly attributed to the
shallower planetary boundary layer (PBL) rather than to an increase in the
emissions (Fig. S8a). The sources of ECf in the coarse
(PM10–PM2.5) size fraction are typically related to resuspension
of abrasion products of vehicle tires or brake wear (Bukowieki et al., 2010;
Zhang et al, 2013). The fine part of ECf is due to fossil fuel
burning, here mostly due to traffic exhaust emissions. It is significantly
correlated with NOx (Fig. S8b) and the
ECf/ NOx= 0.020 ratio lies within the reported
slopes (Zotter et al., 2014b, and references therein).
The contribution of OCf to TC decreased during winter (8 %)
but remained roughly stable throughout the rest of the year (22 % in
spring, 21 % in summer and 19 % in autumn, Fig. 2b) with average
concentrations 0.87 ± 0.30, 0.96 ± 0.12, 0.89 ± 0.14 and
0.76 ± 0.10 µg m-3 for winter, spring, summer and
autumn, respectively (Fig. 3a). The low correlation overall observed between
OCf and ECf (Fig. S6b) may indicate that a fraction
of OCf is not directly emitted but formed as secondary OC (SOC) from
fossil-fuel-related emissions (e.g. traffic). This is supported by low
OCf/ ECf ratios in winter (on average
0.7 ± 0.3) and much higher values in spring and summer (on average
2.7 ± 1.1) (Fig. 3a). The low ratios are consistent with tunnel
measurement studies (Li et al., 2016; Chirico et al., 2011; El Haddad et al.,
2009) and the increase in OCf/ ECf in
spring and summer above these values is an indication of anthropogenic SOA
formation. We also note that fossil SOA may be formed by other sources
besides traffic. A recent study revealed that fossil SOA is produced by the
oxidation of volatile chemical products coming from petrochemical sources
(McDonald et al., 2018).
Probability density functions of factor recoveries: hydrocarbon-like
OA (HOA) in grey, biomass burning OA (BBOA) in dark brown, sulfur-containing
OA (SCOA) in blue, primary biological OA (PBOA) in green, anthropogenic
oxygenated OA (AOOA) in purple, summer oxygenated OA (SOOA) in yellow and
winter oxygenated OA (WOOA) in light brown.
Offline AMS/PMF (ME-2) factor profiles: hydrocarbon-like OA (HOA),
biomass burning OA (BBOA), sulfur-containing OA (SCOA), primary biological
OA (PBOA), anthropogenic oxygenated OA (AOOA), summer oxygenated OA (SOOA)
and winter oxygenated OA (WOOA).
Factor (in red for PM10 and blue for PM2.5) and external
marker (in grey markers) time series for the two size fractions: HOC and
NOx, BBOC and levoglucosan, SCOC, PBOC and cellulose, AOOC and
OCf, SOOC and temperature, and WOOC and NH4+. Note that here, different from Fig. 5, the factors are
quantified according to their carbon mass concentration,
with HOC, BBOC, SCOC, PBOC, AOOC, SOOC, and WOOC referring to
hydrocarbon-like organic carbon (OC), biomass burning OC,
sulfur-containing OC, primary biological OC, anthropogenic oxygenated OC, summer oxygenated OC,
and winter oxygenated OC, respectively.
Variability of OM / OC and factor recoveries.
OM / OC RkQ25Q50Q75Q25Q50Q75HOA1.321.331.360.100.110.13BBOA1.761.771.780.600.610.63SCOA2.032.162.200.680.810.89PBOA1.741.761.820.410.420.44AOOA2.122.142.160.720.790.87SOOA1.661.671.680.780.840.94WOOA1.761.791.830.720.780.92Offline AMS analysis results: factor interpretation
In this section, we will interpret the PMF outputs. The factor recoveries for
all factors, Rk, determined as described in Sect. 3.3, are shown in
Fig. 4. Factor mass spectra are displayed in Fig. 5. The contribution of the
different factors to OA is presented in Fig. 6. In addition, for some cases
we will discuss the factor contribution to OC to check the consistency of
our results with previous literature reports. Recovery values determined
and used in this study will also be compared for each factor to previous
values. Median values of the recoveries as well as the OM / OC ratios
with their interquartile range are compiled in Table 2. The Rk values
were in general consistent with previous reports (Daellenbach et al., 2016,
2017; Bozzetti et al., 2016). Here we report for the first time the
recoveries of each SOA factor individually which were in agreement with the
ones reported by Daellenbach et al. (2016). The consistency of the recovery
results with not only previous offline AMS/PMF studies but also with online
AMS measurements (Xu et al., 2017) points out that this method is rather
robust and universal for different datasets.
Hydrocarbon-like OA (HOA), typically associated with traffic emissions, was
constrained using the reference HOA high-resolution profile from Crippa et
al. (2013). The resulting factor profile (Fig. 5) exhibited a low OM / OC
(Table 2) and the time series followed the one from NOx (Fig. 6). As the
offline AMS technique requires water-extracted samples, it is expected that
HOA, which mostly contains water-insoluble material, will be poorly
represented. This is also shown by the low recovery RHOA,median
which was estimated to be 0.11 (Q25=0.10 and Q75=0.13) as
reported in Daellenbach et al. (2016) (Fig. 4). Therefore, the correlation
between HOA and NOx was weak (Fig. S9). However, the HOA/NOx ratio was
0.017 for PM10 and 0.008 for PM2.5 and these values are consistent
with already reported ones in the literature (Daellenbach et al., 2017; Lanz et
al., 2007). In addition, the HOC time series followed a similar yearly cycle
as ECf (Fig. S10a) and the HOC / OCf ratio was
0.37 ± 0.12 (Fig. S10b), in agreement with Zotter et al. (2014a).
Correlations between BBOA and levoglucosan for the two size
fractions (a), BBOC and ECnf for PM10(b),
SCOA and CH3SO2+ for the two size fractions (c) (the
regression lines show a linear relationship), PBOA and cellulose for
PM10(d), AOOC and OCf (the regression fit
was weighted by the standard deviation of AOOC) (e), and SOOA and
daily averaged temperature as well as OCnf/ ECnf
ratio and temperature for PM10(f).
Biomass burning OA (BBOA) was identified by its significant contributions of
the oxygenated fragments C2H4O2+ (at m/z 60) and
C3H5O2+ (at m/z 73), common markers for wood burning
formed by fragmentation of anhydrous sugars (Alfarra et al., 2007) (Fig. 5).
It was also identified by its distinct seasonal variation which exhibited
exclusively high concentrations in winter, reaching up to
20.0 ± 0.7 µg m-3 for PM10 in December 2013 and
12.3 ± 0.5 µg m-3 for PM2.5 in January 2014
(Fig. 6). The median value for the OM / OC ratio was 1.8 and the
RBBOA was consistent with the low end of the reported one by
Daellenbach et al. (2016) (Table 2). The identification of this factor as
BBOA was further confirmed by its remarkable correlation with levoglucosan.
Similar to levoglucosan, this factor did not exhibit a significant difference
between PM2.5 and PM10 concentrations (Fig. S5a), suggesting that
most of these particles are present in the fine mode, consistent with
previous observations (Levin et al., 2010b). The BBOA/levoglucosan ratio was
7.1 for PM10 and 5.8 for PM2.5, which falls into the range reported
by Daellenbach et al. (2017) and was also consistent with the ratio reported
by Bozzetti et al. (2016). The difference of BBOA/levoglucosan for the two
size fractions is due to four samples in BBOA PM10 with high
concentrations. Lastly, BBOC showed a strong correlation with
ECnf, with a slope of 4.9 (Fig. 7b) which fell within the range
of the compiled ECnf/BBOC ratios in Ulevicius et al. (2016).
Season-wise average (± 1
standard deviation) concentrations (in µg m-3) of
different OA factors per size fraction. Note that for the 2 different years the months per season can
vary.
Sulfur-containing OA (SCOA) was identified by its spectral fingerprint which
is described by a high contribution of the fragment CH3SO2+ (at
m/z 79) (Fig. 5) and high OM / OC ratio (Table 2). The
RSCOA (Fig. 4, Table 2) showed a much broader distribution than
the rest of the primary OC recoveries yet more limited towards the strongly
water-soluble fractions compared to Daellenbach et al. (2017). SCOA
concentrations were higher in the coarse fraction compared to PM2.5
(Figs. 6 and 7c, S5) and exhibited higher concentrations during autumn and
winter compared to summer (Table 3). A similar profile had previously been
linked to a marine origin by Crippa et al. (2013) in Paris; however,
Daellenbach et al. (2017) found that SCOA in Switzerland was rather a primary
locally emitted source with no marine origin due to its anti-correlation with
methane sulfonic acid (MSA). Here we confirm that SCOA did not follow the MSA
time series (Fig. S11) but rather the time series of NOx. These
observations suggest that this factor is connected to a primary coarse
particle episodic source related to traffic.
Primary biological OA (PBOA) exhibited significant contributions from the
fragment C2H5O2+ (part of m/z 61) (Fig. 5) and was more
enhanced in summer and spring (Fig. 6). The RPBOA (Fig. 4,
Table 2) met the high end of RPBOA in Bozzetti et al. (2016).
PBOA appeared mostly in the coarse mode (Table 3, Fig. S5). The mass spectral
features, the seasonality and coarse contribution suggested the biological
nature of this factor possibly including plant debris. Additional support of
this interpretation is provided by the correlation of PBOA with cellulose
(Fig. 7d), a polymer mostly found in the cell wall of plants. The correlation
improved if only data from summer and spring were considered. The outliers
here were the late autumn and winter points when BBOA was more important and
PBOA could not as easily be separated by the PMF technique.
One out of the three oxygenated OAs (OOA) was identified as a highly oxidized
factor, due to the significant contribution of the fragment CO2+
(Fig. 5) and the high OM / OC ratio (Table 2) which was consistent with
the reported OM / OC ratio by Turpin et al. (2001) for non-urban
aerosols. This factor peaked mainly in winter and spring and the PM2.5
size fraction exhibited higher concentrations during this period compared to
the coarse size fraction (Table 3, Fig. 6). The water solubility of this
oxygenated factor was high (Fig. 4, Table 2), which is consistent with the
literature values (Daellenbach et al., 2016, 2017) that refer to the sum of
all oxygenated factors, as well as with reported water-soluble fractions for
highly oxidized compounds (Xu et al., 2017). The yearly median concentration
for PM10 was 0.97 µg m-3 (Q25= 0.86 and
Q75=1.09µg m-3), which accounts for approximately
13 % of the total OA. Out of all the possible correlations with external
markers, this factor correlated best with OCf (Fig. 7e);
therefore, we chose to name it anthropogenic OOA (AOOA) (see also discussion
in Sect. 4.4.2). Both AOOC and OCf followed very similar annual
cycles (Fig. S12) with average
AOOC / OCf= 0.97 ± 2.49. This observation along
with the increase in OCf/ ECf as already
discussed in Sect. 4.2 could indicate that this factor is linked to secondary
organic aerosol from traffic emissions or to transported air masses from
industrialized areas. It may also be connected to the oxidation of volatile
chemical products such as pesticides, coatings, printing inks or cleaning
agents (McDonald et al., 2018). Further discussion about AOOC can be found in
Sect. 4.4.
Probability density functions of the fitting coefficients of the
relative fossil contributions: SCOC in blue, AOOC in purple, SOOC in yellow
and WOOC in light brown.
Relative contributions to the fossil OC per factor (PM10)(a) and to the non-fossil OC per factor (PM10)(b):
BBOC in dark brown, SCOCf and SCOCnf in blue, PBOC in
green, AOOCf and AOOCnf in purple, SOOCf
and SOOCnf in yellow, and WOOCf and WOOnf
in light brown. Note that the total non-fossil concentrations (dark green
markers) are on average 6 times higher compared to the fossil ones (dark grey
markers).
Yearly cycles of fossil PM10(a), non-fossil PM10(b), fossil PM2.5(c), and non-fossil PM2.5(d) OC factors: BBOC in dark brown, SCOCf and
SCOCnf in blue, PBOC in green, AOOCf and
AOOCnf in purple, SOOCf and SOOCnf in
yellow, and WOOCf and WOOnf in light brown. Note that
the covered time periods in (a), (b) and (c),
(d) are different.
Averaged contributions of the fossil and non-fossil primary and
secondary OC to the total OC season wise for PM10.
Summer oxygenated OA (SOOA) was mainly identified by the high contribution of
the fragment C2H3O+ (m/z 43) (Fig. 5)
(fC2H3O+=0.15) as well as its seasonal behaviour (Fig. 6).
Like all the oxygenated OA factors, it was highly water soluble (Fig. 4,
Table 2). The highest concentrations were witnessed in July with values of
4.4 µgm-3 for PM10 in 2013 and 4.3 µgm-3
for PM2.5 in 2014. The bulk contribution of this factor was present in
the PM2.5 fraction (Table 3, Fig. S5). The seasonal variability of SOOA
followed the daily temperature average (Fig. 6). In
fact, SOOA exponentially increased with temperature (Fig. 7f). Such behaviour
was also observed in Daellenbach et al. (2017), where they connected this
factor to the oxidation of terpene emissions and therefore to biogenic SOA
formation. The exponential dependence of SOOA with temperature was also
similar to the temperature dependence of the biogenic SOA concentrations
from a Canadian terpene-rich forest, reported by Leaitch et al. (2011). A
similar factor was identified with an online instrument in Zurich during
summer 2011, where the semi-volatile OOA was mainly formed by biogenic
sources as the high temperatures favour the biogenic emissions compared to
the rest (Canonaco et al., 2015). Finally, the O / C ratio (0.37) fell
into the range of the reported O / C ratios measured by chamber-generated
SOA (Aiken et al., 2008), which was similar to biogenic SOA produced in flow
tubes (Heaton et al., 2007).
Named after its seasonal behaviour (Daellenbach et al., 2017), the third
oxygenated factor, winter oxygenated OA (WOOA), exhibited the highest
concentrations during winter. WOOA mass spectrum exhibited elevated
contributions of the fragment C2H3O+ (Fig. 5), but lower
compared to SOOA (for WOOA fC2H3O+=0.11). It also exhibited
a slightly enhanced contribution of the fragment C2H4O2+
which can be an indication that this factor originated from aged biomass
burning emissions. Moreover, a similar mass spectral pattern (peaks of
fragments C3H3O+, C3H5O2+,
C4H5O2+ and C5H7O2+ at m/z 55, 73, 85
and 99, respectively) to the one coming from oxygenated products from a
wood-burning experiment (Bruns et al., 2015) was found. The recovery of this
factor manifested high values (Table 2) and consisted mainly of fine-mode
particles (Fig. S5). WOOA also correlated with NH4+ (Fig. S13),
which is directly connected to the inorganic secondary ions NO3- and
SO42-.
Coupling of offline AMS and 14C analyses
In this section of the paper we will show the combined results of AMS/PMF and
radiocarbon analyses. The first part will elaborate on the technical aspect of the analysis
by presenting the calculation of the contribution of each
factor to the fossil OC. In the second part, a thorough description of each
fossil and non-fossil major source will be given. The time series of each
fossil and non-fossil fraction for the whole AMS dataset is illustrated in
Fig. 10. Contributions of the primary and secondary OC to the total OC will
be also discussed and shown in Fig. 11.
Calculation of fossil and non-fossil fraction per factor
To combine the AMS/PMF with the 14C results, the identified sources from
AMS/PMF were divided into fossil and non-fossil fractions. HOC was fully
assigned to fossil sources assuming that the percentage of biofuel content is
negligible. BBOC and PBOC were considered totally non-fossil. To explore the
fossil and non-fossil nature of the rest of the factors, we performed
multilinear regression using Eq. (17):
OCf,i-HOCi=a⋅SCOCi+b⋅AOOCi+c⋅SOOCi+d⋅WOOCi,
where a, b, c and d are the fitting coefficients, weighted by the
relative uncertainty of OCf,i- HOCi. To investigate the stability
of the solution, we obtained distributions of the fitting coefficients by
performing 100 bootstrap runs where input data were randomly selected
(Fig. 8). The median values (and first and third quartiles) were as follows:
a=0.81 (Q25=0.73, Q75=0.88), b=0.77 (Q25=0.54,
Q75=0.85), c=0.21 (Q25=0.15, Q75=0.26) and d=0.23
(Q25=0.13, Q75=0.39).
We chose to apply the multilinear regression to the fossil fraction because
for the non-fossil part, the errors related to fitting coefficients were very
high and the dependences of the OCnf on the input factors were
not statistically significant (p values > 0.1).
To calculate the non-fossil part of each factor k (kOCnf), we
used the following equation:
kOCnf,i=kOCi-kOCf,i.
This analysis suggests that the major fossil primary sources were HOC and
SCOC (81 % ± 11 % fossil), while AOOC
(77 % ± 23 % fossil) was the only major fossil secondary
source. In terms of the non-fossil sources, the dominating primary sources
included BBOC and PBOC, whereas the most important secondary sources were
SOOC (79 % ± 11 % non-fossil) and WOOC
(77 % ± 23 % non-fossil).
Contribution of fossil and non-fossil, primary and secondary OC to the
total OC
The results point out that 81 % ± 11 % (average and 1 standard
deviation) of SCOC was fossil (SCOCf). Taking into
account the enhanced contribution of SCOC in the coarse size fraction, its
sulfur content and its fossil nature, we assume that this factor is linked
to primary anthropogenic sources related to traffic, such as tire wear, resuspension of
road dust (Bukowiecki et al., 2010), resuspension from asphalt concrete
(Gehrig et al., 2010) or asphalt mixture abrasion (in bituminous binder,
Fullova et al., 2017). The contribution of SCOCf to the OCf was
more important during autumn and winter (up to 62 %, Fig. 9a) in contrast
to spring and summer (on average 9 % ± 5 %), while on average
the contribution to the OCf was 20 % ± 19 %. The
concentrations in winter and autumn were similar and on average for PM10
(PM2.5) 0.22 ± 0.21 µg m-3
(0.03 ± 0.03 µg m-3) (Fig. 10, Table S2), which
accounted for 73 % of the total SCOC for this period. However, the
contribution of SCOCf to the total OC for the coarse size
fraction was not high (5 % ± 8 % on average).
The combined 14C / AMS analysis supported the initial hypothesis
that AOOC was mainly related to the oxidation of fossil fuel combustion
emissions (e.g. traffic), as AOOC was 77 % ± 23 % fossil
(AOOCf) on average. The average contribution of AOOCf
to the OCf was 28 % ± 14 % (Fig. 9a), larger than
SCOCf, while its contribution to the total OC was
10 % ± 5 % for the coarse OC and 7 % ± 7 % of the
fine OC. The yearly cycle exhibited elevated contributions in winter and
spring compared to summer and autumn with average values for PM10:
0.47 ± 0.22, 0.43 ± 0.30, 0.39 ± 0.23 and
0.29 ± 0.23 µg m-3, respectively (Fig. 10, Table S2).
In winter and spring most of the mass concentration came from the PM2.5
size range in contrast to the other two seasons.
The fossil fractions of SOOC (SOOCf) and WOOC (WOOCf)
were low (21 and 23 %, respectively) and could also be attributed to
traffic emissions or less likely (due to low emissions) to aged aerosols from
residential fossil fuel heating. SOOCf was important during
summer with contributions up to 40 % to the OCf and
WOOCf was more distinctively present during a few days in autumn
and winter (up to 35 % to the OCf) in contrast to the rest of
the year (Fig. 9a).
From the non-fossil sources, apart from non-fossil SCOC (SCOCnf)
and non-fossil AOOC (AOOCnf), the rest of the factors exhibited a
very distinct yearly cycle with BBOC contributing up to 86 % to the
OCnf in late autumn and winter (Fig. 9b, yearly average
28 % ± 30 %) and with PBOC and SOOCnf becoming more
important in late spring, summer and early autumn with contributions up to 82
and 57 %, respectively (Fig. 9b).
SOOC was 79 % non-fossil which supported the AMS/PMF results: the
significance of non-fossil SOOC (SOOCnf) during summer can be
attributed to SOA formation from biogenic emissions. The average contribution
of SOOCnf to OCnf was 25 % ± 19 %
(Fig. 9b). SOOCnf was more pronounced in PM2.5 (on average
1.12 ± 0.40 µg m-3 in summer and
0.75 ± 0.35 µg m-3 in spring, Fig. 10, Table S2). This
factor along with PBOC was the main and almost equally important source of OC
during spring and summer, with PBOC contributing to OC in the coarse mode (on
average 35 % ± 16 % from April to August 2014) and
SOOCnf in the fine mode (46 % ± 15 % from April to
August 2014). PBOC made up 30 % ± 18 % of the OCnf
and the average concentrations of PBOCcoarse for 2014 were 1.00 ± 0.23 µg m-3 in summer and
0.56 ± 0.21 µg m-3 in spring.
Non-fossil WOOC (WOOCnf) dominated over WOOCf
(77 % over 23 %). The average yearly contribution to
OCnf was low (6 % ± 6 %, Fig. 9b); however,
WOOCnf,coarse was apparent during the cold period especially in
2013 with concentrations of 0.88 ± 0.74 µgm-3 on average
for winter (0.28 ± 0.28 µg m-3 for autumn) (Fig. 10).
In 2014 the concentrations dropped for winter (autumn) with
0.53 ± 0.43 µg m-3
(0.15 ± 0.13 µg m-3) for PM10 and
0.22 ± 0.19 µg m-3
(0.21 ± 0.21 µg m-3) for PM2.5. Based on its
yearly cycle (Fig. 10b and d) WOOCnf could be linked to aged OA
influenced by wintertime and early spring biomass burning emissions.
Therefore, not only AOOCf but also WOOCnf can be
related to anthropogenic activities. In other studies (Daellenbach et al.,
2017; Bozzetti et al., 2016) this factor was more pronounced; however, in our
case in winter most of the OCnf was related to primary biomass
burning.
Overall for PM10 the non-fossil primary OC contributions were more
important during autumn (57 %) and winter (75 %), whereas in spring
and summer the non-fossil secondary OC contributions became more
pronounced (32 and 40 %, respectively) (Fig. 11). The dominance of the
SOC during the warm period is likely related to the stronger solar radiation
which favours the photo-oxidation of biogenic volatile organic compounds and to
the elevated biogenic volatile organic compounds emissions.
Conclusions
The coupling of offline AMS and 14C analyses allowed a detailed
characterization of the carbonaceous aerosol in the Alpine valley of Magadino
for the years 2013–2014. The seasonal variation along with the two
size-segregated measurements (PM10 and PM2.5) gave insights into
the source apportionment, by for example quantifying the resuspension of road
dust or asphalt concrete and estimating its contribution to the OC or by
identifying SOC based on SOC precursors. More specifically, seven sources
including four primary and three secondary ones were identified. The
non-fossil primary sources were dominating during autumn and winter, with
BBOC exhibiting by far the highest concentrations. During spring and summer
again two non-fossil sources, PBOC in the coarse fraction and
SOOCnf in the fine mode, prevailed over the fossil ones. The
size-segregated measurements and 14C analysis enabled a better
understanding of the primary SCOC factor, which was enhanced in the coarse
fraction and was mainly fossil, suggesting that it may originate from
resuspension of road dust or tire – asphalt abrasion. The results also
showed that SOC was formed mainly by biogenic sources during summer and
anthropogenic sources during winter. However, SOC formed possibly by
oxidation of traffic emissions or volatile chemical products was also
apparent during summer (AOOCf). AOOCf was also
important during winter along with SOC linked to transported non-fossil
carbonaceous aerosols coming from anthropogenic activities such as biomass
burning (WOOCnf).
Data availability
The data are available upon request from the corresponding
author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-18-6187-2018-supplement.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This work is funded by the Swiss Federal Office for the Environment (FOEN),
OSTLUFT and the cantons of Basel, Graubünden, Ticino, Thurgau and Valais.
The LABEX OSUG@2020 (ANR-10-LABX-56) funded analytical instruments at IGE.
Edited by: Eleanor Browne
Reviewed by: two anonymous referees
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