Introduction
Water-soluble organic carbon (WSOC) is a large fraction of atmospheric
organic aerosols (OA), which contributes approximately 10 to 80 % of the
total mass of organic carbon (OC) in aerosols from urban, rural and remote
sites (Zappoli et al., 1999; Weber et al., 2007; Ruellan and Cachier, 2001;
Wozniak et al., 2012; Mayol-Bracero et al., 2002). Only 10 to 20 % of
total mass of WSOC has been resolved at a molecular level, and it consists of
a large variety of chemical species such as mono- and di-carboxylic acids,
carbohydrate derivatives, alcohols, aliphatic and aromatic acids and amino
acids (Fu et al., 2015; Noziere et al., 2015). Recent studies suggest that
the water-soluble fraction of humic like substances (HULIS) is a major
component of WSOC, which exhibits light-absorbing properties (Limbeck et al.,
2005; Andreae and Gelencser, 2006; Laskin et al., 2015). Therefore, WSOC has
significant influences on the Earth's climate either directly by scattering
and absorbing radiation or indirectly by altering the hygroscopic properties
of aerosols and increasing cloud condensation nuclei (CCN) activity
(Asa-Awuku et al., 2011; Cheng et al., 2011; Hecobian et al., 2010).
WSOC can be directly emitted as primary particles mainly from biomass burning
emissions or produced from secondary organic aerosol (SOA) formation
(Sannigrahi et al., 2006; Kondo et al., 2007; Weber et al., 2007; Bozzetti et
al., 2017a, b). Ambient studies provide evidence that SOA formation through
the oxidation of volatile organic compounds (VOCs) and gas to particle
conversion processes may be a prevalent source of WSOC (Kondo et al., 2007;
Weber et al., 2007; Miyazaki et al., 2006; Hecobian et al., 2010). WSOC is
therefore thought to be a good proxy for secondary organic carbon (SOC) in the
absence of biomass burning (Weber et al., 2007). By contrast, water insoluble
OC (WIOC) is thought to be mainly from primary origins with a substantial
contribution from fossil fuel emissions (Miyazaki et al., 2006; Zhang et al.,
2014a).
Due to a large variety of sources and unresolved formation processes of WSOC,
their relative fossil and non-fossil contributions are still poorly
constrained. Radiocarbon (14C) analysis of sub-fractions of organic
aerosols such as OC, WIOC and WSOC enable an unambiguous, precise and
quantitative determination of their fossil and non-fossil sources (Zhang et
al., 2012, 2014a, b; Zong et al., 2016; Cao et al., 2017). Meanwhile, the
application of aerosol mass spectrometer measurement, positive matrix
factorization and multi-linear engine 2 (ME-2) can quantitatively classify
organic aerosols into two major types such as hydrocarbon-like OA (HOA) from
primary fossil fuel combustion and oxygenated organic aerosol (OOA) from
secondary origin (Zhang et al., 2007; Jimenez et al., 2009). Field campaigns
with the aerosol mass spectrometer (AMS) have revealed a predominance of OOA
in various atmospheric environments, although their sources remain poorly
characterized (Zhang et al., 2007; Jimenez et al., 2009). Previous studies
found OOA is strongly correlated with WSOC from urban aerosols in Tokyo,
Japan, the Pearl River Delta (PRD) in South China and Helsinki, Finland,
indicating similar chemical characteristics, sources and formation processes
of OOA and WSOC (Kondo et al., 2007; Xiao et al., 2011; Timonen et al.,
2013). Similarly, HOA is mostly water insoluble and the major portion of
water insoluble OC (WIOC) can be assigned as HOA (Kondo et al., 2007;
Daellenbach et al., 2016). Therefore, 14C measurement of WIOC and WSOC
aerosols may provide new insights into sources and formation processes of
primary and secondary OA, respectively, which also will elucidate the origin
of HOA and OOA as measured by AMS (Zotter et al., 2014b; Zhang et al., 2017).
In this paper, we apply a newly developed method to measure 14C in WSOC
of PM2.5 (particulate matter with an aerodynamic diameter of smaller than
2.5 µm) samples collected at four Chinese megacities during an
extremely severe haze episode in winter 2013 (Y. L. Zhang et al., 2015;
Huang et al., 2014). In conjunction with our previous dataset from the same
campaign, we quantify fossil and non-fossil emissions from primary and
secondary sources of WSOC and WIOC. The dataset is also complemented by
previous 14C-based source apportionment studies conducted in urban,
rural and remote regions in the Northern Hemisphere to gain an overall picture
of the sources of WSOC aerosols.
Materials and methods
Sampling
During January 2013, extremely high concentrations of 24 h PM2.5 (i.e.
often > 100 µg m-3) were identified in several
large cities in eastern China (Huang et al., 2014; Y. L. Zhang et al., 2015). To
investigate sources and formation mechanisms of the haze particles, an
intensive field campaign was carried out in four large cities, Beijing,
Xi'an, Shanghai and Guangzhou, which are representative cities of the
Beijing–Tianjin–Hebei region, central-northwestern region, Yangtze Delta Region,
and Pearl River Delta Region, respectively. The sampling procedures have been
previously described in detail elsewhere (Y. L. Zhang et al., 2015). Briefly,
PM2.5 samples were collected on pre-baked (450 ∘C for 6 h)
quartz filters using high-volume samplers for 24 h at a flow rate of
∼ 1.05 m3 min-1 from 5 to 25 January 2013. The sampling
sites in each city were located at campuses of universities or at research
institutes, at least 100 m away from major emission sources (e.g., roadways,
industry and domestic sources). One field blank sample for each site was
collected and analyzed. The results reported here were corrected for these
field blanks (Zotter et al., 2014a; Cao et al., 2013). All samples were
stored at -20 ∘C before analysis. The PM2.5 mass was
gravimetrically measured with an analytical microbalance before and after
sampling with the same conditions (∼ 12 h)
OC and EC mass determinations
A 1.0 cm2 filter punches were used for OC and EC mass determination
with a OC / EC analyzer (Model4L) using the EUSAAR_2 protocol (Cavalli
et al., 2010). The replicate analysis (n = 6) showed analytical
precision with relative standard deviations smaller than 5, 10, and 5 %
for OC, EC and TC, respectively. The field blank of OC was on average
2.0 ± 1.0 µg cm-2 (equivalent to ∼ 0.5µg m-3),
which was used for blank correction for OC. EC data were not
corrected for field blank, because such a blank was not detectable.
Offline-AMS measurement and PMF source apportionment
The water-soluble extracts from the same samples were analyzed by a
high-resolution time of flight aerosol mass spectrometer (HR-ToF-AMS) and
the resulting mass spectra were used as an inputs for positive matrix
factorization (PMF) for the source apportionment of the WSOC, OC and
PM2.5. The methodology applied and the AMS-PMF results obtained are
detailed in Huang et al. (2014) and will be only briefly described in the
following. Here, only data relative to WSOC are used.
Filter punches (the equivalent of ∼ 4 cm2) were sonicated in 10 mL
ultrapure water (18.2 MΩ cm at 25 ∘C, TOC < 3 ppb)
for 20 min at 30 ∘C. The water extracts were aerosolized and
the resulting particles were dried with a silica gel diffusion dryer before
analysis by the HR-ToF-AMS. For each measurement ten mass spectra were
recorded (AMS V-mode, m/z 12–500), with a collection time for each spectrum
of 1 min.
Online AMS measurements provide quantitative mass spectra of submicron
non-refractory aerosol species, including organic aerosol and ammonium
nitrate and sulfate. However, the offline AMS measurements described herein
cannot be directly related to ambient concentrations due to uncertainties in
nebulization and AMS lens cut-off. Here, we have scaled the organic aerosol
mass spectra to water soluble organic aerosol concentrations (WSOM),
obtained as WSOC times OM / OC ratios. The latter were determined by the high-resolution analysis of the organic aerosol mass spectra, acquired by the
AMS.
The quantitative WSOM mass spectra are used together with other aerosol
species (listed below), collectively referred to as “species” hereafter, as
PMF inputs. PMF solves the bilinear matrix equation as follows:
Xij=∑kGi,kFk,j+Ei,j,
by following a weighted least squares approach. In the equation, i represent
the time index, j a species and k the factor number. Xij is the input
matrix, Gi,k is the matrix of the factor time series, Fk,j is the
matrix of the factor profiles and Ei,j the model residual matrix. PMF
determines Gi,k and Fk,j such that the ratio of the Frobenius norm
of Ei,j over the uncertainty matrix, si,j, used as model input is
minimized.
The species considered as inputs include the quantitative WSOM mass spectra,
organic markers (3 anhydrous sugars, 4 lignin breakdown products, 2 resin
acids, 4 hopanes, 19 polycyclic aromatic hydrocarbons and their oxygenated
derivatives), EC, major ions (Cl-, NO3-, SO42-,
oxalate, methylsulfonic acid, Na+, K+, Mg2+, Ca2+, and
NH4+) and residual PM. The latter is the difference between total
PM2.5 mass and the measured species. It represents our best estimate of
the particulate chemical species not measured here, most likely dominated by
crustal material.
The Source Finder toolkit (SoFi v.4.9) (Canonaco et al., 2013) from the 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 F′k,j
equals the following:
F′k,j=Fk,j±α⋅Fk,j.
The elements that were constrained in Fk,j
matrix can be found in Huang et al. (2014). The factors extracted by ME-2
were interpreted to be related to primary emissions from traffic (TR),
biomass burning (BB), coal burning (CC), cooking emissions (CI) and dust as
well as
from two secondary aerosol fractions. The contributions of the water soluble
organic aerosol related to these different factors were determined by the
multiplying their relative abundance in the factor profiles by the
respective factor time series. The factors WSOM time series were then
divided by the respective OM / OCk calculated from the high-resolution
analysis of the factor mass spectral profile to obtain the WSOCk time
series related to each of the factors. The average OM / OCk are: 1.25,
1.39, 1.49, 1.55, 2.25 and 2.4 for TR, CI, BB, CB, SOA and dust,
respectively. In the following analysis, the mass of WSOCk related to
coal burning and traffic were assigned to the fossil WSOC fraction, while the
mass of WSOCk related to biomass burning and cooking emissions were
assigned to the non-fossil WSOC fraction (see Sect. 2.5). Meanwhile, the
remaining WSOC fractions are assigned to the secondary factors, which can be
from both fossil and non-fossil origins. These were considered collectively and
compared to the unassigned fossil and non-fossil WSOC, to retrieve the
origins of this remaining fraction (see Sect. 2.5).
The AMS2-based source apportionment scheme of WSOC aerosols in
this study. See text for the equations (i.e., Eqs. 4, 5, 9, 10 in
the Sect. 2.5) and the offline AMS & PMF (see the Sect. 2.3).
14C measurement of WSOC
14C content of micro-scale WSOC aerosol samples was measured with a
newly developed method (Zhang et al., 2014c). Briefly, a 16 mm-diameter
punch of each filter was extracted using 10 mL ultrapure water with low TOC
impurity (less than 5 ppb). The water extracts were recovered in the 20 mL
PFA vials and were then pre-frozen at -20 ∘C more than 5 h before
completely dryness in a freeze dryer (Alpha 2-4 LSC, Christ, Germany) for
about 24 to 36 h. The residue was re-dissolved in 50 µL of
ultrapure water three times and transferred into 200 µL tin
capsules (Elementar, Germany). The concentrated samples were heated in the
oven at 55–60 ∘C until completely dry before the 14C
measurements were taken.
WSOC extracts in tin capsules were then converted to CO2 by the
oxidation of the carbon-containing samples using an Elemental Analyzer (EA,
Model Vario Micro, Elementar, Germany) as a combustion unit (up to
1050 ∘C). The resulting CO2 was introduced continuously by a
versatile gas inlet system into a gas ion source of the accelerator mass
spectrometer MICADAS where 14C of CO2 was finally measured (Wacker
et al., 2013; Salazar et al., 2015). The 14C content of OC and EC was
measured in our previous study (Y. L. Zhang et al., 2015). 14C results
were expressed as fraction of modern (fM), i.e., the fraction of
the measured 14C / 12C ratio related to the
14C / 12C ratio of the reference year 1950 (Stuiver, 1977). To
correct excess 14C from nuclear bomb tests in the 1950s and 1960s,
fM values were converted to the fraction of non-fossil
(fNF) (Zotter et al., 2014a; Zhang et al., 2012) as follows:
fNF=fM/fM,ref.
fM,ref is a reference value of fM for non-fossil
carbon sources including biogenic and biomass burning emissions, which were
estimated as 1.08 ± 0.05 (i.e.,
fM,ref = (0.5 × 1.10 + 0.5 × 1.05) (see
details in Zhang et al., 2012) for WSOC samples collected in 2013 according
to the contemporary atmospheric CO2 fM (Levin et al., 2010)
and a tree growth model (Mohn et al., 2008).
AMS2-based source apportionment of WSOC
To better understand the origin of WSOC observed at these sites, WSOC sources
were apportioned into several major sources by a combination of 14C and
PMF source apportionments (see Fig. 1). Here, two “AMS” (i.e., accelerator
mass spectrometer and aerosol mass spectrometer), such a combined approach
was named “AMS2-based source apportionment”.
WSOC concentration from non-fossil (WSOCNF) and fossil
(WSOCF) sources were calculated as follows:
WSOCNF=WSOC⋅fNF(WSOC)WSOCF=WSOC-WSOCNF.
The mass concentration of WSOC was derived from the subtraction of TC mass
measured from a water-extracted filter from that measured with an un-treated
filter (Zhang et al., 2012):
WSOC=TCun-treated-TCwater-extracted.
Based on mass balance, WIOC concentrations from non-fossil (WIOCNF) and
fossil (WIOCF) sources were calculated as follows:
WIOCNF=OCNF-WSOCNFWIOCF=OCF-WSOCF,
where OC concentrations from non-fossil (OCNF) and fossil
(OCF) sources were obtained by mass and 14C measurement of
the OC fraction, which were reported previously (Y. L. Zhang et al., 2015).
The non-fossil and fossil fuel derived WSOC can be apportioned into primary
and secondary OC:
WSOCNF=WSOCPOC,NF+WSOCSOC,NFWSOCF=WSOCPOC,F+WSOCSOC,F.
WSOCPOC,NF can be sub-divided into the following three major primary
emissions including cooking emission (WSOCCI) and biomass burning
(WSOCBB).
WSOCPOC,NF=WSOCCI+WSOCBB.
Similarly, WSOCPOC,F can be sub-divided into the following two major
primary emissions including traffic (WSOCTR) and coal combustion
(WSOCCB).
WSOCPOC,F=WSOCTR+WSOCCB,
where primary fractions such as WSOCCI, WSOCBB,
WSOCTR and WSOCCB are previously estimated by the
off line AMS–PMF approach (Huang et al., 2014; Daellenbach et al., 2016;
Bozzetti et al., 2017a, b).
An uncertainty propagation scheme using a Latin-hypercube sampling (LHS)
model was implemented to properly estimate overall uncertainties, including
measurement uncertainties of the mass determinations of carbon species (i.e.,
OC, EC, TC, WSOC, WIOC) and 14C measurement, blank corrections from
field blanks, and estimation of fM,ref (Y. L. Zhang et al.,
2015).
Linear relationships (p < 0.01) of WSOC with PM2.5
(a) and OC concentrations (b).
Mass concentrations (µg m3) of WSOC from non-fossil
and fossil fuel sources (WSOCNF and WSOCF,
respectively) as well as non-fossil fractions of the WSOC aerosols from
Beijing, Xi'an, Shanghai and Guangzhou during moderately polluted days (MPD)
and heavily polluted days (HPD). Note the different scaling for different
cities.
Relationships of non-fossil derived WSOC (WSOCNF) and
levoglucosan (a), levoglucosan and the fraction of fossil in WSOC
(fF(WSOC)) (b) and levoglucosan and the fraction of
non-fossil in WSOC (fNF(WSOC)) (c).
Mass concentrations (µg m-3) of WIOC from non-fossil
and fossil fuel sources (WIOCNF and WIOCF,
respectively) as well as non-fossil fractions in the WIOC aerosols from
Beijing, Xi'an, Shanghai and Guangzhou during moderately polluted days (MPD)
and heavily polluted days (HPD). Note the different scaling for different
cities.
Relationship between the fraction of non-fossil WIOC and
WSOC (a) and averaged relative contribution (%) to OC from WSOC
and WIOC from non-fossil and fossil sources (b).
Results and discussion
Overall results
During the haze periods of January 2013, the highest daily average PM2.5
concentrations were found in Xi'an (345 µg m-3) followed by
Beijing (158 µg m-3), Shanghai (90 µg m-3)
and Guangzhou (68 µg m-3). These levels were much higher than
the China's national ambient air quality standards (i.e.,
35 µg m-3). Indeed, several studies have already reported the
chemical composition, source and formation mechanism of PM2.5 in many
large cities during the haze events of January 2013 in eastern China. For
examples, Huang et al. (2014) revealed that the secondary aerosol formation
contributed to 44–71 % of OA in Beijing, Xi'an, Shanghai, and Guangzhou
during this extremely haze event in China (Huang et al., 2014). By
14C-based source appointment conducted in the same campaign, Zhang et
al. (2015) have reported that carbonaceous aerosol pollution was driven to a
large (often dominant) extent by SOA formation from both fossil and
biomass-burning sources (Y. L. Zhang et al., 2015). For all four cities, the
24 h average levels of WSOC were significantly correlated with the levels of
PM2.5 and OC (R=0.99, p < 0.01, Fig. 2), suggesting that
WSOC and OA may have similar sources and formation processes and thus have
important implications for OC loadings and associated environmental and
health effects. However, the sources of WSOC remain poorly constrained. In
this study, we measured the 14C content of WSOC aerosols in six samples
(three with the highest, three with average PM mass) for each city to report
on heavily and moderately polluted days (HPD and MPD, respectively) (Y. L.
Zhang et al., 2015). The 14C contents of OC and EC of the same samples
were reported previously (Y. L. Zhang et al., 2015).
Compilation of literature values of relative fossil fuel
contributions (fossil %) to the WSOC aerosols in East/South Asia, USA and
Europe.
Site
Location
Season
Size
WSOC (µg m-3)
WSOC/OC
Fossil %
References
East Asia
Urban
Beijing, China
Winter/2013
PM2.5
19.8
0.49
47
This work
Urban
Xi'an, China
Winter 2013
PM2.5
31.3
0.53
25
This work
Urban
Shanghai, China
Winter 2013
PM2.5
6.5
0.58
52
This work
Urban
Guangzhou, China
Winter 2013
PM2.5
6.6
0.53
32
This work
Urban
Beijing, China
Winter 2014
PM2.5
14.7
0.40
56
Fang et al. (2017)
Urban
Beijing, China
Winter 2011
PM4.3
15
0.50
55
Zhang et al. (2014b)
Urban
Beijing, China
Winter 2013
PM2.5
9.3
0.31
54
Yan et al. (2017)
Urban
Guangzhou, China
Winter 2012/2013
PM2.5
4.1
0.38
33
Liu et al. (2014)
Urban
Guangzhou, China
Winter 2011
PM10
4.5
0.43
28.5
Zhang et al. (2014b)
Urban
Xi'an, China
Autumn 2009
PM2.5
5.1
0.28
31
Pavuluri et al. (2013)
Urban
Xi'an, China
Autumn 2010
TSP
8.1
0.28
29
Pavuluri et al. (2013)
Urban
Wuhan, China
Winter 2013
PM2.5
13.7
0.45
37
Liu et al. (2016)
Urban
Sapporo, Japan
Summer/Autumn 2010
PM3
1
0.43
15
Pavuluri et al. (2013)
Urban
Sapporo, Japan
Summer 2011
TSP
1.1
0.24
12
Pavuluri et al. (2013)
Urban
Sapporo, Japan
Spring 2010
TSP
1.1
0.31
11
Pavuluri et al. (2013)
Urban
Sapporo, Japan
Autumn 2011
TSP
1.8
0.48
18.3
Pavuluri et al. (2013)
Urban
Sapporo, Japan
Winter 2010
TSP
0.9
0.45
40.2
Pavuluri et al. (2013)
Background
Jeju Island, Korea
Winter 2014
PM2.5
2.2
0.66
50
Fang et al. (2017)
Background
Jeju Island, Korea
Spring 2011
PM2.5
2.0
37.5
Kirillova et al. (2014a)
Background
Jeju Island, Korea
Spring 2011
TSP
3.0
25
Kirillova et al. (2014a)
Average
33 ± 14
South Asia
Background
Hainan, China
Annual 2005/2006
PM2.5
3.9
0.54
18
Zhang et al. (2014b)
Background
Hainan, China
Winter 2005/2006
PM2.5
6.2
0.57
14.5
Zhang et al. (2014b)
Background
Hainan, China
Summer 2005/2006
PM2.5
1.4
0.40
17.7
Zhang et al. (2014b)
Background
Hanimaadhoo, Maldives
Annual 2008/2009
TSP
0.5
17
Kirillova et al. (2013)
Background
Sinhagad, India
Annual 2008/2009
TSP
3.0
24
Kirillova et al. (2013)
Background
Hanimaadhoo, Maldives
Spring 2012
PM2.5
0.6
0.62
14
Bosch et al. (2014)
Urban
Delhi, India
Winter 2010/2011
PM2.5
22.0
21
Kirillova et al. (2014b)
Average
18 ± 4
Europe and USA
Urban
Gothenburg, Sweden
Winter 2005
PM2.5
1.1
0.48
23
Szidat et al. (2009)
Urban
Gothenburg, Sweden
Summer 2006
PM2.5
0.8
0.61
30
Szidat et al. (2009)
Rural
Gothenburg, Sweden
Winter 2005
1.2
0.53
27
Szidat et al. (2009)
Rural/semi-urban
Stockholm, Sweden
Summer 2009
TSP
12
Kirillova et al. (2010)
Urban
Zürich, Switzerland
Summer 2002
PM10
2.1
0.54
14
Szidat et al. (2004)
Urban
Zürich, Switzerland
Winter 2008
PM10
2.8
0.60
26.8
Zhang et al. (2013)
Urban
Moleno, Switzerland
Summer 2006
PM10
5.3
0.67
30
Zhang et al. (2013)
Urban
Bern, Switzerland
Winter 2009
PM10
0.39
14
Zhang et al. (2014b)
Urban
Atlanta, USA
Summer 2004
PM2.5
2.3
0.59
26.5
Weber et al. (2007)
Rural
Millbrook, USA
Annual 2006/2007
TSP
0.36
12
Wozniak et al. (2012)
Rural
Harcum, USA
Annual 2006/2007
TSP
0.38
14
Wozniak et al. (2012)
Regional background
Cesar, Netherlands
Annual 2011/2012
PM2.5
2.3
0.65
21
Dusek et al. (2017)
Average
21 ± 8
WSOC on average accounted for 53 ± 8.0 % (ranging from
40–65 %) of OC including all samples from the four sites, which was
consistent with previous estimates. Based on these measurements, the
concentrations of WSOC from non-fossil sources (WSOCNF) spanned
from 1.41 to 45.3µg m-3 with a mean of
10.6 ± 12.1µg m-3, whereas the corresponding range for
WSOC from fossil fuel emissions (WSOCF) was 0.44 to
20.1µg m-3 with a mean of 5.3 ± 4.9µg m-3
(Fig. 3). Similar to PM2.5 levels, the highest concentrations of
WSOCNF and WSOCF were observed in northern China in
Xi'an and Beijing (Xi'an > Beijing), followed by the two
southern sites of Shanghai and Guangzhou. Non-fossil contributions (mean
± standard deviation) to total WSOC were 53 ± 5, 75 ± 4,
48 ± 2 and 68 ± 6 % in Beijing, Xi'an, Shanghai, and
Guangzhou, respectively. Thus, fossil contributions were notably higher in
Beijing and Shanghai than in Xi'an and Guangzhou. Such a trend was also
observed for OC (Y. L. Zhang et al., 2015), suggesting relatively high
contribution from fossil fuel emissions to OC and WSOC due to large coal
usage. Despite these fossil emissions, non-fossil sources were
important or even dominant contributors for all the studied
sites, which may be associated with primary and secondary OA from
regional and local biomass burning emissions. As shown in Fig. 4,
non-fossil WSOC was significantly correlated with levoglucosan, indicating
that a large fraction of non-fossil WSOC was indeed from biomass burning
emissions. In addition, no significant (or only a negative) correlation
(Fig. 4) was found between levoglucosan and fraction of fossil to WSOC,
suggesting that fossil fuel source is very unlikely to be a major or important
contributor of levoglucosan even in the regions (e.g., Xi'an and Beijing in
this study) where coal combustion is important during the cold period
(Y.-L. Zhang et al., 2015). It should also be noted that formation of SOA
derived from biogenic VOCs may also have contributed to WSOCNF in
Guangzhou, where temperatures during the sampling period were significantly
higher (i.e., 5–18 ∘C) than those in other cities (i.e., -12 to
+9 ∘C) (Bozzetti et al., 2017b). Although both fossil and
non-fossil WSOC concentrations were dramatically enhanced during HPD compared
to those during MPD, their relative contributions did not change
significantly in Beijing and Shanghai, whereas a small increasing and
decreasing trend in the non-fossil fraction was found in Xi'an and Guangzhou,
respectively (Fig. 3). This suggests that the source pattern of WSOC in
Beijing and Shanghai remained similar between HPD and MPD, but the increase
in the WSOC concentrations was rather enhanced by additional fossil fuel and
biogenic(or biomass) burning emissions in Guangzhou and Xi'an, respectively. It
should be noted that the meteorological conditions played significant roles in
the haze formation in eastern China during winter 2013, and has already
been well documented (R. Zhang et al., 2014). However, the details sources of
WSOC and WIOC were still unclear.
WSOC versus WIOC
To compare sources of WSOC and WIOC aerosols, the mass concentrations and
14C contents of WIOC were also derived based on mass balance. The
14C-based source apportionment of WIOC and the relationship between
fNF (WSOC) and fNF (WIOC) are presented in Figs. 5 and
6a, respectively. It shows that non-fossil contributions to WSOC were larger
than those of WIOC for nearly all samples in Beijing, Xi'an and Guangzhou. On
average, the majority (60–70 %) of the fossil OC was water insoluble at
these three sites (see Fig. 6b), indicating that fossil-derived OA mostly
consisted of hydrophobic components and thus is less water soluble than OA
from non-fossil sources. This result is consistent with findings reported
elsewhere such as at an urban or rural site in Switzerland (Zhang et al.,
2013), a remote site on Hainan Island, southern China (Zhang et al., 2014a) and
at two rural sites on the east coast of the United States (Wozniak et al.,
2012). Meanwhile, the fossil OC in Shanghai, the dominant fraction of OC, was
more water soluble (Fig. 6b), suggesting an enhanced SOA formation from
fossil VOCs from vehicle emissions and/or coal burning in this city. As
shown in Figure 6b, non-fossil OA was enriched in water-soluble fractions
(i.e., 60 ± 8 %) for all cities, associated with the hydrophilic
properties of biogenic-derived SOA and biomass-burning derived primary
organic aerosol (POA) and SOA, which are composed of a large fraction of
polar and highly oxygenated compounds (Mayol-Bracero et al., 2002; Sullivan
et al., 2011; Noziere et al., 2015). Thus, non-fossil OC has more
water-soluble components than fossil OC does. It should be noted that relative
contributions of WSOCNF and WSOCF are similar in Beijing and
Shanghai, whereas WSOCNF is much higher than WSOCF in
Xi'an and Guangzhou. This suggests larger contribution of non-fossil sources
to WSOC aerosols in Xi'an and Guangzhou than those in Beijing and Shanghai.
Relative contributions (%) to WSOC from biomass burning, as well
as secondary organic carbon (SOC) from fossil and non-fossil sources
(WSOCSOC,F and WSOCSOC,NF, respectively) in different
cities during moderately polluted days (MPD) and heavily polluted days (HPD),
as well as their corresponding excess (Excess = HPD-MPD). The numbers
above the bars refer to the average WSOC concentrations and the SOC fractions
(%) of WSOC.
Box-plot of the fossil contribution (%) to the WSOC aerosols in
East Asia, South Asia, the USA and Europe. The box represents the 25th (lower
line), 50th (middle line) and 75th (top line) percentiles; the empty square
within the box represent the mean values; the end lines of the vertical bars
represent the 10th (below the box) and 90th (above the box) percentiles; the
solid dots represent the maximum and minimum values; the solid diamonds
represent the individual data (Table 1). The data from East Asia is grouped
by winter and non-winter seasons.
High contribution of secondary formation to WSOC
WSOC was further apportioned into fossil sources such as coal burning (CB),
traffic (TR) and SOC (SOC,F) as well as non-fossil sources such as biomass
burning (BB), cooking (CI) and SOC (SOC,NF) using a AMS2 based source
apportionment (see Sect. 2.5 and Fig. 1). SOC dominated WSOC during both the
HPD and MPD with a mean contribution of 67 ± 9 %, highlighting the
importance of SOC formation to the WSOC aerosols in winter pollution
events. This is consistent with our previous findings for total PM2.5
mass and bulk carbonaceous aerosols (i.e., total carbon, sum of OC and EC)
(Huang et al., 2014; Y. L. Zhang et al., 2015). The increase in SOC
contribution to WSOC during HPD compared to MPD can be largely due to fossil
contribution in Beijing but non-fossil emissions in Xi'an. In Shanghai and
Guangzhou, the source pattern of WSOC was not significantly different between
MPD and HPD. Fossil contributions to WSOCSOC were
50 ± 9 % in Beijing, 61 ± 4 % in Shanghai, associated
with SOA from local and transported fossil fuel derived precursors at these
sites (Guo et al., 2014). This contribution drops to 36 ± 9 and
26 ± 9 % in Guangzhou and Xi'an, respectively, due to higher
biomass-burning contribution to SOC. Despite of the general importance of
fossil SOC, formation of non-fossil WSOCSOC becomes especially
relevant during HPD especially in Xi'an (Fig. 7), which may be explained by
competing effects in SOC formation from fossil versus non-fossil precursors.
It can be hypothesized for extremely polluted episodes that more hydrophilic
volatile compounds that were emitted from biomass burning precursors
preferentially form SOC compounds via heterogeneous reaction or processing of
dust particles, compared to highly hydrophobic precursors from fossil sources,
a point that should be subjected to future laboratory and field experiments. The most
important primary sources of WSOC were biomass burning emissions, and their
contributions were higher in Xi'an (26 ± 7 %) and Guangzhou
(25 ± 6 %) than those found in Beijing (17 ± 6 %) and
Shanghai (17 ± 5 %). The remaining primary sources such as coal
combustion, cooking and traffic were generally very small contributors of
WSOC due to lower water solubility, although coal combustion could exceed
10 % in Beijing. It should be noted that WSOC was dominated by SOC
formation with a mean contribution of 61± 10 and 72 ± 12 %
(average for all four cities) to non-fossil and fossil fuel derived WSOC,
respectively.
Summary and implications
Our study demonstrates that non-fossil emissions are generally a dominant
contributor of WSOC aerosols during extreme haze events in representative
major cities of China, which is in agreement with WSOC source information
identified in aerosols with different size fractions (e.g., TSP, PM10
and PM2.5) observed in the Northern Hemisphere at urban, rural,
semi-urban, and background sites in East/South Asia, Europe and USA
(Table 1). The 14C-based source apportionment database shows a mean
non-fossil fraction of 73 ± 11 % across all sites. This
overwhelming non-fossil contribution to WSOC is consistently observed
throughout the year, which is associated with seasonally dependent
biomass burning emissions and/or biogenic-derived SOC formation. Our study
provides evidence that the presence of oxidized OA, which is to a large
extent water soluble, in the Northern Hemisphere (Zhang et al., 2007) is
mainly derived from biogenic derived SOA and/or biomass burning sources. The
overall importance of non-fossil emissions to the WSOC aerosols results from
large contributions of SOC formation from biogenic precursors (e.g., most
likely during summer) and relatively high water-solubility of primary biomass
burning particles (e.g., most likely during winter) compared to those emitted
from fossil fuel emissions such as coal combustion and vehicle exhaust.
Despite the importance of non-fossil sources, a significant fossil
fraction is also observed in the WSOC aerosols from polluted regions in East
Asia and sites influenced by East Asian continental outflow (Table 1,
Fig. 8). This fossil contribution is apparently higher than in this region
than in the USA and Europe, which is due to large industrial and residential
coal usage as well as vehicle emissions. From our observation, the increases
in the fossil fractions of WSOC were mostly from SOC formation. Since WSOC
has hygroscopic properties, our findings suggest that SOC formation from
non-fossil emissions have significant implications on aerosol-induced climate
effects. In addition, fossil-derived SOC formation may also become important
in polluted regions with large amounts of fossil fuel emissions, such as in
China and other emerging countries. Low combustion efficiencies and
consequently high emission factors in most of the combustion processes in
China may further be responsible for increased concentrations of fossil
precursors which may be oxidized to form water-soluble SOA in the atmosphere
and contribute substantially to the WSOC aerosols. The enhanced WSOC levels
may also originate from aging of fossil POA during the long-range
transport of aerosols (Kirillova et al., 2014a). It is also interesting to
note that fossil contribution during winter in East Asia is generally higher
than those in the rest of the year, although a relatively large fossil fraction
could be occasionally found as well. Such seasonal dependence was not
observed in other regions, suggesting the importance of fossil contribution
to WSOC due to increasing coal combustions during winter in China. This study
provides a more detailed source apportionment of WSOC, which could improve
modelling of climate and health effects as well as the understanding of
atmospheric chemistry of WSOC in the polluted atmosphere such as China and
provide a scientific basis for policy decisions on air pollution emissions
mitigation.