To mitigate haze pollution in China, a better
understanding of the sources of carbonaceous aerosols is required due to the
complexity in multiple emissions and atmospheric processes. Here we combined
the analysis of radiocarbon and the stable isotope 13C to investigate
the sources and formation of carbonaceous aerosols collected in two Chinese
megacities (Beijing and Xi'an) during severe haze events of a “red alarm”
level from December 2016 to January 2017. The haze periods with daily
PM2.5 concentrations as high as ∼ 400 µg m-3
were compared to subsequent clean periods (i.e., PM2.5 less than median concentrations during the winter 2016/2017) with PM2.5 concentrations below 100 µg m-3 in Xi'an and below 20 µg m-3 in Beijing. In Xi'an, liquid fossil fuel combustion was
the dominant source of elemental carbon (EC; 44 %–57 %), followed by
biomass burning (25 %–29 %) and coal combustion (17 %–29 %). In
Beijing, coal combustion contributed 45 %–61 % of EC, and biomass
burning (17 %–24 %) and liquid fossil fuel combustion (22 %–33 %)
contributed less. Non-fossil sources contributed 51 %–56 % of organic
carbon (OC) in Xi'an, and fossil sources contributed 63 %–69 % of OC in
Beijing. Secondary OC (SOC) was largely contributed by non-fossil sources in
Xi'an (56±6 %) and by fossil sources in Beijing (75±10 %), especially during haze periods. The fossil vs. non-fossil
contributions to OC and EC did not change drastically during haze events in
both Xi'an and Beijing. However, compared to clean periods, the contribution
of coal combustion to EC during haze periods increased in Xi'an and
decreased in Beijing. During clean periods, primary OC from biomass burning
and fossil sources constituted ∼ 70 % of OC in Xi'an and
∼ 53 % of OC in Beijing. From clean to haze periods, the
contribution of SOC to total OC increased in Xi'an but decreased in
Beijing, suggesting that the contribution of secondary organic aerosol formation
to increased OC during haze periods was more efficient in Xi'an than in
Beijing. In Beijing, the high SOC fraction in total OC during clean periods
was mainly due to an elevated contribution from non-fossil SOC. In Xi'an, a
slight day–night difference was observed during the clean period with
enhanced fossil contributions to OC and EC during the day. This day–night
difference was negligible during severe haze periods, likely due to the enhanced
accumulation of pollutants under stagnant weather conditions.
Introduction
Severe haze pollution with high PM2.5 (i.e., particulate matter with
aerodynamic diameter ≤ 2.5 µm) concentrations and reduced
visibility occurs frequently during winter in China (An et al., 2019). Field
measurements show that carbonaceous aerosol contributes a significant
fraction of PM2.5 loading during severe haze events in China (Huang
et al., 2014; Elser et al., 2016; Liu et al., 2016). Therefore, a better
understanding of the sources and atmospheric processes of carbonaceous
aerosols is needed for mitigating haze pollution. Many previous studies
focus solely on Beijing, the capital of China. However, studies on other
megacities are also needed for comparison, as well as for a more
comprehensive understanding of haze pollution in China.
Carbonaceous aerosol constituents are separated into elemental carbon (EC)
and organic carbon (OC) fractions differing in their thermal refractiveness
with EC being thermally refractory and OC weakly refractory (Pöschl,
2003, 2005; Petzold et al., 2013). EC is emitted as primary particles from
incomplete combustion sources (i.e., biomass burning and fossil fuel
combustion). Unlike EC, OC can either be emitted as primary OC (POC) from
combustion sources and non-combustion sources (e.g., biogenic emissions) or
formed in the atmosphere as secondary OC (SOC) via the reaction of gas
precursors (Hallquist et al., 2009; Jimenez et al., 2009). The sources and
abundance of different carbon fractions in carbonaceous aerosols vary
considerably in different Chinese cities as a result of complex interplay
between meteorology, local and regional emissions sources, and atmospheric
processes (Zhang et al., 2008; Cui et al., 2015; Tie et al., 2017; An et
al., 2019). Therefore, quantifying the sources of carbonaceous aerosol in
China is a challenging task.
Radiocarbon (14C) analysis of carbonaceous aerosols is the most direct
and effective method to distinguish their main sources, exploiting the fact
that OC and EC of fossil origins (i.e., vehicle emissions, coal combustion)
do not contain 14C (Heal, 2014; Cao et al., 2017; Dusek et al., 2013).
The 14C analysis of OC and EC separately provides a clear-cut division of
carbonaceous aerosols into four major fractions: fossil OC, non-fossil OC
(e.g., OC from biomass burning, biogenic emissions, and cooking), fossil EC
and biomass-burning EC (e.g., Gustafsson et al., 2009; Szidat et al., 2009;
Zotter et al., 2014; Dusek et al., 2017; Ni et al., 2018, 2019a). For
example, Liu et al. (2014) demonstrated that fossil sources including coal
burning and vehicle emissions dominated EC during winter haze events in
Guangzhou, China. Zhang et al. (2015) showed that the elevated carbonaceous
aerosols during the severe haze event in January 2013 in China were by a
large extent driven by SOC from both fossil and non-fossil precursors. In
addition, the analysis of the 13C/12C ratio can refine 14C
source apportionment because coal combustion and vehicle emissions have
different 13C source signatures although they are both completely depleted
of 14C (e.g., Andersson et al., 2015; Li et al., 2016; Winiger et al.,
2016, 2017; Fang et al., 2017, 2018; Ni et al., 2018).
A critical question for effective haze mitigation is whether carbonaceous
aerosols in different Chinese cities have similar characteristics during
haze events. However, there are not many studies highlighting the
differences in sources of primary and secondary carbonaceous aerosols
between cities, especially for studies employing the analysis of 14C or
the stable isotope 13C (e.g., Zhang et al., 2015; Andersson et al.,
2015; Liu et al., 2016). In this work, we compare the severe haze events
reaching the “red alarm” level (i.e., the highest air quality warning level in
China) in two Chinese megacities (Beijing and Xi'an) during December 2016
and January 2017. We present measurements of dual carbon isotopes (i.e.,
14C and the stable carbon isotope 13C) for EC and OC. The sources
of carbonaceous aerosols are elucidated and compared between haze and clean
periods in Beijing and Xi'an with the following main objectives: (1) quantitative
understanding of the difference in EC contribution from the burning of biomass,
coal, and liquid fossil fuel (i.e., vehicle emissions) under different
pollution conditions and (2) constraining the sources of both primary and
secondary OC. Furthermore, the comparison of daytime and nighttime results
in Xi'an yields insight into diurnal variation in sources of carbonaceous
aerosols.
MethodsAerosol collection
To collect PM2.5 samples, high-volume aerosol samplers (flow rate =
1.0 m3 min-1; TE-6070 MFC, Tisch Inc., Cleveland, OH, USA) were
used at an urban background site in Xi'an and Beijing (see Table S1 for
details about the sampling sites). Xi'an is the largest city in northwestern
China with over 8.8 million residents and 2.5 million vehicles in 2016
(Xi'an Municipal Bureau of Statistics and NBS Survey Office in Xi'an, 2017).
Surrounded by the Qinling Mountains to the south and the Loess Plateau to the
north, days with low wind speed occur frequently in Xi'an, promoting the
accumulation of air pollutants. Xi'an is now facing increased serious air
quality issues due to the rapid increase in motor vehicles and energy
consumption in the past 2 decades. Besides residential coal combustion,
biomass burning is also a major emission source in Xi'an and its surrounding
areas (i.e., Guanzhong Plain) for heating and cooking, especially in winter
(Zhang et al., 2014; Xu et al., 2016). Beijing, the capital of China, is a
megacity with over 21 million residents and 5.7 million vehicles in 2016
(Beijing Municipal Bureau of Statistics and NBS Survey Office in Beijing,
2017). Beijing is located in the Beijing–Tianjin–Hebei (BTH) region, the most
economically developed region in northern China. However, the rapid economic
growth and urbanization associated with heavy coal consumption and the rapid
increase in the usage of vehicles led to the poor air quality in Beijing. Besides
local emissions, regional transport of pollutants between neighboring cities
also contributes to air pollution in Beijing (Zheng et al., 2015; An et al.,
2019). The 12 h integrated (daytime: 08:00 to 20:00 CST, China standard time; nighttime: 20:00 to 08:00 CST of the following day) PM2.5
was sampled on pre-combusted quartz filters (20.3 cm × 25.4 cm; QM-A,
Whatman Inc., Clifton, NJ, USA) in Xi'an from 1 to 10 January
2017. In Beijing, the 24 h integrated (10:00 to 10:00 CST the
following day) PM2.5 was collected from 2 December 2016 to 8 January
2017. Field blanks were collected by exposing filters to ambient air for 15 min. Immediately after collection, the filters were transferred into
pre-combusted aluminum foils and stored at -18∘C.
Concentration measurements of OC and EC
The IMPROVE_A protocol (Chow et al., 2007) was implemented on
a carbon analyzer (DRI Model 2001, Atmoslytic Inc., USA) for the measurement of
carbon concentrations. The relative standard deviations for the replicate
analyses were smaller than 10 % for OC and EC. OC mass was corrected for
field blanks (0.4 µg cm-2). EC was too small to be detected on
field blanks. Acidification to remove potential interferences from
carbonates is not necessary because carbonate carbon in PM2.5 samples is found to be negligible compared to the relatively larger OC
and EC amounts for both mass determination and carbon isotopic analysis
(Supplement Sect. S1).
Analysis of carbon isotope
Six samples from haze and clean days were selected per sampling site for
carbon isotope analysis (Tables S1 and S2). We define clean days at
each site as PM2.5 less than the median concentration in the winter heating
season from 15 November 2016 to 15 March 2017. In Xi'an, there were four
composite samples (two daytime and two nighttime) from haze days and two
composite samples (one daytime and one nighttime) from clean days. In Beijing,
five 24 h samples were selected from haze days and one composite sample from 2 clean days. Each composite sample consists of two filter pieces with similar PM2.5 loadings that agree
within 20 % (Fig. S1).
Stable isotope 13C
Filter samples were placed in a quartz tube with CuO grains. The tube was
subsequently evacuated and sealed before heating for 3 h at 375 ∘C to remove OC. Then the EC was extracted by heating the remaining carbon
for 5 h at 850 ∘C. The 13C/12C ratio of EC was measured
by an isotope mass spectrometer (Finnigan MAT-251; Bremen, Germany) and
expressed in the delta notation:
δ13C=13C/12Csample13C/12CV-PDB-1.
The δ13C values are usually reported in per mil (‰). (13C/12C)V-PDB is the 13C/12C ratio of the
international standard Vienna Pee Dee Belemnite (V-PDB). A
well-characterized standard was measured every working day. Duplicate
analysis of δ13C of EC showed an analytical precision better
than ±0.3 ‰. This method was detailed in Ni et
al. (2019b), in which the impacts of potential charred OC on the isolated EC were
evaluated using an isotope-mass-balance-based sensitivity analysis. We
concluded that the expected differences in δ13CEC are
smaller than 1 ‰ under the assumption that the fraction
of charred OC in the isolated EC is at most 20 %.
Radiocarbon
OC and EC in PM2.5 samples were converted to CO2 using an
aerosol combustion system (ACS; Dusek et al., 2014). The ACS has been
evaluated in two intercomparison studies (Szidat et al., 2013; Zenker et
al., 2017). The isolated CO2 was subsequently reduced to graphite (de
Rooij et al., 2010) before 14C measurements could be conducted with the
accelerator mass spectrometer (AMS) at the Center for Isotope Research (CIO; van der Plicht et al., 2000).
The temperature protocol for OC and EC combustion has been detailed in Ni et al. (2018) and is summarized in Fig. S2. To remove possible interfering gas
(e.g., NOx, halogen, and water vapor) from CO2, a reduction oven
filled with copper grains and silver, a dry-ice–ethanol bath, and a flask
filled with phosphorus pentoxide were installed on the ACS.
Fraction modern (F14C) is used to report the 14C data (Reimer et
al., 2004). F14C relates the 14C/12C ratio of a sample to the
ratio of the unperturbed atmosphere in the reference year 1950:
F14C=14C/12Csample,-2514C/12C1950,-25.
Both ratios are normalized to δ13C of -25 ‰ to remove the effect of isotope fractionation. Practically,
(14C/12C)1950,[-25] equals the 14C/12C ratio
of an oxalic acid standard (OXII) multiplied by a factor of 0.7459.
Contamination during graphitization and AMS measurements was quantified from
the measured F14C of standards (OXII with known F14C of 1.3407 and
Rommenhöller with F14C= 0) which was processed in the same way as samples.
The resulting estimated dead and modern contamination was used to correct
the 14C data according to Santos et al. (2007). The reliability of data
correction was further verified by measuring two secondary standards (i.e.,
IAEA-C7 and IAEA-C8) on the same wheel of samples. The measured values of IAEA-C7
(0.495 ± 0.008) and IAEA-C8 (0.154 ± 0.007) agree with their
respective consensus value (0.4953 ± 0.0012 and 0.1503 ± 0.0017)
within uncertainties.
Equations for 14C source apportionment of EC and OC.
See Sect. 2.4 for details. Respectively, rbb and rfossil are the primary OC/EC ratio
for biomass burning and fossil fuel combustion. The estimation of
rbb and rfossil is presented in Supplement Sect. S2.
F14C is larger than the fraction of non-fossil carbon (i.e.,
fnf(OC) for OC, fbb(EC) for EC) due to the large release of 14C
into the atmosphere from the nuclear bomb tests in the 1960s. To eliminate this
effect, F14C is divided by the F14C of non-fossil sources
(F14Cnf). F14Cnf is estimated as 1.09 ± 0.05 for
OC and 1.10 ± 0.05 for EC (see details in Ni et al., 2019b) using a
tree growth model and the contemporary atmospheric 14CO2 over the
past years (Lewis et al., 2004; Mohn et al., 2008; Levin et al., 2010) with
the assumption that biomass-burning OC and biogenic OC contribute to 85 %
and 15 % of total OC, respectively. Once fnf(OC) and fbb(EC) are
known, carbon concentrations can be apportioned into EC and OC from
non-fossil sources (ECbb, OCnf) and fossil sources (ECfossil,
OCfossil) (Eqs. 3–6 in Table 1). OCnf and OCfossil are
further divided into POC from biomass burning (POCbb), other non-fossil
OC (OCo,nf) (Eqs. 7–8), and primary and secondary fossil OC (POCfossil
and SOCfossil, respectively; Eqs. 9–10). POCbb and POCfossil
are estimated using EC as a tracer of primary emissions (i.e., the EC tracer
method; Turpin and Huntzicker, 1995). Based on OCo,nf and
SOCfossil, total SOC and the fraction of fossil carbon in SOC
(ffossil(SOC)) are estimated using Eqs. (11–12). OCo,nf mainly
includes SOC of non-fossil origins (SOCnf), primary biogenic OC, and
cooking OC. OCo,nf is approximately SOCnf as contributions of
primary biogenic sources and cooking to OCo,nf are likely small (Hu et
al., 2010; Guo et al., 2012). If cooking is prominent, OCo,nf is an
overestimate of SOCnf. To estimate the uncertainties of the source
apportionment results, a Monte Carlo simulation (n=10 000) using Eqs. (3–12) was carried out as described in Supplement Sect. S3. The 14C source
apportionment results are presented in Tables S3 and S4.
The dual carbon isotope signatures of EC were used in a Bayesian Markov
chain Monte Carlo (MCMC) scheme (Andersson, 2011) to conduct the
mass-balance three source apportionment of EC (e.g., Andersson et al., 2015;
Li et al., 2016; Winiger et al., 2016, 2017; Fang et al., 2017, 2018). That
is to say that the F14C and δ13C of ambient EC (F14C(EC)
and δ13CEC) can be explained by the burning of biomass
(bb), coal (coal), and liquid fossil fuel (liq.fossil; i.e.,
vehicle emissions):
F14C(EC)δ13CEC1=F14CnfF14CcoalF14Cliq.fossilδ13Cbbδ13Ccoalδ13Cliq.fossil111fbbfcoalfliq.fossil.F14Ccoal and F14Cliq.fossil are equal to zero since coal
and liquid fossil fuel do not contain 14C, and δ13Cbb,
δ13Ccoal, and δ13Cliq.fossil are δ13C signatures for EC from the three sources. Their values were
established as δ13Cbb (-26.7 ± 1.8 ‰ for C3 plants and -16.4 ± 1.4 ‰ for corn stalk; mean ± SD, standard deviation), δ13Ccoal (-23.4±1.3 ‰), and
δ13Cliq.fossil (-25.5±1.3 ‰), based on critical evaluations of studies from the literature
(Andersson et al., 2015; Ni et al., 2018; and references therein).
Uncertainties in F14C and δ13C source signatures and the
measured F14C(EC) and δ13CEC are considered in
the MCMC technique (Parnell et al., 2010, 2013). MCMC outputs are the
posterior probability density functions for fbb, fcoal, and fliq.fossil (i.e., the relative contribution of each source to EC).
The median and interquartile range (25th–75th percentile) are used as the
best estimate and the uncertainties, respectively.
Mass concentrations of OC and EC from fossil and
non-fossil sources (OCfossil, OCnf, ECfossil, and ECbb),
as well as fraction of non-fossil carbon in OC and EC (fnf(OC) and
fbb(EC), respectively), for daytime and nighttime PM2.5 samples in
Xi'an and 24 h integrated PM2.5 samples in Beijing during haze and
clean periods during the measurement periods (2 December 2016 to 10 January
2017). For each city, “haze” and “clean” are used to represent high and
low pollution events, and clean days at each site are defined as days with
PM2.5 less than the median concentration in the winter heating season from
15 November 2016 to 15 March 2017. Uncertainties of 14C-apportioned
fnf(OC) and fbb(EC) are indicated but are too small to be visible.
The data are shown in Table S3.
Results and discussionFossil and non-fossil contributions to EC and OC
During the measurement periods, the highest daily mass concentrations of
PM2.5 in Xi'an (∼ 250–420 µg m-3) and
Beijing (∼ 210–360 µg m-3; Fig. S1) were 10–17
and 8–14 times higher than the standard of the World Health Organization (25 µg m-3; WHO, 2006), respectively. Using radiocarbon
measurements, we investigated the sources of carbonaceous aerosols in
PM2.5 in both cities during several haze periods and compared them to
clean periods with PM2.5 concentrations below 100 µg m-3 in Xi'an and below 20 µg m-3 in Beijing. In Xi'an, even
during the clean periods that we defined here, daily PM2.5 concentrations were
higher than the Chinese pollution standard of 75 µg m-3,
reflecting severe air quality problems. PM2.5, OC, and EC concentrations
during haze periods were >2 times higher in Xi'an and
>5 times higher in Beijing than those during clean periods,
respectively. OC/EC ratios in Xi'an slightly decreased from ∼ 4 during haze periods to ∼ 3 during clean periods, while OC/EC
ratios in Beijing were lower during haze periods (∼ 3) than
clean periods (∼ 4). This reflects different sources and
formation mechanisms of haze pollution in the two cities. In Xi'an, we
collected day and night PM2.5 samples. No consistent day–night
variations in concentrations of PM2.5, OC, and EC (Figs. 1 and S1) were
observed. This results from the diurnal cycle of human activities (e.g.,
traffic, usage of biomass and coal for heating or cooking) and the
development of the planetary boundary layer height which controls the vertical
mixing and dilution of pollutants.
Radiocarbon (14C) in EC and OC was measured to distinguish their fossil
(mainly coal burning and traffic emissions) and non-fossil (mainly
biomass burning) sources. The most important contributor to EC was fossil fuel
combustion, both in Xi'an and Beijing, contributing 73 ± 2 % in
Xi'an and 80 ± 3 % in Beijing. The remaining EC arose from biomass
burning (27 ± 2 % in Xi'an and 20 ± 3 % in Beijing; Fig. 1).
In Xi'an, the fraction of biomass-burning EC in total EC (fbb(EC)) was
largely constant during haze and clean periods (range: 25 %–29 %)
regardless of the wide concentration range of EC from biomass burning
(ECbb; 1.8–6.4 µg m-3) and fossil fuel combustion
(ECfossil; 4.3–18 µg m-3). This suggests that the increase
in ECfossil and ECbb concentrations during haze periods in Xi'an
is likely caused by the enhanced emissions from both fossil fuel and biomass
burning by a similar factor and due to meteorological conditions favoring
the accumulation of particulate air pollution. The fbb(EC) values in
Beijing (20 ± 3 % with a range of 17 %–24 %) were consistently
smaller than those in Xi'an (range: 25 %–29 %), showing that fossil
sources contribute more strongly to EC in Beijing. Moreover, during haze
periods in Beijing, fbb(EC) increased with increasing total EC
concentrations (Fig. 2).
Scatter plot of the relative contribution of biomass
burning to EC (fbb(EC); %) against EC concentrations.
In Xi'an, OC concentrations from non-fossil sources averaged 29 ± 16 µg m-3 (OCnf; range: 9–49 µg m-3), slightly higher
than those from fossil sources (OCfossil; 24 ± 13 µg m-3; range: 8–40 µg m-3) at 95 % confidence
level (paired t test, p value = 0.01). However, in Beijing, OCnf (12 ± 5 µg m-3; 3–19 µg m-3) was significantly lower
than OCfossil (24 ± 10 µg m-3; 4–33 µg m-3)
(p value = 0.001). Consequently, the relative contribution of OCnf to total OC (fnf(OC)) was much higher in Xi'an (average 54 ± 2 %) than in Beijing (34 ± 3 %). The fnf(OC) value in both cities was
considerably higher than the corresponding fbb(EC) for all samples (Fig. 1). The main reason for larger fnf(OC) than fbb(EC) is that primary
OC/EC ratios from biomass-burning emissions are higher than those from
fossil sources. So even though biomass burning contributes a small portion
of EC, its contribution to primary OC will be much higher. In addition,
other non-combustion sources (e.g., biogenic emissions, cooking fumes) and
secondary formation contribute only to OC and not to EC.
(a) The 14C-based fraction fossil vs. δ13C for EC during haze and clean periods in Xi'an and Beijing, China.
The symbol size is an indicator of EC concentrations. (b) Comparison with previous observations in Xi'an and Beijing, in which
BTH-Beijing is a regional receptor site of Beijing located 100 km
southwest of Beijing. Samples from Cao et al. (2011) are placed on the
x axis because no 14C data were available. The expected 14C and
δ13C endmember ranges for emissions from C3 plant burning,
liquid fossil fuel burning, and coal burning are shown as green, black, and
brown bars, respectively. The δ13C source signatures are
indicated as mean plus or minus the standard deviation (SD) (Sect. 2.4). The δ13C signature of
corn stalk burning (i.e., C4 plant; -16.4 ± 1.4 ‰) is also indicated.
In this study, the ffossil(EC) values in Xi'an during winter 2016/2017
are comparable with those previously measured during winter 2015/2016 and
winter 2008/2009 (Ni et al., 2018, 2019b), as illustrated in Fig. 3b,
pointing to a relatively constant contribution of fossil fuel combustion vs.
biomass burning to EC in Xi'an over the past decade. As shown in Fig. 3b,
the ffossil(EC) values in Beijing during winter 2016/2017 agree with the
values reported at an urban site of Beijing in January 2014 (Fang et al.,
2017). A slightly higher ffossil(EC) in urban Beijing was observed
during February 2010 (Chen et al., 2013). Despite the slight variation of
ffossil(EC) over time, ffossil(EC) in Beijing is generally higher
than that in Xi'an (Fig. 3b). The overall average
ffossil(OC) presented for winter 2016/2017 in Beijing (66 ± 3 %) was
higher than that in Xi'an (46 ± 2 %), which is consistent with previously
reported ffossil(OC) in Beijing and Xi'an (Zhang et al., 2015; Ni et
al., 2019a). Lower ffossil(OC) values in winter were reported in
Chongqing (24 %), and higher ffossil(OC) was observed in Taiyuan
(71 %) during winter 2013/2014 (Ni et al., 2019a). The comparison of
ffossil(EC) and ffossil(OC) in different Chinese cities indicates
that the relative importance of fossil sources in carbonaceous aerosols
varies spatially and can change over the years. In Xi'an, clean periods
showed a slight day–night difference with increased contributions of fossil
sources to EC and OC during the day. During haze periods, especially the
second haze event (XH_day2, XH_night2), this
day–night difference disappeared, which suggests a long residence time of
the pollution particles in the urban atmosphere during haze events.
Overall, our 14C data show that fossil sources contribute more strongly
to EC and OC in Beijing than in Xi'an, which is consistent with previous
observations. Both in Beijing and Xi'an, the fossil vs. non-fossil
contributions to EC and OC did not change drastically during haze and clean
periods. In Xi'an, a slight day–night difference was observed during clean
periods but disappeared during haze periods, suggesting a large
accumulation of particles.
Fossil EC apportioned by stable carbon isotopes: coal vs. liquid fossil
fuel
Besides F14C, the δ13C of EC adds an additional dimension
in which fossil EC can be distinguished into EC from the burning of coal and of
liquid fossil fuel (i.e., vehicle emissions). Considerable geographical
differences in δ13CEC signatures were observed with
more depleted values in Xi'an (-25.1 ± 0.5 ‰;
-25.6 ‰ to -24.4 ‰) relative to
those in Beijing (-24.1 ± 0.4 ‰;
-24.4 ‰ to -23.4 ‰; Fig. 3). The
Xi'an signatures are consistent with the signature of liquid fossil fuel
combustion (δ13Cliq.fossil=-25.5±1.3 ‰; Sect. 2.4), whereas the more enriched values in
Beijing indicate the influence of coal combustion (δ13Ccoal=-23.4±1.3 ‰).
In both Xi'an and Beijing, moderate differences exist in δ13CEC between clean and haze days, pointing to a shift in
combustion sources. In Xi'an, δ13CEC during clean
periods (∼-25.5 ‰) was slightly
depleted compared to that during haze periods (-25.0 ‰ to -24.4 ‰), whereas Beijing exhibited more enriched
δ13CEC during clean periods (-23.4 ‰) than during haze periods (-24.4 ‰ to
-24.1 ‰). This suggests a moderate increase in coal
combustion contribution to EC in Xi'an during haze days and a decrease in
Beijing. In Xi'an, no strong day–night difference in δ13CEC was observed with the largest absolute differences of
0.5 ‰ between XH_day1 and
XH_night1. The day–night differences are small relative to
the uncertainties of the potential sources; for example, the endmember range
for coal combustion is more uncertain (±1.3 ‰).
The small day–night differences in δ13CEC reflect
well-mixed EC emissions.
(a) Fractional contributions of three combustion
sources to EC during haze and clean periods in Xi'an and Beijing.
(b) EC concentrations (µg m-3) from each combustion
source. The data are presented in Tables S5 and S6.
The Bayesian MCMC model takes into account the uncertainties of the δ13C and F14C endmembers and statistically apportions EC into the
fractions of biomass burning (fbb), coal combustion (fcoal), and
liquid fossil fuel combustion (fliq.fossil). The MCMC-derived fbb is
in principle the same as the 14C-based fbb(EC) (Fig. S3). The MCMC
results (Fig. 4) show that there were no strong day–night differences in EC
sources during haze and clean periods in Xi'an. Liquid fossil fuel
combustion was the most important contributor to EC in Xi'an with an increased
contribution during clean periods. In Beijing, coal combustion was the
dominant source of EC, with the relative contribution ranging from 48 %
(median; 31 %–61 %, interquartile range) during haze periods to 61 %
(45 %–71 %) during clean periods. The fbb value was fairly constant
between haze and clean periods with respect to fcoal and
fliq.fossil for all samples. In Xi'an, fbb was comparable to
fcoal during haze days and larger than fcoal during clean days. In
Beijing, biomass-burning EC was the smallest fraction in total EC with
smaller fbb than fcoal during both haze and clean days.
Concentrations of total EC increased by 2 times from clean days
(∼ 7.4 µg m-3) to haze days (18.0 µg m-3)
in Xi'an and 8 times in Beijing (1.6 to 13.5 µg m-3). The increased EC concentrations during haze periods in Xi'an
were attributed to liquid fossil fuel combustion (43 %), coal combustion
(29 %), and biomass burning (28 %). However, in Beijing, coal combustion
contributed most of the increased concentrations of EC (45 %), followed by the
burning of liquid fossil fuel (33 %) and biomass (22 %).
In summary, complementing 14C with δ13C allows for
quantitative constraints on EC sources: EC was dominated by liquid fossil
fuel combustion (i.e., vehicle emissions) in Xi'an and by coal burning in
Beijing, especially during clean periods. In Xi'an, no strong day–night
differences in EC sources were observed during haze and clean periods.
Compared with earlier observations in Xi'an (Fig. 3b), we found that the
δ13CEC values in January 2017 from this study are
comparable with wintertime δ13CEC in 2015/2016 (Ni et al.,
2019b) but much more depleted than wintertime δ13CEC in
2008/2009 (Ni et al., 2018) and January 2003 (Cao et al., 2011). This
suggests that fossil sources of EC in Xi'an have changed in the past decade
with a decreasing relative contribution from coal combustion. This is in line
with recent changes in energy use and the decreasing enrichment factors of
As and Pb (i.e., indicators of coal combustion) in Xi'an, as documented in
recent studies (Xu et al., 2016). As shown in Fig. 3b, in Beijing,
variations in δ13CEC from January 2003 (Cao et al.,
2011) to January 2017 (this study) are much narrower than those in Xi'an,
indicating that EC combustion sources did not change significantly
throughout the years in Beijing. Our δ13CEC values
overlap with those in January 2014 (Fang et al., 2017) and fall into the
range of reported δ13CEC values in urban Beijing (Cao et
al., 2011; Chen et al., 2013) and the regional receptor site of Beijing
(Andersson et al., 2015; Fang et al., 2017).
(a) Concentrations of POCbb, OCo,nf,
POCfossil, and SOCfossil(µg m-3) and the mass ratio
of OCo,nf/POCbb and SOCfossil/POCfossil of each sample.
The error bars indicate the interquartile range (25th–75th percentile) of
the median concentrations. Averaged fractions (b) and concentrations
(c) of POCbb, OCo,nf, POCfossil, and
SOCfossil in total OC during haze and clean periods in Xi'an and
Beijing, China. The data are given in Table S4.
Primary and secondary OC
As explained in Sect. 2.4, OCnf and OCfossil are apportioned
into primary (POCbb, POCfossil) and secondary OC (OCo,nf,
SOCfossil; Fig. 5). The large error bars of their concentrations
reflect the large uncertainties in rbb and rfossil. It should be
noted that OCo,nf is used as an approximation of SOCnf or can
be regarded as an upper limit of SOCnf if cooking is a prominent OC
source.
In Xi'an, both ratios of OCo,nf/POCbb and
SOCfossil/POCfossil increased during haze periods (Fig. 5a).
The OCo,nf/POCbb ratio increased by 2.5 times from 0.33–0.46 during
clean periods to 0.86–1.1 during haze periods; in contrast,
SOCfossil/POCfossil increased by 1.5 times from 0.46–0.50 to
0.62–0.78. This underlines that haze episodes in Xi'an were mainly caused
by additional SOC formation with a larger contribution from non-fossil
sources than fossil sources. As shown in Fig. 5b, the contribution of SOC
(i.e., SOC ≅ OCo,nf+ SOCfossil) to OC increased from
clean periods (28 %–32 %) to haze periods (44 %–48 %); this mainly
resulted from the increased contribution of OCo,nf to total OC (i.e.,
from 14 %–16 % to 26 %–29 %). In Xi'an, the day–night difference
was larger during clean periods with less SOC at night for both absolute
concentration and relative contribution to total OC (Fig. 5b, c).
In contrast, Beijing had the opposite variation trends of
OCo,nf/POCbb and SOCfossil/POCfossil from clean to
haze periods. OCo,nf/POCbb ratios during clean periods
(1.3) were on average 5 times higher than those during haze periods
(0.18–0.33), and SOCfossil/POCfossil ratios during clean
periods (0.71) were slightly higher than those during haze periods
(0.41–0.64). This suggests that in Beijing the increased OC concentrations
during haze periods were mainly derived from elevated concentrations of
POCbb and POCfossil. As shown in Fig. 5b, the high SOC contribution to
total OC was observed during clean periods mainly due to elevated
contribution from OCo,nf. The OCo,nf is not likely attributed to
biogenic OC because the biogenic emissions are very low in winter. As a
result, the elevated contribution from OCo,nf to OC during clean
periods in Beijing could be attributed to regional sources. During clean
periods, concentrations of OC and OCo,nf are small, and the measured
carbon concentrations can reflect regional sources, which are dominated by
secondary sources due to long-range transport. It could also be that the
contribution of cooking OC to OCo,nf can be noticeable during clean
conditions.
Fraction of fossil carbon in EC, OC, and SOC
(ffossil(EC), ffossil(OC), and ffossil(SOC), respectively) during
haze and clean periods in Xi'an and Beijing. Interquartile ranges
(25th–75th percentile) of the median ffossil(SOC) are shown as vertical
bars in purple. Uncertainties of 14C-apportioned ffossil(EC) and
ffossil(OC) are indicated but are too small to be visible. The studied
samples include day and night samples in Xi'an (X) and 24 h integrated
samples in Beijing (B) during haze periods (“H” samples) and clean periods
(“C” samples). For details of each sample (x axis), see Fig. 1 and Table S1.
The fossil fraction of the total SOC can be defined as ffossil(SOC) =SOCfossil/SOC. In Xi'an, around half of SOC was derived from fossil
sources (ffossil(SOC) = 44 ± 6 %), whereas ffossil(SOC) = 75 ± 10 % in Beijing. Using a similar approach with this study,
Zhang et al. (2015) also found that Beijing had higher ffossil(SOC)
(48 %–63 %) than in Xi'an (30 %–35 %) during the January 2013
severe haze events. These findings suggest the important contribution of
fossil sources to SOC in Beijing and non-fossil sources in Xi'an. The
ffossil(SOC) value in Beijing increased during haze periods, whereas the
opposite trend was found in Xi'an (Fig. 6). During haze periods in Beijing,
ffossil(SOC) overlapped with ffossil(EC) and was clearly higher than
ffossil(OC).
Together these results reveal the differences in primary and secondary OC in
two Chinese megacities. The contribution of SOC to total OC increased from
clean to haze periods in Xi'an. In contrast, SOC/OC ratios increased from
haze to clean periods in Beijing mainly due to increased SOC from
non-fossil sources. SOC was dominated by fossil sources in Beijing but by
non-fossil sources in Xi'an, especially during haze periods. In Xi'an, the
day–night difference was larger during clean periods with less SOC at night.
Differences between the fractions of non-fossil carbon in OC and EC
The differences between fnf(OC) and fbb(EC) were smaller in Beijing,
ranging from 11 % to 20 % compared to 25 %–29 % in Xi'an. To
better understand what governs the differences, we express fnf(OC) in
terms of fossil to biomass-burning ratio in EC and primary OC/EC emissions
ratios. Starting from the formulas of fbb(EC) andfnf(OC), we find the following:
14fbbEC=ECbbECbb+ECfossil=11+ECfossilECbb,15fnfOC=OCnfOCnf+OCfossil=POCbb+OCo,nfPOCbb+OCo,nf+POCfossil+SOCfossil.
We find that
fnfOC=11+1+SOCfossil/POCfossil1+OCo,nf/POCbb×POCfossilPOCbb=11+1+SOCfossil/POCfossil1+OCo,nf/POCbb×rfossilrbb×ECfossilECbb,
where rfossil is the weighted average of rcoal and
rvehicle.
Comparing Eqs. (14) and (16), we find that fnf(OC) and
fbb(EC) would be equal if 1+SOCfossil/POCfossil1+OCo,nf/POCbb×rfossilrbb=1. Since
rfossil is usually smaller than rbb, fnf(OC) tends to be larger
than fbb(EC), assuming that SOC formation is comparable for fossil and
non-fossil sources (i.e., 1+SOCfossil/POCfossil1+OCo,nf/,POCbb∼1). With smaller rfossil than
rbb, similar fnf(OC) and fbb(EC) can result from larger
secondary formation from fossil sources than non-fossil sources (i.e.,
1+SOCfossil/POCfossil1+OCo,nf/POCbb>1). However, the coal combustion fossil source has a higher
primary OC to EC ratio than vehicle emissions (i.e.,
rcoal>rvehicle). Therefore, in a city where biomass
burning and coal combustion are the dominant pollution sources,
fnf(OC) andfbb(EC) will be more similar than in a city where the
main sources are biomass burning and vehicle emissions.
Compared to Xi'an, Beijing had significantly smaller differences between
fbb(EC) and fnf(OC) (Fig. 1), which was also observed in previous
studies during the haze event in January 2013 (Zhang et al., 2015).
Comparing Eqs. (14) and (16), this suggests either a strong contribution
from coal combustion in Beijing, large secondary formation from fossil
sources, or both. The stronger contribution of coal combustion to OC in
Beijing than in Xi'an was a direct consequence of a larger proportion of
coal combustion in EC in Beijing, as demonstrated by the Bayesian MCMC
results of EC (Sect. 3.2). The latter was further validated by the variation
of SOC. The ffossil(SOC) in Beijing was higher than that in Xi'an
despite the variations between haze and clean periods (Sect. 3.3). By
combining 14C measurements with other state-of-the-art analytical
techniques (e.g., aerosol mass spectrometry), Huang et al. (2014) also found
that fossil OC was mostly secondary in nature in Beijing, and non-fossil SOC
formation was dominant in Xi'an during a wintertime haze episode (i.e.,
Beijing had a larger ffossil(SOC) than Xi'an). However, atmospheric
mechanisms responsible for the enhancement of fossil-derived secondary
organic aerosol formation in Beijing remain unclear.
Furthermore, as shown in Fig. 1, unlike Xi'an where the differences between fnf(OC)
and fbb(EC) were relatively constant for all samples, in Beijing the
differences between fnf(OC) and fbb(EC) were smaller during haze
periods than clean periods, which was caused by decreased fbb(EC) and slightly
increased fnf(OC) during clean periods. This might indicate a higher
relative contribution from coal combustion and/or fossil-dominated SOC
during haze periods in Beijing. However, the Bayesian MCMC results of EC
show the opposite; i.e., in Beijing the contribution of coal combustion to
EC was lower during haze periods than during clean periods (Sect. 3.2).
Therefore, the only possible explanation is that, during haze periods in
Beijing, SOC was dominated by fossil sources. This is validated by
significantly larger ffossil(SOC) during haze periods (76 %–81 %)
than during clean periods (∼ 55 %; Sect. 3.3).
In conclusion, this section discusses the factors that govern the
differences between fbb(EC) and fnf(OC) and concludes that
smaller differences suggest a stronger contribution from coal combustion
and/or larger secondary formation from fossil sources. This is further
examined and validated by source apportionment results of EC and OC (Sect. 3.1–3.3).
Conclusions
In this study, the sources of carbonaceous aerosol were quantified using a
dual-carbon isotopic approach for PM2.5 samples collected in urban
Xi'an and Beijing that reached the red alarm level during December 2016 and
January 2017. The 14C results showed that fossil sources dominated EC,
contributing on average 73 ± 2 % of EC in Xi'an and 80 ± 3 % of EC in Beijing. The remaining EC was attributed to biomass burning.
In Xi'an, fbb(EC) was fairly constant during haze and clean periods
despite the wide range of EC concentrations. However, in Beijing,
fbb(EC) increased with increasing EC concentrations. Complementing
14C with δ13C in a Bayesian MCMC approach allows for the
separation of fossil sources of EC into coal combustion and liquid fossil
fuel combustion. The MCMC results in Xi'an suggest that liquid fossil fuel
combustion contributed 44 %–49 % of EC during haze periods and
54 %–57 % of EC during clean periods. In Beijing, coal combustion was
the dominant fossil source of EC with a decreasing contribution to EC from
clean periods (∼ 61 %) to haze periods (∼ 48 %).
The 14C measurements of OC showed that the contribution of non-fossil
sources to OC was larger than that to EC and was on average 54 ± 2 % in Xi'an and 34 ± 3 % in Beijing. The differences between the
non-fossil fraction in OC and EC were smaller in Beijing and larger in
Xi'an. In Xi'an, the fraction of SOC in total OC was larger during haze
periods than during clean periods mainly due to increased SOC from
non-fossil sources. Beijing showed the opposite trends with a larger
fraction of SOC in total OC during clean periods than during haze periods
mainly due to the elevated contribution from non-fossil SOC during clean
periods.
SOC was dominated by non-fossil sources in Xi'an but by fossil sources in
Beijing, especially during haze periods. The relative contribution of fossil
sources to SOC (ffossil(SOC)) was consistently higher in Beijing than in
Xi'an. In Beijing, ffossil(SOC) was higher during haze periods
(76 %–81 %) than during clean periods (55 %), whereas an opposite
trend was found in Xi'an, with ffossil(SOC) increasing from
∼ 39 %–43 % during haze periods to ∼ 52 %
during clean periods. In Xi'an, a slight day–night difference was found
during clean periods with increasing fossil contribution to OC and EC
during the day and less SOC at night. During strong haze, this day–night
difference was negligible, suggesting a large accumulation under stagnant
weather conditions during the severe haze periods.
Data availability
Data used to support the findings in this study are archived at the
Institute of Earth Environment, Chinese Academy of Sciences, and are
available upon request from the corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-16041-2020-supplement.
Author contributions
RJH and UD designed the study. JC supervised the project. LY contributed to sample preparation. Isotope measurements were made by HN, MMC,
and JG. Data analysis and interpretation were done by HN, MMC, RJH, and UD.
HN wrote the paper with contributions from all coauthors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge the financial support from the Gratama Foundation. We thank
Marc Bleeker and Henk Been for their help with the 14C measurements.
Special thanks are also given to Anita Aerts-Bijma and Dicky van Zonneveld
for their assistance in 14C data correction at CIO.
Financial support
This research has been supported by the National Key Research and Development Program of China (grant no. 2017YFC0212701), the National Natural Science Foundation of China (grant nos. 41925015, 91644219, and 41877408), the KNAW (grant no. 530-5CDP30), the Chinese Academy of Sciences (grant no. ZDBS-LY-DQC001), and the Cross Innovative Team fund from the State Key Laboratory of Loess and Quaternary Geology (grant no. SKLLQGTD1801).
Review statement
This paper was edited by Willy Maenhaut and reviewed by two anonymous referees.
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