To investigate the characteristics of atmospheric brown
carbon (BrC) in the semiarid region of East Asia, PM2.5 and
size-resolved particles in the urban atmosphere of Xi'an, inland China,
during the winter and summer of 2017 were collected and analyzed for optical
properties and chemical compositions. Methanol extracts (MeOH extracts) were
more light-absorbing than water extracts (H2O extracts) in the optical
wavelength of 300–600 nm and well correlated with nitrophenols, polycyclic
aromatic hydrocarbons (PAHs) and oxygenated PAHs (r>0.78). The
light absorptions (absλ=365nm) of H2O extracts and
MeOH extracts in winter were 28±16 and 49±32 M m-1,
respectively, which are about 10 times higher than those in summer, mainly
due to the enhanced emissions from biomass burning for house heating. Water-extracted BrC predominately occurred in the fine mode (< 2.1 µm) during winter and summer, accounting for 81 % and 65 % of the total
absorption of BrC, respectively. The light absorption and stable carbon
isotope composition measurements showed an increasing ratio of absλ=365nm-MeOH to absλ=550nm-EC along with an enrichment of
13C in PM2.5 during the haze development, indicating an
accumulation of secondarily formed BrC (e.g., nitrophenols) in the aerosol aging
process. Positive matrix factorization (PMF) analysis showed that biomass burning, fossil fuel combustion,
secondary formation, and fugitive dust are the major sources of BrC in the
city, accounting for 55 %, 19 %, 16 %, and 10 % of the total BrC of
PM2.5, respectively.
Introduction
Brown carbon (BrC) is a vital fraction of carbonaceous aerosols and
exhibits strong absorption ability from the near-ultraviolet (UV) to visible
light region. Thus, it has been given extensive investigation in the recent
decades (Laskin et al., 2015; Yan et al., 2018; Gustafsson et al., 2009).
BrC has significant impact on climate change directly by absorbing solar
radiation and indirectly by accelerating snowmelt and affecting the albedo
(Qian et al., 2015; Andreae and Ramanathan, 2013). Based on
the remote sensing observations and chemical transport models (Chung et
al., 2012; Wang et al., 2014; Jo et al., 2016), a non-negligible positive
radiative forcing by BrC was found on a global scale with a range from 0.1
to 0.6 W m-2. Beyond that, BrC also influences atmospheric chemistry
and human health. For example, BrC can shield polycyclic aromatic
hydrocarbons (PAHs) from being oxidized and thus substantially elevate lung
cancer risk from PAHs (Hsu et al., 2014; Yan et al., 2018).
The sources of BrC are complicated since it can be primarily emitted from
incomplete combustion of carbon-containing materials (e.g., biomass, coal
and petroleum products) and secondarily derived from aqueous-phase reaction
(Sun et al., 2017; Gilardoni et al., 2016; Xie et al., 2018; Nakayama et
al., 2013). Biomass burning was found to be a major source of BrC (Chen
and Bond, 2010; Chakrabarty et al., 2010; Saleh et al., 2014), because lignin
is of an unsaturated benzene-like structure, which is a chromophore group.
Previous studies found that BrC also comes from secondary
sources by forming chromophores during the aerosol aging process, e.g.,
high-NOx photooxidation (Liu et al., 2016; Xie et al., 2017), ozonolysis
of aromatic precursors (Lee et al., 2014), and aqueous-phase
photochemical oxidation and polymerization (Smith et al., 2014; Flores et
al., 2014; Bones et al., 2010). BrC products account for a very small weight
fraction of organic aerosol (OA) but have a significant effect on OA
optical properties. For example, nitroaromatic compounds generated by
photooxidation of toluene under high-NOx conditions may account for
40 %–60 % of the total light absorption of toluene SOA
(Lin et al., 2015).
Multiple approaches have been developed to quantify the light absorption
properties of BrC (Moosmuller et al., 2009), and a common and sensitive
approach is the direct measurement of spectrophotometric properties of
aerosol water or filter extracts by using optical instrumentation. The
advantage of this method can avert interference from insoluble absorption
material (e.g., black carbon) (Cheng et al., 2016; Shen et al., 2017) and
supply a high-resolution spectrum over a wide wavelength coverage.
Furthermore, it is favorable for characterization of BrC light-absorbing
components by combing with other analytical techniques, such as mass
spectrometry (MS) (Laskin et al., 2015; Corr et al., 2012; Satish et al.,
2017).
Many studies have been conducted on the BrC optical properties in China, but
most of those were based on PM2.5 and PM10 sample collection and
focused on the bulk aerosol optical properties with no information on the
size distributions (Shen et al., 2017; Huang et al., 2018). Xi'an is a
metropolitan city located in Guanzhong Basin of inland China, which is a
typical semiarid region in East Asia and has been suffering from serious
particle pollution due to the large emission of anthropogenic pollutants
(Wu et al., 2018; Wang et al., 2016; Wu et al., 2019), especially intensive
coal combustion and biomass burning in winter for house heating
(Wang et al., 2017). In this study, both PM2.5 and
size-segregated aerosol samples in Xi'an were collected during the 2017
winter and summer and analyzed for the characteristics of BrC. We firstly
investigated the seasonal variations in chemical composition and
light absorption of BrC in the city, then discussed the size distribution of
BrC and the impact of aerosol aging process on BrC, and finally quantified
its source contributions.
ExperimentSample collection
Aerosol samples were collected on a day–night basis each for 12 h by
using a high-volume (∼1.13 m3 min-1) air sampler
(Tisch Environmental, Inc., OH, USA) from 31 December 2016 to 22 January 2017 (in winter) and from 18 July to 6 August 2017 (in summer). The sampler
was installed on the roof of a three-story building on the campus of the
Institute of Earth Environment, CAS (34.22∘ N,
108.88∘ E), which is located at the urban center of
Xi'an, inland China. Meanwhile, size-resolved aerosols with nine
size bins (cutoff points were 0.43, 0.65, 1.1, 2.1, 3.3, 4.7, 5.8 and 9.0 µm, respectively) were collected by using an Anderson sampler at an
airflow rate of 28.3 L min-1 for 24 h. All samples were collected onto
the pre-baked (450∘ for 6 h) quartz filters and stored in a
freezer (-18∘) prior to analysis.
Chemical analysis
A punch (0.526 cm3) from each PM2.5 filter sample was analyzed for
organic carbon (OC) and elemental carbon (EC) with a DRI model 2001
thermal–optical carbon analyzer (Atmoslytic Inc., Calabasas, CA, USA)
following the IMPROVE-A protocol (Chow et al., 2007).
More details of the method including quality assurance and quality control
(QA–QC) can be found elsewhere (Wang et al., 2010).
Partial filters were cut into pieces and then extracted three times under
sonication with 15 mL Milli-Q pure water (18.2 MΩ). Ten ions such as
SO42-, NO3-, Cl-, NH4+ and K+ were
determined using ion chromatography (Dionex, ICS-1100). Similar extraction
processes were also applied to measure the water-soluble organic carbons
(WSOCs) of the samples, which were determined by using a Shimadzu TOC-5000 carbon
analyzer. The detailed method has been reported by
Wang et al. (2013). In order to analyze the organic
compounds in the samples such as levoglucosan, PAHs, OPAH and nitrophenols,
an aliquot of the filter was extracted with a mixture of methanol and dichloromethane (DCM, 1:5,
v/v), derivatized with bis(trimethylsilyl)trifluoroacetamide (BSTFA, HP
7890A, Agilent Co., USA) coupled with a mass spectrometer (GC–MS) (HP
5975, Agilent Co., USA). Details of sample extraction and derivatization
were documented elsewhere (Wang et al., 2009b; Ren et al., 2017). Stable
carbon isotope composition of total carbon (δ13CTC) was
determined by using an elemental analyzer (EA) (Carlo Erba, NA 1500) coupled
with an isotope ratio mass spectrometer (IRMS, Finnigan MAT DELTA Plus),
more details of the method can be found elsewhere (Cao et
al., 2016).
Light absorption measurements
Brown carbon (BrC) was extracted from 6 cm3 filter samples
for 30 min ultrasonication with 20 mL Milli-Q pure water or methanol. All
extracts were then filtered through 0.45 µm PTFE (for water) and 0.22 µm PES (for methanol) pore syringe filters to remove insoluble
components and filter debris. The light absorption spectra were analyzed
with a UV–visible spectrophotometer (AOE Instruments, China) over a
wavelength range of 190–900 nm (Hecobian et al., 2010). The
absorption coefficient of water or methanol extracts (M m-1) could be
calculated as the following equation (Teich et al., 2017):
absλ=(Aλ-A700)VlVa×L×ln(10),
where Aλ and A700 were the light absorption of the extracts
at the wavelengths of λ and 700 nm, respectively. Vl
represents the volume of the solvent extracting the filter sample, and
Va refers to the volume of air corresponding to the filter punch.
L is the absorbing path length (i.e., 1 cm for the currently used quartz
cuvettes). The ln(10) is converted from base 10 (the form provided by the
spectrophotometer) to natural logarithms. According to previous studies,
the absorption coefficient at 365 nm was used as the brown carbon absorption
in order to avoid disturbance of inorganic salts such as nitrate.
The bulk mass absorption coefficient (MAC, m2 g-1) of the extracts at a
given wavelength can be described by the following equation:
MAC=absλCW(M)SOC,
where CW(M)SOC is the atmospheric concentration of the particulate
water-soluble organic carbon (WSOC) or methanol-soluble organic carbon (MSOC, µgC m-3). In this study, we assumed that OC could be completely
dissolved in methanol solvent and substituted the MSOC for the calculation.
This hypothesis would possibly lead to somewhat underestimation of the MAC
of the methanol extracts, although high extraction efficiency of methanol
solvent had been reported by previous studies (Liu et al.,
2013).
The wavelength dependence of light absorption with respect to the
empirically defined power-law relationship is described by the following
equation (Laskin et al., 2015):
MAC=Kλ-AAE,
where K is a factor that includes aerosol mass concentrations, and AAE denotes the absorption Ångström exponent. In this study, the AAE value of
the filter extracts was determined by a linear regression of log(absλ) versus log(λ) over a wavelength range of 300–450 nm.
PMF, as a receptor model, decomposes the sample matrix into two matrices
(factor contributions and factor profiles) and has been widely used for the
source apportionment of atmospheric pollutants. More details on PMF can be
found on the EPA website (https://www.epa.gov/air-research/epa-positive-matrix-factorization-50-fundamentals-and-user-guide,
last access: 16 May 2019).
In the present work, the mass concentrations of major species (OC, EC, WSOC,
SO42-, NO3-, NH4+ and Ca2+), organic markers
(benzo(b)fluoranthene (BbF), benzo(e)pyrene (BeP), indeno(1,2,3-c,d)pyene
(IP), levoglucosan and nitrophenols) and absλ of water
extracts have been used as the input data to perform the source
apportionment for brown carbon with the EPA PMF 5.0 version; similar reports
have been found elsewhere (Hecobian et al., 2010). The model
was run numerous times with three to seven factors and various combinations of the
concentration and absorption data set. Based on the Q value (Qtrue and
Qrobust) and r, which are indicative of the agreement of the model fit,
four factors were obtained as the optimal solution.
Results and discussionCarbonaceous species in PM2.5 during
summer and winter
Figure 1 shows the temporal variations in the concentrations of PM2.5,
WSOC, OC and absλ=365nm values during the two seasons. WSOC
varied from 5.3 to 67 µgC m-3 in winter with an average of 23±13µgC m-3 (Table 1), which was 4.0 times higher than that
in summer. OC exhibited a seasonal variation similar to that of WSOC with an
average of 41±25µgC m-3 in winter and 8.4±2.4µgC m-3 in summer, respectively. However, WSOC/OC ratio was much
higher in summer (0.70±0.12) than that in winter (0.58±0.13), partly as a result of an enhanced photochemical formation of WSOC
under the intense sunlight conditions. Similar phenomena were also found in
Beijing (Ping et al., 2017), Shanghai (Zhao et al.,
2015a), Tokyo (Miyazaki et al., 2006) and the southeastern US (Ding et al., 2008).
Temporal variations in WSOC, OC, PM2.5 and absλ=365nm of PM2.5 samples extracted by water (H2O extraction) and
methanol (MeOH extraction) during winter (a, c) and
summer (b, d).
Concentrations of organic carbon in PM2.5 and meteorological
conditions during winter and summer of 2017 in Xi'an, inland China.
WinterSummer(i) Mass concentrations of organic matter in PM2.5WSOC (µgC m-3)23±135.8±1.4OC (µgC m-3)41±258.4±2.4PAHs (ng m-3)149±898.1±6.5OPAHs (ng m3)174±9817±8.7Nitrophenols (ng m3)17±120.40±0.27Levoglucosan (ng m-3)739±43229±22(ii) PM2.5 and meteorological parameters PM2.5 (µg m-3)194±14137±16T (∘)2.6±2.931±5.4RH (%)60±2058±19Visibility (km)7.0±7.021±11
PAHs, OPAHs and nitrophenols are ubiquitous in the atmosphere and can be
directly emitted from incomplete combustion of carbon-containing fuels
(e.g., coal, biomass) (Shen et al., 2013; Zhang and Tao, 2009). In
addition, OPAHs and nitrophenols can also be produced from photochemical
reactions (Cochran et al., 2016; Keyte et al., 2013; Yuan et al., 2016).
These compounds are the efficient light-absorbing species, because their
molecular structures consist of chromophores (Lin et al., 2017; Bluvshtein
et al., 2017). Herein, 14 PAHs, 7 OPAHs and 7 nitrophenols were examined
for investigating their effect on BrC absorption. As seen in Fig. S1, the
temporal variations in PAHs, OPAHs and nitrophenols were similar to
levoglucosan, which is the tracer of biomass burning emissions, indicating
that biomass burning is one of the major sources of these compounds.
Concentrations of PAHs, OPAHs and nitrophenols during winter were 149±89, 174±98 and 17±12 ng m-3 (Table 1), respectively, and were 10–43 times higher than
those in summer, which can be explained by increasing emissions from
residential heating during winter in the city and its surrounding regions.
As shown in Supplement Table S1, absλ=365nm extracted by methanol
displays good correlations with PAHs, OPAHs and nitrophenols,
especially in winter (r>0.89), which suggests that those
species are important light absorption contributors for BrC in Xi'an.
Huang et al. (2018) found that PAHs and OPAHs in Xi'an
accounted for, on average, 1.7 % of the overall absorption of
methanol-soluble BrC, but their mass fraction in OC was only 0.35 %. A
recent study reported that biomass burning also emitted nitroaromatic
compounds, particularly nitrophenols, and accounted for 50 %–80 % of the
total visible light absorption (> 400 nm) (Lin et
al., 2017). The robust correlations of the above compounds with the absorption
at λ=365 nm suggest that PAHs, OPAHs and nitrophenol are strong
light-absorbing species.
Light absorption of BrC in water and methanol extractsSeasonal variations in light absorption by BrC
As shown in Fig. 2a and b, the marked feature of BrC in Xi'an is that the
absorption spectrum increased notably from the visible to the ultraviolet
ranges, and the average abs-MeOH at λ=365 nm was 1.5–1.7 times
higher than abs-H2O in the two seasons, indicating that MSOC provided a
more comprehensive estimation for BrC. Due to enhanced emission of BrC,
average absλ=365nm of BrC found in winter was 49±32 M m-1 for MeOH extracts and 28±16 M m-1 for WSOC, which were 9.5-
and 8.1-fold higher than that in summer. This phenomenon was also observed
in previous studies in Xi'an (Shen et al., 2017; Huang et al., 2018) and
other areas of China (Du et al., 2014; Chen et al., 2018). Compared with
other regions (Table 2), the absolute absλ=365nm values in Xi'an
were slightly lower than those in the Indo-Gangetic Plain, India
(Satish et al., 2017; Bachi, 2016), but were considerably
higher than those in Beijing, China (Du et al., 2014); the US
(Zhang et al., 2011); and South Korea (Kim et al., 2016),
suggesting heavy pollution of light-absorbing aerosols in Xi'an.
Furthermore, enhanced absλ=365nm loading in the nighttime was
observed during the two seasons, which can be ascribed to the shallower
boundary layer height and the absence of photo-bleaching processes at night
(Saleh et al., 2013; Zhao et al., 2015b).
Seasonal average values of absλ=365nm, AAE and MAC
extracted by MeOH and H2O. AAE is calculated by a linear regression fit
log (absλ=365nm) versus log(λ) in the wavelength range
of 300–450 nm. The shading indicates the standard deviations.
Comparison of light absorption (absλ=365nm), MAC and
AAE values of water extracts of PM2.5 in Xi'an, China, with those in
other cities.
LocationTimeabsλ=365nm (M m-1) MAC (m2 g-1) AAE ReferencesWinterSummerWinterSummerWinterSummerXi'an, China2016–201749±32a5.2±2.1a1.3±0.03a0.8a±0.1a6.1±9.7a5.5±8.8aThis study28±163.5±1.71.2±0.061.1±0.25.3±8.54.8±7.72008–200946±20a8.3±2.3a1.3a0.7a6.0a6.0aHuang et al. (2018)25±125.0±1.31.71.05.75.7Beijing, China2010–201110±8.63.7±3.81.30.5Du et al. (2014)201110±6.91.27.3Cheng et al. (2016)201314±5.24.6±2.21.50.75.35.8Yan et al. (2015)Nanjing, China2015–20169.4±4.73.3±2.41.00.56.77.3Chen et al. (2018)Guangzhou, China20123.6±1.30.85.3Liu et al. (2018)Delhi, India2010–20111.65.1Kirillova et al. (2014)Indo-Gangetic Plain, India2015–201624±191.2Satish et al. (2017)201140±18b1.3b5.1bBachi et al. (2016)52±27c1.3c5.3cSeoul, South Korea2013–201311a5.8a0.9a1.5a5.5a4.1aKim et al. (2016)7.30.91.00.35.88.7Atlanta, US20100.6±0.41.2–0.23.4Zhang et al. (2011)Los Angeles Basin, US20100.4–1.60.77.6Zhang et al. (2013)
a Solution extracted by MeOH. b Samples collected in daytime. c Samples collected in the night.
Linear regression slopes in the scatter plots of absλ=365nm
values versus WSOC or MSOC represent the average of MAC at 365 nm (i.e.,
MACWSOC and MACMSOC). During winter, there was a slight
disparity between the MACWSOC and MACMSOC with the averages of 1.2±0.06 and 1.3±0.03 m2 g-1 (Fig. 2e), respectively, which
indicates that there are some similar chromophores of BrC between the two
fractions. As seen in Table 2, both MACWSOC and MACMSOC in Xi'an
during the two seasons are higher than those in the US and Korea, suggesting
that BrC in the city was comprised of stronger light-absorbing compounds.
absλ=365nm showed a strong linear correlation with levoglucosan
(r>0.98), suggesting that abundant BrC was largely derived from
biomass burning. As shown in Fig. S2, mass ratios of levoglucosan/mannosan
and levoglucosan/galacosan in the PM2.5 samples are similar to
biomass types (i.e., woods, leaves, wheat straw), again reflecting that
biomass burning combustion in Xi'an and its surrounding regions is probably
the major source of BrC in the city during winter. Compared to winter, the
MAC in summer was slightly lower, which can be in part attributed to the
less abundant light-absorbing PAHs and OPAHs due to no biomass burning for
house heating. Moreover, with increasing photooxidation in summer,
fragmentation reactions would occur and thus decrease light absorption for
BrC aerosols, as reported by Sumlin et al. (2017), because higher levels
of O3 and OH radicals in summer intensify the photooxidation and
diminish the BrC aerosol light absorption by reducing the size of conjugated
molecular systems. Interestingly, we found that the MACWSOC (1.1±0.2 m2 g-1) in summer was significantly enhanced compared to
MACMSOC (0.8±0.1 m2 g-1), which can be ascribed to more non-BrC in the methanol extracts such as phthalates, of which the
abundance relative to OC was about 10 times higher in summer than in winter.
The absλ=365nm showed a poor correlation with levoglucosan
(Table S1), further indicating that the biomass burning was not the dominant
source for BrC in summer.
Absorption Ångström exponents (AAEs), which were
derived from the filter methanol- and water-extracted BrC (AAEWSOC and
AAEMSOC) for wavelengths between 300 and 450 nm, were 6.1±9.7
and 5.3±8.5 (Table 2) in winter, respectively, and resembled that in
Beijing (Cheng et al., 2016), Guangzhou (Liu et al., 2018) and
the Indo-Gangetic Plain (Bachi, 2016), possibly indicating that the
chemical compositions of BrC chromophores in these regions are similar
during winter. As seen in Table 2, unlike those of H2O extracts, the
averaged values of MAC and AAE of MeOH extracts were 40 % and 10 %
higher in winter than in summer, respectively, suggesting that chemical
compositions of BrC are different between the two seasons in the city and
the winter BrC contained more nonpolar compounds that are of stronger
light-absorbing ability.
Aerosol size distribution of BrC
Particles with different sizes are of different chemical compositions, and
thus optical properties of BrC in different sizes of particles are also
different (Zhang et al., 2015; Zhai et al., 2017). However, information on
size distribution of BrC absorption is very limited. In this study, we
mainly focused on the water-extracted samples, because particles deposited
on the filter surface are unevenly distributed, making the quantifications
of OC and EC in the size-segregated samples not accurate enough. As shown in
Fig. S3, there was a good relationship between the absλ=365nm (r>0.98) of the samples collected by the Anderson sampler
and those collected by the high-volume PM2.5 sampler (Fig. S3), suggesting
a good agreement between the two sampling methods.
Size distributions of absλ=365nm and MAC of PM2.5
samples extracted by water during the winter and summer of 2017 in Xi'an. Dpg is the geometric mean diameter of particles.
As shown in Fig. 3, absλ=365nm presented a bimodal pattern
during winter and summer, dominating in the fine mode (Dp < 2.1µm) with relative contributions of 81 % and 65 % to the total absorption
in the two seasons, respectively. These proportions are similar to those
reported for a forest wildfire event, which showed that 93 % of the total
BrC absorption was in the fine particles (0.10 < Dp < 1.0 µm) (Lorenzo et al., 2018). Maximum absorptions were
observed at 1.02 and 0.71 µm (Fig. 3a
and b) in winter and summer, respectively, which is in agreement with the
observations by Lei et al. (2018), who found that the major peaks for BrC
absorption were in the range from 0.5 to 1.0 µm in urban emissions and may
shift toward a smaller size (< 0.4µm) for particles released from
burning experiments (Lei et al., 2018). However, the size
distribution pattern of MAC was different from that of absλ=365nm in Xi'an, which presented a monomodal distribution with a peak in
the fine mode (< 2.1µm) in winter and a bimodal distribution in
summer with two peaks in the fine (< 2.1µm) and coarse
(> 2.1µm) modes (Fig. 3c and d). As seen in
Fig. 3c and d, the fine mode of MAC was around 50 % larger in winter
than that in summer, suggesting that the water-soluble fraction of winter fine
particles was more light-absorbing compared to that in summer, probably due
to the stronger summertime bleaching effect.
Underestimation of BrC absorption by solvent extraction methods
A few studies pointed out that absorption properties of BrC extracted by
bulk solution may not entirely reflect the light absorption by ambient
aerosols. Here, we further calculated the light absorption of the samples
using the Mie theory combined with an imaginary (k, responsible for
absorption) refractive index with assumptions that particles were of
spherical morphology and externally mixed with other light-absorbing
components. The imaginary refractive index could be obtained from MAC using the
following equation (Laskin et al., 2015):
k(λ)=ρλabs4π×WSOC=ρλMAC4π,
where ρ (g cm-3) was particle density and assigned as 1.5; more
details about Mie calculations can be found in the study by
Liu et al. (2013).
As noted above, most BrC aerosols were in the fine mode (< 2.1µm); thus, here we only focused on this fraction for the Mie calculations.
The values of imaginary refractive index in winter remain nearly constant
(0.038–0.048) for different particle sizes at λ=365 nm (Table 3),
which was about 2 times smaller than that (0.093±0.049) over the
Gangetic Plain, India (Shamjad et al., 2017). Values of k in
summer were slightly smaller when compared to those in winter, suggesting that
the aerosols in summer were more aged. Sumlin et al. (2017) found that
k decreases along with the atmospheric aging from 0.029±0.001 to
0.019±0.001 at λ=375 nm. However, k values in this study
were 5.0 times (avg.) higher than those reported from the US
(Liu et al., 2013). This is because PM2.5 particles
in Xi'an, China, are enriched in BrC and the mass absorption coefficient was
considerably higher than that in the US. Figure 4 compares the difference
between absλ=365nm predicted by Mie theory (abs-Mie) and that
extracted by the bulk solution (abs-Measure). Mie theory predicted
absλ=365nm that was 1.5-fold higher than that measured by the bulk
solution, suggesting that the solvent extraction methods, which have
commonly been used for atmospheric BrC measurements, could result in an
underestimation of optical absorption of aerosols. Hence, a factor of 1.5 is
recommended to convert the liquid-based data (at least for the water-soluble
data) reported by this work for estimating optical properties of atmospheric
aerosols in Xi'an and its surrounding regions in order to better quantify
the BrC light absorption.
Complex refractive index (k) of brown carbon from samples extracted
by water in two seasons.
An orthogonal regression analysis for absλ=365nm
between samples predicted by Mie theory and extracted by water for different
particle sizes (Dp < 2.1µm).
The characteristics of BrC with aerosol aging
During the aging process, secondary organic aerosols (SOAs) with strong
chromophores can be generated and efficiently absorb solar radiation (Lin
et al., 2014, 2016). From Fig. 5, it can be found that air
quality in Xi'an during the winter varied from clean (PM2.5 < 75 µg m-3) to polluted conditions (PM2.5 > 75 µg m-3) from the period of 12 January to
19 January. Such a case provides an opportunity to investigate the
changes in light absorption by BrC during the aerosol aging process.
Temporal variations in PM2.5, meteorological parameters,
absλ=365nm of W(M)SOC and organic compounds in the period of
10–20 January. The cyan shadow indicates a haze period from
12 to 19 January 2017 with a daily PM2.5 > 75 µg m-3.
As shown in Fig. 5a and b, absλ=365nm extracted by water and
MeOH in Xi'an during the campaign showed an increasing trend from
12 to 19 January, which is similar to PM2.5 loadings but
opposite to the visibility, indicating that BrC is one of the important
factors leading to visibility deterioration. From Fig. 5b, it can also
be seen that light absorption of water extracts dominated over the total BrC
absorption, especially in daytime and showed a variation pattern similar to
the PM2.5 (Fig. 5a) and WSOC loadings (Fig. 5c), indicating a
continuous formation of secondary BrC during the aerosol aging process. To
illustrate this point, the stable carbon isotopic composition (δ13CTC) of total carbon (TC) in the samples was measured. WSOC/OC showed a positive correlation with the δ13CTC,
demonstrating an aging process of aerosols during the haze development from
12 to 19 January, although it was weak (r=0.47, n=17). Similar conclusions were also reported by Yang et al. (2004) and
Pavuluri et al. (2015). From Fig. 5c, increasing trends of OPAHs
and nitrophenols were observed during the haze development, suggesting that
more SOAs with chromophores were generated during such an aerosol aging
process, because these compounds are also of secondary origin. To exclude
the possible impact of the changes in BrC source emissions, the values of
PAHs/OC and levoglucosan/OC were applied in this study, because PAHs and
levoglucosan emission factors are different for different
sources (Nguyen-Duy and Chang, 2017). As shown in Fig. S4, both
values indistinctively changed during the aerosol aging process, indicating
that the increasing absλ=365nm values were not caused by the changes in
source emissions. Moreover, we found that MACMSOC values during the
age process also increased (Fig. 5a), further suggesting that the
bleaching effect on light-absorbing BrC was reducing during the haze
developing process.
EC is one of the major light-absorbing aerosols in the atmosphere
(Collier et al., 2018; Peng et al., 2016). To further discuss the changes
of BrC during the aerosol aging process, we compared the mass absorption
efficiency of EC at λ=550 nm (7.5±1.2 m2 g-1) with BrC
by using the method reported from Yan et al. (2015) and
Kirillova et al. (2014). As shown in Fig. 5c, the concentrations of
EC have a slight change in the haze period, so the changes in light
absorption of EC remained nearly constant. However, the ratio of
absλ=365nm-MeOH/absλ=550nm-EC increasingly became
larger along with the visibility deterioration from 12 to
19 January (Fig. 5b), while the mass ratios of PAHs / EC, OPAHs / EC and
nitrophenols / EC during the period showed a significant negative correlation
with visibility (Fig. S5), further suggesting that the impairment of
the visibility from BrC was getting more significant during the haze development
process.
Linear fit regressions for the ratio of light absorption of
methanol extracts to light absorption of EC (absλ=365nm-MeOH/absλ=550nm-EC) with (a, b)δ13C and (c, d) the ratio of relative abundance of
nitrophenol to EC (nitrophenol / EC) in the daytime and nighttime PM2.5 samples
collected during the haze period of 12 to 19 January
(corresponding to the cyan shadow in Fig. 5) in Xi'an.
During the haze development process, organic aerosols usually become
more aged and enriched in heavier 13C due to the kinetic isotopic
effect (KIE) (Wang et al., 2010). As shown in Fig. 6a and b, δ13C of PM2.5 samples presented a strong positive correlation with
absλ=365nm-MeOH (r=0.82) in the daytime, while there was no
such correlation in the nighttime during the haze period of
12–19 January, indicating a daytime formation of secondary BrC. From
Fig. 6c and d, we also found that the correlation of absλ=365nm-MeOH/absλ=550nm-EC ratio with nitrophenol was much
stronger in daytime than in nighttime, which is opposite to the correlation
of absλ=365nm-MeOH/absλ=550nm-EC ratio with
PAHs. Nitrophenols can be produced from secondary photooxidation of phenol
with NOx, while PAHs are produced solely from direct emissions, especially
from coal and biomass burning for house heating. The opposite diurnal
correlations of the absλ=365nm-MeOH/ absλ=550nm-EC
ratio with nitrophenols and PAHs again revealed an enhanced formation of
secondary BrC during the aerosol aging process.
Positive matrix factorization (PMF) analysis for BrC source
apportionment
In the current work, the EPA PMF 5.0 model was used for identifying the
possible sources of BrC. Because the number of the collected samples in each
season was not large enough, data from the two seasons were merged together
to form a data set of 80×12 (80 samples with 12 species) in order
to obtain an accurate analysis according to the PMF user guide. The resolved
source profiles (factors) represented the sources that influenced
variability in the selected components throughout two seasons in Xi'an.
A similar approach was also reported by Zhang et al. (2010). With
several iterative tests, a solution with four factors was identified as the
optimal solution. As shown in Table S2, the values of Qtrue and
Qrobust were consistent, which indicates that the model fits the
input data well. Furthermore, the correlation coefficient between input and
model values ranged from 0.82 to 0.99 with an average of 0.96, also implying
that the model fit well. This assessment method was widely used in previous
studies (Ren et al., 2017; Wang et al., 2009a).
Factor profiles resolved by PMF mode during the winter and summer
sampling period. The bars represent the concentrations of species and the
dots represent the contributions of species appointed to the factors (the
summer and winter samples were merged together for the PMF analysis due to
the limited number of samples).
Figure 7 shows the factor profiles resolved by the model. Factor 01 was
characterized by high levels of BeF (52 %), BeP (57 %) and IP (67 %),
which were primarily derived from coal combustion and vehicle exhausts
(Kong et al., 2010; Ma et al., 2010; Harrison et al., 1996); further,
relatively high OC (29 %) and EC (25 %) associated with this factor are
well-known tracers of exhaust emissions (Zong et al., 2016), so we
identified factor 01 as the source from fossil fuel combustion. Factor 02
(fugitive dust) shows a high contribution of Ca2+ (69 %) and a moderate
loading of EC (39 %). Ca, as one of the most abundant crustal elements, is
largely from construction work, resuspended dust or soil sources (Chow et
al., 2004; Han et al., 2007). In addition, EC is a well-known tracer of
vehicular emissions (Dorado et al., 2003), so this factor can
be attributed to the impact of vehicles passing with higher speeds, leading
to resuspended non-tailpipe particles. Moreover, the concentrations of
Ca2+ in the night were almost higher than those during the daytime,
with averages of 1.8±1.56 and 1.43±0.85µg m-3,
respectively. This is consistent with the time for transporting the construction
waste by lorry. Thus, factor 02 was identified as fugitive dust. Factor 03
was identified as secondary formation, as it is associated with high
loadings of NO3- (63 %), SO42- (73 %) and
NH4+ (69 %) and a moderate loading of OC and WSOC, indicating
the presence of secondary inorganic and organic aerosols. Factor 04
showed high loadings with nitrophenols, levoglucosan and abs-MeOH and was
identified as biomass burning, because levoglucosan is the tracer for
biomass burning smoke, and nitrophenols can be produced in the aging process
of biomass burning plumes.
Source apportionment for airborne fine particulate BrC in Xi'an
during the campaign.
Figure 8 shows the contributions of the above sources to the light
absorption at λ=365 nm, which also represents the fraction of BrC
for the factors. Biomass burning was the primary source of the BrC,
accounting for 55 % of the total BrC in the city, which is coincided with
the results discussed in the Sect. 3.2.1. A significant fraction (about
19 %) of BrC was associated with fossil fuel combustion. The fraction of
secondary BrC was about 16 %, which was enhanced during the summer due to
the efficient photochemical formation of secondary chromophores. The AAE
value of total BrC, closed to the aged SOA-AAE (4.7–5.3) (Bones
et al., 2010), can also verify it. The remaining fraction of BrC was derived
from the fugitive dust in the city. The results of BrC source apportionment
for the Xi'an samples are in line with the work by Shen et al. (2017) and also similar to the results obtained in Beijing by using
radiocarbon fingerprinting (Yan et al.,
2017).
Conclusions
This study investigated the seasonality of the light absorption
characteristics of BrC in Xi'an. The light absorption coefficient (MAC) of
methanol extracts at 365 nm was 1.5–1.7-fold higher than
water extracts in the two seasons, suggesting nonpolar compounds in the
city are of stronger light-absorbing ability than that of polar compounds.
The strong correlation of levoglucosan with BrC and the diagnostic ratios of
levoglucosan / mannosan and levoglucosan / galacosan revealed that the
wintertime abundant BrC (absλ=365nm-MeOH of 49.18±31.67 M m-1) in Xi'an was mainly derived from the residential biofuel combustion for
house heating in the city and its surrounding region. Size distribution
results showed that 81 % and 65 % of BrC occurred in the fine mode
(< 2.1 µm) during winter and summer, respectively, which is
characterized by a monomodal size distribution with a peak in winter and a
bimodal size distribution in summer with two peaks in the fine and coarse
modes. The fine mode of MAC is 50 % higher in winter than in summer, suggesting that the light-absorbing ability of wintertime
fine particles is stronger, due to the abundant occurrence of PAHs and other
aromatic compounds in the fine mode.
The linear correlation between the ratio of absλ=365nm-MeOHO/absλ=550nm-EC and the enrichment of 13C
during the haze development indicated an accumulation of secondary BrC in
the aerosol aging process. The daytime strong correlation of the ratio of
absλ=365nm-MeOHO/absλ=550nm-EC with nitrophenols in
the haze event further revealed that such an enhanced production of
secondary BrC is related to the photooxidation of aromatic compounds with
NOx. Source apportionment by using PMF showed that 55 % of the BrC was
associated with biomass burning in the city during the campaign, with 19 % and 16 % of BrC derived from fossil fuel combustion and secondary formation,
respectively.
Data availability
Data can be accessed by contacting the
corresponding author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-2017-2020-supplement.
Author contributions
GW designed the experiment. CW, JiaL, JinL and CC collected the samples. CW and ZZ conducted the experiments. CW, GW and JC performed the data interpretation, and CW and GW wrote the paper. SG, YX, XL, GX, XW and FC contributed to the paper with useful scientific discussions or comments.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Multiphase chemistry of secondary aerosol formation under severe haze”. It is not associated with a conference.
Acknowledgements
This work was financially supported by the National Key R&D
Programme “Quantitative Relationship and Regulation Principle between Regional
Oxidation Capacity of Atmospheric and Air Quality” (no. 2017YFC0210000) and the a
program from the National Nature Science Foundation of China (no. 41773117).
Financial support
This research has been supported by the Quantitative Relationship and Regulation Principle between Regional Oxidation Capacity of Atmospheric and Air Quality (grant no. 2017YFC0210000) and the program from the National Nature Science Foundation of China (grant no. 41773117).
Review statement
This paper was edited by Aijun Ding and reviewed by three anonymous referees.
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