Introduction
Carbonaceous aerosols, an important component of fine particulate matter
(PM2.5, particles with aerodynamic diameter <2.5 µm) in
almost all environments, have been identified as critical contributors to
severe air pollution events (Cao et al., 2003; R. J. Huang et al., 2014; Elser
et al., 2016; Liu et al., 2016a). In urban areas in China, they typically
constitute 20 %–50 % of PM2.5 mass (Cao et al., 2007; R. J. Huang et
al., 2014; Tao et al., 2017). Carbonaceous aerosols are of importance
because they have adverse impacts on human health (Nel, 2005; Cao et al.,
2012; Lelieveld et al., 2015) and climate (Chung and Seinfeld, 2002; Bond
et al., 2013), in addition to air quality (Watson, 2002). Carbonaceous
aerosols contain a large number of organic species and are operationally
divided into organic carbon (OC) and elemental carbon (EC) (Pöschl,
2005). EC can significantly absorb incoming solar radiation and is the most
important light-absorbing aerosol component (Bond et al., 2013). On the
other hand, OC mainly scatters light, but there is also OC found with light
absorbing properties, referred to as brown carbon (Pöschl, 2005; Laskin
et al., 2015). Carbonaceous aerosols are believed to contribute large
uncertainties in climate radiative forcing (IPCC, 2013). EC and OC are
mainly emitted from incomplete combustion of biomass (e.g., wood, crop
residues, and grass) and fossil fuels (e.g., coal, gasoline, and diesel).
Biomass burning is the only non-fossil source for EC, but OC also has other
sources, for example, biogenic emissions and cooking. Unlike EC that is
exclusively emitted as primary aerosols, OC includes both primary and
secondary OC, where secondary OC is formed in the atmosphere via atmospheric
oxidation of volatile organic compounds from non-fossil (e.g., biomass
burning, biogenic emissions, and cooking) and fossil sources (Jacobson et
al., 2000; Pöschl, 2005; Hallquist et al., 2009). So far, sources and
evolution of carbonaceous aerosols remain poorly characterized. A better
understanding of OC and EC sources is important for the mitigation of
particulate air pollution and improvement of our understanding of their role in
climate radiative forcing.
Radiocarbon (14C) analyses of OC and EC allow a quantitative and
unambiguous measurement of their fossil and non-fossil contributions, based
on the fact that emissions from fossil sources are 14C-free, whereas
non-fossil emissions contain the contemporary 14C content (e.g.,
Szidat, 2009; Dusek et al., 2013, 2017). The 14C/12C ratio of an
aerosol sample is usually reported as fraction modern (F14C). F14C
relates the 14C/12C ratio of the sample to the ratio of the
unperturbed atmosphere in the reference year of 1950 (Stuiver and Polach,
1977; Mook and van der Plicht, 1999; Reimer et al., 2004). In practice, this
is usually done by relating the 14C/12C ratio of the sample to the
ratio of oxalic acid OXII calibration material multiplied by a factor of
0.7459:
F14C=(14C/12C)sample,[-25](14C/12C)1950,[-25]=(14C/12C)sample,[-25]0.7459×(14C/12C)OXII,[-25],
where the 14C/12C ratios of the sample and standard are both
corrected for machine background and normalized for fractionation to δ13C=-25‰ to correct for isotopic
fractionation during sample pre-treatment and measurements. Aerosol carbon
from living material should have F14C∼1 in an
undisturbed atmosphere, and carbon from fossil sources has F14C=0.
However, F14C values of the contemporary (or non-fossil) carbon sources
are bigger than 1 due to the nuclear bomb tests that nearly doubled the
14CO2 in the atmosphere in the 1960s and 1970s. Currently,
F14C of the atmospheric CO2 is approximately 1.04 (Levin et al.,
2010). This value is decreasing every year because the 14CO2
produced by bomb testing is taken up by oceans and the biosphere and diluted
by 14C-free CO2 produced by fossil fuel burning. For biogenic
aerosols, aerosols emitted from cooking as well as annual crops,
F14C is close to the value of current atmospheric CO2.
F14C of wood burning is higher than that because a significant
fraction of carbon in the wood burned today was fixed during times when
atmospheric 14C/12C ratios were substantially higher than today.
Estimates of F14C for wood burning are based on tree-growth models
(e.g., Lewis et al., 2004; Mohn et al., 2008) and found to range from 1.08
to 1.30 (Szidat et al., 2006; Genberg et al., 2011; Gilardoni et al., 2011;
Minguillón et al., 2011; Dusek et al., 2013). When F14C is measured
on OC and EC separately, contributions from non-fossil and fossil sources to
carbonaceous aerosols can be separated. Previous 14C measurements of
carbonaceous aerosols in China found that EC in urban areas is dominated by
fossil sources, which account for 66 %–87 % of EC mass, whereas OC is more
influenced by non-fossil sources, with fossil sources accounting for only
35 %–67 % (Table 1). Despite a fair number of 14C studies in China in
recent years, only a few 14C datasets have so far reported annual results
and seasonal variations of OC and EC (Y. L. Zhang et al., 2014a, 2015b, 2017).
Relative fossil source contribution to OC and EC
(ffossil(OC) and ffossil(EC) in percentage) in China.
Location
Site type
PM
Season
Year
ffossil(OC)
ffossil(EC)
Reference
fraction
Beijing
urban
PM2.5
winter
2009/2010
83±4
Chen et al. (2013)
Beijing
urban
PM2.5
spring
2013
41±4
67±7
Liu et al. (2016a)
Beijing
rural
PM2.5
winter
2007
80–87
Sun et al. (2012)
Beijing
rural
PM2.5
summer
2007
80–87
Sun et al. (2012)
Beijing
urban
PM2.5
winter
2013
67±3
Yan et al. (2017)
Beijing
urban
PM2.5
summer
2013
36±13
Yan et al. (2017)
Beijing
urban
PM4
annual
2010/2011
79±6
Zhang et al. (2015b)
Beijing
urban
PM2.5
winter
2013
58±5
76±4
Zhang et al. (2015a)
Beijing
urban
PM1
annual
2013/2014
48±12
82±7
Zhang et al. (2017)
Guangzhou
urban
PM2.5
winter
2012/2013
37±4
71±10
J. Liu et al. (2014)
Guangzhou
urban
PM2.5
spring
2013
46±6
80±5
Liu et al. (2016a)
Guangzhou
urban
PM10
winter
2011
42
Y. L. Zhang et al. (2014b)
Guangzhou
urban
PM2.5
winter
2013
35±7
69
Zhang et al. (2015a)
Shanghai
urban
PM2.5
winter
2009/2010
83±4
Chen et al. (2013)
Shanghai
urban
PM2.5
winter
2013
49±2
79±4
Zhang et al. (2015a)
Xiamen
urban
PM2.5
winter
2009/2010
87±3
Chen et al. (2013)
Xi'an
urban
PM2.5
winter
2013
38±3
78±3
Zhang et al. (2015a)
Xi'an
urban
PM2.5
annual
2008/2009
46±8
83±5
This studya
Wuhan
urban
PM2.5
winter
2013
38±5
74±8
Liu et al. (2016b)
North China Plain (NCP)
urban
PM2.5
winter
2013
73–75
Andersson et al. (2015)
Yangtze River Delta (YRD)
urban
PM2.5
winter
2013
66–69
Andersson et al. (2015)
Pearl River Delta (PRD)
urban
PM2.5
winter
2013
67–70
Andersson et al. (2015)
Ningbo
background
PM2.5
annual
2009/2010
77±15
Liu et al. (2013)
Hainan
background
PM2.5
annual
2005/2006
19±10
38±11
Y. L. Zhang et al. (2014a)
a The ffossil(OC) and ffossil(EC) values in this study are calculated
from the F14C data (see details in Sect. 2.5).
In addition to 14C source apportionment, analysis of the stable carbon
isotope composition (namely the 13C/12C ratio, expressed as
δ13C in Eq. 2) can provide further information regarding
sources and atmospheric processing of carbonaceous aerosol (Bosch et al.,
2014; Kirillova et al., 2014b; Andersson et al., 2015; Masalaite et al.,
2017). Different emission sources have their own source signature:
carbonaceous aerosol from coal combustion is enriched in 13C (i.e., has
higher δ13C values of ∼-25 ‰
to -21 ‰) compared to aerosol from liquid fossil fuel
combustion (δ13C∼-28‰ to
-24 ‰) and from burning of C3 plants (δ13C
∼-35 ‰ to -24 ‰)
(Andersson et al., 2015, and references therein). Complementing 14C
source apportionment with 13C measurements allows a better constraint
of the contribution of different emission sources to carbonaceous aerosols
(Kirillova et al., 2013, 2014a; Bosch et al., 2014; Andersson et al., 2015;
Winiger et al., 2015, 2016; Bikkina et al., 2016, 2017; Yan et al., 2017).
For example, EC is inert to chemical or physical transformations; thus the
δ13CEC preserves the signature of emission sources (L. Huang
et al., 2006; Andersson et al., 2015; Winiger et al., 2015, 2016). EC from
fossil sources (e.g., coal combustion, liquid fossil fuel burning) can be
first separated from biomass burning by F14C of EC. Further, δ13C of EC allows separation of fossil sources into coal and liquid
fossil fuel burning (Andersson et al., 2015; Winiger et al., 2015, 2016),
due to their different source signatures. Typical δ13C values
for EC from previous studies are summarized in Table S1 in the Supplement. The interpretation
of the stable carbon isotope signature for OC source apportionment is more
difficult because OC is chemically reactive and δ13C
signatures of OC are not only determined by the source signatures but also
influenced by atmospheric processing. During formation of secondary organic
aerosol (SOA), molecules depleted in heavy isotopes are expected to react
faster, leading to SOA depleted in δ13C compared to its gaseous
precursors, if the precursor is only partially reacted (Anderson et al.,
2004; Irei et al., 2006; Fisseha et al., 2009). For example, Irei et al. (2006)
found that the δ13C values of particulate SOA formed by
OH-radical-induced reactions of toluene ranged from -32.9 ‰ to -32.2 ‰, on average 5.8 ‰
lighter than those of parent toluene, when the 7 %–29 %
toluene was reacted. On the other hand, photochemical aging of
particulate organics leads to δ13C enrichment in the remaining
aerosols due to a faster loss of the lighter carbon isotope 12C (Irei
et al., 2011; Pavuluri and Kawamura, 2016). For example, Bosch et al. (2014)
observed the more enriched δ13C signature of water-soluble OC
(-20.8±0.7 ‰) than EC (-25.8±0.3 ‰) at a receptor station for the South Asian outflow,
due to aging of OC during the long-range transport of aerosols.
We present, to the best of our knowledge, the first 1-year radiocarbon and
stable carbon isotopic measurements to constrain OC and EC sources in China.
PM2.5 samples were collected in Xi'an (33∘29′–34∘44′ N,
107∘40′–109∘49′ E), one of
the most polluted megacities in the world (Zhang and Cao, 2015a). The aims
of this study are (1) to quantify the contributions from fossil and
non-fossil sources to OC and EC by radiocarbon measurements; (2) to further
distinguish the fossil sources of EC into coal and liquid fossil fuel
combustion by complementing radiocarbon with the stable carbon signature; (3) to
assess the sources and atmospheric processing of OC qualitatively using its
stable carbon signature. Further, mass concentrations and source
contributions of primary OC are estimated based on the apportioned EC and
compared with measured OC mass concentrations and source contributions to
give insights into OC sources and formation mechanisms (4).
Methods
Sampling
Sampling was carried out at Xi'an High-Tech Zone (34.23∘ N,
108.88∘ E; ∼10 m above the ground), on a building
rooftop of the Institute of Earth Environment, Chinese Academy of Sciences.
The sampling site is surrounded by a residential area ∼15 km
south of downtown and has no major industrial activities. Details about the
sampling site can be found elsewhere (Bandowe et al., 2014; T. Zhang et al.,
2014).
PM2.5 samples of 24 h duration (from 10:00 to 10:00 the next day, local standard time, LST) were collected every sixth day from 5 July 2008 to 27 June 2009
using a high-volume sampler (TE-6070 MFC, Tisch Inc., Cleveland,
OH, USA) operating at 1.0 m3min-1. PM2.5 samples were
collected on Whatman quartz fiber filters (20.3cm×25.4cm,
Whatman QM/A, Clifton, NJ, USA) that were pre-baked at 900 ∘C for
3 h to remove absorbed organic vapors (Watson et al., 2009; Chow et al.,
2010). After sampling, we immediately removed the filter from the sampler.
All filters were packed in pre-baked aluminum foil, sealed in polyethylene
bags, and stored at -18∘C in a freezer. To be consistent with previous
studies (Han et al., 2016; T. Zhang et al., 2014), we designated 15 November to 14 March
as winter, 15 March to 31 May as spring, 1 June to 31 August as
summer, and 1 September to 14 November as autumn, based on the
meteorological characteristics and the typical residential heating period.
In total, 58 PM2.5 samples were collected, with 13 in spring,
15 in summer, 12 in autumn, and 18 in winter. Six samples with varying
PM2.5 mass and carbonaceous aerosol loading were selected per season
for 14C analysis. We selected the samples carefully to cover periods of
low, medium, and high PM2.5 concentrations to get samples
representative of the various pollution conditions that did occur in each
season. The 24 selected samples are highlighted in Fig. S1 in the Supplement with their OC and
EC concentrations. In total, there are 48 radiocarbon data, including 24 for
OC and 24 for EC. Details on sample selection for 14C analysis are
presented in the Supplement S1.
Organic carbon (OC), elemental carbon (EC), and source markers'
measurement
Filter pieces of 0.5 cm2 were used to measure OC and EC using a Desert
Research Institute (DRI) Model 2001 Thermal/Optical Carbon Analyzer
(Atmoslytic Inc., Calabasas, CA, USA) following the IMPROVE_A
(Interagency Monitoring of Protected Visual Environments) thermal/optical
reflectance (TOR) protocol (Chow et al., 1993, 2007, 2011). Details of the
OC/EC measurement were described in Cao et al. (2005). The differences
between the replicated analyses for the same sample (n=10) are smaller
than 5 % for TC, 5 % for OC, and 10 % for EC, respectively.
Organic markers including levoglucosan, picene, and hopanes were quantified
using gas chromatography–mass spectrometry (GC/MS). Water-soluble potassium
(K+) was measured in water extracts using ion chromatography (Dionex
600, Thermal Scientific-Dionex, Sunnyvale, CA, USA). Details on the
measurements are described in the Supplement S2.
Stable carbon isotopic composition of OC and EC
The stable carbon isotopic composition of OC and EC was determined using a
Finnigan MAT 251 mass spectrometer with a dual inlet system (Bremen,
Germany) at the Stable Isotope Laboratory at the Institute of Earth
Environment, Chinese Academy of Sciences. For OC, filter pieces were heated
at 375 ∘C for 3 h in a vacuum-sealed quartz tube in the presence
of CuO catalyst grains. The evolved CO2 from OC was isolated by a
series of cold traps and quantified manometrically. The stable carbon
isotopic composition of the CO2 was determined as δ13COC by offline analysis with a Finnigan MAT-251 mass
spectrometer. Extraction of EC was done by heating the carbon that remained
on the filters at 850 ∘C for 5 h. The resulting CO2 was
purified in cold traps and then quantified as the EC fraction. The isotopic
ratios of the purified CO2 of EC were measured and determined as
δ13CEC. A routine laboratory working standard with a known
δ13C value was measured every day. The quantitative levels of
13C and 12C isotopes were characterized using a ratio of peak
intensities of m/z 45 (13C16O2) and 44
(12C16O2) in the mass spectrum of CO2. Samples were
analyzed at least in duplicate, and all replicates showed differences less
than 0.3 ‰. δ13C values are reported in
the delta notation as per mil (‰)
differences with respect to the international standard Vienna Pee Dee
Belemnite (V-PDB):
δ13C‰=13C/12Csample13C/12CV-PDB-1×1000.
V-PDB is the primary reference material for measuring natural variations of
13C isotopic content. It is composed of calcium carbonate from a
Cretaceous belemnite rostrum of the Pee Dee Formation in South Carolina,
USA. Its absolute isotope ratio 13C/12C (or
(13C/12C)V-PDB) is 0.0112372, and it is established as δ13C value =0. Details of stable carbon isotope measurements are
described in our previous studies (Cao et al., 2008, 2011, 2013).
Radiocarbon (14C) measurement of OC and EC
Combustion of OC, EC, and standards
OC and EC were
extracted separately by our aerosol combustion system (ACS) (Dusek et al.,
2014). In brief, the ACS consists of a combustion tube, in which aerosol filter
pieces are combusted at different temperatures in pure O2, and a
purification line at which the resulting CO2 is isolated and separated
from other gases, such as water vapor and NOx. The purified CO2 is
then stored in flame-sealed ampoules until graphitization.
OC is combusted by heating filter pieces at 375 ∘C for 10 min. EC
is combusted after complete OC removal. To remove OC completely,
water-soluble OC is first removed from the filter by water extraction (Dusek
et al., 2014) to minimize charring of organic material (Yu et al., 2002).
Subsequently, most water-insoluble OC is removed by heating the filter
pieces at 375 ∘C for 10 min. Then the oven temperature is
increased to 450 ∘C for 3 min, and in this step a mixture of the
most refractory OC and less refractory EC is removed from the filter. The
remaining EC is then combusted by heating at 650 ∘C for
5 min
(Zenker et al., 2017).
Two standards with known 14C content are combusted as quality control
for the combustion process: an oxalic acid standard and a graphite standard.
Small amounts of solid standard material are directly put on the filter
holder of the combustion tube and heated at 650 ∘C for 10 min. In
the further 14C analysis, the CO2 derived from combustion of the
standards is treated exactly like the samples. Therefore, the contamination
introduced by the combustion process can be estimated from the deviation of
measured values from the nominal values of the standards. The contamination
is below 1.5 µgC per combustion, which is relatively small
compared with
the samples ranging between 50 and 270 µgC in this study.
14C analysis of OC and EC
Graphitization
and AMS measurements were conducted at the Centre for Isotope
Research (CIO) at the University of Groningen. The extracted CO2 is
reduced to graphite by reaction with H2 (g) at a molecular ratio
H2/CO2 of 2 using a porous iron pellet as a catalyst at 550 ∘C
(de Rooij et al., 2010). The water vapor from the reduction
reaction is cryogenically removed using Peltier cooling elements. The yield
of graphite is higher than 90 % for samples of >50 µgC. Graphite
formed on the iron pellet is then pressed into a 1.5 mm target holder, which
is introduced into the AMS system for subsequent measurement. The AMS system
(van der Plicht et al., 2000) is dedicated to 14C analysis, and
simultaneously measures 14C/12C and 13C/12C ratios.
Varying amounts of reference materials covering the range of sample mass are
graphitized and analyzed together with samples in the same wheel of AMS. Two
such materials with known 14C content are used: the oxalic acid OXII
calibration material (F14C=1.3406) and a 14C-free CO2 gas
(F14C=0). The differences between measured and nominal F14C
values are used to correct the sample values (de Rooij et al., 2010; Dusek
et al., 2014) for contamination during graphitization and AMS measurement
(Supplement S3). The modern carbon contamination is between 0.35 and 0.50 µgC,
and the fossil carbon contamination is around 2 µgC for
samples bigger than 100 µgC.
Source apportionment methodology using 14C
F14C of EC (F14C(EC)) was converted to the fraction of
biomass burning (fbb(EC)) by dividing with F14C of biomass burning
(F14Cbb=1.10±0.05; Lewis et al., 2004; Mohn et al.,
2008; Palstra and Meijer, 2014) given that biomass burning is the only
non-fossil source of EC, to eliminate the effect from nuclear bomb tests in
the 1960s. EC is primarily produced from biomass burning (ECbb) and
fossil fuel combustion (ECfossil), and absolute EC concentrations from
each source can be estimated once fbb(EC) is known:
ECbb=EC×fbbEC,ECfossil=EC-ECbb.
Analogously, F14C of OC (F14C(OC)) was converted to
the fraction of non-fossil OC (fnf(OC)) by dividing the F14C of
non-fossil sources including both biogenic and biomass burning
(F14Cnf=1.09±0.05; Lewis et al., 2004; Levin et al.,
2010; Y. L. Zhang et al., 2014a). The lower limit of 1.04 corresponds to
current biospheric sources as the source of OC, and the upper limit corresponds
to burning of wood as the main source of OC, with only little input from
annual crops. OC can be apportioned between OC from non-fossil sources
(OCnf) and from fossil-dominated combustion sources (OCfossil)
using fnf(OC):
OCnf=OC×fnfOC,OCfossil=OC-OCnf.
A Monte Carlo simulation with 10 000 individual calculations was conducted
to propagate uncertainties. For each individual calculation,
F14C(OC), F14C(EC), OC, and EC concentrations are
randomly chosen from a normal distribution symmetric around the measured
values, with the experimental uncertainties as standard deviation (SD). Random
values for for F14Cbb and F14Cnf are chosen from a triangular frequency
distribution, with its maximum at the central value, and 0 at the lower limit
and upper limit. In this way 10 000 different estimations of fbb(EC),
fnf(OC), ECbb, ECfossil, OCnf, and OCfossil can be
calculated. The derived average represents the best estimate, and the
standard deviation represents the combined uncertainties.
Source apportionment of EC using Bayesian statistics
F14C and δ13C signatures of EC and a mass balance
calculation were used in combination with a Bayesian Markov chain Monte
Carlo (MCMC) scheme to further constrain EC sources into biomass burning
(fbb), liquid fossil fuel combustion (fliq.fossil), and coal combustion
(fcoal):
F14C(EC)=F14Cbb×fbb+F14Cliq.fossil×fliq.fossil+F14Ccoal×fcoal,fbb+fliq.fossil+fcoal=1,δ13CEC=δ13Cbb×fbb+δ13Cliq.fossil×fliq.fossil+δ13Ccoal×fcoal,
where f represents the fraction of EC mass contributed by a given source, and
subscripts denote investigated sources; “bb” denotes biomass
burning, “liq.fossil” is liquid fossil, and “coal” is fossil
coal. F14C(EC) is included in this model which allows
the contribution from biomass (fbb) to be separated from fossil sources (fliq.fossil and
fcoal). F14Cbb is the F14C of biomass burning (1.10±0.05 as mentioned in Sect. 2.5). F14Cliq.fossil and
F14Ccoal are zero due to the long-term decay. δ13Cbb, δ13Cliq.fossil, and δ13Ccoal are the δ13C signature of EC emitted from
biomass burning, liquid fossil fuel combustion, and coal combustion,
respectively. The means and the standard deviations for δ13Cbb (-26.7±1.8 ‰ for C3 plants, and
-16.4±1.4 ‰ for corn stalk), δ13Cliq.fossil (-25.5±1.3 ‰), and
δ13Ccoal (-23.4±1.3 ‰)
are presented in Table S1 (Andersson et al., 2015, and reference therein;
Sect. 4.3.1), and serve as input for MCMC. The source endmembers for δ13C are less well constrained than for F14C, as δ13C
varies with fuel types and combustion conditions. For example, the range of
possible δ13C values for liquid fossil fuel combustion overlaps
to a small extent with the range for coal combustion, although liquid fossil
fuels are usually more depleted than coal. The MCMC technique takes into
account the variability in the source signatures of F14C and δ13C (Table S1), where δ13C introduces a larger
uncertainty than F14C. Uncertainties of both source endmembers for each
source and the measured ambient δ13CEC and
F14C(EC) are propagated.
MCMC-driven Bayesian approaches have been recently implemented to account
for multiple sources of uncertainties and variabilities for isotope-based
source apportionment applications (Parnell et al., 2010; Andersson, 2011).
MCMC works by repeatedly guessing the values of the source contributions and
finding those values which fit the data best. The initial guesses are usually
poor and are discarded as part of an initial phase known as the burn-in.
Subsequent iterations are then stored and used for the posterior
distribution. MCMC was implemented in the freely available R software
(https://cran.r-project.org/, last access: 15 May 2016), using the simmr package
(https://CRAN.R-project.org/package=simmr, last access: 14 June 2017). Convergence diagnostics were
created to make sure the model has converged properly. The simulation for
each sample was run with 10 000 iterations, using a burn-in of 1000 steps,
and a data thinning of 100.
Results
Temporal variation of OC and EC mass concentrations
During the sampling period, extremely high OC and EC mass
concentrations were sometimes observed (Fig. S1). OC mass concentrations
ranged from 3.3 to 67.0 µgm-3, with an
average of 21.5 µgm-3. EC mass concentrations ranged from 2 to
16 µgm-3, with an average of 7.6 µgm-3 (Table S2). OC and EC mass concentrations were
comparable to those reported in previous studies for Xi'an, which had an
average of 19.7±10.7 µgm-3 (average±standard
deviation) OC and 8.0±4.7 µgm-3 EC from March 2012
to March 2013 (Han et al., 2016).
OC and EC concentrations showed a clear seasonal variation, with higher
concentrations in cold periods than those in warm periods. The differences
between winter and summer concentrations were significant (p<0.05).
The mean winter to summer concentration ratios were 3 for OC and 1.5 for EC.
Similar seasonal trends of OC and EC were also observed in Xi'an, China, in
earlier studies (e.g., Han et al., 2016; Niu et al., 2016).
(a) Temporal variation of EC mass concentrations from
biomass burning (ECbb) and fossil fuel combustion (ECfossil), and
fraction of biomass burning contribution to EC (fbb(EC)). (b) Temporal
variation of OC mass concentrations from non-fossil sources
(OCnf) and fossil sources (OCfossil), and fraction of
non-fossil OC to total OC (fnf(OC)).
Temporal variation of fossil and non-fossil fractions of OC and
EC
To investigate the sources of OC and EC, 24 samples representing
different loadings of carbonaceous aerosols from different seasons were
selected for radiocarbon measurement (Supplement S1, Fig. S2, Table S3).
The highest biomass burning contribution to EC (fbb(EC)) of 46 %
was detected on 25 January 2009 (Fig. 1a). This can be related to
enhanced biomass burning emissions indicated by the comparably high
biomass-indicative levoglucosan/EC ratio, and relatively low fossil fuel
associated Σhopanes/EC ratio and picene/EC ratio (Supplement S2 and
Fig. S3), along with unfavorable meteorological conditions (e.g.,
substantially low wind speed (∼1 ms-1) and low temperature
(-0.5 ∘C)). The highest non-fossil contribution to OC (fnf(OC))
of 70 % was observed on the same day. Note that 25 January 2009
was Chinese New Year's Eve with many fireworks. Since the influence of
fireworks on the F14C signature is not known yet, the following source
apportionment will not include Chinese New Year's Eve.
EC is predominantly influenced by fossil sources, with the relative contribution
of fossil fuel to EC (ffossil(EC)) ranging from 71 % to 89 %, with
an annual average of 83±5 %. Lower ffossil(EC) values were observed
in winter (77±5 %) compared with other seasons. This is due to
the substantial contribution from biomass burning to EC in winter, with a
larger fbb(EC) in winter (23±5 %) than other seasons (14±2 %, 16±1 %, and 18±5 % in summer, spring,
and autumn, respectively; Fig. 1a). This is consistent with the evaluated
levoglucosan/EC ratios observed in winter (96 ngµg-1), 1.6 times higher
than that of the yearly average (Fig. S3). The lowest fbb(EC) in summer (14±2 %) suggests the importance of fossil fuel combustion for EC
concentrations. Since the residential usage of coal in summer is much
reduced compared with other seasons, we can expect higher contribution from
vehicle emissions than coal burning to fossil EC in summer. EC
concentrations from fossil fuel (ECfossil) varied by a factor of 4,
ranging from 3.1 to 11.6 µgm-3, with a mean
of 6.7±2.0 µgm-3, which was 4 times higher than
averaged biomass burning EC concentrations (ECbb=1.5±0.9 µgm-3).
A stronger variation was observed in the ECbb, varying 9-fold from 0.5 to 4.7 µgm-3 (Tables S4, S5).
Stable carbon signatures (δ13C) in OC and EC
for the samples selected for 14C measurements. The δ13C
signatures of burning C3 plants (green rectangle), liquid fossil fuel (e.g., oil, diesel,
and gasoline, black rectangle), and coal (brown rectangle) are indicated as
mean±standard deviation in Table S1. The δ13C endmember
ranges for C4 plant burning (-16.4±1.4 ‰; Table S1)
are much more enriched than other sources, and are not shown in this
figure.
The relative contribution of non-fossil sources to OC
(fnf(OC)) ranged from 31 % to 66 %, with an annual average of
54±8 %, which was larger than that to EC (yearly average of 17±5 %). Higher fnf(OC) was observed in winter (62±5 %) and autumn (57±4 %), compared to summer and spring, when
about half of OC was contributed by non-fossil sources (48±3 %
and 48±8 %, respectively; Table S5). The lowest fnf(OC) of
31 % was detected on 28 April 2009 (Fig. 1b), caused by the enhanced
fossil emissions indicated by the highest Σhopanes/EC ratio (5 ngµg-1; Fig. S3).
Averaged OC concentration from non-fossil sources
(OCnf) was 12±10 µgm-3, ranging from 2.3 to 38.6 µgm-3. OC concentrations from fossil sources
(OCfossil) varied from 3.2 to 20.4 µgm-3, with an average of 9.0±4.8 µgm-3. Clear
seasonal variations were seen in OC concentrations both from fossil and
non-fossil sources, with maxima in winter (OCfossil=13.2±6.0 µgm-3, OCnf=23.3±13.3 µgm-3)
and minima in summer (OCfossil=5.5±1.0 µgm-3, OCnf=5.1±1.4 µgm-3) because of
enhanced fossil and non-fossil activities in winter, mainly biomass burning
and domestic coal burning (Cao et al., 2009, 2011; Han et al., 2010, 2016).
13C signature of OC and EC
The δ13CEC preserves the signature of emission sources, as
EC is inert to chemical or physical transformations (Huang et al., 2006;
Andersson et al., 2015; Winiger et al., 2015, 2016). Major EC sources in
Xi'an include biomass burning, coal combustion, and liquid fossil fuel
(e.g., diesel and gasoline) combustion (i.e., vehicular emissions) (Cao et
al., 2005, 2009, 2011; Han et al., 2010; Wang et al., 2016). C3 plants and
C4 plants, biomass subtypes, have a different δ13C signature.
Aerosols from burning C4 plants are more enriched in δ13C
(-16.4±1.4 ‰) than C3 plants (-26.7±1.8 ‰, Table S1). C3 plants are the dominant
biomass type (e.g., wood, wheat straw) in northern China (Cheng et al.,
2013; Cao et al., 2016). This is also evident from our observation that
δ13C values of the ambient aerosol fall within the range of
C3
plant, coal, and liquid fossil fuel combustion (i.e., vehicular emissions; Fig. 2).
The annually averaged δ13CEC is -24.9±1.1 ‰, varying between -26.5 ‰ and
-22.8 ‰. Considerable seasonal variation is observed,
suggesting a shift among combustion sources. The δ13CEC
signature for winter (-23.2±0.4 ‰) is clearly
located in the δ13C range for coal combustion (-23.4±1.3 ‰, Table S1), and is more enriched compared to
other seasons. This indicates a strong influence of coal combustion in
winter, but the 14C values indicate that coal combustion cannot be the
only source of EC. Moreover, the δ13CEC values in winter
ranging from -23.7 ‰ to -22.8 ‰ are
at the higher (i.e., enriched) end of coal combustion, indicating some
additional contributions from C4 plants, such as corn stalk burning. In
northern China, large quantities of coal are used for heating during a
formal residential “heating season” in winter (Cao et al., 2007), and in
rural Xi'an, burning corn stalks (C4 plant) in the “heated kang” (Zhuang et
al., 2009) is a traditional way of heating in winter (Sun et al., 2017).
The most depleted δ13CEC values in summer (-25.9±0.5 ‰) and spring (-25.4±0.4 ‰) fall into the overlap of liquid fossil fuel emission
(-25.5±1.3 ‰) and C3 plant combustion (-26.7±1.8 ‰, Fig. 2), when little or no coal is used
for residential heating but there are some coal emissions from industries.
As the biomass burning contribution to EC in summer and spring is relatively
low (14±2 % and 16±1 %, respectively), we can expect
that liquid fossil fuel combustion dominates EC emissions. δ13CEC signatures in autumn (-25.1±0.7 ‰) fall in the overlapped area of C3 plant,
liquid fossil
fuel, and coal, implying that EC is influenced by the mixed sources.
δ13COC was in general similar to δ13CEC.
This suggests that biogenic OC is probably not very important, as could be
expected from the high TC concentrations. 14C analysis indicates a
considerably higher fraction of non-fossil OC than non-fossil EC, and it
would seem that this is mainly related to the biomass burning, which has
higher OC/EC ratios than fossil fuel burning. If the contribution of
biogenic OC plays an important role, then the biogenic δ13C
signatures should be relatively similar to the source mixture of EC, which
is rather unlikely, especially as this source mixture is not constant.
δ13COC varies from -27.4 ‰ to -23.2 ‰,
with an annual average of -25.3±1.2 ‰ (Fig. 2). This range overlaps with C3 plants, liquid
fossil fuel, and coal combustion. Influence from marine sources (-21±2 ‰; Chesselet et al., 1981; Miyazaki et al., 2011)
should be minimal, as Xi'an is a far inland city in China. δ13COC shows a similar seasonal variation pattern to δ13CEC. δ13COC is most enriched in winter (-24.1±0.8 ‰), followed by autumn (-24.9±0.8 ‰), summer (-25.7±0.9 ‰),
and spring (-26.6±0.6 ‰). In addition to source
mixtures, atmospheric processing also influences δ13COC
(Irei et al., 2006, 2011; Fisseha et al., 2009). In spring, δ13COC is much more depleted than δ13CEC (1.1 ‰–2.4 ‰), indicating the importance of the secondary
formation of OC (e.g., from volatile organic compound precursors) in
addition to primary sources (Anderson et al., 2004; Iannone et al., 2010).
In summer and autumn 2008, δ13COC was very similar to
δ13CEC (Table S3), and showed strong correlations
(r2=0.90), indicating that OC originates from a similar source
mixture as EC. There are no depleted δ13COC values in
summer and autumn as would be expected from significant secondary OC
formation. In summer this could be partially due to the high temperature:
(i) high temperature favors equilibrium shifts to the gas phase, and the SOA less efficiently partitions to the particle phase; (ii) aging
processes also intensify which causes enriched δ13COC in
the particle phase. This is further discussed in Sect. 4.5.
Discussion
Aerosol characteristics in Xi'an compared to other Chinese
cities
There are few annual 14C measurements in China (Table 1). The annual
average ffossil(EC) derived from 14C data in Xi'an is 83 %. This
falls in the range of annual ffossil(EC) measured in China, depending on
the location. Comparable annual ffossil(EC) was reported at an urban
site of Beijing (79±6%; Zhang et al., 2015b; 82±7 %;
Zhang et al., 2017) and a background receptor site of Ningbo (77±15 %; Liu et al., 2013). Much lower ffossil(EC) was found at a
regional background site in Hainan (38±11 %; Y. L. Zhang et al.,
2014a). The big differences between the two background sites are due to
different air mass transport to the receptor site. The background site in
Ningbo was more often influenced by air masses transported from highly
urbanized regions of eastern China associated with lots of fossil fuel
combustion, whereas the decreased fossil contribution observed in Hainan
could be attributed to enhanced open burning of biomass in Southeast Asia or
southeastern China.
In this study, ffossil(EC) was lowest in winter (77 %). This is
comparable with previously reported ffossil(EC) in Xi'an at the same sampling
site during winter 2013 (78±3 %; Zhang et al., 2015a), Shanghai
(79±4 %; Zhang et al., 2015a), Wuhan (74±8 %; Liu et
al., 2016b), North China Plain (73 %–75 %; Andersson et al., 2015), and
Guangzhou (71±10 %; J. Liu et al., 2014). Higher
ffossil(EC) in winter was reported for Beijing (80 %–87%; Sun et al.,
2012; 83±4 %; Chen et al., 2013) and Xiamen (87±3 %;
Chen et al., 2013). Lower winter ffossil(EC) was observed in Guangzhou
(69 %; Zhang et al., 2015a), Yangtze River Delta (66 %–69 %; Andersson
et al., 2015), and Pearl River Delta (67 %–70 %; Andersson et al., 2015),
indicating different influence of biomass burning emissions over China
during winter. 14C measurements in other seasons are still very scarce
in China.
The annual average ffossil(OC) in Xi'an is 46 %, with the lowest
values in winter (38 %) and the highest in summer (52 %). The annual
average ffossil(OC) in this study is comparable to the results found
at
an urban site of Beijing (48±12 %) (Zhang et al., 2017), but
higher than 19±10 % at a background site of Hainan (Y. L. Zhang et
al., 2014a). Similar contributions from fossil sources to OC were reported
for the same sampling site in Xi'an in winter 2013 (38±3%; Zhang
et al., 2015a), Wuhan in January 2013 (38±5 %; Liu et al.,
2016b), and Guangzhou in winter 2012/2013 (37±4 %; J. Liu et al.,
2014). A higher fossil contribution to OC was found in Beijing with
ffossil(OC) of 58±5 % in winter 2013 and 59±6 % in
winter 2013/2014 (Zhang et al., 2015a, 2017), and in Shanghai with
ffossil(OC) of 49±2 % in winter 2013 (Zhang et al., 2015a).
Previous studies in Beijing observed different seasonal trends, with a higher
contribution of fossil sources in winter (higher ffossil(OC)) than in
other seasons (Yan et al., 2017; Zhang et al., 2017). This is consistent
with findings using online aerosol mass spectrometer analysis in winter
2013/2014 (Elser et al., 2016) that organic matter in Xi'an was found to
be dominated by biomass burning, in contrast to Beijing where it is
dominated by coal burning. This implies different pollution patterns over
Chinese cities.
The δ13CEC is most enriched in winter (-23.2±0.4 ‰),
and most depleted in summer (-25.9±0.5 ‰). This is consistent with previous studies in
northern China, with the winter–summer difference ranging from 0.76 ‰ to
2.79 ‰ for all the seven northern Chinese cities (e.g., Cao
et al., 2011; Table S6), supporting the important influence on EC from coal
combustion in winter. By contrast, no notable difference between winter and
summer δ13CEC is reported in southern China, where there
is no official heating season. (e.g., Ho et al., 2006; Cao et al., 2011;
Table S6). δ13COC showed a seasonal variation pattern
similar to δ13CEC. δ13COC is most
enriched in winter (-24.1±0.8 ‰), comparable
with previously reported winter data in northern China, for example, Beijing
(-24.26±0.29 ‰) by Yan et al. (2017), and seven
northern cities in China (-25.54 ‰ to -23.08 ‰) by Cao et al. (2011), but our winter δ13COC is more enriched than those found in southern China, for
example, Hong Kong (-26.9±0.6 ‰) by Ho et al. (2006), and seven southern cities in China (-26.62 ‰ to
-25.79 ‰) by Cao et al. (2011) (Table S6). The
differences in northern and southern China reveal the influence of coal burning
on OC.
Correlation between F14C(EC) and
K+/EC
ratios and levoglucosan/EC ratios in summer. Data in other seasons are
presented in Fig. S5.
Correlations between F14C(EC) and biomass burning
markers
In 14C-based source apportionment, biomass burning is considered the
only source of non-fossil EC. Here we evaluate F14C(EC) with other
biomass burning markers, including levoglucosan and water-soluble potassium
(K+). In summer, a very strong positive correlation (r2=0.96)
was found between F14C(EC) and K+/EC ratios, in contrast to
the significant negative correlation (r2=0.98) between
F14C(EC) and levoglucosan/EC ratios (Fig. 3). Previous studies
have found that burning of crop residues emitted more K+ than
levoglucosan, with significantly lower levoglucosan/K+ ratios than
burning of wood (Cheng et al., 2013; Zhu et al., 2017). The
levoglucosan/K+ ratio for wood is 24.0±1.8, much higher than
those for crop residues (0.10±0.00 for wheat straw, 0.21±0.08 for corn straw, 0.62±0.32 for rice straw; Cheng et al., 2013).
Emissions from crop residue burning therefore increase both the fraction of
EC from non-fossil sources and K+. This results in a positive
correlation between K+/EC ratios and F14C(EC). At the same
time emissions from crop residue burning contain relatively little
levoglucosan, and atmospheric levoglucosan concentrations are expected to be
dominated by wood burning emissions. If wood burning emissions stay
relatively constant, an increase in crop burning emissions will increase EC
concentrations, but have little effect on levoglucosan concentrations,
leading to lower levoglucosan/EC ratios. The significant positive
correlation of F14C(EC) with K+/EC ratios coinciding with a
negative correlation of F14C(EC) with levoglucosan/EC ratios in
summer therefore suggests strong impacts from crop residues' burning and
little influence from wood burning on the variability of EC. Variable crop
burning activities superimposed on a relatively constant background
contribution from wood burning can explain the observed correlations. In
summer, extensive open burning in croplands is also detected in the MODIS
fire counts map (NASA, 2017) (Fig. S4), when farmers in the surrounding area
of Xi'an (i.e., Guanzhong Plain) burned crop residues in fields. No
significant correlations of F14C(EC) with K+/EC or
levoglucosan/EC were found in other seasons (Fig. S5), suggesting a changing
mixture of biomass subtypes with different levoglucosan/K+ ratios. In
this case, the same amount of non-fossil carbon contribution in EC (i.e., same
F14C(EC)) can be associated with very different K+/EC and
levoglucosan/EC ratios, depending on which type of biomass is dominating at
a given time.
Two-dimensional isotope-based source characterization
plot of OC and EC in different seasons. The fraction fossil
(ffossil(EC) and ffossil(OC)) were calculated using radiocarbon
data. The expected δ13C and 14C endmember ranges for
biomass burning emissions, liquid fossil fuel combustion, and coal
combustion are shown as green, black, and brown bars, respectively, within
the 14C-based endmember ranges for non-fossil (dark green rectangle,
bottom) and fossil fuel combustion (grey rectangle, top). The δ13C signatures of C3 plants (green rectangle), liquid fossil fuel (e.g.,
oil, diesel, and gasoline, black rectangle), and coal (brown rectangle) are
indicated as mean±standard deviation in Table S1. The δ13C signature of C4 plants burning is -16.4±1.4 ‰ and is not shown on the x axis.
δ13C/F14C-based statistical source apportionment of
EC
Figure 4 shows 14C-based ffossil(EC) against δ13CEC together with the isotopic signature of their source
endmembers. The source endmembers for δ13C are less well
constrained than for 14C. For example, δ13C values for
liquid fossil fuel combustion overlap with δ13C values for both
coal and C3 plant combustion. In contrast to δ13C, fbb and
ffossil are clearly different and the uncertainties in the
endmembers are related to the combined uncertainties of 14C
measurements and the factor used to eliminate the bomb test effect
(F14Cbb; see Sect. 2.5). All data points fall reasonably well
within the “source triangle” of C3 plant, liquid fossil fuel (e.g.,
traffic or vehicular emission), and coal combustion, except that δ13CEC values in winter are on the higher (i.e., enriched) end of coal
combustion, indicating the possible influence of C4 plants' combustion as
discussed above in Sect. 3.3.
Selection of δ13C endmembers for aerosols from corn stalk
burning in the study area
To incorporate the possible contribution from C4 plants to source
apportionment, we need to estimate the δ13C signature of
aerosols emitted by C4 biomass burning. Corn stalk is the dominant C4 plant
in Xi'an and its surrounding areas (Guanzhong Plain), with little sugarcane
and other C4 plants (Sun et al., 2017; Zhu et al., 2017). Estimates of
δ13C of corn stalk burning emissions range from -19.3 ‰ to -13.6 ‰ (Chen et al., 2012;
Kawashima and Haneishi, 2012; G. Liu et al., 2014; Guo et al., 2016).
δ13C values of aerosols from corn stalk burning were compiled
from literature (Fig. S6). The mean was computed as the average of the
different datasets, and standard deviation analogously calculated. The δ13C source signature for corn stalk burning is -16.4±1.4 ‰ (Fig. S6).
MCMC4 resultsa from the F14C- and δ13C-based Bayesian source apportionment calculations of EC (median,
interquartile range (25th–75th percentile), and 95 % credible
intervals).
Seasons
Summer
Autumn
Winterc
Spring
Annualc
Biomass burningb (combination
Median
0.135
0.177
0.239
0.156
0.173
of C3 & C4 plants)
25th–75th percentile
(0.129–0.142)
(0.16–0.197)
(0.22–0.26)
(0.153–0.159)
(0.165–0.18)
95 % credible intervals
(0.114–0.159)
(0.117–0.249)
(0.172–0.332)
(0.145–0.166)
(0.15–0.195)
Coal combustion
Median
0.085
0.153
0.446
0.136
0.11
25th–75th percentile
(0.045–0.15)
(0.083–0.261)
(0.294–0.582)
(0.075–0.219)
(0.063–0.18)
95 % credible intervals
(0.012–0.412)
(0.02–0.589)
(0.074–0.739)
(0.019–0.492)
(0.016–0.353)
Liquid fossil fuel combustion
Median
0.779
0.666
0.307
0.707
0.717
25th–75th percentile
(0.713–0.82)
(0.555–0.74)
(0.18–0.457)
(0.627–0.768)
(0.647–0.765)
95 % credible intervals
(0.452–0.858)
(0.226–0.824)
(0.039–0.684)
(0.357–0.826)
(0.468–0.815)
a Results from the four-source (C3 biomass, C4 biomass, coal, and liquid
fossil fuel) MCMC4 model.b Contribution of biomass burning is calculated through a posteriori combination of
the PDF for C3 plants and that for C4 plants (Fig. S8). Median and quartile
ranges for C3 andC4 plants' burning to EC are shown in Table S8.c Sample taken from Chinese New Year's Eve (25 January 2009) was excluded.
Influence of C4 biomass on EC source apportionment
Bayesian Markov chain Monte Carlo techniques (MCMC) were used to account for
the variability of the isotope signatures from the different sources
(Andersson et al., 2015; Winiger et al., 2015; Fang et al., 2017). Results
from a four-source (C3 biomass, C4 biomass, coal, and liquid fossil fuel)
MCMC4 model and a three-source (C3 biomass, coal, and liquid fossil fuel)
MCMC3 model were compared to underscore the influence of C4 biomass on
source apportionment. The results of the Bayesian calculations are the
posterior probability density functions (PDFs) for the relative contributions
from the sources (Figs. S7, S8). For MCMC4, we calculated an a posteriori
combination of the PDFs for C3 biomass and C4 biomass, and denoted the combined PDF
as biomass burning, to better compare results with MCMC3.
Sources of EC in different seasons. Results from the
F14C- and δ13C-based Bayesian source apportionment
calculations of EC. The numbers in the bars represent the median
contribution of liquid fossil fuel, coal, and biomass burning. (a) Results
from the MCMC3 model, including C3 plants as biomass, coal, and liquid fossil
fuel. (b) Impact of C4 plants burning on EC source apportionment is tested
by including C4 biomass in the calculations (MCMC4). Including C4 plants
in the calculations does not affect the contribution of biomass burning to EC.
The relative fraction of C3 and C4 plants in biomass burning is shown in
Fig. S10. In winter, the sample taken on Chinese New Year's Eve (25 January 2009) was excluded.
To estimate seasonal source contributions to EC, we combined all the data
points from each season in the MCMC calculations. Yearly source
apportionment was conducted by combining all the data points, to improve the
precision of the estimated source contributions. The median was used to
represent the best estimate of the contribution of any particular source to
EC. Uncertainties of this best estimate are expressed as an interquartile range
and the 95 % range of the corresponding PDF. For both MCMC4 and MCMC3, the
MCMC-derived fraction of biomass burning EC (fbb; median with
interquartile range calculated by Eq. 7) is similar to that obtained from
radiocarbon data (fbb(EC); average with 1 standard deviation by Eq. 3)
as both of them are well constrained by F14C (Tables 2, S5,
S7, Fig. S9). Compared to MCMC4, MCMC3 overestimated the contributions
from coal combustion and underestimated the contributions from liquid
fossil fuel combustion (Fig. 5). In MCMC3, the δ13C signature
for biomass burning (δ13Cbb) is taken from C3 plants only
(-26.7±1.8 ‰), and is therefore more depleted
compared to the δ13Cbb of combined C3 (-26.7±1.8 ‰) and C4 (-16.4±1.4 ‰)
signatures in MCMC4. With the same fbb in both MCMC3 and MCMC4, MCMC3
calculations apportion a bigger fraction of EC to δ13C-enriched
coal combustion in order to explain the enriched winter δ13CEC. As a result, the MCMC3-derived contribution of liquid fossil
fuel combustion to EC was only 14 % in winter, 5 times lower than in
summer. This implies the absolute EC concentrations from liquid fossil fuel
combustion were much smaller in winter than in summer, considering that the
total EC concentrations in winter were only 1.5 times higher than those in
summer. This is inconsistent with our expectation that absolute EC
concentrations from liquid fossil fuel combustion should be roughly constant
throughout the year, or even higher in winter due to unfavorable
meteorological conditions. If we do not include C4 biomass in the calculations,
coal combustion contributions will be overestimated, and combustion of
liquid fossil fuel be underestimated, especially in winter when δ13CEC values are most enriched combined with the highest contribution from
biomass burning to EC.
MCMC4 calculations reveal that on a yearly average the highest contribution
to EC is from liquid fossil sources (median, 72 %; interquartile range,
65 %–77 %; Table 2), followed by biomass burning (17 %, 16 %–18 %),
and coal combustion (11 %, 6 %–18 %). However, source patterns
changed substantially between different seasons. Coal combustion was the
dominant contributor to EC concentrations in winter, with a median of 45 %
(29 %–58 %). Contrary to winter, EC in other seasons was mainly
derived from liquid fossil usage, accounting for 67 % (56 %–74 %),
71 % (63 %–77 %), and 77 % (71 %–82 %) of EC in autumn, spring, and
summer, respectively. The larger contribution from coal combustion in winter
was associated with the extensive coal use for residential heating and
cooking in Xi'an, in addition to contributions from coal-fired industries
and power plants. This is in line with the findings from δ13C
results. We consider that EC from coal-fired industries and power plants is
much lower than that from residential coal combustion because they have
high combustion efficiency and widely used dust removal facilities. For
example, a previous study reported that EC emission factors (amount of emitted EC per kg fuel) from residential coal combustion are up to 3 orders of
magnitude higher than those from industries and power plants (Zhang et al.,
2008). However, relative contributions from fossil combustion (fcoal+fliq.fossil)
were on average lower in winter than in other seasons
(warm period), implying that contributions from biomass burning were also
important for the EC increment in winter. By subtracting mean ECbb and
ECfossil in the warm period from those in winter, the excess ECbb
and ECfossil were 1.2 and 0.8 µgm-3, respectively. Biomass burning contributed on average 60 % of the EC
increment in winter.
Estimated primary OC based on MCMC4 results. (a) Measured OC concentrations (blue line and diamond symbols) with
observational uncertainties (vertical bar) and estimated OC mass
(OCpri,e, circle and triangular symbols) from apportioned EC and
OC/EC ratios for different sources (Eq. 10). (b) 14C-based fraction
of non-fossil OC (fnf(OC)) and modeled non-fossil fraction in
OCpri,e(fbb(OCpri,e)) derived from Eq. (11). The interquartile
range (25th–75th percentile) of the median OCpri,e and fbb(OCpri,e)
is shown by purple (A), red (B), and green (C) vertical bars. “A” and “B”
denote different OC/EC ratios applied to primary biomass burning emissions
(rbb): A: rbb=5 (3–7, minimum–maximum); B: rbb=4
(3–5); “C” denotes 80 % rliq.fossil applied in summer with
rbb=5 (3–7). fnf(OC) uncertainties are indicated but are too small to
be visible.
Estimating mass concentrations and sources of primary OC
Comparing concentrations and sources of primary OC to total OC can give
insights into the importance of secondary formation and other chemical
processes, such as photochemical loss mechanisms. Based on the EC
concentrations from biomass, coal, and liquid fossil fuel combustion derived
from the MCMC4 model, the total primary OC mass concentrations due to these
three major combustion sources can be estimated (OCpri,e; OC
primary, estimated). The respective EC concentrations apportioned to each
source are multiplied by the characteristic primary OC/EC ratios for each
source (Eq. 10). The non-fossil fraction (i.e., biomass burning) in
OCpri,e(fbb(OCpri,e)) is approximated by Eq. (11):
OCpri,e=POCbb,e+POCcoal,e+POCliq.fossil,e=rbb×fbb+rcoal×fcoal+rliq.fossil×fliq.fossil×EC,fbbOCpri,e=POCbb,eOCpri,e=rbb×fbbrbb×fbb+rcoal×fcoal+rliq.fossil×fliq.fossil,
where POCbb,e, POCcoal,e, and POCliq.fossil,e are estimated
primary OC mass concentrations from biomass burning, coal combustion, and
liquid fossil fuel combustion, respectively; rbb, rcoal, and
rliq.fossil are OC/EC ratios for primary emissions from biomass
burning, coal combustion, and liquid fossil fuel combustion, respectively.
The selection of rbb (5±2), rcoal (2.38±0.44),
and rliq.fossil (0.85±0.16) is done through a literature
search and is described in the Supplement S4; fbb, fcoal, and
fliq.fossil are the relative contribution to EC from the combustion of
biomass, coal, and liquid fossil fuel derived from the MCMC4 model. EC denotes
EC mass concentrations (µgm-3).
A Monte Carlo simulation with 10 000 individual calculations of OCpri,e
and fbb(OCpri,e) was conducted to propagate uncertainties. For each
individual calculation input, EC concentrations are randomly chosen from a
normal distribution symmetric around the measured values with uncertainties
as standard deviation; the random values for rbb, rcoal, and
rliq.fossil are taken from a triangular distribution, which has its
maximum at the central value and 0 at the upper and lower limits. For
fbb, fcoal, and fliq.fossil, the PDF derived from
the MCMC4 model was used (Fig. S11). Then 10 000 different estimations of
OCpri,e and fbb(OCpri,e) were calculated. The derived median
represents the best estimate, and interquartile ranges (25th–75th
percentile) were calculated to represent the combined uncertainties.
The observed OC concentrations and non-fossil fractions fnf(OC) as well
as estimated OCpri,e and fbb(OCpri,e) are shown in Fig. 6.
OCpri,e tracks the observed concentrations and seasonality
of OC very well, with a correlation of r2=0.71 (p<0.05).
OCpri,e values are only substantially lower than OC when observed OC
concentrations >25 µgm-3 (Fig. 6a). Observed OC
mass concentrations that exceed OCpri,e can be explained by
the contribution from secondary OC from coal combustion (SOCcoal) and
liquid fossil fuel usage (SOCliq.fossil) and by other non-fossil OC
(OCo,nf). OCo,nf includes secondary OC from biomass burning and
biogenic sources (SOCnf; SOC non-fossil), and primary OC from
vegetative detritus, bioaerosols, resuspended soil organic matter, or
cooking. Therefore,
Observed OC concentrations-OCpri,e=OCo,nf+SOCcoal+SOCliq.fossil.
In most cases, the contributions to PM2.5 from vegetative detritus,
bioaerosols, and soil dust in the air are likely small because their sizes
are usually much larger than 2.5 µm. For example, Guo et al. (2012)
estimated that vegetative detritus only accounts for ∼1 %
of OC in PM2.5 in Beijing, China, using chemical mass balance
(CMB)
modeling and a tracer-yield method. Thus, this fraction of OC can be ignored
(i.e., OCo,nf≈SOCnf). A previous 14C study in
Xi'an during severe winter pollution days in 2013 also reveals that
increased total carbon (TC=OC+EC) was mainly driven by enhanced SOC
from fossil and non-fossil sources (Zhang et al., 2015a), that is
SOCcoal, SOCliq.fossil, and SOCnf, all of which are not
modeled in OCpri,e.
OCpri,e was higher than the total observed OC in summer 2008, which
may indicate an overestimate of primary OC/EC ratios, or loss of OC due to
photochemical processing. Xi'an is one of the four “stove cities” in
China. In summer, daily average temperature was 25–31 ∘C, and
occasionally exceeded 38 ∘C. At these temperatures, semi-volatile
OC from emission sources becomes volatilized more quickly owing to higher
temperatures, leading to lower primary OC/EC ratios than other seasons.
These low OC/EC ratios in summer are commonly observed in urban China (e.g.,
median, 2.7; interquartile range, 1.9–4) from an overview of PM2.5
composition in China by Tao et al., 2017). This evaporation can be
compounded by loss through photochemical reactions that lead to
the fragmentation of organic compounds.
Observed and estimated OC concentrations. Modeled
OCe,min is the sum of OCpri,e and OCo,nf. OCo,nf
accounts for the differences between fnf(OC) and
fbb(OCpri,e), with an unrealistic assumption of no secondary fossil
OC, leading to minimum addition to OCpri,e. The coral area shows the
POCbb,e and OCo,nf, green area the POCcoal,e, and blue
area the POCliq.fossil,e. Estimation is based on MCMC4
results for EC source apportionment and primary OC/EC ratios corresponding
to case (A) in Fig. 6.
On the other hand, the estimated fbb(OCpri,e) is consistently lower
than observed 14C-based fnf(OC), and weak correlation was observed
(r2=0.31). Differences between the non-fossil carbon fraction in
primary aerosol (fbb(OCpri,e)) and in the total organic aerosol
fnf(OC) can in principle be expected due to secondary organic aerosol
formation. A higher fraction of non-fossil carbon in total OC than in
estimated primary OC implies that non-fossil sources contribute more
strongly to SOC formation than fossil sources. Some previous observations
support this hypothesis. Zhang et al. (2015a) also reported that the
relative contribution of OCo,nf is ∼2 times higher
than that of SOCcoal and SOCliq.fossil in January 2013 at the same
sampling site. In winter, OCo,nf is likely dominated by SOC from
biomass burning emissions, while contributions from biogenic SOC are small.
In spring and summer, additional contributions from biogenic SOC can further
elevate fnf(OC) compared to fbb(OCpri,e).
However, considering both fnf(OC) and OC concentrations, this simple
model of total OC as the sum of primary and secondary OC leads to an
apparent contradiction for spring and summer observations. OCpri,e
is already equal to or exceeds the total measured OC concentrations, whereas
additional SOC is necessary to explain the observed higher fnf(OC).
Spring and summer temperatures in Xi'an are generally high, which favors
active photochemistry. The resulting loss of OC due to photochemistry
probably also needs to be considered to explain the observations.
Differences between observed and estimated primary OC concentrations and
sources
The estimated OCpri,e concentrations are comparable to the observed
OC concentrations, except for samples with observed OC concentrations
>25 µgm-3. However, fbb(OCpri,e) is
considerably lower than the observed fnf(OC). It is worth investigating
whether this might be due to the model assumptions, for example, the
OC/EC
emission ratios used for the primary sources. OC/EC ratios are known to be
dependent on the measurement protocol applied to the samples (Chow et al.,
2001, 2004). For example, Han et al. (2016) found that for fresh biomass
burning emissions, OC/EC ratios from EUSAAR_2 (Cavalli et al.,
2010) are 2 times higher than those from IMPROVE_A (Chow et
al., 2007). According to Eq. (11), underestimated rbb or
overestimated rcoal and rliq.fossil would result in a fbb(OCpri,e)
that is biased towards low values. Impacts of rbb on
fbb(OCpri,e) are presented in Fig. 6b. With higher rbb=5
(3–7, minimum–maximum; our best estimate from the literature review
presented in the Supplement S4) compared to rbb=4 (3–5),
fbb(OCpri,e) only increases by 4 % to 7 %. Any further
increase of rbb would result in a modeled OCpri,e that is
substantially higher than total measured OC.
On the other hand, rliq.fossil of 0.85±0.16 was
applied without considering its seasonal variations. However, it is found
that rliq.fossil is lower in summer compared with other
seasons, which is related to increased volatilization of semi-volatile
organic compounds and faster catalyst and engine warm-up times in summer
(Xie et al., 2017). X. H. H. Huang et al. (2014) found OC/EC ratios from fresh
vehicular emissions in summer to be ∼80 % of the yearly
average, based on the lowest 5 % OC/EC ratios measured in a roadside
environment in Hongkong, China. The fbb(OCpri,e) would
increase 3 % to 5 % in summer, if we apply 80 % of the yearly
average rliq.fossil for the summer (Fig. 6b), which is also not a
substantial increase. In summary, it is not feasible to model the observed
fnf(OC) by primary emissions, even though the total OC concentrations
are in the range of modeled primary OC for spring and summer. Moreover, in
spring, δ13COC is lower than δ13CEC (Fig. 2).
This points to a depleted OC source, which could be an indication
of secondary formation of OC. In summary, the isotopic composition of OC
makes a predominantly primary origin very unlikely.
A more realistic model for OC concentrations and fnf(OC) needs to
account for OCo,nf, SOCcoal, and SOCliq.fossil:
fnf(OC)=POCbb,e+OCo,nfOCpri,e+OCo,nf+SOCcoal+SOCliq.fossil.
Then the estimated total OC (OCe) will be
OCe=OCpri,e+OCo,nf+SOCcoal+SOCliq.fossil.
As a sensitivity study with minimum addition to OCpri,e (thus minimum
OCe, OCe,min), we make the unrealistic assumption that there is
no SOC from coal and liquid fossil fuel combustion (SOCcoal=0,
SOCliq.fossil=0). Only the required OCo,nf is added until
the modeled fnf(OC) is equal to the measured one. Figure 7 presents the
modeled OCe,min and observed OC concentrations. Nearly half of
OCe,min values are higher than observed OC, and especially in summer, the OC
concentrations are consistently overestimated. For many of the data points
in fall and spring there is a reasonable agreement between model and
measurements. There are only a few haze episodes in winter, for which additional
SOC formation would be required to explain observed OC concentrations.
However, a previous study in winter 2013 at the same sampling site found the
secondary fossil OC was 0.75–1.6 times that of primary fossil OC (Zhang et
al., 2015a), which indicates that fossil SOC is likely also of
importance. If we also include SOCcoal and SOCliq.fossil, this
leads to a further overestimate of absolute OC concentrations, if we simply
estimate total OC as the sum of primary and secondary OC. Therefore, the
more reasonable explanation is OC loss. The primary OC/EC ratios do not
preserve the characteristics of sources any more in a warm period due to
active photochemistry under high temperature and humidity. The conclusion
will not change if we apply EC apportionment results from MCMC3 (Figs. S12, S13).
Comparison of EC source apportionment in winter 2008/2009
with two other studies in winter 2012/2013 at the same sampling site.
a Positive matrix factorization (PMF) receptor model simulation.
Changes in emission sources in Xi'an, China (2008/2009 vs. 2012/2013)
EC is a primary emission product, and thus changes in EC sources can reflect
the changes in emission sources. The contribution from biomass burning to EC
was 24 % (median; interquartile range 22 %–26 %) in winter 2008/2009
(Fig. 8, Table 2) with no considerable change in fbb(EC) between
polluted days and clean days (Fig. 1a, except Chinese New Year's Eve).
Taking into account the uncertainties, comparable contributions were also
reported at the same sampling site for winter 2012/2013 based on 14C
measurements (22±3 %; Zhang et al., 2015a), and positive matrix
factorization (PMF) receptor model simulation (20.1±7.9 %; Wang
et al., 2016) (Fig. 8). This suggests that from 2008 to 2013, biomass
burning contributions to EC remained rather stable, although with a slight
decrease from 24 % (22 %–26 %) to 20 % (SD=7.9 %).
Biomass burning in Xi'an mainly includes open burning of crop
residues and household usage of crop residues and wood. The slight decrease
can be explained by more strict rules to minimize crop open burning, but
implementation of regulations was still weak and slow. Moreover, there are
no regulations yet that target household biomass usage (Zhang and Cao,
2015b).
The contribution of coal combustion to EC decreased from 45 % (29 %–58 %)
in winter 2008/2009 to 33.9 % (SD=23.8 %) in winter 2012/2013,
with an increased contribution from vehicle emission from 31 %
(18 %–46 %) to 46 % (SD=25.1 %) (Fig. 8). For EC source
apportionment, it is noted that the quartile range for 2008/2009 values
overlaps range for 2012/2013 values (average±SD). Compared to the
uncertainties of 14C measurements, the uncertainties of PMF results are
always larger, making the overlapped ranges very likely. However, comparing
the probability distribution functions for both cases gives a more complete
picture. Figures S14 and S15 show the PDF of the relative source
contributions to EC from coal combustion and vehicle emissions,
respectively. For the PDF by Wang et al. (2016), we assume normal
distribution as their source apportionment results are not known and given
in the form of average±SD. As shown in Figs. S14 and S15, though
with some overlaps, the PDF of the relative source contribution of coal
combustion (vehicle emissions) does clearly shift to the lower side (higher
side) from the year 2008/2009 to 2012/2013.
Vehicle emissions become increasingly important and coal combustion less so from 2008 to 2013. This change could not be detected from 14C
measurements alone, since the total fossil contribution to EC stayed
relatively constant. Further apportionment of fossil sources into coal
combustion and vehicle emissions could be achieved by combining 14C
measurements with δ13C (Andersson et al., 2015; Winiger et al.,
2016) or organic source markers (Zhang et al., 2015b).
The decreased contribution from coal combustion to EC from 2008 to 2013
resulted from the stepwise replacement of coal by natural gas for
residential heating and cooking since the second half of the 2000s. Natural
gas usage in Xi'an increased by 94 % from 2009 to 2013 (Xi'an Municipal Bureau of Statistics
and NBS Survey Office in Xi'an, 2010, 2014). Although coal combustion in Xi'an had been increasing
from 6.6 million tons in 2008 to 10.3 million tons in 2013, the proportion of
coal used as energy reduced from 71 % to 66 % (Xi'an Municipal Bureau of Statistics and NBS Survey Office in Xi'an, 2009, 2014). The reinforcement of environmental laws and
regulations and the encouragement of using high-efficiency improved coal burners
and high-quality coals are important factors as well. The decreased coal
combustion emissions are also evidenced from the declined Fe-referenced
enrichment factors (EFs, normalized to composition of earth crust) of As and
Pb. As and Pb can indicate coal combustion, as Pb-containing gasoline has
been forbidden since 2000 in Xi'an (Xu et al., 2012). Annual EFs of As and
Pb dropped from 802 and 804 in 2008 to 465 and 490 in 2010, respectively (Xu
et al., 2016).
Vehicular emissions to EC increased from 31 % to 46 % (an absolute
relative increase by roughly 50 %) from 2008 to 2013 (Fig. 8). This is
supported by increasing levels of NO2 in urban Xi'an, which is another
indicator for the contribution of vehicular emissions to air pollution. The
NO2 concentrations in Xi'an increased by 15.5 % from 2006 to 2010
(Xu et al., 2016). The increased vehicular contribution likely resulted from
a strong increase in civil vehicles. The processing (registration) of civil
vehicles increased > 2-fold from 0.9 million units in 2008 to 1.9 million
units in 2013 (Xi'an Municipal Bureau of Statistics and NBS Survey Office in Xi'an, 2009, 2014). However,
vehicular contributions to EC and NO2 concentrations have not increased
to the same extent as the increase in vehicle numbers. This can be
attributed to the upgrade of vehicle emission standards from National II to
National III for light-duty gasoline and heavy-duty diesel vehicles in 2007
and for heavy-duty gasoline vehicles in 2010 in Xi'an (GB18352.3-2005, 2005;
GB17691-2005, 2005), which somewhat offset the increase of vehicle numbers.
Conclusions
Sources of OC and EC in Xi'an, China, are constrained based on a full year of
radiocarbon and the stable isotope 13C measurements for the year 2008–2009.
Radiocarbon measurement reveals that EC is dominated by fossil sources, with
contributions ranging from 71 % to 89 %, with an average of 83±5 %. Compared with EC, OC has a much higher contribution from non-fossil
sources (54±8 %), with a higher contribution in winter (62±5 %). Fossil contributions to OC and EC in this study fall within the
range of published values from other 14C-based source apportionments in
Chinese cities. In this study, the non-fossil contribution to OC in winter
(fnf(OC)=62±5 %) was observed to be higher than in
summer (48±3 %) in Xi'an. A different seasonal variation pattern
for fnf(OC) was reported in Beijing, where the fossil contribution to
OC was higher in winter than in summer (Yan et al., 2017; Zhang et al.,
2017). This implies that different pollution patterns exist in individual
Chinese cities.
In summer, a strong positive correlation was found between
F14C(EC) and K+/EC ratios, and a significant negative
correlation between F14C(EC) and levoglucosan/EC ratios. This
suggests that the burning of crop residues, with significant lower
levoglucosan/K+ ratios than wood, accounted for most of the variability
in non-fossil EC in the summer. No significant correlations of
F14C(EC) with K+/EC or levoglucosan/EC were found in other
seasons (Fig. S5), suggesting a variable mixture of biomass subtypes.
To further refine EC sources, radiocarbon and stable carbon signatures are
combined and used in a Bayesian Markov chain Monte Carlo (MCMC) approach, in
which the burning of C4 plants is included as a subtype of biomass burning. The
MCMC results indicate that coal combustion dominated EC in winter, and
that liquid fossil fuel combustion dominated EC in other seasons. However,
increased contributions from biomass burning were important for the EC
increment in winter as well. Comparisons with the results of other studies
at the same sampling site in winter suggest that the sources of fossil EC
have changed from 2008/2009 to 2013/2014, with decreasing contributions from
coal burning and increasing contributions from motor vehicles. This is
consistent with recent changes in Xi'an: changes in energy consumption, and
the expansion of the civil vehicular fleet, resulting from urbanization and
economic improvement.
δ13COC exhibited similar values to δ13CEC,
and showed strong correlations (r2=0.90) in summer and autumn,
indicating similar source mixtures to EC and the influence of high temperature
on the atmospheric processing of OC. In spring, δ13COC is more
depleted than δ13CEC, indicating the possible importance
of the secondary formation of OC (e.g., from volatile organic compound
precursors) in addition to primary sources. Comparing the observations (OC
mass, 14C-based fnf(OC)) with estimated total primary OC
concentrations related to combustion sources (i.e., estimated by apportioned
EC and corresponding OC to EC ratios) and the non-fossil fraction in the
estimated primary OC makes it possible to provide some insights into the
importance of secondary formation and other chemical processes, such as
photochemical loss mechanisms. It is found that estimated primary OC mass
follows the observed total OC concentrations and seasonality (r2=0.71), but source contributions to total OC differ from the estimated source
contributions to primary OC (r2=0.31). The estimated primary
OC is similar to the observed OC concentrations, except for samples
with observed OC concentrations >25 µgm-3. However,
the non-fossil fraction in estimated primary OC is significantly lower than
the observed fnf(OC). These differences can be explained by the
contribution of other non-fossil primary OC (excluding biomass burning), or
secondary non-fossil OC, which are not included in the estimation. But we
cannot reconcile the differences between observed and estimated non-fossil
OC fraction without overestimating the absolute OC concentrations,
especially in summer. Therefore, we hypothesize that OC loss due to active
photochemistry cannot be neglected, especially not in summer.